diff options
| author | Calvin <calvin@EESI> | 2013-03-26 13:44:35 -0400 |
|---|---|---|
| committer | Calvin <calvin@EESI> | 2013-03-26 13:44:35 -0400 |
| commit | eac04614526d7d619822bbabf46543053e5e7200 (patch) | |
| tree | 5e4be62de68126cb9569b03ece6666ff391ea417 /src | |
| parent | 94da049a53423a9aca04d8376b75347ca7eadcbe (diff) | |
renamed a bunch of stuff
Diffstat (limited to 'src')
| -rw-r--r-- | src/feast.py | 412 | ||||
| -rw-r--r-- | src/test/create_digits_data.py | 16 | ||||
| -rw-r--r-- | src/test/digit.txt | 1797 | ||||
| -rw-r--r-- | src/test/import_data.py | 58 | ||||
| -rw-r--r-- | src/test/test.py | 114 |
5 files changed, 2397 insertions, 0 deletions
diff --git a/src/feast.py b/src/feast.py new file mode 100644 index 0000000..0d2fee6 --- /dev/null +++ b/src/feast.py @@ -0,0 +1,412 @@ +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 + + + + + diff --git a/src/test/create_digits_data.py b/src/test/create_digits_data.py new file mode 100644 index 0000000..891eea8 --- /dev/null +++ b/src/test/create_digits_data.py @@ -0,0 +1,16 @@ +#!/usr/bin/env python +from sklearn import datasets + +digits = datasets.load_digits() # load the data from scikits +data = digits.images.reshape((digits.images.shape[0], -1)) +labels = digits.target # extract the labels + +fw = open('digit.txt', 'w') + +for n in range(len(data)): + mstr = '' + for x in data[n]: + mstr += str(x) + '\t' + fw.write(mstr + str(labels[n]) + '\n') + +fw.close()
\ No newline at end of file diff --git a/src/test/digit.txt b/src/test/digit.txt new file mode 100644 index 0000000..ed966df --- /dev/null +++ b/src/test/digit.txt @@ -0,0 +1,1797 @@ +0.0 0.0 5.0 13.0 9.0 1.0 0.0 0.0 0.0 0.0 13.0 15.0 10.0 15.0 5.0 0.0 0.0 3.0 15.0 2.0 0.0 11.0 8.0 0.0 0.0 4.0 12.0 0.0 0.0 8.0 8.0 0.0 0.0 5.0 8.0 0.0 0.0 9.0 8.0 0.0 0.0 4.0 11.0 0.0 1.0 12.0 7.0 0.0 0.0 2.0 14.0 5.0 10.0 12.0 0.0 0.0 0.0 0.0 6.0 13.0 10.0 0.0 0.0 0.0 0 +0.0 0.0 0.0 12.0 13.0 5.0 0.0 0.0 0.0 0.0 0.0 11.0 16.0 9.0 0.0 0.0 0.0 0.0 3.0 15.0 16.0 6.0 0.0 0.0 0.0 7.0 15.0 16.0 16.0 2.0 0.0 0.0 0.0 0.0 1.0 16.0 16.0 3.0 0.0 0.0 0.0 0.0 1.0 16.0 16.0 6.0 0.0 0.0 0.0 0.0 1.0 16.0 16.0 6.0 0.0 0.0 0.0 0.0 0.0 11.0 16.0 10.0 0.0 0.0 1 +0.0 0.0 0.0 4.0 15.0 12.0 0.0 0.0 0.0 0.0 3.0 16.0 15.0 14.0 0.0 0.0 0.0 0.0 8.0 13.0 8.0 16.0 0.0 0.0 0.0 0.0 1.0 6.0 15.0 11.0 0.0 0.0 0.0 1.0 8.0 13.0 15.0 1.0 0.0 0.0 0.0 9.0 16.0 16.0 5.0 0.0 0.0 0.0 0.0 3.0 13.0 16.0 16.0 11.0 5.0 0.0 0.0 0.0 0.0 3.0 11.0 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16.0 8.0 0.0 0.0 1.0 8.0 12.0 14.0 12.0 1.0 0.0 8 diff --git a/src/test/import_data.py b/src/test/import_data.py new file mode 100644 index 0000000..6d4bd9e --- /dev/null +++ b/src/test/import_data.py @@ -0,0 +1,58 @@ + + + + +################################################################## +################################################################## +################################################################## +def read_digits(fname='digit.txt'): + ''' + read_digits(fname='digit.txt') + + read a data file that contains the features and class labels. + each row of the file is a feature vector with the class + label appended. + ''' + import csv + import numpy as np + + fw = csv.reader(open(fname,'rb'), delimiter='\t') + data = [] + for line in fw: + data.append( [float(x) for x in line] ) + data = np.array(data) + labels = data[:,len(data.transpose())-1] + data = data[:,:len(data.transpose())-1] + return data, labels +################################################################## +################################################################## +################################################################## + + + +################################################################## +################################################################## +################################################################## +def uniform_data(n_observations = 1000, n_features = 50, n_relevant = 5): + import numpy as np + xmax = 10 + xmin = 0 + data = np.random.randint(xmax + 1, size = (n_features, n_observations)) + labels = np.zeros(n_observations) + delta = n_relevant * (xmax - xmin) / 2.0 + + for m in range(n_observations): + zz = 0.0 + for k in range(n_relevant): + zz += data[k, m] + if zz > delta: + labels[m] = 1 + else: + labels[m] = 2 + data = data.transpose() + + return data, labels + +################################################################## +################################################################## +################################################################## diff --git a/src/test/test.py b/src/test/test.py new file mode 100644 index 0000000..0a9633a --- /dev/null +++ b/src/test/test.py @@ -0,0 +1,114 @@ +#!/usr/bin/env python +from feast import * +import numpy as np +import import_data + + +def check_result(selected_features, n_relevant): + selected_features = sorted(selected_features) + success = True + for k in range(n_relevant): + if k != selected_features[k]: + success = False + return success + + + +n_relevant = 5 +data_source = 'uniform' # set the data set we want to test + + +if data_source == 'uniform': + data, labels = import_data.uniform_data(n_relevant = n_relevant) +elif data_source == 'digits': + data, labels = import_data.read_digits('digit.txt') + +n_observations = len(data) # number of samples in the data set +n_features = len(data.transpose()) # number of features in the data set +n_select = 15 # how many features to select +method = 'JMI' # feature selection algorithm + + +print '---> Information' +print ' :n_observations - ' + str(n_observations) +print ' :n_features - ' + str(n_features) +print ' :n_select - ' + str(n_select) +print ' :algorithm - ' + str(method) +print ' ' +print '---> Running unit tests on FEAST 4 Python... ' + + +################################################################# +################################################################# +print ' Running BetaGamma... ' +sf = BetaGamma(data, labels, n_select, beta=0.5, gamma=0.5) +if check_result(sf, n_relevant) == True: + print ' BetaGamma passed!' +else: + print ' BetaGamma failed!' + + +################################################################# +################################################################# +print ' Running CMIM... ' +sf = CMIM(data, labels, n_select) +if check_result(sf, n_relevant) == True: + print ' CMIM passed!' +else: + print ' CMIM failed!' + + +################################################################# +################################################################# +print ' Running CondMI... ' +sf = CondMI(data, labels, n_select) +if check_result(sf, n_relevant) == True: + print ' CondMI passed!' +else: + print ' CondMI failed!' + + +################################################################# +################################################################# +print ' Running DISR... ' +sf = DISR(data, labels, n_select) +if check_result(sf, n_relevant) == True: + print ' DISR passed!' +else: + print ' DISR failed!' + + +################################################################# +################################################################# +print ' Running ICAP... ' +sf = ICAP(data, labels, n_select) +if check_result(sf, n_relevant) == True: + print ' ICAP passed!' +else: + print ' ICAP failed!' + + +################################################################# +################################################################# +print ' Running JMI... ' +sf = JMI(data, labels, n_select) +if check_result(sf, n_relevant) == True: + print ' JMI passed!' +else: + print ' JMI failed!' + + +################################################################# +################################################################# +print ' Running mRMR... ' +sf = mRMR(data, labels, n_select) +if check_result(sf, n_relevant) == True: + print ' mRMR passed!' +else: + print ' mRMR failed!' + +print '---> Done unit tests!' + + + + |