def deserialize_tree(tree_dict, n_features, n_classes, n_outputs):
tree_dict['nodes'] = [tuple(lst) for lst in tree_dict['nodes']]
names = [
'left_child', 'right_child', 'feature', 'threshold', 'impurity',
'n_node_samples', 'weighted_n_node_samples'
]
tree_dict['nodes'] = np.array(tree_dict['nodes'],
dtype=np.dtype({
'names':
names,
'formats':
tree_dict['nodes_dtype']
}))
tree_dict['values'] = np.array(tree_dict['values'])
tree = Tree(n_features, np.array([n_classes], dtype=np.intp), n_outputs)
tree.__setstate__(tree_dict)
return tree
def estimators_(self):
if hasattr(self, '_cached_estimators_'):
if self._cached_estimators_:
return self._cached_estimators_
if LooseVersion(sklearn_version) >= LooseVersion("0.22"):
check_is_fitted(self)
else:
check_is_fitted(self, 'daal_model_')
# convert model to estimators
est = DecisionTreeRegressor(
criterion=self.criterion,
max_depth=self.max_depth,
min_samples_split=self.min_samples_split,
min_samples_leaf=self.min_samples_leaf,
min_weight_fraction_leaf=self.min_weight_fraction_leaf,
max_features=self.max_features,
max_leaf_nodes=self.max_leaf_nodes,
min_impurity_decrease=self.min_impurity_decrease,
min_impurity_split=self.min_impurity_split,
random_state=None)
# we need to set est.tree_ field with Trees constructed from Intel(R) DAAL solution
estimators_ = []
for i in range(self.n_estimators):
est_i = clone(est)
est_i.n_features_ = self.n_features_
est_i.n_outputs_ = self.n_outputs_
tree_i_state_class = daal4py.getTreeState(self.daal_model_, i)
tree_i_state_dict = {
'max_depth' : tree_i_state_class.max_depth,
'node_count' : tree_i_state_class.node_count,
'nodes' : tree_i_state_class.node_ar,
'values': tree_i_state_class.value_ar }
est_i.tree_ = Tree(self.n_features_, np.array([1], dtype=np.intp), self.n_outputs_)
est_i.tree_.__setstate__(tree_i_state_dict)
estimators_.append(est_i)
return estimators_
def _estimators_(self):
if hasattr(self, '_cached_estimators_'):
if self._cached_estimators_:
return self._cached_estimators_
if LooseVersion(sklearn_version) >= LooseVersion("0.22"):
check_is_fitted(self)
else:
check_is_fitted(self, 'daal_model_')
classes_ = self.classes_[0]
n_classes_ = self.n_classes_[0]
# convert model to estimators
params = {
'criterion': self.criterion,
'max_depth': self.max_depth,
'min_samples_split': self.min_samples_split,
'min_samples_leaf': self.min_samples_leaf,
'min_weight_fraction_leaf': self.min_weight_fraction_leaf,
'max_features': self.max_features,
'max_leaf_nodes': self.max_leaf_nodes,
'min_impurity_decrease': self.min_impurity_decrease,
'random_state': None,
}
if not sklearn_check_version('1.0'):
params['min_impurity_split'] = self.min_impurity_split
est = DecisionTreeClassifier(**params)
# we need to set est.tree_ field with Trees constructed from Intel(R)
# oneAPI Data Analytics Library solution
estimators_ = []
random_state_checked = check_random_state(self.random_state)
for i in range(self.n_estimators):
# print("Tree #{}".format(i))
est_i = clone(est)
est_i.set_params(random_state=random_state_checked.randint(
np.iinfo(np.int32).max))
if sklearn_check_version('1.0'):
est_i.n_features_in_ = self.n_features_in_
else:
est_i.n_features_ = self.n_features_in_
est_i.n_outputs_ = self.n_outputs_
est_i.classes_ = classes_
est_i.n_classes_ = n_classes_
# treeState members: 'class_count', 'leaf_count', 'max_depth',
# 'node_ar', 'node_count', 'value_ar'
tree_i_state_class = daal4py.getTreeState(self.daal_model_, i,
n_classes_)
# node_ndarray = tree_i_state_class.node_ar
# value_ndarray = tree_i_state_class.value_ar
# value_shape = (node_ndarray.shape[0], self.n_outputs_,
# n_classes_)
# assert np.allclose(
# value_ndarray, value_ndarray.astype(np.intc, casting='unsafe')
# ), "Value array is non-integer"
tree_i_state_dict = {
'max_depth': tree_i_state_class.max_depth,
'node_count': tree_i_state_class.node_count,
'nodes': tree_i_state_class.node_ar,
'values': tree_i_state_class.value_ar
}
est_i.tree_ = Tree(self.n_features_in_,
np.array([n_classes_], dtype=np.intp),
self.n_outputs_)
est_i.tree_.__setstate__(tree_i_state_dict)
estimators_.append(est_i)
self._cached_estimators_ = estimators_
return estimators_
def fit(self,
X,
y,
sample_mask=None,
X_argsorted=None,
check_input=True,
sample_weight=None):
"""Build a decision tree from the training set (X, y).
Parameters
----------
X : array-like, shape = [n_samples, n_features]
The training input samples. Use ``dtype=np.float32`` for maximum
efficiency.
y : array-like, shape = [n_samples] or [n_samples, n_outputs]
The target values (integers that correspond to classes in
classification, real numbers in regression).
Use ``dtype=np.float64`` and ``order='C'`` for maximum
efficiency.
sample_weight : array-like, shape = [n_samples] or 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. In the case of
classification, splits are also ignored if they would result in any
single class carrying a negative weight in either child node.
check_input : boolean, (default=True)
Allow to bypass several input checking.
Don't use this parameter unless you know what you do.
Returns
-------
self : object
Returns self.
"""
random_state = check_random_state(self.random_state)
# Deprecations
if sample_mask is not None:
warn(
"The sample_mask parameter is deprecated as of version 0.14 "
"and will be removed in 0.16.", DeprecationWarning)
if X_argsorted is not None:
warn(
"The X_argsorted parameter is deprecated as of version 0.14 "
"and will be removed in 0.16.", DeprecationWarning)
# Convert data
if check_input:
X, = check_arrays(X,
dtype=DTYPE,
sparse_format="dense",
check_ccontiguous=True)
# Determine output settings
n_samples, self.n_features_ = X.shape
is_classification = isinstance(self, ClassifierMixin)
y = np.atleast_1d(y)
if y.ndim == 1:
# reshape is necessary to preserve the data contiguity against vs
# [:, np.newaxis] that does not.
y = np.reshape(y, (-1, 1))
self.n_outputs_ = y.shape[1]
if is_classification:
y = np.copy(y)
self.classes_ = []
self.n_classes_ = []
for k in xrange(self.n_outputs_):
classes_k, y[:, k] = unique(y[:, k], return_inverse=True)
self.classes_.append(classes_k)
self.n_classes_.append(classes_k.shape[0])
else:
self.classes_ = [None] * self.n_outputs_
self.n_classes_ = [1] * self.n_outputs_
self.n_classes_ = np.array(self.n_classes_, dtype=np.intp)
if getattr(y, "dtype", None) != DOUBLE or not y.flags.contiguous:
y = np.ascontiguousarray(y, dtype=DOUBLE)
# Check parameters
max_depth = (2**31) - 1 if self.max_depth is None else self.max_depth
if isinstance(self.max_features, six.string_types):
if self.max_features == "auto":
if is_classification:
max_features = max(1, int(np.sqrt(self.n_features_)))
else:
max_features = self.n_features_
elif self.max_features == "sqrt":
max_features = max(1, int(np.sqrt(self.n_features_)))
elif self.max_features == "log2":
max_features = max(1, int(np.log2(self.n_features_)))
else:
raise ValueError(
'Invalid value for max_features. Allowed string '
'values are "auto", "sqrt" or "log2".')
elif self.max_features is None:
max_features = self.n_features_
elif isinstance(self.max_features, (numbers.Integral, np.integer)):
max_features = self.max_features
else: # float
max_features = int(self.max_features * self.n_features_)
if len(y) != n_samples:
raise ValueError("Number of labels=%d does not match "
"number of samples=%d" % (len(y), n_samples))
if self.min_samples_split <= 0:
raise ValueError("min_samples_split must be greater than zero.")
if self.min_samples_leaf <= 0:
raise ValueError("min_samples_leaf must be greater than zero.")
if max_depth <= 0:
raise ValueError("max_depth must be greater than zero. ")
if not (0 < max_features <= self.n_features_):
raise ValueError("max_features must be in (0, n_features]")
if sample_weight is not None:
if (getattr(sample_weight, "dtype", None) != DOUBLE
or not sample_weight.flags.contiguous):
sample_weight = np.ascontiguousarray(sample_weight,
dtype=DOUBLE)
if len(sample_weight.shape) > 1:
raise ValueError("Sample weights array has more "
"than one dimension: %d" %
len(sample_weight.shape))
if len(sample_weight) != n_samples:
raise ValueError("Number of weights=%d does not match "
"number of samples=%d" %
(len(sample_weight), n_samples))
# Set min_samples_split sensibly
min_samples_split = max(self.min_samples_split,
2 * self.min_samples_leaf)
# Build tree
criterion = self.criterion
if not isinstance(criterion, Criterion):
if is_classification:
criterion = CRITERIA_CLF[self.criterion](self.n_outputs_,
self.n_classes_)
else:
criterion = CRITERIA_REG[self.criterion](self.n_outputs_)
splitter = self.splitter
if not isinstance(self.splitter, Splitter):
splitter = SPLITTERS[self.splitter](criterion, max_features,
self.min_samples_leaf,
random_state)
self.criterion_ = criterion
self.splitter_ = splitter
self.tree_ = Tree(self.n_features_, self.n_classes_, self.n_outputs_,
splitter, max_depth, min_samples_split,
self.min_samples_leaf, random_state)
self.tree_.build(X, y, sample_weight=sample_weight)
if self.n_outputs_ == 1:
self.n_classes_ = self.n_classes_[0]
self.classes_ = self.classes_[0]
return self
def __init__(self, max_depth=3, min_samples_leaf=25, debug=False):
self.max_depth = max_depth
self.min_samples_leaf = min_samples_leaf
self.tree = Tree(np.size(X, axis=1), np.array([1]), 1)
self.debug = debug
def fit(self, X, y, sample_weight=None, check_input=True,
X_idx_sorted=None):
random_state = check_random_state(self.random_state)
if self.ccp_alpha < 0.0:
raise ValueError(
"ccp_alpha must be greater than or equal to 0")
if check_input:
# Need to validate separately here.
# We can't pass multi_ouput=True because that would allow y to be
# csr.
check_X_params = dict(dtype=DTYPE, accept_sparse="csc")
check_y_params = dict(ensure_2d=False, dtype=None)
X, y = self._validate_data(X, y,
validate_separately=(check_X_params,
check_y_params))
if issparse(X):
X.sort_indices()
if X.indices.dtype != np.intc or X.indptr.dtype != np.intc:
raise ValueError("No support for np.int64 index based "
"sparse matrices")
# Determine output settings
n_samples, self.n_features_ = X.shape
is_classification = is_classifier(self)
y = np.atleast_1d(y)
expanded_class_weight = None
if y.ndim == 1:
# reshape is necessary to preserve the data contiguity against vs
# [:, np.newaxis] that does not.
y = np.reshape(y, (-1, 1))
self.n_outputs_ = y.shape[1]
if is_classification:
check_classification_targets(y)
y = np.copy(y)
# print(y)
self.classes_ = []
self.n_classes_ = []
if self.class_weight is not None:
y_original = np.copy(y)
y_encoded = np.zeros(y.shape, dtype=np.int)
for k in range(self.n_outputs_):
classes_k, y_encoded[:, k] = np.unique(y[:, k],
return_inverse=True)
self.classes_.append(classes_k)
self.n_classes_.append(classes_k.shape[0])
y = y_encoded
if self.class_weight is not None:
expanded_class_weight = compute_sample_weight(
self.class_weight, y_original)
self.n_classes_ = np.array(self.n_classes_, dtype=np.intp)
if getattr(y, "dtype", None) != DOUBLE or not y.flags.contiguous:
y = np.ascontiguousarray(y, dtype=DOUBLE)
# Check parameters
max_depth = (np.iinfo(np.int32).max if self.max_depth is None
else self.max_depth)
max_leaf_nodes = (-1 if self.max_leaf_nodes is None
else self.max_leaf_nodes)
if isinstance(self.min_samples_leaf, numbers.Integral):
if not 1 <= self.min_samples_leaf:
raise ValueError("min_samples_leaf must be at least 1 "
"or in (0, 0.5], got %s"
% self.min_samples_leaf)
min_samples_leaf = self.min_samples_leaf
else: # float
if not 0. < self.min_samples_leaf <= 0.5:
raise ValueError("min_samples_leaf must be at least 1 "
"or in (0, 0.5], got %s"
% self.min_samples_leaf)
min_samples_leaf = int(ceil(self.min_samples_leaf * n_samples))
if isinstance(self.min_samples_split, numbers.Integral):
if not 2 <= self.min_samples_split:
raise ValueError("min_samples_split must be an integer "
"greater than 1 or a float in (0.0, 1.0]; "
"got the integer %s"
% self.min_samples_split)
min_samples_split = self.min_samples_split
else: # float
if not 0. < self.min_samples_split <= 1.:
raise ValueError("min_samples_split must be an integer "
"greater than 1 or a float in (0.0, 1.0]; "
"got the float %s"
% self.min_samples_split)
min_samples_split = int(
ceil(self.min_samples_split * n_samples))
min_samples_split = max(2, min_samples_split)
min_samples_split = max(min_samples_split, 2 * min_samples_leaf)
if isinstance(self.max_features, str):
if self.max_features == "auto":
if is_classification:
max_features = max(1, int(np.sqrt(self.n_features_)))
else:
max_features = self.n_features_
elif self.max_features == "sqrt":
max_features = max(1, int(np.sqrt(self.n_features_)))
elif self.max_features == "log2":
max_features = max(1, int(np.log2(self.n_features_)))
else:
raise ValueError("Invalid value for max_features. "
"Allowed string values are 'auto', "
"'sqrt' or 'log2'.")
elif self.max_features is None:
max_features = self.n_features_
elif isinstance(self.max_features, numbers.Integral):
max_features = self.max_features
else: # float
if self.max_features > 0.0:
max_features = max(1,
int(self.max_features * self.n_features_))
else:
max_features = 0
self.max_features_ = max_features
if len(y) != n_samples:
raise ValueError("Number of labels=%d does not match "
"number of samples=%d" % (len(y), n_samples))
if not 0 <= self.min_weight_fraction_leaf <= 0.5:
raise ValueError("min_weight_fraction_leaf must in [0, 0.5]")
if max_depth <= 0:
raise ValueError("max_depth must be greater than zero. ")
if not (0 < max_features <= self.n_features_):
raise ValueError("max_features must be in (0, n_features]")
if not isinstance(max_leaf_nodes, numbers.Integral):
raise ValueError("max_leaf_nodes must be integral number but was "
"%r" % max_leaf_nodes)
if -1 < max_leaf_nodes < 2:
raise ValueError(("max_leaf_nodes {0} must be either None "
"or larger than 1").format(max_leaf_nodes))
if sample_weight is not None:
sample_weight = _check_sample_weight(sample_weight, X, DOUBLE)
if expanded_class_weight is not None:
if sample_weight is not None:
sample_weight = sample_weight * expanded_class_weight
else:
sample_weight = expanded_class_weight
# Set min_weight_leaf from min_weight_fraction_leaf
if sample_weight is None:
min_weight_leaf = (self.min_weight_fraction_leaf *
n_samples)
else:
min_weight_leaf = (self.min_weight_fraction_leaf *
np.sum(sample_weight))
min_impurity_split = self.min_impurity_split
if min_impurity_split is not None:
warnings.warn("The min_impurity_split parameter is deprecated. "
"Its default value has changed from 1e-7 to 0 in "
"version 0.23, and it will be removed in 0.25. "
"Use the min_impurity_decrease parameter instead.",
FutureWarning)
if min_impurity_split < 0.:
raise ValueError("min_impurity_split must be greater than "
"or equal to 0")
else:
min_impurity_split = 0
if self.min_impurity_decrease < 0.:
raise ValueError("min_impurity_decrease must be greater than "
"or equal to 0")
if self.presort != 'deprecated':
warnings.warn("The parameter 'presort' is deprecated and has no "
"effect. It will be removed in v0.24. You can "
"suppress this warning by not passing any value "
"to the 'presort' parameter.",
FutureWarning)
# Build tree
criterion = self.criterion
if not isinstance(criterion, Criterion):
if is_classification:
criterion = CRITERIA_CLF[self.criterion](self.n_outputs_,
self.n_classes_)
else:
criterion = CRITERIA_REG[self.criterion](self.n_outputs_,
n_samples)
SPLITTERS = SPARSE_SPLITTERS if issparse(X) else DENSE_SPLITTERS
splitter = self.splitter
if not isinstance(self.splitter, Splitter):
splitter = SPLITTERS[self.splitter](criterion,
self.max_features_,
min_samples_leaf,
min_weight_leaf,
random_state)
if is_classifier(self):
self.tree_ = Tree(self.n_features_,
self.n_classes_, self.n_outputs_)
else:
self.tree_ = Tree(self.n_features_,
# TODO: tree should't need this in this case
np.array([1] * self.n_outputs_,
dtype=np.intp),
self.n_outputs_)
# Use BestFirst if max_leaf_nodes given; use DepthFirst otherwise
if max_leaf_nodes < 0:
builder = DepthFirstTreeBuilder(splitter, min_samples_split,
min_samples_leaf,
min_weight_leaf,
max_depth,
self.min_impurity_decrease,
min_impurity_split)
else:
builder = BestFirstTreeBuilder(splitter, min_samples_split,
min_samples_leaf,
min_weight_leaf,
max_depth,
max_leaf_nodes,
self.min_impurity_decrease,
min_impurity_split)
builder.build(self.tree_, X, y, sample_weight, X_idx_sorted)
# print(self.tree_.children_left.shape)
if self.n_outputs_ == 1 and is_classifier(self):
self.n_classes_ = self.n_classes_[0]
self.classes_ = self.classes_[0]
# print(self.tree_.weighted_n_node_samples)
e = self.e
# print(e)
# for i in range(self.tree_.value.shape[0]):
#
# for j in range(self.tree_.value.shape[2]):
#
# self.e = e /((self.tree_.value[i][0][j] + max_depth))
# #print(self.tree_.value[i][0][j])
# self.tree_.value[i][0][j] = self.addNoise(self.tree_.value[i][0][j])
# #print(self.tree_.value[i][0][j])
# print(self.tree_.value[0][0])
for i in range(self.tree_.value.shape[0]):
fr = np.sum(self.tree_.value[i][0])
self.e = e / (fr + max_depth)
self.tree_.value[i][0] = self.addNoise(self.tree_.value[i][0])
self._prune_tree()
# print(self.tree_.value[0][0])
return self
def daal_fit(self, X, y):
self._check_daal_supported_parameters()
_supported_dtypes_ = [np.single, np.double]
X = check_array(X, dtype=_supported_dtypes_)
y = np.atleast_1d(y)
if y.ndim == 2 and y.shape[1] == 1:
warnings.warn(
"A column-vector y was passed when a 1d array was"
" expected. Please change the shape of y to "
"(n_samples,), for example using ravel().",
DataConversionWarning,
stacklevel=2)
check_consistent_length(X, y)
if y.ndim == 1:
# reshape is necessary to preserve the data contiguity against vs
# [:, np.newaxis] that does not.
y = np.reshape(y, (-1, 1))
self.n_outputs_ = y.shape[1]
if self.n_outputs_ != 1:
_class_name = self.__class__.__name__
raise ValueError(
_class_name +
" does not currently support multi-output data. Consider using OneHotEncoder"
)
y = check_array(y, ensure_2d=False, dtype=None)
y, _ = self._validate_y_class_weight(y)
self.n_classes_ = self.n_classes_[0]
self.classes_ = self.classes_[0]
self.n_features_ = X.shape[1]
rs_ = check_random_state(self.random_state)
seed_ = rs_.randint(0, np.iinfo('i').max)
if self.n_classes_ < 2:
raise ValueError(
"Training data only contain information about one class.")
# create algorithm
X_fptype = getFPType(X)
daal_engine_ = daal4py.engines_mt2203(seed=seed_, fptype=X_fptype)
_featuresPerNode = _to_absolute_max_features(self.max_features,
X.shape[1],
is_classification=False)
dfc_algorithm = daal4py.decision_forest_classification_training(
nClasses=int(self.n_classes_),
fptype=X_fptype,
method='defaultDense',
nTrees=int(self.n_estimators),
observationsPerTreeFraction=1,
featuresPerNode=int(_featuresPerNode),
maxTreeDepth=int(0 if self.max_depth is None else self.max_depth),
minObservationsInLeafNode=1,
engine=daal_engine_,
impurityThreshold=float(0.0 if self.min_impurity_split is None else
self.min_impurity_split),
varImportance="MDI",
resultsToCompute="",
memorySavingMode=False,
bootstrap=bool(self.bootstrap))
# compute
dfc_trainingResult = dfc_algorithm.compute(X, y)
# get resulting model
model = dfc_trainingResult.model
self.daal_model_ = model
# convert model to estimators
est = DecisionTreeClassifier(
criterion=self.criterion,
max_depth=self.max_depth,
min_samples_split=self.min_samples_split,
min_samples_leaf=self.min_samples_leaf,
min_weight_fraction_leaf=self.min_weight_fraction_leaf,
max_features=self.max_features,
max_leaf_nodes=self.max_leaf_nodes,
min_impurity_decrease=self.min_impurity_decrease,
min_impurity_split=self.min_impurity_split,
random_state=None)
# we need to set est.tree_ field with Trees constructed from Intel(R) DAAL solution
estimators_ = []
for i in range(self.n_estimators):
# print("Tree #{}".format(i))
est_i = clone(est)
est_i.n_features_ = self.n_features_
est_i.n_outputs_ = self.n_outputs_
est_i.classes_ = self.classes_
est_i.n_classes_ = self.n_classes_
# treeState members: 'class_count', 'leaf_count', 'max_depth', 'node_ar', 'node_count', 'value_ar'
tree_i_state_class = daal4py.getTreeState(model, i,
self.n_classes_)
node_ndarray = tree_i_state_class.node_ar
value_ndarray = tree_i_state_class.value_ar
value_shape = (node_ndarray.shape[0], self.n_outputs_,
self.n_classes_)
# assert np.allclose(value_ndarray, value_ndarray.astype(np.intc, casting='unsafe')), "Value array is non-integer"
tree_i_state_dict = {
'max_depth': tree_i_state_class.max_depth,
'node_count': tree_i_state_class.node_count,
'nodes': tree_i_state_class.node_ar,
'values': tree_i_state_class.value_ar
}
#
est_i.tree_ = Tree(self.n_features_,
np.array([self.n_classes_], dtype=np.intp),
self.n_outputs_)
est_i.tree_.__setstate__(tree_i_state_dict)
estimators_.append(est_i)
self.estimators_ = estimators_
# compute oob_score_
if self.oob_score:
self._set_oob_score(X, y)
return self
def daal_fit(self, X, y):
self._check_daal_supported_parameters()
_supported_dtypes_ = [np.double, np.single]
X = check_array(X, dtype=_supported_dtypes_)
y = np.atleast_1d(y)
if y.ndim == 2 and y.shape[1] == 1:
warnings.warn(
"A column-vector y was passed when a 1d array was"
" expected. Please change the shape of y to "
"(n_samples,), for example using ravel().",
DataConversionWarning,
stacklevel=2)
y = check_array(y, ensure_2d=False, dtype=X.dtype)
check_consistent_length(X, y)
if y.ndim == 1:
# reshape is necessary to preserve the data contiguity against vs
# [:, np.newaxis] that does not.
y = np.reshape(y, (-1, 1))
self.n_outputs_ = y.shape[1]
self.n_features_ = X.shape[1]
rs_ = check_random_state(self.random_state)
if not self.bootstrap and self.oob_score:
raise ValueError("Out of bag estimation only available"
" if bootstrap=True")
X_fptype = getFPType(X)
seed_ = rs_.randint(0, np.iinfo('i').max)
daal_engine = daal4py.engines_mt2203(seed=seed_, fptype=X_fptype)
_featuresPerNode = _to_absolute_max_features(self.max_features,
X.shape[1],
is_classification=False)
# create algorithm
dfr_algorithm = daal4py.decision_forest_regression_training(
fptype=getFPType(X),
method='defaultDense',
nTrees=int(self.n_estimators),
observationsPerTreeFraction=1,
featuresPerNode=int(_featuresPerNode),
maxTreeDepth=int(0 if self.max_depth is None else self.max_depth),
minObservationsInLeafNode=1,
engine=daal_engine,
impurityThreshold=float(0.0 if self.min_impurity_split is None else
self.min_impurity_split),
varImportance="MDI",
resultsToCompute="",
memorySavingMode=False,
bootstrap=bool(self.bootstrap))
dfr_trainingResult = dfr_algorithm.compute(X, y)
# get resulting model
model = dfr_trainingResult.model
self.daal_model_ = model
# convert model to estimators
est = DecisionTreeRegressor(
criterion=self.criterion,
max_depth=self.max_depth,
min_samples_split=self.min_samples_split,
min_samples_leaf=self.min_samples_leaf,
min_weight_fraction_leaf=self.min_weight_fraction_leaf,
max_features=self.max_features,
max_leaf_nodes=self.max_leaf_nodes,
min_impurity_decrease=self.min_impurity_decrease,
min_impurity_split=self.min_impurity_split,
random_state=None)
# we need to set est.tree_ field with Trees constructed from Intel(R) DAAL solution
estimators_ = []
for i in range(self.n_estimators):
est_i = clone(est)
est_i.n_features_ = self.n_features_
est_i.n_outputs_ = self.n_outputs_
tree_i_state_class = daal4py.getTreeState(model, i)
tree_i_state_dict = {
'max_depth': tree_i_state_class.max_depth,
'node_count': tree_i_state_class.node_count,
'nodes': tree_i_state_class.node_ar,
'values': tree_i_state_class.value_ar
}
est_i.tree_ = Tree(self.n_features_, np.array([1], dtype=np.intp),
self.n_outputs_)
est_i.tree_.__setstate__(tree_i_state_dict)
estimators_.append(est_i)
self.estimators_ = estimators_
# compute oob_score_
if self.oob_score:
self._set_oob_score(X, y)
return self
if getattr(y_train, "dtype", None) != DOUBLE or not y_train.flags.contigous:
y_train = np.ascontiguousarray(y_train, dtype=DOUBLE)
max_depth = (np.iinfo(np.int32).max if max_depth is None else max_depth)
max_leaf_nodes = (-1 if max_leaf_nodes is None else max_leaf_nodes)
max_features = max(1, int(np.sqrt(n_features_)))
criterion = CRITERIA_CLF[criterion](n_outputs_, n_classes_)
SPLITTERS = DENSE_SPLITTERS
splitter = SPLITTERS[splitter](criterion, max_features, min_samples_leaf,
min_weight_leaf, random_state)
tree_ = Tree(n_features_, n_classes_, n_outputs_)
builder = DepthFirstTreeBuilder(splitter, min_samples_split, min_samples_leaf,
min_weight_leaf, max_depth,
min_impurity_decrease, min_impurity_split)
builder.build(tree_, X_train, y_train)
classes_ = classes_[0]
n_classes_ = np.atleast_1d(n_classes_)
pruned_tree = Tree(n_features_, n_classes_, n_outputs_)
_build_pruned_tree_ccp(pruned_tree, tree_, 0)
tree_ = pruned_tree
X_test = check_array(X_test, dtype=DTYPE, accept_sparse="csr")
def fit(self,
X,
y,
sample_weight=None,
check_input=True,
X_idx_sorted=None):
"""Build a survival tree from the training set (X, y).
Parameters
----------
X : array-like, shape = (n_samples, n_features)
Data matrix
y : structured array, shape = (n_samples,)
A structured array containing the binary event indicator
as first field, and time of event or time of censoring as
second field.
check_input : boolean, default: True
Allow to bypass several input checking.
Don't use this parameter unless you know what you do.
X_idx_sorted : array-like, shape = (n_samples, n_features), optional
The indexes of the sorted training input samples. If many tree
are grown on the same dataset, this allows the ordering to be
cached between trees. If None, the data will be sorted here.
Don't use this parameter unless you know what to do.
Returns
-------
self
"""
random_state = check_random_state(self.random_state)
if check_input:
X, event, time = check_arrays_survival(X, y)
time = time.astype(np.float64)
self.event_times_ = np.unique(time[event])
y_numeric = np.empty((X.shape[0], 2), dtype=np.float64)
y_numeric[:, 0] = time
y_numeric[:, 1] = event.astype(np.float64)
else:
y_numeric, self.event_times_ = y
n_samples, self.n_features_ = X.shape
params = self._check_params(n_samples)
self.n_outputs_ = self.event_times_.shape[0]
# one "class" for CHF, one for survival function
self.n_classes_ = np.ones(self.n_outputs_, dtype=np.intp) * 2
# Build tree
criterion = LogrankCriterion(self.n_outputs_, n_samples,
self.event_times_)
splitter = self.splitter
if not isinstance(self.splitter, Splitter):
splitter = DENSE_SPLITTERS[self.splitter](
criterion, self.max_features_, params["min_samples_leaf"],
params["min_weight_leaf"], random_state)
self.tree_ = Tree(self.n_features_, self.n_classes_, self.n_outputs_)
# Use BestFirst if max_leaf_nodes given; use DepthFirst otherwise
if params["max_leaf_nodes"] < 0:
builder = DepthFirstTreeBuilder(
splitter,
params["min_samples_split"],
params["min_samples_leaf"],
params["min_weight_leaf"],
params["max_depth"],
0.0, # min_impurity_decrease
params["min_impurity_split"])
else:
builder = BestFirstTreeBuilder(
splitter,
params["min_samples_split"],
params["min_samples_leaf"],
params["min_weight_leaf"],
params["max_depth"],
params["max_leaf_nodes"],
0.0, # min_impurity_decrease
params["min_impurity_split"])
builder.build(self.tree_, X, y_numeric, sample_weight, X_idx_sorted)
return self
def digitize2tree(bins, right=False):
"""
Builds a decision tree which returns the same result as
`lambda x: numpy.digitize(x, bins, right=right)`
(see :epkg:`numpy:digitize`).
:param bins: array of bins. It has to be 1-dimensional and monotonic.
:param right: Indicating whether the intervals include the right
or the left bin edge. Default behavior is (right==False)
indicating that the interval does not include the right edge.
The left bin end is open in this case, i.e.,
`bins[i-1] <= x < bins[i]` is the default behavior for
monotonically increasing bins.
:return: decision tree
.. note::
The implementation of decision trees in :epkg:`scikit-learn`
only allows one type of decision (`<=`). That's why the
function throws an exception when `right=False`. However,
this could be overcome by using :epkg:`ONNX` where all
kind of decision rules are implemented. Default value for
right is still *False* to follow *numpy* API even though
this value raises an exception in *digitize2tree*.
The following example shows what the tree looks like.
.. runpython::
:showcode:
import numpy
from sklearn.tree import export_text
from mlinsights.mltree import digitize2tree
x = numpy.array([0.2, 6.4, 3.0, 1.6])
bins = numpy.array([0.0, 1.0, 2.5, 4.0, 7.0])
expected = numpy.digitize(x, bins, right=True)
tree = digitize2tree(bins, right=True)
pred = tree.predict(x.reshape((-1, 1)))
print("Comparison with numpy:")
print(expected, pred)
print("Tree:")
print(export_text(tree, feature_names=['x']))
See also example :ref:`l-example-digitize`.
.. versionadded:: 0.4
"""
if not right:
raise RuntimeError(
"right must be True not right=%r" % right)
ascending = len(bins) <= 1 or bins[0] < bins[1]
if not ascending:
bins2 = bins[::-1]
cl = digitize2tree(bins2, right=right)
n = len(bins)
for i in range(cl.tree_.value.shape[0]):
cl.tree_.value[i, 0, 0] = n - cl.tree_.value[i, 0, 0]
return cl
tree = Tree(1, numpy.array([1], dtype=numpy.intp), 1)
values = []
UNUSED = numpy.nan
n_nodes = []
def add_root(index):
if index < 0 or index >= len(bins):
raise IndexError( # pragma: no cover
"Unexpected index %d / len(bins)=%d." % (
index, len(bins)))
parent = -1
is_left = False
is_leaf = False
threshold = bins[index]
n = tree_add_node(
tree, parent, is_left, is_leaf, 0, threshold, 0, 1, 1.)
values.append(UNUSED)
n_nodes.append(n)
return n
def add_nodes(parent, i, j, is_left):
# add for bins[i:j] (j excluded)
if is_left:
# it means j is the parent split
if i == j:
# leaf
n = tree_add_node(tree, parent, is_left, True, 0, 0, 0, 1, 1.)
n_nodes.append(n)
values.append(i)
return n
if i + 1 == j:
# split
values.append(UNUSED)
th = bins[i]
n = tree_add_node(tree, parent, is_left,
False, 0, th, 0, 1, 1.)
n_nodes.append(n)
add_nodes(n, i, i, True)
add_nodes(n, i, j, False)
return n
if i + 1 < j:
# split
values.append(UNUSED)
index = (i + j) // 2
th = bins[index]
n = tree_add_node(tree, parent, is_left,
False, 0, th, 0, 1, 1.)
n_nodes.append(n)
add_nodes(n, i, index, True)
add_nodes(n, index, j, False)
return n
else:
# it means i is the parent split
if i + 1 == j:
# leaf
values.append(j)
n = tree_add_node(tree, parent, is_left, True, 0, 0, 0, 1, 1.)
n_nodes.append(n)
return n
if i + 1 < j:
# split
values.append(UNUSED)
index = (i + j) // 2
th = bins[index]
n = tree_add_node(tree, parent, is_left,
False, 0, th, 0, 1, 1.)
n_nodes.append(n)
add_nodes(n, i, index, True)
add_nodes(n, index, j, False)
return n
raise NotImplementedError( # pragma: no cover
"Unexpected case where i=%r, j=%r, is_left=%r." % (
i, j, is_left))
index = len(bins) // 2
add_root(index)
add_nodes(0, 0, index, True)
add_nodes(0, index, len(bins), False)
cl = DecisionTreeRegressor()
cl.tree_ = tree
cl.tree_.value[:, 0, 0] = numpy.array( # pylint: disable=E1137
values, dtype=numpy.float64)
cl.n_outputs = 1
cl.n_outputs_ = 1
try:
# scikit-learn >= 0.24
cl.n_features_in_ = 1
except AttributeError:
# scikit-learn < 0.24
cl.n_features_ = 1
try:
# for scikit-learn<=0.23.2
cl.n_features_ = 1
except AttributeError:
pass
return cl
def fit(self, X, y, sample_weight=None, check_input=True,
X_idx_sorted="deprecated"):
"""Build a survival tree from the training set (X, y).
Parameters
----------
X : array-like, shape = (n_samples, n_features)
Data matrix
y : structured array, shape = (n_samples,)
A structured array containing the binary event indicator
as first field, and time of event or time of censoring as
second field.
check_input : boolean, 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
Returns
-------
self
"""
random_state = check_random_state(self.random_state)
if check_input:
X, event, time = check_arrays_survival(X, y)
time = time.astype(np.float64)
self.event_times_ = np.unique(time[event])
y_numeric = np.empty((X.shape[0], 2), dtype=np.float64)
y_numeric[:, 0] = time
y_numeric[:, 1] = event.astype(np.float64)
else:
y_numeric, self.event_times_ = y
n_samples, self.n_features_ = X.shape
self.n_features_in_ = self.n_features_
params = self._check_params(n_samples)
if not isinstance(X_idx_sorted, str) or X_idx_sorted != "deprecated":
warnings.warn(
"The parameter 'X_idx_sorted' is deprecated and has no "
"effect. It will be removed in sklearn 1.1 (renaming of 0.26). "
"You can suppress this warning by not passing any value to the "
"'X_idx_sorted' parameter.",
FutureWarning
)
self.n_outputs_ = self.event_times_.shape[0]
# one "class" for CHF, one for survival function
self.n_classes_ = np.ones(self.n_outputs_, dtype=np.intp) * 2
# Build tree
criterion = LogrankCriterion(self.n_outputs_, n_samples, self.event_times_)
splitter = self.splitter
if not isinstance(self.splitter, Splitter):
splitter = DENSE_SPLITTERS[self.splitter](
criterion,
self.max_features_,
params["min_samples_leaf"],
params["min_weight_leaf"],
random_state)
self.tree_ = Tree(self.n_features_, self.n_classes_, self.n_outputs_)
# Use BestFirst if max_leaf_nodes given; use DepthFirst otherwise
if params["max_leaf_nodes"] < 0:
builder = DepthFirstTreeBuilder(splitter,
params["min_samples_split"],
params["min_samples_leaf"],
params["min_weight_leaf"],
params["max_depth"],
0.0, # min_impurity_decrease
params["min_impurity_split"])
else:
builder = BestFirstTreeBuilder(splitter,
params["min_samples_split"],
params["min_samples_leaf"],
params["min_weight_leaf"],
params["max_depth"],
params["max_leaf_nodes"],
0.0, # min_impurity_decrease
params["min_impurity_split"])
builder.build(self.tree_, X, y_numeric, sample_weight)
return self
def fit(self,
X,
y,
sample_weight=None,
check_input=True,
X_idx_sorted=None):
"""Build a newsvendor decision tree regressor from the training set (X, y).
Method is based on [1] and was adapted to enable usage of the newsvendor criterion
Parameters
----------
X : {array-like, sparse matrix} of shape (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 sparse ``csc_matrix``.
y : array-like of shape (n_samples,) or (n_samples, n_outputs)
The target values (real numbers). Use ``dtype=np.float64`` and
``order='C'`` for maximum efficiency.
sample_weight : array-like of shape (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.
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 : array-like of shape (n_samples, n_features), \
default=None
The indexes of the sorted training input samples. If many tree
are grown on the same dataset, this allows the ordering to be
cached between trees. If None, the data will be sorted here.
Don't use this parameter unless you know what to do.
Returns
-------
self : NewsvendorDecisionTreeRegressor
Fitted estimator.
References
----------
[1] scikit-learn, BaseDecisionTree.fit()
<https://github.com/scikit-learn/scikit-learn/blob/master/sklearn/tree/_classes.py>
"""
random_state = check_random_state(self.random_state)
if self.ccp_alpha < 0.0:
raise ValueError("ccp_alpha must be greater than or equal to 0")
# Need to validate separately here.
# We can't pass multi_ouput=True because that would allow y to be
# csr.
check_X_params = dict(dtype=DTYPE, accept_sparse="csc")
check_y_params = dict(ensure_2d=False, dtype=None)
X, y = self._validate_data(X,
y,
validate_separately=(check_X_params,
check_y_params))
if issparse(X):
X.sort_indices()
if X.indices.dtype != np.intc or X.indptr.dtype != np.intc:
raise ValueError("No support for np.int64 index based "
"sparse matrices")
# Determine output settings
n_samples, self.n_features_ = X.shape
y = np.atleast_1d(y)
expanded_class_weight = None
if y.ndim == 1:
# reshape is necessary to preserve the data contiguity against vs
# [:, np.newaxis] that does not.
y = np.reshape(y, (-1, 1))
self.n_outputs_ = y.shape[1]
if getattr(y, "dtype", None) != DOUBLE or not y.flags.contiguous:
y = np.ascontiguousarray(y, dtype=DOUBLE)
# Check parameters
self.cu_, self.co_ = check_cu_co(self.cu, self.co, self.n_outputs_)
max_depth = (np.iinfo(np.int32).max
if self.max_depth is None else self.max_depth)
max_leaf_nodes = (-1 if self.max_leaf_nodes is None else
self.max_leaf_nodes)
if isinstance(self.min_samples_leaf, numbers.Integral):
if not 1 <= self.min_samples_leaf:
raise ValueError("min_samples_leaf must be at least 1 "
"or in (0, 0.5], got %s" %
self.min_samples_leaf)
min_samples_leaf = self.min_samples_leaf
else: # float
if not 0. < self.min_samples_leaf <= 0.5:
raise ValueError("min_samples_leaf must be at least 1 "
"or in (0, 0.5], got %s" %
self.min_samples_leaf)
min_samples_leaf = int(ceil(self.min_samples_leaf * n_samples))
if isinstance(self.min_samples_split, numbers.Integral):
if not 2 <= self.min_samples_split:
raise ValueError("min_samples_split must be an integer "
"greater than 1 or a float in (0.0, 1.0]; "
"got the integer %s" % self.min_samples_split)
min_samples_split = self.min_samples_split
else: # float
if not 0. < self.min_samples_split <= 1.:
raise ValueError("min_samples_split must be an integer "
"greater than 1 or a float in (0.0, 1.0]; "
"got the float %s" % self.min_samples_split)
min_samples_split = int(ceil(self.min_samples_split * n_samples))
min_samples_split = max(2, min_samples_split)
min_samples_split = max(min_samples_split, 2 * min_samples_leaf)
if isinstance(self.max_features, str):
if self.max_features == "auto":
max_features = self.n_features_
elif self.max_features == "sqrt":
max_features = max(1, int(np.sqrt(self.n_features_)))
elif self.max_features == "log2":
max_features = max(1, int(np.log2(self.n_features_)))
else:
raise ValueError("Invalid value for max_features. "
"Allowed string values are 'auto', "
"'sqrt' or 'log2'.")
elif self.max_features is None:
max_features = self.n_features_
elif isinstance(self.max_features, numbers.Integral):
max_features = self.max_features
else: # float
if self.max_features > 0.0:
max_features = max(1,
int(self.max_features * self.n_features_))
else:
max_features = 0
self.max_features_ = max_features
if len(y) != n_samples:
raise ValueError("Number of labels=%d does not match "
"number of samples=%d" % (len(y), n_samples))
if not 0 <= self.min_weight_fraction_leaf <= 0.5:
raise ValueError("min_weight_fraction_leaf must in [0, 0.5]")
if max_depth <= 0:
raise ValueError("max_depth must be greater than zero. ")
if not (0 < max_features <= self.n_features_):
raise ValueError("max_features must be in (0, n_features]")
if not isinstance(max_leaf_nodes, numbers.Integral):
raise ValueError("max_leaf_nodes must be integral number but was "
"%r" % max_leaf_nodes)
if -1 < max_leaf_nodes < 2:
raise ValueError(("max_leaf_nodes {0} must be either None "
"or larger than 1").format(max_leaf_nodes))
if sample_weight is not None:
sample_weight = _check_sample_weight(sample_weight, X, DOUBLE)
if expanded_class_weight is not None:
if sample_weight is not None:
sample_weight = sample_weight * expanded_class_weight
else:
sample_weight = expanded_class_weight
# Set min_weight_leaf from min_weight_fraction_leaf
if sample_weight is None:
min_weight_leaf = (self.min_weight_fraction_leaf * n_samples)
else:
min_weight_leaf = (self.min_weight_fraction_leaf *
np.sum(sample_weight))
min_impurity_split = self.min_impurity_split
if min_impurity_split is not None:
warnings.warn(
"The min_impurity_split parameter is deprecated. "
"Its default value has changed from 1e-7 to 0 in "
"version 0.23, and it will be removed in 0.25. "
"Use the min_impurity_decrease parameter instead.",
FutureWarning)
if min_impurity_split < 0.:
raise ValueError("min_impurity_split must be greater than "
"or equal to 0")
else:
min_impurity_split = 0
if self.min_impurity_decrease < 0.:
raise ValueError("min_impurity_decrease must be greater than "
"or equal to 0")
# Build tree
criterion = NewsvendorCriterion(self.n_outputs_, n_samples, self.cu_,
self.co_)
SPLITTERS = SPARSE_SPLITTERS if issparse(X) else DENSE_SPLITTERS
splitter = self.splitter
if not isinstance(self.splitter, Splitter):
splitter = SPLITTERS[self.splitter](criterion, self.max_features_,
min_samples_leaf,
min_weight_leaf, random_state)
self.tree_ = Tree(
self.n_features_,
# TODO: tree should't need this in this case
np.array([1] * self.n_outputs_, dtype=np.intp),
self.n_outputs_)
# Use BestFirst if max_leaf_nodes given; use DepthFirst otherwise
if max_leaf_nodes < 0:
builder = DepthFirstTreeBuilder(splitter, min_samples_split,
min_samples_leaf, min_weight_leaf,
max_depth,
self.min_impurity_decrease,
min_impurity_split)
else:
builder = BestFirstTreeBuilder(splitter, min_samples_split,
min_samples_leaf, min_weight_leaf,
max_depth, max_leaf_nodes,
self.min_impurity_decrease,
min_impurity_split)
builder.build(self.tree_, X, y, sample_weight, X_idx_sorted=None)
self._prune_tree()
return self
