MLVisualizationTools 0.7.4

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.

We support graphing with matplotlib and plotly. We implicity support all major ML libraries, such as tensorflow and sklearn.

You can use the built in 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.

If you are running on a notebook that doesn't have dash support (like kaggle), you might need pip install MLVisualizationTools[ngrok-tunneling]

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.visualize(model, data)

Functions

MLVisualizationTools connects a variety of smaller functions.

Steps:

1. Start with a ML Model and Dataframe with features
2. Analyzer
3. Interface / Interface Raw (if you don't have a dataframe)
4. Colorizers (optional)
5. Apply Training Data Points (Optional)
6. Colorize data points (Optional)
7. Graphs

Analyzers take a ml 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.

Data Interfaces render training data points on top of the graph to make it easier to tell if the model trained properly.

Graphs turn these output grids into a visual representation.

Sample

from MLVisualizationTools import Analytics, Interfaces, Graphs, Colorizers, DataInterfaces

#Displays plotly graphs with max variance inputs to model

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

grid = Interfaces.predictionGrid(model, maxvar[0], maxvar[1], df)
grid = Colorizers.binary(grid)
grid = DataInterfaces.addPercentageData(grid, df, str('OutputKey'))
fig = Graphs.plotlyGraph(grid)
fig.show()

Prebuilt Examples

Prebuilt examples run off of the pretrained model and dataset packaged with this library. They include:

• Demo: a basic demo of library functionality that renders 2 plots
• MatplotlibDemo: Demo but with matplotlib instead of plotly
• DashDemo: Non-jupyter notebook version of an interactive dash website demo
• DashNotebookDemo: Notebook version of an interactive website demo
• DashKaggleDemo: Notebook version of an dash demo that works in kaggle notebooks
• DataOverlayDemo: Demonstrates data overlay features

See MLVisualizationTools/Examples for more examples. Use example.main() to run the examples and set parameters such as themes.

Tensorflow Compatibility

MLVisualizationTools is distributed with a pretrained tensorflow model to make running examples quick and easy. It is not needed for main library functions.

For version 2.0 through 2.4, we load a v2.0 model. For version 2.5+ we load a v2.5 model.

If this causes compatibility issues you can still use the main library on your models. If you need an example model, retrain it with TrainTitanicModel.py

scikit-learn Compatibility

See SklearnDemo.py

Sklearn can be used exactly like TF because it has the same .predict(X) -> Y interface.

Support for more ML Libraries

We support any ML library that has a predict() call that takes a pd Dataframe with features. If this doesn't work, use a wrapper class like in this example:

import pandas as pd

class ModelWrapper:
    def __init(self, model):
        self.model = model

    def predict(self, dataframe: pd.DataFrame):
        ... #Do whatever code you need here

Remove Feature Testing

See RemoveFeatureDemo.py

Tests if features can be removed from dataset without significantly affecting accuracy. Replaces each dataset column with mean and compares to baseline accuracy.