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binary_classifier_perceptron.py
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binary_classifier_perceptron.py
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#! /usr/bin/env python
import sys
import math
import matplotlib.pyplot as plt
training_files = ["Data/ocr_fold0_sm_train.txt", "Data/ocr_fold1_sm_train.txt", "Data/ocr_fold2_sm_train.txt", "Data/ocr_fold3_sm_train.txt", "Data/ocr_fold4_sm_train.txt", "Data/ocr_fold5_sm_train.txt", "Data/ocr_fold6_sm_train.txt", "Data/ocr_fold7_sm_train.txt", "Data/ocr_fold8_sm_train.txt", "Data/ocr_fold9_sm_train.txt"]
test_files = ["Data/ocr_fold0_sm_test.txt", "Data/ocr_fold1_sm_test.txt", "Data/ocr_fold2_sm_test.txt", "Data/ocr_fold3_sm_test.txt", "Data/ocr_fold4_sm_test.txt", "Data/ocr_fold5_sm_test.txt", "Data/ocr_fold6_sm_test.txt", "Data/ocr_fold7_sm_test.txt", "Data/ocr_fold8_sm_test.txt", "Data/ocr_fold9_sm_test.txt"]
VECTOR_SIZE = 128
vowels = ['a', 'e', 'i', 'o' ,'u']
TRAINING_ITERATIONS = 50
if len(sys.argv) > 1:
TRAINING_ITERATIONS = int(sys.argv[1])
learning_mistakes_per_fold = []
learning_successes_per_fold = []
training_mistakes_per_fold = []
training_successes_per_fold = []
training_accuracy_per_fold = []
testing_mistakes_per_fold = []
testing_successes_per_fold = []
testing_accuracy_per_fold = []
def parse_file_line(line):
if len(line) < 4:
return None
line_split = line.strip().split('\t')
pixel_values = line_split[1]
pixel_vector = convert_string_to_int_list(pixel_values)
y_hat = -1
if line_split[2] in vowels:
y_hat = 1
return pixel_vector, y_hat
def dot_product(vector1, vector2):
result = 0
for i in xrange(VECTOR_SIZE):
result += vector1[i] * vector2[i]
return result
def modulus(vector):
sum_of_squares = 0
for x in vector:
sum_of_squares += x*x
return math.sqrt(sum_of_squares)
def perceptron_train(train_vector, y_hat):
global learning_mistakes, learning_successes
prediction = dot_product(weight_vector, train_vector)
if (prediction * y_hat) <= 0:
learning_mistakes += 1
for i in xrange(VECTOR_SIZE):
weight_vector[i] += y_hat * train_vector[i]
else:
learning_successes += 1
def test(train_vector, y_hat):
global testing_successes, testing_mistakes
prediction = dot_product(weight_vector, train_vector)
if (prediction * y_hat) <= 0:
testing_mistakes += 1
else:
testing_successes += 1
def convert_string_to_int_list(pixel_values):
pixel_values = pixel_values[2:]
pixel_vector = []
for x in pixel_values:
pixel_vector.append(int(x))
return pixel_vector
def average_per_fold(fold_list):
#accepts a list of lists containing y_hats of different interations per fold, and returns a list of averages
average_array = []
for i in range(len(fold_list[0])):
sum_of_elements = 0
for j in range(len(fold_list)):
sum_of_elements += fold_list[j][i]
average_array.append(sum_of_elements / len(fold_list))
return average_array
for fold in range(len(training_files)):
weight_vector = [0] * VECTOR_SIZE
cached_weight_vector = [0] * VECTOR_SIZE
weight_vector_array = []
cached_weight_vector_array = []
learning_mistakes_array = []
learning_successes_array = []
training_mistakes_array = []
training_successes_array = []
training_accuracy_array = []
testing_mistakes_array = []
testing_successes_array = []
testing_accuracy_array = []
for x in range(TRAINING_ITERATIONS):
learning_mistakes = 0
learning_successes = 0
training_mistakes = 0
training_successes = 0
testing_mistakes = 0
testing_successes = 0
with open(training_files[fold], 'r') as f:
for line in f:
if len(line) > 4:
pixel_vector, y_hat = parse_file_line(line)
perceptron_train(pixel_vector, y_hat)
weight_vector_array.append(weight_vector[:])
learning_mistakes_array.append(learning_mistakes)
learning_successes_array.append(learning_successes)
#Calculate accuracy on training data
final_weight_vector = weight_vector_array[-1]
with open(training_files[fold], 'r') as f:
for line in f:
if len(line) > 4:
pixel_vector, y_hat = parse_file_line(line)
if dot_product(final_weight_vector, pixel_vector) < 0:
training_mistakes += 1
else:
training_successes += 1
training_mistakes_array.append(training_mistakes)
training_successes_array.append(training_successes)
training_accuracy_array.append((training_successes * 100) / float(training_mistakes + training_successes))
with open(test_files[fold], 'r') as f:
for line in f:
if len(line) > 4:
pixel_vector, y_hat = parse_file_line(line)
test(pixel_vector, y_hat)
testing_mistakes_array.append(testing_mistakes)
testing_successes_array.append(testing_successes)
testing_accuracy_array.append((testing_successes * 100) / float(testing_mistakes + testing_successes))
learning_mistakes_per_fold.append(learning_mistakes_array)
learning_successes_per_fold.append(learning_successes_array)
training_mistakes_per_fold.append(training_mistakes_array)
training_successes_per_fold.append(training_successes_array)
training_accuracy_per_fold.append(training_accuracy_array)
testing_mistakes_per_fold.append(testing_mistakes_array)
testing_successes_per_fold.append(testing_successes_array)
testing_accuracy_per_fold.append(testing_accuracy_array)
print average_per_fold(learning_mistakes_per_fold)
print average_per_fold(training_accuracy_per_fold)
print average_per_fold(testing_accuracy_per_fold)
plt.plot(average_per_fold(learning_mistakes_per_fold))
plt.ylabel('Mistakes')
plt.xlabel('Iterations')
plt.title('Learning Curve')
plt.show()
plt.plot(average_per_fold(training_accuracy_per_fold), label='Training Accuracy')
plt.plot(average_per_fold(testing_accuracy_per_fold), label='Testing Accuracy')
plt.xlabel('Accuracy')
plt.title('Accuracy Curve for Perceptron')
plt.legend(loc = 1)
plt.show()