sketched results reader - removed first tier learners optimization

src/FPEC_andrea.py

@@ -85,7 +85,6 @@ if __name__ == '__main__':
     lXtr, lytr = data.training()
     lXte, lyte = data.test()
 
-
     if op.set_c != -1:
         meta_parameters = None
     else:
@@ -152,7 +151,7 @@ if __name__ == '__main__':
                            config=config,
                            first_tier_learner=get_learner(calibrate=True),
                            meta_learner=get_learner(calibrate=False, kernel='rbf'),
-                           first_tier_parameters=get_params(dense=False),
+                           first_tier_parameters=None,   # get_params(dense=False),-->first_tier should not be optimized
                            meta_parameters=get_params(dense=True),
                            n_jobs=op.n_jobs)
 

src/data/embeddings.py

@@ -220,6 +220,7 @@ class StorageEmbeddings:
             optimal_n = get_optimal_dim(self.lang_U, 'U')
             self.lang_U = run_pca(optimal_n, self.lang_U)
         elif max_label_space < nC:
+            print(f'Applying PCA to unsupervised matrix U')
             self.lang_U = run_pca(max_label_space, self.lang_U)
 
         return
@@ -258,7 +259,8 @@ class StorageEmbeddings:
             print(f'Applying PCA(n_components={i}')
             for lang in languages:
                 self.lang_S[lang] = stacked_pca.transform(self.lang_S[lang])
-        elif max_label_space < nC:
+        elif max_label_space <= nC:
+            print(f'Computing PCA on Supervised Matrix PCA(n_components:{max_label_space})')
             self.lang_S = run_pca(max_label_space, self.lang_S)
 
         return
@@ -276,7 +278,6 @@ class StorageEmbeddings:
             self._add_emebeddings_supervised(docs, labels, config['reduction'], config['max_label_space'], vocs)
         return self
 
-
     def predict(self, config, docs):
         if config['supervised'] and config['unsupervised']:
             return self._concatenate_embeddings(docs)
@@ -289,4 +290,3 @@ class StorageEmbeddings:
             for lang in docs.keys():
                 _r[lang] = docs[lang].dot(self.lang_U[lang])
         return _r
-

src/learning/learners.py

@@ -549,4 +549,4 @@ class PolylingualEmbeddingsClassifier:
         return _joblib_transform_multiling(self.model.predict_proba, lWEte, n_jobs=self.n_jobs)
 
     def best_params(self):
-        return self.model.best_params()
+        return self.model.best_params()

src/results/results_manager.py

@@ -0,0 +1,7 @@
+import pandas as pd
+import numpy as np
+
+df = pd.read_csv("/home/andreapdr/funneling_pdr/src/results/results.csv", delimiter='\t')
+pivot = pd.pivot_table(df, values=['time', 'macrof1', 'microf1', 'macrok', 'microk'], index=['embed'], aggfunc=[np.mean, np.std])
+print(pivot)
+print('Finished ...')

src/util/decompositions.py

@@ -47,4 +47,4 @@ def get_optimal_dim(X, embed_type):
     plt.axvline(best_n, color='r', label='optimal N')
     plt.legend()
     plt.show()
-    return best_n
+    return best_n

src/util/util.py

@@ -0,0 +1,2 @@
+def fill_missing_classes(lXtr, lytr):
+    pass