def test_retry_max(self):
db_engine = None
trainer = None
# set up a basic model training run
# TODO abstract the setup of a basic model training run where
# we don't worry about the specific values used? it would make
# tests like this require a bit less noise to read past
with testing.postgresql.Postgresql() as postgresql:
db_engine = create_engine(postgresql.url())
ensure_db(db_engine)
init_engine(db_engine)
trainer = ModelTrainer(
project_path='econ-dev/inspections',
experiment_hash=None,
model_storage_engine=InMemoryModelStorageEngine(
project_path=''),
db_engine=db_engine,
model_grouper=ModelGrouper())
# the postgres server goes out of scope here and thus no longer exists
with patch('time.sleep') as time_mock:
with self.assertRaises(sqlalchemy.exc.OperationalError):
trainer.train_models(grid_config(), dict(),
sample_matrix_store())
# we want to make sure that we are using the retrying module sanely
# as opposed to matching the exact # of calls specified by the code
assert len(time_mock.mock_calls) > 5
def test_retry_recovery(self):
db_engine = None
trainer = None
port = None
with rig_engines() as (db_engine, project_storage):
port = db_engine.url.port
trainer = ModelTrainer(
experiment_hash=None,
model_storage_engine=project_storage.model_storage_engine(),
db_engine=db_engine,
model_grouper=ModelGrouper(),
)
matrix_store = get_matrix_store(project_storage)
# start without a database server
# then bring it back up after the first sleep
# use self so it doesn't go out of scope too early and shut down
self.new_server = None
def replace_db(arg):
self.new_server = testing.postgresql.Postgresql(port=port)
db_engine = create_engine(self.new_server.url())
ensure_db(db_engine)
init_engine(db_engine)
get_matrix_store(project_storage)
with patch("time.sleep") as time_mock:
time_mock.side_effect = replace_db
try:
trainer.train_models(grid_config(), dict(), matrix_store)
finally:
if self.new_server is not None:
self.new_server.stop()
assert len(time_mock.mock_calls) == 1
def test_retry_recovery(self):
grid_config = {
'sklearn.ensemble.AdaBoostClassifier': {
'n_estimators': [10]
},
}
engine = None
trainer = None
port = None
with testing.postgresql.Postgresql() as postgresql:
port = postgresql.settings['port']
engine = create_engine(postgresql.url())
ensure_db(engine)
trainer = ModelTrainer(
project_path='econ-dev/inspections',
experiment_hash=None,
model_storage_engine=InMemoryModelStorageEngine(project_path=''),
db_engine=engine,
model_group_keys=['label_name', 'label_timespan']
)
matrix = pandas.DataFrame.from_dict({
'entity_id': [1, 2],
'feature_one': [3, 4],
'feature_two': [5, 6],
'label': ['good', 'bad']
})
matrix_store = InMemoryMatrixStore(matrix, {
'label_timespan': '1d',
'end_time': datetime.datetime.now(),
'feature_start_time': datetime.date(2012, 12, 20),
'label_name': 'label',
'metta-uuid': '1234',
'feature_names': ['ft1', 'ft2'],
'indices': ['entity_id'],
})
# start without a database server
# then bring it back up after the first sleep
# use self so it doesn't go out of scope too early and shut down
self.new_server = None
def replace_db(arg):
self.new_server = testing.postgresql.Postgresql(port=port)
engine = create_engine(self.new_server.url())
ensure_db(engine)
with patch('time.sleep') as time_mock:
time_mock.side_effect = replace_db
try:
trainer.train_models(grid_config, dict(), matrix_store)
finally:
if self.new_server is not None:
self.new_server.stop()
assert len(time_mock.mock_calls) == 1
def test_custom_groups(grid_config, db_engine_with_results_schema, project_storage):
model_storage_engine = project_storage.model_storage_engine()
experiment_hash = save_experiment_and_get_hash(
config={'foo': 'bar'},
db_engine=db_engine_with_results_schema
)
run_id = initialize_tracking_and_get_run_id(
experiment_hash,
experiment_class_path="",
random_seed=5,
experiment_kwargs={},
db_engine=db_engine_with_results_schema
)
trainer = ModelTrainer(
experiment_hash=experiment_hash,
model_storage_engine=model_storage_engine,
model_grouper=ModelGrouper(["class_path"]),
db_engine=db_engine_with_results_schema,
run_id=run_id,
)
# create training set
model_ids = trainer.train_models(
grid_config=grid_config,
misc_db_parameters=dict(),
matrix_store=get_matrix_store(project_storage),
)
# expect only one model group now
records = [
row[0]
for row in db_engine_with_results_schema.execute(
"select distinct model_group_id from triage_metadata.models"
)
]
assert len(records) == 1
assert records[0] == model_ids[0]
def test_custom_groups(sample_matrix_store, grid_config):
with testing.postgresql.Postgresql() as postgresql:
engine = create_engine(postgresql.url())
ensure_db(engine)
init_engine(engine)
with mock_s3():
s3_conn = boto3.resource('s3')
s3_conn.create_bucket(Bucket='econ-dev')
MatrixFactory(matrix_uuid="1234")
session.commit()
# create training set
project_path = 'econ-dev/inspections'
model_storage_engine = S3ModelStorageEngine(project_path)
trainer = ModelTrainer(
project_path=project_path,
experiment_hash=None,
model_storage_engine=model_storage_engine,
model_grouper=ModelGrouper(['class_path']),
db_engine=engine,
)
model_ids = trainer.train_models(grid_config=grid_config,
misc_db_parameters=dict(),
matrix_store=sample_matrix_store)
# expect only one model group now
records = [
row[0] for row in engine.execute(
'select distinct model_group_id from model_metadata.models'
)
]
assert len(records) == 1
assert records[0] == model_ids[0]
def test_retry_max(self):
grid_config = {
'sklearn.ensemble.AdaBoostClassifier': {
'n_estimators': [10]
},
}
engine = None
trainer = None
# set up a basic model training run
# TODO abstract the setup of a basic model training run where
# we don't worry about the specific values used? it would make
# tests like this require a bit less noise to read past
with testing.postgresql.Postgresql() as postgresql:
engine = create_engine(postgresql.url())
ensure_db(engine)
trainer = ModelTrainer(
project_path='econ-dev/inspections',
experiment_hash=None,
model_storage_engine=InMemoryModelStorageEngine(project_path=''),
db_engine=engine,
model_group_keys=['label_name', 'label_timespan']
)
matrix = pandas.DataFrame.from_dict({
'entity_id': [1, 2],
'feature_one': [3, 4],
'feature_two': [5, 6],
'label': ['good', 'bad']
})
matrix_store = InMemoryMatrixStore(matrix, {
'label_timespan': '1d',
'end_time': datetime.datetime.now(),
'feature_start_time': datetime.date(2012, 12, 20),
'label_name': 'label',
'metta-uuid': '1234',
'feature_names': ['ft1', 'ft2'],
'indices': ['entity_id'],
})
# the postgres server goes out of scope here and thus no longer exists
with patch('time.sleep') as time_mock:
with self.assertRaises(sqlalchemy.exc.OperationalError):
trainer.train_models(grid_config, dict(), matrix_store)
# we want to make sure that we are using the retrying module sanely
# as opposed to matching the exact # of calls specified by the code
assert len(time_mock.mock_calls) > 5
def test_retry_max(self):
db_engine = None
trainer = None
# set up a basic model training run
with rig_engines() as (db_engine, project_storage):
trainer = ModelTrainer(
experiment_hash=None,
model_storage_engine=project_storage.model_storage_engine(),
db_engine=db_engine,
model_grouper=ModelGrouper())
matrix_store = get_matrix_store(project_storage)
# the postgres server goes out of scope here and thus no longer exists
with patch('time.sleep') as time_mock:
with self.assertRaises(sqlalchemy.exc.OperationalError):
trainer.train_models(grid_config(), dict(), matrix_store)
# we want to make sure that we are using the retrying module sanely
# as opposed to matching the exact # of calls specified by the code
assert len(time_mock.mock_calls) > 5
def test_retry_recovery(self):
db_engine = None
trainer = None
port = None
with testing.postgresql.Postgresql() as postgresql:
port = postgresql.settings['port']
db_engine = create_engine(postgresql.url())
ensure_db(db_engine)
init_engine(db_engine)
trainer = ModelTrainer(
project_path='econ-dev/inspections',
experiment_hash=None,
model_storage_engine=InMemoryModelStorageEngine(
project_path=''),
db_engine=db_engine,
model_grouper=ModelGrouper())
# start without a database server
# then bring it back up after the first sleep
# use self so it doesn't go out of scope too early and shut down
self.new_server = None
def replace_db(arg):
self.new_server = testing.postgresql.Postgresql(port=port)
db_engine = create_engine(self.new_server.url())
ensure_db(db_engine)
init_engine(db_engine)
# Creates a matrix entry in the matrices table with uuid from train_metadata
MatrixFactory(matrix_uuid="1234")
session.commit()
with patch('time.sleep') as time_mock:
time_mock.side_effect = replace_db
try:
trainer.train_models(grid_config(), dict(),
sample_matrix_store())
finally:
if self.new_server is not None:
self.new_server.stop()
assert len(time_mock.mock_calls) == 1
def test_custom_groups(grid_config):
with rig_engines() as (db_engine, project_storage):
# create training set
model_storage_engine = project_storage.model_storage_engine()
trainer = ModelTrainer(
experiment_hash=None,
model_storage_engine=model_storage_engine,
model_grouper=ModelGrouper(['class_path']),
db_engine=db_engine,
)
model_ids = trainer.train_models(
grid_config=grid_config,
misc_db_parameters=dict(),
matrix_store=get_matrix_store(project_storage))
# expect only one model group now
records = [
row[0] for row in db_engine.execute(
'select distinct model_group_id from model_metadata.models')
]
assert len(records) == 1
assert records[0] == model_ids[0]
def test_custom_groups(grid_config, db_engine_with_results_schema,
project_storage):
model_storage_engine = project_storage.model_storage_engine()
trainer = ModelTrainer(
experiment_hash=None,
model_storage_engine=model_storage_engine,
model_grouper=ModelGrouper(["class_path"]),
db_engine=db_engine_with_results_schema,
)
# create training set
model_ids = trainer.train_models(
grid_config=grid_config,
misc_db_parameters=dict(),
matrix_store=get_matrix_store(project_storage),
)
# expect only one model group now
records = [
row[0] for row in db_engine_with_results_schema.execute(
"select distinct model_group_id from triage_metadata.models")
]
assert len(records) == 1
assert records[0] == model_ids[0]
def test_integration():
with rig_engines() as (db_engine, project_storage):
train_store = get_matrix_store(
project_storage,
matrix_creator(),
matrix_metadata_creator(matrix_type="train"),
)
as_of_dates = [datetime.date(2016, 12, 21), datetime.date(2017, 1, 21)]
test_stores = []
for as_of_date in as_of_dates:
matrix_store = get_matrix_store(
project_storage,
pandas.DataFrame.from_dict({
"entity_id": [3],
"feature_one": [8],
"feature_two": [5],
"label": [0],
}).set_index("entity_id"),
matrix_metadata_creator(end_time=as_of_date,
indices=["entity_id"]),
)
test_stores.append(matrix_store)
model_storage_engine = ModelStorageEngine(project_storage)
experiment_hash = save_experiment_and_get_hash({}, db_engine)
# instantiate pipeline objects
trainer = ModelTrainer(
experiment_hash=experiment_hash,
model_storage_engine=model_storage_engine,
db_engine=db_engine,
)
predictor = Predictor(model_storage_engine, db_engine)
model_evaluator = ModelEvaluator([{
"metrics": ["precision@"],
"thresholds": {
"top_n": [5]
}
}], [{}], db_engine)
# run the pipeline
grid_config = {
"sklearn.linear_model.LogisticRegression": {
"C": [0.00001, 0.0001],
"penalty": ["l1", "l2"],
"random_state": [2193],
}
}
model_ids = trainer.train_models(grid_config=grid_config,
misc_db_parameters=dict(),
matrix_store=train_store)
for model_id in model_ids:
for as_of_date, test_store in zip(as_of_dates, test_stores):
predictions_proba = predictor.predict(
model_id,
test_store,
misc_db_parameters=dict(),
train_matrix_columns=["feature_one", "feature_two"],
)
model_evaluator.evaluate(predictions_proba, test_store,
model_id)
# assert
# 1. that the predictions table entries are present and
# can be linked to the original models
records = [
row for row in db_engine.execute(
"""select entity_id, model_id, as_of_date
from test_results.predictions
join model_metadata.models using (model_id)
order by 3, 2""")
]
assert records == [
(3, 1, datetime.datetime(2016, 12, 21)),
(3, 2, datetime.datetime(2016, 12, 21)),
(3, 3, datetime.datetime(2016, 12, 21)),
(3, 4, datetime.datetime(2016, 12, 21)),
(3, 1, datetime.datetime(2017, 1, 21)),
(3, 2, datetime.datetime(2017, 1, 21)),
(3, 3, datetime.datetime(2017, 1, 21)),
(3, 4, datetime.datetime(2017, 1, 21)),
]
# that evaluations are there
records = [
row for row in db_engine.execute("""
select model_id, evaluation_start_time, metric, parameter
from test_results.evaluations order by 2, 1""")
]
assert records == [
(1, datetime.datetime(2016, 12, 21), "precision@", "5_abs"),
(2, datetime.datetime(2016, 12, 21), "precision@", "5_abs"),
(3, datetime.datetime(2016, 12, 21), "precision@", "5_abs"),
(4, datetime.datetime(2016, 12, 21), "precision@", "5_abs"),
(1, datetime.datetime(2017, 1, 21), "precision@", "5_abs"),
(2, datetime.datetime(2017, 1, 21), "precision@", "5_abs"),
(3, datetime.datetime(2017, 1, 21), "precision@", "5_abs"),
(4, datetime.datetime(2017, 1, 21), "precision@", "5_abs"),
]
def test_model_trainer(sample_matrix_store, grid_config):
with testing.postgresql.Postgresql() as postgresql:
db_engine = create_engine(postgresql.url())
ensure_db(db_engine)
init_engine(db_engine)
with mock_s3():
s3_conn = boto3.resource('s3')
s3_conn.create_bucket(Bucket='econ-dev')
# Creates a matrix entry in the matrices table with uuid from metadata above
MatrixFactory(matrix_uuid="1234")
session.commit()
project_path = 'econ-dev/inspections'
model_storage_engine = S3ModelStorageEngine(project_path)
trainer = ModelTrainer(
project_path=project_path,
experiment_hash=None,
model_storage_engine=model_storage_engine,
model_grouper=ModelGrouper(),
db_engine=db_engine,
)
model_ids = trainer.train_models(grid_config=grid_config,
misc_db_parameters=dict(),
matrix_store=sample_matrix_store)
# assert
# 1. that the models and feature importances table entries are present
records = [
row for row in db_engine.execute(
'select * from train_results.feature_importances')
]
assert len(records) == 4 * 2 # maybe exclude entity_id? yes
records = [
row for row in db_engine.execute(
'select model_hash from model_metadata.models')
]
assert len(records) == 4
hashes = [row[0] for row in records]
# 2. that the model groups are distinct
records = [
row for row in db_engine.execute(
'select distinct model_group_id from model_metadata.models'
)
]
assert len(records) == 4
# 3. that the model sizes are saved in the table and all are < 1 kB
records = [
row for row in db_engine.execute(
'select model_size from model_metadata.models')
]
assert len(records) == 4
for i in records:
size = i[0]
assert size < 1
# 4. that all four models are cached
model_pickles = [
model_storage_engine.get_store(model_hash).load()
for model_hash in hashes
]
assert len(model_pickles) == 4
assert len([x for x in model_pickles if x is not None]) == 4
# 5. that their results can have predictions made on it
test_matrix = pandas.DataFrame.from_dict({
'entity_id': [3, 4],
'feature_one': [4, 4],
'feature_two': [6, 5],
})
test_matrix = InMemoryMatrixStore(matrix=test_matrix, metadata=sample_metadata())\
.matrix
for model_pickle in model_pickles:
predictions = model_pickle.predict(test_matrix)
assert len(predictions) == 2
# 6. when run again, same models are returned
new_model_ids = trainer.train_models(
grid_config=grid_config,
misc_db_parameters=dict(),
matrix_store=sample_matrix_store)
assert len([
row for row in db_engine.execute(
'select model_hash from model_metadata.models')
]) == 4
assert model_ids == new_model_ids
# 7. if replace is set, update non-unique attributes and feature importances
max_batch_run_time = [
row[0] for row in db_engine.execute(
'select max(batch_run_time) from model_metadata.models')
][0]
trainer = ModelTrainer(
project_path=project_path,
experiment_hash=None,
model_storage_engine=model_storage_engine,
model_grouper=ModelGrouper(
model_group_keys=['label_name', 'label_timespan']),
db_engine=db_engine,
replace=True)
new_model_ids = trainer.train_models(
grid_config=grid_config,
misc_db_parameters=dict(),
matrix_store=sample_matrix_store,
)
assert model_ids == new_model_ids
assert [
row['model_id'] for row in db_engine.execute(
'select model_id from model_metadata.models order by 1 asc'
)
] == model_ids
new_max_batch_run_time = [
row[0] for row in db_engine.execute(
'select max(batch_run_time) from model_metadata.models')
][0]
assert new_max_batch_run_time > max_batch_run_time
records = [
row for row in db_engine.execute(
'select * from train_results.feature_importances')
]
assert len(records) == 4 * 2 # maybe exclude entity_id? yes
# 8. if the cache is missing but the metadata is still there, reuse the metadata
for row in db_engine.execute(
'select model_hash from model_metadata.models'):
model_storage_engine.get_store(row[0]).delete()
new_model_ids = trainer.train_models(
grid_config=grid_config,
misc_db_parameters=dict(),
matrix_store=sample_matrix_store)
assert model_ids == sorted(new_model_ids)
# 9. that the generator interface works the same way
new_model_ids = trainer.generate_trained_models(
grid_config=grid_config,
misc_db_parameters=dict(),
matrix_store=sample_matrix_store)
assert model_ids == \
sorted([model_id for model_id in new_model_ids])
def test_reuse_model_random_seeds(grid_config, default_model_trainer):
trainer = default_model_trainer
db_engine = trainer.db_engine
project_storage = trainer.model_storage_engine.project_storage
model_storage_engine = trainer.model_storage_engine
# re-using the random seeds requires the association between experiments and models
# to exist, which we're not getting in these tests since we aren't using the experiment
# architecture, so back-fill these associations after each train_models() run
def update_experiment_models(db_engine):
sql = """
INSERT INTO triage_metadata.experiment_models(experiment_hash,model_hash)
SELECT er.run_hash, m.model_hash
FROM triage_metadata.models m
LEFT JOIN triage_metadata.triage_runs er
ON m.built_in_triage_run = er.id
LEFT JOIN triage_metadata.experiment_models em
ON m.model_hash = em.model_hash
AND er.run_hash = em.experiment_hash
WHERE em.experiment_hash IS NULL
"""
db_engine.execute(sql)
db_engine.execute('COMMIT;')
random.seed(5)
model_ids = trainer.train_models(
grid_config=grid_config,
misc_db_parameters=dict(),
matrix_store=get_matrix_store(project_storage),
)
update_experiment_models(db_engine)
# simulate running a new experiment where the experiment hash has changed
# (e.g. because the model grid is different), but experiment seed is the
# same, so previously-trained models should not get new seeds
experiment_hash = save_experiment_and_get_hash(
config={'baz': 'qux'},
db_engine=db_engine
)
run_id = initialize_tracking_and_get_run_id(
experiment_hash,
experiment_class_path="",
random_seed=5,
experiment_kwargs={},
db_engine=db_engine
)
trainer = ModelTrainer(
experiment_hash=experiment_hash,
model_storage_engine=model_storage_engine,
db_engine=db_engine,
model_grouper=ModelGrouper(),
run_id=run_id,
)
new_grid = grid_config.copy()
new_grid['sklearn.tree.DecisionTreeClassifier']['min_samples_split'] = [3,10,100]
random.seed(5)
new_model_ids = trainer.train_models(
grid_config=new_grid,
misc_db_parameters=dict(),
matrix_store=get_matrix_store(project_storage),
)
update_experiment_models(db_engine)
# should have received 5 models
assert len(new_model_ids) == 6
# all the original model ids should be in the new set
assert len(set(new_model_ids) & set(model_ids)) == len(model_ids)
# however, we should NOT re-use the random seeds (and so get new model_ids)
# if the experiment-level seed is different
experiment_hash = save_experiment_and_get_hash(
config={'lorem': 'ipsum'},
db_engine=db_engine
)
run_id = initialize_tracking_and_get_run_id(
experiment_hash,
experiment_class_path="",
random_seed=42,
experiment_kwargs={},
db_engine=db_engine
)
trainer = ModelTrainer(
experiment_hash=experiment_hash,
model_storage_engine=model_storage_engine,
db_engine=db_engine,
model_grouper=ModelGrouper(),
run_id=run_id,
)
random.seed(42) # different from above
newer_model_ids = trainer.train_models(
grid_config=new_grid,
misc_db_parameters=dict(),
matrix_store=get_matrix_store(project_storage),
)
update_experiment_models(db_engine)
# should get entirely new models now (different IDs)
assert len(newer_model_ids) == 6
assert len(set(new_model_ids) & set(newer_model_ids)) == 0
def test_integration():
with rig_engines() as (db_engine, project_storage):
train_store = get_matrix_store(
project_storage, matrix_creator(),
matrix_metadata_creator(matrix_type='train'))
as_of_dates = [datetime.date(2016, 12, 21), datetime.date(2017, 1, 21)]
test_stores = []
for as_of_date in as_of_dates:
matrix_store = get_matrix_store(
project_storage,
pandas.DataFrame.from_dict({
'entity_id': [3],
'feature_one': [8],
'feature_two': [5],
'label': [0]
}).set_index('entity_id'),
matrix_metadata_creator(end_time=as_of_date,
indices=['entity_id']))
test_stores.append(matrix_store)
model_storage_engine = ModelStorageEngine(project_storage)
experiment_hash = save_experiment_and_get_hash({}, db_engine)
# instantiate pipeline objects
trainer = ModelTrainer(
experiment_hash=experiment_hash,
model_storage_engine=model_storage_engine,
db_engine=db_engine,
)
predictor = Predictor(model_storage_engine, db_engine)
model_evaluator = ModelEvaluator([{
'metrics': ['precision@'],
'thresholds': {
'top_n': [5]
}
}], [{}], db_engine)
# run the pipeline
grid_config = {
'sklearn.linear_model.LogisticRegression': {
'C': [0.00001, 0.0001],
'penalty': ['l1', 'l2'],
'random_state': [2193]
}
}
model_ids = trainer.train_models(grid_config=grid_config,
misc_db_parameters=dict(),
matrix_store=train_store)
for model_id in model_ids:
for as_of_date, test_store in zip(as_of_dates, test_stores):
predictions_proba = predictor.predict(
model_id,
test_store,
misc_db_parameters=dict(),
train_matrix_columns=['feature_one', 'feature_two'])
model_evaluator.evaluate(
predictions_proba,
test_store,
model_id,
)
# assert
# 1. that the predictions table entries are present and
# can be linked to the original models
records = [
row for row in db_engine.execute(
'''select entity_id, model_id, as_of_date
from test_results.predictions
join model_metadata.models using (model_id)
order by 3, 2''')
]
assert records == [
(3, 1, datetime.datetime(2016, 12, 21)),
(3, 2, datetime.datetime(2016, 12, 21)),
(3, 3, datetime.datetime(2016, 12, 21)),
(3, 4, datetime.datetime(2016, 12, 21)),
(3, 1, datetime.datetime(2017, 1, 21)),
(3, 2, datetime.datetime(2017, 1, 21)),
(3, 3, datetime.datetime(2017, 1, 21)),
(3, 4, datetime.datetime(2017, 1, 21)),
]
# that evaluations are there
records = [
row for row in db_engine.execute('''
select model_id, evaluation_start_time, metric, parameter
from test_results.evaluations order by 2, 1''')
]
assert records == [
(1, datetime.datetime(2016, 12, 21), 'precision@', '5_abs'),
(2, datetime.datetime(2016, 12, 21), 'precision@', '5_abs'),
(3, datetime.datetime(2016, 12, 21), 'precision@', '5_abs'),
(4, datetime.datetime(2016, 12, 21), 'precision@', '5_abs'),
(1, datetime.datetime(2017, 1, 21), 'precision@', '5_abs'),
(2, datetime.datetime(2017, 1, 21), 'precision@', '5_abs'),
(3, datetime.datetime(2017, 1, 21), 'precision@', '5_abs'),
(4, datetime.datetime(2017, 1, 21), 'precision@', '5_abs'),
]
def test_model_trainer(grid_config, default_model_trainer):
trainer = default_model_trainer
db_engine = trainer.db_engine
project_storage = trainer.model_storage_engine.project_storage
model_storage_engine = trainer.model_storage_engine
def set_test_seed():
random.seed(5)
set_test_seed()
model_ids = trainer.train_models(
grid_config=grid_config,
misc_db_parameters=dict(),
matrix_store=get_matrix_store(project_storage),
)
# assert
# 1. that the models and feature importances table entries are present
records = [
row for row in db_engine.execute(
"select * from train_results.feature_importances")
]
assert len(records) == 4 * 2 # maybe exclude entity_id? yes
records = [
row for row in db_engine.execute(
"select model_hash from triage_metadata.models")
]
assert len(records) == 4
hashes = [row[0] for row in records]
# 2. that the model groups are distinct
records = [
row for row in db_engine.execute(
"select distinct model_group_id from triage_metadata.models")
]
assert len(records) == 4
# 2. that the random seeds are distinct
records = [
row for row in db_engine.execute(
"select distinct random_seed from triage_metadata.models")
]
assert len(records) == 4
# 3. that the model sizes are saved in the table and all are < 1 kB
records = [
row for row in db_engine.execute(
"select model_size from triage_metadata.models")
]
assert len(records) == 4
for i in records:
size = i[0]
assert size < 1
# 4. that all four models are cached
model_pickles = [
model_storage_engine.load(model_hash) for model_hash in hashes
]
assert len(model_pickles) == 4
assert len([x for x in model_pickles if x is not None]) == 4
# 5. that their results can have predictions made on it
test_matrix = pd.DataFrame.from_dict({
"entity_id": [3, 4],
"feature_one": [4, 4],
"feature_two": [6, 5]
}).set_index("entity_id")
for model_pickle in model_pickles:
predictions = model_pickle.predict(test_matrix)
assert len(predictions) == 2
# 6. when run again with the same starting seed, same models are returned
set_test_seed()
new_model_ids = trainer.train_models(
grid_config=grid_config,
misc_db_parameters=dict(),
matrix_store=get_matrix_store(project_storage),
)
assert (len([
row for row in db_engine.execute(
"select model_hash from triage_metadata.models")
]) == 4)
assert model_ids == new_model_ids
# 7. if replace is set, update non-unique attributes and feature importances
max_batch_run_time = [
row[0] for row in db_engine.execute(
"select max(batch_run_time) from triage_metadata.models")
][0]
trainer = ModelTrainer(
experiment_hash=None,
model_storage_engine=model_storage_engine,
model_grouper=ModelGrouper(
model_group_keys=["label_name", "label_timespan"]),
db_engine=db_engine,
replace=True,
)
set_test_seed()
new_model_ids = trainer.train_models(
grid_config=grid_config,
misc_db_parameters=dict(),
matrix_store=get_matrix_store(project_storage),
)
assert model_ids == new_model_ids
assert [
row["model_id"] for row in db_engine.execute(
"select model_id from triage_metadata.models order by 1 asc")
] == model_ids
new_max_batch_run_time = [
row[0] for row in db_engine.execute(
"select max(batch_run_time) from triage_metadata.models")
][0]
assert new_max_batch_run_time > max_batch_run_time
records = [
row for row in db_engine.execute(
"select * from train_results.feature_importances")
]
assert len(records) == 4 * 2 # maybe exclude entity_id? yes
# 8. if the cache is missing but the metadata is still there, reuse the metadata
set_test_seed()
for row in db_engine.execute(
"select model_hash from triage_metadata.models"):
model_storage_engine.delete(row[0])
new_model_ids = trainer.train_models(
grid_config=grid_config,
misc_db_parameters=dict(),
matrix_store=get_matrix_store(project_storage),
)
assert model_ids == sorted(new_model_ids)
# 9. that the generator interface works the same way
set_test_seed()
new_model_ids = trainer.generate_trained_models(
grid_config=grid_config,
misc_db_parameters=dict(),
matrix_store=get_matrix_store(project_storage),
)
assert model_ids == sorted([model_id for model_id in new_model_ids])
def test_integration():
with testing.postgresql.Postgresql() as postgresql:
db_engine = create_engine(postgresql.url())
ensure_db(db_engine)
init_engine(db_engine)
with mock_s3():
s3_conn = boto3.resource('s3')
s3_conn.create_bucket(Bucket='econ-dev')
project_path = 'econ-dev/inspections'
# create train and test matrices
train_matrix = pandas.DataFrame.from_dict({
'entity_id': [1, 2],
'feature_one': [3, 4],
'feature_two': [5, 6],
'label': [7, 8]
}).set_index('entity_id')
train_metadata = {
'feature_start_time': datetime.date(2012, 12, 20),
'end_time': datetime.date(2016, 12, 20),
'label_name': 'label',
'label_timespan': '1y',
'feature_names': ['ft1', 'ft2'],
'metta-uuid': '1234',
'indices': ['entity_id'],
'matrix_type': 'train'
}
# Creates a matrix entry in the matrices table with uuid from train_metadata
MatrixFactory(matrix_uuid="1234")
session.commit()
train_store = InMemoryMatrixStore(train_matrix, sample_metadata())
as_of_dates = [
datetime.date(2016, 12, 21),
datetime.date(2017, 1, 21)
]
test_stores = [
InMemoryMatrixStore(
pandas.DataFrame.from_dict({
'entity_id': [3],
'feature_one': [8],
'feature_two': [5],
'label': [5]
}), {
'label_name': 'label',
'label_timespan': '1y',
'end_time': as_of_date,
'metta-uuid': '1234',
'indices': ['entity_id'],
'matrix_type': 'test',
'as_of_date_frequency': '1month'
}) for as_of_date in as_of_dates
]
model_storage_engine = S3ModelStorageEngine(project_path)
experiment_hash = save_experiment_and_get_hash({}, db_engine)
# instantiate pipeline objects
trainer = ModelTrainer(
project_path=project_path,
experiment_hash=experiment_hash,
model_storage_engine=model_storage_engine,
db_engine=db_engine,
)
predictor = Predictor(project_path, model_storage_engine,
db_engine)
model_evaluator = ModelEvaluator([{
'metrics': ['precision@'],
'thresholds': {
'top_n': [5]
}
}], [{}], db_engine)
# run the pipeline
grid_config = {
'sklearn.linear_model.LogisticRegression': {
'C': [0.00001, 0.0001],
'penalty': ['l1', 'l2'],
'random_state': [2193]
}
}
model_ids = trainer.train_models(grid_config=grid_config,
misc_db_parameters=dict(),
matrix_store=train_store)
for model_id in model_ids:
for as_of_date, test_store in zip(as_of_dates, test_stores):
predictions_proba = predictor.predict(
model_id,
test_store,
misc_db_parameters=dict(),
train_matrix_columns=['feature_one', 'feature_two'])
model_evaluator.evaluate(
predictions_proba,
test_store,
model_id,
)
# assert
# 1. that the predictions table entries are present and
# can be linked to the original models
records = [
row for row in db_engine.execute(
'''select entity_id, model_id, as_of_date
from test_results.test_predictions
join model_metadata.models using (model_id)
order by 3, 2''')
]
assert records == [
(3, 1, datetime.datetime(2016, 12, 21)),
(3, 2, datetime.datetime(2016, 12, 21)),
(3, 3, datetime.datetime(2016, 12, 21)),
(3, 4, datetime.datetime(2016, 12, 21)),
(3, 1, datetime.datetime(2017, 1, 21)),
(3, 2, datetime.datetime(2017, 1, 21)),
(3, 3, datetime.datetime(2017, 1, 21)),
(3, 4, datetime.datetime(2017, 1, 21)),
]
# that evaluations are there
records = [
row for row in db_engine.execute('''
select model_id, evaluation_start_time, metric, parameter
from test_results.test_evaluations order by 2, 1''')
]
assert records == [
(1, datetime.datetime(2016, 12, 21), 'precision@', '5_abs'),
(2, datetime.datetime(2016, 12, 21), 'precision@', '5_abs'),
(3, datetime.datetime(2016, 12, 21), 'precision@', '5_abs'),
(4, datetime.datetime(2016, 12, 21), 'precision@', '5_abs'),
(1, datetime.datetime(2017, 1, 21), 'precision@', '5_abs'),
(2, datetime.datetime(2017, 1, 21), 'precision@', '5_abs'),
(3, datetime.datetime(2017, 1, 21), 'precision@', '5_abs'),
(4, datetime.datetime(2017, 1, 21), 'precision@', '5_abs'),
]
def test_model_trainer(grid_config):
with rig_engines() as (db_engine, project_storage):
# Creates a matrix entry in the matrices table with uuid from metadata above
model_storage_engine = project_storage.model_storage_engine()
trainer = ModelTrainer(
experiment_hash=None,
model_storage_engine=model_storage_engine,
model_grouper=ModelGrouper(),
db_engine=db_engine,
)
model_ids = trainer.train_models(
grid_config=grid_config,
misc_db_parameters=dict(),
matrix_store=get_matrix_store(project_storage),
)
# assert
# 1. that the models and feature importances table entries are present
records = [
row for row in db_engine.execute(
'select * from train_results.feature_importances')
]
assert len(records) == 4 * 2 # maybe exclude entity_id? yes
records = [
row for row in db_engine.execute(
'select model_hash from model_metadata.models')
]
assert len(records) == 4
hashes = [row[0] for row in records]
# 2. that the model groups are distinct
records = [
row for row in db_engine.execute(
'select distinct model_group_id from model_metadata.models')
]
assert len(records) == 4
# 3. that the model sizes are saved in the table and all are < 1 kB
records = [
row for row in db_engine.execute(
'select model_size from model_metadata.models')
]
assert len(records) == 4
for i in records:
size = i[0]
assert size < 1
# 4. that all four models are cached
model_pickles = [
model_storage_engine.load(model_hash) for model_hash in hashes
]
assert len(model_pickles) == 4
assert len([x for x in model_pickles if x is not None]) == 4
# 5. that their results can have predictions made on it
test_matrix = pandas.DataFrame.from_dict({
'entity_id': [3, 4],
'feature_one': [4, 4],
'feature_two': [6, 5],
}).set_index('entity_id')
for model_pickle in model_pickles:
predictions = model_pickle.predict(test_matrix)
assert len(predictions) == 2
# 6. when run again, same models are returned
new_model_ids = trainer.train_models(
grid_config=grid_config,
misc_db_parameters=dict(),
matrix_store=get_matrix_store(project_storage))
assert len([
row for row in db_engine.execute(
'select model_hash from model_metadata.models')
]) == 4
assert model_ids == new_model_ids
# 7. if replace is set, update non-unique attributes and feature importances
max_batch_run_time = [
row[0] for row in db_engine.execute(
'select max(batch_run_time) from model_metadata.models')
][0]
trainer = ModelTrainer(
experiment_hash=None,
model_storage_engine=model_storage_engine,
model_grouper=ModelGrouper(
model_group_keys=['label_name', 'label_timespan']),
db_engine=db_engine,
replace=True)
new_model_ids = trainer.train_models(
grid_config=grid_config,
misc_db_parameters=dict(),
matrix_store=get_matrix_store(project_storage))
assert model_ids == new_model_ids
assert [
row['model_id'] for row in db_engine.execute(
'select model_id from model_metadata.models order by 1 asc')
] == model_ids
new_max_batch_run_time = [
row[0] for row in db_engine.execute(
'select max(batch_run_time) from model_metadata.models')
][0]
assert new_max_batch_run_time > max_batch_run_time
records = [
row for row in db_engine.execute(
'select * from train_results.feature_importances')
]
assert len(records) == 4 * 2 # maybe exclude entity_id? yes
# 8. if the cache is missing but the metadata is still there, reuse the metadata
for row in db_engine.execute(
'select model_hash from model_metadata.models'):
model_storage_engine.delete(row[0])
new_model_ids = trainer.train_models(
grid_config=grid_config,
misc_db_parameters=dict(),
matrix_store=get_matrix_store(project_storage))
assert model_ids == sorted(new_model_ids)
# 9. that the generator interface works the same way
new_model_ids = trainer.generate_trained_models(
grid_config=grid_config,
misc_db_parameters=dict(),
matrix_store=get_matrix_store(project_storage))
assert model_ids == \
sorted([model_id for model_id in new_model_ids])
def test_model_trainer():
with testing.postgresql.Postgresql() as postgresql:
engine = create_engine(postgresql.url())
ensure_db(engine)
grid_config = {
'sklearn.linear_model.LogisticRegression': {
'C': [0.00001, 0.0001],
'penalty': ['l1', 'l2'],
'random_state': [2193]
}
}
with mock_s3():
s3_conn = boto3.resource('s3')
s3_conn.create_bucket(Bucket='econ-dev')
# create training set
matrix = pandas.DataFrame.from_dict({
'entity_id': [1, 2],
'feature_one': [3, 4],
'feature_two': [5, 6],
'label': ['good', 'bad']
})
metadata = {
'feature_start_time': datetime.date(2012, 12, 20),
'end_time': datetime.date(2016, 12, 20),
'label_name': 'label',
'label_timespan': '1y',
'metta-uuid': '1234',
'feature_names': ['ft1', 'ft2'],
'indices': ['entity_id'],
}
project_path = 'econ-dev/inspections'
model_storage_engine = S3ModelStorageEngine(project_path)
trainer = ModelTrainer(
project_path=project_path,
experiment_hash=None,
model_storage_engine=model_storage_engine,
db_engine=engine,
model_group_keys=['label_name', 'label_timespan']
)
matrix_store = InMemoryMatrixStore(matrix, metadata)
model_ids = trainer.train_models(
grid_config=grid_config,
misc_db_parameters=dict(),
matrix_store=matrix_store
)
# assert
# 1. that the models and feature importances table entries are present
records = [
row for row in
engine.execute('select * from results.feature_importances')
]
assert len(records) == 4 * 2 # maybe exclude entity_id? yes
records = [
row for row in
engine.execute('select model_hash from results.models')
]
assert len(records) == 4
hashes = [row[0] for row in records]
# 2. that the model groups are distinct
records = [
row for row in
engine.execute('select distinct model_group_id from results.models')
]
assert len(records) == 4
# 3. that all four models are cached
model_pickles = [
model_storage_engine.get_store(model_hash).load()
for model_hash in hashes
]
assert len(model_pickles) == 4
assert len([x for x in model_pickles if x is not None]) == 4
# 4. that their results can have predictions made on it
test_matrix = pandas.DataFrame.from_dict({
'entity_id': [3, 4],
'feature_one': [4, 4],
'feature_two': [6, 5],
})
test_matrix = InMemoryMatrixStore(matrix=test_matrix, metadata=metadata).matrix
for model_pickle in model_pickles:
predictions = model_pickle.predict(test_matrix)
assert len(predictions) == 2
# 5. when run again, same models are returned
new_model_ids = trainer.train_models(
grid_config=grid_config,
misc_db_parameters=dict(),
matrix_store=matrix_store
)
assert len([
row for row in
engine.execute('select model_hash from results.models')
]) == 4
assert model_ids == new_model_ids
# 6. if replace is set, update non-unique attributes and feature importances
max_batch_run_time = [
row[0] for row in
engine.execute('select max(batch_run_time) from results.models')
][0]
trainer = ModelTrainer(
project_path=project_path,
experiment_hash=None,
model_storage_engine=model_storage_engine,
db_engine=engine,
model_group_keys=['label_name', 'label_timespan'],
replace=True
)
new_model_ids = trainer.train_models(
grid_config=grid_config,
misc_db_parameters=dict(),
matrix_store=matrix_store,
)
assert model_ids == new_model_ids
assert [
row['model_id'] for row in
engine.execute('select model_id from results.models order by 1 asc')
] == model_ids
new_max_batch_run_time = [
row[0] for row in
engine.execute('select max(batch_run_time) from results.models')
][0]
assert new_max_batch_run_time > max_batch_run_time
records = [
row for row in
engine.execute('select * from results.feature_importances')
]
assert len(records) == 4 * 2 # maybe exclude entity_id? yes
# 7. if the cache is missing but the metadata is still there, reuse the metadata
for row in engine.execute('select model_hash from results.models'):
model_storage_engine.get_store(row[0]).delete()
new_model_ids = trainer.train_models(
grid_config=grid_config,
misc_db_parameters=dict(),
matrix_store=matrix_store
)
assert model_ids == sorted(new_model_ids)
# 8. that the generator interface works the same way
new_model_ids = trainer.generate_trained_models(
grid_config=grid_config,
misc_db_parameters=dict(),
matrix_store=matrix_store
)
assert model_ids == \
sorted([model_id for model_id in new_model_ids])
