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feast/sdk/python/feast/inference.py at master · npow/feast · GitHub
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import
re
from
typing
import
List
from
feast
import
(
BigQuerySource
,
Entity
,
Feature
,
FileSource
,
RedshiftSource
,
SnowflakeSource
,
)
from
feast
.
data_source
import
DataSource
,
RequestDataSource
from
feast
.
errors
import
RegistryInferenceFailure
from
feast
.
feature_view
import
FeatureView
from
feast
.
repo_config
import
RepoConfig
from
feast
.
value_type
import
ValueType
def
update_entities_with_inferred_types_from_feature_views
(
entities
:
List
[
Entity
],
feature_views
:
List
[
FeatureView
],
config
:
RepoConfig
)
->
None
:
"""
Infers the types of the entities by examining the schemas of feature view batch sources.
Args:
entities: The entities to be updated.
feature_views: A list containing feature views associated with the entities.
config: The config for the current feature store.
"""
incomplete_entities
=
{
entity
.
name
:
entity
for
entity
in
entities
if
entity
.
value_type
==
ValueType
.
UNKNOWN
}
incomplete_entities_keys
=
incomplete_entities
.
keys
()
for
view
in
feature_views
:
if
not
(
incomplete_entities_keys
&
set
(
view
.
entities
)):
continue
# skip if view doesn't contain any entities that need inference
col_names_and_types
=
view
.
batch_source
.
get_table_column_names_and_types
(
config
)
for
entity_name
in
view
.
entities
:
if
entity_name
in
incomplete_entities
:
entity
=
incomplete_entities
[
entity_name
]
# get entity information from information extracted from the view batch source
extracted_entity_name_type_pairs
=
list
(
filter
(
lambda
tup
:
tup
[
0
]
==
entity
.
join_key
,
col_names_and_types
,)
)
if
len
(
extracted_entity_name_type_pairs
)
==
0
:
# Doesn't mention inference error because would also be an error without inferencing
raise
ValueError
(
f"""No column in the batch source for the
{
view
.
name
}
feature view matches
its entity's name."""
)
inferred_value_type
=
view
.
batch_source
.
source_datatype_to_feast_value_type
()(
extracted_entity_name_type_pairs
[
0
][
1
]
)
if
(
entity
.
value_type
!=
ValueType
.
UNKNOWN
and
entity
.
value_type
!=
inferred_value_type
)
or
(
len
(
extracted_entity_name_type_pairs
)
>
1
):
raise
RegistryInferenceFailure
(
"Entity"
,
f"""Entity value_type inference failed for
{
entity_name
}
entity.
Multiple viable matches.
"""
,
)
entity
.
value_type
=
inferred_value_type
def
update_data_sources_with_inferred_event_timestamp_col
(
data_sources
:
List
[
DataSource
],
config
:
RepoConfig
)
->
None
:
ERROR_MSG_PREFIX
=
"Unable to infer DataSource event_timestamp_column"
for
data_source
in
data_sources
:
if
isinstance
(
data_source
,
RequestDataSource
):
continue
if
(
data_source
.
event_timestamp_column
is
None
or
data_source
.
event_timestamp_column
==
""
):
# prepare right match pattern for data source
ts_column_type_regex_pattern
=
""
# TODO(adchia): Move Spark source inference out of this logic
if
(
isinstance
(
data_source
,
FileSource
)
or
"SparkSource"
==
data_source
.
__class__
.
__name__
):
ts_column_type_regex_pattern
=
r"^timestamp"
elif
isinstance
(
data_source
,
BigQuerySource
):
ts_column_type_regex_pattern
=
"TIMESTAMP|DATETIME"
elif
isinstance
(
data_source
,
RedshiftSource
):
ts_column_type_regex_pattern
=
"TIMESTAMP[A-Z]*"
elif
isinstance
(
data_source
,
SnowflakeSource
):
ts_column_type_regex_pattern
=
"TIMESTAMP_[A-Z]*"
else
:
raise
RegistryInferenceFailure
(
"DataSource"
,
f"""
DataSource inferencing of event_timestamp_column is currently only supported
for FileSource, SparkSource, BigQuerySource, RedshiftSource, and SnowflakeSource.
Attempting to infer from
{
data_source
}
.
"""
,
)
# for informing the type checker
assert
(
isinstance
(
data_source
,
FileSource
)
or
isinstance
(
data_source
,
BigQuerySource
)
or
isinstance
(
data_source
,
RedshiftSource
)
or
isinstance
(
data_source
,
SnowflakeSource
)
or
"SparkSource"
==
data_source
.
__class__
.
__name__
)
# loop through table columns to find singular match
event_timestamp_column
,
matched_flag
=
None
,
False
for
(
col_name
,
col_datatype
,
)
in
data_source
.
get_table_column_names_and_types
(
config
):
if
re
.
match
(
ts_column_type_regex_pattern
,
col_datatype
):
if
matched_flag
:
raise
RegistryInferenceFailure
(
"DataSource"
,
f"""
{
ERROR_MSG_PREFIX
}
due to multiple possible columns satisfying
the criteria.
{
ts_column_type_regex_pattern
}
{
col_name
}
"""
,
)
matched_flag
=
True
event_timestamp_column
=
col_name
if
matched_flag
:
assert
event_timestamp_column
data_source
.
event_timestamp_column
=
event_timestamp_column
else
:
raise
RegistryInferenceFailure
(
"DataSource"
,
f"""
{
ERROR_MSG_PREFIX
}
due to an absence of columns that satisfy the criteria.
"""
,
)
def
update_feature_views_with_inferred_features
(
fvs
:
List
[
FeatureView
],
entities
:
List
[
Entity
],
config
:
RepoConfig
)
->
None
:
"""
Infers the set of features associated to each FeatureView and updates the FeatureView with those features.
Inference occurs through considering each column of the underlying data source as a feature except columns that are
associated with the data source's timestamp columns and the FeatureView's entity columns.
Args:
fvs: The feature views to be updated.
entities: A list containing entities associated with the feature views.
config: The config for the current feature store.
"""
entity_name_to_join_key_map
=
{
entity
.
name
:
entity
.
join_key
for
entity
in
entities
}
for
fv
in
fvs
:
if
not
fv
.
features
:
columns_to_exclude
=
{
fv
.
batch_source
.
event_timestamp_column
,
fv
.
batch_source
.
created_timestamp_column
,
}
|
{
entity_name_to_join_key_map
[
entity_name
]
for
entity_name
in
fv
.
entities
}
if
fv
.
batch_source
.
event_timestamp_column
in
fv
.
batch_source
.
field_mapping
:
columns_to_exclude
.
add
(
fv
.
batch_source
.
field_mapping
[
fv
.
batch_source
.
event_timestamp_column
]
)
if
(
fv
.
batch_source
.
created_timestamp_column
in
fv
.
batch_source
.
field_mapping
):
columns_to_exclude
.
add
(
fv
.
batch_source
.
field_mapping
[
fv
.
batch_source
.
created_timestamp_column
]
)
for
(
col_name
,
col_datatype
,
)
in
fv
.
batch_source
.
get_table_column_names_and_types
(
config
):
if
col_name
not
in
columns_to_exclude
and
not
re
.
match
(
"^__|__$"
,
col_name
,
# double underscores often signal an internal-use column
):
feature_name
=
(
fv
.
batch_source
.
field_mapping
[
col_name
]
if
col_name
in
fv
.
batch_source
.
field_mapping
else
col_name
)
fv
.
features
.
append
(
Feature
(
feature_name
,
fv
.
batch_source
.
source_datatype_to_feast_value_type
()(
col_datatype
),
)
)
if
not
fv
.
features
:
raise
RegistryInferenceFailure
(
"FeatureView"
,
f"Could not infer Features for the FeatureView named
{
fv
.
name
}
."
,
)
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