def linear_model(x_train, x_test, y_train, y_test): G = linear_regression(x_train, y_train) y_pred, rmse, mae, r2 = regression_predictor(G, x_test, y_test) val = cross_validation_regressor(model, x_train, y_train) stats = pd.DataFrame([(val, mae, rmse, r2)], columns=['cross_val', 'rmse', 'mae', 'r2']) return G, y_pred, stats
def linear_model(x_train, x_test, y_train, y_test): model = linear_regression(x_train, y_train) val = cross_validation_regressor(model, x_train, y_train) y_pred, mse, mae, r2 = regression_predictor(model, x_test, y_test) rmse = (mse) d = {'0': val, '1': mae, '2': rmse, '3': r2} stats = pd.DataFrame(d, index=d.keys()) stats.reset_index(drop=True, inplace=True) return model, y_pred, stats
def linear_model(x_train, x_test, y_train, y_test): model = linear_regression(x_train, y_train) val = cross_validation_regressor(model, x_train, y_train) y_pred, mse, mae, r2 = regression_predictor(model, x_test, y_test) stats = pd.DataFrame(np.array([val, mae, mse, r2]).reshape(1, 4), columns=['v', 'm', 's', 'r'], index=[0]) return model, y_pred, stats
def linear_model(x_train, x_test, y_train, y_test): model = linear_regression(x_train, y_train) val = cross_validation_regressor(model, x_train, y_train) y_pred, mse, mae, r2 = regression_predictor(model, x_test, y_test) stats = pd.DataFrame() stats['CV_score'] = val, val stats['MAE'] = mae stats['MSE'] = mse stats['r2'] = r2 #stats.set_index('Name',inplace=True) return model, y_pred, stats
def linear_model(x_train, x_test, y_train, y_test): model = linear_regression(x_train, y_train) val = cross_validation_regressor(model, x_train, y_train) y_pred, mse, mae, r2 = regression_predictor(model, x_test, y_test) d = { 'cross_validation': [val], 'rmse': [mse], 'mae': [mae], 'rsquared': [r2] } stats = pd.DataFrame(data=d) return model, y_pred, stats
def linear_model(x_train, x_test, y_train, y_test): G = linear_regression(x_train, y_train) c_val = cross_validation_regressor(G, x_train, y_train) y_pred, mse, mae, r2 = regression_predictor(G, x_test, y_test) my_dict = {'c_val': c_val, 'mse': mse, 'mae': mae, 'r2': r2} stats = pd.DataFrame(my_dict, index=[0]) return G, y_pred, stats
from sklearn.metrics import mean_squared_error, mean_absolute_error, r2_score from greyatomlib.multivariate_regression_project.q01_load_data.build import load_data from greyatomlib.multivariate_regression_project.q02_data_split.build import split_dataset from greyatomlib.multivariate_regression_project.q03_data_encoding.build import label_encode from greyatomlib.multivariate_regression_project.q05_linear_regression_model.build import linear_regression from greyatomlib.multivariate_regression_project.q06_cross_validation.build import cross_validation_regressor df = load_data('data/student-mat.csv') x_train, x_test, y_train, y_test = split_dataset(df) x_train, x_test = label_encode(x_train, x_test) model = linear_regression(x_train, y_train) val = cross_validation_regressor(model, x_train, y_train) y_pred, mse, mae, r2 = regression_predictor(model, x_test, y_test) class Test_regression_predictor(TestCase): def test_args(self): # Input parameters tests args = getfullargspec(regression_predictor) self.assertEqual(len(args[0]), 3, "Expected arguments %d, Given %d" % (2, len(args[0]))) def test_y_pred_type(self): self.assertIsInstance( y_pred, np.ndarray, "Expected data type for 'return value' is float you are returning\
def linear_model(x_train, x_test, y_train, y_test): G = linear_regression(x_train, y_train) stats = pd.DataFrame([(val,mae,mse,r2)], columns = ['cross_val','rmse','mae','r2']) return G, y_pred, stats
import matplotlib.pyplot as plt import pylab import scipy.stats as stats from greyatomlib.multivariate_regression_project.q01_load_data.build import load_data from greyatomlib.multivariate_regression_project.q02_data_split.build import split_dataset from greyatomlib.multivariate_regression_project.q05_linear_regression_model.build import linear_regression from greyatomlib.multivariate_regression_project.q07_regression_pred.build import regression_predictor #from greyatomlib.linear_regression.q05_residuals.build import residuals #from greyatomlib.multivariate_regression_project.q06_cross_validation import cross_validation_regressor from sklearn.linear_model import LinearRegression from greyatomlib.multivariate_regression_project.q03_data_encoding.build import label_encode df = load_data('data/student-mat.csv') x_train, x_test, y_train, y_test = split_dataset(df) x_train, x_test = label_encode(x_train, x_test) lin_reg = linear_regression(x_train, y_train) y_pred, _, __, ___ = regression_predictor(lin_reg, x_test, y_test) def plot_residuals(y_test, y_pred, name): error_residuals = y_test - y_pred stats.probplot(error_residuals, dist="norm", plot=pylab) return pylab.show() #plot_residuals(y_test,y_pred,'name')