skmultiflow.data.TemporalDataStream

class skmultiflow.data.TemporalDataStream(data, y=None, time=None, sample_weight=None, sample_delay=0, target_idx=- 1, n_targets=1, cat_features=None, name=None, allow_nan=False, ordered=True)

Create a temporal stream from a data source.

TemporalDataStream takes the whole data set containing the X (features), time (timestamps) and Y (targets).

Parameters

data: numpy.ndarray or pandas.DataFrame

The features and targets or only the features if they are passed in the y parameter.

time: numpy.ndarray(dtype=datetime64) or pandas.Series (Default=None)

The timestamp column of each instance. If its a pandas.Series, it will be converted into a numpy.ndarray. If None, delay by number of samples is considered and sample_delay must be int.

sample_weight: numpy.ndarray or pandas.Series, optional (Default=None)

Sample weights.

sample_delay: numpy.ndarray, pandas.Series, numpy.timedelta64 or int, optional (Default=0)

Options per data type used:

numpy.timedelta64: Samples delay in time, the time-offset between the event time and when the label is available, e.g., numpy.timedelta64(1,”D”) for a 1-day delay)

numpy.ndarray[numpy.datetime64]: array with the timestamps when each sample will be available

pandas.Series: series with the timestamps when each sample will be available

int: the delay in number of samples.

y: numpy.ndarray or pandas.DataFrame, optional (Default=None)

The targets.

target_idx: int, optional (default=-1)

The column index from which the targets start.

n_targets: int, optional (default=1)

The number of targets.

cat_features: list, optional (default=None)

A list of indices corresponding to the location of categorical features.

name: str, optional (default=None)

A string to id the data.

ordered: bool, optional (default=True)

If True, consider that data, y, and time are already ordered by timestamp. Otherwise, the data is ordered based on time timestamps (time cannot be None).

allow_nan: bool, optional (default=False)

If True, allows NaN values in the data. Otherwise, an error is raised.

Notes

The stream object provides upon request a number of samples, in a way such that old samples cannot be accessed at a later time. This is done to correctly simulate the stream context.

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)	Checks if stream has more samples.
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 the estimated number of remaining samples.
next_sample(self[, batch_size])	Get next sample.
prepare_for_use()	Prepare the stream for use.
print_df(self)	Prints all the samples in the stream.
reset(self)	Resets the estimator to its initial state.
restart(self)	Restarts the stream.
set_params(self, **params)	Set the parameters of this estimator.

Attributes

X	Return the features’ columns.
cat_features_idx	Get the list of the categorical features index.
data	Return the data set used to generate the stream.
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	Get the number of targets.
target_idx	Get the number of the column where Y begins.
target_names	Retrieve the names of the targets
target_values	Retrieve all target_values in the stream for each target.
y	Return the targets’ columns.

property X

Return the features’ columns.

Returns

np.ndarray:

the features’ columns

property cat_features_idx

Get the list of the categorical features index.

Returns

list:

List of categorical features index.

property data

Return the data set used to generate the stream.

Returns

pd.DataFrame:

Data set.

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)

Checks if stream has more samples.

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 the estimated number of remaining samples.

Returns

int

Remaining number of samples.

property n_targets

Get the number of targets.

Returns

int:

The number of targets.

next_sample(self, batch_size=1)

Get next sample.

If there is enough instances to supply at least batch_size samples, those are returned. If there aren’t a tuple of (None, None) is returned.

Parameters

batch_size: int

The number of instances to return.

Returns

tuple or tuple list

Returns the next batch_size instances (sample_x, sample_y, sample_time, sample_delay (if available), sample_weight (if available)). For general purposes the return can be treated as a numpy.ndarray.

static prepare_for_use()

Prepare the stream for use.

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

print_df(self)

Prints all the samples in the stream.

reset(self)

Resets the estimator to its initial state.

Returns

self

restart(self)

Restarts the stream.

It basically server the purpose of reinitializing the stream to its 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_idx

Get the number of the column where Y begins.

Returns

int:

The number of the column where Y begins.

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

property y

Return the targets’ columns.

Returns

np.ndarray:

the targets’ columns