tsfresh_feature_extractor_operator¶
Operator to extract features from a dataframe
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class
tasrif.processing_pipeline.tsfresh.tsfresh_feature_extractor_operator.TSFreshFeatureExtractorOperator(seq_id_col='seq_id', date_feature_name='time', value_col='Steps', features={'agg_linear_trend': [{'attr': 'slope', 'chunk_len': 50, 'f_agg': 'mean'}, {'attr': 'slope', 'chunk_len': 10, 'f_agg': 'var'}, {'attr': 'slope', 'chunk_len': 5, 'f_agg': 'max'}, {'attr': 'slope', 'chunk_len': 5, 'f_agg': 'mean'}, {'attr': 'rvalue', 'chunk_len': 5, 'f_agg': 'max'}, {'attr': 'slope', 'chunk_len': 50, 'f_agg': 'var'}, {'attr': 'rvalue', 'chunk_len': 5, 'f_agg': 'mean'}, {'attr': 'rvalue', 'chunk_len': 5, 'f_agg': 'var'}, {'attr': 'slope', 'chunk_len': 10, 'f_agg': 'mean'}, {'attr': 'intercept', 'chunk_len': 5, 'f_agg': 'mean'}, {'attr': 'slope', 'chunk_len': 50, 'f_agg': 'max'}, {'attr': 'slope', 'chunk_len': 5, 'f_agg': 'var'}, {'attr': 'rvalue', 'chunk_len': 10, 'f_agg': 'var'}, {'attr': 'slope', 'chunk_len': 10, 'f_agg': 'max'}, {'attr': 'intercept', 'chunk_len': 5, 'f_agg': 'var'}, {'attr': 'rvalue', 'chunk_len': 10, 'f_agg': 'max'}, {'attr': 'intercept', 'chunk_len': 5, 'f_agg': 'max'}, {'attr': 'rvalue', 'chunk_len': 10, 'f_agg': 'mean'}, {'attr': 'intercept', 'chunk_len': 10, 'f_agg': 'mean'}, {'attr': 'intercept', 'chunk_len': 10, 'f_agg': 'var'}, {'attr': 'intercept', 'chunk_len': 10, 'f_agg': 'max'}, {'attr': 'rvalue', 'chunk_len': 50, 'f_agg': 'max'}], 'cwt_coefficients': [{'coeff': 3, 'w': 2, 'widths': 2, 5, 10, 20}, {'coeff': 7, 'w': 2, 'widths': 2, 5, 10, 20}], 'energy_ratio_by_chunks': [{'num_segments': 10, 'segment_focus': 9}], 'fft_coefficient': [{'attr': 'imag', 'coeff': 1}, {'attr': 'imag', 'coeff': 8}], 'first_location_of_maximum': None, 'index_mass_quantile': [{'q': 0.4}, {'q': 0.7}, {'q': 0.6}, {'q': 0.8}, {'q': 0.3}], 'last_location_of_maximum': None, 'linear_trend': [{'attr': 'rvalue'}, {'attr': 'slope'}, {'attr': 'intercept'}]})¶ From a timeseries dataframe of participants, this function generates two dataframes:
>>> import numpy as np >>> import pandas as pd >>> >>> from tasrif.processing_pipeline.tsfresh import TSFreshFeatureExtractorOperator >>> >>> df = pd.DataFrame([ ... ["2020-02-16 15:15:00",98.86667,105.0,105.0,105.0,282.5,0], ... ["2020-02-16 15:30:00",87.26667,94.0,94.0,94.0,275.0,0], ... ["2020-02-16 15:45:00",94.53333,94.0,94.0,94.0,250.0,0], ... ["2020-02-16 16:00:00",86.666664,95.0,95.0,95.0,235.0,0], ... ["2020-02-16 16:15:00",86.86667,84.0,84.0,84.0,206.5,0], ... ["2020-02-16 16:30:00",88.53333,101.0,101.0,101.0,191.0,0], ... ["2020-02-16 16:45:00",76.066666,68.0,68.0,68.0,166.5,0], ... ["2020-02-19 08:00:00",85.333336,100.0,100.0,100.0,108.0,1], ... ["2020-02-19 08:15:00",86.933334,83.0,83.0,83.0,110.0,1], ... ["2020-02-19 08:30:00",86.73333,84.0,84.0,84.0,116.0,1], ... ["2020-02-19 08:45:00",93.066666,84.0,84.0,84.0,163.0,1], ... ["2020-02-19 09:00:00",95.13333,98.0,98.0,98.0,183.5,1], ... ["2020-02-19 09:15:00",89.2,110.0,110.0,110.0,177.0,1], ... ["2020-02-19 09:30:00",85.933334,79.0,79.0,79.0,171.0,1], ... ["2020-02-21 00:15:00",88.6,103.0,103.0,103.0,153.0,2], ... ["2020-02-21 00:30:00",83.53333,85.0,85.0,85.0,139.0,2], ... ["2020-02-21 00:45:00",86.2,84.0,84.0,84.0,131.0,2], ... ["2020-02-21 01:00:00",85.933334,82.0,82.0,82.0,124.0,2], ... ["2020-02-21 01:15:00",88.933334,89.0,89.0,89.0,120.0,2], ... ["2020-02-21 01:30:00",78.53333,77.0,77.0,77.0,119.0,2], ... ["2020-02-21 01:45:00",76.13333,79.0,79.0,79.0,116.0,2]], ... columns=['dateTime','HeartRate','Calories','Steps','Distance','CGM','seq_id'] ... ) >>> >>> df['dateTime'] = pd.to_datetime(df['dateTime']) >>> df >>> >>> operator = TSFreshFeatureExtractorOperator(seq_id_col="seq_id", date_feature_name='dateTime', >>> value_col='Steps') >>> features = operator.process(df)[0] >>> features[features.columns[2:4]] Steps__agg_linear_trend__attr_"slope"__chunk_len_5__f_agg_"max" Steps__agg_linear_trend__attr_"slope"__chunk_len_5__f_agg_"mean" 0 -4.0 -9.9 1 10.0 4.7 2 -24.0 -10.6
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__init__(seq_id_col='seq_id', date_feature_name='time', value_col='Steps', features={'agg_linear_trend': [{'attr': 'slope', 'chunk_len': 50, 'f_agg': 'mean'}, {'attr': 'slope', 'chunk_len': 10, 'f_agg': 'var'}, {'attr': 'slope', 'chunk_len': 5, 'f_agg': 'max'}, {'attr': 'slope', 'chunk_len': 5, 'f_agg': 'mean'}, {'attr': 'rvalue', 'chunk_len': 5, 'f_agg': 'max'}, {'attr': 'slope', 'chunk_len': 50, 'f_agg': 'var'}, {'attr': 'rvalue', 'chunk_len': 5, 'f_agg': 'mean'}, {'attr': 'rvalue', 'chunk_len': 5, 'f_agg': 'var'}, {'attr': 'slope', 'chunk_len': 10, 'f_agg': 'mean'}, {'attr': 'intercept', 'chunk_len': 5, 'f_agg': 'mean'}, {'attr': 'slope', 'chunk_len': 50, 'f_agg': 'max'}, {'attr': 'slope', 'chunk_len': 5, 'f_agg': 'var'}, {'attr': 'rvalue', 'chunk_len': 10, 'f_agg': 'var'}, {'attr': 'slope', 'chunk_len': 10, 'f_agg': 'max'}, {'attr': 'intercept', 'chunk_len': 5, 'f_agg': 'var'}, {'attr': 'rvalue', 'chunk_len': 10, 'f_agg': 'max'}, {'attr': 'intercept', 'chunk_len': 5, 'f_agg': 'max'}, {'attr': 'rvalue', 'chunk_len': 10, 'f_agg': 'mean'}, {'attr': 'intercept', 'chunk_len': 10, 'f_agg': 'mean'}, {'attr': 'intercept', 'chunk_len': 10, 'f_agg': 'var'}, {'attr': 'intercept', 'chunk_len': 10, 'f_agg': 'max'}, {'attr': 'rvalue', 'chunk_len': 50, 'f_agg': 'max'}], 'cwt_coefficients': [{'coeff': 3, 'w': 2, 'widths': 2, 5, 10, 20}, {'coeff': 7, 'w': 2, 'widths': 2, 5, 10, 20}], 'energy_ratio_by_chunks': [{'num_segments': 10, 'segment_focus': 9}], 'fft_coefficient': [{'attr': 'imag', 'coeff': 1}, {'attr': 'imag', 'coeff': 8}], 'first_location_of_maximum': None, 'index_mass_quantile': [{'q': 0.4}, {'q': 0.7}, {'q': 0.6}, {'q': 0.8}, {'q': 0.3}], 'last_location_of_maximum': None, 'linear_trend': [{'attr': 'rvalue'}, {'attr': 'slope'}, {'attr': 'intercept'}]})¶ Creates a new instance of TSFreshFeatureExtractorOperator
- Parameters
seq_id_col (str) – label column in the dataframe
date_feature_name (str) – time column in the dataframe
value_col (str or list) – column(s) to extract features from
features (dict) – list of features to be passed to tsfresh extract_features method. (see https://tsfresh.readthedocs.io/en/latest/api/tsfresh.feature_extraction.html) for more details
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process(*data_frames)¶ Processes the passed data frame as per the configuration define in the constructor. Currently uses custom parameters for the extraction of specific features.
- Parameters
*data_frames (list of pd.DataFrame) – Variable number of pandas dataframes to be processed
- Returns
- pd.DataFrame -or- list[pd.DataFrame]
Processed dataframe(s) resulting from applying the operator
- Raises
ValueError – Occurs when one of self.value_col is not a column within the *data_frames
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