# PDStoolkit
### Table of Contents
1. [Project Description](#desc)
2. [Documentation & Tutorials](#docs)
3. [Package Contents](#content)
4. [Installation](#install)
5. [Usage](#usage)
## Description <a name="desc"></a>
The PDStoolkit (Process Data Science Toolkit) package has been created to provide easy-to-use modules to help quickly build data-based solutions for process systems such as those for process monitoring, modeling, fault diagnosis, system identification, etc. Current modules in the package are wrappers around pre-existing Sklearn's classes and provide several additional methods to facilitate a process data scientist's job. Details on these are provided in the following section. More modules relevant for process data science will be added over time.
## Documentation and Tutorials <a name="docs"></a>
- PDStoolkit_Manual.pdf (in Github repository) provides some quick information on the algorithms implemented in the package
- Class documentations are provided in the 'docs' folder in Github (Source Code) repository
- Tutorials are provided in the 'tutorials' folder in Github (Source Code) repository
- The blog post (https://mlforpse.com/intro-to-pdstoolkit-python-package/) gives some perspective behind the motivation for development of PDStoolkit package
- Theoretical and conceptual details on specific algorithms can be found in our book (https://leanpub.com/machineLearningPSE)
## Package Contents <a name="content"></a>
The main modules in the package currently are:
- **PDS_PCA: Principal Component analysis for Process Data Science**
- This class is a child of [sklearn.decomposition.PCA class](http://scikit-learn.org/stable/modules/generated/sklearn.decomposition.PCA.html)
- The following additional methods are provided
- *computeMetrics*: computes the monitoring indices (Q or SPE, T2) for the supplied data
- *computeThresholds*: computes the thresholds / control limits for the monitoring indices from training data
- *draw_monitoring_charts*: draws the monitoring charts for the training or test data
- *detect_abnormalities*: detects if the observations are abnormal or normal samples
- *get_contributions*: returns abnormality contributions for T2 and SPE for an observation sample
- **PDS_PLS: Partial Least Squares regression for Process Data Science**
- This class is a child of [sklearn.cross_decomposition.PLSRegression class](http://scikit-learn.org/stable/modules/generated/sklearn.cross_decomposition.PLSRegression.html)
- The following additional methods are provided
- *computeMetrics*: computes the monitoring indices (SPEx, SPEy, T2) for the supplied data
- *computeThresholds*: computes the thresholds / control limits for the monitoring indices from training data
- *draw_monitoring_charts*: draws the monitoring charts for the training or test data
- *detect_abnormalities*: detects if the observations are abnormal or normal samples
- **PDS_DPCA: Dynamic Principal Component analysis for Process Data Science**
- This class is a child of [sklearn.decomposition.PCA class](http://scikit-learn.org/stable/modules/generated/sklearn.decomposition.PCA.html)
- The following additional methods are provided
- *computeMetrics*: computes the monitoring indices (Q or SPE, T2) for the supplied data
- *computeThresholds*: computes the thresholds / control limits for the monitoring indices from training data
- *draw_monitoring_charts*: draws the monitoring charts for the training or test data
- *detect_abnormalities*: detects if the observations are abnormal or normal samples
- **PDS_DPLS: Dynamic Partial Least Squares regression for Process Data Science**
- This class is a child of [sklearn.cross_decomposition.PLSRegression class](http://scikit-learn.org/stable/modules/generated/sklearn.cross_decomposition.PLSRegression.html)
- The following additional methods are provided
- *computeMetrics*: computes the monitoring indices (SPEx, SPEy, T2) for the supplied data
- *computeThresholds*: computes the thresholds / control limits for the monitoring indices from training data
- *draw_monitoring_charts*: draws the monitoring charts for the training or test data
- *detect_abnormalities*: detects if the observations are abnormal or normal samples
- **PDS_CVA: Canonical Variate Analysis for Process Data Science**
- This class is written from scratch
- The following additional methods are provided
- *computeMetrics*: computes the monitoring indices (Ts2, Te2, Q) for the supplied data
- *computeThresholds*: computes the thresholds / control limits for the monitoring indices from training data
- *draw_monitoring_charts*: draws the monitoring charts for the training or test data
- *detect_abnormalities*: detects if the observations are abnormal or normal samples
## Instalation <a name="install"></a>
Installation from Pypi:
pip install PDStoolkit
Import modules
from PDStoolkit import PDS_PCA
from PDStoolkit import PDS_PLS
## Usage <a name="usage"></a>
The following code builds a PCA-based process monitoirng model using PDS-PCA class and uses it for subsequent fault detectiona and fault diagnosis on test data. For details on data and results, see the ProcessMonitoring_PCA notebook in the tutorials folder.
```
# imports
from PDStoolkit import PDS_PCA
# fit PDS_PCA model
pca = PDS_PCA()
pca.fit(data_train_normal, autoFindNLatents=True)
T2_train, SPE_train = pca.computeMetrics(data_train_normal, isTrainingData=True)
T2_CL, SPE_CL = pca.computeThresholds(method='statistical', alpha=0.01)
pca.draw_monitoring_charts(title='training data')
# fault detectiona and fault diagnosis on test data
pca.detect_abnormalities(data_test_normal, title='test data')
T2_contri, SPE_contri = pca.get_contributions(data_test_normal)
```
### License
All code is provided under a BSD 3-clause license. See LICENSE file for more information.
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"description": "# PDStoolkit\r\n\r\n### Table of Contents\r\n1. [Project Description](#desc)\r\n2. [Documentation & Tutorials](#docs)\r\n3. [Package Contents](#content)\r\n4. [Installation](#install)\r\n5. [Usage](#usage)\r\n\r\n## Description <a name=\"desc\"></a>\r\nThe PDStoolkit (Process Data Science Toolkit) package has been created to provide easy-to-use modules to help quickly build data-based solutions for process systems such as those for process monitoring, modeling, fault diagnosis, system identification, etc. Current modules in the package are wrappers around pre-existing Sklearn's classes and provide several additional methods to facilitate a process data scientist's job. Details on these are provided in the following section. More modules relevant for process data science will be added over time.\r\n\r\n## Documentation and Tutorials <a name=\"docs\"></a>\r\n- PDStoolkit_Manual.pdf (in Github repository) provides some quick information on the algorithms implemented in the package\r\n- Class documentations are provided in the 'docs' folder in Github (Source Code) repository\r\n- Tutorials are provided in the 'tutorials' folder in Github (Source Code) repository\r\n- The blog post (https://mlforpse.com/intro-to-pdstoolkit-python-package/) gives some perspective behind the motivation for development of PDStoolkit package \r\n- Theoretical and conceptual details on specific algorithms can be found in our book (https://leanpub.com/machineLearningPSE) \r\n\r\n## Package Contents <a name=\"content\"></a>\r\nThe main modules in the package currently are:\r\n\r\n - **PDS_PCA: Principal Component analysis for Process Data Science**\r\n - This class is a child of [sklearn.decomposition.PCA class](http://scikit-learn.org/stable/modules/generated/sklearn.decomposition.PCA.html) \r\n - The following additional methods are provided\r\n - *computeMetrics*: computes the monitoring indices (Q or SPE, T2) for the supplied data\r\n - *computeThresholds*: computes the thresholds / control limits for the monitoring indices from training data\r\n - *draw_monitoring_charts*: draws the monitoring charts for the training or test data\r\n - *detect_abnormalities*: detects if the observations are abnormal or normal samples\r\n - *get_contributions*: returns abnormality contributions for T2 and SPE for an observation sample\r\n\t \r\n - **PDS_PLS: Partial Least Squares regression for Process Data Science**\r\n - This class is a child of [sklearn.cross_decomposition.PLSRegression class](http://scikit-learn.org/stable/modules/generated/sklearn.cross_decomposition.PLSRegression.html) \r\n - The following additional methods are provided\r\n - *computeMetrics*: computes the monitoring indices (SPEx, SPEy, T2) for the supplied data\r\n - *computeThresholds*: computes the thresholds / control limits for the monitoring indices from training data\r\n - *draw_monitoring_charts*: draws the monitoring charts for the training or test data\r\n - *detect_abnormalities*: detects if the observations are abnormal or normal samples\r\n\t \r\n - **PDS_DPCA: Dynamic Principal Component analysis for Process Data Science**\r\n - This class is a child of [sklearn.decomposition.PCA class](http://scikit-learn.org/stable/modules/generated/sklearn.decomposition.PCA.html) \r\n - The following additional methods are provided\r\n - *computeMetrics*: computes the monitoring indices (Q or SPE, T2) for the supplied data\r\n - *computeThresholds*: computes the thresholds / control limits for the monitoring indices from training data\r\n - *draw_monitoring_charts*: draws the monitoring charts for the training or test data\r\n - *detect_abnormalities*: detects if the observations are abnormal or normal samples\r\n \r\n - **PDS_DPLS: Dynamic Partial Least Squares regression for Process Data Science**\r\n - This class is a child of [sklearn.cross_decomposition.PLSRegression class](http://scikit-learn.org/stable/modules/generated/sklearn.cross_decomposition.PLSRegression.html) \r\n - The following additional methods are provided\r\n - *computeMetrics*: computes the monitoring indices (SPEx, SPEy, T2) for the supplied data\r\n - *computeThresholds*: computes the thresholds / control limits for the monitoring indices from training data\r\n - *draw_monitoring_charts*: draws the monitoring charts for the training or test data\r\n - *detect_abnormalities*: detects if the observations are abnormal or normal samples\r\n \r\n - **PDS_CVA: Canonical Variate Analysis for Process Data Science**\r\n - This class is written from scratch \r\n - The following additional methods are provided\r\n - *computeMetrics*: computes the monitoring indices (Ts2, Te2, Q) for the supplied data\r\n - *computeThresholds*: computes the thresholds / control limits for the monitoring indices from training data\r\n - *draw_monitoring_charts*: draws the monitoring charts for the training or test data\r\n - *detect_abnormalities*: detects if the observations are abnormal or normal samples\r\n \r\n## Instalation <a name=\"install\"></a>\r\nInstallation from Pypi:\r\n\r\n pip install PDStoolkit\r\n\r\nImport modules\r\n\r\n from PDStoolkit import PDS_PCA\r\n from PDStoolkit import PDS_PLS\r\n\r\n## Usage <a name=\"usage\"></a>\r\nThe following code builds a PCA-based process monitoirng model using PDS-PCA class and uses it for subsequent fault detectiona and fault diagnosis on test data. For details on data and results, see the ProcessMonitoring_PCA notebook in the tutorials folder.\r\n\r\n```\r\n# imports\r\nfrom PDStoolkit import PDS_PCA\r\n\r\n# fit PDS_PCA model\r\npca = PDS_PCA()\r\npca.fit(data_train_normal, autoFindNLatents=True)\r\n\r\nT2_train, SPE_train = pca.computeMetrics(data_train_normal, isTrainingData=True)\r\nT2_CL, SPE_CL = pca.computeThresholds(method='statistical', alpha=0.01)\r\npca.draw_monitoring_charts(title='training data')\r\n\r\n# fault detectiona and fault diagnosis on test data\r\npca.detect_abnormalities(data_test_normal, title='test data')\r\nT2_contri, SPE_contri = pca.get_contributions(data_test_normal)\r\n```\r\n \r\n### License\r\nAll code is provided under a BSD 3-clause license. See LICENSE file for more information.\r\n",
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