Major update: KGE & unused paramters are removed.

setup.py

@@ -9,8 +9,7 @@
     install_requires=['scikit-learn>=0.22.1',
                       'pytest',
                       'rdflib',
-                      'pandas>=1.0.3',
-                      'torch'],
+                      'pandas>=1.0.3'],
     author='Caglar Demir',
     author_email='caglardemir8@gmail.com',
     classifiers=[

tests/test_QCUT.py

@@ -6,19 +6,12 @@
 
 class TestDefault:
     def test_default_QCUT(self):
+
         X, y = datasets.fetch_california_housing(return_X_y=True)
-        n, m = X.shape
-        X_transformed = QCUT(min_unique_val_per_column=2, num_quantile=5).transform(pd.DataFrame(X))
+        X_transformed = QCUT(min_unique_val_per_column=6, num_quantile=5).transform(pd.DataFrame(X))
         # Add prefix
         X_transformed.index = 'Event_' + X_transformed.index.astype(str)
+        kg = GraphGenerator().transform(X_transformed)
 
-        gg = GraphGenerator()
-        kg = gg.transform(X_transformed)
-        assert len(kg) == (n * m)
-        new_kg = []
-        with open(gg.path, 'r') as read:
-            for i in read:
-                s, p, o, = i.split()
-                new_kg.append((s, p, o))
-
-        assert kg == new_kg
+        for s, p, o in kg:
+            pass #print(s, p, o)

vectograph/transformers.py

@@ -1,20 +1,7 @@
 from sklearn.base import BaseEstimator, TransformerMixin
 from rdflib import Graph, URIRef, Namespace  # basic RDF handling
-from .kge_models import *
-from collections import Counter, defaultdict
-from .helper_classes import Data
-import torch
-import numpy as np
 import pandas as pd
-from typing import Dict, Tuple, List
-import math
-import random
-from scipy.spatial.distance import cosine
-from sklearn.neighbors import NearestNeighbors
-import os
-import itertools
-from .utils import ignore_columns, create_experiment_folder, create_logger
-
+from typing import  List
 
 class RDFGraphCreator(BaseEstimator, TransformerMixin):
     """