Awesome
PYTSDA-TDengine
Official Website: https://www.taosdata.com/cn/
TDengine Version：1.6.5.9 , Any question about TDengine, wechat : 13720014098
PYTSDA-TDengine is an end-to end time series data analysis Python system with TDengine. PYTSDA-TDengine provides algorithms which meet the demands for users in time series data analysis fields, w/wo data science or machine learning background. PYTSDA-TDengine gives the ability to execute machine learning algorithms in-database without moving data out of the database server or over the network. It also provides access to a wide range of time series data analysis algorithms, including statistical analysis and more recent deep learning based approaches.
PYTSDA-TDengine is featured for:
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Full Stack Service which supports operations and maintenances from light-weight SQL based database to back-end machine learning algorithms and makes the throughput speed faster;
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State-of-the-art Time Series Data Analysis Approaches including Statistical/Machine Learning/Deep Learning models with unified APIs and detailed documentation;
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Powerful Data Analysis Mechanism which supports time-series data analysis with flexible time-slice(sliding-window) segmentation.
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Automated Machine Learning PYTSDA-TDengine describes the first attempt to incorporate automated machine learning with time series data, and belongs to one of the first attempts to extend automated machine learning concepts into real-world data mining tasks.
The Full API Reference can be found in handbook.
API Demo:
from utils.import_algorithm import algorithm_selection
from utils.utilities import output_performance,connect_server,query_data
# connect to the database
conn,cursor=connect_server(host, user, password)
# query data from specific time range
data = query_data(database_name,table_name,start_time,end_time)
# train the anomaly detection algorithm
clf = algorithm_selection(algorithm_name)
clf.fit(X_train)
# get outlier result and scores
prediction_result = clf.predict(X_test)
outlierness_score = clf.decision_function(test)
#visualize the prediction_result
visualize_distribution(X_test,prediction_result,outlierness_score)
Quick Start
python demo.py --ground_truth --visualize_distribution
Results are shown as
connect to TDengine success
Load dataset and table
Loading cost: 0.151061 seconds
Load data successful
Start processing:
100%|████████████████████████████████████| 10/10 [00:00<00:00, 14.02it/s]
==============================
Results in Algorithm dagmm are:
accuracy_score: 0.98
precision_score: 0.99
recall_score: 0.99
f1_score: 0.99
roc_auc_score: 0.99
processing time: 15.330137 seconds
==============================
connection is closed
Installation
To install the package, please use the pip installation as follows:
pip install pyodds
pip install git+git@github.com:datamllab/PyODDS.git
Note: PYTSDA-TDengine is only compatible with Python 3.6 and above.
Required Dependencies
- pandas>=0.25.0
- taos==1.4.15
- tensorflow==2.0.0b1
- numpy>=1.16.4
- seaborn>=0.9.0
- torch>=1.1.0
- luminol==0.4
- tqdm>=4.35.0
- matplotlib>=3.1.1
- scikit_learn>=0.21.3
目前测试的环境：
pandas =1.0.3
tensorflow =2.1.0
numpy=1.18.1
seaborn=0.10.1
torch=1.4.0+cpu
luminol=0.4
hyperopt=0.2.4
tqdm=4.46.0
matplotlib=3.1.3
scikit_learn=0.23.1
To compile and package the JDBC driver source code, you should have a Java jdk-8 or higher and Apache Maven 2.7 or higher installed. To install openjdk-8 on Ubuntu:
sudo apt-get install openjdk-8-jdk
To install Apache Maven on Ubuntu:
sudo apt-get install maven
To install the TDengine as the back-end database service, please refer to this instruction.
To enable the Python client APIs for TDengine, please follow this handbook.
To insure the locale in config file is valid:
sudo locale-gen "en_US.UTF-8"
export LC_ALL="en_US.UTF-8"
locale
To start the service after installation, in a terminal, use:
taosd
Implemented Algorithms
Statistical Based Methods
| Methods | Algorithm | Class API |
|---|---|---|
| CBLOF | Clustering-Based Local Outlier Factor | :class:algo.cblof.CBLOF |
| HBOS | Histogram-based Outlier Score | :class:algo.hbos.HBOS |
| IFOREST | Isolation Forest | :class:algo.iforest.IFOREST |
| KNN | k-Nearest Neighbors | :class:algo.knn.KNN |
| LOF | Local Outlier Factor | :class:algo.cblof.CBLOF |
| OCSVM | One-Class Support Vector Machines | :class:algo.ocsvm.OCSVM |
| PCA | Principal Component Analysis | :class:algo.pca.PCA |
| RobustCovariance | Robust Covariance | :class:algo.robustcovariance.RCOV |
| SOD | Subspace Outlier Detection | :class:algo.sod.SOD |
Deep Learning Based Methods
| Methods | Algorithm | Class API |
|---|---|---|
| autoencoder | Outlier detection using replicator neural networks | :class:algo.autoencoder.AUTOENCODER |
| dagmm | Deep autoencoding gaussian mixture model for unsupervised anomaly detection | :class:algo.dagmm.DAGMM |
Time Serie Methods
| Methods | Algorithm | Class API |
|---|---|---|
| lstmad | Long short term memory networks for anomaly detection in time series | :class:algo.lstm_ad.LSTMAD |
| lstmencdec | LSTM-based encoder-decoder for multi-sensor anomaly detection | :class:algo.lstm_enc_dec_axl.LSTMED |
| luminol | Linkedin's luminol | :class:algo.luminol.LUMINOL |
APIs Cheatsheet
The Full API Reference can be found in handbook.
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connect_server(hostname,username,password): Connect to Apache backend TDengine Service.
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query_data(connection,cursor,database_name,table_name,start_time,end_time): Query data from table table_name in database database_name within a given time range.
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algorithm_selection(algorithm_name,contamination): Select an algorithm as detector.
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fit(X): Fit X to detector.
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predict(X): Predict if instance in X is outlier or not.
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decision_function(X): Output the anomaly score of instances in X.
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output_performance(algorithm_name,ground_truth,prediction_result,outlierness_score): Output the prediction result as evaluation matrix in Accuracy, Precision, Recall, F1 Score, ROC-AUC Score, Cost time.
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visualize_distribution(X,prediction_result,outlierness_score): Visualize the detection result with the the data distribution.
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visualize_outlierscore(outlierness_score,prediction_result,contamination) Visualize the detection result with the outlier score.
License
<!-- Biblatex entry: -->You may use this software under the MIT License.
Environment configuration
<!-- Biblatex entry: -->Linux：TDengine Server 1.6.5.9
Windows：TDengine 1.5.5.9 client & Python Connector https://www.taosdata.com/cn/documentation/connector/#Python-Connector and follows：
1 anaconda 安装 清华Anaconda镜像地址：https://mirrors.tuna.tsinghua.edu.cn/help/anaconda/
2 全部装上吧 https://github.com/Shawshank-Smile/pyodds pandas =1.0.3 tensorflow =2.1.0 numpy=1.18.1 seaborn=0.10.1 torch=1.4.0+cpu luminol=0.4 hyperopt=0.2.4 tqdm=4.46.0 matplotlib=3.1.3 scikit_learn=0.23.1
3 https://blog.csdn.net/ANNILingMo/article/details/88032599 https://www.cnblogs.com/andrew-address/p/12733669.html （这个是可以的） Torch 没有安装成功，先不搞了。如果需要安装，参考以上的链接吧。
4 安装vscode
5 vscode 使用anaconda的环境 https://blog.csdn.net/m0_45161766/article/details/105729025 https://www.cnblogs.com/lataku/p/10743257.html
6 vscode 配置 git https://www.cnblogs.com/ostrich-sunshine/p/11329444.html
7 Git安装教程（windows） https://www.cnblogs.com/wj-1314/p/7993819.html
8 git配置 https://www.cnblogs.com/lucy-xyy/p/11733317.html https://www.cnblogs.com/ashidamana/p/6122619.html https://www.cnblogs.com/wj-1314/p/7992543.html