feat: use proper python logging

pysr/__init__.py

@@ -1,5 +1,12 @@
+import logging
 import os
 
+pysr_logger = logging.getLogger("pysr")
+pysr_logger.setLevel(logging.INFO)
+handler = logging.StreamHandler()
+handler.setLevel(logging.INFO)
+pysr_logger.addHandler(handler)
+
 if os.environ.get("PYSR_USE_BEARTYPE", "0") == "1":
     from beartype.claw import beartype_this_package
 

pysr/feature_selection.py

@@ -1,5 +1,6 @@
 """Functions for doing feature selection during preprocessing."""
 
+import logging
 from typing import cast
 
 import numpy as np
@@ -8,6 +9,8 @@
 
 from .utils import ArrayLike
 
+pysr_logger = logging.getLogger(__name__)
+
 
 def run_feature_selection(
     X: ndarray,
@@ -44,7 +47,7 @@ def _handle_feature_selection(
 ):
     if select_k_features is not None:
         selection = run_feature_selection(X, y, select_k_features)
-        print(f"Using features {[variable_names[i] for i in selection]}")
+        pysr_logger.info(f"Using features {[variable_names[i] for i in selection]}")
         X = X[:, selection]
     else:
         selection = None

pysr/sr.py

@@ -1,6 +1,7 @@
 """Define the PySRRegressor scikit-learn interface."""
 
 import copy
+import logging
 import os
 import pickle as pkl
 import re
@@ -67,6 +68,8 @@
 
 ALREADY_RAN = False
 
+pysr_logger = logging.getLogger(__name__)
+
 
 def _process_constraints(
     binary_operators: list[str],
@@ -1107,7 +1110,7 @@ def from_file(
 
         pkl_filename = Path(run_directory) / "checkpoint.pkl"
         if pkl_filename.exists():
-            print(f"Attempting to load model from {pkl_filename}...")
+            pysr_logger.info(f"Attempting to load model from {pkl_filename}...")
             assert binary_operators is None
             assert unary_operators is None
             assert n_features_in is None
@@ -1129,9 +1132,9 @@ def from_file(
 
             return model
         else:
-            print(
-                f"Checkpoint file {pkl_filename} does not exist. "
-                "Attempting to recreate model from scratch..."
+            pysr_logger.info(
+                "Checkpoint file %s does not exist. Attempting to recreate model from scratch...",
+                pkl_filename,
             )
             csv_filename = Path(run_directory) / "hall_of_fame.csv"
             csv_filename_bak = Path(run_directory) / "hall_of_fame.csv.bak"
@@ -1232,12 +1235,16 @@ def __getstate__(self) -> dict[str, Any]:
         )
         state_keys_containing_lambdas = ["extra_sympy_mappings", "extra_torch_mappings"]
         for state_key in state_keys_containing_lambdas:
-            if state[state_key] is not None and show_pickle_warning:
-                warnings.warn(
-                    f"`{state_key}` cannot be pickled and will be removed from the "
-                    "serialized instance. When loading the model, please redefine "
-                    f"`{state_key}` at runtime."
-                )
+            warn_msg = (
+                f"`{state_key}` cannot be pickled and will be removed from the "
+                "serialized instance. When loading the model, please redefine "
+                f"`{state_key}` at runtime."
+            )
+            if state[state_key] is not None:
+                if show_pickle_warning:
+                    warnings.warn(warn_msg)
+                else:
+                    pysr_logger.debug(warn_msg)
         state_keys_to_clear = state_keys_containing_lambdas
         state_keys_to_clear.append("logger_")
         pickled_state = {
@@ -1280,7 +1287,7 @@ def _checkpoint(self):
             try:
                 pkl.dump(self, f)
             except Exception as e:
-                print(f"Error checkpointing model: {e}")
+                pysr_logger.debug(f"Error checkpointing model: {e}")
         self.show_pickle_warnings_ = True
 
     def get_pkl_filename(self) -> Path:
@@ -1752,7 +1759,7 @@ def _pre_transform_training_data(
             self.selection_mask_ = selection_mask
             self.feature_names_in_ = _check_feature_names_in(self, variable_names)
             self.display_feature_names_in_ = self.feature_names_in_
-            print(f"Using features {self.feature_names_in_}")
+            pysr_logger.info(f"Using features {self.feature_names_in_}")
 
         # Denoising transformation
         if self.denoise:
@@ -1824,7 +1831,7 @@ def _run(
 
         # Start julia backend processes
         if not ALREADY_RAN and runtime_params.update_verbosity != 0:
-            print("Compiling Julia backend...")
+            pysr_logger.info("Compiling Julia backend...")
 
         parallelism, numprocs = _map_parallelism_params(
             self.parallelism, self.procs, getattr(self, "multithreading", None)