def basic_integration_test(
cohort_names,
feature_group_create_rules,
feature_group_mix_rules,
expected_matrix_multiplier,
expected_group_lists,
):
with testing.postgresql.Postgresql() as postgresql:
db_engine = create_engine(postgresql.url())
Base.metadata.create_all(db_engine)
populate_source_data(db_engine)
with TemporaryDirectory() as temp_dir:
chopper = Timechop(
feature_start_time=datetime(2010, 1, 1),
feature_end_time=datetime(2014, 1, 1),
label_start_time=datetime(2011, 1, 1),
label_end_time=datetime(2014, 1, 1),
model_update_frequency="1year",
training_label_timespans=["6months"],
test_label_timespans=["6months"],
training_as_of_date_frequencies="1day",
test_as_of_date_frequencies="3months",
max_training_histories=["1months"],
test_durations=["1months"],
)
entity_date_table_generator = EntityDateTableGenerator(
db_engine=db_engine,
entity_date_table_name="cohort_abcd",
query="select distinct(entity_id) from events")
label_generator = LabelGenerator(
db_engine=db_engine,
query=sample_config()["label_config"]["query"])
feature_generator = FeatureGenerator(
db_engine=db_engine,
features_schema_name="features",
replace=True)
feature_dictionary_creator = FeatureDictionaryCreator(
db_engine=db_engine, features_schema_name="features")
feature_group_creator = FeatureGroupCreator(
feature_group_create_rules)
feature_group_mixer = FeatureGroupMixer(feature_group_mix_rules)
project_storage = ProjectStorage(temp_dir)
planner = Planner(
feature_start_time=datetime(2010, 1, 1),
label_names=["outcome"],
label_types=["binary"],
cohort_names=cohort_names,
user_metadata={},
)
builder = MatrixBuilder(
engine=db_engine,
db_config={
"features_schema_name": "features",
"labels_schema_name": "public",
"labels_table_name": "labels",
"cohort_table_name": "cohort_abcd",
},
experiment_hash=None,
matrix_storage_engine=project_storage.matrix_storage_engine(),
replace=True,
)
# chop time
split_definitions = chopper.chop_time()
num_split_matrices = sum(1 + len(split["test_matrices"])
for split in split_definitions)
# generate as_of_times for feature/label/state generation
all_as_of_times = []
for split in split_definitions:
all_as_of_times.extend(split["train_matrix"]["as_of_times"])
for test_matrix in split["test_matrices"]:
all_as_of_times.extend(test_matrix["as_of_times"])
all_as_of_times = list(set(all_as_of_times))
# generate entity_date state table
entity_date_table_generator.generate_entity_date_table(
as_of_dates=all_as_of_times)
# create labels table
label_generator.generate_all_labels(
labels_table="labels",
as_of_dates=all_as_of_times,
label_timespans=["6months"],
)
# create feature table tasks
# we would use FeatureGenerator#create_all_tables but want to use
# the tasks dict directly to create a feature dict
aggregations = feature_generator.aggregations(
feature_aggregation_config=[
{
"prefix":
"cat",
"from_obj":
"cat_complaints",
"knowledge_date_column":
"as_of_date",
"aggregates": [{
"quantity": "cat_sightings",
"metrics": ["count", "avg"],
"imputation": {
"all": {
"type": "mean"
}
},
}],
"intervals": ["1y"],
"groups": ["entity_id"],
},
{
"prefix":
"dog",
"from_obj":
"dog_complaints",
"knowledge_date_column":
"as_of_date",
"aggregates_imputation": {
"count": {
"type": "constant",
"value": 7
},
"sum": {
"type": "mean"
},
"avg": {
"type": "zero"
},
},
"aggregates": [{
"quantity": "dog_sightings",
"metrics": ["count", "avg"]
}],
"intervals": ["1y"],
"groups": ["entity_id"],
},
],
feature_dates=all_as_of_times,
state_table=entity_date_table_generator.entity_date_table_name,
)
feature_table_agg_tasks = feature_generator.generate_all_table_tasks(
aggregations, task_type="aggregation")
# create feature aggregation tables
feature_generator.process_table_tasks(feature_table_agg_tasks)
feature_table_imp_tasks = feature_generator.generate_all_table_tasks(
aggregations, task_type="imputation")
# create feature imputation tables
feature_generator.process_table_tasks(feature_table_imp_tasks)
# build feature dictionaries from feature tables and
# subsetting config
master_feature_dict = feature_dictionary_creator.feature_dictionary(
feature_table_names=feature_table_imp_tasks.keys(),
index_column_lookup=feature_generator.index_column_lookup(
aggregations),
)
feature_dicts = feature_group_mixer.generate(
feature_group_creator.subsets(master_feature_dict))
# figure out what matrices need to be built
_, matrix_build_tasks = planner.generate_plans(
split_definitions, feature_dicts)
# go and build the matrices
builder.build_all_matrices(matrix_build_tasks)
# super basic assertion: did matrices we expect get created?
matrices_records = list(
db_engine.execute(
"""select matrix_uuid, num_observations, matrix_type
from triage_metadata.matrices
"""))
matrix_directory = os.path.join(temp_dir, "matrices")
matrices = [
path for path in os.listdir(matrix_directory) if ".csv" in path
]
metadatas = [
path for path in os.listdir(matrix_directory)
if ".yaml" in path
]
assert len(matrices) == num_split_matrices * \
expected_matrix_multiplier
assert len(metadatas) == num_split_matrices * \
expected_matrix_multiplier
assert len(matrices) == len(matrices_records)
feature_group_name_lists = []
for metadata_path in metadatas:
with open(os.path.join(matrix_directory, metadata_path)) as f:
metadata = yaml.full_load(f)
feature_group_name_lists.append(metadata["feature_groups"])
for matrix_uuid, num_observations, matrix_type in matrices_records:
assert matrix_uuid in matrix_build_tasks # the hashes of the matrices
assert type(num_observations) is int
assert matrix_type == matrix_build_tasks[matrix_uuid][
"matrix_type"]
def deep_unique_tuple(l):
return set([tuple(i) for i in l])
assert deep_unique_tuple(
feature_group_name_lists) == deep_unique_tuple(
expected_group_lists)
def basic_integration_test(state_filters, feature_group_create_rules,
feature_group_mix_rules, expected_matrix_multiplier,
expected_group_lists):
with testing.postgresql.Postgresql() as postgresql:
db_engine = create_engine(postgresql.url())
Base.metadata.create_all(db_engine)
populate_source_data(db_engine)
with TemporaryDirectory() as temp_dir:
chopper = Timechop(
feature_start_time=datetime(2010, 1, 1),
feature_end_time=datetime(2014, 1, 1),
label_start_time=datetime(2011, 1, 1),
label_end_time=datetime(2014, 1, 1),
model_update_frequency='1year',
training_label_timespans=['6months'],
test_label_timespans=['6months'],
training_as_of_date_frequencies='1day',
test_as_of_date_frequencies='3months',
max_training_histories=['1months'],
test_durations=['1months'],
)
state_table_generator = StateTableGeneratorFromDense(
db_engine=db_engine,
experiment_hash='abcd',
dense_state_table='states',
)
label_generator = LabelGenerator(
db_engine=db_engine,
query=sample_config()['label_config']['query'])
feature_generator = FeatureGenerator(
db_engine=db_engine,
features_schema_name='features',
replace=True,
)
feature_dictionary_creator = FeatureDictionaryCreator(
db_engine=db_engine, features_schema_name='features')
feature_group_creator = FeatureGroupCreator(
feature_group_create_rules)
feature_group_mixer = FeatureGroupMixer(feature_group_mix_rules)
project_storage = ProjectStorage(temp_dir)
planner = Planner(
feature_start_time=datetime(2010, 1, 1),
label_names=['outcome'],
label_types=['binary'],
states=state_filters,
user_metadata={},
)
builder = MatrixBuilder(
engine=db_engine,
db_config={
'features_schema_name': 'features',
'labels_schema_name': 'public',
'labels_table_name': 'labels',
'sparse_state_table_name': 'tmp_sparse_states_abcd',
},
matrix_storage_engine=project_storage.matrix_storage_engine(),
replace=True)
# chop time
split_definitions = chopper.chop_time()
num_split_matrices = sum(1 + len(split['test_matrices'])
for split in split_definitions)
# generate as_of_times for feature/label/state generation
all_as_of_times = []
for split in split_definitions:
all_as_of_times.extend(split['train_matrix']['as_of_times'])
for test_matrix in split['test_matrices']:
all_as_of_times.extend(test_matrix['as_of_times'])
all_as_of_times = list(set(all_as_of_times))
# generate sparse state table
state_table_generator.generate_sparse_table(
as_of_dates=all_as_of_times)
# create labels table
label_generator.generate_all_labels(labels_table='labels',
as_of_dates=all_as_of_times,
label_timespans=['6months'])
# create feature table tasks
# we would use FeatureGenerator#create_all_tables but want to use
# the tasks dict directly to create a feature dict
aggregations = feature_generator.aggregations(
feature_aggregation_config=[{
'prefix':
'cat',
'from_obj':
'cat_complaints',
'knowledge_date_column':
'as_of_date',
'aggregates': [{
'quantity': 'cat_sightings',
'metrics': ['count', 'avg'],
'imputation': {
'all': {
'type': 'mean'
}
}
}],
'intervals': ['1y'],
'groups': ['entity_id']
}, {
'prefix':
'dog',
'from_obj':
'dog_complaints',
'knowledge_date_column':
'as_of_date',
'aggregates_imputation': {
'count': {
'type': 'constant',
'value': 7
},
'sum': {
'type': 'mean'
},
'avg': {
'type': 'zero'
}
},
'aggregates': [{
'quantity': 'dog_sightings',
'metrics': ['count', 'avg'],
}],
'intervals': ['1y'],
'groups': ['entity_id']
}],
feature_dates=all_as_of_times,
state_table=state_table_generator.sparse_table_name)
feature_table_agg_tasks = feature_generator.generate_all_table_tasks(
aggregations, task_type='aggregation')
# create feature aggregation tables
feature_generator.process_table_tasks(feature_table_agg_tasks)
feature_table_imp_tasks = feature_generator.generate_all_table_tasks(
aggregations, task_type='imputation')
# create feature imputation tables
feature_generator.process_table_tasks(feature_table_imp_tasks)
# build feature dictionaries from feature tables and
# subsetting config
master_feature_dict = feature_dictionary_creator.feature_dictionary(
feature_table_names=feature_table_imp_tasks.keys(),
index_column_lookup=feature_generator.index_column_lookup(
aggregations))
feature_dicts = feature_group_mixer.generate(
feature_group_creator.subsets(master_feature_dict))
# figure out what matrices need to be built
_, matrix_build_tasks =\
planner.generate_plans(
split_definitions,
feature_dicts
)
# go and build the matrices
builder.build_all_matrices(matrix_build_tasks)
# super basic assertion: did matrices we expect get created?
matrices_records = list(
db_engine.execute(
'''select matrix_uuid, num_observations, matrix_type
from model_metadata.matrices
'''))
matrix_directory = os.path.join(temp_dir, 'matrices')
matrices = [
path for path in os.listdir(matrix_directory) if '.csv' in path
]
metadatas = [
path for path in os.listdir(matrix_directory)
if '.yaml' in path
]
assert len(matrices) == num_split_matrices * \
expected_matrix_multiplier
assert len(metadatas) == num_split_matrices * \
expected_matrix_multiplier
assert len(matrices) == len(matrices_records)
feature_group_name_lists = []
for metadata_path in metadatas:
with open(os.path.join(matrix_directory, metadata_path)) as f:
metadata = yaml.load(f)
feature_group_name_lists.append(metadata['feature_groups'])
for matrix_uuid, num_observations, matrix_type in matrices_records:
assert matrix_uuid in matrix_build_tasks #the hashes of the matrices
assert type(num_observations) is int
assert matrix_type == matrix_build_tasks[matrix_uuid][
'matrix_type']
def deep_unique_tuple(l):
return set([tuple(i) for i in l])
assert deep_unique_tuple(
feature_group_name_lists) == deep_unique_tuple(
expected_group_lists)
