Returns the optimized pipeline's score on the given testing data using the user-specified scoring function.
-The default scoring function for TPOTClassifier is 'mean_squared_error'.
+The default scoring function for TPOTRegressor is 'mean_squared_error'.
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+++ b/docs/index.html
@@ -213,5 +213,5 @@
diff --git a/docs/search/search_index.json b/docs/search/search_index.json
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@@ -87,7 +87,7 @@
},
{
"location": "/api/",
- "text": "Classification\n\n\nclass\n tpot.\nTPOTClassifier\n(\ngenerations\n=100, \npopulation_size\n=100,\n \noffspring_size\n=None, \nmutation_rate\n=0.9,\n \ncrossover_rate\n=0.1,\n \nscoring\n='accuracy', \ncv\n=5,\n \nsubsample\n=1.0, \nn_jobs\n=1,\n \nmax_time_mins\n=None, \nmax_eval_time_mins\n=5,\n \nrandom_state\n=None, \nconfig_dict\n=None,\n \ntemplate\n=None,\n \nwarm_start\n=False,\n \nmemory\n=None,\n \nuse_dask\n=False,\n \nperiodic_checkpoint_folder\n=None,\n \nearly_stop\n=None,\n \nverbosity\n=0,\n \ndisable_update_check\n=False\n)\n\n\n\nsource\n\n\n\nAutomated machine learning for supervised classification tasks.\n\n\nThe TPOTClassifier performs an intelligent search over machine learning pipelines that can contain supervised classification models,\npreprocessors, feature selection techniques, and any other estimator or transformer that follows the \nscikit-learn API\n.\nThe TPOTClassifier will also search over the hyperparameters of all objects in the pipeline.\n\n\nBy default, TPOTClassifier will search over a broad range of supervised classification algorithms, transformers, and their parameters.\nHowever, the algorithms, transformers, and hyperparameters that the TPOTClassifier searches over can be fully customized using the \nconfig_dict\n parameter.\n\n\nRead more in the \nUser Guide\n.\n\n\n\n\n\n\nParameters:\n\n\n\n\ngenerations\n: int or None optional (default=100)\n\n\nNumber of iterations to the run pipeline optimization process. It must be a positive number or None. If None, the parameter \nmax_time_mins\n must be defined as the runtime limit.\n\n\nGenerally, TPOT will work better when you give it more generations (and therefore time) to optimize the pipeline.\n\n\nTPOT will evaluate \npopulation_size\n + \ngenerations\n \u00d7 \noffspring_size\n pipelines in total.\n\n\n\n\npopulation_size\n: int, optional (default=100)\n\n\nNumber of individuals to retain in the genetic programming population every generation. Must be a positive number.\n\n\nGenerally, TPOT will work better when you give it more individuals with which to optimize the pipeline.\n\n\n\n\noffspring_size\n: int, optional (default=None)\n\n\nNumber 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.\n\n\n\n\nmutation_rate\n: float, optional (default=0.9)\n\n\nMutation 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.\n\n\n\nmutation_rate\n + \ncrossover_rate\n cannot exceed 1.0.\n\n\nWe recommend using the default parameter unless you understand how the mutation rate affects GP algorithms.\n\n\n\n\ncrossover_rate\n: float, optional (default=0.1)\n\n\nCrossover 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.\n\n\n\nmutation_rate\n + \ncrossover_rate\n cannot exceed 1.0.\n\n\nWe recommend using the default parameter unless you understand how the crossover rate affects GP algorithms.\n\n\n\n\nscoring\n: string or callable, optional (default='accuracy')\n\n\nFunction used to evaluate the quality of a given pipeline for the classification problem. The following built-in scoring functions can be used:\n\n\n'accuracy', 'adjusted_rand_score', 'average_precision', 'balanced_accuracy', 'f1', 'f1_macro', 'f1_micro', 'f1_samples', 'f1_weighted', 'neg_log_loss','precision',\n'precision_macro', 'precision_micro', 'precision_samples', 'precision_weighted', 'recall', 'recall_macro', 'recall_micro', 'recall_samples', 'recall_weighted', 'roc_auc'\n\n\nIf you would like to use a custom scorer, you can pass the callable object/function with signature \nscorer(estimator, X, y)\n.\n\n\nSee the section on \nscoring functions\n for more details.\n\n\n\n\n\ncv\n: int, cross-validation generator, or an iterable, optional (default=5)\n\n\nCross-validation strategy used when evaluating pipelines.\n\n\nPossible inputs:\n\n\n\ninteger, to specify the number of folds in a StratifiedKFold,\n\n\nAn object to be used as a cross-validation generator, or\n\n\nAn iterable yielding train/test splits.\n\n\n\n\n\nsubsample\n: float, optional (default=1.0)\n\n\nFraction of training samples that are used during the TPOT optimization process. Must be in the range (0.0, 1.0].\n\n\nSetting \nsubsample\n=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\n\n\n\nn_jobs\n: integer, optional (default=1)\n\n\nNumber of processes to use in parallel for evaluating pipelines during the TPOT optimization process.\n\n\nSetting \nn_jobs\n=-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.\n\n\n\n\nmax_time_mins\n: integer or None, optional (default=None)\n\n\nHow many minutes TPOT has to optimize the pipeline.\n\n\nIf not None, this setting will allow TPOT to run until \nmax_time_mins\n minutes elapsed and then stop. TPOT will stop earlier if \ngenerations\n is set and all generations are already evaluated.\n\n\n\n\nmax_eval_time_mins\n: float, optional (default=5)\n\n\nHow many minutes TPOT has to evaluate a single pipeline.\n\n\nSetting this parameter to higher values will allow TPOT to evaluate more complex pipelines, but will also allow TPOT to run longer. Use this parameter to help prevent TPOT from wasting time on evaluating time-consuming pipelines.\n\n\n\n\nrandom_state\n: integer or None, optional (default=None)\n\n\nThe seed of the pseudo random number generator used in TPOT.\n\n\nUse this parameter to make sure that TPOT will give you the same results each time you run it against the same data set with that seed.\n\n\n\n\nconfig_dict\n: Python dictionary, string, or None, optional (default=None)\n\n\nA configuration dictionary for customizing the operators and parameters that TPOT searches in the optimization process.\n\n\nPossible inputs are:\n\n\n\nPython dictionary, TPOT will use your custom configuration,\n\n\nstring 'TPOT light', TPOT will use a built-in configuration with only fast models and preprocessors, or\n\n\nstring 'TPOT MDR', TPOT will use a built-in configuration specialized for genomic studies, or\n\n\nstring '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\n\n\nNone, TPOT will use the default TPOTClassifier configuration.\n\n\n\nSee the \nbuilt-in configurations\n section for the list of configurations included with TPOT, and the \ncustom configuration\n section for more information and examples of how to create your own TPOT configurations.\n\n\n\n\ntemplate\n: string (default=None)\n\n\nTemplate of predefined pipeline structure. The option is for specifying a desired structure for the machine learning pipeline evaluated in TPOT.\n\n\nSo far this option only supports linear pipeline structure. Each step in the pipeline should be a main class of operators (Selector, Transformer, Classifier) 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) 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.\n\nSee the \n template option in tpot\n section for more details.\n\n\n\n\nwarm_start\n: boolean, optional (default=False)\n\n\nFlag indicating whether the TPOT instance will reuse the population from previous calls to \nfit()\n.\n\n\nSetting \nwarm_start\n=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.\n\n\n\n\nmemory\n: a joblib.Memory object or string, optional (default=None)\n\n\nIf 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 \nscikit-learn documentation\n\n\n\nPossible inputs are:\n\n\n\nString 'auto': TPOT uses memory caching with a temporary directory and cleans it up upon shutdown, or\n\n\nPath of a caching directory, TPOT uses memory caching with the provided directory and TPOT does NOT clean the caching directory up upon shutdown, or\n\n\nMemory object, TPOT uses the instance of joblib.Memory for memory caching and TPOT does NOT clean the caching directory up upon shutdown, or\n\n\nNone, TPOT does not use memory caching.\n\n\n\n\n\n\n\nuse_dask\n: boolean, optional (default: False)\n\n\nWhether to use Dask-ML's pipeline optimiziations. This avoid re-fitting\nthe same estimator on the same split of data multiple times. It\nwill also provide more detailed diagnostics when using Dask's\ndistributed scheduler.\n\n\nSee \navoid repeated work\n for more details.\n\n\n\n\nperiodic_checkpoint_folder\n: path string, optional (default: None)\n\n\nIf supplied, a folder in which TPOT will periodically save pipelines in pareto front so far while optimizing.\n\nCurrently once per generation but not more often than once per 30 seconds.\n\nUseful in multiple cases:\n\n\n\nSudden death before TPOT could save optimized pipeline\n\n\nTrack its progress\n\n\nGrab pipelines while it's still optimizing\n\n\n\n\n\n\n\nearly_stop\n: integer, optional (default: None)\n\n\nHow many generations TPOT checks whether there is no improvement in optimization process.\n\n\nEnds the optimization process if there is no improvement in the given number of generations.\n\n\n\n\nverbosity\n: integer, optional (default=0)\n\n\nHow much information TPOT communicates while it's running.\n\n\nPossible inputs are:\n\n\n\n0, TPOT will print nothing,\n\n\n1, TPOT will print minimal information,\n\n\n2, TPOT will print more information and provide a progress bar, or\n\n\n3, TPOT will print everything and provide a progress bar.\n\n\n\n\n\n\n\ndisable_update_check\n: boolean, optional (default=False)\n\n\nFlag indicating whether the TPOT version checker should be disabled.\n\n\nThe update checker will tell you when a new version of TPOT has been released.\n\n\n\n\n\n\n\n\n\n\nAttributes:\n\n\n\n\nfitted_pipeline_\n: scikit-learn Pipeline object\n\n\nThe best pipeline that TPOT discovered during the pipeline optimization process, fitted on the entire training dataset.\n\n\n\n\npareto_front_fitted_pipelines_\n: Python dictionary\n\n\nDictionary 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.\n\n\nThe TPOT Pareto front provides a trade-off between pipeline complexity (i.e., the number of steps in the pipeline) and the predictive performance of the pipeline.\n\n\nNote: \npareto_front_fitted_pipelines_\n is only available when \nverbosity\n=3.\n\n\n\n\nevaluated_individuals_\n: Python dictionary\n\n\nDictionary 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).\n\n\nThis attribute is primarily for internal use, but may be useful for looking at the other pipelines that TPOT evaluated.\n\n\n\n\n\n\n\n\n\n\nExample\n\n\nfrom tpot import TPOTClassifier\nfrom sklearn.datasets import load_digits\nfrom sklearn.model_selection import train_test_split\n\ndigits = load_digits()\nX_train, X_test, y_train, y_test = train_test_split(digits.data, digits.target,\n train_size=0.75, test_size=0.25)\n\ntpot = TPOTClassifier(generations=5, population_size=50, verbosity=2)\ntpot.fit(X_train, y_train)\nprint(tpot.score(X_test, y_test))\ntpot.export('tpot_digits_pipeline.py')\n\n\n\n\nFunctions\n\n\n\n\n\n\nfit\n(features, classes[, sample_weight, groups])\n\n\nRun the TPOT optimization process on the given training data.\n\n\n\n\n\n\n\npredict\n(features)\n\n\nUse the optimized pipeline to predict the classes for a feature set.\n\n\n\n\n\n\n\npredict_proba\n(features)\n\n\nUse the optimized pipeline to estimate the class probabilities for a feature set.\n\n\n\n\n\n\n\nscore\n(testing_features, testing_classes)\n\n\nReturns the optimized pipeline's score on the given testing data using the user-specified scoring function.\n\n\n\n\n\n\n\nexport\n(output_file_name)\n\n\nExport the optimized pipeline as Python code.\n\n\n\n\n\n\n\n\n\nfit(features, classes, sample_weight=None, groups=None)\n\n\n\n\n\nRun the TPOT optimization process on the given training data.\n\n\nUses 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.\n\n\n\n\n\n\n\nParameters:\n\n\n\n\nfeatures\n: array-like {n_samples, n_features}\n\n\nFeature matrix\n\n\nTPOT and all scikit-learn algorithms assume that the features will be numerical and there will be no missing values.\nAs such, when a feature matrix is provided to TPOT, all missing values will automatically be replaced (i.e., imputed)\nusing \nmedian value imputation\n.\n\n\nIf you wish to use a different imputation strategy than median imputation, please make sure to apply imputation to your feature set prior to passing it to TPOT.\n\n\n\n\nclasses\n: array-like {n_samples}\n\n\nList of class labels for prediction\n\n\n\n\nsample_weight\n: array-like {n_samples}, optional\n\n\nPer-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.\n\n\n\n\ngroups\n: array-like, with shape {n_samples, }, optional\n\n\nGroup labels for the samples used when performing cross-validation.\n\n\nThis parameter should only be used in conjunction with sklearn's Group cross-validation functions, such as \nsklearn.model_selection.GroupKFold\n.\n\n\n\n\n\n\n\n\n\nReturns:\n\n\n\n\nself\n: object\n\n\nReturns a copy of the fitted TPOT object\n\n\n\n\n\n\n\n\n\n\n\n\n\n\npredict(features)\n\n\n\n\n\nUse the optimized pipeline to predict the classes for a feature set.\n\n\n\n\n\n\n\nParameters:\n\n\n\n\nfeatures\n: array-like {n_samples, n_features}\n\n\nFeature matrix\n\n\n\n\n\n\n\n\n\nReturns:\n\n\n\n\npredictions\n: array-like {n_samples}\n\n\nPredicted classes for the samples in the feature matrix\n\n\n\n\n\n\n\n\n\n\n\n\n\n\npredict_proba(features)\n\n\n\n\n\nUse the optimized pipeline to estimate the class probabilities for a feature set.\n\n\nNote: This function will only work for pipelines whose final classifier supports the \npredict_proba\n function. TPOT will raise an error otherwise.\n\n\n\n\n\n\n\nParameters:\n\n\n\n\nfeatures\n: array-like {n_samples, n_features}\n\n\nFeature matrix\n\n\n\n\n\n\n\n\n\nReturns:\n\n\n\n\npredictions\n: array-like {n_samples, n_classes}\n\n\nThe class probabilities of the input samples\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nscore(testing_features, testing_classes)\n\n\n\n\n\nReturns the optimized pipeline's score on the given testing data using the user-specified scoring function.\n\n\nThe default scoring function for TPOTClassifier is 'accuracy'.\n\n\n\n\n\n\n\nParameters:\n\n\n\n\ntesting_features\n: array-like {n_samples, n_features}\n\n\nFeature matrix of the testing set\n\n\n\n\ntesting_classes\n: array-like {n_samples}\n\n\nList of class labels for prediction in the testing set\n\n\n\n\n\n\n\n\n\nReturns:\n\n\n\n\naccuracy_score\n: float\n\n\nThe estimated test set accuracy according to the user-specified scoring function.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nexport(output_file_name, data_file_path)\n\n\n\n\n\nExport the optimized pipeline as Python code.\n\n\nSee the \nusage documentation\n for example usage of the export function.\n\n\n\n\n\n\n\nParameters:\n\n\n\n\noutput_file_name\n: string\n\n\nString containing the path and file name of the desired output file\n\n\n\ndata_file_path\n: string\n\n\nBy 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.\n\n\n\n\n\n\n\nReturns:\n\n\n\n\nexported_code_string\n: string\n\n\nThe whole pipeline text as a string should be returned if output_file_name is not specified.\n\n\n\n\n\n\n\n\n\n\n\n\nRegression\n\n\nclass\n tpot.\nTPOTRegressor\n(\ngenerations\n=100, \npopulation_size\n=100,\n \noffspring_size\n=None, \nmutation_rate\n=0.9,\n \ncrossover_rate\n=0.1,\n \nscoring\n='neg_mean_squared_error', \ncv\n=5,\n \nsubsample\n=1.0, \nn_jobs\n=1,\n \nmax_time_mins\n=None, \nmax_eval_time_mins\n=5,\n \nrandom_state\n=None, \nconfig_dict\n=None,\n \ntemplate\n=None,\n \nwarm_start\n=False,\n \nmemory\n=None,\n \nuse_dask\n=False,\n \nperiodic_checkpoint_folder\n=None,\n \nearly_stop\n=None,\n \nverbosity\n=0,\n \ndisable_update_check\n=False\n)\n\n\n\nsource\n\n\n\nAutomated machine learning for supervised regression tasks.\n\n\nThe TPOTRegressor performs an intelligent search over machine learning pipelines that can contain supervised regression models,\npreprocessors, feature selection techniques, and any other estimator or transformer that follows the \nscikit-learn API\n.\nThe TPOTRegressor will also search over the hyperparameters of all objects in the pipeline.\n\n\nBy default, TPOTRegressor will search over a broad range of supervised regression models, transformers, and their hyperparameters.\nHowever, the models, transformers, and parameters that the TPOTRegressor searches over can be fully customized using the \nconfig_dict\n parameter.\n\n\nRead more in the \nUser Guide\n.\n\n\n\n\n\n\nParameters:\n\n\n\n\ngenerations\n: int or None, optional (default=100)\n\n\nNumber of iterations to the run pipeline optimization process. It must be a positive number or None. If None, the parameter \nmax_time_mins\n must be defined as the runtime limit.\n\n\nGenerally, TPOT will work better when you give it more generations (and therefore time) to optimize the pipeline.\n\n\nTPOT will evaluate \npopulation_size\n + \ngenerations\n \u00d7 \noffspring_size\n pipelines in total.\n\n\n\n\npopulation_size\n: int, optional (default=100)\n\n\nNumber of individuals to retain in the genetic programming population every generation. Must be a positive number.\n\n\nGenerally, TPOT will work better when you give it more individuals with which to optimize the pipeline.\n\n\n\n\noffspring_size\n: int, optional (default=None)\n\n\nNumber 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.\n\n\n\n\nmutation_rate\n: float, optional (default=0.9)\n\n\nMutation 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.\n\n\n\nmutation_rate\n + \ncrossover_rate\n cannot exceed 1.0.\n\n\nWe recommend using the default parameter unless you understand how the mutation rate affects GP algorithms.\n\n\n\n\ncrossover_rate\n: float, optional (default=0.1)\n\n\nCrossover 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.\n\n\n\nmutation_rate\n + \ncrossover_rate\n cannot exceed 1.0.\n\n\nWe recommend using the default parameter unless you understand how the crossover rate affects GP algorithms.\n\n\n\n\nscoring\n: string or callable, optional (default='neg_mean_squared_error')\n\n\nFunction used to evaluate the quality of a given pipeline for the regression problem. The following built-in scoring functions can be used:\n\n\n'neg_median_absolute_error', 'neg_mean_absolute_error', 'neg_mean_squared_error', 'r2'\n\n\nNote that we recommend using the \nneg\n version of mean squared error and related metrics so TPOT will minimize (instead of maximize) the metric.\n\n\nIf you would like to use a custom scorer, you can pass the callable object/function with signature \nscorer(estimator, X, y)\n.\n\n\nSee the section on \nscoring functions\n for more details.\n\n\n\n\ncv\n: int, cross-validation generator, or an iterable, optional (default=5)\n\n\nCross-validation strategy used when evaluating pipelines.\n\n\nPossible inputs:\n\n\n\ninteger, to specify the number of folds in a KFold,\n\n\nAn object to be used as a cross-validation generator, or\n\n\nAn iterable yielding train/test splits.\n\n\n\n\n\n\n\nsubsample\n: float, optional (default=1.0)\n\n\nFraction of training samples that are used during the TPOT optimization process. Must be in the range (0.0, 1.0].\n\n\nSetting \nsubsample\n=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\n\n\n\nn_jobs\n: integer, optional (default=1)\n\n\nNumber of processes to use in parallel for evaluating pipelines during the TPOT optimization process.\n\n\nSetting \nn_jobs\n=-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\n\n\n\n\nmax_time_mins\n: integer or None, optional (default=None)\n\n\nHow many minutes TPOT has to optimize the pipeline.\n\n\nIf not None, this setting will allow TPOT to run until \nmax_time_mins\n minutes elapsed and then stop. TPOT will stop earlier if \ngenerations\n is set and all generations are already evaluated.\n\n\n\n\nmax_eval_time_mins\n: float, optional (default=5)\n\n\nHow many minutes TPOT has to evaluate a single pipeline.\n\n\nSetting this parameter to higher values will allow TPOT to evaluate more complex pipelines, but will also allow TPOT to run longer. Use this parameter to help prevent TPOT from wasting time on evaluating time-consuming pipelines.\n\n\n\n\nrandom_state\n: integer or None, optional (default=None)\n\n\nThe seed of the pseudo random number generator used in TPOT.\n\n\nUse this parameter to make sure that TPOT will give you the same results each time you run it against the same data set with that seed.\n\n\n\n\nconfig_dict\n: Python dictionary, string, or None, optional (default=None)\n\n\nA configuration dictionary for customizing the operators and parameters that TPOT searches in the optimization process.\n\n\nPossible inputs are:\n\n\n\nPython dictionary, TPOT will use your custom configuration,\n\n\nstring 'TPOT light', TPOT will use a built-in configuration with only fast models and preprocessors, or\n\n\nstring 'TPOT MDR', TPOT will use a built-in configuration specialized for genomic studies, or\n\n\nstring '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\n\n\nNone, TPOT will use the default TPOTRegressor configuration.\n\n\n\nSee the \nbuilt-in configurations\n section for the list of configurations included with TPOT, and the \ncustom configuration\n section for more information and examples of how to create your own TPOT configurations.\n\n\n\n\ntemplate\n: string (default=None)\n\n\nTemplate of predefined pipeline structure. The option is for specifying a desired structure for the machine learning pipeline evaluated in TPOT.\n\n\nSo far this option only supports linear pipeline structure. Each step in the pipeline should be a main class of operators (Selector, Transformer 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) 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-Regressor\". By default value of template is None, TPOT generates tree-based pipeline randomly.\n\nSee the \n template option in tpot\n section for more details.\n\n\n\n\nwarm_start\n: boolean, optional (default=False)\n\n\nFlag indicating whether the TPOT instance will reuse the population from previous calls to \nfit()\n.\n\n\nSetting \nwarm_start\n=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.\n\n\n\n\nmemory\n: a joblib.Memory object or string, optional (default=None)\n\n\nIf 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 \nscikit-learn documentation\n\n\n\nPossible inputs are:\n\n\n\nString 'auto': TPOT uses memory caching with a temporary directory and cleans it up upon shutdown, or\n\n\nPath of a caching directory, TPOT uses memory caching with the provided directory and TPOT does NOT clean the caching directory up upon shutdown, or\n\n\nMemory object, TPOT uses the instance of joblib.Memory for memory caching and TPOT does NOT clean the caching directory up upon shutdown, or\n\n\nNone, TPOT does not use memory caching.\n\n\n\n\n\n\n\nuse_dask\n: boolean, optional (default: False)\n\n\nWhether to use Dask-ML's pipeline optimiziations. This avoid re-fitting\nthe same estimator on the same split of data multiple times. It\nwill also provide more detailed diagnostics when using Dask's\ndistributed scheduler.\n\n\nSee \navoid repeated work\n for more details.\n\n\n\n\nperiodic_checkpoint_folder\n: path string, optional (default: None)\n\n\nIf supplied, a folder in which TPOT will periodically save pipelines in pareto front so far while optimizing.\n\nCurrently once per generation but not more often than once per 30 seconds.\n\nUseful in multiple cases:\n\n\n\nSudden death before TPOT could save optimized pipeline\n\n\nTrack its progress\n\n\nGrab pipelines while it's still optimizing\n\n\n\n\n\n\n\nearly_stop\n: integer, optional (default: None)\n\n\nHow many generations TPOT checks whether there is no improvement in optimization process.\n\n\nEnds the optimization process if there is no improvement in the given number of generations.\n\n\n\n\nverbosity\n: integer, optional (default=0)\n\n\nHow much information TPOT communicates while it's running.\n\n\nPossible inputs are:\n\n\n\n0, TPOT will print nothing,\n\n\n1, TPOT will print minimal information,\n\n\n2, TPOT will print more information and provide a progress bar, or\n\n\n3, TPOT will print everything and provide a progress bar.\n\n\n\n\n\n\n\ndisable_update_check\n: boolean, optional (default=False)\n\n\nFlag indicating whether the TPOT version checker should be disabled.\n\n\nThe update checker will tell you when a new version of TPOT has been released.\n\n\n\n\n\n\n\n\n\n\nAttributes:\n\n\n\n\nfitted_pipeline_\n: scikit-learn Pipeline object\n\n\nThe best pipeline that TPOT discovered during the pipeline optimization process, fitted on the entire training dataset.\n\n\n\n\npareto_front_fitted_pipelines_\n: Python dictionary\n\n\nDictionary 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.\n\n\nThe TPOT Pareto front provides a trade-off between pipeline complexity (i.e., the number of steps in the pipeline) and the predictive performance of the pipeline.\n\n\nNote: \n_pareto_front_fitted_pipelines\n is only available when \nverbosity\n=3.\n\n\n\n\nevaluated_individuals_\n: Python dictionary\n\n\nDictionary 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).\n\n\nThis attribute is primarily for internal use, but may be useful for looking at the other pipelines that TPOT evaluated.\n\n\n\n\n\n\n\n\n\n\nExample\n\n\nfrom tpot import TPOTRegressor\nfrom sklearn.datasets import load_boston\nfrom sklearn.model_selection import train_test_split\n\ndigits = load_boston()\nX_train, X_test, y_train, y_test = train_test_split(digits.data, digits.target,\n train_size=0.75, test_size=0.25)\n\ntpot = TPOTRegressor(generations=5, population_size=50, verbosity=2)\ntpot.fit(X_train, y_train)\nprint(tpot.score(X_test, y_test))\ntpot.export('tpot_boston_pipeline.py')\n\n\n\n\nFunctions\n\n\n\n\n\n\nfit\n(features, target[, sample_weight, groups])\n\n\nRun the TPOT optimization process on the given training data.\n\n\n\n\n\n\n\npredict\n(features)\n\n\nUse the optimized pipeline to predict the target values for a feature set.\n\n\n\n\n\n\n\nscore\n(testing_features, testing_target)\n\n\nReturns the optimized pipeline's score on the given testing data using the user-specified scoring function.\n\n\n\n\n\n\n\nexport\n(output_file_name)\n\n\nExport the optimized pipeline as Python code.\n\n\n\n\n\n\n\n\n\nfit(features, target, sample_weight=None, groups=None)\n\n\n\n\n\nRun the TPOT optimization process on the given training data.\n\n\nUses 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.\n\n\n\n\n\n\n\nParameters:\n\n\n\n\nfeatures\n: array-like {n_samples, n_features}\n\n\nFeature matrix\n\n\nTPOT and all scikit-learn algorithms assume that the features will be numerical and there will be no missing values.\nAs such, when a feature matrix is provided to TPOT, all missing values will automatically be replaced (i.e., imputed)\nusing \nmedian value imputation\n.\n\n\nIf you wish to use a different imputation strategy than median imputation, please make sure to apply imputation to your feature set prior to passing it to TPOT.\n\n\n\n\ntarget\n: array-like {n_samples}\n\n\nList of target labels for prediction\n\n\n\n\nsample_weight\n: array-like {n_samples}, optional\n\n\nPer-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.\n\n\n\n\ngroups\n: array-like, with shape {n_samples, }, optional\n\n\nGroup labels for the samples used when performing cross-validation.\n\n\nThis parameter should only be used in conjunction with sklearn's Group cross-validation functions, such as \nsklearn.model_selection.GroupKFold\n.\n\n\n\n\n\n\n\n\n\nReturns:\n\n\n\n\nself\n: object\n\n\nReturns a copy of the fitted TPOT object\n\n\n\n\n\n\n\n\n\n\n\n\n\n\npredict(features)\n\n\n\n\n\nUse the optimized pipeline to predict the target values for a feature set.\n\n\n\n\n\n\n\nParameters:\n\n\n\n\nfeatures\n: array-like {n_samples, n_features}\n\n\nFeature matrix\n\n\n\n\n\n\n\n\n\nReturns:\n\n\n\n\npredictions\n: array-like {n_samples}\n\n\nPredicted target values for the samples in the feature matrix\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nscore(testing_features, testing_target)\n\n\n\n\n\nReturns the optimized pipeline's score on the given testing data using the user-specified scoring function.\n\n\nThe default scoring function for TPOTClassifier is 'mean_squared_error'.\n\n\n\n\n\n\n\nParameters:\n\n\n\n\ntesting_features\n: array-like {n_samples, n_features}\n\n\nFeature matrix of the testing set\n\n\n\n\ntesting_target\n: array-like {n_samples}\n\n\nList of target labels for prediction in the testing set\n\n\n\n\n\n\n\n\n\nReturns:\n\n\n\n\naccuracy_score\n: float\n\n\nThe estimated test set accuracy according to the user-specified scoring function.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nexport(output_file_name)\n\n\n\n\n\nExport the optimized pipeline as Python code.\n\n\nSee the \nusage documentation\n for example usage of the export function.\n\n\n\n\n\n\n\nParameters:\n\n\n\n\noutput_file_name\n: string\n\n\nString containing the path and file name of the desired output file\n\n\n\ndata_file_path\n: string\n\n\nBy 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.\n\n\n\n\n\n\n\nReturns:\n\n\n\n\nexported_code_string\n: string\n\n\nThe whole pipeline text as a string should be returned if output_file_name is not specified.",
+ "text": "Classification\n\n\nclass\n tpot.\nTPOTClassifier\n(\ngenerations\n=100, \npopulation_size\n=100,\n \noffspring_size\n=None, \nmutation_rate\n=0.9,\n \ncrossover_rate\n=0.1,\n \nscoring\n='accuracy', \ncv\n=5,\n \nsubsample\n=1.0, \nn_jobs\n=1,\n \nmax_time_mins\n=None, \nmax_eval_time_mins\n=5,\n \nrandom_state\n=None, \nconfig_dict\n=None,\n \ntemplate\n=None,\n \nwarm_start\n=False,\n \nmemory\n=None,\n \nuse_dask\n=False,\n \nperiodic_checkpoint_folder\n=None,\n \nearly_stop\n=None,\n \nverbosity\n=0,\n \ndisable_update_check\n=False\n)\n\n\n\nsource\n\n\n\nAutomated machine learning for supervised classification tasks.\n\n\nThe TPOTClassifier performs an intelligent search over machine learning pipelines that can contain supervised classification models,\npreprocessors, feature selection techniques, and any other estimator or transformer that follows the \nscikit-learn API\n.\nThe TPOTClassifier will also search over the hyperparameters of all objects in the pipeline.\n\n\nBy default, TPOTClassifier will search over a broad range of supervised classification algorithms, transformers, and their parameters.\nHowever, the algorithms, transformers, and hyperparameters that the TPOTClassifier searches over can be fully customized using the \nconfig_dict\n parameter.\n\n\nRead more in the \nUser Guide\n.\n\n\n\n\n\n\nParameters:\n\n\n\n\ngenerations\n: int or None optional (default=100)\n\n\nNumber of iterations to the run pipeline optimization process. It must be a positive number or None. If None, the parameter \nmax_time_mins\n must be defined as the runtime limit.\n\n\nGenerally, TPOT will work better when you give it more generations (and therefore time) to optimize the pipeline.\n\n\nTPOT will evaluate \npopulation_size\n + \ngenerations\n \u00d7 \noffspring_size\n pipelines in total.\n\n\n\n\npopulation_size\n: int, optional (default=100)\n\n\nNumber of individuals to retain in the genetic programming population every generation. Must be a positive number.\n\n\nGenerally, TPOT will work better when you give it more individuals with which to optimize the pipeline.\n\n\n\n\noffspring_size\n: int, optional (default=None)\n\n\nNumber 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.\n\n\n\n\nmutation_rate\n: float, optional (default=0.9)\n\n\nMutation 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.\n\n\n\nmutation_rate\n + \ncrossover_rate\n cannot exceed 1.0.\n\n\nWe recommend using the default parameter unless you understand how the mutation rate affects GP algorithms.\n\n\n\n\ncrossover_rate\n: float, optional (default=0.1)\n\n\nCrossover 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.\n\n\n\nmutation_rate\n + \ncrossover_rate\n cannot exceed 1.0.\n\n\nWe recommend using the default parameter unless you understand how the crossover rate affects GP algorithms.\n\n\n\n\nscoring\n: string or callable, optional (default='accuracy')\n\n\nFunction used to evaluate the quality of a given pipeline for the classification problem. The following built-in scoring functions can be used:\n\n\n'accuracy', 'adjusted_rand_score', 'average_precision', 'balanced_accuracy', 'f1', 'f1_macro', 'f1_micro', 'f1_samples', 'f1_weighted', 'neg_log_loss','precision',\n'precision_macro', 'precision_micro', 'precision_samples', 'precision_weighted', 'recall', 'recall_macro', 'recall_micro', 'recall_samples', 'recall_weighted', 'roc_auc'\n\n\nIf you would like to use a custom scorer, you can pass the callable object/function with signature \nscorer(estimator, X, y)\n.\n\n\nSee the section on \nscoring functions\n for more details.\n\n\n\n\n\ncv\n: int, cross-validation generator, or an iterable, optional (default=5)\n\n\nCross-validation strategy used when evaluating pipelines.\n\n\nPossible inputs:\n\n\n\ninteger, to specify the number of folds in a StratifiedKFold,\n\n\nAn object to be used as a cross-validation generator, or\n\n\nAn iterable yielding train/test splits.\n\n\n\n\n\nsubsample\n: float, optional (default=1.0)\n\n\nFraction of training samples that are used during the TPOT optimization process. Must be in the range (0.0, 1.0].\n\n\nSetting \nsubsample\n=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\n\n\n\nn_jobs\n: integer, optional (default=1)\n\n\nNumber of processes to use in parallel for evaluating pipelines during the TPOT optimization process.\n\n\nSetting \nn_jobs\n=-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.\n\n\n\n\nmax_time_mins\n: integer or None, optional (default=None)\n\n\nHow many minutes TPOT has to optimize the pipeline.\n\n\nIf not None, this setting will allow TPOT to run until \nmax_time_mins\n minutes elapsed and then stop. TPOT will stop earlier if \ngenerations\n is set and all generations are already evaluated.\n\n\n\n\nmax_eval_time_mins\n: float, optional (default=5)\n\n\nHow many minutes TPOT has to evaluate a single pipeline.\n\n\nSetting this parameter to higher values will allow TPOT to evaluate more complex pipelines, but will also allow TPOT to run longer. Use this parameter to help prevent TPOT from wasting time on evaluating time-consuming pipelines.\n\n\n\n\nrandom_state\n: integer or None, optional (default=None)\n\n\nThe seed of the pseudo random number generator used in TPOT.\n\n\nUse this parameter to make sure that TPOT will give you the same results each time you run it against the same data set with that seed.\n\n\n\n\nconfig_dict\n: Python dictionary, string, or None, optional (default=None)\n\n\nA configuration dictionary for customizing the operators and parameters that TPOT searches in the optimization process.\n\n\nPossible inputs are:\n\n\n\nPython dictionary, TPOT will use your custom configuration,\n\n\nstring 'TPOT light', TPOT will use a built-in configuration with only fast models and preprocessors, or\n\n\nstring 'TPOT MDR', TPOT will use a built-in configuration specialized for genomic studies, or\n\n\nstring '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\n\n\nNone, TPOT will use the default TPOTClassifier configuration.\n\n\n\nSee the \nbuilt-in configurations\n section for the list of configurations included with TPOT, and the \ncustom configuration\n section for more information and examples of how to create your own TPOT configurations.\n\n\n\n\ntemplate\n: string (default=None)\n\n\nTemplate of predefined pipeline structure. The option is for specifying a desired structure for the machine learning pipeline evaluated in TPOT.\n\n\nSo far this option only supports linear pipeline structure. Each step in the pipeline should be a main class of operators (Selector, Transformer, Classifier) 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) 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.\n\nSee the \n template option in tpot\n section for more details.\n\n\n\n\nwarm_start\n: boolean, optional (default=False)\n\n\nFlag indicating whether the TPOT instance will reuse the population from previous calls to \nfit()\n.\n\n\nSetting \nwarm_start\n=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.\n\n\n\n\nmemory\n: a joblib.Memory object or string, optional (default=None)\n\n\nIf 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 \nscikit-learn documentation\n\n\n\nPossible inputs are:\n\n\n\nString 'auto': TPOT uses memory caching with a temporary directory and cleans it up upon shutdown, or\n\n\nPath of a caching directory, TPOT uses memory caching with the provided directory and TPOT does NOT clean the caching directory up upon shutdown, or\n\n\nMemory object, TPOT uses the instance of joblib.Memory for memory caching and TPOT does NOT clean the caching directory up upon shutdown, or\n\n\nNone, TPOT does not use memory caching.\n\n\n\n\n\n\n\nuse_dask\n: boolean, optional (default: False)\n\n\nWhether to use Dask-ML's pipeline optimiziations. This avoid re-fitting\nthe same estimator on the same split of data multiple times. It\nwill also provide more detailed diagnostics when using Dask's\ndistributed scheduler.\n\n\nSee \navoid repeated work\n for more details.\n\n\n\n\nperiodic_checkpoint_folder\n: path string, optional (default: None)\n\n\nIf supplied, a folder in which TPOT will periodically save pipelines in pareto front so far while optimizing.\n\nCurrently once per generation but not more often than once per 30 seconds.\n\nUseful in multiple cases:\n\n\n\nSudden death before TPOT could save optimized pipeline\n\n\nTrack its progress\n\n\nGrab pipelines while it's still optimizing\n\n\n\n\n\n\n\nearly_stop\n: integer, optional (default: None)\n\n\nHow many generations TPOT checks whether there is no improvement in optimization process.\n\n\nEnds the optimization process if there is no improvement in the given number of generations.\n\n\n\n\nverbosity\n: integer, optional (default=0)\n\n\nHow much information TPOT communicates while it's running.\n\n\nPossible inputs are:\n\n\n\n0, TPOT will print nothing,\n\n\n1, TPOT will print minimal information,\n\n\n2, TPOT will print more information and provide a progress bar, or\n\n\n3, TPOT will print everything and provide a progress bar.\n\n\n\n\n\n\n\ndisable_update_check\n: boolean, optional (default=False)\n\n\nFlag indicating whether the TPOT version checker should be disabled.\n\n\nThe update checker will tell you when a new version of TPOT has been released.\n\n\n\n\n\n\n\n\n\n\nAttributes:\n\n\n\n\nfitted_pipeline_\n: scikit-learn Pipeline object\n\n\nThe best pipeline that TPOT discovered during the pipeline optimization process, fitted on the entire training dataset.\n\n\n\n\npareto_front_fitted_pipelines_\n: Python dictionary\n\n\nDictionary 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.\n\n\nThe TPOT Pareto front provides a trade-off between pipeline complexity (i.e., the number of steps in the pipeline) and the predictive performance of the pipeline.\n\n\nNote: \npareto_front_fitted_pipelines_\n is only available when \nverbosity\n=3.\n\n\n\n\nevaluated_individuals_\n: Python dictionary\n\n\nDictionary 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).\n\n\nThis attribute is primarily for internal use, but may be useful for looking at the other pipelines that TPOT evaluated.\n\n\n\n\n\n\n\n\n\n\nExample\n\n\nfrom tpot import TPOTClassifier\nfrom sklearn.datasets import load_digits\nfrom sklearn.model_selection import train_test_split\n\ndigits = load_digits()\nX_train, X_test, y_train, y_test = train_test_split(digits.data, digits.target,\n train_size=0.75, test_size=0.25)\n\ntpot = TPOTClassifier(generations=5, population_size=50, verbosity=2)\ntpot.fit(X_train, y_train)\nprint(tpot.score(X_test, y_test))\ntpot.export('tpot_digits_pipeline.py')\n\n\n\n\nFunctions\n\n\n\n\n\n\nfit\n(features, classes[, sample_weight, groups])\n\n\nRun the TPOT optimization process on the given training data.\n\n\n\n\n\n\n\npredict\n(features)\n\n\nUse the optimized pipeline to predict the classes for a feature set.\n\n\n\n\n\n\n\npredict_proba\n(features)\n\n\nUse the optimized pipeline to estimate the class probabilities for a feature set.\n\n\n\n\n\n\n\nscore\n(testing_features, testing_classes)\n\n\nReturns the optimized pipeline's score on the given testing data using the user-specified scoring function.\n\n\n\n\n\n\n\nexport\n(output_file_name)\n\n\nExport the optimized pipeline as Python code.\n\n\n\n\n\n\n\n\n\nfit(features, classes, sample_weight=None, groups=None)\n\n\n\n\n\nRun the TPOT optimization process on the given training data.\n\n\nUses 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.\n\n\n\n\n\n\n\nParameters:\n\n\n\n\nfeatures\n: array-like {n_samples, n_features}\n\n\nFeature matrix\n\n\nTPOT and all scikit-learn algorithms assume that the features will be numerical and there will be no missing values.\nAs such, when a feature matrix is provided to TPOT, all missing values will automatically be replaced (i.e., imputed)\nusing \nmedian value imputation\n.\n\n\nIf you wish to use a different imputation strategy than median imputation, please make sure to apply imputation to your feature set prior to passing it to TPOT.\n\n\n\n\nclasses\n: array-like {n_samples}\n\n\nList of class labels for prediction\n\n\n\n\nsample_weight\n: array-like {n_samples}, optional\n\n\nPer-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.\n\n\n\n\ngroups\n: array-like, with shape {n_samples, }, optional\n\n\nGroup labels for the samples used when performing cross-validation.\n\n\nThis parameter should only be used in conjunction with sklearn's Group cross-validation functions, such as \nsklearn.model_selection.GroupKFold\n.\n\n\n\n\n\n\n\n\n\nReturns:\n\n\n\n\nself\n: object\n\n\nReturns a copy of the fitted TPOT object\n\n\n\n\n\n\n\n\n\n\n\n\n\n\npredict(features)\n\n\n\n\n\nUse the optimized pipeline to predict the classes for a feature set.\n\n\n\n\n\n\n\nParameters:\n\n\n\n\nfeatures\n: array-like {n_samples, n_features}\n\n\nFeature matrix\n\n\n\n\n\n\n\n\n\nReturns:\n\n\n\n\npredictions\n: array-like {n_samples}\n\n\nPredicted classes for the samples in the feature matrix\n\n\n\n\n\n\n\n\n\n\n\n\n\n\npredict_proba(features)\n\n\n\n\n\nUse the optimized pipeline to estimate the class probabilities for a feature set.\n\n\nNote: This function will only work for pipelines whose final classifier supports the \npredict_proba\n function. TPOT will raise an error otherwise.\n\n\n\n\n\n\n\nParameters:\n\n\n\n\nfeatures\n: array-like {n_samples, n_features}\n\n\nFeature matrix\n\n\n\n\n\n\n\n\n\nReturns:\n\n\n\n\npredictions\n: array-like {n_samples, n_classes}\n\n\nThe class probabilities of the input samples\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nscore(testing_features, testing_classes)\n\n\n\n\n\nReturns the optimized pipeline's score on the given testing data using the user-specified scoring function.\n\n\nThe default scoring function for TPOTClassifier is 'accuracy'.\n\n\n\n\n\n\n\nParameters:\n\n\n\n\ntesting_features\n: array-like {n_samples, n_features}\n\n\nFeature matrix of the testing set\n\n\n\n\ntesting_classes\n: array-like {n_samples}\n\n\nList of class labels for prediction in the testing set\n\n\n\n\n\n\n\n\n\nReturns:\n\n\n\n\naccuracy_score\n: float\n\n\nThe estimated test set accuracy according to the user-specified scoring function.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nexport(output_file_name, data_file_path)\n\n\n\n\n\nExport the optimized pipeline as Python code.\n\n\nSee the \nusage documentation\n for example usage of the export function.\n\n\n\n\n\n\n\nParameters:\n\n\n\n\noutput_file_name\n: string\n\n\nString containing the path and file name of the desired output file\n\n\n\ndata_file_path\n: string\n\n\nBy 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.\n\n\n\n\n\n\n\nReturns:\n\n\n\n\nexported_code_string\n: string\n\n\nThe whole pipeline text as a string should be returned if output_file_name is not specified.\n\n\n\n\n\n\n\n\n\n\n\n\nRegression\n\n\nclass\n tpot.\nTPOTRegressor\n(\ngenerations\n=100, \npopulation_size\n=100,\n \noffspring_size\n=None, \nmutation_rate\n=0.9,\n \ncrossover_rate\n=0.1,\n \nscoring\n='neg_mean_squared_error', \ncv\n=5,\n \nsubsample\n=1.0, \nn_jobs\n=1,\n \nmax_time_mins\n=None, \nmax_eval_time_mins\n=5,\n \nrandom_state\n=None, \nconfig_dict\n=None,\n \ntemplate\n=None,\n \nwarm_start\n=False,\n \nmemory\n=None,\n \nuse_dask\n=False,\n \nperiodic_checkpoint_folder\n=None,\n \nearly_stop\n=None,\n \nverbosity\n=0,\n \ndisable_update_check\n=False\n)\n\n\n\nsource\n\n\n\nAutomated machine learning for supervised regression tasks.\n\n\nThe TPOTRegressor performs an intelligent search over machine learning pipelines that can contain supervised regression models,\npreprocessors, feature selection techniques, and any other estimator or transformer that follows the \nscikit-learn API\n.\nThe TPOTRegressor will also search over the hyperparameters of all objects in the pipeline.\n\n\nBy default, TPOTRegressor will search over a broad range of supervised regression models, transformers, and their hyperparameters.\nHowever, the models, transformers, and parameters that the TPOTRegressor searches over can be fully customized using the \nconfig_dict\n parameter.\n\n\nRead more in the \nUser Guide\n.\n\n\n\n\n\n\nParameters:\n\n\n\n\ngenerations\n: int or None, optional (default=100)\n\n\nNumber of iterations to the run pipeline optimization process. It must be a positive number or None. If None, the parameter \nmax_time_mins\n must be defined as the runtime limit.\n\n\nGenerally, TPOT will work better when you give it more generations (and therefore time) to optimize the pipeline.\n\n\nTPOT will evaluate \npopulation_size\n + \ngenerations\n \u00d7 \noffspring_size\n pipelines in total.\n\n\n\n\npopulation_size\n: int, optional (default=100)\n\n\nNumber of individuals to retain in the genetic programming population every generation. Must be a positive number.\n\n\nGenerally, TPOT will work better when you give it more individuals with which to optimize the pipeline.\n\n\n\n\noffspring_size\n: int, optional (default=None)\n\n\nNumber 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.\n\n\n\n\nmutation_rate\n: float, optional (default=0.9)\n\n\nMutation 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.\n\n\n\nmutation_rate\n + \ncrossover_rate\n cannot exceed 1.0.\n\n\nWe recommend using the default parameter unless you understand how the mutation rate affects GP algorithms.\n\n\n\n\ncrossover_rate\n: float, optional (default=0.1)\n\n\nCrossover 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.\n\n\n\nmutation_rate\n + \ncrossover_rate\n cannot exceed 1.0.\n\n\nWe recommend using the default parameter unless you understand how the crossover rate affects GP algorithms.\n\n\n\n\nscoring\n: string or callable, optional (default='neg_mean_squared_error')\n\n\nFunction used to evaluate the quality of a given pipeline for the regression problem. The following built-in scoring functions can be used:\n\n\n'neg_median_absolute_error', 'neg_mean_absolute_error', 'neg_mean_squared_error', 'r2'\n\n\nNote that we recommend using the \nneg\n version of mean squared error and related metrics so TPOT will minimize (instead of maximize) the metric.\n\n\nIf you would like to use a custom scorer, you can pass the callable object/function with signature \nscorer(estimator, X, y)\n.\n\n\nSee the section on \nscoring functions\n for more details.\n\n\n\n\ncv\n: int, cross-validation generator, or an iterable, optional (default=5)\n\n\nCross-validation strategy used when evaluating pipelines.\n\n\nPossible inputs:\n\n\n\ninteger, to specify the number of folds in a KFold,\n\n\nAn object to be used as a cross-validation generator, or\n\n\nAn iterable yielding train/test splits.\n\n\n\n\n\n\n\nsubsample\n: float, optional (default=1.0)\n\n\nFraction of training samples that are used during the TPOT optimization process. Must be in the range (0.0, 1.0].\n\n\nSetting \nsubsample\n=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\n\n\n\nn_jobs\n: integer, optional (default=1)\n\n\nNumber of processes to use in parallel for evaluating pipelines during the TPOT optimization process.\n\n\nSetting \nn_jobs\n=-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\n\n\n\n\nmax_time_mins\n: integer or None, optional (default=None)\n\n\nHow many minutes TPOT has to optimize the pipeline.\n\n\nIf not None, this setting will allow TPOT to run until \nmax_time_mins\n minutes elapsed and then stop. TPOT will stop earlier if \ngenerations\n is set and all generations are already evaluated.\n\n\n\n\nmax_eval_time_mins\n: float, optional (default=5)\n\n\nHow many minutes TPOT has to evaluate a single pipeline.\n\n\nSetting this parameter to higher values will allow TPOT to evaluate more complex pipelines, but will also allow TPOT to run longer. Use this parameter to help prevent TPOT from wasting time on evaluating time-consuming pipelines.\n\n\n\n\nrandom_state\n: integer or None, optional (default=None)\n\n\nThe seed of the pseudo random number generator used in TPOT.\n\n\nUse this parameter to make sure that TPOT will give you the same results each time you run it against the same data set with that seed.\n\n\n\n\nconfig_dict\n: Python dictionary, string, or None, optional (default=None)\n\n\nA configuration dictionary for customizing the operators and parameters that TPOT searches in the optimization process.\n\n\nPossible inputs are:\n\n\n\nPython dictionary, TPOT will use your custom configuration,\n\n\nstring 'TPOT light', TPOT will use a built-in configuration with only fast models and preprocessors, or\n\n\nstring 'TPOT MDR', TPOT will use a built-in configuration specialized for genomic studies, or\n\n\nstring '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\n\n\nNone, TPOT will use the default TPOTRegressor configuration.\n\n\n\nSee the \nbuilt-in configurations\n section for the list of configurations included with TPOT, and the \ncustom configuration\n section for more information and examples of how to create your own TPOT configurations.\n\n\n\n\ntemplate\n: string (default=None)\n\n\nTemplate of predefined pipeline structure. The option is for specifying a desired structure for the machine learning pipeline evaluated in TPOT.\n\n\nSo far this option only supports linear pipeline structure. Each step in the pipeline should be a main class of operators (Selector, Transformer 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) 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-Regressor\". By default value of template is None, TPOT generates tree-based pipeline randomly.\n\nSee the \n template option in tpot\n section for more details.\n\n\n\n\nwarm_start\n: boolean, optional (default=False)\n\n\nFlag indicating whether the TPOT instance will reuse the population from previous calls to \nfit()\n.\n\n\nSetting \nwarm_start\n=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.\n\n\n\n\nmemory\n: a joblib.Memory object or string, optional (default=None)\n\n\nIf 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 \nscikit-learn documentation\n\n\n\nPossible inputs are:\n\n\n\nString 'auto': TPOT uses memory caching with a temporary directory and cleans it up upon shutdown, or\n\n\nPath of a caching directory, TPOT uses memory caching with the provided directory and TPOT does NOT clean the caching directory up upon shutdown, or\n\n\nMemory object, TPOT uses the instance of joblib.Memory for memory caching and TPOT does NOT clean the caching directory up upon shutdown, or\n\n\nNone, TPOT does not use memory caching.\n\n\n\n\n\n\n\nuse_dask\n: boolean, optional (default: False)\n\n\nWhether to use Dask-ML's pipeline optimiziations. This avoid re-fitting\nthe same estimator on the same split of data multiple times. It\nwill also provide more detailed diagnostics when using Dask's\ndistributed scheduler.\n\n\nSee \navoid repeated work\n for more details.\n\n\n\n\nperiodic_checkpoint_folder\n: path string, optional (default: None)\n\n\nIf supplied, a folder in which TPOT will periodically save pipelines in pareto front so far while optimizing.\n\nCurrently once per generation but not more often than once per 30 seconds.\n\nUseful in multiple cases:\n\n\n\nSudden death before TPOT could save optimized pipeline\n\n\nTrack its progress\n\n\nGrab pipelines while it's still optimizing\n\n\n\n\n\n\n\nearly_stop\n: integer, optional (default: None)\n\n\nHow many generations TPOT checks whether there is no improvement in optimization process.\n\n\nEnds the optimization process if there is no improvement in the given number of generations.\n\n\n\n\nverbosity\n: integer, optional (default=0)\n\n\nHow much information TPOT communicates while it's running.\n\n\nPossible inputs are:\n\n\n\n0, TPOT will print nothing,\n\n\n1, TPOT will print minimal information,\n\n\n2, TPOT will print more information and provide a progress bar, or\n\n\n3, TPOT will print everything and provide a progress bar.\n\n\n\n\n\n\n\ndisable_update_check\n: boolean, optional (default=False)\n\n\nFlag indicating whether the TPOT version checker should be disabled.\n\n\nThe update checker will tell you when a new version of TPOT has been released.\n\n\n\n\n\n\n\n\n\n\nAttributes:\n\n\n\n\nfitted_pipeline_\n: scikit-learn Pipeline object\n\n\nThe best pipeline that TPOT discovered during the pipeline optimization process, fitted on the entire training dataset.\n\n\n\n\npareto_front_fitted_pipelines_\n: Python dictionary\n\n\nDictionary 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.\n\n\nThe TPOT Pareto front provides a trade-off between pipeline complexity (i.e., the number of steps in the pipeline) and the predictive performance of the pipeline.\n\n\nNote: \n_pareto_front_fitted_pipelines\n is only available when \nverbosity\n=3.\n\n\n\n\nevaluated_individuals_\n: Python dictionary\n\n\nDictionary 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).\n\n\nThis attribute is primarily for internal use, but may be useful for looking at the other pipelines that TPOT evaluated.\n\n\n\n\n\n\n\n\n\n\nExample\n\n\nfrom tpot import TPOTRegressor\nfrom sklearn.datasets import load_boston\nfrom sklearn.model_selection import train_test_split\n\ndigits = load_boston()\nX_train, X_test, y_train, y_test = train_test_split(digits.data, digits.target,\n train_size=0.75, test_size=0.25)\n\ntpot = TPOTRegressor(generations=5, population_size=50, verbosity=2)\ntpot.fit(X_train, y_train)\nprint(tpot.score(X_test, y_test))\ntpot.export('tpot_boston_pipeline.py')\n\n\n\n\nFunctions\n\n\n\n\n\n\nfit\n(features, target[, sample_weight, groups])\n\n\nRun the TPOT optimization process on the given training data.\n\n\n\n\n\n\n\npredict\n(features)\n\n\nUse the optimized pipeline to predict the target values for a feature set.\n\n\n\n\n\n\n\nscore\n(testing_features, testing_target)\n\n\nReturns the optimized pipeline's score on the given testing data using the user-specified scoring function.\n\n\n\n\n\n\n\nexport\n(output_file_name)\n\n\nExport the optimized pipeline as Python code.\n\n\n\n\n\n\n\n\n\nfit(features, target, sample_weight=None, groups=None)\n\n\n\n\n\nRun the TPOT optimization process on the given training data.\n\n\nUses 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.\n\n\n\n\n\n\n\nParameters:\n\n\n\n\nfeatures\n: array-like {n_samples, n_features}\n\n\nFeature matrix\n\n\nTPOT and all scikit-learn algorithms assume that the features will be numerical and there will be no missing values.\nAs such, when a feature matrix is provided to TPOT, all missing values will automatically be replaced (i.e., imputed)\nusing \nmedian value imputation\n.\n\n\nIf you wish to use a different imputation strategy than median imputation, please make sure to apply imputation to your feature set prior to passing it to TPOT.\n\n\n\n\ntarget\n: array-like {n_samples}\n\n\nList of target labels for prediction\n\n\n\n\nsample_weight\n: array-like {n_samples}, optional\n\n\nPer-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.\n\n\n\n\ngroups\n: array-like, with shape {n_samples, }, optional\n\n\nGroup labels for the samples used when performing cross-validation.\n\n\nThis parameter should only be used in conjunction with sklearn's Group cross-validation functions, such as \nsklearn.model_selection.GroupKFold\n.\n\n\n\n\n\n\n\n\n\nReturns:\n\n\n\n\nself\n: object\n\n\nReturns a copy of the fitted TPOT object\n\n\n\n\n\n\n\n\n\n\n\n\n\n\npredict(features)\n\n\n\n\n\nUse the optimized pipeline to predict the target values for a feature set.\n\n\n\n\n\n\n\nParameters:\n\n\n\n\nfeatures\n: array-like {n_samples, n_features}\n\n\nFeature matrix\n\n\n\n\n\n\n\n\n\nReturns:\n\n\n\n\npredictions\n: array-like {n_samples}\n\n\nPredicted target values for the samples in the feature matrix\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nscore(testing_features, testing_target)\n\n\n\n\n\nReturns the optimized pipeline's score on the given testing data using the user-specified scoring function.\n\n\nThe default scoring function for TPOTRegressor is 'mean_squared_error'.\n\n\n\n\n\n\n\nParameters:\n\n\n\n\ntesting_features\n: array-like {n_samples, n_features}\n\n\nFeature matrix of the testing set\n\n\n\n\ntesting_target\n: array-like {n_samples}\n\n\nList of target labels for prediction in the testing set\n\n\n\n\n\n\n\n\n\nReturns:\n\n\n\n\naccuracy_score\n: float\n\n\nThe estimated test set accuracy according to the user-specified scoring function.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nexport(output_file_name)\n\n\n\n\n\nExport the optimized pipeline as Python code.\n\n\nSee the \nusage documentation\n for example usage of the export function.\n\n\n\n\n\n\n\nParameters:\n\n\n\n\noutput_file_name\n: string\n\n\nString containing the path and file name of the desired output file\n\n\n\ndata_file_path\n: string\n\n\nBy 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.\n\n\n\n\n\n\n\nReturns:\n\n\n\n\nexported_code_string\n: string\n\n\nThe whole pipeline text as a string should be returned if output_file_name is not specified.",
"title": "TPOT API"
},
{
@@ -97,7 +97,7 @@
},
{
"location": "/api/#regression",
- "text": "class tpot. TPOTRegressor ( generations =100, population_size =100,\n offspring_size =None, mutation_rate =0.9,\n crossover_rate =0.1,\n scoring ='neg_mean_squared_error', cv =5,\n subsample =1.0, n_jobs =1,\n max_time_mins =None, max_eval_time_mins =5,\n random_state =None, config_dict =None,\n template =None,\n warm_start =False,\n memory =None,\n use_dask =False,\n periodic_checkpoint_folder =None,\n early_stop =None,\n verbosity =0,\n disable_update_check =False ) source Automated machine learning for supervised regression tasks. The TPOTRegressor performs an intelligent search over machine learning pipelines that can contain supervised regression models,\npreprocessors, feature selection techniques, and any other estimator or transformer that follows the scikit-learn API .\nThe TPOTRegressor will also search over the hyperparameters of all objects in the pipeline. By default, TPOTRegressor will search over a broad range of supervised regression models, transformers, and their hyperparameters.\nHowever, the models, transformers, and parameters that the TPOTRegressor searches over can be fully customized using the config_dict parameter. Read more in the User Guide . Parameters: generations : int or None, optional (default=100) \nNumber 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. \nGenerally, TPOT will work better when you give it more generations (and therefore time) to optimize the pipeline. \nTPOT will evaluate population_size + generations \u00d7 offspring_size pipelines in total. population_size : int, optional (default=100) \nNumber of individuals to retain in the genetic programming population every generation. Must be a positive number. \nGenerally, TPOT will work better when you give it more individuals with which to optimize the pipeline. offspring_size : int, optional (default=None) \nNumber 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) \nMutation 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. \nWe recommend using the default parameter unless you understand how the mutation rate affects GP algorithms. crossover_rate : float, optional (default=0.1) \nCrossover 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. \nWe recommend using the default parameter unless you understand how the crossover rate affects GP algorithms. scoring : string or callable, optional (default='neg_mean_squared_error') \nFunction used to evaluate the quality of a given pipeline for the regression problem. The following built-in scoring functions can be used: \n'neg_median_absolute_error', 'neg_mean_absolute_error', 'neg_mean_squared_error', 'r2' \nNote that we recommend using the neg version of mean squared error and related metrics so TPOT will minimize (instead of maximize) the metric. \nIf you would like to use a custom scorer, you can pass the callable object/function with signature scorer(estimator, X, y) . \nSee the section on scoring functions for more details. cv : int, cross-validation generator, or an iterable, optional (default=5) \nCross-validation strategy used when evaluating pipelines. \nPossible inputs: integer, to specify the number of folds in a KFold, An object to be used as a cross-validation generator, or An iterable yielding train/test splits. subsample : float, optional (default=1.0) \nFraction of training samples that are used during the TPOT optimization process. Must be in the range (0.0, 1.0]. \nSetting 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) \nNumber of processes to use in parallel for evaluating pipelines during the TPOT optimization process. \nSetting 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) \nHow many minutes TPOT has to optimize the pipeline. \nIf not None, this setting will allow TPOT to run until 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) \nHow many minutes TPOT has to evaluate a single pipeline. \nSetting this parameter to higher values will allow TPOT to evaluate more complex pipelines, but will also allow TPOT to run longer. Use this parameter to help prevent TPOT from wasting time on evaluating time-consuming pipelines. random_state : integer or None, optional (default=None) \nThe seed of the pseudo random number generator used in TPOT. \nUse this parameter to make sure that TPOT will give you the same results each time you run it against the same data set with that seed. config_dict : Python dictionary, string, or None, optional (default=None) \nA configuration dictionary for customizing the operators and parameters that TPOT searches in the optimization process. \nPossible inputs are: 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. \nSee 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) \nTemplate of predefined pipeline structure. The option is for specifying a desired structure for the machine learning pipeline evaluated in TPOT. \nSo far this option only supports linear pipeline structure. Each step in the pipeline should be a main class of operators (Selector, Transformer 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) 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-Regressor\". By default value of template is None, TPOT generates tree-based pipeline randomly.\n\nSee the template option in tpot section for more details. warm_start : boolean, optional (default=False) \nFlag indicating whether the TPOT instance will reuse the population from previous calls to fit() . \nSetting 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) \nIf 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 \nPossible inputs are: 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) \nWhether to use Dask-ML's pipeline optimiziations. This avoid re-fitting\nthe same estimator on the same split of data multiple times. It\nwill also provide more detailed diagnostics when using Dask's\ndistributed scheduler. \nSee avoid repeated work for more details. periodic_checkpoint_folder : path string, optional (default: None) \nIf supplied, a folder in which TPOT will periodically save pipelines in pareto front so far while optimizing. \nCurrently once per generation but not more often than once per 30 seconds. \nUseful in multiple cases: Sudden death before TPOT could save optimized pipeline Track its progress Grab pipelines while it's still optimizing early_stop : integer, optional (default: None) \nHow many generations TPOT checks whether there is no improvement in optimization process. \nEnds the optimization process if there is no improvement in the given number of generations. verbosity : integer, optional (default=0) \nHow much information TPOT communicates while it's running. \nPossible inputs are: 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) \nFlag indicating whether the TPOT version checker should be disabled. \nThe update checker will tell you when a new version of TPOT has been released. Attributes: fitted_pipeline_ : scikit-learn Pipeline object \nThe best pipeline that TPOT discovered during the pipeline optimization process, fitted on the entire training dataset. pareto_front_fitted_pipelines_ : Python dictionary \nDictionary 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. \nThe TPOT Pareto front provides a trade-off between pipeline complexity (i.e., the number of steps in the pipeline) and the predictive performance of the pipeline. \nNote: _pareto_front_fitted_pipelines is only available when verbosity =3. evaluated_individuals_ : Python dictionary \nDictionary 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). \nThis attribute is primarily for internal use, but may be useful for looking at the other pipelines that TPOT evaluated. Example from tpot import TPOTRegressor\nfrom sklearn.datasets import load_boston\nfrom sklearn.model_selection import train_test_split\n\ndigits = load_boston()\nX_train, X_test, y_train, y_test = train_test_split(digits.data, digits.target,\n train_size=0.75, test_size=0.25)\n\ntpot = TPOTRegressor(generations=5, population_size=50, verbosity=2)\ntpot.fit(X_train, y_train)\nprint(tpot.score(X_test, y_test))\ntpot.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. fit(features, target, sample_weight=None, groups=None) \nRun the TPOT optimization process on the given training data. \nUses 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} \nFeature matrix \nTPOT and all scikit-learn algorithms assume that the features will be numerical and there will be no missing values.\nAs such, when a feature matrix is provided to TPOT, all missing values will automatically be replaced (i.e., imputed)\nusing median value imputation . \nIf you wish to use a different imputation strategy than median imputation, please make sure to apply imputation to your feature set prior to passing it to TPOT. target : array-like {n_samples} \nList of target labels for prediction sample_weight : array-like {n_samples}, optional \nPer-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 \nGroup labels for the samples used when performing cross-validation. \nThis parameter should only be used in conjunction with sklearn's Group cross-validation functions, such as sklearn.model_selection.GroupKFold . Returns: self : object \nReturns a copy of the fitted TPOT object predict(features) \nUse the optimized pipeline to predict the target values for a feature set. Parameters: features : array-like {n_samples, n_features} \nFeature matrix Returns: predictions : array-like {n_samples} \nPredicted target values for the samples in the feature matrix score(testing_features, testing_target) \nReturns the optimized pipeline's score on the given testing data using the user-specified scoring function. \nThe default scoring function for TPOTClassifier is 'mean_squared_error'. Parameters: testing_features : array-like {n_samples, n_features} \nFeature matrix of the testing set testing_target : array-like {n_samples} \nList of target labels for prediction in the testing set Returns: accuracy_score : float \nThe estimated test set accuracy according to the user-specified scoring function. export(output_file_name) \nExport the optimized pipeline as Python code. \nSee the usage documentation for example usage of the export function. Parameters: output_file_name : string \nString containing the path and file name of the desired output file data_file_path : string \nBy 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 \nThe whole pipeline text as a string should be returned if output_file_name is not specified.",
+ "text": "class tpot. TPOTRegressor ( generations =100, population_size =100,\n offspring_size =None, mutation_rate =0.9,\n crossover_rate =0.1,\n scoring ='neg_mean_squared_error', cv =5,\n subsample =1.0, n_jobs =1,\n max_time_mins =None, max_eval_time_mins =5,\n random_state =None, config_dict =None,\n template =None,\n warm_start =False,\n memory =None,\n use_dask =False,\n periodic_checkpoint_folder =None,\n early_stop =None,\n verbosity =0,\n disable_update_check =False ) source Automated machine learning for supervised regression tasks. The TPOTRegressor performs an intelligent search over machine learning pipelines that can contain supervised regression models,\npreprocessors, feature selection techniques, and any other estimator or transformer that follows the scikit-learn API .\nThe TPOTRegressor will also search over the hyperparameters of all objects in the pipeline. By default, TPOTRegressor will search over a broad range of supervised regression models, transformers, and their hyperparameters.\nHowever, the models, transformers, and parameters that the TPOTRegressor searches over can be fully customized using the config_dict parameter. Read more in the User Guide . Parameters: generations : int or None, optional (default=100) \nNumber 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. \nGenerally, TPOT will work better when you give it more generations (and therefore time) to optimize the pipeline. \nTPOT will evaluate population_size + generations \u00d7 offspring_size pipelines in total. population_size : int, optional (default=100) \nNumber of individuals to retain in the genetic programming population every generation. Must be a positive number. \nGenerally, TPOT will work better when you give it more individuals with which to optimize the pipeline. offspring_size : int, optional (default=None) \nNumber 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) \nMutation 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. \nWe recommend using the default parameter unless you understand how the mutation rate affects GP algorithms. crossover_rate : float, optional (default=0.1) \nCrossover 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. \nWe recommend using the default parameter unless you understand how the crossover rate affects GP algorithms. scoring : string or callable, optional (default='neg_mean_squared_error') \nFunction used to evaluate the quality of a given pipeline for the regression problem. The following built-in scoring functions can be used: \n'neg_median_absolute_error', 'neg_mean_absolute_error', 'neg_mean_squared_error', 'r2' \nNote that we recommend using the neg version of mean squared error and related metrics so TPOT will minimize (instead of maximize) the metric. \nIf you would like to use a custom scorer, you can pass the callable object/function with signature scorer(estimator, X, y) . \nSee the section on scoring functions for more details. cv : int, cross-validation generator, or an iterable, optional (default=5) \nCross-validation strategy used when evaluating pipelines. \nPossible inputs: integer, to specify the number of folds in a KFold, An object to be used as a cross-validation generator, or An iterable yielding train/test splits. subsample : float, optional (default=1.0) \nFraction of training samples that are used during the TPOT optimization process. Must be in the range (0.0, 1.0]. \nSetting 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) \nNumber of processes to use in parallel for evaluating pipelines during the TPOT optimization process. \nSetting 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) \nHow many minutes TPOT has to optimize the pipeline. \nIf not None, this setting will allow TPOT to run until 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) \nHow many minutes TPOT has to evaluate a single pipeline. \nSetting this parameter to higher values will allow TPOT to evaluate more complex pipelines, but will also allow TPOT to run longer. Use this parameter to help prevent TPOT from wasting time on evaluating time-consuming pipelines. random_state : integer or None, optional (default=None) \nThe seed of the pseudo random number generator used in TPOT. \nUse this parameter to make sure that TPOT will give you the same results each time you run it against the same data set with that seed. config_dict : Python dictionary, string, or None, optional (default=None) \nA configuration dictionary for customizing the operators and parameters that TPOT searches in the optimization process. \nPossible inputs are: 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. \nSee 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) \nTemplate of predefined pipeline structure. The option is for specifying a desired structure for the machine learning pipeline evaluated in TPOT. \nSo far this option only supports linear pipeline structure. Each step in the pipeline should be a main class of operators (Selector, Transformer 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) 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-Regressor\". By default value of template is None, TPOT generates tree-based pipeline randomly.\n\nSee the template option in tpot section for more details. warm_start : boolean, optional (default=False) \nFlag indicating whether the TPOT instance will reuse the population from previous calls to fit() . \nSetting 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) \nIf 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 \nPossible inputs are: 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) \nWhether to use Dask-ML's pipeline optimiziations. This avoid re-fitting\nthe same estimator on the same split of data multiple times. It\nwill also provide more detailed diagnostics when using Dask's\ndistributed scheduler. \nSee avoid repeated work for more details. periodic_checkpoint_folder : path string, optional (default: None) \nIf supplied, a folder in which TPOT will periodically save pipelines in pareto front so far while optimizing. \nCurrently once per generation but not more often than once per 30 seconds. \nUseful in multiple cases: Sudden death before TPOT could save optimized pipeline Track its progress Grab pipelines while it's still optimizing early_stop : integer, optional (default: None) \nHow many generations TPOT checks whether there is no improvement in optimization process. \nEnds the optimization process if there is no improvement in the given number of generations. verbosity : integer, optional (default=0) \nHow much information TPOT communicates while it's running. \nPossible inputs are: 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) \nFlag indicating whether the TPOT version checker should be disabled. \nThe update checker will tell you when a new version of TPOT has been released. Attributes: fitted_pipeline_ : scikit-learn Pipeline object \nThe best pipeline that TPOT discovered during the pipeline optimization process, fitted on the entire training dataset. pareto_front_fitted_pipelines_ : Python dictionary \nDictionary 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. \nThe TPOT Pareto front provides a trade-off between pipeline complexity (i.e., the number of steps in the pipeline) and the predictive performance of the pipeline. \nNote: _pareto_front_fitted_pipelines is only available when verbosity =3. evaluated_individuals_ : Python dictionary \nDictionary 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). \nThis attribute is primarily for internal use, but may be useful for looking at the other pipelines that TPOT evaluated. Example from tpot import TPOTRegressor\nfrom sklearn.datasets import load_boston\nfrom sklearn.model_selection import train_test_split\n\ndigits = load_boston()\nX_train, X_test, y_train, y_test = train_test_split(digits.data, digits.target,\n train_size=0.75, test_size=0.25)\n\ntpot = TPOTRegressor(generations=5, population_size=50, verbosity=2)\ntpot.fit(X_train, y_train)\nprint(tpot.score(X_test, y_test))\ntpot.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. fit(features, target, sample_weight=None, groups=None) \nRun the TPOT optimization process on the given training data. \nUses 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} \nFeature matrix \nTPOT and all scikit-learn algorithms assume that the features will be numerical and there will be no missing values.\nAs such, when a feature matrix is provided to TPOT, all missing values will automatically be replaced (i.e., imputed)\nusing median value imputation . \nIf you wish to use a different imputation strategy than median imputation, please make sure to apply imputation to your feature set prior to passing it to TPOT. target : array-like {n_samples} \nList of target labels for prediction sample_weight : array-like {n_samples}, optional \nPer-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 \nGroup labels for the samples used when performing cross-validation. \nThis parameter should only be used in conjunction with sklearn's Group cross-validation functions, such as sklearn.model_selection.GroupKFold . Returns: self : object \nReturns a copy of the fitted TPOT object predict(features) \nUse the optimized pipeline to predict the target values for a feature set. Parameters: features : array-like {n_samples, n_features} \nFeature matrix Returns: predictions : array-like {n_samples} \nPredicted target values for the samples in the feature matrix score(testing_features, testing_target) \nReturns the optimized pipeline's score on the given testing data using the user-specified scoring function. \nThe default scoring function for TPOTRegressor is 'mean_squared_error'. Parameters: testing_features : array-like {n_samples, n_features} \nFeature matrix of the testing set testing_target : array-like {n_samples} \nList of target labels for prediction in the testing set Returns: accuracy_score : float \nThe estimated test set accuracy according to the user-specified scoring function. export(output_file_name) \nExport the optimized pipeline as Python code. \nSee the usage documentation for example usage of the export function. Parameters: output_file_name : string \nString containing the path and file name of the desired output file data_file_path : string \nBy 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 \nThe whole pipeline text as a string should be returned if output_file_name is not specified.",
"title": "Regression"
},
{
diff --git a/docs/sitemap.xml b/docs/sitemap.xml
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--- a/docs/sitemap.xml
+++ b/docs/sitemap.xml
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http://epistasislab.github.io/tpot/
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http://epistasislab.github.io/tpot/installing/
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http://epistasislab.github.io/tpot/using/
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http://epistasislab.github.io/tpot/api/
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http://epistasislab.github.io/tpot/examples/
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http://epistasislab.github.io/tpot/contributing/
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http://epistasislab.github.io/tpot/releases/
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http://epistasislab.github.io/tpot/citing/
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http://epistasislab.github.io/tpot/support/
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http://epistasislab.github.io/tpot/related/
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diff --git a/docs_sources/api.md b/docs_sources/api.md
index 2e20357e..895a2294 100644
--- a/docs_sources/api.md
+++ b/docs_sources/api.md
@@ -887,7 +887,7 @@ score(testing_features, testing_target)
Returns the optimized pipeline's score on the given testing data using the user-specified scoring function.
-The default scoring function for TPOTClassifier is 'mean_squared_error'.
+The default scoring function for TPOTRegressor is 'mean_squared_error'.