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Copy pathfeatures.py
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209 lines (186 loc) · 5.93 KB
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import json
from datetime import datetime
from typing import List
import click
import pandas as pd
from feast.repo_operations import create_feature_store
@click.group(name="features")
def features_cmd():
"""
Access features
"""
pass
@features_cmd.command(name="list")
@click.option(
"--output",
type=click.Choice(["table", "json"], case_sensitive=False),
default="table",
show_default=True,
help="Output format",
)
@click.pass_context
def features_list(ctx: click.Context, output: str):
"""
List all features
"""
store = create_feature_store(ctx)
feature_views = [
*store.list_batch_feature_views(),
*store.list_on_demand_feature_views(),
*store.list_stream_feature_views(),
]
feature_list = []
for fv in feature_views:
for feature in fv.features:
feature_list.append([feature.name, fv.name, str(feature.dtype)])
if output == "json":
json_output = [
{"feature_name": fn, "feature_view": fv, "dtype": dt}
for fv, fn, dt in feature_list
]
click.echo(json.dumps(json_output, indent=4))
else:
from tabulate import tabulate
click.echo(
tabulate(
feature_list,
headers=["Feature", "Feature View", "Data Type"],
tablefmt="plain",
)
)
@features_cmd.command("describe")
@click.argument("feature_name", type=str)
@click.pass_context
def describe_feature(ctx: click.Context, feature_name: str):
"""
Describe a specific feature by name
"""
store = create_feature_store(ctx)
feature_views = [
*store.list_batch_feature_views(),
*store.list_on_demand_feature_views(),
*store.list_stream_feature_views(),
]
feature_details = []
for fv in feature_views:
for feature in fv.features:
if feature.name == feature_name:
feature_details.append(
{
"Feature Name": feature.name,
"Feature View": fv.name,
"Data Type": str(feature.dtype),
"Description": getattr(feature, "description", "N/A"),
"Online Store": getattr(fv, "online", "N/A"),
"Source": json.loads(str(getattr(fv, "batch_source", "N/A"))),
}
)
if not feature_details:
click.echo(f"Feature '{feature_name}' not found in any feature view.")
return
click.echo(json.dumps(feature_details, indent=4))
@click.command("get-online-features")
@click.option(
"--entities",
"-e",
type=str,
multiple=True,
required=True,
help="Entity key-value pairs (e.g., driver_id=1001)",
)
@click.option(
"--features",
"-f",
multiple=True,
required=True,
help="Features to retrieve. (e.g.,feature-view:feature-name) ex: driver_hourly_stats:conv_rate",
)
@click.pass_context
def get_online_features(ctx: click.Context, entities: List[str], features: List[str]):
"""
Fetch online feature values for a given entity ID
"""
store = create_feature_store(ctx)
entity_dict: dict[str, List[str]] = {}
for entity in entities:
try:
key, value = entity.split("=")
if key not in entity_dict:
entity_dict[key] = []
entity_dict[key].append(value)
except ValueError:
click.echo(f"Invalid entity format: {entity}. Use key=value format.")
return
entity_rows = [
dict(zip(entity_dict.keys(), values)) for values in zip(*entity_dict.values())
]
feature_vector = store.get_online_features(
features=list(features),
entity_rows=entity_rows,
).to_dict()
click.echo(json.dumps(feature_vector, indent=4))
@click.command(name="get-historical-features")
@click.option(
"--dataframe",
"-d",
type=str,
help='JSON string containing entities and timestamps. Example: \'[{"event_timestamp": "2025-03-29T12:00:00", "driver_id": 1001}]\'',
)
@click.option(
"--features",
"-f",
multiple=True,
help="Features to retrieve. feature-view:feature-name ex: driver_hourly_stats:conv_rate",
)
@click.option(
"--start-date",
"-s",
type=str,
help="Start date for historical feature retrieval. Format: YYYY-MM-DD HH:MM:SS",
)
@click.option(
"--end-date",
"-e",
type=str,
help="End date for historical feature retrieval. Format: YYYY-MM-DD HH:MM:SS",
)
@click.pass_context
def get_historical_features(
ctx: click.Context,
dataframe: str,
features: List[str],
start_date: str,
end_date: str,
):
"""
Fetch historical feature values for a given entity ID
"""
store = create_feature_store(ctx)
if not dataframe and not start_date and not end_date:
click.echo(
"Either --dataframe or --start-date and/or --end-date must be provided."
)
return
if dataframe and (start_date or end_date):
click.echo("Cannot specify both --dataframe and --start-date/--end-date.")
return
entity_df = None
if dataframe:
try:
entity_list = json.loads(dataframe)
if not isinstance(entity_list, list):
raise ValueError("Entities must be a list of dictionaries.")
entity_df = pd.DataFrame(entity_list)
entity_df["event_timestamp"] = pd.to_datetime(entity_df["event_timestamp"])
except Exception as e:
click.echo(f"Error parsing entities JSON: {e}", err=True)
return
feature_vector = store.get_historical_features(
entity_df=entity_df,
features=list(features),
start_date=datetime.strptime(start_date, "%Y-%m-%d %H:%M:%S")
if start_date
else None,
end_date=datetime.strptime(end_date, "%Y-%m-%d %H:%M:%S") if end_date else None,
).to_df()
click.echo(feature_vector.to_json(orient="records", indent=4))