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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1 +# -*- encoding: utf-8 -*-
2 +
3 +import os
4 +from time import time
5 +import argparse
6 +from sklearn.naive_bayes import MultinomialNB
7 +from sklearn.tree import DecisionTreeClassifier
8 +from sklearn.svm import SVC
9 +from sklearn.linear_model import Perceptron
10 +from sklearn.metrics import accuracy_score, precision_score, recall_score, f1_score, confusion_matrix, \
11 + classification_report
12 +
13 +# Author:
14 +# Carlos Méndez-Cruz
15 +
16 +# Goal: training and evaluation using Iris dataset
17 +
18 +# Arguments:
19 +# 1) --inputPath Path to read input files.
20 +# 2) --inputTrainingData File to read training data.
21 +# 3) --inputTrainingClasses File to read training true classes.
22 +# 4) --inputEvaluationData File to read evaluation data.
23 +# 5) --inputEvaluationClasses File to read evaluation true classes.
24 +# 6) --outputPath Path to place output files.
25 +# 7) --outputFile File to place evaluation report.
26 +# 8) --classifier Classifier: MultinomialNB, SVM, DecisionTree, Perceptron.
27 +
28 +# Ouput:
29 +# 1) Evaluation report.
30 +
31 +# Execution:
32 +# python training-evaluation-iris-v1.py
33 +# --inputPath /home/clasificacion/iris-dataset
34 +# --inputTrainingData training-ata.txt
35 +# --inputTrainingClasses training-Classes.txt
36 +# --inputEvaluationData evaluation-data.txt
37 +# --inputEvaluationClasses evaluation-classes.txt
38 +# --outputPath /home/classification/reports
39 +# --outputFile report-iris-svm.txt
40 +# --classifier SVM
41 +
42 +###########################################################
43 +# MAIN PROGRAM #
44 +###########################################################
45 +
46 +if __name__ == "__main__":
47 + parser = argparse.ArgumentParser(description='Training and testing Iris data set.')
48 + parser.add_argument("--inputPath", dest="inputPath",
49 + help="Path to read input files", metavar="PATH")
50 + parser.add_argument("--inputTrainingData", dest="inputTrainingData",
51 + help="File to read training data", metavar="FILE")
52 + parser.add_argument("--inputTrainingClasses", dest="inputTrainingClasses",
53 + help="File to read training true classes", metavar="FILE")
54 + parser.add_argument("--inputEvaluationData", dest="inputEvaluationData",
55 + help="File to read evaluation data", metavar="FILE")
56 + parser.add_argument("--inputEvaluationClasses", dest="inputEvaluationClasses",
57 + help="File to read evaluation true classes", metavar="FILE")
58 + parser.add_argument("--outputPath", dest="outputPath",
59 + help="Path to place output files", metavar="PATH")
60 + parser.add_argument("--outputFile", dest="outputFile",
61 + help="File to write evaluation report", metavar="FILE")
62 + parser.add_argument("--classifier", dest="classifier",
63 + help="Classifier", metavar="CLASSIFIER")
64 +
65 + args = parser.parse_args()
66 +
67 + # Printing parameter
68 + print('-------------------------------- PARAMETERS --------------------------------')
69 + print("Path to read input files: " + str(args.inputPath))
70 + print("File to read training data: " + str(args.inputTrainingData))
71 + print("File to read training true classes: " + str(args.inputTrainingClasses))
72 + print("File to read evaluation data: " + str(args.inputEvaluationData))
73 + print("File to read evaluation true classes: " + str(args.inputEvaluationClasses))
74 + print("Path to place output files: " + str(args.outputPath))
75 + print("File to write evaluation report: " + str(args.outputFile))
76 + print("Classifier: " + str(args.outputFile))
77 +
78 + # Start time
79 + t0 = time()
80 +
81 + print(" Reading training and evaluation data, and true classes...")
82 + trueTrainingClasses = []
83 + trueEvaluationClasses = []
84 + with open(os.path.join(args.inputPath, args.inputTrainingClasses), encoding='utf8', mode='r') \
85 + as classFile:
86 + for line in classFile:
87 + line = line.strip('\r\n')
88 + trueTrainingClasses.append(line)
89 +
90 + with open(os.path.join(args.inputPath, args.inputEvaluationClasses), encoding='utf8', mode='r') \
91 + as classFile:
92 + for line in classFile:
93 + line = line.strip('\r\n')
94 + trueEvaluationClasses.append(line)
95 + # print(trueEvaluationClasses)
96 +
97 + dataTraining = []
98 + dataEvaluation = []
99 + with open(os.path.join(args.inputPath, args.inputTrainingData), encoding='utf8', mode='r') \
100 + as dataFile:
101 + for line in dataFile:
102 + listTemp = []
103 + listFloat = []
104 + line = line.strip('\r\n')
105 + listTemp = line.split('\t')
106 + for elem in listTemp:
107 + listFloat.append(float(elem))
108 + dataTraining.append(listFloat)
109 + # print(dataTraining)
110 +
111 + with open(os.path.join(args.inputPath, args.inputEvaluationData), encoding='utf8', mode='r') \
112 + as dataFile:
113 + for line in dataFile:
114 + listTemp = []
115 + listFloat = []
116 + line = line.strip('\r\n')
117 + listTemp = line.split('\t')
118 + for elem in listTemp:
119 + listFloat.append(float(elem))
120 + dataEvaluation.append(listFloat)
121 + # print(dataEvaluation)
122 + print(" Reading data and true classes done!")
123 +
124 + if args.classifier == "MultinomialNB":
125 + classifier = MultinomialNB()
126 + elif args.classifier == "SVM":
127 + classifier = SVC()
128 + elif args.classifier == "DecisionTree":
129 + classifier = DecisionTreeClassifier()
130 + elif args.classifier == "Perceptron":
131 + classifier = Perceptron()
132 +
133 + print(" Training...")
134 + classifier.fit(dataTraining, trueTrainingClasses)
135 + print(" Prediction...")
136 + y_pred = classifier.predict(dataEvaluation)
137 + if args.classifier in ["Perceptron", "SVM"]:
138 + confidence_scores = classifier.decision_function(dataEvaluation)
139 + print(" Training and predition done!")
140 +
141 + print(" Saving evaluation report...")
142 + with open(os.path.join(args.outputPath, args.outputFile), mode='w', encoding='utf8') as oFile:
143 + oFile.write('********** EVALUATION REPORT **********\n')
144 + oFile.write('Classifier: {}\n'.format(args.classifier))
145 + oFile.write('Accuracy: {}\n'.format(accuracy_score(trueEvaluationClasses, y_pred)))
146 + oFile.write('Precision: {}\n'.format(precision_score(trueEvaluationClasses, y_pred, average='weighted')))
147 + oFile.write('Recall: {}\n'.format(recall_score(trueEvaluationClasses, y_pred, average='weighted')))
148 + oFile.write('F-score: {}\n'.format(f1_score(trueEvaluationClasses, y_pred, average='weighted')))
149 + oFile.write('Confusion matrix: \n')
150 + oFile.write(str(confusion_matrix(trueEvaluationClasses, y_pred)) + '\n')
151 + oFile.write('Classification report: \n')
152 + oFile.write(classification_report(trueEvaluationClasses, y_pred) + '\n')
153 + if args.classifier in ["Perceptron", "SVM"]:
154 + oFile.write('\nWeights assigned to the features: \n')
155 + oFile.write("{}".format(classifier.coef_))
156 + oFile.write('Confidence scores: \n')
157 + oFile.write("{}".format(confidence_scores))
158 + if args.classifier == "SVM":
159 + oFile.write('Number of support vectors per class: {}\n'.format(classifier.n_support_))
160 + oFile.write('Support vectors: {}\n'.format(classifier.support_vectors_))
161 +
162 + print(" Saving evaluation report done!")
163 +
164 + print("Training and evaluation done in: %fs" % (time() - t0))