def fit_transform(self, X, y):
predictions, scores = cv_predict_transform(self.model_estimators, X, y,
self.prediction_function,
self.verbose)
self.scores = scores
self.predictions = predictions
return predictions
if __name__ == "__main__":
from prepare_data import subjects, get_nii_data, load_stimuli
_, _, stimuli = load_stimuli()
data = get_nii_data(subjects[0])
from multi_select_k_best import MultiSelectKBest
from sklearn.feature_selection import SelectKBest
from sklearn.feature_selection import f_classif
from sklearn.linear_model import LogisticRegression
from sklearn.pipeline import Pipeline
## A first pipeline using globally selected features
## and l2 logistic regression
selector = MultiSelectKBest(f_classif, k=500)
estimators = [LogisticRegression(C=C, penalty='l2')
for C in 2. ** np.arange(-24, 0, 2)]
import numpy as np
if __name__ == "__main__":
# re-generate layer 1 results
from layer_1_predictor import CvPredictTransform
from prepare_data import subjects, get_nii_data, load_stimuli
_, _, stimuli = load_stimuli()
data = get_nii_data(subjects[2])
from multi_select_k_best import MultiSelectKBest
from sklearn.feature_selection import SelectKBest
from sklearn.feature_selection import f_classif
from sklearn.linear_model import LogisticRegression
from sklearn.svm import SVC
from sklearn.pipeline import Pipeline
## A first pipeline using globally selected features
## and l2 logistic regression
selector = MultiSelectKBest(f_classif, k=100)
estimators = [LogisticRegression(C=C, penalty='l2')
for C in [10]] #]2. ** np.arange(-24, 0, 2)]
# estimators = [SVC(C=C, probability=True, kernel="linear")
# for C in 2. ** np.arange(-24, 2, 2)]
first_layer_predictor = CvPredictTransform(model_estimators=estimators)
pipeline = Pipeline([('feature_reduction', selector),
('first_layer_prediction', first_layer_predictor)])
def fit_transform(self, X, y):
predictions, scores = cv_predict_transform(self.model_estimators, X, y,
self.prediction_function,
self.verbose)
self.scores = scores
self.predictions = predictions
return predictions
if __name__ == "__main__":
from prepare_data import subjects, get_nii_data, load_stimuli
_, _, stimuli = load_stimuli()
data = get_nii_data(subjects[0])
from multi_select_k_best import MultiSelectKBest
from sklearn.feature_selection import SelectKBest
from sklearn.feature_selection import f_classif
from sklearn.linear_model import LogisticRegression
from sklearn.pipeline import Pipeline
scaler = StandardScaler()
## A first pipeline using globally selected features
## and l2 logistic regression
selector = MultiSelectKBest(f_classif, k=300)
estimators = [
LogisticRegression(C=C, penalty='l2', intercept_scaling=100)
import numpy as np
if __name__ == "__main__":
# re-generate layer 1 results
from layer_1_predictor import CvPredictTransform
from prepare_data import subjects, get_nii_data, load_stimuli
_, _, stimuli = load_stimuli()
data = get_nii_data(subjects[2])
from multi_select_k_best import MultiSelectKBest
from sklearn.feature_selection import SelectKBest
from sklearn.feature_selection import f_classif
from sklearn.linear_model import LogisticRegression
from sklearn.svm import SVC
from sklearn.pipeline import Pipeline
## A first pipeline using globally selected features
## and l2 logistic regression
selector = MultiSelectKBest(f_classif, k=100)
estimators = [LogisticRegression(C=C, penalty='l2')
for C in [10]]#]2. ** np.arange(-24, 0, 2)]
# estimators = [SVC(C=C, probability=True, kernel="linear")
# for C in 2. ** np.arange(-24, 2, 2)]
first_layer_predictor = CvPredictTransform(model_estimators=estimators)
pipeline = Pipeline([('feature_reduction', selector),
