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code_for_hw3_part2.py
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code_for_hw3_part2.py
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# Implement perceptron, average perceptron, and pegasos
import numpy as np
import matplotlib.pyplot as plt
from matplotlib import colors
from matplotlib.image import imread
print("Importing code_for_hw03 (part 2, imported as hw3)")
######################################################################
# Plotting
def tidy_plot(xmin, xmax, ymin, ymax, center = False, title = None,
xlabel = None, ylabel = None):
plt.ion()
plt.figure(facecolor="white")
ax = plt.subplot()
if center:
ax.spines['left'].set_position('zero')
ax.spines['right'].set_color('none')
ax.spines['bottom'].set_position('zero')
ax.spines['top'].set_color('none')
ax.spines['left'].set_smart_bounds(True)
ax.spines['bottom'].set_smart_bounds(True)
ax.xaxis.set_ticks_position('bottom')
ax.yaxis.set_ticks_position('left')
else:
ax.spines["top"].set_visible(False)
ax.spines["right"].set_visible(False)
ax.get_xaxis().tick_bottom()
ax.get_yaxis().tick_left()
eps = .05
plt.xlim(xmin-eps, xmax+eps)
plt.ylim(ymin-eps, ymax+eps)
if title: ax.set_title(title)
if xlabel: ax.set_xlabel(xlabel)
if ylabel: ax.set_ylabel(ylabel)
return ax
def plot_separator(ax, th, th_0):
xmin, xmax = ax.get_xlim()
ymin,ymax = ax.get_ylim()
pts = []
eps = 1.0e-6
# xmin boundary crossing is when xmin th[0] + y th[1] + th_0 = 0
# that is, y = (-th_0 - xmin th[0]) / th[1]
if abs(th[1,0]) > eps:
pts += [np.array([x, (-th_0 - x * th[0,0]) / th[1,0]]) \
for x in (xmin, xmax)]
if abs(th[0,0]) > 1.0e-6:
pts += [np.array([(-th_0 - y * th[1,0]) / th[0,0], y]) \
for y in (ymin, ymax)]
in_pts = []
for p in pts:
if (xmin-eps) <= p[0] <= (xmax+eps) and \
(ymin-eps) <= p[1] <= (ymax+eps):
duplicate = False
for p1 in in_pts:
if np.max(np.abs(p - p1)) < 1.0e-6:
duplicate = True
if not duplicate:
in_pts.append(p)
if in_pts and len(in_pts) >= 2:
# Plot separator
vpts = np.vstack(in_pts)
ax.plot(vpts[:,0], vpts[:,1], 'k-', lw=2)
# Plot normal
vmid = 0.5*(in_pts[0] + in_pts[1])
scale = np.sum(th*th)**0.5
diff = in_pts[0] - in_pts[1]
dist = max(xmax-xmin, ymax-ymin)
vnrm = vmid + (dist/10)*(th.T[0]/scale)
vpts = np.vstack([vmid, vnrm])
ax.plot(vpts[:,0], vpts[:,1], 'k-', lw=2)
# Try to keep limits from moving around
ax.set_xlim((xmin, xmax))
ax.set_ylim((ymin, ymax))
else:
print('Separator not in plot range')
def plot_data(data, labels, ax = None, clear = False,
xmin = None, xmax = None, ymin = None, ymax = None):
if ax is None:
if xmin == None: xmin = np.min(data[0, :]) - 0.5
if xmax == None: xmax = np.max(data[0, :]) + 0.5
if ymin == None: ymin = np.min(data[1, :]) - 0.5
if ymax == None: ymax = np.max(data[1, :]) + 0.5
ax = tidy_plot(xmin, xmax, ymin, ymax)
x_range = xmax - xmin; y_range = ymax - ymin
if .1 < x_range / y_range < 10:
ax.set_aspect('equal')
xlim, ylim = ax.get_xlim(), ax.get_ylim()
elif clear:
xlim, ylim = ax.get_xlim(), ax.get_ylim()
ax.clear()
else:
xlim, ylim = ax.get_xlim(), ax.get_ylim()
colors = np.choose(labels > 0, cv(['r', 'g']))[0]
ax.scatter(data[0,:], data[1,:], c = colors,
marker = 'o', s=50, edgecolors = 'none')
# Seems to occasionally mess up the limits
ax.set_xlim(xlim); ax.set_ylim(ylim)
ax.grid(True, which='both')
#ax.axhline(y=0, color='k')
#ax.axvline(x=0, color='k')
return ax
######################################################################
# Utilities
# Takes a list of numbers and returns a column vector: n x 1
def cv(value_list):
return np.transpose(rv(value_list))
# Takes a list of numbers and returns a row vector: 1 x n
def rv(value_list):
return np.array([value_list])
# x is dimension d by n
# th is dimension d by m
# th0 is dimension 1 by m
# return matrix of y values for each column of x and theta: dimension m x n
def y(x, th, th0):
return np.dot(np.transpose(th), x) + np.transpose(th0)
def length(d_by_m):
return np.sum(d_by_m * d_by_m, axis = 0, keepdims = True)**0.5
# x is dimension d by n
# th is dimension d by m
# th0 is dimension 1 by m
# return matrix of signed dist for each column of x and theta: dimension m x n
def signed_dist(x, th, th0):
return y(x, th, th0) / np.transpose(length(th))
######################################################################
# Perceptron code
# data is dimension d by n
# labels is dimension 1 by n
# T is a positive integer number of steps to run
# Perceptron algorithm with offset.
# data is dimension d by n
# labels is dimension 1 by n
# T is a positive integer number of steps to run
def perceptron(data, labels, params = {}, hook = None):
# if T not in params, default to 50
T = params.get('T', 50)
(d, n) = data.shape
theta = np.zeros((d, 1)); theta_0 = np.zeros((1, 1))
for t in range(T):
for i in range(n):
x = data[:,i:i+1]
y = labels[:,i:i+1]
if y * positive(x, theta, theta_0) <= 0.0:
theta = theta + y * x
theta_0 = theta_0 + y
if hook: hook((theta, theta_0))
return theta, theta_0
def averaged_perceptron(data, labels, params = {}, hook = None):
T = params.get('T', 50)
(d, n) = data.shape
theta = np.zeros((d, 1)); theta_0 = np.zeros((1, 1))
theta_sum = theta.copy()
theta_0_sum = theta_0.copy()
for t in range(T):
for i in range(n):
x = data[:,i:i+1]
y = labels[:,i:i+1]
if y * positive(x, theta, theta_0) <= 0.0:
theta = theta + y * x
theta_0 = theta_0 + y
if hook: hook((theta, theta_0))
theta_sum = theta_sum + theta
theta_0_sum = theta_0_sum + theta_0
theta_avg = theta_sum / (T*n)
theta_0_avg = theta_0_sum / (T*n)
if hook: hook((theta_avg, theta_0_avg))
return theta_avg, theta_0_avg
def positive(x, th, th0):
return np.sign(th.T@x + th0)
def score(data, labels, th, th0):
return np.sum(positive(data, th, th0) == labels)
def eval_classifier(learner, data_train, labels_train, data_test, labels_test):
th, th0 = learner(data_train, labels_train)
return score(data_test, labels_test, th, th0)/data_test.shape[1]
def xval_learning_alg(learner, data, labels, k):
_, n = data.shape
idx = list(range(n))
np.random.seed(0)
np.random.shuffle(idx)
data, labels = data[:,idx], labels[:,idx]
s_data = np.array_split(data, k, axis=1)
s_labels = np.array_split(labels, k, axis=1)
score_sum = 0
for i in range(k):
data_train = np.concatenate(s_data[:i] + s_data[i+1:], axis=1)
labels_train = np.concatenate(s_labels[:i] + s_labels[i+1:], axis=1)
data_test = np.array(s_data[i])
labels_test = np.array(s_labels[i])
score_sum += eval_classifier(learner, data_train, labels_train,
data_test, labels_test)
return score_sum/k
######################################################################
# Tests
def test_linear_classifier(dataFun, learner, learner_params = {},
draw = True, refresh = True, pause = True):
data, labels = dataFun()
d, n = data.shape
if draw:
ax = plot_data(data, labels)
def hook(params):
(th, th0) = params
if refresh: plot_data(data, labels, ax, clear = True)
plot_separator(ax, th, th0)
print('th', th.T, 'th0', th0)
if pause: input('go?')
else:
hook = None
th, th0 = learner(data, labels, hook = hook, params = learner_params)
print("Final score", float(score(data, labels, th, th0)) / n)
print("Params", np.transpose(th), th0)
######################################################################
# For auto dataset
def load_auto_data(path_data):
"""
Returns a list of dict with keys:
"""
numeric_fields = {'mpg', 'cylinders', 'displacement', 'horsepower', 'weight',
'acceleration', 'model_year', 'origin'}
data = []
with open(path_data) as f_data:
for datum in csv.DictReader(f_data, delimiter='\t'):
for field in list(datum.keys()):
if field in numeric_fields and datum[field]:
datum[field] = float(datum[field])
data.append(datum)
return data
# Feature transformations
def std_vals(data, f):
vals = [entry[f] for entry in data]
avg = sum(vals)/len(vals)
dev = [(entry[f] - avg)**2 for entry in data]
sd = (sum(dev)/len(vals))**0.5
return (avg, sd)
def standard(v, std):
return [(v-std[0])/std[1]]
def raw(x):
return [x]
def one_hot(v, entries):
vec = len(entries)*[0]
vec[entries.index(v)] = 1
return vec
# The class (mpg) added to the front of features
def auto_data_and_labels(auto_data, features):
features = [('mpg', raw)] + features
std = {f:std_vals(auto_data, f) for (f, phi) in features if phi==standard}
entries = {f:list(set([entry[f] for entry in auto_data])) \
for (f, phi) in features if phi==one_hot}
print('avg and std', std)
print('entries in one_hot field', entries)
vals = []
for entry in auto_data:
phis = []
for (f, phi) in features:
if phi == standard:
phis.extend(phi(entry[f], std[f]))
elif phi == one_hot:
phis.extend(phi(entry[f], entries[f]))
else:
phis.extend(phi(entry[f]))
vals.append(np.array([phis]))
data_labels = np.vstack(vals)
return data_labels[:, 1:].T, data_labels[:, 0:1].T
######################################################################
# For food review dataset
from string import punctuation, digits, printable
import csv
def load_review_data(path_data):
"""
Returns a list of dict with keys:
* sentiment: +1 or -1 if the review was positive or negative, respectively
* text: the text of the review
"""
basic_fields = {'sentiment', 'text'}
data = []
with open(path_data) as f_data:
for datum in csv.DictReader(f_data, delimiter='\t'):
for field in list(datum.keys()):
if field not in basic_fields:
del datum[field]
if datum['sentiment']:
datum['sentiment'] = int(datum['sentiment'])
data.append(datum)
return data
printable = set(printable)
def clean(s):
return filter(lambda x: x in printable, s)
def extract_words(input_string):
"""
Helper function for bag_of_words()
Inputs a text string
Returns a list of lowercase words in the string.
Punctuation and digits are separated out into their own words.
"""
for c in punctuation + digits:
input_string = input_string.replace(c, ' ' + c + ' ')
# return [ps.stem(w) for w in input_string.lower().split()]
return input_string.lower().split()
def bag_of_words(texts):
"""
Inputs a list of string reviews
Returns a dictionary of unique unigrams occurring over the input
Feel free to change this code as guided by Section 3 (e.g. remove stopwords, add bigrams etc.)
"""
dictionary = {} # maps word to unique index
for text in texts:
word_list = extract_words(text)
for word in word_list:
if word not in dictionary:
dictionary[word] = len(dictionary)
return dictionary
def extract_bow_feature_vectors(reviews, dictionary):
"""
Inputs a list of string reviews
Inputs the dictionary of words as given by bag_of_words
Returns the bag-of-words feature matrix representation of the data.
The returned matrix is of shape (n, m), where n is the number of reviews
and m the total number of entries in the dictionary.
"""
num_reviews = len(reviews)
feature_matrix = np.zeros([num_reviews, len(dictionary)])
for i, text in enumerate(reviews):
word_list = extract_words(text)
for word in word_list:
if word in dictionary:
feature_matrix[i, dictionary[word]] = 1
# We want the feature vectors as columns
return feature_matrix.T
def reverse_dict(d):
return {v: k for k, v in d.items()}
######################################################################
# For MNIST dataset
# NOTE you should use this function to evaluate your MNIST results!
def get_classification_accuracy(data, labels):
"""
@param data (d,n) array
@param labels (1,n) array
"""
return xval_learning_alg(lambda data, labels: perceptron(data, labels, {"T": 50}), data, labels, 10)
def load_mnist_data(labels):
"""
@param labels list of labels from {0, 1,...,9}
@return dict: label (int) -> [[image1], [image2], ...]
"""
data = {}
for label in labels:
images = load_mnist_single("mnist/mnist_train{}.png".format(label))
y = np.array([[label] * len(images)])
data[label] = {
"images": images,
"labels": y
}
return data
def load_mnist_single(path_data):
"""
@return list of images (first row of large picture)
"""
img = imread(path_data) # 2156 x 2156 (m,n)
m, n = img.shape
side_len = 28 # standard mnist
n_img = int(m / 28)
imgs = [] # list of single images
for i in range(n_img):
start_ind = i*side_len
end_ind = start_ind + side_len
current_img = img[start_ind:end_ind, :side_len] # 28 by 28
current_img = current_img / 255 # normalization!!!
imgs.append(current_img)
return imgs
#-----------------------------------------------------------------------------
print("Imported tidy_plot, plot_separator, plot_data, plot_nonlin_sep, cv, rv, y, positive, score")
print(" xval_learning_alg, eval_classifier")
print("Tests: test_linear_classifier")
print("Dataset tools: load_auto_data, std_vals, standard, raw, one_hot, auto_data_and_labels")
print(" load_review_data, clean, extract_words, bag_of_words, extract_bow_feature_vectors")
print(" load_mnist_data, load_mnist_single")