def test_df_values(self):
est1 = dpp.RobustScaler()
est2 = dpp.RobustScaler()
result_ar = est1.fit_transform(X)
result_df = est2.fit_transform(df)
assert_eq_ar(result_ar, result_df.values)
for attr in ['scale_', 'center_']:
assert_eq_ar(getattr(est1, attr), getattr(est2, attr))
assert_eq_ar(est1.transform(X), est2.transform(X))
assert_eq_ar(est1.transform(df).values, est2.transform(X))
assert_eq_ar(est1.transform(X), est2.transform(df).values)
# different data types
df['0'] = df['0'].astype('float32')
result_ar = est1.fit_transform(X)
result_df = est2.fit_transform(df)
assert_eq_ar(result_ar, result_df.values)
def test_fit(self):
a = dpp.RobustScaler()
b = spp.RobustScaler()
# bigger data to make percentile more reliable
# and not centered around 0 to make rtol work
X, y = make_classification(n_samples=1000, chunks=200, random_state=0)
X = X + 3
a.fit(X)
b.fit(X.compute())
assert_estimator_equal(a, b, rtol=0.2)
def test_transform(self):
a = dpp.RobustScaler()
b = spp.RobustScaler()
a.fit(X)
b.fit(X.compute())
# overwriting dask-ml's fitted attributes to have them exactly equal
# (the approximate equality is tested above)
a.scale_ = b.scale_
a.center_ = b.center_
assert_eq_ar(a.transform(X).compute(), b.transform(X.compute()))
def test_df_values(self):
est1 = dpp.RobustScaler()
est2 = dpp.RobustScaler()
result_ar = est1.fit_transform(X)
result_df = est2.fit_transform(df)
if hasattr(result_df, "values"):
result_df = result_df.values
assert_eq_ar(result_ar, result_df)
for attr in ["scale_", "center_"]:
assert_eq_ar(getattr(est1, attr), getattr(est2, attr))
assert_eq_ar(est1.transform(X), est2.transform(X))
assert_eq_ar(est1.transform(df).values, est2.transform(X))
assert_eq_ar(est1.transform(X), est2.transform(df).values)
# different data types
df["0"] = df["0"].astype("float32")
result_ar = est1.fit_transform(X)
result_df = est2.fit_transform(df)
if hasattr(result_df, "values"):
result_df = result_df.values
assert_eq_ar(result_ar, result_df)
def test_inverse_transform(self):
a = dpp.RobustScaler()
assert_eq_ar(
a.inverse_transform(a.fit_transform(X)).compute(), X.compute())
def test_inverse_transform(self):
a = dpp.RobustScaler()
result = a.inverse_transform(a.fit_transform(X))
assert dask.is_dask_collection(result)
assert_eq_ar(result, X)
