3. Using Pytorch with CapyMOA#

  • This notebook demonstrates how use Pytorch with CapyMOA.

  • It contains examples showing

    • How to define a Pytorch Network to be used with CapyMOA

    • How a simple Pytorch model can be used in a CapyMOA Instance loop

    • How to define a Pytorch CapyMOA Classifier based on CapyMOA Classifier framework and how to use it with prequential_evaluation()

    • How to use a Pytorch dataset with a CapyMOA classifier

  • Tutorial 6: Exploring Advanced Features includes an example using TensorBoard and a PyTorchClassifier


More information about CapyMOA can be found in https://www.capymoa.org

last update on 03/05/2024

1. Setup#

  • Sets random seed for reproducibility

  • Sets Pytorch network

1.1 Set random seeds#

[1]:
import random
random_seed=1
random.seed(random_seed)

1.2 Define network structure#

  • Here, network uses the CPU device

[2]:
import torch
from torch import nn

torch.manual_seed(random_seed)
torch.use_deterministic_algorithms(True)

# Get cpu device for training.
device = ("cpu")
print(f"Using {device} device")

# Define model
class NeuralNetwork(nn.Module):
    def __init__(self, input_size=0, number_of_classes=0):
        super().__init__()
        self.flatten = nn.Flatten()
        self.linear_relu_stack = nn.Sequential(
            nn.Linear(input_size, 512),
            nn.ReLU(),
            nn.Linear(512, 512),
            nn.ReLU(),
            nn.Linear(512, number_of_classes)
        )

    def forward(self, x):
        x = self.flatten(x)
        logits = self.linear_relu_stack(x)
        return logits

Using cpu device

1.3 Using instance loop#

  • Model is initialized after receiving the first instance

[3]:
from capymoa.evaluation import ClassificationEvaluator
from capymoa.datasets import ElectricityTiny

elec_stream = ElectricityTiny()

# Creating the evaluator
evaluator = ClassificationEvaluator(schema=elec_stream.get_schema())

model = None
optimizer = None
loss_fn = nn.CrossEntropyLoss()

i = 0
while elec_stream.has_more_instances():
    i += 1
    instance = elec_stream.next_instance()
    if model is None:
        moa_instance = instance.java_instance.getData()
        # initialize the model and send it to the device
        model = NeuralNetwork(input_size=elec_stream.get_schema().get_num_attributes(),
                              number_of_classes=elec_stream.get_schema().get_num_classes()).to(device)
        # set the optimizer
        optimizer = torch.optim.SGD(model.parameters(), lr=1e-3)
        print(model)

    X = torch.tensor(instance.x, dtype=torch.float32)
    y = torch.tensor(instance.y_index, dtype=torch.long)
    # set the device and add a dimension to the tensor
    X, y = torch.unsqueeze(X.to(device), 0), torch.unsqueeze(y.to(device),0)

    # turn off gradient collection for test
    with torch.no_grad():
        pred = model(X)
        prediction = torch.argmax(pred)

    # update evaluator with predicted class
    evaluator.update(instance.y_index, prediction.item())

    # Compute prediction error
    pred = model(X)
    loss = loss_fn(pred, y)

    # Backpropagation
    loss.backward()
    optimizer.step()
    optimizer.zero_grad()

    if i % 500 == 0:
        print(f'Accuracy at {i} : {evaluator.accuracy()}')

print(f'Accuracy at {i} : {evaluator.accuracy()}')
NeuralNetwork(
  (flatten): Flatten(start_dim=1, end_dim=-1)
  (linear_relu_stack): Sequential(
    (0): Linear(in_features=6, out_features=512, bias=True)
    (1): ReLU()
    (2): Linear(in_features=512, out_features=512, bias=True)
    (3): ReLU()
    (4): Linear(in_features=512, out_features=2, bias=True)
  )
)
Accuracy at 500 : 50.4
Accuracy at 1000 : 55.2
Accuracy at 1500 : 61.199999999999996
Accuracy at 2000 : 61.1
Accuracy at 2000 : 61.1

2. PyTorchClassifier#

  • Defining a PyTorchClassifier using CapyMOA API makes it compatibility with CapyMOA functions like prequential_evaluation() without losing the flexibility of specifying the architecture and the training method

  • Model is initialized after receiving the first instance

  • PyTorchClassifier is based on capymoa.base Classifier abstract class

  • Important: We can access information about the stream through any of its instances. See set_model(self, instance) for an example:

...
moa_instance = instance.java_instance.getData()
self.model = NeuralNetwork(input_size=moa_instance.get_num_attributes(),
                                       number_of_classes=moa_instance.get_num_classes()).to(self.device)
...
[4]:
from capymoa.base import Classifier
import numpy as np

class PyTorchClassifier(Classifier):
    def __init__(self, schema=None, random_seed=1, nn_model: nn.Module = None, optimizer=None, loss_fn=nn.CrossEntropyLoss(), device=("cpu"), lr=1e-3):
        super().__init__(schema, random_seed)
        self.model = None
        self.optimizer = None
        self.loss_fn = loss_fn
        self.lr = lr
        self.device = device

        torch.manual_seed(random_seed)

        if nn_model is None:
            self.set_model(None)
        else:
            self.model = nn_model.to(device)
        if optimizer is None:
            if self.model is not None:
                self.optimizer = torch.optim.SGD(self.model.parameters(), lr=lr)
        else:
            self.optimizer = optimizer

    def __str__(self):
        return str(self.model)

    def CLI_help(self):
        return str('schema=None, random_seed=1, nn_model: nn.Module = None, optimizer=None, loss_fn=nn.CrossEntropyLoss(), device=("cpu"), lr=1e-3')

    def set_model(self, instance):
        if self.schema is None:
            moa_instance = instance.java_instance.getData()
            self.model = NeuralNetwork(input_size=moa_instance.get_num_attributes(),
                                       number_of_classes=moa_instance.get_num_classes()).to(self.device)
        elif instance is not None:
            self.model = NeuralNetwork(input_size=self.schema.get_num_attributes(),
                                       number_of_classes=self.schema.get_num_classes()).to(self.device)

    def train(self, instance):
        if self.model is None:
            self.set_model(instance)

        X = torch.tensor(instance.x, dtype=torch.float32)
        y = torch.tensor(instance.y_index, dtype=torch.long)
        # set the device and add a dimension to the tensor
        X, y = torch.unsqueeze(X.to(self.device), 0), torch.unsqueeze(y.to(self.device),0)

        # Compute prediction error
        pred = self.model(X)
        loss = self.loss_fn(pred, y)

        # Backpropagation
        loss.backward()
        self.optimizer.step()
        self.optimizer.zero_grad()

    def predict(self, instance):
        return np.argmax(self.predict_proba(instance))

    def predict_proba(self, instance):
        if self.model is None:
            self.set_model(instance)
        X = torch.unsqueeze(torch.tensor(instance.x, dtype=torch.float32).to(self.device), 0)
        # turn off gradient collection
        with torch.no_grad():
            pred = np.asarray(self.model(X).numpy(), dtype=np.double)
        return pred

2.2 Using PyTorchClassifier + prequential_evaluation#

  • We can access information about the stream through the schema directly, from the example below:

...
nn_model=NeuralNetwork(input_size=elec_stream.get_schema().get_num_attributes(),
                       number_of_classes=elec_stream.get_schema().get_num_classes()).to(device)
...
[5]:
from capymoa.evaluation import prequential_evaluation

## Opening a file as a stream
elec_stream = ElectricityTiny()

# Creating a learner
simple_pyTorch_classifier = PyTorchClassifier(
    schema=elec_stream.get_schema(),
    nn_model=NeuralNetwork(input_size=elec_stream.get_schema().get_num_attributes(),
                           number_of_classes=elec_stream.get_schema().get_num_classes()).to(device)
)

evaluator = prequential_evaluation(stream=elec_stream, learner=simple_pyTorch_classifier, window_size=4500, optimise=False)

evaluator['cumulative'].accuracy()
[5]:
62.849999999999994

3. How to use a Pytorch dataset with a CapyMOA classifier#

  • One may want to use various Pytorch datasets with different CapyMOA classifiers

  • In this example we use Pytorch Dataset + prequential evaluation + CapyMOA Classifier

Observation: Using a learner like Online Bagging without any feature extraction is not going to yield meaningful performance

[6]:
from capymoa.classifier import OnlineBagging
from capymoa.stream import PytorchStream
from capymoa.evaluation import prequential_evaluation
from capymoa.evaluation.visualization import plot_windowed_results

from torchvision import datasets
from torchvision.transforms import ToTensor

pytorchDtaset = datasets.FashionMNIST(
            root="data",
            train=True,
            download=True,
            transform=ToTensor()
        )
pytorch_stream = PytorchStream(dataset=pytorchDtaset)

# Creating a learner
ob_learner = OnlineBagging(schema=pytorch_stream.get_schema(), ensemble_size=5)

results_ob_learner = prequential_evaluation(stream=pytorch_stream, learner=ob_learner, window_size=100, max_instances=1000)

print(results_ob_learner['cumulative'].accuracy())
display(results_ob_learner['windowed'].metrics_per_window())
plot_windowed_results(results_ob_learner)
Downloading http://fashion-mnist.s3-website.eu-central-1.amazonaws.com/train-images-idx3-ubyte.gz
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Downloading http://fashion-mnist.s3-website.eu-central-1.amazonaws.com/train-labels-idx1-ubyte.gz
Downloading http://fashion-mnist.s3-website.eu-central-1.amazonaws.com/train-labels-idx1-ubyte.gz to data/FashionMNIST/raw/train-labels-idx1-ubyte.gz
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Downloading http://fashion-mnist.s3-website.eu-central-1.amazonaws.com/t10k-images-idx3-ubyte.gz
Downloading http://fashion-mnist.s3-website.eu-central-1.amazonaws.com/t10k-images-idx3-ubyte.gz to data/FashionMNIST/raw/t10k-images-idx3-ubyte.gz
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Downloading http://fashion-mnist.s3-website.eu-central-1.amazonaws.com/t10k-labels-idx1-ubyte.gz
Downloading http://fashion-mnist.s3-website.eu-central-1.amazonaws.com/t10k-labels-idx1-ubyte.gz to data/FashionMNIST/raw/t10k-labels-idx1-ubyte.gz
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43.3
classified instances classifications correct (percent) Kappa Statistic (percent) Kappa Temporal Statistic (percent) Kappa M Statistic (percent) F1 Score (percent) F1 Score for class 0 (percent) F1 Score for class 1 (percent) F1 Score for class 2 (percent) F1 Score for class 3 (percent) ... Recall for class 0 (percent) Recall for class 1 (percent) Recall for class 2 (percent) Recall for class 3 (percent) Recall for class 4 (percent) Recall for class 5 (percent) Recall for class 6 (percent) Recall for class 7 (percent) Recall for class 8 (percent) Recall for class 9 (percent)
0 100.0 16.0 5.127626 -1.204819 -1.204819 NaN 22.222222 30.769231 46.153846 NaN ... 66.666667 18.181818 33.333333 0.000000 0.000000 0.000000 0.000000 12.500000 0.000000 18.181818
1 200.0 41.0 33.288105 32.954545 30.588235 45.879666 39.130435 57.142857 36.363636 NaN ... 75.000000 40.000000 22.222222 0.000000 22.222222 22.222222 63.636364 23.076923 75.000000 12.500000
2 300.0 29.0 20.996996 21.978022 21.978022 NaN 24.390244 72.727273 51.851852 NaN ... 62.500000 57.142857 53.846154 0.000000 9.090909 18.181818 33.333333 11.111111 18.181818 50.000000
3 400.0 48.0 41.775837 43.478261 42.222222 54.216958 36.363636 70.588235 22.222222 15.384615 ... 72.727273 54.545455 25.000000 8.333333 25.000000 66.666667 50.000000 63.636364 66.666667 33.333333
4 500.0 51.0 45.591828 45.555556 42.352941 56.932958 39.024390 50.000000 23.529412 22.222222 ... 88.888889 40.000000 16.666667 12.500000 50.000000 75.000000 25.000000 62.500000 78.571429 54.545455
5 600.0 48.0 42.164387 39.534884 36.585366 NaN 38.888889 50.000000 47.058824 NaN ... 70.000000 33.333333 40.000000 0.000000 50.000000 71.428571 46.153846 50.000000 62.500000 61.538462
6 700.0 46.0 39.366719 36.470588 29.870130 NaN 42.105263 80.000000 50.000000 NaN ... 88.888889 66.666667 57.142857 0.000000 61.538462 25.000000 33.333333 22.222222 68.750000 42.857143
7 800.0 55.0 49.301487 49.438202 47.058824 NaN 58.823529 96.551724 60.000000 NaN ... 90.909091 93.333333 50.000000 0.000000 66.666667 53.846154 27.272727 20.000000 63.636364 37.500000
8 900.0 50.0 43.813912 45.652174 42.528736 48.261276 66.666667 81.818182 15.384615 62.500000 ... 87.500000 69.230769 16.666667 45.454545 16.666667 50.000000 33.333333 30.000000 44.444444 50.000000
9 1000.0 49.0 43.118447 46.875000 42.045455 54.455830 59.259259 85.714286 55.555556 22.222222 ... 88.888889 85.714286 50.000000 12.500000 75.000000 66.666667 27.272727 12.500000 87.500000 12.500000

10 rows × 38 columns

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