Python sklearn.linear_model.RandomizedLogisticRegression() Examples

The following are 6 code examples for showing how to use sklearn.linear_model.RandomizedLogisticRegression(). These examples are extracted from open source projects. You can vote up the ones you like or vote down the ones you don't like, and go to the original project or source file by following the links above each example.

You may check out the related API usage on the sidebar.

You may also want to check out all available functions/classes of the module sklearn.linear_model , or try the search function .

Example 1
Project: rasa_lookup_demo   Author: RasaHQ   File: create_ngrams.py    License: Apache License 2.0 8 votes vote down vote up
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 
Example 2
Project: rasa_lookup_demo   Author: RasaHQ   File: create_ngrams.py    License: Apache License 2.0 5 votes vote down vote up
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 
Example 3
Project: rasa_nlu   Author: weizhenzhao   File: ngram_featurizer.py    License: Apache License 2.0 5 votes vote down vote up
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 
Example 4
Project: Rasa_NLU_Chi   Author: crownpku   File: ngram_featurizer.py    License: Apache License 2.0 5 votes vote down vote up
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 
Example 5
Project: pandas-ml   Author: pandas-ml   File: test_linear_model.py    License: BSD 3-Clause "New" or "Revised" License 5 votes vote down vote up
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) 
Example 6
Project: ProFET   Author: ddofer   File: PipeTasks.py    License: GNU General Public License v3.0 5 votes vote down vote up
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!