seems to be working :D
This commit is contained in:
parent
4150f4351f
commit
2a685cec1e
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@ -5,6 +5,7 @@ from os.path import join
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from quapy.data import LabelledCollection
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from quapy.protocol import AbstractProtocol
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import json
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def load_txt_sample(path, parse_columns, verbose=False, max_lines=None):
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@ -40,24 +41,70 @@ def load_txt_sample(path, parse_columns, verbose=False, max_lines=None):
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return X, y
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def load_json_sample(path, class_name, max_lines=-1):
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obj = json.load(open(path, 'rt'))
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keys = [f'{id}' for id in range(len(obj['text'].keys()))]
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text = [obj['text'][id] for id in keys]
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classes = [obj[class_name][id] for id in keys]
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if max_lines is not None and max_lines>0:
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text = text[:max_lines]
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classes = classes[:max_lines]
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return text, classes
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class TextRankings:
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def __init__(self, path, class_name):
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self.obj = json.load(open(path, 'rt'))
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self.class_name = class_name
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def get_sample_Xy(self, sample_id, max_lines=-1):
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sample_id = str(sample_id)
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O = self.obj
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docs_ids = [doc_id for doc_id, query_id in O['qid'].items() if query_id == sample_id]
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texts = [O['text'][doc_id] for doc_id in docs_ids]
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labels = [O['continent'][doc_id] for doc_id in docs_ids]
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if max_lines > 0 and len(texts) > max_lines:
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ranks = [int(O['rank'][doc_id]) for doc_id in docs_ids]
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sel = np.argsort(ranks)[:max_lines]
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texts = np.asarray(texts)[sel]
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labels = np.asarray(labels)[sel]
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return texts, labels
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def get_query_id_from_path(path, prefix='training', posfix='200SPLIT'):
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qid = path
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qid = qid[:qid.index(posfix)]
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qid = qid[qid.index(prefix)+len(prefix):]
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return qid
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class RetrievedSamples(AbstractProtocol):
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def __init__(self, path_dir: str, load_fn, vectorizer, max_train_lines=None, max_test_lines=None, classes=None):
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def __init__(self, path_dir: str, load_fn, vectorizer, max_train_lines=None, max_test_lines=None, classes=None, class_name=None):
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self.path_dir = path_dir
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self.load_fn = load_fn
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self.vectorizer = vectorizer
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self.max_train_lines = max_train_lines
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self.max_test_lines = max_test_lines
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self.classes=classes
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assert class_name is not None, 'class name should be specified'
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self.class_name = class_name
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self.text_samples = TextRankings(join(self.path_dir, 'testRankingsRetrieval.json'), class_name=class_name)
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def __call__(self):
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for file in glob(join(self.path_dir, 'test_rankings', 'test_rankingstraining_rankings_*.txt')):
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X, y = self.load_fn(file.replace('test_', 'training_'), parse_columns=True, max_lines=self.max_train_lines)
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for file in glob(join(self.path_dir, 'training*SPLIT.json')):
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X, y = self.load_fn(file, class_name=self.class_name, max_lines=self.max_train_lines)
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X = self.vectorizer.transform(X)
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train_sample = LabelledCollection(X, y, classes=self.classes)
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X, y = self.load_fn(file, parse_columns=True, max_lines=self.max_test_lines)
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query_id = get_query_id_from_path(file)
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X, y = self.text_samples.get_sample_Xy(query_id, max_lines=self.max_test_lines)
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# if len(X)!=qp.environ['SAMPLE_SIZE']:
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# print(f'[warning]: file {file} contains {len(X)} instances (expected: {qp.environ["SAMPLE_SIZE"]})')
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# assert len(X) == qp.environ['SAMPLE_SIZE'], f'unexpected sample size for file {file}, found {len(X)}'
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@ -1,3 +1,5 @@
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from collections import defaultdict
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import numpy as np
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import pandas as pd
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from sklearn.feature_extraction.text import TfidfVectorizer
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@ -7,7 +9,8 @@ from sklearn.svm import LinearSVC
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import quapy as qp
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import quapy.functional as F
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from Retrieval.commons import RetrievedSamples, load_txt_sample
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from Retrieval.commons import RetrievedSamples, load_txt_sample, load_json_sample
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from Retrieval.tabular import Table
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from method.non_aggregative import MaximumLikelihoodPrevalenceEstimation
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from quapy.method.aggregative import ClassifyAndCount, EMQ, ACC, PCC, PACC, KDEyML
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from quapy.protocol import AbstractProtocol
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@ -27,6 +30,7 @@ Por ahora 1000 en tr y 100 en test
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Parece que ahora hay muy poco shift
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"""
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def cls(classifier_trained=None):
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if classifier_trained is None:
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# return LinearSVC()
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@ -37,31 +41,31 @@ def cls(classifier_trained=None):
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def methods(classifier_trained=None):
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yield ('CC', ClassifyAndCount(cls(classifier_trained)))
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yield ('PCC', PCC(cls(classifier_trained)))
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yield ('ACC', ACC(cls(classifier_trained), val_split=5, n_jobs=-1))
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yield ('PACC', PACC(cls(classifier_trained), val_split=5, n_jobs=-1))
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yield ('EMQ', EMQ(cls(classifier_trained), exact_train_prev=True))
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yield ('EMQh', EMQ(cls(classifier_trained), exact_train_prev=False))
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yield ('EMQ-BCTS', EMQ(cls(classifier_trained), exact_train_prev=False, recalib='bcts'))
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yield ('EMQ-TS', EMQ(cls(classifier_trained), exact_train_prev=False, recalib='ts'))
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yield ('EMQ-NBVS', EMQ(cls(classifier_trained), exact_train_prev=False, recalib='nbvs'))
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# yield ('EMQ-BCTS', EMQ(cls(classifier_trained), exact_train_prev=False, recalib='bcts'))
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# yield ('EMQ-TS', EMQ(cls(classifier_trained), exact_train_prev=False, recalib='ts'))
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# yield ('EMQ-NBVS', EMQ(cls(classifier_trained), exact_train_prev=False, recalib='nbvs'))
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# yield ('EMQ-VS', EMQ(cls(classifier_trained), exact_train_prev=False, recalib='vs'))
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yield ('PCC', PCC(cls(classifier_trained)))
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yield ('ACC', ACC(cls(classifier_trained), val_split=5, n_jobs=-1))
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yield ('KDE001', KDEyML(cls(classifier_trained), val_split=5, n_jobs=-1, bandwidth=0.001))
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yield ('KDE005', KDEyML(cls(classifier_trained), val_split=5, n_jobs=-1, bandwidth=0.005)) # <-- wow!
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yield ('KDE01', KDEyML(cls(classifier_trained), val_split=5, n_jobs=-1, bandwidth=0.01))
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yield ('KDE02', KDEyML(cls(classifier_trained), val_split=5, n_jobs=-1, bandwidth=0.02))
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yield ('KDE03', KDEyML(cls(classifier_trained), val_split=5, n_jobs=-1, bandwidth=0.03))
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yield ('KDE05', KDEyML(cls(classifier_trained), val_split=5, n_jobs=-1, bandwidth=0.05))
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# yield ('KDE001', KDEyML(cls(classifier_trained), val_split=5, n_jobs=-1, bandwidth=0.001))
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# yield ('KDE005', KDEyML(cls(classifier_trained), val_split=5, n_jobs=-1, bandwidth=0.005)) # <-- wow!
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# yield ('KDE01', KDEyML(cls(classifier_trained), val_split=5, n_jobs=-1, bandwidth=0.01))
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# yield ('KDE02', KDEyML(cls(classifier_trained), val_split=5, n_jobs=-1, bandwidth=0.02))
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# yield ('KDE03', KDEyML(cls(classifier_trained), val_split=5, n_jobs=-1, bandwidth=0.03))
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# yield ('KDE05', KDEyML(cls(classifier_trained), val_split=5, n_jobs=-1, bandwidth=0.05))
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yield ('KDE07', KDEyML(cls(classifier_trained), val_split=5, n_jobs=-1, bandwidth=0.07))
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yield ('KDE10', KDEyML(cls(classifier_trained), val_split=5, n_jobs=-1, bandwidth=0.10))
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# yield ('KDE10', KDEyML(cls(classifier_trained), val_split=5, n_jobs=-1, bandwidth=0.10))
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yield ('MLPE', MaximumLikelihoodPrevalenceEstimation())
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def train_classifier():
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tfidf = TfidfVectorizer(sublinear_tf=True, min_df=10)
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training = LabelledCollection.load(train_path, loader_func=load_txt_sample, verbose=True, parse_columns=False)
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training = LabelledCollection.load(train_path, loader_func=load_json_sample, class_name=CLASS_NAME)
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if REDUCE_TR > 0:
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if REDUCE_TR > 0 and len(training) > REDUCE_TR:
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print('Reducing the number of documents in the training to', REDUCE_TR)
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training = training.sampling(REDUCE_TR, *training.prevalence())
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@ -89,69 +93,77 @@ def train_classifier():
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return tfidf, classifier_trained
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def reduceAtK(data: LabelledCollection, k):
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X, y = data.Xy
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X = X[:k]
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y = y[:k]
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return LabelledCollection(X, y, classes=data.classes_)
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RANK_AT_K = 1000
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RANK_AT_K = -1
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REDUCE_TR = 50000
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qp.environ['SAMPLE_SIZE'] = RANK_AT_K
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data_path = './50_50_split_trec'
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train_path = join(data_path, 'train_50_50_continent.txt')
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data_path = './newExperimentalSetup'
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train_path = join(data_path, 'train3000samples.json')
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tfidf, classifier_trained = qp.util.pickled_resource('classifier.pkl', train_classifier)
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trained=True
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experiment_prot = RetrievedSamples(data_path,
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load_fn=load_txt_sample,
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vectorizer=tfidf,
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max_train_lines=None,
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max_test_lines=RANK_AT_K, classes=classifier_trained.classes_)
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Ks = [10, 50, 100, 250, 500, 1000, 2000]
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# Ks = [500]
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result_mae_dict = {}
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result_mrae_dict = {}
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for method_name, quantifier in methods(classifier_trained):
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# print('Starting with method=', method_name)
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for CLASS_NAME in ['continent']: #, 'gender', 'gender_category', 'occupations', 'source_countries', 'source_subcont_regions', 'years_category', 'relative_pageviews_category']:
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mae_errors = []
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mrae_errors = []
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pbar = tqdm(experiment_prot(), total=49)
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for train, test in pbar:
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if train is not None:
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try:
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tfidf, classifier_trained = qp.util.pickled_resource(f'classifier_{CLASS_NAME}.pkl', train_classifier)
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trained=True
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# print(train.prevalence())
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# print(test.prevalence())
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if trained and method_name!='MLPE':
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quantifier.fit(train, val_split=train, fit_classifier=False)
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else:
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quantifier.fit(train)
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estim_prev = quantifier.quantify(test.instances)
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experiment_prot = RetrievedSamples(data_path,
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load_fn=load_json_sample,
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vectorizer=tfidf,
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max_train_lines=None,
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max_test_lines=RANK_AT_K, classes=classifier_trained.classes_, class_name=CLASS_NAME)
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mae = qp.error.mae(test.prevalence(), estim_prev)
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mae_errors.append(mae)
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method_names = [name for name, *other in methods()]
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benchmarks = [f'{CLASS_NAME}@{k}' for k in Ks]
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table_mae = Table(benchmarks, method_names, color_mode='global')
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table_mrae = Table(benchmarks, method_names, color_mode='global')
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mrae = qp.error.mrae(test.prevalence(), estim_prev)
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mrae_errors.append(mrae)
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for method_name, quantifier in methods(classifier_trained):
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# print('Starting with method=', method_name)
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# print()
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# print('Training prevalence:', F.strprev(train.prevalence()), 'shape', train.X.shape)
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# print('Test prevalence:', F.strprev(test.prevalence()), 'shape', test.X.shape)
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# print('Estim prevalence:', F.strprev(estim_prev))
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mae_errors = {k:[] for k in Ks}
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mrae_errors = {k:[] for k in Ks}
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except Exception as e:
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print(f'wow, something happened here! skipping; {e}')
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else:
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print('skipping one!')
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pbar = tqdm(experiment_prot(), total=49)
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for train, test in pbar:
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if train is not None:
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try:
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if trained and method_name!='MLPE':
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quantifier.fit(train, val_split=train, fit_classifier=False)
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else:
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quantifier.fit(train)
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pbar.set_description(f'{method_name}\tmae={np.mean(mae_errors):.4f}\tmrae={np.mean(mrae_errors):.4f}')
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print()
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result_mae_dict[method_name] = np.mean(mae_errors)
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result_mrae_dict[method_name] = np.mean(mrae_errors)
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for k in Ks:
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test_k = reduceAtK(test, k)
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estim_prev = quantifier.quantify(test_k.instances)
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mae_errors[k].append(qp.error.mae(test_k.prevalence(), estim_prev))
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mrae_errors[k].append(qp.error.mrae(test_k.prevalence(), estim_prev, eps=(1./(2*k))))
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except Exception as e:
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print(f'wow, something happened here! skipping; {e}')
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else:
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print('skipping one!')
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# pbar.set_description(f'{method_name}\tmae={np.mean(mae_errors):.4f}\tmrae={np.mean(mrae_errors):.4f}')
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pbar.set_description(f'{method_name}')
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for k in Ks:
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table_mae.add(benchmark=f'{CLASS_NAME}@{k}', method=method_name, values=mae_errors[k])
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table_mrae.add(benchmark=f'{CLASS_NAME}@{k}', method=method_name, values=mrae_errors[k])
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table_mae.latexPDF('./latex', 'table_mae.tex')
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table_mrae.latexPDF('./latex', 'table_mrae.tex')
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print('Results\n'+('-'*100))
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for method_name in result_mae_dict.keys():
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MAE = result_mae_dict[method_name]
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MRAE = result_mrae_dict[method_name]
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print(f'{method_name}\t{MAE=:.5f}\t{MRAE=:.5f}')
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@ -0,0 +1,427 @@
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import os.path
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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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from pathlib import Path
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from os.path import join
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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, color_mode='local', maxtone=50):
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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.color_mode = color_mode
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self.maxtone = maxtone
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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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minval = {}
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maxval = {}
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if self.color_mode == 'global':
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filled_cols_idx = np.argwhere(self.map['fill'])
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col_means = [self.map['mean'][i, j] for i, j in filled_cols_idx]
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if len(filled_cols_idx) > 0:
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global_minval = min(col_means)
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global_maxval = max(col_means)
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for i in range(self.nbenchmarks):
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minval[i] = global_minval
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maxval[i] = global_maxval
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elif self.color_mode == 'local':
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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)}' + '}'
|
||||
Loading…
Reference in New Issue