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train.py
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train.py
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#!/usr/bin/python3
"""
Train Python base Predictions
"""
import os
import pandas as pd
from joblib import dump
from sklearn.discriminant_analysis import LinearDiscriminantAnalysis
from sklearn.neural_network import MLPClassifier
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)
# Serialize model
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)
# Serialize model
dump(clf_NN, MODEL_PATH_NN)
if __name__ == '__main__':
train()