def GridSearch(makePolynomialFeatures):
for i in range(1):
print("*" * 20, i)
X_train, X_test, y_train, y_test = getPlantsPropulsionData(
splitData=True, makePolynomialFeatures=makePolynomialFeatures)
AdaBoostGS(X_train, X_test, y_train, y_test)
DecisionTreeGS(X_train, X_test, y_train, y_test)
ElasticNetRegressorGS(X_train, X_test, y_train, y_test)
ExtraTreeGS(X_train, X_test, y_train, y_test)
GradientBoostingGS(X_train, X_test, y_train, y_test)
LarsRegressorGS(X_train, X_test, y_train, y_test)
LassoRegressorGS(X_train, X_test, y_train, y_test)
LinearSVRRegressorGS(X_train, X_test, y_train, y_test)
NeuralNetGS(X_train, X_test, y_train, y_test)
NuSVRRegressorGS(X_train, X_test, y_train, y_test)
PoissonRegGS(X_train, X_test, y_train, y_test)
RidgeRegressorGS(X_train, X_test, y_train, y_test)
SGD_GS(X_train, X_test, y_train, y_test)
SVRRegressorGS(X_train, X_test, y_train, y_test)
XgBoostGS(X_train, X_test, y_train, y_test)
grid_reg = GridSearchCV(
reg,
param_grid=grid_values,
scoring=['neg_mean_squared_error', 'neg_mean_absolute_error', 'r2'],
refit='r2',
n_jobs=-1,
cv=2,
verbose=100)
grid_reg.fit(X_train, y_train)
reg = grid_reg.best_estimator_
reg.fit(X_train, y_train)
y_pred = reg.predict(X_test)
printMetrics(y_true=y_test, y_pred=y_pred)
val_metrics = getMetrics(y_true=y_test, y_pred=y_pred)
y_pred = reg.predict(X=X_train)
metrics = getMetrics(y_true=y_train, y_pred=y_pred)
printMetrics(y_true=y_train, y_pred=y_pred)
best_params: dict = grid_reg.best_params_
saveBestParams(nameOfModel="LarsRegressorGS", best_params=best_params)
logSave(nameOfModel="LarsRegressorGS",
reg=reg,
metrics=metrics,
val_metrics=val_metrics)
X_train, X_test, y_train, y_test = getPlantsPropulsionData(
splitData=True, makePolynomialFeatures=True)
LarsRegressor(X_train, X_test, y_train, y_test)