from sklearn.ensemble import RandomForestClassifier as RFC
from immuneML.ml_methods.SklearnMethod import SklearnMethod
from scripts.specification_util import update_docs_per_mapping
[docs]class RandomForestClassifier(SklearnMethod):
"""
This is a wrapper of scikit-learn’s RandomForestClassifier class. Please see the
`scikit-learn documentation <https://scikit-learn.org/stable/modules/generated/sklearn.ensemble.RandomForestClassifier.html>`_
of RandomForestClassifier for the parameters.
Note: if you are interested in plotting the coefficients of the random forest classifier model,
consider running the :ref:`Coefficients` report.
For usage instructions, check :py:obj:`~immuneML.ml_methods.SklearnMethod.SklearnMethod`.
YAML specification:
.. indent with spaces
.. code-block:: yaml
my_random_forest_classifier: # user-defined method name
RandomForestClassifier: # name of the ML method
# sklearn parameters (same names as in original sklearn class)
random_state: 100 # always use this value for random state
n_estimators: [10, 50, 100] # find the optimal number of trees in the forest
# Additional parameter that determines whether to print convergence warnings
show_warnings: True
# if any of the parameters under RandomForestClassifier is a list and model_selection_cv is True,
# a grid search will be done over the given parameters, using the number of folds specified in model_selection_n_folds,
# and the optimal model will be selected
model_selection_cv: True
model_selection_n_folds: 5
# alternative way to define ML method with default values:
my_default_random_forest: RandomForestClassifier
"""
def __init__(self, parameter_grid: dict = None, parameters: dict = None):
parameters = parameters if parameters is not None else {}
if parameter_grid is not None:
parameter_grid = parameter_grid
else:
parameter_grid = {"n_estimators": [10, 50, 100]}
super(RandomForestClassifier, self).__init__(parameter_grid=parameter_grid, parameters=parameters)
def _get_ml_model(self, cores_for_training: int = 2, X=None):
params = self._parameters.copy()
params["n_jobs"] = cores_for_training
return RFC(**params)
[docs] def can_predict_proba(self) -> bool:
return True
[docs] def get_params(self):
params = self.model.get_params(deep=True)
params["feature_importances"] = self.model.feature_importances_.tolist()
return params
[docs] @staticmethod
def get_documentation():
doc = str(RandomForestClassifier.__doc__)
mapping = {
"For usage instructions, check :py:obj:`~immuneML.ml_methods.SklearnMethod.SklearnMethod`.": SklearnMethod.get_usage_documentation("RandomForestClassifier"),
}
doc = update_docs_per_mapping(doc, mapping)
return doc