163 lines
4.2 KiB
Python
163 lines
4.2 KiB
Python
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from email import header
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from typing import Tuple
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import statistics as stats
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import numpy as np
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import pandas as pd
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def _in_div(s):
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return "<div>" + s + "</div>\n"
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def _header_footer(s):
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return (
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"""
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<html>
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<head>
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<style>
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.dataframe {
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tr:hover {
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background-color: aquamarine;
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}
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}
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</style>
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</head>
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<body>
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""" +
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s +
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"</body></html>"
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)
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class EvaluationReport:
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def __init__(self, prefix=None):
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self.base = []
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self.dict = {}
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self._grouped = False
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self._grouped_base = []
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self._grouped_dict = {}
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self._dataframe = None
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self.prefix = prefix if prefix is not None else "default"
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self._times = {}
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self._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.base.append(base)
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for k, v in row.items():
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if (k, self.prefix) in self.dict:
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self.dict[(k, self.prefix)].append(v)
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else:
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self.dict[(k, self.prefix)] = [v]
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self._grouped = False
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self._dataframe = 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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@property
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def grouped(self):
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if self._grouped:
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return self._grouped_dict
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self._grouped_base = []
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self._grouped_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.base):
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if ind < (len(self.base) - 1) and bp == self.base[ind + 1]:
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continue
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self._grouped_base.append(bp)
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for col in self.dict.keys():
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self._grouped_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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self._grouped = True
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return self._grouped_dict
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@property
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def gbase(self):
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self.grouped
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return self._grouped_base
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def get_dataframe(self, metrics=None):
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if self._dataframe is None:
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self_columns = sorted(self.columns, key=lambda c: c[0])
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self._dataframe = pd.DataFrame(
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self.grouped,
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index=self.gbase,
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columns=pd.MultiIndex.from_tuples(self_columns),
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)
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df = pd.DataFrame(self._dataframe)
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if metrics is not None:
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df = df.drop(
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[(c0, c1) for (c0, c1) in df.columns if c0 not in metrics], axis=1
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)
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if len(set(k0 for k0, k1 in df.columns)) == 1:
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df = df.droplevel(0, axis=1)
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return df
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def merge(self, other):
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if not all(v1 == v2 for v1, v2 in zip(self.base, other.base)):
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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.base = self.base
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report.dict = self.dict | other.dict
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return report
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@property
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def times(self):
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return self._times
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@times.setter
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def times(self, val):
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self._times = val
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@property
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def prevs(self):
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return self._prevs
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@prevs.setter
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def prevs(self, val):
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self._prevs = val
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@property
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def target(self):
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return self._target
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@target.setter
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def target(self, val):
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self._target = val
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def to_html(self, *metrics):
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res = _in_div("target: " + self.target)
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for k,v in self.prevs.items():
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res += _in_div(f"{k}: {str(v)}")
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for k,v in self.times.items():
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res += _in_div(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(metrics=m).to_html() + "\n\n"
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return _header_footer(res)
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@staticmethod
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def combine_reports(*args):
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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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return er
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