bixai 0.1.6

Package that helps you make some machine learning models easier to understand

bixai

The bixai is a package made for NLO to make an attempt at understanding drivers behind models

It contains multiple modules to analyse different problems:

1. Decomposition for logistic regressions over time
2. Multivariate logistic regression with impact of variables
3. Visualize a decision tree
4. Time-series forecasting with regressions and random forest

Installation

Use the package manager pip to install bixai.

pip install bixai

Usage Decomposition for logistic regressions over time

# import modules
from bixai.generate_example_data import GenerateData
from bixai.creating_dataset import CreatingDataSet
from bixai.logistic_regression_decomp import LogisticDecomposition
from random import randint
from sklearn.linear_model import LogisticRegression
import plotly.io as pio
pio.renderers.default = 'browser'

# Generate data
example_data = GenerateData(10000)
df_example = example_data.generate_dataset()

# Using CreatingDataSet to clean the data
getting_data = CreatingDataSet(df_example, {})

# The sample size you want from the data and the variables (X and y) to be used
subset_size = 5000 
X_vars = ['percentage_gelezen_mails', 'geslacht', 'leeftijd', 'maanden_lid', 'kanaal_instroom', 'actie_instroom',
          'contact_vorm']
y = 'churn'

# Create the test/train
X_train, X_test, y_train, y_test = getting_data.get_train_test(y, X_vars, divided_by_max=False, scale_data=True,
                                                               add_random_int=False, add_random_cont=False, set_seed=2,
                                                               size=subset_size, test_size=0.25, random_state=12,
                                                               with_mean=True, with_std=True)

# Logistic Regression Model
model_churn = LogisticRegression().fit(X_train, y_train)

# Add a random variable to split on for this example (this should come from own data)
split_var = [randint(2018, 2022) for p in range(0, len(X_train))] 
vars_to_show = []
model_decomp = LogisticDecomposition(model_churn)
decomposition_results = model_decomp.decomposition_logistic(X_train, split_var=split_var, plot=True, y=[],
                                                             X_vars_to_show=vars_to_show,
                                                             scaling_to_mean_odds_model=True,
                                                             scaling_to_actual_odds=False)

License

Copyright (c) 2023 Rumiko Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the "Software"), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions: The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software.