Merge remote-tracking branch 'origin/master'

EP/Cora_node_classification/random_nodes_for_node_classification.py

+"""
+@requirements: cora.py
+@info: Will be imported from node_classification.
+"""
+
+
 import cora
 import pickle as pkl
 import random
@@ -21,7 +27,7 @@ def get_random_num_nodes(set_elm, num, seed):
     random.seed(seed)
     return set(random.sample(set_elm, num))
 
-def get_num_random_nodes_for_all_classes_read(path = "/home/utaemon/SP/graph.pkl", num = 20, seed = 1):
+def get_num_random_nodes_for_all_classes_read(path = "graph.pkl", num = 20, seed = 1):
     """get specific number of nodes per class, same number for all classes"""
     cora_dict = dict_of_node_classes_read(path)
     sampled_random_id_set = set()
@@ -29,17 +35,3 @@ def get_num_random_nodes_for_all_classes_read(path = "/home/utaemon/SP/graph.pkl
         for id in get_random_num_nodes(cora_dict[key], num, seed):
             sampled_random_id_set.add(id)
     return sampled_random_id_set
-
-
-#unused
-'''
-def get_num_of_random_nodes(path = "/home/utaemon/SP/graph.pkl", seed=0, num = 3):
-    """Get random nodes."""
-    random.seed(seed)
-    cora_nodes = set(read_graph(path).nodes)
-    return set(random.sample(cora_nodes, num))
-
-def pickle_output(method = get_num_random_nodes_for_all_classes_read(), output = "random_nodes.pkl"):
-    with open(output, "wb") as file:
-        pkl.dump(method, file)
-'''
\ No newline at end of file

EP/node_classification_1000.txt

-C=0.1
-
-max
-0.652
-avg
-0.5900960000000008
-
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-
-
-C Bestimmung
-/usr/bin/python3 /home/utaemon/PycharmProjects/Softwareprojekt/node_classification.py
-Iteration/Random Seed: 0
-C = 0.01
-0.558
-C = 0.1
-0.564
-C = 0.5
-0.564
-C = 1.0
-0.561
-C = 5.0
-0.542
-C = 10.0
-0.544
-Iteration/Random Seed: 1
-C = 0.01
-0.642
-C = 0.1
-0.635
-C = 0.5
-0.628
-C = 1.0
-0.619
-C = 5.0
-0.611
-C = 10.0
-0.61
-Iteration/Random Seed: 2
-C = 0.01
-0.549
-C = 0.1
-0.57
-C = 0.5
-0.553
-C = 1.0
-0.548
-C = 5.0
-0.539
-C = 10.0
-0.538
-Iteration/Random Seed: 3
-C = 0.01
-0.629
-C = 0.1
-0.624
-C = 0.5
-0.608
-C = 1.0
-0.603
-C = 5.0
-0.578
-C = 10.0
-0.575
-Iteration/Random Seed: 4
-C = 0.01
-0.62
-C = 0.1
-0.638
-C = 0.5
-0.632
-C = 1.0
-0.632
-C = 5.0
-0.629
-C = 10.0
-0.631
-Iteration/Random Seed: 5
-C = 0.01
-0.595
-C = 0.1
-0.615
-C = 0.5
-0.62
-C = 1.0
-0.622
-C = 5.0
-0.616
-C = 10.0
-0.61
-Iteration/Random Seed: 6
-C = 0.01
-0.607
-C = 0.1
-0.626
-C = 0.5
-0.618
-C = 1.0
-0.61
-C = 5.0
-0.598
-C = 10.0
-0.589
-Iteration/Random Seed: 7
-C = 0.01
-0.596
-C = 0.1
-0.603
-C = 0.5
-0.587
-C = 1.0
-0.581
-C = 5.0
-0.563
-C = 10.0
-0.561
-Iteration/Random Seed: 8
-C = 0.01
-0.602
-C = 0.1
-0.619
-C = 0.5
-0.617
-C = 1.0
-0.611
-C = 5.0
-0.608
-C = 10.0
-0.605
-Iteration/Random Seed: 9
-C = 0.01
-0.58
-C = 0.1
-0.595
-C = 0.5
-0.585
-C = 1.0
-0.577
-C = 5.0
-0.563
-C = 10.0
-0.56
-
-Process finished with exit code 0
-

Senseval_Prep/Senseval3/cl23.mrg

+*x*x*x*x*x*x*x*x*x*x*x*x*x*x*x*x*x*x*x*x*x*x*x*x*x*x*x*x*x*x*x*x*x*x*x*x*x*
+*x*x*x*x*x*x*x*x*x*x*x*x*x*x*x*x*x*x*x*x*x*x*x*x*x*x*x*x*x*x*x*x*x*x*x*x*x*
+*x*                                                                     *x*
+*x*            Copyright (C) 1995 University of Pennsylvania            *x*
+*x*                                                                     *x*
+*x*    The data in this file are part of a preliminary version of the   *x*
+*x*    Penn Treebank Corpus and should not be redistributed.  Any       *x*
+*x*    research using this corpus or based on it should acknowledge     *x*
+*x*    that fact, as well as the preliminary nature of the corpus.      *x*
+*x*                                                                     *x*
+*x*x*x*x*x*x*x*x*x*x*x*x*x*x*x*x*x*x*x*x*x*x*x*x*x*x*x*x*x*x*x*x*x*x*x*x*x*
+*x*x*x*x*x*x*x*x*x*x*x*x*x*x*x*x*x*x*x*x*x*x*x*x*x*x*x*x*x*x*x*x*x*x*x*x*x*
 
 ( (S 
     (NP-SBJ (DT That) )

Senseval_Prep/senseval_preprocessing.py

@@ -29,6 +29,9 @@ def get_stopword_list(stop_path):
         stopli = []
         for line in f:
             stopli.append(line[:-1])
+        stopli.append("n't")
+        stopli.append("'m")
+        stopli.append("whether")
         return stopli
 
 def get_infos(tree_path):