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Template search spaces

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/.

DynamicUnionPipeline

Bases: SearchSpace

Source code in tpot2/search_spaces/pipelines/dynamicunion.py 
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class DynamicUnionPipeline(SearchSpace):
    def __init__(self, search_space : SearchSpace, max_estimators=None, allow_repeats=False ) -> None:
        """
        Takes in a list of search spaces. will produce a pipeline of Sequential length. Each step in the pipeline will correspond to the the search space provided in the same index.
        """

        self.search_space = search_space
        self.max_estimators = max_estimators
        self.allow_repeats = allow_repeats

    def generate(self, rng=None):
        rng = np.random.default_rng(rng)
        return DynamicUnionPipelineIndividual(self.search_space, max_estimators=self.max_estimators, allow_repeats=self.allow_repeats, rng=rng)

__init__(search_space, max_estimators=None, allow_repeats=False)

Takes in a list of search spaces. will produce a pipeline of Sequential length. Each step in the pipeline will correspond to the the search space provided in the same index.

Source code in tpot2/search_spaces/pipelines/dynamicunion.py 
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def __init__(self, search_space : SearchSpace, max_estimators=None, allow_repeats=False ) -> None:
    """
    Takes in a list of search spaces. will produce a pipeline of Sequential length. Each step in the pipeline will correspond to the the search space provided in the same index.
    """

    self.search_space = search_space
    self.max_estimators = max_estimators
    self.allow_repeats = allow_repeats

DynamicUnionPipelineIndividual

Bases: SklearnIndividual

Takes in one search space. Will produce a FeatureUnion of up to max_estimators number of steps. The output of the FeatureUnion will the all of the steps concatenated together.

Source code in tpot2/search_spaces/pipelines/dynamicunion.py 
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class DynamicUnionPipelineIndividual(SklearnIndividual):
    """
    Takes in one search space.
    Will produce a FeatureUnion of up to max_estimators number of steps.
    The output of the FeatureUnion will the all of the steps concatenated together.

    """

    def __init__(self, search_space : SearchSpace, max_estimators=None, allow_repeats=False, rng=None) -> None:
        super().__init__()
        self.search_space = search_space

        if max_estimators is None:
            self.max_estimators = np.inf
        else:
            self.max_estimators = max_estimators

        self.allow_repeats = allow_repeats

        self.union_dict = {}

        if self.max_estimators == np.inf:
            init_max = 3
        else:
            init_max = self.max_estimators

        rng = np.random.default_rng(rng)

        for _ in range(rng.integers(1, init_max)):
            self._mutate_add_step(rng)


    def mutate(self, rng=None):
        rng = np.random.default_rng(rng)
        mutation_funcs = [self._mutate_add_step, self._mutate_remove_step, self._mutate_replace_step, self._mutate_note]
        rng.shuffle(mutation_funcs)
        for mutation_func in mutation_funcs:
            if mutation_func(rng):
                return True

    def _mutate_add_step(self, rng):
        rng = np.random.default_rng(rng)
        max_attempts = 10
        if len(self.union_dict) < self.max_estimators:
            for _ in range(max_attempts):
                new_step = self.search_space.generate(rng)
                if new_step.unique_id() not in self.union_dict:
                    self.union_dict[new_step.unique_id()] = new_step
                    return True
        return False

    def _mutate_remove_step(self, rng):
        rng = np.random.default_rng(rng)
        if len(self.union_dict) > 1:
            self.union_dict.pop( rng.choice(list(self.union_dict.keys())))  
            return True
        return False

    def _mutate_replace_step(self, rng):
        rng = np.random.default_rng(rng)        
        changed = self._mutate_remove_step(rng) or self._mutate_add_step(rng)
        return changed

    #TODO mutate one step or multiple?
    def _mutate_note(self, rng):
        rng = np.random.default_rng(rng)
        changed = False
        values = list(self.union_dict.values())
        for step in values:
            if rng.random() < 0.5:
                changed = step.mutate(rng) or changed

        self.union_dict = {step.unique_id(): step for step in values}

        return changed


    def crossover(self, other, rng=None):
        rng = np.random.default_rng(rng)

        cx_funcs = [self._crossover_swap_multiple_nodes, self._crossover_node]
        rng.shuffle(cx_funcs)
        for cx_func in cx_funcs:
            if cx_func(other, rng):
                return True

        return False


    def _crossover_swap_multiple_nodes(self, other, rng):
        rng = np.random.default_rng(rng)
        self_values = list(self.union_dict.values())
        other_values = list(other.union_dict.values())

        rng.shuffle(self_values)
        rng.shuffle(other_values)

        self_idx = rng.integers(0,len(self_values))
        other_idx = rng.integers(0,len(other_values))

        #Note that this is not one-point-crossover since the sequence doesn't matter. this is just a quick way to swap multiple random items
        self_values[:self_idx], other_values[:other_idx] = other_values[:other_idx], self_values[:self_idx]

        self.union_dict = {step.unique_id(): step for step in self_values}
        other.union_dict = {step.unique_id(): step for step in other_values}

        return True


    def _crossover_node(self, other, rng):
        rng = np.random.default_rng(rng)

        changed = False
        self_values = list(self.union_dict.values())
        other_values = list(other.union_dict.values())

        rng.shuffle(self_values)
        rng.shuffle(other_values)

        for self_step, other_step in zip(self_values, other_values):
            if rng.random() < 0.5:
                changed = self_step.crossover(other_step, rng) or changed

        self.union_dict = {step.unique_id(): step for step in self_values}
        other.union_dict = {step.unique_id(): step for step in other_values}

        return changed

    def export_pipeline(self, **kwargs):
        values = list(self.union_dict.values())
        return sklearn.pipeline.make_union(*[step.export_pipeline(**kwargs) for step in values])

    def unique_id(self):
        values = list(self.union_dict.values())
        l = [step.unique_id() for step in values]
        # if all items are strings, then sort them
        if all([isinstance(x, str) for x in l]):
            l.sort()
        l = ["FeatureUnion"] + l
        return TupleIndex(frozenset(l))

EstimatorNodeIndividual

Bases: SklearnIndividual

Note that ConfigurationSpace does not support None as a parameter. Instead, use the special string "". TPOT will automatically replace instances of this string with the Python None.

Parameters:

Name Type Description Default
method type

The class of the estimator to be used

 required
space ConfigurationSpace | dict

The hyperparameter space to be used. If a dict is passed, hyperparameters are fixed and not learned.

 required
Source code in tpot2/search_spaces/nodes/estimator_node.py 
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class EstimatorNodeIndividual(SklearnIndividual):
    """
    Note that ConfigurationSpace does not support None as a parameter. Instead, use the special string "<NONE>". TPOT will automatically replace instances of this string with the Python None. 

    Parameters
    ----------
    method : type
        The class of the estimator to be used

    space : ConfigurationSpace|dict
        The hyperparameter space to be used. If a dict is passed, hyperparameters are fixed and not learned.

    """
    def __init__(self, method: type, 
                        space: ConfigurationSpace|dict, #TODO If a dict is passed, hyperparameters are fixed and not learned. Is this confusing? Should we make a second node type?
                        hyperparameter_parser: callable = None,
                        rng=None) -> None:
        super().__init__()
        self.method = method
        self.space = space

        if hyperparameter_parser is None:
            self.hyperparameter_parser = default_hyperparameter_parser
        else:
            self.hyperparameter_parser = hyperparameter_parser

        if isinstance(space, dict):
            self.hyperparameters = space
        else:
            rng = np.random.default_rng(rng)
            self.space.seed(rng.integers(0, 2**32))
            self.hyperparameters = dict(self.space.sample_configuration())

    def mutate(self, rng=None):
        if isinstance(self.space, dict): 
            return False

        rng = np.random.default_rng(rng)
        self.space.seed(rng.integers(0, 2**32))
        self.hyperparameters = dict(self.space.sample_configuration())
        return True

    def crossover(self, other, rng=None):
        if isinstance(self.space, dict):
            return False

        rng = np.random.default_rng(rng)
        if self.method != other.method:
            return False

        #loop through hyperparameters, randomly swap items in self.hyperparameters with items in other.hyperparameters
        for hyperparameter in self.space:
            if rng.choice([True, False]):
                if hyperparameter in other.hyperparameters:
                    self.hyperparameters[hyperparameter] = other.hyperparameters[hyperparameter]

        return True



    @final #this method should not be overridden, instead override hyperparameter_parser
    def export_pipeline(self, **kwargs):
        return self.method(**self.hyperparameter_parser(self.hyperparameters))

    def unique_id(self):
        #return a dictionary of the method and the hyperparameters
        method_str = self.method.__name__
        params = list(self.hyperparameters.keys())
        params = sorted(params)

        id_str = f"{method_str}({', '.join([f'{param}={self.hyperparameters[param]}' for param in params])})"

        return id_str

FSSIndividual

Bases: SklearnIndividual

Source code in tpot2/search_spaces/nodes/fss_node.py 
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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 
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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 
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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 
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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

FeatureSetSelector

Bases: BaseEstimator, SelectorMixin

Select predefined feature subsets.

Source code in tpot2/builtin_modules/feature_set_selector.py 
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class FeatureSetSelector(BaseEstimator, SelectorMixin):
    """
    Select predefined feature subsets.


    """

    def __init__(self, sel_subset=None, name=None):
        """Create a FeatureSetSelector object.

        Parameters
        ----------
        sel_subset: list or int
            If X is a dataframe, items in sel_subset list must correspond to column names
            If X is a numpy array, items in sel_subset list must correspond to column indexes
            int: index of a single column
        Returns
        -------
        None

        """
        self.name = name
        self.sel_subset = sel_subset


    def fit(self, X, y=None):
        """Fit FeatureSetSelector for feature selection

        Parameters
        ----------
        X: array-like of shape (n_samples, n_features)
            The training input samples.
        y: array-like, shape (n_samples,)
            The target values (integers that correspond to classes in classification, real numbers in regression).

        Returns
        -------
        self: object
            Returns a copy of the estimator
        """
        if isinstance(self.sel_subset, int) or isinstance(self.sel_subset, str):
            self.sel_subset = [self.sel_subset]

        #generate  self.feat_list_idx
        if isinstance(X, pd.DataFrame):
            self.feature_names_in_ = X.columns.tolist()
            self.feat_list_idx = sorted([self.feature_names_in_.index(feat) for feat in self.sel_subset])


        elif isinstance(X, np.ndarray):
            self.feature_names_in_ = None#list(range(X.shape[1]))

            self.feat_list_idx = sorted(self.sel_subset)

        n_features = X.shape[1]
        self.mask = np.zeros(n_features, dtype=bool)
        self.mask[np.asarray(self.feat_list_idx)] = True

        return self

    #TODO keep returned as dataframe if input is dataframe? may not be consistent with sklearn

    # def transform(self, X):

    def _get_tags(self):
        tags = {"allow_nan": True, "requires_y": False}
        return tags

    def _get_support_mask(self):
        """
        Get the boolean mask indicating which features are selected
        Returns
        -------
        support : boolean array of shape [# input features]
            An element is True iff its corresponding feature is selected for
            retention.
        """
        return self.mask

__init__(sel_subset=None, name=None)

Create a FeatureSetSelector object.

Parameters:

Name Type Description Default
sel_subset

If X is a dataframe, items in sel_subset list must correspond to column names If X is a numpy array, items in sel_subset list must correspond to column indexes int: index of a single column

 None

Returns:

Type Description
None
Source code in tpot2/builtin_modules/feature_set_selector.py 
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def __init__(self, sel_subset=None, name=None):
    """Create a FeatureSetSelector object.

    Parameters
    ----------
    sel_subset: list or int
        If X is a dataframe, items in sel_subset list must correspond to column names
        If X is a numpy array, items in sel_subset list must correspond to column indexes
        int: index of a single column
    Returns
    -------
    None

    """
    self.name = name
    self.sel_subset = sel_subset

fit(X, y=None)

Fit FeatureSetSelector for feature selection

Parameters:

Name Type Description Default
X

The training input samples.

 required
y

The target values (integers that correspond to classes in classification, real numbers in regression).

 None

Returns:

Name Type Description
self object

Returns a copy of the estimator
Source code in tpot2/builtin_modules/feature_set_selector.py 
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def fit(self, X, y=None):
    """Fit FeatureSetSelector for feature selection

    Parameters
    ----------
    X: array-like of shape (n_samples, n_features)
        The training input samples.
    y: array-like, shape (n_samples,)
        The target values (integers that correspond to classes in classification, real numbers in regression).

    Returns
    -------
    self: object
        Returns a copy of the estimator
    """
    if isinstance(self.sel_subset, int) or isinstance(self.sel_subset, str):
        self.sel_subset = [self.sel_subset]

    #generate  self.feat_list_idx
    if isinstance(X, pd.DataFrame):
        self.feature_names_in_ = X.columns.tolist()
        self.feat_list_idx = sorted([self.feature_names_in_.index(feat) for feat in self.sel_subset])


    elif isinstance(X, np.ndarray):
        self.feature_names_in_ = None#list(range(X.shape[1]))

        self.feat_list_idx = sorted(self.sel_subset)

    n_features = X.shape[1]
    self.mask = np.zeros(n_features, dtype=bool)
    self.mask[np.asarray(self.feat_list_idx)] = True

    return self

GeneticFeatureSelectorNode

Bases: SearchSpace

Source code in tpot2/search_spaces/nodes/genetic_feature_selection.py 
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class GeneticFeatureSelectorNode(SearchSpace):
    def __init__(self,                     
                    n_features,
                    start_p=0.2,
                    mutation_rate = 0.1,
                    crossover_rate = 0.1,
                    mutation_rate_rate = 0, # These are still experimental but seem to help. Theory is that it takes slower steps as it gets closer to the optimal solution.
                    crossover_rate_rate = 0,# Otherwise is mutation_rate is too small, it takes forever, and if its too large, it never converges.
                    ):
        """
        A node that generates a GeneticFeatureSelectorIndividual. Uses genetic algorithm to select novel subsets of features.

        Parameters
        ----------
        n_features : int
            Number of features in the dataset.
        start_p : float
            Probability of selecting a given feature for the initial subset of features.
        mutation_rate : float
            Probability of adding/removing a feature from the subset of features.
        crossover_rate : float
            Probability of swapping a feature between two subsets of features.
        mutation_rate_rate : float
            Probability of changing the mutation rate. (experimental)
        crossover_rate_rate : float
            Probability of changing the crossover rate. (experimental)

        """

        self.n_features = n_features
        self.start_p = start_p
        self.mutation_rate = mutation_rate
        self.crossover_rate = crossover_rate
        self.mutation_rate_rate = mutation_rate_rate
        self.crossover_rate_rate = crossover_rate_rate


    def generate(self, rng=None) -> SklearnIndividual:
        return GeneticFeatureSelectorIndividual(   mask=self.n_features,
                                                    start_p=self.start_p,
                                                    mutation_rate=self.mutation_rate,
                                                    crossover_rate=self.crossover_rate,
                                                    mutation_rate_rate=self.mutation_rate_rate,
                                                    crossover_rate_rate=self.crossover_rate_rate,
                                                    rng=rng
                                                )

__init__(n_features, start_p=0.2, mutation_rate=0.1, crossover_rate=0.1, mutation_rate_rate=0, crossover_rate_rate=0)

A node that generates a GeneticFeatureSelectorIndividual. Uses genetic algorithm to select novel subsets of features.

Parameters:

Name Type Description Default
n_features int

Number of features in the dataset.

 required
start_p float

Probability of selecting a given feature for the initial subset of features.

 0.2
mutation_rate float

Probability of adding/removing a feature from the subset of features.

 0.1
crossover_rate float

Probability of swapping a feature between two subsets of features.

 0.1
mutation_rate_rate float

Probability of changing the mutation rate. (experimental)

 0
crossover_rate_rate float

Probability of changing the crossover rate. (experimental)

 0
Source code in tpot2/search_spaces/nodes/genetic_feature_selection.py 
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def __init__(self,                     
                n_features,
                start_p=0.2,
                mutation_rate = 0.1,
                crossover_rate = 0.1,
                mutation_rate_rate = 0, # These are still experimental but seem to help. Theory is that it takes slower steps as it gets closer to the optimal solution.
                crossover_rate_rate = 0,# Otherwise is mutation_rate is too small, it takes forever, and if its too large, it never converges.
                ):
    """
    A node that generates a GeneticFeatureSelectorIndividual. Uses genetic algorithm to select novel subsets of features.

    Parameters
    ----------
    n_features : int
        Number of features in the dataset.
    start_p : float
        Probability of selecting a given feature for the initial subset of features.
    mutation_rate : float
        Probability of adding/removing a feature from the subset of features.
    crossover_rate : float
        Probability of swapping a feature between two subsets of features.
    mutation_rate_rate : float
        Probability of changing the mutation rate. (experimental)
    crossover_rate_rate : float
        Probability of changing the crossover rate. (experimental)

    """

    self.n_features = n_features
    self.start_p = start_p
    self.mutation_rate = mutation_rate
    self.crossover_rate = crossover_rate
    self.mutation_rate_rate = mutation_rate_rate
    self.crossover_rate_rate = crossover_rate_rate

GraphKey

A class that can be used as a key for a graph.

Parameters:

Name Type Description Default
graph Graph

The graph to use as a key. Node Attributes are used for the hash.

 required
matched_label str

The node attribute to consider for the hash.

 'label'
Source code in tpot2/search_spaces/pipelines/graph.py 
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class GraphKey():
    '''
    A class that can be used as a key for a graph.

    Parameters
    ----------
    graph : (nx.Graph)
        The graph to use as a key. Node Attributes are used for the hash.
    matched_label : (str)
        The node attribute to consider for the hash.
    '''

    def __init__(self, graph, matched_label='label') -> None:#['hyperparameters', 'method_class']) -> None:


        self.graph = graph
        self.matched_label = matched_label
        self.node_match = partial(node_match, matched_labels=[matched_label])
        self.key = int(nx.weisfeiler_lehman_graph_hash(self.graph, node_attr=self.matched_label),16) #hash(tuple(sorted([val for (node, val) in self.graph.degree()])))


    #If hash is different, node is definitely different
    # https://arxiv.org/pdf/2002.06653.pdf
    def __hash__(self) -> int:

        return self.key

    #If hash is same, use __eq__ to know if they are actually different
    def __eq__(self, other):
        return nx.is_isomorphic(self.graph, other.graph, node_match=self.node_match)

GraphPipelineIndividual

Bases: SklearnIndividual

Defines a search space of pipelines in the shape of a Directed Acyclic Graphs. The search spaces for root, leaf, and inner nodes can be defined separately if desired. Each graph will have a single root serving as the final estimator which is drawn from the root_search_space. If the leaf_search_space is defined, all leaves in the pipeline will be drawn from that search space. If the leaf_search_space is not defined, all leaves will be drawn from the inner_search_space. Nodes that are not leaves or roots will be drawn from the inner_search_space. If the inner_search_space is not defined, there will be no inner nodes.

cross_val_predict_cv, method, memory, and use_label_encoder are passed to the GraphPipeline object when the pipeline is exported and not directly used in the search space.

Exports to a GraphPipeline object.

Parameters:

Name Type Description Default
root_search_space SearchSpace

The search space for the root node of the graph. This node will be the final estimator in the pipeline.

 required
inner_search_space SearchSpace

The search space for the inner nodes of the graph. If not defined, there will be no inner nodes.

 None
leaf_search_space SearchSpace

The search space for the leaf nodes of the graph. If not defined, the leaf nodes will be drawn from the inner_search_space.

 None
crossover_same_depth bool

If True, crossover will only occur between nodes at the same depth in the graph. If False, crossover will occur between nodes at any depth.

 False
cross_val_predict_cv Union[int, Callable]

Determines the cross-validation splitting strategy used in inner classifiers or regressors

 0
method str

The prediction method to use for the inner classifiers or regressors. If 'auto', it will try to use predict_proba, decision_function, or predict in that order.

 'auto'
memory

Used to cache the input and outputs of nodes to prevent refitting or computationally heavy transformations. By default, no caching is performed. If a string is given, it is the path to the caching directory.

 required
use_label_encoder bool

If True, the label encoder is used to encode the labels to be 0 to N. If False, the label encoder is not used. Mainly useful for classifiers (XGBoost) that require labels to be ints from 0 to N. Can also be a sklearn.preprocessing.LabelEncoder object. If so, that label encoder is used.

 False
rng

Seed for sampling the first graph instance.

 None
Source code in tpot2/search_spaces/pipelines/graph.py 
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class GraphPipelineIndividual(SklearnIndividual):
    """
        Defines a search space of pipelines in the shape of a Directed Acyclic Graphs. The search spaces for root, leaf, and inner nodes can be defined separately if desired.
        Each graph will have a single root serving as the final estimator which is drawn from the `root_search_space`. If the `leaf_search_space` is defined, all leaves 
        in the pipeline will be drawn from that search space. If the `leaf_search_space` is not defined, all leaves will be drawn from the `inner_search_space`.
        Nodes that are not leaves or roots will be drawn from the `inner_search_space`. If the `inner_search_space` is not defined, there will be no inner nodes.

        `cross_val_predict_cv`, `method`, `memory`, and `use_label_encoder` are passed to the GraphPipeline object when the pipeline is exported and not directly used in the search space.

        Exports to a GraphPipeline object.

        Parameters
        ----------

        root_search_space: SearchSpace
            The search space for the root node of the graph. This node will be the final estimator in the pipeline.

        inner_search_space: SearchSpace, optional
            The search space for the inner nodes of the graph. If not defined, there will be no inner nodes.

        leaf_search_space: SearchSpace, optional
            The search space for the leaf nodes of the graph. If not defined, the leaf nodes will be drawn from the inner_search_space.

        crossover_same_depth: bool, optional
            If True, crossover will only occur between nodes at the same depth in the graph. If False, crossover will occur between nodes at any depth.

        cross_val_predict_cv: int, cross-validation generator or an iterable, optional
            Determines the cross-validation splitting strategy used in inner classifiers or regressors

        method: str, optional
            The prediction method to use for the inner classifiers or regressors. If 'auto', it will try to use predict_proba, decision_function, or predict in that order.

        memory: str or object with the joblib.Memory interface, optional
            Used to cache the input and outputs of nodes to prevent refitting or computationally heavy transformations. By default, no caching is performed. If a string is given, it is the path to the caching directory.

        use_label_encoder: bool, optional
            If True, the label encoder is used to encode the labels to be 0 to N. If False, the label encoder is not used.
            Mainly useful for classifiers (XGBoost) that require labels to be ints from 0 to N.
            Can also be a sklearn.preprocessing.LabelEncoder object. If so, that label encoder is used.

        rng: int, RandomState instance or None, optional
            Seed for sampling the first graph instance. 

        """

    def __init__(
            self,  
            root_search_space: SearchSpace, 
            leaf_search_space: SearchSpace = None, 
            inner_search_space: SearchSpace = None, 
            max_size: int = np.inf,
            crossover_same_depth: bool = False,
            cross_val_predict_cv: Union[int, Callable] = 0, #signature function(estimator, X, y=none)
            method: str = 'auto',
            use_label_encoder: bool = False,
            rng=None):

        super().__init__()

        self.__debug = False

        rng = np.random.default_rng(rng)

        self.root_search_space = root_search_space
        self.leaf_search_space = leaf_search_space
        self.inner_search_space = inner_search_space
        self.max_size = max_size
        self.crossover_same_depth = crossover_same_depth

        self.cross_val_predict_cv = cross_val_predict_cv
        self.method = method
        self.use_label_encoder = use_label_encoder

        self.root = self.root_search_space.generate(rng)
        self.graph = nx.DiGraph()
        self.graph.add_node(self.root)

        if self.leaf_search_space is not None:
            self.leaf = self.leaf_search_space.generate(rng)
            self.graph.add_node(self.leaf)
            self.graph.add_edge(self.root, self.leaf)

        if self.inner_search_space is None and self.leaf_search_space is None:
            self.mutate_methods_list = [self._mutate_node]
            self.crossover_methods_list = [self._crossover_swap_branch,]#[self._crossover_swap_branch, self._crossover_swap_node, self._crossover_take_branch]  #TODO self._crossover_nodes, 

        else:
            self.mutate_methods_list = [self._mutate_insert_leaf, self._mutate_insert_inner_node, self._mutate_remove_node, self._mutate_node, self._mutate_insert_bypass_node]
            self.crossover_methods_list = [self._crossover_swap_branch, self._crossover_nodes, self._crossover_take_branch ]#[self._crossover_swap_branch, self._crossover_swap_node, self._crossover_take_branch]  #TODO self._crossover_nodes, 

        self.merge_duplicated_nodes_toggle = True

        self.graphkey = None


    def mutate(self, rng=None):
        rng = np.random.default_rng(rng)
        rng.shuffle(self.mutate_methods_list)
        for mutate_method in self.mutate_methods_list:
            if mutate_method(rng=rng):

                if self.merge_duplicated_nodes_toggle:
                    self._merge_duplicated_nodes()

                if self.__debug:
                    print(mutate_method)

                    if self.root not in self.graph.nodes:
                        print('lost root something went wrong with ', mutate_method)

                    if len(self.graph.predecessors(self.root)) > 0:
                        print('root has parents ', mutate_method)

                    if any([n in nx.ancestors(self.graph,n) for n in self.graph.nodes]):
                        print('a node is connecting to itself...')

                    if self.__debug:
                        try:
                            nx.find_cycle(self.graph)
                            print('something went wrong with ', mutate_method)
                        except:
                            pass

                self.graphkey = None

        return False




    def _mutate_insert_leaf(self, rng=None):
        rng = np.random.default_rng(rng)
        if self.max_size > self.graph.number_of_nodes():
            sorted_nodes_list = list(self.graph.nodes)
            rng.shuffle(sorted_nodes_list) #TODO: sort by number of children and/or parents? bias model one way or another
            for node in sorted_nodes_list:
                #if leafs are protected, check if node is a leaf
                #if node is a leaf, skip because we don't want to add node on top of node
                if (self.leaf_search_space is not None #if leafs are protected
                    and   len(list(self.graph.successors(node))) == 0 #if node is leaf
                    and  len(list(self.graph.predecessors(node))) > 0 #except if node is root, in which case we want to add a leaf even if it happens to be a leaf too
                    ):

                    continue

                #If node *is* the root or is not a leaf, add leaf node. (dont want to add leaf on top of leaf)
                if self.leaf_search_space is not None:
                    new_node = self.leaf_search_space.generate(rng)
                else:
                    new_node = self.inner_search_space.generate(rng)

                self.graph.add_node(new_node)
                self.graph.add_edge(node, new_node)
                return True

        return False

    def _mutate_insert_inner_node(self, rng=None):
        """
        Finds an edge in the graph and inserts a new node between the two nodes. Removes the edge between the two nodes.
        """
        rng = np.random.default_rng(rng)
        if self.max_size > self.graph.number_of_nodes():
            sorted_nodes_list = list(self.graph.nodes)
            sorted_nodes_list2 = list(self.graph.nodes)
            rng.shuffle(sorted_nodes_list) #TODO: sort by number of children and/or parents? bias model one way or another
            rng.shuffle(sorted_nodes_list2)
            for node in sorted_nodes_list:
                #loop through children of node
                for child_node in list(self.graph.successors(node)):

                    if child_node is not node and child_node not in nx.ancestors(self.graph, node):
                        if self.leaf_search_space is not None:
                            #If if we are protecting leafs, dont add connection into a leaf
                            if len(list(nx.descendants(self.graph,node))) ==0 :
                                continue

                        new_node = self.inner_search_space.generate(rng)

                        self.graph.add_node(new_node)
                        self.graph.add_edges_from([(node, new_node), (new_node, child_node)])
                        self.graph.remove_edge(node, child_node)
                        return True

        return False


    def _mutate_remove_node(self, rng=None):
        '''
        Removes a randomly chosen node and connects its parents to its children.
        If the node is the only leaf for an inner node and 'leaf_search_space' is not none, we do not remove it.
        '''
        rng = np.random.default_rng(rng)
        nodes_list = list(self.graph.nodes)
        nodes_list.remove(self.root)
        leaves = get_leaves(self.graph)

        while len(nodes_list) > 0:
            node = rng.choice(nodes_list)
            nodes_list.remove(node)

            if self.leaf_search_space is not None and len(list(nx.descendants(self.graph,node))) == 0 : #if the node is a leaf
                if len(leaves) <= 1:
                    continue #dont remove the last leaf
                leaf_parents = self.graph.predecessors(node)

                # if any of the parents of the node has one one child, continue
                if any([len(list(self.graph.successors(lp))) < 2 for lp in leaf_parents]): #dont remove a leaf if it is the only input into another node.
                    continue

                remove_and_stitch(self.graph, node)
                remove_nodes_disconnected_from_node(self.graph, self.root)
                return True

            else:
                remove_and_stitch(self.graph, node)
                remove_nodes_disconnected_from_node(self.graph, self.root)
                return True

        return False



    def _mutate_node(self, rng=None):
        '''
        Mutates the hyperparameters for a randomly chosen node in the graph.
        '''
        rng = np.random.default_rng(rng)
        sorted_nodes_list = list(self.graph.nodes)
        rng.shuffle(sorted_nodes_list)
        completed_one = False
        for node in sorted_nodes_list:
            if node.mutate(rng):
                return True
        return False

    def _mutate_remove_edge(self, rng=None):
        '''
        Deletes an edge as long as deleting that edge does not make the graph disconnected.
        '''
        rng = np.random.default_rng(rng)
        sorted_nodes_list = list(self.graph.nodes)
        rng.shuffle(sorted_nodes_list)
        for child_node in sorted_nodes_list:
            parents = list(self.graph.predecessors(child_node))
            if len(parents) > 1: # if it has more than one parent, you can remove an edge (if this is the only child of a node, it will become a leaf)

                for parent_node in parents:
                    # if removing the egde will make the parent_node a leaf node, skip
                    if self.leaf_search_space is not None and len(list(self.graph.successors(parent_node))) < 2:
                        continue

                    self.graph.remove_edge(parent_node, child_node)
                    return True
        return False   

    def _mutate_add_edge(self, rng=None):
        '''
        Randomly add an edge from a node to another node that is not an ancestor of the first node.
        '''
        rng = np.random.default_rng(rng)
        sorted_nodes_list = list(self.graph.nodes)
        rng.shuffle(sorted_nodes_list)
        for child_node in sorted_nodes_list:
            for parent_node in sorted_nodes_list:
                if self.leaf_search_space is not None:
                    if len(list(self.graph.successors(parent_node))) == 0:
                        continue

                # skip if
                # - parent and child are the same node
                # - edge already exists
                # - child is an ancestor of parent
                if  (child_node is not parent_node) and not self.graph.has_edge(parent_node,child_node) and (child_node not in nx.ancestors(self.graph, parent_node)):
                    self.graph.add_edge(parent_node,child_node)
                    return True

        return False

    def _mutate_insert_bypass_node(self, rng=None):
        """
        Pick two nodes (doesn't necessarily need to be connected). Create a new node. connect one node to the new node and the new node to the other node.
        Does not remove any edges.
        """
        rng = np.random.default_rng(rng)
        if self.max_size > self.graph.number_of_nodes():
            sorted_nodes_list = list(self.graph.nodes)
            sorted_nodes_list2 = list(self.graph.nodes)
            rng.shuffle(sorted_nodes_list) #TODO: sort by number of children and/or parents? bias model one way or another
            rng.shuffle(sorted_nodes_list2)
            for node in sorted_nodes_list:
                for child_node in sorted_nodes_list2:
                    if child_node is not node and child_node not in nx.ancestors(self.graph, node):
                        if self.leaf_search_space is not None:
                            #If if we are protecting leafs, dont add connection into a leaf
                            if len(list(nx.descendants(self.graph,node))) ==0 :
                                continue

                        new_node = self.inner_search_space.generate(rng)

                        self.graph.add_node(new_node)
                        self.graph.add_edges_from([(node, new_node), (new_node, child_node)])
                        return True

        return False


    def crossover(self, ind2, rng=None):
        '''
        self is the first individual, ind2 is the second individual
        If crossover_same_depth, it will select graphindividuals at the same recursive depth.
        Otherwise, it will select graphindividuals randomly from the entire graph and its subgraphs.

        This does not impact graphs without subgraphs. And it does not impacts nodes that are not graphindividuals. Cros
        '''

        rng = np.random.default_rng(rng)

        rng.shuffle(self.crossover_methods_list)

        finished = False

        for crossover_method in self.crossover_methods_list:
            if crossover_method(ind2, rng=rng):
                self._merge_duplicated_nodes()
                finished = True
                break

        if self.__debug:
            try:
                nx.find_cycle(self.graph)
                print('something went wrong with ', crossover_method)
            except:
                pass

        if finished:
            self.graphkey = None

        return finished


    def _crossover_swap_branch(self, G2, rng=None):
        '''
        swaps a branch from parent1 with a branch from parent2. does not modify parent2
        '''
        rng = np.random.default_rng(rng)

        if self.crossover_same_depth:
            pair_gen = select_nodes_same_depth(self.graph, self.root, G2.graph, G2.root, rng=rng)
        else:
            pair_gen = select_nodes_randomly(self.graph, G2.graph, rng=rng)

        for node1, node2 in pair_gen:
            #TODO: if root is in inner_search_space, then do use it?
            if node1 is self.root or node2 is G2.root: #dont want to add root as inner node
                continue

            #check if node1 is a leaf and leafs are protected, don't add an input to the leave
            if self.leaf_search_space is not None: #if we are protecting leaves,
                node1_is_leaf = len(list(self.graph.successors(node1))) == 0
                node2_is_leaf = len(list(G2.graph.successors(node2))) == 0
                #if not ((node1_is_leaf and node1_is_leaf) or (not node1_is_leaf and not node2_is_leaf)): #if node1 is a leaf
                #if (node1_is_leaf and (not node2_is_leaf)) or ( (not node1_is_leaf) and node2_is_leaf):
                if not node1_is_leaf:
                    #only continue if node1 and node2 are both leaves or both not leaves
                    continue

            temp_graph_1 = self.graph.copy()
            temp_graph_1.remove_node(node1)
            remove_nodes_disconnected_from_node(temp_graph_1, self.root)

            #isolating the branch
            branch2 = G2.graph.copy()
            n2_descendants = nx.descendants(branch2,node2)
            for n in list(branch2.nodes):
                if n not in n2_descendants and n is not node2: #removes all nodes not in the branch
                    branch2.remove_node(n)

            branch2 = copy.deepcopy(branch2)
            branch2_root = get_roots(branch2)[0]
            temp_graph_1.add_edges_from(branch2.edges)
            for p in list(self.graph.predecessors(node1)):
                temp_graph_1.add_edge(p,branch2_root)

            if temp_graph_1.number_of_nodes() > self.max_size:
                continue

            self.graph = temp_graph_1

            return True
        return False


    def _crossover_take_branch(self, G2, rng=None):
        '''
        Takes a subgraph from Parent2 and add it to a randomly chosen node in Parent1.
        '''
        rng = np.random.default_rng(rng)

        if self.crossover_same_depth:
            pair_gen = select_nodes_same_depth(self.graph, self.root, G2.graph, G2.root, rng=rng)
        else:
            pair_gen = select_nodes_randomly(self.graph, G2.graph, rng=rng)

        for node1, node2 in pair_gen:
            #TODO: if root is in inner_search_space, then do use it?
            if node2 is G2.root: #dont want to add root as inner node
                continue


            #check if node1 is a leaf and leafs are protected, don't add an input to the leave
            if self.leaf_search_space is not None and len(list(self.graph.successors(node1))) == 0:
                continue

            #icheck if node2 is graph individual
            # if isinstance(node2,GraphIndividual):
            #     if not ((isinstance(node2,GraphIndividual) and ("Recursive" in self.inner_search_space or "Recursive" in self.leaf_search_space))):
            #         continue

            #isolating the branch
            branch2 = G2.graph.copy()
            n2_descendants = nx.descendants(branch2,node2)
            for n in list(branch2.nodes):
                if n not in n2_descendants and n is not node2: #removes all nodes not in the branch
                    branch2.remove_node(n)

            #if node1 plus node2 branch has more than max_children, skip
            if branch2.number_of_nodes() + self.graph.number_of_nodes() > self.max_size:
                continue

            branch2 = copy.deepcopy(branch2)
            branch2_root = get_roots(branch2)[0]
            self.graph.add_edges_from(branch2.edges)
            self.graph.add_edge(node1,branch2_root)

            return True
        return False



    def _crossover_nodes(self, G2, rng=None):
        '''
        Swaps the hyperparamters of one randomly chosen node in Parent1 with the hyperparameters of randomly chosen node in Parent2.
        '''
        rng = np.random.default_rng(rng)

        if self.crossover_same_depth:
            pair_gen = select_nodes_same_depth(self.graph, self.root, G2.graph, G2.root, rng=rng)
        else:
            pair_gen = select_nodes_randomly(self.graph, G2.graph, rng=rng)

        for node1, node2 in pair_gen:

            #if both nodes are leaves
            if len(list(self.graph.successors(node1)))==0 and len(list(G2.graph.successors(node2)))==0:
                if node1.crossover(node2):
                    return True


            #if both nodes are inner nodes
            if len(list(self.graph.successors(node1)))>0 and len(list(G2.graph.successors(node2)))>0:
                if len(list(self.graph.predecessors(node1)))>0 and len(list(G2.graph.predecessors(node2)))>0:
                    if node1.crossover(node2):
                        return True

            #if both nodes are root nodes
            if node1 is self.root and node2 is G2.root:
                if node1.crossover(node2):
                    return True


        return False

    #not including the nodes, just their children
    #Finds leaves attached to nodes and swaps them
    def _crossover_swap_leaf_at_node(self, G2, rng=None):
        rng = np.random.default_rng(rng)

        if self.crossover_same_depth:
            pair_gen = select_nodes_same_depth(self.graph, self.root, G2.graph, G2.root, rng=rng)
        else:
            pair_gen = select_nodes_randomly(self.graph, G2.graph, rng=rng)

        success = False
        for node1, node2 in pair_gen:
            # if leaves are protected node1 and node2 must both be leaves or both be inner nodes
            if self.leaf_search_space is not None and not (len(list(self.graph.successors(node1)))==0 ^ len(list(G2.graph.successors(node2)))==0):
                continue
            #self_leafs = [c for c in nx.descendants(self.graph,node1) if len(list(self.graph.successors(c)))==0 and c is not node1]
            node_leafs = [c for c in nx.descendants(G2.graph,node2) if len(list(G2.graph.successors(c)))==0 and c is not node2]

            # if len(self_leafs) >0:
            #     for c in self_leafs:
            #         if random.choice([True,False]):
            #             self.graph.remove_node(c)
            #             G2.graph.add_edge(node2, c)
            #             success = True

            if len(node_leafs) >0:
                for c in node_leafs:
                    if rng.choice([True,False]):
                        G2.graph.remove_node(c)
                        self.graph.add_edge(node1, c)
                        success = True

        return success



    #TODO edit so that G2 is not modified
    def _crossover_swap_node(self, G2, rng=None):
        '''
        Swaps randomly chosen node from Parent1 with a randomly chosen node from Parent2.
        '''
        rng = np.random.default_rng(rng)

        if self.crossover_same_depth:
            pair_gen = select_nodes_same_depth(self.graph, self.root, G2.graph, G2.root, rng=rng)
        else:
            pair_gen = select_nodes_randomly(self.graph, G2.graph, rng=rng)

        for node1, node2 in pair_gen:
            if node1 is self.root or node2 is G2.root: #TODO: allow root
                continue

            #if leaves are protected
            if self.leaf_search_space is not None:
                #if one node is a leaf, the other must be a leaf
                if not((len(list(self.graph.successors(node1)))==0) ^ (len(list(G2.graph.successors(node2)))==0)):
                    continue #only continue if both are leaves, or both are not leaves


            n1_s = self.graph.successors(node1)
            n1_p = self.graph.predecessors(node1)

            n2_s = G2.graph.successors(node2)
            n2_p = G2.graph.predecessors(node2)

            self.graph.remove_node(node1)
            G2.graph.remove_node(node2)

            self.graph.add_node(node2)

            self.graph.add_edges_from([ (node2, n) for n in n1_s])
            G2.graph.add_edges_from([ (node1, n) for n in n2_s])

            self.graph.add_edges_from([ (n, node2) for n in n1_p])
            G2.graph.add_edges_from([ (n, node1) for n in n2_p])

            return True

        return False


    def _merge_duplicated_nodes(self):

        graph_changed = False
        merged = False
        while(not merged):
            node_list = list(self.graph.nodes)
            merged = True
            for node, other_node in itertools.product(node_list, node_list):
                if node is other_node:
                    continue

                #If nodes are same class/hyperparameters
                if node.unique_id() == other_node.unique_id():
                    node_children = set(self.graph.successors(node))
                    other_node_children = set(self.graph.successors(other_node))
                    #if nodes have identical children, they can be merged
                    if node_children == other_node_children:
                        for other_node_parent in list(self.graph.predecessors(other_node)):
                            if other_node_parent not in self.graph.predecessors(node):
                                self.graph.add_edge(other_node_parent,node)

                        self.graph.remove_node(other_node)
                        merged=False
                        graph_changed = True
                        break

        return graph_changed


    def export_pipeline(self, memory=None, **kwargs):
        estimator_graph = self.graph.copy()

        #mapping = {node:node.method_class(**node.hyperparameters) for node in estimator_graph}
        label_remapping = {}
        label_to_instance = {}

        for node in estimator_graph:
            this_pipeline_node = node.export_pipeline(memory=memory, **kwargs)
            found_unique_label = False
            i=1
            while not found_unique_label:
                label = "{0}_{1}".format(this_pipeline_node.__class__.__name__, i)
                if label not in label_to_instance:
                    found_unique_label = True
                else:
                    i+=1

            label_remapping[node] = label
            label_to_instance[label] = this_pipeline_node

        estimator_graph = nx.relabel_nodes(estimator_graph, label_remapping)

        for label, instance in label_to_instance.items():
            estimator_graph.nodes[label]["instance"] = instance

        return tpot2.GraphPipeline(graph=estimator_graph, memory=memory, use_label_encoder=self.use_label_encoder, method=self.method, cross_val_predict_cv=self.cross_val_predict_cv)


    def plot(self):
        G = self.graph.reverse()
        #TODO clean this up
        try:
            pos = nx.planar_layout(G)  # positions for all nodes
        except:
            pos = nx.shell_layout(G)
        # nodes
        options = {'edgecolors': 'tab:gray', 'node_size': 800, 'alpha': 0.9}
        nodelist = list(G.nodes)
        node_color = [plt.cm.Set1(G.nodes[n]['recursive depth']) for n in G]

        fig, ax = plt.subplots()

        nx.draw(G, pos, nodelist=nodelist, node_color=node_color, ax=ax,  **options)


        '''edgelist = []
        for n in n1.node_set:
            for child in n.children:
                edgelist.append((n,child))'''

        # edges
        #nx.draw_networkx_edges(G, pos, width=3.0, arrows=True)
        '''nx.draw_networkx_edges(
            G,
            pos,
            edgelist=[edgelist],
            width=8,
            alpha=0.5,
            edge_color='tab:red',
        )'''



        # some math labels
        labels = {}
        for i, n in enumerate(G.nodes):
            labels[n] = n.method_class.__name__ + "\n" + str(n.hyperparameters)


        nx.draw_networkx_labels(G, pos, labels,ax=ax, font_size=7, font_color='black')

        plt.tight_layout()
        plt.axis('off')
        plt.show()


    def unique_id(self):
        if self.graphkey is None:
            #copy self.graph
            new_graph = self.graph.copy()
            for n in new_graph.nodes:
                new_graph.nodes[n]['label'] = n.unique_id()

            new_graph = nx.convert_node_labels_to_integers(new_graph)
            self.graphkey = GraphKey(new_graph)

        return self.graphkey

crossover(ind2, rng=None)

self is the first individual, ind2 is the second individual If crossover_same_depth, it will select graphindividuals at the same recursive depth. Otherwise, it will select graphindividuals randomly from the entire graph and its subgraphs.

This does not impact graphs without subgraphs. And it does not impacts nodes that are not graphindividuals. Cros

Source code in tpot2/search_spaces/pipelines/graph.py 
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def crossover(self, ind2, rng=None):
    '''
    self is the first individual, ind2 is the second individual
    If crossover_same_depth, it will select graphindividuals at the same recursive depth.
    Otherwise, it will select graphindividuals randomly from the entire graph and its subgraphs.

    This does not impact graphs without subgraphs. And it does not impacts nodes that are not graphindividuals. Cros
    '''

    rng = np.random.default_rng(rng)

    rng.shuffle(self.crossover_methods_list)

    finished = False

    for crossover_method in self.crossover_methods_list:
        if crossover_method(ind2, rng=rng):
            self._merge_duplicated_nodes()
            finished = True
            break

    if self.__debug:
        try:
            nx.find_cycle(self.graph)
            print('something went wrong with ', crossover_method)
        except:
            pass

    if finished:
        self.graphkey = None

    return finished

GraphSearchPipeline

Bases: SearchSpace

Source code in tpot2/search_spaces/pipelines/graph.py 
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class GraphSearchPipeline(SearchSpace):
    def __init__(self, 
        root_search_space: SearchSpace, 
        leaf_search_space: SearchSpace = None, 
        inner_search_space: SearchSpace = None, 
        max_size: int = np.inf,
        crossover_same_depth: bool = False,
        cross_val_predict_cv: Union[int, Callable] = 0, #signature function(estimator, X, y=none)
        method: str = 'auto',
        use_label_encoder: bool = False):

        """
        Defines a search space of pipelines in the shape of a Directed Acyclic Graphs. The search spaces for root, leaf, and inner nodes can be defined separately if desired.
        Each graph will have a single root serving as the final estimator which is drawn from the `root_search_space`. If the `leaf_search_space` is defined, all leaves 
        in the pipeline will be drawn from that search space. If the `leaf_search_space` is not defined, all leaves will be drawn from the `inner_search_space`.
        Nodes that are not leaves or roots will be drawn from the `inner_search_space`. If the `inner_search_space` is not defined, there will be no inner nodes.

        `cross_val_predict_cv`, `method`, `memory`, and `use_label_encoder` are passed to the GraphPipeline object when the pipeline is exported and not directly used in the search space.

        Exports to a GraphPipeline object.

        Parameters
        ----------

        root_search_space: SearchSpace
            The search space for the root node of the graph. This node will be the final estimator in the pipeline.

        inner_search_space: SearchSpace, optional
            The search space for the inner nodes of the graph. If not defined, there will be no inner nodes.

        leaf_search_space: SearchSpace, optional
            The search space for the leaf nodes of the graph. If not defined, the leaf nodes will be drawn from the inner_search_space.

        crossover_same_depth: bool, optional
            If True, crossover will only occur between nodes at the same depth in the graph. If False, crossover will occur between nodes at any depth.

        cross_val_predict_cv : int, default=0
            Number of folds to use for the cross_val_predict function for inner classifiers and regressors. Estimators will still be fit on the full dataset, but the following node will get the outputs from cross_val_predict.

            - 0-1 : When set to 0 or 1, the cross_val_predict function will not be used. The next layer will get the outputs from fitting and transforming the full dataset.
            - >=2 : When fitting pipelines with inner classifiers or regressors, they will still be fit on the full dataset.
                    However, the output to the next node will come from cross_val_predict with the specified number of folds.

        method: str, optional
            The prediction method to use for the inner classifiers or regressors. If 'auto', it will try to use predict_proba, decision_function, or predict in that order.

        memory: str or object with the joblib.Memory interface, optional
            Used to cache the input and outputs of nodes to prevent refitting or computationally heavy transformations. By default, no caching is performed. If a string is given, it is the path to the caching directory.

        use_label_encoder: bool, optional
            If True, the label encoder is used to encode the labels to be 0 to N. If False, the label encoder is not used.
            Mainly useful for classifiers (XGBoost) that require labels to be ints from 0 to N.
            Can also be a sklearn.preprocessing.LabelEncoder object. If so, that label encoder is used.

        """


        self.root_search_space = root_search_space
        self.leaf_search_space = leaf_search_space
        self.inner_search_space = inner_search_space
        self.max_size = max_size
        self.crossover_same_depth = crossover_same_depth

        self.cross_val_predict_cv = cross_val_predict_cv
        self.method = method
        self.use_label_encoder = use_label_encoder

    def generate(self, rng=None):
        rng = np.random.default_rng(rng)
        ind =  GraphPipelineIndividual(self.root_search_space, self.leaf_search_space, self.inner_search_space, self.max_size, self.crossover_same_depth, 
                                       self.cross_val_predict_cv, self.method, self.use_label_encoder, rng=rng)  
            # if user specified limit, grab a random number between that limit

        if self.max_size is None or self.max_size == np.inf:
            n_nodes = rng.integers(1, 5)
        else:
            n_nodes = min(rng.integers(1, self.max_size), 5)

        starting_ops = []
        if self.inner_search_space is not None:
            starting_ops.append(ind._mutate_insert_inner_node)
        if self.leaf_search_space is not None or self.inner_search_space is not None:
            starting_ops.append(ind._mutate_insert_leaf)
            n_nodes -= 1

        if len(starting_ops) > 0:
            for _ in range(n_nodes-1):
                func = rng.choice(starting_ops)
                func(rng=rng)

        ind._merge_duplicated_nodes()

        return ind

__init__(root_search_space, leaf_search_space=None, inner_search_space=None, max_size=np.inf, crossover_same_depth=False, cross_val_predict_cv=0, method='auto', use_label_encoder=False)

Defines a search space of pipelines in the shape of a Directed Acyclic Graphs. The search spaces for root, leaf, and inner nodes can be defined separately if desired. Each graph will have a single root serving as the final estimator which is drawn from the root_search_space. If the leaf_search_space is defined, all leaves in the pipeline will be drawn from that search space. If the leaf_search_space is not defined, all leaves will be drawn from the inner_search_space. Nodes that are not leaves or roots will be drawn from the inner_search_space. If the inner_search_space is not defined, there will be no inner nodes.

cross_val_predict_cv, method, memory, and use_label_encoder are passed to the GraphPipeline object when the pipeline is exported and not directly used in the search space.

Exports to a GraphPipeline object.

Parameters:

Name Type Description Default
root_search_space SearchSpace

The search space for the root node of the graph. This node will be the final estimator in the pipeline.

 required
inner_search_space SearchSpace

The search space for the inner nodes of the graph. If not defined, there will be no inner nodes.

 None
leaf_search_space SearchSpace

The search space for the leaf nodes of the graph. If not defined, the leaf nodes will be drawn from the inner_search_space.

 None
crossover_same_depth bool

If True, crossover will only occur between nodes at the same depth in the graph. If False, crossover will occur between nodes at any depth.

 False
cross_val_predict_cv int

Number of folds to use for the cross_val_predict function for inner classifiers and regressors. Estimators will still be fit on the full dataset, but the following node will get the outputs from cross_val_predict.

  • 0-1 : When set to 0 or 1, the cross_val_predict function will not be used. The next layer will get the outputs from fitting and transforming the full dataset.

  • =2 : When fitting pipelines with inner classifiers or regressors, they will still be fit on the full dataset. However, the output to the next node will come from cross_val_predict with the specified number of folds.

 0
method str

The prediction method to use for the inner classifiers or regressors. If 'auto', it will try to use predict_proba, decision_function, or predict in that order.

 'auto'
memory

Used to cache the input and outputs of nodes to prevent refitting or computationally heavy transformations. By default, no caching is performed. If a string is given, it is the path to the caching directory.

 required
use_label_encoder bool

If True, the label encoder is used to encode the labels to be 0 to N. If False, the label encoder is not used. Mainly useful for classifiers (XGBoost) that require labels to be ints from 0 to N. Can also be a sklearn.preprocessing.LabelEncoder object. If so, that label encoder is used.

 False
Source code in tpot2/search_spaces/pipelines/graph.py 
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def __init__(self, 
    root_search_space: SearchSpace, 
    leaf_search_space: SearchSpace = None, 
    inner_search_space: SearchSpace = None, 
    max_size: int = np.inf,
    crossover_same_depth: bool = False,
    cross_val_predict_cv: Union[int, Callable] = 0, #signature function(estimator, X, y=none)
    method: str = 'auto',
    use_label_encoder: bool = False):

    """
    Defines a search space of pipelines in the shape of a Directed Acyclic Graphs. The search spaces for root, leaf, and inner nodes can be defined separately if desired.
    Each graph will have a single root serving as the final estimator which is drawn from the `root_search_space`. If the `leaf_search_space` is defined, all leaves 
    in the pipeline will be drawn from that search space. If the `leaf_search_space` is not defined, all leaves will be drawn from the `inner_search_space`.
    Nodes that are not leaves or roots will be drawn from the `inner_search_space`. If the `inner_search_space` is not defined, there will be no inner nodes.

    `cross_val_predict_cv`, `method`, `memory`, and `use_label_encoder` are passed to the GraphPipeline object when the pipeline is exported and not directly used in the search space.

    Exports to a GraphPipeline object.

    Parameters
    ----------

    root_search_space: SearchSpace
        The search space for the root node of the graph. This node will be the final estimator in the pipeline.

    inner_search_space: SearchSpace, optional
        The search space for the inner nodes of the graph. If not defined, there will be no inner nodes.

    leaf_search_space: SearchSpace, optional
        The search space for the leaf nodes of the graph. If not defined, the leaf nodes will be drawn from the inner_search_space.

    crossover_same_depth: bool, optional
        If True, crossover will only occur between nodes at the same depth in the graph. If False, crossover will occur between nodes at any depth.

    cross_val_predict_cv : int, default=0
        Number of folds to use for the cross_val_predict function for inner classifiers and regressors. Estimators will still be fit on the full dataset, but the following node will get the outputs from cross_val_predict.

        - 0-1 : When set to 0 or 1, the cross_val_predict function will not be used. The next layer will get the outputs from fitting and transforming the full dataset.
        - >=2 : When fitting pipelines with inner classifiers or regressors, they will still be fit on the full dataset.
                However, the output to the next node will come from cross_val_predict with the specified number of folds.

    method: str, optional
        The prediction method to use for the inner classifiers or regressors. If 'auto', it will try to use predict_proba, decision_function, or predict in that order.

    memory: str or object with the joblib.Memory interface, optional
        Used to cache the input and outputs of nodes to prevent refitting or computationally heavy transformations. By default, no caching is performed. If a string is given, it is the path to the caching directory.

    use_label_encoder: bool, optional
        If True, the label encoder is used to encode the labels to be 0 to N. If False, the label encoder is not used.
        Mainly useful for classifiers (XGBoost) that require labels to be ints from 0 to N.
        Can also be a sklearn.preprocessing.LabelEncoder object. If so, that label encoder is used.

    """


    self.root_search_space = root_search_space
    self.leaf_search_space = leaf_search_space
    self.inner_search_space = inner_search_space
    self.max_size = max_size
    self.crossover_same_depth = crossover_same_depth

    self.cross_val_predict_cv = cross_val_predict_cv
    self.method = method
    self.use_label_encoder = use_label_encoder

MaskSelector

Bases: BaseEstimator, SelectorMixin

Select predefined feature subsets.

Source code in tpot2/search_spaces/nodes/genetic_feature_selection.py 
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class MaskSelector(BaseEstimator, SelectorMixin):
    """Select predefined feature subsets."""

    def __init__(self, mask, set_output_transform=None):
        self.mask = mask
        self.set_output_transform = set_output_transform
        if set_output_transform is not None:
            self.set_output(transform=set_output_transform)

    def fit(self, X, y=None):
        self.n_features_in_ = X.shape[1]
        if isinstance(X, pd.DataFrame):
            self.feature_names_in_ = X.columns
        #     self.set_output(transform="pandas")
        self.is_fitted_ = True #so sklearn knows it's fitted
        return self

    def _get_tags(self):
        tags = {"allow_nan": True, "requires_y": False}
        return tags

    def _get_support_mask(self):
        return np.array(self.mask)

    def get_feature_names_out(self, input_features=None):
        return self.feature_names_in_[self.get_support()]

SequentialPipeline

Bases: SearchSpace

Source code in tpot2/search_spaces/pipelines/sequential.py 
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class SequentialPipeline(SearchSpace):
    def __init__(self, search_spaces : List[SearchSpace] ) -> None:
        """
        Takes in a list of search spaces. will produce a pipeline of Sequential length. Each step in the pipeline will correspond to the the search space provided in the same index.
        """

        self.search_spaces = search_spaces

    def generate(self, rng=None):
        rng = np.random.default_rng(rng)
        return SequentialPipelineIndividual(self.search_spaces, rng=rng)

__init__(search_spaces)

Takes in a list of search spaces. will produce a pipeline of Sequential length. Each step in the pipeline will correspond to the the search space provided in the same index.

Source code in tpot2/search_spaces/pipelines/sequential.py 
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def __init__(self, search_spaces : List[SearchSpace] ) -> None:
    """
    Takes in a list of search spaces. will produce a pipeline of Sequential length. Each step in the pipeline will correspond to the the search space provided in the same index.
    """

    self.search_spaces = search_spaces

SklearnIndividual

Bases: BaseIndividual

Source code in tpot2/search_spaces/base.py 
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class SklearnIndividual(tpot2.BaseIndividual):

    def __init_subclass__(cls):
        cls.crossover = cls.validate_same_type(cls.crossover)


    def __init__(self,) -> None:
        super().__init__()

    def mutate(self, rng=None):
        return

    def crossover(self, other, rng=None, **kwargs):
        return 

    @final
    def validate_same_type(func):

        def wrapper(self, other, rng=None, **kwargs):
            if not isinstance(other, type(self)):
                return False
            return func(self, other, rng=rng, **kwargs)

        return wrapper

    def export_pipeline(self, **kwargs) -> BaseEstimator:
        return

    def unique_id(self):
        """
        Returns a unique identifier for the individual. Used for preventing duplicate individuals from being evaluated.
        """
        return self

    #TODO currently TPOT2 population class manually uses the unique_id to generate the index for the population data frame.
    #alternatively, the index could be the individual itself, with the __eq__ and __hash__ methods implemented.

    # Though this breaks the graphpipeline. When a mutation is called, it changes the __eq__ and __hash__ outputs.
    # Since networkx uses the hash and eq to determine if a node is already in the graph, this causes the graph thing that 
    # This is a new node not in the graph. But this could be changed if when the graphpipeline mutates nodes, 
    # it "replaces" the existing node with the mutated node. This would require a change in the graphpipeline class.

    # def __eq__(self, other):
    #     return self.unique_id() == other.unique_id()

    # def __hash__(self):
    #     return hash(self.unique_id())

    #number of components in the pipeline
    def get_size(self):
        return 1

    @final
    def export_flattened_graphpipeline(self, **graphpipeline_kwargs) -> tpot2.GraphPipeline:
        return flatten_to_graphpipeline(self.export_pipeline(), **graphpipeline_kwargs)

unique_id()

Returns a unique identifier for the individual. Used for preventing duplicate individuals from being evaluated.

Source code in tpot2/search_spaces/base.py 
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def unique_id(self):
    """
    Returns a unique identifier for the individual. Used for preventing duplicate individuals from being evaluated.
    """
    return self

TreePipeline

Bases: SearchSpace

Source code in tpot2/search_spaces/pipelines/tree.py 
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class TreePipeline(SearchSpace):
    def __init__(self, root_search_space : SearchSpace, 
                        leaf_search_space : SearchSpace = None, 
                        inner_search_space : SearchSpace =None, 
                        min_size: int = 2, 
                        max_size: int = 10,
                        crossover_same_depth=False) -> None:

        """
        Generates a pipeline of variable length. Pipeline will have a tree structure similar to TPOT1.

        """

        self.search_space = root_search_space
        self.leaf_search_space = leaf_search_space
        self.inner_search_space = inner_search_space
        self.min_size = min_size
        self.max_size = max_size
        self.crossover_same_depth = crossover_same_depth

    def generate(self, rng=None):
        rng = np.random.default_rng(rng)
        return TreePipelineIndividual(self.search_space, self.leaf_search_space, self.inner_search_space, self.min_size, self.max_size, self.crossover_same_depth, rng=rng)

__init__(root_search_space, leaf_search_space=None, inner_search_space=None, min_size=2, max_size=10, crossover_same_depth=False)

Generates a pipeline of variable length. Pipeline will have a tree structure similar to TPOT1.

Source code in tpot2/search_spaces/pipelines/tree.py 
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def __init__(self, root_search_space : SearchSpace, 
                    leaf_search_space : SearchSpace = None, 
                    inner_search_space : SearchSpace =None, 
                    min_size: int = 2, 
                    max_size: int = 10,
                    crossover_same_depth=False) -> None:

    """
    Generates a pipeline of variable length. Pipeline will have a tree structure similar to TPOT1.

    """

    self.search_space = root_search_space
    self.leaf_search_space = leaf_search_space
    self.inner_search_space = inner_search_space
    self.min_size = min_size
    self.max_size = max_size
    self.crossover_same_depth = crossover_same_depth

TupleIndex

TPOT2 uses tuples to create a unique id for some pipeline search spaces. However, tuples sometimes don't interact correctly with pandas indexes. This class is a wrapper around a tuple that allows it to be used as a key in a dictionary, without it being an itereable.

An alternative could be to make unique id return a string, but this would not work with graphpipelines, which require a special object. This class allows linear pipelines to contain graph pipelines while still being able to be used as a key in a dictionary.

Source code in tpot2/search_spaces/tuple_index.py 
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class TupleIndex():
    """
    TPOT2 uses tuples to create a unique id for some pipeline search spaces. However, tuples sometimes don't interact correctly with pandas indexes.
    This class is a wrapper around a tuple that allows it to be used as a key in a dictionary, without it being an itereable.

    An alternative could be to make unique id return a string, but this would not work with graphpipelines, which require a special object.
    This class allows linear pipelines to contain graph pipelines while still being able to be used as a key in a dictionary.

    """
    def __init__(self, tup):
        self.tup = tup

    def __eq__(self,other) -> bool:
        return self.tup == other

    def __hash__(self) -> int:
        return self.tup.__hash__()

    def __str__(self) -> str:
        return self.tup.__str__()

    def __repr__(self) -> str:
        return self.tup.__repr__()

UnionPipeline

Bases: SearchSpace

Source code in tpot2/search_spaces/pipelines/union.py 
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class UnionPipeline(SearchSpace):
    def __init__(self, search_spaces : List[SearchSpace] ) -> None:
        """
        Takes in a list of search spaces. will produce a pipeline of Sequential length. Each step in the pipeline will correspond to the the search space provided in the same index.
        """

        self.search_spaces = search_spaces

    def generate(self, rng=None):
        rng = np.random.default_rng(rng)
        return UnionPipelineIndividual(self.search_spaces, rng=rng)

__init__(search_spaces)

Takes in a list of search spaces. will produce a pipeline of Sequential length. Each step in the pipeline will correspond to the the search space provided in the same index.

Source code in tpot2/search_spaces/pipelines/union.py 
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def __init__(self, search_spaces : List[SearchSpace] ) -> None:
    """
    Takes in a list of search spaces. will produce a pipeline of Sequential length. Each step in the pipeline will correspond to the the search space provided in the same index.
    """

    self.search_spaces = search_spaces

UnionPipelineIndividual

Bases: SklearnIndividual

Takes in a list of search spaces. each space is a list of SearchSpaces. Will produce a FeatureUnion pipeline. Each step in the pipeline will correspond to the the search space provided in the same index. The resulting pipeline will be a FeatureUnion of the steps in the pipeline.

Source code in tpot2/search_spaces/pipelines/union.py 
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class UnionPipelineIndividual(SklearnIndividual):
    """
    Takes in a list of search spaces. each space is a list of SearchSpaces.
    Will produce a FeatureUnion pipeline. Each step in the pipeline will correspond to the the search space provided in the same index.
    The resulting pipeline will be a FeatureUnion of the steps in the pipeline.

    """

    def __init__(self, search_spaces : List[SearchSpace], rng=None) -> None:
        super().__init__()
        self.search_spaces = search_spaces

        self.pipeline = []
        for space in self.search_spaces:
            self.pipeline.append(space.generate(rng))

    def mutate(self, rng=None):
        rng = np.random.default_rng(rng)
        step = rng.choice(self.pipeline)
        return step.mutate(rng)


    def crossover(self, other, rng=None):
        #swap a random step in the pipeline with the corresponding step in the other pipeline
        rng = np.random.default_rng(rng)

        cx_funcs = [self._crossover_node, self._crossover_swap_node]
        rng.shuffle(cx_funcs)
        for cx_func in cx_funcs:
            if cx_func(other, rng):
                return True

        return False

    def _crossover_swap_node(self, other, rng):
        rng = np.random.default_rng(rng)
        idx = rng.integers(1,len(self.pipeline))

        self.pipeline[idx], other.pipeline[idx] = other.pipeline[idx], self.pipeline[idx]
        return True

    def _crossover_node(self, other, rng):
        rng = np.random.default_rng(rng)

        crossover_success = False
        for idx in range(len(self.pipeline)):
            if rng.random() < 0.5:
                if self.pipeline[idx].crossover(other.pipeline[idx], rng):
                    crossover_success = True

        return crossover_success

    def export_pipeline(self, **kwargs):
        return sklearn.pipeline.make_union(*[step.export_pipeline(**kwargs) for step in self.pipeline])

    def unique_id(self):
        l = [step.unique_id() for step in self.pipeline]
        l = ["FeatureUnion"] + l
        return TupleIndex(tuple(l))

WrapperPipeline

Bases: SearchSpace

Source code in tpot2/search_spaces/pipelines/wrapper.py 
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class WrapperPipeline(SearchSpace):
    def __init__(
            self, 
            method: type, 
            space: ConfigurationSpace,
            estimator_search_space: SearchSpace,
            hyperparameter_parser: callable = None, 
            wrapped_param_name: str = None
            ) -> None:

        """
        This search space is for wrapping a sklearn estimator with a method that takes another estimator and hyperparameters as arguments.
        For example, this can be used with sklearn.ensemble.BaggingClassifier or sklearn.ensemble.AdaBoostClassifier.

        """


        self.estimator_search_space = estimator_search_space
        self.method = method
        self.space = space
        self.hyperparameter_parser=hyperparameter_parser
        self.wrapped_param_name = wrapped_param_name

    def generate(self, rng=None):
        rng = np.random.default_rng(rng)
        return WrapperPipelineIndividual(method=self.method, space=self.space, estimator_search_space=self.estimator_search_space, hyperparameter_parser=self.hyperparameter_parser, wrapped_param_name=self.wrapped_param_name,  rng=rng)

__init__(method, space, estimator_search_space, hyperparameter_parser=None, wrapped_param_name=None)

This search space is for wrapping a sklearn estimator with a method that takes another estimator and hyperparameters as arguments. For example, this can be used with sklearn.ensemble.BaggingClassifier or sklearn.ensemble.AdaBoostClassifier.

Source code in tpot2/search_spaces/pipelines/wrapper.py 
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def __init__(
        self, 
        method: type, 
        space: ConfigurationSpace,
        estimator_search_space: SearchSpace,
        hyperparameter_parser: callable = None, 
        wrapped_param_name: str = None
        ) -> None:

    """
    This search space is for wrapping a sklearn estimator with a method that takes another estimator and hyperparameters as arguments.
    For example, this can be used with sklearn.ensemble.BaggingClassifier or sklearn.ensemble.AdaBoostClassifier.

    """


    self.estimator_search_space = estimator_search_space
    self.method = method
    self.space = space
    self.hyperparameter_parser=hyperparameter_parser
    self.wrapped_param_name = wrapped_param_name

get_template_search_spaces(search_space, classification=True, inner_predictors=None, cross_val_predict_cv=None, **get_search_space_params)

Returns a search space which can be optimized by TPOT.

Parameters:

Name Type Description Default
search_space

The default search space to use. If a string, it should be one of the following: - 'linear': A search space for linear pipelines - 'linear-light': A search space for linear pipelines with a smaller, faster search space - 'graph': A search space for graph pipelines - 'graph-light': A search space for graph pipelines with a smaller, faster search space - 'mdr': A search space for MDR pipelines If a SearchSpace object, it should be a valid search space object for TPOT.

 required
classification

Whether the problem is a classification problem or a regression problem.

 True
inner_predictors

Whether to include additional classifiers/regressors before the final classifier/regressor (allowing for ensembles). Defaults to False for 'linear-light' and 'graph-light' search spaces, and True otherwise. (Not used for 'mdr' search space)

 None
cross_val_predict_cv

The number of folds to use for cross_val_predict. Defaults to 0 for 'linear-light' and 'graph-light' search spaces, and 5 otherwise. (Not used for 'mdr' search space)

 None
get_search_space_params

Additional parameters to pass to the get_search_space function.

 {}
Source code in tpot2/config/template_search_spaces.py 
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def get_template_search_spaces(search_space, classification=True, inner_predictors=None, cross_val_predict_cv=None, **get_search_space_params):
    """
    Returns a search space which can be optimized by TPOT.

    Parameters
    ----------
    search_space: str or SearchSpace
        The default search space to use. If a string, it should be one of the following:
            - 'linear': A search space for linear pipelines
            - 'linear-light': A search space for linear pipelines with a smaller, faster search space
            - 'graph': A search space for graph pipelines
            - 'graph-light': A search space for graph pipelines with a smaller, faster search space
            - 'mdr': A search space for MDR pipelines
        If a SearchSpace object, it should be a valid search space object for TPOT.

    classification: bool, default=True
        Whether the problem is a classification problem or a regression problem.

    inner_predictors: bool, default=None
        Whether to include additional classifiers/regressors before the final classifier/regressor (allowing for ensembles). 
        Defaults to False for 'linear-light' and 'graph-light' search spaces, and True otherwise. (Not used for 'mdr' search space)

    cross_val_predict_cv: int, default=None
        The number of folds to use for cross_val_predict. 
        Defaults to 0 for 'linear-light' and 'graph-light' search spaces, and 5 otherwise. (Not used for 'mdr' search space)

    get_search_space_params: dict
        Additional parameters to pass to the get_search_space function.

    """
    if inner_predictors is None:
        if search_space == "light" or search_space == "graph_light":
            inner_predictors = False
        else:
            inner_predictors = True

    if cross_val_predict_cv is None:
        if search_space == "light" or search_space == "graph_light":
            cross_val_predict_cv = 0
        else:
            if classification:
                cross_val_predict_cv = sklearn.model_selection.StratifiedKFold(n_splits=5, shuffle=True, random_state=42)
            else:
                cross_val_predict_cv = sklearn.model_selection.KFold(n_splits=5, shuffle=True, random_state=42)

    if isinstance(search_space, str):
        if search_space == "linear":
            return get_linear_search_space(classification, inner_predictors, cross_val_predict_cv=cross_val_predict_cv, **get_search_space_params)
        elif search_space == "graph":
            return get_graph_search_space(classification, inner_predictors, cross_val_predict_cv=cross_val_predict_cv, **get_search_space_params)
        elif search_space == "graph-light":
            return get_graph_search_space_light(classification, inner_predictors, cross_val_predict_cv=cross_val_predict_cv, **get_search_space_params)
        elif search_space == "linear-light":
            return get_light_search_space(classification, inner_predictors, cross_val_predict_cv=cross_val_predict_cv, **get_search_space_params)
        elif search_space == "mdr":
            return get_mdr_search_space(classification, **get_search_space_params)
        else:
            raise ValueError("Invalid search space")
    else:
        return search_space