Update name

README.md

@@ -72,7 +72,7 @@ The code includes the following modules:
 * eval.py -- Calculate performance metrics from results, each being the average of 10 seeds
 * utils.py  
   * GIFPloter() -- Auxiliary tool for online plot
-  * GetIndicator() -- Auxiliary tool for evaluating metric 
+  * CompPerformMetrics() -- Auxiliary tool for evaluating metric 
   * Sampling() -- Sampling in the latent space for generating new data on the learned manifold 
 
 ## Running the code
@@ -103,7 +103,7 @@ python main.py -MultiRun
   python eval.py -M ML-Enc
   python eval.py -M ML-AE
   ```
-The evaluation metrics are available in `./pic/indicators.csv`
+The evaluation metrics are available in `./pic/PerformMetrics.csv`
 
 6. To test the generalization to unseen data
   ```

dataset.py

@@ -113,7 +113,7 @@ def LoadData(data_name='SwissRoll', data_num=1500, seed=0, noise=0.0, device=tor
         train_data = train_data / 2
 
     # Load 7Mnist Dataset
-    if data_name == '7MNIST':
+    if data_name == 'MNIST_7':
 
         train_data = torchvisiondatasets.MNIST(
             '~/data', train=True, download=True,
@@ -138,7 +138,7 @@ def LoadData(data_name='SwissRoll', data_num=1500, seed=0, noise=0.0, device=tor
             train_label = train_label[mask][data_num:data_num*2]
 
     # Load 10Mnist Dataset
-    if data_name == '10MNIST':
+    if data_name == 'MNIST_10':
 
         train_data = torchvisiondatasets.MNIST(
             '~/data', train=True, download=True,

eval.py

@@ -1,7 +1,7 @@
 import csv
 import argparse
 import numpy as np
-from utils import GetIndicator
+from utils import CompPerformMetrics
 
 if __name__ == "__main__":
 
@@ -19,10 +19,10 @@
 
         if args.mode == 'ML-Enc':
             data9 = np.loadtxt(path + '9.txt')
-            indicator = GetIndicator(data=data0, latent=data9, lat=[data8])
+            indicator = CompPerformMetrics(data=data0, latent=data9, lat=[data8])
         if args.mode == 'ML-AE':
             data18 = np.loadtxt(path + '18.txt')
-            indicator = GetIndicator(data=data0, latent=data18, lat=[data8, data11])    
+            indicator = CompPerformMetrics(data=data0, latent=data18, lat=[data8, data11])    
 
         out_seeds.append(np.array(list(indicator.values())))
         print(indicator)
@@ -31,7 +31,7 @@
     out_seeds = out_seeds.mean(axis=0)
 
     # Save metrics results to a csv file
-    outFile = open('./pic/indicators.csv','a+', newline='')
+    outFile = open('./pic/PerformMetrics.csv','a+', newline='')
     writer = csv.writer(outFile, dialect='excel')
 
     names = []

main.py

@@ -164,7 +164,7 @@ def InlinePlot(model, batch_size, datas, labels, path, name, indicator=False, mo
             label_point = np.concatenate((label_point, label.cpu().detach().numpy()), axis=0)
 
     # Plotting a new fig for the current epoch
-    if param['DATASET'] != '10MNIST':
+    if param['DATASET'] != 'MNIST_10':
         gif_ploter.AddNewFig(
             latent_point, 
             label_point,
@@ -176,7 +176,7 @@ def InlinePlot(model, batch_size, datas, labels, path, name, indicator=False, mo
     # Used for metrics evaluation and executed at the completion of the entire training process.
     if indicator:
         latent_index = 2 * len(param['NetworkStructure']) - 3
-        indicator = GetIndicator(
+        indicator = CompPerformMetrics(
                         datas.reshape(datas.shape[0], -1), 
                         torch.tensor(latent_point[latent_index], device=device),
                         dataset = param['DATASET']
@@ -190,7 +190,7 @@ def InlinePlot(model, batch_size, datas, labels, path, name, indicator=False, mo
         np.savetxt(path + '/out/label.txt', label_point)
 
         # Save the metrics to a csv file
-        outFile = open(path + '/indicators.csv','a+', newline='')
+        outFile = open(path + '/PerformMetrics.csv','a+', newline='')
         writer = csv.writer(outFile, dialect='excel')
         names = []
         results = []
@@ -226,7 +226,7 @@ def SetParam():
     parser.add_argument("-N", "--name", default=None, type=str)   # File names where data and figs are stored
     parser.add_argument("-PP", "--ParamPath", default='None', type=str)   # Path for an existing parameter
     parser.add_argument("-M", "--Mode", default='ML-AE', type=str)
-    parser.add_argument("-D", "--DATASET", default='SwissRoll', type=str, choices=['SwissRoll', 'SCurve', '7MNIST', '10MNIST', 'Spheres5500'])
+    parser.add_argument("-D", "--DATASET", default='SwissRoll', type=str, choices=['SwissRoll', 'SCurve', 'MNIST_7', 'MNIST_10', 'Spheres5500'])
     parser.add_argument("-LR", "--LEARNINGRATE", default=1e-3, type=float)
     parser.add_argument("-B", "--BATCHSIZE", default=800, type=int)
     parser.add_argument("-RB", "--RegularB", default=3, type=float)   # Boundary parameters for push-away Loss
@@ -243,10 +243,10 @@ def SetParam():
     parser.add_argument("-MultiRun", "--Train_MultiRun", default=False, action='store_true')
     args = parser.parse_args()
 
-    if args.DATASET == '7MNIST':
-        args.ParamPath = './param/7mnist.json'
-    if args.DATASET == '10MNIST':
-        args.ParamPath = './param/10mnist.json'
+    if args.DATASET == 'MNIST_7':
+        args.ParamPath = './param/mnist_7.json'
+    if args.DATASET == 'MNIST_10':
+        args.ParamPath = './param/mnist_10.json'
     if args.DATASET == 'Spheres5500':
         args.ParamPath = './param/spheres5500.json'
     if args.ParamPath is not 'None':
@@ -362,7 +362,7 @@ def Train_MultiRun():
 
         # Plotting the final results and evaluating the metrics
         InlinePlot(Model, param['BATCHSIZE'], train_data, train_label, path, name='Train', indicator=True, mode=param['Mode'])
-        if param['DATASET'] != '10MNIST':
+        if param['DATASET'] != 'MNIST_10':
             gif_ploter.SaveGIF(path=path)
 
         # Testing the generalizability of the model to out-of-samples

param/10mnist.json

@@ -2,7 +2,7 @@
   "name": null,
   "ParamPath": "None",
   "Mode": "ML-AE",
-  "DATASET": "10MNIST",
+  "DATASET": "MNIST_10",
   "LEARNINGRATE": 0.001,
   "BATCHSIZE": 8000,
   "RegularB": 3,

param/7mnist.json

@@ -2,7 +2,7 @@
   "name": null,
   "ParamPath": "None",
   "Mode": "ML-AE",
-  "DATASET": "7MNIST",
+  "DATASET": "MNIST_10",
   "LEARNINGRATE": 0.001,
   "BATCHSIZE": 8000,
   "RegularB": 2.2,

utils.py

@@ -213,7 +213,7 @@ def Plot_Generation(self, input_data, latent, rec_data, latent_gen, gen_data, la
         plt.close() 
 
 
-def GetIndicator(data, latent, lat=None, dataset='None'):
+def CompPerformMetrics(data, latent, lat=None, dataset='None'):
 
     """
     function used to evaluate metrics