def main(): data = MNISTSeven("../data/mnist_seven.csv", 3000, 1000, 1000) myPerceptronClassifier = Perceptron(data.trainingSet, data.validationSet, data.testSet, learningRate=0.01, epochs=10) # Train the classifiers print("=========================") print("Training..") print("\nTraining the Perceptron..") myPerceptronClassifier.train() print("Done..") # Do the recognizer perceptronPred = myPerceptronClassifier.evaluate() # Report the result print("=========================") evaluator = Evaluator() print("\nResult of the Perceptron recognizer:") #evaluator.printComparison(data.testSet, perceptronPred) evaluator.printAccuracy(data.testSet, perceptronPred)
def main(): data = MNISTSeven("../data/mnist_seven.csv", 3000, 1000, 1000, oneHot=True) myStupidClassifier = StupidRecognizer(data.trainingSet, data.validationSet, data.testSet) myPerceptronClassifier = Perceptron(data.trainingSet, data.validationSet, data.testSet, learningRate=0.005, epochs=30) myLRClassifier = LogisticRegression(data.trainingSet, data.validationSet, data.testSet, learningRate=0.005, epochs=30) # Train the classifiers print("=========================") print("Training..") print("\nStupid Classifier has been training..") myStupidClassifier.train() print("Done..") print("\nPerceptron has been training..") myPerceptronClassifier.train() print("Done..") print("\nLogistic Regression has been training..") myLRClassifier.train() print("Done..") # Do the recognizer # Explicitly specify the test set to be evaluated stupidPred = myStupidClassifier.evaluate() perceptronPred = myPerceptronClassifier.evaluate() lrPred = myLRClassifier.evaluate() # Report the result print("=========================") evaluator = Evaluator() print("Result of the stupid recognizer:") # evaluator.printComparison(data.testSet, stupidPred) evaluator.printAccuracy(data.testSet, stupidPred) print("\nResult of the Perceptron recognizer:") # evaluator.printComparison(data.testSet, perceptronPred) evaluator.printAccuracy(data.testSet, perceptronPred) print("\nResult of the Logistic Regression recognizer:") # evaluator.printComparison(data.testSet, perceptronPred) evaluator.printAccuracy(data.testSet, lrPred)
def main(): data = MNISTSeven("../data/mnist_seven.csv", 3000, 1000, 1000) myStupidClassifier = StupidRecognizer(data.trainingSet, data.validationSet, data.testSet) # Uncomment this to make your Perceptron evaluated myPerceptronClassifier = Perceptron( data.trainingSet, data.validationSet, data.testSet, learningRate=1.0, #0.005, epochs=1 #30 ) # Train the classifiers print("=========================") print("Training..") print("\nStupid Classifier has been training..") myStupidClassifier.train() print("Done..") print("\nPerceptron has been training..") myPerceptronClassifier.train() print("Done..") # Do the recognizer # Explicitly specify the test set to be evaluated stupidPred = myStupidClassifier.evaluate() # Uncomment this to make your Perceptron evaluated perceptronPred = myPerceptronClassifier.evaluate() # Report the result print("=========================") evaluator = Evaluator() print("Result of the stupid recognizer:") #evaluator.printComparison(data.testSet, stupidPred) evaluator.printAccuracy(data.testSet, stupidPred) print("\nResult of the Perceptron recognizer:") #evaluator.printComparison(data.testSet, perceptronPred) # Uncomment this to make your Perceptron evaluated evaluator.printAccuracy(data.testSet, perceptronPred) evaluator.printConfusionMatrix(data.testSet, perceptronPred) evaluator.printClassificationResult(data.testSet, perceptronPred, ['class 0', 'class 1']) #target_names)
def __init__(self): self.iris = pd.read_csv('https://archive.ics.uci.edu/ml/' 'machine-learning-databases/iris/iris.data', header=None) print(self.iris.head()) print('----------------------') print(self.iris.tail) print('----------------------') print(self.iris.columns) ''' [150 rows x 5 columns]> ---------------------- Int64Index([0, 1, 2, 3, 4], dtype='int64') ''' # Iris-setosa 와 versicolor 선택 (MCP 는 이진분류만 할 수 있다) t = self.iris.iloc[0:100,4].values self.y = np.where(t == 'Iris-setosa', -1, 1) # 꽃받침 길이, 꽃잎 추출 self.X = self.iris.iloc[0:100, [0,2]].values self.clf = Perceptron(eta = 0.1, n_iter=10)
def main(): data = MNISTSeven("../data/mnist_seven.csv", 3000, 1000, 1000) myStupidClassifier = StupidRecognizer(data.trainingSet, data.validationSet, data.testSet) # parameters learnRate = 0.005 maxEpochs = 20 #epochNumber = 30 xEpochs = [] yAccuracyPerceptron = [] yAccuracyLogistic = [] # loop for gathering data for graph plotting for epochNumber in xrange(1, maxEpochs + 1): myPerceptronClassifier = Perceptron( data.trainingSet, data.validationSet, data.testSet, learningRate=learnRate, #0.005, epochs=epochNumber) # Uncomment this to run Logistic Neuron Layer myLRClassifier = LogisticRegression( data.trainingSet, data.validationSet, data.testSet, learningRate=learnRate, #0.005, epochs=epochNumber #30 ) # Train the classifiers print("=========================") print("Training..") print("\nStupid Classifier has been training..") myStupidClassifier.train() print("Done..") print("\nPerceptron has been training..") myPerceptronClassifier.train() print("Done..") print("\nLogistic Regression has been training..") myLRClassifier.train() print("Done..") # Do the recognizer # Explicitly specify the test set to be evaluated stupidPred = myStupidClassifier.evaluate() perceptronPred = myPerceptronClassifier.evaluate() lrPred = myLRClassifier.evaluate() # Report the result print("=========================") evaluator = Evaluator() print("Result of the stupid recognizer:") #evaluator.printComparison(data.testSet, stupidPred) evaluator.printAccuracy(data.testSet, stupidPred) print("\nResult of the Perceptron recognizer:") #evaluator.printComparison(data.testSet, perceptronPred) evaluator.printAccuracy(data.testSet, perceptronPred) print("\nResult of the Logistic Regression recognizer:") #evaluator.printComparison(data.testSet, lrPred) evaluator.printAccuracy(data.testSet, lrPred) # accumulate plotting data xEpochs.append(epochNumber) yAccuracyPerceptron.append( accuracy_score(data.testSet.label, perceptronPred) * 100) yAccuracyLogistic.append( accuracy_score(data.testSet.label, lrPred) * 100) # === end of for loop === # plot the graph plt.plot(xEpochs, yAccuracyPerceptron, marker='o', label='Perceptron') plt.plot(xEpochs, yAccuracyLogistic, marker='o', color='r', label='Logistic Neuron') plt.xlabel('Number of epochs') plt.ylabel('Accuracy [%]') plt.title( 'Performance on different epochs\n(using: testSet | learningRate: ' + str(learnRate) + ')') #plt.legend() plt.legend(loc=4) #plt.legend(bbox_to_anchor=(0., 1.02, 1., .102), loc=3, ncol=2, mode="expand", borderaxespad=0.) #plt.legend(bbox_to_anchor=(1.05, 1), loc=2, borderaxespad=0.) plt.show()
def main(): data = MNISTSeven("data/mnist_seven.csv", 3000, 1000, 1000, oneHot=True) myStupidClassifier = StupidRecognizer(data.trainingSet, data.validationSet, data.testSet) myPerceptronClassifier = Perceptron(data.trainingSet, data.validationSet, data.testSet, learningRate=0.005, epochs=30) myLRClassifier = LogisticRegression(data.trainingSet, data.validationSet, data.testSet, learningRate=0.005, epochs=30) # Report the result # print("=========================") evaluator = Evaluator() # Train the classifiers print("=========================") print("Training..") # print("\nStupid Classifier has been training..") # myStupidClassifier.train() # print("Done..") # print("\nPerceptron has been training..") # myPerceptronClassifier.train() # print("Done..") # print("\nLogistic Regression has been training..") # myLRClassifier.train() # print("Done..") myMLP = MultilayerPerceptron(data.trainingSet, data.validationSet, data.testSet, learningRate=0.01, epochs=30, loss="ce", outputActivation="softmax", weight_decay=0.1) print("\nMLP has been training..") myMLP.train() print("Done..") # Do the recognizer # Explicitly specify the test set to be evaluated # stupidPred = myStupidClassifier.evaluate() # perceptronPred = myPerceptronClassifier.evaluate() # lrPred = myLRClassifier.evaluate() mlpPred = myMLP.evaluate(data.validationSet) # # Report the result # print("=========================") # evaluator = Evaluator() # print("Result of the stupid recognizer:") # #evaluator.printComparison(data.testSet, stupidPred) # evaluator.printAccuracy(data.testSet, stupidPred) # print("\nResult of the Perceptron recognizer:") # #evaluator.printComparison(data.testSet, perceptronPred) # evaluator.printAccuracy(data.testSet, perceptronPred) # print("\nResult of the Logistic Regression recognizer:") # #evaluator.printComparison(data.testSet, lrPred) # evaluator.printAccuracy(data.testSet, lrPred) print("\nResult of the Multilayer Perceptron recognizer:") #evaluator.printComparison(data.testSet, lrPred) # evaluator.printAccuracy(data.testSet, mlpPred) plot = PerformancePlot("MLP validation") plot.draw_performance_epoch(myMLP.performances, myMLP.epochs)
def classify_one(): data = MNISTSeven("../data/mnist_seven.csv", 3000, 1000, 1000, one_hot=True, target_digit='7') # NOTE: # Comment out the MNISTSeven instantiation above and # uncomment the following to work with full MNIST task # data = MNISTSeven("../data/mnist_seven.csv", 3000, 1000, 1000, # one_hot=False) # NOTE: # Other 1-digit classifiers do not make sense now for comparison purpose # So you should comment them out, let alone the MLP training and evaluation # Train the classifiers # print("=========================") print("Training..") # Stupid Classifier myStupidClassifier = StupidRecognizer(data.training_set, data.validation_set, data.test_set) print("\nStupid Classifier has been training..") myStupidClassifier.train() print("Done..") # Do the recognizer # Explicitly specify the test set to be evaluated stupidPred = myStupidClassifier.evaluate() # Perceptron myPerceptronClassifier = Perceptron(data.training_set, data.validation_set, data.test_set, learning_rate=0.005, epochs=10) print("\nPerceptron has been training..") myPerceptronClassifier.train() print("Done..") # Do the recognizer # Explicitly specify the test set to be evaluated perceptronPred = myPerceptronClassifier.evaluate() # Logistic Regression myLRClassifier = LogisticRegression(data.training_set, data.validation_set, data.test_set, learning_rate=0.20, epochs=30) print("\nLogistic Regression has been training..") myLRClassifier.train() print("Done..") # Do the recognizer # Explicitly specify the test set to be evaluated lrPred = myLRClassifier.evaluate() # Logistic Regression myMLPClassifier = MultilayerPerceptron(data.training_set, data.validation_set, data.test_set, learning_rate=0.30, epochs=50) print("\nMultilayer Perceptron has been training..") myMLPClassifier.train() print("Done..") # Do the recognizer # Explicitly specify the test set to be evaluated mlpPred = myMLPClassifier.evaluate() # Report the result # print("=========================") evaluator = Evaluator() print("Result of the stupid recognizer:") # evaluator.printComparison(data.testSet, stupidPred) evaluator.printAccuracy(data.test_set, stupidPred) print("\nResult of the Perceptron recognizer (on test set):") # evaluator.printComparison(data.testSet, perceptronPred) evaluator.printAccuracy(data.test_set, perceptronPred) print("\nResult of the Logistic Regression recognizer (on test set):") # evaluator.printComparison(data.testSet, perceptronPred) evaluator.printAccuracy(data.test_set, lrPred) print("\nResult of the Multi-layer Perceptron recognizer (on test set):") # evaluator.printComparison(data.testSet, perceptronPred) evaluator.printAccuracy(data.test_set, mlpPred) # Draw plot = PerformancePlot("Logistic Regression") plot.draw_performance_epoch(myLRClassifier.performances, myLRClassifier.epochs)
X = pd.DataFrame(iris.data, columns=iris.feature_names).iloc[0:100, [0,2]].values y = iris.target[0:100] y = np.where(y == 0, 1, -1) plt.scatter(df.iloc[:49, 0], df.iloc[:49, 2], color='red', marker='o', label='setosa') plt.scatter(df.iloc[50:101,0], df.iloc[50:101, 2], color='blue', marker='x', label='versicolor') plt.scatter(df.iloc[102:,0], df.iloc[102:, 2], color='green', marker='^', label='virginica') plt.xlabel('sepal length[cm]') plt.ylabel('petal length[cm]') plt.legend(loc='upper left') plt.show() ppn = Perceptron(eta=0.01, n_iter=10) ppn.fit(X,y) plt.plot(range(1, len(ppn.errors_) + 1), ppn.errors_, marker='o') plt.xlabel('Epoch') plt.ylabel('Number of errors') plt.show() from model.plot import plot_decision_regions plot_decision_regions(X, y, classifier=ppn) plt.xlabel('sepal length [cm]') plt.ylabel('petal length [cm]') plt.legend(loc='upper left')