From 237e69b73217d07aa84797ee1e321580ac8c7c77 Mon Sep 17 00:00:00 2001 From: Arthur Lu Date: Fri, 4 Feb 2022 08:50:56 +0000 Subject: [PATCH] fix minor bugs in RandomForest.py --- analysis-master/tra_analysis/RandomForest.py | 13 ++++++++----- 1 file changed, 8 insertions(+), 5 deletions(-) diff --git a/analysis-master/tra_analysis/RandomForest.py b/analysis-master/tra_analysis/RandomForest.py index 02afe73c..11f02f68 100644 --- a/analysis-master/tra_analysis/RandomForest.py +++ b/analysis-master/tra_analysis/RandomForest.py @@ -4,9 +4,12 @@ # this should be imported as a python module using 'from tra_analysis import RandomForest' # setup: -__version__ = "1.0.2" +__version__ = "1.0.3" __changelog__ = """changelog: + 1.0.3: + - updated RandomForestClassifier and RandomForestRegressor parameters to match sklearn v 1.0.2 + - changed default values for kwargs to rely on sklearn 1.0.2: - optimized imports 1.0.1: @@ -28,19 +31,19 @@ __all__ = [ import sklearn, sklearn.ensemble, sklearn.naive_bayes from . import ClassificationMetric, RegressionMetric -def random_forest_classifier(data, labels, test_size, n_estimators, criterion="gini", max_depth=None, min_samples_split=2, min_samples_leaf=1, min_weight_fraction_leaf=0.0, max_features="auto", max_leaf_nodes=None, min_impurity_decrease=0.0, min_impurity_split=None, bootstrap=True, oob_score=False, n_jobs=None, random_state=None, verbose=0, warm_start=False, class_weight=None): +def random_forest_classifier(data, labels, test_size, n_estimators, **kwargs): data_train, data_test, labels_train, labels_test = sklearn.model_selection.train_test_split(data, labels, test_size=test_size, random_state=1) - kernel = sklearn.ensemble.RandomForestClassifier(n_estimators = n_estimators, criterion = criterion, max_depth = max_depth, min_samples_split = min_samples_split, min_samples_leaf = min_samples_leaf, min_weight_fraction_leaf = min_weight_fraction_leaf, max_leaf_nodes = max_leaf_nodes, min_impurity_decrease = min_impurity_decrease, bootstrap = bootstrap, oob_score = oob_score, n_jobs = n_jobs, random_state = random_state, verbose = verbose, warm_start = warm_start, class_weight = class_weight) + kernel = sklearn.ensemble.RandomForestClassifier(n_estimators = n_estimators, **kwargs) kernel.fit(data_train, labels_train) predictions = kernel.predict(data_test) return kernel, ClassificationMetric(predictions, labels_test) -def random_forest_regressor(data, outputs, test_size, n_estimators, criterion="mse", max_depth=None, min_samples_split=2, min_samples_leaf=1, min_weight_fraction_leaf=0.0, max_features="auto", max_leaf_nodes=None, min_impurity_decrease=0.0, min_impurity_split=None, bootstrap=True, oob_score=False, n_jobs=None, random_state=None, verbose=0, warm_start=False): +def random_forest_regressor(data, outputs, test_size, n_estimators, **kwargs): data_train, data_test, outputs_train, outputs_test = sklearn.model_selection.train_test_split(data, outputs, test_size=test_size, random_state=1) - kernel = sklearn.ensemble.RandomForestRegressor(n_estimators = n_estimators, criterion = criterion, max_depth = max_depth, min_samples_split = min_samples_split, min_weight_fraction_leaf = min_weight_fraction_leaf, max_features = max_features, max_leaf_nodes = max_leaf_nodes, min_impurity_decrease = min_impurity_decrease, min_impurity_split = min_impurity_split, bootstrap = bootstrap, oob_score = oob_score, n_jobs = n_jobs, random_state = random_state, verbose = verbose, warm_start = warm_start) + kernel = sklearn.ensemble.RandomForestRegressor(n_estimators = n_estimators, **kwargs) kernel.fit(data_train, outputs_train) predictions = kernel.predict(data_test)