def setIndex(self, cb, index):
if (cb.currentText() == "Charger mon fichier ..."):
fileName, _ = QFileDialog.getOpenFileName(
self, "Ouvrir un fichier", "../Classifier/TimeSeriesFiles",
"Text files (*.txt)")
if fileName:
QApplication.setOverrideCursor(Qt.WaitCursor)
App.fileNameTS = fileName
App.X_train, _ = importTS.fileImportTS(App.fileNameTS)
index.setMaximum(len(App.X_train))
QApplication.restoreOverrideCursor()
index.setEnabled(True)
else:
QApplication.setOverrideCursor(Qt.WaitCursor)
App.X_train, _, _, _ = importTS.dataImport(cb.currentText())
index.setMaximum(len(App.X_train))
QApplication.restoreOverrideCursor()
index.setEnabled(True)
def saveClassifier(self):
fileName, _ = QFileDialog.getSaveFileName(
self, "Sauvegarder un fichier",
"../Classifier/SaveClassifierFiles", "Saved classifiers (*.sav)")
if fileName:
QApplication.setOverrideCursor(Qt.WaitCursor)
if self.cb_Classifier_SelectTS.currentText(
) == "Charger mon fichier ...":
X_train, Y_train = importTS.fileImportTS(App.fileNameTS)
if self.cb_Classifier.currentText() == "Learning Shapelet":
classifier = LearningClassifier.learningShapeletClassifier(
X_train, Y_train)
LearningClassifier.saveClassifierLS(classifier, fileName)
elif self.cb_Classifier.currentText() == "1NN-DTW":
classifier = LearningClassifier.NN1_DTWClassifier(
X_train, Y_train)
LearningClassifier.saveClassifier1NN(classifier, fileName)
else:
classifier = LearningClassifier.NN1_Classifier(
X_train, Y_train)
LearningClassifier.saveClassifier1NN(classifier, fileName)
else:
X_train, Y_train, _, _ = importTS.dataImport(
self.cb_Classifier_SelectTS.currentText())
if self.cb_Classifier.currentText() == "Learning Shapelet":
classifier = LearningClassifier.learningShapeletClassifier(
X_train, Y_train)
LearningClassifier.saveClassifierLS(classifier, fileName)
elif self.cb_Classifier.currentText() == "1NN-DTW":
classifier = LearningClassifier.NN1_DTWClassifier(
X_train, Y_train)
LearningClassifier.saveClassifier1NN(classifier, fileName)
else:
classifier = LearningClassifier.NN1_Classifier(
X_train, Y_train)
LearningClassifier.saveClassifier1NN(classifier, fileName)
QApplication.restoreOverrideCursor()
QMessageBox.information(self, 'Classifieur sauvegardé !',
"Le classifieur a été sauvegardé !",
QMessageBox.Ok)
import sys
sys.path.insert(0, "../Classifier")
import importTS
import LearningClassifier
import lime_timeseries
from matplotlib.backends.backend_qt5agg import FigureCanvasQTAgg as FigureCanvas
import pandas as pd
from sklearn.neighbors import KNeighborsClassifier as KNN
import matplotlib.pyplot as plt
import math
"""Importation de la ST Trace
X_train et Y_train servent à l'entrainement du classifieur. Les ST non étiqueté sont dans X_test."""
X_train, Y_train, X_test, Y_test = importTS.dataImport("Trace")
for i in range(30):
s = "b"
if(Y_train[i]==4):
s="r"
if(Y_train[i]==1):
s="g"
if(Y_train[i]==2):
s="c"
if(Y_train[i]==3):
s="y"
plt.plot(X_train[i].ravel(),s)
plt.show()
"""Construction classifieur 1NN-DTW. En vrai, on va le loader"""
"--local",
type=str,
help="indicate that the TS file <input_file> is on the computer")
# default = pas dans la ligne de commande
# const = dans la ligne mais pas renseigné
args = parser.parse_args()
#print(args)
import importTS
import LearningClassifier
if args.local:
X_train, Y_train = importTS.fileImportTS(args.local)
else:
X_train, Y_train, _, _ = importTS.dataImport(args.input_file)
fileName = "./SaveClassifierFiles/"
if args.output:
fileName += args.output
else:
fileName += args.input_file
fileName += ".sav"
if args.classifier_type == "LS":
classifier = LearningClassifier.learningShapeletClassifier(
X_train, Y_train)
LearningClassifier.saveClassifierLS(classifier, fileName)
elif args.classifier_type == "1NN-DTW":
classifier = LearningClassifier.NN1_DTWClassifier(X_train, Y_train)
LearningClassifier.saveClassifier1NN(classifier, fileName)
import LearningClassifier
import lime_timeseries
import pandas as pd
from sklearn.neighbors import KNeighborsClassifier as KNN
import matplotlib.pyplot as plt
import math
import re
import ExplanationWindow
from matplotlib.backends.backend_qt5agg import FigureCanvasQTAgg as FigureCanvas
"""if args.perso:
X_train, Y_train = importTS.fileImportTS(args.perso)
else:
X_train, Y_train, X_test, Y_test= importTS.dataImport(args.input_file)
"""
X_train, Y_train, X_test, Y_test = importTS.dataImport(args.input_file)
if args.features == 0:
args.features = args.cuts
cltype = args.classifier[len(args.classifier) - 8:]
#print(cltype)
if cltype == "_1NN.sav":
cl = LearningClassifier.loadClassifieur1NN(args.classifier)
else:
cl = LearningClassifier.loadClassifieurLS(args.classifier)
myTs = X_test[args.index].ravel()
myTSexp = lime_timeseries.TSExplainer()
exp = myTSexp.explain_instance(myTs, cl, X_train, args.cuts, args.features,
args.samples)
import importTS
import LearningClassifier
import numpy as np
import pandas as pd
from sklearn.metrics import accuracy_score
from tslearn.preprocessing import TimeSeriesScalerMinMax
"Fichier de test"
X_train, Y_train, X_test, Y_test = importTS.dataImport("Beef")
print("Résultat attendu : " + str(Y_test[1]))
"Test LS"
clLS = LearningClassifier.learningShapeletClassifier(X_train, Y_train)
print("Résultat LS : " + str(clLS.predict(X_test)))
predicted_labels = clLS.predict(X_test)
print("Correct classification rate :", accuracy_score(Y_test,
predicted_labels))
LearningClassifier.saveClassifierLS(clLS, "LS")
loadclLS, labelsave = LearningClassifier.loadClassifieurLS("_LS_.sav")
newlabel = loadclLS.predict(X_test)
print(newlabel)
print("Résulat classifieur LS save : " +
str(labelsave.inverse_transform(newlabel)))
print("Correct classification rate :",
accuracy_score(Y_test, labelsave.inverse_transform(newlabel)))
"""
"Test 1NN-DTW"