我们从Python开源项目中，提取了以下7个代码示例，用于说明如何使用sklearn.linear_model.RandomizedLogisticRegression()。
def get_feature_selection_model_from_name(type_of_estimator, model_name): model_map = { 'classifier': { 'SelectFromModel': SelectFromModel(RandomForestClassifier(n_jobs=-1, max_depth=10, n_estimators=15), threshold='20*mean'), 'RFECV': RFECV(estimator=RandomForestClassifier(n_jobs=-1), step=0.1), 'GenericUnivariateSelect': GenericUnivariateSelect(), 'RandomizedSparse': RandomizedLogisticRegression(), 'KeepAll': 'KeepAll' }, 'regressor': { 'SelectFromModel': SelectFromModel(RandomForestRegressor(n_jobs=-1, max_depth=10, n_estimators=15), threshold='0.7*mean'), 'RFECV': RFECV(estimator=RandomForestRegressor(n_jobs=-1), step=0.1), 'GenericUnivariateSelect': GenericUnivariateSelect(), 'RandomizedSparse': RandomizedLasso(), 'KeepAll': 'KeepAll' } } return model_map[type_of_estimator][model_name]
def LogisticRegression(result): # dd = pd.DataFrame(Variance) dd = result # dd['flag'] = df_flag from random import shuffle data = dd.as_matrix() shuffle(data) p = 0.8 # train/test ratio m,n = np.shape(data) train = data[:int(m*p),:] test = data[int(m*p):,:] data = result df_flag = result.pop('df') from sklearn.linear_model import LogisticRegression as LR from sklearn.linear_model import RandomizedLogisticRegression as RLR x = result.values y = df_flag.values rlr = RLR() #????????????? rlr.fit(x, y) #???? rlr.get_support() #?????????????? print(u'??????????????') print(u'??????%s' % ','.join(data.columns[rlr.get_support()])) x = data[data.columns[rlr.get_support()]].as_matrix() # lr = LR() # ???????? lr.fit(x, y) # ?????????????? print(u'????') print(u'???????%s' % lr.score(x, y))
def log_reg_feat_selection(X_train, y_train, X_valid, y_valid, random_state): """ Feature selection based on the scores given to the features by the RandomizedLogisticRegression algorithm. """ rlr = RandomizedLogisticRegression(C=[0.001, 0.01, 0.1, 1.], sample_fraction=0.7, n_resampling=200, selection_threshold=0.25, verbose=5, n_jobs=-1, random_state=0) rlr.fit(X_train, y_train) np.save('save/feat_sel_log_reg.npy', rlr.scores_) return rlr.scores_
def stabilty_index(self, clf): # sklearn implements stability selection in # RandomizedLogisticRegression class only clf.fit(self.features, self.labels) # map Feature scores between 0 and 1. to the feature names stabledict = {k: v for k, v in zip(self.features.columns.tolist(), map(lambda x: round(x, 4), clf.scores_ ) ) } return stabledict
def _get_clfs(self): clf_dict = {"rlrclf": RandomizedLogisticRegression(), "rfclf": RandomForestClassifier(criterion='entropy'), "dtrclf": DecisionTreeClassifier(criterion='entropy'), "lrclf": LogisticRegression() } return clf_dict
def _sort_applicable_ngrams(self, list_of_ngrams, examples, labels): """Given an intent classification problem and a list of ngrams, creates ordered list of most useful ngrams.""" if list_of_ngrams: from sklearn import linear_model, preprocessing import numpy as np # filter examples where we do not have enough labeled instances for cv usable_labels = [] for label in np.unique(labels): lab_sents = np.array(examples)[np.array(labels) == label] if len(lab_sents) < self.min_intent_examples_for_ngram_classification: continue usable_labels.append(label) mask = [label in usable_labels for label in labels] if any(mask) and len(usable_labels) >= 2: try: examples = np.array(examples)[mask] labels = np.array(labels)[mask] X = np.array(self._ngrams_in_sentences(examples, list_of_ngrams)) intent_encoder = preprocessing.LabelEncoder() intent_encoder.fit(labels) y = intent_encoder.transform(labels) clf = linear_model.RandomizedLogisticRegression(C=1) clf.fit(X, y) scores = clf.scores_ sort_idx = [i[0] for i in sorted(enumerate(scores), key=lambda x: -1 * x[1])] return np.array(list_of_ngrams)[sort_idx] except ValueError as e: if "needs samples of at least 2 classes" in str(e): # we got unlucky during the random sampling :( and selected a slice that only contains one class return [] else: raise e else: # there is no example we can use for the cross validation return [] else: return []
