def test_Averaged__init__():
c1 = Averaged(sgdc_1)
c2 = Averaged(sgdc_2)
assert c1._name != c2._name
assert Averaged(sgdc_1)._name == c1._name
with pytest.raises(TypeError):
Averaged("not an estimator")
def test_fit_dask_array():
dXc = da.from_array(Xc, chunks=200)
dyc = da.from_array(yc, chunks=200)
c = Averaged(sgdc_1)
fit_test(c, dXc, dyc, SGDClassifier)
dXr = da.from_array(Xr, chunks=200)
dyr = da.from_array(yr, chunks=200)
c = Averaged(sgdr)
fit_test(c, dXr, dyr, SGDRegressor)
def test_fit_dask_matrix():
X_bag = db.from_sequence([Xc[0:200], Xc[200:400], Xc[400:]])
y_bag = db.from_sequence([yc[0:200], yc[200:400], yc[400:]])
dX = dm.from_bag(X_bag)
dy = dm.from_bag(y_bag)
c = Averaged(sgdc_1)
fit_test(c, dX, dy, SGDClassifier)
def test_score():
dX = da.from_array(Xc, chunks=200)
dy = da.from_array(yc, chunks=200)
c = Averaged(sgdc_1)
fit = c.fit(dX, dy)
s = fit.score(Xc, yc)
assert isinstance(s, Delayed)
res = s.compute()
assert isinstance(res, float)
s = fit.score(dX, dy)
assert isinstance(s, Delayed)
res = s.compute()
assert isinstance(res, float)
will_error = c.score(Xc, yc)
with pytest.raises(NotFittedError):
will_error.compute()
def test_predict():
dX = da.from_array(Xc, chunks=200)
dy = da.from_array(yc, chunks=200)
c = Averaged(sgdc_1)
fit = c.fit(dX, dy)
pred = fit.predict(Xc)
assert isinstance(pred, Delayed)
res = pred.compute()
assert isinstance(res, np.ndarray)
pred = fit.predict(dX)
assert isinstance(pred, da.Array)
res = pred.compute()
assert isinstance(res, np.ndarray)
will_error = c.predict(Xc)
with pytest.raises(NotFittedError):
will_error.compute()
def test_set_params():
c = Averaged(sgdc_1)
c2 = c.set_params(penalty='l2')
assert isinstance(c2, Averaged)
c3 = Averaged(sgdc_2)
assert c2._name == c3._name # set_params name equivalent to init
# Check no mutation
assert c2.get_params()['penalty'] == 'l2'
assert c2.compute().penalty == 'l2'
assert c.get_params()['penalty'] == 'l1'
assert c.compute().penalty == 'l1'
def test_tokenize_Averaged():
c1 = Averaged(sgdc_1)
c2 = Averaged(sgdc_2)
assert tokenize(c1) == tokenize(c1)
assert tokenize(c1) != tokenize(c2)
assert tokenize(c1) != tokenize(Wrapped(sgdc_1))
def test_Averaged_from_sklearn():
c1 = Averaged(sgdc_1)
assert Averaged.from_sklearn(sgdc_1)._name == c1._name
assert Averaged.from_sklearn(c1) is c1
def test_repr():
c = Averaged(sgdc_1)
res = repr(c)
assert res.startswith('Averaged')
def test_dir():
c = Averaged(sgdc_1)
attrs = dir(c)
assert 'penalty' in attrs
def test_getattr():
c = Averaged(sgdc_1)
assert c.penalty == sgdc_1.penalty
with pytest.raises(AttributeError):
c.not_a_real_parameter
def test_setattr():
c = Averaged(sgdc_1)
with pytest.raises(AttributeError):
c.penalty = 'l2'
def test_get_params():
c = Averaged(sgdc_1)
assert c.get_params() == sgdc_1.get_params()
assert c.get_params(deep=False) == sgdc_1.get_params(deep=False)
def test__estimator_type():
c = Averaged(sgdc_1)
assert c._estimator_type == sgdc_1._estimator_type
def test_clone():
c = Averaged(sgdc_1)
c2 = clone(c)
assert isinstance(c2, Averaged)
assert c._name == c2._name
assert c._est is not c2._est