Object oriented approach for machine learning

Data

Data used in this project is airline_sentiment_analysis.csv containing 14000+ rows and 2 columns named test contaning test reviews and column airline sentiment contaning sentiment associated with each test respectively.

Setting Up

• python 3.x

Dependencies

• scikit-learn
• pandas
• numpy
• imblearn
• pyramid
• tensorflow (Optional)

To install the depenencies

$ pip install -r requirements.txt

Training

• The ML class in ml.py file present in src folder implements different options to get started with model traning.
• A GridSearchCV traning can be perfomed with all the listed models in ml.py by calling the train_gridsearch() method provided the data after intializing the Sentiment_Analysis class in pipeline.py

import pipeline

SA = pipeline.Sentiment_Analysis()
SA.train_gridsearch(df,sample_data=True,sampling_ratio=(.4,.7),n_splits=5,test_size=.2)

• A custom model of choice can be trained with the same data processing pipeline by calling the train_model method and passing the model as argument after initializing the Sentiment_Analysis class in pipeline.py

import pipeline

model = "Compiled model of your choice"
SA = pipeline.Sentiment_Analysis()
SA.train_model(df,model,sample_data=True,sampling_ratio=(.4,.7),test_size=.2)

• Both traning methods accepts sampling_data as boolean, whether to perform SMOTE and Under sampling of majority class can be provied as a tuple to sampling_ratio and a test split size

Quick train

$ cd src
$ python train.py

Inference

• Inference on models can be performed by calling the model_inference method in pipeline.py.
• When class Sentiment_Analysis initialized in inference mode providing targets are optional, targets can be provied incase there is requirement for scoring.

import pipeline

model = "pretrained model"
SA = Sentiment_Analysis(test=True)
SA.model_inference(model,df,targets=None)

$ cd src
$ python inference.py

Swagger UI

API endpoints built using pyramid web framework along with Swagger UI API documentation is implemented to to serve the pretained ML models

Run the pyramid app server

$ cd src
$ python app.py

• pyramid server becomes available at http://localhost:5000
• Swagger UI can be accessed at http://localhost:5000/docs/