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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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9 changes: 8 additions & 1 deletion src/m5py/main.py
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Original file line number Diff line number Diff line change
Expand Up @@ -17,6 +17,7 @@
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

Expand Down Expand Up @@ -210,8 +211,14 @@ 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)

# Get the correct fit method name based on the sklearn version used
sklearn_version_tuple = tuple(map(int, sklearn_version.split('.')))
fit_method_name = "fit" if sklearn_version_tuple <= (1, 3) else "_fit"

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