def get_classifier(self, classifier_name):
modelmap = {
"naive": NaiveBayesClassifier(),
"svm" : SVMClassifier(),
'random': RandomForestClf()
# "grid": GridSearchClassifier()
}
if classifier_name in modelmap:
return modelmap[classifier_name]
else:
raise Exception("Unrecognized model: {}".format(classifier_name))
from classifiers.naive_bayes_classifier import NaiveBayesClassifier
from classifiers.svm_classifier import SVMClassifier
import argparse
datasets = {
'ag_news': AgNews(),
'yahoo_answers': YahooAnswers(),
'yelp_review_polarity': YelpReviewPolarity(),
}
classifiers = {
'cnn': CNNClassifier(),
'lstm': LSTMClassifier(),
'character_level_cnn': CharacterLevelCNNClassifier(),
'naive_bayes': NaiveBayesClassifier(),
'svm': SVMClassifier()
}
def main(classifier_str, dataset_str):
dataset = datasets[dataset_str]
classifier = classifiers[classifier_str]
classifier.load(dataset)
classifier.fit()
classifier.evaluate()
# TODO grid search
if __name__ == "__main__":
parser = argparse.ArgumentParser()
def main():
if sys.argv[1] == 'logistic_regression':
if sys.argv[2] in ['accuracy', 'confusion_matrix']:
kfold = 5
model = LogisticRegressionClassifier(approch=sys.argv[3])
model.train()
model.evaluate(label="Training", metrics=sys.argv[2])
model.evaluate(label="Testing", metrics=sys.argv[2])
hyperparameters = {
'penalty': ['l1', 'l2'],
'C': np.logspace(0, 4, 10)
}
model.setting_model(hyperparameters, kfold, sys.argv[2])
elif sys.argv[1] == 'random_forest':
if sys.argv[2] in ['accuracy', 'confusion_matrix']:
kfold = 5
model = RandomForestAlgorithmClassifier(approch=sys.argv[3])
model.train()
model.evaluate(label="Training", metrics=sys.argv[2])
model.evaluate(label="Testing", metrics=sys.argv[2])
hyperparameters = {
'max_depth': np.linspace(10, 100, num=10), # best=50
'n_estimators': range(88, 91)
}
model.setting_model(hyperparameters, kfold, sys.argv[2])
elif sys.argv[1] == 'svm':
if sys.argv[2] in ['accuracy', 'confusion_matrix']:
kfold = 5
model = SVMClassifier(approch=sys.argv[3])
model.train()
model.evaluate(label="Training", metrics=sys.argv[2])
model.evaluate(label="Testing", metrics=sys.argv[2])
hyperparameters = {
'C': [0.1, 1, 10, 100, 1000],
'gamma': [1, 0.1, 0.01, 0.001, 0.0001],
'kernel': ['rbf', 'sigmoid']
}
model.setting_model(hyperparameters, kfold, sys.argv[2])
elif sys.argv[1] == 'adaboost':
if sys.argv[2] in ['accuracy', 'confusion_matrix']:
kfold = 5
model = AdaBoostAlgorithmClassifier(approch=sys.argv[3])
model.train()
model.evaluate(label="Training", metrics=sys.argv[2])
model.evaluate(label="Testing", metrics=sys.argv[2])
hyperparameters = {
'base_estimator': [
tree.DecisionTreeClassifier(max_depth=n)
for n in range(13, 14)
],
'n_estimators': [50, 55, 60, 65, 70, 80, 85],
'algorithm': ['SAMME.R', 'SAMME'],
'learning_rate':
np.geomspace(0.01, 1, num=3)
}
model.setting_model(hyperparameters, kfold, sys.argv[2])
elif sys.argv[1] == 'neural_network':
if sys.argv[2] in ['accuracy', 'confusion_matrix']:
kfold = 5
model = NeuralnetworkClassifier(approch=sys.argv[3])
model.train()
model.evaluate(label="Training", metrics=sys.argv[2])
model.evaluate(label="Testing", metrics=sys.argv[2])
hyperparameters = {
'hidden_layer_sizes': [(5, ), (5, 5)],
'activation': ['relu'],
'solver': ['identity', 'relu', 'tanh'],
'alpha': [1e-5, 3e-4],
'learning_rate_init': [1e-2, 1e-3]
}
model.setting_model(hyperparameters, kfold, sys.argv[2])
elif sys.argv[1] == 'linear_discriminant':
if sys.argv[2] in ['accuracy', 'confusion_matrix']:
kfold = 5
model = LinearDiscriminantClassifier(approch=sys.argv[3])
model.train()
model.evaluate(label="Training", metrics=sys.argv[2])
model.evaluate(label="Testing", metrics=sys.argv[2])
hyperparameters = {
'penalty': ['l1', 'l2'],
'multi_class': ['auto'],
'solver': ['liblinear', 'identity', 'relu', 'tanh'],
'max_iter': [10000]
}
model.setting_model(hyperparameters, kfold, sys.argv[2])
