def test6(self):
ridge_sk = sklearn.linear_model.Ridge(alpha=1000)
ridge_sk.fit(self.x_train.T, self.y_train.ravel())
preds_ridgesk = ridge_sk.predict(self.x_test.T)
r_squared_sk = r_squared(self.y_test, preds_ridgesk)
ridge_obj2 = RidgeRegression(degree=1, regParam=1000)
ridge_obj2.fit_iterative_optimizer(xtrain=self.x_train,
ytrain=self.y_train,
num_epochs=200,
learn_rate=0.1)
preds_ridge = ridge_obj2.predict_linear_regression(self.x_test)
r_squared_own = r_squared(self.y_test, preds_ridge)
self.assertLessEqual(abs(r_squared_own - r_squared_sk), 0.07)
def test5(self):
lasso_sk = sklearn.linear_model.Lasso(alpha=1)
lasso_sk.fit(self.x_train.T, self.y_train.ravel())
preds_lassosk = lasso_sk.predict(self.x_test.T)
r_squared_sk = r_squared(self.y_test, preds_lassosk)
lasso_obj2 = LassoRegression(degree=1, regParam=1)
lasso_obj2.fit_iterative_optimizer(xtrain=self.x_train,
ytrain=self.y_train,
num_epochs=15,
learn_rate=0.15)
preds_lasso = lasso_obj2.predict_linear_regression(self.x_test)
r_squared_own = r_squared(self.y_test, preds_lasso)
self.assertLessEqual(abs(r_squared_own - r_squared_sk), 0.08)
def test4(self):
lin_reg = sklearn.linear_model.LinearRegression()
lin_reg.fit(self.x_train.T, self.y_train.ravel())
preds_linreg = lin_reg.predict(self.x_test.T)
r_squared_sk = r_squared(self.y_test, preds_linreg)
lin_regOwn = LinearRegression(degree=1)
lin_regOwn.fit_iterative_optimizer(xtrain=self.x_train,
ytrain=self.y_train,
num_epochs=50,
learn_rate=0.15)
preds_lrOwn = lin_regOwn.predict_linear_regression(self.x_test)
r_squared_own = r_squared(self.y_test, preds_lrOwn)
self.assertLessEqual(abs(r_squared_own - r_squared_sk), 0.07)
def test9(self):
ridge_estimator = RidgeRegression(degree=2, regParam=55)
ridge_estimator.fit_iterative_optimizer(xtrain=self.x_train,
ytrain=self.y_train,
num_epochs=275,
learn_rate=0.01)
preds = ridge_estimator.predict_linear_regression(self.x_test)
r_squared_val = r_squared(self.y_test, preds)
self.assertGreaterEqual(r_squared_val, 0.85)
def test7(self):
degree_2 = LinearRegression(degree=2)
degree_2.fit_iterative_optimizer(xtrain=self.x_train,
ytrain=self.y_train,
num_epochs=275,
learn_rate=0.01,
ret_train_loss=True)
deg_2 = degree_2.predict_linear_regression(self.x_test)
r_squared_val = r_squared(self.y_test, deg_2)
self.assertGreaterEqual(r_squared_val, 0.8)
def test3(self):
ridge_obj = RidgeRegression(degree=1, regParam=1000)
ridge_obj.fit_iterative_optimizer(xtrain=self.x_train,
ytrain=self.y_train,
xvalid=self.x_valid,
yvalid=self.y_valid,
num_epochs=100,
ret_train_loss=True,
learn_rate=0.1)
preds2 = ridge_obj.predict_linear_regression(self.x_test)
r_squared_val = r_squared(self.y_test, preds2)
self.assertGreaterEqual(r_squared_val, 0.5)
def test1(self) -> None:
lr_obj = LinearRegression(degree=1)
lr_obj.fit_iterative_optimizer(xtrain=self.x_train,
ytrain=self.y_train,
xvalid=self.x_valid,
yvalid=self.y_valid,
num_epochs=100,
ret_train_loss=True,
learn_rate=0.1)
preds = lr_obj.predict_linear_regression(self.x_test)
r_squared_val = r_squared(self.y_test, preds)
self.assertGreaterEqual(r_squared_val, 0.5)