module `ml.neural_tree`#

Short summary#

module mlstatpy.ml.neural_tree

Conversion from tree to neural network.

Classes#

class	truncated documentation
`BaseNeuralTreeNet`	Classifier or regressor following scikit-learn API.
`NeuralTreeNet`	Node ensemble.
`NeuralTreeNetClassifier`	Classifier following scikit-learn API.
`NeuralTreeNetRegressor`	Regressor following scikit-learn API.

Functions#

function	truncated documentation
`label_class_to_softmax_output`	Converts a binary class label into a matrix with two columns of probabilities.

Properties#

property	truncated documentation
`_repr_html_`	HTML representation of estimator. This is redundant with the logic of _repr_mimebundle_. The latter should …
`_repr_html_`	HTML representation of estimator. This is redundant with the logic of _repr_mimebundle_. The latter should …
`_repr_html_`	HTML representation of estimator. This is redundant with the logic of _repr_mimebundle_. The latter should …
`shape`	Returns the shape of the coefficients.
`training_weights`	Returns the weights.

Static Methods#

staticmethod	truncated documentation
`_create_from_tree_compact`	Implements strategy “compact”. See @see meth create_from_tree.
`_create_from_tree_one`	Implements strategy “one”. See @see meth create_from_tree.
`create_from_tree`	Creates a `NeuralTreeNet` instance from a DecisionTreeClassifier
`onnx_converter`	Converts this model into ONNX.
`onnx_converter`	Converts this model into ONNX.
`onnx_converter`	Converts this model into ONNX.
`onnx_shape_calculator`	Shape calculator when converting this model into ONNX. See :epkg:`skearn-onnx`.
`onnx_shape_calculator`	Shape calculator when converting this model into ONNX. See :epkg:`skearn-onnx`.
`onnx_shape_calculator`	Shape calculator when converting this model into ONNX. See :epkg:`skearn-onnx`.

Methods#

method	truncated documentation
`__getitem__`	Retrieves node and attributes for node i.
`__init__`
`__init__`
`__init__`
`__init__`
`__len__`	Returns the number of nodes
`__repr__`	usual
`_common_loss_dloss`	Common beginning to methods loss, dlossds, dlossdw.
`_get_output_node_attr`	Retrieves the output nodes. nb_last is the number of expected outputs.
`_predict_one`
`_update_members`	Updates internal members.
`append`	Appends a node into the graph.
`clear`	Clear all nodes
`copy`
`decision_function`	Returns the classification probabilities.
`decision_function`	Returns the classification probabilities.
`decision_function`	Returns the classification probabilities.
`dlossds`	Computes the loss derivative against the inputs.
`fill_cache`	Creates a cache with intermediate results.
`fit`	Trains the estimator.
`fit`	Trains the estimator.
`fit`	Trains the estimator.
`gradient_backward`	Computes the gradient in X.
`loss`	Computes the loss due to prediction error. Returns a float.
`predict`
`predict`	Returns the predicted classes.
`predict`	Returns the predicted classes.
`predict_proba`	Returns the classification probabilities.
`to_dot`	Exports the neural network into dot.
`update_training_weights`	Updates weights.

Documentation#

Conversion from tree to neural network.

source on GitHub

class mlstatpy.ml.neural_tree.BaseNeuralTreeNet(estimator, optimizer=None, max_iter=100, early_th=None, verbose=False, lr=None, lr_schedule=None, l1=0.0, l2=0.0, momentum=0.9)#

Bases : BaseEstimator

Classifier or regressor following scikit-learn API.

Paramètres:

estimator – instance of NeuralTreeNet.
X – training set
y – training labels
optimizer – optimizer, by default, it is SGDOptimizer.
max_iter – number maximum of iterations
early_th – early stopping threshold
verbose – more verbose
lr – to overwrite learning_rate_init if optimizer is None (unused otherwise)
lr_schedule – to overwrite lr_schedule if optimizer is None (unused otherwise)
l1 – L1 regularization if optimizer is None (unused otherwise)
l2 – L2 regularization if optimizer is None (unused otherwise)
momentum – used if optimizer is None

source on GitHub

__init__(estimator, optimizer=None, max_iter=100, early_th=None, verbose=False, lr=None, lr_schedule=None, l1=0.0, l2=0.0, momentum=0.9)#

decision_function(X)#

Returns the classification probabilities.

Paramètres:: X – inputs
Renvoie:: probabilities

source on GitHub

fit(X, y, sample_weights=None)#

Trains the estimator.

Paramètres:

X – input features
y – expected classes (binary)
sample_weights – sample weights

Renvoie:

self

source on GitHub

static onnx_converter()#

Converts this model into ONNX.

source on GitHub

static onnx_shape_calculator()#

Shape calculator when converting this model into ONNX. See :epkg:`skearn-onnx`.

source on GitHub

class mlstatpy.ml.neural_tree.NeuralTreeNet(dim, empty=True)#

Bases : _TrainingAPI

Node ensemble.

Paramètres:

dim – space dimension
empty – empty network, other adds an identity node

<<<

import numpy
from mlstatpy.ml.neural_tree import NeuralTreeNode, NeuralTreeNet

w1 = numpy.array([-0.5, 0.8, -0.6])

neu = NeuralTreeNode(w1[1:], bias=w1[0], activation='sigmoid')
net = NeuralTreeNet(2, empty=True)
net.append(neu, numpy.arange(2))

ide = NeuralTreeNode(numpy.array([1.]),
                     bias=numpy.array([0.]),
                     activation='identity')

net.append(ide, numpy.arange(2, 3))

X = numpy.abs(numpy.random.randn(10, 2))
pred = net.predict(X)
print(pred)

>>>

    [[0.024 1.468 0.204 0.204]
     [0.617 0.655 0.401 0.401]
     [1.045 1.32  0.388 0.388]
     [1.844 0.177 0.705 0.705]
     [0.007 0.382 0.327 0.327]
     [1.463 0.579 0.58  0.58 ]
     [0.688 0.184 0.485 0.485]
     [0.847 0.6   0.454 0.454]
     [1.017 0.387 0.52  0.52 ]
     [0.178 0.413 0.353 0.353]]

source on GitHub

__getitem__(i)#: Retrieves node and attributes for node i.

__init__(dim, empty=True)#

__len__()#: Returns the number of nodes

__repr__()#: usual

_common_loss_dloss(X, y, cache=None)#

Common beginning to methods loss, dlossds, dlossdw.

source on GitHub

static _create_from_tree_compact(tree, k=1.0)#: Implements strategy “compact”. See @see meth create_from_tree.

static _create_from_tree_one(tree, k=1.0)#: Implements strategy “one”. See @see meth create_from_tree.

_get_output_node_attr(nb_last)#

Retrieves the output nodes. nb_last is the number of expected outputs.

source on GitHub

_predict_one(X)#

_update_members(node=None, attr=None)#: Updates internal members.

append(node, inputs)#

Appends a node into the graph.

Paramètres:

node – node to add
inputs – index of input nodes

source on GitHub

clear()#: Clear all nodes

static create_from_tree(tree, k=1.0, arch='one')#

Creates a NeuralTreeNet instance from a DecisionTreeClassifier

Paramètres:

tree – DecisionTreeClassifier
k – slant of the sigmoïd
arch – architecture, see below

Renvoie:

NeuralTreeNet

The function only works for binary problems. Available architecture: * “one”: the method adds nodes with one output, there

is no soecific definition of layers,

“compact”: the adds two nodes, the first computes the threshold, the second one computes the leaves output, a final node merges all outputs into one

See notebook Un arbre de décision en réseaux de neurones for examples.

source on GitHub

dlossds(X, y, cache=None)#

Computes the loss derivative against the inputs.

source on GitHub

fill_cache(X)#

Creates a cache with intermediate results.

source on GitHub

gradient_backward(graddx, X, inputs=False, cache=None)#

Computes the gradient in X.

Paramètres:

graddx – existing gradient against the inputs
X – computes the gradient in X
inputs – if False, derivative against the coefficients, otherwise against the inputs.
cache – cache intermediate results to avoid more computation

Renvoie:

gradient

source on GitHub

loss(X, y, cache=None)#

Computes the loss due to prediction error. Returns a float.

source on GitHub

property shape#: Returns the shape of the coefficients.

to_dot(X=None)#

Exports the neural network into dot.

Paramètres:: X – input as an example

source on GitHub

property training_weights#: Returns the weights.

update_training_weights(X, add=True)#

Updates weights.

Paramètres:

grad – vector to add to the weights such as gradient
add – addition or replace

source on GitHub

class mlstatpy.ml.neural_tree.NeuralTreeNetClassifier(estimator, optimizer=None, max_iter=100, early_th=None, verbose=False, lr=None, lr_schedule=None, l1=0.0, l2=0.0, momentum=0.9)#

Bases : ClassifierMixin, BaseNeuralTreeNet

Classifier following scikit-learn API.

Paramètres:

estimator – instance of NeuralTreeNet.
X – training set
y – training labels
optimizer – optimizer, by default, it is SGDOptimizer.
max_iter – number maximum of iterations
early_th – early stopping threshold
verbose – more verbose
lr – to overwrite learning_rate_init if optimizer is None (unused otherwise)
lr_schedule – to overwrite lr_schedule if optimizer is None (unused otherwise)
l1 – L1 regularization if optimizer is None (unused otherwise)
l2 – L2 regularization if optimizer is None (unused otherwise)
momentum – used if optimizer is None

source on GitHub

__init__(estimator, optimizer=None, max_iter=100, early_th=None, verbose=False, lr=None, lr_schedule=None, l1=0.0, l2=0.0, momentum=0.9)#

predict(X)#

Returns the predicted classes.

Paramètres:: X – inputs
Renvoie:: classes

source on GitHub

predict_proba(X)#

Returns the classification probabilities.

Paramètres:: X – inputs
Renvoie:: probabilities

source on GitHub

class mlstatpy.ml.neural_tree.NeuralTreeNetRegressor(estimator, optimizer=None, max_iter=100, early_th=None, verbose=False, lr=None, lr_schedule=None, l1=0.0, l2=0.0, momentum=0.9)#

Bases : RegressorMixin, BaseNeuralTreeNet

Regressor following scikit-learn API.

Paramètres:

estimator – instance of NeuralTreeNet.
X – training set
y – training labels
optimizer – optimizer, by default, it is SGDOptimizer.
max_iter – number maximum of iterations
early_th – early stopping threshold
verbose – more verbose
lr – to overwrite learning_rate_init if optimizer is None (unused otherwise)
lr_schedule – to overwrite lr_schedule if optimizer is None (unused otherwise)
l1 – L1 regularization if optimizer is None (unused otherwise)
l2 – L2 regularization if optimizer is None (unused otherwise)
momentum – used if optimizer is None

source on GitHub

__init__(estimator, optimizer=None, max_iter=100, early_th=None, verbose=False, lr=None, lr_schedule=None, l1=0.0, l2=0.0, momentum=0.9)#

predict(X)#

Returns the predicted classes.

Paramètres:: X – inputs
Renvoie:: classes

source on GitHub

mlstatpy.ml.neural_tree.label_class_to_softmax_output(y_label)#

Converts a binary class label into a matrix with two columns of probabilities.

<<<

import numpy
from mlstatpy.ml.neural_tree import label_class_to_softmax_output

y_label = numpy.array([0, 1, 0, 0])
soft_y = label_class_to_softmax_output(y_label)
print(soft_y)

>>>

    [[1. 0.]
     [0. 1.]
     [1. 0.]
     [1. 0.]]

source on GitHub

Liens

Contenu

Information

module `ml.neural_tree`#

Short summary#

Classes#

Functions#

Properties#

Static Methods#

Methods#

Documentation#

Liens

Contenu

Information

module ml.neural_tree#

Short summary#

Classes#

Functions#

Properties#

Static Methods#

Methods#

Documentation#

module `ml.neural_tree`#