from collections import defaultdict
from collections import namedtuple
import csv
import matplotlib
matplotlib.use("PDF")
import matplotlib.pyplot as plt
import numpy as np
class GrowingHistogram(object):
def __init__(self, bin_width):
self.bin_width = float(bin_width)
self.bins = defaultdict(int)
def accumulate(self, value):
bin_idx = int(np.floor(value / self.bin_width))
self.bins[bin_idx] += 1
def histogram(self):
offset_idx = 0
n_bins = max(self.bins.iterkeys()) + 1
min_idx = min(self.bins.iterkeys)
if min_idx < 0:
offset_idx += np.abs(min_idx)
n_bins += np.abs(min_idx)
counts = [0] * n_bins
lowerbounds = [0.] * (n_bins + 1)
for bin_idx, count in self.bins.iteritems():
idx = offset_idx + bin_idx
counts[idx] = count
lowerbounds[idx] = bin_idx * self.bin_width
lowerbounds[-1] = n_bins * self.bin_width
return counts, lowerbounds
def read_ratings(flname):
Rating = namedtuple("Rating", ["user_id", "movie_id", "rating", "timestamp"])
with open(flname) as fl:
for ln in fl:
user_id, movie_id, rating, timestamp = ln.strip().split("::")
yield Rating(user_id=int(user_id) - 1,
movie_id=int(movie_id) - 1,
rating=float(rating),
timestamp=int(timestamp))
def plot_correlation(flname, title, x_label, y_label, dataset):
"""
Scatter plot with line of best fit
dataset - tuple of (x_values, y_values)
"""
plt.clf()
plt.hold(True)
plt.scatter(dataset[0], dataset[1], alpha=0.7, color="k")
xs = np.array(dataset[0])
ys = np.array(dataset[1])
A = np.vstack([xs, np.ones(len(xs))]).T
m, c = np.linalg.lstsq(A, ys)[0]
plt.plot(xs, m*xs + c, "c-")
plt.xlabel(x_label, fontsize=16)
plt.ylabel(y_label, fontsize=16)
plt.xlim([0.25, max(dataset[0])])
plt.ylim([10., max(dataset[1])])
plt.title(title, fontsize=18)
plt.savefig(flname, DPI=200)
def plot_histogram(flname, title, x_label, datasets):
"""
Histogram
dataset - list tuples of (frequencies, bins, style)
"""
plt.clf()
plt.hold(True)
max_bin = 0
for frequencies, bins, style in datasets:
xs = []
ys = []
max_bin = max(max_bin, max(bins))
for i, f in enumerate(frequencies):
xs.append(bins[i])
xs.append(bins[i+1])
ys.append(f)
ys.append(f)
plt.plot(xs, ys, style)
plt.xlabel(x_label, fontsize=16)
plt.ylabel("Occurrences", fontsize=16)
plt.xlim([0, max_bin + 1])
plt.title(title, fontsize=18)
plt.savefig(flname, DPI=200)
def plot_aucs_nnzs(flname, model_bits, aucs, nzs):
fig, ax1 = plt.subplots()
ax1.plot(model_bits, aucs, 'c-')
ax1.set_xlabel('Hashed Features (log_2)', fontsize=16)
# Make the y-axis label and tick labels match the line color.
ax1.set_ylabel('AUC', color='c', fontsize=16)
for tl in ax1.get_yticklabels():
tl.set_color('c')
ax2 = ax1.twinx()
ax2.plot(model_bits, nzs, 'k-')
ax2.set_ylabel('Non-zero Weights', color='k', fontsize=16)
for tl in ax2.get_yticklabels():
tl.set_color('k')
fig.subplots_adjust(right=0.8)
fig.savefig(flname, DPI=200)
