Metadata-Version: 2.1
Name: MLVisualizationTools
Version: 0.1.6
Summary: A set of functions and demos to make machine learning projects easier to understand through effective visualizations.
Home-page: https://github.com/RobertJN64/MLVisualizationTools
Author: Robert Nies
Author-email: robertjnies@gamil.com
License: MIT
Platform: UNKNOWN
Classifier: Programming Language :: Python :: 3
Classifier: Programming Language :: Python :: 3 :: Only
Classifier: Programming Language :: Python :: 3.6
Classifier: Programming Language :: Python :: 3.7
Classifier: Programming Language :: Python :: 3.8
Classifier: Programming Language :: Python :: 3.9
Requires-Python: >=3.6
Description-Content-Type: text/markdown
Provides-Extra: dash
Provides-Extra: dash-notebook
Provides-Extra: kaggle-notebook
License-File: LICENSE

# 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.

If you are running on a kaggle notebook, you might need 
`pip install MLVisualizationTools[kaggle-notebook]`

## Express

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

```python
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](/src/MLVisualizationTools/examples) for more examples.

```python
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)
grid = Colorizers.Binary(grid)
fig = Graphs.PlotlyGrid(grid, maxvar[0].name, maxvar[1].name)
fig.show()
```

