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Diffstat (limited to 'python/feast.py')
| -rw-r--r-- | python/feast.py | 412 |
1 files changed, 0 insertions, 412 deletions
diff --git a/python/feast.py b/python/feast.py deleted file mode 100644 index 0d2fee6..0000000 --- a/python/feast.py +++ /dev/null @@ -1,412 +0,0 @@ -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: - print "Error: could not find libFSToolbox" - exit() - - -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) - - # cast as C types - c_n_observations = c_int(n_observations) - c_n_select = c_int(n_select) - c_n_features = c_int(n_features) - c_beta = c_double(beta) - c_gamma = c_double(gamma) - - libFSToolbox.BetaGamma.restype = POINTER(c_double * n_select) - features = libFSToolbox.BetaGamma(c_n_select, - c_n_observations, - c_n_features, - data.ctypes.data_as(POINTER(c_double)), - labels.ctypes.data_as(POINTER(c_double)), - output.ctypes.data_as(POINTER(c_double)), - c_beta, - 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) - - return selected_features - - - -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 - output = np.zeros(n_select) - - # cast as C types - c_n_observations = c_int(n_observations) - 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, - c_n_observations, - c_n_features, - data.ctypes.data_as(POINTER(c_double)), - labels.ctypes.data_as(POINTER(c_double)), - output.ctypes.data_as(POINTER(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) - - return selected_features - - - -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) - - # cast as C types - c_n_observations = c_int(n_observations) - 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, - c_n_observations, - c_n_features, - data.ctypes.data_as(POINTER(c_double)), - labels.ctypes.data_as(POINTER(c_double)), - output.ctypes.data_as(POINTER(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) - - return selected_features - - - - - - -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) - - # cast as C types - c_n_observations = c_int(n_observations) - 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, - c_n_observations, - c_n_features, - data.ctypes.data_as(POINTER(c_double)), - labels.ctypes.data_as(POINTER(c_double)), - output.ctypes.data_as(POINTER(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) - - return selected_features - - - - -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) - - # cast as C types - c_n_observations = c_int(n_observations) - 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, - c_n_observations, - c_n_features, - data.ctypes.data_as(POINTER(c_double)), - labels.ctypes.data_as(POINTER(c_double)), - output.ctypes.data_as(POINTER(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) - - return selected_features - - - - - -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 - output = np.zeros(n_select) - - # cast as C types - c_n_observations = c_int(n_observations) - 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, - c_n_observations, - c_n_features, - data.ctypes.data_as(POINTER(c_double)), - labels.ctypes.data_as(POINTER(c_double)), - output.ctypes.data_as(POINTER(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) - - return selected_features - -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 - output = np.zeros(n_select) - - # cast as C types - c_n_observations = c_int(n_observations) - 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, - c_n_observations, - c_n_features, - data.ctypes.data_as(POINTER(c_double)), - labels.ctypes.data_as(POINTER(c_double)), - output.ctypes.data_as(POINTER(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) - - return selected_features - - - - - |