def test_all_regressors(): x, y = make_friedman2(10000) x_train, y_train, x_test, y_test = test_helpers.split_dataset(x, y) #print y_test[:100] ols = LinearRegression() ols.fit(x_train, y_train) ols_pred = ols.predict(x_test) #print ols_pred[:100] ols_mse = mean_square_error(y_test, ols_pred) for fn in regressors: print fn model = fn(x_train, y_train) print model pred = model.predict(x_test) #print pred[:100] mse = mean_square_error(y_test, pred) print "OLS MSE:", ols_mse, " Current MSE:", mse print "Ratio:", mse / ols_mse assert ols_mse > 1.1 * mse
def test_all_regressors(): x, y = make_friedman2(10000) x_train, y_train, x_test, y_test = test_helpers.split_dataset(x,y) #print y_test[:100] ols = LinearRegression() ols.fit(x_train, y_train) ols_pred = ols.predict(x_test) #print ols_pred[:100] ols_mse = mean_square_error(y_test, ols_pred) for fn in regressors: print fn model = fn(x_train,y_train) print model pred = model.predict(x_test) #print pred[:100] mse = mean_square_error(y_test, pred) print "OLS MSE:", ols_mse, " Current MSE:", mse print "Ratio:", mse / ols_mse assert ols_mse > 1.1*mse
import recipes import numpy as np import sklearn.datasets from test_helpers import split_dataset iris = sklearn.datasets.load_iris() x_train, y_train, x_test, y_test = split_dataset(iris.data, iris.target) classifiers = [ recipes.train_svm_tree, recipes.train_sgd_tree, #recipes.train_svm_forest, #recipes.train_sgd_forest, recipes.train_random_forest, recipes.train_clustered_svm, recipes.train_clustered_svm_ensemble ] def test_all_classifiers(): for model_constructor in classifiers: print model_constructor model = model_constructor(x_train, y_train) print model pred = model.predict(x_test) num_incorrect = np.sum(pred != y_test) print "Expected num_incorrect <= 20, got:", num_incorrect assert num_incorrect <= 15