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############# This is not an Officially Supported Google Product! #########################
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#### Automatically Build Variant Interpretable Machine Learning Models (Auto_ViML) ######
#### Developed by Ramadurai Seshadri ######
###### Version 0.60 #########
##### Updated bug fix and downsampling improved. Aug 14,2019 #######
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#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. #######
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#### 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 "SHAP" library for SHAP values which provides more interpretability. #####
#### But if you don't have it, Auto_ViML will skip it and show you the regular feature importances. ###
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#### INPUTS: ###
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#### 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 3 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 #####
#### 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. #####
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#### OUTPUTS: #####
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#### 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! ##########################
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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 autovimlTo 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- Copy or download this entire directory of files to any local directory using git clone or any download methods.
In the same directory, open a Jupyter Notebook and use this line to import the .py file:
from autoviml.Auto_ViML import Auto_ViMLLoad 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', Boosting_Flag=False, Binning_Flag=False,
Add_Poly=False, Stacking_Flag=False,Imbalanced_Flag=False, verbose=0)
“This is not an official Google product”.
Licensed under the Apache License, Version 2.0 (the “License”).