progress

tiger.py

@@ -18,6 +18,7 @@ REFERENCE_TRANSCRIPTS = ('gencode.v19.pc_transcripts.fa.gz', 'gencode.v19.lncRNA
 BATCH_SIZE_COMPUTE = 500
 BATCH_SIZE_SCAN = 20
 BATCH_SIZE_TRANSCRIPTS = 50
+UNIT_INTERVAL_MAP = 'exp-lin-exp'
 
 # configure GPUs
 for gpu in tf.config.list_physical_devices('GPU'):
@@ -101,27 +102,37 @@ def process_data(transcript_seq: str):
 
 def prediction_transform(predictions: np.array, **params):
 
-    # regime indices
-    active_saturation = predictions < params['a']
-    linear_regime = (params['a'] <= predictions) & (predictions <= params['c'])
-    inactive_saturation = params['c'] < predictions
+    if UNIT_INTERVAL_MAP == 'sigmoid':
+        return 1 - 1 / (1 + np.exp(params['a'] * predictions + params['b']))
 
-    # linear regime
-    slope = (params['d'] - params['b']) / (params['c'] - params['a'])
-    intercept = -params['a'] * slope + params['b']
-    predictions[linear_regime] = slope * predictions[linear_regime] + intercept
+    elif UNIT_INTERVAL_MAP == 'min-max':
+        return 1 - (predictions - params['a']) / (params['b'] - params['a'])
 
-    # active saturation regime
-    alpha = slope / params['b']
-    beta = alpha * params['a'] - np.log(params['b'])
-    predictions[active_saturation] = np.exp(alpha * predictions[active_saturation] - beta)
+    elif UNIT_INTERVAL_MAP == 'exp-lin-exp':
+        # regime indices
+        active_saturation = predictions < params['a']
+        linear_regime = (params['a'] <= predictions) & (predictions <= params['c'])
+        inactive_saturation = params['c'] < predictions
 
-    # inactive saturation regime
-    alpha = slope / (1 - params['d'])
-    beta = -alpha * params['c'] - np.log(1 - params['d'])
-    predictions[inactive_saturation] = 1 - np.exp(-alpha * predictions[inactive_saturation] - beta)
+        # linear regime
+        slope = (params['d'] - params['b']) / (params['c'] - params['a'])
+        intercept = -params['a'] * slope + params['b']
+        predictions[linear_regime] = slope * predictions[linear_regime] + intercept
 
-    return 1 - predictions
+        # active saturation regime
+        alpha = slope / params['b']
+        beta = alpha * params['a'] - np.log(params['b'])
+        predictions[active_saturation] = np.exp(alpha * predictions[active_saturation] - beta)
+
+        # inactive saturation regime
+        alpha = slope / (1 - params['d'])
+        beta = -alpha * params['c'] - np.log(1 - params['d'])
+        predictions[inactive_saturation] = 1 - np.exp(-alpha * predictions[inactive_saturation] - beta)
+
+        return 1 - predictions
+
+    else:
+        raise NotImplementedError
 
 
 def titration_ratio(guide: np.array, parent: np.array):