18426
12269
sklearn.pipeline.Pipeline(step_0=automl.components.feature_preprocessing.multi_column_label_encoder.MultiColumnLabelEncoderComponent,step_1=automl.util.sklearn.StackingEstimator(estimator=sklearn.ensemble._hist_gradient_boosting.gradient_boosting.HistGradientBoostingClassifier),step_2=sklearn.discriminant_analysis.LinearDiscriminantAnalysis)
sklearn.Pipeline(MultiColumnLabelEncoderComponent,StackingEstimator,LinearDiscriminantAnalysis)
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``.
2020-05-20T18:11:49
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"}}, {"oml-python:serialized_object": "component_reference", "value": {"key": "step_2", "step_name": "step_2"}}]
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_2
17705
12269
sklearn.discriminant_analysis.LinearDiscriminantAnalysis
sklearn.LinearDiscriminantAnalysis
sklearn.discriminant_analysis.LinearDiscriminantAnalysis
4
openml==0.10.2,sklearn==0.22.1
Linear Discriminant Analysis
A classifier with a linear decision boundary, generated by fitting class
conditional densities to the data and using Bayes' rule.
The model fits a Gaussian density to each class, assuming that all classes
share the same covariance matrix.
The fitted model can also be used to reduce the dimensionality of the input
by projecting it to the most discriminative directions.
.. versionadded:: 0.17
*LinearDiscriminantAnalysis*.
2020-05-18T19:46:37
English
sklearn==0.22.1
numpy>=1.6.1
scipy>=0.9
n_components
int
346
Number of components (<= min(n_classes - 1, n_features)) for
dimensionality reduction. If None, will be set to
min(n_classes - 1, n_features)
priors
array
null
Class priors
shrinkage
string or float
0.4904334135320484
Shrinkage parameter, possible values:
- None: no shrinkage (default)
- 'auto': automatic shrinkage using the Ledoit-Wolf lemma
- float between 0 and 1: fixed shrinkage parameter
Note that shrinkage works only with 'lsqr' and 'eigen' solvers
solver
string
"eigen"
Solver to use, possible values:
- 'svd': Singular value decomposition (default)
Does not compute the covariance matrix, therefore this solver is
recommended for data with a large number of features
- 'lsqr': Least squares solution, can be combined with shrinkage
- 'eigen': Eigenvalue decomposition, can be combined with shrinkage
store_covariance
bool
false
Additionally compute class covariance matrix (default False), used
only in 'svd' solver
.. versionadded:: 0.17
tol
float
0.0012994132677570124
Threshold used for rank estimation in SVD solver
.. versionadded:: 0.17
openml-python
python
scikit-learn
sklearn
sklearn_0.22.1
step_0
17711
12269
automl.components.feature_preprocessing.multi_column_label_encoder.MultiColumnLabelEncoderComponent
automl.MultiColumnLabelEncoderComponent
automl.components.feature_preprocessing.multi_column_label_encoder.MultiColumnLabelEncoderComponent
1
automl==0.0.1,openml==0.10.2,sklearn==0.22.1
MultiColumnLabelEncoderComponent
A ColumnEncoder that can handle missing values and multiple categorical columns.
2020-05-18T21:55:00
English
sklearn==0.22.1
numpy>=1.6.1
scipy>=0.9
columns
List
null
List of column to be encoded
openml-python
python
scikit-learn
sklearn
sklearn_0.22.1
step_1
17928
12269
automl.util.sklearn.StackingEstimator(estimator=sklearn.ensemble._hist_gradient_boosting.gradient_boosting.HistGradientBoostingClassifier)
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.
2020-05-19T04:07:24
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
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
openml-python
python
scikit-learn
sklearn
sklearn_0.22.1
openml-python
python
scikit-learn
sklearn
sklearn_0.22.1