eval.py

@@ -66,7 +66,6 @@ def load_model(model_path, device):
         config = json.load(f)
 
     trained_encoder = SentenceTransformer(model_path, device=str(device))
-    trained_encoder.to(device)
     trained_head = load_head(
         model_path, "coordinate_head.pt", trained_encoder, config, device
     )
@@ -77,7 +76,6 @@ def load_model(model_path, device):
         base_encoder = SentenceTransformer(initial_encoder_path, device=str(device))
     else:
         base_encoder = SentenceTransformer(config["model_name"], device=str(device))
-    base_encoder.to(device)
     initial_head_path = os.path.join(model_path, "initial_coordinate_head.pt")
     initial_head = None
     if os.path.exists(initial_head_path):
@@ -122,18 +120,8 @@ def make_prediction_plot(results, plot_file):
             color="0.75",
             linewidth=0.4,
             alpha=0.35,
-            zorder=1,
         )
 
-    ax.scatter(
-        results["predicted_longitude"],
-        results["predicted_latitude"],
-        s=12,
-        color="#d62728",
-        alpha=0.45,
-        label="predicted",
-        zorder=2,
-    )
     ax.scatter(
         results["longitude"],
         results["latitude"],
@@ -141,7 +129,14 @@ def make_prediction_plot(results, plot_file):
         color="#1f77b4",
         alpha=0.75,
         label="actual",
-        zorder=3,
+    )
+    ax.scatter(
+        results["predicted_longitude"],
+        results["predicted_latitude"],
+        s=12,
+        color="#d62728",
+        alpha=0.45,
+        label="predicted",
     )
     ax.set_xlabel("longitude")
     ax.set_ylabel("latitude")

prepare_training_data.py

@@ -3,7 +3,6 @@ import argparse
 import pandas as pd
 
 INPUT_COLUMNS = ["intersection", "text_on_sign_exact", "latitude", "longitude"]
-EXCLUDED_INTERSECTIONS = {"56th-pena"}
 
 
 def parse_args():
@@ -58,7 +57,6 @@ def load_raw_data(path):
     data = data.dropna(subset=INPUT_COLUMNS)
     data["text_on_sign_exact"] = data["text_on_sign_exact"].astype(str).str.strip()
     data = data[data["text_on_sign_exact"] != ""]
-    data = data[~data["intersection"].isin(EXCLUDED_INTERSECTIONS)]
     return data