turicreate.activity_classifier.ActivityClassifier.predict_topk

ActivityClassifier.predict_topk(dataset, output_type='probability', k=3, output_frequency='per_row')

Return top-k predictions for the dataset, using the trained model. Predictions are returned as an SFrame with three columns: prediction_id, class, and probability, or rank, depending on the output_type parameter.

Parameters:
dataset : SFrame

Dataset of new observations. Must include columns with the same names as the features and session id used for model training, but does not require a target column. Additional columns are ignored.

output_type : {‘probability’, ‘rank’}, optional

Choose the return type of the prediction:

  • probability: Probability associated with each label in the prediction.
  • rank : Rank associated with each label in the prediction.
k : int, optional

Number of classes to return for each input example.

output_frequency : {‘per_row’, ‘per_window’}, optional

The frequency of the predictions which is one of:

  • ‘per_row’: Each prediction is returned prediction_window times.
  • ‘per_window’: Return a single prediction for each prediction_window rows in dataset per session_id.
Returns:
out : SFrame

An SFrame with model predictions.

See also

predict, classify, evaluate

Examples

>>> pred = m.predict_topk(validation_data, k=3)
>>> pred
+---------------+-------+-------------------+
|     row_id    | class |    probability    |
+---------------+-------+-------------------+
|       0       |   4   |   0.995623886585  |
|       0       |   9   |  0.0038311756216  |
|       0       |   7   | 0.000301006948575 |
|       1       |   1   |   0.928708016872  |
|       1       |   3   |  0.0440889261663  |
|       1       |   2   |  0.0176190119237  |
|       2       |   3   |   0.996967732906  |
|       2       |   2   |  0.00151345680933 |
|       2       |   7   | 0.000637513934635 |
|       3       |   1   |   0.998070061207  |
|      ...      |  ...  |        ...        |
+---------------+-------+-------------------+