|
1
|
|
|
#!/usr/bin/env python |
|
2
|
|
|
# coding=utf-8 |
|
3
|
|
|
import datetime |
|
4
|
|
|
import sys |
|
5
|
|
|
|
|
6
|
|
|
if sys.version_info[0] == 2: |
|
7
|
|
|
from Queue import Queue, Empty |
|
8
|
|
|
else: |
|
9
|
|
|
from queue import Queue, Empty |
|
10
|
|
|
|
|
11
|
|
|
|
|
12
|
|
|
class MetricsLogger(object): |
|
13
|
|
|
"""MetricsLogger collects metrics measured during experiments. |
|
14
|
|
|
|
|
15
|
|
|
MetricsLogger is the (only) part of the Metrics API. |
|
16
|
|
|
An instance of the class should be created for the Run class, such that the |
|
17
|
|
|
log_scalar_metric method is accessible from running experiments using |
|
18
|
|
|
_run.metrics.log_scalar_metric. |
|
19
|
|
|
""" |
|
20
|
|
|
|
|
21
|
|
|
def __init__(self): |
|
22
|
|
|
# Create a message queue that remembers |
|
23
|
|
|
# calls of the log_scalar_metric |
|
24
|
|
|
self._logged_metrics = Queue() |
|
25
|
|
|
self._metric_step_counter = {} |
|
26
|
|
|
"""Remembers the last number of each metric.""" |
|
27
|
|
|
|
|
28
|
|
|
def log_scalar_metric(self, metric_name, value, step=None): |
|
29
|
|
|
""" |
|
30
|
|
|
Add a new measurement. |
|
31
|
|
|
|
|
32
|
|
|
The measurement will be processed by the MongoDB observer |
|
33
|
|
|
during a heartbeat event. |
|
34
|
|
|
Other observers are not yet supported. |
|
35
|
|
|
|
|
36
|
|
|
:param metric_name: The name of the metric, e.g. training.loss. |
|
37
|
|
|
:param value: The measured value. |
|
38
|
|
|
:param step: The step number (integer), e.g. the iteration number |
|
39
|
|
|
If not specified, an internal counter for each metric |
|
40
|
|
|
is used, incremented by one. |
|
41
|
|
|
""" |
|
42
|
|
|
if step is None: |
|
43
|
|
|
step = self._metric_step_counter.get(metric_name, -1) + 1 |
|
44
|
|
|
self._logged_metrics.put( |
|
45
|
|
|
ScalarMetricLogEntry(metric_name, step, |
|
46
|
|
|
datetime.datetime.utcnow(), |
|
47
|
|
|
value)) |
|
48
|
|
|
self._metric_step_counter[metric_name] = step |
|
49
|
|
|
|
|
50
|
|
|
def get_last_metrics(self): |
|
51
|
|
|
"""Read all measurement events since last call of the method. |
|
52
|
|
|
|
|
53
|
|
|
:return List[ScalarMetricLogEntry] |
|
54
|
|
|
""" |
|
55
|
|
|
read_up_to = self._logged_metrics.qsize() |
|
56
|
|
|
messages = [] |
|
57
|
|
|
for i in range(read_up_to): |
|
58
|
|
|
try: |
|
59
|
|
|
messages.append(self._logged_metrics.get_nowait()) |
|
60
|
|
|
except Empty: |
|
61
|
|
|
pass |
|
62
|
|
|
return messages |
|
63
|
|
|
|
|
64
|
|
|
|
|
65
|
|
|
class ScalarMetricLogEntry(): |
|
66
|
|
|
"""Container for measurements of scalar metrics. |
|
67
|
|
|
|
|
68
|
|
|
There is exactly one ScalarMetricLogEntry per logged scalar metric value. |
|
69
|
|
|
""" |
|
70
|
|
|
|
|
71
|
|
|
def __init__(self, name, step, timestamp, value): |
|
72
|
|
|
self.name = name |
|
73
|
|
|
self.step = step |
|
74
|
|
|
self.timestamp = timestamp |
|
75
|
|
|
self.value = value |
|
76
|
|
|
|
|
77
|
|
|
|
|
78
|
|
|
def linearize_metrics(logged_metrics): |
|
79
|
|
|
""" |
|
80
|
|
|
Group metrics by name. |
|
81
|
|
|
|
|
82
|
|
|
Takes a list of individual measurements, possibly belonging |
|
83
|
|
|
to different metrics and groups them by name. |
|
84
|
|
|
|
|
85
|
|
|
:param logged_metrics: A list of ScalarMetricLogEntries |
|
86
|
|
|
:return: Measured values grouped by the metric name: |
|
87
|
|
|
{"metric_name1": {"steps": [0,1,2], "values": [4, 5, 6], |
|
88
|
|
|
"timestamps": [datetime, datetime, datetime]}, |
|
89
|
|
|
"metric_name2": {...}} |
|
90
|
|
|
""" |
|
91
|
|
|
metrics_by_name = {} |
|
92
|
|
|
for metric_entry in logged_metrics: |
|
93
|
|
|
if metric_entry.name not in metrics_by_name: |
|
94
|
|
|
metrics_by_name[metric_entry.name] = { |
|
95
|
|
|
"steps": [], |
|
96
|
|
|
"values": [], |
|
97
|
|
|
"timestamps": [], |
|
98
|
|
|
"name": metric_entry.name |
|
99
|
|
|
} |
|
100
|
|
|
metrics_by_name[metric_entry.name]["steps"] \ |
|
101
|
|
|
.append(metric_entry.step) |
|
102
|
|
|
metrics_by_name[metric_entry.name]["values"] \ |
|
103
|
|
|
.append(metric_entry.value) |
|
104
|
|
|
metrics_by_name[metric_entry.name]["timestamps"] \ |
|
105
|
|
|
.append(metric_entry.timestamp) |
|
106
|
|
|
return metrics_by_name |
|
107
|
|
|
|
IDSIA /
sacred
