ITEM Discount Z Score to CUSTOMER X PRODUCT item Discount 14d

SDK code to create ITEM_Discount_Z_Score_to_CUSTOMER_X_PRODUCT_item_Discount_14d¶

Feature description:

Z-Score of the item Discount in relation to the distribution of item Discount among all items with the same customer_x_product as that item over a 14d period.

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 = ['14d']

windows = ['14d']

Get view from table¶

See SDK reference for views

In [ ]:

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

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

Create Lookup feature¶

See SDK reference for features

See SDK reference to declare a lookup feature

In [ ]:

# Create lookup feature from Discount column for item entity.
item_discount =\
invoiceitems_view["Discount"].as_feature("ITEM_Discount")

# Create lookup feature from Discount column for item entity. item_discount =\ invoiceitems_view["Discount"].as_feature("ITEM_Discount")

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 customer_x_product entity (['GroceryCustomerGuid',
# 'GroceryProductGuid']).
invoiceitems_view_by_customer_x_product =\
invoiceitems_view.groupby(['GroceryCustomerGuid', 'GroceryProductGuid'])

# Group INVOICEITEMS view by customer_x_product entity (['GroceryCustomerGuid', # 'GroceryProductGuid']). invoiceitems_view_by_customer_x_product =\ invoiceitems_view.groupby(['GroceryCustomerGuid', 'GroceryProductGuid'])

In [ ]:

# Get Avg of Discount for the customer_x_product over time.
feature_group =\
invoiceitems_view_by_customer_x_product.aggregate_over(
    "Discount", method="avg",
    feature_names=[
        "CUSTOMER_X_PRODUCT_Avg_of_item_Discount"
        + "_" + w for w in windows
    ],
    windows=windows
)
# Get CUSTOMER_X_PRODUCT_Avg_of_item_Discount_14d object from feature group.
customer_x_product_avg_of_item_discount_14d =\
feature_group["CUSTOMER_X_PRODUCT_Avg_of_item_Discount_14d"]

# Get Avg of Discount for the customer_x_product over time. feature_group =\ invoiceitems_view_by_customer_x_product.aggregate_over( "Discount", method="avg", feature_names=[ "CUSTOMER_X_PRODUCT_Avg_of_item_Discount" + "_" + w for w in windows ], windows=windows ) # Get CUSTOMER_X_PRODUCT_Avg_of_item_Discount_14d object from feature group. customer_x_product_avg_of_item_discount_14d =\ feature_group["CUSTOMER_X_PRODUCT_Avg_of_item_Discount_14d"]

In [ ]:

# Get Std of Discount for the customer_x_product over time.
feature_group =\
invoiceitems_view_by_customer_x_product.aggregate_over(
    "Discount", method="std",
    feature_names=[
        "CUSTOMER_X_PRODUCT_Std_of_item_Discount"
        + "_" + w for w in windows
    ],
    windows=windows
)
# Get CUSTOMER_X_PRODUCT_Std_of_item_Discount_14d object from feature group.
customer_x_product_std_of_item_discount_14d =\
feature_group["CUSTOMER_X_PRODUCT_Std_of_item_Discount_14d"]

# Get Std of Discount for the customer_x_product over time. feature_group =\ invoiceitems_view_by_customer_x_product.aggregate_over( "Discount", method="std", feature_names=[ "CUSTOMER_X_PRODUCT_Std_of_item_Discount" + "_" + w for w in windows ], windows=windows ) # Get CUSTOMER_X_PRODUCT_Std_of_item_Discount_14d object from feature group. customer_x_product_std_of_item_discount_14d =\ feature_group["CUSTOMER_X_PRODUCT_Std_of_item_Discount_14d"]

Compare lookup with aggregation¶

See list of feature transforms

In [ ]:

# Get the Z-Score of the item Discount in relation to the distribution of item Discount among all
# items with the same customer_x_product as that item over a 14d period.
item_discount_z_score_to_customer_x_product_item_discount_14d = (
    item_discount
    - customer_x_product_avg_of_item_discount_14d
) / customer_x_product_std_of_item_discount_14d
# Give a name to new feature
item_discount_z_score_to_customer_x_product_item_discount_14d.name = \
"ITEM_Discount_Z_Score_to_CUSTOMER_X_PRODUCT_item_Discount_14d"

# Get the Z-Score of the item Discount in relation to the distribution of item Discount among all # items with the same customer_x_product as that item over a 14d period. item_discount_z_score_to_customer_x_product_item_discount_14d = ( item_discount - customer_x_product_avg_of_item_discount_14d ) / customer_x_product_std_of_item_discount_14d # Give a name to new feature item_discount_z_score_to_customer_x_product_item_discount_14d.name = \ "ITEM_Discount_Z_Score_to_CUSTOMER_X_PRODUCT_item_Discount_14d"

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'
item_discount_z_score_to_customer_x_product_item_discount_14d.primary_entity

#Check the primary entity of the feature' item_discount_z_score_to_customer_x_product_item_discount_14d.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 ITEM_Discount_Z_Score_to_CUSTOMER_X_PRODUCT_item_Discount_14d
item_discount_z_score_to_customer_x_product_item_discount_14d.preview(
	preview_table
)

#Preview ITEM_Discount_Z_Score_to_CUSTOMER_X_PRODUCT_item_Discount_14d item_discount_z_score_to_customer_x_product_item_discount_14d.preview( preview_table )

Save feature¶

See SDK reference to save feature

In [ ]:

# Save feature
item_discount_z_score_to_customer_x_product_item_discount_14d.save()

# Save feature item_discount_z_score_to_customer_x_product_item_discount_14d.save()

Add description and see feature definition file¶

See SDK reference for the definition file of a feature

In [ ]:

# Add description
item_discount_z_score_to_customer_x_product_item_discount_14d.update_description(
	"Z-Score of the item Discount in relation to the distribution of item "
	"Discount among all items with the same customer_x_product as that item"
	" over a 14d period."
)
# See feature definition file
item_discount_z_score_to_customer_x_product_item_discount_14d.definition

# Add description item_discount_z_score_to_customer_x_product_item_discount_14d.update_description( "Z-Score of the item Discount in relation to the distribution of item " "Discount among all items with the same customer_x_product as that item" " over a 14d period." ) # See feature definition file item_discount_z_score_to_customer_x_product_item_discount_14d.definition