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test_parallel_post_fit.py
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test_parallel_post_fit.py
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import dask
import dask.array as da
import dask.dataframe as dd
import numpy as np
import pytest
import sklearn.datasets
from sklearn.decomposition import PCA
from sklearn.ensemble import GradientBoostingClassifier
from sklearn.linear_model import LinearRegression, LogisticRegression
from dask_ml.datasets import make_classification
from dask_ml.utils import assert_eq_ar, assert_estimator_equal
from dask_ml.wrappers import ParallelPostFit
def test_it_works():
clf = ParallelPostFit(GradientBoostingClassifier())
X, y = make_classification(n_samples=1000, chunks=100)
X_, y_ = dask.compute(X, y)
clf.fit(X_, y_)
assert isinstance(clf.predict(X), da.Array)
assert isinstance(clf.predict_proba(X), da.Array)
result = clf.score(X, y)
expected = clf.estimator.score(X_, y_)
assert result == expected
def test_no_method_raises():
clf = ParallelPostFit(LinearRegression())
X, y = make_classification(chunks=50)
clf.fit(X, y)
with pytest.raises(AttributeError) as m:
clf.predict_proba(X)
assert m.match("The wrapped estimator (.|\n)* 'predict_proba' method.")
def test_laziness():
clf = ParallelPostFit(LinearRegression())
X, y = make_classification(chunks=50)
clf.fit(X, y)
x = clf.score(X, y, compute=False)
assert dask.is_dask_collection(x)
assert 0 < x.compute() < 1
@pytest.mark.parametrize("kind", ["numpy", "dask.dataframe", "dask.array"])
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(*dask.compute(X, y))
wrap.fit(*dask.compute(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)
result = wrap.predict_log_proba(X)
expected = base.predict_log_proba(X)
assert_eq_ar(result, expected)
@pytest.mark.parametrize("kind", ["numpy", "dask.dataframe", "dask.array"])
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_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)
def test_auto_rechunk():
clf = ParallelPostFit(GradientBoostingClassifier())
X, y = make_classification(n_samples=1000, n_features=20, chunks=100)
X = X.rechunk({0: 100, 1: 10})
clf.fit(X, y)
assert clf.predict(X).compute().shape == (1000,)
assert clf.predict_proba(X).compute().shape == (1000, 2)
assert clf.score(X, y) == clf.score(X.compute(), y.compute())
X, y = make_classification(n_samples=1000, n_features=20, chunks=100)
X = X.rechunk({0: 100, 1: 10})
X._chunks = (tuple(np.nan for _ in X.chunks[0]), X.chunks[1])
clf.predict(X)