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import statistics
import pandas as pd
from tabulate import tabulate
class ProcessedMetricsPrinter:
def print_data_frame(self, name, processed_metrics):
print(f"metrics for {name}")
data_frame = self.get_data_frame(processed_metrics)
print(tabulate(data_frame, showindex=False, headers=data_frame.columns, tablefmt="grid"))
def combine_processed_metrics(self, processed_metrics_list):
r"""
A method that merges the value arrays of the keys in the dictionary
of processed metrics.
Args:
processed_metrics_list (list): a list containing dictionaries with
recorded metrics as keys, and the values are lists of elapsed times.
Returns::
A merged dictionary that is created from the list of dictionaries passed
into the method.
Examples::
>>> instance = ProcessedMetricsPrinter()
>>> dict_1 = trainer1.get_processed_metrics()
>>> dict_2 = trainer2.get_processed_metrics()
>>> print(dict_1)
{
"forward_metric_type,forward_pass" : [.0429, .0888]
}
>>> print(dict_2)
{
"forward_metric_type,forward_pass" : [.0111, .0222]
}
>>> processed_metrics_list = [dict_1, dict_2]
>>> result = instance.combine_processed_metrics(processed_metrics_list)
>>> print(result)
{
"forward_metric_type,forward_pass" : [.0429, .0888, .0111, .0222]
}
"""
processed_metric_totals = {}
for processed_metrics in processed_metrics_list:
for metric_name, values in processed_metrics.items():
if metric_name not in processed_metric_totals:
processed_metric_totals[metric_name] = []
processed_metric_totals[metric_name] += values
return processed_metric_totals
def get_data_frame(self, processed_metrics):
df = pd.DataFrame(
columns=['name', 'min', 'max', 'mean', 'variance', 'stdev']
)
for metric_name in sorted(processed_metrics.keys()):
values = processed_metrics[metric_name]
row = {
"name": metric_name,
"min": min(values),
"max": max(values),
"mean": statistics.mean(values),
"variance": statistics.variance(values),
"stdev": statistics.stdev(values)
}
df = df.append(row, ignore_index=True)
return df
def print_metrics(self, name, rank_metrics_list):
if rank_metrics_list:
metrics_list = []
for rank, metric in rank_metrics_list:
self.print_data_frame(f"{name}={rank}", metric)
metrics_list.append(metric)
combined_metrics = self.combine_processed_metrics(metrics_list)
self.print_data_frame(f"all {name}", combined_metrics)
def save_to_file(self, data_frame, file_name):
file_name = f"data_frames/{file_name}.csv"
data_frame.to_csv(file_name, encoding='utf-8', index=False)
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