| library googleapis.prediction.v1_6; |
| |
| import "dart:core" as core; |
| import "dart:collection" as collection; |
| import "dart:async" as async; |
| import "dart:convert" as convert; |
| |
| import "package:crypto/crypto.dart" as crypto; |
| import 'package:http/http.dart' as http; |
| import '../src/common_internal.dart' as common_internal; |
| import '../common/common.dart' as common; |
| |
| export '../common/common.dart' show ApiRequestError; |
| export '../common/common.dart' show DetailedApiRequestError; |
| |
| /** |
| * Lets you access a cloud hosted machine learning service that makes it easy to |
| * build smart apps |
| */ |
| class PredictionApi { |
| /** Manage your data and permissions in Google Cloud Storage */ |
| static const DevstorageFullControlScope = "https://www.googleapis.com/auth/devstorage.full_control"; |
| |
| /** View your data in Google Cloud Storage */ |
| static const DevstorageReadOnlyScope = "https://www.googleapis.com/auth/devstorage.read_only"; |
| |
| /** Manage your data in Google Cloud Storage */ |
| static const DevstorageReadWriteScope = "https://www.googleapis.com/auth/devstorage.read_write"; |
| |
| /** Manage your data in the Google Prediction API */ |
| static const PredictionScope = "https://www.googleapis.com/auth/prediction"; |
| |
| |
| final common_internal.ApiRequester _requester; |
| |
| HostedmodelsResourceApi get hostedmodels => new HostedmodelsResourceApi(_requester); |
| TrainedmodelsResourceApi get trainedmodels => new TrainedmodelsResourceApi(_requester); |
| |
| PredictionApi(http.Client client) : |
| _requester = new common_internal.ApiRequester(client, "https://www.googleapis.com/", "/prediction/v1.6/projects/"); |
| } |
| |
| |
| /** Not documented yet. */ |
| class HostedmodelsResourceApi { |
| final common_internal.ApiRequester _requester; |
| |
| HostedmodelsResourceApi(common_internal.ApiRequester client) : |
| _requester = client; |
| |
| /** |
| * Submit input and request an output against a hosted model. |
| * |
| * [request] - The metadata request object. |
| * |
| * Request parameters: |
| * |
| * [project] - The project associated with the model. |
| * |
| * [hostedModelName] - The name of a hosted model. |
| * |
| * Completes with a [Output]. |
| * |
| * Completes with a [common.ApiRequestError] if the API endpoint returned an |
| * error. |
| * |
| * If the used [http.Client] completes with an error when making a REST call, |
| * this method will complete with the same error. |
| */ |
| async.Future<Output> predict(Input request, core.String project, core.String hostedModelName) { |
| var _url = null; |
| var _queryParams = new core.Map(); |
| var _uploadMedia = null; |
| var _uploadOptions = null; |
| var _downloadOptions = common.DownloadOptions.Metadata; |
| var _body = null; |
| |
| if (request != null) { |
| _body = convert.JSON.encode((request).toJson()); |
| } |
| if (project == null) { |
| throw new core.ArgumentError("Parameter project is required."); |
| } |
| if (hostedModelName == null) { |
| throw new core.ArgumentError("Parameter hostedModelName is required."); |
| } |
| |
| |
| _url = common_internal.Escaper.ecapeVariable('$project') + '/hostedmodels/' + common_internal.Escaper.ecapeVariable('$hostedModelName') + '/predict'; |
| |
| var _response = _requester.request(_url, |
| "POST", |
| body: _body, |
| queryParams: _queryParams, |
| uploadOptions: _uploadOptions, |
| uploadMedia: _uploadMedia, |
| downloadOptions: _downloadOptions); |
| return _response.then((data) => new Output.fromJson(data)); |
| } |
| |
| } |
| |
| |
| /** Not documented yet. */ |
| class TrainedmodelsResourceApi { |
| final common_internal.ApiRequester _requester; |
| |
| TrainedmodelsResourceApi(common_internal.ApiRequester client) : |
| _requester = client; |
| |
| /** |
| * Get analysis of the model and the data the model was trained on. |
| * |
| * Request parameters: |
| * |
| * [project] - The project associated with the model. |
| * |
| * [id] - The unique name for the predictive model. |
| * |
| * Completes with a [Analyze]. |
| * |
| * Completes with a [common.ApiRequestError] if the API endpoint returned an |
| * error. |
| * |
| * If the used [http.Client] completes with an error when making a REST call, |
| * this method will complete with the same error. |
| */ |
| async.Future<Analyze> analyze(core.String project, core.String id) { |
| var _url = null; |
| var _queryParams = new core.Map(); |
| var _uploadMedia = null; |
| var _uploadOptions = null; |
| var _downloadOptions = common.DownloadOptions.Metadata; |
| var _body = null; |
| |
| if (project == null) { |
| throw new core.ArgumentError("Parameter project is required."); |
| } |
| if (id == null) { |
| throw new core.ArgumentError("Parameter id is required."); |
| } |
| |
| |
| _url = common_internal.Escaper.ecapeVariable('$project') + '/trainedmodels/' + common_internal.Escaper.ecapeVariable('$id') + '/analyze'; |
| |
| var _response = _requester.request(_url, |
| "GET", |
| body: _body, |
| queryParams: _queryParams, |
| uploadOptions: _uploadOptions, |
| uploadMedia: _uploadMedia, |
| downloadOptions: _downloadOptions); |
| return _response.then((data) => new Analyze.fromJson(data)); |
| } |
| |
| /** |
| * Delete a trained model. |
| * |
| * Request parameters: |
| * |
| * [project] - The project associated with the model. |
| * |
| * [id] - The unique name for the predictive model. |
| * |
| * Completes with a [common.ApiRequestError] if the API endpoint returned an |
| * error. |
| * |
| * If the used [http.Client] completes with an error when making a REST call, |
| * this method will complete with the same error. |
| */ |
| async.Future delete(core.String project, core.String id) { |
| var _url = null; |
| var _queryParams = new core.Map(); |
| var _uploadMedia = null; |
| var _uploadOptions = null; |
| var _downloadOptions = common.DownloadOptions.Metadata; |
| var _body = null; |
| |
| if (project == null) { |
| throw new core.ArgumentError("Parameter project is required."); |
| } |
| if (id == null) { |
| throw new core.ArgumentError("Parameter id is required."); |
| } |
| |
| _downloadOptions = null; |
| |
| _url = common_internal.Escaper.ecapeVariable('$project') + '/trainedmodels/' + common_internal.Escaper.ecapeVariable('$id'); |
| |
| var _response = _requester.request(_url, |
| "DELETE", |
| body: _body, |
| queryParams: _queryParams, |
| uploadOptions: _uploadOptions, |
| uploadMedia: _uploadMedia, |
| downloadOptions: _downloadOptions); |
| return _response.then((data) => null); |
| } |
| |
| /** |
| * Check training status of your model. |
| * |
| * Request parameters: |
| * |
| * [project] - The project associated with the model. |
| * |
| * [id] - The unique name for the predictive model. |
| * |
| * Completes with a [Insert2]. |
| * |
| * Completes with a [common.ApiRequestError] if the API endpoint returned an |
| * error. |
| * |
| * If the used [http.Client] completes with an error when making a REST call, |
| * this method will complete with the same error. |
| */ |
| async.Future<Insert2> get(core.String project, core.String id) { |
| var _url = null; |
| var _queryParams = new core.Map(); |
| var _uploadMedia = null; |
| var _uploadOptions = null; |
| var _downloadOptions = common.DownloadOptions.Metadata; |
| var _body = null; |
| |
| if (project == null) { |
| throw new core.ArgumentError("Parameter project is required."); |
| } |
| if (id == null) { |
| throw new core.ArgumentError("Parameter id is required."); |
| } |
| |
| |
| _url = common_internal.Escaper.ecapeVariable('$project') + '/trainedmodels/' + common_internal.Escaper.ecapeVariable('$id'); |
| |
| var _response = _requester.request(_url, |
| "GET", |
| body: _body, |
| queryParams: _queryParams, |
| uploadOptions: _uploadOptions, |
| uploadMedia: _uploadMedia, |
| downloadOptions: _downloadOptions); |
| return _response.then((data) => new Insert2.fromJson(data)); |
| } |
| |
| /** |
| * Train a Prediction API model. |
| * |
| * [request] - The metadata request object. |
| * |
| * Request parameters: |
| * |
| * [project] - The project associated with the model. |
| * |
| * Completes with a [Insert2]. |
| * |
| * Completes with a [common.ApiRequestError] if the API endpoint returned an |
| * error. |
| * |
| * If the used [http.Client] completes with an error when making a REST call, |
| * this method will complete with the same error. |
| */ |
| async.Future<Insert2> insert(Insert request, core.String project) { |
| var _url = null; |
| var _queryParams = new core.Map(); |
| var _uploadMedia = null; |
| var _uploadOptions = null; |
| var _downloadOptions = common.DownloadOptions.Metadata; |
| var _body = null; |
| |
| if (request != null) { |
| _body = convert.JSON.encode((request).toJson()); |
| } |
| if (project == null) { |
| throw new core.ArgumentError("Parameter project is required."); |
| } |
| |
| |
| _url = common_internal.Escaper.ecapeVariable('$project') + '/trainedmodels'; |
| |
| var _response = _requester.request(_url, |
| "POST", |
| body: _body, |
| queryParams: _queryParams, |
| uploadOptions: _uploadOptions, |
| uploadMedia: _uploadMedia, |
| downloadOptions: _downloadOptions); |
| return _response.then((data) => new Insert2.fromJson(data)); |
| } |
| |
| /** |
| * List available models. |
| * |
| * Request parameters: |
| * |
| * [project] - The project associated with the model. |
| * |
| * [maxResults] - Maximum number of results to return. |
| * |
| * [pageToken] - Pagination token. |
| * |
| * Completes with a [List]. |
| * |
| * Completes with a [common.ApiRequestError] if the API endpoint returned an |
| * error. |
| * |
| * If the used [http.Client] completes with an error when making a REST call, |
| * this method will complete with the same error. |
| */ |
| async.Future<List> list(core.String project, {core.int maxResults, core.String pageToken}) { |
| var _url = null; |
| var _queryParams = new core.Map(); |
| var _uploadMedia = null; |
| var _uploadOptions = null; |
| var _downloadOptions = common.DownloadOptions.Metadata; |
| var _body = null; |
| |
| if (project == null) { |
| throw new core.ArgumentError("Parameter project is required."); |
| } |
| if (maxResults != null) { |
| _queryParams["maxResults"] = ["${maxResults}"]; |
| } |
| if (pageToken != null) { |
| _queryParams["pageToken"] = [pageToken]; |
| } |
| |
| |
| _url = common_internal.Escaper.ecapeVariable('$project') + '/trainedmodels/list'; |
| |
| var _response = _requester.request(_url, |
| "GET", |
| body: _body, |
| queryParams: _queryParams, |
| uploadOptions: _uploadOptions, |
| uploadMedia: _uploadMedia, |
| downloadOptions: _downloadOptions); |
| return _response.then((data) => new List.fromJson(data)); |
| } |
| |
| /** |
| * Submit model id and request a prediction. |
| * |
| * [request] - The metadata request object. |
| * |
| * Request parameters: |
| * |
| * [project] - The project associated with the model. |
| * |
| * [id] - The unique name for the predictive model. |
| * |
| * Completes with a [Output]. |
| * |
| * Completes with a [common.ApiRequestError] if the API endpoint returned an |
| * error. |
| * |
| * If the used [http.Client] completes with an error when making a REST call, |
| * this method will complete with the same error. |
| */ |
| async.Future<Output> predict(Input request, core.String project, core.String id) { |
| var _url = null; |
| var _queryParams = new core.Map(); |
| var _uploadMedia = null; |
| var _uploadOptions = null; |
| var _downloadOptions = common.DownloadOptions.Metadata; |
| var _body = null; |
| |
| if (request != null) { |
| _body = convert.JSON.encode((request).toJson()); |
| } |
| if (project == null) { |
| throw new core.ArgumentError("Parameter project is required."); |
| } |
| if (id == null) { |
| throw new core.ArgumentError("Parameter id is required."); |
| } |
| |
| |
| _url = common_internal.Escaper.ecapeVariable('$project') + '/trainedmodels/' + common_internal.Escaper.ecapeVariable('$id') + '/predict'; |
| |
| var _response = _requester.request(_url, |
| "POST", |
| body: _body, |
| queryParams: _queryParams, |
| uploadOptions: _uploadOptions, |
| uploadMedia: _uploadMedia, |
| downloadOptions: _downloadOptions); |
| return _response.then((data) => new Output.fromJson(data)); |
| } |
| |
| /** |
| * Add new data to a trained model. |
| * |
| * [request] - The metadata request object. |
| * |
| * Request parameters: |
| * |
| * [project] - The project associated with the model. |
| * |
| * [id] - The unique name for the predictive model. |
| * |
| * Completes with a [Insert2]. |
| * |
| * Completes with a [common.ApiRequestError] if the API endpoint returned an |
| * error. |
| * |
| * If the used [http.Client] completes with an error when making a REST call, |
| * this method will complete with the same error. |
| */ |
| async.Future<Insert2> update(Update request, core.String project, core.String id) { |
| var _url = null; |
| var _queryParams = new core.Map(); |
| var _uploadMedia = null; |
| var _uploadOptions = null; |
| var _downloadOptions = common.DownloadOptions.Metadata; |
| var _body = null; |
| |
| if (request != null) { |
| _body = convert.JSON.encode((request).toJson()); |
| } |
| if (project == null) { |
| throw new core.ArgumentError("Parameter project is required."); |
| } |
| if (id == null) { |
| throw new core.ArgumentError("Parameter id is required."); |
| } |
| |
| |
| _url = common_internal.Escaper.ecapeVariable('$project') + '/trainedmodels/' + common_internal.Escaper.ecapeVariable('$id'); |
| |
| var _response = _requester.request(_url, |
| "PUT", |
| body: _body, |
| queryParams: _queryParams, |
| uploadOptions: _uploadOptions, |
| uploadMedia: _uploadMedia, |
| downloadOptions: _downloadOptions); |
| return _response.then((data) => new Insert2.fromJson(data)); |
| } |
| |
| } |
| |
| |
| |
| /** Not documented yet. */ |
| class AnalyzeDataDescriptionFeaturesCategoricalValues { |
| /** Number of times this feature had this value. */ |
| core.String count; |
| |
| /** The category name. */ |
| core.String value; |
| |
| |
| AnalyzeDataDescriptionFeaturesCategoricalValues(); |
| |
| AnalyzeDataDescriptionFeaturesCategoricalValues.fromJson(core.Map _json) { |
| if (_json.containsKey("count")) { |
| count = _json["count"]; |
| } |
| if (_json.containsKey("value")) { |
| value = _json["value"]; |
| } |
| } |
| |
| core.Map toJson() { |
| var _json = new core.Map(); |
| if (count != null) { |
| _json["count"] = count; |
| } |
| if (value != null) { |
| _json["value"] = value; |
| } |
| return _json; |
| } |
| } |
| |
| |
| /** Description of the categorical values of this feature. */ |
| class AnalyzeDataDescriptionFeaturesCategorical { |
| /** Number of categorical values for this feature in the data. */ |
| core.String count; |
| |
| /** List of all the categories for this feature in the data set. */ |
| core.List<AnalyzeDataDescriptionFeaturesCategoricalValues> values; |
| |
| |
| AnalyzeDataDescriptionFeaturesCategorical(); |
| |
| AnalyzeDataDescriptionFeaturesCategorical.fromJson(core.Map _json) { |
| if (_json.containsKey("count")) { |
| count = _json["count"]; |
| } |
| if (_json.containsKey("values")) { |
| values = _json["values"].map((value) => new AnalyzeDataDescriptionFeaturesCategoricalValues.fromJson(value)).toList(); |
| } |
| } |
| |
| core.Map toJson() { |
| var _json = new core.Map(); |
| if (count != null) { |
| _json["count"] = count; |
| } |
| if (values != null) { |
| _json["values"] = values.map((value) => (value).toJson()).toList(); |
| } |
| return _json; |
| } |
| } |
| |
| |
| /** Description of the numeric values of this feature. */ |
| class AnalyzeDataDescriptionFeaturesNumeric { |
| /** Number of numeric values for this feature in the data set. */ |
| core.String count; |
| |
| /** Mean of the numeric values of this feature in the data set. */ |
| core.String mean; |
| |
| /** Variance of the numeric values of this feature in the data set. */ |
| core.String variance; |
| |
| |
| AnalyzeDataDescriptionFeaturesNumeric(); |
| |
| AnalyzeDataDescriptionFeaturesNumeric.fromJson(core.Map _json) { |
| if (_json.containsKey("count")) { |
| count = _json["count"]; |
| } |
| if (_json.containsKey("mean")) { |
| mean = _json["mean"]; |
| } |
| if (_json.containsKey("variance")) { |
| variance = _json["variance"]; |
| } |
| } |
| |
| core.Map toJson() { |
| var _json = new core.Map(); |
| if (count != null) { |
| _json["count"] = count; |
| } |
| if (mean != null) { |
| _json["mean"] = mean; |
| } |
| if (variance != null) { |
| _json["variance"] = variance; |
| } |
| return _json; |
| } |
| } |
| |
| |
| /** Description of multiple-word text values of this feature. */ |
| class AnalyzeDataDescriptionFeaturesText { |
| /** Number of multiple-word text values for this feature. */ |
| core.String count; |
| |
| |
| AnalyzeDataDescriptionFeaturesText(); |
| |
| AnalyzeDataDescriptionFeaturesText.fromJson(core.Map _json) { |
| if (_json.containsKey("count")) { |
| count = _json["count"]; |
| } |
| } |
| |
| core.Map toJson() { |
| var _json = new core.Map(); |
| if (count != null) { |
| _json["count"] = count; |
| } |
| return _json; |
| } |
| } |
| |
| |
| /** Not documented yet. */ |
| class AnalyzeDataDescriptionFeatures { |
| /** Description of the categorical values of this feature. */ |
| AnalyzeDataDescriptionFeaturesCategorical categorical; |
| |
| /** The feature index. */ |
| core.String index; |
| |
| /** Description of the numeric values of this feature. */ |
| AnalyzeDataDescriptionFeaturesNumeric numeric; |
| |
| /** Description of multiple-word text values of this feature. */ |
| AnalyzeDataDescriptionFeaturesText text; |
| |
| |
| AnalyzeDataDescriptionFeatures(); |
| |
| AnalyzeDataDescriptionFeatures.fromJson(core.Map _json) { |
| if (_json.containsKey("categorical")) { |
| categorical = new AnalyzeDataDescriptionFeaturesCategorical.fromJson(_json["categorical"]); |
| } |
| if (_json.containsKey("index")) { |
| index = _json["index"]; |
| } |
| if (_json.containsKey("numeric")) { |
| numeric = new AnalyzeDataDescriptionFeaturesNumeric.fromJson(_json["numeric"]); |
| } |
| if (_json.containsKey("text")) { |
| text = new AnalyzeDataDescriptionFeaturesText.fromJson(_json["text"]); |
| } |
| } |
| |
| core.Map toJson() { |
| var _json = new core.Map(); |
| if (categorical != null) { |
| _json["categorical"] = (categorical).toJson(); |
| } |
| if (index != null) { |
| _json["index"] = index; |
| } |
| if (numeric != null) { |
| _json["numeric"] = (numeric).toJson(); |
| } |
| if (text != null) { |
| _json["text"] = (text).toJson(); |
| } |
| return _json; |
| } |
| } |
| |
| |
| /** Description of the output values in the data set. */ |
| class AnalyzeDataDescriptionOutputFeatureNumeric { |
| /** Number of numeric output values in the data set. */ |
| core.String count; |
| |
| /** Mean of the output values in the data set. */ |
| core.String mean; |
| |
| /** Variance of the output values in the data set. */ |
| core.String variance; |
| |
| |
| AnalyzeDataDescriptionOutputFeatureNumeric(); |
| |
| AnalyzeDataDescriptionOutputFeatureNumeric.fromJson(core.Map _json) { |
| if (_json.containsKey("count")) { |
| count = _json["count"]; |
| } |
| if (_json.containsKey("mean")) { |
| mean = _json["mean"]; |
| } |
| if (_json.containsKey("variance")) { |
| variance = _json["variance"]; |
| } |
| } |
| |
| core.Map toJson() { |
| var _json = new core.Map(); |
| if (count != null) { |
| _json["count"] = count; |
| } |
| if (mean != null) { |
| _json["mean"] = mean; |
| } |
| if (variance != null) { |
| _json["variance"] = variance; |
| } |
| return _json; |
| } |
| } |
| |
| |
| /** Not documented yet. */ |
| class AnalyzeDataDescriptionOutputFeatureText { |
| /** Number of times the output label occurred in the data set. */ |
| core.String count; |
| |
| /** The output label. */ |
| core.String value; |
| |
| |
| AnalyzeDataDescriptionOutputFeatureText(); |
| |
| AnalyzeDataDescriptionOutputFeatureText.fromJson(core.Map _json) { |
| if (_json.containsKey("count")) { |
| count = _json["count"]; |
| } |
| if (_json.containsKey("value")) { |
| value = _json["value"]; |
| } |
| } |
| |
| core.Map toJson() { |
| var _json = new core.Map(); |
| if (count != null) { |
| _json["count"] = count; |
| } |
| if (value != null) { |
| _json["value"] = value; |
| } |
| return _json; |
| } |
| } |
| |
| |
| /** Description of the output value or label. */ |
| class AnalyzeDataDescriptionOutputFeature { |
| /** Description of the output values in the data set. */ |
| AnalyzeDataDescriptionOutputFeatureNumeric numeric; |
| |
| /** Description of the output labels in the data set. */ |
| core.List<AnalyzeDataDescriptionOutputFeatureText> text; |
| |
| |
| AnalyzeDataDescriptionOutputFeature(); |
| |
| AnalyzeDataDescriptionOutputFeature.fromJson(core.Map _json) { |
| if (_json.containsKey("numeric")) { |
| numeric = new AnalyzeDataDescriptionOutputFeatureNumeric.fromJson(_json["numeric"]); |
| } |
| if (_json.containsKey("text")) { |
| text = _json["text"].map((value) => new AnalyzeDataDescriptionOutputFeatureText.fromJson(value)).toList(); |
| } |
| } |
| |
| core.Map toJson() { |
| var _json = new core.Map(); |
| if (numeric != null) { |
| _json["numeric"] = (numeric).toJson(); |
| } |
| if (text != null) { |
| _json["text"] = text.map((value) => (value).toJson()).toList(); |
| } |
| return _json; |
| } |
| } |
| |
| |
| /** Description of the data the model was trained on. */ |
| class AnalyzeDataDescription { |
| /** Description of the input features in the data set. */ |
| core.List<AnalyzeDataDescriptionFeatures> features; |
| |
| /** Description of the output value or label. */ |
| AnalyzeDataDescriptionOutputFeature outputFeature; |
| |
| |
| AnalyzeDataDescription(); |
| |
| AnalyzeDataDescription.fromJson(core.Map _json) { |
| if (_json.containsKey("features")) { |
| features = _json["features"].map((value) => new AnalyzeDataDescriptionFeatures.fromJson(value)).toList(); |
| } |
| if (_json.containsKey("outputFeature")) { |
| outputFeature = new AnalyzeDataDescriptionOutputFeature.fromJson(_json["outputFeature"]); |
| } |
| } |
| |
| core.Map toJson() { |
| var _json = new core.Map(); |
| if (features != null) { |
| _json["features"] = features.map((value) => (value).toJson()).toList(); |
| } |
| if (outputFeature != null) { |
| _json["outputFeature"] = (outputFeature).toJson(); |
| } |
| return _json; |
| } |
| } |
| |
| |
| /** Description of the model. */ |
| class AnalyzeModelDescription { |
| /** |
| * An output confusion matrix. This shows an estimate for how this model will |
| * do in predictions. This is first indexed by the true class label. For each |
| * true class label, this provides a pair {predicted_label, count}, where |
| * count is the estimated number of times the model will predict the predicted |
| * label given the true label. Will not output if more then 100 classes |
| * (Categorical models only). |
| */ |
| core.Map<core.String, core.Map<core.String, core.String>> confusionMatrix; |
| |
| /** A list of the confusion matrix row totals. */ |
| core.Map<core.String, core.String> confusionMatrixRowTotals; |
| |
| /** Basic information about the model. */ |
| Insert2 modelinfo; |
| |
| |
| AnalyzeModelDescription(); |
| |
| AnalyzeModelDescription.fromJson(core.Map _json) { |
| if (_json.containsKey("confusionMatrix")) { |
| confusionMatrix = _json["confusionMatrix"]; |
| } |
| if (_json.containsKey("confusionMatrixRowTotals")) { |
| confusionMatrixRowTotals = _json["confusionMatrixRowTotals"]; |
| } |
| if (_json.containsKey("modelinfo")) { |
| modelinfo = new Insert2.fromJson(_json["modelinfo"]); |
| } |
| } |
| |
| core.Map toJson() { |
| var _json = new core.Map(); |
| if (confusionMatrix != null) { |
| _json["confusionMatrix"] = confusionMatrix; |
| } |
| if (confusionMatrixRowTotals != null) { |
| _json["confusionMatrixRowTotals"] = confusionMatrixRowTotals; |
| } |
| if (modelinfo != null) { |
| _json["modelinfo"] = (modelinfo).toJson(); |
| } |
| return _json; |
| } |
| } |
| |
| |
| /** Not documented yet. */ |
| class Analyze { |
| /** Description of the data the model was trained on. */ |
| AnalyzeDataDescription dataDescription; |
| |
| /** List of errors with the data. */ |
| core.List<core.Map<core.String, core.String>> errors; |
| |
| /** The unique name for the predictive model. */ |
| core.String id; |
| |
| /** What kind of resource this is. */ |
| core.String kind; |
| |
| /** Description of the model. */ |
| AnalyzeModelDescription modelDescription; |
| |
| /** A URL to re-request this resource. */ |
| core.String selfLink; |
| |
| |
| Analyze(); |
| |
| Analyze.fromJson(core.Map _json) { |
| if (_json.containsKey("dataDescription")) { |
| dataDescription = new AnalyzeDataDescription.fromJson(_json["dataDescription"]); |
| } |
| if (_json.containsKey("errors")) { |
| errors = _json["errors"]; |
| } |
| if (_json.containsKey("id")) { |
| id = _json["id"]; |
| } |
| if (_json.containsKey("kind")) { |
| kind = _json["kind"]; |
| } |
| if (_json.containsKey("modelDescription")) { |
| modelDescription = new AnalyzeModelDescription.fromJson(_json["modelDescription"]); |
| } |
| if (_json.containsKey("selfLink")) { |
| selfLink = _json["selfLink"]; |
| } |
| } |
| |
| core.Map toJson() { |
| var _json = new core.Map(); |
| if (dataDescription != null) { |
| _json["dataDescription"] = (dataDescription).toJson(); |
| } |
| if (errors != null) { |
| _json["errors"] = errors; |
| } |
| if (id != null) { |
| _json["id"] = id; |
| } |
| if (kind != null) { |
| _json["kind"] = kind; |
| } |
| if (modelDescription != null) { |
| _json["modelDescription"] = (modelDescription).toJson(); |
| } |
| if (selfLink != null) { |
| _json["selfLink"] = selfLink; |
| } |
| return _json; |
| } |
| } |
| |
| |
| /** Input to the model for a prediction. */ |
| class InputInput { |
| /** |
| * A list of input features, these can be strings or doubles. |
| * |
| * The values for Object must be JSON objects. It can consist of `num`, |
| * `String`, `bool` and `null` as well as `Map` and `List` values. |
| */ |
| core.List<core.Object> csvInstance; |
| |
| |
| InputInput(); |
| |
| InputInput.fromJson(core.Map _json) { |
| if (_json.containsKey("csvInstance")) { |
| csvInstance = _json["csvInstance"]; |
| } |
| } |
| |
| core.Map toJson() { |
| var _json = new core.Map(); |
| if (csvInstance != null) { |
| _json["csvInstance"] = csvInstance; |
| } |
| return _json; |
| } |
| } |
| |
| |
| /** Not documented yet. */ |
| class Input { |
| /** Input to the model for a prediction. */ |
| InputInput input; |
| |
| |
| Input(); |
| |
| Input.fromJson(core.Map _json) { |
| if (_json.containsKey("input")) { |
| input = new InputInput.fromJson(_json["input"]); |
| } |
| } |
| |
| core.Map toJson() { |
| var _json = new core.Map(); |
| if (input != null) { |
| _json["input"] = (input).toJson(); |
| } |
| return _json; |
| } |
| } |
| |
| |
| /** Not documented yet. */ |
| class InsertTrainingInstances { |
| /** |
| * The input features for this instance. |
| * |
| * The values for Object must be JSON objects. It can consist of `num`, |
| * `String`, `bool` and `null` as well as `Map` and `List` values. |
| */ |
| core.List<core.Object> csvInstance; |
| |
| /** The generic output value - could be regression or class label. */ |
| core.String output; |
| |
| |
| InsertTrainingInstances(); |
| |
| InsertTrainingInstances.fromJson(core.Map _json) { |
| if (_json.containsKey("csvInstance")) { |
| csvInstance = _json["csvInstance"]; |
| } |
| if (_json.containsKey("output")) { |
| output = _json["output"]; |
| } |
| } |
| |
| core.Map toJson() { |
| var _json = new core.Map(); |
| if (csvInstance != null) { |
| _json["csvInstance"] = csvInstance; |
| } |
| if (output != null) { |
| _json["output"] = output; |
| } |
| return _json; |
| } |
| } |
| |
| |
| /** Not documented yet. */ |
| class Insert { |
| /** The unique name for the predictive model. */ |
| core.String id; |
| |
| /** Type of predictive model (classification or regression). */ |
| core.String modelType; |
| |
| /** The Id of the model to be copied over. */ |
| core.String sourceModel; |
| |
| /** Google storage location of the training data file. */ |
| core.String storageDataLocation; |
| |
| /** Google storage location of the preprocessing pmml file. */ |
| core.String storagePMMLLocation; |
| |
| /** Google storage location of the pmml model file. */ |
| core.String storagePMMLModelLocation; |
| |
| /** Instances to train model on. */ |
| core.List<InsertTrainingInstances> trainingInstances; |
| |
| /** |
| * A class weighting function, which allows the importance weights for class |
| * labels to be specified (Categorical models only). |
| */ |
| core.List<core.Map<core.String, core.double>> utility; |
| |
| |
| Insert(); |
| |
| Insert.fromJson(core.Map _json) { |
| if (_json.containsKey("id")) { |
| id = _json["id"]; |
| } |
| if (_json.containsKey("modelType")) { |
| modelType = _json["modelType"]; |
| } |
| if (_json.containsKey("sourceModel")) { |
| sourceModel = _json["sourceModel"]; |
| } |
| if (_json.containsKey("storageDataLocation")) { |
| storageDataLocation = _json["storageDataLocation"]; |
| } |
| if (_json.containsKey("storagePMMLLocation")) { |
| storagePMMLLocation = _json["storagePMMLLocation"]; |
| } |
| if (_json.containsKey("storagePMMLModelLocation")) { |
| storagePMMLModelLocation = _json["storagePMMLModelLocation"]; |
| } |
| if (_json.containsKey("trainingInstances")) { |
| trainingInstances = _json["trainingInstances"].map((value) => new InsertTrainingInstances.fromJson(value)).toList(); |
| } |
| if (_json.containsKey("utility")) { |
| utility = _json["utility"]; |
| } |
| } |
| |
| core.Map toJson() { |
| var _json = new core.Map(); |
| if (id != null) { |
| _json["id"] = id; |
| } |
| if (modelType != null) { |
| _json["modelType"] = modelType; |
| } |
| if (sourceModel != null) { |
| _json["sourceModel"] = sourceModel; |
| } |
| if (storageDataLocation != null) { |
| _json["storageDataLocation"] = storageDataLocation; |
| } |
| if (storagePMMLLocation != null) { |
| _json["storagePMMLLocation"] = storagePMMLLocation; |
| } |
| if (storagePMMLModelLocation != null) { |
| _json["storagePMMLModelLocation"] = storagePMMLModelLocation; |
| } |
| if (trainingInstances != null) { |
| _json["trainingInstances"] = trainingInstances.map((value) => (value).toJson()).toList(); |
| } |
| if (utility != null) { |
| _json["utility"] = utility; |
| } |
| return _json; |
| } |
| } |
| |
| |
| /** Model metadata. */ |
| class Insert2ModelInfo { |
| /** |
| * Estimated accuracy of model taking utility weights into account |
| * (Categorical models only). |
| */ |
| core.String classWeightedAccuracy; |
| |
| /** |
| * A number between 0.0 and 1.0, where 1.0 is 100% accurate. This is an |
| * estimate, based on the amount and quality of the training data, of the |
| * estimated prediction accuracy. You can use this is a guide to decide |
| * whether the results are accurate enough for your needs. This estimate will |
| * be more reliable if your real input data is similar to your training data |
| * (Categorical models only). |
| */ |
| core.String classificationAccuracy; |
| |
| /** |
| * An estimated mean squared error. The can be used to measure the quality of |
| * the predicted model (Regression models only). |
| */ |
| core.String meanSquaredError; |
| |
| /** Type of predictive model (CLASSIFICATION or REGRESSION). */ |
| core.String modelType; |
| |
| /** Number of valid data instances used in the trained model. */ |
| core.String numberInstances; |
| |
| /** Number of class labels in the trained model (Categorical models only). */ |
| core.String numberLabels; |
| |
| |
| Insert2ModelInfo(); |
| |
| Insert2ModelInfo.fromJson(core.Map _json) { |
| if (_json.containsKey("classWeightedAccuracy")) { |
| classWeightedAccuracy = _json["classWeightedAccuracy"]; |
| } |
| if (_json.containsKey("classificationAccuracy")) { |
| classificationAccuracy = _json["classificationAccuracy"]; |
| } |
| if (_json.containsKey("meanSquaredError")) { |
| meanSquaredError = _json["meanSquaredError"]; |
| } |
| if (_json.containsKey("modelType")) { |
| modelType = _json["modelType"]; |
| } |
| if (_json.containsKey("numberInstances")) { |
| numberInstances = _json["numberInstances"]; |
| } |
| if (_json.containsKey("numberLabels")) { |
| numberLabels = _json["numberLabels"]; |
| } |
| } |
| |
| core.Map toJson() { |
| var _json = new core.Map(); |
| if (classWeightedAccuracy != null) { |
| _json["classWeightedAccuracy"] = classWeightedAccuracy; |
| } |
| if (classificationAccuracy != null) { |
| _json["classificationAccuracy"] = classificationAccuracy; |
| } |
| if (meanSquaredError != null) { |
| _json["meanSquaredError"] = meanSquaredError; |
| } |
| if (modelType != null) { |
| _json["modelType"] = modelType; |
| } |
| if (numberInstances != null) { |
| _json["numberInstances"] = numberInstances; |
| } |
| if (numberLabels != null) { |
| _json["numberLabels"] = numberLabels; |
| } |
| return _json; |
| } |
| } |
| |
| |
| /** Not documented yet. */ |
| class Insert2 { |
| /** Insert time of the model (as a RFC 3339 timestamp). */ |
| core.DateTime created; |
| |
| /** The unique name for the predictive model. */ |
| core.String id; |
| |
| /** What kind of resource this is. */ |
| core.String kind; |
| |
| /** Model metadata. */ |
| Insert2ModelInfo modelInfo; |
| |
| /** Type of predictive model (CLASSIFICATION or REGRESSION). */ |
| core.String modelType; |
| |
| /** A URL to re-request this resource. */ |
| core.String selfLink; |
| |
| /** Google storage location of the training data file. */ |
| core.String storageDataLocation; |
| |
| /** Google storage location of the preprocessing pmml file. */ |
| core.String storagePMMLLocation; |
| |
| /** Google storage location of the pmml model file. */ |
| core.String storagePMMLModelLocation; |
| |
| /** Training completion time (as a RFC 3339 timestamp). */ |
| core.DateTime trainingComplete; |
| |
| /** |
| * The current status of the training job. This can be one of following: |
| * RUNNING; DONE; ERROR; ERROR: TRAINING JOB NOT FOUND |
| */ |
| core.String trainingStatus; |
| |
| |
| Insert2(); |
| |
| Insert2.fromJson(core.Map _json) { |
| if (_json.containsKey("created")) { |
| created = core.DateTime.parse(_json["created"]); |
| } |
| if (_json.containsKey("id")) { |
| id = _json["id"]; |
| } |
| if (_json.containsKey("kind")) { |
| kind = _json["kind"]; |
| } |
| if (_json.containsKey("modelInfo")) { |
| modelInfo = new Insert2ModelInfo.fromJson(_json["modelInfo"]); |
| } |
| if (_json.containsKey("modelType")) { |
| modelType = _json["modelType"]; |
| } |
| if (_json.containsKey("selfLink")) { |
| selfLink = _json["selfLink"]; |
| } |
| if (_json.containsKey("storageDataLocation")) { |
| storageDataLocation = _json["storageDataLocation"]; |
| } |
| if (_json.containsKey("storagePMMLLocation")) { |
| storagePMMLLocation = _json["storagePMMLLocation"]; |
| } |
| if (_json.containsKey("storagePMMLModelLocation")) { |
| storagePMMLModelLocation = _json["storagePMMLModelLocation"]; |
| } |
| if (_json.containsKey("trainingComplete")) { |
| trainingComplete = core.DateTime.parse(_json["trainingComplete"]); |
| } |
| if (_json.containsKey("trainingStatus")) { |
| trainingStatus = _json["trainingStatus"]; |
| } |
| } |
| |
| core.Map toJson() { |
| var _json = new core.Map(); |
| if (created != null) { |
| _json["created"] = (created).toIso8601String(); |
| } |
| if (id != null) { |
| _json["id"] = id; |
| } |
| if (kind != null) { |
| _json["kind"] = kind; |
| } |
| if (modelInfo != null) { |
| _json["modelInfo"] = (modelInfo).toJson(); |
| } |
| if (modelType != null) { |
| _json["modelType"] = modelType; |
| } |
| if (selfLink != null) { |
| _json["selfLink"] = selfLink; |
| } |
| if (storageDataLocation != null) { |
| _json["storageDataLocation"] = storageDataLocation; |
| } |
| if (storagePMMLLocation != null) { |
| _json["storagePMMLLocation"] = storagePMMLLocation; |
| } |
| if (storagePMMLModelLocation != null) { |
| _json["storagePMMLModelLocation"] = storagePMMLModelLocation; |
| } |
| if (trainingComplete != null) { |
| _json["trainingComplete"] = (trainingComplete).toIso8601String(); |
| } |
| if (trainingStatus != null) { |
| _json["trainingStatus"] = trainingStatus; |
| } |
| return _json; |
| } |
| } |
| |
| |
| /** Not documented yet. */ |
| class List { |
| /** List of models. */ |
| core.List<Insert2> items; |
| |
| /** What kind of resource this is. */ |
| core.String kind; |
| |
| /** Pagination token to fetch the next page, if one exists. */ |
| core.String nextPageToken; |
| |
| /** A URL to re-request this resource. */ |
| core.String selfLink; |
| |
| |
| List(); |
| |
| List.fromJson(core.Map _json) { |
| if (_json.containsKey("items")) { |
| items = _json["items"].map((value) => new Insert2.fromJson(value)).toList(); |
| } |
| if (_json.containsKey("kind")) { |
| kind = _json["kind"]; |
| } |
| if (_json.containsKey("nextPageToken")) { |
| nextPageToken = _json["nextPageToken"]; |
| } |
| if (_json.containsKey("selfLink")) { |
| selfLink = _json["selfLink"]; |
| } |
| } |
| |
| core.Map toJson() { |
| var _json = new core.Map(); |
| if (items != null) { |
| _json["items"] = items.map((value) => (value).toJson()).toList(); |
| } |
| if (kind != null) { |
| _json["kind"] = kind; |
| } |
| if (nextPageToken != null) { |
| _json["nextPageToken"] = nextPageToken; |
| } |
| if (selfLink != null) { |
| _json["selfLink"] = selfLink; |
| } |
| return _json; |
| } |
| } |
| |
| |
| /** Not documented yet. */ |
| class OutputOutputMulti { |
| /** The class label. */ |
| core.String label; |
| |
| /** The probability of the class label. */ |
| core.String score; |
| |
| |
| OutputOutputMulti(); |
| |
| OutputOutputMulti.fromJson(core.Map _json) { |
| if (_json.containsKey("label")) { |
| label = _json["label"]; |
| } |
| if (_json.containsKey("score")) { |
| score = _json["score"]; |
| } |
| } |
| |
| core.Map toJson() { |
| var _json = new core.Map(); |
| if (label != null) { |
| _json["label"] = label; |
| } |
| if (score != null) { |
| _json["score"] = score; |
| } |
| return _json; |
| } |
| } |
| |
| |
| /** Not documented yet. */ |
| class Output { |
| /** The unique name for the predictive model. */ |
| core.String id; |
| |
| /** What kind of resource this is. */ |
| core.String kind; |
| |
| /** The most likely class label (Categorical models only). */ |
| core.String outputLabel; |
| |
| /** |
| * A list of class labels with their estimated probabilities (Categorical |
| * models only). |
| */ |
| core.List<OutputOutputMulti> outputMulti; |
| |
| /** The estimated regression value (Regression models only). */ |
| core.double outputValue; |
| |
| /** A URL to re-request this resource. */ |
| core.String selfLink; |
| |
| |
| Output(); |
| |
| Output.fromJson(core.Map _json) { |
| if (_json.containsKey("id")) { |
| id = _json["id"]; |
| } |
| if (_json.containsKey("kind")) { |
| kind = _json["kind"]; |
| } |
| if (_json.containsKey("outputLabel")) { |
| outputLabel = _json["outputLabel"]; |
| } |
| if (_json.containsKey("outputMulti")) { |
| outputMulti = _json["outputMulti"].map((value) => new OutputOutputMulti.fromJson(value)).toList(); |
| } |
| if (_json.containsKey("outputValue")) { |
| outputValue = _json["outputValue"]; |
| } |
| if (_json.containsKey("selfLink")) { |
| selfLink = _json["selfLink"]; |
| } |
| } |
| |
| core.Map toJson() { |
| var _json = new core.Map(); |
| if (id != null) { |
| _json["id"] = id; |
| } |
| if (kind != null) { |
| _json["kind"] = kind; |
| } |
| if (outputLabel != null) { |
| _json["outputLabel"] = outputLabel; |
| } |
| if (outputMulti != null) { |
| _json["outputMulti"] = outputMulti.map((value) => (value).toJson()).toList(); |
| } |
| if (outputValue != null) { |
| _json["outputValue"] = outputValue; |
| } |
| if (selfLink != null) { |
| _json["selfLink"] = selfLink; |
| } |
| return _json; |
| } |
| } |
| |
| |
| /** Not documented yet. */ |
| class Update { |
| /** |
| * The input features for this instance. |
| * |
| * The values for Object must be JSON objects. It can consist of `num`, |
| * `String`, `bool` and `null` as well as `Map` and `List` values. |
| */ |
| core.List<core.Object> csvInstance; |
| |
| /** The generic output value - could be regression or class label. */ |
| core.String output; |
| |
| |
| Update(); |
| |
| Update.fromJson(core.Map _json) { |
| if (_json.containsKey("csvInstance")) { |
| csvInstance = _json["csvInstance"]; |
| } |
| if (_json.containsKey("output")) { |
| output = _json["output"]; |
| } |
| } |
| |
| core.Map toJson() { |
| var _json = new core.Map(); |
| if (csvInstance != null) { |
| _json["csvInstance"] = csvInstance; |
| } |
| if (output != null) { |
| _json["output"] = output; |
| } |
| return _json; |
| } |
| } |
| |
| |
