diff options
| author | Calvin <calvin@EESI> | 2013-03-26 13:54:21 -0400 |
|---|---|---|
| committer | Calvin <calvin@EESI> | 2013-03-26 13:54:21 -0400 |
| commit | 63f3535a58191997dd2f9704dda635a3bf3f8fce (patch) | |
| tree | e79c10f48aa47c13ef83c54ca5836ff46760e8fa /src | |
| parent | eac04614526d7d619822bbabf46543053e5e7200 (diff) | |
moved out of src, added a basic setup.py
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, 0 insertions, 2397 deletions
diff --git a/src/feast.py b/src/feast.py deleted file mode 100644 index 0d2fee6..0000000 --- a/src/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 - - - - - diff --git a/src/test/create_digits_data.py b/src/test/create_digits_data.py deleted file mode 100644 index 891eea8..0000000 --- a/src/test/create_digits_data.py +++ /dev/null @@ -1,16 +0,0 @@ -#!/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 deleted file mode 100644 index ed966df..0000000 --- a/src/test/digit.txt +++ /dev/null @@ -1,1797 +0,0 @@ -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 deleted file mode 100644 index 6d4bd9e..0000000 --- a/src/test/import_data.py +++ /dev/null @@ -1,58 +0,0 @@ - - - - -################################################################## -################################################################## -################################################################## -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 deleted file mode 100644 index 0a9633a..0000000 --- a/src/test/test.py +++ /dev/null @@ -1,114 +0,0 @@ -#!/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!' - - - - |