analyze_stats.py

#!/usr/bin/env python
"""Computes the result of a stats cPickle file.

A stats cPickle file has the following format:

- List of N elements, each representing a track.
- Each position (or track) contains the rank index of the covers
    corresponding to this position.

The results this script computes are:
- Mean Average Precision (MAP)
- Average Rank per track
- Average Rank per clique
- Precision at k (default k=10)

Plotting:
- Rank histograms (one or two stats files)

----
Authors: 
Uri Nieto (oriol@nyu.edu)
Eric J. Humphrey (ejhumphrey@nyu.edu)

----
License:
This code is distributed under the GNU LESSER PUBLIC LICENSE 
(LGPL, see www.gnu.org).

Copyright (c) 2012-2013 MARL@NYU.
All rights reserved.

Redistribution and use in source and binary forms, with or without
modification, are permitted provided that the following conditions are met:

  a. Redistributions of source code must retain the above copyright notice,
     this list of conditions and the following disclaimer.
  b. Redistributions in binary form must reproduce the above copyright
     notice, this list of conditions and the following disclaimer in the
     documentation and/or other materials provided with the distribution.
  c. Neither the name of MARL, NYU nor the names of its contributors
     may be used to endorse or promote products derived from this software
     without specific prior written permission.


THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS"
AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE
ARE DISCLAIMED. IN NO EVENT SHALL THE REGENTS OR CONTRIBUTORS BE LIABLE FOR
ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL
DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR
SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER
CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT
LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY
OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH
DAMAGE.
"""

import argparse
import cPickle
import numpy as np
import pylab as plt
import utils

def get_top_ranked(stats):
    tr = np.zeros(len(stats))
    for i,s in enumerate(stats):
        try:
            if not np.isnan(s[0]) or s[0] != np.inf:
                tr[i] = s[0]
        except:
            continue
    return tr

def get_average_rank(stats):
    tr = np.zeros(len(stats))
    for i,s in enumerate(stats):
        try:
            if not np.isnan(s[0]) or s[0] != np.inf:
                tr[i] = np.mean(s)
        except:
            continue
    return tr

def average_rank_per_track(stats):
    mean_r = []
    for s in stats:
        try:
            for rank in s:
                if not np.isnan(rank) or rank != np.inf:
                    mean_r.append(rank)
        except:
            continue
    return np.mean(mean_r)

def average_rank_per_clique(stats):
    mean_r = []
    for s in stats:
        try:
            mean_r.append(np.mean(s))
            if np.isnan(mean_r[-1]) or mean_r[-1] == np.inf:
                mean_r = mean_r[:-1]
        except:
            continue
    return np.mean(mean_r)

def precision_at_k(ranks, k):
    if k == 0: return 1.0
    ranks = np.asarray(ranks)
    relevant = len(np.where(ranks <= k)[0])
    return relevant / float(k)

def average_precision(stats, q, ver=False):
    try:
        nrel = len(stats[q]) # Number of relevant docs
    except:
        return np.nan
    ap = []
    for k in stats[q]:
        pk = precision_at_k(stats[q], k)
        ap.append(pk)
    return np.sum(ap) / float(nrel)

def average_precision_at_k(stats, k):
    precision = []
    for s in stats:
        precision.append(precision_at_k(s,k))
    return np.mean(precision)

def mean_average_precision(stats):
    Q = len(stats) # Number of queries
    ma_p = []
    for q in xrange(Q):
        ap = average_precision(stats, q)
        if np.isnan(ap):
            continue
        ma_p.append(ap)
    return np.mean(ma_p)

def mean_per_clique_count(stats, N=None):
    if N is None:
        N = len(stats)
    means = np.zeros(N)
    for n in xrange(1,N):
        m = []
        k = 0
        for s in stats:
            try:
                if len(s) == n:
                    k += 1
                    m.append(np.mean(s))
            except:
                continue
        if len(m) != 0:
            means[n] = np.mean(m)
    return means

##### PLOTTING

def compute_rank_histogram_buckets(stats):
    ranks = []
    for s in stats:
        try:
            for rank in s:
                ranks.append(rank)
        except:
            continue
    # Calculate histogram
    """
    hist = np.zeros(5) #1-10, 11-25, 26-50, 51-100, 101+
    for r in ranks:
        if r <= 10:
            hist[0] += 1
        elif r > 10 and r <= 25:
            hist[1] += 1
        elif r > 25 and r <= 50:
            hist[2] += 1
        elif r > 50 and r <= 100:
            hist[3] += 1
        elif r > 100:
            hist[4] += 1
    """
    hist = np.zeros(5) #1, 2, 3-5, 6-10, 11+
    for r in ranks:
        if r <= 1:
            hist[0] += 1
        elif r > 1 and r <= 2:
            hist[1] += 1
        elif r > 2 and r <= 5:
            hist[2] += 1
        elif r > 5 and r <= 10:
            hist[3] += 1
        elif r > 10:
            hist[4] += 1

    # Probability Density Function:
    hist = hist.astype(float)
    hist /= float(hist.sum())

    return hist

def plot_rank_histogram(stats, bins=5):
    hist = compute_rank_histogram_buckets(stats)

    # Plot histogram as PDF
    plt.bar(xrange(0,bins), hist, align="center")
    plt.title("Rank Histogram")
    plt.xlabel("Ranks")
    plt.ylabel("Normalized Count")
    plt.xticks(xrange(0,5), ("1-10", "11-25", "26-50", "51-100", "101+"))
    plt.show()

def plot_rank_histograms(stats1, stats2, bins=5, test=True):

    hist1 = compute_rank_histogram_buckets(stats1)
    hist2 = compute_rank_histogram_buckets(stats2)

    if test:
        label1 = "k-means(2045) + LDA(50)"
        label2 = "2D-FMC + PCA(200)"
        title = "Rank Histogram of the test set on the MSD"
    else:
        label1 = "k-means(2045) + LDA(200)"
        label2 = "2D-FMC + PCA(200)"
        title = "Rank Histogram of the train set"

    fig = plt.figure()
    ax = fig.gca()
    width = 0.45
    ax.bar(np.arange(5)-width/2, hist1, width=width, color='b', 
        label=label1, align="center")
    ax.bar(np.arange(5)+width/2, hist2, width=width, color='g', 
        label=label2, align="center")

    # Plot histogram as PDF
    plt.title(title)
    plt.xlabel("Ranks")
    plt.ylabel("Normalized Count")
    #plt.xticks(xrange(0,5), ("1-10", "11-25", "26-50", "51-100", "101+"))
    plt.xticks(xrange(0,5), ("1", "2", "3-5", "6-10", "11+"))
    plt.legend(loc="upper left")
    plt.show()

def plot_precision_at_k_histograms(stats1, stats2, K=[1,3,5,10], test=True):
    P1 = [average_precision_at_k(stats1, k) for k in K]
    P2 = [average_precision_at_k(stats2, k) for k in K]

    if test:
        label1 = "k-means(2045) + LDA(50)"
        label2 = "2D-FMC + PCA(200)"
        title = "Precision @ k of the test set on the MSD"
    else:
        label1 = "k-means(2045) + LDA(200)"
        label2 = "2D-FMC + PCA(200)"
        title = "Precision @ k of the train set"

    fig = plt.figure()
    ax = fig.gca()
    width = 0.45
    ax.bar(np.arange(len(K))-width/2, P1, width=width, color='0.75', 
        label=label1, align="center")
    ax.bar(np.arange(len(K))+width/2, P2, width=width, color='0.9', 
        label=label2, align="center", hatch='//')

    # Plot histogram as PDF
    #plt.title(title)
    plt.xlabel("k")
    plt.ylabel("Precision @ k")
    plt.xticks(xrange(0,len(K)), ("1", "3", "5", "10"))
    ylabels = np.arange(0,3.,0.5)*10
    plt.yticks(np.arange(0,3.,0.5)*.1, ylabels.astype(int))
    plt.legend(loc="upper right")
    plt.show()


def process(statsfile, k, optfile=None):
    stats = utils.load_pickle(statsfile)
    track_ar = average_rank_per_track(stats)
    clique_ar = average_rank_per_clique(stats)
    ma_p = mean_average_precision(stats)
    #k_p = average_precision(stats, k, ver=True)
    k_p = average_precision_at_k(stats, k)

    # Set up logger
    logger = utils.configure_logger()

    # print results
    logger.info("Number of queries: %d" % len(stats))
    logger.info("Average Rank per Track: %.3f" % track_ar)
    logger.info("Average Rank per Clique: %.3f" % clique_ar)
    logger.info("Mean Average Precision: %.2f %%" % (ma_p * 100))
    logger.info("Precision at %d: %.2f %%" % (k, k_p * 100))
    
    if optfile is not None:
        stats2 = utils.load_pickle(optfile)
        #plot_rank_histograms(stats, stats2, test=False) 
        plot_precision_at_k_histograms(stats, stats2, K=[1,3,5,10], test=False)
    else:
        plot_rank_histogram(stats)

def main():
    # Args parser
    parser = argparse.ArgumentParser(description=
                "Analyzes the stats of a stats pickle file",
                formatter_class=argparse.ArgumentDefaultsHelpFormatter)
    parser.add_argument("statsfile", action="store",
                        help="stats file")
    parser.add_argument("-k", action="store", dest="k", default=10, type=int,
                        help="Compute Precision at k")
    parser.add_argument("-s", action="store", dest="optfile", default=None,
                        help="Optional stats file to make compare with")
    args = parser.parse_args()

    # Process
    process(args.statsfile, k=args.k, optfile=args.optfile)



if __name__ == "__main__":
    main()