48 lines
1.6 KiB
Python
48 lines
1.6 KiB
Python
from sklearn.model_selection import LeaveOneGroupOut, cross_val_score
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class ModelValidation:
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def __init__(self, X, y, group_variable=None, cv_name="loso"):
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self.model = None
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self.cv = None
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idx_common = X.index.intersection(y.index)
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self.y = y.loc[idx_common, "NA"]
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# TODO Handle the case of multiple labels.
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self.X = X.loc[idx_common]
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self.groups = self.y.index.get_level_values(group_variable)
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self.cv_name = cv_name
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print("ModelValidation initialized.")
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def set_cv_method(self):
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if self.cv_name == "loso":
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self.cv = LeaveOneGroupOut()
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self.cv.get_n_splits(X=self.X, y=self.y, groups=self.groups)
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print("Validation method set.")
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def cross_validate(self):
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print("Running cross validation ...")
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if self.model is None:
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raise TypeError(
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"Please, specify a machine learning model first, by setting the .model attribute. "
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"E.g. self.model = sklearn.linear_model.LinearRegression()"
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)
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if self.cv is None:
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raise TypeError(
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"Please, specify a cross validation method first, by using set_cv_method() first."
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)
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if self.X.isna().any().any() or self.y.isna().any().any():
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raise ValueError(
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"NaNs were found in either X or y. Please, check your data before continuing."
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)
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return cross_val_score(
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estimator=self.model,
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X=self.X,
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y=self.y,
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groups=self.groups,
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cv=self.cv,
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n_jobs=-1,
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scoring="r2",
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)
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