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Refactor .neural.fitness
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@knakamura13
knakamura13 committed Sep 4, 2024
1 parent b087739 commit e7ae4af
Showing 1 changed file with 64 additions and 85 deletions.
149 changes: 64 additions & 85 deletions src/mlrose_ky/neural/fitness/network_weights.py
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Original file line number Diff line number Diff line change
Expand Up @@ -3,6 +3,8 @@
# Author: Genevieve Hayes
# License: BSD 3-clause

from typing import Callable

import numpy as np
import sklearn.metrics as skm

Expand All @@ -11,75 +13,77 @@


class NetworkWeights:
"""Fitness function for neural network weights optimization problem.
"""
Fitness function for neural network weights optimization problem.
Parameters
----------
X: np.ndarray
X : np.ndarray
Numpy array containing feature dataset with each row representing a
single observation.
y: np.ndarray
y : np.ndarray
Numpy array containing true values of data labels.
Length must be same as length of X.
node_list: list[int]
Length must be the same as the length of X.
node_list : list[int]
Number of nodes in each layer, including the input and output layers.
activation: callable
activation : Callabe
Activation function for each of the hidden layers with the signature
:code:`activation(x, deriv)`, where setting deriv is a boolean that
`activation(x, deriv)`, where setting deriv is a boolean that
determines whether to return the activation function or its derivative.
bias: bool, default: True
bias : bool, default=True
Whether a bias term is included in the network.
is_classifier: bool, default: True
is_classifier : bool, default=True
Whether the network is for classification or regression. Set True for
classification and False for regression.
learning_rate : float, default=0.1
The learning rate for gradient descent updates.
"""

def __init__(self, X, y, node_list: list[int], activation, bias=True, is_classifier=True, learning_rate=0.1):
# Ensure the activation function has the correct signature
def __init__(
self,
X: np.ndarray,
y: np.ndarray,
node_list: list[int],
activation: Callable,
bias: bool = True,
is_classifier: bool = True,
learning_rate: float = 0.1,
):
if not callable(activation):
raise TypeError("Activation function must be callable.")
try:
activation(np.array([0.1]), deriv=False) # Test the function signature
activation(np.array([0.1]), deriv=False)
except TypeError:
raise TypeError("Activation function must accept two arguments: 'x' and 'deriv'.")

# Check for empty dataset
if X.size == 0 or y.size == 0:
raise ValueError("X and y cannot be empty.")

# Make sure y is an array and not a list
y = np.array(y)

# Convert y to 2D if necessary
if len(np.shape(y)) == 1:
y = np.reshape(y, [len(y), 1])

# Verify X and y are the same length
if not np.shape(X)[0] == np.shape(y)[0]:
raise Exception("""The length of X and y must be equal.""")
if np.shape(X)[0] != np.shape(y)[0]:
raise ValueError(f"The length of X ({np.shape(X)[0]}) and y ({np.shape(y)[0]}) must be equal.")

if len(node_list) < 2:
raise Exception("""node_list must contain at least 2 elements.""")
raise ValueError("node_list must contain at least 2 elements.")

if not np.shape(X)[1] == (node_list[0] - bias):
raise Exception("""The number of columns in X must equal %d""" % ((node_list[0] - bias),))
if np.shape(X)[1] != (node_list[0] - bias):
raise ValueError(f"The number of columns in X must equal {node_list[0] - bias}.")

if not np.shape(y)[1] == node_list[-1]:
raise Exception("""The number of columns in y must equal %d""" % (node_list[-1],))
if np.shape(y)[1] != node_list[-1]:
raise ValueError(f"The number of columns in y must equal {node_list[-1]}.")

if not isinstance(bias, bool):
raise Exception("""bias must be True or False.""")
raise ValueError("bias must be True or False.")

if not isinstance(is_classifier, bool):
raise Exception("""is_classifier must be True or False.""")
raise ValueError("is_classifier must be True or False.")

if learning_rate <= 0:
raise Exception("""learning_rate must be greater than 0.""")
raise ValueError("learning_rate must be greater than 0.")

self.X = X
self.y_true = y
Expand All @@ -89,14 +93,9 @@ def __init__(self, X, y, node_list: list[int], activation, bias=True, is_classif
self.is_classifier = is_classifier
self.learning_rate = learning_rate

# Determine appropriate loss function and output activation function
if self.is_classifier:
self.loss = skm.log_loss

if np.shape(self.y_true)[1] == 1:
self.output_activation = act.sigmoid
else:
self.output_activation = act.softmax
self.output_activation = act.sigmoid if np.shape(self.y_true)[1] == 1 else act.softmax
else:
self.loss = skm.mean_squared_error
self.output_activation = act.identity
Expand All @@ -106,107 +105,87 @@ def __init__(self, X, y, node_list: list[int], activation, bias=True, is_classif
self.weights = []
self.prob_type = "continuous"

nodes = 0
for i in range(len(node_list) - 1):
nodes += node_list[i] * node_list[i + 1]

self.nodes = nodes
self.nodes = sum(node_list[i] * node_list[i + 1] for i in range(len(node_list) - 1))

def evaluate(self, state):
"""Evaluate the fitness of a state.
def evaluate(self, state: np.ndarray) -> float:
"""
Evaluate the fitness of a state.
Parameters
----------
state: np.ndarray
state : np.ndarray
State array for evaluation.
Returns
-------
fitness: float
fitness : float
Value of fitness function.
"""
if not len(state) == self.nodes:
raise Exception("""state must have length %d""" % (self.nodes,))
if len(state) != self.nodes:
raise ValueError(f"state must have length {self.nodes}, got {len(state)}.")

self.inputs_list = []
self.weights: list = list(unflatten_weights(state, self.node_list))
self.weights = list(unflatten_weights(state, self.node_list))

# Add bias column to inputs matrix, if required
if self.bias:
ones = np.ones([np.shape(self.X)[0], 1])
inputs = np.hstack((self.X, ones))

else:
inputs = self.X

# Pass data through network
for i in range(len(self.weights)):
# Multiple inputs by weights
outputs = np.dot(inputs, self.weights[i])
self.inputs_list.append(inputs)

# Transform outputs to get inputs for next layer (or final preds)
if i < len(self.weights) - 1:
inputs = self.activation(outputs)
else:
self.y_pred = self.output_activation(outputs)
inputs = self.activation(outputs) if i < len(self.weights) - 1 else self.output_activation(outputs)

# Evaluate loss function
fitness = self.loss(self.y_true, self.y_pred)
self.y_pred = inputs
return self.loss(self.y_true, self.y_pred)

return fitness

def get_output_activation(self):
"""Return the activation function for the output layer.
def get_output_activation(self) -> Callable:
"""
Return the activation function for the output layer.
Returns
-------
self.output_activation: callable
Callable
Activation function for the output layer.
"""
return self.output_activation

def get_prob_type(self):
"""Return the problem type.
def get_prob_type(self) -> str:
"""
Return the problem type.
Returns
-------
self.prob_type: str
Specifies problem type as 'discrete', 'continuous', 'tsp', or
'either'.
str
Problem type as 'discrete', 'continuous', 'tsp', or 'either'.
"""
return self.prob_type

def calculate_updates(self):
"""Calculate gradient descent updates.
def calculate_updates(self) -> list[np.ndarray]:
"""
Calculate gradient descent updates.
Returns
-------
updates_list: list
list of np.ndarray
List of back propagation weight updates.
"""
delta_list: list = []
updates_list = []
updates_list: list = []

# Work backwards from final layer
for i in range(len(self.inputs_list) - 1, -1, -1):
# Final layer
if i == len(self.inputs_list) - 1:
delta = self.y_pred - self.y_true
# Hidden layers
else:
dot = np.dot(delta_list[-1], np.transpose(self.weights[i + 1]))
activation = self.activation(self.inputs_list[i + 1], deriv=True)
delta = dot * activation

delta_list.append(delta)

# Calculate updates
updates = -1.0 * self.learning_rate * np.dot(np.transpose(self.inputs_list[i]), delta)

updates = -self.learning_rate * np.dot(np.transpose(self.inputs_list[i]), delta)
updates_list.append(updates)

# Reverse order of updates list
updates_list = updates_list[::-1]

return updates_list
return updates_list[::-1]

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