def test_multiclass():
X, y = sklearn.datasets.make_classification(n_classes=3, n_informative=4)
X = da.from_array(X, chunks=50)
y = da.from_array(y, chunks=50)
if SK_GE_020:
kwargs = {"multi_class": "auto"}
else:
kwargs = {}
clf = ParallelPostFit(
LogisticRegression(random_state=0, n_jobs=1, solver="lbfgs", **kwargs)
)
clf.fit(X, y)
result = clf.predict(X)
expected = clf.estimator.predict(X)
assert isinstance(result, da.Array)
assert_eq_ar(result, expected)
result = clf.predict_proba(X)
expected = clf.estimator.predict_proba(X)
assert isinstance(result, da.Array)
assert_eq_ar(result, expected)
def test_predict(kind):
X, y = make_classification(chunks=100)
if kind == "numpy":
X, y = dask.compute(X, y)
elif kind == "dask.dataframe":
X = dd.from_dask_array(X)
y = dd.from_dask_array(y)
base = LogisticRegression(random_state=0, n_jobs=1, solver="lbfgs")
wrap = ParallelPostFit(
LogisticRegression(random_state=0, n_jobs=1, solver="lbfgs"))
base.fit(X, y)
wrap.fit(X, y)
assert_estimator_equal(wrap.estimator, base)
result = wrap.predict(X)
expected = base.predict(X)
assert_eq_ar(result, expected)
result = wrap.predict_proba(X)
expected = base.predict_proba(X)
assert_eq_ar(result, expected)
def test_sparse_inputs():
X = csr_matrix((3, 4))
y = np.asarray([0, 0, 1], dtype=np.int32)
base = SGDClassifier(tol=1e-3)
base = base.fit(X, y)
wrap = ParallelPostFit(base)
X_da = da.from_array(X, chunks=(1, 4))
result = wrap.predict(X_da).compute()
expected = base.predict(X)
assert_eq_ar(result, expected)
def test_multiclass():
X, y = make_classification(chunks=50, n_classes=3, n_informative=4)
clf = ParallelPostFit(LogisticRegression(random_state=0))
clf.fit(X, y)
result = clf.predict(X)
expected = clf.estimator.predict(X)
assert isinstance(result, da.Array)
assert_eq_ar(result, expected)
result = clf.predict_proba(X)
expected = clf.estimator.predict_proba(X)
assert isinstance(result, da.Array)
assert_eq_ar(result, expected)
def test_transform_meta_override():
X = pd.DataFrame({"cat_s": ["a", "b", "c", "d"]})
dd_X = dd.from_pandas(X, npartitions=2)
base = OneHotEncoder(sparse=False)
base.fit(pd.DataFrame(X))
# Failure when not proving transform_meta
# because of value dependent model
wrap = ParallelPostFit(base)
with pytest.raises(ValueError):
wrap.transform(dd_X)
wrap = ParallelPostFit(base,
transform_meta=np.array([[0, 0, 0, 0]],
dtype=np.float64))
result = wrap.transform(dd_X)
expected = base.transform(X)
assert_eq_ar(result, expected)
def test_transform(kind):
X, y = make_classification(chunks=100)
if kind == "numpy":
X, y = dask.compute(X, y)
elif kind == "dask.dataframe":
X = dd.from_dask_array(X)
y = dd.from_dask_array(y)
base = PCA(random_state=0)
wrap = ParallelPostFit(PCA(random_state=0))
base.fit(*dask.compute(X, y))
wrap.fit(*dask.compute(X, y))
assert_estimator_equal(wrap.estimator, base)
result = base.transform(*dask.compute(X))
expected = wrap.transform(X)
assert_eq_ar(result, expected)
def test_predict_meta_override():
X = pd.DataFrame({"c_0": [1, 2, 3, 4]})
y = np.array([1, 2, 3, 4])
base = CategoricalNB()
base.fit(pd.DataFrame(X), y)
dd_X = dd.from_pandas(X, npartitions=2)
dd_X._meta = pd.DataFrame({"c_0": [5]})
# Failure when not proving predict_meta
# because of value dependent model
wrap = ParallelPostFit(base)
with pytest.raises(ValueError):
wrap.predict(dd_X)
# Success when providing meta over-ride
wrap = ParallelPostFit(base, predict_meta=np.array([1]))
result = wrap.predict(dd_X)
expected = base.predict(X)
assert_eq_ar(result, expected)
def test_multiclass():
X, y = sklearn.datasets.make_classification(n_classes=3, n_informative=4)
X = da.from_array(X, chunks=50)
y = da.from_array(y, chunks=50)
clf = ParallelPostFit(
LogisticRegression(random_state=0,
n_jobs=1,
solver="lbfgs",
multi_class="auto"))
clf.fit(*dask.compute(X, y))
result = clf.predict(X)
expected = clf.estimator.predict(X)
assert isinstance(result, da.Array)
assert_eq_ar(result, expected)
result = clf.predict_proba(X)
expected = clf.estimator.predict_proba(X)
assert isinstance(result, da.Array)
assert_eq_ar(result, expected)
result = clf.predict_log_proba(X)
expected = clf.estimator.predict_log_proba(X)
assert_eq_ar(result, expected)
