def sample_metta_csv_diff_order(directory):
"""Stores matrix and metadata in a metta-data-like form
The train and test matrices will have different column orders
Args:
directory (str)
"""
train_dict = OrderedDict([
('entity_id', [1, 2]),
('k_feature', [0.5, 0.4]),
('m_feature', [0.4, 0.5]),
('label', [0, 1])
])
train_matrix = pandas.DataFrame.from_dict(train_dict)
train_metadata = {
'feature_start_time': datetime.date(2014, 1, 1),
'end_time': datetime.date(2015, 1, 1),
'matrix_id': 'train_matrix',
'label_name': 'label',
'label_timespan': '3month',
'indices': ['entity_id'],
}
test_dict = OrderedDict([
('entity_id', [3, 4]),
('m_feature', [0.4, 0.5]),
('k_feature', [0.5, 0.4]),
('label', [0, 1])
])
test_matrix = pandas.DataFrame.from_dict(test_dict)
test_metadata = {
'feature_start_time': datetime.date(2015, 1, 1),
'end_time': datetime.date(2016, 1, 1),
'matrix_id': 'test_matrix',
'label_name': 'label',
'label_timespan': '3month',
'indices': ['entity_id'],
}
train_uuid, test_uuid = metta.archive_train_test(
train_config=train_metadata,
df_train=train_matrix,
test_config=test_metadata,
df_test=test_matrix,
directory=directory,
format='csv'
)
train_store = CSVMatrixStore(
matrix_path=os.path.join(directory, '{}.csv'.format(train_uuid)),
metadata_path=os.path.join(directory, '{}.yaml'.format(train_uuid))
)
test_store = CSVMatrixStore(
matrix_path=os.path.join(directory, '{}.csv'.format(test_uuid)),
metadata_path=os.path.join(directory, '{}.yaml'.format(test_uuid))
)
return train_store, test_store
def test_as_of_dates_entity_index(self):
data = {
"entity_id": [1, 2],
"feature_one": [0.5, 0.6],
"feature_two": [0.5, 0.6],
}
with tempfile.TemporaryDirectory() as tmpdir:
project_storage = ProjectStorage(tmpdir)
matrix_store = CSVMatrixStore(project_storage, [], "test")
matrix_store.matrix = pd.DataFrame.from_dict(data)
matrix_store.metadata = {"end_time": "2016-01-01", "indices": ["entity_id"]}
self.assertEqual(matrix_store.as_of_dates, ["2016-01-01"])
def test_as_of_dates_entity_index(project_storage):
data = {
"entity_id": [1, 2],
"feature_one": [0.5, 0.6],
"feature_two": [0.5, 0.6],
"label": [0, 1],
}
df = pd.DataFrame.from_dict(data)
labels = df.pop("label")
matrix_store = CSVMatrixStore(project_storage, [], "test")
matrix_store.matrix_label_tuple = df, labels
matrix_store.metadata = {"end_time": "2016-01-01", "indices": ["entity_id"], "label_name": "label"}
assert matrix_store.as_of_dates == ["2016-01-01"]
def test_as_of_dates(project_storage):
data = {
"entity_id": [1, 2, 1, 2],
"feature_one": [0.5, 0.6, 0.5, 0.6],
"feature_two": [0.5, 0.6, 0.5, 0.6],
"as_of_date": [
pd.Timestamp(2016, 1, 1),
pd.Timestamp(2016, 1, 1),
pd.Timestamp(2017, 1, 1),
pd.Timestamp(2017, 1, 1),
],
"label": [1, 0, 1, 0],
}
df = pd.DataFrame.from_dict(data)
matrix_store = CSVMatrixStore(
project_storage,
[],
"test",
matrix=df,
metadata={
"indices": ["entity_id", "as_of_date"],
"label_name": "label"
},
)
assert matrix_store.as_of_dates == [
datetime.date(2016, 1, 1),
datetime.date(2017, 1, 1),
]
def test_as_of_dates_entity_index(self):
data = {
'entity_id': [1, 2],
'feature_one': [0.5, 0.6],
'feature_two': [0.5, 0.6],
}
with tempfile.TemporaryDirectory() as tmpdir:
project_storage = ProjectStorage(tmpdir)
matrix_store = CSVMatrixStore(project_storage, [], 'test')
matrix_store.matrix = pd.DataFrame.from_dict(data)
matrix_store.metadata = {
'end_time': '2016-01-01',
'indices': ['entity_id']
}
self.assertEqual(matrix_store.as_of_dates, ['2016-01-01'])
def matrix_stores():
df = pd.DataFrame.from_dict(DATA_DICT).set_index(MatrixStore.indices)
with tempfile.TemporaryDirectory() as tmpdir:
project_storage = ProjectStorage(tmpdir)
tmpcsv = os.path.join(tmpdir, "df.csv.gz")
tmpyaml = os.path.join(tmpdir, "df.yaml")
with open(tmpyaml, "w") as outfile:
yaml.dump(METADATA, outfile, default_flow_style=False)
df.to_csv(tmpcsv, compression="gzip")
csv = CSVMatrixStore(project_storage, [], "df")
# first test with caching
with csv.cache():
yield csv
# with the caching out of scope they will be nuked
# and this last version will not have any cache
yield csv
def matrix_store(self):
data_dict = OrderedDict([('entity_id', [1, 2]),
('k_feature', [0.5, 0.4]),
('m_feature', [0.4, 0.5]), ('label', [0, 1])])
df = pd.DataFrame.from_dict(data_dict)
metadata = {
'label_name': 'label',
'indices': ['entity_id'],
}
inmemory = InMemoryMatrixStore(matrix=df, metadata=metadata)
with tempfile.TemporaryDirectory() as tmpdir:
tmpcsv = os.path.join(tmpdir, 'df.csv')
tmpyaml = os.path.join(tmpdir, 'metadata.yaml')
tmphdf = os.path.join(tmpdir, 'df.h5')
with open(tmpyaml, 'w') as outfile:
yaml.dump(metadata, outfile, default_flow_style=False)
df.to_csv(tmpcsv)
df.to_hdf(tmphdf, 'matrix')
csv = CSVMatrixStore(matrix_path=tmpcsv, metadata_path=tmpyaml)
hdf = HDFMatrixStore(matrix_path=tmphdf, metadata_path=tmpyaml)
assert csv.matrix.to_dict() == inmemory.matrix.to_dict()
assert hdf.matrix.to_dict() == inmemory.matrix.to_dict()
assert csv.metadata == inmemory.metadata
assert hdf.metadata == inmemory.metadata
assert csv.head_of_matrix.to_dict(
) == inmemory.head_of_matrix.to_dict()
assert hdf.head_of_matrix.to_dict(
) == inmemory.head_of_matrix.to_dict()
assert csv.empty == inmemory.empty
assert hdf.empty == inmemory.empty
assert csv.labels().to_dict() == inmemory.labels().to_dict()
assert hdf.labels().to_dict() == inmemory.labels().to_dict()
matrix_store = [inmemory, csv, hdf]
return matrix_store
def matrix_stores():
df = pd.DataFrame.from_dict(DATA_DICT).set_index(["entity_id"])
with tempfile.TemporaryDirectory() as tmpdir:
project_storage = ProjectStorage(tmpdir)
tmpcsv = os.path.join(tmpdir, "df.csv")
tmpyaml = os.path.join(tmpdir, "df.yaml")
tmphdf = os.path.join(tmpdir, "df.h5")
with open(tmpyaml, "w") as outfile:
yaml.dump(METADATA, outfile, default_flow_style=False)
df.to_csv(tmpcsv)
df.to_hdf(tmphdf, "matrix")
csv = CSVMatrixStore(project_storage, [], "df")
hdf = HDFMatrixStore(project_storage, [], "df")
assert csv.design_matrix.equals(hdf.design_matrix)
# first test with caching
with csv.cache(), hdf.cache():
yield csv
yield hdf
# with the caching out of scope they will be nuked
# and these last two versions will not have any cache
yield csv
yield hdf
def matrix_stores(self):
df = pd.DataFrame.from_dict(self.data_dict).set_index(["entity_id"])
with tempfile.TemporaryDirectory() as tmpdir:
project_storage = ProjectStorage(tmpdir)
tmpcsv = os.path.join(tmpdir, "df.csv")
tmpyaml = os.path.join(tmpdir, "df.yaml")
tmphdf = os.path.join(tmpdir, "df.h5")
with open(tmpyaml, "w") as outfile:
yaml.dump(self.metadata, outfile, default_flow_style=False)
df.to_csv(tmpcsv)
df.to_hdf(tmphdf, "matrix")
csv = CSVMatrixStore(project_storage, [], "df")
hdf = HDFMatrixStore(project_storage, [], "df")
assert csv.matrix.equals(hdf.matrix)
yield from [csv, hdf]
def test_as_of_dates_entity_date_index(project_storage):
data = {
"entity_id": [1, 2, 1, 2],
"feature_one": [0.5, 0.6, 0.5, 0.6],
"feature_two": [0.5, 0.6, 0.5, 0.6],
"as_of_date": ["2016-01-01", "2016-01-01", "2017-01-01", "2017-01-01"],
"label": [1, 0, 1, 0]
}
df = pd.DataFrame.from_dict(data)
matrix_store = CSVMatrixStore(
project_storage,
[],
"test",
matrix=df,
metadata={"indices": ["entity_id", "as_of_date"], "label_name": "label"}
)
assert matrix_store.as_of_dates == ["2016-01-01", "2017-01-01"]
def test_s3_save(self):
with mock_s3():
client = boto3.client("s3")
client.create_bucket(Bucket="fake-matrix-bucket", ACL="public-read-write")
example = next(self.matrix_stores())
project_storage = ProjectStorage("s3://fake-matrix-bucket")
tosave = CSVMatrixStore(project_storage, [], "test")
tosave.matrix = example.matrix
tosave.metadata = example.metadata
tosave.save()
tocheck = CSVMatrixStore(project_storage, [], "test")
assert tocheck.metadata == example.metadata
assert tocheck.matrix.to_dict() == example.matrix.to_dict()
def test_s3_save():
with mock_s3():
client = boto3.client("s3")
client.create_bucket(Bucket="fake-matrix-bucket", ACL="public-read-write")
for example in matrix_stores():
if not isinstance(example, CSVMatrixStore):
continue
project_storage = ProjectStorage("s3://fake-matrix-bucket")
tosave = CSVMatrixStore(project_storage, [], "test")
tosave.metadata = example.metadata
tosave.matrix_label_tuple = example.matrix_label_tuple
tosave.save()
tocheck = CSVMatrixStore(project_storage, [], "test")
assert tocheck.metadata == example.metadata
assert tocheck.design_matrix.to_dict() == example.design_matrix.to_dict()
def test_s3_save(self):
with mock_s3():
import boto3
client = boto3.client('s3')
client.create_bucket(Bucket='fake-matrix-bucket',
ACL='public-read-write')
example = next(self.matrix_stores())
project_storage = ProjectStorage('s3://fake-matrix-bucket')
tosave = CSVMatrixStore(project_storage, [], 'test')
tosave.matrix = example.matrix
tosave.metadata = example.metadata
tosave.save()
tocheck = CSVMatrixStore(project_storage, [], 'test')
assert tocheck.metadata == example.metadata
assert tocheck.matrix.to_dict() == example.matrix.to_dict()
def test_s3_save(self):
with mock_s3():
import boto3
client = boto3.client('s3')
client.create_bucket(Bucket='fake-matrix-bucket',
ACL='public-read-write')
matrix_store_list = self.matrix_store()
for matrix_store in matrix_store_list:
if isinstance(matrix_store, CSVMatrixStore):
matrix_store.save(project_path='s3://fake-matrix-bucket',
name='test')
# CSV
csv = CSVMatrixStore(
matrix_path='s3://fake-matrix-bucket/test.csv',
metadata_path='s3://fake-matrix-bucket/test.yaml')
assert csv.metadata == matrix_store_list[0].metadata
assert csv.matrix.to_dict(
) == matrix_store_list[0].matrix.to_dict()
