def test_dtype_match(): rng = np.random.RandomState(0) alpha = 1.0 n_samples, n_features = 6, 5 X_64 = rng.randn(n_samples, n_features) y_64 = rng.randn(n_samples) X_32 = X_64.astype(np.float32) y_32 = y_64.astype(np.float32) solvers = ["svd", "sparse_cg", "cholesky", "lsqr"] for solver in solvers: # Check type consistency 32bits ridge_32 = Ridge(alpha=alpha, solver=solver) ridge_32.fit(X_32, y_32) coef_32 = ridge_32.coef_ # Check type consistency 64 bits ridge_64 = Ridge(alpha=alpha, solver=solver) ridge_64.fit(X_64, y_64) coef_64 = ridge_64.coef_ # Do the actual checks at once for easier debug assert coef_32.dtype == X_32.dtype assert coef_64.dtype == X_64.dtype assert ridge_32.predict(X_32).dtype == X_32.dtype assert ridge_64.predict(X_64).dtype == X_64.dtype assert_almost_equal(ridge_32.coef_, ridge_64.coef_, decimal=5)
def test_dtype_match(solver): rng = np.random.RandomState(0) alpha = 1.0 n_samples, n_features = 6, 5 X_64 = rng.randn(n_samples, n_features) y_64 = rng.randn(n_samples) X_32 = X_64.astype(np.float32) y_32 = y_64.astype(np.float32) # Check type consistency 32bits ridge_32 = Ridge(alpha=alpha, solver=solver, max_iter=500, tol=1e-10,) ridge_32.fit(X_32, y_32) coef_32 = ridge_32.coef_ # Check type consistency 64 bits ridge_64 = Ridge(alpha=alpha, solver=solver, max_iter=500, tol=1e-10,) ridge_64.fit(X_64, y_64) coef_64 = ridge_64.coef_ # Do the actual checks at once for easier debug assert coef_32.dtype == X_32.dtype assert coef_64.dtype == X_64.dtype assert ridge_32.predict(X_32).dtype == X_32.dtype assert ridge_64.predict(X_64).dtype == X_64.dtype assert_allclose(ridge_32.coef_, ridge_64.coef_, rtol=1e-4)
def test_dtype_match_cholesky(): # Test different alphas in cholesky solver to ensure full coverage. # This test is separated from test_dtype_match for clarity. rng = np.random.RandomState(0) alpha = (1.0, 0.5) n_samples, n_features, n_target = 6, 7, 2 X_64 = rng.randn(n_samples, n_features) y_64 = rng.randn(n_samples, n_target) X_32 = X_64.astype(np.float32) y_32 = y_64.astype(np.float32) # Check type consistency 32bits ridge_32 = Ridge(alpha=alpha, solver='cholesky') ridge_32.fit(X_32, y_32) coef_32 = ridge_32.coef_ # Check type consistency 64 bits ridge_64 = Ridge(alpha=alpha, solver='cholesky') ridge_64.fit(X_64, y_64) coef_64 = ridge_64.coef_ # Do all the checks at once, like this is easier to debug assert coef_32.dtype == X_32.dtype assert coef_64.dtype == X_64.dtype assert ridge_32.predict(X_32).dtype == X_32.dtype assert ridge_64.predict(X_64).dtype == X_64.dtype assert_almost_equal(ridge_32.coef_, ridge_64.coef_, decimal=5)
def test_sparse_design_with_sample_weights(): # Sample weights must work with sparse matrices n_sampless = [2, 3] n_featuress = [3, 2] rng = np.random.RandomState(42) sparse_matrix_converters = [sp.coo_matrix, sp.csr_matrix, sp.csc_matrix, sp.lil_matrix, sp.dok_matrix ] sparse_ridge = Ridge(alpha=1., fit_intercept=False) dense_ridge = Ridge(alpha=1., fit_intercept=False) for n_samples, n_features in zip(n_sampless, n_featuress): X = rng.randn(n_samples, n_features) y = rng.randn(n_samples) sample_weights = rng.randn(n_samples) ** 2 + 1 for sparse_converter in sparse_matrix_converters: X_sparse = sparse_converter(X) sparse_ridge.fit(X_sparse, y, sample_weight=sample_weights) dense_ridge.fit(X, y, sample_weight=sample_weights) assert_array_almost_equal(sparse_ridge.coef_, dense_ridge.coef_, decimal=6)
def test_raises_value_error_if_sample_weights_greater_than_1d(): # Sample weights must be either scalar or 1D n_sampless = [2, 3] n_featuress = [3, 2] rng = np.random.RandomState(42) for n_samples, n_features in zip(n_sampless, n_featuress): X = rng.randn(n_samples, n_features) y = rng.randn(n_samples) sample_weights_OK = rng.randn(n_samples) ** 2 + 1 sample_weights_OK_1 = 1.0 sample_weights_OK_2 = 2.0 sample_weights_not_OK = sample_weights_OK[:, np.newaxis] sample_weights_not_OK_2 = sample_weights_OK[np.newaxis, :] ridge = Ridge(alpha=1) # make sure the "OK" sample weights actually work ridge.fit(X, y, sample_weights_OK) ridge.fit(X, y, sample_weights_OK_1) ridge.fit(X, y, sample_weights_OK_2) def fit_ridge_not_ok(): ridge.fit(X, y, sample_weights_not_OK) def fit_ridge_not_ok_2(): ridge.fit(X, y, sample_weights_not_OK_2) assert_raise_message(ValueError, "Sample weights must be 1D array or scalar", fit_ridge_not_ok) assert_raise_message(ValueError, "Sample weights must be 1D array or scalar", fit_ridge_not_ok_2)
def _test_ridge_loo(filter_): # test that can work with both dense or sparse matrices n_samples = X_diabetes.shape[0] ret = [] ridge_gcv = _RidgeGCV(fit_intercept=False) ridge = Ridge(fit_intercept=False) # generalized cross-validation (efficient leave-one-out) K, v, Q = ridge_gcv._pre_compute(X_diabetes, y_diabetes) errors, c = ridge_gcv._errors(v, Q, y_diabetes, 1.0) values, c = ridge_gcv._values(K, v, Q, y_diabetes, 1.0) # brute-force leave-one-out: remove one example at a time errors2 = [] values2 = [] for i in range(n_samples): sel = np.arange(n_samples) != i X_new = X_diabetes[sel] y_new = y_diabetes[sel] ridge.fit(X_new, y_new) value = ridge.predict([X_diabetes[i]])[0] error = (y_diabetes[i] - value) ** 2 errors2.append(error) values2.append(value) # check that efficient and brute-force LOO give same results assert_almost_equal(errors, errors2) assert_almost_equal(values, values2) # check best alpha ridge_gcv.fit(filter_(X_diabetes), y_diabetes) best_alpha = ridge_gcv.best_alpha ret.append(best_alpha) # check that we get same best alpha with custom loss_func ridge_gcv2 = _RidgeGCV(fit_intercept=False, loss_func=mean_squared_error) ridge_gcv2.fit(filter_(X_diabetes), y_diabetes) assert_equal(ridge_gcv2.best_alpha, best_alpha) # check that we get same best alpha with sample weights ridge_gcv.fit(filter_(X_diabetes), y_diabetes, sample_weight=np.ones(n_samples)) assert_equal(ridge_gcv.best_alpha, best_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_array_almost_equal(np.vstack((y_pred, y_pred)).T, Y_pred, decimal=5) return ret
def _test_tolerance(filter_): ridge = Ridge(tol=1e-5, fit_intercept=False) ridge.fit(filter_(X_diabetes), y_diabetes) score = ridge.score(filter_(X_diabetes), y_diabetes) ridge2 = Ridge(tol=1e-3, fit_intercept=False) ridge2.fit(filter_(X_diabetes), y_diabetes) score2 = ridge2.score(filter_(X_diabetes), y_diabetes) assert score >= score2
def _test_tolerance(filter_): ridge = Ridge(tol=1e-5) ridge.fit(filter_(X_diabetes), y_diabetes) score = ridge.score(filter_(X_diabetes), y_diabetes) ridge2 = Ridge(tol=1e-3) ridge2.fit(filter_(X_diabetes), y_diabetes) score2 = ridge2.score(filter_(X_diabetes), y_diabetes) assert_true(score >= score2)
def test_ridge_singular(): # test on a singular matrix rng = np.random.RandomState(0) n_samples, n_features = 6, 6 y = rng.randn(n_samples // 2) y = np.concatenate((y, y)) X = rng.randn(n_samples // 2, n_features) X = np.concatenate((X, X), axis=0) ridge = Ridge(alpha=0) ridge.fit(X, y) assert_greater(ridge.score(X, y), 0.9)
def _test_multi_ridge_diabetes(filter_): # simulate several responses Y = np.vstack((y_diabetes, y_diabetes)).T n_features = X_diabetes.shape[1] ridge = Ridge(fit_intercept=False) ridge.fit(filter_(X_diabetes), Y) assert_equal(ridge.coef_.shape, (2, n_features)) Y_pred = ridge.predict(filter_(X_diabetes)) ridge.fit(filter_(X_diabetes), y_diabetes) y_pred = ridge.predict(filter_(X_diabetes)) assert_array_almost_equal(np.vstack((y_pred, y_pred)).T, Y_pred, decimal=3)
def test_ridge(): # Ridge regression convergence test using score # TODO: for this test to be robust, we should use a dataset instead # of np.random. rng = np.random.RandomState(0) alpha = 1.0 for solver in ("svd", "sparse_cg", "cholesky", "lsqr"): # With more samples than features n_samples, n_features = 6, 5 y = rng.randn(n_samples) X = rng.randn(n_samples, n_features) ridge = Ridge(alpha=alpha, solver=solver) ridge.fit(X, y) assert_equal(ridge.coef_.shape, (X.shape[1], )) assert_greater(ridge.score(X, y), 0.47) if solver == "cholesky": # Currently the only solver to support sample_weight. ridge.fit(X, y, sample_weight=np.ones(n_samples)) assert_greater(ridge.score(X, y), 0.47) # With more features than samples n_samples, n_features = 5, 10 y = rng.randn(n_samples) X = rng.randn(n_samples, n_features) ridge = Ridge(alpha=alpha, solver=solver) ridge.fit(X, y) assert_greater(ridge.score(X, y), .9) if solver == "cholesky": # Currently the only solver to support sample_weight. ridge.fit(X, y, sample_weight=np.ones(n_samples)) assert_greater(ridge.score(X, y), 0.9)
def test_ridge(): """Ridge regression convergence test using score TODO: for this test to be robust, we should use a dataset instead of np.random. """ alpha = 1.0 # With more samples than features n_samples, n_features = 6, 5 y = np.random.randn(n_samples) X = np.random.randn(n_samples, n_features) ridge = Ridge(alpha=alpha) ridge.fit(X, y) assert_equal(ridge.coef_.shape, (X.shape[1],)) assert ridge.score(X, y) > 0.5 ridge.fit(X, y, sample_weight=np.ones(n_samples)) assert ridge.score(X, y) > 0.5 # With more features than samples n_samples, n_features = 5, 10 y = np.random.randn(n_samples) X = np.random.randn(n_samples, n_features) ridge = Ridge(alpha=alpha) ridge.fit(X, y) assert ridge.score(X, y) > 0.9 ridge.fit(X, y, sample_weight=np.ones(n_samples)) assert ridge.score(X, y) > 0.9
def test_ridge(): """Ridge regression convergence test using score TODO: for this test to be robust, we should use a dataset instead of np.random. """ alpha = 1.0 for solver in ("sparse_cg", "dense_cholesky", "lsqr"): # With more samples than features n_samples, n_features = 6, 5 y = rng.randn(n_samples) X = rng.randn(n_samples, n_features) ridge = Ridge(alpha=alpha, solver=solver) ridge.fit(X, y) assert_equal(ridge.coef_.shape, (X.shape[1], )) assert_greater(ridge.score(X, y), 0.47) ridge.fit(X, y, sample_weight=np.ones(n_samples)) assert_greater(ridge.score(X, y), 0.47) # With more features than samples n_samples, n_features = 5, 10 y = rng.randn(n_samples) X = rng.randn(n_samples, n_features) ridge = Ridge(alpha=alpha, solver=solver) ridge.fit(X, y) assert_greater(ridge.score(X, y), .9) ridge.fit(X, y, sample_weight=np.ones(n_samples)) assert_greater(ridge.score(X, y), 0.9)
def test_fit_simple_backupsklearn(): df = pd.read_csv("./open_data/simple.txt", delim_whitespace=True) X = np.array(df.iloc[:, :df.shape[1] - 1], dtype='float32', order='C') y = np.array(df.iloc[:, df.shape[1] - 1], dtype='float32', order='C') Solver = h2o4gpu.Ridge enet = Solver(glm_stop_early=False) print("h2o4gpu fit()") enet.fit(X, y) print("h2o4gpu predict()") print(enet.predict(X)) print("h2o4gpu score()") print(enet.score(X,y)) enet_wrapper = Solver(normalize=True, random_state=1234) print("h2o4gpu scikit wrapper fit()") enet_wrapper.fit(X, y) print("h2o4gpu scikit wrapper predict()") print(enet_wrapper.predict(X)) print("h2o4gpu scikit wrapper score()") print(enet_wrapper.score(X, y)) from sklearn.linear_model.ridge import Ridge enet_sk = Ridge(normalize=True, random_state=1234) print("Scikit fit()") enet_sk.fit(X, y) print("Scikit predict()") print(enet_sk.predict(X)) print("Scikit score()") print(enet_sk.score(X, y)) enet_sk_coef = csr_matrix(enet_sk.coef_, dtype=np.float32).toarray() print(enet_sk.coef_) print(enet_sk_coef) print(enet_wrapper.coef_) print(enet_sk.intercept_) print(enet_wrapper.intercept_) print(enet_sk.n_iter_) print(enet_wrapper.n_iter_) print("Coeffs, intercept, and n_iters should match") assert np.allclose(enet_wrapper.coef_, enet_sk_coef) assert np.allclose(enet_wrapper.intercept_, enet_sk.intercept_)
def test_ridge_sample_weights(): rng = np.random.RandomState(0) for solver in ("cholesky", ): for n_samples, n_features in ((6, 5), (5, 10)): for alpha in (1.0, 1e-2): y = rng.randn(n_samples) X = rng.randn(n_samples, n_features) sample_weight = 1 + rng.rand(n_samples) coefs = ridge_regression(X, y, alpha=alpha, sample_weight=sample_weight, solver=solver) # Sample weight can be implemented via a simple rescaling # for the square loss. coefs2 = ridge_regression( X * np.sqrt(sample_weight)[:, np.newaxis], y * np.sqrt(sample_weight), alpha=alpha, solver=solver) assert_array_almost_equal(coefs, coefs2) # Test for fit_intercept = True est = Ridge(alpha=alpha, solver=solver) est.fit(X, y, sample_weight=sample_weight) # Check using Newton's Method # Quadratic function should be solved in a single step. # Initialize sample_weight = np.sqrt(sample_weight) X_weighted = sample_weight[:, np.newaxis] * ( np.column_stack((np.ones(n_samples), X))) y_weighted = y * sample_weight # Gradient is (X*coef-y)*X + alpha*coef_[1:] # Remove coef since it is initialized to zero. grad = -np.dot(y_weighted, X_weighted) # Hessian is (X.T*X) + alpha*I except that the first # diagonal element should be zero, since there is no # penalization of intercept. diag = alpha * np.ones(n_features + 1) diag[0] = 0. hess = np.dot(X_weighted.T, X_weighted) hess.flat[::n_features + 2] += diag coef_ = - np.dot(linalg.inv(hess), grad) assert_almost_equal(coef_[0], est.intercept_) assert_array_almost_equal(coef_[1:], est.coef_)
def test_ridge_intercept(): # Test intercept with multiple targets GH issue #708 rng = np.random.RandomState(0) n_samples, n_features = 5, 10 X = rng.randn(n_samples, n_features) y = rng.randn(n_samples) Y = np.c_[y, 1. + y] ridge = Ridge() ridge.fit(X, y) intercept = ridge.intercept_ ridge.fit(X, Y) assert_almost_equal(ridge.intercept_[0], intercept) assert_almost_equal(ridge.intercept_[1], intercept + 1.)
def test_n_iter(): # Test that self.n_iter_ is correct. n_targets = 2 X, y = X_diabetes, y_diabetes y_n = np.tile(y, (n_targets, 1)).T for max_iter in range(1, 4): for solver in ('sag', 'saga', 'lsqr'): reg = Ridge(solver=solver, max_iter=max_iter, tol=1e-12) reg.fit(X, y_n) assert_array_equal(reg.n_iter_, np.tile(max_iter, n_targets)) for solver in ('sparse_cg', 'svd', 'cholesky'): reg = Ridge(solver=solver, max_iter=1, tol=1e-1) reg.fit(X, y_n) assert_equal(reg.n_iter_, None)
def BasisPrediction(degree, function): # Basis function Prediction res_pred = [] res_mse = [] for d in degree: #title = 'KRSS, ' + str(typ) + ", degree: " + str(d) + ", lambda: 0.1" K = kernel_ridge_Basis(train_x, function, d) K_test = kernel_ridge_Basis(test_x, function, d) clf = Ridge() clf.fit(K, train_y) pred_y = clf.predict(K_test) res_pred.append(pred_y) mse = compute_MSE(test_y, pred_y) print('mse: ', mse) res_mse.append(mse) return res_pred, res_mse
def test_n_iter(): # Test that self.n_iter_ is correct. n_targets = 2 X, y = X_diabetes, y_diabetes y_n = np.tile(y, (n_targets, 1)).T for max_iter in range(1, 4): for solver in ('sag', 'saga', 'lsqr'): reg = Ridge(solver=solver, max_iter=max_iter, tol=1e-12) reg.fit(X, y_n) assert_array_equal(reg.n_iter_, np.tile(max_iter, n_targets)) for solver in ('sparse_cg', 'svd', 'cholesky'): reg = Ridge(solver=solver, max_iter=1, tol=1e-1) reg.fit(X, y_n) assert_equal(reg.n_iter_, None)
def test_ridge_intercept(): # Test intercept with multiple targets GH issue #708 rng = np.random.RandomState(0) n_samples, n_features = 5, 10 X = rng.randn(n_samples, n_features) y = rng.randn(n_samples) Y = np.c_[y, 1. + y] ridge = Ridge() ridge.fit(X, y) intercept = ridge.intercept_ ridge.fit(X, Y) assert_almost_equal(ridge.intercept_[0], intercept) assert_almost_equal(ridge.intercept_[1], intercept + 1.)
def test_ridge_fit_intercept_sparse(): X, y = make_regression(n_samples=1000, n_features=2, n_informative=2, bias=10.0, random_state=42) X_csr = sp.csr_matrix(X) dense = Ridge(alpha=1.0, tol=1.0e-15, solver="sag", fit_intercept=True) sparse = Ridge(alpha=1.0, tol=1.0e-15, solver="sag", fit_intercept=True) dense.fit(X, y) sparse.fit(X_csr, y) assert_almost_equal(dense.intercept_, sparse.intercept_) assert_array_almost_equal(dense.coef_, sparse.coef_) # test the solver switch and the corresponding warning sparse = Ridge(alpha=1.0, tol=1.0e-15, solver="lsqr", fit_intercept=True) assert_warns(UserWarning, sparse.fit, X_csr, y) assert_almost_equal(dense.intercept_, sparse.intercept_) assert_array_almost_equal(dense.coef_, sparse.coef_)
def test_ridge_sample_weights(): # TODO: loop over sparse data as well # Note: parametrizing this test with pytest results in failed # assertions, meaning that is is not extremely robust rng = np.random.RandomState(0) param_grid = product((1.0, 1e-2), (True, False), ('svd', 'cholesky', 'lsqr', 'sparse_cg')) for n_samples, n_features in ((6, 5), (5, 10)): y = rng.randn(n_samples) X = rng.randn(n_samples, n_features) sample_weight = 1.0 + rng.rand(n_samples) for (alpha, intercept, solver) in param_grid: # Ridge with explicit sample_weight est = Ridge(alpha=alpha, fit_intercept=intercept, solver=solver, tol=1e-6) est.fit(X, y, sample_weight=sample_weight) coefs = est.coef_ inter = est.intercept_ # Closed form of the weighted regularized least square # theta = (X^T W X + alpha I)^(-1) * X^T W y W = np.diag(sample_weight) if intercept is False: X_aug = X I = np.eye(n_features) else: dummy_column = np.ones(shape=(n_samples, 1)) X_aug = np.concatenate((dummy_column, X), axis=1) I = np.eye(n_features + 1) I[0, 0] = 0 cf_coefs = linalg.solve( X_aug.T.dot(W).dot(X_aug) + alpha * I, X_aug.T.dot(W).dot(y)) if intercept is False: assert_array_almost_equal(coefs, cf_coefs) else: assert_array_almost_equal(coefs, cf_coefs[1:]) assert_almost_equal(inter, cf_coefs[0])
def test_ridge_fit_intercept_sparse(): X, y = make_regression(n_samples=1000, n_features=2, n_informative=2, bias=10., random_state=42) X_csr = sp.csr_matrix(X) dense = Ridge(alpha=1., tol=1.e-15, solver='sag', fit_intercept=True) sparse = Ridge(alpha=1., tol=1.e-15, solver='sag', fit_intercept=True) dense.fit(X, y) sparse.fit(X_csr, y) assert_almost_equal(dense.intercept_, sparse.intercept_) assert_array_almost_equal(dense.coef_, sparse.coef_) # test the solver switch and the corresponding warning sparse = Ridge(alpha=1., tol=1.e-15, solver='lsqr', fit_intercept=True) assert_warns(UserWarning, sparse.fit, X_csr, y) assert_almost_equal(dense.intercept_, sparse.intercept_) assert_array_almost_equal(dense.coef_, sparse.coef_)
def test_ridge_fit_intercept_sparse_sag(): X, y = _make_sparse_offset_regression( n_features=5, n_samples=20, random_state=0, X_offset=5.) X_csr = sp.csr_matrix(X) params = dict(alpha=1., solver='sag', fit_intercept=True, tol=1e-10, max_iter=100000) dense_ridge = Ridge(**params) sparse_ridge = Ridge(**params) dense_ridge.fit(X, y) with pytest.warns(None) as record: sparse_ridge.fit(X_csr, y) assert len(record) == 0 assert np.allclose(dense_ridge.intercept_, sparse_ridge.intercept_, rtol=1e-4) assert np.allclose(dense_ridge.coef_, sparse_ridge.coef_, rtol=1e-4) with pytest.warns(UserWarning, match='"sag" solver requires.*'): Ridge(solver='sag').fit(X_csr, y)
def test_ridge_vs_lstsq(): """On alpha=0., Ridge and OLS yield the same solution.""" # we need more samples than features n_samples, n_features = 5, 4 y = rng.randn(n_samples) X = rng.randn(n_samples, n_features) ridge = Ridge(alpha=0., fit_intercept=False) ols = LinearRegression(fit_intercept=False) ridge.fit(X, y) ols.fit(X, y) assert_almost_equal(ridge.coef_, ols.coef_) ridge.fit(X, y) ols.fit(X, y) assert_almost_equal(ridge.coef_, ols.coef_)
def test_ridge_vs_lstsq(): """On alpha=0., Ridge and OLS yield the same solution.""" # we need more samples than features n_samples, n_features = 5, 4 y = rng.randn(n_samples) X = rng.randn(n_samples, n_features) ridge = Ridge(alpha=0., fit_intercept=False) ols = LinearRegression(fit_intercept=False) ridge.fit(X, y) ols.fit(X, y) assert_almost_equal(ridge.coef_, ols.coef_) ridge.fit(X, y) ols.fit(X, y) assert_almost_equal(ridge.coef_, ols.coef_)
def test_ridge_sample_weights(): # TODO: loop over sparse data as well # Note: parametrizing this test with pytest results in failed # assertions, meaning that is is not extremely robust rng = np.random.RandomState(0) param_grid = product((1.0, 1e-2), (True, False), ('svd', 'cholesky', 'lsqr', 'sparse_cg')) for n_samples, n_features in ((6, 5), (5, 10)): y = rng.randn(n_samples) X = rng.randn(n_samples, n_features) sample_weight = 1.0 + rng.rand(n_samples) for (alpha, intercept, solver) in param_grid: # Ridge with explicit sample_weight est = Ridge(alpha=alpha, fit_intercept=intercept, solver=solver, tol=1e-6) est.fit(X, y, sample_weight=sample_weight) coefs = est.coef_ inter = est.intercept_ # Closed form of the weighted regularized least square # theta = (X^T W X + alpha I)^(-1) * X^T W y W = np.diag(sample_weight) if intercept is False: X_aug = X I = np.eye(n_features) else: dummy_column = np.ones(shape=(n_samples, 1)) X_aug = np.concatenate((dummy_column, X), axis=1) I = np.eye(n_features + 1) I[0, 0] = 0 cf_coefs = linalg.solve(X_aug.T.dot(W).dot(X_aug) + alpha * I, X_aug.T.dot(W).dot(y)) if intercept is False: assert_array_almost_equal(coefs, cf_coefs) else: assert_array_almost_equal(coefs, cf_coefs[1:]) assert_almost_equal(inter, cf_coefs[0])
def test_toy_ridge_object(): # Test BayesianRegression ridge classifier # TODO: test also n_samples > n_features X = np.array([[1], [2]]) Y = np.array([1, 2]) clf = Ridge(alpha=0.0) clf.fit(X, Y) X_test = [[1], [2], [3], [4]] assert_almost_equal(clf.predict(X_test), [1., 2, 3, 4]) assert_equal(len(clf.coef_.shape), 1) assert_equal(type(clf.intercept_), np.float64) Y = np.vstack((Y, Y)).T clf.fit(X, Y) X_test = [[1], [2], [3], [4]] assert_equal(len(clf.coef_.shape), 2) assert_equal(type(clf.intercept_), np.ndarray)
def test_ridge_fit_intercept_sparse(): X, y = make_regression(n_samples=1000, n_features=2, n_informative=2, bias=10., random_state=42) X_csr = sp.csr_matrix(X) for solver in ['sag', 'sparse_cg']: dense = Ridge(alpha=1., tol=1.e-15, solver=solver, fit_intercept=True) sparse = Ridge(alpha=1., tol=1.e-15, solver=solver, fit_intercept=True) dense.fit(X, y) with pytest.warns(None) as record: sparse.fit(X_csr, y) assert len(record) == 0 assert_almost_equal(dense.intercept_, sparse.intercept_) assert_array_almost_equal(dense.coef_, sparse.coef_) # test the solver switch and the corresponding warning for solver in ['saga', 'lsqr']: sparse = Ridge(alpha=1., tol=1.e-15, solver=solver, fit_intercept=True) assert_raises_regex(ValueError, "In Ridge,", sparse.fit, X_csr, y)
def test_toy_ridge_object(): # Test BayesianRegression ridge classifier # TODO: test also n_samples > n_features X = np.array([[1], [2]]) Y = np.array([1, 2]) reg = Ridge(alpha=0.0) reg.fit(X, Y) X_test = [[1], [2], [3], [4]] assert_almost_equal(reg.predict(X_test), [1., 2, 3, 4]) assert len(reg.coef_.shape) == 1 assert type(reg.intercept_) == np.float64 Y = np.vstack((Y, Y)).T reg.fit(X, Y) X_test = [[1], [2], [3], [4]] assert len(reg.coef_.shape) == 2 assert type(reg.intercept_) == np.ndarray
def test_toy_ridge_object(): # Test BayesianRegression ridge classifier # TODO: test also n_samples > n_features X = np.array([[1], [2]]) Y = np.array([1, 2]) clf = Ridge(alpha=0.0) clf.fit(X, Y) X_test = [[1], [2], [3], [4]] assert_almost_equal(clf.predict(X_test), [1., 2, 3, 4]) assert_equal(len(clf.coef_.shape), 1) assert_equal(type(clf.intercept_), np.float64) Y = np.vstack((Y, Y)).T clf.fit(X, Y) X_test = [[1], [2], [3], [4]] assert_equal(len(clf.coef_.shape), 2) assert_equal(type(clf.intercept_), np.ndarray)
def test_ridge_fit_intercept_sparse(): X, y = make_regression(n_samples=1000, n_features=2, n_informative=2, bias=10., random_state=42) X_csr = sp.csr_matrix(X) for solver in ['sag', 'sparse_cg']: dense = Ridge(alpha=1., tol=1.e-15, solver=solver, fit_intercept=True) sparse = Ridge(alpha=1., tol=1.e-15, solver=solver, fit_intercept=True) dense.fit(X, y) with pytest.warns(None) as record: sparse.fit(X_csr, y) assert len(record) == 0 assert_almost_equal(dense.intercept_, sparse.intercept_) assert_array_almost_equal(dense.coef_, sparse.coef_) # test the solver switch and the corresponding warning for solver in ['saga', 'lsqr']: sparse = Ridge(alpha=1., tol=1.e-15, solver=solver, fit_intercept=True) assert_raises_regex(ValueError, "In Ridge,", sparse.fit, X_csr, y)
def test_dtype_match(solver): rng = np.random.RandomState(0) alpha = 1.0 n_samples, n_features = 6, 5 X_64 = rng.randn(n_samples, n_features) y_64 = rng.randn(n_samples) X_32 = X_64.astype(np.float32) y_32 = y_64.astype(np.float32) # Check type consistency 32bits ridge_32 = Ridge( alpha=alpha, solver=solver, max_iter=500, tol=1e-10, ) ridge_32.fit(X_32, y_32) coef_32 = ridge_32.coef_ # Check type consistency 64 bits ridge_64 = Ridge( alpha=alpha, solver=solver, max_iter=500, tol=1e-10, ) ridge_64.fit(X_64, y_64) coef_64 = ridge_64.coef_ # Do the actual checks at once for easier debug assert coef_32.dtype == X_32.dtype assert coef_64.dtype == X_64.dtype assert ridge_32.predict(X_32).dtype == X_32.dtype assert ridge_64.predict(X_64).dtype == X_64.dtype if solver == "sparse_cg" and sys.platform == "darwin": pytest.xfail( "Known failures on MacOS, See " "https://github.com/scikit-learn/scikit-learn/issues/13868") assert_allclose(ridge_32.coef_, ridge_64.coef_, rtol=1e-4)
def test_raises_value_error_if_sample_weights_greater_than_1d(): """Sample weights must be either scalar or 1D""" n_sampless = [2, 3] n_featuress = [3, 2] rng = np.random.RandomState(42) for n_samples, n_features in zip(n_sampless, n_featuress): X = rng.randn(n_samples, n_features) y = rng.randn(n_samples) sample_weights_OK = rng.randn(n_samples) ** 2 + 1 sample_weights_OK_1 = 1. sample_weights_OK_2 = 2. sample_weights_not_OK = sample_weights_OK[:, np.newaxis] sample_weights_not_OK_2 = sample_weights_OK[np.newaxis, :] ridge = Ridge(alpha=1) # make sure the "OK" sample weights actually work ridge.fit(X, y, sample_weights_OK) ridge.fit(X, y, sample_weights_OK_1) ridge.fit(X, y, sample_weights_OK_2) def fit_ridge_not_ok(): ridge.fit(X, y, sample_weights_not_OK) def fit_ridge_not_ok_2(): ridge.fit(X, y, sample_weights_not_OK_2) assert_raise_message(ValueError, "Sample weights must be 1D array or scalar", fit_ridge_not_ok) assert_raise_message(ValueError, "Sample weights must be 1D array or scalar", fit_ridge_not_ok_2)
class RidgeImpl(): def __init__(self, alpha=1.0, fit_intercept=True, normalize=False, copy_X=True, max_iter=None, tol=0.001, solver='auto', random_state=None): self._hyperparams = { 'alpha': alpha, 'fit_intercept': fit_intercept, 'normalize': normalize, 'copy_X': copy_X, 'max_iter': max_iter, 'tol': tol, 'solver': solver, 'random_state': random_state} def fit(self, X, y=None): self._sklearn_model = SKLModel(**self._hyperparams) if (y is not None): self._sklearn_model.fit(X, y) else: self._sklearn_model.fit(X) return self def predict(self, X): return self._sklearn_model.predict(X)
def eval_aggr_shifts(X, y, ignore_rows): eps = 1e-6 pred = [] real = [] for inst_n in ignore_rows: X = np.concatenate((X[:inst_n], X[inst_n+1:])) y = np.concatenate((y[:inst_n], y[inst_n+1:])) n = X.shape[0] for inst_n in range(n): x_i = X[inst_n] y_i = y[inst_n] X_train = np.concatenate((X[:inst_n], X[inst_n+1:])) y_train = np.concatenate((y[:inst_n], y[inst_n+1:])) y_train = np.array([max(eps, min(1 - eps, val)) for val in y_train]) y_train = np.log(y_train / (1 - y_train)) model = Ridge(alpha=.2, fit_intercept=True, normalize=True) #model = Lasso(alpha=.001, fit_intercept=True, normalize=True) model.fit(X_train, y_train) y_hat = model.predict(x_i.reshape(1, -1))[0] y_i1 = max(eps, min(1 - eps, y_i)) y_i1 = np.log(y_i1 / (1 - y_i1)) print('inst: ' + str(inst_n) + ', prediction: ' + str(y_hat) + ', err: ' + str(y_hat - y_i1)) pred.append(1 / (1 + exp(-y_hat))) real.append(y_i) model = Ridge(alpha=.2, fit_intercept=True, normalize=True) model.fit(X, y) return pred, real, model.coef_
def test_ridge_fit_intercept_sparse(solver): X, y = _make_sparse_offset_regression(n_features=20, random_state=0) X_csr = sp.csr_matrix(X) # for now only sparse_cg can correctly fit an intercept with sparse X with # default tol and max_iter. # sag is tested separately in test_ridge_fit_intercept_sparse_sag # because it requires more iterations and should raise a warning if default # max_iter is used. # other solvers raise an exception, as checked in # test_ridge_fit_intercept_sparse_error # # "auto" should switch to "sparse_cg" when X is sparse # so the reference we use for both ("auto" and "sparse_cg") is # Ridge(solver="sparse_cg"), fitted using the dense representation (note # that "sparse_cg" can fit sparse or dense data) dense_ridge = Ridge(solver='sparse_cg') sparse_ridge = Ridge(solver=solver) dense_ridge.fit(X, y) with pytest.warns(None) as record: sparse_ridge.fit(X_csr, y) assert len(record) == 0 assert np.allclose(dense_ridge.intercept_, sparse_ridge.intercept_) assert np.allclose(dense_ridge.coef_, sparse_ridge.coef_)
def test_dtype_match(solver): rng = np.random.RandomState(0) alpha = 1.0 n_samples, n_features = 6, 5 X_64 = rng.randn(n_samples, n_features) y_64 = rng.randn(n_samples) X_32 = X_64.astype(np.float32) y_32 = y_64.astype(np.float32) # Check type consistency 32bits ridge_32 = Ridge( alpha=alpha, solver=solver, max_iter=500, tol=1e-10, ) ridge_32.fit(X_32, y_32) coef_32 = ridge_32.coef_ # Check type consistency 64 bits ridge_64 = Ridge( alpha=alpha, solver=solver, max_iter=500, tol=1e-10, ) ridge_64.fit(X_64, y_64) coef_64 = ridge_64.coef_ # Do the actual checks at once for easier debug assert coef_32.dtype == X_32.dtype assert coef_64.dtype == X_64.dtype assert ridge_32.predict(X_32).dtype == X_32.dtype assert ridge_64.predict(X_64).dtype == X_64.dtype assert_allclose(ridge_32.coef_, ridge_64.coef_, rtol=1e-4)
def test_ridge(): """Ridge regression convergence test using score TODO: for this test to be robust, we should use a dataset instead of np.random. """ rng = np.random.RandomState(0) alpha = 1.0 for solver in ("svd", "sparse_cg", "cholesky", "lsqr"): # With more samples than features n_samples, n_features = 6, 5 y = rng.randn(n_samples) X = rng.randn(n_samples, n_features) ridge = Ridge(alpha=alpha, solver=solver) ridge.fit(X, y) assert_equal(ridge.coef_.shape, (X.shape[1], )) assert_greater(ridge.score(X, y), 0.47) if solver == "cholesky": # Currently the only solver to support sample_weight. ridge.fit(X, y, sample_weight=np.ones(n_samples)) assert_greater(ridge.score(X, y), 0.47) # With more features than samples n_samples, n_features = 5, 10 y = rng.randn(n_samples) X = rng.randn(n_samples, n_features) ridge = Ridge(alpha=alpha, solver=solver) ridge.fit(X, y) assert_greater(ridge.score(X, y), .9) if solver == "cholesky": # Currently the only solver to support sample_weight. ridge.fit(X, y, sample_weight=np.ones(n_samples)) assert_greater(ridge.score(X, y), 0.9)
def test_ridge_shapes(): # Test shape of coef_ and intercept_ rng = np.random.RandomState(0) n_samples, n_features = 5, 10 X = rng.randn(n_samples, n_features) y = rng.randn(n_samples) Y1 = y[:, np.newaxis] Y = np.c_[y, 1 + y] ridge = Ridge() ridge.fit(X, y) assert_equal(ridge.coef_.shape, (n_features, )) assert_equal(ridge.intercept_.shape, ()) ridge.fit(X, Y1) assert_equal(ridge.coef_.shape, (1, n_features)) assert_equal(ridge.intercept_.shape, (1, )) ridge.fit(X, Y) assert_equal(ridge.coef_.shape, (2, n_features)) assert_equal(ridge.intercept_.shape, (2, ))
def test_ridge_shapes(): # Test shape of coef_ and intercept_ rng = np.random.RandomState(0) n_samples, n_features = 5, 10 X = rng.randn(n_samples, n_features) y = rng.randn(n_samples) Y1 = y[:, np.newaxis] Y = np.c_[y, 1 + y] ridge = Ridge() ridge.fit(X, y) assert_equal(ridge.coef_.shape, (n_features,)) assert_equal(ridge.intercept_.shape, ()) ridge.fit(X, Y1) assert_equal(ridge.coef_.shape, (1, n_features)) assert_equal(ridge.intercept_.shape, (1, )) ridge.fit(X, Y) assert_equal(ridge.coef_.shape, (2, n_features)) assert_equal(ridge.intercept_.shape, (2, ))
def test_sparse_cg_max_iter(): reg = Ridge(solver="sparse_cg", max_iter=1) reg.fit(X_diabetes, y_diabetes) assert_equal(reg.coef_.shape[0], X_diabetes.shape[1])
def main(num_pts, num_children, learning_rate=1.5, learning_scale=0.8, rand_seed=0): top_node = Node(SqLoss, parent=None, name="root", input_dim=0) child_nodes = [Node(SqLoss, parent=top_node, input_dim=FEATURE_DIM, name='Child {:d}'.format(i)) for i in xrange(num_children)] #child_nodes = [] # for i in xrange(num_children): # func = linear_features # if i % 2 == 0: # func = square_features # child_nodes.append(Node(None, parent=top_node, input_dim=FEATURE_DIM, predict_func=func, # name='Child {:d}'.format(i))) validation_set = [pt for pt in dataset(500, seed=rand_seed + 1)] batch_set = [pt for pt in dataset(num_pts, seed=rand_seed)] from sklearn.linear_model.ridge import Ridge batch_learner = Ridge(alpha=1e-15, fit_intercept=False) batch_learner.fit(np.vstack([pt.x for pt in batch_set]), np.array([pt.y for pt in batch_set])) batch_pred = batch_learner.predict(np.vstack([pt.x for pt in validation_set])) Yval = np.array([pt.y for pt in validation_set]) # THIS HAS TO BE THE SAME LOSS AS THE TOP NODE! mean_batch_err = np.mean([top_node.loss(pred, val) for (pred, val) in zip(batch_pred, Yval)]) #err = batch_pred - Yval; mean_batch_err = np.mean(0.5*err*err) print('Batch err: {:.4g}'.format(mean_batch_err)) npprint = partial(np.array_str, precision=3) multiprocess = num_children >= 75 if multiprocess: from pathos.multiprocessing import ProcessingPool as Pool from pathos.multiprocessing import cpu_count #p = Pool(int(ceil(0.75*cpu_count()))) p = Pool(cpu_count()) val_helper = partial(predict_layer, child_nodes=child_nodes, top_node=top_node) learner_weights = np.array([node.w for node in child_nodes]) disp_num_child = 15 if num_children < disp_num_child: print('Child learner weights: {}'.format(npprint(learner_weights.ravel()))) validation_preds = [] per_iter_learner_weights = [] print 'Starting Online Boosting...' for i, pt in enumerate(dataset(num_pts, seed=rand_seed)): per_iter_learner_weights.append(learner_weights) # Compute loss on Validation set if multiprocess: val_results = p.map(val_helper, validation_set) else: val_results = [predict_layer(val_pt, child_nodes, top_node) for val_pt in validation_set] val_psums, val_losses = zip(*val_results) val_preds = [psum[-1] for psum in val_psums] validation_preds.append(val_preds) avg_val_loss = np.mean(val_losses) # Compute the partial sums, loss on current data point partial_sums, top_loss = predict_layer(pt, child_nodes, top_node) # get the gradient of the top loss at each partial sum true_val = pt.y offset_partials = partial_sums.copy() offset_partials[1:] = partial_sums[:-1] offset_partials[0] = 0 dlosses = [node.dloss(pred_val, true_val) for pred_val, node in zip(offset_partials, child_nodes)] step_size = learning_scale / np.power((i + 1), learning_rate) learner_weights = np.array([node.grad_step(pt.x, loss, step_size) for (node, loss) in zip(child_nodes, dlosses)]) if i < 1 or i == num_pts - 1 or (i < num_children and num_children < disp_num_child)\ or i % min(int(ceil(num_pts * 0.05)), 25) == 0 or avg_val_loss > 1e3: print('Iteration {:d}/{:d}: (x={:.2g},y={:.2g})'.format(i + 1, num_pts, pt.x, pt.y)) print(' Avg validation loss on pt: {:.4g} vs Batch: {:.4g}'.format(avg_val_loss, mean_batch_err)) print(' Top layer loss on pt: {:.4g}'.format(top_loss)) if num_children < disp_num_child: print(' Child learner weights: {}'.format(npprint(learner_weights.ravel()))) print(' Partial sums: {}'.format(npprint(partial_sums))) print(' Took descent step of step size {:.4g}...'.format(step_size)) # endfor return validation_set, validation_preds, batch_pred, batch_set, per_iter_learner_weights
def _test_ridge_diabetes(filter_): ridge = Ridge(fit_intercept=False) ridge.fit(filter_(X_diabetes), y_diabetes) return np.round(ridge.score(filter_(X_diabetes), y_diabetes), 5)
def _test_ridge_loo(filter_): # test that can work with both dense or sparse matrices n_samples = X_diabetes.shape[0] ret = [] ridge_gcv = _RidgeGCV(fit_intercept=False) ridge = Ridge(alpha=1.0, fit_intercept=False) # generalized cross-validation (efficient leave-one-out) decomp = ridge_gcv._pre_compute(X_diabetes, y_diabetes) errors, c = ridge_gcv._errors(1.0, y_diabetes, *decomp) values, c = ridge_gcv._values(1.0, y_diabetes, *decomp) # brute-force leave-one-out: remove one example at a time errors2 = [] values2 = [] for i in range(n_samples): sel = np.arange(n_samples) != i X_new = X_diabetes[sel] y_new = y_diabetes[sel] ridge.fit(X_new, y_new) value = ridge.predict([X_diabetes[i]])[0] error = (y_diabetes[i] - value) ** 2 errors2.append(error) values2.append(value) # check that efficient and brute-force LOO give same results assert_almost_equal(errors, errors2) assert_almost_equal(values, values2) # generalized cross-validation (efficient leave-one-out, # SVD variation) decomp = ridge_gcv._pre_compute_svd(X_diabetes, y_diabetes) errors3, c = ridge_gcv._errors_svd(ridge.alpha, y_diabetes, *decomp) values3, c = ridge_gcv._values_svd(ridge.alpha, y_diabetes, *decomp) # check that efficient and SVD efficient LOO give same results assert_almost_equal(errors, errors3) assert_almost_equal(values, values3) # 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_equal(ridge_gcv2.alpha_, 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_equal(ridge_gcv3.alpha_, alpha_) # check that we get same best alpha with a scorer scorer = get_scorer('mean_squared_error') ridge_gcv4 = RidgeCV(fit_intercept=False, scoring=scorer) ridge_gcv4.fit(filter_(X_diabetes), y_diabetes) assert_equal(ridge_gcv4.alpha_, alpha_) # check that we get same best alpha with sample weights ridge_gcv.fit(filter_(X_diabetes), y_diabetes, sample_weight=np.ones(n_samples)) assert_equal(ridge_gcv.alpha_, 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_array_almost_equal(np.vstack((y_pred, y_pred)).T, Y_pred, decimal=5) return ret
train0 = extract_predictor(train_dataset, True) train1 = extract_target(train_dataset) test0 = extract_predictor(test_dataset, False) results = [] for cnt in range(1000): projected0 = [] projected1 = [] for i in xrange(len(train0)): if random.random() < 0.4: continue projected0.append(train0[i]) projected1.append(train1[i]) print "now fitting the model", cnt, "with len", len(projected0) model = Ridge() model.fit(projected0, projected1) predictions=model.predict(test0) results.append(list(predictions)) final_result = [] for ind in xrange(len(results[0])): cand = [] for i in xrange(len(results)): cand.append(results[i][ind]) final_result.append(sum(sorted(cand)[100:-100])*1.0/(len(cand)-200)) #predictions=model.predict(valid_dataset) #Evaluate the quality of the prediction #print sklearn.metrics.mean_absolute_error(predictions,valid_target)
model2_train1 += virtual_test1 print "now saving the result" ff = open('virtual_train_data.json', 'w') ff.write(json.dumps([model2_train0, model2_train1])) ff.close() if sys.argv[1] == "second": ff = open('virtual_train_data.json', 'r') model2_train0, model2_train1 = json.loads(ff.read()) ff.close() print "opened train0 and train1 with each length", len(model2_train0), len(model2_train1) print model2_train0[0] print model2_train1[0] ff = open('intermediate_result.json', 'r') model2_test0, _ = json.loads(ff.read()) print model2_test0[0] model2 = Ridge() print "start fitting 2nd model" model2.fit(model2_train0, model2_train1) print "start predicting" predictions=model2.predict(model2_test0) print "saving the predicted result into the file" f = open('result.csv', 'w') f.write("ID;COTIS\n"); for ind, prd in enumerate(predictions): f.write(my_ids[ind] + ';' + str(prd) + '\n') f.close() print "all tasks completed"
#print(krrs_trigpredictions) for lst in krrs_trigpredictions: print(compute_MSE(np.array(lst).reshape(200, 1), test_y.reshape(200, 1))) berr_polypredictions = [] for deg in [1, 2, 4, 6]: train_x_new = [] for i in range(train_x.shape[0]): train_x_new.append([train_x[i]**j for j in range(deg + 1)]) test_x_new = [] for i in range(test_x.shape[0]): test_x_new.append([test_x[i]**j for j in range(deg + 1)]) clf = Ridge() clf.fit(train_x_new, train_y) pred_y = clf.predict(test_x_new) #print(pred_y) berr_polypredictions.append(pred_y) #print(berr_polypredictions) for lst in berr_polypredictions: print(compute_MSE(np.array(lst).reshape(200, 1), test_y.reshape(200, 1))) def trig_expansion(train_x, degree): res = [1] d = 0.5 for de in range(degree): res.append(np.sin(de * d * train_x)) res.append(np.cos(de * d * train_x))
def test_sparse_cg_max_iter(): reg = Ridge(solver="sparse_cg", max_iter=1) reg.fit(X_diabetes, y_diabetes) assert_equal(reg.coef_.shape[0], X_diabetes.shape[1])
def _test_ridge_diabetes(filter_): ridge = Ridge(fit_intercept=False) ridge.fit(filter_(X_diabetes), y_diabetes) return np.round(ridge.score(filter_(X_diabetes), y_diabetes), 5)
def _test_ridge_loo(filter_): # test that can work with both dense or sparse matrices n_samples = X_diabetes.shape[0] ret = [] ridge_gcv = _RidgeGCV(fit_intercept=False) ridge = Ridge(alpha=1.0, fit_intercept=False) # generalized cross-validation (efficient leave-one-out) decomp = ridge_gcv._pre_compute(X_diabetes, y_diabetes) errors, c = ridge_gcv._errors(1.0, y_diabetes, *decomp) values, c = ridge_gcv._values(1.0, y_diabetes, *decomp) # brute-force leave-one-out: remove one example at a time errors2 = [] values2 = [] for i in range(n_samples): sel = np.arange(n_samples) != i X_new = X_diabetes[sel] y_new = y_diabetes[sel] ridge.fit(X_new, y_new) value = ridge.predict([X_diabetes[i]])[0] error = (y_diabetes[i] - value)**2 errors2.append(error) values2.append(value) # check that efficient and brute-force LOO give same results assert_almost_equal(errors, errors2) assert_almost_equal(values, values2) # generalized cross-validation (efficient leave-one-out, # SVD variation) decomp = ridge_gcv._pre_compute_svd(X_diabetes, y_diabetes) errors3, c = ridge_gcv._errors_svd(ridge.alpha, y_diabetes, *decomp) values3, c = ridge_gcv._values_svd(ridge.alpha, y_diabetes, *decomp) # check that efficient and SVD efficient LOO give same results assert_almost_equal(errors, errors3) assert_almost_equal(values, values3) # 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_equal(ridge_gcv2.alpha_, 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_equal(ridge_gcv3.alpha_, alpha_) # check that we get same best alpha with a scorer scorer = get_scorer('mean_squared_error') ridge_gcv4 = RidgeCV(fit_intercept=False, scoring=scorer) ridge_gcv4.fit(filter_(X_diabetes), y_diabetes) assert_equal(ridge_gcv4.alpha_, alpha_) # check that we get same best alpha with sample weights ridge_gcv.fit(filter_(X_diabetes), y_diabetes, sample_weight=np.ones(n_samples)) assert_equal(ridge_gcv.alpha_, 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_array_almost_equal(np.vstack((y_pred, y_pred)).T, Y_pred, decimal=5) return ret
def trainModel(param,feat_folder,feat_name): #read data from folder print 'now we read data from folder:%s'%(feat_folder) #start cv print 'now we need to generate cross_validation' accuracy_cv = [] for i in range(0,2): print 'this is the run:%d cross-validation'%(i+1) testIndex = loadCVIndex("%s/test.run%d.txt"%("../data/feat/combine",(i+1))) #if we use xgboost to train model ,we need to use svmlib format if param['task'] in ['regression']: #with xgb we will dump the file with CV,and we will read data train_data = xgb.DMatrix("%s/run%d/train.svm.txt"%(feat_folder,(i+1))) valid_data = xgb.DMatrix("%s/run%d/test.svm.txt"%(feat_folder,(i+1))) watchlist = [(train_data,'train'),(valid_data,'valid')] bst = xgb.train(param,train_data,int(param['num_round']),watchlist) pred = bst.predict(valid_data) elif param['task'] in ['clf_skl_lr']: train_data,train_label = load_svmlight_file("%s/run%d/train.svm.txt"%(feat_folder,(i+1))) test_data,test_label = load_svmlight_file("%s/run%d/test.svm.txt"%(feat_folder,(i+1))) train_data = train_data.tocsr() test_data = test_data.tocsr() clf = LogisticRegression() clf.fit(train_data,train_label) pred = clf.predict(test_data) elif param['task'] == "reg_skl_rf": ## regression with sklearn random forest regressor train_data,train_label = load_svmlight_file("%s/run%d/train.svm.txt"%(feat_folder,(i+1))) test_data,test_label = load_svmlight_file("%s/run%d/test.svm.txt"%(feat_folder,(i+1))) rf = RandomForestRegressor(n_estimators=param['n_estimators'], max_features=param['max_features'], n_jobs=param['n_jobs'], random_state=param['random_state']) rf.fit(train_data, test_label) pred = rf.predict(test_data) elif param['task'] == "reg_skl_etr": ## regression with sklearn extra trees regressor train_data,train_label = load_svmlight_file("%s/run%d/train.svm.txt"%(feat_folder,(i+1))) test_data,test_label = load_svmlight_file("%s/run%d/test.svm.txt"%(feat_folder,(i+1))) etr = ExtraTreesRegressor(n_estimators=param['n_estimators'], max_features=param['max_features'], n_jobs=param['n_jobs'], random_state=param['random_state']) etr.fit(train_data,test_label) pred = etr.predict(test_data) elif param['task'] in ['reg_skl_gbm'] : train_data,train_label = load_svmlight_file("%s/run%d/train.svm.txt"%(feat_folder,(i+1))) test_data,test_label = load_svmlight_file("%s/run%d/test.svm.txt"%(feat_folder,(i+1))) gbm = GradientBoostingClassifier(n_estimators=int(param['n_estimators']), learning_rate=param['learning_rate'], max_features=param['max_features'], max_depth=param['max_depth'], subsample=param['subsample'], random_state=param['random_state']) feat_names.remove('cid') gbm.fit(train_data,train_label) pred = gbm.predict(test_data) elif param['task'] in ['reg_skl_ridge']: train_data,train_label = load_svmlight_file("%s/run%d/train.svm.txt"%(feat_folder,(i+1))) test_data,test_label = load_svmlight_file("%s/run%d/test.svm.txt"%(feat_folder,(i+1))) train_data = train_data.tocsr() test_data = test_data.tocsr() ridge = Ridge(alpha=param["alpha"], normalize=True) ridge.fit(train_data,train_label) predraw = ridge.predict(test_data) print predraw predrank = predraw.argsort().argsort() trainIndex = loadCVIndex("%s/train.run%d.txt"%("../data/feat/combine",(i+1))) cdf = creatCDF(train, trainIndex) pred = getScore(predrank,cdf) print pred """ elif param['task'] in ['regression']: elif param['task'] in ['reg_skl_gbm'] : gbm = GradientBoostingClassifier(n_estimators=int(param['n_estimators']), learning_rate=param['learning_rate'], max_features=param['max_features'], max_depth=param['max_depth'], subsample=param['subsample'], random_state=param['random_state']) feat_names.remove('cid') gbm.fit(train_data[feat_names],train_data['cid']) pred = gbm.predict(valid_data[feat_names]) elif param['task'] in ['reg_skl_ridge']: feat_names.remove('cid') ridge = Ridge(alpha=param["alpha"], normalize=True) ridge.fit(train_data[feat_names],train_data['cid']) pred = ridge.predict(valid_data[feat_names]) """ #now we use the the accuracy to limit our model acc = accuracy_model(pred,train.iloc[testIndex]['cid']) print "the model accurary:%s"%(acc) accuracy_cv.append(acc) #here we will count the accuracy_cv_mean = np.mean(accuracy_cv) accuracy_cv_std = np.std(accuracy_cv) print 'the accuracy for %.6f'%(accuracy_cv_mean) return {'loss':-accuracy_cv_mean,'attachments':{'std':accuracy_cv_std},'status': STATUS_OK}