def test_forest_reg_model(data, test): import numpy as np from mle_training.utils import data_preprocess as preprocess housing = data # Fit missing value imputer on train data preprocess.fit(train_data=housing) # Transform train and test data X_train, y_train = preprocess.transform(data=housing) # Fit model and score on training set forest_model = train_score.forest_reg_model(X=X_train, y=y_train) y_pred_model = forest_model.predict(test)[0] assert np.round(y_pred_model, 3) == 134796.667
def test_model_predict(data): import numpy as np from mle_training.utils import data_preprocess as preprocess housing = data # Fit missing value imputer on train data preprocess.fit(train_data=housing) # Transform train and test data X_train, y_train = preprocess.transform(data=housing) from mle_training import train_score # train and score module # Fit model and score on training set lin_model = train_score.linear_reg_model(X=X_train, y=y_train) # Score trained model on test set (can be a model stored in a pickle file) y_pred = predict.model_predict(model=lin_model, X=[X_train.iloc[0, :]])[0] assert np.round(y_pred, 3) == 136237.050