MDE Class

MDE is an object-oriented class implementation. MDE class arguments can be specified through the MDE class constructor. A command line interface (CLI) is also supported for the MDE and Evaluate classes with CLI parameters configured through command line arguments.

The MDE class can be imported as a module and executed with dimx.Run() or from the command line with theManifoldDimExpand.py wrapper as shown in the examples.

Class and application parameters are detailed in the parameters table.

MDE Class Constructor

MDE class constructor

MDE( dataFrame=None, dataFile=None, dataName=None, removeTime=False,
     noTime=False, columnNames=[], initDataColumns=[], removeColumns=[],
     D=3, target=None, lib=[], pred=[], Tp=1, tau=-1, exclusionRadius=0,
     sample=20, pLibSizes=[10, 15, 85, 90], noCCM=False, ccmSlope=0.01,
     ccmSeed=None, E=0, crossMapRhoMin=0.5, embedDimRhoMin=0.5, maxE=15,
     firstEMax=False, timeDelay=0, cores=5, mpMethod=None, chunksize=1,
     outDir='./', outFile=None, outCSV=None, logFile=None, consoleOut=True,
     verbose=False, debug=False, plot=False, title=None, args=None )

Required Arguments

target : target variable

dataFrame or dataFile : Multivariate observations

Notes

MDE includes all columns in the dataFrame when scanning observables. To avoid including the target or other columns list them in removeColumns, for example: removeColumns=[index, FWD, Left_Right]. To explicitly list columns to scan columnNames can be used.

If the EDM/CCM library (lib) and prediction (pred) row indices are not specified they default to all observation rows in the dataFrame.

ccmSlope determines the criteria for CCM convergence. It is the slope of a linear regression of CCM predictive correlation onto [0,1] normalized CCM library sizes specified as percentiles in pLibSizes.

crossMapRhoMin and embedDimRhoMin are minimum thresholds of predictive correlation to accept a candidate observation vector as valid (crossMapRhoMin) and qualify the embedding dimension for CCM (embedDimRhoMin). Higher values make MDE more selective. Default values of 0.5 may be too high for specific data/systems.

The embedding dimension needed for CCM is automatically determined if parameter E=0, the default. Otherwise the specified value of E is used.

To account for unobserved variables N = timeDelay vectors of the top observables can be added to the manifold.

MDE Class Methods

Run()

Run a configured MDE class instance.

MDE Applications

Evaluate

Evaluate MDE discovered observables predicting the target. Compare to PCA and diffusion map decompositions.

Evaluate( dataFrame=None, dataFile=None, outFile=None, mde_columns=[], 
          columns_range=[], i_columns=[], columnMatch=[], removeColumns=[],
          removeTime=False, initDataColumns=[], predictVar=None, library=[],
          prediction=[], E=0, tau=-1, Tp=0, components=3, dmap_k=5,
          dmap_epsilon='bgh', dmap_alpha=0.5, plot=False, plotRho=False,
          minMax=False, maxN=7, figsize=(8, 8), xlim=None, verbose=False,
          args=None )

MDE CLI ManifoldDimExpand.py:

ManifoldDimExpand.py is an executable program that instantiates and runs an MDE class instance. Parameters are detailed in the parameters table and can be listed with the -h command line option.

./ManifoldDimExpand.py -d ../data/Fly80XY_norm_1061.csv 
-rc index FWD Left_Right -D 10 -t FWD -l 1 300 -p 301 600
-C 10 -ccs 0.01 -emin 0.5 -P -title "MDE FWD" -v