ITEM TotalCost Z Score to OVERALL item TotalCost 14d
SDK code to create ITEM_TotalCost_Z_Score_to_OVERALL_item_TotalCost_14d¶
Feature description:
Z-Score of the item TotalCost in relation to the distribution of item TotalCost among all items over a 14d period.
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import featurebyte as fb
fb.use_profile("tutorial")
import featurebyte as fb
fb.use_profile("tutorial")
Activate catalog¶
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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']
windows = ['14d']
Get view from table¶
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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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# Create lookup feature from TotalCost column for item entity.
item_totalcost =\
invoiceitems_view["TotalCost"].as_feature("ITEM_TotalCost")
# Create lookup feature from TotalCost column for item entity.
item_totalcost =\
invoiceitems_view["TotalCost"].as_feature("ITEM_TotalCost")
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
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# Group INVOICEITEMS view without any groupby key for aggregates on all data.
invoiceitems_view_by_overall =\
invoiceitems_view.groupby([])
# Group INVOICEITEMS view without any groupby key for aggregates on all data.
invoiceitems_view_by_overall =\
invoiceitems_view.groupby([])
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# Get Avg of TotalCost over time.
feature_group =\
invoiceitems_view_by_overall.aggregate_over(
"TotalCost", method="avg",
feature_names=[
"OVERALL_Avg_of_item_TotalCost"
+ "_" + w for w in windows
],
windows=windows
)
# Get OVERALL_Avg_of_item_TotalCost_14d object from feature group.
overall_avg_of_item_totalcost_14d =\
feature_group["OVERALL_Avg_of_item_TotalCost_14d"]
# Get Avg of TotalCost over time.
feature_group =\
invoiceitems_view_by_overall.aggregate_over(
"TotalCost", method="avg",
feature_names=[
"OVERALL_Avg_of_item_TotalCost"
+ "_" + w for w in windows
],
windows=windows
)
# Get OVERALL_Avg_of_item_TotalCost_14d object from feature group.
overall_avg_of_item_totalcost_14d =\
feature_group["OVERALL_Avg_of_item_TotalCost_14d"]
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# Get Std of TotalCost over time.
feature_group =\
invoiceitems_view_by_overall.aggregate_over(
"TotalCost", method="std",
feature_names=[
"OVERALL_Std_of_item_TotalCost"
+ "_" + w for w in windows
],
windows=windows
)
# Get OVERALL_Std_of_item_TotalCost_14d object from feature group.
overall_std_of_item_totalcost_14d =\
feature_group["OVERALL_Std_of_item_TotalCost_14d"]
# Get Std of TotalCost over time.
feature_group =\
invoiceitems_view_by_overall.aggregate_over(
"TotalCost", method="std",
feature_names=[
"OVERALL_Std_of_item_TotalCost"
+ "_" + w for w in windows
],
windows=windows
)
# Get OVERALL_Std_of_item_TotalCost_14d object from feature group.
overall_std_of_item_totalcost_14d =\
feature_group["OVERALL_Std_of_item_TotalCost_14d"]
Compare lookup with aggregation¶
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# Get the Z-Score of the item TotalCost in relation to the distribution of item TotalCost among all
# items over a 14d period.
item_totalcost_z_score_to_overall_item_totalcost_14d = (
item_totalcost
- overall_avg_of_item_totalcost_14d
) / overall_std_of_item_totalcost_14d
# Give a name to new feature
item_totalcost_z_score_to_overall_item_totalcost_14d.name = \
"ITEM_TotalCost_Z_Score_to_OVERALL_item_TotalCost_14d"
# Get the Z-Score of the item TotalCost in relation to the distribution of item TotalCost among all
# items over a 14d period.
item_totalcost_z_score_to_overall_item_totalcost_14d = (
item_totalcost
- overall_avg_of_item_totalcost_14d
) / overall_std_of_item_totalcost_14d
# Give a name to new feature
item_totalcost_z_score_to_overall_item_totalcost_14d.name = \
"ITEM_TotalCost_Z_Score_to_OVERALL_item_TotalCost_14d"
Preview feature¶
Read on the feature primary entity concept
Read on the serving entity concept
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#Check the primary entity of the feature'
item_totalcost_z_score_to_overall_item_totalcost_14d.primary_entity
#Check the primary entity of the feature'
item_totalcost_z_score_to_overall_item_totalcost_14d.primary_entity
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#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"
)
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#Preview ITEM_TotalCost_Z_Score_to_OVERALL_item_TotalCost_14d
item_totalcost_z_score_to_overall_item_totalcost_14d.preview(
preview_table
)
#Preview ITEM_TotalCost_Z_Score_to_OVERALL_item_TotalCost_14d
item_totalcost_z_score_to_overall_item_totalcost_14d.preview(
preview_table
)
Save feature¶
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# Save feature
item_totalcost_z_score_to_overall_item_totalcost_14d.save()
# Save feature
item_totalcost_z_score_to_overall_item_totalcost_14d.save()
Add description and see feature definition file¶
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# Add description
item_totalcost_z_score_to_overall_item_totalcost_14d.update_description(
"Z-Score of the item TotalCost in relation to the distribution of item "
"TotalCost among all items over a 14d period."
)
# See feature definition file
item_totalcost_z_score_to_overall_item_totalcost_14d.definition
# Add description
item_totalcost_z_score_to_overall_item_totalcost_14d.update_description(
"Z-Score of the item TotalCost in relation to the distribution of item "
"TotalCost among all items over a 14d period."
)
# See feature definition file
item_totalcost_z_score_to_overall_item_totalcost_14d.definition