train.py

#!/usr/bin/python3
# train.py
# Richardson Saint Bonheur



import platform; print(platform.platform())
import sys; print("Python", sys.version)
import numpy; print("Numpy", numpy.__version__)
import scipy; print ("Scipy", scipy.__version__)


import os

import numpy as np
from sklearn.discriminant_analysis import LinearDiscriminantAnalysis
from sklearn.neural_network import MLPClassifier
import pandas as pd
from joblib import dump
from sklearn import preprocessing 



def train():


    # Load Directory paths for persisting model

    MODEL_DIR = os.environ["MODEL_DIR"]
    MODEL_FILE_LDA = os.environ["MODEL_FILE_LDA"]
    MODEL_FILE_NN = os.environ["MODEL_FILE_NN"]
    MODEL_PATH_LDA = os.path.join(MODEL_DIR, MODEL_FILE_LDA)
    MODEL_PATH_NN = os.path.join(MODEL_DIR, MODEL_FILE_NN)


    # Load Read and Normalize training data

    training = "./train.csv"

    data_train = pd.read_csv(training)


    y_train = data_train['# Letter'].values
    X_train = data_train.drop(data_train.loc[:, 'Line':'# Letter'].columns, axis = 1)


    print("Shape of the training data")
    print(X_train.shape)
    print(y_train.shape)

    # Data normalization (0,1)
    X_train = preprocessing.normalize(X_train, norm='l2')
    
    # Models training
    
    # Linear Discrimant Analysis (Default parameters)
    clf_lda = LinearDiscriminantAnalysis()
    clf_lda.fit(X_train, y_train)


    # Save model
    from joblib import dump
    dump(clf_lda, MODEL_PATH_LDA)


    # Neural Networks multi-layer perceptron (MLP) algorithm
    clf_NN = MLPClassifier(solver='adam', activation='relu', alpha=0.0001, hidden_layer_sizes=(500,), random_state=0, max_iter=1000)
    clf_NN.fit(X_train, y_train)
       
    # Secord model
    from joblib import dump, load
    dump(clf_NN, MODEL_PATH_NN)
        
if __name__ == '__main__':
    train()