def regression():
# Generate a random regression problem
X, y = make_regression(n_samples=5000, n_features=25, n_informative=25,
n_targets=1, random_state=100, noise=0.05)
y *= 0.01
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.1,
random_state=1111)
model = NeuralNet(
layers=[
Dense(64, Parameters(init='normal')),
Activation('linear'),
Dense(32, Parameters(init='normal')),
Activation('linear'),
Dense(1),
],
loss='mse',
optimizer=Adam(),
metric='mse',
batch_size=256,
max_epochs=15,
)
model.fit(X_train, y_train)
predictions = model.predict(X_test)
print("regression mse", mean_squared_error(y_test, predictions.flatten()))
def regression():
# Generate a random regression problem
X, y = make_regression(
n_samples=500, n_features=5, n_informative=5, n_targets=1, noise=0.05, random_state=1111, bias=0.5
)
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.25, random_state=1111)
model = knn.KNNRegressor(k=5, distance_func=distance.euclidean)
model.fit(X_train, y_train)
predictions = model.predict(X_test)
print("regression mse", mean_squared_error(y_test, predictions))
def regression():
# Generate a random regression problem
X, y = make_regression(
n_samples=500, n_features=5, n_informative=5, n_targets=1, noise=0.05, random_state=1111, bias=0.5
)
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.1, random_state=1111)
model = RandomForestRegressor(n_estimators=50, max_depth=10, max_features=3)
model.fit(X_train, y_train)
predictions = model.predict(X_test)
print("regression, mse: %s" % mean_squared_error(y_test.flatten(), predictions.flatten()))
def regression():
# Generate a random regression problem
X, y = make_regression(n_samples=10000, n_features=100,
n_informative=75, n_targets=1, noise=0.05,
random_state=1111, bias=0.5)
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.25,
random_state=1111)
model = LinearRegression(lr=0.01, max_iters=2000, penalty='l2', C=0.03)
model.fit(X_train, y_train)
predictions = model.predict(X_test)
print('regression mse', mean_squared_error(y_test, predictions))
def regression():
# Generate a random regression problem
X, y = make_regression(n_samples=500, n_features=5,
n_informative=5, n_targets=1,
noise=0.05, random_state=1111, bias=0.5)
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.25,
random_state=1111)
model = knn.KNNRegressor(k=5, distance_func=distance.euclidean)
model.fit(X_train, y_train)
predictions = model.predict(X_test)
print('regression mse', mean_squared_error(y_test, predictions))
def regression():
# Generate a random regression problem
X, y = make_regression(n_samples=500, n_features=5, n_informative=5,
n_targets=1, noise=0.05, random_state=1111,
bias=0.5)
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.1,
random_state=1111)
model = GradientBoostingRegressor(n_estimators=25, max_depth=5,
max_features=3, )
model.fit(X_train, y_train)
predictions = model.predict(X_test)
print('regression, mse: %s'
% mean_squared_error(y_test.flatten(), predictions.flatten()))
def regression():
X, y = make_regression(n_samples=10000,
n_features=100,
n_informative=75,
n_targets=1,
noise=0.05,
random_state=8888,
bias=0.5)
X_train, X_test, y_train, y_test = train_test_split(X,
y,
test_size=0.25,
random_state=1111)
model = LinearRegression(lr=0.001, max_iters=2000, penalty='l2', C=0.03)
model.fit(X_train, y_train)
predictions = model.predict(X_test)
print('Regression MSE', mean_squared_error(y_test, predictions))
def test_mlp():
model = NeuralNet(
layers=[
Dense(16, Parameters(init='normal')),
Activation('linear'),
Dense(8, Parameters(init='normal')),
Activation('linear'),
Dense(1),
],
loss='mse',
optimizer=Adam(),
metric='mse',
batch_size=64,
max_epochs=150,
)
model.fit(X_train, y_train)
predictions = model.predict(X_test)
assert mean_squared_error(y_test, predictions.flatten()) < 1.0
def test_mlp():
model = NeuralNet(
layers=[
Dense(16, Parameters(init='normal')),
Activation('linear'),
Dense(8, Parameters(init='normal')),
Activation('linear'),
Dense(1),
],
loss='mse',
optimizer=Adam(),
metric='mse',
batch_size=64,
max_epochs=150,
)
model.fit(X_train, y_train)
predictions = model.predict(X_test)
assert mean_squared_error(y_test, predictions.flatten()) < 1.0
def regression():
# Generate a random regression problem
X, y = make_regression(n_samples=10000,
n_features=100,
n_informative=75,
n_targets=1,
noise=0.05,
random_state=1111,
bias=0.5)
X_train, X_test, y_train, y_test = train_test_split(X,
y,
test_size=0.25,
random_state=1111)
model = LinearRegression(lr=0.01, max_iters=2000, penalty="l2", C=0.03)
model.fit(X_train, y_train)
predictions = model.predict(X_test)
print("regression mse", mean_squared_error(y_test, predictions))
def regression():
X, y = make_regression(n_samples=500,
n_features=5,
n_informative=5,
n_targets=1,
noise=0.05,
random_state=1111,
bias=0.5)
X_train, X_test, y_train, y_test = train_test_split(X,
y,
test_size=0.1,
random_state=1111)
model = GradientBoostingRegressor(
n_estimators=25,
max_depth=5,
max_features=3,
)
model.fit(X_train, y_train)
predictions = model.predict(X_test)
print('regression, mse: %s' %
mean_squared_error(y_test.flatten(), predictions.flatten()))
def test_knn():
model = KNNRegressor(k=5)
model.fit(X_train, y_train)
predictions = model.predict(X_test)
assert mean_squared_error(y_test, predictions) < 10000
def test_linear():
model = LinearRegression(lr=0.01, max_iters=2000, penalty='l2', C=0.03)
model.fit(X_train, y_train)
predictions = model.predict(X_test)
assert mean_squared_error(y_test, predictions) < 0.25
def test_knn():
model = KNNRegressor(k=5)
model.fit(X_train, y_train)
predictions = model.predict(X_test)
assert mean_squared_error(y_test, predictions) < 10000
def test_linear():
model = LinearRegression(lr=0.01, max_iters=2000, penalty='l2', C=0.03)
model.fit(X_train, y_train)
predictions = model.predict(X_test)
assert mean_squared_error(y_test, predictions) < 0.25
