kiraplenkin/woe_scoring

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

WOE-Scoring

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

Quickstart

1. Install the package:

pip install woe-scoring

2. Use WOETransformer:

import pandas as pd
from woe_scoring import WOETransformer
from sklearn.model_selection import train_test_split
from sklearn.linear_model import LogisticRegression

df = pd.read_csv("titanic_data.csv")
train, test = train_test_split(
    df, test_size=0.3, random_state=42, stratify=df["Survived"]
)

special_cols = [
    "PassengerId",
    "Survived",
    "Name",
    "Ticket",
    "Cabin",
]

cat_cols = [
    "Pclass",
    "Sex",
    "SibSp",
    "Parch",
    "Embarked",
]

encoder = WOETransformer(
    max_bins=8,
    min_pct_group=0.1,
    diff_woe_threshold=0.1,
    cat_features=cat_cols,
    special_cols=special_cols,
    n_jobs=-1,
    merge_type="chi2",
)

encoder.fit(train, train["Survived"])
encoder.save_to_file("train_dict.json")

encoder.load_woe_iv_dict("train_dict.json")
encoder.refit(train, train["Survived"])

enc_train = encoder.transform(train)
enc_test = encoder.transform(test)

model = LogisticRegression()
model.fit(enc_train, train["Survived"])
test_proba = model.predict_proba(enc_test)[:, 1]

3. Use CreateModel:

import pandas as pd
from woe_scoring import CreateModel
from sklearn.model_selection import train_test_split

df = pd.read_csv("titanic_data.csv")
train, test = train_test_split(
    df, test_size=0.3, random_state=42, stratify=df["Survived"]
)

special_cols = [
    "PassengerId",
    "Survived",
    "Name",
    "Ticket",
    "Cabin",
]

model = CreateModel(
    max_vars=5,
    special_cols=special_cols,
    selection_method="sfs",
    model_type="sklearn",
    gini_threshold=5.0,
    n_jobs=-1,
    random_state=42,
    class_weight="balanced",
    cv=3,
)
model.fit(train, train["Survived"])
test_proba = model.predict_proba(test[model.feature_names_])

print(model.coef_, model.intercept_)
print(model.feature_names_)