From 1e857f0420c6423fb7453ed3cbc6a1d062e97bf3 Mon Sep 17 00:00:00 2001 From: Calvin Date: Fri, 5 Apr 2013 13:51:26 -0400 Subject: added basic docs generated with epydocs, and stripped down --- feast-module.html | 864 ++++++++++++++++++++++++++++++++++++++++++++++++++++++ 1 file changed, 864 insertions(+) create mode 100644 feast-module.html (limited to 'feast-module.html') diff --git a/feast-module.html b/feast-module.html new file mode 100644 index 0000000..557d352 --- /dev/null +++ b/feast-module.html @@ -0,0 +1,864 @@ + + + + + feast + + + + + + + + + + + + + + + + + + + + + + + +
   Home      Trees      Indices      Help    + + +
PyFeast
+ + + + + +
+ + Module feast + + + + +
+
+ +

Module feast

source code

+
+
+The FEAST module provides an interface between the C-library
+for feature selection to Python. 
+
+References: 
+1) G. Brown, A. Pocock, M.-J. Zhao, and M. Lujan, "Conditional
+    likelihood maximization: A unifying framework for information
+    theoretic feature selection," Journal of Machine Learning 
+    Research, vol. 13, pp. 27-66, 2012.
+
+
+ +
+

Version: + 0.2.0 +

+

Author: + Calvin Morrison +

+

Copyright: + Copyright 2013, EESI Laboratory +

+

License: + GPL +

+
+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
+ Functions
+ list + + + + + + +
BetaGamma(data, + labels, + n_select, + beta=1.0, + gamma=1.0)
+ This algorithm implements conditional mutual information feature + select, such that beta and gamma control the weight attached to the + redundant mutual and conditional mutual information, respectively.		 + source code + +
+ +
+ list + + + + + + +
CIFE(data, + labels, + n_select)
+ This function implements the Condred feature selection algorithm.		 + source code + +
+ +
+ list + + + + + + +
CMIM(data, + labels, + n_select)
+ This function implements the conditional mutual information + maximization feature selection algorithm.		 + source code + +
+ +
+   + + + + + + +
CondMI(data, + labels, + n_select)
+ This function implements the conditional mutual information + maximization feature selection algorithm.		 + source code + +
+ +
+ list + + + + + + +
Condred(data, + labels, + n_select)
+ This function implements the Condred feature selection algorithm.		 + source code + +
+ +
+ list + + + + + + +
DISR(data, + labels, + n_select)
+ This function implements the double input symmetrical relevance + feature selection algorithm.		 + source code + +
+ +
+ list + + + + + + +
ICAP(data, + labels, + n_select)
+ This function implements the interaction capping feature selection + algorithm.		 + source code + +
+ +
+ list + + + + + + +
JMI(data, + labels, + n_select)
+ This function implements the joint mutual information feature + selection algorithm.		 + source code + +
+ +
+ list + + + + + + +
MIFS(data, + labels, + n_select)
+ This function implements the MIFS algorithm.		 + source code + +
+ +
+ list + + + + + + +
MIM(data, + labels, + n_select)
+ This function implements the MIM algorithm.		 + source code + +
+ +
+ list + + + + + + +
mRMR(data, + labels, + n_select)
+ This funciton implements the max-relevance min-redundancy feature + selection algorithm.		 + source code + +
+ +
+ tuple + + + + + + +
check_data(data, + labels)
+ Check dimensions of the data and the labels.		 + source code + +
+ +
+ + + + + + + + + + + + + + + + + + + + + + + + +
+ Variables
+   + + __credits__ = ['Calvin Morrison', 'Gregory Ditzler'] +
+   + + __maintainer__ = 'Calvin Morrison' +
+   + + __email__ = 'mutantturkey@gmail.com' +
+   + + __status__ = 'Release' +
+   + + libFSToolbox = <CDLL 'libFSToolbox.so', handle 2be1240 at 2b4b... +
+   + + __package__ = None +
+ + + + + + +
+ Function Details
+ +
+ +
+ + +
+

BetaGamma(data, + labels, + n_select, + beta=1.0, + gamma=1.0) +

+ 		source code  +
+ +

This algorithm implements conditional mutual information feature + select, such that beta and gamma control the weight attached to the + redundant mutual and conditional mutual information, respectively.

+
+
Parameters:
+
    +
  • data (ndarray) - data in a Numpy array such that len(data) = n_observations, and + len(data.transpose()) = n_features (REQUIRED)
    • +
  • labels (ndarray) - labels represented in a numpy list with n_observations as the + number of elements. That is len(labels) = len(data) = + n_observations. (REQUIRED)
    • +
  • n_select (integer) - number of features to select. (REQUIRED)
    • +
  • beta (float between 0 and 1.0) - penalty attacted to I(X_j;X_k)
    • +
  • gamma (float between 0 and 1.0) - positive weight attached to the conditional redundancy term + I(X_k;X_j|Y)
    • +
+
Returns: list
+
features in the order they were selected.
+
+
+
+ +
+ +
+ + +
+

CIFE(data, + labels, + n_select) +

+ 		source code  +
+ +

This function implements the Condred feature selection algorithm. beta + = 1; gamma = 1;

+
+
Parameters:
+
    +
  • data (ndarray) - A Numpy array such that len(data) = n_observations, and + len(data.transpose()) = n_features
    • +
  • labels (ndarray) - labels represented in a numpy list with n_observations as the + number of elements. That is len(labels) = len(data) = + n_observations.
    • +
  • n_select (integer) - number of features to select.
    • +
+
Returns: list
+
+
+
+ +
+ +
+ + +
+

CMIM(data, + labels, + n_select) +

+ 		source code  +
+ +

This function implements the conditional mutual information + maximization feature selection algorithm. Note that this implementation + does not allow for the weighting of the redundancy terms that BetaGamma + will allow you to do.

+
+
Parameters:
+
    +
  • data (ndarray) - A Numpy array such that len(data) = n_observations, and + len(data.transpose()) = n_features
    • +
  • labels (ndarray) - labels represented in a numpy array with n_observations as the + number of elements. That is len(labels) = len(data) = + n_observations.
    • +
  • n_select (integer) - number of features to select.
    • +
+
Returns: list
+
features in the order that they were selected.
+
+
+
+ +
+ +
+ + +
+

CondMI(data, + labels, + n_select) +

+ 		source code  +
+ +

This function implements the conditional mutual information + maximization feature selection algorithm.

+
+
Parameters:
+
    +
  • data (ndarray) - data in a Numpy array such that len(data) = n_observations, and + len(data.transpose()) = n_features
    • +
  • labels (ndarray) - represented in a numpy list with n_observations as the number of + elements. That is len(labels) = len(data) = n_observations.
    • +
  • n_select (integer) - number of features to select.
    • +
+
Returns:
+
features in the order they were selected. @rtype list
+
+
+
+ +
+ +
+ + +
+

Condred(data, + labels, + n_select) +

+ 		source code  +
+ +

This function implements the Condred feature selection algorithm. beta + = 0; gamma = 1;

+
+
Parameters:
+
    +
  • data (ndarray) - data in a Numpy array such that len(data) = n_observations, and + len(data.transpose()) = n_features
    • +
  • labels (ndarray) - labels represented in a numpy list with n_observations as the + number of elements. That is len(labels) = len(data) = + n_observations.
    • +
  • n_select (integer) - number of features to select.
    • +
+
Returns: list
+
the features in the order they were selected.
+
+
+
+ +
+ +
+ + +
+

DISR(data, + labels, + n_select) +

+ 		source code  +
+ +

This function implements the double input symmetrical relevance + feature selection algorithm.

+
+
Parameters:
+
    +
  • data (ndarray) - data in a Numpy array such that len(data) = n_observations, and + len(data.transpose()) = n_features
    • +
  • labels (ndarray) - labels represented in a numpy list with n_observations as the + number of elements. That is len(labels) = len(data) = + n_observations.
    • +
  • n_select (integer) - number of features to select. (REQUIRED)
    • +
+
Returns: list
+
the features in the order they were selected.
+
+
+
+ +
+ +
+ + +
+

ICAP(data, + labels, + n_select) +

+ 		source code  +
+ +

This function implements the interaction capping feature selection + algorithm.

+
+
Parameters:
+
    +
  • data (ndarray) - data in a Numpy array such that len(data) = n_observations, and + len(data.transpose()) = n_features
    • +
  • labels (ndarray) - labels represented in a numpy list with n_observations as the + number of elements. That is len(labels) = len(data) = + n_observations.
    • +
  • n_select (integer) - number of features to select. (REQUIRED)
    • +
+
Returns: list
+
the features in the order they were selected.
+
+
+
+ +
+ +
+ + +
+

JMI(data, + labels, + n_select) +

+ 		source code  +
+ +

This function implements the joint mutual information feature + selection algorithm.

+
+
Parameters:
+
    +
  • data (ndarray) - data in a Numpy array such that len(data) = n_observations, and + len(data.transpose()) = n_features
    • +
  • labels (ndarray) - labels represented in a numpy list with n_observations as the + number of elements. That is len(labels) = len(data) = + n_observations.
    • +
  • n_select (integer) - number of features to select. (REQUIRED)
    • +
+
Returns: list
+
the features in the order they were selected.
+
+
+
+ +
+ +
+ + +
+

MIFS(data, + labels, + n_select) +

+ 		source code  +
+ +

This function implements the MIFS algorithm. beta = 1; gamma = 0;

+
+
Parameters:
+
    +
  • data (ndarray) - data in a Numpy array such that len(data) = n_observations, and + len(data.transpose()) = n_features
    • +
  • labels (ndarray) - labels represented in a numpy list with n_observations as the + number of elements. That is len(labels) = len(data) = + n_observations.
    • +
  • n_select (integer) - number of features to select. (REQUIRED)
    • +
+
Returns: list
+
the features in the order they were selected.
+
+
+
+ +
+ +
+ + +
+

MIM(data, + labels, + n_select) +

+ 		source code  +
+ +

This function implements the MIM algorithm. beta = 0; gamma = 0;

+
+
Parameters:
+
    +
  • data (ndarray) - data in a Numpy array such that len(data) = n_observations, and + len(data.transpose()) = n_features
    • +
  • labels (ndarray) - labels represented in a numpy list with n_observations as the + number of elements. That is len(labels) = len(data) = + n_observations.
    • +
  • n_select (integer) - number of features to select. (REQUIRED)
    • +
+
Returns: list
+
the features in the order they were selected.
+
+
+
+ +
+ +
+ + +
+

mRMR(data, + labels, + n_select) +

+ 		source code  +
+ +

This funciton implements the max-relevance min-redundancy feature + selection algorithm.

+
+
Parameters:
+
    +
  • data (ndarray) - data in a Numpy array such that len(data) = n_observations, and + len(data.transpose()) = n_features
    • +
  • labels (ndarray) - labels represented in a numpy list with n_observations as the + number of elements. That is len(labels) = len(data) = + n_observations.
    • +
  • n_select (integer) - number of features to select. (REQUIRED)
    • +
+
Returns: list
+
the features in the order they were selected.
+
+
+
+ +
+ +
+ + +
+

check_data(data, + labels) +

+ 		source code  +
+ +

Check dimensions of the data and the labels. Raise and exception if + there is a problem.

+

Data and Labels are automatically cast as doubles before calling the + feature selection functions

+
+
Parameters:
+
    +
  • data - the data
    • +
  • labels - the labels
    • +
+
Returns: tuple
+
+
+
+
+ + + + + + +
+ Variables Details
+ +
+ +
+

libFSToolbox

+ +
+
+
+
Value:
+ 
+<CDLL 'libFSToolbox.so', handle 2be1240 at 2b4bc10>
+
+
+
+
+
+
+ + + + + + + + + + + + + + + + + + +
   Home      Trees      Indices      Help    + + +
PyFeast
+ + + + + +
+ Generated by Epydoc 3.0.1 on Fri Apr 5 13:44:32 2013 + + http://epydoc.sourceforge.net +
+ + + + -- cgit v1.2.3