forked from moreo/QuaPy
refactoring main, argparse, etc
This commit is contained in:
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b051ed4781
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833476ebf8
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@ -1,182 +1,115 @@
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import os.path
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import sys
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import sklearn
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from sklearn.base import BaseEstimator
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from sklearn.calibration import CalibratedClassifierCV
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from sklearn.linear_model import LogisticRegression
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from sklearn.svm import LinearSVC
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from sklearn.metrics import f1_score
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import functions as fn
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import quapy as qp
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from method.base import BaseQuantifier
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from quapy.data import LabelledCollection
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from quapy.method.aggregative import EMQ, CC, PACC, PCC, HDy, ACC
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from quapy import functional as F
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import numpy as np
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from itertools import chain
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import argparse
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from data import LabelledCollection
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def split_from_index(collection: LabelledCollection, index: np.ndarray):
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in_index_set = set(index)
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out_index_set = set(range(len(collection))) - in_index_set
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out_index = np.asarray(list(out_index_set), dtype=int)
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return collection.sampling_from_index(index), collection.sampling_from_index(out_index)
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def eval_classifier(learner, test:LabelledCollection):
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predictions = learner.predict(test.instances)
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true_labels = test.labels
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# f1 = f1_score(true_labels, predictions, average='macro')
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f1 = f1_score(true_labels, predictions, average='binary')
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# f1 = (true_labels==predictions).mean()
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return f1
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def relevance_sampling_index(pool: LabelledCollection, classifier: BaseEstimator, k: int):
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prob = classifier.predict_proba(pool.instances)[:, 1].flatten()
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top_relevant_idx = np.argsort(-prob)[:k]
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return top_relevant_idx
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def main(args):
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datasetname = args.dataset
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k = args.k
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init_nD = args.initsize
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init_prev = [1-args.initprev, args.initprev]
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sampling_fn = getattr(fn, args.sampling)
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max_iterations = args.iter
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outputdir = './results'
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collection = qp.util.pickled_resource(f'./dataset/{datasetname}.pkl', fn.create_dataset, datasetname)
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nD = len(collection)
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with qp.util.temp_seed(args.seed):
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# initial labelled data selection
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idx = collection.sampling_index(init_nD, *init_prev)
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train, pool = fn.split_from_index(collection, idx)
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first_train = LabelledCollection(train.instances, train.labels)
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# recall_target = 0.99
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qp.util.create_if_not_exist(outputdir)
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i = 0
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with open(os.path.join(outputdir, fn.experiment_name(args)), 'wt') as foo:
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def tee(msg):
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foo.write(msg + '\n')
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foo.flush()
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print(msg)
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tee('it\t%\ttr-size\tte-size\ttr-prev\tte-prev\tte-estim\tte-estimCC\tR\tRhat\tRhatCC\tShift\tAE\tAE_CC\tMF1_Q\tMF1_Clf')
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while True:
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pool_p_hat_cc, classifier = fn.estimate_prev_CC(train, pool)
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pool_p_hat, q_classifier = fn.estimate_prev_Q(train, pool, args.quantifier)
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f1_clf = eval_classifier(classifier, pool)
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f1_q = eval_classifier(q_classifier, pool)
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tr_p = train.prevalence()
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te_p = pool.prevalence()
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nDtr = len(train)
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nDte = len(pool)
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r_hat_cc = fn.recall(tr_p, pool_p_hat_cc, nDtr, nDte)
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r_hat = fn.recall(tr_p, pool_p_hat, nDtr, nDte)
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r = fn.recall(tr_p, te_p, nDtr, nDte)
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tr_te_shift = qp.error.ae(tr_p, te_p)
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progress = 100 * nDtr / nD
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q_ae = qp.error.ae(te_p, pool_p_hat)
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cc_ae = qp.error.ae(te_p, pool_p_hat_cc)
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tee(f'{i}\t{progress:.2f}\t{nDtr}\t{nDte}\t{tr_p[1]:.3f}\t{te_p[1]:.3f}\t{pool_p_hat[1]:.3f}\t{pool_p_hat_cc[1]:.3f}'
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f'\t{r:.3f}\t{r_hat:.3f}\t{r_hat_cc:.3f}\t{tr_te_shift:.5f}\t{q_ae:.4f}\t{cc_ae:.4f}\t{f1_q:.3f}\t{f1_clf:.3f}')
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if nDte < k:
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print('[stop] too few documents remaining')
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break
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elif i+1 == max_iterations:
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print('[stop] maximum number of iterations reached')
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break
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top_relevant_idx = sampling_fn(pool, classifier, k, progress)
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selected, pool = fn.split_from_index(pool, top_relevant_idx)
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train = train + selected
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i += 1
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def uncertainty_sampling_index(pool: LabelledCollection, classifier: BaseEstimator, k: int):
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prob = classifier.predict_proba(pool.instances)[:, 1].flatten()
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top_uncertain_idx = np.argsort(np.abs(prob - 0.5))[:k]
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return top_uncertain_idx
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if __name__=='__main__':
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parser = argparse.ArgumentParser(description='e-Discovery')
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parser.add_argument('--dataset', metavar='DATASET', type=str, help='Dataset name',
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default='RCV1.C4')
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parser.add_argument('--quantifier', metavar='METHOD', type=str, help='Quantification method',
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default='EMQ')
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parser.add_argument('--sampling', metavar='SAMPLING', type=str, help='Sampling criterion',
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default='relevance_sampling')
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parser.add_argument('--iter', metavar='INT', type=int, help='number of iterations (-1 to set no limit)',
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default=-1)
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parser.add_argument('--k', metavar='BATCH', type=int, help='number of documents in a batch',
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default=100)
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parser.add_argument('--initsize', metavar='SIZE', type=int, help='number of labelled documents at the beginning',
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default=1000)
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parser.add_argument('--initprev', metavar='PREV', type=float,
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help='prevalence of the initial sample (-1 for uniform sampling)',
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default=0.5)
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parser.add_argument('--seed', metavar='SEED', type=int,
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help='random seed',
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default=1)
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args = parser.parse_args()
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assert 0 < args.initprev < 1, 'wrong value for initsize; should be in (0., 1.)'
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def mix_rel_unc_sampling_index(pool: LabelledCollection, classifier: BaseEstimator, k: int):
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relevance_idx = relevance_sampling_index(pool, classifier, k)
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uncertanty_idx = uncertainty_sampling_index(pool, classifier, k)
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interleave_idx = np.asarray(list(chain.from_iterable(zip(relevance_idx, uncertanty_idx))))
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_, unique_idx = np.unique(interleave_idx, return_index=True)
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top_interleaved_idx = interleave_idx[np.sort(unique_idx)][:k]
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return top_interleaved_idx
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def negative_sampling_index(pool: LabelledCollection, classifier: BaseEstimator, k: int):
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prob = classifier.predict_proba(pool.instances)[:, 0].flatten()
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top_relevant_idx = np.argsort(-prob)[:k]
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return top_relevant_idx
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def recall(train_prev, pool_prev, train_size, pool_size):
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frac_tr_pos = train_prev[1]
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frac_te_pos = pool_prev[1]
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recall = (frac_tr_pos * train_size) / (frac_tr_pos * train_size + frac_te_pos * pool_size)
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return recall
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def NewClassifier():
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# return CalibratedClassifierCV(LinearSVC(class_weight='balanced'))
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return LogisticRegression(class_weight=None)
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def NewQuantifier():
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return EMQ(CalibratedClassifierCV(NewClassifier()))
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def create_dataset(datasetname):
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if datasetname == 'imdb.10K.75p':
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data = qp.datasets.fetch_reviews('imdb', tfidf=True, min_df=5)
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collection = data.training.sampling(10000, 0.75)
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return collection
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elif datasetname == 'RCV1.C4':
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X, y = sklearn.datasets.fetch_rcv1(subset='train', return_X_y=True)
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y = y.toarray()
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prev = y.mean(axis=0).flatten()
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# choose the first category having a positive prevalence between [0.1,0.2] (realistic scenario for e-Discovery)
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# this category happens to be the cat with id 4
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target_cat = np.argwhere(np.logical_and(prev > 0.1, prev < 0.2)).flatten()[0]
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print('chosen cat', target_cat)
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y = y[:, target_cat].flatten()
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return LabelledCollection(X, y)
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def estimate_prev_CC(train, pool):
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q = CC(NewClassifier()).fit(train)
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return q.quantify(pool.instances), q.learner
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def estimate_prev_Q(train, pool, classifier):
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# q = qp.model_selection.GridSearchQ(
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# ACC(LogisticRegression()),
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# param_grid={'C':np.logspace(-3,3,7), 'class_weight':[None, 'balanced']},
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# sample_size=len(train),
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# protocol='app',
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# n_prevpoints=21,
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# n_repetitions=10)
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q = NewQuantifier()
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# q = ACC(NewClassifier())
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# borrow (supposedly negative) pool documents
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# train_pos = train.counts()[1]
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# train_negs = train.counts()[0]
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# neg_idx = negative_sampling_index(pool, classifier, max(train_pos-train_negs, 5))
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# neg_sample = pool.sampling_from_index(neg_idx)
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# train_augmented = train + LabelledCollection(neg_sample.instances, [0]*len(neg_sample))
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# q.fit(train_augmented)
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q.fit(train)
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# q.fit(first_train)
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# bootstrap_prev = qp.evaluation.natural_prevalence_prediction(q, pool, sample_size=len(train), n_repetitions=50)[1].mean(axis=0).flatten()
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prev = q.quantify(pool.instances)
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return prev, None
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# return q.quantify(pool_instances), None
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def tee(msg):
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foo.write(msg + '\n')
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foo.flush()
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print(msg)
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datasetname = 'RCV1.C4'
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collection = qp.util.pickled_resource(f'./dataset/{datasetname}.pkl', create_dataset, datasetname)
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nD = len(collection)
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# initial labelled data selection
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init_nD = 1000
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init_prev = [0.5, 0.5]
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idx = collection.sampling_index(init_nD, *init_prev)
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train, pool = split_from_index(collection, idx)
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#first_train = LabelledCollection(train.instances, train.labels)
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k = 100
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recall_target = 0.99
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outputdir = './results'
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qp.util.create_if_not_exist(outputdir)
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# sampling_fn, sampling_name = relevance_sampling_index, 'relevance'
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sampling_fn, sampling_name = mix_rel_unc_sampling_index, 'mix'
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q_name = NewQuantifier().__class__.__name__
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experiment_suffix = f'{sampling_name}_{q_name}'
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i = 0
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with open(os.path.join(outputdir, f'{datasetname}_{experiment_suffix}.csv'), 'wt') as foo:
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tee('it\t%\ttr-size\tte-size\ttr-prev\tte-prev\tte-estim\tte-estimCC\tR\tRhat\tRhatCC\tShift\tAE\tAE_CC')
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while True:
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pool_p_hat_cc, classifier = estimate_prev_CC(train, pool)
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pool_p_hat, _ = estimate_prev_Q(train, pool, classifier)
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tr_p = train.prevalence()
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te_p = pool.prevalence()
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nDtr = len(train)
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nDte = len(pool)
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r_hat_cc = recall(tr_p, pool_p_hat_cc, nDtr, nDte)
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r_hat = recall(tr_p, pool_p_hat, nDtr, nDte)
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r = recall(tr_p, te_p, nDtr, nDte)
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tr_te_shift = qp.error.ae(tr_p, te_p)
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progress = 100 * nDtr / nD
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q_ae = qp.error.ae(te_p, pool_p_hat)
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cc_ae = qp.error.ae(te_p, pool_p_hat_cc)
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tee(
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f'{i}\t{progress:.2f}\t{nDtr}\t{nDte}\t{tr_p[1]:.3f}\t{te_p[1]:.3f}\t{pool_p_hat[1]:.3f}\t{pool_p_hat_cc[1]:.3f}'
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f'\t{r:.3f}\t{r_hat:.3f}\t{r_hat_cc:.3f}\t{tr_te_shift:.5f}\t{q_ae:.4f}\t{cc_ae:.4f}')
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if nDte < k:
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break
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top_relevant_idx = sampling_fn(pool, classifier, k)
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selected, pool = split_from_index(pool, top_relevant_idx)
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train = train + selected
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i += 1
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main(args)
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@ -5,16 +5,23 @@ import sys, os, pathlib
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assert len(sys.argv) == 3, f'wrong args, syntax is: python {sys.argv[0]} <result_input_path> <dynamic (0|1)>'
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file = sys.argv[1] #'./results/RCV1.C4.csv'
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file = sys.argv[1]
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loop = bool(int(sys.argv[2]))
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plotname = pathlib.Path(file).name.replace(".csv", ".png")
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if not loop:
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plt.rcParams['figure.figsize'] = [12, 12]
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plt.rcParams['figure.dpi'] = 200
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# plot the data
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fig, axs = plt.subplots(3)
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fig, axs = plt.subplots(5)
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try:
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while True:
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aXn = 0
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df = pd.read_csv(file, sep='\t')
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xs = df['it']
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@ -22,29 +29,49 @@ try:
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y_r = df['R']
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y_rhat = df['Rhat']
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y_rhatCC = df['RhatCC']
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label='R'
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axs[0].plot(xs, y_rhat, label='$\hat{'+label+'}$')
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axs[0].plot(xs, y_rhatCC, label='$\hat{'+label+'}_{CC}$')
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axs[0].plot(xs, y_r, label=label)
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axs[0].legend()
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axs[0].grid()
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axs[aXn].plot(xs, y_rhat, label='$\hat{R}_{Q}$')
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axs[aXn].plot(xs, y_rhatCC, label='$\hat{R}_{CC}$')
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axs[aXn].plot(xs, y_r, label='$R$')
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axs[aXn].legend()
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axs[aXn].grid()
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axs[aXn].set_ylabel('Recall estimation')
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axs[aXn].set_ylim(0,1)
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aXn+=1
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y_r = df['te-prev']
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y_rhat = df['te-estim']
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y_rhatCC = df['te-estimCC']
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label='P'
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axs[1].plot(xs, y_rhat, label='$\hat{'+label+'}$')
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axs[1].plot(xs, y_rhatCC, label='$\hat{'+label+'}_{CC}$')
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axs[1].plot(xs, y_r, label=label)
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axs[1].legend()
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axs[1].grid()
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axs[aXn].plot(xs, y_rhat, label='te-$\hat{Pr}(\oplus)_{Q}$')
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axs[aXn].plot(xs, y_rhatCC, label='te-$\hat{Pr}(\oplus)_{CC}$')
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axs[aXn].plot(xs, y_r, label='te-$Pr(\oplus)$')
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axs[aXn].legend()
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axs[aXn].grid()
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axs[aXn].set_ylabel('Prevalence estimation')
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aXn += 1
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y_ae = df['AE']
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y_ae_cc = df['AE_CC']
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axs[2].plot(xs, y_ae, label='AE')
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axs[2].plot(xs, y_ae_cc, label='AE-CC')
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axs[2].legend()
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axs[2].grid()
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axs[aXn].plot(xs, y_ae, label='AE$_{Q}$')
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axs[aXn].plot(xs, y_ae_cc, label='AE$_{CC}$')
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axs[aXn].legend()
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axs[aXn].grid()
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axs[aXn].set_ylabel('Quantification error')
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aXn += 1
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axs[aXn].plot(xs, df['Shift'], label='tr-te shift (AE)')
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axs[aXn].plot(xs, df['tr-prev'], label='tr-$Pr(\oplus)$')
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axs[aXn].plot(xs, df['te-prev'], label='te-$Pr(\oplus)$')
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axs[aXn].legend()
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axs[aXn].grid()
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axs[aXn].set_ylabel('Train-Test Shift')
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aXn += 1
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axs[aXn].plot(xs, df['MF1_Q'], label='$F_1(clf(Q))$')
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axs[aXn].plot(xs, df['MF1_Clf'], label='$F_1(clf(CC))$')
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axs[aXn].legend()
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axs[aXn].grid()
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axs[aXn].set_ylabel('Classifiers performance')
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aXn += 1
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os.makedirs('./plots', exist_ok=True)
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plt.savefig(f'./plots/{plotname}')
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@ -53,9 +80,8 @@ try:
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break
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else:
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plt.pause(.5)
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axs[0].cla()
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axs[1].cla()
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axs[2].cla()
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for i in range(aXn):
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axs[i].cla()
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except KeyboardInterrupt:
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print("\n[exit]")
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@ -143,7 +143,7 @@ class LabelledCollection:
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else:
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raise NotImplementedError('unsupported operation for collection types')
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labels = np.concatenate([self.labels, other.labels])
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return LabelledCollection(join_instances, labels)
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return LabelledCollection(join_instances, labels, classes_=self.classes_)
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@property
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def Xy(self):
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