small fixes

data_generation.py

@@ -111,6 +111,16 @@ def synthetic_cjs():
                            np.concatenate((np.random.normal(4, 1, (100, 1)), np.random.normal(5, 1, (100, 1))),
                                           axis=1)), axis=0)
 
+def blobs():
+    # 2d
+    n = 4000
+    l = int(n/2)
+    blob1 = np.random.normal(0, 1, (l, 2)) - np.concatenate((np.ones((l, 1)), np.zeros((l, 1))), axis=1)
+
+    blob2 = np.random.normal(0, 1, (l, 2)) + np.concatenate((np.ones((l, 1)), np.zeros((l, 1))), axis=1)
+
+    return np.concatenate((blob1, blob2), axis=0)
+
 
 def append_irrelevant_features(file, n):
     if n == 0:
@@ -127,28 +137,40 @@ def generate():
     # -------generating dataset
     # data = synthetic_cube_in_cube(rows, rel_features, irrel_features, 'l')
     # data__ = synthetic_cjs()
+    data = correlated_data(rows, rel_features + irrel_features, 1, func1)
     #
     # # add zeroes as default class
-    # data = np.concatenate((data, np.zeros((rows, 1))), axis=1)
+    data = np.concatenate((data, np.zeros((rows, 1))), axis=1)
     # -------appending irrelevant features to existing dataset
-    data = append_irrelevant_features(source, irrel_features)
+    # data = append_irrelevant_features(source, irrel_features)
     # storing to disk
     pd.DataFrame(data).to_csv(file, sep=';', header=False, index=False, float_format='%.2f')
 
 
 if __name__ == '__main__':
-    for j in [3,4,5,10]:
-        for i in [1,2,3,4,5,10]:
-        #     file = 'synthetic_cases/synthetic_cube_in_cube_10.csv'
-            file = 'synthetic_cases/synthetic_cube_in_cube_' + str(j) + '_' + str(i) + '.csv'
-            source = 'synthetic_cases/synthetic_cube_in_cube_' + str(j) + '.csv'
+    # for j in [3,4,5,10]:
+    #     for i in [1,2,3,4,5,10]:
+    #     #     file = 'synthetic_cases/synthetic_cube_in_cube_10.csv'
+    #         file = 'synthetic_cases/synthetic_cube_in_cube_' + str(j) + '_' + str(i) + '.csv'
+    #         source = 'synthetic_cases/synthetic_cube_in_cube_' + str(j) + '.csv'
+    #
+    #         if os.path.isfile(file):
+    #             raise ValueError(file + " already exists!")
+    #
+    #         # parameters
+    #         rows = 20000
+    #         rel_features = 10
+    #         irrel_features = i
+    #
+    #         generate()
+    file = 'synthetic_cases/uds_test_4.csv'
 
-            if os.path.isfile(file):
-                raise ValueError(file + " already exists!")
+    if os.path.isfile(file):
+        raise ValueError(file + " already exists!")
 
-            # parameters
-            rows = 20000
-            rel_features = 10
-            irrel_features = i
+    # parameters
+    rows = 4000
+    rel_features = 2
+    irrel_features = 2
 
-            generate()
+    generate()

experiments_logging.py

@@ -65,5 +65,5 @@ def write_cut_file(name, disc_intervals):
 if __name__ == '__main__':
     # rows = 20000
     # data = np.concatenate((synthetic_cube_in_cube(rows, 2, 0), np.zeros((rows, 1))), axis=1)
-    data = pd.read_csv("synthetic_cases/synthetic_cube_in_cube_5.csv", delimiter=";", header=None, na_values='?')
+    data = pd.read_csv("synthetic_cases/synthetic_cube_in_sparse_cube_3_0.csv", delimiter=";", header=None, na_values='?')
     plot_data_3d(data)

main.py

@@ -85,6 +85,9 @@ def compute_distances(bin_map, curr, data, dim_maxes,
         subspace = sm.het_beam_search(data, curr, k, beam_width, delta, cor_measure)
     else:
         raise ValueError("there is no such method!")
+
+    # if len(subspace) == 0:
+
     # todo the rest of the methods
     data = data.copy().loc[:, subspace]
     dim_maxes = dim_maxes[subspace]

subspace_mining.py

@@ -74,6 +74,9 @@ def __init__(self, subspace, score):
     def __lt__(self, other):
         return self.score < other.score
 
+    def __repr__(self):
+        return '(' + str(self.subspace) + ', ' + str(self.score) + ')'
+
 
 def best_first(data, curr, k, cor_measure):
     dims = set(data.columns.tolist())

uds.py

@@ -52,13 +52,12 @@ def dim_optimal_disc(curr, prev, I, data):
             min_cost = None
             arg_min = None
             for j in range(l - 1, i):
-                temp_cost = (support[i] - support[j]) / support[i] * f[i][j + 1] + support[j] / support[i] * val[j][
-                    l - 1]
+                temp_cost = (support[i] - support[j]) / support[i] * f[i][j + 1] \
+                            + support[j] / support[i] * val[j][l - 1]
                 if not min_cost or temp_cost < min_cost:
                     min_cost = temp_cost
                     arg_min = j
 
-            # val[i][l]
             val[i].append(min_cost)
             disc = b[arg_min][l - 1].copy()
             disc.append(merged_bins[i][arg_min + 1])
@@ -125,8 +124,11 @@ def compute_uds(data):
         opt_score = None
         for l, score in enumerate(scores):
             temp_I = extend_I(I, discs[l])
-            temp_cost = score / CEs[dim] + entropy(temp_I, len(data)) / (
-                math.log(UDS_BETA, 2) + sum([math.log(e + 1, 2) for e in es]))
+            temp_cost = score / CEs[dim] + (entropy(temp_I, len(data)) / (
+                # todo old
+                math.log(UDS_BETA, 2) + sum([math.log(e + 1, 2) for e in es])))
+                # math.log(l + 1, 2) + sum([math.log(e + 1, 2) for e in es]))
+                #                             if math.log(l + 1, 2) + sum([math.log(e + 1, 2) for e in es]) != 0 else 0)
             if not opt_cost or temp_cost < opt_cost:
                 opt_cost = temp_cost
                 opt_score = score
@@ -148,14 +150,12 @@ def compute_uds(data):
 
 if __name__ == "__main__":
 
-    data = pd.read_csv('synthetic_cases/uds_test.csv', delimiter=';', header=None)
+    data = pd.read_csv('synthetic_cases/synthetic_3d_parity_problem.csv', delimiter=';', usecols=[0,1,2], header=None) #0.0716387590375
     # data = pd.DataFrame(correlated_data(4000, 20, 10, dg.func2))
     # data = pd.DataFrame(generate_correlated_data(1000, 10, 2, func1))
     # data = pd.DataFrame(generate_uncorrelated_data(4000, 20))
     # classLabels = data.pop(len(data.columns) - 1)
     uds = compute_uds(data)
 
     print(uds)
-    # print(es)
-
-
+    # print(es)