diff options
Diffstat (limited to 'feast.py')
| -rw-r--r-- | feast.py | 145 |
1 files changed, 55 insertions, 90 deletions
@@ -1,4 +1,4 @@ -''' +""" The FEAST module provides an interface between the C-library for feature selection to Python. @@ -8,7 +8,7 @@ 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"] @@ -21,14 +21,10 @@ __status__ = "Release" import numpy as np import ctypes as c -try: - libFSToolbox = c.CDLL("libFSToolbox.so"); -except: - raise Exception("Error: could not load libFSToolbox.so") - +libFSToolbox = c.CDLL("libFSToolbox.so"); def 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 @@ -52,7 +48,7 @@ def BetaGamma(data, labels, n_select, beta=1.0, gamma=1.0): @type gamma: float between 0 and 1.0 @return: features in the order they were selected. @rtype: list - ''' + """ data, labels = check_data(data, labels) # python values @@ -77,20 +73,14 @@ def BetaGamma(data, labels, n_select, beta=1.0, gamma=1.0): c_gamma ) - # 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) - + selected_features.append(i) return selected_features - def CIFE(data, labels, n_select): - ''' + """ This function implements the Condred feature selection algorithm. beta = 1; gamma = 1; @@ -105,15 +95,11 @@ def CIFE(data, labels, n_select): @type n_select: integer @return selected_features: features in the order they were selected. @rtype: list - ''' - + """ return BetaGamma(data, labels, n_select, beta=1.0, gamma=1.0) - - - def 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 @@ -130,7 +116,7 @@ def CMIM(data, labels, n_select): @type n_select: integer @return: features in the order that they were selected. @rtype: list - ''' + """ data, labels = check_data(data, labels) # python values @@ -151,21 +137,16 @@ def CMIM(data, labels, n_select): output.ctypes.data_as(c.POINTER(c.c_double)) ) - - # 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) + selected_features.append(i) return selected_features def CondMI(data, labels, n_select): - ''' + """ This function implements the conditional mutual information maximization feature selection algorithm. @@ -180,7 +161,7 @@ def CondMI(data, labels, n_select): @type n_select: integer @return: features in the order they were selected. @rtype list - ''' + """ data, labels = check_data(data, labels) # python values @@ -200,21 +181,16 @@ def CondMI(data, labels, n_select): labels.ctypes.data_as(c.POINTER(c.c_double)), output.ctypes.data_as(c.POINTER(c.c_double)) ) - - # 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) + selected_features.append(i) return selected_features def Condred(data, labels, n_select): - ''' + """ This function implements the Condred feature selection algorithm. beta = 0; gamma = 1; @@ -229,15 +205,14 @@ def Condred(data, labels, n_select): @type n_select: integer @return: the features in the order they were selected. @rtype: list - ''' + """ data, labels = check_data(data, labels) - return BetaGamma(data, labels, n_select, beta=0.0, gamma=1.0) def DISR(data, labels, n_select): - ''' + """ This function implements the double input symmetrical relevance feature selection algorithm. @@ -252,7 +227,7 @@ def DISR(data, labels, n_select): @type n_select: integer @return: the features in the order they were selected. @rtype: list - ''' + """ data, labels = check_data(data, labels) # python values @@ -272,23 +247,15 @@ def DISR(data, labels, n_select): labels.ctypes.data_as(c.POINTER(c.c_double)), output.ctypes.data_as(c.POINTER(c.c_double)) ) - - # 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) + selected_features.append(i) return selected_features - - - def ICAP(data, labels, n_select): - ''' + """ This function implements the interaction capping feature selection algorithm. @@ -303,7 +270,7 @@ def ICAP(data, labels, n_select): @type n_select: integer @return: the features in the order they were selected. @rtype: list - ''' + """ data, labels = check_data(data, labels) # python values @@ -323,24 +290,15 @@ def ICAP(data, labels, n_select): labels.ctypes.data_as(c.POINTER(c.c_double)), output.ctypes.data_as(c.POINTER(c.c_double)) ) - - # 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) + selected_features.append(i) return selected_features - - - - def JMI(data, labels, n_select): - ''' + """ This function implements the joint mutual information feature selection algorithm. @@ -355,7 +313,7 @@ def JMI(data, labels, n_select): @type n_select: integer @return: the features in the order they were selected. @rtype: list - ''' + """ data, labels = check_data(data, labels) # python values @@ -376,21 +334,15 @@ def JMI(data, labels, n_select): output.ctypes.data_as(c.POINTER(c.c_double)) ) - - # 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) - + selected_features.append(i) return selected_features def MIFS(data, labels, n_select): - ''' + """ This function implements the MIFS algorithm. beta = 1; gamma = 0; @@ -405,13 +357,12 @@ def MIFS(data, labels, n_select): @type n_select: integer @return: the features in the order they were selected. @rtype: list - ''' - + """ return BetaGamma(data, labels, n_select, beta=0.0, gamma=0.0) def MIM(data, labels, n_select): - ''' + """ This function implements the MIM algorithm. beta = 0; gamma = 0; @@ -426,15 +377,35 @@ def MIM(data, labels, n_select): @type n_select: integer @return: the features in the order they were selected. @rtype: list - ''' + """ data, labels = check_data(data, labels) + + # python values + n_observations, n_features = data.shape + output = np.zeros(n_select) - return BetaGamma(data, labels, n_select, beta=0.0, gamma=0.0) + # cast as C types + c_n_observations = c.c_int(n_observations) + c_n_select = c.c_int(n_select) + c_n_features = c.c_int(n_features) + libFSToolbox.MIM.restype = c.POINTER(c.c_double * n_select) + features = libFSToolbox.MIM(c_n_select, + c_n_observations, + c_n_features, + data.ctypes.data_as(c.POINTER(c.c_double)), + labels.ctypes.data_as(c.POINTER(c.c_double)), + output.ctypes.data_as(c.POINTER(c.c_double)) + ) + + selected_features = [] + for i in features.contents: + selected_features.append(i) + return selected_features def mRMR(data, labels, n_select): - ''' + """ This funciton implements the max-relevance min-redundancy feature selection algorithm. @@ -449,7 +420,7 @@ def mRMR(data, labels, n_select): @type n_select: integer @return: the features in the order they were selected. @rtype: list - ''' + """ data, labels = check_data(data, labels) # python values @@ -470,19 +441,13 @@ def mRMR(data, labels, n_select): output.ctypes.data_as(c.POINTER(c.c_double)) ) - - # 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) - + selected_features.append(i) return selected_features def check_data(data, labels): - ''' + """ Check dimensions of the data and the labels. Raise and exception if there is a problem. @@ -493,7 +458,7 @@ def check_data(data, labels): @param labels: the labels @return (data, labels): ndarray of floats @rtype: tuple - ''' + """ if isinstance(data, np.ndarray) is False: raise Exception("data must be an numpy ndarray.") |