def batch():
print("Tesing the performance of LinearRegression(batch)...")
# Train model
reg = LinearRegression()
reg.fit(X=X_train, y=y_train, lr=0.02, epochs=5000)
# Model evaluation
get_r2(reg, X_test, y_test)
def batch():
print("Tesing the performance of LinearRegression(batch)...")
# Train model
reg = LinearRegression()
reg.fit(data=data_train, label=label_train, learning_rate=0.1, epochs=1000)
# Model evaluation
get_r2(reg, data_test, label_test)
print(reg)
def stochastic():
print("Tesing the performance of LinearRegression(stochastic)...")
# Train model
reg = LinearRegression()
reg.fit(data=data_train, label=label_train, learning_rate=0.05, epochs=50,
method="stochastic", sample_rate=0.6)
# Model evaluation
get_r2(reg, data_test, label_test)
print(reg)
def main():
print("Tesing the performance of KNN regressor...")
# Load data
X, y = load_boston_house_prices()
X = min_max_scale(X)
# Split data randomly, train set rate 70%
X_train, X_test, y_train, y_test = train_test_split(X, y, random_state=10)
# Train model
reg = KNeighborsRegressor()
reg.fit(X=X_train, y=y_train, k_neighbors=3)
# Model evaluation
get_r2(reg, X_test, y_test)
def stochastic():
print("Tesing the performance of LinearRegression(stochastic)...")
# Train model
reg = LinearRegression()
reg.fit(X=X_train,
y=y_train,
lr=0.001,
epochs=1000,
method="stochastic",
sample_rate=0.5)
# Model evaluation
get_r2(reg, X_test, y_test)
def main():
print("Tesing the performance of RegressionTree...")
# Load data
X, y = load_boston_house_prices()
# Split data randomly, train set rate 70%
X_train, X_test, y_train, y_test = train_test_split(X, y, random_state=10)
# Train model
reg = RegressionTree()
reg.fit(X=X_train, y=y_train, max_depth=5)
# Show rules
reg.rules
# Model evaluation
get_r2(reg, X_test, y_test)
def main():
print("Tesing the performance of Ridge Regressor(stochastic)...")
# Load data
X, y = load_boston_house_prices()
X = min_max_scale(X)
# Split data randomly, train set rate 70%
X_train, X_test, y_train, y_test = train_test_split(X, y, random_state=10)
# Train model
reg = Ridge()
reg.fit(X=X_train, y=y_train, lr=0.001, epochs=1000,
method="stochastic", sample_rate=0.5, alpha=1e-7)
# Model evaluation
get_r2(reg, X_test, y_test)
def main():
print("Tesing the performance of GBDT regressor...")
# Load data
X, y = load_boston_house_prices()
# Split data randomly, train set rate 70%
X_train, X_test, y_train, y_test = train_test_split(X, y, random_state=10)
# Train model
reg = GradientBoostingRegressor()
reg.fit(X=X_train,
y=y_train,
n_estimators=4,
lr=0.5,
max_depth=3,
min_samples_split=2)
# Model evaluation
get_r2(reg, X_test, y_test)
def main():
"""Tesing the performance of RegressionTree
"""
print("Tesing the performance of RegressionTree...")
# Load data
data, label = load_boston_house_prices()
# Split data randomly, train set rate 70%
data_train, data_test, label_train, label_test = train_test_split(
data, label, random_state=200)
# Train model
reg = RegressionTree()
reg.fit(data=data_train, label=label_train, max_depth=5)
# Show rules
print(reg)
# Model evaluation
get_r2(reg, data_test, label_test)
def main():
"""Tesing the performance of MLP.
"""
print("Tesing the performance of MLP....")
# Load data
data, label = load_boston_house_prices()
data = min_max_scale(data)
# Split data randomly, train set rate 70%
data_train, data_test, label_train, label_test = train_test_split(
data, label, random_state=20)
# Train model
reg = MLP()
reg.fit(data=data_train, label=label_train, n_hidden=8,
epochs=500, batch_size=8, learning_rate=0.0008)
# Model evaluation
get_r2(reg, data_test, label_test)
print(reg)