def test_card_without_combiner():
transer = WOETransformer()
woe_X = transer.fit_transform(df, target)
card = ScoreCard(transer=transer)
card.fit(woe_X, target)
score, sub = card.predict(df, return_sub=True)
assert score[404] == pytest.approx(460.9789823549386, FUZZ_THRESHOLD)
def test_card_combiner_str_not_match():
c = combiner.export()
c['C'] = [['A'], ['B'], ['C']]
com = Combiner().load(c)
bins = com.transform(df)
woe_transer = WOETransformer()
woe = woe_transer.fit_transform(bins, target)
card = ScoreCard(
combiner=com,
transer=woe_transer,
)
with pytest.raises(Exception) as e:
# will raise an exception when fitting a card
card.fit(woe, target)
assert '\'C\' is not matched' in str(e.value)
def test_card_combiner_str_not_match():
c = combiner.export()
c['C'] = [['A'], ['B'], ['C']]
com = Combiner().set_rules(c)
bins = com.transform(df)
woe_transer = WOETransformer()
woe = woe_transer.fit_transform(bins, target)
model = LogisticRegression()
model.fit(woe, target)
with pytest.raises(Exception) as e:
# will raise an exception when create a card
card = ScoreCard(
combiner=com,
transer=woe_transer,
model=model,
)
assert '\'C\' is not matched' in str(e.value)
print('AUC:', AUC(prob_dev, y))
prob_off = lr.predict_proba(offx)[:, 1]
print('测试集')
print('F1:', F1(prob_off, offy))
print('KS:', KS(prob_off, offy))
print('AUC:', AUC(prob_off, offy))
print('模型PSI:', toad.metrics.PSI(prob_dev, prob_off))
print('特征PSI:', '\n', toad.metrics.PSI(x, offx).sort_values(0))
off_bucket = toad.metrics.KS_bucket(prob_off,
offy,
bucket=10,
method='quantile')
print('off_bucket', off_bucket)
from toad.scorecard import ScoreCard
card = ScoreCard(combiner=combiner,
transer=t,
class_weight='balanced',
C=0.1,
base_score=600,
base_odds=35,
pdo=60,
rate=2)
card.fit(x, y)
final_card = card.export(to_frame=True)
final_card.to_csv('D:/0.学习/python/屁屁和铭仔的数据之路/xjh_toad_train_rule.csv',
sep=',',
index=None)
def test_card_map():
config = card.export()
card_from_map = ScoreCard(card=config)
score = card_from_map.predict(df)
assert score[404] == TEST_SCORE
def test_export_frame():
card = ScoreCard(card=card_config)
frame = card.export(to_frame=True)
rows = frame[(frame['name'] == 'B')
& (frame['value'] == 'else')].reset_index()
assert rows.loc[0, 'score'] == 500
def test_generate_testing_frame():
card = ScoreCard(card=card_config)
frame = card.testing_frame()
assert frame.loc[4, 'B'] == 'E'
def test_card_map_with_else():
card_from_map = ScoreCard(card=card_config)
score = card_from_map.predict(df)
assert score[80] == 1000
'else': 500,
},
'C': {
'A': 200,
'B': 100,
},
}
combiner = Combiner()
bins = combiner.fit_transform(df, target, n_bins=5)
woe_transer = WOETransformer()
woe = woe_transer.fit_transform(bins, target)
# create a score card
card = ScoreCard(
combiner=combiner,
transer=woe_transer,
)
card.fit(woe, target)
FUZZ_THRESHOLD = 1e-4
TEST_SCORE = pytest.approx(453.58, FUZZ_THRESHOLD)
def test_proba_to_score():
model = LogisticRegression()
model.fit(woe, target)
proba = model.predict_proba(woe)[:, 1]
score = card.proba_to_score(proba)
assert score[404] == TEST_SCORE
def test_export_frame():
card = ScoreCard(card = card_config)
frame = card.export(to_frame = True)
assert frame.loc[6, 'value'] == 'else'
},
}
combiner = Combiner()
bins = combiner.fit_transform(df, target, n_bins = 5)
woe_transer = WOETransformer()
woe = woe_transer.fit_transform(bins, target)
model = LogisticRegression()
# fit model by woe
model.fit(woe, target)
# create a score card
card = ScoreCard(
combiner = combiner,
transer = woe_transer,
model = model,
)
FUZZ_THRESHOLD = 1e-4
TEST_SCORE = pytest.approx(453.58, FUZZ_THRESHOLD)
def test_proba_to_score():
proba = model.predict_proba(woe)[:,1]
score = card.proba_to_score(proba)
assert score[404] == TEST_SCORE
def test_predict():
score = card.predict(df)