Compute true Jaccard similarity for candidate pairs

algods/algods.py

@@ -1,13 +1,14 @@
 import argparse
+from tqdm import tqdm
 import unicodedata
 import sys
 
 import numpy as np
 
 SHINGLE_SIZE = 5  # Known as k
-PERMUTATIONS_COUNT = 800
-BAND_COUNT = 400
-ROWS_PER_BAND = 2
+PERMUTATIONS_COUNT = 640000
+BAND_COUNT = 160000
+ROWS_PER_BAND = 4
 assert PERMUTATIONS_COUNT == BAND_COUNT * ROWS_PER_BAND
 
 
@@ -63,7 +64,7 @@ def compute_signature_matrix(shingles: np.ndarray, permutations_count: int) -> n
 
     signature_matrix = np.inf * np.ones((permutations_count, docs_count))
 
-    for permutation_id in range(permutations_count):
+    for permutation_id in tqdm(range(permutations_count)):
         permutation = np.random.permutation(shingles)
         signature_matrix[permutation_id] = permutation.argmax(0)
 
@@ -106,19 +107,14 @@ def parse(stream, similarity: float) -> None:
                         candidate_pairs.add((doc_a, doc_b))
 
     candidate_pairs = sorted(candidate_pairs)
+
     for doc_a, doc_b in candidate_pairs:
-        print(doc_a, doc_b)
-
-    exit(1)
-
-    # Calculate true jaccard similarity
-    shingles_sets = [set(x for x in range(len(shingles)) if shingles[x, doc_id]) for doc_id in range(len(docs))]
-    for i, doc1 in enumerate(shingles_sets):
-        for j in range(i + 1, len(docs)):
-            doc2 = shingles_sets[j]
-            d = jaccard_similarity(doc1, doc2)
+        # Compute true jaccard similarity
+        shingles_a = set(x for x in range(len(shingles)) if shingles[x, doc_a])
+        shingles_b = set(x for x in range(len(shingles)) if shingles[x, doc_b])
+        d = jaccard_similarity(shingles_a, shingles_b)
         if d >= similarity:
-                print(f"{i} {j} {d:.06f}")
+            print(f"{doc_a} {doc_b} {d:.06f}")
 
 
 def main():