Skip to content

Fss node

This file is part of the TPOT library.

The current version of TPOT was developed at Cedars-Sinai by: - Pedro Henrique Ribeiro (https://github.com/perib, https://www.linkedin.com/in/pedro-ribeiro/) - Anil Saini (anil.saini@cshs.org) - Jose Hernandez (jgh9094@gmail.com) - Jay Moran (jay.moran@cshs.org) - Nicholas Matsumoto (nicholas.matsumoto@cshs.org) - Hyunjun Choi (hyunjun.choi@cshs.org) - Miguel E. Hernandez (miguel.e.hernandez@cshs.org) - Jason Moore (moorejh28@gmail.com)

The original version of TPOT was primarily developed at the University of Pennsylvania by: - Randal S. Olson (rso@randalolson.com) - Weixuan Fu (weixuanf@upenn.edu) - Daniel Angell (dpa34@drexel.edu) - Jason Moore (moorejh28@gmail.com) - and many more generous open-source contributors

TPOT is free software: you can redistribute it and/or modify it under the terms of the GNU Lesser General Public License as published by the Free Software Foundation, either version 3 of the License, or (at your option) any later version.

TPOT is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU Lesser General Public License for more details.

You should have received a copy of the GNU Lesser General Public License along with TPOT. If not, see http://www.gnu.org/licenses/.

FSSIndividual

Bases: SklearnIndividual

Source code in tpot2/search_spaces/nodes/fss_node.py 
 51
 52
 53
 54
 55
 56
 57
 58
 59
 60
 61
 62
 63
 64
 65
 66
 67
 68
 69
 70
 71
 72
 73
 74
 75
 76
 77
 78
 79
 80
 81
 82
 83
 84
 85
 86
 87
 88
 89
 90
 91
 92
 93
 94
 95
 96
 97
 98
 99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
class FSSIndividual(SklearnIndividual):
    def __init__(   self,
                    subsets,
                    rng=None,
                ):

        """
        An individual for representing a specific FeatureSetSelector. 
        The FeatureSetSelector selects a feature list of list of predefined feature subsets.

        This instance will select one set initially. Mutation and crossover can swap the selected subset with another.

        Parameters
        ----------
        subsets : str or list, default=None
            Sets the subsets that the FeatureSetSeletor will select from if set as an option in one of the configuration dictionaries. 
            Features are defined by column names if using a Pandas data frame, or ints corresponding to indexes if using numpy arrays.
            - str : If a string, it is assumed to be a path to a csv file with the subsets. 
                The first column is assumed to be the name of the subset and the remaining columns are the features in the subset.
            - list or np.ndarray : If a list or np.ndarray, it is assumed to be a list of subsets (i.e a list of lists).
            - dict : A dictionary where keys are the names of the subsets and the values are the list of features.
            - int : If an int, it is assumed to be the number of subsets to generate. Each subset will contain one feature.
            - None : If None, each column will be treated as a subset. One column will be selected per subset.
        rng : int, np.random.Generator, optional
            The random number generator. The default is None.
            Only used to select the first subset.

        Returns
        -------
        None    
        """

        subsets = subsets
        rng = np.random.default_rng(rng)

        if isinstance(subsets, str):
            df = pd.read_csv(subsets,header=None,index_col=0)
            df['features'] = df.apply(lambda x: list([x[c] for c in df.columns]),axis=1)
            self.subset_dict = {}
            for row in df.index:
                self.subset_dict[row] = df.loc[row]['features']
        elif isinstance(subsets, dict):
            self.subset_dict = subsets
        elif isinstance(subsets, list) or isinstance(subsets, np.ndarray):
            self.subset_dict = {str(i):subsets[i] for i in range(len(subsets))}
        elif isinstance(subsets, int):
            self.subset_dict = {"{0}".format(i):i for i in range(subsets)}
        else:
            raise ValueError("Subsets must be a string, dictionary, list, int, or numpy array")

        self.names_list = list(self.subset_dict.keys())


        self.selected_subset_name = rng.choice(self.names_list)
        self.sel_subset = self.subset_dict[self.selected_subset_name]


    def mutate(self, rng=None):
        rng = np.random.default_rng(rng)
        #get list of names not including the current one
        names = [name for name in self.names_list if name != self.selected_subset_name]
        self.selected_subset_name = rng.choice(names)
        self.sel_subset = self.subset_dict[self.selected_subset_name]


    def crossover(self, other, rng=None):
        self.selected_subset_name = other.selected_subset_name
        self.sel_subset = other.sel_subset

    def export_pipeline(self, **kwargs):
        return FeatureSetSelector(sel_subset=self.sel_subset, name=self.selected_subset_name)


    def unique_id(self):
        id_str = "FeatureSetSelector({0})".format(self.selected_subset_name)
        return id_str

__init__(subsets, rng=None)

An individual for representing a specific FeatureSetSelector. The FeatureSetSelector selects a feature list of list of predefined feature subsets.

This instance will select one set initially. Mutation and crossover can swap the selected subset with another.

Parameters:

Name Type Description Default
subsets str or list

Sets the subsets that the FeatureSetSeletor will select from if set as an option in one of the configuration dictionaries. Features are defined by column names if using a Pandas data frame, or ints corresponding to indexes if using numpy arrays. - str : If a string, it is assumed to be a path to a csv file with the subsets. The first column is assumed to be the name of the subset and the remaining columns are the features in the subset. - list or np.ndarray : If a list or np.ndarray, it is assumed to be a list of subsets (i.e a list of lists). - dict : A dictionary where keys are the names of the subsets and the values are the list of features. - int : If an int, it is assumed to be the number of subsets to generate. Each subset will contain one feature. - None : If None, each column will be treated as a subset. One column will be selected per subset.

 None
rng (int, Generator)

The random number generator. The default is None. Only used to select the first subset.

 None

Returns:

Type Description
None
Source code in tpot2/search_spaces/nodes/fss_node.py 
 52
 53
 54
 55
 56
 57
 58
 59
 60
 61
 62
 63
 64
 65
 66
 67
 68
 69
 70
 71
 72
 73
 74
 75
 76
 77
 78
 79
 80
 81
 82
 83
 84
 85
 86
 87
 88
 89
 90
 91
 92
 93
 94
 95
 96
 97
 98
 99
100
101
102
103
104
105
def __init__(   self,
                subsets,
                rng=None,
            ):

    """
    An individual for representing a specific FeatureSetSelector. 
    The FeatureSetSelector selects a feature list of list of predefined feature subsets.

    This instance will select one set initially. Mutation and crossover can swap the selected subset with another.

    Parameters
    ----------
    subsets : str or list, default=None
        Sets the subsets that the FeatureSetSeletor will select from if set as an option in one of the configuration dictionaries. 
        Features are defined by column names if using a Pandas data frame, or ints corresponding to indexes if using numpy arrays.
        - str : If a string, it is assumed to be a path to a csv file with the subsets. 
            The first column is assumed to be the name of the subset and the remaining columns are the features in the subset.
        - list or np.ndarray : If a list or np.ndarray, it is assumed to be a list of subsets (i.e a list of lists).
        - dict : A dictionary where keys are the names of the subsets and the values are the list of features.
        - int : If an int, it is assumed to be the number of subsets to generate. Each subset will contain one feature.
        - None : If None, each column will be treated as a subset. One column will be selected per subset.
    rng : int, np.random.Generator, optional
        The random number generator. The default is None.
        Only used to select the first subset.

    Returns
    -------
    None    
    """

    subsets = subsets
    rng = np.random.default_rng(rng)

    if isinstance(subsets, str):
        df = pd.read_csv(subsets,header=None,index_col=0)
        df['features'] = df.apply(lambda x: list([x[c] for c in df.columns]),axis=1)
        self.subset_dict = {}
        for row in df.index:
            self.subset_dict[row] = df.loc[row]['features']
    elif isinstance(subsets, dict):
        self.subset_dict = subsets
    elif isinstance(subsets, list) or isinstance(subsets, np.ndarray):
        self.subset_dict = {str(i):subsets[i] for i in range(len(subsets))}
    elif isinstance(subsets, int):
        self.subset_dict = {"{0}".format(i):i for i in range(subsets)}
    else:
        raise ValueError("Subsets must be a string, dictionary, list, int, or numpy array")

    self.names_list = list(self.subset_dict.keys())


    self.selected_subset_name = rng.choice(self.names_list)
    self.sel_subset = self.subset_dict[self.selected_subset_name]

FSSNode

Bases: SearchSpace

Source code in tpot2/search_spaces/nodes/fss_node.py 
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
class FSSNode(SearchSpace):
    def __init__(self,                     
                    subsets,
                ):
        """
        A search space for a FeatureSetSelector. 
        The FeatureSetSelector selects a feature list of list of predefined feature subsets.

        Parameters
        ----------
        subsets : str or list, default=None
            Sets the subsets that the FeatureSetSeletor will select from if set as an option in one of the configuration dictionaries. 
            Features are defined by column names if using a Pandas data frame, or ints corresponding to indexes if using numpy arrays.
            - str : If a string, it is assumed to be a path to a csv file with the subsets. 
                The first column is assumed to be the name of the subset and the remaining columns are the features in the subset.
            - list or np.ndarray : If a list or np.ndarray, it is assumed to be a list of subsets (i.e a list of lists).
            - dict : A dictionary where keys are the names of the subsets and the values are the list of features.
            - int : If an int, it is assumed to be the number of subsets to generate. Each subset will contain one feature.
            - None : If None, each column will be treated as a subset. One column will be selected per subset.

        Returns
        -------
        None    

        """

        self.subsets = subsets

    def generate(self, rng=None) -> SklearnIndividual:
        return FSSIndividual(   
            subsets=self.subsets,
            rng=rng,
            )

__init__(subsets)

A search space for a FeatureSetSelector. The FeatureSetSelector selects a feature list of list of predefined feature subsets.

Parameters:

Name Type Description Default
subsets str or list

Sets the subsets that the FeatureSetSeletor will select from if set as an option in one of the configuration dictionaries. Features are defined by column names if using a Pandas data frame, or ints corresponding to indexes if using numpy arrays. - str : If a string, it is assumed to be a path to a csv file with the subsets. The first column is assumed to be the name of the subset and the remaining columns are the features in the subset. - list or np.ndarray : If a list or np.ndarray, it is assumed to be a list of subsets (i.e a list of lists). - dict : A dictionary where keys are the names of the subsets and the values are the list of features. - int : If an int, it is assumed to be the number of subsets to generate. Each subset will contain one feature. - None : If None, each column will be treated as a subset. One column will be selected per subset.

 None

Returns:

Type Description
None
Source code in tpot2/search_spaces/nodes/fss_node.py 
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
def __init__(self,                     
                subsets,
            ):
    """
    A search space for a FeatureSetSelector. 
    The FeatureSetSelector selects a feature list of list of predefined feature subsets.

    Parameters
    ----------
    subsets : str or list, default=None
        Sets the subsets that the FeatureSetSeletor will select from if set as an option in one of the configuration dictionaries. 
        Features are defined by column names if using a Pandas data frame, or ints corresponding to indexes if using numpy arrays.
        - str : If a string, it is assumed to be a path to a csv file with the subsets. 
            The first column is assumed to be the name of the subset and the remaining columns are the features in the subset.
        - list or np.ndarray : If a list or np.ndarray, it is assumed to be a list of subsets (i.e a list of lists).
        - dict : A dictionary where keys are the names of the subsets and the values are the list of features.
        - int : If an int, it is assumed to be the number of subsets to generate. Each subset will contain one feature.
        - None : If None, each column will be treated as a subset. One column will be selected per subset.

    Returns
    -------
    None    

    """

    self.subsets = subsets