change README and add paths

scripts/node_classification/README.md

+# AUTHORS
+Lyuba Dimitrova, Nadia Arslan, Nicolas Weber, Utaemon Toyota
+
+# PROJECT
+Softwareprojekt WS2018/19
+Betreuerin: Prof. Dr. Anette Frank
+Graph Embedding Propagation
+
+# Cora Node Classification
+To evaluate the trained graph and the embeddings the task of node classification will be executed. First, the data of cora will be imported into a networkX graph, which will be saved in a pickle file to use it for the training of the embeddings with our EP-SP algorithm. Afterwards the trained embeddings will be evaluated with LibLinear L2-Logistic Regression provided from sklearn over a transductive setting with 1000 random nodes for validation, 1000 random nodes for testing and 20 random nodes per class for training. For each iteration where the sets are newly splitted the random seed is set to to the iteration number.
+Graph building is provided on cora.py, the evaluation on node_classification.py.
+
+# Required Data
+- Cora Graph saved in data/cora/graph/
+- Embeddings for node classification data/cora/embeddings/
+
+# Dependencies
+-pickle
+-numpy
+-sklearn	for evaluation
+-random		for getting random test, trainings and validation sets
+-sys
+-argparse
+-heapq		for getting a heatmap from confusion matrix
+-sklearn	for confusion matrix and f1 score
+
+# Running instructions
+python3 nc_experiments.py [-g] [-e] [-s] [-i] [-n]
+        -g / --graph        Path to pickled networkX-graph
+        -e / --embeddings   Path to pickled embeddings
+        -s / --seed         Seed for randomization. If this argument is called, only a node classification for this specific seed will be executed
+        -i / --iterations   Number of iterations of node classification. Counter of iteration is equal to random seed
+        -n / --number       Number of instances per class for training
+        -c / --regularization   Inverse of regularization strength

scripts/preprocessing/cora/README.md

+# AUTHORS
+Lyuba Dimitrova, Nadia Arslan, Nicolas Weber, Utaemon Toyota
+
+# PROJECT
+Softwareprojekt WS2018/19
+Betreuerin: Prof. Dr. Anette Frank
+Graph Embedding Propagation
+
+# Building Cora Graph
+With this skript a networkX graph will be created from the raw data.
+Cause of using numpy for bow-array-representations the data has to be saved in pickle format, and not f.e. json.
+
+# Required Data
+- Cora raw data saved in /data/cora/raw/
+
+# Dependencies
+-networkx	for building the graph
+-numpy		to save one-hot vocabulary vectors
+-pickle		to save data in a pickle file
+
+# Running instructions
+python3 cora.py [-n] [-e] [-o]
+	-n / --nodes	Path to cora file containing nodes
+	-e / --edges	Path to cora file containing edges
+	-o / --output	Path where the graph should be saved

scripts/preprocessing/cora/cora.py

@@ -5,12 +5,10 @@ Arrays are initialized in normal or uniform random format (default = normal).
 
 
 #Usage
-get_graph(path_nodes="/home/utaemon/SP/cora/cora.content", path_edges="/home/utaemon/SP/cora/cora.cites")
+get_graph(path_nodes="/../../data/cora/raw/cora.content", path_edges="/../../data/cora/raw/cora.cites")
 -> return graph with nodes and edges
 To write the graph informations in file:
-def write_graph_to_file(path_nodes="/home/utaemon/SP/cora/cora.content", path_edges="/home/utaemon/SP/cora/cora.cites", path_output_graph = "/home/utaemon/SP/")
-To write the dictionary with initalizing Embeddings in file:
-def write_dict_to_file(rand_type="normal_random", dimension = 128, quantity=1433, path_output_emb = "/home/utaemon/SP/")
+def write_graph_to_file(path_nodes="/../../data/cora/raw/cora.content", path_edges="/../../data/cora/raw/cora.cites", path_output_graph = "/../../data/cora/graph/")
 """
 
 import networkx as nx