spark-calibration 1.0.5

Calibratiing model scores/probabilites with pyspark dataframes

Model calibration with pyspark

This package provides a Betacal class which allows the user to fit the default beta calibration model and predict calibrated scores

Setup

spark-calibration is uploaded to PyPi and can be installed with this command:

pip install spark-calibration

Usage

Training

train_df should be a pyspark dataframe with score and label columns

from spark_calibration import Betacal
from spark_calibration import display_classification_calib_metrics
from spark_calibration import plot_calibration_curve


bc = Betacal(parameters="abm")

# training
train_df = spark.read.parquet("s3://train/")
bc.fit(train_df)


# Get the logistic regression model and individual coefficients
print(bc.lr_model, a, b)

# a,b -> coefficients of lr model
# lr_model -> pyspark ml logistic regression model

Prediction

test_df is a pyspark dataframe with score as one of the columns. The predict function adds a new column prediction which has the calibrated score

test_df = spark.read.parquet("s3://test/")
test_df = bc.predict(test_df)

Pre post calibration metrics comparison

The test_df should have score, prediction & label columns. The display_classification_calib_metrics functions displays brier_score_loss, log_loss, area_under_PR_curve and area_under_ROC_curve

display_classification_calib_metrics(test_df)

plot the calibration curve

Computes true, predicted probabilites (pre & post calibration) using quantile binning strategy with 50 bins and plots the calibration curve

plot_calibration_curve(test_df)