Cleanup of eval.py

eval.py

@@ -1,9 +1,9 @@
 #!/usr/bin/env python
 # -*- coding: utf-8 -*-
-# 
+#
 # Copyright (c) 2016-present, Facebook, Inc.
 # All rights reserved.
-# 
+#
 # This source code is licensed under the BSD-style license found in the
 # LICENSE file in the root directory of this source tree. An additional grant
 # of patent rights can be found in the PATENTS file in the same directory.
@@ -14,122 +14,60 @@
 from __future__ import print_function
 from __future__ import unicode_literals
 import numpy as np
-import heapq
 from scipy import stats
 import sys
 import os
 import math
-
 import argparse
 
+def similarity(v1, v2):
+    n1 = np.linalg.norm(v1)
+    n2 = np.linalg.norm(v2)
+    return np.dot(v1, v2) / n1 / n2
+
 parser = argparse.ArgumentParser(description='Process some integers.')
 parser.add_argument('--model', '-m', dest='modelPath', action='store', required=True, help='path to model')
 parser.add_argument('--data', '-d', dest='dataPath', action='store', required=True, help='path to data')
-
 args = parser.parse_args()
 
-try:
-    f = open(args.modelPath, 'r')
-except IOError:
-    sys.exit(0)
-
-
-embeds = {}
-for i, line in enumerate(f):
+vectors = {}
+fin = open(args.modelPath, 'r')
+for i, line in enumerate(fin):
     try:
         tab = line.decode('utf8').split()
         vec = np.array(tab[1:], dtype=float)
         word = tab[0]
-        #word = tab[0].replace('í', 'i').replace('á', 'a').replace('ó', 'o').replace('ñ', 'n').replace('é', 'e').replace('ú', 'u')
-        if not word in embeds:
-            embeds[word] = vec
+        if not word in vectors:
+            vectors[word] = vec
     except ValueError:
         continue
     except UnicodeDecodeError:
         continue
-
-def levenshtein(s1, s2):
-    if len(s1) < len(s2):
-        return levenshtein(s2, s1)
-
-    # len(s1) >= len(s2)
-    if len(s2) == 0:
-        return len(s1)
-
-    previous_row = range(len(s2) + 1)
-    for i, c1 in enumerate(s1):
-        current_row = [i + 1]
-        for j, c2 in enumerate(s2):
-            insertions = previous_row[j + 1] + 1 # j+1 instead of j since previous_row and current_row are one character longer
-            deletions = current_row[j] + 1       # than s2
-            substitutions = previous_row[j] + (c1 != c2)
-            current_row.append(min(insertions, deletions, substitutions))
-        previous_row = current_row
-
-    return previous_row[-1]
-
-def findNearest(query, embeds):
-    me = 100
-    for w,vec in embeds.iteritems():
-        e = levenshtein(query, w)
-        if e < me:
-            me = e
-            nw = w
-            # print("{0:s} {1:s} {2:f}".format(query, w, e))
-    return nw
-
-def similarity(v1, v2):
-    n1 = np.linalg.norm(v1)
-    n2 = np.linalg.norm(v2)
-    dp = np.dot(v1, v2)
-    d = dp / n1 / n2
-    return d
-
-f = open(args.dataPath, 'r')
-
-doEdit = False
+fin.close()
 
 mysim = []
 gold = []
-mysimDrop = []
-goldDrop = []
 drop = 0.0
 nwords = 0.0
 
-for line in f:
-    zz = line.decode('utf8').split()
-    z1 = zz[0].lower()
-    z2 = zz[1].lower()
-    score = float(zz[2])
+fin = open(args.dataPath, 'r')
+for line in fin:
+    tline = line.decode('utf8').split()
+    word1 = tline[0].lower()
+    word2 = tline[1].lower()
     nwords = nwords + 1.0
 
-    if (z1 in embeds) and (z2 in embeds):
-        v1 = embeds[z1]
-        v2 = embeds[z2]
+    if (word1 in vectors) and (word2 in vectors):
+        v1 = vectors[word1]
+        v2 = vectors[word2]
         d = similarity(v1, v2)
         mysim.append(d)
-        gold.append(float(zz[2]))
-    elif (doEdit):
-        if (z1 in embeds):
-            w1 = z1
-        else:
-            w1 = findNearest(z1, embeds)
-
-        if (z2 in embeds):
-            w2 = z2
-        else:
-            w2 = findNearest(z2, embeds)
-        v1 = embeds[w1]
-        v2 = embeds[w2]
-        d = similarity(v1, v2)
-        mysimDrop.append(d)
-        goldDrop.append(score)
-        drop = drop + 1.0
-        sys.stdout.write(str(drop) + " ")
-        sys.stdout.flush()
+        gold.append(float(tline[2]))
     else:
         drop = drop + 1.0
+fin.close()
 
-pr = stats.spearmanr(mysim, gold)
+corr = stats.spearmanr(mysim, gold)
 dataset = os.path.basename(args.dataPath)
-print("{0:20s} & {2:2.0f}\% & {1:2.0f}".format(dataset, pr[0] * 100, math.ceil(drop / nwords * 100.0)))
+print("{0:20s}: {1:2.0f}  (OOV: {2:2.0f}%)"
+      .format(dataset, corr[0] * 100, math.ceil(drop / nwords * 100.0)))