def test_scm1d():
df = pd.read_csv('scm1d.csv')
target = df.loc[:, df.columns.str.contains('L')]
df.drop(target.columns, axis=1, inplace=True)
df, target = df.to_numpy(), target.to_numpy()
X_train, X_test, y_train, y_test = train_test_split(df,
target,
test_size=1658.0 /
8145.0,
random_state=42,
shuffle=True)
gb = GradientBoostingRegressor(
l2_regularization=0.07054193143238725,
min_samples_leaf=23,
learning_rate=0.12336530854190006,
max_iter=1999,
n_iter_no_change=None,
)
# scaler = StandardScaler()
# scaler.fit(X_train)
# train_scaled = scaler.transform(X_train)
# test_scaled = scaler.transform(X_test)
# scalery = StandardScaler()
# scalery.fit(y_train)
# y_Train = scalery.transform(y_train)
# y_Test = scalery.transform(y_test)
gb.fit(X_train, y_train)
y_preds = gb.predict_multi(X_test)
r2 = r2_score(y_test, y_preds, multioutput='uniform_average')
print(r2)
def test_atp1d():
df = pd.read_csv('atp1d.csv')
target = df.loc[:, df.columns.str.startswith('LBL')]
df.drop(target.columns, axis=1, inplace=True)
df, target = df.to_numpy(), target.to_numpy()
X_train, X_test, y_train, y_test = train_test_split(df,
target,
test_size=0.5,
random_state=42,
shuffle=True)
gb = GradientBoostingRegressor(l2_regularization=0.003391634274257872,
min_samples_leaf=10,
learning_rate=0.1088115324113492,
max_iter=199,
n_iter_no_change=20)
scaler = StandardScaler()
scaler.fit(X_train)
train_scaled = scaler.transform(X_train)
test_scaled = scaler.transform(X_test)
scalery = StandardScaler()
scalery.fit(y_train)
y_Train = scalery.transform(y_train)
y_Test = scalery.transform(y_test)
gb.fit(train_scaled, y_Train)
y_preds = gb.predict_multi(test_scaled)
r2 = r2_score(y_Test, y_preds, multioutput='uniform_average')
print(r2)
def test_wq():
df = pd.read_csv('water-quality.csv')
target = df.loc[:, df.columns.str.startswith('x')]
df.drop(target.columns, axis=1, inplace=True)
df, target = df.to_numpy(), target.to_numpy()
X_train, X_test, y_train, y_test = train_test_split(df,
target,
test_size=0.5,
random_state=42,
shuffle=True)
gb = GradientBoostingRegressor(l2_regularization=0.07509314619453317,
min_samples_leaf=15,
learning_rate=0.01948991297099692,
max_iter=300,
n_iter_no_change=17)
scaler = StandardScaler()
scaler.fit(X_train)
train_scaled = scaler.transform(X_train)
test_scaled = scaler.transform(X_test)
scalery = StandardScaler()
scalery.fit(y_train)
y_Train = scalery.transform(y_train)
y_Test = scalery.transform(y_test)
gb.fit(train_scaled, y_Train)
y_preds = gb.predict_multi(test_scaled)
r2 = r2_score(y_Test, y_preds, multioutput='uniform_average')
print(r2)
def test_edm():
df = pd.read_csv('edm.csv')
target = df.loc[:, ['DFlow', 'DGap']]
df.drop(target.columns, axis=1, inplace=True)
df, target = df.to_numpy(), target.to_numpy()
X_train, X_test, y_train, y_test = train_test_split(df,
target,
test_size=0.5,
random_state=42,
shuffle=True)
gb = GradientBoostingRegressor(
l2_regularization=0.880826520747869,
min_samples_leaf=12,
learning_rate=0.22445307581959334,
max_iter=279,
n_iter_no_change=23,
)
scaler = StandardScaler()
scaler.fit(X_train)
train_scaled = scaler.transform(X_train)
test_scaled = scaler.transform(X_test)
scalery = StandardScaler()
scalery.fit(y_train)
y_Train = scalery.transform(y_train)
y_Test = scalery.transform(y_test)
gb.fit(train_scaled, y_Train)
y_preds = gb.predict_multi(test_scaled)
r2 = r2_score(y_Test, y_preds, multioutput='uniform_average')
print(r2)
def test_scm20d():
df = pd.read_csv('scm20d.csv')
target = df.loc[:, df.columns.str.contains('L')]
df.drop(target.columns, axis=1, inplace=True)
df, target = df.to_numpy(), target.to_numpy()
c = 1503.0 / 7463.0
X_train, X_test, y_train, y_test = train_test_split(df,
target,
test_size=c,
random_state=42,
shuffle=True)
gb = GradientBoostingRegressor(
l2_regularization=0.8640187696889217,
min_samples_leaf=19,
learning_rate=0.1164232801613771,
max_iter=1998,
n_iter_no_change=None,
)
scaler = StandardScaler()
scaler.fit(X_train)
train_scaled = scaler.transform(X_train)
test_scaled = scaler.transform(X_test)
scalery = StandardScaler()
scalery.fit(y_train)
y_Train = scalery.transform(y_train)
y_Test = scalery.transform(y_test)
gb.fit(train_scaled, y_Train)
y_preds = gb.predict_multi(test_scaled)
r2 = r2_score(y_Test, y_preds, multioutput='uniform_average')
print(r2)