How to use feast - 10 common examples

parse_dates=["event_timestamp"],
    )

    for i, row in feature_values.iterrows():
        feature_row = FeatureRow()
        feature_row.entityKey = row["id"]
        feature_row.entityName = entity_name

        timestamp = Timestamp()
        timestamp.FromJsonString(row["event_timestamp"].strftime("%Y-%m-%dT%H:%M:%SZ"))
        feature_row.eventTimestamp.CopyFrom(timestamp)

        for info in feature_infos:
            feature = Feature()
            feature.id = info["id"]
            feature_value = Value()
            feature_name = info["name"]
            if info["dtype"] is "Int64":
                feature_value.int64Val = row[feature_name]
            elif info["dtype"] is "Int32":
                feature_value.int32Val = row[feature_name]
            elif info["dtype"] is np.float64:
                feature_value.doubleVal = row[feature_name]
            else:
                raise RuntimeError(
                    f"Unsupported dtype: {info['dtype']}\n"
                    "Supported valueType: INT32, INT64, FLOAT, DOUBLE\n"
                    "Please update your feature specs in testdata/feature_specs folder"
                )
            feature.value.CopyFrom(feature_value)
            feature_row.features.extend([feature])

def test_all_types_register_feature_set_success(client):
    all_types_fs_expected = FeatureSet(
        name="all_types",
        entities=[Entity(name="user_id", dtype=ValueType.INT64)],
        features=[
            Feature(name="float_feature", dtype=ValueType.FLOAT),
            Feature(name="int64_feature", dtype=ValueType.INT64),
            Feature(name="int32_feature", dtype=ValueType.INT32),
            Feature(name="string_feature", dtype=ValueType.STRING),
            Feature(name="bytes_feature", dtype=ValueType.BYTES),
            Feature(name="bool_feature", dtype=ValueType.BOOL),
            Feature(name="double_feature", dtype=ValueType.DOUBLE),
            Feature(name="double_list_feature", dtype=ValueType.DOUBLE_LIST),
            Feature(name="float_list_feature", dtype=ValueType.FLOAT_LIST),
            Feature(name="int64_list_feature", dtype=ValueType.INT64_LIST),
            Feature(name="int32_list_feature", dtype=ValueType.INT32_LIST),
            Feature(name="string_list_feature",
                    dtype=ValueType.STRING_LIST),

historical_fs = FeatureSet(
            "historical",
            features=[Feature("feature_value5", ValueType.STRING)],
            entities=[Entity("entity_id", ValueType.INT64)],
            max_age=Duration(seconds=100),
        )
    client.apply(historical_fs)

    fs1 = FeatureSet(
            "feature_set_1",
            features=[Feature("feature_value6", ValueType.STRING)],
            entities=[Entity("entity_id", ValueType.INT64)],
            max_age=Duration(seconds=100),
        )

    fs2 = FeatureSet(
        "feature_set_2",
        features=[Feature("other_feature_value7", ValueType.INT64)],
        entities=[Entity("other_entity_id", ValueType.INT64)],
        max_age=Duration(seconds=100),
    )
    client.apply(fs1)
    client.apply(fs2)

"file_feature_set",
            features=[Feature("feature_value1", ValueType.STRING)],
            entities=[Entity("entity_id", ValueType.INT64)],
            max_age=Duration(seconds=100),
        )
    client.apply(file_fs1)

    gcs_fs1 = FeatureSet(
            "gcs_feature_set",
            features=[Feature("feature_value2", ValueType.STRING)],
            entities=[Entity("entity_id", ValueType.INT64)],
            max_age=Duration(seconds=100),
        )
    client.apply(gcs_fs1)

    proc_time_fs = FeatureSet(
            "processing_time",
            features=[Feature("feature_value3", ValueType.STRING)],
            entities=[Entity("entity_id", ValueType.INT64)],
            max_age=Duration(seconds=100),
        )
    client.apply(proc_time_fs)

    add_cols_fs = FeatureSet(
            "additional_columns",
            features=[Feature("feature_value4", ValueType.STRING)],
            entities=[Entity("entity_id", ValueType.INT64)],
            max_age=Duration(seconds=100),
        )
    client.apply(add_cols_fs)

    historical_fs = FeatureSet(

"gcs_feature_set",
            features=[Feature("feature_value2", ValueType.STRING)],
            entities=[Entity("entity_id", ValueType.INT64)],
            max_age=Duration(seconds=100),
        )
    client.apply(gcs_fs1)

    proc_time_fs = FeatureSet(
            "processing_time",
            features=[Feature("feature_value3", ValueType.STRING)],
            entities=[Entity("entity_id", ValueType.INT64)],
            max_age=Duration(seconds=100),
        )
    client.apply(proc_time_fs)

    add_cols_fs = FeatureSet(
            "additional_columns",
            features=[Feature("feature_value4", ValueType.STRING)],
            entities=[Entity("entity_id", ValueType.INT64)],
            max_age=Duration(seconds=100),
        )
    client.apply(add_cols_fs)

    historical_fs = FeatureSet(
            "historical",
            features=[Feature("feature_value5", ValueType.STRING)],
            entities=[Entity("entity_id", ValueType.INT64)],
            max_age=Duration(seconds=100),
        )
    client.apply(historical_fs)

    fs1 = FeatureSet(

def test_basic_register_feature_set_success(client):
    # Load feature set from file
    cust_trans_fs_expected = FeatureSet.from_yaml("basic/cust_trans_fs.yaml")

    client.set_project(PROJECT_NAME)

    # Register feature set
    client.apply(cust_trans_fs_expected)

    cust_trans_fs_actual = client.get_feature_set(name="customer_transactions")

    assert cust_trans_fs_actual == cust_trans_fs_expected

    if cust_trans_fs_actual is None:
        raise Exception(
            "Client cannot retrieve 'customer_transactions' FeatureSet "
            "after registration. Either Feast Core does not save the "

"additional_columns",
            features=[Feature("feature_value4", ValueType.STRING)],
            entities=[Entity("entity_id", ValueType.INT64)],
            max_age=Duration(seconds=100),
        )
    client.apply(add_cols_fs)

    historical_fs = FeatureSet(
            "historical",
            features=[Feature("feature_value5", ValueType.STRING)],
            entities=[Entity("entity_id", ValueType.INT64)],
            max_age=Duration(seconds=100),
        )
    client.apply(historical_fs)

    fs1 = FeatureSet(
            "feature_set_1",
            features=[Feature("feature_value6", ValueType.STRING)],
            entities=[Entity("entity_id", ValueType.INT64)],
            max_age=Duration(seconds=100),
        )

    fs2 = FeatureSet(
        "feature_set_2",
        features=[Feature("other_feature_value7", ValueType.INT64)],
        entities=[Entity("other_entity_id", ValueType.INT64)],
        max_age=Duration(seconds=100),
    )
    client.apply(fs1)
    client.apply(fs2)

entities=[Entity("entity_id", ValueType.INT64)],
            max_age=Duration(seconds=100),
        )
    client.apply(gcs_fs1)

    proc_time_fs = FeatureSet(
            "processing_time",
            features=[Feature("feature_value3", ValueType.STRING)],
            entities=[Entity("entity_id", ValueType.INT64)],
            max_age=Duration(seconds=100),
        )
    client.apply(proc_time_fs)

    add_cols_fs = FeatureSet(
            "additional_columns",
            features=[Feature("feature_value4", ValueType.STRING)],
            entities=[Entity("entity_id", ValueType.INT64)],
            max_age=Duration(seconds=100),
        )
    client.apply(add_cols_fs)

    historical_fs = FeatureSet(
            "historical",
            features=[Feature("feature_value5", ValueType.STRING)],
            entities=[Entity("entity_id", ValueType.INT64)],
            max_age=Duration(seconds=100),
        )
    client.apply(historical_fs)

    fs1 = FeatureSet(
            "feature_set_1",
            features=[Feature("feature_value6", ValueType.STRING)],

features=[Feature("feature_value5", ValueType.STRING)],
            entities=[Entity("entity_id", ValueType.INT64)],
            max_age=Duration(seconds=100),
        )
    client.apply(historical_fs)

    fs1 = FeatureSet(
            "feature_set_1",
            features=[Feature("feature_value6", ValueType.STRING)],
            entities=[Entity("entity_id", ValueType.INT64)],
            max_age=Duration(seconds=100),
        )

    fs2 = FeatureSet(
        "feature_set_2",
        features=[Feature("other_feature_value7", ValueType.INT64)],
        entities=[Entity("other_entity_id", ValueType.INT64)],
        max_age=Duration(seconds=100),
    )
    client.apply(fs1)
    client.apply(fs2)

entities=[Entity("entity_id", ValueType.INT64)],
            max_age=Duration(seconds=100),
        )
    client.apply(file_fs1)

    gcs_fs1 = FeatureSet(
            "gcs_feature_set",
            features=[Feature("feature_value2", ValueType.STRING)],
            entities=[Entity("entity_id", ValueType.INT64)],
            max_age=Duration(seconds=100),
        )
    client.apply(gcs_fs1)

    proc_time_fs = FeatureSet(
            "processing_time",
            features=[Feature("feature_value3", ValueType.STRING)],
            entities=[Entity("entity_id", ValueType.INT64)],
            max_age=Duration(seconds=100),
        )
    client.apply(proc_time_fs)

    add_cols_fs = FeatureSet(
            "additional_columns",
            features=[Feature("feature_value4", ValueType.STRING)],
            entities=[Entity("entity_id", ValueType.INT64)],
            max_age=Duration(seconds=100),
        )
    client.apply(add_cols_fs)

    historical_fs = FeatureSet(
            "historical",
            features=[Feature("feature_value5", ValueType.STRING)],