def cross_validate():
train, test = load_cross_validation()
u = train.pca()
X = train.get_pca_features(u)
Y = train.get_labels()
X2 = test.get_pca_features(u)
Y2 = test.get_labels()
rf = RandomForestRegressor(n_jobs=-1)
model = rf.fit(X, Y)
print('Cross validation score: %f' % loss(Y2, model.predict(X2)))
def cross_validate():
train, test = load_cross_validation()
u = train.pca()
X = train.get_pca_features(u)
Y = train.get_labels()
X2 = test.get_pca_features(u)
Y2 = test.get_labels()
rf = RandomForestRegressor(n_jobs=-1)
model = rf.fit(X, Y)
print('Cross validation score: %f' % loss(Y2, model.predict(X2)))
def cross_validate():
print('Cross validate bayes model')
train, test = load_cross_validation()
X = train.get_features()
Y = train.get_labels()
X2 = test.get_features()
Y2 = test.get_labels()
kmeans = KMeans(n_clusters=8)
clf = kmeans.fit(X, train.get_multi_labels())
score = check_score(Y, to_labels(clf.predict(X)))
print("Train dataset score %f" % score)
score = check_score(Y2, to_labels(clf.predict(X2)))
print("test dataset score %f" % score)
def cross_validate():
print('Cross validate kmeans model')
train, test = load_cross_validation()
X = train.get_features()
Y = train.get_labels()
X2 = test.get_features()
Y2 = test.get_labels()
kmeans = KMeans(n_clusters=8)
clf = kmeans.fit(X, train.get_multi_labels())
score = check_score(Y, to_labels(clf.predict(X)))
print("Train dataset score %f" % score)
score = check_score(Y2, to_labels(clf.predict(X2)))
print("test dataset score %f" % score)
def cross_validate():
print('Cross validate bayes model')
train, test = load_cross_validation()
X = train.get_features()
Y = train.get_labels()
X2 = test.get_features()
Y2 = test.get_labels()
gnb = bayes.MultinomialNB()
clf = gnb.fit(X, train.get_multi_labels())
score = check_score(Y, to_labels(clf.predict(X)))
print("Train dataset score %f" % score)
score = check_score(Y2, to_labels(clf.predict(X2)))
print("test dataset score %f" % score)
def cross_validate():
print('Cross validate bayes model')
train, test = load_cross_validation()
X = train.get_features()
Y = train.get_labels()
X2 = test.get_features()
Y2 = test.get_labels()
gnb = bayes.MultinomialNB()
clf = gnb.fit(X, train.get_multi_labels())
score = check_score(Y, to_labels(clf.predict(X)))
print("Train dataset score %f" % score)
score = check_score(Y2, to_labels(clf.predict(X2)))
print("test dataset score %f" % score)
def cross_validate(params):
print('Cross validate with params')
print(params)
network = NeuralNetwork(params)
train, test = load_cross_validation(0.8)
u = train.pca()
if params.pca:
X = train.get_pca_features(u)
else:
X = train.get_features()
Y = train.get_labels()
if params.pca:
X2 = test.get_pca_features(u)
else:
X2 = test.get_features()
Y2 = test.get_labels()
network.fit(X, Y, X2, Y2)
score = network.check_score(X, Y)
print("Train dataset score %f" % (score / len(X)))
score = network.check_score(X2, Y2)
print("test dataset score %f" % (score / len(X2)))
make_submission(network, params, u)
def cross_validate(params):
print('Cross validate with params')
print(params)
network = NeuralNetwork(params)
train, test = load_cross_validation(0.8)
u = train.pca()
if params.pca:
X = train.get_pca_features(u)
else:
X = train.get_features()
Y = train.get_labels()
if params.pca:
X2 = test.get_pca_features(u)
else:
X2 = test.get_features()
Y2 = test.get_labels()
network.fit(X, Y, X2, Y2)
score = network.check_score(X, Y)
print("Train dataset score %f" % (score/len(X)))
score = network.check_score(X2, Y2)
print("test dataset score %f" % (score/len(X2)))
make_submission(network, params, u)
