forked from moreo/QuaPy
adding tables
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daba2c9fb4
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13eb682e53
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import numpy as np
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import itertools
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from scipy.stats import ttest_ind_from_stats, wilcoxon
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class Table:
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VALID_TESTS = [None, "wilcoxon", "ttest"]
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def __init__(self, benchmarks, methods, lower_is_better=True, ttest='ttest', prec_mean=3,
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clean_zero=False, show_std=False, prec_std=3, average=True, missing=None, missing_str='--',
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color=True):
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assert ttest in self.VALID_TESTS, f'unknown test, valid are {self.VALID_TESTS}'
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self.benchmarks = np.asarray(benchmarks)
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self.benchmark_index = {row: i for i, row in enumerate(benchmarks)}
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self.methods = np.asarray(methods)
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self.method_index = {col: j for j, col in enumerate(methods)}
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self.map = {}
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# keyed (#rows,#cols)-ndarrays holding computations from self.map['values']
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self._addmap('values', dtype=object)
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self.lower_is_better = lower_is_better
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self.ttest = ttest
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self.prec_mean = prec_mean
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self.clean_zero = clean_zero
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self.show_std = show_std
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self.prec_std = prec_std
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self.add_average = average
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self.missing = missing
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self.missing_str = missing_str
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self.color = color
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self.touch()
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@property
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def nbenchmarks(self):
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return len(self.benchmarks)
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@property
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def nmethods(self):
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return len(self.methods)
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def touch(self):
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self._modif = True
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def update(self):
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if self._modif:
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self.compute()
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def _getfilled(self):
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return np.argwhere(self.map['fill'])
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@property
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def values(self):
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return self.map['values']
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def _indexes(self):
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return itertools.product(range(self.nbenchmarks), range(self.nmethods))
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def _addmap(self, map, dtype, func=None):
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self.map[map] = np.empty((self.nbenchmarks, self.nmethods), dtype=dtype)
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if func is None:
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return
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m = self.map[map]
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f = func
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indexes = self._indexes() if map == 'fill' else self._getfilled()
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for i, j in indexes:
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m[i, j] = f(self.values[i, j])
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def _addrank(self):
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for i in range(self.nbenchmarks):
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filled_cols_idx = np.argwhere(self.map['fill'][i]).flatten()
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col_means = [self.map['mean'][i, j] for j in filled_cols_idx]
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ranked_cols_idx = filled_cols_idx[np.argsort(col_means)]
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if not self.lower_is_better:
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ranked_cols_idx = ranked_cols_idx[::-1]
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self.map['rank'][i, ranked_cols_idx] = np.arange(1, len(filled_cols_idx) + 1)
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def _addcolor(self):
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for i in range(self.nbenchmarks):
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filled_cols_idx = np.argwhere(self.map['fill'][i]).flatten()
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if filled_cols_idx.size == 0:
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continue
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col_means = [self.map['mean'][i, j] for j in filled_cols_idx]
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minval = min(col_means)
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maxval = max(col_means)
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for col_idx in filled_cols_idx:
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val = self.map['mean'][i, col_idx]
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norm = (maxval - minval)
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if norm > 0:
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normval = (val - minval) / norm
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else:
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normval = 0.5
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if self.lower_is_better:
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normval = 1 - normval
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self.map['color'][i, col_idx] = color_red2green_01(normval)
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def _run_ttest(self, row, col1, col2):
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mean1 = self.map['mean'][row, col1]
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std1 = self.map['std'][row, col1]
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nobs1 = self.map['nobs'][row, col1]
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mean2 = self.map['mean'][row, col2]
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std2 = self.map['std'][row, col2]
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nobs2 = self.map['nobs'][row, col2]
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_, p_val = ttest_ind_from_stats(mean1, std1, nobs1, mean2, std2, nobs2)
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return p_val
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def _run_wilcoxon(self, row, col1, col2):
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values1 = self.map['values'][row, col1]
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values2 = self.map['values'][row, col2]
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_, p_val = wilcoxon(values1, values2)
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return p_val
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def _add_statistical_test(self):
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if self.ttest is None:
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return
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self.some_similar = [False] * self.nmethods
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for i in range(self.nbenchmarks):
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filled_cols_idx = np.argwhere(self.map['fill'][i]).flatten()
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if len(filled_cols_idx) <= 1:
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continue
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col_means = [self.map['mean'][i, j] for j in filled_cols_idx]
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best_pos = filled_cols_idx[np.argmin(col_means)]
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for j in filled_cols_idx:
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if j == best_pos:
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continue
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if self.ttest == 'ttest':
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p_val = self._run_ttest(i, best_pos, j)
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else:
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p_val = self._run_wilcoxon(i, best_pos, j)
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pval_outcome = pval_interpretation(p_val)
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self.map['ttest'][i, j] = pval_outcome
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if pval_outcome != 'Diff':
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self.some_similar[j] = True
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def compute(self):
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self._addmap('fill', dtype=bool, func=lambda x: x is not None)
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self._addmap('mean', dtype=float, func=np.mean)
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self._addmap('std', dtype=float, func=np.std)
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self._addmap('nobs', dtype=float, func=len)
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self._addmap('rank', dtype=int, func=None)
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self._addmap('color', dtype=object, func=None)
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self._addmap('ttest', dtype=object, func=None)
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self._addmap('latex', dtype=object, func=None)
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self._addrank()
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self._addcolor()
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self._add_statistical_test()
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if self.add_average:
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self._addave()
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self._modif = False
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def _is_column_full(self, col):
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return all(self.map['fill'][:, self.method_index[col]])
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def _addave(self):
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ave = Table(['ave'], self.methods, lower_is_better=self.lower_is_better, ttest=self.ttest, average=False,
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missing=self.missing, missing_str=self.missing_str)
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for col in self.methods:
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values = None
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if self._is_column_full(col):
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if self.ttest == 'ttest':
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values = np.asarray(self.map['mean'][:, self.method_index[col]])
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else: # wilcoxon
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values = np.concatenate(self.values[:, self.method_index[col]])
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ave.add('ave', col, values)
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self.average = ave
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def add(self, benchmark, method, values):
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if values is not None:
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values = np.asarray(values)
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if values.ndim == 0:
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values = values.flatten()
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rid, cid = self._coordinates(benchmark, method)
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if self.map['values'][rid, cid] is None:
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self.map['values'][rid, cid] = values
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elif values is not None:
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self.map['values'][rid, cid] = np.concatenate([self.map['values'][rid, cid], values])
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self.touch()
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def get(self, benchmark, method, attr='mean'):
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self.update()
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assert attr in self.map, f'unknwon attribute {attr}'
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rid, cid = self._coordinates(benchmark, method)
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if self.map['fill'][rid, cid]:
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v = self.map[attr][rid, cid]
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if v is None or (isinstance(v, float) and np.isnan(v)):
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return self.missing
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return v
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else:
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return self.missing
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def _coordinates(self, benchmark, method):
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assert benchmark in self.benchmark_index, f'benchmark {benchmark} out of range'
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assert method in self.method_index, f'method {method} out of range'
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rid = self.benchmark_index[benchmark]
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cid = self.method_index[method]
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return rid, cid
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def get_average(self, method, attr='mean'):
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self.update()
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if self.add_average:
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return self.average.get('ave', method, attr=attr)
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return None
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def get_color(self, benchmark, method):
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color = self.get(benchmark, method, attr='color')
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if color is None:
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return ''
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return color
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def latexCell(self, benchmark, method):
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self.update()
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i, j = self._coordinates(benchmark, method)
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if self.map['fill'][i, j] == False:
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return self.missing_str
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mean = self.map['mean'][i, j]
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l = f" {mean:.{self.prec_mean}f}"
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if self.clean_zero:
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l = l.replace(' 0.', '.')
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isbest = self.map['rank'][i, j] == 1
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if isbest:
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l = "\\textbf{" + l.strip() + "}"
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stat = ''
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if self.ttest is not None and self.some_similar[j]:
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test_label = self.map['ttest'][i, j]
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if test_label == 'Sim':
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stat = '^{\dag\phantom{\dag}}'
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elif test_label == 'Same':
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stat = '^{\ddag}'
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elif isbest or test_label == 'Diff':
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stat = '^{\phantom{\ddag}}'
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std = ''
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if self.show_std:
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std = self.map['std'][i, j]
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std = f" {std:.{self.prec_std}f}"
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if self.clean_zero:
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std = std.replace(' 0.', '.')
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std = f" \pm {std:{self.prec_std}}"
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if stat != '' or std != '':
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l = f'{l}${stat}{std}$'
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if self.color:
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l += ' ' + self.map['color'][i, j]
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return l
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def latexTabular(self, benchmark_replace={}, method_replace={}, average=True):
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tab = ' & '
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tab += ' & '.join([method_replace.get(col, col) for col in self.methods])
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tab += ' \\\\\hline\n'
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for row in self.benchmarks:
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rowname = benchmark_replace.get(row, row)
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tab += rowname + ' & '
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tab += self.latexRow(row)
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if average:
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tab += '\hline\n'
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tab += 'Average & '
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tab += self.latexAverage()
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return tab
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def latexRow(self, benchmark, endl='\\\\\hline\n'):
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s = [self.latexCell(benchmark, col) for col in self.methods]
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s = ' & '.join(s)
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s += ' ' + endl
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return s
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def latexAverage(self, endl='\\\\\hline\n'):
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if self.add_average:
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return self.average.latexRow('ave', endl=endl)
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def getRankTable(self):
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t = Table(benchmarks=self.benchmarks, methods=self.methods, prec_mean=0, average=True)
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for rid, cid in self._getfilled():
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row = self.benchmarks[rid]
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col = self.methods[cid]
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t.add(row, col, self.get(row, col, 'rank'))
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t.compute()
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return t
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def dropMethods(self, methods):
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drop_index = [self.method_index[m] for m in methods]
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new_methods = np.delete(self.methods, drop_index)
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new_index = {col: j for j, col in enumerate(new_methods)}
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self.map['values'] = self.values[:, np.asarray([self.method_index[m] for m in new_methods], dtype=int)]
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self.methods = new_methods
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self.method_index = new_index
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self.touch()
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def pval_interpretation(p_val):
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if 0.005 >= p_val:
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return 'Diff'
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elif 0.05 >= p_val > 0.005:
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return 'Sim'
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elif p_val > 0.05:
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return 'Same'
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def color_red2green_01(val, maxtone=50):
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if np.isnan(val): return None
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assert 0 <= val <= 1, f'val {val} out of range [0,1]'
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# rescale to [-1,1]
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val = val * 2 - 1
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if val < 0:
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color = 'red'
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tone = maxtone * (-val)
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else:
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color = 'green'
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tone = maxtone * val
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return '\cellcolor{' + color + f'!{int(tone)}' + '}'
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