picture for the htbd presentation

glucose_ts/data/tf_data_windows.py

@@ -117,7 +117,6 @@ def sample_time_series_dataset(
             for cluster_idx in range(cluster_length)
         ]
 
-
     def custom_generator():
         for combination in combinations:
 
@@ -127,7 +126,9 @@ def sample_time_series_dataset(
                         time_series[cluster_idx * cluster_length + sample]
                         for cluster_idx, sample in zip(
                             input_clusters,
-                            combination[:math.ceil(num_clusters * cut_off_rate)]
+                            combination[
+                                :math.ceil(num_clusters * cut_off_rate)
+                            ]
                         )
                     ],
                     dtype=tf.float32,
@@ -137,7 +138,9 @@ def sample_time_series_dataset(
                         time_series[cluster_idx * cluster_length + sample]
                         for cluster_idx, sample in zip(
                             label_clusters,
-                            combination[math.ceil(num_clusters * cut_off_rate):]
+                            combination[
+                                math.ceil(num_clusters * cut_off_rate):
+                            ]
                         )
                     ],
                     dtype=tf.float32,
@@ -152,7 +155,6 @@ def sample_time_series_dataset(
     ).batch(batch_size)
 
 
-
 def create_validation_dataset(
         data,
         cut_off,

glucose_ts/models/lstm.py

@@ -29,7 +29,8 @@ class VariableLSTM(tf.keras.Model):
         self.lstm_cell = tf.keras.layers.LSTMCell(
             units,
             kernel_regularizer=tf.keras.regularizers.L2(lstm_regularization),
-            recurrent_regularizer=tf.keras.regularizers.L2(lstm_regularization),
+            recurrent_regularizer=tf.keras.regularizers.L2(
+                lstm_regularization),
             dropout=lstm_dropout,
         )
         # Also wrap the LSTMCell in an RNN to make the warmup easier
@@ -58,7 +59,8 @@ class VariableLSTM(tf.keras.Model):
             )
             + [tf.keras.layers.Dense(
                 1,
-                kernel_regularizer=tf.keras.regularizers.L2(dense_regularization),
+                kernel_regularizer=tf.keras.regularizers.L2(
+                    dense_regularization),
             )]
         )
 
@@ -75,7 +77,10 @@ class VariableLSTM(tf.keras.Model):
         inputs, labels = data
 
         with tf.GradientTape() as tape:
-            initial_prediction, initial_state = self.warmup(inputs, training=True)
+            initial_prediction, initial_state = self.warmup(
+                inputs,
+                training=True
+            )
 
             predictions_array = [initial_prediction]
             state = initial_state
@@ -141,20 +146,3 @@ class VariableLSTM(tf.keras.Model):
 
         predictions = predictions_array.stack()
         return tf.transpose(predictions, [1, 0, 2])
-
-    # def test_step(self, data):
-    #     # Unpack the data
-    #     x, y = data
-    #     # Compute predictions
-    #     y_pred = self(x, training=False)
-    #
-    #     # redo normalization
-    #     original_y = y * tf.cast(self.norm_std, dtype=y.dtype) + tf.cast(self.norm_mean, dtype=y.dtype)
-    #     original_y_pred = y_pred * tf.cast(self.norm_std, dtype=y_pred.dtype) + tf.cast(self.norm_mean, dtype=y_pred.dtype)
-    #     # Updates the metrics tracking the loss
-    #     self.compiled_loss(original_y, original_y_pred, regularization_losses=self.losses)
-    #     # Update the metrics.
-    #     self.compiled_metrics.update_state(original_y, original_y_pred)
-    #     # Return a dict mapping metric names to current value.
-    #     # Note that it will include the loss (tracked in self.metrics).
-    #     return {m.name: m.result() for m in self.metrics}

glucose_ts/models/utils.py

@@ -105,5 +105,3 @@ def cross_validation(
         performance.append(model.evaluate(validation_dataset))
 
     return performance
-
-

presentations/htbd_presentation.tex

@@ -49,9 +49,8 @@
 \end{frame}
 
 
-\section{Overview}
 
-\subsection{Spectroscopy}
+\section{Spectroscopy}
 
 \begin{frame}{Raman Spectra}
 \smallHead
@@ -88,7 +87,7 @@
 
 \end{frame}
 
-\subsection{Image Analysis}
+\section{Image Analysis}
 
 \begin{frame}{Microscopic Image Analysis}
 \smallHead

tests/data/test_tf_data_windows.py

@@ -290,10 +290,10 @@ def test_sample_time_series_dataset_three():
         for label, expected in zip(
             labels,
             [
-                tf.constant([[[0], [2]],], dtype=tf.float32),
-                tf.constant([[[1], [4]],], dtype=tf.float32),
-                tf.constant([[[0], [3]],], dtype=tf.float32),
-                tf.constant([[[1], [5]],], dtype=tf.float32),
+                tf.constant([[[0], [2]], ], dtype=tf.float32),
+                tf.constant([[[1], [4]], ], dtype=tf.float32),
+                tf.constant([[[0], [3]], ], dtype=tf.float32),
+                tf.constant([[[1], [5]], ], dtype=tf.float32),
             ]
         )
     ])