diff --git a/Ordinal/gen_tables.py b/Ordinal/gen_tables.py
new file mode 100644
index 0000000..50adb8e
--- /dev/null
+++ b/Ordinal/gen_tables.py
@@ -0,0 +1,52 @@
+import pandas as pd
+from os.path import join
+import os
+from glob import glob
+from pathlib import Path
+
+from Ordinal.main import quantifiers
+from Ordinal.tabular import Table
+
+domain = 'Books-tfidf'
+prot = 'app'
+outpath = f'./tables/{domain}/{prot}/results.tex'
+
+resultpath = join('./results', domain, prot)
+
+methods = [qname for qname, *_ in quantifiers()]
+methods += [m+'-r' for m in methods]
+
+table = Table(benchmarks=['low', 'mid', 'high'],
+ methods=methods,
+ prec_mean=4,
+ show_std=True,
+ prec_std=4)
+
+
+for resultfile in glob(f'{resultpath}/*.csv'):
+ df = pd.read_csv(resultfile)
+ nmd = df['nmd'].values
+ resultname = Path(resultfile).name
+ method, drift, *other = resultname.replace('.csv', '').split('.')
+ if other:
+ method += '-r'
+
+ table.add(drift, method, nmd)
+
+os.makedirs(Path(outpath).parent, exist_ok=True)
+
+tabular = """
+ \\resizebox{\\textwidth}{!}{%
+ \\begin{tabular}{|c||""" + ('c|' * (table.nbenchmarks+1)) + """} \hline
+ """
+tabular += table.latexTabularT()
+tabular += """
+ \end{tabular}%
+ }"""
+
+with open(outpath, 'wt') as foo:
+ foo.write(tabular)
+ foo.write('\n')
+
+print('[done]')
+
diff --git a/Ordinal/main.py b/Ordinal/main.py
index dbdadeb..1659c2a 100644
--- a/Ordinal/main.py
+++ b/Ordinal/main.py
@@ -1,89 +1,151 @@
+import itertools
+
from sklearn.feature_extraction.text import TfidfVectorizer
from sklearn.linear_model import LogisticRegression
import quapy as qp
import numpy as np
from Ordinal.model import OrderedLogisticRegression, StackedClassifier, RegressionQuantification, RegressorClassifier
-from quapy.method.aggregative import PACC, CC, EMQ, PCC, ACC
+from quapy.method.aggregative import PACC, CC, EMQ, PCC, ACC, SLD, HDy
from quapy.data import LabelledCollection
from os.path import join
+import os
from utils import load_samples, load_samples_pkl
from evaluation import nmd, mnmd
from time import time
import pickle
from tqdm import tqdm
-
-domain = 'Books-tfidf'
-datapath = './data'
-protocol = 'app'
-drift = 'high'
-
-train = pickle.load(open(join(datapath, domain, 'training_data.pkl'), 'rb'))
+import mord
def load_test_samples():
ids = np.load(join(datapath, domain, protocol, f'{drift}drift.test.id.npy'))
ids = set(ids)
- for sample in tqdm(load_samples_pkl(join(datapath, domain, protocol, 'test_samples'), filter=ids), total=len(ids)):
+ pklpath = join(datapath, domain, protocol, 'test_samples')
+ for sample in tqdm(load_samples_pkl(pklpath, filter=ids), total=len(ids)):
yield sample.instances, sample.prevalence()
def load_dev_samples():
ids = np.load(join(datapath, domain, protocol, f'{drift}drift.dev.id.npy'))
ids = set(ids)
- for sample in tqdm(load_samples_pkl(join(datapath, domain, protocol, 'dev_samples'), filter=ids), total=len(ids)):
+ pklpath = join(datapath, domain, protocol, 'dev_samples')
+ for sample in tqdm(load_samples_pkl(pklpath, filter=ids), total=len(ids)):
yield sample.instances, sample.prevalence()
-print('fitting the quantifier')
+class LAD(mord.LAD):
+ def fit(self, X, y):
+ self.classes_ = sorted(np.unique(y))
+ return super().fit(X, y)
-# q = EMQ(LogisticRegression(class_weight='balanced'))
-# q = PACC(LogisticRegression(class_weight='balanced'))
-q = PACC(OrderedLogisticRegression())
-# q = PACC(StackedClassifier(LogisticRegression(class_weight='balanced')))
-# q = RegressionQuantification(PCC(LogisticRegression(class_weight='balanced')), val_samples_generator=load_dev_samples)
-# q = ACC(RegressorClassifier())
-param_grid = {'C': np.logspace(-3,3,7), 'class_weight': [None, 'balanced']}
-# param_grid = {'C': np.logspace(-3,3,14)}
-# param_grid = {'alpha':np.logspace(-8, 6, 15)}
+class OrdinalRidge(mord.OrdinalRidge):
+ def fit(self, X, y):
+ self.classes_ = sorted(np.unique(y))
+ return super().fit(X, y)
-# q = qp.model_selection.GridSearchQ(
-# q,
-# param_grid,
-# 1000,
-# 'gen',
-# error=mnmd,
-# val_split=load_dev_samples,
-# n_jobs=-1,
-# refit=False,
-# verbose=True)
-q.fit(train)
+def quantifiers():
+ params_LR = {'C': np.logspace(-3,3,7), 'class_weight': [None, 'balanced']}
+ params_OLR = {'alpha':np.logspace(-3, 3, 7)}
+ params_SVR = {'C': np.logspace(-3,3,7)}
+ # params_SVR = {'C': np.logspace(0, 1, 2)}
-# q = RegressionQuantification(q, val_samples_generator=load_dev_samples)
-# q.fit(None)
+ # baselines
+ yield 'CC(LR)', CC(LogisticRegression()), params_LR
+ yield 'PCC(LR)', PCC(LogisticRegression()), params_LR
+ yield 'ACC(LR)', ACC(LogisticRegression()), params_LR
+ yield 'PACC(LR)', PACC(LogisticRegression()), params_LR
+ #yield 'HDy(LR)', HDy(LogisticRegression()), params_LR
+ yield 'SLD(LR)', EMQ(LogisticRegression()), params_LR
-print('[done]')
+ # with order-aware classifiers
+ # threshold-based ordinal regression (see https://pythonhosted.org/mord/)
+ yield 'CC(OLR-AT)', CC(mord.LogisticAT()), params_OLR
+ yield 'PCC(OLR-AT)', PCC(mord.LogisticAT()), params_OLR
+ yield 'ACC(OLR-AT)', ACC(mord.LogisticAT()), params_OLR
+ yield 'PACC(OLR-AT)', PACC(mord.LogisticAT()), params_OLR
+ #yield 'HDy(OLR-AT)', HDy(mord.LogisticAT()), params_OLR
+ yield 'SLD(OLR-AT)', EMQ(mord.LogisticAT()), params_OLR
+ # other options include mord.LogisticIT(alpha=1.), mord.LogisticSE(alpha=1.)
-report = qp.evaluation.gen_prevalence_report(q, gen_fn=load_test_samples, error_metrics=[nmd])
-mean_nmd = report['nmd'].mean()
-std_nmd = report['nmd'].std()
-print(f'{mean_nmd:.4f} +-{std_nmd:.4f}')
+ # regression-based ordinal regression (see https://pythonhosted.org/mord/)
+ # I am using my implementation, which caters for predict_proba (linear distance to the two closest classes, 0 in the rest)
+ # the other implementation has OrdinalRidge(alpha=1.0) and LAD(C=1.0) with my wrapper classes for having the nclasses_; those do
+ # not implement predict_proba nor decision_score
+ yield 'CC(SVR)', CC(RegressorClassifier()), params_SVR
+ # yield 'PCC(SVR)', PCC(RegressorClassifier()), params_SVR
+ # yield 'PCC-cal(SVR)', PCC(RegressorClassifier()), params_SVR
+ # yield 'ACC(SVR)', ACC(RegressorClassifier()), params_SVR
+ # yield 'PACC(SVR)', PACC(RegressorClassifier()), params_SVR
+ #yield 'HDy(SVR)', HDy(RegressorClassifier()), params_SVR
+ # yield 'SLD(SVR)', EMQ(RegressorClassifier()), params_SVR
-q = RegressionQuantification(q, val_samples_generator=load_dev_samples)
-q.fit(None)
-report = qp.evaluation.gen_prevalence_report(q, gen_fn=load_test_samples, error_metrics=[nmd])
-mean_nmd = report['nmd'].mean()
-std_nmd = report['nmd'].std()
-print(f'[regression-correction] {mean_nmd:.4f} +-{std_nmd:.4f}')
+def run_experiment(params):
+ qname, q, param_grid, drift = params
+ resultfile = join(resultpath, f'{qname}.{drift}.csv')
+ if os.path.exists(resultfile):
+ print(f'result file {resultfile} already exists: continue')
+ return None
+
+ print(f'fitting {qname} for {drift}-drift')
+
+ q = qp.model_selection.GridSearchQ(
+ q,
+ param_grid,
+ sample_size=1000,
+ protocol='gen',
+ error=mnmd,
+ val_split=load_dev_samples,
+ n_jobs=-1,
+ refit=False,
+ verbose=True).fit(train)
+
+ hyperparams = f'{qname}\t{drift}\t{q.best_params_}'
+
+ print('[done]')
+
+ report = qp.evaluation.gen_prevalence_report(q, gen_fn=load_test_samples, error_metrics=[nmd])
+ mean_nmd = report['nmd'].mean()
+ std_nmd = report['nmd'].std()
+ print(f'{qname}: {mean_nmd:.4f} +-{std_nmd:.4f}')
+ report.to_csv(resultfile, index=False)
+
+ print('[learning regressor-based adjustment]')
+ q = RegressionQuantification(q.best_model(), val_samples_generator=load_dev_samples)
+ q.fit(None)
+
+ report = qp.evaluation.gen_prevalence_report(q, gen_fn=load_test_samples, error_metrics=[nmd])
+ mean_nmd = report['nmd'].mean()
+ std_nmd = report['nmd'].std()
+ print(f'[{qname} regression-correction] {mean_nmd:.4f} +-{std_nmd:.4f}')
+ resultfile = join(resultpath, f'{qname}.{drift}.reg.csv')
+ report.to_csv(resultfile, index=False)
+
+ return hyperparams
+
+
+if __name__ == '__main__':
+ domain = 'Books-tfidf'
+ datapath = './data'
+ protocol = 'app'
+ resultpath = join('./results', domain, protocol)
+ os.makedirs(resultpath, exist_ok=True)
+
+ train = pickle.load(open(join(datapath, domain, 'training_data.pkl'), 'rb'))
+
+ with open(join(resultpath, 'hyper.txt'), 'at') as foo:
+ for drift in ['low', 'mid', 'high']:
+ params = [(*qs, drift) for qs in quantifiers()]
+ hypers = qp.util.parallel(run_experiment, params, n_jobs=-2)
+ for h in hypers:
+ if h is not None:
+ foo.write(h)
+ foo.write('\n')
+
-# drift='high'
-# report = qp.evaluation.gen_prevalence_report(q, gen_fn=load_test_samples, error_metrics=[nmd])
-# mean_nmd = report['nmd'].mean()
-# std_nmd = report['nmd'].std()
-# print(f'{mean_nmd:.4f} +-{std_nmd:.4f}')
diff --git a/Ordinal/model.py b/Ordinal/model.py
index fb0c985..0c63123 100644
--- a/Ordinal/model.py
+++ b/Ordinal/model.py
@@ -118,11 +118,12 @@ class RegressionQuantification:
def quantify(self, instances):
Xs = self.base_quantifier.quantify(instances).reshape(1, -1)
# Xs = self.norm.transform(Xs)
- Xs = self.reg.predict(Xs)
+ Xs = self.reg.predict(Xs).flatten()
# Xs = self.norm.inverse_transform(Xs)
+ Xs = np.clip(Xs, 0, 1)
adjusted = Xs / Xs.sum()
# adjusted = np.clip(Xs, 0, 1)
- adjusted = adjusted.flatten()
+ adjusted = adjusted
return adjusted
def get_params(self, deep=True):
@@ -133,13 +134,13 @@ class RegressionQuantification:
class RegressorClassifier(BaseEstimator, ClassifierMixin):
- def __init__(self):
- self.regressor = LinearSVR()
- # self.regressor = SVR()
- # self.regressor = Ridge(normalize=True)
-
+ def __init__(self, C=1.0):
+ self.C = C
def fit(self, X, y):
+ self.regressor = LinearSVR(C=self.C)
+ # self.regressor = SVR()
+ # self.regressor = Ridge(normalize=True)
self.nclasses = len(np.unique(y))
self.regressor.fit(X, y)
return self
@@ -151,13 +152,20 @@ class RegressorClassifier(BaseEstimator, ClassifierMixin):
c[c>(self.nclasses-1)]=self.nclasses-1
return c.astype(np.int)
- def predict_proba(self, X):
+ # def predict_proba(self, X):
+ # r = self.regressor.predict(X)
+ # nC = len(self.classes_)
+ # r = np.clip(r, 0, nC - 1)
+ # dists = np.abs(np.tile(np.arange(nC), (len(r), 1)) - r.reshape(-1,1))
+ # invdist = 1 - dists
+ # invdist[invdist < 0] = 0
+ # return invdist
+
+ def decision_function(self, X):
r = self.regressor.predict(X)
nC = len(self.classes_)
- r = np.clip(r, 0, nC - 1)
dists = np.abs(np.tile(np.arange(nC), (len(r), 1)) - r.reshape(-1,1))
invdist = 1 - dists
- invdist[invdist < 0] = 0
return invdist
@property
@@ -165,8 +173,9 @@ class RegressorClassifier(BaseEstimator, ClassifierMixin):
return np.arange(self.nclasses)
def get_params(self, deep=True):
- return self.regressor.get_params()
+ return {'C':self.C}
def set_params(self, **params):
- self.regressor.set_params(**params)
+ self.C = params['C']
+
diff --git a/Ordinal/tabular.py b/Ordinal/tabular.py
new file mode 100644
index 0000000..cdb1d9c
--- /dev/null
+++ b/Ordinal/tabular.py
@@ -0,0 +1,347 @@
+import numpy as np
+import itertools
+from scipy.stats import ttest_ind_from_stats, wilcoxon
+
+
+class Table:
+ VALID_TESTS = [None, "wilcoxon", "ttest"]
+
+ def __init__(self, benchmarks, methods, lower_is_better=True, significance_test='ttest', prec_mean=3,
+ clean_zero=False, show_std=False, prec_std=3, average=True, missing=None, missing_str='--',
+ color=True):
+ assert significance_test in self.VALID_TESTS, f'unknown test, valid are {self.VALID_TESTS}'
+
+ self.benchmarks = np.asarray(benchmarks)
+ self.benchmark_index = {row: i for i, row in enumerate(benchmarks)}
+
+ self.methods = np.asarray(methods)
+ self.method_index = {col: j for j, col in enumerate(methods)}
+
+ self.map = {}
+ # keyed (#rows,#cols)-ndarrays holding computations from self.map['values']
+ self._addmap('values', dtype=object)
+ self.lower_is_better = lower_is_better
+ self.ttest = significance_test
+ self.prec_mean = prec_mean
+ self.clean_zero = clean_zero
+ self.show_std = show_std
+ self.prec_std = prec_std
+ self.add_average = average
+ self.missing = missing
+ self.missing_str = missing_str
+ self.color = color
+
+ self.touch()
+
+ @property
+ def nbenchmarks(self):
+ return len(self.benchmarks)
+
+ @property
+ def nmethods(self):
+ return len(self.methods)
+
+ def touch(self):
+ self._modif = True
+
+ def update(self):
+ if self._modif:
+ self.compute()
+
+ def _getfilled(self):
+ return np.argwhere(self.map['fill'])
+
+ @property
+ def values(self):
+ return self.map['values']
+
+ def _indexes(self):
+ return itertools.product(range(self.nbenchmarks), range(self.nmethods))
+
+ def _addmap(self, map, dtype, func=None):
+ self.map[map] = np.empty((self.nbenchmarks, self.nmethods), dtype=dtype)
+ if func is None:
+ return
+ m = self.map[map]
+ f = func
+ indexes = self._indexes() if map == 'fill' else self._getfilled()
+ for i, j in indexes:
+ m[i, j] = f(self.values[i, j])
+
+ def _addrank(self):
+ for i in range(self.nbenchmarks):
+ filled_cols_idx = np.argwhere(self.map['fill'][i]).flatten()
+ col_means = [self.map['mean'][i, j] for j in filled_cols_idx]
+ ranked_cols_idx = filled_cols_idx[np.argsort(col_means)]
+ if not self.lower_is_better:
+ ranked_cols_idx = ranked_cols_idx[::-1]
+ self.map['rank'][i, ranked_cols_idx] = np.arange(1, len(filled_cols_idx) + 1)
+
+ def _addcolor(self):
+ for i in range(self.nbenchmarks):
+ filled_cols_idx = np.argwhere(self.map['fill'][i]).flatten()
+ if filled_cols_idx.size == 0:
+ continue
+ col_means = [self.map['mean'][i, j] for j in filled_cols_idx]
+ minval = min(col_means)
+ maxval = max(col_means)
+ for col_idx in filled_cols_idx:
+ val = self.map['mean'][i, col_idx]
+ norm = (maxval - minval)
+ if norm > 0:
+ normval = (val - minval) / norm
+ else:
+ normval = 0.5
+ if self.lower_is_better:
+ normval = 1 - normval
+ self.map['color'][i, col_idx] = color_red2green_01(normval)
+
+ def _run_ttest(self, row, col1, col2):
+ mean1 = self.map['mean'][row, col1]
+ std1 = self.map['std'][row, col1]
+ nobs1 = self.map['nobs'][row, col1]
+ mean2 = self.map['mean'][row, col2]
+ std2 = self.map['std'][row, col2]
+ nobs2 = self.map['nobs'][row, col2]
+ _, p_val = ttest_ind_from_stats(mean1, std1, nobs1, mean2, std2, nobs2)
+ return p_val
+
+ def _run_wilcoxon(self, row, col1, col2):
+ values1 = self.map['values'][row, col1]
+ values2 = self.map['values'][row, col2]
+ _, p_val = wilcoxon(values1, values2)
+ return p_val
+
+ def _add_statistical_test(self):
+ if self.ttest is None:
+ return
+ self.some_similar = [False] * self.nmethods
+ for i in range(self.nbenchmarks):
+ filled_cols_idx = np.argwhere(self.map['fill'][i]).flatten()
+ if len(filled_cols_idx) <= 1:
+ continue
+ col_means = [self.map['mean'][i, j] for j in filled_cols_idx]
+ best_pos = filled_cols_idx[np.argmin(col_means)]
+
+ for j in filled_cols_idx:
+ if j == best_pos:
+ continue
+ if self.ttest == 'ttest':
+ p_val = self._run_ttest(i, best_pos, j)
+ else:
+ p_val = self._run_wilcoxon(i, best_pos, j)
+
+ pval_outcome = pval_interpretation(p_val)
+ self.map['ttest'][i, j] = pval_outcome
+ if pval_outcome != 'Diff':
+ self.some_similar[j] = True
+
+ def compute(self):
+ self._addmap('fill', dtype=bool, func=lambda x: x is not None)
+ self._addmap('mean', dtype=float, func=np.mean)
+ self._addmap('std', dtype=float, func=np.std)
+ self._addmap('nobs', dtype=float, func=len)
+ self._addmap('rank', dtype=int, func=None)
+ self._addmap('color', dtype=object, func=None)
+ self._addmap('ttest', dtype=object, func=None)
+ self._addmap('latex', dtype=object, func=None)
+ self._addrank()
+ self._addcolor()
+ self._add_statistical_test()
+ if self.add_average:
+ self._addave()
+ self._modif = False
+
+ def _is_column_full(self, col):
+ return all(self.map['fill'][:, self.method_index[col]])
+
+ def _addave(self):
+ ave = Table(['ave'], self.methods, lower_is_better=self.lower_is_better, significance_test=self.ttest, average=False,
+ missing=self.missing, missing_str=self.missing_str, prec_mean=self.prec_mean, prec_std=self.prec_std,
+ show_std=self.show_std)
+ for col in self.methods:
+ values = None
+ if self._is_column_full(col):
+ if self.ttest == 'ttest':
+ values = np.asarray(self.map['mean'][:, self.method_index[col]])
+ else: # wilcoxon
+ values = np.concatenate(self.values[:, self.method_index[col]])
+ ave.add('ave', col, values)
+ self.average = ave
+
+ def add(self, benchmark, method, values):
+ if values is not None:
+ values = np.asarray(values)
+ if values.ndim == 0:
+ values = values.flatten()
+ rid, cid = self._coordinates(benchmark, method)
+ if self.map['values'][rid, cid] is None:
+ self.map['values'][rid, cid] = values
+ elif values is not None:
+ self.map['values'][rid, cid] = np.concatenate([self.map['values'][rid, cid], values])
+ self.touch()
+
+ def get(self, benchmark, method, attr='mean'):
+ self.update()
+ assert attr in self.map, f'unknwon attribute {attr}'
+ rid, cid = self._coordinates(benchmark, method)
+ if self.map['fill'][rid, cid]:
+ v = self.map[attr][rid, cid]
+ if v is None or (isinstance(v, float) and np.isnan(v)):
+ return self.missing
+ return v
+ else:
+ return self.missing
+
+ def _coordinates(self, benchmark, method):
+ assert benchmark in self.benchmark_index, f'benchmark {benchmark} out of range'
+ assert method in self.method_index, f'method {method} out of range'
+ rid = self.benchmark_index[benchmark]
+ cid = self.method_index[method]
+ return rid, cid
+
+ def get_average(self, method, attr='mean'):
+ self.update()
+ if self.add_average:
+ return self.average.get('ave', method, attr=attr)
+ return None
+
+ def get_color(self, benchmark, method):
+ color = self.get(benchmark, method, attr='color')
+ if color is None:
+ return ''
+ return color
+
+ def latexCell(self, benchmark, method):
+ self.update()
+ i, j = self._coordinates(benchmark, method)
+ if self.map['fill'][i, j] == False:
+ return self.missing_str
+
+ mean = self.map['mean'][i, j]
+ l = f" {mean:.{self.prec_mean}f}"
+ if self.clean_zero:
+ l = l.replace(' 0.', '.')
+
+ isbest = self.map['rank'][i, j] == 1
+ if isbest:
+ l = "\\textbf{" + l.strip() + "}"
+
+ stat = ''
+ if self.ttest is not None: # and self.some_similar[j]:
+ test_label = self.map['ttest'][i, j]
+ if test_label == 'Sim':
+ stat = '^{\dag\phantom{\dag}}'
+ elif test_label == 'Same':
+ stat = '^{\ddag}'
+ elif isbest or test_label == 'Diff':
+ stat = '^{\phantom{\ddag}}'
+
+ std = ''
+ if self.show_std:
+ std = self.map['std'][i, j]
+ std = f" {std:.{self.prec_std}f}"
+ if self.clean_zero:
+ std = std.replace(' 0.', '.')
+ std = f" \pm {std:{self.prec_std}}"
+
+ if stat != '' or std != '':
+ l = f'{l}${stat}{std}$'
+
+ if self.color:
+ l += ' ' + self.map['color'][i, j]
+
+ return l
+
+ def latexTabular(self, benchmark_replace={}, method_replace={}, average=True):
+ tab = ' & '
+ tab += ' & '.join([method_replace.get(col, col) for col in self.methods])
+ tab += ' \\\\\hline\n'
+ for row in self.benchmarks:
+ rowname = benchmark_replace.get(row, row)
+ tab += rowname + ' & '
+ tab += self.latexRow(row)
+
+ if average:
+ tab += '\hline\n'
+ tab += 'Average & '
+ tab += self.latexAverage()
+ return tab
+
+ def latexTabularT(self, benchmark_replace={}, method_replace={}, average=True, side=False):
+ def withside(label):
+ return '\side{'+label+'}' if side else label
+
+ tab = ' & '
+ tab += ' & '.join([withside(benchmark_replace.get(col, col)) for col in self.benchmarks])
+ if average:
+ tab += ' & ' + withside('Ave')
+ tab += ' \\\\\hline\n'
+ for row in self.methods:
+ rowname = method_replace.get(row, row)
+ tab += rowname + ' & '
+ tab += self.latexRowT(row, endl='')
+ if average:
+ tab += ' & '
+ tab += self.average.latexCell('ave', row)
+ tab += '\\\\\hline\n'
+ return tab
+
+ def latexRow(self, benchmark, endl='\\\\\hline\n'):
+ s = [self.latexCell(benchmark, col) for col in self.methods]
+ s = ' & '.join(s)
+ s += ' ' + endl
+ return s
+
+ def latexRowT(self, method, endl='\\\\\hline\n'):
+ s = [self.latexCell(benchmark, method) for benchmark in self.benchmarks]
+ s = ' & '.join(s)
+ s += ' ' + endl
+ return s
+
+ def latexAverage(self, endl='\\\\\hline\n'):
+ if self.add_average:
+ return self.average.latexRow('ave', endl=endl)
+
+ def getRankTable(self):
+ t = Table(benchmarks=self.benchmarks, methods=self.methods, prec_mean=0, average=True)
+ for rid, cid in self._getfilled():
+ row = self.benchmarks[rid]
+ col = self.methods[cid]
+ t.add(row, col, self.get(row, col, 'rank'))
+ t.compute()
+ return t
+
+ def dropMethods(self, methods):
+ drop_index = [self.method_index[m] for m in methods]
+ new_methods = np.delete(self.methods, drop_index)
+ new_index = {col: j for j, col in enumerate(new_methods)}
+
+ self.map['values'] = self.values[:, np.asarray([self.method_index[m] for m in new_methods], dtype=int)]
+ self.methods = new_methods
+ self.method_index = new_index
+ self.touch()
+
+
+def pval_interpretation(p_val):
+ if 0.005 >= p_val:
+ return 'Diff'
+ elif 0.05 >= p_val > 0.005:
+ return 'Sim'
+ elif p_val > 0.05:
+ return 'Same'
+
+
+def color_red2green_01(val, maxtone=50):
+ if np.isnan(val): return None
+ assert 0 <= val <= 1, f'val {val} out of range [0,1]'
+
+ # rescale to [-1,1]
+ val = val * 2 - 1
+ if val < 0:
+ color = 'red'
+ tone = maxtone * (-val)
+ else:
+ color = 'green'
+ tone = maxtone * val
+ return '\cellcolor{' + color + f'!{int(tone)}' + '}'
\ No newline at end of file
diff --git a/Ordinal/utils.py b/Ordinal/utils.py
index f9544fc..88a278e 100644
--- a/Ordinal/utils.py
+++ b/Ordinal/utils.py
@@ -15,8 +15,6 @@ def load_samples(path_dir, classes):
def load_samples_pkl(path_dir, filter=None):
nsamples = len(glob(join(path_dir, f'*.pkl')))
for id in range(nsamples):
- if filter is not None:
- if id not in filter:
- continue
- yield pickle.load(open(join(path_dir, f'{id}.pkl'), 'rb'))
+ if (filter is None) or id in filter:
+ yield pickle.load(open(join(path_dir, f'{id}.pkl'), 'rb'))
diff --git a/quapy/method/aggregative.py b/quapy/method/aggregative.py
index c0280a2..f2fba73 100644
--- a/quapy/method/aggregative.py
+++ b/quapy/method/aggregative.py
@@ -183,7 +183,7 @@ def _training_helper(learner,
if not hasattr(learner, 'predict_proba'):
print(f'The learner {learner.__class__.__name__} does not seem to be probabilistic. '
f'The learner will be calibrated.')
- learner = CalibratedClassifierCV(learner, cv=5)
+ learner = CalibratedClassifierCV(learner, cv=5, ensemble=True)
if val_split is not None:
if isinstance(val_split, float):
if not (0 < val_split < 1):