9. Create window aggregate features

Create window aggregate features¶

Next feature type we will consider is window aggregate feature. These are features generated by aggregating data within specific time frame.

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)

16:42:36 | INFO     | Using configuration file at: /Users/viktor/.featurebyte/config.yaml
16:42:36 | INFO     | Active profile: tutorial (https://tutorials.featurebyte.com/api/v1)
16:42:36 | INFO     | SDK version: 0.6.0.dev121
16:42:36 | INFO     | No catalog activated.
16:42:36 | INFO     | 11 feature lists, 66 features deployed
16:42:36 | INFO     | Using profile: tutorial
16:42:36 | INFO     | Using configuration file at: /Users/viktor/.featurebyte/config.yaml
16:42:36 | INFO     | Active profile: tutorial (https://tutorials.featurebyte.com/api/v1)
16:42:37 | INFO     | SDK version: 0.6.0.dev121
16:42:37 | INFO     | No catalog activated.
16:42:38 | INFO     | 11 feature lists, 66 features deployed
16:42:38 | INFO     | Catalog activated: Grocery Dataset Tutorial

In [2]:

# Get view from GROCERYINVOICE event table.
groceryinvoice_view = catalog.get_view("GROCERYINVOICE")

# Get view from GROCERYINVOICE event table. groceryinvoice_view = catalog.get_view("GROCERYINVOICE")

Do window aggregation from GROCERYINVOICE¶

In [3]:

# Group GROCERYINVOICE view by customer entity (GroceryCustomerGuid).
groceryinvoice_view_by_customer = groceryinvoice_view.groupby(['GroceryCustomerGuid'])

# Group GROCERYINVOICE view by customer entity (GroceryCustomerGuid). groceryinvoice_view_by_customer = groceryinvoice_view.groupby(['GroceryCustomerGuid'])

In [4]:

# Get Latest invoice Amount for the customer
customer_latest_invoice_amount = groceryinvoice_view_by_customer.aggregate_over(
    "Amount", method="latest",
    feature_names=["CUSTOMER_Latest_invoice_Amount"],
    windows=[None]
)["CUSTOMER_Latest_invoice_Amount"]

# Get Latest invoice Amount for the customer customer_latest_invoice_amount = groceryinvoice_view_by_customer.aggregate_over( "Amount", method="latest", feature_names=["CUSTOMER_Latest_invoice_Amount"], windows=[None] )["CUSTOMER_Latest_invoice_Amount"]

In [5]:

# Set desired windows
windows = ['14d', '28d']

# Set desired windows windows = ['14d', '28d']

In [6]:

# Get Count of invoices for the customer
customer_count_of_invoice_14d_28d = groceryinvoice_view_by_customer.aggregate_over(
    method="count",
    feature_names=[
        "CUSTOMER_Count_of_invoice"
        + "_" + w for w in windows
    ],
    windows=windows
)

# Get Count of invoices for the customer customer_count_of_invoice_14d_28d = groceryinvoice_view_by_customer.aggregate_over( method="count", feature_names=[ "CUSTOMER_Count_of_invoice" + "_" + w for w in windows ], windows=windows )

In [7]:

# Get Avg of Amount for the customer over time.
customer_avg_of_invoice_amount_14d_28d = groceryinvoice_view_by_customer.aggregate_over(
    "Amount", method="avg",
    feature_names=[
        "CUSTOMER_Avg_of_invoice_Amount"
        + "_" + w for w in windows
    ],
    windows=windows
)

# Get Avg of Amount for the customer over time. customer_avg_of_invoice_amount_14d_28d = groceryinvoice_view_by_customer.aggregate_over( "Amount", method="avg", feature_names=[ "CUSTOMER_Avg_of_invoice_Amount" + "_" + w for w in windows ], windows=windows )

In [8]:

# Get Std of Amount for the customer over time.
customer_std_of_invoice_amount_14d_28d = groceryinvoice_view_by_customer.aggregate_over(
    "Amount", method="std",
    feature_names=[
        "CUSTOMER_Std_of_invoice_Amount"
        + "_" + w for w in windows
    ],
    windows=windows
)

# Get Std of Amount for the customer over time. customer_std_of_invoice_amount_14d_28d = groceryinvoice_view_by_customer.aggregate_over( "Amount", method="std", feature_names=[ "CUSTOMER_Std_of_invoice_Amount" + "_" + w for w in windows ], windows=windows )

Preview a feature group¶

In [9]:

feature_group = fb.FeatureGroup([
    customer_latest_invoice_amount,
    customer_count_of_invoice_14d_28d,
    customer_avg_of_invoice_amount_14d_28d,
    customer_std_of_invoice_amount_14d_28d,
])

feature_group = fb.FeatureGroup([ customer_latest_invoice_amount, customer_count_of_invoice_14d_28d, customer_avg_of_invoice_amount_14d_28d, customer_std_of_invoice_amount_14d_28d, ])

In [10]:

# Check the primary entity of the feature group
feature_group.primary_entity

# Check the primary entity of the feature group feature_group.primary_entity

Out[10]:

[<featurebyte.api.entity.Entity at 0x13b95d600>
 {
   '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 [11]:

# 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 [12]:

# Preview feature_group
feature_group.preview(preview_table)

# Preview feature_group feature_group.preview(preview_table)

Out[12]:

	POINT_IN_TIME	GROCERYCUSTOMERGUID	CUSTOMER_Latest_invoice_Amount	CUSTOMER_Count_of_invoice_14d	CUSTOMER_Count_of_invoice_28d	CUSTOMER_Avg_of_invoice_Amount_14d	CUSTOMER_Avg_of_invoice_Amount_28d	CUSTOMER_Std_of_invoice_Amount_14d	CUSTOMER_Std_of_invoice_Amount_28d
0	2022-11-28 11:36:31	d4559f7d-eb28-42c6-b47d-847de24952c2	6.72	0	1	NaN	6.720000	NaN	0.000000
1	2022-10-09 15:47:55	3f8c7c4c-f2c2-408e-a08e-622de3d3a0b9	12.28	0	0	NaN	NaN	NaN	NaN
2	2022-09-14 15:42:42	35390325-8443-43c1-a934-18db923d9a47	10.02	0	4	NaN	26.415000	NaN	19.563101
3	2022-12-26 18:39:46	4eb4ee84-ee13-4eec-9c26-61b6eb4ba35b	53.09	5	10	15.626000	26.450000	19.000809	26.837185
4	2022-12-06 08:47:43	e42fa5f3-7737-4c6a-9ef4-856f113e60bd	21.74	3	6	15.560000	10.555000	6.681337	7.307384
5	2022-11-09 12:14:40	8440debb-6abc-4adc-8c6c-749928141fd0	15.30	1	1	15.300000	15.300000	0.000000	0.000000
6	2022-10-12 17:32:15	8a54e527-e9a4-47a9-a28f-8b3c6ecc02db	14.55	2	4	14.560000	15.050000	0.010000	10.109369
7	2023-01-01 11:51:28	cea213d4-36e4-48c3-ae8d-c7a25911e11c	0.89	12	29	3.209167	5.220345	4.154236	8.163223
8	2023-02-05 15:48:23	3b4f2821-b761-40e9-a32a-5f09685cc597	11.43	4	4	11.317500	11.317500	4.526297	4.526297
9	2023-03-10 16:15:46	91a64566-e212-4e36-8f23-c1f1f324a301	2.00	6	8	4.568333	10.431250	2.832763	11.335775

Save features into catalog¶

With feature groups we can do it in one call.

In [13]:

feature_group.save()

feature_group.save()

Done! |████████████████████████████████████████| 100% in 13.0s (0.08%/s)        
Loading Feature(s) |████████████████████████████████████████| 7/7 [100%] in 0.6s

Add description and see feature definition files¶

In [14]:

# Add description
customer_latest_invoice_amount.update_description("Latest invoice Amount for the customer")
# See feature definition file
customer_latest_invoice_amount.definition

# Add description customer_latest_invoice_amount.update_description("Latest invoice Amount for the customer") # See feature definition file customer_latest_invoice_amount.definition

Out[14]:

# 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"]
output = feat
output.save(_id=ObjectId("6564b8f0a69f1a9a43bdfd7d"))

In [15]:

# Add description
customer_count_of_invoice_14d = customer_count_of_invoice_14d_28d["CUSTOMER_Count_of_invoice_14d"]
customer_count_of_invoice_14d.update_description(
    "Sum of invoice Amount for the customer over a 14d period."
)
customer_count_of_invoice_28d = customer_count_of_invoice_14d_28d["CUSTOMER_Count_of_invoice_28d"]
customer_count_of_invoice_28d.update_description(
    "Count of invoice for the customer over a 28d period."
)
# See feature definition file
customer_count_of_invoice_28d.definition

# Add description customer_count_of_invoice_14d = customer_count_of_invoice_14d_28d["CUSTOMER_Count_of_invoice_14d"] customer_count_of_invoice_14d.update_description( "Sum of invoice Amount for the customer over a 14d period." ) customer_count_of_invoice_28d = customer_count_of_invoice_14d_28d["CUSTOMER_Count_of_invoice_28d"] customer_count_of_invoice_28d.update_description( "Count of invoice for the customer over a 28d period." ) # See feature definition file customer_count_of_invoice_28d.definition

Out[15]:

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


# 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=[],
)
grouped = event_view.groupby(
    by_keys=["GroceryCustomerGuid"], category=None
).aggregate_over(
    value_column=None,
    method="count",
    windows=["28d"],
    feature_names=["CUSTOMER_Count_of_invoice_28d"],
    feature_job_setting=FeatureJobSetting(
        blind_spot="120s", frequency="3600s", time_modulo_frequency="120s"
    ),
    skip_fill_na=True,
)
feat = grouped["CUSTOMER_Count_of_invoice_28d"]
feat_1 = feat.copy()
feat_1[feat.isnull()] = 0
feat_1.name = "CUSTOMER_Count_of_invoice_28d"
output = feat_1
output.save(_id=ObjectId("6564b8f0a69f1a9a43bdfd80"))

In [16]:

# Add description
customer_avg_of_invoice_amount_14d = customer_avg_of_invoice_amount_14d_28d["CUSTOMER_Avg_of_invoice_Amount_14d"]
customer_avg_of_invoice_amount_14d.update_description(
    "Avg of invoice Amount for the customer over a 14d period."
)
customer_avg_of_invoice_amount_28d = customer_avg_of_invoice_amount_14d_28d["CUSTOMER_Avg_of_invoice_Amount_28d"]
customer_avg_of_invoice_amount_28d.update_description(
    "Avg of invoice Amount for the customer over a 28d period."
)
# See feature definition file
customer_avg_of_invoice_amount_28d.definition

# Add description customer_avg_of_invoice_amount_14d = customer_avg_of_invoice_amount_14d_28d["CUSTOMER_Avg_of_invoice_Amount_14d"] customer_avg_of_invoice_amount_14d.update_description( "Avg of invoice Amount for the customer over a 14d period." ) customer_avg_of_invoice_amount_28d = customer_avg_of_invoice_amount_14d_28d["CUSTOMER_Avg_of_invoice_Amount_28d"] customer_avg_of_invoice_amount_28d.update_description( "Avg of invoice Amount for the customer over a 28d period." ) # See feature definition file customer_avg_of_invoice_amount_28d.definition

Out[16]:

# 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="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 = grouped["CUSTOMER_Avg_of_invoice_Amount_28d"]
output = feat
output.save(_id=ObjectId("6564b8f0a69f1a9a43bdfd83"))

In [17]:

# Add description
customer_std_of_invoice_amount_14d = customer_std_of_invoice_amount_14d_28d["CUSTOMER_Std_of_invoice_Amount_14d"]
customer_std_of_invoice_amount_14d.update_description(
    "Std of invoice Amount for the customer over a 14d period."
)
customer_std_of_invoice_amount_28d = customer_std_of_invoice_amount_14d_28d["CUSTOMER_Std_of_invoice_Amount_28d"]
customer_std_of_invoice_amount_28d.update_description(
    "Std of invoice Amount for the customer over a 28d period."
)

# See feature definition file
customer_std_of_invoice_amount_28d.definition

# Add description customer_std_of_invoice_amount_14d = customer_std_of_invoice_amount_14d_28d["CUSTOMER_Std_of_invoice_Amount_14d"] customer_std_of_invoice_amount_14d.update_description( "Std of invoice Amount for the customer over a 14d period." ) customer_std_of_invoice_amount_28d = customer_std_of_invoice_amount_14d_28d["CUSTOMER_Std_of_invoice_Amount_28d"] customer_std_of_invoice_amount_28d.update_description( "Std of invoice Amount for the customer over a 28d period." ) # See feature definition file customer_std_of_invoice_amount_28d.definition

Out[17]:

# 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="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"]
output = feat
output.save(_id=ObjectId("6564b8f0a69f1a9a43bdfd85"))

Concepts in this tutorial¶

• Aggregate Features
• Window Aggregate Features

SDK reference for¶

• View.groupby()
• Groupby.aggregate_over()
• List of aggregate functions
• FeatureGroup
• FeatureGroup.save()

In [ ]: