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evaluator.py
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evaluator.py
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#!/usr/bin/python
import numpy as np
from knn import KNN
from ann import ANN
from sklearn import datasets, cross_validation, metrics
class Evaluator:
""" object to keep track of results and print out metrics """
def __init__(self):
self.Ytrue = []
self.Ypredict = []
def append_results(self, Ytrue, Ypredict):
self.Ytrue.append(Ytrue)
self.Ypredict.append(Ypredict)
def avg_f1_score(self):
scores = [metrics.f1_score(self.Ytrue[i],self.Ypredict[i]) for i in range(len(self.Ytrue))]
return np.mean(scores)
def print_reports(self):
for i in range(len(self.Ytrue)):
print metrics.classification_report(Ytrue,Ypredict)
def crossvalidate(function, X, Y, k=10):
""" cross validate by splitting data into k pieces """
m = len(Y)
kfold = cross_validation.KFold(m, k=k)
evaluator = Evaluator()
for train_index, test_index in kfold:
Xtrain, Xtest = X[train_index], X[test_index]
Ytrain, Ytest = Y[train_index], Y[test_index]
Ypredict = function(Xtrain,Ytrain,Xtest,Ytest)
evaluator.append_results(Ytest,Ypredict)
return evaluator
def compare_functions():
digits = datasets.load_digits()
X = digits.data
Y = digits.target
classes = list(set(Y))
# compare the KNN and ANN using
def knncv(Xtrain, Ytrain, Xtest, Ytest):
knn = KNN(Xtrain,Ytrain)
m = len(Ytest)
Ypredict = np.zeros(m)
for i in xrange(m):
x,y = Xtest[i],Ytest[i]
results = knn.predict(x,k=4,classes=classes)
prediction = results.argmax()
Ypredict[i] = prediction
return Ypredict
evaluator = crossvalidate(knncv,X,Y,k=3)
print "KNN avg f1 scores", evaluator.avg_f1_score()
def anncv(Xtrain, Ytrain, Xtest, Ytest):
inputsize = Xtrain.shape[1]
hiddensize = 12
outputsize = len(classes)
ann = ANN(inputsize,hiddensize,outputsize)
mtrain = len(Ytrain)
# for this example, im only training with the first 12 examples
for n in xrange(400):
for i in xrange(mtrain):
x,y = Xtrain[i],Ytrain[i]
target = np.zeros(len(classes))
target[classes.index(y)] = 1
ann.train(x,target,alpha=0.1,momentum=0.2)
mtest = len(Ytest)
Ypredict = np.zeros(mtest)
for i in xrange(mtest):
x,y = Xtest[i],Ytest[i]
results = ann.predict(x)
prediction = results.argmax()
Ypredict[i] = prediction
return Ypredict
evaluator = crossvalidate(anncv,X,Y,k=3)
print "ANN avg f1 scores", evaluator.avg_f1_score()
if __name__ == '__main__':
compare_functions()