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deep-river-0.2.2
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مشاهده بیشترتوضیحات
Online Deep Learning for river
| ویژگی | مقدار |
|---|---|
| سیستم عامل | - |
| نام فایل | deep-river-0.2.2 |
| نام | deep-river |
| نسخه کتابخانه | 0.2.2 |
| نگهدارنده | [] |
| ایمیل نگهدارنده | [] |
| نویسنده | Cedric Kulbach |
| ایمیل نویسنده | cedric.kulbach@googlemail.com |
| آدرس صفحه اصلی | https://online-ml.github.io/deep-river/ |
| آدرس اینترنتی | https://pypi.org/project/deep-river/ |
| مجوز | BSD-3 |
<p align="center">
<img height="150px" src="https://raw.githubusercontent.com/online-ml/deep-river/master/docs/img/logo.png" alt="incremental dl logo">
</p>
<p align="center">
<img alt="PyPI" src="https://img.shields.io/pypi/v/deep-river">
<a href="https://codecov.io/gh/online-ml/deep-river" >
<img src="https://codecov.io/gh/online-ml/deep-river/branch/master/graph/badge.svg?token=ZKUIISZAYA"/>
</a>
<img alt="PyPI - Downloads" src="https://img.shields.io/pypi/dm/deep-river">
<img alt="GitHub" src="https://img.shields.io/github/license/online-ml/deep-river">
</p>
<p align="center">
deep-river is a Python library for online deep learning.
deep-river's ambition is to enable <a href="https://www.wikiwand.com/en/Online_machine_learning">online machine learning</a> for neural networks.
It combines the <a href="https://www.riverml.xyz">river</a> API with the capabilities of designing neural networks based on <a href="https://pytorch.org">PyTorch</a>.
</p>
## 📚 [Documentation](https://online-ml.github.io/deep-river/)
The [documentation](https://online-ml.github.io/deep-river/) contains an overview of all features of this repository as well as the repository's full features list. In each of these, the git repo reference is listed in a section that shows examples of the features and functionality.
## 💈 Installation
```shell
pip install deep-river
```
or
```shell
pip install "river[deep]"
```
You can install the latest development version from GitHub as so:
```shell
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 <a href="https://www.riverml.xyz">river API</a> 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 <a href="https://online-ml.github.io/deep-river">Documentation</a>.
### Classification
```python
>>> 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
```
### Multi Target Regression
```python
>>> from river import evaluate, compose
>>> from river import metrics
>>> from river import preprocessing
>>> from river import stream
>>> from sklearn import datasets
>>> from torch import nn
>>> from deep_river.regression.multioutput import MultiTargetRegressor
>>> class MyModule(nn.Module):
... def __init__(self, n_features):
... super(MyModule, self).__init__()
... self.dense0 = nn.Linear(n_features, 3)
...
... def forward(self, X, **kwargs):
... X = self.dense0(X)
... return X
>>> dataset = stream.iter_sklearn_dataset(
... dataset=datasets.load_linnerud(),
... shuffle=True,
... seed=42
... )
>>> model = compose.Pipeline(
... preprocessing.StandardScaler(),
... MultiTargetRegressor(
... module=MyModule,
... loss_fn='mse',
... lr=0.3,
... optimizer_fn='sgd',
... ))
>>> metric = metrics.multioutput.MicroAverage(metrics.MAE())
>>> ev = evaluate.progressive_val_score(dataset, model, metric)
>>> print(f"MicroAverage(MAE): {metric.get():.2f}")
MicroAverage(MAE): 28.36
```
### Anomaly Detection
```python
>>> 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
<p align="center">
<img src="https://upload.wikimedia.org/wikipedia/de/thumb/4/44/Fzi_logo.svg/1200px-Fzi_logo.svg.png?raw=true" alt="FZI Logo" height="200"/>
</p>
نیازمندی
| مقدار | نام |
|---|---|
| ~=1.0.2 | scikit-learn |
| ~=1.13.0 | torch |
| ~=1.3.2 | pandas |
| ~=1.24.0 | numpy |
| ~=0.15.0 | river |
| ~=4.61.2 | tqdm |
| ~=4.1.0 | ordered-set |
| ~=0.0.2 | torchviz |
| - | dataclasses |
| >=0.10.1 | graphviz |
| >=3.0.2 | matplotlib |
| >=0.990 | mypy |
| >=2.20.0 | pre-commit |
| >=7.2.0 | pytest |
| >=4.0.0 | pytest-cov |
| >=0.22.1 | scikit-learn |
| >=22.10.0 | black |
| >=5.0.4 | flake8 |
| >=5.10.1 | isort |
| >=1.0.0 | jupyter |
| ==3.2.0 | pyupgrade |
| >=2.0.2 | flask |
| >=6.9.0 | ipykernel |
| >=0.5.3 | mike |
| >=1.2.3 | mkdocs |
| >=2.7.0 | mkdocs-awesome-pages-plugin |
| >=0.3.5 | mkdocs-gen-files |
| >=0.0.8 | mkdocs-charts-plugin |
| >=0.4.1 | mkdocs-literate-nav |
| >=8.1.11 | mkdocs-material |
| >=0.19.0 | mkdocstrings[python] |
| >=0.5.0 | pytkdocs[numpy-style] |
| >=0.1.0 | ipython-genutils |
| >=0.20.0 | mkdocs-jupyter |
| >=6.4.2 | nbconvert |
| >=1.2 | numpydoc |
| >=3.2.2 | spacy |
| >=3.0.3 | jinja2 |
| - | dominate |
| - | jupyter-client |
| - | mkdocs-charts-plugin |
| - | python-slugify |
| ==2.3.1 | watermark |
| ~=1.0.2 | scikit-learn |
| ~=1.13.0 | torch |
| ~=1.3.2 | pandas |
| ~=1.24.0 | numpy |
| ~=0.15.0 | river |
| ~=4.61.2 | tqdm |
| ~=4.1.0 | ordered-set |
| ~=0.0.2 | torchviz |
| >=0.10.1 | graphviz |
| >=3.0.2 | matplotlib |
| >=0.990 | mypy |
| >=2.20.0 | pre-commit |
| >=7.2.0 | pytest |
| >=4.0.0 | pytest-cov |
| >=0.22.1 | scikit-learn |
| >=22.10.0 | black |
| >=5.0.4 | flake8 |
| >=5.10.1 | isort |
| >=1.0.0 | jupyter |
| ==3.2.0 | pyupgrade |
| ~=1.0.2 | scikit-learn |
| ~=1.13.0 | torch |
| ~=1.3.2 | pandas |
| ~=1.24.0 | numpy |
| ~=0.15.0 | river |
| ~=4.61.2 | tqdm |
| ~=4.1.0 | ordered-set |
| ~=0.0.2 | torchviz |
| >=2.0.2 | flask |
| >=6.9.0 | ipykernel |
| >=0.5.3 | mike |
| >=1.2.3 | mkdocs |
| >=2.7.0 | mkdocs-awesome-pages-plugin |
| >=0.3.5 | mkdocs-gen-files |
| >=0.0.8 | mkdocs-charts-plugin |
| >=0.4.1 | mkdocs-literate-nav |
| >=8.1.11 | mkdocs-material |
| >=0.19.0 | mkdocstrings[python] |
| >=0.5.0 | pytkdocs[numpy-style] |
| >=0.1.0 | ipython-genutils |
| >=0.20.0 | mkdocs-jupyter |
| >=6.4.2 | nbconvert |
| >=1.2 | numpydoc |
| >=3.2.2 | spacy |
| >=3.0.3 | jinja2 |
| - | dominate |
| - | jupyter-client |
| - | mkdocs-charts-plugin |
| - | python-slugify |
| ==2.3.1 | watermark |
زبان مورد نیاز
| مقدار | نام |
|---|---|
| >=3.6.0 | Python |
نحوه نصب
نصب پکیج whl deep-river-0.2.2:
pip install deep-river-0.2.2.whl
نصب پکیج tar.gz deep-river-0.2.2:
pip install deep-river-0.2.2.tar.gz