from mletrics.stability import psi
from mletrics.classification import ks
pip install mletrics
import numpy as np
import pandas as pd
from sklearn.pipeline import Pipeline
from sklearn.impute import SimpleImputer
from sklearn.compose import ColumnTransformer
from sklearn.preprocessing import OneHotEncoder
from scikitplot.metrics import plot_ks_statistic
from sklearn.ensemble import RandomForestClassifier
from sklearn.model_selection import train_test_split
from pathlib import Path
p = Path('..')
df = pd.read_csv(p/'datasets/titanic.csv')
df.head()
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PassengerId | Survived | Pclass | Name | Sex | Age | SibSp | Parch | Ticket | Fare | Cabin | Embarked | |
---|---|---|---|---|---|---|---|---|---|---|---|---|
0 | 1 | 0 | 3 | Braund, Mr. Owen Harris | male | 22.0 | 1 | 0 | A/5 21171 | 7.2500 | NaN | S |
1 | 2 | 1 | 1 | Cumings, Mrs. John Bradley (Florence Briggs Th... | female | 38.0 | 1 | 0 | PC 17599 | 71.2833 | C85 | C |
2 | 3 | 1 | 3 | Heikkinen, Miss. Laina | female | 26.0 | 0 | 0 | STON/O2. 3101282 | 7.9250 | NaN | S |
3 | 4 | 1 | 1 | Futrelle, Mrs. Jacques Heath (Lily May Peel) | female | 35.0 | 1 | 0 | 113803 | 53.1000 | C123 | S |
4 | 5 | 0 | 3 | Allen, Mr. William Henry | male | 35.0 | 0 | 0 | 373450 | 8.0500 | NaN | S |
cat_vars = ['Pclass', 'Sex', 'Embarked']
num_vars = ['Age', 'SibSp', 'Fare']
features = cat_vars + num_vars
target = 'Survived'
X = df[features].copy()
y = df[target].copy()
num_pipe = Pipeline(steps=[
('imputer', SimpleImputer(strategy='constant', fill_value=-999))
])
cat_pipe = Pipeline(steps=[
('imputer', SimpleImputer(strategy='constant', fill_value='missing')),
('ohe', OneHotEncoder(sparse=False, handle_unknown='ignore'))
])
transformers = ColumnTransformer(transformers=[
('numeric', num_pipe, num_vars),
('categoric', cat_pipe, cat_vars)
])
model = Pipeline(steps=[
('transformers', transformers),
('model', RandomForestClassifier(random_state=42, max_depth=3))
])
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)
model.fit(X_train, y_train)
y_proba_train = model.predict_proba(X_train)[:,1]
y_proba_test = model.predict_proba(X_test)[:,1]
calculate psi value for the model probability between train and test
psi(y_proba_train, y_proba_test)
0.06001324825109782
- PSI < 0.1 - No change. You can continue using existing model.
- PSI >= 0.1 but less than 0.2 - Slight change is required.
- PSI >= 0.2 - Significant change is required. Ideally, you should not use this model any more.
Reference: https://www.listendata.com/2015/05/population-stability-index.html
ks(y_test, y_proba_test)
0.5886743886743887
Comparing with the
ks
calculated by scikit-plot
:
plot_ks_statistic(y_test, np.column_stack([1-y_proba_test, y_proba_test]))
<AxesSubplot:title={'center':'KS Statistic Plot'}, xlabel='Threshold', ylabel='Percentage below threshold'>