1// Code generated by smithy-go-codegen DO NOT EDIT. 2 3package types 4 5import ( 6 smithydocument "github.com/aws/smithy-go/document" 7 "time" 8) 9 10// Specifies a categorical hyperparameter and it's range of tunable values. This 11// object is part of the ParameterRanges object. 12type CategoricalParameterRange struct { 13 14 // The name of the categorical hyperparameter to tune. 15 // 16 // This member is required. 17 Name *string 18 19 // A list of the tunable categories for the hyperparameter. 20 // 21 // This member is required. 22 Values []string 23 24 noSmithyDocumentSerde 25} 26 27// Specifies a continuous hyperparameter and it's range of tunable values. This 28// object is part of the ParameterRanges object. 29type ContinuousParameterRange struct { 30 31 // The maximum tunable value of the hyperparameter. 32 // 33 // This member is required. 34 MaxValue *float64 35 36 // The minimum tunable value of the hyperparameter. 37 // 38 // This member is required. 39 MinValue *float64 40 41 // The name of the hyperparameter to tune. 42 // 43 // This member is required. 44 Name *string 45 46 // The scale that hyperparameter tuning uses to search the hyperparameter range. 47 // Valid values: Auto Amazon Forecast hyperparameter tuning chooses the best scale 48 // for the hyperparameter. Linear Hyperparameter tuning searches the values in the 49 // hyperparameter range by using a linear scale. Logarithmic Hyperparameter tuning 50 // searches the values in the hyperparameter range by using a logarithmic scale. 51 // Logarithmic scaling works only for ranges that have values greater than 0. 52 // ReverseLogarithmic hyperparameter tuning searches the values in the 53 // hyperparameter range by using a reverse logarithmic scale. Reverse logarithmic 54 // scaling works only for ranges that are entirely within the range 0 <= x < 1.0. 55 // For information about choosing a hyperparameter scale, see Hyperparameter 56 // Scaling 57 // (http://docs.aws.amazon.com/sagemaker/latest/dg/automatic-model-tuning-define-ranges.html#scaling-type). 58 // One of the following values: 59 ScalingType ScalingType 60 61 noSmithyDocumentSerde 62} 63 64// The destination for an export job. Provide an S3 path, an AWS Identity and 65// Access Management (IAM) role that allows Amazon Forecast to access the location, 66// and an AWS Key Management Service (KMS) key (optional). 67type DataDestination struct { 68 69 // The path to an Amazon Simple Storage Service (Amazon S3) bucket along with the 70 // credentials to access the bucket. 71 // 72 // This member is required. 73 S3Config *S3Config 74 75 noSmithyDocumentSerde 76} 77 78// Provides a summary of the dataset group properties used in the ListDatasetGroups 79// operation. To get the complete set of properties, call the DescribeDatasetGroup 80// operation, and provide the DatasetGroupArn. 81type DatasetGroupSummary struct { 82 83 // When the dataset group was created. 84 CreationTime *time.Time 85 86 // The Amazon Resource Name (ARN) of the dataset group. 87 DatasetGroupArn *string 88 89 // The name of the dataset group. 90 DatasetGroupName *string 91 92 // When the dataset group was created or last updated from a call to the 93 // UpdateDatasetGroup operation. While the dataset group is being updated, 94 // LastModificationTime is the current time of the ListDatasetGroups call. 95 LastModificationTime *time.Time 96 97 noSmithyDocumentSerde 98} 99 100// Provides a summary of the dataset import job properties used in the 101// ListDatasetImportJobs operation. To get the complete set of properties, call the 102// DescribeDatasetImportJob operation, and provide the DatasetImportJobArn. 103type DatasetImportJobSummary struct { 104 105 // When the dataset import job was created. 106 CreationTime *time.Time 107 108 // The location of the training data to import and an AWS Identity and Access 109 // Management (IAM) role that Amazon Forecast can assume to access the data. The 110 // training data must be stored in an Amazon S3 bucket. If encryption is used, 111 // DataSource includes an AWS Key Management Service (KMS) key. 112 DataSource *DataSource 113 114 // The Amazon Resource Name (ARN) of the dataset import job. 115 DatasetImportJobArn *string 116 117 // The name of the dataset import job. 118 DatasetImportJobName *string 119 120 // The last time the resource was modified. The timestamp depends on the status of 121 // the job: 122 // 123 // * CREATE_PENDING - The CreationTime. 124 // 125 // * CREATE_IN_PROGRESS - The 126 // current timestamp. 127 // 128 // * CREATE_STOPPING - The current timestamp. 129 // 130 // * CREATE_STOPPED 131 // - When the job stopped. 132 // 133 // * ACTIVE or CREATE_FAILED - When the job finished or 134 // failed. 135 LastModificationTime *time.Time 136 137 // If an error occurred, an informational message about the error. 138 Message *string 139 140 // The status of the dataset import job. States include: 141 // 142 // * ACTIVE 143 // 144 // * 145 // CREATE_PENDING, CREATE_IN_PROGRESS, CREATE_FAILED 146 // 147 // * DELETE_PENDING, 148 // DELETE_IN_PROGRESS, DELETE_FAILED 149 // 150 // * CREATE_STOPPING, CREATE_STOPPED 151 Status *string 152 153 noSmithyDocumentSerde 154} 155 156// Provides a summary of the dataset properties used in the ListDatasets operation. 157// To get the complete set of properties, call the DescribeDataset operation, and 158// provide the DatasetArn. 159type DatasetSummary struct { 160 161 // When the dataset was created. 162 CreationTime *time.Time 163 164 // The Amazon Resource Name (ARN) of the dataset. 165 DatasetArn *string 166 167 // The name of the dataset. 168 DatasetName *string 169 170 // The dataset type. 171 DatasetType DatasetType 172 173 // The domain associated with the dataset. 174 Domain Domain 175 176 // When you create a dataset, LastModificationTime is the same as CreationTime. 177 // While data is being imported to the dataset, LastModificationTime is the current 178 // time of the ListDatasets call. After a CreateDatasetImportJob operation has 179 // finished, LastModificationTime is when the import job completed or failed. 180 LastModificationTime *time.Time 181 182 noSmithyDocumentSerde 183} 184 185// The source of your training data, an AWS Identity and Access Management (IAM) 186// role that allows Amazon Forecast to access the data and, optionally, an AWS Key 187// Management Service (KMS) key. This object is submitted in the 188// CreateDatasetImportJob request. 189type DataSource struct { 190 191 // The path to the training data stored in an Amazon Simple Storage Service (Amazon 192 // S3) bucket along with the credentials to access the data. 193 // 194 // This member is required. 195 S3Config *S3Config 196 197 noSmithyDocumentSerde 198} 199 200// An AWS Key Management Service (KMS) key and an AWS Identity and Access 201// Management (IAM) role that Amazon Forecast can assume to access the key. You can 202// specify this optional object in the CreateDataset and CreatePredictor requests. 203type EncryptionConfig struct { 204 205 // The Amazon Resource Name (ARN) of the KMS key. 206 // 207 // This member is required. 208 KMSKeyArn *string 209 210 // The ARN of the IAM role that Amazon Forecast can assume to access the AWS KMS 211 // key. Passing a role across AWS accounts is not allowed. If you pass a role that 212 // isn't in your account, you get an InvalidInputException error. 213 // 214 // This member is required. 215 RoleArn *string 216 217 noSmithyDocumentSerde 218} 219 220// Provides detailed error metrics to evaluate the performance of a predictor. This 221// object is part of the Metrics object. 222type ErrorMetric struct { 223 224 // The Forecast type used to compute WAPE, MAPE, MASE, and RMSE. 225 ForecastType *string 226 227 // The Mean Absolute Percentage Error (MAPE) 228 MAPE *float64 229 230 // The Mean Absolute Scaled Error (MASE) 231 MASE *float64 232 233 // The root-mean-square error (RMSE). 234 RMSE *float64 235 236 // The weighted absolute percentage error (WAPE). 237 WAPE *float64 238 239 noSmithyDocumentSerde 240} 241 242// Parameters that define how to split a dataset into training data and testing 243// data, and the number of iterations to perform. These parameters are specified in 244// the predefined algorithms but you can override them in the CreatePredictor 245// request. 246type EvaluationParameters struct { 247 248 // The point from the end of the dataset where you want to split the data for model 249 // training and testing (evaluation). Specify the value as the number of data 250 // points. The default is the value of the forecast horizon. BackTestWindowOffset 251 // can be used to mimic a past virtual forecast start date. This value must be 252 // greater than or equal to the forecast horizon and less than half of the 253 // TARGET_TIME_SERIES dataset length. ForecastHorizon <= BackTestWindowOffset < 1/2 254 // * TARGET_TIME_SERIES dataset length 255 BackTestWindowOffset *int32 256 257 // The number of times to split the input data. The default is 1. Valid values are 258 // 1 through 5. 259 NumberOfBacktestWindows *int32 260 261 noSmithyDocumentSerde 262} 263 264// The results of evaluating an algorithm. Returned as part of the 265// GetAccuracyMetrics response. 266type EvaluationResult struct { 267 268 // The Amazon Resource Name (ARN) of the algorithm that was evaluated. 269 AlgorithmArn *string 270 271 // The array of test windows used for evaluating the algorithm. The 272 // NumberOfBacktestWindows from the EvaluationParameters object determines the 273 // number of windows in the array. 274 TestWindows []WindowSummary 275 276 noSmithyDocumentSerde 277} 278 279// Provides featurization (transformation) information for a dataset field. This 280// object is part of the FeaturizationConfig object. For example: { 281// 282// "AttributeName": "demand", 283// 284// FeaturizationPipeline [ { 285// 286// 287// "FeaturizationMethodName": "filling", 288// 289// "FeaturizationMethodParameters": 290// {"aggregation": "avg", "backfill": "nan"} 291// 292// } ] 293// 294// } 295type Featurization struct { 296 297 // The name of the schema attribute that specifies the data field to be featurized. 298 // Amazon Forecast supports the target field of the TARGET_TIME_SERIES and the 299 // RELATED_TIME_SERIES datasets. For example, for the RETAIL domain, the target is 300 // demand, and for the CUSTOM domain, the target is target_value. For more 301 // information, see howitworks-missing-values. 302 // 303 // This member is required. 304 AttributeName *string 305 306 // An array of one FeaturizationMethod object that specifies the feature 307 // transformation method. 308 FeaturizationPipeline []FeaturizationMethod 309 310 noSmithyDocumentSerde 311} 312 313// In a CreatePredictor operation, the specified algorithm trains a model using the 314// specified dataset group. You can optionally tell the operation to modify data 315// fields prior to training a model. These modifications are referred to as 316// featurization. You define featurization using the FeaturizationConfig object. 317// You specify an array of transformations, one for each field that you want to 318// featurize. You then include the FeaturizationConfig object in your 319// CreatePredictor request. Amazon Forecast applies the featurization to the 320// TARGET_TIME_SERIES and RELATED_TIME_SERIES datasets before model training. You 321// can create multiple featurization configurations. For example, you might call 322// the CreatePredictor operation twice by specifying different featurization 323// configurations. 324type FeaturizationConfig struct { 325 326 // The frequency of predictions in a forecast. Valid intervals are Y (Year), M 327 // (Month), W (Week), D (Day), H (Hour), 30min (30 minutes), 15min (15 minutes), 328 // 10min (10 minutes), 5min (5 minutes), and 1min (1 minute). For example, "Y" 329 // indicates every year and "5min" indicates every five minutes. The frequency must 330 // be greater than or equal to the TARGET_TIME_SERIES dataset frequency. When a 331 // RELATED_TIME_SERIES dataset is provided, the frequency must be equal to the 332 // RELATED_TIME_SERIES dataset frequency. 333 // 334 // This member is required. 335 ForecastFrequency *string 336 337 // An array of featurization (transformation) information for the fields of a 338 // dataset. 339 Featurizations []Featurization 340 341 // An array of dimension (field) names that specify how to group the generated 342 // forecast. For example, suppose that you are generating a forecast for item sales 343 // across all of your stores, and your dataset contains a store_id field. If you 344 // want the sales forecast for each item by store, you would specify store_id as 345 // the dimension. All forecast dimensions specified in the TARGET_TIME_SERIES 346 // dataset don't need to be specified in the CreatePredictor request. All forecast 347 // dimensions specified in the RELATED_TIME_SERIES dataset must be specified in the 348 // CreatePredictor request. 349 ForecastDimensions []string 350 351 noSmithyDocumentSerde 352} 353 354// Provides information about the method that featurizes (transforms) a dataset 355// field. The method is part of the FeaturizationPipeline of the Featurization 356// object. The following is an example of how you specify a FeaturizationMethod 357// object. { 358// "FeaturizationMethodName": "filling", 359// 360// 361// "FeaturizationMethodParameters": {"aggregation": "sum", "middlefill": "zero", 362// "backfill": "zero"} 363// 364// } 365type FeaturizationMethod struct { 366 367 // The name of the method. The "filling" method is the only supported method. 368 // 369 // This member is required. 370 FeaturizationMethodName FeaturizationMethodName 371 372 // The method parameters (key-value pairs), which are a map of override parameters. 373 // Specify these parameters to override the default values. Related Time Series 374 // attributes do not accept aggregation parameters. The following list shows the 375 // parameters and their valid values for the "filling" featurization method for a 376 // Target Time Series dataset. Bold signifies the default value. 377 // 378 // * aggregation: 379 // sum, avg, first, min, max 380 // 381 // * frontfill: none 382 // 383 // * middlefill: zero, nan (not a 384 // number), value, median, mean, min, max 385 // 386 // * backfill: zero, nan, value, median, 387 // mean, min, max 388 // 389 // The following list shows the parameters and their valid values 390 // for a Related Time Series featurization method (there are no defaults): 391 // 392 // * 393 // middlefill: zero, value, median, mean, min, max 394 // 395 // * backfill: zero, value, 396 // median, mean, min, max 397 // 398 // * futurefill: zero, value, median, mean, min, max 399 // 400 // To 401 // set a filling method to a specific value, set the fill parameter to value and 402 // define the value in a corresponding _value parameter. For example, to set 403 // backfilling to a value of 2, include the following: "backfill": "value" and 404 // "backfill_value":"2". 405 FeaturizationMethodParameters map[string]string 406 407 noSmithyDocumentSerde 408} 409 410// Describes a filter for choosing a subset of objects. Each filter consists of a 411// condition and a match statement. The condition is either IS or IS_NOT, which 412// specifies whether to include or exclude the objects that match the statement, 413// respectively. The match statement consists of a key and a value. 414type Filter struct { 415 416 // The condition to apply. To include the objects that match the statement, specify 417 // IS. To exclude matching objects, specify IS_NOT. 418 // 419 // This member is required. 420 Condition FilterConditionString 421 422 // The name of the parameter to filter on. 423 // 424 // This member is required. 425 Key *string 426 427 // The value to match. 428 // 429 // This member is required. 430 Value *string 431 432 noSmithyDocumentSerde 433} 434 435// Provides a summary of the forecast export job properties used in the 436// ListForecastExportJobs operation. To get the complete set of properties, call 437// the DescribeForecastExportJob operation, and provide the listed 438// ForecastExportJobArn. 439type ForecastExportJobSummary struct { 440 441 // When the forecast export job was created. 442 CreationTime *time.Time 443 444 // The path to the Amazon Simple Storage Service (Amazon S3) bucket where the 445 // forecast is exported. 446 Destination *DataDestination 447 448 // The Amazon Resource Name (ARN) of the forecast export job. 449 ForecastExportJobArn *string 450 451 // The name of the forecast export job. 452 ForecastExportJobName *string 453 454 // The last time the resource was modified. The timestamp depends on the status of 455 // the job: 456 // 457 // * CREATE_PENDING - The CreationTime. 458 // 459 // * CREATE_IN_PROGRESS - The 460 // current timestamp. 461 // 462 // * CREATE_STOPPING - The current timestamp. 463 // 464 // * CREATE_STOPPED 465 // - When the job stopped. 466 // 467 // * ACTIVE or CREATE_FAILED - When the job finished or 468 // failed. 469 LastModificationTime *time.Time 470 471 // If an error occurred, an informational message about the error. 472 Message *string 473 474 // The status of the forecast export job. States include: 475 // 476 // * ACTIVE 477 // 478 // * 479 // CREATE_PENDING, CREATE_IN_PROGRESS, CREATE_FAILED 480 // 481 // * CREATE_STOPPING, 482 // CREATE_STOPPED 483 // 484 // * DELETE_PENDING, DELETE_IN_PROGRESS, DELETE_FAILED 485 // 486 // The Status 487 // of the forecast export job must be ACTIVE before you can access the forecast in 488 // your S3 bucket. 489 Status *string 490 491 noSmithyDocumentSerde 492} 493 494// Provides a summary of the forecast properties used in the ListForecasts 495// operation. To get the complete set of properties, call the DescribeForecast 496// operation, and provide the ForecastArn that is listed in the summary. 497type ForecastSummary struct { 498 499 // When the forecast creation task was created. 500 CreationTime *time.Time 501 502 // The Amazon Resource Name (ARN) of the dataset group that provided the data used 503 // to train the predictor. 504 DatasetGroupArn *string 505 506 // The ARN of the forecast. 507 ForecastArn *string 508 509 // The name of the forecast. 510 ForecastName *string 511 512 // The last time the resource was modified. The timestamp depends on the status of 513 // the job: 514 // 515 // * CREATE_PENDING - The CreationTime. 516 // 517 // * CREATE_IN_PROGRESS - The 518 // current timestamp. 519 // 520 // * CREATE_STOPPING - The current timestamp. 521 // 522 // * CREATE_STOPPED 523 // - When the job stopped. 524 // 525 // * ACTIVE or CREATE_FAILED - When the job finished or 526 // failed. 527 LastModificationTime *time.Time 528 529 // If an error occurred, an informational message about the error. 530 Message *string 531 532 // The ARN of the predictor used to generate the forecast. 533 PredictorArn *string 534 535 // The status of the forecast. States include: 536 // 537 // * ACTIVE 538 // 539 // * CREATE_PENDING, 540 // CREATE_IN_PROGRESS, CREATE_FAILED 541 // 542 // * CREATE_STOPPING, CREATE_STOPPED 543 // 544 // * 545 // DELETE_PENDING, DELETE_IN_PROGRESS, DELETE_FAILED 546 // 547 // The Status of the forecast 548 // must be ACTIVE before you can query or export the forecast. 549 Status *string 550 551 noSmithyDocumentSerde 552} 553 554// Configuration information for a hyperparameter tuning job. You specify this 555// object in the CreatePredictor request. A hyperparameter is a parameter that 556// governs the model training process. You set hyperparameters before training 557// starts, unlike model parameters, which are determined during training. The 558// values of the hyperparameters effect which values are chosen for the model 559// parameters. In a hyperparameter tuning job, Amazon Forecast chooses the set of 560// hyperparameter values that optimize a specified metric. Forecast accomplishes 561// this by running many training jobs over a range of hyperparameter values. The 562// optimum set of values depends on the algorithm, the training data, and the 563// specified metric objective. 564type HyperParameterTuningJobConfig struct { 565 566 // Specifies the ranges of valid values for the hyperparameters. 567 ParameterRanges *ParameterRanges 568 569 noSmithyDocumentSerde 570} 571 572// The data used to train a predictor. The data includes a dataset group and any 573// supplementary features. You specify this object in the CreatePredictor request. 574type InputDataConfig struct { 575 576 // The Amazon Resource Name (ARN) of the dataset group. 577 // 578 // This member is required. 579 DatasetGroupArn *string 580 581 // An array of supplementary features. The only supported feature is a holiday 582 // calendar. 583 SupplementaryFeatures []SupplementaryFeature 584 585 noSmithyDocumentSerde 586} 587 588// Specifies an integer hyperparameter and it's range of tunable values. This 589// object is part of the ParameterRanges object. 590type IntegerParameterRange struct { 591 592 // The maximum tunable value of the hyperparameter. 593 // 594 // This member is required. 595 MaxValue *int32 596 597 // The minimum tunable value of the hyperparameter. 598 // 599 // This member is required. 600 MinValue *int32 601 602 // The name of the hyperparameter to tune. 603 // 604 // This member is required. 605 Name *string 606 607 // The scale that hyperparameter tuning uses to search the hyperparameter range. 608 // Valid values: Auto Amazon Forecast hyperparameter tuning chooses the best scale 609 // for the hyperparameter. Linear Hyperparameter tuning searches the values in the 610 // hyperparameter range by using a linear scale. Logarithmic Hyperparameter tuning 611 // searches the values in the hyperparameter range by using a logarithmic scale. 612 // Logarithmic scaling works only for ranges that have values greater than 0. 613 // ReverseLogarithmic Not supported for IntegerParameterRange. Reverse logarithmic 614 // scaling works only for ranges that are entirely within the range 0 <= x < 1.0. 615 // For information about choosing a hyperparameter scale, see Hyperparameter 616 // Scaling 617 // (http://docs.aws.amazon.com/sagemaker/latest/dg/automatic-model-tuning-define-ranges.html#scaling-type). 618 // One of the following values: 619 ScalingType ScalingType 620 621 noSmithyDocumentSerde 622} 623 624// Provides metrics that are used to evaluate the performance of a predictor. This 625// object is part of the WindowSummary object. 626type Metrics struct { 627 628 // The average value of all weighted quantile losses. 629 AverageWeightedQuantileLoss *float64 630 631 // Provides detailed error metrics for each forecast type. Metrics include 632 // root-mean square-error (RMSE), mean absolute percentage error (MAPE), mean 633 // absolute scaled error (MASE), and weighted average percentage error (WAPE). 634 ErrorMetrics []ErrorMetric 635 636 // The root-mean-square error (RMSE). 637 // 638 // Deprecated: This property is deprecated, please refer to ErrorMetrics for both 639 // RMSE and WAPE 640 RMSE *float64 641 642 // An array of weighted quantile losses. Quantiles divide a probability 643 // distribution into regions of equal probability. The distribution in this case is 644 // the loss function. 645 WeightedQuantileLosses []WeightedQuantileLoss 646 647 noSmithyDocumentSerde 648} 649 650// Specifies the categorical, continuous, and integer hyperparameters, and their 651// ranges of tunable values. The range of tunable values determines which values 652// that a hyperparameter tuning job can choose for the specified hyperparameter. 653// This object is part of the HyperParameterTuningJobConfig object. 654type ParameterRanges struct { 655 656 // Specifies the tunable range for each categorical hyperparameter. 657 CategoricalParameterRanges []CategoricalParameterRange 658 659 // Specifies the tunable range for each continuous hyperparameter. 660 ContinuousParameterRanges []ContinuousParameterRange 661 662 // Specifies the tunable range for each integer hyperparameter. 663 IntegerParameterRanges []IntegerParameterRange 664 665 noSmithyDocumentSerde 666} 667 668// Provides a summary of the predictor backtest export job properties used in the 669// ListPredictorBacktestExportJobs operation. To get a complete set of properties, 670// call the DescribePredictorBacktestExportJob operation, and provide the listed 671// PredictorBacktestExportJobArn. 672type PredictorBacktestExportJobSummary struct { 673 674 // When the predictor backtest export job was created. 675 CreationTime *time.Time 676 677 // The destination for an export job. Provide an S3 path, an AWS Identity and 678 // Access Management (IAM) role that allows Amazon Forecast to access the location, 679 // and an AWS Key Management Service (KMS) key (optional). 680 Destination *DataDestination 681 682 // The last time the resource was modified. The timestamp depends on the status of 683 // the job: 684 // 685 // * CREATE_PENDING - The CreationTime. 686 // 687 // * CREATE_IN_PROGRESS - The 688 // current timestamp. 689 // 690 // * CREATE_STOPPING - The current timestamp. 691 // 692 // * CREATE_STOPPED 693 // - When the job stopped. 694 // 695 // * ACTIVE or CREATE_FAILED - When the job finished or 696 // failed. 697 LastModificationTime *time.Time 698 699 // Information about any errors that may have occurred during the backtest export. 700 Message *string 701 702 // The Amazon Resource Name (ARN) of the predictor backtest export job. 703 PredictorBacktestExportJobArn *string 704 705 // The name of the predictor backtest export job. 706 PredictorBacktestExportJobName *string 707 708 // The status of the predictor backtest export job. States include: 709 // 710 // * ACTIVE 711 // 712 // * 713 // CREATE_PENDING, CREATE_IN_PROGRESS, CREATE_FAILED 714 // 715 // * CREATE_STOPPING, 716 // CREATE_STOPPED 717 // 718 // * DELETE_PENDING, DELETE_IN_PROGRESS, DELETE_FAILED 719 Status *string 720 721 noSmithyDocumentSerde 722} 723 724// The algorithm used to perform a backtest and the status of those tests. 725type PredictorExecution struct { 726 727 // The ARN of the algorithm used to test the predictor. 728 AlgorithmArn *string 729 730 // An array of test windows used to evaluate the algorithm. The 731 // NumberOfBacktestWindows from the object determines the number of windows in the 732 // array. 733 TestWindows []TestWindowSummary 734 735 noSmithyDocumentSerde 736} 737 738// Contains details on the backtests performed to evaluate the accuracy of the 739// predictor. The tests are returned in descending order of accuracy, with the most 740// accurate backtest appearing first. You specify the number of backtests to 741// perform when you call the operation. 742type PredictorExecutionDetails struct { 743 744 // An array of the backtests performed to evaluate the accuracy of the predictor 745 // against a particular algorithm. The NumberOfBacktestWindows from the object 746 // determines the number of windows in the array. 747 PredictorExecutions []PredictorExecution 748 749 noSmithyDocumentSerde 750} 751 752// Provides a summary of the predictor properties that are used in the 753// ListPredictors operation. To get the complete set of properties, call the 754// DescribePredictor operation, and provide the listed PredictorArn. 755type PredictorSummary struct { 756 757 // When the model training task was created. 758 CreationTime *time.Time 759 760 // The Amazon Resource Name (ARN) of the dataset group that contains the data used 761 // to train the predictor. 762 DatasetGroupArn *string 763 764 // The last time the resource was modified. The timestamp depends on the status of 765 // the job: 766 // 767 // * CREATE_PENDING - The CreationTime. 768 // 769 // * CREATE_IN_PROGRESS - The 770 // current timestamp. 771 // 772 // * CREATE_STOPPING - The current timestamp. 773 // 774 // * CREATE_STOPPED 775 // - When the job stopped. 776 // 777 // * ACTIVE or CREATE_FAILED - When the job finished or 778 // failed. 779 LastModificationTime *time.Time 780 781 // If an error occurred, an informational message about the error. 782 Message *string 783 784 // The ARN of the predictor. 785 PredictorArn *string 786 787 // The name of the predictor. 788 PredictorName *string 789 790 // The status of the predictor. States include: 791 // 792 // * ACTIVE 793 // 794 // * CREATE_PENDING, 795 // CREATE_IN_PROGRESS, CREATE_FAILED 796 // 797 // * DELETE_PENDING, DELETE_IN_PROGRESS, 798 // DELETE_FAILED 799 // 800 // * CREATE_STOPPING, CREATE_STOPPED 801 // 802 // The Status of the predictor 803 // must be ACTIVE before you can use the predictor to create a forecast. 804 Status *string 805 806 noSmithyDocumentSerde 807} 808 809// The path to the file(s) in an Amazon Simple Storage Service (Amazon S3) bucket, 810// and an AWS Identity and Access Management (IAM) role that Amazon Forecast can 811// assume to access the file(s). Optionally, includes an AWS Key Management Service 812// (KMS) key. This object is part of the DataSource object that is submitted in the 813// CreateDatasetImportJob request, and part of the DataDestination object. 814type S3Config struct { 815 816 // The path to an Amazon Simple Storage Service (Amazon S3) bucket or file(s) in an 817 // Amazon S3 bucket. 818 // 819 // This member is required. 820 Path *string 821 822 // The ARN of the AWS Identity and Access Management (IAM) role that Amazon 823 // Forecast can assume to access the Amazon S3 bucket or files. If you provide a 824 // value for the KMSKeyArn key, the role must allow access to the key. Passing a 825 // role across AWS accounts is not allowed. If you pass a role that isn't in your 826 // account, you get an InvalidInputException error. 827 // 828 // This member is required. 829 RoleArn *string 830 831 // The Amazon Resource Name (ARN) of an AWS Key Management Service (KMS) key. 832 KMSKeyArn *string 833 834 noSmithyDocumentSerde 835} 836 837// Defines the fields of a dataset. You specify this object in the CreateDataset 838// request. 839type Schema struct { 840 841 // An array of attributes specifying the name and type of each field in a dataset. 842 Attributes []SchemaAttribute 843 844 noSmithyDocumentSerde 845} 846 847// An attribute of a schema, which defines a dataset field. A schema attribute is 848// required for every field in a dataset. The Schema object contains an array of 849// SchemaAttribute objects. 850type SchemaAttribute struct { 851 852 // The name of the dataset field. 853 AttributeName *string 854 855 // The data type of the field. 856 AttributeType AttributeType 857 858 noSmithyDocumentSerde 859} 860 861// Provides statistics for each data field imported into to an Amazon Forecast 862// dataset with the CreateDatasetImportJob operation. 863type Statistics struct { 864 865 // For a numeric field, the average value in the field. 866 Avg *float64 867 868 // The number of values in the field. If the response value is -1, refer to 869 // CountLong. 870 Count *int32 871 872 // The number of distinct values in the field. If the response value is -1, refer 873 // to CountDistinctLong. 874 CountDistinct *int32 875 876 // The number of distinct values in the field. CountDistinctLong is used instead of 877 // CountDistinct if the value is greater than 2,147,483,647. 878 CountDistinctLong *int64 879 880 // The number of values in the field. CountLong is used instead of Count if the 881 // value is greater than 2,147,483,647. 882 CountLong *int64 883 884 // The number of NAN (not a number) values in the field. If the response value is 885 // -1, refer to CountNanLong. 886 CountNan *int32 887 888 // The number of NAN (not a number) values in the field. CountNanLong is used 889 // instead of CountNan if the value is greater than 2,147,483,647. 890 CountNanLong *int64 891 892 // The number of null values in the field. If the response value is -1, refer to 893 // CountNullLong. 894 CountNull *int32 895 896 // The number of null values in the field. CountNullLong is used instead of 897 // CountNull if the value is greater than 2,147,483,647. 898 CountNullLong *int64 899 900 // For a numeric field, the maximum value in the field. 901 Max *string 902 903 // For a numeric field, the minimum value in the field. 904 Min *string 905 906 // For a numeric field, the standard deviation. 907 Stddev *float64 908 909 noSmithyDocumentSerde 910} 911 912// Describes a supplementary feature of a dataset group. This object is part of the 913// InputDataConfig object. Forecast supports the Weather Index and Holidays 914// built-in featurizations. Weather Index The Amazon Forecast Weather Index is a 915// built-in featurization that incorporates historical and projected weather 916// information into your model. The Weather Index supplements your datasets with 917// over two years of historical weather data and up to 14 days of projected weather 918// data. For more information, see Amazon Forecast Weather Index 919// (https://docs.aws.amazon.com/forecast/latest/dg/weather.html). Holidays Holidays 920// is a built-in featurization that incorporates a feature-engineered dataset of 921// national holiday information into your model. It provides native support for the 922// holiday calendars of 66 countries. To view the holiday calendars, refer to the 923// Jollyday (http://jollyday.sourceforge.net/data.html) library. For more 924// information, see Holidays Featurization 925// (https://docs.aws.amazon.com/forecast/latest/dg/holidays.html). 926type SupplementaryFeature struct { 927 928 // The name of the feature. Valid values: "holiday" and "weather". 929 // 930 // This member is required. 931 Name *string 932 933 // Weather Index To enable the Weather Index, set the value to "true" Holidays To 934 // enable Holidays, specify a country with one of the following two-letter country 935 // codes: 936 // 937 // * "AL" - ALBANIA 938 // 939 // * "AR" - ARGENTINA 940 // 941 // * "AT" - AUSTRIA 942 // 943 // * "AU" - 944 // AUSTRALIA 945 // 946 // * "BA" - BOSNIA HERZEGOVINA 947 // 948 // * "BE" - BELGIUM 949 // 950 // * "BG" - BULGARIA 951 // 952 // * 953 // "BO" - BOLIVIA 954 // 955 // * "BR" - BRAZIL 956 // 957 // * "BY" - BELARUS 958 // 959 // * "CA" - CANADA 960 // 961 // * "CL" - 962 // CHILE 963 // 964 // * "CO" - COLOMBIA 965 // 966 // * "CR" - COSTA RICA 967 // 968 // * "HR" - CROATIA 969 // 970 // * "CZ" - CZECH 971 // REPUBLIC 972 // 973 // * "DK" - DENMARK 974 // 975 // * "EC" - ECUADOR 976 // 977 // * "EE" - ESTONIA 978 // 979 // * "ET" - 980 // ETHIOPIA 981 // 982 // * "FI" - FINLAND 983 // 984 // * "FR" - FRANCE 985 // 986 // * "DE" - GERMANY 987 // 988 // * "GR" - 989 // GREECE 990 // 991 // * "HU" - HUNGARY 992 // 993 // * "IS" - ICELAND 994 // 995 // * "IN" - INDIA 996 // 997 // * "IE" - IRELAND 998 // 999 // * 1000 // "IT" - ITALY 1001 // 1002 // * "JP" - JAPAN 1003 // 1004 // * "KZ" - KAZAKHSTAN 1005 // 1006 // * "KR" - KOREA 1007 // 1008 // * "LV" - 1009 // LATVIA 1010 // 1011 // * "LI" - LIECHTENSTEIN 1012 // 1013 // * "LT" - LITHUANIA 1014 // 1015 // * "LU" - LUXEMBOURG 1016 // 1017 // * "MK" 1018 // - MACEDONIA 1019 // 1020 // * "MT" - MALTA 1021 // 1022 // * "MX" - MEXICO 1023 // 1024 // * "MD" - MOLDOVA 1025 // 1026 // * "ME" - 1027 // MONTENEGRO 1028 // 1029 // * "NL" - NETHERLANDS 1030 // 1031 // * "NZ" - NEW ZEALAND 1032 // 1033 // * "NI" - NICARAGUA 1034 // 1035 // * 1036 // "NG" - NIGERIA 1037 // 1038 // * "NO" - NORWAY 1039 // 1040 // * "PA" - PANAMA 1041 // 1042 // * "PY" - PARAGUAY 1043 // 1044 // * "PE" - 1045 // PERU 1046 // 1047 // * "PL" - POLAND 1048 // 1049 // * "PT" - PORTUGAL 1050 // 1051 // * "RO" - ROMANIA 1052 // 1053 // * "RU" - RUSSIA 1054 // 1055 // * 1056 // "RS" - SERBIA 1057 // 1058 // * "SK" - SLOVAKIA 1059 // 1060 // * "SI" - SLOVENIA 1061 // 1062 // * "ZA" - SOUTH AFRICA 1063 // 1064 // * 1065 // "ES" - SPAIN 1066 // 1067 // * "SE" - SWEDEN 1068 // 1069 // * "CH" - SWITZERLAND 1070 // 1071 // * "UA" - UKRAINE 1072 // 1073 // * "AE" - 1074 // UNITED ARAB EMIRATES 1075 // 1076 // * "US" - UNITED STATES 1077 // 1078 // * "UK" - UNITED KINGDOM 1079 // 1080 // * "UY" - 1081 // URUGUAY 1082 // 1083 // * "VE" - VENEZUELA 1084 // 1085 // This member is required. 1086 Value *string 1087 1088 noSmithyDocumentSerde 1089} 1090 1091// The optional metadata that you apply to a resource to help you categorize and 1092// organize them. Each tag consists of a key and an optional value, both of which 1093// you define. The following basic restrictions apply to tags: 1094// 1095// * Maximum number of 1096// tags per resource - 50. 1097// 1098// * For each resource, each tag key must be unique, and 1099// each tag key can have only one value. 1100// 1101// * Maximum key length - 128 Unicode 1102// characters in UTF-8. 1103// 1104// * Maximum value length - 256 Unicode characters in 1105// UTF-8. 1106// 1107// * If your tagging schema is used across multiple services and resources, 1108// remember that other services may have restrictions on allowed characters. 1109// Generally allowed characters are: letters, numbers, and spaces representable in 1110// UTF-8, and the following characters: + - = . _ : / @. 1111// 1112// * Tag keys and values are 1113// case sensitive. 1114// 1115// * Do not use aws:, AWS:, or any upper or lowercase combination 1116// of such as a prefix for keys as it is reserved for AWS use. You cannot edit or 1117// delete tag keys with this prefix. Values can have this prefix. If a tag value 1118// has aws as its prefix but the key does not, then Forecast considers it to be a 1119// user tag and will count against the limit of 50 tags. Tags with only the key 1120// prefix of aws do not count against your tags per resource limit. 1121type Tag struct { 1122 1123 // One part of a key-value pair that makes up a tag. A key is a general label that 1124 // acts like a category for more specific tag values. 1125 // 1126 // This member is required. 1127 Key *string 1128 1129 // The optional part of a key-value pair that makes up a tag. A value acts as a 1130 // descriptor within a tag category (key). 1131 // 1132 // This member is required. 1133 Value *string 1134 1135 noSmithyDocumentSerde 1136} 1137 1138// The status, start time, and end time of a backtest, as well as a failure reason 1139// if applicable. 1140type TestWindowSummary struct { 1141 1142 // If the test failed, the reason why it failed. 1143 Message *string 1144 1145 // The status of the test. Possible status values are: 1146 // 1147 // * ACTIVE 1148 // 1149 // * 1150 // CREATE_IN_PROGRESS 1151 // 1152 // * CREATE_FAILED 1153 Status *string 1154 1155 // The time at which the test ended. 1156 TestWindowEnd *time.Time 1157 1158 // The time at which the test began. 1159 TestWindowStart *time.Time 1160 1161 noSmithyDocumentSerde 1162} 1163 1164// The weighted loss value for a quantile. This object is part of the Metrics 1165// object. 1166type WeightedQuantileLoss struct { 1167 1168 // The difference between the predicted value and the actual value over the 1169 // quantile, weighted (normalized) by dividing by the sum over all quantiles. 1170 LossValue *float64 1171 1172 // The quantile. Quantiles divide a probability distribution into regions of equal 1173 // probability. For example, if the distribution was divided into 5 regions of 1174 // equal probability, the quantiles would be 0.2, 0.4, 0.6, and 0.8. 1175 Quantile *float64 1176 1177 noSmithyDocumentSerde 1178} 1179 1180// The metrics for a time range within the evaluation portion of a dataset. This 1181// object is part of the EvaluationResult object. The TestWindowStart and 1182// TestWindowEnd parameters are determined by the BackTestWindowOffset parameter of 1183// the EvaluationParameters object. 1184type WindowSummary struct { 1185 1186 // The type of evaluation. 1187 // 1188 // * SUMMARY - The average metrics across all windows. 1189 // 1190 // * 1191 // COMPUTED - The metrics for the specified window. 1192 EvaluationType EvaluationType 1193 1194 // The number of data points within the window. 1195 ItemCount *int32 1196 1197 // Provides metrics used to evaluate the performance of a predictor. 1198 Metrics *Metrics 1199 1200 // The timestamp that defines the end of the window. 1201 TestWindowEnd *time.Time 1202 1203 // The timestamp that defines the start of the window. 1204 TestWindowStart *time.Time 1205 1206 noSmithyDocumentSerde 1207} 1208 1209type noSmithyDocumentSerde = smithydocument.NoSerde 1210