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#!/usr/bin/env python |
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# coding=utf-8 |
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import datetime |
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import sys |
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import sacred.optional as opt |
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if sys.version_info[0] == 2: |
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from Queue import Queue, Empty |
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else: |
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from queue import Queue, Empty |
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class MetricsLogger(object): |
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"""MetricsLogger collects metrics measured during experiments. |
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MetricsLogger is the (only) part of the Metrics API. |
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An instance of the class should be created for the Run class, such that the |
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log_scalar_metric method is accessible from running experiments using |
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_run.metrics.log_scalar_metric. |
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""" |
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def __init__(self): |
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# Create a message queue that remembers |
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# calls of the log_scalar_metric |
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self._logged_metrics = Queue() |
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self._metric_step_counter = {} |
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"""Remembers the last number of each metric.""" |
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def log_scalar_metric(self, metric_name, value, step=None): |
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""" |
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Add a new measurement. |
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The measurement will be processed by the MongoDB observer |
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during a heartbeat event. |
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Other observers are not yet supported. |
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:param metric_name: The name of the metric, e.g. training.loss. |
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:param value: The measured value. |
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:param step: The step number (integer), e.g. the iteration number |
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If not specified, an internal counter for each metric |
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is used, incremented by one. |
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""" |
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if opt.has_numpy: |
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np = opt.np |
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if isinstance(value, np.generic): |
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value = np.asscalar(value) |
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if step is None: |
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step = self._metric_step_counter.get(metric_name, -1) + 1 |
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self._logged_metrics.put( |
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ScalarMetricLogEntry(metric_name, step, |
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datetime.datetime.utcnow(), |
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value)) |
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self._metric_step_counter[metric_name] = step |
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def get_last_metrics(self): |
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"""Read all measurement events since last call of the method. |
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:return List[ScalarMetricLogEntry] |
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""" |
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read_up_to = self._logged_metrics.qsize() |
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messages = [] |
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for i in range(read_up_to): |
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try: |
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messages.append(self._logged_metrics.get_nowait()) |
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except Empty: |
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pass |
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return messages |
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class ScalarMetricLogEntry(): |
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"""Container for measurements of scalar metrics. |
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There is exactly one ScalarMetricLogEntry per logged scalar metric value. |
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""" |
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def __init__(self, name, step, timestamp, value): |
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self.name = name |
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self.step = step |
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self.timestamp = timestamp |
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self.value = value |
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def linearize_metrics(logged_metrics): |
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""" |
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Group metrics by name. |
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Takes a list of individual measurements, possibly belonging |
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to different metrics and groups them by name. |
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:param logged_metrics: A list of ScalarMetricLogEntries |
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:return: Measured values grouped by the metric name: |
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{"metric_name1": {"steps": [0,1,2], "values": [4, 5, 6], |
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"timestamps": [datetime, datetime, datetime]}, |
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"metric_name2": {...}} |
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""" |
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metrics_by_name = {} |
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for metric_entry in logged_metrics: |
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if metric_entry.name not in metrics_by_name: |
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metrics_by_name[metric_entry.name] = { |
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"steps": [], |
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"values": [], |
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"timestamps": [], |
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"name": metric_entry.name |
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} |
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metrics_by_name[metric_entry.name]["steps"] \ |
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.append(metric_entry.step) |
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metrics_by_name[metric_entry.name]["values"] \ |
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.append(metric_entry.value) |
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metrics_by_name[metric_entry.name]["timestamps"] \ |
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.append(metric_entry.timestamp) |
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return metrics_by_name |
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IDSIA /
sacred
