Adapt bag of members

libs/models/tsom_plus_som.py

@@ -5,13 +5,13 @@
 
 
 class TSOMPlusSOM:
-    def __init__(self, member_features, index_members_of_group, params_tsom, params_som):
+    def __init__(self, member_features, group_features, params_tsom, params_som):
         self.params_tsom = params_tsom
         self.params_som = params_som
 
         self.params_tsom['X'] = member_features
-        self.index_members_of_group = index_members_of_group  # グループ数の確認
-        self.group_num = len(self.index_members_of_group)
+        self.group_features = group_features  # グループ数の確認
+        self.group_num = len(self.group_features)
 
     def fit(self, tsom_epoch_num, kernel_width, som_epoch_num):
         self._fit_1st_TSOM(tsom_epoch_num)
@@ -25,14 +25,23 @@ def _fit_1st_TSOM(self, tsom_epoch_num):
     def _fit_KDE(self, kernel_width):  # 学習した後の潜在空間からKDEで確率分布を作る
         prob_data = np.zeros((self.group_num, self.tsom.K1))  # group数*ノード数
         # グループごとにKDEを適用
-        for i in range(self.group_num):
-            Dist = dist.cdist(self.tsom.Zeta1, self.tsom.Z1[self.index_members_of_group[i], :],
-                              'sqeuclidean')  # KxNの距離行列を計算
-            H = np.exp(-Dist / (2 * kernel_width * kernel_width))  # KxNの学習量行列を計算
-            prob = np.sum(H, axis=1)
-            prob_sum = np.sum(prob)
-            prob = prob / prob_sum
-            prob_data[i, :] = prob
+        if isinstance(self.group_features, np.ndarray) and self.group_features.ndim == 2:
+            # group_featuresがbag of membersで与えられた時の処理
+            distance = dist.cdist(self.tsom.Zeta1, self.tsom.Z1, 'sqeuclidean')  # K1 x num_members
+            H = np.exp(-0.5 * distance / (kernel_width * kernel_width))  # KxN
+            prob_data = self.group_features @ H.T  # num_group x K1
+            prob_data = prob_data / prob_data.sum(axis=1)[:, None]
+        else:
+            # group_featuresがlist of listsもしくはlist of arraysで与えられた時の処理
+            for i in range(self.group_num):
+                Dist = dist.cdist(self.tsom.Zeta1,
+                                  self.tsom.Z1[self.group_features[i], :],
+                                  'sqeuclidean')  # KxNの距離行列を計算
+                H = np.exp(-Dist / (2 * kernel_width * kernel_width))  # KxNの学習量行列を計算
+                prob = np.sum(H, axis=1)
+                prob_sum = np.sum(prob)
+                prob = prob / prob_sum
+                prob_data[i, :] = prob
         self.params_som['X'] = prob_data
         self.params_som['metric'] = "KLdivergence"
 

tests/plus_TSOM/test_plusTSOM.py

@@ -0,0 +1,140 @@
+import unittest
+
+import numpy as np
+
+from libs.models.tsom_plus_som import TSOMPlusSOM
+from tests.plus_TSOM.plus_TSOM_watanabe import TSOMPlusSOMWatanabe
+
+
+class TestTSOMPlusSOM(unittest.TestCase):
+    def create_artficial_data(self,n_samples,n_features,n_groups,n_samples_per_group):
+        x = np.random.normal(0.0,1.0,(n_samples,n_features))
+        if isinstance(n_samples_per_group,int):
+            n_samples_per_group = np.ones(n_groups,int) * n_samples_per_group
+        index_members_of_group = []
+        for n_samples_in_the_group in n_samples_per_group:
+            index_members_of_group.append(np.random.randint(0,n_samples,n_samples_in_the_group))
+        return x, index_members_of_group
+
+    def test_plusTSOM_ishida_vs_test_plusTSOM_watanabe(self):
+        seed = 100
+        np.random.seed(seed)
+        n_samples = 1000
+        n_groups = 10  # group数
+        n_features = 3  # 各メンバーの特徴数
+        n_samples_per_group = np.random.randint(1,30,n_groups)  # 各グループにメンバーに何人いるのか
+        member_features,index_members_of_group = self.create_artficial_data(n_samples,
+                                                                           n_features,
+                                                                           n_groups,
+                                                                           n_samples_per_group)
+        # 1stTSOMの初期値
+        Z1 = np.random.rand(n_samples, 2) * 2.0 - 1.0
+        Z2 = np.random.rand(n_features, 2) * 2.0 - 1.0
+        init_TSOM = [Z1, Z2]
+        init_SOM = np.random.rand(n_groups, 2) * 2.0 - 1.0
+
+
+        params_tsom = {'latent_dim': [2, 2],
+                       'resolution': [10, 10],
+                       'SIGMA_MAX': [1.0, 1.0],
+                       'SIGMA_MIN': [0.1, 0.1],
+                       'TAU': [50, 50],
+                       'init': init_TSOM}
+        params_som = {'latent_dim': 2,
+                      'resolution': 10,
+                      'sigma_max': 2.0,
+                      'sigma_min': 0.5,
+                      'tau': 50,
+                      'init': init_SOM}
+        tsom_epoch_num = 50
+        som_epoch_num = 50
+        kernel_width = 0.3
+
+        htsom_ishida = TSOMPlusSOM(member_features=member_features,
+                                   group_features=index_members_of_group,
+                                   params_tsom=params_tsom,
+                                   params_som=params_som)
+        htsom_watanabe = TSOMPlusSOMWatanabe(member_features=member_features,
+                                             index_members_of_group=index_members_of_group,
+                                             params_tsom=params_tsom,
+                                             params_som=params_som)
+
+        htsom_ishida.fit(tsom_epoch_num=tsom_epoch_num,
+                         kernel_width=kernel_width,
+                         som_epoch_num=som_epoch_num)
+        htsom_watanabe.fit(tsom_epoch_num=tsom_epoch_num,
+                           kernel_width=kernel_width,
+                           som_epoch_num=som_epoch_num)
+
+        np.testing.assert_allclose(htsom_ishida.tsom.history['y'], htsom_watanabe.tsom.history['y'])
+        np.testing.assert_allclose(htsom_ishida.tsom.history['z1'], htsom_watanabe.tsom.history['z1'])
+        np.testing.assert_allclose(htsom_ishida.tsom.history['z2'], htsom_watanabe.tsom.history['z2'])
+        np.testing.assert_allclose(htsom_ishida.params_som['X'], htsom_watanabe.params_som['X'])
+        np.testing.assert_allclose(htsom_ishida.som.history['y'], htsom_watanabe.som.history['y'])
+        np.testing.assert_allclose(htsom_ishida.som.history['z'], htsom_watanabe.som.history['z'])
+
+    def _transform_list_to_bag(self,list_of_indexes,num_members):
+        bag_of_members = np.empty((0,num_members))
+        for indexes in list_of_indexes:
+            one_hot_vectors = np.eye(num_members)[indexes]
+            one_bag = one_hot_vectors.sum(axis=0)[None,:]
+            bag_of_members=np.append(bag_of_members,one_bag,axis=0)
+        return bag_of_members
+    def test_matching_index_member_as_list_or_bag(self):
+        seed = 100
+        np.random.seed(seed)
+        n_members = 100
+        n_groups = 10  # group数
+        n_features = 3  # 各メンバーの特徴数
+        n_samples_per_group = np.random.randint(1,50,n_groups)  # 各グループにメンバーに何人いるのか
+        member_features,index_members_of_group = self.create_artficial_data(n_members,
+                                                                            n_features,
+                                                                            n_groups,
+                                                                            n_samples_per_group)
+        bag_of_members = self._transform_list_to_bag(index_members_of_group, n_members)
+
+        Z1 = np.random.rand(n_members, 2) * 2.0 - 1.0
+        Z2 = np.random.rand(n_features, 2) * 2.0 - 1.0
+        init_TSOM = [Z1, Z2]
+        init_SOM = np.random.rand(n_groups, 2) * 2.0 - 1.0
+        params_tsom = {'latent_dim': [2, 2],
+                       'resolution': [10, 10],
+                       'SIGMA_MAX': [1.0, 1.0],
+                       'SIGMA_MIN': [0.1, 0.1],
+                       'TAU': [50, 50],
+                       'init': init_TSOM}
+        params_som = {'latent_dim': 2,
+                      'resolution': 10,
+                      'sigma_max': 2.0,
+                      'sigma_min': 0.5,
+                      'tau': 50,
+                      'init': init_SOM}
+        tsom_epoch_num = 50
+        som_epoch_num = 50
+        kernel_width = 0.3
+
+        tsom_plus_som_input_list = TSOMPlusSOM(member_features=member_features,
+                                               group_features=index_members_of_group,
+                                               params_tsom=params_tsom,
+                                               params_som=params_som)
+        tsom_plus_som_input_bag = TSOMPlusSOM(member_features=member_features,
+                                              group_features=bag_of_members,
+                                              params_tsom=params_tsom,
+                                              params_som=params_som)
+
+        tsom_plus_som_input_list.fit(tsom_epoch_num=tsom_epoch_num,
+                         kernel_width=kernel_width,
+                         som_epoch_num=som_epoch_num)
+        tsom_plus_som_input_bag.fit(tsom_epoch_num=tsom_epoch_num,
+                           kernel_width=kernel_width,
+                           som_epoch_num=som_epoch_num)
+
+        np.testing.assert_allclose(tsom_plus_som_input_list.tsom.history['y'], tsom_plus_som_input_bag.tsom.history['y'])
+        np.testing.assert_allclose(tsom_plus_som_input_list.tsom.history['z1'], tsom_plus_som_input_bag.tsom.history['z1'])
+        np.testing.assert_allclose(tsom_plus_som_input_list.tsom.history['z2'], tsom_plus_som_input_bag.tsom.history['z2'])
+        np.testing.assert_allclose(tsom_plus_som_input_list.params_som['X'], tsom_plus_som_input_bag.params_som['X'])
+        np.testing.assert_allclose(tsom_plus_som_input_list.som.history['y'], tsom_plus_som_input_bag.som.history['y'])
+        np.testing.assert_allclose(tsom_plus_som_input_list.som.history['z'], tsom_plus_som_input_bag.som.history['z'])
+
+if __name__ == "__main__":
+    unittest.main()

tutorials/TSOM_plus_SOM/fit_TSOM_plus_SOM.py

@@ -2,7 +2,7 @@
 import numpy as np
 from libs.datasets.artificial.kura_tsom import load_kura_tsom
 import matplotlib.pyplot as plt
-from libs.models.TSOMPlusSOM import TSOMPlusSOM
+from libs.models.tsom_plus_som import TSOMPlusSOM
 from mpl_toolkits.mplot3d import Axes3D
 from libs.visualization.som.Grad_norm import Grad_Norm
 
@@ -27,11 +27,13 @@
     input_data[int(2 * i), :, :] = group1
     input_data[int(2 * i + 1), :, :] = group2
 
-# fig = plt.figure()
-# ax = fig.add_subplot(1, 1, 1, projection="3d")
-# for i in range(group_num):
-#     ax.scatter(input_data[i, :, 0], input_data[i, :, 1], input_data[i, :, 2])
-# plt.show()
+#観測データの描画
+fig = plt.figure()
+ax = fig.add_subplot(1, 1, 1, projection="3d")
+for i in range(group_num):
+    ax.scatter(input_data[i, :, 0], input_data[i, :, 1], input_data[i, :, 2],label="group"+str(i+1))
+    plt.legend(bbox_to_anchor=(1, 1), loc='upper right', borderaxespad=0, fontsize=10)
+plt.show()
 input_data = input_data.reshape(-1, 3)
 
 # グループラベルの作成
@@ -55,7 +57,7 @@
 # +型階層TSOMのclass読み込み
 # group_label以降の変数ははlatent_dim,resolution,sigma_max,sigma_min,tauでSOMとTSOMでまとめている
 tsom_plus_som = TSOMPlusSOM(member_features=input_data,
-                            index_members_of_group=group_label,
+                            group_features=group_label,
                             params_tsom=params_tsom,
                             params_som=params_som)