feast/sdk/python/feast/utils.py at master · superjcd/feast · GitHub

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import os

import typing

from collections import defaultdict

from datetime import datetime

from pathlib import Path

from typing import Dict, List, Optional, Tuple, Union

import pandas as pd

import pyarrow

from dask import dataframe as dd

from dateutil.tz import tzlocal

from pytz import utc

from feast.constants import FEAST_FS_YAML_FILE_PATH_ENV_NAME

from feast.entity import Entity

from feast.protos.feast.types.EntityKey_pb2 import EntityKey as EntityKeyProto

from feast.protos.feast.types.Value_pb2 import Value as ValueProto

from feast.type_map import python_values_to_proto_values

from feast.value_type import ValueType

if typing.TYPE_CHECKING:

from feast.feature_view import FeatureView

from feast.on_demand_feature_view import OnDemandFeatureView

def make_tzaware(t: datetime) -> datetime:

"""We assume tz-naive datetimes are UTC"""

if t.tzinfo is None:

return t.replace(tzinfo=utc)

else:

return t

def make_df_tzaware(t: pd.DataFrame) -> pd.DataFrame:

"""Make all datetime type columns tzaware; leave everything else intact."""

df = t.copy() # don't modify incoming dataframe inplace

for column in df.columns:

if pd.api.types.is_datetime64_any_dtype(df[column]):

df[column] = pd.to_datetime(df[column], utc=True)

return df

def to_naive_utc(ts: datetime) -> datetime:

if ts.tzinfo is None:

return ts

else:

return ts.astimezone(utc).replace(tzinfo=None)

def maybe_local_tz(t: datetime) -> datetime:

if t.tzinfo is None:

return t.replace(tzinfo=tzlocal())

else:

return t

def get_default_yaml_file_path(repo_path: Path) -> Path:

if FEAST_FS_YAML_FILE_PATH_ENV_NAME in os.environ:

yaml_path = os.environ[FEAST_FS_YAML_FILE_PATH_ENV_NAME]

return Path(yaml_path)

else:

return repo_path / "feature_store.yaml"

def _get_requested_feature_views_to_features_dict(

feature_refs: List[str],

feature_views: List["FeatureView"],

on_demand_feature_views: List["OnDemandFeatureView"],

) -> Tuple[Dict["FeatureView", List[str]], Dict["OnDemandFeatureView", List[str]]]:

"""Create a dict of FeatureView -> List[Feature] for all requested features.

Set full_feature_names to True to have feature names prefixed by their feature view name."""

feature_views_to_feature_map: Dict["FeatureView", List[str]] = defaultdict(list)

on_demand_feature_views_to_feature_map: Dict[

"OnDemandFeatureView", List[str]

] = defaultdict(list)

for ref in feature_refs:

ref_parts = ref.split(":")

feature_view_from_ref = ref_parts[0]

feature_from_ref = ref_parts[1]

found = False

for fv in feature_views:

if fv.projection.name_to_use() == feature_view_from_ref:

found = True

feature_views_to_feature_map[fv].append(feature_from_ref)

for odfv in on_demand_feature_views:

if odfv.projection.name_to_use() == feature_view_from_ref:

found = True

on_demand_feature_views_to_feature_map[odfv].append(feature_from_ref)

if not found:

raise ValueError(f"Could not find feature view from reference {ref}")

return feature_views_to_feature_map, on_demand_feature_views_to_feature_map

def _get_column_names(

feature_view: "FeatureView", entities: List[Entity]

) -> Tuple[List[str], List[str], str, Optional[str]]:

"""

If a field mapping exists, run it in reverse on the join keys,

feature names, event timestamp column, and created timestamp column

to get the names of the relevant columns in the offline feature store table.

Returns:

Tuple containing the list of reverse-mapped join_keys,

reverse-mapped feature names, reverse-mapped event timestamp column,

and reverse-mapped created timestamp column that will be passed into

the query to the offline store.

"""

# if we have mapped fields, use the original field names in the call to the offline store

timestamp_field = feature_view.batch_source.timestamp_field

feature_names = [feature.name for feature in feature_view.features]

created_timestamp_column = feature_view.batch_source.created_timestamp_column

from feast.feature_view import DUMMY_ENTITY_ID

join_keys = [

entity.join_key for entity in entities if entity.join_key != DUMMY_ENTITY_ID

]

if feature_view.batch_source.field_mapping is not None:

reverse_field_mapping = {

v: k for k, v in feature_view.batch_source.field_mapping.items()

}

timestamp_field = (

reverse_field_mapping[timestamp_field]

if timestamp_field in reverse_field_mapping.keys()

else timestamp_field

)

created_timestamp_column = (

reverse_field_mapping[created_timestamp_column]

if created_timestamp_column

and created_timestamp_column in reverse_field_mapping.keys()

else created_timestamp_column

)

join_keys = [

reverse_field_mapping[col] if col in reverse_field_mapping.keys() else col

for col in join_keys

]

feature_names = [

reverse_field_mapping[col] if col in reverse_field_mapping.keys() else col

for col in feature_names

]

# We need to exclude join keys and timestamp columns from the list of features, after they are mapped to

# their final column names via the `field_mapping` field of the source.

feature_names = [

name

for name in feature_names

if name not in join_keys

and name != timestamp_field

and name != created_timestamp_column

]

return (

join_keys,

feature_names,

timestamp_field,

created_timestamp_column,

)

def _run_pyarrow_field_mapping(

table: pyarrow.Table,

field_mapping: Dict[str, str],

) -> pyarrow.Table:

# run field mapping in the forward direction

cols = table.column_names

mapped_cols = [

field_mapping[col] if col in field_mapping.keys() else col for col in cols

]

table = table.rename_columns(mapped_cols)

return table

def _run_dask_field_mapping(

table: dd.DataFrame,

field_mapping: Dict[str, str],

if field_mapping:

# run field mapping in the forward direction

table = table.rename(columns=field_mapping)

table = table.persist()

return table

def _coerce_datetime(ts):

"""

Depending on underlying time resolution, arrow to_pydict() sometimes returns pd

timestamp type (for nanosecond resolution), and sometimes you get standard python datetime

(for microsecond resolution).

While pd timestamp class is a subclass of python datetime, it doesn't always behave the

same way. We convert it to normal datetime so that consumers downstream don't have to deal

with these quirks.

"""

if isinstance(ts, pd.Timestamp):

return ts.to_pydatetime()

else:

return ts

def _convert_arrow_to_proto(

table: Union[pyarrow.Table, pyarrow.RecordBatch],

feature_view: "FeatureView",

join_keys: Dict[str, ValueType],

) -> List[Tuple[EntityKeyProto, Dict[str, ValueProto], datetime, Optional[datetime]]]:

# Avoid ChunkedArrays which guarantees `zero_copy_only` available.

if isinstance(table, pyarrow.Table):

table = table.to_batches()[0]

columns = [

(field.name, field.dtype.to_value_type()) for field in feature_view.features

] + list(join_keys.items())

proto_values_by_column = {

column: python_values_to_proto_values(

table.column(column).to_numpy(zero_copy_only=False), value_type

)

for column, value_type in columns

}

entity_keys = [

EntityKeyProto(

join_keys=join_keys,

entity_values=[proto_values_by_column[k][idx] for k in join_keys],

)

for idx in range(table.num_rows)

]

# Serialize the features per row

feature_dict = {

feature.name: proto_values_by_column[feature.name]

for feature in feature_view.features

}

features = [dict(zip(feature_dict, vars)) for vars in zip(*feature_dict.values())]

# Convert event_timestamps

event_timestamps = [

_coerce_datetime(val)

for val in pd.to_datetime(

table.column(feature_view.batch_source.timestamp_field).to_numpy(

zero_copy_only=False

)

]

# Convert created_timestamps if they exist

if feature_view.batch_source.created_timestamp_column:

created_timestamps = [

_coerce_datetime(val)

for val in pd.to_datetime(

table.column(

feature_view.batch_source.created_timestamp_column

).to_numpy(zero_copy_only=False)

)

]

else:

created_timestamps = [None] * table.num_rows

return list(zip(entity_keys, features, event_timestamps, created_timestamps))

feast/sdk/python/feast/utils.py at master · superjcd/feast · GitHub

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