Open Source Monorail

appengine/monorail/services/spam_svc.py

+# Copyright 2016 The Chromium Authors. All rights reserved.
+# Use of this source code is govered by a BSD-style
+# license that can be found in the LICENSE file or at
+# https://developers.google.com/open-source/licenses/bsd
+
+""" Set of functions for detaling with spam reports.
+"""
+
+import collections
+import httplib2
+import logging
+import settings
+import sys
+import settings
+
+from features import filterrules_helpers
+from framework import sql
+from infra_libs import ts_mon
+
+from apiclient.discovery import build
+from oauth2client.client import GoogleCredentials
+from apiclient.errors import Error as ApiClientError
+from oauth2client.client import Error as Oauth2ClientError
+
+SPAMREPORT_TABLE_NAME = 'SpamReport'
+SPAMVERDICT_TABLE_NAME = 'SpamVerdict'
+ISSUE_TABLE = 'Issue'
+
+REASON_MANUAL = 'manual'
+REASON_THRESHOLD = 'threshold'
+REASON_CLASSIFIER = 'classifier'
+
+SPAMREPORT_COLS = ['issue_id', 'reported_user_id', 'user_id']
+MANUALVERDICT_COLS = ['user_id', 'issue_id', 'is_spam', 'reason', 'project_id']
+THRESHVERDICT_COLS = ['issue_id', 'is_spam', 'reason', 'project_id']
+
+
+class SpamService(object):
+  """The persistence layer for spam reports."""
+  issue_actions = ts_mon.CounterMetric('monorail/spam_svc/issue')
+  comment_actions = ts_mon.CounterMetric('monorail/spam_svc/comment')
+
+  def __init__(self):
+    self.report_tbl = sql.SQLTableManager(SPAMREPORT_TABLE_NAME)
+    self.verdict_tbl = sql.SQLTableManager(SPAMVERDICT_TABLE_NAME)
+    self.issue_tbl = sql.SQLTableManager(ISSUE_TABLE)
+
+    self.prediction_service = None
+    try:
+      credentials = GoogleCredentials.get_application_default()
+      self.prediction_service = build('prediction', 'v1.6',
+                                      http=httplib2.Http(),
+                                      credentials=credentials)
+    except (Oauth2ClientError, ApiClientError):
+      logging.error("Error getting GoogleCredentials: %s" % sys.exc_info()[0])
+
+  def LookupFlaggers(self, cnxn, issue_id):
+    """Returns users who've reported the issue or its comments as spam.
+
+    Returns a tuple. First element is a list of users who flagged the issue;
+    second element is a dictionary of comment id to a list of users who flagged
+    that comment.
+    """
+    rows = self.report_tbl.Select(
+        cnxn, cols=['user_id', 'comment_id'],
+        issue_id=issue_id)
+
+    issue_reporters = []
+    comment_reporters = collections.defaultdict(list)
+    for row in rows:
+      if row[1]:
+        comment_reporters[row[1]].append(row[0])
+      else:
+        issue_reporters.append(row[0])
+
+    return issue_reporters, comment_reporters
+
+  def LookUpFlagCounts(self, cnxn, issue_ids):
+    """Returns a map of issue_id to flag counts"""
+    rows = self.report_tbl.Select(cnxn, cols=['issue_id', 'COUNT(*)'],
+                                  issue_id=issue_ids, group_by=['issue_id'])
+    counts = {}
+    for row in rows:
+      counts[long(row[0])] = row[1]
+    return counts
+
+  def LookUpIssueVerdicts(self, cnxn, issue_ids):
+    """Returns a map of issue_id to most recent spam verdicts"""
+    rows = self.verdict_tbl.Select(cnxn,
+                                   cols=['issue_id', 'reason', 'MAX(created)'],
+                                   issue_id=issue_ids, group_by=['issue_id'])
+    counts = {}
+    for row in rows:
+      counts[long(row[0])] = row[1]
+    return counts
+
+  def LookUpIssueVerdictHistory(self, cnxn, issue_ids):
+    """Returns a map of issue_id to most recent spam verdicts"""
+    rows = self.verdict_tbl.Select(cnxn, cols=[
+        'issue_id', 'reason', 'created', 'is_spam', 'classifier_confidence',
+            'user_id', 'overruled'],
+        issue_id=issue_ids, order_by=[('issue_id', []), ('created', [])])
+
+    # TODO: group by issue_id, make class instead of dict for verdict.
+    verdicts = []
+    for row in rows:
+      verdicts.append({
+        'issue_id': row[0],
+        'reason': row[1],
+        'created': row[2],
+        'is_spam': row[3],
+        'classifier_confidence': row[4],
+        'user_id': row[5],
+        'overruled': row[6],
+      })
+
+    return verdicts
+
+  def FlagIssues(self, cnxn, issue_service, issues, reporting_user_id,
+                 flagged_spam):
+    """Creates or deletes a spam report on an issue."""
+    verdict_updates = []
+    if flagged_spam:
+      rows = [(issue.issue_id, issue.reporter_id, reporting_user_id)
+          for issue in issues]
+      self.report_tbl.InsertRows(cnxn, SPAMREPORT_COLS, rows, ignore=True)
+    else:
+      issue_ids = [issue.issue_id for issue in issues]
+      self.report_tbl.Delete(
+          cnxn, issue_id=issue_ids, user_id=reporting_user_id,
+          comment_id=None)
+
+    project_id = issues[0].project_id
+
+    # Now record new verdicts and update issue.is_spam, if they've changed.
+    ids = [issue.issue_id for issue in issues]
+    counts = self.LookUpFlagCounts(cnxn, ids)
+    previous_verdicts = self.LookUpIssueVerdicts(cnxn, ids)
+
+    for issue_id in counts:
+      # If the flag counts changed enough to toggle the is_spam bit, need to
+      # record a new verdict and update the Issue.
+      if ((flagged_spam and counts[issue_id] >= settings.spam_flag_thresh or
+          not flagged_spam and counts[issue_id] < settings.spam_flag_thresh) and
+          (previous_verdicts[issue_id] != REASON_MANUAL if issue_id in
+           previous_verdicts else True)):
+        verdict_updates.append(issue_id)
+
+    if len(verdict_updates) == 0:
+      return
+
+    # Some of the issues may have exceed the flag threshold, so issue verdicts
+    # and mark as spam in those cases.
+    rows = [(issue_id, flagged_spam, REASON_THRESHOLD, project_id)
+        for issue_id in verdict_updates]
+    self.verdict_tbl.InsertRows(cnxn, THRESHVERDICT_COLS, rows, ignore=True)
+    update_issues = []
+    for issue in issues:
+      if issue.issue_id in verdict_updates:
+        issue.is_spam = flagged_spam
+        update_issues.append(issue)
+
+    if flagged_spam:
+      self.issue_actions.increment_by(len(update_issues), {'type': 'flag'})
+
+    issue_service.UpdateIssues(cnxn, update_issues, update_cols=['is_spam'])
+
+  def FlagComment(self, cnxn, issue_id, comment_id, reported_user_id,
+                  reporting_user_id, flagged_spam):
+    """Creates or deletes a spam report on a comment."""
+    # TODO(seanmccullough): Bulk comment flagging? There's no UI for that.
+    if flagged_spam:
+      self.report_tbl.InsertRow(
+          cnxn, ignore=True, issue_id=issue_id,
+          comment_id=comment_id, reported_user_id=reported_user_id,
+          user_id=reporting_user_id)
+      self.comment_actions.increment({'type': 'flag'})
+    else:
+      self.report_tbl.Delete(
+          cnxn, issue_id=issue_id, comment_id=comment_id,
+          user_id=reporting_user_id)
+
+  def RecordClassifierIssueVerdict(self, cnxn, issue, is_spam, confidence):
+    self.verdict_tbl.InsertRow(cnxn, issue_id=issue.issue_id, is_spam=is_spam,
+        reason=REASON_CLASSIFIER, classifier_confidence=confidence)
+    if is_spam:
+      self.issue_actions.increment({'type': 'classifier'})
+    # This is called at issue creation time, so there's nothing else to do here.
+
+  def RecordManualIssueVerdicts(self, cnxn, issue_service, issues, user_id,
+                                is_spam):
+    rows = [(user_id, issue.issue_id, is_spam, REASON_MANUAL, issue.project_id)
+        for issue in issues]
+    issue_ids = [issue.issue_id for issue in issues]
+
+    # Overrule all previous verdicts.
+    self.verdict_tbl.Update(cnxn, {'overruled': True}, [
+        ('issue_id IN (%s)' % sql.PlaceHolders(issue_ids), issue_ids)
+        ], commit=False)
+
+    self.verdict_tbl.InsertRows(cnxn, MANUALVERDICT_COLS, rows, ignore=True)
+
+    for issue in issues:
+      issue.is_spam = is_spam
+
+    if is_spam:
+      self.issue_actions.increment_by(len(issues), {'type': 'manual'})
+    else:
+      issue_service.AllocateNewLocalIDs(cnxn, issues)
+
+    # This will commit the transaction.
+    issue_service.UpdateIssues(cnxn, issues, update_cols=['is_spam'])
+
+  def RecordManualCommentVerdict(self, cnxn, issue_service, user_service,
+        comment_id, sequence_num, user_id, is_spam):
+    # TODO(seanmccullough): Bulk comment verdicts? There's no UI for that.
+    self.verdict_tbl.InsertRow(cnxn, ignore=True,
+      user_id=user_id, comment_id=comment_id, is_spam=is_spam,
+      reason=REASON_MANUAL)
+    comment = issue_service.GetComment(cnxn, comment_id)
+    comment.is_spam = is_spam
+    issue = issue_service.GetIssue(cnxn, comment.issue_id)
+    issue_service.SoftDeleteComment(cnxn, comment.project_id, issue.local_id,
+                                    sequence_num, user_id, user_service,
+                                    is_spam, True, is_spam)
+    if is_spam:
+      self.comment_actions.increment({'type': 'manual'})
+
+  def RecordClassifierCommentVerdict(self, cnxn, comment, is_spam, confidence):
+    self.verdict_tbl.InsertRow(cnxn, comment_id=comment.id, is_spam=is_spam,
+        reason=REASON_CLASSIFIER, classifier_confidence=confidence,
+        project_id=comment.project_id)
+    if is_spam:
+      self.comment_actions.increment({'type': 'classifier'})
+
+  def ClassifyIssue(self, issue, firstComment):
+    """Classify an issue as either spam or ham.
+
+    Args:
+      issue: the Issue.
+      firstComment: the first Comment on issue.
+
+    Returns a JSON dict of classifier prediction results from
+    the Cloud Prediction API.
+    """
+    # Fail-safe: not spam.
+    result = {'outputLabel': 'ham',
+              'outputMulti': [{'label':'ham', 'score': '1.0'}]}
+    if not self.prediction_service:
+      logging.error("prediction_service not initialized.")
+      return result
+
+    remaining_retries = 3
+    while remaining_retries > 0:
+      try:
+        result = self.prediction_service.trainedmodels().predict(
+            project=settings.classifier_project_id,
+            id=settings.classifier_model_id,
+            body={'input': {
+                'csvInstance': [issue.summary, firstComment.content]}}
+        ).execute()
+        return result
+      except Exception:
+        remaining_retries = remaining_retries - 1
+        logging.error('Error calling prediction API: %s' % sys.exc_info()[0])
+
+    return result
+
+  def ClassifyComment(self, comment_content):
+    """Classify a comment as either spam or ham.
+
+    Args:
+      comment: the comment text.
+
+    Returns a JSON dict of classifier prediction results from
+    the Cloud Prediction API.
+    """
+    # Fail-safe: not spam.
+    result = {'outputLabel': 'ham',
+              'outputMulti': [{'label':'ham', 'score': '1.0'}]}
+    if not self.prediction_service:
+      logging.error("prediction_service not initialized.")
+      return result
+
+    remaining_retries = 3
+    while remaining_retries > 0:
+      try:
+        result = self.prediction_service.trainedmodels().predict(
+            project=settings.classifier_project_id,
+            id=settings.classifier_model_id,
+            # We re-use the issue classifier here, with a blank
+            # description and use the comment content as the body.
+            body={'input': {'csvInstance': ['', comment_content]}}
+        ).execute()
+        return result
+      except Exception:
+        remaining_retries = remaining_retries - 1
+        logging.error('Error calling prediction API: %s' % sys.exc_info()[0])
+
+    return result
+
+  def GetModerationQueue(
+      self, cnxn, _issue_service, project_id, offset=0, limit=10):
+     """Returns list of recent issues with spam verdicts,
+     ranked in ascending order of confidence (so uncertain items are first).
+     """
+     # TODO(seanmccullough): Optimize pagination. This query probably gets
+     # slower as the number of SpamVerdicts grows, regardless of offset
+     # and limit values used here.  Using offset,limit in general may not
+     # be the best way to do this.
+     results = self.verdict_tbl.Select(cnxn,
+         cols=['issue_id', 'is_spam', 'reason', 'classifier_confidence',
+               'created'],
+         where=[
+             ('project_id = %s', [project_id]),
+             ('classifier_confidence <= %s',
+                 [settings.classifier_moderation_thresh]),
+             ('overruled = %s', [False]),
+             ('issue_id IS NOT NULL', []),
+         ],
+         order_by=[
+             ('classifier_confidence ASC', []),
+             ('created ASC', []),
+             ],
+         group_by=['issue_id'],
+         offset=offset,
+         limit=limit,
+         )
+
+     ret = []
+     for row in results:
+       ret.append(ModerationItem(
+         issue_id=long(row[0]),
+         is_spam=row[1] == 1,
+         reason=row[2],
+         classifier_confidence=row[3],
+         verdict_time='%s' % row[4],
+       ))
+
+     count = self.verdict_tbl.SelectValue(cnxn,
+         col='COUNT(*)',
+         where=[
+             ('project_id = %s', [project_id]),
+             ('classifier_confidence <= %s',
+                 [settings.classifier_moderation_thresh]),
+             ('overruled = %s', [False]),
+             ('issue_id IS NOT NULL', []),
+         ])
+
+     return ret, count
+
+  def GetTrainingIssues(self, cnxn, issue_service, since, offset=0, limit=100):
+    """Returns list of recent issues with spam verdicts,
+    ranked in ascending order of confidence (so uncertain items are first).
+    """
+
+    # get all of the manual verdicts in the past day.
+    results = self.verdict_tbl.Select(cnxn,
+        cols=['issue_id'],
+        where=[
+            ('overruled = %s', [False]),
+            ('reason = %s', ['manual']),
+            ('issue_id IS NOT NULL', []),
+            ('created > %s', [since.isoformat()]),
+        ],
+        offset=offset,
+        limit=limit,
+        )
+
+    issue_ids = [long(row[0]) for row in results if row[0]]
+    issues = issue_service.GetIssues(cnxn, issue_ids)
+    comments = issue_service.GetCommentsForIssues(cnxn, issue_ids)
+    first_comments = {}
+    for issue in issues:
+      first_comments[issue.issue_id] = (comments[issue.issue_id][0].content
+          if issue.issue_id in comments else "[Empty]")
+
+    count = self.verdict_tbl.SelectValue(cnxn,
+        col='COUNT(*)',
+        where=[
+            ('overruled = %s', [False]),
+            ('reason = %s', ['manual']),
+            ('issue_id IS NOT NULL', []),
+            ('created > %s', [since.isoformat()]),
+        ])
+
+    return issues, first_comments, count
+
+class ModerationItem:
+  def __init__(self, **kwargs):
+    self.__dict__ = kwargs