def __init__(self, n_estimators=10, criterion="gini", max_depth=None, min_samples_split=2, min_samples_leaf=1, min_weight_fraction_leaf=0., max_features="auto", max_leaf_nodes=None, min_impurity_decrease=0., min_impurity_split=None, bootstrap=True, oob_score=False, n_jobs=1, random_state=None, verbose=0, warm_start=False, class_weight=None): n_estimators = int(n_estimators) _skRandomForestClassifier.__init__( self, n_estimators, criterion, max_depth, min_samples_split, min_samples_leaf, min_weight_fraction_leaf, max_features, max_leaf_nodes, min_impurity_decrease, min_impurity_split, bootstrap, oob_score, n_jobs, random_state, verbose, warm_start, class_weight) BaseWrapperClf.__init__(self)
def __init__(self, eta=0.5, epochs=50, hidden_layers=None, n_classes=None, momentum=0.0, l1=0.0, l2=0.0, dropout=1.0, decrease_const=0.0, minibatches=1, random_seed=None, print_progress=0): epochs = int(epochs) warnings.filterwarnings(module='mlxtend*', action='ignore', category=FutureWarning) warnings.filterwarnings(module='mlxtend*', action='ignore', category=RuntimeWarning) if hidden_layers is None: hidden_layers = [50] _MultiLayerPerceptron.__init__(self, eta, epochs, hidden_layers, n_classes, momentum, l1, l2, dropout, decrease_const, minibatches, random_seed, print_progress) BaseWrapperClf.__init__(self)
def __init__(self, loss="hinge", penalty='l2', alpha=0.0001, l1_ratio=0.15, fit_intercept=True, max_iter=1000, tol=None, shuffle=True, verbose=0, epsilon=DEFAULT_EPSILON, n_jobs=1, random_state=None, learning_rate="optimal", eta0=0.0, power_t=0.5, class_weight=None, warm_start=False, average=False, n_iter=None): _skSGDClassifier.__init__(self, loss, penalty, alpha, l1_ratio, fit_intercept, max_iter, tol, shuffle, verbose, epsilon, n_jobs, random_state, learning_rate, eta0, power_t, class_weight, warm_start, average, n_iter) BaseWrapperClf.__init__(self)
def __init__(self, alpha=1.0, binarize=.0, fit_prior=True, class_prior=None): _skBernoulliNB.__init__(self, alpha, binarize, fit_prior, class_prior) BaseWrapperClf.__init__(self)
def __init__(self, C=1.0, kernel='rbf', degree=3, gamma='auto', coef0=0.0, shrinking=True, probability=True, tol=1e-3, cache_size=200, class_weight=None, verbose=False, max_iter=-1, decision_function_shape='ovr', random_state=None): _SVC.__init__( self, C, kernel, degree, gamma, coef0, shrinking, probability, tol, cache_size, class_weight, verbose, max_iter, decision_function_shape, random_state) BaseWrapperClf.__init__(self)
def __init__(self, base_estimator=None, n_estimators=10, max_samples=1.0, max_features=1.0, bootstrap=True, bootstrap_features=False, oob_score=False, warm_start=False, n_jobs=1, random_state=None, verbose=0): n_estimators = int(n_estimators) _BaggingClassifier.__init__( self, base_estimator, n_estimators, max_samples, max_features, bootstrap, bootstrap_features, oob_score, warm_start, n_jobs, random_state, verbose) BaseWrapperClf.__init__(self)
def __init__(self, max_depth=3, learning_rate=0.1, n_estimators=100, silent=True, objective="binary:logistic", booster='gbtree', n_jobs=1, nthread=None, gamma=0, min_child_weight=1, max_delta_step=0, subsample=1, colsample_bytree=1, colsample_bylevel=1, reg_alpha=0, reg_lambda=1, scale_pos_weight=1, base_score=0.5, random_state=0, seed=None, missing=None, **kwargs): xgb.XGBClassifier.__init__(self, max_depth, learning_rate, n_estimators, silent, objective, booster, n_jobs, nthread, gamma, min_child_weight, max_delta_step, subsample, colsample_bytree, colsample_bylevel, reg_alpha, reg_lambda, scale_pos_weight, base_score, random_state, seed, missing, **kwargs) BaseWrapperClf.__init__(self) warnings.filterwarnings(module='sklearn*', action='ignore', category=DeprecationWarning)
def __init__(self, estimators, voting='hard', weights=None, n_jobs=1, flatten_transform=None): _skVotingClassifier.__init__(self, estimators, voting, weights, n_jobs, flatten_transform) BaseWrapperClf.__init__(self)
def __init__(self, eta=0.1, epochs=50, random_seed=None, print_progress=0): epochs = int(epochs) warnings.filterwarnings(module='mlxtend*', action='ignore', category=FutureWarning) _Perceptron.__init__(self, eta, epochs, random_seed, print_progress) BaseWrapperClf.__init__(self)
def __init__(self, **kwargs): # logging_level = 'Silent' # silent = True # kwargs['silent'] = True warnings.filterwarnings(module='sklearn*', action='ignore', category=DeprecationWarning) BaseWrapperClf.__init__(self) _CatBoostClassifier.__init__(self, **kwargs)
def __init__(self, loss='deviance', learning_rate=0.1, n_estimators=100, subsample=1.0, criterion='friedman_mse', min_samples_split=2, min_samples_leaf=1, min_weight_fraction_leaf=0., max_depth=3, min_impurity_decrease=0., min_impurity_split=None, init=None, random_state=None, max_features=None, verbose=0, max_leaf_nodes=None, warm_start=False, presort='auto'): n_estimators = int(n_estimators) _skGradientBoostingClassifier.__init__( self, loss, learning_rate, n_estimators, subsample, criterion, min_samples_split, min_samples_leaf, min_weight_fraction_leaf, max_depth, min_impurity_decrease, min_impurity_split, init, random_state, max_features, verbose, max_leaf_nodes, warm_start, presort) BaseWrapperClf.__init__(self)
def __init__(self, alphas=(0.1, 1.0, 10.0), fit_intercept=True, normalize=False, scoring=None, cv=None, class_weight=None): _RidgeClassifierCV.__init__(self, alphas, fit_intercept, normalize, scoring, cv, class_weight) BaseWrapperClf.__init__(self)
def __init__(self, hidden_layer_sizes=(100,), activation="relu", solver='adam', alpha=0.0001, batch_size='auto', learning_rate="constant", learning_rate_init=0.001, power_t=0.5, max_iter=200, shuffle=True, random_state=None, tol=1e-4, verbose=False, warm_start=False, momentum=0.9, nesterovs_momentum=True, early_stopping=False, validation_fraction=0.1, beta_1=0.9, beta_2=0.999, epsilon=1e-8): warnings.filterwarnings(module='sklearn*', action='ignore', category=ConvergenceWarning) BaseWrapperClf.__init__(self) _skMLPClassifier.__init__( self, hidden_layer_sizes, activation, solver, alpha, batch_size, learning_rate, learning_rate_init, power_t, max_iter, shuffle, random_state, tol, verbose, warm_start, momentum, nesterovs_momentum, early_stopping, validation_fraction, beta_1, beta_2, epsilon)
def __init__(self, alpha=1.0, fit_intercept=True, normalize=False, copy_X=True, max_iter=None, tol=1e-3, class_weight=None, solver="auto", random_state=None): _RidgeClassifier.__init__(self, alpha, fit_intercept, normalize, copy_X, max_iter, tol, class_weight, solver, random_state) BaseWrapperClf.__init__(self)
def __init__(self, eta=0.01, epochs=50, l2=0.0, minibatches=1, n_classes=None, random_seed=None, print_progress=0): warnings.filterwarnings(module='mlxtend*', action='ignore', category=FutureWarning) epochs = int(epochs) _SoftmaxRegression.__init__(self, eta, epochs, l2, minibatches, n_classes, random_seed, print_progress) BaseWrapperClf.__init__(self)
def __init__(self, eta=0.01, epochs=50, l2_lambda=0.0, minibatches=1, random_seed=None, print_progress=0): epochs = int(epochs) warnings.filterwarnings(module='mlxtend*', action='ignore', category=FutureWarning) _LogisticRegression.__init__(self, eta, epochs, l2_lambda, minibatches, random_seed, print_progress) BaseWrapperClf.__init__(self)
def __init__(self, n_neighbors=5, weights='uniform', algorithm='auto', leaf_size=30, p=2, metric='minkowski', metric_params=None, n_jobs=1, **kwargs): leaf_size = int(leaf_size) n_neighbors = int(n_neighbors) _KNeighborsClassifier.__init__(self, n_neighbors, weights, algorithm, leaf_size, p, metric, metric_params, n_jobs, **kwargs) BaseWrapperClf.__init__(self)
def __init__(self, kernel=None, optimizer="fmin_l_bfgs_b", n_restarts_optimizer=0, max_iter_predict=100, warm_start=False, copy_X_train=True, random_state=None, multi_class="one_vs_rest", n_jobs=1): n_jobs = 4 _skGaussianProcessClassifier.__init__(self, kernel, optimizer, n_restarts_optimizer, max_iter_predict, warm_start, copy_X_train, random_state, multi_class, n_jobs) BaseWrapperClf.__init__(self, )
def __init__(self, penalty='l2', loss='squared_hinge', dual=True, tol=1e-4, C=1.0, multi_class='ovr', fit_intercept=True, intercept_scaling=1, class_weight=None, verbose=0, random_state=None, max_iter=1000): _skLinearSVC.__init__(self, penalty, loss, dual, tol, C, multi_class, fit_intercept, intercept_scaling, class_weight, verbose, random_state, max_iter) BaseWrapperClf.__init__(self)
def __init__(self, classifiers, meta_classifier=None, use_probas=False, average_probas=False, verbose=0, use_features_in_secondary=False, store_train_meta_features=False, use_clones=True): warnings.filterwarnings(module='mlxtend*', action='ignore', category=FutureWarning) if meta_classifier is None: meta_classifier = skBernoulli_NBClf() BaseWrapperClf.__init__(self) _StackingClassifier.__init__(self, classifiers, meta_classifier, use_probas, average_probas, verbose, use_features_in_secondary, store_train_meta_features, use_clones)
def __init__(self, boosting_type="gbdt", num_leaves=31, max_depth=-1, learning_rate=0.1, n_estimators=100, subsample_for_bin=200000, objective=None, class_weight=None, min_split_gain=0., min_child_weight=1e-3, min_child_samples=20, subsample=1., subsample_freq=1, colsample_bytree=1., reg_alpha=0., reg_lambda=0., random_state=None, n_jobs=-1, silent=True, **kwargs): kwargs['verbose'] = -1 num_leaves = int(num_leaves) min_child_samples = int(min_child_samples) n_estimators = int(n_estimators) warnings.filterwarnings(module='sklearn*', action='ignore', category=DeprecationWarning) lightgbm.LGBMClassifier.__init__( self, boosting_type, num_leaves, max_depth, learning_rate, n_estimators, subsample_for_bin, objective, class_weight, min_split_gain, min_child_weight, min_child_samples, subsample, subsample_freq, colsample_bytree, reg_alpha, reg_lambda, random_state, n_jobs, silent, **kwargs) BaseWrapperClf.__init__(self)
def __init__(self, metric='euclidean', shrink_threshold=None): _NearestCentroid.__init__(self, metric, shrink_threshold) BaseWrapperClf.__init__(self)
def __init__(self, priors=None, reg_param=0., store_covariance=False, tol=1.0e-4, store_covariances=None): warnings.filterwarnings(module='sklearn*', action='ignore', category=Warning) BaseWrapperClf.__init__(self) _skQDA.__init__(self, priors, reg_param, store_covariance, tol, store_covariances)
def __init__(self, alpha=1.0, fit_prior=True, class_prior=None): _skMultinomialNB.__init__(self, alpha, fit_prior, class_prior) BaseWrapperClf.__init__(self)
def __init__(self, base_estimator=None, n_estimators=50, learning_rate=1., algorithm='SAMME.R', random_state=None): n_estimators = int(n_estimators) _skAdaBoostClassifier.__init__(self, base_estimator, n_estimators, learning_rate, algorithm, random_state) BaseWrapperClf.__init__(self)
def __init__(self, radius=1.0, weights='uniform', algorithm='auto', leaf_size=30, p=2, metric='minkowski', outlier_label=None, metric_params=None, **kwargs): _RadiusNeighborsClassifier.__init__( self, radius, weights, algorithm, leaf_size, p, metric, outlier_label, metric_params, **kwargs) BaseWrapperClf.__init__(self)
def __init__(self, priors=None): _skGaussianNB.__init__(self, priors) BaseWrapperClf.__init__(self)