Mercurial > repos > shellac > sam_consensus_v3
diff env/lib/python3.9/site-packages/boto/machinelearning/layer1.py @ 0:4f3585e2f14b draft default tip
"planemo upload commit 60cee0fc7c0cda8592644e1aad72851dec82c959"
| author | shellac |
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| date | Mon, 22 Mar 2021 18:12:50 +0000 |
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--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/env/lib/python3.9/site-packages/boto/machinelearning/layer1.py Mon Mar 22 18:12:50 2021 +0000 @@ -0,0 +1,1408 @@ +# 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) +