Mercurial > repos > shellac > guppy_basecaller
diff env/lib/python3.7/site-packages/boto/machinelearning/layer1.py @ 5:9b1c78e6ba9c draft default tip
"planemo upload commit 6c0a8142489327ece472c84e558c47da711a9142"
| author | shellac |
|---|---|
| date | Mon, 01 Jun 2020 08:59:25 -0400 |
| parents | 79f47841a781 |
| children |
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--- a/env/lib/python3.7/site-packages/boto/machinelearning/layer1.py Thu May 14 16:47:39 2020 -0400 +++ /dev/null Thu Jan 01 00:00:00 1970 +0000 @@ -1,1408 +0,0 @@ -# Copyright (c) 2015 Amazon.com, Inc. or its affiliates. All Rights Reserved -# -# Permission is hereby granted, free of charge, to any person obtaining a -# copy of this software and associated documentation files (the -# "Software"), to deal in the Software without restriction, including -# without limitation the rights to use, copy, modify, merge, publish, dis- -# tribute, sublicense, and/or sell copies of the Software, and to permit -# persons to whom the Software is furnished to do so, subject to the fol- -# lowing conditions: -# -# The above copyright notice and this permission notice shall be included -# in all copies or substantial portions of the Software. -# -# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS -# OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABIL- -# ITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT -# SHALL THE AUTHOR BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, -# WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, -# OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS -# IN THE SOFTWARE. -# - -import boto -from boto.compat import json, urlsplit -from boto.connection import AWSQueryConnection -from boto.regioninfo import RegionInfo -from boto.exception import JSONResponseError -from boto.machinelearning import exceptions - - -class MachineLearningConnection(AWSQueryConnection): - """ - Definition of the public APIs exposed by Amazon Machine Learning - """ - APIVersion = "2014-12-12" - AuthServiceName = 'machinelearning' - DefaultRegionName = "us-east-1" - DefaultRegionEndpoint = "machinelearning.us-east-1.amazonaws.com" - ServiceName = "MachineLearning" - TargetPrefix = "AmazonML_20141212" - ResponseError = JSONResponseError - - _faults = { - "InternalServerException": exceptions.InternalServerException, - "LimitExceededException": exceptions.LimitExceededException, - "ResourceNotFoundException": exceptions.ResourceNotFoundException, - "IdempotentParameterMismatchException": exceptions.IdempotentParameterMismatchException, - "PredictorNotMountedException": exceptions.PredictorNotMountedException, - "InvalidInputException": exceptions.InvalidInputException, - } - - - def __init__(self, **kwargs): - region = kwargs.pop('region', None) - if not region: - region = RegionInfo(self, self.DefaultRegionName, - self.DefaultRegionEndpoint) - - if 'host' not in kwargs or kwargs['host'] is None: - kwargs['host'] = region.endpoint - - super(MachineLearningConnection, self).__init__(**kwargs) - self.region = region - self.auth_region_name = self.region.name - - def _required_auth_capability(self): - return ['hmac-v4'] - - def create_batch_prediction(self, batch_prediction_id, ml_model_id, - batch_prediction_data_source_id, output_uri, - batch_prediction_name=None): - """ - Generates predictions for a group of observations. The - observations to process exist in one or more data files - referenced by a `DataSource`. This operation creates a new - `BatchPrediction`, and uses an `MLModel` and the data files - referenced by the `DataSource` as information sources. - - `CreateBatchPrediction` is an asynchronous operation. In - response to `CreateBatchPrediction`, Amazon Machine Learning - (Amazon ML) immediately returns and sets the `BatchPrediction` - status to `PENDING`. After the `BatchPrediction` completes, - Amazon ML sets the status to `COMPLETED`. - - You can poll for status updates by using the - GetBatchPrediction operation and checking the `Status` - parameter of the result. After the `COMPLETED` status appears, - the results are available in the location specified by the - `OutputUri` parameter. - - :type batch_prediction_id: string - :param batch_prediction_id: A user-supplied ID that uniquely identifies - the `BatchPrediction`. - - :type batch_prediction_name: string - :param batch_prediction_name: A user-supplied name or description of - the `BatchPrediction`. `BatchPredictionName` can only use the UTF-8 - character set. - - :type ml_model_id: string - :param ml_model_id: The ID of the `MLModel` that will generate - predictions for the group of observations. - - :type batch_prediction_data_source_id: string - :param batch_prediction_data_source_id: The ID of the `DataSource` that - points to the group of observations to predict. - - :type output_uri: string - :param output_uri: The location of an Amazon Simple Storage Service - (Amazon S3) bucket or directory to store the batch prediction - results. The following substrings are not allowed in the s3 key - portion of the "outputURI" field: ':', '//', '/./', '/../'. - Amazon ML needs permissions to store and retrieve the logs on your - behalf. For information about how to set permissions, see the - `Amazon Machine Learning Developer Guide`_. - - """ - params = { - 'BatchPredictionId': batch_prediction_id, - 'MLModelId': ml_model_id, - 'BatchPredictionDataSourceId': batch_prediction_data_source_id, - 'OutputUri': output_uri, - } - if batch_prediction_name is not None: - params['BatchPredictionName'] = batch_prediction_name - return self.make_request(action='CreateBatchPrediction', - body=json.dumps(params)) - - def create_data_source_from_rds(self, data_source_id, rds_data, role_arn, - data_source_name=None, - compute_statistics=None): - """ - Creates a `DataSource` object from an ` Amazon Relational - Database Service`_ (Amazon RDS). A `DataSource` references - data that can be used to perform CreateMLModel, - CreateEvaluation, or CreateBatchPrediction operations. - - `CreateDataSourceFromRDS` is an asynchronous operation. In - response to `CreateDataSourceFromRDS`, Amazon Machine Learning - (Amazon ML) immediately returns and sets the `DataSource` - status to `PENDING`. After the `DataSource` is created and - ready for use, Amazon ML sets the `Status` parameter to - `COMPLETED`. `DataSource` in `COMPLETED` or `PENDING` status - can only be used to perform CreateMLModel, CreateEvaluation, - or CreateBatchPrediction operations. - - If Amazon ML cannot accept the input source, it sets the - `Status` parameter to `FAILED` and includes an error message - in the `Message` attribute of the GetDataSource operation - response. - - :type data_source_id: string - :param data_source_id: A user-supplied ID that uniquely identifies the - `DataSource`. Typically, an Amazon Resource Number (ARN) becomes - the ID for a `DataSource`. - - :type data_source_name: string - :param data_source_name: A user-supplied name or description of the - `DataSource`. - - :type rds_data: dict - :param rds_data: - The data specification of an Amazon RDS `DataSource`: - - - + DatabaseInformation - - - + `DatabaseName ` - Name of the Amazon RDS database. - + ` InstanceIdentifier ` - Unique identifier for the Amazon RDS - database instance. - - + DatabaseCredentials - AWS Identity and Access Management (IAM) - credentials that are used to connect to the Amazon RDS database. - + ResourceRole - Role (DataPipelineDefaultResourceRole) assumed by an - Amazon Elastic Compute Cloud (EC2) instance to carry out the copy - task from Amazon RDS to Amazon S3. For more information, see `Role - templates`_ for data pipelines. - + ServiceRole - Role (DataPipelineDefaultRole) assumed by the AWS Data - Pipeline service to monitor the progress of the copy task from - Amazon RDS to Amazon Simple Storage Service (S3). For more - information, see `Role templates`_ for data pipelines. - + SecurityInfo - Security information to use to access an Amazon RDS - instance. You need to set up appropriate ingress rules for the - security entity IDs provided to allow access to the Amazon RDS - instance. Specify a [ `SubnetId`, `SecurityGroupIds`] pair for a - VPC-based Amazon RDS instance. - + SelectSqlQuery - Query that is used to retrieve the observation data - for the `Datasource`. - + S3StagingLocation - Amazon S3 location for staging RDS data. The data - retrieved from Amazon RDS using `SelectSqlQuery` is stored in this - location. - + DataSchemaUri - Amazon S3 location of the `DataSchema`. - + DataSchema - A JSON string representing the schema. This is not - required if `DataSchemaUri` is specified. - + DataRearrangement - A JSON string representing the splitting - requirement of a `Datasource`. Sample - ` "{\"randomSeed\":\"some- - random-seed\", - \"splitting\":{\"percentBegin\":10,\"percentEnd\":60}}"` - - :type role_arn: string - :param role_arn: The role that Amazon ML assumes on behalf of the user - to create and activate a data pipeline in the users account and - copy data (using the `SelectSqlQuery`) query from Amazon RDS to - Amazon S3. - - :type compute_statistics: boolean - :param compute_statistics: The compute statistics for a `DataSource`. - The statistics are generated from the observation data referenced - by a `DataSource`. Amazon ML uses the statistics internally during - an `MLModel` training. This parameter must be set to `True` if the - ``DataSource `` needs to be used for `MLModel` training. - - """ - params = { - 'DataSourceId': data_source_id, - 'RDSData': rds_data, - 'RoleARN': role_arn, - } - if data_source_name is not None: - params['DataSourceName'] = data_source_name - if compute_statistics is not None: - params['ComputeStatistics'] = compute_statistics - return self.make_request(action='CreateDataSourceFromRDS', - body=json.dumps(params)) - - def create_data_source_from_redshift(self, data_source_id, data_spec, - role_arn, data_source_name=None, - compute_statistics=None): - """ - Creates a `DataSource` from `Amazon Redshift`_. A `DataSource` - references data that can be used to perform either - CreateMLModel, CreateEvaluation or CreateBatchPrediction - operations. - - `CreateDataSourceFromRedshift` is an asynchronous operation. - In response to `CreateDataSourceFromRedshift`, Amazon Machine - Learning (Amazon ML) immediately returns and sets the - `DataSource` status to `PENDING`. After the `DataSource` is - created and ready for use, Amazon ML sets the `Status` - parameter to `COMPLETED`. `DataSource` in `COMPLETED` or - `PENDING` status can only be used to perform CreateMLModel, - CreateEvaluation, or CreateBatchPrediction operations. - - If Amazon ML cannot accept the input source, it sets the - `Status` parameter to `FAILED` and includes an error message - in the `Message` attribute of the GetDataSource operation - response. - - The observations should exist in the database hosted on an - Amazon Redshift cluster and should be specified by a - `SelectSqlQuery`. Amazon ML executes ` Unload`_ command in - Amazon Redshift to transfer the result set of `SelectSqlQuery` - to `S3StagingLocation.` - - After the `DataSource` is created, it's ready for use in - evaluations and batch predictions. If you plan to use the - `DataSource` to train an `MLModel`, the `DataSource` requires - another item -- a recipe. A recipe describes the observation - variables that participate in training an `MLModel`. A recipe - describes how each input variable will be used in training. - Will the variable be included or excluded from training? Will - the variable be manipulated, for example, combined with - another variable or split apart into word combinations? The - recipe provides answers to these questions. For more - information, see the Amazon Machine Learning Developer Guide. - - :type data_source_id: string - :param data_source_id: A user-supplied ID that uniquely identifies the - `DataSource`. - - :type data_source_name: string - :param data_source_name: A user-supplied name or description of the - `DataSource`. - - :type data_spec: dict - :param data_spec: - The data specification of an Amazon Redshift `DataSource`: - - - + DatabaseInformation - - - + `DatabaseName ` - Name of the Amazon Redshift database. - + ` ClusterIdentifier ` - Unique ID for the Amazon Redshift cluster. - - + DatabaseCredentials - AWS Identity abd Access Management (IAM) - credentials that are used to connect to the Amazon Redshift - database. - + SelectSqlQuery - Query that is used to retrieve the observation data - for the `Datasource`. - + S3StagingLocation - Amazon Simple Storage Service (Amazon S3) - location for staging Amazon Redshift data. The data retrieved from - Amazon Relational Database Service (Amazon RDS) using - `SelectSqlQuery` is stored in this location. - + DataSchemaUri - Amazon S3 location of the `DataSchema`. - + DataSchema - A JSON string representing the schema. This is not - required if `DataSchemaUri` is specified. - + DataRearrangement - A JSON string representing the splitting - requirement of a `Datasource`. Sample - ` "{\"randomSeed\":\"some- - random-seed\", - \"splitting\":{\"percentBegin\":10,\"percentEnd\":60}}"` - - :type role_arn: string - :param role_arn: A fully specified role Amazon Resource Name (ARN). - Amazon ML assumes the role on behalf of the user to create the - following: - - - + A security group to allow Amazon ML to execute the `SelectSqlQuery` - query on an Amazon Redshift cluster - + An Amazon S3 bucket policy to grant Amazon ML read/write permissions - on the `S3StagingLocation` - - :type compute_statistics: boolean - :param compute_statistics: The compute statistics for a `DataSource`. - The statistics are generated from the observation data referenced - by a `DataSource`. Amazon ML uses the statistics internally during - `MLModel` training. This parameter must be set to `True` if the - ``DataSource `` needs to be used for `MLModel` training - - """ - params = { - 'DataSourceId': data_source_id, - 'DataSpec': data_spec, - 'RoleARN': role_arn, - } - if data_source_name is not None: - params['DataSourceName'] = data_source_name - if compute_statistics is not None: - params['ComputeStatistics'] = compute_statistics - return self.make_request(action='CreateDataSourceFromRedshift', - body=json.dumps(params)) - - def create_data_source_from_s3(self, data_source_id, data_spec, - data_source_name=None, - compute_statistics=None): - """ - Creates a `DataSource` object. A `DataSource` references data - that can be used to perform CreateMLModel, CreateEvaluation, - or CreateBatchPrediction operations. - - `CreateDataSourceFromS3` is an asynchronous operation. In - response to `CreateDataSourceFromS3`, Amazon Machine Learning - (Amazon ML) immediately returns and sets the `DataSource` - status to `PENDING`. After the `DataSource` is created and - ready for use, Amazon ML sets the `Status` parameter to - `COMPLETED`. `DataSource` in `COMPLETED` or `PENDING` status - can only be used to perform CreateMLModel, CreateEvaluation or - CreateBatchPrediction operations. - - If Amazon ML cannot accept the input source, it sets the - `Status` parameter to `FAILED` and includes an error message - in the `Message` attribute of the GetDataSource operation - response. - - The observation data used in a `DataSource` should be ready to - use; that is, it should have a consistent structure, and - missing data values should be kept to a minimum. The - observation data must reside in one or more CSV files in an - Amazon Simple Storage Service (Amazon S3) bucket, along with a - schema that describes the data items by name and type. The - same schema must be used for all of the data files referenced - by the `DataSource`. - - After the `DataSource` has been created, it's ready to use in - evaluations and batch predictions. If you plan to use the - `DataSource` to train an `MLModel`, the `DataSource` requires - another item: a recipe. A recipe describes the observation - variables that participate in training an `MLModel`. A recipe - describes how each input variable will be used in training. - Will the variable be included or excluded from training? Will - the variable be manipulated, for example, combined with - another variable, or split apart into word combinations? The - recipe provides answers to these questions. For more - information, see the `Amazon Machine Learning Developer - Guide`_. - - :type data_source_id: string - :param data_source_id: A user-supplied identifier that uniquely - identifies the `DataSource`. - - :type data_source_name: string - :param data_source_name: A user-supplied name or description of the - `DataSource`. - - :type data_spec: dict - :param data_spec: - The data specification of a `DataSource`: - - - + DataLocationS3 - Amazon Simple Storage Service (Amazon S3) location - of the observation data. - + DataSchemaLocationS3 - Amazon S3 location of the `DataSchema`. - + DataSchema - A JSON string representing the schema. This is not - required if `DataSchemaUri` is specified. - + DataRearrangement - A JSON string representing the splitting - requirement of a `Datasource`. Sample - ` "{\"randomSeed\":\"some- - random-seed\", - \"splitting\":{\"percentBegin\":10,\"percentEnd\":60}}"` - - :type compute_statistics: boolean - :param compute_statistics: The compute statistics for a `DataSource`. - The statistics are generated from the observation data referenced - by a `DataSource`. Amazon ML uses the statistics internally during - an `MLModel` training. This parameter must be set to `True` if the - ``DataSource `` needs to be used for `MLModel` training - - """ - params = { - 'DataSourceId': data_source_id, - 'DataSpec': data_spec, - } - if data_source_name is not None: - params['DataSourceName'] = data_source_name - if compute_statistics is not None: - params['ComputeStatistics'] = compute_statistics - return self.make_request(action='CreateDataSourceFromS3', - body=json.dumps(params)) - - def create_evaluation(self, evaluation_id, ml_model_id, - evaluation_data_source_id, evaluation_name=None): - """ - Creates a new `Evaluation` of an `MLModel`. An `MLModel` is - evaluated on a set of observations associated to a - `DataSource`. Like a `DataSource` for an `MLModel`, the - `DataSource` for an `Evaluation` contains values for the - Target Variable. The `Evaluation` compares the predicted - result for each observation to the actual outcome and provides - a summary so that you know how effective the `MLModel` - functions on the test data. Evaluation generates a relevant - performance metric such as BinaryAUC, RegressionRMSE or - MulticlassAvgFScore based on the corresponding `MLModelType`: - `BINARY`, `REGRESSION` or `MULTICLASS`. - - `CreateEvaluation` is an asynchronous operation. In response - to `CreateEvaluation`, Amazon Machine Learning (Amazon ML) - immediately returns and sets the evaluation status to - `PENDING`. After the `Evaluation` is created and ready for - use, Amazon ML sets the status to `COMPLETED`. - - You can use the GetEvaluation operation to check progress of - the evaluation during the creation operation. - - :type evaluation_id: string - :param evaluation_id: A user-supplied ID that uniquely identifies the - `Evaluation`. - - :type evaluation_name: string - :param evaluation_name: A user-supplied name or description of the - `Evaluation`. - - :type ml_model_id: string - :param ml_model_id: The ID of the `MLModel` to evaluate. - The schema used in creating the `MLModel` must match the schema of the - `DataSource` used in the `Evaluation`. - - :type evaluation_data_source_id: string - :param evaluation_data_source_id: The ID of the `DataSource` for the - evaluation. The schema of the `DataSource` must match the schema - used to create the `MLModel`. - - """ - params = { - 'EvaluationId': evaluation_id, - 'MLModelId': ml_model_id, - 'EvaluationDataSourceId': evaluation_data_source_id, - } - if evaluation_name is not None: - params['EvaluationName'] = evaluation_name - return self.make_request(action='CreateEvaluation', - body=json.dumps(params)) - - def create_ml_model(self, ml_model_id, ml_model_type, - training_data_source_id, ml_model_name=None, - parameters=None, recipe=None, recipe_uri=None): - """ - Creates a new `MLModel` using the data files and the recipe as - information sources. - - An `MLModel` is nearly immutable. Users can only update the - `MLModelName` and the `ScoreThreshold` in an `MLModel` without - creating a new `MLModel`. - - `CreateMLModel` is an asynchronous operation. In response to - `CreateMLModel`, Amazon Machine Learning (Amazon ML) - immediately returns and sets the `MLModel` status to - `PENDING`. After the `MLModel` is created and ready for use, - Amazon ML sets the status to `COMPLETED`. - - You can use the GetMLModel operation to check progress of the - `MLModel` during the creation operation. - - CreateMLModel requires a `DataSource` with computed - statistics, which can be created by setting - `ComputeStatistics` to `True` in CreateDataSourceFromRDS, - CreateDataSourceFromS3, or CreateDataSourceFromRedshift - operations. - - :type ml_model_id: string - :param ml_model_id: A user-supplied ID that uniquely identifies the - `MLModel`. - - :type ml_model_name: string - :param ml_model_name: A user-supplied name or description of the - `MLModel`. - - :type ml_model_type: string - :param ml_model_type: The category of supervised learning that this - `MLModel` will address. Choose from the following types: - - + Choose `REGRESSION` if the `MLModel` will be used to predict a - numeric value. - + Choose `BINARY` if the `MLModel` result has two possible values. - + Choose `MULTICLASS` if the `MLModel` result has a limited number of - values. - - - For more information, see the `Amazon Machine Learning Developer - Guide`_. - - :type parameters: map - :param parameters: - A list of the training parameters in the `MLModel`. The list is - implemented as a map of key/value pairs. - - The following is the current set of training parameters: - - - + `sgd.l1RegularizationAmount` - Coefficient regularization L1 norm. It - controls overfitting the data by penalizing large coefficients. - This tends to drive coefficients to zero, resulting in sparse - feature set. If you use this parameter, start by specifying a small - value such as 1.0E-08. The value is a double that ranges from 0 to - MAX_DOUBLE. The default is not to use L1 normalization. The - parameter cannot be used when `L2` is specified. Use this parameter - sparingly. - + `sgd.l2RegularizationAmount` - Coefficient regularization L2 norm. It - controls overfitting the data by penalizing large coefficients. - This tends to drive coefficients to small, nonzero values. If you - use this parameter, start by specifying a small value such as - 1.0E-08. The valuseis a double that ranges from 0 to MAX_DOUBLE. - The default is not to use L2 normalization. This cannot be used - when `L1` is specified. Use this parameter sparingly. - + `sgd.maxPasses` - Number of times that the training process traverses - the observations to build the `MLModel`. The value is an integer - that ranges from 1 to 10000. The default value is 10. - + `sgd.maxMLModelSizeInBytes` - Maximum allowed size of the model. - Depending on the input data, the size of the model might affect its - performance. The value is an integer that ranges from 100000 to - 2147483648. The default value is 33554432. - - :type training_data_source_id: string - :param training_data_source_id: The `DataSource` that points to the - training data. - - :type recipe: string - :param recipe: The data recipe for creating `MLModel`. You must specify - either the recipe or its URI. If you dont specify a recipe or its - URI, Amazon ML creates a default. - - :type recipe_uri: string - :param recipe_uri: The Amazon Simple Storage Service (Amazon S3) - location and file name that contains the `MLModel` recipe. You must - specify either the recipe or its URI. If you dont specify a recipe - or its URI, Amazon ML creates a default. - - """ - params = { - 'MLModelId': ml_model_id, - 'MLModelType': ml_model_type, - 'TrainingDataSourceId': training_data_source_id, - } - if ml_model_name is not None: - params['MLModelName'] = ml_model_name - if parameters is not None: - params['Parameters'] = parameters - if recipe is not None: - params['Recipe'] = recipe - if recipe_uri is not None: - params['RecipeUri'] = recipe_uri - return self.make_request(action='CreateMLModel', - body=json.dumps(params)) - - def create_realtime_endpoint(self, ml_model_id): - """ - Creates a real-time endpoint for the `MLModel`. The endpoint - contains the URI of the `MLModel`; that is, the location to - send real-time prediction requests for the specified - `MLModel`. - - :type ml_model_id: string - :param ml_model_id: The ID assigned to the `MLModel` during creation. - - """ - params = {'MLModelId': ml_model_id, } - return self.make_request(action='CreateRealtimeEndpoint', - body=json.dumps(params)) - - def delete_batch_prediction(self, batch_prediction_id): - """ - Assigns the DELETED status to a `BatchPrediction`, rendering - it unusable. - - After using the `DeleteBatchPrediction` operation, you can use - the GetBatchPrediction operation to verify that the status of - the `BatchPrediction` changed to DELETED. - - The result of the `DeleteBatchPrediction` operation is - irreversible. - - :type batch_prediction_id: string - :param batch_prediction_id: A user-supplied ID that uniquely identifies - the `BatchPrediction`. - - """ - params = {'BatchPredictionId': batch_prediction_id, } - return self.make_request(action='DeleteBatchPrediction', - body=json.dumps(params)) - - def delete_data_source(self, data_source_id): - """ - Assigns the DELETED status to a `DataSource`, rendering it - unusable. - - After using the `DeleteDataSource` operation, you can use the - GetDataSource operation to verify that the status of the - `DataSource` changed to DELETED. - - The results of the `DeleteDataSource` operation are - irreversible. - - :type data_source_id: string - :param data_source_id: A user-supplied ID that uniquely identifies the - `DataSource`. - - """ - params = {'DataSourceId': data_source_id, } - return self.make_request(action='DeleteDataSource', - body=json.dumps(params)) - - def delete_evaluation(self, evaluation_id): - """ - Assigns the `DELETED` status to an `Evaluation`, rendering it - unusable. - - After invoking the `DeleteEvaluation` operation, you can use - the GetEvaluation operation to verify that the status of the - `Evaluation` changed to `DELETED`. - - The results of the `DeleteEvaluation` operation are - irreversible. - - :type evaluation_id: string - :param evaluation_id: A user-supplied ID that uniquely identifies the - `Evaluation` to delete. - - """ - params = {'EvaluationId': evaluation_id, } - return self.make_request(action='DeleteEvaluation', - body=json.dumps(params)) - - def delete_ml_model(self, ml_model_id): - """ - Assigns the DELETED status to an `MLModel`, rendering it - unusable. - - After using the `DeleteMLModel` operation, you can use the - GetMLModel operation to verify that the status of the - `MLModel` changed to DELETED. - - The result of the `DeleteMLModel` operation is irreversible. - - :type ml_model_id: string - :param ml_model_id: A user-supplied ID that uniquely identifies the - `MLModel`. - - """ - params = {'MLModelId': ml_model_id, } - return self.make_request(action='DeleteMLModel', - body=json.dumps(params)) - - def delete_realtime_endpoint(self, ml_model_id): - """ - Deletes a real time endpoint of an `MLModel`. - - :type ml_model_id: string - :param ml_model_id: The ID assigned to the `MLModel` during creation. - - """ - params = {'MLModelId': ml_model_id, } - return self.make_request(action='DeleteRealtimeEndpoint', - body=json.dumps(params)) - - def describe_batch_predictions(self, filter_variable=None, eq=None, - gt=None, lt=None, ge=None, le=None, - ne=None, prefix=None, sort_order=None, - next_token=None, limit=None): - """ - Returns a list of `BatchPrediction` operations that match the - search criteria in the request. - - :type filter_variable: string - :param filter_variable: - Use one of the following variables to filter a list of - `BatchPrediction`: - - - + `CreatedAt` - Sets the search criteria to the `BatchPrediction` - creation date. - + `Status` - Sets the search criteria to the `BatchPrediction` status. - + `Name` - Sets the search criteria to the contents of the - `BatchPrediction` ** ** `Name`. - + `IAMUser` - Sets the search criteria to the user account that invoked - the `BatchPrediction` creation. - + `MLModelId` - Sets the search criteria to the `MLModel` used in the - `BatchPrediction`. - + `DataSourceId` - Sets the search criteria to the `DataSource` used in - the `BatchPrediction`. - + `DataURI` - Sets the search criteria to the data file(s) used in the - `BatchPrediction`. The URL can identify either a file or an Amazon - Simple Storage Solution (Amazon S3) bucket or directory. - - :type eq: string - :param eq: The equal to operator. The `BatchPrediction` results will - have `FilterVariable` values that exactly match the value specified - with `EQ`. - - :type gt: string - :param gt: The greater than operator. The `BatchPrediction` results - will have `FilterVariable` values that are greater than the value - specified with `GT`. - - :type lt: string - :param lt: The less than operator. The `BatchPrediction` results will - have `FilterVariable` values that are less than the value specified - with `LT`. - - :type ge: string - :param ge: The greater than or equal to operator. The `BatchPrediction` - results will have `FilterVariable` values that are greater than or - equal to the value specified with `GE`. - - :type le: string - :param le: The less than or equal to operator. The `BatchPrediction` - results will have `FilterVariable` values that are less than or - equal to the value specified with `LE`. - - :type ne: string - :param ne: The not equal to operator. The `BatchPrediction` results - will have `FilterVariable` values not equal to the value specified - with `NE`. - - :type prefix: string - :param prefix: - A string that is found at the beginning of a variable, such as `Name` - or `Id`. - - For example, a `Batch Prediction` operation could have the `Name` - `2014-09-09-HolidayGiftMailer`. To search for this - `BatchPrediction`, select `Name` for the `FilterVariable` and any - of the following strings for the `Prefix`: - - - + 2014-09 - + 2014-09-09 - + 2014-09-09-Holiday - - :type sort_order: string - :param sort_order: A two-value parameter that determines the sequence - of the resulting list of `MLModel`s. - - + `asc` - Arranges the list in ascending order (A-Z, 0-9). - + `dsc` - Arranges the list in descending order (Z-A, 9-0). - - - Results are sorted by `FilterVariable`. - - :type next_token: string - :param next_token: An ID of the page in the paginated results. - - :type limit: integer - :param limit: The number of pages of information to include in the - result. The range of acceptable values is 1 through 100. The - default value is 100. - - """ - params = {} - if filter_variable is not None: - params['FilterVariable'] = filter_variable - if eq is not None: - params['EQ'] = eq - if gt is not None: - params['GT'] = gt - if lt is not None: - params['LT'] = lt - if ge is not None: - params['GE'] = ge - if le is not None: - params['LE'] = le - if ne is not None: - params['NE'] = ne - if prefix is not None: - params['Prefix'] = prefix - if sort_order is not None: - params['SortOrder'] = sort_order - if next_token is not None: - params['NextToken'] = next_token - if limit is not None: - params['Limit'] = limit - return self.make_request(action='DescribeBatchPredictions', - body=json.dumps(params)) - - def describe_data_sources(self, filter_variable=None, eq=None, gt=None, - lt=None, ge=None, le=None, ne=None, - prefix=None, sort_order=None, next_token=None, - limit=None): - """ - Returns a list of `DataSource` that match the search criteria - in the request. - - :type filter_variable: string - :param filter_variable: - Use one of the following variables to filter a list of `DataSource`: - - - + `CreatedAt` - Sets the search criteria to `DataSource` creation - dates. - + `Status` - Sets the search criteria to `DataSource` statuses. - + `Name` - Sets the search criteria to the contents of `DataSource` ** - ** `Name`. - + `DataUri` - Sets the search criteria to the URI of data files used to - create the `DataSource`. The URI can identify either a file or an - Amazon Simple Storage Service (Amazon S3) bucket or directory. - + `IAMUser` - Sets the search criteria to the user account that invoked - the `DataSource` creation. - - :type eq: string - :param eq: The equal to operator. The `DataSource` results will have - `FilterVariable` values that exactly match the value specified with - `EQ`. - - :type gt: string - :param gt: The greater than operator. The `DataSource` results will - have `FilterVariable` values that are greater than the value - specified with `GT`. - - :type lt: string - :param lt: The less than operator. The `DataSource` results will have - `FilterVariable` values that are less than the value specified with - `LT`. - - :type ge: string - :param ge: The greater than or equal to operator. The `DataSource` - results will have `FilterVariable` values that are greater than or - equal to the value specified with `GE`. - - :type le: string - :param le: The less than or equal to operator. The `DataSource` results - will have `FilterVariable` values that are less than or equal to - the value specified with `LE`. - - :type ne: string - :param ne: The not equal to operator. The `DataSource` results will - have `FilterVariable` values not equal to the value specified with - `NE`. - - :type prefix: string - :param prefix: - A string that is found at the beginning of a variable, such as `Name` - or `Id`. - - For example, a `DataSource` could have the `Name` - `2014-09-09-HolidayGiftMailer`. To search for this `DataSource`, - select `Name` for the `FilterVariable` and any of the following - strings for the `Prefix`: - - - + 2014-09 - + 2014-09-09 - + 2014-09-09-Holiday - - :type sort_order: string - :param sort_order: A two-value parameter that determines the sequence - of the resulting list of `DataSource`. - - + `asc` - Arranges the list in ascending order (A-Z, 0-9). - + `dsc` - Arranges the list in descending order (Z-A, 9-0). - - - Results are sorted by `FilterVariable`. - - :type next_token: string - :param next_token: The ID of the page in the paginated results. - - :type limit: integer - :param limit: The maximum number of `DataSource` to include in the - result. - - """ - params = {} - if filter_variable is not None: - params['FilterVariable'] = filter_variable - if eq is not None: - params['EQ'] = eq - if gt is not None: - params['GT'] = gt - if lt is not None: - params['LT'] = lt - if ge is not None: - params['GE'] = ge - if le is not None: - params['LE'] = le - if ne is not None: - params['NE'] = ne - if prefix is not None: - params['Prefix'] = prefix - if sort_order is not None: - params['SortOrder'] = sort_order - if next_token is not None: - params['NextToken'] = next_token - if limit is not None: - params['Limit'] = limit - return self.make_request(action='DescribeDataSources', - body=json.dumps(params)) - - def describe_evaluations(self, filter_variable=None, eq=None, gt=None, - lt=None, ge=None, le=None, ne=None, prefix=None, - sort_order=None, next_token=None, limit=None): - """ - Returns a list of `DescribeEvaluations` that match the search - criteria in the request. - - :type filter_variable: string - :param filter_variable: - Use one of the following variable to filter a list of `Evaluation` - objects: - - - + `CreatedAt` - Sets the search criteria to the `Evaluation` creation - date. - + `Status` - Sets the search criteria to the `Evaluation` status. - + `Name` - Sets the search criteria to the contents of `Evaluation` ** - ** `Name`. - + `IAMUser` - Sets the search criteria to the user account that invoked - an `Evaluation`. - + `MLModelId` - Sets the search criteria to the `MLModel` that was - evaluated. - + `DataSourceId` - Sets the search criteria to the `DataSource` used in - `Evaluation`. - + `DataUri` - Sets the search criteria to the data file(s) used in - `Evaluation`. The URL can identify either a file or an Amazon - Simple Storage Solution (Amazon S3) bucket or directory. - - :type eq: string - :param eq: The equal to operator. The `Evaluation` results will have - `FilterVariable` values that exactly match the value specified with - `EQ`. - - :type gt: string - :param gt: The greater than operator. The `Evaluation` results will - have `FilterVariable` values that are greater than the value - specified with `GT`. - - :type lt: string - :param lt: The less than operator. The `Evaluation` results will have - `FilterVariable` values that are less than the value specified with - `LT`. - - :type ge: string - :param ge: The greater than or equal to operator. The `Evaluation` - results will have `FilterVariable` values that are greater than or - equal to the value specified with `GE`. - - :type le: string - :param le: The less than or equal to operator. The `Evaluation` results - will have `FilterVariable` values that are less than or equal to - the value specified with `LE`. - - :type ne: string - :param ne: The not equal to operator. The `Evaluation` results will - have `FilterVariable` values not equal to the value specified with - `NE`. - - :type prefix: string - :param prefix: - A string that is found at the beginning of a variable, such as `Name` - or `Id`. - - For example, an `Evaluation` could have the `Name` - `2014-09-09-HolidayGiftMailer`. To search for this `Evaluation`, - select `Name` for the `FilterVariable` and any of the following - strings for the `Prefix`: - - - + 2014-09 - + 2014-09-09 - + 2014-09-09-Holiday - - :type sort_order: string - :param sort_order: A two-value parameter that determines the sequence - of the resulting list of `Evaluation`. - - + `asc` - Arranges the list in ascending order (A-Z, 0-9). - + `dsc` - Arranges the list in descending order (Z-A, 9-0). - - - Results are sorted by `FilterVariable`. - - :type next_token: string - :param next_token: The ID of the page in the paginated results. - - :type limit: integer - :param limit: The maximum number of `Evaluation` to include in the - result. - - """ - params = {} - if filter_variable is not None: - params['FilterVariable'] = filter_variable - if eq is not None: - params['EQ'] = eq - if gt is not None: - params['GT'] = gt - if lt is not None: - params['LT'] = lt - if ge is not None: - params['GE'] = ge - if le is not None: - params['LE'] = le - if ne is not None: - params['NE'] = ne - if prefix is not None: - params['Prefix'] = prefix - if sort_order is not None: - params['SortOrder'] = sort_order - if next_token is not None: - params['NextToken'] = next_token - if limit is not None: - params['Limit'] = limit - return self.make_request(action='DescribeEvaluations', - body=json.dumps(params)) - - def describe_ml_models(self, filter_variable=None, eq=None, gt=None, - lt=None, ge=None, le=None, ne=None, prefix=None, - sort_order=None, next_token=None, limit=None): - """ - Returns a list of `MLModel` that match the search criteria in - the request. - - :type filter_variable: string - :param filter_variable: - Use one of the following variables to filter a list of `MLModel`: - - - + `CreatedAt` - Sets the search criteria to `MLModel` creation date. - + `Status` - Sets the search criteria to `MLModel` status. - + `Name` - Sets the search criteria to the contents of `MLModel` ** ** - `Name`. - + `IAMUser` - Sets the search criteria to the user account that invoked - the `MLModel` creation. - + `TrainingDataSourceId` - Sets the search criteria to the `DataSource` - used to train one or more `MLModel`. - + `RealtimeEndpointStatus` - Sets the search criteria to the `MLModel` - real-time endpoint status. - + `MLModelType` - Sets the search criteria to `MLModel` type: binary, - regression, or multi-class. - + `Algorithm` - Sets the search criteria to the algorithm that the - `MLModel` uses. - + `TrainingDataURI` - Sets the search criteria to the data file(s) used - in training a `MLModel`. The URL can identify either a file or an - Amazon Simple Storage Service (Amazon S3) bucket or directory. - - :type eq: string - :param eq: The equal to operator. The `MLModel` results will have - `FilterVariable` values that exactly match the value specified with - `EQ`. - - :type gt: string - :param gt: The greater than operator. The `MLModel` results will have - `FilterVariable` values that are greater than the value specified - with `GT`. - - :type lt: string - :param lt: The less than operator. The `MLModel` results will have - `FilterVariable` values that are less than the value specified with - `LT`. - - :type ge: string - :param ge: The greater than or equal to operator. The `MLModel` results - will have `FilterVariable` values that are greater than or equal to - the value specified with `GE`. - - :type le: string - :param le: The less than or equal to operator. The `MLModel` results - will have `FilterVariable` values that are less than or equal to - the value specified with `LE`. - - :type ne: string - :param ne: The not equal to operator. The `MLModel` results will have - `FilterVariable` values not equal to the value specified with `NE`. - - :type prefix: string - :param prefix: - A string that is found at the beginning of a variable, such as `Name` - or `Id`. - - For example, an `MLModel` could have the `Name` - `2014-09-09-HolidayGiftMailer`. To search for this `MLModel`, - select `Name` for the `FilterVariable` and any of the following - strings for the `Prefix`: - - - + 2014-09 - + 2014-09-09 - + 2014-09-09-Holiday - - :type sort_order: string - :param sort_order: A two-value parameter that determines the sequence - of the resulting list of `MLModel`. - - + `asc` - Arranges the list in ascending order (A-Z, 0-9). - + `dsc` - Arranges the list in descending order (Z-A, 9-0). - - - Results are sorted by `FilterVariable`. - - :type next_token: string - :param next_token: The ID of the page in the paginated results. - - :type limit: integer - :param limit: The number of pages of information to include in the - result. The range of acceptable values is 1 through 100. The - default value is 100. - - """ - params = {} - if filter_variable is not None: - params['FilterVariable'] = filter_variable - if eq is not None: - params['EQ'] = eq - if gt is not None: - params['GT'] = gt - if lt is not None: - params['LT'] = lt - if ge is not None: - params['GE'] = ge - if le is not None: - params['LE'] = le - if ne is not None: - params['NE'] = ne - if prefix is not None: - params['Prefix'] = prefix - if sort_order is not None: - params['SortOrder'] = sort_order - if next_token is not None: - params['NextToken'] = next_token - if limit is not None: - params['Limit'] = limit - return self.make_request(action='DescribeMLModels', - body=json.dumps(params)) - - def get_batch_prediction(self, batch_prediction_id): - """ - Returns a `BatchPrediction` that includes detailed metadata, - status, and data file information for a `Batch Prediction` - request. - - :type batch_prediction_id: string - :param batch_prediction_id: An ID assigned to the `BatchPrediction` at - creation. - - """ - params = {'BatchPredictionId': batch_prediction_id, } - return self.make_request(action='GetBatchPrediction', - body=json.dumps(params)) - - def get_data_source(self, data_source_id, verbose=None): - """ - Returns a `DataSource` that includes metadata and data file - information, as well as the current status of the - `DataSource`. - - `GetDataSource` provides results in normal or verbose format. - The verbose format adds the schema description and the list of - files pointed to by the DataSource to the normal format. - - :type data_source_id: string - :param data_source_id: The ID assigned to the `DataSource` at creation. - - :type verbose: boolean - :param verbose: Specifies whether the `GetDataSource` operation should - return `DataSourceSchema`. - If true, `DataSourceSchema` is returned. - - If false, `DataSourceSchema` is not returned. - - """ - params = {'DataSourceId': data_source_id, } - if verbose is not None: - params['Verbose'] = verbose - return self.make_request(action='GetDataSource', - body=json.dumps(params)) - - def get_evaluation(self, evaluation_id): - """ - Returns an `Evaluation` that includes metadata as well as the - current status of the `Evaluation`. - - :type evaluation_id: string - :param evaluation_id: The ID of the `Evaluation` to retrieve. The - evaluation of each `MLModel` is recorded and cataloged. The ID - provides the means to access the information. - - """ - params = {'EvaluationId': evaluation_id, } - return self.make_request(action='GetEvaluation', - body=json.dumps(params)) - - def get_ml_model(self, ml_model_id, verbose=None): - """ - Returns an `MLModel` that includes detailed metadata, and data - source information as well as the current status of the - `MLModel`. - - `GetMLModel` provides results in normal or verbose format. - - :type ml_model_id: string - :param ml_model_id: The ID assigned to the `MLModel` at creation. - - :type verbose: boolean - :param verbose: Specifies whether the `GetMLModel` operation should - return `Recipe`. - If true, `Recipe` is returned. - - If false, `Recipe` is not returned. - - """ - params = {'MLModelId': ml_model_id, } - if verbose is not None: - params['Verbose'] = verbose - return self.make_request(action='GetMLModel', - body=json.dumps(params)) - - def predict(self, ml_model_id, record, predict_endpoint): - """ - Generates a prediction for the observation using the specified - `MLModel`. - - - Not all response parameters will be populated because this is - dependent on the type of requested model. - - :type ml_model_id: string - :param ml_model_id: A unique identifier of the `MLModel`. - - :type record: map - :param record: A map of variable name-value pairs that represent an - observation. - - :type predict_endpoint: string - :param predict_endpoint: The endpoint to send the predict request to. - - """ - predict_host = urlsplit(predict_endpoint).hostname - if predict_host is None: - predict_host = predict_endpoint - - params = { - 'MLModelId': ml_model_id, - 'Record': record, - 'PredictEndpoint': predict_host, - } - return self.make_request(action='Predict', - body=json.dumps(params), - host=predict_host) - - def update_batch_prediction(self, batch_prediction_id, - batch_prediction_name): - """ - Updates the `BatchPredictionName` of a `BatchPrediction`. - - You can use the GetBatchPrediction operation to view the - contents of the updated data element. - - :type batch_prediction_id: string - :param batch_prediction_id: The ID assigned to the `BatchPrediction` - during creation. - - :type batch_prediction_name: string - :param batch_prediction_name: A new user-supplied name or description - of the `BatchPrediction`. - - """ - params = { - 'BatchPredictionId': batch_prediction_id, - 'BatchPredictionName': batch_prediction_name, - } - return self.make_request(action='UpdateBatchPrediction', - body=json.dumps(params)) - - def update_data_source(self, data_source_id, data_source_name): - """ - Updates the `DataSourceName` of a `DataSource`. - - You can use the GetDataSource operation to view the contents - of the updated data element. - - :type data_source_id: string - :param data_source_id: The ID assigned to the `DataSource` during - creation. - - :type data_source_name: string - :param data_source_name: A new user-supplied name or description of the - `DataSource` that will replace the current description. - - """ - params = { - 'DataSourceId': data_source_id, - 'DataSourceName': data_source_name, - } - return self.make_request(action='UpdateDataSource', - body=json.dumps(params)) - - def update_evaluation(self, evaluation_id, evaluation_name): - """ - Updates the `EvaluationName` of an `Evaluation`. - - You can use the GetEvaluation operation to view the contents - of the updated data element. - - :type evaluation_id: string - :param evaluation_id: The ID assigned to the `Evaluation` during - creation. - - :type evaluation_name: string - :param evaluation_name: A new user-supplied name or description of the - `Evaluation` that will replace the current content. - - """ - params = { - 'EvaluationId': evaluation_id, - 'EvaluationName': evaluation_name, - } - return self.make_request(action='UpdateEvaluation', - body=json.dumps(params)) - - def update_ml_model(self, ml_model_id, ml_model_name=None, - score_threshold=None): - """ - Updates the `MLModelName` and the `ScoreThreshold` of an - `MLModel`. - - You can use the GetMLModel operation to view the contents of - the updated data element. - - :type ml_model_id: string - :param ml_model_id: The ID assigned to the `MLModel` during creation. - - :type ml_model_name: string - :param ml_model_name: A user-supplied name or description of the - `MLModel`. - - :type score_threshold: float - :param score_threshold: The `ScoreThreshold` used in binary - classification `MLModel` that marks the boundary between a positive - prediction and a negative prediction. - Output values greater than or equal to the `ScoreThreshold` receive a - positive result from the `MLModel`, such as `True`. Output values - less than the `ScoreThreshold` receive a negative response from the - `MLModel`, such as `False`. - - """ - params = {'MLModelId': ml_model_id, } - if ml_model_name is not None: - params['MLModelName'] = ml_model_name - if score_threshold is not None: - params['ScoreThreshold'] = score_threshold - return self.make_request(action='UpdateMLModel', - body=json.dumps(params)) - - def make_request(self, action, body, host=None): - headers = { - 'X-Amz-Target': '%s.%s' % (self.TargetPrefix, action), - 'Host': self.region.endpoint, - 'Content-Type': 'application/x-amz-json-1.1', - 'Content-Length': str(len(body)), - } - http_request_kwargs = { - 'method':'POST', 'path':'/', 'auth_path':'/', 'params':{}, - 'headers': headers, 'data':body - } - if host is not None: - headers['Host'] = host - http_request_kwargs['host'] = host - http_request = self.build_base_http_request(**http_request_kwargs) - response = self._mexe(http_request, sender=None, - override_num_retries=10) - response_body = response.read().decode('utf-8') - boto.log.debug(response_body) - if response.status == 200: - if response_body: - return json.loads(response_body) - else: - json_body = json.loads(response_body) - fault_name = json_body.get('__type', None) - exception_class = self._faults.get(fault_name, self.ResponseError) - raise exception_class(response.status, response.reason, - body=json_body) -