def dominance(self, data, tournament=None, sort_by='corr'):
"""Mean (across eras) of fraction of models bested per era"""
columns = ['corr', 'mse']
mpe, regions = metrics_per_era(data, self, tournament, columns=columns)
dfs = []
for i, col in enumerate(columns):
pivot = mpe.pivot(index='era', columns='pair', values=col)
pairs = pivot.columns.tolist()
a = pivot.values
n = a.shape[1] - 1.0
if n == 0:
raise ValueError("Must have at least two pairs")
m = []
for j in range(pivot.shape[1]):
if col == 'corr':
z = (a[:, j].reshape(-1, 1) < a).sum(axis=1) / n
else:
z = (a[:, j].reshape(-1, 1) > a).sum(axis=1) / n
m.append(z.mean())
df = pd.DataFrame(data=m, index=pairs, columns=[col])
dfs.append(df)
df = pd.concat(dfs, axis=1)
df = add_split_pairs(df)
df = df.sort_values([sort_by], ascending=[False])
return df
def compare(self, data, prediction, tournament=None):
"Compare performance of predictions with the same names"
pairs = []
for pair in self.pairs(as_str=False):
if pair in prediction:
pairs.append(pair)
cols = [
'logloss1', 'logloss2', 'win1', 'corr', 'maxdiff', 'ystd1', 'ystd2'
]
comp = pd.DataFrame(columns=cols, index=pairs)
if len(pairs) == 0:
return comp
ids = data.ids
df1 = self.loc[ids]
df2 = prediction.loc[ids]
p1 = self[pairs]
p2 = prediction[pairs]
m1 = p1.metrics_per_era(data,
tournament,
metrics=['logloss'],
era_as_str=False)
m2 = p2.metrics_per_era(data,
tournament,
metrics=['logloss'],
era_as_str=False)
for i, pair in enumerate(pairs):
m1i = m1[(m1.name == pair[0])
& (m1.tournament == nx.tournament_str(pair[1]))]
m2i = m2[(m2.name == pair[0])
& (m2.tournament == nx.tournament_str(pair[1]))]
if (m1i.index != m2i.index).any():
raise IndexError("Can only handle aligned eras")
logloss1 = m1i.logloss.mean()
logloss2 = m2i.logloss.mean()
win1 = (m1i.logloss < m2i.logloss).mean()
y1 = df1[pair].y.reshape(-1)
y2 = df2[pair].y.reshape(-1)
corr = np.corrcoef(y1, y2)[0, 1]
maxdiff = np.abs(y1 - y2).max()
ystd1 = y1.std()
ystd2 = y2.std()
m = [logloss1, logloss2, win1, corr, maxdiff, ystd1, ystd2]
comp.iloc[i] = m
comp = add_split_pairs(comp)
return comp
