Algorithms have at least one input and one output. All algorithm endpoints are organized in groups. Groups are used by the platform to indicate which inputs and outputs are synchronized together. The first group is automatically synchronized with the channel defined by the block in which the algorithm is deployed.
| Endpoint Name | Data Format | Nature |
|---|---|---|
| scores | tutorial/multiclass_probe_scores/1 | Input |
Analyzers may produce any number of results. Once experiments using this analyzer are done, you may display the results or filter experiments using criteria based on them.
| Name | Type |
|---|---|
| number_of_negatives | int32 |
| roc | plot/scatter/1 |
| number_of_positives | int32 |
| cer | float32 |
xxxxxxxxxximport bobimport numpyclass Algorithm: def __init__(self): self.positives = [] self.negatives = [] def process(self, inputs, output): # accumulate the scores data = inputs['scores'].data real_id = data.real_identity estimated_id = data.estimated_identity if real_id == estimated_id: self.positives.append(data.score) else: self.negatives.append(data.score) # once all values are received, evaluate the scores if not(inputs.hasMoreData()): cer = len(self.negatives) / float(len(self.negatives) + len(self.positives)) roc_points = bob.measure.roc(self.negatives, self.positives, 100) # writes the output back to the platform output.write({ 'cer': numpy.float32(cer), 'number_of_positives': numpy.int32(len(self.positives)), 'number_of_negatives': numpy.int32(len(self.negatives)), 'roc': { "data": [ { "label": "roc", "x": roc_points[0], "y": roc_points[1], } ] }, }) return TrueThe code for this algorithm in Python
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This table shows the number of times this algorithm has been successfully run using the given environment. Note this does not provide sufficient information to evaluate if the algorithm will run when submitted to different conditions.