testDataFiles = [
'ada_regress_test.csv',
# 'knn_regress_test.csv',
'log_regress_test.csv',
'NN_test.csv',
'rf_regress_test.csv',
# 'svm_regress_test.csv'
]
model_size = 0.7
y_blend_train = data.loadTrainingBlend()['ylabels']
x_blend_train = np.zeros((len(trainingDataFiles), len(y_blend_train)))
x_blend_train = np.transpose([loadModelOut(model, model_size) for model in trainingDataFiles])
x_all = len(data.allTest()['xlabels'])
x_test = np.zeros((len(testDataFiles), x_all))
x_test = np.transpose([loadModelOut(model, 0) for model in testDataFiles])
# parameters = {'C': np.logspace(-4.0, 4.0, 20),
# # 'kernel': ['rbf'],
# 'kernel': ['linear', 'poly', 'rbf'],
# # 'degree': [2]
# 'degree': np.arange(0.0, 4.0, 1),
# }
parameters = {'C': np.logspace(-2, 2, 10),
# 'solver' : ['newton-cg', 'lbfgs', 'liblinear']
}
print "Training score: "
print nn_class.score(x1, y1)
print "Test score : "
print nn_class.score(x_23, y_23)
# cross_val_scores = cross_validation.cross_val_score(estimator=nn_class,\
# X=x_train, y=y_train, cv=kf_total, n_jobs=1)
# print "cross val scores: "
# print cross_val_scores
x_all_test = data.allTest()['xlabels']
f = open('nn_regress_test.csv', 'w+')
f.write('Id,Prediction\n')
y_test = nn_class.predict(x_all_test)
for i in range(len(y_test)):
f.write(str(i+1) + ',' + str(y_test[i]) + '\n')
f.close()
g = open('nn_regress_params.txt', 'w+')
g.write(str(nn_class.best_estimator_.get_params()))
g.close()
x_all_train = data.allTrain()['xlabels']
h = open('nn_regress_train.csv', 'w+')