⬛ Python Individual Conditional Expectation Plot Toolbox

Last update: Dec 30, 2022

Overview

⬛ PyCEbox

Python Individual Conditional Expectation Plot Toolbox

A Python implementation of individual conditional expecation plots inspired by R's ICEbox. Individual conditional expectation plots were introduced in Peeking Inside the Black Box: Visualizing Statistical Learning with Plots of Individual Conditional Expectation (arXiv:1309.6392).

Quickstart

pycebox is available on PyPI and can be installed with pip install pycebox.

The tutorial recreates the first example in the above paper using pycebox.

Development

For easy development and prototyping using IPython notebooks, a Docker environment is included. To run an IPython notebook with access to your development version of pycebox, run PORT=8889 sh ./start_container.sh. A Jupyter notebook server with access to your development version of pycebox should be available at http://localhost:8889/tree.

To run the pycebox's tests in your development container

Access a bash shell on the container with docker exec -it pycebox bash.
Change to the pycebox directory with cd ../pycebox
Run the tests with pytest test/test.py

Documentation

For details of pycebox's API, consult the documentation.

License

This library is distributed under the MIT License.

Comments

Typo in ice_plot() regarding _get_quantiles()

There is a typo in the ice_plot() function when calling the _get_quantiles() function. In lines 124 and 137, the ice_plot() calls __get_quantiles() (which is undefined) instead of _get_quantiles(), which results in an error if trying to use quantiles or center the ICE curves.

opened by savvastj 6
Using predicted probabilities for binary classification

Is there any way to give some form of predict_proba function to the ice() function in order to see the probability as opposed to the prediction?

Thanks! Nema

opened by nemasobhani 1
Plot mistake

There is a problem in the visualization part. When I am trying to plot the graph in the example, I see the following mistake:

TypeError Traceback (most recent call last) in 12 ice_plot(ice_df, frac_to_plot=0.1, 13 color_by='x3', cmap=PuOr, ---> 14 ax=ice_ax); 15 16 ice_ax.set_xlabel('$X_2$');

C:\ProgramData\Anaconda3\lib\site-packages\pycebox\ice.py in ice_plot(ice_data, frac_to_plot, plot_points, point_kwargs, x_quantile, plot_pdp, centered, centered_quantile, color_by, cmap, ax, pdp_kwargs, **kwargs) 128 if frac_to_plot < 1.: 129 n_cols = ice_data.shape[1] --> 130 icols = np.random.choice(n_cols, size=frac_to_plot * n_cols, replace=False) 131 plot_ice_data = ice_data.iloc[:, icols] 132 else:

mtrand.pyx in mtrand.RandomState.choice()

TypeError: 'float' object cannot be interpreted as an integer

opened by karakol15 4
"frac_to_plot" parameter in ice_plot

Hey Austin,

This package rocks, thanks for publishing it!

I have a question and a potential small bug in the ice_plot method, specifically on the "frac_to_plot" parameter.

It is my understanding that you simply take the fraction and multiply by the number of columns, and then pass this to the "size" parameter of np.random.choice(). I think we should make sure that the number being passed is an integer, not a float. Otherwise np.random.choice() will not accept a float as a parameter for "size".

Current: icols = np.random.choice(n_cols, size=frac_to_plot * n_cols, replace=False)

Fix: icols = np.random.choice(n_cols, size=int(frac_to_plot * n_cols), replace=False)

Best, Andrew

opened by andrew-cho 1
Extended use to classification models, fixed typecast bug
Extended use to classification models by allowing predict_proba to be passed to the ice_plot function.

Fixed 'type error when size is non-int' error for np.random.choice function
opened by sanjifr3 0
Averaging ICE plots across multiple runs/folds of a model

Hi Austin,

I was wondering if it is possible to average across multiple runs/folds of the same model.

I am trying at the moment, but the resulting ICE plots do not make sense. The per run plots make sense but when I average them across both runs and folds the data gets screwed.

Cheers,

Dan

opened by danieltudosiu 0

Releases(0.0.1)

0.0.1(Jan 8, 2017)

Source code(tar.gz)
Source code(zip)

Owner

Austin Rochford

Chief Data Scientist @ Kibo Commerce, recovering mathematician, enthusiastic Bayesian

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⬛ Python Individual Conditional Expectation Plot Toolbox

Related tags

Overview

⬛ PyCEbox

Quickstart

Development

Documentation

License

Comments

Typo in ice_plot() regarding _get_quantiles()

Using predicted probabilities for binary classification

Plot mistake

"frac_to_plot" parameter in ice_plot

Extended use to classification models, fixed typecast bug

Averaging ICE plots across multiple runs/folds of a model

Releases(0.0.1)

0.0.1(Jan 8, 2017)

Owner

Austin Rochford

Algorithms for monitoring and explaining machine learning models

TensorFlowTTS: Real-Time State-of-the-art Speech Synthesis for Tensorflow 2 (supported including English, Korean, Chinese, German and Easy to adapt for other languages)

A collection of research papers and software related to explainability in graph machine learning.

Visualizer for neural network, deep learning, and machine learning models

Delve is a Python package for analyzing the inference dynamics of your PyTorch model.

Visualizer for neural network, deep learning, and machine learning models

Lucid library adapted for PyTorch

pytorch implementation of "Distilling a Neural Network Into a Soft Decision Tree"

A collection of infrastructure and tools for research in neural network interpretability.

An intuitive library to add plotting functionality to scikit-learn objects.

A python library for decision tree visualization and model interpretation.

L2X - Code for replicating the experiments in the paper Learning to Explain: An Information-Theoretic Perspective on Model Interpretation.

Code for "High-Precision Model-Agnostic Explanations" paper

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