from abc import ABCMeta, abstractmethod
import os
import torch
import torch.nn as nn
from typing import Union, Tuple
import pandas as pd
import numpy as np
from ._base import ClassifierNN
from ._base import RegressorNN
from ..config import Config as cfg
[docs]class CNNClassifier(ClassifierNN):
"""
A class used to represent a Convolutional Neural Network (CNN) for classification tasks.
This class inherits from the `ClassifierNN` class.
Attributes:
model_type (str): A string that represents the type of the model (default is "cnn").
Args:
hidden_size (Union[int, List[int]]): A list of integers that represents the number of nodes in each hidden layer or
a single integer that represents the number of nodes in a single hidden layer.
num_filters (int): The number of filters in the convolutional layer.
kernel_size (int): The size of the kernel in the convolutional layer.
pool_size (int): The size of the pooling layer.
Methods:
__init__(self, **kwargs): Initializes the CNNClassifier object with given or default parameters.
_init_model(self): Initializes the convolutional and fully connected layers of the model based on configuration.
forward(x: torch.Tensor) -> torch.Tensor: Defines the forward pass of the CNN.
"""
model_type = "cnn"
[docs] def __init__(self, **kwargs):
"""
Initializes the CNNClassifier object with given or default parameters.
"""
super().__init__(**kwargs)
[docs] def _init_model(self):
"""
Initializes the convolutional and fully connected layers of the model based on configuration.
"""
# Create the convolutional layers
# Check if hidden_sizes is a single integer and, if so, converts it to a list
if isinstance(cfg.nn.hidden_size, int):
self.hidden_sizes = [cfg.nn.hidden_size]
else:
self.hidden_sizes = cfg.nn.hidden_size
# Initializes a list to store the layers of the neural network
layers = [nn.Conv1d(1, cfg.nn.num_filters, cfg.nn.kernel_size), # 1D convolutional layer
nn.ReLU(), # Activation function
nn.MaxPool1d(cfg.nn.pool_size), # Max pooling layer
nn.Flatten()] # Flatten layer for transforming the output for use in FC layers
# Calculates the input size for the first FC layer after flattening
current_input_size = cfg.nn.num_filters * ((self.input_size - cfg.nn.kernel_size + 1) \
// cfg.nn.pool_size)
# Creates the FC layers of the neural network
for hidden_size in self.hidden_sizes:
layers.append(nn.Linear(current_input_size, hidden_size)) # Linear (FC) layer
layers.append(nn.ReLU()) # Activation function
layers.append(nn.Dropout(cfg.comm.dropout)) # Dropout layer for regularization
current_input_size = hidden_size
# Adds the output FC layer
layers.append(nn.Linear(current_input_size, self.output_size))
# Creates the neural network as a sequential model based on the layers list
self.layers = nn.Sequential(*layers)
[docs] def forward(self, x: torch.Tensor) -> torch.Tensor:
"""
Defines the forward pass of the CNN.
:param x: The input tensor.
:returns: The output tensor, corresponding to the predicted target variable(s).
"""
# Ensures the model has been fitted before making predictions
if self.layers is None:
raise RuntimeError("You must call fit before calling predict")
return self.layers(x)
[docs]class CNNRegressor(RegressorNN):
"""
A class used to represent a Convolutional Neural Network (CNN) for regression tasks.
This class inherits from the `RegressorNN` class.
Attributes:
model_type (str): A string that represents the type of the model (default is "cnn").
Args:
hidden_size (Union[int, List[int]]): A list of integers that represents the number of nodes in each hidden layer or
a single integer that represents the number of nodes in a single hidden layer.
num_filters (int): The number of filters in the convolutional layer.
kernel_size (int): The size of the kernel in the convolutional layer.
pool_size (int): The size of the pooling layer.
Methods:
__init__(self, **kwargs): Initializes the CNNRegressor object with given or default parameters.
_init_model(self): Initializes the convolutional and fully connected layers of the model based on configuration.
forward(x: torch.Tensor) -> torch.Tensor: Defines the forward pass of the CNN.
"""
model_type = "cnn"
[docs] def __init__(self, **kwargs):
"""
Initializes the CNNRegressor object with given or default parameters.
"""
super().__init__(**kwargs)
[docs] def _init_model(self):
"""
Initializes the convolutional and fully connected layers of the model based on configuration.
"""
# Create the convolutional layers
# Check if hidden_sizes is a single integer and, if so, converts it to a list
if isinstance(cfg.nn.hidden_size, int):
self.hidden_sizes = [cfg.nn.hidden_size]
else:
self.hidden_sizes = cfg.nn.hidden_size
# Initializes a list to store the layers of the neural network
layers = [nn.Conv1d(1, cfg.nn.num_filters, cfg.nn.kernel_size), # 1D convolutional layer
nn.ReLU(), # Activation function
nn.MaxPool1d(cfg.nn.pool_size), # Max pooling layer
nn.Flatten()] # Flatten layer for transforming the output for use in FC layers
# Calculates the input size for the first FC layer after flattening
current_input_size = cfg.nn.num_filters * ((self.input_size - cfg.nn.kernel_size + 1) \
// cfg.nn.pool_size)
# Creates the FC layers of the neural network
for hidden_size in self.hidden_sizes:
layers.append(nn.Linear(current_input_size, hidden_size)) # Linear (FC) layer
layers.append(nn.ReLU()) # Activation function
layers.append(nn.Dropout(cfg.comm.dropout)) # Dropout layer for regularization
current_input_size = hidden_size
# Adds the output FC layer
layers.append(nn.Linear(current_input_size, self.output_size))
# Creates the neural network as a sequential model based on the layers list
self.layers = nn.Sequential(*layers)
[docs] def forward(self, x: torch.Tensor) -> torch.Tensor:
"""
Defines the forward pass of the CNN.
:param x: The input tensor.
:returns: The output tensor, corresponding to the predicted target variable(s).
"""
# Ensures the model has been fitted before making predictions
if self.layers is None:
raise RuntimeError("You must call fit before calling predict")
return self.layers(x)