diff options
Diffstat (limited to 'feast.py')
| -rw-r--r-- | feast.py | 233 |
1 files changed, 100 insertions, 133 deletions
@@ -29,28 +29,29 @@ except: 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 + This algorithm 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) = + @param 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 + @type data: ndarray + @param 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 + @type labels: ndarray + @param n_select: number of features to select. (REQUIRED) + @type n_select: integer + @param beta: penalty attacted to I(X_j;X_k) + @type beta: float between 0 and 1.0 + @param 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. + @type gamma: float between 0 and 1.0 + @return: features in the order they were selected. + @rtype: list ''' data, labels = check_data(data, labels) @@ -90,23 +91,20 @@ def BetaGamma(data, labels, n_select, beta=1.0, gamma=1.0): def CIFE(data, labels, n_select): ''' - CIFE(data, labels, n_select) - This function implements the Condred feature selection algorithm. beta = 1; gamma = 1; - Input - :data - data in a Numpy array such that len(data) = + @param data: A Numpy array such that len(data) = n_observations, and len(data.transpose()) = n_features - (REQUIRED) - :labels - labels represented in a numpy list with + @type data: ndarray + @param 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. + @type labels: ndarray + @param n_select: number of features to 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) @@ -116,25 +114,22 @@ def CIFE(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) = + @param data: A Numpy array such that len(data) = n_observations, and len(data.transpose()) = n_features - (REQUIRED) - :labels - labels represented in a numpy list with + @type data: ndarray + @param labels: labels represented in a numpy array 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. + @type labels: ndarray + @param n_select: number of features to select. + @type n_select: integer + @return: features in the order that they were selected. + @rtype: list ''' data, labels = check_data(data, labels) @@ -171,23 +166,20 @@ def CMIM(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. + @param data: data in a Numpy array such that len(data) = n_observations, + and len(data.transpose()) = n_features + @type data: ndarray + @param labels: represented in a numpy list with + n_observations as the number of elements. That is + len(labels) = len(data) = n_observations. + @type labels: ndarray + @param n_select: number of features to select. + @type n_select: integer + @return: features in the order they were selected. + @rtype list ''' data, labels = check_data(data, labels) @@ -223,23 +215,20 @@ def CondMI(data, labels, n_select): def Condred(data, labels, n_select): ''' - Condred(data, labels, n_select) - This function implements the Condred feature selection algorithm. beta = 0; gamma = 1; - Input - :data - data in a Numpy array such that len(data) = + @param 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 + @type data: ndarray + @param 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. + @type labels: ndarray + @param n_select: number of features to select. + @type n_select: integer + @return: the features in the order they were selected. + @rtype: list ''' data, labels = check_data(data, labels) @@ -249,23 +238,20 @@ def Condred(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) = + @param 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 + @type data: ndarray + @param 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. + @type labels: ndarray + @param n_select: number of features to select. (REQUIRED) + @type n_select: integer + @return: the features in the order they were selected. + @rtype: list ''' data, labels = check_data(data, labels) @@ -303,23 +289,20 @@ 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) = + @param 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 + @type data: ndarray + @param 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. + @type labels: ndarray + @param n_select: number of features to select. (REQUIRED) + @type n_select: integer + @return: the features in the order they were selected. + @rtype: list ''' data, labels = check_data(data, labels) @@ -358,23 +341,20 @@ 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) = + @param 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 + @type data: ndarray + @param 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. + @type labels: ndarray + @param n_select: number of features to select. (REQUIRED) + @type n_select: integer + @return: the features in the order they were selected. + @rtype: list ''' data, labels = check_data(data, labels) @@ -411,23 +391,20 @@ def JMI(data, labels, n_select): def MIFS(data, labels, n_select): ''' - MIFS(data, labels, n_select) - This function implements the MIFS algorithm. beta = 1; gamma = 0; - Input - :data - data in a Numpy array such that len(data) = + @param 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 + @type data: ndarray + @param 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. + @type labels: ndarray + @param n_select: number of features to select. (REQUIRED) + @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) @@ -435,23 +412,20 @@ def MIFS(data, labels, n_select): def MIM(data, labels, n_select): ''' - MIM(data, labels, n_select) - This function implements the MIM algorithm. beta = 0; gamma = 0; - Input - :data - data in a Numpy array such that len(data) = + @param 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 + @type data: ndarray + @param 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. + @type labels: ndarray + @param n_select: number of features to select. (REQUIRED) + @type n_select: integer + @return: the features in the order they were selected. + @rtype: list ''' data, labels = check_data(data, labels) @@ -461,23 +435,20 @@ def MIM(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) = + @param 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 + @type data: ndarray + @param 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. + @type labels: ndarray + @param n_select: number of features to select. (REQUIRED) + @type n_select: integer + @return: the features in the order they were selected. + @rtype: list ''' data, labels = check_data(data, labels) @@ -512,20 +483,16 @@ def mRMR(data, labels, n_select): def check_data(data, labels): ''' - check_data(data, labels) - Check dimensions of the data and the labels. Raise and exception if there is a problem. Data and Labels are automatically cast as doubles before calling the feature selection functions - Input - :data - :labels - Output - :data - :labels + @param data: the data + @param labels: the labels + @return (data, labels): ndarray of floats + @rtype: tuple ''' if isinstance(data, np.ndarray) is False: |