rfwtools.extractor.autoregressive

This module provides autogressive feature extraction tools.

Typically, these will be used by DataSet.produce_feature_set(). However there is no reason why these can’t be run externally.

Basic Usage Example:

from rfwtools.data_set import DataSet
from rfwtools.extractor.autoregressive import autoregressive_extractor

# Setup a DataSet object and get some example data to work with
ds = DataSet()
ds.load_example_set_csv("my_example_set.csv")

# Get a single example to work on
ex = ds.example_set.loc[0, 'example']

# Run on one example with defaults
autoregressive_extractor(ex)
# Run on one example with only 2 signals being processed
autoregressive_extractor(ex, signals=['1_GMES', '1_PMES'])
# Run on one example, but only include values before the fault on set.
autoregressive_extractor(ex, query="Time < 0")

# Run this on every example in the example set and produce a corresponding feature set for pre-fault signal data.
ds.produce_feature_set(autoregressive_extractor, query="Time < 0")

Functions

autoregressive_extractor(ex[, normalize, ...])	Uses statsmodels to generate autoregressive model of each waveform.
autoregressive_extractor_faulted_cavity(ex)	Generates AR features for waveforms of the cavity labeled as faulted.