ks-metric 0.2.0

Kolmogorov-Smirnov metric for machine learning

Kolmogorov-Smirnov metric (ks metric) is derived from K-S test. K-S test measures the distance between two plotted cumulative distribution functions (CDF). To use it as a metric for classification machine learning problem we see the distance of plotted CDF of target and non-target. The model that produces the greatest amount of separability between target and non-target distribution would be considered the better model.

Installation

The package requires: pandas and numpy.

To install the package, execute:

$ python setup.py install

or

pip install ks_metric

Usage

To get the KS score :

from sklearn.datasets import load_breast_cancer
from sklearn.model_selection import train_test_split
from sklearn.linear_model import LogisticRegression

from ks_metric import ks_score

data = load_breast_cancer()
X, y = data['data'], data['target']
X_train, X_test, y_train, y_test = train_test_split( X, y, test_size=0.33, random_state=42)

clf = LogisticRegression(random_state=0, max_iter=10000).fit(X_train, y_train)
ks_score(y_train, clf.predict_proba(X_train)[:,1])

KS table :

from ks_metric import ks_table

ks_table(y_train, clf.predict_proba(X_train)[:,1])

KS scorer (for hyperparameter search) :

from sklearn.model_selection import GridSearchCV
from ks_metric import ks_scorer

clf = GridSearchCV(estimator=LogisticRegression(), param_grid={'C':[0.01,0.1,1]}, scoring=ks_scorer)

see the example notebook for detailed usage.