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classifier_analysis.py
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classifier_analysis.py
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import numpy as np
import multiprocessing
from transformers import FilterSimu
n_jobs = multiprocessing.cpu_count()
def classifier_analysis(X, label, methodType):
from sklearn.preprocessing import StandardScaler
from sklearn.model_selection import ShuffleSplit
from sklearn.model_selection import GridSearchCV
from sklearn.pipeline import Pipeline
#rng = None
rng = np.random.RandomState(1)
if methodType == 0:
# random forest
from sklearn.ensemble import RandomForestClassifier
classifier = RandomForestClassifier(n_estimators=10, criterion='gini', max_depth=None, min_samples_split=2,
min_samples_leaf=1, min_weight_fraction_leaf=0.0, max_features='auto',
max_leaf_nodes=None, min_impurity_decrease=0.0, min_impurity_split=None,
bootstrap=True, oob_score=False, n_jobs=n_jobs, random_state=rng, verbose=0,
warm_start=False, class_weight=None)
param_grid = {
'filter__threshold': [0.95, 0.97, 0.99],
'classifier__n_estimators': [5, 10, 20],
'classifier__max_depth': [None, 10, 5, 3],
'classifier__max_features': ['auto', 10, 5]
}
elif methodType == 1:
# adaboost
from sklearn.ensemble import AdaBoostClassifier
classifier = AdaBoostClassifier(base_estimator=None, n_estimators=50, learning_rate=1.0, algorithm='SAMME.R', random_state=rng)
param_grid = {
'filter__threshold': [0.95, 0.97, 0.99],
'classifier__n_estimators': [5, 10, 20],
'classifier__learning_rate': [0.8, 0.9, 1.0]
}
elif methodType == 2:
# GBC
from sklearn.ensemble import GradientBoostingClassifier
classifier = GradientBoostingClassifier(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=0.0, max_depth=3, min_impurity_decrease=0.0,
min_impurity_split=None, init=None, random_state=rng, max_features=None,
verbose=0, max_leaf_nodes=None, warm_start=False, presort='auto')
param_grid = {
'filter__threshold': [0.95, 0.97, 0.99],
'classifier__n_estimators': [50, 100, 150],
'classifier__max_depth': [None, 10, 5, 3],
'classifier__learning_rate': [0.8, 0.9, 1.0]
}
elif methodType == 3:
# logtistic regression
from sklearn.linear_model import LogisticRegression
classifier = LogisticRegression(penalty='l2', dual=False, tol=0.0001, C=1.0, fit_intercept=True,
intercept_scaling=1, class_weight=None, random_state=rng, solver='saga',
max_iter=100, multi_class='multinomial', verbose=0, warm_start=False, n_jobs=n_jobs)
param_grid = {
'filter__threshold': [0.95, 0.97, 0.99],
'classifier__penalty': ['l1', 'l2'],
'classifier__C': [0.9, 1.0, 1.1]
}
elif methodType == 4:
# SVM
from sklearn.svm import SVC
classifier = SVC(C=1.0, kernel='rbf', degree=3, gamma='auto', coef0=0.0, shrinking=True, probability=False,
tol=0.001, cache_size=200, class_weight=None, verbose=False, max_iter=-1,
decision_function_shape='ovr', random_state=rng)
param_grid = {
'filter__threshold': [0.95, 0.97, 0.99],
'classifier__kernel': ['linear', 'poly', 'rbf', 'sigmoid'],
'classifier__C': [0.9, 1.0, 1.1]
}
elif methodType == 5:
# MLP
from sklearn.neural_network import MLPClassifier
classifier = MLPClassifier(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=0.0001, 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-08)
param_grid = {
'filter__threshold': [0.95, 0.97, 0.99],
'classifier__hidden_layer_sizes': [(100, ), (50, ), (20, )],
'classifier__learning_rate_init': [0.0001, 0.001, 0.01]
}
elif methodType == 6:
# linear SVM
from sklearn.svm import LinearSVC
classifier = LinearSVC(penalty='l2', loss='squared_hinge', dual=False, tol=0.0001, C=1.0, multi_class='ovr',
fit_intercept=True, intercept_scaling=1, class_weight=None, verbose=0, random_state=rng,
max_iter=1000)
param_grid = {
'filter__threshold': [0.95, 0.97, 0.99],
'classifier__penalty': ['l1', 'l2'],
'classifier__C': [0.9, 1.0, 1.1]
}
elif methodType == 7:
# Bernoulli Naive Bayes
from sklearn.naive_bayes import BernoulliNB
classifier = BernoulliNB(alpha=1.0, binarize=0.0, fit_prior=True, class_prior=None)
param_grid = {
'filter__threshold': [0.95, 0.97, 0.99],
'classifier__alpha': [0.90, 0.95, 1.0],
'classifier__fit_prior': [True, False]
}
elif methodType == 8:
# multinomial Naive Bayes
from sklearn.naive_bayes import MultinomialNB
classifier = MultinomialNB(alpha=1.0, fit_prior=True, class_prior=None)
param_grid = {
'classifier__alpha': [0.90, 0.95, 1.0],
'classifier__fit_prior': [True, False]
}
else:
return
if methodType == 8:
pipe = Pipeline([
('classifier', classifier)
])
else:
pipe = Pipeline([
('scale', StandardScaler()),
('filter', FilterSimu()),
('classifier', classifier)
])
grid = GridSearchCV(pipe, cv=ShuffleSplit(n_splits=4, test_size=0.25, random_state=rng), n_jobs=1, param_grid=param_grid)
grid.fit(X, label)
best_estimator = grid.best_estimator_
#mean_scores = np.array(grid.cv_results_['mean_test_score'])
#mean_tscores = np.array(grid.cv_results_['mean_train_score'])
#print mean_scores
#print mean_tscores
print grid.best_params_
score = grid.best_score_
#print grid.cv_results_['params']
return best_estimator, grid.predict(X), score