skmultiflow.data.RegressionGenerator

class skmultiflow.data.RegressionGenerator(n_samples=40000, n_features=100, n_informative=10, n_targets=1, random_state=None)

Creates a regression stream.

This generator creates a stream of samples for a regression problem. It uses the make_regression function from scikit-learn, which creates a batch setting regression problem. These samples are then sequentially fed by the next_sample function.

Parameters

n_samples: int (Default: 40000)

Total amount of samples to generate.

n_features: int (Default: 100)

Number of features to generate.

n_informative: int (Default: 10)

Number of relevant features, in other words, the number of features that influence the class label.

n_targets: int (Default: 1)

Number of target_values (outputs) to generate.

random_state: int, RandomState instance or None, optional (default=None)

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.

Notes

This is a wrapper for scikit-lean’s make_regression

Examples

>>> # Imports
>>> from skmultiflow.data.regression_generator import RegressionGenerator
>>> # Setting up the stream
>>> stream = RegressionGenerator(n_samples=100, n_features=20, n_targets=4, n_informative=6,
... random_state=0)
>>> # Retrieving one sample
>>> stream.next_sample()
(array([[ 0.16422776,  0.56729028, -0.76149221,  0.38728048, -1.69810582,
      0.85792392, -0.2226751 , -0.98551074,  1.46657872,  1.64813493,
      0.03863055,  1.14110187, -1.6567151 , -0.29183736, -1.02250684,
     -1.47183501, -1.61647419,  0.85255194, -2.25556423, -0.35343175]]),
     array([[-227.21175382, -208.69356686, -430.10330937, -439.69284148]]))
>>> # Retrieving 10 samples
>>> stream.next_sample(10)
(array([[-0.30309825,  0.44103291,  0.41287082, -0.14456682,  0.3595044 ,
     -0.1983989 ,  0.17879287, -0.40594173, -1.14761094,  1.38526155,
     -0.93788023,  0.0941923 ,  0.43310795,  0.28912051,  1.06458514,
      0.7243685 ,  0.24078751, -0.35811408, -0.36159928, -0.7994224 ],
    [ 1.04297759,  0.41409135, -0.94893281,  0.16464381,  1.04008625,
      0.13191176, -0.50723446, -0.32656098,  0.76877064, -0.52261942,
      0.38909397, -1.98056559,  1.17104106, -0.03926799,  1.47376482,
     -0.00820988,  1.04156839, -0.42132759,  0.88518754,  0.15466883],
    [-0.83912419, -1.01177408,  0.75746833, -0.6432576 ,  1.58776152,
     -0.01005647,  0.08496814, -0.0451133 , -1.04059923,  0.85053068,
     -0.14876615,  1.23800694,  0.0960042 ,  1.86668315,  0.99675964,
      0.07912172, -1.37305354, -0.31560312, -1.13359283, -1.60643969],
    [ 0.9508337 ,  0.55929898,  1.30718385, -1.64134861,  1.39053397,
     -0.46744101, -1.06369559, -0.33868219,  0.85910419,  1.05417791,
     -0.49579549, -0.86015338,  1.21657771,  0.67755703,  0.06606026,
      2.03476254,  0.57275137, -0.80962658, -0.15503581, -0.43109634],
    [-0.80149689, -0.64718143,  1.99795608, -0.96460642,  1.32646164,
     -0.85654931,  0.47224715,  0.93639854,  2.59442459,  0.27117018,
     -0.76211451, -1.5415874 , -0.88778014, -1.42191987, -0.21252304,
     -0.52564059, -0.1753164 , -0.40403229,  0.05989468,  0.9304085 ],
    [-0.21120598, -0.12040664, -1.74418776,  0.87569568, -0.46931074,
      1.66060756, -1.47931598,  1.02122474, -2.8022028 ,  2.45122972,
     -0.48024204, -1.41660348, -0.52325094, -0.44876701,  1.94709864,
      0.70869527, -0.7214313 , -1.18842442, -1.36516288, -0.33210228],
    [ 0.49949823, -0.06205313,  1.76992139, -0.03093626, -1.1046166 ,
     -0.16821422,  1.25916713,  0.26902407,  1.32435875,  1.26741165,
     -0.56643985,  0.3779101 , -0.30769128,  2.52636824, -0.79550055,
      0.52491786, -1.49567952, -0.17220079,  1.57886519,  0.70411102],
    [ 0.8640523 , -2.23960406, -0.5854312 , -0.91307922, -0.22260568,
     -0.26164545,  0.40149906,  0.93674246, -0.20289684, -2.36958691,
      0.24211796, -0.18224478, -0.88872026, -1.27968917, -0.88897136,
      1.41232771,  0.06485611, -0.10988278, -1.68121822,  1.22487056],
    [ 0.61645931,  0.53659652,  0.08595197, -1.96273201, -0.89636972,
      0.75194659,  0.40469546,  0.87096178, -1.19498681,  1.29614987,
     -1.13900819,  0.56298972, -1.21440138, -0.45408036,  0.64796779,
     -0.87797062,  0.8805112 , -0.50040967,  1.58482053,  0.19145087],
    [ 1.30184623, -0.62808756,  1.13689136,  1.02017271, -0.11054066,
      0.09772497, -0.48102712, -1.04525337, -0.39944903,  0.68981816,
      0.28634369,  0.58295368,  0.60884383, -0.1359497 ,  1.53637705,
      1.21114529, -1.06001582,  0.37005589, -0.69204985,  2.3039167 ]]),
      array([[  31.59103587,   19.35028127,   33.49418263,   22.27335009],
      [ 153.04501993,  245.02067196,  338.82484458,  365.47183945],
      [  43.14398252,   47.75322041,    1.17298222,   44.35274394],
      [  93.58627672,  -65.01446316,   79.20394868,   46.55266948],
      [  -9.74401621, -137.01970244, -144.66863494, -123.09407564],
      [ -51.78237536,  103.64689371,  -37.00451143,  -15.08925677],
      [ -32.06049627, -127.04540624,  -21.14164295,  -80.71667   ],
      [-121.50880042, -197.05839429, -278.61694828, -291.47192161],
      [ -72.53226633, -280.00028587,  -44.57428097, -166.31003398],
      [  41.74351609,  220.43038917,  151.95222469,  182.65729147]]))

>>> stream.n_remaining_samples()
89
>>> stream.has_more_samples()
True

Methods

get_data_info(self)	Retrieves minimum information from the stream
get_info(self)	Collects and returns the information about the configuration of the estimator
get_params(self[, deep])	Get parameters for this estimator.
has_more_samples(self)	Returns
is_restartable(self)	Determine if the stream is restartable.
last_sample(self)	Retrieves last batch_size samples in the stream.
n_remaining_samples(self)	Returns
next_sample(self[, batch_size])	Returns next sample from the stream.
prepare_for_use()	Prepare the stream for use.
reset(self)	Resets the estimator to its initial state.
restart(self)	Restart the stream to the initial state.
set_params(self, **params)	Set the parameters of this estimator.

Attributes

feature_names	Retrieve the names of the features.
n_cat_features	Retrieve the number of integer features.
n_features	Retrieve the number of features.
n_num_features	Retrieve the number of numerical features.
n_targets	Retrieve the number of targets
target_names	Retrieve the names of the targets
target_values	Retrieve all target_values in the stream for each target.

property feature_names

Retrieve the names of the features.

Returns

list

names of the features

get_data_info(self)

Retrieves minimum information from the stream

Used by evaluator methods to id the stream.

The default format is: ‘Stream name - n_targets, n_classes, n_features’.

Returns

string

Stream data information

get_info(self)

Collects and returns the information about the configuration of the estimator

Returns

string

Configuration of the estimator.

get_params(self, deep=True)

Get parameters for this estimator.

Parameters

deepboolean, optional

If True, will return the parameters for this estimator and contained subobjects that are estimators.

Returns

paramsmapping of string to any

Parameter names mapped to their values.

has_more_samples(self)

Returns

Boolean

True if stream has more samples.

is_restartable(self)

Determine if the stream is restartable.

Returns

Bool

True if stream is restartable.

last_sample(self)

Retrieves last batch_size samples in the stream.

Returns

tuple or tuple list

A numpy.ndarray of shape (batch_size, n_features) and an array-like of shape (batch_size, n_targets), representing the next batch_size samples.

property n_cat_features

Retrieve the number of integer features.

Returns

int

The number of integer features in the stream.

property n_features

Retrieve the number of features.

Returns

int

The total number of features.

property n_num_features

Retrieve the number of numerical features.

Returns

int

The number of numerical features in the stream.

n_remaining_samples(self)

Returns

int

Number of samples remaining.

property n_targets

Retrieve the number of targets

Returns

int

the number of targets in the stream.

next_sample(self, batch_size=1)

Returns next sample from the stream.

Parameters

batch_size: int (optional, default=1)

The number of sample to return.

Returns

tuple or tuple list

Return a tuple with the features matrix and the labels matrix for the batch_size samples that were requested.

static prepare_for_use()

Prepare the stream for use.

Deprecated in v0.5.0 and will be removed in v0.7.0

reset(self)

Resets the estimator to its initial state.

Returns

self

restart(self)

Restart the stream to the initial state.

set_params(self, **params)

Set the parameters of this estimator.

The method works on simple estimators as well as on nested objects (such as pipelines). The latter have parameters of the form <component>__<parameter> so that it’s possible to update each component of a nested object.

Returns

self

property target_names

Retrieve the names of the targets

Returns

list

the names of the targets in the stream.

property target_values

Retrieve all target_values in the stream for each target.

Returns

list

list of lists of all target_values for each target