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Select between ._fit() of .fit() depending on the sklearn version used #16

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merged 5 commits into from
Jan 22, 2024
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Select between ._fit() of .fit() depending on the sklearn version used #16

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a9e56da
Select between ._fit() of .fit() depending on the sklearn version used
Jan 17, 2024
7680f1e
Check sklearn version with packaging.version.Version
Jan 22, 2024
6e4d77f
Update changelog
Jan 22, 2024
6aa54c2
Update src/m5py/main.py
smarie Jan 22, 2024
10e132a
Update src/m5py/main.py
smarie Jan 22, 2024
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5 changes: 5 additions & 0 deletions docs/changelog.md
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Original file line number Diff line number Diff line change
@@ -1,5 +1,10 @@
# Changelog

### 0.3.2 - Fixed compliance with sklearn 1.3.0

* Fixed `AttributeError: 'super' object has no attribute 'fit' `.
PR [#16](https://github.com/smarie/python-m5p/pull/16) by [lccatala](https://github.com/lccatala)

### 0.3.1 - Fixed compliance with sklearn 1.1.0

* Fixed `TypeError: fit() got an unexpected keyword argument 'X_idx_sorted'`.
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15 changes: 14 additions & 1 deletion src/m5py/main.py
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Expand Up @@ -17,9 +17,15 @@
from sklearn.tree._tree import DOUBLE
from sklearn.utils import check_array
from sklearn.utils.validation import check_is_fitted
from sklearn import __version__ as sklearn_version

from m5py.linreg_utils import linreg_model_to_text, DeNormalizableMixIn, DeNormalizableLinearRegression

from packaging.version import Version

SKLEARN_VERSION = Version(sklearn_version)
SKLEARN13_OR_GREATER = SKLEARN_VERSION >= Version("1.3.0")


__all__ = ["M5Base", "M5Prime"]

Expand Down Expand Up @@ -210,8 +216,15 @@ def fit(self, X, y: np.ndarray, sample_weight=None, check_input=True, X_idx_sort
if self.use_smoothing not in [False, np.bool_(False), "installed", "on_prediction"]:
raise ValueError("use_smoothing: Unexpected value: %s, please report it as issue." % self.use_smoothing)


# (1) Build the initial tree as usual
super(M5Base, self).fit(X, y, sample_weight=sample_weight, check_input=check_input)

# Get the correct fit method name based on the sklearn version used
fit_method_name = "_fit" if SKLEARN13_OR_GREATER else "fit"

fit_method = getattr(super(M5Base, self), fit_method_name)
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Nitpick: readability: please move all new lines after the comment, or all lines before the comment.

fit_method(X, y, sample_weight=sample_weight, check_input=check_input)


if self.debug_prints:
logger.debug("(debug_prints) Initial tree:")
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