deep-river is a Python library for online deep learning. deep-river's ambition is to enable online machine learning for neural networks. It combines the river API with the capabilities of designing neural networks based on PyTorch.

💈 Installation

pip install deep-river

or

pip install "river[deep]"

You can install the latest development version from GitHub as so:

pip install https://github.com/online-ml/deep-river/archive/refs/heads/master.zip

🍫 Quickstart

We build the development of neural networks on top of the river API and refer to the rivers design principles. The following example creates a simple MLP architecture based on PyTorch and incrementally predicts and trains on the website phishing dataset. For further examples check out the Documentation.

Classification

>>> from river import metrics, datasets, preprocessing, compose
>>> from deep_river import classification
>>> from torch import nn
>>> from torch import optim
>>> from torch import manual_seed

>>> _ = manual_seed(42)

>>> class MyModule(nn.Module):
... def __init__(self, n_features):
...     super(MyModule, self).__init__()
...     self.dense0 = nn.Linear(n_features, 5)
...     self.nonlin = nn.ReLU()
...     self.dense1 = nn.Linear(5, 2)
...     self.softmax = nn.Softmax(dim=-1)
...
... def forward(self, X, **kwargs):
...     X = self.nonlin(self.dense0(X))
...     X = self.nonlin(self.dense1(X))
...     X = self.softmax(X)
...     return X

>>> model_pipeline = compose.Pipeline(
...     preprocessing.StandardScaler(),
...     classification.Classifier(module=MyModule, loss_fn='binary_cross_entropy', optimizer_fn='adam')
... )

>>> dataset = datasets.Phishing()
>>> metric = metrics.Accuracy()

>>> for x, y in dataset:
...     y_pred = model_pipeline.predict_one(x)  # make a prediction
...     metric = metric.update(y, y_pred)  # update the metric
...     model_pipeline = model_pipeline.learn_one(x, y)  # make the model learn
>>>     print(f"Accuracy: {metric.get():.4f}")
Accuracy: 0.6728

Anomaly Detection

>>> from deep_river.anomaly import Autoencoder
>>> from river import metrics
>>> from river.datasets import CreditCard
>>> from torch import nn
>>> import math
>>> from river.compose import Pipeline
>>> from river.preprocessing import MinMaxScaler

>>> dataset = CreditCard().take(5000)
>>> metric = metrics.ROCAUC(n_thresholds=50)

>>> class MyAutoEncoder(nn.Module):
... def __init__(self, n_features, latent_dim=3):
...     super(MyAutoEncoder, self).__init__()
...     self.linear1 = nn.Linear(n_features, latent_dim)
...     self.nonlin = nn.LeakyReLU()
...     self.linear2 = nn.Linear(latent_dim, n_features)
...     self.sigmoid = nn.Sigmoid()
...
... def forward(self, X, **kwargs):
...     X = self.linear1(X)
...     X = self.nonlin(X)
...     X = self.linear2(X)
...     return self.sigmoid(X)

>>> ae = Autoencoder(module=MyAutoEncoder, lr=0.005)
>>> scaler = MinMaxScaler()
>>> model = Pipeline(scaler, ae)

>>> for x, y in dataset:
... score = model.score_one(x)
... model = model.learn_one(x=x)
... metric = metric.update(y, score)
...
>>> print(f"ROCAUC: {metric.get():.4f}")
ROCAUC: 0.7447

🏫 Affiliations