dfp_inference_pipe

This module function allows for the consolidation of multiple dfp pipeline modules relevant to the inference process into a single module.

Parameter

Type

Description

Example Value

Default Value

batching_options dictionary Options for batching files. See below -
cache_dir string Directory used for caching intermediate results. “/tmp/cache” -
detection_criteria dictionary Criteria for filtering detections. - -
inference_options dictionary Options for configuring the inference process. See below -
preprocessing_options dictionary Options for preprocessing data. - -
stream_aggregation_options dictionary Options for aggregating data by stream. See below -
timestamp_column_name string Name of the column containing timestamps. “timestamp” -
user_splitting_options dictionary Options for splitting data by user. See below -
write_to_file_options dictionary Options for writing results to a file. - -

batching_options

Parameter

Type

Description

Example Value

Default Value

end_time string End time of the time range to process. “2022-01-01T00:00:00Z” -
iso_date_regex_pattern string ISO date regex pattern. “\d{4}-\d{2}-\d{2}T\d{2}:\d{2}:\d{2}Z” -
parser_kwargs dict Keyword arguments to pass to the parser. - -
period string Time period to batch the data. “1D” -
sampling_rate_s float Sampling rate in seconds. “1.0” -
start_time string Start time of the time range to process. “2021-01-01T00:00:00Z” -

user_splitting_options

Parameter

Type

Description

Example Value

Default Value

fallback_username string Fallback user to use if no model is found for a user. “generic_user” generic_user
include_generic boolean Include generic models in the results. true true
include_individual boolean Include individual models in the results. true false
only_users list List of users to include in the results. [“user_a”,”user_b”] -
skip_users list List of users to exclude from the results. [“user_c”] -
userid_column_name string Column “name for the user ID.” user_id

Parameter

Type

Description

Example Value

Default Value

cache_mode string The user ID to use if the user ID is not found “batch” batch
min_history int Minimum history to trigger a new training event 1 1
max_history int Maximum history to include in a new training event 0 0
timestamp_column_name string Name of the column containing timestamps “timestamp” timestamp
aggregation_span string Lookback timespan for training data in a new training event “60d” 60d
cache_to_disk bool Whether or not to cache streaming data to disk false false
cache_dir string Directory to use for caching streaming data “./.cache” ./.cache

Parameter

Type

Description

Example Value

Default Value

model_name_formatter string Formatter for model names “user_{username}_model” [Required]
fallback_username string Fallback user to use if no model is found for a user “generic_user” generic_user
timestamp_column_name string Name of the timestamp column “timestamp” timestamp
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{ "timestamp_column_name": "timestamp", "cache_dir": "/tmp/cache", "batching_options": { "end_time": "2022-01-01T00:00:00Z", "iso_date_regex_pattern": "\\d{4}-\\d{2}-\\d{2}T\\d{2}:\\d{2}:\\d{2}Z", "parser_kwargs": {}, "period": "1D", "sampling_rate_s": 1.0, "start_time": "2021-01-01T00:00:00Z" }, "user_splitting_options": { "fallback_username": "generic", "include_generic": true, "include_individual": true, "only_users": [ "user_a", "user_b" ], "skip_users": [ "user_c" ], "userid_column_name": "user_id" }, "stream_aggregation_options": { "timestamp_column_name": "timestamp", "cache_mode": "MEMORY", "trigger_on_min_history": true, "aggregation_span": "1D", "trigger_on_min_increment": true, "cache_to_disk": false }, "preprocessing_options": {}, "inference_options": { "model_name_formatter": "{model_name}", "fallback_username": "generic", "timestamp_column_name": "timestamp" }, "detection_criteria": {}, "write_to_file_options": {} }

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