seems to be working :D

Retrieval/commons.py

@@ -5,6 +5,7 @@ from os.path import join
 
 from quapy.data import LabelledCollection
 from quapy.protocol import AbstractProtocol
+import json
 
 
 def load_txt_sample(path, parse_columns, verbose=False, max_lines=None):
@@ -40,24 +41,70 @@ def load_txt_sample(path, parse_columns, verbose=False, max_lines=None):
     return X, y
 
 
+def load_json_sample(path, class_name, max_lines=-1):
+    obj = json.load(open(path, 'rt'))
+    keys = [f'{id}' for id in range(len(obj['text'].keys()))]
+    text = [obj['text'][id] for id in keys]
+    classes = [obj[class_name][id] for id in keys]
+    if max_lines is not None and max_lines>0:
+        text = text[:max_lines]
+        classes = classes[:max_lines]
+    return text, classes
+
+
+class TextRankings:
+
+    def __init__(self, path, class_name):
+        self.obj = json.load(open(path, 'rt'))
+        self.class_name = class_name
+
+    def get_sample_Xy(self, sample_id, max_lines=-1):
+        sample_id = str(sample_id)
+        O = self.obj
+        docs_ids = [doc_id for doc_id, query_id in O['qid'].items() if query_id == sample_id]
+        texts = [O['text'][doc_id] for doc_id in docs_ids]
+        labels = [O['continent'][doc_id] for doc_id in docs_ids]
+        if max_lines > 0 and len(texts) > max_lines:
+            ranks = [int(O['rank'][doc_id]) for doc_id in docs_ids]
+            sel = np.argsort(ranks)[:max_lines]
+            texts = np.asarray(texts)[sel]
+            labels = np.asarray(labels)[sel]
+
+        return texts, labels
+
+
+def get_query_id_from_path(path, prefix='training', posfix='200SPLIT'):
+    qid = path
+    qid = qid[:qid.index(posfix)]
+    qid = qid[qid.index(prefix)+len(prefix):]
+    return qid
+
+
 class RetrievedSamples(AbstractProtocol):
 
-    def __init__(self, path_dir: str, load_fn, vectorizer, max_train_lines=None, max_test_lines=None, classes=None):
+    def __init__(self, path_dir: str, load_fn, vectorizer, max_train_lines=None, max_test_lines=None, classes=None, class_name=None):
         self.path_dir = path_dir
         self.load_fn = load_fn
         self.vectorizer = vectorizer
         self.max_train_lines = max_train_lines
         self.max_test_lines = max_test_lines
         self.classes=classes
+        assert class_name is not None, 'class name should be specified'
+        self.class_name = class_name
+        self.text_samples = TextRankings(join(self.path_dir, 'testRankingsRetrieval.json'), class_name=class_name)
+
 
     def __call__(self):
-        for file in glob(join(self.path_dir, 'test_rankings', 'test_rankingstraining_rankings_*.txt')):
 
-            X, y = self.load_fn(file.replace('test_', 'training_'), parse_columns=True, max_lines=self.max_train_lines)
+        for file in glob(join(self.path_dir, 'training*SPLIT.json')):
+
+            X, y = self.load_fn(file, class_name=self.class_name, max_lines=self.max_train_lines)
             X = self.vectorizer.transform(X)
             train_sample = LabelledCollection(X, y, classes=self.classes)
 
-            X, y = self.load_fn(file, parse_columns=True, max_lines=self.max_test_lines)
+            query_id = get_query_id_from_path(file)
+            X, y = self.text_samples.get_sample_Xy(query_id, max_lines=self.max_test_lines)
+
             # if len(X)!=qp.environ['SAMPLE_SIZE']:
             #     print(f'[warning]: file {file} contains {len(X)} instances (expected: {qp.environ["SAMPLE_SIZE"]})')
             # assert len(X) == qp.environ['SAMPLE_SIZE'], f'unexpected sample size for file {file}, found {len(X)}'

Retrieval/fifth.py

@@ -1,3 +1,5 @@
+from collections import defaultdict
+
 import numpy as np
 import pandas as pd
 from sklearn.feature_extraction.text import TfidfVectorizer
@@ -7,7 +9,8 @@ from sklearn.svm import LinearSVC
 
 import quapy as qp
 import quapy.functional as F
-from Retrieval.commons import RetrievedSamples, load_txt_sample
+from Retrieval.commons import RetrievedSamples, load_txt_sample, load_json_sample
+from Retrieval.tabular import Table
 from method.non_aggregative import MaximumLikelihoodPrevalenceEstimation
 from quapy.method.aggregative import ClassifyAndCount, EMQ, ACC, PCC, PACC, KDEyML
 from quapy.protocol import AbstractProtocol
@@ -27,6 +30,7 @@ Por ahora 1000 en tr y 100 en test
 Parece que ahora hay muy poco shift  
 """
 
+
 def cls(classifier_trained=None):
     if classifier_trained is None:
         # return LinearSVC()
@@ -37,31 +41,31 @@ def cls(classifier_trained=None):
 
 def methods(classifier_trained=None):
     yield ('CC', ClassifyAndCount(cls(classifier_trained)))
+    yield ('PCC', PCC(cls(classifier_trained)))
+    yield ('ACC', ACC(cls(classifier_trained), val_split=5, n_jobs=-1))
     yield ('PACC', PACC(cls(classifier_trained), val_split=5, n_jobs=-1))
     yield ('EMQ', EMQ(cls(classifier_trained), exact_train_prev=True))
     yield ('EMQh', EMQ(cls(classifier_trained), exact_train_prev=False))
-    yield ('EMQ-BCTS', EMQ(cls(classifier_trained), exact_train_prev=False, recalib='bcts'))
+    # yield ('EMQ-BCTS', EMQ(cls(classifier_trained), exact_train_prev=False, recalib='bcts'))
-    yield ('EMQ-TS', EMQ(cls(classifier_trained), exact_train_prev=False, recalib='ts'))
+    # yield ('EMQ-TS', EMQ(cls(classifier_trained), exact_train_prev=False, recalib='ts'))
-    yield ('EMQ-NBVS', EMQ(cls(classifier_trained), exact_train_prev=False, recalib='nbvs'))
+    # yield ('EMQ-NBVS', EMQ(cls(classifier_trained), exact_train_prev=False, recalib='nbvs'))
     # yield ('EMQ-VS', EMQ(cls(classifier_trained), exact_train_prev=False, recalib='vs'))
-    yield ('PCC', PCC(cls(classifier_trained)))
+    # yield ('KDE001', KDEyML(cls(classifier_trained), val_split=5, n_jobs=-1, bandwidth=0.001))
-    yield ('ACC', ACC(cls(classifier_trained), val_split=5, n_jobs=-1))
+    # yield ('KDE005', KDEyML(cls(classifier_trained), val_split=5, n_jobs=-1, bandwidth=0.005)) # <-- wow!
-    yield ('KDE001', KDEyML(cls(classifier_trained), val_split=5, n_jobs=-1, bandwidth=0.001))
+    # yield ('KDE01', KDEyML(cls(classifier_trained), val_split=5, n_jobs=-1, bandwidth=0.01))
-    yield ('KDE005', KDEyML(cls(classifier_trained), val_split=5, n_jobs=-1, bandwidth=0.005)) # <-- wow!
+    # yield ('KDE02', KDEyML(cls(classifier_trained), val_split=5, n_jobs=-1, bandwidth=0.02))
-    yield ('KDE01', KDEyML(cls(classifier_trained), val_split=5, n_jobs=-1, bandwidth=0.01))
+    # yield ('KDE03', KDEyML(cls(classifier_trained), val_split=5, n_jobs=-1, bandwidth=0.03))
-    yield ('KDE02', KDEyML(cls(classifier_trained), val_split=5, n_jobs=-1, bandwidth=0.02))
+    # yield ('KDE05', KDEyML(cls(classifier_trained), val_split=5, n_jobs=-1, bandwidth=0.05))
-    yield ('KDE03', KDEyML(cls(classifier_trained), val_split=5, n_jobs=-1, bandwidth=0.03))
-    yield ('KDE05', KDEyML(cls(classifier_trained), val_split=5, n_jobs=-1, bandwidth=0.05))
     yield ('KDE07', KDEyML(cls(classifier_trained), val_split=5, n_jobs=-1, bandwidth=0.07))
-    yield ('KDE10', KDEyML(cls(classifier_trained), val_split=5, n_jobs=-1, bandwidth=0.10))
+    # yield ('KDE10', KDEyML(cls(classifier_trained), val_split=5, n_jobs=-1, bandwidth=0.10))
     yield ('MLPE', MaximumLikelihoodPrevalenceEstimation())
 
 
 def train_classifier():
     tfidf = TfidfVectorizer(sublinear_tf=True, min_df=10)
-    training = LabelledCollection.load(train_path, loader_func=load_txt_sample, verbose=True, parse_columns=False)
+    training = LabelledCollection.load(train_path, loader_func=load_json_sample, class_name=CLASS_NAME)
 
-    if REDUCE_TR > 0:
+    if REDUCE_TR > 0 and len(training) > REDUCE_TR:
         print('Reducing the number of documents in the training to', REDUCE_TR)
         training = training.sampling(REDUCE_TR, *training.prevalence())
 
@@ -89,69 +93,77 @@ def train_classifier():
     return tfidf, classifier_trained
 
 
+def reduceAtK(data: LabelledCollection, k):
+    X, y = data.Xy
+    X = X[:k]
+    y = y[:k]
+    return LabelledCollection(X, y, classes=data.classes_)
 
-RANK_AT_K = 1000
+
+RANK_AT_K = -1
 REDUCE_TR = 50000
 qp.environ['SAMPLE_SIZE'] = RANK_AT_K
 
-data_path = './50_50_split_trec'
+data_path = './newExperimentalSetup'
-train_path = join(data_path, 'train_50_50_continent.txt')
+train_path = join(data_path, 'train3000samples.json')
 
-tfidf, classifier_trained = qp.util.pickled_resource('classifier.pkl', train_classifier)
+
+Ks = [10, 50, 100, 250, 500, 1000, 2000]
+# Ks = [500]
+
+for CLASS_NAME in ['continent']: #, 'gender', 'gender_category', 'occupations', 'source_countries', 'source_subcont_regions', 'years_category', 'relative_pageviews_category']:
+
+    tfidf, classifier_trained = qp.util.pickled_resource(f'classifier_{CLASS_NAME}.pkl', train_classifier)
     trained=True
 
     experiment_prot = RetrievedSamples(data_path,
-                           load_fn=load_txt_sample,
+                               load_fn=load_json_sample,
                                vectorizer=tfidf,
                                max_train_lines=None,
-                           max_test_lines=RANK_AT_K, classes=classifier_trained.classes_)
+                               max_test_lines=RANK_AT_K, classes=classifier_trained.classes_, class_name=CLASS_NAME)
+
+    method_names = [name for name, *other in methods()]
+    benchmarks = [f'{CLASS_NAME}@{k}' for k in Ks]
+    table_mae = Table(benchmarks, method_names, color_mode='global')
+    table_mrae = Table(benchmarks, method_names, color_mode='global')
 
-result_mae_dict = {}
-result_mrae_dict = {}
     for method_name, quantifier in methods(classifier_trained):
         # print('Starting with method=', method_name)
 
-    mae_errors = []
+        mae_errors = {k:[] for k in Ks}
-    mrae_errors = []
+        mrae_errors = {k:[] for k in Ks}
+
         pbar = tqdm(experiment_prot(), total=49)
         for train, test in pbar:
             if train is not None:
                 try:
-
-                # print(train.prevalence())
-                # print(test.prevalence())
                     if trained and method_name!='MLPE':
                         quantifier.fit(train, val_split=train, fit_classifier=False)
                     else:
                         quantifier.fit(train)
-                estim_prev = quantifier.quantify(test.instances)
 
-                mae = qp.error.mae(test.prevalence(), estim_prev)
+                    for k in Ks:
-                mae_errors.append(mae)
+                        test_k = reduceAtK(test, k)
+                        estim_prev = quantifier.quantify(test_k.instances)
 
-                mrae = qp.error.mrae(test.prevalence(), estim_prev)
+                        mae_errors[k].append(qp.error.mae(test_k.prevalence(), estim_prev))
-                mrae_errors.append(mrae)
+                        mrae_errors[k].append(qp.error.mrae(test_k.prevalence(), estim_prev, eps=(1./(2*k))))
-
-                # print()
-                # print('Training prevalence:', F.strprev(train.prevalence()), 'shape', train.X.shape)
-                # print('Test prevalence:', F.strprev(test.prevalence()), 'shape', test.X.shape)
-                # print('Estim prevalence:', F.strprev(estim_prev))
 
                 except Exception as e:
                     print(f'wow, something happened here! skipping; {e}')
             else:
                 print('skipping one!')
 
-        pbar.set_description(f'{method_name}\tmae={np.mean(mae_errors):.4f}\tmrae={np.mean(mrae_errors):.4f}')
+            # pbar.set_description(f'{method_name}\tmae={np.mean(mae_errors):.4f}\tmrae={np.mean(mrae_errors):.4f}')
-    print()
+            pbar.set_description(f'{method_name}')
-    result_mae_dict[method_name] = np.mean(mae_errors)
+
-    result_mrae_dict[method_name] = np.mean(mrae_errors)
+        for k in Ks:
+            table_mae.add(benchmark=f'{CLASS_NAME}@{k}', method=method_name, values=mae_errors[k])
+            table_mrae.add(benchmark=f'{CLASS_NAME}@{k}', method=method_name, values=mrae_errors[k])
+
+    table_mae.latexPDF('./latex', 'table_mae.tex')
+    table_mrae.latexPDF('./latex', 'table_mrae.tex')
 
-print('Results\n'+('-'*100))
-for method_name in result_mae_dict.keys():
-    MAE = result_mae_dict[method_name]
-    MRAE = result_mrae_dict[method_name]
-    print(f'{method_name}\t{MAE=:.5f}\t{MRAE=:.5f}')
 
 
 

Retrieval/tabular.py

@@ -0,0 +1,427 @@
+import os.path
+import numpy as np
+import itertools
+from scipy.stats import ttest_ind_from_stats, wilcoxon
+from pathlib import Path
+from os.path import join
+
+
+class Table:
+    VALID_TESTS = [None, "wilcoxon", "ttest"]
+
+    def __init__(self, benchmarks, methods, lower_is_better=True, ttest='ttest', prec_mean=3,
+                 clean_zero=False, show_std=False, prec_std=3, average=True, missing=None, missing_str='--',
+                 color=True, color_mode='local', maxtone=50):
+        assert ttest 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 = ttest
+        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.color_mode = color_mode
+        self.maxtone = maxtone
+        
+        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):
+        minval = {}
+        maxval = {}
+
+        if self.color_mode == 'global':
+            filled_cols_idx = np.argwhere(self.map['fill'])
+            col_means = [self.map['mean'][i, j] for i, j in filled_cols_idx]
+            if len(filled_cols_idx) > 0:
+                global_minval = min(col_means)
+                global_maxval = max(col_means)
+                for i in range(self.nbenchmarks):
+                    minval[i] = global_minval
+                    maxval[i] = global_maxval
+        elif self.color_mode == 'local':
+            for i in range(self.nbenchmarks):
+                filled_cols_idx = np.argwhere(self.map['fill'][i, i + 1])
+                if len(filled_cols_idx)>0:
+                    col_means = [self.map['mean'][i, j] for j in filled_cols_idx]
+                    minval[i] = min(col_means)
+                    maxval[i] = max(col_means)
+
+        else:
+            print(f'color mode {self.color_mode} not understood, valid ones are "local" and "global"; skip')
+            return
+
+
+        for i in range(self.nbenchmarks):
+            filled_cols_idx = np.argwhere(self.map['fill'][i]).flatten()
+            for col_idx in filled_cols_idx:
+                val = self.map['mean'][i,col_idx]
+                if i not in maxval or i not in minval:
+                    continue
+                norm = (maxval[i] - minval[i])
+                if norm > 0:
+                    normval = (val - minval[i]) / norm
+                else:
+                    normval = 0.5
+
+                if self.lower_is_better:
+                    normval = 1 - normval
+
+                normval = np.clip(normval, 0,1)
+
+                self.map['color'][i, col_idx] = color_red2green_01(normval, self.maxtone)
+
+    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]
+        try:
+            _, p_val = wilcoxon(values1, values2)
+        except ValueError:
+            p_val = 0
+        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,
+                    ttest=self.ttest,
+                    average=False,
+                    missing=self.missing,
+                    missing_str=self.missing_str,
+                    prec_mean=self.prec_mean,
+                    prec_std=self.prec_std,
+                    clean_zero=self.clean_zero,
+                    show_std=self.show_std,
+                    color=self.color,
+                    maxtone=self.maxtone)
+        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]])
+                    values = np.concatenate(self.values[:, self.method_index[col]])
+                else:  # wilcoxon
+                    # values = np.asarray(self.map['mean'][:, self.method_index[col]])
+                    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)
+        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 latex(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 None else '^{\phantom{\ddag}}'
+        if self.ttest is not None and self.some_similar[j]:
+            test_label = self.map['ttest'][i,j]
+            if test_label == 'Sim':
+                stat = '^{\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 latexPDF(self, path, name:str, *args, **kwargs):
+        if not name.endswith('.tex'):
+            name += '.tex'
+
+        self.latexSaveDocument(join(path, name), *args, **kwargs)
+
+        print("[Tables Done] runing latex")
+        os.chdir(path)
+        os.system('pdflatex '+name)
+        basename = name.replace('.tex', '')
+        os.system(f'rm {basename}.aux {basename}.bbl {basename}.blg {basename}.log {basename}.out {basename}.dvi')
+        os.chdir('..')
+
+    def latexSaveDocument(self, path, *args, **kwargs):
+        document = self.latexDocument(*args, **kwargs)
+        parent = Path(path).parent
+        os.makedirs(parent, exist_ok=True)
+        with open(path, 'wt') as foo:
+            foo.write(document)
+        print('text file save at ', path)
+
+    def latexDocument(self, *args, **kwargs):
+        document = """
+\\documentclass[10pt,a4paper]{article}
+\\usepackage[utf8]{inputenc}
+\\usepackage{amsmath}
+\\usepackage{amsfonts}
+\\usepackage{amssymb}
+\\usepackage{graphicx}
+\\usepackage{xcolor}
+\\usepackage{colortbl}
+        
+\\begin{document}
+        """
+        document += self.latexTable(*args, **kwargs)
+        document += "\n\end{document}\n"
+        return document
+
+    def latexTable(self, benchmark_replace={}, method_replace={}, aslines=False, endl='\\\\\hline', resizebox=True):
+        table = """
+        \\begin{table}
+        \center
+        %%%\\resizebox{\\textwidth}{!}{% \n
+        """
+        table += "\n\\begin{tabular}{|c"+"|c" * self.nmethods + "|}\n"
+        table += self.latexTabular(benchmark_replace, method_replace, aslines, endl)
+        table += "\n\\end{tabular}\n"
+        table += """
+        %%%}%
+        \end{table}
+        """
+        if resizebox:
+            table = table.replace("%%%", "")
+        return table
+
+    def latexTabular(self, benchmark_replace={}, method_replace={}, aslines=False, endl='\\\\\hline'):
+        lines = []
+        l = '\multicolumn{1}{c|}{} & '
+        l += ' & '.join([method_replace.get(col, col) for col in self.methods])
+        l += ' \\\\\hline'
+        lines.append(l)
+
+        for row in self.benchmarks:
+            rowname = benchmark_replace.get(row, row)
+            l = rowname + ' & '
+            l += self.latexRow(row, endl=endl)
+            lines.append(l)
+
+        if self.add_average:
+            # l += '\hline\n'
+            l = '\hline \n \\textit{Average} & '
+            l += self.latexAverage(endl=endl)
+            lines.append(l)
+        if not aslines:
+            lines='\n'.join(lines)
+        return lines
+
+    def latexRow(self, benchmark, endl='\\\\\hline\n'):
+        s = [self.latex(benchmark, col) for col in self.methods]
+        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, prec_mean=0):
+        t = Table(benchmarks=self.benchmarks, methods=self.methods, prec_mean=prec_mean, average=True, maxtone=self.maxtone, ttest=None)
+        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)}' + '}'