import logging
import os
import sklearn
from sklearn.ensemble import ExtraTreesClassifier, ExtraTreesRegressor
from supervised.algorithms.algorithm import BaseAlgorithm
from supervised.algorithms.sklearn import (
SklearnTreesEnsembleClassifierAlgorithm,
SklearnTreesEnsembleRegressorAlgorithm,
)
from supervised.algorithms.registry import AlgorithmsRegistry
from supervised.algorithms.registry import (
BINARY_CLASSIFICATION,
MULTICLASS_CLASSIFICATION,
REGRESSION,
)
from supervised.utils.config import LOG_LEVEL
logger = logging.getLogger(__name__)
logger.setLevel(LOG_LEVEL)
class ExtraTreesAlgorithm(SklearnTreesEnsembleClassifierAlgorithm):
algorithm_name = "Extra Trees Classifier"
algorithm_short_name = "Extra Trees"
def __init__(self, params):
super(ExtraTreesAlgorithm, self).__init__(params)
logger.debug("ExtraTreesAlgorithm.__init__")
self.library_version = sklearn.__version__
self.trees_in_step = additional.get("trees_in_step", 100)
self.max_steps = additional.get("max_steps", 50)
self.early_stopping_rounds = additional.get("early_stopping_rounds", 50)
self.model = ExtraTreesClassifier(
n_estimators=self.trees_in_step,
criterion=params.get("criterion", "gini"),
max_features=params.get("max_features", 0.6),
min_samples_split=params.get("min_samples_split", 30),
warm_start=True,
n_jobs=-1,
random_state=params.get("seed", 1),
)
def file_extension(self):
return "extra_trees"
class ExtraTreesRegressorAlgorithm(SklearnTreesEnsembleRegressorAlgorithm):
algorithm_name = "Extra Trees Regressor"
algorithm_short_name = "Extra Trees"
def __init__(self, params):
super(ExtraTreesRegressorAlgorithm, self).__init__(params)
logger.debug("ExtraTreesRegressorAlgorithm.__init__")
self.library_version = sklearn.__version__
self.trees_in_step = regression_additional.get("trees_in_step", 100)
self.max_steps = regression_additional.get("max_steps", 50)
self.early_stopping_rounds = regression_additional.get(
"early_stopping_rounds", 50
)
self.model = ExtraTreesRegressor(
n_estimators=self.trees_in_step,
criterion=params.get("criterion", "mse"),
max_features=params.get("max_features", 0.8),
min_samples_split=params.get("min_samples_split", 4),
warm_start=True,
n_jobs=-1,
random_state=params.get("seed", 1),
)
def file_extension(self):
return "extra_trees"
# For binary classification target should be 0, 1. There should be no NaNs in target.
et_params = {
"criterion": ["gini", "entropy"],
"max_features": [0.5, 0.6, 0.7, 0.8, 0.9, 1.0],
"min_samples_split": [10, 20, 30, 40, 50],
}
classification_default_params = {
"criterion": "gini",
"max_features": 0.6,
"min_samples_split": 30,
}
additional = {
"trees_in_step": 100,
"max_steps": 50,
"early_stopping_rounds": 50,
"max_rows_limit": None,
"max_cols_limit": None,
}
required_preprocessing = [
"missing_values_inputation",
"convert_categorical",
"target_as_integer",
]
AlgorithmsRegistry.add(
BINARY_CLASSIFICATION,
ExtraTreesAlgorithm,
et_params,
required_preprocessing,
additional,
classification_default_params,
)
AlgorithmsRegistry.add(
MULTICLASS_CLASSIFICATION,
ExtraTreesAlgorithm,
et_params,
required_preprocessing,
additional,
classification_default_params,
)
#
# REGRESSION
#
regression_et_params = {
"criterion": [
"mse"
], # remove "mae" because it slows down a lot https://github.com/scikit-learn/scikit-learn/issues/9626
"max_features": [0.5, 0.6, 0.7, 0.8, 0.9, 1.0],
"min_samples_split": [10, 20, 30, 40, 50],
}
regression_default_params = {
"criterion": "mse",
"max_features": 0.6,
"min_samples_split": 30,
}
regression_additional = {
"trees_in_step": 100,
"max_steps": 50,
"early_stopping_rounds": 50,
"max_rows_limit": None,
"max_cols_limit": None,
}
regression_required_preprocessing = [
"missing_values_inputation",
"convert_categorical",
"target_scale",
]
AlgorithmsRegistry.add(
REGRESSION,
ExtraTreesRegressorAlgorithm,
regression_et_params,
regression_required_preprocessing,
regression_additional,
regression_default_params,
)
