def main():
classifier1 = RandomForestClassifier(n_estimators = 100, max_features=0.5, max_depth=5.0)
classifier2 = DecisionTreeClassifier(max_depth = 10, criterion = 'entropy', random_state = 0)
classifier3 = KNeighborsClassifier(n_neighbors = 5, p = 2, metric = 'minkowski')
classifier4 = SVC(kernel = 'rbf', C = 10.0, random_state = 0, gamma = 0.10)
classifier5 = LogisticRegression(penalty = 'l2', C = 1.0, random_state = 0)
classifier6 = GaussianNB()
print("Reading in the training data")
train = data_io.get_train_df()
print ("Cleaning data. Check here for imputation, One hot encoding and factorization procedures..")
train = FeatureConverter().clean_data(train)
train.drop(['PassengerId'], axis = 1, inplace = True)
#print train.head()
train = train.values
eclf = EnsembleClassifier(clfs = [classifier1, classifier2, classifier3, classifier5, classifier6], voting = 'hard')
#eclf = EnsembleClassifier(clfs = [classifier2], voting = 'hard')
scores = cross_val_score(estimator = eclf, X = train[0:,1:], y = train[0:,0], cv = 10, scoring = 'roc_auc')
print("Accuracy: %0.4f (+/- %0.3f)" % (scores.mean(), scores.std()))
eclf.fit(train[0:,1:],train[0:,0])
print("Saving the classifier")
data_io.save_model(eclf)
def main():
classifier1 = RandomForestClassifier(n_estimators = 100, max_features=0.5, max_depth=5.0)
classifier2 = DecisionTreeClassifier(max_depth = 10, criterion = 'entropy', random_state = 0)
classifier3 = KNeighborsClassifier(n_neighbors = 5, p = 2, metric = 'minkowski')
classifier4 = SVC(kernel = 'rbf', C = 10.0, random_state = 0, gamma = 0.10)
classifier5 = LogisticRegression(penalty = 'l2', C = 1.0, random_state = 0)
classifier6 = GaussianNB()
classifier7 = GradientBoostingClassifier(n_estimators=100, learning_rate=1.0, max_depth=1, random_state=0)
print("Reading in the training data")
train = data_io.get_train_df()
print ("Cleaning data. Check here for imputation, One hot encoding and factorization procedures..")
train = FeatureConverter().clean_data(train)
train.drop(['Id'], axis = 1, inplace = True)
#print train.head()
train = train.values
#eclf = EnsembleClassifier(clfs = [classifier1, classifier2, classifier3, classifier5, classifier6], voting = 'hard')
#eclf = EnsembleClassifier(clfs = [classifier1], voting = 'hard')
eclf = classifier3
#scores = cross_val_score(estimator = eclf, X = train[0:,0:-1], y = train[0:,-1], cv = 10, scoring = 'roc_auc')
#print("Accuracy: %0.4f (+/- %0.3f)" % (scores.mean(), scores.std()))
eclf.fit(train[0:,0:-1],train[0:,-1])
# importances = eclf.feature_importances_
# indices = np.argsort(importances)[::-1]
# for f in range(train[0:,0:-1].shape[1]):
# print("%d. feature %d (%f)" % (f + 1, indices[f], importances[indices[f]]))
#
print("Saving the classifier")
data_io.save_model(eclf)
def main():
print("Loading the test data")
classifier = data_io.load_model()
print ("Load test data. And Clean..")
test = data_io.get_test_df()
test = FeatureConverter().clean_data(test)
passengerIds = test['Id']
test.drop(['Id'], axis = 1, inplace = True)
test = test.values
print("Making predictions")
predictions = classifier.predict(test).astype(int)
#predictions = predictions.reshape(len(predictions), 1)
print("Writing predictions to file")
data_io.write_submission(predictions, passengerIds, ['Id', 'Cover_Type'])
def main():
print("Loading the test data")
classifier = data_io.load_model()
print("Load test data. And Clean..")
test = data_io.get_test_df()
test = FeatureConverter().clean_data(test)
passengerIds = test['Id']
test.drop(['Id'], axis=1, inplace=True)
test = test.values
print("Making predictions")
predictions = classifier.predict(test).astype(int)
#predictions = predictions.reshape(len(predictions), 1)
print("Writing predictions to file")
data_io.write_submission(predictions, passengerIds, ['Id', 'Cover_Type'])
svc_params_grid = \
{
'C': [0.001, 0.1, 1.0, 10.0, 100.0],
'kernel': ['linear', 'rbf'],
'gamma': [0.001, 0.1, 1.0, 10.0, 100.0]
}
if __name__=="__main__":
print("Reading in the training data")
train = data_io.get_train_df()
print ("Cleaning data. Check here for imputation, One hot encoding and factorization procedures..")
train = FeatureConverter().clean_data(train)
train.drop(['PassengerId'], axis = 1, inplace = True)
#print train.head()
train = train.values
grid_search = GridSearchCV(RandomForestClassifier(n_estimators = 100), rf_params_grid, cv = 5, verbose = 1)
grid_search.fit(train[0:,1:],train[0:,0])
print grid_search.best_params_
grid_search = GridSearchCV(LogisticRegression(random_state = 0), lr_params_grid, cv = 5, verbose = 1)
grid_search.fit(train[0:,1:],train[0:,0])
print grid_search.best_params_
grid_search = GridSearchCV(SVC(random_state = 0), svc_params_grid, cv = 5, verbose = 1)
grid_search.fit(train[0:,1:],train[0:,0])
{
'C': [0.001, 0.1, 1.0, 10.0, 100.0],
'kernel': ['linear', 'rbf'],
'gamma': [0.001, 0.1, 1.0, 10.0, 100.0]
}
if __name__ == "__main__":
print("Reading in the training data")
train = data_io.get_train_df()
print(
"Cleaning data. Check here for imputation, One hot encoding and factorization procedures.."
)
train = FeatureConverter().clean_data(train)
train.drop(['PassengerId'], axis=1, inplace=True)
#print train.head()
train = train.values
grid_search = GridSearchCV(RandomForestClassifier(n_estimators=100),
rf_params_grid,
cv=5,
verbose=1)
grid_search.fit(train[0:, 1:], train[0:, 0])
print grid_search.best_params_
grid_search = GridSearchCV(LogisticRegression(random_state=0),
lr_params_grid,
cv=5,
verbose=1)
