Estimator
TPOTEstimator
¶
Bases: BaseEstimator
Source code in tpot2/tpot_estimator/estimator.py
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classes_
property
¶
The classes labels. Only exist if the last step is a classifier.
__init__(scorers, scorers_weights, classification, cv=5, other_objective_functions=[], other_objective_functions_weights=[], objective_function_names=None, bigger_is_better=True, hyperparameter_probability=1, hyper_node_probability=0, hyperparameter_alpha=1, max_size=np.inf, linear_pipeline=False, root_config_dict='Auto', inner_config_dict=['selectors', 'transformers'], leaf_config_dict=None, cross_val_predict_cv=0, categorical_features=None, subsets=None, memory=None, preprocessing=False, population_size=50, initial_population_size=None, population_scaling=0.5, generations_until_end_population=1, generations=None, max_time_seconds=3600, max_eval_time_seconds=60 * 10, validation_strategy='none', validation_fraction=0.2, disable_label_encoder=False, early_stop=None, scorers_early_stop_tol=0.001, other_objectives_early_stop_tol=None, threshold_evaluation_early_stop=None, threshold_evaluation_scaling=0.5, selection_evaluation_early_stop=None, selection_evaluation_scaling=0.5, min_history_threshold=20, survival_percentage=1, crossover_probability=0.2, mutate_probability=0.7, mutate_then_crossover_probability=0.05, crossover_then_mutate_probability=0.05, survival_selector=survival_select_NSGA2, parent_selector=tournament_selection_dominated, budget_range=None, budget_scaling=0.5, generations_until_end_budget=1, stepwise_steps=5, optuna_optimize_pareto_front=False, optuna_optimize_pareto_front_trials=100, optuna_optimize_pareto_front_timeout=60 * 10, optuna_storage='sqlite:///optuna.db', n_jobs=1, memory_limit='4GB', client=None, processes=True, warm_start=False, subset_column=None, periodic_checkpoint_folder=None, callback=None, verbose=0, scatter=True, random_state=None)
¶
An sklearn baseestimator that uses genetic programming to optimize a pipeline.
Parameters:
Name | Type | Description | Default |
---|---|---|---|
scorers |
(list, scorer)
|
A scorer or list of scorers to be used in the cross-validation process. see https://scikit-learn.org/stable/modules/model_evaluation.html |
required |
scorers_weights |
list
|
A list of weights to be applied to the scorers during the optimization process. |
required |
classification |
bool
|
If True, the problem is treated as a classification problem. If False, the problem is treated as a regression problem. Used to determine the CV strategy. |
required |
cv |
(int, cross - validator)
|
|
5
|
other_objective_functions |
list
|
A list of other objective functions to apply to the pipeline. The function takes a single parameter for the graphpipeline estimator and returns either a single score or a list of scores. |
[]
|
other_objective_functions_weights |
list
|
A list of weights to be applied to the other objective functions. |
[]
|
objective_function_names |
list
|
A list of names to be applied to the objective functions. If None, will use the names of the objective functions. |
None
|
bigger_is_better |
bool
|
If True, the objective function is maximized. If False, the objective function is minimized. Use negative weights to reverse the direction. |
True
|
max_size |
int
|
The maximum number of nodes of the pipelines to be generated. |
np.inf
|
linear_pipeline |
bool
|
If True, the pipelines generated will be linear. If False, the pipelines generated will be directed acyclic graphs. |
False
|
root_config_dict |
dict
|
The configuration dictionary to use for the root node of the model. If 'auto', will use "classifiers" if classification=True, else "regressors". - 'selectors' : A selection of sklearn Selector methods. - 'classifiers' : A selection of sklearn Classifier methods. - 'regressors' : A selection of sklearn Regressor methods. - 'transformers' : A selection of sklearn Transformer methods. - 'arithmetic_transformer' : A selection of sklearn Arithmetic Transformer methods that replicate symbolic classification/regression operators. - 'passthrough' : A node that just passes though the input. Useful for passing through raw inputs into inner nodes. - 'feature_set_selector' : A selector that pulls out specific subsets of columns from the data. Only well defined as a leaf. Subsets are set with the subsets parameter. - 'skrebate' : Includes ReliefF, SURF, SURFstar, MultiSURF. - 'MDR' : Includes MDR. - 'ContinuousMDR' : Includes ContinuousMDR. - 'genetic encoders' : Includes Genetic Encoder methods as used in AutoQTL. - 'FeatureEncodingFrequencySelector': Includes FeatureEncodingFrequencySelector method as used in AutoQTL. - list : a list of strings out of the above options to include the corresponding methods in the configuration dictionary. |
'auto'
|
inner_config_dict |
dict
|
The configuration dictionary to use for the inner nodes of the model generation. Default ["selectors", "transformers"] - 'selectors' : A selection of sklearn Selector methods. - 'classifiers' : A selection of sklearn Classifier methods. - 'regressors' : A selection of sklearn Regressor methods. - 'transformers' : A selection of sklearn Transformer methods. - 'arithmetic_transformer' : A selection of sklearn Arithmetic Transformer methods that replicate symbolic classification/regression operators. - 'passthrough' : A node that just passes though the input. Useful for passing through raw inputs into inner nodes. - 'feature_set_selector' : A selector that pulls out specific subsets of columns from the data. Only well defined as a leaf. Subsets are set with the subsets parameter. - 'skrebate' : Includes ReliefF, SURF, SURFstar, MultiSURF. - 'MDR' : Includes MDR. - 'ContinuousMDR' : Includes ContinuousMDR. - 'genetic encoders' : Includes Genetic Encoder methods as used in AutoQTL. - 'FeatureEncodingFrequencySelector': Includes FeatureEncodingFrequencySelector method as used in AutoQTL. - list : a list of strings out of the above options to include the corresponding methods in the configuration dictionary. - None : If None and max_depth>1, the root_config_dict will be used for the inner nodes as well. |
["selectors", "transformers"]
|
leaf_config_dict |
dict
|
The configuration dictionary to use for the leaf node of the model. If set, leaf nodes must be from this dictionary. Otherwise leaf nodes will be generated from the root_config_dict. Default None - 'selectors' : A selection of sklearn Selector methods. - 'classifiers' : A selection of sklearn Classifier methods. - 'regressors' : A selection of sklearn Regressor methods. - 'transformers' : A selection of sklearn Transformer methods. - 'arithmetic_transformer' : A selection of sklearn Arithmetic Transformer methods that replicate symbolic classification/regression operators. - 'passthrough' : A node that just passes though the input. Useful for passing through raw inputs into inner nodes. - 'feature_set_selector' : A selector that pulls out specific subsets of columns from the data. Only well defined as a leaf. Subsets are set with the subsets parameter. - 'skrebate' : Includes ReliefF, SURF, SURFstar, MultiSURF. - 'MDR' : Includes MDR. - 'ContinuousMDR' : Includes ContinuousMDR. - 'genetic encoders' : Includes Genetic Encoder methods as used in AutoQTL. - 'FeatureEncodingFrequencySelector': Includes FeatureEncodingFrequencySelector method as used in AutoQTL. - list : a list of strings out of the above options to include the corresponding methods in the configuration dictionary. - None : If None, a leaf will not be required (i.e. the pipeline can be a single root node). Leaf nodes will be generated from the inner_config_dict. |
None
|
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
|
categorical_features |
Categorical columns to inpute and/or one hot encode during the preprocessing step. Used only if preprocessing is not False. - None : If None, TPOT2 will automatically use object columns in pandas dataframes as objects for one hot encoding in preprocessing. - List of categorical features. If X is a dataframe, this should be a list of column names. If X is a numpy array, this should be a list of column indices |
None
|
|
subsets |
str or list
|
Sets the subsets that the FeatureSetSeletor will select from if set as an option in one of the configuration dictionaries. - 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. - None : If None, each column will be treated as a subset. One column will be selected per subset. If subsets is None, each column will be treated as a subset. One column will be selected per subset. |
None
|
memory |
If supplied, pipeline will cache each transformer after calling fit. This feature is used to avoid computing the fit transformers within a pipeline if the parameters and input data are identical with another fitted pipeline during optimization process. - String 'auto': TPOT uses memory caching with a temporary directory and cleans it up upon shutdown. - String path of a caching directory TPOT uses memory caching with the provided directory and TPOT does NOT clean the caching directory up upon shutdown. If the directory does not exist, TPOT will create it. - Memory object: TPOT uses the instance of joblib.Memory for memory caching, and TPOT does NOT clean the caching directory up upon shutdown. - None: TPOT does not use memory caching. |
None
|
|
preprocessing |
(bool or BaseEstimator / Pipeline)
|
EXPERIMENTAL A pipeline that will be used to preprocess the data before CV. - bool : If True, will use a default preprocessing pipeline. - Pipeline : If an instance of a pipeline is given, will use that pipeline as the preprocessing pipeline. |
False
|
population_size |
int
|
Size of the population |
50
|
initial_population_size |
int
|
Size of the initial population. If None, population_size will be used. |
None
|
population_scaling |
int
|
Scaling factor to use when determining how fast we move the threshold moves from the start to end percentile. |
0.5
|
generations_until_end_population |
int
|
Number of generations until the population size reaches population_size |
1
|
generations |
int
|
Number of generations to run |
50
|
max_time_seconds |
float
|
Maximum time to run the optimization. If none or inf, will run until the end of the generations. |
float("inf")
|
max_eval_time_seconds |
float
|
Maximum time to evaluate a single individual. If none or inf, there will be no time limit per evaluation. |
60*5
|
validation_strategy |
str
|
EXPERIMENTAL The validation strategy to use for selecting the final pipeline from the population. TPOT2 may overfit the cross validation score. A second validation set can be used to select the final pipeline. - 'auto' : Automatically determine the validation strategy based on the dataset shape. - 'reshuffled' : Use the same data for cross validation and final validation, but with different splits for the folds. This is the default for small datasets. - 'split' : Use a separate validation set for final validation. Data will be split according to validation_fraction. This is the default for medium datasets. - 'none' : Do not use a separate validation set for final validation. Select based on the original cross-validation score. This is the default for large datasets. |
'none'
|
validation_fraction |
float
|
EXPERIMENTAL The fraction of the dataset to use for the validation set when validation_strategy is 'split'. Must be between 0 and 1. |
0.2
|
disable_label_encoder |
bool
|
If True, TPOT will check if the target needs to be relabeled to be sequential ints from 0 to N. This is necessary for XGBoost compatibility. If the labels need to be encoded, TPOT2 will use sklearn.preprocessing.LabelEncoder to encode the labels. The encoder can be accessed via the self.label_encoder_ attribute. If False, no additional label encoders will be used. |
False
|
early_stop |
int
|
Number of generations without improvement before early stopping. All objectives must have converged within the tolerance for this to be triggered. |
None
|
scorers_early_stop_tol |
-list of floats list of tolerances for each scorer. If the difference between the best score and the current score is less than the tolerance, the individual is considered to have converged If an index of the list is None, that item will not be used for early stopping -int If an int is given, it will be used as the tolerance for all objectives |
0.001
|
|
other_objectives_early_stop_tol |
-list of floats list of tolerances for each of the other objective function. If the difference between the best score and the current score is less than the tolerance, the individual is considered to have converged If an index of the list is None, that item will not be used for early stopping -int If an int is given, it will be used as the tolerance for all objectives |
None
|
|
threshold_evaluation_early_stop |
list[start, end]
|
starting and ending percentile to use as a threshold for the evaluation early stopping. Values between 0 and 100. |
None
|
threshold_evaluation_scaling |
float [0,inf)
|
A scaling factor to use when determining how fast we move the threshold moves from the start to end percentile. Must be greater than zero. Higher numbers will move the threshold to the end faster. |
0.5
|
selection_evaluation_early_stop |
list
|
A lower and upper percent of the population size to select each round of CV. Values between 0 and 1. |
None
|
selection_evaluation_scaling |
float
|
A scaling factor to use when determining how fast we move the threshold moves from the start to end percentile. Must be greater than zero. Higher numbers will move the threshold to the end faster. |
0.5
|
min_history_threshold |
int
|
The minimum number of previous scores needed before using threshold early stopping. |
0
|
survival_percentage |
float
|
Percentage of the population size to utilize for mutation and crossover at the beginning of the generation. The rest are discarded. Individuals are selected with the selector passed into survival_selector. The value of this parameter must be between 0 and 1, inclusive. For example, if the population size is 100 and the survival percentage is .5, 50 individuals will be selected with NSGA2 from the existing population. These will be used for mutation and crossover to generate the next 100 individuals for the next generation. The remainder are discarded from the live population. In the next generation, there will now be the 50 parents + the 100 individuals for a total of 150. Surivival percentage is based of the population size parameter and not the existing population size (current population size when using successive halving). Therefore, in the next generation we will still select 50 individuals from the currently existing 150. |
1
|
crossover_probability |
float
|
Probability of generating a new individual by crossover between two individuals. |
.2
|
mutate_probability |
float
|
Probability of generating a new individual by crossover between one individuals. |
.7
|
mutate_then_crossover_probability |
float
|
Probability of generating a new individual by mutating two individuals followed by crossover. |
.05
|
crossover_then_mutate_probability |
float
|
Probability of generating a new individual by crossover between two individuals followed by a mutation of the resulting individual. |
.05
|
survival_selector |
function
|
Function to use to select individuals for survival. Must take a matrix of scores and return selected indexes. Used to selected population_size * survival_percentage individuals at the start of each generation to use for mutation and crossover. |
survival_select_NSGA2
|
parent_selector |
function
|
Function to use to select pairs parents for crossover and individuals for mutation. Must take a matrix of scores and return selected indexes. |
parent_select_NSGA2
|
budget_range |
list[start, end]
|
A starting and ending budget to use for the budget scaling. |
None
|
budget_scaling |
A scaling factor to use when determining how fast we move the budget from the start to end budget. |
0.5
|
|
generations_until_end_budget |
int
|
The number of generations to run before reaching the max budget. |
1
|
stepwise_steps |
int
|
The number of staircase steps to take when scaling the budget and population size. |
1
|
n_jobs |
int
|
Number of processes to run in parallel. |
1
|
memory_limit |
str
|
Memory limit for each job. See Dask LocalCluster documentation for more information. |
"4GB"
|
client |
Client
|
A dask client to use for parallelization. If not None, this will override the n_jobs and memory_limit parameters. If None, will create a new client with num_workers=n_jobs and memory_limit=memory_limit. |
None
|
processes |
bool
|
If True, will use multiprocessing to parallelize the optimization process. If False, will use threading. True seems to perform better. However, False is required for interactive debugging. |
True
|
warm_start |
bool
|
If True, will use the continue the evolutionary algorithm from the last generation of the previous run. |
False
|
subset_column |
str or int
|
EXPERIMENTAL The column to use for the subset selection. Must also pass in unique_subset_values to GraphIndividual to function. |
None
|
periodic_checkpoint_folder |
str
|
Folder to save the population to periodically. If None, no periodic saving will be done. If provided, training will resume from this checkpoint. |
None
|
callback |
CallBackInterface
|
Callback object. Not implemented |
None
|
verbose |
int
|
How much information to print during the optimization process. Higher values include the information from lower values. 0. nothing 1. progress bar
|
1
|
random_state |
(int, None)
|
A seed for reproducability of experiments. This value will be passed to numpy.random.default_rng() to create an instnce of the genrator to pass to other classes
|
None
|
Attributes:
Name | Type | Description |
---|---|---|
fitted_pipeline_ |
GraphPipeline
|
A fitted instance of the GraphPipeline that inherits from sklearn BaseEstimator. This is fitted on the full X, y passed to fit. |
evaluated_individuals |
A pandas data frame containing data for all evaluated individuals in the run.
|
Columns: - objective functions : The first few columns correspond to the passed in scorers and objective functions - Parents : A tuple containing the indexes of the pipelines used to generate the pipeline of that row. If NaN, this pipeline was generated randomly in the initial population. - Variation_Function : Which variation function was used to mutate or crossover the parents. If NaN, this pipeline was generated randomly in the initial population. - Individual : The internal representation of the individual that is used during the evolutionary algorithm. This is not an sklearn BaseEstimator. - Generation : The generation the pipeline first appeared. - Pareto_Front : The nondominated front that this pipeline belongs to. 0 means that its scores is not strictly dominated by any other individual. To save on computational time, the best frontier is updated iteratively each generation. The pipelines with the 0th pareto front do represent the exact best frontier. However, the pipelines with pareto front >= 1 are only in reference to the other pipelines in the final population. All other pipelines are set to NaN. - Instance : The unfitted GraphPipeline BaseEstimator. - validation objective functions : Objective function scores evaluated on the validation set. - Validation_Pareto_Front : The full pareto front calculated on the validation set. This is calculated for all pipelines with Pareto_Front equal to 0. Unlike the Pareto_Front which only calculates the frontier and the final population, the Validation Pareto Front is calculated for all pipelines tested on the validation set. |
pareto_front |
The same pandas dataframe as evaluated individuals, but containing only the frontier pareto front pipelines.
|
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Source code in tpot2/tpot_estimator/estimator.py
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