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