Implemented funnelling architecture

refactor/data/datamodule.py

@@ -105,21 +105,25 @@ class RecurrentDataModule(pl.LightningDataModule):
         if stage == 'fit' or stage is None:
             l_train_index, l_train_target = self.multilingualIndex.l_train()
             # Debug settings: reducing number of samples
-            l_train_index = {l: train[:50] for l, train in l_train_index.items()}
-            l_train_target = {l: target[:50] for l, target in l_train_target.items()}
+            l_train_index = {l: train[:5] for l, train in l_train_index.items()}
+            l_train_target = {l: target[:5] for l, target in l_train_target.items()}
 
             self.training_dataset = RecurrentDataset(l_train_index, l_train_target,
                                                      lPad_index=self.multilingualIndex.l_pad())
 
             l_val_index, l_val_target = self.multilingualIndex.l_val()
             # Debug settings: reducing number of samples
-            l_val_index = {l: train[:50] for l, train in l_val_index.items()}
-            l_val_target = {l: target[:50] for l, target in l_val_target.items()}
+            l_val_index = {l: train[:5] for l, train in l_val_index.items()}
+            l_val_target = {l: target[:5] for l, target in l_val_target.items()}
 
             self.val_dataset = RecurrentDataset(l_val_index, l_val_target,
                                                 lPad_index=self.multilingualIndex.l_pad())
         if stage == 'test' or stage is None:
             l_test_index, l_test_target = self.multilingualIndex.l_test()
+            # Debug settings: reducing number of samples
+            l_test_index = {l: train[:5] for l, train in l_test_index.items()}
+            l_test_target = {l: target[:5] for l, target in l_test_target.items()}
+
             self.test_dataset = RecurrentDataset(l_test_index, l_test_target,
                                                  lPad_index=self.multilingualIndex.l_pad())
 
@@ -145,8 +149,8 @@ class BertDataModule(RecurrentDataModule):
         if stage == 'fit' or stage is None:
             l_train_raw, l_train_target = self.multilingualIndex.l_train_raw()
             # Debug settings: reducing number of samples
-            # l_train_raw = {l: train[:50] for l, train in l_train_raw.items()}
-            # l_train_target = {l: target[:50] for l, target in l_train_target.items()}
+            l_train_raw = {l: train[:5] for l, train in l_train_raw.items()}
+            l_train_target = {l: target[:5] for l, target in l_train_target.items()}
 
             l_train_index = self.tokenize(l_train_raw, max_len=self.max_len)
             self.training_dataset = RecurrentDataset(l_train_index, l_train_target,
@@ -154,8 +158,8 @@ class BertDataModule(RecurrentDataModule):
 
             l_val_raw, l_val_target = self.multilingualIndex.l_val_raw()
             # Debug settings: reducing number of samples
-            # l_val_raw = {l: train[:50] for l, train in l_val_raw.items()}
-            # l_val_target = {l: target[:50] for l, target in l_val_target.items()}
+            l_val_raw = {l: train[:5] for l, train in l_val_raw.items()}
+            l_val_target = {l: target[:5] for l, target in l_val_target.items()}
 
             l_val_index = self.tokenize(l_val_raw, max_len=self.max_len)
             self.val_dataset = RecurrentDataset(l_val_index, l_val_target,
@@ -163,6 +167,10 @@ class BertDataModule(RecurrentDataModule):
 
         if stage == 'test' or stage is None:
             l_test_raw, l_test_target = self.multilingualIndex.l_test_raw()
+            # Debug settings: reducing number of samples
+            l_test_raw = {l: train[:5] for l, train in l_test_raw.items()}
+            l_test_target = {l: target[:5] for l, target in l_test_target.items()}
+
             l_test_index = self.tokenize(l_test_raw, max_len=self.max_len)
             self.test_dataset = RecurrentDataset(l_test_index, l_test_target,
                                                  lPad_index=self.multilingualIndex.l_pad())

refactor/funnelling.py

@@ -0,0 +1,95 @@
+from models.learners import *
+from view_generators import VanillaFunGen
+from util.common import _normalize
+
+
+class DocEmbedderList:
+    def __init__(self, embedder_list, probabilistic=True):
+        """
+        Class that takes care of calling fit and transform function for every init embedder.
+        :param embedder_list: list of embedders to be deployed
+        :param probabilistic: whether to recast view generators output to vectors of posterior probabilities or not
+        """
+        assert len(embedder_list) != 0, 'Embedder list cannot be empty!'
+        self.embedders = embedder_list
+        self.probabilistic = probabilistic
+        if probabilistic:
+            _tmp = []
+            for embedder in self.embedders:
+                if isinstance(embedder, VanillaFunGen):
+                    _tmp.append(embedder)
+                else:
+                    _tmp.append(FeatureSet2Posteriors(embedder))
+        self.embedders = _tmp
+
+    def fit(self, lX, ly):
+        for embedder in self.embedders:
+            embedder.fit(lX, ly)
+        return self
+
+    def transform(self, lX):
+        langs = sorted(lX.keys())
+        lZparts = {lang: None for lang in langs}
+
+        for embedder in self.embedders:
+            lZ = embedder.transform(lX)
+            for lang in langs:
+                Z = lZ[lang]
+                if lZparts[lang] is None:
+                    lZparts[lang] = Z
+                else:
+                    lZparts[lang] += Z
+        n_embedders = len(self.embedders)
+        return {lang: lZparts[lang]/n_embedders for lang in langs}
+
+    def fit_transform(self, lX, ly):
+        return self.fit(lX, ly).transform(lX)
+
+
+class FeatureSet2Posteriors:
+    def __init__(self, embedder, l2=True, n_jobs=-1):
+        self.embedder = embedder
+        self.l2 = l2
+        self.n_jobs = n_jobs
+        self.prob_classifier = MetaClassifier(
+            SVC(kernel='rbf', gamma='auto', probability=True, cache_size=1000, random_state=1), n_jobs=n_jobs)
+
+    def fit(self, lX, ly):
+        lZ = self.embedder.fit_transform(lX, ly)
+        self.prob_classifier.fit(lZ, ly)
+        return self
+
+    def transform(self, lX):
+        lP = self.predict_proba(lX)
+        lP = _normalize(lP, self.l2)
+        return lP
+
+    def fit_transform(self, lX, ly):
+        return self.fit(lX, ly).transform(lX)
+
+    def predict(self, lX):
+        lZ = self.embedder.transform(lX)
+        return self.prob_classifier.predict(lZ)
+
+    def predict_proba(self, lX):
+        lZ = self.embedder.transform(lX)
+        return self.prob_classifier.predict_proba(lZ)
+
+
+class Funnelling:
+    def __init__(self, first_tier: DocEmbedderList, 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.n_jobs = n_jobs
+
+    def fit(self, lX, ly):
+        print('## Fitting first-tier learners!')
+        lZ = self.first_tier.fit_transform(lX, ly)
+        print('## Fitting meta-learner!')
+        self.meta.fit(lZ, ly)
+
+    def predict(self, lX):
+        lZ = self.first_tier.transform(lX)
+        ly = self.meta.predict(lZ)
+        return ly

refactor/main.py

@@ -1,8 +1,9 @@
 from argparse import ArgumentParser
-from util.embeddings_manager import MuseLoader
-from view_generators import RecurrentGen, BertGen
+from funnelling import *
+from view_generators import *
 from data.dataset_builder import MultilingualDataset
 from util.common import MultilingualIndex
+from util.evaluation import evaluate
 from time import time
 
 
@@ -25,21 +26,38 @@ def main(args):
     lMuse = MuseLoader(langs=sorted(lX.keys()), cache=EMBEDDINGS_PATH)
     multilingualIndex.index(lX, ly, lXte, lyte, l_pretrained_vocabulary=lMuse.vocabulary())
 
-    # gFun = VanillaFunGen(base_learner=get_learner(calibrate=True), n_jobs=N_JOBS)
-    # gFun = MuseGen(muse_dir='/home/andreapdr/funneling_pdr/embeddings', n_jobs=N_JOBS)
-    # gFun = WordClassGen(n_jobs=N_JOBS)
-    # gFun = RecurrentGen(multilingualIndex, pretrained_embeddings=lMuse, wce=False, batch_size=256,
-    #                     nepochs=50, gpus=args.gpus, n_jobs=N_JOBS)
-    gFun = BertGen(multilingualIndex, batch_size=4, nepochs=1, gpus=args.gpus, n_jobs=N_JOBS)
+    # 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)
 
+    docEmbedders = DocEmbedderList([museEmbedder, wceEmbedder])
+
+    gfun = Funnelling(first_tier=docEmbedders)
+
+    # Training ---------------------------------------
+    print('\n[Training Generalized Funnelling]')
     time_init = time()
-    gFun.fit(lX, ly)
+    time_tr = time()
+    gfun.fit(lX, ly)
+    time_tr = round(time() - time_tr, 3)
+    print(f'Training completed in {time_tr} seconds!')
 
-    # print('Projecting...')
-    # y_ = gFun.transform(lX)
+    # Testing ----------------------------------------
+    print('\n[Testing Generalized Funnelling]')
+    time_te = time()
+    ly_ = gfun.predict(lXte)
 
-    train_time = round(time() - time_init, 3)
-    exit(f'Executed! Training time: {train_time}!')
+    l_eval = evaluate(ly_true=ly, ly_pred=ly_)
+    print(l_eval)
+
+    time_te = round(time() - time_te, 3)
+    print(f'Testing completed in {time_te} seconds!')
+
+    overall_time = round(time() - time_init, 3)
+    exit(f'\nExecuted in: {overall_time } seconds!')
 
 
 if __name__ == '__main__':

refactor/models/learners.py

@@ -5,6 +5,7 @@ from sklearn.multiclass import OneVsRestClassifier
 from sklearn.model_selection import GridSearchCV
 from sklearn.svm import SVC
 from joblib import Parallel, delayed
+from util.standardizer import StandardizeTransformer
 
 
 def get_learner(calibrate=False, kernel='linear', C=1):
@@ -156,7 +157,6 @@ class MonolingualClassifier:
             self.model = GridSearchCV(self.model, param_grid=self.parameters, refit=True, cv=5, n_jobs=self.n_jobs,
                                       error_score=0, verbose=10)
 
-        # print(f'fitting: {self.model} on matrices of shape X={X.shape} Y={y.shape}')
         print(f'fitting: Mono-lingual Classifier on matrices of shape X={X.shape} Y={y.shape}')
         self.model.fit(X, y)
         if isinstance(self.model, GridSearchCV):
@@ -183,3 +183,40 @@ class MonolingualClassifier:
 
     def best_params(self):
         return self.best_params_
+
+
+class MetaClassifier:
+
+    def __init__(self, meta_learner, meta_parameters=None, n_jobs=-1, standardize_range=None):
+        self.n_jobs = n_jobs
+        self.model = MonolingualClassifier(base_learner=meta_learner, parameters=meta_parameters, n_jobs=n_jobs)
+        self.standardize_range = standardize_range
+
+    def fit(self, lZ, ly):
+        tinit = time.time()
+        Z, y = self.stack(lZ, ly)
+
+        self.standardizer = StandardizeTransformer(range=self.standardize_range)
+        Z = self.standardizer.fit_transform(Z)
+
+        print('fitting the Z-space of shape={}'.format(Z.shape))
+        self.model.fit(Z, y)
+        self.time = time.time() - tinit
+
+    def stack(self, lZ, ly=None):
+        langs = list(lZ.keys())
+        Z = np.vstack([lZ[lang] for lang in langs])
+        if ly is not None:
+            y = np.vstack([ly[lang] for lang in langs])
+            return Z, y
+        else:
+            return Z
+
+    def predict(self, lZ):
+        lZ = _joblib_transform_multiling(self.standardizer.transform, lZ, n_jobs=self.n_jobs)
+        return _joblib_transform_multiling(self.model.predict, lZ, n_jobs=self.n_jobs)
+
+    def predict_proba(self, lZ):
+        lZ = _joblib_transform_multiling(self.standardizer.transform, lZ, n_jobs=self.n_jobs)
+        return _joblib_transform_multiling(self.model.predict_proba, lZ, n_jobs=self.n_jobs)
+

refactor/models/pl_bert.py

@@ -45,7 +45,6 @@ class BertModel(pl.LightningModule):
     def training_step(self, train_batch, batch_idx):
         X, y, _, batch_langs = train_batch
         X = torch.cat(X).view([X[0].shape[0], len(X)])
-        # y = y.type(torch.cuda.FloatTensor)
         y = y.type(torch.FloatTensor)
         y = y.to('cuda' if self.gpus else 'cpu')
         logits, _ = self.forward(X)
@@ -64,18 +63,6 @@ class BertModel(pl.LightningModule):
         lX, ly = self._reconstruct_dict(predictions, y, batch_langs)
         return {'loss': loss, 'pred': lX, 'target': ly}
 
-    def _reconstruct_dict(self, predictions, y, batch_langs):
-        reconstructed_x = {lang: [] for lang in set(batch_langs)}
-        reconstructed_y = {lang: [] for lang in set(batch_langs)}
-        for i, pred in enumerate(predictions):
-            reconstructed_x[batch_langs[i]].append(pred)
-            reconstructed_y[batch_langs[i]].append(y[i])
-        for k, v in reconstructed_x.items():
-            reconstructed_x[k] = torch.cat(v).view(-1, predictions.shape[1])
-        for k, v in reconstructed_y.items():
-            reconstructed_y[k] = torch.cat(v).view(-1, predictions.shape[1])
-        return reconstructed_x, reconstructed_y
-
     def training_epoch_end(self, outputs):
         langs = []
         for output in outputs:
@@ -114,7 +101,6 @@ class BertModel(pl.LightningModule):
     def validation_step(self, val_batch, batch_idx):
         X, y, _, batch_langs = val_batch
         X = torch.cat(X).view([X[0].shape[0], len(X)])
-        # y = y.type(torch.cuda.FloatTensor)
         y = y.type(torch.FloatTensor)
         y = y.to('cuda' if self.gpus else 'cpu')
         logits, _ = self.forward(X)
@@ -134,7 +120,6 @@ class BertModel(pl.LightningModule):
     def test_step(self, test_batch, batch_idx):
         X, y, _, batch_langs = test_batch
         X = torch.cat(X).view([X[0].shape[0], len(X)])
-        # y = y.type(torch.cuda.FloatTensor)
         y = y.type(torch.FloatTensor)
         y = y.to('cuda' if self.gpus else 'cpu')
         logits, _ = self.forward(X)
@@ -164,3 +149,16 @@ class BertModel(pl.LightningModule):
         optimizer = AdamW(optimizer_grouped_parameters, lr=lr)
         scheduler = StepLR(optimizer, step_size=25, gamma=0.1)
         return [optimizer], [scheduler]
+
+    @staticmethod
+    def _reconstruct_dict(predictions, y, batch_langs):
+        reconstructed_x = {lang: [] for lang in set(batch_langs)}
+        reconstructed_y = {lang: [] for lang in set(batch_langs)}
+        for i, pred in enumerate(predictions):
+            reconstructed_x[batch_langs[i]].append(pred)
+            reconstructed_y[batch_langs[i]].append(y[i])
+        for k, v in reconstructed_x.items():
+            reconstructed_x[k] = torch.cat(v).view(-1, predictions.shape[1])
+        for k, v in reconstructed_y.items():
+            reconstructed_y[k] = torch.cat(v).view(-1, predictions.shape[1])
+        return reconstructed_x, reconstructed_y

refactor/models/pl_gru.py

@@ -164,15 +164,6 @@ class RecurrentModel(pl.LightningModule):
         re_lX = self._reconstruct_dict(predictions, ly)
         return {'loss': loss, 'pred': re_lX, 'target': ly}
 
-    def _reconstruct_dict(self, X, ly):
-        reconstructed = {}
-        _start = 0
-        for lang in sorted(ly.keys()):
-            lang_batchsize = len(ly[lang])
-            reconstructed[lang] = X[_start:_start+lang_batchsize]
-            _start += lang_batchsize
-        return reconstructed
-    
     def training_epoch_end(self, outputs):
         # outputs is a of n dicts of m elements, where n is equal to the number of epoch steps and m is batchsize.
         # here we save epoch level metric values and compute them specifically for each language
@@ -265,3 +256,13 @@ class RecurrentModel(pl.LightningModule):
         optimizer = AdamW(self.parameters(), lr=1e-3)
         scheduler = StepLR(optimizer, step_size=25, gamma=0.5)
         return [optimizer], [scheduler]
+
+    @staticmethod
+    def _reconstruct_dict(X, ly):
+        reconstructed = {}
+        _start = 0
+        for lang in sorted(ly.keys()):
+            lang_batchsize = len(ly[lang])
+            reconstructed[lang] = X[_start:_start+lang_batchsize]
+            _start += lang_batchsize
+        return reconstructed

refactor/util/common.py

@@ -311,8 +311,8 @@ def index(data, vocab, known_words, analyzer, unk_index, out_of_vocabulary):
     unk_count = 0
     knw_count = 0
     out_count = 0
-    pbar = tqdm(data, desc=f'indexing')
-    for text in pbar:
+    # pbar = tqdm(data, desc=f'indexing')
+    for text in data:
         words = analyzer(text)
         index = []
         for word in words:

refactor/util/standardizer.py

@@ -0,0 +1,36 @@
+import numpy as np
+
+
+class StandardizeTransformer:
+    def __init__(self, axis=0, range=None):
+        """
+
+        :param axis:
+        :param range:
+        """
+        assert range is None or isinstance(range, slice), 'wrong format for range, should either be None or a slice'
+        self.axis = axis
+        self.yetfit = False
+        self.range = range
+
+    def fit(self, X):
+        print('Applying z-score standardization...')
+        std=np.std(X, axis=self.axis, ddof=1)
+        self.std = np.clip(std, 1e-5, None)
+        self.mean = np.mean(X, axis=self.axis)
+        if self.range is not None:
+            ones = np.ones_like(self.std)
+            zeros = np.zeros_like(self.mean)
+            ones[self.range] = self.std[self.range]
+            zeros[self.range] = self.mean[self.range]
+            self.std = ones
+            self.mean = zeros
+        self.yetfit=True
+        return self
+
+    def transform(self, X):
+        if not self.yetfit: 'transform called before fit'
+        return (X - self.mean) / self.std
+
+    def fit_transform(self, X):
+        return self.fit(X).transform(X)

refactor/view_generators.py

@@ -55,6 +55,7 @@ class VanillaFunGen(ViewGen):
         self.vectorizer = TfidfVectorizerMultilingual(sublinear_tf=True, use_idf=True)
 
     def fit(self, lX, lY):
+        print('# Fitting VanillaFunGen...')
         lX = self.vectorizer.fit_transform(lX)
         self.doc_projector.fit(lX, lY)
         return self
@@ -84,6 +85,7 @@ class MuseGen(ViewGen):
         self.vectorizer = TfidfVectorizerMultilingual(sublinear_tf=True, use_idf=True)
 
     def fit(self, lX, ly):
+        print('# Fitting MuseGen...')
         self.vectorizer.fit(lX)
         self.langs = sorted(lX.keys())
         self.lMuse = MuseLoader(langs=self.langs, cache=self.muse_dir)
@@ -105,7 +107,6 @@ class MuseGen(ViewGen):
 
 
 class WordClassGen(ViewGen):
-
     def __init__(self, n_jobs=-1):
         """
         generates document representation via Word-Class-Embeddings.
@@ -119,6 +120,7 @@ class WordClassGen(ViewGen):
         self.vectorizer = TfidfVectorizerMultilingual(sublinear_tf=True, use_idf=True)
 
     def fit(self, lX, ly):
+        print('# Fitting WordClassGen...')
         lX = self.vectorizer.fit_transform(lX)
         self.langs = sorted(lX.keys())
         wce = Parallel(n_jobs=self.n_jobs)(
@@ -171,7 +173,7 @@ class RecurrentGen(ViewGen):
         self.multilingualIndex.train_val_split(val_prop=0.2, max_val=2000, seed=1)
         self.multilingualIndex.embedding_matrices(self.pretrained, supervised=self.wce)
         self.model = self._init_model()
-        self.logger = TensorBoardLogger(save_dir='tb_logs', name='rnn_dev', default_hp_metric=False)
+        self.logger = TensorBoardLogger(save_dir='tb_logs', name='rnn', default_hp_metric=False)
         # self.logger = CSVLogger(save_dir='csv_logs', name='rnn_dev')
 
     def _init_model(self):
@@ -205,6 +207,7 @@ class RecurrentGen(ViewGen):
         :param ly:
         :return:
         """
+        print('# Fitting RecurrentGen...')
         recurrentDataModule = RecurrentDataModule(self.multilingualIndex, batchsize=self.batch_size)
         trainer = Trainer(gradient_clip_val=1e-1, gpus=self.gpus, logger=self.logger, max_epochs=self.nepochs,
                           checkpoint_callback=False)
@@ -241,7 +244,6 @@ class RecurrentGen(ViewGen):
 
 
 class BertGen(ViewGen):
-
     def __init__(self, multilingualIndex, batch_size=128, nepochs=50, gpus=0, n_jobs=-1, stored_path=None):
         super().__init__()
         self.multilingualIndex = multilingualIndex
@@ -251,13 +253,14 @@ class BertGen(ViewGen):
         self.n_jobs = n_jobs
         self.stored_path = stored_path
         self.model = self._init_model()
-        self.logger = TensorBoardLogger(save_dir='tb_logs', name='bert_dev', default_hp_metric=False)
+        self.logger = TensorBoardLogger(save_dir='tb_logs', name='bert', default_hp_metric=False)
 
     def _init_model(self):
         output_size = self.multilingualIndex.get_target_dim()
         return BertModel(output_size=output_size, stored_path=self.stored_path, gpus=self.gpus)
 
     def fit(self, lX, ly):
+        print('# Fitting BertGen...')
         self.multilingualIndex.train_val_split(val_prop=0.2, max_val=2000, seed=1)
         bertDataModule = BertDataModule(self.multilingualIndex, batchsize=self.batch_size, max_len=512)
         trainer = Trainer(gradient_clip_val=1e-1, max_epochs=self.nepochs, gpus=self.gpus,
@@ -272,11 +275,14 @@ class BertGen(ViewGen):
         self.model.to('cuda' if self.gpus else 'cpu')
         self.model.eval()
         time_init = time()
-        l_emebds = self.model.encode(data)
-        pass
+        l_emebds = self.model.encode(data)  # TODO
+        transform_time = round(time() - time_init, 3)
+        print(f'Executed! Transform took: {transform_time}')
+        exit('BERT VIEWGEN TRANSFORM NOT IMPLEMENTED!')
+        return l_emebds
 
     def fit_transform(self, lX, ly):
         # we can assume that we have already indexed data for transform() since we are first calling fit()
-        pass
+        return self.fit(lX, ly).transform(lX)