Carlos-Francisco Méndez-Cruz / laigen-supervised-learning

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Carlos-Francisco Méndez-Cruz
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Classification Iris data set

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# -*- encoding: utf-8 -*-
import os
from time import time
import argparse
from sklearn.naive_bayes import MultinomialNB
from sklearn.tree import DecisionTreeClassifier
from sklearn.svm import SVC
from sklearn.linear_model import Perceptron
from sklearn.metrics import accuracy_score, precision_score, recall_score, f1_score, confusion_matrix, \
classification_report
# Author:
# Carlos Méndez-Cruz
# Goal: training and evaluation using Iris dataset
# Arguments:
# 1) --inputPath Path to read input files.
# 2) --inputTrainingData File to read training data.
# 3) --inputTrainingClasses File to read training true classes.
# 4) --inputEvaluationData File to read evaluation data.
# 5) --inputEvaluationClasses File to read evaluation true classes.
# 6) --outputPath Path to place output files.
# 7) --outputFile File to place evaluation report.
# 8) --classifier Classifier: MultinomialNB, SVM, DecisionTree, Perceptron.
# Ouput:
# 1) Evaluation report.
# Execution:
# python training-evaluation-iris-v1.py
# --inputPath /home/clasificacion/iris-dataset
# --inputTrainingData training-ata.txt
# --inputTrainingClasses training-Classes.txt
# --inputEvaluationData evaluation-data.txt
# --inputEvaluationClasses evaluation-classes.txt
# --outputPath /home/classification/reports
# --outputFile report-iris-svm.txt
# --classifier SVM
###########################################################
# MAIN PROGRAM #
###########################################################
if __name__ == "__main__":
parser = argparse.ArgumentParser(description='Training and testing Iris data set.')
parser.add_argument("--inputPath", dest="inputPath",
help="Path to read input files", metavar="PATH")
parser.add_argument("--inputTrainingData", dest="inputTrainingData",
help="File to read training data", metavar="FILE")
parser.add_argument("--inputTrainingClasses", dest="inputTrainingClasses",
help="File to read training true classes", metavar="FILE")
parser.add_argument("--inputEvaluationData", dest="inputEvaluationData",
help="File to read evaluation data", metavar="FILE")
parser.add_argument("--inputEvaluationClasses", dest="inputEvaluationClasses",
help="File to read evaluation true classes", metavar="FILE")
parser.add_argument("--outputPath", dest="outputPath",
help="Path to place output files", metavar="PATH")
parser.add_argument("--outputFile", dest="outputFile",
help="File to write evaluation report", metavar="FILE")
parser.add_argument("--classifier", dest="classifier",
help="Classifier", metavar="CLASSIFIER")
args = parser.parse_args()
# Printing parameter
print('-------------------------------- PARAMETERS --------------------------------')
print("Path to read input files: " + str(args.inputPath))
print("File to read training data: " + str(args.inputTrainingData))
print("File to read training true classes: " + str(args.inputTrainingClasses))
print("File to read evaluation data: " + str(args.inputEvaluationData))
print("File to read evaluation true classes: " + str(args.inputEvaluationClasses))
print("Path to place output files: " + str(args.outputPath))
print("File to write evaluation report: " + str(args.outputFile))
print("Classifier: " + str(args.outputFile))
# Start time
t0 = time()
print(" Reading training and evaluation data, and true classes...")
trueTrainingClasses = []
trueEvaluationClasses = []
with open(os.path.join(args.inputPath, args.inputTrainingClasses), encoding='utf8', mode='r') \
as classFile:
for line in classFile:
line = line.strip('\r\n')
trueTrainingClasses.append(line)
with open(os.path.join(args.inputPath, args.inputEvaluationClasses), encoding='utf8', mode='r') \
as classFile:
for line in classFile:
line = line.strip('\r\n')
trueEvaluationClasses.append(line)
# print(trueEvaluationClasses)
dataTraining = []
dataEvaluation = []
with open(os.path.join(args.inputPath, args.inputTrainingData), encoding='utf8', mode='r') \
as dataFile:
for line in dataFile:
listTemp = []
listFloat = []
line = line.strip('\r\n')
listTemp = line.split('\t')
for elem in listTemp:
listFloat.append(float(elem))
dataTraining.append(listFloat)
# print(dataTraining)
with open(os.path.join(args.inputPath, args.inputEvaluationData), encoding='utf8', mode='r') \
as dataFile:
for line in dataFile:
listTemp = []
listFloat = []
line = line.strip('\r\n')
listTemp = line.split('\t')
for elem in listTemp:
listFloat.append(float(elem))
dataEvaluation.append(listFloat)
# print(dataEvaluation)
print(" Reading data and true classes done!")
if args.classifier == "MultinomialNB":
classifier = MultinomialNB()
elif args.classifier == "SVM":
classifier = SVC()
elif args.classifier == "DecisionTree":
classifier = DecisionTreeClassifier()
elif args.classifier == "Perceptron":
classifier = Perceptron()
print(" Training...")
classifier.fit(dataTraining, trueTrainingClasses)
print(" Prediction...")
y_pred = classifier.predict(dataEvaluation)
if args.classifier in ["Perceptron", "SVM"]:
confidence_scores = classifier.decision_function(dataEvaluation)
print(" Training and predition done!")
print(" Saving evaluation report...")
with open(os.path.join(args.outputPath, args.outputFile), mode='w', encoding='utf8') as oFile:
oFile.write('********** EVALUATION REPORT **********\n')
oFile.write('Classifier: {}\n'.format(args.classifier))
oFile.write('Accuracy: {}\n'.format(accuracy_score(trueEvaluationClasses, y_pred)))
oFile.write('Precision: {}\n'.format(precision_score(trueEvaluationClasses, y_pred, average='weighted')))
oFile.write('Recall: {}\n'.format(recall_score(trueEvaluationClasses, y_pred, average='weighted')))
oFile.write('F-score: {}\n'.format(f1_score(trueEvaluationClasses, y_pred, average='weighted')))
oFile.write('Confusion matrix: \n')
oFile.write(str(confusion_matrix(trueEvaluationClasses, y_pred)) + '\n')
oFile.write('Classification report: \n')
oFile.write(classification_report(trueEvaluationClasses, y_pred) + '\n')
if args.classifier in ["Perceptron", "SVM"]:
oFile.write('\nWeights assigned to the features: \n')
oFile.write("{}".format(classifier.coef_))
oFile.write('Confidence scores: \n')
oFile.write("{}".format(confidence_scores))
if args.classifier == "SVM":
oFile.write('Number of support vectors per class: {}\n'.format(classifier.n_support_))
oFile.write('Support vectors: {}\n'.format(classifier.support_vectors_))
print(" Saving evaluation report done!")
print("Training and evaluation done in: %fs" % (time() - t0))