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')
