showing experiments

Census/adjacentconcat_4.py

@@ -52,7 +52,7 @@ methods     = [f'{q_name}-cat' for q_name in q_names]
 table = Table(name='adjacentconcat', benchmarks=benchmarks, methods=methods, stat_test=None, color_mode='local')
 table.format.mean_prec = 4
 table.format.show_std = False
-table.format.sta = False
+# table.format = False
 table.format.remove_zero = True
 
 for q_name, q in quantifiers():

Census/adjacentmedianoptim_4.2.py

@@ -14,9 +14,11 @@ from copy import deepcopy
 
 np.set_printoptions(linewidth=np.inf)
 
+
 def classifier():
     return LogisticRegression()
 
+
 def quantifiers():
     cls = classifier()
     # yield 'MLPE', MLPE()
@@ -26,7 +28,7 @@ def quantifiers():
     yield 'PACC', PACC(cls)
     yield 'MS', MS(cls)
     # yield 'MS2', MS2(cls)
-    # yield 'SLD', EMQ(cls)
+    yield 'SLD', EMQ(cls)
 
 
 survey_y = './data/survey_y.csv'

Census/allconcat_3.py

@@ -12,9 +12,11 @@ import quapy as qp
 
 np.set_printoptions(linewidth=np.inf)
 
+
 def classifier():
     return LogisticRegressionCV()
 
+
 def quantifiers():
     cls = classifier()
     yield 'MLPE', MLPE()
@@ -50,7 +52,7 @@ methods     = [f'{q_name}-cat' for q_name in q_names]
 table = Table(name='allconcat', benchmarks=benchmarks, methods=methods, stat_test=None, color_mode='local')
 table.format.mean_prec = 4
 table.format.show_std = False
-table.format.sta = False
+table.format.stat_test = False
 table.format.remove_zero = True
 
 for q_name, q in quantifiers():

Census/classification_accuracy_1.py

@@ -1,15 +1,18 @@
 import numpy as np
-from sklearn.svm import SVC
+from sklearn.linear_model import LogisticRegressionCV, LogisticRegression
+from sklearn.svm import SVC, LinearSVC
 
 from commons import *
 from table import Table
+from tqdm import tqdm
 
 
 np.set_printoptions(linewidth=np.inf)
 
 def classifiers():
-    yield 'LR-opt', LogisticRegressionCV(class_weight='balanced', Cs=10)
-    yield 'LR-def', LogisticRegressionCV()
+    yield 'LR-opt', LogisticRegressionCV(Cs=10)
+    yield 'LR-opt-bal', LogisticRegressionCV(class_weight='balanced', Cs=10)
+    yield 'LR-def', LogisticRegression()
     yield 'SVM-linear', LinearSVC()
     yield 'SVM-rbf', SVC(kernel='rbf')
 
@@ -37,7 +40,7 @@ for cls_name, c in classifiers():
     table = Table(name=cls_name, benchmarks=benchmarks, methods=methods, stat_test=None, color_mode='local', lower_is_better=False)
     table.format.mean_prec = 4
     table.format.show_std = False
-    table.format.sta = False
+    table.format.stat_test = False
     table.format.remove_zero = True
 
     for i, (Ai, Xi, yi) in tqdm(enumerate(trains), total=n_areas):

Census/commons.py

@@ -18,8 +18,8 @@ def load_csv(file, use_yhat=True):
     X = df[covariates].values
     A = df[cod_area].values
 
-    # for i, cov in enumerate(covariates):
-    #     print(f'values of col {i} "{cov}" {np.unique(X[:,i])}')
+    for i, cov in enumerate(covariates):
+        print(f'values of col {i} "{cov}" {np.unique(X[:,i])}')
 
     if y_true in df.columns:
         y = df[y_true].values

Census/pairwise_2.py

@@ -3,7 +3,7 @@ from sklearn.linear_model import LogisticRegressionCV
 
 from quapy.data import LabelledCollection
 from quapy.method.non_aggregative import MaximumLikelihoodPrevalenceEstimation as MLPE
-from quapy.method.aggregative import CC, PCC, ACC, PACC, EMQ
+from quapy.method.aggregative import CC, PCC, ACC, PACC, EMQ, SLD
 from commons import *
 from table import Table
 from tqdm import tqdm
@@ -22,6 +22,7 @@ def quantifiers():
     yield 'PCC', PCC(cls)
     yield 'ACC', ACC(cls)
     yield 'PACC', PACC(cls)
+    yield 'SLD', SLD(cls)
 
 
 survey_y = './data/survey_y.csv'
@@ -69,6 +70,8 @@ for q_name, q in quantifiers():
     for test in benchmarks:
         values = table.get_benchmark_values(test)
         table.add(benchmark=test, method='Best', v=min(values))
+        table.add(benchmark=test, method='Worst', v=max(values))
+        table.add(benchmark=test, method='AVE', v=np.mean(values))
 
     tables.append(table)
 

Census/table.py

@@ -430,7 +430,7 @@ class Table:
         return Table.LatexPDF(pdf_path, tables=[self], tabular_dir=tabular_dir, *args, **kwargs)
 
     @classmethod
-    def Document(self, tex_path, tables:List['Table'], tabular_dir='tables', *args, **kwargs):
+    def Document(self, tex_path, tables:List['Table'], tabular_dir='tables', landscape=True, *args, **kwargs):
         lines = []
         lines.append('\\documentclass[10pt,a4paper]{article}')
         lines.append('\\usepackage[utf8]{inputenc}')
@@ -440,11 +440,14 @@ class Table:
         lines.append('\\usepackage{graphicx}')
         lines.append('\\usepackage{xcolor}')
         lines.append('\\usepackage{colortbl}')
+        if landscape:
+            lines.append('\\usepackage[landscape]{geometry}')
         lines.append('')
         lines.append('\\begin{document}')
         for table in tables:
             lines.append('')
             lines.append(table.table(os.path.join(Path(tex_path).parent, tabular_dir, table.name + '_table.tex'), *args, **kwargs))
+            lines.append('\n\\newpage\n')
         lines.append('\\end{document}')
 
         document = '\n'.join(lines)
@@ -472,5 +475,6 @@ class Table:
         os.chdir(dir)
         os.system('pdflatex ' + Path(tex_path).name)
         basename = Path(tex_path).name.replace('.tex', '')
-        os.system(f'rm {basename}.aux {basename}.bbl {basename}.blg {basename}.log {basename}.out {basename}.dvi')
+        os.system(f'rm {basename}.aux {basename}.log')
         os.chdir(pwd)
+        print('[Done]')