A Python Automated Machine Learning tool that optimizes machine learning pipelines using genetic programming.
LGPL-3.0 License
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Full Changelog: https://github.com/EpistasisLab/tpot/compare/v0.12.1...v0.12.2
Published by jay-m-dev about 1 year ago
Fixes issue with runs terminating too early
Published by nickotto over 1 year ago
Published by weixuanfu almost 4 years ago
Resampler
from imblearn
Published by weixuanfu almost 4 years ago
Published by weixuanfu almost 4 years ago
Published by weixuanfu almost 4 years ago
Published by weixuanfu almost 4 years ago
template
optionPublished by weixuanfu about 4 years ago
Published by weixuanfu over 4 years ago
Pytorch
as an optional dependencyPublished by weixuanfu over 4 years ago
tpot.builtins.PytorchLRClassifier
and tpot.builtins.PytorchMLPClassifier
for classification tasks only)log_file
parameter's behaviorPublished by weixuanfu over 4 years ago
-log
option in command line interface to save process log to a file.Published by weixuanfu over 4 years ago
early_stop
parameter does not work properlyOneHotEncoder
can refit to different datasetsevaluated_individuals_
cannot record correct generation info.log_file
to output logs to a file instead of sys.stdout
Published by weixuanfu almost 5 years ago
warm_start
now saves both Primitive Sets and evaluated_pipelines_ from previous runs;max_min_mins
or KeyboardInterrupt
) ;warm_start
is True;max_time_mins
cannot stop optimization process when search space is limited.Published by weixuanfu almost 5 years ago
score_func(y_true, y_pred)
for scoring parameter
has been dropped.StackingEstimator
for not stacking NaN/Infinity predication probabilities.warm_start=True
when max_time_mins
is not default value.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.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.SGDClassifier
and SGDRegressor
into TPOT default configs.Published by weixuanfu over 5 years ago
template
parameter is changed to None
instead.Published by weixuanfu over 5 years ago
data_file_path
option into expert
function for replacing 'PATH/TO/DATA/FILE'
to customized dataset path in exported scripts. (Related issue #838)Published by weixuanfu over 5 years ago
template
option to specify a desired structure for machine learning pipeline in TPOT. Check TPOT API (it will be updated once it is merge to master branch).FeatureSetSelector
operator into TPOT for feature selection based on priori export knowledge. Please check our preprint paper for more details (Note: it was named DatasetSelector
in 1st version paper but we will rename to FeatureSetSelector in next version of the paper)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. It is related to the issue #846.memory
parameter can create memory cache directory if it does not exist. It is related to the issue #837.Published by weixuanfu over 5 years ago
max_time_mins
parameter doesn't work when use_dask=True
in TPOT 0.9.5ImportError
if operators in the TPOT configuration are not available when verbosity>2
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.TPOTRegressor
Published by weixuanfu about 6 years ago
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.
We refined parameters in VarianceThreshold and FeatureAgglomeration.
TPOT now supports using memory caching within a Pipeline via a optional memory
parameter.
We improved documentation of TPOT.