[Predator] Add TouchCrashedFileMetaFeature.

appengine/findit/crash/loglinear/changelist_classifier.py

 # Copyright 2016 The Chromium Authors. All rights reserved.
 # Use of this source code is governed by a BSD-style license that can be
 # found in the LICENSE file.
 
 from collections import defaultdict
 import logging
-import math
 
 from common.chrome_dependency_fetcher import ChromeDependencyFetcher
 from crash import changelist_classifier
+from crash.changelist_classifier import StackInfo
 from crash.crash_report_with_dependencies import CrashReportWithDependencies
-from crash.loglinear.changelist_features.min_distance import MinDistanceFeature
-from crash.loglinear.changelist_features.top_frame_index import (
-    TopFrameIndexFeature)
-from crash.loglinear.feature import WrapperMetaFeature
 from crash.loglinear.model import UnnormalizedLogLinearModel
-from crash.stacktrace import CallStack
-from crash.stacktrace import Stacktrace
-from crash.suspect import StackInfo
 
 
 class LogLinearChangelistClassifier(object):
   """A ``LogLinearModel``-based implementation of CL classification."""
 
   def __init__(self, get_repository, meta_feature, meta_weight,
                top_n_frames=7, top_n_suspects=3):
-    """Args:
+    """
+    Args:
       get_repository (callable): a function from DEP urls to ``Repository``
         objects, so we can get changelogs and blame for each dep. Notably,
         to keep the code here generic, we make no assumptions about
         which subclass of ``Repository`` this function returns. Thus,
         it is up to the caller to decide what class to return and handle
         any other arguments that class may require (e.g., an http client
         for ``GitilesRepository``).
       meta_feature (MetaFeature): All features.
       meta_weight (MetaWeight): All weights. the weights for the features.
         The keys of the dictionary are the names of the feature that weight is
@@ skipping 101 matching lines @@
       suspect.confidence = score
       # features is ``MetaFeatureValue`` object containing all feature values.
       features = features_given_report(suspect)
       suspect.reasons = features.reason
       suspect.changed_files = [changed_file.ToDict()
                                for changed_file in features.changed_files]
       scored_suspects.append(suspect)
 
     scored_suspects.sort(key=lambda suspect: suspect.confidence)
     return scored_suspects[:self._top_n_suspects]