pydrift 0.2.15
How do we measure the degradation of a machine learning process? Why does the performance of our predictive models decrease? Maybe it is that a data source has changed (one or more variables) or maybe what changes is the relationship of these variables with the target we want to predict. `pydrift` tries to facilitate this task to the data scientist, performing this kind of checks and somehow measuring that degradation.
License: MIT
Requires Python: >=3.6.1

# Welcome to `pydrift` 0.2.15

How do we measure the degradation of a machine learning process? Why does the performance of our predictive models decrease? Maybe it is that a data source has changed (one or more variables) or maybe what changes is the relationship of these variables with the target we want to predict. `pydrift` tries to facilitate this task to the data scientist, performing this kind of checks and somehow measuring that degradation.

# Install `pydrift`

With **pip**:

`pip install pydrift`

With **conda**:

`conda install -c conda-forge pydrift`

With **poetry**

`poetry add pydrift`

# Structure

This is intended to be user-friendly. pydrift is divided into **DataDriftChecker** and **ModelDriftChecker**:

- **DataDriftChecker**: searches for drift in the variables, check if their distributions have changed
- **ModelDriftChecker**: searches for drift in the relationship of the variables with the target, checks that the model behaves the same way for both data sets

Both can use a discriminative model (defined by parent class **DriftChecker**), where the target would be binary in belonging to one of the two sets, 1 if it is the left one and 0 on the contrary. If the model is not able to differentiate given the two sets, there is no difference!

![Class inheritance](https://raw.githubusercontent.com/sergiocalde94/pydrift/master/images/class_inheritance.png)

It also exists `InterpretableDrift` and `DriftCheckerEstimator`:
 
- **InterpretableDrift**: manages all of the stuff related to interpretability of drifting. It can show us the features distribution or the most important features when we are training a discriminative model or our predictive one
- **DriftCheckerEstimator**: allows `pydrift` to be used as a sklearn estimator, it works lonely or in a pipeline, like any sklearn estimator

# Usage

You can take a look to the `notebooks` folder where you can find one example for generic `DriftChecker`, one for `DataDriftChecker` and other one for `ModelDriftChecker`. 

# Correct Notebooks Render

Because `pydrift` uses plotly and GitHub performs a static render of the notebooks figures do not show correctly. For a rich view of the notebook, you can visit  [nbviewer](http://nbviewer.jupyter.org/) and paste the link to the notebook you want to show, for example if you want to render **1-Titanic-Drift-Demo.ipynb** you have to paste https://github.com/sergiocalde94/pydrift/blob/master/notebooks/1-Titanic-Drift-Demo.ipynb into nbviewer.  

# More Info

For more info check the docs available [here](https://sergiocalde94.github.io/pydrift/)

More demos and code improvements will coming, **if you want to contribute you can contact me (sergiocalde94@gmail.com)**, in the future I will upload a file to explain how this would work.