Python linear_model.RandomizedLogisticRegression方法代碼示例

# 需要導入模塊: from sklearn import linear_model [as 別名]
# 或者: from sklearn.linear_model import RandomizedLogisticRegression [as 別名]
def run_logreg(X_train, y_train, selection_threshold=0.2):
    print("\nrunning logistic regression...")
    print("using a selection threshold of {}".format(selection_threshold))
    pipe = Pipeline(
        [
            (
                "feature_selection",
                RandomizedLogisticRegression(selection_threshold=selection_threshold),
            ),
            ("classification", LogisticRegression()),
        ]
    )
    pipe.fit(X_train, y_train)
    print("training accuracy : {}".format(pipe.score(X_train, y_train)))
    print("testing accuracy : {}".format(pipe.score(X_test, y_test)))
    return pipe 

# 需要導入模塊: from sklearn import linear_model [as 別名]
# 或者: from sklearn.linear_model import RandomizedLogisticRegression [as 別名]
def get_features(X_train, y_train, names, selection_threshold=0.2):
    print("\ngetting features with randomized logistic regression...")
    print("using a selection threshold of {}".format(selection_threshold))
    randomized_logistic = RandomizedLogisticRegression(
        selection_threshold=selection_threshold
    )
    randomized_logistic.fit(X_train, y_train)
    mask = randomized_logistic.get_support()
    features = np.array(names)[mask]
    print("found {} ngrams:".format(len([f for f in features])))
    print([f for f in features])
    return features 

# 需要導入模塊: from sklearn import linear_model [as 別名]
# 或者: from sklearn.linear_model import RandomizedLogisticRegression [as 別名]
def _rank_ngrams_using_cv(self, examples, labels, list_of_ngrams):
        from sklearn import linear_model

        X = np.array(self._ngrams_in_sentences(examples, list_of_ngrams))
        y = self.encode_labels(labels)

        clf = linear_model.RandomizedLogisticRegression(C=1)
        clf.fit(X, y)

        # sort the ngrams according to the classification score
        scores = clf.scores_
        sorted_idxs = sorted(enumerate(scores), key=lambda x: -1 * x[1])
        sorted_ngrams = [list_of_ngrams[i[0]] for i in sorted_idxs]

        return sorted_ngrams 

# 需要導入模塊: from sklearn import linear_model [as 別名]
# 或者: from sklearn.linear_model import RandomizedLogisticRegression [as 別名]
def test_objectmapper(self):
        df = pdml.ModelFrame([])
        self.assertIs(df.linear_model.ARDRegression, lm.ARDRegression)
        self.assertIs(df.linear_model.BayesianRidge, lm.BayesianRidge)
        self.assertIs(df.linear_model.ElasticNet, lm.ElasticNet)
        self.assertIs(df.linear_model.ElasticNetCV, lm.ElasticNetCV)

        self.assertIs(df.linear_model.HuberRegressor, lm.HuberRegressor)

        self.assertIs(df.linear_model.Lars, lm.Lars)
        self.assertIs(df.linear_model.LarsCV, lm.LarsCV)
        self.assertIs(df.linear_model.Lasso, lm.Lasso)
        self.assertIs(df.linear_model.LassoCV, lm.LassoCV)
        self.assertIs(df.linear_model.LassoLars, lm.LassoLars)
        self.assertIs(df.linear_model.LassoLarsCV, lm.LassoLarsCV)
        self.assertIs(df.linear_model.LassoLarsIC, lm.LassoLarsIC)

        self.assertIs(df.linear_model.LinearRegression, lm.LinearRegression)
        self.assertIs(df.linear_model.LogisticRegression, lm.LogisticRegression)
        self.assertIs(df.linear_model.LogisticRegressionCV, lm.LogisticRegressionCV)
        self.assertIs(df.linear_model.MultiTaskLasso, lm.MultiTaskLasso)
        self.assertIs(df.linear_model.MultiTaskElasticNet, lm.MultiTaskElasticNet)
        self.assertIs(df.linear_model.MultiTaskLassoCV, lm.MultiTaskLassoCV)
        self.assertIs(df.linear_model.MultiTaskElasticNetCV, lm.MultiTaskElasticNetCV)

        self.assertIs(df.linear_model.OrthogonalMatchingPursuit, lm.OrthogonalMatchingPursuit)
        self.assertIs(df.linear_model.OrthogonalMatchingPursuitCV, lm.OrthogonalMatchingPursuitCV)
        self.assertIs(df.linear_model.PassiveAggressiveClassifier, lm.PassiveAggressiveClassifier)
        self.assertIs(df.linear_model.PassiveAggressiveRegressor, lm.PassiveAggressiveRegressor)

        self.assertIs(df.linear_model.Perceptron, lm.Perceptron)
        self.assertIs(df.linear_model.RandomizedLasso, lm.RandomizedLasso)
        self.assertIs(df.linear_model.RandomizedLogisticRegression, lm.RandomizedLogisticRegression)
        self.assertIs(df.linear_model.RANSACRegressor, lm.RANSACRegressor)
        self.assertIs(df.linear_model.Ridge, lm.Ridge)
        self.assertIs(df.linear_model.RidgeClassifier, lm.RidgeClassifier)
        self.assertIs(df.linear_model.RidgeClassifierCV, lm.RidgeClassifierCV)
        self.assertIs(df.linear_model.RidgeCV, lm.RidgeCV)
        self.assertIs(df.linear_model.SGDClassifier, lm.SGDClassifier)
        self.assertIs(df.linear_model.SGDRegressor, lm.SGDRegressor)
        self.assertIs(df.linear_model.TheilSenRegressor, lm.TheilSenRegressor) 

# 需要導入模塊: from sklearn import linear_model [as 別名]
# 或者: from sklearn.linear_model import RandomizedLogisticRegression [as 別名]
def GetKFeatures(filename, method='RFE',kbest=30,alpha=0.01, reduceMatrix = True):
    '''
    Gets best features using chosen method
    (K-best, RFE, RFECV,'L1' (RandomizedLogisticRegression),'Tree' (ExtraTreesClassifier), mrmr),
    then prints top K features' names (from featNames).
    If reduceMatrix =  True, then also returns X reduced to the K best features.

    Available methods' names are: 'RFE','RFECV','RandomizedLogisticRegression','K-best','ExtraTreesClassifier'..
    Note, that effectiveyl, Any scikit learn method could be used, if correctly imported..
    '''
    #est = method()
    '''
    Gets the K-best features (filtered by FDR, then select best ranked by t-test , more advanced options can be implemented).
    Save the data/matrix with the resulting/kept features to a new output file, "REDUCED_Feat.csv"
    '''
    features, labels, lb_encoder,featureNames = load_data(filename)
    X, y = features, labels

    # change the names as ints back to strings
    class_names=lb_encoder.inverse_transform(y)
    print("Data and labels imported. PreFilter Feature matrix shape:")
    print(X.shape)

    selectK = SelectKBest(k=kbest)
    selectK.fit(X,y)
    selectK_mask=selectK.get_support()
    K_featnames = featureNames[selectK_mask]
    print('X After K filter:',X.shape)
    print("K_featnames: %s" %(K_featnames))
    if reduceMatrix ==True :
        Reduced_df = pd.read_csv(filename, index_col=0)
        Reduced_df = Reduced_df[Reduced_df.columns[selectK_mask]]
        Reduced_df.to_csv('REDUCED_Feat.csv')
        print('Saved to REDUCED_Feat.csv')
        return Reduced_df

#WORKS! But unreadable with too many features!