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bc656fe207
Author | SHA1 | Date |
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Alejandro Moreo Fernandez | bc656fe207 | |
Alejandro Moreo Fernandez | 985f430d52 |
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import itertools
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import os.path
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import pickle
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from collections import defaultdict
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from pathlib import Path
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import numpy as np
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from sklearn.feature_extraction.text import TfidfVectorizer
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from sklearn.linear_model import LogisticRegression
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from sklearn.model_selection import GridSearchCV
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from sklearn.svm import LinearSVC
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import quapy as qp
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from Retrieval.commons import RetrievedSamples, load_sample
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from method.non_aggregative import MaximumLikelihoodPrevalenceEstimation as Naive
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from quapy.method.aggregative import ClassifyAndCount, EMQ, ACC, PCC, PACC, KDEyML
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from quapy.data.base import LabelledCollection
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from os.path import join
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from tqdm import tqdm
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from result_table.src.table import Table
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"""
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"""
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data_home = 'data'
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datasets = ['continent', 'gender', 'years_category', 'relative_pageviews_category', 'num_sitelinks_category']
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param_grid = {'C': np.logspace(-4, 4, 9), 'class_weight': ['balanced', None]}
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# param_grid = {'C': np.logspace(-1, 1, 2)}
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classifiers = [
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('LR', LogisticRegression(max_iter=5000), param_grid),
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('SVM', LinearSVC(), param_grid)
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]
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def benchmark_name(class_name):
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return class_name.replace('_', '\_')
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table = Table(name=f'accuracy', benchmarks=[benchmark_name(d) for d in datasets])
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table.format.show_std = False
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table.format.stat_test = None
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table.format.lower_is_better = False
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for class_name, (cls_name, cls, grid) in itertools.product(datasets, classifiers):
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train_data_path = join(data_home, class_name, 'FULL', 'classifier_training.json') # <-------- fixed classifier
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texts, labels = load_sample(train_data_path, class_name=class_name)
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tfidf = TfidfVectorizer(sublinear_tf=True, min_df=3)
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Xtr = tfidf.fit_transform(texts)
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print(f'Xtr shape={Xtr.shape}')
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print('training classifier...', end='')
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classifier = GridSearchCV(
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cls,
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param_grid=grid,
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n_jobs=-1,
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cv=5,
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verbose=10
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)
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classifier.fit(Xtr, labels)
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classifier_acc = classifier.best_score_
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classifier_acc_per_fold = classifier.cv_results_['mean_test_score'][classifier.best_index_]
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print(f'[done] best-params={classifier.best_params_} got {classifier_acc:.4f} score, per fold {classifier_acc_per_fold}')
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table.add(benchmark=benchmark_name(class_name), method=cls_name, v=classifier_acc_per_fold)
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Table.LatexPDF(f'./latex/classifier_Acc.pdf', tables=[table])
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@ -8,116 +8,102 @@ 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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# print('reading', path)
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if verbose:
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print(f'loading {path}...', end='')
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df = pd.read_csv(path, sep='\t')
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if verbose:
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print('[done]')
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X = df['text'].values
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y = df['continent'].values
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def load_sample(path, class_name):
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"""
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Loads a sample json as a dataframe and returns text and labels for
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the given class_name
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if parse_columns:
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rank = df['rank'].values
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scores = df['score'].values
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rank = rank[y != 'Antarctica']
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scores = scores[y != 'Antarctica']
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X = X[y!='Antarctica']
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y = y[y!='Antarctica']
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if parse_columns:
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order = np.argsort(rank)
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X = X[order]
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y = y[order]
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rank = rank[order]
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scores = scores[order]
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if max_lines is not None:
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X = X[:max_lines]
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y = y[:max_lines]
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return X, y
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:param path: path to a json file
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:param class_name: string representing the target class
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:return: texts, labels for class_name
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"""
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df = pd.read_json(path)
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text = df.text.values
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labels = df[class_name].values
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return text, labels
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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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#print(list(obj.keys()))
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#import sys; sys.exit(0)
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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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def get_text_label_score(df, class_name, vectorizer=None, filter_classes=None):
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text = df.text.values
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labels = df[class_name].values
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rel_score = df.score.values
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if filter_classes is not None:
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idx = np.isin(labels, filter_classes)
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text = text[idx]
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labels = labels[idx]
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rel_score = rel_score[idx]
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if vectorizer is not None:
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text = vectorizer.transform(text)
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order = np.argsort(-rel_score)
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return text[order], labels[order], rel_score[order]
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class TextRankings:
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class RetrievedSamples:
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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[self.class_name][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, 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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def __init__(self,
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class_home: str,
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test_rankings_path: str,
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vectorizer,
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class_name,
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classes=None
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):
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self.class_home = class_home
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self.test_rankings_df = pd.read_json(test_rankings_path)
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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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self.classes=classes
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def __call__(self):
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tests_df = self.test_rankings_df
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class_name = self.class_name
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vectorizer = self.vectorizer
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for file in glob(join(self.path_dir, 'training*SPLIT.json')):
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for file in self._list_queries():
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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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# loads the training sample
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train_df = pd.read_json(file)
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Xtr, ytr, score_tr = get_text_label_score(train_df, class_name, vectorizer, filter_classes=self.classes)
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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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# loads the test sample
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query_id = self._get_query_id_from_path(file)
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sel_df = tests_df[tests_df.qid == int(query_id)]
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Xte, yte, score_te = get_text_label_score(sel_df, class_name, vectorizer, filter_classes=self.classes)
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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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X = self.vectorizer.transform(X)
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try:
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test_sample = LabelledCollection(X, y, classes=train_sample.classes_)
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except ValueError as e:
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print(f'file {file} caused error {e}')
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yield None, None
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yield (Xtr, ytr, score_tr), (Xte, yte, score_te)
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def _list_queries(self):
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return sorted(glob(join(self.class_home, 'training_Query*200SPLIT.json')))
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# def _get_test_sample(self, query_id, max_lines=-1):
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# df = self.test_rankings_df
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# sel_df = df[df.qid==int(query_id)]
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# return get_text_label_score(sel_df)
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# texts = sel_df.text.values
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# try:
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# labels = sel_df[self.class_name].values
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# except KeyError as e:
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# print(f'error: key {self.class_name} not found in test rankings')
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# raise e
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# if max_lines > 0 and len(texts) > max_lines:
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# ranks = sel_df.rank.values
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# idx = np.argsort(ranks)[:max_lines]
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# texts = np.asarray(texts)[idx]
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# labels = np.asarray(labels)[idx]
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# return texts, labels
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def total(self):
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return len(self._list_queries())
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def _get_query_id_from_path(self, path):
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prefix = 'training_Query-'
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posfix = 'Sample-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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# print('train #classes:', train_sample.n_classes, train_sample.prevalence())
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# print('test #classes:', test_sample.n_classes, test_sample.prevalence())
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yield train_sample, test_sample
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@ -0,0 +1,245 @@
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import os.path
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import pickle
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from collections import defaultdict
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from pathlib import Path
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import numpy as np
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from sklearn.feature_extraction.text import TfidfVectorizer
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from sklearn.linear_model import LogisticRegression
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from sklearn.model_selection import GridSearchCV
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from sklearn.svm import LinearSVC
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import quapy as qp
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from Retrieval.commons import RetrievedSamples, load_sample
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from method.non_aggregative import MaximumLikelihoodPrevalenceEstimation as Naive
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from quapy.method.aggregative import ClassifyAndCount, EMQ, ACC, PCC, PACC, KDEyML
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from quapy.data.base import LabelledCollection
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from os.path import join
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from tqdm import tqdm
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from result_table.src.table import Table
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"""
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In this sixth experiment, we have a collection C of >6M documents.
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We split C in two equally-sized pools TrPool, TePool
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I have randomly split the collection in 50% train and 50% split. In each split we have approx. 3.25 million documents.
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We have 5 categories we can evaluate over: Continent, Years_Category, Num_Site_Links, Relative Pageviews and Gender.
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From the training set I have created smaller subsets for each category:
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100K, 500K, 1M and FULL (3.25M)
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For each category and subset, I have created a training set called: "classifier_training.json". This is the "base" training set for the classifier. In this set we have 500 documents per group in a category. (For example: Male 500, Female 500, Unknown 500). Let me know if you think we need more.
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To "bias" the quantifier towards a query, I have executed the queries (97) on the different training sets and retrieved the 200 most relevant documents per group.
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For example: (Male 200, Female 200, Unknown 200)
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Sometimes this is infeasible, we should probably discuss this at some point.
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You can find the results for every query in a file named:
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"training_Query-[QID]Sample-200SPLIT.json"
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Test:
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To evaluate our approach, I have executed the queries on the test split. You can find the results for all 97 queries up till k=1000 in this file.
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testRanking_Results.json
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"""
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def methods(classifier, class_name):
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kde_param = {
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'continent': 0.18,
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'gender': 0.12,
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'years_category':0.09
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}
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yield ('Naive', Naive())
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yield ('NaiveQuery', Naive())
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yield ('CC', ClassifyAndCount(classifier))
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# yield ('PCC', PCC(classifier))
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# yield ('ACC', ACC(classifier, val_split=5, n_jobs=-1))
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yield ('PACC', PACC(classifier, val_split=5, n_jobs=-1))
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# yield ('EMQ', EMQ(classifier, exact_train_prev=True))
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# yield ('EMQ-Platt', EMQ(classifier, exact_train_prev=True, recalib='platt'))
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# yield ('EMQh', EMQ(classifier, exact_train_prev=False))
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# yield ('EMQ-BCTS', EMQ(classifier, exact_train_prev=True, recalib='bcts'))
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# yield ('EMQ-TS', EMQ(classifier, exact_train_prev=False, recalib='ts'))
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# yield ('EMQ-NBVS', EMQ(classifier, exact_train_prev=False, recalib='nbvs'))
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# yield ('EMQ-VS', EMQ(classifier, exact_train_prev=False, recalib='vs'))
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# yield ('KDE001', KDEyML(classifier, val_split=5, n_jobs=-1, bandwidth=0.001))
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# yield ('KDE005', KDEyML(classifier, val_split=5, n_jobs=-1, bandwidth=0.005)) # <-- wow!
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# yield ('KDE01', KDEyML(classifier, val_split=5, n_jobs=-1, bandwidth=0.01))
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# yield ('KDE02', KDEyML(classifier, val_split=5, n_jobs=-1, bandwidth=0.02))
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# yield ('KDE03', KDEyML(classifier, val_split=5, n_jobs=-1, bandwidth=0.03))
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# yield ('KDE-silver', KDEyML(classifier, val_split=5, n_jobs=-1, bandwidth='silverman'))
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# yield ('KDE-scott', KDEyML(classifier, val_split=5, n_jobs=-1, bandwidth='scott'))
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yield ('KDE-opt', KDEyML(classifier, val_split=5, n_jobs=-1, bandwidth=kde_param[class_name]))
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yield ('KDE01', KDEyML(classifier, val_split=5, n_jobs=-1, bandwidth=0.01))
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yield ('KDE02', KDEyML(classifier, val_split=5, n_jobs=-1, bandwidth=0.02))
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yield ('KDE03', KDEyML(classifier, val_split=5, n_jobs=-1, bandwidth=0.03))
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yield ('KDE04', KDEyML(classifier, val_split=5, n_jobs=-1, bandwidth=0.04))
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yield ('KDE05', KDEyML(classifier, val_split=5, n_jobs=-1, bandwidth=0.05))
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yield ('KDE07', KDEyML(classifier, val_split=5, n_jobs=-1, bandwidth=0.07))
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# yield ('KDE10', KDEyML(classifier, val_split=5, n_jobs=-1, bandwidth=0.10))
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def train_classifier(train_path):
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"""
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Trains a classifier. To do so, it loads the training set, transforms it into a tfidf representation.
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The classifier is Logistic Regression, with hyperparameters C (range [0.001, 0.01, ..., 1000]) and
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class_weight (range {'balanced', None}) optimized via 5FCV.
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:return: the tfidf-vectorizer and the classifier trained
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"""
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texts, labels = load_sample(train_path, class_name=class_name)
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tfidf = TfidfVectorizer(sublinear_tf=True, min_df=3)
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Xtr = tfidf.fit_transform(texts)
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print(f'Xtr shape={Xtr.shape}')
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print('training classifier...', end='')
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classifier = LogisticRegression(max_iter=5000)
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classifier = GridSearchCV(
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classifier,
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param_grid={'C': np.logspace(-4, 4, 9), 'class_weight': ['balanced', None]},
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n_jobs=-1,
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cv=5
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)
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classifier.fit(Xtr, labels)
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classifier = classifier.best_estimator_
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classifier_acc = classifier.best_score_
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print(f'[done] best-params={classifier.best_params_} got {classifier_acc:.4f} score')
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training = LabelledCollection(Xtr, labels)
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print('training classes:', training.classes_)
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print('training prevalence:', training.prevalence())
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return tfidf, classifier
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def reduceAtK(data: LabelledCollection, k):
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# if k > len(data):
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# print(f'[warning] {k=}>{len(data)=}')
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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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def benchmark_name(class_name, k):
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scape_class_name = class_name.replace('_', '\_')
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return f'{scape_class_name}@{k}'
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def run_experiment():
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results = {
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'mae': {k: [] for k in Ks},
|
||||
'mrae': {k: [] for k in Ks}
|
||||
}
|
||||
|
||||
pbar = tqdm(experiment_prot(), total=experiment_prot.total())
|
||||
for train, test in pbar:
|
||||
Xtr, ytr, score_tr = train
|
||||
Xte, yte, score_te = test
|
||||
|
||||
if HALF:
|
||||
n = len(ytr) // 2
|
||||
train_col = LabelledCollection(Xtr[:n], ytr[:n], classes=classifier_trained.classes_)
|
||||
else:
|
||||
train_col = LabelledCollection(Xtr, ytr, classes=classifier_trained.classes_)
|
||||
|
||||
if method_name not in ['Naive', 'NaiveQuery']:
|
||||
quantifier.fit(train_col, val_split=train_col, fit_classifier=False)
|
||||
elif method_name == 'Naive':
|
||||
quantifier.fit(train_col)
|
||||
|
||||
test_col = LabelledCollection(Xte, yte, classes=classifier_trained.classes_)
|
||||
for k in Ks:
|
||||
test_k = reduceAtK(test_col, k)
|
||||
if method_name == 'NaiveQuery':
|
||||
train_k = reduceAtK(train_col, k)
|
||||
quantifier.fit(train_k)
|
||||
|
||||
estim_prev = quantifier.quantify(test_k.instances)
|
||||
|
||||
mae = qp.error.mae(test_k.prevalence(), estim_prev)
|
||||
mrae = qp.error.mrae(test_k.prevalence(), estim_prev, eps=(1. / (2 * k)))
|
||||
|
||||
results['mae'][k].append(mae)
|
||||
results['mrae'][k].append(mrae)
|
||||
|
||||
pbar.set_description(f'{method_name}')
|
||||
|
||||
return results
|
||||
|
||||
|
||||
data_home = 'data'
|
||||
|
||||
HALF=True
|
||||
exp_posfix = '_half'
|
||||
|
||||
method_names = [name for name, *other in methods(None, 'continent')]
|
||||
|
||||
Ks = [5, 10, 25, 50, 75, 100, 250, 500, 750, 1000]
|
||||
|
||||
for class_name in ['gender', 'continent', 'years_category']: # 'relative_pageviews_category', 'num_sitelinks_category']:
|
||||
tables_mae, tables_mrae = [], []
|
||||
|
||||
benchmarks = [benchmark_name(class_name, k) for k in Ks]
|
||||
|
||||
for data_size in ['10K', '50K', '100K', '500K', '1M', 'FULL']:
|
||||
|
||||
table_mae = Table(name=f'{class_name}-{data_size}-mae', benchmarks=benchmarks, methods=method_names)
|
||||
table_mrae = Table(name=f'{class_name}-{data_size}-mrae', benchmarks=benchmarks, methods=method_names)
|
||||
table_mae.format.mean_prec = 5
|
||||
table_mae.format.remove_zero = True
|
||||
table_mae.format.color_mode = 'global'
|
||||
|
||||
tables_mae.append(table_mae)
|
||||
tables_mrae.append(table_mrae)
|
||||
|
||||
class_home = join(data_home, class_name, data_size)
|
||||
# train_data_path = join(class_home, 'classifier_training.json')
|
||||
# classifier_path = join('classifiers', data_size, f'classifier_{class_name}.pkl')
|
||||
train_data_path = join(data_home, class_name, 'FULL', 'classifier_training.json') # <-------- fixed classifier
|
||||
classifier_path = join('classifiers', 'FULL', f'classifier_{class_name}.pkl') # <------------ fixed classifier
|
||||
test_rankings_path = join(data_home, 'testRanking_Results.json')
|
||||
results_home = join('results'+exp_posfix, class_name, data_size)
|
||||
|
||||
tfidf, classifier_trained = qp.util.pickled_resource(classifier_path, train_classifier, train_data_path)
|
||||
|
||||
experiment_prot = RetrievedSamples(
|
||||
class_home,
|
||||
test_rankings_path,
|
||||
vectorizer=tfidf,
|
||||
class_name=class_name,
|
||||
classes=classifier_trained.classes_
|
||||
)
|
||||
for method_name, quantifier in methods(classifier_trained, class_name):
|
||||
|
||||
results_path = join(results_home, method_name + '.pkl')
|
||||
if os.path.exists(results_path):
|
||||
print(f'Method {method_name=} already computed')
|
||||
results = pickle.load(open(results_path, 'rb'))
|
||||
else:
|
||||
results = run_experiment()
|
||||
|
||||
os.makedirs(Path(results_path).parent, exist_ok=True)
|
||||
pickle.dump(results, open(results_path, 'wb'), pickle.HIGHEST_PROTOCOL)
|
||||
|
||||
for k in Ks:
|
||||
table_mae.add(benchmark=benchmark_name(class_name, k), method=method_name, v=results['mae'][k])
|
||||
table_mrae.add(benchmark=benchmark_name(class_name, k), method=method_name, v=results['mrae'][k])
|
||||
|
||||
# Table.LatexPDF(f'./latex{exp_posfix}/{class_name}{exp_posfix}.pdf', tables=tables_mae+tables_mrae)
|
||||
Table.LatexPDF(f'./latex{exp_posfix}/{class_name}{exp_posfix}.pdf', tables=tables_mrae)
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
|
@ -0,0 +1,77 @@
|
|||
import itertools
|
||||
import os.path
|
||||
import pickle
|
||||
from collections import defaultdict
|
||||
from pathlib import Path
|
||||
|
||||
import numpy as np
|
||||
from sklearn.feature_extraction.text import TfidfVectorizer
|
||||
from sklearn.linear_model import LogisticRegression
|
||||
from sklearn.model_selection import GridSearchCV
|
||||
from sklearn.svm import LinearSVC
|
||||
|
||||
import quapy as qp
|
||||
from Retrieval.commons import RetrievedSamples, load_sample
|
||||
from quapy.protocol import UPP
|
||||
from quapy.method.non_aggregative import MaximumLikelihoodPrevalenceEstimation as Naive
|
||||
from quapy.model_selection import GridSearchQ
|
||||
from quapy.method.aggregative import ClassifyAndCount, EMQ, ACC, PCC, PACC, KDEyML
|
||||
from quapy.data.base import LabelledCollection
|
||||
|
||||
from os.path import join
|
||||
from tqdm import tqdm
|
||||
|
||||
from result_table.src.table import Table
|
||||
|
||||
"""
|
||||
|
||||
"""
|
||||
|
||||
data_home = 'data'
|
||||
|
||||
datasets = ['continent', 'gender', 'years_category'] #, 'relative_pageviews_category', 'num_sitelinks_category']
|
||||
|
||||
for class_name in datasets:
|
||||
|
||||
train_data_path = join(data_home, class_name, 'FULL', 'classifier_training.json') # <-------- fixed classifier
|
||||
texts, labels = load_sample(train_data_path, class_name=class_name)
|
||||
|
||||
classifier_path = join('classifiers', 'FULL', f'classifier_{class_name}.pkl')
|
||||
tfidf, classifier_trained = pickle.load(open(classifier_path, 'rb'))
|
||||
classifier_hyper = classifier_trained.get_params()
|
||||
print(f'{classifier_hyper=}')
|
||||
|
||||
X = tfidf.transform(texts)
|
||||
print(f'Xtr shape={X.shape}')
|
||||
|
||||
pool = LabelledCollection(X, labels)
|
||||
train, val = pool.split_stratified(train_prop=0.5, random_state=0)
|
||||
q = KDEyML(LogisticRegression())
|
||||
classifier_hyper = {'classifier__C':[classifier_hyper['C'], 0.00000001], 'classifier__class_weight':[classifier_hyper['class_weight']]}
|
||||
quantifier_hyper = {'bandwidth': np.linspace(0.01, 0.2, 20)}
|
||||
hyper = {**classifier_hyper, **quantifier_hyper}
|
||||
qp.environ['SAMPLE_SIZE'] = 100
|
||||
modsel = GridSearchQ(
|
||||
model=q,
|
||||
param_grid=hyper,
|
||||
protocol=UPP(val, sample_size=100),
|
||||
n_jobs=-1,
|
||||
error='mrae',
|
||||
verbose=True
|
||||
)
|
||||
modsel.fit(train)
|
||||
|
||||
print(class_name)
|
||||
print(f'{modsel.best_params_}')
|
||||
print(f'{modsel.best_score_}')
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
|
@ -0,0 +1,105 @@
|
|||
import os.path
|
||||
import pickle
|
||||
from collections import defaultdict
|
||||
from itertools import zip_longest
|
||||
from pathlib import Path
|
||||
|
||||
import numpy as np
|
||||
import pandas as pd
|
||||
from sklearn.feature_extraction.text import TfidfVectorizer
|
||||
from sklearn.linear_model import LogisticRegression
|
||||
from sklearn.model_selection import GridSearchCV
|
||||
from sklearn.svm import LinearSVC
|
||||
|
||||
import quapy as qp
|
||||
import quapy.functional as F
|
||||
from Retrieval.commons import RetrievedSamples, load_sample
|
||||
from method.non_aggregative import MaximumLikelihoodPrevalenceEstimation as Naive
|
||||
from quapy.method.aggregative import ClassifyAndCount, EMQ, ACC, PCC, PACC, KDEyML
|
||||
from quapy.protocol import AbstractProtocol
|
||||
from quapy.data.base import LabelledCollection
|
||||
|
||||
from glob import glob
|
||||
from os.path import join
|
||||
from tqdm import tqdm
|
||||
|
||||
from result_table.src.table import Table
|
||||
import numpy as np
|
||||
import matplotlib.pyplot as plt
|
||||
|
||||
"""
|
||||
Plots the distribution of (predicted) relevance score for the test samples and for the training samples wrt:
|
||||
- training pool size (100K, 500K, 1M, FULL)
|
||||
- rank
|
||||
"""
|
||||
|
||||
|
||||
data_home = 'data'
|
||||
Ks = [5, 10, 25, 50, 75, 100, 250, 500, 750, 1000]
|
||||
|
||||
for class_name in ['num_sitelinks_category', 'relative_pageviews_category', 'years_category', 'continent', 'gender']:
|
||||
test_added = False
|
||||
Mtrs, Mtes, source = [], [], []
|
||||
for data_size in ['10K', '50K', '100K', '500K', '1M', 'FULL']:
|
||||
|
||||
class_home = join(data_home, class_name, data_size)
|
||||
classifier_path = join('classifiers', 'FULL', f'classifier_{class_name}.pkl')
|
||||
test_rankings_path = join(data_home, 'testRanking_Results.json')
|
||||
|
||||
_, classifier = pickle.load(open(classifier_path, 'rb'))
|
||||
|
||||
experiment_prot = RetrievedSamples(
|
||||
class_home,
|
||||
test_rankings_path,
|
||||
vectorizer=None,
|
||||
class_name=class_name,
|
||||
classes=classifier.classes_
|
||||
)
|
||||
|
||||
Mtr = []
|
||||
Mte = []
|
||||
pbar = tqdm(experiment_prot(), total=experiment_prot.total())
|
||||
for train, test in pbar:
|
||||
Xtr, ytr, score_tr = train
|
||||
Xte, yte, score_te = test
|
||||
Mtr.append(score_tr)
|
||||
Mte.append(score_te)
|
||||
|
||||
Mtrs.append(Mtr)
|
||||
if not test_added:
|
||||
Mtes.append(Mte)
|
||||
test_added = True
|
||||
source.append(data_size)
|
||||
|
||||
fig, ax = plt.subplots()
|
||||
train_source = ['train-'+s for s in source]
|
||||
Ms = list(zip(Mtrs, train_source))+list(zip(Mtes, ['test']))
|
||||
|
||||
for M, source in Ms:
|
||||
M = np.asarray(list(zip_longest(*M, fillvalue=np.nan))).T
|
||||
|
||||
num_rep, num_docs = M.shape
|
||||
|
||||
mean_values = np.nanmean(M, axis=0)
|
||||
n_filled = np.count_nonzero(~np.isnan(M), axis=0)
|
||||
std_errors = np.nanstd(M, axis=0) / np.sqrt(n_filled)
|
||||
|
||||
line = ax.plot(range(num_docs), mean_values, '-', label=source, color=None)
|
||||
color = line[-1].get_color()
|
||||
ax.fill_between(range(num_docs), mean_values - std_errors, mean_values + std_errors, alpha=0.3, color=color)
|
||||
|
||||
|
||||
ax.set_xlabel('Doc. Rank')
|
||||
ax.set_ylabel('Rel. Score')
|
||||
ax.set_title(class_name)
|
||||
|
||||
ax.legend()
|
||||
|
||||
# plt.show()
|
||||
os.makedirs('plots', exist_ok=True)
|
||||
plotpath = f'plots/{class_name}.pdf'
|
||||
print(f'saving plot in {plotpath}')
|
||||
plt.savefig(plotpath)
|
||||
|
||||
|
||||
|
|
@ -1,427 +0,0 @@
|
|||
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)}' + '}'
|
|
@ -0,0 +1,27 @@
|
|||
import pandas as pd
|
||||
|
||||
from os.path import join
|
||||
|
||||
from Retrieval.commons import load_json_sample
|
||||
from quapy.data import LabelledCollection
|
||||
|
||||
data_home = 'data'
|
||||
CLASS_NAME = 'continent'
|
||||
datasize = '100K'
|
||||
|
||||
file_path = join(data_home, CLASS_NAME, datasize, 'training_Query-84Sample-200SPLIT.json')
|
||||
|
||||
text, classes = load_json_sample(file_path, CLASS_NAME)
|
||||
|
||||
|
||||
data = LabelledCollection(text, classes)
|
||||
print(data.classes_)
|
||||
print(data.prevalence())
|
||||
print('done')
|
||||
|
||||
test_ranking_path = join(data_home, 'testRanking_Results.json')
|
||||
# obj = json.load(open(test_ranking_path))
|
||||
|
||||
|
||||
df = pd.read_json(test_ranking_path)
|
||||
print('done')
|
|
@ -1,66 +0,0 @@
|
|||
import numpy as np
|
||||
import pandas as pd
|
||||
from sklearn.feature_extraction.text import TfidfVectorizer
|
||||
from sklearn.linear_model import LogisticRegression, LogisticRegressionCV
|
||||
from sklearn.metrics import make_scorer, f1_score
|
||||
from sklearn.svm import LinearSVC
|
||||
|
||||
from quapy.data.base import LabelledCollection
|
||||
from sklearn.model_selection import cross_val_score, GridSearchCV
|
||||
|
||||
from os.path import join
|
||||
|
||||
"""
|
||||
In this experiment, I simply try to understand whether the learning task can be learned or not.
|
||||
The problem is that we are quantifying the categories based on the alphabetical order (of what?).
|
||||
"""
|
||||
|
||||
def load_txt_sample(path, parse_columns, verbose=False, max_lines=None):
|
||||
if verbose:
|
||||
print(f'loading {path}...', end='')
|
||||
df = pd.read_csv(path, sep='\t')
|
||||
if verbose:
|
||||
print('[done]')
|
||||
X = df['text'].values
|
||||
y = df['continent'].values
|
||||
|
||||
if parse_columns:
|
||||
rank = df['rank'].values
|
||||
scores = df['score'].values
|
||||
order = np.argsort(rank)
|
||||
X = X[order]
|
||||
y = y[order]
|
||||
rank = rank[order]
|
||||
scores = scores[order]
|
||||
|
||||
if max_lines is not None:
|
||||
X = X[:max_lines]
|
||||
y = y[:max_lines]
|
||||
|
||||
return X, y
|
||||
|
||||
data_path = './50_50_split_trec'
|
||||
train_path = join(data_path, 'train_50_50_continent.txt')
|
||||
|
||||
tfidf = TfidfVectorizer(sublinear_tf=True, min_df=10)
|
||||
data = LabelledCollection.load(train_path, loader_func=load_txt_sample, verbose=True, parse_columns=False)
|
||||
data = data.sampling(20000)
|
||||
train, test = data.split_stratified()
|
||||
train.instances = tfidf.fit_transform(train.instances)
|
||||
test.instances = tfidf.transform(test.instances)
|
||||
|
||||
# svm = LinearSVC()
|
||||
# cls = GridSearchCV(svm, param_grid={'C':np.logspace(-3,3,7), 'class_weight':['balanced', None]})
|
||||
cls = LogisticRegression()
|
||||
cls.fit(*train.Xy)
|
||||
|
||||
# score = cross_val_score(LogisticRegressionCV(), *data.Xy, scoring=make_scorer(f1_score, average='macro'), n_jobs=-1, cv=5)
|
||||
# print(score)
|
||||
# print(np.mean(score))
|
||||
|
||||
y_pred = cls.predict(test.instances)
|
||||
macrof1 = f1_score(y_true=test.labels, y_pred=y_pred, average='macro')
|
||||
microf1 = f1_score(y_true=test.labels, y_pred=y_pred, average='micro')
|
||||
|
||||
print('macro', macrof1)
|
||||
print('micro', microf1)
|
|
@ -141,6 +141,19 @@ def uniform_prevalence_sampling(n_classes, size=1):
|
|||
return u
|
||||
|
||||
|
||||
def uniform_prevalence(n_classes):
|
||||
"""
|
||||
Returns a vector representing the uniform distribution for `n_classes`
|
||||
|
||||
:param n_classes: number of classes
|
||||
:return: np.ndarray with all values 1/n_classes
|
||||
"""
|
||||
assert isinstance(n_classes, int) and n_classes>0, \
|
||||
(f'param {n_classes} not understood; must be a positive integer representing the '
|
||||
f'number of classes ')
|
||||
return np.full(shape=n_classes, fill_value=1./n_classes)
|
||||
|
||||
|
||||
uniform_simplex_sampling = uniform_prevalence_sampling
|
||||
|
||||
|
||||
|
|
|
@ -62,7 +62,13 @@ class KDEBase:
|
|||
:param bandwidth: float, the bandwidth of the kernel
|
||||
:return: a list of KernelDensity objects, each fitted with the corresponding class-specific covariates
|
||||
"""
|
||||
return [self.get_kde_function(X[y == cat], bandwidth) for cat in classes]
|
||||
class_cond_X = []
|
||||
for cat in classes:
|
||||
selX = X[y==cat]
|
||||
if selX.size==0:
|
||||
selX = [F.uniform_prevalence(len(classes))]
|
||||
class_cond_X.append(selX)
|
||||
return [self.get_kde_function(X_cond_yi, bandwidth) for X_cond_yi in class_cond_X]
|
||||
|
||||
|
||||
|
||||
|
|
|
@ -2,7 +2,7 @@ from abc import ABC, abstractmethod
|
|||
from copy import deepcopy
|
||||
from typing import Callable, Union
|
||||
import numpy as np
|
||||
from abstention.calibration import NoBiasVectorScaling, TempScaling, VectorScaling
|
||||
from abstention.calibration import NoBiasVectorScaling, TempScaling, VectorScaling, PlattScaling
|
||||
from scipy import optimize
|
||||
from sklearn.base import BaseEstimator
|
||||
from sklearn.calibration import CalibratedClassifierCV
|
||||
|
@ -636,19 +636,34 @@ class EMQ(AggregativeSoftQuantifier):
|
|||
calibrator = TempScaling()
|
||||
elif self.recalib == 'vs':
|
||||
calibrator = VectorScaling()
|
||||
elif self.recalib == 'platt':
|
||||
calibrator = CalibratedClassifierCV(estimator=self.classifier, cv='prefit')
|
||||
else:
|
||||
raise ValueError('invalid param argument for recalibration method; available ones are '
|
||||
'"nbvs", "bcts", "ts", and "vs".')
|
||||
|
||||
if not np.issubdtype(y.dtype, np.number):
|
||||
y = np.searchsorted(data.classes_, y)
|
||||
|
||||
if self.recalib == 'platt':
|
||||
self.classifier = calibrator.fit(*data.Xy)
|
||||
else:
|
||||
print(classif_predictions.prevalence())
|
||||
try:
|
||||
self.calibration_function = calibrator(P, np.eye(data.n_classes)[y], posterior_supplied=True)
|
||||
except RuntimeError as e:
|
||||
print(e)
|
||||
print('defaults to I')
|
||||
self.calibration_function = lambda P:P
|
||||
|
||||
if self.exact_train_prev:
|
||||
self.train_prevalence = data.prevalence()
|
||||
else:
|
||||
train_posteriors = classif_predictions.X
|
||||
if self.recalib is not None:
|
||||
if self.recalib == 'platt':
|
||||
train_posteriors = self.classifier.predict_proba(train_posteriors)
|
||||
else:
|
||||
train_posteriors = self.calibration_function(train_posteriors)
|
||||
self.train_prevalence = F.prevalence_from_probabilities(train_posteriors)
|
||||
|
||||
|
|
Loading…
Reference in New Issue