diff options
Diffstat (limited to 'python/feast.py')
| -rw-r--r-- | python/feast.py | 245 |
1 files changed, 224 insertions, 21 deletions
diff --git a/python/feast.py b/python/feast.py index c30c405..0d2fee6 100644 --- a/python/feast.py +++ b/python/feast.py @@ -1,6 +1,32 @@ import numpy as np from ctypes import * + +''' + 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. + + + __author__ = "Calvin Morrison" + __copyright__ = "Copyright 2013, EESI Laboratory" + __credits__ = ["Calvin Morrison", "Gregory Ditzler"] + __license__ = "GPL" + __version__ = "0.1.0" + __maintainer__ = "Calvin Morrison" + __email__ = "mutantturkey@gmail.com" + __status__ = "Release" +''' + +# I listed the function definitions in alphabetical order. Lets +# keep this up. + + try: libFSToolbox = CDLL("libFSToolbox.so"); except: @@ -8,9 +34,31 @@ except: exit() - -def BetaGamma(data, labels, n_select, beta=2.0, gamma=2.0): - +def BetaGamma(data, labels, n_select, beta=1.0, gamma=1.0): + ''' + BetaGamma(data, labels, n_select, beta=1.0, gamma=1.0) + + This algotihm implements conditional mutual information + feature select, such that beta and gamma control the + weight attached to the redundant mutual and conditional + mutual information, respectively. + + Input + :data - data in a Numpy array such that len(data) = + n_observations, and len(data.transpose()) = n_features + (REQUIRED) + :labels - 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 - number of features to select. (REQUIRED) + :beta - penalty attacted to I(X_j;X_k) + :gamma - positive weight attached to the conditional + redundancy term I(X_k;X_j|Y) + Output + :selected_features - returns a list containing the features + in the order they were selected. + ''' # python values n_observations, n_features = data.shape output = np.zeros(n_select) @@ -36,11 +84,37 @@ def BetaGamma(data, labels, n_select, beta=2.0, gamma=2.0): # turn our output into a list selected_features = [] for i in features.contents: + # recall that feast was implemented with Matlab in mind, so the + # authors assumed the indexing started a one; however, in Python + # the indexing starts at zero. selected_features.append(i - 1) return selected_features -def JMI(data, labels, n_select): + + +def CMIM(data, labels, n_select): + ''' + CMIM(data, labels, n_select) + + 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. + + Input + :data - data in a Numpy array such that len(data) = + n_observations, and len(data.transpose()) = n_features + (REQUIRED) + :labels - 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 - number of features to select. (REQUIRED) + Output + :selected_features - returns a list containing the features + in the order they were selected. + ''' # python values n_observations, n_features = data.shape @@ -51,8 +125,8 @@ def JMI(data, labels, n_select): c_n_select = c_int(n_select) c_n_features = c_int(n_features) - libFSToolbox.JMI.restype = POINTER(c_double * n_select) - features = libFSToolbox.JMI(c_n_select, + libFSToolbox.CMIM.restype = POINTER(c_double * n_select) + features = libFSToolbox.CMIM(c_n_select, c_n_observations, c_n_features, data.ctypes.data_as(POINTER(c_double)), @@ -64,12 +138,35 @@ def JMI(data, labels, n_select): # turn our output into a list selected_features = [] for i in features.contents: + # recall that feast was implemented with Matlab in mind, so the + # authors assumed the indexing started a one; however, in Python + # the indexing starts at zero. selected_features.append(i - 1) return selected_features -def mRMR_D(data, labels, n_select): + +def CondMI(data, labels, n_select): + ''' + CondMI(data, labels, n_select) + + This function implements the conditional mutual information + maximization feature selection algorithm. + + Input + :data - data in a Numpy array such that len(data) = + n_observations, and len(data.transpose()) = n_features + (REQUIRED) + :labels - 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 - number of features to select. (REQUIRED) + Output + :selected_features - returns a list containing the features + in the order they were selected. + ''' # python values n_observations, n_features = data.shape output = np.zeros(n_select) @@ -79,8 +176,8 @@ def mRMR_D(data, labels, n_select): c_n_select = c_int(n_select) c_n_features = c_int(n_features) - libFSToolbox.mRMR_D.restype = POINTER(c_double * n_select) - features = libFSToolbox.mRMR_D(c_n_select, + libFSToolbox.CondMI.restype = POINTER(c_double * n_select) + features = libFSToolbox.CondMI(c_n_select, c_n_observations, c_n_features, data.ctypes.data_as(POINTER(c_double)), @@ -92,12 +189,38 @@ def mRMR_D(data, labels, n_select): # turn our output into a list selected_features = [] for i in features.contents: + # recall that feast was implemented with Matlab in mind, so the + # authors assumed the indexing started a one; however, in Python + # the indexing starts at zero. selected_features.append(i - 1) return selected_features -def CMIM(data, labels, n_select): + + + + +def DISR(data, labels, n_select): + ''' + DISR(data, labels, n_select) + + This function implements the double input symmetrical relevance + feature selection algorithm. + + Input + :data - data in a Numpy array such that len(data) = + n_observations, and len(data.transpose()) = n_features + (REQUIRED) + :labels - 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 - number of features to select. (REQUIRED) + Output + :selected_features - returns a list containing the features + in the order they were selected. + ''' # python values n_observations, n_features = data.shape output = np.zeros(n_select) @@ -107,8 +230,8 @@ def CMIM(data, labels, n_select): c_n_select = c_int(n_select) c_n_features = c_int(n_features) - libFSToolbox.CMIM.restype = POINTER(c_double * n_select) - features = libFSToolbox.CMIM(c_n_select, + libFSToolbox.DISR.restype = POINTER(c_double * n_select) + features = libFSToolbox.DISR(c_n_select, c_n_observations, c_n_features, data.ctypes.data_as(POINTER(c_double)), @@ -120,12 +243,36 @@ def CMIM(data, labels, n_select): # turn our output into a list selected_features = [] for i in features.contents: + # recall that feast was implemented with Matlab in mind, so the + # authors assumed the indexing started a one; however, in Python + # the indexing starts at zero. selected_features.append(i - 1) return selected_features -def DISR(data, labels, n_select): + + +def ICAP(data, labels, n_select): + ''' + ICAP(data, labels, n_select) + + This function implements the interaction capping feature + selection algorithm. + + Input + :data - data in a Numpy array such that len(data) = + n_observations, and len(data.transpose()) = n_features + (REQUIRED) + :labels - 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 - number of features to select. (REQUIRED) + Output + :selected_features - returns a list containing the features + in the order they were selected. + ''' # python values n_observations, n_features = data.shape output = np.zeros(n_select) @@ -135,8 +282,8 @@ def DISR(data, labels, n_select): c_n_select = c_int(n_select) c_n_features = c_int(n_features) - libFSToolbox.DISR.restype = POINTER(c_double * n_select) - features = libFSToolbox.DISR(c_n_select, + libFSToolbox.ICAP.restype = POINTER(c_double * n_select) + features = libFSToolbox.ICAP(c_n_select, c_n_observations, c_n_features, data.ctypes.data_as(POINTER(c_double)), @@ -148,11 +295,37 @@ def DISR(data, labels, n_select): # turn our output into a list selected_features = [] for i in features.contents: + # recall that feast was implemented with Matlab in mind, so the + # authors assumed the indexing started a one; however, in Python + # the indexing starts at zero. selected_features.append(i - 1) return selected_features -def ICAP(data, labels, n_select): + + + + +def JMI(data, labels, n_select): + ''' + JMI(data, labels, n_select) + + This function implements the joint mutual information feature + selection algorithm. + + Input + :data - data in a Numpy array such that len(data) = + n_observations, and len(data.transpose()) = n_features + (REQUIRED) + :labels - 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 - number of features to select. (REQUIRED) + Output + :selected_features - returns a list containing the features + in the order they were selected. + ''' # python values n_observations, n_features = data.shape @@ -163,8 +336,8 @@ def ICAP(data, labels, n_select): c_n_select = c_int(n_select) c_n_features = c_int(n_features) - libFSToolbox.ICAP.restype = POINTER(c_double * n_select) - features = libFSToolbox.ICAP(c_n_select, + libFSToolbox.JMI.restype = POINTER(c_double * n_select) + features = libFSToolbox.JMI(c_n_select, c_n_observations, c_n_features, data.ctypes.data_as(POINTER(c_double)), @@ -176,11 +349,33 @@ def ICAP(data, labels, n_select): # turn our output into a list selected_features = [] for i in features.contents: + # recall that feast was implemented with Matlab in mind, so the + # authors assumed the indexing started a one; however, in Python + # the indexing starts at zero. selected_features.append(i - 1) return selected_features -def CondMI(data, labels, n_select): +def mRMR(data, labels, n_select): + ''' + mRMR(data, labels, n_select) + + This funciton implements the max-relevance min-redundancy feature + selection algorithm. + + Input + :data - data in a Numpy array such that len(data) = + n_observations, and len(data.transpose()) = n_features + (REQUIRED) + :labels - 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 - number of features to select. (REQUIRED) + Output + :selected_features - returns a list containing the features + in the order they were selected. + ''' # python values n_observations, n_features = data.shape @@ -191,8 +386,8 @@ def CondMI(data, labels, n_select): c_n_select = c_int(n_select) c_n_features = c_int(n_features) - libFSToolbox.CondMI.restype = POINTER(c_double * n_select) - features = libFSToolbox.CondMI(c_n_select, + libFSToolbox.mRMR_D.restype = POINTER(c_double * n_select) + features = libFSToolbox.mRMR_D(c_n_select, c_n_observations, c_n_features, data.ctypes.data_as(POINTER(c_double)), @@ -204,6 +399,14 @@ def CondMI(data, labels, n_select): # turn our output into a list selected_features = [] for i in features.contents: + # recall that feast was implemented with Matlab in mind, so the + # authors assumed the indexing started a one; however, in Python + # the indexing starts at zero. selected_features.append(i - 1) return selected_features + + + + + |