CUSTOMER vs OVERALL invoice Amount across invoice Weekdays 28d

SDK code to create CUSTOMER_vs_OVERALL_invoice_Amount_across_invoice_Weekdays_28d¶

Feature description:

Similarity between the customer and all customers measured by the Cosine Similarity between the Distribution representing the cumulative Amount of invoice, categorized by their respective invoice's Weekday, over 28d for both entities.

In [ ]:

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

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

Activate catalog¶

See SDK reference for catalogs

In [ ]:

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

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

Set windows for aggregation¶

In [ ]:

windows = ['28d']

windows = ['28d']

Get view from table¶

See SDK reference for views

In [ ]:

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

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

Extract Date parts¶

See SDK reference for view columns

See list of datetime transforms

In [ ]:

# Extract day_of_week date part from Timestamp column for GROCERYINVOICE view.
groceryinvoice_view["Weekday of invoice"] = groceryinvoice_view["Timestamp"].dt.day_of_week

# Extract day_of_week date part from Timestamp column for GROCERYINVOICE view. groceryinvoice_view["Weekday of invoice"] = groceryinvoice_view["Timestamp"].dt.day_of_week

Do window aggregation from GROCERYINVOICE¶

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 GROCERYINVOICE view by customer entity (GroceryCustomerGuid) across different Weekday of
# invoices.
groceryinvoice_view_by_customer_across_weekday_of_invoice =\
groceryinvoice_view.groupby(
    ['GroceryCustomerGuid'], category="Weekday of invoice"
)

# Group GROCERYINVOICE view by customer entity (GroceryCustomerGuid) across different Weekday of # invoices. groceryinvoice_view_by_customer_across_weekday_of_invoice =\ groceryinvoice_view.groupby( ['GroceryCustomerGuid'], category="Weekday of invoice" )

In [ ]:

# Distribution representing the cumulative Amount of invoice, categorized by their respective
# invoice's Weekday, for the customer over time.
feature_group =\
groceryinvoice_view_by_customer_across_weekday_of_invoice.aggregate_over(
    "Amount", method=fb.AggFunc.SUM,
    feature_names=[
        "CUSTOMER_invoice_Amount_across_invoice_Weekdays"
        + "_" + w for w in windows
    ],
    windows=windows
)
# Get CUSTOMER_invoice_Amount_across_invoice_Weekdays_28d object from feature group.
customer_invoice_amount_across_invoice_weekdays_28d =\
feature_group["CUSTOMER_invoice_Amount_across_invoice_Weekdays_28d"]

# Distribution representing the cumulative Amount of invoice, categorized by their respective # invoice's Weekday, for the customer over time. feature_group =\ groceryinvoice_view_by_customer_across_weekday_of_invoice.aggregate_over( "Amount", method=fb.AggFunc.SUM, feature_names=[ "CUSTOMER_invoice_Amount_across_invoice_Weekdays" + "_" + w for w in windows ], windows=windows ) # Get CUSTOMER_invoice_Amount_across_invoice_Weekdays_28d object from feature group. customer_invoice_amount_across_invoice_weekdays_28d =\ feature_group["CUSTOMER_invoice_Amount_across_invoice_Weekdays_28d"]

In [ ]:

# Group GROCERYINVOICE view across different Weekday of invoices.
groceryinvoice_view_by_overall_across_weekday_of_invoice =\
groceryinvoice_view.groupby(
    [], category="Weekday of invoice"
)

# Group GROCERYINVOICE view across different Weekday of invoices. groceryinvoice_view_by_overall_across_weekday_of_invoice =\ groceryinvoice_view.groupby( [], category="Weekday of invoice" )

In [ ]:

# Distribution representing the cumulative Amount of invoice, categorized by their respective
# invoice's Weekday, over time.
feature_group =\
groceryinvoice_view_by_overall_across_weekday_of_invoice.aggregate_over(
    "Amount", method=fb.AggFunc.SUM,
    feature_names=[
        "OVERALL_invoice_Amount_across_invoice_Weekdays"
        + "_" + w for w in windows
    ],
    windows=windows
)
# Get OVERALL_invoice_Amount_across_invoice_Weekdays_28d object from feature group.
overall_invoice_amount_across_invoice_weekdays_28d =\
feature_group["OVERALL_invoice_Amount_across_invoice_Weekdays_28d"]

# Distribution representing the cumulative Amount of invoice, categorized by their respective # invoice's Weekday, over time. feature_group =\ groceryinvoice_view_by_overall_across_weekday_of_invoice.aggregate_over( "Amount", method=fb.AggFunc.SUM, feature_names=[ "OVERALL_invoice_Amount_across_invoice_Weekdays" + "_" + w for w in windows ], windows=windows ) # Get OVERALL_invoice_Amount_across_invoice_Weekdays_28d object from feature group. overall_invoice_amount_across_invoice_weekdays_28d =\ feature_group["OVERALL_invoice_Amount_across_invoice_Weekdays_28d"]

Derive Similarity feature across entities¶

See list of feature transforms

In [ ]:

# Derive Similarity feature from cosine similarity between
# CUSTOMER_invoice_Amount_across_invoice_Weekdays_28d
# and OVERALL_invoice_Amount_across_invoice_Weekdays_28d
customer_vs_overall_invoice_amount_across_invoice_weekdays_28d =\
customer_invoice_amount_across_invoice_weekdays_28d.cd.cosine_similarity(
        overall_invoice_amount_across_invoice_weekdays_28d
    )
# Give a name to new feature
customer_vs_overall_invoice_amount_across_invoice_weekdays_28d.name = \
"CUSTOMER_vs_OVERALL_invoice_Amount_across_invoice_Weekdays_28d"

# Derive Similarity feature from cosine similarity between # CUSTOMER_invoice_Amount_across_invoice_Weekdays_28d # and OVERALL_invoice_Amount_across_invoice_Weekdays_28d customer_vs_overall_invoice_amount_across_invoice_weekdays_28d =\ customer_invoice_amount_across_invoice_weekdays_28d.cd.cosine_similarity( overall_invoice_amount_across_invoice_weekdays_28d ) # Give a name to new feature customer_vs_overall_invoice_amount_across_invoice_weekdays_28d.name = \ "CUSTOMER_vs_OVERALL_invoice_Amount_across_invoice_Weekdays_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'
customer_vs_overall_invoice_amount_across_invoice_weekdays_28d.primary_entity

#Check the primary entity of the feature' customer_vs_overall_invoice_amount_across_invoice_weekdays_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 CUSTOMER_vs_OVERALL_invoice_Amount_across_invoice_Weekdays_28d
customer_vs_overall_invoice_amount_across_invoice_weekdays_28d.preview(
	preview_table
)

#Preview CUSTOMER_vs_OVERALL_invoice_Amount_across_invoice_Weekdays_28d customer_vs_overall_invoice_amount_across_invoice_weekdays_28d.preview( preview_table )

Save feature¶

See SDK reference to save feature

In [ ]:

# Save feature
customer_vs_overall_invoice_amount_across_invoice_weekdays_28d.save()

# Save feature customer_vs_overall_invoice_amount_across_invoice_weekdays_28d.save()

Add description and see feature definition file¶

See SDK reference for the definition file of a feature

In [ ]:

# Add description
customer_vs_overall_invoice_amount_across_invoice_weekdays_28d.update_description(
	"Similarity between the customer and all customers measured by the "
	"Cosine Similarity between the Distribution representing the cumulative"
	" Amount of invoice, categorized by their respective invoice's Weekday,"
	" over 28d for both entities."
)
# See feature definition file
customer_vs_overall_invoice_amount_across_invoice_weekdays_28d.definition

# Add description customer_vs_overall_invoice_amount_across_invoice_weekdays_28d.update_description( "Similarity between the customer and all customers measured by the " "Cosine Similarity between the Distribution representing the cumulative" " Amount of invoice, categorized by their respective invoice's Weekday," " over 28d for both entities." ) # See feature definition file customer_vs_overall_invoice_amount_across_invoice_weekdays_28d.definition