• API Reference - CalibrationScore

Calibration Score

import warnings

import pandas as pd
from sklearn.datasets import load_iris
from sklearn.linear_model import LogisticRegression
from sklearn.model_selection import train_test_split

from deepchecks.tabular.checks import CalibrationScore
from deepchecks.tabular.datasets.classification import adult


def custom_formatwarning(msg, *args, **kwargs):
    # ignore everything except the message
    return str(msg) + '\n'

warnings.formatwarning = custom_formatwarning

Binary Classification

Load data

The dataset is the adult dataset which can be downloaded from the UCI machine learning repository.

Dua, D. and Graff, C. (2019). UCI Machine Learning Repository [http://archive.ics.uci.edu/ml]. Irvine, CA: University of California, School of Information and Computer Science.

from urllib.request import urlopen

from sklearn.preprocessing import LabelEncoder

label_name = 'income'

from deepchecks.tabular import Dataset

train_ds, test_ds = adult.load_data()

model = adult.load_fitted_model()

check = CalibrationScore()
check.run(test_ds, model)

Multi-class classification

iris = load_iris(as_frame=True)
clf = LogisticRegression()
frame = iris.frame
X = iris.data
y = iris.target
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.5, random_state=55)
clf.fit(X_train, y_train)
ds = Dataset(pd.concat([X_test, y_test], axis=1),
            features=iris.feature_names,
            label='target')

check = CalibrationScore()
check.run(ds, clf)

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