leaf_engine.adapt.pipeline.adapt_postprocessing_pipeline

Attributes

AdaptPostprocessingException
BENEFIT_CAPS
BENEFIT_TYPE
DATA_DRIVEN_BENEFIT_ASSUMPTIONS
MANUAL_BENEFIT_ASSUMPTIONS
MARKET_VARIANCE_IMPLEMENTABILITY
MIN_LANE_LENGTH
NETWORK_BENEFIT_IMPLEMENTABILITY

Functions

get_annualization(→ float)	Returns annualization factor.
get_business_impact(→ pandas.DataFrame)
get_business_objectives(→ pandas.DataFrame)	Builds high-level overview of business objectives.
get_co2_emissions(→ int)	Converts total shipment miles to million kilograms CO2.
get_company_stats(→ pandas.DataFrame)	Builds high-level overview of total benefits.
get_daily_pattern_benefit(→ pandas.DataFrame)	Computes lane-level daily flex pattern benefit.
get_flex_lane_summary(→ pandas.DataFrame)	Formats lane level summary to match expected output.
get_lane_summary(→ pandas.DataFrame)	Gets and formats lane-level summary from explorer context.
get_market_rate_variance_benefit(→ pandas.DataFrame)	Computes lane-level market rate variance benefit.
get_network_benefit(→ pandas.DataFrame)	Aggregates network moves and computes lane-level network benefit.
get_network_moves(→ pandas.DataFrame)	Gets and formats network moves from actions context.
get_observations(→ pandas.DataFrame)	Gets and aggregates observations from observation context.
get_portfolio(→ pandas.DataFrame)	Builds adapt portfolio output.
get_ships(→ pandas.DataFrame)	Gets shipments DataFrame from data_context.
get_total_loads(→ int)	Computes number of unique shipments.
get_total_spend(→ float)	Computes total shipment spend.
postprocessing_pipeline(etl_cxt, data_cxt, action_cxt, ...)	Runs postprocessing logic.

Module Contents

leaf_engine.adapt.pipeline.adapt_postprocessing_pipeline.get_annualization(ships_df: pandas.DataFrame) → float

Returns annualization factor.

Parameters:

ships_df (pandas.DataFrame) –

Return type:

float

leaf_engine.adapt.pipeline.adapt_postprocessing_pipeline.get_business_impact(case_df: pandas.DataFrame) → pandas.DataFrame

Parameters:

case_df (pandas.DataFrame) –

Return type:

pandas.DataFrame

leaf_engine.adapt.pipeline.adapt_postprocessing_pipeline.get_business_objectives(ships_df: pandas.DataFrame, case_df: pandas.DataFrame) → pandas.DataFrame

Builds high-level overview of business objectives.

Parameters:

• ships_df (pandas.DataFrame) –

• case_df (pandas.DataFrame) –

Return type:

pandas.DataFrame

leaf_engine.adapt.pipeline.adapt_postprocessing_pipeline.get_co2_emissions(ships_df: pandas.DataFrame) → int

Converts total shipment miles to million kilograms CO2.

Parameters:

ships_df (pandas.DataFrame) –

Return type:

int

leaf_engine.adapt.pipeline.adapt_postprocessing_pipeline.get_company_stats(ships_df: pandas.DataFrame) → pandas.DataFrame

Builds high-level overview of total benefits.

Parameters:

ships_df (pandas.DataFrame) –

Return type:

pandas.DataFrame

leaf_engine.adapt.pipeline.adapt_postprocessing_pipeline.get_daily_pattern_benefit(ships_df, action_context: leaf_engine.adapt.context.adapt_contexts.AdaptActionContext) → pandas.DataFrame

Computes lane-level daily flex pattern benefit.

Parameters:

action_context (leaf_engine.adapt.context.adapt_contexts.AdaptActionContext) –

Return type:

pandas.DataFrame

leaf_engine.adapt.pipeline.adapt_postprocessing_pipeline.get_flex_lane_summary(case_df: pandas.DataFrame) → pandas.DataFrame

Formats lane level summary to match expected output.

Parameters:

case_df (pandas.DataFrame) –

Return type:

pandas.DataFrame

leaf_engine.adapt.pipeline.adapt_postprocessing_pipeline.get_lane_summary(etl_context: leaf_engine.adapt.context.adapt_contexts.AdaptETLContext, explorer_context: leaf_engine.adapt.context.adapt_contexts.AdaptExplorerContext) → pandas.DataFrame

Gets and formats lane-level summary from explorer context.

Parameters:

• etl_context (leaf_engine.adapt.context.adapt_contexts.AdaptETLContext) –

• explorer_context (leaf_engine.adapt.context.adapt_contexts.AdaptExplorerContext) –

Return type:

pandas.DataFrame

leaf_engine.adapt.pipeline.adapt_postprocessing_pipeline.get_market_rate_variance_benefit(observation_context: leaf_engine.adapt.context.adapt_contexts.AdaptObservationContext) → pandas.DataFrame

Computes lane-level market rate variance benefit.

Parameters:

observation_context (leaf_engine.adapt.context.adapt_contexts.AdaptObservationContext) –

Return type:

pandas.DataFrame

leaf_engine.adapt.pipeline.adapt_postprocessing_pipeline.get_network_benefit(ships_df: pandas.DataFrame, action_context: leaf_engine.adapt.context.adapt_contexts.AdaptActionContext) → pandas.DataFrame

Aggregates network moves and computes lane-level network benefit.

Parameters:

• ships_df (pandas.DataFrame) –

• action_context (leaf_engine.adapt.context.adapt_contexts.AdaptActionContext) –

Return type:

pandas.DataFrame

leaf_engine.adapt.pipeline.adapt_postprocessing_pipeline.get_network_moves(action_context: leaf_engine.adapt.context.adapt_contexts.AdaptActionContext) → pandas.DataFrame

Gets and formats network moves from actions context.

Parameters:

action_context (leaf_engine.adapt.context.adapt_contexts.AdaptActionContext) –

Return type:

pandas.DataFrame

leaf_engine.adapt.pipeline.adapt_postprocessing_pipeline.get_observations(observation_context: leaf_engine.adapt.context.adapt_contexts.AdaptObservationContext) → pandas.DataFrame

Gets and aggregates observations from observation context.

Parameters:

observation_context (leaf_engine.adapt.context.adapt_contexts.AdaptObservationContext) –

Return type:

pandas.DataFrame

leaf_engine.adapt.pipeline.adapt_postprocessing_pipeline.get_portfolio(ships_df: pandas.DataFrame, case_df: pandas.DataFrame) → pandas.DataFrame

Builds adapt portfolio output.

Parameters:

• ships_df (pandas.DataFrame) –

• case_df (pandas.DataFrame) –

Return type:

pandas.DataFrame

leaf_engine.adapt.pipeline.adapt_postprocessing_pipeline.get_ships(data_context: leaf_engine.adapt.context.adapt_contexts.AdaptDataContext) → pandas.DataFrame

Gets shipments DataFrame from data_context.

Parameters:

data_context (leaf_engine.adapt.context.adapt_contexts.AdaptDataContext) –

Return type:

pandas.DataFrame

leaf_engine.adapt.pipeline.adapt_postprocessing_pipeline.get_total_loads(ships_df: pandas.DataFrame) → int

Computes number of unique shipments.

Parameters:

ships_df (pandas.DataFrame) –

Return type:

int

leaf_engine.adapt.pipeline.adapt_postprocessing_pipeline.get_total_spend(ships_df: pandas.DataFrame) → float

Computes total shipment spend.

Parameters:

ships_df (pandas.DataFrame) –

Return type:

float

leaf_engine.adapt.pipeline.adapt_postprocessing_pipeline.postprocessing_pipeline(etl_cxt: leaf_engine.adapt.context.adapt_contexts.AdaptETLContext, data_cxt: leaf_engine.adapt.context.adapt_contexts.AdaptDataContext, action_cxt: leaf_engine.adapt.context.adapt_contexts.AdaptActionContext, observation_cxt: leaf_engine.adapt.context.adapt_contexts.AdaptObservationContext, explorer_cxt: leaf_engine.adapt.context.adapt_contexts.AdaptExplorerContext)

Runs postprocessing logic.

Parameters:

• etl_cxt (leaf_engine.adapt.context.adapt_contexts.AdaptETLContext) –

• data_cxt (leaf_engine.adapt.context.adapt_contexts.AdaptDataContext) –

• action_cxt (leaf_engine.adapt.context.adapt_contexts.AdaptActionContext) –

• observation_cxt (leaf_engine.adapt.context.adapt_contexts.AdaptObservationContext) –

• explorer_cxt (leaf_engine.adapt.context.adapt_contexts.AdaptExplorerContext) –

leaf_engine.adapt.pipeline.adapt_postprocessing_pipeline.AdaptPostprocessingException

leaf_engine.adapt.pipeline.adapt_postprocessing_pipeline.BENEFIT_CAPS

leaf_engine.adapt.pipeline.adapt_postprocessing_pipeline.BENEFIT_TYPE

leaf_engine.adapt.pipeline.adapt_postprocessing_pipeline.DATA_DRIVEN_BENEFIT_ASSUMPTIONS = ['network_moves', 'market_rate_variance', 'daily_flex_contract_priced']

leaf_engine.adapt.pipeline.adapt_postprocessing_pipeline.MANUAL_BENEFIT_ASSUMPTIONS

leaf_engine.adapt.pipeline.adapt_postprocessing_pipeline.MARKET_VARIANCE_IMPLEMENTABILITY = 0.5

leaf_engine.adapt.pipeline.adapt_postprocessing_pipeline.MIN_LANE_LENGTH = 250

leaf_engine.adapt.pipeline.adapt_postprocessing_pipeline.NETWORK_BENEFIT_IMPLEMENTABILITY = 0.1