module ml.neural_tree

Short summary

module mlstatpy.ml.neural_tree

Conversion from tree to neural network.

source on GitHub

Classes

class	truncated documentation
NeuralTreeNet	Node ensemble.

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
shape	Returns the shape of the coefficients.
training_weights	Returns the weights.

Static Methods

staticmethod	truncated documentation
_create_from_tree_compact	Implements strategy one. 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

Methods

method	truncated documentation
__getitem__	Retrieves node and attributes for node i.
__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
dlossds	Computes the loss derivative against the inputs.
fill_cache	Creates a cache with intermediate results.
gradient_backward	Computes the gradient in X.
loss	Computes the loss due to prediction error. Returns a float.
predict
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.NeuralTreeNet(dim, empty=True)

Bases : mlstatpy.ml._neural_tree_api._TrainingAPI

Node ensemble.

<<<

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)

>>>

    [[0.088 0.085 0.382 0.382]
     [1.031 1.53  0.356 0.356]
     [0.134 1.022 0.268 0.268]
     [0.381 0.117 0.434 0.434]
     [1.049 0.205 0.554 0.554]
     [1.672 0.848 0.581 0.581]
     [1.321 1.038 0.483 0.483]
     [1.332 0.616 0.549 0.549]
     [0.156 0.268 0.369 0.369]
     [1.2   0.781 0.498 0.498]]

source on GitHub

Paramètres

• dim – space dimension

• empty – empty network, other adds an identity node

source on GitHub

__getitem__(i)

Retrieves node and attributes for node i.

__init__(dim, empty=True)

Paramètres

• dim – space dimension

• empty – empty network, other adds an identity node

source on GitHub

__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 one. 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

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