def test_adagrad_hinge_multiclass():
clf = AdaGradClassifier(alpha=1e-2,
n_iter=100,
loss="hinge",
random_state=0)
clf.fit(X, y)
assert_almost_equal(clf.score(X, y), 0.960, 3)
def test_adagrad_elastic_hinge():
clf = AdaGradClassifier(alpha=0.5,
l1_ratio=0.85,
n_iter=10,
random_state=0)
clf.fit(X_bin, y_bin)
assert_equal(clf.score(X_bin, y_bin), 1.0)
def test_adagrad_elastic_hinge():
clf = AdaGradClassifier(alpha=0.5,
l1_ratio=0.85,
n_iter=10,
random_state=0)
clf.fit(X_bin, y_bin)
assert not hasattr(clf, "predict_proba")
assert clf.score(X_bin, y_bin) == 1.0
def test_adagrad_hinge_multiclass():
clf = AdaGradClassifier(alpha=1e-2,
n_iter=100,
loss="hinge",
random_state=0)
clf.fit(X, y)
assert not hasattr(clf, "predict_proba")
np.testing.assert_almost_equal(clf.score(X, y), 0.940, 3)
def test_adagrad_elastic_log():
clf = AdaGradClassifier(alpha=0.1,
l1_ratio=0.85,
loss="log",
n_iter=10,
random_state=0)
clf.fit(X_bin, y_bin)
assert clf.score(X_bin, y_bin) == 1.0
check_predict_proba(clf, X_bin)
def test_adagrad_elastic_smooth_hinge(bin_train_data):
X_bin, y_bin = bin_train_data
clf = AdaGradClassifier(alpha=0.5,
l1_ratio=0.85,
loss="smooth_hinge",
n_iter=10,
random_state=0)
clf.fit(X_bin, y_bin)
assert not hasattr(clf, "predict_proba")
assert clf.score(X_bin, y_bin) == 1.0
def test_adagrad_callback():
class Callback(object):
def __init__(self, X, y):
self.X = X
self.y = y
self.acc = []
def __call__(self, clf, t):
alpha1 = clf.l1_ratio * clf.alpha
alpha2 = (1 - clf.l1_ratio) * clf.alpha
_proj_elastic_all(clf.eta, t, clf.g_sum_[0], clf.g_norms_[0], alpha1, alpha2, 0, clf.coef_[0])
score = clf.score(self.X, self.y)
self.acc.append(score)
cb = Callback(X_bin, y_bin)
clf = AdaGradClassifier(alpha=0.5, l1_ratio=0.85, n_iter=10, callback=cb, random_state=0)
clf.fit(X_bin, y_bin)
assert_equal(cb.acc[-1], 1.0)
def test_adagrad_callback():
class Callback(object):
def __init__(self, X, y):
self.X = X
self.y = y
self.acc = []
def __call__(self, clf, t):
alpha1 = clf.l1_ratio * clf.alpha
alpha2 = (1 - clf.l1_ratio) * clf.alpha
_proj_elastic_all(clf.eta, t, clf.g_sum_[0], clf.g_norms_[0],
alpha1, alpha2, 0, clf.coef_[0])
score = clf.score(self.X, self.y)
self.acc.append(score)
cb = Callback(X_bin, y_bin)
clf = AdaGradClassifier(alpha=0.5,
l1_ratio=0.85,
n_iter=10,
callback=cb,
random_state=0)
clf.fit(X_bin, y_bin)
assert_equal(cb.acc[-1], 1.0)
def test_lightning_binary_class():
estimator = AdaGradClassifier(random_state=1)
utils.get_binary_classification_model_trainer()(estimator)
assembler = assemblers.SklearnLinearModelAssembler(estimator)
actual = assembler.assemble()
feature_weight_mul = [
ast.BinNumExpr(ast.FeatureRef(0), ast.NumVal(0.1605265174),
ast.BinNumOpType.MUL),
ast.BinNumExpr(ast.FeatureRef(1), ast.NumVal(0.1045225083),
ast.BinNumOpType.MUL),
ast.BinNumExpr(ast.FeatureRef(2), ast.NumVal(0.6237391536),
ast.BinNumOpType.MUL),
ast.BinNumExpr(ast.FeatureRef(3), ast.NumVal(0.1680225811),
ast.BinNumOpType.MUL),
ast.BinNumExpr(ast.FeatureRef(4), ast.NumVal(0.0011013688),
ast.BinNumOpType.MUL),
ast.BinNumExpr(ast.FeatureRef(5), ast.NumVal(-0.0027528486),
ast.BinNumOpType.MUL),
ast.BinNumExpr(ast.FeatureRef(6), ast.NumVal(-0.0058878714),
ast.BinNumOpType.MUL),
ast.BinNumExpr(ast.FeatureRef(7), ast.NumVal(-0.0023719811),
ast.BinNumOpType.MUL),
ast.BinNumExpr(ast.FeatureRef(8), ast.NumVal(0.0019944105),
ast.BinNumOpType.MUL),
ast.BinNumExpr(ast.FeatureRef(9), ast.NumVal(0.0009924456),
ast.BinNumOpType.MUL),
ast.BinNumExpr(ast.FeatureRef(10), ast.NumVal(0.0003994860),
ast.BinNumOpType.MUL),
ast.BinNumExpr(ast.FeatureRef(11), ast.NumVal(0.0124697033),
ast.BinNumOpType.MUL),
ast.BinNumExpr(ast.FeatureRef(12), ast.NumVal(-0.0123674096),
ast.BinNumOpType.MUL),
ast.BinNumExpr(ast.FeatureRef(13), ast.NumVal(-0.2844204905),
ast.BinNumOpType.MUL),
ast.BinNumExpr(ast.FeatureRef(14), ast.NumVal(0.0000273704),
ast.BinNumOpType.MUL),
ast.BinNumExpr(ast.FeatureRef(15), ast.NumVal(-0.0007498013),
ast.BinNumOpType.MUL),
ast.BinNumExpr(ast.FeatureRef(16), ast.NumVal(-0.0010784399),
ast.BinNumOpType.MUL),
ast.BinNumExpr(ast.FeatureRef(17), ast.NumVal(-0.0001848694),
ast.BinNumOpType.MUL),
ast.BinNumExpr(ast.FeatureRef(18), ast.NumVal(0.0000632254),
ast.BinNumOpType.MUL),
ast.BinNumExpr(ast.FeatureRef(19), ast.NumVal(-0.0000369618),
ast.BinNumOpType.MUL),
ast.BinNumExpr(ast.FeatureRef(20), ast.NumVal(0.1520223021),
ast.BinNumOpType.MUL),
ast.BinNumExpr(ast.FeatureRef(21), ast.NumVal(0.0925348635),
ast.BinNumOpType.MUL),
ast.BinNumExpr(ast.FeatureRef(22), ast.NumVal(0.4861047372),
ast.BinNumOpType.MUL),
ast.BinNumExpr(ast.FeatureRef(23), ast.NumVal(-0.2798670185),
ast.BinNumOpType.MUL),
ast.BinNumExpr(ast.FeatureRef(24), ast.NumVal(0.0009925506),
ast.BinNumOpType.MUL),
ast.BinNumExpr(ast.FeatureRef(25), ast.NumVal(-0.0103414976),
ast.BinNumOpType.MUL),
ast.BinNumExpr(ast.FeatureRef(26), ast.NumVal(-0.0155024577),
ast.BinNumOpType.MUL),
ast.BinNumExpr(ast.FeatureRef(27), ast.NumVal(-0.0038881538),
ast.BinNumOpType.MUL),
ast.BinNumExpr(ast.FeatureRef(28), ast.NumVal(0.0010126166),
ast.BinNumOpType.MUL),
ast.BinNumExpr(ast.FeatureRef(29), ast.NumVal(0.0002312558),
ast.BinNumOpType.MUL),
]
expected = assemblers.utils.apply_op_to_expressions(
ast.BinNumOpType.ADD, ast.NumVal(0.0), *feature_weight_mul)
assert utils.cmp_exprs(actual, expected)
def test_adagrad_elastic_hinge():
clf = AdaGradClassifier(alpha=0.5, l1_ratio=0.85, n_iter=10, random_state=0)
clf.fit(X_bin, y_bin)
assert_equal(clf.score(X_bin, y_bin), 1.0)
def test_adagrad_classes_multiclass(train_data):
X, y = train_data
clf = AdaGradClassifier()
assert not hasattr(clf, 'classes_')
clf.fit(X, y)
assert list(clf.classes_) == [0, 1, 2]
def test_lightning_multi_class():
estimator = AdaGradClassifier(random_state=1)
utils.get_classification_model_trainer()(estimator)
assembler = assemblers.SklearnLinearModelAssembler(estimator)
actual = assembler.assemble()
expected = ast.VectorVal([
ast.BinNumExpr(
ast.BinNumExpr(
ast.BinNumExpr(
ast.BinNumExpr(
ast.NumVal(0.0),
ast.BinNumExpr(ast.FeatureRef(0),
ast.NumVal(0.0895848274),
ast.BinNumOpType.MUL),
ast.BinNumOpType.ADD),
ast.BinNumExpr(ast.FeatureRef(1), ast.NumVal(0.3258329434),
ast.BinNumOpType.MUL),
ast.BinNumOpType.ADD),
ast.BinNumExpr(ast.FeatureRef(2), ast.NumVal(-0.4900856238),
ast.BinNumOpType.MUL), ast.BinNumOpType.ADD),
ast.BinNumExpr(ast.FeatureRef(3), ast.NumVal(-0.2214482506),
ast.BinNumOpType.MUL), ast.BinNumOpType.ADD),
ast.BinNumExpr(
ast.BinNumExpr(
ast.BinNumExpr(
ast.BinNumExpr(
ast.NumVal(0.0),
ast.BinNumExpr(ast.FeatureRef(0),
ast.NumVal(-0.1074247041),
ast.BinNumOpType.MUL),
ast.BinNumOpType.ADD),
ast.BinNumExpr(ast.FeatureRef(1),
ast.NumVal(-0.1693225196),
ast.BinNumOpType.MUL),
ast.BinNumOpType.ADD),
ast.BinNumExpr(ast.FeatureRef(2), ast.NumVal(0.0357417324),
ast.BinNumOpType.MUL), ast.BinNumOpType.ADD),
ast.BinNumExpr(ast.FeatureRef(3), ast.NumVal(-0.0161614171),
ast.BinNumOpType.MUL), ast.BinNumOpType.ADD),
ast.BinNumExpr(
ast.BinNumExpr(
ast.BinNumExpr(
ast.BinNumExpr(
ast.NumVal(0.0),
ast.BinNumExpr(ast.FeatureRef(0),
ast.NumVal(-0.1825063678),
ast.BinNumOpType.MUL),
ast.BinNumOpType.ADD),
ast.BinNumExpr(ast.FeatureRef(1),
ast.NumVal(-0.2185655665),
ast.BinNumOpType.MUL),
ast.BinNumOpType.ADD),
ast.BinNumExpr(ast.FeatureRef(2), ast.NumVal(0.3053017646),
ast.BinNumOpType.MUL), ast.BinNumOpType.ADD),
ast.BinNumExpr(ast.FeatureRef(3), ast.NumVal(0.2175198459),
ast.BinNumOpType.MUL), ast.BinNumOpType.ADD)
])
assert utils.cmp_exprs(actual, expected)
def test_adagrad_elastic_smooth_hinge():
clf = AdaGradClassifier(alpha=0.5, l1_ratio=0.85, loss="smooth_hinge", n_iter=10, random_state=0)
clf.fit(X_bin, y_bin)
assert not hasattr(clf, "predict_proba")
assert_equal(clf.score(X_bin, y_bin), 1.0)
def test_adagrad_classes_binary(bin_train_data):
X_bin, y_bin = bin_train_data
clf = AdaGradClassifier()
assert not hasattr(clf, 'classes_')
clf.fit(X_bin, y_bin)
assert list(clf.classes_) == [-1, 1]
def test_lightning_multi_class():
estimator = AdaGradClassifier(random_state=1)
utils.get_classification_model_trainer()(estimator)
assembler = assemblers.SklearnLinearModelAssembler(estimator)
actual = assembler.assemble()
expected = ast.VectorVal([
ast.BinNumExpr(
ast.BinNumExpr(
ast.BinNumExpr(
ast.BinNumExpr(
ast.NumVal(0.0),
ast.BinNumExpr(ast.FeatureRef(0),
ast.NumVal(0.0935146297),
ast.BinNumOpType.MUL),
ast.BinNumOpType.ADD),
ast.BinNumExpr(ast.FeatureRef(1), ast.NumVal(0.3213921354),
ast.BinNumOpType.MUL),
ast.BinNumOpType.ADD),
ast.BinNumExpr(ast.FeatureRef(2), ast.NumVal(-0.4855914264),
ast.BinNumOpType.MUL), ast.BinNumOpType.ADD),
ast.BinNumExpr(ast.FeatureRef(3), ast.NumVal(-0.2214295302),
ast.BinNumOpType.MUL), ast.BinNumOpType.ADD),
ast.BinNumExpr(
ast.BinNumExpr(
ast.BinNumExpr(
ast.BinNumExpr(
ast.NumVal(0.0),
ast.BinNumExpr(ast.FeatureRef(0),
ast.NumVal(-0.1103262586),
ast.BinNumOpType.MUL),
ast.BinNumOpType.ADD),
ast.BinNumExpr(ast.FeatureRef(1),
ast.NumVal(-0.1662457692),
ast.BinNumOpType.MUL),
ast.BinNumOpType.ADD),
ast.BinNumExpr(ast.FeatureRef(2), ast.NumVal(0.0379823341),
ast.BinNumOpType.MUL), ast.BinNumOpType.ADD),
ast.BinNumExpr(ast.FeatureRef(3), ast.NumVal(-0.0128634938),
ast.BinNumOpType.MUL), ast.BinNumOpType.ADD),
ast.BinNumExpr(
ast.BinNumExpr(
ast.BinNumExpr(
ast.BinNumExpr(
ast.NumVal(0.0),
ast.BinNumExpr(ast.FeatureRef(0),
ast.NumVal(-0.1685751402),
ast.BinNumOpType.MUL),
ast.BinNumOpType.ADD),
ast.BinNumExpr(ast.FeatureRef(1),
ast.NumVal(-0.2045901693),
ast.BinNumOpType.MUL),
ast.BinNumOpType.ADD),
ast.BinNumExpr(ast.FeatureRef(2), ast.NumVal(0.2932121798),
ast.BinNumOpType.MUL), ast.BinNumOpType.ADD),
ast.BinNumExpr(ast.FeatureRef(3), ast.NumVal(0.2138148665),
ast.BinNumOpType.MUL), ast.BinNumOpType.ADD)
])
assert utils.cmp_exprs(actual, expected)
def test_lightning_multi_class():
estimator = AdaGradClassifier(random_state=1)
utils.get_classification_model_trainer()(estimator)
assembler = assemblers.SklearnLinearModelAssembler(estimator)
actual = assembler.assemble()
expected = ast.VectorVal([
ast.BinNumExpr(
ast.BinNumExpr(
ast.BinNumExpr(
ast.BinNumExpr(
ast.NumVal(0.0),
ast.BinNumExpr(
ast.FeatureRef(0),
ast.NumVal(0.09686334892116512),
ast.BinNumOpType.MUL),
ast.BinNumOpType.ADD),
ast.BinNumExpr(
ast.FeatureRef(1),
ast.NumVal(0.32572202133211947),
ast.BinNumOpType.MUL),
ast.BinNumOpType.ADD),
ast.BinNumExpr(
ast.FeatureRef(2),
ast.NumVal(-0.48444233646554424),
ast.BinNumOpType.MUL),
ast.BinNumOpType.ADD),
ast.BinNumExpr(
ast.FeatureRef(3),
ast.NumVal(-0.219719145605816),
ast.BinNumOpType.MUL),
ast.BinNumOpType.ADD),
ast.BinNumExpr(
ast.BinNumExpr(
ast.BinNumExpr(
ast.BinNumExpr(
ast.NumVal(0.0),
ast.BinNumExpr(
ast.FeatureRef(0),
ast.NumVal(-0.1089136473832088),
ast.BinNumOpType.MUL),
ast.BinNumOpType.ADD),
ast.BinNumExpr(
ast.FeatureRef(1),
ast.NumVal(-0.16956003333433572),
ast.BinNumOpType.MUL),
ast.BinNumOpType.ADD),
ast.BinNumExpr(
ast.FeatureRef(2),
ast.NumVal(0.0365531256007199),
ast.BinNumOpType.MUL),
ast.BinNumOpType.ADD),
ast.BinNumExpr(
ast.FeatureRef(3),
ast.NumVal(-0.01016100116780896),
ast.BinNumOpType.MUL),
ast.BinNumOpType.ADD),
ast.BinNumExpr(
ast.BinNumExpr(
ast.BinNumExpr(
ast.BinNumExpr(
ast.NumVal(0.0),
ast.BinNumExpr(
ast.FeatureRef(0),
ast.NumVal(-0.16690339219780817),
ast.BinNumOpType.MUL),
ast.BinNumOpType.ADD),
ast.BinNumExpr(
ast.FeatureRef(1),
ast.NumVal(-0.19466284646233858),
ast.BinNumOpType.MUL),
ast.BinNumOpType.ADD),
ast.BinNumExpr(
ast.FeatureRef(2),
ast.NumVal(0.2953585236360389),
ast.BinNumOpType.MUL),
ast.BinNumOpType.ADD),
ast.BinNumExpr(
ast.FeatureRef(3),
ast.NumVal(0.21288203082531384),
ast.BinNumOpType.MUL),
ast.BinNumOpType.ADD)])
assert utils.cmp_exprs(actual, expected)
def test_adagrad_classes_multiclass():
clf = AdaGradClassifier()
assert not hasattr(clf, 'classes_')
clf.fit(X, y)
assert_equal(list(clf.classes_), [0, 1, 2])
def test_adagrad_elastic_log():
clf = AdaGradClassifier(alpha=0.1, l1_ratio=0.85, loss="log", n_iter=10, random_state=0)
clf.fit(X_bin, y_bin)
assert_equal(clf.score(X_bin, y_bin), 1.0)
check_predict_proba(clf, X_bin)
def test_adagrad_classes_multiclass():
clf = AdaGradClassifier()
assert not hasattr(clf, "classes_")
clf.fit(X, y)
assert_equal(list(clf.classes_), [0, 1, 2])
def test_adagrad_classes_binary():
clf = AdaGradClassifier()
assert not hasattr(clf, "classes_")
clf.fit(X_bin, y_bin)
assert_equal(list(clf.classes_), [-1, 1])
def test_adagrad_hinge_multiclass():
clf = AdaGradClassifier(alpha=1e-2, n_iter=100, loss="hinge", random_state=0)
clf.fit(X, y)
assert not hasattr(clf, "predict_proba")
assert_almost_equal(clf.score(X, y), 0.960, 3)
def test_adagrad_classes_binary():
clf = AdaGradClassifier()
assert not hasattr(clf, 'classes_')
clf.fit(X_bin, y_bin)
assert_equal(list(clf.classes_), [-1, 1])
clf2 = SDCAClassifier(loss="squared_hinge",
alpha=alpha,
max_iter=100,
n_calls=X.shape[0] / 2,
random_state=0,
tol=tol)
clf3 = CDClassifier(loss="squared_hinge",
alpha=alpha,
C=1.0 / X.shape[0],
max_iter=50,
n_calls=X.shape[1] / 3,
random_state=0,
tol=tol)
clf4 = AdaGradClassifier(loss="squared_hinge",
alpha=alpha,
eta=eta_adagrad,
n_iter=100,
n_calls=X.shape[0] / 2,
random_state=0)
clf5 = SAGAClassifier(loss="squared_hinge",
alpha=alpha,
max_iter=100,
random_state=0,
tol=tol)
clf6 = SAGClassifier(loss="squared_hinge",
alpha=alpha,
max_iter=100,
random_state=0,
tol=tol)
plt.figure()
def test_lightning_binary_class():
estimator = AdaGradClassifier(random_state=1)
utils.get_binary_classification_model_trainer()(estimator)
assembler = assemblers.SklearnLinearModelAssembler(estimator)
actual = assembler.assemble()
feature_weight_mul = [
ast.BinNumExpr(
ast.FeatureRef(0),
ast.NumVal(0.16218889967390476),
ast.BinNumOpType.MUL),
ast.BinNumExpr(
ast.FeatureRef(1),
ast.NumVal(0.10012761963766906),
ast.BinNumOpType.MUL),
ast.BinNumExpr(
ast.FeatureRef(2),
ast.NumVal(0.6289276652681673),
ast.BinNumOpType.MUL),
ast.BinNumExpr(
ast.FeatureRef(3),
ast.NumVal(0.17618420156072845),
ast.BinNumOpType.MUL),
ast.BinNumExpr(
ast.FeatureRef(4),
ast.NumVal(0.0010492096607182045),
ast.BinNumOpType.MUL),
ast.BinNumExpr(
ast.FeatureRef(5),
ast.NumVal(-0.0029135563693806913),
ast.BinNumOpType.MUL),
ast.BinNumExpr(
ast.FeatureRef(6),
ast.NumVal(-0.005923882409142498),
ast.BinNumOpType.MUL),
ast.BinNumExpr(
ast.FeatureRef(7),
ast.NumVal(-0.0023293599172479755),
ast.BinNumOpType.MUL),
ast.BinNumExpr(
ast.FeatureRef(8),
ast.NumVal(0.0020808828960210517),
ast.BinNumOpType.MUL),
ast.BinNumExpr(
ast.FeatureRef(9),
ast.NumVal(0.0009846430705550103),
ast.BinNumOpType.MUL),
ast.BinNumExpr(
ast.FeatureRef(10),
ast.NumVal(0.0010399810925427265),
ast.BinNumOpType.MUL),
ast.BinNumExpr(
ast.FeatureRef(11),
ast.NumVal(0.011203056917272093),
ast.BinNumOpType.MUL),
ast.BinNumExpr(
ast.FeatureRef(12),
ast.NumVal(-0.007271351370867731),
ast.BinNumOpType.MUL),
ast.BinNumExpr(
ast.FeatureRef(13),
ast.NumVal(-0.26333437096804224),
ast.BinNumOpType.MUL),
ast.BinNumExpr(
ast.FeatureRef(14),
ast.NumVal(1.8533543368532444e-05),
ast.BinNumOpType.MUL),
ast.BinNumExpr(
ast.FeatureRef(15),
ast.NumVal(-0.0008266341686278445),
ast.BinNumOpType.MUL),
ast.BinNumExpr(
ast.FeatureRef(16),
ast.NumVal(-0.0011090316301215724),
ast.BinNumOpType.MUL),
ast.BinNumExpr(
ast.FeatureRef(17),
ast.NumVal(-0.0001910857095336291),
ast.BinNumOpType.MUL),
ast.BinNumExpr(
ast.FeatureRef(18),
ast.NumVal(0.00010735116208006556),
ast.BinNumOpType.MUL),
ast.BinNumExpr(
ast.FeatureRef(19),
ast.NumVal(-4.076097659514017e-05),
ast.BinNumOpType.MUL),
ast.BinNumExpr(
ast.FeatureRef(20),
ast.NumVal(0.15300712110146406),
ast.BinNumOpType.MUL),
ast.BinNumExpr(
ast.FeatureRef(21),
ast.NumVal(0.06316277258339074),
ast.BinNumOpType.MUL),
ast.BinNumExpr(
ast.FeatureRef(22),
ast.NumVal(0.495291178977687),
ast.BinNumOpType.MUL),
ast.BinNumExpr(
ast.FeatureRef(23),
ast.NumVal(-0.29589136204657845),
ast.BinNumOpType.MUL),
ast.BinNumExpr(
ast.FeatureRef(24),
ast.NumVal(0.000771932729567487),
ast.BinNumOpType.MUL),
ast.BinNumExpr(
ast.FeatureRef(25),
ast.NumVal(-0.011877978242492428),
ast.BinNumOpType.MUL),
ast.BinNumExpr(
ast.FeatureRef(26),
ast.NumVal(-0.01678004536869617),
ast.BinNumOpType.MUL),
ast.BinNumExpr(
ast.FeatureRef(27),
ast.NumVal(-0.004070431062579625),
ast.BinNumOpType.MUL),
ast.BinNumExpr(
ast.FeatureRef(28),
ast.NumVal(0.001158641497209262),
ast.BinNumOpType.MUL),
ast.BinNumExpr(
ast.FeatureRef(29),
ast.NumVal(0.00010737287732588742),
ast.BinNumOpType.MUL),
]
expected = assemblers.utils.apply_op_to_expressions(
ast.BinNumOpType.ADD,
ast.NumVal(0.0),
*feature_weight_mul)
assert utils.cmp_exprs(actual, expected)
def test_lightning_binary_class():
estimator = AdaGradClassifier(random_state=1)
utils.get_binary_classification_model_trainer()(estimator)
assembler = assemblers.SklearnLinearModelAssembler(estimator)
actual = assembler.assemble()
feature_weight_mul = [
ast.BinNumExpr(ast.FeatureRef(0), ast.NumVal(0.1617602138),
ast.BinNumOpType.MUL),
ast.BinNumExpr(ast.FeatureRef(1), ast.NumVal(0.0931034793),
ast.BinNumOpType.MUL),
ast.BinNumExpr(ast.FeatureRef(2), ast.NumVal(0.6279180888),
ast.BinNumOpType.MUL),
ast.BinNumExpr(ast.FeatureRef(3), ast.NumVal(0.1856722189),
ast.BinNumOpType.MUL),
ast.BinNumExpr(ast.FeatureRef(4), ast.NumVal(0.0009999878),
ast.BinNumOpType.MUL),
ast.BinNumExpr(ast.FeatureRef(5), ast.NumVal(-0.0028974470),
ast.BinNumOpType.MUL),
ast.BinNumExpr(ast.FeatureRef(6), ast.NumVal(-0.0059948515),
ast.BinNumOpType.MUL),
ast.BinNumExpr(ast.FeatureRef(7), ast.NumVal(-0.0024173728),
ast.BinNumOpType.MUL),
ast.BinNumExpr(ast.FeatureRef(8), ast.NumVal(0.0020429247),
ast.BinNumOpType.MUL),
ast.BinNumExpr(ast.FeatureRef(9), ast.NumVal(0.0009604400),
ast.BinNumOpType.MUL),
ast.BinNumExpr(ast.FeatureRef(10), ast.NumVal(0.0010933747),
ast.BinNumOpType.MUL),
ast.BinNumExpr(ast.FeatureRef(11), ast.NumVal(0.0078588761),
ast.BinNumOpType.MUL),
ast.BinNumExpr(ast.FeatureRef(12), ast.NumVal(-0.0069150246),
ast.BinNumOpType.MUL),
ast.BinNumExpr(ast.FeatureRef(13), ast.NumVal(-0.2583249885),
ast.BinNumOpType.MUL),
ast.BinNumExpr(ast.FeatureRef(14), ast.NumVal(0.0000097479),
ast.BinNumOpType.MUL),
ast.BinNumExpr(ast.FeatureRef(15), ast.NumVal(-0.0007210600),
ast.BinNumOpType.MUL),
ast.BinNumExpr(ast.FeatureRef(16), ast.NumVal(-0.0011295195),
ast.BinNumOpType.MUL),
ast.BinNumExpr(ast.FeatureRef(17), ast.NumVal(-0.0001966115),
ast.BinNumOpType.MUL),
ast.BinNumExpr(ast.FeatureRef(18), ast.NumVal(0.0001358314),
ast.BinNumOpType.MUL),
ast.BinNumExpr(ast.FeatureRef(19), ast.NumVal(-0.0000378118),
ast.BinNumOpType.MUL),
ast.BinNumExpr(ast.FeatureRef(20), ast.NumVal(0.1555921773),
ast.BinNumOpType.MUL),
ast.BinNumExpr(ast.FeatureRef(21), ast.NumVal(0.0621307817),
ast.BinNumOpType.MUL),
ast.BinNumExpr(ast.FeatureRef(22), ast.NumVal(0.5138354949),
ast.BinNumOpType.MUL),
ast.BinNumExpr(ast.FeatureRef(23), ast.NumVal(-0.2418579612),
ast.BinNumOpType.MUL),
ast.BinNumExpr(ast.FeatureRef(24), ast.NumVal(0.0007953821),
ast.BinNumOpType.MUL),
ast.BinNumExpr(ast.FeatureRef(25), ast.NumVal(-0.0110760214),
ast.BinNumOpType.MUL),
ast.BinNumExpr(ast.FeatureRef(26), ast.NumVal(-0.0162178044),
ast.BinNumOpType.MUL),
ast.BinNumExpr(ast.FeatureRef(27), ast.NumVal(-0.0040277699),
ast.BinNumOpType.MUL),
ast.BinNumExpr(ast.FeatureRef(28), ast.NumVal(0.0015067033),
ast.BinNumOpType.MUL),
ast.BinNumExpr(ast.FeatureRef(29), ast.NumVal(0.0001536614),
ast.BinNumOpType.MUL),
]
expected = assemblers.utils.apply_op_to_expressions(
ast.BinNumOpType.ADD, ast.NumVal(0.0), *feature_weight_mul)
assert utils.cmp_exprs(actual, expected)
