def test_dummy_axis0():
est = Dummy(axis=0)
est.fit(X)
X_predicted = est.predict(X).toarray()
X_expected = [[2, 0, 0, 1], [2, 0, 10. / 3, 0], [0, 4, 10. / 3, 0],
[0, 0, 10. / 3, 0], [2, 0, 0, 0]]
assert_array_almost_equal(X_predicted, X_expected)
def test_dummy_axis1():
est = Dummy()
est.fit(X)
X_predicted = est.predict(X).toarray()
X_expected = [[2, 0, 0, 2], [3.5, 0, 3.5, 0], [0, 3.5, 3.5, 0],
[0, 0, 2, 0], [1, 0, 0, 0]]
assert_array_almost_equal(X_predicted, X_expected)
def test_dummy_axis0():
est = Dummy(axis=0)
est.fit(X)
X_predicted = est.predict(X).toarray()
X_expected = [[2, 0, 0, 1],
[2, 0, 10./3, 0],
[0, 4, 10./3, 0],
[0, 0, 10./3, 0],
[2, 0, 0, 0]]
assert_array_almost_equal(X_predicted, X_expected)
def test_dummy_axis1():
est = Dummy()
est.fit(X)
X_predicted = est.predict(X).toarray()
X_expected = [[2, 0, 0, 2],
[3.5, 0, 3.5, 0],
[0, 3.5, 3.5, 0],
[0, 0, 2, 0],
[1, 0, 0, 0]]
assert_array_almost_equal(X_predicted, X_expected)
cv = ShuffleSplit(n_iter=3, train_size=0.75, random_state=0)
for alpha in alphas:
mf = ExplicitMF(n_components=30, max_iter=10, alpha=alpha)
# mf = DictMF(n_components=30, n_epochs=10, alpha=alpha, verbose=1,
# batch_size=10, normalize=True,
# fit_intercept=True,
# random_state=0,
# learning_rate=.5)
mf_scores.append(cross_val_score(mf, X, cv))
# Array of size n_alphas x n_folds.
mf_scores = np.array(mf_scores)
dummy = Dummy()
dummy_scores = cross_val_score(dummy, X, cv)
dummy = Dummy(axis=0)
dummy_scores2 = cross_val_score(dummy, X, cv)
plt.figure()
plt.plot(alphas, mf_scores.mean(axis=1), label="Matrix Factorization")
plt.plot(alphas, [dummy_scores.mean()] * len(alphas), label="User mean")
plt.plot(alphas, [dummy_scores2.mean()] * len(alphas), label="Movie mean")
plt.xlabel("alpha")
plt.xscale("log")
plt.ylabel("RMSE")
plt.legend()
plt.savefig('cross_val.pdf')