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dante-verification
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calibration not improves

This commit is contained in:
Alejandro Moreo Fernandez 2020-06-16 13:48:15 +02:00
parent d1d4e08314
commit 8d45db86b9
4 changed files with 5 additions and 5 deletions
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2
src/author_identification.py
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@ -57,7 +57,7 @@ def main():
print('Fitting the Verificator') print('Fitting the Verificator')
if args.C is None: if args.C is None:
params = {'C': np.logspace(-4, +3, 8)} params = {'C': np.logspace(-3, +3, 7)}
C = 1. C = 1.
else: else:
params = None params = None

0
src/experiments.sh Normal file → Executable file
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4
src/model.py
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@ -36,8 +36,8 @@ class AuthorshipVerificator:
print(f'Best params: {self.estimator.best_params_} (cross-validation F1={f1_mean:.3f})') print(f'Best params: {self.estimator.best_params_} (cross-validation F1={f1_mean:.3f})')
self.estimator = self.estimator.best_estimator_ self.estimator = self.estimator.best_estimator_
self.estimator = CalibratedClassifierCV(base_estimator=self.estimator, cv=self.nfolds, ensemble=False) #self.estimator = CalibratedClassifierCV(base_estimator=self.estimator, cv=self.nfolds, ensemble=False)
self.estimator.fit(X, y) #self.estimator.fit(X, y)
return self return self

4
src/util/calibration.py
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@ -23,7 +23,7 @@ from sklearn.base import (BaseEstimator, ClassifierMixin, RegressorMixin, clone,
from sklearn.preprocessing import label_binarize, LabelBinarizer from sklearn.preprocessing import label_binarize, LabelBinarizer
from sklearn.utils import check_array, indexable, column_or_1d from sklearn.utils import check_array, indexable, column_or_1d
from sklearn.utils.validation import check_is_fitted, check_consistent_length from sklearn.utils.validation import check_is_fitted, check_consistent_length
from sklearn.utils.validation import _check_sample_weight #from sklearn.utils.validation import _check_sample_weight
from sklearn.isotonic import IsotonicRegression from sklearn.isotonic import IsotonicRegression
from sklearn.svm import LinearSVC from sklearn.svm import LinearSVC
from sklearn.model_selection import check_cv, cross_val_predict from sklearn.model_selection import check_cv, cross_val_predict
@ -586,4 +586,4 @@ def calibration_curve(y_true, y_prob, *, normalize=False, n_bins=5,
prob_true = bin_true[nonzero] / bin_total[nonzero] prob_true = bin_true[nonzero] / bin_total[nonzero]
prob_pred = bin_sums[nonzero] / bin_total[nonzero] prob_pred = bin_sums[nonzero] / bin_total[nonzero]
return prob_true, prob_pred return prob_true, prob_pred