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ezhdiezhdi
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Increase test coverage (#167)
1 parent 1c0a478 commit 88a02cd

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4 files changed

+236
-32
lines changed

4 files changed

+236
-32
lines changed

‎sklift/tests/test_datasets.py‎

Lines changed: 27 additions & 16 deletions
Original file line numberDiff line numberDiff line change
@@ -34,24 +34,23 @@ def test_fetch_lenta(lenta_dataset):
3434
assert data.target.shape == lenta_dataset['target.shape']
3535
assert data.treatment.shape == lenta_dataset['treatment.shape']
3636

37-
38-
# @pytest.fixture
39-
# def x5_dataset() -> dict:
40-
# data = {'keys': ['data', 'target', 'treatment', 'DESCR', 'feature_names', 'target_name', 'treatment_name'],
37+
#@pytest.fixture
38+
#def x5_dataset() -> dict:
39+
# data = {'keys': ['data', 'target', 'treatment', 'DESCR', 'feature_names', 'target_name', 'treatment_name'],
4140
# 'data.keys': ['clients', 'train', 'purchases'], 'clients.shape': (400162, 5),
42-
# 'train.shape': (200039, 1), 'target.shape': (200039,), 'treatment.shape': (200039,)}
43-
# return data
44-
#
41+
# 'train.shape': (200039, 1), 'target.shape': (200039,), 'treatment.shape': (200039,)}
42+
# return data
43+
4544
#
46-
# def test_fetch_x5(x5_dataset):
47-
# data = fetch_x5()
48-
# assert isinstance(data, sklearn.utils.Bunch)
49-
# assert set(data.keys()) == set(x5_dataset['keys'])
50-
# assert set(data.data.keys()) == set(x5_dataset['data.keys'])
51-
# assert data.data.clients.shape == x5_dataset['clients.shape']
52-
# assert data.data.train.shape == x5_dataset['train.shape']
53-
# assert data.target.shape == x5_dataset['target.shape']
54-
# assert data.treatment.shape == x5_dataset['treatment.shape']
45+
#def test_fetch_x5(x5_dataset):
46+
# data = fetch_x5()
47+
# assert isinstance(data, sklearn.utils.Bunch)
48+
# assert set(data.keys()) == set(x5_dataset['keys'])
49+
# assert set(data.data.keys()) == set(x5_dataset['data.keys'])
50+
# assert data.data.clients.shape == x5_dataset['clients.shape']
51+
# assert data.data.train.shape == x5_dataset['train.shape']
52+
# assert data.target.shape == x5_dataset['target.shape']
53+
# assert data.treatment.shape == x5_dataset['treatment.shape']
5554

5655

5756
@pytest.fixture
@@ -85,6 +84,14 @@ def test_fetch_criteo10(
8584
assert data.target.shape == target_shape
8685
assert data.treatment.shape == treatment_shape
8786

87+
@pytest.mark.parametrize(
88+
'target_col, treatment_col',
89+
[('visit','new_trmnt'), ('new_target','treatment')]
90+
)
91+
def test_fetch_criteo_errors(target_col, treatment_col):
92+
with pytest.raises(ValueError):
93+
fetch_criteo(target_col=target_col, treatment_col=treatment_col)
94+
8895

8996
@pytest.fixture
9097
def hillstrom_dataset() -> dict:
@@ -111,6 +118,10 @@ def test_fetch_hillstrom(
111118
assert data.target.shape == target_shape
112119
assert data.treatment.shape == hillstrom_dataset['treatment.shape']
113120

121+
def test_fetch_hillstrom_error():
122+
with pytest.raises(ValueError):
123+
fetch_hillstrom(target_col='new_target')
124+
114125

115126
@pytest.fixture
116127
def megafon_dataset() -> dict:

‎sklift/tests/test_metrics.py‎

Lines changed: 115 additions & 2 deletions
Original file line numberDiff line numberDiff line change
@@ -7,10 +7,11 @@
77

88
from sklearn.utils._testing import assert_array_almost_equal
99

10+
from ..metrics import make_uplift_scorer
1011
from ..metrics import uplift_curve, uplift_auc_score, perfect_uplift_curve
1112
from ..metrics import qini_curve, qini_auc_score, perfect_qini_curve
1213
from ..metrics import (uplift_at_k, response_rate_by_percentile,
13-
weighted_average_uplift, uplift_by_percentile, treatment_balance_curve)
14+
weighted_average_uplift, uplift_by_percentile, treatment_balance_curve, average_squared_deviation)
1415

1516

1617
def make_predictions(binary):
@@ -221,6 +222,12 @@ def test_perfect_qini_curve_hard():
221222

222223
assert_array_almost_equal(x_actual, np.array([0., 0., 3.]))
223224
assert_array_almost_equal(y_actual, np.array([0.0, 0.0, 0.0]))
225+
226+
def test_perfect_qini_curve_error():
227+
y_true, uplift, treatment = make_predictions(binary=True)
228+
with pytest.raises(TypeError):
229+
perfect_qini_curve(y_true, treatment, negative_effect=5)
230+
224231

225232

226233
def test_qini_auc_score():
@@ -255,11 +262,33 @@ def test_qini_auc_score():
255262
treatment = [1, 0, 1]
256263
assert_array_almost_equal(qini_auc_score(y_true, uplift, treatment), 0.75)
257264

265+
def test_qini_auc_score_error():
266+
y_true = [1, 0]
267+
uplift = [0.1, 0.3]
268+
treatment = [0, 1]
269+
with pytest.raises(TypeError):
270+
qini_auc_score(y_true, uplift, treatment, negative_effect=5)
271+
258272

259273
def test_uplift_at_k():
260274
y_true, uplift, treatment = make_predictions(binary=True)
261275

262276
assert_array_almost_equal(uplift_at_k(y_true, uplift, treatment, strategy='by_group', k=1), np.array([0.]))
277+
#assert_array_almost_equal(uplift_at_k(y_true, uplift, treatment, strategy='overall', k=2), np.array([0.]))
278+
279+
@pytest.mark.parametrize(
280+
"strategy, k",
281+
[
282+
('new_strategy', 1),
283+
('by_group', -0.5),
284+
('by_group', '1'),
285+
('by_group', 2)
286+
]
287+
)
288+
def test_uplift_at_k_errors(strategy, k):
289+
y_true, uplift, treatment = make_predictions(binary=True)
290+
with pytest.raises(ValueError):
291+
uplift_at_k(y_true, uplift, treatment, strategy, k)
263292

264293

265294
@pytest.mark.parametrize(
@@ -277,6 +306,19 @@ def test_response_rate_by_percentile(strategy, group, response_rate):
277306
assert_array_almost_equal(response_rate_by_percentile(y_true, uplift, treatment, group, strategy, bins=1),
278307
response_rate)
279308

309+
@pytest.mark.parametrize(
310+
"strategy, group, bins",
311+
[
312+
('new_strategy', 'control', 1),
313+
('by_group', 'ctrl', 1),
314+
('by_group', 'control', 0.5),
315+
('by_group', 'control', 9999)
316+
]
317+
)
318+
def test_response_rate_by_percentile_errors(strategy, group, bins):
319+
y_true, uplift, treatment = make_predictions(binary=True)
320+
with pytest.raises(ValueError):
321+
response_rate_by_percentile(y_true, uplift, treatment, group=group, strategy=strategy, bins=bins)
280322

281323
@pytest.mark.parametrize(
282324
"strategy, weighted_average",
@@ -289,7 +331,21 @@ def test_weighted_average_uplift(strategy, weighted_average):
289331
y_true, uplift, treatment = make_predictions(binary=True)
290332

291333
assert_array_almost_equal(weighted_average_uplift(y_true, uplift, treatment, strategy, bins=1), weighted_average)
334+
292335

336+
@pytest.mark.parametrize(
337+
"strategy, bins",
338+
[
339+
('new_strategy', 1),
340+
('by_group', 0.5),
341+
('by_group', 9999)
342+
]
343+
)
344+
def test_weighted_average_uplift_errors(strategy, bins):
345+
y_true, uplift, treatment = make_predictions(binary=True)
346+
with pytest.raises(ValueError):
347+
weighted_average_uplift(y_true, uplift, treatment, strategy=strategy, bins=bins)
348+
293349

294350
@pytest.mark.parametrize(
295351
"strategy, bins, std, total, string_percentiles, data",
@@ -307,11 +363,68 @@ def test_uplift_by_percentile(strategy, bins, std, total, string_percentiles, da
307363

308364
assert_array_almost_equal(
309365
uplift_by_percentile(y_true, uplift, treatment, strategy, bins, std, total, string_percentiles), data)
366+
367+
@pytest.mark.parametrize(
368+
"strategy, bins, std, total, string_percentiles",
369+
[
370+
('new_strategy', 1, True, True, True),
371+
('by_group', 0.5, True, True, True),
372+
('by_group', 9999, True, True, True),
373+
('by_group', 1, 2, True, True),
374+
('by_group', 1, True, True, 2),
375+
('by_group', 1, True, 2, True)
376+
]
377+
)
378+
def test_uplift_by_percentile_errors(strategy, bins, std, total, string_percentiles):
379+
y_true, uplift, treatment = make_predictions(binary=True)
380+
with pytest.raises(ValueError):
381+
uplift_by_percentile(y_true, uplift, treatment, strategy, bins, std, total, string_percentiles)
310382

311383

312384
def test_treatment_balance_curve():
313385
y_true, uplift, treatment = make_predictions(binary=True)
314386

315387
idx, balance = treatment_balance_curve(uplift, treatment, winsize=2)
316388
assert_array_almost_equal(idx, np.array([1., 100.]))
317-
assert_array_almost_equal(balance, np.array([1., 0.5]))
389+
assert_array_almost_equal(balance, np.array([1., 0.5]))
390+
391+
@pytest.mark.parametrize(
392+
"strategy",
393+
[
394+
('overall'),
395+
('by_group')
396+
]
397+
)
398+
def test_average_squared_deviation(strategy):
399+
y_true, uplift, treatment = make_predictions(binary=True)
400+
assert (average_squared_deviation(y_true, uplift, treatment, y_true, uplift, treatment, strategy, bins=1) == 0)
401+
402+
@pytest.mark.parametrize(
403+
"strategy, bins",
404+
[
405+
('new_strategy', 1),
406+
('by_group', 0.5),
407+
('by_group', 9999)
408+
]
409+
)
410+
def test_average_squared_deviation_errors(strategy, bins):
411+
y_true, uplift, treatment = make_predictions(binary=True)
412+
with pytest.raises(ValueError):
413+
average_squared_deviation(y_true, uplift, treatment, y_true, uplift, treatment, strategy=strategy, bins=bins)
414+
415+
def test_metric_name_error():
416+
with pytest.raises(ValueError):
417+
make_uplift_scorer('new_scorer', [0, 1])
418+
419+
def test_make_scorer_error():
420+
with pytest.raises(TypeError):
421+
make_uplift_scorer('qini_auc_score', [])
422+
423+
424+
425+
426+
427+
428+
429+
430+

‎sklift/tests/test_models.py‎

Lines changed: 48 additions & 0 deletions
Original file line numberDiff line numberDiff line change
@@ -1,4 +1,5 @@
11
import pytest
2+
import numpy as np
23
from sklearn.linear_model import LogisticRegression, LinearRegression
34
from sklearn.pipeline import Pipeline
45
from sklearn.preprocessing import StandardScaler
@@ -43,3 +44,50 @@ def test_shape_regression(model, random_xy_dataset_regr):
4344
assert model.fit(X, y, treat).predict(X).shape[0] == y.shape[0]
4445
pipe = Pipeline(steps=[("scaler", StandardScaler()), ("clf", model)])
4546
assert pipe.fit(X, y, clf__treatment=treat).predict(X).shape[0] == y.shape[0]
47+
48+
@pytest.mark.parametrize(
49+
"model",
50+
[
51+
SoloModel(LogisticRegression(), method='dummy'),
52+
SoloModel(LogisticRegression(), method='treatment_interaction'),
53+
]
54+
)
55+
def test_solomodel_fit_error(model):
56+
X, y, treatment = [[1., 0., 0.],[1., 0., 0.],[1., 0., 0.]], [1., 2., 3.], [0., 1., 0.]
57+
with pytest.raises(TypeError):
58+
model.fit(X, y, treatment)
59+
60+
@pytest.mark.parametrize(
61+
"model",
62+
[
63+
SoloModel(LogisticRegression(), method='dummy'),
64+
SoloModel(LogisticRegression(), method='treatment_interaction'),
65+
]
66+
)
67+
def test_solomodel_pred_error(model):
68+
X_train, y_train, treat_train = (np.array([[5.1, 3.5, 1.4, 0.2], [4.9, 3.0, 1.4, 0.2], [4.7, 3.2, 1.3, 0.2]]),
69+
np.array([0.0, 0.0, 1.0]), np.array([0.0, 1.0, 1.0]))
70+
model.fit(X_train, y_train, treat_train)
71+
with pytest.raises(TypeError):
72+
model.predict(1)
73+
74+
@pytest.mark.parametrize("method", ['method'])
75+
def test_solomodel_method_error(method):
76+
with pytest.raises(ValueError):
77+
SoloModel(LogisticRegression(), method=method)
78+
79+
def test_classtransformation_fit_error():
80+
X, y, treatment = [[1., 0., 0.],[1., 0., 0.],[1., 0., 0.]], [1., 2., 3.], [0., 1., 0.]
81+
with pytest.raises(ValueError):
82+
ClassTransformation(LogisticRegression()).fit(X, y, treatment)
83+
84+
@pytest.mark.parametrize("method", ['method'])
85+
def test_twomodels_method_error(method):
86+
with pytest.raises(ValueError):
87+
TwoModels(LinearRegression(), LinearRegression(), method=method)
88+
89+
def test_same_estimator_error():
90+
est = LinearRegression()
91+
with pytest.raises(ValueError):
92+
TwoModels(est, est)
93+

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