From f267f47b3042053f45d0e41f81e37d6eae7d6fa5 Mon Sep 17 00:00:00 2001 From: paxcema Date: Tue, 19 Mar 2024 10:22:23 +0000 Subject: [PATCH] Rebuilt the docs --- .buildinfo | 2 +- _modules/api/high_level.html | 4 +- _modules/api/json_ai.html | 4 +- _modules/api/predictor.html | 4 +- _modules/api/types.html | 4 +- _modules/index.html | 4 +- _modules/lightwood/analysis/analyze.html | 4 +- _modules/lightwood/analysis/base.html | 4 +- _modules/lightwood/analysis/explain.html | 4 +- .../lightwood/analysis/helpers/acc_stats.html | 4 +- .../analysis/helpers/conf_stats.html | 4 +- .../analysis/helpers/feature_importance.html | 4 +- _modules/lightwood/analysis/nc/calibrate.html | 4 +- .../analysis/nn_conf/temp_scale.html | 4 +- _modules/lightwood/data/encoded_ds.html | 4 +- .../lightwood/data/timeseries_analyzer.html | 4 +- .../lightwood/data/timeseries_transform.html | 4 +- _modules/lightwood/encoder/array/array.html | 4 +- .../lightwood/encoder/array/ts_cat_array.html | 4 +- .../lightwood/encoder/array/ts_num_array.html | 4 +- _modules/lightwood/encoder/base.html | 4 +- .../encoder/categorical/autoencoder.html | 4 +- .../lightwood/encoder/categorical/binary.html | 4 +- .../encoder/categorical/multihot.html | 4 +- .../lightwood/encoder/categorical/onehot.html | 4 +- .../encoder/categorical/simple_label.html | 4 +- .../lightwood/encoder/datetime/datetime.html | 4 +- .../datetime/datetime_sin_normalizer.html | 4 +- .../lightwood/encoder/image/img_2_vec.html | 4 +- .../lightwood/encoder/numeric/numeric.html | 4 +- .../lightwood/encoder/numeric/ts_numeric.html | 4 +- .../lightwood/encoder/text/pretrained.html | 4 +- _modules/lightwood/encoder/text/short.html | 4 +- _modules/lightwood/encoder/text/vocab.html | 4 +- .../lightwood/encoder/time_series/ts.html | 4 +- _modules/lightwood/ensemble/base.html | 4 +- _modules/lightwood/ensemble/best_of.html | 4 +- _modules/lightwood/ensemble/embed.html | 4 +- _modules/lightwood/ensemble/identity.html | 4 +- .../lightwood/ensemble/mean_ensemble.html | 4 +- .../lightwood/ensemble/mode_ensemble.html | 4 +- .../lightwood/ensemble/stacked_ensemble.html | 4 +- .../ensemble/ts_stacked_ensemble.html | 4 +- .../ensemble/weighted_mean_ensemble.html | 4 +- _modules/lightwood/helpers/device.html | 4 +- _modules/lightwood/helpers/general.html | 4 +- _modules/lightwood/helpers/torch.html | 4 +- _modules/lightwood/helpers/ts.html | 4 +- _modules/lightwood/mixer/arima.html | 4 +- _modules/lightwood/mixer/base.html | 4 +- _modules/lightwood/mixer/ets.html | 4 +- _modules/lightwood/mixer/neural.html | 4 +- _modules/lightwood/mixer/neural_ts.html | 4 +- _modules/lightwood/mixer/prophet.html | 4 +- _modules/lightwood/mixer/random_forest.html | 4 +- _modules/lightwood/mixer/regression.html | 4 +- _modules/lightwood/mixer/sktime.html | 4 +- _modules/lightwood/mixer/tabtransformer.html | 4 +- _modules/lightwood/mixer/unit.html | 4 +- _modules/lightwood/mixer/xgboost.html | 4 +- _modules/lightwood/mixer/xgboost_array.html | 4 +- .../custom_cleaner/custom_cleaner.ipynb.txt | 138 +- .../custom_encoder_rulebased.ipynb.txt | 178 +-- .../custom_explainer.ipynb.txt | 1394 ++++++++--------- .../custom_mixer/custom_mixer.ipynb.txt | 262 ++-- .../custom_splitter/custom_splitter.ipynb.txt | 230 +-- .../tutorial_data_analysis.ipynb.txt | 144 +- .../tutorial_time_series.ipynb.txt | 373 +++-- .../tutorial_update_models.ipynb.txt | 328 ++-- _static/documentation_options.js | 2 +- analysis.html | 4 +- api.html | 4 +- api/dtype.html | 4 +- api/encode.html | 4 +- api/high_level.html | 4 +- api/json_ai.html | 4 +- api/predictor.html | 4 +- api/types.html | 4 +- data.html | 4 +- encoder.html | 4 +- ensemble.html | 4 +- genindex.html | 4 +- helpers.html | 4 +- index.html | 6 +- lightwood_philosophy.html | 4 +- mixer.html | 4 +- objects.inv | Bin 8566 -> 8566 bytes py-modindex.html | 4 +- search.html | 4 +- searchindex.js | 2 +- tutorials.html | 4 +- tutorials/README.html | 4 +- tutorials/custom_cleaner/custom_cleaner.html | 62 +- tutorials/custom_cleaner/custom_cleaner.ipynb | 138 +- .../custom_encoder_rulebased.html | 94 +- .../custom_encoder_rulebased.ipynb | 178 +-- .../custom_explainer/custom_explainer.html | 1317 ++++++++-------- .../custom_explainer/custom_explainer.ipynb | 1394 ++++++++--------- tutorials/custom_mixer/custom_mixer.html | 218 +-- tutorials/custom_mixer/custom_mixer.ipynb | 262 ++-- .../custom_splitter/custom_splitter.html | 154 +- .../custom_splitter/custom_splitter.ipynb | 230 +-- .../tutorial_data_analysis.html | 44 +- .../tutorial_data_analysis.ipynb | 144 +- .../tutorial_time_series.html | 294 ++-- .../tutorial_time_series.ipynb | 373 +++-- .../tutorial_update_models.html | 275 ++-- .../tutorial_update_models.ipynb | 328 ++-- 108 files changed, 4429 insertions(+), 4451 deletions(-) diff --git a/.buildinfo b/.buildinfo index 70d57055b..8008575dd 100644 --- a/.buildinfo +++ b/.buildinfo @@ -1,4 +1,4 @@ # Sphinx build info version 1 # This file hashes the configuration used when building these files. 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- 24.3.3.1 + 24.3.3.0
diff --git a/_modules/api/json_ai.html b/_modules/api/json_ai.html index 359f7dd2c..8fd039e42 100644 --- a/_modules/api/json_ai.html +++ b/_modules/api/json_ai.html @@ -3,7 +3,7 @@ - api.json_ai — lightwood 24.3.3.1 documentation + api.json_ai — lightwood 24.3.3.0 documentation @@ -38,7 +38,7 @@
- 24.3.3.1 + 24.3.3.0
diff --git a/_modules/api/predictor.html b/_modules/api/predictor.html index aaf1ccce8..de7253a5b 100644 --- a/_modules/api/predictor.html +++ b/_modules/api/predictor.html @@ -3,7 +3,7 @@ - api.predictor — lightwood 24.3.3.1 documentation + api.predictor — lightwood 24.3.3.0 documentation @@ -38,7 +38,7 @@
- 24.3.3.1 + 24.3.3.0
diff --git a/_modules/api/types.html b/_modules/api/types.html index 73b465b8f..e9e280dbd 100644 --- a/_modules/api/types.html +++ b/_modules/api/types.html @@ -3,7 +3,7 @@ - api.types — lightwood 24.3.3.1 documentation + api.types — lightwood 24.3.3.0 documentation @@ -38,7 +38,7 @@
- 24.3.3.1 + 24.3.3.0
diff --git a/_modules/index.html b/_modules/index.html index 964c8b51f..bd828c2c4 100644 --- a/_modules/index.html +++ b/_modules/index.html @@ -3,7 +3,7 @@ - Overview: module code — lightwood 24.3.3.1 documentation + Overview: module code — lightwood 24.3.3.0 documentation @@ -38,7 +38,7 @@
- 24.3.3.1 + 24.3.3.0
diff --git a/_modules/lightwood/analysis/analyze.html b/_modules/lightwood/analysis/analyze.html index 21daedb60..b45ea864e 100644 --- a/_modules/lightwood/analysis/analyze.html +++ b/_modules/lightwood/analysis/analyze.html @@ -3,7 +3,7 @@ - lightwood.analysis.analyze — lightwood 24.3.3.1 documentation + lightwood.analysis.analyze — lightwood 24.3.3.0 documentation @@ -38,7 +38,7 @@
- 24.3.3.1 + 24.3.3.0
diff --git a/_modules/lightwood/analysis/base.html b/_modules/lightwood/analysis/base.html index 5a52b8bce..fba96cac9 100644 --- a/_modules/lightwood/analysis/base.html +++ b/_modules/lightwood/analysis/base.html @@ -3,7 +3,7 @@ - lightwood.analysis.base — lightwood 24.3.3.1 documentation + lightwood.analysis.base — lightwood 24.3.3.0 documentation @@ -38,7 +38,7 @@
- 24.3.3.1 + 24.3.3.0
diff --git a/_modules/lightwood/analysis/explain.html b/_modules/lightwood/analysis/explain.html index 837f67766..1e52f2ed7 100644 --- a/_modules/lightwood/analysis/explain.html +++ b/_modules/lightwood/analysis/explain.html @@ -3,7 +3,7 @@ - lightwood.analysis.explain — lightwood 24.3.3.1 documentation + lightwood.analysis.explain — lightwood 24.3.3.0 documentation @@ -38,7 +38,7 @@
- 24.3.3.1 + 24.3.3.0
diff --git a/_modules/lightwood/analysis/helpers/acc_stats.html b/_modules/lightwood/analysis/helpers/acc_stats.html index 58a5bd3b0..441656991 100644 --- a/_modules/lightwood/analysis/helpers/acc_stats.html +++ b/_modules/lightwood/analysis/helpers/acc_stats.html @@ -3,7 +3,7 @@ - lightwood.analysis.helpers.acc_stats — lightwood 24.3.3.1 documentation + lightwood.analysis.helpers.acc_stats — lightwood 24.3.3.0 documentation @@ -38,7 +38,7 @@
- 24.3.3.1 + 24.3.3.0
diff --git a/_modules/lightwood/analysis/helpers/conf_stats.html b/_modules/lightwood/analysis/helpers/conf_stats.html index b23c68670..665eed0e3 100644 --- a/_modules/lightwood/analysis/helpers/conf_stats.html +++ b/_modules/lightwood/analysis/helpers/conf_stats.html @@ -3,7 +3,7 @@ - lightwood.analysis.helpers.conf_stats — lightwood 24.3.3.1 documentation + lightwood.analysis.helpers.conf_stats — lightwood 24.3.3.0 documentation @@ -38,7 +38,7 @@
- 24.3.3.1 + 24.3.3.0
diff --git a/_modules/lightwood/analysis/helpers/feature_importance.html b/_modules/lightwood/analysis/helpers/feature_importance.html index 7d7873c77..f55f57041 100644 --- a/_modules/lightwood/analysis/helpers/feature_importance.html +++ b/_modules/lightwood/analysis/helpers/feature_importance.html @@ -3,7 +3,7 @@ - lightwood.analysis.helpers.feature_importance — lightwood 24.3.3.1 documentation + lightwood.analysis.helpers.feature_importance — lightwood 24.3.3.0 documentation @@ -38,7 +38,7 @@
- 24.3.3.1 + 24.3.3.0
diff --git a/_modules/lightwood/analysis/nc/calibrate.html b/_modules/lightwood/analysis/nc/calibrate.html index d99934a4d..c1f717d2a 100644 --- a/_modules/lightwood/analysis/nc/calibrate.html +++ b/_modules/lightwood/analysis/nc/calibrate.html @@ -3,7 +3,7 @@ - lightwood.analysis.nc.calibrate — lightwood 24.3.3.1 documentation + lightwood.analysis.nc.calibrate — lightwood 24.3.3.0 documentation @@ -38,7 +38,7 @@
- 24.3.3.1 + 24.3.3.0
diff --git a/_modules/lightwood/analysis/nn_conf/temp_scale.html b/_modules/lightwood/analysis/nn_conf/temp_scale.html index 2ac6e3037..8abb6f706 100644 --- a/_modules/lightwood/analysis/nn_conf/temp_scale.html +++ b/_modules/lightwood/analysis/nn_conf/temp_scale.html @@ -3,7 +3,7 @@ - lightwood.analysis.nn_conf.temp_scale — lightwood 24.3.3.1 documentation + lightwood.analysis.nn_conf.temp_scale — lightwood 24.3.3.0 documentation @@ -38,7 +38,7 @@
- 24.3.3.1 + 24.3.3.0
diff --git a/_modules/lightwood/data/encoded_ds.html b/_modules/lightwood/data/encoded_ds.html index a12c727d2..2546ae259 100644 --- a/_modules/lightwood/data/encoded_ds.html +++ b/_modules/lightwood/data/encoded_ds.html @@ -3,7 +3,7 @@ - lightwood.data.encoded_ds — lightwood 24.3.3.1 documentation + lightwood.data.encoded_ds — lightwood 24.3.3.0 documentation @@ -38,7 +38,7 @@
- 24.3.3.1 + 24.3.3.0
diff --git a/_modules/lightwood/data/timeseries_analyzer.html b/_modules/lightwood/data/timeseries_analyzer.html index 4edea64ec..23961d022 100644 --- a/_modules/lightwood/data/timeseries_analyzer.html +++ b/_modules/lightwood/data/timeseries_analyzer.html @@ -3,7 +3,7 @@ - lightwood.data.timeseries_analyzer — lightwood 24.3.3.1 documentation + lightwood.data.timeseries_analyzer — lightwood 24.3.3.0 documentation @@ -38,7 +38,7 @@
- 24.3.3.1 + 24.3.3.0
diff --git a/_modules/lightwood/data/timeseries_transform.html b/_modules/lightwood/data/timeseries_transform.html index 023d845e3..4f74ef288 100644 --- a/_modules/lightwood/data/timeseries_transform.html +++ b/_modules/lightwood/data/timeseries_transform.html @@ -3,7 +3,7 @@ - lightwood.data.timeseries_transform — lightwood 24.3.3.1 documentation + lightwood.data.timeseries_transform — lightwood 24.3.3.0 documentation @@ -38,7 +38,7 @@
- 24.3.3.1 + 24.3.3.0
diff --git a/_modules/lightwood/encoder/array/array.html b/_modules/lightwood/encoder/array/array.html index b291be367..322443a25 100644 --- a/_modules/lightwood/encoder/array/array.html +++ b/_modules/lightwood/encoder/array/array.html @@ -3,7 +3,7 @@ - lightwood.encoder.array.array — lightwood 24.3.3.1 documentation + lightwood.encoder.array.array — lightwood 24.3.3.0 documentation @@ -38,7 +38,7 @@
- 24.3.3.1 + 24.3.3.0
diff --git a/_modules/lightwood/encoder/array/ts_cat_array.html b/_modules/lightwood/encoder/array/ts_cat_array.html index ffd9515a3..ff8c14371 100644 --- a/_modules/lightwood/encoder/array/ts_cat_array.html +++ b/_modules/lightwood/encoder/array/ts_cat_array.html @@ -3,7 +3,7 @@ - lightwood.encoder.array.ts_cat_array — lightwood 24.3.3.1 documentation + lightwood.encoder.array.ts_cat_array — lightwood 24.3.3.0 documentation @@ -38,7 +38,7 @@
- 24.3.3.1 + 24.3.3.0
diff --git a/_modules/lightwood/encoder/array/ts_num_array.html b/_modules/lightwood/encoder/array/ts_num_array.html index e244d43fd..76c718706 100644 --- a/_modules/lightwood/encoder/array/ts_num_array.html +++ b/_modules/lightwood/encoder/array/ts_num_array.html @@ -3,7 +3,7 @@ - lightwood.encoder.array.ts_num_array — lightwood 24.3.3.1 documentation + lightwood.encoder.array.ts_num_array — lightwood 24.3.3.0 documentation @@ -38,7 +38,7 @@
- 24.3.3.1 + 24.3.3.0
diff --git a/_modules/lightwood/encoder/base.html b/_modules/lightwood/encoder/base.html index 7d664f671..cb027ab7d 100644 --- a/_modules/lightwood/encoder/base.html +++ b/_modules/lightwood/encoder/base.html @@ -3,7 +3,7 @@ - lightwood.encoder.base — lightwood 24.3.3.1 documentation + lightwood.encoder.base — lightwood 24.3.3.0 documentation @@ -38,7 +38,7 @@
- 24.3.3.1 + 24.3.3.0
diff --git a/_modules/lightwood/encoder/categorical/autoencoder.html b/_modules/lightwood/encoder/categorical/autoencoder.html index da1dc803c..15dd7536a 100644 --- a/_modules/lightwood/encoder/categorical/autoencoder.html +++ b/_modules/lightwood/encoder/categorical/autoencoder.html @@ -3,7 +3,7 @@ - lightwood.encoder.categorical.autoencoder — lightwood 24.3.3.1 documentation + lightwood.encoder.categorical.autoencoder — lightwood 24.3.3.0 documentation @@ -38,7 +38,7 @@
- 24.3.3.1 + 24.3.3.0
diff --git a/_modules/lightwood/encoder/categorical/binary.html b/_modules/lightwood/encoder/categorical/binary.html index 4b6b6ec91..bad99a2ef 100644 --- a/_modules/lightwood/encoder/categorical/binary.html +++ b/_modules/lightwood/encoder/categorical/binary.html @@ -3,7 +3,7 @@ - lightwood.encoder.categorical.binary — lightwood 24.3.3.1 documentation + lightwood.encoder.categorical.binary — lightwood 24.3.3.0 documentation @@ -38,7 +38,7 @@
- 24.3.3.1 + 24.3.3.0
diff --git a/_modules/lightwood/encoder/categorical/multihot.html b/_modules/lightwood/encoder/categorical/multihot.html index b2168dd04..d621f91c4 100644 --- a/_modules/lightwood/encoder/categorical/multihot.html +++ b/_modules/lightwood/encoder/categorical/multihot.html @@ -3,7 +3,7 @@ - lightwood.encoder.categorical.multihot — lightwood 24.3.3.1 documentation + lightwood.encoder.categorical.multihot — lightwood 24.3.3.0 documentation @@ -38,7 +38,7 @@
- 24.3.3.1 + 24.3.3.0
diff --git a/_modules/lightwood/encoder/categorical/onehot.html b/_modules/lightwood/encoder/categorical/onehot.html index 78b2e3a50..e67226b20 100644 --- a/_modules/lightwood/encoder/categorical/onehot.html +++ b/_modules/lightwood/encoder/categorical/onehot.html @@ -3,7 +3,7 @@ - lightwood.encoder.categorical.onehot — lightwood 24.3.3.1 documentation + lightwood.encoder.categorical.onehot — lightwood 24.3.3.0 documentation @@ -38,7 +38,7 @@
- 24.3.3.1 + 24.3.3.0
diff --git a/_modules/lightwood/encoder/categorical/simple_label.html b/_modules/lightwood/encoder/categorical/simple_label.html index 6c373b7f1..e270e3560 100644 --- a/_modules/lightwood/encoder/categorical/simple_label.html +++ b/_modules/lightwood/encoder/categorical/simple_label.html @@ -3,7 +3,7 @@ - lightwood.encoder.categorical.simple_label — lightwood 24.3.3.1 documentation + lightwood.encoder.categorical.simple_label — lightwood 24.3.3.0 documentation @@ -38,7 +38,7 @@
- 24.3.3.1 + 24.3.3.0
diff --git a/_modules/lightwood/encoder/datetime/datetime.html b/_modules/lightwood/encoder/datetime/datetime.html index d675ceb56..57a2c265a 100644 --- a/_modules/lightwood/encoder/datetime/datetime.html +++ b/_modules/lightwood/encoder/datetime/datetime.html @@ -3,7 +3,7 @@ - lightwood.encoder.datetime.datetime — lightwood 24.3.3.1 documentation + lightwood.encoder.datetime.datetime — lightwood 24.3.3.0 documentation @@ -38,7 +38,7 @@
- 24.3.3.1 + 24.3.3.0
diff --git a/_modules/lightwood/encoder/datetime/datetime_sin_normalizer.html b/_modules/lightwood/encoder/datetime/datetime_sin_normalizer.html index 275c6cf89..daf57fa19 100644 --- a/_modules/lightwood/encoder/datetime/datetime_sin_normalizer.html +++ b/_modules/lightwood/encoder/datetime/datetime_sin_normalizer.html @@ -3,7 +3,7 @@ - lightwood.encoder.datetime.datetime_sin_normalizer — lightwood 24.3.3.1 documentation + lightwood.encoder.datetime.datetime_sin_normalizer — lightwood 24.3.3.0 documentation @@ -38,7 +38,7 @@
- 24.3.3.1 + 24.3.3.0
diff --git a/_modules/lightwood/encoder/image/img_2_vec.html b/_modules/lightwood/encoder/image/img_2_vec.html index 870b532bc..0b5d45917 100644 --- a/_modules/lightwood/encoder/image/img_2_vec.html +++ b/_modules/lightwood/encoder/image/img_2_vec.html @@ -3,7 +3,7 @@ - lightwood.encoder.image.img_2_vec — lightwood 24.3.3.1 documentation + lightwood.encoder.image.img_2_vec — lightwood 24.3.3.0 documentation @@ -38,7 +38,7 @@
- 24.3.3.1 + 24.3.3.0
diff --git a/_modules/lightwood/encoder/numeric/numeric.html b/_modules/lightwood/encoder/numeric/numeric.html index c166c76f4..fe79af1fa 100644 --- a/_modules/lightwood/encoder/numeric/numeric.html +++ b/_modules/lightwood/encoder/numeric/numeric.html @@ -3,7 +3,7 @@ - lightwood.encoder.numeric.numeric — lightwood 24.3.3.1 documentation + lightwood.encoder.numeric.numeric — lightwood 24.3.3.0 documentation @@ -38,7 +38,7 @@
- 24.3.3.1 + 24.3.3.0
diff --git a/_modules/lightwood/encoder/numeric/ts_numeric.html b/_modules/lightwood/encoder/numeric/ts_numeric.html index 02fd294e9..8a2c8f63b 100644 --- a/_modules/lightwood/encoder/numeric/ts_numeric.html +++ b/_modules/lightwood/encoder/numeric/ts_numeric.html @@ -3,7 +3,7 @@ - lightwood.encoder.numeric.ts_numeric — lightwood 24.3.3.1 documentation + lightwood.encoder.numeric.ts_numeric — lightwood 24.3.3.0 documentation @@ -38,7 +38,7 @@
- 24.3.3.1 + 24.3.3.0
diff --git a/_modules/lightwood/encoder/text/pretrained.html b/_modules/lightwood/encoder/text/pretrained.html index b3e75b69d..0ce420543 100644 --- a/_modules/lightwood/encoder/text/pretrained.html +++ b/_modules/lightwood/encoder/text/pretrained.html @@ -3,7 +3,7 @@ - lightwood.encoder.text.pretrained — lightwood 24.3.3.1 documentation + lightwood.encoder.text.pretrained — lightwood 24.3.3.0 documentation @@ -38,7 +38,7 @@
- 24.3.3.1 + 24.3.3.0
diff --git a/_modules/lightwood/encoder/text/short.html b/_modules/lightwood/encoder/text/short.html index 8b3753893..b39fa1690 100644 --- a/_modules/lightwood/encoder/text/short.html +++ b/_modules/lightwood/encoder/text/short.html @@ -3,7 +3,7 @@ - lightwood.encoder.text.short — lightwood 24.3.3.1 documentation + lightwood.encoder.text.short — lightwood 24.3.3.0 documentation @@ -38,7 +38,7 @@
- 24.3.3.1 + 24.3.3.0
diff --git a/_modules/lightwood/encoder/text/vocab.html b/_modules/lightwood/encoder/text/vocab.html index 323850e7a..ae09f9d49 100644 --- a/_modules/lightwood/encoder/text/vocab.html +++ b/_modules/lightwood/encoder/text/vocab.html @@ -3,7 +3,7 @@ - lightwood.encoder.text.vocab — lightwood 24.3.3.1 documentation + lightwood.encoder.text.vocab — lightwood 24.3.3.0 documentation @@ -38,7 +38,7 @@
- 24.3.3.1 + 24.3.3.0
diff --git a/_modules/lightwood/encoder/time_series/ts.html b/_modules/lightwood/encoder/time_series/ts.html index 53cf61adb..19b28e931 100644 --- a/_modules/lightwood/encoder/time_series/ts.html +++ b/_modules/lightwood/encoder/time_series/ts.html @@ -3,7 +3,7 @@ - lightwood.encoder.time_series.ts — lightwood 24.3.3.1 documentation + lightwood.encoder.time_series.ts — lightwood 24.3.3.0 documentation @@ -38,7 +38,7 @@
- 24.3.3.1 + 24.3.3.0
diff --git a/_modules/lightwood/ensemble/base.html b/_modules/lightwood/ensemble/base.html index da8d1c746..154a3d6a0 100644 --- a/_modules/lightwood/ensemble/base.html +++ b/_modules/lightwood/ensemble/base.html @@ -3,7 +3,7 @@ - lightwood.ensemble.base — lightwood 24.3.3.1 documentation + lightwood.ensemble.base — lightwood 24.3.3.0 documentation @@ -38,7 +38,7 @@
- 24.3.3.1 + 24.3.3.0
diff --git a/_modules/lightwood/ensemble/best_of.html b/_modules/lightwood/ensemble/best_of.html index 6192eefbe..26959debf 100644 --- a/_modules/lightwood/ensemble/best_of.html +++ b/_modules/lightwood/ensemble/best_of.html @@ -3,7 +3,7 @@ - lightwood.ensemble.best_of — lightwood 24.3.3.1 documentation + lightwood.ensemble.best_of — lightwood 24.3.3.0 documentation @@ -38,7 +38,7 @@
- 24.3.3.1 + 24.3.3.0
diff --git a/_modules/lightwood/ensemble/embed.html b/_modules/lightwood/ensemble/embed.html index 33031c307..eddaa4d2c 100644 --- a/_modules/lightwood/ensemble/embed.html +++ b/_modules/lightwood/ensemble/embed.html @@ -3,7 +3,7 @@ - lightwood.ensemble.embed — lightwood 24.3.3.1 documentation + lightwood.ensemble.embed — lightwood 24.3.3.0 documentation @@ -38,7 +38,7 @@
- 24.3.3.1 + 24.3.3.0
diff --git a/_modules/lightwood/ensemble/identity.html b/_modules/lightwood/ensemble/identity.html index e323e6a12..1cc863f15 100644 --- a/_modules/lightwood/ensemble/identity.html +++ b/_modules/lightwood/ensemble/identity.html @@ -3,7 +3,7 @@ - lightwood.ensemble.identity — lightwood 24.3.3.1 documentation + lightwood.ensemble.identity — lightwood 24.3.3.0 documentation @@ -38,7 +38,7 @@
- 24.3.3.1 + 24.3.3.0
diff --git a/_modules/lightwood/ensemble/mean_ensemble.html b/_modules/lightwood/ensemble/mean_ensemble.html index 881e1cedd..6a2509596 100644 --- a/_modules/lightwood/ensemble/mean_ensemble.html +++ b/_modules/lightwood/ensemble/mean_ensemble.html @@ -3,7 +3,7 @@ - lightwood.ensemble.mean_ensemble — lightwood 24.3.3.1 documentation + lightwood.ensemble.mean_ensemble — lightwood 24.3.3.0 documentation @@ -38,7 +38,7 @@
- 24.3.3.1 + 24.3.3.0
diff --git a/_modules/lightwood/ensemble/mode_ensemble.html b/_modules/lightwood/ensemble/mode_ensemble.html index 6c14897cf..122babd2c 100644 --- a/_modules/lightwood/ensemble/mode_ensemble.html +++ b/_modules/lightwood/ensemble/mode_ensemble.html @@ -3,7 +3,7 @@ - lightwood.ensemble.mode_ensemble — lightwood 24.3.3.1 documentation + lightwood.ensemble.mode_ensemble — lightwood 24.3.3.0 documentation @@ -38,7 +38,7 @@
- 24.3.3.1 + 24.3.3.0
diff --git a/_modules/lightwood/ensemble/stacked_ensemble.html b/_modules/lightwood/ensemble/stacked_ensemble.html index 0ff785bcf..a6661de54 100644 --- a/_modules/lightwood/ensemble/stacked_ensemble.html +++ b/_modules/lightwood/ensemble/stacked_ensemble.html @@ -3,7 +3,7 @@ - lightwood.ensemble.stacked_ensemble — lightwood 24.3.3.1 documentation + lightwood.ensemble.stacked_ensemble — lightwood 24.3.3.0 documentation @@ -38,7 +38,7 @@
- 24.3.3.1 + 24.3.3.0
diff --git a/_modules/lightwood/ensemble/ts_stacked_ensemble.html b/_modules/lightwood/ensemble/ts_stacked_ensemble.html index 4507cb2bb..47e3e3fad 100644 --- a/_modules/lightwood/ensemble/ts_stacked_ensemble.html +++ b/_modules/lightwood/ensemble/ts_stacked_ensemble.html @@ -3,7 +3,7 @@ - lightwood.ensemble.ts_stacked_ensemble — lightwood 24.3.3.1 documentation + lightwood.ensemble.ts_stacked_ensemble — lightwood 24.3.3.0 documentation @@ -38,7 +38,7 @@
- 24.3.3.1 + 24.3.3.0
diff --git a/_modules/lightwood/ensemble/weighted_mean_ensemble.html b/_modules/lightwood/ensemble/weighted_mean_ensemble.html index 747c4ddd0..5e0d7ae3a 100644 --- a/_modules/lightwood/ensemble/weighted_mean_ensemble.html +++ b/_modules/lightwood/ensemble/weighted_mean_ensemble.html @@ -3,7 +3,7 @@ - lightwood.ensemble.weighted_mean_ensemble — lightwood 24.3.3.1 documentation + lightwood.ensemble.weighted_mean_ensemble — lightwood 24.3.3.0 documentation @@ -38,7 +38,7 @@
- 24.3.3.1 + 24.3.3.0
diff --git a/_modules/lightwood/helpers/device.html b/_modules/lightwood/helpers/device.html index 436c62704..3c890ec03 100644 --- a/_modules/lightwood/helpers/device.html +++ b/_modules/lightwood/helpers/device.html @@ -3,7 +3,7 @@ - lightwood.helpers.device — lightwood 24.3.3.1 documentation + lightwood.helpers.device — lightwood 24.3.3.0 documentation @@ -38,7 +38,7 @@
- 24.3.3.1 + 24.3.3.0
diff --git a/_modules/lightwood/helpers/general.html b/_modules/lightwood/helpers/general.html index d309bea54..396bb9adb 100644 --- a/_modules/lightwood/helpers/general.html +++ b/_modules/lightwood/helpers/general.html @@ -3,7 +3,7 @@ - lightwood.helpers.general — lightwood 24.3.3.1 documentation + lightwood.helpers.general — lightwood 24.3.3.0 documentation @@ -38,7 +38,7 @@
- 24.3.3.1 + 24.3.3.0
diff --git a/_modules/lightwood/helpers/torch.html b/_modules/lightwood/helpers/torch.html index a2882439f..d0fcdf956 100644 --- a/_modules/lightwood/helpers/torch.html +++ b/_modules/lightwood/helpers/torch.html @@ -3,7 +3,7 @@ - lightwood.helpers.torch — lightwood 24.3.3.1 documentation + lightwood.helpers.torch — lightwood 24.3.3.0 documentation @@ -38,7 +38,7 @@
- 24.3.3.1 + 24.3.3.0
diff --git a/_modules/lightwood/helpers/ts.html b/_modules/lightwood/helpers/ts.html index 2d0d6211e..ec1d5ca36 100644 --- a/_modules/lightwood/helpers/ts.html +++ b/_modules/lightwood/helpers/ts.html @@ -3,7 +3,7 @@ - lightwood.helpers.ts — lightwood 24.3.3.1 documentation + lightwood.helpers.ts — lightwood 24.3.3.0 documentation @@ -38,7 +38,7 @@
- 24.3.3.1 + 24.3.3.0
diff --git a/_modules/lightwood/mixer/arima.html b/_modules/lightwood/mixer/arima.html index 72e61f459..3b0af3664 100644 --- a/_modules/lightwood/mixer/arima.html +++ b/_modules/lightwood/mixer/arima.html @@ -3,7 +3,7 @@ - lightwood.mixer.arima — lightwood 24.3.3.1 documentation + lightwood.mixer.arima — lightwood 24.3.3.0 documentation @@ -38,7 +38,7 @@
- 24.3.3.1 + 24.3.3.0
diff --git a/_modules/lightwood/mixer/base.html b/_modules/lightwood/mixer/base.html index 0a07a9b63..d7439dfd5 100644 --- a/_modules/lightwood/mixer/base.html +++ b/_modules/lightwood/mixer/base.html @@ -3,7 +3,7 @@ - lightwood.mixer.base — lightwood 24.3.3.1 documentation + lightwood.mixer.base — lightwood 24.3.3.0 documentation @@ -38,7 +38,7 @@
- 24.3.3.1 + 24.3.3.0
diff --git a/_modules/lightwood/mixer/ets.html b/_modules/lightwood/mixer/ets.html index a51e2cd3e..6f820cbaf 100644 --- a/_modules/lightwood/mixer/ets.html +++ b/_modules/lightwood/mixer/ets.html @@ -3,7 +3,7 @@ - lightwood.mixer.ets — lightwood 24.3.3.1 documentation + lightwood.mixer.ets — lightwood 24.3.3.0 documentation @@ -38,7 +38,7 @@
- 24.3.3.1 + 24.3.3.0
diff --git a/_modules/lightwood/mixer/neural.html b/_modules/lightwood/mixer/neural.html index b9c1c4689..0d624c989 100644 --- a/_modules/lightwood/mixer/neural.html +++ b/_modules/lightwood/mixer/neural.html @@ -3,7 +3,7 @@ - lightwood.mixer.neural — lightwood 24.3.3.1 documentation + lightwood.mixer.neural — lightwood 24.3.3.0 documentation @@ -38,7 +38,7 @@
- 24.3.3.1 + 24.3.3.0
diff --git a/_modules/lightwood/mixer/neural_ts.html b/_modules/lightwood/mixer/neural_ts.html index c02d2b706..392342fbd 100644 --- a/_modules/lightwood/mixer/neural_ts.html +++ b/_modules/lightwood/mixer/neural_ts.html @@ -3,7 +3,7 @@ - lightwood.mixer.neural_ts — lightwood 24.3.3.1 documentation + lightwood.mixer.neural_ts — lightwood 24.3.3.0 documentation @@ -38,7 +38,7 @@
- 24.3.3.1 + 24.3.3.0
diff --git a/_modules/lightwood/mixer/prophet.html b/_modules/lightwood/mixer/prophet.html index 90d2eb925..da7c68431 100644 --- a/_modules/lightwood/mixer/prophet.html +++ b/_modules/lightwood/mixer/prophet.html @@ -3,7 +3,7 @@ - lightwood.mixer.prophet — lightwood 24.3.3.1 documentation + lightwood.mixer.prophet — lightwood 24.3.3.0 documentation @@ -38,7 +38,7 @@
- 24.3.3.1 + 24.3.3.0
diff --git a/_modules/lightwood/mixer/random_forest.html b/_modules/lightwood/mixer/random_forest.html index de55ec0ed..c63f09249 100644 --- a/_modules/lightwood/mixer/random_forest.html +++ b/_modules/lightwood/mixer/random_forest.html @@ -3,7 +3,7 @@ - lightwood.mixer.random_forest — lightwood 24.3.3.1 documentation + lightwood.mixer.random_forest — lightwood 24.3.3.0 documentation @@ -38,7 +38,7 @@
- 24.3.3.1 + 24.3.3.0
diff --git a/_modules/lightwood/mixer/regression.html b/_modules/lightwood/mixer/regression.html index 3c278f5ed..dfb215772 100644 --- a/_modules/lightwood/mixer/regression.html +++ b/_modules/lightwood/mixer/regression.html @@ -3,7 +3,7 @@ - lightwood.mixer.regression — lightwood 24.3.3.1 documentation + lightwood.mixer.regression — lightwood 24.3.3.0 documentation @@ -38,7 +38,7 @@
- 24.3.3.1 + 24.3.3.0
diff --git a/_modules/lightwood/mixer/sktime.html b/_modules/lightwood/mixer/sktime.html index 04aa1f7c1..6fe1d100f 100644 --- a/_modules/lightwood/mixer/sktime.html +++ b/_modules/lightwood/mixer/sktime.html @@ -3,7 +3,7 @@ - lightwood.mixer.sktime — lightwood 24.3.3.1 documentation + lightwood.mixer.sktime — lightwood 24.3.3.0 documentation @@ -38,7 +38,7 @@
- 24.3.3.1 + 24.3.3.0
diff --git a/_modules/lightwood/mixer/tabtransformer.html b/_modules/lightwood/mixer/tabtransformer.html index 5be98074b..788315707 100644 --- a/_modules/lightwood/mixer/tabtransformer.html +++ b/_modules/lightwood/mixer/tabtransformer.html @@ -3,7 +3,7 @@ - lightwood.mixer.tabtransformer — lightwood 24.3.3.1 documentation + lightwood.mixer.tabtransformer — lightwood 24.3.3.0 documentation @@ -38,7 +38,7 @@
- 24.3.3.1 + 24.3.3.0
diff --git a/_modules/lightwood/mixer/unit.html b/_modules/lightwood/mixer/unit.html index a870e0e44..2e388f52c 100644 --- a/_modules/lightwood/mixer/unit.html +++ b/_modules/lightwood/mixer/unit.html @@ -3,7 +3,7 @@ - lightwood.mixer.unit — lightwood 24.3.3.1 documentation + lightwood.mixer.unit — lightwood 24.3.3.0 documentation @@ -38,7 +38,7 @@
- 24.3.3.1 + 24.3.3.0
diff --git a/_modules/lightwood/mixer/xgboost.html b/_modules/lightwood/mixer/xgboost.html index 6b863d151..86954f194 100644 --- a/_modules/lightwood/mixer/xgboost.html +++ b/_modules/lightwood/mixer/xgboost.html @@ -3,7 +3,7 @@ - lightwood.mixer.xgboost — lightwood 24.3.3.1 documentation + lightwood.mixer.xgboost — lightwood 24.3.3.0 documentation @@ -38,7 +38,7 @@
- 24.3.3.1 + 24.3.3.0
diff --git a/_modules/lightwood/mixer/xgboost_array.html b/_modules/lightwood/mixer/xgboost_array.html index 64b998905..36504cd23 100644 --- a/_modules/lightwood/mixer/xgboost_array.html +++ b/_modules/lightwood/mixer/xgboost_array.html @@ -3,7 +3,7 @@ - lightwood.mixer.xgboost_array — lightwood 24.3.3.1 documentation + lightwood.mixer.xgboost_array — lightwood 24.3.3.0 documentation @@ -38,7 +38,7 @@
- 24.3.3.1 + 24.3.3.0
diff --git a/_sources/tutorials/custom_cleaner/custom_cleaner.ipynb.txt b/_sources/tutorials/custom_cleaner/custom_cleaner.ipynb.txt index 249dd26a0..8d8e06416 100644 --- a/_sources/tutorials/custom_cleaner/custom_cleaner.ipynb.txt +++ b/_sources/tutorials/custom_cleaner/custom_cleaner.ipynb.txt @@ -31,10 +31,10 @@ "id": "happy-wheat", "metadata": { "execution": { - "iopub.execute_input": "2024-03-19T10:01:24.293027Z", - "iopub.status.busy": "2024-03-19T10:01:24.292490Z", - "iopub.status.idle": "2024-03-19T10:01:26.832255Z", - "shell.execute_reply": "2024-03-19T10:01:26.831481Z" + "iopub.execute_input": "2024-03-19T10:19:37.224393Z", + "iopub.status.busy": "2024-03-19T10:19:37.224196Z", + "iopub.status.idle": "2024-03-19T10:19:39.740153Z", + "shell.execute_reply": "2024-03-19T10:19:39.739489Z" } }, "outputs": [ @@ -42,14 +42,14 @@ "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:lightwood-2670:No torchvision detected, image helpers not supported.\u001b[0m\n" + "\u001b[32mINFO:lightwood-2943:No torchvision detected, image helpers not supported.\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:lightwood-2670:No torchvision/pillow detected, image encoder not supported\u001b[0m\n" + "\u001b[32mINFO:lightwood-2943:No torchvision/pillow detected, image encoder not supported\u001b[0m\n" ] } ], @@ -87,10 +87,10 @@ "id": "recognized-parish", "metadata": { "execution": { - "iopub.execute_input": "2024-03-19T10:01:26.835698Z", - "iopub.status.busy": "2024-03-19T10:01:26.835149Z", - "iopub.status.idle": "2024-03-19T10:01:28.275017Z", - "shell.execute_reply": "2024-03-19T10:01:28.274348Z" + "iopub.execute_input": "2024-03-19T10:19:39.743708Z", + "iopub.status.busy": "2024-03-19T10:19:39.743020Z", + "iopub.status.idle": "2024-03-19T10:19:40.746691Z", + "shell.execute_reply": "2024-03-19T10:19:40.746040Z" } }, "outputs": [ @@ -221,10 +221,10 @@ "id": "chicken-truth", "metadata": { "execution": { - "iopub.execute_input": "2024-03-19T10:01:28.277742Z", - "iopub.status.busy": "2024-03-19T10:01:28.277344Z", - "iopub.status.idle": "2024-03-19T10:01:43.768327Z", - "shell.execute_reply": "2024-03-19T10:01:43.767789Z" + "iopub.execute_input": "2024-03-19T10:19:40.749243Z", + "iopub.status.busy": "2024-03-19T10:19:40.749038Z", + "iopub.status.idle": "2024-03-19T10:19:56.275041Z", + "shell.execute_reply": "2024-03-19T10:19:56.274470Z" } }, "outputs": [ @@ -232,98 +232,98 @@ "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:lightwood-2670:Dropping features: ['url_legal', 'license', 'standard_error']\u001b[0m\n" + "\u001b[32mINFO:lightwood-2943:Dropping features: ['url_legal', 'license', 'standard_error']\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:type_infer-2670:Analyzing a sample of 2478\u001b[0m\n" + "\u001b[32mINFO:type_infer-2943:Analyzing a sample of 2478\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:type_infer-2670:from a total population of 2834, this is equivalent to 87.4% of your data.\u001b[0m\n" + "\u001b[32mINFO:type_infer-2943:from a total population of 2834, this is equivalent to 87.4% of your data.\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:type_infer-2670:Infering type for: id\u001b[0m\n" + "\u001b[32mINFO:type_infer-2943:Infering type for: id\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:type_infer-2670:Doing text detection for column: id\u001b[0m\n" + "\u001b[32mINFO:type_infer-2943:Doing text detection for column: id\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:type_infer-2670:Column id has data type categorical\u001b[0m\n" + "\u001b[32mINFO:type_infer-2943:Column id has data type categorical\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:type_infer-2670:Infering type for: excerpt\u001b[0m\n" + "\u001b[32mINFO:type_infer-2943:Infering type for: excerpt\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:type_infer-2670:Doing text detection for column: excerpt\u001b[0m\n" + "\u001b[32mINFO:type_infer-2943:Doing text detection for column: excerpt\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:type_infer-2670:Infering type for: target\u001b[0m\n" + "\u001b[32mINFO:type_infer-2943:Infering type for: target\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:type_infer-2670:Column target has data type float\u001b[0m\n" + "\u001b[32mINFO:type_infer-2943:Column target has data type float\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[33mWARNING:type_infer-2670:Column id is an identifier of type \"Hash-like identifier\"\u001b[0m\n" + "\u001b[33mWARNING:type_infer-2943:Column id is an identifier of type \"Hash-like identifier\"\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:dataprep_ml-2670:Starting statistical analysis\u001b[0m\n" + "\u001b[32mINFO:dataprep_ml-2943:Starting statistical analysis\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:dataprep_ml-2670:Dropping features: ['id']\u001b[0m\n" + "\u001b[32mINFO:dataprep_ml-2943:Dropping features: ['id']\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:dataprep_ml-2670:Finished statistical analysis\u001b[0m\n" + "\u001b[32mINFO:dataprep_ml-2943:Finished statistical analysis\u001b[0m\n" ] } ], @@ -360,10 +360,10 @@ "id": "designed-condition", "metadata": { "execution": { - "iopub.execute_input": "2024-03-19T10:01:43.771095Z", - "iopub.status.busy": "2024-03-19T10:01:43.770716Z", - "iopub.status.idle": "2024-03-19T10:01:43.774857Z", - "shell.execute_reply": "2024-03-19T10:01:43.774220Z" + "iopub.execute_input": "2024-03-19T10:19:56.277921Z", + "iopub.status.busy": "2024-03-19T10:19:56.277484Z", + "iopub.status.idle": "2024-03-19T10:19:56.281731Z", + "shell.execute_reply": "2024-03-19T10:19:56.281046Z" } }, "outputs": [ @@ -434,7 +434,7 @@ " \"unbias_target\": true,\n", " \"seconds_per_mixer\": 21384.0,\n", " \"seconds_per_encoder\": 85536.0,\n", - " \"expected_additional_time\": 15.479900598526001,\n", + " \"expected_additional_time\": 15.515635967254639,\n", " \"time_aim\": 259200,\n", " \"target_weights\": null,\n", " \"positive_domain\": false,\n", @@ -518,10 +518,10 @@ "id": "325d8f1b", "metadata": { "execution": { - "iopub.execute_input": "2024-03-19T10:01:43.777689Z", - "iopub.status.busy": "2024-03-19T10:01:43.777267Z", - "iopub.status.idle": "2024-03-19T10:01:43.782425Z", - "shell.execute_reply": "2024-03-19T10:01:43.781792Z" + "iopub.execute_input": "2024-03-19T10:19:56.284376Z", + "iopub.status.busy": "2024-03-19T10:19:56.284179Z", + "iopub.status.idle": "2024-03-19T10:19:56.289596Z", + "shell.execute_reply": "2024-03-19T10:19:56.288979Z" } }, "outputs": [ @@ -646,10 +646,10 @@ "id": "f030f8ca", "metadata": { "execution": { - "iopub.execute_input": "2024-03-19T10:01:43.785011Z", - "iopub.status.busy": "2024-03-19T10:01:43.784642Z", - "iopub.status.idle": "2024-03-19T10:01:43.787855Z", - "shell.execute_reply": "2024-03-19T10:01:43.787217Z" + "iopub.execute_input": "2024-03-19T10:19:56.292040Z", + "iopub.status.busy": "2024-03-19T10:19:56.291667Z", + "iopub.status.idle": "2024-03-19T10:19:56.294751Z", + "shell.execute_reply": "2024-03-19T10:19:56.294232Z" } }, "outputs": [], @@ -711,10 +711,10 @@ "id": "floating-patent", "metadata": { "execution": { - "iopub.execute_input": "2024-03-19T10:01:43.790373Z", - "iopub.status.busy": "2024-03-19T10:01:43.790020Z", - "iopub.status.idle": "2024-03-19T10:01:43.999248Z", - "shell.execute_reply": "2024-03-19T10:01:43.998578Z" + "iopub.execute_input": "2024-03-19T10:19:56.297253Z", + "iopub.status.busy": "2024-03-19T10:19:56.296883Z", + "iopub.status.idle": "2024-03-19T10:19:56.504807Z", + "shell.execute_reply": "2024-03-19T10:19:56.504139Z" } }, "outputs": [ @@ -795,7 +795,7 @@ " \"unbias_target\": True,\n", " \"seconds_per_mixer\": 21384.0,\n", " \"seconds_per_encoder\": 85536.0,\n", - " \"expected_additional_time\": 15.479900598526001,\n", + " \"expected_additional_time\": 15.515635967254639,\n", " \"time_aim\": 259200,\n", " \"target_weights\": None,\n", " \"positive_domain\": False,\n", @@ -825,7 +825,7 @@ " self.accuracy_functions = [\"r2_score\"]\n", " self.identifiers = {\"id\": \"Hash-like identifier\"}\n", " self.dtype_dict = {\"excerpt\": \"rich_text\", \"target\": \"float\"}\n", - " self.lightwood_version = \"24.3.3.1\"\n", + " self.lightwood_version = \"24.3.3.0\"\n", " self.pred_args = PredictionArguments()\n", "\n", " # Any feature-column dependencies\n", @@ -1449,10 +1449,10 @@ "id": "violent-guard", "metadata": { "execution": { - "iopub.execute_input": "2024-03-19T10:01:44.002116Z", - "iopub.status.busy": "2024-03-19T10:01:44.001616Z", - "iopub.status.idle": "2024-03-19T10:01:44.009223Z", - "shell.execute_reply": "2024-03-19T10:01:44.008614Z" + "iopub.execute_input": "2024-03-19T10:19:56.507600Z", + "iopub.status.busy": "2024-03-19T10:19:56.507376Z", + "iopub.status.idle": "2024-03-19T10:19:56.515176Z", + "shell.execute_reply": "2024-03-19T10:19:56.514556Z" } }, "outputs": [], @@ -1467,10 +1467,10 @@ "id": "closing-episode", "metadata": { "execution": { - "iopub.execute_input": "2024-03-19T10:01:44.011918Z", - "iopub.status.busy": "2024-03-19T10:01:44.011529Z", - "iopub.status.idle": "2024-03-19T10:01:44.145820Z", - "shell.execute_reply": "2024-03-19T10:01:44.145162Z" + "iopub.execute_input": "2024-03-19T10:19:56.517713Z", + "iopub.status.busy": "2024-03-19T10:19:56.517509Z", + "iopub.status.idle": "2024-03-19T10:19:56.648899Z", + "shell.execute_reply": "2024-03-19T10:19:56.648351Z" }, "scrolled": false }, @@ -1479,70 +1479,70 @@ "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:dataprep_ml-2670:Starting statistical analysis\u001b[0m\n" + "\u001b[32mINFO:dataprep_ml-2943:Starting statistical analysis\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:dataprep_ml-2670:Dropping features: ['id']\u001b[0m\n" + "\u001b[32mINFO:dataprep_ml-2943:Dropping features: ['id']\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:dataprep_ml-2670:Finished statistical analysis\u001b[0m\n" + "\u001b[32mINFO:dataprep_ml-2943:Finished statistical analysis\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[37mDEBUG:lightwood-2670: `analyze_data` runtime: 0.05 seconds\u001b[0m\n" + "\u001b[37mDEBUG:lightwood-2943: `analyze_data` runtime: 0.05 seconds\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:dataprep_ml-2670:Cleaning the data\u001b[0m\n" + "\u001b[32mINFO:dataprep_ml-2943:Cleaning the data\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:dataprep_ml-2670:Dropping features: ['id']\u001b[0m\n" + "\u001b[32mINFO:dataprep_ml-2943:Dropping features: ['id']\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:lightwood-2670:Cleaning column =excerpt\u001b[0m\n" + "\u001b[32mINFO:lightwood-2943:Cleaning column =excerpt\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:lightwood-2670:Cleaning column =target\u001b[0m\n" + "\u001b[32mINFO:lightwood-2943:Cleaning column =target\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:lightwood-2670:Converted target into strictly non-negative\u001b[0m\n" + "\u001b[32mINFO:lightwood-2943:Converted target into strictly non-negative\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[37mDEBUG:lightwood-2670: `preprocess` runtime: 0.07 seconds\u001b[0m\n" + "\u001b[37mDEBUG:lightwood-2943: `preprocess` runtime: 0.07 seconds\u001b[0m\n" ] }, { @@ -1632,10 +1632,10 @@ "id": "major-stake", "metadata": { "execution": { - "iopub.execute_input": "2024-03-19T10:01:44.148552Z", - "iopub.status.busy": "2024-03-19T10:01:44.148159Z", - "iopub.status.idle": "2024-03-19T10:01:44.152736Z", - "shell.execute_reply": "2024-03-19T10:01:44.152083Z" + "iopub.execute_input": "2024-03-19T10:19:56.651477Z", + "iopub.status.busy": "2024-03-19T10:19:56.651208Z", + "iopub.status.idle": "2024-03-19T10:19:56.655938Z", + "shell.execute_reply": "2024-03-19T10:19:56.655250Z" } }, "outputs": [ diff --git a/_sources/tutorials/custom_encoder_rulebased/custom_encoder_rulebased.ipynb.txt b/_sources/tutorials/custom_encoder_rulebased/custom_encoder_rulebased.ipynb.txt index 3d78b315d..48cb8219f 100644 --- a/_sources/tutorials/custom_encoder_rulebased/custom_encoder_rulebased.ipynb.txt +++ b/_sources/tutorials/custom_encoder_rulebased/custom_encoder_rulebased.ipynb.txt @@ -43,10 +43,10 @@ "id": "raising-adventure", "metadata": { "execution": { - "iopub.execute_input": "2024-03-19T10:01:05.526289Z", - "iopub.status.busy": "2024-03-19T10:01:05.526091Z", - "iopub.status.idle": "2024-03-19T10:01:08.110087Z", - "shell.execute_reply": "2024-03-19T10:01:08.109384Z" + "iopub.execute_input": "2024-03-19T10:19:18.781052Z", + "iopub.status.busy": "2024-03-19T10:19:18.780864Z", + "iopub.status.idle": "2024-03-19T10:19:21.287724Z", + "shell.execute_reply": "2024-03-19T10:19:21.287069Z" } }, "outputs": [ @@ -54,14 +54,14 @@ "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:lightwood-2588:No torchvision detected, image helpers not supported.\u001b[0m\n" + "\u001b[32mINFO:lightwood-2865:No torchvision detected, image helpers not supported.\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:lightwood-2588:No torchvision/pillow detected, image encoder not supported\u001b[0m\n" + "\u001b[32mINFO:lightwood-2865:No torchvision/pillow detected, image encoder not supported\u001b[0m\n" ] } ], @@ -93,10 +93,10 @@ "id": "technical-government", "metadata": { "execution": { - "iopub.execute_input": "2024-03-19T10:01:08.113518Z", - "iopub.status.busy": "2024-03-19T10:01:08.112978Z", - "iopub.status.idle": "2024-03-19T10:01:08.570602Z", - "shell.execute_reply": "2024-03-19T10:01:08.569921Z" + "iopub.execute_input": "2024-03-19T10:19:21.290994Z", + "iopub.status.busy": "2024-03-19T10:19:21.290532Z", + "iopub.status.idle": "2024-03-19T10:19:21.602551Z", + "shell.execute_reply": "2024-03-19T10:19:21.601915Z" } }, "outputs": [ @@ -243,10 +243,10 @@ "id": "absent-maker", "metadata": { "execution": { - "iopub.execute_input": "2024-03-19T10:01:08.573521Z", - "iopub.status.busy": "2024-03-19T10:01:08.573084Z", - "iopub.status.idle": "2024-03-19T10:01:19.617025Z", - "shell.execute_reply": "2024-03-19T10:01:19.616272Z" + "iopub.execute_input": "2024-03-19T10:19:21.605129Z", + "iopub.status.busy": "2024-03-19T10:19:21.604941Z", + "iopub.status.idle": "2024-03-19T10:19:32.650571Z", + "shell.execute_reply": "2024-03-19T10:19:32.649852Z" } }, "outputs": [ @@ -254,161 +254,161 @@ "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:type_infer-2588:Analyzing a sample of 6920\u001b[0m\n" + "\u001b[32mINFO:type_infer-2865:Analyzing a sample of 6920\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:type_infer-2588:from a total population of 10668, this is equivalent to 64.9% of your data.\u001b[0m\n" + "\u001b[32mINFO:type_infer-2865:from a total population of 10668, this is equivalent to 64.9% of your data.\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:type_infer-2588:Using 3 processes to deduct types.\u001b[0m\n" + "\u001b[32mINFO:type_infer-2865:Using 3 processes to deduct types.\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:type_infer-2588:Infering type for: year\u001b[0m\n" + "\u001b[32mINFO:type_infer-2865:Infering type for: year\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:type_infer-2588:Infering type for: price\u001b[0m\n" + "\u001b[32mINFO:type_infer-2865:Infering type for: price\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:type_infer-2588:Column price has data type integer\u001b[0m\n" + "\u001b[32mINFO:type_infer-2865:Column year has data type integer\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:type_infer-2588:Column year has data type integer\u001b[0m\n" + "\u001b[32mINFO:type_infer-2865:Column price has data type integer\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:type_infer-2588:Infering type for: transmission\u001b[0m\n" + "\u001b[32mINFO:type_infer-2865:Infering type for: transmission\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:type_infer-2588:Infering type for: mileage\u001b[0m\n" + "\u001b[32mINFO:type_infer-2865:Infering type for: mileage\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:type_infer-2588:Column mileage has data type integer\u001b[0m\n" + "\u001b[32mINFO:type_infer-2865:Infering type for: model\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:type_infer-2588:Infering type for: fuelType\u001b[0m\n" + "\u001b[32mINFO:type_infer-2865:Column mileage has data type integer\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:type_infer-2588:Infering type for: model\u001b[0m\n" + "\u001b[32mINFO:type_infer-2865:Infering type for: fuelType\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:type_infer-2588:Column fuelType has data type categorical\u001b[0m\n" + "\u001b[32mINFO:type_infer-2865:Column model has data type categorical\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:type_infer-2588:Infering type for: tax\u001b[0m\n" + "\u001b[32mINFO:type_infer-2865:Infering type for: tax\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:type_infer-2588:Column tax has data type integer\u001b[0m\n" + "\u001b[32mINFO:type_infer-2865:Column tax has data type integer\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:type_infer-2588:Infering type for: mpg\u001b[0m\n" + "\u001b[32mINFO:type_infer-2865:Infering type for: mpg\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:type_infer-2588:Column mpg has data type float\u001b[0m\n" + "\u001b[32mINFO:type_infer-2865:Column mpg has data type float\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:type_infer-2588:Infering type for: engineSize\u001b[0m\n" + "\u001b[32mINFO:type_infer-2865:Infering type for: engineSize\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:type_infer-2588:Column engineSize has data type float\u001b[0m\n" + "\u001b[32mINFO:type_infer-2865:Column engineSize has data type float\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:type_infer-2588:Column transmission has data type categorical\u001b[0m\n" + "\u001b[32mINFO:type_infer-2865:Column transmission has data type categorical\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:type_infer-2588:Column model has data type categorical\u001b[0m\n" + "\u001b[32mINFO:type_infer-2865:Column fuelType has data type categorical\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:dataprep_ml-2588:Starting statistical analysis\u001b[0m\n" + "\u001b[32mINFO:dataprep_ml-2865:Starting statistical analysis\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:dataprep_ml-2588:Finished statistical analysis\u001b[0m\n" + "\u001b[32mINFO:dataprep_ml-2865:Finished statistical analysis\u001b[0m\n" ] } ], @@ -437,10 +437,10 @@ "id": "coastal-paragraph", "metadata": { "execution": { - "iopub.execute_input": "2024-03-19T10:01:19.620277Z", - "iopub.status.busy": "2024-03-19T10:01:19.620037Z", - "iopub.status.idle": "2024-03-19T10:01:19.625297Z", - "shell.execute_reply": "2024-03-19T10:01:19.624612Z" + "iopub.execute_input": "2024-03-19T10:19:32.653652Z", + "iopub.status.busy": "2024-03-19T10:19:32.653387Z", + "iopub.status.idle": "2024-03-19T10:19:32.657936Z", + "shell.execute_reply": "2024-03-19T10:19:32.657299Z" } }, "outputs": [ @@ -545,7 +545,7 @@ " \"unbias_target\": true,\n", " \"seconds_per_mixer\": 21384.0,\n", " \"seconds_per_encoder\": 85536.0,\n", - " \"expected_additional_time\": 11.03162956237793,\n", + " \"expected_additional_time\": 11.033989667892456,\n", " \"time_aim\": 259200,\n", " \"target_weights\": null,\n", " \"positive_domain\": false,\n", @@ -643,10 +643,10 @@ "id": "e03db1b0", "metadata": { "execution": { - "iopub.execute_input": "2024-03-19T10:01:19.627937Z", - "iopub.status.busy": "2024-03-19T10:01:19.627718Z", - "iopub.status.idle": "2024-03-19T10:01:19.633288Z", - "shell.execute_reply": "2024-03-19T10:01:19.632627Z" + "iopub.execute_input": "2024-03-19T10:19:32.660713Z", + "iopub.status.busy": "2024-03-19T10:19:32.660350Z", + "iopub.status.idle": "2024-03-19T10:19:32.665576Z", + "shell.execute_reply": "2024-03-19T10:19:32.664956Z" } }, "outputs": [ @@ -766,10 +766,10 @@ "id": "e30866c1", "metadata": { "execution": { - "iopub.execute_input": "2024-03-19T10:01:19.635995Z", - "iopub.status.busy": "2024-03-19T10:01:19.635568Z", - "iopub.status.idle": "2024-03-19T10:01:19.638949Z", - "shell.execute_reply": "2024-03-19T10:01:19.638424Z" + "iopub.execute_input": "2024-03-19T10:19:32.667988Z", + "iopub.status.busy": "2024-03-19T10:19:32.667682Z", + "iopub.status.idle": "2024-03-19T10:19:32.670900Z", + "shell.execute_reply": "2024-03-19T10:19:32.670280Z" } }, "outputs": [], @@ -828,10 +828,10 @@ "id": "elementary-fusion", "metadata": { "execution": { - "iopub.execute_input": "2024-03-19T10:01:19.641687Z", - "iopub.status.busy": "2024-03-19T10:01:19.641294Z", - "iopub.status.idle": "2024-03-19T10:01:19.644573Z", - "shell.execute_reply": "2024-03-19T10:01:19.644028Z" + "iopub.execute_input": "2024-03-19T10:19:32.673575Z", + "iopub.status.busy": "2024-03-19T10:19:32.673201Z", + "iopub.status.idle": "2024-03-19T10:19:32.676378Z", + "shell.execute_reply": "2024-03-19T10:19:32.675835Z" } }, "outputs": [], @@ -857,10 +857,10 @@ "id": "inappropriate-james", "metadata": { "execution": { - "iopub.execute_input": "2024-03-19T10:01:19.647157Z", - "iopub.status.busy": "2024-03-19T10:01:19.646820Z", - "iopub.status.idle": "2024-03-19T10:01:20.020948Z", - "shell.execute_reply": "2024-03-19T10:01:20.020282Z" + "iopub.execute_input": "2024-03-19T10:19:32.678830Z", + "iopub.status.busy": "2024-03-19T10:19:32.678489Z", + "iopub.status.idle": "2024-03-19T10:19:33.022380Z", + "shell.execute_reply": "2024-03-19T10:19:33.021731Z" } }, "outputs": [], @@ -891,10 +891,10 @@ "id": "palestinian-harvey", "metadata": { "execution": { - "iopub.execute_input": "2024-03-19T10:01:20.024242Z", - "iopub.status.busy": "2024-03-19T10:01:20.023718Z", - "iopub.status.idle": "2024-03-19T10:01:21.197476Z", - "shell.execute_reply": "2024-03-19T10:01:21.196767Z" + "iopub.execute_input": "2024-03-19T10:19:33.025713Z", + "iopub.status.busy": "2024-03-19T10:19:33.025246Z", + "iopub.status.idle": "2024-03-19T10:19:34.232444Z", + "shell.execute_reply": "2024-03-19T10:19:34.231782Z" } }, "outputs": [ @@ -902,133 +902,133 @@ "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:dataprep_ml-2588:Starting statistical analysis\u001b[0m\n" + "\u001b[32mINFO:dataprep_ml-2865:Starting statistical analysis\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:dataprep_ml-2588:Finished statistical analysis\u001b[0m\n" + "\u001b[32mINFO:dataprep_ml-2865:Finished statistical analysis\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[37mDEBUG:lightwood-2588: `analyze_data` runtime: 0.44 seconds\u001b[0m\n" + "\u001b[37mDEBUG:lightwood-2865: `analyze_data` runtime: 0.44 seconds\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:dataprep_ml-2588:Cleaning the data\u001b[0m\n" + "\u001b[32mINFO:dataprep_ml-2865:Cleaning the data\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[37mDEBUG:lightwood-2588: `preprocess` runtime: 0.13 seconds\u001b[0m\n" + "\u001b[37mDEBUG:lightwood-2865: `preprocess` runtime: 0.14 seconds\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:dataprep_ml-2588:Splitting the data into train/test\u001b[0m\n" + "\u001b[32mINFO:dataprep_ml-2865:Splitting the data into train/test\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[37mDEBUG:lightwood-2588: `split` runtime: 0.0 seconds\u001b[0m\n" + "\u001b[37mDEBUG:lightwood-2865: `split` runtime: 0.0 seconds\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[37mDEBUG:dataprep_ml-2588:Preparing sequentially...\u001b[0m\n" + "\u001b[37mDEBUG:dataprep_ml-2865:Preparing sequentially...\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[37mDEBUG:dataprep_ml-2588:Preparing encoder for year...\u001b[0m\n" + "\u001b[37mDEBUG:dataprep_ml-2865:Preparing encoder for year...\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[37mDEBUG:dataprep_ml-2588:Preparing encoder for mileage...\u001b[0m\n" + "\u001b[37mDEBUG:dataprep_ml-2865:Preparing encoder for mileage...\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[37mDEBUG:dataprep_ml-2588:Preparing encoder for tax...\u001b[0m\n" + "\u001b[37mDEBUG:dataprep_ml-2865:Preparing encoder for tax...\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[37mDEBUG:dataprep_ml-2588:Preparing encoder for mpg...\u001b[0m\n" + "\u001b[37mDEBUG:dataprep_ml-2865:Preparing encoder for mpg...\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[37mDEBUG:dataprep_ml-2588:Preparing encoder for engineSize...\u001b[0m\n" + "\u001b[37mDEBUG:dataprep_ml-2865:Preparing encoder for engineSize...\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:lightwood-2588:Categories Detected = 1\u001b[0m\n" + "\u001b[32mINFO:lightwood-2865:Categories Detected = 1\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:lightwood-2588:Categories Detected = 1\u001b[0m\n" + "\u001b[32mINFO:lightwood-2865:Categories Detected = 1\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:lightwood-2588:Categories Detected = 1\u001b[0m\n" + "\u001b[32mINFO:lightwood-2865:Categories Detected = 1\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[37mDEBUG:lightwood-2588: `prepare` runtime: 0.02 seconds\u001b[0m\n" + "\u001b[37mDEBUG:lightwood-2865: `prepare` runtime: 0.01 seconds\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:dataprep_ml-2588:Featurizing the data\u001b[0m\n" + "\u001b[32mINFO:dataprep_ml-2865:Featurizing the data\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[37mDEBUG:lightwood-2588: `featurize` runtime: 0.57 seconds\u001b[0m\n" + "\u001b[37mDEBUG:lightwood-2865: `featurize` runtime: 0.6 seconds\u001b[0m\n" ] } ], @@ -1063,10 +1063,10 @@ "id": "silent-dealing", "metadata": { "execution": { - "iopub.execute_input": "2024-03-19T10:01:21.200339Z", - "iopub.status.busy": "2024-03-19T10:01:21.200121Z", - "iopub.status.idle": "2024-03-19T10:01:21.209729Z", - "shell.execute_reply": "2024-03-19T10:01:21.209058Z" + "iopub.execute_input": "2024-03-19T10:19:34.235295Z", + "iopub.status.busy": "2024-03-19T10:19:34.234902Z", + "iopub.status.idle": "2024-03-19T10:19:34.243659Z", + "shell.execute_reply": "2024-03-19T10:19:34.243047Z" } }, "outputs": [ @@ -1168,10 +1168,10 @@ "id": "superior-mobility", "metadata": { "execution": { - "iopub.execute_input": "2024-03-19T10:01:21.212465Z", - "iopub.status.busy": "2024-03-19T10:01:21.212022Z", - "iopub.status.idle": "2024-03-19T10:01:21.215924Z", - "shell.execute_reply": "2024-03-19T10:01:21.215168Z" + "iopub.execute_input": "2024-03-19T10:19:34.246255Z", + "iopub.status.busy": "2024-03-19T10:19:34.245806Z", + "iopub.status.idle": "2024-03-19T10:19:34.249222Z", + "shell.execute_reply": "2024-03-19T10:19:34.248595Z" } }, "outputs": [ diff --git a/_sources/tutorials/custom_explainer/custom_explainer.ipynb.txt b/_sources/tutorials/custom_explainer/custom_explainer.ipynb.txt index eecd2b95e..83308e7f0 100644 --- a/_sources/tutorials/custom_explainer/custom_explainer.ipynb.txt +++ b/_sources/tutorials/custom_explainer/custom_explainer.ipynb.txt @@ -30,10 +30,10 @@ "execution_count": 1, "metadata": { "execution": { - "iopub.execute_input": "2024-03-19T10:00:15.559135Z", - "iopub.status.busy": "2024-03-19T10:00:15.558584Z", - "iopub.status.idle": "2024-03-19T10:00:19.257005Z", - "shell.execute_reply": "2024-03-19T10:00:19.256212Z" + "iopub.execute_input": "2024-03-19T10:18:29.526658Z", + "iopub.status.busy": "2024-03-19T10:18:29.526465Z", + "iopub.status.idle": "2024-03-19T10:18:33.446169Z", + "shell.execute_reply": "2024-03-19T10:18:33.445420Z" } }, "outputs": [ @@ -49,20 +49,20 @@ "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:lightwood-2252:No torchvision detected, image helpers not supported.\u001b[0m\n" + "\u001b[32mINFO:lightwood-2540:No torchvision detected, image helpers not supported.\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:lightwood-2252:No torchvision/pillow detected, image encoder not supported\u001b[0m\n" + "\u001b[32mINFO:lightwood-2540:No torchvision/pillow detected, image encoder not supported\u001b[0m\n" ] }, { "data": { "text/plain": [ - "'24.3.3.1'" + "'24.3.3.0'" ] }, "execution_count": 1, @@ -95,10 +95,10 @@ "execution_count": 2, "metadata": { "execution": { - "iopub.execute_input": "2024-03-19T10:00:19.260083Z", - "iopub.status.busy": "2024-03-19T10:00:19.259562Z", - "iopub.status.idle": "2024-03-19T10:00:19.288162Z", - "shell.execute_reply": "2024-03-19T10:00:19.287634Z" + "iopub.execute_input": "2024-03-19T10:18:33.449296Z", + "iopub.status.busy": "2024-03-19T10:18:33.448773Z", + "iopub.status.idle": "2024-03-19T10:18:33.477488Z", + "shell.execute_reply": "2024-03-19T10:18:33.476992Z" } }, "outputs": [], @@ -124,17 +124,17 @@ "execution_count": 3, "metadata": { "execution": { - "iopub.execute_input": "2024-03-19T10:00:19.290619Z", - "iopub.status.busy": "2024-03-19T10:00:19.290230Z", - "iopub.status.idle": "2024-03-19T10:00:19.294294Z", - "shell.execute_reply": "2024-03-19T10:00:19.293649Z" + "iopub.execute_input": "2024-03-19T10:18:33.479802Z", + "iopub.status.busy": "2024-03-19T10:18:33.479458Z", + "iopub.status.idle": "2024-03-19T10:18:33.483414Z", + "shell.execute_reply": "2024-03-19T10:18:33.482792Z" } }, "outputs": [ { "data": { "text/plain": [ - "<__main__.ModelCorrelationHeatmap at 0x7e3a82a68b80>" + "<__main__.ModelCorrelationHeatmap at 0x7867834b34f0>" ] }, "execution_count": 3, @@ -160,10 +160,10 @@ "execution_count": 4, "metadata": { "execution": { - "iopub.execute_input": "2024-03-19T10:00:19.296816Z", - "iopub.status.busy": "2024-03-19T10:00:19.296435Z", - "iopub.status.idle": "2024-03-19T10:00:19.300040Z", - "shell.execute_reply": "2024-03-19T10:00:19.299392Z" + "iopub.execute_input": "2024-03-19T10:18:33.485704Z", + "iopub.status.busy": "2024-03-19T10:18:33.485508Z", + "iopub.status.idle": "2024-03-19T10:18:33.489342Z", + "shell.execute_reply": "2024-03-19T10:18:33.488807Z" } }, "outputs": [], @@ -192,10 +192,10 @@ "execution_count": 5, "metadata": { "execution": { - "iopub.execute_input": "2024-03-19T10:00:19.302574Z", - "iopub.status.busy": "2024-03-19T10:00:19.302202Z", - "iopub.status.idle": "2024-03-19T10:00:19.305830Z", - "shell.execute_reply": "2024-03-19T10:00:19.305214Z" + "iopub.execute_input": "2024-03-19T10:18:33.491908Z", + "iopub.status.busy": "2024-03-19T10:18:33.491538Z", + "iopub.status.idle": "2024-03-19T10:18:33.495137Z", + "shell.execute_reply": "2024-03-19T10:18:33.494525Z" } }, "outputs": [], @@ -238,10 +238,10 @@ "execution_count": 6, "metadata": { "execution": { - "iopub.execute_input": "2024-03-19T10:00:19.308255Z", - "iopub.status.busy": "2024-03-19T10:00:19.307880Z", - "iopub.status.idle": "2024-03-19T10:00:19.312478Z", - "shell.execute_reply": "2024-03-19T10:00:19.311865Z" + "iopub.execute_input": "2024-03-19T10:18:33.497660Z", + "iopub.status.busy": "2024-03-19T10:18:33.497270Z", + "iopub.status.idle": "2024-03-19T10:18:33.501828Z", + "shell.execute_reply": "2024-03-19T10:18:33.501275Z" } }, "outputs": [ @@ -335,10 +335,10 @@ "execution_count": 7, "metadata": { "execution": { - "iopub.execute_input": "2024-03-19T10:00:19.315118Z", - "iopub.status.busy": "2024-03-19T10:00:19.314617Z", - "iopub.status.idle": "2024-03-19T10:00:19.600156Z", - "shell.execute_reply": "2024-03-19T10:00:19.599496Z" + "iopub.execute_input": "2024-03-19T10:18:33.504364Z", + "iopub.status.busy": "2024-03-19T10:18:33.503988Z", + "iopub.status.idle": "2024-03-19T10:18:33.633265Z", + "shell.execute_reply": "2024-03-19T10:18:33.632631Z" } }, "outputs": [ @@ -346,126 +346,126 @@ "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:type_infer-2252:Analyzing a sample of 222\u001b[0m\n" + "\u001b[32mINFO:type_infer-2540:Analyzing a sample of 222\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:type_infer-2252:from a total population of 225, this is equivalent to 98.7% of your data.\u001b[0m\n" + "\u001b[32mINFO:type_infer-2540:from a total population of 225, this is equivalent to 98.7% of your data.\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:type_infer-2252:Infering type for: Population\u001b[0m\n" + "\u001b[32mINFO:type_infer-2540:Infering type for: Population\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:type_infer-2252:Column Population has data type integer\u001b[0m\n" + "\u001b[32mINFO:type_infer-2540:Column Population has data type integer\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:type_infer-2252:Infering type for: Area (sq. mi.)\u001b[0m\n" + "\u001b[32mINFO:type_infer-2540:Infering type for: Area (sq. mi.)\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:type_infer-2252:Column Area (sq. mi.) has data type integer\u001b[0m\n" + "\u001b[32mINFO:type_infer-2540:Column Area (sq. mi.) has data type integer\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:type_infer-2252:Infering type for: Pop. Density \u001b[0m\n" + "\u001b[32mINFO:type_infer-2540:Infering type for: Pop. Density \u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:type_infer-2252:Column Pop. Density has data type float\u001b[0m\n" + "\u001b[32mINFO:type_infer-2540:Column Pop. Density has data type float\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:type_infer-2252:Infering type for: GDP ($ per capita)\u001b[0m\n" + "\u001b[32mINFO:type_infer-2540:Infering type for: GDP ($ per capita)\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:type_infer-2252:Column GDP ($ per capita) has data type integer\u001b[0m\n" + "\u001b[32mINFO:type_infer-2540:Column GDP ($ per capita) has data type integer\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:type_infer-2252:Infering type for: Literacy (%)\u001b[0m\n" + "\u001b[32mINFO:type_infer-2540:Infering type for: Literacy (%)\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:type_infer-2252:Column Literacy (%) has data type float\u001b[0m\n" + "\u001b[32mINFO:type_infer-2540:Column Literacy (%) has data type float\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:type_infer-2252:Infering type for: Infant mortality \u001b[0m\n" + "\u001b[32mINFO:type_infer-2540:Infering type for: Infant mortality \u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:type_infer-2252:Column Infant mortality has data type float\u001b[0m\n" + "\u001b[32mINFO:type_infer-2540:Column Infant mortality has data type float\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:type_infer-2252:Infering type for: Development Index\u001b[0m\n" + "\u001b[32mINFO:type_infer-2540:Infering type for: Development Index\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:type_infer-2252:Column Development Index has data type categorical\u001b[0m\n" + "\u001b[32mINFO:type_infer-2540:Column Development Index has data type categorical\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:dataprep_ml-2252:Starting statistical analysis\u001b[0m\n" + "\u001b[32mINFO:dataprep_ml-2540:Starting statistical analysis\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:dataprep_ml-2252:Finished statistical analysis\u001b[0m\n" + "\u001b[32mINFO:dataprep_ml-2540:Finished statistical analysis\u001b[0m\n" ] } ], @@ -506,10 +506,10 @@ "execution_count": 8, "metadata": { "execution": { - "iopub.execute_input": "2024-03-19T10:00:19.603006Z", - "iopub.status.busy": "2024-03-19T10:00:19.602470Z", - "iopub.status.idle": "2024-03-19T10:00:19.606780Z", - "shell.execute_reply": "2024-03-19T10:00:19.606159Z" + "iopub.execute_input": "2024-03-19T10:18:33.636041Z", + "iopub.status.busy": "2024-03-19T10:18:33.635608Z", + "iopub.status.idle": "2024-03-19T10:18:33.639918Z", + "shell.execute_reply": "2024-03-19T10:18:33.639318Z" } }, "outputs": [ @@ -540,10 +540,10 @@ "execution_count": 9, "metadata": { "execution": { - "iopub.execute_input": "2024-03-19T10:00:19.609412Z", - "iopub.status.busy": "2024-03-19T10:00:19.608955Z", - "iopub.status.idle": "2024-03-19T10:00:25.098019Z", - "shell.execute_reply": "2024-03-19T10:00:25.097446Z" + "iopub.execute_input": "2024-03-19T10:18:33.642288Z", + "iopub.status.busy": "2024-03-19T10:18:33.641938Z", + "iopub.status.idle": "2024-03-19T10:18:39.783485Z", + "shell.execute_reply": "2024-03-19T10:18:39.782886Z" }, "scrolled": false }, @@ -552,182 +552,182 @@ "name": "stderr", "output_type": "stream", "text": [ - 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"\u001b[32mINFO:lightwood-2252:XGBoost mixer does not have a `partial_fit` implementation\u001b[0m\n" + "\u001b[32mINFO:lightwood-2540:XGBoost mixer does not have a `partial_fit` implementation\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[37mDEBUG:lightwood-2252: `fit_mixer` runtime: 0.05 seconds\u001b[0m\n" + "\u001b[37mDEBUG:lightwood-2540: `fit_mixer` runtime: 0.06 seconds\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[33mWARNING:dataprep_ml-2252:Exception: Unspported categorical type for regression when training mixer: \u001b[0m\n" + "\u001b[33mWARNING:dataprep_ml-2540:Exception: Unspported categorical type for regression when training mixer: \u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:lightwood-2252:Started fitting RandomForest model\u001b[0m\n" + "\u001b[32mINFO:lightwood-2540:Started fitting RandomForest model\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:lightwood-2252:RandomForest based correlation of (train data): 1.0\u001b[0m\n" + "\u001b[32mINFO:lightwood-2540:RandomForest based correlation of (train data): 1.0\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:lightwood-2252:RandomForest based correlation of (dev data): 1.0\u001b[0m\n" + "\u001b[32mINFO:lightwood-2540:RandomForest based correlation of (dev data): 1.0\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[37mDEBUG:lightwood-2252: `fit_mixer` runtime: 0.11 seconds\u001b[0m\n" + "\u001b[37mDEBUG:lightwood-2540: `fit_mixer` runtime: 0.6 seconds\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:dataprep_ml-2252:Ensembling the mixer\u001b[0m\n" + "\u001b[32mINFO:dataprep_ml-2540:Ensembling the mixer\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:lightwood-2252:Mixer: Neural got accuracy: 0.922\u001b[0m\n" + "\u001b[32mINFO:lightwood-2540:Mixer: Neural got accuracy: 0.922\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:lightwood-2252:Mixer: XGBoostMixer got accuracy: 1.0\u001b[0m\n" + "\u001b[32mINFO:lightwood-2540:Mixer: XGBoostMixer got accuracy: 1.0\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:lightwood-2252:Mixer: RandomForest got accuracy: 1.0\u001b[0m\n" + "\u001b[32mINFO:lightwood-2540:Mixer: RandomForest got accuracy: 1.0\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:lightwood-2252:Picked best mixer: RandomForest\u001b[0m\n" + "\u001b[32mINFO:lightwood-2540:Picked best mixer: RandomForest\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[37mDEBUG:lightwood-2252: `fit` runtime: 4.81 seconds\u001b[0m\n" + "\u001b[37mDEBUG:lightwood-2540: `fit` runtime: 5.46 seconds\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:dataprep_ml-2252:[Learn phase 7/8] - Ensemble analysis\u001b[0m\n" + "\u001b[32mINFO:dataprep_ml-2540:[Learn phase 7/8] - Ensemble analysis\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:dataprep_ml-2252:Analyzing the ensemble of mixers\u001b[0m\n" + "\u001b[32mINFO:dataprep_ml-2540:Analyzing the ensemble of mixers\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:lightwood-2252:The block ICP is now running its analyze() method\u001b[0m\n" + "\u001b[32mINFO:lightwood-2540:The block ICP is now running its analyze() method\u001b[0m\n" ] }, { @@ -5084,63 +5084,63 @@ "text": [ "/opt/hostedtoolcache/Python/3.9.18/x64/lib/python3.9/site-packages/sklearn/preprocessing/_encoders.py:975: FutureWarning: `sparse` was renamed to `sparse_output` in version 1.2 and will be removed in 1.4. `sparse_output` is ignored unless you leave `sparse` to its default value.\n", " warnings.warn(\n", - "\u001b[32mINFO:lightwood-2252:The block ConfStats is now running its analyze() method\u001b[0m\n" + "\u001b[32mINFO:lightwood-2540:The block ConfStats is now running its analyze() method\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:lightwood-2252:The block AccStats is now running its analyze() method\u001b[0m\n" + "\u001b[32mINFO:lightwood-2540:The block AccStats is now running its analyze() method\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:lightwood-2252:The block PermutationFeatureImportance is now running its analyze() method\u001b[0m\n" + "\u001b[32mINFO:lightwood-2540:The block PermutationFeatureImportance is now running its analyze() method\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:lightwood-2252:[PFI] Using a random sample (1000 rows out of 22).\u001b[0m\n" + "\u001b[32mINFO:lightwood-2540:[PFI] Using a random sample (1000 rows out of 22).\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:lightwood-2252:[PFI] Set to consider first 10 columns out of 6: ['Population', 'Area (sq. mi.)', 'Pop. Density ', 'GDP ($ per capita)', 'Literacy (%)', 'Infant mortality '].\u001b[0m\n" + "\u001b[32mINFO:lightwood-2540:[PFI] Set to consider first 10 columns out of 6: ['Population', 'Area (sq. mi.)', 'Pop. Density ', 'GDP ($ per capita)', 'Literacy (%)', 'Infant mortality '].\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:lightwood-2252:The block ModelCorrelationHeatmap is now running its analyze() method\u001b[0m\n" + "\u001b[32mINFO:lightwood-2540:The block ModelCorrelationHeatmap is now running its analyze() method\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[37mDEBUG:lightwood-2252: `analyze_ensemble` runtime: 0.2 seconds\u001b[0m\n" + "\u001b[37mDEBUG:lightwood-2540: `analyze_ensemble` runtime: 0.2 seconds\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:dataprep_ml-2252:[Learn phase 8/8] - Adjustment on validation requested\u001b[0m\n" + "\u001b[32mINFO:dataprep_ml-2540:[Learn phase 8/8] - Adjustment on validation requested\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:dataprep_ml-2252:Updating the mixers\u001b[0m\n" + "\u001b[32mINFO:dataprep_ml-2540:Updating the mixers\u001b[0m\n" ] }, { @@ -5148,77 +5148,71 @@ "output_type": "stream", "text": [ "/opt/hostedtoolcache/Python/3.9.18/x64/lib/python3.9/site-packages/torch/cuda/amp/grad_scaler.py:126: UserWarning: torch.cuda.amp.GradScaler is enabled, but CUDA is not available. Disabling.\n", - " warnings.warn(\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "\u001b[32mINFO:lightwood-2252:Loss @ epoch 1: 0.033697554686417185\u001b[0m\n" + " warnings.warn(\n", + "\u001b[32mINFO:lightwood-2540:Loss @ epoch 1: 0.033697554686417185\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:lightwood-2252:Loss @ epoch 2: 0.033981192080924906\u001b[0m\n" + "\u001b[32mINFO:lightwood-2540:Loss @ epoch 2: 0.033981192080924906\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:lightwood-2252:Loss @ epoch 3: 0.037426896315688886\u001b[0m\n" + "\u001b[32mINFO:lightwood-2540:Loss @ epoch 3: 0.037426896315688886\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:lightwood-2252:Loss @ epoch 4: 0.04428015494098266\u001b[0m\n" + "\u001b[32mINFO:lightwood-2540:Loss @ epoch 4: 0.04428015494098266\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:lightwood-2252:Loss @ epoch 5: 0.061086510928968586\u001b[0m\n" + "\u001b[32mINFO:lightwood-2540:Loss @ epoch 5: 0.061086510928968586\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:lightwood-2252:Loss @ epoch 6: 0.03466159128583968\u001b[0m\n" + "\u001b[32mINFO:lightwood-2540:Loss @ epoch 6: 0.03466159128583968\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:lightwood-2252:Loss @ epoch 7: 0.03769115870818496\u001b[0m\n" + "\u001b[32mINFO:lightwood-2540:Loss @ epoch 7: 0.03769115870818496\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:lightwood-2252:XGBoost mixer does not have a `partial_fit` implementation\u001b[0m\n" + "\u001b[32mINFO:lightwood-2540:XGBoost mixer does not have a `partial_fit` implementation\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[37mDEBUG:lightwood-2252: `adjust` runtime: 0.06 seconds\u001b[0m\n" + "\u001b[37mDEBUG:lightwood-2540: `adjust` runtime: 0.06 seconds\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[37mDEBUG:lightwood-2252: `learn` runtime: 5.17 seconds\u001b[0m\n" + "\u001b[37mDEBUG:lightwood-2540: `learn` runtime: 5.82 seconds\u001b[0m\n" ] } ], @@ -5243,10 +5237,10 @@ "execution_count": 10, "metadata": { "execution": { - "iopub.execute_input": "2024-03-19T10:00:25.100883Z", - "iopub.status.busy": "2024-03-19T10:00:25.100499Z", - "iopub.status.idle": "2024-03-19T10:00:25.908381Z", - "shell.execute_reply": "2024-03-19T10:00:25.907666Z" + "iopub.execute_input": "2024-03-19T10:18:39.786210Z", + "iopub.status.busy": "2024-03-19T10:18:39.785843Z", + "iopub.status.idle": "2024-03-19T10:18:40.609969Z", + "shell.execute_reply": "2024-03-19T10:18:40.609129Z" } }, "outputs": [ diff --git a/_sources/tutorials/custom_mixer/custom_mixer.ipynb.txt b/_sources/tutorials/custom_mixer/custom_mixer.ipynb.txt index 6e8465696..f3d0b2af7 100644 --- a/_sources/tutorials/custom_mixer/custom_mixer.ipynb.txt +++ b/_sources/tutorials/custom_mixer/custom_mixer.ipynb.txt @@ -46,10 +46,10 @@ "execution_count": 1, "metadata": { "execution": { - "iopub.execute_input": "2024-03-19T10:00:58.382482Z", - "iopub.status.busy": "2024-03-19T10:00:58.382268Z", - "iopub.status.idle": "2024-03-19T10:00:58.391179Z", - "shell.execute_reply": "2024-03-19T10:00:58.390533Z" + "iopub.execute_input": "2024-03-19T10:19:12.145057Z", + "iopub.status.busy": "2024-03-19T10:19:12.144662Z", + "iopub.status.idle": "2024-03-19T10:19:12.153051Z", + "shell.execute_reply": "2024-03-19T10:19:12.152456Z" } }, "outputs": [ @@ -133,10 +133,10 @@ "execution_count": 2, "metadata": { "execution": { - "iopub.execute_input": "2024-03-19T10:00:58.429652Z", - "iopub.status.busy": "2024-03-19T10:00:58.429170Z", - "iopub.status.idle": "2024-03-19T10:01:01.451092Z", - "shell.execute_reply": "2024-03-19T10:01:01.450296Z" + "iopub.execute_input": "2024-03-19T10:19:12.190696Z", + "iopub.status.busy": "2024-03-19T10:19:12.190280Z", + "iopub.status.idle": "2024-03-19T10:19:14.877692Z", + "shell.execute_reply": "2024-03-19T10:19:14.877086Z" } }, "outputs": [ @@ -144,238 +144,238 @@ "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:lightwood-2557:No torchvision detected, image helpers not supported.\u001b[0m\n" + "\u001b[32mINFO:lightwood-2835:No torchvision detected, image helpers not supported.\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:lightwood-2557:No torchvision/pillow detected, image encoder not supported\u001b[0m\n" + "\u001b[32mINFO:lightwood-2835:No torchvision/pillow detected, image encoder not supported\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:type_infer-2557:Analyzing a sample of 298\u001b[0m\n" + "\u001b[32mINFO:type_infer-2835:Analyzing a sample of 298\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:type_infer-2557:from a total population of 303, this is equivalent to 98.3% of your data.\u001b[0m\n" + "\u001b[32mINFO:type_infer-2835:from a total population of 303, this is equivalent to 98.3% of your data.\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:type_infer-2557:Infering type for: age\u001b[0m\n" + "\u001b[32mINFO:type_infer-2835:Infering type for: age\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:type_infer-2557:Column age has data type integer\u001b[0m\n" + "\u001b[32mINFO:type_infer-2835:Column age has data type integer\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:type_infer-2557:Infering type for: sex\u001b[0m\n" + "\u001b[32mINFO:type_infer-2835:Infering type for: sex\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:type_infer-2557:Column sex has data type binary\u001b[0m\n" + "\u001b[32mINFO:type_infer-2835:Column sex has data type binary\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:type_infer-2557:Infering type for: cp\u001b[0m\n" + "\u001b[32mINFO:type_infer-2835:Infering type for: cp\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:type_infer-2557:Column cp has data type categorical\u001b[0m\n" + "\u001b[32mINFO:type_infer-2835:Column cp has data type categorical\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:type_infer-2557:Infering type for: trestbps\u001b[0m\n" + "\u001b[32mINFO:type_infer-2835:Infering type for: trestbps\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:type_infer-2557:Column trestbps has data type integer\u001b[0m\n" + "\u001b[32mINFO:type_infer-2835:Column trestbps has data type integer\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:type_infer-2557:Infering type for: chol\u001b[0m\n" + "\u001b[32mINFO:type_infer-2835:Infering type for: chol\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:type_infer-2557:Column chol has data type integer\u001b[0m\n" + "\u001b[32mINFO:type_infer-2835:Column chol has data type integer\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:type_infer-2557:Infering type for: fbs\u001b[0m\n" + "\u001b[32mINFO:type_infer-2835:Infering type for: fbs\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:type_infer-2557:Column fbs has data type binary\u001b[0m\n" + "\u001b[32mINFO:type_infer-2835:Column fbs has data type binary\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:type_infer-2557:Infering type for: restecg\u001b[0m\n" + "\u001b[32mINFO:type_infer-2835:Infering type for: restecg\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:type_infer-2557:Column restecg has data type categorical\u001b[0m\n" + "\u001b[32mINFO:type_infer-2835:Column restecg has data type categorical\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:type_infer-2557:Infering type for: thalach\u001b[0m\n" + "\u001b[32mINFO:type_infer-2835:Infering type for: thalach\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:type_infer-2557:Column thalach has data type integer\u001b[0m\n" + "\u001b[32mINFO:type_infer-2835:Column thalach has data type integer\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:type_infer-2557:Infering type for: exang\u001b[0m\n" + "\u001b[32mINFO:type_infer-2835:Infering type for: exang\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:type_infer-2557:Column exang has data type binary\u001b[0m\n" + "\u001b[32mINFO:type_infer-2835:Column exang has data type binary\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:type_infer-2557:Infering type for: oldpeak\u001b[0m\n" + "\u001b[32mINFO:type_infer-2835:Infering type for: oldpeak\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:type_infer-2557:Column oldpeak has data type float\u001b[0m\n" + "\u001b[32mINFO:type_infer-2835:Column oldpeak has data type float\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:type_infer-2557:Infering type for: slope\u001b[0m\n" + "\u001b[32mINFO:type_infer-2835:Infering type for: slope\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:type_infer-2557:Column slope has data type categorical\u001b[0m\n" + "\u001b[32mINFO:type_infer-2835:Column slope has data type categorical\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:type_infer-2557:Infering type for: ca\u001b[0m\n" + "\u001b[32mINFO:type_infer-2835:Infering type for: ca\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:type_infer-2557:Column ca has data type categorical\u001b[0m\n" + "\u001b[32mINFO:type_infer-2835:Column ca has data type categorical\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:type_infer-2557:Infering type for: thal\u001b[0m\n" + "\u001b[32mINFO:type_infer-2835:Infering type for: thal\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:type_infer-2557:Column thal has data type categorical\u001b[0m\n" + "\u001b[32mINFO:type_infer-2835:Column thal has data type categorical\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:type_infer-2557:Infering type for: target\u001b[0m\n" + "\u001b[32mINFO:type_infer-2835:Infering type for: target\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:type_infer-2557:Column target has data type binary\u001b[0m\n" + "\u001b[32mINFO:type_infer-2835:Column target has data type binary\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:dataprep_ml-2557:Starting statistical analysis\u001b[0m\n" + "\u001b[32mINFO:dataprep_ml-2835:Starting statistical analysis\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:dataprep_ml-2557:Finished statistical analysis\u001b[0m\n" + "\u001b[32mINFO:dataprep_ml-2835:Finished statistical analysis\u001b[0m\n" ] }, { @@ -502,7 +502,7 @@ " \"unbias_target\": true,\n", " \"seconds_per_mixer\": 42768.0,\n", " \"seconds_per_encoder\": null,\n", - " \"expected_additional_time\": 0.06622028350830078,\n", + " \"expected_additional_time\": 0.0670318603515625,\n", " \"time_aim\": 259200,\n", " \"target_weights\": null,\n", " \"positive_domain\": false,\n", @@ -571,10 +571,10 @@ "execution_count": 3, "metadata": { "execution": { - "iopub.execute_input": "2024-03-19T10:01:01.453881Z", - "iopub.status.busy": "2024-03-19T10:01:01.453596Z", - "iopub.status.idle": "2024-03-19T10:01:01.457319Z", - "shell.execute_reply": "2024-03-19T10:01:01.456736Z" + "iopub.execute_input": "2024-03-19T10:19:14.880531Z", + "iopub.status.busy": "2024-03-19T10:19:14.880082Z", + "iopub.status.idle": "2024-03-19T10:19:14.883668Z", + "shell.execute_reply": "2024-03-19T10:19:14.882989Z" } }, "outputs": [], @@ -603,10 +603,10 @@ "execution_count": 4, "metadata": { "execution": { - "iopub.execute_input": "2024-03-19T10:01:01.460092Z", - "iopub.status.busy": "2024-03-19T10:01:01.459568Z", - "iopub.status.idle": "2024-03-19T10:01:01.802614Z", - "shell.execute_reply": "2024-03-19T10:01:01.801895Z" + "iopub.execute_input": "2024-03-19T10:19:14.886296Z", + "iopub.status.busy": "2024-03-19T10:19:14.885870Z", + "iopub.status.idle": "2024-03-19T10:19:15.202701Z", + "shell.execute_reply": "2024-03-19T10:19:15.202006Z" } }, "outputs": [], @@ -622,10 +622,10 @@ "execution_count": 5, "metadata": { "execution": { - "iopub.execute_input": "2024-03-19T10:01:01.805773Z", - "iopub.status.busy": "2024-03-19T10:01:01.805533Z", - "iopub.status.idle": "2024-03-19T10:01:02.425305Z", - "shell.execute_reply": "2024-03-19T10:01:02.424755Z" + "iopub.execute_input": "2024-03-19T10:19:15.206083Z", + "iopub.status.busy": "2024-03-19T10:19:15.205491Z", + "iopub.status.idle": "2024-03-19T10:19:15.814277Z", + "shell.execute_reply": "2024-03-19T10:19:15.813632Z" } }, "outputs": [ @@ -633,308 +633,308 @@ "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:dataprep_ml-2557:[Learn phase 1/8] - Statistical analysis\u001b[0m\n" + "\u001b[32mINFO:dataprep_ml-2835:[Learn phase 1/8] - Statistical analysis\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:dataprep_ml-2557:Starting statistical analysis\u001b[0m\n" + "\u001b[32mINFO:dataprep_ml-2835:Starting statistical analysis\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:dataprep_ml-2557:Finished statistical analysis\u001b[0m\n" + "\u001b[32mINFO:dataprep_ml-2835:Finished statistical analysis\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[37mDEBUG:lightwood-2557: `analyze_data` runtime: 0.03 seconds\u001b[0m\n" + "\u001b[37mDEBUG:lightwood-2835: `analyze_data` runtime: 0.04 seconds\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:dataprep_ml-2557:[Learn phase 2/8] - Data preprocessing\u001b[0m\n" + "\u001b[32mINFO:dataprep_ml-2835:[Learn phase 2/8] - Data preprocessing\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:dataprep_ml-2557:Cleaning the data\u001b[0m\n" + "\u001b[32mINFO:dataprep_ml-2835:Cleaning the data\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[37mDEBUG:lightwood-2557: `preprocess` runtime: 0.01 seconds\u001b[0m\n" + "\u001b[37mDEBUG:lightwood-2835: `preprocess` runtime: 0.01 seconds\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:dataprep_ml-2557:[Learn phase 3/8] - Data splitting\u001b[0m\n" + "\u001b[32mINFO:dataprep_ml-2835:[Learn phase 3/8] - 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"\u001b[37mDEBUG:dataprep_ml-2557:Preparing encoder for oldpeak...\u001b[0m\n" + "\u001b[37mDEBUG:dataprep_ml-2835:Preparing encoder for oldpeak...\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[37mDEBUG:dataprep_ml-2557:Preparing encoder for slope...\u001b[0m\n" + "\u001b[37mDEBUG:dataprep_ml-2835:Preparing encoder for slope...\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[37mDEBUG:lightwood-2557:Encoding UNKNOWN categories as index 0\u001b[0m\n" + "\u001b[37mDEBUG:lightwood-2835:Encoding UNKNOWN categories as index 0\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[37mDEBUG:dataprep_ml-2557:Preparing encoder for ca...\u001b[0m\n" + "\u001b[37mDEBUG:dataprep_ml-2835:Preparing encoder for ca...\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[37mDEBUG:lightwood-2557:Encoding UNKNOWN categories as index 0\u001b[0m\n" + "\u001b[37mDEBUG:lightwood-2835:Encoding UNKNOWN categories as index 0\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[37mDEBUG:dataprep_ml-2557:Preparing encoder for thal...\u001b[0m\n" + "\u001b[37mDEBUG:dataprep_ml-2835:Preparing encoder for thal...\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[37mDEBUG:lightwood-2557:Encoding UNKNOWN categories as index 0\u001b[0m\n" + "\u001b[37mDEBUG:lightwood-2835:Encoding UNKNOWN categories as index 0\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[37mDEBUG:lightwood-2557: `prepare` runtime: 0.02 seconds\u001b[0m\n" + "\u001b[37mDEBUG:lightwood-2835: `prepare` runtime: 0.02 seconds\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:dataprep_ml-2557:[Learn phase 5/8] - Feature generation\u001b[0m\n" + "\u001b[32mINFO:dataprep_ml-2835:[Learn phase 5/8] - Feature generation\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:dataprep_ml-2557:Featurizing the data\u001b[0m\n" + "\u001b[32mINFO:dataprep_ml-2835:Featurizing the data\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[37mDEBUG:lightwood-2557: `featurize` runtime: 0.09 seconds\u001b[0m\n" + "\u001b[37mDEBUG:lightwood-2835: `featurize` runtime: 0.09 seconds\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:dataprep_ml-2557:[Learn phase 6/8] - Mixer training\u001b[0m\n" + "\u001b[32mINFO:dataprep_ml-2835:[Learn phase 6/8] - Mixer training\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:dataprep_ml-2557:Training the mixers\u001b[0m\n" + "\u001b[32mINFO:dataprep_ml-2835:Training the mixers\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[37mDEBUG:lightwood-2557: `fit_mixer` runtime: 0.12 seconds\u001b[0m\n" + "\u001b[37mDEBUG:lightwood-2835: `fit_mixer` runtime: 0.12 seconds\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:dataprep_ml-2557:Ensembling the mixer\u001b[0m\n" + "\u001b[32mINFO:dataprep_ml-2835:Ensembling the mixer\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:lightwood-2557:Mixer: RandomForestMixer got accuracy: 0.798\u001b[0m\n" + "\u001b[32mINFO:lightwood-2835:Mixer: RandomForestMixer got accuracy: 0.798\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:lightwood-2557:Picked best mixer: RandomForestMixer\u001b[0m\n" + "\u001b[32mINFO:lightwood-2835:Picked best mixer: RandomForestMixer\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[37mDEBUG:lightwood-2557: `fit` runtime: 0.13 seconds\u001b[0m\n" + "\u001b[37mDEBUG:lightwood-2835: `fit` runtime: 0.13 seconds\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:dataprep_ml-2557:[Learn phase 7/8] - Ensemble analysis\u001b[0m\n" + "\u001b[32mINFO:dataprep_ml-2835:[Learn phase 7/8] - Ensemble analysis\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:dataprep_ml-2557:Analyzing the ensemble of mixers\u001b[0m\n" + "\u001b[32mINFO:dataprep_ml-2835:Analyzing the ensemble of mixers\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:lightwood-2557:The block ICP is now running its analyze() method\u001b[0m\n" + "\u001b[32mINFO:lightwood-2835:The block ICP is now running its analyze() method\u001b[0m\n" ] }, { @@ -943,35 +943,35 @@ "text": [ "/opt/hostedtoolcache/Python/3.9.18/x64/lib/python3.9/site-packages/sklearn/preprocessing/_encoders.py:975: FutureWarning: `sparse` was renamed to `sparse_output` in version 1.2 and will be removed in 1.4. `sparse_output` is ignored unless you leave `sparse` to its default value.\n", " warnings.warn(\n", - "\u001b[32mINFO:lightwood-2557:The block ConfStats is now running its analyze() method\u001b[0m\n" + "\u001b[32mINFO:lightwood-2835:The block ConfStats is now running its analyze() method\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:lightwood-2557:The block AccStats is now running its analyze() method\u001b[0m\n" + "\u001b[32mINFO:lightwood-2835:The block AccStats is now running its analyze() method\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:lightwood-2557:The block PermutationFeatureImportance is now running its analyze() method\u001b[0m\n" + "\u001b[32mINFO:lightwood-2835:The block PermutationFeatureImportance is now running its analyze() method\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:lightwood-2557:[PFI] Using a random sample (1000 rows out of 31).\u001b[0m\n" + "\u001b[32mINFO:lightwood-2835:[PFI] Using a random sample (1000 rows out of 31).\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:lightwood-2557:[PFI] Set to consider first 10 columns out of 10: ['age', 'sex', 'cp', 'trestbps', 'chol', 'fbs', 'restecg', 'thalach', 'exang', 'oldpeak'].\u001b[0m\n" + "\u001b[32mINFO:lightwood-2835:[PFI] Set to consider first 10 columns out of 10: ['age', 'sex', 'cp', 'trestbps', 'chol', 'fbs', 'restecg', 'thalach', 'exang', 'oldpeak'].\u001b[0m\n" ] }, { @@ -994,35 +994,35 @@ "name": "stderr", "output_type": "stream", "text": [ - "\u001b[37mDEBUG:lightwood-2557: `analyze_ensemble` runtime: 0.27 seconds\u001b[0m\n" + "\u001b[37mDEBUG:lightwood-2835: `analyze_ensemble` runtime: 0.26 seconds\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:dataprep_ml-2557:[Learn phase 8/8] - Adjustment on validation requested\u001b[0m\n" + "\u001b[32mINFO:dataprep_ml-2835:[Learn phase 8/8] - Adjustment on validation requested\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:dataprep_ml-2557:Updating the mixers\u001b[0m\n" + "\u001b[32mINFO:dataprep_ml-2835:Updating the mixers\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[37mDEBUG:lightwood-2557: `adjust` runtime: 0.04 seconds\u001b[0m\n" + "\u001b[37mDEBUG:lightwood-2835: `adjust` runtime: 0.04 seconds\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[37mDEBUG:lightwood-2557: `learn` runtime: 0.62 seconds\u001b[0m\n" + "\u001b[37mDEBUG:lightwood-2835: `learn` runtime: 0.6 seconds\u001b[0m\n" ] } ], @@ -1042,10 +1042,10 @@ "execution_count": 6, "metadata": { "execution": { - "iopub.execute_input": "2024-03-19T10:01:02.427974Z", - "iopub.status.busy": "2024-03-19T10:01:02.427763Z", - "iopub.status.idle": "2024-03-19T10:01:02.550761Z", - "shell.execute_reply": "2024-03-19T10:01:02.550053Z" + "iopub.execute_input": "2024-03-19T10:19:15.817082Z", + "iopub.status.busy": "2024-03-19T10:19:15.816625Z", + "iopub.status.idle": "2024-03-19T10:19:15.933674Z", + "shell.execute_reply": "2024-03-19T10:19:15.933075Z" } }, "outputs": [ @@ -1053,35 +1053,35 @@ "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:dataprep_ml-2557:[Predict phase 1/4] - Data preprocessing\u001b[0m\n" + "\u001b[32mINFO:dataprep_ml-2835:[Predict phase 1/4] - Data preprocessing\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:dataprep_ml-2557:Cleaning the data\u001b[0m\n" + "\u001b[32mINFO:dataprep_ml-2835:Cleaning the data\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[37mDEBUG:lightwood-2557: `preprocess` runtime: 0.01 seconds\u001b[0m\n" + "\u001b[37mDEBUG:lightwood-2835: `preprocess` runtime: 0.01 seconds\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:dataprep_ml-2557:[Predict phase 2/4] - Feature generation\u001b[0m\n" + "\u001b[32mINFO:dataprep_ml-2835:[Predict phase 2/4] - Feature generation\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:dataprep_ml-2557:Featurizing the data\u001b[0m\n" + "\u001b[32mINFO:dataprep_ml-2835:Featurizing the data\u001b[0m\n" ] }, { @@ -1104,91 +1104,91 @@ " outputs = ufunc(*inputs)\n", "/opt/hostedtoolcache/Python/3.9.18/x64/lib/python3.9/site-packages/numpy/lib/function_base.py:2455: RuntimeWarning: invalid value encountered in _none_fn (vectorized)\n", " outputs = ufunc(*inputs)\n", - "\u001b[37mDEBUG:lightwood-2557: `featurize` runtime: 0.02 seconds\u001b[0m\n" + "\u001b[37mDEBUG:lightwood-2835: `featurize` runtime: 0.02 seconds\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:dataprep_ml-2557:[Predict phase 3/4] - Calling ensemble\u001b[0m\n" + "\u001b[32mINFO:dataprep_ml-2835:[Predict phase 3/4] - Calling ensemble\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[37mDEBUG:lightwood-2557: `_timed_call` runtime: 0.01 seconds\u001b[0m\n" + "\u001b[37mDEBUG:lightwood-2835: `_timed_call` runtime: 0.01 seconds\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:dataprep_ml-2557:[Predict phase 4/4] - Analyzing output\u001b[0m\n" + "\u001b[32mINFO:dataprep_ml-2835:[Predict phase 4/4] - Analyzing output\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:lightwood-2557:The block ICP is now running its explain() method\u001b[0m\n" + "\u001b[32mINFO:lightwood-2835:The block ICP is now running its explain() method\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:lightwood-2557:The block ConfStats is now running its explain() method\u001b[0m\n" + "\u001b[32mINFO:lightwood-2835:The block ConfStats is now running its explain() method\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:lightwood-2557:ConfStats.explain() has not been implemented, no modifications will be done to the data insights.\u001b[0m\n" + "\u001b[32mINFO:lightwood-2835:ConfStats.explain() has not been implemented, no modifications will be done to the data insights.\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:lightwood-2557:The block AccStats is now running its explain() method\u001b[0m\n" + "\u001b[32mINFO:lightwood-2835:The block AccStats is now running its explain() method\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:lightwood-2557:AccStats.explain() has not been implemented, no modifications will be done to the data insights.\u001b[0m\n" + "\u001b[32mINFO:lightwood-2835:AccStats.explain() has not been implemented, no modifications will be done to the data insights.\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:lightwood-2557:The block PermutationFeatureImportance is now running its explain() method\u001b[0m\n" + "\u001b[32mINFO:lightwood-2835:The block PermutationFeatureImportance is now running its explain() method\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:lightwood-2557:PermutationFeatureImportance.explain() has not been implemented, no modifications will be done to the data insights.\u001b[0m\n" + "\u001b[32mINFO:lightwood-2835:PermutationFeatureImportance.explain() has not been implemented, no modifications will be done to the data insights.\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[37mDEBUG:lightwood-2557: `explain` runtime: 0.01 seconds\u001b[0m\n" + "\u001b[37mDEBUG:lightwood-2835: `explain` runtime: 0.01 seconds\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[37mDEBUG:lightwood-2557: `predict` runtime: 0.05 seconds\u001b[0m\n" + "\u001b[37mDEBUG:lightwood-2835: `predict` runtime: 0.05 seconds\u001b[0m\n" ] }, { diff --git a/_sources/tutorials/custom_splitter/custom_splitter.ipynb.txt b/_sources/tutorials/custom_splitter/custom_splitter.ipynb.txt index 95440ba2f..9c3d05589 100644 --- a/_sources/tutorials/custom_splitter/custom_splitter.ipynb.txt +++ b/_sources/tutorials/custom_splitter/custom_splitter.ipynb.txt @@ -28,10 +28,10 @@ "id": "interim-discussion", "metadata": { "execution": { - "iopub.execute_input": "2024-03-19T10:01:47.239674Z", - "iopub.status.busy": "2024-03-19T10:01:47.239449Z", - "iopub.status.idle": "2024-03-19T10:01:50.061642Z", - "shell.execute_reply": "2024-03-19T10:01:50.060971Z" + "iopub.execute_input": "2024-03-19T10:19:59.610275Z", + "iopub.status.busy": "2024-03-19T10:19:59.609809Z", + "iopub.status.idle": "2024-03-19T10:20:02.362199Z", + "shell.execute_reply": "2024-03-19T10:20:02.361538Z" } }, "outputs": [ @@ -39,14 +39,14 @@ "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:lightwood-2709:No torchvision detected, image helpers not supported.\u001b[0m\n" + "\u001b[32mINFO:lightwood-2978:No torchvision detected, image helpers not supported.\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:lightwood-2709:No torchvision/pillow detected, image encoder not supported\u001b[0m\n" + "\u001b[32mINFO:lightwood-2978:No torchvision/pillow detected, image encoder not supported\u001b[0m\n" ] } ], @@ -87,10 +87,10 @@ "id": "foreign-orchestra", "metadata": { "execution": { - "iopub.execute_input": "2024-03-19T10:01:50.065028Z", - "iopub.status.busy": "2024-03-19T10:01:50.064509Z", - "iopub.status.idle": "2024-03-19T10:01:56.279725Z", - "shell.execute_reply": "2024-03-19T10:01:56.279000Z" + "iopub.execute_input": "2024-03-19T10:20:02.365602Z", + "iopub.status.busy": "2024-03-19T10:20:02.365149Z", + "iopub.status.idle": "2024-03-19T10:20:07.452547Z", + "shell.execute_reply": "2024-03-19T10:20:07.451832Z" } }, "outputs": [ @@ -316,10 +316,10 @@ "id": "cathedral-mills", "metadata": { "execution": { - "iopub.execute_input": "2024-03-19T10:01:56.282558Z", - "iopub.status.busy": "2024-03-19T10:01:56.282154Z", - "iopub.status.idle": "2024-03-19T10:01:56.638853Z", - "shell.execute_reply": "2024-03-19T10:01:56.638168Z" + "iopub.execute_input": "2024-03-19T10:20:07.455462Z", + "iopub.status.busy": "2024-03-19T10:20:07.454974Z", + "iopub.status.idle": "2024-03-19T10:20:07.804222Z", + "shell.execute_reply": "2024-03-19T10:20:07.803562Z" } }, "outputs": [ @@ -374,10 +374,10 @@ "id": "medieval-zambia", "metadata": { "execution": { - "iopub.execute_input": "2024-03-19T10:01:56.641742Z", - "iopub.status.busy": "2024-03-19T10:01:56.641343Z", - "iopub.status.idle": "2024-03-19T10:03:06.100696Z", - "shell.execute_reply": "2024-03-19T10:03:06.099959Z" + "iopub.execute_input": "2024-03-19T10:20:07.806996Z", + "iopub.status.busy": "2024-03-19T10:20:07.806608Z", + "iopub.status.idle": "2024-03-19T10:21:16.393872Z", + "shell.execute_reply": "2024-03-19T10:21:16.393237Z" } }, "outputs": [ @@ -385,469 +385,469 @@ "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:type_infer-2709:Analyzing a sample of 18424\u001b[0m\n" + "\u001b[32mINFO:type_infer-2978:Analyzing a sample of 18424\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:type_infer-2709:from a total population of 284807, this is equivalent to 6.5% of your data.\u001b[0m\n" + "\u001b[32mINFO:type_infer-2978:from a total population of 284807, this is equivalent to 6.5% of your data.\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:type_infer-2709:Using 3 processes to deduct types.\u001b[0m\n" + "\u001b[32mINFO:type_infer-2978:Using 3 processes to deduct types.\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:type_infer-2709:Infering type for: Time\u001b[0m\n" + "\u001b[32mINFO:type_infer-2978:Infering type for: Time\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:type_infer-2709:Infering type for: V3\u001b[0m\n" + "\u001b[32mINFO:type_infer-2978:Infering type for: V3\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:type_infer-2709:Infering type for: V6\u001b[0m\n" + "\u001b[32mINFO:type_infer-2978:Infering type for: V6\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:type_infer-2709:Column Time has data type integer\u001b[0m\n" + "\u001b[32mINFO:type_infer-2978:Column Time has data type integer\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - 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"\u001b[32mINFO:type_infer-2709:Column V28 has data type float\u001b[0m\n" + "\u001b[32mINFO:type_infer-2978:Infering type for: Amount\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:type_infer-2709:Infering type for: Amount\u001b[0m\n" + "\u001b[32mINFO:type_infer-2978:Column V26 has data type float\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:type_infer-2709:Column Amount has data type float\u001b[0m\n" + "\u001b[32mINFO:type_infer-2978:Column Amount has data type float\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:dataprep_ml-2709:Starting statistical analysis\u001b[0m\n" + "\u001b[32mINFO:dataprep_ml-2978:Starting statistical analysis\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:dataprep_ml-2709:Finished statistical analysis\u001b[0m\n" + "\u001b[32mINFO:dataprep_ml-2978:Finished statistical analysis\u001b[0m\n" ] } ], @@ -901,10 +901,10 @@ "id": "4411ee53", "metadata": { "execution": { - "iopub.execute_input": "2024-03-19T10:03:06.103501Z", - "iopub.status.busy": "2024-03-19T10:03:06.103277Z", - "iopub.status.idle": "2024-03-19T10:03:06.108507Z", - "shell.execute_reply": "2024-03-19T10:03:06.107869Z" + "iopub.execute_input": "2024-03-19T10:21:16.397004Z", + "iopub.status.busy": "2024-03-19T10:21:16.396578Z", + "iopub.status.idle": "2024-03-19T10:21:16.402082Z", + "shell.execute_reply": "2024-03-19T10:21:16.401471Z" } }, "outputs": [ @@ -996,10 +996,10 @@ "id": "34092d12", "metadata": { "execution": { - "iopub.execute_input": "2024-03-19T10:03:06.111188Z", - "iopub.status.busy": "2024-03-19T10:03:06.110800Z", - "iopub.status.idle": "2024-03-19T10:03:06.114233Z", - "shell.execute_reply": "2024-03-19T10:03:06.113596Z" + "iopub.execute_input": "2024-03-19T10:21:16.404572Z", + "iopub.status.busy": "2024-03-19T10:21:16.404205Z", + "iopub.status.idle": "2024-03-19T10:21:16.407304Z", + "shell.execute_reply": "2024-03-19T10:21:16.406807Z" } }, "outputs": [], @@ -1055,10 +1055,10 @@ "id": "alleged-concentrate", "metadata": { "execution": { - "iopub.execute_input": "2024-03-19T10:03:06.116867Z", - "iopub.status.busy": "2024-03-19T10:03:06.116499Z", - "iopub.status.idle": "2024-03-19T10:03:06.498297Z", - "shell.execute_reply": "2024-03-19T10:03:06.497622Z" + "iopub.execute_input": "2024-03-19T10:21:16.409972Z", + "iopub.status.busy": "2024-03-19T10:21:16.409527Z", + "iopub.status.idle": "2024-03-19T10:21:16.768862Z", + "shell.execute_reply": "2024-03-19T10:21:16.768199Z" } }, "outputs": [ @@ -1139,7 +1139,7 @@ " \"unbias_target\": True,\n", " \"seconds_per_mixer\": 42768.0,\n", " \"seconds_per_encoder\": None,\n", - " \"expected_additional_time\": 69.44483995437622,\n", + " \"expected_additional_time\": 68.57310724258423,\n", " \"time_aim\": 259200,\n", " \"target_weights\": None,\n", " \"positive_domain\": False,\n", @@ -1201,7 +1201,7 @@ " \"Amount\": \"float\",\n", " \"Class\": \"binary\",\n", " }\n", - " self.lightwood_version = \"24.3.3.1\"\n", + " self.lightwood_version = \"24.3.3.0\"\n", " self.pred_args = PredictionArguments()\n", "\n", " # Any feature-column dependencies\n", @@ -1902,10 +1902,10 @@ "id": "organic-london", "metadata": { "execution": { - "iopub.execute_input": "2024-03-19T10:03:06.501241Z", - "iopub.status.busy": "2024-03-19T10:03:06.500710Z", - "iopub.status.idle": "2024-03-19T10:03:06.509031Z", - "shell.execute_reply": "2024-03-19T10:03:06.508404Z" + "iopub.execute_input": "2024-03-19T10:21:16.771656Z", + "iopub.status.busy": "2024-03-19T10:21:16.771437Z", + "iopub.status.idle": "2024-03-19T10:21:16.779773Z", + "shell.execute_reply": "2024-03-19T10:21:16.779119Z" } }, "outputs": [], @@ -1920,10 +1920,10 @@ "id": "fabulous-prime", "metadata": { "execution": { - "iopub.execute_input": "2024-03-19T10:03:06.511868Z", - "iopub.status.busy": "2024-03-19T10:03:06.511453Z", - "iopub.status.idle": "2024-03-19T10:03:26.916363Z", - "shell.execute_reply": "2024-03-19T10:03:26.915678Z" + "iopub.execute_input": "2024-03-19T10:21:16.782156Z", + "iopub.status.busy": "2024-03-19T10:21:16.781962Z", + "iopub.status.idle": "2024-03-19T10:21:37.022779Z", + "shell.execute_reply": "2024-03-19T10:21:37.022012Z" } }, "outputs": [ @@ -1931,28 +1931,28 @@ "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:dataprep_ml-2709:Cleaning the data\u001b[0m\n" + "\u001b[32mINFO:dataprep_ml-2978:Cleaning the data\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[37mDEBUG:lightwood-2709: `preprocess` runtime: 18.83 seconds\u001b[0m\n" + "\u001b[37mDEBUG:lightwood-2978: `preprocess` runtime: 18.68 seconds\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:dataprep_ml-2709:Splitting the data into train/test\u001b[0m\n" + "\u001b[32mINFO:dataprep_ml-2978:Splitting the data into train/test\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[37mDEBUG:lightwood-2709: `split` runtime: 1.57 seconds\u001b[0m\n" + "\u001b[37mDEBUG:lightwood-2978: `split` runtime: 1.55 seconds\u001b[0m\n" ] } ], @@ -1968,10 +1968,10 @@ "id": "suspended-biography", "metadata": { "execution": { - "iopub.execute_input": "2024-03-19T10:03:26.919067Z", - "iopub.status.busy": "2024-03-19T10:03:26.918775Z", - "iopub.status.idle": "2024-03-19T10:03:28.191841Z", - "shell.execute_reply": "2024-03-19T10:03:28.191123Z" + "iopub.execute_input": "2024-03-19T10:21:37.025718Z", + "iopub.status.busy": "2024-03-19T10:21:37.025482Z", + "iopub.status.idle": "2024-03-19T10:21:38.292751Z", + "shell.execute_reply": "2024-03-19T10:21:38.292052Z" } }, "outputs": [ diff --git a/_sources/tutorials/tutorial_data_analysis/tutorial_data_analysis.ipynb.txt b/_sources/tutorials/tutorial_data_analysis/tutorial_data_analysis.ipynb.txt index 3b0933da3..cd5dc5d77 100644 --- a/_sources/tutorials/tutorial_data_analysis/tutorial_data_analysis.ipynb.txt +++ b/_sources/tutorials/tutorial_data_analysis/tutorial_data_analysis.ipynb.txt @@ -35,10 +35,10 @@ "execution_count": 1, "metadata": { "execution": { - "iopub.execute_input": "2024-03-19T10:00:49.651494Z", - "iopub.status.busy": "2024-03-19T10:00:49.650877Z", - "iopub.status.idle": "2024-03-19T10:00:49.989665Z", - "shell.execute_reply": "2024-03-19T10:00:49.988766Z" + "iopub.execute_input": "2024-03-19T10:19:03.938511Z", + "iopub.status.busy": "2024-03-19T10:19:03.938316Z", + "iopub.status.idle": "2024-03-19T10:19:04.265900Z", + "shell.execute_reply": "2024-03-19T10:19:04.265235Z" } }, "outputs": [ @@ -175,10 +175,10 @@ "execution_count": 2, "metadata": { "execution": { - "iopub.execute_input": "2024-03-19T10:00:50.028410Z", - "iopub.status.busy": "2024-03-19T10:00:50.027800Z", - "iopub.status.idle": "2024-03-19T10:00:52.344636Z", - "shell.execute_reply": "2024-03-19T10:00:52.344000Z" + "iopub.execute_input": "2024-03-19T10:19:04.305139Z", + "iopub.status.busy": "2024-03-19T10:19:04.304727Z", + "iopub.status.idle": "2024-03-19T10:19:06.536521Z", + "shell.execute_reply": "2024-03-19T10:19:06.535864Z" } }, "outputs": [ @@ -186,14 +186,14 @@ "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:lightwood-2515:No torchvision detected, image helpers not supported.\u001b[0m\n" + "\u001b[32mINFO:lightwood-2801:No torchvision detected, image helpers not supported.\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:lightwood-2515:No torchvision/pillow detected, image encoder not supported\u001b[0m\n" + "\u001b[32mINFO:lightwood-2801:No torchvision/pillow detected, image encoder not supported\u001b[0m\n" ] } ], @@ -215,10 +215,10 @@ "execution_count": 3, "metadata": { "execution": { - "iopub.execute_input": "2024-03-19T10:00:52.348266Z", - "iopub.status.busy": "2024-03-19T10:00:52.347545Z", - "iopub.status.idle": "2024-03-19T10:00:52.353326Z", - "shell.execute_reply": "2024-03-19T10:00:52.352769Z" + "iopub.execute_input": "2024-03-19T10:19:06.539697Z", + "iopub.status.busy": "2024-03-19T10:19:06.539214Z", + "iopub.status.idle": "2024-03-19T10:19:06.544551Z", + "shell.execute_reply": "2024-03-19T10:19:06.543867Z" } }, "outputs": [ @@ -270,10 +270,10 @@ "execution_count": 4, "metadata": { "execution": { - "iopub.execute_input": "2024-03-19T10:00:52.355839Z", - "iopub.status.busy": "2024-03-19T10:00:52.355431Z", - "iopub.status.idle": "2024-03-19T10:00:52.380446Z", - "shell.execute_reply": "2024-03-19T10:00:52.379821Z" + "iopub.execute_input": "2024-03-19T10:19:06.547045Z", + "iopub.status.busy": "2024-03-19T10:19:06.546702Z", + "iopub.status.idle": "2024-03-19T10:19:06.570850Z", + "shell.execute_reply": "2024-03-19T10:19:06.570337Z" } }, "outputs": [ @@ -281,112 +281,112 @@ "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:type_infer-2515:Analyzing a sample of 222\u001b[0m\n" + "\u001b[32mINFO:type_infer-2801:Analyzing a sample of 222\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:type_infer-2515:from a total population of 225, this is equivalent to 98.7% of your data.\u001b[0m\n" + "\u001b[32mINFO:type_infer-2801:from a total population of 225, this is equivalent to 98.7% of your data.\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:type_infer-2515:Infering type for: Population\u001b[0m\n" + "\u001b[32mINFO:type_infer-2801:Infering type for: Population\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:type_infer-2515:Column Population has data type integer\u001b[0m\n" + "\u001b[32mINFO:type_infer-2801:Column Population has data type integer\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:type_infer-2515:Infering type for: Area (sq. mi.)\u001b[0m\n" + "\u001b[32mINFO:type_infer-2801:Infering type for: Area (sq. mi.)\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:type_infer-2515:Column Area (sq. mi.) has data type integer\u001b[0m\n" + "\u001b[32mINFO:type_infer-2801:Column Area (sq. mi.) has data type integer\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:type_infer-2515:Infering type for: Pop. Density \u001b[0m\n" + "\u001b[32mINFO:type_infer-2801:Infering type for: Pop. Density \u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:type_infer-2515:Column Pop. Density has data type float\u001b[0m\n" + "\u001b[32mINFO:type_infer-2801:Column Pop. Density has data type float\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:type_infer-2515:Infering type for: GDP ($ per capita)\u001b[0m\n" + "\u001b[32mINFO:type_infer-2801:Infering type for: GDP ($ per capita)\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:type_infer-2515:Column GDP ($ per capita) has data type integer\u001b[0m\n" + "\u001b[32mINFO:type_infer-2801:Column GDP ($ per capita) has data type integer\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:type_infer-2515:Infering type for: Literacy (%)\u001b[0m\n" + "\u001b[32mINFO:type_infer-2801:Infering type for: Literacy (%)\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:type_infer-2515:Column Literacy (%) has data type float\u001b[0m\n" + "\u001b[32mINFO:type_infer-2801:Column Literacy (%) has data type float\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:type_infer-2515:Infering type for: Infant mortality \u001b[0m\n" + "\u001b[32mINFO:type_infer-2801:Infering type for: Infant mortality \u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:type_infer-2515:Column Infant mortality has data type float\u001b[0m\n" + "\u001b[32mINFO:type_infer-2801:Column Infant mortality has data type float\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:type_infer-2515:Infering type for: Development Index\u001b[0m\n" + "\u001b[32mINFO:type_infer-2801:Infering type for: Development Index\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:type_infer-2515:Column Development Index has data type categorical\u001b[0m\n" + "\u001b[32mINFO:type_infer-2801:Column Development Index has data type categorical\u001b[0m\n" ] }, { @@ -421,10 +421,10 @@ "execution_count": 5, "metadata": { "execution": { - "iopub.execute_input": "2024-03-19T10:00:52.383003Z", - "iopub.status.busy": "2024-03-19T10:00:52.382644Z", - "iopub.status.idle": "2024-03-19T10:00:52.386819Z", - "shell.execute_reply": "2024-03-19T10:00:52.386243Z" + "iopub.execute_input": "2024-03-19T10:19:06.573173Z", + "iopub.status.busy": "2024-03-19T10:19:06.572977Z", + "iopub.status.idle": "2024-03-19T10:19:06.577284Z", + "shell.execute_reply": "2024-03-19T10:19:06.576743Z" } }, "outputs": [ @@ -474,10 +474,10 @@ "execution_count": 6, "metadata": { "execution": { - "iopub.execute_input": "2024-03-19T10:00:52.389447Z", - "iopub.status.busy": "2024-03-19T10:00:52.388990Z", - "iopub.status.idle": "2024-03-19T10:00:52.416923Z", - "shell.execute_reply": "2024-03-19T10:00:52.416426Z" + "iopub.execute_input": "2024-03-19T10:19:06.579864Z", + "iopub.status.busy": "2024-03-19T10:19:06.579460Z", + "iopub.status.idle": "2024-03-19T10:19:06.605049Z", + "shell.execute_reply": "2024-03-19T10:19:06.604425Z" } }, "outputs": [ @@ -485,14 +485,14 @@ "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:dataprep_ml-2515:Starting statistical analysis\u001b[0m\n" + "\u001b[32mINFO:dataprep_ml-2801:Starting statistical analysis\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:dataprep_ml-2515:Finished statistical analysis\u001b[0m\n" + "\u001b[32mINFO:dataprep_ml-2801:Finished statistical analysis\u001b[0m\n" ] } ], @@ -520,10 +520,10 @@ "execution_count": 7, "metadata": { "execution": { - "iopub.execute_input": "2024-03-19T10:00:52.419438Z", - "iopub.status.busy": "2024-03-19T10:00:52.419082Z", - "iopub.status.idle": "2024-03-19T10:00:52.423513Z", - "shell.execute_reply": "2024-03-19T10:00:52.422896Z" + "iopub.execute_input": "2024-03-19T10:19:06.607583Z", + "iopub.status.busy": "2024-03-19T10:19:06.607232Z", + "iopub.status.idle": "2024-03-19T10:19:06.611429Z", + "shell.execute_reply": "2024-03-19T10:19:06.610739Z" } }, "outputs": [ @@ -569,10 +569,10 @@ "execution_count": 8, "metadata": { "execution": { - "iopub.execute_input": "2024-03-19T10:00:52.426009Z", - "iopub.status.busy": "2024-03-19T10:00:52.425648Z", - "iopub.status.idle": "2024-03-19T10:00:52.429578Z", - "shell.execute_reply": "2024-03-19T10:00:52.428980Z" + "iopub.execute_input": "2024-03-19T10:19:06.613932Z", + "iopub.status.busy": "2024-03-19T10:19:06.613555Z", + "iopub.status.idle": "2024-03-19T10:19:06.617700Z", + "shell.execute_reply": "2024-03-19T10:19:06.617161Z" } }, "outputs": [ @@ -612,10 +612,10 @@ "execution_count": 9, "metadata": { "execution": { - "iopub.execute_input": "2024-03-19T10:00:52.432021Z", - "iopub.status.busy": "2024-03-19T10:00:52.431822Z", - "iopub.status.idle": "2024-03-19T10:00:52.436360Z", - "shell.execute_reply": "2024-03-19T10:00:52.435714Z" + "iopub.execute_input": "2024-03-19T10:19:06.620208Z", + "iopub.status.busy": "2024-03-19T10:19:06.619835Z", + "iopub.status.idle": "2024-03-19T10:19:06.624339Z", + "shell.execute_reply": "2024-03-19T10:19:06.623720Z" }, "scrolled": false }, @@ -673,10 +673,10 @@ "execution_count": 10, "metadata": { "execution": { - "iopub.execute_input": "2024-03-19T10:00:52.438828Z", - "iopub.status.busy": "2024-03-19T10:00:52.438629Z", - "iopub.status.idle": "2024-03-19T10:00:52.442719Z", - "shell.execute_reply": "2024-03-19T10:00:52.442066Z" + "iopub.execute_input": "2024-03-19T10:19:06.626914Z", + "iopub.status.busy": "2024-03-19T10:19:06.626544Z", + "iopub.status.idle": "2024-03-19T10:19:06.630569Z", + "shell.execute_reply": "2024-03-19T10:19:06.629918Z" } }, "outputs": [ @@ -700,10 +700,10 @@ "execution_count": 11, "metadata": { "execution": { - "iopub.execute_input": "2024-03-19T10:00:52.445081Z", - "iopub.status.busy": "2024-03-19T10:00:52.444718Z", - "iopub.status.idle": "2024-03-19T10:00:52.449280Z", - "shell.execute_reply": "2024-03-19T10:00:52.448653Z" + "iopub.execute_input": "2024-03-19T10:19:06.632918Z", + "iopub.status.busy": "2024-03-19T10:19:06.632572Z", + "iopub.status.idle": "2024-03-19T10:19:06.636980Z", + "shell.execute_reply": "2024-03-19T10:19:06.636416Z" }, "scrolled": false }, @@ -786,10 +786,10 @@ "execution_count": 12, "metadata": { "execution": { - "iopub.execute_input": "2024-03-19T10:00:52.451758Z", - "iopub.status.busy": "2024-03-19T10:00:52.451357Z", - "iopub.status.idle": "2024-03-19T10:00:52.455554Z", - "shell.execute_reply": "2024-03-19T10:00:52.454881Z" + "iopub.execute_input": "2024-03-19T10:19:06.639388Z", + "iopub.status.busy": "2024-03-19T10:19:06.639170Z", + "iopub.status.idle": "2024-03-19T10:19:06.642788Z", + "shell.execute_reply": "2024-03-19T10:19:06.642142Z" } }, "outputs": [ @@ -841,10 +841,10 @@ "execution_count": 13, "metadata": { "execution": { - "iopub.execute_input": "2024-03-19T10:00:52.458008Z", - "iopub.status.busy": "2024-03-19T10:00:52.457628Z", - "iopub.status.idle": "2024-03-19T10:00:55.130170Z", - "shell.execute_reply": "2024-03-19T10:00:55.129435Z" + "iopub.execute_input": "2024-03-19T10:19:06.645291Z", + "iopub.status.busy": "2024-03-19T10:19:06.645097Z", + "iopub.status.idle": "2024-03-19T10:19:09.211358Z", + "shell.execute_reply": "2024-03-19T10:19:09.210753Z" }, "scrolled": false }, diff --git a/_sources/tutorials/tutorial_time_series/tutorial_time_series.ipynb.txt b/_sources/tutorials/tutorial_time_series/tutorial_time_series.ipynb.txt index 7f6e9cda1..0079b3d1d 100644 --- a/_sources/tutorials/tutorial_time_series/tutorial_time_series.ipynb.txt +++ b/_sources/tutorials/tutorial_time_series/tutorial_time_series.ipynb.txt @@ -24,10 +24,10 @@ "execution_count": 1, "metadata": { "execution": { - "iopub.execute_input": "2024-03-19T10:00:29.052255Z", - "iopub.status.busy": "2024-03-19T10:00:29.052061Z", - "iopub.status.idle": "2024-03-19T10:00:29.633972Z", - "shell.execute_reply": "2024-03-19T10:00:29.633267Z" + "iopub.execute_input": "2024-03-19T10:18:43.858095Z", + "iopub.status.busy": "2024-03-19T10:18:43.857906Z", + "iopub.status.idle": "2024-03-19T10:18:44.259716Z", + "shell.execute_reply": "2024-03-19T10:18:44.259089Z" } }, "outputs": [ @@ -162,10 +162,10 @@ "execution_count": 2, "metadata": { "execution": { - "iopub.execute_input": "2024-03-19T10:00:29.671158Z", - "iopub.status.busy": "2024-03-19T10:00:29.670709Z", - "iopub.status.idle": "2024-03-19T10:00:31.910461Z", - "shell.execute_reply": "2024-03-19T10:00:31.909732Z" + "iopub.execute_input": "2024-03-19T10:18:44.297952Z", + "iopub.status.busy": "2024-03-19T10:18:44.297543Z", + "iopub.status.idle": "2024-03-19T10:18:46.516497Z", + "shell.execute_reply": "2024-03-19T10:18:46.515851Z" } }, "outputs": [ @@ -173,14 +173,14 @@ "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:lightwood-2383:No torchvision detected, image helpers not supported.\u001b[0m\n" + "\u001b[32mINFO:lightwood-2669:No torchvision detected, image helpers not supported.\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:lightwood-2383:No torchvision/pillow detected, image encoder not supported\u001b[0m\n" + "\u001b[32mINFO:lightwood-2669:No torchvision/pillow detected, image encoder not supported\u001b[0m\n" ] } ], @@ -193,10 +193,10 @@ "execution_count": 3, "metadata": { "execution": { - "iopub.execute_input": "2024-03-19T10:00:31.913739Z", - "iopub.status.busy": "2024-03-19T10:00:31.913449Z", - "iopub.status.idle": "2024-03-19T10:00:31.917298Z", - "shell.execute_reply": "2024-03-19T10:00:31.916694Z" + "iopub.execute_input": "2024-03-19T10:18:46.519651Z", + "iopub.status.busy": "2024-03-19T10:18:46.519150Z", + "iopub.status.idle": "2024-03-19T10:18:46.522788Z", + "shell.execute_reply": "2024-03-19T10:18:46.522177Z" } }, "outputs": [], @@ -223,10 +223,10 @@ "execution_count": 4, "metadata": { "execution": { - "iopub.execute_input": "2024-03-19T10:00:31.919961Z", - "iopub.status.busy": "2024-03-19T10:00:31.919505Z", - "iopub.status.idle": "2024-03-19T10:00:31.923256Z", - "shell.execute_reply": "2024-03-19T10:00:31.922624Z" + "iopub.execute_input": "2024-03-19T10:18:46.525334Z", + "iopub.status.busy": "2024-03-19T10:18:46.524970Z", + "iopub.status.idle": "2024-03-19T10:18:46.528771Z", + "shell.execute_reply": "2024-03-19T10:18:46.528137Z" } }, "outputs": [ @@ -261,10 +261,10 @@ "execution_count": 5, "metadata": { "execution": { - "iopub.execute_input": "2024-03-19T10:00:31.925848Z", - "iopub.status.busy": "2024-03-19T10:00:31.925662Z", - "iopub.status.idle": "2024-03-19T10:00:35.934404Z", - "shell.execute_reply": "2024-03-19T10:00:35.933746Z" + "iopub.execute_input": "2024-03-19T10:18:46.531381Z", + "iopub.status.busy": "2024-03-19T10:18:46.531001Z", + "iopub.status.idle": "2024-03-19T10:18:50.680970Z", + "shell.execute_reply": "2024-03-19T10:18:50.680334Z" } }, "outputs": [ @@ -272,49 +272,49 @@ "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:type_infer-2383:Analyzing a sample of 2467\u001b[0m\n" + "\u001b[32mINFO:type_infer-2669:Analyzing a sample of 2467\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:type_infer-2383:from a total population of 2820, this is equivalent to 87.5% of your data.\u001b[0m\n" + "\u001b[32mINFO:type_infer-2669:from a total population of 2820, this is equivalent to 87.5% of your data.\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:type_infer-2383:Infering type for: Month\u001b[0m\n" + "\u001b[32mINFO:type_infer-2669:Infering type for: Month\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:type_infer-2383:Column Month has data type date\u001b[0m\n" + "\u001b[32mINFO:type_infer-2669:Column Month has data type date\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:type_infer-2383:Infering type for: Sunspots\u001b[0m\n" + "\u001b[32mINFO:type_infer-2669:Infering type for: Sunspots\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:type_infer-2383:Column Sunspots has data type float\u001b[0m\n" + "\u001b[32mINFO:type_infer-2669:Column Sunspots has data type float\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:dataprep_ml-2383:Starting statistical analysis\u001b[0m\n" + "\u001b[32mINFO:dataprep_ml-2669:Starting statistical analysis\u001b[0m\n" ] }, { @@ -323,7 +323,7 @@ "text": [ "/opt/hostedtoolcache/Python/3.9.18/x64/lib/python3.9/site-packages/dataprep_ml/cleaners.py:163: UserWarning: The argument 'infer_datetime_format' is deprecated and will be removed in a future version. A strict version of it is now the default, see https://pandas.pydata.org/pdeps/0004-consistent-to-datetime-parsing.html. You can safely remove this argument.\n", " result = pd.to_datetime(element,\n", - "\u001b[32mINFO:dataprep_ml-2383:Finished statistical analysis\u001b[0m\n" + "\u001b[32mINFO:dataprep_ml-2669:Finished statistical analysis\u001b[0m\n" ] } ], @@ -360,10 +360,10 @@ "execution_count": 6, "metadata": { "execution": { - "iopub.execute_input": "2024-03-19T10:00:35.937460Z", - "iopub.status.busy": "2024-03-19T10:00:35.937029Z", - "iopub.status.idle": "2024-03-19T10:00:37.993986Z", - "shell.execute_reply": "2024-03-19T10:00:37.993278Z" + "iopub.execute_input": "2024-03-19T10:18:50.684122Z", + "iopub.status.busy": "2024-03-19T10:18:50.683672Z", + "iopub.status.idle": "2024-03-19T10:18:52.624199Z", + "shell.execute_reply": "2024-03-19T10:18:52.623532Z" } }, "outputs": [ @@ -371,14 +371,14 @@ "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:dataprep_ml-2383:[Learn phase 1/8] - Statistical analysis\u001b[0m\n" + "\u001b[32mINFO:dataprep_ml-2669:[Learn phase 1/8] - Statistical analysis\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:dataprep_ml-2383:Starting statistical analysis\u001b[0m\n" + "\u001b[32mINFO:dataprep_ml-2669:Starting statistical analysis\u001b[0m\n" ] }, { @@ -387,28 +387,28 @@ "text": [ "/opt/hostedtoolcache/Python/3.9.18/x64/lib/python3.9/site-packages/dataprep_ml/cleaners.py:163: UserWarning: The argument 'infer_datetime_format' is deprecated and will be removed in a future version. A strict version of it is now the default, see https://pandas.pydata.org/pdeps/0004-consistent-to-datetime-parsing.html. You can safely remove this argument.\n", " result = pd.to_datetime(element,\n", - "\u001b[32mINFO:dataprep_ml-2383:Finished statistical analysis\u001b[0m\n" + "\u001b[32mINFO:dataprep_ml-2669:Finished statistical analysis\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[37mDEBUG:lightwood-2383: `analyze_data` runtime: 0.05 seconds\u001b[0m\n" + "\u001b[37mDEBUG:lightwood-2669: `analyze_data` runtime: 0.05 seconds\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:dataprep_ml-2383:[Learn phase 2/8] - Data preprocessing\u001b[0m\n" + "\u001b[32mINFO:dataprep_ml-2669:[Learn phase 2/8] - Data preprocessing\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:dataprep_ml-2383:Cleaning the data\u001b[0m\n" + "\u001b[32mINFO:dataprep_ml-2669:Cleaning the data\u001b[0m\n" ] }, { @@ -417,133 +417,133 @@ "text": [ "/opt/hostedtoolcache/Python/3.9.18/x64/lib/python3.9/site-packages/dataprep_ml/cleaners.py:163: UserWarning: The argument 'infer_datetime_format' is deprecated and will be removed in a future version. A strict version of it is now the default, see https://pandas.pydata.org/pdeps/0004-consistent-to-datetime-parsing.html. You can safely remove this argument.\n", " result = pd.to_datetime(element,\n", - "\u001b[32mINFO:dataprep_ml-2383:Transforming timeseries data\u001b[0m\n" + "\u001b[32mINFO:dataprep_ml-2669:Transforming timeseries data\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[37mDEBUG:lightwood-2383: `preprocess` runtime: 0.09 seconds\u001b[0m\n" + "\u001b[37mDEBUG:lightwood-2669: `preprocess` runtime: 0.09 seconds\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:dataprep_ml-2383:[Learn phase 3/8] - Data splitting\u001b[0m\n" + "\u001b[32mINFO:dataprep_ml-2669:[Learn phase 3/8] - Data splitting\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:dataprep_ml-2383:Splitting the data into train/test\u001b[0m\n" + "\u001b[32mINFO:dataprep_ml-2669:Splitting the data into train/test\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[37mDEBUG:lightwood-2383: `split` runtime: 0.0 seconds\u001b[0m\n" + "\u001b[37mDEBUG:lightwood-2669: `split` runtime: 0.0 seconds\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:dataprep_ml-2383:[Learn phase 4/8] - Preparing encoders\u001b[0m\n" + "\u001b[32mINFO:dataprep_ml-2669:[Learn phase 4/8] - Preparing encoders\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[37mDEBUG:dataprep_ml-2383:Preparing sequentially...\u001b[0m\n" + "\u001b[37mDEBUG:dataprep_ml-2669:Preparing sequentially...\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[37mDEBUG:lightwood-2383: `prepare` runtime: 0.05 seconds\u001b[0m\n" + "\u001b[37mDEBUG:lightwood-2669: `prepare` runtime: 0.05 seconds\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:dataprep_ml-2383:[Learn phase 5/8] - Feature generation\u001b[0m\n" + "\u001b[32mINFO:dataprep_ml-2669:[Learn phase 5/8] - Feature generation\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:dataprep_ml-2383:Featurizing the data\u001b[0m\n" + "\u001b[32mINFO:dataprep_ml-2669:Featurizing the data\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[37mDEBUG:lightwood-2383: `featurize` runtime: 0.05 seconds\u001b[0m\n" + "\u001b[37mDEBUG:lightwood-2669: `featurize` runtime: 0.05 seconds\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:dataprep_ml-2383:[Learn phase 6/8] - Mixer training\u001b[0m\n" + "\u001b[32mINFO:dataprep_ml-2669:[Learn phase 6/8] - Mixer training\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:dataprep_ml-2383:Training the mixers\u001b[0m\n" + "\u001b[32mINFO:dataprep_ml-2669:Training the mixers\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[33mWARNING:lightwood-2383:XGBoost running on CPU\u001b[0m\n" + "\u001b[33mWARNING:lightwood-2669:XGBoost running on CPU\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[33mWARNING:lightwood-2383:XGBoost running on CPU\u001b[0m\n" + "\u001b[33mWARNING:lightwood-2669:XGBoost running on CPU\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[33mWARNING:lightwood-2383:XGBoost running on CPU\u001b[0m\n" + "\u001b[33mWARNING:lightwood-2669:XGBoost running on CPU\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[33mWARNING:lightwood-2383:XGBoost running on CPU\u001b[0m\n" + "\u001b[33mWARNING:lightwood-2669:XGBoost running on CPU\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[33mWARNING:lightwood-2383:XGBoost running on CPU\u001b[0m\n" + "\u001b[33mWARNING:lightwood-2669:XGBoost running on CPU\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[33mWARNING:lightwood-2383:XGBoost running on CPU\u001b[0m\n" + "\u001b[33mWARNING:lightwood-2669:XGBoost running on CPU\u001b[0m\n" ] }, { @@ -558,12 +558,12 @@ "name": "stdout", "output_type": "stream", "text": [ - "[10:00:36] WARNING: ../src/learner.cc:339: No visible GPU is found, setting `gpu_id` to -1\n", - "[10:00:36] WARNING: ../src/learner.cc:339: No visible GPU is found, setting `gpu_id` to -1\n", - "[10:00:36] WARNING: ../src/learner.cc:339: No visible GPU is found, setting `gpu_id` to -1\n", - "[10:00:36] WARNING: ../src/learner.cc:339: No visible GPU is found, setting `gpu_id` to -1\n", - "[10:00:36] WARNING: ../src/learner.cc:339: No visible GPU is found, setting `gpu_id` to -1\n", - "[10:00:36] WARNING: ../src/learner.cc:339: No visible GPU is found, setting `gpu_id` to -1\n" + "[10:18:50] WARNING: ../src/learner.cc:339: No visible GPU is found, setting `gpu_id` to -1\n", + "[10:18:50] WARNING: ../src/learner.cc:339: No visible GPU is found, setting `gpu_id` to -1\n", + "[10:18:50] WARNING: ../src/learner.cc:339: No visible GPU is found, setting `gpu_id` to -1\n", + "[10:18:50] WARNING: ../src/learner.cc:339: No visible GPU is found, setting `gpu_id` to -1\n", + "[10:18:50] WARNING: ../src/learner.cc:339: No visible GPU is found, setting `gpu_id` to -1\n", + "[10:18:50] WARNING: ../src/learner.cc:339: No visible GPU is found, setting `gpu_id` to -1\n" ] }, { @@ -575,63 +575,63 @@ "Consider using one of the following signatures instead:\n", "\taddcmul_(Tensor tensor1, Tensor tensor2, *, Number value) (Triggered internally at ../torch/csrc/utils/python_arg_parser.cpp:1630.)\n", " exp_avg_sq.mul_(beta2).addcmul_(1 - beta2, grad, grad)\n", - "\u001b[32mINFO:lightwood-2383:Loss of 9.051180630922318 with learning rate 0.0001\u001b[0m\n" + "\u001b[32mINFO:lightwood-2669:Loss of 9.051180630922318 with learning rate 0.0001\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:lightwood-2383:Loss of 9.014871209859848 with learning rate 0.0005\u001b[0m\n" + "\u001b[32mINFO:lightwood-2669:Loss of 9.014871209859848 with learning rate 0.0005\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:lightwood-2383:Loss of 8.969509482383728 with learning rate 0.001\u001b[0m\n" + "\u001b[32mINFO:lightwood-2669:Loss of 8.969509482383728 with learning rate 0.001\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:lightwood-2383:Loss of 8.879052013158798 with learning rate 0.002\u001b[0m\n" + "\u001b[32mINFO:lightwood-2669:Loss of 8.879052013158798 with learning rate 0.002\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:lightwood-2383:Loss of 8.788950502872467 with learning rate 0.003\u001b[0m\n" + "\u001b[32mINFO:lightwood-2669:Loss of 8.788950502872467 with learning rate 0.003\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:lightwood-2383:Loss of 8.611965209245682 with learning rate 0.005\u001b[0m\n" + "\u001b[32mINFO:lightwood-2669:Loss of 8.611965209245682 with learning rate 0.005\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:lightwood-2383:Loss of 8.195775926113129 with learning rate 0.01\u001b[0m\n" + "\u001b[32mINFO:lightwood-2669:Loss of 8.195775926113129 with learning rate 0.01\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:lightwood-2383:Loss of 6.255893141031265 with learning rate 0.05\u001b[0m\n" + "\u001b[32mINFO:lightwood-2669:Loss of 6.255893141031265 with learning rate 0.05\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:lightwood-2383:Found learning rate of: 0.05\u001b[0m\n" + "\u001b[32mINFO:lightwood-2669:Found learning rate of: 0.05\u001b[0m\n" ] }, { @@ -640,105 +640,105 @@ "text": [ "/opt/hostedtoolcache/Python/3.9.18/x64/lib/python3.9/site-packages/torch/cuda/amp/grad_scaler.py:126: UserWarning: torch.cuda.amp.GradScaler is enabled, but CUDA is not available. Disabling.\n", " warnings.warn(\n", - "\u001b[32mINFO:lightwood-2383:Loss @ epoch 1: 0.5818348675966263\u001b[0m\n" + "\u001b[32mINFO:lightwood-2669:Loss @ epoch 1: 0.5818348675966263\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:lightwood-2383:Loss @ epoch 2: 0.4797109067440033\u001b[0m\n" + "\u001b[32mINFO:lightwood-2669:Loss @ epoch 2: 0.4797109067440033\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:lightwood-2383:Loss @ epoch 3: 0.48386093974113464\u001b[0m\n" + "\u001b[32mINFO:lightwood-2669:Loss @ epoch 3: 0.48386093974113464\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:lightwood-2383:Loss @ epoch 4: 0.49511992931365967\u001b[0m\n" + "\u001b[32mINFO:lightwood-2669:Loss @ epoch 4: 0.49511992931365967\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:lightwood-2383:Loss @ epoch 5: 0.39475560188293457\u001b[0m\n" + "\u001b[32mINFO:lightwood-2669:Loss @ epoch 5: 0.39475560188293457\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:lightwood-2383:Loss @ epoch 6: 0.39592696726322174\u001b[0m\n" + "\u001b[32mINFO:lightwood-2669:Loss @ epoch 6: 0.39592696726322174\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:lightwood-2383:Loss @ epoch 7: 0.3622782379388809\u001b[0m\n" + "\u001b[32mINFO:lightwood-2669:Loss @ epoch 7: 0.3622782379388809\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:lightwood-2383:Loss @ epoch 8: 0.38170479238033295\u001b[0m\n" + "\u001b[32mINFO:lightwood-2669:Loss @ epoch 8: 0.38170479238033295\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:lightwood-2383:Loss @ epoch 9: 0.5138543993234634\u001b[0m\n" + "\u001b[32mINFO:lightwood-2669:Loss @ epoch 9: 0.5138543993234634\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:lightwood-2383:Loss @ epoch 10: 0.6360723078250885\u001b[0m\n" + "\u001b[32mINFO:lightwood-2669:Loss @ epoch 10: 0.6360723078250885\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:lightwood-2383:Loss @ epoch 1: 0.29868809472430835\u001b[0m\n" + "\u001b[32mINFO:lightwood-2669:Loss @ epoch 1: 0.29868809472430835\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:lightwood-2383:Loss @ epoch 2: 0.30318967591632495\u001b[0m\n" + "\u001b[32mINFO:lightwood-2669:Loss @ epoch 2: 0.30318967591632495\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[37mDEBUG:lightwood-2383: `fit_mixer` runtime: 0.99 seconds\u001b[0m\n" + "\u001b[37mDEBUG:lightwood-2669: `fit_mixer` runtime: 0.9 seconds\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:lightwood-2383:Started fitting LGBM models for array prediction\u001b[0m\n" + "\u001b[32mINFO:lightwood-2669:Started fitting LGBM models for array prediction\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:lightwood-2383:Started fitting XGBoost model\u001b[0m\n" + "\u001b[32mINFO:lightwood-2669:Started fitting XGBoost model\u001b[0m\n" ] }, { @@ -752,14 +752,14 @@ "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:lightwood-2383:A single GBM iteration takes 0.1 seconds\u001b[0m\n" + "\u001b[32mINFO:lightwood-2669:A single GBM iteration takes 0.1 seconds\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:lightwood-2383:Training XGBoost with 57023 iterations given 7127.986500263214 seconds constraint\u001b[0m\n" + "\u001b[32mINFO:lightwood-2669:Training XGBoost with 57023 iterations given 7127.987701892853 seconds constraint\u001b[0m\n" ] }, { @@ -871,7 +871,7 @@ "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:lightwood-2383:Started fitting XGBoost model\u001b[0m\n" + "\u001b[32mINFO:lightwood-2669:Started fitting XGBoost model\u001b[0m\n" ] }, { @@ -885,14 +885,14 @@ "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:lightwood-2383:A single GBM iteration takes 0.1 seconds\u001b[0m\n" + "\u001b[32mINFO:lightwood-2669:A single GBM iteration takes 0.1 seconds\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:lightwood-2383:Training XGBoost with 57023 iterations given 7127.988530635834 seconds constraint\u001b[0m\n" + "\u001b[32mINFO:lightwood-2669:Training XGBoost with 57023 iterations given 7127.988470077515 seconds constraint\u001b[0m\n" ] }, { @@ -997,7 +997,7 @@ "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:lightwood-2383:Started fitting XGBoost model\u001b[0m\n" + "\u001b[32mINFO:lightwood-2669:Started fitting XGBoost model\u001b[0m\n" ] }, { @@ -1011,14 +1011,14 @@ "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:lightwood-2383:A single GBM iteration takes 0.1 seconds\u001b[0m\n" + "\u001b[32mINFO:lightwood-2669:A single GBM iteration takes 0.1 seconds\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:lightwood-2383:Training XGBoost with 57023 iterations given 7127.987628936768 seconds constraint\u001b[0m\n" + "\u001b[32mINFO:lightwood-2669:Training XGBoost with 57023 iterations given 7127.988441467285 seconds constraint\u001b[0m\n" ] }, { @@ -1088,35 +1088,35 @@ "name": "stdout", "output_type": "stream", "text": [ - "[9]\tvalidation_0-rmse:19.12589" + "[9]\tvalidation_0-rmse:19.12589\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "\n" + "[10]\tvalidation_0-rmse:19.34977\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "[10]\tvalidation_0-rmse:19.34977\n" + "[11]\tvalidation_0-rmse:19.43217\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "[11]\tvalidation_0-rmse:19.43217\n" + "[12]\tvalidation_0-rmse:19.48230\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:lightwood-2383:Started fitting XGBoost model\u001b[0m\n" + "\u001b[32mINFO:lightwood-2669:Started fitting XGBoost model\u001b[0m\n" ] }, { @@ -1130,14 +1130,14 @@ "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:lightwood-2383:A single GBM iteration takes 0.1 seconds\u001b[0m\n" + "\u001b[32mINFO:lightwood-2669:A single GBM iteration takes 0.1 seconds\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:lightwood-2383:Training XGBoost with 57023 iterations given 7127.988447189331 seconds constraint\u001b[0m\n" + "\u001b[32mINFO:lightwood-2669:Training XGBoost with 57023 iterations given 7127.98904633522 seconds constraint\u001b[0m\n" ] }, { @@ -1242,7 +1242,7 @@ "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:lightwood-2383:Started fitting XGBoost model\u001b[0m\n" + "\u001b[32mINFO:lightwood-2669:Started fitting XGBoost model\u001b[0m\n" ] }, { @@ -1256,14 +1256,14 @@ "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:lightwood-2383:A single GBM iteration takes 0.1 seconds\u001b[0m\n" + "\u001b[32mINFO:lightwood-2669:A single GBM iteration takes 0.1 seconds\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:lightwood-2383:Training XGBoost with 57023 iterations given 7127.988327264786 seconds constraint\u001b[0m\n" + "\u001b[32mINFO:lightwood-2669:Training XGBoost with 57023 iterations given 7127.98738694191 seconds constraint\u001b[0m\n" ] }, { @@ -1305,14 +1305,7 @@ "name": "stdout", "output_type": "stream", "text": [ - "[5]\tvalidation_0-rmse:22.35045" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n" + "[5]\tvalidation_0-rmse:22.35045\n" ] }, { @@ -1371,11 +1364,18 @@ "[13]\tvalidation_0-rmse:22.31415\n" ] }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[14]\tvalidation_0-rmse:22.31000\n" + ] + }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:lightwood-2383:Started fitting XGBoost model\u001b[0m\n" + "\u001b[32mINFO:lightwood-2669:Started fitting XGBoost model\u001b[0m\n" ] }, { @@ -1389,14 +1389,14 @@ "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:lightwood-2383:A single GBM iteration takes 0.1 seconds\u001b[0m\n" + "\u001b[32mINFO:lightwood-2669:A single GBM iteration takes 0.1 seconds\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:lightwood-2383:Training XGBoost with 57023 iterations given 7127.988114356995 seconds constraint\u001b[0m\n" + "\u001b[32mINFO:lightwood-2669:Training XGBoost with 57023 iterations given 7127.989282369614 seconds constraint\u001b[0m\n" ] }, { @@ -1480,14 +1480,7 @@ "name": "stdout", "output_type": "stream", "text": [ - "[11]\tvalidation_0-rmse:21.84587" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n" + "[11]\tvalidation_0-rmse:21.84587\n" ] }, { @@ -1515,119 +1508,119 @@ "name": "stderr", "output_type": "stream", "text": [ - "\u001b[37mDEBUG:lightwood-2383: `fit_mixer` runtime: 0.51 seconds\u001b[0m\n" + "\u001b[37mDEBUG:lightwood-2669: `fit_mixer` runtime: 0.49 seconds\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:dataprep_ml-2383:Ensembling the mixer\u001b[0m\n" + "\u001b[32mINFO:dataprep_ml-2669:Ensembling the mixer\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:lightwood-2383:Mixer: NeuralTs got accuracy: 0.875\u001b[0m\n" + "\u001b[32mINFO:lightwood-2669:Mixer: NeuralTs got accuracy: 0.875\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[33mWARNING:lightwood-2383:This model does not output probability estimates\u001b[0m\n" + "\u001b[33mWARNING:lightwood-2669:This model does not output probability estimates\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:lightwood-2383:Mixer: XGBoostArrayMixer got accuracy: 0.869\u001b[0m\n" + "\u001b[32mINFO:lightwood-2669:Mixer: XGBoostArrayMixer got accuracy: 0.869\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:lightwood-2383:Picked best mixer: NeuralTs\u001b[0m\n" + "\u001b[32mINFO:lightwood-2669:Picked best mixer: NeuralTs\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[37mDEBUG:lightwood-2383: `fit` runtime: 1.55 seconds\u001b[0m\n" + "\u001b[37mDEBUG:lightwood-2669: `fit` runtime: 1.44 seconds\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:dataprep_ml-2383:[Learn phase 7/8] - Ensemble analysis\u001b[0m\n" + "\u001b[32mINFO:dataprep_ml-2669:[Learn phase 7/8] - Ensemble analysis\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:dataprep_ml-2383:Analyzing the ensemble of mixers\u001b[0m\n" + "\u001b[32mINFO:dataprep_ml-2669:Analyzing the ensemble of mixers\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:lightwood-2383:The block ICP is now running its analyze() method\u001b[0m\n" + "\u001b[32mINFO:lightwood-2669:The block ICP is now running its analyze() method\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:lightwood-2383:The block ConfStats is now running its analyze() method\u001b[0m\n" + "\u001b[32mINFO:lightwood-2669:The block ConfStats is now running its analyze() method\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:lightwood-2383:The block AccStats is now running its analyze() method\u001b[0m\n" + "\u001b[32mINFO:lightwood-2669:The block AccStats is now running its analyze() method\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:lightwood-2383:The block PermutationFeatureImportance is now running its analyze() method\u001b[0m\n" + "\u001b[32mINFO:lightwood-2669:The block PermutationFeatureImportance is now running its analyze() method\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[33mWARNING:lightwood-2383:Block 'PermutationFeatureImportance' does not support time series nor text encoding, skipping...\u001b[0m\n" + "\u001b[33mWARNING:lightwood-2669:Block 'PermutationFeatureImportance' does not support time series nor text encoding, skipping...\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[37mDEBUG:lightwood-2383: `analyze_ensemble` runtime: 0.16 seconds\u001b[0m\n" + "\u001b[37mDEBUG:lightwood-2669: `analyze_ensemble` runtime: 0.15 seconds\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:dataprep_ml-2383:[Learn phase 8/8] - Adjustment on validation requested\u001b[0m\n" + "\u001b[32mINFO:dataprep_ml-2669:[Learn phase 8/8] - Adjustment on validation requested\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:dataprep_ml-2383:Updating the mixers\u001b[0m\n" + "\u001b[32mINFO:dataprep_ml-2669:Updating the mixers\u001b[0m\n" ] }, { @@ -1642,77 +1635,77 @@ "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:lightwood-2383:Loss @ epoch 1: 0.29626286526521045\u001b[0m\n" + "\u001b[32mINFO:lightwood-2669:Loss @ epoch 1: 0.29626286526521045\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:lightwood-2383:Loss @ epoch 2: 0.2954987535874049\u001b[0m\n" + "\u001b[32mINFO:lightwood-2669:Loss @ epoch 2: 0.2954987535874049\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:lightwood-2383:Updating array of LGBM models...\u001b[0m\n" + "\u001b[32mINFO:lightwood-2669:Updating array of LGBM models...\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:lightwood-2383:XGBoost mixer does not have a `partial_fit` implementation\u001b[0m\n" + "\u001b[32mINFO:lightwood-2669:XGBoost mixer does not have a `partial_fit` implementation\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:lightwood-2383:XGBoost mixer does not have a `partial_fit` implementation\u001b[0m\n" + "\u001b[32mINFO:lightwood-2669:XGBoost mixer does not have a `partial_fit` implementation\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:lightwood-2383:XGBoost mixer does not have a `partial_fit` implementation\u001b[0m\n" + "\u001b[32mINFO:lightwood-2669:XGBoost mixer does not have a `partial_fit` implementation\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:lightwood-2383:XGBoost mixer does not have a `partial_fit` implementation\u001b[0m\n" + "\u001b[32mINFO:lightwood-2669:XGBoost mixer does not have a `partial_fit` implementation\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:lightwood-2383:XGBoost mixer does not have a `partial_fit` implementation\u001b[0m\n" + "\u001b[32mINFO:lightwood-2669:XGBoost mixer does not have a `partial_fit` implementation\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:lightwood-2383:XGBoost mixer does not have a `partial_fit` implementation\u001b[0m\n" + "\u001b[32mINFO:lightwood-2669:XGBoost mixer does not have a `partial_fit` implementation\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[37mDEBUG:lightwood-2383: `adjust` runtime: 0.09 seconds\u001b[0m\n" + "\u001b[37mDEBUG:lightwood-2669: `adjust` runtime: 0.09 seconds\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[37mDEBUG:lightwood-2383: `learn` runtime: 2.05 seconds\u001b[0m\n" + "\u001b[37mDEBUG:lightwood-2669: `learn` runtime: 1.94 seconds\u001b[0m\n" ] } ], @@ -1734,10 +1727,10 @@ "execution_count": 7, "metadata": { "execution": { - "iopub.execute_input": "2024-03-19T10:00:37.996875Z", - "iopub.status.busy": "2024-03-19T10:00:37.996662Z", - "iopub.status.idle": "2024-03-19T10:00:38.223083Z", - "shell.execute_reply": "2024-03-19T10:00:38.222403Z" + "iopub.execute_input": "2024-03-19T10:18:52.627058Z", + "iopub.status.busy": "2024-03-19T10:18:52.626672Z", + "iopub.status.idle": "2024-03-19T10:18:52.849567Z", + "shell.execute_reply": "2024-03-19T10:18:52.848941Z" } }, "outputs": [ @@ -1745,20 +1738,20 @@ "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:dataprep_ml-2383:[Predict phase 1/4] - Data preprocessing\u001b[0m\n" + "\u001b[32mINFO:dataprep_ml-2669:[Predict phase 1/4] - Data preprocessing\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "/tmp/d20805f5018ce0107de047b65b88c485a326e1d1a6f5f89517108424359285157.py:584: SettingWithCopyWarning: \n", + "/tmp/f73a2317178f784e4c57c843f563cceab09655f97903f47417108435306748986.py:584: SettingWithCopyWarning: \n", "A value is trying to be set on a copy of a slice from a DataFrame.\n", "Try using .loc[row_indexer,col_indexer] = value instead\n", "\n", "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", " data[col] = [None] * len(data)\n", - "\u001b[32mINFO:dataprep_ml-2383:Cleaning the data\u001b[0m\n" + "\u001b[32mINFO:dataprep_ml-2669:Cleaning the data\u001b[0m\n" ] }, { @@ -1767,119 +1760,119 @@ "text": [ "/opt/hostedtoolcache/Python/3.9.18/x64/lib/python3.9/site-packages/dataprep_ml/cleaners.py:163: UserWarning: The argument 'infer_datetime_format' is deprecated and will be removed in a future version. A strict version of it is now the default, see https://pandas.pydata.org/pdeps/0004-consistent-to-datetime-parsing.html. You can safely remove this argument.\n", " result = pd.to_datetime(element,\n", - "\u001b[32mINFO:dataprep_ml-2383:Transforming timeseries data\u001b[0m\n" + "\u001b[32mINFO:dataprep_ml-2669:Transforming timeseries data\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[37mDEBUG:lightwood-2383: `preprocess` runtime: 0.02 seconds\u001b[0m\n" + "\u001b[37mDEBUG:lightwood-2669: `preprocess` runtime: 0.02 seconds\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:dataprep_ml-2383:[Predict phase 2/4] - Feature generation\u001b[0m\n" + "\u001b[32mINFO:dataprep_ml-2669:[Predict phase 2/4] - Feature generation\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:dataprep_ml-2383:Featurizing the data\u001b[0m\n" + "\u001b[32mINFO:dataprep_ml-2669:Featurizing the data\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[37mDEBUG:lightwood-2383: `featurize` runtime: 0.01 seconds\u001b[0m\n" + "\u001b[37mDEBUG:lightwood-2669: `featurize` runtime: 0.01 seconds\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:dataprep_ml-2383:[Predict phase 3/4] - Calling ensemble\u001b[0m\n" + "\u001b[32mINFO:dataprep_ml-2669:[Predict phase 3/4] - Calling ensemble\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[37mDEBUG:lightwood-2383: `_timed_call` runtime: 0.09 seconds\u001b[0m\n" + "\u001b[37mDEBUG:lightwood-2669: `_timed_call` runtime: 0.09 seconds\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:dataprep_ml-2383:[Predict phase 4/4] - Analyzing output\u001b[0m\n" + "\u001b[32mINFO:dataprep_ml-2669:[Predict phase 4/4] - Analyzing output\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:lightwood-2383:The block ICP is now running its explain() method\u001b[0m\n" + "\u001b[32mINFO:lightwood-2669:The block ICP is now running its explain() method\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:lightwood-2383:The block ConfStats is now running its explain() method\u001b[0m\n" + "\u001b[32mINFO:lightwood-2669:The block ConfStats is now running its explain() method\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:lightwood-2383:ConfStats.explain() has not been implemented, no modifications will be done to the data insights.\u001b[0m\n" + "\u001b[32mINFO:lightwood-2669:ConfStats.explain() has not been implemented, no modifications will be done to the data insights.\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:lightwood-2383:The block AccStats is now running its explain() method\u001b[0m\n" + "\u001b[32mINFO:lightwood-2669:The block AccStats is now running its explain() method\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:lightwood-2383:AccStats.explain() has not been implemented, no modifications will be done to the data insights.\u001b[0m\n" + "\u001b[32mINFO:lightwood-2669:AccStats.explain() has not been implemented, no modifications will be done to the data insights.\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:lightwood-2383:The block PermutationFeatureImportance is now running its explain() method\u001b[0m\n" + "\u001b[32mINFO:lightwood-2669:The block PermutationFeatureImportance is now running its explain() method\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:lightwood-2383:PermutationFeatureImportance.explain() has not been implemented, no modifications will be done to the data insights.\u001b[0m\n" + "\u001b[32mINFO:lightwood-2669:PermutationFeatureImportance.explain() has not been implemented, no modifications will be done to the data insights.\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[37mDEBUG:lightwood-2383: `explain` runtime: 0.09 seconds\u001b[0m\n" + "\u001b[37mDEBUG:lightwood-2669: `explain` runtime: 0.09 seconds\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[37mDEBUG:lightwood-2383: `predict` runtime: 0.22 seconds\u001b[0m\n" + "\u001b[37mDEBUG:lightwood-2669: `predict` runtime: 0.22 seconds\u001b[0m\n" ] } ], @@ -1899,10 +1892,10 @@ "execution_count": 8, "metadata": { "execution": { - "iopub.execute_input": "2024-03-19T10:00:38.226342Z", - "iopub.status.busy": "2024-03-19T10:00:38.226147Z", - "iopub.status.idle": "2024-03-19T10:00:38.237734Z", - "shell.execute_reply": "2024-03-19T10:00:38.237155Z" + "iopub.execute_input": "2024-03-19T10:18:52.852090Z", + "iopub.status.busy": "2024-03-19T10:18:52.851741Z", + "iopub.status.idle": "2024-03-19T10:18:52.862630Z", + "shell.execute_reply": "2024-03-19T10:18:52.862011Z" } }, "outputs": [ @@ -2007,10 +2000,10 @@ "execution_count": 9, "metadata": { "execution": { - "iopub.execute_input": "2024-03-19T10:00:38.240237Z", - "iopub.status.busy": "2024-03-19T10:00:38.239843Z", - "iopub.status.idle": "2024-03-19T10:00:38.630482Z", - "shell.execute_reply": "2024-03-19T10:00:38.629820Z" + "iopub.execute_input": "2024-03-19T10:18:52.865241Z", + "iopub.status.busy": "2024-03-19T10:18:52.864888Z", + "iopub.status.idle": "2024-03-19T10:18:53.249782Z", + "shell.execute_reply": "2024-03-19T10:18:53.249067Z" } }, "outputs": [], @@ -2023,10 +2016,10 @@ "execution_count": 10, "metadata": { "execution": { - "iopub.execute_input": "2024-03-19T10:00:38.633703Z", - "iopub.status.busy": "2024-03-19T10:00:38.633127Z", - "iopub.status.idle": "2024-03-19T10:00:38.827401Z", - "shell.execute_reply": "2024-03-19T10:00:38.826665Z" + "iopub.execute_input": "2024-03-19T10:18:53.252801Z", + "iopub.status.busy": "2024-03-19T10:18:53.252497Z", + "iopub.status.idle": "2024-03-19T10:18:53.440309Z", + "shell.execute_reply": "2024-03-19T10:18:53.439662Z" } }, "outputs": [ diff --git a/_sources/tutorials/tutorial_update_models/tutorial_update_models.ipynb.txt b/_sources/tutorials/tutorial_update_models/tutorial_update_models.ipynb.txt index 581b499cf..7e69b9956 100644 --- a/_sources/tutorials/tutorial_update_models/tutorial_update_models.ipynb.txt +++ b/_sources/tutorials/tutorial_update_models/tutorial_update_models.ipynb.txt @@ -21,10 +21,10 @@ "execution_count": 1, "metadata": { "execution": { - "iopub.execute_input": "2024-03-19T10:00:41.914113Z", - "iopub.status.busy": "2024-03-19T10:00:41.913919Z", - "iopub.status.idle": "2024-03-19T10:00:44.497058Z", - "shell.execute_reply": "2024-03-19T10:00:44.496340Z" + "iopub.execute_input": "2024-03-19T10:18:56.494093Z", + "iopub.status.busy": "2024-03-19T10:18:56.493634Z", + "iopub.status.idle": "2024-03-19T10:18:59.001389Z", + "shell.execute_reply": "2024-03-19T10:18:59.000734Z" } }, "outputs": [ @@ -32,14 +32,14 @@ "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:lightwood-2429:No torchvision detected, image helpers not supported.\u001b[0m\n" + "\u001b[32mINFO:lightwood-2715:No torchvision detected, image helpers not supported.\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:lightwood-2429:No torchvision/pillow detected, image encoder not supported\u001b[0m\n" + "\u001b[32mINFO:lightwood-2715:No torchvision/pillow detected, image encoder not supported\u001b[0m\n" ] } ], @@ -53,10 +53,10 @@ "execution_count": 2, "metadata": { "execution": { - "iopub.execute_input": "2024-03-19T10:00:44.500008Z", - "iopub.status.busy": "2024-03-19T10:00:44.499726Z", - "iopub.status.idle": "2024-03-19T10:00:44.741388Z", - "shell.execute_reply": "2024-03-19T10:00:44.740724Z" + "iopub.execute_input": "2024-03-19T10:18:59.004694Z", + "iopub.status.busy": "2024-03-19T10:18:59.004232Z", + "iopub.status.idle": "2024-03-19T10:18:59.160672Z", + "shell.execute_reply": "2024-03-19T10:18:59.160035Z" } }, "outputs": [ @@ -98,10 +98,10 @@ "execution_count": 3, "metadata": { "execution": { - "iopub.execute_input": "2024-03-19T10:00:44.744380Z", - "iopub.status.busy": "2024-03-19T10:00:44.743940Z", - "iopub.status.idle": "2024-03-19T10:00:46.186503Z", - "shell.execute_reply": "2024-03-19T10:00:46.185809Z" + "iopub.execute_input": "2024-03-19T10:18:59.163388Z", + "iopub.status.busy": "2024-03-19T10:18:59.162992Z", + "iopub.status.idle": "2024-03-19T10:19:00.590655Z", + "shell.execute_reply": "2024-03-19T10:19:00.589988Z" }, "scrolled": true }, @@ -110,364 +110,364 @@ "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:type_infer-2429:Analyzing a sample of 979\u001b[0m\n" + "\u001b[32mINFO:type_infer-2715:Analyzing a sample of 979\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:type_infer-2429:from a total population of 1030, this is equivalent to 95.0% of your data.\u001b[0m\n" + "\u001b[32mINFO:type_infer-2715:from a total population of 1030, this is equivalent to 95.0% of your data.\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:type_infer-2429:Using 3 processes to deduct types.\u001b[0m\n" + "\u001b[32mINFO:type_infer-2715:Using 3 processes to deduct types.\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:type_infer-2429:Infering type for: cement\u001b[0m\n" + "\u001b[32mINFO:type_infer-2715:Infering type for: slag\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:type_infer-2429:Infering type for: slag\u001b[0m\n" + "\u001b[32mINFO:type_infer-2715:Infering type for: cement\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:type_infer-2429:Column cement has data type float\u001b[0m\n" + "\u001b[32mINFO:type_infer-2715:Column slag has data type float\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:type_infer-2429:Infering type for: flyAsh\u001b[0m\n" + "\u001b[32mINFO:type_infer-2715:Infering type for: flyAsh\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:type_infer-2429:Column slag has data type float\u001b[0m\n" + "\u001b[32mINFO:type_infer-2715:Column cement has data type float\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:type_infer-2429:Infering type for: water\u001b[0m\n" + "\u001b[32mINFO:type_infer-2715:Infering type for: water\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:type_infer-2429:Column flyAsh has data type float\u001b[0m\n" + "\u001b[32mINFO:type_infer-2715:Column flyAsh has data type float\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:type_infer-2429:Infering type for: superPlasticizer\u001b[0m\n" + "\u001b[32mINFO:type_infer-2715:Infering type for: superPlasticizer\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:type_infer-2429:Column water has data type float\u001b[0m\n" + "\u001b[32mINFO:type_infer-2715:Column water has data type float\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:type_infer-2429:Infering type for: coarseAggregate\u001b[0m\n" + "\u001b[32mINFO:type_infer-2715:Infering type for: coarseAggregate\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:type_infer-2429:Column superPlasticizer has data type float\u001b[0m\n" + "\u001b[32mINFO:type_infer-2715:Column superPlasticizer has data type float\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:type_infer-2429:Infering type for: fineAggregate\u001b[0m\n" + "\u001b[32mINFO:type_infer-2715:Infering type for: fineAggregate\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:type_infer-2429:Column coarseAggregate has data type float\u001b[0m\n" + "\u001b[32mINFO:type_infer-2715:Column coarseAggregate has data type float\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:type_infer-2429:Infering type for: age\u001b[0m\n" + "\u001b[32mINFO:type_infer-2715:Column fineAggregate has data type float\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:type_infer-2429:Column age has data type integer\u001b[0m\n" + "\u001b[32mINFO:type_infer-2715:Infering type for: age\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:type_infer-2429:Column fineAggregate has data type float\u001b[0m\n" + "\u001b[32mINFO:type_infer-2715:Infering type for: concrete_strength\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:type_infer-2429:Infering type for: id\u001b[0m\n" + "\u001b[32mINFO:type_infer-2715:Column age has data type integer\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:type_infer-2429:Infering type for: concrete_strength\u001b[0m\n" + "\u001b[32mINFO:type_infer-2715:Column concrete_strength has data type float\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:type_infer-2429:Column concrete_strength has data type float\u001b[0m\n" + "\u001b[32mINFO:type_infer-2715:Infering type for: id\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:type_infer-2429:Column id has data type integer\u001b[0m\n" + "\u001b[32mINFO:type_infer-2715:Column id has data type integer\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:dataprep_ml-2429:Starting statistical analysis\u001b[0m\n" + "\u001b[32mINFO:dataprep_ml-2715:Starting statistical analysis\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:dataprep_ml-2429:Finished statistical analysis\u001b[0m\n" + "\u001b[32mINFO:dataprep_ml-2715:Finished statistical analysis\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:dataprep_ml-2429:[Learn phase 1/8] - Statistical analysis\u001b[0m\n" + "\u001b[32mINFO:dataprep_ml-2715:[Learn phase 1/8] - Statistical analysis\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:dataprep_ml-2429:Starting statistical analysis\u001b[0m\n" + "\u001b[32mINFO:dataprep_ml-2715:Starting statistical analysis\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:dataprep_ml-2429:Finished statistical analysis\u001b[0m\n" + "\u001b[32mINFO:dataprep_ml-2715:Finished statistical analysis\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[37mDEBUG:lightwood-2429: `analyze_data` runtime: 0.02 seconds\u001b[0m\n" + "\u001b[37mDEBUG:lightwood-2715: `analyze_data` runtime: 0.02 seconds\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:dataprep_ml-2429:[Learn phase 2/8] - Data preprocessing\u001b[0m\n" + "\u001b[32mINFO:dataprep_ml-2715:[Learn phase 2/8] - Data preprocessing\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:dataprep_ml-2429:Cleaning the data\u001b[0m\n" + "\u001b[32mINFO:dataprep_ml-2715:Cleaning the data\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[37mDEBUG:lightwood-2429: `preprocess` runtime: 0.01 seconds\u001b[0m\n" + "\u001b[37mDEBUG:lightwood-2715: `preprocess` runtime: 0.01 seconds\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:dataprep_ml-2429:[Learn phase 3/8] - Data splitting\u001b[0m\n" + "\u001b[32mINFO:dataprep_ml-2715:[Learn phase 3/8] - Data splitting\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:dataprep_ml-2429:Splitting the data into train/test\u001b[0m\n" + "\u001b[32mINFO:dataprep_ml-2715:Splitting the data into train/test\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[37mDEBUG:lightwood-2429: `split` runtime: 0.0 seconds\u001b[0m\n" + "\u001b[37mDEBUG:lightwood-2715: `split` runtime: 0.0 seconds\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:dataprep_ml-2429:[Learn phase 4/8] - Preparing encoders\u001b[0m\n" + "\u001b[32mINFO:dataprep_ml-2715:[Learn phase 4/8] - Preparing encoders\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[37mDEBUG:dataprep_ml-2429:Preparing sequentially...\u001b[0m\n" + "\u001b[37mDEBUG:dataprep_ml-2715:Preparing sequentially...\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[37mDEBUG:dataprep_ml-2429:Preparing encoder for id...\u001b[0m\n" + "\u001b[37mDEBUG:dataprep_ml-2715:Preparing encoder for id...\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[37mDEBUG:dataprep_ml-2429:Preparing encoder for cement...\u001b[0m\n" + "\u001b[37mDEBUG:dataprep_ml-2715:Preparing encoder for cement...\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[37mDEBUG:dataprep_ml-2429:Preparing encoder for slag...\u001b[0m\n" + "\u001b[37mDEBUG:dataprep_ml-2715:Preparing encoder for slag...\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[37mDEBUG:dataprep_ml-2429:Preparing encoder for flyAsh...\u001b[0m\n" + "\u001b[37mDEBUG:dataprep_ml-2715:Preparing encoder for flyAsh...\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[37mDEBUG:dataprep_ml-2429:Preparing encoder for water...\u001b[0m\n" + "\u001b[37mDEBUG:dataprep_ml-2715:Preparing encoder for water...\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[37mDEBUG:dataprep_ml-2429:Preparing encoder for superPlasticizer...\u001b[0m\n" + "\u001b[37mDEBUG:dataprep_ml-2715:Preparing encoder for superPlasticizer...\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[37mDEBUG:dataprep_ml-2429:Preparing encoder for coarseAggregate...\u001b[0m\n" + "\u001b[37mDEBUG:dataprep_ml-2715:Preparing encoder for coarseAggregate...\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[37mDEBUG:dataprep_ml-2429:Preparing encoder for fineAggregate...\u001b[0m\n" + "\u001b[37mDEBUG:dataprep_ml-2715:Preparing encoder for fineAggregate...\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[37mDEBUG:dataprep_ml-2429:Preparing encoder for age...\u001b[0m\n" + "\u001b[37mDEBUG:dataprep_ml-2715:Preparing encoder for age...\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[37mDEBUG:lightwood-2429: `prepare` runtime: 0.01 seconds\u001b[0m\n" + "\u001b[37mDEBUG:lightwood-2715: `prepare` runtime: 0.01 seconds\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:dataprep_ml-2429:[Learn phase 5/8] - Feature generation\u001b[0m\n" + "\u001b[32mINFO:dataprep_ml-2715:[Learn phase 5/8] - Feature generation\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:dataprep_ml-2429:Featurizing the data\u001b[0m\n" + "\u001b[32mINFO:dataprep_ml-2715:Featurizing the data\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[37mDEBUG:lightwood-2429: `featurize` runtime: 0.06 seconds\u001b[0m\n" + "\u001b[37mDEBUG:lightwood-2715: `featurize` runtime: 0.06 seconds\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:dataprep_ml-2429:[Learn phase 6/8] - Mixer training\u001b[0m\n" + "\u001b[32mINFO:dataprep_ml-2715:[Learn phase 6/8] - Mixer training\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:dataprep_ml-2429:Training the mixers\u001b[0m\n" + "\u001b[32mINFO:dataprep_ml-2715:Training the mixers\u001b[0m\n" ] }, { @@ -487,63 +487,63 @@ "Consider using one of the following signatures instead:\n", "\taddcmul_(Tensor tensor1, Tensor tensor2, *, Number value) (Triggered internally at ../torch/csrc/utils/python_arg_parser.cpp:1630.)\n", " exp_avg_sq.mul_(beta2).addcmul_(1 - beta2, grad, grad)\n", - "\u001b[32mINFO:lightwood-2429:Loss of 39.99637508392334 with learning rate 0.0001\u001b[0m\n" + "\u001b[32mINFO:lightwood-2715:Loss of 39.99637508392334 with learning rate 0.0001\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:lightwood-2429:Loss of 21.826460361480713 with learning rate 0.0005\u001b[0m\n" + "\u001b[32mINFO:lightwood-2715:Loss of 21.826460361480713 with learning rate 0.0005\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:lightwood-2429:Loss of 15.12899512052536 with learning rate 0.001\u001b[0m\n" + "\u001b[32mINFO:lightwood-2715:Loss of 15.12899512052536 with learning rate 0.001\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:lightwood-2429:Loss of 15.062753021717072 with learning rate 0.002\u001b[0m\n" + "\u001b[32mINFO:lightwood-2715:Loss of 15.062753021717072 with learning rate 0.002\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:lightwood-2429:Loss of 26.490495562553406 with learning rate 0.003\u001b[0m\n" + "\u001b[32mINFO:lightwood-2715:Loss of 26.490495562553406 with learning rate 0.003\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:lightwood-2429:Loss of 33.6572003364563 with learning rate 0.005\u001b[0m\n" + "\u001b[32mINFO:lightwood-2715:Loss of 33.6572003364563 with learning rate 0.005\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:lightwood-2429:Loss of 303.60721158981323 with learning rate 0.01\u001b[0m\n" + "\u001b[32mINFO:lightwood-2715:Loss of 303.60721158981323 with learning rate 0.01\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:lightwood-2429:Loss of nan with learning rate 0.05\u001b[0m\n" + "\u001b[32mINFO:lightwood-2715:Loss of nan with learning rate 0.05\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:lightwood-2429:Found learning rate of: 0.002\u001b[0m\n" + "\u001b[32mINFO:lightwood-2715:Found learning rate of: 0.002\u001b[0m\n" ] }, { @@ -552,161 +552,161 @@ "text": [ "/opt/hostedtoolcache/Python/3.9.18/x64/lib/python3.9/site-packages/torch/cuda/amp/grad_scaler.py:126: UserWarning: torch.cuda.amp.GradScaler is enabled, but CUDA is not available. Disabling.\n", " warnings.warn(\n", - "\u001b[32mINFO:lightwood-2429:Loss @ epoch 1: 0.11838734149932861\u001b[0m\n" + "\u001b[32mINFO:lightwood-2715:Loss @ epoch 1: 0.11838734149932861\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:lightwood-2429:Loss @ epoch 2: 0.4641949534416199\u001b[0m\n" + "\u001b[32mINFO:lightwood-2715:Loss @ epoch 2: 0.4641949534416199\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:lightwood-2429:Loss @ epoch 3: 0.3976145386695862\u001b[0m\n" + "\u001b[32mINFO:lightwood-2715:Loss @ epoch 3: 0.3976145386695862\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:lightwood-2429:Loss @ epoch 4: 0.3706841468811035\u001b[0m\n" + "\u001b[32mINFO:lightwood-2715:Loss @ epoch 4: 0.3706841468811035\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:lightwood-2429:Loss @ epoch 5: 0.2367912232875824\u001b[0m\n" + "\u001b[32mINFO:lightwood-2715:Loss @ epoch 5: 0.2367912232875824\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:lightwood-2429:Loss @ epoch 6: 0.22560915350914001\u001b[0m\n" + "\u001b[32mINFO:lightwood-2715:Loss @ epoch 6: 0.22560915350914001\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:lightwood-2429:Loss @ epoch 7: 0.12089195847511292\u001b[0m\n" + "\u001b[32mINFO:lightwood-2715:Loss @ epoch 7: 0.12089195847511292\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[37mDEBUG:lightwood-2429: `fit_mixer` runtime: 0.59 seconds\u001b[0m\n" + "\u001b[37mDEBUG:lightwood-2715: `fit_mixer` runtime: 0.58 seconds\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:dataprep_ml-2429:Ensembling the mixer\u001b[0m\n" + "\u001b[32mINFO:dataprep_ml-2715:Ensembling the mixer\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:lightwood-2429:Mixer: Neural got accuracy: 0.238\u001b[0m\n" + "\u001b[32mINFO:lightwood-2715:Mixer: Neural got accuracy: 0.238\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:lightwood-2429:Picked best mixer: Neural\u001b[0m\n" + "\u001b[32mINFO:lightwood-2715:Picked best mixer: Neural\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[37mDEBUG:lightwood-2429: `fit` runtime: 0.6 seconds\u001b[0m\n" + "\u001b[37mDEBUG:lightwood-2715: `fit` runtime: 0.59 seconds\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:dataprep_ml-2429:[Learn phase 7/8] - Ensemble analysis\u001b[0m\n" + "\u001b[32mINFO:dataprep_ml-2715:[Learn phase 7/8] - Ensemble analysis\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:dataprep_ml-2429:Analyzing the ensemble of mixers\u001b[0m\n" + "\u001b[32mINFO:dataprep_ml-2715:Analyzing the ensemble of mixers\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:lightwood-2429:The block ICP is now running its analyze() method\u001b[0m\n" + "\u001b[32mINFO:lightwood-2715:The block ICP is now running its analyze() method\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:lightwood-2429:The block ConfStats is now running its analyze() method\u001b[0m\n" + "\u001b[32mINFO:lightwood-2715:The block ConfStats is now running its analyze() method\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:lightwood-2429:The block AccStats is now running its analyze() method\u001b[0m\n" + "\u001b[32mINFO:lightwood-2715:The block AccStats is now running its analyze() method\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:lightwood-2429:The block PermutationFeatureImportance is now running its analyze() method\u001b[0m\n" + "\u001b[32mINFO:lightwood-2715:The block PermutationFeatureImportance is now running its analyze() method\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:lightwood-2429:[PFI] Using a random sample (1000 rows out of 10).\u001b[0m\n" + "\u001b[32mINFO:lightwood-2715:[PFI] Using a random sample (1000 rows out of 10).\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:lightwood-2429:[PFI] Set to consider first 10 columns out of 9: ['id', 'cement', 'slag', 'flyAsh', 'water', 'superPlasticizer', 'coarseAggregate', 'fineAggregate', 'age'].\u001b[0m\n" + "\u001b[32mINFO:lightwood-2715:[PFI] Set to consider first 10 columns out of 9: ['id', 'cement', 'slag', 'flyAsh', 'water', 'superPlasticizer', 'coarseAggregate', 'fineAggregate', 'age'].\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[37mDEBUG:lightwood-2429: `analyze_ensemble` runtime: 0.15 seconds\u001b[0m\n" + "\u001b[37mDEBUG:lightwood-2715: `analyze_ensemble` runtime: 0.15 seconds\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:dataprep_ml-2429:[Learn phase 8/8] - Adjustment on validation requested\u001b[0m\n" + "\u001b[32mINFO:dataprep_ml-2715:[Learn phase 8/8] - Adjustment on validation requested\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:dataprep_ml-2429:Updating the mixers\u001b[0m\n" + "\u001b[32mINFO:dataprep_ml-2715:Updating the mixers\u001b[0m\n" ] }, { @@ -714,22 +714,28 @@ "output_type": "stream", "text": [ "/opt/hostedtoolcache/Python/3.9.18/x64/lib/python3.9/site-packages/torch/cuda/amp/grad_scaler.py:126: UserWarning: torch.cuda.amp.GradScaler is enabled, but CUDA is not available. Disabling.\n", - " warnings.warn(\n", - "\u001b[32mINFO:lightwood-2429:Loss @ epoch 1: 0.1678172747294108\u001b[0m\n" + " warnings.warn(\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\u001b[32mINFO:lightwood-2715:Loss @ epoch 1: 0.1678172747294108\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[37mDEBUG:lightwood-2429: `adjust` runtime: 0.03 seconds\u001b[0m\n" + "\u001b[37mDEBUG:lightwood-2715: `adjust` runtime: 0.03 seconds\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[37mDEBUG:lightwood-2429: `learn` runtime: 0.88 seconds\u001b[0m\n" + "\u001b[37mDEBUG:lightwood-2715: `learn` runtime: 0.87 seconds\u001b[0m\n" ] } ], @@ -766,10 +772,10 @@ "execution_count": 4, "metadata": { "execution": { - "iopub.execute_input": "2024-03-19T10:00:46.189375Z", - "iopub.status.busy": "2024-03-19T10:00:46.189162Z", - "iopub.status.idle": "2024-03-19T10:00:46.330971Z", - "shell.execute_reply": "2024-03-19T10:00:46.330313Z" + "iopub.execute_input": "2024-03-19T10:19:00.593904Z", + "iopub.status.busy": "2024-03-19T10:19:00.593304Z", + "iopub.status.idle": "2024-03-19T10:19:00.734274Z", + "shell.execute_reply": "2024-03-19T10:19:00.733642Z" } }, "outputs": [ @@ -777,126 +783,126 @@ "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:dataprep_ml-2429:[Predict phase 1/4] - Data preprocessing\u001b[0m\n" + "\u001b[32mINFO:dataprep_ml-2715:[Predict phase 1/4] - Data preprocessing\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:dataprep_ml-2429:Cleaning the data\u001b[0m\n" + "\u001b[32mINFO:dataprep_ml-2715:Cleaning the data\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[37mDEBUG:lightwood-2429: `preprocess` runtime: 0.01 seconds\u001b[0m\n" + "\u001b[37mDEBUG:lightwood-2715: `preprocess` runtime: 0.01 seconds\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:dataprep_ml-2429:[Predict phase 2/4] - Feature generation\u001b[0m\n" + "\u001b[32mINFO:dataprep_ml-2715:[Predict phase 2/4] - Feature generation\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:dataprep_ml-2429:Featurizing the data\u001b[0m\n" + "\u001b[32mINFO:dataprep_ml-2715:Featurizing the data\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[37mDEBUG:lightwood-2429: `featurize` runtime: 0.03 seconds\u001b[0m\n" + "\u001b[37mDEBUG:lightwood-2715: `featurize` runtime: 0.03 seconds\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:dataprep_ml-2429:[Predict phase 3/4] - Calling ensemble\u001b[0m\n" + "\u001b[32mINFO:dataprep_ml-2715:[Predict phase 3/4] - Calling ensemble\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[37mDEBUG:lightwood-2429: `_timed_call` runtime: 0.03 seconds\u001b[0m\n" + "\u001b[37mDEBUG:lightwood-2715: `_timed_call` runtime: 0.03 seconds\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:dataprep_ml-2429:[Predict phase 4/4] - Analyzing output\u001b[0m\n" + "\u001b[32mINFO:dataprep_ml-2715:[Predict phase 4/4] - Analyzing output\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:lightwood-2429:The block ICP is now running its explain() method\u001b[0m\n" + "\u001b[32mINFO:lightwood-2715:The block ICP is now running its explain() method\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:lightwood-2429:The block ConfStats is now running its explain() method\u001b[0m\n" + "\u001b[32mINFO:lightwood-2715:The block ConfStats is now running its explain() method\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:lightwood-2429:ConfStats.explain() has not been implemented, no modifications will be done to the data insights.\u001b[0m\n" + "\u001b[32mINFO:lightwood-2715:ConfStats.explain() has not been implemented, no modifications will be done to the data insights.\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:lightwood-2429:The block AccStats is now running its explain() method\u001b[0m\n" + "\u001b[32mINFO:lightwood-2715:The block AccStats is now running its explain() method\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:lightwood-2429:AccStats.explain() has not been implemented, no modifications will be done to the data insights.\u001b[0m\n" + "\u001b[32mINFO:lightwood-2715:AccStats.explain() has not been implemented, no modifications will be done to the data insights.\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:lightwood-2429:The block PermutationFeatureImportance is now running its explain() method\u001b[0m\n" + "\u001b[32mINFO:lightwood-2715:The block PermutationFeatureImportance is now running its explain() method\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:lightwood-2429:PermutationFeatureImportance.explain() has not been implemented, no modifications will be done to the data insights.\u001b[0m\n" + "\u001b[32mINFO:lightwood-2715:PermutationFeatureImportance.explain() has not been implemented, no modifications will be done to the data insights.\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[37mDEBUG:lightwood-2429: `explain` runtime: 0.05 seconds\u001b[0m\n" + "\u001b[37mDEBUG:lightwood-2715: `explain` runtime: 0.05 seconds\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[37mDEBUG:lightwood-2429: `predict` runtime: 0.13 seconds\u001b[0m\n" + "\u001b[37mDEBUG:lightwood-2715: `predict` runtime: 0.13 seconds\u001b[0m\n" ] }, { @@ -1090,10 +1096,10 @@ "execution_count": 5, "metadata": { "execution": { - "iopub.execute_input": "2024-03-19T10:00:46.333752Z", - "iopub.status.busy": "2024-03-19T10:00:46.333296Z", - "iopub.status.idle": "2024-03-19T10:00:46.445392Z", - "shell.execute_reply": "2024-03-19T10:00:46.444779Z" + "iopub.execute_input": "2024-03-19T10:19:00.737098Z", + "iopub.status.busy": "2024-03-19T10:19:00.736628Z", + "iopub.status.idle": "2024-03-19T10:19:00.845511Z", + "shell.execute_reply": "2024-03-19T10:19:00.844939Z" } }, "outputs": [ @@ -1101,35 +1107,35 @@ "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:dataprep_ml-2429:Cleaning the data\u001b[0m\n" + "\u001b[32mINFO:dataprep_ml-2715:Cleaning the data\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[37mDEBUG:lightwood-2429: `preprocess` runtime: 0.02 seconds\u001b[0m\n" + "\u001b[37mDEBUG:lightwood-2715: `preprocess` runtime: 0.02 seconds\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:dataprep_ml-2429:Cleaning the data\u001b[0m\n" + "\u001b[32mINFO:dataprep_ml-2715:Cleaning the data\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[37mDEBUG:lightwood-2429: `preprocess` runtime: 0.01 seconds\u001b[0m\n" + "\u001b[37mDEBUG:lightwood-2715: `preprocess` runtime: 0.01 seconds\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:dataprep_ml-2429:Updating the mixers\u001b[0m\n" + "\u001b[32mINFO:dataprep_ml-2715:Updating the mixers\u001b[0m\n" ] }, { @@ -1144,14 +1150,14 @@ "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:lightwood-2429:Loss @ epoch 1: 0.10915952424208324\u001b[0m\n" + "\u001b[32mINFO:lightwood-2715:Loss @ epoch 1: 0.10915952424208324\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[37mDEBUG:lightwood-2429: `adjust` runtime: 0.11 seconds\u001b[0m\n" + "\u001b[37mDEBUG:lightwood-2715: `adjust` runtime: 0.11 seconds\u001b[0m\n" ] } ], @@ -1164,10 +1170,10 @@ "execution_count": 6, "metadata": { "execution": { - "iopub.execute_input": "2024-03-19T10:00:46.448524Z", - "iopub.status.busy": "2024-03-19T10:00:46.448061Z", - "iopub.status.idle": "2024-03-19T10:00:46.587972Z", - "shell.execute_reply": "2024-03-19T10:00:46.587381Z" + "iopub.execute_input": "2024-03-19T10:19:00.848107Z", + "iopub.status.busy": "2024-03-19T10:19:00.847689Z", + "iopub.status.idle": "2024-03-19T10:19:00.985350Z", + "shell.execute_reply": "2024-03-19T10:19:00.984820Z" } }, "outputs": [ @@ -1175,126 +1181,126 @@ "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:dataprep_ml-2429:[Predict phase 1/4] - Data preprocessing\u001b[0m\n" + "\u001b[32mINFO:dataprep_ml-2715:[Predict phase 1/4] - Data preprocessing\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:dataprep_ml-2429:Cleaning the data\u001b[0m\n" + "\u001b[32mINFO:dataprep_ml-2715:Cleaning the data\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[37mDEBUG:lightwood-2429: `preprocess` runtime: 0.01 seconds\u001b[0m\n" + "\u001b[37mDEBUG:lightwood-2715: `preprocess` runtime: 0.01 seconds\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - 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Lightwood

Release:
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24.3.3.1

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Mar 19, 2024

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[[13, "iii-model-building-and-training"]], "Strengths and drawbacks": [[13, "strengths-and-drawbacks"]], "Mixers": [[14, "mixers"]], "Tutorials": [[15, "tutorials"]], "How to make a tutorial notebook?": [[16, "how-to-make-a-tutorial-notebook"]], "Using your own pre-processing methods in Lightwood": [[17, "Using-your-own-pre-processing-methods-in-Lightwood"]], "Date: 2021.10.07": [[17, "Date:-2021.10.07"], [21, "Date:-2021.10.07"]], "1) Load your data": [[17, "1)-Load-your-data"], [18, "1)-Load-your-data"], [21, "1)-Load-your-data"]], "2) Create a JSON-AI default object": [[17, "2)-Create-a-JSON-AI-default-object"], [21, "2)-Create-a-JSON-AI-default-object"]], "3) Build your own cleaner module": [[17, "3)-Build-your-own-cleaner-module"]], "Place your custom module in ~/lightwood_modules or /etc/lightwood_modules": [[17, "Place-your-custom-module-in-~/lightwood_modules-or-/etc/lightwood_modules"]], "4) Introduce your custom cleaner in JSON-AI": [[17, "4)-Introduce-your-custom-cleaner-in-JSON-AI"]], "5) Generate Python code representing your ML pipeline": [[17, "5)-Generate-Python-code-representing-your-ML-pipeline"], [21, "5)-Generate-Python-code-representing-your-ML-pipeline"]], "6) Call python to run your code and see your preprocessed outputs": [[17, "6)-Call-python-to-run-your-code-and-see-your-preprocessed-outputs"], [21, "6)-Call-python-to-run-your-code-and-see-your-preprocessed-outputs"]], "Custom Encoder: Rule-Based": [[18, "Custom-Encoder:-Rule-Based"]], "2) Generate JSON-AI Syntax": [[18, "2)-Generate-JSON-AI-Syntax"]], "3) Create your custom encoder (LabelEncoder).": [[18, "3)-Create-your-custom-encoder-(LabelEncoder)."]], "LabelEncoder": [[18, "LabelEncoder"]], "4) Edit JSON-AI": [[18, "4)-Edit-JSON-AI"]], "5) Generate code and your predictor from JSON-AI": [[18, "5)-Generate-code-and-your-predictor-from-JSON-AI"]], "Tutorial - Implementing a custom analysis block in Lightwood": [[19, "Tutorial---Implementing-a-custom-analysis-block-in-Lightwood"]], "Objective": [[19, "Objective"], [20, "Objective"], [22, "Objective"]], "Step 1: figuring out what we need": [[19, "Step-1:-figuring-out-what-we-need"]], "Step 2: Implementing the custom analysis block": [[19, "Step-2:-Implementing-the-custom-analysis-block"]], "Step 3: Exposing the block to Lightwood": [[19, "Step-3:-Exposing-the-block-to-Lightwood"]], "Step 4: Final test run": [[19, "Step-4:-Final-test-run"]], "Tutorial - Implementing a custom mixer in Lightwood": [[20, "Tutorial---Implementing-a-custom-mixer-in-Lightwood"]], "Step 1: The Mixer Interface": [[20, "Step-1:-The-Mixer-Interface"]], "Step 2: Writing our mixer": [[20, "Step-2:-Writing-our-mixer"]], "Step 3: Using our mixer": [[20, "Step-3:-Using-our-mixer"]], "Build your own training/testing split": [[21, "Build-your-own-training/testing-split"]], "3) Build your own splitter module": [[21, "3)-Build-your-own-splitter-module"]], "Place your custom module in ~/lightwood_modules": [[21, "Place-your-custom-module-in-~/lightwood_modules"]], "4) Introduce your custom splitter in JSON-AI": [[21, "4)-Introduce-your-custom-splitter-in-JSON-AI"]], "Tutorial - Introduction to Lightwood\u2019s statistical analysis": [[22, "Tutorial---Introduction-to-Lightwood's-statistical-analysis"]], "Step 1: load the dataset and define the predictive task": [[22, "Step-1:-load-the-dataset-and-define-the-predictive-task"]], "Step 2: Run the statistical analysis": [[22, "Step-2:-Run-the-statistical-analysis"]], "Step 3: Peeking inside": [[22, "Step-3:-Peeking-inside"]], "Amount of missing information": [[22, "Amount-of-missing-information"]], "Buckets per column": [[22, "Buckets-per-column"]], "Bias per column": [[22, "Bias-per-column"]], "Column histograms": [[22, "Column-histograms"]], "Final thoughts": [[22, "Final-thoughts"]], "Tutorial - Time series forecasting": [[23, "Tutorial---Time-series-forecasting"]], "Load data": [[23, "Load-data"]], "Define the predictive task": [[23, "Define-the-predictive-task"]], "Generate the predictor object": [[23, "Generate-the-predictor-object"]], "Train": [[23, "Train"]], "Predict": [[23, "Predict"]], "Visualizing a forecast": [[23, "Visualizing-a-forecast"]], "Conclusion": [[23, "Conclusion"], [24, "Conclusion"]], "Initial model training": [[24, "Initial-model-training"]], "Updating the predictor": [[24, "Updating-the-predictor"]], "BaseMixer.partial_fit()": [[24, "BaseMixer.partial_fit()"]], "PredictorInterface.adjust()": [[24, "PredictorInterface.adjust()"]]}, "indexentries": {"accstats (class in analysis)": [[0, "analysis.AccStats"]], "baseanalysisblock (class in analysis)": [[0, "analysis.BaseAnalysisBlock"]], "confstats (class in analysis)": [[0, "analysis.ConfStats"]], "icp (class in analysis)": [[0, "analysis.ICP"]], "permutationfeatureimportance (class in analysis)": [[0, "analysis.PermutationFeatureImportance"]], "tempscaler (class in analysis)": [[0, "analysis.TempScaler"]], "analysis": [[0, "module-analysis"]], "analyze() (analysis.accstats method)": [[0, "analysis.AccStats.analyze"]], "analyze() (analysis.baseanalysisblock method)": [[0, "analysis.BaseAnalysisBlock.analyze"]], "analyze() (analysis.confstats method)": [[0, "analysis.ConfStats.analyze"]], "analyze() (analysis.icp method)": [[0, "analysis.ICP.analyze"]], "analyze() (analysis.permutationfeatureimportance method)": [[0, "analysis.PermutationFeatureImportance.analyze"]], "analyze() (analysis.tempscaler method)": [[0, "analysis.TempScaler.analyze"]], "explain() (analysis.baseanalysisblock method)": [[0, "analysis.BaseAnalysisBlock.explain"]], "explain() (analysis.icp method)": [[0, "analysis.ICP.explain"]], "explain() (analysis.tempscaler method)": [[0, "analysis.TempScaler.explain"]], "explain() (in module analysis)": [[0, "analysis.explain"]], "model_analyzer() (in module analysis)": [[0, "analysis.model_analyzer"]], "module": [[0, "module-analysis"], [4, "module-api.high_level"], [5, 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8, 9, 14, 17, 18, 19, 20, 21, 22, 23, 24], "behavior": [6, 7, 8, 9, 13, 14, 18, 19], "wrap": [6, 9, 10, 24], "predictionargu": [6, 7, 8, 12, 17, 20, 21], "ha": [6, 7, 9, 11, 12, 14, 16, 17, 18, 19, 20, 21, 22, 23, 24], "explicitli": [6, 7, 8, 9, 17, 18, 19, 21], "With": [6, 18], "path": [6, 9, 12, 13, 17, 21], "stratifi": [6, 21], "fraction": [6, 9], "strict": [6, 17, 21, 23], "list": [6, 7, 8, 9, 10, 11, 14, 17, 18, 19, 21, 22], "which": [6, 7, 9, 12, 13, 14, 17, 18, 19, 20, 21, 23], "hold": 6, "rais": [6, 11, 17, 20, 21], "error": [6, 7, 9, 14, 17, 20, 21], "otherwis": [6, 9, 11, 14, 16, 17], "skip": [6, 14, 17, 20, 21, 23], "n_metric": 6, "cell": [6, 11, 19], "consist": [7, 9, 18, 23], "high": [7, 9, 19, 22, 23], "scienc": [7, 12, 18], "up": [7, 13, 16, 17, 19, 21, 23], "being": [7, 13, 14], "repres": [7, 9, 11, 12, 13, 18], "argument": [7, 8, 14, 17, 18, 21, 22, 23, 24], "constructor": 7, "timeseriesset": [7, 8, 9, 12, 14, 17, 19, 21, 22], "is_timeseri": [7, 17, 18, 20, 21, 22], 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"abl": [7, 18, 19, 22], "mani": [7, 8, 9, 14, 17, 18, 21, 22, 23], "context": [7, 8, 11], "non": [7, 9, 17, 21], "histor": [7, 8, 9], "shall": [7, 21], "without": [7, 9, 12, 13, 17, 18, 21, 24], "through": [7, 9, 11, 13, 14, 17, 19, 21, 23, 24], "integ": [7, 9, 14, 17, 18, 19, 20, 21, 22, 24], "float": [7, 9, 13, 14, 17, 18, 19, 20, 21, 22, 23, 24], "incomplet": 7, "less": [7, 9, 21], "observ": [7, 8, 9, 14, 21, 22, 23], "datetim": [7, 13, 23], "histori": 7, "period": [7, 14], "length": [7, 8, 9, 11, 14], "interv": [7, 8, 13, 23], "timeseries_analyz": [7, 8, 12, 14], "detect_period": 7, "document": [7, 12, 14, 17, 19, 21, 22, 23], "daili": 7, "7": [7, 17, 18, 19, 20, 21, 22, 23, 24], "obj": 7, "python": [7, 9, 11, 12, 13, 14, 18, 19, 20, 22, 23, 24], "mandatori": 7, "from_json": [7, 12], "encode_json": 7, "to_json": [7, 12, 17, 18, 20, 21], "pct_invalid": [7, 17, 18, 20, 21, 22], "unbias_target": [7, 17, 18, 20, 21, 22], "seconds_per_mix": [7, 17, 18, 20, 21, 22, 24], 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"mixer.SkTime.partial_fit"]], "partial_fit() (mixer.unit method)": [[14, "mixer.Unit.partial_fit"]], "partial_fit() (mixer.xgboostarraymixer method)": [[14, "mixer.XGBoostArrayMixer.partial_fit"]], "partial_fit() (mixer.xgboostmixer method)": [[14, "mixer.XGBoostMixer.partial_fit"]], "supports_proba (mixer.xgboostmixer attribute)": [[14, "mixer.XGBoostMixer.supports_proba"]]}}) \ No newline at end of file diff --git a/tutorials.html b/tutorials.html index b08b41569..b9d0a8c6b 100644 --- a/tutorials.html +++ b/tutorials.html @@ -4,7 +4,7 @@ - Tutorials — lightwood 24.3.3.1 documentation + Tutorials — lightwood 24.3.3.0 documentation @@ -41,7 +41,7 @@
- 24.3.3.1 + 24.3.3.0
diff --git a/tutorials/README.html b/tutorials/README.html index 0f11f4d99..ce8388e49 100644 --- a/tutorials/README.html +++ b/tutorials/README.html @@ -4,7 +4,7 @@ - How to make a tutorial notebook? — lightwood 24.3.3.1 documentation + How to make a tutorial notebook? — lightwood 24.3.3.0 documentation @@ -39,7 +39,7 @@
- 24.3.3.1 + 24.3.3.0
diff --git a/tutorials/custom_cleaner/custom_cleaner.html b/tutorials/custom_cleaner/custom_cleaner.html index 322eb3a74..b9e0917ec 100644 --- a/tutorials/custom_cleaner/custom_cleaner.html +++ b/tutorials/custom_cleaner/custom_cleaner.html @@ -4,7 +4,7 @@ - Using your own pre-processing methods in Lightwood — lightwood 24.3.3.1 documentation + Using your own pre-processing methods in Lightwood — lightwood 24.3.3.0 documentation @@ -42,7 +42,7 @@
- 24.3.3.1 + 24.3.3.0
@@ -257,7 +257,7 @@

2) Create a JSON-AI default object
-INFO:lightwood-2670:Dropping features: ['url_legal', 'license', 'standard_error']
+INFO:lightwood-2943:Dropping features: ['url_legal', 'license', 'standard_error']
 

Lightwood, as it processes the data, will provide the user a few pieces of information.

@@ -449,7 +449,7 @@

2) Create a JSON-AI default object5) Generate Python code representing your ML pipeline6) Call python to run your code and see your preprocessed outputs
-INFO:dataprep_ml-2670:Starting statistical analysis
+INFO:dataprep_ml-2943:Starting statistical analysis
 

diff --git a/tutorials/custom_cleaner/custom_cleaner.ipynb b/tutorials/custom_cleaner/custom_cleaner.ipynb index 249dd26a0..8d8e06416 100644 --- a/tutorials/custom_cleaner/custom_cleaner.ipynb +++ b/tutorials/custom_cleaner/custom_cleaner.ipynb @@ -31,10 +31,10 @@ "id": "happy-wheat", "metadata": { "execution": { - "iopub.execute_input": "2024-03-19T10:01:24.293027Z", - "iopub.status.busy": "2024-03-19T10:01:24.292490Z", - "iopub.status.idle": "2024-03-19T10:01:26.832255Z", - "shell.execute_reply": "2024-03-19T10:01:26.831481Z" + "iopub.execute_input": "2024-03-19T10:19:37.224393Z", + "iopub.status.busy": "2024-03-19T10:19:37.224196Z", + "iopub.status.idle": "2024-03-19T10:19:39.740153Z", + "shell.execute_reply": "2024-03-19T10:19:39.739489Z" } }, "outputs": [ @@ -42,14 +42,14 @@ "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:lightwood-2670:No torchvision detected, image helpers not supported.\u001b[0m\n" + "\u001b[32mINFO:lightwood-2943:No torchvision detected, image helpers not supported.\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:lightwood-2670:No torchvision/pillow detected, image encoder not supported\u001b[0m\n" + "\u001b[32mINFO:lightwood-2943:No torchvision/pillow detected, image encoder not supported\u001b[0m\n" ] } ], @@ -87,10 +87,10 @@ "id": "recognized-parish", "metadata": { "execution": { - "iopub.execute_input": "2024-03-19T10:01:26.835698Z", - "iopub.status.busy": "2024-03-19T10:01:26.835149Z", - "iopub.status.idle": "2024-03-19T10:01:28.275017Z", - "shell.execute_reply": "2024-03-19T10:01:28.274348Z" + "iopub.execute_input": "2024-03-19T10:19:39.743708Z", + "iopub.status.busy": "2024-03-19T10:19:39.743020Z", + "iopub.status.idle": "2024-03-19T10:19:40.746691Z", + "shell.execute_reply": "2024-03-19T10:19:40.746040Z" } }, "outputs": [ @@ -221,10 +221,10 @@ "id": "chicken-truth", "metadata": { "execution": { - "iopub.execute_input": "2024-03-19T10:01:28.277742Z", - "iopub.status.busy": "2024-03-19T10:01:28.277344Z", - "iopub.status.idle": "2024-03-19T10:01:43.768327Z", - "shell.execute_reply": "2024-03-19T10:01:43.767789Z" + "iopub.execute_input": "2024-03-19T10:19:40.749243Z", + "iopub.status.busy": "2024-03-19T10:19:40.749038Z", + "iopub.status.idle": "2024-03-19T10:19:56.275041Z", + "shell.execute_reply": "2024-03-19T10:19:56.274470Z" } }, "outputs": [ @@ -232,98 +232,98 @@ "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:lightwood-2670:Dropping features: ['url_legal', 'license', 'standard_error']\u001b[0m\n" + "\u001b[32mINFO:lightwood-2943:Dropping features: ['url_legal', 'license', 'standard_error']\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:type_infer-2670:Analyzing a sample of 2478\u001b[0m\n" + "\u001b[32mINFO:type_infer-2943:Analyzing a sample of 2478\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:type_infer-2670:from a total population of 2834, this is equivalent to 87.4% of your data.\u001b[0m\n" + "\u001b[32mINFO:type_infer-2943:from a total population of 2834, this is equivalent to 87.4% of your data.\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:type_infer-2670:Infering type for: id\u001b[0m\n" + "\u001b[32mINFO:type_infer-2943:Infering type for: id\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:type_infer-2670:Doing text detection for column: id\u001b[0m\n" + "\u001b[32mINFO:type_infer-2943:Doing text detection for column: id\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:type_infer-2670:Column id has data type categorical\u001b[0m\n" + "\u001b[32mINFO:type_infer-2943:Column id has data type categorical\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:type_infer-2670:Infering type for: excerpt\u001b[0m\n" + "\u001b[32mINFO:type_infer-2943:Infering type for: excerpt\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:type_infer-2670:Doing text detection for column: excerpt\u001b[0m\n" + "\u001b[32mINFO:type_infer-2943:Doing text detection for column: excerpt\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:type_infer-2670:Infering type for: target\u001b[0m\n" + "\u001b[32mINFO:type_infer-2943:Infering type for: target\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:type_infer-2670:Column target has data type float\u001b[0m\n" + "\u001b[32mINFO:type_infer-2943:Column target has data type float\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[33mWARNING:type_infer-2670:Column id is an identifier of type \"Hash-like identifier\"\u001b[0m\n" + "\u001b[33mWARNING:type_infer-2943:Column id is an identifier of type \"Hash-like identifier\"\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:dataprep_ml-2670:Starting statistical analysis\u001b[0m\n" + "\u001b[32mINFO:dataprep_ml-2943:Starting statistical analysis\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:dataprep_ml-2670:Dropping features: ['id']\u001b[0m\n" + "\u001b[32mINFO:dataprep_ml-2943:Dropping features: ['id']\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:dataprep_ml-2670:Finished statistical analysis\u001b[0m\n" + "\u001b[32mINFO:dataprep_ml-2943:Finished statistical analysis\u001b[0m\n" ] } ], @@ -360,10 +360,10 @@ "id": "designed-condition", "metadata": { "execution": { - "iopub.execute_input": "2024-03-19T10:01:43.771095Z", - "iopub.status.busy": "2024-03-19T10:01:43.770716Z", - "iopub.status.idle": "2024-03-19T10:01:43.774857Z", - "shell.execute_reply": "2024-03-19T10:01:43.774220Z" + "iopub.execute_input": "2024-03-19T10:19:56.277921Z", + "iopub.status.busy": "2024-03-19T10:19:56.277484Z", + "iopub.status.idle": "2024-03-19T10:19:56.281731Z", + "shell.execute_reply": "2024-03-19T10:19:56.281046Z" } }, "outputs": [ @@ -434,7 +434,7 @@ " \"unbias_target\": true,\n", " \"seconds_per_mixer\": 21384.0,\n", " \"seconds_per_encoder\": 85536.0,\n", - " \"expected_additional_time\": 15.479900598526001,\n", + " \"expected_additional_time\": 15.515635967254639,\n", " \"time_aim\": 259200,\n", " \"target_weights\": null,\n", " \"positive_domain\": false,\n", @@ -518,10 +518,10 @@ "id": "325d8f1b", "metadata": { "execution": { - "iopub.execute_input": "2024-03-19T10:01:43.777689Z", - "iopub.status.busy": "2024-03-19T10:01:43.777267Z", - "iopub.status.idle": "2024-03-19T10:01:43.782425Z", - "shell.execute_reply": "2024-03-19T10:01:43.781792Z" + "iopub.execute_input": "2024-03-19T10:19:56.284376Z", + "iopub.status.busy": "2024-03-19T10:19:56.284179Z", + "iopub.status.idle": "2024-03-19T10:19:56.289596Z", + "shell.execute_reply": "2024-03-19T10:19:56.288979Z" } }, "outputs": [ @@ -646,10 +646,10 @@ "id": "f030f8ca", "metadata": { "execution": { - "iopub.execute_input": "2024-03-19T10:01:43.785011Z", - "iopub.status.busy": "2024-03-19T10:01:43.784642Z", - "iopub.status.idle": "2024-03-19T10:01:43.787855Z", - "shell.execute_reply": "2024-03-19T10:01:43.787217Z" + "iopub.execute_input": "2024-03-19T10:19:56.292040Z", + "iopub.status.busy": "2024-03-19T10:19:56.291667Z", + "iopub.status.idle": "2024-03-19T10:19:56.294751Z", + "shell.execute_reply": "2024-03-19T10:19:56.294232Z" } }, "outputs": [], @@ -711,10 +711,10 @@ "id": "floating-patent", "metadata": { "execution": { - "iopub.execute_input": "2024-03-19T10:01:43.790373Z", - "iopub.status.busy": "2024-03-19T10:01:43.790020Z", - "iopub.status.idle": "2024-03-19T10:01:43.999248Z", - "shell.execute_reply": "2024-03-19T10:01:43.998578Z" + "iopub.execute_input": "2024-03-19T10:19:56.297253Z", + "iopub.status.busy": "2024-03-19T10:19:56.296883Z", + "iopub.status.idle": "2024-03-19T10:19:56.504807Z", + "shell.execute_reply": "2024-03-19T10:19:56.504139Z" } }, "outputs": [ @@ -795,7 +795,7 @@ " \"unbias_target\": True,\n", " \"seconds_per_mixer\": 21384.0,\n", " \"seconds_per_encoder\": 85536.0,\n", - " \"expected_additional_time\": 15.479900598526001,\n", + " \"expected_additional_time\": 15.515635967254639,\n", " \"time_aim\": 259200,\n", " \"target_weights\": None,\n", " \"positive_domain\": False,\n", @@ -825,7 +825,7 @@ " self.accuracy_functions = [\"r2_score\"]\n", " self.identifiers = {\"id\": \"Hash-like identifier\"}\n", " self.dtype_dict = {\"excerpt\": \"rich_text\", \"target\": \"float\"}\n", - " self.lightwood_version = \"24.3.3.1\"\n", + " self.lightwood_version = \"24.3.3.0\"\n", " self.pred_args = PredictionArguments()\n", "\n", " # Any feature-column dependencies\n", @@ -1449,10 +1449,10 @@ "id": "violent-guard", "metadata": { "execution": { - "iopub.execute_input": "2024-03-19T10:01:44.002116Z", - "iopub.status.busy": "2024-03-19T10:01:44.001616Z", - "iopub.status.idle": "2024-03-19T10:01:44.009223Z", - "shell.execute_reply": "2024-03-19T10:01:44.008614Z" + "iopub.execute_input": "2024-03-19T10:19:56.507600Z", + "iopub.status.busy": "2024-03-19T10:19:56.507376Z", + "iopub.status.idle": "2024-03-19T10:19:56.515176Z", + "shell.execute_reply": "2024-03-19T10:19:56.514556Z" } }, "outputs": [], @@ -1467,10 +1467,10 @@ "id": "closing-episode", "metadata": { "execution": { - "iopub.execute_input": "2024-03-19T10:01:44.011918Z", - "iopub.status.busy": "2024-03-19T10:01:44.011529Z", - "iopub.status.idle": "2024-03-19T10:01:44.145820Z", - "shell.execute_reply": "2024-03-19T10:01:44.145162Z" + "iopub.execute_input": "2024-03-19T10:19:56.517713Z", + "iopub.status.busy": "2024-03-19T10:19:56.517509Z", + "iopub.status.idle": "2024-03-19T10:19:56.648899Z", + "shell.execute_reply": "2024-03-19T10:19:56.648351Z" }, "scrolled": false }, @@ -1479,70 +1479,70 @@ "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:dataprep_ml-2670:Starting statistical analysis\u001b[0m\n" + "\u001b[32mINFO:dataprep_ml-2943:Starting statistical analysis\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:dataprep_ml-2670:Dropping features: ['id']\u001b[0m\n" + "\u001b[32mINFO:dataprep_ml-2943:Dropping features: ['id']\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:dataprep_ml-2670:Finished statistical analysis\u001b[0m\n" + "\u001b[32mINFO:dataprep_ml-2943:Finished statistical analysis\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[37mDEBUG:lightwood-2670: `analyze_data` runtime: 0.05 seconds\u001b[0m\n" + "\u001b[37mDEBUG:lightwood-2943: `analyze_data` runtime: 0.05 seconds\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:dataprep_ml-2670:Cleaning the data\u001b[0m\n" + "\u001b[32mINFO:dataprep_ml-2943:Cleaning the data\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:dataprep_ml-2670:Dropping features: ['id']\u001b[0m\n" + "\u001b[32mINFO:dataprep_ml-2943:Dropping features: ['id']\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:lightwood-2670:Cleaning column =excerpt\u001b[0m\n" + "\u001b[32mINFO:lightwood-2943:Cleaning column =excerpt\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:lightwood-2670:Cleaning column =target\u001b[0m\n" + "\u001b[32mINFO:lightwood-2943:Cleaning column =target\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:lightwood-2670:Converted target into strictly non-negative\u001b[0m\n" + "\u001b[32mINFO:lightwood-2943:Converted target into strictly non-negative\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[37mDEBUG:lightwood-2670: `preprocess` runtime: 0.07 seconds\u001b[0m\n" + "\u001b[37mDEBUG:lightwood-2943: `preprocess` runtime: 0.07 seconds\u001b[0m\n" ] }, { @@ -1632,10 +1632,10 @@ "id": "major-stake", "metadata": { "execution": { - "iopub.execute_input": "2024-03-19T10:01:44.148552Z", - "iopub.status.busy": "2024-03-19T10:01:44.148159Z", - "iopub.status.idle": "2024-03-19T10:01:44.152736Z", - "shell.execute_reply": "2024-03-19T10:01:44.152083Z" + "iopub.execute_input": "2024-03-19T10:19:56.651477Z", + "iopub.status.busy": "2024-03-19T10:19:56.651208Z", + "iopub.status.idle": "2024-03-19T10:19:56.655938Z", + "shell.execute_reply": "2024-03-19T10:19:56.655250Z" } }, "outputs": [ diff --git a/tutorials/custom_encoder_rulebased/custom_encoder_rulebased.html b/tutorials/custom_encoder_rulebased/custom_encoder_rulebased.html index 42f3be556..855bcc4ad 100644 --- a/tutorials/custom_encoder_rulebased/custom_encoder_rulebased.html +++ b/tutorials/custom_encoder_rulebased/custom_encoder_rulebased.html @@ -4,7 +4,7 @@ - Custom Encoder: Rule-Based — lightwood 24.3.3.1 documentation + Custom Encoder: Rule-Based — lightwood 24.3.3.0 documentation @@ -42,7 +42,7 @@
- 24.3.3.1 + 24.3.3.0
@@ -282,7 +282,7 @@

2) Generate JSON-AI Syntax
-INFO:type_infer-2588:Analyzing a sample of 6920
+INFO:type_infer-2865:Analyzing a sample of 6920
 

Let’s take a look at our JSON-AI and print to file.

@@ -572,7 +572,7 @@

2) Generate JSON-AI Syntax
-INFO:dataprep_ml-2588:Starting statistical analysis
+INFO:dataprep_ml-2865:Starting statistical analysis
 

The splitter creates 3 data-splits, a “train”, “dev”, and “test” set. The featurize command from the predictor allows us to convert the cleaned data into features. We can access this as follows:

diff --git a/tutorials/custom_encoder_rulebased/custom_encoder_rulebased.ipynb b/tutorials/custom_encoder_rulebased/custom_encoder_rulebased.ipynb index 3d78b315d..48cb8219f 100644 --- a/tutorials/custom_encoder_rulebased/custom_encoder_rulebased.ipynb +++ b/tutorials/custom_encoder_rulebased/custom_encoder_rulebased.ipynb @@ -43,10 +43,10 @@ "id": "raising-adventure", "metadata": { "execution": { - "iopub.execute_input": "2024-03-19T10:01:05.526289Z", - "iopub.status.busy": "2024-03-19T10:01:05.526091Z", - "iopub.status.idle": "2024-03-19T10:01:08.110087Z", - "shell.execute_reply": "2024-03-19T10:01:08.109384Z" + "iopub.execute_input": "2024-03-19T10:19:18.781052Z", + "iopub.status.busy": "2024-03-19T10:19:18.780864Z", + "iopub.status.idle": "2024-03-19T10:19:21.287724Z", + "shell.execute_reply": "2024-03-19T10:19:21.287069Z" } }, "outputs": [ @@ -54,14 +54,14 @@ "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:lightwood-2588:No torchvision detected, image helpers not supported.\u001b[0m\n" + "\u001b[32mINFO:lightwood-2865:No torchvision detected, image helpers not supported.\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:lightwood-2588:No torchvision/pillow detected, image encoder not supported\u001b[0m\n" + "\u001b[32mINFO:lightwood-2865:No torchvision/pillow detected, image encoder not supported\u001b[0m\n" ] } ], @@ -93,10 +93,10 @@ "id": "technical-government", "metadata": { "execution": { - "iopub.execute_input": "2024-03-19T10:01:08.113518Z", - "iopub.status.busy": "2024-03-19T10:01:08.112978Z", - "iopub.status.idle": "2024-03-19T10:01:08.570602Z", - "shell.execute_reply": "2024-03-19T10:01:08.569921Z" + "iopub.execute_input": "2024-03-19T10:19:21.290994Z", + "iopub.status.busy": "2024-03-19T10:19:21.290532Z", + "iopub.status.idle": "2024-03-19T10:19:21.602551Z", + "shell.execute_reply": "2024-03-19T10:19:21.601915Z" } }, "outputs": [ @@ -243,10 +243,10 @@ "id": "absent-maker", "metadata": { "execution": { - "iopub.execute_input": "2024-03-19T10:01:08.573521Z", - "iopub.status.busy": "2024-03-19T10:01:08.573084Z", - "iopub.status.idle": "2024-03-19T10:01:19.617025Z", - "shell.execute_reply": "2024-03-19T10:01:19.616272Z" + "iopub.execute_input": "2024-03-19T10:19:21.605129Z", + "iopub.status.busy": "2024-03-19T10:19:21.604941Z", + "iopub.status.idle": "2024-03-19T10:19:32.650571Z", + "shell.execute_reply": "2024-03-19T10:19:32.649852Z" } }, "outputs": [ @@ -254,161 +254,161 @@ "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:type_infer-2588:Analyzing a sample of 6920\u001b[0m\n" + "\u001b[32mINFO:type_infer-2865:Analyzing a sample of 6920\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:type_infer-2588:from a total population of 10668, this is equivalent to 64.9% of your data.\u001b[0m\n" + "\u001b[32mINFO:type_infer-2865:from a total population of 10668, this is equivalent to 64.9% of your data.\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:type_infer-2588:Using 3 processes to deduct types.\u001b[0m\n" + "\u001b[32mINFO:type_infer-2865:Using 3 processes to deduct types.\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:type_infer-2588:Infering type for: year\u001b[0m\n" + "\u001b[32mINFO:type_infer-2865:Infering type for: year\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:type_infer-2588:Infering type for: price\u001b[0m\n" + "\u001b[32mINFO:type_infer-2865:Infering type for: price\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:type_infer-2588:Column price has data type integer\u001b[0m\n" + "\u001b[32mINFO:type_infer-2865:Column year has data type integer\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:type_infer-2588:Column year has data type integer\u001b[0m\n" + "\u001b[32mINFO:type_infer-2865:Column price has data type integer\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:type_infer-2588:Infering type for: transmission\u001b[0m\n" + "\u001b[32mINFO:type_infer-2865:Infering type for: transmission\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:type_infer-2588:Infering type for: mileage\u001b[0m\n" + "\u001b[32mINFO:type_infer-2865:Infering type for: mileage\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:type_infer-2588:Column mileage has data type integer\u001b[0m\n" + "\u001b[32mINFO:type_infer-2865:Infering type for: model\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:type_infer-2588:Infering type for: fuelType\u001b[0m\n" + "\u001b[32mINFO:type_infer-2865:Column mileage has data type integer\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:type_infer-2588:Infering type for: model\u001b[0m\n" + "\u001b[32mINFO:type_infer-2865:Infering type for: fuelType\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:type_infer-2588:Column fuelType has data type categorical\u001b[0m\n" + "\u001b[32mINFO:type_infer-2865:Column model has data type categorical\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:type_infer-2588:Infering type for: tax\u001b[0m\n" + "\u001b[32mINFO:type_infer-2865:Infering type for: tax\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:type_infer-2588:Column tax has data type integer\u001b[0m\n" + "\u001b[32mINFO:type_infer-2865:Column tax has data type integer\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:type_infer-2588:Infering type for: mpg\u001b[0m\n" + "\u001b[32mINFO:type_infer-2865:Infering type for: mpg\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:type_infer-2588:Column mpg has data type float\u001b[0m\n" + "\u001b[32mINFO:type_infer-2865:Column mpg has data type float\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:type_infer-2588:Infering type for: engineSize\u001b[0m\n" + "\u001b[32mINFO:type_infer-2865:Infering type for: engineSize\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:type_infer-2588:Column engineSize has data type float\u001b[0m\n" + "\u001b[32mINFO:type_infer-2865:Column engineSize has data type float\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:type_infer-2588:Column transmission has data type categorical\u001b[0m\n" + "\u001b[32mINFO:type_infer-2865:Column transmission has data type categorical\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:type_infer-2588:Column model has data type categorical\u001b[0m\n" + "\u001b[32mINFO:type_infer-2865:Column fuelType has data type categorical\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:dataprep_ml-2588:Starting statistical analysis\u001b[0m\n" + "\u001b[32mINFO:dataprep_ml-2865:Starting statistical analysis\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:dataprep_ml-2588:Finished statistical analysis\u001b[0m\n" + "\u001b[32mINFO:dataprep_ml-2865:Finished statistical analysis\u001b[0m\n" ] } ], @@ -437,10 +437,10 @@ "id": "coastal-paragraph", "metadata": { "execution": { - "iopub.execute_input": "2024-03-19T10:01:19.620277Z", - "iopub.status.busy": "2024-03-19T10:01:19.620037Z", - "iopub.status.idle": "2024-03-19T10:01:19.625297Z", - "shell.execute_reply": "2024-03-19T10:01:19.624612Z" + "iopub.execute_input": "2024-03-19T10:19:32.653652Z", + "iopub.status.busy": "2024-03-19T10:19:32.653387Z", + "iopub.status.idle": "2024-03-19T10:19:32.657936Z", + "shell.execute_reply": "2024-03-19T10:19:32.657299Z" } }, "outputs": [ @@ -545,7 +545,7 @@ " \"unbias_target\": true,\n", " \"seconds_per_mixer\": 21384.0,\n", " \"seconds_per_encoder\": 85536.0,\n", - " \"expected_additional_time\": 11.03162956237793,\n", + " \"expected_additional_time\": 11.033989667892456,\n", " \"time_aim\": 259200,\n", " \"target_weights\": null,\n", " \"positive_domain\": false,\n", @@ -643,10 +643,10 @@ "id": "e03db1b0", "metadata": { "execution": { - "iopub.execute_input": "2024-03-19T10:01:19.627937Z", - "iopub.status.busy": "2024-03-19T10:01:19.627718Z", - "iopub.status.idle": "2024-03-19T10:01:19.633288Z", - "shell.execute_reply": "2024-03-19T10:01:19.632627Z" + "iopub.execute_input": "2024-03-19T10:19:32.660713Z", + "iopub.status.busy": "2024-03-19T10:19:32.660350Z", + "iopub.status.idle": "2024-03-19T10:19:32.665576Z", + "shell.execute_reply": "2024-03-19T10:19:32.664956Z" } }, "outputs": [ @@ -766,10 +766,10 @@ "id": "e30866c1", "metadata": { "execution": { - "iopub.execute_input": "2024-03-19T10:01:19.635995Z", - "iopub.status.busy": "2024-03-19T10:01:19.635568Z", - "iopub.status.idle": "2024-03-19T10:01:19.638949Z", - "shell.execute_reply": "2024-03-19T10:01:19.638424Z" + "iopub.execute_input": "2024-03-19T10:19:32.667988Z", + "iopub.status.busy": "2024-03-19T10:19:32.667682Z", + "iopub.status.idle": "2024-03-19T10:19:32.670900Z", + "shell.execute_reply": "2024-03-19T10:19:32.670280Z" } }, "outputs": [], @@ -828,10 +828,10 @@ "id": "elementary-fusion", "metadata": { "execution": { - "iopub.execute_input": "2024-03-19T10:01:19.641687Z", - "iopub.status.busy": "2024-03-19T10:01:19.641294Z", - "iopub.status.idle": "2024-03-19T10:01:19.644573Z", - "shell.execute_reply": "2024-03-19T10:01:19.644028Z" + "iopub.execute_input": "2024-03-19T10:19:32.673575Z", + "iopub.status.busy": "2024-03-19T10:19:32.673201Z", + "iopub.status.idle": "2024-03-19T10:19:32.676378Z", + "shell.execute_reply": "2024-03-19T10:19:32.675835Z" } }, "outputs": [], @@ -857,10 +857,10 @@ "id": "inappropriate-james", "metadata": { "execution": { - "iopub.execute_input": "2024-03-19T10:01:19.647157Z", - "iopub.status.busy": "2024-03-19T10:01:19.646820Z", - "iopub.status.idle": "2024-03-19T10:01:20.020948Z", - "shell.execute_reply": "2024-03-19T10:01:20.020282Z" + "iopub.execute_input": "2024-03-19T10:19:32.678830Z", + "iopub.status.busy": "2024-03-19T10:19:32.678489Z", + "iopub.status.idle": "2024-03-19T10:19:33.022380Z", + "shell.execute_reply": "2024-03-19T10:19:33.021731Z" } }, "outputs": [], @@ -891,10 +891,10 @@ "id": "palestinian-harvey", "metadata": { "execution": { - "iopub.execute_input": "2024-03-19T10:01:20.024242Z", - "iopub.status.busy": "2024-03-19T10:01:20.023718Z", - "iopub.status.idle": "2024-03-19T10:01:21.197476Z", - "shell.execute_reply": "2024-03-19T10:01:21.196767Z" + "iopub.execute_input": "2024-03-19T10:19:33.025713Z", + "iopub.status.busy": "2024-03-19T10:19:33.025246Z", + "iopub.status.idle": "2024-03-19T10:19:34.232444Z", + "shell.execute_reply": "2024-03-19T10:19:34.231782Z" } }, "outputs": [ @@ -902,133 +902,133 @@ "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:dataprep_ml-2588:Starting statistical analysis\u001b[0m\n" + "\u001b[32mINFO:dataprep_ml-2865:Starting statistical analysis\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:dataprep_ml-2588:Finished statistical analysis\u001b[0m\n" + "\u001b[32mINFO:dataprep_ml-2865:Finished statistical analysis\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[37mDEBUG:lightwood-2588: `analyze_data` runtime: 0.44 seconds\u001b[0m\n" + "\u001b[37mDEBUG:lightwood-2865: `analyze_data` runtime: 0.44 seconds\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:dataprep_ml-2588:Cleaning the data\u001b[0m\n" + "\u001b[32mINFO:dataprep_ml-2865:Cleaning the data\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[37mDEBUG:lightwood-2588: `preprocess` runtime: 0.13 seconds\u001b[0m\n" + "\u001b[37mDEBUG:lightwood-2865: `preprocess` runtime: 0.14 seconds\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:dataprep_ml-2588:Splitting the data into train/test\u001b[0m\n" + "\u001b[32mINFO:dataprep_ml-2865:Splitting the data into train/test\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[37mDEBUG:lightwood-2588: `split` runtime: 0.0 seconds\u001b[0m\n" + "\u001b[37mDEBUG:lightwood-2865: `split` runtime: 0.0 seconds\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[37mDEBUG:dataprep_ml-2588:Preparing sequentially...\u001b[0m\n" + "\u001b[37mDEBUG:dataprep_ml-2865:Preparing sequentially...\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[37mDEBUG:dataprep_ml-2588:Preparing encoder for year...\u001b[0m\n" + "\u001b[37mDEBUG:dataprep_ml-2865:Preparing encoder for year...\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[37mDEBUG:dataprep_ml-2588:Preparing encoder for mileage...\u001b[0m\n" + "\u001b[37mDEBUG:dataprep_ml-2865:Preparing encoder for mileage...\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[37mDEBUG:dataprep_ml-2588:Preparing encoder for tax...\u001b[0m\n" + "\u001b[37mDEBUG:dataprep_ml-2865:Preparing encoder for tax...\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[37mDEBUG:dataprep_ml-2588:Preparing encoder for mpg...\u001b[0m\n" + "\u001b[37mDEBUG:dataprep_ml-2865:Preparing encoder for mpg...\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[37mDEBUG:dataprep_ml-2588:Preparing encoder for engineSize...\u001b[0m\n" + "\u001b[37mDEBUG:dataprep_ml-2865:Preparing encoder for engineSize...\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:lightwood-2588:Categories Detected = 1\u001b[0m\n" + "\u001b[32mINFO:lightwood-2865:Categories Detected = 1\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:lightwood-2588:Categories Detected = 1\u001b[0m\n" + "\u001b[32mINFO:lightwood-2865:Categories Detected = 1\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:lightwood-2588:Categories Detected = 1\u001b[0m\n" + "\u001b[32mINFO:lightwood-2865:Categories Detected = 1\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[37mDEBUG:lightwood-2588: `prepare` runtime: 0.02 seconds\u001b[0m\n" + "\u001b[37mDEBUG:lightwood-2865: `prepare` runtime: 0.01 seconds\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:dataprep_ml-2588:Featurizing the data\u001b[0m\n" + "\u001b[32mINFO:dataprep_ml-2865:Featurizing the data\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[37mDEBUG:lightwood-2588: `featurize` runtime: 0.57 seconds\u001b[0m\n" + "\u001b[37mDEBUG:lightwood-2865: `featurize` runtime: 0.6 seconds\u001b[0m\n" ] } ], @@ -1063,10 +1063,10 @@ "id": "silent-dealing", "metadata": { "execution": { - "iopub.execute_input": "2024-03-19T10:01:21.200339Z", - "iopub.status.busy": "2024-03-19T10:01:21.200121Z", - "iopub.status.idle": "2024-03-19T10:01:21.209729Z", - "shell.execute_reply": "2024-03-19T10:01:21.209058Z" + "iopub.execute_input": "2024-03-19T10:19:34.235295Z", + "iopub.status.busy": "2024-03-19T10:19:34.234902Z", + "iopub.status.idle": "2024-03-19T10:19:34.243659Z", + "shell.execute_reply": "2024-03-19T10:19:34.243047Z" } }, "outputs": [ @@ -1168,10 +1168,10 @@ "id": "superior-mobility", "metadata": { "execution": { - "iopub.execute_input": "2024-03-19T10:01:21.212465Z", - "iopub.status.busy": "2024-03-19T10:01:21.212022Z", - "iopub.status.idle": "2024-03-19T10:01:21.215924Z", - "shell.execute_reply": "2024-03-19T10:01:21.215168Z" + "iopub.execute_input": "2024-03-19T10:19:34.246255Z", + "iopub.status.busy": "2024-03-19T10:19:34.245806Z", + "iopub.status.idle": "2024-03-19T10:19:34.249222Z", + "shell.execute_reply": "2024-03-19T10:19:34.248595Z" } }, "outputs": [ diff --git a/tutorials/custom_explainer/custom_explainer.html b/tutorials/custom_explainer/custom_explainer.html index 96c25ae0c..f90a8ec73 100644 --- a/tutorials/custom_explainer/custom_explainer.html +++ b/tutorials/custom_explainer/custom_explainer.html @@ -4,7 +4,7 @@ - Tutorial - Implementing a custom analysis block in Lightwood — lightwood 24.3.3.1 documentation + Tutorial - Implementing a custom analysis block in Lightwood — lightwood 24.3.3.0 documentation @@ -42,7 +42,7 @@
- 24.3.3.1 + 24.3.3.0
@@ -188,7 +188,7 @@

Step 1: figuring out what we need
-<__main__.ModelCorrelationHeatmap at 0x7e3a82a68b80>
+<__main__.ModelCorrelationHeatmap at 0x7867834b34f0>
 

Right now, our newly created analysis block doesn’t do much, apart from returning the info and insights (row_insights and global_insights) exactly as it received them from the previous block.

@@ -344,7 +344,7 @@

Step 4: Final test run
-INFO:type_infer-2252:Analyzing a sample of 222
+INFO:type_infer-2540:Analyzing a sample of 222
 

We can take a look at the respective Json AI key just to confirm our newly added analysis block is in there:

@@ -520,7 +520,7 @@

Step 4: Final test run
-INFO:dataprep_ml-2252:[Learn phase 1/8] - Statistical analysis
+INFO:dataprep_ml-2540:[Learn phase 1/8] - Statistical analysis
 

@@ -528,7 +528,7 @@

Step 4: Final test run
-INFO:dataprep_ml-2252:Starting statistical analysis
+INFO:dataprep_ml-2540:Starting statistical analysis
 

@@ -536,7 +536,7 @@

Step 4: Final test run
-INFO:dataprep_ml-2252:Finished statistical analysis
+INFO:dataprep_ml-2540:Finished statistical analysis
 

@@ -544,7 +544,7 @@

Step 4: Final test run
-DEBUG:lightwood-2252: `analyze_data` runtime: 0.02 seconds
+DEBUG:lightwood-2540: `analyze_data` runtime: 0.02 seconds
 

@@ -552,7 +552,7 @@

Step 4: Final test run
-INFO:dataprep_ml-2252:[Learn phase 2/8] - Data preprocessing
+INFO:dataprep_ml-2540:[Learn phase 2/8] - Data preprocessing
 

@@ -560,7 +560,7 @@

Step 4: Final test run
-INFO:dataprep_ml-2252:Cleaning the data
+INFO:dataprep_ml-2540:Cleaning the data
 

@@ -568,7 +568,7 @@

Step 4: Final test run
-DEBUG:lightwood-2252: `preprocess` runtime: 0.01 seconds
+DEBUG:lightwood-2540: `preprocess` runtime: 0.01 seconds
 

@@ -576,7 +576,7 @@

Step 4: Final test run
-INFO:dataprep_ml-2252:[Learn phase 3/8] - Data splitting
+INFO:dataprep_ml-2540:[Learn phase 3/8] - Data splitting
 

@@ -584,7 +584,7 @@

Step 4: Final test run
-INFO:dataprep_ml-2252:Splitting the data into train/test
+INFO:dataprep_ml-2540:Splitting the data into train/test
 

@@ -592,7 +592,7 @@

Step 4: Final test run
-DEBUG:lightwood-2252: `split` runtime: 0.01 seconds
+DEBUG:lightwood-2540: `split` runtime: 0.01 seconds
 

@@ -600,7 +600,7 @@

Step 4: Final test run
-INFO:dataprep_ml-2252:[Learn phase 4/8] - Preparing encoders
+INFO:dataprep_ml-2540:[Learn phase 4/8] - Preparing encoders
 

@@ -608,7 +608,7 @@

Step 4: Final test run
-DEBUG:dataprep_ml-2252:Preparing sequentially...
+DEBUG:dataprep_ml-2540:Preparing sequentially...
 

@@ -616,7 +616,7 @@

Step 4: Final test run
-DEBUG:dataprep_ml-2252:Preparing encoder for Population...
+DEBUG:dataprep_ml-2540:Preparing encoder for Population...
 

@@ -624,7 +624,7 @@

Step 4: Final test run
-DEBUG:dataprep_ml-2252:Preparing encoder for Area (sq. mi.)...
+DEBUG:dataprep_ml-2540:Preparing encoder for Area (sq. mi.)...
 

@@ -632,7 +632,7 @@

Step 4: Final test run
-DEBUG:dataprep_ml-2252:Preparing encoder for Pop. Density ...
+DEBUG:dataprep_ml-2540:Preparing encoder for Pop. Density ...
 

@@ -640,7 +640,7 @@

Step 4: Final test run
-DEBUG:dataprep_ml-2252:Preparing encoder for GDP ($ per capita)...
+DEBUG:dataprep_ml-2540:Preparing encoder for GDP ($ per capita)...
 

@@ -648,7 +648,7 @@

Step 4: Final test run
-DEBUG:dataprep_ml-2252:Preparing encoder for Literacy (%)...
+DEBUG:dataprep_ml-2540:Preparing encoder for Literacy (%)...
 

@@ -656,7 +656,7 @@

Step 4: Final test run
-DEBUG:dataprep_ml-2252:Preparing encoder for Infant mortality ...
+DEBUG:dataprep_ml-2540:Preparing encoder for Infant mortality ...
 

@@ -664,7 +664,7 @@

Step 4: Final test run
-DEBUG:lightwood-2252:Encoding UNKNOWN categories as index 0
+DEBUG:lightwood-2540:Encoding UNKNOWN categories as index 0
 

@@ -672,7 +672,7 @@

Step 4: Final test run
-DEBUG:lightwood-2252: `prepare` runtime: 0.01 seconds
+DEBUG:lightwood-2540: `prepare` runtime: 0.01 seconds
 

@@ -680,7 +680,7 @@

Step 4: Final test run
-INFO:dataprep_ml-2252:[Learn phase 5/8] - Feature generation
+INFO:dataprep_ml-2540:[Learn phase 5/8] - Feature generation
 

@@ -688,7 +688,7 @@

Step 4: Final test run
-INFO:dataprep_ml-2252:Featurizing the data
+INFO:dataprep_ml-2540:Featurizing the data
 

@@ -696,7 +696,7 @@

Step 4: Final test run
-DEBUG:lightwood-2252: `featurize` runtime: 0.05 seconds
+DEBUG:lightwood-2540: `featurize` runtime: 0.05 seconds
 

@@ -704,7 +704,7 @@

Step 4: Final test run
-INFO:dataprep_ml-2252:[Learn phase 6/8] - Mixer training
+INFO:dataprep_ml-2540:[Learn phase 6/8] - Mixer training
 

@@ -712,7 +712,7 @@

Step 4: Final test run
-INFO:dataprep_ml-2252:Training the mixers
+INFO:dataprep_ml-2540:Training the mixers
 

@@ -720,7 +720,7 @@

Step 4: Final test run
-WARNING:lightwood-2252:XGBoost running on CPU
+WARNING:lightwood-2540:XGBoost running on CPU
 

@@ -737,7 +737,7 @@

Step 4: Final test run
-[10:00:20] WARNING: ../src/learner.cc:339: No visible GPU is found, setting `gpu_id` to -1
+[10:18:34] WARNING: ../src/learner.cc:339: No visible GPU is found, setting `gpu_id` to -1
 

@@ -750,7 +750,7 @@

Step 4: Final test runINFO:lightwood-2252:Loss of 18.69619858264923 with learning rate 0.0001 +INFO:lightwood-2540:Loss of 18.69619858264923 with learning rate 0.0001

@@ -758,7 +758,7 @@

Step 4: Final test run
-INFO:lightwood-2252:Loss of 16.93891429901123 with learning rate 0.0005
+INFO:lightwood-2540:Loss of 16.93891429901123 with learning rate 0.0005
 

@@ -766,7 +766,7 @@

Step 4: Final test run
-INFO:lightwood-2252:Loss of 16.197376608848572 with learning rate 0.001
+INFO:lightwood-2540:Loss of 16.197376608848572 with learning rate 0.001
 

@@ -774,7 +774,7 @@

Step 4: Final test run
-INFO:lightwood-2252:Loss of 16.06481909751892 with learning rate 0.002
+INFO:lightwood-2540:Loss of 16.06481909751892 with learning rate 0.002
 

@@ -782,7 +782,7 @@

Step 4: Final test run
-INFO:lightwood-2252:Loss of 16.472004413604736 with learning rate 0.003
+INFO:lightwood-2540:Loss of 16.472004413604736 with learning rate 0.003
 

@@ -790,7 +790,7 @@

Step 4: Final test run
-INFO:lightwood-2252:Loss of 18.28026556968689 with learning rate 0.005
+INFO:lightwood-2540:Loss of 18.28026556968689 with learning rate 0.005
 

@@ -798,7 +798,7 @@

Step 4: Final test run
-INFO:lightwood-2252:Loss of 26.746760368347168 with learning rate 0.01
+INFO:lightwood-2540:Loss of 26.746760368347168 with learning rate 0.01
 

@@ -806,7 +806,7 @@

Step 4: Final test run
-INFO:lightwood-2252:Loss of 101.83524441719055 with learning rate 0.05
+INFO:lightwood-2540:Loss of 101.83524441719055 with learning rate 0.05
 

@@ -814,7 +814,7 @@

Step 4: Final test run
-INFO:lightwood-2252:Found learning rate of: 0.002
+INFO:lightwood-2540:Found learning rate of: 0.002
 

@@ -824,7 +824,7 @@

Step 4: Final test run
 /opt/hostedtoolcache/Python/3.9.18/x64/lib/python3.9/site-packages/torch/cuda/amp/grad_scaler.py:126: UserWarning: torch.cuda.amp.GradScaler is enabled, but CUDA is not available.  Disabling.
   warnings.warn(
-INFO:lightwood-2252:Loss @ epoch 1: 1.319209337234497
+INFO:lightwood-2540:Loss @ epoch 1: 1.319209337234497
 

@@ -832,7 +832,7 @@

Step 4: Final test run
-INFO:lightwood-2252:Loss @ epoch 2: 1.3220206499099731
+INFO:lightwood-2540:Loss @ epoch 2: 1.3220206499099731
 

@@ -840,7 +840,7 @@

Step 4: Final test run
-INFO:lightwood-2252:Loss @ epoch 3: 1.3063435554504395
+INFO:lightwood-2540:Loss @ epoch 3: 1.3063435554504395
 

@@ -848,7 +848,7 @@

Step 4: Final test run
-INFO:lightwood-2252:Loss @ epoch 4: 1.2932535409927368
+INFO:lightwood-2540:Loss @ epoch 4: 1.2932535409927368
 

@@ -856,7 +856,7 @@

Step 4: Final test run
-INFO:lightwood-2252:Loss @ epoch 5: 1.2823516130447388
+INFO:lightwood-2540:Loss @ epoch 5: 1.2823516130447388
 

@@ -864,7 +864,7 @@

Step 4: Final test run
-INFO:lightwood-2252:Loss @ epoch 6: 1.2705544233322144
+INFO:lightwood-2540:Loss @ epoch 6: 1.2705544233322144
 

@@ -872,7 +872,7 @@

Step 4: Final test run
-INFO:lightwood-2252:Loss @ epoch 7: 1.2418551445007324
+INFO:lightwood-2540:Loss @ epoch 7: 1.2418551445007324
 

@@ -880,7 +880,7 @@

Step 4: Final test run
-INFO:lightwood-2252:Loss @ epoch 8: 1.2208324670791626
+INFO:lightwood-2540:Loss @ epoch 8: 1.2208324670791626
 

@@ -888,7 +888,7 @@

Step 4: Final test run
-INFO:lightwood-2252:Loss @ epoch 9: 1.197828769683838
+INFO:lightwood-2540:Loss @ epoch 9: 1.197828769683838
 

@@ -896,7 +896,7 @@

Step 4: Final test run
-INFO:lightwood-2252:Loss @ epoch 10: 1.1781431436538696
+INFO:lightwood-2540:Loss @ epoch 10: 1.1781431436538696
 

@@ -904,7 +904,7 @@

Step 4: Final test run
-INFO:lightwood-2252:Loss @ epoch 11: 1.161504864692688
+INFO:lightwood-2540:Loss @ epoch 11: 1.161504864692688
 

@@ -912,7 +912,7 @@

Step 4: Final test run
-INFO:lightwood-2252:Loss @ epoch 12: 1.1442031860351562
+INFO:lightwood-2540:Loss @ epoch 12: 1.1442031860351562
 

@@ -920,7 +920,7 @@

Step 4: Final test run
-INFO:lightwood-2252:Loss @ epoch 13: 1.1058541536331177
+INFO:lightwood-2540:Loss @ epoch 13: 1.1058541536331177
 

@@ -928,7 +928,7 @@

Step 4: Final test run
-INFO:lightwood-2252:Loss @ epoch 14: 1.0935649871826172
+INFO:lightwood-2540:Loss @ epoch 14: 1.0935649871826172
 

@@ -936,7 +936,7 @@

Step 4: Final test run
-INFO:lightwood-2252:Loss @ epoch 15: 1.0802721977233887
+INFO:lightwood-2540:Loss @ epoch 15: 1.0802721977233887
 

@@ -944,7 +944,7 @@

Step 4: Final test run
-INFO:lightwood-2252:Loss @ epoch 16: 1.0696258544921875
+INFO:lightwood-2540:Loss @ epoch 16: 1.0696258544921875
 

@@ -952,7 +952,7 @@

Step 4: Final test run
-INFO:lightwood-2252:Loss @ epoch 17: 1.0607414245605469
+INFO:lightwood-2540:Loss @ epoch 17: 1.0607414245605469
 

@@ -960,7 +960,7 @@

Step 4: Final test run
-INFO:lightwood-2252:Loss @ epoch 18: 1.0493905544281006
+INFO:lightwood-2540:Loss @ epoch 18: 1.0493905544281006
 

@@ -968,7 +968,7 @@

Step 4: Final test run
-INFO:lightwood-2252:Loss @ epoch 19: 1.020617961883545
+INFO:lightwood-2540:Loss @ epoch 19: 1.020617961883545
 

@@ -976,7 +976,7 @@

Step 4: Final test run
-INFO:lightwood-2252:Loss @ epoch 20: 1.0081787109375
+INFO:lightwood-2540:Loss @ epoch 20: 1.0081787109375
 

@@ -984,7 +984,7 @@

Step 4: Final test run
-INFO:lightwood-2252:Loss @ epoch 21: 0.9943330883979797
+INFO:lightwood-2540:Loss @ epoch 21: 0.9943330883979797
 

@@ -992,7 +992,7 @@

Step 4: Final test run
-INFO:lightwood-2252:Loss @ epoch 22: 0.9842473268508911
+INFO:lightwood-2540:Loss @ epoch 22: 0.9842473268508911
 

@@ -1000,7 +1000,7 @@

Step 4: Final test run
-INFO:lightwood-2252:Loss @ epoch 23: 0.9762145280838013
+INFO:lightwood-2540:Loss @ epoch 23: 0.9762145280838013
 

@@ -1008,7 +1008,7 @@

Step 4: Final test run
-INFO:lightwood-2252:Loss @ epoch 24: 0.9653865098953247
+INFO:lightwood-2540:Loss @ epoch 24: 0.9653865098953247
 

@@ -1016,7 +1016,7 @@

Step 4: Final test run
-INFO:lightwood-2252:Loss @ epoch 25: 0.9380742311477661
+INFO:lightwood-2540:Loss @ epoch 25: 0.9380742311477661
 

@@ -1024,7 +1024,7 @@

Step 4: Final test run
-INFO:lightwood-2252:Loss @ epoch 26: 0.9271669387817383
+INFO:lightwood-2540:Loss @ epoch 26: 0.9271669387817383
 

@@ -1032,7 +1032,7 @@

Step 4: Final test run
-INFO:lightwood-2252:Loss @ epoch 27: 0.9147073030471802
+INFO:lightwood-2540:Loss @ epoch 27: 0.9147073030471802
 

@@ -1040,7 +1040,7 @@

Step 4: Final test run
-INFO:lightwood-2252:Loss @ epoch 28: 0.9064992070198059
+INFO:lightwood-2540:Loss @ epoch 28: 0.9064992070198059
 

@@ -1048,7 +1048,7 @@

Step 4: Final test run
-INFO:lightwood-2252:Loss @ epoch 29: 0.900122344493866
+INFO:lightwood-2540:Loss @ epoch 29: 0.900122344493866
 

@@ -1056,7 +1056,7 @@

Step 4: Final test run
-INFO:lightwood-2252:Loss @ epoch 30: 0.8903173208236694
+INFO:lightwood-2540:Loss @ epoch 30: 0.8903173208236694
 

@@ -1064,7 +1064,7 @@

Step 4: Final test run
-INFO:lightwood-2252:Loss @ epoch 31: 0.8648637533187866
+INFO:lightwood-2540:Loss @ epoch 31: 0.8648637533187866
 

@@ -1072,7 +1072,7 @@

Step 4: Final test run
-INFO:lightwood-2252:Loss @ epoch 32: 0.8549227118492126
+INFO:lightwood-2540:Loss @ epoch 32: 0.8549227118492126
 

@@ -1080,7 +1080,7 @@

Step 4: Final test run
-INFO:lightwood-2252:Loss @ epoch 33: 0.8434366583824158
+INFO:lightwood-2540:Loss @ epoch 33: 0.8434366583824158
 

@@ -1088,7 +1088,7 @@

Step 4: Final test run
-INFO:lightwood-2252:Loss @ epoch 34: 0.8365358114242554
+INFO:lightwood-2540:Loss @ epoch 34: 0.8365358114242554
 

@@ -1096,7 +1096,7 @@

Step 4: Final test run
-INFO:lightwood-2252:Loss @ epoch 35: 0.831310510635376
+INFO:lightwood-2540:Loss @ epoch 35: 0.831310510635376
 

@@ -1104,7 +1104,7 @@

Step 4: Final test run
-INFO:lightwood-2252:Loss @ epoch 36: 0.8222441673278809
+INFO:lightwood-2540:Loss @ epoch 36: 0.8222441673278809
 

@@ -1112,7 +1112,7 @@

Step 4: Final test run
-INFO:lightwood-2252:Loss @ epoch 37: 0.7981722950935364
+INFO:lightwood-2540:Loss @ epoch 37: 0.7981722950935364
 

@@ -1120,7 +1120,7 @@

Step 4: Final test run
-INFO:lightwood-2252:Loss @ epoch 38: 0.789170503616333
+INFO:lightwood-2540:Loss @ epoch 38: 0.789170503616333
 

@@ -1128,7 +1128,7 @@

Step 4: Final test run
-INFO:lightwood-2252:Loss @ epoch 39: 0.7787192463874817
+INFO:lightwood-2540:Loss @ epoch 39: 0.7787192463874817
 

@@ -1136,7 +1136,7 @@

Step 4: Final test run
-INFO:lightwood-2252:Loss @ epoch 40: 0.7730110287666321
+INFO:lightwood-2540:Loss @ epoch 40: 0.7730110287666321
 

@@ -1144,7 +1144,7 @@

Step 4: Final test run
-INFO:lightwood-2252:Loss @ epoch 41: 0.7687097787857056
+INFO:lightwood-2540:Loss @ epoch 41: 0.7687097787857056
 

@@ -1152,7 +1152,7 @@

Step 4: Final test run
-INFO:lightwood-2252:Loss @ epoch 42: 0.7602015137672424
+INFO:lightwood-2540:Loss @ epoch 42: 0.7602015137672424
 

@@ -1160,7 +1160,7 @@

Step 4: Final test run
-INFO:lightwood-2252:Loss @ epoch 43: 0.7373268604278564
+INFO:lightwood-2540:Loss @ epoch 43: 0.7373268604278564
 

@@ -1168,7 +1168,7 @@

Step 4: Final test run
-INFO:lightwood-2252:Loss @ epoch 44: 0.7292225956916809
+INFO:lightwood-2540:Loss @ epoch 44: 0.7292225956916809
 

@@ -1176,7 +1176,7 @@

Step 4: Final test run
-INFO:lightwood-2252:Loss @ epoch 45: 0.7197889685630798
+INFO:lightwood-2540:Loss @ epoch 45: 0.7197889685630798
 

@@ -1184,7 +1184,7 @@

Step 4: Final test run
-INFO:lightwood-2252:Loss @ epoch 46: 0.7151773571968079
+INFO:lightwood-2540:Loss @ epoch 46: 0.7151773571968079
 

@@ -1192,7 +1192,7 @@

Step 4: Final test run
-INFO:lightwood-2252:Loss @ epoch 47: 0.7117206454277039
+INFO:lightwood-2540:Loss @ epoch 47: 0.7117206454277039
 

@@ -1200,7 +1200,7 @@

Step 4: Final test run
-INFO:lightwood-2252:Loss @ epoch 48: 0.7038285136222839
+INFO:lightwood-2540:Loss @ epoch 48: 0.7038285136222839
 

@@ -1208,7 +1208,7 @@

Step 4: Final test run
-INFO:lightwood-2252:Loss @ epoch 49: 0.682073175907135
+INFO:lightwood-2540:Loss @ epoch 49: 0.682073175907135
 

@@ -1216,7 +1216,7 @@

Step 4: Final test run
-INFO:lightwood-2252:Loss @ epoch 50: 0.674643874168396
+INFO:lightwood-2540:Loss @ epoch 50: 0.674643874168396
 

@@ -1224,7 +1224,7 @@

Step 4: Final test run
-INFO:lightwood-2252:Loss @ epoch 51: 0.6659626364707947
+INFO:lightwood-2540:Loss @ epoch 51: 0.6659626364707947
 

@@ -1232,7 +1232,7 @@

Step 4: Final test run
-INFO:lightwood-2252:Loss @ epoch 52: 0.6620772480964661
+INFO:lightwood-2540:Loss @ epoch 52: 0.6620772480964661
 

@@ -1240,7 +1240,7 @@

Step 4: Final test run
-INFO:lightwood-2252:Loss @ epoch 53: 0.6590715646743774
+INFO:lightwood-2540:Loss @ epoch 53: 0.6590715646743774
 

@@ -1248,7 +1248,7 @@

Step 4: Final test run
-INFO:lightwood-2252:Loss @ epoch 54: 0.6515910625457764
+INFO:lightwood-2540:Loss @ epoch 54: 0.6515910625457764
 

@@ -1256,7 +1256,7 @@

Step 4: Final test run
-INFO:lightwood-2252:Loss @ epoch 55: 0.6308077573776245
+INFO:lightwood-2540:Loss @ epoch 55: 0.6308077573776245
 

@@ -1264,7 +1264,7 @@

Step 4: Final test run
-INFO:lightwood-2252:Loss @ epoch 56: 0.6241987347602844
+INFO:lightwood-2540:Loss @ epoch 56: 0.6241987347602844
 

@@ -1272,7 +1272,7 @@

Step 4: Final test run
-INFO:lightwood-2252:Loss @ epoch 57: 0.6163835525512695
+INFO:lightwood-2540:Loss @ epoch 57: 0.6163835525512695
 

@@ -1280,7 +1280,7 @@

Step 4: Final test run
-INFO:lightwood-2252:Loss @ epoch 58: 0.6131908297538757
+INFO:lightwood-2540:Loss @ epoch 58: 0.6131908297538757
 

@@ -1288,7 +1288,7 @@

Step 4: Final test run
-INFO:lightwood-2252:Loss @ epoch 59: 0.6106155514717102
+INFO:lightwood-2540:Loss @ epoch 59: 0.6106155514717102
 

@@ -1296,7 +1296,7 @@

Step 4: Final test run
-INFO:lightwood-2252:Loss @ epoch 60: 0.6036757826805115
+INFO:lightwood-2540:Loss @ epoch 60: 0.6036757826805115
 

@@ -1304,7 +1304,7 @@

Step 4: Final test run
-INFO:lightwood-2252:Loss @ epoch 61: 0.5848420262336731
+INFO:lightwood-2540:Loss @ epoch 61: 0.5848420262336731
 

@@ -1312,7 +1312,7 @@

Step 4: Final test run
-INFO:lightwood-2252:Loss @ epoch 62: 0.5793871879577637
+INFO:lightwood-2540:Loss @ epoch 62: 0.5793871879577637
 

@@ -1320,7 +1320,7 @@

Step 4: Final test run
-INFO:lightwood-2252:Loss @ epoch 63: 0.5726662278175354
+INFO:lightwood-2540:Loss @ epoch 63: 0.5726662278175354
 

@@ -1328,7 +1328,7 @@

Step 4: Final test run
-INFO:lightwood-2252:Loss @ epoch 64: 0.5703645348548889
+INFO:lightwood-2540:Loss @ epoch 64: 0.5703645348548889
 

@@ -1336,7 +1336,7 @@

Step 4: Final test run
-INFO:lightwood-2252:Loss @ epoch 65: 0.5684641003608704
+INFO:lightwood-2540:Loss @ epoch 65: 0.5684641003608704
 

@@ -1344,7 +1344,7 @@

Step 4: Final test run
-INFO:lightwood-2252:Loss @ epoch 66: 0.5622180104255676
+INFO:lightwood-2540:Loss @ epoch 66: 0.5622180104255676
 

@@ -1352,7 +1352,7 @@

Step 4: Final test run
-INFO:lightwood-2252:Loss @ epoch 67: 0.5449516773223877
+INFO:lightwood-2540:Loss @ epoch 67: 0.5449516773223877
 

@@ -1360,7 +1360,7 @@

Step 4: Final test run
-INFO:lightwood-2252:Loss @ epoch 68: 0.5401747226715088
+INFO:lightwood-2540:Loss @ epoch 68: 0.5401747226715088
 

@@ -1368,7 +1368,7 @@

Step 4: Final test run
-INFO:lightwood-2252:Loss @ epoch 69: 0.5341063141822815
+INFO:lightwood-2540:Loss @ epoch 69: 0.5341063141822815
 

@@ -1376,7 +1376,7 @@

Step 4: Final test run
-INFO:lightwood-2252:Loss @ epoch 70: 0.5322306752204895
+INFO:lightwood-2540:Loss @ epoch 70: 0.5322306752204895
 

@@ -1384,7 +1384,7 @@

Step 4: Final test run
-INFO:lightwood-2252:Loss @ epoch 71: 0.5305425524711609
+INFO:lightwood-2540:Loss @ epoch 71: 0.5305425524711609
 

@@ -1392,7 +1392,7 @@

Step 4: Final test run
-INFO:lightwood-2252:Loss @ epoch 72: 0.5246548056602478
+INFO:lightwood-2540:Loss @ epoch 72: 0.5246548056602478
 

@@ -1400,7 +1400,7 @@

Step 4: Final test run
-INFO:lightwood-2252:Loss @ epoch 73: 0.5083626508712769
+INFO:lightwood-2540:Loss @ epoch 73: 0.5083626508712769
 

@@ -1408,7 +1408,7 @@

Step 4: Final test run
-INFO:lightwood-2252:Loss @ epoch 74: 0.5040708184242249
+INFO:lightwood-2540:Loss @ epoch 74: 0.5040708184242249
 

@@ -1416,7 +1416,7 @@

Step 4: Final test run
-INFO:lightwood-2252:Loss @ epoch 75: 0.49863335490226746
+INFO:lightwood-2540:Loss @ epoch 75: 0.49863335490226746
 

@@ -1424,7 +1424,7 @@

Step 4: Final test run
-INFO:lightwood-2252:Loss @ epoch 76: 0.49717265367507935
+INFO:lightwood-2540:Loss @ epoch 76: 0.49717265367507935
 

@@ -1432,7 +1432,7 @@

Step 4: Final test run
-INFO:lightwood-2252:Loss @ epoch 77: 0.49564701318740845
+INFO:lightwood-2540:Loss @ epoch 77: 0.49564701318740845
 

@@ -1440,7 +1440,7 @@

Step 4: Final test run
-INFO:lightwood-2252:Loss @ epoch 78: 0.4900944232940674
+INFO:lightwood-2540:Loss @ epoch 78: 0.4900944232940674
 

@@ -1448,7 +1448,7 @@

Step 4: Final test run
-INFO:lightwood-2252:Loss @ epoch 79: 0.47473227977752686
+INFO:lightwood-2540:Loss @ epoch 79: 0.47473227977752686
 

@@ -1456,7 +1456,7 @@

Step 4: Final test run
-INFO:lightwood-2252:Loss @ epoch 80: 0.4708785116672516
+INFO:lightwood-2540:Loss @ epoch 80: 0.4708785116672516
 

@@ -1464,7 +1464,7 @@

Step 4: Final test run
-INFO:lightwood-2252:Loss @ epoch 81: 0.46578508615493774
+INFO:lightwood-2540:Loss @ epoch 81: 0.46578508615493774
 

@@ -1472,7 +1472,7 @@

Step 4: Final test run
-INFO:lightwood-2252:Loss @ epoch 82: 0.4644494950771332
+INFO:lightwood-2540:Loss @ epoch 82: 0.4644494950771332
 

@@ -1480,7 +1480,7 @@

Step 4: Final test run
-INFO:lightwood-2252:Loss @ epoch 83: 0.4629424810409546
+INFO:lightwood-2540:Loss @ epoch 83: 0.4629424810409546
 

@@ -1488,7 +1488,7 @@

Step 4: Final test run
-INFO:lightwood-2252:Loss @ epoch 84: 0.4576236307621002
+INFO:lightwood-2540:Loss @ epoch 84: 0.4576236307621002
 

@@ -1496,7 +1496,7 @@

Step 4: Final test run
-INFO:lightwood-2252:Loss @ epoch 85: 0.44295966625213623
+INFO:lightwood-2540:Loss @ epoch 85: 0.44295966625213623
 

@@ -1504,7 +1504,7 @@

Step 4: Final test run
-INFO:lightwood-2252:Loss @ epoch 86: 0.4393518269062042
+INFO:lightwood-2540:Loss @ epoch 86: 0.4393518269062042
 

@@ -1512,7 +1512,7 @@

Step 4: Final test run
-INFO:lightwood-2252:Loss @ epoch 87: 0.4346559941768646
+INFO:lightwood-2540:Loss @ epoch 87: 0.4346559941768646
 

@@ -1520,7 +1520,7 @@

Step 4: Final test run
-INFO:lightwood-2252:Loss @ epoch 88: 0.43358123302459717
+INFO:lightwood-2540:Loss @ epoch 88: 0.43358123302459717
 

@@ -1528,7 +1528,7 @@

Step 4: Final test run
-INFO:lightwood-2252:Loss @ epoch 89: 0.43231165409088135
+INFO:lightwood-2540:Loss @ epoch 89: 0.43231165409088135
 

@@ -1536,7 +1536,7 @@

Step 4: Final test run
-INFO:lightwood-2252:Loss @ epoch 90: 0.42753666639328003
+INFO:lightwood-2540:Loss @ epoch 90: 0.42753666639328003
 

@@ -1544,7 +1544,7 @@

Step 4: Final test run
-INFO:lightwood-2252:Loss @ epoch 91: 0.41425880789756775
+INFO:lightwood-2540:Loss @ epoch 91: 0.41425880789756775
 

@@ -1552,7 +1552,7 @@

Step 4: Final test run
-INFO:lightwood-2252:Loss @ epoch 92: 0.4113253951072693
+INFO:lightwood-2540:Loss @ epoch 92: 0.4113253951072693
 

@@ -1560,7 +1560,7 @@

Step 4: Final test run
-INFO:lightwood-2252:Loss @ epoch 93: 0.4074642062187195
+INFO:lightwood-2540:Loss @ epoch 93: 0.4074642062187195
 

@@ -1568,7 +1568,7 @@

Step 4: Final test run
-INFO:lightwood-2252:Loss @ epoch 94: 0.40698081254959106
+INFO:lightwood-2540:Loss @ epoch 94: 0.40698081254959106
 

@@ -1576,7 +1576,7 @@

Step 4: Final test run
-INFO:lightwood-2252:Loss @ epoch 95: 0.4062195420265198
+INFO:lightwood-2540:Loss @ epoch 95: 0.4062195420265198
 

@@ -1584,7 +1584,7 @@

Step 4: Final test run
-INFO:lightwood-2252:Loss @ epoch 96: 0.4020974636077881
+INFO:lightwood-2540:Loss @ epoch 96: 0.4020974636077881
 

@@ -1592,7 +1592,7 @@

Step 4: Final test run
-INFO:lightwood-2252:Loss @ epoch 97: 0.3904534876346588
+INFO:lightwood-2540:Loss @ epoch 97: 0.3904534876346588
 

@@ -1600,7 +1600,7 @@

Step 4: Final test run
-INFO:lightwood-2252:Loss @ epoch 98: 0.3879585564136505
+INFO:lightwood-2540:Loss @ epoch 98: 0.3879585564136505
 

@@ -1608,7 +1608,7 @@

Step 4: Final test run
-INFO:lightwood-2252:Loss @ epoch 99: 0.38447296619415283
+INFO:lightwood-2540:Loss @ epoch 99: 0.38447296619415283
 

@@ -1616,7 +1616,7 @@

Step 4: Final test run
-INFO:lightwood-2252:Loss @ epoch 100: 0.3841344118118286
+INFO:lightwood-2540:Loss @ epoch 100: 0.3841344118118286
 

@@ -1624,7 +1624,7 @@

Step 4: Final test run
-INFO:lightwood-2252:Loss @ epoch 101: 0.3833732604980469
+INFO:lightwood-2540:Loss @ epoch 101: 0.3833732604980469
 

@@ -1632,7 +1632,7 @@

Step 4: Final test run
-INFO:lightwood-2252:Loss @ epoch 102: 0.37956172227859497
+INFO:lightwood-2540:Loss @ epoch 102: 0.37956172227859497
 

@@ -1640,7 +1640,7 @@

Step 4: Final test run
-INFO:lightwood-2252:Loss @ epoch 103: 0.36883822083473206
+INFO:lightwood-2540:Loss @ epoch 103: 0.36883822083473206
 

@@ -1648,7 +1648,7 @@

Step 4: Final test run
-INFO:lightwood-2252:Loss @ epoch 104: 0.3664571940898895
+INFO:lightwood-2540:Loss @ epoch 104: 0.3664571940898895
 

@@ -1656,7 +1656,7 @@

Step 4: Final test run
-INFO:lightwood-2252:Loss @ epoch 105: 0.36335548758506775
+INFO:lightwood-2540:Loss @ epoch 105: 0.36335548758506775
 

@@ -1664,7 +1664,7 @@

Step 4: Final test run
-INFO:lightwood-2252:Loss @ epoch 106: 0.36316758394241333
+INFO:lightwood-2540:Loss @ epoch 106: 0.36316758394241333
 

@@ -1672,7 +1672,7 @@

Step 4: Final test run
-INFO:lightwood-2252:Loss @ epoch 107: 0.3625164330005646
+INFO:lightwood-2540:Loss @ epoch 107: 0.3625164330005646
 

@@ -1680,7 +1680,7 @@

Step 4: Final test run
-INFO:lightwood-2252:Loss @ epoch 108: 0.359012246131897
+INFO:lightwood-2540:Loss @ epoch 108: 0.359012246131897
 

@@ -1688,7 +1688,7 @@

Step 4: Final test run
-INFO:lightwood-2252:Loss @ epoch 109: 0.34912365674972534
+INFO:lightwood-2540:Loss @ epoch 109: 0.34912365674972534
 

@@ -1696,7 +1696,7 @@

Step 4: Final test run
-INFO:lightwood-2252:Loss @ epoch 110: 0.34696850180625916
+INFO:lightwood-2540:Loss @ epoch 110: 0.34696850180625916
 

@@ -1704,7 +1704,7 @@

Step 4: Final test run
-INFO:lightwood-2252:Loss @ epoch 111: 0.3441202938556671
+INFO:lightwood-2540:Loss @ epoch 111: 0.3441202938556671
 

@@ -1712,7 +1712,7 @@

Step 4: Final test run
-INFO:lightwood-2252:Loss @ epoch 112: 0.34398093819618225
+INFO:lightwood-2540:Loss @ epoch 112: 0.34398093819618225
 

@@ -1720,7 +1720,7 @@

Step 4: Final test run
-INFO:lightwood-2252:Loss @ epoch 113: 0.3432472050189972
+INFO:lightwood-2540:Loss @ epoch 113: 0.3432472050189972
 

@@ -1728,7 +1728,7 @@

Step 4: Final test run
-INFO:lightwood-2252:Loss @ epoch 114: 0.3399496376514435
+INFO:lightwood-2540:Loss @ epoch 114: 0.3399496376514435
 

@@ -1736,7 +1736,7 @@

Step 4: Final test run
-INFO:lightwood-2252:Loss @ epoch 115: 0.3308461308479309
+INFO:lightwood-2540:Loss @ epoch 115: 0.3308461308479309
 

@@ -1744,7 +1744,7 @@

Step 4: Final test run
-INFO:lightwood-2252:Loss @ epoch 116: 0.3289041221141815
+INFO:lightwood-2540:Loss @ epoch 116: 0.3289041221141815
 

@@ -1752,7 +1752,7 @@

Step 4: Final test run
-INFO:lightwood-2252:Loss @ epoch 117: 0.32627207040786743
+INFO:lightwood-2540:Loss @ epoch 117: 0.32627207040786743
 

@@ -1760,7 +1760,7 @@

Step 4: Final test run
-INFO:lightwood-2252:Loss @ epoch 118: 0.3261372447013855
+INFO:lightwood-2540:Loss @ epoch 118: 0.3261372447013855
 

@@ -1768,7 +1768,7 @@

Step 4: Final test run
-INFO:lightwood-2252:Loss @ epoch 119: 0.32546231150627136
+INFO:lightwood-2540:Loss @ epoch 119: 0.32546231150627136
 

@@ -1776,7 +1776,7 @@

Step 4: Final test run
-INFO:lightwood-2252:Loss @ epoch 120: 0.3224080502986908
+INFO:lightwood-2540:Loss @ epoch 120: 0.3224080502986908
 

@@ -1784,7 +1784,7 @@

Step 4: Final test run
-INFO:lightwood-2252:Loss @ epoch 121: 0.314008891582489
+INFO:lightwood-2540:Loss @ epoch 121: 0.314008891582489
 

@@ -1792,7 +1792,7 @@

Step 4: Final test run
-INFO:lightwood-2252:Loss @ epoch 122: 0.31220486760139465
+INFO:lightwood-2540:Loss @ epoch 122: 0.31220486760139465
 

@@ -1800,7 +1800,7 @@

Step 4: Final test run
-INFO:lightwood-2252:Loss @ epoch 123: 0.3098214566707611
+INFO:lightwood-2540:Loss @ epoch 123: 0.3098214566707611
 

@@ -1808,7 +1808,7 @@

Step 4: Final test run
-INFO:lightwood-2252:Loss @ epoch 124: 0.30979809165000916
+INFO:lightwood-2540:Loss @ epoch 124: 0.30979809165000916
 

@@ -1816,7 +1816,7 @@

Step 4: Final test run
-INFO:lightwood-2252:Loss @ epoch 125: 0.3090403079986572
+INFO:lightwood-2540:Loss @ epoch 125: 0.3090403079986572
 

@@ -1824,7 +1824,7 @@

Step 4: Final test run
-INFO:lightwood-2252:Loss @ epoch 126: 0.30612480640411377
+INFO:lightwood-2540:Loss @ epoch 126: 0.30612480640411377
 

@@ -1832,7 +1832,7 @@

Step 4: Final test run
-INFO:lightwood-2252:Loss @ epoch 127: 0.29819032549858093
+INFO:lightwood-2540:Loss @ epoch 127: 0.29819032549858093
 

@@ -1840,7 +1840,7 @@

Step 4: Final test run
-INFO:lightwood-2252:Loss @ epoch 128: 0.29648637771606445
+INFO:lightwood-2540:Loss @ epoch 128: 0.29648637771606445
 

@@ -1848,7 +1848,7 @@

Step 4: Final test run
-INFO:lightwood-2252:Loss @ epoch 129: 0.2943042516708374
+INFO:lightwood-2540:Loss @ epoch 129: 0.2943042516708374
 

@@ -1856,7 +1856,7 @@

Step 4: Final test run
-INFO:lightwood-2252:Loss @ epoch 130: 0.29420480132102966
+INFO:lightwood-2540:Loss @ epoch 130: 0.29420480132102966
 

@@ -1864,7 +1864,7 @@

Step 4: Final test run
-INFO:lightwood-2252:Loss @ epoch 131: 0.2934538424015045
+INFO:lightwood-2540:Loss @ epoch 131: 0.2934538424015045
 

@@ -1872,7 +1872,7 @@

Step 4: Final test run
-INFO:lightwood-2252:Loss @ epoch 132: 0.2907505929470062
+INFO:lightwood-2540:Loss @ epoch 132: 0.2907505929470062
 

@@ -1880,7 +1880,7 @@

Step 4: Final test run
-INFO:lightwood-2252:Loss @ epoch 133: 0.2835786044597626
+INFO:lightwood-2540:Loss @ epoch 133: 0.2835786044597626
 

@@ -1888,7 +1888,7 @@

Step 4: Final test run
-INFO:lightwood-2252:Loss @ epoch 134: 0.28203263878822327
+INFO:lightwood-2540:Loss @ epoch 134: 0.28203263878822327
 

@@ -1896,7 +1896,7 @@

Step 4: Final test run
-INFO:lightwood-2252:Loss @ epoch 135: 0.2801313102245331
+INFO:lightwood-2540:Loss @ epoch 135: 0.2801313102245331
 

@@ -1904,7 +1904,7 @@

Step 4: Final test run
-INFO:lightwood-2252:Loss @ epoch 136: 0.2801584303379059
+INFO:lightwood-2540:Loss @ epoch 136: 0.2801584303379059
 

@@ -1912,7 +1912,7 @@

Step 4: Final test run
-INFO:lightwood-2252:Loss @ epoch 137: 0.27946653962135315
+INFO:lightwood-2540:Loss @ epoch 137: 0.27946653962135315
 

@@ -1920,7 +1920,7 @@

Step 4: Final test run
-INFO:lightwood-2252:Loss @ epoch 138: 0.2770102620124817
+INFO:lightwood-2540:Loss @ epoch 138: 0.2770102620124817
 

@@ -1928,7 +1928,7 @@

Step 4: Final test run
-INFO:lightwood-2252:Loss @ epoch 139: 0.2705138921737671
+INFO:lightwood-2540:Loss @ epoch 139: 0.2705138921737671
 

@@ -1936,7 +1936,7 @@

Step 4: Final test run
-INFO:lightwood-2252:Loss @ epoch 140: 0.2689667046070099
+INFO:lightwood-2540:Loss @ epoch 140: 0.2689667046070099
 

@@ -1944,7 +1944,7 @@

Step 4: Final test run
-INFO:lightwood-2252:Loss @ epoch 141: 0.26713839173316956
+INFO:lightwood-2540:Loss @ epoch 141: 0.26713839173316956
 

@@ -1952,7 +1952,7 @@

Step 4: Final test run
-INFO:lightwood-2252:Loss @ epoch 142: 0.26722976565361023
+INFO:lightwood-2540:Loss @ epoch 142: 0.26722976565361023
 

@@ -1960,7 +1960,7 @@

Step 4: Final test run
-INFO:lightwood-2252:Loss @ epoch 143: 0.26659274101257324
+INFO:lightwood-2540:Loss @ epoch 143: 0.26659274101257324
 

@@ -1968,7 +1968,7 @@

Step 4: Final test run
-INFO:lightwood-2252:Loss @ epoch 144: 0.26436734199523926
+INFO:lightwood-2540:Loss @ epoch 144: 0.26436734199523926
 

@@ -1976,7 +1976,7 @@

Step 4: Final test run
-INFO:lightwood-2252:Loss @ epoch 145: 0.2585783302783966
+INFO:lightwood-2540:Loss @ epoch 145: 0.2585783302783966
 

@@ -1984,7 +1984,7 @@

Step 4: Final test run
-INFO:lightwood-2252:Loss @ epoch 146: 0.25718021392822266
+INFO:lightwood-2540:Loss @ epoch 146: 0.25718021392822266
 

@@ -1992,7 +1992,7 @@

Step 4: Final test run
-INFO:lightwood-2252:Loss @ epoch 147: 0.25569674372673035
+INFO:lightwood-2540:Loss @ epoch 147: 0.25569674372673035
 

@@ -2000,7 +2000,7 @@

Step 4: Final test run
-INFO:lightwood-2252:Loss @ epoch 148: 0.25572293996810913
+INFO:lightwood-2540:Loss @ epoch 148: 0.25572293996810913
 

@@ -2008,7 +2008,7 @@

Step 4: Final test run
-INFO:lightwood-2252:Loss @ epoch 149: 0.254925012588501
+INFO:lightwood-2540:Loss @ epoch 149: 0.254925012588501
 

@@ -2016,7 +2016,7 @@

Step 4: Final test run
-INFO:lightwood-2252:Loss @ epoch 150: 0.25273722410202026
+INFO:lightwood-2540:Loss @ epoch 150: 0.25273722410202026
 

@@ -2024,7 +2024,7 @@

Step 4: Final test run
-INFO:lightwood-2252:Loss @ epoch 151: 0.24740025401115417
+INFO:lightwood-2540:Loss @ epoch 151: 0.24740025401115417
 

@@ -2032,7 +2032,7 @@

Step 4: Final test run
-INFO:lightwood-2252:Loss @ epoch 152: 0.24594709277153015
+INFO:lightwood-2540:Loss @ epoch 152: 0.24594709277153015
 

@@ -2040,7 +2040,7 @@

Step 4: Final test run
-INFO:lightwood-2252:Loss @ epoch 153: 0.2445458471775055
+INFO:lightwood-2540:Loss @ epoch 153: 0.2445458471775055
 

@@ -2048,7 +2048,7 @@

Step 4: Final test run
-INFO:lightwood-2252:Loss @ epoch 154: 0.2445453554391861
+INFO:lightwood-2540:Loss @ epoch 154: 0.2445453554391861
 

@@ -2056,7 +2056,7 @@

Step 4: Final test run
-INFO:lightwood-2252:Loss @ epoch 155: 0.24368290603160858
+INFO:lightwood-2540:Loss @ epoch 155: 0.24368290603160858
 

@@ -2064,7 +2064,7 @@

Step 4: Final test run
-INFO:lightwood-2252:Loss @ epoch 156: 0.2416052669286728
+INFO:lightwood-2540:Loss @ epoch 156: 0.2416052669286728
 

@@ -2072,7 +2072,7 @@

Step 4: Final test run
-INFO:lightwood-2252:Loss @ epoch 157: 0.23683445155620575
+INFO:lightwood-2540:Loss @ epoch 157: 0.23683445155620575
 

@@ -2080,7 +2080,7 @@

Step 4: Final test run
-INFO:lightwood-2252:Loss @ epoch 158: 0.23561015725135803
+INFO:lightwood-2540:Loss @ epoch 158: 0.23561015725135803
 

@@ -2088,7 +2088,7 @@

Step 4: Final test run
-INFO:lightwood-2252:Loss @ epoch 159: 0.2342897355556488
+INFO:lightwood-2540:Loss @ epoch 159: 0.2342897355556488
 

@@ -2096,7 +2096,7 @@

Step 4: Final test run
-INFO:lightwood-2252:Loss @ epoch 160: 0.2342986762523651
+INFO:lightwood-2540:Loss @ epoch 160: 0.2342986762523651
 

@@ -2104,7 +2104,7 @@

Step 4: Final test run
-INFO:lightwood-2252:Loss @ epoch 161: 0.2334001511335373
+INFO:lightwood-2540:Loss @ epoch 161: 0.2334001511335373
 

@@ -2112,7 +2112,7 @@

Step 4: Final test run
-INFO:lightwood-2252:Loss @ epoch 162: 0.23141320049762726
+INFO:lightwood-2540:Loss @ epoch 162: 0.23141320049762726
 

@@ -2120,7 +2120,7 @@

Step 4: Final test run
-INFO:lightwood-2252:Loss @ epoch 163: 0.22705355286598206
+INFO:lightwood-2540:Loss @ epoch 163: 0.22705355286598206
 

@@ -2128,7 +2128,7 @@

Step 4: Final test run
-INFO:lightwood-2252:Loss @ epoch 164: 0.22583813965320587
+INFO:lightwood-2540:Loss @ epoch 164: 0.22583813965320587
 

@@ -2136,7 +2136,7 @@

Step 4: Final test run
-INFO:lightwood-2252:Loss @ epoch 165: 0.22455710172653198
+INFO:lightwood-2540:Loss @ epoch 165: 0.22455710172653198
 

@@ -2144,7 +2144,7 @@

Step 4: Final test run
-INFO:lightwood-2252:Loss @ epoch 166: 0.2245052307844162
+INFO:lightwood-2540:Loss @ epoch 166: 0.2245052307844162
 

@@ -2152,7 +2152,7 @@

Step 4: Final test run
-INFO:lightwood-2252:Loss @ epoch 167: 0.22359803318977356
+INFO:lightwood-2540:Loss @ epoch 167: 0.22359803318977356
 

@@ -2160,7 +2160,7 @@

Step 4: Final test run
-INFO:lightwood-2252:Loss @ epoch 168: 0.22173909842967987
+INFO:lightwood-2540:Loss @ epoch 168: 0.22173909842967987
 

@@ -2168,7 +2168,7 @@

Step 4: Final test run
-INFO:lightwood-2252:Loss @ epoch 169: 0.2178291231393814
+INFO:lightwood-2540:Loss @ epoch 169: 0.2178291231393814
 

@@ -2176,7 +2176,7 @@

Step 4: Final test run
-INFO:lightwood-2252:Loss @ epoch 170: 0.21670299768447876
+INFO:lightwood-2540:Loss @ epoch 170: 0.21670299768447876
 

@@ -2184,7 +2184,7 @@

Step 4: Final test run
-INFO:lightwood-2252:Loss @ epoch 171: 0.21559178829193115
+INFO:lightwood-2540:Loss @ epoch 171: 0.21559178829193115
 

@@ -2192,7 +2192,7 @@

Step 4: Final test run
-INFO:lightwood-2252:Loss @ epoch 172: 0.21557293832302094
+INFO:lightwood-2540:Loss @ epoch 172: 0.21557293832302094
 

@@ -2200,7 +2200,7 @@

Step 4: Final test run
-INFO:lightwood-2252:Loss @ epoch 173: 0.21463343501091003
+INFO:lightwood-2540:Loss @ epoch 173: 0.21463343501091003
 

@@ -2208,7 +2208,7 @@

Step 4: Final test run
-INFO:lightwood-2252:Loss @ epoch 174: 0.21291333436965942
+INFO:lightwood-2540:Loss @ epoch 174: 0.21291333436965942
 

@@ -2216,7 +2216,7 @@

Step 4: Final test run
-INFO:lightwood-2252:Loss @ epoch 175: 0.20953477919101715
+INFO:lightwood-2540:Loss @ epoch 175: 0.20953477919101715
 

@@ -2224,7 +2224,7 @@

Step 4: Final test run
-INFO:lightwood-2252:Loss @ epoch 176: 0.20840951800346375
+INFO:lightwood-2540:Loss @ epoch 176: 0.20840951800346375
 

@@ -2232,7 +2232,7 @@

Step 4: Final test run
-INFO:lightwood-2252:Loss @ epoch 177: 0.20733794569969177
+INFO:lightwood-2540:Loss @ epoch 177: 0.20733794569969177
 

@@ -2240,7 +2240,7 @@

Step 4: Final test run
-INFO:lightwood-2252:Loss @ epoch 178: 0.20730628073215485
+INFO:lightwood-2540:Loss @ epoch 178: 0.20730628073215485
 

@@ -2248,7 +2248,7 @@

Step 4: Final test run
-INFO:lightwood-2252:Loss @ epoch 179: 0.20635393261909485
+INFO:lightwood-2540:Loss @ epoch 179: 0.20635393261909485
 

@@ -2256,7 +2256,7 @@

Step 4: Final test run
-INFO:lightwood-2252:Loss @ epoch 180: 0.20470596849918365
+INFO:lightwood-2540:Loss @ epoch 180: 0.20470596849918365
 

@@ -2264,7 +2264,7 @@

Step 4: Final test run
-INFO:lightwood-2252:Loss @ epoch 181: 0.2016059160232544
+INFO:lightwood-2540:Loss @ epoch 181: 0.2016059160232544
 

@@ -2272,7 +2272,7 @@

Step 4: Final test run
-INFO:lightwood-2252:Loss @ epoch 182: 0.2004680186510086
+INFO:lightwood-2540:Loss @ epoch 182: 0.2004680186510086
 

@@ -2280,7 +2280,7 @@

Step 4: Final test run
-INFO:lightwood-2252:Loss @ epoch 183: 0.1995442509651184
+INFO:lightwood-2540:Loss @ epoch 183: 0.1995442509651184
 

@@ -2288,7 +2288,7 @@

Step 4: Final test run
-INFO:lightwood-2252:Loss @ epoch 184: 0.1995476931333542
+INFO:lightwood-2540:Loss @ epoch 184: 0.1995476931333542
 

@@ -2296,7 +2296,7 @@

Step 4: Final test run
-INFO:lightwood-2252:Loss @ epoch 185: 0.1985597461462021
+INFO:lightwood-2540:Loss @ epoch 185: 0.1985597461462021
 

@@ -2304,7 +2304,7 @@

Step 4: Final test run
-INFO:lightwood-2252:Loss @ epoch 186: 0.19704405963420868
+INFO:lightwood-2540:Loss @ epoch 186: 0.19704405963420868
 

@@ -2312,7 +2312,7 @@

Step 4: Final test run
-INFO:lightwood-2252:Loss @ epoch 187: 0.19429439306259155
+INFO:lightwood-2540:Loss @ epoch 187: 0.19429439306259155
 

@@ -2320,7 +2320,7 @@

Step 4: Final test run
-INFO:lightwood-2252:Loss @ epoch 188: 0.1931215077638626
+INFO:lightwood-2540:Loss @ epoch 188: 0.1931215077638626
 

@@ -2328,7 +2328,7 @@

Step 4: Final test run
-INFO:lightwood-2252:Loss @ epoch 189: 0.19224728643894196
+INFO:lightwood-2540:Loss @ epoch 189: 0.19224728643894196
 

@@ -2336,7 +2336,7 @@

Step 4: Final test run
-INFO:lightwood-2252:Loss @ epoch 190: 0.1922168731689453
+INFO:lightwood-2540:Loss @ epoch 190: 0.1922168731689453
 

@@ -2344,7 +2344,7 @@

Step 4: Final test run
-INFO:lightwood-2252:Loss @ epoch 191: 0.19120150804519653
+INFO:lightwood-2540:Loss @ epoch 191: 0.19120150804519653
 

@@ -2352,7 +2352,7 @@

Step 4: Final test run
-INFO:lightwood-2252:Loss @ epoch 192: 0.1897118091583252
+INFO:lightwood-2540:Loss @ epoch 192: 0.1897118091583252
 

@@ -2360,7 +2360,7 @@

Step 4: Final test run
-INFO:lightwood-2252:Loss @ epoch 193: 0.187192901968956
+INFO:lightwood-2540:Loss @ epoch 193: 0.187192901968956
 

@@ -2368,7 +2368,7 @@

Step 4: Final test run
-INFO:lightwood-2252:Loss @ epoch 194: 0.18604235351085663
+INFO:lightwood-2540:Loss @ epoch 194: 0.18604235351085663
 

@@ -2376,7 +2376,7 @@

Step 4: Final test run
-INFO:lightwood-2252:Loss @ epoch 195: 0.18525990843772888
+INFO:lightwood-2540:Loss @ epoch 195: 0.18525990843772888
 

@@ -2384,7 +2384,7 @@

Step 4: Final test run
-INFO:lightwood-2252:Loss @ epoch 196: 0.18517257273197174
+INFO:lightwood-2540:Loss @ epoch 196: 0.18517257273197174
 

@@ -2392,7 +2392,7 @@

Step 4: Final test run
-INFO:lightwood-2252:Loss @ epoch 197: 0.1841844767332077
+INFO:lightwood-2540:Loss @ epoch 197: 0.1841844767332077
 

@@ -2400,7 +2400,7 @@

Step 4: Final test run
-INFO:lightwood-2252:Loss @ epoch 198: 0.18275843560695648
+INFO:lightwood-2540:Loss @ epoch 198: 0.18275843560695648
 

@@ -2408,7 +2408,7 @@

Step 4: Final test run
-INFO:lightwood-2252:Loss @ epoch 199: 0.18052375316619873
+INFO:lightwood-2540:Loss @ epoch 199: 0.18052375316619873
 

@@ -2416,7 +2416,7 @@

Step 4: Final test run
-INFO:lightwood-2252:Loss @ epoch 200: 0.1795222908258438
+INFO:lightwood-2540:Loss @ epoch 200: 0.1795222908258438
 

@@ -2424,7 +2424,7 @@

Step 4: Final test run
-INFO:lightwood-2252:Loss @ epoch 201: 0.17878693342208862
+INFO:lightwood-2540:Loss @ epoch 201: 0.17878693342208862
 

@@ -2432,7 +2432,7 @@

Step 4: Final test run
-INFO:lightwood-2252:Loss @ epoch 202: 0.17881926894187927
+INFO:lightwood-2540:Loss @ epoch 202: 0.17881926894187927
 

@@ -2440,7 +2440,7 @@

Step 4: Final test run
-INFO:lightwood-2252:Loss @ epoch 203: 0.17786364257335663
+INFO:lightwood-2540:Loss @ epoch 203: 0.17786364257335663
 

@@ -2448,7 +2448,7 @@

Step 4: Final test run
-INFO:lightwood-2252:Loss @ epoch 204: 0.17654098570346832
+INFO:lightwood-2540:Loss @ epoch 204: 0.17654098570346832
 

@@ -2456,7 +2456,7 @@

Step 4: Final test run
-INFO:lightwood-2252:Loss @ epoch 205: 0.17458021640777588
+INFO:lightwood-2540:Loss @ epoch 205: 0.17458021640777588
 

@@ -2464,7 +2464,7 @@

Step 4: Final test run
-INFO:lightwood-2252:Loss @ epoch 206: 0.17358489334583282
+INFO:lightwood-2540:Loss @ epoch 206: 0.17358489334583282
 

@@ -2472,7 +2472,7 @@

Step 4: Final test run
-INFO:lightwood-2252:Loss @ epoch 207: 0.1728101521730423
+INFO:lightwood-2540:Loss @ epoch 207: 0.1728101521730423
 

@@ -2480,7 +2480,7 @@

Step 4: Final test run
-INFO:lightwood-2252:Loss @ epoch 208: 0.17278429865837097
+INFO:lightwood-2540:Loss @ epoch 208: 0.17278429865837097
 

@@ -2488,7 +2488,7 @@

Step 4: Final test run
-INFO:lightwood-2252:Loss @ epoch 209: 0.17180292308330536
+INFO:lightwood-2540:Loss @ epoch 209: 0.17180292308330536
 

@@ -2496,7 +2496,7 @@

Step 4: Final test run
-INFO:lightwood-2252:Loss @ epoch 210: 0.17050613462924957
+INFO:lightwood-2540:Loss @ epoch 210: 0.17050613462924957
 

@@ -2504,7 +2504,7 @@

Step 4: Final test run
-INFO:lightwood-2252:Loss @ epoch 211: 0.16873842477798462
+INFO:lightwood-2540:Loss @ epoch 211: 0.16873842477798462
 

@@ -2512,7 +2512,7 @@

Step 4: Final test run
-INFO:lightwood-2252:Loss @ epoch 212: 0.1677248477935791
+INFO:lightwood-2540:Loss @ epoch 212: 0.1677248477935791
 

@@ -2520,7 +2520,7 @@

Step 4: Final test run
-INFO:lightwood-2252:Loss @ epoch 213: 0.16707710921764374
+INFO:lightwood-2540:Loss @ epoch 213: 0.16707710921764374
 

@@ -2528,7 +2528,7 @@

Step 4: Final test run
-INFO:lightwood-2252:Loss @ epoch 214: 0.1671123504638672
+INFO:lightwood-2540:Loss @ epoch 214: 0.1671123504638672
 

@@ -2536,7 +2536,7 @@

Step 4: Final test run
-INFO:lightwood-2252:Loss @ epoch 215: 0.16612616181373596
+INFO:lightwood-2540:Loss @ epoch 215: 0.16612616181373596
 

@@ -2544,7 +2544,7 @@

Step 4: Final test run
-INFO:lightwood-2252:Loss @ epoch 216: 0.16487975418567657
+INFO:lightwood-2540:Loss @ epoch 216: 0.16487975418567657
 

@@ -2552,7 +2552,7 @@

Step 4: Final test run
-INFO:lightwood-2252:Loss @ epoch 217: 0.16339382529258728
+INFO:lightwood-2540:Loss @ epoch 217: 0.16339382529258728
 

@@ -2560,7 +2560,7 @@

Step 4: Final test run
-INFO:lightwood-2252:Loss @ epoch 218: 0.1624278575181961
+INFO:lightwood-2540:Loss @ epoch 218: 0.1624278575181961
 

@@ -2568,7 +2568,7 @@

Step 4: Final test run
-INFO:lightwood-2252:Loss @ epoch 219: 0.16172048449516296
+INFO:lightwood-2540:Loss @ epoch 219: 0.16172048449516296
 

@@ -2576,7 +2576,7 @@

Step 4: Final test run
-INFO:lightwood-2252:Loss @ epoch 220: 0.16165515780448914
+INFO:lightwood-2540:Loss @ epoch 220: 0.16165515780448914
 

@@ -2584,7 +2584,7 @@

Step 4: Final test run
-INFO:lightwood-2252:Loss @ epoch 221: 0.16061937808990479
+INFO:lightwood-2540:Loss @ epoch 221: 0.16061937808990479
 

@@ -2592,7 +2592,7 @@

Step 4: Final test run
-INFO:lightwood-2252:Loss @ epoch 222: 0.1594206690788269
+INFO:lightwood-2540:Loss @ epoch 222: 0.1594206690788269
 

@@ -2600,7 +2600,7 @@

Step 4: Final test run
-INFO:lightwood-2252:Loss @ epoch 223: 0.15802235901355743
+INFO:lightwood-2540:Loss @ epoch 223: 0.15802235901355743
 

@@ -2608,7 +2608,7 @@

Step 4: Final test run
-INFO:lightwood-2252:Loss @ epoch 224: 0.15704363584518433
+INFO:lightwood-2540:Loss @ epoch 224: 0.15704363584518433
 

@@ -2616,7 +2616,7 @@

Step 4: Final test run
-INFO:lightwood-2252:Loss @ epoch 225: 0.15640243887901306
+INFO:lightwood-2540:Loss @ epoch 225: 0.15640243887901306
 

@@ -2624,7 +2624,7 @@

Step 4: Final test run
-INFO:lightwood-2252:Loss @ epoch 226: 0.15635541081428528
+INFO:lightwood-2540:Loss @ epoch 226: 0.15635541081428528
 

@@ -2632,7 +2632,7 @@

Step 4: Final test run
-INFO:lightwood-2252:Loss @ epoch 227: 0.15536457300186157
+INFO:lightwood-2540:Loss @ epoch 227: 0.15536457300186157
 

@@ -2640,7 +2640,7 @@

Step 4: Final test run
-INFO:lightwood-2252:Loss @ epoch 228: 0.154209703207016
+INFO:lightwood-2540:Loss @ epoch 228: 0.154209703207016
 

@@ -2648,7 +2648,7 @@

Step 4: Final test run
-INFO:lightwood-2252:Loss @ epoch 229: 0.15291643142700195
+INFO:lightwood-2540:Loss @ epoch 229: 0.15291643142700195
 

@@ -2656,7 +2656,7 @@

Step 4: Final test run
-INFO:lightwood-2252:Loss @ epoch 230: 0.15191468596458435
+INFO:lightwood-2540:Loss @ epoch 230: 0.15191468596458435
 

@@ -2664,7 +2664,7 @@

Step 4: Final test run
-INFO:lightwood-2252:Loss @ epoch 231: 0.15118129551410675
+INFO:lightwood-2540:Loss @ epoch 231: 0.15118129551410675
 

@@ -2672,7 +2672,7 @@

Step 4: Final test run
-INFO:lightwood-2252:Loss @ epoch 232: 0.151133194565773
+INFO:lightwood-2540:Loss @ epoch 232: 0.151133194565773
 

@@ -2680,7 +2680,7 @@

Step 4: Final test run
-INFO:lightwood-2252:Loss @ epoch 233: 0.1501670926809311
+INFO:lightwood-2540:Loss @ epoch 233: 0.1501670926809311
 

@@ -2688,7 +2688,7 @@

Step 4: Final test run
-INFO:lightwood-2252:Loss @ epoch 234: 0.14912192523479462
+INFO:lightwood-2540:Loss @ epoch 234: 0.14912192523479462
 

@@ -2696,7 +2696,7 @@

Step 4: Final test run
-INFO:lightwood-2252:Loss @ epoch 235: 0.1481197327375412
+INFO:lightwood-2540:Loss @ epoch 235: 0.1481197327375412
 

@@ -2704,7 +2704,7 @@

Step 4: Final test run
-INFO:lightwood-2252:Loss @ epoch 236: 0.14712536334991455
+INFO:lightwood-2540:Loss @ epoch 236: 0.14712536334991455
 

@@ -2712,7 +2712,7 @@

Step 4: Final test run
-INFO:lightwood-2252:Loss @ epoch 237: 0.14660944044589996
+INFO:lightwood-2540:Loss @ epoch 237: 0.14660944044589996
 

@@ -2720,7 +2720,7 @@

Step 4: Final test run
-INFO:lightwood-2252:Loss @ epoch 238: 0.14649781584739685
+INFO:lightwood-2540:Loss @ epoch 238: 0.14649781584739685
 

@@ -2728,7 +2728,7 @@

Step 4: Final test run
-INFO:lightwood-2252:Loss @ epoch 239: 0.145524799823761
+INFO:lightwood-2540:Loss @ epoch 239: 0.145524799823761
 

@@ -2736,7 +2736,7 @@

Step 4: Final test run
-INFO:lightwood-2252:Loss @ epoch 240: 0.14443959295749664
+INFO:lightwood-2540:Loss @ epoch 240: 0.14443959295749664
 

@@ -2744,7 +2744,7 @@

Step 4: Final test run
-INFO:lightwood-2252:Loss @ epoch 241: 0.14341002702713013
+INFO:lightwood-2540:Loss @ epoch 241: 0.14341002702713013
 

@@ -2752,7 +2752,7 @@

Step 4: Final test run
-INFO:lightwood-2252:Loss @ epoch 242: 0.14249812066555023
+INFO:lightwood-2540:Loss @ epoch 242: 0.14249812066555023
 

@@ -2760,7 +2760,7 @@

Step 4: Final test run
-INFO:lightwood-2252:Loss @ epoch 243: 0.14185366034507751
+INFO:lightwood-2540:Loss @ epoch 243: 0.14185366034507751
 

@@ -2768,7 +2768,7 @@

Step 4: Final test run
-INFO:lightwood-2252:Loss @ epoch 244: 0.1419423222541809
+INFO:lightwood-2540:Loss @ epoch 244: 0.1419423222541809
 

@@ -2776,7 +2776,7 @@

Step 4: Final test run
-INFO:lightwood-2252:Loss @ epoch 245: 0.1409326195716858
+INFO:lightwood-2540:Loss @ epoch 245: 0.1409326195716858
 

@@ -2784,7 +2784,7 @@

Step 4: Final test run
-INFO:lightwood-2252:Loss @ epoch 246: 0.1399424970149994
+INFO:lightwood-2540:Loss @ epoch 246: 0.1399424970149994
 

@@ -2792,7 +2792,7 @@

Step 4: Final test run
-INFO:lightwood-2252:Loss @ epoch 247: 0.13920661807060242
+INFO:lightwood-2540:Loss @ epoch 247: 0.13920661807060242
 

@@ -2800,7 +2800,7 @@

Step 4: Final test run
-INFO:lightwood-2252:Loss @ epoch 248: 0.13832959532737732
+INFO:lightwood-2540:Loss @ epoch 248: 0.13832959532737732
 

@@ -2808,7 +2808,7 @@

Step 4: Final test run
-INFO:lightwood-2252:Loss @ epoch 249: 0.13784818351268768
+INFO:lightwood-2540:Loss @ epoch 249: 0.13784818351268768
 

@@ -2816,7 +2816,7 @@

Step 4: Final test run
-INFO:lightwood-2252:Loss @ epoch 250: 0.13769637048244476
+INFO:lightwood-2540:Loss @ epoch 250: 0.13769637048244476
 

@@ -2824,7 +2824,7 @@

Step 4: Final test run
-INFO:lightwood-2252:Loss @ epoch 251: 0.1367887407541275
+INFO:lightwood-2540:Loss @ epoch 251: 0.1367887407541275
 

@@ -2832,7 +2832,7 @@

Step 4: Final test run
-INFO:lightwood-2252:Loss @ epoch 252: 0.13588252663612366
+INFO:lightwood-2540:Loss @ epoch 252: 0.13588252663612366
 

@@ -2840,7 +2840,7 @@

Step 4: Final test run
-INFO:lightwood-2252:Loss @ epoch 253: 0.13521170616149902
+INFO:lightwood-2540:Loss @ epoch 253: 0.13521170616149902
 

@@ -2848,7 +2848,7 @@

Step 4: Final test run
-INFO:lightwood-2252:Loss @ epoch 254: 0.13428467512130737
+INFO:lightwood-2540:Loss @ epoch 254: 0.13428467512130737
 

@@ -2856,7 +2856,7 @@

Step 4: Final test run
-INFO:lightwood-2252:Loss @ epoch 255: 0.13384407758712769
+INFO:lightwood-2540:Loss @ epoch 255: 0.13384407758712769
 

@@ -2864,7 +2864,7 @@

Step 4: Final test run
-INFO:lightwood-2252:Loss @ epoch 256: 0.13372991979122162
+INFO:lightwood-2540:Loss @ epoch 256: 0.13372991979122162
 

@@ -2872,7 +2872,7 @@

Step 4: Final test run
-INFO:lightwood-2252:Loss @ epoch 257: 0.13274942338466644
+INFO:lightwood-2540:Loss @ epoch 257: 0.13274942338466644
 

@@ -2880,7 +2880,7 @@

Step 4: Final test run
-INFO:lightwood-2252:Loss @ epoch 258: 0.13186557590961456
+INFO:lightwood-2540:Loss @ epoch 258: 0.13186557590961456
 

@@ -2888,7 +2888,7 @@

Step 4: Final test run
-INFO:lightwood-2252:Loss @ epoch 259: 0.13135230541229248
+INFO:lightwood-2540:Loss @ epoch 259: 0.13135230541229248
 

@@ -2896,7 +2896,7 @@

Step 4: Final test run
-INFO:lightwood-2252:Loss @ epoch 260: 0.13046588003635406
+INFO:lightwood-2540:Loss @ epoch 260: 0.13046588003635406
 

@@ -2904,7 +2904,7 @@

Step 4: Final test run
-INFO:lightwood-2252:Loss @ epoch 261: 0.12980172038078308
+INFO:lightwood-2540:Loss @ epoch 261: 0.12980172038078308
 

@@ -2912,7 +2912,7 @@

Step 4: Final test run
-INFO:lightwood-2252:Loss @ epoch 262: 0.12982229888439178
+INFO:lightwood-2540:Loss @ epoch 262: 0.12982229888439178
 

@@ -2920,7 +2920,7 @@

Step 4: Final test run
-INFO:lightwood-2252:Loss @ epoch 263: 0.12882845103740692
+INFO:lightwood-2540:Loss @ epoch 263: 0.12882845103740692
 

@@ -2928,7 +2928,7 @@

Step 4: Final test run
-INFO:lightwood-2252:Loss @ epoch 264: 0.12798714637756348
+INFO:lightwood-2540:Loss @ epoch 264: 0.12798714637756348
 

@@ -2936,7 +2936,7 @@

Step 4: Final test run
-INFO:lightwood-2252:Loss @ epoch 265: 0.12758088111877441
+INFO:lightwood-2540:Loss @ epoch 265: 0.12758088111877441
 

@@ -2944,7 +2944,7 @@

Step 4: Final test run
-INFO:lightwood-2252:Loss @ epoch 266: 0.12660843133926392
+INFO:lightwood-2540:Loss @ epoch 266: 0.12660843133926392
 

@@ -2952,7 +2952,7 @@

Step 4: Final test run
-INFO:lightwood-2252:Loss @ epoch 267: 0.1261577606201172
+INFO:lightwood-2540:Loss @ epoch 267: 0.1261577606201172
 

@@ -2960,7 +2960,7 @@

Step 4: Final test run
-INFO:lightwood-2252:Loss @ epoch 268: 0.1260918229818344
+INFO:lightwood-2540:Loss @ epoch 268: 0.1260918229818344
 

@@ -2968,7 +2968,7 @@

Step 4: Final test run
-INFO:lightwood-2252:Loss @ epoch 269: 0.12515920400619507
+INFO:lightwood-2540:Loss @ epoch 269: 0.12515920400619507
 

@@ -2976,7 +2976,7 @@

Step 4: Final test run
-INFO:lightwood-2252:Loss @ epoch 270: 0.12436933070421219
+INFO:lightwood-2540:Loss @ epoch 270: 0.12436933070421219
 

@@ -2984,7 +2984,7 @@

Step 4: Final test run
-INFO:lightwood-2252:Loss @ epoch 271: 0.12405422329902649
+INFO:lightwood-2540:Loss @ epoch 271: 0.12405422329902649
 

@@ -2992,7 +2992,7 @@

Step 4: Final test run
-INFO:lightwood-2252:Loss @ epoch 272: 0.12305409461259842
+INFO:lightwood-2540:Loss @ epoch 272: 0.12305409461259842
 

@@ -3000,7 +3000,7 @@

Step 4: Final test run
-INFO:lightwood-2252:Loss @ epoch 273: 0.12272939085960388
+INFO:lightwood-2540:Loss @ epoch 273: 0.12272939085960388
 

@@ -3008,7 +3008,7 @@

Step 4: Final test run
-INFO:lightwood-2252:Loss @ epoch 274: 0.12267134338617325
+INFO:lightwood-2540:Loss @ epoch 274: 0.12267134338617325
 

@@ -3016,7 +3016,7 @@

Step 4: Final test run
-INFO:lightwood-2252:Loss @ epoch 275: 0.12182944267988205
+INFO:lightwood-2540:Loss @ epoch 275: 0.12182944267988205
 

@@ -3024,7 +3024,7 @@

Step 4: Final test run
-INFO:lightwood-2252:Loss @ epoch 276: 0.12103450298309326
+INFO:lightwood-2540:Loss @ epoch 276: 0.12103450298309326
 

@@ -3032,7 +3032,7 @@

Step 4: Final test run
-INFO:lightwood-2252:Loss @ epoch 277: 0.12083345651626587
+INFO:lightwood-2540:Loss @ epoch 277: 0.12083345651626587
 

@@ -3040,7 +3040,7 @@

Step 4: Final test run
-INFO:lightwood-2252:Loss @ epoch 278: 0.11998103559017181
+INFO:lightwood-2540:Loss @ epoch 278: 0.11998103559017181
 

@@ -3048,7 +3048,7 @@

Step 4: Final test run
-INFO:lightwood-2252:Loss @ epoch 279: 0.11937755346298218
+INFO:lightwood-2540:Loss @ epoch 279: 0.11937755346298218
 

@@ -3056,7 +3056,7 @@

Step 4: Final test run
-INFO:lightwood-2252:Loss @ epoch 280: 0.1195112019777298
+INFO:lightwood-2540:Loss @ epoch 280: 0.1195112019777298
 

@@ -3064,7 +3064,7 @@

Step 4: Final test run
-INFO:lightwood-2252:Loss @ epoch 281: 0.1185888797044754
+INFO:lightwood-2540:Loss @ epoch 281: 0.1185888797044754
 

@@ -3072,7 +3072,7 @@

Step 4: Final test run
-INFO:lightwood-2252:Loss @ epoch 282: 0.11789504438638687
+INFO:lightwood-2540:Loss @ epoch 282: 0.11789504438638687
 

@@ -3080,7 +3080,7 @@

Step 4: Final test run
-INFO:lightwood-2252:Loss @ epoch 283: 0.11783000081777573
+INFO:lightwood-2540:Loss @ epoch 283: 0.11783000081777573
 

@@ -3088,7 +3088,7 @@

Step 4: Final test run
-INFO:lightwood-2252:Loss @ epoch 284: 0.11681754887104034
+INFO:lightwood-2540:Loss @ epoch 284: 0.11681754887104034
 

@@ -3096,7 +3096,7 @@

Step 4: Final test run
-INFO:lightwood-2252:Loss @ epoch 285: 0.11649196594953537
+INFO:lightwood-2540:Loss @ epoch 285: 0.11649196594953537
 

@@ -3104,7 +3104,7 @@

Step 4: Final test run
-INFO:lightwood-2252:Loss @ epoch 286: 0.11648327857255936
+INFO:lightwood-2540:Loss @ epoch 286: 0.11648327857255936
 

@@ -3112,7 +3112,7 @@

Step 4: Final test run
-INFO:lightwood-2252:Loss @ epoch 287: 0.11562418192625046
+INFO:lightwood-2540:Loss @ epoch 287: 0.11562418192625046
 

@@ -3120,7 +3120,7 @@

Step 4: Final test run
-INFO:lightwood-2252:Loss @ epoch 288: 0.11489420384168625
+INFO:lightwood-2540:Loss @ epoch 288: 0.11489420384168625
 

@@ -3128,7 +3128,7 @@

Step 4: Final test run
-INFO:lightwood-2252:Loss @ epoch 289: 0.11485717445611954
+INFO:lightwood-2540:Loss @ epoch 289: 0.11485717445611954
 

@@ -3136,7 +3136,7 @@

Step 4: Final test run
-INFO:lightwood-2252:Loss @ epoch 290: 0.11407709866762161
+INFO:lightwood-2540:Loss @ epoch 290: 0.11407709866762161
 

@@ -3144,7 +3144,7 @@

Step 4: Final test run
-INFO:lightwood-2252:Loss @ epoch 291: 0.11348505318164825
+INFO:lightwood-2540:Loss @ epoch 291: 0.11348505318164825
 

@@ -3152,7 +3152,7 @@

Step 4: Final test run
-INFO:lightwood-2252:Loss @ epoch 292: 0.11358898878097534
+INFO:lightwood-2540:Loss @ epoch 292: 0.11358898878097534
 

@@ -3160,7 +3160,7 @@

Step 4: Final test run
-INFO:lightwood-2252:Loss @ epoch 293: 0.11268813163042068
+INFO:lightwood-2540:Loss @ epoch 293: 0.11268813163042068
 

@@ -3168,7 +3168,7 @@

Step 4: Final test run
-INFO:lightwood-2252:Loss @ epoch 294: 0.11207651346921921
+INFO:lightwood-2540:Loss @ epoch 294: 0.11207651346921921
 

@@ -3176,7 +3176,7 @@

Step 4: Final test run
-INFO:lightwood-2252:Loss @ epoch 295: 0.11220688372850418
+INFO:lightwood-2540:Loss @ epoch 295: 0.11220688372850418
 

@@ -3184,7 +3184,7 @@

Step 4: Final test run
-INFO:lightwood-2252:Loss @ epoch 296: 0.11118005961179733
+INFO:lightwood-2540:Loss @ epoch 296: 0.11118005961179733
 

@@ -3192,7 +3192,7 @@

Step 4: Final test run
-INFO:lightwood-2252:Loss @ epoch 297: 0.11089354008436203
+INFO:lightwood-2540:Loss @ epoch 297: 0.11089354008436203
 

@@ -3200,7 +3200,7 @@

Step 4: Final test run
-INFO:lightwood-2252:Loss @ epoch 298: 0.11088859289884567
+INFO:lightwood-2540:Loss @ epoch 298: 0.11088859289884567
 

@@ -3208,7 +3208,7 @@

Step 4: Final test run
-INFO:lightwood-2252:Loss @ epoch 299: 0.1100316271185875
+INFO:lightwood-2540:Loss @ epoch 299: 0.1100316271185875
 

@@ -3216,7 +3216,7 @@

Step 4: Final test run
-INFO:lightwood-2252:Loss @ epoch 300: 0.1093800961971283
+INFO:lightwood-2540:Loss @ epoch 300: 0.1093800961971283
 

@@ -3224,7 +3224,7 @@

Step 4: Final test run
-INFO:lightwood-2252:Loss @ epoch 301: 0.10955681651830673
+INFO:lightwood-2540:Loss @ epoch 301: 0.10955681651830673
 

@@ -3232,7 +3232,7 @@

Step 4: Final test run
-INFO:lightwood-2252:Loss @ epoch 302: 0.10869839787483215
+INFO:lightwood-2540:Loss @ epoch 302: 0.10869839787483215
 

@@ -3240,7 +3240,7 @@

Step 4: Final test run
-INFO:lightwood-2252:Loss @ epoch 303: 0.10815789550542831
+INFO:lightwood-2540:Loss @ epoch 303: 0.10815789550542831
 

@@ -3248,7 +3248,7 @@

Step 4: Final test run
-INFO:lightwood-2252:Loss @ epoch 304: 0.10832306742668152
+INFO:lightwood-2540:Loss @ epoch 304: 0.10832306742668152
 

@@ -3256,7 +3256,7 @@

Step 4: Final test run
-INFO:lightwood-2252:Loss @ epoch 305: 0.10742544382810593
+INFO:lightwood-2540:Loss @ epoch 305: 0.10742544382810593
 

@@ -3264,7 +3264,7 @@

Step 4: Final test run
-INFO:lightwood-2252:Loss @ epoch 306: 0.10682710260152817
+INFO:lightwood-2540:Loss @ epoch 306: 0.10682710260152817
 

@@ -3272,7 +3272,7 @@

Step 4: Final test run
-INFO:lightwood-2252:Loss @ epoch 307: 0.10698221623897552
+INFO:lightwood-2540:Loss @ epoch 307: 0.10698221623897552
 

@@ -3280,7 +3280,7 @@

Step 4: Final test run
-INFO:lightwood-2252:Loss @ epoch 308: 0.10616409033536911
+INFO:lightwood-2540:Loss @ epoch 308: 0.10616409033536911
 

@@ -3288,7 +3288,7 @@

Step 4: Final test run
-INFO:lightwood-2252:Loss @ epoch 309: 0.10568025708198547
+INFO:lightwood-2540:Loss @ epoch 309: 0.10568025708198547
 

@@ -3296,7 +3296,7 @@

Step 4: Final test run
-INFO:lightwood-2252:Loss @ epoch 310: 0.10574078559875488
+INFO:lightwood-2540:Loss @ epoch 310: 0.10574078559875488
 

@@ -3304,7 +3304,7 @@

Step 4: Final test run
-INFO:lightwood-2252:Loss @ epoch 311: 0.10493995994329453
+INFO:lightwood-2540:Loss @ epoch 311: 0.10493995994329453
 

@@ -3312,7 +3312,7 @@

Step 4: Final test run
-INFO:lightwood-2252:Loss @ epoch 312: 0.10438455641269684
+INFO:lightwood-2540:Loss @ epoch 312: 0.10438455641269684
 

@@ -3320,7 +3320,7 @@

Step 4: Final test run
-INFO:lightwood-2252:Loss @ epoch 313: 0.10449497401714325
+INFO:lightwood-2540:Loss @ epoch 313: 0.10449497401714325
 

@@ -3328,7 +3328,7 @@

Step 4: Final test run
-INFO:lightwood-2252:Loss @ epoch 314: 0.10372297465801239
+INFO:lightwood-2540:Loss @ epoch 314: 0.10372297465801239
 

@@ -3336,7 +3336,7 @@

Step 4: Final test run
-INFO:lightwood-2252:Loss @ epoch 315: 0.10320515185594559
+INFO:lightwood-2540:Loss @ epoch 315: 0.10320515185594559
 

@@ -3344,7 +3344,7 @@

Step 4: Final test run
-INFO:lightwood-2252:Loss @ epoch 316: 0.10330332815647125
+INFO:lightwood-2540:Loss @ epoch 316: 0.10330332815647125
 

@@ -3352,7 +3352,7 @@

Step 4: Final test run
-INFO:lightwood-2252:Loss @ epoch 317: 0.10239537805318832
+INFO:lightwood-2540:Loss @ epoch 317: 0.10239537805318832
 

@@ -3360,7 +3360,7 @@

Step 4: Final test run
-INFO:lightwood-2252:Loss @ epoch 318: 0.10185065120458603
+INFO:lightwood-2540:Loss @ epoch 318: 0.10185065120458603
 

@@ -3368,7 +3368,7 @@

Step 4: Final test run
-INFO:lightwood-2252:Loss @ epoch 319: 0.10217708349227905
+INFO:lightwood-2540:Loss @ epoch 319: 0.10217708349227905
 

@@ -3376,7 +3376,7 @@

Step 4: Final test run
-INFO:lightwood-2252:Loss @ epoch 320: 0.10135672241449356
+INFO:lightwood-2540:Loss @ epoch 320: 0.10135672241449356
 

@@ -3384,7 +3384,7 @@

Step 4: Final test run
-INFO:lightwood-2252:Loss @ epoch 321: 0.10087659955024719
+INFO:lightwood-2540:Loss @ epoch 321: 0.10087659955024719
 

@@ -3392,7 +3392,7 @@

Step 4: Final test run
-INFO:lightwood-2252:Loss @ epoch 322: 0.10087589174509048
+INFO:lightwood-2540:Loss @ epoch 322: 0.10087589174509048
 

@@ -3400,7 +3400,7 @@

Step 4: Final test run
-INFO:lightwood-2252:Loss @ epoch 323: 0.10005565732717514
+INFO:lightwood-2540:Loss @ epoch 323: 0.10005565732717514
 

@@ -3408,7 +3408,7 @@

Step 4: Final test run
-INFO:lightwood-2252:Loss @ epoch 324: 0.09949999302625656
+INFO:lightwood-2540:Loss @ epoch 324: 0.09949999302625656
 

@@ -3416,7 +3416,7 @@

Step 4: Final test run
-INFO:lightwood-2252:Loss @ epoch 325: 0.09970266371965408
+INFO:lightwood-2540:Loss @ epoch 325: 0.09970266371965408
 

@@ -3424,7 +3424,7 @@

Step 4: Final test run
-INFO:lightwood-2252:Loss @ epoch 326: 0.09918338060379028
+INFO:lightwood-2540:Loss @ epoch 326: 0.09918338060379028
 

@@ -3432,7 +3432,7 @@

Step 4: Final test run
-INFO:lightwood-2252:Loss @ epoch 327: 0.09840800613164902
+INFO:lightwood-2540:Loss @ epoch 327: 0.09840800613164902
 

@@ -3440,7 +3440,7 @@

Step 4: Final test run
-INFO:lightwood-2252:Loss @ epoch 328: 0.09882311522960663
+INFO:lightwood-2540:Loss @ epoch 328: 0.09882311522960663
 

@@ -3448,7 +3448,7 @@

Step 4: Final test run
-INFO:lightwood-2252:Loss @ epoch 329: 0.09775345772504807
+INFO:lightwood-2540:Loss @ epoch 329: 0.09775345772504807
 

@@ -3456,7 +3456,7 @@

Step 4: Final test run
-INFO:lightwood-2252:Loss @ epoch 330: 0.09729817509651184
+INFO:lightwood-2540:Loss @ epoch 330: 0.09729817509651184
 

@@ -3464,7 +3464,7 @@

Step 4: Final test run
-INFO:lightwood-2252:Loss @ epoch 331: 0.09763044863939285
+INFO:lightwood-2540:Loss @ epoch 331: 0.09763044863939285
 

@@ -3472,7 +3472,7 @@

Step 4: Final test run
-INFO:lightwood-2252:Loss @ epoch 332: 0.0967596173286438
+INFO:lightwood-2540:Loss @ epoch 332: 0.0967596173286438
 

@@ -3480,7 +3480,7 @@

Step 4: Final test run
-INFO:lightwood-2252:Loss @ epoch 333: 0.09642492234706879
+INFO:lightwood-2540:Loss @ epoch 333: 0.09642492234706879
 

@@ -3488,7 +3488,7 @@

Step 4: Final test run
-INFO:lightwood-2252:Loss @ epoch 334: 0.09656761586666107
+INFO:lightwood-2540:Loss @ epoch 334: 0.09656761586666107
 

@@ -3496,7 +3496,7 @@

Step 4: Final test run
-INFO:lightwood-2252:Loss @ epoch 335: 0.09573261439800262
+INFO:lightwood-2540:Loss @ epoch 335: 0.09573261439800262
 

@@ -3504,7 +3504,7 @@

Step 4: Final test run
-INFO:lightwood-2252:Loss @ epoch 336: 0.09523642063140869
+INFO:lightwood-2540:Loss @ epoch 336: 0.09523642063140869
 

@@ -3512,7 +3512,7 @@

Step 4: Final test run
-INFO:lightwood-2252:Loss @ epoch 337: 0.09568659961223602
+INFO:lightwood-2540:Loss @ epoch 337: 0.09568659961223602
 

@@ -3520,7 +3520,7 @@

Step 4: Final test run
-INFO:lightwood-2252:Loss @ epoch 338: 0.09509280323982239
+INFO:lightwood-2540:Loss @ epoch 338: 0.09509280323982239
 

@@ -3528,7 +3528,7 @@

Step 4: Final test run
-INFO:lightwood-2252:Loss @ epoch 339: 0.09460369497537613
+INFO:lightwood-2540:Loss @ epoch 339: 0.09460369497537613
 

@@ -3536,7 +3536,7 @@

Step 4: Final test run
-INFO:lightwood-2252:Loss @ epoch 340: 0.09476538747549057
+INFO:lightwood-2540:Loss @ epoch 340: 0.09476538747549057
 

@@ -3544,7 +3544,7 @@

Step 4: Final test run
-INFO:lightwood-2252:Loss @ epoch 341: 0.09388881921768188
+INFO:lightwood-2540:Loss @ epoch 341: 0.09388881921768188
 

@@ -3552,7 +3552,7 @@

Step 4: Final test run
-INFO:lightwood-2252:Loss @ epoch 342: 0.09349637478590012
+INFO:lightwood-2540:Loss @ epoch 342: 0.09349637478590012
 

@@ -3560,7 +3560,7 @@

Step 4: Final test run
-INFO:lightwood-2252:Loss @ epoch 343: 0.09398090839385986
+INFO:lightwood-2540:Loss @ epoch 343: 0.09398090839385986
 

@@ -3568,7 +3568,7 @@

Step 4: Final test run
-INFO:lightwood-2252:Loss @ epoch 344: 0.09314301609992981
+INFO:lightwood-2540:Loss @ epoch 344: 0.09314301609992981
 

@@ -3576,7 +3576,7 @@

Step 4: Final test run
-INFO:lightwood-2252:Loss @ epoch 345: 0.09281699359416962
+INFO:lightwood-2540:Loss @ epoch 345: 0.09281699359416962
 

@@ -3584,7 +3584,7 @@

Step 4: Final test run
-INFO:lightwood-2252:Loss @ epoch 346: 0.09290202707052231
+INFO:lightwood-2540:Loss @ epoch 346: 0.09290202707052231
 

@@ -3592,7 +3592,7 @@

Step 4: Final test run
-INFO:lightwood-2252:Loss @ epoch 347: 0.09209518879652023
+INFO:lightwood-2540:Loss @ epoch 347: 0.09209518879652023
 

@@ -3600,7 +3600,7 @@

Step 4: Final test run
-INFO:lightwood-2252:Loss @ epoch 348: 0.09171803295612335
+INFO:lightwood-2540:Loss @ epoch 348: 0.09171803295612335
 

@@ -3608,7 +3608,7 @@

Step 4: Final test run
-INFO:lightwood-2252:Loss @ epoch 349: 0.09221566468477249
+INFO:lightwood-2540:Loss @ epoch 349: 0.09221566468477249
 

@@ -3616,7 +3616,7 @@

Step 4: Final test run
-INFO:lightwood-2252:Loss @ epoch 350: 0.09150414168834686
+INFO:lightwood-2540:Loss @ epoch 350: 0.09150414168834686
 

@@ -3624,7 +3624,7 @@

Step 4: Final test run
-INFO:lightwood-2252:Loss @ epoch 351: 0.0910501629114151
+INFO:lightwood-2540:Loss @ epoch 351: 0.0910501629114151
 

@@ -3632,7 +3632,7 @@

Step 4: Final test run
-INFO:lightwood-2252:Loss @ epoch 352: 0.09118885546922684
+INFO:lightwood-2540:Loss @ epoch 352: 0.09118885546922684
 

@@ -3640,7 +3640,7 @@

Step 4: Final test run
-INFO:lightwood-2252:Loss @ epoch 353: 0.09043896198272705
+INFO:lightwood-2540:Loss @ epoch 353: 0.09043896198272705
 

@@ -3648,7 +3648,7 @@

Step 4: Final test run
-INFO:lightwood-2252:Loss @ epoch 354: 0.09006913751363754
+INFO:lightwood-2540:Loss @ epoch 354: 0.09006913751363754
 

@@ -3656,7 +3656,7 @@

Step 4: Final test run
-INFO:lightwood-2252:Loss @ epoch 355: 0.09049264341592789
+INFO:lightwood-2540:Loss @ epoch 355: 0.09049264341592789
 

@@ -3664,7 +3664,7 @@

Step 4: Final test run
-INFO:lightwood-2252:Loss @ epoch 356: 0.0898597463965416
+INFO:lightwood-2540:Loss @ epoch 356: 0.0898597463965416
 

@@ -3672,7 +3672,7 @@

Step 4: Final test run
-INFO:lightwood-2252:Loss @ epoch 357: 0.08943390846252441
+INFO:lightwood-2540:Loss @ epoch 357: 0.08943390846252441
 

@@ -3680,7 +3680,7 @@

Step 4: Final test run
-INFO:lightwood-2252:Loss @ epoch 358: 0.0896739661693573
+INFO:lightwood-2540:Loss @ epoch 358: 0.0896739661693573
 

@@ -3688,7 +3688,7 @@

Step 4: Final test run
-INFO:lightwood-2252:Loss @ epoch 359: 0.08882326632738113
+INFO:lightwood-2540:Loss @ epoch 359: 0.08882326632738113
 

@@ -3696,7 +3696,7 @@

Step 4: Final test run
-INFO:lightwood-2252:Loss @ epoch 360: 0.08850156515836716
+INFO:lightwood-2540:Loss @ epoch 360: 0.08850156515836716
 

@@ -3704,7 +3704,7 @@

Step 4: Final test run
-INFO:lightwood-2252:Loss @ epoch 361: 0.08897048979997635
+INFO:lightwood-2540:Loss @ epoch 361: 0.08897048979997635
 

@@ -3712,7 +3712,7 @@

Step 4: Final test run
-INFO:lightwood-2252:Loss @ epoch 362: 0.08849596232175827
+INFO:lightwood-2540:Loss @ epoch 362: 0.08849596232175827
 

@@ -3720,7 +3720,7 @@

Step 4: Final test run
-INFO:lightwood-2252:Loss @ epoch 363: 0.08790712803602219
+INFO:lightwood-2540:Loss @ epoch 363: 0.08790712803602219
 

@@ -3728,7 +3728,7 @@

Step 4: Final test run
-INFO:lightwood-2252:Loss @ epoch 364: 0.08821234852075577
+INFO:lightwood-2540:Loss @ epoch 364: 0.08821234852075577
 

@@ -3736,7 +3736,7 @@

Step 4: Final test run
-INFO:lightwood-2252:Loss @ epoch 365: 0.08732891082763672
+INFO:lightwood-2540:Loss @ epoch 365: 0.08732891082763672
 

@@ -3744,7 +3744,7 @@

Step 4: Final test run
-INFO:lightwood-2252:Loss @ epoch 366: 0.08704856038093567
+INFO:lightwood-2540:Loss @ epoch 366: 0.08704856038093567
 

@@ -3752,7 +3752,7 @@

Step 4: Final test run
-INFO:lightwood-2252:Loss @ epoch 367: 0.08765564113855362
+INFO:lightwood-2540:Loss @ epoch 367: 0.08765564113855362
 

@@ -3760,7 +3760,7 @@

Step 4: Final test run
-INFO:lightwood-2252:Loss @ epoch 368: 0.08696923404932022
+INFO:lightwood-2540:Loss @ epoch 368: 0.08696923404932022
 

@@ -3768,7 +3768,7 @@

Step 4: Final test run
-INFO:lightwood-2252:Loss @ epoch 369: 0.08649873733520508
+INFO:lightwood-2540:Loss @ epoch 369: 0.08649873733520508
 

@@ -3776,7 +3776,7 @@

Step 4: Final test run
-INFO:lightwood-2252:Loss @ epoch 370: 0.08676613122224808
+INFO:lightwood-2540:Loss @ epoch 370: 0.08676613122224808
 

@@ -3784,7 +3784,7 @@

Step 4: Final test run
-INFO:lightwood-2252:Loss @ epoch 371: 0.08599219471216202
+INFO:lightwood-2540:Loss @ epoch 371: 0.08599219471216202
 

@@ -3792,7 +3792,7 @@

Step 4: Final test run
-INFO:lightwood-2252:Loss @ epoch 372: 0.08565033972263336
+INFO:lightwood-2540:Loss @ epoch 372: 0.08565033972263336
 

@@ -3800,7 +3800,7 @@

Step 4: Final test run
-INFO:lightwood-2252:Loss @ epoch 373: 0.08618329465389252
+INFO:lightwood-2540:Loss @ epoch 373: 0.08618329465389252
 

@@ -3808,7 +3808,7 @@

Step 4: Final test run
-INFO:lightwood-2252:Loss @ epoch 374: 0.08559156954288483
+INFO:lightwood-2540:Loss @ epoch 374: 0.08559156954288483
 

@@ -3816,7 +3816,7 @@

Step 4: Final test run
-INFO:lightwood-2252:Loss @ epoch 375: 0.08509930223226547
+INFO:lightwood-2540:Loss @ epoch 375: 0.08509930223226547
 

@@ -3824,7 +3824,7 @@

Step 4: Final test run
-INFO:lightwood-2252:Loss @ epoch 376: 0.08543801307678223
+INFO:lightwood-2540:Loss @ epoch 376: 0.08543801307678223
 

@@ -3832,7 +3832,7 @@

Step 4: Final test run
-INFO:lightwood-2252:Loss @ epoch 377: 0.084554523229599
+INFO:lightwood-2540:Loss @ epoch 377: 0.084554523229599
 

@@ -3840,7 +3840,7 @@

Step 4: Final test run
-INFO:lightwood-2252:Loss @ epoch 378: 0.08425222337245941
+INFO:lightwood-2540:Loss @ epoch 378: 0.08425222337245941
 

@@ -3848,7 +3848,7 @@

Step 4: Final test run
-INFO:lightwood-2252:Loss @ epoch 379: 0.08496475219726562
+INFO:lightwood-2540:Loss @ epoch 379: 0.08496475219726562
 

@@ -3856,7 +3856,7 @@

Step 4: Final test run
-INFO:lightwood-2252:Loss @ epoch 380: 0.08428442478179932
+INFO:lightwood-2540:Loss @ epoch 380: 0.08428442478179932
 

@@ -3864,7 +3864,7 @@

Step 4: Final test run
-INFO:lightwood-2252:Loss @ epoch 381: 0.08389458060264587
+INFO:lightwood-2540:Loss @ epoch 381: 0.08389458060264587
 

@@ -3872,7 +3872,7 @@

Step 4: Final test run
-INFO:lightwood-2252:Loss @ epoch 382: 0.08416417241096497
+INFO:lightwood-2540:Loss @ epoch 382: 0.08416417241096497
 

@@ -3880,7 +3880,7 @@

Step 4: Final test run
-INFO:lightwood-2252:Loss @ epoch 383: 0.08331726491451263
+INFO:lightwood-2540:Loss @ epoch 383: 0.08331726491451263
 

@@ -3888,7 +3888,7 @@

Step 4: Final test run
-INFO:lightwood-2252:Loss @ epoch 384: 0.08304726332426071
+INFO:lightwood-2540:Loss @ epoch 384: 0.08304726332426071
 

@@ -3896,7 +3896,7 @@

Step 4: Final test run
-INFO:lightwood-2252:Loss @ epoch 385: 0.0837259590625763
+INFO:lightwood-2540:Loss @ epoch 385: 0.0837259590625763
 

@@ -3904,7 +3904,7 @@

Step 4: Final test run
-INFO:lightwood-2252:Loss @ epoch 386: 0.08301664143800735
+INFO:lightwood-2540:Loss @ epoch 386: 0.08301664143800735
 

@@ -3912,7 +3912,7 @@

Step 4: Final test run
-INFO:lightwood-2252:Loss @ epoch 387: 0.08279375731945038
+INFO:lightwood-2540:Loss @ epoch 387: 0.08279375731945038
 

@@ -3920,7 +3920,7 @@

Step 4: Final test run
-INFO:lightwood-2252:Loss @ epoch 388: 0.08285657316446304
+INFO:lightwood-2540:Loss @ epoch 388: 0.08285657316446304
 

@@ -3928,7 +3928,7 @@

Step 4: Final test run
-INFO:lightwood-2252:Loss @ epoch 389: 0.0822003185749054
+INFO:lightwood-2540:Loss @ epoch 389: 0.0822003185749054
 

@@ -3936,7 +3936,7 @@

Step 4: Final test run
-INFO:lightwood-2252:Loss @ epoch 390: 0.08189017325639725
+INFO:lightwood-2540:Loss @ epoch 390: 0.08189017325639725
 

@@ -3944,7 +3944,7 @@

Step 4: Final test run
-INFO:lightwood-2252:Loss @ epoch 391: 0.08244460821151733
+INFO:lightwood-2540:Loss @ epoch 391: 0.08244460821151733
 

@@ -3952,7 +3952,7 @@

Step 4: Final test run
-INFO:lightwood-2252:Loss @ epoch 392: 0.08176209777593613
+INFO:lightwood-2540:Loss @ epoch 392: 0.08176209777593613
 

@@ -3960,7 +3960,7 @@

Step 4: Final test run
-INFO:lightwood-2252:Loss @ epoch 393: 0.08143384009599686
+INFO:lightwood-2540:Loss @ epoch 393: 0.08143384009599686
 

@@ -3968,7 +3968,7 @@

Step 4: Final test run
-INFO:lightwood-2252:Loss @ epoch 394: 0.08153267949819565
+INFO:lightwood-2540:Loss @ epoch 394: 0.08153267949819565
 

@@ -3976,7 +3976,7 @@

Step 4: Final test run
-INFO:lightwood-2252:Loss @ epoch 395: 0.08074252307415009
+INFO:lightwood-2540:Loss @ epoch 395: 0.08074252307415009
 

@@ -3984,7 +3984,7 @@

Step 4: Final test run
-INFO:lightwood-2252:Loss @ epoch 396: 0.0804641842842102
+INFO:lightwood-2540:Loss @ epoch 396: 0.0804641842842102
 

@@ -3992,7 +3992,7 @@

Step 4: Final test run
-INFO:lightwood-2252:Loss @ epoch 397: 0.08112648874521255
+INFO:lightwood-2540:Loss @ epoch 397: 0.08112648874521255
 

@@ -4000,7 +4000,7 @@

Step 4: Final test run
-INFO:lightwood-2252:Loss @ epoch 398: 0.0804068073630333
+INFO:lightwood-2540:Loss @ epoch 398: 0.0804068073630333
 

@@ -4008,7 +4008,7 @@

Step 4: Final test run
-INFO:lightwood-2252:Loss @ epoch 399: 0.08000007271766663
+INFO:lightwood-2540:Loss @ epoch 399: 0.08000007271766663
 

@@ -4016,7 +4016,7 @@

Step 4: Final test run
-INFO:lightwood-2252:Loss @ epoch 400: 0.08030638843774796
+INFO:lightwood-2540:Loss @ epoch 400: 0.08030638843774796
 

@@ -4024,7 +4024,7 @@

Step 4: Final test run
-INFO:lightwood-2252:Loss @ epoch 401: 0.07946185022592545
+INFO:lightwood-2540:Loss @ epoch 401: 0.07946185022592545
 

@@ -4032,7 +4032,7 @@

Step 4: Final test run
-INFO:lightwood-2252:Loss @ epoch 402: 0.07926557213068008
+INFO:lightwood-2540:Loss @ epoch 402: 0.07926557213068008
 

@@ -4040,7 +4040,7 @@

Step 4: Final test run
-INFO:lightwood-2252:Loss @ epoch 403: 0.07995376735925674
+INFO:lightwood-2540:Loss @ epoch 403: 0.07995376735925674
 

@@ -4048,7 +4048,7 @@

Step 4: Final test run
-INFO:lightwood-2252:Loss @ epoch 404: 0.07914069294929504
+INFO:lightwood-2540:Loss @ epoch 404: 0.07914069294929504
 

@@ -4056,7 +4056,7 @@

Step 4: Final test run
-INFO:lightwood-2252:Loss @ epoch 405: 0.07901032269001007
+INFO:lightwood-2540:Loss @ epoch 405: 0.07901032269001007
 

@@ -4064,7 +4064,7 @@

Step 4: Final test run
-INFO:lightwood-2252:Loss @ epoch 406: 0.07910943776369095
+INFO:lightwood-2540:Loss @ epoch 406: 0.07910943776369095
 

@@ -4072,7 +4072,7 @@

Step 4: Final test run
-INFO:lightwood-2252:Loss @ epoch 407: 0.07840055227279663
+INFO:lightwood-2540:Loss @ epoch 407: 0.07840055227279663
 

@@ -4080,7 +4080,7 @@

Step 4: Final test run
-INFO:lightwood-2252:Loss @ epoch 408: 0.07814037799835205
+INFO:lightwood-2540:Loss @ epoch 408: 0.07814037799835205
 

@@ -4088,7 +4088,7 @@

Step 4: Final test run
-INFO:lightwood-2252:Loss @ epoch 409: 0.07874786853790283
+INFO:lightwood-2540:Loss @ epoch 409: 0.07874786853790283
 

@@ -4096,7 +4096,7 @@

Step 4: Final test run
-INFO:lightwood-2252:Loss @ epoch 410: 0.07819069921970367
+INFO:lightwood-2540:Loss @ epoch 410: 0.07819069921970367
 

@@ -4104,7 +4104,7 @@

Step 4: Final test run
-INFO:lightwood-2252:Loss @ epoch 411: 0.07780887931585312
+INFO:lightwood-2540:Loss @ epoch 411: 0.07780887931585312
 

@@ -4112,7 +4112,7 @@

Step 4: Final test run
-INFO:lightwood-2252:Loss @ epoch 412: 0.07802116870880127
+INFO:lightwood-2540:Loss @ epoch 412: 0.07802116870880127
 

@@ -4120,7 +4120,7 @@

Step 4: Final test run
-INFO:lightwood-2252:Loss @ epoch 413: 0.0772867277264595
+INFO:lightwood-2540:Loss @ epoch 413: 0.0772867277264595
 

@@ -4128,7 +4128,7 @@

Step 4: Final test run
-INFO:lightwood-2252:Loss @ epoch 414: 0.07709880918264389
+INFO:lightwood-2540:Loss @ epoch 414: 0.07709880918264389
 

@@ -4136,7 +4136,7 @@

Step 4: Final test run
-INFO:lightwood-2252:Loss @ epoch 415: 0.0776868537068367
+INFO:lightwood-2540:Loss @ epoch 415: 0.0776868537068367
 

@@ -4144,7 +4144,7 @@

Step 4: Final test run
-INFO:lightwood-2252:Loss @ epoch 416: 0.07716330885887146
+INFO:lightwood-2540:Loss @ epoch 416: 0.07716330885887146
 

@@ -4152,7 +4152,7 @@

Step 4: Final test run
-INFO:lightwood-2252:Loss @ epoch 417: 0.07688125967979431
+INFO:lightwood-2540:Loss @ epoch 417: 0.07688125967979431
 

@@ -4160,7 +4160,7 @@

Step 4: Final test run
-INFO:lightwood-2252:Loss @ epoch 418: 0.07698465138673782
+INFO:lightwood-2540:Loss @ epoch 418: 0.07698465138673782
 

@@ -4168,7 +4168,7 @@

Step 4: Final test run
-INFO:lightwood-2252:Loss @ epoch 419: 0.0762372612953186
+INFO:lightwood-2540:Loss @ epoch 419: 0.0762372612953186
 

@@ -4176,7 +4176,7 @@

Step 4: Final test run
-INFO:lightwood-2252:Loss @ epoch 420: 0.07603802531957626
+INFO:lightwood-2540:Loss @ epoch 420: 0.07603802531957626
 

@@ -4184,7 +4184,7 @@

Step 4: Final test run
-INFO:lightwood-2252:Loss @ epoch 421: 0.07675285637378693
+INFO:lightwood-2540:Loss @ epoch 421: 0.07675285637378693
 

@@ -4192,7 +4192,7 @@

Step 4: Final test run
-INFO:lightwood-2252:Loss @ epoch 422: 0.07623977214097977
+INFO:lightwood-2540:Loss @ epoch 422: 0.07623977214097977
 

@@ -4200,7 +4200,7 @@

Step 4: Final test run
-INFO:lightwood-2252:Loss @ epoch 423: 0.07567108422517776
+INFO:lightwood-2540:Loss @ epoch 423: 0.07567108422517776
 

@@ -4208,7 +4208,7 @@

Step 4: Final test run
-INFO:lightwood-2252:Loss @ epoch 424: 0.07615751028060913
+INFO:lightwood-2540:Loss @ epoch 424: 0.07615751028060913
 

@@ -4216,7 +4216,7 @@

Step 4: Final test run
-INFO:lightwood-2252:Loss @ epoch 425: 0.07526733726263046
+INFO:lightwood-2540:Loss @ epoch 425: 0.07526733726263046
 

@@ -4224,7 +4224,7 @@

Step 4: Final test run
-INFO:lightwood-2252:Loss @ epoch 426: 0.07509555667638779
+INFO:lightwood-2540:Loss @ epoch 426: 0.07509555667638779
 

@@ -4232,7 +4232,7 @@

Step 4: Final test run
-INFO:lightwood-2252:Loss @ epoch 427: 0.07569493353366852
+INFO:lightwood-2540:Loss @ epoch 427: 0.07569493353366852
 

@@ -4240,7 +4240,7 @@

Step 4: Final test run
-INFO:lightwood-2252:Loss @ epoch 428: 0.07537294924259186
+INFO:lightwood-2540:Loss @ epoch 428: 0.07537294924259186
 

@@ -4248,7 +4248,7 @@

Step 4: Final test run
-INFO:lightwood-2252:Loss @ epoch 429: 0.07467805594205856
+INFO:lightwood-2540:Loss @ epoch 429: 0.07467805594205856
 

@@ -4256,7 +4256,7 @@

Step 4: Final test run
-INFO:lightwood-2252:Loss @ epoch 430: 0.07528648525476456
+INFO:lightwood-2540:Loss @ epoch 430: 0.07528648525476456
 

@@ -4264,7 +4264,7 @@

Step 4: Final test run
-INFO:lightwood-2252:Loss @ epoch 431: 0.07435967028141022
+INFO:lightwood-2540:Loss @ epoch 431: 0.07435967028141022
 

@@ -4272,7 +4272,7 @@

Step 4: Final test run
-INFO:lightwood-2252:Loss @ epoch 432: 0.07422596961259842
+INFO:lightwood-2540:Loss @ epoch 432: 0.07422596961259842
 

@@ -4280,7 +4280,7 @@

Step 4: Final test run
-INFO:lightwood-2252:Loss @ epoch 433: 0.07503972947597504
+INFO:lightwood-2540:Loss @ epoch 433: 0.07503972947597504
 

@@ -4288,7 +4288,7 @@

Step 4: Final test run
-INFO:lightwood-2252:Loss @ epoch 434: 0.07434249669313431
+INFO:lightwood-2540:Loss @ epoch 434: 0.07434249669313431
 

@@ -4296,7 +4296,7 @@

Step 4: Final test run
-INFO:lightwood-2252:Loss @ epoch 435: 0.07409335672855377
+INFO:lightwood-2540:Loss @ epoch 435: 0.07409335672855377
 

@@ -4304,7 +4304,7 @@

Step 4: Final test run
-INFO:lightwood-2252:Loss @ epoch 436: 0.07420685887336731
+INFO:lightwood-2540:Loss @ epoch 436: 0.07420685887336731
 

@@ -4312,7 +4312,7 @@

Step 4: Final test run
-INFO:lightwood-2252:Loss @ epoch 437: 0.0735834538936615
+INFO:lightwood-2540:Loss @ epoch 437: 0.0735834538936615
 

@@ -4320,7 +4320,7 @@

Step 4: Final test run
-INFO:lightwood-2252:Loss @ epoch 438: 0.07333341240882874
+INFO:lightwood-2540:Loss @ epoch 438: 0.07333341240882874
 

@@ -4328,7 +4328,7 @@

Step 4: Final test run
-INFO:lightwood-2252:Loss @ epoch 439: 0.07391082495450974
+INFO:lightwood-2540:Loss @ epoch 439: 0.07391082495450974
 

@@ -4336,7 +4336,7 @@

Step 4: Final test run
-INFO:lightwood-2252:Loss @ epoch 440: 0.07348911464214325
+INFO:lightwood-2540:Loss @ epoch 440: 0.07348911464214325
 

@@ -4344,7 +4344,7 @@

Step 4: Final test run
-INFO:lightwood-2252:Loss @ epoch 441: 0.07308389991521835
+INFO:lightwood-2540:Loss @ epoch 441: 0.07308389991521835
 

@@ -4352,7 +4352,7 @@

Step 4: Final test run
-INFO:lightwood-2252:Loss @ epoch 442: 0.07328886538743973
+INFO:lightwood-2540:Loss @ epoch 442: 0.07328886538743973
 

@@ -4360,7 +4360,7 @@

Step 4: Final test run
-INFO:lightwood-2252:Loss @ epoch 443: 0.0725550651550293
+INFO:lightwood-2540:Loss @ epoch 443: 0.0725550651550293
 

@@ -4368,7 +4368,7 @@

Step 4: Final test run
-INFO:lightwood-2252:Loss @ epoch 444: 0.07240220904350281
+INFO:lightwood-2540:Loss @ epoch 444: 0.07240220904350281
 

@@ -4376,7 +4376,7 @@

Step 4: Final test run
-INFO:lightwood-2252:Loss @ epoch 445: 0.07308465242385864
+INFO:lightwood-2540:Loss @ epoch 445: 0.07308465242385864
 

@@ -4384,7 +4384,7 @@

Step 4: Final test run
-INFO:lightwood-2252:Loss @ epoch 446: 0.07288312911987305
+INFO:lightwood-2540:Loss @ epoch 446: 0.07288312911987305
 

@@ -4392,7 +4392,7 @@

Step 4: Final test run
-INFO:lightwood-2252:Loss @ epoch 447: 0.0722663402557373
+INFO:lightwood-2540:Loss @ epoch 447: 0.0722663402557373
 

@@ -4400,7 +4400,7 @@

Step 4: Final test run
-INFO:lightwood-2252:Loss @ epoch 448: 0.07264856994152069
+INFO:lightwood-2540:Loss @ epoch 448: 0.07264856994152069
 

@@ -4408,7 +4408,7 @@

Step 4: Final test run
-INFO:lightwood-2252:Loss @ epoch 449: 0.07182618230581284
+INFO:lightwood-2540:Loss @ epoch 449: 0.07182618230581284
 

@@ -4416,7 +4416,7 @@

Step 4: Final test run
-INFO:lightwood-2252:Loss @ epoch 450: 0.07167533785104752
+INFO:lightwood-2540:Loss @ epoch 450: 0.07167533785104752
 

@@ -4424,7 +4424,7 @@

Step 4: Final test run
-INFO:lightwood-2252:Loss @ epoch 451: 0.07241341471672058
+INFO:lightwood-2540:Loss @ epoch 451: 0.07241341471672058
 

@@ -4432,7 +4432,7 @@

Step 4: Final test run
-INFO:lightwood-2252:Loss @ epoch 452: 0.07208056002855301
+INFO:lightwood-2540:Loss @ epoch 452: 0.07208056002855301
 

@@ -4440,7 +4440,7 @@

Step 4: Final test run
-INFO:lightwood-2252:Loss @ epoch 453: 0.07154601812362671
+INFO:lightwood-2540:Loss @ epoch 453: 0.07154601812362671
 

@@ -4448,7 +4448,7 @@

Step 4: Final test run
-INFO:lightwood-2252:Loss @ epoch 454: 0.07190731167793274
+INFO:lightwood-2540:Loss @ epoch 454: 0.07190731167793274
 

@@ -4456,7 +4456,7 @@

Step 4: Final test run
-INFO:lightwood-2252:Loss @ epoch 455: 0.0710812360048294
+INFO:lightwood-2540:Loss @ epoch 455: 0.0710812360048294
 

@@ -4464,7 +4464,7 @@

Step 4: Final test run
-INFO:lightwood-2252:Loss @ epoch 456: 0.07096673548221588
+INFO:lightwood-2540:Loss @ epoch 456: 0.07096673548221588
 

@@ -4472,7 +4472,7 @@

Step 4: Final test run
-INFO:lightwood-2252:Loss @ epoch 457: 0.0718337818980217
+INFO:lightwood-2540:Loss @ epoch 457: 0.0718337818980217
 

@@ -4480,7 +4480,7 @@

Step 4: Final test run
-INFO:lightwood-2252:Loss @ epoch 458: 0.07134897261857986
+INFO:lightwood-2540:Loss @ epoch 458: 0.07134897261857986
 

@@ -4488,7 +4488,7 @@

Step 4: Final test run
-INFO:lightwood-2252:Loss @ epoch 459: 0.07083813846111298
+INFO:lightwood-2540:Loss @ epoch 459: 0.07083813846111298
 

@@ -4496,7 +4496,7 @@

Step 4: Final test run
-INFO:lightwood-2252:Loss @ epoch 460: 0.07124733179807663
+INFO:lightwood-2540:Loss @ epoch 460: 0.07124733179807663
 

@@ -4504,7 +4504,7 @@

Step 4: Final test run
-INFO:lightwood-2252:Loss @ epoch 461: 0.0705094262957573
+INFO:lightwood-2540:Loss @ epoch 461: 0.0705094262957573
 

@@ -4512,7 +4512,7 @@

Step 4: Final test run
-INFO:lightwood-2252:Loss @ epoch 462: 0.07036501169204712
+INFO:lightwood-2540:Loss @ epoch 462: 0.07036501169204712
 

@@ -4520,7 +4520,7 @@

Step 4: Final test run
-INFO:lightwood-2252:Loss @ epoch 463: 0.07111788541078568
+INFO:lightwood-2540:Loss @ epoch 463: 0.07111788541078568
 

@@ -4528,7 +4528,7 @@

Step 4: Final test run
-INFO:lightwood-2252:Loss @ epoch 464: 0.07069509476423264
+INFO:lightwood-2540:Loss @ epoch 464: 0.07069509476423264
 

@@ -4536,7 +4536,7 @@

Step 4: Final test run
-INFO:lightwood-2252:Loss @ epoch 465: 0.07026039808988571
+INFO:lightwood-2540:Loss @ epoch 465: 0.07026039808988571
 

@@ -4544,7 +4544,7 @@

Step 4: Final test run
-INFO:lightwood-2252:Loss @ epoch 466: 0.07056906819343567
+INFO:lightwood-2540:Loss @ epoch 466: 0.07056906819343567
 

@@ -4552,7 +4552,7 @@

Step 4: Final test run
-INFO:lightwood-2252:Loss @ epoch 467: 0.06981150805950165
+INFO:lightwood-2540:Loss @ epoch 467: 0.06981150805950165
 

@@ -4560,7 +4560,7 @@

Step 4: Final test run
-INFO:lightwood-2252:Loss @ epoch 468: 0.06967213749885559
+INFO:lightwood-2540:Loss @ epoch 468: 0.06967213749885559
 

@@ -4568,7 +4568,7 @@

Step 4: Final test run
-INFO:lightwood-2252:Loss @ epoch 469: 0.0704450011253357
+INFO:lightwood-2540:Loss @ epoch 469: 0.0704450011253357
 

@@ -4576,7 +4576,7 @@

Step 4: Final test run
-INFO:lightwood-2252:Loss @ epoch 470: 0.07002224773168564
+INFO:lightwood-2540:Loss @ epoch 470: 0.07002224773168564
 

@@ -4584,7 +4584,7 @@

Step 4: Final test run
-INFO:lightwood-2252:Loss @ epoch 471: 0.06954890489578247
+INFO:lightwood-2540:Loss @ epoch 471: 0.06954890489578247
 

@@ -4592,7 +4592,7 @@

Step 4: Final test run
-INFO:lightwood-2252:Loss @ epoch 472: 0.07001929730176926
+INFO:lightwood-2540:Loss @ epoch 472: 0.07001929730176926
 

@@ -4600,7 +4600,7 @@

Step 4: Final test run
-INFO:lightwood-2252:Loss @ epoch 473: 0.06918215751647949
+INFO:lightwood-2540:Loss @ epoch 473: 0.06918215751647949
 

@@ -4608,7 +4608,7 @@

Step 4: Final test run
-INFO:lightwood-2252:Loss @ epoch 474: 0.06905678659677505
+INFO:lightwood-2540:Loss @ epoch 474: 0.06905678659677505
 

@@ -4616,7 +4616,7 @@

Step 4: Final test run
-INFO:lightwood-2252:Loss @ epoch 475: 0.06994140148162842
+INFO:lightwood-2540:Loss @ epoch 475: 0.06994140148162842
 

@@ -4624,7 +4624,7 @@

Step 4: Final test run
-INFO:lightwood-2252:Loss @ epoch 476: 0.06957031041383743
+INFO:lightwood-2540:Loss @ epoch 476: 0.06957031041383743
 

@@ -4632,7 +4632,7 @@

Step 4: Final test run
-INFO:lightwood-2252:Loss @ epoch 477: 0.06890591233968735
+INFO:lightwood-2540:Loss @ epoch 477: 0.06890591233968735
 

@@ -4640,7 +4640,7 @@

Step 4: Final test run
-INFO:lightwood-2252:Loss @ epoch 478: 0.06942413747310638
+INFO:lightwood-2540:Loss @ epoch 478: 0.06942413747310638
 

@@ -4648,7 +4648,7 @@

Step 4: Final test run
-INFO:lightwood-2252:Loss @ epoch 479: 0.068662129342556
+INFO:lightwood-2540:Loss @ epoch 479: 0.068662129342556
 

@@ -4656,7 +4656,7 @@

Step 4: Final test run
-INFO:lightwood-2252:Loss @ epoch 480: 0.0685315951704979
+INFO:lightwood-2540:Loss @ epoch 480: 0.0685315951704979
 

@@ -4664,7 +4664,7 @@

Step 4: Final test run
-INFO:lightwood-2252:Loss @ epoch 481: 0.06919320672750473
+INFO:lightwood-2540:Loss @ epoch 481: 0.06919320672750473
 

@@ -4672,7 +4672,7 @@

Step 4: Final test run
-INFO:lightwood-2252:Loss @ epoch 482: 0.06884051114320755
+INFO:lightwood-2540:Loss @ epoch 482: 0.06884051114320755
 

@@ -4680,7 +4680,7 @@

Step 4: Final test run
-INFO:lightwood-2252:Loss @ epoch 483: 0.06852498650550842
+INFO:lightwood-2540:Loss @ epoch 483: 0.06852498650550842
 

@@ -4688,7 +4688,7 @@

Step 4: Final test run
-INFO:lightwood-2252:Loss @ epoch 484: 0.06881336867809296
+INFO:lightwood-2540:Loss @ epoch 484: 0.06881336867809296
 

@@ -4696,7 +4696,7 @@

Step 4: Final test run
-INFO:lightwood-2252:Loss @ epoch 485: 0.0681278333067894
+INFO:lightwood-2540:Loss @ epoch 485: 0.0681278333067894
 

@@ -4704,7 +4704,7 @@

Step 4: Final test run
-INFO:lightwood-2252:Loss @ epoch 486: 0.06801153719425201
+INFO:lightwood-2540:Loss @ epoch 486: 0.06801153719425201
 

@@ -4712,7 +4712,7 @@

Step 4: Final test run
-INFO:lightwood-2252:Loss @ epoch 487: 0.0688665509223938
+INFO:lightwood-2540:Loss @ epoch 487: 0.0688665509223938
 

@@ -4720,7 +4720,7 @@

Step 4: Final test run
-INFO:lightwood-2252:Loss @ epoch 488: 0.06848578155040741
+INFO:lightwood-2540:Loss @ epoch 488: 0.06848578155040741
 

@@ -4728,7 +4728,7 @@

Step 4: Final test run
-INFO:lightwood-2252:Loss @ epoch 489: 0.0680362805724144
+INFO:lightwood-2540:Loss @ epoch 489: 0.0680362805724144
 

@@ -4736,7 +4736,7 @@

Step 4: Final test run
-INFO:lightwood-2252:Loss @ epoch 490: 0.0685308426618576
+INFO:lightwood-2540:Loss @ epoch 490: 0.0685308426618576
 

@@ -4744,7 +4744,7 @@

Step 4: Final test run
-INFO:lightwood-2252:Loss @ epoch 491: 0.06770123541355133
+INFO:lightwood-2540:Loss @ epoch 491: 0.06770123541355133
 

@@ -4752,7 +4752,7 @@

Step 4: Final test run
-INFO:lightwood-2252:Loss @ epoch 492: 0.06760372221469879
+INFO:lightwood-2540:Loss @ epoch 492: 0.06760372221469879
 

@@ -4760,7 +4760,7 @@

Step 4: Final test run
-INFO:lightwood-2252:Loss @ epoch 493: 0.06856502592563629
+INFO:lightwood-2540:Loss @ epoch 493: 0.06856502592563629
 

@@ -4768,7 +4768,7 @@

Step 4: Final test run
-INFO:lightwood-2252:Loss @ epoch 494: 0.0679614394903183
+INFO:lightwood-2540:Loss @ epoch 494: 0.0679614394903183
 

@@ -4776,7 +4776,7 @@

Step 4: Final test run
-INFO:lightwood-2252:Loss @ epoch 495: 0.0675961971282959
+INFO:lightwood-2540:Loss @ epoch 495: 0.0675961971282959
 

@@ -4784,7 +4784,7 @@

Step 4: Final test run
-INFO:lightwood-2252:Loss @ epoch 496: 0.06795072555541992
+INFO:lightwood-2540:Loss @ epoch 496: 0.06795072555541992
 

@@ -4792,7 +4792,7 @@

Step 4: Final test run
-INFO:lightwood-2252:Loss @ epoch 497: 0.06731095910072327
+INFO:lightwood-2540:Loss @ epoch 497: 0.06731095910072327
 

@@ -4800,7 +4800,7 @@

Step 4: Final test run
-INFO:lightwood-2252:Loss @ epoch 498: 0.06714644283056259
+INFO:lightwood-2540:Loss @ epoch 498: 0.06714644283056259
 

@@ -4808,7 +4808,7 @@

Step 4: Final test run
-INFO:lightwood-2252:Loss @ epoch 499: 0.06786693632602692
+INFO:lightwood-2540:Loss @ epoch 499: 0.06786693632602692
 

@@ -4816,7 +4816,7 @@

Step 4: Final test run
-INFO:lightwood-2252:Loss @ epoch 500: 0.06758256256580353
+INFO:lightwood-2540:Loss @ epoch 500: 0.06758256256580353
 

@@ -4824,7 +4824,7 @@

Step 4: Final test run
-INFO:lightwood-2252:Loss @ epoch 501: 0.06698315590620041
+INFO:lightwood-2540:Loss @ epoch 501: 0.06698315590620041
 

@@ -4832,7 +4832,7 @@

Step 4: Final test run
-INFO:lightwood-2252:Loss @ epoch 502: 0.06747950613498688
+INFO:lightwood-2540:Loss @ epoch 502: 0.06747950613498688
 

@@ -4840,7 +4840,7 @@

Step 4: Final test run
-INFO:lightwood-2252:Loss @ epoch 503: 0.06655343621969223
+INFO:lightwood-2540:Loss @ epoch 503: 0.06655343621969223
 

@@ -4848,7 +4848,7 @@

Step 4: Final test run
-INFO:lightwood-2252:Loss @ epoch 504: 0.06652842462062836
+INFO:lightwood-2540:Loss @ epoch 504: 0.06652842462062836
 

@@ -4856,7 +4856,7 @@

Step 4: Final test run
-INFO:lightwood-2252:Loss @ epoch 505: 0.06745205074548721
+INFO:lightwood-2540:Loss @ epoch 505: 0.06745205074548721
 

@@ -4864,7 +4864,7 @@

Step 4: Final test run
-INFO:lightwood-2252:Loss @ epoch 506: 0.0668550580739975
+INFO:lightwood-2540:Loss @ epoch 506: 0.0668550580739975
 

@@ -4872,7 +4872,7 @@

Step 4: Final test run
-INFO:lightwood-2252:Loss @ epoch 507: 0.06666403263807297
+INFO:lightwood-2540:Loss @ epoch 507: 0.06666403263807297
 

@@ -4880,7 +4880,7 @@

Step 4: Final test run
-INFO:lightwood-2252:Loss @ epoch 508: 0.06683854013681412
+INFO:lightwood-2540:Loss @ epoch 508: 0.06683854013681412
 

@@ -4888,7 +4888,7 @@

Step 4: Final test run
-INFO:lightwood-2252:Loss @ epoch 509: 0.06626935303211212
+INFO:lightwood-2540:Loss @ epoch 509: 0.06626935303211212
 

@@ -4896,7 +4896,7 @@

Step 4: Final test run
-INFO:lightwood-2252:Loss @ epoch 510: 0.06613652408123016
+INFO:lightwood-2540:Loss @ epoch 510: 0.06613652408123016
 

@@ -4904,7 +4904,7 @@

Step 4: Final test run
-INFO:lightwood-2252:Loss @ epoch 511: 0.06672576069831848
+INFO:lightwood-2540:Loss @ epoch 511: 0.06672576069831848
 

@@ -4912,7 +4912,7 @@

Step 4: Final test run
-INFO:lightwood-2252:Loss @ epoch 512: 0.0666651502251625
+INFO:lightwood-2540:Loss @ epoch 512: 0.0666651502251625
 

@@ -4920,7 +4920,7 @@

Step 4: Final test run
-INFO:lightwood-2252:Loss @ epoch 513: 0.06582488119602203
+INFO:lightwood-2540:Loss @ epoch 513: 0.06582488119602203
 

@@ -4928,7 +4928,7 @@

Step 4: Final test run
-INFO:lightwood-2252:Loss @ epoch 514: 0.06652247160673141
+INFO:lightwood-2540:Loss @ epoch 514: 0.06652247160673141
 

@@ -4936,7 +4936,7 @@

Step 4: Final test run
-INFO:lightwood-2252:Loss @ epoch 515: 0.06558185815811157
+INFO:lightwood-2540:Loss @ epoch 515: 0.06558185815811157
 

@@ -4944,7 +4944,7 @@

Step 4: Final test run
-INFO:lightwood-2252:Loss @ epoch 516: 0.0655498206615448
+INFO:lightwood-2540:Loss @ epoch 516: 0.0655498206615448
 

@@ -4952,7 +4952,7 @@

Step 4: Final test run
-INFO:lightwood-2252:Loss @ epoch 517: 0.06624851375818253
+INFO:lightwood-2540:Loss @ epoch 517: 0.06624851375818253
 

@@ -4960,7 +4960,7 @@

Step 4: Final test run
-INFO:lightwood-2252:Loss @ epoch 518: 0.06601088494062424
+INFO:lightwood-2540:Loss @ epoch 518: 0.06601088494062424
 

@@ -4968,7 +4968,7 @@

Step 4: Final test run
-INFO:lightwood-2252:Loss @ epoch 519: 0.06545697897672653
+INFO:lightwood-2540:Loss @ epoch 519: 0.06545697897672653
 

@@ -4976,7 +4976,7 @@

Step 4: Final test run
-INFO:lightwood-2252:Loss @ epoch 520: 0.0659414529800415
+INFO:lightwood-2540:Loss @ epoch 520: 0.0659414529800415
 

@@ -4984,7 +4984,7 @@

Step 4: Final test run
-INFO:lightwood-2252:Loss @ epoch 521: 0.06516807526350021
+INFO:lightwood-2540:Loss @ epoch 521: 0.06516807526350021
 

@@ -4992,7 +4992,7 @@

Step 4: Final test run
-INFO:lightwood-2252:Loss @ epoch 522: 0.06501934677362442
+INFO:lightwood-2540:Loss @ epoch 522: 0.06501934677362442
 

@@ -5000,7 +5000,7 @@

Step 4: Final test run
-INFO:lightwood-2252:Loss @ epoch 523: 0.06574487686157227
+INFO:lightwood-2540:Loss @ epoch 523: 0.06574487686157227
 

@@ -5008,7 +5008,7 @@

Step 4: Final test run
-INFO:lightwood-2252:Loss @ epoch 524: 0.06553597748279572
+INFO:lightwood-2540:Loss @ epoch 524: 0.06553597748279572
 

@@ -5016,7 +5016,7 @@

Step 4: Final test run
-INFO:lightwood-2252:Loss @ epoch 525: 0.06504649668931961
+INFO:lightwood-2540:Loss @ epoch 525: 0.06504649668931961
 

@@ -5024,7 +5024,7 @@

Step 4: Final test run
-INFO:lightwood-2252:Loss @ epoch 526: 0.06540416181087494
+INFO:lightwood-2540:Loss @ epoch 526: 0.06540416181087494
 

@@ -5032,7 +5032,7 @@

Step 4: Final test run
-INFO:lightwood-2252:Loss @ epoch 527: 0.06479271501302719
+INFO:lightwood-2540:Loss @ epoch 527: 0.06479271501302719
 

@@ -5040,7 +5040,7 @@

Step 4: Final test run
-INFO:lightwood-2252:Loss @ epoch 528: 0.06469936668872833
+INFO:lightwood-2540:Loss @ epoch 528: 0.06469936668872833
 

@@ -5048,7 +5048,7 @@

Step 4: Final test run
-INFO:lightwood-2252:Loss @ epoch 529: 0.0654490739107132
+INFO:lightwood-2540:Loss @ epoch 529: 0.0654490739107132
 

@@ -5056,7 +5056,7 @@

Step 4: Final test run
-INFO:lightwood-2252:Loss @ epoch 530: 0.06509881466627121
+INFO:lightwood-2540:Loss @ epoch 530: 0.06509881466627121
 

@@ -5064,7 +5064,7 @@

Step 4: Final test run
-INFO:lightwood-2252:Loss @ epoch 531: 0.06460769474506378
+INFO:lightwood-2540:Loss @ epoch 531: 0.06460769474506378
 

@@ -5072,7 +5072,7 @@

Step 4: Final test run
-INFO:lightwood-2252:Loss @ epoch 532: 0.06506450474262238
+INFO:lightwood-2540:Loss @ epoch 532: 0.06506450474262238
 

@@ -5080,7 +5080,7 @@

Step 4: Final test run
-INFO:lightwood-2252:Loss @ epoch 533: 0.06425388902425766
+INFO:lightwood-2540:Loss @ epoch 533: 0.06425388902425766
 

@@ -5088,7 +5088,7 @@

Step 4: Final test run
-INFO:lightwood-2252:Loss @ epoch 534: 0.06419297307729721
+INFO:lightwood-2540:Loss @ epoch 534: 0.06419297307729721
 

@@ -5096,7 +5096,7 @@

Step 4: Final test run
-INFO:lightwood-2252:Loss @ epoch 535: 0.06507144123315811
+INFO:lightwood-2540:Loss @ epoch 535: 0.06507144123315811
 

@@ -5104,7 +5104,7 @@

Step 4: Final test run
-INFO:lightwood-2252:Loss @ epoch 536: 0.06475593149662018
+INFO:lightwood-2540:Loss @ epoch 536: 0.06475593149662018
 

@@ -5112,7 +5112,7 @@

Step 4: Final test run
-INFO:lightwood-2252:Loss @ epoch 537: 0.0640476867556572
+INFO:lightwood-2540:Loss @ epoch 537: 0.0640476867556572
 

@@ -5120,7 +5120,7 @@

Step 4: Final test run
-INFO:lightwood-2252:Loss @ epoch 538: 0.06452148407697678
+INFO:lightwood-2540:Loss @ epoch 538: 0.06452148407697678
 

@@ -5128,7 +5128,7 @@

Step 4: Final test run
-INFO:lightwood-2252:Loss @ epoch 539: 0.063988097012043
+INFO:lightwood-2540:Loss @ epoch 539: 0.063988097012043
 

@@ -5136,7 +5136,7 @@

Step 4: Final test run
-INFO:lightwood-2252:Loss @ epoch 540: 0.06390102207660675
+INFO:lightwood-2540:Loss @ epoch 540: 0.06390102207660675
 

@@ -5144,7 +5144,7 @@

Step 4: Final test run
-INFO:lightwood-2252:Loss @ epoch 541: 0.06427431106567383
+INFO:lightwood-2540:Loss @ epoch 541: 0.06427431106567383
 

@@ -5152,7 +5152,7 @@

Step 4: Final test run
-INFO:lightwood-2252:Loss @ epoch 542: 0.06461699306964874
+INFO:lightwood-2540:Loss @ epoch 542: 0.06461699306964874
 

@@ -5160,7 +5160,7 @@

Step 4: Final test run
-INFO:lightwood-2252:Loss @ epoch 543: 0.06366197764873505
+INFO:lightwood-2540:Loss @ epoch 543: 0.06366197764873505
 

@@ -5168,7 +5168,7 @@

Step 4: Final test run
-INFO:lightwood-2252:Loss @ epoch 544: 0.06439769268035889
+INFO:lightwood-2540:Loss @ epoch 544: 0.06439769268035889
 

@@ -5176,7 +5176,7 @@

Step 4: Final test run
-INFO:lightwood-2252:Loss @ epoch 545: 0.06354749947786331
+INFO:lightwood-2540:Loss @ epoch 545: 0.06354749947786331
 

@@ -5184,7 +5184,7 @@

Step 4: Final test run
-INFO:lightwood-2252:Loss @ epoch 546: 0.06346575170755386
+INFO:lightwood-2540:Loss @ epoch 546: 0.06346575170755386
 

@@ -5192,7 +5192,7 @@

Step 4: Final test run
-INFO:lightwood-2252:Loss @ epoch 547: 0.06415951251983643
+INFO:lightwood-2540:Loss @ epoch 547: 0.06415951251983643
 

@@ -5200,7 +5200,7 @@

Step 4: Final test run
-INFO:lightwood-2252:Loss @ epoch 548: 0.06416907906532288
+INFO:lightwood-2540:Loss @ epoch 548: 0.06416907906532288
 

@@ -5208,7 +5208,7 @@

Step 4: Final test run
-INFO:lightwood-2252:Loss @ epoch 549: 0.06350232660770416
+INFO:lightwood-2540:Loss @ epoch 549: 0.06350232660770416
 

@@ -5216,7 +5216,7 @@

Step 4: Final test run
-INFO:lightwood-2252:Loss @ epoch 1: 0.03389815576374531
+INFO:lightwood-2540:Loss @ epoch 1: 0.03389815576374531
 

@@ -5224,7 +5224,7 @@

Step 4: Final test run
-INFO:lightwood-2252:Loss @ epoch 2: 0.033698095567524435
+INFO:lightwood-2540:Loss @ epoch 2: 0.033698095567524435
 

@@ -5232,7 +5232,7 @@

Step 4: Final test run
-INFO:lightwood-2252:Loss @ epoch 3: 0.0372611828148365
+INFO:lightwood-2540:Loss @ epoch 3: 0.0372611828148365
 

@@ -5240,7 +5240,7 @@

Step 4: Final test run
-INFO:lightwood-2252:Loss @ epoch 4: 0.0382374182343483
+INFO:lightwood-2540:Loss @ epoch 4: 0.0382374182343483
 

@@ -5248,7 +5248,7 @@

Step 4: Final test run
-INFO:lightwood-2252:Loss @ epoch 5: 0.03677316829562187
+INFO:lightwood-2540:Loss @ epoch 5: 0.03677316829562187
 

@@ -5256,7 +5256,7 @@

Step 4: Final test run
-INFO:lightwood-2252:Loss @ epoch 6: 0.04194173291325569
+INFO:lightwood-2540:Loss @ epoch 6: 0.04194173291325569
 

@@ -5264,7 +5264,7 @@

Step 4: Final test run
-INFO:lightwood-2252:Loss @ epoch 7: 0.04046095162630081
+INFO:lightwood-2540:Loss @ epoch 7: 0.04046095162630081
 

@@ -5272,7 +5272,7 @@

Step 4: Final test run
-DEBUG:lightwood-2252: `fit_mixer` runtime: 4.58 seconds
+DEBUG:lightwood-2540: `fit_mixer` runtime: 4.76 seconds
 

@@ -5280,7 +5280,7 @@

Step 4: Final test run
-INFO:lightwood-2252:Started fitting XGBoost model
+INFO:lightwood-2540:Started fitting XGBoost model
 

@@ -5296,7 +5296,7 @@

Step 4: Final test run
-INFO:lightwood-2252:A single GBM iteration takes 0.1 seconds
+INFO:lightwood-2540:A single GBM iteration takes 0.1 seconds
 

@@ -5304,7 +5304,7 @@

Step 4: Final test run
-INFO:lightwood-2252:Training XGBoost with 131 iterations given 16.484190421104433 seconds constraint
+INFO:lightwood-2540:Training XGBoost with 131 iterations given 16.479915187358856 seconds constraint
 

@@ -5568,7 +5568,7 @@

Step 4: Final test run
-INFO:lightwood-2252:XGBoost mixer does not have a `partial_fit` implementation
+INFO:lightwood-2540:XGBoost mixer does not have a `partial_fit` implementation
 

@@ -5576,7 +5576,7 @@

Step 4: Final test run
-DEBUG:lightwood-2252: `fit_mixer` runtime: 0.05 seconds
+DEBUG:lightwood-2540: `fit_mixer` runtime: 0.06 seconds
 

@@ -5584,7 +5584,7 @@

Step 4: Final test run
-WARNING:dataprep_ml-2252:Exception: Unspported categorical type for regression when training mixer: <lightwood.mixer.regression.Regression object at 0x7e3a81ff7970>
+WARNING:dataprep_ml-2540:Exception: Unspported categorical type for regression when training mixer: <lightwood.mixer.regression.Regression object at 0x786782fd67f0>
 

@@ -5592,7 +5592,7 @@

Step 4: Final test run
-INFO:lightwood-2252:Started fitting RandomForest model
+INFO:lightwood-2540:Started fitting RandomForest model
 

@@ -5600,7 +5600,7 @@

Step 4: Final test run
-INFO:lightwood-2252:RandomForest based correlation of (train data): 1.0
+INFO:lightwood-2540:RandomForest based correlation of (train data): 1.0
 

@@ -5608,7 +5608,7 @@

Step 4: Final test run
-INFO:lightwood-2252:RandomForest based correlation of (dev data): 1.0
+INFO:lightwood-2540:RandomForest based correlation of (dev data): 1.0
 

@@ -5616,7 +5616,7 @@

Step 4: Final test run
-DEBUG:lightwood-2252: `fit_mixer` runtime: 0.11 seconds
+DEBUG:lightwood-2540: `fit_mixer` runtime: 0.6 seconds
 

@@ -5624,7 +5624,7 @@

Step 4: Final test run
-INFO:dataprep_ml-2252:Ensembling the mixer
+INFO:dataprep_ml-2540:Ensembling the mixer
 

@@ -5632,7 +5632,7 @@

Step 4: Final test run
-INFO:lightwood-2252:Mixer: Neural got accuracy: 0.922
+INFO:lightwood-2540:Mixer: Neural got accuracy: 0.922
 

@@ -5640,7 +5640,7 @@

Step 4: Final test run
-INFO:lightwood-2252:Mixer: XGBoostMixer got accuracy: 1.0
+INFO:lightwood-2540:Mixer: XGBoostMixer got accuracy: 1.0
 

@@ -5648,7 +5648,7 @@

Step 4: Final test run
-INFO:lightwood-2252:Mixer: RandomForest got accuracy: 1.0
+INFO:lightwood-2540:Mixer: RandomForest got accuracy: 1.0
 

@@ -5656,7 +5656,7 @@

Step 4: Final test run
-INFO:lightwood-2252:Picked best mixer: RandomForest
+INFO:lightwood-2540:Picked best mixer: RandomForest
 

@@ -5664,7 +5664,7 @@

Step 4: Final test run
-DEBUG:lightwood-2252: `fit` runtime: 4.81 seconds
+DEBUG:lightwood-2540: `fit` runtime: 5.46 seconds
 

@@ -5672,7 +5672,7 @@

Step 4: Final test run
-INFO:dataprep_ml-2252:[Learn phase 7/8] - Ensemble analysis
+INFO:dataprep_ml-2540:[Learn phase 7/8] - Ensemble analysis
 

@@ -5680,7 +5680,7 @@

Step 4: Final test run
-INFO:dataprep_ml-2252:Analyzing the ensemble of mixers
+INFO:dataprep_ml-2540:Analyzing the ensemble of mixers
 

@@ -5688,7 +5688,7 @@

Step 4: Final test run
-INFO:lightwood-2252:The block ICP is now running its analyze() method
+INFO:lightwood-2540:The block ICP is now running its analyze() method
 

@@ -5698,7 +5698,7 @@

Step 4: Final test run
 /opt/hostedtoolcache/Python/3.9.18/x64/lib/python3.9/site-packages/sklearn/preprocessing/_encoders.py:975: FutureWarning: `sparse` was renamed to `sparse_output` in version 1.2 and will be removed in 1.4. `sparse_output` is ignored unless you leave `sparse` to its default value.
   warnings.warn(
-INFO:lightwood-2252:The block ConfStats is now running its analyze() method
+INFO:lightwood-2540:The block ConfStats is now running its analyze() method
 

@@ -5706,7 +5706,7 @@

Step 4: Final test run
-INFO:lightwood-2252:The block AccStats is now running its analyze() method
+INFO:lightwood-2540:The block AccStats is now running its analyze() method
 

@@ -5714,7 +5714,7 @@

Step 4: Final test run
-INFO:lightwood-2252:The block PermutationFeatureImportance is now running its analyze() method
+INFO:lightwood-2540:The block PermutationFeatureImportance is now running its analyze() method
 

@@ -5722,7 +5722,7 @@

Step 4: Final test run
-INFO:lightwood-2252:[PFI] Using a random sample (1000 rows out of 22).
+INFO:lightwood-2540:[PFI] Using a random sample (1000 rows out of 22).
 

@@ -5730,7 +5730,7 @@

Step 4: Final test run
-INFO:lightwood-2252:[PFI] Set to consider first 10 columns out of 6: ['Population', 'Area (sq. mi.)', 'Pop. Density ', 'GDP ($ per capita)', 'Literacy (%)', 'Infant mortality '].
+INFO:lightwood-2540:[PFI] Set to consider first 10 columns out of 6: ['Population', 'Area (sq. mi.)', 'Pop. Density ', 'GDP ($ per capita)', 'Literacy (%)', 'Infant mortality '].
 

@@ -5738,7 +5738,7 @@

Step 4: Final test run
-INFO:lightwood-2252:The block ModelCorrelationHeatmap is now running its analyze() method
+INFO:lightwood-2540:The block ModelCorrelationHeatmap is now running its analyze() method
 

@@ -5746,7 +5746,7 @@

Step 4: Final test run
-DEBUG:lightwood-2252: `analyze_ensemble` runtime: 0.2 seconds
+DEBUG:lightwood-2540: `analyze_ensemble` runtime: 0.2 seconds
 

@@ -5754,7 +5754,7 @@

Step 4: Final test run
-INFO:dataprep_ml-2252:[Learn phase 8/8] - Adjustment on validation requested
+INFO:dataprep_ml-2540:[Learn phase 8/8] - Adjustment on validation requested
 

@@ -5762,7 +5762,7 @@

Step 4: Final test run
-INFO:dataprep_ml-2252:Updating the mixers
+INFO:dataprep_ml-2540:Updating the mixers
 

@@ -5772,6 +5772,7 @@

Step 4: Final test run
 /opt/hostedtoolcache/Python/3.9.18/x64/lib/python3.9/site-packages/torch/cuda/amp/grad_scaler.py:126: UserWarning: torch.cuda.amp.GradScaler is enabled, but CUDA is not available.  Disabling.
   warnings.warn(
+INFO:lightwood-2540:Loss @ epoch 1: 0.033697554686417185
 

@@ -5779,7 +5780,7 @@

Step 4: Final test run
-INFO:lightwood-2252:Loss @ epoch 1: 0.033697554686417185
+INFO:lightwood-2540:Loss @ epoch 2: 0.033981192080924906
 

@@ -5787,7 +5788,7 @@

Step 4: Final test run
-INFO:lightwood-2252:Loss @ epoch 2: 0.033981192080924906
+INFO:lightwood-2540:Loss @ epoch 3: 0.037426896315688886
 

@@ -5795,7 +5796,7 @@

Step 4: Final test run
-INFO:lightwood-2252:Loss @ epoch 3: 0.037426896315688886
+INFO:lightwood-2540:Loss @ epoch 4: 0.04428015494098266
 

@@ -5803,7 +5804,7 @@

Step 4: Final test run
-INFO:lightwood-2252:Loss @ epoch 4: 0.04428015494098266
+INFO:lightwood-2540:Loss @ epoch 5: 0.061086510928968586
 

@@ -5811,7 +5812,7 @@

Step 4: Final test run
-INFO:lightwood-2252:Loss @ epoch 5: 0.061086510928968586
+INFO:lightwood-2540:Loss @ epoch 6: 0.03466159128583968
 

@@ -5819,7 +5820,7 @@

Step 4: Final test run
-INFO:lightwood-2252:Loss @ epoch 6: 0.03466159128583968
+INFO:lightwood-2540:Loss @ epoch 7: 0.03769115870818496
 

@@ -5827,7 +5828,7 @@

Step 4: Final test run
-INFO:lightwood-2252:Loss @ epoch 7: 0.03769115870818496
+INFO:lightwood-2540:XGBoost mixer does not have a `partial_fit` implementation
 

@@ -5835,15 +5836,7 @@

Step 4: Final test run
-INFO:lightwood-2252:XGBoost mixer does not have a `partial_fit` implementation
-
-

-
-
-
-
-
-DEBUG:lightwood-2252: `adjust` runtime: 0.06 seconds
+DEBUG:lightwood-2540: `adjust` runtime: 0.06 seconds
 
@@ -5851,7 +5844,7 @@

Step 4: Final test run
-DEBUG:lightwood-2252: `learn` runtime: 5.17 seconds
+DEBUG:lightwood-2540: `learn` runtime: 5.82 seconds
 

Finally, we can visualize the mixer correlation matrix:

diff --git a/tutorials/custom_explainer/custom_explainer.ipynb b/tutorials/custom_explainer/custom_explainer.ipynb index eecd2b95e..83308e7f0 100644 --- a/tutorials/custom_explainer/custom_explainer.ipynb +++ b/tutorials/custom_explainer/custom_explainer.ipynb @@ -30,10 +30,10 @@ "execution_count": 1, "metadata": { "execution": { - "iopub.execute_input": "2024-03-19T10:00:15.559135Z", - "iopub.status.busy": "2024-03-19T10:00:15.558584Z", - "iopub.status.idle": "2024-03-19T10:00:19.257005Z", - "shell.execute_reply": "2024-03-19T10:00:19.256212Z" + "iopub.execute_input": "2024-03-19T10:18:29.526658Z", + "iopub.status.busy": "2024-03-19T10:18:29.526465Z", + "iopub.status.idle": "2024-03-19T10:18:33.446169Z", + "shell.execute_reply": "2024-03-19T10:18:33.445420Z" } }, "outputs": [ @@ -49,20 +49,20 @@ "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:lightwood-2252:No torchvision detected, image helpers not supported.\u001b[0m\n" + "\u001b[32mINFO:lightwood-2540:No torchvision detected, image helpers not supported.\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:lightwood-2252:No torchvision/pillow detected, image encoder not supported\u001b[0m\n" + "\u001b[32mINFO:lightwood-2540:No torchvision/pillow detected, image encoder not supported\u001b[0m\n" ] }, { "data": { "text/plain": [ - "'24.3.3.1'" + "'24.3.3.0'" ] }, "execution_count": 1, @@ -95,10 +95,10 @@ "execution_count": 2, "metadata": { "execution": { - "iopub.execute_input": "2024-03-19T10:00:19.260083Z", - "iopub.status.busy": "2024-03-19T10:00:19.259562Z", - "iopub.status.idle": "2024-03-19T10:00:19.288162Z", - "shell.execute_reply": "2024-03-19T10:00:19.287634Z" + "iopub.execute_input": "2024-03-19T10:18:33.449296Z", + "iopub.status.busy": "2024-03-19T10:18:33.448773Z", + "iopub.status.idle": "2024-03-19T10:18:33.477488Z", + "shell.execute_reply": "2024-03-19T10:18:33.476992Z" } }, "outputs": [], @@ -124,17 +124,17 @@ "execution_count": 3, "metadata": { "execution": { - "iopub.execute_input": "2024-03-19T10:00:19.290619Z", - "iopub.status.busy": "2024-03-19T10:00:19.290230Z", - "iopub.status.idle": "2024-03-19T10:00:19.294294Z", - "shell.execute_reply": "2024-03-19T10:00:19.293649Z" + "iopub.execute_input": "2024-03-19T10:18:33.479802Z", + "iopub.status.busy": "2024-03-19T10:18:33.479458Z", + "iopub.status.idle": "2024-03-19T10:18:33.483414Z", + "shell.execute_reply": "2024-03-19T10:18:33.482792Z" } }, "outputs": [ { "data": { "text/plain": [ - "<__main__.ModelCorrelationHeatmap at 0x7e3a82a68b80>" + "<__main__.ModelCorrelationHeatmap at 0x7867834b34f0>" ] }, "execution_count": 3, @@ -160,10 +160,10 @@ "execution_count": 4, "metadata": { "execution": { - "iopub.execute_input": "2024-03-19T10:00:19.296816Z", - "iopub.status.busy": "2024-03-19T10:00:19.296435Z", - "iopub.status.idle": "2024-03-19T10:00:19.300040Z", - "shell.execute_reply": "2024-03-19T10:00:19.299392Z" + "iopub.execute_input": "2024-03-19T10:18:33.485704Z", + "iopub.status.busy": "2024-03-19T10:18:33.485508Z", + "iopub.status.idle": "2024-03-19T10:18:33.489342Z", + "shell.execute_reply": "2024-03-19T10:18:33.488807Z" } }, "outputs": [], @@ -192,10 +192,10 @@ "execution_count": 5, "metadata": { "execution": { - "iopub.execute_input": "2024-03-19T10:00:19.302574Z", - "iopub.status.busy": "2024-03-19T10:00:19.302202Z", - "iopub.status.idle": "2024-03-19T10:00:19.305830Z", - "shell.execute_reply": "2024-03-19T10:00:19.305214Z" + "iopub.execute_input": "2024-03-19T10:18:33.491908Z", + "iopub.status.busy": "2024-03-19T10:18:33.491538Z", + "iopub.status.idle": "2024-03-19T10:18:33.495137Z", + "shell.execute_reply": "2024-03-19T10:18:33.494525Z" } }, "outputs": [], @@ -238,10 +238,10 @@ "execution_count": 6, "metadata": { "execution": { - "iopub.execute_input": "2024-03-19T10:00:19.308255Z", - "iopub.status.busy": "2024-03-19T10:00:19.307880Z", - "iopub.status.idle": "2024-03-19T10:00:19.312478Z", - "shell.execute_reply": "2024-03-19T10:00:19.311865Z" + "iopub.execute_input": "2024-03-19T10:18:33.497660Z", + "iopub.status.busy": "2024-03-19T10:18:33.497270Z", + "iopub.status.idle": "2024-03-19T10:18:33.501828Z", + "shell.execute_reply": "2024-03-19T10:18:33.501275Z" } }, "outputs": [ @@ -335,10 +335,10 @@ "execution_count": 7, "metadata": { "execution": { - "iopub.execute_input": "2024-03-19T10:00:19.315118Z", - "iopub.status.busy": "2024-03-19T10:00:19.314617Z", - "iopub.status.idle": "2024-03-19T10:00:19.600156Z", - "shell.execute_reply": "2024-03-19T10:00:19.599496Z" + "iopub.execute_input": "2024-03-19T10:18:33.504364Z", + "iopub.status.busy": "2024-03-19T10:18:33.503988Z", + "iopub.status.idle": "2024-03-19T10:18:33.633265Z", + "shell.execute_reply": "2024-03-19T10:18:33.632631Z" } }, "outputs": [ @@ -346,126 +346,126 @@ "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:type_infer-2252:Analyzing a sample of 222\u001b[0m\n" + "\u001b[32mINFO:type_infer-2540:Analyzing a sample of 222\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:type_infer-2252:from a total population of 225, this is equivalent to 98.7% of your data.\u001b[0m\n" + "\u001b[32mINFO:type_infer-2540:from a total population of 225, this is equivalent to 98.7% of your data.\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:type_infer-2252:Infering type for: Population\u001b[0m\n" + "\u001b[32mINFO:type_infer-2540:Infering type for: Population\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:type_infer-2252:Column Population has data type integer\u001b[0m\n" + "\u001b[32mINFO:type_infer-2540:Column Population has data type integer\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:type_infer-2252:Infering type for: Area (sq. mi.)\u001b[0m\n" + "\u001b[32mINFO:type_infer-2540:Infering type for: Area (sq. mi.)\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:type_infer-2252:Column Area (sq. mi.) has data type integer\u001b[0m\n" + "\u001b[32mINFO:type_infer-2540:Column Area (sq. mi.) has data type integer\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:type_infer-2252:Infering type for: Pop. Density \u001b[0m\n" + "\u001b[32mINFO:type_infer-2540:Infering type for: Pop. Density \u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:type_infer-2252:Column Pop. Density has data type float\u001b[0m\n" + "\u001b[32mINFO:type_infer-2540:Column Pop. Density has data type float\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:type_infer-2252:Infering type for: GDP ($ per capita)\u001b[0m\n" + "\u001b[32mINFO:type_infer-2540:Infering type for: GDP ($ per capita)\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:type_infer-2252:Column GDP ($ per capita) has data type integer\u001b[0m\n" + "\u001b[32mINFO:type_infer-2540:Column GDP ($ per capita) has data type integer\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:type_infer-2252:Infering type for: Literacy (%)\u001b[0m\n" + "\u001b[32mINFO:type_infer-2540:Infering type for: Literacy (%)\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:type_infer-2252:Column Literacy (%) has data type float\u001b[0m\n" + "\u001b[32mINFO:type_infer-2540:Column Literacy (%) has data type float\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:type_infer-2252:Infering type for: Infant mortality \u001b[0m\n" + "\u001b[32mINFO:type_infer-2540:Infering type for: Infant mortality \u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:type_infer-2252:Column Infant mortality has data type float\u001b[0m\n" + "\u001b[32mINFO:type_infer-2540:Column Infant mortality has data type float\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:type_infer-2252:Infering type for: Development Index\u001b[0m\n" + "\u001b[32mINFO:type_infer-2540:Infering type for: Development Index\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:type_infer-2252:Column Development Index has data type categorical\u001b[0m\n" + "\u001b[32mINFO:type_infer-2540:Column Development Index has data type categorical\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:dataprep_ml-2252:Starting statistical analysis\u001b[0m\n" + "\u001b[32mINFO:dataprep_ml-2540:Starting statistical analysis\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:dataprep_ml-2252:Finished statistical analysis\u001b[0m\n" + "\u001b[32mINFO:dataprep_ml-2540:Finished statistical analysis\u001b[0m\n" ] } ], @@ -506,10 +506,10 @@ "execution_count": 8, "metadata": { "execution": { - "iopub.execute_input": "2024-03-19T10:00:19.603006Z", - "iopub.status.busy": "2024-03-19T10:00:19.602470Z", - "iopub.status.idle": "2024-03-19T10:00:19.606780Z", - "shell.execute_reply": "2024-03-19T10:00:19.606159Z" + "iopub.execute_input": "2024-03-19T10:18:33.636041Z", + "iopub.status.busy": "2024-03-19T10:18:33.635608Z", + "iopub.status.idle": "2024-03-19T10:18:33.639918Z", + "shell.execute_reply": "2024-03-19T10:18:33.639318Z" } }, "outputs": [ @@ -540,10 +540,10 @@ "execution_count": 9, "metadata": { "execution": { - "iopub.execute_input": "2024-03-19T10:00:19.609412Z", - "iopub.status.busy": "2024-03-19T10:00:19.608955Z", - "iopub.status.idle": "2024-03-19T10:00:25.098019Z", - "shell.execute_reply": "2024-03-19T10:00:25.097446Z" + "iopub.execute_input": "2024-03-19T10:18:33.642288Z", + "iopub.status.busy": "2024-03-19T10:18:33.641938Z", + "iopub.status.idle": "2024-03-19T10:18:39.783485Z", + "shell.execute_reply": "2024-03-19T10:18:39.782886Z" }, "scrolled": false }, @@ -552,182 +552,182 @@ "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:dataprep_ml-2252:[Learn phase 1/8] - Statistical analysis\u001b[0m\n" + "\u001b[32mINFO:dataprep_ml-2540:[Learn phase 1/8] - Statistical analysis\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:dataprep_ml-2252:Starting statistical analysis\u001b[0m\n" + "\u001b[32mINFO:dataprep_ml-2540:Starting statistical analysis\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:dataprep_ml-2252:Finished statistical analysis\u001b[0m\n" + "\u001b[32mINFO:dataprep_ml-2540:Finished statistical analysis\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[37mDEBUG:lightwood-2252: `analyze_data` runtime: 0.02 seconds\u001b[0m\n" + "\u001b[37mDEBUG:lightwood-2540: `analyze_data` runtime: 0.02 seconds\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:dataprep_ml-2252:[Learn phase 2/8] - Data preprocessing\u001b[0m\n" + "\u001b[32mINFO:dataprep_ml-2540:[Learn phase 2/8] - Data preprocessing\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:dataprep_ml-2252:Cleaning the data\u001b[0m\n" + "\u001b[32mINFO:dataprep_ml-2540:Cleaning the data\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[37mDEBUG:lightwood-2252: `preprocess` runtime: 0.01 seconds\u001b[0m\n" + "\u001b[37mDEBUG:lightwood-2540: `preprocess` runtime: 0.01 seconds\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:dataprep_ml-2252:[Learn phase 3/8] - Data splitting\u001b[0m\n" + "\u001b[32mINFO:dataprep_ml-2540:[Learn phase 3/8] - Data splitting\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:dataprep_ml-2252:Splitting the data into train/test\u001b[0m\n" + "\u001b[32mINFO:dataprep_ml-2540:Splitting the data into train/test\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[37mDEBUG:lightwood-2252: `split` runtime: 0.01 seconds\u001b[0m\n" + "\u001b[37mDEBUG:lightwood-2540: `split` runtime: 0.01 seconds\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:dataprep_ml-2252:[Learn phase 4/8] - Preparing encoders\u001b[0m\n" + "\u001b[32mINFO:dataprep_ml-2540:[Learn phase 4/8] - Preparing encoders\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[37mDEBUG:dataprep_ml-2252:Preparing sequentially...\u001b[0m\n" + "\u001b[37mDEBUG:dataprep_ml-2540:Preparing sequentially...\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[37mDEBUG:dataprep_ml-2252:Preparing encoder for Population...\u001b[0m\n" + "\u001b[37mDEBUG:dataprep_ml-2540:Preparing encoder for Population...\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[37mDEBUG:dataprep_ml-2252:Preparing encoder for Area (sq. mi.)...\u001b[0m\n" + "\u001b[37mDEBUG:dataprep_ml-2540:Preparing encoder for Area (sq. mi.)...\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[37mDEBUG:dataprep_ml-2252:Preparing encoder for Pop. 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"\u001b[32mINFO:lightwood-2252:Loss @ epoch 7: 0.04046095162630081\u001b[0m\n" + "\u001b[32mINFO:lightwood-2540:Loss @ epoch 7: 0.04046095162630081\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[37mDEBUG:lightwood-2252: `fit_mixer` runtime: 4.58 seconds\u001b[0m\n" + "\u001b[37mDEBUG:lightwood-2540: `fit_mixer` runtime: 4.76 seconds\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:lightwood-2252:Started fitting XGBoost model\u001b[0m\n" + "\u001b[32mINFO:lightwood-2540:Started fitting XGBoost model\u001b[0m\n" ] }, { @@ -4732,14 +4732,14 @@ "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:lightwood-2252:A single GBM iteration takes 0.1 seconds\u001b[0m\n" + "\u001b[32mINFO:lightwood-2540:A single GBM iteration takes 0.1 seconds\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:lightwood-2252:Training XGBoost with 131 iterations given 16.484190421104433 seconds constraint\u001b[0m\n" + "\u001b[32mINFO:lightwood-2540:Training XGBoost with 131 iterations given 16.479915187358856 seconds constraint\u001b[0m\n" ] }, { @@ -4970,112 +4970,112 @@ "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:lightwood-2252:XGBoost mixer does not have a `partial_fit` implementation\u001b[0m\n" + "\u001b[32mINFO:lightwood-2540:XGBoost mixer does not have a `partial_fit` implementation\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[37mDEBUG:lightwood-2252: `fit_mixer` runtime: 0.05 seconds\u001b[0m\n" + "\u001b[37mDEBUG:lightwood-2540: `fit_mixer` runtime: 0.06 seconds\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[33mWARNING:dataprep_ml-2252:Exception: Unspported categorical type for regression when training mixer: \u001b[0m\n" + "\u001b[33mWARNING:dataprep_ml-2540:Exception: Unspported categorical type for regression when training mixer: \u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:lightwood-2252:Started fitting RandomForest model\u001b[0m\n" + "\u001b[32mINFO:lightwood-2540:Started fitting RandomForest model\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:lightwood-2252:RandomForest based correlation of (train data): 1.0\u001b[0m\n" + "\u001b[32mINFO:lightwood-2540:RandomForest based correlation of (train data): 1.0\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:lightwood-2252:RandomForest based correlation of (dev data): 1.0\u001b[0m\n" + "\u001b[32mINFO:lightwood-2540:RandomForest based correlation of (dev data): 1.0\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[37mDEBUG:lightwood-2252: `fit_mixer` runtime: 0.11 seconds\u001b[0m\n" + "\u001b[37mDEBUG:lightwood-2540: `fit_mixer` runtime: 0.6 seconds\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:dataprep_ml-2252:Ensembling the mixer\u001b[0m\n" + "\u001b[32mINFO:dataprep_ml-2540:Ensembling the mixer\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:lightwood-2252:Mixer: Neural got accuracy: 0.922\u001b[0m\n" + "\u001b[32mINFO:lightwood-2540:Mixer: Neural got accuracy: 0.922\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:lightwood-2252:Mixer: XGBoostMixer got accuracy: 1.0\u001b[0m\n" + "\u001b[32mINFO:lightwood-2540:Mixer: XGBoostMixer got accuracy: 1.0\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:lightwood-2252:Mixer: RandomForest got accuracy: 1.0\u001b[0m\n" + "\u001b[32mINFO:lightwood-2540:Mixer: RandomForest got accuracy: 1.0\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:lightwood-2252:Picked best mixer: RandomForest\u001b[0m\n" + "\u001b[32mINFO:lightwood-2540:Picked best mixer: RandomForest\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[37mDEBUG:lightwood-2252: `fit` runtime: 4.81 seconds\u001b[0m\n" + "\u001b[37mDEBUG:lightwood-2540: `fit` runtime: 5.46 seconds\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:dataprep_ml-2252:[Learn phase 7/8] - Ensemble analysis\u001b[0m\n" + "\u001b[32mINFO:dataprep_ml-2540:[Learn phase 7/8] - Ensemble analysis\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:dataprep_ml-2252:Analyzing the ensemble of mixers\u001b[0m\n" + "\u001b[32mINFO:dataprep_ml-2540:Analyzing the ensemble of mixers\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:lightwood-2252:The block ICP is now running its analyze() method\u001b[0m\n" + "\u001b[32mINFO:lightwood-2540:The block ICP is now running its analyze() method\u001b[0m\n" ] }, { @@ -5084,63 +5084,63 @@ "text": [ "/opt/hostedtoolcache/Python/3.9.18/x64/lib/python3.9/site-packages/sklearn/preprocessing/_encoders.py:975: FutureWarning: `sparse` was renamed to `sparse_output` in version 1.2 and will be removed in 1.4. `sparse_output` is ignored unless you leave `sparse` to its default value.\n", " warnings.warn(\n", - "\u001b[32mINFO:lightwood-2252:The block ConfStats is now running its analyze() method\u001b[0m\n" + "\u001b[32mINFO:lightwood-2540:The block ConfStats is now running its analyze() method\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:lightwood-2252:The block AccStats is now running its analyze() method\u001b[0m\n" + "\u001b[32mINFO:lightwood-2540:The block AccStats is now running its analyze() method\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:lightwood-2252:The block PermutationFeatureImportance is now running its analyze() method\u001b[0m\n" + "\u001b[32mINFO:lightwood-2540:The block PermutationFeatureImportance is now running its analyze() method\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:lightwood-2252:[PFI] Using a random sample (1000 rows out of 22).\u001b[0m\n" + "\u001b[32mINFO:lightwood-2540:[PFI] Using a random sample (1000 rows out of 22).\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:lightwood-2252:[PFI] Set to consider first 10 columns out of 6: ['Population', 'Area (sq. mi.)', 'Pop. Density ', 'GDP ($ per capita)', 'Literacy (%)', 'Infant mortality '].\u001b[0m\n" + "\u001b[32mINFO:lightwood-2540:[PFI] Set to consider first 10 columns out of 6: ['Population', 'Area (sq. mi.)', 'Pop. Density ', 'GDP ($ per capita)', 'Literacy (%)', 'Infant mortality '].\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:lightwood-2252:The block ModelCorrelationHeatmap is now running its analyze() method\u001b[0m\n" + "\u001b[32mINFO:lightwood-2540:The block ModelCorrelationHeatmap is now running its analyze() method\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[37mDEBUG:lightwood-2252: `analyze_ensemble` runtime: 0.2 seconds\u001b[0m\n" + "\u001b[37mDEBUG:lightwood-2540: `analyze_ensemble` runtime: 0.2 seconds\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:dataprep_ml-2252:[Learn phase 8/8] - Adjustment on validation requested\u001b[0m\n" + "\u001b[32mINFO:dataprep_ml-2540:[Learn phase 8/8] - Adjustment on validation requested\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:dataprep_ml-2252:Updating the mixers\u001b[0m\n" + "\u001b[32mINFO:dataprep_ml-2540:Updating the mixers\u001b[0m\n" ] }, { @@ -5148,77 +5148,71 @@ "output_type": "stream", "text": [ "/opt/hostedtoolcache/Python/3.9.18/x64/lib/python3.9/site-packages/torch/cuda/amp/grad_scaler.py:126: UserWarning: torch.cuda.amp.GradScaler is enabled, but CUDA is not available. Disabling.\n", - " warnings.warn(\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "\u001b[32mINFO:lightwood-2252:Loss @ epoch 1: 0.033697554686417185\u001b[0m\n" + " warnings.warn(\n", + "\u001b[32mINFO:lightwood-2540:Loss @ epoch 1: 0.033697554686417185\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:lightwood-2252:Loss @ epoch 2: 0.033981192080924906\u001b[0m\n" + "\u001b[32mINFO:lightwood-2540:Loss @ epoch 2: 0.033981192080924906\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:lightwood-2252:Loss @ epoch 3: 0.037426896315688886\u001b[0m\n" + "\u001b[32mINFO:lightwood-2540:Loss @ epoch 3: 0.037426896315688886\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:lightwood-2252:Loss @ epoch 4: 0.04428015494098266\u001b[0m\n" + "\u001b[32mINFO:lightwood-2540:Loss @ epoch 4: 0.04428015494098266\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:lightwood-2252:Loss @ epoch 5: 0.061086510928968586\u001b[0m\n" + "\u001b[32mINFO:lightwood-2540:Loss @ epoch 5: 0.061086510928968586\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:lightwood-2252:Loss @ epoch 6: 0.03466159128583968\u001b[0m\n" + "\u001b[32mINFO:lightwood-2540:Loss @ epoch 6: 0.03466159128583968\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:lightwood-2252:Loss @ epoch 7: 0.03769115870818496\u001b[0m\n" + "\u001b[32mINFO:lightwood-2540:Loss @ epoch 7: 0.03769115870818496\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:lightwood-2252:XGBoost mixer does not have a `partial_fit` implementation\u001b[0m\n" + "\u001b[32mINFO:lightwood-2540:XGBoost mixer does not have a `partial_fit` implementation\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[37mDEBUG:lightwood-2252: `adjust` runtime: 0.06 seconds\u001b[0m\n" + "\u001b[37mDEBUG:lightwood-2540: `adjust` runtime: 0.06 seconds\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[37mDEBUG:lightwood-2252: `learn` runtime: 5.17 seconds\u001b[0m\n" + "\u001b[37mDEBUG:lightwood-2540: `learn` runtime: 5.82 seconds\u001b[0m\n" ] } ], @@ -5243,10 +5237,10 @@ "execution_count": 10, "metadata": { "execution": { - "iopub.execute_input": "2024-03-19T10:00:25.100883Z", - "iopub.status.busy": "2024-03-19T10:00:25.100499Z", - "iopub.status.idle": "2024-03-19T10:00:25.908381Z", - "shell.execute_reply": "2024-03-19T10:00:25.907666Z" + "iopub.execute_input": "2024-03-19T10:18:39.786210Z", + "iopub.status.busy": "2024-03-19T10:18:39.785843Z", + "iopub.status.idle": "2024-03-19T10:18:40.609969Z", + "shell.execute_reply": "2024-03-19T10:18:40.609129Z" } }, "outputs": [ diff --git a/tutorials/custom_mixer/custom_mixer.html b/tutorials/custom_mixer/custom_mixer.html index eb04b61bb..40a8bca41 100644 --- a/tutorials/custom_mixer/custom_mixer.html +++ b/tutorials/custom_mixer/custom_mixer.html @@ -4,7 +4,7 @@ - Tutorial - Implementing a custom mixer in Lightwood — lightwood 24.3.3.1 documentation + Tutorial - Implementing a custom mixer in Lightwood — lightwood 24.3.3.0 documentation @@ -42,7 +42,7 @@
- 24.3.3.1 + 24.3.3.0
@@ -210,7 +210,7 @@

Step 3: Using our mixer
-INFO:lightwood-2557:No torchvision detected, image helpers not supported.
+INFO:lightwood-2835:No torchvision detected, image helpers not supported.
 

@@ -218,7 +218,7 @@

Step 3: Using our mixer
-INFO:lightwood-2557:No torchvision/pillow detected, image encoder not supported
+INFO:lightwood-2835:No torchvision/pillow detected, image encoder not supported
 

@@ -226,7 +226,7 @@

Step 3: Using our mixer
-INFO:type_infer-2557:Analyzing a sample of 298
+INFO:type_infer-2835:Analyzing a sample of 298
 

@@ -234,7 +234,7 @@

Step 3: Using our mixer
-INFO:type_infer-2557:from a total population of 303, this is equivalent to 98.3% of your data.
+INFO:type_infer-2835:from a total population of 303, this is equivalent to 98.3% of your data.
 

@@ -242,7 +242,7 @@

Step 3: Using our mixer
-INFO:type_infer-2557:Infering type for: age
+INFO:type_infer-2835:Infering type for: age
 

@@ -250,7 +250,7 @@

Step 3: Using our mixer
-INFO:type_infer-2557:Column age has data type integer
+INFO:type_infer-2835:Column age has data type integer
 

@@ -258,7 +258,7 @@

Step 3: Using our mixer
-INFO:type_infer-2557:Infering type for: sex
+INFO:type_infer-2835:Infering type for: sex
 

@@ -266,7 +266,7 @@

Step 3: Using our mixer
-INFO:type_infer-2557:Column sex has data type binary
+INFO:type_infer-2835:Column sex has data type binary
 

@@ -274,7 +274,7 @@

Step 3: Using our mixer
-INFO:type_infer-2557:Infering type for: cp
+INFO:type_infer-2835:Infering type for: cp
 

@@ -282,7 +282,7 @@

Step 3: Using our mixer
-INFO:type_infer-2557:Column cp has data type categorical
+INFO:type_infer-2835:Column cp has data type categorical
 

@@ -290,7 +290,7 @@

Step 3: Using our mixer
-INFO:type_infer-2557:Infering type for: trestbps
+INFO:type_infer-2835:Infering type for: trestbps
 

@@ -298,7 +298,7 @@

Step 3: Using our mixer
-INFO:type_infer-2557:Column trestbps has data type integer
+INFO:type_infer-2835:Column trestbps has data type integer
 

@@ -306,7 +306,7 @@

Step 3: Using our mixer
-INFO:type_infer-2557:Infering type for: chol
+INFO:type_infer-2835:Infering type for: chol
 

@@ -314,7 +314,7 @@

Step 3: Using our mixer
-INFO:type_infer-2557:Column chol has data type integer
+INFO:type_infer-2835:Column chol has data type integer
 

@@ -322,7 +322,7 @@

Step 3: Using our mixer
-INFO:type_infer-2557:Infering type for: fbs
+INFO:type_infer-2835:Infering type for: fbs
 

@@ -330,7 +330,7 @@

Step 3: Using our mixer
-INFO:type_infer-2557:Column fbs has data type binary
+INFO:type_infer-2835:Column fbs has data type binary
 

@@ -338,7 +338,7 @@

Step 3: Using our mixer
-INFO:type_infer-2557:Infering type for: restecg
+INFO:type_infer-2835:Infering type for: restecg
 

@@ -346,7 +346,7 @@

Step 3: Using our mixer
-INFO:type_infer-2557:Column restecg has data type categorical
+INFO:type_infer-2835:Column restecg has data type categorical
 

@@ -354,7 +354,7 @@

Step 3: Using our mixer
-INFO:type_infer-2557:Infering type for: thalach
+INFO:type_infer-2835:Infering type for: thalach
 

@@ -362,7 +362,7 @@

Step 3: Using our mixer
-INFO:type_infer-2557:Column thalach has data type integer
+INFO:type_infer-2835:Column thalach has data type integer
 

@@ -370,7 +370,7 @@

Step 3: Using our mixer
-INFO:type_infer-2557:Infering type for: exang
+INFO:type_infer-2835:Infering type for: exang
 

@@ -378,7 +378,7 @@

Step 3: Using our mixer
-INFO:type_infer-2557:Column exang has data type binary
+INFO:type_infer-2835:Column exang has data type binary
 

@@ -386,7 +386,7 @@

Step 3: Using our mixer
-INFO:type_infer-2557:Infering type for: oldpeak
+INFO:type_infer-2835:Infering type for: oldpeak
 

@@ -394,7 +394,7 @@

Step 3: Using our mixer
-INFO:type_infer-2557:Column oldpeak has data type float
+INFO:type_infer-2835:Column oldpeak has data type float
 

@@ -402,7 +402,7 @@

Step 3: Using our mixer
-INFO:type_infer-2557:Infering type for: slope
+INFO:type_infer-2835:Infering type for: slope
 

@@ -410,7 +410,7 @@

Step 3: Using our mixer
-INFO:type_infer-2557:Column slope has data type categorical
+INFO:type_infer-2835:Column slope has data type categorical
 

@@ -418,7 +418,7 @@

Step 3: Using our mixer
-INFO:type_infer-2557:Infering type for: ca
+INFO:type_infer-2835:Infering type for: ca
 

@@ -426,7 +426,7 @@

Step 3: Using our mixer
-INFO:type_infer-2557:Column ca has data type categorical
+INFO:type_infer-2835:Column ca has data type categorical
 

@@ -434,7 +434,7 @@

Step 3: Using our mixer
-INFO:type_infer-2557:Infering type for: thal
+INFO:type_infer-2835:Infering type for: thal
 

@@ -442,7 +442,7 @@

Step 3: Using our mixer
-INFO:type_infer-2557:Column thal has data type categorical
+INFO:type_infer-2835:Column thal has data type categorical
 

@@ -450,7 +450,7 @@

Step 3: Using our mixer
-INFO:type_infer-2557:Infering type for: target
+INFO:type_infer-2835:Infering type for: target
 

@@ -458,7 +458,7 @@

Step 3: Using our mixer
-INFO:type_infer-2557:Column target has data type binary
+INFO:type_infer-2835:Column target has data type binary
 

@@ -466,7 +466,7 @@

Step 3: Using our mixer
-INFO:dataprep_ml-2557:Starting statistical analysis
+INFO:dataprep_ml-2835:Starting statistical analysis
 

@@ -474,7 +474,7 @@

Step 3: Using our mixer
-INFO:dataprep_ml-2557:Finished statistical analysis
+INFO:dataprep_ml-2835:Finished statistical analysis
 

@@ -602,7 +602,7 @@

Step 3: Using our mixerStep 3: Using our mixer
-INFO:dataprep_ml-2557:[Learn phase 1/8] - Statistical analysis
+INFO:dataprep_ml-2835:[Learn phase 1/8] - Statistical analysis
 

@@ -687,7 +687,7 @@

Step 3: Using our mixer
-INFO:dataprep_ml-2557:Starting statistical analysis
+INFO:dataprep_ml-2835:Starting statistical analysis
 

@@ -695,7 +695,7 @@

Step 3: Using our mixer
-INFO:dataprep_ml-2557:Finished statistical analysis
+INFO:dataprep_ml-2835:Finished statistical analysis
 

@@ -703,7 +703,7 @@

Step 3: Using our mixer
-DEBUG:lightwood-2557: `analyze_data` runtime: 0.03 seconds
+DEBUG:lightwood-2835: `analyze_data` runtime: 0.04 seconds
 

@@ -711,7 +711,7 @@

Step 3: Using our mixer
-INFO:dataprep_ml-2557:[Learn phase 2/8] - Data preprocessing
+INFO:dataprep_ml-2835:[Learn phase 2/8] - Data preprocessing
 

@@ -719,7 +719,7 @@

Step 3: Using our mixer
-INFO:dataprep_ml-2557:Cleaning the data
+INFO:dataprep_ml-2835:Cleaning the data
 

@@ -727,7 +727,7 @@

Step 3: Using our mixer
-DEBUG:lightwood-2557: `preprocess` runtime: 0.01 seconds
+DEBUG:lightwood-2835: `preprocess` runtime: 0.01 seconds
 

@@ -735,7 +735,7 @@

Step 3: Using our mixer
-INFO:dataprep_ml-2557:[Learn phase 3/8] - Data splitting
+INFO:dataprep_ml-2835:[Learn phase 3/8] - Data splitting
 

@@ -743,7 +743,7 @@

Step 3: Using our mixer
-INFO:dataprep_ml-2557:Splitting the data into train/test
+INFO:dataprep_ml-2835:Splitting the data into train/test
 

@@ -751,7 +751,7 @@

Step 3: Using our mixer
-DEBUG:lightwood-2557: `split` runtime: 0.01 seconds
+DEBUG:lightwood-2835: `split` runtime: 0.01 seconds
 

@@ -759,7 +759,7 @@

Step 3: Using our mixer
-INFO:dataprep_ml-2557:[Learn phase 4/8] - Preparing encoders
+INFO:dataprep_ml-2835:[Learn phase 4/8] - Preparing encoders
 

@@ -767,7 +767,7 @@

Step 3: Using our mixer
-DEBUG:dataprep_ml-2557:Preparing sequentially...
+DEBUG:dataprep_ml-2835:Preparing sequentially...
 

@@ -775,7 +775,7 @@

Step 3: Using our mixer
-DEBUG:dataprep_ml-2557:Preparing encoder for age...
+DEBUG:dataprep_ml-2835:Preparing encoder for age...
 

@@ -783,7 +783,7 @@

Step 3: Using our mixer
-DEBUG:dataprep_ml-2557:Preparing encoder for sex...
+DEBUG:dataprep_ml-2835:Preparing encoder for sex...
 

@@ -791,7 +791,7 @@

Step 3: Using our mixer
-DEBUG:dataprep_ml-2557:Preparing encoder for cp...
+DEBUG:dataprep_ml-2835:Preparing encoder for cp...
 

@@ -799,7 +799,7 @@

Step 3: Using our mixer
-DEBUG:lightwood-2557:Encoding UNKNOWN categories as index 0
+DEBUG:lightwood-2835:Encoding UNKNOWN categories as index 0
 

@@ -807,7 +807,7 @@

Step 3: Using our mixer
-DEBUG:dataprep_ml-2557:Preparing encoder for trestbps...
+DEBUG:dataprep_ml-2835:Preparing encoder for trestbps...
 

@@ -815,7 +815,7 @@

Step 3: Using our mixer
-DEBUG:dataprep_ml-2557:Preparing encoder for chol...
+DEBUG:dataprep_ml-2835:Preparing encoder for chol...
 

@@ -823,7 +823,7 @@

Step 3: Using our mixer
-DEBUG:dataprep_ml-2557:Preparing encoder for fbs...
+DEBUG:dataprep_ml-2835:Preparing encoder for fbs...
 

@@ -831,7 +831,7 @@

Step 3: Using our mixer
-DEBUG:dataprep_ml-2557:Preparing encoder for restecg...
+DEBUG:dataprep_ml-2835:Preparing encoder for restecg...
 

@@ -839,7 +839,7 @@

Step 3: Using our mixer
-DEBUG:lightwood-2557:Encoding UNKNOWN categories as index 0
+DEBUG:lightwood-2835:Encoding UNKNOWN categories as index 0
 

@@ -847,7 +847,7 @@

Step 3: Using our mixer
-DEBUG:dataprep_ml-2557:Preparing encoder for thalach...
+DEBUG:dataprep_ml-2835:Preparing encoder for thalach...
 

@@ -855,7 +855,7 @@

Step 3: Using our mixer
-DEBUG:dataprep_ml-2557:Preparing encoder for exang...
+DEBUG:dataprep_ml-2835:Preparing encoder for exang...
 

@@ -863,7 +863,7 @@

Step 3: Using our mixer
-DEBUG:dataprep_ml-2557:Preparing encoder for oldpeak...
+DEBUG:dataprep_ml-2835:Preparing encoder for oldpeak...
 

@@ -871,7 +871,7 @@

Step 3: Using our mixer
-DEBUG:dataprep_ml-2557:Preparing encoder for slope...
+DEBUG:dataprep_ml-2835:Preparing encoder for slope...
 

@@ -879,7 +879,7 @@

Step 3: Using our mixer
-DEBUG:lightwood-2557:Encoding UNKNOWN categories as index 0
+DEBUG:lightwood-2835:Encoding UNKNOWN categories as index 0
 

@@ -887,7 +887,7 @@

Step 3: Using our mixer
-DEBUG:dataprep_ml-2557:Preparing encoder for ca...
+DEBUG:dataprep_ml-2835:Preparing encoder for ca...
 

@@ -895,7 +895,7 @@

Step 3: Using our mixer
-DEBUG:lightwood-2557:Encoding UNKNOWN categories as index 0
+DEBUG:lightwood-2835:Encoding UNKNOWN categories as index 0
 

@@ -903,7 +903,7 @@

Step 3: Using our mixer
-DEBUG:dataprep_ml-2557:Preparing encoder for thal...
+DEBUG:dataprep_ml-2835:Preparing encoder for thal...
 

@@ -911,7 +911,7 @@

Step 3: Using our mixer
-DEBUG:lightwood-2557:Encoding UNKNOWN categories as index 0
+DEBUG:lightwood-2835:Encoding UNKNOWN categories as index 0
 

@@ -919,7 +919,7 @@

Step 3: Using our mixer
-DEBUG:lightwood-2557: `prepare` runtime: 0.02 seconds
+DEBUG:lightwood-2835: `prepare` runtime: 0.02 seconds
 

@@ -927,7 +927,7 @@

Step 3: Using our mixer
-INFO:dataprep_ml-2557:[Learn phase 5/8] - Feature generation
+INFO:dataprep_ml-2835:[Learn phase 5/8] - Feature generation
 

@@ -935,7 +935,7 @@

Step 3: Using our mixer
-INFO:dataprep_ml-2557:Featurizing the data
+INFO:dataprep_ml-2835:Featurizing the data
 

@@ -943,7 +943,7 @@

Step 3: Using our mixer
-DEBUG:lightwood-2557: `featurize` runtime: 0.09 seconds
+DEBUG:lightwood-2835: `featurize` runtime: 0.09 seconds
 

@@ -951,7 +951,7 @@

Step 3: Using our mixer
-INFO:dataprep_ml-2557:[Learn phase 6/8] - Mixer training
+INFO:dataprep_ml-2835:[Learn phase 6/8] - Mixer training
 

@@ -959,7 +959,7 @@

Step 3: Using our mixer
-INFO:dataprep_ml-2557:Training the mixers
+INFO:dataprep_ml-2835:Training the mixers
 

@@ -967,7 +967,7 @@

Step 3: Using our mixer
-DEBUG:lightwood-2557: `fit_mixer` runtime: 0.12 seconds
+DEBUG:lightwood-2835: `fit_mixer` runtime: 0.12 seconds
 

@@ -975,7 +975,7 @@

Step 3: Using our mixer
-INFO:dataprep_ml-2557:Ensembling the mixer
+INFO:dataprep_ml-2835:Ensembling the mixer
 

@@ -983,7 +983,7 @@

Step 3: Using our mixer
-INFO:lightwood-2557:Mixer: RandomForestMixer got accuracy: 0.798
+INFO:lightwood-2835:Mixer: RandomForestMixer got accuracy: 0.798
 

@@ -991,7 +991,7 @@

Step 3: Using our mixer
-INFO:lightwood-2557:Picked best mixer: RandomForestMixer
+INFO:lightwood-2835:Picked best mixer: RandomForestMixer
 

@@ -999,7 +999,7 @@

Step 3: Using our mixer
-DEBUG:lightwood-2557: `fit` runtime: 0.13 seconds
+DEBUG:lightwood-2835: `fit` runtime: 0.13 seconds
 

@@ -1007,7 +1007,7 @@

Step 3: Using our mixer
-INFO:dataprep_ml-2557:[Learn phase 7/8] - Ensemble analysis
+INFO:dataprep_ml-2835:[Learn phase 7/8] - Ensemble analysis
 

@@ -1015,7 +1015,7 @@

Step 3: Using our mixer
-INFO:dataprep_ml-2557:Analyzing the ensemble of mixers
+INFO:dataprep_ml-2835:Analyzing the ensemble of mixers
 

@@ -1023,7 +1023,7 @@

Step 3: Using our mixer
-INFO:lightwood-2557:The block ICP is now running its analyze() method
+INFO:lightwood-2835:The block ICP is now running its analyze() method
 

@@ -1033,7 +1033,7 @@

Step 3: Using our mixer
 /opt/hostedtoolcache/Python/3.9.18/x64/lib/python3.9/site-packages/sklearn/preprocessing/_encoders.py:975: FutureWarning: `sparse` was renamed to `sparse_output` in version 1.2 and will be removed in 1.4. `sparse_output` is ignored unless you leave `sparse` to its default value.
   warnings.warn(
-INFO:lightwood-2557:The block ConfStats is now running its analyze() method
+INFO:lightwood-2835:The block ConfStats is now running its analyze() method
 

@@ -1041,7 +1041,7 @@

Step 3: Using our mixer
-INFO:lightwood-2557:The block AccStats is now running its analyze() method
+INFO:lightwood-2835:The block AccStats is now running its analyze() method
 

@@ -1049,7 +1049,7 @@

Step 3: Using our mixer
-INFO:lightwood-2557:The block PermutationFeatureImportance is now running its analyze() method
+INFO:lightwood-2835:The block PermutationFeatureImportance is now running its analyze() method
 

@@ -1057,7 +1057,7 @@

Step 3: Using our mixer
-INFO:lightwood-2557:[PFI] Using a random sample (1000 rows out of 31).
+INFO:lightwood-2835:[PFI] Using a random sample (1000 rows out of 31).
 

@@ -1065,7 +1065,7 @@

Step 3: Using our mixer
-INFO:lightwood-2557:[PFI] Set to consider first 10 columns out of 10: ['age', 'sex', 'cp', 'trestbps', 'chol', 'fbs', 'restecg', 'thalach', 'exang', 'oldpeak'].
+INFO:lightwood-2835:[PFI] Set to consider first 10 columns out of 10: ['age', 'sex', 'cp', 'trestbps', 'chol', 'fbs', 'restecg', 'thalach', 'exang', 'oldpeak'].
 

@@ -1091,7 +1091,7 @@

Step 3: Using our mixer
-DEBUG:lightwood-2557: `analyze_ensemble` runtime: 0.27 seconds
+DEBUG:lightwood-2835: `analyze_ensemble` runtime: 0.26 seconds
 

@@ -1099,7 +1099,7 @@

Step 3: Using our mixer
-INFO:dataprep_ml-2557:[Learn phase 8/8] - Adjustment on validation requested
+INFO:dataprep_ml-2835:[Learn phase 8/8] - Adjustment on validation requested
 

@@ -1107,7 +1107,7 @@

Step 3: Using our mixer
-INFO:dataprep_ml-2557:Updating the mixers
+INFO:dataprep_ml-2835:Updating the mixers
 

@@ -1115,7 +1115,7 @@

Step 3: Using our mixer
-DEBUG:lightwood-2557: `adjust` runtime: 0.04 seconds
+DEBUG:lightwood-2835: `adjust` runtime: 0.04 seconds
 

@@ -1123,7 +1123,7 @@

Step 3: Using our mixer
-DEBUG:lightwood-2557: `learn` runtime: 0.62 seconds
+DEBUG:lightwood-2835: `learn` runtime: 0.6 seconds
 

Finally, we can use the trained predictor to make some predictions, or save it to a pickle for later use

@@ -1147,7 +1147,7 @@

Step 3: Using our mixer
-INFO:dataprep_ml-2557:[Predict phase 1/4] - Data preprocessing
+INFO:dataprep_ml-2835:[Predict phase 1/4] - Data preprocessing
 
diff --git a/tutorials/custom_mixer/custom_mixer.ipynb b/tutorials/custom_mixer/custom_mixer.ipynb index 6e8465696..f3d0b2af7 100644 --- a/tutorials/custom_mixer/custom_mixer.ipynb +++ b/tutorials/custom_mixer/custom_mixer.ipynb @@ -46,10 +46,10 @@ "execution_count": 1, "metadata": { "execution": { - "iopub.execute_input": "2024-03-19T10:00:58.382482Z", - "iopub.status.busy": "2024-03-19T10:00:58.382268Z", - "iopub.status.idle": "2024-03-19T10:00:58.391179Z", - "shell.execute_reply": "2024-03-19T10:00:58.390533Z" + "iopub.execute_input": "2024-03-19T10:19:12.145057Z", + "iopub.status.busy": "2024-03-19T10:19:12.144662Z", + "iopub.status.idle": "2024-03-19T10:19:12.153051Z", + "shell.execute_reply": "2024-03-19T10:19:12.152456Z" } }, "outputs": [ @@ -133,10 +133,10 @@ "execution_count": 2, "metadata": { "execution": { - "iopub.execute_input": "2024-03-19T10:00:58.429652Z", - "iopub.status.busy": "2024-03-19T10:00:58.429170Z", - "iopub.status.idle": "2024-03-19T10:01:01.451092Z", - "shell.execute_reply": "2024-03-19T10:01:01.450296Z" + "iopub.execute_input": "2024-03-19T10:19:12.190696Z", + "iopub.status.busy": "2024-03-19T10:19:12.190280Z", + "iopub.status.idle": "2024-03-19T10:19:14.877692Z", + "shell.execute_reply": "2024-03-19T10:19:14.877086Z" } }, "outputs": [ @@ -144,238 +144,238 @@ "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:lightwood-2557:No torchvision detected, image helpers not supported.\u001b[0m\n" + "\u001b[32mINFO:lightwood-2835:No torchvision detected, image helpers not supported.\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:lightwood-2557:No torchvision/pillow detected, image encoder not supported\u001b[0m\n" + "\u001b[32mINFO:lightwood-2835:No torchvision/pillow detected, image encoder not supported\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:type_infer-2557:Analyzing a sample of 298\u001b[0m\n" + "\u001b[32mINFO:type_infer-2835:Analyzing a sample of 298\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:type_infer-2557:from a total population of 303, this is equivalent to 98.3% of your data.\u001b[0m\n" + "\u001b[32mINFO:type_infer-2835:from a total population of 303, this is equivalent to 98.3% of your data.\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:type_infer-2557:Infering type for: age\u001b[0m\n" + "\u001b[32mINFO:type_infer-2835:Infering type for: age\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:type_infer-2557:Column age has data type integer\u001b[0m\n" + "\u001b[32mINFO:type_infer-2835:Column age has data type integer\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:type_infer-2557:Infering type for: sex\u001b[0m\n" + "\u001b[32mINFO:type_infer-2835:Infering type for: sex\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:type_infer-2557:Column sex has data type binary\u001b[0m\n" + "\u001b[32mINFO:type_infer-2835:Column sex has data type binary\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:type_infer-2557:Infering type for: cp\u001b[0m\n" + "\u001b[32mINFO:type_infer-2835:Infering type for: cp\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:type_infer-2557:Column cp has data type categorical\u001b[0m\n" + "\u001b[32mINFO:type_infer-2835:Column cp has data type categorical\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:type_infer-2557:Infering type for: trestbps\u001b[0m\n" + "\u001b[32mINFO:type_infer-2835:Infering type for: trestbps\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:type_infer-2557:Column trestbps has data type integer\u001b[0m\n" + "\u001b[32mINFO:type_infer-2835:Column trestbps has data type integer\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:type_infer-2557:Infering type for: chol\u001b[0m\n" + "\u001b[32mINFO:type_infer-2835:Infering type for: chol\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:type_infer-2557:Column chol has data type integer\u001b[0m\n" + "\u001b[32mINFO:type_infer-2835:Column chol has data type integer\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:type_infer-2557:Infering type for: fbs\u001b[0m\n" + "\u001b[32mINFO:type_infer-2835:Infering type for: fbs\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:type_infer-2557:Column fbs has data type binary\u001b[0m\n" + "\u001b[32mINFO:type_infer-2835:Column fbs has data type binary\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:type_infer-2557:Infering type for: restecg\u001b[0m\n" + "\u001b[32mINFO:type_infer-2835:Infering type for: restecg\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:type_infer-2557:Column restecg has data type categorical\u001b[0m\n" + "\u001b[32mINFO:type_infer-2835:Column restecg has data type categorical\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:type_infer-2557:Infering type for: thalach\u001b[0m\n" + "\u001b[32mINFO:type_infer-2835:Infering type for: thalach\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:type_infer-2557:Column thalach has data type integer\u001b[0m\n" + "\u001b[32mINFO:type_infer-2835:Column thalach has data type integer\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:type_infer-2557:Infering type for: exang\u001b[0m\n" + "\u001b[32mINFO:type_infer-2835:Infering type for: exang\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:type_infer-2557:Column exang has data type binary\u001b[0m\n" + "\u001b[32mINFO:type_infer-2835:Column exang has data type binary\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:type_infer-2557:Infering type for: oldpeak\u001b[0m\n" + "\u001b[32mINFO:type_infer-2835:Infering type for: oldpeak\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:type_infer-2557:Column oldpeak has data type float\u001b[0m\n" + "\u001b[32mINFO:type_infer-2835:Column oldpeak has data type float\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:type_infer-2557:Infering type for: slope\u001b[0m\n" + "\u001b[32mINFO:type_infer-2835:Infering type for: slope\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:type_infer-2557:Column slope has data type categorical\u001b[0m\n" + "\u001b[32mINFO:type_infer-2835:Column slope has data type categorical\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:type_infer-2557:Infering type for: ca\u001b[0m\n" + "\u001b[32mINFO:type_infer-2835:Infering type for: ca\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:type_infer-2557:Column ca has data type categorical\u001b[0m\n" + "\u001b[32mINFO:type_infer-2835:Column ca has data type categorical\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:type_infer-2557:Infering type for: thal\u001b[0m\n" + "\u001b[32mINFO:type_infer-2835:Infering type for: thal\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:type_infer-2557:Column thal has data type categorical\u001b[0m\n" + "\u001b[32mINFO:type_infer-2835:Column thal has data type categorical\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:type_infer-2557:Infering type for: target\u001b[0m\n" + "\u001b[32mINFO:type_infer-2835:Infering type for: target\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:type_infer-2557:Column target has data type binary\u001b[0m\n" + "\u001b[32mINFO:type_infer-2835:Column target has data type binary\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:dataprep_ml-2557:Starting statistical analysis\u001b[0m\n" + "\u001b[32mINFO:dataprep_ml-2835:Starting statistical analysis\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:dataprep_ml-2557:Finished statistical analysis\u001b[0m\n" + "\u001b[32mINFO:dataprep_ml-2835:Finished statistical analysis\u001b[0m\n" ] }, { @@ -502,7 +502,7 @@ " \"unbias_target\": true,\n", " \"seconds_per_mixer\": 42768.0,\n", " \"seconds_per_encoder\": null,\n", - " \"expected_additional_time\": 0.06622028350830078,\n", + " \"expected_additional_time\": 0.0670318603515625,\n", " \"time_aim\": 259200,\n", " \"target_weights\": null,\n", " \"positive_domain\": false,\n", @@ -571,10 +571,10 @@ "execution_count": 3, "metadata": { "execution": { - "iopub.execute_input": "2024-03-19T10:01:01.453881Z", - "iopub.status.busy": "2024-03-19T10:01:01.453596Z", - "iopub.status.idle": "2024-03-19T10:01:01.457319Z", - "shell.execute_reply": "2024-03-19T10:01:01.456736Z" + "iopub.execute_input": "2024-03-19T10:19:14.880531Z", + "iopub.status.busy": "2024-03-19T10:19:14.880082Z", + "iopub.status.idle": "2024-03-19T10:19:14.883668Z", + "shell.execute_reply": "2024-03-19T10:19:14.882989Z" } }, "outputs": [], @@ -603,10 +603,10 @@ "execution_count": 4, "metadata": { "execution": { - "iopub.execute_input": "2024-03-19T10:01:01.460092Z", - "iopub.status.busy": "2024-03-19T10:01:01.459568Z", - "iopub.status.idle": "2024-03-19T10:01:01.802614Z", - "shell.execute_reply": "2024-03-19T10:01:01.801895Z" + "iopub.execute_input": "2024-03-19T10:19:14.886296Z", + "iopub.status.busy": "2024-03-19T10:19:14.885870Z", + "iopub.status.idle": "2024-03-19T10:19:15.202701Z", + "shell.execute_reply": "2024-03-19T10:19:15.202006Z" } }, "outputs": [], @@ -622,10 +622,10 @@ "execution_count": 5, "metadata": { "execution": { - "iopub.execute_input": "2024-03-19T10:01:01.805773Z", - "iopub.status.busy": "2024-03-19T10:01:01.805533Z", - "iopub.status.idle": "2024-03-19T10:01:02.425305Z", - "shell.execute_reply": "2024-03-19T10:01:02.424755Z" + "iopub.execute_input": "2024-03-19T10:19:15.206083Z", + "iopub.status.busy": "2024-03-19T10:19:15.205491Z", + "iopub.status.idle": "2024-03-19T10:19:15.814277Z", + "shell.execute_reply": "2024-03-19T10:19:15.813632Z" } }, "outputs": [ @@ -633,308 +633,308 @@ "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:dataprep_ml-2557:[Learn phase 1/8] - Statistical analysis\u001b[0m\n" + "\u001b[32mINFO:dataprep_ml-2835:[Learn phase 1/8] - Statistical analysis\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:dataprep_ml-2557:Starting statistical analysis\u001b[0m\n" + "\u001b[32mINFO:dataprep_ml-2835:Starting statistical analysis\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:dataprep_ml-2557:Finished statistical analysis\u001b[0m\n" + "\u001b[32mINFO:dataprep_ml-2835:Finished statistical analysis\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - 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"\u001b[32mINFO:lightwood-2557:Mixer: RandomForestMixer got accuracy: 0.798\u001b[0m\n" + "\u001b[32mINFO:lightwood-2835:Mixer: RandomForestMixer got accuracy: 0.798\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:lightwood-2557:Picked best mixer: RandomForestMixer\u001b[0m\n" + "\u001b[32mINFO:lightwood-2835:Picked best mixer: RandomForestMixer\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[37mDEBUG:lightwood-2557: `fit` runtime: 0.13 seconds\u001b[0m\n" + "\u001b[37mDEBUG:lightwood-2835: `fit` runtime: 0.13 seconds\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:dataprep_ml-2557:[Learn phase 7/8] - Ensemble analysis\u001b[0m\n" + "\u001b[32mINFO:dataprep_ml-2835:[Learn phase 7/8] - Ensemble analysis\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:dataprep_ml-2557:Analyzing the ensemble of mixers\u001b[0m\n" + "\u001b[32mINFO:dataprep_ml-2835:Analyzing the ensemble of mixers\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:lightwood-2557:The block ICP is now running its analyze() method\u001b[0m\n" + "\u001b[32mINFO:lightwood-2835:The block ICP is now running its analyze() method\u001b[0m\n" ] }, { @@ -943,35 +943,35 @@ "text": [ "/opt/hostedtoolcache/Python/3.9.18/x64/lib/python3.9/site-packages/sklearn/preprocessing/_encoders.py:975: FutureWarning: `sparse` was renamed to `sparse_output` in version 1.2 and will be removed in 1.4. `sparse_output` is ignored unless you leave `sparse` to its default value.\n", " warnings.warn(\n", - "\u001b[32mINFO:lightwood-2557:The block ConfStats is now running its analyze() method\u001b[0m\n" + "\u001b[32mINFO:lightwood-2835:The block ConfStats is now running its analyze() method\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:lightwood-2557:The block AccStats is now running its analyze() method\u001b[0m\n" + "\u001b[32mINFO:lightwood-2835:The block AccStats is now running its analyze() method\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:lightwood-2557:The block PermutationFeatureImportance is now running its analyze() method\u001b[0m\n" + "\u001b[32mINFO:lightwood-2835:The block PermutationFeatureImportance is now running its analyze() method\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:lightwood-2557:[PFI] Using a random sample (1000 rows out of 31).\u001b[0m\n" + "\u001b[32mINFO:lightwood-2835:[PFI] Using a random sample (1000 rows out of 31).\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:lightwood-2557:[PFI] Set to consider first 10 columns out of 10: ['age', 'sex', 'cp', 'trestbps', 'chol', 'fbs', 'restecg', 'thalach', 'exang', 'oldpeak'].\u001b[0m\n" + "\u001b[32mINFO:lightwood-2835:[PFI] Set to consider first 10 columns out of 10: ['age', 'sex', 'cp', 'trestbps', 'chol', 'fbs', 'restecg', 'thalach', 'exang', 'oldpeak'].\u001b[0m\n" ] }, { @@ -994,35 +994,35 @@ "name": "stderr", "output_type": "stream", "text": [ - "\u001b[37mDEBUG:lightwood-2557: `analyze_ensemble` runtime: 0.27 seconds\u001b[0m\n" + "\u001b[37mDEBUG:lightwood-2835: `analyze_ensemble` runtime: 0.26 seconds\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:dataprep_ml-2557:[Learn phase 8/8] - Adjustment on validation requested\u001b[0m\n" + "\u001b[32mINFO:dataprep_ml-2835:[Learn phase 8/8] - Adjustment on validation requested\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:dataprep_ml-2557:Updating the mixers\u001b[0m\n" + "\u001b[32mINFO:dataprep_ml-2835:Updating the mixers\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[37mDEBUG:lightwood-2557: `adjust` runtime: 0.04 seconds\u001b[0m\n" + "\u001b[37mDEBUG:lightwood-2835: `adjust` runtime: 0.04 seconds\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[37mDEBUG:lightwood-2557: `learn` runtime: 0.62 seconds\u001b[0m\n" + "\u001b[37mDEBUG:lightwood-2835: `learn` runtime: 0.6 seconds\u001b[0m\n" ] } ], @@ -1042,10 +1042,10 @@ "execution_count": 6, "metadata": { "execution": { - "iopub.execute_input": "2024-03-19T10:01:02.427974Z", - "iopub.status.busy": "2024-03-19T10:01:02.427763Z", - "iopub.status.idle": "2024-03-19T10:01:02.550761Z", - "shell.execute_reply": "2024-03-19T10:01:02.550053Z" + "iopub.execute_input": "2024-03-19T10:19:15.817082Z", + "iopub.status.busy": "2024-03-19T10:19:15.816625Z", + "iopub.status.idle": "2024-03-19T10:19:15.933674Z", + "shell.execute_reply": "2024-03-19T10:19:15.933075Z" } }, "outputs": [ @@ -1053,35 +1053,35 @@ "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:dataprep_ml-2557:[Predict phase 1/4] - Data preprocessing\u001b[0m\n" + "\u001b[32mINFO:dataprep_ml-2835:[Predict phase 1/4] - Data preprocessing\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:dataprep_ml-2557:Cleaning the data\u001b[0m\n" + "\u001b[32mINFO:dataprep_ml-2835:Cleaning the data\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[37mDEBUG:lightwood-2557: `preprocess` runtime: 0.01 seconds\u001b[0m\n" + "\u001b[37mDEBUG:lightwood-2835: `preprocess` runtime: 0.01 seconds\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:dataprep_ml-2557:[Predict phase 2/4] - Feature generation\u001b[0m\n" + "\u001b[32mINFO:dataprep_ml-2835:[Predict phase 2/4] - Feature generation\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:dataprep_ml-2557:Featurizing the data\u001b[0m\n" + "\u001b[32mINFO:dataprep_ml-2835:Featurizing the data\u001b[0m\n" ] }, { @@ -1104,91 +1104,91 @@ " outputs = ufunc(*inputs)\n", "/opt/hostedtoolcache/Python/3.9.18/x64/lib/python3.9/site-packages/numpy/lib/function_base.py:2455: RuntimeWarning: invalid value encountered in _none_fn (vectorized)\n", " outputs = ufunc(*inputs)\n", - "\u001b[37mDEBUG:lightwood-2557: `featurize` runtime: 0.02 seconds\u001b[0m\n" + "\u001b[37mDEBUG:lightwood-2835: `featurize` runtime: 0.02 seconds\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:dataprep_ml-2557:[Predict phase 3/4] - Calling ensemble\u001b[0m\n" + "\u001b[32mINFO:dataprep_ml-2835:[Predict phase 3/4] - Calling ensemble\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[37mDEBUG:lightwood-2557: `_timed_call` runtime: 0.01 seconds\u001b[0m\n" + "\u001b[37mDEBUG:lightwood-2835: `_timed_call` runtime: 0.01 seconds\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:dataprep_ml-2557:[Predict phase 4/4] - Analyzing output\u001b[0m\n" + "\u001b[32mINFO:dataprep_ml-2835:[Predict phase 4/4] - Analyzing output\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:lightwood-2557:The block ICP is now running its explain() method\u001b[0m\n" + "\u001b[32mINFO:lightwood-2835:The block ICP is now running its explain() method\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:lightwood-2557:The block ConfStats is now running its explain() method\u001b[0m\n" + "\u001b[32mINFO:lightwood-2835:The block ConfStats is now running its explain() method\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:lightwood-2557:ConfStats.explain() has not been implemented, no modifications will be done to the data insights.\u001b[0m\n" + "\u001b[32mINFO:lightwood-2835:ConfStats.explain() has not been implemented, no modifications will be done to the data insights.\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:lightwood-2557:The block AccStats is now running its explain() method\u001b[0m\n" + "\u001b[32mINFO:lightwood-2835:The block AccStats is now running its explain() method\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:lightwood-2557:AccStats.explain() has not been implemented, no modifications will be done to the data insights.\u001b[0m\n" + "\u001b[32mINFO:lightwood-2835:AccStats.explain() has not been implemented, no modifications will be done to the data insights.\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:lightwood-2557:The block PermutationFeatureImportance is now running its explain() method\u001b[0m\n" + "\u001b[32mINFO:lightwood-2835:The block PermutationFeatureImportance is now running its explain() method\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:lightwood-2557:PermutationFeatureImportance.explain() has not been implemented, no modifications will be done to the data insights.\u001b[0m\n" + "\u001b[32mINFO:lightwood-2835:PermutationFeatureImportance.explain() has not been implemented, no modifications will be done to the data insights.\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[37mDEBUG:lightwood-2557: `explain` runtime: 0.01 seconds\u001b[0m\n" + "\u001b[37mDEBUG:lightwood-2835: `explain` runtime: 0.01 seconds\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[37mDEBUG:lightwood-2557: `predict` runtime: 0.05 seconds\u001b[0m\n" + "\u001b[37mDEBUG:lightwood-2835: `predict` runtime: 0.05 seconds\u001b[0m\n" ] }, { diff --git a/tutorials/custom_splitter/custom_splitter.html b/tutorials/custom_splitter/custom_splitter.html index 315c33231..eeeef2fc7 100644 --- a/tutorials/custom_splitter/custom_splitter.html +++ b/tutorials/custom_splitter/custom_splitter.html @@ -4,7 +4,7 @@ - Build your own training/testing split — lightwood 24.3.3.1 documentation + Build your own training/testing split — lightwood 24.3.3.0 documentation @@ -42,7 +42,7 @@
- 24.3.3.1 + 24.3.3.0
@@ -129,7 +129,7 @@

Date: 2021.10.07
-INFO:lightwood-2709:No torchvision detected, image helpers not supported.
+INFO:lightwood-2978:No torchvision detected, image helpers not supported.
 

@@ -383,7 +383,7 @@

2) Create a JSON-AI default object
-INFO:type_infer-2709:Analyzing a sample of 18424
+INFO:type_infer-2978:Analyzing a sample of 18424
 

Lightwood looks at each of the many columns and indicates they are mostly float, with exception of “Class” which is binary.

@@ -1141,7 +1141,7 @@

5) Generate Python code representing your ML pipeline5) Generate Python code representing your ML pipeline6) Call python to run your code and see your preprocessed outputs
-INFO:dataprep_ml-2709:Cleaning the data
+INFO:dataprep_ml-2978:Cleaning the data
 
diff --git a/tutorials/custom_splitter/custom_splitter.ipynb b/tutorials/custom_splitter/custom_splitter.ipynb index 95440ba2f..9c3d05589 100644 --- a/tutorials/custom_splitter/custom_splitter.ipynb +++ b/tutorials/custom_splitter/custom_splitter.ipynb @@ -28,10 +28,10 @@ "id": "interim-discussion", "metadata": { "execution": { - "iopub.execute_input": "2024-03-19T10:01:47.239674Z", - "iopub.status.busy": "2024-03-19T10:01:47.239449Z", - "iopub.status.idle": "2024-03-19T10:01:50.061642Z", - "shell.execute_reply": "2024-03-19T10:01:50.060971Z" + "iopub.execute_input": "2024-03-19T10:19:59.610275Z", + "iopub.status.busy": "2024-03-19T10:19:59.609809Z", + "iopub.status.idle": "2024-03-19T10:20:02.362199Z", + "shell.execute_reply": "2024-03-19T10:20:02.361538Z" } }, "outputs": [ @@ -39,14 +39,14 @@ "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:lightwood-2709:No torchvision detected, image helpers not supported.\u001b[0m\n" + "\u001b[32mINFO:lightwood-2978:No torchvision detected, image helpers not supported.\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:lightwood-2709:No torchvision/pillow detected, image encoder not supported\u001b[0m\n" + "\u001b[32mINFO:lightwood-2978:No torchvision/pillow detected, image encoder not supported\u001b[0m\n" ] } ], @@ -87,10 +87,10 @@ "id": "foreign-orchestra", "metadata": { "execution": { - "iopub.execute_input": "2024-03-19T10:01:50.065028Z", - "iopub.status.busy": "2024-03-19T10:01:50.064509Z", - "iopub.status.idle": "2024-03-19T10:01:56.279725Z", - "shell.execute_reply": "2024-03-19T10:01:56.279000Z" + "iopub.execute_input": "2024-03-19T10:20:02.365602Z", + "iopub.status.busy": "2024-03-19T10:20:02.365149Z", + "iopub.status.idle": "2024-03-19T10:20:07.452547Z", + "shell.execute_reply": "2024-03-19T10:20:07.451832Z" } }, "outputs": [ @@ -316,10 +316,10 @@ "id": "cathedral-mills", "metadata": { "execution": { - "iopub.execute_input": "2024-03-19T10:01:56.282558Z", - "iopub.status.busy": "2024-03-19T10:01:56.282154Z", - "iopub.status.idle": "2024-03-19T10:01:56.638853Z", - "shell.execute_reply": "2024-03-19T10:01:56.638168Z" + "iopub.execute_input": "2024-03-19T10:20:07.455462Z", + "iopub.status.busy": "2024-03-19T10:20:07.454974Z", + "iopub.status.idle": "2024-03-19T10:20:07.804222Z", + "shell.execute_reply": "2024-03-19T10:20:07.803562Z" } }, "outputs": [ @@ -374,10 +374,10 @@ "id": "medieval-zambia", "metadata": { "execution": { - "iopub.execute_input": "2024-03-19T10:01:56.641742Z", - "iopub.status.busy": "2024-03-19T10:01:56.641343Z", - "iopub.status.idle": "2024-03-19T10:03:06.100696Z", - "shell.execute_reply": "2024-03-19T10:03:06.099959Z" + "iopub.execute_input": "2024-03-19T10:20:07.806996Z", + "iopub.status.busy": "2024-03-19T10:20:07.806608Z", + "iopub.status.idle": "2024-03-19T10:21:16.393872Z", + "shell.execute_reply": "2024-03-19T10:21:16.393237Z" } }, "outputs": [ @@ -385,469 +385,469 @@ "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:type_infer-2709:Analyzing a sample of 18424\u001b[0m\n" + "\u001b[32mINFO:type_infer-2978:Analyzing a sample of 18424\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:type_infer-2709:from a total population of 284807, this is equivalent to 6.5% of your data.\u001b[0m\n" + "\u001b[32mINFO:type_infer-2978:from a total population of 284807, this is equivalent to 6.5% of your data.\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:type_infer-2709:Using 3 processes to deduct types.\u001b[0m\n" + "\u001b[32mINFO:type_infer-2978:Using 3 processes to deduct types.\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:type_infer-2709:Infering type for: Time\u001b[0m\n" + "\u001b[32mINFO:type_infer-2978:Infering type for: Time\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:type_infer-2709:Infering type for: V3\u001b[0m\n" + "\u001b[32mINFO:type_infer-2978:Infering type for: V3\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:type_infer-2709:Infering type for: V6\u001b[0m\n" + "\u001b[32mINFO:type_infer-2978:Infering type for: V6\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:type_infer-2709:Column Time has data type integer\u001b[0m\n" + "\u001b[32mINFO:type_infer-2978:Column Time has data type integer\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - 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"\u001b[32mINFO:type_infer-2709:Infering type for: V20\u001b[0m\n" + "\u001b[32mINFO:type_infer-2978:Infering type for: V27\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:type_infer-2709:Column V24 has data type float\u001b[0m\n" + "\u001b[32mINFO:type_infer-2978:Column V21 has data type float\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:type_infer-2709:Infering type for: V25\u001b[0m\n" + "\u001b[32mINFO:type_infer-2978:Infering type for: V22\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:type_infer-2709:Column V22 has data type float\u001b[0m\n" + "\u001b[32mINFO:type_infer-2978:Column V22 has data type float\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:type_infer-2709:Infering type for: V23\u001b[0m\n" + "\u001b[32mINFO:type_infer-2978:Infering type for: V23\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - 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"\u001b[32mINFO:type_infer-2709:Infering type for: Class\u001b[0m\n" + "\u001b[32mINFO:type_infer-2978:Infering type for: Class\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:type_infer-2709:Column Class has data type binary\u001b[0m\n" + "\u001b[32mINFO:type_infer-2978:Column Class has data type binary\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:type_infer-2709:Column V27 has data type float\u001b[0m\n" + "\u001b[32mINFO:type_infer-2978:Column V25 has data type float\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:type_infer-2709:Infering type for: V28\u001b[0m\n" + "\u001b[32mINFO:type_infer-2978:Infering type for: V26\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:type_infer-2709:Column V26 has data type float\u001b[0m\n" + "\u001b[32mINFO:type_infer-2978:Column V28 has data type float\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:type_infer-2709:Column V28 has data type float\u001b[0m\n" + "\u001b[32mINFO:type_infer-2978:Infering type for: Amount\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:type_infer-2709:Infering type for: Amount\u001b[0m\n" + "\u001b[32mINFO:type_infer-2978:Column V26 has data type float\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:type_infer-2709:Column Amount has data type float\u001b[0m\n" + "\u001b[32mINFO:type_infer-2978:Column Amount has data type float\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:dataprep_ml-2709:Starting statistical analysis\u001b[0m\n" + "\u001b[32mINFO:dataprep_ml-2978:Starting statistical analysis\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:dataprep_ml-2709:Finished statistical analysis\u001b[0m\n" + "\u001b[32mINFO:dataprep_ml-2978:Finished statistical analysis\u001b[0m\n" ] } ], @@ -901,10 +901,10 @@ "id": "4411ee53", "metadata": { "execution": { - "iopub.execute_input": "2024-03-19T10:03:06.103501Z", - "iopub.status.busy": "2024-03-19T10:03:06.103277Z", - "iopub.status.idle": "2024-03-19T10:03:06.108507Z", - "shell.execute_reply": "2024-03-19T10:03:06.107869Z" + "iopub.execute_input": "2024-03-19T10:21:16.397004Z", + "iopub.status.busy": "2024-03-19T10:21:16.396578Z", + "iopub.status.idle": "2024-03-19T10:21:16.402082Z", + "shell.execute_reply": "2024-03-19T10:21:16.401471Z" } }, "outputs": [ @@ -996,10 +996,10 @@ "id": "34092d12", "metadata": { "execution": { - "iopub.execute_input": "2024-03-19T10:03:06.111188Z", - "iopub.status.busy": "2024-03-19T10:03:06.110800Z", - "iopub.status.idle": "2024-03-19T10:03:06.114233Z", - "shell.execute_reply": "2024-03-19T10:03:06.113596Z" + "iopub.execute_input": "2024-03-19T10:21:16.404572Z", + "iopub.status.busy": "2024-03-19T10:21:16.404205Z", + "iopub.status.idle": "2024-03-19T10:21:16.407304Z", + "shell.execute_reply": "2024-03-19T10:21:16.406807Z" } }, "outputs": [], @@ -1055,10 +1055,10 @@ "id": "alleged-concentrate", "metadata": { "execution": { - "iopub.execute_input": "2024-03-19T10:03:06.116867Z", - "iopub.status.busy": "2024-03-19T10:03:06.116499Z", - "iopub.status.idle": "2024-03-19T10:03:06.498297Z", - "shell.execute_reply": "2024-03-19T10:03:06.497622Z" + "iopub.execute_input": "2024-03-19T10:21:16.409972Z", + "iopub.status.busy": "2024-03-19T10:21:16.409527Z", + "iopub.status.idle": "2024-03-19T10:21:16.768862Z", + "shell.execute_reply": "2024-03-19T10:21:16.768199Z" } }, "outputs": [ @@ -1139,7 +1139,7 @@ " \"unbias_target\": True,\n", " \"seconds_per_mixer\": 42768.0,\n", " \"seconds_per_encoder\": None,\n", - " \"expected_additional_time\": 69.44483995437622,\n", + " \"expected_additional_time\": 68.57310724258423,\n", " \"time_aim\": 259200,\n", " \"target_weights\": None,\n", " \"positive_domain\": False,\n", @@ -1201,7 +1201,7 @@ " \"Amount\": \"float\",\n", " \"Class\": \"binary\",\n", " }\n", - " self.lightwood_version = \"24.3.3.1\"\n", + " self.lightwood_version = \"24.3.3.0\"\n", " self.pred_args = PredictionArguments()\n", "\n", " # Any feature-column dependencies\n", @@ -1902,10 +1902,10 @@ "id": "organic-london", "metadata": { "execution": { - "iopub.execute_input": "2024-03-19T10:03:06.501241Z", - "iopub.status.busy": "2024-03-19T10:03:06.500710Z", - "iopub.status.idle": "2024-03-19T10:03:06.509031Z", - "shell.execute_reply": "2024-03-19T10:03:06.508404Z" + "iopub.execute_input": "2024-03-19T10:21:16.771656Z", + "iopub.status.busy": "2024-03-19T10:21:16.771437Z", + "iopub.status.idle": "2024-03-19T10:21:16.779773Z", + "shell.execute_reply": "2024-03-19T10:21:16.779119Z" } }, "outputs": [], @@ -1920,10 +1920,10 @@ "id": "fabulous-prime", "metadata": { "execution": { - "iopub.execute_input": "2024-03-19T10:03:06.511868Z", - "iopub.status.busy": "2024-03-19T10:03:06.511453Z", - "iopub.status.idle": "2024-03-19T10:03:26.916363Z", - "shell.execute_reply": "2024-03-19T10:03:26.915678Z" + "iopub.execute_input": "2024-03-19T10:21:16.782156Z", + "iopub.status.busy": "2024-03-19T10:21:16.781962Z", + "iopub.status.idle": "2024-03-19T10:21:37.022779Z", + "shell.execute_reply": "2024-03-19T10:21:37.022012Z" } }, "outputs": [ @@ -1931,28 +1931,28 @@ "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:dataprep_ml-2709:Cleaning the data\u001b[0m\n" + "\u001b[32mINFO:dataprep_ml-2978:Cleaning the data\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[37mDEBUG:lightwood-2709: `preprocess` runtime: 18.83 seconds\u001b[0m\n" + "\u001b[37mDEBUG:lightwood-2978: `preprocess` runtime: 18.68 seconds\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:dataprep_ml-2709:Splitting the data into train/test\u001b[0m\n" + "\u001b[32mINFO:dataprep_ml-2978:Splitting the data into train/test\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[37mDEBUG:lightwood-2709: `split` runtime: 1.57 seconds\u001b[0m\n" + "\u001b[37mDEBUG:lightwood-2978: `split` runtime: 1.55 seconds\u001b[0m\n" ] } ], @@ -1968,10 +1968,10 @@ "id": "suspended-biography", "metadata": { "execution": { - "iopub.execute_input": "2024-03-19T10:03:26.919067Z", - "iopub.status.busy": "2024-03-19T10:03:26.918775Z", - "iopub.status.idle": "2024-03-19T10:03:28.191841Z", - "shell.execute_reply": "2024-03-19T10:03:28.191123Z" + "iopub.execute_input": "2024-03-19T10:21:37.025718Z", + "iopub.status.busy": "2024-03-19T10:21:37.025482Z", + "iopub.status.idle": "2024-03-19T10:21:38.292751Z", + "shell.execute_reply": "2024-03-19T10:21:38.292052Z" } }, "outputs": [ diff --git a/tutorials/tutorial_data_analysis/tutorial_data_analysis.html b/tutorials/tutorial_data_analysis/tutorial_data_analysis.html index a90fd6637..f3211cdfd 100644 --- a/tutorials/tutorial_data_analysis/tutorial_data_analysis.html +++ b/tutorials/tutorial_data_analysis/tutorial_data_analysis.html @@ -4,7 +4,7 @@ - Tutorial - Introduction to Lightwood’s statistical analysis — lightwood 24.3.3.1 documentation + Tutorial - Introduction to Lightwood’s statistical analysis — lightwood 24.3.3.0 documentation @@ -42,7 +42,7 @@
- 24.3.3.1 + 24.3.3.0

Let’s see how this object has been populated. ProblemDefinition is a Python dataclass, so it comes with some convenient tools to achieve this:

@@ -290,7 +290,7 @@

Step 1: load the dataset and define the predictive task
-INFO:type_infer-2515:Analyzing a sample of 222
+INFO:type_infer-2801:Analyzing a sample of 222
 

diff --git a/tutorials/tutorial_data_analysis/tutorial_data_analysis.ipynb b/tutorials/tutorial_data_analysis/tutorial_data_analysis.ipynb index 3b0933da3..cd5dc5d77 100644 --- a/tutorials/tutorial_data_analysis/tutorial_data_analysis.ipynb +++ b/tutorials/tutorial_data_analysis/tutorial_data_analysis.ipynb @@ -35,10 +35,10 @@ "execution_count": 1, "metadata": { "execution": { - "iopub.execute_input": "2024-03-19T10:00:49.651494Z", - "iopub.status.busy": "2024-03-19T10:00:49.650877Z", - "iopub.status.idle": "2024-03-19T10:00:49.989665Z", - "shell.execute_reply": "2024-03-19T10:00:49.988766Z" + "iopub.execute_input": "2024-03-19T10:19:03.938511Z", + "iopub.status.busy": "2024-03-19T10:19:03.938316Z", + "iopub.status.idle": "2024-03-19T10:19:04.265900Z", + "shell.execute_reply": "2024-03-19T10:19:04.265235Z" } }, "outputs": [ @@ -175,10 +175,10 @@ "execution_count": 2, "metadata": { "execution": { - "iopub.execute_input": "2024-03-19T10:00:50.028410Z", - "iopub.status.busy": "2024-03-19T10:00:50.027800Z", - "iopub.status.idle": "2024-03-19T10:00:52.344636Z", - "shell.execute_reply": "2024-03-19T10:00:52.344000Z" + "iopub.execute_input": "2024-03-19T10:19:04.305139Z", + "iopub.status.busy": "2024-03-19T10:19:04.304727Z", + "iopub.status.idle": "2024-03-19T10:19:06.536521Z", + "shell.execute_reply": "2024-03-19T10:19:06.535864Z" } }, "outputs": [ @@ -186,14 +186,14 @@ "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:lightwood-2515:No torchvision detected, image helpers not supported.\u001b[0m\n" + "\u001b[32mINFO:lightwood-2801:No torchvision detected, image helpers not supported.\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:lightwood-2515:No torchvision/pillow detected, image encoder not supported\u001b[0m\n" + "\u001b[32mINFO:lightwood-2801:No torchvision/pillow detected, image encoder not supported\u001b[0m\n" ] } ], @@ -215,10 +215,10 @@ "execution_count": 3, "metadata": { "execution": { - "iopub.execute_input": "2024-03-19T10:00:52.348266Z", - "iopub.status.busy": "2024-03-19T10:00:52.347545Z", - "iopub.status.idle": "2024-03-19T10:00:52.353326Z", - "shell.execute_reply": "2024-03-19T10:00:52.352769Z" + "iopub.execute_input": "2024-03-19T10:19:06.539697Z", + "iopub.status.busy": "2024-03-19T10:19:06.539214Z", + "iopub.status.idle": "2024-03-19T10:19:06.544551Z", + "shell.execute_reply": "2024-03-19T10:19:06.543867Z" } }, "outputs": [ @@ -270,10 +270,10 @@ "execution_count": 4, "metadata": { "execution": { - "iopub.execute_input": "2024-03-19T10:00:52.355839Z", - "iopub.status.busy": "2024-03-19T10:00:52.355431Z", - "iopub.status.idle": "2024-03-19T10:00:52.380446Z", - "shell.execute_reply": "2024-03-19T10:00:52.379821Z" + "iopub.execute_input": "2024-03-19T10:19:06.547045Z", + "iopub.status.busy": "2024-03-19T10:19:06.546702Z", + "iopub.status.idle": "2024-03-19T10:19:06.570850Z", + "shell.execute_reply": "2024-03-19T10:19:06.570337Z" } }, "outputs": [ @@ -281,112 +281,112 @@ "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:type_infer-2515:Analyzing a sample of 222\u001b[0m\n" + "\u001b[32mINFO:type_infer-2801:Analyzing a sample of 222\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:type_infer-2515:from a total population of 225, this is equivalent to 98.7% of your data.\u001b[0m\n" + "\u001b[32mINFO:type_infer-2801:from a total population of 225, this is equivalent to 98.7% of your data.\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:type_infer-2515:Infering type for: Population\u001b[0m\n" + "\u001b[32mINFO:type_infer-2801:Infering type for: Population\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:type_infer-2515:Column Population has data type integer\u001b[0m\n" + "\u001b[32mINFO:type_infer-2801:Column Population has data type integer\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:type_infer-2515:Infering type for: Area (sq. mi.)\u001b[0m\n" + "\u001b[32mINFO:type_infer-2801:Infering type for: Area (sq. mi.)\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:type_infer-2515:Column Area (sq. mi.) has data type integer\u001b[0m\n" + "\u001b[32mINFO:type_infer-2801:Column Area (sq. mi.) has data type integer\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:type_infer-2515:Infering type for: Pop. Density \u001b[0m\n" + "\u001b[32mINFO:type_infer-2801:Infering type for: Pop. Density \u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:type_infer-2515:Column Pop. Density has data type float\u001b[0m\n" + "\u001b[32mINFO:type_infer-2801:Column Pop. Density has data type float\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:type_infer-2515:Infering type for: GDP ($ per capita)\u001b[0m\n" + "\u001b[32mINFO:type_infer-2801:Infering type for: GDP ($ per capita)\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:type_infer-2515:Column GDP ($ per capita) has data type integer\u001b[0m\n" + "\u001b[32mINFO:type_infer-2801:Column GDP ($ per capita) has data type integer\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:type_infer-2515:Infering type for: Literacy (%)\u001b[0m\n" + "\u001b[32mINFO:type_infer-2801:Infering type for: Literacy (%)\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:type_infer-2515:Column Literacy (%) has data type float\u001b[0m\n" + "\u001b[32mINFO:type_infer-2801:Column Literacy (%) has data type float\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:type_infer-2515:Infering type for: Infant mortality \u001b[0m\n" + "\u001b[32mINFO:type_infer-2801:Infering type for: Infant mortality \u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:type_infer-2515:Column Infant mortality has data type float\u001b[0m\n" + "\u001b[32mINFO:type_infer-2801:Column Infant mortality has data type float\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:type_infer-2515:Infering type for: Development Index\u001b[0m\n" + "\u001b[32mINFO:type_infer-2801:Infering type for: Development Index\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:type_infer-2515:Column Development Index has data type categorical\u001b[0m\n" + "\u001b[32mINFO:type_infer-2801:Column Development Index has data type categorical\u001b[0m\n" ] }, { @@ -421,10 +421,10 @@ "execution_count": 5, "metadata": { "execution": { - "iopub.execute_input": "2024-03-19T10:00:52.383003Z", - "iopub.status.busy": "2024-03-19T10:00:52.382644Z", - "iopub.status.idle": "2024-03-19T10:00:52.386819Z", - "shell.execute_reply": "2024-03-19T10:00:52.386243Z" + "iopub.execute_input": "2024-03-19T10:19:06.573173Z", + "iopub.status.busy": "2024-03-19T10:19:06.572977Z", + "iopub.status.idle": "2024-03-19T10:19:06.577284Z", + "shell.execute_reply": "2024-03-19T10:19:06.576743Z" } }, "outputs": [ @@ -474,10 +474,10 @@ "execution_count": 6, "metadata": { "execution": { - "iopub.execute_input": "2024-03-19T10:00:52.389447Z", - "iopub.status.busy": "2024-03-19T10:00:52.388990Z", - "iopub.status.idle": "2024-03-19T10:00:52.416923Z", - "shell.execute_reply": "2024-03-19T10:00:52.416426Z" + "iopub.execute_input": "2024-03-19T10:19:06.579864Z", + "iopub.status.busy": "2024-03-19T10:19:06.579460Z", + "iopub.status.idle": "2024-03-19T10:19:06.605049Z", + "shell.execute_reply": "2024-03-19T10:19:06.604425Z" } }, "outputs": [ @@ -485,14 +485,14 @@ "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:dataprep_ml-2515:Starting statistical analysis\u001b[0m\n" + "\u001b[32mINFO:dataprep_ml-2801:Starting statistical analysis\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:dataprep_ml-2515:Finished statistical analysis\u001b[0m\n" + "\u001b[32mINFO:dataprep_ml-2801:Finished statistical analysis\u001b[0m\n" ] } ], @@ -520,10 +520,10 @@ "execution_count": 7, "metadata": { "execution": { - "iopub.execute_input": "2024-03-19T10:00:52.419438Z", - "iopub.status.busy": "2024-03-19T10:00:52.419082Z", - "iopub.status.idle": "2024-03-19T10:00:52.423513Z", - "shell.execute_reply": "2024-03-19T10:00:52.422896Z" + "iopub.execute_input": "2024-03-19T10:19:06.607583Z", + "iopub.status.busy": "2024-03-19T10:19:06.607232Z", + "iopub.status.idle": "2024-03-19T10:19:06.611429Z", + "shell.execute_reply": "2024-03-19T10:19:06.610739Z" } }, "outputs": [ @@ -569,10 +569,10 @@ "execution_count": 8, "metadata": { "execution": { - "iopub.execute_input": "2024-03-19T10:00:52.426009Z", - "iopub.status.busy": "2024-03-19T10:00:52.425648Z", - "iopub.status.idle": "2024-03-19T10:00:52.429578Z", - "shell.execute_reply": "2024-03-19T10:00:52.428980Z" + "iopub.execute_input": "2024-03-19T10:19:06.613932Z", + "iopub.status.busy": "2024-03-19T10:19:06.613555Z", + "iopub.status.idle": "2024-03-19T10:19:06.617700Z", + "shell.execute_reply": "2024-03-19T10:19:06.617161Z" } }, "outputs": [ @@ -612,10 +612,10 @@ "execution_count": 9, "metadata": { "execution": { - "iopub.execute_input": "2024-03-19T10:00:52.432021Z", - "iopub.status.busy": "2024-03-19T10:00:52.431822Z", - "iopub.status.idle": "2024-03-19T10:00:52.436360Z", - "shell.execute_reply": "2024-03-19T10:00:52.435714Z" + "iopub.execute_input": "2024-03-19T10:19:06.620208Z", + "iopub.status.busy": "2024-03-19T10:19:06.619835Z", + "iopub.status.idle": "2024-03-19T10:19:06.624339Z", + "shell.execute_reply": "2024-03-19T10:19:06.623720Z" }, "scrolled": false }, @@ -673,10 +673,10 @@ "execution_count": 10, "metadata": { "execution": { - "iopub.execute_input": "2024-03-19T10:00:52.438828Z", - "iopub.status.busy": "2024-03-19T10:00:52.438629Z", - "iopub.status.idle": "2024-03-19T10:00:52.442719Z", - "shell.execute_reply": "2024-03-19T10:00:52.442066Z" + "iopub.execute_input": "2024-03-19T10:19:06.626914Z", + "iopub.status.busy": "2024-03-19T10:19:06.626544Z", + "iopub.status.idle": "2024-03-19T10:19:06.630569Z", + "shell.execute_reply": "2024-03-19T10:19:06.629918Z" } }, "outputs": [ @@ -700,10 +700,10 @@ "execution_count": 11, "metadata": { "execution": { - "iopub.execute_input": "2024-03-19T10:00:52.445081Z", - "iopub.status.busy": "2024-03-19T10:00:52.444718Z", - "iopub.status.idle": "2024-03-19T10:00:52.449280Z", - "shell.execute_reply": "2024-03-19T10:00:52.448653Z" + "iopub.execute_input": "2024-03-19T10:19:06.632918Z", + "iopub.status.busy": "2024-03-19T10:19:06.632572Z", + "iopub.status.idle": "2024-03-19T10:19:06.636980Z", + "shell.execute_reply": "2024-03-19T10:19:06.636416Z" }, "scrolled": false }, @@ -786,10 +786,10 @@ "execution_count": 12, "metadata": { "execution": { - "iopub.execute_input": "2024-03-19T10:00:52.451758Z", - "iopub.status.busy": "2024-03-19T10:00:52.451357Z", - "iopub.status.idle": "2024-03-19T10:00:52.455554Z", - "shell.execute_reply": "2024-03-19T10:00:52.454881Z" + "iopub.execute_input": "2024-03-19T10:19:06.639388Z", + "iopub.status.busy": "2024-03-19T10:19:06.639170Z", + "iopub.status.idle": "2024-03-19T10:19:06.642788Z", + "shell.execute_reply": "2024-03-19T10:19:06.642142Z" } }, "outputs": [ @@ -841,10 +841,10 @@ "execution_count": 13, "metadata": { "execution": { - "iopub.execute_input": "2024-03-19T10:00:52.458008Z", - "iopub.status.busy": "2024-03-19T10:00:52.457628Z", - "iopub.status.idle": "2024-03-19T10:00:55.130170Z", - "shell.execute_reply": "2024-03-19T10:00:55.129435Z" + "iopub.execute_input": "2024-03-19T10:19:06.645291Z", + "iopub.status.busy": "2024-03-19T10:19:06.645097Z", + "iopub.status.idle": "2024-03-19T10:19:09.211358Z", + "shell.execute_reply": "2024-03-19T10:19:09.210753Z" }, "scrolled": false }, diff --git a/tutorials/tutorial_time_series/tutorial_time_series.html b/tutorials/tutorial_time_series/tutorial_time_series.html index 6e3ef4fb6..f81deba90 100644 --- a/tutorials/tutorial_time_series/tutorial_time_series.html +++ b/tutorials/tutorial_time_series/tutorial_time_series.html @@ -4,7 +4,7 @@ - Tutorial - Time series forecasting — lightwood 24.3.3.1 documentation + Tutorial - Time series forecasting — lightwood 24.3.3.0 documentation @@ -42,7 +42,7 @@
- 24.3.3.1 + 24.3.3.0
@@ -216,7 +216,7 @@

Define the predictive task
-INFO:lightwood-2383:No torchvision detected, image helpers not supported.
+INFO:lightwood-2669:No torchvision detected, image helpers not supported.
 

@@ -372,7 +372,7 @@

Train
-INFO:dataprep_ml-2383:[Learn phase 1/8] - Statistical analysis
+INFO:dataprep_ml-2669:[Learn phase 1/8] - Statistical analysis
 
-INFO:dataprep_ml-2383:Starting statistical analysis
+INFO:dataprep_ml-2669:Starting statistical analysis
 
-DEBUG:lightwood-2383: `analyze_data` runtime: 0.05 seconds
+DEBUG:lightwood-2669: `analyze_data` runtime: 0.05 seconds
 
-INFO:dataprep_ml-2383:[Learn phase 2/8] - Data preprocessing
+INFO:dataprep_ml-2669:[Learn phase 2/8] - Data preprocessing
 
-INFO:dataprep_ml-2383:Cleaning the data
+INFO:dataprep_ml-2669:Cleaning the data
 
-DEBUG:lightwood-2383: `preprocess` runtime: 0.09 seconds
+DEBUG:lightwood-2669: `preprocess` runtime: 0.09 seconds
 
-INFO:dataprep_ml-2383:[Learn phase 3/8] - Data splitting
+INFO:dataprep_ml-2669:[Learn phase 3/8] - Data splitting
 
-INFO:dataprep_ml-2383:Splitting the data into train/test
+INFO:dataprep_ml-2669:Splitting the data into train/test
 
-DEBUG:lightwood-2383: `split` runtime: 0.0 seconds
+DEBUG:lightwood-2669: `split` runtime: 0.0 seconds
 
-INFO:dataprep_ml-2383:[Learn phase 4/8] - Preparing encoders
+INFO:dataprep_ml-2669:[Learn phase 4/8] - Preparing encoders
 
-DEBUG:dataprep_ml-2383:Preparing sequentially...
+DEBUG:dataprep_ml-2669:Preparing sequentially...
 
-DEBUG:lightwood-2383: `prepare` runtime: 0.05 seconds
+DEBUG:lightwood-2669: `prepare` runtime: 0.05 seconds
 
-INFO:dataprep_ml-2383:[Learn phase 5/8] - Feature generation
+INFO:dataprep_ml-2669:[Learn phase 5/8] - Feature generation
 
-INFO:dataprep_ml-2383:Featurizing the data
+INFO:dataprep_ml-2669:Featurizing the data
 
-DEBUG:lightwood-2383: `featurize` runtime: 0.05 seconds
+DEBUG:lightwood-2669: `featurize` runtime: 0.05 seconds
 
-INFO:dataprep_ml-2383:[Learn phase 6/8] - Mixer training
+INFO:dataprep_ml-2669:[Learn phase 6/8] - Mixer training
 
-INFO:dataprep_ml-2383:Training the mixers
+INFO:dataprep_ml-2669:Training the mixers
 
-WARNING:lightwood-2383:XGBoost running on CPU
+WARNING:lightwood-2669:XGBoost running on CPU
 
-WARNING:lightwood-2383:XGBoost running on CPU
+WARNING:lightwood-2669:XGBoost running on CPU
 
-WARNING:lightwood-2383:XGBoost running on CPU
+WARNING:lightwood-2669:XGBoost running on CPU
 
-WARNING:lightwood-2383:XGBoost running on CPU
+WARNING:lightwood-2669:XGBoost running on CPU
 
-WARNING:lightwood-2383:XGBoost running on CPU
+WARNING:lightwood-2669:XGBoost running on CPU
 
-WARNING:lightwood-2383:XGBoost running on CPU
+WARNING:lightwood-2669:XGBoost running on CPU
 
-[10:00:36] WARNING: ../src/learner.cc:339: No visible GPU is found, setting `gpu_id` to -1
-[10:00:36] WARNING: ../src/learner.cc:339: No visible GPU is found, setting `gpu_id` to -1
-[10:00:36] WARNING: ../src/learner.cc:339: No visible GPU is found, setting `gpu_id` to -1
-[10:00:36] WARNING: ../src/learner.cc:339: No visible GPU is found, setting `gpu_id` to -1
-[10:00:36] WARNING: ../src/learner.cc:339: No visible GPU is found, setting `gpu_id` to -1
-[10:00:36] WARNING: ../src/learner.cc:339: No visible GPU is found, setting `gpu_id` to -1
+[10:18:50] WARNING: ../src/learner.cc:339: No visible GPU is found, setting `gpu_id` to -1
+[10:18:50] WARNING: ../src/learner.cc:339: No visible GPU is found, setting `gpu_id` to -1
+[10:18:50] WARNING: ../src/learner.cc:339: No visible GPU is found, setting `gpu_id` to -1
+[10:18:50] WARNING: ../src/learner.cc:339: No visible GPU is found, setting `gpu_id` to -1
+[10:18:50] WARNING: ../src/learner.cc:339: No visible GPU is found, setting `gpu_id` to -1
+[10:18:50] WARNING: ../src/learner.cc:339: No visible GPU is found, setting `gpu_id` to -1
 
-INFO:lightwood-2383:Loss of 9.014871209859848 with learning rate 0.0005
+INFO:lightwood-2669:Loss of 9.014871209859848 with learning rate 0.0005
 
-INFO:lightwood-2383:Loss of 8.969509482383728 with learning rate 0.001
+INFO:lightwood-2669:Loss of 8.969509482383728 with learning rate 0.001
 
-INFO:lightwood-2383:Loss of 8.879052013158798 with learning rate 0.002
+INFO:lightwood-2669:Loss of 8.879052013158798 with learning rate 0.002
 
-INFO:lightwood-2383:Loss of 8.788950502872467 with learning rate 0.003
+INFO:lightwood-2669:Loss of 8.788950502872467 with learning rate 0.003
 
-INFO:lightwood-2383:Loss of 8.611965209245682 with learning rate 0.005
+INFO:lightwood-2669:Loss of 8.611965209245682 with learning rate 0.005
 
-INFO:lightwood-2383:Loss of 8.195775926113129 with learning rate 0.01
+INFO:lightwood-2669:Loss of 8.195775926113129 with learning rate 0.01
 
-INFO:lightwood-2383:Loss of 6.255893141031265 with learning rate 0.05
+INFO:lightwood-2669:Loss of 6.255893141031265 with learning rate 0.05
 
-INFO:lightwood-2383:Found learning rate of: 0.05
+INFO:lightwood-2669:Found learning rate of: 0.05
 
-INFO:lightwood-2383:Loss @ epoch 2: 0.4797109067440033
+INFO:lightwood-2669:Loss @ epoch 2: 0.4797109067440033
 
-INFO:lightwood-2383:Loss @ epoch 3: 0.48386093974113464
+INFO:lightwood-2669:Loss @ epoch 3: 0.48386093974113464
 
-INFO:lightwood-2383:Loss @ epoch 4: 0.49511992931365967
+INFO:lightwood-2669:Loss @ epoch 4: 0.49511992931365967
 
-INFO:lightwood-2383:Loss @ epoch 5: 0.39475560188293457
+INFO:lightwood-2669:Loss @ epoch 5: 0.39475560188293457
 
-INFO:lightwood-2383:Loss @ epoch 6: 0.39592696726322174
+INFO:lightwood-2669:Loss @ epoch 6: 0.39592696726322174
 
-INFO:lightwood-2383:Loss @ epoch 7: 0.3622782379388809
+INFO:lightwood-2669:Loss @ epoch 7: 0.3622782379388809
 
-INFO:lightwood-2383:Loss @ epoch 8: 0.38170479238033295
+INFO:lightwood-2669:Loss @ epoch 8: 0.38170479238033295
 
-INFO:lightwood-2383:Loss @ epoch 9: 0.5138543993234634
+INFO:lightwood-2669:Loss @ epoch 9: 0.5138543993234634
 
-INFO:lightwood-2383:Loss @ epoch 10: 0.6360723078250885
+INFO:lightwood-2669:Loss @ epoch 10: 0.6360723078250885
 
-INFO:lightwood-2383:Loss @ epoch 1: 0.29868809472430835
+INFO:lightwood-2669:Loss @ epoch 1: 0.29868809472430835
 
-INFO:lightwood-2383:Loss @ epoch 2: 0.30318967591632495
+INFO:lightwood-2669:Loss @ epoch 2: 0.30318967591632495
 
-DEBUG:lightwood-2383: `fit_mixer` runtime: 0.99 seconds
+DEBUG:lightwood-2669: `fit_mixer` runtime: 0.9 seconds
 
-INFO:lightwood-2383:Started fitting LGBM models for array prediction
+INFO:lightwood-2669:Started fitting LGBM models for array prediction
 
-INFO:lightwood-2383:Started fitting XGBoost model
+INFO:lightwood-2669:Started fitting XGBoost model
 
-INFO:lightwood-2383:A single GBM iteration takes 0.1 seconds
+INFO:lightwood-2669:A single GBM iteration takes 0.1 seconds
 
-INFO:lightwood-2383:Training XGBoost with 57023 iterations given 7127.986500263214 seconds constraint
+INFO:lightwood-2669:Training XGBoost with 57023 iterations given 7127.987701892853 seconds constraint
 
-INFO:lightwood-2383:Started fitting XGBoost model
+INFO:lightwood-2669:Started fitting XGBoost model
 
-INFO:lightwood-2383:A single GBM iteration takes 0.1 seconds
+INFO:lightwood-2669:A single GBM iteration takes 0.1 seconds
 
-INFO:lightwood-2383:Training XGBoost with 57023 iterations given 7127.988530635834 seconds constraint
+INFO:lightwood-2669:Training XGBoost with 57023 iterations given 7127.988470077515 seconds constraint
 
-INFO:lightwood-2383:Started fitting XGBoost model
+INFO:lightwood-2669:Started fitting XGBoost model
 
-INFO:lightwood-2383:A single GBM iteration takes 0.1 seconds
+INFO:lightwood-2669:A single GBM iteration takes 0.1 seconds
 
-INFO:lightwood-2383:Training XGBoost with 57023 iterations given 7127.987628936768 seconds constraint
+INFO:lightwood-2669:Training XGBoost with 57023 iterations given 7127.988441467285 seconds constraint
 
-
+[10]    validation_0-rmse:19.34977
 
-[10]    validation_0-rmse:19.34977
+[11]    validation_0-rmse:19.43217
 
-[11]    validation_0-rmse:19.43217
+[12]    validation_0-rmse:19.48230
 
-INFO:lightwood-2383:Started fitting XGBoost model
+INFO:lightwood-2669:Started fitting XGBoost model
 
-INFO:lightwood-2383:A single GBM iteration takes 0.1 seconds
+INFO:lightwood-2669:A single GBM iteration takes 0.1 seconds
 
-INFO:lightwood-2383:Training XGBoost with 57023 iterations given 7127.988447189331 seconds constraint
+INFO:lightwood-2669:Training XGBoost with 57023 iterations given 7127.98904633522 seconds constraint
 
-INFO:lightwood-2383:Started fitting XGBoost model
+INFO:lightwood-2669:Started fitting XGBoost model
 
-INFO:lightwood-2383:A single GBM iteration takes 0.1 seconds
+INFO:lightwood-2669:A single GBM iteration takes 0.1 seconds
 
-INFO:lightwood-2383:Training XGBoost with 57023 iterations given 7127.988327264786 seconds constraint
+INFO:lightwood-2669:Training XGBoost with 57023 iterations given 7127.98738694191 seconds constraint
 
-
-
-
-
-
-
 
-INFO:lightwood-2383:Training XGBoost with 57023 iterations given 7127.988114356995 seconds constraint
+INFO:lightwood-2669:Training XGBoost with 57023 iterations given 7127.989282369614 seconds constraint
 
-
-
-
-
-
-
 
-DEBUG:lightwood-2383: `fit_mixer` runtime: 0.51 seconds
+DEBUG:lightwood-2669: `fit_mixer` runtime: 0.49 seconds
 
-INFO:dataprep_ml-2383:Ensembling the mixer
+INFO:dataprep_ml-2669:Ensembling the mixer
 
-INFO:lightwood-2383:Mixer: NeuralTs got accuracy: 0.875
+INFO:lightwood-2669:Mixer: NeuralTs got accuracy: 0.875
 
-WARNING:lightwood-2383:This model does not output probability estimates
+WARNING:lightwood-2669:This model does not output probability estimates
 
-INFO:lightwood-2383:Mixer: XGBoostArrayMixer got accuracy: 0.869
+INFO:lightwood-2669:Mixer: XGBoostArrayMixer got accuracy: 0.869
 
-INFO:lightwood-2383:Picked best mixer: NeuralTs
+INFO:lightwood-2669:Picked best mixer: NeuralTs
 
-DEBUG:lightwood-2383: `fit` runtime: 1.55 seconds
+DEBUG:lightwood-2669: `fit` runtime: 1.44 seconds
 
-INFO:dataprep_ml-2383:[Learn phase 7/8] - Ensemble analysis
+INFO:dataprep_ml-2669:[Learn phase 7/8] - Ensemble analysis
 
-INFO:dataprep_ml-2383:Analyzing the ensemble of mixers
+INFO:dataprep_ml-2669:Analyzing the ensemble of mixers
 
-INFO:lightwood-2383:The block ICP is now running its analyze() method
+INFO:lightwood-2669:The block ICP is now running its analyze() method
 
-INFO:lightwood-2383:The block ConfStats is now running its analyze() method
+INFO:lightwood-2669:The block ConfStats is now running its analyze() method
 
-INFO:lightwood-2383:The block AccStats is now running its analyze() method
+INFO:lightwood-2669:The block AccStats is now running its analyze() method
 
-INFO:lightwood-2383:The block PermutationFeatureImportance is now running its analyze() method
+INFO:lightwood-2669:The block PermutationFeatureImportance is now running its analyze() method
 
-WARNING:lightwood-2383:Block 'PermutationFeatureImportance' does not support time series nor text encoding, skipping...
+WARNING:lightwood-2669:Block 'PermutationFeatureImportance' does not support time series nor text encoding, skipping...
 
-DEBUG:lightwood-2383: `analyze_ensemble` runtime: 0.16 seconds
+DEBUG:lightwood-2669: `analyze_ensemble` runtime: 0.15 seconds
 
-INFO:dataprep_ml-2383:[Learn phase 8/8] - Adjustment on validation requested
+INFO:dataprep_ml-2669:[Learn phase 8/8] - Adjustment on validation requested
 
-INFO:dataprep_ml-2383:Updating the mixers
+INFO:dataprep_ml-2669:Updating the mixers
 
-INFO:lightwood-2383:Loss @ epoch 1: 0.29626286526521045
+INFO:lightwood-2669:Loss @ epoch 1: 0.29626286526521045
 
-INFO:lightwood-2383:Loss @ epoch 2: 0.2954987535874049
+INFO:lightwood-2669:Loss @ epoch 2: 0.2954987535874049
 
-INFO:lightwood-2383:Updating array of LGBM models...
+INFO:lightwood-2669:Updating array of LGBM models...
 
-INFO:lightwood-2383:XGBoost mixer does not have a `partial_fit` implementation
+INFO:lightwood-2669:XGBoost mixer does not have a `partial_fit` implementation
 
-INFO:lightwood-2383:XGBoost mixer does not have a `partial_fit` implementation
+INFO:lightwood-2669:XGBoost mixer does not have a `partial_fit` implementation
 
-INFO:lightwood-2383:XGBoost mixer does not have a `partial_fit` implementation
+INFO:lightwood-2669:XGBoost mixer does not have a `partial_fit` implementation
 
-INFO:lightwood-2383:XGBoost mixer does not have a `partial_fit` implementation
+INFO:lightwood-2669:XGBoost mixer does not have a `partial_fit` implementation
 
-INFO:lightwood-2383:XGBoost mixer does not have a `partial_fit` implementation
+INFO:lightwood-2669:XGBoost mixer does not have a `partial_fit` implementation
 
-INFO:lightwood-2383:XGBoost mixer does not have a `partial_fit` implementation
+INFO:lightwood-2669:XGBoost mixer does not have a `partial_fit` implementation
 
-DEBUG:lightwood-2383: `adjust` runtime: 0.09 seconds
+DEBUG:lightwood-2669: `adjust` runtime: 0.09 seconds
 
-DEBUG:lightwood-2383: `learn` runtime: 2.05 seconds
+DEBUG:lightwood-2669: `learn` runtime: 1.94 seconds
 
@@ -1922,7 +1914,7 @@

Predict
-INFO:dataprep_ml-2383:[Predict phase 1/4] - Data preprocessing
+INFO:dataprep_ml-2669:[Predict phase 1/4] - Data preprocessing
 

Let’s check how a single row might look:

diff --git a/tutorials/tutorial_time_series/tutorial_time_series.ipynb b/tutorials/tutorial_time_series/tutorial_time_series.ipynb index 7f6e9cda1..0079b3d1d 100644 --- a/tutorials/tutorial_time_series/tutorial_time_series.ipynb +++ b/tutorials/tutorial_time_series/tutorial_time_series.ipynb @@ -24,10 +24,10 @@ "execution_count": 1, "metadata": { "execution": { - "iopub.execute_input": "2024-03-19T10:00:29.052255Z", - "iopub.status.busy": "2024-03-19T10:00:29.052061Z", - "iopub.status.idle": "2024-03-19T10:00:29.633972Z", - "shell.execute_reply": "2024-03-19T10:00:29.633267Z" + "iopub.execute_input": "2024-03-19T10:18:43.858095Z", + "iopub.status.busy": "2024-03-19T10:18:43.857906Z", + "iopub.status.idle": "2024-03-19T10:18:44.259716Z", + "shell.execute_reply": "2024-03-19T10:18:44.259089Z" } }, "outputs": [ @@ -162,10 +162,10 @@ "execution_count": 2, "metadata": { "execution": { - "iopub.execute_input": "2024-03-19T10:00:29.671158Z", - "iopub.status.busy": "2024-03-19T10:00:29.670709Z", - "iopub.status.idle": "2024-03-19T10:00:31.910461Z", - "shell.execute_reply": "2024-03-19T10:00:31.909732Z" + "iopub.execute_input": "2024-03-19T10:18:44.297952Z", + "iopub.status.busy": "2024-03-19T10:18:44.297543Z", + "iopub.status.idle": "2024-03-19T10:18:46.516497Z", + "shell.execute_reply": "2024-03-19T10:18:46.515851Z" } }, "outputs": [ @@ -173,14 +173,14 @@ "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:lightwood-2383:No torchvision detected, image helpers not supported.\u001b[0m\n" + "\u001b[32mINFO:lightwood-2669:No torchvision detected, image helpers not supported.\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:lightwood-2383:No torchvision/pillow detected, image encoder not supported\u001b[0m\n" + "\u001b[32mINFO:lightwood-2669:No torchvision/pillow detected, image encoder not supported\u001b[0m\n" ] } ], @@ -193,10 +193,10 @@ "execution_count": 3, "metadata": { "execution": { - "iopub.execute_input": "2024-03-19T10:00:31.913739Z", - "iopub.status.busy": "2024-03-19T10:00:31.913449Z", - "iopub.status.idle": "2024-03-19T10:00:31.917298Z", - "shell.execute_reply": "2024-03-19T10:00:31.916694Z" + "iopub.execute_input": "2024-03-19T10:18:46.519651Z", + "iopub.status.busy": "2024-03-19T10:18:46.519150Z", + "iopub.status.idle": "2024-03-19T10:18:46.522788Z", + "shell.execute_reply": "2024-03-19T10:18:46.522177Z" } }, "outputs": [], @@ -223,10 +223,10 @@ "execution_count": 4, "metadata": { "execution": { - "iopub.execute_input": "2024-03-19T10:00:31.919961Z", - "iopub.status.busy": "2024-03-19T10:00:31.919505Z", - "iopub.status.idle": "2024-03-19T10:00:31.923256Z", - "shell.execute_reply": "2024-03-19T10:00:31.922624Z" + "iopub.execute_input": "2024-03-19T10:18:46.525334Z", + "iopub.status.busy": "2024-03-19T10:18:46.524970Z", + "iopub.status.idle": "2024-03-19T10:18:46.528771Z", + "shell.execute_reply": "2024-03-19T10:18:46.528137Z" } }, "outputs": [ @@ -261,10 +261,10 @@ "execution_count": 5, "metadata": { "execution": { - "iopub.execute_input": "2024-03-19T10:00:31.925848Z", - "iopub.status.busy": "2024-03-19T10:00:31.925662Z", - "iopub.status.idle": "2024-03-19T10:00:35.934404Z", - "shell.execute_reply": "2024-03-19T10:00:35.933746Z" + "iopub.execute_input": "2024-03-19T10:18:46.531381Z", + "iopub.status.busy": "2024-03-19T10:18:46.531001Z", + "iopub.status.idle": "2024-03-19T10:18:50.680970Z", + "shell.execute_reply": "2024-03-19T10:18:50.680334Z" } }, "outputs": [ @@ -272,49 +272,49 @@ "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:type_infer-2383:Analyzing a sample of 2467\u001b[0m\n" + "\u001b[32mINFO:type_infer-2669:Analyzing a sample of 2467\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:type_infer-2383:from a total population of 2820, this is equivalent to 87.5% of your data.\u001b[0m\n" + "\u001b[32mINFO:type_infer-2669:from a total population of 2820, this is equivalent to 87.5% of your data.\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:type_infer-2383:Infering type for: Month\u001b[0m\n" + "\u001b[32mINFO:type_infer-2669:Infering type for: Month\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:type_infer-2383:Column Month has data type date\u001b[0m\n" + "\u001b[32mINFO:type_infer-2669:Column Month has data type date\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:type_infer-2383:Infering type for: Sunspots\u001b[0m\n" + "\u001b[32mINFO:type_infer-2669:Infering type for: Sunspots\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:type_infer-2383:Column Sunspots has data type float\u001b[0m\n" + "\u001b[32mINFO:type_infer-2669:Column Sunspots has data type float\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:dataprep_ml-2383:Starting statistical analysis\u001b[0m\n" + "\u001b[32mINFO:dataprep_ml-2669:Starting statistical analysis\u001b[0m\n" ] }, { @@ -323,7 +323,7 @@ "text": [ "/opt/hostedtoolcache/Python/3.9.18/x64/lib/python3.9/site-packages/dataprep_ml/cleaners.py:163: UserWarning: The argument 'infer_datetime_format' is deprecated and will be removed in a future version. A strict version of it is now the default, see https://pandas.pydata.org/pdeps/0004-consistent-to-datetime-parsing.html. You can safely remove this argument.\n", " result = pd.to_datetime(element,\n", - "\u001b[32mINFO:dataprep_ml-2383:Finished statistical analysis\u001b[0m\n" + "\u001b[32mINFO:dataprep_ml-2669:Finished statistical analysis\u001b[0m\n" ] } ], @@ -360,10 +360,10 @@ "execution_count": 6, "metadata": { "execution": { - "iopub.execute_input": "2024-03-19T10:00:35.937460Z", - "iopub.status.busy": "2024-03-19T10:00:35.937029Z", - "iopub.status.idle": "2024-03-19T10:00:37.993986Z", - "shell.execute_reply": "2024-03-19T10:00:37.993278Z" + "iopub.execute_input": "2024-03-19T10:18:50.684122Z", + "iopub.status.busy": "2024-03-19T10:18:50.683672Z", + "iopub.status.idle": "2024-03-19T10:18:52.624199Z", + "shell.execute_reply": "2024-03-19T10:18:52.623532Z" } }, "outputs": [ @@ -371,14 +371,14 @@ "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:dataprep_ml-2383:[Learn phase 1/8] - Statistical analysis\u001b[0m\n" + "\u001b[32mINFO:dataprep_ml-2669:[Learn phase 1/8] - Statistical analysis\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:dataprep_ml-2383:Starting statistical analysis\u001b[0m\n" + "\u001b[32mINFO:dataprep_ml-2669:Starting statistical analysis\u001b[0m\n" ] }, { @@ -387,28 +387,28 @@ "text": [ "/opt/hostedtoolcache/Python/3.9.18/x64/lib/python3.9/site-packages/dataprep_ml/cleaners.py:163: UserWarning: The argument 'infer_datetime_format' is deprecated and will be removed in a future version. A strict version of it is now the default, see https://pandas.pydata.org/pdeps/0004-consistent-to-datetime-parsing.html. You can safely remove this argument.\n", " result = pd.to_datetime(element,\n", - "\u001b[32mINFO:dataprep_ml-2383:Finished statistical analysis\u001b[0m\n" + "\u001b[32mINFO:dataprep_ml-2669:Finished statistical analysis\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[37mDEBUG:lightwood-2383: `analyze_data` runtime: 0.05 seconds\u001b[0m\n" + "\u001b[37mDEBUG:lightwood-2669: `analyze_data` runtime: 0.05 seconds\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:dataprep_ml-2383:[Learn phase 2/8] - Data preprocessing\u001b[0m\n" + "\u001b[32mINFO:dataprep_ml-2669:[Learn phase 2/8] - Data preprocessing\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:dataprep_ml-2383:Cleaning the data\u001b[0m\n" + "\u001b[32mINFO:dataprep_ml-2669:Cleaning the data\u001b[0m\n" ] }, { @@ -417,133 +417,133 @@ "text": [ "/opt/hostedtoolcache/Python/3.9.18/x64/lib/python3.9/site-packages/dataprep_ml/cleaners.py:163: UserWarning: The argument 'infer_datetime_format' is deprecated and will be removed in a future version. A strict version of it is now the default, see https://pandas.pydata.org/pdeps/0004-consistent-to-datetime-parsing.html. You can safely remove this argument.\n", " result = pd.to_datetime(element,\n", - "\u001b[32mINFO:dataprep_ml-2383:Transforming timeseries data\u001b[0m\n" + "\u001b[32mINFO:dataprep_ml-2669:Transforming timeseries data\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[37mDEBUG:lightwood-2383: `preprocess` runtime: 0.09 seconds\u001b[0m\n" + "\u001b[37mDEBUG:lightwood-2669: `preprocess` runtime: 0.09 seconds\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:dataprep_ml-2383:[Learn phase 3/8] - Data splitting\u001b[0m\n" + "\u001b[32mINFO:dataprep_ml-2669:[Learn phase 3/8] - Data splitting\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:dataprep_ml-2383:Splitting the data into train/test\u001b[0m\n" + "\u001b[32mINFO:dataprep_ml-2669:Splitting the data into train/test\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[37mDEBUG:lightwood-2383: `split` runtime: 0.0 seconds\u001b[0m\n" + "\u001b[37mDEBUG:lightwood-2669: `split` runtime: 0.0 seconds\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:dataprep_ml-2383:[Learn phase 4/8] - Preparing encoders\u001b[0m\n" + "\u001b[32mINFO:dataprep_ml-2669:[Learn phase 4/8] - Preparing encoders\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[37mDEBUG:dataprep_ml-2383:Preparing sequentially...\u001b[0m\n" + "\u001b[37mDEBUG:dataprep_ml-2669:Preparing sequentially...\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[37mDEBUG:lightwood-2383: `prepare` runtime: 0.05 seconds\u001b[0m\n" + "\u001b[37mDEBUG:lightwood-2669: `prepare` runtime: 0.05 seconds\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:dataprep_ml-2383:[Learn phase 5/8] - Feature generation\u001b[0m\n" + "\u001b[32mINFO:dataprep_ml-2669:[Learn phase 5/8] - Feature generation\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:dataprep_ml-2383:Featurizing the data\u001b[0m\n" + "\u001b[32mINFO:dataprep_ml-2669:Featurizing the data\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[37mDEBUG:lightwood-2383: `featurize` runtime: 0.05 seconds\u001b[0m\n" + "\u001b[37mDEBUG:lightwood-2669: `featurize` runtime: 0.05 seconds\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:dataprep_ml-2383:[Learn phase 6/8] - Mixer training\u001b[0m\n" + "\u001b[32mINFO:dataprep_ml-2669:[Learn phase 6/8] - Mixer training\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:dataprep_ml-2383:Training the mixers\u001b[0m\n" + "\u001b[32mINFO:dataprep_ml-2669:Training the mixers\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[33mWARNING:lightwood-2383:XGBoost running on CPU\u001b[0m\n" + "\u001b[33mWARNING:lightwood-2669:XGBoost running on CPU\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[33mWARNING:lightwood-2383:XGBoost running on CPU\u001b[0m\n" + "\u001b[33mWARNING:lightwood-2669:XGBoost running on CPU\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[33mWARNING:lightwood-2383:XGBoost running on CPU\u001b[0m\n" + "\u001b[33mWARNING:lightwood-2669:XGBoost running on CPU\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[33mWARNING:lightwood-2383:XGBoost running on CPU\u001b[0m\n" + "\u001b[33mWARNING:lightwood-2669:XGBoost running on CPU\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[33mWARNING:lightwood-2383:XGBoost running on CPU\u001b[0m\n" + "\u001b[33mWARNING:lightwood-2669:XGBoost running on CPU\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[33mWARNING:lightwood-2383:XGBoost running on CPU\u001b[0m\n" + "\u001b[33mWARNING:lightwood-2669:XGBoost running on CPU\u001b[0m\n" ] }, { @@ -558,12 +558,12 @@ "name": "stdout", "output_type": "stream", "text": [ - "[10:00:36] WARNING: ../src/learner.cc:339: No visible GPU is found, setting `gpu_id` to -1\n", - "[10:00:36] WARNING: ../src/learner.cc:339: No visible GPU is found, setting `gpu_id` to -1\n", - "[10:00:36] WARNING: ../src/learner.cc:339: No visible GPU is found, setting `gpu_id` to -1\n", - "[10:00:36] WARNING: ../src/learner.cc:339: No visible GPU is found, setting `gpu_id` to -1\n", - "[10:00:36] WARNING: ../src/learner.cc:339: No visible GPU is found, setting `gpu_id` to -1\n", - "[10:00:36] WARNING: ../src/learner.cc:339: No visible GPU is found, setting `gpu_id` to -1\n" + "[10:18:50] WARNING: ../src/learner.cc:339: No visible GPU is found, setting `gpu_id` to -1\n", + "[10:18:50] WARNING: ../src/learner.cc:339: No visible GPU is found, setting `gpu_id` to -1\n", + "[10:18:50] WARNING: ../src/learner.cc:339: No visible GPU is found, setting `gpu_id` to -1\n", + "[10:18:50] WARNING: ../src/learner.cc:339: No visible GPU is found, setting `gpu_id` to -1\n", + "[10:18:50] WARNING: ../src/learner.cc:339: No visible GPU is found, setting `gpu_id` to -1\n", + "[10:18:50] WARNING: ../src/learner.cc:339: No visible GPU is found, setting `gpu_id` to -1\n" ] }, { @@ -575,63 +575,63 @@ "Consider using one of the following signatures instead:\n", "\taddcmul_(Tensor tensor1, Tensor tensor2, *, Number value) (Triggered internally at ../torch/csrc/utils/python_arg_parser.cpp:1630.)\n", " exp_avg_sq.mul_(beta2).addcmul_(1 - beta2, grad, grad)\n", - "\u001b[32mINFO:lightwood-2383:Loss of 9.051180630922318 with learning rate 0.0001\u001b[0m\n" + "\u001b[32mINFO:lightwood-2669:Loss of 9.051180630922318 with learning rate 0.0001\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:lightwood-2383:Loss of 9.014871209859848 with learning rate 0.0005\u001b[0m\n" + "\u001b[32mINFO:lightwood-2669:Loss of 9.014871209859848 with learning rate 0.0005\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:lightwood-2383:Loss of 8.969509482383728 with learning rate 0.001\u001b[0m\n" + "\u001b[32mINFO:lightwood-2669:Loss of 8.969509482383728 with learning rate 0.001\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:lightwood-2383:Loss of 8.879052013158798 with learning rate 0.002\u001b[0m\n" + "\u001b[32mINFO:lightwood-2669:Loss of 8.879052013158798 with learning rate 0.002\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:lightwood-2383:Loss of 8.788950502872467 with learning rate 0.003\u001b[0m\n" + "\u001b[32mINFO:lightwood-2669:Loss of 8.788950502872467 with learning rate 0.003\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:lightwood-2383:Loss of 8.611965209245682 with learning rate 0.005\u001b[0m\n" + "\u001b[32mINFO:lightwood-2669:Loss of 8.611965209245682 with learning rate 0.005\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:lightwood-2383:Loss of 8.195775926113129 with learning rate 0.01\u001b[0m\n" + "\u001b[32mINFO:lightwood-2669:Loss of 8.195775926113129 with learning rate 0.01\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:lightwood-2383:Loss of 6.255893141031265 with learning rate 0.05\u001b[0m\n" + "\u001b[32mINFO:lightwood-2669:Loss of 6.255893141031265 with learning rate 0.05\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:lightwood-2383:Found learning rate of: 0.05\u001b[0m\n" + "\u001b[32mINFO:lightwood-2669:Found learning rate of: 0.05\u001b[0m\n" ] }, { @@ -640,105 +640,105 @@ "text": [ "/opt/hostedtoolcache/Python/3.9.18/x64/lib/python3.9/site-packages/torch/cuda/amp/grad_scaler.py:126: UserWarning: torch.cuda.amp.GradScaler is enabled, but CUDA is not available. Disabling.\n", " warnings.warn(\n", - "\u001b[32mINFO:lightwood-2383:Loss @ epoch 1: 0.5818348675966263\u001b[0m\n" + "\u001b[32mINFO:lightwood-2669:Loss @ epoch 1: 0.5818348675966263\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:lightwood-2383:Loss @ epoch 2: 0.4797109067440033\u001b[0m\n" + "\u001b[32mINFO:lightwood-2669:Loss @ epoch 2: 0.4797109067440033\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:lightwood-2383:Loss @ epoch 3: 0.48386093974113464\u001b[0m\n" + "\u001b[32mINFO:lightwood-2669:Loss @ epoch 3: 0.48386093974113464\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:lightwood-2383:Loss @ epoch 4: 0.49511992931365967\u001b[0m\n" + "\u001b[32mINFO:lightwood-2669:Loss @ epoch 4: 0.49511992931365967\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:lightwood-2383:Loss @ epoch 5: 0.39475560188293457\u001b[0m\n" + "\u001b[32mINFO:lightwood-2669:Loss @ epoch 5: 0.39475560188293457\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:lightwood-2383:Loss @ epoch 6: 0.39592696726322174\u001b[0m\n" + "\u001b[32mINFO:lightwood-2669:Loss @ epoch 6: 0.39592696726322174\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:lightwood-2383:Loss @ epoch 7: 0.3622782379388809\u001b[0m\n" + "\u001b[32mINFO:lightwood-2669:Loss @ epoch 7: 0.3622782379388809\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:lightwood-2383:Loss @ epoch 8: 0.38170479238033295\u001b[0m\n" + "\u001b[32mINFO:lightwood-2669:Loss @ epoch 8: 0.38170479238033295\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:lightwood-2383:Loss @ epoch 9: 0.5138543993234634\u001b[0m\n" + "\u001b[32mINFO:lightwood-2669:Loss @ epoch 9: 0.5138543993234634\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:lightwood-2383:Loss @ epoch 10: 0.6360723078250885\u001b[0m\n" + "\u001b[32mINFO:lightwood-2669:Loss @ epoch 10: 0.6360723078250885\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:lightwood-2383:Loss @ epoch 1: 0.29868809472430835\u001b[0m\n" + "\u001b[32mINFO:lightwood-2669:Loss @ epoch 1: 0.29868809472430835\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:lightwood-2383:Loss @ epoch 2: 0.30318967591632495\u001b[0m\n" + "\u001b[32mINFO:lightwood-2669:Loss @ epoch 2: 0.30318967591632495\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[37mDEBUG:lightwood-2383: `fit_mixer` runtime: 0.99 seconds\u001b[0m\n" + "\u001b[37mDEBUG:lightwood-2669: `fit_mixer` runtime: 0.9 seconds\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:lightwood-2383:Started fitting LGBM models for array prediction\u001b[0m\n" + "\u001b[32mINFO:lightwood-2669:Started fitting LGBM models for array prediction\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:lightwood-2383:Started fitting XGBoost model\u001b[0m\n" + "\u001b[32mINFO:lightwood-2669:Started fitting XGBoost model\u001b[0m\n" ] }, { @@ -752,14 +752,14 @@ "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:lightwood-2383:A single GBM iteration takes 0.1 seconds\u001b[0m\n" + "\u001b[32mINFO:lightwood-2669:A single GBM iteration takes 0.1 seconds\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:lightwood-2383:Training XGBoost with 57023 iterations given 7127.986500263214 seconds constraint\u001b[0m\n" + "\u001b[32mINFO:lightwood-2669:Training XGBoost with 57023 iterations given 7127.987701892853 seconds constraint\u001b[0m\n" ] }, { @@ -871,7 +871,7 @@ "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:lightwood-2383:Started fitting XGBoost model\u001b[0m\n" + "\u001b[32mINFO:lightwood-2669:Started fitting XGBoost model\u001b[0m\n" ] }, { @@ -885,14 +885,14 @@ "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:lightwood-2383:A single GBM iteration takes 0.1 seconds\u001b[0m\n" + "\u001b[32mINFO:lightwood-2669:A single GBM iteration takes 0.1 seconds\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:lightwood-2383:Training XGBoost with 57023 iterations given 7127.988530635834 seconds constraint\u001b[0m\n" + "\u001b[32mINFO:lightwood-2669:Training XGBoost with 57023 iterations given 7127.988470077515 seconds constraint\u001b[0m\n" ] }, { @@ -997,7 +997,7 @@ "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:lightwood-2383:Started fitting XGBoost model\u001b[0m\n" + "\u001b[32mINFO:lightwood-2669:Started fitting XGBoost model\u001b[0m\n" ] }, { @@ -1011,14 +1011,14 @@ "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:lightwood-2383:A single GBM iteration takes 0.1 seconds\u001b[0m\n" + "\u001b[32mINFO:lightwood-2669:A single GBM iteration takes 0.1 seconds\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:lightwood-2383:Training XGBoost with 57023 iterations given 7127.987628936768 seconds constraint\u001b[0m\n" + "\u001b[32mINFO:lightwood-2669:Training XGBoost with 57023 iterations given 7127.988441467285 seconds constraint\u001b[0m\n" ] }, { @@ -1088,35 +1088,35 @@ "name": "stdout", "output_type": "stream", "text": [ - "[9]\tvalidation_0-rmse:19.12589" + "[9]\tvalidation_0-rmse:19.12589\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "\n" + "[10]\tvalidation_0-rmse:19.34977\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "[10]\tvalidation_0-rmse:19.34977\n" + "[11]\tvalidation_0-rmse:19.43217\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "[11]\tvalidation_0-rmse:19.43217\n" + "[12]\tvalidation_0-rmse:19.48230\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:lightwood-2383:Started fitting XGBoost model\u001b[0m\n" + "\u001b[32mINFO:lightwood-2669:Started fitting XGBoost model\u001b[0m\n" ] }, { @@ -1130,14 +1130,14 @@ "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:lightwood-2383:A single GBM iteration takes 0.1 seconds\u001b[0m\n" + "\u001b[32mINFO:lightwood-2669:A single GBM iteration takes 0.1 seconds\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:lightwood-2383:Training XGBoost with 57023 iterations given 7127.988447189331 seconds constraint\u001b[0m\n" + "\u001b[32mINFO:lightwood-2669:Training XGBoost with 57023 iterations given 7127.98904633522 seconds constraint\u001b[0m\n" ] }, { @@ -1242,7 +1242,7 @@ "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:lightwood-2383:Started fitting XGBoost model\u001b[0m\n" + "\u001b[32mINFO:lightwood-2669:Started fitting XGBoost model\u001b[0m\n" ] }, { @@ -1256,14 +1256,14 @@ "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:lightwood-2383:A single GBM iteration takes 0.1 seconds\u001b[0m\n" + "\u001b[32mINFO:lightwood-2669:A single GBM iteration takes 0.1 seconds\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:lightwood-2383:Training XGBoost with 57023 iterations given 7127.988327264786 seconds constraint\u001b[0m\n" + "\u001b[32mINFO:lightwood-2669:Training XGBoost with 57023 iterations given 7127.98738694191 seconds constraint\u001b[0m\n" ] }, { @@ -1305,14 +1305,7 @@ "name": "stdout", "output_type": "stream", "text": [ - "[5]\tvalidation_0-rmse:22.35045" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n" + "[5]\tvalidation_0-rmse:22.35045\n" ] }, { @@ -1371,11 +1364,18 @@ "[13]\tvalidation_0-rmse:22.31415\n" ] }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[14]\tvalidation_0-rmse:22.31000\n" + ] + }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:lightwood-2383:Started fitting XGBoost model\u001b[0m\n" + "\u001b[32mINFO:lightwood-2669:Started fitting XGBoost model\u001b[0m\n" ] }, { @@ -1389,14 +1389,14 @@ "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:lightwood-2383:A single GBM iteration takes 0.1 seconds\u001b[0m\n" + "\u001b[32mINFO:lightwood-2669:A single GBM iteration takes 0.1 seconds\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:lightwood-2383:Training XGBoost with 57023 iterations given 7127.988114356995 seconds constraint\u001b[0m\n" + "\u001b[32mINFO:lightwood-2669:Training XGBoost with 57023 iterations given 7127.989282369614 seconds constraint\u001b[0m\n" ] }, { @@ -1480,14 +1480,7 @@ "name": "stdout", "output_type": "stream", "text": [ - "[11]\tvalidation_0-rmse:21.84587" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n" + "[11]\tvalidation_0-rmse:21.84587\n" ] }, { @@ -1515,119 +1508,119 @@ "name": "stderr", "output_type": "stream", "text": [ - "\u001b[37mDEBUG:lightwood-2383: `fit_mixer` runtime: 0.51 seconds\u001b[0m\n" + "\u001b[37mDEBUG:lightwood-2669: `fit_mixer` runtime: 0.49 seconds\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:dataprep_ml-2383:Ensembling the mixer\u001b[0m\n" + "\u001b[32mINFO:dataprep_ml-2669:Ensembling the mixer\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:lightwood-2383:Mixer: NeuralTs got accuracy: 0.875\u001b[0m\n" + "\u001b[32mINFO:lightwood-2669:Mixer: NeuralTs got accuracy: 0.875\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[33mWARNING:lightwood-2383:This model does not output probability estimates\u001b[0m\n" + "\u001b[33mWARNING:lightwood-2669:This model does not output probability estimates\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:lightwood-2383:Mixer: XGBoostArrayMixer got accuracy: 0.869\u001b[0m\n" + "\u001b[32mINFO:lightwood-2669:Mixer: XGBoostArrayMixer got accuracy: 0.869\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:lightwood-2383:Picked best mixer: NeuralTs\u001b[0m\n" + "\u001b[32mINFO:lightwood-2669:Picked best mixer: NeuralTs\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[37mDEBUG:lightwood-2383: `fit` runtime: 1.55 seconds\u001b[0m\n" + "\u001b[37mDEBUG:lightwood-2669: `fit` runtime: 1.44 seconds\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:dataprep_ml-2383:[Learn phase 7/8] - Ensemble analysis\u001b[0m\n" + "\u001b[32mINFO:dataprep_ml-2669:[Learn phase 7/8] - Ensemble analysis\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:dataprep_ml-2383:Analyzing the ensemble of mixers\u001b[0m\n" + "\u001b[32mINFO:dataprep_ml-2669:Analyzing the ensemble of mixers\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:lightwood-2383:The block ICP is now running its analyze() method\u001b[0m\n" + "\u001b[32mINFO:lightwood-2669:The block ICP is now running its analyze() method\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:lightwood-2383:The block ConfStats is now running its analyze() method\u001b[0m\n" + "\u001b[32mINFO:lightwood-2669:The block ConfStats is now running its analyze() method\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:lightwood-2383:The block AccStats is now running its analyze() method\u001b[0m\n" + "\u001b[32mINFO:lightwood-2669:The block AccStats is now running its analyze() method\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:lightwood-2383:The block PermutationFeatureImportance is now running its analyze() method\u001b[0m\n" + "\u001b[32mINFO:lightwood-2669:The block PermutationFeatureImportance is now running its analyze() method\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[33mWARNING:lightwood-2383:Block 'PermutationFeatureImportance' does not support time series nor text encoding, skipping...\u001b[0m\n" + "\u001b[33mWARNING:lightwood-2669:Block 'PermutationFeatureImportance' does not support time series nor text encoding, skipping...\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[37mDEBUG:lightwood-2383: `analyze_ensemble` runtime: 0.16 seconds\u001b[0m\n" + "\u001b[37mDEBUG:lightwood-2669: `analyze_ensemble` runtime: 0.15 seconds\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:dataprep_ml-2383:[Learn phase 8/8] - Adjustment on validation requested\u001b[0m\n" + "\u001b[32mINFO:dataprep_ml-2669:[Learn phase 8/8] - Adjustment on validation requested\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:dataprep_ml-2383:Updating the mixers\u001b[0m\n" + "\u001b[32mINFO:dataprep_ml-2669:Updating the mixers\u001b[0m\n" ] }, { @@ -1642,77 +1635,77 @@ "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:lightwood-2383:Loss @ epoch 1: 0.29626286526521045\u001b[0m\n" + "\u001b[32mINFO:lightwood-2669:Loss @ epoch 1: 0.29626286526521045\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:lightwood-2383:Loss @ epoch 2: 0.2954987535874049\u001b[0m\n" + "\u001b[32mINFO:lightwood-2669:Loss @ epoch 2: 0.2954987535874049\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:lightwood-2383:Updating array of LGBM models...\u001b[0m\n" + "\u001b[32mINFO:lightwood-2669:Updating array of LGBM models...\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:lightwood-2383:XGBoost mixer does not have a `partial_fit` implementation\u001b[0m\n" + "\u001b[32mINFO:lightwood-2669:XGBoost mixer does not have a `partial_fit` implementation\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:lightwood-2383:XGBoost mixer does not have a `partial_fit` implementation\u001b[0m\n" + "\u001b[32mINFO:lightwood-2669:XGBoost mixer does not have a `partial_fit` implementation\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:lightwood-2383:XGBoost mixer does not have a `partial_fit` implementation\u001b[0m\n" + "\u001b[32mINFO:lightwood-2669:XGBoost mixer does not have a `partial_fit` implementation\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:lightwood-2383:XGBoost mixer does not have a `partial_fit` implementation\u001b[0m\n" + "\u001b[32mINFO:lightwood-2669:XGBoost mixer does not have a `partial_fit` implementation\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:lightwood-2383:XGBoost mixer does not have a `partial_fit` implementation\u001b[0m\n" + "\u001b[32mINFO:lightwood-2669:XGBoost mixer does not have a `partial_fit` implementation\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:lightwood-2383:XGBoost mixer does not have a `partial_fit` implementation\u001b[0m\n" + "\u001b[32mINFO:lightwood-2669:XGBoost mixer does not have a `partial_fit` implementation\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[37mDEBUG:lightwood-2383: `adjust` runtime: 0.09 seconds\u001b[0m\n" + "\u001b[37mDEBUG:lightwood-2669: `adjust` runtime: 0.09 seconds\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[37mDEBUG:lightwood-2383: `learn` runtime: 2.05 seconds\u001b[0m\n" + "\u001b[37mDEBUG:lightwood-2669: `learn` runtime: 1.94 seconds\u001b[0m\n" ] } ], @@ -1734,10 +1727,10 @@ "execution_count": 7, "metadata": { "execution": { - "iopub.execute_input": "2024-03-19T10:00:37.996875Z", - "iopub.status.busy": "2024-03-19T10:00:37.996662Z", - "iopub.status.idle": "2024-03-19T10:00:38.223083Z", - "shell.execute_reply": "2024-03-19T10:00:38.222403Z" + "iopub.execute_input": "2024-03-19T10:18:52.627058Z", + "iopub.status.busy": "2024-03-19T10:18:52.626672Z", + "iopub.status.idle": "2024-03-19T10:18:52.849567Z", + "shell.execute_reply": "2024-03-19T10:18:52.848941Z" } }, "outputs": [ @@ -1745,20 +1738,20 @@ "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:dataprep_ml-2383:[Predict phase 1/4] - Data preprocessing\u001b[0m\n" + "\u001b[32mINFO:dataprep_ml-2669:[Predict phase 1/4] - Data preprocessing\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "/tmp/d20805f5018ce0107de047b65b88c485a326e1d1a6f5f89517108424359285157.py:584: SettingWithCopyWarning: \n", + "/tmp/f73a2317178f784e4c57c843f563cceab09655f97903f47417108435306748986.py:584: SettingWithCopyWarning: \n", "A value is trying to be set on a copy of a slice from a DataFrame.\n", "Try using .loc[row_indexer,col_indexer] = value instead\n", "\n", "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", " data[col] = [None] * len(data)\n", - "\u001b[32mINFO:dataprep_ml-2383:Cleaning the data\u001b[0m\n" + "\u001b[32mINFO:dataprep_ml-2669:Cleaning the data\u001b[0m\n" ] }, { @@ -1767,119 +1760,119 @@ "text": [ "/opt/hostedtoolcache/Python/3.9.18/x64/lib/python3.9/site-packages/dataprep_ml/cleaners.py:163: UserWarning: The argument 'infer_datetime_format' is deprecated and will be removed in a future version. A strict version of it is now the default, see https://pandas.pydata.org/pdeps/0004-consistent-to-datetime-parsing.html. You can safely remove this argument.\n", " result = pd.to_datetime(element,\n", - "\u001b[32mINFO:dataprep_ml-2383:Transforming timeseries data\u001b[0m\n" + "\u001b[32mINFO:dataprep_ml-2669:Transforming timeseries data\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[37mDEBUG:lightwood-2383: `preprocess` runtime: 0.02 seconds\u001b[0m\n" + "\u001b[37mDEBUG:lightwood-2669: `preprocess` runtime: 0.02 seconds\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:dataprep_ml-2383:[Predict phase 2/4] - Feature generation\u001b[0m\n" + "\u001b[32mINFO:dataprep_ml-2669:[Predict phase 2/4] - Feature generation\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:dataprep_ml-2383:Featurizing the data\u001b[0m\n" + "\u001b[32mINFO:dataprep_ml-2669:Featurizing the data\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[37mDEBUG:lightwood-2383: `featurize` runtime: 0.01 seconds\u001b[0m\n" + "\u001b[37mDEBUG:lightwood-2669: `featurize` runtime: 0.01 seconds\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:dataprep_ml-2383:[Predict phase 3/4] - Calling ensemble\u001b[0m\n" + "\u001b[32mINFO:dataprep_ml-2669:[Predict phase 3/4] - Calling ensemble\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[37mDEBUG:lightwood-2383: `_timed_call` runtime: 0.09 seconds\u001b[0m\n" + "\u001b[37mDEBUG:lightwood-2669: `_timed_call` runtime: 0.09 seconds\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:dataprep_ml-2383:[Predict phase 4/4] - Analyzing output\u001b[0m\n" + "\u001b[32mINFO:dataprep_ml-2669:[Predict phase 4/4] - Analyzing output\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:lightwood-2383:The block ICP is now running its explain() method\u001b[0m\n" + "\u001b[32mINFO:lightwood-2669:The block ICP is now running its explain() method\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:lightwood-2383:The block ConfStats is now running its explain() method\u001b[0m\n" + "\u001b[32mINFO:lightwood-2669:The block ConfStats is now running its explain() method\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:lightwood-2383:ConfStats.explain() has not been implemented, no modifications will be done to the data insights.\u001b[0m\n" + "\u001b[32mINFO:lightwood-2669:ConfStats.explain() has not been implemented, no modifications will be done to the data insights.\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:lightwood-2383:The block AccStats is now running its explain() method\u001b[0m\n" + "\u001b[32mINFO:lightwood-2669:The block AccStats is now running its explain() method\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:lightwood-2383:AccStats.explain() has not been implemented, no modifications will be done to the data insights.\u001b[0m\n" + "\u001b[32mINFO:lightwood-2669:AccStats.explain() has not been implemented, no modifications will be done to the data insights.\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:lightwood-2383:The block PermutationFeatureImportance is now running its explain() method\u001b[0m\n" + "\u001b[32mINFO:lightwood-2669:The block PermutationFeatureImportance is now running its explain() method\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:lightwood-2383:PermutationFeatureImportance.explain() has not been implemented, no modifications will be done to the data insights.\u001b[0m\n" + "\u001b[32mINFO:lightwood-2669:PermutationFeatureImportance.explain() has not been implemented, no modifications will be done to the data insights.\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[37mDEBUG:lightwood-2383: `explain` runtime: 0.09 seconds\u001b[0m\n" + "\u001b[37mDEBUG:lightwood-2669: `explain` runtime: 0.09 seconds\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[37mDEBUG:lightwood-2383: `predict` runtime: 0.22 seconds\u001b[0m\n" + "\u001b[37mDEBUG:lightwood-2669: `predict` runtime: 0.22 seconds\u001b[0m\n" ] } ], @@ -1899,10 +1892,10 @@ "execution_count": 8, "metadata": { "execution": { - "iopub.execute_input": "2024-03-19T10:00:38.226342Z", - "iopub.status.busy": "2024-03-19T10:00:38.226147Z", - "iopub.status.idle": "2024-03-19T10:00:38.237734Z", - "shell.execute_reply": "2024-03-19T10:00:38.237155Z" + "iopub.execute_input": "2024-03-19T10:18:52.852090Z", + "iopub.status.busy": "2024-03-19T10:18:52.851741Z", + "iopub.status.idle": "2024-03-19T10:18:52.862630Z", + "shell.execute_reply": "2024-03-19T10:18:52.862011Z" } }, "outputs": [ @@ -2007,10 +2000,10 @@ "execution_count": 9, "metadata": { "execution": { - "iopub.execute_input": "2024-03-19T10:00:38.240237Z", - "iopub.status.busy": "2024-03-19T10:00:38.239843Z", - "iopub.status.idle": "2024-03-19T10:00:38.630482Z", - "shell.execute_reply": "2024-03-19T10:00:38.629820Z" + "iopub.execute_input": "2024-03-19T10:18:52.865241Z", + "iopub.status.busy": "2024-03-19T10:18:52.864888Z", + "iopub.status.idle": "2024-03-19T10:18:53.249782Z", + "shell.execute_reply": "2024-03-19T10:18:53.249067Z" } }, "outputs": [], @@ -2023,10 +2016,10 @@ "execution_count": 10, "metadata": { "execution": { - "iopub.execute_input": "2024-03-19T10:00:38.633703Z", - "iopub.status.busy": "2024-03-19T10:00:38.633127Z", - "iopub.status.idle": "2024-03-19T10:00:38.827401Z", - "shell.execute_reply": "2024-03-19T10:00:38.826665Z" + "iopub.execute_input": "2024-03-19T10:18:53.252801Z", + "iopub.status.busy": "2024-03-19T10:18:53.252497Z", + "iopub.status.idle": "2024-03-19T10:18:53.440309Z", + "shell.execute_reply": "2024-03-19T10:18:53.439662Z" } }, "outputs": [ diff --git a/tutorials/tutorial_update_models/tutorial_update_models.html b/tutorials/tutorial_update_models/tutorial_update_models.html index 992e128cd..55fd4cf41 100644 --- a/tutorials/tutorial_update_models/tutorial_update_models.html +++ b/tutorials/tutorial_update_models/tutorial_update_models.html @@ -4,7 +4,7 @@ - Introduction — lightwood 24.3.3.1 documentation + Introduction — lightwood 24.3.3.0 documentation @@ -42,7 +42,7 @@
- 24.3.3.1 + 24.3.3.0
@@ -110,7 +110,7 @@

Initial model training
-INFO:lightwood-2429:No torchvision detected, image helpers not supported.
+INFO:lightwood-2715:No torchvision detected, image helpers not supported.
 

+
+
+
+
+
+DEBUG:lightwood-2715: `adjust` runtime: 0.03 seconds
 
@@ -1210,7 +1217,7 @@

PredictorInterf

-INFO:dataprep_ml-2429:Cleaning the data
+INFO:dataprep_ml-2715:Cleaning the data
 
@@ -1218,7 +1225,7 @@

PredictorInterf

-DEBUG:lightwood-2429: `preprocess` runtime: 0.02 seconds
+DEBUG:lightwood-2715: `preprocess` runtime: 0.02 seconds
 
@@ -1226,7 +1233,7 @@

PredictorInterf

-INFO:dataprep_ml-2429:Cleaning the data
+INFO:dataprep_ml-2715:Cleaning the data
 
@@ -1234,7 +1241,7 @@

PredictorInterf

-DEBUG:lightwood-2429: `preprocess` runtime: 0.01 seconds
+DEBUG:lightwood-2715: `preprocess` runtime: 0.01 seconds
 
@@ -1242,7 +1249,7 @@

PredictorInterf

-INFO:dataprep_ml-2429:Updating the mixers
+INFO:dataprep_ml-2715:Updating the mixers
 
@@ -1259,7 +1266,7 @@

PredictorInterf

-INFO:lightwood-2429:Loss @ epoch 1: 0.10915952424208324
+INFO:lightwood-2715:Loss @ epoch 1: 0.10915952424208324
 
@@ -1267,7 +1274,7 @@

PredictorInterf

-DEBUG:lightwood-2429: `adjust` runtime: 0.11 seconds
+DEBUG:lightwood-2715: `adjust` runtime: 0.11 seconds
 
@@ -1284,7 +1291,7 @@

PredictorInterf

-INFO:dataprep_ml-2429:[Predict phase 1/4] - Data preprocessing
+INFO:dataprep_ml-2715:[Predict phase 1/4] - Data preprocessing
 
@@ -1292,7 +1299,7 @@

PredictorInterf

-INFO:dataprep_ml-2429:Cleaning the data
+INFO:dataprep_ml-2715:Cleaning the data
 
@@ -1300,7 +1307,7 @@

PredictorInterf

-DEBUG:lightwood-2429: `preprocess` runtime: 0.01 seconds
+DEBUG:lightwood-2715: `preprocess` runtime: 0.01 seconds
 
@@ -1308,7 +1315,7 @@

PredictorInterf

-INFO:dataprep_ml-2429:[Predict phase 2/4] - Feature generation
+INFO:dataprep_ml-2715:[Predict phase 2/4] - Feature generation
 
@@ -1316,7 +1323,7 @@

PredictorInterf

-INFO:dataprep_ml-2429:Featurizing the data
+INFO:dataprep_ml-2715:Featurizing the data
 
@@ -1324,7 +1331,7 @@

PredictorInterf

-DEBUG:lightwood-2429: `featurize` runtime: 0.03 seconds
+DEBUG:lightwood-2715: `featurize` runtime: 0.03 seconds
 
@@ -1332,7 +1339,7 @@

PredictorInterf

-INFO:dataprep_ml-2429:[Predict phase 3/4] - Calling ensemble
+INFO:dataprep_ml-2715:[Predict phase 3/4] - Calling ensemble
 
@@ -1340,7 +1347,7 @@

PredictorInterf

-DEBUG:lightwood-2429: `_timed_call` runtime: 0.03 seconds
+DEBUG:lightwood-2715: `_timed_call` runtime: 0.03 seconds
 
@@ -1348,7 +1355,7 @@

PredictorInterf

-INFO:dataprep_ml-2429:[Predict phase 4/4] - Analyzing output
+INFO:dataprep_ml-2715:[Predict phase 4/4] - Analyzing output
 
@@ -1356,7 +1363,7 @@

PredictorInterf

-INFO:lightwood-2429:The block ICP is now running its explain() method
+INFO:lightwood-2715:The block ICP is now running its explain() method
 
@@ -1364,7 +1371,7 @@

PredictorInterf

-INFO:lightwood-2429:The block ConfStats is now running its explain() method
+INFO:lightwood-2715:The block ConfStats is now running its explain() method
 
@@ -1372,7 +1379,7 @@

PredictorInterf

-INFO:lightwood-2429:ConfStats.explain() has not been implemented, no modifications will be done to the data insights.
+INFO:lightwood-2715:ConfStats.explain() has not been implemented, no modifications will be done to the data insights.
 
@@ -1380,7 +1387,7 @@

PredictorInterf

-INFO:lightwood-2429:The block AccStats is now running its explain() method
+INFO:lightwood-2715:The block AccStats is now running its explain() method
 
@@ -1388,7 +1395,7 @@

PredictorInterf

-INFO:lightwood-2429:AccStats.explain() has not been implemented, no modifications will be done to the data insights.
+INFO:lightwood-2715:AccStats.explain() has not been implemented, no modifications will be done to the data insights.
 
@@ -1396,7 +1403,7 @@

PredictorInterf

-INFO:lightwood-2429:The block PermutationFeatureImportance is now running its explain() method
+INFO:lightwood-2715:The block PermutationFeatureImportance is now running its explain() method
 
@@ -1404,7 +1411,7 @@

PredictorInterf

-INFO:lightwood-2429:PermutationFeatureImportance.explain() has not been implemented, no modifications will be done to the data insights.
+INFO:lightwood-2715:PermutationFeatureImportance.explain() has not been implemented, no modifications will be done to the data insights.
 
@@ -1412,7 +1419,7 @@

PredictorInterf

-DEBUG:lightwood-2429: `explain` runtime: 0.05 seconds
+DEBUG:lightwood-2715: `explain` runtime: 0.05 seconds
 
@@ -1420,7 +1427,7 @@

PredictorInterf

-DEBUG:lightwood-2429: `predict` runtime: 0.13 seconds
+DEBUG:lightwood-2715: `predict` runtime: 0.13 seconds
 
diff --git a/tutorials/tutorial_update_models/tutorial_update_models.ipynb b/tutorials/tutorial_update_models/tutorial_update_models.ipynb index 581b499cf..7e69b9956 100644 --- a/tutorials/tutorial_update_models/tutorial_update_models.ipynb +++ b/tutorials/tutorial_update_models/tutorial_update_models.ipynb @@ -21,10 +21,10 @@ "execution_count": 1, "metadata": { "execution": { - "iopub.execute_input": "2024-03-19T10:00:41.914113Z", - "iopub.status.busy": "2024-03-19T10:00:41.913919Z", - "iopub.status.idle": "2024-03-19T10:00:44.497058Z", - "shell.execute_reply": "2024-03-19T10:00:44.496340Z" + "iopub.execute_input": "2024-03-19T10:18:56.494093Z", + "iopub.status.busy": "2024-03-19T10:18:56.493634Z", + "iopub.status.idle": "2024-03-19T10:18:59.001389Z", + "shell.execute_reply": "2024-03-19T10:18:59.000734Z" } }, "outputs": [ @@ -32,14 +32,14 @@ "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:lightwood-2429:No torchvision detected, image helpers not supported.\u001b[0m\n" + "\u001b[32mINFO:lightwood-2715:No torchvision detected, image helpers not supported.\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:lightwood-2429:No torchvision/pillow detected, image encoder not supported\u001b[0m\n" + "\u001b[32mINFO:lightwood-2715:No torchvision/pillow detected, image encoder not supported\u001b[0m\n" ] } ], @@ -53,10 +53,10 @@ "execution_count": 2, "metadata": { "execution": { - "iopub.execute_input": "2024-03-19T10:00:44.500008Z", - "iopub.status.busy": "2024-03-19T10:00:44.499726Z", - "iopub.status.idle": "2024-03-19T10:00:44.741388Z", - "shell.execute_reply": "2024-03-19T10:00:44.740724Z" + "iopub.execute_input": "2024-03-19T10:18:59.004694Z", + "iopub.status.busy": "2024-03-19T10:18:59.004232Z", + "iopub.status.idle": "2024-03-19T10:18:59.160672Z", + "shell.execute_reply": "2024-03-19T10:18:59.160035Z" } }, "outputs": [ @@ -98,10 +98,10 @@ "execution_count": 3, "metadata": { "execution": { - "iopub.execute_input": "2024-03-19T10:00:44.744380Z", - "iopub.status.busy": "2024-03-19T10:00:44.743940Z", - "iopub.status.idle": "2024-03-19T10:00:46.186503Z", - "shell.execute_reply": "2024-03-19T10:00:46.185809Z" + "iopub.execute_input": "2024-03-19T10:18:59.163388Z", + "iopub.status.busy": "2024-03-19T10:18:59.162992Z", + "iopub.status.idle": "2024-03-19T10:19:00.590655Z", + "shell.execute_reply": "2024-03-19T10:19:00.589988Z" }, "scrolled": true }, @@ -110,364 +110,364 @@ "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:type_infer-2429:Analyzing a sample of 979\u001b[0m\n" + "\u001b[32mINFO:type_infer-2715:Analyzing a sample of 979\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:type_infer-2429:from a total population of 1030, this is equivalent to 95.0% of your data.\u001b[0m\n" + "\u001b[32mINFO:type_infer-2715:from a total population of 1030, this is equivalent to 95.0% of your data.\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:type_infer-2429:Using 3 processes to deduct types.\u001b[0m\n" + "\u001b[32mINFO:type_infer-2715:Using 3 processes to deduct types.\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:type_infer-2429:Infering type for: cement\u001b[0m\n" + "\u001b[32mINFO:type_infer-2715:Infering type for: slag\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:type_infer-2429:Infering type for: slag\u001b[0m\n" + "\u001b[32mINFO:type_infer-2715:Infering type for: cement\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:type_infer-2429:Column cement has data type float\u001b[0m\n" + "\u001b[32mINFO:type_infer-2715:Column slag has data type float\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:type_infer-2429:Infering type for: flyAsh\u001b[0m\n" + "\u001b[32mINFO:type_infer-2715:Infering type for: flyAsh\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:type_infer-2429:Column slag has data type float\u001b[0m\n" + "\u001b[32mINFO:type_infer-2715:Column cement has data type float\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:type_infer-2429:Infering type for: water\u001b[0m\n" + "\u001b[32mINFO:type_infer-2715:Infering type for: water\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:type_infer-2429:Column flyAsh has data type float\u001b[0m\n" + "\u001b[32mINFO:type_infer-2715:Column flyAsh has data type float\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:type_infer-2429:Infering type for: superPlasticizer\u001b[0m\n" + "\u001b[32mINFO:type_infer-2715:Infering type for: superPlasticizer\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:type_infer-2429:Column water has data type float\u001b[0m\n" + "\u001b[32mINFO:type_infer-2715:Column water has data type float\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:type_infer-2429:Infering type for: coarseAggregate\u001b[0m\n" + "\u001b[32mINFO:type_infer-2715:Infering type for: coarseAggregate\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:type_infer-2429:Column superPlasticizer has data type float\u001b[0m\n" + "\u001b[32mINFO:type_infer-2715:Column superPlasticizer has data type float\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:type_infer-2429:Infering type for: fineAggregate\u001b[0m\n" + "\u001b[32mINFO:type_infer-2715:Infering type for: fineAggregate\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:type_infer-2429:Column coarseAggregate has data type float\u001b[0m\n" + "\u001b[32mINFO:type_infer-2715:Column coarseAggregate has data type float\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:type_infer-2429:Infering type for: age\u001b[0m\n" + "\u001b[32mINFO:type_infer-2715:Column fineAggregate has data type float\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:type_infer-2429:Column age has data type integer\u001b[0m\n" + "\u001b[32mINFO:type_infer-2715:Infering type for: age\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:type_infer-2429:Column fineAggregate has data type float\u001b[0m\n" + "\u001b[32mINFO:type_infer-2715:Infering type for: concrete_strength\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:type_infer-2429:Infering type for: id\u001b[0m\n" + "\u001b[32mINFO:type_infer-2715:Column age has data type integer\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:type_infer-2429:Infering type for: concrete_strength\u001b[0m\n" + "\u001b[32mINFO:type_infer-2715:Column concrete_strength has data type float\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:type_infer-2429:Column concrete_strength has data type float\u001b[0m\n" + "\u001b[32mINFO:type_infer-2715:Infering type for: id\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:type_infer-2429:Column id has data type integer\u001b[0m\n" + "\u001b[32mINFO:type_infer-2715:Column id has data type integer\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:dataprep_ml-2429:Starting statistical analysis\u001b[0m\n" + "\u001b[32mINFO:dataprep_ml-2715:Starting statistical analysis\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:dataprep_ml-2429:Finished statistical analysis\u001b[0m\n" + "\u001b[32mINFO:dataprep_ml-2715:Finished statistical analysis\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - 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"\u001b[37mDEBUG:dataprep_ml-2429:Preparing encoder for age...\u001b[0m\n" + "\u001b[37mDEBUG:dataprep_ml-2715:Preparing encoder for age...\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[37mDEBUG:lightwood-2429: `prepare` runtime: 0.01 seconds\u001b[0m\n" + "\u001b[37mDEBUG:lightwood-2715: `prepare` runtime: 0.01 seconds\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:dataprep_ml-2429:[Learn phase 5/8] - Feature generation\u001b[0m\n" + "\u001b[32mINFO:dataprep_ml-2715:[Learn phase 5/8] - Feature generation\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:dataprep_ml-2429:Featurizing the data\u001b[0m\n" + "\u001b[32mINFO:dataprep_ml-2715:Featurizing the data\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[37mDEBUG:lightwood-2429: `featurize` runtime: 0.06 seconds\u001b[0m\n" + "\u001b[37mDEBUG:lightwood-2715: `featurize` runtime: 0.06 seconds\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:dataprep_ml-2429:[Learn phase 6/8] - Mixer training\u001b[0m\n" + "\u001b[32mINFO:dataprep_ml-2715:[Learn phase 6/8] - Mixer training\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:dataprep_ml-2429:Training the mixers\u001b[0m\n" + "\u001b[32mINFO:dataprep_ml-2715:Training the mixers\u001b[0m\n" ] }, { @@ -487,63 +487,63 @@ "Consider using one of the following signatures instead:\n", "\taddcmul_(Tensor tensor1, Tensor tensor2, *, Number value) (Triggered internally at ../torch/csrc/utils/python_arg_parser.cpp:1630.)\n", " exp_avg_sq.mul_(beta2).addcmul_(1 - beta2, grad, grad)\n", - "\u001b[32mINFO:lightwood-2429:Loss of 39.99637508392334 with learning rate 0.0001\u001b[0m\n" + "\u001b[32mINFO:lightwood-2715:Loss of 39.99637508392334 with learning rate 0.0001\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:lightwood-2429:Loss of 21.826460361480713 with learning rate 0.0005\u001b[0m\n" + "\u001b[32mINFO:lightwood-2715:Loss of 21.826460361480713 with learning rate 0.0005\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:lightwood-2429:Loss of 15.12899512052536 with learning rate 0.001\u001b[0m\n" + "\u001b[32mINFO:lightwood-2715:Loss of 15.12899512052536 with learning rate 0.001\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:lightwood-2429:Loss of 15.062753021717072 with learning rate 0.002\u001b[0m\n" + "\u001b[32mINFO:lightwood-2715:Loss of 15.062753021717072 with learning rate 0.002\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:lightwood-2429:Loss of 26.490495562553406 with learning rate 0.003\u001b[0m\n" + "\u001b[32mINFO:lightwood-2715:Loss of 26.490495562553406 with learning rate 0.003\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:lightwood-2429:Loss of 33.6572003364563 with learning rate 0.005\u001b[0m\n" + "\u001b[32mINFO:lightwood-2715:Loss of 33.6572003364563 with learning rate 0.005\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:lightwood-2429:Loss of 303.60721158981323 with learning rate 0.01\u001b[0m\n" + "\u001b[32mINFO:lightwood-2715:Loss of 303.60721158981323 with learning rate 0.01\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:lightwood-2429:Loss of nan with learning rate 0.05\u001b[0m\n" + "\u001b[32mINFO:lightwood-2715:Loss of nan with learning rate 0.05\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:lightwood-2429:Found learning rate of: 0.002\u001b[0m\n" + "\u001b[32mINFO:lightwood-2715:Found learning rate of: 0.002\u001b[0m\n" ] }, { @@ -552,161 +552,161 @@ "text": [ "/opt/hostedtoolcache/Python/3.9.18/x64/lib/python3.9/site-packages/torch/cuda/amp/grad_scaler.py:126: UserWarning: torch.cuda.amp.GradScaler is enabled, but CUDA is not available. Disabling.\n", " warnings.warn(\n", - "\u001b[32mINFO:lightwood-2429:Loss @ epoch 1: 0.11838734149932861\u001b[0m\n" + "\u001b[32mINFO:lightwood-2715:Loss @ epoch 1: 0.11838734149932861\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:lightwood-2429:Loss @ epoch 2: 0.4641949534416199\u001b[0m\n" + "\u001b[32mINFO:lightwood-2715:Loss @ epoch 2: 0.4641949534416199\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:lightwood-2429:Loss @ epoch 3: 0.3976145386695862\u001b[0m\n" + "\u001b[32mINFO:lightwood-2715:Loss @ epoch 3: 0.3976145386695862\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:lightwood-2429:Loss @ epoch 4: 0.3706841468811035\u001b[0m\n" + "\u001b[32mINFO:lightwood-2715:Loss @ epoch 4: 0.3706841468811035\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:lightwood-2429:Loss @ epoch 5: 0.2367912232875824\u001b[0m\n" + "\u001b[32mINFO:lightwood-2715:Loss @ epoch 5: 0.2367912232875824\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - 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"\u001b[32mINFO:lightwood-2429:Picked best mixer: Neural\u001b[0m\n" + "\u001b[32mINFO:lightwood-2715:Picked best mixer: Neural\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[37mDEBUG:lightwood-2429: `fit` runtime: 0.6 seconds\u001b[0m\n" + "\u001b[37mDEBUG:lightwood-2715: `fit` runtime: 0.59 seconds\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:dataprep_ml-2429:[Learn phase 7/8] - Ensemble analysis\u001b[0m\n" + "\u001b[32mINFO:dataprep_ml-2715:[Learn phase 7/8] - Ensemble analysis\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:dataprep_ml-2429:Analyzing the ensemble of mixers\u001b[0m\n" + "\u001b[32mINFO:dataprep_ml-2715:Analyzing the ensemble of mixers\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:lightwood-2429:The block ICP is now running its analyze() method\u001b[0m\n" + "\u001b[32mINFO:lightwood-2715:The block ICP is now running its analyze() method\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:lightwood-2429:The block ConfStats is now running its analyze() method\u001b[0m\n" + "\u001b[32mINFO:lightwood-2715:The block ConfStats is now running its analyze() method\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:lightwood-2429:The block AccStats is now running its analyze() method\u001b[0m\n" + "\u001b[32mINFO:lightwood-2715:The block AccStats is now running its analyze() method\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:lightwood-2429:The block PermutationFeatureImportance is now running its analyze() method\u001b[0m\n" + "\u001b[32mINFO:lightwood-2715:The block PermutationFeatureImportance is now running its analyze() method\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:lightwood-2429:[PFI] Using a random sample (1000 rows out of 10).\u001b[0m\n" + "\u001b[32mINFO:lightwood-2715:[PFI] Using a random sample (1000 rows out of 10).\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:lightwood-2429:[PFI] Set to consider first 10 columns out of 9: ['id', 'cement', 'slag', 'flyAsh', 'water', 'superPlasticizer', 'coarseAggregate', 'fineAggregate', 'age'].\u001b[0m\n" + "\u001b[32mINFO:lightwood-2715:[PFI] Set to consider first 10 columns out of 9: ['id', 'cement', 'slag', 'flyAsh', 'water', 'superPlasticizer', 'coarseAggregate', 'fineAggregate', 'age'].\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[37mDEBUG:lightwood-2429: `analyze_ensemble` runtime: 0.15 seconds\u001b[0m\n" + "\u001b[37mDEBUG:lightwood-2715: `analyze_ensemble` runtime: 0.15 seconds\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:dataprep_ml-2429:[Learn phase 8/8] - Adjustment on validation requested\u001b[0m\n" + "\u001b[32mINFO:dataprep_ml-2715:[Learn phase 8/8] - Adjustment on validation requested\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:dataprep_ml-2429:Updating the mixers\u001b[0m\n" + "\u001b[32mINFO:dataprep_ml-2715:Updating the mixers\u001b[0m\n" ] }, { @@ -714,22 +714,28 @@ "output_type": "stream", "text": [ "/opt/hostedtoolcache/Python/3.9.18/x64/lib/python3.9/site-packages/torch/cuda/amp/grad_scaler.py:126: UserWarning: torch.cuda.amp.GradScaler is enabled, but CUDA is not available. Disabling.\n", - " warnings.warn(\n", - "\u001b[32mINFO:lightwood-2429:Loss @ epoch 1: 0.1678172747294108\u001b[0m\n" + " warnings.warn(\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\u001b[32mINFO:lightwood-2715:Loss @ epoch 1: 0.1678172747294108\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[37mDEBUG:lightwood-2429: `adjust` runtime: 0.03 seconds\u001b[0m\n" + "\u001b[37mDEBUG:lightwood-2715: `adjust` runtime: 0.03 seconds\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[37mDEBUG:lightwood-2429: `learn` runtime: 0.88 seconds\u001b[0m\n" + "\u001b[37mDEBUG:lightwood-2715: `learn` runtime: 0.87 seconds\u001b[0m\n" ] } ], @@ -766,10 +772,10 @@ "execution_count": 4, "metadata": { "execution": { - "iopub.execute_input": "2024-03-19T10:00:46.189375Z", - "iopub.status.busy": "2024-03-19T10:00:46.189162Z", - "iopub.status.idle": "2024-03-19T10:00:46.330971Z", - "shell.execute_reply": "2024-03-19T10:00:46.330313Z" + "iopub.execute_input": "2024-03-19T10:19:00.593904Z", + "iopub.status.busy": "2024-03-19T10:19:00.593304Z", + "iopub.status.idle": "2024-03-19T10:19:00.734274Z", + "shell.execute_reply": "2024-03-19T10:19:00.733642Z" } }, "outputs": [ @@ -777,126 +783,126 @@ "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:dataprep_ml-2429:[Predict phase 1/4] - Data preprocessing\u001b[0m\n" + "\u001b[32mINFO:dataprep_ml-2715:[Predict phase 1/4] - Data preprocessing\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:dataprep_ml-2429:Cleaning the data\u001b[0m\n" + "\u001b[32mINFO:dataprep_ml-2715:Cleaning the data\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[37mDEBUG:lightwood-2429: `preprocess` runtime: 0.01 seconds\u001b[0m\n" + "\u001b[37mDEBUG:lightwood-2715: `preprocess` runtime: 0.01 seconds\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:dataprep_ml-2429:[Predict phase 2/4] - Feature generation\u001b[0m\n" + "\u001b[32mINFO:dataprep_ml-2715:[Predict phase 2/4] - Feature generation\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:dataprep_ml-2429:Featurizing the data\u001b[0m\n" + "\u001b[32mINFO:dataprep_ml-2715:Featurizing the data\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[37mDEBUG:lightwood-2429: `featurize` runtime: 0.03 seconds\u001b[0m\n" + "\u001b[37mDEBUG:lightwood-2715: `featurize` runtime: 0.03 seconds\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:dataprep_ml-2429:[Predict phase 3/4] - Calling ensemble\u001b[0m\n" + "\u001b[32mINFO:dataprep_ml-2715:[Predict phase 3/4] - Calling ensemble\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[37mDEBUG:lightwood-2429: `_timed_call` runtime: 0.03 seconds\u001b[0m\n" + "\u001b[37mDEBUG:lightwood-2715: `_timed_call` runtime: 0.03 seconds\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:dataprep_ml-2429:[Predict phase 4/4] - Analyzing output\u001b[0m\n" + "\u001b[32mINFO:dataprep_ml-2715:[Predict phase 4/4] - Analyzing output\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:lightwood-2429:The block ICP is now running its explain() method\u001b[0m\n" + "\u001b[32mINFO:lightwood-2715:The block ICP is now running its explain() method\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:lightwood-2429:The block ConfStats is now running its explain() method\u001b[0m\n" + "\u001b[32mINFO:lightwood-2715:The block ConfStats is now running its explain() method\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:lightwood-2429:ConfStats.explain() has not been implemented, no modifications will be done to the data insights.\u001b[0m\n" + "\u001b[32mINFO:lightwood-2715:ConfStats.explain() has not been implemented, no modifications will be done to the data insights.\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:lightwood-2429:The block AccStats is now running its explain() method\u001b[0m\n" + "\u001b[32mINFO:lightwood-2715:The block AccStats is now running its explain() method\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:lightwood-2429:AccStats.explain() has not been implemented, no modifications will be done to the data insights.\u001b[0m\n" + "\u001b[32mINFO:lightwood-2715:AccStats.explain() has not been implemented, no modifications will be done to the data insights.\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:lightwood-2429:The block PermutationFeatureImportance is now running its explain() method\u001b[0m\n" + "\u001b[32mINFO:lightwood-2715:The block PermutationFeatureImportance is now running its explain() method\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:lightwood-2429:PermutationFeatureImportance.explain() has not been implemented, no modifications will be done to the data insights.\u001b[0m\n" + "\u001b[32mINFO:lightwood-2715:PermutationFeatureImportance.explain() has not been implemented, no modifications will be done to the data insights.\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[37mDEBUG:lightwood-2429: `explain` runtime: 0.05 seconds\u001b[0m\n" + "\u001b[37mDEBUG:lightwood-2715: `explain` runtime: 0.05 seconds\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[37mDEBUG:lightwood-2429: `predict` runtime: 0.13 seconds\u001b[0m\n" + "\u001b[37mDEBUG:lightwood-2715: `predict` runtime: 0.13 seconds\u001b[0m\n" ] }, { @@ -1090,10 +1096,10 @@ "execution_count": 5, "metadata": { "execution": { - "iopub.execute_input": "2024-03-19T10:00:46.333752Z", - "iopub.status.busy": "2024-03-19T10:00:46.333296Z", - "iopub.status.idle": "2024-03-19T10:00:46.445392Z", - "shell.execute_reply": "2024-03-19T10:00:46.444779Z" + "iopub.execute_input": "2024-03-19T10:19:00.737098Z", + "iopub.status.busy": "2024-03-19T10:19:00.736628Z", + "iopub.status.idle": "2024-03-19T10:19:00.845511Z", + "shell.execute_reply": "2024-03-19T10:19:00.844939Z" } }, "outputs": [ @@ -1101,35 +1107,35 @@ "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:dataprep_ml-2429:Cleaning the data\u001b[0m\n" + "\u001b[32mINFO:dataprep_ml-2715:Cleaning the data\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[37mDEBUG:lightwood-2429: `preprocess` runtime: 0.02 seconds\u001b[0m\n" + "\u001b[37mDEBUG:lightwood-2715: `preprocess` runtime: 0.02 seconds\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:dataprep_ml-2429:Cleaning the data\u001b[0m\n" + "\u001b[32mINFO:dataprep_ml-2715:Cleaning the data\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[37mDEBUG:lightwood-2429: `preprocess` runtime: 0.01 seconds\u001b[0m\n" + "\u001b[37mDEBUG:lightwood-2715: `preprocess` runtime: 0.01 seconds\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:dataprep_ml-2429:Updating the mixers\u001b[0m\n" + "\u001b[32mINFO:dataprep_ml-2715:Updating the mixers\u001b[0m\n" ] }, { @@ -1144,14 +1150,14 @@ "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:lightwood-2429:Loss @ epoch 1: 0.10915952424208324\u001b[0m\n" + "\u001b[32mINFO:lightwood-2715:Loss @ epoch 1: 0.10915952424208324\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[37mDEBUG:lightwood-2429: `adjust` runtime: 0.11 seconds\u001b[0m\n" + "\u001b[37mDEBUG:lightwood-2715: `adjust` runtime: 0.11 seconds\u001b[0m\n" ] } ], @@ -1164,10 +1170,10 @@ "execution_count": 6, "metadata": { "execution": { - "iopub.execute_input": "2024-03-19T10:00:46.448524Z", - "iopub.status.busy": "2024-03-19T10:00:46.448061Z", - "iopub.status.idle": "2024-03-19T10:00:46.587972Z", - "shell.execute_reply": "2024-03-19T10:00:46.587381Z" + "iopub.execute_input": "2024-03-19T10:19:00.848107Z", + "iopub.status.busy": "2024-03-19T10:19:00.847689Z", + "iopub.status.idle": "2024-03-19T10:19:00.985350Z", + "shell.execute_reply": "2024-03-19T10:19:00.984820Z" } }, "outputs": [ @@ -1175,126 +1181,126 @@ "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:dataprep_ml-2429:[Predict phase 1/4] - Data preprocessing\u001b[0m\n" + "\u001b[32mINFO:dataprep_ml-2715:[Predict phase 1/4] - Data preprocessing\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:dataprep_ml-2429:Cleaning the data\u001b[0m\n" + "\u001b[32mINFO:dataprep_ml-2715:Cleaning the data\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[37mDEBUG:lightwood-2429: `preprocess` runtime: 0.01 seconds\u001b[0m\n" + "\u001b[37mDEBUG:lightwood-2715: `preprocess` runtime: 0.01 seconds\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:dataprep_ml-2429:[Predict phase 2/4] - Feature generation\u001b[0m\n" + "\u001b[32mINFO:dataprep_ml-2715:[Predict phase 2/4] - Feature generation\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:dataprep_ml-2429:Featurizing the data\u001b[0m\n" + "\u001b[32mINFO:dataprep_ml-2715:Featurizing the data\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[37mDEBUG:lightwood-2429: `featurize` runtime: 0.03 seconds\u001b[0m\n" + "\u001b[37mDEBUG:lightwood-2715: `featurize` runtime: 0.03 seconds\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:dataprep_ml-2429:[Predict phase 3/4] - Calling ensemble\u001b[0m\n" + "\u001b[32mINFO:dataprep_ml-2715:[Predict phase 3/4] - Calling ensemble\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[37mDEBUG:lightwood-2429: `_timed_call` runtime: 0.03 seconds\u001b[0m\n" + "\u001b[37mDEBUG:lightwood-2715: `_timed_call` runtime: 0.03 seconds\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:dataprep_ml-2429:[Predict phase 4/4] - Analyzing output\u001b[0m\n" + "\u001b[32mINFO:dataprep_ml-2715:[Predict phase 4/4] - Analyzing output\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:lightwood-2429:The block ICP is now running its explain() method\u001b[0m\n" + "\u001b[32mINFO:lightwood-2715:The block ICP is now running its explain() method\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:lightwood-2429:The block ConfStats is now running its explain() method\u001b[0m\n" + "\u001b[32mINFO:lightwood-2715:The block ConfStats is now running its explain() method\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:lightwood-2429:ConfStats.explain() has not been implemented, no modifications will be done to the data insights.\u001b[0m\n" + "\u001b[32mINFO:lightwood-2715:ConfStats.explain() has not been implemented, no modifications will be done to the data insights.\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:lightwood-2429:The block AccStats is now running its explain() method\u001b[0m\n" + "\u001b[32mINFO:lightwood-2715:The block AccStats is now running its explain() method\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:lightwood-2429:AccStats.explain() has not been implemented, no modifications will be done to the data insights.\u001b[0m\n" + "\u001b[32mINFO:lightwood-2715:AccStats.explain() has not been implemented, no modifications will be done to the data insights.\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:lightwood-2429:The block PermutationFeatureImportance is now running its explain() method\u001b[0m\n" + "\u001b[32mINFO:lightwood-2715:The block PermutationFeatureImportance is now running its explain() method\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:lightwood-2429:PermutationFeatureImportance.explain() has not been implemented, no modifications will be done to the data insights.\u001b[0m\n" + "\u001b[32mINFO:lightwood-2715:PermutationFeatureImportance.explain() has not been implemented, no modifications will be done to the data insights.\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[37mDEBUG:lightwood-2429: `explain` runtime: 0.05 seconds\u001b[0m\n" + "\u001b[37mDEBUG:lightwood-2715: `explain` runtime: 0.05 seconds\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[37mDEBUG:lightwood-2429: `predict` runtime: 0.13 seconds\u001b[0m\n" + "\u001b[37mDEBUG:lightwood-2715: `predict` runtime: 0.13 seconds\u001b[0m\n" ] }, { @@ -1458,10 +1464,10 @@ "execution_count": 7, "metadata": { "execution": { - "iopub.execute_input": "2024-03-19T10:00:46.590739Z", - "iopub.status.busy": "2024-03-19T10:00:46.590321Z", - "iopub.status.idle": "2024-03-19T10:00:46.596077Z", - "shell.execute_reply": "2024-03-19T10:00:46.595343Z" + "iopub.execute_input": "2024-03-19T10:19:00.987763Z", + "iopub.status.busy": "2024-03-19T10:19:00.987563Z", + "iopub.status.idle": "2024-03-19T10:19:00.992792Z", + "shell.execute_reply": "2024-03-19T10:19:00.992130Z" } }, "outputs": [