wehub-resource-sync
62 lines
1.7 KiB
Python
62 lines
1.7 KiB
Python
try:
|
|
from sklearn.model_selection import train_test_split
|
|
except ImportError:
|
|
from sklearn.cross_validation import train_test_split
|
|
from sklearn.datasets import make_regression
|
|
|
|
from mla.knn import KNNRegressor
|
|
from mla.linear_models import LinearRegression
|
|
from mla.metrics.metrics import mean_squared_error
|
|
from mla.neuralnet import NeuralNet
|
|
from mla.neuralnet.layers import Activation, Dense
|
|
from mla.neuralnet.optimizers import Adam
|
|
from mla.neuralnet.parameters import Parameters
|
|
|
|
# Generate a random regression problem
|
|
X, y = make_regression(
|
|
n_samples=1000,
|
|
n_features=10,
|
|
n_informative=10,
|
|
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
|
|
)
|
|
|
|
|
|
def test_linear():
|
|
model = LinearRegression(lr=0.01, max_iters=2000, penalty="l2", C=0.003)
|
|
model.fit(X_train, y_train)
|
|
predictions = model.predict(X_test)
|
|
assert mean_squared_error(y_test, predictions) < 0.25
|
|
|
|
|
|
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_knn():
|
|
model = KNNRegressor(k=5)
|
|
model.fit(X_train, y_train)
|
|
predictions = model.predict(X_test)
|
|
assert mean_squared_error(y_test, predictions) < 10000
|