kiraplenkin/woe_scoring

Monotone Weight Of Evidence Transformer and LogisticRegression model with scikit-learn API

woe_scoring's Issues

Wrong len of bins

Position of missing bin if only two bins

Refactor transform method

Min 2 bin for percent option in max_bins

Add lxml to requirements

Add optim parameters to logreg

Fix check_mono func

Num binning ->
while (_mono_flags(bad_rates) is False) and (len(bad_rates) > 2): and to top

_mono_flags ->
return True in [positive_mono_diff, negative_mono_diff]

Bins refactoring

bins.extend(
np.nanquantile(x, quantile / max_bins, axis=0)
for quantile in range(1, max_bins)
)

->

_, nbins = pd.qcut(x, q= max_bins, retbins=True)
bins += list(nbins)

Add default path for saving reports

Save scorecard func

Req model_results information

Change _mono_flags

Change to numpy.diff

Refit bug

Set threshold diff_woe in _check_diff_woe as parameter

Change cat-binning

q_list = list(sorted(set(q_list)))

new_bins = [copy.deepcopy(bad_rates[0]["bin"])]
start = 1
for i in range(len(q_list) - 1):
    for n in range(start, len(bad_rates)):
        if (bad_rate_list[n] >= q_list[i]) & (
            bad_rate_list[n] < q_list[i + 1]
        ):
            try:
                new_bins[i] += bad_rates[n]["bin"]
                start += 1
            except IndexError:
                new_bins.append([])
                new_bins[i] += bad_rates[n]["bin"]
                start += 1

AttributeError: 'WaldTestResults' object has no attribute 'as_text'

Class CreateModel
Method fit

save_report = True

with open("./model_wald.txt", "w") as fh:
                fh.write(temp_model.wald_test_terms().as_text())

max_bins = -1

Add max_bins=-1

Bug with checking Wald threshold

retrain = False
to_drop = []
for i, pvalue in enumerate(self.model.wald_test_terms().table["pvalue"]):
    if pvalue > 0.05:
        to_drop.append(self.model.wald_test_terms().table.index[i])
        retrain = True
if retrain:
    self.feature_names_ = [feature for feature in self.feature_names_ if feature not in to_drop]
    self.model = sm.Logit(y, sm.add_constant(x[self.feature_names_])).fit()

Bug calc woe

(good + 0.5 / all_good) / (bad + 0.5 / all_bad)

Optimize cat_bining func

Rename "WOE_" to prefix

Calculate all bad and all good without nan

all_bad = y[~pd.isna(x)].sum()
all_good = len(y[~pd.isna(x)]) - all_bad

Copy of dataset

Save reports

434 if self.save_reports:
435 try:
436 with open(

AttributeError: 'CreateModel' object has no attribute 'save_reports'

Create model should return new feature_names_

Bug in mono check

while (_mono_flags(bad_rates) is False) and (len(bad_rates) > 2):

Add compare data type for cat features in refit func

try: X = X.astype(float) except ValueError: X = X.astype(object)

Add parallel and Multicore Computing

Add documentation

Fix model.predict_proba(oot)