https://github.com/abhimishra91/hackathon-framework

Framework that will be used for Data Hackathon
https://github.com/abhimishra91/hackathon-framework

data-science framework kaggle-competition

Last synced: 10 months ago
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Framework that will be used for Data Hackathon

• Host: GitHub
• URL: https://github.com/abhimishra91/hackathon-framework
• Owner: abhimishra91
• License: mit
• Created: 2020-05-27T02:41:13.000Z (almost 6 years ago)
• Default Branch: master
• Last Pushed: 2021-01-21T06:05:00.000Z (about 5 years ago)
• Last Synced: 2025-01-25T09:42:46.003Z (about 1 year ago)
• Topics: data-science, framework, kaggle-competition
• Language: Python
• Size: 61.5 KB
• Stars: 2
• Watchers: 2
• Forks: 1
• Open Issues: 0
• Metadata Files:
  • Readme: README.md
  • License: LICENSE

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README

          
# Hackathon Framework

Objective of this project to enable quick experimentation in Data Analytics projects with minimal cookie cutter programming.

Getting rid of all the fit_transforms.! 

---

***NOTE***

- This is a work in progress. Underlying modules are in process of development.

- As this project matures there will be changes in the scripts such as `train.py` and `predict.py`

- TODO

    * Create modules for `tuning`, `stacking`

    * Removal of some of the modules that are redundant

---

The framework is designed to make the Data Science flow easier to perform, by encapsulating different techniques for each step within 1 method.

There are classes for each of the below listed steps:

 - Feature Evaluation

    * Report to give an intution of the dataset

 

 - Feature Engineering

    * Modules to perform feature transformations on Categorical and Numerical Dataset.

    * Various applicable techniques are encoded within these modules and are accesed with an argument.

 

 - Fefature Generation

    * Module to create new features based on different techniques

   

 - Cross Validation

    * Stratified Folding both for Regression and Classification

 - Training

    * Run multiple models using 1 class.

    * Evaluating and Saving the results in an organized manner

 - Tuning

    * Hyper-parameter tuning of multiple models, based on json arguments for parameter values.

 

 - Prediction

 - Evaluating the model

  

  

## Steps to use the framework

  

1. Clone the repo.

2. Create 3 folders `input` and `model` and `tuneq`.

3. Save the training, testing and sample submission file in `input` folder. 

4. The outputs generated from training such as trained model, encoders and oof_preds will be saved in `model` folder.

5. The parameters for fine tuning the models should be saved in the `tune` folder.

6. Update the `config.py` to point it to the correct path for data, model and tuning.

7. Update the `dispatcher.py` with model/models you want to run your dataset on.

8. Use the sample notebook to understand how to use this framework after this intial configuration is completed.

## Description of Files and their Purpose

- `config.py`: Config file to give path of all the datasets and other standard configuration items. Such as csv files path, random seed etc.

 

- `feature_eval.py`: This script and the class inside is used to analyze the dataframe and its columns to get the following output:

	 - min, max and unique values of each column

	 - histogram/ distribution of each column

	 - corelation of columns using a heat map

	 

- `feature_gen.py`: Encapsulates method to generate new features. Currently implemented the `Polynomial features` method from sklearn.

    Returns Dataframe with new features. 

- `feature_impute.py`: Encapsulates the method to impute blank values in a dataframe.

    Currently, it supports 3 imputation methods:

    - Simple Imputer

    - Model Based Imputer: Extra Trees or knn

    - Knn based imputer

    - Returns updated Dataframe

- `cross_validation.py`: This class is used to perform cross validation on any dataframe based on the type of problem statement. It is used to create cross validated dataset.

- `categorical.py`: This class can be used for encoding of categorical features in a given dataframe.

	- Inputs : Dataframe, Categorical Columns List, Type of Encoding

	- Output: Encoded Dataframe

	- Supported Encoding Techniques:

	    - Lable Encoding

	    - Binary Encoding

	    - One Hot Encoding

- `numerical.py`: This class can be used for encoding of numerical features in a given dataframe.

	- Inputs : Dataframe, Categorical Columns List, Type of Encoding

	- Output: Encoded Dataframe, Transformer Object for later use. 

	- Support Techniques:

	    - Standard Scaler

	    - Min-Max Scaler

	    - Power Tranformer

	    - Log Transformer

	

- `metrics.py`: This class can be used to evaluate the results of given predictions and actual value. 

- `dispatcher.py`: Python File with Models and parameters. They have been designed to supply the models to `engine.py` for training on a given dataset

- `engine.py`: This script encapsulates the method to train and evaluate the multiple models simultaneously

    - Leverages on `dispatcher.py` and `metrics.py` for model and metrics

    - The results for each fold are also saved in the `models` folder as `oof_predictions.csv` for each model.

    - **To Do** Stacking module to suporrt stacking of multiple models

- **Scripts to be ignored for now**:

    - `train.py`: For training

    - `predict.py`: For prediction

    - `tune.py`: For tuning h-parameter

    - `create_folds.py`: To create folded datframe