chore: add information about sessions

into the mfcc json file

Handle_emg_data.py

@@ -528,12 +528,12 @@ class NN_handler:
                                         4: [],
                                         5: []
                                         }
-        # Should med 4 sessions * (~150, 208) of mfcc samples per person. One DataFrame per subject
+        # Should med 4 sessions * (~150, 208) of mfcc samples per person. One [DataFrame, session_length_list] per subject
-        self.mfcc_samples_per_subject = {1: None,  
+        self.mfcc_samples_per_subject = {1: [],  
-                                         2: None, 
+                                         2: [], 
-                                         3: None,
+                                         3: [],
-                                         4: None,
+                                         4: [],
-                                         5: None
+                                         5: []
                                          }
 
     # GET method for reg_samples_dict
@@ -627,8 +627,8 @@ class NN_handler:
 
     # Takes in all EMG session Dataframe and creates DataFrame of MFCC samples
     # Input: DataFrame(shape[1]=16, EMG data)
-    # Output: DataFrame(merged MFCC data, shape: (n, 13*16))
+    # Output: DataFrame(merged MFCC data, shape: (n, 13*16)), length of session datapoints
-    def make_mfcc_df_from_session_df(self, session_df) -> DataFrame:
+    def make_mfcc_df_from_session_df(self, session_df):
         session_df.rename(columns={0:'timestamp'}, inplace=True)
         samplerate = get_samplerate(session_df)
         attach_func = lambda list_1, list_2: list_1.extend(list_2)
@@ -645,16 +645,19 @@ class NN_handler:
             mfcc_i = DataFrame(mfcc_i).dropna()
             mfcc_i['combined'] = mfcc_i.values.tolist()
             df = result_df.combine(mfcc_i['combined'], attach_func)
+        
+        session_length = (len(result_df.index)) # Add the length of session data points
 
-        return result_df
+        return result_df, session_length
 
     # Merges MFCC data from all sessions and stores the sample data in 
     # the NN_handler's mfcc_samples_per_subject dict
     # Input: None(NN_handler)
-    # Output: None -> stores in NN_handler
+    # Output: None -> stores in NN_handler [samples, session_length_list] for each subject
     def store_mfcc_samples(self) -> None:
         for subject_nr in range(5):
             subj_samples = []
+            session_length_list = []
             for session_nr in range(4):
                 list_of_emg = self.get_emg_list(subject_nr+1, session_nr+1)
                 tot_session_df = self.make_subj_sample(list_of_emg)
@@ -663,11 +666,12 @@ class NN_handler:
                 if tot_session_df.isnull().values.any():
                     print('NaN in: subject', subject_nr+1, 'session:', session_nr+1, 'where? HERE')
                 
-                mfcc_df_i = self.make_mfcc_df_from_session_df(tot_session_df)
+                mfcc_df_i, session_length = self.make_mfcc_df_from_session_df(tot_session_df)
                 subj_samples.append(mfcc_df_i)
+                session_length_list.append(session_length)            
             
             result_df = pd.concat(subj_samples, axis=0, ignore_index=True)
-            self.mfcc_samples_per_subject[subject_nr+1] = result_df
+            self.mfcc_samples_per_subject[subject_nr+1] = [result_df, session_length_list]
 
 
     # Makes MFCC data from reg_samples_per_subject and stores it in a json file
@@ -735,7 +739,9 @@ class NN_handler:
             data = {
                 "mapping": [],
                 "labels": [],
-                "mfcc": []
+                "mfcc": [],
+
+                "session_lengths": []
             }
 
             raw_data_dict = self.get_mfcc_samples_dict()
@@ -746,13 +752,15 @@ class NN_handler:
                 # save subject label in the mapping
                 subject_label = 'Subject ' + str(key)
                 print("\nProcessing: {}".format(subject_label))
-                data["mapping"].append(subject_label)
+                data["mapping"].append(subject_label)       # Subject label
+                data["session_lengths"].append(value[1])   # List[subject][session_length_list]
 
                 # process all samples per subject
-                for i, sample in enumerate(value):
+                for i, sample in enumerate(value[0]):
 
-                    data["labels"].append(key-1)
+                    data["labels"].append(key-1)    # Subject nr
-                    data["mfcc"].append(sample)
+                    data["mfcc"].append(sample[0])  # MFCC sample on same index
+                     
                     print("sample:{} is done".format(i+1))
                     #print(np.array(mfcc_data).shape)
 

Neural_Network_Analysis.py

@@ -13,7 +13,7 @@ DATA_PATH_MFCC = str(Path.cwd()) + "/mfcc_data.json"
 
 # Loads data from the json file and reshapes X_data(samples, 1, 208) and y_data(samples, 1)
 # Input: JSON path
-# Ouput: X(mfcc data), y(labels)
+# Ouput: X(mfcc data), y(labels), session_lengths
 def load_data_from_json(data_path): 
 
     with open(data_path, "r") as fp:
@@ -27,11 +27,13 @@ def load_data_from_json(data_path):
     y = np.array(data["labels"])
     y = y.reshape(y.shape[0], 1)
     #print(y.shape)
+
+    session_lengths = data['session_lengths']
     
 
     print("Data succesfully loaded!")
 
-    return X, y
+    return X, y, session_lengths
 
 # Plots the training history with two subplots. First training and test accuracy, and then 
 # loss with respect to epochs
@@ -62,17 +64,56 @@ def plot_history(history):
 
     plt.show()
 
-# Takes in data and labels, and splits it into train, validation and test sets
+# Takes in data and labels, and splits it into train, validation and test sets by percentage
 # Input: Data, labels, whether to shuffle, % validatiion, % test
 # Ouput: X_train, X_validation, X_test, y_train, y_validation, y_test
 def prepare_datasets_percentsplit(X, y, shuffle_vars:bool, validation_size=0.2, test_size=0.25,):
 
-    # create train, validation and test split
+    # Create train, validation and test split
     X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=test_size, shuffle=shuffle_vars)
     X_train, X_validation, y_train, y_validation = train_test_split(X_train, y_train, test_size=validation_size, shuffle=shuffle_vars)
 
     return X_train, X_validation, X_test, y_train, y_validation, y_test
 
+# Takes in data, labels, and session_lengths and splits it into train and test sets by session_index
+# Input: Data, labels, session_lengths, test_session_index
+# Ouput: X_train, X_test, y_train, y_test
+def prepare_datasets_sessions(X, y, session_lengths, test_session_index=4, nr_subjects=5):
+
+    subject_starting_index = 0
+    X_train = np.empty((1, 1, 208))
+    y_train = np.empty((1, 208))
+    X_test = np.empty((1, 1, 208))
+    y_test = np.empty((1, 208))
+
+    for i in range(nr_subjects):
+        
+        start_test_index = sum(session_lengths[i][:test_session_index])
+        end_test_index = start_test_index + session_lengths[i][test_session_index-1]
+        end_subject_index = sum(session_lengths[i])
+        if start_test_index == subject_starting_index:
+            X_test.append(X[start_test_index:end_test_index])
+            y_test.append(y[start_test_index:end_test_index])
+            X_train.append(X[end_test_index:end_subject_index])
+            y_train.append(y[end_test_index:end_subject_index])
+            
+        elif end_test_index == end_subject_index:
+            X_train.append(X[subject_starting_index:start_test_index])
+            y_train.append(y[subject_starting_index:start_test_index])
+            X_test.append(X[start_test_index:end_test_index])
+            y_test.append(y[start_test_index:end_test_index])
+        else:
+            X_train.append(X[subject_starting_index:start_test_index])
+            y_train.append(y[subject_starting_index:start_test_index])
+            X_test.append(X[start_test_index:end_test_index])
+            y_test.append(y[start_test_index:end_test_index])
+            X_train.append(X[end_test_index:end_subject_index])
+            y_train.append(y[end_test_index:end_subject_index])
+        subject_starting_index = end_subject_index
+
+
+    return X_train, X_test, y_train, y_test
+
 # Creates a RNN_LSTM neural network model
 # Input: input shape, classes of classification
 # Ouput: model:Keras.model
@@ -119,14 +160,14 @@ def train(model, batch_size, epochs, X_train, X_validation, y_train, y_validatio
 if __name__ == "__main__":
 
     # Load data
-    X, y = load_data_from_json(DATA_PATH_MFCC)
+    X, y, session_lengths = load_data_from_json(DATA_PATH_MFCC)
 
     # Get prepared data: train, validation, and test
-    X_train, X_validation, X_test, y_train, y_validation, y_test = prepare_datasets_percentsplit(X, y,
+    (X_train, X_validation, 
-                                                                                                validation_size=0.2, 
+    X_test, y_train, 
-                                                                                                test_size=0.25,  
+    y_validation, 
-                                                                                                shuffle_vars=True)
+    y_test) = prepare_datasets_percentsplit(X, y, validation_size=0.2, test_size=0.25,  shuffle_vars=True)
-    print(X_train.shape)
+    #print(X_train.shape)
 
     # Make model
     model = RNN_LSTM(input_shape=(1, 208))