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aursad-0.2.4
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مشاهده بیشترتوضیحات
Accompanying library to the AURSAD dataset
| ویژگی | مقدار |
|---|---|
| سیستم عامل | - |
| نام فایل | aursad-0.2.4 |
| نام | aursad |
| نسخه کتابخانه | 0.2.4 |
| نگهدارنده | [] |
| ایمیل نگهدارنده | [] |
| نویسنده | Błażej Leporowski |
| ایمیل نویسنده | bleporowski@outlook.com |
| آدرس صفحه اصلی | https://github.com/CptPirx/robo-package |
| آدرس اینترنتی | https://pypi.org/project/aursad/ |
| مجوز | - |
# AURSAD
<div align="left">
[comment]: <> ( <a href='https://ride.readthedocs.io/en/latest/?badge=latest'>)
[comment]: <> ( <img src='https://readthedocs.org/projects/ride/badge/?version=latest' alt='Documentation Status' height="20"/>)
[comment]: <> ( </a>)
[comment]: <> ( <a href="https://codecov.io/gh/LukasHedegaard/ride">)
[comment]: <> ( <img src="https://codecov.io/gh/LukasHedegaard/ride/branch/main/graph/badge.svg?token=SJ59JOWNAC" height="20"/>)
[comment]: <> ( </a>)
<a href="https://opensource.org/licenses/MIT">
<img src="https://img.shields.io/badge/License-MIT-yellow.svg" height="20">
</a>
</div>
A python library for the AURSAD dataset.
Detailed [description](https://arxiv.org/abs/2102.01409) of the dataset and [download](https://zenodo.org/record/4487073).
The library contains several useful functionalities for preprocessing the dataset for ML applications:
* Creating numpy training and test datasets for sampled data
* Creating [Keras TimeSeries generators](https://www.tensorflow.org/api_docs/python/tf/keras/preprocessing/sequence/TimeseriesGenerator)
for sliding window data
* Prediction and classification mode
* In the default prediction mode, the target label of window is the label of the next sample. This can be
used to train a sliding window model that predicts the class of the next sample based on the window.
* In the classification mode, the target label of a window is the most common label in that window.
* Filtering the dataset
* Removing undesired columns as outlined in the paper
* 3 different types of labeling
* Full sample labeling where loosening and tightening motions are labeled together
* Separate sample labeling where loosening motion is given its own label
* 'Tighten' sample labeling, when only the tightening parts of the whole process are labeled as normal/anomalies,
loosening and movement parts of the motion get its own separate labels
* Binary labels -> every anomaly is given the same label
* Subsampling the data
* Dimensionality reduction using PCA or ANOVA F-values
* One-hot label encoding
* Zero padding the samples to equalise their length
* Z-score standardisation
* Taking data only from screwdriver sensors
### Dataset
The dataset contains 2045 samples in total. The robot was sampled with frequency of 100 Hz.
| Type | Label | Samples | % |
|--------------------------|-------|---------|----|
| Normal operation | 0 | 1420 | 69 |
| Damaged screw | 1 | 221 | 11 |
| Extra assembly component | 2 | 183 | 9 |
| Missing screw | 3 | 218 | 11 |
| Damaged thread samples | 4 | 3 | 0 |
Additionally, there are 2049 supplementary samples describing the loosening/screw picking motion, labeled 5.
## Installation
AURSAD has been tested on Windows 10 and Python 3.8.
### PIP installation
To install from pip with required dependencies use:
```bash
pip install aursad
```
### Source installation
To install latest version from github, clone the source from the project repository and install with setup.py:
```bash
git clone https://github.com/CptPirx/robo-package
cd robo-package
python setup.py install --user
```
## Instructions
The package presents to user two methods: get_dataset_numpy() and get_dataset_generator().
#### Sampling
```bash
def get_dataset_numpy(path, onehot_labels=True, reduce_dimensionality=False, reduce_method='PCA', n_dimensions=60,
subsample_data=True, subsample_freq=2, train_size=0.7, random_state=42, normal_samples=1,
damaged_samples=1, assembly_samples=1, missing_samples=1, damaged_thread_samples=0,
loosening_samples=1, move_samples=1, drop_extra_columns=True, pad_data=True,
label_type='partial', binary_labels=False, standardize=False, screwdriver_only=False):
"""
Create numpy dataset from input file
:param assembly_samples: float,
percentage of extra assembly samples to take
:param binary_labels: bool,
if True all anomalies are labeled the same
:param damaged_samples: float,
percentage of damaged samples to take
:param damaged_thread_samples: float,
percentage of damaged thread samples to take
:param drop_extra_columns: bool,
drop the extra columns as outlined in the paper
:param label_type: string,
'full', 'partial' or 'tighten'
:param loosening_samples: float,
percentage of loosening samples to take
:param missing_samples: float,
percentage of missing samples to take
:param move_samples: float,
percentage of movement samples to take
:param n_dimensions: int,
the target number of dimensions
:param normal_samples: float,
percentage of normal samples to take
:param onehot_labels: bool,
output onehot encoded labels
:param pad_data: bool,
if True pad data to equal length samples, if False return data in continuous form
:param path: path to the data
:param random_state: int,
random state for train_test split
:param reduce_dimensionality: bool,
reduce dimensionality of the dataset
:param reduce_method: string,
dimensionality reduction method to be used
:param screwdriver_only: bool,
take only the 4 dimensions from the screwdriver sensors
:param standardize: bool,
if True apply z-score standardisation
:param subsample_data: bool,
reduce number of events by taking every subsample_freq event
:param subsample_freq: int,
the frequency of subsampling
:param train_size: float,
percentage of data as training data
:return: 4 np arrays,
train and test data & labels
"""
```
Sample usage:
```bash
import aursad
data_path = 'C:/Users/my_path/robot_data.h5'
train_x, train_y, test_x, test_y = aursad.get_dataset_numpy(data_path)
```
#### Sliding window
```bash
def get_dataset_generator(path, window_size=100, reduce_dimensionality=False, reduce_method='PCA', n_dimensions=60,
subsample_data=True, subsample_freq=2, train_size=0.7, random_state=42, normal_samples=1,
damaged_samples=1, assembly_samples=1, missing_samples=1, damaged_thread_samples=0,
loosening_samples=1, move_samples=1,drop_extra_columns=True, label_type='partial',
batch_size=256, binary_labels=False, standardize=False, screwdriver_only=False,
onehot_labels=True):
"""
Create Keras sliding window generator from input file
:param assembly_samples: float,
percentage of extra assembly samples to take
:param batch_size: int,
batch size for the sliding window generator
:param binary_labels: bool,
if True all anomalies are labeled the same
:param damaged_samples: float,
percentage of damaged samples to take
:param damaged_thread_samples: float,
percentage of damaged thread samples to take
:param drop_extra_columns: bool,
drop the extra columns as outlined in the paper
:param label_type: string,
'full', 'partial' or 'tighten'
:param loosening_samples: float,
percentage of loosening samples to take
:param missing_samples: float,
percentage of missing samples to take
:param move_samples: float,
percentage of movement samples to take
:param n_dimensions: int,
the target number of dimensions
:param normal_samples: float,
percentage of normal samples to take
:param onehot_labels: bool,
output onehot encoded labels
:param path: path to the data
:param prediction_mode: bool,
if True the target of a window [x_0, x_100] is label of x_101, if False, the target is the most common label in [x_0, x_100]
:param random_state: int,
random state for train_test split
:param reduce_dimensionality: bool,
reduce dimensionality of the dataset
:param reduce_method: string,
dimensionality reduction method to be used
:param screwdriver_only: bool,
take only the 4 dimensions from the screwdriver sensors
:param standardize: bool,
if True apply z-score standardisation
:param subsample_data: bool,
reduce number of events by taking every subsample_freq event
:param subsample_freq: int,
the frequency of subsampling
:param train_size: float,
percentage of data as training data
:param window_size: int,
size of the sliding window
:return: 4 np arrays,
train and test data & labels
:return: keras TimeSeries generators,
train and test generators
"""
```
Sample usage:
```bash
import aursad
data_path = 'C:/Users/my_path/robot_data.h5'
train_x, train_y, test_x, test_y, train_generator, test_generator = aursad.get_dataset_generator(data_path)
```
نیازمندی
| مقدار | نام |
|---|---|
| - | tqdm |
| - | sklearn |
| - | numpy |
| - | pandas |
| - | tensorflow |
زبان مورد نیاز
| مقدار | نام |
|---|---|
| >=3.6 | Python |
نحوه نصب
نصب پکیج whl aursad-0.2.4:
pip install aursad-0.2.4.whl
نصب پکیج tar.gz aursad-0.2.4:
pip install aursad-0.2.4.tar.gz