A tool for visualizing the structure and performance of Random Forests
A tool for visualizing the structure and performance of Random Forests (and other ensemble methods based on decision trees).
Install and update RFVis via pip:
$ pip install rfvis
This will allow you interactively visualize a fitted Random Forest (RF) in your browser. To directly generate SVG files from your model you also need to install Node.js, see Command Line Interface for more information.
Command Line API
RFVis offers a command line tool to either generate SVG files directly from
your input data (
rfvis cli <data>) or to spin up a web-based GUI for a more
interactive analysis (
rfvis gui <data>).
To see all available commands run:
$ rfvis --help Usage: rfvis [OPTIONS] COMMAND [ARGS]... A tool for visualizing the structure and performance of Random Forests Options: --version Show the version and exit. --help Show this message and exit. Commands: cli Command line interface to generate SVGs. gui Web-based graphical user interface.
Graphical User Interface
To interactively analyze your forest with the web-based GUI run:
$ rfvis gui /path/to/data * Running on http://127.0.0.1:8080/ (Press CTRL+C to quit)
You can now open up your browser at http://localhost:8080 to see something like this:
Command Line Interface
$ rfvis cli /path/to/data >> Exported "/dev/random-forest-visualization/tree-0.svg" >> Exported "/dev/random-forest-visualization/tree-1.svg" >> Exported "/dev/random-forest-visualization/tree-2.svg" >> Exported "/dev/random-forest-visualization/tree-3.svg" ...
Get a full list of available options with
$ rfvis cli --help Usage: rfvis cli [OPTIONS] FOREST_JSON Web-based graphical user interface. As Python is unable to render React components, we make a subprocess call to a small Node.js application which will do the rendering and also store the created SVG files. This command requires that Node.js is installed on your system! FOREST_JSON: Path to the JSON file that contains the forest's data. Options: -o, --out PATH Output path of the SVG files. [default: (current working directory)] -w, --width INTEGER Width of the SVG. [default: 800] -h, --height INTEGER Height of the SVG. [default: 800] --trunk-length INTEGER Length of the trunk which influences the overall tree size. [default: 100] --display-depth INTEGER Maximum depth of the tree rendering. Cut of leaves are visualized as pie chart consolidation nodes. --branch-color [Impurity] Coloring of the branches. [default: Impurity] --leaf-color [Impurity|Best Class] Coloring of the leaves. [default: Impurity] --help Show this message and exit.
The data for the Command Line API must be available on your filesystem as a JSON file for the forest and additionally one CSV file per tree. Both data formats will be extended with properties in the future, this is just the minimal set.
You can find a working example under
forest.json holds all information about the ensemble model:
- name (string): Name of your forest, will be displayed in the GUI
- error (float): The error (e.g. the out-of-bag or validation error) of the entire ensemble model, will be displayed in the GUI
- n_samples (int): Number of samples the model was trained on
- correlationMatrix (float): Correlation between the single trees within
the model. Has dimensions
Nis the number of trees. This will be used to compute the forest map.
- classes: The output classes
- name (string): Name of the class
- color (int, int, int): RGB values in the range of 0-255 which determine the color of the class in the visualization
- trees: The trees in the forest
- error (float): The error (again could be either the out-of-bag or validation error) of the single tree
- data (string): Relative path to the CSV file containing the tree data
For each tree specified in the
forest.json RFVis expects a CSV file where one
entry represents one node in the tree. An entry has the following format:
- id (int): ID of the node
- depth (int) Depth of the node in the tree (starting at
- n_node_samples (int): Number of training samples reaching the node
- impurity (float): Impurity of the node (
- value (int): Class distribution within the node, i.e. every entry
represents the amount of samples within the node that respond to a specific
class. The index corresponds to the indices in
RFVis also offers a Python API which works directly on a scikit-learn RandomForestClassifier.
You can find a working example under
rfvis.gui() visualizes a fitted RandomForestClassifier in a web based graphical user interface.
The server runs in a separate process and is available at
gui(model, data=None, target=None, name=None, class_names=None, class_colors=None, port=8080)
- model (sklearn.ensemble.RandomForestClassifier): The model to visualize.
- data (array-like, shape=(n_samples, n_features)): The training input samples that were used to fit the model. Used to compute the out-of-bag error and correlation of the individual trees. If not provided, the forest view will have no significance.
- target (array-like, shape=n_samples): The target values (class labels) that were used to fit the model. Used to compute the out-of-bag error and correlation of the individual trees. If not provided, the forest view will have no significance.
- name (str): Optional name of the model which will be displayed in the frontend.
- class_names (List[str]): Optional list of names of the target classes
- class_colors (List[str]): Optional list of browser interpretable colors for the target classes. See https://developer.mozilla.org/en-US/docs/Web/CSS/color_value.
- port (int): Port on which the frontend will run on. Defaults to 8080.
- process (multiprocessing.Process): Subprocess that runs the server. Can be terminated with process.terminate().
The repository contains a
Pipfile for conveniently creating a virtualenv
for development. Just install pipenv
$ pipenv install
You can now e.g. start the server on the default port 8080 via:
$ pipenv run rfvis gui <path_to_forest_json>
Note that you need to build the frontend bundle first before you can
actually see the application working on
To build the frontend you need Node.js installed. First install all
dev-dependencies by running the following
from within the
$ npm install
Now you can build a production-ready bundle via:
$ npm run build
If you have the Python server running you should now be able to see the
For developing on the frontend more conveniently run:
$ npm start
To start a development server with hot reloading at
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