Module imodels.tree.cart_ccp
Expand source code
from sklearn.tree import DecisionTreeClassifier, export_text, DecisionTreeRegressor
from sklearn.base import BaseEstimator
from sklearn import datasets
from sklearn.datasets import make_friedman1,make_friedman2,make_friedman3
from imodels.util.tree import compute_tree_complexity
from copy import deepcopy
class DecisionTreeClassifierCCP(DecisionTreeClassifier):
def __init__(self, estimator_: BaseEstimator, desired_complexity: int = 1):
self.desired_complexity = desired_complexity
#print('est', estimator_)
self.estimator_ = estimator_
def fit(self,X,y,sample_weight=None,*args, **kwargs):
path = self.estimator_.cost_complexity_pruning_path(X,y)
ccp_alphas, impurities = path.ccp_alphas, path.impurities
complexities = {}
for alpha in ccp_alphas:
est_params = self.estimator_.get_params()
est_params['ccp_alpha'] = alpha
copied_estimator = deepcopy(self.estimator_).set_params(**est_params)
copied_estimator.fit(X, y)
complexities[alpha] = self._get_complexity(copied_estimator)
closest_alpha, closest_leaves = min(complexities.items(), key=lambda x: abs(self.desired_complexity - x[1]))
params_for_fitting = self.estimator_.get_params()
params_for_fitting['ccp_alpha'] = closest_alpha
self.estimator_.set_params(**params_for_fitting)
self.estimator_.fit(X,y,*args, **kwargs)
def _get_complexity(self,BaseEstimator):
return compute_tree_complexity(BaseEstimator.tree_)
def predict_proba(self, *args, **kwargs):
if hasattr(self.estimator_, 'predict_proba'):
return self.estimator_.predict_proba(*args, **kwargs)
else:
return NotImplemented
def predict(self,X,*args, **kwargs):
return self.estimator_.predict(X,*args, **kwargs)
def score(self, *args, **kwargs):
if hasattr(self.estimator_, 'score'):
return self.estimator_.score(*args, **kwargs)
else:
return NotImplemented
class DecisionTreeRegressorCCP(BaseEstimator):
def __init__(self, estimator_: BaseEstimator, desired_complexity: int = 1):
self.desired_complexity = desired_complexity
#print('est', estimator_)
self.estimator_ = estimator_
def fit(self,X,y,sample_weight=None,*args, **kwargs):
path = self.estimator_.cost_complexity_pruning_path(X,y)
ccp_alphas, impurities = path.ccp_alphas, path.impurities
complexities = {}
for alpha in ccp_alphas:
est_params = self.estimator_.get_params()
est_params['ccp_alpha'] = alpha
copied_estimator = deepcopy(self.estimator_).set_params(**est_params)
copied_estimator.fit(X, y)
complexities[alpha] = self._get_complexity(copied_estimator)
closest_alpha, closest_leaves = min(complexities.items(), key=lambda x: abs(self.desired_complexity - x[1]))
params_for_fitting = self.estimator_.get_params()
params_for_fitting['ccp_alpha'] = closest_alpha
self.estimator_.set_params(**params_for_fitting)
self.estimator_.fit(X,y,*args, **kwargs)
def _get_complexity(self,BaseEstimator):
return compute_tree_complexity(BaseEstimator.tree_)
def predict(self,X,*args, **kwargs):
return self.estimator_.predict(X,*args, **kwargs)
def score(self, *args, **kwargs):
if hasattr(self.estimator_, 'score'):
return self.estimator_.score(*args, **kwargs)
else:
return NotImplemented
if __name__ == '__main__':
m = DecisionTreeClassifierCCP(estimator_=DecisionTreeClassifier(min_samples_leaf = 5),desired_complexity = 10)
#X,y = make_friedman1()
X, y = datasets.load_breast_cancer(return_X_y=True)
m.fit(X,y)
m.predict(X)
m.score(X,y)
Classes
class DecisionTreeClassifierCCP (estimator_: sklearn.base.BaseEstimator, desired_complexity: int = 1)
-
A decision tree classifier.
Read more in the :ref:
User Guide <tree>
.Parameters
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_depth
:int
, 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_split
:int
orfloat
, 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 andceil(min_samples_split * n_samples)
are the minimum number of samples for each split.
Changed in version: 0.18
Added float values for fractions.
- If int, then consider
min_samples_leaf
:int
orfloat
, 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 andceil(min_samples_leaf * n_samples)
are the minimum number of samples for each node.
Changed in version: 0.18
Added float values for fractions.
- If int, then consider
min_weight_fraction_leaf
:float
, 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_features
:int, float
or{"auto", "sqrt", "log2"}
, default=None
-
The number of features to consider when looking for the best split:
- If int, then consider <code>max\_features</code> features at each split. - If float, then <code>max\_features</code> 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_state
:int, RandomState instance
orNone
, 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 :term:Glossary <random_state>
for details. max_leaf_nodes
:int
, 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_decrease
:float
, 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.Added in version: 0.19
class_weight
:dict, list
ofdict
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_alpha
:non-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 :ref:minimal_cost_complexity_pruning
for details.Added in version: 0.22
Attributes
classes_
:ndarray
ofshape (n_classes,)
orlist
ofndarray
- The classes labels (single output problem), or a list of arrays of class labels (multi-output problem).
feature_importances_
:ndarray
ofshape (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 :func:
sklearn.inspection.permutation_importance
as an alternative. max_features_
:int
- The inferred value of max_features.
n_classes_
:int
orlist
ofint
- 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. Usen_features_in_
instead. n_features_in_
:int
-
Number of features seen during :term:
fit
.Added in version: 0.24
feature_names_in_
:ndarray
ofshape (
n_features_in_,)
-
Names of features seen during :term:
fit
. Defined only whenX
has feature names that are all strings.Added 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 :ref:sphx_glr_auto_examples_tree_plot_unveil_tree_structure.py
for basic usage of these attributes.
See Also
DecisionTreeRegressor
- A decision tree regressor.
Notes
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 :meth:
predict
method operates using the :func:numpy.argmax
function on the outputs of :meth:predict_proba
. This means that in case the highest predicted probabilities are tied, the classifier will predict the tied class with the lowest index in :term:classes_
.References
.. [1] https://en.wikipedia.org/wiki/Decision_tree_learning
.. [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
Examples
>>> 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) ... # doctest: +SKIP ... array([ 1. , 0.93..., 0.86..., 0.93..., 0.93..., 0.93..., 0.93..., 1. , 0.93..., 1. ])
Expand source code
class DecisionTreeClassifierCCP(DecisionTreeClassifier): def __init__(self, estimator_: BaseEstimator, desired_complexity: int = 1): self.desired_complexity = desired_complexity #print('est', estimator_) self.estimator_ = estimator_ def fit(self,X,y,sample_weight=None,*args, **kwargs): path = self.estimator_.cost_complexity_pruning_path(X,y) ccp_alphas, impurities = path.ccp_alphas, path.impurities complexities = {} for alpha in ccp_alphas: est_params = self.estimator_.get_params() est_params['ccp_alpha'] = alpha copied_estimator = deepcopy(self.estimator_).set_params(**est_params) copied_estimator.fit(X, y) complexities[alpha] = self._get_complexity(copied_estimator) closest_alpha, closest_leaves = min(complexities.items(), key=lambda x: abs(self.desired_complexity - x[1])) params_for_fitting = self.estimator_.get_params() params_for_fitting['ccp_alpha'] = closest_alpha self.estimator_.set_params(**params_for_fitting) self.estimator_.fit(X,y,*args, **kwargs) def _get_complexity(self,BaseEstimator): return compute_tree_complexity(BaseEstimator.tree_) def predict_proba(self, *args, **kwargs): if hasattr(self.estimator_, 'predict_proba'): return self.estimator_.predict_proba(*args, **kwargs) else: return NotImplemented def predict(self,X,*args, **kwargs): return self.estimator_.predict(X,*args, **kwargs) def score(self, *args, **kwargs): if hasattr(self.estimator_, 'score'): return self.estimator_.score(*args, **kwargs) else: return NotImplemented
Ancestors
- sklearn.tree._classes.DecisionTreeClassifier
- sklearn.base.ClassifierMixin
- sklearn.tree._classes.BaseDecisionTree
- sklearn.base.MultiOutputMixin
- sklearn.base.BaseEstimator
Methods
def fit(self, X, y, sample_weight=None, *args, **kwargs)
-
Build a decision tree classifier from the training set (X, y).
Parameters
X
:{array-like, sparse matrix}
ofshape (n_samples, n_features)
- The training input samples. Internally, it will be converted to
dtype=np.float32
and if a sparse matrix is provided to a sparsecsc_matrix
. y
:array-like
ofshape (n_samples,)
or(n_samples, n_outputs)
- The target values (class labels) as integers or strings.
sample_weight
:array-like
ofshape (n_samples,)
, default=None
- Sample weights. If None, then samples are equally weighted. Splits that would create child nodes with net zero or negative weight are ignored while searching for a split in each node. Splits are also ignored if they would result in any single class carrying a negative weight in either child node.
check_input
:bool
, default=True
- Allow to bypass several input checking. Don't use this parameter unless you know what you do.
X_idx_sorted
:deprecated
, default="deprecated"
-
This parameter is deprecated and has no effect. It will be removed in 1.1 (renaming of 0.26).
Deprecated since version: 0.24
Returns
self
:DecisionTreeClassifier
- Fitted estimator.
Expand source code
def fit(self,X,y,sample_weight=None,*args, **kwargs): path = self.estimator_.cost_complexity_pruning_path(X,y) ccp_alphas, impurities = path.ccp_alphas, path.impurities complexities = {} for alpha in ccp_alphas: est_params = self.estimator_.get_params() est_params['ccp_alpha'] = alpha copied_estimator = deepcopy(self.estimator_).set_params(**est_params) copied_estimator.fit(X, y) complexities[alpha] = self._get_complexity(copied_estimator) closest_alpha, closest_leaves = min(complexities.items(), key=lambda x: abs(self.desired_complexity - x[1])) params_for_fitting = self.estimator_.get_params() params_for_fitting['ccp_alpha'] = closest_alpha self.estimator_.set_params(**params_for_fitting) self.estimator_.fit(X,y,*args, **kwargs)
def predict(self, X, *args, **kwargs)
-
Predict class or regression value for X.
For a classification model, the predicted class for each sample in X is returned. For a regression model, the predicted value based on X is returned.
Parameters
X
:{array-like, sparse matrix}
ofshape (n_samples, n_features)
- The input samples. Internally, it will be converted to
dtype=np.float32
and if a sparse matrix is provided to a sparsecsr_matrix
. check_input
:bool
, default=True
- Allow to bypass several input checking. Don't use this parameter unless you know what you do.
Returns
y
:array-like
ofshape (n_samples,)
or(n_samples, n_outputs)
- The predicted classes, or the predict values.
Expand source code
def predict(self,X,*args, **kwargs): return self.estimator_.predict(X,*args, **kwargs)
def predict_proba(self, *args, **kwargs)
-
Predict class probabilities of the input samples X.
The predicted class probability is the fraction of samples of the same class in a leaf.
Parameters
X
:{array-like, sparse matrix}
ofshape (n_samples, n_features)
- The input samples. Internally, it will be converted to
dtype=np.float32
and if a sparse matrix is provided to a sparsecsr_matrix
. check_input
:bool
, default=True
- Allow to bypass several input checking. Don't use this parameter unless you know what you do.
Returns
proba
:ndarray
ofshape (n_samples, n_classes)
orlist
ofn_outputs such arrays if n_outputs > 1
- The class probabilities of the input samples. The order of the
classes corresponds to that in the attribute :term:
classes_
.
Expand source code
def predict_proba(self, *args, **kwargs): if hasattr(self.estimator_, 'predict_proba'): return self.estimator_.predict_proba(*args, **kwargs) else: return NotImplemented
def score(self, *args, **kwargs)
-
Return the mean accuracy on the given test data and labels.
In multi-label classification, this is the subset accuracy which is a harsh metric since you require for each sample that each label set be correctly predicted.
Parameters
X
:array-like
ofshape (n_samples, n_features)
- Test samples.
y
:array-like
ofshape (n_samples,)
or(n_samples, n_outputs)
- True labels for
X
. sample_weight
:array-like
ofshape (n_samples,)
, default=None
- Sample weights.
Returns
score
:float
- Mean accuracy of
self.predict(X)
wrt.y
.
Expand source code
def score(self, *args, **kwargs): if hasattr(self.estimator_, 'score'): return self.estimator_.score(*args, **kwargs) else: return NotImplemented
class DecisionTreeRegressorCCP (estimator_: sklearn.base.BaseEstimator, desired_complexity: int = 1)
-
Base class for all estimators in scikit-learn.
Notes
All estimators should specify all the parameters that can be set at the class level in their
__init__
as explicit keyword arguments (no*args
or**kwargs
).Expand source code
class DecisionTreeRegressorCCP(BaseEstimator): def __init__(self, estimator_: BaseEstimator, desired_complexity: int = 1): self.desired_complexity = desired_complexity #print('est', estimator_) self.estimator_ = estimator_ def fit(self,X,y,sample_weight=None,*args, **kwargs): path = self.estimator_.cost_complexity_pruning_path(X,y) ccp_alphas, impurities = path.ccp_alphas, path.impurities complexities = {} for alpha in ccp_alphas: est_params = self.estimator_.get_params() est_params['ccp_alpha'] = alpha copied_estimator = deepcopy(self.estimator_).set_params(**est_params) copied_estimator.fit(X, y) complexities[alpha] = self._get_complexity(copied_estimator) closest_alpha, closest_leaves = min(complexities.items(), key=lambda x: abs(self.desired_complexity - x[1])) params_for_fitting = self.estimator_.get_params() params_for_fitting['ccp_alpha'] = closest_alpha self.estimator_.set_params(**params_for_fitting) self.estimator_.fit(X,y,*args, **kwargs) def _get_complexity(self,BaseEstimator): return compute_tree_complexity(BaseEstimator.tree_) def predict(self,X,*args, **kwargs): return self.estimator_.predict(X,*args, **kwargs) def score(self, *args, **kwargs): if hasattr(self.estimator_, 'score'): return self.estimator_.score(*args, **kwargs) else: return NotImplemented
Ancestors
- sklearn.base.BaseEstimator
Methods
def fit(self, X, y, sample_weight=None, *args, **kwargs)
-
Expand source code
def fit(self,X,y,sample_weight=None,*args, **kwargs): path = self.estimator_.cost_complexity_pruning_path(X,y) ccp_alphas, impurities = path.ccp_alphas, path.impurities complexities = {} for alpha in ccp_alphas: est_params = self.estimator_.get_params() est_params['ccp_alpha'] = alpha copied_estimator = deepcopy(self.estimator_).set_params(**est_params) copied_estimator.fit(X, y) complexities[alpha] = self._get_complexity(copied_estimator) closest_alpha, closest_leaves = min(complexities.items(), key=lambda x: abs(self.desired_complexity - x[1])) params_for_fitting = self.estimator_.get_params() params_for_fitting['ccp_alpha'] = closest_alpha self.estimator_.set_params(**params_for_fitting) self.estimator_.fit(X,y,*args, **kwargs)
def predict(self, X, *args, **kwargs)
-
Expand source code
def predict(self,X,*args, **kwargs): return self.estimator_.predict(X,*args, **kwargs)
def score(self, *args, **kwargs)
-
Expand source code
def score(self, *args, **kwargs): if hasattr(self.estimator_, 'score'): return self.estimator_.score(*args, **kwargs) else: return NotImplemented