imputegap.recovery.manager package¶

Module contents¶

class imputegap.recovery.manager.TimeSeries(verbose=True)[source]¶

Bases: object

Class for managing and manipulating time series data.

This class allows importing, normalizing, and visualizing time series datasets. It also provides methods to contaminate the datasets with missing values and plot results.

Methods¶

__init__() :

Initializes the TimeSeries object.

import_matrix(data=None) :

Imports a matrix of time series data.

load_series(data, nbr_series=None, nbr_val=None, header=False, normalizer=”z_score”, replace_nan=False, reverse=False, verbose=True):

Loads time series data from a file or predefined dataset.

print(limit=10, view_by_series=False) :

Prints a limited number of time series from the dataset.

print_results(metrics, algorithm=””) :

Prints the results of the imputation process.

normalize(normalizer=”z_score”, data=None, verbose=True):

Normalizes the time series dataset.

plot(input_data, incomp_data=None, recov_data=None, max_series=None, max_values=None, size=(16, 8), save_path=””, display=True) :

Plots the time series data, including raw, contaminated, or imputed data.

import_matrix(data=None)[source]¶

Imports a matrix of time series data.

The data can be provided as a list or a NumPy array. The format is (Series, Values), where series are separated by space, and values are separated by newline characters.

Parameters¶

datalist or numpy.ndarray, optional

The matrix of time series data to import.

Returns¶

TimeSeries

The TimeSeries object with the imported data.

load_series(data, nbr_series=None, nbr_val=None, header=False, normalizer='z_score', replace_nan=False, reverse=False, verbose=True)[source]¶

Loads time series data from a file or predefined dataset.

The data is loaded as a matrix of shape (Values, Series). You can limit the number of series or values per series for computational efficiency.

Parameters¶

datastr

The file path or name of a predefined dataset (e.g., ‘bafu.txt’).

nbr_seriesint, optional

The maximum number of series to load.

nbr_valint, optional

The maximum number of values per series.

headerbool, optional

Whether the dataset has a header. Default is False.

normalizerstr, optional

The normalization technique to use. Options are “z_score” or “min_max”. Default is “z_score”. To keep the raw data, set normalizer to None | normalizer=None

replace_nanbool, optional

The Dataset has already NaN values that needs to be replaced by 0 values.

reverse: bool, optional

Order of the 1st dimension of the dataset, series or values/timestamps. Default is False e.g. True : (50, 1000) / 50 sensors (lines) of 10000 values/timestamps (cols) e.g. False : (1000, 50) / 1000 values/timestamps (lines) for 50 sensors (cols)

verbosebool, optional

Display information print (default: True).

Returns¶

TimeSeries

The TimeSeries object with the loaded data.

Example¶

>>> ts.load_series(utils.search_path("eeg-alcohol"), nbr_series=50, nbr_val=100)

normalize(normalizer='z_score', data=None, verbose=True)[source]¶

Normalize the time series dataset.

Supported normalization techniques are “z_score” and “min_max”. The method also logs the execution time for the normalization process.

Parameters¶

normalizerstr, optional

The normalization technique to use. Options are “z_score” or “min_max”. Default is “z_score”.

datadarray, optional

Matrix to normalize (outside of the object).

verbosebool, optional

Whether to display the contamination information (default is False).

Returns¶

numpy.ndarray

The normalized time series data.

Example¶

>>> ts.normalize(normalizer="z_score")

plot(input_data, incomp_data=None, recov_data=None, nbr_series=None, nbr_val=None, series_range=None, subplot=False, size=(16, 8), algorithm=None, save_path='./imputegap_assets', style='default', cont_rate=None, grid=True, reverse=True, legends=True, display=True, verbose=True)[source]¶

Plot the time series data, including raw, contaminated, or imputed data.

Parameters¶

input_datanumpy.ndarray

The original time series data without contamination.

incomp_datanumpy.ndarray, optional

The contaminated time series data.

recov_datanumpy.ndarray, optional

The imputed time series data.

nbr_seriesint, optional

The maximum number of series to plot.

nbr_valint, optional

The maximum number of values per series to plot.

series_rangeint, optional

The index of a specific series to plot. If set, only this series will be plotted.

subplotbool, optional

Print one time series by subplot or all in the same plot.

sizetuple, optional

Size of the plot in inches. Default is (16, 8).

algorithmstr, optional

Name of the algorithm used for imputation.

save_pathstr, optional

Path to save the plot locally.

stylestr, optional

Name of the style used for the plot (“default” / “mono”: specific series more visible).

cont_ratestr, optional

Percentage of contamination in each series to plot.

gridbool, optional

Whether to plot in a grid or not.

reversebool, optional

Reverse the plot to see timestamps as x axis and values as y axis.

legends: bool, optional

Display or not the legend in the plot (default is True).

displaybool, optional

Whether to display the plot. Default is True.

verbosebool, optional

Whether to display the plot information. Default is True.

Returns¶

str or None

The file path of the saved plot, if applicable.

Example¶

>>> ts.plot(input_data=ts.data, nbr_series=9, nbr_val=100, save_path="./imputegap_assets") # plain data
>>> ts.plot(ts.data, ts_m, nbr_series=9, subplot=True, save_path="./imputegap_assets") # contamination
>>> ts.plot(input_data=ts.data, incomp_data=ts_m, recov_data=imputer.recov_data, nbr_series=9, subplot=True, save_path="./imputegap_assets") # imputation

print(nbr_val=10, nbr_series=7, view_by_series=True)[source]¶

Prints a limited number of time series from the dataset.

Parameters¶

nbr_val : int, optional The number of timestamps to print. Default is 15. Use -1 for no restriction. nbr_series : int, optional The number of series to print. Default is 10. Use -1 for no restriction. view_by_series : bool, optional Whether to view by series (True) or by values (False).

Returns¶

None

print_results(metrics, algorithm='', text='Results')[source]¶

Prints the results of the imputation process.

Parameters¶

metricsdict

A dictionary containing the imputation metrics to display.

algorithmstr, optional

The name of the algorithm used for imputation.

algorithmstr, optional

Output text to help the user.

Returns¶

None

Example¶

>>> ts.print_results(imputer.metrics, imputer.algorithm)

range(starting_series, ending_series)[source]¶

Select a subset of series from the dataset within a given range.

Parameters¶

starting_seriesint

The index of the first series to keep (inclusive).

ending_seriesint

The index of the last series to keep (inclusive).

shift(id_series, shift_value=0.01)[source]¶

Shift the values of a series.

Parameters¶

id_seriesint

The index of the series to shift

shift_valuefloat

Values of shift (vertically) (default: 0.01).

imputegap.recovery.manager.select_backend()[source]¶