fgsld
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from sklearn.calibration import CalibratedClassifierCV
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from sklearn.calibration import CalibratedClassifierCV
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from sklearn.svm import LinearSVC
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from sklearn.svm import LinearSVC
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from fgsld.fgsld_quantifiers import FakeFGLSD
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from NewMethods.fgsld.fine_grained_sld import FineGrainedSLD
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from method.aggregative import EMQ, CC
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from method.aggregative import EMQ, CC
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from quapy.data import LabelledCollection
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from quapy.method.base import BaseQuantifier
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import quapy as qp
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import quapy as qp
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import quapy.functional as F
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from sklearn.linear_model import LogisticRegression
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class FakeFGLSD(BaseQuantifier):
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def __init__(self, learner, nbins, isomerous):
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self.learner = learner
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self.nbins = nbins
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self.isomerous = isomerous
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def fit(self, data: LabelledCollection):
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self.Xtr, self.ytr = data.Xy
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self.learner.fit(self.Xtr, self.ytr)
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return self
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def quantify(self, instances):
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tr_priors = F.prevalence_from_labels(self.ytr, n_classes=2)
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fgsld = FineGrainedSLD(self.Xtr, instances, self.ytr, tr_priors, self.learner, n_bins=self.nbins)
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priors, posteriors = fgsld.run(self.isomerous)
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return priors
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def get_params(self, deep=True):
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pass
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def set_params(self, **parameters):
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pass
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qp.environ['SAMPLE_SIZE'] = 500
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qp.environ['SAMPLE_SIZE'] = 500
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dataset = qp.datasets.fetch_reviews('hp')
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dataset = qp.datasets.fetch_reviews('kindle')
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qp.data.preprocessing.text2tfidf(dataset, min_df=5, inplace=True)
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qp.data.preprocessing.text2tfidf(dataset, min_df=5, inplace=True)
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training = dataset.training
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training = dataset.training
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@ -50,8 +20,10 @@ method_names, true_prevs, estim_prevs, tr_prevs = [], [], [], []
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for model, model_name in [
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for model, model_name in [
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(CC(cls), 'CC'),
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(CC(cls), 'CC'),
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(FakeFGLSD(cls, nbins=1, isomerous=False), 'FGSLD-1'),
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# (FakeFGLSD(cls, nbins=5, isomerous=False, recompute_bins=False), 'FGSLD-isometric-stat-5'),
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(FakeFGLSD(cls, nbins=2, isomerous=False), 'FGSLD-2'),
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(FakeFGLSD(cls, nbins=5, isomerous=True, recompute_bins=True), 'FGSLD-isometric-dyn-5'),
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# (FakeFGLSD(cls, nbins=5, isomerous=True, recompute_bins=False), 'FGSLD-isomerous-stat-5'),
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# (FakeFGLSD(cls, nbins=10, isomerous=True, recompute_bins=True), 'FGSLD-isomerous-dyn-10'),
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#(FakeFGLSD(cls, nbins=5, isomerous=False), 'FGSLD-5'),
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#(FakeFGLSD(cls, nbins=5, isomerous=False), 'FGSLD-5'),
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#(FakeFGLSD(cls, nbins=10, isomerous=False), 'FGSLD-10'),
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#(FakeFGLSD(cls, nbins=10, isomerous=False), 'FGSLD-10'),
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#(FakeFGLSD(cls, nbins=50, isomerous=False), 'FGSLD-50'),
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#(FakeFGLSD(cls, nbins=50, isomerous=False), 'FGSLD-50'),
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@ -64,7 +36,7 @@ for model, model_name in [
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print('running ', model_name)
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print('running ', model_name)
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model.fit(training)
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model.fit(training)
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true_prev, estim_prev = qp.evaluation.artificial_sampling_prediction(
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true_prev, estim_prev = qp.evaluation.artificial_sampling_prediction(
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model, test, qp.environ['SAMPLE_SIZE'], n_repetitions=10, n_prevpoints=21, n_jobs=-1
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model, test, qp.environ['SAMPLE_SIZE'], n_repetitions=5, n_prevpoints=11, n_jobs=-1
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)
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)
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method_names.append(model_name)
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method_names.append(model_name)
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true_prevs.append(true_prev)
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true_prevs.append(true_prev)
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@ -1,6 +1,8 @@
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import numpy as np
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import numpy as np
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from metrics import isomerous_bins, isometric_bins
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from metrics import isomerous_bins, isometric_bins
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from em import History, get_measures_single_history
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from em import History, get_measures_single_history
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from sklearn.model_selection import cross_val_predict
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import math
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class FineGrainedSLD:
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class FineGrainedSLD:
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@ -8,13 +10,13 @@ class FineGrainedSLD:
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self.y_tr = y_tr
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self.y_tr = y_tr
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self.clf = clf
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self.clf = clf
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self.tr_priors = tr_priors
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self.tr_priors = tr_priors
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self.tr_preds = clf.predict_proba(x_tr)
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self.te_preds = clf.predict_proba(x_te)
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self.te_preds = clf.predict_proba(x_te)
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self.tr_preds = cross_val_predict(clf, x_tr, y_tr, method='predict_proba', n_jobs=10)
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self.n_bins = n_bins
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self.n_bins = n_bins
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self.history: [History] = []
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self.history: [History] = []
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self.multi_class = False
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self.multi_class = False
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def run(self, isomerous_binning, epsilon=1e-6, compute_bins_at_every_iter=False, return_posteriors_hist=False):
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def run(self, isomerous_binning, epsilon=1e-6, compute_bins_at_every_iter=True, return_posteriors_hist=False):
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"""
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"""
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Run the FGSLD algorithm.
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Run the FGSLD algorithm.
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@ -24,8 +26,8 @@ class FineGrainedSLD:
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:param return_posteriors_hist: whether to return posteriors at every iteration or not.
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:param return_posteriors_hist: whether to return posteriors at every iteration or not.
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:return: If `return_posteriors_hist` is true, the returned posteriors will be a list of numpy arrays, else a single numpy array with posteriors at last iteration.
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:return: If `return_posteriors_hist` is true, the returned posteriors will be a list of numpy arrays, else a single numpy array with posteriors at last iteration.
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"""
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"""
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smoothing_tr = 1 / (2 * self.y_tr.shape[0])
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smoothing_tr = 1 / (2 * self.tr_preds.shape[0])
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smoothing_te = smoothing_tr
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smoothing_te = 1 / (2 * self.te_preds.shape[0])
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s = 0
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s = 0
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tr_bin_priors = np.zeros((self.n_bins, self.tr_preds.shape[1]), dtype=np.float)
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tr_bin_priors = np.zeros((self.n_bins, self.tr_preds.shape[1]), dtype=np.float)
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te_bin_priors = np.zeros((self.n_bins, self.te_preds.shape[1]), dtype=np.float)
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te_bin_priors = np.zeros((self.n_bins, self.te_preds.shape[1]), dtype=np.float)
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@ -53,15 +55,22 @@ class FineGrainedSLD:
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for i, bin_ in enumerate(bins):
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for i, bin_ in enumerate(bins):
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if bin_.shape[0] == 0:
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if bin_.shape[0] == 0:
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continue
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continue
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te = te_bin_priors[i][label_idx]
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tr = tr_bin_priors[i][label_idx]
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# local_min = (math.floor(tr * 10) / 10)
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# local_max = local_min + .1
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# trans = lambda l: min(max((l - local_min) / 1, 0), 1)
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trans = lambda l: l
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self.te_preds[:, label_idx][bin_] = (te_preds_cp[:, label_idx][bin_]) * \
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self.te_preds[:, label_idx][bin_] = (te_preds_cp[:, label_idx][bin_]) * \
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(te_bin_priors[i][label_idx] / te_bin_priors_prev[i][label_idx])
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(trans(te) / trans(tr))
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# Normalization step
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# Normalization step
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self.te_preds = (self.te_preds.T / self.te_preds.sum(axis=1)).T
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self.te_preds = (self.te_preds / self.te_preds.sum(axis=1, keepdims=True))
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val = 0
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val = 0
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for label_idx in range(te_bin_priors.shape[1]):
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for label_idx in range(te_bin_priors.shape[1]):
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if (temp := max(abs((te_bin_priors[:, label_idx] / te_bin_priors_prev[:, label_idx]) - 1))) > val:
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temp = max(abs((te_bin_priors[:, label_idx] / te_bin_priors_prev[:, label_idx]) - 1))
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if temp > val:
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val = temp
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val = temp
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s += 1
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s += 1
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if return_posteriors_hist:
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if return_posteriors_hist:
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Binary file not shown.
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Before Width: | Height: | Size: 162 KiB After Width: | Height: | Size: 163 KiB |
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@ -4,7 +4,6 @@ from os import makedirs
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import sys, os
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import sys, os
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import pickle
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import pickle
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from experiments import result_path
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from experiments import result_path
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from gen_tables import save_table, experiment_errors
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from tabular import Table
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from tabular import Table
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import argparse
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import argparse
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'Average': 'Average'
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'Average': 'Average'
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}
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}
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def save_table(path, table):
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print(f'saving results in {path}')
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with open(path, 'wt') as foo:
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foo.write(table)
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def experiment_errors(path, dataset, method, loss):
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path = result_path(path, dataset, method, 'm'+loss if not loss.startswith('m') else loss)
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if os.path.exists(path):
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true_prevs, estim_prevs, _, _, _, _ = pickle.load(open(path, 'rb'))
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err_fn = getattr(qp.error, loss)
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errors = err_fn(true_prevs, estim_prevs)
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return errors
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return None
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def nicerm(key):
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def nicerm(key):
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return '\mathrm{'+nice[key]+'}'
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return '\mathrm{'+nice[key]+'}'
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from sklearn.linear_model import LogisticRegression
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import quapy as qp
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from classification.methods import PCALR
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from method.meta import QuaNet
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from quapy.method.aggregative import *
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from NewMethods.methods import *
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from experiments import run, SAMPLE_SIZE
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import numpy as np
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import itertools
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from joblib import Parallel, delayed
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import settings
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import argparse
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import torch
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parser = argparse.ArgumentParser(description='Run experiments for Tweeter Sentiment Quantification')
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parser.add_argument('results', metavar='RESULT_PATH', type=str, help='path to the directory where to store the results')
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#parser.add_argument('svmperfpath', metavar='SVMPERF_PATH', type=str, help='path to the directory with svmperf')
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args = parser.parse_args()
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def quantification_models():
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def newLR():
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return LogisticRegression(max_iter=1000, solver='lbfgs', n_jobs=-1)
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__C_range = np.logspace(-4, 5, 10)
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lr_params = {'C': __C_range, 'class_weight': [None, 'balanced']}
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svmperf_params = {'C': __C_range}
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#yield 'paccsld', PACCSLD(newLR()), lr_params
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yield 'hdysld', OneVsAll(HDySLD(newLR())), lr_params # <-- promising!
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#device = 'cuda' if torch.cuda.is_available() else 'cpu'
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#print(f'Running QuaNet in {device}')
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#yield 'quanet', QuaNet(PCALR(**newLR().get_params()), SAMPLE_SIZE, device=device), lr_params
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if __name__ == '__main__':
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print(f'Result folder: {args.results}')
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np.random.seed(0)
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optim_losses = ['mae']
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datasets = qp.datasets.TWITTER_SENTIMENT_DATASETS_TRAIN
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models = quantification_models()
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results = Parallel(n_jobs=settings.N_JOBS)(
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delayed(run)(experiment) for experiment in itertools.product(optim_losses, datasets, models)
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)
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import multiprocessing
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import multiprocessing
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N_JOBS = -2 #multiprocessing.cpu_count()
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N_JOBS = -2 #multiprocessing.cpu_count()
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SAMPLE_SIZE = 100
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ENSEMBLE_N_JOBS=1
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SAMPLE_SIZE = 100
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AE RAE
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SemEval13 SVM-KLD 0.0722 0.1720
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SVM-NKLD 0.0714 0.2756
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SVM-QBETA2 0.0782 0.2775
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LR-CC 0.0996 0.3095
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LR-EM 0.1191 0.3923
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LR-PCC 0.0344 0.1506
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LR-ACC 0.0806 0.2479
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LR-PACC 0.0812 0.2626
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SemEval14 SVM-KLD 0.0843 0.2268
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SVM-NKLD 0.0836 0.3367
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SVM-QBETA2 0.1018 0.3680
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LR-CC 0.1043 0.3212
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LR-EM 0.0807 0.3517
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LR-PCC 0.1001 0.4277
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LR-ACC 0.0581 0.2360
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LR-PACC 0.0533 0.2573
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SemEval15 SVM-KLD 0.1185 0.3789
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SVM-NKLD 0.1155 0.4720
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SVM-QBETA2 0.1263 0.4762
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LR-CC 0.1101 0.2879
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LR-EM 0.1204 0.2949
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LR-PCC 0.0460 0.1973
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LR-ACC 0.1064 0.2971
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LR-PACC 0.1013 0.2729
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SemEval16 SVM-KLD 0.0385 0.1512
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SVM-NKLD 0.0830 0.3249
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SVM-QBETA2 0.1201 0.5156
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LR-CC 0.0500 0.1771
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LR-EM 0.0646 0.2126
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LR-PCC 0.0379 0.1553
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LR-ACC 0.0542 0.2246
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LR-PACC 0.0864 0.3504
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Sanders SVM-KLD 0.0134 0.0630
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SVM-NKLD 0.0950 0.3965
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SVM-QBETA2 0.1098 0.4360
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LR-CC 0.0671 0.2682
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LR-EM 0.0715 0.2849
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LR-PCC 0.0150 0.0602
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LR-ACC 0.0338 0.1306
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LR-PACC 0.0301 0.1173
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SST SVM-KLD 0.0413 0.1458
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SVM-NKLD 0.0749 0.2497
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SVM-QBETA2 0.0671 0.2343
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LR-CC 0.0330 0.1239
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LR-EM 0.0369 0.1190
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LR-PCC 0.0282 0.1068
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LR-ACC 0.0492 0.1689
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LR-PACC 0.0841 0.2302
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OMD SVM-KLD 0.0305 0.0999
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SVM-NKLD 0.0437 0.1279
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SVM-QBETA2 0.0624 0.1826
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LR-CC 0.0524 0.1527
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LR-EM 0.0648 0.1886
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LR-PCC 0.0046 0.0095
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LR-ACC 0.0239 0.0753
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LR-PACC 0.0100 0.0293
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HCR SVM-KLD 0.0414 0.2191
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SVM-NKLD 0.0604 0.2324
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SVM-QBETA2 0.1272 0.4600
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LR-CC 0.0525 0.1817
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LR-EM 0.0895 0.3093
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LR-PCC 0.0055 0.0202
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LR-ACC 0.0240 0.1026
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LR-PACC 0.0329 0.1436
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GASP SVM-KLD 0.0171 0.0529
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SVM-NKLD 0.0503 0.3416
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SVM-QBETA2 0.0640 0.4402
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LR-CC 0.0189 0.1297
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LR-EM 0.0231 0.1589
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LR-PCC 0.0097 0.0682
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LR-ACC 0.0150 0.1038
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LR-PACC 0.0087 0.0597
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WA SVM-KLD 0.0647 0.1957
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SVM-NKLD 0.0393 0.1357
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SVM-QBETA2 0.0798 0.2332
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LR-CC 0.0434 0.1270
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LR-EM 0.0391 0.1145
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LR-PCC 0.0338 0.0990
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LR-ACC 0.0407 0.1197
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LR-PACC 0.0277 0.0815
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|
||||||
WB SVM-KLD 0.0613 0.1791
|
|
||||||
SVM-NKLD 0.0534 0.1756
|
|
||||||
SVM-QBETA2 0.0249 0.0774
|
|
||||||
LR-CC 0.0132 0.0399
|
|
||||||
LR-EM 0.0244 0.0773
|
|
||||||
LR-PCC 0.0123 0.0390
|
|
||||||
LR-ACC 0.0230 0.0719
|
|
||||||
LR-PACC 0.0165 0.0515
|
|
||||||
|
|
@ -1,35 +0,0 @@
|
||||||
import numpy as np
|
|
||||||
import quapy as qp
|
|
||||||
import settings
|
|
||||||
import os
|
|
||||||
import pickle
|
|
||||||
from glob import glob
|
|
||||||
import itertools
|
|
||||||
import pathlib
|
|
||||||
|
|
||||||
qp.environ['SAMPLE_SIZE'] = settings.SAMPLE_SIZE
|
|
||||||
|
|
||||||
resultdir = './results'
|
|
||||||
methods = ['*']
|
|
||||||
|
|
||||||
|
|
||||||
def evaluate_results(methods, datasets, error_name):
|
|
||||||
results_str = []
|
|
||||||
all = []
|
|
||||||
error = qp.error.from_name(error_name)
|
|
||||||
for method, dataset in itertools.product(methods, datasets):
|
|
||||||
for experiment in glob(f'{resultdir}/{dataset}-{method}-{error_name}.pkl'):
|
|
||||||
true_prevalences, estim_prevalences, tr_prev, te_prev, te_prev_estim, best_params = \
|
|
||||||
pickle.load(open(experiment, 'rb'))
|
|
||||||
result = error(true_prevalences, estim_prevalences)
|
|
||||||
string = f'{pathlib.Path(experiment).name}: {result:.3f}'
|
|
||||||
results_str.append(string)
|
|
||||||
all.append(result)
|
|
||||||
results_str = sorted(results_str)
|
|
||||||
for r in results_str:
|
|
||||||
print(r)
|
|
||||||
print()
|
|
||||||
print(f'Ave: {np.mean(all):.3f}')
|
|
||||||
|
|
||||||
|
|
||||||
evaluate_results(methods=['epacc*mae1k'], datasets=['*'], error_name='mae')
|
|
||||||
|
|
@ -1,214 +0,0 @@
|
||||||
from sklearn.linear_model import LogisticRegression
|
|
||||||
import quapy as qp
|
|
||||||
from classification.methods import PCALR
|
|
||||||
from method.meta import QuaNet
|
|
||||||
from method.non_aggregative import MaximumLikelihoodPrevalenceEstimation
|
|
||||||
from quapy.method.aggregative import CC, ACC, PCC, PACC, EMQ, OneVsAll, SVMQ, SVMKLD, SVMNKLD, SVMAE, SVMRAE, HDy
|
|
||||||
from quapy.method.meta import EPACC, EEMQ
|
|
||||||
import quapy.functional as F
|
|
||||||
import numpy as np
|
|
||||||
import os
|
|
||||||
import pickle
|
|
||||||
import itertools
|
|
||||||
from joblib import Parallel, delayed
|
|
||||||
import settings
|
|
||||||
import argparse
|
|
||||||
import torch
|
|
||||||
import shutil
|
|
||||||
|
|
||||||
|
|
||||||
qp.environ['SAMPLE_SIZE'] = settings.SAMPLE_SIZE
|
|
||||||
|
|
||||||
def newLR():
|
|
||||||
return LogisticRegression(max_iter=1000, solver='lbfgs', n_jobs=-1)
|
|
||||||
|
|
||||||
__C_range = np.logspace(-4, 5, 10)
|
|
||||||
lr_params = {'C': __C_range, 'class_weight': [None, 'balanced']}
|
|
||||||
svmperf_params = {'C': __C_range}
|
|
||||||
|
|
||||||
def quantification_models():
|
|
||||||
# methods tested in Gao & Sebastiani 2016
|
|
||||||
yield 'cc', CC(newLR()), lr_params
|
|
||||||
yield 'acc', ACC(newLR()), lr_params
|
|
||||||
yield 'pcc', PCC(newLR()), lr_params
|
|
||||||
yield 'pacc', PACC(newLR()), lr_params
|
|
||||||
yield 'sld', EMQ(newLR()), lr_params
|
|
||||||
yield 'svmq', OneVsAll(SVMQ(args.svmperfpath)), svmperf_params
|
|
||||||
yield 'svmkld', OneVsAll(SVMKLD(args.svmperfpath)), svmperf_params
|
|
||||||
yield 'svmnkld', OneVsAll(SVMNKLD(args.svmperfpath)), svmperf_params
|
|
||||||
|
|
||||||
# methods added
|
|
||||||
yield 'svmmae', OneVsAll(SVMAE(args.svmperfpath)), svmperf_params
|
|
||||||
yield 'svmmrae', OneVsAll(SVMRAE(args.svmperfpath)), svmperf_params
|
|
||||||
yield 'hdy', OneVsAll(HDy(newLR())), lr_params
|
|
||||||
|
|
||||||
|
|
||||||
def quantification_cuda_models():
|
|
||||||
device = 'cuda' if torch.cuda.is_available() else 'cpu'
|
|
||||||
print(f'Running QuaNet in {device}')
|
|
||||||
learner = PCALR(**newLR().get_params())
|
|
||||||
yield 'quanet', QuaNet(learner, settings.SAMPLE_SIZE, checkpointdir=args.checkpointdir, device=device), lr_params
|
|
||||||
|
|
||||||
|
|
||||||
def quantification_ensembles():
|
|
||||||
param_mod_sel = {
|
|
||||||
'sample_size': settings.SAMPLE_SIZE,
|
|
||||||
'n_prevpoints': 21,
|
|
||||||
'n_repetitions': 5,
|
|
||||||
'verbose': False
|
|
||||||
}
|
|
||||||
common={
|
|
||||||
'max_sample_size': 1000,
|
|
||||||
'n_jobs': settings.ENSEMBLE_N_JOBS,
|
|
||||||
'param_grid': lr_params,
|
|
||||||
'param_mod_sel': param_mod_sel,
|
|
||||||
'val_split': 0.4,
|
|
||||||
'min_pos': 10
|
|
||||||
}
|
|
||||||
|
|
||||||
# hyperparameters will be evaluated within each quantifier of the ensemble, and so the typical model selection
|
|
||||||
# will be skipped (by setting hyperparameters to None)
|
|
||||||
hyper_none = None
|
|
||||||
#yield 'epaccmaeptr', EPACC(newLR(), optim='mae', policy='ptr', **common), hyper_none
|
|
||||||
yield 'epaccmaemae1k', EPACC(newLR(), optim='mae', policy='mae', **common), hyper_none
|
|
||||||
# yield 'esldmaeptr', EEMQ(newLR(), optim='mae', policy='ptr', **common), hyper_none
|
|
||||||
# yield 'esldmaemae', EEMQ(newLR(), optim='mae', policy='mae', **common), hyper_none
|
|
||||||
|
|
||||||
#yield 'epaccmraeptr', EPACC(newLR(), optim='mrae', policy='ptr', **common), hyper_none
|
|
||||||
#yield 'epaccmraemrae', EPACC(newLR(), optim='mrae', policy='mrae', **common), hyper_none
|
|
||||||
#yield 'esldmraeptr', EEMQ(newLR(), optim='mrae', policy='ptr', **common), hyper_none
|
|
||||||
#yield 'esldmraemrae', EEMQ(newLR(), optim='mrae', policy='mrae', **common), hyper_none
|
|
||||||
|
|
||||||
|
|
||||||
def evaluate_experiment(true_prevalences, estim_prevalences):
|
|
||||||
print('\nEvaluation Metrics:\n'+'='*22)
|
|
||||||
for eval_measure in [qp.error.mae, qp.error.mrae]:
|
|
||||||
err = eval_measure(true_prevalences, estim_prevalences)
|
|
||||||
print(f'\t{eval_measure.__name__}={err:.4f}')
|
|
||||||
print()
|
|
||||||
|
|
||||||
|
|
||||||
def evaluate_method_point_test(true_prev, estim_prev):
|
|
||||||
print('\nPoint-Test evaluation:\n' + '=' * 22)
|
|
||||||
print(f'true-prev={F.strprev(true_prev)}, estim-prev={F.strprev(estim_prev)}')
|
|
||||||
for eval_measure in [qp.error.mae, qp.error.mrae]:
|
|
||||||
err = eval_measure(true_prev, estim_prev)
|
|
||||||
print(f'\t{eval_measure.__name__}={err:.4f}')
|
|
||||||
|
|
||||||
|
|
||||||
def result_path(path, dataset_name, model_name, optim_loss):
|
|
||||||
return os.path.join(path, f'{dataset_name}-{model_name}-{optim_loss}.pkl')
|
|
||||||
|
|
||||||
|
|
||||||
def is_already_computed(dataset_name, model_name, optim_loss):
|
|
||||||
if dataset_name=='semeval':
|
|
||||||
check_datasets = ['semeval13', 'semeval14', 'semeval15']
|
|
||||||
else:
|
|
||||||
check_datasets = [dataset_name]
|
|
||||||
return all(os.path.exists(result_path(args.results, name, model_name, optim_loss)) for name in check_datasets)
|
|
||||||
|
|
||||||
|
|
||||||
def save_results(dataset_name, model_name, optim_loss, *results):
|
|
||||||
rpath = result_path(args.results, dataset_name, model_name, optim_loss)
|
|
||||||
qp.util.create_parent_dir(rpath)
|
|
||||||
with open(rpath, 'wb') as foo:
|
|
||||||
pickle.dump(tuple(results), foo, pickle.HIGHEST_PROTOCOL)
|
|
||||||
|
|
||||||
|
|
||||||
def run(experiment):
|
|
||||||
|
|
||||||
optim_loss, dataset_name, (model_name, model, hyperparams) = experiment
|
|
||||||
|
|
||||||
if is_already_computed(dataset_name, model_name, optim_loss=optim_loss):
|
|
||||||
print(f'result for dataset={dataset_name} model={model_name} loss={optim_loss} already computed.')
|
|
||||||
return
|
|
||||||
elif (optim_loss == 'mae' and 'mrae' in model_name) or (optim_loss=='mrae' and 'mae' in model_name):
|
|
||||||
print(f'skipping model={model_name} for optim_loss={optim_loss}')
|
|
||||||
return
|
|
||||||
else:
|
|
||||||
print(f'running dataset={dataset_name} model={model_name} loss={optim_loss}')
|
|
||||||
|
|
||||||
benchmark_devel = qp.datasets.fetch_twitter(dataset_name, for_model_selection=True, min_df=5, pickle=True)
|
|
||||||
benchmark_devel.stats()
|
|
||||||
|
|
||||||
# model selection (hyperparameter optimization for a quantification-oriented loss)
|
|
||||||
if hyperparams is not None:
|
|
||||||
model_selection = qp.model_selection.GridSearchQ(
|
|
||||||
model,
|
|
||||||
param_grid=hyperparams,
|
|
||||||
sample_size=settings.SAMPLE_SIZE,
|
|
||||||
n_prevpoints=21,
|
|
||||||
n_repetitions=5,
|
|
||||||
error=optim_loss,
|
|
||||||
refit=False,
|
|
||||||
timeout=60*60,
|
|
||||||
verbose=True
|
|
||||||
)
|
|
||||||
model_selection.fit(benchmark_devel.training, benchmark_devel.test)
|
|
||||||
model = model_selection.best_model()
|
|
||||||
best_params = model_selection.best_params_
|
|
||||||
else:
|
|
||||||
best_params = {}
|
|
||||||
|
|
||||||
# model evaluation
|
|
||||||
test_names = [dataset_name] if dataset_name != 'semeval' else ['semeval13', 'semeval14', 'semeval15']
|
|
||||||
for test_no, test_name in enumerate(test_names):
|
|
||||||
benchmark_eval = qp.datasets.fetch_twitter(test_name, for_model_selection=False, min_df=5, pickle=True)
|
|
||||||
if test_no == 0:
|
|
||||||
print('fitting the selected model')
|
|
||||||
# fits the model only the first time
|
|
||||||
model.fit(benchmark_eval.training)
|
|
||||||
|
|
||||||
true_prevalences, estim_prevalences = qp.evaluation.artificial_sampling_prediction(
|
|
||||||
model,
|
|
||||||
test=benchmark_eval.test,
|
|
||||||
sample_size=settings.SAMPLE_SIZE,
|
|
||||||
n_prevpoints=21,
|
|
||||||
n_repetitions=25,
|
|
||||||
n_jobs=-1 if isinstance(model, qp.method.meta.Ensemble) else 1
|
|
||||||
)
|
|
||||||
test_estim_prevalence = model.quantify(benchmark_eval.test.instances)
|
|
||||||
test_true_prevalence = benchmark_eval.test.prevalence()
|
|
||||||
|
|
||||||
evaluate_experiment(true_prevalences, estim_prevalences)
|
|
||||||
evaluate_method_point_test(test_true_prevalence, test_estim_prevalence)
|
|
||||||
save_results(test_name, model_name, optim_loss,
|
|
||||||
true_prevalences, estim_prevalences,
|
|
||||||
benchmark_eval.training.prevalence(), test_true_prevalence, test_estim_prevalence,
|
|
||||||
best_params)
|
|
||||||
|
|
||||||
#if isinstance(model, QuaNet):
|
|
||||||
#model.clean_checkpoint_dir()
|
|
||||||
|
|
||||||
|
|
||||||
if __name__ == '__main__':
|
|
||||||
parser = argparse.ArgumentParser(description='Run experiments for Tweeter Sentiment Quantification')
|
|
||||||
parser.add_argument('results', metavar='RESULT_PATH', type=str,
|
|
||||||
help='path to the directory where to store the results')
|
|
||||||
parser.add_argument('--svmperfpath', metavar='SVMPERF_PATH', type=str, default='./svm_perf_quantification',
|
|
||||||
help='path to the directory with svmperf')
|
|
||||||
parser.add_argument('--checkpointdir', metavar='PATH', type=str, default='./checkpoint',
|
|
||||||
help='path to the directory where to dump QuaNet checkpoints')
|
|
||||||
args = parser.parse_args()
|
|
||||||
|
|
||||||
print(f'Result folder: {args.results}')
|
|
||||||
np.random.seed(0)
|
|
||||||
|
|
||||||
optim_losses = ['mae', 'mrae']
|
|
||||||
datasets = qp.datasets.TWITTER_SENTIMENT_DATASETS_TRAIN
|
|
||||||
|
|
||||||
models = quantification_models()
|
|
||||||
qp.util.parallel(run, itertools.product(optim_losses, datasets, models), n_jobs=settings.N_JOBS)
|
|
||||||
|
|
||||||
models = quantification_cuda_models()
|
|
||||||
qp.util.parallel(run, itertools.product(optim_losses, datasets, models), n_jobs=settings.CUDA_N_JOBS)
|
|
||||||
|
|
||||||
models = quantification_ensembles()
|
|
||||||
qp.util.parallel(run, itertools.product(optim_losses, datasets, models), n_jobs=1)
|
|
||||||
# Parallel(n_jobs=1)(
|
|
||||||
# delayed(run)(experiment) for experiment in itertools.product(optim_losses, datasets, models)
|
|
||||||
# )
|
|
||||||
|
|
||||||
#shutil.rmtree(args.checkpointdir, ignore_errors=True)
|
|
||||||
|
|
||||||
|
|
||||||
|
|
@ -1,95 +0,0 @@
|
||||||
import quapy as qp
|
|
||||||
import settings
|
|
||||||
import os
|
|
||||||
import pathlib
|
|
||||||
import pickle
|
|
||||||
from glob import glob
|
|
||||||
import sys
|
|
||||||
from TweetSentQuant.util import nicename
|
|
||||||
from os.path import join
|
|
||||||
|
|
||||||
|
|
||||||
qp.environ['SAMPLE_SIZE'] = settings.SAMPLE_SIZE
|
|
||||||
plotext='png'
|
|
||||||
|
|
||||||
resultdir = './results'
|
|
||||||
plotdir = './plots'
|
|
||||||
os.makedirs(plotdir, exist_ok=True)
|
|
||||||
|
|
||||||
def gather_results(methods, error_name):
|
|
||||||
method_names, true_prevs, estim_prevs, tr_prevs = [], [], [], []
|
|
||||||
for method in methods:
|
|
||||||
for experiment in glob(f'{resultdir}/*-{method}-m{error_name}.pkl'):
|
|
||||||
true_prevalences, estim_prevalences, tr_prev, te_prev, te_prev_estim, best_params = pickle.load(open(experiment, 'rb'))
|
|
||||||
method_names.append(nicename(method))
|
|
||||||
true_prevs.append(true_prevalences)
|
|
||||||
estim_prevs.append(estim_prevalences)
|
|
||||||
tr_prevs.append(tr_prev)
|
|
||||||
return method_names, true_prevs, estim_prevs, tr_prevs
|
|
||||||
|
|
||||||
|
|
||||||
def plot_error_by_drift(methods, error_name, logscale=False, path=None):
|
|
||||||
print('plotting error by drift')
|
|
||||||
if path is not None:
|
|
||||||
path = join(path, f'error_by_drift_{error_name}.{plotext}')
|
|
||||||
method_names, true_prevs, estim_prevs, tr_prevs = gather_results(methods, error_name)
|
|
||||||
qp.plot.error_by_drift(
|
|
||||||
method_names,
|
|
||||||
true_prevs,
|
|
||||||
estim_prevs,
|
|
||||||
tr_prevs,
|
|
||||||
n_bins=20,
|
|
||||||
error_name=error_name,
|
|
||||||
show_std=False,
|
|
||||||
logscale=logscale,
|
|
||||||
title=f'Quantification error as a function of distribution shift',
|
|
||||||
savepath=path
|
|
||||||
)
|
|
||||||
|
|
||||||
|
|
||||||
def diagonal_plot(methods, error_name, path=None):
|
|
||||||
print('plotting diagonal plots')
|
|
||||||
if path is not None:
|
|
||||||
path = join(path, f'diag_{error_name}')
|
|
||||||
method_names, true_prevs, estim_prevs, tr_prevs = gather_results(methods, error_name)
|
|
||||||
qp.plot.binary_diagonal(method_names, true_prevs, estim_prevs, pos_class=0, title='Negative', legend=False, show_std=False, savepath=f'{path}_neg.{plotext}')
|
|
||||||
qp.plot.binary_diagonal(method_names, true_prevs, estim_prevs, pos_class=1, title='Neutral', legend=False, show_std=False, savepath=f'{path}_neu.{plotext}')
|
|
||||||
qp.plot.binary_diagonal(method_names, true_prevs, estim_prevs, pos_class=2, title='Positive', legend=True, show_std=False, savepath=f'{path}_pos.{plotext}')
|
|
||||||
|
|
||||||
|
|
||||||
def binary_bias_global(methods, error_name, path=None):
|
|
||||||
print('plotting bias global')
|
|
||||||
if path is not None:
|
|
||||||
path = join(path, f'globalbias_{error_name}')
|
|
||||||
method_names, true_prevs, estim_prevs, tr_prevs = gather_results(methods, error_name)
|
|
||||||
qp.plot.binary_bias_global(method_names, true_prevs, estim_prevs, pos_class=0, title='Negative', savepath=f'{path}_neg.{plotext}')
|
|
||||||
qp.plot.binary_bias_global(method_names, true_prevs, estim_prevs, pos_class=1, title='Neutral', savepath=f'{path}_neu.{plotext}')
|
|
||||||
qp.plot.binary_bias_global(method_names, true_prevs, estim_prevs, pos_class=2, title='Positive', savepath=f'{path}_pos.{plotext}')
|
|
||||||
|
|
||||||
|
|
||||||
def binary_bias_bins(methods, error_name, path=None):
|
|
||||||
print('plotting bias local')
|
|
||||||
if path is not None:
|
|
||||||
path = join(path, f'localbias_{error_name}')
|
|
||||||
method_names, true_prevs, estim_prevs, tr_prevs = gather_results(methods, error_name)
|
|
||||||
qp.plot.binary_bias_bins(method_names, true_prevs, estim_prevs, pos_class=0, title='Negative', legend=False, savepath=f'{path}_neg.{plotext}')
|
|
||||||
qp.plot.binary_bias_bins(method_names, true_prevs, estim_prevs, pos_class=1, title='Neutral', legend=False, savepath=f'{path}_neu.{plotext}')
|
|
||||||
qp.plot.binary_bias_bins(method_names, true_prevs, estim_prevs, pos_class=2, title='Positive', legend=True, savepath=f'{path}_pos.{plotext}')
|
|
||||||
|
|
||||||
|
|
||||||
gao_seb_methods = ['cc', 'acc', 'pcc', 'pacc', 'sld', 'svmq', 'svmkld', 'svmnkld']
|
|
||||||
new_methods_ae = ['svmmae' , 'epaccmaeptr', 'epaccmaemae', 'hdy', 'quanet']
|
|
||||||
new_methods_rae = ['svmmrae' , 'epaccmraeptr', 'epaccmraemrae', 'hdy', 'quanet']
|
|
||||||
|
|
||||||
plot_error_by_drift(gao_seb_methods+new_methods_ae, error_name='ae', path=plotdir)
|
|
||||||
plot_error_by_drift(gao_seb_methods+new_methods_rae, error_name='rae', logscale=True, path=plotdir)
|
|
||||||
|
|
||||||
diagonal_plot(gao_seb_methods+new_methods_ae, error_name='ae', path=plotdir)
|
|
||||||
diagonal_plot(gao_seb_methods+new_methods_rae, error_name='rae', path=plotdir)
|
|
||||||
|
|
||||||
binary_bias_global(gao_seb_methods+new_methods_ae, error_name='ae', path=plotdir)
|
|
||||||
binary_bias_global(gao_seb_methods+new_methods_rae, error_name='rae', path=plotdir)
|
|
||||||
|
|
||||||
#binary_bias_bins(gao_seb_methods+new_methods_ae, error_name='ae', path=plotdir)
|
|
||||||
#binary_bias_bins(gao_seb_methods+new_methods_rae, error_name='rae', path=plotdir)
|
|
||||||
|
|
||||||
|
|
@ -1,145 +0,0 @@
|
||||||
import quapy as qp
|
|
||||||
import numpy as np
|
|
||||||
from os import makedirs
|
|
||||||
import sys, os
|
|
||||||
import pickle
|
|
||||||
import argparse
|
|
||||||
from TweetSentQuant.util import nicename, get_ranks_from_Gao_Sebastiani
|
|
||||||
import settings
|
|
||||||
from experiments import result_path
|
|
||||||
from tabular import Table
|
|
||||||
|
|
||||||
tables_path = './tables'
|
|
||||||
MAXTONE = 50 # sets the intensity of the maximum color reached by the worst (red) and best (green) results
|
|
||||||
|
|
||||||
makedirs(tables_path, exist_ok=True)
|
|
||||||
|
|
||||||
qp.environ['SAMPLE_SIZE'] = settings.SAMPLE_SIZE
|
|
||||||
|
|
||||||
|
|
||||||
def save_table(path, table):
|
|
||||||
print(f'saving results in {path}')
|
|
||||||
with open(path, 'wt') as foo:
|
|
||||||
foo.write(table)
|
|
||||||
|
|
||||||
|
|
||||||
def experiment_errors(path, dataset, method, loss):
|
|
||||||
path = result_path(path, dataset, method, 'm'+loss if not loss.startswith('m') else loss)
|
|
||||||
if os.path.exists(path):
|
|
||||||
true_prevs, estim_prevs, _, _, _, _ = pickle.load(open(path, 'rb'))
|
|
||||||
err_fn = getattr(qp.error, loss)
|
|
||||||
errors = err_fn(true_prevs, estim_prevs)
|
|
||||||
return errors
|
|
||||||
return None
|
|
||||||
|
|
||||||
|
|
||||||
|
|
||||||
if __name__ == '__main__':
|
|
||||||
parser = argparse.ArgumentParser(description='Generate tables for Tweeter Sentiment Quantification')
|
|
||||||
parser.add_argument('results', metavar='RESULT_PATH', type=str,
|
|
||||||
help='path to the directory where to store the results')
|
|
||||||
args = parser.parse_args()
|
|
||||||
|
|
||||||
datasets = qp.datasets.TWITTER_SENTIMENT_DATASETS_TEST
|
|
||||||
evaluation_measures = [qp.error.ae, qp.error.rae]
|
|
||||||
gao_seb_methods = ['cc', 'acc', 'pcc', 'pacc', 'sld', 'svmq', 'svmkld', 'svmnkld']
|
|
||||||
new_methods = ['hdy', 'quanet']
|
|
||||||
|
|
||||||
gao_seb_ranks, gao_seb_results = get_ranks_from_Gao_Sebastiani()
|
|
||||||
|
|
||||||
for i, eval_func in enumerate(evaluation_measures):
|
|
||||||
|
|
||||||
# Tables evaluation scores for AE and RAE (two tables)
|
|
||||||
# ----------------------------------------------------
|
|
||||||
|
|
||||||
eval_name = eval_func.__name__
|
|
||||||
added_methods = ['svmm' + eval_name, f'epaccm{eval_name}ptr', f'epaccm{eval_name}m{eval_name}'] + new_methods
|
|
||||||
methods = gao_seb_methods + added_methods
|
|
||||||
nold_methods = len(gao_seb_methods)
|
|
||||||
nnew_methods = len(added_methods)
|
|
||||||
|
|
||||||
# fill data table
|
|
||||||
table = Table(benchmarks=datasets, methods=methods)
|
|
||||||
for dataset in datasets:
|
|
||||||
for method in methods:
|
|
||||||
table.add(dataset, method, experiment_errors(args.results, dataset, method, eval_name))
|
|
||||||
|
|
||||||
# write the latex table
|
|
||||||
# tabular = """
|
|
||||||
# \\begin{tabularx}{\\textwidth}{|c||""" + ('Y|'*nold_methods)+ '|' + ('Y|'*nnew_methods) + """} \hline
|
|
||||||
# & \multicolumn{"""+str(nold_methods)+"""}{c||}{Methods tested in~\cite{Gao:2016uq}} &
|
|
||||||
# \multicolumn{"""+str(nnew_methods)+"""}{c|}{} \\\\ \hline
|
|
||||||
# """
|
|
||||||
tabular = """
|
|
||||||
\\resizebox{\\textwidth}{!}{%
|
|
||||||
\\begin{tabular}{|c||""" + ('c|' * nold_methods) + '|' + ('c|' * nnew_methods) + """} \hline
|
|
||||||
& \multicolumn{""" + str(nold_methods) + """}{c||}{Methods tested in~\cite{Gao:2016uq}} &
|
|
||||||
\multicolumn{""" + str(nnew_methods) + """}{c|}{} \\\\ \hline
|
|
||||||
"""
|
|
||||||
rowreplace={dataset: nicename(dataset) for dataset in datasets}
|
|
||||||
colreplace={method: nicename(method, eval_name, side=True) for method in methods}
|
|
||||||
|
|
||||||
tabular += table.latexTabular(benchmark_replace=rowreplace, method_replace=colreplace)
|
|
||||||
tabular += """
|
|
||||||
\end{tabular}%
|
|
||||||
}
|
|
||||||
"""
|
|
||||||
|
|
||||||
save_table(f'./tables/tab_results_{eval_name}.new.tex', tabular)
|
|
||||||
|
|
||||||
# Tables ranks for AE and RAE (two tables)
|
|
||||||
# ----------------------------------------------------
|
|
||||||
methods = gao_seb_methods
|
|
||||||
|
|
||||||
table.dropMethods(added_methods)
|
|
||||||
|
|
||||||
# fill the data table
|
|
||||||
ranktable = Table(benchmarks=datasets, methods=methods, missing='--')
|
|
||||||
for dataset in datasets:
|
|
||||||
for method in methods:
|
|
||||||
ranktable.add(dataset, method, values=table.get(dataset, method, 'rank'))
|
|
||||||
|
|
||||||
# write the latex table
|
|
||||||
tabular = """
|
|
||||||
\\resizebox{\\textwidth}{!}{%
|
|
||||||
\\begin{tabular}{|c||""" + ('c|' * len(gao_seb_methods)) + """} \hline
|
|
||||||
& \multicolumn{""" + str(nold_methods) + """}{c|}{Methods tested in~\cite{Gao:2016uq}} \\\\ \hline
|
|
||||||
"""
|
|
||||||
for method in methods:
|
|
||||||
tabular += ' & ' + nicename(method, eval_name, side=True)
|
|
||||||
tabular += "\\\\\hline\n"
|
|
||||||
|
|
||||||
for dataset in datasets:
|
|
||||||
tabular += nicename(dataset) + ' '
|
|
||||||
for method in methods:
|
|
||||||
newrank = ranktable.get(dataset, method)
|
|
||||||
oldrank = gao_seb_ranks[f'{dataset}-{method}-{eval_name}']
|
|
||||||
if newrank != '--':
|
|
||||||
newrank = f'{int(newrank)}'
|
|
||||||
color = ranktable.get_color(dataset, method)
|
|
||||||
if color == '--':
|
|
||||||
color = ''
|
|
||||||
tabular += ' & ' + f'{newrank}' + f' ({oldrank}) ' + color
|
|
||||||
tabular += '\\\\\hline\n'
|
|
||||||
tabular += '\hline\n'
|
|
||||||
|
|
||||||
tabular += 'Average '
|
|
||||||
for method in methods:
|
|
||||||
newrank = ranktable.get_average(method)
|
|
||||||
oldrank = gao_seb_ranks[f'Average-{method}-{eval_name}']
|
|
||||||
if newrank != '--':
|
|
||||||
newrank = f'{newrank:.1f}'
|
|
||||||
oldrank = f'{oldrank:.1f}'
|
|
||||||
color = ranktable.get_average(method, 'color')
|
|
||||||
if color == '--':
|
|
||||||
color = ''
|
|
||||||
tabular += ' & ' + f'{newrank}' + f' ({oldrank}) ' + color
|
|
||||||
tabular += '\\\\\hline\n'
|
|
||||||
tabular += """
|
|
||||||
\end{tabular}%
|
|
||||||
}
|
|
||||||
"""
|
|
||||||
|
|
||||||
save_table(f'./tables/tab_rank_{eval_name}.new.tex', tabular)
|
|
||||||
|
|
||||||
print("[Done]")
|
|
||||||
|
|
@ -1,8 +0,0 @@
|
||||||
import multiprocessing
|
|
||||||
|
|
||||||
N_JOBS = -2 #multiprocessing.cpu_count()
|
|
||||||
CUDA_N_JOBS = 2
|
|
||||||
ENSEMBLE_N_JOBS = -2
|
|
||||||
|
|
||||||
SAMPLE_SIZE = 100
|
|
||||||
|
|
||||||
|
|
@ -1,318 +0,0 @@
|
||||||
import numpy as np
|
|
||||||
import itertools
|
|
||||||
from scipy.stats import ttest_ind_from_stats, wilcoxon
|
|
||||||
|
|
||||||
|
|
||||||
class Table:
|
|
||||||
VALID_TESTS = [None, "wilcoxon", "ttest"]
|
|
||||||
|
|
||||||
def __init__(self, benchmarks, methods, lower_is_better=True, ttest='ttest', prec_mean=3,
|
|
||||||
clean_zero=False, show_std=False, prec_std=3, average=True, missing=None, missing_str='--', color=True):
|
|
||||||
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.touch()
|
|
||||||
|
|
||||||
@property
|
|
||||||
def nbenchmarks(self):
|
|
||||||
return len(self.benchmarks)
|
|
||||||
|
|
||||||
@property
|
|
||||||
def nmethods(self):
|
|
||||||
return len(self.methods)
|
|
||||||
|
|
||||||
def touch(self):
|
|
||||||
self._modif = True
|
|
||||||
|
|
||||||
def update(self):
|
|
||||||
if self._modif:
|
|
||||||
self.compute()
|
|
||||||
|
|
||||||
def _getfilled(self):
|
|
||||||
return np.argwhere(self.map['fill'])
|
|
||||||
|
|
||||||
@property
|
|
||||||
def values(self):
|
|
||||||
return self.map['values']
|
|
||||||
|
|
||||||
def _indexes(self):
|
|
||||||
return itertools.product(range(self.nbenchmarks), range(self.nmethods))
|
|
||||||
|
|
||||||
def _addmap(self, map, dtype, func=None):
|
|
||||||
self.map[map] = np.empty((self.nbenchmarks, self.nmethods), dtype=dtype)
|
|
||||||
if func is None:
|
|
||||||
return
|
|
||||||
m = self.map[map]
|
|
||||||
f = func
|
|
||||||
indexes = self._indexes() if map == 'fill' else self._getfilled()
|
|
||||||
for i, j in indexes:
|
|
||||||
m[i, j] = f(self.values[i, j])
|
|
||||||
|
|
||||||
def _addrank(self):
|
|
||||||
for i in range(self.nbenchmarks):
|
|
||||||
filled_cols_idx = np.argwhere(self.map['fill'][i]).flatten()
|
|
||||||
col_means = [self.map['mean'][i,j] for j in filled_cols_idx]
|
|
||||||
ranked_cols_idx = filled_cols_idx[np.argsort(col_means)]
|
|
||||||
if not self.lower_is_better:
|
|
||||||
ranked_cols_idx = ranked_cols_idx[::-1]
|
|
||||||
self.map['rank'][i, ranked_cols_idx] = np.arange(1, len(filled_cols_idx)+1)
|
|
||||||
|
|
||||||
def _addcolor(self):
|
|
||||||
for i in range(self.nbenchmarks):
|
|
||||||
filled_cols_idx = np.argwhere(self.map['fill'][i]).flatten()
|
|
||||||
if filled_cols_idx.size==0:
|
|
||||||
continue
|
|
||||||
col_means = [self.map['mean'][i,j] for j in filled_cols_idx]
|
|
||||||
minval = min(col_means)
|
|
||||||
maxval = max(col_means)
|
|
||||||
for col_idx in filled_cols_idx:
|
|
||||||
val = self.map['mean'][i,col_idx]
|
|
||||||
norm = (maxval - minval)
|
|
||||||
if norm > 0:
|
|
||||||
normval = (val - minval) / norm
|
|
||||||
else:
|
|
||||||
normval = 0.5
|
|
||||||
if self.lower_is_better:
|
|
||||||
normval = 1 - normval
|
|
||||||
self.map['color'][i, col_idx] = color_red2green_01(normval)
|
|
||||||
|
|
||||||
def _run_ttest(self, row, col1, col2):
|
|
||||||
mean1 = self.map['mean'][row, col1]
|
|
||||||
std1 = self.map['std'][row, col1]
|
|
||||||
nobs1 = self.map['nobs'][row, col1]
|
|
||||||
mean2 = self.map['mean'][row, col2]
|
|
||||||
std2 = self.map['std'][row, col2]
|
|
||||||
nobs2 = self.map['nobs'][row, col2]
|
|
||||||
_, p_val = ttest_ind_from_stats(mean1, std1, nobs1, mean2, std2, nobs2)
|
|
||||||
return p_val
|
|
||||||
|
|
||||||
def _run_wilcoxon(self, row, col1, col2):
|
|
||||||
values1 = self.map['values'][row, col1]
|
|
||||||
values2 = self.map['values'][row, col2]
|
|
||||||
_, p_val = wilcoxon(values1, values2)
|
|
||||||
return p_val
|
|
||||||
|
|
||||||
def _add_statistical_test(self):
|
|
||||||
if self.ttest is None:
|
|
||||||
return
|
|
||||||
self.some_similar = [False]*self.nmethods
|
|
||||||
for i in range(self.nbenchmarks):
|
|
||||||
filled_cols_idx = np.argwhere(self.map['fill'][i]).flatten()
|
|
||||||
if len(filled_cols_idx) <= 1:
|
|
||||||
continue
|
|
||||||
col_means = [self.map['mean'][i,j] for j in filled_cols_idx]
|
|
||||||
best_pos = filled_cols_idx[np.argmin(col_means)]
|
|
||||||
|
|
||||||
for j in filled_cols_idx:
|
|
||||||
if j==best_pos:
|
|
||||||
continue
|
|
||||||
if self.ttest == 'ttest':
|
|
||||||
p_val = self._run_ttest(i, best_pos, j)
|
|
||||||
else:
|
|
||||||
p_val = self._run_wilcoxon(i, best_pos, j)
|
|
||||||
|
|
||||||
pval_outcome = pval_interpretation(p_val)
|
|
||||||
self.map['ttest'][i, j] = pval_outcome
|
|
||||||
if pval_outcome != 'Diff':
|
|
||||||
self.some_similar[j] = True
|
|
||||||
|
|
||||||
def compute(self):
|
|
||||||
self._addmap('fill', dtype=bool, func=lambda x: x is not None)
|
|
||||||
self._addmap('mean', dtype=float, func=np.mean)
|
|
||||||
self._addmap('std', dtype=float, func=np.std)
|
|
||||||
self._addmap('nobs', dtype=float, func=len)
|
|
||||||
self._addmap('rank', dtype=int, func=None)
|
|
||||||
self._addmap('color', dtype=object, func=None)
|
|
||||||
self._addmap('ttest', dtype=object, func=None)
|
|
||||||
self._addmap('latex', dtype=object, func=None)
|
|
||||||
self._addrank()
|
|
||||||
self._addcolor()
|
|
||||||
self._add_statistical_test()
|
|
||||||
if self.add_average:
|
|
||||||
self._addave()
|
|
||||||
self._modif = False
|
|
||||||
|
|
||||||
def _is_column_full(self, col):
|
|
||||||
return all(self.map['fill'][:, self.method_index[col]])
|
|
||||||
|
|
||||||
def _addave(self):
|
|
||||||
ave = Table(['ave'], self.methods, lower_is_better=self.lower_is_better, ttest=self.ttest, average=False,
|
|
||||||
missing=self.missing, missing_str=self.missing_str)
|
|
||||||
for col in self.methods:
|
|
||||||
values = None
|
|
||||||
if self._is_column_full(col):
|
|
||||||
if self.ttest == 'ttest':
|
|
||||||
values = np.asarray(self.map['mean'][:, self.method_index[col]])
|
|
||||||
else: # wilcoxon
|
|
||||||
values = np.concatenate(self.values[:, self.method_index[col]])
|
|
||||||
ave.add('ave', col, values)
|
|
||||||
self.average = ave
|
|
||||||
|
|
||||||
def add(self, benchmark, method, values):
|
|
||||||
if values is not None:
|
|
||||||
values = np.asarray(values)
|
|
||||||
if values.ndim==0:
|
|
||||||
values = values.flatten()
|
|
||||||
rid, cid = self._coordinates(benchmark, method)
|
|
||||||
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 not None and self.some_similar[j]:
|
|
||||||
test_label = self.map['ttest'][i,j]
|
|
||||||
if test_label == 'Sim':
|
|
||||||
stat = '^{\dag\phantom{\dag}}'
|
|
||||||
elif test_label == 'Same':
|
|
||||||
stat = '^{\ddag}'
|
|
||||||
elif isbest or test_label == 'Diff':
|
|
||||||
stat = '^{\phantom{\ddag}}'
|
|
||||||
|
|
||||||
std = ''
|
|
||||||
if self.show_std:
|
|
||||||
std = self.map['std'][i,j]
|
|
||||||
std = f" {std:.{self.prec_std}f}"
|
|
||||||
if self.clean_zero:
|
|
||||||
std = std.replace(' 0.', '.')
|
|
||||||
std = f" \pm {std:{self.prec_std}}"
|
|
||||||
|
|
||||||
if stat!='' or std!='':
|
|
||||||
l = f'{l}${stat}{std}$'
|
|
||||||
|
|
||||||
if self.color:
|
|
||||||
l += ' ' + self.map['color'][i,j]
|
|
||||||
|
|
||||||
return l
|
|
||||||
|
|
||||||
def latexTabular(self, benchmark_replace={}, method_replace={}, average=True):
|
|
||||||
tab = ' & '
|
|
||||||
tab += ' & '.join([method_replace.get(col, col) for col in self.methods])
|
|
||||||
tab += ' \\\\\hline\n'
|
|
||||||
for row in self.benchmarks:
|
|
||||||
rowname = benchmark_replace.get(row, row)
|
|
||||||
tab += rowname + ' & '
|
|
||||||
tab += self.latexRow(row)
|
|
||||||
|
|
||||||
if average:
|
|
||||||
tab += '\hline\n'
|
|
||||||
tab += 'Average & '
|
|
||||||
tab += self.latexAverage()
|
|
||||||
return tab
|
|
||||||
|
|
||||||
def 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):
|
|
||||||
t = Table(benchmarks=self.benchmarks, methods=self.methods, prec_mean=0, average=True)
|
|
||||||
for rid, cid in self._getfilled():
|
|
||||||
row = self.benchmarks[rid]
|
|
||||||
col = self.methods[cid]
|
|
||||||
t.add(row, col, self.get(row, col, 'rank'))
|
|
||||||
t.compute()
|
|
||||||
return t
|
|
||||||
|
|
||||||
def dropMethods(self, methods):
|
|
||||||
drop_index = [self.method_index[m] for m in methods]
|
|
||||||
new_methods = np.delete(self.methods, drop_index)
|
|
||||||
new_index = {col:j for j, col in enumerate(new_methods)}
|
|
||||||
|
|
||||||
self.map['values'] = self.values[:,np.asarray([self.method_index[m] for m in new_methods], dtype=int)]
|
|
||||||
self.methods = new_methods
|
|
||||||
self.method_index = new_index
|
|
||||||
self.touch()
|
|
||||||
|
|
||||||
|
|
||||||
def pval_interpretation(p_val):
|
|
||||||
if 0.005 >= p_val:
|
|
||||||
return 'Diff'
|
|
||||||
elif 0.05 >= p_val > 0.005:
|
|
||||||
return 'Sim'
|
|
||||||
elif p_val > 0.05:
|
|
||||||
return 'Same'
|
|
||||||
|
|
||||||
|
|
||||||
def color_red2green_01(val, maxtone=50):
|
|
||||||
if np.isnan(val): return None
|
|
||||||
assert 0 <= val <= 1, f'val {val} out of range [0,1]'
|
|
||||||
|
|
||||||
# rescale to [-1,1]
|
|
||||||
val = val * 2 - 1
|
|
||||||
if val < 0:
|
|
||||||
color = 'red'
|
|
||||||
tone = maxtone * (-val)
|
|
||||||
else:
|
|
||||||
color = 'green'
|
|
||||||
tone = maxtone * val
|
|
||||||
return '\cellcolor{' + color + f'!{int(tone)}' + '}'
|
|
||||||
|
|
||||||
|
|
@ -1,89 +0,0 @@
|
||||||
import numpy as np
|
|
||||||
|
|
||||||
|
|
||||||
nice = {
|
|
||||||
'mae':'AE',
|
|
||||||
'mrae':'RAE',
|
|
||||||
'ae':'AE',
|
|
||||||
'rae':'RAE',
|
|
||||||
'svmkld': 'SVM(KLD)',
|
|
||||||
'svmnkld': 'SVM(NKLD)',
|
|
||||||
'svmq': 'SVM(Q)',
|
|
||||||
'svmae': 'SVM(AE)',
|
|
||||||
'svmnae': 'SVM(NAE)',
|
|
||||||
'svmmae': 'SVM(AE)',
|
|
||||||
'svmmrae': 'SVM(RAE)',
|
|
||||||
'quanet': 'QuaNet',
|
|
||||||
'hdy': 'HDy',
|
|
||||||
'dys': 'DyS',
|
|
||||||
'epaccmaeptr': 'E(PACC)$_\mathrm{Ptr}$',
|
|
||||||
'epaccmaemae': 'E(PACC)$_\mathrm{AE}$',
|
|
||||||
'epaccmraeptr': 'E(PACC)$_\mathrm{Ptr}$',
|
|
||||||
'epaccmraemrae': 'E(PACC)$_\mathrm{RAE}$',
|
|
||||||
'svmperf':'',
|
|
||||||
'sanders': 'Sanders',
|
|
||||||
'semeval13': 'SemEval13',
|
|
||||||
'semeval14': 'SemEval14',
|
|
||||||
'semeval15': 'SemEval15',
|
|
||||||
'semeval16': 'SemEval16',
|
|
||||||
'Average': 'Average'
|
|
||||||
}
|
|
||||||
|
|
||||||
|
|
||||||
def nicerm(key):
|
|
||||||
return '\mathrm{'+nice[key]+'}'
|
|
||||||
|
|
||||||
|
|
||||||
def nicename(method, eval_name=None, side=False):
|
|
||||||
m = nice.get(method, method.upper())
|
|
||||||
if eval_name is not None:
|
|
||||||
o = '$^{' + nicerm(eval_name) + '}$'
|
|
||||||
m = (m+o).replace('$$','')
|
|
||||||
if side:
|
|
||||||
m = '\side{'+m+'}'
|
|
||||||
return m
|
|
||||||
|
|
||||||
|
|
||||||
def load_Gao_Sebastiani_previous_results():
|
|
||||||
def rename(method):
|
|
||||||
old2new = {
|
|
||||||
'kld': 'svmkld',
|
|
||||||
'nkld': 'svmnkld',
|
|
||||||
'qbeta2': 'svmq',
|
|
||||||
'em': 'sld'
|
|
||||||
}
|
|
||||||
return old2new.get(method, method)
|
|
||||||
|
|
||||||
gao_seb_results = {}
|
|
||||||
with open('./Gao_Sebastiani_results.txt', 'rt') as fin:
|
|
||||||
lines = fin.readlines()
|
|
||||||
for line in lines[1:]:
|
|
||||||
line = line.strip()
|
|
||||||
parts = line.lower().split()
|
|
||||||
if len(parts) == 4:
|
|
||||||
dataset, method, ae, rae = parts
|
|
||||||
else:
|
|
||||||
method, ae, rae = parts
|
|
||||||
learner, method = method.split('-')
|
|
||||||
method = rename(method)
|
|
||||||
gao_seb_results[f'{dataset}-{method}-ae'] = float(ae)
|
|
||||||
gao_seb_results[f'{dataset}-{method}-rae'] = float(rae)
|
|
||||||
return gao_seb_results
|
|
||||||
|
|
||||||
|
|
||||||
def get_ranks_from_Gao_Sebastiani():
|
|
||||||
gao_seb_results = load_Gao_Sebastiani_previous_results()
|
|
||||||
datasets = set([key.split('-')[0] for key in gao_seb_results.keys()])
|
|
||||||
methods = np.sort(np.unique([key.split('-')[1] for key in gao_seb_results.keys()]))
|
|
||||||
ranks = {}
|
|
||||||
for metric in ['ae', 'rae']:
|
|
||||||
for dataset in datasets:
|
|
||||||
scores = [gao_seb_results[f'{dataset}-{method}-{metric}'] for method in methods]
|
|
||||||
order = np.argsort(scores)
|
|
||||||
sorted_methods = methods[order]
|
|
||||||
for i, method in enumerate(sorted_methods):
|
|
||||||
ranks[f'{dataset}-{method}-{metric}'] = i+1
|
|
||||||
for method in methods:
|
|
||||||
rankave = np.mean([ranks[f'{dataset}-{method}-{metric}'] for dataset in datasets])
|
|
||||||
ranks[f'Average-{method}-{metric}'] = rankave
|
|
||||||
return ranks, gao_seb_results
|
|
||||||
Loading…
Reference in New Issue