130 lines
3.7 KiB
Python
130 lines
3.7 KiB
Python
import math
|
|
import statistics as stats
|
|
from typing import List, Tuple
|
|
|
|
import numpy as np
|
|
import pandas as pd
|
|
|
|
from quacc import plot
|
|
from quacc.utils import fmt_line_md
|
|
|
|
|
|
class EvaluationReport:
|
|
def __init__(self, prefix=None):
|
|
self._prevs = []
|
|
self._dict = {}
|
|
self._g_prevs = None
|
|
self._g_dict = None
|
|
self.name = prefix if prefix is not None else "default"
|
|
self.times = {}
|
|
self.train_prevs = {}
|
|
self.target = "default"
|
|
|
|
def append_row(self, base: np.ndarray | Tuple, **row):
|
|
if isinstance(base, np.ndarray):
|
|
base = tuple(base.tolist())
|
|
self._prevs.append(base)
|
|
for k, v in row.items():
|
|
if (k, self.name) in self._dict:
|
|
self._dict[(k, self.name)].append(v)
|
|
else:
|
|
self._dict[(k, self.name)] = [v]
|
|
self._g_prevs = None
|
|
|
|
@property
|
|
def columns(self):
|
|
return self._dict.keys()
|
|
|
|
def groupby_prevs(self, metric: str = None):
|
|
if self._g_dict is None:
|
|
self._g_prevs = []
|
|
self._g_dict = {k: [] for k in self._dict.keys()}
|
|
|
|
last_end = 0
|
|
for ind, bp in enumerate(self._prevs):
|
|
if ind < (len(self._prevs) - 1) and bp == self._prevs[ind + 1]:
|
|
continue
|
|
|
|
self._g_prevs.append(bp)
|
|
for col in self._dict.keys():
|
|
self._g_dict[col].append(
|
|
stats.mean(self._dict[col][last_end : ind + 1])
|
|
)
|
|
|
|
last_end = ind + 1
|
|
|
|
filtered_g_dict = self._g_dict
|
|
if metric is not None:
|
|
filtered_g_dict = {
|
|
c1: ls for ((c0, c1), ls) in self._g_dict.items() if c0 == metric
|
|
}
|
|
|
|
return self._g_prevs, filtered_g_dict
|
|
|
|
def get_dataframe(self, metric="acc"):
|
|
g_prevs, g_dict = self.groupby_prevs(metric=metric)
|
|
return pd.DataFrame(
|
|
g_dict,
|
|
index=g_prevs,
|
|
columns=g_dict.keys(),
|
|
)
|
|
|
|
def get_plot(self, mode="delta", metric="acc"):
|
|
g_prevs, g_dict = self.groupby_prevs(metric=metric)
|
|
t_prev = int(round(self.train_prevs["train"][0] * 100))
|
|
title = f"{self.name}_{t_prev}_{metric}"
|
|
plot.plot_delta(g_prevs, g_dict, metric, title)
|
|
|
|
def to_md(self, *metrics):
|
|
res = ""
|
|
for k, v in self.train_prevs.items():
|
|
res += fmt_line_md(f"{k}: {str(v)}")
|
|
for k, v in self.times.items():
|
|
res += fmt_line_md(f"{k}: {v:.3f}s")
|
|
res += "\n"
|
|
for m in metrics:
|
|
res += self.get_dataframe(metric=m).to_html() + "\n\n"
|
|
self.get_plot(metric=m)
|
|
|
|
return res
|
|
|
|
def merge(self, other):
|
|
if not all(v1 == v2 for v1, v2 in zip(self._prevs, other._prevs)):
|
|
raise ValueError("other has not same base prevalences of self")
|
|
|
|
if len(set(self._dict.keys()).intersection(set(other._dict.keys()))) > 0:
|
|
raise ValueError("self and other have matching keys")
|
|
|
|
report = EvaluationReport()
|
|
report._prevs = self._prevs
|
|
report._dict = self._dict | other._dict
|
|
return report
|
|
|
|
@staticmethod
|
|
def combine_reports(*args, name="default"):
|
|
er = args[0]
|
|
for r in args[1:]:
|
|
er = er.merge(r)
|
|
|
|
er.name = name
|
|
return er
|
|
|
|
|
|
class DatasetReport:
|
|
def __init__(self, name):
|
|
self.name = name
|
|
self.ers: List[EvaluationReport] = []
|
|
|
|
def add(self, er: EvaluationReport):
|
|
self.ers.append(er)
|
|
|
|
def to_md(self, *metrics):
|
|
res = f"{self.name}\n\n"
|
|
for er in self.ers:
|
|
res += f"{er.to_md(*metrics)}\n\n"
|
|
|
|
return res
|
|
|
|
def __iter__(self):
|
|
return (er for er in self.ers)
|