10. Derive features from other features

Derive feature from other features¶

Features can also be created based on other features.

In this tutorial we will create a feature that measures the Z-Score of the Latest invoice Amount for the customer compared to that customer's invoices over a 28d period. This feature is derived from 3 features we created previously: CUSTOMER_Latest_invoice_Amount, CUSTOMER_Avg_of_invoice_Amount_28d and CUSTOMER_Std_of_invoice_Amount_28d.

In [1]:

import featurebyte as fb

# Set your profile to the tutorial environment
fb.use_profile("tutorial")

catalog_name = "Grocery Dataset Tutorial"
catalog = fb.Catalog.activate(catalog_name)

import featurebyte as fb # Set your profile to the tutorial environment fb.use_profile("tutorial") catalog_name = "Grocery Dataset Tutorial" catalog = fb.Catalog.activate(catalog_name)

16:43:20 | INFO     | Using configuration file at: /Users/viktor/.featurebyte/config.yaml
16:43:20 | INFO     | Active profile: tutorial (https://tutorials.featurebyte.com/api/v1)
16:43:20 | INFO     | SDK version: 0.6.0.dev121
16:43:20 | INFO     | No catalog activated.
16:43:20 | INFO     | 10 feature lists, 59 features deployed
16:43:20 | INFO     | Using profile: tutorial
16:43:21 | INFO     | Using configuration file at: /Users/viktor/.featurebyte/config.yaml
16:43:21 | INFO     | Active profile: tutorial (https://tutorials.featurebyte.com/api/v1)
16:43:21 | INFO     | SDK version: 0.6.0.dev121
16:43:21 | INFO     | No catalog activated.
16:43:21 | INFO     | 10 feature lists, 59 features deployed
16:43:21 | INFO     | Catalog activated: Grocery Dataset Tutorial

Get features we previously created and saved¶

In [2]:

customer_latest_invoice_amount = catalog.get_feature("CUSTOMER_Latest_invoice_Amount")
customer_avg_of_invoice_amount_28d = catalog.get_feature("CUSTOMER_Avg_of_invoice_Amount_28d")
customer_std_of_invoice_amount_28d = catalog.get_feature("CUSTOMER_Std_of_invoice_Amount_28d")

customer_latest_invoice_amount = catalog.get_feature("CUSTOMER_Latest_invoice_Amount") customer_avg_of_invoice_amount_28d = catalog.get_feature("CUSTOMER_Avg_of_invoice_Amount_28d") customer_std_of_invoice_amount_28d = catalog.get_feature("CUSTOMER_Std_of_invoice_Amount_28d")

Derive a z-score feature from saved features¶

In [3]:

customer_latest_invoice_amount_Z_score_to_invoice_amount_28d = (
    customer_latest_invoice_amount
    - customer_avg_of_invoice_amount_28d
) / customer_std_of_invoice_amount_28d
# Give a name to new feature
customer_latest_invoice_amount_Z_score_to_invoice_amount_28d.name = \
"CUSTOMER_Latest_invoice_Amount_Z_Score_to_invoice_Amount_28d"

customer_latest_invoice_amount_Z_score_to_invoice_amount_28d = ( customer_latest_invoice_amount - customer_avg_of_invoice_amount_28d ) / customer_std_of_invoice_amount_28d # Give a name to new feature customer_latest_invoice_amount_Z_score_to_invoice_amount_28d.name = \ "CUSTOMER_Latest_invoice_Amount_Z_Score_to_invoice_Amount_28d"

Preview feature¶

In [4]:

customer_latest_invoice_amount_Z_score_to_invoice_amount_28d.primary_entity

customer_latest_invoice_amount_Z_score_to_invoice_amount_28d.primary_entity

Out[4]:

[<featurebyte.api.entity.Entity at 0x149b56c80>
 {
   'name': 'customer',
   'created_at': '2023-11-27T15:39:09.477000',
   'updated_at': '2023-11-27T15:39:19.968000',
   'description': None,
   'serving_names': [
     'GROCERYCUSTOMERGUID'
   ],
   'catalog_name': 'Grocery Dataset Tutorial'
 }]

In [5]:

# Get observation table: 'Preview Table with 10 Customers'
preview_table = catalog.get_observation_table("Preview Table with 10 Customers").to_pandas()

# Get observation table: 'Preview Table with 10 Customers' preview_table = catalog.get_observation_table("Preview Table with 10 Customers").to_pandas()

Downloading table |████████████████████████████████████████| 10/10 [100%] in 0.1

In [6]:

# Preview CUSTOMER_Age
customer_latest_invoice_amount_Z_score_to_invoice_amount_28d.preview(preview_table)

# Preview CUSTOMER_Age customer_latest_invoice_amount_Z_score_to_invoice_amount_28d.preview(preview_table)

Out[6]:

	POINT_IN_TIME	GROCERYCUSTOMERGUID	CUSTOMER_Latest_invoice_Amount_Z_Score_to_invoice_Amount_28d
0	2022-11-28 11:36:31	d4559f7d-eb28-42c6-b47d-847de24952c2	NaN
1	2022-10-09 15:47:55	3f8c7c4c-f2c2-408e-a08e-622de3d3a0b9	NaN
2	2022-09-14 15:42:42	35390325-8443-43c1-a934-18db923d9a47	-0.838057
3	2022-12-26 18:39:46	4eb4ee84-ee13-4eec-9c26-61b6eb4ba35b	0.992653
4	2022-12-06 08:47:43	e42fa5f3-7737-4c6a-9ef4-856f113e60bd	1.530644
5	2022-11-09 12:14:40	8440debb-6abc-4adc-8c6c-749928141fd0	NaN
6	2022-10-12 17:32:15	8a54e527-e9a4-47a9-a28f-8b3c6ecc02db	-0.049459
7	2023-01-01 11:51:28	cea213d4-36e4-48c3-ae8d-c7a25911e11c	-0.530470
8	2023-02-05 15:48:23	3b4f2821-b761-40e9-a32a-5f09685cc597	0.024855
9	2023-03-10 16:15:46	91a64566-e212-4e36-8f23-c1f1f324a301	-0.743774

Save feature¶

In [7]:

customer_latest_invoice_amount_Z_score_to_invoice_amount_28d.save()

customer_latest_invoice_amount_Z_score_to_invoice_amount_28d.save()

Done! |████████████████████████████████████████| 100% in 6.5s (0.16%/s)         

Add description and view definition file¶

In [8]:

# Add description
customer_latest_invoice_amount_Z_score_to_invoice_amount_28d.update_description(
	"Z-Score of the Latest invoice Amount for the customer compared to customer invoices over a 28d period"
)
# See feature definition file
customer_latest_invoice_amount_Z_score_to_invoice_amount_28d.definition

# Add description customer_latest_invoice_amount_Z_score_to_invoice_amount_28d.update_description( "Z-Score of the Latest invoice Amount for the customer compared to customer invoices over a 28d period" ) # See feature definition file customer_latest_invoice_amount_Z_score_to_invoice_amount_28d.definition

Out[8]:

# Generated by SDK version: 0.6.0.dev121
from bson import ObjectId
from featurebyte import ColumnCleaningOperation
from featurebyte import DisguisedValueImputation
from featurebyte import EventTable
from featurebyte import FeatureJobSetting
from featurebyte import ValueBeyondEndpointImputation


# event_table name: "GROCERYINVOICE"
event_table = EventTable.get_by_id(ObjectId("6564b7ebbeba6c193e0fe3bc"))
event_view = event_table.get_view(
    view_mode="manual",
    drop_column_names=["record_available_at"],
    column_cleaning_operations=[
        ColumnCleaningOperation(
            column_name="Amount",
            cleaning_operations=[
                DisguisedValueImputation(
                    imputed_value=None, disguised_values=[-99.0, -98.0]
                ),
                ValueBeyondEndpointImputation(
                    type="less_than", end_point=0.0, imputed_value=0.0
                ),
                ValueBeyondEndpointImputation(
                    type="greater_than", end_point=2000.0, imputed_value=2000.0
                ),
            ],
        )
    ],
)
grouped = event_view.groupby(
    by_keys=["GroceryCustomerGuid"], category=None
).aggregate_over(
    value_column="Amount",
    method="latest",
    windows=[None],
    feature_names=["CUSTOMER_Latest_invoice_Amount"],
    feature_job_setting=FeatureJobSetting(
        blind_spot="120s", frequency="3600s", time_modulo_frequency="120s"
    ),
    skip_fill_na=True,
)
feat = grouped["CUSTOMER_Latest_invoice_Amount"]
grouped_1 = event_view.groupby(
    by_keys=["GroceryCustomerGuid"], category=None
).aggregate_over(
    value_column="Amount",
    method="avg",
    windows=["28d"],
    feature_names=["CUSTOMER_Avg_of_invoice_Amount_28d"],
    feature_job_setting=FeatureJobSetting(
        blind_spot="120s", frequency="3600s", time_modulo_frequency="120s"
    ),
    skip_fill_na=True,
)
feat_1 = grouped_1["CUSTOMER_Avg_of_invoice_Amount_28d"]
grouped_2 = event_view.groupby(
    by_keys=["GroceryCustomerGuid"], category=None
).aggregate_over(
    value_column="Amount",
    method="std",
    windows=["28d"],
    feature_names=["CUSTOMER_Std_of_invoice_Amount_28d"],
    feature_job_setting=FeatureJobSetting(
        blind_spot="120s", frequency="3600s", time_modulo_frequency="120s"
    ),
    skip_fill_na=True,
)
feat_2 = grouped_2["CUSTOMER_Std_of_invoice_Amount_28d"]
feat_3 = (feat - feat_1) / feat_2
feat_3.name = "CUSTOMER_Latest_invoice_Amount_Z_Score_to_invoice_Amount_28d"
output = feat_3
output.save(_id=ObjectId("6564b91c69ee318ce739930c"))

Concepts in this tutorial¶

• Feature Transforms

SDK reference for¶

• Catalog.get_feature()