theft-package |
Tools for Handling Extraction of Features from Time-series |
calculate_features |
Compute features on an input time series dataset |
calculate_interval |
Calculate interval summaries with a measure of central tendency of classification results |
check_vector_quality |
Check for presence of NAs and non-numerics in a vector |
compare_features |
Conduct statistical testing on time-series feature classification performance to identify top features or compare entire sets |
feature_list |
All features available in theft in tidy format |
filter_duplicates |
Remove duplicate features that exist in multiple feature sets and retain a reproducible random selection of one of them |
filter_good_features |
Filter resample data sets according to good feature list |
find_good_features |
Helper function to find features in both train and test set that are "good" |
fit_models |
Fit classification model and compute key metrics |
get_rescale_vals |
Calculate central tendency and spread values for all numeric columns in a dataset |
init_theft |
Communicate to R the Python virtual environment containing the relevant libraries for calculating features |
install_python_pkgs |
Download and install all the relevant Python packages into a target location |
make_title |
Helper function for converting to title case |
maxabs_scaler |
Rescales a numeric vector using maximum absolute scaling |
minmax_scaler |
Rescales a numeric vector into the unit interval [0,1] |
normalise |
Scale each feature vector into a user-specified range for visualisation and modelling |
normalize |
Scale each feature vector into a user-specified range for visualisation and modelling |
plot.feature_calculations |
Produce a plot for a feature_calculations object |
plot.low_dimension |
Produce a plot for a low_dimension object |
process_hctsa_file |
Load in hctsa formatted MATLAB files of time series data into a tidy format ready for feature extraction |
reduce_dims |
Project a feature matrix into a low dimensional representation using PCA or t-SNE |
resampled_ttest |
Compute correlated t-statistic and p-value for resampled data from correctR package |
resample_data |
Helper function to create a resampled dataset |
rescale_zscore |
Calculate z-score for all columns in a dataset using train set central tendency and spread |
robustsigmoid_scaler |
Rescales a numeric vector using an outlier-robust Sigmoidal transformation |
select_stat_cols |
Helper function to select only the relevant columns for statistical testing |
sigmoid_scaler |
Rescales a numeric vector using a Sigmoidal transformation |
simData |
Sample of randomly-generated time series to produce function tests and vignettes |
stat_test |
Calculate p-values for feature sets or features relative to an empirical null or each other using resampled t-tests |
theft |
Tools for Handling Extraction of Features from Time-series |
tsfeature_classifier |
Fit classifiers using time-series features using a resample-based approach and get a fast understanding of performance |
zscore_scaler |
Rescales a numeric vector into z-scores |