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dpyacl-0.3.3
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Distributed Python Active Learning library
| ویژگی | مقدار |
|---|---|
| سیستم عامل | - |
| نام فایل | dpyacl-0.3.3 |
| نام | dpyacl |
| نسخه کتابخانه | 0.3.3 |
| نگهدارنده | [] |
| ایمیل نگهدارنده | [] |
| نویسنده | Alfredo Lorie |
| ایمیل نویسنده | a24lorie@gmail.com |
| آدرس صفحه اصلی | https://github.com/a24lorie/DPyACL |
| آدرس اینترنتی | https://pypi.org/project/dpyacl/ |
| مجوز | GNU |
***
DPyACL
Distributed Python Framework for Active Learning
May 2020
Alfredo Lorie Bernardo
version 0.3.3
***
# Introduction
`DPyACL` is a flexible Distributed Active Learning library written in Python, aimed to make active learning experiments
simpler and faster. Its leverage Dask distributed features to execute active learning experiments computations among a
cluster of computers, allowing to speed up computation and tackle scenarios where data doesn't fit in a single computer.
It also has been developed with a modular object-oriented design to provide an intuitive, ease of use interface and
to allow reuse, modification, and extensibility. It also offers full compatibility with libraries like NumPy, SciPy,
Pandas, Scikit-learn and Keras. This library is available in PyPI and distributed under the GNU license.4
Up to date, DPyACL heavily uses Dask library to implement in a distributed and parallel fashion the the most significant
strategies strategies that have appeared on the single_label-label.
For future releases, we hope to include strategies strategies related with multi-label learning paradigms.
# Download
GitHub: <https://github.com/a24lorie/DPyACL>
# Using DPyACL
The fastest way to use `DPyACL` is from a Jupyter Notebook.
## Preparing an experiment
When defining an Active Learning experiment `DPyACL` offers set pre-defined components that can be configured and
combined by the user to better fit its needs. The required components to setup and experiment are listed below
1. **The Dataset**
2. **Labelled and unlabelled sets**: Optional - The experiment might be configured to randomly choose an initial labeled and unlabeled sets
2. **An Experiment**: HoldOut and KFold experiments are provided
3. **The AL scenario**: The current release provides a Pool Based Scenario
4. **The Machine Learning Technique**: It can be a machine learning technique from any library that provides an API compatible with the
**fit**, **predict** and **predict_proba** definitions. Sklearn, Dask-ML, Keras are compatible
5. **The Evaluation Method(s)**
7. **The Query Strategy**
5. **The Stopping Criteria**
8. **The Oracle**: The current release provides a Simulated Oracle
### Configuring the experiment
```python
ml_technique = LogisticRegression(solver='liblinear')
stopping_criteria = MaxIteration(50)
query_strategy = QueryMarginSampling()
performance_metrics = [
Accuracy(),
F1(average='macro'),
Precision(average='macro'),
Recall(average='macro')]
experiment = HoldOutExperiment(
client=None,
X=_X,
Y=_y,
scenario_type=PoolBasedSamplingScenario,
train_idx=train_idx,
test_idx=test_idx,
label_idx=label_idx,
unlabel_idx=unlabel_idx,
ml_technique=ml_technique,
performance_metrics=performance_metrics,
query_strategy=query_strategy,
oracle=SimulatedOracle(labels=_y),
stopping_criteria=stopping_criteria,
self_partition=False
)
```
### Execute the experiment
```python
result = experiment.evaluate(verbose=True)
```
### Analyze the experiment results
```python
query_analyser = ExperimentAnalyserFactory.experiment_analyser(
performance_metrics= [metric.metric_name for metric in performance_metrics],
method_name=query_strategy.query_function_name,
method_results=result,
type="queries"
)
# get a brief description of the experiment
query_analyser.plot_learning_curves(title='Active Learning experiment results')
```
# Contribution
If you find a bug, send a [pull request](https://github.com/a24lorie/PyACL/pulls) and we'll discuss things. If you are not familiar with "***pull request***" term I recommend reading the following [article](https://yangsu.github.io/pull-request-tutorial/) for better understanding
نحوه نصب
نصب پکیج whl dpyacl-0.3.3:
pip install dpyacl-0.3.3.whl
نصب پکیج tar.gz dpyacl-0.3.3:
pip install dpyacl-0.3.3.tar.gz