Revised recording and revising of caesar parameter data

diff --git a/.gitignore b/.gitignore
index 7e77dc726dfffc3515eac5131a1fb0d469152573..0546add21afd94c4049024b4460cc795e622f65a 100644 (file)
--- a/.gitignore
+++ b/.gitignore
@@ -40,3 +40,6 @@ nosetests.xml
 
 # Sublime text
 *.sublime-workspace
+
+# Logs
+*.log

diff --git a/caesar_break_parameter_trials.csv b/caesar_break_parameter_trials.csv
index 6f71f0779797eb302a00da9557f6b23fd20447ae..435081f3704839e5b269003730a41477198db456 100644 (file)
--- a/caesar_break_parameter_trials.csv
+++ b/caesar_break_parameter_trials.csv
@@ -1,93 +1,14 @@
-,message_length
-scoring, 300, 100, 50, 30, 20, 10, 5
-Pletters, 0.9994, 0.9994, 0.9994, 0.9966, 0.9778, 0.8174, 0.4712
-Pletters, 0.9994, 0.9994, 0.9994, 0.9966, 0.9778, 0.8174, 0.4712
-Pletters, 0.9994, 0.9994, 0.9994, 0.9966, 0.9778, 0.8174, 0.4712
-Pletters, 0.9994, 0.9994, 0.9994, 0.9966, 0.9778, 0.8174, 0.4712
-Pletters, 0.9994, 0.9994, 0.9994, 0.9966, 0.9778, 0.8174, 0.4712
-Pletters, 0.9994, 0.9994, 0.9994, 0.9966, 0.9778, 0.8174, 0.4712
-Pletters, 0.9994, 0.9994, 0.9994, 0.9966, 0.9778, 0.8174, 0.4712
-cosine_distance + euclidean_scaled, 0.9996, 0.9996, 0.9974, 0.9836, 0.9356, 0.7124, 0.4218
-cosine_distance + euclidean_scaled, 0.9996, 0.9996, 0.9974, 0.9836, 0.9356, 0.7124, 0.4218
-cosine_distance + euclidean_scaled, 0.9996, 0.9996, 0.9974, 0.9836, 0.9356, 0.7124, 0.4218
-cosine_distance + euclidean_scaled, 0.9996, 0.9996, 0.9974, 0.9836, 0.9356, 0.7124, 0.4218
-cosine_distance + euclidean_scaled, 0.9996, 0.9996, 0.9974, 0.9836, 0.9356, 0.7124, 0.4218
-cosine_distance + euclidean_scaled, 0.9996, 0.9996, 0.9974, 0.9836, 0.9356, 0.7124, 0.4218
-cosine_distance + euclidean_scaled, 0.9996, 0.9996, 0.9974, 0.9836, 0.9356, 0.7124, 0.4218
-cosine_distance + normalised, 0.9994, 0.9996, 0.998, 0.9836, 0.934, 0.7186, 0.4402
-cosine_distance + normalised, 0.9994, 0.9996, 0.998, 0.9836, 0.934, 0.7186, 0.4402
-cosine_distance + normalised, 0.9994, 0.9996, 0.998, 0.9836, 0.934, 0.7186, 0.4402
-cosine_distance + normalised, 0.9994, 0.9996, 0.998, 0.9836, 0.934, 0.7186, 0.4402
-cosine_distance + normalised, 0.9994, 0.9996, 0.998, 0.9836, 0.934, 0.7186, 0.4402
-cosine_distance + normalised, 0.9994, 0.9996, 0.998, 0.9836, 0.934, 0.7186, 0.4402
-cosine_distance + normalised, 0.9994, 0.9996, 0.998, 0.9836, 0.934, 0.7186, 0.4402
-geometric_mean + euclidean_scaled, 0.9996, 0.9996, 0.99, 0.9506, 0.8892, 0.6562, 0.4368
-geometric_mean + euclidean_scaled, 0.9996, 0.9996, 0.99, 0.9506, 0.8892, 0.6562, 0.4368
-geometric_mean + euclidean_scaled, 0.9996, 0.9996, 0.99, 0.9506, 0.8892, 0.6562, 0.4368
-geometric_mean + euclidean_scaled, 0.9996, 0.9996, 0.99, 0.9506, 0.8892, 0.6562, 0.4368
-geometric_mean + euclidean_scaled, 0.9996, 0.9996, 0.99, 0.9506, 0.8892, 0.6562, 0.4368
-geometric_mean + euclidean_scaled, 0.9996, 0.9996, 0.99, 0.9506, 0.8892, 0.6562, 0.4368
-geometric_mean + euclidean_scaled, 0.9996, 0.9996, 0.99, 0.9506, 0.8892, 0.6562, 0.4368
-geometric_mean + normalised, 0.9996, 0.9992, 0.9902, 0.9222, 0.9408, 0.7062, 0.4568
-geometric_mean + normalised, 0.9996, 0.9992, 0.9902, 0.9222, 0.9408, 0.7062, 0.4568
-geometric_mean + normalised, 0.9996, 0.9992, 0.9902, 0.9222, 0.9408, 0.7062, 0.4568
-geometric_mean + normalised, 0.9996, 0.9992, 0.9902, 0.9222, 0.9408, 0.7062, 0.4568
-geometric_mean + normalised, 0.9996, 0.9992, 0.9902, 0.9222, 0.9408, 0.7062, 0.4568
-geometric_mean + normalised, 0.9996, 0.9992, 0.9902, 0.9222, 0.9408, 0.7062, 0.4568
-geometric_mean + normalised, 0.9996, 0.9992, 0.9902, 0.9222, 0.9408, 0.7062, 0.4568
-harmonic_mean + euclidean_scaled, 0.4688, 0.5122, 0.6894, 0.5948, 0.5258, 0.4426, 0.3642
-harmonic_mean + euclidean_scaled, 0.4688, 0.5122, 0.6894, 0.5948, 0.5258, 0.4426, 0.3642
-harmonic_mean + euclidean_scaled, 0.4688, 0.5122, 0.6894, 0.5948, 0.5258, 0.4426, 0.3642
-harmonic_mean + euclidean_scaled, 0.4688, 0.5122, 0.6894, 0.5948, 0.5258, 0.4426, 0.3642
-harmonic_mean + euclidean_scaled, 0.4688, 0.5122, 0.6894, 0.5948, 0.5258, 0.4426, 0.3642
-harmonic_mean + euclidean_scaled, 0.4688, 0.5122, 0.6894, 0.5948, 0.5258, 0.4426, 0.3642
-harmonic_mean + euclidean_scaled, 0.4688, 0.5122, 0.6894, 0.5948, 0.5258, 0.4426, 0.3642
-harmonic_mean + normalised, 0.8134, 0.8368, 0.7672, 0.2674, 0.8608, 0.6736, 0.453
-harmonic_mean + normalised, 0.8134, 0.8368, 0.7672, 0.2674, 0.8608, 0.6736, 0.453
-harmonic_mean + normalised, 0.8134, 0.8368, 0.7672, 0.2674, 0.8608, 0.6736, 0.453
-harmonic_mean + normalised, 0.8134, 0.8368, 0.7672, 0.2674, 0.8608, 0.6736, 0.453
-harmonic_mean + normalised, 0.8134, 0.8368, 0.7672, 0.2674, 0.8608, 0.6736, 0.453
-harmonic_mean + normalised, 0.8134, 0.8368, 0.7672, 0.2674, 0.8608, 0.6736, 0.453
-harmonic_mean + normalised, 0.8134, 0.8368, 0.7672, 0.2674, 0.8608, 0.6736, 0.453
-l1 + euclidean_scaled, 0.9998, 0.9994, 0.9984, 0.9904, 0.9502, 0.7558, 0.4348
-l1 + euclidean_scaled, 0.9998, 0.9994, 0.9984, 0.9904, 0.9502, 0.7558, 0.4348
-l1 + euclidean_scaled, 0.9998, 0.9994, 0.9984, 0.9904, 0.9502, 0.7558, 0.4348
-l1 + euclidean_scaled, 0.9998, 0.9994, 0.9984, 0.9904, 0.9502, 0.7558, 0.4348
-l1 + euclidean_scaled, 0.9998, 0.9994, 0.9984, 0.9904, 0.9502, 0.7558, 0.4348
-l1 + euclidean_scaled, 0.9998, 0.9994, 0.9984, 0.9904, 0.9502, 0.7558, 0.4348
-l1 + euclidean_scaled, 0.9998, 0.9994, 0.9984, 0.9904, 0.9502, 0.7558, 0.4348
-l1 + normalised, 0.9998, 0.9998, 0.9986, 0.9882, 0.955, 0.7252, 0.4432
-l1 + normalised, 0.9998, 0.9998, 0.9986, 0.9882, 0.955, 0.7252, 0.4432
-l1 + normalised, 0.9998, 0.9998, 0.9986, 0.9882, 0.955, 0.7252, 0.4432
-l1 + normalised, 0.9998, 0.9998, 0.9986, 0.9882, 0.955, 0.7252, 0.4432
-l1 + normalised, 0.9998, 0.9998, 0.9986, 0.9882, 0.955, 0.7252, 0.4432
-l1 + normalised, 0.9998, 0.9998, 0.9986, 0.9882, 0.955, 0.7252, 0.4432
-l1 + normalised, 0.9998, 0.9998, 0.9986, 0.9882, 0.955, 0.7252, 0.4432
-l2 + euclidean_scaled, 0.9996, 0.9988, 0.9992, 0.9786, 0.9368, 0.712, 0.4336
-l2 + euclidean_scaled, 0.9996, 0.9988, 0.9992, 0.9786, 0.9368, 0.712, 0.4336
-l2 + euclidean_scaled, 0.9996, 0.9988, 0.9992, 0.9786, 0.9368, 0.712, 0.4336
-l2 + euclidean_scaled, 0.9996, 0.9988, 0.9992, 0.9786, 0.9368, 0.712, 0.4336
-l2 + euclidean_scaled, 0.9996, 0.9988, 0.9992, 0.9786, 0.9368, 0.712, 0.4336
-l2 + euclidean_scaled, 0.9996, 0.9988, 0.9992, 0.9786, 0.9368, 0.712, 0.4336
-l2 + euclidean_scaled, 0.9996, 0.9988, 0.9992, 0.9786, 0.9368, 0.712, 0.4336
-l2 + normalised, 0.9998, 0.999, 0.998, 0.9818, 0.933, 0.709, 0.4356
-l2 + normalised, 0.9998, 0.999, 0.998, 0.9818, 0.933, 0.709, 0.4356
-l2 + normalised, 0.9998, 0.999, 0.998, 0.9818, 0.933, 0.709, 0.4356
-l2 + normalised, 0.9998, 0.999, 0.998, 0.9818, 0.933, 0.709, 0.4356
-l2 + normalised, 0.9998, 0.999, 0.998, 0.9818, 0.933, 0.709, 0.4356
-l2 + normalised, 0.9998, 0.999, 0.998, 0.9818, 0.933, 0.709, 0.4356
-l2 + normalised, 0.9998, 0.999, 0.998, 0.9818, 0.933, 0.709, 0.4356
-l3 + euclidean_scaled, 0.9996, 0.999, 0.996, 0.9684, 0.8934, 0.6282, 0.4084
-l3 + euclidean_scaled, 0.9996, 0.999, 0.996, 0.9684, 0.8934, 0.6282, 0.4084
-l3 + euclidean_scaled, 0.9996, 0.999, 0.996, 0.9684, 0.8934, 0.6282, 0.4084
-l3 + euclidean_scaled, 0.9996, 0.999, 0.996, 0.9684, 0.8934, 0.6282, 0.4084
-l3 + euclidean_scaled, 0.9996, 0.999, 0.996, 0.9684, 0.8934, 0.6282, 0.4084
-l3 + euclidean_scaled, 0.9996, 0.999, 0.996, 0.9684, 0.8934, 0.6282, 0.4084
-l3 + euclidean_scaled, 0.9996, 0.999, 0.996, 0.9684, 0.8934, 0.6282, 0.4084
-l3 + normalised, 1.0, 0.9986, 0.9932, 0.963, 0.8696, 0.594, 0.4122
-l3 + normalised, 1.0, 0.9986, 0.9932, 0.963, 0.8696, 0.594, 0.4122
-l3 + normalised, 1.0, 0.9986, 0.9932, 0.963, 0.8696, 0.594, 0.4122
-l3 + normalised, 1.0, 0.9986, 0.9932, 0.963, 0.8696, 0.594, 0.4122
-l3 + normalised, 1.0, 0.9986, 0.9932, 0.963, 0.8696, 0.594, 0.4122
-l3 + normalised, 1.0, 0.9986, 0.9932, 0.963, 0.8696, 0.594, 0.4122
-l3 + normalised, 1.0, 0.9986, 0.9932, 0.963, 0.8696, 0.594, 0.4122
+"name",300,100,50,30,20,10,5\r
+"Pletters",4996,4999,4999,4982,4887,4081,2400\r
+"cosine_distance + euclidean_scaled",5000,4997,4995,4921,4671,3568,2184\r
+"cosine_distance + normalised",4998,4998,4993,4921,4667,3538,2152\r
+"geometric_mean + euclidean_scaled",4994,4996,4953,4770,4457,3208,2192\r
+"geometric_mean + normalised",4996,4997,4939,4574,4681,3607,2293\r
+"harmonic_mean + euclidean_scaled",2381,2555,3445,2991,2651,2125,1769\r
+"harmonic_mean + normalised",4033,4177,3772,1347,4352,3371,2255\r
+"l1 + euclidean_scaled",4997,4998,4993,4967,4779,3760,2252\r
+"l1 + normalised",4996,4996,4997,4953,4756,3668,2174\r
+"l2 + euclidean_scaled",4996,4997,4987,4906,4700,3592,2158\r
+"l2 + normalised",4999,4999,4988,4915,4676,3585,2129\r
+"l3 + euclidean_scaled",5000,4999,4973,4807,4456,3160,1970\r
+"l3 + normalised",4999,4999,4966,4782,4388,2968,1986\r

diff --git a/find_best_caesar_break_parameters.py b/find_best_caesar_break_parameters.py
index 9ed53488dde8161ea13b1b6caa35b8aa60eb0fa0..5f04e56a70becb4122cbe4726f7459d840127b82 100644 (file)
--- a/find_best_caesar_break_parameters.py
+++ b/find_best_caesar_break_parameters.py
@@ -3,6 +3,7 @@ import collections
 from cipher import *
 from cipherbreak import *
 import itertools
+import csv
 
 corpus = sanitise(''.join([open('shakespeare.txt', 'r').read(), 
     open('sherlock-holmes.txt', 'r').read(), 
@@ -11,14 +12,6 @@ corpus_length = len(corpus)
 
 euclidean_scaled_english_counts = norms.euclidean_scale(english_counts)
 
-# def frequency_compare(text, target_frequency, frequency_scaling, metric):
-#     counts = frequency_scaling(frequencies(text))
-#     return -1 * metric(target_frequency, counts)
-
-# def euclidean_compare(text):
-#     return frequency_compare(text, norms.euclidean_scale(english_counts),
-#             norms.euclidean_scale, norms.euclidean_distance)
-
 metrics = [{'func': norms.l1, 'invert': True, 'name': 'l1'}, 
     {'func': norms.l2, 'invert': True, 'name': 'l2'},
     {'func': norms.l3, 'invert': True, 'name': 'l3'},
@@ -48,7 +41,6 @@ def make_frequency_compare_function(target_frequency, frequency_scaling, metric,
         return score
     return frequency_compare
 
-
 def scoring_functions():
     return [{'func': make_frequency_compare_function(s['corpus_frequency'], 
                 s['scaling'], m['func'], m['invert']),
@@ -65,6 +57,7 @@ def eval_one_score(scoring_function, message_length):
     print(scoring_function['name'], message_length)
     if scoring_function['name'] not in scores:
         scores[scoring_function['name']] = collections.defaultdict(int)
+        scores[scoring_function['name']]['name'] = scoring_function['name']
     for _ in range(trials):
         sample_start = random.randint(0, corpus_length - message_length)
         sample = corpus[sample_start:(sample_start + message_length)]
@@ -77,14 +70,11 @@ def eval_one_score(scoring_function, message_length):
 
 def show_results():
     with open('caesar_break_parameter_trials.csv', 'w') as f:
-        print(',message_length', file = f)
-        print('scoring,', ', '.join([str(l) for l in message_lengths]), file = f)
+        writer = csv.DictWriter(f, ['name'] + message_lengths, 
+            quoting=csv.QUOTE_NONNUMERIC)
+        writer.writeheader()
         for scoring in sorted(scores.keys()):
-            for length in message_lengths:
-                print(scoring, end='', sep='', file=f)
-                for l in message_lengths:
-                    print(',', scores[scoring][l] / trials, end='', file=f)
-                print('', file = f)
+            writer.writerow(scores[scoring])
 
 eval_scores()
 show_results()

diff --git a/plot-caesar-parameters.ipynb b/plot-caesar-parameters.ipynb
new file mode 100644 (file)
index 0000000..59b6c6e
--- /dev/null
+++ b/plot-caesar-parameters.ipynb
@@ -0,0 +1,249 @@
+{
+ "metadata": {
+  "name": ""
+ },
+ "nbformat": 3,
+ "nbformat_minor": 0,
+ "worksheets": [
+  {
+   "cells": [
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "import csv\n",
+      "import matplotlib.pyplot as plt\n",
+      "%matplotlib inline"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [],
+     "prompt_number": 1
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "with open('caesar_break_parameter_trials.csv') as c:\n",
+      "    results = [l for l in csv.DictReader(c, quoting=csv.QUOTE_NONNUMERIC)]\n",
+      "results"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [
+      {
+       "metadata": {},
+       "output_type": "pyout",
+       "prompt_number": 2,
+       "text": [
+        "[{5.0: 2400.0,\n",
+        "  20.0: 4887.0,\n",
+        "  50.0: 4999.0,\n",
+        "  100.0: 4999.0,\n",
+        "  'name': 'Pletters',\n",
+        "  10.0: 4081.0,\n",
+        "  300.0: 4996.0,\n",
+        "  30.0: 4982.0},\n",
+        " {5.0: 2184.0,\n",
+        "  20.0: 4671.0,\n",
+        "  50.0: 4995.0,\n",
+        "  100.0: 4997.0,\n",
+        "  'name': 'cosine_distance + euclidean_scaled',\n",
+        "  10.0: 3568.0,\n",
+        "  300.0: 5000.0,\n",
+        "  30.0: 4921.0},\n",
+        " {5.0: 2152.0,\n",
+        "  20.0: 4667.0,\n",
+        "  50.0: 4993.0,\n",
+        "  100.0: 4998.0,\n",
+        "  'name': 'cosine_distance + normalised',\n",
+        "  10.0: 3538.0,\n",
+        "  300.0: 4998.0,\n",
+        "  30.0: 4921.0},\n",
+        " {5.0: 2192.0,\n",
+        "  20.0: 4457.0,\n",
+        "  50.0: 4953.0,\n",
+        "  100.0: 4996.0,\n",
+        "  'name': 'geometric_mean + euclidean_scaled',\n",
+        "  10.0: 3208.0,\n",
+        "  300.0: 4994.0,\n",
+        "  30.0: 4770.0},\n",
+        " {5.0: 2293.0,\n",
+        "  20.0: 4681.0,\n",
+        "  50.0: 4939.0,\n",
+        "  100.0: 4997.0,\n",
+        "  'name': 'geometric_mean + normalised',\n",
+        "  10.0: 3607.0,\n",
+        "  300.0: 4996.0,\n",
+        "  30.0: 4574.0},\n",
+        " {5.0: 1769.0,\n",
+        "  20.0: 2651.0,\n",
+        "  50.0: 3445.0,\n",
+        "  100.0: 2555.0,\n",
+        "  'name': 'harmonic_mean + euclidean_scaled',\n",
+        "  10.0: 2125.0,\n",
+        "  300.0: 2381.0,\n",
+        "  30.0: 2991.0},\n",
+        " {5.0: 2255.0,\n",
+        "  20.0: 4352.0,\n",
+        "  50.0: 3772.0,\n",
+        "  100.0: 4177.0,\n",
+        "  'name': 'harmonic_mean + normalised',\n",
+        "  10.0: 3371.0,\n",
+        "  300.0: 4033.0,\n",
+        "  30.0: 1347.0},\n",
+        " {5.0: 2252.0,\n",
+        "  20.0: 4779.0,\n",
+        "  50.0: 4993.0,\n",
+        "  100.0: 4998.0,\n",
+        "  'name': 'l1 + euclidean_scaled',\n",
+        "  10.0: 3760.0,\n",
+        "  300.0: 4997.0,\n",
+        "  30.0: 4967.0},\n",
+        " {5.0: 2174.0,\n",
+        "  20.0: 4756.0,\n",
+        "  50.0: 4997.0,\n",
+        "  100.0: 4996.0,\n",
+        "  'name': 'l1 + normalised',\n",
+        "  10.0: 3668.0,\n",
+        "  300.0: 4996.0,\n",
+        "  30.0: 4953.0},\n",
+        " {5.0: 2158.0,\n",
+        "  20.0: 4700.0,\n",
+        "  50.0: 4987.0,\n",
+        "  100.0: 4997.0,\n",
+        "  'name': 'l2 + euclidean_scaled',\n",
+        "  10.0: 3592.0,\n",
+        "  300.0: 4996.0,\n",
+        "  30.0: 4906.0},\n",
+        " {5.0: 2129.0,\n",
+        "  20.0: 4676.0,\n",
+        "  50.0: 4988.0,\n",
+        "  100.0: 4999.0,\n",
+        "  'name': 'l2 + normalised',\n",
+        "  10.0: 3585.0,\n",
+        "  300.0: 4999.0,\n",
+        "  30.0: 4915.0},\n",
+        " {5.0: 1970.0,\n",
+        "  20.0: 4456.0,\n",
+        "  50.0: 4973.0,\n",
+        "  100.0: 4999.0,\n",
+        "  'name': 'l3 + euclidean_scaled',\n",
+        "  10.0: 3160.0,\n",
+        "  300.0: 5000.0,\n",
+        "  30.0: 4807.0},\n",
+        " {5.0: 1986.0,\n",
+        "  20.0: 4388.0,\n",
+        "  50.0: 4966.0,\n",
+        "  100.0: 4999.0,\n",
+        "  'name': 'l3 + normalised',\n",
+        "  10.0: 2968.0,\n",
+        "  300.0: 4999.0,\n",
+        "  30.0: 4782.0}]"
+       ]
+      }
+     ],
+     "prompt_number": 2
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "for r in results:\n",
+      "    plt.plot(sorted([k for k in r.keys() if k != 'name']), sorted([r[k] for k in r.keys() if k != 'name']))\n",
+      "plt.legend([r['name'] for r in results], loc='center', bbox_to_anchor=(2, 0.5))"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [
+      {
+       "metadata": {},
+       "output_type": "pyout",
+       "prompt_number": 3,
+       "text": [
+        "<matplotlib.legend.Legend at 0x7fefd4091f90>"
+       ]
+      },
+      {
+       "metadata": {},
+       "output_type": "display_data",
+       "png": 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GDw8PvLy8OHToEGlpaeTm5hIQEADAiBEj+P77729zVcpVjF/6+WQsNsZetLqQ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gAAAg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\nSQghhBBCCCGq8f8DDYHDkOhsPi4AAAAASUVORK5CYII=\n",
+       "text": [
+        "<matplotlib.figure.Figure at 0x7fefd402c050>"
+       ]
+      }
+     ],
+     "prompt_number": 3
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "for r in results:\n",
+      "    plt.plot(sorted([k for k in [5.0, 10.0, 20.0, 30.0]]), sorted([r[k] for k in [5.0, 10.0, 20.0, 30.0]]))\n",
+      "plt.legend([r['name'] for r in results], loc='center', bbox_to_anchor=(2, 0.5))"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [
+      {
+       "metadata": {},
+       "output_type": "pyout",
+       "prompt_number": 4,
+       "text": [
+        "<matplotlib.legend.Legend at 0x7fefd4091810>"
+       ]
+      },
+      {
+       "metadata": {},
+       "output_type": "display_data",
+       "png": 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Hyck5H5J++w0efBCefx66dQNX16t//vR0+OAD+OgjaNECXpxcRmbgMt7d+w4h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\n",
+       "text": [
+        "<matplotlib.figure.Figure at 0x7fefd40917d0>"
+       ]
+      }
+     ],
+     "prompt_number": 4
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "bests = {}\n",
+      "for l in [k for k in results[0].keys() if k != 'name']:\n",
+      "    bests[l] = max(results, key=lambda r: r[l])['name']\n",
+      "bests"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [
+      {
+       "metadata": {},
+       "output_type": "pyout",
+       "prompt_number": 5,
+       "text": [
+        "{5.0: 'Pletters',\n",
+        " 10.0: 'Pletters',\n",
+        " 20.0: 'Pletters',\n",
+        " 30.0: 'Pletters',\n",
+        " 50.0: 'Pletters',\n",
+        " 100.0: 'Pletters',\n",
+        " 300.0: 'cosine_distance + euclidean_scaled'}"
+       ]
+      }
+     ],
+     "prompt_number": 5
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [],
+     "language": "python",
+     "metadata": {},
+     "outputs": []
+    }
+   ],
+   "metadata": {}
+  }
+ ]
+}
\ No newline at end of file