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)
'avg_cur_bal': [6515.0, 10622.0, 19486.0, 36453.0]
}
comb.set_rules(rules)
train_b = comb.transform(train_s, labels=True)
test_b = comb.transform(test_s, labels=True)
for i in columns[~columns.isin(['split', 'loan_status'])]:
data_i = pd.concat([train_b[i], train_s['loan_status']], axis=1)
bin_plot(data_i, x=i, target='loan_status')
plt.show()
print('分箱完成', '\n' * 2)
# ----------------------------------------------------------------------------------------------------------------------------------------------------
# WOE编码
print('WOE编码'.center(62, '—'))
WOE = WOETransformer()
train_w = WOE.fit_transform(train_b, y='loan_status')
test_w = WOE.fit_transform(test_b, y='loan_status')
print('WOE编码完成', '\n' * 2)
train_w.to_csv('train_w.csv', index=False)
test_w.to_csv('test_w.csv', index=False)
# ----------------------------------------------------------------------------------------------------------------------------------------------------
# 特征筛选B
print('特征第二次筛选'.center(60, '—'))
train_w = pd.read_csv('train_w.csv')
test_w = pd.read_csv('test_w.csv')
train_s2, drops = select(train_w,
target='loan_status',
iv=0.005,
corr=0.8,
},
'B': {
','.join(list('ABCD')): 200,
','.join(list('EF')): 400,
'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)