18433
12269
sklearn.pipeline.Pipeline(step_0=automl.components.feature_preprocessing.multi_column_label_encoder.MultiColumnLabelEncoderComponent,step_1=automl.util.sklearn.StackingEstimator(estimator=sklearn.tree._classes.DecisionTreeClassifier),step_2=sklearn.ensemble._hist_gradient_boosting.gradient_boosting.HistGradientBoostingClassifier)
sklearn.Pipeline(MultiColumnLabelEncoderComponent,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``.
2020-05-20T18:32:34
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
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
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
17924
12269
automl.util.sklearn.StackingEstimator(estimator=sklearn.tree._classes.DecisionTreeClassifier)
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.
2020-05-19T04:03:39
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
17504
11295
sklearn.tree._classes.DecisionTreeClassifier
sklearn.DecisionTreeClassifier
sklearn.tree._classes.DecisionTreeClassifier
3
openml==0.10.2,sklearn==0.22.1
A decision tree classifier.
2020-02-08T19:46:35
English
sklearn==0.22.1
numpy>=1.6.1
scipy>=0.9
ccp_alpha
non
0.0
Complexity parameter used for Minimal Cost-Complexity Pruning. The
subtree with the largest cost complexity that is smaller than
``ccp_alpha`` will be chosen. By default, no pruning is performed. See
:ref:`minimal_cost_complexity_pruning` for details
.. versionadded:: 0.22
class_weight
dict
null
Weights associated with classes in the form ``{class_label: weight}``
If None, all classes are supposed to have weight one. For
multi-output problems, a list of dicts can be provided in the same
order as the columns of y
Note that for multioutput (including multilabel) weights should be
defined for each class of every column in its own dict. For example,
for four-class multilabel classification weights should be
[{0: 1, 1: 1}, {0: 1, 1: 5}, {0: 1, 1: 1}, {0: 1, 1: 1}] instead of
[{1:1}, {2:5}, {3:1}, {4:1}]
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))``
For multi-output, the weights of each column of y will be multiplied
Note that these weights will be multiplied with sample_weight (passed
through the fit method) if sample_weight is specified
criterion
"gini"
max_depth
int
null
The maximum depth of the tree. If None, then nodes are expanded until
all leaves are pure or until all leaves contain less than
min_samples_split samples
max_features
int
null
The number of features to consider when looking for the best split:
- If int, then consider `max_features` features at each split
- If float, then `max_features` is a fraction and
`int(max_features * n_features)` features are considered at each
split
- If "auto", then `max_features=sqrt(n_features)`
- If "sqrt", then `max_features=sqrt(n_features)`
- If "log2", then `max_features=log2(n_features)`
- If None, then `max_features=n_features`
Note: the search for a split does not stop until at least one
valid partition of the node samples is found, even if it requires to
effectively inspect more than ``max_features`` features
max_leaf_nodes
int
null
Grow a tree with ``max_leaf_nodes`` in best-first fashion
Best nodes are defined as relative reduction in impurity
If None then unlimited number of leaf nodes
min_impurity_decrease
float
0.0
A node will be split if this split induces a decrease of the impurity
greater than or equal to this value
The weighted impurity decrease equation is the following::
N_t / N * (impurity - N_t_R / N_t * right_impurity
- N_t_L / N_t * left_impurity)
where ``N`` is the total number of samples, ``N_t`` is the number of
samples at the current node, ``N_t_L`` is the number of samples in the
left child, and ``N_t_R`` is the number of samples in the right child
``N``, ``N_t``, ``N_t_R`` and ``N_t_L`` all refer to the weighted sum,
if ``sample_weight`` is passed
.. versionadded:: 0.19
min_impurity_split
float
null
Threshold for early stopping in tree growth. A node will split
if its impurity is above the threshold, otherwise it is a leaf
.. deprecated:: 0.19
``min_impurity_split`` has been deprecated in favor of
``min_impurity_decrease`` in 0.19. The default value of
``min_impurity_split`` will change from 1e-7 to 0 in 0.23 and it
will be removed in 0.25. Use ``min_impurity_decrease`` instead
min_samples_leaf
int or float
1
The minimum number of samples required to be at a leaf node
A split point at any depth will only be considered if it leaves at
least ``min_samples_leaf`` training samples in each of the left and
right branches. This may have the effect of smoothing the model,
especially in regression
- If int, then consider `min_samples_leaf` as the minimum number
- If float, then `min_samples_leaf` is a fraction and
`ceil(min_samples_leaf * n_samples)` are the minimum
number of samples for each node
.. versionchanged:: 0.18
Added float values for fractions
min_samples_split
int or float
2
The minimum number of samples required to split an internal node:
- If int, then consider `min_samples_split` as the minimum number
- If float, then `min_samples_split` is a fraction and
`ceil(min_samples_split * n_samples)` are the minimum
number of samples for each split
.. versionchanged:: 0.18
Added float values for fractions
min_weight_fraction_leaf
float
0.0
The minimum weighted fraction of the sum total of weights (of all
the input samples) required to be at a leaf node. Samples have
equal weight when sample_weight is not provided
presort
deprecated
"deprecated"
This parameter is deprecated and will be removed in v0.24
.. deprecated:: 0.22
random_state
int or RandomState
null
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`
splitter
"best"
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