Clean up use of area/probability variants

confusion_matrix.py

@@ -116,7 +116,7 @@ def main():
                 args.feature,
                 progress,
                 model.get(args.model,
-                          feature.model_output_type(args.feature),
+                          feature.result_type(args.feature),
                           args.load_model),
                 model_version,
                 args.batch_size,

database.py

@@ -2,6 +2,8 @@
 import logging
 import sqlite3
 
+import feature
+
 
 log = logging.getLogger('database')
 
@@ -416,25 +418,49 @@ def mark_checked(cursor, z, x, y):
 
 
 def training_tiles(cursor, feature_name):
-    cursor.execute('''select tile_hash, score, 0
-                      from training_set
-                      natural join tile_positions
-                      natural join true_score
-                      where feature_name = ?
-                   ''',
-                   [feature_name])
+    if feature.result_type(feature_name) == 'probability':
+        cursor.execute('''select tile_hash, has_feature, 0
+                          from training_set
+                          natural join tile_positions
+                          natural join has_feature
+                          where feature_name = ?
+                       ''',
+                       [feature_name])
+    elif feature.result_type(feature_name) == 'area':
+        cursor.execute('''select tile_hash, score, 0
+                          from training_set
+                          natural join tile_positions
+                          natural join true_score
+                          where feature_name = ?
+                       ''',
+                       [feature_name])
+    else:
+        raise RuntimeError
+
     return cursor.fetchall()
 
 
 def validation_tiles(cursor, feature_name):
-    cursor.execute('''select tile_hash, true_score.score, scores.score
-                      from validation_set
-                      natural join tile_positions
-                      natural left join true_score
-                      natural left join scores
-                      where feature_name = ?
-                   ''',
-                   [feature_name])
+    if feature.result_type(feature_name) == 'probability':
+        cursor.execute('''select tile_hash, has_feature, score
+                          from validation_set
+                          natural join tile_positions
+                          natural left join has_feature
+                          natural left join scores
+                          where feature_name = ?
+                       ''',
+                       [feature_name])
+    elif feature.result_type(feature_name) == 'area':
+        cursor.execute('''select tile_hash, true_score.score, scores.score
+                          from validation_set
+                          natural join tile_positions
+                          natural left join true_score
+                          natural left join scores
+                          where feature_name = ?
+                       ''',
+                       [feature_name])
+    else:
+        raise RuntimeError
     return cursor.fetchall()
 
 
@@ -451,6 +477,7 @@ def validation_tiles_for_scoring(cursor, current_model, feature_name):
                    [current_model, feature_name])
     return cursor.fetchall()
 
+
 def set_true_score(cursor, tile_hash, feature_name, true_score):
     assert type(tile_hash) == str
     assert type(feature_name) == str

feature.py

@@ -43,7 +43,7 @@ def overpass_query(feature):
                '''
 
 
-def model_output_type(feature):
+def result_type(feature):
     return {
             'large_solar': 'probability',
             'playground': 'probability',

model.py

@@ -112,7 +112,7 @@ def inception_resnet_v2(input_layer, input_shape):
             )
 
 
-def get(model_type, output_type, weights_from=None, learning_rate=1e-4):
+def get(model_type, result_type, weights_from=None, learning_rate=1e-4):
     input_shape = (256, 256, 3)
     inputs = keras.layers.Input(input_shape)
 
@@ -132,9 +132,9 @@ def get(model_type, output_type, weights_from=None, learning_rate=1e-4):
     dense_1 = keras.layers.Dense(dense_width, activation='relu')(flatten)
     dense_2 = keras.layers.Dense(dense_width, activation='relu')(dense_1)
 
-    if output_type == 'probability':
+    if result_type == 'probability':
         dense_3 = keras.layers.Dense(1, activation='sigmoid')(dense_2)
-    elif output_type == 'area':
+    elif result_type == 'area':
         dense_3 = keras.layers.Dense(1, activation='relu')(dense_2)
 
     model = keras.models.Model(inputs=inputs, outputs=dense_3)

score_tiles.py

@@ -146,7 +146,7 @@ def main():
     image_dir = pathlib.Path(args.tile_path)
 
     m = model.get(args.model,
-                  feature.model_output_type(args.feature),
+                  feature.result_type(args.feature),
                   args.load_model)
 
     progress = Progress()

train.py

@@ -6,6 +6,7 @@
 import tensorflow as tf
 
 import database
+import feature
 import model
 import util
 
@@ -183,7 +184,7 @@ def main():
         validation_data = validation_data.batch(args.batch_size)
 
     m = model.get(args.model,
-                  feature.model_output_type(args.feature_name),
+                  feature.result_type(args.feature),
                   args.load_model,
                   args.learning_rate)
 

web.py

@@ -7,7 +7,6 @@
 import sqlite3
 
 import database
-import feature
 import util