def evaluate_offline_job():
filesystem, path = FileSource.create_filesystem_and_path(
data_source.path, data_source.file_options.s3_endpoint_override
)
source_df = pd.read_parquet(path, filesystem=filesystem)
# Make sure all timestamp fields are tz-aware. We default tz-naive fields to UTC
source_df[event_timestamp_column] = source_df[event_timestamp_column].apply(
lambda x: x if x.tzinfo is not None else x.replace(tzinfo=pytz.utc)
)
if created_timestamp_column:
source_df[created_timestamp_column] = source_df[
created_timestamp_column
].apply(
lambda x: x if x.tzinfo is not None else x.replace(tzinfo=pytz.utc)
)
source_columns = set(source_df.columns)
if not set(join_key_columns).issubset(source_columns):
raise FeastJoinKeysDuringMaterialization(
data_source.path, set(join_key_columns), source_columns
)
ts_columns = (
[event_timestamp_column, created_timestamp_column]
if created_timestamp_column
else [event_timestamp_column]
)
source_df.sort_values(by=ts_columns, inplace=True)
filtered_df = source_df[
(source_df[event_timestamp_column] >= start_date)
& (source_df[event_timestamp_column] < end_date)
]
columns_to_extract = set(
join_key_columns + feature_name_columns + ts_columns
)
if join_key_columns:
last_values_df = filtered_df.drop_duplicates(
join_key_columns, keep="last", ignore_index=True
)
else:
last_values_df = filtered_df
last_values_df[DUMMY_ENTITY_ID] = DUMMY_ENTITY_VAL
columns_to_extract.add(DUMMY_ENTITY_ID)
return last_values_df[columns_to_extract]
def persist(self, storage: SavedDatasetStorage):
assert isinstance(storage, SavedDatasetFileStorage)
filesystem, path = FileSource.create_filesystem_and_path(
storage.file_options.uri,
storage.file_options.s3_endpoint_override,
)
if path.endswith(".parquet"):
pyarrow.parquet.write_table(self.to_arrow(),
where=path,
filesystem=filesystem)
else:
# otherwise assume destination is directory
pyarrow.parquet.write_to_dataset(self.to_arrow(),
root_path=path,
filesystem=filesystem)
def evaluate_historical_retrieval():
# Make sure all event timestamp fields are tz-aware. We default tz-naive fields to UTC
entity_df[entity_df_event_timestamp_col] = entity_df[
entity_df_event_timestamp_col].apply(
lambda x: x
if x.tzinfo is not None else x.replace(tzinfo=pytz.utc))
# Create a copy of entity_df to prevent modifying the original
entity_df_with_features = entity_df.copy()
# Convert event timestamp column to datetime and normalize time zone to UTC
# This is necessary to avoid issues with pd.merge_asof
entity_df_with_features[
entity_df_event_timestamp_col] = pd.to_datetime(
entity_df_with_features[entity_df_event_timestamp_col],
utc=True)
# Sort event timestamp values
entity_df_with_features = entity_df_with_features.sort_values(
entity_df_event_timestamp_col)
# Load feature view data from sources and join them incrementally
for feature_view, features in feature_views_to_features.items():
event_timestamp_column = (
feature_view.batch_source.event_timestamp_column)
created_timestamp_column = (
feature_view.batch_source.created_timestamp_column)
# Read offline parquet data in pyarrow format.
filesystem, path = FileSource.create_filesystem_and_path(
feature_view.batch_source.path,
feature_view.batch_source.file_options.
s3_endpoint_override,
)
table = pyarrow.parquet.read_table(path, filesystem=filesystem)
# Rename columns by the field mapping dictionary if it exists
if feature_view.batch_source.field_mapping is not None:
table = _run_field_mapping(
table, feature_view.batch_source.field_mapping)
# Rename entity columns by the join_key_map dictionary if it exists
if feature_view.projection.join_key_map:
table = _run_field_mapping(
table, feature_view.projection.join_key_map)
# Convert pyarrow table to pandas dataframe. Note, if the underlying data has missing values,
# pandas will convert those values to np.nan if the dtypes are numerical (floats, ints, etc.) or boolean
# If the dtype is 'object', then missing values are inferred as python `None`s.
# More details at:
# https://pandas.pydata.org/pandas-docs/stable/user_guide/missing_data.html#values-considered-missing
df_to_join = table.to_pandas()
# Make sure all timestamp fields are tz-aware. We default tz-naive fields to UTC
df_to_join[event_timestamp_column] = df_to_join[
event_timestamp_column].apply(lambda x: x
if x.tzinfo is not None else
x.replace(tzinfo=pytz.utc))
if created_timestamp_column:
df_to_join[created_timestamp_column] = df_to_join[
created_timestamp_column].apply(
lambda x: x if x.tzinfo is not None else x.replace(
tzinfo=pytz.utc))
# Sort dataframe by the event timestamp column
df_to_join = df_to_join.sort_values(event_timestamp_column)
# Build a list of all the features we should select from this source
feature_names = []
for feature in features:
# Modify the separator for feature refs in column names to double underscore. We are using
# double underscore as separator for consistency with other databases like BigQuery,
# where there are very few characters available for use as separators
if full_feature_names:
formatted_feature_name = (
f"{feature_view.projection.name_to_use()}__{feature}"
)
else:
formatted_feature_name = feature
# Add the feature name to the list of columns
feature_names.append(formatted_feature_name)
# Ensure that the source dataframe feature column includes the feature view name as a prefix
df_to_join.rename(
columns={feature: formatted_feature_name},
inplace=True,
)
# Build a list of entity columns to join on (from the right table)
join_keys = []
for entity_name in feature_view.entities:
entity = registry.get_entity(entity_name, project)
join_key = feature_view.projection.join_key_map.get(
entity.join_key, entity.join_key)
join_keys.append(join_key)
right_entity_columns = join_keys
right_entity_key_columns = [event_timestamp_column
] + right_entity_columns
# Remove all duplicate entity keys (using created timestamp)
right_entity_key_sort_columns = right_entity_key_columns
if created_timestamp_column:
# If created_timestamp is available, use it to dedupe deterministically
right_entity_key_sort_columns = right_entity_key_sort_columns + [
created_timestamp_column
]
df_to_join.sort_values(by=right_entity_key_sort_columns,
inplace=True)
df_to_join.drop_duplicates(
right_entity_key_sort_columns,
keep="last",
ignore_index=True,
inplace=True,
)
# Select only the columns we need to join from the feature dataframe
df_to_join = df_to_join[right_entity_key_columns +
feature_names]
# Do point in-time-join between entity_df and feature dataframe
entity_df_with_features = pd.merge_asof(
entity_df_with_features,
df_to_join,
left_on=entity_df_event_timestamp_col,
right_on=event_timestamp_column,
by=right_entity_columns or None,
tolerance=feature_view.ttl,
)
# Remove right (feature table/view) event_timestamp column.
if event_timestamp_column != entity_df_event_timestamp_col:
entity_df_with_features.drop(
columns=[event_timestamp_column], inplace=True)
# Ensure that we delete dataframes to free up memory
del df_to_join
# Move "event_timestamp" column to front
current_cols = entity_df_with_features.columns.tolist()
current_cols.remove(entity_df_event_timestamp_col)
entity_df_with_features = entity_df_with_features[
[entity_df_event_timestamp_col] + current_cols]
return entity_df_with_features
