def batch():
print("Tesing the accuracy of LinearRegression(batch)...")
# Train model
reg = LinearRegression()
reg.fit(X=X_train, y=y_train, lr=0.02, epochs=5000)
# Model accuracy
get_r2(reg, X_test, y_test)
def stochastic():
print("Tesing the accuracy 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 accuracy
get_r2(reg, X_test, y_test)
def main():
print("Tesing the accuracy 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=4)
# Show rules
reg.print_rules()
# Model accuracy
get_r2(reg, X_test, y_test)
def main():
print("Tesing the accuracy 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=2,
min_samples_split=2)
# Model accuracy
get_r2(reg, X_test, y_test)
def __init__(self, batch_size, block_size, hidden_size, threshold,
**kargs):
nn.Module.__init__(self)
B, M, H = batch_size, block_size, hidden_size
self.p = nn.Linear(H, 1)
self.p.bias.data.fill_(-1.) # force initial low p
self.sigma = nn.Sigmoid()
self.threshold = threshold
# global variables
self.updates = 0
#self.exit_ = torch.zeros(B, M, 1).float().cuda()
self.run = torch.ones(B, M, 1).bool().cuda()
# helper
self.index_run = torch.arange(B * M).reshape(B, M, 1).cuda()
self.align = torch.arange(M).cuda()
# buffer
self.unpack_h = torch.zeros(B, M, H).cuda()
self.weighted_h = torch.zeros(B, M, H).cuda()
self.acc_p = torch.zeros(B, M, 1).cuda()
self.remainders = torch.zeros(B, M, 1).cuda()
# power of 2
self.r2 = get_r2(M).cuda()
def __init__(self, M):
nn.Module.__init__(self)
self.x = (torch.arange(M) - M // 2).float().cuda()
self.r2 = get_r2(M).float().cuda()
self.M = M