code: def test_input_validation_classifier():
      """Check that guarded input validation raises errors"""
  
      # Check too much proba
      est = SymbolicClassifier(p_point_mutation=0.5)
      assert_raises(ValueError, est.fit, cancer.data, cancer.target)
  
      # Check invalid init_method
      est = SymbolicClassifier(init_method="ni")
      assert_raises(ValueError, est.fit, cancer.data, cancer.target)
  
      # Check invalid const_ranges
      est = SymbolicClassifier(const_range=2)
      assert_raises(ValueError, est.fit, cancer.data, cancer.target)
      est = SymbolicClassifier(const_range=[2, 2])
      assert_raises(ValueError, est.fit, cancer.data, cancer.target)
      est = SymbolicClassifier(const_range=(2, 2, 2))
      assert_raises(ValueError, est.fit, cancer.data, cancer.target)
      est = SymbolicClassifier(const_range="ni")
      assert_raises(ValueError, est.fit, cancer.data, cancer.target)
      # And check acceptable, but strange, representations of const_range
      est = SymbolicClassifier(
          population_size=100, generations=1, const_range=(2, 2)
      )
      est.fit(cancer.data, cancer.target)
      est = SymbolicClassifier(
          population_size=100, generations=1, const_range=None
      )
      est.fit(cancer.data, cancer.target)
      est = SymbolicClassifier(
          population_size=100, generations=1, const_range=(4, 2)
      )
      est.fit(cancer.data, cancer.target)
  
      # Check invalid init_depth
      est = SymbolicClassifier(init_depth=2)
      assert_raises(ValueError, est.fit, cancer.data, cancer.target)
      est = SymbolicClassifier(init_depth=2)
      assert_raises(ValueError, est.fit, cancer.data, cancer.target)
      est = SymbolicClassifier(init_depth=[2, 2])
      assert_raises(ValueError, est.fit, cancer.data, cancer.target)
      est = SymbolicClassifier(init_depth=(2, 2, 2))
      assert_raises(ValueError, est.fit, cancer.data, cancer.target)
      est = SymbolicClassifier(init_depth="ni")
      assert_raises(ValueError, est.fit, cancer.data, cancer.target)
      est = SymbolicClassifier(init_depth=(4, 2))
      assert_raises(ValueError, est.fit, cancer.data, cancer.target)
      # And check acceptable, but strange, representations of init_depth
      est = SymbolicClassifier(
          population_size=100, generations=1, init_depth=(2, 2)
      )
      est.fit(cancer.data, cancer.target)
  
      # Check classifier metrics
      for m in ["log loss"]:
          est = SymbolicClassifier(
              population_size=100, generations=1, metric=m
          )
          est.fit(cancer.data, cancer.target)
      # And check a fake one
      est = SymbolicClassifier(metric="the larch")
      assert_raises(ValueError, est.fit, cancer.data, cancer.target)
  
      # Check classifier transformers
      for t in ["sigmoid"]:
          est = SymbolicClassifier(
              population_size=100, generations=1, transformer=t
          )
          est.fit(cancer.data, cancer.target)
      # And check an incompatible one with wrong arity
      est = SymbolicClassifier(transformer=sub2)
      assert_raises(ValueError, est.fit, cancer.data, cancer.target)
      # And check a fake one
      est = SymbolicClassifier(transformer="the larch")
      assert_raises(ValueError, est.fit, cancer.data, cancer.target)
  
imports: import pickle
  import pytest
  import sys
  from io import StringIO
  import numpy
  from scipy.stats import pearsonr, spearmanr
  from sklearn.datasets import load_diabetes, load_breast_cancer
  from sklearn.metrics import mean_absolute_error
  from sklearn.model_selection import GridSearchCV
  from sklearn.pipeline import make_pipeline
  from sklearn.preprocessing import StandardScaler
  from sklearn.tree import DecisionTreeRegressor
  from sklearn.utils._testing import assert_almost_equal
  from sklearn.utils._testing import assert_array_equal
  from sklearn.utils._testing import assert_array_almost_equal
  from sklearn.utils._testing import assert_raises
  from sklearn.utils.validation import check_random_state
  from gplearn.genetic import SymbolicClassifier, SymbolicRegressor
  from gplearn.genetic import SymbolicTransformer
  from gplearn.fitness import weighted_pearson, weighted_spearman
  from gplearn._program import _Program
  from gplearn.fitness import _fitness_map
  from gplearn.functions import add2, sub2, mul2, div2, sqrt1, log1, abs1, max2, min2
  from gplearn.functions import _Function
label_desc: Use image node_id 13 for calling a global function with example usage: test_input_validation_classifier() without return types