IsolationForestAnomalyDetector
Versions
v1.0.0
Basic Information
Class Name: IsolationForestAnomalyDetector
Title: Isolation Forest Anomaly Detector
Version: 1.0.0
Author: Simon Vedder
Organization: OneStream
Creation Date: 2024-04-18
Default Routine Memory Capacity: 2.0 GB
Tags
Anomaly, Time Series, Unsupervised, ML
Description
Short Description
Constructor for the Isolation Forest Anomaly Detection Routine
Long Description
This routine employs an unsupervised anomaly detection technique known as the isolation forest. Primarily designed for pinpointing point anomalies, this method utilizes a tree-based structure to effectively isolate outliers in the dataset. Optimal performance of this technique is achieved when applied to datasets with effective numeric features, as the algorithm depends heavily on quantitative attributes to identify deviations. Prior to fitting the model, non-numeric features are pruned.
Use Cases
1. Fraud Detection
Isolation Forest serves as a powerful machine learning tool for detecting fraud within financial datasets that are updated with critical daily transaction metrics. This technique shines in scenarios where data is extensive and anomalies, though infrequent, could have severe consequences, such as in cases of financial fraud. Our hypothetical dataset includes daily summaries of financial activities, such as total money spent, total number of transactions, and potentially other features like average transaction value or geographic distribution of transactions. Isolation Forest uses these daily features to identify data points that deviate significantly from typical patterns observed in the dataset. The model isolates anomalies by randomly selecting features and split values, effectively separating unusual observations from normal ones more quickly than conventional methods. This method is particularly useful in environments where fraud patterns can evolve, and new types of fraud might emerge, thereby requiring a flexible and adaptive approach to anomaly detection.
2. Inventory Management
Isolation Forest serves as an effective tool for detecting anomalies in inventory management within the retail sector. This approach is particularly advantageous in environments with large datasets where inventory levels across multiple store locations or product categories need constant monitoring to ensure operational efficiency and prevent losses. Retailers often maintain complex datasets that track daily inventory changes, including shipments, sales, and returns for possibly thousands of SKUs across various locations. Each day's inventory activity is recorded and summarized to monitor key metrics such as stock levels, rate of inventory turnover, and discrepancies between expected and actual stock counts. Isolation Forest analyzes these inventory metrics to identify data points that stand out from normal patterns, which could indicate issues like stockouts, overstock, or logistical errors. Anomalies detected by the model might suggest various issues such as theft, supplier errors, or inefficiencies in stock management.
Routine Methods
1. Init (Constructor)
- Method:
__init__-
Type: Constructor
-
Memory Capacity: 2.0 GB
-
Allow In-Memory Execution: No
-
Read Only: No
-
Method Limits: N/A
-
Outputs Dynamic Artifacts: No
-
Short Description:
- Constructor for the Isolation Forest Anomaly Detection Routine.
-
Detailed Description:
- The Isolation Forest constructor is used to set the group name and hyperparameters for this anomaly detection instance.
-
Inputs:
- Required Input
- Hyperparameters: The hyperparameters for the Isolation Forest Anomaly Detector.
- Name:
hyper_parameters - Tooltip:
- Validation Constraints:
- This input may be subject to other validation constraints at runtime.
- Validation Constraints:
- Type: IsolationForestAnomalyDetectorHyperParameters
- Name:
- Group Name: Group name for your anomaly detector, used as an identifier in the output artifact.
- Name:
group_name - Tooltip:
- Validation Constraints:
- This input may be subject to other validation constraints at runtime.
- Validation Constraints:
- Type: str
- Name:
- Hyperparameters: The hyperparameters for the Isolation Forest Anomaly Detector.
- Required Input
-
Artifacts: No artifacts are returned by this method
-
2. Fit (Method)
- Method:
fit-
Type: Method
-
Memory Capacity: 2.0 GB
-
Allow In-Memory Execution: No
-
Read Only: No
-
Method Limits: On a dataset with 20K targets, 6M rows, and 6 columns, this method completed in about two hours with 100GB of memory allocated. The size of this dataset is approximately the largest dataset this method can handle.
-
Outputs Dynamic Artifacts: No
-
Short Description:
- Fit the isolation forest model.
-
Detailed Description:
- The fit method will combine the feature and target dataset and then fit an isolation forest model for each individual target.
-
Inputs:
- Required Input
- Source Data Definition: The source data definition to use.
- Name:
source_data_definition - Tooltip:
- Validation Constraints:
- This input may be subject to other validation constraints at runtime.
- Validation Constraints:
- Type: TimeSeriesTableDefinition
- Name:
- Feature Data Definition: The feature data definition to use.
- Name:
feature_data_definitions - Tooltip:
- Validation Constraints:
- This input may be subject to other validation constraints at runtime.
- Validation Constraints:
- Type: list[TimeSeriesTableDefinition]
- Name:
- Date Range: The date range to fit anomalies on.
- Name:
time_range - Tooltip:
- Detail:
- If None, entire dataset will be used.
- Validation Constraints:
- This input may be subject to other validation constraints at runtime.
- Detail:
- Type: Optional[StartDateEndDateDefinition]
- Name:
- Source Data Definition: The source data definition to use.
- Required Input
-
Artifacts: No artifacts are returned by this method
-
3. Predict (Method)
- Method:
predict-
Type: Method
-
Memory Capacity: 2.0 GB
-
Allow In-Memory Execution: No
-
Read Only: No
-
Method Limits: On a dataset with 20K targets, 6M rows, and 6 columns, this method completed in about three and a half hours with 100GB of memory allocated. The size of this dataset is approximately the largest dataset this method can handle.
-
Outputs Dynamic Artifacts: No
-
Short Description:
- Find Anomalies.
-
Detailed Description:
- This method will predict anomalies using the isolation forest model that was fit in the fit method. It uses the saved columns from the fit method to determine which columns will be used to predict for each target
-
Inputs:
- Required Input
- Source Data Definition: The source data definition to use.
- Name:
source_data_definition - Tooltip:
- Validation Constraints:
- This input may be subject to other validation constraints at runtime.
- Validation Constraints:
- Type: TimeSeriesTableDefinition
- Name:
- Feature Data Definition: The feature data definition to use.
- Name:
feature_data_definitions - Tooltip:
- Validation Constraints:
- This input may be subject to other validation constraints at runtime.
- Validation Constraints:
- Type: list[TimeSeriesTableDefinition]
- Name:
- Date Range: The date range to predict anomalies on.
- Name:
time_range - Tooltip:
- Detail:
- If None, entire dataset will be used.
- Validation Constraints:
- This input may be subject to other validation constraints at runtime.
- Detail:
- Type: Optional[StartDateEndDateDefinition]
- Name:
- State Info Definition: The snapshot name and description. These will be used as identifiers in the snapshot artifact.
- Name:
state_info - Tooltip:
- Validation Constraints:
- This input may be subject to other validation constraints at runtime.
- Validation Constraints:
- Type: StateInfoDefinition
- Name:
- Source Data Definition: The source data definition to use.
- Required Input
-
Artifacts:
-
AnomalySnapshot: Parquet file containing data about your anomaly detection run.
- Qualified Key Annotation:
anomaly_snapshot - Aggregate Artifact:
False - In-Memory Json Accessible:
False - File Annotations:
artifacts_/@anomaly_snapshot/data_/data_<int>.parquet- A partitioned set of parquet files where each file will have no more than 1000000 rows.
- Qualified Key Annotation:
-
Specific Anomaly Dates: Parquet file containing data about the specific dates an anomaly was detected.
- Qualified Key Annotation:
anomaly_dates - Aggregate Artifact:
False - In-Memory Json Accessible:
False - File Annotations:
artifacts_/@anomaly_dates/data_/data_<int>.parquet- A partitioned set of parquet files where each file will have no more than 1000000 rows.
- Qualified Key Annotation:
-
Specific Anomaly Instances: Parquet file containing data about the specific anomaly instances that were detected.
- Qualified Key Annotation:
anomaly_instance - Aggregate Artifact:
False - In-Memory Json Accessible:
False - File Annotations:
artifacts_/@anomaly_instance/data_/data_<int>.parquet- A partitioned set of parquet files where each file will have no more than 1000000 rows.
- Qualified Key Annotation:
-
-
Interface Definitions
1. Anomaly Detection Interface
An interface class requiring fit and predict methods to be implemented.
This BaseRoutineInterface class enforces a common interface for all anomaly detection routines. The interface requires each anomaly detection routine to implement a fit method and a predict method with the same input parameters. Each concrete class will have constructor methods where hyperparameters specific to the anomaly detection algorithm may be set, however, this interface does not enforce any specific constructor method.
Interface Methods:
1. Fit
Method Name: fit
Short Description: Abstract Fit Method
Detailed Description: This specifies the necessary input and output parameters for the fit method on all anomaly detection routines. The input parameters contain a source data definition and time range to fit an anomaly detector to.
Inputs:
| Property | Type | Required | Description |
|---|---|---|---|
source_data_definition | #/$defs/TimeSeriesTableDefinition | Yes | The source data definition to use. |
feature_data_definitions | array | Yes | The feature data definition to use. |
time_range | `#/$defs/StartDateEndDateDefinition | null` | No |
Input Schema (JSON):
{
"$defs": {
"CubeViewTabularConnection": {
"description": "Tabular connection backed by an OneStream cube view.\n\nStores only the cube view name. At materialize time the cube view is\nextracted fresh via the DataFlow XBR endpoint and the result is loaded\nthrough DuckDB into a DataFrame.\n\nAnalogous to SqlTabularConnection (pick a table) or FileTabularConnection\n(pick a file): the user selects a pre-configured artifact, not raw config.\n\nThe OneStream application is taken from the SessionInfo (``si.app_name``) -- the single source\nof truth stamped at the request boundary / derived from the job's IAM entrypoint -- so the\nconnection no longer collects an application name from the user.\n\nExample:\n >>> conn = CubeViewTabularConnection(cube_view_name=\"Retail Source Data\")\n >>> df = conn.materialize_tabular_pandas_dataframe()",
"properties": {
"cube_view_name": {
"description": "The name of the OneStream cube view to use as the data source.",
"field_type": "input",
"input_component": {
"component_type": "combobox",
"show_search": true
},
"list_render_mode": null,
"long_description": null,
"options_callback": "xperiflow.source.app.routines.pbm.store.conn.cubeviewtable:CubeViewTabularConnection.get_cube_view_bound_options",
"options_callback_kwargs": null,
"state_name": "cube_view_name",
"title": "Cube View",
"tooltip": "Validation Constraints:\nThis input may be subject to other validation constraints at runtime.",
"type": "string"
}
},
"required": [
"cube_view_name"
],
"title": "CubeViewTabularConnection",
"type": "object"
},
"FileExtensions_": {
"description": "File Extensions.",
"enum": [
".csv",
".tsv",
".psv",
".parquet",
".xlsx"
],
"title": "FileExtensions_",
"type": "string"
},
"FileTabularConnection": {
"properties": {
"connection_key": {
"$ref": "#/$defs/MetaFileSystemConnectionKey",
"description": "The MetaFileSystem connection key.",
"field_type": "input",
"input_component": {
"component_type": "combobox",
"show_search": true
},
"list_render_mode": null,
"long_description": null,
"options_callback": null,
"options_callback_kwargs": null,
"state_name": "connection_key",
"title": "Connection Key",
"tooltip": "Validation Constraints:\nThis input may be subject to other validation constraints at runtime."
},
"file_path": {
"description": "The full file path to the file to ingest.",
"field_type": "input",
"input_component": {
"component_type": "combobox",
"show_search": true
},
"list_render_mode": null,
"long_description": null,
"options_callback": "xperiflow.source.app.routines.pbm.store.conn.filetable:FileTabularConnection.get_file_path_bound_options",
"options_callback_kwargs": null,
"state_name": "file_path",
"title": "File Path",
"tooltip": "Validation Constraints:\nThis input may be subject to other validation constraints at runtime.",
"type": "string"
}
},
"required": [
"connection_key",
"file_path"
],
"title": "FileTabularConnection",
"type": "object"
},
"MetaFileSystemConnectionKey": {
"enum": [
"sql-server-routine",
"sql-server-shared"
],
"title": "MetaFileSystemConnectionKey",
"type": "string"
},
"PartitionedFileTabularConnection": {
"properties": {
"connection_key": {
"$ref": "#/$defs/MetaFileSystemConnectionKey",
"description": "The MetaFileSystem connection key.",
"field_type": "input",
"input_component": {
"component_type": "combobox",
"show_search": true
},
"list_render_mode": null,
"long_description": null,
"options_callback": null,
"options_callback_kwargs": null,
"state_name": "connection_key",
"title": "Connection Key",
"tooltip": "Validation Constraints:\nThis input may be subject to other validation constraints at runtime."
},
"file_type": {
"$ref": "#/$defs/FileExtensions_",
"description": "The type of files to read from the directory.",
"field_type": "input",
"input_component": {
"component_type": "combobox",
"show_search": true
},
"list_render_mode": null,
"long_description": null,
"options_callback": null,
"options_callback_kwargs": null,
"state_name": "file_info",
"title": "File Type",
"tooltip": "Validation Constraints:\nThis input may be subject to other validation constraints at runtime."
},
"directory_path": {
"description": "The full directory path containing partitioned tabular files.",
"field_type": "input",
"input_component": {
"component_type": "combobox",
"show_search": true
},
"list_render_mode": null,
"long_description": null,
"options_callback": "xperiflow.source.app.routines.pbm.store.conn.partitionedfiletable:PartitionedFileTabularConnection.get_directory_path_bound_options",
"options_callback_kwargs": null,
"state_name": "file_info",
"title": "Directory Path",
"tooltip": "Validation Constraints:\nThis input may be subject to other validation constraints at runtime.",
"type": "string"
}
},
"required": [
"connection_key",
"file_type",
"directory_path"
],
"title": "PartitionedFileTabularConnection",
"type": "object"
},
"SqlTabularConnection": {
"properties": {
"database_resource": {
"description": "The name of the database resource to connect to.",
"field_type": "input",
"input_component": {
"component_type": "combobox",
"show_search": true
},
"list_render_mode": null,
"long_description": null,
"options_callback": "xperiflow.source.app.routines.pbm.store.conn.sqltable:SqlTabularConnection.get_database_resources",
"options_callback_kwargs": null,
"state_name": "database_resource",
"title": "Database Resource",
"tooltip": "Validation Constraints:\nThis input may be subject to other validation constraints at runtime.",
"type": "string"
},
"database_name": {
"description": "The name of the database to connect to.",
"field_type": "input",
"input_component": {
"component_type": "combobox",
"show_search": true
},
"list_render_mode": null,
"long_description": null,
"options_callback": "xperiflow.source.app.routines.pbm.store.conn.sqltable:SqlTabularConnection.get_database_schemas",
"options_callback_kwargs": null,
"state_name": "database_name",
"title": "Database Name",
"tooltip": "Detail:\nNote: If you don\u2019t see the database name that you are looking for in this list, it is recommended that you first move the data to be used within a database that is available within this list.\n\nValidation Constraints:\nThis input may be subject to other validation constraints at runtime.",
"type": "string"
},
"table_name": {
"description": "The name of the table to use.",
"field_type": "input",
"input_component": {
"component_type": "combobox",
"show_search": true
},
"list_render_mode": null,
"long_description": null,
"options_callback": "xperiflow.source.app.routines.pbm.store.conn.sqltable:SqlTabularConnection.get_tables",
"options_callback_kwargs": null,
"state_name": "table_name",
"title": "Table Name",
"tooltip": "Validation Constraints:\nThis input may be subject to other validation constraints at runtime.",
"type": "string"
}
},
"required": [
"database_resource",
"database_name",
"table_name"
],
"title": "SqlTabularConnection",
"type": "object"
},
"StartDateEndDateDefinition": {
"properties": {
"start_date": {
"description": "The inclusive start of the date range (MM/DD/YYYY).",
"field_type": "input",
"format": "date-time",
"input_component": {
"component_type": "dateselector",
"max_date": null,
"min_date": null
},
"list_render_mode": null,
"long_description": null,
"options_callback": null,
"options_callback_kwargs": null,
"state_name": "date_selection",
"title": "Start Date",
"tooltip": "Validation Constraints:\nThis input may be subject to other validation constraints at runtime.",
"type": "string"
},
"end_date": {
"description": "The inclusive end of the date range (MM/DD/YYYY).",
"field_type": "input",
"format": "date-time",
"input_component": {
"component_type": "dateselector",
"max_date": null,
"min_date": null
},
"list_render_mode": null,
"long_description": null,
"options_callback": null,
"options_callback_kwargs": null,
"state_name": "date_selection",
"title": "End Date",
"tooltip": "Detail:\nNote, the Seasonal ARIMA Anomaly Detector Routine treats the end date as exclusive.\n\nValidation Constraints:\nThis input may be subject to other validation constraints at runtime.",
"type": "string"
}
},
"required": [
"start_date",
"end_date"
],
"title": "StartDateEndDateDefinition",
"type": "object"
},
"TabularConnection": {
"description": "A shared parameter base model dedication to tabular connections.",
"properties": {
"tabular_connection": {
"anyOf": [
{
"$ref": "#/$defs/SqlTabularConnection"
},
{
"$ref": "#/$defs/FileTabularConnection"
},
{
"$ref": "#/$defs/PartitionedFileTabularConnection"
},
{
"$ref": "#/$defs/CubeViewTabularConnection"
}
],
"description": "The connection type to use to access the source data.",
"field_type": "input",
"input_component": {
"component_type": "combobox",
"show_search": true
},
"list_render_mode": null,
"long_description": null,
"options_callback": null,
"options_callback_kwargs": null,
"state_name": "connection",
"title": "Connection",
"tooltip": "Validation Constraints:\nThis input may be subject to other validation constraints at runtime."
}
},
"required": [
"tabular_connection"
],
"title": "TabularConnection",
"type": "object"
},
"TimeSeriesTableDefinition": {
"description": "A parameter base model dedicated to loading tabular time series data.",
"properties": {
"data_connection": {
"$ref": "#/$defs/TabularConnection",
"description": "The connection to the source data.",
"field_type": "input",
"input_component": {
"component_type": "combobox",
"show_search": true
},
"list_render_mode": null,
"long_description": null,
"options_callback": null,
"options_callback_kwargs": null,
"state_name": "source_connection",
"title": "Connection",
"tooltip": "Validation Constraints:\nThis input may be subject to other validation constraints at runtime."
},
"dimension_columns": {
"description": "The columns to use as dimensions.",
"field_type": "input",
"input_component": {
"component_type": "combobox",
"show_search": true
},
"items": {
"type": "string"
},
"list_render_mode": null,
"long_description": null,
"minItems": 1,
"options_callback": "xperiflow.source.app.routines.pbm.store.tsf.tstable:TimeSeriesTableDefinition.get_table_columns",
"options_callback_kwargs": null,
"state_name": "column_selection",
"title": "Dimension Columns",
"tooltip": "Validation Constraints:\nThe input must have a minimum length of 1.\n\nThis input may be subject to other validation constraints at runtime.",
"type": "array"
},
"date_column": {
"description": "The column to use as the date.",
"field_type": "input",
"input_component": {
"component_type": "combobox",
"show_search": true
},
"list_render_mode": null,
"long_description": null,
"options_callback": "xperiflow.source.app.routines.pbm.store.tsf.tstable:TimeSeriesTableDefinition.get_table_columns",
"options_callback_kwargs": null,
"state_name": "column_selection",
"title": "Date Column",
"tooltip": "Detail:\nThe date column must in a DateTime readable format.\n\nValidation Constraints:\nThis input may be subject to other validation constraints at runtime.",
"type": "string"
},
"value_column": {
"description": "The column to use as the value.",
"field_type": "input",
"input_component": {
"component_type": "combobox",
"show_search": true
},
"list_render_mode": null,
"long_description": null,
"options_callback": "xperiflow.source.app.routines.pbm.store.tsf.tstable:TimeSeriesTableDefinition.get_table_columns",
"options_callback_kwargs": null,
"state_name": "column_selection",
"title": "Value Column",
"tooltip": "Detail:\nThe value column must be a numeric (int, float, double, decimal, etc.) column.\n\nValidation Constraints:\nThis input may be subject to other validation constraints at runtime.",
"type": "string"
}
},
"required": [
"data_connection",
"dimension_columns",
"date_column",
"value_column"
],
"title": "TimeSeriesTableDefinition",
"type": "object"
}
},
"properties": {
"source_data_definition": {
"$ref": "#/$defs/TimeSeriesTableDefinition",
"description": "The source data definition to use.",
"field_type": "input",
"input_component": {
"component_type": "combobox",
"show_search": true
},
"list_render_mode": null,
"long_description": null,
"options_callback": null,
"options_callback_kwargs": null,
"state_name": "SourceDefinition",
"title": "Source Data Definition",
"tooltip": "Validation Constraints:\nThis input may be subject to other validation constraints at runtime."
},
"feature_data_definitions": {
"description": "The feature data definition to use.",
"field_type": "input",
"input_component": {
"component_type": "combobox",
"show_search": true
},
"items": {
"$ref": "#/$defs/TimeSeriesTableDefinition"
},
"list_render_mode": null,
"long_description": null,
"options_callback": null,
"options_callback_kwargs": null,
"state_name": "FeatureDefinition",
"title": "Feature Data Definition",
"tooltip": "Validation Constraints:\nThis input may be subject to other validation constraints at runtime.",
"type": "array"
},
"time_range": {
"anyOf": [
{
"$ref": "#/$defs/StartDateEndDateDefinition"
},
{
"type": "null"
}
],
"default": null,
"description": "The date range to fit anomalies on.",
"field_type": "input",
"input_component": {
"component_type": "combobox",
"show_search": true
},
"list_render_mode": null,
"long_description": null,
"options_callback": null,
"options_callback_kwargs": null,
"state_name": "DateRange",
"title": "Date Range",
"tooltip": "Detail:\nIf None, entire dataset will be used.\n\nValidation Constraints:\nThis input may be subject to other validation constraints at runtime."
}
},
"required": [
"source_data_definition",
"feature_data_definitions"
],
"title": "AnomalyDetectionFitParameters",
"type": "object"
}
Artifacts: No artifacts are returned by this method
2. Predict
Method Name: predict
Short Description: Abstract Predict Method
Detailed Description: This specifies the necessary input and output parameters for the predict method on all anomaly detection routines. The input parameters contain a source data definition and a time range to detect anomalies.
Inputs:
| Property | Type | Required | Description |
|---|---|---|---|
source_data_definition | #/$defs/TimeSeriesTableDefinition | Yes | The source data definition to use. |
feature_data_definitions | array | Yes | The feature data definition to use. |
time_range | `#/$defs/StartDateEndDateDefinition | null` | No |
state_info | #/$defs/StateInfoDefinition | Yes | The snapshot name and description. These will be used as identifiers in the snapshot artifact. |
Input Schema (JSON):
{
"$defs": {
"CubeViewTabularConnection": {
"description": "Tabular connection backed by an OneStream cube view.\n\nStores only the cube view name. At materialize time the cube view is\nextracted fresh via the DataFlow XBR endpoint and the result is loaded\nthrough DuckDB into a DataFrame.\n\nAnalogous to SqlTabularConnection (pick a table) or FileTabularConnection\n(pick a file): the user selects a pre-configured artifact, not raw config.\n\nThe OneStream application is taken from the SessionInfo (``si.app_name``) -- the single source\nof truth stamped at the request boundary / derived from the job's IAM entrypoint -- so the\nconnection no longer collects an application name from the user.\n\nExample:\n >>> conn = CubeViewTabularConnection(cube_view_name=\"Retail Source Data\")\n >>> df = conn.materialize_tabular_pandas_dataframe()",
"properties": {
"cube_view_name": {
"description": "The name of the OneStream cube view to use as the data source.",
"field_type": "input",
"input_component": {
"component_type": "combobox",
"show_search": true
},
"list_render_mode": null,
"long_description": null,
"options_callback": "xperiflow.source.app.routines.pbm.store.conn.cubeviewtable:CubeViewTabularConnection.get_cube_view_bound_options",
"options_callback_kwargs": null,
"state_name": "cube_view_name",
"title": "Cube View",
"tooltip": "Validation Constraints:\nThis input may be subject to other validation constraints at runtime.",
"type": "string"
}
},
"required": [
"cube_view_name"
],
"title": "CubeViewTabularConnection",
"type": "object"
},
"FileExtensions_": {
"description": "File Extensions.",
"enum": [
".csv",
".tsv",
".psv",
".parquet",
".xlsx"
],
"title": "FileExtensions_",
"type": "string"
},
"FileTabularConnection": {
"properties": {
"connection_key": {
"$ref": "#/$defs/MetaFileSystemConnectionKey",
"description": "The MetaFileSystem connection key.",
"field_type": "input",
"input_component": {
"component_type": "combobox",
"show_search": true
},
"list_render_mode": null,
"long_description": null,
"options_callback": null,
"options_callback_kwargs": null,
"state_name": "connection_key",
"title": "Connection Key",
"tooltip": "Validation Constraints:\nThis input may be subject to other validation constraints at runtime."
},
"file_path": {
"description": "The full file path to the file to ingest.",
"field_type": "input",
"input_component": {
"component_type": "combobox",
"show_search": true
},
"list_render_mode": null,
"long_description": null,
"options_callback": "xperiflow.source.app.routines.pbm.store.conn.filetable:FileTabularConnection.get_file_path_bound_options",
"options_callback_kwargs": null,
"state_name": "file_path",
"title": "File Path",
"tooltip": "Validation Constraints:\nThis input may be subject to other validation constraints at runtime.",
"type": "string"
}
},
"required": [
"connection_key",
"file_path"
],
"title": "FileTabularConnection",
"type": "object"
},
"MetaFileSystemConnectionKey": {
"enum": [
"sql-server-routine",
"sql-server-shared"
],
"title": "MetaFileSystemConnectionKey",
"type": "string"
},
"PartitionedFileTabularConnection": {
"properties": {
"connection_key": {
"$ref": "#/$defs/MetaFileSystemConnectionKey",
"description": "The MetaFileSystem connection key.",
"field_type": "input",
"input_component": {
"component_type": "combobox",
"show_search": true
},
"list_render_mode": null,
"long_description": null,
"options_callback": null,
"options_callback_kwargs": null,
"state_name": "connection_key",
"title": "Connection Key",
"tooltip": "Validation Constraints:\nThis input may be subject to other validation constraints at runtime."
},
"file_type": {
"$ref": "#/$defs/FileExtensions_",
"description": "The type of files to read from the directory.",
"field_type": "input",
"input_component": {
"component_type": "combobox",
"show_search": true
},
"list_render_mode": null,
"long_description": null,
"options_callback": null,
"options_callback_kwargs": null,
"state_name": "file_info",
"title": "File Type",
"tooltip": "Validation Constraints:\nThis input may be subject to other validation constraints at runtime."
},
"directory_path": {
"description": "The full directory path containing partitioned tabular files.",
"field_type": "input",
"input_component": {
"component_type": "combobox",
"show_search": true
},
"list_render_mode": null,
"long_description": null,
"options_callback": "xperiflow.source.app.routines.pbm.store.conn.partitionedfiletable:PartitionedFileTabularConnection.get_directory_path_bound_options",
"options_callback_kwargs": null,
"state_name": "file_info",
"title": "Directory Path",
"tooltip": "Validation Constraints:\nThis input may be subject to other validation constraints at runtime.",
"type": "string"
}
},
"required": [
"connection_key",
"file_type",
"directory_path"
],
"title": "PartitionedFileTabularConnection",
"type": "object"
},
"SqlTabularConnection": {
"properties": {
"database_resource": {
"description": "The name of the database resource to connect to.",
"field_type": "input",
"input_component": {
"component_type": "combobox",
"show_search": true
},
"list_render_mode": null,
"long_description": null,
"options_callback": "xperiflow.source.app.routines.pbm.store.conn.sqltable:SqlTabularConnection.get_database_resources",
"options_callback_kwargs": null,
"state_name": "database_resource",
"title": "Database Resource",
"tooltip": "Validation Constraints:\nThis input may be subject to other validation constraints at runtime.",
"type": "string"
},
"database_name": {
"description": "The name of the database to connect to.",
"field_type": "input",
"input_component": {
"component_type": "combobox",
"show_search": true
},
"list_render_mode": null,
"long_description": null,
"options_callback": "xperiflow.source.app.routines.pbm.store.conn.sqltable:SqlTabularConnection.get_database_schemas",
"options_callback_kwargs": null,
"state_name": "database_name",
"title": "Database Name",
"tooltip": "Detail:\nNote: If you don\u2019t see the database name that you are looking for in this list, it is recommended that you first move the data to be used within a database that is available within this list.\n\nValidation Constraints:\nThis input may be subject to other validation constraints at runtime.",
"type": "string"
},
"table_name": {
"description": "The name of the table to use.",
"field_type": "input",
"input_component": {
"component_type": "combobox",
"show_search": true
},
"list_render_mode": null,
"long_description": null,
"options_callback": "xperiflow.source.app.routines.pbm.store.conn.sqltable:SqlTabularConnection.get_tables",
"options_callback_kwargs": null,
"state_name": "table_name",
"title": "Table Name",
"tooltip": "Validation Constraints:\nThis input may be subject to other validation constraints at runtime.",
"type": "string"
}
},
"required": [
"database_resource",
"database_name",
"table_name"
],
"title": "SqlTabularConnection",
"type": "object"
},
"StartDateEndDateDefinition": {
"properties": {
"start_date": {
"description": "The inclusive start of the date range (MM/DD/YYYY).",
"field_type": "input",
"format": "date-time",
"input_component": {
"component_type": "dateselector",
"max_date": null,
"min_date": null
},
"list_render_mode": null,
"long_description": null,
"options_callback": null,
"options_callback_kwargs": null,
"state_name": "date_selection",
"title": "Start Date",
"tooltip": "Validation Constraints:\nThis input may be subject to other validation constraints at runtime.",
"type": "string"
},
"end_date": {
"description": "The inclusive end of the date range (MM/DD/YYYY).",
"field_type": "input",
"format": "date-time",
"input_component": {
"component_type": "dateselector",
"max_date": null,
"min_date": null
},
"list_render_mode": null,
"long_description": null,
"options_callback": null,
"options_callback_kwargs": null,
"state_name": "date_selection",
"title": "End Date",
"tooltip": "Detail:\nNote, the Seasonal ARIMA Anomaly Detector Routine treats the end date as exclusive.\n\nValidation Constraints:\nThis input may be subject to other validation constraints at runtime.",
"type": "string"
}
},
"required": [
"start_date",
"end_date"
],
"title": "StartDateEndDateDefinition",
"type": "object"
},
"StateInfoDefinition": {
"properties": {
"snapshot_name": {
"description": "The name of the anomaly detector instance.",
"field_type": "input",
"input_component": {
"component_type": "textbox",
"height": null,
"multiline": false
},
"list_render_mode": null,
"long_description": null,
"options_callback": null,
"options_callback_kwargs": null,
"state_name": "snapshot_info",
"title": "Name",
"tooltip": "Validation Constraints:\nThis input may be subject to other validation constraints at runtime.",
"type": "string"
},
"snapshot_description": {
"description": "The description of your anomaly detector instance.",
"field_type": "input",
"input_component": {
"component_type": "textbox",
"height": null,
"multiline": false
},
"list_render_mode": null,
"long_description": null,
"options_callback": null,
"options_callback_kwargs": null,
"state_name": "snapshot_info",
"title": "Snapshot Description",
"tooltip": "Validation Constraints:\nThis input may be subject to other validation constraints at runtime.",
"type": "string"
}
},
"required": [
"snapshot_name",
"snapshot_description"
],
"title": "StateInfoDefinition",
"type": "object"
},
"TabularConnection": {
"description": "A shared parameter base model dedication to tabular connections.",
"properties": {
"tabular_connection": {
"anyOf": [
{
"$ref": "#/$defs/SqlTabularConnection"
},
{
"$ref": "#/$defs/FileTabularConnection"
},
{
"$ref": "#/$defs/PartitionedFileTabularConnection"
},
{
"$ref": "#/$defs/CubeViewTabularConnection"
}
],
"description": "The connection type to use to access the source data.",
"field_type": "input",
"input_component": {
"component_type": "combobox",
"show_search": true
},
"list_render_mode": null,
"long_description": null,
"options_callback": null,
"options_callback_kwargs": null,
"state_name": "connection",
"title": "Connection",
"tooltip": "Validation Constraints:\nThis input may be subject to other validation constraints at runtime."
}
},
"required": [
"tabular_connection"
],
"title": "TabularConnection",
"type": "object"
},
"TimeSeriesTableDefinition": {
"description": "A parameter base model dedicated to loading tabular time series data.",
"properties": {
"data_connection": {
"$ref": "#/$defs/TabularConnection",
"description": "The connection to the source data.",
"field_type": "input",
"input_component": {
"component_type": "combobox",
"show_search": true
},
"list_render_mode": null,
"long_description": null,
"options_callback": null,
"options_callback_kwargs": null,
"state_name": "source_connection",
"title": "Connection",
"tooltip": "Validation Constraints:\nThis input may be subject to other validation constraints at runtime."
},
"dimension_columns": {
"description": "The columns to use as dimensions.",
"field_type": "input",
"input_component": {
"component_type": "combobox",
"show_search": true
},
"items": {
"type": "string"
},
"list_render_mode": null,
"long_description": null,
"minItems": 1,
"options_callback": "xperiflow.source.app.routines.pbm.store.tsf.tstable:TimeSeriesTableDefinition.get_table_columns",
"options_callback_kwargs": null,
"state_name": "column_selection",
"title": "Dimension Columns",
"tooltip": "Validation Constraints:\nThe input must have a minimum length of 1.\n\nThis input may be subject to other validation constraints at runtime.",
"type": "array"
},
"date_column": {
"description": "The column to use as the date.",
"field_type": "input",
"input_component": {
"component_type": "combobox",
"show_search": true
},
"list_render_mode": null,
"long_description": null,
"options_callback": "xperiflow.source.app.routines.pbm.store.tsf.tstable:TimeSeriesTableDefinition.get_table_columns",
"options_callback_kwargs": null,
"state_name": "column_selection",
"title": "Date Column",
"tooltip": "Detail:\nThe date column must in a DateTime readable format.\n\nValidation Constraints:\nThis input may be subject to other validation constraints at runtime.",
"type": "string"
},
"value_column": {
"description": "The column to use as the value.",
"field_type": "input",
"input_component": {
"component_type": "combobox",
"show_search": true
},
"list_render_mode": null,
"long_description": null,
"options_callback": "xperiflow.source.app.routines.pbm.store.tsf.tstable:TimeSeriesTableDefinition.get_table_columns",
"options_callback_kwargs": null,
"state_name": "column_selection",
"title": "Value Column",
"tooltip": "Detail:\nThe value column must be a numeric (int, float, double, decimal, etc.) column.\n\nValidation Constraints:\nThis input may be subject to other validation constraints at runtime.",
"type": "string"
}
},
"required": [
"data_connection",
"dimension_columns",
"date_column",
"value_column"
],
"title": "TimeSeriesTableDefinition",
"type": "object"
}
},
"description": "\"Note that only most recent fit will be utilized in predictions.\"",
"properties": {
"source_data_definition": {
"$ref": "#/$defs/TimeSeriesTableDefinition",
"description": "The source data definition to use.",
"field_type": "input",
"input_component": {
"component_type": "combobox",
"show_search": true
},
"list_render_mode": null,
"long_description": null,
"options_callback": null,
"options_callback_kwargs": null,
"state_name": "SourceDefinition",
"title": "Source Data Definition",
"tooltip": "Validation Constraints:\nThis input may be subject to other validation constraints at runtime."
},
"feature_data_definitions": {
"description": "The feature data definition to use.",
"field_type": "input",
"input_component": {
"component_type": "combobox",
"show_search": true
},
"items": {
"$ref": "#/$defs/TimeSeriesTableDefinition"
},
"list_render_mode": null,
"long_description": null,
"options_callback": null,
"options_callback_kwargs": null,
"state_name": "FeatureDefinition",
"title": "Feature Data Definition",
"tooltip": "Validation Constraints:\nThis input may be subject to other validation constraints at runtime.",
"type": "array"
},
"time_range": {
"anyOf": [
{
"$ref": "#/$defs/StartDateEndDateDefinition"
},
{
"type": "null"
}
],
"default": null,
"description": "The date range to predict anomalies on.",
"field_type": "input",
"input_component": {
"component_type": "combobox",
"show_search": true
},
"list_render_mode": null,
"long_description": null,
"options_callback": null,
"options_callback_kwargs": null,
"state_name": "DateRange",
"title": "Date Range",
"tooltip": "Detail:\nIf None, entire dataset will be used.\n\nValidation Constraints:\nThis input may be subject to other validation constraints at runtime."
},
"state_info": {
"$ref": "#/$defs/StateInfoDefinition",
"description": "The snapshot name and description. These will be used as identifiers in the snapshot artifact.",
"field_type": "input",
"input_component": {
"component_type": "combobox",
"show_search": true
},
"list_render_mode": null,
"long_description": null,
"options_callback": null,
"options_callback_kwargs": null,
"state_name": "StateInfoDefinition",
"title": "State Info Definition",
"tooltip": "Validation Constraints:\nThis input may be subject to other validation constraints at runtime."
}
},
"required": [
"source_data_definition",
"feature_data_definitions",
"state_info"
],
"title": "AnomalyDetectionPredictParameters",
"type": "object"
}
Artifacts:
| Property | Type | Required | Description |
|---|---|---|---|
anomaly_snapshot | unknown | Yes | Parquet file containing data about your anomaly detection run. |
anomaly_dates | unknown | Yes | Parquet file containing data about the specific dates an anomaly was detected. |
anomaly_instance | unknown | Yes | Parquet file containing data about the specific anomaly instances that were detected. |
Artifact Schema (JSON):
{
"additionalProperties": true,
"properties": {
"anomaly_snapshot": {
"description": "Parquet file containing data about your anomaly detection run.",
"io_factory_kwargs": {},
"preview_factory_kwargs": null,
"preview_factory_type": null,
"statistic_factory_kwargs": null,
"statistic_factory_type": null,
"title": "AnomalySnapshot"
},
"anomaly_dates": {
"description": "Parquet file containing data about the specific dates an anomaly was detected.",
"io_factory_kwargs": {},
"preview_factory_kwargs": null,
"preview_factory_type": null,
"statistic_factory_kwargs": null,
"statistic_factory_type": null,
"title": "Specific Anomaly Dates"
},
"anomaly_instance": {
"description": "Parquet file containing data about the specific anomaly instances that were detected.",
"io_factory_kwargs": {},
"preview_factory_kwargs": null,
"preview_factory_type": null,
"statistic_factory_kwargs": null,
"statistic_factory_type": null,
"title": "Specific Anomaly Instances"
}
},
"required": [
"anomaly_snapshot",
"anomaly_dates",
"anomaly_instance"
],
"title": "AnomalyDetectionArtifacts",
"type": "object"
}
Developer Docs
Routine Typename: IsolationForestAnomalyDetector
| Method Name | Artifact Keys |
|---|---|
__init__ | N/A |
fit | N/A |
predict | anomaly_snapshot, anomaly_dates, anomaly_instance |