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erge branch 'main' of https://gitlab.constantvzw.org/anais_berck/random_forest

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Guillaume Slizewicz 2 years ago
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*.pyc
commenting_code_model/visualizer/random_forest_model_*
commenting_code_model/random_forest_model_*
__pycache__

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README.md
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# Random_forest
Exploring random forest algorithm during residency Meise
Exploring random forest algorithm during residency Meise.
## Getting started
In this repository experiments with the random forest algorithm: a commented implementation to develop an understanding of its workings and a visualizer of the generated models.
To make it easy for you to get started with GitLab, here's a list of recommended next steps.
The implementation was taken from [Machine Learning Mastery](https://machinelearningmastery.com/implement-random-forest-scratch-python/)
Already a pro? Just edit this README.md and make it your own. Want to make it easy? [Use the template at the bottom](#editing-this-readme)!
## Add your files
## Files
- [ ] [Create](https://docs.gitlab.com/ee/user/project/repository/web_editor.html#create-a-file) or [upload](https://docs.gitlab.com/ee/user/project/repository/web_editor.html#upload-a-file) files
- [ ] [Add files using the command line](https://docs.gitlab.com/ee/gitlab-basics/add-file.html#add-a-file-using-the-command-line) or push an existing Git repository with the following command:
The repository has an annotated version of the algorithm, a dataset with trainingdata and a visualizer.
```
cd existing_repo
git remote add origin https://gitlab.constantvzw.org/anais_berck/random_forest.git
git branch -M main
git push -uf origin main
```
## Integrate with your tools
- [ ] [Set up project integrations](https://gitlab.constantvzw.org/anais_berck/random_forest/-/settings/integrations)
## Collaborate with your team
- [ ] [Invite team members and collaborators](https://docs.gitlab.com/ee/user/project/members/)
- [ ] [Create a new merge request](https://docs.gitlab.com/ee/user/project/merge_requests/creating_merge_requests.html)
- [ ] [Automatically close issues from merge requests](https://docs.gitlab.com/ee/user/project/issues/managing_issues.html#closing-issues-automatically)
- [ ] [Enable merge request approvals](https://docs.gitlab.com/ee/user/project/merge_requests/approvals/)
- [ ] [Automatically merge when pipeline succeeds](https://docs.gitlab.com/ee/user/project/merge_requests/merge_when_pipeline_succeeds.html)
## Test and Deploy
Use the built-in continuous integration in GitLab.
- [ ] [Get started with GitLab CI/CD](https://docs.gitlab.com/ee/ci/quick_start/index.html)
- [ ] [Analyze your code for known vulnerabilities with Static Application Security Testing(SAST)](https://docs.gitlab.com/ee/user/application_security/sast/)
- [ ] [Deploy to Kubernetes, Amazon EC2, or Amazon ECS using Auto Deploy](https://docs.gitlab.com/ee/topics/autodevops/requirements.html)
- [ ] [Use pull-based deployments for improved Kubernetes management](https://docs.gitlab.com/ee/user/clusters/agent/)
- [ ] [Set up protected environments](https://docs.gitlab.com/ee/ci/environments/protected_environments.html)
*commenting_code_model/random_forest_model_altered.py* an implementation of the Random Forest model. Commented during [Anaïs Bercks' residency in Meise](https://algoliterarypublishing.net/activities.html), and slighty altered to store the generated model into a json file.
***
*commenting_code_model/iris_data.csv* data set on Iris petals
# Editing this README
*commenting_code_model/visualizer/visualizer.py* the script visualizing a model generated by the *random_forest_model_altered.py* script.
When you're ready to make this README your own, just edit this file and use the handy template below (or feel free to structure it however you want - this is just a starting point!). Thank you to [makeareadme.com](https://www.makeareadme.com/) for this template.
## Running
## Suggestions for a good README
Every project is different, so consider which of these sections apply to yours. The sections used in the template are suggestions for most open source projects. Also keep in mind that while a README can be too long and detailed, too long is better than too short. If you think your README is too long, consider utilizing another form of documentation rather than cutting out information.
Run the script *commenting_code_model/random_forest_model_altered.py* to generate random forests. The generated models will be json files in the same directory as the model.
## Name
Choose a self-explaining name for your project.
Optionally run the *commenting_code_model/visualizer/visualizer.py* to generate visualizations. Those visualizations will the placed in the visualizer folder.
## Description
Let people know what your project can do specifically. Provide context and add a link to any reference visitors might be unfamiliar with. A list of Features or a Background subsection can also be added here. If there are alternatives to your project, this is a good place to list differentiating factors.
## Requirements
## Badges
On some READMEs, you may see small images that convey metadata, such as whether or not all the tests are passing for the project. You can use Shields to add some to your README. Many services also have instructions for adding a badge.
The implementations are written in python and require python 3, find information on how to [install python here](https://www.python.org/downloads/)
## Visuals
Depending on what you are making, it can be a good idea to include screenshots or even a video (you'll frequently see GIFs rather than actual videos). Tools like ttygif can help, but check out Asciinema for a more sophisticated method.
The visualizer uses graphviz to generate the visualization. Find information on how to [install graphviz here](https://graphviz.org/download/)
## Installation
Within a particular ecosystem, there may be a common way of installing things, such as using Yarn, NuGet, or Homebrew. However, consider the possibility that whoever is reading your README is a novice and would like more guidance. Listing specific steps helps remove ambiguity and gets people to using your project as quickly as possible. If it only runs in a specific context like a particular programming language version or operating system or has dependencies that have to be installed manually, also add a Requirements subsection.
The visualizer also requires the [graphviz python bindings](https://pypi.org/project/graphviz). The easiest way to install them is through pip:
## Usage
Use examples liberally, and show the expected output if you can. It's helpful to have inline the smallest example of usage that you can demonstrate, while providing links to more sophisticated examples if they are too long to reasonably include in the README.
## Support
Tell people where they can go to for help. It can be any combination of an issue tracker, a chat room, an email address, etc.
## Roadmap
If you have ideas for releases in the future, it is a good idea to list them in the README.
## Contributing
State if you are open to contributions and what your requirements are for accepting them.
For people who want to make changes to your project, it's helpful to have some documentation on how to get started. Perhaps there is a script that they should run or some environment variables that they need to set. Make these steps explicit. These instructions could also be useful to your future self.
You can also document commands to lint the code or run tests. These steps help to ensure high code quality and reduce the likelihood that the changes inadvertently break something. Having instructions for running tests is especially helpful if it requires external setup, such as starting a Selenium server for testing in a browser.
## Authors and acknowledgment
Show your appreciation to those who have contributed to the project.
## License
For open source projects, say how it is licensed.
## Project status
If you have run out of energy or time for your project, put a note at the top of the README saying that development has slowed down or stopped completely. Someone may choose to fork your project or volunteer to step in as a maintainer or owner, allowing your project to keep going. You can also make an explicit request for maintainers.
```
pip3 install graphviz
```

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commenting_code_model/random_forest_model_altered.py
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@ -7,7 +7,12 @@ from random import randrange
from csv import reader
from math import sqrt
import json
import os.path
# Get the directory of the current script to use in importing data
# and exporting the model.
basepath = os.path.dirname(os.path.realpath(__file__))
# Load a CSV file. Definition of the function to read the csv and create dataset here
def load_csv(filename):
dataset = list()
@ -290,16 +295,17 @@ def bagging_predict(trees, row):
return max(set(predictions), key=predictions.count)
# Random Forest Algorithm
def random_forest(train, test, max_depth, min_size, sample_size, n_trees, n_features):
def random_forest(train, test, max_depth, min_size, sample_size, n_trees, n_features, model_path=None):
trees = list()
for i in range(n_trees):
for _ in range(n_trees):
sample = subsample(train, sample_size)
# building a tree / root is dictionary with index, value, left/right)
tree = build_tree(sample, max_depth, min_size, n_features)
trees.append(tree)
with open('random_forest_model.json', 'w') as outfile:
json.dump(trees, outfile, indent = 6)
if model_path:
with open(model_path, 'w') as outfile:
json.dump(trees, outfile, indent = 6)
# prediction using one of the folds we separated in the beginning, forest votes on every row of test data
predictions = [bagging_predict(trees, row) for row in test]
# returns votes/predictions of the forest
@ -311,7 +317,7 @@ def random_forest(train, test, max_depth, min_size, sample_size, n_trees, n_feat
seed(2)
# load and prepare data
# filename = 'sonar_csv.csv'
filename = 'iris_data.csv'
filename = os.path.join(basepath, 'iris_data.csv')
dataset = load_csv(filename)
#print(dataset)
# convert string attributes to integers
@ -331,8 +337,10 @@ sample_size = 1.0
# it specifies the size of the subset of features for the folds, where the size is close to the square root of the total number of features
n_features = int(sqrt(len(dataset[0])-1))
# it tries forest of 1 tree, 5 trees, 10 trees
for n_trees in [1, 5, 10]:
scores = evaluate_algorithm(dataset, random_forest, n_folds, max_depth, min_size, sample_size, n_trees, n_features)
model_path = os.path.join(basepath, 'random_forest_model_{}-trees.json'.format(n_trees))
scores = evaluate_algorithm(dataset, random_forest, n_folds, max_depth, min_size, sample_size, n_trees, n_features, model_path)
print('Trees: %d' % n_trees)
print('Scores: %s' % scores)
print('Mean Accuracy: %.3f%%' % (sum(scores)/float(len(scores))))
@ -344,8 +352,8 @@ for n_trees in [1, 5, 10]:
#### pickle trees
#### use: unpickle trees + bagging_predict with new data
with open('random_forest_model.json', 'r') as infile:
trees = json.load(infile)
prediction = bagging_predict(trees, dataset[23])
# this gives a number, you have to reorganise model to get back the string of the class
print(prediction)
# with open('random_forest_model.json', 'r') as infile:
# trees = json.load(infile)
# prediction = bagging_predict(trees, dataset[23])
# # this gives a number, you have to reorganise model to get back the string of the class
# print(prediction)

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commenting_code_model/visualizer/requirements.txt
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graphviz

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commenting_code_model/visualizer/visualizer.py
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@ -59,16 +59,36 @@ def make_graph (graphname):
graph.attr('graph', splines='line', rankdir='BT')
return graph
def visualize (tree, graphname, generate_node_name = make_name_generator(length=3)):
def visualize (tree, graphname, generate_node_name = make_name_generator(length=3), directory=None):
graph = make_graph(graphname)
visualize_node(graph, generate_node_name, tree)
graph.render(graphname)
graph.render(graphname, directory=directory)
if __name__ == '__main__':
import json
import os.path
import glob
basepath = os.path.dirname(os.path.realpath(__file__))
globpath = os.path.realpath(os.path.join(basepath, '..', 'random_forest_model_*-trees.json'))
models = glob.glob(globpath)
# Search for exported models
for modelpath in models:
print("Found {}".format(modelpath))
with open('../random_forest_model.json', 'r') as file_in:
forest = json.load(file_in)
# Open model
with open(modelpath, 'r') as file_in:
# Parse the forest
forest = json.load(file_in)
modelname, _ = os.path.splitext(os.path.basename(modelpath))
graphnamepattern = '{}_tree_{{}}'.format(modelname)
for idx, tree in enumerate(forest):
visualize(tree, 'random-tree-{}'.format(idx))
# Walk through the forest and visualize the trees
for idx, tree in enumerate(forest):
graphname = graphnamepattern.format(idx, len(forest))
print('Visualizing tree {} of {}'.format(idx, len(forest)))
visualize(tree, graphname, directory=basepath)
print()
print('Graphs placed in: {}'.format(basepath))
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