Added images for blog, refactored code

[cipher-tools.git] / caesar_break_parameter_trials.py

diff --git a/caesar_break_parameter_trials.py b/caesar_break_parameter_trials.py
new file mode 100644 (file)
index 0000000..490c258
--- /dev/null
+++ b/caesar_break_parameter_trials.py
@@ -0,0 +1,89 @@
+import random
+import csv
+from support.utilities import *
+from support.language_models import *
+from support.norms import *
+from cipher.caesar import *
+
+trials = 100
+
+corpus = sanitise(cat([
+    open('support/shakespeare.txt').read(), 
+    open('support/sherlock-holmes.txt').read(), 
+    open('support/war-and-peace.txt').read()
+    ]))
+corpus_length = len(corpus)
+
+euclidean_scaled_english_counts = euclidean_scale(english_counts)
+
+metrics = [{'func': l1, 'invert': True, 'name': 'l1'}, 
+    {'func': l2, 'invert': True, 'name': 'l2'},
+    {'func': l3, 'invert': True, 'name': 'l3'},
+    {'func': cosine_similarity, 'invert': False, 'name': 'cosine_similarity'}]
+
+scalings = [{'corpus_frequency': normalised_english_counts, 
+         'scaling': normalise,
+         'name': 'normalised'},
+        {'corpus_frequency': euclidean_scaled_english_counts, 
+         'scaling': euclidean_scale,
+         'name': 'euclidean_scaled'}]
+
+message_lengths = [100, 50, 30, 20, 10, 5]
+
+def make_frequency_compare_function(
+        target_frequency, frequency_scaling, metric, invert):
+    def frequency_compare(text):
+        counts = frequency_scaling(frequencies(text))
+        if invert:
+            score = -1 * metric(target_frequency, counts)
+        else:
+            score = metric(target_frequency, counts)
+        return score
+    return frequency_compare
+
+models = (
+    [ {'func': make_frequency_compare_function(
+            s['corpus_frequency'], s['scaling'], 
+            m['func'], m['invert']),
+       'name': '{} + {}'.format(m['name'], s['name'])}
+        for m in metrics
+        for s in scalings ] 
+    + 
+    [{'func': Pletters, 'name': 'Pletters'}, 
+     {'func': Pbigrams, 'name': 'Pbigrams'},
+     {'func': Ptrigrams, 'name': 'Ptrigrams'}]
+)
+
+def random_ciphertext(message_length):
+    sample_start = random.randint(0, corpus_length - message_length)
+    sample = corpus[sample_start:(sample_start + message_length)]
+    key = random.randint(1, 25)
+    ciphertext = caesar_encipher(sample, key)
+    return key, ciphertext
+
+
+def eval_models():
+    return {m['name']: {l: eval_one_model(m, l) for l in message_lengths}
+               for m in models}
+
+def eval_one_model(model, message_length):
+    print(model['name'], message_length)
+    successes = 0
+    for _ in range(trials):
+        key, ciphertext = random_ciphertext(message_length)
+        found_key, _ = caesar_break(ciphertext, model['func'])
+        if found_key == key:
+            successes += 1 
+    return successes
+
+def write_results(scores):
+    with open('caesar_break_parameter_trials.csv', 'w') as f:
+        writer = csv.DictWriter(f, ['name'] + message_lengths, 
+            quoting=csv.QUOTE_NONNUMERIC)
+        writer.writeheader()
+        for scoring in sorted(scores):
+            scores[scoring]['name'] = scoring
+            writer.writerow(scores[scoring])
+
+scores = eval_models()
+write_results(scores)