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Showing content from https://github.com/predict-idlab/tsflex/releases/tag/v0.2.2 below:

Release tsflex v0.2.2 · predict-idlab/tsflex · GitHub

• 🔥 Now also supports feature-extraction on numeric-index data (and thus not only time-based data)
• 💚 Seamless integration with tsfresh, check out the example below:

from tsfresh.feature_extraction import MinimalFCParameters; import scipy.stats as ss

from tsflex.features import FeatureCollection, MultipleFeatureDescriptors
from tsflex.features.integrations import tsfresh_settings_wrapper
from tsflex.utils.data import load_empatica_data

# Load sequence-indexed data (in this case a time-index)
df_tmp, df_acc = load_empatica_data(['tmp', 'acc'])

# Construct your feature extraction configuration & extract features
fc = FeatureCollection(
    MultipleFeatureDescriptors(
        functions=tsfresh_settings_wrapper(MinimalFCParameters()) + [ss.skew],
        series_names=["TMP", "ACC_x", "ACC_y"],
        windows=["5min", "15min"],
        strides="5min"
    )
)

fc.calculate(data=[df_tmp, df_acc], return_df=True)

• ⚡ Optimized strided-rolling feature-extraction, see the newly generated benchmark ⬇️

• Added FeatureCollection.reduce() which comes in really handy when feature selection is performed in your machine-learning pipeline
• 🐻 chunk_data() now also supports DataFrame-dicts as input, which can be more convenient when having DataFrames with a lot of columns for which you want to specify the sample-frequencies.
• 🌻 SeriesPipeline is now more compose-like as it now accepts SeriesPipeline instances

• 🧵 Changed pathos ➡️ multiprocess as multiprocessing back-end
• 🔧 Moved the bound_method argument to FeatureCollection.calculate()
• 📝 Rewrote strided-rolling back-end in a more OO manner (introduced the segmenter module), which complies with our roadmap of providing more segmenting functionality

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