Removed unused files

Author: daenuprobst

data/az/az-2048-3-true-props.pkl.gz

@@ -24,15 +24,15 @@ def save_results(
 
 def predict_az():
     root_path = Path(__file__).resolve().parent
-    az_file_path = Path(root_path, "../../data/az/az-2048-3-true-props.pkl")
+    az_file_path = Path(root_path, "../../data/az/az-2048-3-true.pkl")
 
     data = pickle.load(open(az_file_path, "rb"))
 
     r2s = []
     maes = []
 
     for i, split in enumerate(data):
-        print(f"Evaluating split {i+1}/10 ...")
+        print(f"Evaluating split {i + 1}/10 ...")
         X_train, y_train, X_valid, y_valid, X_test, y_test = (
             split["train"]["X"],
             split["train"]["y"],

data/az/az-2048-3-true.pkl.gz

@@ -0,0 +1,66 @@
+import pickle
+from pathlib import Path
+from typing import Tuple
+from statistics import stdev
+import numpy as np
+from xgboost import XGBRegressor
+from catboost import CatBoostRegressor
+from sklearn import svm
+from sklearn.ensemble import RandomForestRegressor
+from sklearn.metrics import r2_score, mean_absolute_error
+
+
+def save_results(
+    set_name: str,
+    split_id: str,
+    file_name: str,
+    ground_truth: np.ndarray,
+    prediction: np.ndarray,
+) -> None:
+    with open(f"{set_name}_{split_id}_{file_name}.csv", "w+") as f:
+        for gt, pred in zip(ground_truth, prediction):
+            f.write(f"{set_name},{split_id},{file_name},{gt},{pred}\n")
+
+
+def predict_az():
+    root_path = Path(__file__).resolve().parent
+    az_file_path = Path(root_path, "../../data/az/az-2048-3-true.pkl")
+
+    data = pickle.load(open(az_file_path, "rb"))
+
+    r2s = []
+    maes = []
+
+    for i, split in enumerate(data):
+        print(f"Evaluating split {i + 1}/10 ...")
+        X_train, y_train, X_valid, y_valid, X_test, y_test = (
+            split["train"]["X"],
+            split["train"]["y"],
+            split["valid"]["X"],
+            split["valid"]["y"],
+            split["test"]["X"],
+            split["test"]["y"],
+        )
+
+        X_train = np.concatenate((X_train, X_valid))
+        y_train = np.concatenate((y_train, y_valid))
+        model = RandomForestRegressor(n_estimators=1000, random_state=42)
+        model.fit(X_train, y_train)
+        y_pred = model.predict(X_test)
+
+        r_squared = r2_score(y_test, y_pred)
+        mae = mean_absolute_error(y_test, y_pred)
+        print(f"Test {i + 1}", r_squared, mae / 100)
+        r2s.append(r_squared)
+        maes.append(mae)
+
+    print("Tests R2:", sum(r2s) / len(r2s), stdev(r2s))
+    print("Tests MAE:", sum(maes) / (100 * len(maes)), stdev(maes) / 100)
+
+
+def main():
+    predict_az()
+
+
+if __name__ == "__main__":
+    main()