def test_regression_metrics(n_samples=50):
y_true = np.arange(n_samples)
y_pred = y_true + 1
assert_almost_equal(mean_squared_error(y_true, y_pred), 1.)
assert_almost_equal(
mean_squared_log_error(y_true, y_pred),
mean_squared_error(np.log(1 + y_true), np.log(1 + y_pred)))
assert_almost_equal(mean_absolute_error(y_true, y_pred), 1.)
assert_almost_equal(median_absolute_error(y_true, y_pred), 1.)
assert_almost_equal(max_error(y_true, y_pred), 1.)
assert_almost_equal(r2_score(y_true, y_pred), 0.995, 2)
assert_almost_equal(explained_variance_score(y_true, y_pred), 1.)
assert_almost_equal(mean_tweedie_deviance(y_true, y_pred, p=0),
mean_squared_error(y_true, y_pred))
# Tweedie deviance needs positive y_pred, except for p=0,
# p>=2 needs positive y_true
# results evaluated by sympy
y_true = np.arange(1, 1 + n_samples)
y_pred = 2 * y_true
n = n_samples
assert_almost_equal(mean_tweedie_deviance(y_true, y_pred, p=-1),
5 / 12 * n * (n**2 + 2 * n + 1))
assert_almost_equal(mean_tweedie_deviance(y_true, y_pred, p=1),
(n + 1) * (1 - np.log(2)))
assert_almost_equal(mean_tweedie_deviance(y_true, y_pred, p=2),
2 * np.log(2) - 1)
assert_almost_equal(mean_tweedie_deviance(y_true, y_pred, p=3 / 2),
((6 * np.sqrt(2) - 8) / n) * np.sqrt(y_true).sum())
assert_almost_equal(mean_tweedie_deviance(y_true, y_pred, p=3),
np.sum(1 / y_true) / (4 * n))
def test_regression_custom_weights():
y_true = [[1, 2], [2.5, -1], [4.5, 3], [5, 7]]
y_pred = [[1, 1], [2, -1], [5, 4], [5, 6.5]]
msew = mean_squared_error(y_true, y_pred, multioutput=[0.4, 0.6])
rmsew = mean_squared_error(y_true,
y_pred,
multioutput=[0.4, 0.6],
squared=False)
maew = mean_absolute_error(y_true, y_pred, multioutput=[0.4, 0.6])
rw = r2_score(y_true, y_pred, multioutput=[0.4, 0.6])
evsw = explained_variance_score(y_true, y_pred, multioutput=[0.4, 0.6])
assert_almost_equal(msew, 0.39, decimal=2)
assert_almost_equal(rmsew, 0.62, decimal=2)
assert_almost_equal(maew, 0.475, decimal=3)
assert_almost_equal(rw, 0.94, decimal=2)
assert_almost_equal(evsw, 0.94, decimal=2)
# Handling msle separately as it does not accept negative inputs.
y_true = np.array([[0.5, 1], [1, 2], [7, 6]])
y_pred = np.array([[0.5, 2], [1, 2.5], [8, 8]])
msle = mean_squared_log_error(y_true, y_pred, multioutput=[0.3, 0.7])
msle2 = mean_squared_error(np.log(1 + y_true),
np.log(1 + y_pred),
multioutput=[0.3, 0.7])
assert_almost_equal(msle, msle2, decimal=2)
def test_regression_metrics_at_limits():
assert_almost_equal(mean_squared_error([0.], [0.]), 0.00, 2)
assert_almost_equal(mean_squared_error([0.], [0.], squared=False), 0.00, 2)
assert_almost_equal(mean_squared_log_error([0.], [0.]), 0.00, 2)
assert_almost_equal(mean_absolute_error([0.], [0.]), 0.00, 2)
assert_almost_equal(median_absolute_error([0.], [0.]), 0.00, 2)
assert_almost_equal(max_error([0.], [0.]), 0.00, 2)
assert_almost_equal(explained_variance_score([0.], [0.]), 1.00, 2)
assert_almost_equal(r2_score([0., 1], [0., 1]), 1.00, 2)
assert_raises_regex(
ValueError, "Mean Squared Logarithmic Error cannot be "
"used when targets contain negative values.", mean_squared_log_error,
[-1.], [-1.])
assert_raises_regex(
ValueError, "Mean Squared Logarithmic Error cannot be "
"used when targets contain negative values.", mean_squared_log_error,
[1., 2., 3.], [1., -2., 3.])
assert_raises_regex(
ValueError, "Mean Squared Logarithmic Error cannot be "
"used when targets contain negative values.", mean_squared_log_error,
[1., -2., 3.], [1., 2., 3.])
# Tweedie deviance error
p = -1.2
assert_allclose(mean_tweedie_deviance([0], [1.], p=p),
2. / (2. - p),
rtol=1e-3)
with pytest.raises(ValueError,
match="can only be used on strictly positive y_pred."):
mean_tweedie_deviance([0.], [0.], p=p)
assert_almost_equal(mean_tweedie_deviance([0.], [0.], p=0), 0.00, 2)
msg = "only be used on non-negative y_true and strictly positive y_pred."
with pytest.raises(ValueError, match=msg):
mean_tweedie_deviance([0.], [0.], p=1.0)
p = 1.5
assert_allclose(mean_tweedie_deviance([0.], [1.], p=p), 2. / (2. - p))
msg = "only be used on non-negative y_true and strictly positive y_pred."
with pytest.raises(ValueError, match=msg):
mean_tweedie_deviance([0.], [0.], p=p)
p = 2.
assert_allclose(mean_tweedie_deviance([1.], [1.], p=p), 0.00, atol=1e-8)
msg = "can only be used on strictly positive y_true and y_pred."
with pytest.raises(ValueError, match=msg):
mean_tweedie_deviance([0.], [0.], p=p)
p = 3.
assert_allclose(mean_tweedie_deviance([1.], [1.], p=p), 0.00, atol=1e-8)
msg = "can only be used on strictly positive y_true and y_pred."
with pytest.raises(ValueError, match=msg):
mean_tweedie_deviance([0.], [0.], p=p)
with pytest.raises(ValueError,
match="deviance is only defined for p<=0 and p>=1."):
mean_tweedie_deviance([0.], [0.], p=0.5)
def test_regression_multioutput_array():
y_true = [[1, 2], [2.5, -1], [4.5, 3], [5, 7]]
y_pred = [[1, 1], [2, -1], [5, 4], [5, 6.5]]
mse = mean_squared_error(y_true, y_pred, multioutput='raw_values')
mae = mean_absolute_error(y_true, y_pred, multioutput='raw_values')
r = r2_score(y_true, y_pred, multioutput='raw_values')
evs = explained_variance_score(y_true, y_pred, multioutput='raw_values')
assert_array_almost_equal(mse, [0.125, 0.5625], decimal=2)
assert_array_almost_equal(mae, [0.25, 0.625], decimal=2)
assert_array_almost_equal(r, [0.95, 0.93], decimal=2)
assert_array_almost_equal(evs, [0.95, 0.93], decimal=2)
# mean_absolute_error and mean_squared_error are equal because
# it is a binary problem.
y_true = [[0, 0]] * 4
y_pred = [[1, 1]] * 4
mse = mean_squared_error(y_true, y_pred, multioutput='raw_values')
mae = mean_absolute_error(y_true, y_pred, multioutput='raw_values')
r = r2_score(y_true, y_pred, multioutput='raw_values')
assert_array_almost_equal(mse, [1., 1.], decimal=2)
assert_array_almost_equal(mae, [1., 1.], decimal=2)
assert_array_almost_equal(r, [0., 0.], decimal=2)
r = r2_score([[0, -1], [0, 1]], [[2, 2], [1, 1]], multioutput='raw_values')
assert_array_almost_equal(r, [0, -3.5], decimal=2)
assert np.mean(r) == r2_score([[0, -1], [0, 1]], [[2, 2], [1, 1]],
multioutput='uniform_average')
evs = explained_variance_score([[0, -1], [0, 1]], [[2, 2], [1, 1]],
multioutput='raw_values')
assert_array_almost_equal(evs, [0, -1.25], decimal=2)
# Checking for the condition in which both numerator and denominator is
# zero.
y_true = [[1, 3], [-1, 2]]
y_pred = [[1, 4], [-1, 1]]
r2 = r2_score(y_true, y_pred, multioutput='raw_values')
assert_array_almost_equal(r2, [1., -3.], decimal=2)
assert np.mean(r2) == r2_score(y_true,
y_pred,
multioutput='uniform_average')
evs = explained_variance_score(y_true, y_pred, multioutput='raw_values')
assert_array_almost_equal(evs, [1., -3.], decimal=2)
assert np.mean(evs) == explained_variance_score(y_true, y_pred)
# Handling msle separately as it does not accept negative inputs.
y_true = np.array([[0.5, 1], [1, 2], [7, 6]])
y_pred = np.array([[0.5, 2], [1, 2.5], [8, 8]])
msle = mean_squared_log_error(y_true, y_pred, multioutput='raw_values')
msle2 = mean_squared_error(np.log(1 + y_true),
np.log(1 + y_pred),
multioutput='raw_values')
assert_array_almost_equal(msle, msle2, decimal=2)
def _test_ridge_loo(filter_):
# test that can work with both dense or sparse matrices
n_samples = X_diabetes.shape[0]
ret = []
fit_intercept = filter_ == DENSE_FILTER
ridge_gcv = _RidgeGCV(fit_intercept=fit_intercept)
# check best alpha
ridge_gcv.fit(filter_(X_diabetes), y_diabetes)
alpha_ = ridge_gcv.alpha_
ret.append(alpha_)
# check that we get same best alpha with custom loss_func
f = ignore_warnings
scoring = make_scorer(mean_squared_error, greater_is_better=False)
ridge_gcv2 = RidgeCV(fit_intercept=False, scoring=scoring)
f(ridge_gcv2.fit)(filter_(X_diabetes), y_diabetes)
assert ridge_gcv2.alpha_ == pytest.approx(alpha_)
# check that we get same best alpha with custom score_func
func = lambda x, y: -mean_squared_error(x, y)
scoring = make_scorer(func)
ridge_gcv3 = RidgeCV(fit_intercept=False, scoring=scoring)
f(ridge_gcv3.fit)(filter_(X_diabetes), y_diabetes)
assert ridge_gcv3.alpha_ == pytest.approx(alpha_)
# check that we get same best alpha with a scorer
scorer = get_scorer('neg_mean_squared_error')
ridge_gcv4 = RidgeCV(fit_intercept=False, scoring=scorer)
ridge_gcv4.fit(filter_(X_diabetes), y_diabetes)
assert ridge_gcv4.alpha_ == pytest.approx(alpha_)
# check that we get same best alpha with sample weights
if filter_ == DENSE_FILTER:
ridge_gcv.fit(filter_(X_diabetes),
y_diabetes,
sample_weight=np.ones(n_samples))
assert ridge_gcv.alpha_ == pytest.approx(alpha_)
# simulate several responses
Y = np.vstack((y_diabetes, y_diabetes)).T
ridge_gcv.fit(filter_(X_diabetes), Y)
Y_pred = ridge_gcv.predict(filter_(X_diabetes))
ridge_gcv.fit(filter_(X_diabetes), y_diabetes)
y_pred = ridge_gcv.predict(filter_(X_diabetes))
assert_allclose(np.vstack((y_pred, y_pred)).T, Y_pred, rtol=1e-5)
return ret
def test_multioutput_regression():
y_true = np.array([[1, 0, 0, 1], [0, 1, 1, 1], [1, 1, 0, 1]])
y_pred = np.array([[0, 0, 0, 1], [1, 0, 1, 1], [0, 0, 0, 1]])
error = mean_squared_error(y_true, y_pred)
assert_almost_equal(error, (1. / 3 + 2. / 3 + 2. / 3) / 4.)
error = mean_squared_error(y_true, y_pred, squared=False)
assert_almost_equal(error, 0.645, decimal=2)
error = mean_squared_log_error(y_true, y_pred)
assert_almost_equal(error, 0.200, decimal=2)
# mean_absolute_error and mean_squared_error are equal because
# it is a binary problem.
error = mean_absolute_error(y_true, y_pred)
assert_almost_equal(error, (1. + 2. / 3) / 4.)
error = r2_score(y_true, y_pred, multioutput='variance_weighted')
assert_almost_equal(error, 1. - 5. / 2)
error = r2_score(y_true, y_pred, multioutput='uniform_average')
assert_almost_equal(error, -.875)
def test_base_chain_crossval_fit_and_predict():
# Fit chain with cross_val_predict and verify predict
# performance
X, Y = generate_multilabel_dataset_with_correlations()
for chain in [
ClassifierChain(LogisticRegression()),
RegressorChain(Ridge())
]:
chain.fit(X, Y)
chain_cv = clone(chain).set_params(cv=3)
chain_cv.fit(X, Y)
Y_pred_cv = chain_cv.predict(X)
Y_pred = chain.predict(X)
assert Y_pred_cv.shape == Y_pred.shape
assert not np.all(Y_pred == Y_pred_cv)
if isinstance(chain, ClassifierChain):
assert jaccard_score(Y, Y_pred_cv, average='samples') > .4
else:
assert mean_squared_error(Y, Y_pred_cv) < .25
# Split the targets into training/testing sets
diabetes_y_train = diabetes_y[:-20]
diabetes_y_test = diabetes_y[-20:]
# Create linear regression object
regr = linear_model.LinearRegression()
# Train the model using the training sets
regr.fit(diabetes_X_train, diabetes_y_train)
# Make predictions using the testing set
diabetes_y_pred = regr.predict(diabetes_X_test)
# The coefficients
print('Coefficients: \n', regr.coef_)
# The mean squared error
print('Mean squared error: %.2f' %
mean_squared_error(diabetes_y_test, diabetes_y_pred))
# The coefficient of determination: 1 is perfect prediction
print('Coefficient of determination: %.2f' %
r2_score(diabetes_y_test, diabetes_y_pred))
# Plot outputs
plt.scatter(diabetes_X_test, diabetes_y_test, color='black')
plt.plot(diabetes_X_test, diabetes_y_pred, color='blue', linewidth=3)
plt.xticks(())
plt.yticks(())
plt.show()
n_features=10,
coef=True,
random_state=1,
bias=3.5)
coefs = []
errors = []
alphas = np.logspace(-6, 6, 200)
# Train the model with different regularisation strengths
for a in alphas:
clf.set_params(alpha=a)
clf.fit(X, y)
coefs.append(clf.coef_)
errors.append(mean_squared_error(clf.coef_, w))
# Display results
plt.figure(figsize=(20, 6))
plt.subplot(121)
ax = plt.gca()
ax.plot(alphas, coefs)
ax.set_xscale('log')
plt.xlabel('alpha')
plt.ylabel('weights')
plt.title('Ridge coefficients as a function of the regularization')
plt.axis('tight')
plt.subplot(122)
ax = plt.gca()
X_train, y_train = X[:offset], y[:offset]
X_test, y_test = X[offset:], y[offset:]
# #############################################################################
# Fit regression model
params = {
'n_estimators': 500,
'max_depth': 4,
'min_samples_split': 2,
'learning_rate': 0.01,
'loss': 'ls'
}
clf = ensemble.GradientBoostingRegressor(**params)
clf.fit(X_train, y_train)
mse = mean_squared_error(y_test, clf.predict(X_test))
print("MSE: %.4f" % mse)
# #############################################################################
# Plot training deviance
# compute test set deviance
test_score = np.zeros((params['n_estimators'], ), dtype=np.float64)
for i, y_pred in enumerate(clf.staged_predict(X_test)):
test_score[i] = clf.loss_(y_test, y_pred)
plt.figure(figsize=(12, 6))
plt.subplot(1, 2, 1)
plt.title('Deviance')
plt.plot(np.arange(params['n_estimators']) + 1,
lw = 3
x_plot = np.linspace(X.min(), X.max())
for title, this_X, this_y in [('Modeling Errors Only', X, y),
('Corrupt X, Small Deviants', X_errors, y),
('Corrupt y, Small Deviants', X, y_errors),
('Corrupt X, Large Deviants', X_errors_large, y),
('Corrupt y, Large Deviants', X, y_errors_large)
]:
plt.figure(figsize=(5, 4))
plt.plot(this_X[:, 0], this_y, 'b+')
for name, estimator in estimators:
model = make_pipeline(PolynomialFeatures(3), estimator)
model.fit(this_X, this_y)
mse = mean_squared_error(model.predict(X_test), y_test)
y_plot = model.predict(x_plot[:, np.newaxis])
plt.plot(x_plot,
y_plot,
color=colors[name],
linestyle=linestyle[name],
linewidth=lw,
label='%s: error = %.3f' % (name, mse))
legend_title = 'Error of Mean\nAbsolute Deviation\nto Non-corrupt Data'
legend = plt.legend(loc='upper right',
frameon=False,
title=legend_title,
prop=dict(size='x-small'))
plt.xlim(-4, 10.2)
plt.ylim(-2, 10.2)
mean = X_train.mean(axis=0)
X_train = (X_train - mean) / std
X_test = (X_test - mean) / std
std = y_train.std(axis=0)
mean = y_train.mean(axis=0)
y_train = (y_train - mean) / std
y_test = (y_test - mean) / std
gc.collect()
print("- benchmarking ElasticNet")
clf = ElasticNet(alpha=alpha, l1_ratio=0.5, fit_intercept=False)
tstart = time()
clf.fit(X_train, y_train)
elnet_results[i, j,
0] = mean_squared_error(clf.predict(X_test), y_test)
elnet_results[i, j, 1] = time() - tstart
gc.collect()
print("- benchmarking SGD")
clf = SGDRegressor(alpha=alpha / n_train,
fit_intercept=False,
max_iter=max_iter,
learning_rate="invscaling",
eta0=.01,
power_t=0.25,
tol=1e-3)
tstart = time()
clf.fit(X_train, y_train)
sgd_results[i, j, 0] = mean_squared_error(clf.predict(X_test),
