Supress progress output for tests

test/test.py

@@ -1,12 +1,10 @@
 import inspect
 import unittest
-from unittest.mock import patch
 import numpy as np
 from pysr import PySRRegressor
 from pysr.sr import run_feature_selection, _handle_feature_selection
 from sklearn.utils.estimator_checks import check_estimator
 import sympy
-from sympy import lambdify
 import pandas as pd
 import warnings
 
@@ -22,6 +20,7 @@ class TestPipeline(unittest.TestCase):
             inspect.signature(PySRRegressor.__init__).parameters["populations"].default
         )
         self.default_test_kwargs = dict(
+            progress=False,
             model_selection="accuracy",
             niterations=default_niterations * 2,
             populations=default_populations * 2,
@@ -235,6 +234,7 @@ class TestBest(unittest.TestCase):
         self.X = self.rstate.randn(10, 2)
         self.y = np.cos(self.X[:, 0]) ** 2
         self.model = PySRRegressor(
+            progress=False,
             niterations=1,
             extra_sympy_mappings={},
             output_jax_format=False,

test/test_jax.py

@@ -25,6 +25,7 @@ class TestJAX(unittest.TestCase):
         X = pd.DataFrame(np.random.randn(100, 10))
         y = np.ones(X.shape[0])
         model = PySRRegressor(
+            progress=False,
             max_evals=10000,
             output_jax_format=True,
         )
@@ -54,7 +55,7 @@ class TestJAX(unittest.TestCase):
     def test_pipeline(self):
         X = np.random.randn(100, 10)
         y = np.ones(X.shape[0])
-        model = PySRRegressor(max_evals=10000, output_jax_format=True)
+        model = PySRRegressor(progress=False, max_evals=10000, output_jax_format=True)
         model.fit(X, y)
 
         equations = pd.DataFrame(
@@ -83,7 +84,10 @@ class TestJAX(unittest.TestCase):
         y = X["k15"] ** 2 + np.cos(X["k20"])
 
         model = PySRRegressor(
-            unary_operators=["cos"], select_k_features=3, early_stop_condition=1e-5
+            progress=False,
+            unary_operators=["cos"],
+            select_k_features=3,
+            early_stop_condition=1e-5,
         )
         model.fit(X.values, y.values)
         f, parameters = model.jax().values()

test/test_torch.py

@@ -26,6 +26,7 @@ class TestTorch(unittest.TestCase):
         X = pd.DataFrame(np.random.randn(100, 10))
         y = np.ones(X.shape[0])
         model = PySRRegressor(
+            progress=False,
             max_evals=10000,
             model_selection="accuracy",
             extra_sympy_mappings={},
@@ -60,6 +61,7 @@ class TestTorch(unittest.TestCase):
         X = np.random.randn(100, 10)
         y = np.ones(X.shape[0])
         model = PySRRegressor(
+            progress=False,
             max_evals=10000,
             model_selection="accuracy",
             output_torch_format=True,
@@ -114,6 +116,7 @@ class TestTorch(unittest.TestCase):
         X = np.random.randn(100, 3)
         y = np.ones(X.shape[0])
         model = PySRRegressor(
+            progress=False,
             max_evals=10000,
             model_selection="accuracy",
             output_torch_format=True,
@@ -156,6 +159,7 @@ class TestTorch(unittest.TestCase):
         y = X["k15"] ** 2 + np.cos(X["k20"])
 
         model = PySRRegressor(
+            progress=False,
             unary_operators=["cos"],
             select_k_features=3,
             early_stop_condition=1e-5,