def test_csv_parquet_index_alignment(self):
targets = [CSVTarget()]
csv_align_set, _ = prepare_feature_set("csv-align",
"ticker",
quotes,
timestamp_key="time",
targets=targets)
csv_df = csv_align_set.to_dataframe()
features = ["csv-align.*"]
csv_vec = fs.FeatureVector("csv-align-vector", features)
resp = fs.get_offline_features(csv_vec)
csv_vec_df = resp.to_dataframe()
targets = [ParquetTarget()]
parquet_align_set, _ = prepare_feature_set("parquet-align",
"ticker",
quotes,
timestamp_key="time",
targets=targets)
parquet_df = parquet_align_set.to_dataframe()
features = ["parquet-align.*"]
parquet_vec = fs.FeatureVector("parquet-align-vector", features)
resp = fs.get_offline_features(parquet_vec)
parquet_vec_df = resp.to_dataframe()
assert all(csv_df == parquet_df)
assert all(csv_vec_df == parquet_vec_df)
def test_ingest_twice_with_nulls(self):
name = f"test_ingest_twice_with_nulls_{uuid.uuid4()}"
key = "key"
measurements = fs.FeatureSet(
name, entities=[Entity(key)], timestamp_key="my_time"
)
columns = [key, "my_string", "my_time"]
df = pd.DataFrame(
[["mykey1", "hello", pd.Timestamp("2019-01-26 14:52:37")]], columns=columns
)
df.set_index("my_string")
source = DataFrameSource(df)
measurements.set_targets(
targets=[ParquetTarget(partitioned=True)], with_defaults=False,
)
resp1 = fs.ingest(measurements, source)
assert resp1.to_dict() == {
"my_string": {"mykey1": "hello"},
"my_time": {"mykey1": pd.Timestamp("2019-01-26 14:52:37")},
}
features = [
f"{name}.*",
]
vector = fs.FeatureVector("myvector", features)
resp2 = fs.get_offline_features(vector)
resp2 = resp2.to_dataframe()
assert resp2.to_dict() == {"my_string": {"mykey1": "hello"}}
measurements = fs.FeatureSet(
name, entities=[Entity(key)], timestamp_key="my_time"
)
columns = [key, "my_string", "my_time"]
df = pd.DataFrame(
[["mykey2", None, pd.Timestamp("2019-01-26 14:52:37")]], columns=columns
)
df.set_index("my_string")
source = DataFrameSource(df)
measurements.set_targets(
targets=[ParquetTarget(partitioned=True)], with_defaults=False,
)
resp1 = fs.ingest(measurements, source, overwrite=False)
assert resp1.to_dict() == {
"my_string": {"mykey2": None},
"my_time": {"mykey2": pd.Timestamp("2019-01-26 14:52:37")},
}
features = [
f"{name}.*",
]
vector = fs.FeatureVector("myvector", features)
resp2 = fs.get_offline_features(vector)
resp2 = resp2.to_dataframe()
assert resp2.to_dict() == {"my_string": {"mykey1": "hello", "mykey2": None}}
def test_unaggregated_columns(self):
test_base_time = datetime(2020, 12, 1, 17, 33, 15)
data = pd.DataFrame({
"time": [test_base_time, test_base_time - pd.Timedelta(minutes=1)],
"first_name": ["moshe", "yosi"],
"last_name": ["cohen", "levi"],
"bid": [2000, 10],
})
name = f"measurements_{uuid.uuid4()}"
# write to kv
data_set = fs.FeatureSet(name, entities=[Entity("first_name")])
data_set.add_aggregation(
name="bids",
column="bid",
operations=["sum", "max"],
windows="1h",
period="10m",
)
fs.ingest(data_set, data, return_df=True)
features = [f"{name}.bids_sum_1h", f"{name}.last_name"]
vector = fs.FeatureVector("my-vec", features)
svc = fs.get_online_feature_service(vector)
resp = svc.get([{"first_name": "moshe"}])
expected = {"bids_sum_1h": 2000.0, "last_name": "cohen"}
assert resp[0] == expected
svc.close()
def test_parquet_target_vector_overwrite(self):
df1 = pd.DataFrame({"name": ["ABC", "DEF", "GHI"], "value": [1, 2, 3]})
fset = fs.FeatureSet(name="fvec-parquet-fset", entities=[fs.Entity("name")])
fs.ingest(fset, df1)
features = ["fvec-parquet-fset.*"]
fvec = fs.FeatureVector("fvec-parquet", features=features)
target = ParquetTarget()
off1 = fs.get_offline_features(fvec, target=target)
dfout1 = pd.read_parquet(target._target_path)
assert (
df1.set_index(keys="name")
.sort_index()
.equals(off1.to_dataframe().sort_index())
)
assert df1.set_index(keys="name").sort_index().equals(dfout1.sort_index())
df2 = pd.DataFrame({"name": ["JKL", "MNO", "PQR"], "value": [4, 5, 6]})
fs.ingest(fset, df2)
off2 = fs.get_offline_features(fvec, target=target)
dfout2 = pd.read_parquet(target._target_path)
assert (
df2.set_index(keys="name")
.sort_index()
.equals(off2.to_dataframe().sort_index())
)
assert df2.set_index(keys="name").sort_index().equals(dfout2.sort_index())
def test_override_false(self):
df1 = pd.DataFrame({"name": ["ABC", "DEF", "GHI"], "value": [1, 2, 3]})
df2 = pd.DataFrame({"name": ["JKL", "MNO", "PQR"], "value": [4, 5, 6]})
df3 = pd.concat([df1, df2])
fset = fs.FeatureSet(name="override-false", entities=[fs.Entity("name")])
fs.ingest(fset, df1)
features = ["override-false.*"]
fvec = fs.FeatureVector("override-false-vec", features=features)
off1 = fs.get_offline_features(fvec).to_dataframe()
assert df1.set_index(keys="name").sort_index().equals(off1.sort_index())
fs.ingest(fset, df2, overwrite=False)
off2 = fs.get_offline_features(fvec).to_dataframe()
assert df3.set_index(keys="name").sort_index().equals(off2.sort_index())
fs.ingest(fset, df1, targets=[ParquetTarget()])
off1 = fs.get_offline_features(fvec).to_dataframe()
assert df1.set_index(keys="name").sort_index().equals(off1.sort_index())
svc = fs.get_online_feature_service(fvec)
resp = svc.get(entity_rows=[{"name": "PQR"}])
assert resp[0]["value"] == 6
svc.close()
with pytest.raises(mlrun.errors.MLRunInvalidArgumentError):
fs.ingest(fset, df1, targets=[CSVTarget()], overwrite=False)
fset.set_targets(targets=[CSVTarget()])
with pytest.raises(mlrun.errors.MLRunInvalidArgumentError):
fs.ingest(fset, df1, overwrite=False)
def _get_offline_vector(self, features, features_size):
vector = fs.FeatureVector("myvector", features, "stock-quotes.xx")
resp = fs.get_offline_features(
vector,
entity_rows=trades,
entity_timestamp_column="time",
)
assert len(vector.spec.features) == len(
features), "unexpected num of requested features"
assert (len(vector.status.features) == features_size
), "unexpected num of returned features"
assert (len(vector.status.stats) == features_size
), "unexpected num of feature stats"
assert vector.status.label_column == "xx", "unexpected label_column name"
df = resp.to_dataframe()
columns = trades.shape[1] + features_size - 2 # - 2 keys
assert df.shape[1] == columns, "unexpected num of returned df columns"
resp.to_parquet(str(self.results_path / "query.parquet"))
# check simple api without join with other df
resp = fs.get_offline_features(vector)
df = resp.to_dataframe()
assert df.shape[
1] == features_size, "unexpected num of returned df columns"
def test_check_permissions():
data = pd.DataFrame({
"time_stamp": [
pd.Timestamp("2021-06-09 09:30:06.008"),
pd.Timestamp("2021-06-09 10:29:07.009"),
pd.Timestamp("2021-06-09 09:29:08.010"),
],
"data": [10, 20, 30],
"string": ["ab", "cd", "ef"],
})
data_set1 = fs.FeatureSet("fs1", entities=[Entity("string")])
mlrun.db.FileRunDB.verify_authorization = unittest.mock.Mock(
side_effect=mlrun.errors.MLRunAccessDeniedError(""))
try:
fs.preview(
data_set1,
data,
entity_columns=[Entity("string")],
timestamp_key="time_stamp",
)
assert False
except mlrun.errors.MLRunAccessDeniedError:
pass
try:
fs.ingest(data_set1, data, infer_options=fs.InferOptions.default())
assert False
except mlrun.errors.MLRunAccessDeniedError:
pass
features = ["fs1.*"]
feature_vector = fs.FeatureVector("test", features)
try:
fs.get_offline_features(feature_vector,
entity_timestamp_column="time_stamp")
assert False
except mlrun.errors.MLRunAccessDeniedError:
pass
try:
fs.get_online_feature_service(feature_vector)
assert False
except mlrun.errors.MLRunAccessDeniedError:
pass
try:
fs.deploy_ingestion_service(featureset=data_set1)
assert False
except mlrun.errors.MLRunAccessDeniedError:
pass
try:
data_set1.purge_targets()
assert False
except mlrun.errors.MLRunAccessDeniedError:
pass
def test_multiple_entities(self):
name = f"measurements_{uuid.uuid4()}"
current_time = pd.Timestamp.now()
data = pd.DataFrame(
{
"time": [
current_time,
current_time - pd.Timedelta(minutes=1),
current_time - pd.Timedelta(minutes=2),
current_time - pd.Timedelta(minutes=3),
current_time - pd.Timedelta(minutes=4),
current_time - pd.Timedelta(minutes=5),
],
"first_name": ["moshe", "yosi", "yosi", "yosi", "moshe", "yosi"],
"last_name": ["cohen", "levi", "levi", "levi", "cohen", "levi"],
"bid": [2000, 10, 11, 12, 2500, 14],
}
)
# write to kv
data_set = fs.FeatureSet(
name, entities=[Entity("first_name"), Entity("last_name")]
)
data_set.add_aggregation(
name="bids",
column="bid",
operations=["sum", "max"],
windows="1h",
period="10m",
emit_policy=EmitAfterMaxEvent(1),
)
fs.infer_metadata(
data_set,
data, # source
entity_columns=["first_name", "last_name"],
timestamp_key="time",
options=fs.InferOptions.default(),
)
data_set.plot(
str(self.results_path / "pipe.png"), rankdir="LR", with_targets=True
)
fs.ingest(data_set, data, return_df=True)
features = [
f"{name}.bids_sum_1h",
]
vector = fs.FeatureVector("my-vec", features)
svc = fs.get_online_feature_service(vector)
resp = svc.get([{"first_name": "yosi", "last_name": "levi"}])
assert resp[0]["bids_sum_1h"] == 47.0
svc.close()
def test_ordered_pandas_asof_merge(self):
left_set, left = prepare_feature_set(
"left", "ticker", trades, timestamp_key="time"
)
right_set, right = prepare_feature_set(
"right", "ticker", quotes, timestamp_key="time"
)
features = ["left.*", "right.*"]
feature_vector = fs.FeatureVector("test_fv", features, description="test FV")
res = fs.get_offline_features(feature_vector, entity_timestamp_column="time")
res = res.to_dataframe()
assert res.shape[0] == left.shape[0]
def _get_online_features(self, features, features_size):
# test real-time query
vector = fs.FeatureVector("my-vec", features)
svc = fs.get_online_feature_service(vector)
resp = svc.get([{"ticker": "GOOG"}, {"ticker": "MSFT"}])
resp = svc.get([{"ticker": "AAPL"}])
assert (resp[0]["name"] == "Apple Inc" and resp[0]["exchange"]
== "NASDAQ"), "unexpected online result"
resp2 = svc.get([{"ticker": "AAPL"}], as_list=True)
assert (len(resp2[0]) == features_size -
1), "unexpected online vector size" # -1 label
svc.close()
def test_overwrite(self):
df1 = pd.DataFrame({"name": ["ABC", "DEF", "GHI"], "value": [1, 2, 3]})
df2 = pd.DataFrame({"name": ["JKL", "MNO", "PQR"], "value": [4, 5, 6]})
fset = fs.FeatureSet(name="overwrite-fs", entities=[fs.Entity("name")])
fs.ingest(fset, df1, targets=[CSVTarget(), ParquetTarget(), NoSqlTarget()])
features = ["overwrite-fs.*"]
fvec = fs.FeatureVector("overwrite-vec", features=features)
csv_path = fset.get_target_path(name="csv")
csv_df = pd.read_csv(csv_path)
assert (
df1.set_index(keys="name")
.sort_index()
.equals(csv_df.set_index(keys="name").sort_index())
)
parquet_path = fset.get_target_path(name="parquet")
parquet_df = pd.read_parquet(parquet_path)
assert df1.set_index(keys="name").sort_index().equals(parquet_df.sort_index())
svc = fs.get_online_feature_service(fvec)
resp = svc.get(entity_rows=[{"name": "GHI"}])
assert resp[0]["value"] == 3
svc.close()
fs.ingest(fset, df2)
csv_path = fset.get_target_path(name="csv")
csv_df = pd.read_csv(csv_path)
assert (
df1.set_index(keys="name")
.sort_index()
.equals(csv_df.set_index(keys="name").sort_index())
)
parquet_path = fset.get_target_path(name="parquet")
parquet_df = pd.read_parquet(parquet_path)
assert df2.set_index(keys="name").sort_index().equals(parquet_df.sort_index())
svc = fs.get_online_feature_service(fvec)
resp = svc.get(entity_rows=[{"name": "GHI"}])
assert resp[0] is None
resp = svc.get(entity_rows=[{"name": "PQR"}])
assert resp[0]["value"] == 6
svc.close()
def test_none_value(self):
data = pd.DataFrame(
{"first_name": ["moshe", "yossi"], "bid": [2000, 10], "bool": [True, None]}
)
# write to kv
data_set = fs.FeatureSet("tests2", entities=[Entity("first_name")])
fs.ingest(data_set, data, return_df=True)
features = ["tests2.*"]
vector = fs.FeatureVector("my-vec", features)
svc = fs.get_online_feature_service(vector)
resp = svc.get([{"first_name": "yossi"}])
assert resp[0] == {"bid": 10, "bool": None}
svc.close()
def test_realtime_query():
init_store()
features = [
"stock-quotes.bid",
"stock-quotes.asks_sum_5h",
"stock-quotes.ask as mycol",
"stocks.*",
]
features_size = (len(features) + 1 + 1
) # (*) returns 2 features, label adds 1 feature
resp = fs.get_offline_features(
features,
entity_rows=trades,
entity_timestamp_column="time",
label_feature="stock-quotes.xx",
)
vector = resp.vector
assert len(vector.spec.features) == len(
features), "unexpected num of requested features"
assert (len(vector.status.features) == features_size
), "unexpected num of returned features"
assert len(vector.status.stats
) == features_size, "unexpected num of feature stats"
assert vector.status.label_column == "xx", "unexpected label_column name"
df = resp.to_dataframe()
columns = trades.shape[1] + features_size - 2 # - 2 keys
assert df.shape[1] == columns, "unexpected num of returned df columns"
resp.to_parquet(results_dir + "query.parquet")
# test real-time query
vector = fs.FeatureVector("my-vec", features)
svc = fs.get_online_feature_service(vector)
resp = svc.get([{"ticker": "GOOG"}, {"ticker": "MSFT"}])
resp = svc.get([{"ticker": "AAPL"}])
assert (resp[0]["name"] == "Apple Inc"
and resp[0]["exchange"] == "NASDAQ"), "unexpected online result"
resp2 = svc.get([{"ticker": "AAPL"}], as_list=True)
assert (len(resp2[0]) == features_size -
1), "unexpected online vector size" # -1 label
svc.close()
def test_feature_vector_db(self):
name = "fvec-test"
fvec = fs.FeatureVector(name=name)
db = mlrun.get_run_db()
# TODO: Using to_dict due to a bug in httpdb api which will be fixed in another PR
db.create_feature_vector(feature_vector=fvec.to_dict(),
project=self.project_name)
vecs = db.list_feature_vectors(self.project_name, name)
assert len(vecs) == 1, "bad number of results"
feature_vec = db.get_feature_vector(name, self.project_name)
assert feature_vec.metadata.name == name, "bad feature set response"
fs.delete_feature_vector(name, self.project_name)
vecs = db.list_feature_vectors(self.project_name, name)
assert not vecs, "Feature vector should be deleted"
def test_right_not_ordered_pandas_asof_merge():
init_store()
right = quotes.sort_values(by="bid")
left_set, left = prepare_feature_set("left",
"ticker",
trades,
timestamp_key="time")
right_set, right = prepare_feature_set("right",
"ticker",
right,
timestamp_key="time")
features = ["left.*", "right.*"]
feature_vector = fs.FeatureVector("test_fv", features, "test FV")
res = fs.get_offline_features(feature_vector,
entity_timestamp_column="time")
res = res.to_dataframe()
assert res.shape[0] == left.shape[0]
def test_overwrite_specified_nosql_path(self):
df1 = pd.DataFrame({"name": ["ABC", "DEF", "GHI"], "value": [1, 2, 3]})
df2 = pd.DataFrame({"name": ["JKL", "MNO", "PQR"], "value": [4, 5, 6]})
targets = [NoSqlTarget(path="v3io:///bigdata/overwrite-spec")]
fset = fs.FeatureSet(name="overwrite-spec-path",
entities=[fs.Entity("name")])
features = ["overwrite-spec-path.*"]
fvec = fs.FeatureVector("overwrite-spec-path-fvec", features=features)
fs.ingest(fset, df1, targets=targets)
fs.ingest(fset, df2, targets=targets)
svc = fs.get_online_feature_service(fvec)
resp = svc.get(entity_rows=[{"name": "PQR"}])
assert resp[0]["value"] == 6
resp = svc.get(entity_rows=[{"name": "ABC"}])
assert resp[0] is None
svc.close()
def test_offline_features_filter_non_partitioned(self):
data = pd.DataFrame({
"time_stamp": [
pd.Timestamp("2021-06-09 09:30:06.008"),
pd.Timestamp("2021-06-09 10:29:07.009"),
pd.Timestamp("2021-06-09 09:29:08.010"),
],
"data": [10, 20, 30],
"string": ["ab", "cd", "ef"],
})
data_set1 = fs.FeatureSet("fs1", entities=[Entity("string")])
fs.ingest(data_set1, data, infer_options=fs.InferOptions.default())
features = ["fs1.*"]
vector = fs.FeatureVector("vector", features)
resp = fs.get_offline_features(
vector,
entity_timestamp_column="time_stamp",
start_time=datetime(2021, 6, 9, 9, 30),
end_time=datetime(2021, 6, 9, 10, 30),
)
assert len(resp.to_dataframe()) == 2
def test_realtime_query():
init_store()
features = [
"stock-quotes.bid",
"stock-quotes.asks_sum_5h",
"stock-quotes.ask as mycol",
"stocks.*",
]
resp = fs.get_offline_features(features,
entity_rows=trades,
entity_timestamp_column="time")
vector = resp.vector
assert len(vector.spec.features) == len(
features), "unexpected num of requested features"
# stocks (*) returns 2 features
assert (len(vector.status.features) == len(features) +
1), "unexpected num of returned features"
assert (len(vector.status.stats) == len(features) +
1), "unexpected num of feature stats"
df = resp.to_dataframe()
columns = trades.shape[1] + len(features) + 1
assert df.shape[1] == columns, "unexpected num of returned df columns"
resp.to_parquet(results_dir + "query.parquet")
# test real-time query
vector = fs.FeatureVector("my-vec", features)
svc = fs.get_online_feature_service(vector)
resp = svc.get([{"ticker": "GOOG"}, {"ticker": "MSFT"}])
print(resp)
resp = svc.get([{"ticker": "AAPL"}])
assert (resp[0]["ticker"] == "AAPL"
and resp[0]["exchange"] == "NASDAQ"), "unexpected online result"
svc.close()
def test_schedule_on_filtered_by_time(self, partitioned):
name = f"sched-time-{str(partitioned)}"
now = datetime.now()
path = "v3io:///bigdata/bla.parquet"
fsys = fsspec.filesystem(v3iofs.fs.V3ioFS.protocol)
pd.DataFrame({
"time": [
pd.Timestamp("2021-01-10 10:00:00"),
pd.Timestamp("2021-01-10 11:00:00"),
],
"first_name": ["moshe", "yosi"],
"data": [2000, 10],
}).to_parquet(path=path, filesystem=fsys)
cron_trigger = "*/2 * * * *"
source = ParquetSource("myparquet",
path=path,
time_field="time",
schedule=cron_trigger)
feature_set = fs.FeatureSet(
name=name,
entities=[fs.Entity("first_name")],
timestamp_key="time",
engine="spark",
)
if partitioned:
targets = [
NoSqlTarget(),
ParquetTarget(
name="tar1",
path="v3io:///bigdata/fs1/",
partitioned=True,
partition_cols=["time"],
),
]
else:
targets = [
ParquetTarget(name="tar2",
path="v3io:///bigdata/fs2/",
partitioned=False),
NoSqlTarget(),
]
fs.ingest(
feature_set,
source,
run_config=fs.RunConfig(local=False),
targets=targets,
spark_context=self.spark_service,
)
# ingest starts every second minute and it takes ~90 seconds to finish.
if (now.minute % 2) == 0:
sleep(60 - now.second + 60 + 90)
else:
sleep(60 - now.second + 90)
features = [f"{name}.*"]
vec = fs.FeatureVector("sched_test-vec", features)
svc = fs.get_online_feature_service(vec)
resp = svc.get([{"first_name": "yosi"}, {"first_name": "moshe"}])
assert resp[0]["data"] == 10
assert resp[1]["data"] == 2000
pd.DataFrame({
"time": [
pd.Timestamp("2021-01-10 12:00:00"),
pd.Timestamp("2021-01-10 13:00:00"),
now + pd.Timedelta(minutes=10),
pd.Timestamp("2021-01-09 13:00:00"),
],
"first_name": ["moshe", "dina", "katya", "uri"],
"data": [50, 10, 25, 30],
}).to_parquet(path=path)
sleep(120)
resp = svc.get([
{
"first_name": "yosi"
},
{
"first_name": "moshe"
},
{
"first_name": "katya"
},
{
"first_name": "dina"
},
{
"first_name": "uri"
},
])
assert resp[0]["data"] == 10
assert resp[1]["data"] == 50
assert resp[2] is None
assert resp[3]["data"] == 10
assert resp[4] is None
svc.close()
# check offline
resp = fs.get_offline_features(vec)
assert len(resp.to_dataframe() == 4)
assert "uri" not in resp.to_dataframe(
) and "katya" not in resp.to_dataframe()
def feature_selection(context,
df_artifact,
k: int=5,
min_votes: float=0.5,
label_column: str=None,
stat_filters: list=['f_classif', 'mutual_info_classif', 'chi2', 'f_regression'],
model_filters: dict={'LinearSVC': 'LinearSVC',
'LogisticRegression': 'LogisticRegression',
'ExtraTreesClassifier': 'ExtraTreesClassifier'},
max_scaled_scores: bool=True,
sample_ratio: float=None,
output_vector_name: float=None,
ignore_type_errors: bool=False,
is_feature_vector: bool=False):
"""Applies selected feature selection statistical functions
or models on our 'df_artifact'.
Each statistical function or model will vote for it's best K selected features.
If a feature has >= 'min_votes' votes, it will be selected.
:param context: the function context.
:param k: number of top features to select from each statistical
function or model.
:param min_votes: minimal number of votes (from a model or by statistical
function) needed for a feature to be selected.
Can be specified by percentage of votes or absolute
number of votes.
:param label_column: ground-truth (y) labels.
:param stat_filters: statistical functions to apply to the features
(from sklearn.feature_selection).
:param model_filters: models to use for feature evaluation, can be specified by
model name (ex. LinearSVC), formalized json (contains 'CLASS',
'FIT', 'META') or a path to such json file.
:param max_scaled_scores: produce feature scores table scaled with max_scaler.
:param sample_ratio: percentage of the dataset the user whishes to compute the feature selection process on.
:param output_vector_name: creates a new feature vector containing only the identifies features.
:param ignore_type_errors: skips datatypes that are neither float or int within the feature vector.
:param is_feature_vector: bool stating if the data is passed as a feature vector.
"""
# Check if df.meta is valid, if it is, look for a feature vector
if df_artifact.meta:
if df_artifact.meta.kind == mlrun.api.schemas.ObjectKind.feature_vector:
is_feature_vector = True
# Look inside meta.spec.label_feature to identify the label_column if the user did not specify it
if label_column is None:
if is_feature_vector:
label_column = df_artifact.meta.spec.label_feature.split('.')[1]
else:
raise ValueError('No label_column was given, please add a label_column.')
# Use the feature vector as dataframe
df = df_artifact.as_df()
# Ensure k is not bigger than the the total number of features
if k > df.shape[1]:
raise ValueError(f'K cannot be bigger than the total number of features ({df.shape[1]}). Please choose a smaller K.')
elif k < 1:
raise ValueError(f'K cannot be smaller than 1. Please choose a bigger K.')
# Create a sample dataframe of the original feature vector
if sample_ratio:
df = df.groupby(label_column).apply(lambda x: x.sample(frac=sample_ratio)).reset_index(drop=True)
df = df.dropna()
# Set feature vector and labels
y = df.pop(label_column)
X = df
if np.object in list(X.dtypes) and ignore_type_errors is False:
raise ValueError(f"{df.select_dtypes(include=['object']).columns.tolist()} are neither float or int.")
# Create selected statistical estimators
stat_functions_list = {stat_name: SelectKBest(create_class(f'sklearn.feature_selection.{stat_name}'), k)
for stat_name in stat_filters}
requires_abs = ['chi2']
# Run statistic filters
selected_features_agg = {}
stats_df = pd.DataFrame(index=X.columns).dropna()
for stat_name, stat_func in stat_functions_list.items():
try:
params = (X, y) if stat_name in requires_abs else (abs(X), y)
stat = stat_func.fit(*params)
# Collect stat function results
stat_df = pd.DataFrame(index=X.columns,
columns=[stat_name],
data=stat.scores_)
plot_stat(context, stat_name, stat_df)
stats_df = stats_df.join(stat_df)
# Select K Best features
selected_features = X.columns[stat_func.get_support()]
selected_features_agg[stat_name] = selected_features
except Exception as e:
context.logger.info(f"Couldn't calculate {stat_name} because of: {e}")
# Create models from class name / json file / json params
all_sklearn_estimators = dict(all_estimators()) if len(model_filters) > 0 else {}
selected_models = {}
for model_name, model in model_filters.items():
if '.json' in model:
current_model = json.load(open(model, 'r'))
ClassifierClass = create_class(current_model["META"]["class"])
selected_models[model_name] = ClassifierClass(**current_model["CLASS"])
elif model in all_sklearn_estimators:
selected_models[model_name] = all_sklearn_estimators[model_name]()
else:
try:
current_model = json.loads(model) if isinstance(model, str) else current_model
ClassifierClass = create_class(current_model["META"]["class"])
selected_models[model_name] = ClassifierClass(**current_model["CLASS"])
except:
context.logger.info(f'unable to load {model}')
# Run model filters
models_df = pd.DataFrame(index=X.columns)
for model_name, model in selected_models.items():
if model_name == 'LogisticRegression':
model.set_params(solver='liblinear')
# Train model and get feature importance
select_from_model = SelectFromModel(model).fit(X, y)
feature_idx = select_from_model.get_support()
feature_names = X.columns[feature_idx]
selected_features_agg[model_name] = feature_names.tolist()
# Collect model feature importance
if hasattr(select_from_model.estimator_, 'coef_'):
stat_df = select_from_model.estimator_.coef_
elif hasattr(select_from_model.estimator_, 'feature_importances_'):
stat_df = select_from_model.estimator_.feature_importances_
stat_df = pd.DataFrame(index=X.columns,
columns=[model_name],
data=stat_df[0])
models_df = models_df.join(stat_df)
plot_stat(context, model_name, stat_df)
# Create feature_scores DF with stat & model filters scores
result_matrix_df = pd.concat([stats_df, models_df], axis=1, sort=False)
context.log_dataset(key='feature_scores',
df=result_matrix_df,
local_path='feature_scores.parquet',
format='parquet')
if max_scaled_scores:
normalized_df = result_matrix_df.replace([np.inf, -np.inf], np.nan).values
min_max_scaler = MinMaxScaler()
normalized_df = min_max_scaler.fit_transform(normalized_df)
normalized_df = pd.DataFrame(data=normalized_df,
columns=result_matrix_df.columns,
index=result_matrix_df.index)
context.log_dataset(key='max_scaled_scores_feature_scores',
df=normalized_df,
local_path='max_scaled_scores_feature_scores.parquet',
format='parquet')
# Create feature count DataFrame
for test_name in selected_features_agg:
result_matrix_df[test_name] = [1 if x in selected_features_agg[test_name] else 0 for x in X.columns]
result_matrix_df.loc[:, 'num_votes'] = result_matrix_df.sum(axis=1)
context.log_dataset(key='selected_features_count',
df=result_matrix_df,
local_path='selected_features_count.parquet',
format='parquet')
# How many votes are needed for a feature to be selected?
if isinstance(min_votes, int):
votes_needed = min_votes
else:
num_filters = len(stat_filters) + len(model_filters)
votes_needed = int(np.floor(num_filters * max(min(min_votes, 1), 0)))
context.logger.info(f'votes needed to be selected: {votes_needed}')
# Create final feature dataframe
selected_features = result_matrix_df[result_matrix_df.num_votes >= votes_needed].index.tolist()
good_feature_df = df.loc[:, selected_features]
final_df = pd.concat([good_feature_df, y], axis=1)
context.log_dataset(key='selected_features',
df=final_df,
local_path='selected_features.parquet',
format='parquet')
# Creating a new feature vector containing only the identified top features
if is_feature_vector and df_artifact.meta.spec.features and output_vector_name:
# Selecting the top K features from our top feature dataframe
selected_features = result_matrix_df.head(k).index
# Match the selected feature names to the FS Feature annotations
matched_selections = [feature for feature in list(df_artifact.meta.spec.features) for selected in list(selected_features) if feature.endswith(selected)]
# Defining our new feature vector
top_features_fv = fs.FeatureVector(output_vector_name,
matched_selections,
label_feature="labels.label",
description='feature vector composed strictly of our top features')
# Saving
top_features_fv.save()
fs.get_offline_features(top_features_fv, target=ParquetTarget())
# Logging our new feature vector URI
context.log_result('top_features_vector', top_features_fv.uri)
def test_ingest_with_column_conversion(self):
orig_df = source = pd.DataFrame(
{
"time_stamp": [
pd.Timestamp("2002-04-01 04:32:34.000"),
pd.Timestamp("2002-04-01 15:05:37.000"),
pd.Timestamp("2002-03-31 23:46:07.000"),
],
"ssrxbtok": [488441267876, 438975336749, 298802679370],
"nkxuonfx": [0.241233, 0.160264, 0.045345],
"xzvipbmo": [True, False, None],
"bikyseca": ["ONE", "TWO", "THREE"],
"napxsuhp": [True, False, True],
"oegndrxe": [
pd.Timestamp("2002-04-01 04:32:34.000"),
pd.Timestamp("2002-04-01 05:06:34.000"),
pd.Timestamp("2002-04-01 05:38:34.000"),
],
"aatxnkgx": [-227504700006, -470002151801, -33193685176],
"quupyoxi": ["FOUR", "FIVE", "SIX"],
"temdojgz": [0.570031, 0.677182, 0.276053],
},
index=None,
)
fset = fs.FeatureSet(
"rWQTKqbhje",
timestamp_key="time_stamp",
entities=[
Entity("{}".format(k["name"])) for k in [
{
"dtype": "float",
"null_values": False,
"name": "temdojgz",
"df_dtype": "float64",
},
{
"dtype": "str",
"null_values": False,
"name": "bikyseca",
"df_dtype": "object",
},
{
"dtype": "float",
"null_values": False,
"name": "nkxuonfx",
"df_dtype": "float64",
},
]
],
)
fset.graph.to(name="s1", handler="my_func")
ikjqkfcz = ParquetTarget(path="v3io:///bigdata/ifrlsjvxgv",
partitioned=False)
fs.ingest(fset, source, targets=[ikjqkfcz])
features = ["rWQTKqbhje.*"]
vector = fs.FeatureVector("WPAyrYux", features)
vector.spec.with_indexes = False
resp = fs.get_offline_features(vector)
off_df = resp.to_dataframe()
del orig_df["time_stamp"]
if None in list(orig_df.index.names):
orig_df.set_index(["temdojgz", "bikyseca", "nkxuonfx"],
inplace=True)
orig_df = orig_df.sort_values(
by=["temdojgz", "bikyseca", "nkxuonfx"]).sort_index(axis=1)
off_df = off_df.sort_values(
by=["temdojgz", "bikyseca", "nkxuonfx"]).sort_index(axis=1)
pd.testing.assert_frame_equal(
off_df,
orig_df,
check_dtype=True,
check_index_type=True,
check_column_type=True,
check_like=True,
check_names=True,
)
def test_ingest_partitioned_by_key_and_time(
self, key_bucketing_number, partition_cols, time_partitioning_granularity
):
key = "patient_id"
name = f"measurements_{uuid.uuid4()}"
measurements = fs.FeatureSet(name, entities=[Entity(key)])
source = CSVSource(
"mycsv",
path=os.path.relpath(str(self.assets_path / "testdata.csv")),
time_field="timestamp",
)
measurements.set_targets(
targets=[
ParquetTarget(
partitioned=True,
key_bucketing_number=key_bucketing_number,
partition_cols=partition_cols,
time_partitioning_granularity=time_partitioning_granularity,
)
],
with_defaults=False,
)
resp1 = fs.ingest(measurements, source)
features = [
f"{name}.*",
]
vector = fs.FeatureVector("myvector", features)
resp = fs.get_offline_features(vector)
resp2 = resp.to_dataframe()
assert resp1.to_dict() == resp2.to_dict()
file_system = fsspec.filesystem("v3io")
kind = TargetTypes.parquet
path = f"{get_default_prefix_for_target(kind)}/sets/{name}-latest"
path = path.format(name=name, kind=kind, project="system-test-project")
dataset = pq.ParquetDataset(path, filesystem=file_system,)
partitions = [key for key, _ in dataset.pieces[0].partition_keys]
if key_bucketing_number is None:
expected_partitions = []
elif key_bucketing_number == 0:
expected_partitions = ["igzpart_key"]
else:
expected_partitions = [f"igzpart_hash{key_bucketing_number}_key"]
expected_partitions += partition_cols or []
if all(
value is None
for value in [
key_bucketing_number,
partition_cols,
time_partitioning_granularity,
]
):
time_partitioning_granularity = "hour"
if time_partitioning_granularity:
for unit in ["year", "month", "day", "hour"]:
expected_partitions.append(f"igzpart_{unit}")
if unit == time_partitioning_granularity:
break
assert partitions == expected_partitions
resp = fs.get_offline_features(
vector,
start_time=datetime(2020, 12, 1, 17, 33, 15),
end_time=datetime(2020, 12, 1, 17, 33, 16),
entity_timestamp_column="timestamp",
)
resp2 = resp.to_dataframe()
assert len(resp2) == 10
