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SIENTIAPDE-1081

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Enhance documentation across multiple modules with detailed parameter descriptions and usage examples
This commit is contained in:
vitor-aignosi
2025-05-26 16:45:50 -03:00
parent 5326051714
commit 10081b71d2
12 changed files with 294 additions and 92 deletions
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25
laborious/workflows/predictions_batch.py
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@@ -19,19 +19,20 @@ class PredictionsBatch():
2. Loads data using a custom query and executes the prediction process
Args:
input_data (dict[str, Any]): The input data for the workflow.
- input_data (dict[str, Any]): The input data for the workflow.
Contains the following keys:
schedule_name (str): The name of the schedule.
model_name (str): The name of the model.
model_id (int): The id of the model.
query (str): The SQL query to be executed to load data.
schema (dict, optional): The schema definition for the data.
table_name (str, optional): The name of the table to process.
input_filters (dict, optional): Filters to be applied during prediction.
mlflow_transform_filters (dict, optional): Filters to be applied during prediction.
mlflow_predict_filters (dict, optional): Filters to be applied during prediction.
model_retention (int, optional): The model retention period in minutes.
path_priority (list[str]): The path priority.
- schedule_name (str): The name of the schedule.
- model_name (str): The name of the model.
- model_id (int): The id of the model.
- query (str): The SQL query to be executed to load data.
- schema (dict, optional): The schema definition for the data.
- table_name (str, optional): The name of the table to process.
- input_filters (dict, optional): Filters to be applied during prediction.
- mlflow_transform_filters (dict, optional): Filters to be applied
during prediction.
- mlflow_predict_filters (dict, optional): Filters to be applied during prediction.
- model_retention (int, optional): The model retention period in minutes.
- path_priority (list[str]): The path priority.
Returns:
None

22
laborious/workflows/sub_workflows/format_and_export_prediction.py
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@@ -21,17 +21,17 @@ class FormatAndExportPrediction():
Args:
input_data(dict[str, Any]): The input data for the workflow.
Contains the following keys:
path_flag(str): The path flag to determine the type of prediction to format
data(dict[str, Any]): The data to format
prediction_confidence(float): The prediction confidence to be registered
timestamp(str): The timestamp of the prediction, synchronized with the data
model_id(int): The model id of the prediction
model_name(str): The model name of the prediction
model_retention(str): The model retention of the prediction
comment(str): The comment to be registered
schema(str): The schema of the prediction
table_name(str): The table name of the prediction
opc_output_config(dict[str, Any]): The opc output config of the prediction
- path_flag(str): The path flag to determine the type of prediction to format
- data(dict[str, Any]): The data to format
- prediction_confidence(float): The prediction confidence to be registered
- timestamp(str): The timestamp of the prediction, synchronized with the data
- model_id(int): The model id of the prediction
- model_name(str): The model name of the prediction
- model_retention(str): The model retention of the prediction
- comment(str): The comment to be registered
- schema(str): The schema of the prediction
- table_name(str): The table name of the prediction
- opc_output_config(dict[str, Any]): The opc output config of the prediction
Returns:
bool: True if the workflow was successful, False otherwise.

26
laborious/workflows/sub_workflows/prediction_process.py
View File

@@ -19,19 +19,21 @@ class PredictionProcess():
2. Loads data using a custom query and executes the prediction process
Args:
input_data (dict[str, Any]): The input data for the workflow.
- input_data (dict[str, Any]): The input data for the workflow.
Contains the following keys:
data (dict[str, Any]): The data to be used for the prediction.
schema (str): The schema of the table.
table_name (str): The name of the table.
model_id (int): The id of the model.
input_filters (dict, optional): Filters to be applied during prediction.
mlflow_transform_filters (dict, optional): Filters to be applied during prediction.
mlflow_predict_filters (dict, optional): Filters to be applied during prediction.
model_name (str): The name of the model.
model_retention (int, optional): The model retention period in minutes.
path_priority (list[str]): The path priority.
opc_output_config (dict[str, Any]): The opc output config of the prediction.
- data (dict[str, Any]): The data to be used for the prediction.
- schema (str): The schema of the table.
- table_name (str): The name of the table.
- model_id (int): The id of the model.
- input_filters (dict, optional): Filters to be applied during prediction.
- mlflow_transform_filters (dict, optional): Filters to be
applied during prediction.
- mlflow_predict_filters (dict, optional): Filters to be
applied during prediction.
- model_name (str): The name of the model.
- model_retention (int, optional): The model retention period in minutes.
- path_priority (list[str]): The path priority.
- opc_output_config (dict[str, Any]): The opc output config of the prediction.
Returns:
None