MLVisualizationTools 0.1.3

A set of functions and demos to make machine learning projects easier to understand through effective visualizations.

MLVisualizationTools

MLVisualizationTools is a python library to make machine learning more understandable through the use of effective visualizations.

It supports tensorflow, matplotlib, and plotly, with support for more ml libraries coming soon.

You can use the builtin apps to quickly anaylyze your existing models, or build custom projects using the modular sets of functions.

Installation

pip install MLVisualizationTools

Depending on your use case, tensorflow, plotly and matplotlib might need to be installed.

pip install tensorflow pip install plotly pip install matplotlib

To use interactive webapps, use the pip install MLVisualizationTools[dash] or pip install MLVisualizationTools[dash-notebook] flags on install.

Express

To get started using MLVisualizationTools, run one of the prebuilt apps.

import MLVisualizationTools.express.DashModelVisualizer as App

model = ... #your keras model
data = ... #your pandas dataframe with features

App.main(model, data)

Functions

MLVisualizationTools connects a variety of smaller functions.

Steps:

1. Keras Model and Dataframe with features
2. Analyzer
3. Interface / Interface Raw (if you don't have a dataframe)
4. Colorizers (optional)
5. Graphs

Analyzers take a keras model and return information about the inputs such as which ones have high variance.

Interfaces take parameters and construct a multidimensional grid of values based on plugging these numbers into the model.

(Raw interfaces allow you to use interfaces by specifying column data instead of a pandas dataframe. Column data is a list with a dict with name, min, max, and mean values for each feature column)

Colorizers mark points as being certain colors, typically above or below 0.5.

Graphs turn these output grids into a visual representation.

Examples

See MLVisualizationTools/Examples for more examples.

from MLVisualizationTools import Analytics, Interfaces, Graphs, Colorizers

#Displays plotly graphs with max variance inputs to model

model = ... #your model
df = ... #your dataframe
AR = Analytics.Tensorflow(model, df)
maxvar = AR.maxVariance()

grid = Interfaces.TensorflowGrid(model, maxvar[0].name, maxvar[1].name, df, ["Survived"])
grid = Colorizers.Binary(grid)
fig = Graphs.PlotlyGrid(grid, maxvar[0].name, maxvar[1].name)
fig.show()