From daba2c9fb47c3cd4162e7abfbed2f54549b861e0 Mon Sep 17 00:00:00 2001
From: Alex Moreo <alejandro.moreo@isti.cnr.it>
Date: Fri, 27 Aug 2021 13:57:33 +0200
Subject: [PATCH] adding tables generation

---
 MultiLabel/gentables.py | 75 +++++++++++++++++++++++++++++++++++++++++
 1 file changed, 75 insertions(+)
 create mode 100644 MultiLabel/gentables.py

diff --git a/MultiLabel/gentables.py b/MultiLabel/gentables.py
new file mode 100644
index 0000000..5124289
--- /dev/null
+++ b/MultiLabel/gentables.py
@@ -0,0 +1,75 @@
+import argparse
+from sklearn.calibration import CalibratedClassifierCV
+from sklearn.linear_model import LogisticRegression
+import itertools
+from sklearn.multioutput import ClassifierChain
+from tqdm import tqdm
+from skmultilearn.dataset import load_dataset, available_data_sets
+from scipy.sparse import csr_matrix
+import quapy as qp
+from MultiLabel.main import load_results
+from MultiLabel.mlclassification import MultilabelStackedClassifier
+from MultiLabel.mldata import MultilabelledCollection
+from MultiLabel.mlquantification import MultilabelNaiveQuantifier, MLCC, MLPCC, MLRegressionQuantification, \
+    MLACC, \
+    MLPACC, MultilabelNaiveAggregativeQuantifier
+from MultiLabel.tabular import Table
+from method.aggregative import PACC, CC, EMQ, PCC, ACC, HDy
+import numpy as np
+from data.dataset  import Dataset
+from mlevaluation import ml_natural_prevalence_prediction, ml_artificial_prevalence_prediction, check_error_str
+import sys
+import os
+import pickle
+
+models = ['NaiveCC', 'NaivePCC', 'NaiveACC', 'NaivePACC', 'NaiveHDy', 'NaiveSLD']
+datasets = sorted(set([x[0] for x in available_data_sets().keys()]))
+
+
+def generate_table(path, protocol, error):
+    print(f'generating {path}')
+    table = Table(datasets, models)
+    for dataset, model in itertools.product(datasets, models):
+        result_path = f'{opt.results}/{dataset}_{model}.pkl'
+        if os.path.exists(result_path):
+            result = load_results(result_path)
+            true_prevs, estim_prevs = result[protocol]
+            scores = np.asarray([error(trues, estims) for trues, estims in zip(true_prevs, estim_prevs)]).flatten()
+            table.add(dataset, model, scores)
+
+    tabular = """
+    \\resizebox{\\textwidth}{!}{%
+            \\begin{tabular}{|c||""" + ('c|' * len(models)) + """} \hline
+            """
+    dataset_replace = {'tmc2007_500': 'tmc2007\_500'}
+    method_replace = {}
+
+    tabular += table.latexTabular(benchmark_replace=dataset_replace, method_replace=method_replace)
+    tabular += """
+        \end{tabular}%
+        }
+    """
+    with open(path, 'wt') as foo:
+        foo.write(tabular)
+
+
+if __name__ == '__main__':
+    parser = argparse.ArgumentParser(description='Experiments for multi-label quantification')
+    parser.add_argument('--results', type=str, default='./results', metavar='str',
+                        help=f'path where to store the results')
+    parser.add_argument('--tablepath', type=str, default='./tables', metavar='str',
+                        help=f'path where to store the tables')
+    opt = parser.parse_args()
+
+    os.makedirs(opt.results, exist_ok=True)
+    os.makedirs(opt.tablepath, exist_ok=True)
+
+    eval_error = qp.error.ae
+    generate_table(f'{opt.tablepath}/npp.ae.tex', protocol='npp', error=eval_error)
+    generate_table(f'{opt.tablepath}/app.ae.tex', protocol='app', error=eval_error)
+
+
+
+
+
+