Pipeline(steps=[('featureunion-1',\n",
" FeatureUnion(transformer_list=[('pipeline-1',\n",
" Pipeline(steps=[('columntransformer',\n",
" ColumnTransformer(transformers=[('passthrough',\n",
" 'passthrough',\n",
" <sklearn.compose._column_transformer.make_column_selector object at 0x7d34eb307cd0>)])),\n",
" ('simpleimputer',\n",
" SimpleImputer(fill_value='missing',\n",
" strategy='constant')),\n",
" ('onehotencode...\n",
" VarianceThreshold(threshold=0.1557560591318)),\n",
" ('featureunion-2',\n",
" FeatureUnion(transformer_list=[('pipeline-1',\n",
" Pipeline(steps=[('passthrough',\n",
" Passthrough())])),\n",
" ('pipeline-2',\n",
" Pipeline(steps=[('polynomialfeatures',\n",
" PolynomialFeatures())])),\n",
" ('pipeline-3',\n",
" Pipeline(steps=[('zerocount',\n",
" ZeroCount())]))])),\n",
" ('randomforestclassifier',\n",
" RandomForestClassifier(criterion='log_loss',\n",
" n_estimators=80))]) In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. Pipeline?Documentation for Pipeline iFitted Pipeline(steps=[('featureunion-1',\n",
" FeatureUnion(transformer_list=[('pipeline-1',\n",
" Pipeline(steps=[('columntransformer',\n",
" ColumnTransformer(transformers=[('passthrough',\n",
" 'passthrough',\n",
" <sklearn.compose._column_transformer.make_column_selector object at 0x7d34eb307cd0>)])),\n",
" ('simpleimputer',\n",
" SimpleImputer(fill_value='missing',\n",
" strategy='constant')),\n",
" ('onehotencode...\n",
" VarianceThreshold(threshold=0.1557560591318)),\n",
" ('featureunion-2',\n",
" FeatureUnion(transformer_list=[('pipeline-1',\n",
" Pipeline(steps=[('passthrough',\n",
" Passthrough())])),\n",
" ('pipeline-2',\n",
" Pipeline(steps=[('polynomialfeatures',\n",
" PolynomialFeatures())])),\n",
" ('pipeline-3',\n",
" Pipeline(steps=[('zerocount',\n",
" ZeroCount())]))])),\n",
" ('randomforestclassifier',\n",
" RandomForestClassifier(criterion='log_loss',\n",
" n_estimators=80))]) featureunion-1: FeatureUnion?Documentation for featureunion-1: FeatureUnion FeatureUnion(transformer_list=[('pipeline-1',\n",
" Pipeline(steps=[('columntransformer',\n",
" ColumnTransformer(transformers=[('passthrough',\n",
" 'passthrough',\n",
" <sklearn.compose._column_transformer.make_column_selector object at 0x7d34eb307cd0>)])),\n",
" ('simpleimputer',\n",
" SimpleImputer(fill_value='missing',\n",
" strategy='constant')),\n",
" ('onehotencoder',\n",
" OneHotEncoder(drop='first',\n",
" sparse_output=False))])),\n",
" ('pipeline-2',\n",
" Pipeline(steps=[('columntransformer',\n",
" ColumnTransformer(transformers=[('passthrough',\n",
" 'passthrough',\n",
" <sklearn.compose._column_transformer.make_column_selector object at 0x7d34eb307d30>)])),\n",
" ('simpleimputer',\n",
" SimpleImputer(strategy='median'))]))]) featureunion-2: FeatureUnion?Documentation for featureunion-2: FeatureUnion FeatureUnion(transformer_list=[('pipeline-1',\n",
" Pipeline(steps=[('passthrough',\n",
" Passthrough())])),\n",
" ('pipeline-2',\n",
" Pipeline(steps=[('polynomialfeatures',\n",
" PolynomialFeatures())])),\n",
" ('pipeline-3',\n",
" Pipeline(steps=[('zerocount', ZeroCount())]))]) )])),\n",
" ('simpleimputer',\n",
" SimpleImputer(fill_value='missing',\n",
" strategy='constant')),\n",
" ('onehotencode...\n",
" VarianceThreshold(threshold=0.1557560591318)),\n",
" ('featureunion-2',\n",
" FeatureUnion(transformer_list=[('pipeline-1',\n",
" Pipeline(steps=[('passthrough',\n",
" Passthrough())])),\n",
" ('pipeline-2',\n",
" Pipeline(steps=[('polynomialfeatures',\n",
" PolynomialFeatures())])),\n",
" ('pipeline-3',\n",
" Pipeline(steps=[('zerocount',\n",
" ZeroCount())]))])),\n",
" ('randomforestclassifier',\n",
" RandomForestClassifier(criterion='log_loss',\n",
" n_estimators=80))])"
]
},
"execution_count": 5,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"est.fitted_pipeline_"
]
},
{
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"metadata": {},
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{
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================================================
FILE: Tutorial/simple_fss.csv
================================================
one,a,b,c
two,d,e,f
three,g,h,i
================================================
FILE: docs/archived/api.md
================================================
⚠️ Warning
This documentation is for the archived version of TPOT, which is no longer maintained. For the latest version, click here .
# TPOT API
## Classification
class tpot.TPOTClassifier (generations =100, population_size =100,
offspring_size =None, mutation_rate =0.9,
crossover_rate =0.1,
scoring ='accuracy', cv =5,
subsample =1.0, n_jobs =1,
max_time_mins =None, max_eval_time_mins =5,
random_state =None, config_dict =None,
template =None,
warm_start =False,
memory =None,
use_dask =False,
periodic_checkpoint_folder =None,
early_stop =None,
verbosity =0,
disable_update_check =False,
log_file =None
Parameters:
generations : int or None optional (default=100)
Number of iterations to the run pipeline optimization process. It must be a positive number or None. If None, the parameter max_time_mins must be defined as the runtime limit.
population_size + generations × offspring_size pipelines in total.
population_size : int, optional (default=100)
Number of individuals to retain in the genetic programming population every generation. Must be a positive number.
offspring_size : int, optional (default=None)
Number of offspring to produce in each genetic programming generation. Must be a positive number. By default, the number of offspring is equal to the number of population size.
mutation_rate : float, optional (default=0.9)
Mutation rate for the genetic programming algorithm in the range [0.0, 1.0]. This parameter tells the GP algorithm how many pipelines to apply random changes to every generation.
mutation_rate + crossover_rate cannot exceed 1.0.
crossover_rate : float, optional (default=0.1)
Crossover rate for the genetic programming algorithm in the range [0.0, 1.0]. This parameter tells the genetic programming algorithm how many pipelines to "breed" every generation.
mutation_rate + crossover_rate cannot exceed 1.0.
scoring : string or callable, optional (default='accuracy')
Function used to evaluate the quality of a given pipeline for the classification problem. The following built-in scoring functions can be used:
scorer(estimator, X, y) .
scoring functions for more details.
cv : int, cross-validation generator, or an iterable, optional (default=5)
Cross-validation strategy used when evaluating pipelines.
integer, to specify the number of folds in an unshuffled StratifiedKFold,
An object to be used as a cross-validation generator, or
An iterable yielding train/test splits.
subsample : float, optional (default=1.0)
Fraction of training samples that are used during the TPOT optimization process. Must be in the range (0.0, 1.0].
subsample =0.5 tells TPOT to use a random subsample of half of the training data. This subsample will remain the same during the entire pipeline optimization process.
n_jobs : integer, optional (default=1)
Number of processes to use in parallel for evaluating pipelines during the TPOT optimization process.
n_jobs =-1 will use as many cores as available on the computer. For n_jobs below -1, (n_cpus + 1 + n_jobs) are used. Thus for n_jobs = -2, all CPUs but one are used. Beware that using multiple processes on the same machine may cause memory issues for large datasets.
max_time_mins : integer or None, optional (default=None)
How many minutes TPOT has to optimize the pipeline.
max_time_mins minutes elapsed and then stop. TPOT will stop earlier if generations is set and all generations are already evaluated.
max_eval_time_mins : float, optional (default=5)
How many minutes TPOT has to evaluate a single pipeline.
random_state : integer or None, optional (default=None)
The seed of the pseudo random number generator used in TPOT.
config_dict : Python dictionary, string, or None, optional (default=None)
A configuration dictionary for customizing the operators and parameters that TPOT searches in the optimization process.
Python dictionary, TPOT will use your custom configuration,
string 'TPOT light', TPOT will use a built-in configuration with only fast models and preprocessors, or
string 'TPOT MDR', TPOT will use a built-in configuration specialized for genomic studies, or
string 'TPOT sparse': TPOT will use a configuration dictionary with a one-hot encoder and the operators normally included in TPOT that also support sparse matrices, or
None, TPOT will use the default TPOTClassifier configuration.
See the built-in configurations section for the list of configurations included with TPOT, and the custom configuration section for more information and examples of how to create your own TPOT configurations.
template : string (default=None)
Template of predefined pipeline structure. The option is for specifying a desired structure for the machine learning pipeline evaluated in TPOT.
template option in tpot section for more details.
warm_start : boolean, optional (default=False)
Flag indicating whether the TPOT instance will reuse the population from previous calls to fit() .
warm_start =True can be useful for running TPOT for a short time on a dataset, checking the results, then resuming the TPOT run from where it left off.
memory : a joblib.Memory object or string, optional (default=None)
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. More details about memory caching in scikit-learn documentation
String 'auto': TPOT uses memory caching with a temporary directory and cleans it up upon shutdown, or
Path of a caching directory, TPOT uses memory caching with the provided directory and TPOT does NOT clean the caching directory up upon shutdown, or
Memory object, TPOT uses the instance of joblib.Memory for memory caching and TPOT does NOT clean the caching directory up upon shutdown, or
None, TPOT does not use memory caching.
use_dask : boolean, optional (default: False)
Whether to use Dask-ML's pipeline optimiziations. This avoid re-fitting
the same estimator on the same split of data multiple times. It
will also provide more detailed diagnostics when using Dask's
distributed scheduler.
avoid repeated work for more details.
periodic_checkpoint_folder : path string, optional (default: None)
If supplied, a folder in which TPOT will periodically save pipelines in pareto front so far while optimizing.
Sudden death before TPOT could save optimized pipeline
Track its progress
Grab pipelines while it's still optimizing
early_stop : integer, optional (default: None)
How many generations TPOT checks whether there is no improvement in optimization process.
verbosity : integer, optional (default=0)
How much information TPOT communicates while it's running.
0, TPOT will print nothing,
1, TPOT will print minimal information,
2, TPOT will print more information and provide a progress bar, or
3, TPOT will print everything and provide a progress bar.
disable_update_check : boolean, optional (default=False)
Flag indicating whether the TPOT version checker should be disabled.
log_file : file-like class (io.TextIOWrapper or io.StringIO) or string, optional (default: None)
Save progress content to a file.
If it is a string for the path and file name of the desired output file,
TPOT will create the file and write log into it.
If it is None, TPOT will output log into sys.stdout
Attributes:
fitted_pipeline_ : scikit-learn Pipeline object
The best pipeline that TPOT discovered during the pipeline optimization process, fitted on the entire training dataset.
pareto_front_fitted_pipelines_ : Python dictionary
Dictionary containing the all pipelines on the TPOT Pareto front, where the key is the string representation of the pipeline and the value is the corresponding pipeline fitted on the entire training dataset.
pareto_front_fitted_pipelines_ is only available when verbosity =3.
evaluated_individuals_ : Python dictionary
Dictionary containing all pipelines that were evaluated during the pipeline optimization process, where the key is the string representation of the pipeline and the value is a tuple containing (# of steps in pipeline, accuracy metric for the pipeline).
Example
```Python
from tpot import TPOTClassifier
from sklearn.datasets import load_digits
from sklearn.model_selection import train_test_split
digits = load_digits()
X_train, X_test, y_train, y_test = train_test_split(digits.data, digits.target,
train_size=0.75, test_size=0.25)
tpot = TPOTClassifier(generations=5, population_size=50, verbosity=2)
tpot.fit(X_train, y_train)
print(tpot.score(X_test, y_test))
tpot.export('tpot_digits_pipeline.py')
```
Functions
fit (features, classes[, sample_weight, groups])Run the TPOT optimization process on the given training data.
predict (features)Use the optimized pipeline to predict the classes for a feature set.
predict_proba (features)Use the optimized pipeline to estimate the class probabilities for a feature set.
score (testing_features, testing_classes)Returns the optimized pipeline's score on the given testing data using the user-specified scoring function.
export (output_file_name)Export the optimized pipeline as Python code.
Run the TPOT optimization process on the given training data.
Uses genetic programming to optimize a machine learning pipeline that maximizes the score on the provided features and target. This pipeline optimization procedure uses internal k-fold cross-validaton to avoid overfitting on the provided data. At the end of the pipeline optimization procedure, the best pipeline is then trained on the entire set of provided samples.
Parameters:
features : array-like {n_samples, n_features}
Feature matrix
median value imputation .
classes : array-like {n_samples}
List of class labels for prediction
sample_weight : array-like {n_samples}, optional
Per-sample weights. Higher weights indicate more importance. If specified, sample_weight will be passed to any pipeline element whose fit() function accepts a sample_weight argument. By default, using sample_weight does not affect tpot's scoring functions, which determine preferences between pipelines.
groups : array-like, with shape {n_samples, }, optional
Group labels for the samples used when performing cross-validation.
sklearn.model_selection.GroupKFold .
Returns:
self : object
Returns a copy of the fitted TPOT object
Use the optimized pipeline to predict the classes for a feature set.
Parameters:
features : array-like {n_samples, n_features}
Feature matrix
Returns:
predictions : array-like {n_samples}
Predicted classes for the samples in the feature matrix
Use the optimized pipeline to estimate the class probabilities for a feature set.
Note: This function will only work for pipelines whose final classifier supports the
predict_proba function. TPOT will raise an error otherwise.
Parameters:
features : array-like {n_samples, n_features}
Feature matrix
Returns:
predictions : array-like {n_samples, n_classes}
The class probabilities of the input samples
Returns the optimized pipeline's score on the given testing data using the user-specified scoring function.
The default scoring function for TPOTClassifier is 'accuracy'.
Parameters:
testing_features : array-like {n_samples, n_features}
Feature matrix of the testing set
testing_classes : array-like {n_samples}
List of class labels for prediction in the testing set
Returns:
accuracy_score : float
The estimated test set accuracy according to the user-specified scoring function.
Export the optimized pipeline as Python code.
See the
usage documentation for example usage of the export function.
Parameters:
output_file_name : string
String containing the path and file name of the desired output file
data_file_path : string
By default, the path of input dataset is 'PATH/TO/DATA/FILE' by default. If data_file_path is another string, the path will be replaced.
Returns:
exported_code_string : string
The whole pipeline text as a string should be returned if output_file_name is not specified.
class tpot.TPOTRegressor (generations =100, population_size =100,
offspring_size =None, mutation_rate =0.9,
crossover_rate =0.1,
scoring ='neg_mean_squared_error', cv =5,
subsample =1.0, n_jobs =1,
max_time_mins =None, max_eval_time_mins =5,
random_state =None, config_dict =None,
template =None,
warm_start =False,
memory =None,
use_dask =False,
periodic_checkpoint_folder =None,
early_stop =None,
verbosity =0,
disable_update_check =False
Parameters:
generations : int or None, optional (default=100)
Number of iterations to the run pipeline optimization process. It must be a positive number or None. If None, the parameter max_time_mins must be defined as the runtime limit.
population_size + generations × offspring_size pipelines in total.
population_size : int, optional (default=100)
Number of individuals to retain in the genetic programming population every generation. Must be a positive number.
offspring_size : int, optional (default=None)
Number of offspring to produce in each genetic programming generation. Must be a positive number. By default, the number of offspring is equal to the number of population size.
mutation_rate : float, optional (default=0.9)
Mutation rate for the genetic programming algorithm in the range [0.0, 1.0]. This parameter tells the GP algorithm how many pipelines to apply random changes to every generation.
mutation_rate + crossover_rate cannot exceed 1.0.
crossover_rate : float, optional (default=0.1)
Crossover rate for the genetic programming algorithm in the range [0.0, 1.0]. This parameter tells the genetic programming algorithm how many pipelines to "breed" every generation.
mutation_rate + crossover_rate cannot exceed 1.0.
scoring : string or callable, optional (default='neg_mean_squared_error')
Function used to evaluate the quality of a given pipeline for the regression problem. The following built-in scoring functions can be used:
neg version of mean squared error and related metrics so TPOT will minimize (instead of maximize) the metric.
scorer(estimator, X, y) .
scoring functions for more details.
cv : int, cross-validation generator, or an iterable, optional (default=5)
Cross-validation strategy used when evaluating pipelines.
integer, to specify the number of folds in an unshuffled KFold,
An object to be used as a cross-validation generator, or
An iterable yielding train/test splits.
subsample : float, optional (default=1.0)
Fraction of training samples that are used during the TPOT optimization process. Must be in the range (0.0, 1.0].
subsample =0.5 tells TPOT to use a random subsample of half of the training data. This subsample will remain the same during the entire pipeline optimization process.
n_jobs : integer, optional (default=1)
Number of processes to use in parallel for evaluating pipelines during the TPOT optimization process.
n_jobs =-1 will use as many cores as available on the computer. For n_jobs below -1, (n_cpus + 1 + n_jobs) are used. Thus for n_jobs = -2, all CPUs but one are used. Beware that using multiple processes on the same machine may cause memory issues for large datasets
max_time_mins : integer or None, optional (default=None)
How many minutes TPOT has to optimize the pipeline.
max_time_mins minutes elapsed and then stop. TPOT will stop earlier if generations is set and all generations are already evaluated.
max_eval_time_mins : float, optional (default=5)
How many minutes TPOT has to evaluate a single pipeline.
random_state : integer or None, optional (default=None)
The seed of the pseudo random number generator used in TPOT.
config_dict : Python dictionary, string, or None, optional (default=None)
A configuration dictionary for customizing the operators and parameters that TPOT searches in the optimization process.
Python dictionary, TPOT will use your custom configuration,
string 'TPOT light', TPOT will use a built-in configuration with only fast models and preprocessors, or
string 'TPOT MDR', TPOT will use a built-in configuration specialized for genomic studies, or
string 'TPOT sparse': TPOT will use a configuration dictionary with a one-hot encoder and the operators normally included in TPOT that also support sparse matrices, or
None, TPOT will use the default TPOTRegressor configuration.
See the built-in configurations section for the list of configurations included with TPOT, and the custom configuration section for more information and examples of how to create your own TPOT configurations.
template : string (default=None)
Template of predefined pipeline structure. The option is for specifying a desired structure for the machine learning pipeline evaluated in TPOT.
template option in tpot section for more details.
warm_start : boolean, optional (default=False)
Flag indicating whether the TPOT instance will reuse the population from previous calls to fit() .
warm_start =True can be useful for running TPOT for a short time on a dataset, checking the results, then resuming the TPOT run from where it left off.
memory : a joblib.Memory object or string, optional (default=None)
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. More details about memory caching in scikit-learn documentation
String 'auto': TPOT uses memory caching with a temporary directory and cleans it up upon shutdown, or
Path of a caching directory, TPOT uses memory caching with the provided directory and TPOT does NOT clean the caching directory up upon shutdown, or
Memory object, TPOT uses the instance of joblib.Memory for memory caching and TPOT does NOT clean the caching directory up upon shutdown, or
None, TPOT does not use memory caching.
use_dask : boolean, optional (default: False)
Whether to use Dask-ML's pipeline optimiziations. This avoid re-fitting
the same estimator on the same split of data multiple times. It
will also provide more detailed diagnostics when using Dask's
distributed scheduler.
avoid repeated work for more details.
periodic_checkpoint_folder : path string, optional (default: None)
If supplied, a folder in which TPOT will periodically save pipelines in pareto front so far while optimizing.
Sudden death before TPOT could save optimized pipeline
Track its progress
Grab pipelines while it's still optimizing
early_stop : integer, optional (default: None)
How many generations TPOT checks whether there is no improvement in optimization process.
verbosity : integer, optional (default=0)
How much information TPOT communicates while it's running.
0, TPOT will print nothing,
1, TPOT will print minimal information,
2, TPOT will print more information and provide a progress bar, or
3, TPOT will print everything and provide a progress bar.
disable_update_check : boolean, optional (default=False)
Flag indicating whether the TPOT version checker should be disabled.
Attributes:
fitted_pipeline_ : scikit-learn Pipeline object
The best pipeline that TPOT discovered during the pipeline optimization process, fitted on the entire training dataset.
pareto_front_fitted_pipelines_ : Python dictionary
Dictionary containing the all pipelines on the TPOT Pareto front, where the key is the string representation of the pipeline and the value is the corresponding pipeline fitted on the entire training dataset.
_pareto_front_fitted_pipelines is only available when verbosity =3.
evaluated_individuals_ : Python dictionary
Dictionary containing all pipelines that were evaluated during the pipeline optimization process, where the key is the string representation of the pipeline and the value is a tuple containing (# of steps in pipeline, accuracy metric for the pipeline).
Example
```Python
from tpot import TPOTRegressor
from sklearn.datasets import load_boston
from sklearn.model_selection import train_test_split
digits = load_boston()
X_train, X_test, y_train, y_test = train_test_split(digits.data, digits.target,
train_size=0.75, test_size=0.25)
tpot = TPOTRegressor(generations=5, population_size=50, verbosity=2)
tpot.fit(X_train, y_train)
print(tpot.score(X_test, y_test))
tpot.export('tpot_boston_pipeline.py')
```
Functions
fit (features, target[, sample_weight, groups])Run the TPOT optimization process on the given training data.
predict (features)Use the optimized pipeline to predict the target values for a feature set.
score (testing_features, testing_target)Returns the optimized pipeline's score on the given testing data using the user-specified scoring function.
export (output_file_name)Export the optimized pipeline as Python code.
Run the TPOT optimization process on the given training data.
Uses genetic programming to optimize a machine learning pipeline that maximizes the score on the provided features and target. This pipeline optimization procedure uses internal k-fold cross-validaton to avoid overfitting on the provided data. At the end of the pipeline optimization procedure, the best pipeline is then trained on the entire set of provided samples.
Parameters:
features : array-like {n_samples, n_features}
Feature matrix
median value imputation .
target : array-like {n_samples}
List of target labels for prediction
sample_weight : array-like {n_samples}, optional
Per-sample weights. Higher weights indicate more importance. If specified, sample_weight will be passed to any pipeline element whose fit() function accepts a sample_weight argument. By default, using sample_weight does not affect tpot's scoring functions, which determine preferences between pipelines.
groups : array-like, with shape {n_samples, }, optional
Group labels for the samples used when performing cross-validation.
sklearn.model_selection.GroupKFold .
Returns:
self : object
Returns a copy of the fitted TPOT object
Use the optimized pipeline to predict the target values for a feature set.
Parameters:
features : array-like {n_samples, n_features}
Feature matrix
Returns:
predictions : array-like {n_samples}
Predicted target values for the samples in the feature matrix
Returns the optimized pipeline's score on the given testing data using the user-specified scoring function.
The default scoring function for TPOTRegressor is 'mean_squared_error'.
Parameters:
testing_features : array-like {n_samples, n_features}
Feature matrix of the testing set
testing_target : array-like {n_samples}
List of target labels for prediction in the testing set
Returns:
accuracy_score : float
The estimated test set accuracy according to the user-specified scoring function.
Export the optimized pipeline as Python code.
See the
usage documentation for example usage of the export function.
Parameters:
output_file_name : string
String containing the path and file name of the desired output file
data_file_path : string
By default, the path of input dataset is 'PATH/TO/DATA/FILE' by default. If data_file_path is another string, the path will be replaced.
Returns:
exported_code_string : string
The whole pipeline text as a string should be returned if output_file_name is not specified.
⚠️ Warning
This documentation is for the archived version of TPOT, which is no longer maintained. For the latest version, click here .
# Citing TPOT
If you use TPOT in a scientific publication, please consider citing at least one of the following papers:
Trang T. Le, Weixuan Fu and Jason H. Moore (2020). [Scaling tree-based automated machine learning to biomedical big data with a feature set selector](https://academic.oup.com/bioinformatics/article/36/1/250/5511404). *Bioinformatics*.36(1): 250-256.
BibTeX entry:
```bibtex
@article{le2020scaling,
title={Scaling tree-based automated machine learning to biomedical big data with a feature set selector},
author={Le, Trang T and Fu, Weixuan and Moore, Jason H},
journal={Bioinformatics},
volume={36},
number={1},
pages={250--256},
year={2020},
publisher={Oxford University Press}
}
```
Randal S. Olson, Ryan J. Urbanowicz, Peter C. Andrews, Nicole A. Lavender, La Creis Kidd, and Jason H. Moore (2016). [Automating biomedical data science through tree-based pipeline optimization](http://link.springer.com/chapter/10.1007/978-3-319-31204-0_9). *Applications of Evolutionary Computation*, pages 123-137.
BibTeX entry:
```bibtex
@inbook{Olson2016EvoBio,
author={Olson, Randal S. and Urbanowicz, Ryan J. and Andrews, Peter C. and Lavender, Nicole A. and Kidd, La Creis and Moore, Jason H.},
editor={Squillero, Giovanni and Burelli, Paolo},
chapter={Automating Biomedical Data Science Through Tree-Based Pipeline Optimization},
title={Applications of Evolutionary Computation: 19th European Conference, EvoApplications 2016, Porto, Portugal, March 30 -- April 1, 2016, Proceedings, Part I},
year={2016},
publisher={Springer International Publishing},
pages={123--137},
isbn={978-3-319-31204-0},
doi={10.1007/978-3-319-31204-0_9},
url={http://dx.doi.org/10.1007/978-3-319-31204-0_9}
}
```
Evaluation of a Tree-based Pipeline Optimization Tool for Automating Data Science
Randal S. Olson, Nathan Bartley, Ryan J. Urbanowicz, and Jason H. Moore (2016). [Evaluation of a Tree-based Pipeline Optimization Tool for Automating Data Science](http://dl.acm.org/citation.cfm?id=2908918). *Proceedings of GECCO 2016*, pages 485-492.
BibTeX entry:
```bibtex
@inproceedings{OlsonGECCO2016,
author = {Olson, Randal S. and Bartley, Nathan and Urbanowicz, Ryan J. and Moore, Jason H.},
title = {Evaluation of a Tree-based Pipeline Optimization Tool for Automating Data Science},
booktitle = {Proceedings of the Genetic and Evolutionary Computation Conference 2016},
series = {GECCO '16},
year = {2016},
isbn = {978-1-4503-4206-3},
location = {Denver, Colorado, USA},
pages = {485--492},
numpages = {8},
url = {http://doi.acm.org/10.1145/2908812.2908918},
doi = {10.1145/2908812.2908918},
acmid = {2908918},
publisher = {ACM},
address = {New York, NY, USA},
}
```
Alternatively, you can cite the repository directly with the following DOI:
[DOI](https://zenodo.org/badge/latestdoi/20747/rhiever/tpot)
================================================
FILE: docs/archived/contributing.md
================================================
⚠️ Warning
This documentation is for the archived version of TPOT, which is no longer maintained. For the latest version, click here .
# Contribution Guide
We welcome you to [check the existing issues](https://github.com/EpistasisLab/tpot/issues/) for bugs or enhancements to work on. If you have an idea for an extension to TPOT, please [file a new issue](https://github.com/EpistasisLab/tpot/issues/new) so we can discuss it.
## Project layout
The latest stable release of TPOT is on the [master branch](https://github.com/EpistasisLab/tpot/tree/master), whereas the latest version of TPOT in development is on the [development branch](https://github.com/EpistasisLab/tpot/tree/development). Make sure you are looking at and working on the correct branch if you're looking to contribute code.
In terms of directory structure:
* All of TPOT's code sources are in the `tpot` directory
* The documentation sources are in the `docs_sources` directory
* Images in the documentation are in the `images` directory
* Tutorials for TPOT are in the `tutorials` directory
* Unit tests for TPOT are in the `tests.py` file
Make sure to familiarize yourself with the project layout before making any major contributions, and especially make sure to send all code changes to the `development` branch.
## How to contribute
The preferred way to contribute to TPOT is to fork the
[main repository](https://github.com/EpistasisLab/tpot/) on
GitHub:
1. Fork the [project repository](https://github.com/EpistasisLab/tpot):
click on the 'Fork' button near the top of the page. This creates
a copy of the code under your account on the GitHub server.
2. Clone this copy to your local disk:
$ git clone git@github.com:YourUsername/tpot.git
$ cd tpot
3. Create a branch to hold your changes:
$ git checkout -b my-contribution
4. Make sure your local environment is setup correctly for development. Installation instructions are almost identical to [the user instructions](installing.md) except that TPOT should *not* be installed. If you have TPOT installed on your computer then make sure you are using a virtual environment that does not have TPOT installed. Furthermore, you should make sure you have installed the `nose` package into your development environment so that you can test changes locally.
$ conda install nose
5. Start making changes on your newly created branch, remembering to never work on the ``master`` branch! Work on this copy on your computer using Git to do the version control.
6. Once some changes are saved locally, you can use your tweaked version of TPOT by navigating to the project's base directory and running TPOT directly from the command line:
$ python -m tpot.driver
or by running script that imports and uses the TPOT module with code similar to `from tpot import TPOTClassifier`
7. To check your changes haven't broken any existing tests and to check new tests you've added pass run the following (note, you must have the `nose` package installed within your dev environment for this to work):
$ nosetests -s -v
8. When you're done editing and local testing, run:
$ git add modified_files
$ git commit
to record your changes in Git, then push them to GitHub with:
$ git push -u origin my-contribution
Finally, go to the web page of your fork of the TPOT repo, and click 'Pull Request' (PR) to send your changes to the maintainers for review. Make sure that you send your PR to the `development` branch, as the `master` branch is reserved for the latest stable release. This will start the CI server to check all the project's unit tests run and send an email to the maintainers.
(If any of the above seems like magic to you, then look up the
[Git documentation](http://git-scm.com/documentation) on the web.)
## Before submitting your pull request
Before you submit a pull request for your contribution, please work through this checklist to make sure that you have done everything necessary so we can efficiently review and accept your changes.
If your contribution changes TPOT in any way:
* Update the [documentation](https://github.com/EpistasisLab/tpot/tree/master/docs_sources) so all of your changes are reflected there.
* Update the [README](https://github.com/EpistasisLab/tpot/blob/master/README.md) if anything there has changed.
If your contribution involves any code changes:
* Update the [project unit tests](https://github.com/EpistasisLab/tpot/tree/master/tests) to test your code changes.
* Make sure that your code is properly commented with [docstrings](https://www.python.org/dev/peps/pep-0257/) and comments explaining your rationale behind non-obvious coding practices.
* If your code affected any of the pipeline operators, make sure that the corresponding [export functionality](https://github.com/EpistasisLab/tpot/blob/master/tpot/export_utils.py) reflects those changes.
If your contribution requires a new library dependency:
* Double-check that the new dependency is easy to install via `pip` or Anaconda and supports both Python 2 and 3. If the dependency requires a complicated installation, then we most likely won't merge your changes because we want to keep TPOT easy to install.
* Add the required version of the library to [.travis.yml](https://github.com/EpistasisLab/tpot/blob/master/.travis.yml#L7)
* Add a line to pip install the library to [.travis_install.sh](https://github.com/EpistasisLab/tpot/blob/master/ci/.travis_install.sh#L46)
* Add a line to print the version of the library to [.travis_install.sh](https://github.com/EpistasisLab/tpot/blob/master/ci/.travis_install.sh#L63)
* Similarly add a line to print the version of the library to [.travis_test.sh](https://github.com/EpistasisLab/tpot/blob/master/ci/.travis_test.sh#L13)
## After submitting your pull request
After submitting your pull request, [Travis-CI](https://travis-ci.com/) will automatically run unit tests on your changes and make sure that your updated code builds and runs on Python 2 and 3. We also use services that automatically check code quality and test coverage.
Check back shortly after submitting your pull request to make sure that your code passes these checks. If any of the checks come back with a red X, then do your best to address the errors.
================================================
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================================================
.md-grid {
max-width: 100%;
}
================================================
FILE: docs/archived/examples.md
================================================
⚠️ Warning
This documentation is for the archived version of TPOT, which is no longer maintained. For the latest version, click here .
# Overview
The following sections illustrate the usage of TPOT with various datasets, each
belonging to a typical class of machine learning tasks.
| Dataset | Task | Task class | Dataset description | Jupyter notebook |
| ------- | ----------------------- | ---------------------- |:-------------------:|:------------------------------------------------------------------------------------------:|
| Iris | flower classification | classification | [link](https://archive.ics.uci.edu/ml/datasets/iris) | [link](https://github.com/EpistasisLab/tpot/blob/master/tutorials/IRIS.ipynb) |
| Optical Recognition of Handwritten Digits | digit recognition | (image) classification | [link](https://scikit-learn.org/stable/datasets/index.html#digits-dataset) | [link](https://github.com/EpistasisLab/tpot/blob/master/tutorials/Digits.ipynb) |
| Boston | housing prices modeling | regression | [link](https://www.cs.toronto.edu/~delve/data/boston/bostonDetail.html) | N/A |
| Titanic | survival analysis | classification | [link](https://www.kaggle.com/c/titanic/data) | [link](https://github.com/EpistasisLab/tpot/blob/master/tutorials/Titanic_Kaggle.ipynb) |
| Bank Marketing | subscription prediction | classification | [link](https://archive.ics.uci.edu/ml/datasets/Bank+Marketing) | [link](https://github.com/EpistasisLab/tpot/blob/master/tutorials/Portuguese%20Bank%20Marketing/Portuguese%20Bank%20Marketing%20Strategy.ipynb) |
| MAGIC Gamma Telescope | event detection | classification | [link](https://archive.ics.uci.edu/ml/datasets/MAGIC+Gamma+Telescope) | [link](https://github.com/EpistasisLab/tpot/blob/master/tutorials/MAGIC%20Gamma%20Telescope/MAGIC%20Gamma%20Telescope.ipynb) |
| cuML Classification Example | random classification problem | classification | [link](https://scikit-learn.org/stable/modules/generated/sklearn.datasets.make_classification.html) | [link](https://github.com/EpistasisLab/tpot/blob/master/tutorials/cuML_Classification_Example.ipynb) |
| cuML Regression Example | random regression problem | regression | [link](https://scikit-learn.org/stable/modules/generated/sklearn.datasets.make_regression.html) | [link](https://github.com/EpistasisLab/tpot/blob/master/tutorials/cuML_Regression_Example.ipynb) |
**Notes:**
- For details on how the `fit()`, `score()` and `export()` methods work, refer to the [usage documentation](/using/).
- Upon re-running the experiments, your resulting pipelines _may_ differ (to some extent) from the ones demonstrated here.
## Iris flower classification
The following code illustrates how TPOT can be employed for performing a simple _classification task_ over the Iris dataset.
```Python
from tpot import TPOTClassifier
from sklearn.datasets import load_iris
from sklearn.model_selection import train_test_split
import numpy as np
iris = load_iris()
X_train, X_test, y_train, y_test = train_test_split(iris.data.astype(np.float64),
iris.target.astype(np.float64), train_size=0.75, test_size=0.25, random_state=42)
tpot = TPOTClassifier(generations=5, population_size=50, verbosity=2, random_state=42)
tpot.fit(X_train, y_train)
print(tpot.score(X_test, y_test))
tpot.export('tpot_iris_pipeline.py')
```
Running this code should discover a pipeline (exported as `tpot_iris_pipeline.py`) that achieves about 97% test accuracy:
```Python
import numpy as np
import pandas as pd
from sklearn.model_selection import train_test_split
from sklearn.neighbors import KNeighborsClassifier
from sklearn.pipeline import make_pipeline
from sklearn.preprocessing import Normalizer
from tpot.export_utils import set_param_recursive
# NOTE: Make sure that the outcome column is labeled 'target' in the data file
tpot_data = pd.read_csv('PATH/TO/DATA/FILE', sep='COLUMN_SEPARATOR', dtype=np.float64)
features = tpot_data.drop('target', axis=1)
training_features, testing_features, training_target, testing_target = \
train_test_split(features, tpot_data['target'], random_state=42)
# Average CV score on the training set was: 0.9826086956521738
exported_pipeline = make_pipeline(
Normalizer(norm="l2"),
KNeighborsClassifier(n_neighbors=5, p=2, weights="distance")
)
# Fix random state for all the steps in exported pipeline
set_param_recursive(exported_pipeline.steps, 'random_state', 42)
exported_pipeline.fit(training_features, training_target)
results = exported_pipeline.predict(testing_features)
```
## Digits dataset
Below is a minimal working example with the optical recognition of handwritten digits dataset, which is an _image classification problem_.
```Python
from tpot import TPOTClassifier
from sklearn.datasets import load_digits
from sklearn.model_selection import train_test_split
digits = load_digits()
X_train, X_test, y_train, y_test = train_test_split(digits.data, digits.target,
train_size=0.75, test_size=0.25, random_state=42)
tpot = TPOTClassifier(generations=5, population_size=50, verbosity=2, random_state=42)
tpot.fit(X_train, y_train)
print(tpot.score(X_test, y_test))
tpot.export('tpot_digits_pipeline.py')
```
Running this code should discover a pipeline (exported as `tpot_digits_pipeline.py`) that achieves about 98% test accuracy:
```Python
import numpy as np
import pandas as pd
from sklearn.ensemble import RandomForestClassifier
from sklearn.linear_model import LogisticRegression
from sklearn.model_selection import train_test_split
from sklearn.pipeline import make_pipeline, make_union
from sklearn.preprocessing import PolynomialFeatures
from tpot.builtins import StackingEstimator
from tpot.export_utils import set_param_recursive
# NOTE: Make sure that the outcome column is labeled 'target' in the data file
tpot_data = pd.read_csv('PATH/TO/DATA/FILE', sep='COLUMN_SEPARATOR', dtype=np.float64)
features = tpot_data.drop('target', axis=1)
training_features, testing_features, training_target, testing_target = \
train_test_split(features, tpot_data['target'], random_state=42)
# Average CV score on the training set was: 0.9799428471757372
exported_pipeline = make_pipeline(
PolynomialFeatures(degree=2, include_bias=False, interaction_only=False),
StackingEstimator(estimator=LogisticRegression(C=0.1, dual=False, penalty="l1")),
RandomForestClassifier(bootstrap=True, criterion="entropy", max_features=0.35000000000000003, min_samples_leaf=20, min_samples_split=19, n_estimators=100)
)
# Fix random state for all the steps in exported pipeline
set_param_recursive(exported_pipeline.steps, 'random_state', 42)
exported_pipeline.fit(training_features, training_target)
results = exported_pipeline.predict(testing_features)
```
## Boston housing prices modeling
The following code illustrates how TPOT can be employed for performing a _regression task_ over the Boston housing prices dataset.
```Python
from tpot import TPOTRegressor
from sklearn.datasets import load_boston
from sklearn.model_selection import train_test_split
housing = load_boston()
X_train, X_test, y_train, y_test = train_test_split(housing.data, housing.target,
train_size=0.75, test_size=0.25, random_state=42)
tpot = TPOTRegressor(generations=5, population_size=50, verbosity=2, random_state=42)
tpot.fit(X_train, y_train)
print(tpot.score(X_test, y_test))
tpot.export('tpot_boston_pipeline.py')
```
Running this code should discover a pipeline (exported as `tpot_boston_pipeline.py`) that achieves at least 10 mean squared error (MSE) on the test set:
```Python
import numpy as np
import pandas as pd
from sklearn.ensemble import ExtraTreesRegressor
from sklearn.model_selection import train_test_split
from sklearn.pipeline import make_pipeline
from sklearn.preprocessing import PolynomialFeatures
from tpot.export_utils import set_param_recursive
# NOTE: Make sure that the outcome column is labeled 'target' in the data file
tpot_data = pd.read_csv('PATH/TO/DATA/FILE', sep='COLUMN_SEPARATOR', dtype=np.float64)
features = tpot_data.drop('target', axis=1)
training_features, testing_features, training_target, testing_target = \
train_test_split(features, tpot_data['target'], random_state=42)
# Average CV score on the training set was: -10.812040755234403
exported_pipeline = make_pipeline(
PolynomialFeatures(degree=2, include_bias=False, interaction_only=False),
ExtraTreesRegressor(bootstrap=False, max_features=0.5, min_samples_leaf=2, min_samples_split=3, n_estimators=100)
)
# Fix random state for all the steps in exported pipeline
set_param_recursive(exported_pipeline.steps, 'random_state', 42)
exported_pipeline.fit(training_features, training_target)
results = exported_pipeline.predict(testing_features)
```
## Titanic survival analysis
To see the TPOT applied the Titanic Kaggle dataset, see the Jupyter notebook [here](https://github.com/EpistasisLab/tpot/blob/master/tutorials/Titanic_Kaggle.ipynb). This example shows how to take a messy dataset and preprocess it such that it can be used in scikit-learn and TPOT.
## Portuguese Bank Marketing
The corresponding Jupyter notebook, containing the associated data preprocessing and analysis, can be found [here](https://github.com/EpistasisLab/tpot/blob/master/tutorials/Portuguese%20Bank%20Marketing/Portuguese%20Bank%20Marketing%20Stratergy.ipynb).
## MAGIC Gamma Telescope
The corresponding Jupyter notebook, containing the associated data preprocessing and analysis, can be found [here](https://github.com/EpistasisLab/tpot/blob/master/tutorials/MAGIC%20Gamma%20Telescope/MAGIC%20Gamma%20Telescope.ipynb).
## Neural network classifier using TPOT-NN
By loading the TPOT-NN configuration dictionary , PyTorch estimators will be included for classification. Users can also create their own NN configuration dictionary that includes `tpot.builtins.PytorchLRClassifier` and/or `tpot.builtins.PytorchMLPClassifier`, or they can specify them using a template string, as shown in the following example:
```Python
from tpot import TPOTClassifier
from sklearn.datasets import make_blobs
from sklearn.model_selection import train_test_split
X, y = make_blobs(n_samples=100, centers=2, n_features=3, random_state=42)
X_train, X_test, y_train, y_test = train_test_split(X, y, train_size=0.75, test_size=0.25)
clf = TPOTClassifier(config_dict='TPOT NN', template='Selector-Transformer-PytorchLRClassifier',
verbosity=2, population_size=10, generations=10)
clf.fit(X_train, y_train)
print(clf.score(X_test, y_test))
clf.export('tpot_nn_demo_pipeline.py')
```
This example is somewhat trivial, but it should result in nearly 100% classification accuracy.
================================================
FILE: docs/archived/index.md
================================================
⚠️ Warning
This documentation is for the archived version of TPOT, which is no longer maintained. For the latest version, click here .
Consider TPOT your **Data Science Assistant**. TPOT is a Python Automated Machine Learning tool that optimizes machine learning pipelines using genetic programming.
An example machine learning pipeline
An example TPOT pipeline
⚠️ Warning
This documentation is for the archived version of TPOT, which is no longer maintained. For the latest version, click here .
# Installation
TPOT is built on top of several existing Python libraries, including:
* [NumPy](http://www.numpy.org/)
* [SciPy](https://www.scipy.org/)
* [scikit-learn](http://www.scikit-learn.org/)
* [DEAP](https://github.com/DEAP/deap)
* [update_checker](https://github.com/bboe/update_checker)
* [tqdm](https://github.com/tqdm/tqdm)
* [stopit](https://github.com/glenfant/stopit)
* [pandas](http://pandas.pydata.org)
* [joblib](https://joblib.readthedocs.io/en/latest/)
* [xgboost](https://xgboost.readthedocs.io/en/latest/)
Most of the necessary Python packages can be installed via the [Anaconda Python distribution](https://www.anaconda.com/products/individual), which we strongly recommend that you use. **Support for Python 3.4 and below has been officially dropped since version 0.11.0.**
You can install TPOT using `pip` or `conda-forge`.
## pip
NumPy, SciPy, scikit-learn, pandas, joblib, and PyTorch can be installed in Anaconda via the command:
```Shell
conda install numpy scipy scikit-learn pandas joblib pytorch
```
DEAP, update_checker, tqdm, stopit and xgboost can be installed with `pip` via the command:
```Shell
pip install deap update_checker tqdm stopit xgboost
```
**Windows users: pip installation may not work on some Windows environments, and it may cause unexpected errors.** If you have issues installing XGBoost, check the [XGBoost installation documentation](http://xgboost.readthedocs.io/en/latest/build.html).
If you plan to use [Dask](http://dask.pydata.org/en/latest/) for parallel training, make sure to install [dask[delay] and dask[dataframe]](https://docs.dask.org/en/latest/install.html) and [dask_ml](https://dask-ml.readthedocs.io/en/latest/install.html). **It is noted that dask-ml>=1.7 requires distributed>=2.4.0 and scikit-learn>=0.23.0.**
```Shell
pip install dask[delayed] dask[dataframe] dask-ml fsspec>=0.3.3 distributed>=2.10.0
```
If you plan to use the [TPOT-MDR configuration](https://arxiv.org/abs/1702.01780), make sure to install [scikit-mdr](https://github.com/EpistasisLab/scikit-mdr) and [scikit-rebate](https://github.com/EpistasisLab/scikit-rebate):
```Shell
pip install scikit-mdr skrebate
```
To enable support for [PyTorch](https://pytorch.org/)-based neural networks (TPOT-NN), you will need to install PyTorch. TPOT-NN will work with either CPU or GPU PyTorch, but we strongly recommend using a GPU version, if possible, as CPU PyTorch models tend to train very slowly.
We recommend following [PyTorch's installation instructions](https://pytorch.org/get-started/locally/) customized for your operating system and Python distribution.
Finally to install TPOT itself, run the following command:
```Shell
pip install tpot
```
## conda-forge
To install tpot and its core dependencies you can use:
```Shell
conda install -c conda-forge tpot
```
To install additional dependencies you can use:
```Shell
conda install -c conda-forge tpot xgboost dask dask-ml scikit-mdr skrebate
```
As mentioned above, we recommend following [PyTorch's installation instructions](https://pytorch.org/get-started/locally/) for installing it to enable support for [PyTorch](https://pytorch.org/)-based neural networks (TPOT-NN).
## Installation for using TPOT-cuML configuration
With "TPOT cuML" configuration (see built-in configurations ), TPOT will search over a restricted configuration using the GPU-accelerated estimators in [RAPIDS cuML](https://github.com/rapidsai/cuml) and [DMLC XGBoost](https://github.com/dmlc/xgboost). **This configuration requires an NVIDIA Pascal architecture or better GPU with [compute capability 6.0+](https://developer.nvidia.com/cuda-gpus), and that the library cuML is installed.** With this configuration, all model training and predicting will be GPU-accelerated. This configuration is particularly useful for medium-sized and larger datasets on which CPU-based estimators are a common bottleneck, and works for both the `TPOTClassifier` and `TPOTRegressor`.
Please download this conda environment yml file to install TPOT for using TPOT-cuML configuration.
```
conda env create -f tpot-cuml.yml -n tpot-cuml
conda activate tpot-cuml
```
## Installation problems
Please [file a new issue](https://github.com/EpistasisLab/tpot/issues/new) if you run into installation problems.
================================================
FILE: docs/archived/related.md
================================================
⚠️ Warning
This documentation is for the archived version of TPOT, which is no longer maintained. For the latest version, click here .
Other Automated Machine Learning (AutoML) tools and related projects:
Name
Language
License
Description
Auto-WEKA Java
GPL-v3
Automated model selection and hyper-parameter tuning for Weka models.
auto-sklearn Python
BSD-3-Clause
An automated machine learning toolkit and a drop-in replacement for a scikit-learn estimator.
auto_ml Python
MIT
Automated machine learning for analytics & production. Supports manual feature type declarations.
H2O AutoML Java with Python, Scala & R APIs and web GUI
Apache 2.0
Automated: data prep, hyperparameter tuning, random grid search and stacked ensembles in a distributed ML platform.
devol Python
MIT
Automated deep neural network design via genetic programming.
MLBox Python
BSD-3-Clause
Accurate hyper-parameter optimization in high-dimensional space with support for distributed computing.
Recipe C
GPL-v3
Machine-learning pipeline optimization through genetic programming. Uses grammars to define pipeline structure.
Xcessiv Python
Apache 2.0
A web-based application for quick, scalable, and automated hyper-parameter tuning and stacked ensembling in Python.
GAMA Python
Apache 2.0
Machine-learning pipeline optimization through asynchronous evaluation based genetic programming.
================================================
FILE: docs/archived/releases.md
================================================
⚠️ Warning
This documentation is for the archived version of TPOT, which is no longer maintained. For the latest version, click here .
# Release Notes
## Version 0.12.0
- Fix numpy compatibility
- Dask optimizations
- Minor bug fixes
## Version 0.11.7
- Fix compatibility issue with scikit-learn 0.24 and xgboost 1.3.0
- Fix a bug causing that TPOT does not work when classifying more than 50 classes
- Add initial support `Resampler` from `imblearn`
- Fix minor bugs
## Version 0.11.6
- Fix a bug causing point mutation function does not work properly with using `template` option
- Add a new built configuration called "TPOT cuML" which TPOT will search over a restricted configuration using the GPU-accelerated estimators in [RAPIDS cuML](https://github.com/rapidsai/cuml) and [DMLC XGBoost](https://github.com/dmlc/xgboost). **This configuration requires an NVIDIA Pascal architecture or better GPU with [compute capability 6.0+](https://developer.nvidia.com/cuda-gpus), and that the library cuML is installed.**
- Add string path support for log/log_file parameter
- Fix a bug in version 0.11.5 causing no update in stdout after each generation
- Fix minor bugs
## Version 0.11.5
- Make `Pytorch` as an optional dependency
- Refine installation documentation
## Version 0.11.4
- Add a new built configuration "TPOT NN" which includes all operators in "Default TPOT" plus additional neural network estimators written in PyTorch (currently `tpot.builtins.PytorchLRClassifier` and `tpot.builtins.PytorchMLPClassifier` for classification tasks only)
- Refine `log_file` parameter's behavior
## Version 0.11.3
- Fix a bug in TPOTRegressor in v0.11.2
- Add `-log` option in command line interface to save process log to a file.
## Version 0.11.2
- Fix `early_stop` parameter does not work properly
- TPOT built-in `OneHotEncoder` can refit to different datasets
- Fix the issue that the attribute `evaluated_individuals_` cannot record correct generation info.
- Add a new parameter `log_file` to output logs to a file instead of `sys.stdout`
- Fix some code quality issues and mistakes in documentations
- Fix minor bugs
## Version 0.11.1
- Fix compatibility issue with scikit-learn v0.22
- `warm_start` now saves both Primitive Sets and evaluated_pipelines_ from previous runs;
- Fix the error that TPOT assign wrong fitness scores to non-evaluated pipelines (interrupted by `max_min_mins` or `KeyboardInterrupt`) ;
- Fix the bug that mutation operator cannot generate new pipeline when template is not default value and `warm_start` is True;
- Fix the bug that `max_time_mins` cannot stop optimization process when search space is limited.
- Fix a bug in exported codes when the exported pipeline is only 1 estimator
- Fix spelling mistakes in documentations
- Fix some code quality issues
## Version 0.11.0
- **Support for Python 3.4 and below has been officially dropped.** Also support for scikit-learn 0.20 or below has been dropped.
- The support of a metric function with the signature `score_func(y_true, y_pred)` for `scoring parameter` has been dropped.
- Refine `StackingEstimator` for not stacking NaN/Infinity predication probabilities.
- Fix a bug that population doesn't persist by `warm_start=True` when `max_time_mins` is not default value.
- Now the `random_state` parameter in TPOT is used for pipeline evaluation instead of using a fixed random seed of 42 before. The `set_param_recursive` function has been moved to `export_utils.py` and it can be used in exported codes for setting `random_state` recursively in scikit-learn Pipeline. It is used to set `random_state` in `fitted_pipeline_` attribute and exported pipelines.
- TPOT can independently use `generations` and `max_time_mins` to limit the optimization process through using one of the parameters or both.
- `.export()` function will return string of exported pipeline if output filename is not specified.
- Add [`SGDClassifier`](https://scikit-learn.org/stable/modules/generated/sklearn.linear_model.SGDClassifier.html) and [`SGDRegressor`](https://scikit-learn.org/stable/modules/generated/sklearn.linear_model.SGDRegressor.html) into TPOT default configs.
- Documentation has been updated
- Fix minor bugs.
## Version 0.10.2
- **TPOT v0.10.2 is the last version to support Python 2.7 and Python 3.4.**
- Minor updates for fixing compatibility issues with the latest version of scikit-learn (version > 0.21) and xgboost (v0.90)
- Default value of `template` parameter is changed to `None` instead.
- Fix errors in documentation
## Version 0.10.1
- Add `data_file_path` option into `expert` function for replacing `'PATH/TO/DATA/FILE'` to customized dataset path in exported scripts. (Related issue #838)
- Change python version in CI tests to 3.7
- Add CI tests for macOS.
## Version 0.10.0
- Add a new `template` option to specify a desired structure for machine learning pipeline in TPOT. Check [TPOT API](https://epistasislab.github.io/tpot/api/) (it will be updated once it is merge to master branch).
- Add `FeatureSetSelector` operator into TPOT for feature selection based on *priori* export knowledge. Please check our [preprint paper](https://www.biorxiv.org/content/10.1101/502484v1.article-info) for more details (*Note: it was named `DatasetSelector` in 1st version paper but we will rename to FeatureSetSelector in next version of the paper*)
- Refine `n_jobs` parameter to accept value below -1. For n_jobs below -1, (n_cpus + 1 + n_jobs) are used. Thus for n_jobs = -2, all CPUs but one are used.
- Now `memory` parameter can create memory cache directory if it does not exist.
- Fix minor bugs.
## Version 0.9.6
- Fix a bug causing that `max_time_mins` parameter doesn't work when `use_dask=True` in TPOT 0.9.5
- Now TPOT saves best pareto values best pareto pipeline s in checkpoint folder
- TPOT raises `ImportError` if operators in the TPOT configuration are not available when `verbosity>2`
- Thank @PGijsbers for the suggestions. Now TPOT can save scores of individuals already evaluated in any generation even the evaluation process of that generation is interrupted/stopped. But it is noted that, in this case, TPOT will raise this **warning message**: `WARNING: TPOT may not provide a good pipeline if TPOT is stopped/interrupted in a early generation.`, because the pipelines in early generation, e.g. 1st generation, are evolved/modified very limited times via evolutionary algorithm.
- Fix bugs in configuration of `TPOTRegressor`
- Error fixes in documentation
## Version 0.9.5
- **TPOT now supports integration with Dask for parallelization + smart caching**. Big thanks to the Dask dev team for making this happen!
- TPOT now supports for imputation/sparse matrices into `predict` and `predict_proba` functions.
- `TPOTClassifier` and `TPOTRegressor` now follows scikit-learn estimator API.
- We refined scoring parameter in TPOT API for accepting [`Scorer` object](http://jaquesgrobler.github.io/online-sklearn-build/modules/generated/sklearn.metrics.Scorer.html).
- We refined parameters in VarianceThreshold and FeatureAgglomeration.
- TPOT now supports using memory caching within a Pipeline via an optional `memory` parameter.
- We improved documentation of TPOT.
## Version 0.9
* **TPOT now supports sparse matrices** with a new built-in TPOT configuration, "TPOT sparse". We are using a custom OneHotEncoder implementation that supports missing values and continuous features.
* We have added an "early stopping" option for stopping the optimization process if no improvement is made within a set number of generations. Look up the `early_stop` parameter to access this functionality.
* TPOT now reduces the number of duplicated pipelines between generations, which saves you time during the optimization process.
* TPOT now supports custom scoring functions via the command-line mode.
* We have added a new optional argument, `periodic_checkpoint_folder`, that allows TPOT to periodically save the best pipeline so far to a local folder during optimization process.
* TPOT no longer uses `sklearn.externals.joblib` when `n_jobs=1` to avoid the potential freezing issue [that scikit-learn suffers from](http://scikit-learn.org/stable/faq.html#why-do-i-sometime-get-a-crash-freeze-with-n-jobs-1-under-osx-or-linux).
* We have added `pandas` as a dependency to read input datasets instead of `numpy.recfromcsv`. NumPy's `recfromcsv` function is unable to parse datasets with complex data types.
* Fixed a bug that `DEFAULT` in the parameter(s) of nested estimator raises `KeyError` when exporting pipelines.
* Fixed a bug related to setting `random_state` in nested estimators. The issue would happen with pipeline with `SelectFromModel` (`ExtraTreesClassifier` as nested estimator) or `StackingEstimator` if nested estimator has `random_state` parameter.
* Fixed a bug in the missing value imputation function in TPOT to impute along columns instead rows.
* Refined input checking for sparse matrices in TPOT.
* Refined the TPOT pipeline mutation operator.
## Version 0.8
* **TPOT now detects whether there are missing values in your dataset** and replaces them with the median value of the column.
* TPOT now allows you to set a `group` parameter in the `fit` function so you can use the [GroupKFold](http://scikit-learn.org/stable/modules/generated/sklearn.model_selection.GroupKFold.html) cross-validation strategy.
* TPOT now allows you to set a subsample ratio of the training instance with the `subsample` parameter. For example, setting `subsample`=0.5 tells TPOT to create a fixed subsample of half of the training data for the pipeline optimization process. This parameter can be useful for speeding up the pipeline optimization process, but may give less accurate performance estimates from cross-validation.
* **TPOT now has more [built-in configurations](/using/#built-in-tpot-configurations)**, including TPOT MDR and TPOT light, for both classification and regression problems.
* `TPOTClassifier` and `TPOTRegressor` now expose three useful internal attributes, `fitted_pipeline_`, `pareto_front_fitted_pipelines_`, and `evaluated_individuals_`. These attributes are described in the [API documentation](/api/).
* Oh, **TPOT now has [thorough API documentation](/api/)**. Check it out!
* Fixed a reproducibility issue where setting `random_seed` didn't necessarily result in the same results every time. This bug was present since TPOT v0.7.
* Refined input checking in TPOT.
* Removed Python 2 uncompliant code.
## Version 0.7
* **TPOT now has multiprocessing support.** TPOT allows you to use multiple processes in parallel to accelerate the pipeline optimization process in TPOT with the `n_jobs` parameter.
* TPOT now allows you to **customize the operators and parameters considered during the optimization process**, which can be accomplished with the new `config_dict` parameter. The format of this customized dictionary can be found in the [online documentation](/using/#customizing-tpots-operators-and-parameters), along with a list of [built-in configurations](/using/#built-in-tpot-configurations).
* TPOT now allows you to **specify a time limit for evaluating a single pipeline** (default limit is 5 minutes) in optimization process with the `max_eval_time_mins` parameter, so TPOT won't spend hours evaluating overly-complex pipelines.
* We tweaked TPOT's underlying evolutionary optimization algorithm to work even better, including using the [mu+lambda algorithm](http://deap.readthedocs.io/en/master/api/algo.html#deap.algorithms.eaMuPlusLambda). This algorithm gives you more control of how many pipelines are generated every iteration with the `offspring_size` parameter.
* Refined the default operators and parameters in TPOT, so TPOT 0.7 should work even better than 0.6.
* TPOT now supports sample weights in the fitness function if some if your samples are more important to classify correctly than others. The sample weights option works the same as in scikit-learn, e.g., `tpot.fit(x_train, y_train, sample_weights=sample_weights)`.
* The default scoring metric in TPOT has been changed from balanced accuracy to accuracy, the same default metric for classification algorithms in scikit-learn. Balanced accuracy can still be used by setting `scoring='balanced_accuracy'` when creating a TPOT instance.
## Version 0.6
* **TPOT now supports regression problems!** We have created two separate `TPOTClassifier` and `TPOTRegressor` classes to support classification and regression problems, respectively. The [command-line interface](/using/#tpot-on-the-command-line) also supports this feature through the `-mode` parameter.
* TPOT now allows you to **specify a time limit** for the optimization process with the `max_time_mins` parameter, so you don't need to guess how long TPOT will take any more to recommend a pipeline to you.
* Added a new operator that performs feature selection using [ExtraTrees](http://scikit-learn.org/stable/modules/ensemble.html#extremely-randomized-trees) feature importance scores.
* **[XGBoost](https://github.com/dmlc/xgboost) has been added as an optional dependency to TPOT.** If you have XGBoost installed, TPOT will automatically detect your installation and use the `XGBoostClassifier` and `XGBoostRegressor` in its pipelines.
* TPOT now offers a verbosity level of 3 ("science mode"), which outputs the entire Pareto front instead of only the current best score. This feature may be useful for users looking to make a trade-off between pipeline complexity and score.
## Version 0.5
* Major refactor: Each operator is defined in a separate class file. Hooray for easier-to-maintain code!
* TPOT now **exports directly to scikit-learn Pipelines** instead of hacky code.
* Internal representation of individuals now uses scikit-learn pipelines.
* Parameters for each operator have been optimized so TPOT spends less time exploring useless parameters.
* We have removed pandas as a dependency and instead use numpy matrices to store the data.
* TPOT now uses **k-fold cross-validation** when evaluating pipelines, with a default k = 3. This k parameter can be tuned when creating a new TPOT instance.
* Improved **scoring function support**: Even though TPOT uses balanced accuracy by default, you can now have TPOT use [any of the scoring functions](http://scikit-learn.org/stable/modules/model_evaluation.html#common-cases-predefined-values) that `cross_val_score` supports.
* Added the scikit-learn [Normalizer](http://scikit-learn.org/stable/modules/generated/sklearn.preprocessing.Normalizer.html) preprocessor.
* [Minor text fixes.](http://knowyourmeme.com/memes/pokemon-go-updates-controversy)
## Version 0.4
In TPOT 0.4, we've made some major changes to the internals of TPOT and added some convenience functions. We've summarized the changes below.
Added new sklearn models and preprocessors
AdaBoostClassifier
BernoulliNB
ExtraTreesClassifier
GaussianNB
MultinomialNB
LinearSVC
PassiveAggressiveClassifier
GradientBoostingClassifier
RBFSampler
FastICA
FeatureAgglomeration
Nystroem
Added operator that inserts virtual features for the count of features with values of zero
Reworked parameterization of TPOT operators
Reduced parameter search space with information from a scikit-learn benchmark
TPOT no longer generates arbitrary parameter values, but uses a fixed parameter set instead
Removed XGBoost as a dependency
Too many users were having install issues with XGBoost
Replaced with scikit-learn's GradientBoostingClassifier
Improved descriptiveness of TPOT command line parameter documentation
Removed min/max/avg details during fit() when verbosity > 1
Replaced with tqdm progress bar
Added tqdm as a dependency
Added fit_predict() convenience function
Added get_params() function so TPOT can operate in scikit-learn's cross_val_score & related functions
## Version 0.3
* We revised the internal optimization process of TPOT to make it more efficient, in particular in regards to the model parameters that TPOT optimizes over.
## Version 0.2
* TPOT now has the ability to export the optimized pipelines to sklearn code.
* Logistic regression, SVM, and k-nearest neighbors classifiers were added as pipeline operators. Previously, TPOT only included decision tree and random forest classifiers.
* TPOT can now use arbitrary scoring functions for the optimization process.
* TPOT now performs multi-objective Pareto optimization to balance model complexity (i.e., # of pipeline operators) and the score of the pipeline.
## Version 0.1
* First public release of TPOT.
* Optimizes pipelines with decision trees and random forest classifiers as the model, and uses a handful of feature preprocessors.
================================================
FILE: docs/archived/support.md
================================================
⚠️ Warning
This documentation is for the archived version of TPOT, which is no longer maintained. For the latest version, click here .
TPOT was developed in the [Computational Genetics Lab](http://epistasis.org/) at the [University of Pennsylvania](https://www.upenn.edu/) with funding from the [NIH](http://www.nih.gov/) under grant R01 AI117694. We are incredibly grateful for the support of the NIH and the University of Pennsylvania during the development of this project.
The TPOT logo was designed by Todd Newmuis, who generously donated his time to the project.
================================================
FILE: docs/archived/using.md
================================================
⚠️ Warning
This documentation is for the archived version of TPOT, which is no longer maintained. For the latest version, click here .
# Using TPOT
## What to expect from AutoML software
Automated machine learning (AutoML) takes a higher-level approach to machine learning than most practitioners are used to,
so we've gathered a handful of guidelines on what to expect when running AutoML software such as TPOT.
AutoML algorithms aren't intended to run for only a few minutes
Of course, you *can* run TPOT for only a few minutes and it will find a reasonably good pipeline for your dataset.
However, if you don't run TPOT for long enough, it may not find the best possible pipeline for your dataset. It may even not
find any suitable pipeline at all, in which case a `RuntimeError('A pipeline has not yet been optimized. Please call fit() first.')`
will be raised.
Often it is worthwhile to run multiple instances of TPOT in parallel for a long time (hours to days) to allow TPOT to thoroughly search
the pipeline space for your dataset.
AutoML algorithms can take a long time to finish their search
AutoML algorithms aren't as simple as fitting one model on the dataset; they are considering multiple machine learning algorithms
(random forests, linear models, SVMs, etc.) in a pipeline with multiple preprocessing steps (missing value imputation, scaling,
PCA, feature selection, etc.), the hyperparameters for all of the models and preprocessing steps, as well as multiple ways
to ensemble or stack the algorithms within the pipeline.
As such, TPOT will take a while to run on larger datasets, but it's important to realize why. With the default TPOT settings
(100 generations with 100 population size), TPOT will evaluate 10,000 pipeline configurations before finishing.
To put this number into context, think about a grid search of 10,000 hyperparameter combinations for a machine learning algorithm
and how long that grid search will take. That is 10,000 model configurations to evaluate with 10-fold cross-validation,
which means that roughly 100,000 models are fit and evaluated on the training data in one grid search.
That's a time-consuming procedure, even for simpler models like decision trees.
Typical TPOT runs will take hours to days to finish (unless it's a small dataset), but you can always interrupt
the run partway through and see the best results so far. TPOT also [provides](/tpot/api/) a `warm_start` parameter that
lets you restart a TPOT run from where it left off.
AutoML algorithms can recommend different solutions for the same dataset
If you're working with a reasonably complex dataset or run TPOT for a short amount of time, different TPOT runs
may result in different pipeline recommendations. TPOT's optimization algorithm is stochastic in nature, which means
that it uses randomness (in part) to search the possible pipeline space. When two TPOT runs recommend different
pipelines, this means that the TPOT runs didn't converge due to lack of time *or* that multiple pipelines
perform more-or-less the same on your dataset.
This is actually an advantage over fixed grid search techniques: TPOT is meant to be an assistant that gives
you ideas on how to solve a particular machine learning problem by exploring pipeline configurations that you
might have never considered, then leaves the fine-tuning to more constrained parameter tuning techniques such
as grid search.
## TPOT with code
We've taken care to design the TPOT interface to be as similar as possible to scikit-learn.
TPOT can be imported just like any regular Python module. To import TPOT, type:
```Python
from tpot import TPOTClassifier
```
then create an instance of TPOT as follows:
```Python
pipeline_optimizer = TPOTClassifier()
```
It's also possible to use TPOT for regression problems with the `TPOTRegressor` class. Other than the class name,
a `TPOTRegressor` is used the same way as a `TPOTClassifier`. You can read more about the `TPOTClassifier` and `TPOTRegressor` classes in the [API documentation](/tpot/api/).
Some example code with custom TPOT parameters might look like:
```Python
pipeline_optimizer = TPOTClassifier(generations=5, population_size=20, cv=5,
random_state=42, verbosity=2)
```
Now TPOT is ready to optimize a pipeline for you. You can tell TPOT to optimize a pipeline based on a data set with the `fit` function:
```Python
pipeline_optimizer.fit(X_train, y_train)
```
The `fit` function initializes the genetic programming algorithm to find the highest-scoring pipeline based on average k-fold cross-validation
Then, the pipeline is trained on the entire set of provided samples, and the TPOT instance can be used as a fitted model.
You can then proceed to evaluate the final pipeline on the testing set with the `score` function:
```Python
print(pipeline_optimizer.score(X_test, y_test))
```
Finally, you can tell TPOT to export the corresponding Python code for the optimized pipeline to a text file with the `export` function:
```Python
pipeline_optimizer.export('tpot_exported_pipeline.py')
```
Once this code finishes running, `tpot_exported_pipeline.py` will contain the Python code for the optimized pipeline.
Below is a full example script using TPOT to optimize a pipeline, score it, and export the best pipeline to a file.
```Python
from tpot import TPOTClassifier
from sklearn.datasets import load_digits
from sklearn.model_selection import train_test_split
digits = load_digits()
X_train, X_test, y_train, y_test = train_test_split(digits.data, digits.target,
train_size=0.75, test_size=0.25)
pipeline_optimizer = TPOTClassifier(generations=5, population_size=20, cv=5,
random_state=42, verbosity=2)
pipeline_optimizer.fit(X_train, y_train)
print(pipeline_optimizer.score(X_test, y_test))
pipeline_optimizer.export('tpot_exported_pipeline.py')
```
Check our [examples](/tpot/examples/) to see TPOT applied to some specific data sets.
## TPOT on the command line
To use TPOT via the command line, enter the following command with a path to the data file:
```Shell
tpot /path_to/data_file.csv
```
An example command-line call to TPOT may look like:
```Shell
tpot data/mnist.csv -is , -target class -o tpot_exported_pipeline.py -g 5 -p 20 -cv 5 -s 42 -v 2
```
TPOT offers several arguments that can be provided at the command line. To see brief descriptions of these arguments,
enter the following command:
```Shell
tpot --help
```
Detailed descriptions of the command-line arguments are below.
Argument
Parameter
Valid values
Effect
-is
INPUT_SEPARATOR
Any string
Character used to separate columns in the input file.
-target
TARGET_NAME
Any string
Name of the target column in the input file.
-mode
TPOT_MODE
['classification', 'regression']
Whether TPOT is being used for a supervised classification or regression problem.
-o
OUTPUT_FILE
String path to a file
File to export the code for the final optimized pipeline.
-g
GENERATIONS
Any positive integer or None
Number of iterations to run the pipeline optimization process. It must be a positive number or None. If None, the parameter max_time_mins must be defined as the runtime limit. Generally, TPOT will work better when you give it more generations (and therefore time) to optimize the pipeline.
-p
POPULATION_SIZE
Any positive integer
Number of individuals to retain in the GP population every generation. Generally, TPOT will work better when you give it more individuals (and therefore time) to optimize the pipeline.
-os
OFFSPRING_SIZE
Any positive integer
Number of offspring to produce in each GP generation.
-mr
MUTATION_RATE
[0.0, 1.0]
GP mutation rate in the range [0.0, 1.0]. This tells the GP algorithm how many pipelines to apply random changes to every generation.
-xr
CROSSOVER_RATE
[0.0, 1.0]
GP crossover rate in the range [0.0, 1.0]. This tells the GP algorithm how many pipelines to "breed" every generation.
-scoring
SCORING_FN
'accuracy', 'adjusted_rand_score', 'average_precision', 'balanced_accuracy',
Function used to evaluate the quality of a given pipeline for the problem. By default, accuracy is used for classification and mean squared error (MSE) is used for regression.
scoring functions for more details.
-cv
CV
Any integer > 1
Number of folds to evaluate each pipeline over in k-fold cross-validation during the TPOT optimization process.
-sub
SUBSAMPLE
(0.0, 1.0]
Subsample ratio of the training instance. Setting it to 0.5 means that TPOT randomly collects half of training samples for pipeline optimization process.
-njobs
NUM_JOBS
Any positive integer or -1
Number of CPUs for evaluating pipelines in parallel during the TPOT optimization process.
-maxtime
MAX_TIME_MINS
Any positive integer
How many minutes TPOT has to optimize the pipeline.
-maxeval
MAX_EVAL_MINS
Any positive float
How many minutes TPOT has to evaluate a single pipeline.
-s
RANDOM_STATE
Any positive integer
Random number generator seed for reproducibility.
-config
CONFIG_FILE
String or file path
Operators and parameter configurations in TPOT:
Path for configuration file: TPOT will use the path to a configuration file for customizing the operators and parameters that TPOT uses in the optimization process
string 'TPOT light', TPOT will use a built-in configuration with only fast models and preprocessors
string 'TPOT MDR', TPOT will use a built-in configuration specialized for genomic studies
string 'TPOT sparse': TPOT will use a configuration dictionary with a one-hot encoder and the operators normally included in TPOT that also support sparse matrices.
See the built-in configurations section for the list of configurations included with TPOT, and the custom configuration section for more information and examples of how to create your own TPOT configurations.
-template
TEMPLATE
String
Template of predefined pipeline structure. The option is for specifying a desired structure for the machine learning pipeline evaluated in TPOT. So far this option only supports linear pipeline structure. Each step in the pipeline should be a main class of operators (Selector, Transformer, Classifier or Regressor) or a specific operator (e.g. `SelectPercentile`) defined in TPOT operator configuration. If one step is a main class, TPOT will randomly assign all subclass operators (subclasses of [`SelectorMixin`](https://github.com/scikit-learn/scikit-learn/blob/master/sklearn/feature_selection/base.py#L17), [`TransformerMixin`](https://scikit-learn.org/stable/modules/generated/sklearn.base.TransformerMixin.html), [`ClassifierMixin`](https://scikit-learn.org/stable/modules/generated/sklearn.base.ClassifierMixin.html) or [`RegressorMixin`](https://scikit-learn.org/stable/modules/generated/sklearn.base.RegressorMixin.html) in scikit-learn) to that step. Steps in the template are delimited by "-", e.g. "SelectPercentile-Transformer-Classifier". By default value of template is None, TPOT generates tree-based pipeline randomly.
See the template option in tpot section for more details.
-memory
MEMORY
String or file path
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. Memory caching mode in TPOT:
Path for a caching directory: TPOT uses memory caching with the provided directory and TPOT does NOT clean the caching directory up upon shutdown.
string 'auto': TPOT uses memory caching with a temporary directory and cleans it up upon shutdown.
-cf
CHECKPOINT_FOLDER
Folder path
If supplied, a folder you created, in which tpot will periodically save pipelines in pareto front so far while optimizing.
sudden death before tpot could save an optimized pipeline
progress tracking
grabbing a pipeline while tpot is working
-es
EARLY_STOP
Any positive integer
How many generations TPOT checks whether there is no improvement in optimization process.
-v
VERBOSITY
{0, 1, 2, 3}
How much information TPOT communicates while it is running.
-log
LOG
Folder path
Save progress content to a file.
--no-update-check
Flag indicating whether the TPOT version checker should be disabled.
--version
Show TPOT's version number and exit.
--help
Show TPOT's help documentation and exit.
## Scoring functions
TPOT makes use of `sklearn.model_selection.cross_val_score` for evaluating pipelines, and as such offers the same support for scoring functions. There are two ways to make use of scoring functions with TPOT:
- You can pass in a string to the `scoring` parameter from the list above. Any other strings will cause TPOT to throw an exception.
- You can pass the callable object/function with signature `scorer(estimator, X, y)`, where `estimator` is trained estimator to use for scoring, `X` are features that will be passed to `estimator.predict` and `y` are target values for `X`. To do this, you should implement your own function. See the example below for further explanation.
```Python
from tpot import TPOTClassifier
from sklearn.datasets import load_digits
from sklearn.model_selection import train_test_split
from sklearn.metrics import make_scorer
digits = load_digits()
X_train, X_test, y_train, y_test = train_test_split(digits.data, digits.target,
train_size=0.75, test_size=0.25)
# Make a custom metric function
def my_custom_accuracy(y_true, y_pred):
return float(sum(y_pred == y_true)) / len(y_true)
# Make a custom a scorer from the custom metric function
# Note: greater_is_better=False in make_scorer below would mean that the scoring function should be minimized.
my_custom_scorer = make_scorer(my_custom_accuracy, greater_is_better=True)
tpot = TPOTClassifier(generations=5, population_size=20, verbosity=2,
scoring=my_custom_scorer)
tpot.fit(X_train, y_train)
print(tpot.score(X_test, y_test))
tpot.export('tpot_digits_pipeline.py')
```
* **my_module.scorer_name**: You can also use a custom `score_func(y_true, y_pred)` or `scorer(estimator, X, y)` function through the command line by adding the argument `-scoring my_module.scorer` to your command-line call. TPOT will import your module and use the custom scoring function from there. TPOT will include your current working directory when importing the module, so you can place it in the same directory where you are going to run TPOT.
Example: `-scoring sklearn.metrics.auc` will use the function auc from sklearn.metrics module.
## Built-in TPOT configurations
TPOT comes with a handful of default operators and parameter configurations that we believe work well for optimizing machine learning pipelines. Below is a list of the current built-in configurations that come with TPOT.
Configuration Name
Description
Operators
Default TPOT
TPOT will search over a broad range of preprocessors, feature constructors, feature selectors, models, and parameters to find a series of operators that minimize the error of the model predictions. Some of these operators are complex and may take a long time to run, especially on larger datasets.
Note: This is the default configuration for TPOT. To use this configuration, use the default value (None) for the config_dict parameter.
Classification
Regression
TPOT light
TPOT will search over a restricted range of preprocessors, feature constructors, feature selectors, models, and parameters to find a series of operators that minimize the error of the model predictions. Only simpler and fast-running operators will be used in these pipelines, so TPOT light is useful for finding quick and simple pipelines for a classification or regression problem.
Classification
Regression
TPOT MDR
TPOT will search over a series of feature selectors and Multifactor Dimensionality Reduction models to find a series of operators that maximize prediction accuracy. The TPOT MDR configuration is specialized for genome-wide association studies (GWAS) , and is described in detail online here .
Classification
Regression
TPOT sparse
TPOT uses a configuration dictionary with a one-hot encoder and the operators normally included in TPOT that also support sparse matrices.
Classification
Regression
TPOT NN
TPOT uses the same configuration as "Default TPOT" plus additional neural network estimators written in PyTorch (currently only `tpot.builtins.PytorchLRClassifier` and `tpot.builtins.PytorchMLPClassifier`).
Classification
TPOT cuML
TPOT will search over a restricted configuration using the GPU-accelerated estimators in RAPIDS cuML and DMLC XGBoost . This configuration requires an NVIDIA Pascal architecture or better GPU with compute capability 6.0+, and that the library cuML is installed. With this configuration, all model training and predicting will be GPU-accelerated.
Classification
Regression
To use any of these configurations, simply pass the string name of the configuration to the `config_dict` parameter (or `-config` on the command line). For example, to use the "TPOT light" configuration:
```Python
from tpot import TPOTClassifier
from sklearn.datasets import load_digits
from sklearn.model_selection import train_test_split
digits = load_digits()
X_train, X_test, y_train, y_test = train_test_split(digits.data, digits.target,
train_size=0.75, test_size=0.25)
tpot = TPOTClassifier(generations=5, population_size=20, verbosity=2,
config_dict='TPOT light')
tpot.fit(X_train, y_train)
print(tpot.score(X_test, y_test))
tpot.export('tpot_digits_pipeline.py')
```
## Customizing TPOT's operators and parameters
Beyond the default configurations that come with TPOT, in some cases it is useful to limit the algorithms and parameters that TPOT considers. For that reason, we allow users to provide TPOT with a custom configuration for its operators and parameters.
The custom TPOT configuration must be in nested dictionary format, where the first level key is the path and name of the operator (e.g., `sklearn.naive_bayes.MultinomialNB`) and the second level key is the corresponding parameter name for that operator (e.g., `fit_prior`). The second level key should point to a list of parameter values for that parameter, e.g., `'fit_prior': [True, False]`.
For a simple example, the configuration could be:
```Python
tpot_config = {
'sklearn.naive_bayes.GaussianNB': {
},
'sklearn.naive_bayes.BernoulliNB': {
'alpha': [1e-3, 1e-2, 1e-1, 1., 10., 100.],
'fit_prior': [True, False]
},
'sklearn.naive_bayes.MultinomialNB': {
'alpha': [1e-3, 1e-2, 1e-1, 1., 10., 100.],
'fit_prior': [True, False]
}
}
```
in which case TPOT would only consider pipelines containing `GaussianNB`, `BernoulliNB`, `MultinomialNB`, and tune those algorithm's parameters in the ranges provided. This dictionary can be passed directly within the code to the `TPOTClassifier`/`TPOTRegressor` `config_dict` parameter, described above. For example:
```Python
from tpot import TPOTClassifier
from sklearn.datasets import load_digits
from sklearn.model_selection import train_test_split
digits = load_digits()
X_train, X_test, y_train, y_test = train_test_split(digits.data, digits.target,
train_size=0.75, test_size=0.25)
tpot_config = {
'sklearn.naive_bayes.GaussianNB': {
},
'sklearn.naive_bayes.BernoulliNB': {
'alpha': [1e-3, 1e-2, 1e-1, 1., 10., 100.],
'fit_prior': [True, False]
},
'sklearn.naive_bayes.MultinomialNB': {
'alpha': [1e-3, 1e-2, 1e-1, 1., 10., 100.],
'fit_prior': [True, False]
}
}
tpot = TPOTClassifier(generations=5, population_size=20, verbosity=2,
config_dict=tpot_config)
tpot.fit(X_train, y_train)
print(tpot.score(X_test, y_test))
tpot.export('tpot_digits_pipeline.py')
```
Command-line users must create a separate `.py` file with the custom configuration and provide the path to the file to the `tpot` call. For example, if the simple example configuration above is saved in `tpot_classifier_config.py`, that configuration could be used on the command line with the command:
```
tpot data/mnist.csv -is , -target class -config tpot_classifier_config.py -g 5 -p 20 -v 2 -o tpot_exported_pipeline.py
```
When using the command-line interface, the configuration file specified in the `-config` parameter *must* name its custom TPOT configuration `tpot_config`. Otherwise, TPOT will not be able to locate the configuration dictionary.
For more detailed examples of how to customize TPOT's operator configuration, see the default configurations for [classification](https://github.com/EpistasisLab/tpot/blob/master/tpot/config/classifier.py) and [regression](https://github.com/EpistasisLab/tpot/blob/master/tpot/config/regressor.py) in TPOT's source code.
Note that you must have all of the corresponding packages for the operators installed on your computer, otherwise TPOT will not be able to use them. For example, if XGBoost is not installed on your computer, then TPOT will simply not import nor use XGBoost in the pipelines it considers.
## Template option in TPOT
Template option provides a way to specify a desired structure for machine learning pipeline, which may reduce TPOT computation time and potentially provide more interpretable results. Current implementation only supports linear pipelines.
Below is a simple example to use `template` option. The pipelines generated/evaluated in TPOT will follow this structure: 1st step is a feature selector (a subclass of [`SelectorMixin`](https://github.com/scikit-learn/scikit-learn/blob/master/sklearn/feature_selection/base.py#L17)), 2nd step is a feature transformer (a subclass of [`TransformerMixin`](https://scikit-learn.org/stable/modules/generated/sklearn.base.TransformerMixin.html)) and 3rd step is a classifier for classification (a subclass of [`ClassifierMixin`](https://scikit-learn.org/stable/modules/generated/sklearn.base.ClassifierMixin.html)). The last step must be `Classifier` for `TPOTClassifier`'s template but `Regressor` for `TPOTRegressor`. **Note: although `SelectorMixin` is subclass of `TransformerMixin` in scikit-learn, but `Transformer` in this option excludes those subclasses of `SelectorMixin`.**
```Python
tpot_obj = TPOTClassifier(
template='Selector-Transformer-Classifier'
)
```
If a specific operator, e.g. `SelectPercentile`, is preferred for usage in the 1st step of the pipeline, the template can be defined like 'SelectPercentile-Transformer-Classifier'.
## FeatureSetSelector in TPOT
`FeatureSetSelector` is a special new operator in TPOT. This operator enables feature selection based on *priori* expert knowledge. For example, in RNA-seq gene expression analysis, this operator can be used to select one or more gene (feature) set(s) based on GO (Gene Ontology) terms or annotated gene sets Molecular Signatures Database ([MSigDB](http://software.broadinstitute.org/gsea/msigdb/index.jsp)) in the 1st step of pipeline via `template` option above, in order to reduce dimensions and TPOT computation time. This operator requires a dataset list in csv format. In this csv file, there are only three columns: 1st column is feature set names, 2nd column is the total number of features in one set and 3rd column is a list of feature names (if input X is pandas.DataFrame) or indexes (if input X is numpy.ndarray) delimited by ";". Below is an example how to use this operator in TPOT.
Please check our [preprint paper](https://www.biorxiv.org/content/10.1101/502484v1.article-info) for more details.
```Python
from tpot import TPOTClassifier
import numpy as np
import pandas as pd
from tpot.config import classifier_config_dict
test_data = pd.read_csv("https://raw.githubusercontent.com/EpistasisLab/tpot/master/tests/tests.csv")
test_X = test_data.drop("class", axis=1)
test_y = test_data['class']
# add FeatureSetSelector into tpot configuration
classifier_config_dict['tpot.builtins.FeatureSetSelector'] = {
'subset_list': ['https://raw.githubusercontent.com/EpistasisLab/tpot/master/tests/subset_test.csv'],
'sel_subset': [0,1] # select only one feature set, a list of index of subset in the list above
#'sel_subset': list(combinations(range(3), 2)) # select two feature sets
}
tpot = TPOTClassifier(generations=5,
population_size=50, verbosity=2,
template='FeatureSetSelector-Transformer-Classifier',
config_dict=classifier_config_dict)
tpot.fit(test_X, test_y)
```
## Pipeline caching in TPOT
With the `memory` parameter, pipelines can cache the results of each transformer after fitting them. This feature is used to avoid repeated computation by transformers within a pipeline if the parameters and input data are identical to another fitted pipeline during optimization process. TPOT allows users to specify a custom directory path or [`joblib.Memory`](https://joblib.readthedocs.io/en/latest/generated/joblib.Memory.html) in case they want to re-use the memory cache in future TPOT runs (or a `warm_start` run).
There are three methods for enabling memory caching in TPOT:
```Python
from tpot import TPOTClassifier
from tempfile import mkdtemp
from joblib import Memory
from shutil import rmtree
# Method 1, auto mode: TPOT uses memory caching with a temporary directory and cleans it up upon shutdown
tpot = TPOTClassifier(memory='auto')
# Method 2, with a custom directory for memory caching
tpot = TPOTClassifier(memory='/to/your/path')
# Method 3, with a Memory object
cachedir = mkdtemp() # Create a temporary folder
memory = Memory(cachedir=cachedir, verbose=0)
tpot = TPOTClassifier(memory=memory)
# Clear the cache directory when you don't need it anymore
rmtree(cachedir)
```
**Note: TPOT does NOT clean up memory caches if users set a custom directory path or Memory object. We recommend that you clean up the memory caches when you don't need it anymore.**
## Crash/freeze issue with n_jobs > 1 under OSX or Linux
Internally, TPOT uses [joblib](http://joblib.readthedocs.io/) to fit estimators in parallel.
This is the same parallelization framework used by scikit-learn. But it may crash/freeze with n_jobs > 1 under OSX or Linux [as scikit-learn does](http://scikit-learn.org/stable/faq.html#why-do-i-sometime-get-a-crash-freeze-with-n-jobs-1-under-osx-or-linux), especially with large datasets.
One solution is to configure Python's `multiprocessing` module to use the `forkserver` start method (instead of the default `fork`) to manage the process pools. You can enable the `forkserver` mode globally for your program by putting the following codes into your main script:
```Python
import multiprocessing
# other imports, custom code, load data, define model...
if __name__ == '__main__':
multiprocessing.set_start_method('forkserver')
# call scikit-learn utils or tpot utils with n_jobs > 1 here
```
More information about these start methods can be found in the [multiprocessing documentation](https://docs.python.org/3/library/multiprocessing.html#contexts-and-start-methods).
## Parallel Training with Dask
For large problems or working on Jupyter notebook, we highly recommend that you can distribute the work on a [Dask](http://dask.pydata.org/en/latest/) cluster.
The [dask-examples binder](https://mybinder.org/v2/gh/dask/dask-examples/master?filepath=machine-learning%2Ftpot.ipynb) has a runnable example
with a small dask cluster.
To use your Dask cluster to fit a TPOT model, specify the ``use_dask`` keyword when you create the TPOT estimator. **Note: if `use_dask=True`, TPOT will use as many cores as available on the your Dask cluster. If `n_jobs` is specified, then it will control the chunk size (10*`n_jobs` if it is less then offspring size) of parallel training.**
```python
estimator = TPOTEstimator(use_dask=True, n_jobs=-1)
```
This will use all the workers on your cluster to do the training, and use [Dask-ML's pipeline rewriting](https://dask-ml.readthedocs.io/en/latest/hyper-parameter-search.html#avoid-repeated-work) to avoid re-fitting estimators multiple times on the same set of data.
It will also provide fine-grained diagnostics in the [distributed scheduler UI](https://distributed.readthedocs.io/en/latest/web.html).
Alternatively, Dask implements a joblib backend.
You can instruct TPOT to use the distributed backend during training by specifying a `joblib.parallel_backend`:
```python
import joblib
import distributed.joblib
from dask.distributed import Client
# connect to the cluster
client = Client('schedueler-address')
# create the estimator normally
estimator = TPOTClassifier(n_jobs=-1)
# perform the fit in this context manager
with joblib.parallel_backend("dask"):
estimator.fit(X, y)
```
See [dask's distributed joblib integration](https://distributed.readthedocs.io/en/latest/joblib.html) for more.
## Neural Networks in TPOT (`tpot.nn`)
Support for neural network models and deep learning is an experimental feature newly added to TPOT. Available neural network architectures are provided by the `tpot.nn` module. Unlike regular `sklearn` estimators, these models need to be written by hand, and must also inherit the appropriate base classes provided by `sklearn` for all of their built-in modules. In other words, they need implement methods like `.fit()`, `fit_transform()`, `get_params()`, etc., as described in detail on [Developing scikit-learn estimators](https://scikit-learn.org/stable/developers/develop.html).
### Telling TPOT to use built-in PyTorch neural network models
Mainly due to the issues described below, TPOT won't use its neural network models unless you explicitly tell it to do so. This is done as follows:
- Use `import tpot.nn` before instantiating any TPOT estimators.
- Use a configuration dictionary that includes one or more `tpot.nn` estimators, either by writing one manually, including one from a file, or by importing the configuration in `tpot/config/classifier_nn.py`. A very simple example that will force TPOT to only use a PyTorch-based logistic regression classifier as its main estimator is as follows:
```python
tpot_config = {
'tpot.nn.PytorchLRClassifier': {
'learning_rate': [1e-3, 1e-2, 1e-1, 0.5, 1.]
}
}
```
- Alternatively, use a template string including `PytorchLRClassifier` or `PytorchMLPClassifier` while loading the TPOT-NN configuration dictionary.
Neural network models are notorious for being extremely sensitive to their initialization parameters, so you may need to heavily adjust `tpot.nn` configuration dictionaries in order to attain good performance on your dataset.
A simple example of using TPOT-NN is shown in [examples](/tpot/examples/).
### Important caveats
- Neural network models (especially when they reach moderately large sizes) take a notoriously large amount of time and computing power to train. You should expect `tpot.nn` neural networks to train several orders of magnitude slower than their `sklearn` alternatives. This can be alleviated somewhat by training the models on computers with CUDA-enabled GPUs.
- TPOT will occasionally learn pipelines that stack several `sklearn` estimators. Mathematically, these can be nearly identical to some deep learning models. For example, by stacking several `sklearn.linear_model.LogisticRegression`s, you end up with a very close approximation of a Multilayer Perceptron; one of the simplest and most well known deep learning architectures. TPOT's genetic programming algorithms generally optimize these 'networks' much faster than PyTorch, which typically uses a more brute-force convex optimization approach.
- The problem of 'black box' model introspection is one of the most substantial criticisms and challenges of deep learning. This problem persists in `tpot.nn`, whereas TPOT's default estimators often are far easier to introspect.
================================================
FILE: docs/cite.md
================================================
# Citing TPOT
If you use TPOT in a scientific publication, please consider citing at least one of the following papers:
Hernandez, J. G., Saini, A. K., Ghosh, A., & Moore, J. H. (2025). [The tree-based pipeline optimization tool: Tackling biomedical research problems with genetic programming and automated machine learning](https://www.cell.com/patterns/fulltext/S2666-3899(25)00162-X). Patterns, 6(7).
BibTeX entry:
```bibtext
@article{hernandez2025tree,
title={The tree-based pipeline optimization tool: Tackling biomedical research problems with genetic programming and automated machine learning},
author={Hernandez, Jose Guadalupe and Saini, Anil Kumar and Ghosh, Attri and Moore, Jason H},
journal={Patterns},
volume={6},
number={7},
year={2025},
publisher={Elsevier}
}
```
Ribeiro, P., Saini, A., Moran, J., Matsumoto, N., Choi, H., Hernandez, M., & Moore, J. H. (2024). [TPOT2: A New Graph-Based Implementation of the Tree-Based Pipeline Optimization Tool for Automated Machine Learning](https://link.springer.com/chapter/10.1007/978-981-99-8413-8_1). In Genetic programming theory and practice XX (pp. 1-17). Singapore: Springer Nature Singapore.
BitTex entry:
```bibtex
@incollection{ribeiro2024tpot2,
title={TPOT2: A New Graph-Based Implementation of the Tree-Based Pipeline Optimization Tool for Automated Machine Learning},
author={Ribeiro, Pedro and Saini, Anil and Moran, Jay and Matsumoto, Nicholas and Choi, Hyunjun and Hernandez, Miguel and Moore, Jason H},
booktitle={Genetic programming theory and practice XX},
pages={1--17},
year={2024},
publisher={Springer}
}
```
Randal S. Olson, Ryan J. Urbanowicz, Peter C. Andrews, Nicole A. Lavender, La Creis Kidd, and Jason H. Moore (2016). [Automating biomedical data science through tree-based pipeline optimization](http://link.springer.com/chapter/10.1007/978-3-319-31204-0_9). *Applications of Evolutionary Computation*, pages 123-137.
BibTeX entry:
```bibtex
@inbook{Olson2016EvoBio,
author={Olson, Randal S. and Urbanowicz, Ryan J. and Andrews, Peter C. and Lavender, Nicole A. and Kidd, La Creis and Moore, Jason H.},
editor={Squillero, Giovanni and Burelli, Paolo},
chapter={Automating Biomedical Data Science Through Tree-Based Pipeline Optimization},
title={Applications of Evolutionary Computation: 19th European Conference, EvoApplications 2016, Porto, Portugal, March 30 -- April 1, 2016, Proceedings, Part I},
year={2016},
publisher={Springer International Publishing},
pages={123--137},
isbn={978-3-319-31204-0},
doi={10.1007/978-3-319-31204-0_9},
url={http://dx.doi.org/10.1007/978-3-319-31204-0_9}
}
```
Randal S. Olson, Nathan Bartley, Ryan J. Urbanowicz, and Jason H. Moore (2016). [Evaluation of a Tree-based Pipeline Optimization Tool for Automating Data Science](http://dl.acm.org/citation.cfm?id=2908918). *Proceedings of GECCO 2016*, pages 485-492.
BibTeX entry:
```bibtex
@inproceedings{OlsonGECCO2016,
author = {Olson, Randal S. and Bartley, Nathan and Urbanowicz, Ryan J. and Moore, Jason H.},
title = {Evaluation of a Tree-based Pipeline Optimization Tool for Automating Data Science},
booktitle = {Proceedings of the Genetic and Evolutionary Computation Conference 2016},
series = {GECCO '16},
year = {2016},
isbn = {978-1-4503-4206-3},
location = {Denver, Colorado, USA},
pages = {485--492},
numpages = {8},
url = {http://doi.acm.org/10.1145/2908812.2908918},
doi = {10.1145/2908812.2908918},
acmid = {2908918},
publisher = {ACM},
address = {New York, NY, USA},
}
```
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FILE: docs/contribute.md
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# Contributing
We welcome you to check the existing issues for bugs or enhancements to work on. If you have an idea for an extension to TPOT, please file a new issue so we can discuss it.
# Contribution Guide
We welcome you to [check the existing issues](https://github.com/EpistasisLab/tpot/issues/) for bugs or enhancements to work on. If you have an idea for an extension to TPOT, please [file a new issue](https://github.com/EpistasisLab/tpot/issues/new) so we can discuss it.
## Project layout
Both the latest stable release and the development version of TPOT are on the [main branch](https://github.com/EpistasisLab/tpot/tree/main). Ensure you are working from the correct commit when contributing.
In terms of directory structure:
* All of TPOT's code sources are in the `tpot` directory
* The documentation sources are in the `docs_sources` directory
* Images in the documentation are in the `images` directory
* Tutorials for TPOT are in the `tutorials` directory
* Unit tests for TPOT are in the `tests.py` file
Make sure to familiarize yourself with the project layout before making any major contributions, and especially make sure to send all code changes to the `main` branch.
## How to contribute
The preferred way to contribute to TPOT is to fork the
[main repository](https://github.com/EpistasisLab/tpot/) on
GitHub:
1. Fork the [project repository](https://github.com/EpistasisLab/tpot):
click on the 'Fork' button near the top of the page. This creates
a copy of the code under your account on the GitHub server.
2. Clone this copy to your local disk:
$ git clone git@github.com:YourUsername/tpot.git
$ cd tpot
3. Create a branch to hold your changes:
$ git checkout main
$ git pull origin main
$ git checkout -b my-contribution
4. Make sure your local environment is setup correctly for development. Set up your local environment following the [Developer Installation instructions](installing.md#developerlatest-branch-installation). Ensure you also install `pytest` (`conda install pytest` or `pip install pytest`) into your development environment so that you can test changes locally.
5. Start making changes on your newly created branch. Work on this copy on your computer using Git to do the version control.
6. Check your changes haven't broken any existing tests and pass all your new tests. Navigate the terminal into the `tpot/tpot/` folder and run the command `pytest` to start all tests. (note, you must have the `pytest` package installed within your dev environment for this to work):
$ pytest
7. When you're done editing and local testing, run:
$ git add modified_files
$ git commit
to record your changes in Git, then push them to GitHub with:
$ git push -u origin my-contribution
Finally, go to the web page of your fork of the TPOT repo, and click 'Pull Request' (PR) to send your changes to the maintainers for review. Make sure that you send your PR to the `main` branch. This will start the CI server to check all the project's unit tests run and send an email to the maintainers.
(If any of the above seems like magic to you, then look up the
[Git documentation](http://git-scm.com/documentation) on the web.)
## Before submitting your pull request
Before you submit a pull request for your contribution, please work through this checklist to make sure that you have done everything necessary so we can efficiently review and accept your changes.
If your contribution changes TPOT in any way:
* Update the [documentation](https://github.com/EpistasisLab/tpot/tree/main/docs) so all of your changes are reflected there.
* Update the [README](https://github.com/EpistasisLab/tpot/blob/main/README.md) if anything there has changed.
If your contribution involves any code changes:
* Update the [project unit tests](https://github.com/EpistasisLab/tpot/tree/main/tpot/tests) to test your code changes.
* Make sure that your code is properly commented with [docstrings](https://www.python.org/dev/peps/pep-0257/) and comments explaining your rationale behind non-obvious coding practices.
If your contribution requires a new library dependency:
* Double-check that the new dependency is easy to install via `pip` or Anaconda. If the dependency requires a complicated installation, then we most likely won't merge your changes because we want to keep TPOT easy to install.
## After submitting your pull request
After submitting your pull request, GitHub will automatically run unit tests on your changes and make sure that your updated code builds and runs. We also use services that automatically check code quality and test coverage.
Check back shortly after submitting your pull request to make sure that your code passes these checks. If any of the checks come back with a red X, then do your best to address the errors.
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.md-grid {
max-width: 100%;
}
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FILE: docs/index.md
================================================
{%
include-markdown "../README.md"
%}
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================================================
# Installation
TPOT requires a working installation of Python.
### Creating a conda environment (optional)
We recommend using conda environments for installing TPOT, though it would work equally well if manually installed without it.
[More information on making anaconda environments found here.](https://conda.io/projects/conda/en/latest/user-guide/tasks/manage-environments.html)
```
conda create --name tpotenv python=3.13
conda activate tpotenv
```
### Note for M1 Mac or other Arm-based CPU users
You need to install the lightgbm package directly from conda using the following command before installing TPOT.
This is to ensure that you get the version that is compatible with your system.
```
conda install --yes -c conda-forge 'lightgbm>=3.3.3'
```
### Developer/Latest Branch Installation
```
pip install -e /path/to/tpotrepo
```
If you downloaded with git pull, then the repository folder will be named TPOT. (Note: this folder is the one that includes setup.py inside of it and not the folder of the same name inside it).
If you downloaded as a zip, the folder may be called tpot-main.
================================================
FILE: docs/related.md
================================================
Other Automated Machine Learning (AutoML) tools and related projects:
Name
Language
License
Description
Auto-WEKA Java
GPL-v3
Automated model selection and hyper-parameter tuning for Weka models.
auto-sklearn Python
BSD-3-Clause
An automated machine learning toolkit and a drop-in replacement for a scikit-learn estimator.
auto_ml Python
MIT
Automated machine learning for analytics & production. Supports manual feature type declarations.
H2O AutoML Java with Python, Scala & R APIs and web GUI
Apache 2.0
Automated: data prep, hyperparameter tuning, random grid search and stacked ensembles in a distributed ML platform.
devol Python
MIT
Automated deep neural network design via genetic programming.
MLBox Python
BSD-3-Clause
Accurate hyper-parameter optimization in high-dimensional space with support for distributed computing.
Recipe C
GPL-v3
Machine-learning pipeline optimization through genetic programming. Uses grammars to define pipeline structure.
Xcessiv Python
Apache 2.0
A web-based application for quick, scalable, and automated hyper-parameter tuning and stacked ensembling in Python.
GAMA Python
Apache 2.0
Machine-learning pipeline optimization through asynchronous evaluation based genetic programming.
PyMoo Python
Apache 2.0
Multi-objective optimization in Python.
Karoo GP Python
MIT
A Python based genetic programming application suite with support for symbolic regression and classification.
MABE C++
See here A Python based genetic programming application suite with support for symbolic regression and classification.
SBBFramework Python
BSD-2-Clause
Python implementation of Symbiotic Bid-Based (SBB) framework for problem decomposition using Genetic Programming (GP).
Tiny GP Python
GPL-v3
A minimalistic program implementing Koza-style (tree-based) genetic programming to solve a symbolic regression problem.
Baikal Python
BSD-3-Clause
A graph-based functional API for building complex scikit-learn pipelines.
skdag Python
MIT
A more flexible alternative to scikit-learn Pipelines.
d6tflow Python
MIT
A python library which makes building complex data science workflows easy, fast and intuitive.
================================================
FILE: docs/requirements_docs.txt
================================================
griffe==1.3.1
mike==2.1.3
mkdocs==1.6.1
mkdocs-include-markdown-plugin==6.2.2
mkdocs-jupyter==0.25.0
mkdocs-material==9.5.35
mkdocstrings==0.26.1
mkdocstrings-python==1.11.1
nbconvert==7.16.5
================================================
FILE: docs/scripts/build_docs_sources.sh
================================================
#!/bin/bash
function iterate_files() {
local directory="$1"
base_dir="docs/documentation"
for file in "$directory"/*; do
if [ -f "$file" ] && [[ "$file" == *.py ]] && [ "$(basename "$file")" != "__init__.py" ] && \
! echo "$file" | grep -q "test" && [ "$(basename "$file")" != "graph_utils.py" ]; then
directories=$base_dir/$(dirname "$file")
file_name=$(basename "$file")
md_file=$directories/"${file_name%.*}".md
mkdir -p $directories && touch $md_file
include_line=$(dirname "$file")
include_line="${include_line//\//.}"."${file_name%.*}"
echo "::: $include_line" > $md_file
elif [ -d "$file" ]; then
iterate_files "$file"
fi
done
}
iterate_files "tpot"
================================================
FILE: docs/scripts/build_mkdocs.sh
================================================
#!/bin/bash
cat > mkdocs.yml <> mkdocs.yml
echo " - tpot_api/estimator.md" >> mkdocs.yml
echo " - tpot_api/classifier.md" >> mkdocs.yml
echo " - tpot_api/regressor.md" >> mkdocs.yml
echo " - Examples:" >> mkdocs.yml
for file in docs/Tutorial/*.ipynb; do
base=$(basename $file .ipynb)
echo " - Tutorial/$base.ipynb" >> mkdocs.yml
done
echo " - Documentation:" >> mkdocs.yml
function iterate_source_files() {
local directory="$1"
for file in "$directory"/*; do
if [ -f "$file" ] && [[ "$file" == *.md ]]; then
slash_count=$(echo "$file" | grep -o '/' | wc -l)
num_spaces=$((slash_count * 2))
spaces=$(printf "%*s" $num_spaces)
echo "$spaces- ${file#*/}" >> mkdocs.yml
fi
done
for file in "$directory"/*; do
if [ -d "$file" ]; then
slash_count=$(echo "$file" | grep -o '/' | wc -l)
num_spaces=$((slash_count * 2))
spaces=$(printf "%*s" $num_spaces)
last_dir=$(basename "$file")
echo "$spaces- $last_dir:" >> mkdocs.yml
iterate_source_files "$file"
fi
done
}
iterate_source_files "docs/documentation"
# make these static instead
# for file in docs/*.md; do
# base=$(basename $file .md)
# if [ "$base" == "index" ]; then
# continue
# fi
# echo " - $base.md" >> mkdocs.yml
# done
echo " - contribute.md" >> mkdocs.yml
echo " - cite.md" >> mkdocs.yml
echo " - support.md" >> mkdocs.yml
echo " - related.md" >> mkdocs.yml
# moved to the top
# # test docstring
# # echo " - Tutorials:" >> mkdocs.yml
# for file in docs/tutorial/*.ipynb; do
# base=$(basename $file .ipynb)
# echo " - tutorial/$base.ipynb" >> mkdocs.yml
# done
================================================
FILE: docs/scripts/build_tutorial_toc_not_used.sh
================================================
#!/bin/bash
for file in docs/tutorial/*.html; do
base=$(basename "$file" .html)
echo "
" > "docs/tutorial/$base.md"
done
================================================
FILE: docs/support.md
================================================
# Support
TPOT was developed in the [Artificial Intelligence Innovation (A2I) Lab](http://epistasis.org/) at Cedars-Sinai with funding from the [NIH](http://www.nih.gov/) under grants U01 AG066833 and R01 LM010098. We are incredibly grateful for the support of the NIH and the Cedars-Sinai during the development of this project.
The TPOT logo was designed by Todd Newmuis, who generously donated his time to the project.
================================================
FILE: docs/tpot_api/classifier.md
================================================
::: tpot.tpot_estimator.templates.tpottemplates.TPOTClassifier
================================================
FILE: docs/tpot_api/estimator.md
================================================
::: tpot.tpot_estimator.estimator
================================================
FILE: docs/tpot_api/regressor.md
================================================
::: tpot.tpot_estimator.templates.tpottemplates.TPOTRegressor
================================================
FILE: docs/using.md
================================================
# Using TPOT
See the Tutorials Folder for more instructions and examples.
## Best Practices
### 1
TPOT uses dask for parallel processing. When Python is parallelized, each module is imported within each processes. Therefore it is important to protect all code within a `if __name__ == "__main__"` when running TPOT from a script. This is not required when running TPOT from a notebook.
For example:
```
#my_analysis.py
import tpot
if __name__ == "__main__":
X, y = load_my_data()
est = tpot.TPOTClassifier()
est.fit(X,y)
#rest of analysis
```
### 2
When designing custom objective functions, avoid the use of global variables.
Don't Do:
```
global_X = [[1,2],[4,5]]
global_y = [0,1]
def foo(est):
return my_scorer(est, X=global_X, y=global_y)
```
Instead use a partial
```
from functools import partial
def foo_scorer(est, X, y):
return my_scorer(est, X, y)
if __name__=='__main__':
X = [[1,2],[4,5]]
y = [0,1]
final_scorer = partial(foo_scorer, X=X, y=y)
```
Similarly when using lambda functions.
Dont Do:
```
def new_objective(est, a, b)
#definition
a = 100
b = 20
bad_function = lambda est : new_objective(est=est, a=a, b=b)
```
Do:
```
def new_objective(est, a, b)
#definition
a = 100
b = 20
good_function = lambda est, a=a, b=b : new_objective(est=est, a=a, b=b)
```
## Tips
TPOT will not check if your data is correctly formatted. It will assume that you have passed in operators that can handle the type of data that was passed in. For instance, if you pass in a pandas dataframe with categorical features and missing data, then you should also include in your configuration operators that can handle those feautures of the data. Alternatively, if you pass in `preprocessing = True`, TPOT will impute missing values, one hot encode categorical features, then standardize the data. (Note that this is currently fitted and transformed on the entire training set before splitting for CV. Later there will be an option to apply per fold, and have the parameters be learnable.)
Setting `verbose` to 5 can be helpful during debugging as it will print out the error generated by failing pipelines.
================================================
FILE: mkdocs_archived.yml
================================================
site_name: TPOT
site_url: http://epistasislab.github.io/tpot
site_author: Randal S. Olson
site_description: Documentation for TPOT, a Python Automated Machine Learning tool that optimizes machine learning pipelines using genetic programming.
repo_url: https://github.com/epistasislab/tpot
edit_uri: edit/master/docs/archived/
docs_dir: docs/archived/
site_dir: target/archived_site
#theme: readthedocs
theme:
name: material
logo: assets/tpot-logo.jpg
favicon: assets/favicon.ico
features:
- toc.integrate
- navigation.top
palette:
# light mode
- scheme: default
primary: grey
toggle:
icon: material/brightness-7
name: Switch to dark mode
# dark mode
- scheme: slate
primary: grey
toggle:
icon: material/brightness-4
name: Switch to light mode
extra:
version:
provider: mike
extra_css:
- css/archived.css
markdown_extensions:
- tables
- fenced_code
- pymdownx.highlight:
anchor_linenums: true
- pymdownx.inlinehilite
- pymdownx.snippets
- pymdownx.superfences
plugins:
- include-markdown
copyright: Developed by Randal S. Olson and others at the University of Pennsylvania
nav:
- Home: index.md
- Installation: installing.md
- Using TPOT: using.md
- TPOT API: api.md
- Examples: examples.md
- Contributing: contributing.md
- Release Notes: releases.md
- Citing TPOT: citing.md
- Support: support.md
- Related: related.md
================================================
FILE: pyproject.toml
================================================
[build-system]
requires = ["setuptools>=61", "setuptools_scm>=7.0", "wheel"]
build-backend = "setuptools.build_meta"
[project]
name = "TPOT"
description = "Tree-based Pipeline Optimization Tool"
readme = "README.md"
requires-python = ">=3.10,<3.14"
license = { text = "LGPL-3.0" }
authors = [
{ name = "Pedro Ribeiro" }
]
keywords = [
"pipeline optimization",
"hyperparameter optimization",
"data science",
"machine learning",
"genetic programming",
"evolutionary computation"
]
classifiers = [
"Intended Audience :: Science/Research",
"Programming Language :: Python :: 3.10",
"Programming Language :: Python :: 3.11",
"Programming Language :: Python :: 3.12",
"Programming Language :: Python :: 3.13",
"Topic :: Scientific/Engineering :: Artificial Intelligence"
]
dependencies = [
"numpy>=1.26.4",
"scipy>=1.3.1",
"scikit-learn>=1.6",
"update_checker>=0.16",
"tqdm>=4.36.1",
"stopit>=1.1.1",
"pandas>=2.2.0",
"joblib>=1.1.1",
"xgboost>=3.0.0",
"matplotlib>=3.6.2",
"traitlets>=5.8.0",
"lightgbm>=3.3.3",
"optuna>=3.0.5",
"networkx>=3.0",
"dask>=2024.4.2",
"distributed>=2024.4.2",
"dask-expr>=1.0.12",
"dask-jobqueue>=0.8.5",
"func_timeout>=4.3.5",
"configspace>=1.1.1",
"dill>=0.3.9",
"seaborn>=0.13.2",
]
dynamic = ["version"]
[project.optional-dependencies]
skrebate = ["skrebate>=0.3.4"]
mdr = ["scikit-mdr>=0.4.4"]
sklearnex = ["scikit-learn-intelex>=2023.2.1"]
amltk = ["amltk>=1.12.1"]
testing = [
"pytest>=6.0",
"pytest-cov>=2.0",
"mypy>=0.910",
"flake8>=3.9",
"tox>=3.24"
]
[project.urls]
Homepage = "https://github.com/EpistasisLab/tpot"
[project.scripts]
tpot = "tpot:main"
[tool.setuptools]
packages = ["tpot"]
zip-safe = true
[tool.setuptools.package-data]
tpot = ["py.typed"]
[tool.flake8]
max-line-length = 120
[tool.setuptools_scm]
# setuptools_scm gets the version from Git tags, e.g git tag v1.1.0
# then python -m build embeds the version into the package
================================================
FILE: tox.ini
================================================
[tox]
minversion = 3.28.0
# flake8 and mypy outputs severla errors, so we disable them for now
# envlist = py310, flake8, mypy
envlist = py310, py311, py312, py313
isolated_build = true
skip_missing_interpreters = true
[gh-actions]
python =
3.10: py310
3.11: py311
3.12: py312
3.13: py313
# 3.10: py310, flake8, mypy
[testenv]
setenv =
PYTHONPATH = {toxinidir}
extras = testing
deps =
setuptools>=65.0.0
commands =
pytest --basetemp={envtmpdir}
[testenv:flake8]
basepython = python3.10
deps = flake8
commands = flake8 tpot
[testenv:mypy]
basepython = python3.10
extras = testing
deps =
setuptools>=65.0.0
commands = mypy tpot
================================================
FILE: tpot/__init__.py
================================================
"""
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)
- Gabriel Ketron (gabriel.ketron@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 is supported for np arrays")
if self.columns == "categorical":
self.columns_ = list(X.select_dtypes(exclude='number').columns)
elif self.columns == "numeric":
self.columns_ = [col for col in X.columns if is_numeric_dtype(X[col])]
elif self.columns == "auto":
self.columns_ = auto_select_categorical_features(X)
elif self.columns == "all":
if isinstance(X, pd.DataFrame):
self.columns_ = X.columns
else:
self.columns_ = list(range(X.shape[1]))
elif isinstance(self.columns, list):
self.columns_ = self.columns
else:
raise ValueError(f"Invalid value for columns: {self.columns}")
if len(self.columns_) == 0:
return self
self.enc = sklearn.preprocessing.OneHotEncoder( categories='auto',
drop = self.drop,
handle_unknown = self.handle_unknown,
sparse_output = self.sparse_output,
min_frequency = self.min_frequency,
max_categories = self.max_categories)
#TODO make this more consistent with sklearn baseimputer/baseencoder
if isinstance(X, pd.DataFrame):
self.enc.set_output(transform="pandas")
for col in X.columns:
# check if the column name is not a string
if not isinstance(col, str):
# if it's not a string, rename the column with "X" prefix
X.rename(columns={col: f"X{col}"}, inplace=True)
if len(self.columns_) == X.shape[1]:
X_sel = self.enc.fit(X)
else:
X_sel, X_not_sel = _X_selected(X, self.columns_)
X_sel = self.enc.fit(X_sel)
return self
def transform(self, X):
"""Transform X using one-hot encoding.
Parameters
----------
X : array-like or sparse matrix, shape=(n_samples, n_features)
Dense array or sparse matrix.
Returns
-------
X_out : sparse matrix if sparse=True else a 2-d array, dtype=int
Transformed input.
"""
if len(self.columns_) == 0:
return X
#TODO make this more consistent with sklearn baseimputer/baseencoder
if isinstance(X, pd.DataFrame):
for col in X.columns:
# check if the column name is not a string
if not isinstance(col, str):
# if it's not a string, rename the column with "X" prefix
X.rename(columns={col: f"X{col}"}, inplace=True)
if len(self.columns_) == X.shape[1]:
return self.enc.transform(X)
else:
X_sel, X_not_sel= _X_selected(X, self.columns_)
X_sel = self.enc.transform(X_sel)
#If X is dataframe
if isinstance(X, pd.DataFrame):
X_sel = pd.DataFrame(X_sel, columns=self.enc.get_feature_names_out())
return pd.concat([X_not_sel.reset_index(drop=True), X_sel.reset_index(drop=True)], axis=1)
else:
return np.hstack((X_not_sel, X_sel))
class ColumnOrdinalEncoder(TransformerMixin, BaseEstimator ):
def __init__(self, columns='auto', handle_unknown='error', unknown_value = -1, encoded_missing_value = np.nan, min_frequency=None,max_categories=None):
'''
Parameters
----------
columns : str, list, default='auto'
Determines which columns to onehot encode with sklearn.preprocessing.OneHotEncoder.
- 'auto' : Automatically select categorical features based on columns with less than 10 unique values
- 'categorical' : Automatically select categorical features
- 'numeric' : Automatically select numeric features
- 'all' : Select all features
- list : A list of columns to select
drop, handle_unknown, sparse_output, min_frequency, max_categories : see sklearn.preprocessing.OneHotEncoder
'''
self.columns = columns
self.handle_unknown = handle_unknown
self.unknown_value = unknown_value
self.encoded_missing_value = encoded_missing_value
self.min_frequency = min_frequency
self.max_categories = max_categories
def fit(self, X, y=None):
"""Fit OneHotEncoder to X, then transform X.
Equivalent to self.fit(X).transform(X), but more convenient and more
efficient. See fit for the parameters, transform for the return value.
Parameters
----------
X : array-like or sparse matrix, shape=(n_samples, n_features)
Dense array or sparse matrix.
y: array-like {n_samples,} (Optional, ignored)
Feature labels
"""
if (self.columns == "categorical" or self.columns == "numeric") and not isinstance(X, pd.DataFrame):
raise ValueError(f"Invalid value for columns: {self.columns}. "
"Only 'all' or is supported for np arrays")
if self.columns == "categorical":
self.columns_ = list(X.select_dtypes(exclude='number').columns)
elif self.columns == "numeric":
self.columns_ = [col for col in X.columns if is_numeric_dtype(X[col])]
elif self.columns == "auto":
self.columns_ = auto_select_categorical_features(X)
elif self.columns == "all":
if isinstance(X, pd.DataFrame):
self.columns_ = X.columns
else:
self.columns_ = list(range(X.shape[1]))
elif isinstance(self.columns, list):
self.columns_ = self.columns
else:
raise ValueError(f"Invalid value for columns: {self.columns}")
if len(self.columns_) == 0:
return self
self.enc = sklearn.preprocessing.OrdinalEncoder(categories='auto',
handle_unknown = self.handle_unknown,
unknown_value = self.unknown_value,
encoded_missing_value = self.encoded_missing_value,
min_frequency = self.min_frequency,
max_categories = self.max_categories)
#TODO make this more consistent with sklearn baseimputer/baseencoder
'''
if isinstance(X, pd.DataFrame):
self.enc.set_output(transform="pandas")
for col in X.columns:
# check if the column name is not a string
if not isinstance(col, str):
# if it's not a string, rename the column with "X" prefix
X.rename(columns={col: f"X{col}"}, inplace=True)
'''
if len(self.columns_) == X.shape[1]:
X_sel = self.enc.fit(X)
else:
X_sel, X_not_sel = _X_selected(X, self.columns_)
X_sel = self.enc.fit(X_sel)
return self
def transform(self, X):
"""Transform X using one-hot encoding.
Parameters
----------
X : array-like or sparse matrix, shape=(n_samples, n_features)
Dense array or sparse matrix.
Returns
-------
X_out : sparse matrix if sparse=True else a 2-d array, dtype=int
Transformed input.
"""
if len(self.columns_) == 0:
return X
#TODO make this more consistent with sklearn baseimputer/baseencoder
'''
if isinstance(X, pd.DataFrame):
for col in X.columns:
# check if the column name is not a string
if not isinstance(col, str):
# if it's not a string, rename the column with "X" prefix
X.rename(columns={col: f"X{col}"}, inplace=True)
'''
if len(self.columns_) == X.shape[1]:
return self.enc.transform(X)
else:
X_sel, X_not_sel= _X_selected(X, self.columns_)
X_sel = self.enc.transform(X_sel)
#If X is dataframe
if isinstance(X, pd.DataFrame):
X_sel = pd.DataFrame(X_sel, columns=self.enc.get_feature_names_out())
return pd.concat([X_not_sel.reset_index(drop=True), X_sel.reset_index(drop=True)], axis=1)
else:
return np.hstack((X_not_sel, X_sel))
================================================
FILE: tpot/builtin_modules/estimatortransformer.py
================================================
"""
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)
- Gabriel Ketron (gabriel.ketron@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 is supported for np arrays")
if self.columns == "categorical":
self.columns_ = list(X.select_dtypes(exclude='number').columns)
elif self.columns == "numeric":
self.columns_ = [col for col in X.columns if is_numeric_dtype(X[col])]
elif self.columns == "all":
if isinstance(X, pd.DataFrame):
self.columns_ = X.columns
else:
self.columns_ = list(range(X.shape[1]))
elif isinstance(self.columns, list):
self.columns_ = self.columns
else:
raise ValueError(f"Invalid value for columns: {self.columns}")
if len(self.columns_) == 0:
return self
self.imputer = sklearn.impute.SimpleImputer(missing_values=self.missing_values,
strategy=self.strategy,
fill_value=self.fill_value,
copy=self.copy,
add_indicator=self.add_indicator,
keep_empty_features=self.keep_empty_features)
if isinstance(X, pd.DataFrame):
self.imputer.set_output(transform="pandas")
if isinstance(X, pd.DataFrame):
self.imputer.fit(X[self.columns_], y)
else:
self.imputer.fit(X[:, self.columns_], y)
return self
def transform(self, X):
if len(self.columns_) == 0:
return X
if isinstance(X, pd.DataFrame):
X = X.copy()
X[self.columns_] = self.imputer.transform(X[self.columns_])
return X
else:
X = np.copy(X)
X[:, self.columns_] = self.imputer.transform(X[:, self.columns_])
return X
================================================
FILE: tpot/builtin_modules/nn.py
================================================
# -*- coding: utf-8 -*-
"""
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)
- Gabriel Ketron (gabriel.ketron@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 ". 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
class EstimatorNode(SearchSpace):
def __init__(self, method, space, hyperparameter_parser=default_hyperparameter_parser):
self.method = method
self.space = space
self.hyperparameter_parser = hyperparameter_parser
def generate(self, rng=None):
return EstimatorNodeIndividual(self.method, self.space, hyperparameter_parser=self.hyperparameter_parser, rng=rng)
================================================
FILE: tpot/search_spaces/nodes/estimator_node_gradual.py
================================================
"""
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)
- Gabriel Ketron (gabriel.ketron@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 ". 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
self.hyperparameters = gradual_hyperparameter_update(params=self.hyperparameters, configspace=self.space, rng=rng)
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
def gradual_hyperparameter_update(params:dict, configspace:ConfigurationSpace, rng=None):
rng = np.random.default_rng(rng)
configspace.seed(rng.integers(0, 2**32))
new_params = dict(configspace.sample_configuration())
for param in list(new_params.keys()):
#if parameter is float, multiply by normal distribution
if param not in params:
continue
try:
if issubclass(type(configspace[param]), ConfigSpace.hyperparameters.hyperparameter.FloatHyperparameter):
if configspace[param].log:
new_params[param] = params[param] * rng.lognormal(0, 1)
else:
new_params[param] = params[param] + rng.normal(0, .1)* (configspace[param].upper-configspace[param].lower)
# if check if above or below min and cap
if new_params[param] < configspace[param].lower:
new_params[param] = configspace[param].lower
elif new_params[param] > configspace[param].upper:
new_params[param] = configspace[param].upper
#if parameter is integer, add normal distribution
elif issubclass(type(configspace[param]), ConfigSpace.hyperparameters.hyperparameter.IntegerHyperparameter):
new_params[param] = params[param] * rng.normal(0, 1)
# if check if above or below min and cap
if new_params[param] < configspace[param].lower:
new_params[param] = configspace[param].lower
elif new_params[param] > configspace[param].upper:
new_params[param] = configspace[param].upper
new_params[param] = int(new_params[param])
# TODO : add support for categorical hyperparameters
else:
new_params[param] = params[param]
except:
pass
return new_params
class EstimatorNode_gradual(SearchSpace):
def __init__(self, method, space, hyperparameter_parser=default_hyperparameter_parser):
self.method = method
self.space = space
self.hyperparameter_parser = hyperparameter_parser
def generate(self, rng=None):
return EstimatorNodeIndividual_gradual(self.method, self.space, hyperparameter_parser=self.hyperparameter_parser, rng=rng)
================================================
FILE: tpot/search_spaces/nodes/fss_node.py
================================================
"""
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)
- Gabriel Ketron (gabriel.ketron@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 scores[next_point_index], axis=1)
nondominated_point_mask[next_point_index] = True
is_efficient = is_efficient[nondominated_point_mask] # Remove dominated points
scores = scores[nondominated_point_mask]
next_point_index = np.sum(nondominated_point_mask[:next_point_index])+1
if return_mask:
is_efficient_mask = np.zeros(n_points, dtype = bool)
is_efficient_mask[is_efficient] = True
return is_efficient_mask
else:
return is_efficient
def get_pareto_frontier(df, column_names, weights):
# dftmp = df[~df[column_names].isin(invalid_values).any(axis=1)]
dftmp = df[df[column_names].notnull().all(axis=1)]
if "Budget" in dftmp.columns:
#get rows with the max budget
dftmp = dftmp[dftmp["Budget"]==dftmp["Budget"].max()]
indexes = dftmp[~dftmp[column_names].isna().any(axis=1)].index.values
weighted_scores = df.loc[indexes][column_names].to_numpy() * weights
mask = is_pareto_efficient(weighted_scores, return_mask = True)
df["Pareto_Front"] = np.nan #TODO this will get deprecated
df.loc[indexes[mask], "Pareto_Front"] = 1
def get_pareto_front(df, column_names, weights):
dftmp = df[df[column_names].notnull().all(axis=1)]
if "Budget" in dftmp.columns:
#get rows with the max budget
dftmp = dftmp[dftmp["Budget"]==dftmp["Budget"].max()]
indexes = dftmp[~dftmp[column_names].isna().any(axis=1)].index.values
weighted_scores = df.loc[indexes][column_names].to_numpy() * weights
pareto_fronts = tpot.selectors.nondominated_sorting(weighted_scores)
df = pd.DataFrame(index=df.index,columns=["Pareto_Front"], data=[])
df["Pareto_Front"] = np.nan
for i, front in enumerate(pareto_fronts):
for index in front:
df.loc[indexes[index], "Pareto_Front"] = i+1
return df["Pareto_Front"]
An example machine learning pipeline
An example TPOT pipeline