kbfreder

# import function, or patch it:
  # Note: may need to install mlxtend
  
try:
    from sklearn.inspection import permutation_importance
except ImportError:
    print("Problem importing permutation_importance -- patching")
    from mlxtend.evaluate import feature_importance_permutation
    def permutation_importance(estimator, X, y, scoring='r2', n_repeats=5):
        """

kbfreder

from sklearn.model_selection import GridSearchCV
from sklearn.metrics import make_scorer 
# import Random Forest

param_grid = {'n_estimators': [50, 100, 200],
                 'max_depth':[None, 10, 15, 20], 
                 'criterion': ['gini', 'entropy'],
                 'min_impurity_decrease': [0, 1e7, 1e5]}
scorer = make_scorer(accuracy_score)

kbfreder

# import Random Forest

base_rf = RandomForestClassifier(n_estimators=100, max_depth=None)
base_rf.fit(train_X, train_y)
depths = [est.tree_.max_depth for est in base_rf.estimators_]

kbfreder

# this is a custom module
import assess_clf_models as acm

from sklearn.linear_model import LogisticRegression
from sklearn.tree import DecisionTreeClassifier
from sklearn.ensemble import GradientBoostingClassifier, RandomForestClassifier, BaggingClassifier
from catboost import CatBoostClassifier

from sklearn.compose import ColumnTransformer
from sklearn.preprocessing import StandardScaler, OneHotEncoder, FunctionTransformer