moved dataloader function get_dataset

dataManager/utils.py

@@ -0,0 +1,78 @@
+from os.path import expanduser
+from dataManager.gFunDataset import gFunDataset
+from dataManager.multiNewsDataset import MultiNewsDataset
+from dataManager.amazonDataset import AmazonDataset
+
+
+def get_dataset(dataset_name, args):
+    assert dataset_name in [
+        "multinews",
+        "amazon",
+        "rcv1-2",
+        "glami",
+        "cls",
+    ], "dataset not supported"
+
+    RCV_DATAPATH = expanduser(
+        "~/datasets/rcv1-2/rcv1-2_doclist_trByLang1000_teByLang1000_processed_run0.pickle"
+    )
+    JRC_DATAPATH = expanduser(
+        "~/datasets/jrc/jrc_doclist_1958-2005vs2006_all_top300_noparallel_processed_run0.pickle"
+    )
+    CLS_DATAPATH = expanduser("~/datasets/cls-acl10-processed/cls-acl10-processed.pkl")
+
+    MULTINEWS_DATAPATH = expanduser("~/datasets/MultiNews/20110730/")
+
+    GLAMI_DATAPATH = expanduser("~/datasets/GLAMI-1M-dataset")
+
+    if dataset_name == "multinews":
+        # TODO: convert to gFunDataset
+        raise NotImplementedError
+        dataset = MultiNewsDataset(
+            expanduser(MULTINEWS_DATAPATH),
+            excluded_langs=["ar", "pe", "pl", "tr", "ua"],
+        )
+    elif dataset_name == "amazon":
+        # TODO: convert to gFunDataset
+        raise NotImplementedError
+        dataset = AmazonDataset(
+            domains=args.domains,
+            nrows=args.nrows,
+            min_count=args.min_count,
+            max_labels=args.max_labels,
+        )
+    elif dataset_name == "jrc":
+        dataset = gFunDataset(
+            dataset_dir=JRC_DATAPATH,
+            is_textual=True,
+            is_visual=False,
+            is_multilabel=True,
+            nrows=args.nrows,
+        )
+    elif dataset_name == "rcv1-2":
+        dataset = gFunDataset(
+            dataset_dir=RCV_DATAPATH,
+            is_textual=True,
+            is_visual=False,
+            is_multilabel=True,
+            nrows=args.nrows,
+        )
+    elif dataset_name == "glami":
+        dataset = gFunDataset(
+            dataset_dir=GLAMI_DATAPATH,
+            is_textual=True,
+            is_visual=True,
+            is_multilabel=False,
+            nrows=args.nrows,
+        )
+    elif dataset_name == "cls":
+        dataset = gFunDataset(
+            dataset_dir=CLS_DATAPATH,
+            is_textual=True,
+            is_visual=False,
+            is_multilabel=False,
+            nrows=args.nrows,
+        )
+    else:
+        raise NotImplementedError
+    return dataset

evaluation/evaluate.py

@@ -5,10 +5,15 @@ from evaluation.metrics import *
 
 def evaluation_metrics(y, y_):
     if len(y.shape) == len(y_.shape) == 1 and len(np.unique(y)) > 2:  # single-label
-        raise NotImplementedError()  # return f1_score(y,y_,average='macro'), f1_score(y,y_,average='micro')
+        raise NotImplementedError()
-    else:  # the metrics I implemented assume multiclass multilabel classification as binary classifiers
+    else:
-        return macroF1(y, y_), microF1(y, y_), macroK(y, y_), microK(y, y_)
+        return (
-        # return macroF1(y, y_), microF1(y, y_), macroK(y, y_), macroAcc(y, y_)
+            macroF1(y, y_),
+            microF1(y, y_),
+            macroK(y, y_),
+            microK(y, y_),
+            # macroAcc(y, y_),
+        )
 
 
 def evaluate(ly_true, ly_pred, metrics=evaluation_metrics, n_jobs=-1):

gfun/generalizedFunnelling.py

@@ -334,7 +334,7 @@ class GeneralizedFunnelling:
                 pickle.dump(self.metaclassifier, f)
         return
 
-    def save_first_tier_learners(self, model_id):
+    def save_first_tier_learners(self):
         for vgf in self.first_tier_learners:
             vgf.save_vgf(model_id=self._model_id)
         return self

main.py

@@ -1,13 +1,7 @@
-import pickle
 from argparse import ArgumentParser
-from os.path import expanduser
 from time import time
 
-from dataManager.amazonDataset import AmazonDataset
+from dataManager.utils import get_dataset
-from dataManager.multilingualDataset import MultilingualDataset
-from dataManager.multiNewsDataset import MultiNewsDataset
-from dataManager.glamiDataset import GlamiDataset
-from dataManager.gFunDataset import gFunDataset
 from evaluation.evaluate import evaluate, log_eval
 from gfun.generalizedFunnelling import GeneralizedFunnelling
 
@@ -25,85 +19,10 @@ TODO:
 """
 
 
-def get_dataset(datasetname, args):
-    assert datasetname in [
-        "multinews",
-        "amazon",
-        "rcv1-2",
-        "glami",
-        "cls",
-    ], "dataset not supported"
-
-    RCV_DATAPATH = expanduser(
-        "~/datasets/rcv1-2/rcv1-2_doclist_trByLang1000_teByLang1000_processed_run0.pickle"
-    )
-    JRC_DATAPATH = expanduser(
-        "~/datasets/jrc/jrc_doclist_1958-2005vs2006_all_top300_noparallel_processed_run0.pickle"
-    )
-    CLS_DATAPATH = expanduser("~/datasets/cls-acl10-processed/cls-acl10-processed.pkl")
-
-    MULTINEWS_DATAPATH = expanduser("~/datasets/MultiNews/20110730/")
-
-    GLAMI_DATAPATH = expanduser("~/datasets/GLAMI-1M-dataset")
-
-    if datasetname == "multinews":
-        # TODO: convert to gFunDataset
-        raise NotImplementedError
-        dataset = MultiNewsDataset(
-            expanduser(MULTINEWS_DATAPATH),
-            excluded_langs=["ar", "pe", "pl", "tr", "ua"],
-        )
-    elif datasetname == "amazon":
-        # TODO: convert to gFunDataset
-        raise NotImplementedError
-        dataset = AmazonDataset(
-            domains=args.domains,
-            nrows=args.nrows,
-            min_count=args.min_count,
-            max_labels=args.max_labels,
-        )
-    elif datasetname == "rcv1-2":
-        dataset = gFunDataset(
-            dataset_dir=RCV_DATAPATH,
-            is_textual=True,
-            is_visual=False,
-            is_multilabel=True,
-            nrows=args.nrows,
-        )
-    elif datasetname == "glami":
-        dataset = gFunDataset(
-            dataset_dir=GLAMI_DATAPATH,
-            is_textual=True,
-            is_visual=True,
-            is_multilabel=False,
-            nrows=args.nrows,
-        )
-    elif datasetname == "cls":
-        dataset = gFunDataset(
-            dataset_dir=CLS_DATAPATH,
-            is_textual=True,
-            is_visual=False,
-            is_multilabel=False,
-            nrows=args.nrows,
-        )
-    else:
-        raise NotImplementedError
-    return dataset
-
-
 def main(args):
     dataset = get_dataset(args.dataset, args)
-    if (
-        isinstance(dataset, MultilingualDataset)
-        or isinstance(dataset, MultiNewsDataset)
-        or isinstance(dataset, GlamiDataset)
-        or isinstance(dataset, gFunDataset)
-    ):
     lX, lY = dataset.training()
     lX_te, lY_te = dataset.test()
-    else:
-        lX = dataset.dX
-        lY = dataset.dY
 
     tinit = time()
 
@@ -140,7 +59,7 @@ def main(args):
         max_length=args.max_length,
         patience=args.patience,
         evaluate_step=args.evaluate_step,
-        device="cuda",
+        device=args.device,
         # Visual Transformer VGF params --------------
         visual_transformer=args.visual_transformer,
         visual_transformer_name=args.visual_transformer_name,
@@ -186,6 +105,7 @@ if __name__ == "__main__":
     parser.add_argument("-l", "--load_trained", type=str, default=None)
     parser.add_argument("--meta", action="store_true")
     parser.add_argument("--nosave", action="store_true")
+    parser.add_argument("--device", type=str, default="cuda")
     # Dataset parameters -------------------
     parser.add_argument("-d", "--dataset", type=str, default="rcv1-2")
     parser.add_argument("--domains", type=str, default="all")