Implemented funnelling architecture

refactor/funnelling.py

@@ -77,10 +77,9 @@ class FeatureSet2Posteriors:
 
 
 class Funnelling:
-    def __init__(self, first_tier: DocEmbedderList, n_jobs=-1):
+    def __init__(self, first_tier: DocEmbedderList, meta_classifier: MetaClassifier, n_jobs=-1):
         self.first_tier = first_tier
-        self.meta = MetaClassifier(
-            SVC(kernel='rbf', gamma='auto', probability=True, cache_size=1000, random_state=1), n_jobs=n_jobs)
+        self.meta = meta_classifier
         self.n_jobs = n_jobs
 
     def fit(self, lX, ly):

refactor/main.py

@@ -2,13 +2,14 @@ from argparse import ArgumentParser
 from funnelling import *
 from view_generators import *
 from data.dataset_builder import MultilingualDataset
-from util.common import MultilingualIndex
+from util.common import MultilingualIndex, get_params
 from util.evaluation import evaluate
 from util.results_csv import CSVlog
 from time import time
 
 
 def main(args):
+    OPTIMC = True # TODO
     N_JOBS = 8
     print('Running refactored...')
 
@@ -27,16 +28,36 @@ def main(args):
     lMuse = MuseLoader(langs=sorted(lX.keys()), cache=EMBEDDINGS_PATH)
     multilingualIndex.index(lX, ly, lXte, lyte, l_pretrained_vocabulary=lMuse.vocabulary())
 
-    # posteriorEmbedder = VanillaFunGen(base_learner=get_learner(calibrate=True), n_jobs=N_JOBS)
-    museEmbedder = MuseGen(muse_dir=EMBEDDINGS_PATH, n_jobs=N_JOBS)
-    wceEmbedder = WordClassGen(n_jobs=N_JOBS)
-    # rnnEmbedder = RecurrentGen(multilingualIndex, pretrained_embeddings=lMuse, wce=False, batch_size=256,
-    #                     nepochs=250, gpus=args.gpus, n_jobs=N_JOBS)
-    # bertEmbedder = BertGen(multilingualIndex, batch_size=4, nepochs=1, gpus=args.gpus, n_jobs=N_JOBS)
+    embedder_list = []
+    if args.X:
+        posteriorEmbedder = VanillaFunGen(base_learner=get_learner(calibrate=True), n_jobs=N_JOBS)
+        embedder_list.append(posteriorEmbedder)
 
-    docEmbedders = DocEmbedderList([museEmbedder, wceEmbedder])
+    if args.M:
+        museEmbedder = MuseGen(muse_dir=EMBEDDINGS_PATH, n_jobs=N_JOBS)
+        embedder_list.append(museEmbedder)
 
-    gfun = Funnelling(first_tier=docEmbedders)
+    if args.W:
+        wceEmbedder = WordClassGen(n_jobs=N_JOBS)
+        embedder_list.append(wceEmbedder)
+
+    if args.G:
+        rnnEmbedder = RecurrentGen(multilingualIndex, pretrained_embeddings=lMuse, wce=False, batch_size=256,
+                                   nepochs=250, gpus=args.gpus, n_jobs=N_JOBS)
+        embedder_list.append(rnnEmbedder)
+
+    if args.B:
+        bertEmbedder = BertGen(multilingualIndex, batch_size=4, nepochs=1, gpus=args.gpus, n_jobs=N_JOBS)
+        embedder_list.append(bertEmbedder)
+
+    # Init DocEmbedderList
+    docEmbedders = DocEmbedderList(embedder_list=embedder_list, probabilistic=True)
+    meta_parameters = None if not OPTIMC else [{'C': [1, 1e3, 1e2, 1e1, 1e-1]}]
+    meta = MetaClassifier(meta_learner=get_learner(calibrate=False, kernel='rbf', C=meta_parameters),
+                          meta_parameters=get_params(optimc=True))
+
+    # Init Funnelling Architecture
+    gfun = Funnelling(first_tier=docEmbedders, meta_classifier=meta)
 
     # Training ---------------------------------------
     print('\n[Training Generalized Funnelling]')
@@ -64,9 +85,9 @@ def main(args):
         print(f'Lang {lang}: macro-F1 = {macrof1:.3f} micro-F1 = {microf1:.3f}')
         results.add_row(method='gfun',
                         setting='TODO',
-                        sif='TODO',
-                        zscore='TRUE',
-                        l2='TRUE',
+                        sif='True',
+                        zscore='True',
+                        l2='True',
                         dataset='TODO',
                         time_tr=time_tr,
                         time_te=time_te,
@@ -84,6 +105,11 @@ def main(args):
 
 if __name__ == '__main__':
     parser = ArgumentParser()
+    parser.add_argument('--X')
+    parser.add_argument('--M')
+    parser.add_argument('--W')
+    parser.add_argument('--G')
+    parser.add_argument('--B')
     parser.add_argument('--gpus', default=None)
     args = parser.parse_args()
     main(args)

refactor/util/common.py

@@ -360,4 +360,12 @@ def pad(index_list, pad_index, max_pad_length=None):
         pad_length = min(pad_length, max_pad_length)
     for i, indexes in enumerate(index_list):
         index_list[i] = [pad_index] * (pad_length - len(indexes)) + indexes[:pad_length]
-    return index_list
+    return index_list
+
+
+def get_params(optimc=False):
+    if not optimc:
+        return None
+    c_range = [1e4, 1e3, 1e2, 1e1, 1, 1e-1]
+    kernel = 'rbf'
+    return [{'kernel': [kernel], 'C': c_range, 'gamma':['auto']}]