def get(self, exploration_id, exploration_version):
"""Handles GET requests."""
try:
exp_services.get_exploration_by_id(exploration_id)
except:
raise self.PageNotFoundException
self.render_json(stats_services.get_exploration_stats(
exploration_id, exploration_version))
def get(self, exploration_id):
"""Handles GET requests."""
current_exploration = exp_fetchers.get_exploration_by_id(
exploration_id)
self.render_json(
stats_services.get_exploration_stats(
exploration_id,
current_exploration.version).to_frontend_dict())
def get(self, exploration_id, exploration_version):
"""Handles GET requests."""
try:
exp_services.get_exploration_by_id(exploration_id)
except:
raise self.PageNotFoundException
self.render_json(stats_services.get_exploration_stats(
exploration_id, exploration_version))
def get(self, exploration_id):
"""Handles GET requests."""
try:
current_exploration = exp_services.get_exploration_by_id(
exploration_id)
except:
raise self.PageNotFoundException
self.render_json(stats_services.get_exploration_stats(
exploration_id, current_exploration.version).to_frontend_dict())
def map(item):
"""Implements the map function (generator).
Computes exploration data for every contributor and owner of the
exploration.
Args:
item: ExpSummaryModel. An instance of ExpSummaryModel.
Yields:
This function may yield as many times as appropriate 2-tuples in the
format (str, dict), where
- str. The unique ID of the user.
- dict: A dict that includes entries for all the explorations that
this user contributes to or owns. Each entry has the following
keys:
- 'exploration_impact_score': float. The impact score of all the
explorations contributed to by the user.
- 'total_plays_for_owned_exp': int. Total plays of all
explorations owned by the user.
- 'average_rating_for_owned_exp': float. Average of average
ratings of all explorations owned by the user.
- 'num_ratings_for_owned_exp': int. Total number of ratings of
all explorations owned by the user.
"""
if item.deleted:
return
exponent = 2.0 / 3
# This is set to False only when the exploration impact score is not
# valid to be calculated.
calculate_exploration_impact_score = True
# Get average rating and value per user.
total_rating = 0
for ratings_value in item.ratings:
total_rating += item.ratings[ratings_value] * int(ratings_value)
sum_of_ratings = sum(item.ratings.itervalues())
average_rating = ((total_rating /
sum_of_ratings) if sum_of_ratings else None)
if average_rating is not None:
value_per_user = average_rating - 2
if value_per_user <= 0:
calculate_exploration_impact_score = False
else:
calculate_exploration_impact_score = False
exploration_stats = stats_services.get_exploration_stats(
item.id, item.version)
# For each state, find the number of first entries to the state.
# This is considered to be approximately equal to the number of
# users who answered the state because very few users enter a state
# and leave without answering anything at all.
answer_count = exploration_stats.get_sum_of_first_hit_counts()
num_starts = exploration_stats.num_starts
# Turn answer count into reach.
reach = answer_count**exponent
exploration_summary = exp_fetchers.get_exploration_summary_by_id(
item.id)
contributors = exploration_summary.contributors_summary
total_commits = sum(contributors.itervalues())
if total_commits == 0:
calculate_exploration_impact_score = False
mapped_owner_ids = []
for contrib_id in contributors:
exploration_data = {}
# Set the value of exploration impact score only if it needs to be
# calculated.
if calculate_exploration_impact_score:
# Find fractional contribution for each contributor.
contribution = (contributors[contrib_id] /
float(total_commits))
# Find score for this specific exploration.
exploration_data.update({
'exploration_impact_score':
(value_per_user * reach * contribution)
})
# If the user is an owner for the exploration, then update dict with
# 'average ratings' and 'total plays' as well.
if contrib_id in exploration_summary.owner_ids:
mapped_owner_ids.append(contrib_id)
# Get num starts (total plays) for the exploration.
exploration_data.update({
'total_plays_for_owned_exp':
num_starts,
})
# Update data with average rating only if it is not None.
if average_rating is not None:
exploration_data.update({
'average_rating_for_owned_exp':
average_rating,
'num_ratings_for_owned_exp':
sum_of_ratings
})
yield (contrib_id, exploration_data)
for owner_id in exploration_summary.owner_ids:
if owner_id not in mapped_owner_ids:
mapped_owner_ids.append(owner_id)
# Get num starts (total plays) for the exploration.
exploration_data = {
'total_plays_for_owned_exp': num_starts,
}
# Update data with average rating only if it is not None.
if average_rating is not None:
exploration_data.update({
'average_rating_for_owned_exp':
average_rating,
'num_ratings_for_owned_exp':
sum_of_ratings
})
yield (owner_id, exploration_data)
def map(item):
"""Implements the map function (generator).
Computes exploration data for every contributor and owner of the
exploration.
Args:
item: ExpSummaryModel. An instance of ExpSummaryModel.
Yields:
This function may yield as many times as appropriate 2-tuples in the
format (str, dict), where
- str. The unique ID of the user.
- dict: A dict that includes entries for all the explorations that
this user contributes to or owns. Each entry has the following
keys:
- 'exploration_impact_score': float. The impact score of all the
explorations contributed to by the user.
- 'total_plays_for_owned_exp': int. Total plays of all
explorations owned by the user.
- 'average_rating_for_owned_exp': float. Average of average
ratings of all explorations owned by the user.
- 'num_ratings_for_owned_exp': int. Total number of ratings of
all explorations owned by the user.
"""
if item.deleted:
return
exponent = 2.0 / 3
# This is set to False only when the exploration impact score is not
# valid to be calculated.
calculate_exploration_impact_score = True
# Get average rating and value per user
total_rating = 0
for ratings_value in item.ratings:
total_rating += item.ratings[ratings_value] * int(ratings_value)
sum_of_ratings = sum(item.ratings.itervalues())
average_rating = ((total_rating /
sum_of_ratings) if sum_of_ratings else None)
if average_rating is not None:
value_per_user = average_rating - 2
if value_per_user <= 0:
calculate_exploration_impact_score = False
else:
calculate_exploration_impact_score = False
if feconf.ENABLE_NEW_STATS_FRAMEWORK:
exploration_stats = stats_services.get_exploration_stats(
item.id, item.version)
# For each state, find the number of first entries to the state.
# This is considered to be approximately equal to the number of
# users who answered the state because very few users enter a state
# and leave without answering anything at all.
answer_count = exploration_stats.get_sum_of_first_hit_counts()
num_starts = exploration_stats.num_starts
else:
statistics = (
stats_jobs_continuous.StatisticsAggregator.get_statistics(
item.id, stats_jobs_continuous.VERSION_ALL))
answer_count = 0
# Find number of users per state (card), and subtract no answer
# This will not count people who have been back to a state twice
# but did not give an answer the second time, but is probably the
# closest we can get with current statistics to "number of users
# who gave an answer" since it is "number of users who always gave
# an answer".
for state_name in statistics['state_hit_counts']:
state_stats = statistics['state_hit_counts'][state_name]
first_entry_count = state_stats.get('first_entry_count', 0)
no_answer_count = state_stats.get('no_answer_count', 0)
answer_count += first_entry_count - no_answer_count
num_starts = statistics['start_exploration_count']
# Turn answer count into reach
reach = answer_count**exponent
exploration_summary = exp_services.get_exploration_summary_by_id(
item.id)
contributors = exploration_summary.contributors_summary
total_commits = sum(contributors.itervalues())
if total_commits == 0:
calculate_exploration_impact_score = False
mapped_owner_ids = []
for contrib_id in contributors:
exploration_data = {}
# Set the value of exploration impact score only if it needs to be
# calculated.
if calculate_exploration_impact_score:
# Find fractional contribution for each contributor
contribution = (contributors[contrib_id] /
float(total_commits))
# Find score for this specific exploration
exploration_data.update({
'exploration_impact_score':
(value_per_user * reach * contribution)
})
# if the user is an owner for the exploration, then update dict with
# 'average ratings' and 'total plays' as well.
if contrib_id in exploration_summary.owner_ids:
mapped_owner_ids.append(contrib_id)
# Get num starts (total plays) for the exploration
exploration_data.update({
'total_plays_for_owned_exp':
num_starts,
})
# Update data with average rating only if it is not None.
if average_rating is not None:
exploration_data.update({
'average_rating_for_owned_exp':
average_rating,
'num_ratings_for_owned_exp':
sum_of_ratings
})
yield (contrib_id, exploration_data)
for owner_id in exploration_summary.owner_ids:
if owner_id not in mapped_owner_ids:
mapped_owner_ids.append(owner_id)
# Get num starts (total plays) for the exploration
exploration_data = {
'total_plays_for_owned_exp': num_starts,
}
# Update data with average rating only if it is not None.
if average_rating is not None:
exploration_data.update({
'average_rating_for_owned_exp':
average_rating,
'num_ratings_for_owned_exp':
sum_of_ratings
})
yield (owner_id, exploration_data)
