neurosynth/analysis/classify.py
def classify(X, y, clf_method='ERF', classifier=None, output='summary_clf',
             cross_val=None, class_weight=None, regularization=None,
             param_grid=None, scoring='accuracy', refit_all=True,
             feat_select=None):
    """ Wrapper for scikit-learn classification functions
    Imlements various types of classification and cross validation """

    # Build classifier
    clf = Classifier(clf_method, classifier, param_grid)

    # Fit & test model with or without cross-validation
    if cross_val is not None:
        score = clf.cross_val_fit(X, y, cross_val, scoring=scoring,
                                  feat_select=feat_select,
                                  class_weight=class_weight)
    else:
        # Does not support scoring function
        score = clf.fit(X, y, class_weight=class_weight).score(X, y)

    # Return some stuff...
    from collections import Counter

    if output == 'clf':
        return clf
    else:
        if output == 'summary':
            output = {'score': score, 'n': dict(Counter(y))}
        elif output == 'summary_clf':
            output = {
                'score': score,
                'n': dict(Counter(y)),
                'clf': clf,
                'features_selected': clf.features_selected,
                'predictions': clf.predictions
            }

        return output
def classify(X, y, clf_method='ERF', classifier=None, output='summary_clf',
             cross_val=None, class_weight=None, regularization=None,
             param_grid=None, scoring='accuracy', refit_all=True,
             feat_select=None):
    # Build classifier
    clf = Classifier(clf_method, classifier, param_grid)

    # Fit & test model with or without cross-validation
    if cross_val is not None:
        score = clf.cross_val_fit(X, y, cross_val, scoring=scoring,
                                  feat_select=feat_select,
                                  class_weight=class_weight)
    else:
        # Does not support scoring function
        score = clf.fit(X, y, class_weight=class_weight).score(X, y)

    # Return some stuff...
    from collections import Counter

    if output == 'clf':
        return clf
    else:
        if output == 'summary':
            output = {'score': score, 'n': dict(Counter(y))}
        elif output == 'summary_clf':
            output = {
                'score': score,
                'n': dict(Counter(y)),
                'clf': clf,
                'features_selected': clf.features_selected,
                'predictions': clf.predictions
            }

        return output
def classify ( X , y , clf_method = 'ERF' , classifier = None , output = 'summary_clf' , cross_val = None , class_weight = None , regularization = None , param_grid = None , scoring = 'accuracy' , refit_all = True , feat_select = None ) : # Build classifier clf = Classifier ( clf_method , classifier , param_grid ) # Fit & test model with or without cross-validation if cross_val is not None : score = clf . cross_val_fit ( X , y , cross_val , scoring = scoring , feat_select = feat_select , class_weight = class_weight ) else : # Does not support scoring function score = clf . fit ( X , y , class_weight = class_weight ) . score ( X , y ) # Return some stuff... from collections import Counter if output == 'clf' : return clf else : if output == 'summary' : output = { 'score' : score , 'n' : dict ( Counter ( y ) ) } elif output == 'summary_clf' : output = { 'score' : score , 'n' : dict ( Counter ( y ) ) , 'clf' : clf , 'features_selected' : clf . features_selected , 'predictions' : clf . predictions } return output
Wrapper for scikit-learn classification functions
    Imlements various types of classification and cross validation
Wrapper for scikit - learn classification functions Imlements various types of classification and cross validation