如何在 mlrun.feature_store 中使用 get_offline_features()？

Question

我正在尝试从现有的功能商店获取功能。在文档 https://docs.mlrun.org/en/latest/api/mlrun.feature_store.html 中，它说您可以将特征向量 uri 或 FeatureVector 对象传递给 mlrun.feature_store.get_offline_features()。
特征存储的 uri 是什么？
在哪里可以找到示例？

Answer 1

在 MLRun 中，特征集是一组一起摄取的特征。特征向量是从特征集中选择的特征（这里有几列，那里有几列，等等）。这非常适合使用通用 entity/key.

将多个数据源连接在一起

可以在下面找到从 MLRun 创建和查询特征集的完整示例：

import mlrun.feature_store as fs
from mlrun import set_environment
import pandas as pd

# Set project - for retrieving features later
set_environment(project="my-project")

# Feature set to ingest
df = pd.DataFrame({
    "key" : [0, 1, 2, 3],
    "value" : ["A", "B", "C", "D"]
})

# Create feature set with desired name and entity/key
fset = fs.FeatureSet("my-feature-set", entities=[fs.Entity("key")])

# Ingest
fs.ingest(featureset=fset, source=df)

# Create feature vector (allows for joining multiple feature sets together)
features = ["my-feature-set.*"] # can also do ["my-feature-set.A", my-feature-set.B", ...]
vector = fs.FeatureVector("my-feature-vector", features)

# Retrieve offline features (vector object)
fs.get_offline_features(vector)

# Retrieve offline features (project + name)
fs.get_offline_features("my-project/my-feature-vector")

# Retrieve offline features as pandas dataframe
fs.get_offline_features("my-project/my-feature-vector").to_dataframe()

您可以在此处的文档中找到更多特征存储示例：https://docs.mlrun.org/en/latest/feature-store/feature-store.html

如何在 mlrun.feature_store 中使用 get_offline_features()？

how to use get_offline_features() in the mlrun.feature_store?

nuclio