167 lines
5.7 KiB
Python
167 lines
5.7 KiB
Python
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# -*- coding: utf-8 -*-
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from __future__ import absolute_import
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import re
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import fractions
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from collections import namedtuple
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LabelScore = namedtuple('LabelScore', 'match model ref precision recall f1')
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class TrainLogParser(object):
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def __init__(self):
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self.state = None
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self.featgen_percent = -2
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self.featgen_num_features = None
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self.featgen_seconds = None
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self.training_seconds = None
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self.storing_seconds = None
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self.iterations = []
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self.last_iteration = None
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self.log = []
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self.events = []
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def feed(self, line):
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# if line != '\n':
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self.log.append(line)
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if self.state is None:
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self.state = 'STARTING'
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self.handle_STARTING(line)
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self.events.append(('start', 0, len(self.log)))
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return 'start'
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event = getattr(self, "handle_" + self.state)(line)
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if event is not None:
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start, end = self.events[-1][2], len(self.log)
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if event in ('prepared', 'optimization_end'):
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end -= 1
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self.events.append((event, start, end))
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return event
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@property
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def last_log(self):
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event, start, end = self.events[-1]
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return ''.join(self.log[start:end])
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def handle_STARTING(self, line):
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if line.startswith('Feature generation'):
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self.state = 'FEATGEN'
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def handle_FEATGEN(self, line):
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if line in "0123456789.10":
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self.featgen_percent += 2
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return 'featgen_progress'
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m = re.match(r"Number of features: (\d+)", line)
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if m:
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self.featgen_num_features = int(m.group(1))
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return None
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if self._seconds(line) is not None:
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self.featgen_seconds = self._seconds(line)
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self.state = 'AFTER_FEATGEN'
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return 'featgen_end'
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def handle_AFTER_FEATGEN(self, line):
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if self._iteration_head(line) is not None:
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self.state = 'ITERATION'
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self.handle_ITERATION(line)
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return 'prepared'
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if 'terminated with error' in line:
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self.state = 'AFTER_ITERATION'
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return 'prepare_error'
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def handle_ITERATION(self, line):
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if self._iteration_head(line) is not None:
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self.last_iteration = {
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'num': self._iteration_head(line),
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'scores': {},
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}
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self.iterations.append(self.last_iteration)
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elif line == '\n':
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self.state = 'AFTER_ITERATION'
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return 'iteration'
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def add_re(key, pattern, typ):
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m = re.match(pattern, line)
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if m:
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self.last_iteration[key] = typ(m.group(1))
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add_re("loss", r"Loss: (\d+\.\d+)", float)
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add_re("feature_norm", r"Feature norm: (\d+\.\d+)", float)
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add_re("error_norm", r"Error norm: (\d+\.\d+)", float)
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add_re("active_features", r"Active features: (\d+)", int)
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add_re("linesearch_trials", r"Line search trials: (\d+)", int)
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add_re("linesearch_step", r"Line search step: (\d+\.\d+)", float)
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add_re("time", r"Seconds required for this iteration: (\d+\.\d+)", float)
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m = re.match(r"Macro-average precision, recall, F1: \((\d\.\d+), (\d\.\d+), (\d\.\d+)\)", line)
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if m:
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self.last_iteration['avg_precision'] = float(m.group(1))
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self.last_iteration['avg_recall'] = float(m.group(2))
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self.last_iteration['avg_f1'] = float(m.group(3))
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m = re.match(r"Item accuracy: (\d+) / (\d+)", line)
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if m:
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acc = fractions.Fraction(int(m.group(1)), int(m.group(2)))
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self.last_iteration['item_accuracy'] = acc
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self.last_iteration['item_accuracy_float'] = float(acc)
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m = re.match(r"Instance accuracy: (\d+) / (\d+)", line)
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if m:
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acc = fractions.Fraction(int(m.group(1)), int(m.group(2)))
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self.last_iteration['instance_accuracy'] = acc
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self.last_iteration['instance_accuracy_float'] = float(acc)
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m = re.match(r"\s{4}(.+): \((\d+), (\d+), (\d+)\) \((\d\.\d+), (\d\.\d+), (\d\.\d+)\)", line)
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if m:
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self.last_iteration['scores'][m.group(1)] = LabelScore(**{
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'match': int(m.group(2)),
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'model': int(m.group(3)),
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'ref': int(m.group(4)),
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'precision': float(m.group(5)),
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'recall': float(m.group(6)),
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'f1': float(m.group(7)),
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})
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m = re.match(r"\s{4}(.+): \(0, 0, 0\) \(\*{6}, \*{6}, \*{6}\)", line)
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if m:
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self.last_iteration['scores'][m.group(1)] = LabelScore(**{
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'match': 0,
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'model': 0,
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'ref': 0,
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'precision': None,
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'recall': None,
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'f1': None,
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})
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def handle_AFTER_ITERATION(self, line):
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if self._iteration_head(line) is not None:
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self.state = 'ITERATION'
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return self.handle_ITERATION(line)
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m = re.match(r"Total seconds required for training: (\d+\.\d+)", line)
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if m:
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self.training_seconds = float(m.group(1))
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if line.startswith('Storing the model'):
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self.state = 'STORING'
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return 'optimization_end'
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def handle_STORING(self, line):
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if line == '\n':
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return 'end'
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elif self._seconds(line):
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self.storing_seconds = self._seconds(line)
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def _iteration_head(self, line):
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m = re.match(r'\*{5} (?:Iteration|Epoch) #(\d+) \*{5}\n', line)
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if m:
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return int(m.group(1))
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def _seconds(self, line):
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m = re.match(r'Seconds required: (\d+\.\d+)', line)
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if m:
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return float(m.group(1))
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