simpleml.models.classifiers.sklearn.tree
Wrapper module around sklearn.tree
Module Contents
Classes
No different than base model. Here just to maintain the pattern |
|
No different than base model. Here just to maintain the pattern |
|
A decision tree classifier. |
|
An extremely randomized tree classifier. |
Attributes
Trees |
|
- class simpleml.models.classifiers.sklearn.tree.SklearnDecisionTreeClassifier(has_external_files=True, external_model_kwargs=None, params=None, fitted=False, pipeline_id=None, **kwargs)[source]
Bases:
simpleml.models.classifiers.sklearn.base_sklearn_classifier.SklearnClassifier
No different than base model. Here just to maintain the pattern Generic Base -> Library Base -> Domain Base -> Individual Models (ex: [Library]Model -> SklearnModel -> SklearnClassifier -> SklearnLogisticRegression)
Need to explicitly separate passthrough kwargs to external models since most do not support arbitrary **kwargs in the constructors
Two supported patterns - full initialization in constructor or stepwise configured before fit and save
- Parameters
- class simpleml.models.classifiers.sklearn.tree.SklearnExtraTreeClassifier(has_external_files=True, external_model_kwargs=None, params=None, fitted=False, pipeline_id=None, **kwargs)[source]
Bases:
simpleml.models.classifiers.sklearn.base_sklearn_classifier.SklearnClassifier
No different than base model. Here just to maintain the pattern Generic Base -> Library Base -> Domain Base -> Individual Models (ex: [Library]Model -> SklearnModel -> SklearnClassifier -> SklearnLogisticRegression)
Need to explicitly separate passthrough kwargs to external models since most do not support arbitrary **kwargs in the constructors
Two supported patterns - full initialization in constructor or stepwise configured before fit and save
- Parameters
- class simpleml.models.classifiers.sklearn.tree.WrappedSklearnDecisionTreeClassifier(*, criterion='gini', splitter='best', max_depth=None, min_samples_split=2, min_samples_leaf=1, min_weight_fraction_leaf=0.0, max_features=None, random_state=None, max_leaf_nodes=None, min_impurity_decrease=0.0, class_weight=None, ccp_alpha=0.0)[source]
Bases:
sklearn.tree.DecisionTreeClassifier
,simpleml.models.classifiers.external_models.ClassificationExternalModelMixin
A decision tree classifier.
Read more in the User Guide.
- criterion{“gini”, “entropy”}, default=”gini”
The function to measure the quality of a split. Supported criteria are “gini” for the Gini impurity and “entropy” for the information gain.
- splitter{“best”, “random”}, default=”best”
The strategy used to choose the split at each node. Supported strategies are “best” to choose the best split and “random” to choose the best random split.
- max_depthint, default=None
The maximum depth of the tree. If None, then nodes are expanded until all leaves are pure or until all leaves contain less than min_samples_split samples.
- min_samples_splitint or float, default=2
The minimum number of samples required to split an internal node:
If int, then consider min_samples_split as the minimum number.
If float, then min_samples_split is a fraction and ceil(min_samples_split * n_samples) are the minimum number of samples for each split.
Changed in version 0.18: Added float values for fractions.
- min_samples_leafint or float, default=1
The minimum number of samples required to be at a leaf node. A split point at any depth will only be considered if it leaves at least
min_samples_leaf
training samples in each of the left and right branches. This may have the effect of smoothing the model, especially in regression.If int, then consider min_samples_leaf as the minimum number.
If float, then min_samples_leaf is a fraction and ceil(min_samples_leaf * n_samples) are the minimum number of samples for each node.
Changed in version 0.18: Added float values for fractions.
- min_weight_fraction_leaffloat, default=0.0
The minimum weighted fraction of the sum total of weights (of all the input samples) required to be at a leaf node. Samples have equal weight when sample_weight is not provided.
- max_featuresint, float or {“auto”, “sqrt”, “log2”}, default=None
The number of features to consider when looking for the best split:
If int, then consider max_features features at each split.
If float, then max_features is a fraction and int(max_features * n_features) features are considered at each split.
If “auto”, then max_features=sqrt(n_features).
If “sqrt”, then max_features=sqrt(n_features).
If “log2”, then max_features=log2(n_features).
If None, then max_features=n_features.
Note: the search for a split does not stop until at least one valid partition of the node samples is found, even if it requires to effectively inspect more than
max_features
features.- random_stateint, RandomState instance or None, default=None
Controls the randomness of the estimator. The features are always randomly permuted at each split, even if
splitter
is set to"best"
. Whenmax_features < n_features
, the algorithm will selectmax_features
at random at each split before finding the best split among them. But the best found split may vary across different runs, even ifmax_features=n_features
. That is the case, if the improvement of the criterion is identical for several splits and one split has to be selected at random. To obtain a deterministic behaviour during fitting,random_state
has to be fixed to an integer. See Glossary for details.- max_leaf_nodesint, default=None
Grow a tree with
max_leaf_nodes
in best-first fashion. Best nodes are defined as relative reduction in impurity. If None then unlimited number of leaf nodes.- min_impurity_decreasefloat, default=0.0
A node will be split if this split induces a decrease of the impurity greater than or equal to this value.
The weighted impurity decrease equation is the following:
N_t / N * (impurity - N_t_R / N_t * right_impurity - N_t_L / N_t * left_impurity)
where
N
is the total number of samples,N_t
is the number of samples at the current node,N_t_L
is the number of samples in the left child, andN_t_R
is the number of samples in the right child.N
,N_t
,N_t_R
andN_t_L
all refer to the weighted sum, ifsample_weight
is passed.New in version 0.19.
- class_weightdict, list of dict or “balanced”, default=None
Weights associated with classes in the form
{class_label: weight}
. If None, all classes are supposed to have weight one. For multi-output problems, a list of dicts can be provided in the same order as the columns of y.Note that for multioutput (including multilabel) weights should be defined for each class of every column in its own dict. For example, for four-class multilabel classification weights should be [{0: 1, 1: 1}, {0: 1, 1: 5}, {0: 1, 1: 1}, {0: 1, 1: 1}] instead of [{1:1}, {2:5}, {3:1}, {4:1}].
The “balanced” mode uses the values of y to automatically adjust weights inversely proportional to class frequencies in the input data as
n_samples / (n_classes * np.bincount(y))
For multi-output, the weights of each column of y will be multiplied.
Note that these weights will be multiplied with sample_weight (passed through the fit method) if sample_weight is specified.
- ccp_alphanon-negative float, default=0.0
Complexity parameter used for Minimal Cost-Complexity Pruning. The subtree with the largest cost complexity that is smaller than
ccp_alpha
will be chosen. By default, no pruning is performed. See minimal_cost_complexity_pruning for details.New in version 0.22.
- classesndarray of shape (n_classes,) or list of ndarray
The classes labels (single output problem), or a list of arrays of class labels (multi-output problem).
- feature_importances_ndarray of shape (n_features,)
The impurity-based feature importances. The higher, the more important the feature. The importance of a feature is computed as the (normalized) total reduction of the criterion brought by that feature. It is also known as the Gini importance 4.
Warning: impurity-based feature importances can be misleading for high cardinality features (many unique values). See
sklearn.inspection.permutation_importance()
as an alternative.- max_features_int
The inferred value of max_features.
- n_classes_int or list of int
The number of classes (for single output problems), or a list containing the number of classes for each output (for multi-output problems).
- n_features_int
The number of features when
fit
is performed.Deprecated since version 1.0: n_features_ is deprecated in 1.0 and will be removed in 1.2. Use n_features_in_ instead.
- n_features_in_int
Number of features seen during fit.
New in version 0.24.
- feature_names_in_ndarray of shape (n_features_in_,)
Names of features seen during fit. Defined only when X has feature names that are all strings.
New in version 1.0.
- n_outputs_int
The number of outputs when
fit
is performed.- tree_Tree instance
The underlying Tree object. Please refer to
help(sklearn.tree._tree.Tree)
for attributes of Tree object and sphx_glr_auto_examples_tree_plot_unveil_tree_structure.py for basic usage of these attributes.
DecisionTreeRegressor : A decision tree regressor.
The default values for the parameters controlling the size of the trees (e.g.
max_depth
,min_samples_leaf
, etc.) lead to fully grown and unpruned trees which can potentially be very large on some data sets. To reduce memory consumption, the complexity and size of the trees should be controlled by setting those parameter values.The
predict()
method operates using thenumpy.argmax()
function on the outputs ofpredict_proba()
. This means that in case the highest predicted probabilities are tied, the classifier will predict the tied class with the lowest index in classes_.- 1
- 2
L. Breiman, J. Friedman, R. Olshen, and C. Stone, “Classification and Regression Trees”, Wadsworth, Belmont, CA, 1984.
- 3
T. Hastie, R. Tibshirani and J. Friedman. “Elements of Statistical Learning”, Springer, 2009.
- 4
L. Breiman, and A. Cutler, “Random Forests”, https://www.stat.berkeley.edu/~breiman/RandomForests/cc_home.htm
>>> from sklearn.datasets import load_iris >>> from sklearn.model_selection import cross_val_score >>> from sklearn.tree import DecisionTreeClassifier >>> clf = DecisionTreeClassifier(random_state=0) >>> iris = load_iris() >>> cross_val_score(clf, iris.data, iris.target, cv=10) ... ... array([ 1. , 0.93..., 0.86..., 0.93..., 0.93..., 0.93..., 0.93..., 1. , 0.93..., 1. ])
- class simpleml.models.classifiers.sklearn.tree.WrappedSklearnExtraTreeClassifier(*, criterion='gini', splitter='random', max_depth=None, min_samples_split=2, min_samples_leaf=1, min_weight_fraction_leaf=0.0, max_features='auto', random_state=None, max_leaf_nodes=None, min_impurity_decrease=0.0, class_weight=None, ccp_alpha=0.0)[source]
Bases:
sklearn.tree.ExtraTreeClassifier
,simpleml.models.classifiers.external_models.ClassificationExternalModelMixin
An extremely randomized tree classifier.
Extra-trees differ from classic decision trees in the way they are built. When looking for the best split to separate the samples of a node into two groups, random splits are drawn for each of the max_features randomly selected features and the best split among those is chosen. When max_features is set 1, this amounts to building a totally random decision tree.
Warning: Extra-trees should only be used within ensemble methods.
Read more in the User Guide.
- criterion{“gini”, “entropy”}, default=”gini”
The function to measure the quality of a split. Supported criteria are “gini” for the Gini impurity and “entropy” for the information gain.
- splitter{“random”, “best”}, default=”random”
The strategy used to choose the split at each node. Supported strategies are “best” to choose the best split and “random” to choose the best random split.
- max_depthint, default=None
The maximum depth of the tree. If None, then nodes are expanded until all leaves are pure or until all leaves contain less than min_samples_split samples.
- min_samples_splitint or float, default=2
The minimum number of samples required to split an internal node:
If int, then consider min_samples_split as the minimum number.
If float, then min_samples_split is a fraction and ceil(min_samples_split * n_samples) are the minimum number of samples for each split.
Changed in version 0.18: Added float values for fractions.
- min_samples_leafint or float, default=1
The minimum number of samples required to be at a leaf node. A split point at any depth will only be considered if it leaves at least
min_samples_leaf
training samples in each of the left and right branches. This may have the effect of smoothing the model, especially in regression.If int, then consider min_samples_leaf as the minimum number.
If float, then min_samples_leaf is a fraction and ceil(min_samples_leaf * n_samples) are the minimum number of samples for each node.
Changed in version 0.18: Added float values for fractions.
- min_weight_fraction_leaffloat, default=0.0
The minimum weighted fraction of the sum total of weights (of all the input samples) required to be at a leaf node. Samples have equal weight when sample_weight is not provided.
- max_featuresint, float, {“auto”, “sqrt”, “log2”} or None, default=”auto”
The number of features to consider when looking for the best split:
If int, then consider max_features features at each split.
If float, then max_features is a fraction and int(max_features * n_features) features are considered at each split.
If “auto”, then max_features=sqrt(n_features).
If “sqrt”, then max_features=sqrt(n_features).
If “log2”, then max_features=log2(n_features).
If None, then max_features=n_features.
Note: the search for a split does not stop until at least one valid partition of the node samples is found, even if it requires to effectively inspect more than
max_features
features.- random_stateint, RandomState instance or None, default=None
Used to pick randomly the max_features used at each split. See Glossary for details.
- max_leaf_nodesint, default=None
Grow a tree with
max_leaf_nodes
in best-first fashion. Best nodes are defined as relative reduction in impurity. If None then unlimited number of leaf nodes.- min_impurity_decreasefloat, default=0.0
A node will be split if this split induces a decrease of the impurity greater than or equal to this value.
The weighted impurity decrease equation is the following:
N_t / N * (impurity - N_t_R / N_t * right_impurity - N_t_L / N_t * left_impurity)
where
N
is the total number of samples,N_t
is the number of samples at the current node,N_t_L
is the number of samples in the left child, andN_t_R
is the number of samples in the right child.N
,N_t
,N_t_R
andN_t_L
all refer to the weighted sum, ifsample_weight
is passed.New in version 0.19.
- class_weightdict, list of dict or “balanced”, default=None
Weights associated with classes in the form
{class_label: weight}
. If None, all classes are supposed to have weight one. For multi-output problems, a list of dicts can be provided in the same order as the columns of y.Note that for multioutput (including multilabel) weights should be defined for each class of every column in its own dict. For example, for four-class multilabel classification weights should be [{0: 1, 1: 1}, {0: 1, 1: 5}, {0: 1, 1: 1}, {0: 1, 1: 1}] instead of [{1:1}, {2:5}, {3:1}, {4:1}].
The “balanced” mode uses the values of y to automatically adjust weights inversely proportional to class frequencies in the input data as
n_samples / (n_classes * np.bincount(y))
For multi-output, the weights of each column of y will be multiplied.
Note that these weights will be multiplied with sample_weight (passed through the fit method) if sample_weight is specified.
- ccp_alphanon-negative float, default=0.0
Complexity parameter used for Minimal Cost-Complexity Pruning. The subtree with the largest cost complexity that is smaller than
ccp_alpha
will be chosen. By default, no pruning is performed. See minimal_cost_complexity_pruning for details.New in version 0.22.
- classesndarray of shape (n_classes,) or list of ndarray
The classes labels (single output problem), or a list of arrays of class labels (multi-output problem).
- max_features_int
The inferred value of max_features.
- n_classes_int or list of int
The number of classes (for single output problems), or a list containing the number of classes for each output (for multi-output problems).
- feature_importances_ndarray of shape (n_features,)
The impurity-based feature importances. The higher, the more important the feature. The importance of a feature is computed as the (normalized) total reduction of the criterion brought by that feature. It is also known as the Gini importance.
Warning: impurity-based feature importances can be misleading for high cardinality features (many unique values). See
sklearn.inspection.permutation_importance()
as an alternative.- n_features_int
The number of features when
fit
is performed.Deprecated since version 1.0: n_features_ is deprecated in 1.0 and will be removed in 1.2. Use n_features_in_ instead.
- n_features_in_int
Number of features seen during fit.
New in version 0.24.
- feature_names_in_ndarray of shape (n_features_in_,)
Names of features seen during fit. Defined only when X has feature names that are all strings.
New in version 1.0.
- n_outputs_int
The number of outputs when
fit
is performed.- tree_Tree instance
The underlying Tree object. Please refer to
help(sklearn.tree._tree.Tree)
for attributes of Tree object and sphx_glr_auto_examples_tree_plot_unveil_tree_structure.py for basic usage of these attributes.
ExtraTreeRegressor : An extremely randomized tree regressor. sklearn.ensemble.ExtraTreesClassifier : An extra-trees classifier. sklearn.ensemble.ExtraTreesRegressor : An extra-trees regressor. sklearn.ensemble.RandomForestClassifier : A random forest classifier. sklearn.ensemble.RandomForestRegressor : A random forest regressor. sklearn.ensemble.RandomTreesEmbedding : An ensemble of
totally random trees.
The default values for the parameters controlling the size of the trees (e.g.
max_depth
,min_samples_leaf
, etc.) lead to fully grown and unpruned trees which can potentially be very large on some data sets. To reduce memory consumption, the complexity and size of the trees should be controlled by setting those parameter values.- 1
P. Geurts, D. Ernst., and L. Wehenkel, “Extremely randomized trees”, Machine Learning, 63(1), 3-42, 2006.
>>> from sklearn.datasets import load_iris >>> from sklearn.model_selection import train_test_split >>> from sklearn.ensemble import BaggingClassifier >>> from sklearn.tree import ExtraTreeClassifier >>> X, y = load_iris(return_X_y=True) >>> X_train, X_test, y_train, y_test = train_test_split( ... X, y, random_state=0) >>> extra_tree = ExtraTreeClassifier(random_state=0) >>> cls = BaggingClassifier(extra_tree, random_state=0).fit( ... X_train, y_train) >>> cls.score(X_test, y_test) 0.8947...