def split(intersections, csv_report):
logging.info('Splitting started')
if csv_report:
if cfg.reporting.use_uuid:
uuid = uuid4()
reporter = CSVReport(cfg.reporting.csv_dir, uuid)
else:
reporter = CSVReport(cfg.reporting.csv_dir, None)
else:
reporter = ExcelReport(cfg.reporting.file)
logging.info('Spark initialization')
sc = SparkContext(cfg.spark.master, 'map_test: split')
sqlContext = SQLContext(sc)
logging.info('Source file reading')
df = sqlContext.read.json(cfg.splitting.source_file)
df = df.withColumn("Date", F.from_utc_timestamp("eventTime", "UTC"))
users_with_event_count = df.groupBy(F.col("entityId").alias("user")).count()
logging.info('Filter users with small number of events')
min_events = 10
users_with_few_events = (users_with_event_count
.filter("count < %d" % (min_events))
.select(F.col("user").alias("user_with_few_events")))
ndf = df.join(users_with_few_events,
F.col("entityId")==F.col("user_with_few_events"),
how="left_outer")
df1 = ndf.filter("user_with_few_events is NULL").drop("user_with_few_events")
logging.info('Split data into train and test')
train_df, test_df = split_data(df)
train_df.write.json(cfg.splitting.train_file, mode="overwrite")
test_df.write.json(cfg.splitting.test_file, mode="overwrite")
train_df = train_df.select("entityId", "event", "targetEntityId").cache()
test_df = test_df.select("entityId", "event", "targetEntityId").cache()
logging.info('Calculation of different stat metrics of datasets')
events_by_type = (df
.groupBy("event")
.count()
.select(F.col("event"), F.col("count").alias("count_total"))
.toPandas())
events_by_type_test = (test_df
.groupBy("event")
.count()
.select(F.col("event"), F.col("count").alias("count_test"))
.toPandas()
.set_index("event"))
events_by_type_train = (train_df
.groupBy("event")
.count()
.select(F.col("event"), F.col("count").alias("count_train"))
.toPandas()
.set_index("event"))
unique_users_by_event = (df
.select(F.col("entityId"), F.col("event"))
.distinct()
.groupBy("event")
.count()
.select(F.col("event"), F.col("count").alias("unique_users_total"))
.toPandas()
.set_index("event"))
unique_users_by_event_train = (train_df
.select(F.col("entityId"), F.col("event"))
.distinct()
.groupBy("event")
.count()
.select(F.col("event"), F.col("count").alias("unique_users_train"))
.toPandas()
.set_index("event"))
unique_users_by_event_test = (test_df
.select(F.col("entityId"), F.col("event"))
.distinct()
.groupBy("event")
.count()
.select(F.col("event"), F.col("count").alias("unique_users_test"))
.toPandas()
.set_index("event"))
unique_items_by_event = (df
.select(F.col("targetEntityId"), F.col("event"))
.distinct()
.groupBy("event")
.count()
.select(F.col("event"), F.col("count").alias("unique_items_total"))
.toPandas()
.set_index("event"))
unique_items_by_event_train = (train_df
.select(F.col("targetEntityId"), F.col("event"))
.distinct()
.groupBy("event")
.count()
.select(F.col("event"), F.col("count").alias("unique_items_train"))
.toPandas()
.set_index("event"))
unique_items_by_event_test = (test_df
.select(F.col("targetEntityId"), F.col("event"))
.distinct()
.groupBy("event")
.count()
.select(F.col("event"), F.col("count").alias("unique_items_test"))
.toPandas()
.set_index("event"))
logging.info('Calculate total counts')
events = df.count()
events_train = train_df.count()
events_test = test_df.count()
unique_users = df.select("entityId").distinct().count()
unique_users_train = train_df.select("entityId").distinct().count()
unique_users_test = test_df.select("entityId").distinct().count()
unique_items = df.select(F.col("targetEntityId")).distinct().count()
unique_items_train = train_df.select(F.col("targetEntityId")).distinct().count()
unique_items_test = test_df.select(F.col("targetEntityId")).distinct().count()
info_df = events_by_type
dfs = [unique_users_by_event, unique_items_by_event,
events_by_type_train, events_by_type_test,
unique_users_by_event_train, unique_users_by_event_test,
unique_items_by_event_train, unique_items_by_event_test]
for data_frame in dfs:
info_df = info_df.join(data_frame, on="event")
n_rows, n_cols = info_df.shape
# totals
info_df.loc[n_rows] = ['ANY EVENT', events, unique_users, unique_items,
events_train, events_test,
unique_users_train, unique_users_test,
unique_items_train, unique_items_test]
info_df.insert(4, 'events per user', info_df.ix[:, 1] / info_df.ix[:, 2])
info_df.insert(5, 'events per item', info_df.ix[:, 1] / info_df.ix[:, 3])
logging.info('Create event stat worksheet')
reporter.start_new_sheet('Events stat')
reporter.report(
['event', 'event count', 'unique users', 'unique items',
'events per user', 'events per item',
'event count train', 'event count test',
'unique users train', 'unique users test',
'unique items train', 'unique items test'],
[column.tolist() for _, column in info_df.iteritems()],
selected_rows=[next(info_df.iteritems())[1].tolist().index(cfg.testing.primary_event)],
cfg=cfg)
reporter.finish_sheet()
if intersections:
logging.info('Start intersections calculation')
reporter.start_new_sheet('Intersections')
columns_for_matrix = cfg.testing.events
logging.info('Process train / train user intersection')
train_train_users = (
train_df
.select(F.col("entityId").alias("user"), F.col("event").alias("event_left"))
.distinct()
.join(train_df.select(F.col("entityId").alias("user"), F.col("event").alias("event_right")).distinct(),
on="user", how="inner")
.groupBy(["event_left", "event_right"])
.count()
.collect())
trtru = mk_intersection_matrix(train_train_users, columns_for_matrix)
reporter.report(
[''] + list(trtru.columns.values),
[trtru.index.tolist()] + [column for _, column in trtru.iteritems()],
title='Train / train user intersection')
logging.info('Process train / test user intersection')
train_test_users = (
train_df
.select(F.col("entityId").alias("user"), F.col("event").alias("event_left"))
.distinct()
.join(test_df.select(F.col("entityId").alias("user"), F.col("event").alias("event_right")).distinct(),
on="user", how="inner")
.groupBy(["event_left", "event_right"])
.count()
.collect())
trtsu = mk_intersection_matrix(train_test_users, columns_for_matrix,
horizontal_suffix=" train", vertical_suffix=" test")
reporter.report(
[''] + list(trtsu.columns.values),
[trtsu.index.tolist()] + [column for _, column in trtsu.iteritems()],
title='Train / test user intersection')
logging.info('Process train / train item intersection')
train_train_items = (
train_df
.select(F.col("targetEntityId").alias("item"), F.col("event").alias("event_left"))
.distinct()
.join(train_df.select(F.col("targetEntityId").alias("item"), F.col("event").alias("event_right")).distinct(),
on="item", how="inner")
.groupBy(["event_left", "event_right"])
.count()
.collect())
trtri = mk_intersection_matrix(train_train_items, columns_for_matrix)
reporter.report(
[''] + list(trtri.columns.values),
[trtri.index.tolist()] + [column for _, column in trtri.iteritems()],
title='Train / train item intersection'
)
logging.info('Process train / test item intersection')
train_test_items = (
train_df
.select(F.col("targetEntityId").alias("item"), F.col("event").alias("event_left"))
.distinct()
.join(test_df.select(F.col("targetEntityId").alias("item"), F.col("event").alias("event_right")).distinct(),
on="item", how="inner")
.groupBy(["event_left", "event_right"])
.count()
.collect())
trtsi = mk_intersection_matrix(train_test_items, columns_for_matrix,
horizontal_suffix=" train", vertical_suffix=" test")
reporter.report(
[''] + list(trtsi.columns.values),
[trtsi.index.tolist()] + [column for _, column in trtsi.iteritems()],
title='Train / test item intersection'
)
reporter.report_config(cfg)
reporter.finish_document()
logging.info('Splitting finished successfully')
def split(intersections, csv_report):
logging.info('Splitting started')
if csv_report:
if cfg.reporting.use_uuid:
uuid = uuid4()
reporter = CSVReport(cfg.reporting.csv_dir, uuid)
else:
reporter = CSVReport(cfg.reporting.csv_dir, None)
else:
reporter = ExcelReport(cfg.reporting.file)
logging.info('Spark initialization')
sc = SparkContext(cfg.spark.master, 'map_test: split')
sqlContext = SQLContext(sc)
logging.info('Source file reading')
df = sqlContext.read.json(cfg.splitting.source_file)
df = df.withColumn("Date", F.from_utc_timestamp("eventTime", "UTC"))
users_with_event_count = df.groupBy(
F.col("entityId").alias("user")).count()
logging.info('Filter users with small number of events')
min_events = 10
users_with_few_events = (users_with_event_count.filter(
"count < %d" % (min_events)).select(
F.col("user").alias("user_with_few_events")))
ndf = df.join(users_with_few_events,
F.col("entityId") == F.col("user_with_few_events"),
how="left_outer")
df1 = ndf.filter("user_with_few_events is NULL").drop(
"user_with_few_events")
logging.info('Split data into train and test')
train_df, test_df = split_data(df)
train_df.write.json(cfg.splitting.train_file, mode="overwrite")
test_df.write.json(cfg.splitting.test_file, mode="overwrite")
train_df = train_df.select("entityId", "event", "targetEntityId").cache()
test_df = test_df.select("entityId", "event", "targetEntityId").cache()
logging.info('Calculation of different stat metrics of datasets')
events_by_type = (df.groupBy("event").count().select(
F.col("event"),
F.col("count").alias("count_total")).toPandas())
events_by_type_test = (test_df.groupBy("event").count().select(
F.col("event"),
F.col("count").alias("count_test")).toPandas().set_index("event"))
events_by_type_train = (train_df.groupBy("event").count().select(
F.col("event"),
F.col("count").alias("count_train")).toPandas().set_index("event"))
unique_users_by_event = (df.select(
F.col("entityId"),
F.col("event")).distinct().groupBy("event").count().select(
F.col("event"),
F.col("count").alias("unique_users_total")).toPandas().set_index(
"event"))
unique_users_by_event_train = (train_df.select(
F.col("entityId"),
F.col("event")).distinct().groupBy("event").count().select(
F.col("event"),
F.col("count").alias("unique_users_train")).toPandas().set_index(
"event"))
unique_users_by_event_test = (test_df.select(
F.col("entityId"),
F.col("event")).distinct().groupBy("event").count().select(
F.col("event"),
F.col("count").alias("unique_users_test")).toPandas().set_index(
"event"))
unique_items_by_event = (df.select(
F.col("targetEntityId"),
F.col("event")).distinct().groupBy("event").count().select(
F.col("event"),
F.col("count").alias("unique_items_total")).toPandas().set_index(
"event"))
unique_items_by_event_train = (train_df.select(
F.col("targetEntityId"),
F.col("event")).distinct().groupBy("event").count().select(
F.col("event"),
F.col("count").alias("unique_items_train")).toPandas().set_index(
"event"))
unique_items_by_event_test = (test_df.select(
F.col("targetEntityId"),
F.col("event")).distinct().groupBy("event").count().select(
F.col("event"),
F.col("count").alias("unique_items_test")).toPandas().set_index(
"event"))
logging.info('Calculate total counts')
events = df.count()
events_train = train_df.count()
events_test = test_df.count()
unique_users = df.select("entityId").distinct().count()
unique_users_train = train_df.select("entityId").distinct().count()
unique_users_test = test_df.select("entityId").distinct().count()
unique_items = df.select(F.col("targetEntityId")).distinct().count()
unique_items_train = train_df.select(
F.col("targetEntityId")).distinct().count()
unique_items_test = test_df.select(
F.col("targetEntityId")).distinct().count()
info_df = events_by_type
dfs = [
unique_users_by_event, unique_items_by_event, events_by_type_train,
events_by_type_test, unique_users_by_event_train,
unique_users_by_event_test, unique_items_by_event_train,
unique_items_by_event_test
]
for data_frame in dfs:
info_df = info_df.join(data_frame, on="event")
n_rows, n_cols = info_df.shape
# totals
info_df.loc[n_rows] = [
'ANY EVENT', events, unique_users, unique_items, events_train,
events_test, unique_users_train, unique_users_test, unique_items_train,
unique_items_test
]
info_df.insert(4, 'events per user', info_df.ix[:, 1] / info_df.ix[:, 2])
info_df.insert(5, 'events per item', info_df.ix[:, 1] / info_df.ix[:, 3])
logging.info('Create event stat worksheet')
reporter.start_new_sheet('Events stat')
reporter.report([
'event', 'event count', 'unique users', 'unique items',
'events per user', 'events per item', 'event count train',
'event count test', 'unique users train', 'unique users test',
'unique items train', 'unique items test'
], [column.tolist() for _, column in info_df.iteritems()],
selected_rows=[
next(info_df.iteritems())[1].tolist().index(
cfg.testing.primary_event)
],
cfg=cfg)
reporter.finish_sheet()
if intersections:
logging.info('Start intersections calculation')
reporter.start_new_sheet('Intersections')
columns_for_matrix = cfg.testing.events
logging.info('Process train / train user intersection')
train_train_users = (train_df.select(
F.col("entityId").alias("user"),
F.col("event").alias("event_left")).distinct().join(
train_df.select(
F.col("entityId").alias("user"),
F.col("event").alias("event_right")).distinct(),
on="user",
how="inner").groupBy(["event_left",
"event_right"]).count().collect())
trtru = mk_intersection_matrix(train_train_users, columns_for_matrix)
reporter.report([''] + list(trtru.columns.values),
[trtru.index.tolist()] +
[column for _, column in trtru.iteritems()],
title='Train / train user intersection')
logging.info('Process train / test user intersection')
train_test_users = (train_df.select(
F.col("entityId").alias("user"),
F.col("event").alias("event_left")).distinct().join(
test_df.select(
F.col("entityId").alias("user"),
F.col("event").alias("event_right")).distinct(),
on="user",
how="inner").groupBy(["event_left",
"event_right"]).count().collect())
trtsu = mk_intersection_matrix(train_test_users,
columns_for_matrix,
horizontal_suffix=" train",
vertical_suffix=" test")
reporter.report([''] + list(trtsu.columns.values),
[trtsu.index.tolist()] +
[column for _, column in trtsu.iteritems()],
title='Train / test user intersection')
logging.info('Process train / train item intersection')
train_train_items = (train_df.select(
F.col("targetEntityId").alias("item"),
F.col("event").alias("event_left")).distinct().join(
train_df.select(
F.col("targetEntityId").alias("item"),
F.col("event").alias("event_right")).distinct(),
on="item",
how="inner").groupBy(["event_left",
"event_right"]).count().collect())
trtri = mk_intersection_matrix(train_train_items, columns_for_matrix)
reporter.report([''] + list(trtri.columns.values),
[trtri.index.tolist()] +
[column for _, column in trtri.iteritems()],
title='Train / train item intersection')
logging.info('Process train / test item intersection')
train_test_items = (train_df.select(
F.col("targetEntityId").alias("item"),
F.col("event").alias("event_left")).distinct().join(
test_df.select(
F.col("targetEntityId").alias("item"),
F.col("event").alias("event_right")).distinct(),
on="item",
how="inner").groupBy(["event_left",
"event_right"]).count().collect())
trtsi = mk_intersection_matrix(train_test_items,
columns_for_matrix,
horizontal_suffix=" train",
vertical_suffix=" test")
reporter.report([''] + list(trtsi.columns.values),
[trtsi.index.tolist()] +
[column for _, column in trtsi.iteritems()],
title='Train / test item intersection')
reporter.report_config(cfg)
reporter.finish_document()
logging.info('Splitting finished successfully')
def test(csv_report,
all,
dummy_test,
separate_test,
all_but_test,
primary_pairs_test,
custom_combos_test,
non_zero_users_from_file):
logging.info('Testing started')
if csv_report:
if cfg.reporting.use_uuid:
uuid = uuid4()
reporter = CSVReport(cfg.reporting.csv_dir, uuid)
else:
reporter = CSVReport(cfg.reporting.csv_dir, None)
else:
reporter = ExcelReport(cfg.reporting.file)
logging.info('Spark context initialization')
sc = SparkContext(cfg.spark.master, 'map_test: test')
sqlContext = SQLContext(sc)
logging.info('Test data reading')
test_df = sqlContext.read.json(cfg.splitting.test_file).select("entityId", "event", "targetEntityId").cache()
test_data = test_df.filter("event = '%s'" % (cfg.testing.primary_event)).collect()
#non_zero_users = set([r[0] for r in test_data][500:650]) # Because actually all our users have 0.0 scores -- too few data
if all or dummy_test:
logging.info('Train data reading')
train_df = sqlContext.read.json(cfg.splitting.train_file).select("entityId", "event", "targetEntityId").cache()
counts = train_df.filter("event = '%s'" % (cfg.testing.primary_event)).groupBy("targetEntityId").count().collect()
sorted_rating = sorted([(row.asDict()['count'], row.asDict()['targetEntityId']) for row in counts], reverse=True)
elements = np.array([item for cnt, item in sorted_rating])
probs = np.array([cnt for cnt, item in sorted_rating])
probs = 1.0 * probs / probs.sum()
logging.info('Process dummy test')
# case 1. Random sampling from items (uniform)
dummy_uniform_res = run_map_test_dummy(test_data, items=elements, probs=probs,
uniform=True, top=False, K=cfg.testing.map_k)
# case 2. Random sampling from items (according to their distribution in training data)
dummy_res = run_map_test_dummy(test_data, items=elements, probs=probs,
uniform=False, top=False, K=cfg.testing.map_k)
# case 3. Top-N items from training data
dummy_top_res = run_map_test_dummy(test_data, items=elements, probs=probs,
uniform=True, top=True, K=cfg.testing.map_k)
reporter.start_new_sheet('Dummy MAP benchmark')
reporter.report(
['', 'Random uniform', 'Random sampled from train', 'Top - N'],
[[('MAP @ %d' % i) for i in range(1, len(dummy_res)+1)]] + [dummy_uniform_res, dummy_res, dummy_top_res],
cfg=cfg
)
reporter.finish_sheet()
logging.info('Process top 20 dummy test')
scores = []
for i in range(20):
scores.append(run_map_test_dummy(test_data, items=elements[i:], uniform=True,
top=True, K=1, no_progress=True)[0])
reporter.start_new_sheet('Top-20 perfomance')
reporter.report(
['Rank', 'MAP@1'],
[list(range(1, 21)), scores],
bold_first_column=False,
cfg=cfg
)
reporter.finish_sheet()
if all or separate_test or all_but_test or primary_pairs_test or custom_combos_test:
logging.info('Non zero users')
if non_zero_users_from_file:
with open(cfg.testing.non_zero_users_file) as input:
non_zero_users = set(input.read().split(','))
else:
_, r_data, _ = run_map_test(test_data, [cfg.testing.primary_event], test=False)
non_zero_users = get_nonzero(r_data)
with open(cfg.testing.non_zero_users_file, 'w') as output:
output.write(','.join(non_zero_users))
if all or separate_test:
logging.info('Process "map separate events" test')
columns = []
for ev in cfg.testing.events:
(r_scores, r_data, ipu) = run_map_test(test_data, [ev], users=non_zero_users, test=False)
columns.append(r_scores + [len(non_zero_users)])
first_column = [('MAP @ %d' % i) for i in range(1, len(columns[0]))] + ['non-zero users']
reporter.start_new_sheet('MAP separate events')
reporter.report(
['event'] + cfg.testing.events,
[first_column] + columns,
selected_columns=[cfg.testing.events.index(cfg.testing.primary_event) + 1],
cfg=cfg
)
reporter.finish_sheet()
if all or all_but_test:
logging.info('Process "map all but..." test')
events_scores = []
for ev in cfg.testing.events:
evs = list(cfg.testing.events)
evs.remove(ev)
(r_scores, r_data, ipu) = run_map_test(test_data, evs, users=non_zero_users, test=False)
events_scores.append(r_scores + [len(non_zero_users)])
evl = cfg.testing.events
all_scores, r_data, ipu = run_map_test(test_data, evl, users=non_zero_users, test=False)
all_scores.append(len(non_zero_users))
first_column = [('MAP @ %d' % i) for i in range(1, len(all_scores))] + ['non-zero users']
reporter.start_new_sheet('MAP all but...')
reporter.report(
['event'] + cfg.testing.events + ['All'],
[first_column] + events_scores + [all_scores],
selected_columns=[cfg.testing.events.index(cfg.testing.primary_event) + 1],
cfg=cfg
)
reporter.finish_sheet()
if all or primary_pairs_test:
logging.info('Process "map pairs with primary" test')
columns = []
events_without_primary = [event for event in cfg.testing.events if event != cfg.testing.primary_event]
for event in events_without_primary:
(r_scores, r_data, ipu) = run_map_test(test_data, [cfg.testing.primary_event, event],
users=non_zero_users, test=False)
columns.append(r_scores + [len(non_zero_users)])
first_column = [('MAP @ %d' % i) for i in range(1, len(columns[0]))] + ['non-zero users']
reporter.start_new_sheet('MAP pairs with primary')
reporter.report(
['event'] + events_without_primary,
[first_column] + columns,
cfg=cfg
)
reporter.finish_sheet()
if all or custom_combos_test:
logging.info('Process "custom combos" test')
columns = []
for event_group in cfg.testing.custom_combos.event_groups:
if len(event_group) == 2 and cfg.testing.primary_event in event_group and primary_pairs_test:
logging.warn("Report for group %s already generated in 'MAP pairs with primary'" % str(event_group))
continue
if len(event_group) == 1 and separate_test:
logging.warn("Report for group %s already generated in 'MAP separate events'" % str(event_group))
continue
if len(event_group) >= len(cfg.testing.events) - 1 and all_but_test:
logging.warn("Report for group %s already generated in 'All but...'" % str(event_group))
continue
(r_scores, r_data, ipu) = run_map_test(test_data, event_group,
users=non_zero_users, test=False)
columns.append(r_scores + [len(non_zero_users)])
first_column = [('MAP @ %d' % i) for i in range(1, len(columns[0]))] + ['non-zero users']
reporter.start_new_sheet('Custom combos')
reporter.report(
['event'] + [str([s.encode('utf-8') for s in group]) for group in cfg.testing.custom_combos.event_groups],
[first_column] + columns,
cfg=cfg
)
reporter.finish_sheet()
reporter.finish_document()
logging.info('Testing finished successfully')
def test(csv_report, all, dummy_test, separate_test, all_but_test,
primary_pairs_test, custom_combos_test, non_zero_users_from_file):
logging.info('Testing started')
if csv_report:
if cfg.reporting.use_uuid:
uuid = uuid4()
reporter = CSVReport(cfg.reporting.csv_dir, uuid)
else:
reporter = CSVReport(cfg.reporting.csv_dir, None)
else:
reporter = ExcelReport(cfg.reporting.file)
logging.info('Spark context initialization')
sc = SparkContext(cfg.spark.master, 'map_test: test')
sqlContext = SQLContext(sc)
logging.info('Test data reading')
test_df = sqlContext.read.json(cfg.splitting.test_file).select(
"entityId", "event", "targetEntityId").cache()
test_data = test_df.filter("event = '%s'" %
(cfg.testing.primary_event)).collect()
#non_zero_users = set([r[0] for r in test_data][500:650]) # Because actually all our users have 0.0 scores -- too few data
if all or dummy_test:
logging.info('Train data reading')
train_df = sqlContext.read.json(cfg.splitting.train_file).select(
"entityId", "event", "targetEntityId").cache()
counts = train_df.filter("event = '%s'" %
(cfg.testing.primary_event)).groupBy(
"targetEntityId").count().collect()
sorted_rating = sorted(
[(row.asDict()['count'], row.asDict()['targetEntityId'])
for row in counts],
reverse=True)
elements = np.array([item for cnt, item in sorted_rating])
probs = np.array([cnt for cnt, item in sorted_rating])
probs = 1.0 * probs / probs.sum()
logging.info('Process dummy test')
# case 1. Random sampling from items (uniform)
dummy_uniform_res = run_map_test_dummy(test_data,
items=elements,
probs=probs,
uniform=True,
top=False,
K=cfg.testing.map_k)
# case 2. Random sampling from items (according to their distribution in training data)
dummy_res = run_map_test_dummy(test_data,
items=elements,
probs=probs,
uniform=False,
top=False,
K=cfg.testing.map_k)
# case 3. Top-N items from training data
dummy_top_res = run_map_test_dummy(test_data,
items=elements,
probs=probs,
uniform=True,
top=True,
K=cfg.testing.map_k)
reporter.start_new_sheet('Dummy MAP benchmark')
reporter.report(
['', 'Random uniform', 'Random sampled from train', 'Top - N'],
[[('MAP @ %d' % i) for i in range(1,
len(dummy_res) + 1)]] +
[dummy_uniform_res, dummy_res, dummy_top_res],
cfg=cfg)
reporter.finish_sheet()
logging.info('Process top 20 dummy test')
scores = []
for i in range(20):
scores.append(
run_map_test_dummy(test_data,
items=elements[i:],
uniform=True,
top=True,
K=1,
no_progress=True)[0])
reporter.start_new_sheet('Top-20 perfomance')
reporter.report(['Rank', 'MAP@1'], [list(range(1, 21)), scores],
bold_first_column=False,
cfg=cfg)
reporter.finish_sheet()
if all or separate_test or all_but_test or primary_pairs_test or custom_combos_test:
logging.info('Non zero users')
if non_zero_users_from_file:
with open(cfg.testing.non_zero_users_file) as input:
non_zero_users = set(input.read().split(','))
else:
_, r_data, _ = run_map_test(test_data, [cfg.testing.primary_event],
test=False)
non_zero_users = get_nonzero(r_data)
with open(cfg.testing.non_zero_users_file, 'w') as output:
output.write(','.join(non_zero_users))
if all or separate_test:
logging.info('Process "map separate events" test')
columns = []
for ev in cfg.testing.events:
(r_scores, r_data, ipu) = run_map_test(test_data, [ev],
users=non_zero_users,
test=False)
columns.append(r_scores + [len(non_zero_users)])
first_column = [('MAP @ %d' % i) for i in range(1, len(columns[0]))
] + ['non-zero users']
reporter.start_new_sheet('MAP separate events')
reporter.report(
['event'] + cfg.testing.events, [first_column] + columns,
selected_columns=[
cfg.testing.events.index(cfg.testing.primary_event) + 1
],
cfg=cfg)
reporter.finish_sheet()
if all or all_but_test:
logging.info('Process "map all but..." test')
events_scores = []
for ev in cfg.testing.events:
evs = list(cfg.testing.events)
evs.remove(ev)
(r_scores, r_data, ipu) = run_map_test(test_data,
evs,
users=non_zero_users,
test=False)
events_scores.append(r_scores + [len(non_zero_users)])
evl = cfg.testing.events
all_scores, r_data, ipu = run_map_test(test_data,
evl,
users=non_zero_users,
test=False)
all_scores.append(len(non_zero_users))
first_column = [('MAP @ %d' % i) for i in range(1, len(all_scores))
] + ['non-zero users']
reporter.start_new_sheet('MAP all but...')
reporter.report(
['event'] + cfg.testing.events + ['All'],
[first_column] + events_scores + [all_scores],
selected_columns=[
cfg.testing.events.index(cfg.testing.primary_event) + 1
],
cfg=cfg)
reporter.finish_sheet()
if all or primary_pairs_test:
logging.info('Process "map pairs with primary" test')
columns = []
events_without_primary = [
event for event in cfg.testing.events
if event != cfg.testing.primary_event
]
for event in events_without_primary:
(r_scores, r_data,
ipu) = run_map_test(test_data, [cfg.testing.primary_event, event],
users=non_zero_users,
test=False)
columns.append(r_scores + [len(non_zero_users)])
first_column = [('MAP @ %d' % i) for i in range(1, len(columns[0]))
] + ['non-zero users']
reporter.start_new_sheet('MAP pairs with primary')
reporter.report(['event'] + events_without_primary,
[first_column] + columns,
cfg=cfg)
reporter.finish_sheet()
if all or custom_combos_test:
logging.info('Process "custom combos" test')
columns = []
for event_group in cfg.testing.custom_combos.event_groups:
if len(
event_group
) == 2 and cfg.testing.primary_event in event_group and primary_pairs_test:
logging.warn(
"Report for group %s already generated in 'MAP pairs with primary'"
% str(event_group))
continue
if len(event_group) == 1 and separate_test:
logging.warn(
"Report for group %s already generated in 'MAP separate events'"
% str(event_group))
continue
if len(event_group) >= len(
cfg.testing.events) - 1 and all_but_test:
logging.warn(
"Report for group %s already generated in 'All but...'" %
str(event_group))
continue
(r_scores, r_data, ipu) = run_map_test(test_data,
event_group,
users=non_zero_users,
test=False)
columns.append(r_scores + [len(non_zero_users)])
first_column = [('MAP @ %d' % i) for i in range(1, len(columns[0]))
] + ['non-zero users']
reporter.start_new_sheet('Custom combos')
reporter.report(['event'] + [
str([s.encode('utf-8') for s in group])
for group in cfg.testing.custom_combos.event_groups
], [first_column] + columns,
cfg=cfg)
reporter.finish_sheet()
reporter.finish_document()
logging.info('Testing finished successfully')