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comparison env/lib/python3.7/site-packages/boto/machinelearning/layer1.py @ 2:6af9afd405e9 draft

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"planemo upload commit 0a63dd5f4d38a1f6944587f52a8cd79874177fc1"
author shellac
date Thu, 14 May 2020 14:56:58 -0400
parents 26e78fe6e8c4
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1 # Copyright (c) 2015 Amazon.com, Inc. or its affiliates. All Rights Reserved
2 #
3 # Permission is hereby granted, free of charge, to any person obtaining a
4 # copy of this software and associated documentation files (the
5 # "Software"), to deal in the Software without restriction, including
6 # without limitation the rights to use, copy, modify, merge, publish, dis-
7 # tribute, sublicense, and/or sell copies of the Software, and to permit
8 # persons to whom the Software is furnished to do so, subject to the fol-
9 # lowing conditions:
10 #
11 # The above copyright notice and this permission notice shall be included
12 # in all copies or substantial portions of the Software.
13 #
14 # THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS
15 # OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABIL-
16 # ITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT
17 # SHALL THE AUTHOR BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY,
18 # WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
19 # OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS
20 # IN THE SOFTWARE.
21 #
22
23 import boto
24 from boto.compat import json, urlsplit
25 from boto.connection import AWSQueryConnection
26 from boto.regioninfo import RegionInfo
27 from boto.exception import JSONResponseError
28 from boto.machinelearning import exceptions
29
30
31 class MachineLearningConnection(AWSQueryConnection):
32 """
33 Definition of the public APIs exposed by Amazon Machine Learning
34 """
35 APIVersion = "2014-12-12"
36 AuthServiceName = 'machinelearning'
37 DefaultRegionName = "us-east-1"
38 DefaultRegionEndpoint = "machinelearning.us-east-1.amazonaws.com"
39 ServiceName = "MachineLearning"
40 TargetPrefix = "AmazonML_20141212"
41 ResponseError = JSONResponseError
42
43 _faults = {
44 "InternalServerException": exceptions.InternalServerException,
45 "LimitExceededException": exceptions.LimitExceededException,
46 "ResourceNotFoundException": exceptions.ResourceNotFoundException,
47 "IdempotentParameterMismatchException": exceptions.IdempotentParameterMismatchException,
48 "PredictorNotMountedException": exceptions.PredictorNotMountedException,
49 "InvalidInputException": exceptions.InvalidInputException,
50 }
51
52
53 def __init__(self, **kwargs):
54 region = kwargs.pop('region', None)
55 if not region:
56 region = RegionInfo(self, self.DefaultRegionName,
57 self.DefaultRegionEndpoint)
58
59 if 'host' not in kwargs or kwargs['host'] is None:
60 kwargs['host'] = region.endpoint
61
62 super(MachineLearningConnection, self).__init__(**kwargs)
63 self.region = region
64 self.auth_region_name = self.region.name
65
66 def _required_auth_capability(self):
67 return ['hmac-v4']
68
69 def create_batch_prediction(self, batch_prediction_id, ml_model_id,
70 batch_prediction_data_source_id, output_uri,
71 batch_prediction_name=None):
72 """
73 Generates predictions for a group of observations. The
74 observations to process exist in one or more data files
75 referenced by a `DataSource`. This operation creates a new
76 `BatchPrediction`, and uses an `MLModel` and the data files
77 referenced by the `DataSource` as information sources.
78
79 `CreateBatchPrediction` is an asynchronous operation. In
80 response to `CreateBatchPrediction`, Amazon Machine Learning
81 (Amazon ML) immediately returns and sets the `BatchPrediction`
82 status to `PENDING`. After the `BatchPrediction` completes,
83 Amazon ML sets the status to `COMPLETED`.
84
85 You can poll for status updates by using the
86 GetBatchPrediction operation and checking the `Status`
87 parameter of the result. After the `COMPLETED` status appears,
88 the results are available in the location specified by the
89 `OutputUri` parameter.
90
91 :type batch_prediction_id: string
92 :param batch_prediction_id: A user-supplied ID that uniquely identifies
93 the `BatchPrediction`.
94
95 :type batch_prediction_name: string
96 :param batch_prediction_name: A user-supplied name or description of
97 the `BatchPrediction`. `BatchPredictionName` can only use the UTF-8
98 character set.
99
100 :type ml_model_id: string
101 :param ml_model_id: The ID of the `MLModel` that will generate
102 predictions for the group of observations.
103
104 :type batch_prediction_data_source_id: string
105 :param batch_prediction_data_source_id: The ID of the `DataSource` that
106 points to the group of observations to predict.
107
108 :type output_uri: string
109 :param output_uri: The location of an Amazon Simple Storage Service
110 (Amazon S3) bucket or directory to store the batch prediction
111 results. The following substrings are not allowed in the s3 key
112 portion of the "outputURI" field: ':', '//', '/./', '/../'.
113 Amazon ML needs permissions to store and retrieve the logs on your
114 behalf. For information about how to set permissions, see the
115 `Amazon Machine Learning Developer Guide`_.
116
117 """
118 params = {
119 'BatchPredictionId': batch_prediction_id,
120 'MLModelId': ml_model_id,
121 'BatchPredictionDataSourceId': batch_prediction_data_source_id,
122 'OutputUri': output_uri,
123 }
124 if batch_prediction_name is not None:
125 params['BatchPredictionName'] = batch_prediction_name
126 return self.make_request(action='CreateBatchPrediction',
127 body=json.dumps(params))
128
129 def create_data_source_from_rds(self, data_source_id, rds_data, role_arn,
130 data_source_name=None,
131 compute_statistics=None):
132 """
133 Creates a `DataSource` object from an ` Amazon Relational
134 Database Service`_ (Amazon RDS). A `DataSource` references
135 data that can be used to perform CreateMLModel,
136 CreateEvaluation, or CreateBatchPrediction operations.
137
138 `CreateDataSourceFromRDS` is an asynchronous operation. In
139 response to `CreateDataSourceFromRDS`, Amazon Machine Learning
140 (Amazon ML) immediately returns and sets the `DataSource`
141 status to `PENDING`. After the `DataSource` is created and
142 ready for use, Amazon ML sets the `Status` parameter to
143 `COMPLETED`. `DataSource` in `COMPLETED` or `PENDING` status
144 can only be used to perform CreateMLModel, CreateEvaluation,
145 or CreateBatchPrediction operations.
146
147 If Amazon ML cannot accept the input source, it sets the
148 `Status` parameter to `FAILED` and includes an error message
149 in the `Message` attribute of the GetDataSource operation
150 response.
151
152 :type data_source_id: string
153 :param data_source_id: A user-supplied ID that uniquely identifies the
154 `DataSource`. Typically, an Amazon Resource Number (ARN) becomes
155 the ID for a `DataSource`.
156
157 :type data_source_name: string
158 :param data_source_name: A user-supplied name or description of the
159 `DataSource`.
160
161 :type rds_data: dict
162 :param rds_data:
163 The data specification of an Amazon RDS `DataSource`:
164
165
166 + DatabaseInformation -
167
168 + `DatabaseName ` - Name of the Amazon RDS database.
169 + ` InstanceIdentifier ` - Unique identifier for the Amazon RDS
170 database instance.
171
172 + DatabaseCredentials - AWS Identity and Access Management (IAM)
173 credentials that are used to connect to the Amazon RDS database.
174 + ResourceRole - Role (DataPipelineDefaultResourceRole) assumed by an
175 Amazon Elastic Compute Cloud (EC2) instance to carry out the copy
176 task from Amazon RDS to Amazon S3. For more information, see `Role
177 templates`_ for data pipelines.
178 + ServiceRole - Role (DataPipelineDefaultRole) assumed by the AWS Data
179 Pipeline service to monitor the progress of the copy task from
180 Amazon RDS to Amazon Simple Storage Service (S3). For more
181 information, see `Role templates`_ for data pipelines.
182 + SecurityInfo - Security information to use to access an Amazon RDS
183 instance. You need to set up appropriate ingress rules for the
184 security entity IDs provided to allow access to the Amazon RDS
185 instance. Specify a [ `SubnetId`, `SecurityGroupIds`] pair for a
186 VPC-based Amazon RDS instance.
187 + SelectSqlQuery - Query that is used to retrieve the observation data
188 for the `Datasource`.
189 + S3StagingLocation - Amazon S3 location for staging RDS data. The data
190 retrieved from Amazon RDS using `SelectSqlQuery` is stored in this
191 location.
192 + DataSchemaUri - Amazon S3 location of the `DataSchema`.
193 + DataSchema - A JSON string representing the schema. This is not
194 required if `DataSchemaUri` is specified.
195 + DataRearrangement - A JSON string representing the splitting
196 requirement of a `Datasource`. Sample - ` "{\"randomSeed\":\"some-
197 random-seed\",
198 \"splitting\":{\"percentBegin\":10,\"percentEnd\":60}}"`
199
200 :type role_arn: string
201 :param role_arn: The role that Amazon ML assumes on behalf of the user
202 to create and activate a data pipeline in the users account and
203 copy data (using the `SelectSqlQuery`) query from Amazon RDS to
204 Amazon S3.
205
206 :type compute_statistics: boolean
207 :param compute_statistics: The compute statistics for a `DataSource`.
208 The statistics are generated from the observation data referenced
209 by a `DataSource`. Amazon ML uses the statistics internally during
210 an `MLModel` training. This parameter must be set to `True` if the
211 ``DataSource `` needs to be used for `MLModel` training.
212
213 """
214 params = {
215 'DataSourceId': data_source_id,
216 'RDSData': rds_data,
217 'RoleARN': role_arn,
218 }
219 if data_source_name is not None:
220 params['DataSourceName'] = data_source_name
221 if compute_statistics is not None:
222 params['ComputeStatistics'] = compute_statistics
223 return self.make_request(action='CreateDataSourceFromRDS',
224 body=json.dumps(params))
225
226 def create_data_source_from_redshift(self, data_source_id, data_spec,
227 role_arn, data_source_name=None,
228 compute_statistics=None):
229 """
230 Creates a `DataSource` from `Amazon Redshift`_. A `DataSource`
231 references data that can be used to perform either
232 CreateMLModel, CreateEvaluation or CreateBatchPrediction
233 operations.
234
235 `CreateDataSourceFromRedshift` is an asynchronous operation.
236 In response to `CreateDataSourceFromRedshift`, Amazon Machine
237 Learning (Amazon ML) immediately returns and sets the
238 `DataSource` status to `PENDING`. After the `DataSource` is
239 created and ready for use, Amazon ML sets the `Status`
240 parameter to `COMPLETED`. `DataSource` in `COMPLETED` or
241 `PENDING` status can only be used to perform CreateMLModel,
242 CreateEvaluation, or CreateBatchPrediction operations.
243
244 If Amazon ML cannot accept the input source, it sets the
245 `Status` parameter to `FAILED` and includes an error message
246 in the `Message` attribute of the GetDataSource operation
247 response.
248
249 The observations should exist in the database hosted on an
250 Amazon Redshift cluster and should be specified by a
251 `SelectSqlQuery`. Amazon ML executes ` Unload`_ command in
252 Amazon Redshift to transfer the result set of `SelectSqlQuery`
253 to `S3StagingLocation.`
254
255 After the `DataSource` is created, it's ready for use in
256 evaluations and batch predictions. If you plan to use the
257 `DataSource` to train an `MLModel`, the `DataSource` requires
258 another item -- a recipe. A recipe describes the observation
259 variables that participate in training an `MLModel`. A recipe
260 describes how each input variable will be used in training.
261 Will the variable be included or excluded from training? Will
262 the variable be manipulated, for example, combined with
263 another variable or split apart into word combinations? The
264 recipe provides answers to these questions. For more
265 information, see the Amazon Machine Learning Developer Guide.
266
267 :type data_source_id: string
268 :param data_source_id: A user-supplied ID that uniquely identifies the
269 `DataSource`.
270
271 :type data_source_name: string
272 :param data_source_name: A user-supplied name or description of the
273 `DataSource`.
274
275 :type data_spec: dict
276 :param data_spec:
277 The data specification of an Amazon Redshift `DataSource`:
278
279
280 + DatabaseInformation -
281
282 + `DatabaseName ` - Name of the Amazon Redshift database.
283 + ` ClusterIdentifier ` - Unique ID for the Amazon Redshift cluster.
284
285 + DatabaseCredentials - AWS Identity abd Access Management (IAM)
286 credentials that are used to connect to the Amazon Redshift
287 database.
288 + SelectSqlQuery - Query that is used to retrieve the observation data
289 for the `Datasource`.
290 + S3StagingLocation - Amazon Simple Storage Service (Amazon S3)
291 location for staging Amazon Redshift data. The data retrieved from
292 Amazon Relational Database Service (Amazon RDS) using
293 `SelectSqlQuery` is stored in this location.
294 + DataSchemaUri - Amazon S3 location of the `DataSchema`.
295 + DataSchema - A JSON string representing the schema. This is not
296 required if `DataSchemaUri` is specified.
297 + DataRearrangement - A JSON string representing the splitting
298 requirement of a `Datasource`. Sample - ` "{\"randomSeed\":\"some-
299 random-seed\",
300 \"splitting\":{\"percentBegin\":10,\"percentEnd\":60}}"`
301
302 :type role_arn: string
303 :param role_arn: A fully specified role Amazon Resource Name (ARN).
304 Amazon ML assumes the role on behalf of the user to create the
305 following:
306
307
308 + A security group to allow Amazon ML to execute the `SelectSqlQuery`
309 query on an Amazon Redshift cluster
310 + An Amazon S3 bucket policy to grant Amazon ML read/write permissions
311 on the `S3StagingLocation`
312
313 :type compute_statistics: boolean
314 :param compute_statistics: The compute statistics for a `DataSource`.
315 The statistics are generated from the observation data referenced
316 by a `DataSource`. Amazon ML uses the statistics internally during
317 `MLModel` training. This parameter must be set to `True` if the
318 ``DataSource `` needs to be used for `MLModel` training
319
320 """
321 params = {
322 'DataSourceId': data_source_id,
323 'DataSpec': data_spec,
324 'RoleARN': role_arn,
325 }
326 if data_source_name is not None:
327 params['DataSourceName'] = data_source_name
328 if compute_statistics is not None:
329 params['ComputeStatistics'] = compute_statistics
330 return self.make_request(action='CreateDataSourceFromRedshift',
331 body=json.dumps(params))
332
333 def create_data_source_from_s3(self, data_source_id, data_spec,
334 data_source_name=None,
335 compute_statistics=None):
336 """
337 Creates a `DataSource` object. A `DataSource` references data
338 that can be used to perform CreateMLModel, CreateEvaluation,
339 or CreateBatchPrediction operations.
340
341 `CreateDataSourceFromS3` is an asynchronous operation. In
342 response to `CreateDataSourceFromS3`, Amazon Machine Learning
343 (Amazon ML) immediately returns and sets the `DataSource`
344 status to `PENDING`. After the `DataSource` is created and
345 ready for use, Amazon ML sets the `Status` parameter to
346 `COMPLETED`. `DataSource` in `COMPLETED` or `PENDING` status
347 can only be used to perform CreateMLModel, CreateEvaluation or
348 CreateBatchPrediction operations.
349
350 If Amazon ML cannot accept the input source, it sets the
351 `Status` parameter to `FAILED` and includes an error message
352 in the `Message` attribute of the GetDataSource operation
353 response.
354
355 The observation data used in a `DataSource` should be ready to
356 use; that is, it should have a consistent structure, and
357 missing data values should be kept to a minimum. The
358 observation data must reside in one or more CSV files in an
359 Amazon Simple Storage Service (Amazon S3) bucket, along with a
360 schema that describes the data items by name and type. The
361 same schema must be used for all of the data files referenced
362 by the `DataSource`.
363
364 After the `DataSource` has been created, it's ready to use in
365 evaluations and batch predictions. If you plan to use the
366 `DataSource` to train an `MLModel`, the `DataSource` requires
367 another item: a recipe. A recipe describes the observation
368 variables that participate in training an `MLModel`. A recipe
369 describes how each input variable will be used in training.
370 Will the variable be included or excluded from training? Will
371 the variable be manipulated, for example, combined with
372 another variable, or split apart into word combinations? The
373 recipe provides answers to these questions. For more
374 information, see the `Amazon Machine Learning Developer
375 Guide`_.
376
377 :type data_source_id: string
378 :param data_source_id: A user-supplied identifier that uniquely
379 identifies the `DataSource`.
380
381 :type data_source_name: string
382 :param data_source_name: A user-supplied name or description of the
383 `DataSource`.
384
385 :type data_spec: dict
386 :param data_spec:
387 The data specification of a `DataSource`:
388
389
390 + DataLocationS3 - Amazon Simple Storage Service (Amazon S3) location
391 of the observation data.
392 + DataSchemaLocationS3 - Amazon S3 location of the `DataSchema`.
393 + DataSchema - A JSON string representing the schema. This is not
394 required if `DataSchemaUri` is specified.
395 + DataRearrangement - A JSON string representing the splitting
396 requirement of a `Datasource`. Sample - ` "{\"randomSeed\":\"some-
397 random-seed\",
398 \"splitting\":{\"percentBegin\":10,\"percentEnd\":60}}"`
399
400 :type compute_statistics: boolean
401 :param compute_statistics: The compute statistics for a `DataSource`.
402 The statistics are generated from the observation data referenced
403 by a `DataSource`. Amazon ML uses the statistics internally during
404 an `MLModel` training. This parameter must be set to `True` if the
405 ``DataSource `` needs to be used for `MLModel` training
406
407 """
408 params = {
409 'DataSourceId': data_source_id,
410 'DataSpec': data_spec,
411 }
412 if data_source_name is not None:
413 params['DataSourceName'] = data_source_name
414 if compute_statistics is not None:
415 params['ComputeStatistics'] = compute_statistics
416 return self.make_request(action='CreateDataSourceFromS3',
417 body=json.dumps(params))
418
419 def create_evaluation(self, evaluation_id, ml_model_id,
420 evaluation_data_source_id, evaluation_name=None):
421 """
422 Creates a new `Evaluation` of an `MLModel`. An `MLModel` is
423 evaluated on a set of observations associated to a
424 `DataSource`. Like a `DataSource` for an `MLModel`, the
425 `DataSource` for an `Evaluation` contains values for the
426 Target Variable. The `Evaluation` compares the predicted
427 result for each observation to the actual outcome and provides
428 a summary so that you know how effective the `MLModel`
429 functions on the test data. Evaluation generates a relevant
430 performance metric such as BinaryAUC, RegressionRMSE or
431 MulticlassAvgFScore based on the corresponding `MLModelType`:
432 `BINARY`, `REGRESSION` or `MULTICLASS`.
433
434 `CreateEvaluation` is an asynchronous operation. In response
435 to `CreateEvaluation`, Amazon Machine Learning (Amazon ML)
436 immediately returns and sets the evaluation status to
437 `PENDING`. After the `Evaluation` is created and ready for
438 use, Amazon ML sets the status to `COMPLETED`.
439
440 You can use the GetEvaluation operation to check progress of
441 the evaluation during the creation operation.
442
443 :type evaluation_id: string
444 :param evaluation_id: A user-supplied ID that uniquely identifies the
445 `Evaluation`.
446
447 :type evaluation_name: string
448 :param evaluation_name: A user-supplied name or description of the
449 `Evaluation`.
450
451 :type ml_model_id: string
452 :param ml_model_id: The ID of the `MLModel` to evaluate.
453 The schema used in creating the `MLModel` must match the schema of the
454 `DataSource` used in the `Evaluation`.
455
456 :type evaluation_data_source_id: string
457 :param evaluation_data_source_id: The ID of the `DataSource` for the
458 evaluation. The schema of the `DataSource` must match the schema
459 used to create the `MLModel`.
460
461 """
462 params = {
463 'EvaluationId': evaluation_id,
464 'MLModelId': ml_model_id,
465 'EvaluationDataSourceId': evaluation_data_source_id,
466 }
467 if evaluation_name is not None:
468 params['EvaluationName'] = evaluation_name
469 return self.make_request(action='CreateEvaluation',
470 body=json.dumps(params))
471
472 def create_ml_model(self, ml_model_id, ml_model_type,
473 training_data_source_id, ml_model_name=None,
474 parameters=None, recipe=None, recipe_uri=None):
475 """
476 Creates a new `MLModel` using the data files and the recipe as
477 information sources.
478
479 An `MLModel` is nearly immutable. Users can only update the
480 `MLModelName` and the `ScoreThreshold` in an `MLModel` without
481 creating a new `MLModel`.
482
483 `CreateMLModel` is an asynchronous operation. In response to
484 `CreateMLModel`, Amazon Machine Learning (Amazon ML)
485 immediately returns and sets the `MLModel` status to
486 `PENDING`. After the `MLModel` is created and ready for use,
487 Amazon ML sets the status to `COMPLETED`.
488
489 You can use the GetMLModel operation to check progress of the
490 `MLModel` during the creation operation.
491
492 CreateMLModel requires a `DataSource` with computed
493 statistics, which can be created by setting
494 `ComputeStatistics` to `True` in CreateDataSourceFromRDS,
495 CreateDataSourceFromS3, or CreateDataSourceFromRedshift
496 operations.
497
498 :type ml_model_id: string
499 :param ml_model_id: A user-supplied ID that uniquely identifies the
500 `MLModel`.
501
502 :type ml_model_name: string
503 :param ml_model_name: A user-supplied name or description of the
504 `MLModel`.
505
506 :type ml_model_type: string
507 :param ml_model_type: The category of supervised learning that this
508 `MLModel` will address. Choose from the following types:
509
510 + Choose `REGRESSION` if the `MLModel` will be used to predict a
511 numeric value.
512 + Choose `BINARY` if the `MLModel` result has two possible values.
513 + Choose `MULTICLASS` if the `MLModel` result has a limited number of
514 values.
515
516
517 For more information, see the `Amazon Machine Learning Developer
518 Guide`_.
519
520 :type parameters: map
521 :param parameters:
522 A list of the training parameters in the `MLModel`. The list is
523 implemented as a map of key/value pairs.
524
525 The following is the current set of training parameters:
526
527
528 + `sgd.l1RegularizationAmount` - Coefficient regularization L1 norm. It
529 controls overfitting the data by penalizing large coefficients.
530 This tends to drive coefficients to zero, resulting in sparse
531 feature set. If you use this parameter, start by specifying a small
532 value such as 1.0E-08. The value is a double that ranges from 0 to
533 MAX_DOUBLE. The default is not to use L1 normalization. The
534 parameter cannot be used when `L2` is specified. Use this parameter
535 sparingly.
536 + `sgd.l2RegularizationAmount` - Coefficient regularization L2 norm. It
537 controls overfitting the data by penalizing large coefficients.
538 This tends to drive coefficients to small, nonzero values. If you
539 use this parameter, start by specifying a small value such as
540 1.0E-08. The valuseis a double that ranges from 0 to MAX_DOUBLE.
541 The default is not to use L2 normalization. This cannot be used
542 when `L1` is specified. Use this parameter sparingly.
543 + `sgd.maxPasses` - Number of times that the training process traverses
544 the observations to build the `MLModel`. The value is an integer
545 that ranges from 1 to 10000. The default value is 10.
546 + `sgd.maxMLModelSizeInBytes` - Maximum allowed size of the model.
547 Depending on the input data, the size of the model might affect its
548 performance. The value is an integer that ranges from 100000 to
549 2147483648. The default value is 33554432.
550
551 :type training_data_source_id: string
552 :param training_data_source_id: The `DataSource` that points to the
553 training data.
554
555 :type recipe: string
556 :param recipe: The data recipe for creating `MLModel`. You must specify
557 either the recipe or its URI. If you dont specify a recipe or its
558 URI, Amazon ML creates a default.
559
560 :type recipe_uri: string
561 :param recipe_uri: The Amazon Simple Storage Service (Amazon S3)
562 location and file name that contains the `MLModel` recipe. You must
563 specify either the recipe or its URI. If you dont specify a recipe
564 or its URI, Amazon ML creates a default.
565
566 """
567 params = {
568 'MLModelId': ml_model_id,
569 'MLModelType': ml_model_type,
570 'TrainingDataSourceId': training_data_source_id,
571 }
572 if ml_model_name is not None:
573 params['MLModelName'] = ml_model_name
574 if parameters is not None:
575 params['Parameters'] = parameters
576 if recipe is not None:
577 params['Recipe'] = recipe
578 if recipe_uri is not None:
579 params['RecipeUri'] = recipe_uri
580 return self.make_request(action='CreateMLModel',
581 body=json.dumps(params))
582
583 def create_realtime_endpoint(self, ml_model_id):
584 """
585 Creates a real-time endpoint for the `MLModel`. The endpoint
586 contains the URI of the `MLModel`; that is, the location to
587 send real-time prediction requests for the specified
588 `MLModel`.
589
590 :type ml_model_id: string
591 :param ml_model_id: The ID assigned to the `MLModel` during creation.
592
593 """
594 params = {'MLModelId': ml_model_id, }
595 return self.make_request(action='CreateRealtimeEndpoint',
596 body=json.dumps(params))
597
598 def delete_batch_prediction(self, batch_prediction_id):
599 """
600 Assigns the DELETED status to a `BatchPrediction`, rendering
601 it unusable.
602
603 After using the `DeleteBatchPrediction` operation, you can use
604 the GetBatchPrediction operation to verify that the status of
605 the `BatchPrediction` changed to DELETED.
606
607 The result of the `DeleteBatchPrediction` operation is
608 irreversible.
609
610 :type batch_prediction_id: string
611 :param batch_prediction_id: A user-supplied ID that uniquely identifies
612 the `BatchPrediction`.
613
614 """
615 params = {'BatchPredictionId': batch_prediction_id, }
616 return self.make_request(action='DeleteBatchPrediction',
617 body=json.dumps(params))
618
619 def delete_data_source(self, data_source_id):
620 """
621 Assigns the DELETED status to a `DataSource`, rendering it
622 unusable.
623
624 After using the `DeleteDataSource` operation, you can use the
625 GetDataSource operation to verify that the status of the
626 `DataSource` changed to DELETED.
627
628 The results of the `DeleteDataSource` operation are
629 irreversible.
630
631 :type data_source_id: string
632 :param data_source_id: A user-supplied ID that uniquely identifies the
633 `DataSource`.
634
635 """
636 params = {'DataSourceId': data_source_id, }
637 return self.make_request(action='DeleteDataSource',
638 body=json.dumps(params))
639
640 def delete_evaluation(self, evaluation_id):
641 """
642 Assigns the `DELETED` status to an `Evaluation`, rendering it
643 unusable.
644
645 After invoking the `DeleteEvaluation` operation, you can use
646 the GetEvaluation operation to verify that the status of the
647 `Evaluation` changed to `DELETED`.
648
649 The results of the `DeleteEvaluation` operation are
650 irreversible.
651
652 :type evaluation_id: string
653 :param evaluation_id: A user-supplied ID that uniquely identifies the
654 `Evaluation` to delete.
655
656 """
657 params = {'EvaluationId': evaluation_id, }
658 return self.make_request(action='DeleteEvaluation',
659 body=json.dumps(params))
660
661 def delete_ml_model(self, ml_model_id):
662 """
663 Assigns the DELETED status to an `MLModel`, rendering it
664 unusable.
665
666 After using the `DeleteMLModel` operation, you can use the
667 GetMLModel operation to verify that the status of the
668 `MLModel` changed to DELETED.
669
670 The result of the `DeleteMLModel` operation is irreversible.
671
672 :type ml_model_id: string
673 :param ml_model_id: A user-supplied ID that uniquely identifies the
674 `MLModel`.
675
676 """
677 params = {'MLModelId': ml_model_id, }
678 return self.make_request(action='DeleteMLModel',
679 body=json.dumps(params))
680
681 def delete_realtime_endpoint(self, ml_model_id):
682 """
683 Deletes a real time endpoint of an `MLModel`.
684
685 :type ml_model_id: string
686 :param ml_model_id: The ID assigned to the `MLModel` during creation.
687
688 """
689 params = {'MLModelId': ml_model_id, }
690 return self.make_request(action='DeleteRealtimeEndpoint',
691 body=json.dumps(params))
692
693 def describe_batch_predictions(self, filter_variable=None, eq=None,
694 gt=None, lt=None, ge=None, le=None,
695 ne=None, prefix=None, sort_order=None,
696 next_token=None, limit=None):
697 """
698 Returns a list of `BatchPrediction` operations that match the
699 search criteria in the request.
700
701 :type filter_variable: string
702 :param filter_variable:
703 Use one of the following variables to filter a list of
704 `BatchPrediction`:
705
706
707 + `CreatedAt` - Sets the search criteria to the `BatchPrediction`
708 creation date.
709 + `Status` - Sets the search criteria to the `BatchPrediction` status.
710 + `Name` - Sets the search criteria to the contents of the
711 `BatchPrediction` ** ** `Name`.
712 + `IAMUser` - Sets the search criteria to the user account that invoked
713 the `BatchPrediction` creation.
714 + `MLModelId` - Sets the search criteria to the `MLModel` used in the
715 `BatchPrediction`.
716 + `DataSourceId` - Sets the search criteria to the `DataSource` used in
717 the `BatchPrediction`.
718 + `DataURI` - Sets the search criteria to the data file(s) used in the
719 `BatchPrediction`. The URL can identify either a file or an Amazon
720 Simple Storage Solution (Amazon S3) bucket or directory.
721
722 :type eq: string
723 :param eq: The equal to operator. The `BatchPrediction` results will
724 have `FilterVariable` values that exactly match the value specified
725 with `EQ`.
726
727 :type gt: string
728 :param gt: The greater than operator. The `BatchPrediction` results
729 will have `FilterVariable` values that are greater than the value
730 specified with `GT`.
731
732 :type lt: string
733 :param lt: The less than operator. The `BatchPrediction` results will
734 have `FilterVariable` values that are less than the value specified
735 with `LT`.
736
737 :type ge: string
738 :param ge: The greater than or equal to operator. The `BatchPrediction`
739 results will have `FilterVariable` values that are greater than or
740 equal to the value specified with `GE`.
741
742 :type le: string
743 :param le: The less than or equal to operator. The `BatchPrediction`
744 results will have `FilterVariable` values that are less than or
745 equal to the value specified with `LE`.
746
747 :type ne: string
748 :param ne: The not equal to operator. The `BatchPrediction` results
749 will have `FilterVariable` values not equal to the value specified
750 with `NE`.
751
752 :type prefix: string
753 :param prefix:
754 A string that is found at the beginning of a variable, such as `Name`
755 or `Id`.
756
757 For example, a `Batch Prediction` operation could have the `Name`
758 `2014-09-09-HolidayGiftMailer`. To search for this
759 `BatchPrediction`, select `Name` for the `FilterVariable` and any
760 of the following strings for the `Prefix`:
761
762
763 + 2014-09
764 + 2014-09-09
765 + 2014-09-09-Holiday
766
767 :type sort_order: string
768 :param sort_order: A two-value parameter that determines the sequence
769 of the resulting list of `MLModel`s.
770
771 + `asc` - Arranges the list in ascending order (A-Z, 0-9).
772 + `dsc` - Arranges the list in descending order (Z-A, 9-0).
773
774
775 Results are sorted by `FilterVariable`.
776
777 :type next_token: string
778 :param next_token: An ID of the page in the paginated results.
779
780 :type limit: integer
781 :param limit: The number of pages of information to include in the
782 result. The range of acceptable values is 1 through 100. The
783 default value is 100.
784
785 """
786 params = {}
787 if filter_variable is not None:
788 params['FilterVariable'] = filter_variable
789 if eq is not None:
790 params['EQ'] = eq
791 if gt is not None:
792 params['GT'] = gt
793 if lt is not None:
794 params['LT'] = lt
795 if ge is not None:
796 params['GE'] = ge
797 if le is not None:
798 params['LE'] = le
799 if ne is not None:
800 params['NE'] = ne
801 if prefix is not None:
802 params['Prefix'] = prefix
803 if sort_order is not None:
804 params['SortOrder'] = sort_order
805 if next_token is not None:
806 params['NextToken'] = next_token
807 if limit is not None:
808 params['Limit'] = limit
809 return self.make_request(action='DescribeBatchPredictions',
810 body=json.dumps(params))
811
812 def describe_data_sources(self, filter_variable=None, eq=None, gt=None,
813 lt=None, ge=None, le=None, ne=None,
814 prefix=None, sort_order=None, next_token=None,
815 limit=None):
816 """
817 Returns a list of `DataSource` that match the search criteria
818 in the request.
819
820 :type filter_variable: string
821 :param filter_variable:
822 Use one of the following variables to filter a list of `DataSource`:
823
824
825 + `CreatedAt` - Sets the search criteria to `DataSource` creation
826 dates.
827 + `Status` - Sets the search criteria to `DataSource` statuses.
828 + `Name` - Sets the search criteria to the contents of `DataSource` **
829 ** `Name`.
830 + `DataUri` - Sets the search criteria to the URI of data files used to
831 create the `DataSource`. The URI can identify either a file or an
832 Amazon Simple Storage Service (Amazon S3) bucket or directory.
833 + `IAMUser` - Sets the search criteria to the user account that invoked
834 the `DataSource` creation.
835
836 :type eq: string
837 :param eq: The equal to operator. The `DataSource` results will have
838 `FilterVariable` values that exactly match the value specified with
839 `EQ`.
840
841 :type gt: string
842 :param gt: The greater than operator. The `DataSource` results will
843 have `FilterVariable` values that are greater than the value
844 specified with `GT`.
845
846 :type lt: string
847 :param lt: The less than operator. The `DataSource` results will have
848 `FilterVariable` values that are less than the value specified with
849 `LT`.
850
851 :type ge: string
852 :param ge: The greater than or equal to operator. The `DataSource`
853 results will have `FilterVariable` values that are greater than or
854 equal to the value specified with `GE`.
855
856 :type le: string
857 :param le: The less than or equal to operator. The `DataSource` results
858 will have `FilterVariable` values that are less than or equal to
859 the value specified with `LE`.
860
861 :type ne: string
862 :param ne: The not equal to operator. The `DataSource` results will
863 have `FilterVariable` values not equal to the value specified with
864 `NE`.
865
866 :type prefix: string
867 :param prefix:
868 A string that is found at the beginning of a variable, such as `Name`
869 or `Id`.
870
871 For example, a `DataSource` could have the `Name`
872 `2014-09-09-HolidayGiftMailer`. To search for this `DataSource`,
873 select `Name` for the `FilterVariable` and any of the following
874 strings for the `Prefix`:
875
876
877 + 2014-09
878 + 2014-09-09
879 + 2014-09-09-Holiday
880
881 :type sort_order: string
882 :param sort_order: A two-value parameter that determines the sequence
883 of the resulting list of `DataSource`.
884
885 + `asc` - Arranges the list in ascending order (A-Z, 0-9).
886 + `dsc` - Arranges the list in descending order (Z-A, 9-0).
887
888
889 Results are sorted by `FilterVariable`.
890
891 :type next_token: string
892 :param next_token: The ID of the page in the paginated results.
893
894 :type limit: integer
895 :param limit: The maximum number of `DataSource` to include in the
896 result.
897
898 """
899 params = {}
900 if filter_variable is not None:
901 params['FilterVariable'] = filter_variable
902 if eq is not None:
903 params['EQ'] = eq
904 if gt is not None:
905 params['GT'] = gt
906 if lt is not None:
907 params['LT'] = lt
908 if ge is not None:
909 params['GE'] = ge
910 if le is not None:
911 params['LE'] = le
912 if ne is not None:
913 params['NE'] = ne
914 if prefix is not None:
915 params['Prefix'] = prefix
916 if sort_order is not None:
917 params['SortOrder'] = sort_order
918 if next_token is not None:
919 params['NextToken'] = next_token
920 if limit is not None:
921 params['Limit'] = limit
922 return self.make_request(action='DescribeDataSources',
923 body=json.dumps(params))
924
925 def describe_evaluations(self, filter_variable=None, eq=None, gt=None,
926 lt=None, ge=None, le=None, ne=None, prefix=None,
927 sort_order=None, next_token=None, limit=None):
928 """
929 Returns a list of `DescribeEvaluations` that match the search
930 criteria in the request.
931
932 :type filter_variable: string
933 :param filter_variable:
934 Use one of the following variable to filter a list of `Evaluation`
935 objects:
936
937
938 + `CreatedAt` - Sets the search criteria to the `Evaluation` creation
939 date.
940 + `Status` - Sets the search criteria to the `Evaluation` status.
941 + `Name` - Sets the search criteria to the contents of `Evaluation` **
942 ** `Name`.
943 + `IAMUser` - Sets the search criteria to the user account that invoked
944 an `Evaluation`.
945 + `MLModelId` - Sets the search criteria to the `MLModel` that was
946 evaluated.
947 + `DataSourceId` - Sets the search criteria to the `DataSource` used in
948 `Evaluation`.
949 + `DataUri` - Sets the search criteria to the data file(s) used in
950 `Evaluation`. The URL can identify either a file or an Amazon
951 Simple Storage Solution (Amazon S3) bucket or directory.
952
953 :type eq: string
954 :param eq: The equal to operator. The `Evaluation` results will have
955 `FilterVariable` values that exactly match the value specified with
956 `EQ`.
957
958 :type gt: string
959 :param gt: The greater than operator. The `Evaluation` results will
960 have `FilterVariable` values that are greater than the value
961 specified with `GT`.
962
963 :type lt: string
964 :param lt: The less than operator. The `Evaluation` results will have
965 `FilterVariable` values that are less than the value specified with
966 `LT`.
967
968 :type ge: string
969 :param ge: The greater than or equal to operator. The `Evaluation`
970 results will have `FilterVariable` values that are greater than or
971 equal to the value specified with `GE`.
972
973 :type le: string
974 :param le: The less than or equal to operator. The `Evaluation` results
975 will have `FilterVariable` values that are less than or equal to
976 the value specified with `LE`.
977
978 :type ne: string
979 :param ne: The not equal to operator. The `Evaluation` results will
980 have `FilterVariable` values not equal to the value specified with
981 `NE`.
982
983 :type prefix: string
984 :param prefix:
985 A string that is found at the beginning of a variable, such as `Name`
986 or `Id`.
987
988 For example, an `Evaluation` could have the `Name`
989 `2014-09-09-HolidayGiftMailer`. To search for this `Evaluation`,
990 select `Name` for the `FilterVariable` and any of the following
991 strings for the `Prefix`:
992
993
994 + 2014-09
995 + 2014-09-09
996 + 2014-09-09-Holiday
997
998 :type sort_order: string
999 :param sort_order: A two-value parameter that determines the sequence
1000 of the resulting list of `Evaluation`.
1001
1002 + `asc` - Arranges the list in ascending order (A-Z, 0-9).
1003 + `dsc` - Arranges the list in descending order (Z-A, 9-0).
1004
1005
1006 Results are sorted by `FilterVariable`.
1007
1008 :type next_token: string
1009 :param next_token: The ID of the page in the paginated results.
1010
1011 :type limit: integer
1012 :param limit: The maximum number of `Evaluation` to include in the
1013 result.
1014
1015 """
1016 params = {}
1017 if filter_variable is not None:
1018 params['FilterVariable'] = filter_variable
1019 if eq is not None:
1020 params['EQ'] = eq
1021 if gt is not None:
1022 params['GT'] = gt
1023 if lt is not None:
1024 params['LT'] = lt
1025 if ge is not None:
1026 params['GE'] = ge
1027 if le is not None:
1028 params['LE'] = le
1029 if ne is not None:
1030 params['NE'] = ne
1031 if prefix is not None:
1032 params['Prefix'] = prefix
1033 if sort_order is not None:
1034 params['SortOrder'] = sort_order
1035 if next_token is not None:
1036 params['NextToken'] = next_token
1037 if limit is not None:
1038 params['Limit'] = limit
1039 return self.make_request(action='DescribeEvaluations',
1040 body=json.dumps(params))
1041
1042 def describe_ml_models(self, filter_variable=None, eq=None, gt=None,
1043 lt=None, ge=None, le=None, ne=None, prefix=None,
1044 sort_order=None, next_token=None, limit=None):
1045 """
1046 Returns a list of `MLModel` that match the search criteria in
1047 the request.
1048
1049 :type filter_variable: string
1050 :param filter_variable:
1051 Use one of the following variables to filter a list of `MLModel`:
1052
1053
1054 + `CreatedAt` - Sets the search criteria to `MLModel` creation date.
1055 + `Status` - Sets the search criteria to `MLModel` status.
1056 + `Name` - Sets the search criteria to the contents of `MLModel` ** **
1057 `Name`.
1058 + `IAMUser` - Sets the search criteria to the user account that invoked
1059 the `MLModel` creation.
1060 + `TrainingDataSourceId` - Sets the search criteria to the `DataSource`
1061 used to train one or more `MLModel`.
1062 + `RealtimeEndpointStatus` - Sets the search criteria to the `MLModel`
1063 real-time endpoint status.
1064 + `MLModelType` - Sets the search criteria to `MLModel` type: binary,
1065 regression, or multi-class.
1066 + `Algorithm` - Sets the search criteria to the algorithm that the
1067 `MLModel` uses.
1068 + `TrainingDataURI` - Sets the search criteria to the data file(s) used
1069 in training a `MLModel`. The URL can identify either a file or an
1070 Amazon Simple Storage Service (Amazon S3) bucket or directory.
1071
1072 :type eq: string
1073 :param eq: The equal to operator. The `MLModel` results will have
1074 `FilterVariable` values that exactly match the value specified with
1075 `EQ`.
1076
1077 :type gt: string
1078 :param gt: The greater than operator. The `MLModel` results will have
1079 `FilterVariable` values that are greater than the value specified
1080 with `GT`.
1081
1082 :type lt: string
1083 :param lt: The less than operator. The `MLModel` results will have
1084 `FilterVariable` values that are less than the value specified with
1085 `LT`.
1086
1087 :type ge: string
1088 :param ge: The greater than or equal to operator. The `MLModel` results
1089 will have `FilterVariable` values that are greater than or equal to
1090 the value specified with `GE`.
1091
1092 :type le: string
1093 :param le: The less than or equal to operator. The `MLModel` results
1094 will have `FilterVariable` values that are less than or equal to
1095 the value specified with `LE`.
1096
1097 :type ne: string
1098 :param ne: The not equal to operator. The `MLModel` results will have
1099 `FilterVariable` values not equal to the value specified with `NE`.
1100
1101 :type prefix: string
1102 :param prefix:
1103 A string that is found at the beginning of a variable, such as `Name`
1104 or `Id`.
1105
1106 For example, an `MLModel` could have the `Name`
1107 `2014-09-09-HolidayGiftMailer`. To search for this `MLModel`,
1108 select `Name` for the `FilterVariable` and any of the following
1109 strings for the `Prefix`:
1110
1111
1112 + 2014-09
1113 + 2014-09-09
1114 + 2014-09-09-Holiday
1115
1116 :type sort_order: string
1117 :param sort_order: A two-value parameter that determines the sequence
1118 of the resulting list of `MLModel`.
1119
1120 + `asc` - Arranges the list in ascending order (A-Z, 0-9).
1121 + `dsc` - Arranges the list in descending order (Z-A, 9-0).
1122
1123
1124 Results are sorted by `FilterVariable`.
1125
1126 :type next_token: string
1127 :param next_token: The ID of the page in the paginated results.
1128
1129 :type limit: integer
1130 :param limit: The number of pages of information to include in the
1131 result. The range of acceptable values is 1 through 100. The
1132 default value is 100.
1133
1134 """
1135 params = {}
1136 if filter_variable is not None:
1137 params['FilterVariable'] = filter_variable
1138 if eq is not None:
1139 params['EQ'] = eq
1140 if gt is not None:
1141 params['GT'] = gt
1142 if lt is not None:
1143 params['LT'] = lt
1144 if ge is not None:
1145 params['GE'] = ge
1146 if le is not None:
1147 params['LE'] = le
1148 if ne is not None:
1149 params['NE'] = ne
1150 if prefix is not None:
1151 params['Prefix'] = prefix
1152 if sort_order is not None:
1153 params['SortOrder'] = sort_order
1154 if next_token is not None:
1155 params['NextToken'] = next_token
1156 if limit is not None:
1157 params['Limit'] = limit
1158 return self.make_request(action='DescribeMLModels',
1159 body=json.dumps(params))
1160
1161 def get_batch_prediction(self, batch_prediction_id):
1162 """
1163 Returns a `BatchPrediction` that includes detailed metadata,
1164 status, and data file information for a `Batch Prediction`
1165 request.
1166
1167 :type batch_prediction_id: string
1168 :param batch_prediction_id: An ID assigned to the `BatchPrediction` at
1169 creation.
1170
1171 """
1172 params = {'BatchPredictionId': batch_prediction_id, }
1173 return self.make_request(action='GetBatchPrediction',
1174 body=json.dumps(params))
1175
1176 def get_data_source(self, data_source_id, verbose=None):
1177 """
1178 Returns a `DataSource` that includes metadata and data file
1179 information, as well as the current status of the
1180 `DataSource`.
1181
1182 `GetDataSource` provides results in normal or verbose format.
1183 The verbose format adds the schema description and the list of
1184 files pointed to by the DataSource to the normal format.
1185
1186 :type data_source_id: string
1187 :param data_source_id: The ID assigned to the `DataSource` at creation.
1188
1189 :type verbose: boolean
1190 :param verbose: Specifies whether the `GetDataSource` operation should
1191 return `DataSourceSchema`.
1192 If true, `DataSourceSchema` is returned.
1193
1194 If false, `DataSourceSchema` is not returned.
1195
1196 """
1197 params = {'DataSourceId': data_source_id, }
1198 if verbose is not None:
1199 params['Verbose'] = verbose
1200 return self.make_request(action='GetDataSource',
1201 body=json.dumps(params))
1202
1203 def get_evaluation(self, evaluation_id):
1204 """
1205 Returns an `Evaluation` that includes metadata as well as the
1206 current status of the `Evaluation`.
1207
1208 :type evaluation_id: string
1209 :param evaluation_id: The ID of the `Evaluation` to retrieve. The
1210 evaluation of each `MLModel` is recorded and cataloged. The ID
1211 provides the means to access the information.
1212
1213 """
1214 params = {'EvaluationId': evaluation_id, }
1215 return self.make_request(action='GetEvaluation',
1216 body=json.dumps(params))
1217
1218 def get_ml_model(self, ml_model_id, verbose=None):
1219 """
1220 Returns an `MLModel` that includes detailed metadata, and data
1221 source information as well as the current status of the
1222 `MLModel`.
1223
1224 `GetMLModel` provides results in normal or verbose format.
1225
1226 :type ml_model_id: string
1227 :param ml_model_id: The ID assigned to the `MLModel` at creation.
1228
1229 :type verbose: boolean
1230 :param verbose: Specifies whether the `GetMLModel` operation should
1231 return `Recipe`.
1232 If true, `Recipe` is returned.
1233
1234 If false, `Recipe` is not returned.
1235
1236 """
1237 params = {'MLModelId': ml_model_id, }
1238 if verbose is not None:
1239 params['Verbose'] = verbose
1240 return self.make_request(action='GetMLModel',
1241 body=json.dumps(params))
1242
1243 def predict(self, ml_model_id, record, predict_endpoint):
1244 """
1245 Generates a prediction for the observation using the specified
1246 `MLModel`.
1247
1248
1249 Not all response parameters will be populated because this is
1250 dependent on the type of requested model.
1251
1252 :type ml_model_id: string
1253 :param ml_model_id: A unique identifier of the `MLModel`.
1254
1255 :type record: map
1256 :param record: A map of variable name-value pairs that represent an
1257 observation.
1258
1259 :type predict_endpoint: string
1260 :param predict_endpoint: The endpoint to send the predict request to.
1261
1262 """
1263 predict_host = urlsplit(predict_endpoint).hostname
1264 if predict_host is None:
1265 predict_host = predict_endpoint
1266
1267 params = {
1268 'MLModelId': ml_model_id,
1269 'Record': record,
1270 'PredictEndpoint': predict_host,
1271 }
1272 return self.make_request(action='Predict',
1273 body=json.dumps(params),
1274 host=predict_host)
1275
1276 def update_batch_prediction(self, batch_prediction_id,
1277 batch_prediction_name):
1278 """
1279 Updates the `BatchPredictionName` of a `BatchPrediction`.
1280
1281 You can use the GetBatchPrediction operation to view the
1282 contents of the updated data element.
1283
1284 :type batch_prediction_id: string
1285 :param batch_prediction_id: The ID assigned to the `BatchPrediction`
1286 during creation.
1287
1288 :type batch_prediction_name: string
1289 :param batch_prediction_name: A new user-supplied name or description
1290 of the `BatchPrediction`.
1291
1292 """
1293 params = {
1294 'BatchPredictionId': batch_prediction_id,
1295 'BatchPredictionName': batch_prediction_name,
1296 }
1297 return self.make_request(action='UpdateBatchPrediction',
1298 body=json.dumps(params))
1299
1300 def update_data_source(self, data_source_id, data_source_name):
1301 """
1302 Updates the `DataSourceName` of a `DataSource`.
1303
1304 You can use the GetDataSource operation to view the contents
1305 of the updated data element.
1306
1307 :type data_source_id: string
1308 :param data_source_id: The ID assigned to the `DataSource` during
1309 creation.
1310
1311 :type data_source_name: string
1312 :param data_source_name: A new user-supplied name or description of the
1313 `DataSource` that will replace the current description.
1314
1315 """
1316 params = {
1317 'DataSourceId': data_source_id,
1318 'DataSourceName': data_source_name,
1319 }
1320 return self.make_request(action='UpdateDataSource',
1321 body=json.dumps(params))
1322
1323 def update_evaluation(self, evaluation_id, evaluation_name):
1324 """
1325 Updates the `EvaluationName` of an `Evaluation`.
1326
1327 You can use the GetEvaluation operation to view the contents
1328 of the updated data element.
1329
1330 :type evaluation_id: string
1331 :param evaluation_id: The ID assigned to the `Evaluation` during
1332 creation.
1333
1334 :type evaluation_name: string
1335 :param evaluation_name: A new user-supplied name or description of the
1336 `Evaluation` that will replace the current content.
1337
1338 """
1339 params = {
1340 'EvaluationId': evaluation_id,
1341 'EvaluationName': evaluation_name,
1342 }
1343 return self.make_request(action='UpdateEvaluation',
1344 body=json.dumps(params))
1345
1346 def update_ml_model(self, ml_model_id, ml_model_name=None,
1347 score_threshold=None):
1348 """
1349 Updates the `MLModelName` and the `ScoreThreshold` of an
1350 `MLModel`.
1351
1352 You can use the GetMLModel operation to view the contents of
1353 the updated data element.
1354
1355 :type ml_model_id: string
1356 :param ml_model_id: The ID assigned to the `MLModel` during creation.
1357
1358 :type ml_model_name: string
1359 :param ml_model_name: A user-supplied name or description of the
1360 `MLModel`.
1361
1362 :type score_threshold: float
1363 :param score_threshold: The `ScoreThreshold` used in binary
1364 classification `MLModel` that marks the boundary between a positive
1365 prediction and a negative prediction.
1366 Output values greater than or equal to the `ScoreThreshold` receive a
1367 positive result from the `MLModel`, such as `True`. Output values
1368 less than the `ScoreThreshold` receive a negative response from the
1369 `MLModel`, such as `False`.
1370
1371 """
1372 params = {'MLModelId': ml_model_id, }
1373 if ml_model_name is not None:
1374 params['MLModelName'] = ml_model_name
1375 if score_threshold is not None:
1376 params['ScoreThreshold'] = score_threshold
1377 return self.make_request(action='UpdateMLModel',
1378 body=json.dumps(params))
1379
1380 def make_request(self, action, body, host=None):
1381 headers = {
1382 'X-Amz-Target': '%s.%s' % (self.TargetPrefix, action),
1383 'Host': self.region.endpoint,
1384 'Content-Type': 'application/x-amz-json-1.1',
1385 'Content-Length': str(len(body)),
1386 }
1387 http_request_kwargs = {
1388 'method':'POST', 'path':'/', 'auth_path':'/', 'params':{},
1389 'headers': headers, 'data':body
1390 }
1391 if host is not None:
1392 headers['Host'] = host
1393 http_request_kwargs['host'] = host
1394 http_request = self.build_base_http_request(**http_request_kwargs)
1395 response = self._mexe(http_request, sender=None,
1396 override_num_retries=10)
1397 response_body = response.read().decode('utf-8')
1398 boto.log.debug(response_body)
1399 if response.status == 200:
1400 if response_body:
1401 return json.loads(response_body)
1402 else:
1403 json_body = json.loads(response_body)
1404 fault_name = json_body.get('__type', None)
1405 exception_class = self._faults.get(fault_name, self.ResponseError)
1406 raise exception_class(response.status, response.reason,
1407 body=json_body)
1408