Added Nguyen benchmark and a method to list datasets in a benchmark

SRToolkit/dataset/srbenchmark.py

@@ -45,7 +45,7 @@ def create_dataset(self, dataset_name: str):
         if dataset_name in self.datasets:
             # Check if dataset exists otherwise download it from an url
             if os.path.exists(self.datasets[dataset_name]["path"]):
-                data = np.load(self.datasets[dataset_name]["path"] + ".npy")
+                data = np.load(self.datasets[dataset_name]["path"])
             else:
                 raise ValueError(f"Could not find dataset {dataset_name} at {self.datasets[dataset_name]['path']}")
 
@@ -64,6 +64,23 @@ def create_dataset(self, dataset_name: str):
         else:
             raise ValueError(f"Dataset {dataset_name} not found")
 
+    def list_datasets(self, verbose=True):
+        datasets = [dataset_name for dataset_name in self.datasets]
+        sorted(datasets, key= lambda dataset_name: self.datasets[dataset_name]["num_variables"], reverse=True)
+
+        if verbose:
+            for d in datasets:
+                if self.datasets[d]["num_variables"] == 1:
+                    variable_str = "1 variable"
+                elif self.datasets[d]["num_variables"] < 1:
+                    variable_str = "Amount of variables unknown"
+                else:
+                    variable_str = f"{self.datasets[d]['num_variables']} variables"
+
+                print(f"{d}:\t{variable_str}, \tExpression: {self.datasets[d]['original_equation']}")
+        return datasets
+
+
     @staticmethod
     def download_benchmark_data(url, directory_path):
         # Check if directory_path exist
@@ -96,10 +113,10 @@ def nguyen(dataset_directory: str):
         sl_1v.add_symbol("sin", symbol_type="fn", precedence=5, np_fn="{} = np.sin({})")
         sl_1v.add_symbol("cos", symbol_type="fn", precedence=5, np_fn="{} = np.cos({})")
         sl_1v.add_symbol("exp", symbol_type="fn", precedence=5, np_fn="{} = np.exp({})")
-        sl_1v.add_symbol("ln", symbol_type="fn", precedence=5, np_fn="{} = np.ln({})")
+        sl_1v.add_symbol("log", symbol_type="fn", precedence=5, np_fn="{} = np.log10({})")
         sl_1v.add_symbol("sqrt", symbol_type="fn", precedence=5, np_fn="{} = np.sqrt({})")
-        sl_1v.add_symbol("^2", symbol_type="fn", precedence=5, np_fn="{} = np.pow({}, 2)")
-        sl_1v.add_symbol("^3", symbol_type="fn", precedence=5, np_fn="{} = np.pow({}, 3)")
+        sl_1v.add_symbol("^2", symbol_type="fn", precedence=5, np_fn="{} = np.power({}, 2)")
+        sl_1v.add_symbol("^3", symbol_type="fn", precedence=5, np_fn="{} = np.power({}, 3)")
         sl_1v.add_symbol("X_0", "var", 5, "X[:, 0]")
 
         sl_2v = copy.copy(sl_1v)
@@ -152,29 +169,6 @@ def nguyen(dataset_directory: str):
 
 
 if __name__ == '__main__':
-    # benchmark = SRBenchmark.nguyen("../../data/nguyen")
-    # a = 0
-    from SRToolkit.utils.expression_compiler import expr_to_executable_function
-
-    equations = [["X_0", "+", "X_0", "^2", "+", "X_0", "^3"],
-                 ["X_0", "+", "X_0", "^2", "+", "X_0", "^3", "+", "X_0", "*", "X_0", "^3"],
-                 ["X_0", "+", "X_0", "^2", "+", "X_0", "^3", "+", "X_0", "*", "X_0", "^3", "+", "X_0", "^2", "*", "X_0", "^3"],
-                 ["X_0", "+", "X_0", "^2", "+", "X_0", "^3", "+", "X_0", "*", "X_0", "^3", "+", "X_0", "^2", "*", "X_0", "^3", "+", "X_0", "^3", "*", "X_0", "^3"],
-                 ["sin", "(", "X_0", "^2", ")", "*", "cos", "(", "X_0", ")", "-", "1"],
-                 ["sin", "(", "X_0", ")", "+", "sin", "(", "X_0", "+", "X_0", "^2", ")"],
-                 ["log", "(", "1", "+", "X_0", ")", "+", "log", "(", "1", "+", "X_0", "^2", ")"],
-                 ["sqrt", "(", "X_0", ")"],
-                 ["sin", "(", "X_0", ")", "+", "sin", "(", "X_1", "^2", ")"],
-                 ["2", "*", "sin", "(", "X_0", ")", "*", "cos", "(", "X_1", ")"]]
-
-    bounds = [(-20, 20), (-20, 20), (-20, 20), (-20, 20), (-20, 20), (-20, 20), (1, 100), (0, 100), (-20, 20), (-20, 20)]
-
-    for i, eq in enumerate(equations):
-        exec_fun = expr_to_executable_function(eq)
-        if i < 8:
-            x = np.random.random((10000, 1)) * (bounds[i][1] - bounds[i][0]) + bounds[i][0]
-        else:
-            x = np.random.random((10000, 2)) * (bounds[i][1] - bounds[i][0]) + bounds[i][0]
-        y = exec_fun(x, None)
-
-        np.save(f"../../data/Nguyen/NG-{i+1}.npy", np.concatenate([x, y[:, np.newaxis]], axis=1))
+    benchmark = SRBenchmark.nguyen("../../data/nguyen")
+    benchmark.list_datasets()
+    a = 0