Feature extraction settings

When starting a new data science project involving time series you probably want to start by extracting a comprehensive set of features. Later you can identify which features are relevant for the task at hand. In the final stages, you probably want to fine tune the parameter of the features to fine tune your models.

You can do all those things with tsfresh. So, you need to know how to control which features are calculated by tsfresh and how one can adjust the parameters. In this section, we will clarify this.

For the lazy: Just let me calculate some features!

To calculate a comprehensive set of features, call the tsfresh.extract_features() method without passing a default_fc_parameters or kind_to_fc_parameters object. This way you will be using the default options, which will use all the feature calculators in this package, that we consider are OK to return by default.

For the advanced: How do I set the parameters for all kind of time series?

After digging deeper into your data, you maybe want to calculate more of a certain type of feature and less of another type. So, you need to use custom settings for the feature extractors. To do that with tsfresh you will have to use a custom settings object:

>>> from tsfresh.feature_extraction import ComprehensiveFCParameters
>>> settings = ComprehensiveFCParameters()
>>> # Set here the options of the settings object as shown in the paragraphs below
>>> # ...
>>> from tsfresh.feature_extraction import extract_features
>>> extract_features(df, default_fc_parameters=settings)

The default_fc_parameters is expected to be a dictionary which maps feature calculator names (the function names you can find in the tsfresh.feature_extraction.feature_calculators file) to a list of dictionaries, which are the parameters with which the function will be called (as key value pairs). Each function-parameter combination that is in this dict will be called during the extraction and will produce a feature. If the function does not take any parameters, the value should be set to None.

For example:

fc_parameters = {
    "length": None,
    "large_standard_deviation": [{"r": 0.05}, {"r": 0.1}]

will produce three features: one by calling the tsfresh.feature_extraction.feature_calculators.length() function without any parameters and two by calling tsfresh.feature_extraction.feature_calculators.large_standard_deviation() with r = 0.05 and r = 0.1.

So you can control which features will be extracted, by adding or removing either keys or parameters from this dict. It is as easy as that. If you decide not to calculate the length feature here, you delete it from the dictionary:

del fc_parameters["length"]

And now, only the two other features are calculated.

For convenience, three dictionaries are predefined and can be used right away:

Theoretically, you could calculate an unlimited number of features with tsfresh by adding entry after entry to the dictionary.

For the ambitious: How do I set the parameters for different type of time series?

It is also possible to control the features to be extracted for the different kinds of time series individually. You can do so by passing a kind_to_fc_parameters parameter to the tsfresh.extract_features() function. It should be a dict mapping from kind names (as string) to fc_parameters objects, which you would normally pass as an argument to the default_fc_parameters parameter.

So, for example the following code snippet:

kind_to_fc_parameters = {
    "temperature": {"mean": None},
    "pressure": {"maximum": None, "minimum": None}

will extract the "mean" feature of the "temperature" time series and the "minimum" and "maximum" of the "pressure" time series.

The kind_to_fc_parameters argument will partly override the default_fc_parameters. So, if you include a kind name in the kind_to_fc_parameters parameter, its value will be used for that kind. Other kinds will still use the default_fc_parameters.

A handy trick: Do I really have to create the dictionary by hand?

Not necessarily. Let’s assume you have a DataFrame of tsfresh features. By using feature selection algorithms you find out that only a subgroup of features is relevant.

Then, we provide the tsfresh.feature_extraction.settings.from_columns() method that constructs the kind_to_fc_parameters dictionary from the column names of this filtered feature matrix to make sure that only relevant features are extracted.

This can save a huge amount of time because you prevent the calculation of unnecessary features. Let’s illustrate this with an example:

# X_tsfresh contains the extracted tsfresh features
X_tsfresh = extract_features(...)

# which are now filtered to only contain relevant features
X_tsfresh_filtered = some_feature_selection(X_tsfresh, y, ....)

# we can easily construct the corresponding settings object
kind_to_fc_parameters = tsfresh.feature_extraction.settings.from_columns(X_tsfresh_filtered)

The above code will construct for you the kind_to_fc_parameters dictionary that corresponds to the features and parameters (!) from the tsfresh features that were filtered by the some_feature_selection feature selection algorithm.