File utils.py - Documentation
January 14, 2019 ยท View on GitHub
Methods - Overview
| name | description |
|---|---|
| save_model | Save a pysster.Model object. |
| load_model | Load a pysster.Model object. |
| save_data | Save a pysster.Data object. |
| load_data | Load a pysster.Data object. |
| annotate_structures | Annotate secondary structure predictions with structural contexts. |
| predict_structures | Predict secondary structures for RNA sequences. |
| get_performance_report | Get a performance overview of a classifier. |
| plot_roc | Get ROC curves for every class. |
| plot_prec_recall | Get Precision-Recall curves for every class. |
| save_as_meme | Save sequence (or structure) motifs in MEME format. |
| run_tomtom | Compare a MEME file against a database using TomTom. |
save_model
def save_model(model, file_path)
Save a pysster.Model object.
This function creates two files: a pickled version of the pysster.Model object and an hdf5 file of the actual keras model (e.g. if file_path is 'model' two files are created: 'model' and 'model.h5')
| parameter | type | description |
|---|---|---|
| model | pysster.Model | A Model object. |
| file_path | str | A file name. |
load_model
def load_model(file_path)
Load a pysster.Model object.
| parameter | type | description |
|---|---|---|
| file_path | str | A file containing a pickled pysster.Model object (file_path.h5 must also exist, see save_model()). |
| returns | type | description |
|---|---|---|
| model | pysster.Model | A Model object. |
save_data
def save_data(data, file_path)
Save a pysster.Data object.
The object will be pickled to disk.
| parameter | type | description |
|---|---|---|
| file_path | str | A file name. |
load_data
def load_data(file_path)
Load a pysster.Data object.
| parameter | type | description |
|---|---|---|
| file_path | str | A file containing a pickled pysster.Data object. |
| returns | type | description |
|---|---|---|
| data | pysster.Data | The Data object loaded from file. |
annotate_structures
def annotate_structures(input_file, output_file)
Annotate secondary structure predictions with structural contexts.
Given dot-bracket strings this function will annote every character as either 'H' (hairpin), 'S' (stem), 'I' (internal loop/bulge), 'M' (multi loop), 'F' (5-prime) or 'T' (3-prime). The input file must be a fasta formatted file and each sequence and structure must span a single line:
>header
CCCCAUAGGGG
((((...)))) (-3.3)
This is the default format of e.g. RNAfold. The output file will contain the annotated string:
>header
CCCCAUAGGGG
SSSSHHHSSSS
| parameter | type | description |
|---|---|---|
| input_file | str | A fasta file containing secondary structure predictions. |
| output_file | str | A fasta file with secondary structure annotations. |
predict_structures
def predict_structures(input_file, output_file, num_processes=None, annotate=False)
Predict secondary structures for RNA sequences.
This is a convenience function to get quick RNA secondary structure predictions. The function will try to use the ViennaRNA python bindings or the RNAfold binary to perform predictions. If neither can be found the function raises a RuntimeError. Using the ViennaRNA python bindings is preferred as it is faster.
Entries of the output file look as follows if annotate = False:
>header
CCCCAUAGGGG
((((...)))) (-3.3)
If annotate = True the annotated structure string instead of the dot-bracket string will be printed:
>header
CCCCAUAGGGG
SSSSHHHSSSS
Have a look at the annotate_structures() function for more information about annotated structure strings.
Warning: Due to the way Python works spinning up additional processes means copying the complete memory of the original process, i.e. if the original processes already uses 5 GB of RAM each additional process will use 5 GB as well.
| parameter | type | description |
|---|---|---|
| input_file | str | A fasta file with RNA sequences. |
| output_file | str | A fasta file with sequences and structures. |
| num_processes | int | The number of parallel processes to use for prediction. (default: number of available cores) |
| annotate | bool | Output the annotated structure string instead of the dot-bracket string. (default: false) |
get_performance_report
def get_performance_report(labels, predictions)
Get a performance overview of a classifier.
The report contains precision, recall, f1-score, ROC-AUC and Precision-Recall-AUC for every class (in a 1 vs. all approach) and weighted averages (weighted by the the number of sequences 'n' in each class).
| parameter | type | description |
|---|---|---|
| labels | numpy.ndarray | A binary matrix of shape (num sequences, num classes) containing the true labels. |
| predictions | numpy.ndarray | A matrix of shape (num sequences, num classes) containing predicted probabilites. |
| returns | type | description |
|---|---|---|
| report | str | Summary table of the above mentioned performance measurements. |
plot_roc
def plot_roc(labels, predictions, file_path)
Get ROC curves for every class.
In the case of more than two classes the comparisons will be performed in a 1 vs. all approach (i.e. you get one curve per class).
| parameter | type | description |
|---|---|---|
| labels | numpy.ndarray | A binary matrix of shape (num sequences, num classes) containing the true labels. |
| predictions | numpy.ndarray | A matrix of shape (num sequences, num classes) containing predicted probabilites. |
| file_path | str | The file the plot should be saved to. |
plot_prec_recall
def plot_prec_recall(labels, predictions, file_path)
Get Precision-Recall curves for every class.
In the case of more than two classes the comparisons will be performed in a 1 vs. rest approach (i.e. you get one curve per class).
| parameter | type | description |
|---|---|---|
| labels | numpy.ndarray | A binary matrix of shape (num sequences, num classes) containing the true labels. |
| predictions | numpy.ndarray | A matrix of shape (num sequences, num classes) containing predicted probabilites. |
| file_path | str | The file the plot should be saved to. |
save_as_meme
def save_as_meme(logos, file_path)
Save sequence (or structure) motifs in MEME format.
| parameter | type | description |
|---|---|---|
| logos | [pysster.Motif] | A list of Motif objects. |
| file_path | str | The name of the output text file. |
run_tomtom
def run_tomtom(motif_file, output_folder, database, options = None)
Compare a MEME file against a database using TomTom.
Default options string: "-min-overlap 5 -verbosity 1 -xalph -evalue -thresh 0.1"
| parameter | type | description |
|---|---|---|
| motif_file | str | A MEME file. |
| output_folder | str | The folder the TomTom output will be saved in. |
| database | str | A MEME file serving as the database to compare against. |
| option | str | Command line options passed to TomTom. |