(Latest Version)

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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