class Classifier:
def __init__(self, classifier_type, **kwargs):
"""
Initialize a classifier for managing learning model.
Args:
classifier_type: the type of learning model. e.g. neural_network
**kwargs: store parameter in a dictionary
"""
self.classifier_type = classifier_type
self.params = kwargs
self.clf = None
self.file = open('result/trial_' + str(datetime.datetime.today()).replace("/", "_", -1) + ".txt", 'w', 0)
def train(self, training_data, testData, classNum, batchSize):
"""
Create a learning model. Train the model with the training data. Print the training accuracy every certain iterations.
If the learning rate is not chosen appropriately, let the user to enter a new
"""
# find the numbers for feature and label
featureNum = training_data.shape[1] - 1
# #this will find all the unique labels automatically, but will have problem when training data is lacking some labels
# labelNum = len(np.unique(training_data[:, :1]))
labelNum = classNum
# get the number of nodes for each layer
if "hidden_layer" in self.params and self.params["hidden_layer"] is not None:
nodeNum = [featureNum] + self.params["hidden_layer"] + [labelNum]
else:
nodeNum = [featureNum, featureNum * 2, labelNum]
# get the mode for initializing the weight
if "weightInitMode" in self.params and self.params["weightInitMode"] is not None:
weightInitMode = self.params["weightInitMode"]
else:
weightInitMode = None
# get the momentum factor
if "momentumFactor" in self.params:
momentumFactor = self.params["momentumFactor"]
else:
momentumFactor = 0.0
self.clf = NeuralNetwork(training_data, nodeNum, weightInitMode, momentumFactor)
iteration = 5
totalIter = 0
testSize = 100000
while iteration > 0:
if iteration < 10:
self.clf.train(iteration, batchSize)
totalIter += iteration
print "---------- Settings ----------"
print "Examples :", training_data.shape[0]
print "Batch size :", batchSize
print "Alpha :", self.clf.getAlpha()
print "Momentum factor :", momentumFactor
print "# of Nodes in all layers :", nodeNum
print "Training iteration so far:", totalIter
self.file.write("\n")
self.file.write("---------- Settings ----------" + "\n")
self.file.write("Examples : " + str(training_data.shape[0]) + "\n")
self.file.write("Batch size : " + str(batchSize) + "\n")
self.file.write("Alpha : " + str(self.clf.getAlpha()) + "\n")
self.file.write("Momentum factor : " + str(momentumFactor) + "\n")
self.file.write("# of Nodes in all layers : " + str(nodeNum) + "\n")
self.file.write("Training iteration so far: " + str(totalIter) + "\n")
self.test(training_data, "training")
self.test(testData, "testing")
iteration = 0
while iteration >= testSize:
self.clf.train(testSize, batchSize)
totalIter += testSize
print "---------- Settings ----------"
print "Examples :", training_data.shape[0]
print "Batch size :", batchSize
print "Alpha :", self.clf.getAlpha()
print "Momentum factor :", momentumFactor
print "# of Nodes in all layers :", nodeNum
print "Training iteration so far:", totalIter
self.file.write("\n")
self.file.write("---------- Settings ----------" + "\n")
self.file.write("Examples : " + str(training_data.shape[0]) + "\n")
self.file.write("Batch size : " + str(batchSize) + "\n")
self.file.write("Alpha : " + str(self.clf.getAlpha()) + "\n")
self.file.write("Momentum factor : " + str(momentumFactor) + "\n")
self.file.write("# of Nodes in all layers : " + str(nodeNum) + "\n")
self.file.write("Training iteration so far: " + str(totalIter) + "\n")
self.test(training_data, "training")
self.test(testData, "testing")
iteration -= testSize
if iteration > 0:
self.clf.train(iteration, batchSize)
totalIter += iteration
print "---------- Settings ----------"
print "Examples :", training_data.shape[0]
print "Batch size :", batchSize
print "Alpha :", self.clf.getAlpha()
print "Momentum factor :", momentumFactor
print "# of Nodes in all layers :", nodeNum
print "Training iteration so far:", totalIter
self.file.write("\n")
self.file.write("---------- Settings ----------" + "\n")
self.file.write("Examples : " + str(training_data.shape[0]) + "\n")
self.file.write("Batch size : " + str(batchSize) + "\n")
self.file.write("Alpha : " + str(self.clf.getAlpha()) + "\n")
self.file.write("Momentum factor : " + str(momentumFactor) + "\n")
self.file.write("# of Nodes in all layers : " + str(nodeNum) + "\n")
self.file.write("Training iteration so far: " + str(totalIter) + "\n")
self.test(training_data, "training")
self.test(testData, "testing")
iteration = 0
print ""
restart = raw_input("Do you want to restart? (Y/N)")
if restart.upper() == "Y":
totalIter = 0
print "Current Alpha is", self.clf.getAlpha()
alpha = raw_input("What alpha ?")
self.clf.setAlpha(float(alpha))
self.clf.initTheta()
self.file.write("\n")
self.file.write("*****************************************************\n")
self.file.write("Re-initialize trail with alpha = " + str(alpha) + "\n")
self.file.write("*****************************************************\n")
print ""
iteration = raw_input("How many iteration do you want to train the model?")
try:
iteration = int(iteration)
except:
iteration = raw_input("Please input an integer")
iteration = 1
print "Total training iterations:", totalIter
def predict(self, data):
"""
"""
return self.clf.predict(data)
def test(self, test_data, mode):
"""
"""
correct = 0
countPrediction = {}
countCorrect = {}
countTotal = Counter(list(test_data[:, 0]))
allPrediction = {}
labels = np.unique(test_data[:, 0])
for label in labels:
countCorrect[label] = 0
countPrediction[label] = 0
allPrediction[label] = 0
for e in test_data:
label = e[0]
pred_label = self.predict(e)
if label == pred_label:
correct += 1
if e[0] in countCorrect:
countCorrect[e[0]] += 1
else:
countCorrect[e[0]] = 1
if pred_label in allPrediction:
allPrediction[pred_label] += 1
else:
allPrediction[pred_label] = 1
if pred_label in countPrediction:
countPrediction[pred_label] += 1
else:
countPrediction[pred_label] = 1
print "---------- Result ----------"
print "Alpha is", self.clf.getAlpha()
print "Count correct", countCorrect
print "All predictions", allPrediction
accuracy = float(correct) / len(test_data)
print "The accuracy for", mode, "is", accuracy
self.file.write("---------- Result ----------" + "\n")
self.file.write("Alpha is " + str(self.clf.getAlpha()) + "\n")
self.file.write("Count correct " + str(countCorrect) + "\n")
self.file.write("All predictions " + str(allPrediction) + "\n")
self.file.write("The accuracy for " + mode + " is " + str(accuracy) + "\n")
def getAttrValue(self, ex):
"""
Find the attribute values for each attribute.
Args:
ex: given examples
Returns: a dictionary where the keys are the attribute indices and the values are the attribute values.
"""
attrValue = {}
for i in range(len(ex[0])):
attrValue[i] = list(set([v for v in ex[:, i]]))
return attrValue
