Phi toolbox for multivariate analysis by Sal Garcia ([email protected], [email protected])
Version 2.0 includes: Batch Analysis
Version 1.0 includes: Principal Components Analysis (PCA), Projection to Latent Structures (PLS), Locally Weighted PLS (LWPLS), Savitzy-Golay derivative and Standard Normal Variate pre-processing for spectra.
A variety of plotting tools for models created with pyphi.
Pyphi requires the following python packages: numpy, scipy, pandas, xlrd, bokeh, matplotlib, pyomo. These can be installed via setup.py below or manually using pip/conda and the requirements.txt file.
- Ensure you have Python 3 installed and accessible via your terminal ("python" command).
- It's strongly encouraged you create a virtual environment using anaconda (
conda create -n your_pyphienv python) or venv (pip -m venv your_pyphienv). You can then activate your environmentconda activate your_pyphienvor venv Windowsyourenv\Scripts\activate.bator venv Linux/macsource yourenv/bin/activate) and then install everything into a sandboxed environment.
- It's strongly encouraged you create a virtual environment using anaconda (
- Download this repository via
git cloneor manually using the download zip button at the top of the page. - Install the pyphi and pyphi_plots modules by opening a terminal window, navigating to the root of this repository, and typing
pip install -r requirements.txt.
To confirm you have a working installation, navigate to the Examples folder and copy the Example_Script_testing_MD_by_NLP.py to the directory of your choice. Run python Example_Script_testing_MD_by_NLP.py, verifying there are no errors logged to the console.
- IPOPT as an executable in your system path or GAMS python module or GAMS executable in yoru system path.
- Windows:
conda install -c conda-forge IPOPT=3.11.1or download from IPOPT releases page, extract and add the IPOPT\bin folder to your system path or add all files to your working directory. - Mac/Linux:
conda install -c conda-forge IPOPT, download from IPOPT releases page, or Compile using coinbrew.
- Windows:
- libhsl with ma57 within library loading path or in the same directory as IPOPT executable.
- Speeds up IPOPT for large problems but requires a free academic or paid industrial license and a local IPOPT installation.
- Must request in advance and building the source code is nontrivial. Expert use only.
- If IPOPT is not detected, pyphi will submit the pyomo models to the NEOS server to solve them remotely.
- To use the NEOS server, the environment variable "NEOS_EMAIL" must be assigned a valid email. This can be done outside of python using set/set/export or use
import os os.environ["NEOS_EMAIL"] = [email protected]in your code.
- To use the NEOS server, the environment variable "NEOS_EMAIL" must be assigned a valid email. This can be done outside of python using set/set/export or use
Adding a folder to your system path:
- Windows: temporary
set PATH=C:\Path\To\ipopt\bin;%PATH%or persistentsetx PATH=C:\Path\To\ipopt\bin;%PATH%. - Mac/Linux:
export PATH=/path/to/ipopt:$PATH, add to .profile/.*rc file to make persistent. - Both via Conda: after activating your environment, use
conda env config vars setand your OS-specific set or export command.
- Added batch predict and data replicator to phi
- I realize I had not updated this section in a long while (sorry)
- Added routine cat_2_matrix to conver categorical classifiers to matrices
- Added Multi-block PLS model
- Fixed small bug un clean_low_variances routine
- Added rotation of loadings so that var(t) for ti>=0 is always larger than var(t) for ti<0
- Enhanced clean_low_variances function to return a list with columns removed from dataframe.
- PLS model estimation using Non-linear programming as described in Journal of Chemometrics, 28(7), pp.575-584.
- PCA model estimation using Non-linear programming as described in Lopez-Negrete et al. J. Chemometrics 2010; 24: 301–311.