From 010b47b62c4dd4e2ff52d5df80185f114c50bda8 Mon Sep 17 00:00:00 2001 From: paxcema Date: Wed, 15 May 2024 12:40:09 +0000 Subject: [PATCH] Rebuilt the docs --- .../custom_cleaner/custom_cleaner.ipynb.txt | 136 +- .../custom_encoder_rulebased.ipynb.txt | 178 +-- .../custom_explainer.ipynb.txt | 1384 ++++++++--------- .../custom_mixer/custom_mixer.ipynb.txt | 262 ++-- .../custom_splitter/custom_splitter.ipynb.txt | 228 +-- .../tutorial_data_analysis.ipynb.txt | 144 +- .../tutorial_time_series.ipynb.txt | 439 +++--- .../tutorial_update_models.ipynb.txt | 320 ++-- searchindex.js | 2 +- tutorials/custom_cleaner/custom_cleaner.html | 56 +- tutorials/custom_cleaner/custom_cleaner.ipynb | 136 +- .../custom_encoder_rulebased.html | 90 +- .../custom_encoder_rulebased.ipynb | 178 +-- .../custom_explainer/custom_explainer.html | 1304 ++++++++-------- .../custom_explainer/custom_explainer.ipynb | 1384 ++++++++--------- tutorials/custom_mixer/custom_mixer.html | 214 +-- tutorials/custom_mixer/custom_mixer.ipynb | 262 ++-- .../custom_splitter/custom_splitter.html | 148 +- .../custom_splitter/custom_splitter.ipynb | 228 +-- .../tutorial_data_analysis.html | 40 +- .../tutorial_data_analysis.ipynb | 144 +- .../tutorial_time_series.html | 264 ++-- .../tutorial_time_series.ipynb | 439 +++--- .../tutorial_update_models.html | 264 ++-- .../tutorial_update_models.ipynb | 320 ++-- 25 files changed, 4227 insertions(+), 4337 deletions(-) diff --git a/_sources/tutorials/custom_cleaner/custom_cleaner.ipynb.txt b/_sources/tutorials/custom_cleaner/custom_cleaner.ipynb.txt index 72ccb0439..e2d0321af 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": "2024-05-15T12:31:38.314866Z", - "iopub.status.busy": "2024-05-15T12:31:38.314666Z", - "iopub.status.idle": "2024-05-15T12:31:41.066114Z", - "shell.execute_reply": "2024-05-15T12:31:41.065470Z" + "iopub.execute_input": "2024-05-15T12:38:44.614888Z", + "iopub.status.busy": "2024-05-15T12:38:44.614690Z", + "iopub.status.idle": "2024-05-15T12:38:47.421978Z", + "shell.execute_reply": "2024-05-15T12:38:47.421222Z" } }, "outputs": [ @@ -42,14 +42,14 @@ "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:lightwood-2845:No torchvision detected, image helpers not supported.\u001b[0m\n" + "\u001b[32mINFO:lightwood-2730:No torchvision detected, image helpers not supported.\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:lightwood-2845:No torchvision/pillow detected, image encoder not supported\u001b[0m\n" + "\u001b[32mINFO:lightwood-2730:No torchvision/pillow detected, image encoder not supported\u001b[0m\n" ] } ], @@ -87,10 +87,10 @@ "id": "recognized-parish", "metadata": { "execution": { - "iopub.execute_input": "2024-05-15T12:31:41.069559Z", - "iopub.status.busy": "2024-05-15T12:31:41.068848Z", - "iopub.status.idle": "2024-05-15T12:31:42.003247Z", - "shell.execute_reply": "2024-05-15T12:31:42.002523Z" + "iopub.execute_input": "2024-05-15T12:38:47.425324Z", + "iopub.status.busy": "2024-05-15T12:38:47.424967Z", + "iopub.status.idle": "2024-05-15T12:38:48.351359Z", + "shell.execute_reply": "2024-05-15T12:38:48.350712Z" } }, "outputs": [ @@ -221,10 +221,10 @@ "id": "chicken-truth", "metadata": { "execution": { - "iopub.execute_input": "2024-05-15T12:31:42.006058Z", - "iopub.status.busy": "2024-05-15T12:31:42.005722Z", - "iopub.status.idle": "2024-05-15T12:31:57.532124Z", - "shell.execute_reply": "2024-05-15T12:31:57.531480Z" + "iopub.execute_input": "2024-05-15T12:38:48.353933Z", + "iopub.status.busy": "2024-05-15T12:38:48.353724Z", + "iopub.status.idle": "2024-05-15T12:39:03.836779Z", + "shell.execute_reply": "2024-05-15T12:39:03.836183Z" } }, "outputs": [ @@ -232,98 +232,98 @@ "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:lightwood-2845:Dropping features: ['url_legal', 'license', 'standard_error']\u001b[0m\n" + "\u001b[32mINFO:lightwood-2730:Dropping features: ['url_legal', 'license', 'standard_error']\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:type_infer-2845:Analyzing a sample of 2478\u001b[0m\n" + "\u001b[32mINFO:type_infer-2730:Analyzing a sample of 2478\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:type_infer-2845:from a total population of 2834, this is equivalent to 87.4% of your data.\u001b[0m\n" + "\u001b[32mINFO:type_infer-2730: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-2845:Infering type for: id\u001b[0m\n" + "\u001b[32mINFO:type_infer-2730:Infering type for: id\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:type_infer-2845:Doing text detection for column: id\u001b[0m\n" + "\u001b[32mINFO:type_infer-2730:Doing text detection for column: id\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:type_infer-2845:Column id has data type categorical\u001b[0m\n" + "\u001b[32mINFO:type_infer-2730:Column id has data type categorical\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:type_infer-2845:Infering type for: excerpt\u001b[0m\n" + "\u001b[32mINFO:type_infer-2730:Infering type for: excerpt\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:type_infer-2845:Doing text detection for column: excerpt\u001b[0m\n" + "\u001b[32mINFO:type_infer-2730:Doing text detection for column: excerpt\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:type_infer-2845:Infering type for: target\u001b[0m\n" + "\u001b[32mINFO:type_infer-2730:Infering type for: target\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:type_infer-2845:Column target has data type float\u001b[0m\n" + "\u001b[32mINFO:type_infer-2730:Column target has data type float\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[33mWARNING:type_infer-2845:Column id is an identifier of type \"Hash-like identifier\"\u001b[0m\n" + "\u001b[33mWARNING:type_infer-2730:Column id is an identifier of type \"Hash-like identifier\"\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:dataprep_ml-2845:Starting statistical analysis\u001b[0m\n" + "\u001b[32mINFO:dataprep_ml-2730:Starting statistical analysis\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:dataprep_ml-2845:Dropping features: ['id']\u001b[0m\n" + "\u001b[32mINFO:dataprep_ml-2730:Dropping features: ['id']\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:dataprep_ml-2845:Finished statistical analysis\u001b[0m\n" + "\u001b[32mINFO:dataprep_ml-2730:Finished statistical analysis\u001b[0m\n" ] } ], @@ -360,10 +360,10 @@ "id": "designed-condition", "metadata": { "execution": { - "iopub.execute_input": "2024-05-15T12:31:57.535376Z", - "iopub.status.busy": "2024-05-15T12:31:57.534790Z", - "iopub.status.idle": "2024-05-15T12:31:57.539994Z", - "shell.execute_reply": "2024-05-15T12:31:57.539305Z" + "iopub.execute_input": "2024-05-15T12:39:03.839556Z", + "iopub.status.busy": "2024-05-15T12:39:03.839259Z", + "iopub.status.idle": "2024-05-15T12:39:03.843934Z", + "shell.execute_reply": "2024-05-15T12:39:03.843268Z" } }, "outputs": [ @@ -434,7 +434,7 @@ " \"unbias_target\": true,\n", " \"seconds_per_mixer\": 21384.0,\n", " \"seconds_per_encoder\": 85536.0,\n", - " \"expected_additional_time\": 15.485530853271484,\n", + " \"expected_additional_time\": 15.443372249603271,\n", " \"time_aim\": 259200,\n", " \"target_weights\": null,\n", " \"positive_domain\": false,\n", @@ -518,10 +518,10 @@ "id": "325d8f1b", "metadata": { "execution": { - "iopub.execute_input": "2024-05-15T12:31:57.542555Z", - "iopub.status.busy": "2024-05-15T12:31:57.542241Z", - "iopub.status.idle": "2024-05-15T12:31:57.547712Z", - "shell.execute_reply": "2024-05-15T12:31:57.547064Z" + "iopub.execute_input": "2024-05-15T12:39:03.846644Z", + "iopub.status.busy": "2024-05-15T12:39:03.846253Z", + "iopub.status.idle": "2024-05-15T12:39:03.851463Z", + "shell.execute_reply": "2024-05-15T12:39:03.850848Z" } }, "outputs": [ @@ -646,10 +646,10 @@ "id": "f030f8ca", "metadata": { "execution": { - "iopub.execute_input": "2024-05-15T12:31:57.550277Z", - "iopub.status.busy": "2024-05-15T12:31:57.549912Z", - "iopub.status.idle": "2024-05-15T12:31:57.553192Z", - "shell.execute_reply": "2024-05-15T12:31:57.552629Z" + "iopub.execute_input": "2024-05-15T12:39:03.853851Z", + "iopub.status.busy": "2024-05-15T12:39:03.853511Z", + "iopub.status.idle": "2024-05-15T12:39:03.856756Z", + "shell.execute_reply": "2024-05-15T12:39:03.856216Z" } }, "outputs": [], @@ -711,10 +711,10 @@ "id": "floating-patent", "metadata": { "execution": { - "iopub.execute_input": "2024-05-15T12:31:57.555666Z", - "iopub.status.busy": "2024-05-15T12:31:57.555289Z", - "iopub.status.idle": "2024-05-15T12:31:57.904645Z", - "shell.execute_reply": "2024-05-15T12:31:57.903931Z" + "iopub.execute_input": "2024-05-15T12:39:03.859167Z", + "iopub.status.busy": "2024-05-15T12:39:03.858968Z", + "iopub.status.idle": "2024-05-15T12:39:04.214810Z", + "shell.execute_reply": "2024-05-15T12:39:04.214113Z" } }, "outputs": [ @@ -795,7 +795,7 @@ " \"unbias_target\": True,\n", " \"seconds_per_mixer\": 21384.0,\n", " \"seconds_per_encoder\": 85536.0,\n", - " \"expected_additional_time\": 15.485530853271484,\n", + " \"expected_additional_time\": 15.443372249603271,\n", " \"time_aim\": 259200,\n", " \"target_weights\": None,\n", " \"positive_domain\": False,\n", @@ -1452,10 +1452,10 @@ "id": "violent-guard", "metadata": { "execution": { - "iopub.execute_input": "2024-05-15T12:31:57.907458Z", - "iopub.status.busy": "2024-05-15T12:31:57.907076Z", - "iopub.status.idle": "2024-05-15T12:31:57.915206Z", - "shell.execute_reply": "2024-05-15T12:31:57.914714Z" + "iopub.execute_input": "2024-05-15T12:39:04.217801Z", + "iopub.status.busy": "2024-05-15T12:39:04.217334Z", + "iopub.status.idle": "2024-05-15T12:39:04.225997Z", + "shell.execute_reply": "2024-05-15T12:39:04.225444Z" } }, "outputs": [], @@ -1470,10 +1470,10 @@ "id": "closing-episode", "metadata": { "execution": { - "iopub.execute_input": "2024-05-15T12:31:57.917761Z", - "iopub.status.busy": "2024-05-15T12:31:57.917396Z", - "iopub.status.idle": "2024-05-15T12:31:58.052073Z", - "shell.execute_reply": "2024-05-15T12:31:58.051450Z" + "iopub.execute_input": "2024-05-15T12:39:04.228433Z", + "iopub.status.busy": "2024-05-15T12:39:04.228041Z", + "iopub.status.idle": "2024-05-15T12:39:04.362314Z", + "shell.execute_reply": "2024-05-15T12:39:04.361709Z" }, "scrolled": false }, @@ -1482,70 +1482,70 @@ "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:dataprep_ml-2845:Starting statistical analysis\u001b[0m\n" + "\u001b[32mINFO:dataprep_ml-2730:Starting statistical analysis\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:dataprep_ml-2845:Dropping features: ['id']\u001b[0m\n" + "\u001b[32mINFO:dataprep_ml-2730:Dropping features: ['id']\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:dataprep_ml-2845:Finished statistical analysis\u001b[0m\n" + "\u001b[32mINFO:dataprep_ml-2730:Finished statistical analysis\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[37mDEBUG:lightwood-2845: `analyze_data` runtime: 0.05 seconds\u001b[0m\n" + "\u001b[37mDEBUG:lightwood-2730: `analyze_data` runtime: 0.05 seconds\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:dataprep_ml-2845:Cleaning the data\u001b[0m\n" + "\u001b[32mINFO:dataprep_ml-2730:Cleaning the data\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:dataprep_ml-2845:Dropping features: ['id']\u001b[0m\n" + "\u001b[32mINFO:dataprep_ml-2730:Dropping features: ['id']\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:lightwood-2845:Cleaning column =excerpt\u001b[0m\n" + "\u001b[32mINFO:lightwood-2730:Cleaning column =excerpt\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:lightwood-2845:Cleaning column =target\u001b[0m\n" + "\u001b[32mINFO:lightwood-2730:Cleaning column =target\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:lightwood-2845:Converted target into strictly non-negative\u001b[0m\n" + "\u001b[32mINFO:lightwood-2730:Converted target into strictly non-negative\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[37mDEBUG:lightwood-2845: `preprocess` runtime: 0.08 seconds\u001b[0m\n" + "\u001b[37mDEBUG:lightwood-2730: `preprocess` runtime: 0.07 seconds\u001b[0m\n" ] }, { @@ -1635,10 +1635,10 @@ "id": "major-stake", "metadata": { "execution": { - "iopub.execute_input": "2024-05-15T12:31:58.054538Z", - "iopub.status.busy": "2024-05-15T12:31:58.054334Z", - "iopub.status.idle": "2024-05-15T12:31:58.059138Z", - "shell.execute_reply": "2024-05-15T12:31:58.058503Z" + "iopub.execute_input": "2024-05-15T12:39:04.364948Z", + "iopub.status.busy": "2024-05-15T12:39:04.364536Z", + "iopub.status.idle": "2024-05-15T12:39:04.369332Z", + "shell.execute_reply": "2024-05-15T12:39:04.368673Z" } }, "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 b8c1aa4b9..8fb88c2df 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": "2024-05-15T12:30:59.516740Z", - "iopub.status.busy": "2024-05-15T12:30:59.516542Z", - "iopub.status.idle": "2024-05-15T12:31:02.311138Z", - "shell.execute_reply": "2024-05-15T12:31:02.310397Z" + "iopub.execute_input": "2024-05-15T12:38:05.532387Z", + "iopub.status.busy": "2024-05-15T12:38:05.531820Z", + "iopub.status.idle": "2024-05-15T12:38:08.368195Z", + "shell.execute_reply": "2024-05-15T12:38:08.367522Z" } }, "outputs": [ @@ -54,14 +54,14 @@ "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:lightwood-2555:No torchvision detected, image helpers not supported.\u001b[0m\n" + "\u001b[32mINFO:lightwood-2443:No torchvision detected, image helpers not supported.\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:lightwood-2555:No torchvision/pillow detected, image encoder not supported\u001b[0m\n" + "\u001b[32mINFO:lightwood-2443:No torchvision/pillow detected, image encoder not supported\u001b[0m\n" ] } ], @@ -93,10 +93,10 @@ "id": "technical-government", "metadata": { "execution": { - "iopub.execute_input": "2024-05-15T12:31:02.314599Z", - "iopub.status.busy": "2024-05-15T12:31:02.314113Z", - "iopub.status.idle": "2024-05-15T12:31:02.537991Z", - "shell.execute_reply": "2024-05-15T12:31:02.537249Z" + "iopub.execute_input": "2024-05-15T12:38:08.371594Z", + "iopub.status.busy": "2024-05-15T12:38:08.371079Z", + "iopub.status.idle": "2024-05-15T12:38:08.597223Z", + "shell.execute_reply": "2024-05-15T12:38:08.596542Z" } }, "outputs": [ @@ -243,10 +243,10 @@ "id": "absent-maker", "metadata": { "execution": { - "iopub.execute_input": "2024-05-15T12:31:02.540787Z", - "iopub.status.busy": "2024-05-15T12:31:02.540376Z", - "iopub.status.idle": "2024-05-15T12:31:13.435988Z", - "shell.execute_reply": "2024-05-15T12:31:13.435371Z" + "iopub.execute_input": "2024-05-15T12:38:08.600162Z", + "iopub.status.busy": "2024-05-15T12:38:08.599740Z", + "iopub.status.idle": "2024-05-15T12:38:19.532578Z", + "shell.execute_reply": "2024-05-15T12:38:19.531845Z" } }, "outputs": [ @@ -254,161 +254,161 @@ "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:type_infer-2555:Analyzing a sample of 6920\u001b[0m\n" + "\u001b[32mINFO:type_infer-2443:Analyzing a sample of 6920\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:type_infer-2555:from a total population of 10668, this is equivalent to 64.9% of your data.\u001b[0m\n" + "\u001b[32mINFO:type_infer-2443: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-2555:Using 3 processes to deduct types.\u001b[0m\n" + "\u001b[32mINFO:type_infer-2443:Using 3 processes to deduct types.\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:type_infer-2555:Infering type for: year\u001b[0m\n" + "\u001b[32mINFO:type_infer-2443:Infering type for: year\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:type_infer-2555:Infering type for: price\u001b[0m\n" + "\u001b[32mINFO:type_infer-2443:Infering type for: price\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:type_infer-2555:Column year has data type integer\u001b[0m\n" + "\u001b[32mINFO:type_infer-2443:Column year has data type integer\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:type_infer-2555:Column price has data type integer\u001b[0m\n" + "\u001b[32mINFO:type_infer-2443:Column price has data type integer\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:type_infer-2555:Infering type for: transmission\u001b[0m\n" + "\u001b[32mINFO:type_infer-2443:Infering type for: transmission\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:type_infer-2555:Infering type for: mileage\u001b[0m\n" + "\u001b[32mINFO:type_infer-2443:Infering type for: mileage\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:type_infer-2555:Infering type for: model\u001b[0m\n" + "\u001b[32mINFO:type_infer-2443:Column mileage has data type integer\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:type_infer-2555:Column mileage has data type integer\u001b[0m\n" + "\u001b[32mINFO:type_infer-2443:Infering type for: fuelType\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:type_infer-2555:Infering type for: fuelType\u001b[0m\n" + "\u001b[32mINFO:type_infer-2443:Infering type for: model\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:type_infer-2555:Column transmission has data type categorical\u001b[0m\n" + "\u001b[32mINFO:type_infer-2443:Column fuelType has data type categorical\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:type_infer-2555:Infering type for: tax\u001b[0m\n" + "\u001b[32mINFO:type_infer-2443:Infering type for: tax\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:type_infer-2555:Column tax has data type integer\u001b[0m\n" + "\u001b[32mINFO:type_infer-2443:Column tax has data type integer\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:type_infer-2555:Infering type for: mpg\u001b[0m\n" + "\u001b[32mINFO:type_infer-2443:Infering type for: mpg\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:type_infer-2555:Column mpg has data type float\u001b[0m\n" + "\u001b[32mINFO:type_infer-2443:Column mpg has data type float\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:type_infer-2555:Infering type for: engineSize\u001b[0m\n" + "\u001b[32mINFO:type_infer-2443:Infering type for: engineSize\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:type_infer-2555:Column engineSize has data type float\u001b[0m\n" + "\u001b[32mINFO:type_infer-2443:Column engineSize has data type float\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:type_infer-2555:Column fuelType has data type categorical\u001b[0m\n" + "\u001b[32mINFO:type_infer-2443:Column transmission has data type categorical\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:type_infer-2555:Column model has data type categorical\u001b[0m\n" + "\u001b[32mINFO:type_infer-2443:Column model has data type categorical\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:dataprep_ml-2555:Starting statistical analysis\u001b[0m\n" + "\u001b[32mINFO:dataprep_ml-2443:Starting statistical analysis\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:dataprep_ml-2555:Finished statistical analysis\u001b[0m\n" + "\u001b[32mINFO:dataprep_ml-2443:Finished statistical analysis\u001b[0m\n" ] } ], @@ -437,10 +437,10 @@ "id": "coastal-paragraph", "metadata": { "execution": { - "iopub.execute_input": "2024-05-15T12:31:13.439193Z", - "iopub.status.busy": "2024-05-15T12:31:13.438672Z", - "iopub.status.idle": "2024-05-15T12:31:13.444013Z", - "shell.execute_reply": "2024-05-15T12:31:13.443379Z" + "iopub.execute_input": "2024-05-15T12:38:19.535763Z", + "iopub.status.busy": "2024-05-15T12:38:19.535442Z", + "iopub.status.idle": "2024-05-15T12:38:19.540827Z", + "shell.execute_reply": "2024-05-15T12:38:19.540187Z" } }, "outputs": [ @@ -545,7 +545,7 @@ " \"unbias_target\": true,\n", " \"seconds_per_mixer\": 21384.0,\n", " \"seconds_per_encoder\": 85536.0,\n", - " \"expected_additional_time\": 10.851930856704712,\n", + " \"expected_additional_time\": 10.889316082000732,\n", " \"time_aim\": 259200,\n", " \"target_weights\": null,\n", " \"positive_domain\": false,\n", @@ -643,10 +643,10 @@ "id": "e03db1b0", "metadata": { "execution": { - "iopub.execute_input": "2024-05-15T12:31:13.446814Z", - "iopub.status.busy": "2024-05-15T12:31:13.446417Z", - "iopub.status.idle": "2024-05-15T12:31:13.451666Z", - "shell.execute_reply": "2024-05-15T12:31:13.451027Z" + "iopub.execute_input": "2024-05-15T12:38:19.543477Z", + "iopub.status.busy": "2024-05-15T12:38:19.543104Z", + "iopub.status.idle": "2024-05-15T12:38:19.548319Z", + "shell.execute_reply": "2024-05-15T12:38:19.547714Z" } }, "outputs": [ @@ -766,10 +766,10 @@ "id": "e30866c1", "metadata": { "execution": { - "iopub.execute_input": "2024-05-15T12:31:13.454205Z", - "iopub.status.busy": "2024-05-15T12:31:13.453822Z", - "iopub.status.idle": "2024-05-15T12:31:13.457126Z", - "shell.execute_reply": "2024-05-15T12:31:13.456554Z" + "iopub.execute_input": "2024-05-15T12:38:19.550981Z", + "iopub.status.busy": "2024-05-15T12:38:19.550558Z", + "iopub.status.idle": "2024-05-15T12:38:19.553867Z", + "shell.execute_reply": "2024-05-15T12:38:19.553338Z" } }, "outputs": [], @@ -828,10 +828,10 @@ "id": "elementary-fusion", "metadata": { "execution": { - "iopub.execute_input": "2024-05-15T12:31:13.459498Z", - "iopub.status.busy": "2024-05-15T12:31:13.459299Z", - "iopub.status.idle": "2024-05-15T12:31:13.462500Z", - "shell.execute_reply": "2024-05-15T12:31:13.461960Z" + "iopub.execute_input": "2024-05-15T12:38:19.556355Z", + "iopub.status.busy": "2024-05-15T12:38:19.556042Z", + "iopub.status.idle": "2024-05-15T12:38:19.559139Z", + "shell.execute_reply": "2024-05-15T12:38:19.558517Z" } }, "outputs": [], @@ -857,10 +857,10 @@ "id": "inappropriate-james", "metadata": { "execution": { - "iopub.execute_input": "2024-05-15T12:31:13.464989Z", - "iopub.status.busy": "2024-05-15T12:31:13.464584Z", - "iopub.status.idle": "2024-05-15T12:31:13.820565Z", - "shell.execute_reply": "2024-05-15T12:31:13.819924Z" + "iopub.execute_input": "2024-05-15T12:38:19.561533Z", + "iopub.status.busy": "2024-05-15T12:38:19.561170Z", + "iopub.status.idle": "2024-05-15T12:38:19.929081Z", + "shell.execute_reply": "2024-05-15T12:38:19.928368Z" } }, "outputs": [], @@ -891,10 +891,10 @@ "id": "palestinian-harvey", "metadata": { "execution": { - "iopub.execute_input": "2024-05-15T12:31:13.823560Z", - "iopub.status.busy": "2024-05-15T12:31:13.823333Z", - "iopub.status.idle": "2024-05-15T12:31:14.973079Z", - "shell.execute_reply": "2024-05-15T12:31:14.972381Z" + "iopub.execute_input": "2024-05-15T12:38:19.932374Z", + "iopub.status.busy": "2024-05-15T12:38:19.932028Z", + "iopub.status.idle": "2024-05-15T12:38:21.057588Z", + "shell.execute_reply": "2024-05-15T12:38:21.056913Z" } }, "outputs": [ @@ -902,133 +902,133 @@ "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:dataprep_ml-2555:Starting statistical analysis\u001b[0m\n" + "\u001b[32mINFO:dataprep_ml-2443:Starting statistical analysis\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:dataprep_ml-2555:Finished statistical analysis\u001b[0m\n" + "\u001b[32mINFO:dataprep_ml-2443:Finished statistical analysis\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[37mDEBUG:lightwood-2555: `analyze_data` runtime: 0.43 seconds\u001b[0m\n" + "\u001b[37mDEBUG:lightwood-2443: `analyze_data` runtime: 0.42 seconds\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:dataprep_ml-2555:Cleaning the data\u001b[0m\n" + "\u001b[32mINFO:dataprep_ml-2443:Cleaning the data\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[37mDEBUG:lightwood-2555: `preprocess` runtime: 0.13 seconds\u001b[0m\n" + "\u001b[37mDEBUG:lightwood-2443: `preprocess` runtime: 0.13 seconds\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:dataprep_ml-2555:Splitting the data into train/test\u001b[0m\n" + "\u001b[32mINFO:dataprep_ml-2443:Splitting the data into train/test\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[37mDEBUG:lightwood-2555: `split` runtime: 0.0 seconds\u001b[0m\n" + "\u001b[37mDEBUG:lightwood-2443: `split` runtime: 0.0 seconds\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[37mDEBUG:dataprep_ml-2555:Preparing sequentially...\u001b[0m\n" + "\u001b[37mDEBUG:dataprep_ml-2443:Preparing sequentially...\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[37mDEBUG:dataprep_ml-2555:Preparing encoder for year...\u001b[0m\n" + "\u001b[37mDEBUG:dataprep_ml-2443:Preparing encoder for year...\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[37mDEBUG:dataprep_ml-2555:Preparing encoder for mileage...\u001b[0m\n" + "\u001b[37mDEBUG:dataprep_ml-2443:Preparing encoder for mileage...\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[37mDEBUG:dataprep_ml-2555:Preparing encoder for tax...\u001b[0m\n" + "\u001b[37mDEBUG:dataprep_ml-2443:Preparing encoder for tax...\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[37mDEBUG:dataprep_ml-2555:Preparing encoder for mpg...\u001b[0m\n" + "\u001b[37mDEBUG:dataprep_ml-2443:Preparing encoder for mpg...\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[37mDEBUG:dataprep_ml-2555:Preparing encoder for engineSize...\u001b[0m\n" + "\u001b[37mDEBUG:dataprep_ml-2443:Preparing encoder for engineSize...\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:lightwood-2555:Categories Detected = 1\u001b[0m\n" + "\u001b[32mINFO:lightwood-2443:Categories Detected = 1\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:lightwood-2555:Categories Detected = 1\u001b[0m\n" + "\u001b[32mINFO:lightwood-2443:Categories Detected = 1\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:lightwood-2555:Categories Detected = 1\u001b[0m\n" + "\u001b[32mINFO:lightwood-2443:Categories Detected = 1\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[37mDEBUG:lightwood-2555: `prepare` runtime: 0.01 seconds\u001b[0m\n" + "\u001b[37mDEBUG:lightwood-2443: `prepare` runtime: 0.02 seconds\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:dataprep_ml-2555:Featurizing the data\u001b[0m\n" + "\u001b[32mINFO:dataprep_ml-2443:Featurizing the data\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[37mDEBUG:lightwood-2555: `featurize` runtime: 0.57 seconds\u001b[0m\n" + "\u001b[37mDEBUG:lightwood-2443: `featurize` runtime: 0.55 seconds\u001b[0m\n" ] } ], @@ -1063,10 +1063,10 @@ "id": "silent-dealing", "metadata": { "execution": { - "iopub.execute_input": "2024-05-15T12:31:14.975668Z", - "iopub.status.busy": "2024-05-15T12:31:14.975462Z", - "iopub.status.idle": "2024-05-15T12:31:14.984083Z", - "shell.execute_reply": "2024-05-15T12:31:14.983497Z" + "iopub.execute_input": "2024-05-15T12:38:21.060331Z", + "iopub.status.busy": "2024-05-15T12:38:21.059995Z", + "iopub.status.idle": "2024-05-15T12:38:21.068779Z", + "shell.execute_reply": "2024-05-15T12:38:21.068148Z" } }, "outputs": [ @@ -1168,10 +1168,10 @@ "id": "superior-mobility", "metadata": { "execution": { - "iopub.execute_input": "2024-05-15T12:31:14.986569Z", - "iopub.status.busy": "2024-05-15T12:31:14.986205Z", - "iopub.status.idle": "2024-05-15T12:31:14.989579Z", - "shell.execute_reply": "2024-05-15T12:31:14.988928Z" + "iopub.execute_input": "2024-05-15T12:38:21.071345Z", + "iopub.status.busy": "2024-05-15T12:38:21.070979Z", + "iopub.status.idle": "2024-05-15T12:38:21.074376Z", + "shell.execute_reply": "2024-05-15T12:38:21.073715Z" } }, "outputs": [ diff --git a/_sources/tutorials/custom_explainer/custom_explainer.ipynb.txt b/_sources/tutorials/custom_explainer/custom_explainer.ipynb.txt index 2a591918b..51b9eca01 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": "2024-05-15T12:31:18.040567Z", - "iopub.status.busy": "2024-05-15T12:31:18.040367Z", - "iopub.status.idle": "2024-05-15T12:31:20.849208Z", - "shell.execute_reply": "2024-05-15T12:31:20.848432Z" + "iopub.execute_input": "2024-05-15T12:38:24.174629Z", + "iopub.status.busy": "2024-05-15T12:38:24.174420Z", + "iopub.status.idle": "2024-05-15T12:38:27.049213Z", + "shell.execute_reply": "2024-05-15T12:38:27.048475Z" } }, "outputs": [ @@ -41,14 +41,14 @@ "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:lightwood-2634:No torchvision detected, image helpers not supported.\u001b[0m\n" + "\u001b[32mINFO:lightwood-2523:No torchvision detected, image helpers not supported.\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:lightwood-2634:No torchvision/pillow detected, image encoder not supported\u001b[0m\n" + "\u001b[32mINFO:lightwood-2523:No torchvision/pillow detected, image encoder not supported\u001b[0m\n" ] }, { @@ -87,10 +87,10 @@ "execution_count": 2, "metadata": { "execution": { - "iopub.execute_input": "2024-05-15T12:31:20.852043Z", - "iopub.status.busy": "2024-05-15T12:31:20.851597Z", - "iopub.status.idle": "2024-05-15T12:31:20.862285Z", - "shell.execute_reply": "2024-05-15T12:31:20.861787Z" + "iopub.execute_input": "2024-05-15T12:38:27.052314Z", + "iopub.status.busy": "2024-05-15T12:38:27.051670Z", + "iopub.status.idle": "2024-05-15T12:38:27.062358Z", + "shell.execute_reply": "2024-05-15T12:38:27.061815Z" } }, "outputs": [], @@ -116,17 +116,17 @@ "execution_count": 3, "metadata": { "execution": { - "iopub.execute_input": "2024-05-15T12:31:20.864582Z", - "iopub.status.busy": "2024-05-15T12:31:20.864386Z", - "iopub.status.idle": "2024-05-15T12:31:20.868451Z", - "shell.execute_reply": "2024-05-15T12:31:20.867794Z" + "iopub.execute_input": "2024-05-15T12:38:27.064834Z", + "iopub.status.busy": "2024-05-15T12:38:27.064464Z", + "iopub.status.idle": "2024-05-15T12:38:27.068750Z", + "shell.execute_reply": "2024-05-15T12:38:27.068172Z" } }, "outputs": [ { "data": { "text/plain": [ - "<__main__.ModelCorrelationHeatmap at 0x7fc16da6f8e0>" + "<__main__.ModelCorrelationHeatmap at 0x7ff43c2f7fd0>" ] }, "execution_count": 3, @@ -152,10 +152,10 @@ "execution_count": 4, "metadata": { "execution": { - "iopub.execute_input": "2024-05-15T12:31:20.870934Z", - "iopub.status.busy": "2024-05-15T12:31:20.870625Z", - "iopub.status.idle": "2024-05-15T12:31:20.874327Z", - "shell.execute_reply": "2024-05-15T12:31:20.873715Z" + "iopub.execute_input": "2024-05-15T12:38:27.071262Z", + "iopub.status.busy": "2024-05-15T12:38:27.070912Z", + "iopub.status.idle": "2024-05-15T12:38:27.074715Z", + "shell.execute_reply": "2024-05-15T12:38:27.074047Z" } }, "outputs": [], @@ -184,10 +184,10 @@ "execution_count": 5, "metadata": { "execution": { - "iopub.execute_input": "2024-05-15T12:31:20.876862Z", - "iopub.status.busy": "2024-05-15T12:31:20.876391Z", - "iopub.status.idle": "2024-05-15T12:31:20.880194Z", - "shell.execute_reply": "2024-05-15T12:31:20.879585Z" + "iopub.execute_input": "2024-05-15T12:38:27.077209Z", + "iopub.status.busy": "2024-05-15T12:38:27.076853Z", + "iopub.status.idle": "2024-05-15T12:38:27.080672Z", + "shell.execute_reply": "2024-05-15T12:38:27.080021Z" } }, "outputs": [], @@ -230,10 +230,10 @@ "execution_count": 6, "metadata": { "execution": { - "iopub.execute_input": "2024-05-15T12:31:20.882736Z", - "iopub.status.busy": "2024-05-15T12:31:20.882391Z", - "iopub.status.idle": "2024-05-15T12:31:20.886907Z", - "shell.execute_reply": "2024-05-15T12:31:20.886269Z" + "iopub.execute_input": "2024-05-15T12:38:27.083193Z", + "iopub.status.busy": "2024-05-15T12:38:27.082821Z", + "iopub.status.idle": "2024-05-15T12:38:27.087268Z", + "shell.execute_reply": "2024-05-15T12:38:27.086667Z" } }, "outputs": [ @@ -327,10 +327,10 @@ "execution_count": 7, "metadata": { "execution": { - "iopub.execute_input": "2024-05-15T12:31:20.889827Z", - "iopub.status.busy": "2024-05-15T12:31:20.889272Z", - "iopub.status.idle": "2024-05-15T12:31:21.057701Z", - "shell.execute_reply": "2024-05-15T12:31:21.057163Z" + "iopub.execute_input": "2024-05-15T12:38:27.089762Z", + "iopub.status.busy": "2024-05-15T12:38:27.089581Z", + "iopub.status.idle": "2024-05-15T12:38:27.268872Z", + "shell.execute_reply": "2024-05-15T12:38:27.268207Z" } }, "outputs": [ @@ -338,126 +338,126 @@ "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:type_infer-2634:Analyzing a sample of 222\u001b[0m\n" + "\u001b[32mINFO:type_infer-2523:Analyzing a sample of 222\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:type_infer-2634:from a total population of 225, this is equivalent to 98.7% of your data.\u001b[0m\n" + "\u001b[32mINFO:type_infer-2523: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-2634:Infering type for: Population\u001b[0m\n" + "\u001b[32mINFO:type_infer-2523:Infering type for: Population\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:type_infer-2634:Column Population has data type integer\u001b[0m\n" + "\u001b[32mINFO:type_infer-2523:Column Population has data type integer\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:type_infer-2634:Infering type for: Area (sq. mi.)\u001b[0m\n" + "\u001b[32mINFO:type_infer-2523:Infering type for: Area (sq. mi.)\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:type_infer-2634:Column Area (sq. mi.) has data type integer\u001b[0m\n" + "\u001b[32mINFO:type_infer-2523:Column Area (sq. mi.) has data type integer\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:type_infer-2634:Infering type for: Pop. Density \u001b[0m\n" + "\u001b[32mINFO:type_infer-2523:Infering type for: Pop. Density \u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:type_infer-2634:Column Pop. Density has data type float\u001b[0m\n" + "\u001b[32mINFO:type_infer-2523:Column Pop. Density has data type float\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:type_infer-2634:Infering type for: GDP ($ per capita)\u001b[0m\n" + "\u001b[32mINFO:type_infer-2523:Infering type for: GDP ($ per capita)\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:type_infer-2634:Column GDP ($ per capita) has data type integer\u001b[0m\n" + "\u001b[32mINFO:type_infer-2523:Column GDP ($ per capita) has data type integer\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:type_infer-2634:Infering type for: Literacy (%)\u001b[0m\n" + "\u001b[32mINFO:type_infer-2523:Infering type for: Literacy (%)\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:type_infer-2634:Column Literacy (%) has data type float\u001b[0m\n" + "\u001b[32mINFO:type_infer-2523:Column Literacy (%) has data type float\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:type_infer-2634:Infering type for: Infant mortality \u001b[0m\n" + "\u001b[32mINFO:type_infer-2523:Infering type for: Infant mortality \u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:type_infer-2634:Column Infant mortality has data type float\u001b[0m\n" + "\u001b[32mINFO:type_infer-2523:Column Infant mortality has data type float\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:type_infer-2634:Infering type for: Development Index\u001b[0m\n" + "\u001b[32mINFO:type_infer-2523:Infering type for: Development Index\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:type_infer-2634:Column Development Index has data type categorical\u001b[0m\n" + "\u001b[32mINFO:type_infer-2523:Column Development Index has data type categorical\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:dataprep_ml-2634:Starting statistical analysis\u001b[0m\n" + "\u001b[32mINFO:dataprep_ml-2523:Starting statistical analysis\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:dataprep_ml-2634:Finished statistical analysis\u001b[0m\n" + "\u001b[32mINFO:dataprep_ml-2523:Finished statistical analysis\u001b[0m\n" ] } ], @@ -498,10 +498,10 @@ "execution_count": 8, "metadata": { "execution": { - "iopub.execute_input": "2024-05-15T12:31:21.060354Z", - "iopub.status.busy": "2024-05-15T12:31:21.059889Z", - "iopub.status.idle": "2024-05-15T12:31:21.064194Z", - "shell.execute_reply": "2024-05-15T12:31:21.063539Z" + "iopub.execute_input": "2024-05-15T12:38:27.271840Z", + "iopub.status.busy": "2024-05-15T12:38:27.271355Z", + "iopub.status.idle": "2024-05-15T12:38:27.275829Z", + "shell.execute_reply": "2024-05-15T12:38:27.275222Z" } }, "outputs": [ @@ -532,10 +532,10 @@ "execution_count": 9, "metadata": { "execution": { - "iopub.execute_input": "2024-05-15T12:31:21.066779Z", - "iopub.status.busy": "2024-05-15T12:31:21.066406Z", - "iopub.status.idle": "2024-05-15T12:31:26.612292Z", - "shell.execute_reply": "2024-05-15T12:31:26.611639Z" + "iopub.execute_input": "2024-05-15T12:38:27.278438Z", + "iopub.status.busy": "2024-05-15T12:38:27.277985Z", + "iopub.status.idle": "2024-05-15T12:38:32.815207Z", + "shell.execute_reply": "2024-05-15T12:38:32.814580Z" }, "scrolled": false }, @@ -544,182 +544,182 @@ "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:dataprep_ml-2634:[Learn phase 1/8] - Statistical analysis\u001b[0m\n" + "\u001b[32mINFO:dataprep_ml-2523:[Learn phase 1/8] - Statistical analysis\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:dataprep_ml-2634:Starting statistical analysis\u001b[0m\n" + "\u001b[32mINFO:dataprep_ml-2523:Starting statistical analysis\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:dataprep_ml-2634:Finished statistical analysis\u001b[0m\n" + "\u001b[32mINFO:dataprep_ml-2523:Finished statistical analysis\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[37mDEBUG:lightwood-2634: `analyze_data` runtime: 0.02 seconds\u001b[0m\n" + "\u001b[37mDEBUG:lightwood-2523: `analyze_data` runtime: 0.02 seconds\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:dataprep_ml-2634:[Learn phase 2/8] - Data preprocessing\u001b[0m\n" + "\u001b[32mINFO:dataprep_ml-2523:[Learn phase 2/8] - Data preprocessing\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:dataprep_ml-2634:Cleaning the data\u001b[0m\n" + "\u001b[32mINFO:dataprep_ml-2523:Cleaning the data\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[37mDEBUG:lightwood-2634: `preprocess` runtime: 0.01 seconds\u001b[0m\n" + "\u001b[37mDEBUG:lightwood-2523: `preprocess` runtime: 0.01 seconds\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:dataprep_ml-2634:[Learn phase 3/8] - Data splitting\u001b[0m\n" + "\u001b[32mINFO:dataprep_ml-2523:[Learn phase 3/8] - Data splitting\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:dataprep_ml-2634:Splitting the data into train/test\u001b[0m\n" + "\u001b[32mINFO:dataprep_ml-2523:Splitting the data into train/test\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[37mDEBUG:lightwood-2634: `split` runtime: 0.01 seconds\u001b[0m\n" + "\u001b[37mDEBUG:lightwood-2523: `split` runtime: 0.01 seconds\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - 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"\u001b[37mDEBUG:lightwood-2634: `prepare` runtime: 0.01 seconds\u001b[0m\n" + "\u001b[37mDEBUG:lightwood-2523: `prepare` runtime: 0.01 seconds\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:dataprep_ml-2634:[Learn phase 5/8] - Feature generation\u001b[0m\n" + "\u001b[32mINFO:dataprep_ml-2523:[Learn phase 5/8] - Feature generation\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:dataprep_ml-2634:Featurizing the data\u001b[0m\n" + "\u001b[32mINFO:dataprep_ml-2523:Featurizing the data\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[37mDEBUG:lightwood-2634: `featurize` runtime: 0.05 seconds\u001b[0m\n" + "\u001b[37mDEBUG:lightwood-2523: `featurize` runtime: 0.05 seconds\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:dataprep_ml-2634:[Learn phase 6/8] - Mixer training\u001b[0m\n" + "\u001b[32mINFO:dataprep_ml-2523:[Learn phase 6/8] - Mixer training\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:dataprep_ml-2634:Training the mixers\u001b[0m\n" + "\u001b[32mINFO:dataprep_ml-2523:Training the mixers\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[33mWARNING:lightwood-2634:XGBoost running on CPU\u001b[0m\n" + "\u001b[33mWARNING:lightwood-2523:XGBoost running on CPU\u001b[0m\n" ] }, { @@ -734,7 +734,7 @@ "name": "stdout", "output_type": "stream", "text": [ - "[12:31:21] WARNING: ../src/learner.cc:339: No visible GPU is found, setting `gpu_id` to -1\n" + "[12:38:27] WARNING: ../src/learner.cc:339: No visible GPU is found, setting `gpu_id` to -1\n" ] }, { @@ -746,3969 +746,3969 @@ "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:1578.)\n", " exp_avg_sq.mul_(beta2).addcmul_(1 - beta2, grad, grad)\n", - "\u001b[32mINFO:lightwood-2634:Loss of 18.69619858264923 with learning rate 0.0001\u001b[0m\n" + "\u001b[32mINFO:lightwood-2523:Loss of 18.69619858264923 with learning rate 0.0001\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:lightwood-2634:Loss of 16.93891429901123 with learning rate 0.0005\u001b[0m\n" + "\u001b[32mINFO:lightwood-2523:Loss of 16.93891429901123 with learning rate 0.0005\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:lightwood-2634:Loss of 16.197376608848572 with learning rate 0.001\u001b[0m\n" + "\u001b[32mINFO:lightwood-2523:Loss of 16.197376608848572 with learning rate 0.001\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:lightwood-2634:Loss of 16.06481909751892 with learning rate 0.002\u001b[0m\n" + "\u001b[32mINFO:lightwood-2523:Loss of 16.06481909751892 with learning rate 0.002\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:lightwood-2634:Loss of 16.472004413604736 with learning rate 0.003\u001b[0m\n" + "\u001b[32mINFO:lightwood-2523:Loss of 16.472004413604736 with learning rate 0.003\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - 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"\u001b[32mINFO:lightwood-2634:XGBoost mixer does not have a `partial_fit` implementation\u001b[0m\n" + "\u001b[32mINFO:lightwood-2523:XGBoost mixer does not have a `partial_fit` implementation\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[37mDEBUG:lightwood-2634: `fit_mixer` runtime: 0.05 seconds\u001b[0m\n" + "\u001b[37mDEBUG:lightwood-2523: `fit_mixer` runtime: 0.05 seconds\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[33mWARNING:dataprep_ml-2634:Exception: Unspported categorical type for regression when training mixer: \u001b[0m\n" + "\u001b[33mWARNING:dataprep_ml-2523:Exception: Unspported categorical type for regression when training mixer: \u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:lightwood-2634:Started fitting RandomForest model\u001b[0m\n" + "\u001b[32mINFO:lightwood-2523:Started fitting RandomForest model\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:lightwood-2634:RandomForest based correlation of (train data): 1.0\u001b[0m\n" + "\u001b[32mINFO:lightwood-2523:RandomForest based correlation of (train data): 1.0\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:lightwood-2634:RandomForest based correlation of (dev data): 1.0\u001b[0m\n" + "\u001b[32mINFO:lightwood-2523:RandomForest based correlation of (dev data): 1.0\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[37mDEBUG:lightwood-2634: `fit_mixer` runtime: 0.14 seconds\u001b[0m\n" + "\u001b[37mDEBUG:lightwood-2523: `fit_mixer` runtime: 0.13 seconds\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:dataprep_ml-2634:Ensembling the mixer\u001b[0m\n" + "\u001b[32mINFO:dataprep_ml-2523:Ensembling the mixer\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:lightwood-2634:Mixer: Neural got accuracy: 0.922\u001b[0m\n" + "\u001b[32mINFO:lightwood-2523:Mixer: Neural got accuracy: 0.922\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:lightwood-2634:Mixer: XGBoostMixer got accuracy: 1.0\u001b[0m\n" + "\u001b[32mINFO:lightwood-2523:Mixer: XGBoostMixer got accuracy: 1.0\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:lightwood-2634:Mixer: RandomForest got accuracy: 1.0\u001b[0m\n" + "\u001b[32mINFO:lightwood-2523:Mixer: RandomForest got accuracy: 1.0\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:lightwood-2634:Picked best mixer: RandomForest\u001b[0m\n" + "\u001b[32mINFO:lightwood-2523:Picked best mixer: RandomForest\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[37mDEBUG:lightwood-2634: `fit` runtime: 4.83 seconds\u001b[0m\n" + "\u001b[37mDEBUG:lightwood-2523: `fit` runtime: 4.81 seconds\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:dataprep_ml-2634:[Learn phase 7/8] - Ensemble analysis\u001b[0m\n" + "\u001b[32mINFO:dataprep_ml-2523:[Learn phase 7/8] - Ensemble analysis\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:dataprep_ml-2634:Analyzing the ensemble of mixers\u001b[0m\n" + "\u001b[32mINFO:dataprep_ml-2523:Analyzing the ensemble of mixers\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:lightwood-2634:The block ICP is now running its analyze() method\u001b[0m\n" + "\u001b[32mINFO:lightwood-2523:The block ICP is now running its analyze() method\u001b[0m\n" ] }, { @@ -5074,63 +5074,63 @@ "text": [ "/opt/hostedtoolcache/Python/3.9.19/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-2634:The block ConfStats is now running its analyze() method\u001b[0m\n" + "\u001b[32mINFO:lightwood-2523:The block ConfStats is now running its analyze() method\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:lightwood-2634:The block AccStats is now running its analyze() method\u001b[0m\n" + "\u001b[32mINFO:lightwood-2523:The block AccStats is now running its analyze() method\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:lightwood-2634:The block PermutationFeatureImportance is now running its analyze() method\u001b[0m\n" + "\u001b[32mINFO:lightwood-2523:The block PermutationFeatureImportance is now running its analyze() method\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:lightwood-2634:[PFI] Using a random sample (1000 rows out of 22).\u001b[0m\n" + "\u001b[32mINFO:lightwood-2523:[PFI] Using a random sample (1000 rows out of 22).\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:lightwood-2634:[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-2523:[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-2634:The block ModelCorrelationHeatmap is now running its analyze() method\u001b[0m\n" + "\u001b[32mINFO:lightwood-2523:The block ModelCorrelationHeatmap is now running its analyze() method\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[37mDEBUG:lightwood-2634: `analyze_ensemble` runtime: 0.2 seconds\u001b[0m\n" + "\u001b[37mDEBUG:lightwood-2523: `analyze_ensemble` runtime: 0.21 seconds\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:dataprep_ml-2634:[Learn phase 8/8] - Adjustment on validation requested\u001b[0m\n" + "\u001b[32mINFO:dataprep_ml-2523:[Learn phase 8/8] - Adjustment on validation requested\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:dataprep_ml-2634:Updating the mixers\u001b[0m\n" + "\u001b[32mINFO:dataprep_ml-2523:Updating the mixers\u001b[0m\n" ] }, { @@ -5145,70 +5145,70 @@ "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:lightwood-2634:Loss @ epoch 1: 0.033697554686417185\u001b[0m\n" + "\u001b[32mINFO:lightwood-2523:Loss @ epoch 1: 0.033697554686417185\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:lightwood-2634:Loss @ epoch 2: 0.033981192080924906\u001b[0m\n" + "\u001b[32mINFO:lightwood-2523:Loss @ epoch 2: 0.033981192080924906\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:lightwood-2634:Loss @ epoch 3: 0.037426896315688886\u001b[0m\n" + "\u001b[32mINFO:lightwood-2523:Loss @ epoch 3: 0.037426896315688886\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:lightwood-2634:Loss @ epoch 4: 0.04428015494098266\u001b[0m\n" + "\u001b[32mINFO:lightwood-2523:Loss @ epoch 4: 0.04428015494098266\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:lightwood-2634:Loss @ epoch 5: 0.061086510928968586\u001b[0m\n" + "\u001b[32mINFO:lightwood-2523:Loss @ epoch 5: 0.061086510928968586\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:lightwood-2634:Loss @ epoch 6: 0.03466159128583968\u001b[0m\n" + "\u001b[32mINFO:lightwood-2523:Loss @ epoch 6: 0.03466159128583968\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:lightwood-2634:Loss @ epoch 7: 0.03769115870818496\u001b[0m\n" + "\u001b[32mINFO:lightwood-2523:Loss @ epoch 7: 0.03769115870818496\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:lightwood-2634:XGBoost mixer does not have a `partial_fit` implementation\u001b[0m\n" + "\u001b[32mINFO:lightwood-2523:XGBoost mixer does not have a `partial_fit` implementation\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[37mDEBUG:lightwood-2634: `adjust` runtime: 0.06 seconds\u001b[0m\n" + "\u001b[37mDEBUG:lightwood-2523: `adjust` runtime: 0.06 seconds\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[37mDEBUG:lightwood-2634: `learn` runtime: 5.2 seconds\u001b[0m\n" + "\u001b[37mDEBUG:lightwood-2523: `learn` runtime: 5.19 seconds\u001b[0m\n" ] } ], @@ -5233,10 +5233,10 @@ "execution_count": 10, "metadata": { "execution": { - "iopub.execute_input": "2024-05-15T12:31:26.615040Z", - "iopub.status.busy": "2024-05-15T12:31:26.614829Z", - "iopub.status.idle": "2024-05-15T12:31:27.123113Z", - "shell.execute_reply": "2024-05-15T12:31:27.122477Z" + "iopub.execute_input": "2024-05-15T12:38:32.818054Z", + "iopub.status.busy": "2024-05-15T12:38:32.817644Z", + "iopub.status.idle": "2024-05-15T12:38:33.321743Z", + "shell.execute_reply": "2024-05-15T12:38:33.321104Z" } }, "outputs": [ diff --git a/_sources/tutorials/custom_mixer/custom_mixer.ipynb.txt b/_sources/tutorials/custom_mixer/custom_mixer.ipynb.txt index 83bb2e0e1..c3dacff27 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": "2024-05-15T12:30:52.389121Z", - "iopub.status.busy": "2024-05-15T12:30:52.388896Z", - "iopub.status.idle": "2024-05-15T12:30:52.397395Z", - "shell.execute_reply": "2024-05-15T12:30:52.396710Z" + "iopub.execute_input": "2024-05-15T12:37:58.233387Z", + "iopub.status.busy": "2024-05-15T12:37:58.232873Z", + "iopub.status.idle": "2024-05-15T12:37:58.241919Z", + "shell.execute_reply": "2024-05-15T12:37:58.241302Z" } }, "outputs": [ @@ -133,10 +133,10 @@ "execution_count": 2, "metadata": { "execution": { - "iopub.execute_input": "2024-05-15T12:30:52.433000Z", - "iopub.status.busy": "2024-05-15T12:30:52.432734Z", - "iopub.status.idle": "2024-05-15T12:30:55.473584Z", - "shell.execute_reply": "2024-05-15T12:30:55.472895Z" + "iopub.execute_input": "2024-05-15T12:37:58.280767Z", + "iopub.status.busy": "2024-05-15T12:37:58.280464Z", + "iopub.status.idle": "2024-05-15T12:38:01.332205Z", + "shell.execute_reply": "2024-05-15T12:38:01.331516Z" } }, "outputs": [ @@ -144,238 +144,238 @@ "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:lightwood-2525:No torchvision detected, image helpers not supported.\u001b[0m\n" + "\u001b[32mINFO:lightwood-2414:No torchvision detected, image helpers not supported.\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:lightwood-2525:No torchvision/pillow detected, image encoder not supported\u001b[0m\n" + "\u001b[32mINFO:lightwood-2414:No torchvision/pillow detected, image encoder not supported\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:type_infer-2525:Analyzing a sample of 298\u001b[0m\n" + "\u001b[32mINFO:type_infer-2414:Analyzing a sample of 298\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:type_infer-2525:from a total population of 303, this is equivalent to 98.3% of your data.\u001b[0m\n" + "\u001b[32mINFO:type_infer-2414: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-2525:Infering type for: age\u001b[0m\n" + "\u001b[32mINFO:type_infer-2414:Infering type for: age\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:type_infer-2525:Column age has data type integer\u001b[0m\n" + "\u001b[32mINFO:type_infer-2414:Column age has data type integer\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:type_infer-2525:Infering type for: sex\u001b[0m\n" + "\u001b[32mINFO:type_infer-2414:Infering type for: sex\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:type_infer-2525:Column sex has data type binary\u001b[0m\n" + "\u001b[32mINFO:type_infer-2414:Column sex has data type binary\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:type_infer-2525:Infering type for: cp\u001b[0m\n" + "\u001b[32mINFO:type_infer-2414:Infering type for: cp\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:type_infer-2525:Column cp has data type categorical\u001b[0m\n" + "\u001b[32mINFO:type_infer-2414:Column cp has data type categorical\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:type_infer-2525:Infering type for: trestbps\u001b[0m\n" + "\u001b[32mINFO:type_infer-2414:Infering type for: trestbps\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:type_infer-2525:Column trestbps has data type integer\u001b[0m\n" + "\u001b[32mINFO:type_infer-2414:Column trestbps has data type integer\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:type_infer-2525:Infering type for: chol\u001b[0m\n" + "\u001b[32mINFO:type_infer-2414:Infering type for: chol\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:type_infer-2525:Column chol has data type integer\u001b[0m\n" + "\u001b[32mINFO:type_infer-2414:Column chol has data type integer\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:type_infer-2525:Infering type for: fbs\u001b[0m\n" + "\u001b[32mINFO:type_infer-2414:Infering type for: fbs\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:type_infer-2525:Column fbs has data type binary\u001b[0m\n" + "\u001b[32mINFO:type_infer-2414:Column fbs has data type binary\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:type_infer-2525:Infering type for: restecg\u001b[0m\n" + "\u001b[32mINFO:type_infer-2414:Infering type for: restecg\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:type_infer-2525:Column restecg has data type categorical\u001b[0m\n" + "\u001b[32mINFO:type_infer-2414:Column restecg has data type categorical\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:type_infer-2525:Infering type for: thalach\u001b[0m\n" + "\u001b[32mINFO:type_infer-2414:Infering type for: thalach\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:type_infer-2525:Column thalach has data type integer\u001b[0m\n" + "\u001b[32mINFO:type_infer-2414:Column thalach has data type integer\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:type_infer-2525:Infering type for: exang\u001b[0m\n" + "\u001b[32mINFO:type_infer-2414:Infering type for: exang\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:type_infer-2525:Column exang has data type binary\u001b[0m\n" + "\u001b[32mINFO:type_infer-2414:Column exang has data type binary\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:type_infer-2525:Infering type for: oldpeak\u001b[0m\n" + "\u001b[32mINFO:type_infer-2414:Infering type for: oldpeak\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:type_infer-2525:Column oldpeak has data type float\u001b[0m\n" + "\u001b[32mINFO:type_infer-2414:Column oldpeak has data type float\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:type_infer-2525:Infering type for: slope\u001b[0m\n" + "\u001b[32mINFO:type_infer-2414:Infering type for: slope\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:type_infer-2525:Column slope has data type categorical\u001b[0m\n" + "\u001b[32mINFO:type_infer-2414:Column slope has data type categorical\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:type_infer-2525:Infering type for: ca\u001b[0m\n" + "\u001b[32mINFO:type_infer-2414:Infering type for: ca\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:type_infer-2525:Column ca has data type categorical\u001b[0m\n" + "\u001b[32mINFO:type_infer-2414:Column ca has data type categorical\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:type_infer-2525:Infering type for: thal\u001b[0m\n" + "\u001b[32mINFO:type_infer-2414:Infering type for: thal\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:type_infer-2525:Column thal has data type categorical\u001b[0m\n" + "\u001b[32mINFO:type_infer-2414:Column thal has data type categorical\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:type_infer-2525:Infering type for: target\u001b[0m\n" + "\u001b[32mINFO:type_infer-2414:Infering type for: target\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:type_infer-2525:Column target has data type binary\u001b[0m\n" + "\u001b[32mINFO:type_infer-2414:Column target has data type binary\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:dataprep_ml-2525:Starting statistical analysis\u001b[0m\n" + "\u001b[32mINFO:dataprep_ml-2414:Starting statistical analysis\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:dataprep_ml-2525:Finished statistical analysis\u001b[0m\n" + "\u001b[32mINFO:dataprep_ml-2414: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\": 0.07092165946960449,\n", + " \"expected_additional_time\": 0.07208943367004395,\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": "2024-05-15T12:30:55.476427Z", - "iopub.status.busy": "2024-05-15T12:30:55.475939Z", - "iopub.status.idle": "2024-05-15T12:30:55.479383Z", - "shell.execute_reply": "2024-05-15T12:30:55.478798Z" + "iopub.execute_input": "2024-05-15T12:38:01.334897Z", + "iopub.status.busy": "2024-05-15T12:38:01.334489Z", + "iopub.status.idle": "2024-05-15T12:38:01.337884Z", + "shell.execute_reply": "2024-05-15T12:38:01.337281Z" } }, "outputs": [], @@ -603,10 +603,10 @@ "execution_count": 4, "metadata": { "execution": { - "iopub.execute_input": "2024-05-15T12:30:55.481884Z", - "iopub.status.busy": "2024-05-15T12:30:55.481529Z", - "iopub.status.idle": "2024-05-15T12:30:55.818438Z", - "shell.execute_reply": "2024-05-15T12:30:55.817741Z" + "iopub.execute_input": "2024-05-15T12:38:01.340400Z", + "iopub.status.busy": "2024-05-15T12:38:01.339924Z", + "iopub.status.idle": "2024-05-15T12:38:01.687957Z", + "shell.execute_reply": "2024-05-15T12:38:01.687306Z" } }, "outputs": [], @@ -622,10 +622,10 @@ "execution_count": 5, "metadata": { "execution": { - "iopub.execute_input": "2024-05-15T12:30:55.821486Z", - "iopub.status.busy": "2024-05-15T12:30:55.821229Z", - "iopub.status.idle": "2024-05-15T12:30:56.441907Z", - "shell.execute_reply": "2024-05-15T12:30:56.441224Z" + "iopub.execute_input": "2024-05-15T12:38:01.691070Z", + "iopub.status.busy": "2024-05-15T12:38:01.690625Z", + "iopub.status.idle": "2024-05-15T12:38:02.309441Z", + "shell.execute_reply": "2024-05-15T12:38:02.308885Z" } }, "outputs": [ @@ -633,308 +633,308 @@ "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:dataprep_ml-2525:[Learn phase 1/8] - Statistical analysis\u001b[0m\n" + "\u001b[32mINFO:dataprep_ml-2414:[Learn phase 1/8] - Statistical analysis\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:dataprep_ml-2525:Starting statistical analysis\u001b[0m\n" + "\u001b[32mINFO:dataprep_ml-2414:Starting statistical analysis\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:dataprep_ml-2525:Finished statistical analysis\u001b[0m\n" + "\u001b[32mINFO:dataprep_ml-2414:Finished statistical analysis\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[37mDEBUG:lightwood-2525: `analyze_data` runtime: 0.03 seconds\u001b[0m\n" + "\u001b[37mDEBUG:lightwood-2414: `analyze_data` runtime: 0.03 seconds\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:dataprep_ml-2525:[Learn phase 2/8] - Data preprocessing\u001b[0m\n" + "\u001b[32mINFO:dataprep_ml-2414:[Learn phase 2/8] - 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"\u001b[37mDEBUG:lightwood-2525: `fit` runtime: 0.13 seconds\u001b[0m\n" + "\u001b[37mDEBUG:lightwood-2414: `fit` runtime: 0.13 seconds\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:dataprep_ml-2525:[Learn phase 7/8] - Ensemble analysis\u001b[0m\n" + "\u001b[32mINFO:dataprep_ml-2414:[Learn phase 7/8] - Ensemble analysis\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:dataprep_ml-2525:Analyzing the ensemble of mixers\u001b[0m\n" + "\u001b[32mINFO:dataprep_ml-2414:Analyzing the ensemble of mixers\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:lightwood-2525:The block ICP is now running its analyze() method\u001b[0m\n" + "\u001b[32mINFO:lightwood-2414:The block ICP is now running its analyze() method\u001b[0m\n" ] }, { @@ -943,35 +943,35 @@ "text": [ "/opt/hostedtoolcache/Python/3.9.19/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-2525:The block ConfStats is now running its analyze() method\u001b[0m\n" + "\u001b[32mINFO:lightwood-2414:The block ConfStats is now running its analyze() method\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:lightwood-2525:The block AccStats is now running its analyze() method\u001b[0m\n" + "\u001b[32mINFO:lightwood-2414:The block AccStats is now running its analyze() method\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:lightwood-2525:The block PermutationFeatureImportance is now running its analyze() method\u001b[0m\n" + "\u001b[32mINFO:lightwood-2414:The block PermutationFeatureImportance is now running its analyze() method\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:lightwood-2525:[PFI] Using a random sample (1000 rows out of 31).\u001b[0m\n" + "\u001b[32mINFO:lightwood-2414:[PFI] Using a random sample (1000 rows out of 31).\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:lightwood-2525:[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-2414:[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-2525: `analyze_ensemble` runtime: 0.27 seconds\u001b[0m\n" + "\u001b[37mDEBUG:lightwood-2414: `analyze_ensemble` runtime: 0.27 seconds\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:dataprep_ml-2525:[Learn phase 8/8] - Adjustment on validation requested\u001b[0m\n" + "\u001b[32mINFO:dataprep_ml-2414:[Learn phase 8/8] - Adjustment on validation requested\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:dataprep_ml-2525:Updating the mixers\u001b[0m\n" + "\u001b[32mINFO:dataprep_ml-2414:Updating the mixers\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[37mDEBUG:lightwood-2525: `adjust` runtime: 0.04 seconds\u001b[0m\n" + "\u001b[37mDEBUG:lightwood-2414: `adjust` runtime: 0.04 seconds\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[37mDEBUG:lightwood-2525: `learn` runtime: 0.62 seconds\u001b[0m\n" + "\u001b[37mDEBUG:lightwood-2414: `learn` runtime: 0.61 seconds\u001b[0m\n" ] } ], @@ -1042,10 +1042,10 @@ "execution_count": 6, "metadata": { "execution": { - "iopub.execute_input": "2024-05-15T12:30:56.444798Z", - "iopub.status.busy": "2024-05-15T12:30:56.444349Z", - "iopub.status.idle": "2024-05-15T12:30:56.568845Z", - "shell.execute_reply": "2024-05-15T12:30:56.568168Z" + "iopub.execute_input": "2024-05-15T12:38:02.312200Z", + "iopub.status.busy": "2024-05-15T12:38:02.311838Z", + "iopub.status.idle": "2024-05-15T12:38:02.432975Z", + "shell.execute_reply": "2024-05-15T12:38:02.432362Z" } }, "outputs": [ @@ -1053,35 +1053,35 @@ "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:dataprep_ml-2525:[Predict phase 1/4] - Data preprocessing\u001b[0m\n" + "\u001b[32mINFO:dataprep_ml-2414:[Predict phase 1/4] - Data preprocessing\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:dataprep_ml-2525:Cleaning the data\u001b[0m\n" + "\u001b[32mINFO:dataprep_ml-2414:Cleaning the data\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[37mDEBUG:lightwood-2525: `preprocess` runtime: 0.01 seconds\u001b[0m\n" + "\u001b[37mDEBUG:lightwood-2414: `preprocess` runtime: 0.01 seconds\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:dataprep_ml-2525:[Predict phase 2/4] - Feature generation\u001b[0m\n" + "\u001b[32mINFO:dataprep_ml-2414:[Predict phase 2/4] - Feature generation\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:dataprep_ml-2525:Featurizing the data\u001b[0m\n" + "\u001b[32mINFO:dataprep_ml-2414:Featurizing the data\u001b[0m\n" ] }, { @@ -1104,91 +1104,91 @@ " outputs = ufunc(*inputs)\n", "/opt/hostedtoolcache/Python/3.9.19/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-2525: `featurize` runtime: 0.02 seconds\u001b[0m\n" + "\u001b[37mDEBUG:lightwood-2414: `featurize` runtime: 0.02 seconds\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:dataprep_ml-2525:[Predict phase 3/4] - Calling ensemble\u001b[0m\n" + "\u001b[32mINFO:dataprep_ml-2414:[Predict phase 3/4] - Calling ensemble\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[37mDEBUG:lightwood-2525: `_timed_call` runtime: 0.01 seconds\u001b[0m\n" + "\u001b[37mDEBUG:lightwood-2414: `_timed_call` runtime: 0.01 seconds\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:dataprep_ml-2525:[Predict phase 4/4] - Analyzing output\u001b[0m\n" + "\u001b[32mINFO:dataprep_ml-2414:[Predict phase 4/4] - Analyzing output\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:lightwood-2525:The block ICP is now running its explain() method\u001b[0m\n" + "\u001b[32mINFO:lightwood-2414:The block ICP is now running its explain() method\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:lightwood-2525:The block ConfStats is now running its explain() method\u001b[0m\n" + "\u001b[32mINFO:lightwood-2414:The block ConfStats is now running its explain() method\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:lightwood-2525:ConfStats.explain() has not been implemented, no modifications will be done to the data insights.\u001b[0m\n" + "\u001b[32mINFO:lightwood-2414: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-2525:The block AccStats is now running its explain() method\u001b[0m\n" + "\u001b[32mINFO:lightwood-2414:The block AccStats is now running its explain() method\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:lightwood-2525:AccStats.explain() has not been implemented, no modifications will be done to the data insights.\u001b[0m\n" + "\u001b[32mINFO:lightwood-2414: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-2525:The block PermutationFeatureImportance is now running its explain() method\u001b[0m\n" + "\u001b[32mINFO:lightwood-2414:The block PermutationFeatureImportance is now running its explain() method\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:lightwood-2525:PermutationFeatureImportance.explain() has not been implemented, no modifications will be done to the data insights.\u001b[0m\n" + "\u001b[32mINFO:lightwood-2414: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-2525: `explain` runtime: 0.01 seconds\u001b[0m\n" + "\u001b[37mDEBUG:lightwood-2414: `explain` runtime: 0.01 seconds\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[37mDEBUG:lightwood-2525: `predict` runtime: 0.06 seconds\u001b[0m\n" + "\u001b[37mDEBUG:lightwood-2414: `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 4c9029b63..cbbf9a8d5 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": "2024-05-15T12:29:04.352246Z", - "iopub.status.busy": "2024-05-15T12:29:04.351721Z", - "iopub.status.idle": "2024-05-15T12:29:13.307917Z", - "shell.execute_reply": "2024-05-15T12:29:13.307226Z" + "iopub.execute_input": "2024-05-15T12:36:11.731772Z", + "iopub.status.busy": "2024-05-15T12:36:11.731340Z", + "iopub.status.idle": "2024-05-15T12:36:18.394584Z", + "shell.execute_reply": "2024-05-15T12:36:18.393802Z" } }, "outputs": [ @@ -39,14 +39,14 @@ "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:lightwood-2259:No torchvision detected, image helpers not supported.\u001b[0m\n" + "\u001b[32mINFO:lightwood-2197:No torchvision detected, image helpers not supported.\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:lightwood-2259:No torchvision/pillow detected, image encoder not supported\u001b[0m\n" + "\u001b[32mINFO:lightwood-2197:No torchvision/pillow detected, image encoder not supported\u001b[0m\n" ] } ], @@ -87,10 +87,10 @@ "id": "foreign-orchestra", "metadata": { "execution": { - "iopub.execute_input": "2024-05-15T12:29:13.311274Z", - "iopub.status.busy": "2024-05-15T12:29:13.310750Z", - "iopub.status.idle": "2024-05-15T12:29:18.428236Z", - "shell.execute_reply": "2024-05-15T12:29:18.427494Z" + "iopub.execute_input": "2024-05-15T12:36:18.397974Z", + "iopub.status.busy": "2024-05-15T12:36:18.397631Z", + "iopub.status.idle": "2024-05-15T12:36:23.491475Z", + "shell.execute_reply": "2024-05-15T12:36:23.490796Z" } }, "outputs": [ @@ -316,10 +316,10 @@ "id": "cathedral-mills", "metadata": { "execution": { - "iopub.execute_input": "2024-05-15T12:29:18.431216Z", - "iopub.status.busy": "2024-05-15T12:29:18.430796Z", - "iopub.status.idle": "2024-05-15T12:29:18.786310Z", - "shell.execute_reply": "2024-05-15T12:29:18.785723Z" + "iopub.execute_input": "2024-05-15T12:36:23.494300Z", + "iopub.status.busy": "2024-05-15T12:36:23.494042Z", + "iopub.status.idle": "2024-05-15T12:36:23.854824Z", + "shell.execute_reply": "2024-05-15T12:36:23.854148Z" } }, "outputs": [ @@ -374,10 +374,10 @@ "id": "medieval-zambia", "metadata": { "execution": { - "iopub.execute_input": "2024-05-15T12:29:18.789121Z", - "iopub.status.busy": "2024-05-15T12:29:18.788667Z", - "iopub.status.idle": "2024-05-15T12:30:27.330359Z", - "shell.execute_reply": "2024-05-15T12:30:27.329696Z" + "iopub.execute_input": "2024-05-15T12:36:23.857705Z", + "iopub.status.busy": "2024-05-15T12:36:23.857253Z", + "iopub.status.idle": "2024-05-15T12:37:32.751150Z", + "shell.execute_reply": "2024-05-15T12:37:32.750449Z" } }, "outputs": [ @@ -385,469 +385,469 @@ "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:type_infer-2259:Analyzing a sample of 18424\u001b[0m\n" + "\u001b[32mINFO:type_infer-2197:Analyzing a sample of 18424\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:type_infer-2259:from a total population of 284807, this is equivalent to 6.5% of your data.\u001b[0m\n" + "\u001b[32mINFO:type_infer-2197: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-2259:Using 3 processes to deduct types.\u001b[0m\n" + "\u001b[32mINFO:type_infer-2197:Using 3 processes to deduct types.\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:type_infer-2259:Infering type for: Time\u001b[0m\n" + "\u001b[32mINFO:type_infer-2197:Infering type for: Time\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:type_infer-2259:Infering type for: V3\u001b[0m\n" + "\u001b[32mINFO:type_infer-2197:Infering type for: V3\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:type_infer-2259:Infering type for: V6\u001b[0m\n" + "\u001b[32mINFO:type_infer-2197:Infering type for: V6\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:type_infer-2259:Column Time has data type integer\u001b[0m\n" + "\u001b[32mINFO:type_infer-2197:Column Time has data type integer\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - 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"\u001b[32mINFO:type_infer-2259:Column V21 has data type float\u001b[0m\n" + "\u001b[32mINFO:type_infer-2197:Infering type for: V19\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:type_infer-2259:Infering type for: V22\u001b[0m\n" + "\u001b[32mINFO:type_infer-2197:Infering type for: V24\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:type_infer-2259:Column V17 has data type float\u001b[0m\n" + "\u001b[32mINFO:type_infer-2197:Column V19 has data type float\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:type_infer-2259:Infering type for: V24\u001b[0m\n" + "\u001b[32mINFO:type_infer-2197:Infering type for: V20\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:type_infer-2259:Column V19 has data type float\u001b[0m\n" + "\u001b[32mINFO:type_infer-2197:Column V21 has data type float\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - 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"\u001b[32mINFO:type_infer-2259:Column V23 has data type float\u001b[0m\n" + "\u001b[32mINFO:type_infer-2197:Column V22 has data type float\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:type_infer-2259:Infering type for: V27\u001b[0m\n" + "\u001b[32mINFO:type_infer-2197:Infering type for: V23\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:type_infer-2259:Column V20 has data type float\u001b[0m\n" + "\u001b[32mINFO:type_infer-2197:Column V27 has data type float\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:type_infer-2259:Infering type for: Class\u001b[0m\n" + "\u001b[32mINFO:type_infer-2197:Infering type for: V28\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:type_infer-2259:Column V25 has data type float\u001b[0m\n" + "\u001b[32mINFO:type_infer-2197:Column V25 has data type float\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:type_infer-2259:Infering type for: V26\u001b[0m\n" + "\u001b[32mINFO:type_infer-2197:Infering type for: V26\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:type_infer-2259:Column Class has data type binary\u001b[0m\n" + "\u001b[32mINFO:type_infer-2197:Column V28 has data type float\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:type_infer-2259:Column V27 has data type float\u001b[0m\n" + "\u001b[32mINFO:type_infer-2197:Infering type for: Amount\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:type_infer-2259:Infering type for: V28\u001b[0m\n" + "\u001b[32mINFO:type_infer-2197:Column V23 has data type float\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:type_infer-2259:Column V26 has data type float\u001b[0m\n" + "\u001b[32mINFO:type_infer-2197:Infering type for: Class\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:type_infer-2259:Column V28 has data type float\u001b[0m\n" + "\u001b[32mINFO:type_infer-2197:Column V26 has data type float\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:type_infer-2259:Infering type for: Amount\u001b[0m\n" + "\u001b[32mINFO:type_infer-2197:Column Amount has data type float\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:type_infer-2259:Column Amount has data type float\u001b[0m\n" + "\u001b[32mINFO:type_infer-2197:Column Class has data type binary\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:dataprep_ml-2259:Starting statistical analysis\u001b[0m\n" + "\u001b[32mINFO:dataprep_ml-2197:Starting statistical analysis\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:dataprep_ml-2259:Finished statistical analysis\u001b[0m\n" + "\u001b[32mINFO:dataprep_ml-2197:Finished statistical analysis\u001b[0m\n" ] } ], @@ -901,10 +901,10 @@ "id": "4411ee53", "metadata": { "execution": { - "iopub.execute_input": "2024-05-15T12:30:27.333499Z", - "iopub.status.busy": "2024-05-15T12:30:27.333282Z", - "iopub.status.idle": "2024-05-15T12:30:27.338468Z", - "shell.execute_reply": "2024-05-15T12:30:27.337924Z" + "iopub.execute_input": "2024-05-15T12:37:32.754539Z", + "iopub.status.busy": "2024-05-15T12:37:32.754103Z", + "iopub.status.idle": "2024-05-15T12:37:32.759217Z", + "shell.execute_reply": "2024-05-15T12:37:32.758527Z" } }, "outputs": [ @@ -996,10 +996,10 @@ "id": "34092d12", "metadata": { "execution": { - "iopub.execute_input": "2024-05-15T12:30:27.340823Z", - "iopub.status.busy": "2024-05-15T12:30:27.340587Z", - "iopub.status.idle": "2024-05-15T12:30:27.343872Z", - "shell.execute_reply": "2024-05-15T12:30:27.343379Z" + "iopub.execute_input": "2024-05-15T12:37:32.761863Z", + "iopub.status.busy": "2024-05-15T12:37:32.761475Z", + "iopub.status.idle": "2024-05-15T12:37:32.764800Z", + "shell.execute_reply": "2024-05-15T12:37:32.764192Z" } }, "outputs": [], @@ -1055,10 +1055,10 @@ "id": "alleged-concentrate", "metadata": { "execution": { - "iopub.execute_input": "2024-05-15T12:30:27.346334Z", - "iopub.status.busy": "2024-05-15T12:30:27.346133Z", - "iopub.status.idle": "2024-05-15T12:30:27.581484Z", - "shell.execute_reply": "2024-05-15T12:30:27.580794Z" + "iopub.execute_input": "2024-05-15T12:37:32.767542Z", + "iopub.status.busy": "2024-05-15T12:37:32.767181Z", + "iopub.status.idle": "2024-05-15T12:37:33.006726Z", + "shell.execute_reply": "2024-05-15T12:37:33.005918Z" } }, "outputs": [ @@ -1139,7 +1139,7 @@ " \"unbias_target\": True,\n", " \"seconds_per_mixer\": 42768.0,\n", " \"seconds_per_encoder\": None,\n", - " \"expected_additional_time\": 68.51064419746399,\n", + " \"expected_additional_time\": 68.86109614372253,\n", " \"time_aim\": 259200,\n", " \"target_weights\": None,\n", " \"positive_domain\": False,\n", @@ -1905,10 +1905,10 @@ "id": "organic-london", "metadata": { "execution": { - "iopub.execute_input": "2024-05-15T12:30:27.583960Z", - "iopub.status.busy": "2024-05-15T12:30:27.583758Z", - "iopub.status.idle": "2024-05-15T12:30:27.591554Z", - "shell.execute_reply": "2024-05-15T12:30:27.591054Z" + "iopub.execute_input": "2024-05-15T12:37:33.009564Z", + "iopub.status.busy": "2024-05-15T12:37:33.009352Z", + "iopub.status.idle": "2024-05-15T12:37:33.017259Z", + "shell.execute_reply": "2024-05-15T12:37:33.016750Z" } }, "outputs": [], @@ -1923,10 +1923,10 @@ "id": "fabulous-prime", "metadata": { "execution": { - "iopub.execute_input": "2024-05-15T12:30:27.594015Z", - "iopub.status.busy": "2024-05-15T12:30:27.593641Z", - "iopub.status.idle": "2024-05-15T12:30:47.763538Z", - "shell.execute_reply": "2024-05-15T12:30:47.762831Z" + "iopub.execute_input": "2024-05-15T12:37:33.020087Z", + "iopub.status.busy": "2024-05-15T12:37:33.019578Z", + "iopub.status.idle": "2024-05-15T12:37:53.359194Z", + "shell.execute_reply": "2024-05-15T12:37:53.358511Z" } }, "outputs": [ @@ -1934,28 +1934,28 @@ "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:dataprep_ml-2259:Cleaning the data\u001b[0m\n" + "\u001b[32mINFO:dataprep_ml-2197:Cleaning the data\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[37mDEBUG:lightwood-2259: `preprocess` runtime: 18.54 seconds\u001b[0m\n" + "\u001b[37mDEBUG:lightwood-2197: `preprocess` runtime: 18.56 seconds\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:dataprep_ml-2259:Splitting the data into train/test\u001b[0m\n" + "\u001b[32mINFO:dataprep_ml-2197:Splitting the data into train/test\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[37mDEBUG:lightwood-2259: `split` runtime: 1.62 seconds\u001b[0m\n" + "\u001b[37mDEBUG:lightwood-2197: `split` runtime: 1.77 seconds\u001b[0m\n" ] } ], @@ -1971,10 +1971,10 @@ "id": "suspended-biography", "metadata": { "execution": { - "iopub.execute_input": "2024-05-15T12:30:47.766563Z", - "iopub.status.busy": "2024-05-15T12:30:47.766019Z", - "iopub.status.idle": "2024-05-15T12:30:49.244243Z", - "shell.execute_reply": "2024-05-15T12:30:49.243541Z" + "iopub.execute_input": "2024-05-15T12:37:53.362317Z", + "iopub.status.busy": "2024-05-15T12:37:53.361876Z", + "iopub.status.idle": "2024-05-15T12:37:54.934620Z", + "shell.execute_reply": "2024-05-15T12:37:54.933895Z" } }, "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 d0872ea95..e2897726b 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": "2024-05-15T12:32:14.309394Z", - "iopub.status.busy": "2024-05-15T12:32:14.309198Z", - "iopub.status.idle": "2024-05-15T12:32:14.629441Z", - "shell.execute_reply": "2024-05-15T12:32:14.628730Z" + "iopub.execute_input": "2024-05-15T12:39:20.516178Z", + "iopub.status.busy": "2024-05-15T12:39:20.515976Z", + "iopub.status.idle": "2024-05-15T12:39:20.842577Z", + "shell.execute_reply": "2024-05-15T12:39:20.841884Z" } }, "outputs": [ @@ -175,10 +175,10 @@ "execution_count": 2, "metadata": { "execution": { - "iopub.execute_input": "2024-05-15T12:32:14.667078Z", - "iopub.status.busy": "2024-05-15T12:32:14.666676Z", - "iopub.status.idle": "2024-05-15T12:32:17.186059Z", - "shell.execute_reply": "2024-05-15T12:32:17.185340Z" + "iopub.execute_input": "2024-05-15T12:39:20.882091Z", + "iopub.status.busy": "2024-05-15T12:39:20.881616Z", + "iopub.status.idle": "2024-05-15T12:39:23.423280Z", + "shell.execute_reply": "2024-05-15T12:39:23.422573Z" } }, "outputs": [ @@ -186,14 +186,14 @@ "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:lightwood-2916:No torchvision detected, image helpers not supported.\u001b[0m\n" + "\u001b[32mINFO:lightwood-2866:No torchvision detected, image helpers not supported.\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:lightwood-2916:No torchvision/pillow detected, image encoder not supported\u001b[0m\n" + "\u001b[32mINFO:lightwood-2866:No torchvision/pillow detected, image encoder not supported\u001b[0m\n" ] } ], @@ -215,10 +215,10 @@ "execution_count": 3, "metadata": { "execution": { - "iopub.execute_input": "2024-05-15T12:32:17.189281Z", - "iopub.status.busy": "2024-05-15T12:32:17.188770Z", - "iopub.status.idle": "2024-05-15T12:32:17.193969Z", - "shell.execute_reply": "2024-05-15T12:32:17.193389Z" + "iopub.execute_input": "2024-05-15T12:39:23.426512Z", + "iopub.status.busy": "2024-05-15T12:39:23.426163Z", + "iopub.status.idle": "2024-05-15T12:39:23.431505Z", + "shell.execute_reply": "2024-05-15T12:39:23.430893Z" } }, "outputs": [ @@ -270,10 +270,10 @@ "execution_count": 4, "metadata": { "execution": { - "iopub.execute_input": "2024-05-15T12:32:17.196483Z", - "iopub.status.busy": "2024-05-15T12:32:17.196106Z", - "iopub.status.idle": "2024-05-15T12:32:17.219249Z", - "shell.execute_reply": "2024-05-15T12:32:17.218661Z" + "iopub.execute_input": "2024-05-15T12:39:23.434201Z", + "iopub.status.busy": "2024-05-15T12:39:23.433737Z", + "iopub.status.idle": "2024-05-15T12:39:23.459713Z", + "shell.execute_reply": "2024-05-15T12:39:23.459082Z" } }, "outputs": [ @@ -281,112 +281,112 @@ "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:type_infer-2916:Analyzing a sample of 222\u001b[0m\n" + "\u001b[32mINFO:type_infer-2866:Analyzing a sample of 222\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:type_infer-2916:from a total population of 225, this is equivalent to 98.7% of your data.\u001b[0m\n" + "\u001b[32mINFO:type_infer-2866: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-2916:Infering type for: Population\u001b[0m\n" + "\u001b[32mINFO:type_infer-2866:Infering type for: Population\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:type_infer-2916:Column Population has data type integer\u001b[0m\n" + "\u001b[32mINFO:type_infer-2866:Column Population has data type integer\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:type_infer-2916:Infering type for: Area (sq. mi.)\u001b[0m\n" + "\u001b[32mINFO:type_infer-2866:Infering type for: Area (sq. mi.)\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:type_infer-2916:Column Area (sq. mi.) has data type integer\u001b[0m\n" + "\u001b[32mINFO:type_infer-2866:Column Area (sq. mi.) has data type integer\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:type_infer-2916:Infering type for: Pop. Density \u001b[0m\n" + "\u001b[32mINFO:type_infer-2866:Infering type for: Pop. Density \u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:type_infer-2916:Column Pop. Density has data type float\u001b[0m\n" + "\u001b[32mINFO:type_infer-2866:Column Pop. Density has data type float\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:type_infer-2916:Infering type for: GDP ($ per capita)\u001b[0m\n" + "\u001b[32mINFO:type_infer-2866:Infering type for: GDP ($ per capita)\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:type_infer-2916:Column GDP ($ per capita) has data type integer\u001b[0m\n" + "\u001b[32mINFO:type_infer-2866:Column GDP ($ per capita) has data type integer\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:type_infer-2916:Infering type for: Literacy (%)\u001b[0m\n" + "\u001b[32mINFO:type_infer-2866:Infering type for: Literacy (%)\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:type_infer-2916:Column Literacy (%) has data type float\u001b[0m\n" + "\u001b[32mINFO:type_infer-2866:Column Literacy (%) has data type float\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:type_infer-2916:Infering type for: Infant mortality \u001b[0m\n" + "\u001b[32mINFO:type_infer-2866:Infering type for: Infant mortality \u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:type_infer-2916:Column Infant mortality has data type float\u001b[0m\n" + "\u001b[32mINFO:type_infer-2866:Column Infant mortality has data type float\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:type_infer-2916:Infering type for: Development Index\u001b[0m\n" + "\u001b[32mINFO:type_infer-2866:Infering type for: Development Index\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:type_infer-2916:Column Development Index has data type categorical\u001b[0m\n" + "\u001b[32mINFO:type_infer-2866:Column Development Index has data type categorical\u001b[0m\n" ] }, { @@ -421,10 +421,10 @@ "execution_count": 5, "metadata": { "execution": { - "iopub.execute_input": "2024-05-15T12:32:17.221782Z", - "iopub.status.busy": "2024-05-15T12:32:17.221448Z", - "iopub.status.idle": "2024-05-15T12:32:17.225621Z", - "shell.execute_reply": "2024-05-15T12:32:17.224989Z" + "iopub.execute_input": "2024-05-15T12:39:23.462354Z", + "iopub.status.busy": "2024-05-15T12:39:23.461965Z", + "iopub.status.idle": "2024-05-15T12:39:23.466294Z", + "shell.execute_reply": "2024-05-15T12:39:23.465608Z" } }, "outputs": [ @@ -474,10 +474,10 @@ "execution_count": 6, "metadata": { "execution": { - "iopub.execute_input": "2024-05-15T12:32:17.228143Z", - "iopub.status.busy": "2024-05-15T12:32:17.227783Z", - "iopub.status.idle": "2024-05-15T12:32:17.253950Z", - "shell.execute_reply": "2024-05-15T12:32:17.253307Z" + "iopub.execute_input": "2024-05-15T12:39:23.468886Z", + "iopub.status.busy": "2024-05-15T12:39:23.468511Z", + "iopub.status.idle": "2024-05-15T12:39:23.495336Z", + "shell.execute_reply": "2024-05-15T12:39:23.494690Z" } }, "outputs": [ @@ -485,14 +485,14 @@ "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:dataprep_ml-2916:Starting statistical analysis\u001b[0m\n" + "\u001b[32mINFO:dataprep_ml-2866:Starting statistical analysis\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:dataprep_ml-2916:Finished statistical analysis\u001b[0m\n" + "\u001b[32mINFO:dataprep_ml-2866:Finished statistical analysis\u001b[0m\n" ] } ], @@ -520,10 +520,10 @@ "execution_count": 7, "metadata": { "execution": { - "iopub.execute_input": "2024-05-15T12:32:17.256430Z", - "iopub.status.busy": "2024-05-15T12:32:17.256226Z", - "iopub.status.idle": "2024-05-15T12:32:17.260696Z", - "shell.execute_reply": "2024-05-15T12:32:17.260064Z" + "iopub.execute_input": "2024-05-15T12:39:23.497946Z", + "iopub.status.busy": "2024-05-15T12:39:23.497565Z", + "iopub.status.idle": "2024-05-15T12:39:23.502155Z", + "shell.execute_reply": "2024-05-15T12:39:23.501523Z" } }, "outputs": [ @@ -569,10 +569,10 @@ "execution_count": 8, "metadata": { "execution": { - "iopub.execute_input": "2024-05-15T12:32:17.263203Z", - "iopub.status.busy": "2024-05-15T12:32:17.262837Z", - "iopub.status.idle": "2024-05-15T12:32:17.266857Z", - "shell.execute_reply": "2024-05-15T12:32:17.266234Z" + "iopub.execute_input": "2024-05-15T12:39:23.504893Z", + "iopub.status.busy": "2024-05-15T12:39:23.504449Z", + "iopub.status.idle": "2024-05-15T12:39:23.509018Z", + "shell.execute_reply": "2024-05-15T12:39:23.508474Z" } }, "outputs": [ @@ -612,10 +612,10 @@ "execution_count": 9, "metadata": { "execution": { - "iopub.execute_input": "2024-05-15T12:32:17.269479Z", - "iopub.status.busy": "2024-05-15T12:32:17.269087Z", - "iopub.status.idle": "2024-05-15T12:32:17.273721Z", - "shell.execute_reply": "2024-05-15T12:32:17.273107Z" + "iopub.execute_input": "2024-05-15T12:39:23.511601Z", + "iopub.status.busy": "2024-05-15T12:39:23.511209Z", + "iopub.status.idle": "2024-05-15T12:39:23.515862Z", + "shell.execute_reply": "2024-05-15T12:39:23.515270Z" }, "scrolled": false }, @@ -673,10 +673,10 @@ "execution_count": 10, "metadata": { "execution": { - "iopub.execute_input": "2024-05-15T12:32:17.276269Z", - "iopub.status.busy": "2024-05-15T12:32:17.275813Z", - "iopub.status.idle": "2024-05-15T12:32:17.279893Z", - "shell.execute_reply": "2024-05-15T12:32:17.279315Z" + "iopub.execute_input": "2024-05-15T12:39:23.518414Z", + "iopub.status.busy": "2024-05-15T12:39:23.517941Z", + "iopub.status.idle": "2024-05-15T12:39:23.522169Z", + "shell.execute_reply": "2024-05-15T12:39:23.521501Z" } }, "outputs": [ @@ -700,10 +700,10 @@ "execution_count": 11, "metadata": { "execution": { - "iopub.execute_input": "2024-05-15T12:32:17.282178Z", - "iopub.status.busy": "2024-05-15T12:32:17.281977Z", - "iopub.status.idle": "2024-05-15T12:32:17.286732Z", - "shell.execute_reply": "2024-05-15T12:32:17.286148Z" + "iopub.execute_input": "2024-05-15T12:39:23.524471Z", + "iopub.status.busy": "2024-05-15T12:39:23.524270Z", + "iopub.status.idle": "2024-05-15T12:39:23.529133Z", + "shell.execute_reply": "2024-05-15T12:39:23.528566Z" }, "scrolled": false }, @@ -786,10 +786,10 @@ "execution_count": 12, "metadata": { "execution": { - "iopub.execute_input": "2024-05-15T12:32:17.289214Z", - "iopub.status.busy": "2024-05-15T12:32:17.288834Z", - "iopub.status.idle": "2024-05-15T12:32:17.292779Z", - "shell.execute_reply": "2024-05-15T12:32:17.292139Z" + "iopub.execute_input": "2024-05-15T12:39:23.531603Z", + "iopub.status.busy": "2024-05-15T12:39:23.531233Z", + "iopub.status.idle": "2024-05-15T12:39:23.535243Z", + "shell.execute_reply": "2024-05-15T12:39:23.534589Z" } }, "outputs": [ @@ -841,10 +841,10 @@ "execution_count": 13, "metadata": { "execution": { - "iopub.execute_input": "2024-05-15T12:32:17.295205Z", - "iopub.status.busy": "2024-05-15T12:32:17.294842Z", - "iopub.status.idle": "2024-05-15T12:32:19.947038Z", - "shell.execute_reply": "2024-05-15T12:32:19.946304Z" + "iopub.execute_input": "2024-05-15T12:39:23.537775Z", + "iopub.status.busy": "2024-05-15T12:39:23.537413Z", + "iopub.status.idle": "2024-05-15T12:39:26.181488Z", + "shell.execute_reply": "2024-05-15T12:39:26.180780Z" }, "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 64fb603ac..51f683c34 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": "2024-05-15T12:32:01.117399Z", - "iopub.status.busy": "2024-05-15T12:32:01.117200Z", - "iopub.status.idle": "2024-05-15T12:32:01.618443Z", - "shell.execute_reply": "2024-05-15T12:32:01.617772Z" + "iopub.execute_input": "2024-05-15T12:39:07.466078Z", + "iopub.status.busy": "2024-05-15T12:39:07.465874Z", + "iopub.status.idle": "2024-05-15T12:39:07.852195Z", + "shell.execute_reply": "2024-05-15T12:39:07.851479Z" } }, "outputs": [ @@ -162,10 +162,10 @@ "execution_count": 2, "metadata": { "execution": { - "iopub.execute_input": "2024-05-15T12:32:01.654728Z", - "iopub.status.busy": "2024-05-15T12:32:01.654340Z", - "iopub.status.idle": "2024-05-15T12:32:04.161035Z", - "shell.execute_reply": "2024-05-15T12:32:04.160343Z" + "iopub.execute_input": "2024-05-15T12:39:07.892100Z", + "iopub.status.busy": "2024-05-15T12:39:07.891629Z", + "iopub.status.idle": "2024-05-15T12:39:10.416172Z", + "shell.execute_reply": "2024-05-15T12:39:10.415460Z" } }, "outputs": [ @@ -173,14 +173,14 @@ "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:lightwood-2872:No torchvision detected, image helpers not supported.\u001b[0m\n" + "\u001b[32mINFO:lightwood-2821:No torchvision detected, image helpers not supported.\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:lightwood-2872:No torchvision/pillow detected, image encoder not supported\u001b[0m\n" + "\u001b[32mINFO:lightwood-2821:No torchvision/pillow detected, image encoder not supported\u001b[0m\n" ] } ], @@ -193,10 +193,10 @@ "execution_count": 3, "metadata": { "execution": { - "iopub.execute_input": "2024-05-15T12:32:04.164053Z", - "iopub.status.busy": "2024-05-15T12:32:04.163790Z", - "iopub.status.idle": "2024-05-15T12:32:04.167552Z", - "shell.execute_reply": "2024-05-15T12:32:04.167026Z" + "iopub.execute_input": "2024-05-15T12:39:10.419295Z", + "iopub.status.busy": "2024-05-15T12:39:10.418970Z", + "iopub.status.idle": "2024-05-15T12:39:10.422725Z", + "shell.execute_reply": "2024-05-15T12:39:10.422113Z" } }, "outputs": [], @@ -223,10 +223,10 @@ "execution_count": 4, "metadata": { "execution": { - "iopub.execute_input": "2024-05-15T12:32:04.170051Z", - "iopub.status.busy": "2024-05-15T12:32:04.169696Z", - "iopub.status.idle": "2024-05-15T12:32:04.173649Z", - "shell.execute_reply": "2024-05-15T12:32:04.173022Z" + "iopub.execute_input": "2024-05-15T12:39:10.425265Z", + "iopub.status.busy": "2024-05-15T12:39:10.424889Z", + "iopub.status.idle": "2024-05-15T12:39:10.428910Z", + "shell.execute_reply": "2024-05-15T12:39:10.428271Z" } }, "outputs": [ @@ -261,10 +261,10 @@ "execution_count": 5, "metadata": { "execution": { - "iopub.execute_input": "2024-05-15T12:32:04.176107Z", - "iopub.status.busy": "2024-05-15T12:32:04.175746Z", - "iopub.status.idle": "2024-05-15T12:32:08.372518Z", - "shell.execute_reply": "2024-05-15T12:32:08.371822Z" + "iopub.execute_input": "2024-05-15T12:39:10.431459Z", + "iopub.status.busy": "2024-05-15T12:39:10.431088Z", + "iopub.status.idle": "2024-05-15T12:39:14.560206Z", + "shell.execute_reply": "2024-05-15T12:39:14.559482Z" } }, "outputs": [ @@ -272,49 +272,49 @@ "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:type_infer-2872:Analyzing a sample of 2467\u001b[0m\n" + "\u001b[32mINFO:type_infer-2821:Analyzing a sample of 2467\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:type_infer-2872:from a total population of 2820, this is equivalent to 87.5% of your data.\u001b[0m\n" + "\u001b[32mINFO:type_infer-2821: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-2872:Infering type for: Month\u001b[0m\n" + "\u001b[32mINFO:type_infer-2821:Infering type for: Month\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:type_infer-2872:Column Month has data type date\u001b[0m\n" + "\u001b[32mINFO:type_infer-2821:Column Month has data type date\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:type_infer-2872:Infering type for: Sunspots\u001b[0m\n" + "\u001b[32mINFO:type_infer-2821:Infering type for: Sunspots\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:type_infer-2872:Column Sunspots has data type float\u001b[0m\n" + "\u001b[32mINFO:type_infer-2821:Column Sunspots has data type float\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:dataprep_ml-2872:Starting statistical analysis\u001b[0m\n" + "\u001b[32mINFO:dataprep_ml-2821:Starting statistical analysis\u001b[0m\n" ] }, { @@ -323,7 +323,7 @@ "text": [ "/opt/hostedtoolcache/Python/3.9.19/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-2872:Finished statistical analysis\u001b[0m\n" + "\u001b[32mINFO:dataprep_ml-2821:Finished statistical analysis\u001b[0m\n" ] } ], @@ -360,10 +360,10 @@ "execution_count": 6, "metadata": { "execution": { - "iopub.execute_input": "2024-05-15T12:32:08.375604Z", - "iopub.status.busy": "2024-05-15T12:32:08.375181Z", - "iopub.status.idle": "2024-05-15T12:32:10.298183Z", - "shell.execute_reply": "2024-05-15T12:32:10.297500Z" + "iopub.execute_input": "2024-05-15T12:39:14.563441Z", + "iopub.status.busy": "2024-05-15T12:39:14.563060Z", + "iopub.status.idle": "2024-05-15T12:39:16.481390Z", + "shell.execute_reply": "2024-05-15T12:39:16.480840Z" } }, "outputs": [ @@ -371,14 +371,14 @@ "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:dataprep_ml-2872:[Learn phase 1/8] - Statistical analysis\u001b[0m\n" + "\u001b[32mINFO:dataprep_ml-2821:[Learn phase 1/8] - Statistical analysis\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:dataprep_ml-2872:Starting statistical analysis\u001b[0m\n" + "\u001b[32mINFO:dataprep_ml-2821:Starting statistical analysis\u001b[0m\n" ] }, { @@ -387,28 +387,28 @@ "text": [ "/opt/hostedtoolcache/Python/3.9.19/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-2872:Finished statistical analysis\u001b[0m\n" + "\u001b[32mINFO:dataprep_ml-2821:Finished statistical analysis\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[37mDEBUG:lightwood-2872: `analyze_data` runtime: 0.06 seconds\u001b[0m\n" + "\u001b[37mDEBUG:lightwood-2821: `analyze_data` runtime: 0.06 seconds\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:dataprep_ml-2872:[Learn phase 2/8] - Data preprocessing\u001b[0m\n" + "\u001b[32mINFO:dataprep_ml-2821:[Learn phase 2/8] - Data preprocessing\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:dataprep_ml-2872:Cleaning the data\u001b[0m\n" + "\u001b[32mINFO:dataprep_ml-2821:Cleaning the data\u001b[0m\n" ] }, { @@ -417,133 +417,133 @@ "text": [ "/opt/hostedtoolcache/Python/3.9.19/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-2872:Transforming timeseries data\u001b[0m\n" + "\u001b[32mINFO:dataprep_ml-2821:Transforming timeseries data\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[37mDEBUG:lightwood-2872: `preprocess` runtime: 0.09 seconds\u001b[0m\n" + "\u001b[37mDEBUG:lightwood-2821: `preprocess` runtime: 0.09 seconds\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:dataprep_ml-2872:[Learn phase 3/8] - Data splitting\u001b[0m\n" + "\u001b[32mINFO:dataprep_ml-2821:[Learn phase 3/8] - Data splitting\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:dataprep_ml-2872:Splitting the data into train/test\u001b[0m\n" + "\u001b[32mINFO:dataprep_ml-2821:Splitting the data into train/test\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[37mDEBUG:lightwood-2872: `split` runtime: 0.0 seconds\u001b[0m\n" + "\u001b[37mDEBUG:lightwood-2821: `split` runtime: 0.0 seconds\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:dataprep_ml-2872:[Learn phase 4/8] - Preparing encoders\u001b[0m\n" + "\u001b[32mINFO:dataprep_ml-2821:[Learn phase 4/8] - Preparing encoders\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[37mDEBUG:dataprep_ml-2872:Preparing sequentially...\u001b[0m\n" + "\u001b[37mDEBUG:dataprep_ml-2821:Preparing sequentially...\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[37mDEBUG:lightwood-2872: `prepare` runtime: 0.05 seconds\u001b[0m\n" + "\u001b[37mDEBUG:lightwood-2821: `prepare` runtime: 0.05 seconds\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:dataprep_ml-2872:[Learn phase 5/8] - Feature generation\u001b[0m\n" + "\u001b[32mINFO:dataprep_ml-2821:[Learn phase 5/8] - Feature generation\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:dataprep_ml-2872:Featurizing the data\u001b[0m\n" + "\u001b[32mINFO:dataprep_ml-2821:Featurizing the data\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[37mDEBUG:lightwood-2872: `featurize` runtime: 0.05 seconds\u001b[0m\n" + "\u001b[37mDEBUG:lightwood-2821: `featurize` runtime: 0.04 seconds\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:dataprep_ml-2872:[Learn phase 6/8] - Mixer training\u001b[0m\n" + "\u001b[32mINFO:dataprep_ml-2821:[Learn phase 6/8] - Mixer training\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:dataprep_ml-2872:Training the mixers\u001b[0m\n" + "\u001b[32mINFO:dataprep_ml-2821:Training the mixers\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[33mWARNING:lightwood-2872:XGBoost running on CPU\u001b[0m\n" + "\u001b[33mWARNING:lightwood-2821:XGBoost running on CPU\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[33mWARNING:lightwood-2872:XGBoost running on CPU\u001b[0m\n" + "\u001b[33mWARNING:lightwood-2821:XGBoost running on CPU\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[33mWARNING:lightwood-2872:XGBoost running on CPU\u001b[0m\n" + "\u001b[33mWARNING:lightwood-2821:XGBoost running on CPU\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[33mWARNING:lightwood-2872:XGBoost running on CPU\u001b[0m\n" + "\u001b[33mWARNING:lightwood-2821:XGBoost running on CPU\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[33mWARNING:lightwood-2872:XGBoost running on CPU\u001b[0m\n" + "\u001b[33mWARNING:lightwood-2821:XGBoost running on CPU\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[33mWARNING:lightwood-2872:XGBoost running on CPU\u001b[0m\n" + "\u001b[33mWARNING:lightwood-2821:XGBoost running on CPU\u001b[0m\n" ] }, { @@ -558,12 +558,12 @@ "name": "stdout", "output_type": "stream", "text": [ - "[12:32:08] WARNING: ../src/learner.cc:339: No visible GPU is found, setting `gpu_id` to -1\n", - "[12:32:08] WARNING: ../src/learner.cc:339: No visible GPU is found, setting `gpu_id` to -1\n", - "[12:32:08] WARNING: ../src/learner.cc:339: No visible GPU is found, setting `gpu_id` to -1\n", - "[12:32:08] WARNING: ../src/learner.cc:339: No visible GPU is found, setting `gpu_id` to -1\n", - "[12:32:08] WARNING: ../src/learner.cc:339: No visible GPU is found, setting `gpu_id` to -1\n", - "[12:32:08] WARNING: ../src/learner.cc:339: No visible GPU is found, setting `gpu_id` to -1\n" + "[12:39:14] WARNING: ../src/learner.cc:339: No visible GPU is found, setting `gpu_id` to -1\n", + "[12:39:14] WARNING: ../src/learner.cc:339: No visible GPU is found, setting `gpu_id` to -1\n", + "[12:39:14] WARNING: ../src/learner.cc:339: No visible GPU is found, setting `gpu_id` to -1\n", + "[12:39:14] WARNING: ../src/learner.cc:339: No visible GPU is found, setting `gpu_id` to -1\n", + "[12:39:14] WARNING: ../src/learner.cc:339: No visible GPU is found, setting `gpu_id` to -1\n", + "[12:39:14] WARNING: ../src/learner.cc:339: No visible GPU is found, setting `gpu_id` to -1\n" ] }, { @@ -575,168 +575,168 @@ "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:1578.)\n", " exp_avg_sq.mul_(beta2).addcmul_(1 - beta2, grad, grad)\n", - "\u001b[32mINFO:lightwood-2872:Loss of 9.051180630922318 with learning rate 0.0001\u001b[0m\n" + "\u001b[32mINFO:lightwood-2821:Loss of 9.051180630922318 with learning rate 0.0001\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:lightwood-2872:Loss of 9.014871209859848 with learning rate 0.0005\u001b[0m\n" + "\u001b[32mINFO:lightwood-2821:Loss of 9.014871209859848 with learning rate 0.0005\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:lightwood-2872:Loss of 8.969509482383728 with learning rate 0.001\u001b[0m\n" + "\u001b[32mINFO:lightwood-2821:Loss of 8.969509482383728 with learning rate 0.001\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:lightwood-2872:Loss of 8.879052013158798 with learning rate 0.002\u001b[0m\n" + "\u001b[32mINFO:lightwood-2821:Loss of 8.879052013158798 with learning rate 0.002\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:lightwood-2872:Loss of 8.788950502872467 with learning rate 0.003\u001b[0m\n" + "\u001b[32mINFO:lightwood-2821:Loss of 8.788950502872467 with learning rate 0.003\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:lightwood-2872:Loss of 8.611965209245682 with learning rate 0.005\u001b[0m\n" + "\u001b[32mINFO:lightwood-2821:Loss of 8.611965209245682 with learning rate 0.005\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:lightwood-2872:Loss of 8.195775926113129 with learning rate 0.01\u001b[0m\n" + "\u001b[32mINFO:lightwood-2821:Loss of 8.195775926113129 with learning rate 0.01\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:lightwood-2872:Loss of 6.255893141031265 with learning rate 0.05\u001b[0m\n" + "\u001b[32mINFO:lightwood-2821:Loss of 6.255893141031265 with learning rate 0.05\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:lightwood-2872:Found learning rate of: 0.05\u001b[0m\n" + "\u001b[32mINFO:lightwood-2821:Found learning rate of: 0.05\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:lightwood-2872:Loss @ epoch 1: 0.5818348675966263\u001b[0m\n" + "\u001b[32mINFO:lightwood-2821:Loss @ epoch 1: 0.5818348675966263\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:lightwood-2872:Loss @ epoch 2: 0.4797109067440033\u001b[0m\n" + "\u001b[32mINFO:lightwood-2821:Loss @ epoch 2: 0.4797109067440033\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:lightwood-2872:Loss @ epoch 3: 0.48386093974113464\u001b[0m\n" + "\u001b[32mINFO:lightwood-2821:Loss @ epoch 3: 0.48386093974113464\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:lightwood-2872:Loss @ epoch 4: 0.49511992931365967\u001b[0m\n" + "\u001b[32mINFO:lightwood-2821:Loss @ epoch 4: 0.49511992931365967\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:lightwood-2872:Loss @ epoch 5: 0.39475560188293457\u001b[0m\n" + "\u001b[32mINFO:lightwood-2821:Loss @ epoch 5: 0.39475560188293457\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:lightwood-2872:Loss @ epoch 6: 0.39592696726322174\u001b[0m\n" + "\u001b[32mINFO:lightwood-2821:Loss @ epoch 6: 0.39592696726322174\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:lightwood-2872:Loss @ epoch 7: 0.3622782379388809\u001b[0m\n" + "\u001b[32mINFO:lightwood-2821:Loss @ epoch 7: 0.3622782379388809\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:lightwood-2872:Loss @ epoch 8: 0.38170479238033295\u001b[0m\n" + "\u001b[32mINFO:lightwood-2821:Loss @ epoch 8: 0.38170479238033295\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:lightwood-2872:Loss @ epoch 9: 0.5138543993234634\u001b[0m\n" + "\u001b[32mINFO:lightwood-2821:Loss @ epoch 9: 0.5138543993234634\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:lightwood-2872:Loss @ epoch 10: 0.6360723078250885\u001b[0m\n" + "\u001b[32mINFO:lightwood-2821:Loss @ epoch 10: 0.6360723078250885\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:lightwood-2872:Loss @ epoch 1: 0.29868809472430835\u001b[0m\n" + "\u001b[32mINFO:lightwood-2821:Loss @ epoch 1: 0.29868809472430835\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:lightwood-2872:Loss @ epoch 2: 0.30318967591632495\u001b[0m\n" + "\u001b[32mINFO:lightwood-2821:Loss @ epoch 2: 0.30318967591632495\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[37mDEBUG:lightwood-2872: `fit_mixer` runtime: 0.87 seconds\u001b[0m\n" + "\u001b[37mDEBUG:lightwood-2821: `fit_mixer` runtime: 0.86 seconds\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:lightwood-2872:Started fitting LGBM models for array prediction\u001b[0m\n" + "\u001b[32mINFO:lightwood-2821:Started fitting LGBM models for array prediction\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:lightwood-2872:Started fitting XGBoost model\u001b[0m\n" + "\u001b[32mINFO:lightwood-2821:Started fitting XGBoost model\u001b[0m\n" ] }, { @@ -750,14 +750,14 @@ "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:lightwood-2872:A single GBM iteration takes 0.1 seconds\u001b[0m\n" + "\u001b[32mINFO:lightwood-2821:A single GBM iteration takes 0.1 seconds\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:lightwood-2872:Training XGBoost with 57023 iterations given 7127.987446546555 seconds constraint\u001b[0m\n" + "\u001b[32mINFO:lightwood-2821:Training XGBoost with 57023 iterations given 7127.987055063248 seconds constraint\u001b[0m\n" ] }, { @@ -858,11 +858,18 @@ "[13]\tvalidation_0-rmse:15.87505\n" ] }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[14]\tvalidation_0-rmse:16.06330\n" + ] + }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:lightwood-2872:Started fitting XGBoost model\u001b[0m\n" + "\u001b[32mINFO:lightwood-2821:Started fitting XGBoost model\u001b[0m\n" ] }, { @@ -876,14 +883,14 @@ "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:lightwood-2872:A single GBM iteration takes 0.1 seconds\u001b[0m\n" + "\u001b[32mINFO:lightwood-2821:A single GBM iteration takes 0.1 seconds\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:lightwood-2872:Training XGBoost with 57023 iterations given 7127.988590955734 seconds constraint\u001b[0m\n" + "\u001b[32mINFO:lightwood-2821:Training XGBoost with 57023 iterations given 7127.986838340759 seconds constraint\u001b[0m\n" ] }, { @@ -984,11 +991,18 @@ "[13]\tvalidation_0-rmse:17.75939\n" ] }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[14]\tvalidation_0-rmse:17.84796\n" + ] + }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:lightwood-2872:Started fitting XGBoost model\u001b[0m\n" + "\u001b[32mINFO:lightwood-2821:Started fitting XGBoost model\u001b[0m\n" ] }, { @@ -1002,14 +1016,14 @@ "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:lightwood-2872:A single GBM iteration takes 0.1 seconds\u001b[0m\n" + "\u001b[32mINFO:lightwood-2821:A single GBM iteration takes 0.1 seconds\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:lightwood-2872:Training XGBoost with 57023 iterations given 7127.988853693008 seconds constraint\u001b[0m\n" + "\u001b[32mINFO:lightwood-2821:Training XGBoost with 57023 iterations given 7127.9879677295685 seconds constraint\u001b[0m\n" ] }, { @@ -1107,7 +1121,7 @@ "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:lightwood-2872:Started fitting XGBoost model\u001b[0m\n" + "\u001b[32mINFO:lightwood-2821:Started fitting XGBoost model\u001b[0m\n" ] }, { @@ -1121,14 +1135,14 @@ "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:lightwood-2872:A single GBM iteration takes 0.1 seconds\u001b[0m\n" + "\u001b[32mINFO:lightwood-2821:A single GBM iteration takes 0.1 seconds\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:lightwood-2872:Training XGBoost with 57023 iterations given 7127.988720655441 seconds constraint\u001b[0m\n" + "\u001b[32mINFO:lightwood-2821:Training XGBoost with 57023 iterations given 7127.9887981414795 seconds constraint\u001b[0m\n" ] }, { @@ -1142,14 +1156,7 @@ "name": "stdout", "output_type": "stream", "text": [ - "[1]\tvalidation_0-rmse:34.13289" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n" + "[1]\tvalidation_0-rmse:34.13289\n" ] }, { @@ -1240,7 +1247,7 @@ "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:lightwood-2872:Started fitting XGBoost model\u001b[0m\n" + "\u001b[32mINFO:lightwood-2821:Started fitting XGBoost model\u001b[0m\n" ] }, { @@ -1254,14 +1261,14 @@ "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:lightwood-2872:A single GBM iteration takes 0.1 seconds\u001b[0m\n" + "\u001b[32mINFO:lightwood-2821:A single GBM iteration takes 0.1 seconds\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:lightwood-2872:Training XGBoost with 57023 iterations given 7127.9883551597595 seconds constraint\u001b[0m\n" + "\u001b[32mINFO:lightwood-2821:Training XGBoost with 57023 iterations given 7127.988205194473 seconds constraint\u001b[0m\n" ] }, { @@ -1289,14 +1296,7 @@ "name": "stdout", "output_type": "stream", "text": [ - "[3]\tvalidation_0-rmse:24.63817" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n" + "[3]\tvalidation_0-rmse:24.63817\n" ] }, { @@ -1338,28 +1338,14 @@ "name": "stdout", "output_type": "stream", "text": [ - "[9]\tvalidation_0-rmse:22.10747" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n" + "[9]\tvalidation_0-rmse:22.10747\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "[10]\tvalidation_0-rmse:22.20352" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n" + "[10]\tvalidation_0-rmse:22.20352\n" ] }, { @@ -1373,42 +1359,21 @@ "name": "stdout", "output_type": "stream", "text": [ - "[12]\tvalidation_0-rmse:22.25308" + "[12]\tvalidation_0-rmse:22.25308\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[13]\tvalidation_0-rmse:22.31415" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[14]\tvalidation_0-rmse:22.31000\n" + "[13]\tvalidation_0-rmse:22.31415\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:lightwood-2872:Started fitting XGBoost model\u001b[0m\n" + "\u001b[32mINFO:lightwood-2821:Started fitting XGBoost model\u001b[0m\n" ] }, { @@ -1422,14 +1387,14 @@ "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:lightwood-2872:A single GBM iteration takes 0.1 seconds\u001b[0m\n" + "\u001b[32mINFO:lightwood-2821:A single GBM iteration takes 0.1 seconds\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:lightwood-2872:Training XGBoost with 57023 iterations given 7127.988573551178 seconds constraint\u001b[0m\n" + "\u001b[32mINFO:lightwood-2821:Training XGBoost with 57023 iterations given 7127.987644195557 seconds constraint\u001b[0m\n" ] }, { @@ -1492,14 +1457,7 @@ "name": "stdout", "output_type": "stream", "text": [ - "[8]\tvalidation_0-rmse:21.74380" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n" + "[8]\tvalidation_0-rmse:21.74380\n" ] }, { @@ -1537,130 +1495,123 @@ "[13]\tvalidation_0-rmse:21.68890\n" ] }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[14]\tvalidation_0-rmse:21.70025\n" - ] - }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[37mDEBUG:lightwood-2872: `fit_mixer` runtime: 0.5 seconds\u001b[0m\n" + "\u001b[37mDEBUG:lightwood-2821: `fit_mixer` runtime: 0.5 seconds\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:dataprep_ml-2872:Ensembling the mixer\u001b[0m\n" + "\u001b[32mINFO:dataprep_ml-2821:Ensembling the mixer\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:lightwood-2872:Mixer: NeuralTs got accuracy: 0.875\u001b[0m\n" + "\u001b[32mINFO:lightwood-2821:Mixer: NeuralTs got accuracy: 0.875\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[33mWARNING:lightwood-2872:This model does not output probability estimates\u001b[0m\n" + "\u001b[33mWARNING:lightwood-2821:This model does not output probability estimates\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:lightwood-2872:Mixer: XGBoostArrayMixer got accuracy: 0.869\u001b[0m\n" + "\u001b[32mINFO:lightwood-2821:Mixer: XGBoostArrayMixer got accuracy: 0.869\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:lightwood-2872:Picked best mixer: NeuralTs\u001b[0m\n" + "\u001b[32mINFO:lightwood-2821:Picked best mixer: NeuralTs\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[37mDEBUG:lightwood-2872: `fit` runtime: 1.41 seconds\u001b[0m\n" + "\u001b[37mDEBUG:lightwood-2821: `fit` runtime: 1.4 seconds\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:dataprep_ml-2872:[Learn phase 7/8] - Ensemble analysis\u001b[0m\n" + "\u001b[32mINFO:dataprep_ml-2821:[Learn phase 7/8] - Ensemble analysis\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:dataprep_ml-2872:Analyzing the ensemble of mixers\u001b[0m\n" + "\u001b[32mINFO:dataprep_ml-2821:Analyzing the ensemble of mixers\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:lightwood-2872:The block ICP is now running its analyze() method\u001b[0m\n" + "\u001b[32mINFO:lightwood-2821:The block ICP is now running its analyze() method\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:lightwood-2872:The block ConfStats is now running its analyze() method\u001b[0m\n" + "\u001b[32mINFO:lightwood-2821:The block ConfStats is now running its analyze() method\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:lightwood-2872:The block AccStats is now running its analyze() method\u001b[0m\n" + "\u001b[32mINFO:lightwood-2821:The block AccStats is now running its analyze() method\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:lightwood-2872:The block PermutationFeatureImportance is now running its analyze() method\u001b[0m\n" + "\u001b[32mINFO:lightwood-2821:The block PermutationFeatureImportance is now running its analyze() method\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[33mWARNING:lightwood-2872:Block 'PermutationFeatureImportance' does not support time series nor text encoding, skipping...\u001b[0m\n" + "\u001b[33mWARNING:lightwood-2821:Block 'PermutationFeatureImportance' does not support time series nor text encoding, skipping...\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[37mDEBUG:lightwood-2872: `analyze_ensemble` runtime: 0.16 seconds\u001b[0m\n" + "\u001b[37mDEBUG:lightwood-2821: `analyze_ensemble` runtime: 0.16 seconds\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:dataprep_ml-2872:[Learn phase 8/8] - Adjustment on validation requested\u001b[0m\n" + "\u001b[32mINFO:dataprep_ml-2821:[Learn phase 8/8] - Adjustment on validation requested\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:dataprep_ml-2872:Updating the mixers\u001b[0m\n" + "\u001b[32mINFO:dataprep_ml-2821:Updating the mixers\u001b[0m\n" ] }, { @@ -1668,84 +1619,78 @@ "output_type": "stream", "text": [ "/opt/hostedtoolcache/Python/3.9.19/x64/lib/python3.9/site-packages/torch/amp/grad_scaler.py:131: 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-2872:Loss @ epoch 1: 0.29626286526521045\u001b[0m\n" + " warnings.warn(\n", + "\u001b[32mINFO:lightwood-2821:Loss @ epoch 1: 0.29626286526521045\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:lightwood-2872:Loss @ epoch 2: 0.2954987535874049\u001b[0m\n" + "\u001b[32mINFO:lightwood-2821:Loss @ epoch 2: 0.2954987535874049\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:lightwood-2872:Updating array of LGBM models...\u001b[0m\n" + "\u001b[32mINFO:lightwood-2821:Updating array of LGBM models...\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:lightwood-2872:XGBoost mixer does not have a `partial_fit` implementation\u001b[0m\n" + "\u001b[32mINFO:lightwood-2821:XGBoost mixer does not have a `partial_fit` implementation\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:lightwood-2872:XGBoost mixer does not have a `partial_fit` implementation\u001b[0m\n" + "\u001b[32mINFO:lightwood-2821:XGBoost mixer does not have a `partial_fit` implementation\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:lightwood-2872:XGBoost mixer does not have a `partial_fit` implementation\u001b[0m\n" + "\u001b[32mINFO:lightwood-2821:XGBoost mixer does not have a `partial_fit` implementation\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:lightwood-2872:XGBoost mixer does not have a `partial_fit` implementation\u001b[0m\n" + "\u001b[32mINFO:lightwood-2821:XGBoost mixer does not have a `partial_fit` implementation\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:lightwood-2872:XGBoost mixer does not have a `partial_fit` implementation\u001b[0m\n" + "\u001b[32mINFO:lightwood-2821:XGBoost mixer does not have a `partial_fit` implementation\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:lightwood-2872:XGBoost mixer does not have a `partial_fit` implementation\u001b[0m\n" + "\u001b[32mINFO:lightwood-2821:XGBoost mixer does not have a `partial_fit` implementation\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[37mDEBUG:lightwood-2872: `adjust` runtime: 0.09 seconds\u001b[0m\n" + "\u001b[37mDEBUG:lightwood-2821: `adjust` runtime: 0.1 seconds\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[37mDEBUG:lightwood-2872: `learn` runtime: 1.92 seconds\u001b[0m\n" + "\u001b[37mDEBUG:lightwood-2821: `learn` runtime: 1.91 seconds\u001b[0m\n" ] } ], @@ -1767,10 +1712,10 @@ "execution_count": 7, "metadata": { "execution": { - "iopub.execute_input": "2024-05-15T12:32:10.301152Z", - "iopub.status.busy": "2024-05-15T12:32:10.300912Z", - "iopub.status.idle": "2024-05-15T12:32:10.529312Z", - "shell.execute_reply": "2024-05-15T12:32:10.528664Z" + "iopub.execute_input": "2024-05-15T12:39:16.484128Z", + "iopub.status.busy": "2024-05-15T12:39:16.483722Z", + "iopub.status.idle": "2024-05-15T12:39:16.713196Z", + "shell.execute_reply": "2024-05-15T12:39:16.712480Z" } }, "outputs": [ @@ -1778,20 +1723,20 @@ "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:dataprep_ml-2872:[Predict phase 1/4] - Data preprocessing\u001b[0m\n" + "\u001b[32mINFO:dataprep_ml-2821:[Predict phase 1/4] - Data preprocessing\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "/tmp/11cf2e3a3fe2d38f7293f27fbfc34dab78123e8d8e63ffa417157763283658414.py:587: SettingWithCopyWarning: \n", + "/tmp/b9f9761599676d055346cd2b368083bd2229f6aecb71536117157767545533166.py:587: 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-2872:Cleaning the data\u001b[0m\n" + "\u001b[32mINFO:dataprep_ml-2821:Cleaning the data\u001b[0m\n" ] }, { @@ -1800,119 +1745,119 @@ "text": [ "/opt/hostedtoolcache/Python/3.9.19/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-2872:Transforming timeseries data\u001b[0m\n" + "\u001b[32mINFO:dataprep_ml-2821:Transforming timeseries data\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[37mDEBUG:lightwood-2872: `preprocess` runtime: 0.02 seconds\u001b[0m\n" + "\u001b[37mDEBUG:lightwood-2821: `preprocess` runtime: 0.02 seconds\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:dataprep_ml-2872:[Predict phase 2/4] - Feature generation\u001b[0m\n" + "\u001b[32mINFO:dataprep_ml-2821:[Predict phase 2/4] - Feature generation\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:dataprep_ml-2872:Featurizing the data\u001b[0m\n" + "\u001b[32mINFO:dataprep_ml-2821:Featurizing the data\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[37mDEBUG:lightwood-2872: `featurize` runtime: 0.01 seconds\u001b[0m\n" + "\u001b[37mDEBUG:lightwood-2821: `featurize` runtime: 0.01 seconds\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:dataprep_ml-2872:[Predict phase 3/4] - Calling ensemble\u001b[0m\n" + "\u001b[32mINFO:dataprep_ml-2821:[Predict phase 3/4] - Calling ensemble\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[37mDEBUG:lightwood-2872: `_timed_call` runtime: 0.09 seconds\u001b[0m\n" + "\u001b[37mDEBUG:lightwood-2821: `_timed_call` runtime: 0.09 seconds\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:dataprep_ml-2872:[Predict phase 4/4] - Analyzing output\u001b[0m\n" + "\u001b[32mINFO:dataprep_ml-2821:[Predict phase 4/4] - Analyzing output\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:lightwood-2872:The block ICP is now running its explain() method\u001b[0m\n" + "\u001b[32mINFO:lightwood-2821:The block ICP is now running its explain() method\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:lightwood-2872:The block ConfStats is now running its explain() method\u001b[0m\n" + "\u001b[32mINFO:lightwood-2821:The block ConfStats is now running its explain() method\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:lightwood-2872:ConfStats.explain() has not been implemented, no modifications will be done to the data insights.\u001b[0m\n" + "\u001b[32mINFO:lightwood-2821: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-2872:The block AccStats is now running its explain() method\u001b[0m\n" + "\u001b[32mINFO:lightwood-2821:The block AccStats is now running its explain() method\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:lightwood-2872:AccStats.explain() has not been implemented, no modifications will be done to the data insights.\u001b[0m\n" + "\u001b[32mINFO:lightwood-2821: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-2872:The block PermutationFeatureImportance is now running its explain() method\u001b[0m\n" + "\u001b[32mINFO:lightwood-2821:The block PermutationFeatureImportance is now running its explain() method\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:lightwood-2872:PermutationFeatureImportance.explain() has not been implemented, no modifications will be done to the data insights.\u001b[0m\n" + "\u001b[32mINFO:lightwood-2821: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-2872: `explain` runtime: 0.09 seconds\u001b[0m\n" + "\u001b[37mDEBUG:lightwood-2821: `explain` runtime: 0.09 seconds\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[37mDEBUG:lightwood-2872: `predict` runtime: 0.22 seconds\u001b[0m\n" + "\u001b[37mDEBUG:lightwood-2821: `predict` runtime: 0.22 seconds\u001b[0m\n" ] } ], @@ -1932,10 +1877,10 @@ "execution_count": 8, "metadata": { "execution": { - "iopub.execute_input": "2024-05-15T12:32:10.531834Z", - "iopub.status.busy": "2024-05-15T12:32:10.531626Z", - "iopub.status.idle": "2024-05-15T12:32:10.542916Z", - "shell.execute_reply": "2024-05-15T12:32:10.542282Z" + "iopub.execute_input": "2024-05-15T12:39:16.715848Z", + "iopub.status.busy": "2024-05-15T12:39:16.715440Z", + "iopub.status.idle": "2024-05-15T12:39:16.726742Z", + "shell.execute_reply": "2024-05-15T12:39:16.726049Z" } }, "outputs": [ @@ -2040,10 +1985,10 @@ "execution_count": 9, "metadata": { "execution": { - "iopub.execute_input": "2024-05-15T12:32:10.545546Z", - "iopub.status.busy": "2024-05-15T12:32:10.545158Z", - "iopub.status.idle": "2024-05-15T12:32:10.951600Z", - "shell.execute_reply": "2024-05-15T12:32:10.950867Z" + "iopub.execute_input": "2024-05-15T12:39:16.729246Z", + "iopub.status.busy": "2024-05-15T12:39:16.728891Z", + "iopub.status.idle": "2024-05-15T12:39:17.132478Z", + "shell.execute_reply": "2024-05-15T12:39:17.131830Z" } }, "outputs": [], @@ -2056,10 +2001,10 @@ "execution_count": 10, "metadata": { "execution": { - "iopub.execute_input": "2024-05-15T12:32:10.954775Z", - "iopub.status.busy": "2024-05-15T12:32:10.954472Z", - "iopub.status.idle": "2024-05-15T12:32:11.144915Z", - "shell.execute_reply": "2024-05-15T12:32:11.144230Z" + "iopub.execute_input": "2024-05-15T12:39:17.135502Z", + "iopub.status.busy": "2024-05-15T12:39:17.135009Z", + "iopub.status.idle": "2024-05-15T12:39:17.315414Z", + "shell.execute_reply": "2024-05-15T12:39:17.314713Z" } }, "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 32ca35f36..baad7f850 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": "2024-05-15T12:31:30.371645Z", - "iopub.status.busy": "2024-05-15T12:31:30.371452Z", - "iopub.status.idle": "2024-05-15T12:31:33.203343Z", - "shell.execute_reply": "2024-05-15T12:31:33.202682Z" + "iopub.execute_input": "2024-05-15T12:38:36.597254Z", + "iopub.status.busy": "2024-05-15T12:38:36.597058Z", + "iopub.status.idle": "2024-05-15T12:38:39.444383Z", + "shell.execute_reply": "2024-05-15T12:38:39.443711Z" } }, "outputs": [ @@ -32,14 +32,14 @@ "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:lightwood-2759:No torchvision detected, image helpers not supported.\u001b[0m\n" + "\u001b[32mINFO:lightwood-2649:No torchvision detected, image helpers not supported.\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:lightwood-2759:No torchvision/pillow detected, image encoder not supported\u001b[0m\n" + "\u001b[32mINFO:lightwood-2649:No torchvision/pillow detected, image encoder not supported\u001b[0m\n" ] } ], @@ -53,10 +53,10 @@ "execution_count": 2, "metadata": { "execution": { - "iopub.execute_input": "2024-05-15T12:31:33.206422Z", - "iopub.status.busy": "2024-05-15T12:31:33.206158Z", - "iopub.status.idle": "2024-05-15T12:31:33.429712Z", - "shell.execute_reply": "2024-05-15T12:31:33.429076Z" + "iopub.execute_input": "2024-05-15T12:38:39.447713Z", + "iopub.status.busy": "2024-05-15T12:38:39.447197Z", + "iopub.status.idle": "2024-05-15T12:38:39.569705Z", + "shell.execute_reply": "2024-05-15T12:38:39.568972Z" } }, "outputs": [ @@ -98,10 +98,10 @@ "execution_count": 3, "metadata": { "execution": { - "iopub.execute_input": "2024-05-15T12:31:33.432194Z", - "iopub.status.busy": "2024-05-15T12:31:33.431990Z", - "iopub.status.idle": "2024-05-15T12:31:34.871737Z", - "shell.execute_reply": "2024-05-15T12:31:34.871077Z" + "iopub.execute_input": "2024-05-15T12:38:39.572420Z", + "iopub.status.busy": "2024-05-15T12:38:39.572012Z", + "iopub.status.idle": "2024-05-15T12:38:41.013683Z", + "shell.execute_reply": "2024-05-15T12:38:41.013049Z" }, "scrolled": true }, @@ -110,364 +110,364 @@ "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:type_infer-2759:Analyzing a sample of 979\u001b[0m\n" + "\u001b[32mINFO:type_infer-2649:Analyzing a sample of 979\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:type_infer-2759:from a total population of 1030, this is equivalent to 95.0% of your data.\u001b[0m\n" + "\u001b[32mINFO:type_infer-2649: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-2759:Using 3 processes to deduct types.\u001b[0m\n" + "\u001b[32mINFO:type_infer-2649:Using 3 processes to deduct types.\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:type_infer-2759:Infering type for: cement\u001b[0m\n" + "\u001b[32mINFO:type_infer-2649:Infering type for: cement\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:type_infer-2759:Infering type for: slag\u001b[0m\n" + "\u001b[32mINFO:type_infer-2649:Infering type for: slag\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:type_infer-2759:Column cement has data type float\u001b[0m\n" + "\u001b[32mINFO:type_infer-2649:Column slag has data type float\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:type_infer-2759:Column 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"name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:dataprep_ml-2759:[Learn phase 5/8] - Feature generation\u001b[0m\n" + "\u001b[32mINFO:dataprep_ml-2649:[Learn phase 5/8] - Feature generation\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:dataprep_ml-2759:Featurizing the data\u001b[0m\n" + "\u001b[32mINFO:dataprep_ml-2649:Featurizing the data\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[37mDEBUG:lightwood-2759: `featurize` runtime: 0.06 seconds\u001b[0m\n" + "\u001b[37mDEBUG:lightwood-2649: `featurize` runtime: 0.06 seconds\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:dataprep_ml-2759:[Learn phase 6/8] - Mixer training\u001b[0m\n" + "\u001b[32mINFO:dataprep_ml-2649:[Learn phase 6/8] - Mixer training\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:dataprep_ml-2759:Training the mixers\u001b[0m\n" + "\u001b[32mINFO:dataprep_ml-2649:Training the mixers\u001b[0m\n" ] }, { @@ -487,224 +487,224 @@ "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:1578.)\n", " exp_avg_sq.mul_(beta2).addcmul_(1 - beta2, grad, grad)\n", - "\u001b[32mINFO:lightwood-2759:Loss of 39.99637508392334 with learning rate 0.0001\u001b[0m\n" + "\u001b[32mINFO:lightwood-2649:Loss of 39.99637508392334 with learning rate 0.0001\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:lightwood-2759:Loss of 21.826460361480713 with learning rate 0.0005\u001b[0m\n" + "\u001b[32mINFO:lightwood-2649:Loss of 21.826460361480713 with learning rate 0.0005\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:lightwood-2759:Loss of 15.12899512052536 with learning rate 0.001\u001b[0m\n" + "\u001b[32mINFO:lightwood-2649:Loss of 15.12899512052536 with learning rate 0.001\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:lightwood-2759:Loss of 15.062753021717072 with learning rate 0.002\u001b[0m\n" + "\u001b[32mINFO:lightwood-2649:Loss of 15.062753021717072 with learning rate 0.002\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:lightwood-2759:Loss of 26.490495562553406 with learning rate 0.003\u001b[0m\n" + "\u001b[32mINFO:lightwood-2649:Loss of 26.490495562553406 with learning rate 0.003\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:lightwood-2759:Loss of 33.6572003364563 with learning rate 0.005\u001b[0m\n" + "\u001b[32mINFO:lightwood-2649:Loss of 33.6572003364563 with learning rate 0.005\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:lightwood-2759:Loss of 303.60721158981323 with learning rate 0.01\u001b[0m\n" + "\u001b[32mINFO:lightwood-2649:Loss of 303.60721158981323 with learning rate 0.01\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:lightwood-2759:Loss of nan with learning rate 0.05\u001b[0m\n" + "\u001b[32mINFO:lightwood-2649:Loss of nan with learning rate 0.05\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:lightwood-2759:Found learning rate of: 0.002\u001b[0m\n" + "\u001b[32mINFO:lightwood-2649:Found learning rate of: 0.002\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:lightwood-2759:Loss @ epoch 1: 0.11838734149932861\u001b[0m\n" + "\u001b[32mINFO:lightwood-2649:Loss @ epoch 1: 0.11838734149932861\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:lightwood-2759:Loss @ epoch 2: 0.4641949534416199\u001b[0m\n" + "\u001b[32mINFO:lightwood-2649:Loss @ epoch 2: 0.4641949534416199\u001b[0m\n" ] }, { "name": "stderr", 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"\u001b[37mDEBUG:lightwood-2759: `fit` runtime: 0.53 seconds\u001b[0m\n" + "\u001b[37mDEBUG:lightwood-2649: `fit` runtime: 0.54 seconds\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:dataprep_ml-2759:[Learn phase 7/8] - Ensemble analysis\u001b[0m\n" + "\u001b[32mINFO:dataprep_ml-2649:[Learn phase 7/8] - Ensemble analysis\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:dataprep_ml-2759:Analyzing the ensemble of mixers\u001b[0m\n" + "\u001b[32mINFO:dataprep_ml-2649:Analyzing the ensemble of mixers\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:lightwood-2759:The block ICP is now running its analyze() method\u001b[0m\n" + "\u001b[32mINFO:lightwood-2649:The block ICP is now running its analyze() method\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:lightwood-2759:The block ConfStats is now running its analyze() 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"\u001b[32mINFO:lightwood-2759:[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-2649:[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-2759: `analyze_ensemble` runtime: 0.15 seconds\u001b[0m\n" + "\u001b[37mDEBUG:lightwood-2649: `analyze_ensemble` runtime: 0.15 seconds\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:dataprep_ml-2759:[Learn phase 8/8] - Adjustment on validation requested\u001b[0m\n" + "\u001b[32mINFO:dataprep_ml-2649:[Learn phase 8/8] - Adjustment on validation requested\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:dataprep_ml-2759:Updating the mixers\u001b[0m\n" + "\u001b[32mINFO:dataprep_ml-2649:Updating the mixers\u001b[0m\n" ] }, { @@ -719,21 +719,21 @@ "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:lightwood-2759:Loss @ epoch 1: 0.1678172747294108\u001b[0m\n" + "\u001b[32mINFO:lightwood-2649:Loss @ epoch 1: 0.1678172747294108\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[37mDEBUG:lightwood-2759: `adjust` runtime: 0.03 seconds\u001b[0m\n" + "\u001b[37mDEBUG:lightwood-2649: `adjust` runtime: 0.03 seconds\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[37mDEBUG:lightwood-2759: `learn` runtime: 0.82 seconds\u001b[0m\n" + "\u001b[37mDEBUG:lightwood-2649: `learn` runtime: 0.83 seconds\u001b[0m\n" ] } ], @@ -770,10 +770,10 @@ "execution_count": 4, "metadata": { "execution": { - "iopub.execute_input": "2024-05-15T12:31:34.874734Z", - "iopub.status.busy": "2024-05-15T12:31:34.874316Z", - "iopub.status.idle": "2024-05-15T12:31:35.015369Z", - "shell.execute_reply": "2024-05-15T12:31:35.014803Z" + "iopub.execute_input": "2024-05-15T12:38:41.016512Z", + "iopub.status.busy": "2024-05-15T12:38:41.016092Z", + "iopub.status.idle": "2024-05-15T12:38:41.160419Z", + "shell.execute_reply": "2024-05-15T12:38:41.159845Z" } }, "outputs": [ @@ -781,126 +781,126 @@ "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:dataprep_ml-2759:[Predict phase 1/4] - Data preprocessing\u001b[0m\n" + "\u001b[32mINFO:dataprep_ml-2649:[Predict phase 1/4] - Data preprocessing\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:dataprep_ml-2759:Cleaning the data\u001b[0m\n" + "\u001b[32mINFO:dataprep_ml-2649:Cleaning the data\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[37mDEBUG:lightwood-2759: `preprocess` runtime: 0.01 seconds\u001b[0m\n" + "\u001b[37mDEBUG:lightwood-2649: `preprocess` runtime: 0.01 seconds\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:dataprep_ml-2759:[Predict phase 2/4] - Feature generation\u001b[0m\n" + "\u001b[32mINFO:dataprep_ml-2649:[Predict phase 2/4] - Feature generation\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:dataprep_ml-2759:Featurizing the data\u001b[0m\n" + "\u001b[32mINFO:dataprep_ml-2649:Featurizing the data\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[37mDEBUG:lightwood-2759: `featurize` runtime: 0.03 seconds\u001b[0m\n" + "\u001b[37mDEBUG:lightwood-2649: `featurize` runtime: 0.03 seconds\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:dataprep_ml-2759:[Predict phase 3/4] - Calling ensemble\u001b[0m\n" + "\u001b[32mINFO:dataprep_ml-2649:[Predict phase 3/4] - Calling ensemble\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[37mDEBUG:lightwood-2759: 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implemented, no modifications will be done to the data insights.\u001b[0m\n" + "\u001b[32mINFO:lightwood-2649: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-2759:The block AccStats is now running its explain() method\u001b[0m\n" + "\u001b[32mINFO:lightwood-2649:The block AccStats is now running its explain() method\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:lightwood-2759:AccStats.explain() has not been implemented, no modifications will be done to the data insights.\u001b[0m\n" + "\u001b[32mINFO:lightwood-2649: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-2759:The block PermutationFeatureImportance is now running its explain() method\u001b[0m\n" + "\u001b[32mINFO:lightwood-2649:The block PermutationFeatureImportance is now running its explain() method\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:lightwood-2759:PermutationFeatureImportance.explain() has not been implemented, no modifications will be done to the data insights.\u001b[0m\n" + "\u001b[32mINFO:lightwood-2649: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-2759: `explain` runtime: 0.05 seconds\u001b[0m\n" + "\u001b[37mDEBUG:lightwood-2649: `explain` runtime: 0.05 seconds\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[37mDEBUG:lightwood-2759: `predict` runtime: 0.13 seconds\u001b[0m\n" + "\u001b[37mDEBUG:lightwood-2649: `predict` runtime: 0.13 seconds\u001b[0m\n" ] }, { @@ -1094,10 +1094,10 @@ "execution_count": 5, "metadata": { "execution": { - "iopub.execute_input": "2024-05-15T12:31:35.017903Z", - "iopub.status.busy": "2024-05-15T12:31:35.017554Z", - "iopub.status.idle": "2024-05-15T12:31:35.127911Z", - "shell.execute_reply": "2024-05-15T12:31:35.127311Z" + "iopub.execute_input": "2024-05-15T12:38:41.162902Z", + "iopub.status.busy": "2024-05-15T12:38:41.162689Z", + "iopub.status.idle": "2024-05-15T12:38:41.276226Z", + "shell.execute_reply": "2024-05-15T12:38:41.275636Z" } }, "outputs": [ @@ -1105,35 +1105,35 @@ "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:dataprep_ml-2759:Cleaning the data\u001b[0m\n" + "\u001b[32mINFO:dataprep_ml-2649:Cleaning the data\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[37mDEBUG:lightwood-2759: `preprocess` runtime: 0.02 seconds\u001b[0m\n" + "\u001b[37mDEBUG:lightwood-2649: `preprocess` runtime: 0.02 seconds\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:dataprep_ml-2759:Cleaning the data\u001b[0m\n" + "\u001b[32mINFO:dataprep_ml-2649:Cleaning the data\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[37mDEBUG:lightwood-2759: `preprocess` runtime: 0.01 seconds\u001b[0m\n" + "\u001b[37mDEBUG:lightwood-2649: `preprocess` runtime: 0.01 seconds\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:dataprep_ml-2759:Updating the mixers\u001b[0m\n" + "\u001b[32mINFO:dataprep_ml-2649:Updating the mixers\u001b[0m\n" ] }, { @@ -1148,14 +1148,14 @@ "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:lightwood-2759:Loss @ epoch 1: 0.10915952424208324\u001b[0m\n" + "\u001b[32mINFO:lightwood-2649:Loss @ epoch 1: 0.10915952424208324\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[37mDEBUG:lightwood-2759: `adjust` runtime: 0.11 seconds\u001b[0m\n" + "\u001b[37mDEBUG:lightwood-2649: `adjust` runtime: 0.11 seconds\u001b[0m\n" ] } ], @@ -1168,10 +1168,10 @@ "execution_count": 6, "metadata": { "execution": { - "iopub.execute_input": "2024-05-15T12:31:35.130571Z", - "iopub.status.busy": "2024-05-15T12:31:35.130366Z", - "iopub.status.idle": "2024-05-15T12:31:35.268592Z", - "shell.execute_reply": "2024-05-15T12:31:35.267965Z" + "iopub.execute_input": "2024-05-15T12:38:41.279047Z", + "iopub.status.busy": "2024-05-15T12:38:41.278590Z", + "iopub.status.idle": "2024-05-15T12:38:41.418298Z", + "shell.execute_reply": "2024-05-15T12:38:41.417642Z" } }, "outputs": [ @@ -1179,126 +1179,126 @@ "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:dataprep_ml-2759:[Predict phase 1/4] - Data preprocessing\u001b[0m\n" + "\u001b[32mINFO:dataprep_ml-2649:[Predict phase 1/4] - Data preprocessing\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:dataprep_ml-2759:Cleaning the data\u001b[0m\n" + "\u001b[32mINFO:dataprep_ml-2649:Cleaning the data\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[37mDEBUG:lightwood-2759: `preprocess` runtime: 0.01 seconds\u001b[0m\n" + "\u001b[37mDEBUG:lightwood-2649: `preprocess` runtime: 0.01 seconds\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:dataprep_ml-2759:[Predict phase 2/4] - Feature generation\u001b[0m\n" + "\u001b[32mINFO:dataprep_ml-2649:[Predict phase 2/4] - Feature generation\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:dataprep_ml-2759:Featurizing the data\u001b[0m\n" + "\u001b[32mINFO:dataprep_ml-2649:Featurizing the data\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[37mDEBUG:lightwood-2759: `featurize` runtime: 0.03 seconds\u001b[0m\n" + "\u001b[37mDEBUG:lightwood-2649: `featurize` runtime: 0.03 seconds\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:dataprep_ml-2759:[Predict phase 3/4] - Calling ensemble\u001b[0m\n" + "\u001b[32mINFO:dataprep_ml-2649:[Predict phase 3/4] - Calling ensemble\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[37mDEBUG:lightwood-2759: `_timed_call` runtime: 0.03 seconds\u001b[0m\n" + "\u001b[37mDEBUG:lightwood-2649: `_timed_call` runtime: 0.03 seconds\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:dataprep_ml-2759:[Predict phase 4/4] - Analyzing output\u001b[0m\n" + "\u001b[32mINFO:dataprep_ml-2649:[Predict phase 4/4] - Analyzing output\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:lightwood-2759:The block ICP is now running its explain() method\u001b[0m\n" + "\u001b[32mINFO:lightwood-2649:The block ICP is now running its explain() method\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:lightwood-2759:The block ConfStats is now running its explain() method\u001b[0m\n" + "\u001b[32mINFO:lightwood-2649:The block ConfStats is now running its explain() method\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:lightwood-2759:ConfStats.explain() has not been implemented, no modifications will be done to the data insights.\u001b[0m\n" + "\u001b[32mINFO:lightwood-2649: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-2759:The block AccStats is now running its explain() method\u001b[0m\n" + "\u001b[32mINFO:lightwood-2649:The block AccStats is now running its explain() method\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:lightwood-2759:AccStats.explain() has not been implemented, no modifications will be done to the data insights.\u001b[0m\n" + "\u001b[32mINFO:lightwood-2649: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-2759:The block PermutationFeatureImportance is now running its explain() method\u001b[0m\n" + "\u001b[32mINFO:lightwood-2649:The block PermutationFeatureImportance is now running its explain() method\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:lightwood-2759:PermutationFeatureImportance.explain() has not been implemented, no modifications will be done to the data insights.\u001b[0m\n" + "\u001b[32mINFO:lightwood-2649: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-2759: `explain` runtime: 0.05 seconds\u001b[0m\n" + "\u001b[37mDEBUG:lightwood-2649: `explain` runtime: 0.05 seconds\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[37mDEBUG:lightwood-2759: `predict` runtime: 0.13 seconds\u001b[0m\n" + "\u001b[37mDEBUG:lightwood-2649: `predict` runtime: 0.13 seconds\u001b[0m\n" ] }, { @@ -1462,10 +1462,10 @@ "execution_count": 7, "metadata": { "execution": { - "iopub.execute_input": "2024-05-15T12:31:35.271408Z", - "iopub.status.busy": "2024-05-15T12:31:35.270879Z", - "iopub.status.idle": "2024-05-15T12:31:35.276560Z", - "shell.execute_reply": "2024-05-15T12:31:35.275925Z" + "iopub.execute_input": "2024-05-15T12:38:41.421175Z", + "iopub.status.busy": "2024-05-15T12:38:41.420697Z", + "iopub.status.idle": "2024-05-15T12:38:41.426502Z", + "shell.execute_reply": "2024-05-15T12:38:41.425844Z" } }, "outputs": [ diff --git a/searchindex.js b/searchindex.js index 01a52c7e5..2bcaddd65 100644 --- a/searchindex.js +++ b/searchindex.js @@ -1 +1 @@ -Search.setIndex({"docnames": ["analysis", "api", "api/dtype", "api/encode", "api/high_level", "api/json_ai", "api/predictor", "api/types", "data", "encoder", "ensemble", "helpers", "index", "lightwood_philosophy", "mixer", "tutorials", "tutorials/README", "tutorials/custom_cleaner/custom_cleaner", "tutorials/custom_encoder_rulebased/custom_encoder_rulebased", "tutorials/custom_explainer/custom_explainer", "tutorials/custom_mixer/custom_mixer", "tutorials/custom_splitter/custom_splitter", "tutorials/tutorial_data_analysis/tutorial_data_analysis", "tutorials/tutorial_time_series/tutorial_time_series", "tutorials/tutorial_update_models/tutorial_update_models"], "filenames": ["analysis.rst", "api.rst", "api/dtype.rst", "api/encode.rst", "api/high_level.rst", "api/json_ai.rst", "api/predictor.rst", "api/types.rst", "data.rst", "encoder.rst", "ensemble.rst", "helpers.rst", "index.rst", "lightwood_philosophy.rst", "mixer.rst", "tutorials.rst", "tutorials/README.md", "tutorials/custom_cleaner/custom_cleaner.ipynb", "tutorials/custom_encoder_rulebased/custom_encoder_rulebased.ipynb", "tutorials/custom_explainer/custom_explainer.ipynb", "tutorials/custom_mixer/custom_mixer.ipynb", "tutorials/custom_splitter/custom_splitter.ipynb", "tutorials/tutorial_data_analysis/tutorial_data_analysis.ipynb", "tutorials/tutorial_time_series/tutorial_time_series.ipynb", "tutorials/tutorial_update_models/tutorial_update_models.ipynb"], "titles": ["Analysis", "API", "Data Types (dtypes)", "Encode your data", "JSON-AI Config", "JSON-AI Config", "Predictor Interface", "Lightwood API Types", "Data", "Encoders", "Ensemble", "Helpers", "Lightwood", "Lightwood Philosophy", "Mixers", "Tutorials", "How to make a tutorial notebook?", "Using your own pre-processing methods in Lightwood", "Custom Encoder: Rule-Based", "Tutorial - Implementing a custom analysis block in Lightwood", "Tutorial - Implementing a custom mixer in Lightwood", "Build your own training/testing split", "Tutorial - Introduction to Lightwood\u2019s statistical analysis", "Tutorial - Time series forecasting", 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"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/custom_cleaner/custom_cleaner.html b/tutorials/custom_cleaner/custom_cleaner.html index 95f730e78..03a3df0a2 100644 --- a/tutorials/custom_cleaner/custom_cleaner.html +++ b/tutorials/custom_cleaner/custom_cleaner.html @@ -125,8 +125,8 @@

Date: 2021.10.07 
-INFO:lightwood-2845:No torchvision detected, image helpers not supported.
-INFO:lightwood-2845:No torchvision/pillow detected, image encoder not supported
+INFO:lightwood-2730:No torchvision detected, image helpers not supported.
+INFO:lightwood-2730:No torchvision/pillow detected, image encoder not supported
@@ -250,20 +250,20 @@

2) Create a JSON-AI default object 
-INFO:lightwood-2845:Dropping features: ['url_legal', 'license', 'standard_error']
-INFO:type_infer-2845:Analyzing a sample of 2478
-INFO:type_infer-2845:from a total population of 2834, this is equivalent to 87.4% of your data.
-INFO:type_infer-2845:Infering type for: id
-INFO:type_infer-2845:Doing text detection for column: id
-INFO:type_infer-2845:Column id has data type categorical
-INFO:type_infer-2845:Infering type for: excerpt
-INFO:type_infer-2845:Doing text detection for column: excerpt
-INFO:type_infer-2845:Infering type for: target
-INFO:type_infer-2845:Column target has data type float
-WARNING:type_infer-2845:Column id is an identifier of type "Hash-like identifier"
-INFO:dataprep_ml-2845:Starting statistical analysis
-INFO:dataprep_ml-2845:Dropping features: ['id']
-INFO:dataprep_ml-2845:Finished statistical analysis
+INFO:lightwood-2730:Dropping features: ['url_legal', 'license', 'standard_error']
+INFO:type_infer-2730:Analyzing a sample of 2478
+INFO:type_infer-2730:from a total population of 2834, this is equivalent to 87.4% of your data.
+INFO:type_infer-2730:Infering type for: id
+INFO:type_infer-2730:Doing text detection for column: id
+INFO:type_infer-2730:Column id has data type categorical
+INFO:type_infer-2730:Infering type for: excerpt
+INFO:type_infer-2730:Doing text detection for column: excerpt
+INFO:type_infer-2730:Infering type for: target
+INFO:type_infer-2730:Column target has data type float
+WARNING:type_infer-2730:Column id is an identifier of type "Hash-like identifier"
+INFO:dataprep_ml-2730:Starting statistical analysis
+INFO:dataprep_ml-2730:Dropping features: ['id']
+INFO:dataprep_ml-2730:Finished statistical analysis

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

@@ -351,7 +351,7 @@

2) Create a JSON-AI default object6) Call python to run your code and see your preprocessed outputs 
-INFO:dataprep_ml-2845:Starting statistical analysis
-INFO:dataprep_ml-2845:Dropping features: ['id']
-INFO:dataprep_ml-2845:Finished statistical analysis
-DEBUG:lightwood-2845: `analyze_data` runtime: 0.05 seconds
-INFO:dataprep_ml-2845:Cleaning the data
-INFO:dataprep_ml-2845:Dropping features: ['id']
-INFO:lightwood-2845:Cleaning column =excerpt
-INFO:lightwood-2845:Cleaning column =target
-INFO:lightwood-2845:Converted target into strictly non-negative
-DEBUG:lightwood-2845: `preprocess` runtime: 0.08 seconds
+INFO:dataprep_ml-2730:Starting statistical analysis
+INFO:dataprep_ml-2730:Dropping features: ['id']
+INFO:dataprep_ml-2730:Finished statistical analysis
+DEBUG:lightwood-2730: `analyze_data` runtime: 0.05 seconds
+INFO:dataprep_ml-2730:Cleaning the data
+INFO:dataprep_ml-2730:Dropping features: ['id']
+INFO:lightwood-2730:Cleaning column =excerpt
+INFO:lightwood-2730:Cleaning column =target
+INFO:lightwood-2730:Converted target into strictly non-negative
+DEBUG:lightwood-2730: `preprocess` runtime: 0.07 seconds
diff --git a/tutorials/custom_cleaner/custom_cleaner.ipynb b/tutorials/custom_cleaner/custom_cleaner.ipynb index 72ccb0439..e2d0321af 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": "2024-05-15T12:31:38.314866Z", - "iopub.status.busy": "2024-05-15T12:31:38.314666Z", - "iopub.status.idle": "2024-05-15T12:31:41.066114Z", - "shell.execute_reply": "2024-05-15T12:31:41.065470Z" + "iopub.execute_input": "2024-05-15T12:38:44.614888Z", + "iopub.status.busy": "2024-05-15T12:38:44.614690Z", + "iopub.status.idle": "2024-05-15T12:38:47.421978Z", + "shell.execute_reply": "2024-05-15T12:38:47.421222Z" } }, "outputs": [ @@ -42,14 +42,14 @@ "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:lightwood-2845:No torchvision detected, image helpers not supported.\u001b[0m\n" + "\u001b[32mINFO:lightwood-2730:No torchvision detected, image helpers not supported.\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:lightwood-2845:No torchvision/pillow detected, image encoder not supported\u001b[0m\n" + "\u001b[32mINFO:lightwood-2730:No torchvision/pillow detected, image encoder not supported\u001b[0m\n" ] } ], @@ -87,10 +87,10 @@ "id": "recognized-parish", "metadata": { "execution": { - "iopub.execute_input": "2024-05-15T12:31:41.069559Z", - "iopub.status.busy": "2024-05-15T12:31:41.068848Z", - "iopub.status.idle": "2024-05-15T12:31:42.003247Z", - "shell.execute_reply": "2024-05-15T12:31:42.002523Z" + "iopub.execute_input": "2024-05-15T12:38:47.425324Z", + "iopub.status.busy": "2024-05-15T12:38:47.424967Z", + "iopub.status.idle": "2024-05-15T12:38:48.351359Z", + "shell.execute_reply": "2024-05-15T12:38:48.350712Z" } }, "outputs": [ @@ -221,10 +221,10 @@ "id": "chicken-truth", "metadata": { "execution": { - "iopub.execute_input": "2024-05-15T12:31:42.006058Z", - "iopub.status.busy": "2024-05-15T12:31:42.005722Z", - "iopub.status.idle": "2024-05-15T12:31:57.532124Z", - "shell.execute_reply": "2024-05-15T12:31:57.531480Z" + "iopub.execute_input": "2024-05-15T12:38:48.353933Z", + "iopub.status.busy": "2024-05-15T12:38:48.353724Z", + "iopub.status.idle": "2024-05-15T12:39:03.836779Z", + "shell.execute_reply": "2024-05-15T12:39:03.836183Z" } }, "outputs": [ @@ -232,98 +232,98 @@ "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:lightwood-2845:Dropping features: ['url_legal', 'license', 'standard_error']\u001b[0m\n" + "\u001b[32mINFO:lightwood-2730:Dropping features: ['url_legal', 'license', 'standard_error']\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:type_infer-2845:Analyzing a sample of 2478\u001b[0m\n" + "\u001b[32mINFO:type_infer-2730:Analyzing a sample of 2478\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:type_infer-2845:from a total population of 2834, this is equivalent to 87.4% of your data.\u001b[0m\n" + "\u001b[32mINFO:type_infer-2730: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-2845:Infering type for: id\u001b[0m\n" + "\u001b[32mINFO:type_infer-2730:Infering type for: id\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:type_infer-2845:Doing text detection for column: id\u001b[0m\n" + "\u001b[32mINFO:type_infer-2730:Doing text detection for column: id\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:type_infer-2845:Column id has data type categorical\u001b[0m\n" + "\u001b[32mINFO:type_infer-2730:Column id has data type categorical\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:type_infer-2845:Infering type for: excerpt\u001b[0m\n" + "\u001b[32mINFO:type_infer-2730:Infering type for: excerpt\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:type_infer-2845:Doing text detection for column: excerpt\u001b[0m\n" + "\u001b[32mINFO:type_infer-2730:Doing text detection for column: excerpt\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:type_infer-2845:Infering type for: target\u001b[0m\n" + "\u001b[32mINFO:type_infer-2730:Infering type for: target\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:type_infer-2845:Column target has data type float\u001b[0m\n" + "\u001b[32mINFO:type_infer-2730:Column target has data type float\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[33mWARNING:type_infer-2845:Column id is an identifier of type \"Hash-like identifier\"\u001b[0m\n" + "\u001b[33mWARNING:type_infer-2730:Column id is an identifier of type \"Hash-like identifier\"\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:dataprep_ml-2845:Starting statistical analysis\u001b[0m\n" + "\u001b[32mINFO:dataprep_ml-2730:Starting statistical analysis\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:dataprep_ml-2845:Dropping features: ['id']\u001b[0m\n" + "\u001b[32mINFO:dataprep_ml-2730:Dropping features: ['id']\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:dataprep_ml-2845:Finished statistical analysis\u001b[0m\n" + "\u001b[32mINFO:dataprep_ml-2730:Finished statistical analysis\u001b[0m\n" ] } ], @@ -360,10 +360,10 @@ "id": "designed-condition", "metadata": { "execution": { - "iopub.execute_input": "2024-05-15T12:31:57.535376Z", - "iopub.status.busy": "2024-05-15T12:31:57.534790Z", - "iopub.status.idle": "2024-05-15T12:31:57.539994Z", - "shell.execute_reply": "2024-05-15T12:31:57.539305Z" + "iopub.execute_input": "2024-05-15T12:39:03.839556Z", + "iopub.status.busy": "2024-05-15T12:39:03.839259Z", + "iopub.status.idle": "2024-05-15T12:39:03.843934Z", + "shell.execute_reply": "2024-05-15T12:39:03.843268Z" } }, "outputs": [ @@ -434,7 +434,7 @@ " \"unbias_target\": true,\n", " \"seconds_per_mixer\": 21384.0,\n", " \"seconds_per_encoder\": 85536.0,\n", - " \"expected_additional_time\": 15.485530853271484,\n", + " \"expected_additional_time\": 15.443372249603271,\n", " \"time_aim\": 259200,\n", " \"target_weights\": null,\n", " \"positive_domain\": false,\n", @@ -518,10 +518,10 @@ "id": "325d8f1b", "metadata": { "execution": { - "iopub.execute_input": "2024-05-15T12:31:57.542555Z", - "iopub.status.busy": "2024-05-15T12:31:57.542241Z", - "iopub.status.idle": "2024-05-15T12:31:57.547712Z", - "shell.execute_reply": "2024-05-15T12:31:57.547064Z" + "iopub.execute_input": "2024-05-15T12:39:03.846644Z", + "iopub.status.busy": "2024-05-15T12:39:03.846253Z", + "iopub.status.idle": "2024-05-15T12:39:03.851463Z", + "shell.execute_reply": "2024-05-15T12:39:03.850848Z" } }, "outputs": [ @@ -646,10 +646,10 @@ "id": "f030f8ca", "metadata": { "execution": { - "iopub.execute_input": "2024-05-15T12:31:57.550277Z", - "iopub.status.busy": "2024-05-15T12:31:57.549912Z", - "iopub.status.idle": "2024-05-15T12:31:57.553192Z", - "shell.execute_reply": "2024-05-15T12:31:57.552629Z" + "iopub.execute_input": "2024-05-15T12:39:03.853851Z", + "iopub.status.busy": "2024-05-15T12:39:03.853511Z", + "iopub.status.idle": "2024-05-15T12:39:03.856756Z", + "shell.execute_reply": "2024-05-15T12:39:03.856216Z" } }, "outputs": [], @@ -711,10 +711,10 @@ "id": "floating-patent", "metadata": { "execution": { - "iopub.execute_input": "2024-05-15T12:31:57.555666Z", - "iopub.status.busy": "2024-05-15T12:31:57.555289Z", - "iopub.status.idle": "2024-05-15T12:31:57.904645Z", - "shell.execute_reply": "2024-05-15T12:31:57.903931Z" + "iopub.execute_input": "2024-05-15T12:39:03.859167Z", + "iopub.status.busy": "2024-05-15T12:39:03.858968Z", + "iopub.status.idle": "2024-05-15T12:39:04.214810Z", + "shell.execute_reply": "2024-05-15T12:39:04.214113Z" } }, "outputs": [ @@ -795,7 +795,7 @@ " \"unbias_target\": True,\n", " \"seconds_per_mixer\": 21384.0,\n", " \"seconds_per_encoder\": 85536.0,\n", - " \"expected_additional_time\": 15.485530853271484,\n", + " \"expected_additional_time\": 15.443372249603271,\n", " \"time_aim\": 259200,\n", " \"target_weights\": None,\n", " \"positive_domain\": False,\n", @@ -1452,10 +1452,10 @@ "id": "violent-guard", "metadata": { "execution": { - "iopub.execute_input": "2024-05-15T12:31:57.907458Z", - "iopub.status.busy": "2024-05-15T12:31:57.907076Z", - "iopub.status.idle": "2024-05-15T12:31:57.915206Z", - "shell.execute_reply": "2024-05-15T12:31:57.914714Z" + "iopub.execute_input": "2024-05-15T12:39:04.217801Z", + "iopub.status.busy": "2024-05-15T12:39:04.217334Z", + "iopub.status.idle": "2024-05-15T12:39:04.225997Z", + "shell.execute_reply": "2024-05-15T12:39:04.225444Z" } }, "outputs": [], @@ -1470,10 +1470,10 @@ "id": "closing-episode", "metadata": { "execution": { - "iopub.execute_input": "2024-05-15T12:31:57.917761Z", - "iopub.status.busy": "2024-05-15T12:31:57.917396Z", - "iopub.status.idle": "2024-05-15T12:31:58.052073Z", - "shell.execute_reply": "2024-05-15T12:31:58.051450Z" + "iopub.execute_input": "2024-05-15T12:39:04.228433Z", + "iopub.status.busy": "2024-05-15T12:39:04.228041Z", + "iopub.status.idle": "2024-05-15T12:39:04.362314Z", + "shell.execute_reply": "2024-05-15T12:39:04.361709Z" }, "scrolled": false }, @@ -1482,70 +1482,70 @@ "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:dataprep_ml-2845:Starting statistical analysis\u001b[0m\n" + "\u001b[32mINFO:dataprep_ml-2730:Starting statistical analysis\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:dataprep_ml-2845:Dropping features: ['id']\u001b[0m\n" + "\u001b[32mINFO:dataprep_ml-2730:Dropping features: ['id']\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:dataprep_ml-2845:Finished statistical analysis\u001b[0m\n" + "\u001b[32mINFO:dataprep_ml-2730:Finished statistical analysis\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[37mDEBUG:lightwood-2845: `analyze_data` runtime: 0.05 seconds\u001b[0m\n" + "\u001b[37mDEBUG:lightwood-2730: `analyze_data` runtime: 0.05 seconds\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:dataprep_ml-2845:Cleaning the data\u001b[0m\n" + "\u001b[32mINFO:dataprep_ml-2730:Cleaning the data\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:dataprep_ml-2845:Dropping features: ['id']\u001b[0m\n" + "\u001b[32mINFO:dataprep_ml-2730:Dropping features: ['id']\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:lightwood-2845:Cleaning column =excerpt\u001b[0m\n" + "\u001b[32mINFO:lightwood-2730:Cleaning column =excerpt\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:lightwood-2845:Cleaning column =target\u001b[0m\n" + "\u001b[32mINFO:lightwood-2730:Cleaning column =target\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:lightwood-2845:Converted target into strictly non-negative\u001b[0m\n" + "\u001b[32mINFO:lightwood-2730:Converted target into strictly non-negative\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[37mDEBUG:lightwood-2845: `preprocess` runtime: 0.08 seconds\u001b[0m\n" + "\u001b[37mDEBUG:lightwood-2730: `preprocess` runtime: 0.07 seconds\u001b[0m\n" ] }, { @@ -1635,10 +1635,10 @@ "id": "major-stake", "metadata": { "execution": { - "iopub.execute_input": "2024-05-15T12:31:58.054538Z", - "iopub.status.busy": "2024-05-15T12:31:58.054334Z", - "iopub.status.idle": "2024-05-15T12:31:58.059138Z", - "shell.execute_reply": "2024-05-15T12:31:58.058503Z" + "iopub.execute_input": "2024-05-15T12:39:04.364948Z", + "iopub.status.busy": "2024-05-15T12:39:04.364536Z", + "iopub.status.idle": "2024-05-15T12:39:04.369332Z", + "shell.execute_reply": "2024-05-15T12:39:04.368673Z" } }, "outputs": [ diff --git a/tutorials/custom_encoder_rulebased/custom_encoder_rulebased.html b/tutorials/custom_encoder_rulebased/custom_encoder_rulebased.html index e5c9982c1..f7cf50556 100644 --- a/tutorials/custom_encoder_rulebased/custom_encoder_rulebased.html +++ b/tutorials/custom_encoder_rulebased/custom_encoder_rulebased.html @@ -126,8 +126,8 @@

Custom Encoder: Rule-Based 
-INFO:lightwood-2555:No torchvision detected, image helpers not supported.
-INFO:lightwood-2555:No torchvision/pillow detected, image encoder not supported
+INFO:lightwood-2443:No torchvision detected, image helpers not supported.
+INFO:lightwood-2443:No torchvision/pillow detected, image encoder not supported

@@ -275,29 +275,29 @@

2) Generate JSON-AI Syntax 
-INFO:type_infer-2555:Analyzing a sample of 6920
-INFO:type_infer-2555:from a total population of 10668, this is equivalent to 64.9% of your data.
-INFO:type_infer-2555:Using 3 processes to deduct types.
-INFO:type_infer-2555:Infering type for: year
-INFO:type_infer-2555:Infering type for: price
-INFO:type_infer-2555:Column year has data type integer
-INFO:type_infer-2555:Column price has data type integer
-INFO:type_infer-2555:Infering type for: transmission
-INFO:type_infer-2555:Infering type for: mileage
-INFO:type_infer-2555:Infering type for: model
-INFO:type_infer-2555:Column mileage has data type integer
-INFO:type_infer-2555:Infering type for: fuelType
-INFO:type_infer-2555:Column transmission has data type categorical
-INFO:type_infer-2555:Infering type for: tax
-INFO:type_infer-2555:Column tax has data type integer
-INFO:type_infer-2555:Infering type for: mpg
-INFO:type_infer-2555:Column mpg has data type float
-INFO:type_infer-2555:Infering type for: engineSize
-INFO:type_infer-2555:Column engineSize has data type float
-INFO:type_infer-2555:Column fuelType has data type categorical
-INFO:type_infer-2555:Column model has data type categorical
-INFO:dataprep_ml-2555:Starting statistical analysis
-INFO:dataprep_ml-2555:Finished statistical analysis
+INFO:type_infer-2443:Analyzing a sample of 6920
+INFO:type_infer-2443:from a total population of 10668, this is equivalent to 64.9% of your data.
+INFO:type_infer-2443:Using 3 processes to deduct types.
+INFO:type_infer-2443:Infering type for: year
+INFO:type_infer-2443:Infering type for: price
+INFO:type_infer-2443:Column year has data type integer
+INFO:type_infer-2443:Column price has data type integer
+INFO:type_infer-2443:Infering type for: transmission
+INFO:type_infer-2443:Infering type for: mileage
+INFO:type_infer-2443:Column mileage has data type integer
+INFO:type_infer-2443:Infering type for: fuelType
+INFO:type_infer-2443:Infering type for: model
+INFO:type_infer-2443:Column fuelType has data type categorical
+INFO:type_infer-2443:Infering type for: tax
+INFO:type_infer-2443:Column tax has data type integer
+INFO:type_infer-2443:Infering type for: mpg
+INFO:type_infer-2443:Column mpg has data type float
+INFO:type_infer-2443:Infering type for: engineSize
+INFO:type_infer-2443:Column engineSize has data type float
+INFO:type_infer-2443:Column transmission has data type categorical
+INFO:type_infer-2443:Column model has data type categorical
+INFO:dataprep_ml-2443:Starting statistical analysis
+INFO:dataprep_ml-2443:Finished statistical analysis

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

@@ -411,7 +411,7 @@

2) Generate JSON-AI Syntax 
-INFO:dataprep_ml-2555:Starting statistical analysis
-INFO:dataprep_ml-2555:Finished statistical analysis
-DEBUG:lightwood-2555: `analyze_data` runtime: 0.43 seconds
-INFO:dataprep_ml-2555:Cleaning the data
-DEBUG:lightwood-2555: `preprocess` runtime: 0.13 seconds
-INFO:dataprep_ml-2555:Splitting the data into train/test
-DEBUG:lightwood-2555: `split` runtime: 0.0 seconds
-DEBUG:dataprep_ml-2555:Preparing sequentially...
-DEBUG:dataprep_ml-2555:Preparing encoder for year...
-DEBUG:dataprep_ml-2555:Preparing encoder for mileage...
-DEBUG:dataprep_ml-2555:Preparing encoder for tax...
-DEBUG:dataprep_ml-2555:Preparing encoder for mpg...
-DEBUG:dataprep_ml-2555:Preparing encoder for engineSize...
-INFO:lightwood-2555:Categories Detected = 1
-INFO:lightwood-2555:Categories Detected = 1
-INFO:lightwood-2555:Categories Detected = 1
-DEBUG:lightwood-2555: `prepare` runtime: 0.01 seconds
-INFO:dataprep_ml-2555:Featurizing the data
-DEBUG:lightwood-2555: `featurize` runtime: 0.57 seconds
+INFO:dataprep_ml-2443:Starting statistical analysis
+INFO:dataprep_ml-2443:Finished statistical analysis
+DEBUG:lightwood-2443: `analyze_data` runtime: 0.42 seconds
+INFO:dataprep_ml-2443:Cleaning the data
+DEBUG:lightwood-2443: `preprocess` runtime: 0.13 seconds
+INFO:dataprep_ml-2443:Splitting the data into train/test
+DEBUG:lightwood-2443: `split` runtime: 0.0 seconds
+DEBUG:dataprep_ml-2443:Preparing sequentially...
+DEBUG:dataprep_ml-2443:Preparing encoder for year...
+DEBUG:dataprep_ml-2443:Preparing encoder for mileage...
+DEBUG:dataprep_ml-2443:Preparing encoder for tax...
+DEBUG:dataprep_ml-2443:Preparing encoder for mpg...
+DEBUG:dataprep_ml-2443:Preparing encoder for engineSize...
+INFO:lightwood-2443:Categories Detected = 1
+INFO:lightwood-2443:Categories Detected = 1
+INFO:lightwood-2443:Categories Detected = 1
+DEBUG:lightwood-2443: `prepare` runtime: 0.02 seconds
+INFO:dataprep_ml-2443:Featurizing the data
+DEBUG:lightwood-2443: `featurize` runtime: 0.55 seconds

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 b8c1aa4b9..8fb88c2df 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": "2024-05-15T12:30:59.516740Z", - "iopub.status.busy": "2024-05-15T12:30:59.516542Z", - "iopub.status.idle": "2024-05-15T12:31:02.311138Z", - "shell.execute_reply": "2024-05-15T12:31:02.310397Z" + "iopub.execute_input": "2024-05-15T12:38:05.532387Z", + "iopub.status.busy": "2024-05-15T12:38:05.531820Z", + "iopub.status.idle": "2024-05-15T12:38:08.368195Z", + "shell.execute_reply": "2024-05-15T12:38:08.367522Z" } }, "outputs": [ @@ -54,14 +54,14 @@ "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:lightwood-2555:No torchvision detected, image helpers not supported.\u001b[0m\n" + "\u001b[32mINFO:lightwood-2443:No torchvision detected, image helpers not supported.\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:lightwood-2555:No torchvision/pillow detected, image encoder not supported\u001b[0m\n" + "\u001b[32mINFO:lightwood-2443:No torchvision/pillow detected, image encoder not supported\u001b[0m\n" ] } ], @@ -93,10 +93,10 @@ "id": "technical-government", "metadata": { "execution": { - "iopub.execute_input": "2024-05-15T12:31:02.314599Z", - "iopub.status.busy": "2024-05-15T12:31:02.314113Z", - "iopub.status.idle": "2024-05-15T12:31:02.537991Z", - "shell.execute_reply": "2024-05-15T12:31:02.537249Z" + "iopub.execute_input": "2024-05-15T12:38:08.371594Z", + "iopub.status.busy": "2024-05-15T12:38:08.371079Z", + "iopub.status.idle": "2024-05-15T12:38:08.597223Z", + "shell.execute_reply": "2024-05-15T12:38:08.596542Z" } }, "outputs": [ @@ -243,10 +243,10 @@ "id": "absent-maker", "metadata": { "execution": { - "iopub.execute_input": "2024-05-15T12:31:02.540787Z", - "iopub.status.busy": "2024-05-15T12:31:02.540376Z", - "iopub.status.idle": "2024-05-15T12:31:13.435988Z", - "shell.execute_reply": "2024-05-15T12:31:13.435371Z" + "iopub.execute_input": "2024-05-15T12:38:08.600162Z", + "iopub.status.busy": "2024-05-15T12:38:08.599740Z", + "iopub.status.idle": "2024-05-15T12:38:19.532578Z", + "shell.execute_reply": "2024-05-15T12:38:19.531845Z" } }, "outputs": [ @@ -254,161 +254,161 @@ "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:type_infer-2555:Analyzing a sample of 6920\u001b[0m\n" + "\u001b[32mINFO:type_infer-2443:Analyzing a sample of 6920\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:type_infer-2555:from a total population of 10668, this is equivalent to 64.9% of your data.\u001b[0m\n" + "\u001b[32mINFO:type_infer-2443: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-2555:Using 3 processes to deduct types.\u001b[0m\n" + "\u001b[32mINFO:type_infer-2443:Using 3 processes to deduct types.\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:type_infer-2555:Infering type for: year\u001b[0m\n" + "\u001b[32mINFO:type_infer-2443:Infering type for: year\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:type_infer-2555:Infering type for: price\u001b[0m\n" + "\u001b[32mINFO:type_infer-2443:Infering type for: price\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:type_infer-2555:Column year has data type integer\u001b[0m\n" + "\u001b[32mINFO:type_infer-2443:Column year has data type integer\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:type_infer-2555:Column price has data type integer\u001b[0m\n" + "\u001b[32mINFO:type_infer-2443:Column price has data type integer\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:type_infer-2555:Infering type for: transmission\u001b[0m\n" + "\u001b[32mINFO:type_infer-2443:Infering type for: transmission\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:type_infer-2555:Infering type for: mileage\u001b[0m\n" + "\u001b[32mINFO:type_infer-2443:Infering type for: mileage\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:type_infer-2555:Infering type for: model\u001b[0m\n" + "\u001b[32mINFO:type_infer-2443:Column mileage has data type integer\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:type_infer-2555:Column mileage has data type integer\u001b[0m\n" + "\u001b[32mINFO:type_infer-2443:Infering type for: fuelType\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:type_infer-2555:Infering type for: fuelType\u001b[0m\n" + "\u001b[32mINFO:type_infer-2443:Infering type for: model\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:type_infer-2555:Column transmission has data type categorical\u001b[0m\n" + "\u001b[32mINFO:type_infer-2443:Column fuelType has data type categorical\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:type_infer-2555:Infering type for: tax\u001b[0m\n" + "\u001b[32mINFO:type_infer-2443:Infering type for: tax\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:type_infer-2555:Column tax has data type integer\u001b[0m\n" + "\u001b[32mINFO:type_infer-2443:Column tax has data type integer\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:type_infer-2555:Infering type for: mpg\u001b[0m\n" + "\u001b[32mINFO:type_infer-2443:Infering type for: mpg\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:type_infer-2555:Column mpg has data type float\u001b[0m\n" + "\u001b[32mINFO:type_infer-2443:Column mpg has data type float\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:type_infer-2555:Infering type for: engineSize\u001b[0m\n" + "\u001b[32mINFO:type_infer-2443:Infering type for: engineSize\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:type_infer-2555:Column engineSize has data type float\u001b[0m\n" + "\u001b[32mINFO:type_infer-2443:Column engineSize has data type float\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:type_infer-2555:Column fuelType has data type categorical\u001b[0m\n" + "\u001b[32mINFO:type_infer-2443:Column transmission has data type categorical\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:type_infer-2555:Column model has data type categorical\u001b[0m\n" + "\u001b[32mINFO:type_infer-2443:Column model has data type categorical\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:dataprep_ml-2555:Starting statistical analysis\u001b[0m\n" + "\u001b[32mINFO:dataprep_ml-2443:Starting statistical analysis\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:dataprep_ml-2555:Finished statistical analysis\u001b[0m\n" + "\u001b[32mINFO:dataprep_ml-2443:Finished statistical analysis\u001b[0m\n" ] } ], @@ -437,10 +437,10 @@ "id": "coastal-paragraph", "metadata": { "execution": { - "iopub.execute_input": "2024-05-15T12:31:13.439193Z", - "iopub.status.busy": "2024-05-15T12:31:13.438672Z", - "iopub.status.idle": "2024-05-15T12:31:13.444013Z", - "shell.execute_reply": "2024-05-15T12:31:13.443379Z" + "iopub.execute_input": "2024-05-15T12:38:19.535763Z", + "iopub.status.busy": "2024-05-15T12:38:19.535442Z", + "iopub.status.idle": "2024-05-15T12:38:19.540827Z", + "shell.execute_reply": "2024-05-15T12:38:19.540187Z" } }, "outputs": [ @@ -545,7 +545,7 @@ " \"unbias_target\": true,\n", " \"seconds_per_mixer\": 21384.0,\n", " \"seconds_per_encoder\": 85536.0,\n", - " \"expected_additional_time\": 10.851930856704712,\n", + " \"expected_additional_time\": 10.889316082000732,\n", " \"time_aim\": 259200,\n", " \"target_weights\": null,\n", " \"positive_domain\": false,\n", @@ -643,10 +643,10 @@ "id": "e03db1b0", "metadata": { "execution": { - "iopub.execute_input": "2024-05-15T12:31:13.446814Z", - "iopub.status.busy": "2024-05-15T12:31:13.446417Z", - "iopub.status.idle": "2024-05-15T12:31:13.451666Z", - "shell.execute_reply": "2024-05-15T12:31:13.451027Z" + "iopub.execute_input": "2024-05-15T12:38:19.543477Z", + "iopub.status.busy": "2024-05-15T12:38:19.543104Z", + "iopub.status.idle": "2024-05-15T12:38:19.548319Z", + "shell.execute_reply": "2024-05-15T12:38:19.547714Z" } }, "outputs": [ @@ -766,10 +766,10 @@ "id": "e30866c1", "metadata": { "execution": { - "iopub.execute_input": "2024-05-15T12:31:13.454205Z", - "iopub.status.busy": "2024-05-15T12:31:13.453822Z", - "iopub.status.idle": "2024-05-15T12:31:13.457126Z", - "shell.execute_reply": "2024-05-15T12:31:13.456554Z" + "iopub.execute_input": "2024-05-15T12:38:19.550981Z", + "iopub.status.busy": "2024-05-15T12:38:19.550558Z", + "iopub.status.idle": "2024-05-15T12:38:19.553867Z", + "shell.execute_reply": "2024-05-15T12:38:19.553338Z" } }, "outputs": [], @@ -828,10 +828,10 @@ "id": "elementary-fusion", "metadata": { "execution": { - "iopub.execute_input": "2024-05-15T12:31:13.459498Z", - "iopub.status.busy": "2024-05-15T12:31:13.459299Z", - "iopub.status.idle": "2024-05-15T12:31:13.462500Z", - "shell.execute_reply": "2024-05-15T12:31:13.461960Z" + "iopub.execute_input": "2024-05-15T12:38:19.556355Z", + "iopub.status.busy": "2024-05-15T12:38:19.556042Z", + "iopub.status.idle": "2024-05-15T12:38:19.559139Z", + "shell.execute_reply": "2024-05-15T12:38:19.558517Z" } }, "outputs": [], @@ -857,10 +857,10 @@ "id": "inappropriate-james", "metadata": { "execution": { - "iopub.execute_input": "2024-05-15T12:31:13.464989Z", - "iopub.status.busy": "2024-05-15T12:31:13.464584Z", - "iopub.status.idle": "2024-05-15T12:31:13.820565Z", - "shell.execute_reply": "2024-05-15T12:31:13.819924Z" + "iopub.execute_input": "2024-05-15T12:38:19.561533Z", + "iopub.status.busy": "2024-05-15T12:38:19.561170Z", + "iopub.status.idle": "2024-05-15T12:38:19.929081Z", + "shell.execute_reply": "2024-05-15T12:38:19.928368Z" } }, "outputs": [], @@ -891,10 +891,10 @@ "id": "palestinian-harvey", "metadata": { "execution": { - "iopub.execute_input": "2024-05-15T12:31:13.823560Z", - "iopub.status.busy": "2024-05-15T12:31:13.823333Z", - "iopub.status.idle": "2024-05-15T12:31:14.973079Z", - "shell.execute_reply": "2024-05-15T12:31:14.972381Z" + "iopub.execute_input": "2024-05-15T12:38:19.932374Z", + "iopub.status.busy": "2024-05-15T12:38:19.932028Z", + "iopub.status.idle": "2024-05-15T12:38:21.057588Z", + "shell.execute_reply": "2024-05-15T12:38:21.056913Z" } }, "outputs": [ @@ -902,133 +902,133 @@ "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:dataprep_ml-2555:Starting statistical analysis\u001b[0m\n" + "\u001b[32mINFO:dataprep_ml-2443:Starting statistical analysis\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:dataprep_ml-2555:Finished statistical analysis\u001b[0m\n" + "\u001b[32mINFO:dataprep_ml-2443:Finished statistical analysis\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[37mDEBUG:lightwood-2555: `analyze_data` runtime: 0.43 seconds\u001b[0m\n" + "\u001b[37mDEBUG:lightwood-2443: `analyze_data` runtime: 0.42 seconds\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:dataprep_ml-2555:Cleaning the data\u001b[0m\n" + "\u001b[32mINFO:dataprep_ml-2443:Cleaning the data\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[37mDEBUG:lightwood-2555: `preprocess` runtime: 0.13 seconds\u001b[0m\n" + "\u001b[37mDEBUG:lightwood-2443: `preprocess` runtime: 0.13 seconds\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:dataprep_ml-2555:Splitting the data into train/test\u001b[0m\n" + "\u001b[32mINFO:dataprep_ml-2443:Splitting the data into train/test\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[37mDEBUG:lightwood-2555: `split` runtime: 0.0 seconds\u001b[0m\n" + "\u001b[37mDEBUG:lightwood-2443: `split` runtime: 0.0 seconds\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[37mDEBUG:dataprep_ml-2555:Preparing sequentially...\u001b[0m\n" + "\u001b[37mDEBUG:dataprep_ml-2443:Preparing sequentially...\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[37mDEBUG:dataprep_ml-2555:Preparing encoder for year...\u001b[0m\n" + "\u001b[37mDEBUG:dataprep_ml-2443:Preparing encoder for year...\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[37mDEBUG:dataprep_ml-2555:Preparing encoder for mileage...\u001b[0m\n" + "\u001b[37mDEBUG:dataprep_ml-2443:Preparing encoder for mileage...\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[37mDEBUG:dataprep_ml-2555:Preparing encoder for tax...\u001b[0m\n" + "\u001b[37mDEBUG:dataprep_ml-2443:Preparing encoder for tax...\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[37mDEBUG:dataprep_ml-2555:Preparing encoder for mpg...\u001b[0m\n" + "\u001b[37mDEBUG:dataprep_ml-2443:Preparing encoder for mpg...\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[37mDEBUG:dataprep_ml-2555:Preparing encoder for engineSize...\u001b[0m\n" + "\u001b[37mDEBUG:dataprep_ml-2443:Preparing encoder for engineSize...\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:lightwood-2555:Categories Detected = 1\u001b[0m\n" + "\u001b[32mINFO:lightwood-2443:Categories Detected = 1\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:lightwood-2555:Categories Detected = 1\u001b[0m\n" + "\u001b[32mINFO:lightwood-2443:Categories Detected = 1\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:lightwood-2555:Categories Detected = 1\u001b[0m\n" + "\u001b[32mINFO:lightwood-2443:Categories Detected = 1\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[37mDEBUG:lightwood-2555: `prepare` runtime: 0.01 seconds\u001b[0m\n" + "\u001b[37mDEBUG:lightwood-2443: `prepare` runtime: 0.02 seconds\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:dataprep_ml-2555:Featurizing the data\u001b[0m\n" + "\u001b[32mINFO:dataprep_ml-2443:Featurizing the data\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[37mDEBUG:lightwood-2555: `featurize` runtime: 0.57 seconds\u001b[0m\n" + "\u001b[37mDEBUG:lightwood-2443: `featurize` runtime: 0.55 seconds\u001b[0m\n" ] } ], @@ -1063,10 +1063,10 @@ "id": "silent-dealing", "metadata": { "execution": { - "iopub.execute_input": "2024-05-15T12:31:14.975668Z", - "iopub.status.busy": "2024-05-15T12:31:14.975462Z", - "iopub.status.idle": "2024-05-15T12:31:14.984083Z", - "shell.execute_reply": "2024-05-15T12:31:14.983497Z" + "iopub.execute_input": "2024-05-15T12:38:21.060331Z", + "iopub.status.busy": "2024-05-15T12:38:21.059995Z", + "iopub.status.idle": "2024-05-15T12:38:21.068779Z", + "shell.execute_reply": "2024-05-15T12:38:21.068148Z" } }, "outputs": [ @@ -1168,10 +1168,10 @@ "id": "superior-mobility", "metadata": { "execution": { - "iopub.execute_input": "2024-05-15T12:31:14.986569Z", - "iopub.status.busy": "2024-05-15T12:31:14.986205Z", - "iopub.status.idle": "2024-05-15T12:31:14.989579Z", - "shell.execute_reply": "2024-05-15T12:31:14.988928Z" + "iopub.execute_input": "2024-05-15T12:38:21.071345Z", + "iopub.status.busy": "2024-05-15T12:38:21.070979Z", + "iopub.status.idle": "2024-05-15T12:38:21.074376Z", + "shell.execute_reply": "2024-05-15T12:38:21.073715Z" } }, "outputs": [ diff --git a/tutorials/custom_explainer/custom_explainer.html b/tutorials/custom_explainer/custom_explainer.html index e52446cd0..d9b727a84 100644 --- a/tutorials/custom_explainer/custom_explainer.html +++ b/tutorials/custom_explainer/custom_explainer.html @@ -118,8 +118,8 @@

Objective 
-INFO:lightwood-2634:No torchvision detected, image helpers not supported.
-INFO:lightwood-2634:No torchvision/pillow detected, image encoder not supported
+INFO:lightwood-2523:No torchvision detected, image helpers not supported.
+INFO:lightwood-2523:No torchvision/pillow detected, image encoder not supported
@@ -172,7 +172,7 @@

Step 1: figuring out what we need 
-<__main__.ModelCorrelationHeatmap at 0x7fc16da6f8e0>
+<__main__.ModelCorrelationHeatmap at 0x7ff43c2f7fd0>

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.

@@ -328,24 +328,24 @@

Step 4: Final test run 
-INFO:type_infer-2634:Analyzing a sample of 222
-INFO:type_infer-2634:from a total population of 225, this is equivalent to 98.7% of your data.
-INFO:type_infer-2634:Infering type for: Population
-INFO:type_infer-2634:Column Population has data type integer
-INFO:type_infer-2634:Infering type for: Area (sq. mi.)
-INFO:type_infer-2634:Column Area (sq. mi.) has data type integer
-INFO:type_infer-2634:Infering type for: Pop. Density 
-INFO:type_infer-2634:Column Pop. Density  has data type float
-INFO:type_infer-2634:Infering type for: GDP ($ per capita)
-INFO:type_infer-2634:Column GDP ($ per capita) has data type integer
-INFO:type_infer-2634:Infering type for: Literacy (%)
-INFO:type_infer-2634:Column Literacy (%) has data type float
-INFO:type_infer-2634:Infering type for: Infant mortality 
-INFO:type_infer-2634:Column Infant mortality  has data type float
-INFO:type_infer-2634:Infering type for: Development Index
-INFO:type_infer-2634:Column Development Index has data type categorical
-INFO:dataprep_ml-2634:Starting statistical analysis
-INFO:dataprep_ml-2634:Finished statistical analysis
+INFO:type_infer-2523:Analyzing a sample of 222
+INFO:type_infer-2523:from a total population of 225, this is equivalent to 98.7% of your data.
+INFO:type_infer-2523:Infering type for: Population
+INFO:type_infer-2523:Column Population has data type integer
+INFO:type_infer-2523:Infering type for: Area (sq. mi.)
+INFO:type_infer-2523:Column Area (sq. mi.) has data type integer
+INFO:type_infer-2523:Infering type for: Pop. Density 
+INFO:type_infer-2523:Column Pop. Density  has data type float
+INFO:type_infer-2523:Infering type for: GDP ($ per capita)
+INFO:type_infer-2523:Column GDP ($ per capita) has data type integer
+INFO:type_infer-2523:Infering type for: Literacy (%)
+INFO:type_infer-2523:Column Literacy (%) has data type float
+INFO:type_infer-2523:Infering type for: Infant mortality 
+INFO:type_infer-2523:Column Infant mortality  has data type float
+INFO:type_infer-2523:Infering type for: Development Index
+INFO:type_infer-2523:Column Development Index has data type categorical
+INFO:dataprep_ml-2523:Starting statistical analysis
+INFO:dataprep_ml-2523:Finished statistical analysis

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

@@ -385,32 +385,32 @@

Step 4: Final test run 
-INFO:dataprep_ml-2634:[Learn phase 1/8] - Statistical analysis
-INFO:dataprep_ml-2634:Starting statistical analysis
-INFO:dataprep_ml-2634:Finished statistical analysis
-DEBUG:lightwood-2634: `analyze_data` runtime: 0.02 seconds
-INFO:dataprep_ml-2634:[Learn phase 2/8] - Data preprocessing
-INFO:dataprep_ml-2634:Cleaning the data
-DEBUG:lightwood-2634: `preprocess` runtime: 0.01 seconds
-INFO:dataprep_ml-2634:[Learn phase 3/8] - Data splitting
-INFO:dataprep_ml-2634:Splitting the data into train/test
-DEBUG:lightwood-2634: `split` runtime: 0.01 seconds
-INFO:dataprep_ml-2634:[Learn phase 4/8] - Preparing encoders
-DEBUG:dataprep_ml-2634:Preparing sequentially...
-DEBUG:dataprep_ml-2634:Preparing encoder for Population...
-DEBUG:dataprep_ml-2634:Preparing encoder for Area (sq. mi.)...
-DEBUG:dataprep_ml-2634:Preparing encoder for Pop. Density ...
-DEBUG:dataprep_ml-2634:Preparing encoder for GDP ($ per capita)...
-DEBUG:dataprep_ml-2634:Preparing encoder for Literacy (%)...
-DEBUG:dataprep_ml-2634:Preparing encoder for Infant mortality ...
-DEBUG:lightwood-2634:Encoding UNKNOWN categories as index 0
-DEBUG:lightwood-2634: `prepare` runtime: 0.01 seconds
-INFO:dataprep_ml-2634:[Learn phase 5/8] - Feature generation
-INFO:dataprep_ml-2634:Featurizing the data
-DEBUG:lightwood-2634: `featurize` runtime: 0.05 seconds
-INFO:dataprep_ml-2634:[Learn phase 6/8] - Mixer training
-INFO:dataprep_ml-2634:Training the mixers
-WARNING:lightwood-2634:XGBoost running on CPU
+INFO:dataprep_ml-2523:[Learn phase 1/8] - Statistical analysis
+INFO:dataprep_ml-2523:Starting statistical analysis
+INFO:dataprep_ml-2523:Finished statistical analysis
+DEBUG:lightwood-2523: `analyze_data` runtime: 0.02 seconds
+INFO:dataprep_ml-2523:[Learn phase 2/8] - Data preprocessing
+INFO:dataprep_ml-2523:Cleaning the data
+DEBUG:lightwood-2523: `preprocess` runtime: 0.01 seconds
+INFO:dataprep_ml-2523:[Learn phase 3/8] - Data splitting
+INFO:dataprep_ml-2523:Splitting the data into train/test
+DEBUG:lightwood-2523: `split` runtime: 0.01 seconds
+INFO:dataprep_ml-2523:[Learn phase 4/8] - Preparing encoders
+DEBUG:dataprep_ml-2523:Preparing sequentially...
+DEBUG:dataprep_ml-2523:Preparing encoder for Population...
+DEBUG:dataprep_ml-2523:Preparing encoder for Area (sq. mi.)...
+DEBUG:dataprep_ml-2523:Preparing encoder for Pop. Density ...
+DEBUG:dataprep_ml-2523:Preparing encoder for GDP ($ per capita)...
+DEBUG:dataprep_ml-2523:Preparing encoder for Literacy (%)...
+DEBUG:dataprep_ml-2523:Preparing encoder for Infant mortality ...
+DEBUG:lightwood-2523:Encoding UNKNOWN categories as index 0
+DEBUG:lightwood-2523: `prepare` runtime: 0.01 seconds
+INFO:dataprep_ml-2523:[Learn phase 5/8] - Feature generation
+INFO:dataprep_ml-2523:Featurizing the data
+DEBUG:lightwood-2523: `featurize` runtime: 0.05 seconds
+INFO:dataprep_ml-2523:[Learn phase 6/8] - Mixer training
+INFO:dataprep_ml-2523:Training the mixers
+WARNING:lightwood-2523:XGBoost running on CPU
 /opt/hostedtoolcache/Python/3.9.19/x64/lib/python3.9/site-packages/torch/amp/grad_scaler.py:131: UserWarning: torch.cuda.amp.GradScaler is enabled, but CUDA is not available.  Disabling.
   warnings.warn(
@@ -420,7 +420,7 @@

Step 4: Final test run 
-[12:31:21] WARNING: ../src/learner.cc:339: No visible GPU is found, setting `gpu_id` to -1
+[12:38:27] WARNING: ../src/learner.cc:339: No visible GPU is found, setting `gpu_id` to -1
@@ -433,573 +433,573 @@

Step 4: Final test runINFO:lightwood-2634:Loss of 18.69619858264923 with learning rate 0.0001 -INFO:lightwood-2634:Loss of 16.93891429901123 with learning rate 0.0005 -INFO:lightwood-2634:Loss of 16.197376608848572 with learning rate 0.001 -INFO:lightwood-2634:Loss of 16.06481909751892 with learning rate 0.002 -INFO:lightwood-2634:Loss of 16.472004413604736 with learning rate 0.003 -INFO:lightwood-2634:Loss of 18.28026556968689 with learning rate 0.005 -INFO:lightwood-2634:Loss of 26.746760368347168 with learning rate 0.01 -INFO:lightwood-2634:Loss of 101.83524441719055 with learning rate 0.05 -INFO:lightwood-2634:Found learning rate of: 0.002 -INFO:lightwood-2634:Loss @ epoch 1: 1.319209337234497 -INFO:lightwood-2634:Loss @ epoch 2: 1.3220206499099731 -INFO:lightwood-2634:Loss @ epoch 3: 1.3063435554504395 -INFO:lightwood-2634:Loss @ epoch 4: 1.2932535409927368 -INFO:lightwood-2634:Loss @ epoch 5: 1.2823516130447388 -INFO:lightwood-2634:Loss @ epoch 6: 1.2705544233322144 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0.1195112019777298 -INFO:lightwood-2634:Loss @ epoch 281: 0.1185888797044754 -INFO:lightwood-2634:Loss @ epoch 282: 0.11789504438638687 -INFO:lightwood-2634:Loss @ epoch 283: 0.11783000081777573 -INFO:lightwood-2634:Loss @ epoch 284: 0.11681754887104034 -INFO:lightwood-2634:Loss @ epoch 285: 0.11649196594953537 -INFO:lightwood-2634:Loss @ epoch 286: 0.11648327857255936 -INFO:lightwood-2634:Loss @ epoch 287: 0.11562418192625046 -INFO:lightwood-2634:Loss @ epoch 288: 0.11489420384168625 -INFO:lightwood-2634:Loss @ epoch 289: 0.11485717445611954 -INFO:lightwood-2634:Loss @ epoch 290: 0.11407709866762161 -INFO:lightwood-2634:Loss @ epoch 291: 0.11348505318164825 -INFO:lightwood-2634:Loss @ epoch 292: 0.11358898878097534 -INFO:lightwood-2634:Loss @ epoch 293: 0.11268813163042068 -INFO:lightwood-2634:Loss @ epoch 294: 0.11207651346921921 -INFO:lightwood-2634:Loss @ epoch 295: 0.11220688372850418 -INFO:lightwood-2634:Loss @ epoch 296: 0.11118005961179733 -INFO:lightwood-2634:Loss @ epoch 297: 0.11089354008436203 -INFO:lightwood-2634:Loss @ epoch 298: 0.11088859289884567 -INFO:lightwood-2634:Loss @ epoch 299: 0.1100316271185875 -INFO:lightwood-2634:Loss @ epoch 300: 0.1093800961971283 -INFO:lightwood-2634:Loss @ epoch 301: 0.10955681651830673 -INFO:lightwood-2634:Loss @ epoch 302: 0.10869839787483215 -INFO:lightwood-2634:Loss @ epoch 303: 0.10815789550542831 -INFO:lightwood-2634:Loss @ epoch 304: 0.10832306742668152 -INFO:lightwood-2634:Loss @ epoch 305: 0.10742544382810593 -INFO:lightwood-2634:Loss @ epoch 306: 0.10682710260152817 -INFO:lightwood-2634:Loss @ epoch 307: 0.10698221623897552 -INFO:lightwood-2634:Loss @ epoch 308: 0.10616409033536911 -INFO:lightwood-2634:Loss @ epoch 309: 0.10568025708198547 -INFO:lightwood-2634:Loss @ epoch 310: 0.10574078559875488 -INFO:lightwood-2634:Loss @ epoch 311: 0.10493995994329453 -INFO:lightwood-2634:Loss @ epoch 312: 0.10438455641269684 -INFO:lightwood-2634:Loss @ epoch 313: 0.10449497401714325 -INFO:lightwood-2634:Loss @ epoch 314: 0.10372297465801239 -INFO:lightwood-2634:Loss @ epoch 315: 0.10320515185594559 -INFO:lightwood-2634:Loss @ epoch 316: 0.10330332815647125 -INFO:lightwood-2634:Loss @ epoch 317: 0.10239537805318832 -INFO:lightwood-2634:Loss @ epoch 318: 0.10185065120458603 -INFO:lightwood-2634:Loss @ epoch 319: 0.10217708349227905 -INFO:lightwood-2634:Loss @ epoch 320: 0.10135672241449356 -INFO:lightwood-2634:Loss @ epoch 321: 0.10087659955024719 -INFO:lightwood-2634:Loss @ epoch 322: 0.10087589174509048 -INFO:lightwood-2634:Loss @ epoch 323: 0.10005565732717514 -INFO:lightwood-2634:Loss @ epoch 324: 0.09949999302625656 -INFO:lightwood-2634:Loss @ epoch 325: 0.09970266371965408 -INFO:lightwood-2634:Loss @ epoch 326: 0.09918338060379028 -INFO:lightwood-2634:Loss @ epoch 327: 0.09840800613164902 -INFO:lightwood-2634:Loss @ epoch 328: 0.09882311522960663 -INFO:lightwood-2634:Loss @ epoch 329: 0.09775345772504807 -INFO:lightwood-2634:Loss @ epoch 330: 0.09729817509651184 -INFO:lightwood-2634:Loss @ epoch 331: 0.09763044863939285 -INFO:lightwood-2634:Loss @ epoch 332: 0.0967596173286438 -INFO:lightwood-2634:Loss @ epoch 333: 0.09642492234706879 -INFO:lightwood-2634:Loss @ epoch 334: 0.09656761586666107 -INFO:lightwood-2634:Loss @ epoch 335: 0.09573261439800262 -INFO:lightwood-2634:Loss @ epoch 336: 0.09523642063140869 -INFO:lightwood-2634:Loss @ epoch 337: 0.09568659961223602 -INFO:lightwood-2634:Loss @ epoch 338: 0.09509280323982239 -INFO:lightwood-2634:Loss @ epoch 339: 0.09460369497537613 -INFO:lightwood-2634:Loss @ epoch 340: 0.09476538747549057 -INFO:lightwood-2634:Loss @ epoch 341: 0.09388881921768188 -INFO:lightwood-2634:Loss @ epoch 342: 0.09349637478590012 -INFO:lightwood-2634:Loss @ epoch 343: 0.09398090839385986 -INFO:lightwood-2634:Loss @ epoch 344: 0.09314301609992981 -INFO:lightwood-2634:Loss @ epoch 345: 0.09281699359416962 -INFO:lightwood-2634:Loss @ epoch 346: 0.09290202707052231 -INFO:lightwood-2634:Loss @ epoch 347: 0.09209518879652023 -INFO:lightwood-2634:Loss @ epoch 348: 0.09171803295612335 -INFO:lightwood-2634:Loss @ epoch 349: 0.09221566468477249 -INFO:lightwood-2634:Loss @ epoch 350: 0.09150414168834686 -INFO:lightwood-2634:Loss @ epoch 351: 0.0910501629114151 -INFO:lightwood-2634:Loss @ epoch 352: 0.09118885546922684 -INFO:lightwood-2634:Loss @ epoch 353: 0.09043896198272705 -INFO:lightwood-2634:Loss @ epoch 354: 0.09006913751363754 -INFO:lightwood-2634:Loss @ epoch 355: 0.09049264341592789 -INFO:lightwood-2634:Loss @ epoch 356: 0.0898597463965416 -INFO:lightwood-2634:Loss @ epoch 357: 0.08943390846252441 -INFO:lightwood-2634:Loss @ epoch 358: 0.0896739661693573 -INFO:lightwood-2634:Loss @ epoch 359: 0.08882326632738113 -INFO:lightwood-2634:Loss @ epoch 360: 0.08850156515836716 -INFO:lightwood-2634:Loss @ epoch 361: 0.08897048979997635 -INFO:lightwood-2634:Loss @ epoch 362: 0.08849596232175827 -INFO:lightwood-2634:Loss @ epoch 363: 0.08790712803602219 -INFO:lightwood-2634:Loss @ epoch 364: 0.08821234852075577 -INFO:lightwood-2634:Loss @ epoch 365: 0.08732891082763672 -INFO:lightwood-2634:Loss @ epoch 366: 0.08704856038093567 -INFO:lightwood-2634:Loss @ epoch 367: 0.08765564113855362 -INFO:lightwood-2634:Loss @ epoch 368: 0.08696923404932022 -INFO:lightwood-2634:Loss @ epoch 369: 0.08649873733520508 -INFO:lightwood-2634:Loss @ epoch 370: 0.08676613122224808 -INFO:lightwood-2634:Loss @ epoch 371: 0.08599219471216202 -INFO:lightwood-2634:Loss @ epoch 372: 0.08565033972263336 -INFO:lightwood-2634:Loss @ epoch 373: 0.08618329465389252 -INFO:lightwood-2634:Loss @ epoch 374: 0.08559156954288483 -INFO:lightwood-2634:Loss @ epoch 375: 0.08509930223226547 -INFO:lightwood-2634:Loss @ epoch 376: 0.08543801307678223 -INFO:lightwood-2634:Loss @ epoch 377: 0.084554523229599 -INFO:lightwood-2634:Loss @ epoch 378: 0.08425222337245941 -INFO:lightwood-2634:Loss @ epoch 379: 0.08496475219726562 -INFO:lightwood-2634:Loss @ epoch 380: 0.08428442478179932 -INFO:lightwood-2634:Loss @ epoch 381: 0.08389458060264587 -INFO:lightwood-2634:Loss @ epoch 382: 0.08416417241096497 -INFO:lightwood-2634:Loss @ epoch 383: 0.08331726491451263 -INFO:lightwood-2634:Loss @ epoch 384: 0.08304726332426071 -INFO:lightwood-2634:Loss @ epoch 385: 0.0837259590625763 -INFO:lightwood-2634:Loss @ epoch 386: 0.08301664143800735 -INFO:lightwood-2634:Loss @ epoch 387: 0.08279375731945038 -INFO:lightwood-2634:Loss @ epoch 388: 0.08285657316446304 -INFO:lightwood-2634:Loss @ epoch 389: 0.0822003185749054 -INFO:lightwood-2634:Loss @ epoch 390: 0.08189017325639725 -INFO:lightwood-2634:Loss @ epoch 391: 0.08244460821151733 -INFO:lightwood-2634:Loss @ epoch 392: 0.08176209777593613 -INFO:lightwood-2634:Loss @ epoch 393: 0.08143384009599686 -INFO:lightwood-2634:Loss @ epoch 394: 0.08153267949819565 -INFO:lightwood-2634:Loss @ epoch 395: 0.08074252307415009 -INFO:lightwood-2634:Loss @ epoch 396: 0.0804641842842102 -INFO:lightwood-2634:Loss @ epoch 397: 0.08112648874521255 -INFO:lightwood-2634:Loss @ epoch 398: 0.0804068073630333 -INFO:lightwood-2634:Loss @ epoch 399: 0.08000007271766663 -INFO:lightwood-2634:Loss @ epoch 400: 0.08030638843774796 -INFO:lightwood-2634:Loss @ epoch 401: 0.07946185022592545 -INFO:lightwood-2634:Loss @ epoch 402: 0.07926557213068008 -INFO:lightwood-2634:Loss @ epoch 403: 0.07995376735925674 -INFO:lightwood-2634:Loss @ epoch 404: 0.07914069294929504 -INFO:lightwood-2634:Loss @ epoch 405: 0.07901032269001007 -INFO:lightwood-2634:Loss @ epoch 406: 0.07910943776369095 -INFO:lightwood-2634:Loss @ epoch 407: 0.07840055227279663 -INFO:lightwood-2634:Loss @ epoch 408: 0.07814037799835205 -INFO:lightwood-2634:Loss @ epoch 409: 0.07874786853790283 -INFO:lightwood-2634:Loss @ epoch 410: 0.07819069921970367 -INFO:lightwood-2634:Loss @ epoch 411: 0.07780887931585312 -INFO:lightwood-2634:Loss @ epoch 412: 0.07802116870880127 -INFO:lightwood-2634:Loss @ epoch 413: 0.0772867277264595 -INFO:lightwood-2634:Loss @ epoch 414: 0.07709880918264389 -INFO:lightwood-2634:Loss @ epoch 415: 0.0776868537068367 -INFO:lightwood-2634:Loss @ epoch 416: 0.07716330885887146 -INFO:lightwood-2634:Loss @ epoch 417: 0.07688125967979431 -INFO:lightwood-2634:Loss @ epoch 418: 0.07698465138673782 -INFO:lightwood-2634:Loss @ epoch 419: 0.0762372612953186 -INFO:lightwood-2634:Loss @ epoch 420: 0.07603802531957626 -INFO:lightwood-2634:Loss @ epoch 421: 0.07675285637378693 -INFO:lightwood-2634:Loss @ epoch 422: 0.07623977214097977 -INFO:lightwood-2634:Loss @ epoch 423: 0.07567108422517776 -INFO:lightwood-2634:Loss @ epoch 424: 0.07615751028060913 -INFO:lightwood-2634:Loss @ epoch 425: 0.07526733726263046 -INFO:lightwood-2634:Loss @ epoch 426: 0.07509555667638779 -INFO:lightwood-2634:Loss @ epoch 427: 0.07569493353366852 -INFO:lightwood-2634:Loss @ epoch 428: 0.07537294924259186 -INFO:lightwood-2634:Loss @ epoch 429: 0.07467805594205856 -INFO:lightwood-2634:Loss @ epoch 430: 0.07528648525476456 -INFO:lightwood-2634:Loss @ epoch 431: 0.07435967028141022 -INFO:lightwood-2634:Loss @ epoch 432: 0.07422596961259842 -INFO:lightwood-2634:Loss @ epoch 433: 0.07503972947597504 -INFO:lightwood-2634:Loss @ epoch 434: 0.07434249669313431 -INFO:lightwood-2634:Loss @ epoch 435: 0.07409335672855377 -INFO:lightwood-2634:Loss @ epoch 436: 0.07420685887336731 -INFO:lightwood-2634:Loss @ epoch 437: 0.0735834538936615 -INFO:lightwood-2634:Loss @ epoch 438: 0.07333341240882874 -INFO:lightwood-2634:Loss @ epoch 439: 0.07391082495450974 -INFO:lightwood-2634:Loss @ epoch 440: 0.07348911464214325 -INFO:lightwood-2634:Loss @ epoch 441: 0.07308389991521835 -INFO:lightwood-2634:Loss @ epoch 442: 0.07328886538743973 -INFO:lightwood-2634:Loss @ epoch 443: 0.0725550651550293 -INFO:lightwood-2634:Loss @ epoch 444: 0.07240220904350281 -INFO:lightwood-2634:Loss @ epoch 445: 0.07308465242385864 -INFO:lightwood-2634:Loss @ epoch 446: 0.07288312911987305 -INFO:lightwood-2634:Loss @ epoch 447: 0.0722663402557373 -INFO:lightwood-2634:Loss @ epoch 448: 0.07264856994152069 -INFO:lightwood-2634:Loss @ epoch 449: 0.07182618230581284 -INFO:lightwood-2634:Loss @ epoch 450: 0.07167533785104752 -INFO:lightwood-2634:Loss @ epoch 451: 0.07241341471672058 -INFO:lightwood-2634:Loss @ epoch 452: 0.07208056002855301 -INFO:lightwood-2634:Loss @ epoch 453: 0.07154601812362671 -INFO:lightwood-2634:Loss @ epoch 454: 0.07190731167793274 -INFO:lightwood-2634:Loss @ epoch 455: 0.0710812360048294 -INFO:lightwood-2634:Loss @ epoch 456: 0.07096673548221588 -INFO:lightwood-2634:Loss @ epoch 457: 0.0718337818980217 -INFO:lightwood-2634:Loss @ epoch 458: 0.07134897261857986 -INFO:lightwood-2634:Loss @ epoch 459: 0.07083813846111298 -INFO:lightwood-2634:Loss @ epoch 460: 0.07124733179807663 -INFO:lightwood-2634:Loss @ epoch 461: 0.0705094262957573 -INFO:lightwood-2634:Loss @ epoch 462: 0.07036501169204712 -INFO:lightwood-2634:Loss @ epoch 463: 0.07111788541078568 -INFO:lightwood-2634:Loss @ epoch 464: 0.07069509476423264 -INFO:lightwood-2634:Loss @ epoch 465: 0.07026039808988571 -INFO:lightwood-2634:Loss @ epoch 466: 0.07056906819343567 -INFO:lightwood-2634:Loss @ epoch 467: 0.06981150805950165 -INFO:lightwood-2634:Loss @ epoch 468: 0.06967213749885559 -INFO:lightwood-2634:Loss @ epoch 469: 0.0704450011253357 -INFO:lightwood-2634:Loss @ epoch 470: 0.07002224773168564 -INFO:lightwood-2634:Loss @ epoch 471: 0.06954890489578247 -INFO:lightwood-2634:Loss @ epoch 472: 0.07001929730176926 -INFO:lightwood-2634:Loss @ epoch 473: 0.06918215751647949 -INFO:lightwood-2634:Loss @ epoch 474: 0.06905678659677505 -INFO:lightwood-2634:Loss @ epoch 475: 0.06994140148162842 -INFO:lightwood-2634:Loss @ epoch 476: 0.06957031041383743 -INFO:lightwood-2634:Loss @ epoch 477: 0.06890591233968735 -INFO:lightwood-2634:Loss @ epoch 478: 0.06942413747310638 -INFO:lightwood-2634:Loss @ epoch 479: 0.068662129342556 -INFO:lightwood-2634:Loss @ epoch 480: 0.0685315951704979 -INFO:lightwood-2634:Loss @ epoch 481: 0.06919320672750473 -INFO:lightwood-2634:Loss @ epoch 482: 0.06884051114320755 -INFO:lightwood-2634:Loss @ epoch 483: 0.06852498650550842 -INFO:lightwood-2634:Loss @ epoch 484: 0.06881336867809296 -INFO:lightwood-2634:Loss @ epoch 485: 0.0681278333067894 -INFO:lightwood-2634:Loss @ epoch 486: 0.06801153719425201 -INFO:lightwood-2634:Loss @ epoch 487: 0.0688665509223938 -INFO:lightwood-2634:Loss @ epoch 488: 0.06848578155040741 -INFO:lightwood-2634:Loss @ epoch 489: 0.0680362805724144 -INFO:lightwood-2634:Loss @ epoch 490: 0.0685308426618576 -INFO:lightwood-2634:Loss @ epoch 491: 0.06770123541355133 -INFO:lightwood-2634:Loss @ epoch 492: 0.06760372221469879 -INFO:lightwood-2634:Loss @ epoch 493: 0.06856502592563629 -INFO:lightwood-2634:Loss @ epoch 494: 0.0679614394903183 -INFO:lightwood-2634:Loss @ epoch 495: 0.0675961971282959 -INFO:lightwood-2634:Loss @ epoch 496: 0.06795072555541992 -INFO:lightwood-2634:Loss @ epoch 497: 0.06731095910072327 -INFO:lightwood-2634:Loss @ epoch 498: 0.06714644283056259 -INFO:lightwood-2634:Loss @ epoch 499: 0.06786693632602692 -INFO:lightwood-2634:Loss @ epoch 500: 0.06758256256580353 -INFO:lightwood-2634:Loss @ epoch 501: 0.06698315590620041 -INFO:lightwood-2634:Loss @ epoch 502: 0.06747950613498688 -INFO:lightwood-2634:Loss @ epoch 503: 0.06655343621969223 -INFO:lightwood-2634:Loss @ epoch 504: 0.06652842462062836 -INFO:lightwood-2634:Loss @ epoch 505: 0.06745205074548721 -INFO:lightwood-2634:Loss @ epoch 506: 0.0668550580739975 -INFO:lightwood-2634:Loss @ epoch 507: 0.06666403263807297 -INFO:lightwood-2634:Loss @ epoch 508: 0.06683854013681412 -INFO:lightwood-2634:Loss @ epoch 509: 0.06626935303211212 -INFO:lightwood-2634:Loss @ epoch 510: 0.06613652408123016 -INFO:lightwood-2634:Loss @ epoch 511: 0.06672576069831848 -INFO:lightwood-2634:Loss @ epoch 512: 0.0666651502251625 -INFO:lightwood-2634:Loss @ epoch 513: 0.06582488119602203 -INFO:lightwood-2634:Loss @ epoch 514: 0.06652247160673141 -INFO:lightwood-2634:Loss @ epoch 515: 0.06558185815811157 -INFO:lightwood-2634:Loss @ epoch 516: 0.0655498206615448 -INFO:lightwood-2634:Loss @ epoch 517: 0.06624851375818253 -INFO:lightwood-2634:Loss @ epoch 518: 0.06601088494062424 -INFO:lightwood-2634:Loss @ epoch 519: 0.06545697897672653 -INFO:lightwood-2634:Loss @ epoch 520: 0.0659414529800415 -INFO:lightwood-2634:Loss @ epoch 521: 0.06516807526350021 -INFO:lightwood-2634:Loss @ epoch 522: 0.06501934677362442 -INFO:lightwood-2634:Loss @ epoch 523: 0.06574487686157227 -INFO:lightwood-2634:Loss @ epoch 524: 0.06553597748279572 -INFO:lightwood-2634:Loss @ epoch 525: 0.06504649668931961 -INFO:lightwood-2634:Loss @ epoch 526: 0.06540416181087494 -INFO:lightwood-2634:Loss @ epoch 527: 0.06479271501302719 -INFO:lightwood-2634:Loss @ epoch 528: 0.06469936668872833 -INFO:lightwood-2634:Loss @ epoch 529: 0.0654490739107132 -INFO:lightwood-2634:Loss @ epoch 530: 0.06509881466627121 -INFO:lightwood-2634:Loss @ epoch 531: 0.06460769474506378 -INFO:lightwood-2634:Loss @ epoch 532: 0.06506450474262238 -INFO:lightwood-2634:Loss @ epoch 533: 0.06425388902425766 -INFO:lightwood-2634:Loss @ epoch 534: 0.06419297307729721 -INFO:lightwood-2634:Loss @ epoch 535: 0.06507144123315811 -INFO:lightwood-2634:Loss @ epoch 536: 0.06475593149662018 -INFO:lightwood-2634:Loss @ epoch 537: 0.0640476867556572 -INFO:lightwood-2634:Loss @ epoch 538: 0.06452148407697678 -INFO:lightwood-2634:Loss @ epoch 539: 0.063988097012043 -INFO:lightwood-2634:Loss @ epoch 540: 0.06390102207660675 -INFO:lightwood-2634:Loss @ epoch 541: 0.06427431106567383 -INFO:lightwood-2634:Loss @ epoch 542: 0.06461699306964874 -INFO:lightwood-2634:Loss @ epoch 543: 0.06366197764873505 -INFO:lightwood-2634:Loss @ epoch 544: 0.06439769268035889 -INFO:lightwood-2634:Loss @ epoch 545: 0.06354749947786331 -INFO:lightwood-2634:Loss @ epoch 546: 0.06346575170755386 -INFO:lightwood-2634:Loss @ epoch 547: 0.06415951251983643 -INFO:lightwood-2634:Loss @ epoch 548: 0.06416907906532288 -INFO:lightwood-2634:Loss @ epoch 549: 0.06350232660770416 -INFO:lightwood-2634:Loss @ epoch 1: 0.03389815576374531 -INFO:lightwood-2634:Loss @ epoch 2: 0.033698095567524435 -INFO:lightwood-2634:Loss @ epoch 3: 0.0372611828148365 -INFO:lightwood-2634:Loss @ epoch 4: 0.0382374182343483 -INFO:lightwood-2634:Loss @ epoch 5: 0.03677316829562187 -INFO:lightwood-2634:Loss @ epoch 6: 0.04194173291325569 -INFO:lightwood-2634:Loss @ epoch 7: 0.04046095162630081 -DEBUG:lightwood-2634: `fit_mixer` runtime: 4.6 seconds -INFO:lightwood-2634:Started fitting XGBoost model +INFO:lightwood-2523:Loss of 18.69619858264923 with learning rate 0.0001 +INFO:lightwood-2523:Loss of 16.93891429901123 with learning rate 0.0005 +INFO:lightwood-2523:Loss of 16.197376608848572 with learning rate 0.001 +INFO:lightwood-2523:Loss of 16.06481909751892 with learning rate 0.002 +INFO:lightwood-2523:Loss of 16.472004413604736 with learning rate 0.003 +INFO:lightwood-2523:Loss of 18.28026556968689 with learning rate 0.005 +INFO:lightwood-2523:Loss of 26.746760368347168 with learning rate 0.01 +INFO:lightwood-2523:Loss of 101.83524441719055 with learning rate 0.05 +INFO:lightwood-2523:Found learning rate of: 0.002 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53: 0.6590715646743774 +INFO:lightwood-2523:Loss @ epoch 54: 0.6515910625457764 +INFO:lightwood-2523:Loss @ epoch 55: 0.6308077573776245 +INFO:lightwood-2523:Loss @ epoch 56: 0.6241987347602844 +INFO:lightwood-2523:Loss @ epoch 57: 0.6163835525512695 +INFO:lightwood-2523:Loss @ epoch 58: 0.6131908297538757 +INFO:lightwood-2523:Loss @ epoch 59: 0.6106155514717102 +INFO:lightwood-2523:Loss @ epoch 60: 0.6036757826805115 +INFO:lightwood-2523:Loss @ epoch 61: 0.5848420262336731 +INFO:lightwood-2523:Loss @ epoch 62: 0.5793871879577637 +INFO:lightwood-2523:Loss @ epoch 63: 0.5726662278175354 +INFO:lightwood-2523:Loss @ epoch 64: 0.5703645348548889 +INFO:lightwood-2523:Loss @ epoch 65: 0.5684641003608704 +INFO:lightwood-2523:Loss @ epoch 66: 0.5622180104255676 +INFO:lightwood-2523:Loss @ epoch 67: 0.5449516773223877 +INFO:lightwood-2523:Loss @ epoch 68: 0.5401747226715088 +INFO:lightwood-2523:Loss @ epoch 69: 0.5341063141822815 +INFO:lightwood-2523:Loss @ epoch 70: 0.5322306752204895 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0.11358898878097534 +INFO:lightwood-2523:Loss @ epoch 293: 0.11268813163042068 +INFO:lightwood-2523:Loss @ epoch 294: 0.11207651346921921 +INFO:lightwood-2523:Loss @ epoch 295: 0.11220688372850418 +INFO:lightwood-2523:Loss @ epoch 296: 0.11118005961179733 +INFO:lightwood-2523:Loss @ epoch 297: 0.11089354008436203 +INFO:lightwood-2523:Loss @ epoch 298: 0.11088859289884567 +INFO:lightwood-2523:Loss @ epoch 299: 0.1100316271185875 +INFO:lightwood-2523:Loss @ epoch 300: 0.1093800961971283 +INFO:lightwood-2523:Loss @ epoch 301: 0.10955681651830673 +INFO:lightwood-2523:Loss @ epoch 302: 0.10869839787483215 +INFO:lightwood-2523:Loss @ epoch 303: 0.10815789550542831 +INFO:lightwood-2523:Loss @ epoch 304: 0.10832306742668152 +INFO:lightwood-2523:Loss @ epoch 305: 0.10742544382810593 +INFO:lightwood-2523:Loss @ epoch 306: 0.10682710260152817 +INFO:lightwood-2523:Loss @ epoch 307: 0.10698221623897552 +INFO:lightwood-2523:Loss @ epoch 308: 0.10616409033536911 +INFO:lightwood-2523:Loss @ epoch 309: 0.10568025708198547 +INFO:lightwood-2523:Loss @ epoch 310: 0.10574078559875488 +INFO:lightwood-2523:Loss @ epoch 311: 0.10493995994329453 +INFO:lightwood-2523:Loss @ epoch 312: 0.10438455641269684 +INFO:lightwood-2523:Loss @ epoch 313: 0.10449497401714325 +INFO:lightwood-2523:Loss @ epoch 314: 0.10372297465801239 +INFO:lightwood-2523:Loss @ epoch 315: 0.10320515185594559 +INFO:lightwood-2523:Loss @ epoch 316: 0.10330332815647125 +INFO:lightwood-2523:Loss @ epoch 317: 0.10239537805318832 +INFO:lightwood-2523:Loss @ epoch 318: 0.10185065120458603 +INFO:lightwood-2523:Loss @ epoch 319: 0.10217708349227905 +INFO:lightwood-2523:Loss @ epoch 320: 0.10135672241449356 +INFO:lightwood-2523:Loss @ epoch 321: 0.10087659955024719 +INFO:lightwood-2523:Loss @ epoch 322: 0.10087589174509048 +INFO:lightwood-2523:Loss @ epoch 323: 0.10005565732717514 +INFO:lightwood-2523:Loss @ epoch 324: 0.09949999302625656 +INFO:lightwood-2523:Loss @ epoch 325: 0.09970266371965408 +INFO:lightwood-2523:Loss @ epoch 326: 0.09918338060379028 +INFO:lightwood-2523:Loss @ epoch 327: 0.09840800613164902 +INFO:lightwood-2523:Loss @ epoch 328: 0.09882311522960663 +INFO:lightwood-2523:Loss @ epoch 329: 0.09775345772504807 +INFO:lightwood-2523:Loss @ epoch 330: 0.09729817509651184 +INFO:lightwood-2523:Loss @ epoch 331: 0.09763044863939285 +INFO:lightwood-2523:Loss @ epoch 332: 0.0967596173286438 +INFO:lightwood-2523:Loss @ epoch 333: 0.09642492234706879 +INFO:lightwood-2523:Loss @ epoch 334: 0.09656761586666107 +INFO:lightwood-2523:Loss @ epoch 335: 0.09573261439800262 +INFO:lightwood-2523:Loss @ epoch 336: 0.09523642063140869 +INFO:lightwood-2523:Loss @ epoch 337: 0.09568659961223602 +INFO:lightwood-2523:Loss @ epoch 338: 0.09509280323982239 +INFO:lightwood-2523:Loss @ epoch 339: 0.09460369497537613 +INFO:lightwood-2523:Loss @ epoch 340: 0.09476538747549057 +INFO:lightwood-2523:Loss @ epoch 341: 0.09388881921768188 +INFO:lightwood-2523:Loss @ epoch 342: 0.09349637478590012 +INFO:lightwood-2523:Loss @ epoch 343: 0.09398090839385986 +INFO:lightwood-2523:Loss @ epoch 344: 0.09314301609992981 +INFO:lightwood-2523:Loss @ epoch 345: 0.09281699359416962 +INFO:lightwood-2523:Loss @ epoch 346: 0.09290202707052231 +INFO:lightwood-2523:Loss @ epoch 347: 0.09209518879652023 +INFO:lightwood-2523:Loss @ epoch 348: 0.09171803295612335 +INFO:lightwood-2523:Loss @ epoch 349: 0.09221566468477249 +INFO:lightwood-2523:Loss @ epoch 350: 0.09150414168834686 +INFO:lightwood-2523:Loss @ epoch 351: 0.0910501629114151 +INFO:lightwood-2523:Loss @ epoch 352: 0.09118885546922684 +INFO:lightwood-2523:Loss @ epoch 353: 0.09043896198272705 +INFO:lightwood-2523:Loss @ epoch 354: 0.09006913751363754 +INFO:lightwood-2523:Loss @ epoch 355: 0.09049264341592789 +INFO:lightwood-2523:Loss @ epoch 356: 0.0898597463965416 +INFO:lightwood-2523:Loss @ epoch 357: 0.08943390846252441 +INFO:lightwood-2523:Loss @ epoch 358: 0.0896739661693573 +INFO:lightwood-2523:Loss @ epoch 359: 0.08882326632738113 +INFO:lightwood-2523:Loss @ epoch 360: 0.08850156515836716 +INFO:lightwood-2523:Loss @ epoch 361: 0.08897048979997635 +INFO:lightwood-2523:Loss @ epoch 362: 0.08849596232175827 +INFO:lightwood-2523:Loss @ epoch 363: 0.08790712803602219 +INFO:lightwood-2523:Loss @ epoch 364: 0.08821234852075577 +INFO:lightwood-2523:Loss @ epoch 365: 0.08732891082763672 +INFO:lightwood-2523:Loss @ epoch 366: 0.08704856038093567 +INFO:lightwood-2523:Loss @ epoch 367: 0.08765564113855362 +INFO:lightwood-2523:Loss @ epoch 368: 0.08696923404932022 +INFO:lightwood-2523:Loss @ epoch 369: 0.08649873733520508 +INFO:lightwood-2523:Loss @ epoch 370: 0.08676613122224808 +INFO:lightwood-2523:Loss @ epoch 371: 0.08599219471216202 +INFO:lightwood-2523:Loss @ epoch 372: 0.08565033972263336 +INFO:lightwood-2523:Loss @ epoch 373: 0.08618329465389252 +INFO:lightwood-2523:Loss @ epoch 374: 0.08559156954288483 +INFO:lightwood-2523:Loss @ epoch 375: 0.08509930223226547 +INFO:lightwood-2523:Loss @ epoch 376: 0.08543801307678223 +INFO:lightwood-2523:Loss @ epoch 377: 0.084554523229599 +INFO:lightwood-2523:Loss @ epoch 378: 0.08425222337245941 +INFO:lightwood-2523:Loss @ epoch 379: 0.08496475219726562 +INFO:lightwood-2523:Loss @ epoch 380: 0.08428442478179932 +INFO:lightwood-2523:Loss @ epoch 381: 0.08389458060264587 +INFO:lightwood-2523:Loss @ epoch 382: 0.08416417241096497 +INFO:lightwood-2523:Loss @ epoch 383: 0.08331726491451263 +INFO:lightwood-2523:Loss @ epoch 384: 0.08304726332426071 +INFO:lightwood-2523:Loss @ epoch 385: 0.0837259590625763 +INFO:lightwood-2523:Loss @ epoch 386: 0.08301664143800735 +INFO:lightwood-2523:Loss @ epoch 387: 0.08279375731945038 +INFO:lightwood-2523:Loss @ epoch 388: 0.08285657316446304 +INFO:lightwood-2523:Loss @ epoch 389: 0.0822003185749054 +INFO:lightwood-2523:Loss @ epoch 390: 0.08189017325639725 +INFO:lightwood-2523:Loss @ epoch 391: 0.08244460821151733 +INFO:lightwood-2523:Loss @ epoch 392: 0.08176209777593613 +INFO:lightwood-2523:Loss @ epoch 393: 0.08143384009599686 +INFO:lightwood-2523:Loss @ epoch 394: 0.08153267949819565 +INFO:lightwood-2523:Loss @ epoch 395: 0.08074252307415009 +INFO:lightwood-2523:Loss @ epoch 396: 0.0804641842842102 +INFO:lightwood-2523:Loss @ epoch 397: 0.08112648874521255 +INFO:lightwood-2523:Loss @ epoch 398: 0.0804068073630333 +INFO:lightwood-2523:Loss @ epoch 399: 0.08000007271766663 +INFO:lightwood-2523:Loss @ epoch 400: 0.08030638843774796 +INFO:lightwood-2523:Loss @ epoch 401: 0.07946185022592545 +INFO:lightwood-2523:Loss @ epoch 402: 0.07926557213068008 +INFO:lightwood-2523:Loss @ epoch 403: 0.07995376735925674 +INFO:lightwood-2523:Loss @ epoch 404: 0.07914069294929504 +INFO:lightwood-2523:Loss @ epoch 405: 0.07901032269001007 +INFO:lightwood-2523:Loss @ epoch 406: 0.07910943776369095 +INFO:lightwood-2523:Loss @ epoch 407: 0.07840055227279663 +INFO:lightwood-2523:Loss @ epoch 408: 0.07814037799835205 +INFO:lightwood-2523:Loss @ epoch 409: 0.07874786853790283 +INFO:lightwood-2523:Loss @ epoch 410: 0.07819069921970367 +INFO:lightwood-2523:Loss @ epoch 411: 0.07780887931585312 +INFO:lightwood-2523:Loss @ epoch 412: 0.07802116870880127 +INFO:lightwood-2523:Loss @ epoch 413: 0.0772867277264595 +INFO:lightwood-2523:Loss @ epoch 414: 0.07709880918264389 +INFO:lightwood-2523:Loss @ epoch 415: 0.0776868537068367 +INFO:lightwood-2523:Loss @ epoch 416: 0.07716330885887146 +INFO:lightwood-2523:Loss @ epoch 417: 0.07688125967979431 +INFO:lightwood-2523:Loss @ epoch 418: 0.07698465138673782 +INFO:lightwood-2523:Loss @ epoch 419: 0.0762372612953186 +INFO:lightwood-2523:Loss @ epoch 420: 0.07603802531957626 +INFO:lightwood-2523:Loss @ epoch 421: 0.07675285637378693 +INFO:lightwood-2523:Loss @ epoch 422: 0.07623977214097977 +INFO:lightwood-2523:Loss @ epoch 423: 0.07567108422517776 +INFO:lightwood-2523:Loss @ epoch 424: 0.07615751028060913 +INFO:lightwood-2523:Loss @ epoch 425: 0.07526733726263046 +INFO:lightwood-2523:Loss @ epoch 426: 0.07509555667638779 +INFO:lightwood-2523:Loss @ epoch 427: 0.07569493353366852 +INFO:lightwood-2523:Loss @ epoch 428: 0.07537294924259186 +INFO:lightwood-2523:Loss @ epoch 429: 0.07467805594205856 +INFO:lightwood-2523:Loss @ epoch 430: 0.07528648525476456 +INFO:lightwood-2523:Loss @ epoch 431: 0.07435967028141022 +INFO:lightwood-2523:Loss @ epoch 432: 0.07422596961259842 +INFO:lightwood-2523:Loss @ epoch 433: 0.07503972947597504 +INFO:lightwood-2523:Loss @ epoch 434: 0.07434249669313431 +INFO:lightwood-2523:Loss @ epoch 435: 0.07409335672855377 +INFO:lightwood-2523:Loss @ epoch 436: 0.07420685887336731 +INFO:lightwood-2523:Loss @ epoch 437: 0.0735834538936615 +INFO:lightwood-2523:Loss @ epoch 438: 0.07333341240882874 +INFO:lightwood-2523:Loss @ epoch 439: 0.07391082495450974 +INFO:lightwood-2523:Loss @ epoch 440: 0.07348911464214325 +INFO:lightwood-2523:Loss @ epoch 441: 0.07308389991521835 +INFO:lightwood-2523:Loss @ epoch 442: 0.07328886538743973 +INFO:lightwood-2523:Loss @ epoch 443: 0.0725550651550293 +INFO:lightwood-2523:Loss @ epoch 444: 0.07240220904350281 +INFO:lightwood-2523:Loss @ epoch 445: 0.07308465242385864 +INFO:lightwood-2523:Loss @ epoch 446: 0.07288312911987305 +INFO:lightwood-2523:Loss @ epoch 447: 0.0722663402557373 +INFO:lightwood-2523:Loss @ epoch 448: 0.07264856994152069 +INFO:lightwood-2523:Loss @ epoch 449: 0.07182618230581284 +INFO:lightwood-2523:Loss @ epoch 450: 0.07167533785104752 +INFO:lightwood-2523:Loss @ epoch 451: 0.07241341471672058 +INFO:lightwood-2523:Loss @ epoch 452: 0.07208056002855301 +INFO:lightwood-2523:Loss @ epoch 453: 0.07154601812362671 +INFO:lightwood-2523:Loss @ epoch 454: 0.07190731167793274 +INFO:lightwood-2523:Loss @ epoch 455: 0.0710812360048294 +INFO:lightwood-2523:Loss @ epoch 456: 0.07096673548221588 +INFO:lightwood-2523:Loss @ epoch 457: 0.0718337818980217 +INFO:lightwood-2523:Loss @ epoch 458: 0.07134897261857986 +INFO:lightwood-2523:Loss @ epoch 459: 0.07083813846111298 +INFO:lightwood-2523:Loss @ epoch 460: 0.07124733179807663 +INFO:lightwood-2523:Loss @ epoch 461: 0.0705094262957573 +INFO:lightwood-2523:Loss @ epoch 462: 0.07036501169204712 +INFO:lightwood-2523:Loss @ epoch 463: 0.07111788541078568 +INFO:lightwood-2523:Loss @ epoch 464: 0.07069509476423264 +INFO:lightwood-2523:Loss @ epoch 465: 0.07026039808988571 +INFO:lightwood-2523:Loss @ epoch 466: 0.07056906819343567 +INFO:lightwood-2523:Loss @ epoch 467: 0.06981150805950165 +INFO:lightwood-2523:Loss @ epoch 468: 0.06967213749885559 +INFO:lightwood-2523:Loss @ epoch 469: 0.0704450011253357 +INFO:lightwood-2523:Loss @ epoch 470: 0.07002224773168564 +INFO:lightwood-2523:Loss @ epoch 471: 0.06954890489578247 +INFO:lightwood-2523:Loss @ epoch 472: 0.07001929730176926 +INFO:lightwood-2523:Loss @ epoch 473: 0.06918215751647949 +INFO:lightwood-2523:Loss @ epoch 474: 0.06905678659677505 +INFO:lightwood-2523:Loss @ epoch 475: 0.06994140148162842 +INFO:lightwood-2523:Loss @ epoch 476: 0.06957031041383743 +INFO:lightwood-2523:Loss @ epoch 477: 0.06890591233968735 +INFO:lightwood-2523:Loss @ epoch 478: 0.06942413747310638 +INFO:lightwood-2523:Loss @ epoch 479: 0.068662129342556 +INFO:lightwood-2523:Loss @ epoch 480: 0.0685315951704979 +INFO:lightwood-2523:Loss @ epoch 481: 0.06919320672750473 +INFO:lightwood-2523:Loss @ epoch 482: 0.06884051114320755 +INFO:lightwood-2523:Loss @ epoch 483: 0.06852498650550842 +INFO:lightwood-2523:Loss @ epoch 484: 0.06881336867809296 +INFO:lightwood-2523:Loss @ epoch 485: 0.0681278333067894 +INFO:lightwood-2523:Loss @ epoch 486: 0.06801153719425201 +INFO:lightwood-2523:Loss @ epoch 487: 0.0688665509223938 +INFO:lightwood-2523:Loss @ epoch 488: 0.06848578155040741 +INFO:lightwood-2523:Loss @ epoch 489: 0.0680362805724144 +INFO:lightwood-2523:Loss @ epoch 490: 0.0685308426618576 +INFO:lightwood-2523:Loss @ epoch 491: 0.06770123541355133 +INFO:lightwood-2523:Loss @ epoch 492: 0.06760372221469879 +INFO:lightwood-2523:Loss @ epoch 493: 0.06856502592563629 +INFO:lightwood-2523:Loss @ epoch 494: 0.0679614394903183 +INFO:lightwood-2523:Loss @ epoch 495: 0.0675961971282959 +INFO:lightwood-2523:Loss @ epoch 496: 0.06795072555541992 +INFO:lightwood-2523:Loss @ epoch 497: 0.06731095910072327 +INFO:lightwood-2523:Loss @ epoch 498: 0.06714644283056259 +INFO:lightwood-2523:Loss @ epoch 499: 0.06786693632602692 +INFO:lightwood-2523:Loss @ epoch 500: 0.06758256256580353 +INFO:lightwood-2523:Loss @ epoch 501: 0.06698315590620041 +INFO:lightwood-2523:Loss @ epoch 502: 0.06747950613498688 +INFO:lightwood-2523:Loss @ epoch 503: 0.06655343621969223 +INFO:lightwood-2523:Loss @ epoch 504: 0.06652842462062836 +INFO:lightwood-2523:Loss @ epoch 505: 0.06745205074548721 +INFO:lightwood-2523:Loss @ epoch 506: 0.0668550580739975 +INFO:lightwood-2523:Loss @ epoch 507: 0.06666403263807297 +INFO:lightwood-2523:Loss @ epoch 508: 0.06683854013681412 +INFO:lightwood-2523:Loss @ epoch 509: 0.06626935303211212 +INFO:lightwood-2523:Loss @ epoch 510: 0.06613652408123016 +INFO:lightwood-2523:Loss @ epoch 511: 0.06672576069831848 +INFO:lightwood-2523:Loss @ epoch 512: 0.0666651502251625 +INFO:lightwood-2523:Loss @ epoch 513: 0.06582488119602203 +INFO:lightwood-2523:Loss @ epoch 514: 0.06652247160673141 +INFO:lightwood-2523:Loss @ epoch 515: 0.06558185815811157 +INFO:lightwood-2523:Loss @ epoch 516: 0.0655498206615448 +INFO:lightwood-2523:Loss @ epoch 517: 0.06624851375818253 +INFO:lightwood-2523:Loss @ epoch 518: 0.06601088494062424 +INFO:lightwood-2523:Loss @ epoch 519: 0.06545697897672653 +INFO:lightwood-2523:Loss @ epoch 520: 0.0659414529800415 +INFO:lightwood-2523:Loss @ epoch 521: 0.06516807526350021 +INFO:lightwood-2523:Loss @ epoch 522: 0.06501934677362442 +INFO:lightwood-2523:Loss @ epoch 523: 0.06574487686157227 +INFO:lightwood-2523:Loss @ epoch 524: 0.06553597748279572 +INFO:lightwood-2523:Loss @ epoch 525: 0.06504649668931961 +INFO:lightwood-2523:Loss @ epoch 526: 0.06540416181087494 +INFO:lightwood-2523:Loss @ epoch 527: 0.06479271501302719 +INFO:lightwood-2523:Loss @ epoch 528: 0.06469936668872833 +INFO:lightwood-2523:Loss @ epoch 529: 0.0654490739107132 +INFO:lightwood-2523:Loss @ epoch 530: 0.06509881466627121 +INFO:lightwood-2523:Loss @ epoch 531: 0.06460769474506378 +INFO:lightwood-2523:Loss @ epoch 532: 0.06506450474262238 +INFO:lightwood-2523:Loss @ epoch 533: 0.06425388902425766 +INFO:lightwood-2523:Loss @ epoch 534: 0.06419297307729721 +INFO:lightwood-2523:Loss @ epoch 535: 0.06507144123315811 +INFO:lightwood-2523:Loss @ epoch 536: 0.06475593149662018 +INFO:lightwood-2523:Loss @ epoch 537: 0.0640476867556572 +INFO:lightwood-2523:Loss @ epoch 538: 0.06452148407697678 +INFO:lightwood-2523:Loss @ epoch 539: 0.063988097012043 +INFO:lightwood-2523:Loss @ epoch 540: 0.06390102207660675 +INFO:lightwood-2523:Loss @ epoch 541: 0.06427431106567383 +INFO:lightwood-2523:Loss @ epoch 542: 0.06461699306964874 +INFO:lightwood-2523:Loss @ epoch 543: 0.06366197764873505 +INFO:lightwood-2523:Loss @ epoch 544: 0.06439769268035889 +INFO:lightwood-2523:Loss @ epoch 545: 0.06354749947786331 +INFO:lightwood-2523:Loss @ epoch 546: 0.06346575170755386 +INFO:lightwood-2523:Loss @ epoch 547: 0.06415951251983643 +INFO:lightwood-2523:Loss @ epoch 548: 0.06416907906532288 +INFO:lightwood-2523:Loss @ epoch 549: 0.06350232660770416 +INFO:lightwood-2523:Loss @ epoch 1: 0.03389815576374531 +INFO:lightwood-2523:Loss @ epoch 2: 0.033698095567524435 +INFO:lightwood-2523:Loss @ epoch 3: 0.0372611828148365 +INFO:lightwood-2523:Loss @ epoch 4: 0.0382374182343483 +INFO:lightwood-2523:Loss @ epoch 5: 0.03677316829562187 +INFO:lightwood-2523:Loss @ epoch 6: 0.04194173291325569 +INFO:lightwood-2523:Loss @ epoch 7: 0.04046095162630081 +DEBUG:lightwood-2523: `fit_mixer` runtime: 4.59 seconds +INFO:lightwood-2523:Started fitting XGBoost model

@@ -1015,8 +1015,8 @@

Step 4: Final test run 
-INFO:lightwood-2634:A single GBM iteration takes 0.1 seconds
-INFO:lightwood-2634:Training XGBoost with 131 iterations given 16.483366186618806 seconds constraint
+INFO:lightwood-2523:A single GBM iteration takes 0.1 seconds
+INFO:lightwood-2523:Training XGBoost with 131 iterations given 16.483406192064287 seconds constraint

@@ -1063,45 +1063,45 @@

Step 4: Final test run 
-INFO:lightwood-2634:XGBoost mixer does not have a `partial_fit` implementation
-DEBUG:lightwood-2634: `fit_mixer` runtime: 0.05 seconds
-WARNING:dataprep_ml-2634:Exception: Unspported categorical type for regression when training mixer: <lightwood.mixer.regression.Regression object at 0x7fc16cbd3c70>
-INFO:lightwood-2634:Started fitting RandomForest model
-INFO:lightwood-2634:RandomForest based correlation of (train data): 1.0
-INFO:lightwood-2634:RandomForest based correlation of (dev data): 1.0
-DEBUG:lightwood-2634: `fit_mixer` runtime: 0.14 seconds
-INFO:dataprep_ml-2634:Ensembling the mixer
-INFO:lightwood-2634:Mixer: Neural got accuracy: 0.922
-INFO:lightwood-2634:Mixer: XGBoostMixer got accuracy: 1.0
-INFO:lightwood-2634:Mixer: RandomForest got accuracy: 1.0
-INFO:lightwood-2634:Picked best mixer: RandomForest
-DEBUG:lightwood-2634: `fit` runtime: 4.83 seconds
-INFO:dataprep_ml-2634:[Learn phase 7/8] - Ensemble analysis
-INFO:dataprep_ml-2634:Analyzing the ensemble of mixers
-INFO:lightwood-2634:The block ICP is now running its analyze() method
+INFO:lightwood-2523:XGBoost mixer does not have a `partial_fit` implementation
+DEBUG:lightwood-2523: `fit_mixer` runtime: 0.05 seconds
+WARNING:dataprep_ml-2523:Exception: Unspported categorical type for regression when training mixer: <lightwood.mixer.regression.Regression object at 0x7ff43a6c0a90>
+INFO:lightwood-2523:Started fitting RandomForest model
+INFO:lightwood-2523:RandomForest based correlation of (train data): 1.0
+INFO:lightwood-2523:RandomForest based correlation of (dev data): 1.0
+DEBUG:lightwood-2523: `fit_mixer` runtime: 0.13 seconds
+INFO:dataprep_ml-2523:Ensembling the mixer
+INFO:lightwood-2523:Mixer: Neural got accuracy: 0.922
+INFO:lightwood-2523:Mixer: XGBoostMixer got accuracy: 1.0
+INFO:lightwood-2523:Mixer: RandomForest got accuracy: 1.0
+INFO:lightwood-2523:Picked best mixer: RandomForest
+DEBUG:lightwood-2523: `fit` runtime: 4.81 seconds
+INFO:dataprep_ml-2523:[Learn phase 7/8] - Ensemble analysis
+INFO:dataprep_ml-2523:Analyzing the ensemble of mixers
+INFO:lightwood-2523:The block ICP is now running its analyze() method
 /opt/hostedtoolcache/Python/3.9.19/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-2634:The block ConfStats is now running its analyze() method
-INFO:lightwood-2634:The block AccStats is now running its analyze() method
-INFO:lightwood-2634:The block PermutationFeatureImportance is now running its analyze() method
-INFO:lightwood-2634:[PFI] Using a random sample (1000 rows out of 22).
-INFO:lightwood-2634:[PFI] Set to consider first 10 columns out of 6: ['Population', 'Area (sq. mi.)', 'Pop. Density ', 'GDP ($ per capita)', 'Literacy (%)', 'Infant mortality '].
-INFO:lightwood-2634:The block ModelCorrelationHeatmap is now running its analyze() method
-DEBUG:lightwood-2634: `analyze_ensemble` runtime: 0.2 seconds
-INFO:dataprep_ml-2634:[Learn phase 8/8] - Adjustment on validation requested
-INFO:dataprep_ml-2634:Updating the mixers
+INFO:lightwood-2523:The block ConfStats is now running its analyze() method
+INFO:lightwood-2523:The block AccStats is now running its analyze() method
+INFO:lightwood-2523:The block PermutationFeatureImportance is now running its analyze() method
+INFO:lightwood-2523:[PFI] Using a random sample (1000 rows out of 22).
+INFO:lightwood-2523:[PFI] Set to consider first 10 columns out of 6: ['Population', 'Area (sq. mi.)', 'Pop. Density ', 'GDP ($ per capita)', 'Literacy (%)', 'Infant mortality '].
+INFO:lightwood-2523:The block ModelCorrelationHeatmap is now running its analyze() method
+DEBUG:lightwood-2523: `analyze_ensemble` runtime: 0.21 seconds
+INFO:dataprep_ml-2523:[Learn phase 8/8] - Adjustment on validation requested
+INFO:dataprep_ml-2523:Updating the mixers
 /opt/hostedtoolcache/Python/3.9.19/x64/lib/python3.9/site-packages/torch/amp/grad_scaler.py:131: UserWarning: torch.cuda.amp.GradScaler is enabled, but CUDA is not available.  Disabling.
   warnings.warn(
-INFO:lightwood-2634:Loss @ epoch 1: 0.033697554686417185
-INFO:lightwood-2634:Loss @ epoch 2: 0.033981192080924906
-INFO:lightwood-2634:Loss @ epoch 3: 0.037426896315688886
-INFO:lightwood-2634:Loss @ epoch 4: 0.04428015494098266
-INFO:lightwood-2634:Loss @ epoch 5: 0.061086510928968586
-INFO:lightwood-2634:Loss @ epoch 6: 0.03466159128583968
-INFO:lightwood-2634:Loss @ epoch 7: 0.03769115870818496
-INFO:lightwood-2634:XGBoost mixer does not have a `partial_fit` implementation
-DEBUG:lightwood-2634: `adjust` runtime: 0.06 seconds
-DEBUG:lightwood-2634: `learn` runtime: 5.2 seconds
+INFO:lightwood-2523:Loss @ epoch 1: 0.033697554686417185
+INFO:lightwood-2523:Loss @ epoch 2: 0.033981192080924906
+INFO:lightwood-2523:Loss @ epoch 3: 0.037426896315688886
+INFO:lightwood-2523:Loss @ epoch 4: 0.04428015494098266
+INFO:lightwood-2523:Loss @ epoch 5: 0.061086510928968586
+INFO:lightwood-2523:Loss @ epoch 6: 0.03466159128583968
+INFO:lightwood-2523:Loss @ epoch 7: 0.03769115870818496
+INFO:lightwood-2523:XGBoost mixer does not have a `partial_fit` implementation
+DEBUG:lightwood-2523: `adjust` runtime: 0.06 seconds
+DEBUG:lightwood-2523: `learn` runtime: 5.19 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 2a591918b..51b9eca01 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": "2024-05-15T12:31:18.040567Z", - "iopub.status.busy": "2024-05-15T12:31:18.040367Z", - "iopub.status.idle": "2024-05-15T12:31:20.849208Z", - "shell.execute_reply": "2024-05-15T12:31:20.848432Z" + "iopub.execute_input": "2024-05-15T12:38:24.174629Z", + "iopub.status.busy": "2024-05-15T12:38:24.174420Z", + "iopub.status.idle": "2024-05-15T12:38:27.049213Z", + "shell.execute_reply": "2024-05-15T12:38:27.048475Z" } }, "outputs": [ @@ -41,14 +41,14 @@ "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:lightwood-2634:No torchvision detected, image helpers not supported.\u001b[0m\n" + "\u001b[32mINFO:lightwood-2523:No torchvision detected, image helpers not supported.\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:lightwood-2634:No torchvision/pillow detected, image encoder not supported\u001b[0m\n" + "\u001b[32mINFO:lightwood-2523:No torchvision/pillow detected, image encoder not supported\u001b[0m\n" ] }, { @@ -87,10 +87,10 @@ "execution_count": 2, "metadata": { "execution": { - "iopub.execute_input": "2024-05-15T12:31:20.852043Z", - "iopub.status.busy": "2024-05-15T12:31:20.851597Z", - "iopub.status.idle": "2024-05-15T12:31:20.862285Z", - "shell.execute_reply": "2024-05-15T12:31:20.861787Z" + "iopub.execute_input": "2024-05-15T12:38:27.052314Z", + "iopub.status.busy": "2024-05-15T12:38:27.051670Z", + "iopub.status.idle": "2024-05-15T12:38:27.062358Z", + "shell.execute_reply": "2024-05-15T12:38:27.061815Z" } }, "outputs": [], @@ -116,17 +116,17 @@ "execution_count": 3, "metadata": { "execution": { - "iopub.execute_input": "2024-05-15T12:31:20.864582Z", - "iopub.status.busy": "2024-05-15T12:31:20.864386Z", - "iopub.status.idle": "2024-05-15T12:31:20.868451Z", - "shell.execute_reply": "2024-05-15T12:31:20.867794Z" + "iopub.execute_input": "2024-05-15T12:38:27.064834Z", + "iopub.status.busy": "2024-05-15T12:38:27.064464Z", + "iopub.status.idle": "2024-05-15T12:38:27.068750Z", + "shell.execute_reply": "2024-05-15T12:38:27.068172Z" } }, "outputs": [ { "data": { "text/plain": [ - "<__main__.ModelCorrelationHeatmap at 0x7fc16da6f8e0>" + "<__main__.ModelCorrelationHeatmap at 0x7ff43c2f7fd0>" ] }, "execution_count": 3, @@ -152,10 +152,10 @@ "execution_count": 4, "metadata": { "execution": { - "iopub.execute_input": "2024-05-15T12:31:20.870934Z", - "iopub.status.busy": "2024-05-15T12:31:20.870625Z", - "iopub.status.idle": "2024-05-15T12:31:20.874327Z", - "shell.execute_reply": "2024-05-15T12:31:20.873715Z" + "iopub.execute_input": "2024-05-15T12:38:27.071262Z", + "iopub.status.busy": "2024-05-15T12:38:27.070912Z", + "iopub.status.idle": "2024-05-15T12:38:27.074715Z", + "shell.execute_reply": "2024-05-15T12:38:27.074047Z" } }, "outputs": [], @@ -184,10 +184,10 @@ "execution_count": 5, "metadata": { "execution": { - "iopub.execute_input": "2024-05-15T12:31:20.876862Z", - "iopub.status.busy": "2024-05-15T12:31:20.876391Z", - "iopub.status.idle": "2024-05-15T12:31:20.880194Z", - "shell.execute_reply": "2024-05-15T12:31:20.879585Z" + "iopub.execute_input": "2024-05-15T12:38:27.077209Z", + "iopub.status.busy": "2024-05-15T12:38:27.076853Z", + "iopub.status.idle": "2024-05-15T12:38:27.080672Z", + "shell.execute_reply": "2024-05-15T12:38:27.080021Z" } }, "outputs": [], @@ -230,10 +230,10 @@ "execution_count": 6, "metadata": { "execution": { - "iopub.execute_input": "2024-05-15T12:31:20.882736Z", - "iopub.status.busy": "2024-05-15T12:31:20.882391Z", - "iopub.status.idle": "2024-05-15T12:31:20.886907Z", - "shell.execute_reply": "2024-05-15T12:31:20.886269Z" + "iopub.execute_input": "2024-05-15T12:38:27.083193Z", + "iopub.status.busy": "2024-05-15T12:38:27.082821Z", + "iopub.status.idle": "2024-05-15T12:38:27.087268Z", + "shell.execute_reply": "2024-05-15T12:38:27.086667Z" } }, "outputs": [ @@ -327,10 +327,10 @@ "execution_count": 7, "metadata": { "execution": { - "iopub.execute_input": "2024-05-15T12:31:20.889827Z", - "iopub.status.busy": "2024-05-15T12:31:20.889272Z", - "iopub.status.idle": "2024-05-15T12:31:21.057701Z", - "shell.execute_reply": "2024-05-15T12:31:21.057163Z" + "iopub.execute_input": "2024-05-15T12:38:27.089762Z", + "iopub.status.busy": "2024-05-15T12:38:27.089581Z", + "iopub.status.idle": "2024-05-15T12:38:27.268872Z", + "shell.execute_reply": "2024-05-15T12:38:27.268207Z" } }, "outputs": [ @@ -338,126 +338,126 @@ "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:type_infer-2634:Analyzing a sample of 222\u001b[0m\n" + "\u001b[32mINFO:type_infer-2523:Analyzing a sample of 222\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:type_infer-2634:from a total population of 225, this is equivalent to 98.7% of your data.\u001b[0m\n" + "\u001b[32mINFO:type_infer-2523: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-2634:Infering type for: Population\u001b[0m\n" + "\u001b[32mINFO:type_infer-2523:Infering type for: Population\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:type_infer-2634:Column Population has data type integer\u001b[0m\n" + "\u001b[32mINFO:type_infer-2523:Column Population has data type integer\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:type_infer-2634:Infering type for: Area (sq. mi.)\u001b[0m\n" + "\u001b[32mINFO:type_infer-2523:Infering type for: Area (sq. mi.)\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:type_infer-2634:Column Area (sq. mi.) has data type integer\u001b[0m\n" + "\u001b[32mINFO:type_infer-2523:Column Area (sq. mi.) has data type integer\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:type_infer-2634:Infering type for: Pop. Density \u001b[0m\n" + "\u001b[32mINFO:type_infer-2523:Infering type for: Pop. Density \u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:type_infer-2634:Column Pop. Density has data type float\u001b[0m\n" + "\u001b[32mINFO:type_infer-2523:Column Pop. Density has data type float\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:type_infer-2634:Infering type for: GDP ($ per capita)\u001b[0m\n" + "\u001b[32mINFO:type_infer-2523:Infering type for: GDP ($ per capita)\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:type_infer-2634:Column GDP ($ per capita) has data type integer\u001b[0m\n" + "\u001b[32mINFO:type_infer-2523:Column GDP ($ per capita) has data type integer\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:type_infer-2634:Infering type for: Literacy (%)\u001b[0m\n" + "\u001b[32mINFO:type_infer-2523:Infering type for: Literacy (%)\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:type_infer-2634:Column Literacy (%) has data type float\u001b[0m\n" + "\u001b[32mINFO:type_infer-2523:Column Literacy (%) has data type float\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:type_infer-2634:Infering type for: Infant mortality \u001b[0m\n" + "\u001b[32mINFO:type_infer-2523:Infering type for: Infant mortality \u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:type_infer-2634:Column Infant mortality has data type float\u001b[0m\n" + "\u001b[32mINFO:type_infer-2523:Column Infant mortality has data type float\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:type_infer-2634:Infering type for: Development Index\u001b[0m\n" + "\u001b[32mINFO:type_infer-2523:Infering type for: Development Index\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:type_infer-2634:Column Development Index has data type categorical\u001b[0m\n" + "\u001b[32mINFO:type_infer-2523:Column Development Index has data type categorical\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:dataprep_ml-2634:Starting statistical analysis\u001b[0m\n" + "\u001b[32mINFO:dataprep_ml-2523:Starting statistical analysis\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:dataprep_ml-2634:Finished statistical analysis\u001b[0m\n" + "\u001b[32mINFO:dataprep_ml-2523:Finished statistical analysis\u001b[0m\n" ] } ], @@ -498,10 +498,10 @@ "execution_count": 8, "metadata": { "execution": { - "iopub.execute_input": "2024-05-15T12:31:21.060354Z", - "iopub.status.busy": "2024-05-15T12:31:21.059889Z", - "iopub.status.idle": "2024-05-15T12:31:21.064194Z", - "shell.execute_reply": "2024-05-15T12:31:21.063539Z" + "iopub.execute_input": "2024-05-15T12:38:27.271840Z", + "iopub.status.busy": "2024-05-15T12:38:27.271355Z", + "iopub.status.idle": "2024-05-15T12:38:27.275829Z", + "shell.execute_reply": "2024-05-15T12:38:27.275222Z" } }, "outputs": [ @@ -532,10 +532,10 @@ "execution_count": 9, "metadata": { "execution": { - "iopub.execute_input": "2024-05-15T12:31:21.066779Z", - "iopub.status.busy": "2024-05-15T12:31:21.066406Z", - "iopub.status.idle": "2024-05-15T12:31:26.612292Z", - "shell.execute_reply": "2024-05-15T12:31:26.611639Z" + "iopub.execute_input": "2024-05-15T12:38:27.278438Z", + "iopub.status.busy": "2024-05-15T12:38:27.277985Z", + "iopub.status.idle": "2024-05-15T12:38:32.815207Z", + "shell.execute_reply": "2024-05-15T12:38:32.814580Z" }, "scrolled": false }, @@ -544,182 +544,182 @@ "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:dataprep_ml-2634:[Learn phase 1/8] - Statistical analysis\u001b[0m\n" + "\u001b[32mINFO:dataprep_ml-2523:[Learn phase 1/8] - Statistical analysis\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:dataprep_ml-2634:Starting statistical analysis\u001b[0m\n" + "\u001b[32mINFO:dataprep_ml-2523:Starting statistical analysis\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:dataprep_ml-2634:Finished statistical analysis\u001b[0m\n" + "\u001b[32mINFO:dataprep_ml-2523:Finished statistical analysis\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[37mDEBUG:lightwood-2634: `analyze_data` runtime: 0.02 seconds\u001b[0m\n" + "\u001b[37mDEBUG:lightwood-2523: `analyze_data` runtime: 0.02 seconds\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:dataprep_ml-2634:[Learn phase 2/8] - Data preprocessing\u001b[0m\n" + "\u001b[32mINFO:dataprep_ml-2523:[Learn phase 2/8] - Data preprocessing\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:dataprep_ml-2634:Cleaning the data\u001b[0m\n" + "\u001b[32mINFO:dataprep_ml-2523:Cleaning the data\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[37mDEBUG:lightwood-2634: `preprocess` runtime: 0.01 seconds\u001b[0m\n" + "\u001b[37mDEBUG:lightwood-2523: `preprocess` runtime: 0.01 seconds\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:dataprep_ml-2634:[Learn phase 3/8] - Data splitting\u001b[0m\n" + "\u001b[32mINFO:dataprep_ml-2523:[Learn phase 3/8] - Data splitting\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:dataprep_ml-2634:Splitting the data into train/test\u001b[0m\n" + "\u001b[32mINFO:dataprep_ml-2523:Splitting the data into train/test\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[37mDEBUG:lightwood-2634: `split` runtime: 0.01 seconds\u001b[0m\n" + "\u001b[37mDEBUG:lightwood-2523: `split` runtime: 0.01 seconds\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:dataprep_ml-2634:[Learn phase 4/8] - Preparing encoders\u001b[0m\n" + "\u001b[32mINFO:dataprep_ml-2523:[Learn phase 4/8] - Preparing encoders\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[37mDEBUG:dataprep_ml-2634:Preparing sequentially...\u001b[0m\n" + "\u001b[37mDEBUG:dataprep_ml-2523:Preparing sequentially...\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[37mDEBUG:dataprep_ml-2634:Preparing encoder for Population...\u001b[0m\n" + "\u001b[37mDEBUG:dataprep_ml-2523:Preparing encoder for Population...\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[37mDEBUG:dataprep_ml-2634:Preparing encoder for Area (sq. mi.)...\u001b[0m\n" + "\u001b[37mDEBUG:dataprep_ml-2523:Preparing encoder for Area (sq. mi.)...\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[37mDEBUG:dataprep_ml-2634:Preparing encoder for Pop. Density ...\u001b[0m\n" + "\u001b[37mDEBUG:dataprep_ml-2523:Preparing encoder for Pop. Density ...\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[37mDEBUG:dataprep_ml-2634:Preparing encoder for GDP ($ per capita)...\u001b[0m\n" + "\u001b[37mDEBUG:dataprep_ml-2523:Preparing encoder for GDP ($ per capita)...\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[37mDEBUG:dataprep_ml-2634:Preparing encoder for Literacy (%)...\u001b[0m\n" + "\u001b[37mDEBUG:dataprep_ml-2523:Preparing encoder for Literacy (%)...\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[37mDEBUG:dataprep_ml-2634:Preparing encoder for Infant mortality ...\u001b[0m\n" + "\u001b[37mDEBUG:dataprep_ml-2523:Preparing encoder for Infant mortality ...\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[37mDEBUG:lightwood-2634:Encoding UNKNOWN categories as index 0\u001b[0m\n" + "\u001b[37mDEBUG:lightwood-2523:Encoding UNKNOWN categories as index 0\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[37mDEBUG:lightwood-2634: `prepare` runtime: 0.01 seconds\u001b[0m\n" + "\u001b[37mDEBUG:lightwood-2523: `prepare` runtime: 0.01 seconds\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:dataprep_ml-2634:[Learn phase 5/8] - Feature generation\u001b[0m\n" + "\u001b[32mINFO:dataprep_ml-2523:[Learn phase 5/8] - Feature generation\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:dataprep_ml-2634:Featurizing the data\u001b[0m\n" + "\u001b[32mINFO:dataprep_ml-2523:Featurizing the data\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[37mDEBUG:lightwood-2634: `featurize` runtime: 0.05 seconds\u001b[0m\n" + "\u001b[37mDEBUG:lightwood-2523: `featurize` runtime: 0.05 seconds\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:dataprep_ml-2634:[Learn phase 6/8] - Mixer training\u001b[0m\n" + "\u001b[32mINFO:dataprep_ml-2523:[Learn phase 6/8] - Mixer training\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:dataprep_ml-2634:Training the mixers\u001b[0m\n" + "\u001b[32mINFO:dataprep_ml-2523:Training the mixers\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[33mWARNING:lightwood-2634:XGBoost running on CPU\u001b[0m\n" + "\u001b[33mWARNING:lightwood-2523:XGBoost running on CPU\u001b[0m\n" ] }, { @@ -734,7 +734,7 @@ "name": "stdout", "output_type": "stream", "text": [ - "[12:31:21] WARNING: ../src/learner.cc:339: No visible GPU is found, setting `gpu_id` to -1\n" + "[12:38:27] WARNING: ../src/learner.cc:339: No visible GPU is found, setting `gpu_id` to -1\n" ] }, { @@ -746,3969 +746,3969 @@ "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:1578.)\n", " exp_avg_sq.mul_(beta2).addcmul_(1 - beta2, grad, grad)\n", - "\u001b[32mINFO:lightwood-2634:Loss of 18.69619858264923 with learning rate 0.0001\u001b[0m\n" + "\u001b[32mINFO:lightwood-2523:Loss of 18.69619858264923 with learning rate 0.0001\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - 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"\u001b[37mDEBUG:lightwood-2634: `fit_mixer` runtime: 4.6 seconds\u001b[0m\n" + "\u001b[37mDEBUG:lightwood-2523: `fit_mixer` runtime: 4.59 seconds\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:lightwood-2634:Started fitting XGBoost model\u001b[0m\n" + "\u001b[32mINFO:lightwood-2523:Started fitting XGBoost model\u001b[0m\n" ] }, { @@ -4722,14 +4722,14 @@ "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:lightwood-2634:A single GBM iteration takes 0.1 seconds\u001b[0m\n" + "\u001b[32mINFO:lightwood-2523:A single GBM iteration takes 0.1 seconds\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:lightwood-2634:Training XGBoost with 131 iterations given 16.483366186618806 seconds constraint\u001b[0m\n" + "\u001b[32mINFO:lightwood-2523:Training XGBoost with 131 iterations given 16.483406192064287 seconds constraint\u001b[0m\n" ] }, { @@ -4960,112 +4960,112 @@ "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:lightwood-2634:XGBoost mixer does not have a `partial_fit` implementation\u001b[0m\n" + "\u001b[32mINFO:lightwood-2523:XGBoost mixer does not have a `partial_fit` implementation\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[37mDEBUG:lightwood-2634: `fit_mixer` runtime: 0.05 seconds\u001b[0m\n" + "\u001b[37mDEBUG:lightwood-2523: `fit_mixer` runtime: 0.05 seconds\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[33mWARNING:dataprep_ml-2634:Exception: Unspported categorical type for regression when training mixer: \u001b[0m\n" + "\u001b[33mWARNING:dataprep_ml-2523:Exception: Unspported categorical type for regression when training mixer: \u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:lightwood-2634:Started fitting RandomForest model\u001b[0m\n" + "\u001b[32mINFO:lightwood-2523:Started fitting RandomForest model\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:lightwood-2634:RandomForest based correlation of (train data): 1.0\u001b[0m\n" + "\u001b[32mINFO:lightwood-2523:RandomForest based correlation of (train data): 1.0\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:lightwood-2634:RandomForest based correlation of (dev data): 1.0\u001b[0m\n" + "\u001b[32mINFO:lightwood-2523:RandomForest based correlation of (dev data): 1.0\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[37mDEBUG:lightwood-2634: `fit_mixer` runtime: 0.14 seconds\u001b[0m\n" + "\u001b[37mDEBUG:lightwood-2523: `fit_mixer` runtime: 0.13 seconds\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:dataprep_ml-2634:Ensembling the mixer\u001b[0m\n" + "\u001b[32mINFO:dataprep_ml-2523:Ensembling the mixer\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:lightwood-2634:Mixer: Neural got accuracy: 0.922\u001b[0m\n" + "\u001b[32mINFO:lightwood-2523:Mixer: Neural got accuracy: 0.922\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:lightwood-2634:Mixer: XGBoostMixer got accuracy: 1.0\u001b[0m\n" + "\u001b[32mINFO:lightwood-2523:Mixer: XGBoostMixer got accuracy: 1.0\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:lightwood-2634:Mixer: RandomForest got accuracy: 1.0\u001b[0m\n" + "\u001b[32mINFO:lightwood-2523:Mixer: RandomForest got accuracy: 1.0\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:lightwood-2634:Picked best mixer: RandomForest\u001b[0m\n" + "\u001b[32mINFO:lightwood-2523:Picked best mixer: RandomForest\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[37mDEBUG:lightwood-2634: `fit` runtime: 4.83 seconds\u001b[0m\n" + "\u001b[37mDEBUG:lightwood-2523: `fit` runtime: 4.81 seconds\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:dataprep_ml-2634:[Learn phase 7/8] - Ensemble analysis\u001b[0m\n" + "\u001b[32mINFO:dataprep_ml-2523:[Learn phase 7/8] - Ensemble analysis\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:dataprep_ml-2634:Analyzing the ensemble of mixers\u001b[0m\n" + "\u001b[32mINFO:dataprep_ml-2523:Analyzing the ensemble of mixers\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:lightwood-2634:The block ICP is now running its analyze() method\u001b[0m\n" + "\u001b[32mINFO:lightwood-2523:The block ICP is now running its analyze() method\u001b[0m\n" ] }, { @@ -5074,63 +5074,63 @@ "text": [ "/opt/hostedtoolcache/Python/3.9.19/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-2634:The block ConfStats is now running its analyze() method\u001b[0m\n" + "\u001b[32mINFO:lightwood-2523:The block ConfStats is now running its analyze() method\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:lightwood-2634:The block AccStats is now running its analyze() method\u001b[0m\n" + "\u001b[32mINFO:lightwood-2523:The block AccStats is now running its analyze() method\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:lightwood-2634:The block PermutationFeatureImportance is now running its analyze() method\u001b[0m\n" + "\u001b[32mINFO:lightwood-2523:The block PermutationFeatureImportance is now running its analyze() method\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:lightwood-2634:[PFI] Using a random sample (1000 rows out of 22).\u001b[0m\n" + "\u001b[32mINFO:lightwood-2523:[PFI] Using a random sample (1000 rows out of 22).\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:lightwood-2634:[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-2523:[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-2634:The block ModelCorrelationHeatmap is now running its analyze() method\u001b[0m\n" + "\u001b[32mINFO:lightwood-2523:The block ModelCorrelationHeatmap is now running its analyze() method\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[37mDEBUG:lightwood-2634: `analyze_ensemble` runtime: 0.2 seconds\u001b[0m\n" + "\u001b[37mDEBUG:lightwood-2523: `analyze_ensemble` runtime: 0.21 seconds\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:dataprep_ml-2634:[Learn phase 8/8] - Adjustment on validation requested\u001b[0m\n" + "\u001b[32mINFO:dataprep_ml-2523:[Learn phase 8/8] - Adjustment on validation requested\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:dataprep_ml-2634:Updating the mixers\u001b[0m\n" + "\u001b[32mINFO:dataprep_ml-2523:Updating the mixers\u001b[0m\n" ] }, { @@ -5145,70 +5145,70 @@ "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:lightwood-2634:Loss @ epoch 1: 0.033697554686417185\u001b[0m\n" + "\u001b[32mINFO:lightwood-2523:Loss @ epoch 1: 0.033697554686417185\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:lightwood-2634:Loss @ epoch 2: 0.033981192080924906\u001b[0m\n" + "\u001b[32mINFO:lightwood-2523:Loss @ epoch 2: 0.033981192080924906\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:lightwood-2634:Loss @ epoch 3: 0.037426896315688886\u001b[0m\n" + "\u001b[32mINFO:lightwood-2523:Loss @ epoch 3: 0.037426896315688886\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:lightwood-2634:Loss @ epoch 4: 0.04428015494098266\u001b[0m\n" + "\u001b[32mINFO:lightwood-2523:Loss @ epoch 4: 0.04428015494098266\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:lightwood-2634:Loss @ epoch 5: 0.061086510928968586\u001b[0m\n" + "\u001b[32mINFO:lightwood-2523:Loss @ epoch 5: 0.061086510928968586\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:lightwood-2634:Loss @ epoch 6: 0.03466159128583968\u001b[0m\n" + "\u001b[32mINFO:lightwood-2523:Loss @ epoch 6: 0.03466159128583968\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:lightwood-2634:Loss @ epoch 7: 0.03769115870818496\u001b[0m\n" + "\u001b[32mINFO:lightwood-2523:Loss @ epoch 7: 0.03769115870818496\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:lightwood-2634:XGBoost mixer does not have a `partial_fit` implementation\u001b[0m\n" + "\u001b[32mINFO:lightwood-2523:XGBoost mixer does not have a `partial_fit` implementation\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[37mDEBUG:lightwood-2634: `adjust` runtime: 0.06 seconds\u001b[0m\n" + "\u001b[37mDEBUG:lightwood-2523: `adjust` runtime: 0.06 seconds\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[37mDEBUG:lightwood-2634: `learn` runtime: 5.2 seconds\u001b[0m\n" + "\u001b[37mDEBUG:lightwood-2523: `learn` runtime: 5.19 seconds\u001b[0m\n" ] } ], @@ -5233,10 +5233,10 @@ "execution_count": 10, "metadata": { "execution": { - "iopub.execute_input": "2024-05-15T12:31:26.615040Z", - "iopub.status.busy": "2024-05-15T12:31:26.614829Z", - "iopub.status.idle": "2024-05-15T12:31:27.123113Z", - "shell.execute_reply": "2024-05-15T12:31:27.122477Z" + "iopub.execute_input": "2024-05-15T12:38:32.818054Z", + "iopub.status.busy": "2024-05-15T12:38:32.817644Z", + "iopub.status.idle": "2024-05-15T12:38:33.321743Z", + "shell.execute_reply": "2024-05-15T12:38:33.321104Z" } }, "outputs": [ diff --git a/tutorials/custom_mixer/custom_mixer.html b/tutorials/custom_mixer/custom_mixer.html index 7f35d5d0f..9e70d1435 100644 --- a/tutorials/custom_mixer/custom_mixer.html +++ b/tutorials/custom_mixer/custom_mixer.html @@ -210,40 +210,40 @@

Step 3: Using our mixer 
-INFO:lightwood-2525:No torchvision detected, image helpers not supported.
-INFO:lightwood-2525:No torchvision/pillow detected, image encoder not supported
-INFO:type_infer-2525:Analyzing a sample of 298
-INFO:type_infer-2525:from a total population of 303, this is equivalent to 98.3% of your data.
-INFO:type_infer-2525:Infering type for: age
-INFO:type_infer-2525:Column age has data type integer
-INFO:type_infer-2525:Infering type for: sex
-INFO:type_infer-2525:Column sex has data type binary
-INFO:type_infer-2525:Infering type for: cp
-INFO:type_infer-2525:Column cp has data type categorical
-INFO:type_infer-2525:Infering type for: trestbps
-INFO:type_infer-2525:Column trestbps has data type integer
-INFO:type_infer-2525:Infering type for: chol
-INFO:type_infer-2525:Column chol has data type integer
-INFO:type_infer-2525:Infering type for: fbs
-INFO:type_infer-2525:Column fbs has data type binary
-INFO:type_infer-2525:Infering type for: restecg
-INFO:type_infer-2525:Column restecg has data type categorical
-INFO:type_infer-2525:Infering type for: thalach
-INFO:type_infer-2525:Column thalach has data type integer
-INFO:type_infer-2525:Infering type for: exang
-INFO:type_infer-2525:Column exang has data type binary
-INFO:type_infer-2525:Infering type for: oldpeak
-INFO:type_infer-2525:Column oldpeak has data type float
-INFO:type_infer-2525:Infering type for: slope
-INFO:type_infer-2525:Column slope has data type categorical
-INFO:type_infer-2525:Infering type for: ca
-INFO:type_infer-2525:Column ca has data type categorical
-INFO:type_infer-2525:Infering type for: thal
-INFO:type_infer-2525:Column thal has data type categorical
-INFO:type_infer-2525:Infering type for: target
-INFO:type_infer-2525:Column target has data type binary
-INFO:dataprep_ml-2525:Starting statistical analysis
-INFO:dataprep_ml-2525:Finished statistical analysis
+INFO:lightwood-2414:No torchvision detected, image helpers not supported.
+INFO:lightwood-2414:No torchvision/pillow detected, image encoder not supported
+INFO:type_infer-2414:Analyzing a sample of 298
+INFO:type_infer-2414:from a total population of 303, this is equivalent to 98.3% of your data.
+INFO:type_infer-2414:Infering type for: age
+INFO:type_infer-2414:Column age has data type integer
+INFO:type_infer-2414:Infering type for: sex
+INFO:type_infer-2414:Column sex has data type binary
+INFO:type_infer-2414:Infering type for: cp
+INFO:type_infer-2414:Column cp has data type categorical
+INFO:type_infer-2414:Infering type for: trestbps
+INFO:type_infer-2414:Column trestbps has data type integer
+INFO:type_infer-2414:Infering type for: chol
+INFO:type_infer-2414:Column chol has data type integer
+INFO:type_infer-2414:Infering type for: fbs
+INFO:type_infer-2414:Column fbs has data type binary
+INFO:type_infer-2414:Infering type for: restecg
+INFO:type_infer-2414:Column restecg has data type categorical
+INFO:type_infer-2414:Infering type for: thalach
+INFO:type_infer-2414:Column thalach has data type integer
+INFO:type_infer-2414:Infering type for: exang
+INFO:type_infer-2414:Column exang has data type binary
+INFO:type_infer-2414:Infering type for: oldpeak
+INFO:type_infer-2414:Column oldpeak has data type float
+INFO:type_infer-2414:Infering type for: slope
+INFO:type_infer-2414:Column slope has data type categorical
+INFO:type_infer-2414:Infering type for: ca
+INFO:type_infer-2414:Column ca has data type categorical
+INFO:type_infer-2414:Infering type for: thal
+INFO:type_infer-2414:Column thal has data type categorical
+INFO:type_infer-2414:Infering type for: target
+INFO:type_infer-2414:Column target has data type binary
+INFO:dataprep_ml-2414:Starting statistical analysis
+INFO:dataprep_ml-2414:Finished statistical analysis
@@ -371,7 +371,7 @@

Step 3: Using our mixerStep 3: Using our mixer 
-INFO:dataprep_ml-2525:[Learn phase 1/8] - Statistical analysis
-INFO:dataprep_ml-2525:Starting statistical analysis
-INFO:dataprep_ml-2525:Finished statistical analysis
-DEBUG:lightwood-2525: `analyze_data` runtime: 0.03 seconds
-INFO:dataprep_ml-2525:[Learn phase 2/8] - Data preprocessing
-INFO:dataprep_ml-2525:Cleaning the data
-DEBUG:lightwood-2525: `preprocess` runtime: 0.01 seconds
-INFO:dataprep_ml-2525:[Learn phase 3/8] - Data splitting
-INFO:dataprep_ml-2525:Splitting the data into train/test
-DEBUG:lightwood-2525: `split` runtime: 0.01 seconds
-INFO:dataprep_ml-2525:[Learn phase 4/8] - Preparing encoders
-DEBUG:dataprep_ml-2525:Preparing sequentially...
-DEBUG:dataprep_ml-2525:Preparing encoder for age...
-DEBUG:dataprep_ml-2525:Preparing encoder for sex...
-DEBUG:dataprep_ml-2525:Preparing encoder for cp...
-DEBUG:lightwood-2525:Encoding UNKNOWN categories as index 0
-DEBUG:dataprep_ml-2525:Preparing encoder for trestbps...
-DEBUG:dataprep_ml-2525:Preparing encoder for chol...
-DEBUG:dataprep_ml-2525:Preparing encoder for fbs...
-DEBUG:dataprep_ml-2525:Preparing encoder for restecg...
-DEBUG:lightwood-2525:Encoding UNKNOWN categories as index 0
-DEBUG:dataprep_ml-2525:Preparing encoder for thalach...
-DEBUG:dataprep_ml-2525:Preparing encoder for exang...
-DEBUG:dataprep_ml-2525:Preparing encoder for oldpeak...
-DEBUG:dataprep_ml-2525:Preparing encoder for slope...
-DEBUG:lightwood-2525:Encoding UNKNOWN categories as index 0
-DEBUG:dataprep_ml-2525:Preparing encoder for ca...
-DEBUG:lightwood-2525:Encoding UNKNOWN categories as index 0
-DEBUG:dataprep_ml-2525:Preparing encoder for thal...
-DEBUG:lightwood-2525:Encoding UNKNOWN categories as index 0
-DEBUG:lightwood-2525: `prepare` runtime: 0.02 seconds
-INFO:dataprep_ml-2525:[Learn phase 5/8] - Feature generation
-INFO:dataprep_ml-2525:Featurizing the data
-DEBUG:lightwood-2525: `featurize` runtime: 0.1 seconds
-INFO:dataprep_ml-2525:[Learn phase 6/8] - Mixer training
-INFO:dataprep_ml-2525:Training the mixers
-DEBUG:lightwood-2525: `fit_mixer` runtime: 0.12 seconds
-INFO:dataprep_ml-2525:Ensembling the mixer
-INFO:lightwood-2525:Mixer: RandomForestMixer got accuracy: 0.798
-INFO:lightwood-2525:Picked best mixer: RandomForestMixer
-DEBUG:lightwood-2525: `fit` runtime: 0.13 seconds
-INFO:dataprep_ml-2525:[Learn phase 7/8] - Ensemble analysis
-INFO:dataprep_ml-2525:Analyzing the ensemble of mixers
-INFO:lightwood-2525:The block ICP is now running its analyze() method
+INFO:dataprep_ml-2414:[Learn phase 1/8] - Statistical analysis
+INFO:dataprep_ml-2414:Starting statistical analysis
+INFO:dataprep_ml-2414:Finished statistical analysis
+DEBUG:lightwood-2414: `analyze_data` runtime: 0.03 seconds
+INFO:dataprep_ml-2414:[Learn phase 2/8] - Data preprocessing
+INFO:dataprep_ml-2414:Cleaning the data
+DEBUG:lightwood-2414: `preprocess` runtime: 0.01 seconds
+INFO:dataprep_ml-2414:[Learn phase 3/8] - Data splitting
+INFO:dataprep_ml-2414:Splitting the data into train/test
+DEBUG:lightwood-2414: `split` runtime: 0.01 seconds
+INFO:dataprep_ml-2414:[Learn phase 4/8] - Preparing encoders
+DEBUG:dataprep_ml-2414:Preparing sequentially...
+DEBUG:dataprep_ml-2414:Preparing encoder for age...
+DEBUG:dataprep_ml-2414:Preparing encoder for sex...
+DEBUG:dataprep_ml-2414:Preparing encoder for cp...
+DEBUG:lightwood-2414:Encoding UNKNOWN categories as index 0
+DEBUG:dataprep_ml-2414:Preparing encoder for trestbps...
+DEBUG:dataprep_ml-2414:Preparing encoder for chol...
+DEBUG:dataprep_ml-2414:Preparing encoder for fbs...
+DEBUG:dataprep_ml-2414:Preparing encoder for restecg...
+DEBUG:lightwood-2414:Encoding UNKNOWN categories as index 0
+DEBUG:dataprep_ml-2414:Preparing encoder for thalach...
+DEBUG:dataprep_ml-2414:Preparing encoder for exang...
+DEBUG:dataprep_ml-2414:Preparing encoder for oldpeak...
+DEBUG:dataprep_ml-2414:Preparing encoder for slope...
+DEBUG:lightwood-2414:Encoding UNKNOWN categories as index 0
+DEBUG:dataprep_ml-2414:Preparing encoder for ca...
+DEBUG:lightwood-2414:Encoding UNKNOWN categories as index 0
+DEBUG:dataprep_ml-2414:Preparing encoder for thal...
+DEBUG:lightwood-2414:Encoding UNKNOWN categories as index 0
+DEBUG:lightwood-2414: `prepare` runtime: 0.02 seconds
+INFO:dataprep_ml-2414:[Learn phase 5/8] - Feature generation
+INFO:dataprep_ml-2414:Featurizing the data
+DEBUG:lightwood-2414: `featurize` runtime: 0.09 seconds
+INFO:dataprep_ml-2414:[Learn phase 6/8] - Mixer training
+INFO:dataprep_ml-2414:Training the mixers
+DEBUG:lightwood-2414: `fit_mixer` runtime: 0.12 seconds
+INFO:dataprep_ml-2414:Ensembling the mixer
+INFO:lightwood-2414:Mixer: RandomForestMixer got accuracy: 0.798
+INFO:lightwood-2414:Picked best mixer: RandomForestMixer
+DEBUG:lightwood-2414: `fit` runtime: 0.13 seconds
+INFO:dataprep_ml-2414:[Learn phase 7/8] - Ensemble analysis
+INFO:dataprep_ml-2414:Analyzing the ensemble of mixers
+INFO:lightwood-2414:The block ICP is now running its analyze() method
 /opt/hostedtoolcache/Python/3.9.19/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-2525:The block ConfStats is now running its analyze() method
-INFO:lightwood-2525:The block AccStats is now running its analyze() method
-INFO:lightwood-2525:The block PermutationFeatureImportance is now running its analyze() method
-INFO:lightwood-2525:[PFI] Using a random sample (1000 rows out of 31).
-INFO:lightwood-2525:[PFI] Set to consider first 10 columns out of 10: ['age', 'sex', 'cp', 'trestbps', 'chol', 'fbs', 'restecg', 'thalach', 'exang', 'oldpeak'].
+INFO:lightwood-2414:The block ConfStats is now running its analyze() method
+INFO:lightwood-2414:The block AccStats is now running its analyze() method
+INFO:lightwood-2414:The block PermutationFeatureImportance is now running its analyze() method
+INFO:lightwood-2414:[PFI] Using a random sample (1000 rows out of 31).
+INFO:lightwood-2414:[PFI] Set to consider first 10 columns out of 10: ['age', 'sex', 'cp', 'trestbps', 'chol', 'fbs', 'restecg', 'thalach', 'exang', 'oldpeak'].
 /opt/hostedtoolcache/Python/3.9.19/x64/lib/python3.9/site-packages/sklearn/metrics/_classification.py:2399: UserWarning: y_pred contains classes not in y_true
   warnings.warn("y_pred contains classes not in y_true")
 /opt/hostedtoolcache/Python/3.9.19/x64/lib/python3.9/site-packages/sklearn/metrics/_classification.py:2399: UserWarning: y_pred contains classes not in y_true
   warnings.warn("y_pred contains classes not in y_true")
-DEBUG:lightwood-2525: `analyze_ensemble` runtime: 0.27 seconds
-INFO:dataprep_ml-2525:[Learn phase 8/8] - Adjustment on validation requested
-INFO:dataprep_ml-2525:Updating the mixers
-DEBUG:lightwood-2525: `adjust` runtime: 0.04 seconds
-DEBUG:lightwood-2525: `learn` runtime: 0.62 seconds
+DEBUG:lightwood-2414: `analyze_ensemble` runtime: 0.27 seconds
+INFO:dataprep_ml-2414:[Learn phase 8/8] - Adjustment on validation requested
+INFO:dataprep_ml-2414:Updating the mixers
+DEBUG:lightwood-2414: `adjust` runtime: 0.04 seconds
+DEBUG:lightwood-2414: `learn` runtime: 0.61 seconds

Finally, we can use the trained predictor to make some predictions, or save it to a pickle for later use

@@ -531,11 +531,11 @@

Step 3: Using our mixer 
-INFO:dataprep_ml-2525:[Predict phase 1/4] - Data preprocessing
-INFO:dataprep_ml-2525:Cleaning the data
-DEBUG:lightwood-2525: `preprocess` runtime: 0.01 seconds
-INFO:dataprep_ml-2525:[Predict phase 2/4] - Feature generation
-INFO:dataprep_ml-2525:Featurizing the data
+INFO:dataprep_ml-2414:[Predict phase 1/4] - Data preprocessing
+INFO:dataprep_ml-2414:Cleaning the data
+DEBUG:lightwood-2414: `preprocess` runtime: 0.01 seconds
+INFO:dataprep_ml-2414:[Predict phase 2/4] - Feature generation
+INFO:dataprep_ml-2414:Featurizing the data
 /opt/hostedtoolcache/Python/3.9.19/x64/lib/python3.9/site-packages/numpy/lib/function_base.py:2455: RuntimeWarning: invalid value encountered in _none_fn (vectorized)
   outputs = ufunc(*inputs)
 /opt/hostedtoolcache/Python/3.9.19/x64/lib/python3.9/site-packages/numpy/lib/function_base.py:2455: RuntimeWarning: invalid value encountered in _none_fn (vectorized)
@@ -552,19 +552,19 @@ 
Step 3: Using our mixerDEBUG:lightwood-2525: `featurize` runtime: 0.02 seconds
-INFO:dataprep_ml-2525:[Predict phase 3/4] - Calling ensemble
-DEBUG:lightwood-2525: `_timed_call` runtime: 0.01 seconds
-INFO:dataprep_ml-2525:[Predict phase 4/4] - Analyzing output
-INFO:lightwood-2525:The block ICP is now running its explain() method
-INFO:lightwood-2525:The block ConfStats is now running its explain() method
-INFO:lightwood-2525:ConfStats.explain() has not been implemented, no modifications will be done to the data insights.
-INFO:lightwood-2525:The block AccStats is now running its explain() method
-INFO:lightwood-2525:AccStats.explain() has not been implemented, no modifications will be done to the data insights.
-INFO:lightwood-2525:The block PermutationFeatureImportance is now running its explain() method
-INFO:lightwood-2525:PermutationFeatureImportance.explain() has not been implemented, no modifications will be done to the data insights.
-DEBUG:lightwood-2525: `explain` runtime: 0.01 seconds
-DEBUG:lightwood-2525: `predict` runtime: 0.06 seconds
+DEBUG:lightwood-2414: `featurize` runtime: 0.02 seconds
+INFO:dataprep_ml-2414:[Predict phase 3/4] - Calling ensemble
+DEBUG:lightwood-2414: `_timed_call` runtime: 0.01 seconds
+INFO:dataprep_ml-2414:[Predict phase 4/4] - Analyzing output
+INFO:lightwood-2414:The block ICP is now running its explain() method
+INFO:lightwood-2414:The block ConfStats is now running its explain() method
+INFO:lightwood-2414:ConfStats.explain() has not been implemented, no modifications will be done to the data insights.
+INFO:lightwood-2414:The block AccStats is now running its explain() method
+INFO:lightwood-2414:AccStats.explain() has not been implemented, no modifications will be done to the data insights.
+INFO:lightwood-2414:The block PermutationFeatureImportance is now running its explain() method
+INFO:lightwood-2414:PermutationFeatureImportance.explain() has not been implemented, no modifications will be done to the data insights.
+DEBUG:lightwood-2414: `explain` runtime: 0.01 seconds
+DEBUG:lightwood-2414: `predict` runtime: 0.05 seconds
diff --git a/tutorials/custom_mixer/custom_mixer.ipynb b/tutorials/custom_mixer/custom_mixer.ipynb index 83bb2e0e1..c3dacff27 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": "2024-05-15T12:30:52.389121Z", - "iopub.status.busy": "2024-05-15T12:30:52.388896Z", - "iopub.status.idle": "2024-05-15T12:30:52.397395Z", - "shell.execute_reply": "2024-05-15T12:30:52.396710Z" + "iopub.execute_input": "2024-05-15T12:37:58.233387Z", + "iopub.status.busy": "2024-05-15T12:37:58.232873Z", + "iopub.status.idle": "2024-05-15T12:37:58.241919Z", + "shell.execute_reply": "2024-05-15T12:37:58.241302Z" } }, "outputs": [ @@ -133,10 +133,10 @@ "execution_count": 2, "metadata": { "execution": { - "iopub.execute_input": "2024-05-15T12:30:52.433000Z", - "iopub.status.busy": "2024-05-15T12:30:52.432734Z", - "iopub.status.idle": "2024-05-15T12:30:55.473584Z", - "shell.execute_reply": "2024-05-15T12:30:55.472895Z" + "iopub.execute_input": "2024-05-15T12:37:58.280767Z", + "iopub.status.busy": "2024-05-15T12:37:58.280464Z", + "iopub.status.idle": "2024-05-15T12:38:01.332205Z", + "shell.execute_reply": "2024-05-15T12:38:01.331516Z" } }, "outputs": [ @@ -144,238 +144,238 @@ "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:lightwood-2525:No torchvision detected, image helpers not supported.\u001b[0m\n" + "\u001b[32mINFO:lightwood-2414:No torchvision detected, image helpers not supported.\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:lightwood-2525:No torchvision/pillow detected, image encoder not supported\u001b[0m\n" + "\u001b[32mINFO:lightwood-2414:No torchvision/pillow detected, image encoder not supported\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:type_infer-2525:Analyzing a sample of 298\u001b[0m\n" + "\u001b[32mINFO:type_infer-2414:Analyzing a sample of 298\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:type_infer-2525:from a total population of 303, this is equivalent to 98.3% of your data.\u001b[0m\n" + "\u001b[32mINFO:type_infer-2414: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-2525:Infering type for: age\u001b[0m\n" + "\u001b[32mINFO:type_infer-2414:Infering type for: age\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:type_infer-2525:Column age has data type integer\u001b[0m\n" + "\u001b[32mINFO:type_infer-2414:Column age has data type integer\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:type_infer-2525:Infering type for: sex\u001b[0m\n" + "\u001b[32mINFO:type_infer-2414:Infering type for: sex\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:type_infer-2525:Column sex has data type binary\u001b[0m\n" + "\u001b[32mINFO:type_infer-2414:Column sex has data type binary\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:type_infer-2525:Infering type for: cp\u001b[0m\n" + "\u001b[32mINFO:type_infer-2414:Infering type for: cp\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:type_infer-2525:Column cp has data type categorical\u001b[0m\n" + "\u001b[32mINFO:type_infer-2414:Column cp has data type categorical\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:type_infer-2525:Infering type for: trestbps\u001b[0m\n" + "\u001b[32mINFO:type_infer-2414:Infering type for: trestbps\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:type_infer-2525:Column trestbps has data type integer\u001b[0m\n" + "\u001b[32mINFO:type_infer-2414:Column trestbps has data type integer\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:type_infer-2525:Infering type for: chol\u001b[0m\n" + "\u001b[32mINFO:type_infer-2414:Infering type for: chol\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:type_infer-2525:Column chol has data type integer\u001b[0m\n" + "\u001b[32mINFO:type_infer-2414:Column chol has data type integer\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:type_infer-2525:Infering type for: fbs\u001b[0m\n" + "\u001b[32mINFO:type_infer-2414:Infering type for: fbs\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:type_infer-2525:Column fbs has data type binary\u001b[0m\n" + "\u001b[32mINFO:type_infer-2414:Column fbs has data type binary\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:type_infer-2525:Infering type for: restecg\u001b[0m\n" + "\u001b[32mINFO:type_infer-2414:Infering type for: restecg\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:type_infer-2525:Column restecg has data type categorical\u001b[0m\n" + "\u001b[32mINFO:type_infer-2414:Column restecg has data type categorical\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:type_infer-2525:Infering type for: thalach\u001b[0m\n" + "\u001b[32mINFO:type_infer-2414:Infering type for: thalach\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:type_infer-2525:Column thalach has data type integer\u001b[0m\n" + "\u001b[32mINFO:type_infer-2414:Column thalach has data type integer\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:type_infer-2525:Infering type for: exang\u001b[0m\n" + "\u001b[32mINFO:type_infer-2414:Infering type for: exang\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:type_infer-2525:Column exang has data type binary\u001b[0m\n" + "\u001b[32mINFO:type_infer-2414:Column exang has data type binary\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:type_infer-2525:Infering type for: oldpeak\u001b[0m\n" + "\u001b[32mINFO:type_infer-2414:Infering type for: oldpeak\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:type_infer-2525:Column oldpeak has data type float\u001b[0m\n" + "\u001b[32mINFO:type_infer-2414:Column oldpeak has data type float\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:type_infer-2525:Infering type for: slope\u001b[0m\n" + "\u001b[32mINFO:type_infer-2414:Infering type for: slope\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:type_infer-2525:Column slope has data type categorical\u001b[0m\n" + "\u001b[32mINFO:type_infer-2414:Column slope has data type categorical\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:type_infer-2525:Infering type for: ca\u001b[0m\n" + "\u001b[32mINFO:type_infer-2414:Infering type for: ca\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:type_infer-2525:Column ca has data type categorical\u001b[0m\n" + "\u001b[32mINFO:type_infer-2414:Column ca has data type categorical\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:type_infer-2525:Infering type for: thal\u001b[0m\n" + "\u001b[32mINFO:type_infer-2414:Infering type for: thal\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:type_infer-2525:Column thal has data type categorical\u001b[0m\n" + "\u001b[32mINFO:type_infer-2414:Column thal has data type categorical\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:type_infer-2525:Infering type for: target\u001b[0m\n" + "\u001b[32mINFO:type_infer-2414:Infering type for: target\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:type_infer-2525:Column target has data type binary\u001b[0m\n" + "\u001b[32mINFO:type_infer-2414:Column target has data type binary\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:dataprep_ml-2525:Starting statistical analysis\u001b[0m\n" + "\u001b[32mINFO:dataprep_ml-2414:Starting statistical analysis\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:dataprep_ml-2525:Finished statistical analysis\u001b[0m\n" + "\u001b[32mINFO:dataprep_ml-2414: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\": 0.07092165946960449,\n", + " \"expected_additional_time\": 0.07208943367004395,\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": "2024-05-15T12:30:55.476427Z", - "iopub.status.busy": "2024-05-15T12:30:55.475939Z", - "iopub.status.idle": "2024-05-15T12:30:55.479383Z", - "shell.execute_reply": "2024-05-15T12:30:55.478798Z" + "iopub.execute_input": "2024-05-15T12:38:01.334897Z", + "iopub.status.busy": "2024-05-15T12:38:01.334489Z", + "iopub.status.idle": "2024-05-15T12:38:01.337884Z", + "shell.execute_reply": "2024-05-15T12:38:01.337281Z" } }, "outputs": [], @@ -603,10 +603,10 @@ "execution_count": 4, "metadata": { "execution": { - "iopub.execute_input": "2024-05-15T12:30:55.481884Z", - "iopub.status.busy": "2024-05-15T12:30:55.481529Z", - "iopub.status.idle": "2024-05-15T12:30:55.818438Z", - "shell.execute_reply": "2024-05-15T12:30:55.817741Z" + "iopub.execute_input": "2024-05-15T12:38:01.340400Z", + "iopub.status.busy": "2024-05-15T12:38:01.339924Z", + "iopub.status.idle": "2024-05-15T12:38:01.687957Z", + "shell.execute_reply": "2024-05-15T12:38:01.687306Z" } }, "outputs": [], @@ -622,10 +622,10 @@ "execution_count": 5, "metadata": { "execution": { - "iopub.execute_input": "2024-05-15T12:30:55.821486Z", - "iopub.status.busy": "2024-05-15T12:30:55.821229Z", - "iopub.status.idle": "2024-05-15T12:30:56.441907Z", - "shell.execute_reply": "2024-05-15T12:30:56.441224Z" + "iopub.execute_input": "2024-05-15T12:38:01.691070Z", + "iopub.status.busy": "2024-05-15T12:38:01.690625Z", + "iopub.status.idle": "2024-05-15T12:38:02.309441Z", + "shell.execute_reply": "2024-05-15T12:38:02.308885Z" } }, "outputs": [ @@ -633,308 +633,308 @@ "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:dataprep_ml-2525:[Learn phase 1/8] - Statistical analysis\u001b[0m\n" + "\u001b[32mINFO:dataprep_ml-2414:[Learn phase 1/8] - Statistical analysis\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:dataprep_ml-2525:Starting statistical analysis\u001b[0m\n" + "\u001b[32mINFO:dataprep_ml-2414:Starting statistical analysis\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:dataprep_ml-2525:Finished statistical analysis\u001b[0m\n" + "\u001b[32mINFO:dataprep_ml-2414:Finished statistical analysis\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[37mDEBUG:lightwood-2525: `analyze_data` runtime: 0.03 seconds\u001b[0m\n" + "\u001b[37mDEBUG:lightwood-2414: `analyze_data` runtime: 0.03 seconds\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:dataprep_ml-2525:[Learn phase 2/8] - Data preprocessing\u001b[0m\n" + "\u001b[32mINFO:dataprep_ml-2414:[Learn phase 2/8] - Data preprocessing\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:dataprep_ml-2525:Cleaning the data\u001b[0m\n" + "\u001b[32mINFO:dataprep_ml-2414:Cleaning the data\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[37mDEBUG:lightwood-2525: `preprocess` runtime: 0.01 seconds\u001b[0m\n" + "\u001b[37mDEBUG:lightwood-2414: `preprocess` runtime: 0.01 seconds\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:dataprep_ml-2525:[Learn phase 3/8] - Data splitting\u001b[0m\n" + "\u001b[32mINFO:dataprep_ml-2414:[Learn phase 3/8] - Data splitting\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:dataprep_ml-2525:Splitting the data into train/test\u001b[0m\n" + "\u001b[32mINFO:dataprep_ml-2414:Splitting the data into train/test\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[37mDEBUG:lightwood-2525: `split` runtime: 0.01 seconds\u001b[0m\n" + "\u001b[37mDEBUG:lightwood-2414: `split` runtime: 0.01 seconds\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:dataprep_ml-2525:[Learn phase 4/8] - Preparing encoders\u001b[0m\n" + "\u001b[32mINFO:dataprep_ml-2414:[Learn phase 4/8] - Preparing encoders\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[37mDEBUG:dataprep_ml-2525:Preparing sequentially...\u001b[0m\n" + "\u001b[37mDEBUG:dataprep_ml-2414:Preparing sequentially...\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[37mDEBUG:dataprep_ml-2525:Preparing encoder for age...\u001b[0m\n" + "\u001b[37mDEBUG:dataprep_ml-2414:Preparing encoder for age...\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[37mDEBUG:dataprep_ml-2525:Preparing encoder for sex...\u001b[0m\n" + "\u001b[37mDEBUG:dataprep_ml-2414:Preparing encoder for sex...\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[37mDEBUG:dataprep_ml-2525:Preparing encoder for cp...\u001b[0m\n" + "\u001b[37mDEBUG:dataprep_ml-2414:Preparing encoder for cp...\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[37mDEBUG:lightwood-2525:Encoding UNKNOWN categories as index 0\u001b[0m\n" + "\u001b[37mDEBUG:lightwood-2414:Encoding UNKNOWN categories as index 0\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[37mDEBUG:dataprep_ml-2525:Preparing encoder for trestbps...\u001b[0m\n" + "\u001b[37mDEBUG:dataprep_ml-2414:Preparing encoder for trestbps...\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[37mDEBUG:dataprep_ml-2525:Preparing encoder for chol...\u001b[0m\n" + "\u001b[37mDEBUG:dataprep_ml-2414:Preparing encoder for chol...\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[37mDEBUG:dataprep_ml-2525:Preparing encoder for fbs...\u001b[0m\n" + "\u001b[37mDEBUG:dataprep_ml-2414:Preparing encoder for fbs...\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[37mDEBUG:dataprep_ml-2525:Preparing encoder for restecg...\u001b[0m\n" + "\u001b[37mDEBUG:dataprep_ml-2414:Preparing encoder for restecg...\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[37mDEBUG:lightwood-2525:Encoding UNKNOWN categories as index 0\u001b[0m\n" + "\u001b[37mDEBUG:lightwood-2414:Encoding UNKNOWN categories as index 0\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[37mDEBUG:dataprep_ml-2525:Preparing encoder for thalach...\u001b[0m\n" + "\u001b[37mDEBUG:dataprep_ml-2414:Preparing encoder for thalach...\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[37mDEBUG:dataprep_ml-2525:Preparing encoder for exang...\u001b[0m\n" + "\u001b[37mDEBUG:dataprep_ml-2414:Preparing encoder for exang...\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[37mDEBUG:dataprep_ml-2525:Preparing encoder for oldpeak...\u001b[0m\n" + "\u001b[37mDEBUG:dataprep_ml-2414:Preparing encoder for oldpeak...\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[37mDEBUG:dataprep_ml-2525:Preparing encoder for slope...\u001b[0m\n" + "\u001b[37mDEBUG:dataprep_ml-2414:Preparing encoder for slope...\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[37mDEBUG:lightwood-2525:Encoding UNKNOWN categories as index 0\u001b[0m\n" + "\u001b[37mDEBUG:lightwood-2414:Encoding UNKNOWN categories as index 0\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[37mDEBUG:dataprep_ml-2525:Preparing encoder for ca...\u001b[0m\n" + "\u001b[37mDEBUG:dataprep_ml-2414:Preparing encoder for ca...\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[37mDEBUG:lightwood-2525:Encoding UNKNOWN categories as index 0\u001b[0m\n" + "\u001b[37mDEBUG:lightwood-2414:Encoding UNKNOWN categories as index 0\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - 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"\u001b[37mDEBUG:lightwood-2525: `featurize` runtime: 0.1 seconds\u001b[0m\n" + "\u001b[37mDEBUG:lightwood-2414: `featurize` runtime: 0.09 seconds\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:dataprep_ml-2525:[Learn phase 6/8] - Mixer training\u001b[0m\n" + "\u001b[32mINFO:dataprep_ml-2414:[Learn phase 6/8] - Mixer training\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:dataprep_ml-2525:Training the mixers\u001b[0m\n" + "\u001b[32mINFO:dataprep_ml-2414:Training the mixers\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[37mDEBUG:lightwood-2525: `fit_mixer` runtime: 0.12 seconds\u001b[0m\n" + "\u001b[37mDEBUG:lightwood-2414: `fit_mixer` runtime: 0.12 seconds\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:dataprep_ml-2525:Ensembling the mixer\u001b[0m\n" + "\u001b[32mINFO:dataprep_ml-2414:Ensembling the mixer\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:lightwood-2525:Mixer: RandomForestMixer got accuracy: 0.798\u001b[0m\n" + "\u001b[32mINFO:lightwood-2414:Mixer: RandomForestMixer got accuracy: 0.798\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:lightwood-2525:Picked best mixer: RandomForestMixer\u001b[0m\n" + "\u001b[32mINFO:lightwood-2414:Picked best mixer: RandomForestMixer\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[37mDEBUG:lightwood-2525: `fit` runtime: 0.13 seconds\u001b[0m\n" + "\u001b[37mDEBUG:lightwood-2414: `fit` runtime: 0.13 seconds\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:dataprep_ml-2525:[Learn phase 7/8] - Ensemble analysis\u001b[0m\n" + "\u001b[32mINFO:dataprep_ml-2414:[Learn phase 7/8] - Ensemble analysis\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:dataprep_ml-2525:Analyzing the ensemble of mixers\u001b[0m\n" + "\u001b[32mINFO:dataprep_ml-2414:Analyzing the ensemble of mixers\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:lightwood-2525:The block ICP is now running its analyze() method\u001b[0m\n" + "\u001b[32mINFO:lightwood-2414:The block ICP is now running its analyze() method\u001b[0m\n" ] }, { @@ -943,35 +943,35 @@ "text": [ "/opt/hostedtoolcache/Python/3.9.19/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-2525:The block ConfStats is now running its analyze() method\u001b[0m\n" + "\u001b[32mINFO:lightwood-2414:The block ConfStats is now running its analyze() method\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:lightwood-2525:The block AccStats is now running its analyze() method\u001b[0m\n" + "\u001b[32mINFO:lightwood-2414:The block AccStats is now running its analyze() method\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:lightwood-2525:The block PermutationFeatureImportance is now running its analyze() method\u001b[0m\n" + "\u001b[32mINFO:lightwood-2414:The block PermutationFeatureImportance is now running its analyze() method\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:lightwood-2525:[PFI] Using a random sample (1000 rows out of 31).\u001b[0m\n" + "\u001b[32mINFO:lightwood-2414:[PFI] Using a random sample (1000 rows out of 31).\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:lightwood-2525:[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-2414:[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-2525: `analyze_ensemble` runtime: 0.27 seconds\u001b[0m\n" + "\u001b[37mDEBUG:lightwood-2414: `analyze_ensemble` runtime: 0.27 seconds\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:dataprep_ml-2525:[Learn phase 8/8] - Adjustment on validation requested\u001b[0m\n" + "\u001b[32mINFO:dataprep_ml-2414:[Learn phase 8/8] - Adjustment on validation requested\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:dataprep_ml-2525:Updating the mixers\u001b[0m\n" + "\u001b[32mINFO:dataprep_ml-2414:Updating the mixers\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[37mDEBUG:lightwood-2525: `adjust` runtime: 0.04 seconds\u001b[0m\n" + "\u001b[37mDEBUG:lightwood-2414: `adjust` runtime: 0.04 seconds\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[37mDEBUG:lightwood-2525: `learn` runtime: 0.62 seconds\u001b[0m\n" + "\u001b[37mDEBUG:lightwood-2414: `learn` runtime: 0.61 seconds\u001b[0m\n" ] } ], @@ -1042,10 +1042,10 @@ "execution_count": 6, "metadata": { "execution": { - "iopub.execute_input": "2024-05-15T12:30:56.444798Z", - "iopub.status.busy": "2024-05-15T12:30:56.444349Z", - "iopub.status.idle": "2024-05-15T12:30:56.568845Z", - "shell.execute_reply": "2024-05-15T12:30:56.568168Z" + "iopub.execute_input": "2024-05-15T12:38:02.312200Z", + "iopub.status.busy": "2024-05-15T12:38:02.311838Z", + "iopub.status.idle": "2024-05-15T12:38:02.432975Z", + "shell.execute_reply": "2024-05-15T12:38:02.432362Z" } }, "outputs": [ @@ -1053,35 +1053,35 @@ "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:dataprep_ml-2525:[Predict phase 1/4] - Data preprocessing\u001b[0m\n" + "\u001b[32mINFO:dataprep_ml-2414:[Predict phase 1/4] - Data preprocessing\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:dataprep_ml-2525:Cleaning the data\u001b[0m\n" + "\u001b[32mINFO:dataprep_ml-2414:Cleaning the data\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[37mDEBUG:lightwood-2525: `preprocess` runtime: 0.01 seconds\u001b[0m\n" + "\u001b[37mDEBUG:lightwood-2414: `preprocess` runtime: 0.01 seconds\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:dataprep_ml-2525:[Predict phase 2/4] - Feature generation\u001b[0m\n" + "\u001b[32mINFO:dataprep_ml-2414:[Predict phase 2/4] - Feature generation\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:dataprep_ml-2525:Featurizing the data\u001b[0m\n" + "\u001b[32mINFO:dataprep_ml-2414:Featurizing the data\u001b[0m\n" ] }, { @@ -1104,91 +1104,91 @@ " outputs = ufunc(*inputs)\n", "/opt/hostedtoolcache/Python/3.9.19/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-2525: `featurize` runtime: 0.02 seconds\u001b[0m\n" + "\u001b[37mDEBUG:lightwood-2414: `featurize` runtime: 0.02 seconds\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:dataprep_ml-2525:[Predict phase 3/4] - Calling ensemble\u001b[0m\n" + "\u001b[32mINFO:dataprep_ml-2414:[Predict phase 3/4] - Calling ensemble\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[37mDEBUG:lightwood-2525: `_timed_call` runtime: 0.01 seconds\u001b[0m\n" + "\u001b[37mDEBUG:lightwood-2414: `_timed_call` runtime: 0.01 seconds\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:dataprep_ml-2525:[Predict phase 4/4] - Analyzing output\u001b[0m\n" + "\u001b[32mINFO:dataprep_ml-2414:[Predict phase 4/4] - Analyzing output\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:lightwood-2525:The block ICP is now running its explain() method\u001b[0m\n" + "\u001b[32mINFO:lightwood-2414:The block ICP is now running its explain() method\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:lightwood-2525:The block ConfStats is now running its explain() method\u001b[0m\n" + "\u001b[32mINFO:lightwood-2414:The block ConfStats is now running its explain() method\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:lightwood-2525:ConfStats.explain() has not been implemented, no modifications will be done to the data insights.\u001b[0m\n" + "\u001b[32mINFO:lightwood-2414: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-2525:The block AccStats is now running its explain() method\u001b[0m\n" + "\u001b[32mINFO:lightwood-2414:The block AccStats is now running its explain() method\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:lightwood-2525:AccStats.explain() has not been implemented, no modifications will be done to the data insights.\u001b[0m\n" + "\u001b[32mINFO:lightwood-2414: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-2525:The block PermutationFeatureImportance is now running its explain() method\u001b[0m\n" + "\u001b[32mINFO:lightwood-2414:The block PermutationFeatureImportance is now running its explain() method\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:lightwood-2525:PermutationFeatureImportance.explain() has not been implemented, no modifications will be done to the data insights.\u001b[0m\n" + "\u001b[32mINFO:lightwood-2414: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-2525: `explain` runtime: 0.01 seconds\u001b[0m\n" + "\u001b[37mDEBUG:lightwood-2414: `explain` runtime: 0.01 seconds\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[37mDEBUG:lightwood-2525: `predict` runtime: 0.06 seconds\u001b[0m\n" + "\u001b[37mDEBUG:lightwood-2414: `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 ddfcc5170..44c6ae9b0 100644 --- a/tutorials/custom_splitter/custom_splitter.html +++ b/tutorials/custom_splitter/custom_splitter.html @@ -129,8 +129,8 @@

Date: 2021.10.07 
-INFO:lightwood-2259:No torchvision detected, image helpers not supported.
-INFO:lightwood-2259:No torchvision/pillow detected, image encoder not supported
+INFO:lightwood-2197:No torchvision detected, image helpers not supported.
+INFO:lightwood-2197:No torchvision/pillow detected, image encoder not supported

@@ -376,73 +376,73 @@

2) Create a JSON-AI default object 
-INFO:type_infer-2259:Analyzing a sample of 18424
-INFO:type_infer-2259:from a total population of 284807, this is equivalent to 6.5% of your data.
-INFO:type_infer-2259:Using 3 processes to deduct types.
-INFO:type_infer-2259:Infering type for: Time
-INFO:type_infer-2259:Infering type for: V3
-INFO:type_infer-2259:Infering type for: V6
-INFO:type_infer-2259:Column Time has data type integer
-INFO:type_infer-2259:Infering type for: V1
-INFO:type_infer-2259:Column V3 has data type float
-INFO:type_infer-2259:Infering type for: V4
-INFO:type_infer-2259:Column V6 has data type float
-INFO:type_infer-2259:Column V4 has data type float
-INFO:type_infer-2259:Infering type for: V7
-INFO:type_infer-2259:Infering type for: V5
-INFO:type_infer-2259:Column V1 has data type float
-INFO:type_infer-2259:Infering type for: V2
-INFO:type_infer-2259:Column V5 has data type float
-INFO:type_infer-2259:Infering type for: V9
-INFO:type_infer-2259:Column V7 has data type float
-INFO:type_infer-2259:Infering type for: V8
-INFO:type_infer-2259:Column V9 has data type float
-INFO:type_infer-2259:Infering type for: V10
-INFO:type_infer-2259:Column V2 has data type float
-INFO:type_infer-2259:Infering type for: V12
-INFO:type_infer-2259:Column V8 has data type float
-INFO:type_infer-2259:Infering type for: V15
-INFO:type_infer-2259:Column V10 has data type float
-INFO:type_infer-2259:Infering type for: V11
-INFO:type_infer-2259:Column V12 has data type float
-INFO:type_infer-2259:Infering type for: V13
-INFO:type_infer-2259:Column V13 has data type float
-INFO:type_infer-2259:Infering type for: V14
-INFO:type_infer-2259:Column V15 has data type float
-INFO:type_infer-2259:Column V11 has data type float
-INFO:type_infer-2259:Infering type for: V16
-INFO:type_infer-2259:Infering type for: V18
-INFO:type_infer-2259:Column V14 has data type float
-INFO:type_infer-2259:Infering type for: V21
-INFO:type_infer-2259:Column V16 has data type float
-INFO:type_infer-2259:Infering type for: V17
-INFO:type_infer-2259:Column V18 has data type float
-INFO:type_infer-2259:Infering type for: V19
-INFO:type_infer-2259:Column V21 has data type float
-INFO:type_infer-2259:Infering type for: V22
-INFO:type_infer-2259:Column V17 has data type float
-INFO:type_infer-2259:Infering type for: V24
-INFO:type_infer-2259:Column V19 has data type float
-INFO:type_infer-2259:Infering type for: V20
-INFO:type_infer-2259:Column V22 has data type float
-INFO:type_infer-2259:Infering type for: V23
-INFO:type_infer-2259:Column V24 has data type float
-INFO:type_infer-2259:Infering type for: V25
-INFO:type_infer-2259:Column V23 has data type float
-INFO:type_infer-2259:Infering type for: V27
-INFO:type_infer-2259:Column V20 has data type float
-INFO:type_infer-2259:Infering type for: Class
-INFO:type_infer-2259:Column V25 has data type float
-INFO:type_infer-2259:Infering type for: V26
-INFO:type_infer-2259:Column Class has data type binary
-INFO:type_infer-2259:Column V27 has data type float
-INFO:type_infer-2259:Infering type for: V28
-INFO:type_infer-2259:Column V26 has data type float
-INFO:type_infer-2259:Column V28 has data type float
-INFO:type_infer-2259:Infering type for: Amount
-INFO:type_infer-2259:Column Amount has data type float
-INFO:dataprep_ml-2259:Starting statistical analysis
-INFO:dataprep_ml-2259:Finished statistical analysis
+INFO:type_infer-2197:Analyzing a sample of 18424
+INFO:type_infer-2197:from a total population of 284807, this is equivalent to 6.5% of your data.
+INFO:type_infer-2197:Using 3 processes to deduct types.
+INFO:type_infer-2197:Infering type for: Time
+INFO:type_infer-2197:Infering type for: V3
+INFO:type_infer-2197:Infering type for: V6
+INFO:type_infer-2197:Column Time has data type integer
+INFO:type_infer-2197:Infering type for: V1
+INFO:type_infer-2197:Column V3 has data type float
+INFO:type_infer-2197:Infering type for: V4
+INFO:type_infer-2197:Column V6 has data type float
+INFO:type_infer-2197:Infering type for: V7
+INFO:type_infer-2197:Column V1 has data type float
+INFO:type_infer-2197:Infering type for: V2
+INFO:type_infer-2197:Column V4 has data type float
+INFO:type_infer-2197:Infering type for: V5
+INFO:type_infer-2197:Column V7 has data type float
+INFO:type_infer-2197:Infering type for: V8
+INFO:type_infer-2197:Column V2 has data type float
+INFO:type_infer-2197:Infering type for: V9
+INFO:type_infer-2197:Column V5 has data type float
+INFO:type_infer-2197:Infering type for: V12
+INFO:type_infer-2197:Column V8 has data type float
+INFO:type_infer-2197:Infering type for: V15
+INFO:type_infer-2197:Column V9 has data type float
+INFO:type_infer-2197:Infering type for: V10
+INFO:type_infer-2197:Column V15 has data type float
+INFO:type_infer-2197:Infering type for: V16
+INFO:type_infer-2197:Column V12 has data type float
+INFO:type_infer-2197:Infering type for: V13
+INFO:type_infer-2197:Column V13 has data type float
+INFO:type_infer-2197:Infering type for: V14
+INFO:type_infer-2197:Column V10 has data type float
+INFO:type_infer-2197:Infering type for: V11
+INFO:type_infer-2197:Column V16 has data type float
+INFO:type_infer-2197:Infering type for: V17
+INFO:type_infer-2197:Column V14 has data type float
+INFO:type_infer-2197:Infering type for: V18
+INFO:type_infer-2197:Column V11 has data type float
+INFO:type_infer-2197:Infering type for: V21
+INFO:type_infer-2197:Column V17 has data type float
+INFO:type_infer-2197:Column V18 has data type float
+INFO:type_infer-2197:Infering type for: V19
+INFO:type_infer-2197:Infering type for: V24
+INFO:type_infer-2197:Column V19 has data type float
+INFO:type_infer-2197:Infering type for: V20
+INFO:type_infer-2197:Column V21 has data type float
+INFO:type_infer-2197:Infering type for: V22
+INFO:type_infer-2197:Column V24 has data type float
+INFO:type_infer-2197:Infering type for: V25
+INFO:type_infer-2197:Column V20 has data type float
+INFO:type_infer-2197:Infering type for: V27
+INFO:type_infer-2197:Column V22 has data type float
+INFO:type_infer-2197:Infering type for: V23
+INFO:type_infer-2197:Column V27 has data type float
+INFO:type_infer-2197:Infering type for: V28
+INFO:type_infer-2197:Column V25 has data type float
+INFO:type_infer-2197:Infering type for: V26
+INFO:type_infer-2197:Column V28 has data type float
+INFO:type_infer-2197:Infering type for: Amount
+INFO:type_infer-2197:Column V23 has data type float
+INFO:type_infer-2197:Infering type for: Class
+INFO:type_infer-2197:Column V26 has data type float
+INFO:type_infer-2197:Column Amount has data type float
+INFO:type_infer-2197:Column Class has data type binary
+INFO:dataprep_ml-2197:Starting statistical analysis
+INFO:dataprep_ml-2197:Finished statistical analysis

Lightwood looks at each of the many columns and indicates they are mostly float, with exception of “Class” which is binary.

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

5) Generate Python code representing your ML pipeline6) Call python to run your code and see your preprocessed outputs 
-INFO:dataprep_ml-2259:Cleaning the data
-DEBUG:lightwood-2259: `preprocess` runtime: 18.54 seconds
-INFO:dataprep_ml-2259:Splitting the data into train/test
-DEBUG:lightwood-2259: `split` runtime: 1.62 seconds
+INFO:dataprep_ml-2197:Cleaning the data
+DEBUG:lightwood-2197: `preprocess` runtime: 18.56 seconds
+INFO:dataprep_ml-2197:Splitting the data into train/test
+DEBUG:lightwood-2197: `split` runtime: 1.77 seconds
diff --git a/tutorials/custom_splitter/custom_splitter.ipynb b/tutorials/custom_splitter/custom_splitter.ipynb index 4c9029b63..cbbf9a8d5 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": "2024-05-15T12:29:04.352246Z", - "iopub.status.busy": "2024-05-15T12:29:04.351721Z", - "iopub.status.idle": "2024-05-15T12:29:13.307917Z", - "shell.execute_reply": "2024-05-15T12:29:13.307226Z" + "iopub.execute_input": "2024-05-15T12:36:11.731772Z", + "iopub.status.busy": "2024-05-15T12:36:11.731340Z", + "iopub.status.idle": "2024-05-15T12:36:18.394584Z", + "shell.execute_reply": "2024-05-15T12:36:18.393802Z" } }, "outputs": [ @@ -39,14 +39,14 @@ "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:lightwood-2259:No torchvision detected, image helpers not supported.\u001b[0m\n" + "\u001b[32mINFO:lightwood-2197:No torchvision detected, image helpers not supported.\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:lightwood-2259:No torchvision/pillow detected, image encoder not supported\u001b[0m\n" + "\u001b[32mINFO:lightwood-2197:No torchvision/pillow detected, image encoder not supported\u001b[0m\n" ] } ], @@ -87,10 +87,10 @@ "id": "foreign-orchestra", "metadata": { "execution": { - "iopub.execute_input": "2024-05-15T12:29:13.311274Z", - "iopub.status.busy": "2024-05-15T12:29:13.310750Z", - "iopub.status.idle": "2024-05-15T12:29:18.428236Z", - "shell.execute_reply": "2024-05-15T12:29:18.427494Z" + "iopub.execute_input": "2024-05-15T12:36:18.397974Z", + "iopub.status.busy": "2024-05-15T12:36:18.397631Z", + "iopub.status.idle": "2024-05-15T12:36:23.491475Z", + "shell.execute_reply": "2024-05-15T12:36:23.490796Z" } }, "outputs": [ @@ -316,10 +316,10 @@ "id": "cathedral-mills", "metadata": { "execution": { - "iopub.execute_input": "2024-05-15T12:29:18.431216Z", - "iopub.status.busy": "2024-05-15T12:29:18.430796Z", - "iopub.status.idle": "2024-05-15T12:29:18.786310Z", - "shell.execute_reply": "2024-05-15T12:29:18.785723Z" + "iopub.execute_input": "2024-05-15T12:36:23.494300Z", + "iopub.status.busy": "2024-05-15T12:36:23.494042Z", + "iopub.status.idle": "2024-05-15T12:36:23.854824Z", + "shell.execute_reply": "2024-05-15T12:36:23.854148Z" } }, "outputs": [ @@ -374,10 +374,10 @@ "id": "medieval-zambia", "metadata": { "execution": { - "iopub.execute_input": "2024-05-15T12:29:18.789121Z", - "iopub.status.busy": "2024-05-15T12:29:18.788667Z", - "iopub.status.idle": "2024-05-15T12:30:27.330359Z", - "shell.execute_reply": "2024-05-15T12:30:27.329696Z" + "iopub.execute_input": "2024-05-15T12:36:23.857705Z", + "iopub.status.busy": "2024-05-15T12:36:23.857253Z", + "iopub.status.idle": "2024-05-15T12:37:32.751150Z", + "shell.execute_reply": "2024-05-15T12:37:32.750449Z" } }, "outputs": [ @@ -385,469 +385,469 @@ "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:type_infer-2259:Analyzing a sample of 18424\u001b[0m\n" + "\u001b[32mINFO:type_infer-2197:Analyzing a sample of 18424\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:type_infer-2259:from a total population of 284807, this is equivalent to 6.5% of your data.\u001b[0m\n" + "\u001b[32mINFO:type_infer-2197: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-2259:Using 3 processes to deduct types.\u001b[0m\n" + "\u001b[32mINFO:type_infer-2197:Using 3 processes to deduct types.\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:type_infer-2259:Infering type for: Time\u001b[0m\n" + "\u001b[32mINFO:type_infer-2197:Infering type for: Time\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:type_infer-2259:Infering type for: V3\u001b[0m\n" + "\u001b[32mINFO:type_infer-2197:Infering type for: V3\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:type_infer-2259:Infering type for: V6\u001b[0m\n" + "\u001b[32mINFO:type_infer-2197:Infering type for: V6\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:type_infer-2259:Column Time has data type integer\u001b[0m\n" + "\u001b[32mINFO:type_infer-2197:Column Time has data type integer\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:type_infer-2259:Infering type for: V1\u001b[0m\n" + "\u001b[32mINFO:type_infer-2197:Infering type for: V1\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:type_infer-2259:Column V3 has data type float\u001b[0m\n" + "\u001b[32mINFO:type_infer-2197:Column V3 has data type float\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:type_infer-2259:Infering type for: V4\u001b[0m\n" + "\u001b[32mINFO:type_infer-2197:Infering type for: V4\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:type_infer-2259:Column V6 has data type float\u001b[0m\n" + "\u001b[32mINFO:type_infer-2197:Column V6 has data type float\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:type_infer-2259:Column V4 has data type float\u001b[0m\n" + "\u001b[32mINFO:type_infer-2197:Infering type for: V7\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:type_infer-2259:Infering type for: V7\u001b[0m\n" + "\u001b[32mINFO:type_infer-2197:Column V1 has data type float\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:type_infer-2259:Infering type for: V5\u001b[0m\n" + "\u001b[32mINFO:type_infer-2197:Infering type for: V2\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:type_infer-2259:Column V1 has data type float\u001b[0m\n" + "\u001b[32mINFO:type_infer-2197:Column V4 has data type float\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:type_infer-2259:Infering type for: V2\u001b[0m\n" + "\u001b[32mINFO:type_infer-2197:Infering type for: V5\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:type_infer-2259:Column V5 has data type float\u001b[0m\n" + "\u001b[32mINFO:type_infer-2197:Column V7 has data type float\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:type_infer-2259:Infering type for: V9\u001b[0m\n" + "\u001b[32mINFO:type_infer-2197:Infering type for: V8\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:type_infer-2259:Column V7 has data type float\u001b[0m\n" + "\u001b[32mINFO:type_infer-2197:Column V2 has data type float\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:type_infer-2259:Infering type for: V8\u001b[0m\n" + "\u001b[32mINFO:type_infer-2197:Infering type for: V9\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:type_infer-2259:Column V9 has data type float\u001b[0m\n" + "\u001b[32mINFO:type_infer-2197:Column V5 has data type float\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:type_infer-2259:Infering type for: V10\u001b[0m\n" + "\u001b[32mINFO:type_infer-2197:Infering type for: V12\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:type_infer-2259:Column V2 has data type float\u001b[0m\n" + "\u001b[32mINFO:type_infer-2197:Column V8 has data type float\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:type_infer-2259:Infering type for: V12\u001b[0m\n" + "\u001b[32mINFO:type_infer-2197:Infering type for: V15\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:type_infer-2259:Column V8 has data type float\u001b[0m\n" + "\u001b[32mINFO:type_infer-2197:Column V9 has data type float\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:type_infer-2259:Infering type for: V15\u001b[0m\n" + "\u001b[32mINFO:type_infer-2197:Infering type for: V10\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:type_infer-2259:Column V10 has data type float\u001b[0m\n" + "\u001b[32mINFO:type_infer-2197:Column V15 has data type float\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:type_infer-2259:Infering type for: V11\u001b[0m\n" + "\u001b[32mINFO:type_infer-2197:Infering type for: V16\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:type_infer-2259:Column V12 has data type float\u001b[0m\n" + "\u001b[32mINFO:type_infer-2197:Column V12 has data type float\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:type_infer-2259:Infering type for: V13\u001b[0m\n" + "\u001b[32mINFO:type_infer-2197:Infering type for: V13\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:type_infer-2259:Column V13 has data type float\u001b[0m\n" + "\u001b[32mINFO:type_infer-2197:Column V13 has data type float\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:type_infer-2259:Infering type for: V14\u001b[0m\n" + "\u001b[32mINFO:type_infer-2197:Infering type for: V14\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:type_infer-2259:Column V15 has data type float\u001b[0m\n" + "\u001b[32mINFO:type_infer-2197:Column V10 has data type float\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:type_infer-2259:Column V11 has data type float\u001b[0m\n" + "\u001b[32mINFO:type_infer-2197:Infering type for: V11\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:type_infer-2259:Infering type for: V16\u001b[0m\n" + "\u001b[32mINFO:type_infer-2197:Column V16 has data type float\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:type_infer-2259:Infering type for: V18\u001b[0m\n" + "\u001b[32mINFO:type_infer-2197:Infering type for: V17\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:type_infer-2259:Column V14 has data type float\u001b[0m\n" + "\u001b[32mINFO:type_infer-2197:Column V14 has data type float\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:type_infer-2259:Infering type for: V21\u001b[0m\n" + "\u001b[32mINFO:type_infer-2197:Infering type for: V18\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:type_infer-2259:Column V16 has data type float\u001b[0m\n" + "\u001b[32mINFO:type_infer-2197:Column V11 has data type float\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:type_infer-2259:Infering type for: V17\u001b[0m\n" + "\u001b[32mINFO:type_infer-2197:Infering type for: V21\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:type_infer-2259:Column V18 has data type float\u001b[0m\n" + "\u001b[32mINFO:type_infer-2197:Column V17 has data type float\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:type_infer-2259:Infering type for: V19\u001b[0m\n" + "\u001b[32mINFO:type_infer-2197:Column V18 has data type float\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:type_infer-2259:Column V21 has data type float\u001b[0m\n" + "\u001b[32mINFO:type_infer-2197:Infering type for: V19\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:type_infer-2259:Infering type for: V22\u001b[0m\n" + "\u001b[32mINFO:type_infer-2197:Infering type for: V24\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:type_infer-2259:Column V17 has data type float\u001b[0m\n" + "\u001b[32mINFO:type_infer-2197:Column V19 has data type float\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:type_infer-2259:Infering type for: V24\u001b[0m\n" + "\u001b[32mINFO:type_infer-2197:Infering type for: V20\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:type_infer-2259:Column V19 has data type float\u001b[0m\n" + "\u001b[32mINFO:type_infer-2197:Column V21 has data type float\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:type_infer-2259:Infering type for: V20\u001b[0m\n" + "\u001b[32mINFO:type_infer-2197:Infering type for: V22\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:type_infer-2259:Column V22 has data type float\u001b[0m\n" + "\u001b[32mINFO:type_infer-2197:Column V24 has data type float\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:type_infer-2259:Infering type for: V23\u001b[0m\n" + "\u001b[32mINFO:type_infer-2197:Infering type for: V25\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:type_infer-2259:Column V24 has data type float\u001b[0m\n" + "\u001b[32mINFO:type_infer-2197:Column V20 has data type float\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:type_infer-2259:Infering type for: V25\u001b[0m\n" + "\u001b[32mINFO:type_infer-2197:Infering type for: V27\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:type_infer-2259:Column V23 has data type float\u001b[0m\n" + "\u001b[32mINFO:type_infer-2197:Column V22 has data type float\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:type_infer-2259:Infering type for: V27\u001b[0m\n" + "\u001b[32mINFO:type_infer-2197:Infering type for: V23\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:type_infer-2259:Column V20 has data type float\u001b[0m\n" + "\u001b[32mINFO:type_infer-2197:Column V27 has data type float\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:type_infer-2259:Infering type for: Class\u001b[0m\n" + "\u001b[32mINFO:type_infer-2197:Infering type for: V28\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:type_infer-2259:Column V25 has data type float\u001b[0m\n" + "\u001b[32mINFO:type_infer-2197:Column V25 has data type float\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:type_infer-2259:Infering type for: V26\u001b[0m\n" + "\u001b[32mINFO:type_infer-2197:Infering type for: V26\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:type_infer-2259:Column Class has data type binary\u001b[0m\n" + "\u001b[32mINFO:type_infer-2197:Column V28 has data type float\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:type_infer-2259:Column V27 has data type float\u001b[0m\n" + "\u001b[32mINFO:type_infer-2197:Infering type for: Amount\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:type_infer-2259:Infering type for: V28\u001b[0m\n" + "\u001b[32mINFO:type_infer-2197:Column V23 has data type float\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:type_infer-2259:Column V26 has data type float\u001b[0m\n" + "\u001b[32mINFO:type_infer-2197:Infering type for: Class\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:type_infer-2259:Column V28 has data type float\u001b[0m\n" + "\u001b[32mINFO:type_infer-2197:Column V26 has data type float\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:type_infer-2259:Infering type for: Amount\u001b[0m\n" + "\u001b[32mINFO:type_infer-2197:Column Amount has data type float\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:type_infer-2259:Column Amount has data type float\u001b[0m\n" + "\u001b[32mINFO:type_infer-2197:Column Class has data type binary\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:dataprep_ml-2259:Starting statistical analysis\u001b[0m\n" + "\u001b[32mINFO:dataprep_ml-2197:Starting statistical analysis\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:dataprep_ml-2259:Finished statistical analysis\u001b[0m\n" + "\u001b[32mINFO:dataprep_ml-2197:Finished statistical analysis\u001b[0m\n" ] } ], @@ -901,10 +901,10 @@ "id": "4411ee53", "metadata": { "execution": { - "iopub.execute_input": "2024-05-15T12:30:27.333499Z", - "iopub.status.busy": "2024-05-15T12:30:27.333282Z", - "iopub.status.idle": "2024-05-15T12:30:27.338468Z", - "shell.execute_reply": "2024-05-15T12:30:27.337924Z" + "iopub.execute_input": "2024-05-15T12:37:32.754539Z", + "iopub.status.busy": "2024-05-15T12:37:32.754103Z", + "iopub.status.idle": "2024-05-15T12:37:32.759217Z", + "shell.execute_reply": "2024-05-15T12:37:32.758527Z" } }, "outputs": [ @@ -996,10 +996,10 @@ "id": "34092d12", "metadata": { "execution": { - "iopub.execute_input": "2024-05-15T12:30:27.340823Z", - "iopub.status.busy": "2024-05-15T12:30:27.340587Z", - "iopub.status.idle": "2024-05-15T12:30:27.343872Z", - "shell.execute_reply": "2024-05-15T12:30:27.343379Z" + "iopub.execute_input": "2024-05-15T12:37:32.761863Z", + "iopub.status.busy": "2024-05-15T12:37:32.761475Z", + "iopub.status.idle": "2024-05-15T12:37:32.764800Z", + "shell.execute_reply": "2024-05-15T12:37:32.764192Z" } }, "outputs": [], @@ -1055,10 +1055,10 @@ "id": "alleged-concentrate", "metadata": { "execution": { - "iopub.execute_input": "2024-05-15T12:30:27.346334Z", - "iopub.status.busy": "2024-05-15T12:30:27.346133Z", - "iopub.status.idle": "2024-05-15T12:30:27.581484Z", - "shell.execute_reply": "2024-05-15T12:30:27.580794Z" + "iopub.execute_input": "2024-05-15T12:37:32.767542Z", + "iopub.status.busy": "2024-05-15T12:37:32.767181Z", + "iopub.status.idle": "2024-05-15T12:37:33.006726Z", + "shell.execute_reply": "2024-05-15T12:37:33.005918Z" } }, "outputs": [ @@ -1139,7 +1139,7 @@ " \"unbias_target\": True,\n", " \"seconds_per_mixer\": 42768.0,\n", " \"seconds_per_encoder\": None,\n", - " \"expected_additional_time\": 68.51064419746399,\n", + " \"expected_additional_time\": 68.86109614372253,\n", " \"time_aim\": 259200,\n", " \"target_weights\": None,\n", " \"positive_domain\": False,\n", @@ -1905,10 +1905,10 @@ "id": "organic-london", "metadata": { "execution": { - "iopub.execute_input": "2024-05-15T12:30:27.583960Z", - "iopub.status.busy": "2024-05-15T12:30:27.583758Z", - "iopub.status.idle": "2024-05-15T12:30:27.591554Z", - "shell.execute_reply": "2024-05-15T12:30:27.591054Z" + "iopub.execute_input": "2024-05-15T12:37:33.009564Z", + "iopub.status.busy": "2024-05-15T12:37:33.009352Z", + "iopub.status.idle": "2024-05-15T12:37:33.017259Z", + "shell.execute_reply": "2024-05-15T12:37:33.016750Z" } }, "outputs": [], @@ -1923,10 +1923,10 @@ "id": "fabulous-prime", "metadata": { "execution": { - "iopub.execute_input": "2024-05-15T12:30:27.594015Z", - "iopub.status.busy": "2024-05-15T12:30:27.593641Z", - "iopub.status.idle": "2024-05-15T12:30:47.763538Z", - "shell.execute_reply": "2024-05-15T12:30:47.762831Z" + "iopub.execute_input": "2024-05-15T12:37:33.020087Z", + "iopub.status.busy": "2024-05-15T12:37:33.019578Z", + "iopub.status.idle": "2024-05-15T12:37:53.359194Z", + "shell.execute_reply": "2024-05-15T12:37:53.358511Z" } }, "outputs": [ @@ -1934,28 +1934,28 @@ "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:dataprep_ml-2259:Cleaning the data\u001b[0m\n" + "\u001b[32mINFO:dataprep_ml-2197:Cleaning the data\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[37mDEBUG:lightwood-2259: `preprocess` runtime: 18.54 seconds\u001b[0m\n" + "\u001b[37mDEBUG:lightwood-2197: `preprocess` runtime: 18.56 seconds\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:dataprep_ml-2259:Splitting the data into train/test\u001b[0m\n" + "\u001b[32mINFO:dataprep_ml-2197:Splitting the data into train/test\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[37mDEBUG:lightwood-2259: `split` runtime: 1.62 seconds\u001b[0m\n" + "\u001b[37mDEBUG:lightwood-2197: `split` runtime: 1.77 seconds\u001b[0m\n" ] } ], @@ -1971,10 +1971,10 @@ "id": "suspended-biography", "metadata": { "execution": { - "iopub.execute_input": "2024-05-15T12:30:47.766563Z", - "iopub.status.busy": "2024-05-15T12:30:47.766019Z", - "iopub.status.idle": "2024-05-15T12:30:49.244243Z", - "shell.execute_reply": "2024-05-15T12:30:49.243541Z" + "iopub.execute_input": "2024-05-15T12:37:53.362317Z", + "iopub.status.busy": "2024-05-15T12:37:53.361876Z", + "iopub.status.idle": "2024-05-15T12:37:54.934620Z", + "shell.execute_reply": "2024-05-15T12:37:54.933895Z" } }, "outputs": [ diff --git a/tutorials/tutorial_data_analysis/tutorial_data_analysis.html b/tutorials/tutorial_data_analysis/tutorial_data_analysis.html index 039a19ba7..b89aea199 100644 --- a/tutorials/tutorial_data_analysis/tutorial_data_analysis.html +++ b/tutorials/tutorial_data_analysis/tutorial_data_analysis.html @@ -222,8 +222,8 @@

Step 1: load the dataset and define the predictive task 
-INFO:lightwood-2916:No torchvision detected, image helpers not supported.
-INFO:lightwood-2916:No torchvision/pillow detected, image encoder not supported
+INFO:lightwood-2866:No torchvision detected, image helpers not supported.
+INFO:lightwood-2866:No torchvision/pillow detected, image encoder not supported

Let’s see how this object has been populated. ProblemDefinition is a Python dataclass, so it comes with some convenient tools to achieve this:

@@ -283,22 +283,22 @@

Step 1: load the dataset and define the predictive task 
-INFO:type_infer-2916:Analyzing a sample of 222
-INFO:type_infer-2916:from a total population of 225, this is equivalent to 98.7% of your data.
-INFO:type_infer-2916:Infering type for: Population
-INFO:type_infer-2916:Column Population has data type integer
-INFO:type_infer-2916:Infering type for: Area (sq. mi.)
-INFO:type_infer-2916:Column Area (sq. mi.) has data type integer
-INFO:type_infer-2916:Infering type for: Pop. Density 
-INFO:type_infer-2916:Column Pop. Density  has data type float
-INFO:type_infer-2916:Infering type for: GDP ($ per capita)
-INFO:type_infer-2916:Column GDP ($ per capita) has data type integer
-INFO:type_infer-2916:Infering type for: Literacy (%)
-INFO:type_infer-2916:Column Literacy (%) has data type float
-INFO:type_infer-2916:Infering type for: Infant mortality 
-INFO:type_infer-2916:Column Infant mortality  has data type float
-INFO:type_infer-2916:Infering type for: Development Index
-INFO:type_infer-2916:Column Development Index has data type categorical
+INFO:type_infer-2866:Analyzing a sample of 222
+INFO:type_infer-2866:from a total population of 225, this is equivalent to 98.7% of your data.
+INFO:type_infer-2866:Infering type for: Population
+INFO:type_infer-2866:Column Population has data type integer
+INFO:type_infer-2866:Infering type for: Area (sq. mi.)
+INFO:type_infer-2866:Column Area (sq. mi.) has data type integer
+INFO:type_infer-2866:Infering type for: Pop. Density 
+INFO:type_infer-2866:Column Pop. Density  has data type float
+INFO:type_infer-2866:Infering type for: GDP ($ per capita)
+INFO:type_infer-2866:Column GDP ($ per capita) has data type integer
+INFO:type_infer-2866:Infering type for: Literacy (%)
+INFO:type_infer-2866:Column Literacy (%) has data type float
+INFO:type_infer-2866:Infering type for: Infant mortality 
+INFO:type_infer-2866:Column Infant mortality  has data type float
+INFO:type_infer-2866:Infering type for: Development Index
+INFO:type_infer-2866:Column Development Index has data type categorical
@@ -360,8 +360,8 @@

Step 2: Run the statistical analysis 
-INFO:dataprep_ml-2916:Starting statistical analysis
-INFO:dataprep_ml-2916:Finished statistical analysis
+INFO:dataprep_ml-2866:Starting statistical analysis
+INFO:dataprep_ml-2866:Finished statistical analysis

diff --git a/tutorials/tutorial_data_analysis/tutorial_data_analysis.ipynb b/tutorials/tutorial_data_analysis/tutorial_data_analysis.ipynb index d0872ea95..e2897726b 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": "2024-05-15T12:32:14.309394Z", - "iopub.status.busy": "2024-05-15T12:32:14.309198Z", - "iopub.status.idle": "2024-05-15T12:32:14.629441Z", - "shell.execute_reply": "2024-05-15T12:32:14.628730Z" + "iopub.execute_input": "2024-05-15T12:39:20.516178Z", + "iopub.status.busy": "2024-05-15T12:39:20.515976Z", + "iopub.status.idle": "2024-05-15T12:39:20.842577Z", + "shell.execute_reply": "2024-05-15T12:39:20.841884Z" } }, "outputs": [ @@ -175,10 +175,10 @@ "execution_count": 2, "metadata": { "execution": { - "iopub.execute_input": "2024-05-15T12:32:14.667078Z", - "iopub.status.busy": "2024-05-15T12:32:14.666676Z", - "iopub.status.idle": "2024-05-15T12:32:17.186059Z", - "shell.execute_reply": "2024-05-15T12:32:17.185340Z" + "iopub.execute_input": "2024-05-15T12:39:20.882091Z", + "iopub.status.busy": "2024-05-15T12:39:20.881616Z", + "iopub.status.idle": "2024-05-15T12:39:23.423280Z", + "shell.execute_reply": "2024-05-15T12:39:23.422573Z" } }, "outputs": [ @@ -186,14 +186,14 @@ "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:lightwood-2916:No torchvision detected, image helpers not supported.\u001b[0m\n" + "\u001b[32mINFO:lightwood-2866:No torchvision detected, image helpers not supported.\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:lightwood-2916:No torchvision/pillow detected, image encoder not supported\u001b[0m\n" + "\u001b[32mINFO:lightwood-2866:No torchvision/pillow detected, image encoder not supported\u001b[0m\n" ] } ], @@ -215,10 +215,10 @@ "execution_count": 3, "metadata": { "execution": { - "iopub.execute_input": "2024-05-15T12:32:17.189281Z", - "iopub.status.busy": "2024-05-15T12:32:17.188770Z", - "iopub.status.idle": "2024-05-15T12:32:17.193969Z", - "shell.execute_reply": "2024-05-15T12:32:17.193389Z" + "iopub.execute_input": "2024-05-15T12:39:23.426512Z", + "iopub.status.busy": "2024-05-15T12:39:23.426163Z", + "iopub.status.idle": "2024-05-15T12:39:23.431505Z", + "shell.execute_reply": "2024-05-15T12:39:23.430893Z" } }, "outputs": [ @@ -270,10 +270,10 @@ "execution_count": 4, "metadata": { "execution": { - "iopub.execute_input": "2024-05-15T12:32:17.196483Z", - "iopub.status.busy": "2024-05-15T12:32:17.196106Z", - "iopub.status.idle": "2024-05-15T12:32:17.219249Z", - "shell.execute_reply": "2024-05-15T12:32:17.218661Z" + "iopub.execute_input": "2024-05-15T12:39:23.434201Z", + "iopub.status.busy": "2024-05-15T12:39:23.433737Z", + "iopub.status.idle": "2024-05-15T12:39:23.459713Z", + "shell.execute_reply": "2024-05-15T12:39:23.459082Z" } }, "outputs": [ @@ -281,112 +281,112 @@ "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:type_infer-2916:Analyzing a sample of 222\u001b[0m\n" + "\u001b[32mINFO:type_infer-2866:Analyzing a sample of 222\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:type_infer-2916:from a total population of 225, this is equivalent to 98.7% of your data.\u001b[0m\n" + "\u001b[32mINFO:type_infer-2866: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-2916:Infering type for: Population\u001b[0m\n" + "\u001b[32mINFO:type_infer-2866:Infering type for: Population\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:type_infer-2916:Column Population has data type integer\u001b[0m\n" + "\u001b[32mINFO:type_infer-2866:Column Population has data type integer\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:type_infer-2916:Infering type for: Area (sq. mi.)\u001b[0m\n" + "\u001b[32mINFO:type_infer-2866:Infering type for: Area (sq. mi.)\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:type_infer-2916:Column Area (sq. mi.) has data type integer\u001b[0m\n" + "\u001b[32mINFO:type_infer-2866:Column Area (sq. mi.) has data type integer\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:type_infer-2916:Infering type for: Pop. Density \u001b[0m\n" + "\u001b[32mINFO:type_infer-2866:Infering type for: Pop. Density \u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:type_infer-2916:Column Pop. Density has data type float\u001b[0m\n" + "\u001b[32mINFO:type_infer-2866:Column Pop. Density has data type float\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:type_infer-2916:Infering type for: GDP ($ per capita)\u001b[0m\n" + "\u001b[32mINFO:type_infer-2866:Infering type for: GDP ($ per capita)\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:type_infer-2916:Column GDP ($ per capita) has data type integer\u001b[0m\n" + "\u001b[32mINFO:type_infer-2866:Column GDP ($ per capita) has data type integer\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:type_infer-2916:Infering type for: Literacy (%)\u001b[0m\n" + "\u001b[32mINFO:type_infer-2866:Infering type for: Literacy (%)\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:type_infer-2916:Column Literacy (%) has data type float\u001b[0m\n" + "\u001b[32mINFO:type_infer-2866:Column Literacy (%) has data type float\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:type_infer-2916:Infering type for: Infant mortality \u001b[0m\n" + "\u001b[32mINFO:type_infer-2866:Infering type for: Infant mortality \u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:type_infer-2916:Column Infant mortality has data type float\u001b[0m\n" + "\u001b[32mINFO:type_infer-2866:Column Infant mortality has data type float\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:type_infer-2916:Infering type for: Development Index\u001b[0m\n" + "\u001b[32mINFO:type_infer-2866:Infering type for: Development Index\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:type_infer-2916:Column Development Index has data type categorical\u001b[0m\n" + "\u001b[32mINFO:type_infer-2866:Column Development Index has data type categorical\u001b[0m\n" ] }, { @@ -421,10 +421,10 @@ "execution_count": 5, "metadata": { "execution": { - "iopub.execute_input": "2024-05-15T12:32:17.221782Z", - "iopub.status.busy": "2024-05-15T12:32:17.221448Z", - "iopub.status.idle": "2024-05-15T12:32:17.225621Z", - "shell.execute_reply": "2024-05-15T12:32:17.224989Z" + "iopub.execute_input": "2024-05-15T12:39:23.462354Z", + "iopub.status.busy": "2024-05-15T12:39:23.461965Z", + "iopub.status.idle": "2024-05-15T12:39:23.466294Z", + "shell.execute_reply": "2024-05-15T12:39:23.465608Z" } }, "outputs": [ @@ -474,10 +474,10 @@ "execution_count": 6, "metadata": { "execution": { - "iopub.execute_input": "2024-05-15T12:32:17.228143Z", - "iopub.status.busy": "2024-05-15T12:32:17.227783Z", - "iopub.status.idle": "2024-05-15T12:32:17.253950Z", - "shell.execute_reply": "2024-05-15T12:32:17.253307Z" + "iopub.execute_input": "2024-05-15T12:39:23.468886Z", + "iopub.status.busy": "2024-05-15T12:39:23.468511Z", + "iopub.status.idle": "2024-05-15T12:39:23.495336Z", + "shell.execute_reply": "2024-05-15T12:39:23.494690Z" } }, "outputs": [ @@ -485,14 +485,14 @@ "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:dataprep_ml-2916:Starting statistical analysis\u001b[0m\n" + "\u001b[32mINFO:dataprep_ml-2866:Starting statistical analysis\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:dataprep_ml-2916:Finished statistical analysis\u001b[0m\n" + "\u001b[32mINFO:dataprep_ml-2866:Finished statistical analysis\u001b[0m\n" ] } ], @@ -520,10 +520,10 @@ "execution_count": 7, "metadata": { "execution": { - "iopub.execute_input": "2024-05-15T12:32:17.256430Z", - "iopub.status.busy": "2024-05-15T12:32:17.256226Z", - "iopub.status.idle": "2024-05-15T12:32:17.260696Z", - "shell.execute_reply": "2024-05-15T12:32:17.260064Z" + "iopub.execute_input": "2024-05-15T12:39:23.497946Z", + "iopub.status.busy": "2024-05-15T12:39:23.497565Z", + "iopub.status.idle": "2024-05-15T12:39:23.502155Z", + "shell.execute_reply": "2024-05-15T12:39:23.501523Z" } }, "outputs": [ @@ -569,10 +569,10 @@ "execution_count": 8, "metadata": { "execution": { - "iopub.execute_input": "2024-05-15T12:32:17.263203Z", - "iopub.status.busy": "2024-05-15T12:32:17.262837Z", - "iopub.status.idle": "2024-05-15T12:32:17.266857Z", - "shell.execute_reply": "2024-05-15T12:32:17.266234Z" + "iopub.execute_input": "2024-05-15T12:39:23.504893Z", + "iopub.status.busy": "2024-05-15T12:39:23.504449Z", + "iopub.status.idle": "2024-05-15T12:39:23.509018Z", + "shell.execute_reply": "2024-05-15T12:39:23.508474Z" } }, "outputs": [ @@ -612,10 +612,10 @@ "execution_count": 9, "metadata": { "execution": { - "iopub.execute_input": "2024-05-15T12:32:17.269479Z", - "iopub.status.busy": "2024-05-15T12:32:17.269087Z", - "iopub.status.idle": "2024-05-15T12:32:17.273721Z", - "shell.execute_reply": "2024-05-15T12:32:17.273107Z" + "iopub.execute_input": "2024-05-15T12:39:23.511601Z", + "iopub.status.busy": "2024-05-15T12:39:23.511209Z", + "iopub.status.idle": "2024-05-15T12:39:23.515862Z", + "shell.execute_reply": "2024-05-15T12:39:23.515270Z" }, "scrolled": false }, @@ -673,10 +673,10 @@ "execution_count": 10, "metadata": { "execution": { - "iopub.execute_input": "2024-05-15T12:32:17.276269Z", - "iopub.status.busy": "2024-05-15T12:32:17.275813Z", - "iopub.status.idle": "2024-05-15T12:32:17.279893Z", - "shell.execute_reply": "2024-05-15T12:32:17.279315Z" + "iopub.execute_input": "2024-05-15T12:39:23.518414Z", + "iopub.status.busy": "2024-05-15T12:39:23.517941Z", + "iopub.status.idle": "2024-05-15T12:39:23.522169Z", + "shell.execute_reply": "2024-05-15T12:39:23.521501Z" } }, "outputs": [ @@ -700,10 +700,10 @@ "execution_count": 11, "metadata": { "execution": { - "iopub.execute_input": "2024-05-15T12:32:17.282178Z", - "iopub.status.busy": "2024-05-15T12:32:17.281977Z", - "iopub.status.idle": "2024-05-15T12:32:17.286732Z", - "shell.execute_reply": "2024-05-15T12:32:17.286148Z" + "iopub.execute_input": "2024-05-15T12:39:23.524471Z", + "iopub.status.busy": "2024-05-15T12:39:23.524270Z", + "iopub.status.idle": "2024-05-15T12:39:23.529133Z", + "shell.execute_reply": "2024-05-15T12:39:23.528566Z" }, "scrolled": false }, @@ -786,10 +786,10 @@ "execution_count": 12, "metadata": { "execution": { - "iopub.execute_input": "2024-05-15T12:32:17.289214Z", - "iopub.status.busy": "2024-05-15T12:32:17.288834Z", - "iopub.status.idle": "2024-05-15T12:32:17.292779Z", - "shell.execute_reply": "2024-05-15T12:32:17.292139Z" + "iopub.execute_input": "2024-05-15T12:39:23.531603Z", + "iopub.status.busy": "2024-05-15T12:39:23.531233Z", + "iopub.status.idle": "2024-05-15T12:39:23.535243Z", + "shell.execute_reply": "2024-05-15T12:39:23.534589Z" } }, "outputs": [ @@ -841,10 +841,10 @@ "execution_count": 13, "metadata": { "execution": { - "iopub.execute_input": "2024-05-15T12:32:17.295205Z", - "iopub.status.busy": "2024-05-15T12:32:17.294842Z", - "iopub.status.idle": "2024-05-15T12:32:19.947038Z", - "shell.execute_reply": "2024-05-15T12:32:19.946304Z" + "iopub.execute_input": "2024-05-15T12:39:23.537775Z", + "iopub.status.busy": "2024-05-15T12:39:23.537413Z", + "iopub.status.idle": "2024-05-15T12:39:26.181488Z", + "shell.execute_reply": "2024-05-15T12:39:26.180780Z" }, "scrolled": false }, diff --git a/tutorials/tutorial_time_series/tutorial_time_series.html b/tutorials/tutorial_time_series/tutorial_time_series.html index 8bba3be74..3ed0f400d 100644 --- a/tutorials/tutorial_time_series/tutorial_time_series.html +++ b/tutorials/tutorial_time_series/tutorial_time_series.html @@ -216,8 +216,8 @@

Define the predictive task 
-INFO:lightwood-2872:No torchvision detected, image helpers not supported.
-INFO:lightwood-2872:No torchvision/pillow detected, image encoder not supported
+INFO:lightwood-2821:No torchvision detected, image helpers not supported.
+INFO:lightwood-2821:No torchvision/pillow detected, image encoder not supported
@@ -285,16 +285,16 @@

Generate the predictor object 
-INFO:type_infer-2872:Analyzing a sample of 2467
-INFO:type_infer-2872:from a total population of 2820, this is equivalent to 87.5% of your data.
-INFO:type_infer-2872:Infering type for: Month
-INFO:type_infer-2872:Column Month has data type date
-INFO:type_infer-2872:Infering type for: Sunspots
-INFO:type_infer-2872:Column Sunspots has data type float
-INFO:dataprep_ml-2872:Starting statistical analysis
+INFO:type_infer-2821:Analyzing a sample of 2467
+INFO:type_infer-2821:from a total population of 2820, this is equivalent to 87.5% of your data.
+INFO:type_infer-2821:Infering type for: Month
+INFO:type_infer-2821:Column Month has data type date
+INFO:type_infer-2821:Infering type for: Sunspots
+INFO:type_infer-2821:Column Sunspots has data type float
+INFO:dataprep_ml-2821:Starting statistical analysis
 /opt/hostedtoolcache/Python/3.9.19/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.
   result = pd.to_datetime(element,
-INFO:dataprep_ml-2872:Finished statistical analysis
+INFO:dataprep_ml-2821:Finished statistical analysis

@@ -316,35 +316,35 @@

Train 
-INFO:dataprep_ml-2872:[Learn phase 1/8] - Statistical analysis
-INFO:dataprep_ml-2872:Starting statistical analysis
+INFO:dataprep_ml-2821:[Learn phase 1/8] - Statistical analysis
+INFO:dataprep_ml-2821:Starting statistical analysis
 /opt/hostedtoolcache/Python/3.9.19/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.
   result = pd.to_datetime(element,
-INFO:dataprep_ml-2872:Finished statistical analysis
-DEBUG:lightwood-2872: `analyze_data` runtime: 0.06 seconds
-INFO:dataprep_ml-2872:[Learn phase 2/8] - Data preprocessing
-INFO:dataprep_ml-2872:Cleaning the data
+INFO:dataprep_ml-2821:Finished statistical analysis
+DEBUG:lightwood-2821: `analyze_data` runtime: 0.06 seconds
+INFO:dataprep_ml-2821:[Learn phase 2/8] - Data preprocessing
+INFO:dataprep_ml-2821:Cleaning the data
 /opt/hostedtoolcache/Python/3.9.19/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.
   result = pd.to_datetime(element,
-INFO:dataprep_ml-2872:Transforming timeseries data
-DEBUG:lightwood-2872: `preprocess` runtime: 0.09 seconds
-INFO:dataprep_ml-2872:[Learn phase 3/8] - Data splitting
-INFO:dataprep_ml-2872:Splitting the data into train/test
-DEBUG:lightwood-2872: `split` runtime: 0.0 seconds
-INFO:dataprep_ml-2872:[Learn phase 4/8] - Preparing encoders
-DEBUG:dataprep_ml-2872:Preparing sequentially...
-DEBUG:lightwood-2872: `prepare` runtime: 0.05 seconds
-INFO:dataprep_ml-2872:[Learn phase 5/8] - Feature generation
-INFO:dataprep_ml-2872:Featurizing the data
-DEBUG:lightwood-2872: `featurize` runtime: 0.05 seconds
-INFO:dataprep_ml-2872:[Learn phase 6/8] - Mixer training
-INFO:dataprep_ml-2872:Training the mixers
-WARNING:lightwood-2872:XGBoost running on CPU
-WARNING:lightwood-2872:XGBoost running on CPU
-WARNING:lightwood-2872:XGBoost running on CPU
-WARNING:lightwood-2872:XGBoost running on CPU
-WARNING:lightwood-2872:XGBoost running on CPU
-WARNING:lightwood-2872:XGBoost running on CPU
+INFO:dataprep_ml-2821:Transforming timeseries data
+DEBUG:lightwood-2821: `preprocess` runtime: 0.09 seconds
+INFO:dataprep_ml-2821:[Learn phase 3/8] - Data splitting
+INFO:dataprep_ml-2821:Splitting the data into train/test
+DEBUG:lightwood-2821: `split` runtime: 0.0 seconds
+INFO:dataprep_ml-2821:[Learn phase 4/8] - Preparing encoders
+DEBUG:dataprep_ml-2821:Preparing sequentially...
+DEBUG:lightwood-2821: `prepare` runtime: 0.05 seconds
+INFO:dataprep_ml-2821:[Learn phase 5/8] - Feature generation
+INFO:dataprep_ml-2821:Featurizing the data
+DEBUG:lightwood-2821: `featurize` runtime: 0.04 seconds
+INFO:dataprep_ml-2821:[Learn phase 6/8] - Mixer training
+INFO:dataprep_ml-2821:Training the mixers
+WARNING:lightwood-2821:XGBoost running on CPU
+WARNING:lightwood-2821:XGBoost running on CPU
+WARNING:lightwood-2821:XGBoost running on CPU
+WARNING:lightwood-2821:XGBoost running on CPU
+WARNING:lightwood-2821:XGBoost running on CPU
+WARNING:lightwood-2821:XGBoost running on CPU
 /opt/hostedtoolcache/Python/3.9.19/x64/lib/python3.9/site-packages/torch/amp/grad_scaler.py:131: UserWarning: torch.cuda.amp.GradScaler is enabled, but CUDA is not available.  Disabling.
   warnings.warn(
@@ -354,12 +354,12 @@

Train 
-[12:32:08] WARNING: ../src/learner.cc:339: No visible GPU is found, setting `gpu_id` to -1
-[12:32:08] WARNING: ../src/learner.cc:339: No visible GPU is found, setting `gpu_id` to -1
-[12:32:08] WARNING: ../src/learner.cc:339: No visible GPU is found, setting `gpu_id` to -1
-[12:32:08] WARNING: ../src/learner.cc:339: No visible GPU is found, setting `gpu_id` to -1
-[12:32:08] WARNING: ../src/learner.cc:339: No visible GPU is found, setting `gpu_id` to -1
-[12:32:08] WARNING: ../src/learner.cc:339: No visible GPU is found, setting `gpu_id` to -1
+[12:39:14] WARNING: ../src/learner.cc:339: No visible GPU is found, setting `gpu_id` to -1
+[12:39:14] WARNING: ../src/learner.cc:339: No visible GPU is found, setting `gpu_id` to -1
+[12:39:14] WARNING: ../src/learner.cc:339: No visible GPU is found, setting `gpu_id` to -1
+[12:39:14] WARNING: ../src/learner.cc:339: No visible GPU is found, setting `gpu_id` to -1
+[12:39:14] WARNING: ../src/learner.cc:339: No visible GPU is found, setting `gpu_id` to -1
+[12:39:14] WARNING: ../src/learner.cc:339: No visible GPU is found, setting `gpu_id` to -1
@@ -372,30 +372,30 @@

Train Consider using one of the following signatures instead: addcmul_(Tensor tensor1, Tensor tensor2, *, Number value) (Triggered internally at ../torch/csrc/utils/python_arg_parser.cpp:1578.) exp_avg_sq.mul_(beta2).addcmul_(1 - beta2, grad, grad) -INFO:lightwood-2872:Loss of 9.051180630922318 with learning rate 0.0001 -INFO:lightwood-2872:Loss of 9.014871209859848 with learning rate 0.0005 -INFO:lightwood-2872:Loss of 8.969509482383728 with learning rate 0.001 -INFO:lightwood-2872:Loss of 8.879052013158798 with learning rate 0.002 -INFO:lightwood-2872:Loss of 8.788950502872467 with learning rate 0.003 -INFO:lightwood-2872:Loss of 8.611965209245682 with learning rate 0.005 -INFO:lightwood-2872:Loss of 8.195775926113129 with learning rate 0.01 -INFO:lightwood-2872:Loss of 6.255893141031265 with learning rate 0.05 -INFO:lightwood-2872:Found learning rate of: 0.05 -INFO:lightwood-2872:Loss @ epoch 1: 0.5818348675966263 -INFO:lightwood-2872:Loss @ epoch 2: 0.4797109067440033 -INFO:lightwood-2872:Loss @ epoch 3: 0.48386093974113464 -INFO:lightwood-2872:Loss @ epoch 4: 0.49511992931365967 -INFO:lightwood-2872:Loss @ epoch 5: 0.39475560188293457 -INFO:lightwood-2872:Loss @ epoch 6: 0.39592696726322174 -INFO:lightwood-2872:Loss @ epoch 7: 0.3622782379388809 -INFO:lightwood-2872:Loss @ epoch 8: 0.38170479238033295 -INFO:lightwood-2872:Loss @ epoch 9: 0.5138543993234634 -INFO:lightwood-2872:Loss @ epoch 10: 0.6360723078250885 -INFO:lightwood-2872:Loss @ epoch 1: 0.29868809472430835 -INFO:lightwood-2872:Loss @ epoch 2: 0.30318967591632495 -DEBUG:lightwood-2872: `fit_mixer` runtime: 0.87 seconds -INFO:lightwood-2872:Started fitting LGBM models for array prediction -INFO:lightwood-2872:Started fitting XGBoost model +INFO:lightwood-2821:Loss of 9.051180630922318 with learning rate 0.0001 +INFO:lightwood-2821:Loss of 9.014871209859848 with learning rate 0.0005 +INFO:lightwood-2821:Loss of 8.969509482383728 with learning rate 0.001 +INFO:lightwood-2821:Loss of 8.879052013158798 with learning rate 0.002 +INFO:lightwood-2821:Loss of 8.788950502872467 with learning rate 0.003 +INFO:lightwood-2821:Loss of 8.611965209245682 with learning rate 0.005 +INFO:lightwood-2821:Loss of 8.195775926113129 with learning rate 0.01 +INFO:lightwood-2821:Loss of 6.255893141031265 with learning rate 0.05 +INFO:lightwood-2821:Found learning rate of: 0.05 +INFO:lightwood-2821:Loss @ epoch 1: 0.5818348675966263 +INFO:lightwood-2821:Loss @ epoch 2: 0.4797109067440033 +INFO:lightwood-2821:Loss @ epoch 3: 0.48386093974113464 +INFO:lightwood-2821:Loss @ epoch 4: 0.49511992931365967 +INFO:lightwood-2821:Loss @ epoch 5: 0.39475560188293457 +INFO:lightwood-2821:Loss @ epoch 6: 0.39592696726322174 +INFO:lightwood-2821:Loss @ epoch 7: 0.3622782379388809 +INFO:lightwood-2821:Loss @ epoch 8: 0.38170479238033295 +INFO:lightwood-2821:Loss @ epoch 9: 0.5138543993234634 +INFO:lightwood-2821:Loss @ epoch 10: 0.6360723078250885 +INFO:lightwood-2821:Loss @ epoch 1: 0.29868809472430835 +INFO:lightwood-2821:Loss @ epoch 2: 0.30318967591632495 +DEBUG:lightwood-2821: `fit_mixer` runtime: 0.86 seconds +INFO:lightwood-2821:Started fitting LGBM models for array prediction +INFO:lightwood-2821:Started fitting XGBoost model

@@ -411,8 +411,8 @@

Train 

-INFO:lightwood-2872:A single GBM iteration takes 0.1 seconds
-INFO:lightwood-2872:Training XGBoost with 57023 iterations given 7127.987446546555 seconds constraint
+INFO:lightwood-2821:A single GBM iteration takes 0.1 seconds
+INFO:lightwood-2821:Training XGBoost with 57023 iterations given 7127.987055063248 seconds constraint
@@ -434,6 +434,7 @@

Train [11] validation_0-rmse:15.89176 [12] validation_0-rmse:15.87901 [13] validation_0-rmse:15.87505 +[14] validation_0-rmse:16.06330

@@ -441,7 +442,7 @@

Train 

-INFO:lightwood-2872:Started fitting XGBoost model
+INFO:lightwood-2821:Started fitting XGBoost model
@@ -457,8 +458,8 @@

Train 

-INFO:lightwood-2872:A single GBM iteration takes 0.1 seconds
-INFO:lightwood-2872:Training XGBoost with 57023 iterations given 7127.988590955734 seconds constraint
+INFO:lightwood-2821:A single GBM iteration takes 0.1 seconds
+INFO:lightwood-2821:Training XGBoost with 57023 iterations given 7127.986838340759 seconds constraint
@@ -480,6 +481,7 @@

Train [11] validation_0-rmse:17.56609 [12] validation_0-rmse:17.71702 [13] validation_0-rmse:17.75939 +[14] validation_0-rmse:17.84796

@@ -487,7 +489,7 @@

Train 

-INFO:lightwood-2872:Started fitting XGBoost model
+INFO:lightwood-2821:Started fitting XGBoost model
@@ -503,8 +505,8 @@

Train 

-INFO:lightwood-2872:A single GBM iteration takes 0.1 seconds
-INFO:lightwood-2872:Training XGBoost with 57023 iterations given 7127.988853693008 seconds constraint
+INFO:lightwood-2821:A single GBM iteration takes 0.1 seconds
+INFO:lightwood-2821:Training XGBoost with 57023 iterations given 7127.9879677295685 seconds constraint
@@ -532,7 +534,7 @@

Train 

-INFO:lightwood-2872:Started fitting XGBoost model
+INFO:lightwood-2821:Started fitting XGBoost model
@@ -548,8 +550,8 @@

Train 

-INFO:lightwood-2872:A single GBM iteration takes 0.1 seconds
-INFO:lightwood-2872:Training XGBoost with 57023 iterations given 7127.988720655441 seconds constraint
+INFO:lightwood-2821:A single GBM iteration takes 0.1 seconds
+INFO:lightwood-2821:Training XGBoost with 57023 iterations given 7127.9887981414795 seconds constraint
@@ -578,7 +580,7 @@

Train 

-INFO:lightwood-2872:Started fitting XGBoost model
+INFO:lightwood-2821:Started fitting XGBoost model
@@ -594,8 +596,8 @@

Train 

-INFO:lightwood-2872:A single GBM iteration takes 0.1 seconds
-INFO:lightwood-2872:Training XGBoost with 57023 iterations given 7127.9883551597595 seconds constraint
+INFO:lightwood-2821:A single GBM iteration takes 0.1 seconds
+INFO:lightwood-2821:Training XGBoost with 57023 iterations given 7127.988205194473 seconds constraint
@@ -617,7 +619,6 @@

Train [11] validation_0-rmse:22.25761 [12] validation_0-rmse:22.25308 [13] validation_0-rmse:22.31415 -[14] validation_0-rmse:22.31000

@@ -625,7 +626,7 @@

Train 

-INFO:lightwood-2872:Started fitting XGBoost model
+INFO:lightwood-2821:Started fitting XGBoost model
@@ -641,8 +642,8 @@

Train 

-INFO:lightwood-2872:A single GBM iteration takes 0.1 seconds
-INFO:lightwood-2872:Training XGBoost with 57023 iterations given 7127.988573551178 seconds constraint
+INFO:lightwood-2821:A single GBM iteration takes 0.1 seconds
+INFO:lightwood-2821:Training XGBoost with 57023 iterations given 7127.987644195557 seconds constraint
@@ -664,7 +665,6 @@

Train [11] validation_0-rmse:21.84587 [12] validation_0-rmse:21.78099 [13] validation_0-rmse:21.68890 -[14] validation_0-rmse:21.70025

@@ -672,36 +672,36 @@

Train 

-DEBUG:lightwood-2872: `fit_mixer` runtime: 0.5 seconds
-INFO:dataprep_ml-2872:Ensembling the mixer
-INFO:lightwood-2872:Mixer: NeuralTs got accuracy: 0.875
-WARNING:lightwood-2872:This model does not output probability estimates
-INFO:lightwood-2872:Mixer: XGBoostArrayMixer got accuracy: 0.869
-INFO:lightwood-2872:Picked best mixer: NeuralTs
-DEBUG:lightwood-2872: `fit` runtime: 1.41 seconds
-INFO:dataprep_ml-2872:[Learn phase 7/8] - Ensemble analysis
-INFO:dataprep_ml-2872:Analyzing the ensemble of mixers
-INFO:lightwood-2872:The block ICP is now running its analyze() method
-INFO:lightwood-2872:The block ConfStats is now running its analyze() method
-INFO:lightwood-2872:The block AccStats is now running its analyze() method
-INFO:lightwood-2872:The block PermutationFeatureImportance is now running its analyze() method
-WARNING:lightwood-2872:Block 'PermutationFeatureImportance' does not support time series nor text encoding, skipping...
-DEBUG:lightwood-2872: `analyze_ensemble` runtime: 0.16 seconds
-INFO:dataprep_ml-2872:[Learn phase 8/8] - Adjustment on validation requested
-INFO:dataprep_ml-2872:Updating the mixers
+DEBUG:lightwood-2821: `fit_mixer` runtime: 0.5 seconds
+INFO:dataprep_ml-2821:Ensembling the mixer
+INFO:lightwood-2821:Mixer: NeuralTs got accuracy: 0.875
+WARNING:lightwood-2821:This model does not output probability estimates
+INFO:lightwood-2821:Mixer: XGBoostArrayMixer got accuracy: 0.869
+INFO:lightwood-2821:Picked best mixer: NeuralTs
+DEBUG:lightwood-2821: `fit` runtime: 1.4 seconds
+INFO:dataprep_ml-2821:[Learn phase 7/8] - Ensemble analysis
+INFO:dataprep_ml-2821:Analyzing the ensemble of mixers
+INFO:lightwood-2821:The block ICP is now running its analyze() method
+INFO:lightwood-2821:The block ConfStats is now running its analyze() method
+INFO:lightwood-2821:The block AccStats is now running its analyze() method
+INFO:lightwood-2821:The block PermutationFeatureImportance is now running its analyze() method
+WARNING:lightwood-2821:Block 'PermutationFeatureImportance' does not support time series nor text encoding, skipping...
+DEBUG:lightwood-2821: `analyze_ensemble` runtime: 0.16 seconds
+INFO:dataprep_ml-2821:[Learn phase 8/8] - Adjustment on validation requested
+INFO:dataprep_ml-2821:Updating the mixers
 /opt/hostedtoolcache/Python/3.9.19/x64/lib/python3.9/site-packages/torch/amp/grad_scaler.py:131: UserWarning: torch.cuda.amp.GradScaler is enabled, but CUDA is not available.  Disabling.
   warnings.warn(
-INFO:lightwood-2872:Loss @ epoch 1: 0.29626286526521045
-INFO:lightwood-2872:Loss @ epoch 2: 0.2954987535874049
-INFO:lightwood-2872:Updating array of LGBM models...
-INFO:lightwood-2872:XGBoost mixer does not have a `partial_fit` implementation
-INFO:lightwood-2872:XGBoost mixer does not have a `partial_fit` implementation
-INFO:lightwood-2872:XGBoost mixer does not have a `partial_fit` implementation
-INFO:lightwood-2872:XGBoost mixer does not have a `partial_fit` implementation
-INFO:lightwood-2872:XGBoost mixer does not have a `partial_fit` implementation
-INFO:lightwood-2872:XGBoost mixer does not have a `partial_fit` implementation
-DEBUG:lightwood-2872: `adjust` runtime: 0.09 seconds
-DEBUG:lightwood-2872: `learn` runtime: 1.92 seconds
+INFO:lightwood-2821:Loss @ epoch 1: 0.29626286526521045
+INFO:lightwood-2821:Loss @ epoch 2: 0.2954987535874049
+INFO:lightwood-2821:Updating array of LGBM models...
+INFO:lightwood-2821:XGBoost mixer does not have a `partial_fit` implementation
+INFO:lightwood-2821:XGBoost mixer does not have a `partial_fit` implementation
+INFO:lightwood-2821:XGBoost mixer does not have a `partial_fit` implementation
+INFO:lightwood-2821:XGBoost mixer does not have a `partial_fit` implementation
+INFO:lightwood-2821:XGBoost mixer does not have a `partial_fit` implementation
+INFO:lightwood-2821:XGBoost mixer does not have a `partial_fit` implementation
+DEBUG:lightwood-2821: `adjust` runtime: 0.1 seconds
+DEBUG:lightwood-2821: `learn` runtime: 1.91 seconds
@@ -721,33 +721,33 @@

Predict 
-INFO:dataprep_ml-2872:[Predict phase 1/4] - Data preprocessing
-/tmp/11cf2e3a3fe2d38f7293f27fbfc34dab78123e8d8e63ffa417157763283658414.py:587: SettingWithCopyWarning:
+INFO:dataprep_ml-2821:[Predict phase 1/4] - Data preprocessing
+/tmp/b9f9761599676d055346cd2b368083bd2229f6aecb71536117157767545533166.py:587: SettingWithCopyWarning:
 A value is trying to be set on a copy of a slice from a DataFrame.
 Try using .loc[row_indexer,col_indexer] = value instead
 
 See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
   data[col] = [None] * len(data)
-INFO:dataprep_ml-2872:Cleaning the data
+INFO:dataprep_ml-2821:Cleaning the data
 /opt/hostedtoolcache/Python/3.9.19/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.
   result = pd.to_datetime(element,
-INFO:dataprep_ml-2872:Transforming timeseries data
-DEBUG:lightwood-2872: `preprocess` runtime: 0.02 seconds
-INFO:dataprep_ml-2872:[Predict phase 2/4] - Feature generation
-INFO:dataprep_ml-2872:Featurizing the data
-DEBUG:lightwood-2872: `featurize` runtime: 0.01 seconds
-INFO:dataprep_ml-2872:[Predict phase 3/4] - Calling ensemble
-DEBUG:lightwood-2872: `_timed_call` runtime: 0.09 seconds
-INFO:dataprep_ml-2872:[Predict phase 4/4] - Analyzing output
-INFO:lightwood-2872:The block ICP is now running its explain() method
-INFO:lightwood-2872:The block ConfStats is now running its explain() method
-INFO:lightwood-2872:ConfStats.explain() has not been implemented, no modifications will be done to the data insights.
-INFO:lightwood-2872:The block AccStats is now running its explain() method
-INFO:lightwood-2872:AccStats.explain() has not been implemented, no modifications will be done to the data insights.
-INFO:lightwood-2872:The block PermutationFeatureImportance is now running its explain() method
-INFO:lightwood-2872:PermutationFeatureImportance.explain() has not been implemented, no modifications will be done to the data insights.
-DEBUG:lightwood-2872: `explain` runtime: 0.09 seconds
-DEBUG:lightwood-2872: `predict` runtime: 0.22 seconds
+INFO:dataprep_ml-2821:Transforming timeseries data
+DEBUG:lightwood-2821: `preprocess` runtime: 0.02 seconds
+INFO:dataprep_ml-2821:[Predict phase 2/4] - Feature generation
+INFO:dataprep_ml-2821:Featurizing the data
+DEBUG:lightwood-2821: `featurize` runtime: 0.01 seconds
+INFO:dataprep_ml-2821:[Predict phase 3/4] - Calling ensemble
+DEBUG:lightwood-2821: `_timed_call` runtime: 0.09 seconds
+INFO:dataprep_ml-2821:[Predict phase 4/4] - Analyzing output
+INFO:lightwood-2821:The block ICP is now running its explain() method
+INFO:lightwood-2821:The block ConfStats is now running its explain() method
+INFO:lightwood-2821:ConfStats.explain() has not been implemented, no modifications will be done to the data insights.
+INFO:lightwood-2821:The block AccStats is now running its explain() method
+INFO:lightwood-2821:AccStats.explain() has not been implemented, no modifications will be done to the data insights.
+INFO:lightwood-2821:The block PermutationFeatureImportance is now running its explain() method
+INFO:lightwood-2821:PermutationFeatureImportance.explain() has not been implemented, no modifications will be done to the data insights.
+DEBUG:lightwood-2821: `explain` runtime: 0.09 seconds
+DEBUG:lightwood-2821: `predict` runtime: 0.22 seconds

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 64fb603ac..51f683c34 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": "2024-05-15T12:32:01.117399Z", - "iopub.status.busy": "2024-05-15T12:32:01.117200Z", - "iopub.status.idle": "2024-05-15T12:32:01.618443Z", - "shell.execute_reply": "2024-05-15T12:32:01.617772Z" + "iopub.execute_input": "2024-05-15T12:39:07.466078Z", + "iopub.status.busy": "2024-05-15T12:39:07.465874Z", + "iopub.status.idle": "2024-05-15T12:39:07.852195Z", + "shell.execute_reply": "2024-05-15T12:39:07.851479Z" } }, "outputs": [ @@ -162,10 +162,10 @@ "execution_count": 2, "metadata": { "execution": { - "iopub.execute_input": "2024-05-15T12:32:01.654728Z", - "iopub.status.busy": "2024-05-15T12:32:01.654340Z", - "iopub.status.idle": "2024-05-15T12:32:04.161035Z", - "shell.execute_reply": "2024-05-15T12:32:04.160343Z" + "iopub.execute_input": "2024-05-15T12:39:07.892100Z", + "iopub.status.busy": "2024-05-15T12:39:07.891629Z", + "iopub.status.idle": "2024-05-15T12:39:10.416172Z", + "shell.execute_reply": "2024-05-15T12:39:10.415460Z" } }, "outputs": [ @@ -173,14 +173,14 @@ "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:lightwood-2872:No torchvision detected, image helpers not supported.\u001b[0m\n" + "\u001b[32mINFO:lightwood-2821:No torchvision detected, image helpers not supported.\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:lightwood-2872:No torchvision/pillow detected, image encoder not supported\u001b[0m\n" + "\u001b[32mINFO:lightwood-2821:No torchvision/pillow detected, image encoder not supported\u001b[0m\n" ] } ], @@ -193,10 +193,10 @@ "execution_count": 3, "metadata": { "execution": { - "iopub.execute_input": "2024-05-15T12:32:04.164053Z", - "iopub.status.busy": "2024-05-15T12:32:04.163790Z", - "iopub.status.idle": "2024-05-15T12:32:04.167552Z", - "shell.execute_reply": "2024-05-15T12:32:04.167026Z" + "iopub.execute_input": "2024-05-15T12:39:10.419295Z", + "iopub.status.busy": "2024-05-15T12:39:10.418970Z", + "iopub.status.idle": "2024-05-15T12:39:10.422725Z", + "shell.execute_reply": "2024-05-15T12:39:10.422113Z" } }, "outputs": [], @@ -223,10 +223,10 @@ "execution_count": 4, "metadata": { "execution": { - "iopub.execute_input": "2024-05-15T12:32:04.170051Z", - "iopub.status.busy": "2024-05-15T12:32:04.169696Z", - "iopub.status.idle": "2024-05-15T12:32:04.173649Z", - "shell.execute_reply": "2024-05-15T12:32:04.173022Z" + "iopub.execute_input": "2024-05-15T12:39:10.425265Z", + "iopub.status.busy": "2024-05-15T12:39:10.424889Z", + "iopub.status.idle": "2024-05-15T12:39:10.428910Z", + "shell.execute_reply": "2024-05-15T12:39:10.428271Z" } }, "outputs": [ @@ -261,10 +261,10 @@ "execution_count": 5, "metadata": { "execution": { - "iopub.execute_input": "2024-05-15T12:32:04.176107Z", - "iopub.status.busy": "2024-05-15T12:32:04.175746Z", - "iopub.status.idle": "2024-05-15T12:32:08.372518Z", - "shell.execute_reply": "2024-05-15T12:32:08.371822Z" + "iopub.execute_input": "2024-05-15T12:39:10.431459Z", + "iopub.status.busy": "2024-05-15T12:39:10.431088Z", + "iopub.status.idle": "2024-05-15T12:39:14.560206Z", + "shell.execute_reply": "2024-05-15T12:39:14.559482Z" } }, "outputs": [ @@ -272,49 +272,49 @@ "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:type_infer-2872:Analyzing a sample of 2467\u001b[0m\n" + "\u001b[32mINFO:type_infer-2821:Analyzing a sample of 2467\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:type_infer-2872:from a total population of 2820, this is equivalent to 87.5% of your data.\u001b[0m\n" + "\u001b[32mINFO:type_infer-2821: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-2872:Infering type for: Month\u001b[0m\n" + "\u001b[32mINFO:type_infer-2821:Infering type for: Month\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:type_infer-2872:Column Month has data type date\u001b[0m\n" + "\u001b[32mINFO:type_infer-2821:Column Month has data type date\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:type_infer-2872:Infering type for: Sunspots\u001b[0m\n" + "\u001b[32mINFO:type_infer-2821:Infering type for: Sunspots\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:type_infer-2872:Column Sunspots has data type float\u001b[0m\n" + "\u001b[32mINFO:type_infer-2821:Column Sunspots has data type float\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:dataprep_ml-2872:Starting statistical analysis\u001b[0m\n" + "\u001b[32mINFO:dataprep_ml-2821:Starting statistical analysis\u001b[0m\n" ] }, { @@ -323,7 +323,7 @@ "text": [ "/opt/hostedtoolcache/Python/3.9.19/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-2872:Finished statistical analysis\u001b[0m\n" + "\u001b[32mINFO:dataprep_ml-2821:Finished statistical analysis\u001b[0m\n" ] } ], @@ -360,10 +360,10 @@ "execution_count": 6, "metadata": { "execution": { - "iopub.execute_input": "2024-05-15T12:32:08.375604Z", - "iopub.status.busy": "2024-05-15T12:32:08.375181Z", - "iopub.status.idle": "2024-05-15T12:32:10.298183Z", - "shell.execute_reply": "2024-05-15T12:32:10.297500Z" + "iopub.execute_input": "2024-05-15T12:39:14.563441Z", + "iopub.status.busy": "2024-05-15T12:39:14.563060Z", + "iopub.status.idle": "2024-05-15T12:39:16.481390Z", + "shell.execute_reply": "2024-05-15T12:39:16.480840Z" } }, "outputs": [ @@ -371,14 +371,14 @@ "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:dataprep_ml-2872:[Learn phase 1/8] - Statistical analysis\u001b[0m\n" + "\u001b[32mINFO:dataprep_ml-2821:[Learn phase 1/8] - Statistical analysis\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:dataprep_ml-2872:Starting statistical analysis\u001b[0m\n" + "\u001b[32mINFO:dataprep_ml-2821:Starting statistical analysis\u001b[0m\n" ] }, { @@ -387,28 +387,28 @@ "text": [ "/opt/hostedtoolcache/Python/3.9.19/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-2872:Finished statistical analysis\u001b[0m\n" + "\u001b[32mINFO:dataprep_ml-2821:Finished statistical analysis\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[37mDEBUG:lightwood-2872: `analyze_data` runtime: 0.06 seconds\u001b[0m\n" + "\u001b[37mDEBUG:lightwood-2821: `analyze_data` runtime: 0.06 seconds\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:dataprep_ml-2872:[Learn phase 2/8] - Data preprocessing\u001b[0m\n" + "\u001b[32mINFO:dataprep_ml-2821:[Learn phase 2/8] - Data preprocessing\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:dataprep_ml-2872:Cleaning the data\u001b[0m\n" + "\u001b[32mINFO:dataprep_ml-2821:Cleaning the data\u001b[0m\n" ] }, { @@ -417,133 +417,133 @@ "text": [ "/opt/hostedtoolcache/Python/3.9.19/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-2872:Transforming timeseries data\u001b[0m\n" + "\u001b[32mINFO:dataprep_ml-2821:Transforming timeseries data\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[37mDEBUG:lightwood-2872: `preprocess` runtime: 0.09 seconds\u001b[0m\n" + "\u001b[37mDEBUG:lightwood-2821: `preprocess` runtime: 0.09 seconds\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:dataprep_ml-2872:[Learn phase 3/8] - Data splitting\u001b[0m\n" + "\u001b[32mINFO:dataprep_ml-2821:[Learn phase 3/8] - Data splitting\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:dataprep_ml-2872:Splitting the data into train/test\u001b[0m\n" + "\u001b[32mINFO:dataprep_ml-2821:Splitting the data into train/test\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[37mDEBUG:lightwood-2872: `split` runtime: 0.0 seconds\u001b[0m\n" + "\u001b[37mDEBUG:lightwood-2821: `split` runtime: 0.0 seconds\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:dataprep_ml-2872:[Learn phase 4/8] - Preparing encoders\u001b[0m\n" + "\u001b[32mINFO:dataprep_ml-2821:[Learn phase 4/8] - Preparing encoders\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[37mDEBUG:dataprep_ml-2872:Preparing sequentially...\u001b[0m\n" + "\u001b[37mDEBUG:dataprep_ml-2821:Preparing sequentially...\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[37mDEBUG:lightwood-2872: `prepare` runtime: 0.05 seconds\u001b[0m\n" + "\u001b[37mDEBUG:lightwood-2821: `prepare` runtime: 0.05 seconds\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:dataprep_ml-2872:[Learn phase 5/8] - Feature generation\u001b[0m\n" + "\u001b[32mINFO:dataprep_ml-2821:[Learn phase 5/8] - Feature generation\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:dataprep_ml-2872:Featurizing the data\u001b[0m\n" + "\u001b[32mINFO:dataprep_ml-2821:Featurizing the data\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[37mDEBUG:lightwood-2872: `featurize` runtime: 0.05 seconds\u001b[0m\n" + "\u001b[37mDEBUG:lightwood-2821: `featurize` runtime: 0.04 seconds\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:dataprep_ml-2872:[Learn phase 6/8] - Mixer training\u001b[0m\n" + "\u001b[32mINFO:dataprep_ml-2821:[Learn phase 6/8] - Mixer training\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:dataprep_ml-2872:Training the mixers\u001b[0m\n" + "\u001b[32mINFO:dataprep_ml-2821:Training the mixers\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[33mWARNING:lightwood-2872:XGBoost running on CPU\u001b[0m\n" + "\u001b[33mWARNING:lightwood-2821:XGBoost running on CPU\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[33mWARNING:lightwood-2872:XGBoost running on CPU\u001b[0m\n" + "\u001b[33mWARNING:lightwood-2821:XGBoost running on CPU\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[33mWARNING:lightwood-2872:XGBoost running on CPU\u001b[0m\n" + "\u001b[33mWARNING:lightwood-2821:XGBoost running on CPU\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[33mWARNING:lightwood-2872:XGBoost running on CPU\u001b[0m\n" + "\u001b[33mWARNING:lightwood-2821:XGBoost running on CPU\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[33mWARNING:lightwood-2872:XGBoost running on CPU\u001b[0m\n" + "\u001b[33mWARNING:lightwood-2821:XGBoost running on CPU\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[33mWARNING:lightwood-2872:XGBoost running on CPU\u001b[0m\n" + "\u001b[33mWARNING:lightwood-2821:XGBoost running on CPU\u001b[0m\n" ] }, { @@ -558,12 +558,12 @@ "name": "stdout", "output_type": "stream", "text": [ - "[12:32:08] WARNING: ../src/learner.cc:339: No visible GPU is found, setting `gpu_id` to -1\n", - "[12:32:08] WARNING: ../src/learner.cc:339: No visible GPU is found, setting `gpu_id` to -1\n", - "[12:32:08] WARNING: ../src/learner.cc:339: No visible GPU is found, setting `gpu_id` to -1\n", - "[12:32:08] WARNING: ../src/learner.cc:339: No visible GPU is found, setting `gpu_id` to -1\n", - "[12:32:08] WARNING: ../src/learner.cc:339: No visible GPU is found, setting `gpu_id` to -1\n", - "[12:32:08] WARNING: ../src/learner.cc:339: No visible GPU is found, setting `gpu_id` to -1\n" + "[12:39:14] WARNING: ../src/learner.cc:339: No visible GPU is found, setting `gpu_id` to -1\n", + "[12:39:14] WARNING: ../src/learner.cc:339: No visible GPU is found, setting `gpu_id` to -1\n", + "[12:39:14] WARNING: ../src/learner.cc:339: No visible GPU is found, setting `gpu_id` to -1\n", + "[12:39:14] WARNING: ../src/learner.cc:339: No visible GPU is found, setting `gpu_id` to -1\n", + "[12:39:14] WARNING: ../src/learner.cc:339: No visible GPU is found, setting `gpu_id` to -1\n", + "[12:39:14] WARNING: ../src/learner.cc:339: No visible GPU is found, setting `gpu_id` to -1\n" ] }, { @@ -575,168 +575,168 @@ "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:1578.)\n", " exp_avg_sq.mul_(beta2).addcmul_(1 - beta2, grad, grad)\n", - "\u001b[32mINFO:lightwood-2872:Loss of 9.051180630922318 with learning rate 0.0001\u001b[0m\n" + "\u001b[32mINFO:lightwood-2821:Loss of 9.051180630922318 with learning rate 0.0001\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:lightwood-2872:Loss of 9.014871209859848 with learning rate 0.0005\u001b[0m\n" + "\u001b[32mINFO:lightwood-2821:Loss of 9.014871209859848 with learning rate 0.0005\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:lightwood-2872:Loss of 8.969509482383728 with learning rate 0.001\u001b[0m\n" + "\u001b[32mINFO:lightwood-2821:Loss of 8.969509482383728 with learning rate 0.001\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:lightwood-2872:Loss of 8.879052013158798 with learning rate 0.002\u001b[0m\n" + "\u001b[32mINFO:lightwood-2821:Loss of 8.879052013158798 with learning rate 0.002\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:lightwood-2872:Loss of 8.788950502872467 with learning rate 0.003\u001b[0m\n" + "\u001b[32mINFO:lightwood-2821:Loss of 8.788950502872467 with learning rate 0.003\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:lightwood-2872:Loss of 8.611965209245682 with learning rate 0.005\u001b[0m\n" + "\u001b[32mINFO:lightwood-2821:Loss of 8.611965209245682 with learning rate 0.005\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:lightwood-2872:Loss of 8.195775926113129 with learning rate 0.01\u001b[0m\n" + "\u001b[32mINFO:lightwood-2821:Loss of 8.195775926113129 with learning rate 0.01\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:lightwood-2872:Loss of 6.255893141031265 with learning rate 0.05\u001b[0m\n" + "\u001b[32mINFO:lightwood-2821:Loss of 6.255893141031265 with learning rate 0.05\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:lightwood-2872:Found learning rate of: 0.05\u001b[0m\n" + "\u001b[32mINFO:lightwood-2821:Found learning rate of: 0.05\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:lightwood-2872:Loss @ epoch 1: 0.5818348675966263\u001b[0m\n" + "\u001b[32mINFO:lightwood-2821:Loss @ epoch 1: 0.5818348675966263\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:lightwood-2872:Loss @ epoch 2: 0.4797109067440033\u001b[0m\n" + "\u001b[32mINFO:lightwood-2821:Loss @ epoch 2: 0.4797109067440033\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:lightwood-2872:Loss @ epoch 3: 0.48386093974113464\u001b[0m\n" + "\u001b[32mINFO:lightwood-2821:Loss @ epoch 3: 0.48386093974113464\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:lightwood-2872:Loss @ epoch 4: 0.49511992931365967\u001b[0m\n" + "\u001b[32mINFO:lightwood-2821:Loss @ epoch 4: 0.49511992931365967\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:lightwood-2872:Loss @ epoch 5: 0.39475560188293457\u001b[0m\n" + "\u001b[32mINFO:lightwood-2821:Loss @ epoch 5: 0.39475560188293457\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:lightwood-2872:Loss @ epoch 6: 0.39592696726322174\u001b[0m\n" + "\u001b[32mINFO:lightwood-2821:Loss @ epoch 6: 0.39592696726322174\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:lightwood-2872:Loss @ epoch 7: 0.3622782379388809\u001b[0m\n" + "\u001b[32mINFO:lightwood-2821:Loss @ epoch 7: 0.3622782379388809\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:lightwood-2872:Loss @ epoch 8: 0.38170479238033295\u001b[0m\n" + "\u001b[32mINFO:lightwood-2821:Loss @ epoch 8: 0.38170479238033295\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:lightwood-2872:Loss @ epoch 9: 0.5138543993234634\u001b[0m\n" + "\u001b[32mINFO:lightwood-2821:Loss @ epoch 9: 0.5138543993234634\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:lightwood-2872:Loss @ epoch 10: 0.6360723078250885\u001b[0m\n" + "\u001b[32mINFO:lightwood-2821:Loss @ epoch 10: 0.6360723078250885\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:lightwood-2872:Loss @ epoch 1: 0.29868809472430835\u001b[0m\n" + "\u001b[32mINFO:lightwood-2821:Loss @ epoch 1: 0.29868809472430835\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:lightwood-2872:Loss @ epoch 2: 0.30318967591632495\u001b[0m\n" + "\u001b[32mINFO:lightwood-2821:Loss @ epoch 2: 0.30318967591632495\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[37mDEBUG:lightwood-2872: `fit_mixer` runtime: 0.87 seconds\u001b[0m\n" + "\u001b[37mDEBUG:lightwood-2821: `fit_mixer` runtime: 0.86 seconds\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:lightwood-2872:Started fitting LGBM models for array prediction\u001b[0m\n" + "\u001b[32mINFO:lightwood-2821:Started fitting LGBM models for array prediction\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:lightwood-2872:Started fitting XGBoost model\u001b[0m\n" + "\u001b[32mINFO:lightwood-2821:Started fitting XGBoost model\u001b[0m\n" ] }, { @@ -750,14 +750,14 @@ "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:lightwood-2872:A single GBM iteration takes 0.1 seconds\u001b[0m\n" + "\u001b[32mINFO:lightwood-2821:A single GBM iteration takes 0.1 seconds\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:lightwood-2872:Training XGBoost with 57023 iterations given 7127.987446546555 seconds constraint\u001b[0m\n" + "\u001b[32mINFO:lightwood-2821:Training XGBoost with 57023 iterations given 7127.987055063248 seconds constraint\u001b[0m\n" ] }, { @@ -858,11 +858,18 @@ "[13]\tvalidation_0-rmse:15.87505\n" ] }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[14]\tvalidation_0-rmse:16.06330\n" + ] + }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:lightwood-2872:Started fitting XGBoost model\u001b[0m\n" + "\u001b[32mINFO:lightwood-2821:Started fitting XGBoost model\u001b[0m\n" ] }, { @@ -876,14 +883,14 @@ "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:lightwood-2872:A single GBM iteration takes 0.1 seconds\u001b[0m\n" + "\u001b[32mINFO:lightwood-2821:A single GBM iteration takes 0.1 seconds\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:lightwood-2872:Training XGBoost with 57023 iterations given 7127.988590955734 seconds constraint\u001b[0m\n" + "\u001b[32mINFO:lightwood-2821:Training XGBoost with 57023 iterations given 7127.986838340759 seconds constraint\u001b[0m\n" ] }, { @@ -984,11 +991,18 @@ "[13]\tvalidation_0-rmse:17.75939\n" ] }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[14]\tvalidation_0-rmse:17.84796\n" + ] + }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:lightwood-2872:Started fitting XGBoost model\u001b[0m\n" + "\u001b[32mINFO:lightwood-2821:Started fitting XGBoost model\u001b[0m\n" ] }, { @@ -1002,14 +1016,14 @@ "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:lightwood-2872:A single GBM iteration takes 0.1 seconds\u001b[0m\n" + "\u001b[32mINFO:lightwood-2821:A single GBM iteration takes 0.1 seconds\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:lightwood-2872:Training XGBoost with 57023 iterations given 7127.988853693008 seconds constraint\u001b[0m\n" + "\u001b[32mINFO:lightwood-2821:Training XGBoost with 57023 iterations given 7127.9879677295685 seconds constraint\u001b[0m\n" ] }, { @@ -1107,7 +1121,7 @@ "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:lightwood-2872:Started fitting XGBoost model\u001b[0m\n" + "\u001b[32mINFO:lightwood-2821:Started fitting XGBoost model\u001b[0m\n" ] }, { @@ -1121,14 +1135,14 @@ "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:lightwood-2872:A single GBM iteration takes 0.1 seconds\u001b[0m\n" + "\u001b[32mINFO:lightwood-2821:A single GBM iteration takes 0.1 seconds\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:lightwood-2872:Training XGBoost with 57023 iterations given 7127.988720655441 seconds constraint\u001b[0m\n" + "\u001b[32mINFO:lightwood-2821:Training XGBoost with 57023 iterations given 7127.9887981414795 seconds constraint\u001b[0m\n" ] }, { @@ -1142,14 +1156,7 @@ "name": "stdout", "output_type": "stream", "text": [ - "[1]\tvalidation_0-rmse:34.13289" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n" + "[1]\tvalidation_0-rmse:34.13289\n" ] }, { @@ -1240,7 +1247,7 @@ "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:lightwood-2872:Started fitting XGBoost model\u001b[0m\n" + "\u001b[32mINFO:lightwood-2821:Started fitting XGBoost model\u001b[0m\n" ] }, { @@ -1254,14 +1261,14 @@ "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:lightwood-2872:A single GBM iteration takes 0.1 seconds\u001b[0m\n" + "\u001b[32mINFO:lightwood-2821:A single GBM iteration takes 0.1 seconds\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:lightwood-2872:Training XGBoost with 57023 iterations given 7127.9883551597595 seconds constraint\u001b[0m\n" + "\u001b[32mINFO:lightwood-2821:Training XGBoost with 57023 iterations given 7127.988205194473 seconds constraint\u001b[0m\n" ] }, { @@ -1289,14 +1296,7 @@ "name": "stdout", "output_type": "stream", "text": [ - "[3]\tvalidation_0-rmse:24.63817" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n" + "[3]\tvalidation_0-rmse:24.63817\n" ] }, { @@ -1338,28 +1338,14 @@ "name": "stdout", "output_type": "stream", "text": [ - "[9]\tvalidation_0-rmse:22.10747" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n" + "[9]\tvalidation_0-rmse:22.10747\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "[10]\tvalidation_0-rmse:22.20352" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n" + "[10]\tvalidation_0-rmse:22.20352\n" ] }, { @@ -1373,42 +1359,21 @@ "name": "stdout", "output_type": "stream", "text": [ - "[12]\tvalidation_0-rmse:22.25308" + "[12]\tvalidation_0-rmse:22.25308\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[13]\tvalidation_0-rmse:22.31415" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[14]\tvalidation_0-rmse:22.31000\n" + "[13]\tvalidation_0-rmse:22.31415\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:lightwood-2872:Started fitting XGBoost model\u001b[0m\n" + "\u001b[32mINFO:lightwood-2821:Started fitting XGBoost model\u001b[0m\n" ] }, { @@ -1422,14 +1387,14 @@ "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:lightwood-2872:A single GBM iteration takes 0.1 seconds\u001b[0m\n" + "\u001b[32mINFO:lightwood-2821:A single GBM iteration takes 0.1 seconds\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:lightwood-2872:Training XGBoost with 57023 iterations given 7127.988573551178 seconds constraint\u001b[0m\n" + "\u001b[32mINFO:lightwood-2821:Training XGBoost with 57023 iterations given 7127.987644195557 seconds constraint\u001b[0m\n" ] }, { @@ -1492,14 +1457,7 @@ "name": "stdout", "output_type": "stream", "text": [ - "[8]\tvalidation_0-rmse:21.74380" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n" + "[8]\tvalidation_0-rmse:21.74380\n" ] }, { @@ -1537,130 +1495,123 @@ "[13]\tvalidation_0-rmse:21.68890\n" ] }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[14]\tvalidation_0-rmse:21.70025\n" - ] - }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[37mDEBUG:lightwood-2872: `fit_mixer` runtime: 0.5 seconds\u001b[0m\n" + "\u001b[37mDEBUG:lightwood-2821: `fit_mixer` runtime: 0.5 seconds\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:dataprep_ml-2872:Ensembling the mixer\u001b[0m\n" + "\u001b[32mINFO:dataprep_ml-2821:Ensembling the mixer\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:lightwood-2872:Mixer: NeuralTs got accuracy: 0.875\u001b[0m\n" + "\u001b[32mINFO:lightwood-2821:Mixer: NeuralTs got accuracy: 0.875\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[33mWARNING:lightwood-2872:This model does not output probability estimates\u001b[0m\n" + "\u001b[33mWARNING:lightwood-2821:This model does not output probability estimates\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:lightwood-2872:Mixer: XGBoostArrayMixer got accuracy: 0.869\u001b[0m\n" + "\u001b[32mINFO:lightwood-2821:Mixer: XGBoostArrayMixer got accuracy: 0.869\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:lightwood-2872:Picked best mixer: NeuralTs\u001b[0m\n" + "\u001b[32mINFO:lightwood-2821:Picked best mixer: NeuralTs\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[37mDEBUG:lightwood-2872: `fit` runtime: 1.41 seconds\u001b[0m\n" + "\u001b[37mDEBUG:lightwood-2821: `fit` runtime: 1.4 seconds\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:dataprep_ml-2872:[Learn phase 7/8] - Ensemble analysis\u001b[0m\n" + "\u001b[32mINFO:dataprep_ml-2821:[Learn phase 7/8] - Ensemble analysis\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:dataprep_ml-2872:Analyzing the ensemble of mixers\u001b[0m\n" + "\u001b[32mINFO:dataprep_ml-2821:Analyzing the ensemble of mixers\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:lightwood-2872:The block ICP is now running its analyze() method\u001b[0m\n" + "\u001b[32mINFO:lightwood-2821:The block ICP is now running its analyze() method\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:lightwood-2872:The block ConfStats is now running its analyze() method\u001b[0m\n" + "\u001b[32mINFO:lightwood-2821:The block ConfStats is now running its analyze() method\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:lightwood-2872:The block AccStats is now running its analyze() method\u001b[0m\n" + "\u001b[32mINFO:lightwood-2821:The block AccStats is now running its analyze() method\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:lightwood-2872:The block PermutationFeatureImportance is now running its analyze() method\u001b[0m\n" + "\u001b[32mINFO:lightwood-2821:The block PermutationFeatureImportance is now running its analyze() method\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[33mWARNING:lightwood-2872:Block 'PermutationFeatureImportance' does not support time series nor text encoding, skipping...\u001b[0m\n" + "\u001b[33mWARNING:lightwood-2821:Block 'PermutationFeatureImportance' does not support time series nor text encoding, skipping...\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[37mDEBUG:lightwood-2872: `analyze_ensemble` runtime: 0.16 seconds\u001b[0m\n" + "\u001b[37mDEBUG:lightwood-2821: `analyze_ensemble` runtime: 0.16 seconds\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:dataprep_ml-2872:[Learn phase 8/8] - Adjustment on validation requested\u001b[0m\n" + "\u001b[32mINFO:dataprep_ml-2821:[Learn phase 8/8] - Adjustment on validation requested\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:dataprep_ml-2872:Updating the mixers\u001b[0m\n" + "\u001b[32mINFO:dataprep_ml-2821:Updating the mixers\u001b[0m\n" ] }, { @@ -1668,84 +1619,78 @@ "output_type": "stream", "text": [ "/opt/hostedtoolcache/Python/3.9.19/x64/lib/python3.9/site-packages/torch/amp/grad_scaler.py:131: 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-2872:Loss @ epoch 1: 0.29626286526521045\u001b[0m\n" + " warnings.warn(\n", + "\u001b[32mINFO:lightwood-2821:Loss @ epoch 1: 0.29626286526521045\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:lightwood-2872:Loss @ epoch 2: 0.2954987535874049\u001b[0m\n" + "\u001b[32mINFO:lightwood-2821:Loss @ epoch 2: 0.2954987535874049\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:lightwood-2872:Updating array of LGBM models...\u001b[0m\n" + "\u001b[32mINFO:lightwood-2821:Updating array of LGBM models...\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:lightwood-2872:XGBoost mixer does not have a `partial_fit` implementation\u001b[0m\n" + "\u001b[32mINFO:lightwood-2821:XGBoost mixer does not have a `partial_fit` implementation\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:lightwood-2872:XGBoost mixer does not have a `partial_fit` implementation\u001b[0m\n" + "\u001b[32mINFO:lightwood-2821:XGBoost mixer does not have a `partial_fit` implementation\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:lightwood-2872:XGBoost mixer does not have a `partial_fit` implementation\u001b[0m\n" + "\u001b[32mINFO:lightwood-2821:XGBoost mixer does not have a `partial_fit` implementation\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:lightwood-2872:XGBoost mixer does not have a `partial_fit` implementation\u001b[0m\n" + "\u001b[32mINFO:lightwood-2821:XGBoost mixer does not have a `partial_fit` implementation\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:lightwood-2872:XGBoost mixer does not have a `partial_fit` implementation\u001b[0m\n" + "\u001b[32mINFO:lightwood-2821:XGBoost mixer does not have a `partial_fit` implementation\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:lightwood-2872:XGBoost mixer does not have a `partial_fit` implementation\u001b[0m\n" + "\u001b[32mINFO:lightwood-2821:XGBoost mixer does not have a `partial_fit` implementation\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[37mDEBUG:lightwood-2872: `adjust` runtime: 0.09 seconds\u001b[0m\n" + "\u001b[37mDEBUG:lightwood-2821: `adjust` runtime: 0.1 seconds\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[37mDEBUG:lightwood-2872: `learn` runtime: 1.92 seconds\u001b[0m\n" + "\u001b[37mDEBUG:lightwood-2821: `learn` runtime: 1.91 seconds\u001b[0m\n" ] } ], @@ -1767,10 +1712,10 @@ "execution_count": 7, "metadata": { "execution": { - "iopub.execute_input": "2024-05-15T12:32:10.301152Z", - "iopub.status.busy": "2024-05-15T12:32:10.300912Z", - "iopub.status.idle": "2024-05-15T12:32:10.529312Z", - "shell.execute_reply": "2024-05-15T12:32:10.528664Z" + "iopub.execute_input": "2024-05-15T12:39:16.484128Z", + "iopub.status.busy": "2024-05-15T12:39:16.483722Z", + "iopub.status.idle": "2024-05-15T12:39:16.713196Z", + "shell.execute_reply": "2024-05-15T12:39:16.712480Z" } }, "outputs": [ @@ -1778,20 +1723,20 @@ "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:dataprep_ml-2872:[Predict phase 1/4] - Data preprocessing\u001b[0m\n" + "\u001b[32mINFO:dataprep_ml-2821:[Predict phase 1/4] - Data preprocessing\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "/tmp/11cf2e3a3fe2d38f7293f27fbfc34dab78123e8d8e63ffa417157763283658414.py:587: SettingWithCopyWarning: \n", + "/tmp/b9f9761599676d055346cd2b368083bd2229f6aecb71536117157767545533166.py:587: 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-2872:Cleaning the data\u001b[0m\n" + "\u001b[32mINFO:dataprep_ml-2821:Cleaning the data\u001b[0m\n" ] }, { @@ -1800,119 +1745,119 @@ "text": [ "/opt/hostedtoolcache/Python/3.9.19/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-2872:Transforming timeseries data\u001b[0m\n" + "\u001b[32mINFO:dataprep_ml-2821:Transforming timeseries data\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[37mDEBUG:lightwood-2872: `preprocess` runtime: 0.02 seconds\u001b[0m\n" + "\u001b[37mDEBUG:lightwood-2821: `preprocess` runtime: 0.02 seconds\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:dataprep_ml-2872:[Predict phase 2/4] - Feature generation\u001b[0m\n" + "\u001b[32mINFO:dataprep_ml-2821:[Predict phase 2/4] - Feature generation\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:dataprep_ml-2872:Featurizing the data\u001b[0m\n" + "\u001b[32mINFO:dataprep_ml-2821:Featurizing the data\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[37mDEBUG:lightwood-2872: `featurize` runtime: 0.01 seconds\u001b[0m\n" + "\u001b[37mDEBUG:lightwood-2821: `featurize` runtime: 0.01 seconds\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:dataprep_ml-2872:[Predict phase 3/4] - Calling ensemble\u001b[0m\n" + "\u001b[32mINFO:dataprep_ml-2821:[Predict phase 3/4] - Calling ensemble\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[37mDEBUG:lightwood-2872: `_timed_call` runtime: 0.09 seconds\u001b[0m\n" + "\u001b[37mDEBUG:lightwood-2821: `_timed_call` runtime: 0.09 seconds\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:dataprep_ml-2872:[Predict phase 4/4] - Analyzing output\u001b[0m\n" + "\u001b[32mINFO:dataprep_ml-2821:[Predict phase 4/4] - Analyzing output\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:lightwood-2872:The block ICP is now running its explain() method\u001b[0m\n" + "\u001b[32mINFO:lightwood-2821:The block ICP is now running its explain() method\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:lightwood-2872:The block ConfStats is now running its explain() method\u001b[0m\n" + "\u001b[32mINFO:lightwood-2821:The block ConfStats is now running its explain() method\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:lightwood-2872:ConfStats.explain() has not been implemented, no modifications will be done to the data insights.\u001b[0m\n" + "\u001b[32mINFO:lightwood-2821: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-2872:The block AccStats is now running its explain() method\u001b[0m\n" + "\u001b[32mINFO:lightwood-2821:The block AccStats is now running its explain() method\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:lightwood-2872:AccStats.explain() has not been implemented, no modifications will be done to the data insights.\u001b[0m\n" + "\u001b[32mINFO:lightwood-2821: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-2872:The block PermutationFeatureImportance is now running its explain() method\u001b[0m\n" + "\u001b[32mINFO:lightwood-2821:The block PermutationFeatureImportance is now running its explain() method\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:lightwood-2872:PermutationFeatureImportance.explain() has not been implemented, no modifications will be done to the data insights.\u001b[0m\n" + "\u001b[32mINFO:lightwood-2821: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-2872: `explain` runtime: 0.09 seconds\u001b[0m\n" + "\u001b[37mDEBUG:lightwood-2821: `explain` runtime: 0.09 seconds\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[37mDEBUG:lightwood-2872: `predict` runtime: 0.22 seconds\u001b[0m\n" + "\u001b[37mDEBUG:lightwood-2821: `predict` runtime: 0.22 seconds\u001b[0m\n" ] } ], @@ -1932,10 +1877,10 @@ "execution_count": 8, "metadata": { "execution": { - "iopub.execute_input": "2024-05-15T12:32:10.531834Z", - "iopub.status.busy": "2024-05-15T12:32:10.531626Z", - "iopub.status.idle": "2024-05-15T12:32:10.542916Z", - "shell.execute_reply": "2024-05-15T12:32:10.542282Z" + "iopub.execute_input": "2024-05-15T12:39:16.715848Z", + "iopub.status.busy": "2024-05-15T12:39:16.715440Z", + "iopub.status.idle": "2024-05-15T12:39:16.726742Z", + "shell.execute_reply": "2024-05-15T12:39:16.726049Z" } }, "outputs": [ @@ -2040,10 +1985,10 @@ "execution_count": 9, "metadata": { "execution": { - "iopub.execute_input": "2024-05-15T12:32:10.545546Z", - "iopub.status.busy": "2024-05-15T12:32:10.545158Z", - "iopub.status.idle": "2024-05-15T12:32:10.951600Z", - "shell.execute_reply": "2024-05-15T12:32:10.950867Z" + "iopub.execute_input": "2024-05-15T12:39:16.729246Z", + "iopub.status.busy": "2024-05-15T12:39:16.728891Z", + "iopub.status.idle": "2024-05-15T12:39:17.132478Z", + "shell.execute_reply": "2024-05-15T12:39:17.131830Z" } }, "outputs": [], @@ -2056,10 +2001,10 @@ "execution_count": 10, "metadata": { "execution": { - "iopub.execute_input": "2024-05-15T12:32:10.954775Z", - "iopub.status.busy": "2024-05-15T12:32:10.954472Z", - "iopub.status.idle": "2024-05-15T12:32:11.144915Z", - "shell.execute_reply": "2024-05-15T12:32:11.144230Z" + "iopub.execute_input": "2024-05-15T12:39:17.135502Z", + "iopub.status.busy": "2024-05-15T12:39:17.135009Z", + "iopub.status.idle": "2024-05-15T12:39:17.315414Z", + "shell.execute_reply": "2024-05-15T12:39:17.314713Z" } }, "outputs": [ diff --git a/tutorials/tutorial_update_models/tutorial_update_models.html b/tutorials/tutorial_update_models/tutorial_update_models.html index 3cfce6424..b4cbe7ef8 100644 --- a/tutorials/tutorial_update_models/tutorial_update_models.html +++ b/tutorials/tutorial_update_models/tutorial_update_models.html @@ -110,8 +110,8 @@

Initial model training 
-INFO:lightwood-2759:No torchvision detected, image helpers not supported.
-INFO:lightwood-2759:No torchvision/pillow detected, image encoder not supported
+INFO:lightwood-2649:No torchvision detected, image helpers not supported.
+INFO:lightwood-2649:No torchvision/pillow detected, image encoder not supported
@@ -181,58 +181,58 @@

Initial model training 
-INFO:type_infer-2759:Analyzing a sample of 979
-INFO:type_infer-2759:from a total population of 1030, this is equivalent to 95.0% of your data.
-INFO:type_infer-2759:Using 3 processes to deduct types.
-INFO:type_infer-2759:Infering type for: cement
-INFO:type_infer-2759:Infering type for: slag
-INFO:type_infer-2759:Column cement has data type float
-INFO:type_infer-2759:Column slag has data type float
-INFO:type_infer-2759:Infering type for: water
-INFO:type_infer-2759:Infering type for: flyAsh
-INFO:type_infer-2759:Column water has data type float
-INFO:type_infer-2759:Column flyAsh has data type float
-INFO:type_infer-2759:Infering type for: superPlasticizer
-INFO:type_infer-2759:Infering type for: coarseAggregate
-INFO:type_infer-2759:Column coarseAggregate has data type float
-INFO:type_infer-2759:Column superPlasticizer has data type float
-INFO:type_infer-2759:Infering type for: fineAggregate
-INFO:type_infer-2759:Infering type for: age
-INFO:type_infer-2759:Column age has data type integer
-INFO:type_infer-2759:Infering type for: concrete_strength
-INFO:type_infer-2759:Column fineAggregate has data type float
-INFO:type_infer-2759:Column concrete_strength has data type float
-INFO:type_infer-2759:Infering type for: id
-INFO:type_infer-2759:Column id has data type integer
-INFO:dataprep_ml-2759:Starting statistical analysis
-INFO:dataprep_ml-2759:Finished statistical analysis
-INFO:dataprep_ml-2759:[Learn phase 1/8] - Statistical analysis
-INFO:dataprep_ml-2759:Starting statistical analysis
-INFO:dataprep_ml-2759:Finished statistical analysis
-DEBUG:lightwood-2759: `analyze_data` runtime: 0.02 seconds
-INFO:dataprep_ml-2759:[Learn phase 2/8] - Data preprocessing
-INFO:dataprep_ml-2759:Cleaning the data
-DEBUG:lightwood-2759: `preprocess` runtime: 0.01 seconds
-INFO:dataprep_ml-2759:[Learn phase 3/8] - Data splitting
-INFO:dataprep_ml-2759:Splitting the data into train/test
-DEBUG:lightwood-2759: `split` runtime: 0.0 seconds
-INFO:dataprep_ml-2759:[Learn phase 4/8] - Preparing encoders
-DEBUG:dataprep_ml-2759:Preparing sequentially...
-DEBUG:dataprep_ml-2759:Preparing encoder for id...
-DEBUG:dataprep_ml-2759:Preparing encoder for cement...
-DEBUG:dataprep_ml-2759:Preparing encoder for slag...
-DEBUG:dataprep_ml-2759:Preparing encoder for flyAsh...
-DEBUG:dataprep_ml-2759:Preparing encoder for water...
-DEBUG:dataprep_ml-2759:Preparing encoder for superPlasticizer...
-DEBUG:dataprep_ml-2759:Preparing encoder for coarseAggregate...
-DEBUG:dataprep_ml-2759:Preparing encoder for fineAggregate...
-DEBUG:dataprep_ml-2759:Preparing encoder for age...
-DEBUG:lightwood-2759: `prepare` runtime: 0.01 seconds
-INFO:dataprep_ml-2759:[Learn phase 5/8] - Feature generation
-INFO:dataprep_ml-2759:Featurizing the data
-DEBUG:lightwood-2759: `featurize` runtime: 0.06 seconds
-INFO:dataprep_ml-2759:[Learn phase 6/8] - Mixer training
-INFO:dataprep_ml-2759:Training the mixers
+INFO:type_infer-2649:Analyzing a sample of 979
+INFO:type_infer-2649:from a total population of 1030, this is equivalent to 95.0% of your data.
+INFO:type_infer-2649:Using 3 processes to deduct types.
+INFO:type_infer-2649:Infering type for: cement
+INFO:type_infer-2649:Infering type for: slag
+INFO:type_infer-2649:Column slag has data type float
+INFO:type_infer-2649:Column cement has data type float
+INFO:type_infer-2649:Infering type for: water
+INFO:type_infer-2649:Infering type for: flyAsh
+INFO:type_infer-2649:Column water has data type float
+INFO:type_infer-2649:Column flyAsh has data type float
+INFO:type_infer-2649:Infering type for: superPlasticizer
+INFO:type_infer-2649:Infering type for: coarseAggregate
+INFO:type_infer-2649:Infering type for: id
+INFO:type_infer-2649:Column coarseAggregate has data type float
+INFO:type_infer-2649:Column superPlasticizer has data type float
+INFO:type_infer-2649:Column id has data type integer
+INFO:type_infer-2649:Infering type for: fineAggregate
+INFO:type_infer-2649:Infering type for: age
+INFO:type_infer-2649:Infering type for: concrete_strength
+INFO:type_infer-2649:Column age has data type integer
+INFO:type_infer-2649:Column fineAggregate has data type float
+INFO:type_infer-2649:Column concrete_strength has data type float
+INFO:dataprep_ml-2649:Starting statistical analysis
+INFO:dataprep_ml-2649:Finished statistical analysis
+INFO:dataprep_ml-2649:[Learn phase 1/8] - Statistical analysis
+INFO:dataprep_ml-2649:Starting statistical analysis
+INFO:dataprep_ml-2649:Finished statistical analysis
+DEBUG:lightwood-2649: `analyze_data` runtime: 0.02 seconds
+INFO:dataprep_ml-2649:[Learn phase 2/8] - Data preprocessing
+INFO:dataprep_ml-2649:Cleaning the data
+DEBUG:lightwood-2649: `preprocess` runtime: 0.01 seconds
+INFO:dataprep_ml-2649:[Learn phase 3/8] - Data splitting
+INFO:dataprep_ml-2649:Splitting the data into train/test
+DEBUG:lightwood-2649: `split` runtime: 0.0 seconds
+INFO:dataprep_ml-2649:[Learn phase 4/8] - Preparing encoders
+DEBUG:dataprep_ml-2649:Preparing sequentially...
+DEBUG:dataprep_ml-2649:Preparing encoder for id...
+DEBUG:dataprep_ml-2649:Preparing encoder for cement...
+DEBUG:dataprep_ml-2649:Preparing encoder for slag...
+DEBUG:dataprep_ml-2649:Preparing encoder for flyAsh...
+DEBUG:dataprep_ml-2649:Preparing encoder for water...
+DEBUG:dataprep_ml-2649:Preparing encoder for superPlasticizer...
+DEBUG:dataprep_ml-2649:Preparing encoder for coarseAggregate...
+DEBUG:dataprep_ml-2649:Preparing encoder for fineAggregate...
+DEBUG:dataprep_ml-2649:Preparing encoder for age...
+DEBUG:lightwood-2649: `prepare` runtime: 0.01 seconds
+INFO:dataprep_ml-2649:[Learn phase 5/8] - Feature generation
+INFO:dataprep_ml-2649:Featurizing the data
+DEBUG:lightwood-2649: `featurize` runtime: 0.06 seconds
+INFO:dataprep_ml-2649:[Learn phase 6/8] - Mixer training
+INFO:dataprep_ml-2649:Training the mixers
 /opt/hostedtoolcache/Python/3.9.19/x64/lib/python3.9/site-packages/torch/amp/grad_scaler.py:131: UserWarning: torch.cuda.amp.GradScaler is enabled, but CUDA is not available.  Disabling.
   warnings.warn(
 /opt/hostedtoolcache/Python/3.9.19/x64/lib/python3.9/site-packages/pytorch_ranger/ranger.py:172: UserWarning: This overload of addcmul_ is deprecated:
@@ -240,43 +240,43 @@ 
Initial model trainingINFO:lightwood-2759:Loss of 39.99637508392334 with learning rate 0.0001
-INFO:lightwood-2759:Loss of 21.826460361480713 with learning rate 0.0005
-INFO:lightwood-2759:Loss of 15.12899512052536 with learning rate 0.001
-INFO:lightwood-2759:Loss of 15.062753021717072 with learning rate 0.002
-INFO:lightwood-2759:Loss of 26.490495562553406 with learning rate 0.003
-INFO:lightwood-2759:Loss of 33.6572003364563 with learning rate 0.005
-INFO:lightwood-2759:Loss of 303.60721158981323 with learning rate 0.01
-INFO:lightwood-2759:Loss of nan with learning rate 0.05
-INFO:lightwood-2759:Found learning rate of: 0.002
-INFO:lightwood-2759:Loss @ epoch 1: 0.11838734149932861
-INFO:lightwood-2759:Loss @ epoch 2: 0.4641949534416199
-INFO:lightwood-2759:Loss @ epoch 3: 0.3976145386695862
-INFO:lightwood-2759:Loss @ epoch 4: 0.3706841468811035
-INFO:lightwood-2759:Loss @ epoch 5: 0.2367912232875824
-INFO:lightwood-2759:Loss @ epoch 6: 0.22560915350914001
-INFO:lightwood-2759:Loss @ epoch 7: 0.12089195847511292
-DEBUG:lightwood-2759: `fit_mixer` runtime: 0.52 seconds
-INFO:dataprep_ml-2759:Ensembling the mixer
-INFO:lightwood-2759:Mixer: Neural got accuracy: 0.238
-INFO:lightwood-2759:Picked best mixer: Neural
-DEBUG:lightwood-2759: `fit` runtime: 0.53 seconds
-INFO:dataprep_ml-2759:[Learn phase 7/8] - Ensemble analysis
-INFO:dataprep_ml-2759:Analyzing the ensemble of mixers
-INFO:lightwood-2759:The block ICP is now running its analyze() method
-INFO:lightwood-2759:The block ConfStats is now running its analyze() method
-INFO:lightwood-2759:The block AccStats is now running its analyze() method
-INFO:lightwood-2759:The block PermutationFeatureImportance is now running its analyze() method
-INFO:lightwood-2759:[PFI] Using a random sample (1000 rows out of 10).
-INFO:lightwood-2759:[PFI] Set to consider first 10 columns out of 9: ['id', 'cement', 'slag', 'flyAsh', 'water', 'superPlasticizer', 'coarseAggregate', 'fineAggregate', 'age'].
-DEBUG:lightwood-2759: `analyze_ensemble` runtime: 0.15 seconds
-INFO:dataprep_ml-2759:[Learn phase 8/8] - Adjustment on validation requested
-INFO:dataprep_ml-2759:Updating the mixers
+INFO:lightwood-2649:Loss of 39.99637508392334 with learning rate 0.0001
+INFO:lightwood-2649:Loss of 21.826460361480713 with learning rate 0.0005
+INFO:lightwood-2649:Loss of 15.12899512052536 with learning rate 0.001
+INFO:lightwood-2649:Loss of 15.062753021717072 with learning rate 0.002
+INFO:lightwood-2649:Loss of 26.490495562553406 with learning rate 0.003
+INFO:lightwood-2649:Loss of 33.6572003364563 with learning rate 0.005
+INFO:lightwood-2649:Loss of 303.60721158981323 with learning rate 0.01
+INFO:lightwood-2649:Loss of nan with learning rate 0.05
+INFO:lightwood-2649:Found learning rate of: 0.002
+INFO:lightwood-2649:Loss @ epoch 1: 0.11838734149932861
+INFO:lightwood-2649:Loss @ epoch 2: 0.4641949534416199
+INFO:lightwood-2649:Loss @ epoch 3: 0.3976145386695862
+INFO:lightwood-2649:Loss @ epoch 4: 0.3706841468811035
+INFO:lightwood-2649:Loss @ epoch 5: 0.2367912232875824
+INFO:lightwood-2649:Loss @ epoch 6: 0.22560915350914001
+INFO:lightwood-2649:Loss @ epoch 7: 0.12089195847511292
+DEBUG:lightwood-2649: `fit_mixer` runtime: 0.53 seconds
+INFO:dataprep_ml-2649:Ensembling the mixer
+INFO:lightwood-2649:Mixer: Neural got accuracy: 0.238
+INFO:lightwood-2649:Picked best mixer: Neural
+DEBUG:lightwood-2649: `fit` runtime: 0.54 seconds
+INFO:dataprep_ml-2649:[Learn phase 7/8] - Ensemble analysis
+INFO:dataprep_ml-2649:Analyzing the ensemble of mixers
+INFO:lightwood-2649:The block ICP is now running its analyze() method
+INFO:lightwood-2649:The block ConfStats is now running its analyze() method
+INFO:lightwood-2649:The block AccStats is now running its analyze() method
+INFO:lightwood-2649:The block PermutationFeatureImportance is now running its analyze() method
+INFO:lightwood-2649:[PFI] Using a random sample (1000 rows out of 10).
+INFO:lightwood-2649:[PFI] Set to consider first 10 columns out of 9: ['id', 'cement', 'slag', 'flyAsh', 'water', 'superPlasticizer', 'coarseAggregate', 'fineAggregate', 'age'].
+DEBUG:lightwood-2649: `analyze_ensemble` runtime: 0.15 seconds
+INFO:dataprep_ml-2649:[Learn phase 8/8] - Adjustment on validation requested
+INFO:dataprep_ml-2649:Updating the mixers
 /opt/hostedtoolcache/Python/3.9.19/x64/lib/python3.9/site-packages/torch/amp/grad_scaler.py:131: UserWarning: torch.cuda.amp.GradScaler is enabled, but CUDA is not available.  Disabling.
   warnings.warn(
-INFO:lightwood-2759:Loss @ epoch 1: 0.1678172747294108
-DEBUG:lightwood-2759: `adjust` runtime: 0.03 seconds
-DEBUG:lightwood-2759: `learn` runtime: 0.82 seconds
+INFO:lightwood-2649:Loss @ epoch 1: 0.1678172747294108
+DEBUG:lightwood-2649: `adjust` runtime: 0.03 seconds
+DEBUG:lightwood-2649: `learn` runtime: 0.83 seconds

@@ -294,24 +294,24 @@

Initial model training 
-INFO:dataprep_ml-2759:[Predict phase 1/4] - Data preprocessing
-INFO:dataprep_ml-2759:Cleaning the data
-DEBUG:lightwood-2759: `preprocess` runtime: 0.01 seconds
-INFO:dataprep_ml-2759:[Predict phase 2/4] - Feature generation
-INFO:dataprep_ml-2759:Featurizing the data
-DEBUG:lightwood-2759: `featurize` runtime: 0.03 seconds
-INFO:dataprep_ml-2759:[Predict phase 3/4] - Calling ensemble
-DEBUG:lightwood-2759: `_timed_call` runtime: 0.03 seconds
-INFO:dataprep_ml-2759:[Predict phase 4/4] - Analyzing output
-INFO:lightwood-2759:The block ICP is now running its explain() method
-INFO:lightwood-2759:The block ConfStats is now running its explain() method
-INFO:lightwood-2759:ConfStats.explain() has not been implemented, no modifications will be done to the data insights.
-INFO:lightwood-2759:The block AccStats is now running its explain() method
-INFO:lightwood-2759:AccStats.explain() has not been implemented, no modifications will be done to the data insights.
-INFO:lightwood-2759:The block PermutationFeatureImportance is now running its explain() method
-INFO:lightwood-2759:PermutationFeatureImportance.explain() has not been implemented, no modifications will be done to the data insights.
-DEBUG:lightwood-2759: `explain` runtime: 0.05 seconds
-DEBUG:lightwood-2759: `predict` runtime: 0.13 seconds
+INFO:dataprep_ml-2649:[Predict phase 1/4] - Data preprocessing
+INFO:dataprep_ml-2649:Cleaning the data
+DEBUG:lightwood-2649: `preprocess` runtime: 0.01 seconds
+INFO:dataprep_ml-2649:[Predict phase 2/4] - Feature generation
+INFO:dataprep_ml-2649:Featurizing the data
+DEBUG:lightwood-2649: `featurize` runtime: 0.03 seconds
+INFO:dataprep_ml-2649:[Predict phase 3/4] - Calling ensemble
+DEBUG:lightwood-2649: `_timed_call` runtime: 0.03 seconds
+INFO:dataprep_ml-2649:[Predict phase 4/4] - Analyzing output
+INFO:lightwood-2649:The block ICP is now running its explain() method
+INFO:lightwood-2649:The block ConfStats is now running its explain() method
+INFO:lightwood-2649:ConfStats.explain() has not been implemented, no modifications will be done to the data insights.
+INFO:lightwood-2649:The block AccStats is now running its explain() method
+INFO:lightwood-2649:AccStats.explain() has not been implemented, no modifications will be done to the data insights.
+INFO:lightwood-2649:The block PermutationFeatureImportance is now running its explain() method
+INFO:lightwood-2649:PermutationFeatureImportance.explain() has not been implemented, no modifications will be done to the data insights.
+DEBUG:lightwood-2649: `explain` runtime: 0.05 seconds
+DEBUG:lightwood-2649: `predict` runtime: 0.13 seconds

@@ -473,15 +473,15 @@

PredictorInterf 

-INFO:dataprep_ml-2759:Cleaning the data
-DEBUG:lightwood-2759: `preprocess` runtime: 0.02 seconds
-INFO:dataprep_ml-2759:Cleaning the data
-DEBUG:lightwood-2759: `preprocess` runtime: 0.01 seconds
-INFO:dataprep_ml-2759:Updating the mixers
+INFO:dataprep_ml-2649:Cleaning the data
+DEBUG:lightwood-2649: `preprocess` runtime: 0.02 seconds
+INFO:dataprep_ml-2649:Cleaning the data
+DEBUG:lightwood-2649: `preprocess` runtime: 0.01 seconds
+INFO:dataprep_ml-2649:Updating the mixers
 /opt/hostedtoolcache/Python/3.9.19/x64/lib/python3.9/site-packages/torch/amp/grad_scaler.py:131: UserWarning: torch.cuda.amp.GradScaler is enabled, but CUDA is not available.  Disabling.
   warnings.warn(
-INFO:lightwood-2759:Loss @ epoch 1: 0.10915952424208324
-DEBUG:lightwood-2759: `adjust` runtime: 0.11 seconds
+INFO:lightwood-2649:Loss @ epoch 1: 0.10915952424208324
+DEBUG:lightwood-2649: `adjust` runtime: 0.11 seconds
@@ -498,24 +498,24 @@

PredictorInterf 

-INFO:dataprep_ml-2759:[Predict phase 1/4] - Data preprocessing
-INFO:dataprep_ml-2759:Cleaning the data
-DEBUG:lightwood-2759: `preprocess` runtime: 0.01 seconds
-INFO:dataprep_ml-2759:[Predict phase 2/4] - Feature generation
-INFO:dataprep_ml-2759:Featurizing the data
-DEBUG:lightwood-2759: `featurize` runtime: 0.03 seconds
-INFO:dataprep_ml-2759:[Predict phase 3/4] - Calling ensemble
-DEBUG:lightwood-2759: `_timed_call` runtime: 0.03 seconds
-INFO:dataprep_ml-2759:[Predict phase 4/4] - Analyzing output
-INFO:lightwood-2759:The block ICP is now running its explain() method
-INFO:lightwood-2759:The block ConfStats is now running its explain() method
-INFO:lightwood-2759:ConfStats.explain() has not been implemented, no modifications will be done to the data insights.
-INFO:lightwood-2759:The block AccStats is now running its explain() method
-INFO:lightwood-2759:AccStats.explain() has not been implemented, no modifications will be done to the data insights.
-INFO:lightwood-2759:The block PermutationFeatureImportance is now running its explain() method
-INFO:lightwood-2759:PermutationFeatureImportance.explain() has not been implemented, no modifications will be done to the data insights.
-DEBUG:lightwood-2759: `explain` runtime: 0.05 seconds
-DEBUG:lightwood-2759: `predict` runtime: 0.13 seconds
+INFO:dataprep_ml-2649:[Predict phase 1/4] - Data preprocessing
+INFO:dataprep_ml-2649:Cleaning the data
+DEBUG:lightwood-2649: `preprocess` runtime: 0.01 seconds
+INFO:dataprep_ml-2649:[Predict phase 2/4] - Feature generation
+INFO:dataprep_ml-2649:Featurizing the data
+DEBUG:lightwood-2649: `featurize` runtime: 0.03 seconds
+INFO:dataprep_ml-2649:[Predict phase 3/4] - Calling ensemble
+DEBUG:lightwood-2649: `_timed_call` runtime: 0.03 seconds
+INFO:dataprep_ml-2649:[Predict phase 4/4] - Analyzing output
+INFO:lightwood-2649:The block ICP is now running its explain() method
+INFO:lightwood-2649:The block ConfStats is now running its explain() method
+INFO:lightwood-2649:ConfStats.explain() has not been implemented, no modifications will be done to the data insights.
+INFO:lightwood-2649:The block AccStats is now running its explain() method
+INFO:lightwood-2649:AccStats.explain() has not been implemented, no modifications will be done to the data insights.
+INFO:lightwood-2649:The block PermutationFeatureImportance is now running its explain() method
+INFO:lightwood-2649:PermutationFeatureImportance.explain() has not been implemented, no modifications will be done to the data insights.
+DEBUG:lightwood-2649: `explain` runtime: 0.05 seconds
+DEBUG:lightwood-2649: `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 32ca35f36..baad7f850 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": "2024-05-15T12:31:30.371645Z", - "iopub.status.busy": "2024-05-15T12:31:30.371452Z", - "iopub.status.idle": "2024-05-15T12:31:33.203343Z", - "shell.execute_reply": "2024-05-15T12:31:33.202682Z" + "iopub.execute_input": "2024-05-15T12:38:36.597254Z", + "iopub.status.busy": "2024-05-15T12:38:36.597058Z", + "iopub.status.idle": "2024-05-15T12:38:39.444383Z", + "shell.execute_reply": "2024-05-15T12:38:39.443711Z" } }, "outputs": [ @@ -32,14 +32,14 @@ "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:lightwood-2759:No torchvision detected, image helpers not supported.\u001b[0m\n" + "\u001b[32mINFO:lightwood-2649:No torchvision detected, image helpers not supported.\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:lightwood-2759:No torchvision/pillow detected, image encoder not supported\u001b[0m\n" + "\u001b[32mINFO:lightwood-2649:No torchvision/pillow detected, image encoder not supported\u001b[0m\n" ] } ], @@ -53,10 +53,10 @@ "execution_count": 2, "metadata": { "execution": { - "iopub.execute_input": "2024-05-15T12:31:33.206422Z", - "iopub.status.busy": "2024-05-15T12:31:33.206158Z", - "iopub.status.idle": "2024-05-15T12:31:33.429712Z", - "shell.execute_reply": "2024-05-15T12:31:33.429076Z" + "iopub.execute_input": "2024-05-15T12:38:39.447713Z", + "iopub.status.busy": "2024-05-15T12:38:39.447197Z", + "iopub.status.idle": "2024-05-15T12:38:39.569705Z", + "shell.execute_reply": "2024-05-15T12:38:39.568972Z" } }, "outputs": [ @@ -98,10 +98,10 @@ "execution_count": 3, "metadata": { "execution": { - "iopub.execute_input": "2024-05-15T12:31:33.432194Z", - "iopub.status.busy": "2024-05-15T12:31:33.431990Z", - "iopub.status.idle": "2024-05-15T12:31:34.871737Z", - "shell.execute_reply": "2024-05-15T12:31:34.871077Z" + "iopub.execute_input": "2024-05-15T12:38:39.572420Z", + "iopub.status.busy": "2024-05-15T12:38:39.572012Z", + "iopub.status.idle": "2024-05-15T12:38:41.013683Z", + "shell.execute_reply": "2024-05-15T12:38:41.013049Z" }, "scrolled": true }, @@ -110,364 +110,364 @@ "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:type_infer-2759:Analyzing a sample of 979\u001b[0m\n" + "\u001b[32mINFO:type_infer-2649:Analyzing a sample of 979\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:type_infer-2759:from a total population of 1030, this is equivalent to 95.0% of your data.\u001b[0m\n" + "\u001b[32mINFO:type_infer-2649: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-2759:Using 3 processes to deduct types.\u001b[0m\n" + "\u001b[32mINFO:type_infer-2649:Using 3 processes to deduct types.\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:type_infer-2759:Infering type for: cement\u001b[0m\n" + "\u001b[32mINFO:type_infer-2649:Infering type for: cement\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:type_infer-2759:Infering type for: slag\u001b[0m\n" + "\u001b[32mINFO:type_infer-2649:Infering type for: slag\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:type_infer-2759:Column cement has data type float\u001b[0m\n" + "\u001b[32mINFO:type_infer-2649:Column slag has data type float\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:type_infer-2759:Column slag has data type float\u001b[0m\n" + "\u001b[32mINFO:type_infer-2649:Column cement has data type float\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:type_infer-2759:Infering type for: water\u001b[0m\n" + "\u001b[32mINFO:type_infer-2649:Infering type for: water\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:type_infer-2759:Infering type for: flyAsh\u001b[0m\n" + "\u001b[32mINFO:type_infer-2649:Infering type for: flyAsh\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:type_infer-2759:Column water has data type float\u001b[0m\n" + "\u001b[32mINFO:type_infer-2649:Column water has data type float\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:type_infer-2759:Column flyAsh has data type float\u001b[0m\n" + "\u001b[32mINFO:type_infer-2649:Column flyAsh has data type float\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:type_infer-2759:Infering type for: superPlasticizer\u001b[0m\n" + "\u001b[32mINFO:type_infer-2649:Infering type for: superPlasticizer\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:type_infer-2759:Infering type for: coarseAggregate\u001b[0m\n" + "\u001b[32mINFO:type_infer-2649:Infering type for: coarseAggregate\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:type_infer-2759:Column coarseAggregate has data type float\u001b[0m\n" + "\u001b[32mINFO:type_infer-2649:Infering type for: id\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:type_infer-2759:Column superPlasticizer has data type float\u001b[0m\n" + "\u001b[32mINFO:type_infer-2649:Column coarseAggregate has data type float\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:type_infer-2759:Infering type for: fineAggregate\u001b[0m\n" + "\u001b[32mINFO:type_infer-2649:Column superPlasticizer has data type float\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:type_infer-2759:Infering type for: age\u001b[0m\n" + "\u001b[32mINFO:type_infer-2649:Column id has data type integer\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:type_infer-2759:Column age has data type integer\u001b[0m\n" + "\u001b[32mINFO:type_infer-2649:Infering type for: fineAggregate\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:type_infer-2759:Infering type for: concrete_strength\u001b[0m\n" + "\u001b[32mINFO:type_infer-2649:Infering type for: age\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:type_infer-2759:Column fineAggregate has data type float\u001b[0m\n" + "\u001b[32mINFO:type_infer-2649:Infering type for: concrete_strength\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:type_infer-2759:Column concrete_strength has data type float\u001b[0m\n" + "\u001b[32mINFO:type_infer-2649:Column age has data type integer\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:type_infer-2759:Infering type for: id\u001b[0m\n" + "\u001b[32mINFO:type_infer-2649:Column fineAggregate has data type float\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:type_infer-2759:Column id has data type integer\u001b[0m\n" + "\u001b[32mINFO:type_infer-2649:Column concrete_strength has data type float\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:dataprep_ml-2759:Starting statistical analysis\u001b[0m\n" + "\u001b[32mINFO:dataprep_ml-2649:Starting statistical analysis\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:dataprep_ml-2759:Finished statistical analysis\u001b[0m\n" + "\u001b[32mINFO:dataprep_ml-2649:Finished statistical analysis\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:dataprep_ml-2759:[Learn phase 1/8] - Statistical analysis\u001b[0m\n" + "\u001b[32mINFO:dataprep_ml-2649:[Learn phase 1/8] - Statistical analysis\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:dataprep_ml-2759:Starting statistical analysis\u001b[0m\n" + "\u001b[32mINFO:dataprep_ml-2649:Starting statistical analysis\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:dataprep_ml-2759:Finished statistical analysis\u001b[0m\n" + "\u001b[32mINFO:dataprep_ml-2649:Finished statistical analysis\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[37mDEBUG:lightwood-2759: `analyze_data` runtime: 0.02 seconds\u001b[0m\n" + "\u001b[37mDEBUG:lightwood-2649: `analyze_data` runtime: 0.02 seconds\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:dataprep_ml-2759:[Learn phase 2/8] - Data preprocessing\u001b[0m\n" + "\u001b[32mINFO:dataprep_ml-2649:[Learn phase 2/8] - 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"\u001b[32mINFO:dataprep_ml-2759:[Learn phase 6/8] - Mixer training\u001b[0m\n" + "\u001b[32mINFO:dataprep_ml-2649:[Learn phase 6/8] - Mixer training\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:dataprep_ml-2759:Training the mixers\u001b[0m\n" + "\u001b[32mINFO:dataprep_ml-2649:Training the mixers\u001b[0m\n" ] }, { @@ -487,224 +487,224 @@ "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:1578.)\n", " exp_avg_sq.mul_(beta2).addcmul_(1 - beta2, grad, grad)\n", - "\u001b[32mINFO:lightwood-2759:Loss of 39.99637508392334 with learning rate 0.0001\u001b[0m\n" + "\u001b[32mINFO:lightwood-2649:Loss of 39.99637508392334 with learning rate 0.0001\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:lightwood-2759:Loss of 21.826460361480713 with learning rate 0.0005\u001b[0m\n" + "\u001b[32mINFO:lightwood-2649:Loss of 21.826460361480713 with learning rate 0.0005\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:lightwood-2759:Loss of 15.12899512052536 with learning rate 0.001\u001b[0m\n" + "\u001b[32mINFO:lightwood-2649:Loss of 15.12899512052536 with learning rate 0.001\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:lightwood-2759:Loss of 15.062753021717072 with learning rate 0.002\u001b[0m\n" + "\u001b[32mINFO:lightwood-2649:Loss of 15.062753021717072 with learning rate 0.002\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:lightwood-2759:Loss of 26.490495562553406 with learning rate 0.003\u001b[0m\n" + "\u001b[32mINFO:lightwood-2649:Loss of 26.490495562553406 with learning rate 0.003\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:lightwood-2759:Loss of 33.6572003364563 with learning rate 0.005\u001b[0m\n" + "\u001b[32mINFO:lightwood-2649:Loss of 33.6572003364563 with learning rate 0.005\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:lightwood-2759:Loss of 303.60721158981323 with learning rate 0.01\u001b[0m\n" + "\u001b[32mINFO:lightwood-2649:Loss of 303.60721158981323 with learning rate 0.01\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:lightwood-2759:Loss of nan with learning rate 0.05\u001b[0m\n" + "\u001b[32mINFO:lightwood-2649:Loss of nan with learning rate 0.05\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:lightwood-2759:Found learning rate of: 0.002\u001b[0m\n" + "\u001b[32mINFO:lightwood-2649:Found learning rate of: 0.002\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:lightwood-2759:Loss @ epoch 1: 0.11838734149932861\u001b[0m\n" + "\u001b[32mINFO:lightwood-2649:Loss @ epoch 1: 0.11838734149932861\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:lightwood-2759:Loss @ epoch 2: 0.4641949534416199\u001b[0m\n" + "\u001b[32mINFO:lightwood-2649:Loss @ epoch 2: 0.4641949534416199\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - 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"\u001b[32mINFO:dataprep_ml-2759:[Learn phase 7/8] - Ensemble analysis\u001b[0m\n" + "\u001b[32mINFO:dataprep_ml-2649:[Learn phase 7/8] - Ensemble analysis\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:dataprep_ml-2759:Analyzing the ensemble of mixers\u001b[0m\n" + "\u001b[32mINFO:dataprep_ml-2649:Analyzing the ensemble of mixers\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:lightwood-2759:The block ICP is now running its analyze() method\u001b[0m\n" + "\u001b[32mINFO:lightwood-2649:The block ICP is now running its analyze() method\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:lightwood-2759:The block ConfStats is now running its analyze() method\u001b[0m\n" + "\u001b[32mINFO:lightwood-2649:The block ConfStats is now running its analyze() method\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:lightwood-2759:The block AccStats is now running its analyze() method\u001b[0m\n" + "\u001b[32mINFO:lightwood-2649:The block AccStats is now running its analyze() method\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - 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"\u001b[37mDEBUG:lightwood-2759: `analyze_ensemble` runtime: 0.15 seconds\u001b[0m\n" + "\u001b[37mDEBUG:lightwood-2649: `analyze_ensemble` runtime: 0.15 seconds\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:dataprep_ml-2759:[Learn phase 8/8] - Adjustment on validation requested\u001b[0m\n" + "\u001b[32mINFO:dataprep_ml-2649:[Learn phase 8/8] - Adjustment on validation requested\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:dataprep_ml-2759:Updating the mixers\u001b[0m\n" + "\u001b[32mINFO:dataprep_ml-2649:Updating the mixers\u001b[0m\n" ] }, { @@ -719,21 +719,21 @@ "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:lightwood-2759:Loss @ epoch 1: 0.1678172747294108\u001b[0m\n" + "\u001b[32mINFO:lightwood-2649:Loss @ epoch 1: 0.1678172747294108\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[37mDEBUG:lightwood-2759: `adjust` runtime: 0.03 seconds\u001b[0m\n" + "\u001b[37mDEBUG:lightwood-2649: `adjust` runtime: 0.03 seconds\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[37mDEBUG:lightwood-2759: `learn` runtime: 0.82 seconds\u001b[0m\n" + "\u001b[37mDEBUG:lightwood-2649: `learn` runtime: 0.83 seconds\u001b[0m\n" ] } ], @@ -770,10 +770,10 @@ "execution_count": 4, "metadata": { "execution": { - "iopub.execute_input": "2024-05-15T12:31:34.874734Z", - "iopub.status.busy": "2024-05-15T12:31:34.874316Z", - "iopub.status.idle": "2024-05-15T12:31:35.015369Z", - "shell.execute_reply": "2024-05-15T12:31:35.014803Z" + "iopub.execute_input": "2024-05-15T12:38:41.016512Z", + "iopub.status.busy": "2024-05-15T12:38:41.016092Z", + "iopub.status.idle": "2024-05-15T12:38:41.160419Z", + "shell.execute_reply": "2024-05-15T12:38:41.159845Z" } }, "outputs": [ @@ -781,126 +781,126 @@ "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:dataprep_ml-2759:[Predict phase 1/4] - Data preprocessing\u001b[0m\n" + "\u001b[32mINFO:dataprep_ml-2649:[Predict phase 1/4] - Data preprocessing\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - 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"\u001b[32mINFO:lightwood-2759:ConfStats.explain() has not been implemented, no modifications will be done to the data insights.\u001b[0m\n" + "\u001b[32mINFO:lightwood-2649: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-2759:The block AccStats is now running its explain() method\u001b[0m\n" + "\u001b[32mINFO:lightwood-2649:The block AccStats is now running its explain() method\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:lightwood-2759:AccStats.explain() has not been implemented, no modifications will be done to the data insights.\u001b[0m\n" + "\u001b[32mINFO:lightwood-2649: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-2759:The block PermutationFeatureImportance is now running its explain() method\u001b[0m\n" + "\u001b[32mINFO:lightwood-2649:The block PermutationFeatureImportance is now running its explain() method\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:lightwood-2759:PermutationFeatureImportance.explain() has not been implemented, no modifications will be done to the data insights.\u001b[0m\n" + "\u001b[32mINFO:lightwood-2649: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-2759: `explain` runtime: 0.05 seconds\u001b[0m\n" + "\u001b[37mDEBUG:lightwood-2649: `explain` runtime: 0.05 seconds\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[37mDEBUG:lightwood-2759: `predict` runtime: 0.13 seconds\u001b[0m\n" + "\u001b[37mDEBUG:lightwood-2649: `predict` runtime: 0.13 seconds\u001b[0m\n" ] }, { @@ -1094,10 +1094,10 @@ "execution_count": 5, "metadata": { "execution": { - "iopub.execute_input": "2024-05-15T12:31:35.017903Z", - "iopub.status.busy": "2024-05-15T12:31:35.017554Z", - "iopub.status.idle": "2024-05-15T12:31:35.127911Z", - "shell.execute_reply": "2024-05-15T12:31:35.127311Z" + "iopub.execute_input": "2024-05-15T12:38:41.162902Z", + "iopub.status.busy": "2024-05-15T12:38:41.162689Z", + "iopub.status.idle": "2024-05-15T12:38:41.276226Z", + "shell.execute_reply": "2024-05-15T12:38:41.275636Z" } }, "outputs": [ @@ -1105,35 +1105,35 @@ "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:dataprep_ml-2759:Cleaning the data\u001b[0m\n" + "\u001b[32mINFO:dataprep_ml-2649:Cleaning the data\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[37mDEBUG:lightwood-2759: `preprocess` runtime: 0.02 seconds\u001b[0m\n" + "\u001b[37mDEBUG:lightwood-2649: `preprocess` runtime: 0.02 seconds\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:dataprep_ml-2759:Cleaning the data\u001b[0m\n" + "\u001b[32mINFO:dataprep_ml-2649:Cleaning the data\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[37mDEBUG:lightwood-2759: `preprocess` runtime: 0.01 seconds\u001b[0m\n" + "\u001b[37mDEBUG:lightwood-2649: `preprocess` runtime: 0.01 seconds\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:dataprep_ml-2759:Updating the mixers\u001b[0m\n" + "\u001b[32mINFO:dataprep_ml-2649:Updating the mixers\u001b[0m\n" ] }, { @@ -1148,14 +1148,14 @@ "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:lightwood-2759:Loss @ epoch 1: 0.10915952424208324\u001b[0m\n" + "\u001b[32mINFO:lightwood-2649:Loss @ epoch 1: 0.10915952424208324\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[37mDEBUG:lightwood-2759: `adjust` runtime: 0.11 seconds\u001b[0m\n" + "\u001b[37mDEBUG:lightwood-2649: `adjust` runtime: 0.11 seconds\u001b[0m\n" ] } ], @@ -1168,10 +1168,10 @@ "execution_count": 6, "metadata": { "execution": { - "iopub.execute_input": "2024-05-15T12:31:35.130571Z", - "iopub.status.busy": "2024-05-15T12:31:35.130366Z", - "iopub.status.idle": "2024-05-15T12:31:35.268592Z", - "shell.execute_reply": "2024-05-15T12:31:35.267965Z" + "iopub.execute_input": "2024-05-15T12:38:41.279047Z", + "iopub.status.busy": "2024-05-15T12:38:41.278590Z", + "iopub.status.idle": "2024-05-15T12:38:41.418298Z", + "shell.execute_reply": "2024-05-15T12:38:41.417642Z" } }, "outputs": [ @@ -1179,126 +1179,126 @@ "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:dataprep_ml-2759:[Predict phase 1/4] - Data preprocessing\u001b[0m\n" + "\u001b[32mINFO:dataprep_ml-2649:[Predict phase 1/4] - Data preprocessing\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:dataprep_ml-2759:Cleaning the data\u001b[0m\n" + "\u001b[32mINFO:dataprep_ml-2649:Cleaning the data\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[37mDEBUG:lightwood-2759: `preprocess` runtime: 0.01 seconds\u001b[0m\n" + "\u001b[37mDEBUG:lightwood-2649: `preprocess` runtime: 0.01 seconds\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:dataprep_ml-2759:[Predict phase 2/4] - Feature generation\u001b[0m\n" + "\u001b[32mINFO:dataprep_ml-2649:[Predict phase 2/4] - Feature generation\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:dataprep_ml-2759:Featurizing the data\u001b[0m\n" + "\u001b[32mINFO:dataprep_ml-2649:Featurizing the data\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[37mDEBUG:lightwood-2759: `featurize` runtime: 0.03 seconds\u001b[0m\n" + "\u001b[37mDEBUG:lightwood-2649: `featurize` runtime: 0.03 seconds\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:dataprep_ml-2759:[Predict phase 3/4] - Calling ensemble\u001b[0m\n" + "\u001b[32mINFO:dataprep_ml-2649:[Predict phase 3/4] - Calling ensemble\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[37mDEBUG:lightwood-2759: `_timed_call` runtime: 0.03 seconds\u001b[0m\n" + "\u001b[37mDEBUG:lightwood-2649: `_timed_call` runtime: 0.03 seconds\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:dataprep_ml-2759:[Predict phase 4/4] - Analyzing output\u001b[0m\n" + "\u001b[32mINFO:dataprep_ml-2649:[Predict phase 4/4] - Analyzing output\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:lightwood-2759:The block ICP is now running its explain() method\u001b[0m\n" + "\u001b[32mINFO:lightwood-2649:The block ICP is now running its explain() method\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:lightwood-2759:The block ConfStats is now running its explain() method\u001b[0m\n" + "\u001b[32mINFO:lightwood-2649:The block ConfStats is now running its explain() method\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:lightwood-2759:ConfStats.explain() has not been implemented, no modifications will be done to the data insights.\u001b[0m\n" + "\u001b[32mINFO:lightwood-2649: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-2759:The block AccStats is now running its explain() method\u001b[0m\n" + "\u001b[32mINFO:lightwood-2649:The block AccStats is now running its explain() method\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:lightwood-2759:AccStats.explain() has not been implemented, no modifications will be done to the data insights.\u001b[0m\n" + "\u001b[32mINFO:lightwood-2649: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-2759:The block PermutationFeatureImportance is now running its explain() method\u001b[0m\n" + "\u001b[32mINFO:lightwood-2649:The block PermutationFeatureImportance is now running its explain() method\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32mINFO:lightwood-2759:PermutationFeatureImportance.explain() has not been implemented, no modifications will be done to the data insights.\u001b[0m\n" + "\u001b[32mINFO:lightwood-2649: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-2759: `explain` runtime: 0.05 seconds\u001b[0m\n" + "\u001b[37mDEBUG:lightwood-2649: `explain` runtime: 0.05 seconds\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[37mDEBUG:lightwood-2759: `predict` runtime: 0.13 seconds\u001b[0m\n" + "\u001b[37mDEBUG:lightwood-2649: `predict` runtime: 0.13 seconds\u001b[0m\n" ] }, { @@ -1462,10 +1462,10 @@ "execution_count": 7, "metadata": { "execution": { - "iopub.execute_input": "2024-05-15T12:31:35.271408Z", - "iopub.status.busy": "2024-05-15T12:31:35.270879Z", - "iopub.status.idle": "2024-05-15T12:31:35.276560Z", - "shell.execute_reply": "2024-05-15T12:31:35.275925Z" + "iopub.execute_input": "2024-05-15T12:38:41.421175Z", + "iopub.status.busy": "2024-05-15T12:38:41.420697Z", + "iopub.status.idle": "2024-05-15T12:38:41.426502Z", + "shell.execute_reply": "2024-05-15T12:38:41.425844Z" } }, "outputs": [