PRODUCT Consistency of item TotalCost across customer Genders 14d vs 28d

SDK code to create PRODUCT_Consistency_of_item_TotalCost_across_customer_Genders_14d_vs_28d¶

Feature description:

Consistency score of the product measured by the Cosine Similarity between the Distribution representing the cumulative TotalCost of item, categorized by their respective customer's Gender, for both the 14d and 28d periods.

In [ ]:

import featurebyte as fb
fb.use_profile("tutorial")

import featurebyte as fb fb.use_profile("tutorial")

Activate catalog¶

See SDK reference for catalogs

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

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

Set windows for aggregation¶

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windows = ['14d', '28d']

windows = ['14d', '28d']

Get view from table¶

See SDK reference for views

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# Get view from INVOICEITEMS item table.
invoiceitems_view = catalog.get_view("INVOICEITEMS")

# Get view from INVOICEITEMS item table. invoiceitems_view = catalog.get_view("INVOICEITEMS")

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# Get view from GROCERYCUSTOMER scd table.
grocerycustomer_view = catalog.get_view("GROCERYCUSTOMER")

# Get view from GROCERYCUSTOMER scd table. grocerycustomer_view = catalog.get_view("GROCERYCUSTOMER")

Join views¶

See SDK reference for views

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# Join GROCERYCUSTOMER view to INVOICEITEMS view.
invoiceitems_view = invoiceitems_view.join(grocerycustomer_view, rsuffix="")

# Join GROCERYCUSTOMER view to INVOICEITEMS view. invoiceitems_view = invoiceitems_view.join(grocerycustomer_view, rsuffix="")

Do window aggregation from INVOICEITEMS¶

See SDK reference for features

See SDK reference to groupby a view

See SDK reference to do aggregation over time

See SDK reference for feature group

See list of aggregation operations

In [ ]:

# Group INVOICEITEMS view by product entity (GroceryProductGuid) across different Genders.
invoiceitems_view_by_product_across_gender =\
invoiceitems_view.groupby(
    ['GroceryProductGuid'], category="Gender"
)

# Group INVOICEITEMS view by product entity (GroceryProductGuid) across different Genders. invoiceitems_view_by_product_across_gender =\ invoiceitems_view.groupby( ['GroceryProductGuid'], category="Gender" )

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# Distribution representing the cumulative TotalCost of item, categorized by their respective
# customer's Gender, for the product over time.
feature_group =\
invoiceitems_view_by_product_across_gender.aggregate_over(
    "TotalCost", method=fb.AggFunc.SUM,
    feature_names=[
        "PRODUCT_item_TotalCost_across_customer_Genders"
        + "_" + w for w in windows
    ],
    windows=windows
)
# Get PRODUCT_item_TotalCost_across_customer_Genders_14d object from feature group.
product_item_totalcost_across_customer_genders_14d =\
feature_group["PRODUCT_item_TotalCost_across_customer_Genders_14d"]
# Get PRODUCT_item_TotalCost_across_customer_Genders_28d object from feature group.
product_item_totalcost_across_customer_genders_28d =\
feature_group["PRODUCT_item_TotalCost_across_customer_Genders_28d"]

# Distribution representing the cumulative TotalCost of item, categorized by their respective # customer's Gender, for the product over time. feature_group =\ invoiceitems_view_by_product_across_gender.aggregate_over( "TotalCost", method=fb.AggFunc.SUM, feature_names=[ "PRODUCT_item_TotalCost_across_customer_Genders" + "_" + w for w in windows ], windows=windows ) # Get PRODUCT_item_TotalCost_across_customer_Genders_14d object from feature group. product_item_totalcost_across_customer_genders_14d =\ feature_group["PRODUCT_item_TotalCost_across_customer_Genders_14d"] # Get PRODUCT_item_TotalCost_across_customer_Genders_28d object from feature group. product_item_totalcost_across_customer_genders_28d =\ feature_group["PRODUCT_item_TotalCost_across_customer_Genders_28d"]

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# Derive Consistency feature from cosine similarity between
# PRODUCT_item_TotalCost_across_customer_Genders_14d
# and PRODUCT_item_TotalCost_across_customer_Genders_28d
product_consistency_of_item_totalcost_across_customer_genders_14d_vs_28d =\
product_item_totalcost_across_customer_genders_14d.cd.cosine_similarity(
        product_item_totalcost_across_customer_genders_28d
    )
# Give a name to new feature
product_consistency_of_item_totalcost_across_customer_genders_14d_vs_28d.name = \
"PRODUCT_Consistency_of_item_TotalCost_across_customer_Genders_14d_vs_28d"

# Derive Consistency feature from cosine similarity between # PRODUCT_item_TotalCost_across_customer_Genders_14d # and PRODUCT_item_TotalCost_across_customer_Genders_28d product_consistency_of_item_totalcost_across_customer_genders_14d_vs_28d =\ product_item_totalcost_across_customer_genders_14d.cd.cosine_similarity( product_item_totalcost_across_customer_genders_28d ) # Give a name to new feature product_consistency_of_item_totalcost_across_customer_genders_14d_vs_28d.name = \ "PRODUCT_Consistency_of_item_TotalCost_across_customer_Genders_14d_vs_28d"

Preview feature¶

Read on the feature primary entity concept

Read on the serving entity concept

See SDK reference for observation table

See SDK reference to preview feature

In [ ]:

#Check the primary entity of the feature'
product_consistency_of_item_totalcost_across_customer_genders_14d_vs_28d.primary_entity

#Check the primary entity of the feature' product_consistency_of_item_totalcost_across_customer_genders_14d_vs_28d.primary_entity

In [ ]:

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

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

In [ ]:

#Preview PRODUCT_Consistency_of_item_TotalCost_across_customer_Genders_14d_vs_28d
product_consistency_of_item_totalcost_across_customer_genders_14d_vs_28d.preview(
	preview_table
)

#Preview PRODUCT_Consistency_of_item_TotalCost_across_customer_Genders_14d_vs_28d product_consistency_of_item_totalcost_across_customer_genders_14d_vs_28d.preview( preview_table )

Save feature¶

See SDK reference to save feature

In [ ]:

# Save feature
product_consistency_of_item_totalcost_across_customer_genders_14d_vs_28d.save()

# Save feature product_consistency_of_item_totalcost_across_customer_genders_14d_vs_28d.save()

Add description and see feature definition file¶

See SDK reference for the definition file of a feature

In [ ]:

# Add description
product_consistency_of_item_totalcost_across_customer_genders_14d_vs_28d.update_description(
	"Consistency score of the product measured by the Cosine Similarity "
	"between the Distribution representing the cumulative TotalCost of "
	"item, categorized by their respective customer's Gender, for both the "
	"14d and 28d periods."
)
# See feature definition file
product_consistency_of_item_totalcost_across_customer_genders_14d_vs_28d.definition

# Add description product_consistency_of_item_totalcost_across_customer_genders_14d_vs_28d.update_description( "Consistency score of the product measured by the Cosine Similarity " "between the Distribution representing the cumulative TotalCost of " "item, categorized by their respective customer's Gender, for both the " "14d and 28d periods." ) # See feature definition file product_consistency_of_item_totalcost_across_customer_genders_14d_vs_28d.definition