Updated generated docs
diff --git a/docs/dyn/prediction.v1.4.trainedmodels.html b/docs/dyn/prediction.v1.4.trainedmodels.html
index 77a55a0..3043289 100644
--- a/docs/dyn/prediction.v1.4.trainedmodels.html
+++ b/docs/dyn/prediction.v1.4.trainedmodels.html
@@ -22,24 +22,191 @@
<dl><dt><a name="Resource-get"><strong>get</strong></a> = method(self, **kwargs)</dt><dd><tt>Check training status of your model.<br>
<br>
Args:<br>
- id: string, The unique name for the predictive model. (required)</tt></dd></dl>
+ id: string, The unique name for the predictive model. (required)<br>
+ <br>
+Returns:<br>
+ An object of the form<br>
+ <br>
+ {<br>
+ "kind": "prediction#training", # What kind of resource this is.<br>
+ "storageDataLocation": "A String", # Google storage location of the training data file.<br>
+ "dataAnalysis": { # Data Analysis.<br>
+ "warnings": [<br>
+ "A String",<br>
+ ],<br>
+ },<br>
+ "trainingStatus": "A String", # The current status of the training job. This can be one of following: RUNNING; DONE; ERROR; ERROR: TRAINING JOB NOT FOUND<br>
+ "modelInfo": { # Model metadata.<br>
+ "confusionMatrixRowTotals": { # A list of the confusion matrix row totals<br>
+ },<br>
+ "numberLabels": "A String", # Number of class labels in the trained model [Categorical models only].<br>
+ "confusionMatrix": { # 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 [Categorical models only].<br>
+ },<br>
+ "meanSquaredError": 3.140000, # An estimated mean squared error. The can be used to measure the quality of the predicted model [Regression models only].<br>
+ "modelType": "A String", # Type of predictive model (CLASSIFICATION or REGRESSION)<br>
+ "numberInstances": "A String", # Number of valid data instances used in the trained model.<br>
+ "classWeightedAccuracy": 3.140000, # Estimated accuracy of model taking utility weights into account [Categorical models only].<br>
+ "classificationAccuracy": 3.140000, # 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].<br>
+ },<br>
+ "storagePMMLLocation": "A String", # Google storage location of the preprocessing pmml file.<br>
+ "id": "A String", # The unique name for the predictive model.<br>
+ "selfLink": "A String", # A URL to re-request this resource.<br>
+ "utility": [ # A class weighting function, which allows the importance weights for class labels to be specified [Categorical models only].<br>
+ { # Class label (string).<br>
+ },<br>
+ ],<br>
+ }</tt></dd></dl>
<dl><dt><a name="Resource-insert"><strong>insert</strong></a> = method(self, **kwargs)</dt><dd><tt>Begin training your model.<br>
<br>
Args:<br>
- body: object, The request body. (required)</tt></dd></dl>
+ body: object, The request body. (required)<br>
+ The object takes the form of:<br>
+ <br>
+{<br>
+ "kind": "prediction#training", # What kind of resource this is.<br>
+ "storageDataLocation": "A String", # Google storage location of the training data file.<br>
+ "dataAnalysis": { # Data Analysis.<br>
+ "warnings": [<br>
+ "A String",<br>
+ ],<br>
+ },<br>
+ "trainingStatus": "A String", # The current status of the training job. This can be one of following: RUNNING; DONE; ERROR; ERROR: TRAINING JOB NOT FOUND<br>
+ "modelInfo": { # Model metadata.<br>
+ "confusionMatrixRowTotals": { # A list of the confusion matrix row totals<br>
+ },<br>
+ "numberLabels": "A String", # Number of class labels in the trained model [Categorical models only].<br>
+ "confusionMatrix": { # 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 [Categorical models only].<br>
+ },<br>
+ "meanSquaredError": 3.140000, # An estimated mean squared error. The can be used to measure the quality of the predicted model [Regression models only].<br>
+ "modelType": "A String", # Type of predictive model (CLASSIFICATION or REGRESSION)<br>
+ "numberInstances": "A String", # Number of valid data instances used in the trained model.<br>
+ "classWeightedAccuracy": 3.140000, # Estimated accuracy of model taking utility weights into account [Categorical models only].<br>
+ "classificationAccuracy": 3.140000, # 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].<br>
+ },<br>
+ "storagePMMLLocation": "A String", # Google storage location of the preprocessing pmml file.<br>
+ "id": "A String", # The unique name for the predictive model.<br>
+ "selfLink": "A String", # A URL to re-request this resource.<br>
+ "utility": [ # A class weighting function, which allows the importance weights for class labels to be specified [Categorical models only].<br>
+ { # Class label (string).<br>
+ },<br>
+ ],<br>
+ }<br>
+ <br>
+ <br>
+Returns:<br>
+ An object of the form<br>
+ <br>
+ {<br>
+ "kind": "prediction#training", # What kind of resource this is.<br>
+ "storageDataLocation": "A String", # Google storage location of the training data file.<br>
+ "dataAnalysis": { # Data Analysis.<br>
+ "warnings": [<br>
+ "A String",<br>
+ ],<br>
+ },<br>
+ "trainingStatus": "A String", # The current status of the training job. This can be one of following: RUNNING; DONE; ERROR; ERROR: TRAINING JOB NOT FOUND<br>
+ "modelInfo": { # Model metadata.<br>
+ "confusionMatrixRowTotals": { # A list of the confusion matrix row totals<br>
+ },<br>
+ "numberLabels": "A String", # Number of class labels in the trained model [Categorical models only].<br>
+ "confusionMatrix": { # 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 [Categorical models only].<br>
+ },<br>
+ "meanSquaredError": 3.140000, # An estimated mean squared error. The can be used to measure the quality of the predicted model [Regression models only].<br>
+ "modelType": "A String", # Type of predictive model (CLASSIFICATION or REGRESSION)<br>
+ "numberInstances": "A String", # Number of valid data instances used in the trained model.<br>
+ "classWeightedAccuracy": 3.140000, # Estimated accuracy of model taking utility weights into account [Categorical models only].<br>
+ "classificationAccuracy": 3.140000, # 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].<br>
+ },<br>
+ "storagePMMLLocation": "A String", # Google storage location of the preprocessing pmml file.<br>
+ "id": "A String", # The unique name for the predictive model.<br>
+ "selfLink": "A String", # A URL to re-request this resource.<br>
+ "utility": [ # A class weighting function, which allows the importance weights for class labels to be specified [Categorical models only].<br>
+ { # Class label (string).<br>
+ },<br>
+ ],<br>
+ }</tt></dd></dl>
<dl><dt><a name="Resource-predict"><strong>predict</strong></a> = method(self, **kwargs)</dt><dd><tt>Submit model id and request a prediction<br>
<br>
Args:<br>
body: object, The request body. (required)<br>
- id: string, The unique name for the predictive model. (required)</tt></dd></dl>
+ The object takes the form of:<br>
+ <br>
+{<br>
+ "input": { # Input to the model for a prediction<br>
+ "csvInstance": [ # A list of input features, these can be strings or doubles.<br>
+ "",<br>
+ ],<br>
+ },<br>
+ }<br>
+ <br>
+ id: string, The unique name for the predictive model. (required)<br>
+ <br>
+Returns:<br>
+ An object of the form<br>
+ <br>
+ {<br>
+ "kind": "prediction#output", # What kind of resource this is.<br>
+ "outputLabel": "A String", # The most likely class label [Categorical models only].<br>
+ "id": "A String", # The unique name for the predictive model.<br>
+ "outputMulti": [ # A list of class labels with their estimated probabilities [Categorical models only].<br>
+ {<br>
+ "score": 3.140000, # The probability of the class label.<br>
+ "label": "A String", # The class label.<br>
+ },<br>
+ ],<br>
+ "outputValue": 3.140000, # The estimated regression value [Regression models only].<br>
+ "selfLink": "A String", # A URL to re-request this resource.<br>
+ }</tt></dd></dl>
<dl><dt><a name="Resource-update"><strong>update</strong></a> = method(self, **kwargs)</dt><dd><tt>Add new data to a trained model.<br>
<br>
Args:<br>
body: object, The request body. (required)<br>
- id: string, A parameter (required)</tt></dd></dl>
+ The object takes the form of:<br>
+ <br>
+{<br>
+ "csvInstance": [ # The input features for this instance<br>
+ "",<br>
+ ],<br>
+ "label": "A String", # The true class label of this instance<br>
+ }<br>
+ <br>
+ id: string, The unique name for the predictive model. (required)<br>
+ <br>
+Returns:<br>
+ An object of the form<br>
+ <br>
+ {<br>
+ "kind": "prediction#training", # What kind of resource this is.<br>
+ "storageDataLocation": "A String", # Google storage location of the training data file.<br>
+ "dataAnalysis": { # Data Analysis.<br>
+ "warnings": [<br>
+ "A String",<br>
+ ],<br>
+ },<br>
+ "trainingStatus": "A String", # The current status of the training job. This can be one of following: RUNNING; DONE; ERROR; ERROR: TRAINING JOB NOT FOUND<br>
+ "modelInfo": { # Model metadata.<br>
+ "confusionMatrixRowTotals": { # A list of the confusion matrix row totals<br>
+ },<br>
+ "numberLabels": "A String", # Number of class labels in the trained model [Categorical models only].<br>
+ "confusionMatrix": { # 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 [Categorical models only].<br>
+ },<br>
+ "meanSquaredError": 3.140000, # An estimated mean squared error. The can be used to measure the quality of the predicted model [Regression models only].<br>
+ "modelType": "A String", # Type of predictive model (CLASSIFICATION or REGRESSION)<br>
+ "numberInstances": "A String", # Number of valid data instances used in the trained model.<br>
+ "classWeightedAccuracy": 3.140000, # Estimated accuracy of model taking utility weights into account [Categorical models only].<br>
+ "classificationAccuracy": 3.140000, # 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].<br>
+ },<br>
+ "storagePMMLLocation": "A String", # Google storage location of the preprocessing pmml file.<br>
+ "id": "A String", # The unique name for the predictive model.<br>
+ "selfLink": "A String", # A URL to re-request this resource.<br>
+ "utility": [ # A class weighting function, which allows the importance weights for class labels to be specified [Categorical models only].<br>
+ { # Class label (string).<br>
+ },<br>
+ ],<br>
+ }</tt></dd></dl>
<hr>
Data descriptors defined here:<br>