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Automatically Build Variant Interpretable ML models fast - now with CatBoost!

Project description

Auto_ViML

Automatically Build Variant Interpretable ML models fast!

#########################################################################################################
#############       This is not an Officially Supported Google Product!         #########################
#########################################################################################################
####       Automatically Build Variant Interpretable Machine Learning Models (Auto_ViML)           ######
####                                Developed by Ramadurai Seshadri                                ######
######                               Version 0.1.472                                               ######
#####   MOST STABLE VERSION WITH CATBOOST, UPGRADES AND BUG FIXES. WORTH UPGRADING!                ######
#####   PLEASE upgrade: pip3 install --upgrade --ignore-installed  --no-deps autoviml             #######
#####   Upgraded with CatBoost and many plots to aid interpretability.         Jan 2,2020       #########
#########################################################################################################
#Copyright 2019 Google LLC                                                                        #######
#                                                                                                 #######
#Licensed under the Apache License, Version 2.0 (the "License");                                  #######
#you may not use this file except in compliance with the License.                                 #######
#You may obtain a copy of the License at                                                          #######
#                                                                                                 #######
#    https://www.apache.org/licenses/LICENSE-2.0                                                  #######
#                                                                                                 #######
#Unless required by applicable law or agreed to in writing, software                              #######
#distributed under the License is distributed on an "AS IS" BASIS,                                #######
#WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.                         #######
#See the License for the specific language governing permissions and                              #######
#limitations under the License.                                                                   #######
#########################################################################################################
####   Auto_ViML was designed for building a High Performance Interpretable Model With Fewest Vars.   ###
####   The "V" in Auto_ViML stands for Variant because it tries Multiple Models and Multiple Features ###
####   to find the Best Performing Model for any data set.The "i" in Auto_ViML stands " Interpretable"###
####   since it selects the fewest Features to build a simpler, more interpretable model. This is key. ##
####   Auto_ViML is built mostly using Scikit-Learn, Numpy, Pandas and Matplotlib. Hence it should run ##
####   on any Python 2 or Python 3 Anaconda installations. You won't have to import any special      ####
####   Libraries other than "CatBoost" and "SHAP" library for SHAP values for interpretability.     #####
####   But if you don't have it, Auto_ViML will skip it and show you the regular feature importances. ###
#########################################################################################################
####   INPUTS:                                                                                        ###
#########################################################################################################
####   train: could be a datapath+filename or a dataframe. It will detect which is which and load it.####
####   test: could be a datapath+filename or a dataframe. If you don't have any, just leave it as "". ###  
####   submission: must be a datapath+filename. If you don't have any, just leave it as empty string.#### 
####   target: name of the target variable in the data set.                                          ####
####   sep: if you have a spearator in the file such as "," or "\t" mention it here. Default is ",". ####
####   scoring_parameter: if you want your own scoring parameter such as "f1" give it here. If not, #####
####       it will assume the appropriate scoring param for the problem and it will build the model.#####
####   hyper_param: Tuning options are GridSearch ('GS') and RandomizedSearch ('RS'). Default is 'GS'.###
####   feature_reduction: Default = 'True' but it can be set to False if you don't want automatic    ####
####         feature_reduction since in Image data sets like digits and MNIST, you get better       #####
####         results when you don't reduce features automatically. You can always try both and see. #####
####   KMeans_Featurizer = True: Adds a cluster label to features based on KMeans. Use for Linear.  #####
####         False (default) = For Random Forests or XGB models, leave it False since it may overfit.####
####   Boosting Flag: you have 4 possible choices (default is False):                               #####
####    None = This will build a Linear Model                                                       #####
####    False = This will build a Random Forest or Extra Trees model (also known as Bagging)        #####
####    True = This will build an XGBoost model                                                     #####
####    CatBoost = THis will build a CatBoost model (provided you have CatBoost installed)          #####
####   Add_Poly: Default is 0. It has 2 additional settings:                                        #####
####    1 = Add interaction variables only such as x1*x2, x2*x3,...x9*10 etc.                       ##### 
####    2 = Add Interactions and Squared variables such as x1**2, x2**2, etc.                       #####
####   Stacking_Flag: Default is False. If set to True, it will add an additional feature which     #####
####         is derived from predictions of another model. This is used in some cases but may result#####
####         in overfitting. So be careful turning this flag "on".                                  #####
####   Binning_Flag: Default is False. It set to True, it will convert the top numeric variables    #####
####         into binned variables through a technique known as "Entropy" binning. This is very     #####
####         helpful for certain datasets (especially hard to build models).                        #####
####   Imbalanced_Flag: Default is False. If set to True, it will downsample the "Majority Class"   #####
####         in an imbalanced dataset and make the "Rare" class at least 5% of the data set. This   #####
####         the ideal threshold in my mind to make a model learn. Do it for Highly Imbalanced data.#####
####   verbose: This has 3 possible states:                                                         #####
####    0 = limited output. Great for running this silently and getting fast results.               #####
####    1 = more charts. Great for knowing how results were and making changes to flags in input.   #####
####    2 = lots of charts and output. Great for reproducing what Auto_ViML does on your own.       #####
#########################################################################################################
####   OUTPUTS:                                                                                     #####
#########################################################################################################
####   model: It will return your trained model                                                     #####
####   features: the fewest number of features in your model to make it perform well                #####
####   train_modified: this is the modified train dataframe after removing and adding features      #####
####   test_modified: this is the modified test dataframe with the same transformations as train    #####
#################               A D D I T I O N A L    N O T E S                              ###########
####   Finally, it writes your submission file to disk in the current directory called "mysubmission.csv"
####   This submission file is ready for you to show it clients or submit it to competitions.       #####     
####   If no submission file was given but as long as you give it a test file name, it will create  #####
####   a submission file for you named "mySubmission.csv".                                          #####
####   Auto_ViML works on any Multi-Class, Multi-Label Data Set. So you can have many target labels #####  
####   You don't have to tell Auto_ViML whether it is a Regression or Classification problem.       #####
####   Suggestions for a Scoring Metric:                                                            #####
####   If you have Binary Class and Multi-Class in a Single Label, Choose Accuracy. It will        ######
####   do very well. If you want something better, try roc_auc even for Multi-Class which works.   ######
####   You can try F1 or Weighted F1 if you want something complex or for Multi-Class.             ######
####   Note that For Imbalanced Classes (<=5% classes), it automatically adds Class Weights.       ######
####   Also, Note that it handles Multi-Label automatically so you can send Train data             ######
####   with multiple Labels (Targets) and it will automatically predict for each Label.            ######
####   Finally this is Meant to Be a Fast Algorithm, so use it for just quick POCs                 ######
####   This is Not Meant for Production Problems. It produces great models but it is not Perfect!  ######
######################### HELP OTHERS! PLEASE CONTRIBUTE! OPEN A PULL REQUEST! ##########################
#########################################################################################################

Prerequsites:

To clone the Auto_ViML, it is better to create a new environment, and install required dependencies:

To install from PyPi:

conda create -n <your_env_name> python=3.7 anaconda
conda activate <your_env_name> # ON WINDOWS: `source activate <your_env_name>` 
pip install autoviml

To install from source:

cd <AutoVIML_Destination>
git clone git@github.com:AutoViML/Auto_ViML.git 
# or download and unzip https://github.com/AutoViML/Auto_ViML/archive/master.zip
conda create -n <your_env_name> python=3.7 anaconda
conda activate <your_env_name> # ON WINDOWS: `source activate <your_env_name>` 
cd Auto_ViML
pip install -r requirements.txt

DOWNLOAD / INSTALLATION

  1. Copy or download this entire directory of files to any local directory using git clone or any download methods.

RUN AUTO_ViML

  1. In the same directory, open a Jupyter Notebook and use this line to import the .py file:
    from autoviml.Auto_ViML import Auto_ViML

  2. Load a data set (any CSV or text file) into a Pandas dataframe and split it into Train and Test dataframes. If you don't have a test dataframe, you can simple assign the test variable below to '' (empty string):

Finally, call Auto_ViML using the train, test dataframes and the name of the target variable in data frame. That's all.

model, features, trainm, testm = Auto_ViML(train, target, test, sample_submission, hyper_param='GS', feature_reduction=True, scoring_parameter='weighted-f1', KMeans_Featurizer=False, Boosting_Flag=False, Binning_Flag=False, Add_Poly=False, Stacking_Flag=False,Imbalanced_Flag=False, verbose=0)

Auto_ViML (pronounced as "auto vimal") Read this Medium article to learn how to use Auto_ViML.

DISCLAIMER

“This is not an official Google product”.

LICENSE

Licensed under the Apache License, Version 2.0 (the “License”).

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