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)

22:00:49 | INFO     | Using configuration file at: /Users/gxav/.featurebyte/config.yaml
22:00:49 | INFO     | Active profile: tutorial (https://tutorials.featurebyte.com/api/v1)
22:00:49 | WARNING  | Remote SDK version (0.5.0.dev6) is different from local (0.5.0.dev1). Update local SDK to avoid unexpected behavior.
22:00:49 | INFO     | No catalog activated.
22:00:49 | INFO     | 6 feature lists, 31 features deployed
22:00:49 | INFO     | Using profile: tutorial
22:00:50 | INFO     | Using configuration file at: /Users/gxav/.featurebyte/config.yaml
22:00:50 | INFO     | Active profile: tutorial (https://tutorials.featurebyte.com/api/v1)
22:00:50 | WARNING  | Remote SDK version (0.5.0.dev6) is different from local (0.5.0.dev1). Update local SDK to avoid unexpected behavior.
22:00:50 | INFO     | No catalog activated.
22:00:51 | INFO     | 6 feature lists, 31 features deployed
22:00:51 | 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 0x7f87a16c5d40>
 {
   'name': 'customer',
   'created_at': '2023-09-11T13:56:58.863000',
   'updated_at': '2023-09-11T13:57:16.943000',
   '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	2022-07-21 12:50:21	dd0c74f0-9ba8-4ca9-bd84-8148095aa38a	1.696160
1	2022-11-10 15:57:20	0401635c-e6ab-4525-bb5d-00aba7f6d0c4	1.706156
2	2022-08-14 19:00:14	54d86ef6-f9b8-40e2-9162-a60bd1b705db	0.931491
3	2022-07-12 08:02:04	4eb4ee84-ee13-4eec-9c26-61b6eb4ba35b	1.567570
4	2022-12-13 08:15:49	1e866814-e5a6-475d-87e3-b53377cc005b	-0.367178
5	2023-04-26 16:52:34	48072b52-39cf-452c-8531-02cc4d0fc32e	-0.521815
6	2023-03-01 11:31:00	081f111a-598b-43ae-a28a-3a5dc3d2a091	-0.341443
7	2023-01-19 16:33:33	f3415165-754c-40b6-af17-06ef952a3fa1	-0.585856
8	2023-04-11 19:07:26	d0ea14bf-038a-4ae5-887e-e2d4d68dd8f6	-0.463131
9	2023-04-10 08:24:27	69d8718e-8c4a-4264-8edf-e0ffc1ef4737	1.813072

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 6.9s (0.15%/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: 0.5.0.dev6
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("64ff1c910d5bfbfb21bce78a"))
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, -98]
                ),
                ValueBeyondEndpointImputation(
                    type="less_than", end_point=0, imputed_value=0
                ),
                ValueBeyondEndpointImputation(
                    type="greater_than", end_point=2000, imputed_value=2000
                ),
            ],
        )
    ],
)
grouped = 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 = grouped["CUSTOMER_Std_of_invoice_Amount_28d"]
grouped_1 = 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_1 = grouped_1["CUSTOMER_Latest_invoice_Amount"]
grouped_2 = 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_2 = grouped_2["CUSTOMER_Avg_of_invoice_Amount_28d"]
feat_3 = (feat_1 - feat_2) / feat
feat_3.name = "CUSTOMER_Latest_invoice_Amount_Z_Score_to_invoice_Amount_28d"
output = feat_3
output.save(_id=ObjectId("64ff1d9a2fa89ef7c7f4f5b8"))

Concepts in this tutorial¶

• Feature Transforms

SDK reference for¶

• Catalog.get_feature()

In [ ]: