From 847747ff72cfb307fbbd0dbcdfcf3ebbbd57b4a6 Mon Sep 17 00:00:00 2001 From: Minura Punchihewa Date: Tue, 3 Dec 2024 15:12:17 +0530 Subject: [PATCH 1/4] bumped sktime to >=0.30.0 and scikit-learn to >=1.5.0 --- pyproject.toml | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/pyproject.toml b/pyproject.toml index 1670b002e..f68030c5f 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -24,10 +24,10 @@ transformers = ">=4.34.0" optuna = ">=3.1.0,<4.0.0" scipy = ">=1.5.4" psutil = ">=5.7.0" -scikit-learn = ">=1.0.0" +scikit-learn = ">=1.5.0" dataclasses_json = ">=0.5.4" dill = "==0.3.6" -sktime = ">=0.25.0,<0.26.0" +sktime = ">=0.30.0" statsforecast = "~=1.6.0" torch_optimizer = "==0.1.0" black = "==24.3.0" From 58c4b0dde852bd11c9aed3e6829b0e711b1acbe1 Mon Sep 17 00:00:00 2001 From: Minura Punchihewa Date: Wed, 4 Dec 2024 17:18:30 +0530 Subject: [PATCH 2/4] updated the poetry.lock file --- poetry.lock | 147 +++++++++++++++++++++++++++++++--------------------- 1 file 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["boto3", "botocore", "mlflow", "moto"] -networks = ["keras-self-attention (>=0.51,<0.52)", "tensorflow (>=2,<=2.14)"] +networks = ["keras-self-attention (>=0.51,<0.52)", "tensorflow (>=2,<2.17)"] +numpy1 = ["numpy (<3.0.0)"] pandas1 = ["pandas (<2.0.0)"] param-est = ["seasonal (>=0.3.1,<0.4)", "statsmodels (>=0.12.1,<0.15)"] -regression = ["numba (>=0.53,<0.59)", "tensorflow (>=2,<=2.14)"] -tests = ["pytest (>=7.4,<7.5)", "pytest-cov (>=4.1,<4.2)", "pytest-randomly (>=3.15,<3.16)", "pytest-timeout (>=2.1,<2.3)", "pytest-xdist (>=3.3,<3.6)"] -transformations = ["esig (>=0.9.7,<0.10)", "filterpy (>=1.4.5,<1.5)", "holidays (>=0.29,<0.42)", "mne (>=1.5,<1.7)", "numba (>=0.53,<0.59)", "pycatch22 (>=0.4,<0.4.4)", "pykalman-bardo (>=0.9.7,<0.10)", "statsmodels (>=0.12.1,<0.15)", "stumpy (>=1.5.1,<1.13)", "tsfresh (>=0.17,<0.21)"] +regression = ["numba (>=0.53,<0.61)", "tensorflow (>=2,<2.17)"] +tests = ["pytest (>=7.4,<8.4)", "pytest-cov (>=4.1,<5.1)", "pytest-randomly (>=3.15,<3.16)", "pytest-timeout (>=2.1,<2.4)", "pytest-xdist (>=3.3,<3.7)"] +transformations = ["esig (>=0.9.7,<0.10)", "filterpy (>=1.4.5,<1.5)", "holidays (>=0.29,<0.57)", "mne (>=1.5,<1.9)", "numba (>=0.53,<0.61)", "pycatch22 (>=0.4,<0.4.6)", "statsmodels (>=0.12.1,<0.15)", "stumpy (>=1.5.1,<1.13)", "temporian (>=0.7.0,!=0.8.0,<0.9.0)", "tsfresh (>=0.17,<0.21)"] [[package]] name = "slicer" @@ -5708,4 +5735,4 @@ xai = ["pyod", "shap", "suod"] [metadata] lock-version = "2.0" python-versions = ">=3.9,<3.12" -content-hash = "b55fe3f8f3b07e78d578cdefbe673e761b4248c666f267706487632378d23fd0" +content-hash = "985acc0dba5919c63b5ac163be5a6cb997c234459ae83a5d1a3392140d20ce58" From db8038ea98c0d3af707fbf59058e425c798ea5a2 Mon Sep 17 00:00:00 2001 From: Minura Punchihewa Date: Wed, 4 Dec 2024 21:48:09 +0530 Subject: [PATCH 3/4] updated use of OneHotEncoder with bumped version --- lightwood/encoder/helpers.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/lightwood/encoder/helpers.py b/lightwood/encoder/helpers.py index ff3359a88..b3fceb173 100644 --- a/lightwood/encoder/helpers.py +++ b/lightwood/encoder/helpers.py @@ -45,7 +45,7 @@ class CatNormalizer: def __init__(self, encoder_class='one_hot'): self.encoder_class = encoder_class if encoder_class == 'one_hot': - self.scaler = OneHotEncoder(sparse=False, handle_unknown='ignore') + self.scaler = OneHotEncoder(sparse_output=False, handle_unknown='ignore') else: self.scaler = OrdinalEncoder() From 404eb1ce7fc74fcc6cf7ff0eea7d1c503678dc92 Mon Sep 17 00:00:00 2001 From: Minura Punchihewa Date: Wed, 4 Dec 2024 22:16:27 +0530 Subject: [PATCH 4/4] updated a few more calls to OneHotEncoder --- lightwood/analysis/nc/calibrate.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/lightwood/analysis/nc/calibrate.py b/lightwood/analysis/nc/calibrate.py index 88d5c93ae..152a8694f 100644 --- a/lightwood/analysis/nc/calibrate.py +++ b/lightwood/analysis/nc/calibrate.py @@ -59,7 +59,7 @@ def analyze(self, info: Dict[str, object], **kwargs) -> Dict[str, object]: all_classes = np.array([ns.encoded_val_data.encoders[ns.target].rev_map[idx] for idx in class_keys]) if data_type != dtype.tags: - enc = OneHotEncoder(sparse=False, handle_unknown='ignore') + enc = OneHotEncoder(sparse_output=False, handle_unknown='ignore') enc.fit(all_classes.reshape(-1, 1)) output['label_encoders'] = enc # needed to repr cat labels inside nonconformist else: