Pipeline

Parameters

	steps steps: list of tuples List of (name of step, estimator) tuples that are to be chained in sequential order. To be compatible with the scikit-learn API, all steps must define `fit`. All non-last steps must also define `transform`. See :ref:`Combining Estimators <combining_estimators>` for more details.	[('scaler', ...), ('som', ...)]
	transform_input transform_input: list of str, default=None The names of the :term:`metadata` parameters that should be transformed by the pipeline before passing it to the step consuming it. This enables transforming some input arguments to ``fit`` (other than ``X``) to be transformed by the steps of the pipeline up to the step which requires them. Requirement is defined via :ref:`metadata routing <metadata_routing>`. For instance, this can be used to pass a validation set through the pipeline. You can only set this if metadata routing is enabled, which you can enable using ``sklearn.set_config(enable_metadata_routing=True)``. .. versionadded:: 1.6	None
	memory memory: str or object with the joblib.Memory interface, default=None Used to cache the fitted transformers of the pipeline. The last step will never be cached, even if it is a transformer. 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. See :ref:`sphx_glr_auto_examples_neighbors_plot_caching_nearest_neighbors.py` for an example on how to enable caching.	None
	verbose verbose: bool, default=False If True, the time elapsed while fitting each step will be printed as it is completed.	False

Fitted attributes

Name	Type	Value
classes_ classes_: ndarray of shape (n_classes,) The classes labels. Only exist if the last step of the pipeline is a classifier.	ndarray[int64](10,)	[0,1,2,...,7,8,9]
n_features_in_ n_features_in_: int Number of features seen during :term:`fit`. Only defined if the underlying first estimator in `steps` exposes such an attribute when fit. .. versionadded:: 0.24	int	64

StandardScaler

?Documentation for StandardScaler

Parameters

	copy copy: bool, default=True If False, try to avoid a copy and do inplace scaling instead. This is not guaranteed to always work inplace; e.g. if the data is not a NumPy array or scipy.sparse CSR matrix, a copy may still be returned.	True
	with_mean with_mean: bool, default=True If True, center the data before scaling. This does not work (and will raise an exception) when attempted on sparse matrices, because centering them entails building a dense matrix which in common use cases is likely to be too large to fit in memory.	True
	with_std with_std: bool, default=True If True, scale the data to unit variance (or equivalently, unit standard deviation).	True

Fitted attributes

Name	Type	Value
mean_ mean_: ndarray of shape (n_features,) or None The mean value for each feature in the training set. Equal to ``None`` when ``with_mean=False`` and ``with_std=False``.	ndarray[float64](64,)	[0. ,0.3 ,5.2 ,...,6.76,2.07,0.36]
n_features_in_ n_features_in_: int Number of features seen during :term:`fit`. .. versionadded:: 0.24	int	64
n_samples_seen_ n_samples_seen_: int or ndarray of shape (n_features,) The number of samples processed by the estimator for each feature. If there are no missing samples, the ``n_samples_seen`` will be an integer, otherwise it will be an array of dtype int. If `sample_weights` are used it will be a float (if no missing data) or an array of dtype float that sums the weights seen so far. Will be reset on new calls to fit, but increments across ``partial_fit`` calls.	float64	1797
scale_ scale_: ndarray of shape (n_features,) or None Per feature relative scaling of the data to achieve zero mean and unit variance. Generally this is calculated using `np.sqrt(var_)`. If a variance is zero, we can't achieve unit variance, and the data is left as-is, giving a scaling factor of 1. `scale_` is equal to `None` when `with_std=False`. .. versionadded:: 0.17 scale_	ndarray[float64](64,)	[1. ,0.91,4.75,...,5.9 ,4.09,1.86]
var_ var_: ndarray of shape (n_features,) or None The variance for each feature in the training set. Used to compute `scale_`. Equal to ``None`` when ``with_mean=False`` and ``with_std=False``.	ndarray[float64](64,)	[ 0. , 0.82,22.6 ,...,34.8 ,16.72, 3.46]

64 features

x10

x11

x12

x13

x14

x15

x16

x17

x18

x19

x20

x21

x22

x23

x24

x25

x26

x27

x28

x29

x30

x31

x32

x33

x34

x35

x36

x37

x38

x39

x40

x41

x42

x43

x44

x45

x46

x47

x48

x49

x50

x51

x52

x53

x54

x55

x56

x57

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x60

x61

x62

x63

SomClassifier

Parameters

	spreading_factor	0.6
	sigma_end	0.5
	random_state	42
	tau_2	0.01
	n_iter	500
	sigma_start	None
	sigma_fine	None
	vertical_growth	False
	decay_function	'exponential'
	neighborhood_function	'gaussian'
	verbose	False
	coarse_training_frac	0.5
	convergence_threshold	1e-05
	max_neurons	100
	metric	'euclidean'
	threshold_method	'se'
	growth_criterion	'quantization_error'
	min_samples_vertical_growth	100
	n_jobs	1

Fitted attributes

Name	Type	Value
classes_	ndarray[int64](10,)	[0,1,2,...,7,8,9]
converged_	bool	True
growing_threshold_	float	598.6
n_features_in_	int	64
n_iter_	int	185
neurons_	list	[(0, 0), (0, 1), (1, 0), (1, 1), ...]
qe_0_	float	1.308e+04
quantization_error_	float	5.095
random_state_	RandomState	RandomState(M...0x7DAC5FE90340
som_	Graph	<networkx.cla...x7dac5fedacf0>
topographic_error_	float	0.1809
weights_	ndarray[float64](29, 64)	[[ 0. ,-0.33,-0.94,..., 0.58,-0.01,-0.17], [ 0. ,-0.31,-0.49,..., 0.56, 0.02,-0.19], [ 0. ,-0.29,-0.43,...,-0.27,-0.35,-0.19], ..., [ 0. ,-0.34,-1. ,..., 1.15, 0.81,-0.14], [ 0. , 0.17, 0.66,..., 0.64,-0.04,-0.19], [ 0. ,-0.31,-0.3 ,...,-0.07, 1.03, 1.84]]