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torch_shallow_neural_classifier.py
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torch_shallow_neural_classifier.py
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import numpy as np
import torch
import torch.nn as nn
import torch.utils.data
from torch_model_base import TorchModelBase
import utils
__author__ = "Christopher Potts"
__version__ = "CS224u, Stanford, Spring 2022"
class TorchShallowNeuralClassifier(TorchModelBase):
def __init__(self,
hidden_dim=50,
hidden_activation=nn.Tanh(),
**base_kwargs):
"""
A model
h = f(xW_xh + b_h)
y = softmax(hW_hy + b_y)
with a cross-entropy loss and f determined by `hidden_activation`.
Parameters
----------
hidden_dim : int
Dimensionality of the hidden layer.
hidden_activation : nn.Module
The non-activation function used by the network for the
hidden layer.
**base_kwargs
For details, see `torch_model_base.py`.
Attributes
----------
loss: nn.CrossEntropyLoss(reduction="mean")
self.params: list
Extends TorchModelBase.params with names for all of the
arguments for this class to support tuning of these values
using `sklearn.model_selection` tools.
"""
self.hidden_dim = hidden_dim
self.hidden_activation = hidden_activation
super().__init__(**base_kwargs)
self.loss = nn.CrossEntropyLoss(reduction="mean")
self.params += ['hidden_dim', 'hidden_activation']
def build_graph(self):
"""
Define the model's computation graph.
Returns
-------
nn.Module
"""
return nn.Sequential(
nn.Linear(self.input_dim, self.hidden_dim),
self.hidden_activation,
nn.Linear(self.hidden_dim, self.n_classes_))
def build_dataset(self, X, y=None):
"""
Define datasets for the model.
Parameters
----------
X : iterable of length `n_examples`
Each element must have the same length.
y: None or iterable of length `n_examples`
Attributes
----------
input_dim : int
Set based on `X.shape[1]` after `X` has been converted to
`np.array`.
Returns
-------
torch.utils.data.TensorDataset` Where `y=None`, the dataset will
yield single tensors `X`. Where `y` is specified, it will yield
`(X, y)` pairs.
"""
X = np.array(X)
self.input_dim = X.shape[1]
X = torch.FloatTensor(X)
if y is None:
dataset = torch.utils.data.TensorDataset(X)
else:
self.classes_ = sorted(set(y))
self.n_classes_ = len(self.classes_)
class2index = dict(zip(self.classes_, range(self.n_classes_)))
y = [class2index[label] for label in y]
y = torch.tensor(y)
dataset = torch.utils.data.TensorDataset(X, y)
return dataset
def score(self, X, y, device=None):
"""
Uses macro-F1 as the score function. Note: this departs from
`sklearn`, where classifiers use accuracy as their scoring
function. Using macro-F1 is more consistent with our course.
This function can be used to evaluate models, but its primary
use is in cross-validation and hyperparameter tuning.
Parameters
----------
X: np.array, shape `(n_examples, n_features)`
y: iterable, shape `len(n_examples)`
These can be the raw labels. They will converted internally
as needed. See `build_dataset`.
device: str or None
Allows the user to temporarily change the device used
during prediction. This is useful if predictions require a
lot of memory and so are better done on the CPU. After
prediction is done, the model is returned to `self.device`.
Returns
-------
float
"""
preds = self.predict(X, device=device)
return utils.safe_macro_f1(y, preds)
def predict_proba(self, X, device=None):
"""
Predicted probabilities for the examples in `X`.
Parameters
----------
X : np.array, shape `(n_examples, n_features)`
device: str or None
Allows the user to temporarily change the device used
during prediction. This is useful if predictions require a
lot of memory and so are better done on the CPU. After
prediction is done, the model is returned to `self.device`.
Returns
-------
np.array, shape `(len(X), self.n_classes_)`
Each row of this matrix will sum to 1.0.
"""
preds = self._predict(X, device=device)
probs = torch.softmax(preds, dim=1).cpu().numpy()
return probs
def predict(self, X, device=None):
"""
Predicted labels for the examples in `X`. These are converted
from the integers that PyTorch needs back to their original
values in `self.classes_`.
Parameters
----------
X : np.array, shape `(n_examples, n_features)`
device: str or None
Allows the user to temporarily change the device used
during prediction. This is useful if predictions require a
lot of memory and so are better done on the CPU. After
prediction is done, the model is returned to `self.device`.
Returns
-------
list, length len(X)
"""
probs = self.predict_proba(X, device=device)
return [self.classes_[i] for i in probs.argmax(axis=1)]
def simple_example():
"""Assess on the digits dataset."""
from sklearn.datasets import load_digits
from sklearn.model_selection import train_test_split
from sklearn.metrics import classification_report, accuracy_score
utils.fix_random_seeds()
digits = load_digits()
X = digits.data
y = digits.target
X_train, X_test, y_train, y_test = train_test_split(
X, y, test_size=0.33, random_state=42)
mod = TorchShallowNeuralClassifier()
print(mod)
mod.fit(X_train, y_train)
preds = mod.predict(X_test)
print("\nClassification report:")
print(classification_report(y_test, preds))
return accuracy_score(y_test, preds)
if __name__ == '__main__':
simple_example()