From 1d1ec433d81c8fe11c30687f7f746459d8040567 Mon Sep 17 00:00:00 2001
From: Skudalen <markusskudal@gmail.com>
Date: Mon, 19 Jul 2021 10:34:33 +0200
Subject: [PATCH] feat: make log possibility for inverse cross-val too

---
 .DS_Store                  | Bin 6148 -> 8196 bytes
 Neural_Network_Analysis.py |  18 +++++++++++++-----
 2 files changed, 13 insertions(+), 5 deletions(-)

diff --git a/.DS_Store b/.DS_Store
index 3576c0f8eef457d53eb19a238be92b358e923238..a1ebc19534b44e2fd65db9f3320a4b168a4b6c8d 100644
GIT binary patch
delta 441
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delta 119
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diff --git a/Neural_Network_Analysis.py b/Neural_Network_Analysis.py
index e5a55e5..63ac9a8 100644
--- a/Neural_Network_Analysis.py
+++ b/Neural_Network_Analysis.py
@@ -289,7 +289,7 @@ def session_cross_validation(model_name:str, X, y, session_lengths, nr_sessions,
 # the average of networks trained om them
 # Input: raw data, session_lengths list, total nr of sessions, batch_size, and nr of epochs 
 # Ouput: tuple(cross validation average, list(result for each dataset(len=nr_sessions)))
-def inverse_session_cross_validation(model_name:str, X, y, session_lengths, nr_sessions, batch_size=64, epochs=30):
+def inverse_session_cross_validation(model_name:str, X, y, session_lengths, nr_sessions, log_to_csv=True, batch_size=64, epochs=30):
     session_training_results = []
     for i in range(nr_sessions):
 
@@ -316,8 +316,9 @@ def inverse_session_cross_validation(model_name:str, X, y, session_lengths, nr_s
         train(model, X_train_session, y_train_session, verbose=1, batch_size=batch_size, epochs=epochs)
         test_loss, test_acc = model.evaluate(X_test_session, y_test_session, verbose=0)
         session_training_results.append(test_acc)
-        #if log_to_csv:
-            #prediction_csv_logger(X_test_session, y_test_session, model_name, model, i)
+        if log_to_csv:
+            custom_path = '/{}_train_session{}_log.csv'
+            prediction_csv_logger(X_test_session, y_test_session, model_name, model, i, custom_path)
         del model
         K.clear_session()
         #print('Session', i, 'as test data gives accuracy:', test_acc)
@@ -329,9 +330,12 @@ def inverse_session_cross_validation(model_name:str, X, y, session_lengths, nr_s
 # Takes in test data and logs input data and the prediction from a model
 # Input: raw data, session_lengths list, total nr of sessions, batch_size, and nr of epochs 
 # Ouput: tuple(cross validation average, list(result for each dataset(len=nr_sessions)))
-def prediction_csv_logger(X, y, model_name, model, session_nr):
+def prediction_csv_logger(X, y, model_name, model, session_nr, custom_path=None):
     
     csv_path = str(Path.cwd()) + '/logs/{}/{}_session{}_log.csv'.format(model_name, model_name, session_nr+1)
+    if custom_path:
+        path = str(Path.cwd()) + '/logs/{}' + custom_path
+        csv_path = path.format(model_name, model_name, session_nr+1)
 
     layerOutput = model.predict(X, verbose=0)
 
@@ -502,16 +506,20 @@ if __name__ == "__main__":
     #'''
     # ----- Inverse cross-validation ------
     # Trained on one session, tested on three
-    average_GRU = inverse_session_cross_validation('GRU', X, y, session_lengths, nr_sessions=NR_SESSIONS, 
+    average_GRU = inverse_session_cross_validation('GRU', X, y, session_lengths, nr_sessions=NR_SESSIONS,
+                                                                        log_to_csv=True, 
                                                                         batch_size=BATCH_SIZE, 
                                                                         epochs=EPOCHS)
     average_LSTM = inverse_session_cross_validation('LSTM', X, y, session_lengths, nr_sessions=NR_SESSIONS,
+                                                                        log_to_csv=True,
                                                                         batch_size=BATCH_SIZE, 
                                                                         epochs=EPOCHS)
     average_FFN = inverse_session_cross_validation('FFN', X, y, session_lengths, nr_sessions=NR_SESSIONS,
+                                                                        log_to_csv=True,
                                                                         batch_size=BATCH_SIZE, 
                                                                         epochs=EPOCHS)
     average_CNN = inverse_session_cross_validation('CNN_1D', X, y, session_lengths, nr_sessions=NR_SESSIONS,
+                                                                        log_to_csv=True,
                                                                         batch_size=BATCH_SIZE, 
                                                                         epochs=EPOCHS)