turicreate.sound_classifier.SoundClassifier.predict_topk

SoundClassifier.predict_topk(dataset, output_type='probability', k=3, verbose=True, batch_size=64)

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

Parameters:

dataset : SFrame | SArray | dict

The audio data to be classified. If dataset is an SFrame, it must have a column with the same name as the feature 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.

verbose : bool, optional

If True, prints progress updates and model details.

batch_size : int, optional

If you are getting memory errors, try decreasing this value. If you have a powerful computer, increasing this value may improve performance.

Returns:

out : SFrame

An SFrame with model predictions.

See also

predict, classify, evaluate

Examples

>>> pred = m.predict_topk(validation_data, k=3)
>>> pred
+------+-------+-------------------+
|  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  |
| ...  |  ...  |        ...        |
+------+-------+-------------------+