Merge pull request #223 from mailgun/maxim/develop

PIP-1509: Optimise sender name check [python3]

requirements.txt

@@ -6,6 +6,6 @@ joblib
 lxml>=2.3.3
 numpy
 regex>=1
-scikit-learn==0.24.1 # pickled versions of classifier, else rebuild
+scikit-learn>=1.0.0
 scipy
 six>=1.10.0

setup.py

@@ -29,7 +29,7 @@ class InstallCommand(install):
 
 
 setup(name='talon',
-      version='1.4.8',
+      version='1.4.10',
       description=("Mailgun library "
                    "to extract message quotations and signatures."),
       long_description=open("README.rst").read(),
@@ -44,21 +44,21 @@ setup(name='talon',
       include_package_data=True,
       zip_safe=True,
       install_requires=[
-          "lxml>=2.3.3",
-          "regex>=1",
+          "lxml",
+          "regex",
           "numpy",
           "scipy",
-          "scikit-learn==0.24.1", # pickled versions of classifier, else rebuild
-          "chardet>=1.0.1",
-          "cchardet>=0.3.5",
+          "scikit-learn>=1.0.0",
+          "chardet",
+          "cchardet",
           "cssselect",
-          "six>=1.10.0",
+          "six",
           "html5lib",
           "joblib",
           ],
       tests_require=[
           "mock",
-          "nose>=1.2.1",
+          "nose",
           "coverage"
           ]
       )

talon/signature/__init__.py

@@ -23,17 +23,14 @@ trained against, don't forget to regenerate:
 from __future__ import absolute_import
 import os
 
-from . import extraction
-from . extraction import extract  #noqa
-from . learning import classifier
-
-
-DATA_DIR = os.path.join(os.path.dirname(__file__), 'data')
-
-EXTRACTOR_FILENAME = os.path.join(DATA_DIR, 'classifier')
-EXTRACTOR_DATA = os.path.join(DATA_DIR, 'train.data')
+from talon.signature import extraction
+from talon.signature.extraction import extract
+from talon.signature.learning import classifier
 
 
 def initialize():
-    extraction.EXTRACTOR = classifier.load(EXTRACTOR_FILENAME,
-                                           EXTRACTOR_DATA)
+    data_dir = os.path.join(os.path.dirname(__file__), 'data')
+    extractor_filename = os.path.join(data_dir, 'classifier')
+    extractor_data_filename = os.path.join(data_dir, 'train.data')
+    extraction.EXTRACTOR = classifier.load(extractor_filename,
+                                           extractor_data_filename)

talon/signature/learning/helpers.py

@@ -102,7 +102,7 @@ def flatten_list(list_to_flatten):
 
 
 def contains_sender_names(sender):
-    '''Returns a functions to search sender\'s name or it\'s part.
+    """Returns a functions to search sender\'s name or it\'s part.
 
     >>> feature = contains_sender_names("Sergey N.  Obukhov <xxx@example.com>")
     >>> feature("Sergey Obukhov")
@@ -115,7 +115,7 @@ def contains_sender_names(sender):
     1
     >>> contains_sender_names("<serobnic@mail.ru>")("serobnic")
     1
-    '''
+    """
     names = '( |$)|'.join(flatten_list([[e, e.capitalize()]
                                         for e in extract_names(sender)]))
     names = names or sender
@@ -140,10 +140,16 @@ def extract_names(sender):
     sender = "".join([char if char.isalpha() else ' ' for char in sender])
     # Remove too short words and words from "black" list i.e.
     # words like `ru`, `gmail`, `com`, `org`, etc.
-    sender = [word for word in sender.split() if len(word) > 1 and
-              not word in BAD_SENDER_NAMES]
-    # Remove duplicates
-    names = list(set(sender))
+    names = list()
+    for word in sender.split():
+        if len(word) < 2:
+            continue
+        if word in BAD_SENDER_NAMES:
+            continue
+        if word in names:
+            continue
+        names.append(word)
+
     return names
 
 
@@ -208,20 +214,26 @@ def many_capitalized_words(s):
 
 
 def has_signature(body, sender):
-    '''Checks if the body has signature. Returns True or False.'''
+    """Checks if the body has signature. Returns True or False."""
     non_empty = [line for line in body.splitlines() if line.strip()]
     candidate = non_empty[-SIGNATURE_MAX_LINES:]
     upvotes = 0
+    sender_check = contains_sender_names(sender)
     for line in candidate:
         # we check lines for sender's name, phone, email and url,
         # those signature lines don't take more then 27 lines
         if len(line.strip()) > 27:
             continue
-        elif contains_sender_names(sender)(line):
+
+        if sender_check(line):
             return True
-        elif (binary_regex_search(RE_RELAX_PHONE)(line) +
+
+        if (binary_regex_search(RE_RELAX_PHONE)(line) +
                 binary_regex_search(RE_EMAIL)(line) +
                 binary_regex_search(RE_URL)(line) == 1):
             upvotes += 1
+
     if upvotes > 1:
         return True
+
+    return False