17887 12269 sklearn.pipeline.Pipeline(step_0=automl.util.sklearn.StackingEstimator(estimator=sklearn.linear_model._stochastic_gradient.SGDClassifier),step_1=sklearn.ensemble._hist_gradient_boosting.gradient_boosting.HistGradientBoostingClassifier) sklearn.Pipeline(StackingEstimator,HistGradientBoostingClassifier) sklearn.pipeline.Pipeline 1 automl==0.0.1,openml==0.10.2,sklearn==0.22.1 Pipeline of transforms with a final estimator. Sequentially apply a list of transforms and a final estimator. Intermediate steps of the pipeline must be 'transforms', that is, they must implement fit and transform methods. The final estimator only needs to implement fit. The transformers in the pipeline can be cached using ``memory`` argument. The purpose of the pipeline is to assemble several steps that can be cross-validated together while setting different parameters. For this, it enables setting parameters of the various steps using their names and the parameter name separated by a '__', as in the example below. A step's estimator may be replaced entirely by setting the parameter with its name to another estimator, or a transformer removed by setting it to 'passthrough' or ``None``. 2020-05-19T03:36:21 English sklearn==0.22.1 numpy>=1.6.1 scipy>=0.9 memory None null Used to cache the fitted transformers of the pipeline. By default, no caching is performed. If a string is given, it is the path to the caching directory. Enabling caching triggers a clone of the transformers before fitting. Therefore, the transformer instance given to the pipeline cannot be inspected directly. Use the attribute ``named_steps`` or ``steps`` to inspect estimators within the pipeline. Caching the transformers is advantageous when fitting is time consuming steps list [{"oml-python:serialized_object": "component_reference", "value": {"key": "step_0", "step_name": "step_0"}}, {"oml-python:serialized_object": "component_reference", "value": {"key": "step_1", "step_name": "step_1"}}] List of (name, transform) tuples (implementing fit/transform) that are chained, in the order in which they are chained, with the last object an estimator verbose bool false If True, the time elapsed while fitting each step will be printed as it is completed. step_1 17709 12269 sklearn.ensemble._hist_gradient_boosting.gradient_boosting.HistGradientBoostingClassifier sklearn.HistGradientBoostingClassifier sklearn.ensemble._hist_gradient_boosting.gradient_boosting.HistGradientBoostingClassifier 7 openml==0.10.2,sklearn==0.22.1 Histogram-based Gradient Boosting Classification Tree. This estimator is much faster than :class:`GradientBoostingClassifier<sklearn.ensemble.GradientBoostingClassifier>` for big datasets (n_samples >= 10 000). This estimator has native support for missing values (NaNs). During training, the tree grower learns at each split point whether samples with missing values should go to the left or right child, based on the potential gain. When predicting, samples with missing values are assigned to the left or right child consequently. If no missing values were encountered for a given feature during training, then samples with missing values are mapped to whichever child has the most samples. This implementation is inspired by `LightGBM <https://github.com/Microsoft/LightGBM>`_. .. note:: This estimator is still **experimental** for now: the predictions and the API might change without any deprecation cycle. To use it, you need to explicitly import ``enable_hist_gradient_boosting``:: >>> # explicit... 2020-05-18T19:47:40 English sklearn==0.22.1 numpy>=1.6.1 scipy>=0.9 l2_regularization float 0.029157851614848844 The L2 regularization parameter. Use 0 for no regularization learning_rate float 0.0002615635618827854 The learning rate, also known as *shrinkage*. This is used as a multiplicative factor for the leaves values. Use ``1`` for no shrinkage loss "auto" max_bins int 219 The maximum number of bins to use for non-missing values. Before training, each feature of the input array `X` is binned into integer-valued bins, which allows for a much faster training stage Features with a small number of unique values may use less than ``max_bins`` bins. In addition to the ``max_bins`` bins, one more bin is always reserved for missing values. Must be no larger than 255 max_depth int or None 9 The maximum depth of each tree. The depth of a tree is the number of nodes to go from the root to the deepest leaf. Must be strictly greater than 1. Depth isn't constrained by default max_iter int 938 The maximum number of iterations of the boosting process, i.e. the maximum number of trees for binary classification. For multiclass classification, `n_classes` trees per iteration are built max_leaf_nodes int or None 107 The maximum number of leaves for each tree. Must be strictly greater than 1. If None, there is no maximum limit min_samples_leaf int 266 The minimum number of samples per leaf. For small datasets with less than a few hundred samples, it is recommended to lower this value since only very shallow trees would be built n_iter_no_change int or None 65 Used to determine when to "early stop". The fitting process is stopped when none of the last ``n_iter_no_change`` scores are better than the ``n_iter_no_change - 1`` -th-to-last one, up to some tolerance. If None or 0, no early-stopping is done random_state int 42 Pseudo-random number generator to control the subsampling in the binning process, and the train/validation data split if early stopping is enabled. See :term:`random_state`. scoring str or callable or None "neg_log_loss" Scoring parameter to use for early stopping. It can be a single string (see :ref:`scoring_parameter`) or a callable (see :ref:`scoring`). If None, the estimator's default scorer is used. If ``scoring='loss'``, early stopping is checked w.r.t the loss value. Only used if ``n_iter_no_change`` is not None tol float or None 0.09169788469283188 The absolute tolerance to use when comparing scores. The higher the tolerance, the more likely we are to early stop: higher tolerance means that it will be harder for subsequent iterations to be considered an improvement upon the reference score verbose: int, optional (default=0) The verbosity level. If not zero, print some information about the fitting process validation_fraction int or float or None 0.19574305926541347 Proportion (or absolute size) of training data to set aside as validation data for early stopping. If None, early stopping is done on the training data verbose 0 warm_start bool false When set to ``True``, reuse the solution of the previous call to fit and add more estimators to the ensemble. For results to be valid, the estimator should be re-trained on the same data only See :term:`the Glossary <warm_start>` openml-python python scikit-learn sklearn sklearn_0.22.1 step_0 17875 12269 automl.util.sklearn.StackingEstimator(estimator=sklearn.linear_model._stochastic_gradient.SGDClassifier) automl.StackingEstimator automl.util.sklearn.StackingEstimator 1 automl==0.0.1,openml==0.10.2,sklearn==0.22.1 StackingEstimator A shallow wrapper around a classification algorithm to implement the transform method. Allows stacking of arbitrary classification algorithms in a pipelines. 2020-05-19T03:30:09 English sklearn==0.22.1 numpy>=1.6.1 scipy>=0.9 estimator PredictionMixin {"oml-python:serialized_object": "component_reference", "value": {"key": "estimator", "step_name": null}} An instance implementing PredictionMixin estimator 17703 12269 sklearn.linear_model._stochastic_gradient.SGDClassifier sklearn.SGDClassifier sklearn.linear_model._stochastic_gradient.SGDClassifier 2 openml==0.10.2,sklearn==0.22.1 Linear classifiers (SVM, logistic regression, a.o.) with SGD training. This estimator implements regularized linear models with stochastic gradient descent (SGD) learning: the gradient of the loss is estimated each sample at a time and the model is updated along the way with a decreasing strength schedule (aka learning rate). SGD allows minibatch (online/out-of-core) learning, see the partial_fit method. For best results using the default learning rate schedule, the data should have zero mean and unit variance. This implementation works with data represented as dense or sparse arrays of floating point values for the features. The model it fits can be controlled with the loss parameter; by default, it fits a linear support vector machine (SVM). The regularizer is a penalty added to the loss function that shrinks model parameters towards the zero vector using either the squared euclidean norm L2 or the absolute norm L1 or a combination of both (Elastic Net). If the parameter update crosses the 0.0 value b... 2020-05-18T19:46:26 English sklearn==0.22.1 numpy>=1.6.1 scipy>=0.9 alpha float 18.576489600940455 Constant that multiplies the regularization term. Defaults to 0.0001 Also used to compute learning_rate when set to 'optimal' average bool or int false When set to True, computes the averaged SGD weights and stores the result in the ``coef_`` attribute. If set to an int greater than 1, averaging will begin once the total number of samples seen reaches average. So ``average=10`` will begin averaging after seeing 10 samples. class_weight dict null Preset for the class_weight fit parameter Weights associated with classes. If not given, all classes are supposed to have weight one The "balanced" mode uses the values of y to automatically adjust weights inversely proportional to class frequencies in the input data as ``n_samples / (n_classes * np.bincount(y))`` early_stopping bool true Whether to use early stopping to terminate training when validation score is not improving. If set to True, it will automatically set aside a stratified fraction of training data as validation and terminate training when validation score is not improving by at least tol for n_iter_no_change consecutive epochs .. versionadded:: 0.20 epsilon float 0.9292126060861614 Epsilon in the epsilon-insensitive loss functions; only if `loss` is 'huber', 'epsilon_insensitive', or 'squared_epsilon_insensitive' For 'huber', determines the threshold at which it becomes less important to get the prediction exactly right For epsilon-insensitive, any differences between the current prediction and the correct label are ignored if they are less than this threshold eta0 double 0.8230008156002588 The initial learning rate for the 'constant', 'invscaling' or 'adaptive' schedules. The default value is 0.0 as eta0 is not used by the default schedule 'optimal' fit_intercept bool false Whether the intercept should be estimated or not. If False, the data is assumed to be already centered. Defaults to True l1_ratio float 0.3888555696189441 The Elastic Net mixing parameter, with 0 <= l1_ratio <= 1 l1_ratio=0 corresponds to L2 penalty, l1_ratio=1 to L1 Defaults to 0.15 learning_rate str "constant" The learning rate schedule: 'constant': eta = eta0 'optimal': [default] eta = 1.0 / (alpha * (t + t0)) where t0 is chosen by a heuristic proposed by Leon Bottou 'invscaling': eta = eta0 / pow(t, power_t) 'adaptive': eta = eta0, as long as the training keeps decreasing Each time n_iter_no_change consecutive epochs fail to decrease the training loss by tol or fail to increase validation score by tol if early_stopping is True, the current learning rate is divided by 5 loss str "modified_huber" The loss function to be used. Defaults to 'hinge', which gives a linear SVM The possible options are 'hinge', 'log', 'modified_huber', 'squared_hinge', 'perceptron', or a regression loss: 'squared_loss', 'huber', 'epsilon_insensitive', or 'squared_epsilon_insensitive' The 'log' loss gives logistic regression, a probabilistic classifier 'modified_huber' is another smooth loss that brings tolerance to outliers as well as probability estimates 'squared_hinge' is like hinge but is quadratically penalized 'perceptron' is the linear loss used by the perceptron algorithm The other losses are designed for regression but can be useful in classification as well; see SGDRegressor for a description penalty : {'l2', 'l1', 'elasticnet'}, default='l2' The penalty (aka regularization term) to be used. Defaults to 'l2' which is the standard regularizer for linear SVM models. 'l1' and 'elasticnet' might bring sparsity to the model (feature selection) not achieva... max_iter int 1657 The maximum number of passes over the training data (aka epochs) It only impacts the behavior in the ``fit`` method, and not the :meth:`partial_fit` method .. versionadded:: 0.19 n_iter_no_change int 44 Number of iterations with no improvement to wait before early stopping .. versionadded:: 0.20 n_jobs int 1 The number of CPUs to use to do the OVA (One Versus All, for multi-class problems) computation ``None`` means 1 unless in a :obj:`joblib.parallel_backend` context ``-1`` means using all processors. See :term:`Glossary <n_jobs>` for more details penalty "elasticnet" power_t double 0.5337479304733836 The exponent for inverse scaling learning rate [default 0.5] random_state int 42 The seed of the pseudo random number generator to use when shuffling the data. If int, random_state is the seed used by the random number generator; If RandomState instance, random_state is the random number generator; If None, the random number generator is the RandomState instance used by `np.random` shuffle bool true Whether or not the training data should be shuffled after each epoch tol float 0.0016762319789051909 The stopping criterion. If it is not None, the iterations will stop when (loss > best_loss - tol) for ``n_iter_no_change`` consecutive epochs .. versionadded:: 0.19 validation_fraction float 0.5789400632046087 The proportion of training data to set aside as validation set for early stopping. Must be between 0 and 1 Only used if early_stopping is True .. versionadded:: 0.20 verbose int 0 The verbosity level warm_start bool false When set to True, reuse the solution of the previous call to fit as initialization, otherwise, just erase the previous solution See :term:`the Glossary <warm_start>` Repeatedly calling fit or partial_fit when warm_start is True can result in a different solution than when calling fit a single time because of the way the data is shuffled If a dynamic learning rate is used, the learning rate is adapted depending on the number of samples already seen. Calling ``fit`` resets this counter, while ``partial_fit`` will result in increasing the existing counter openml-python python scikit-learn sklearn sklearn_0.22.1 openml-python python scikit-learn sklearn sklearn_0.22.1 openml-python python scikit-learn sklearn sklearn_0.22.1