# Source code for selene_sdk.predict.predict_handlers.logit_score_handler

"""
Handles computing and outputting the log fold change scores
"""
from scipy.special import logit

from .handler import PredictionsHandler

[docs]class LogitScoreHandler(PredictionsHandler): """ The logit score handler calculates and records the difference between logit(alt) and logit(ref) predictions (logit(alt) - logit(ref)). For reference, if some event occurs with probability :math:p, then the log-odds is the logit of p, or .. math:: \\mathrm{logit}(p) = \\log\\left(\\frac{p}{1 - p}\\right) = \\log(p) - \\log(1 - p) Parameters ---------- features : list of str List of sequence-level features, in the same order that the model will return its predictions. columns_for_ids : list of str Columns in the file that help to identify the input sequence to which the features data corresponds. output_path_prefix : str Path to the file to which Selene will write the absolute difference scores. The path may contain a filename prefix. Selene will append logits to the end of the prefix. output_format : {'tsv', 'hdf5'} Specify the desired output format. TSV can be specified if you would like the final file to be easily perused. However, saving to a TSV file is much slower than saving to an HDF5 file. output_size : int, optional The total number of rows in the output. Must be specified when the output_format is hdf5. write_mem_limit : int, optional Default is 1500. Specify the amount of memory you can allocate to storing model predictions/scores for this particular handler, in MB. Handler will write to file whenever this memory limit is reached. write_labels : bool, optional Default is True. If you initialize multiple write handlers for the same set of inputs with output format hdf5, set write_label to False on all handlers except 1 so that only 1 handler writes the row labels to an output file. Attributes ---------- needs_base_pred : bool Whether the handler needs the base (reference) prediction as input to compute the final output """ def __init__(self, features, columns_for_ids, output_path_prefix, output_format, output_size=None, write_mem_limit=1500, write_labels=True): """ Constructs a new LogitScoreHandler object. """ super(LogitScoreHandler, self).__init__( features, columns_for_ids, output_path_prefix, output_format, output_size=output_size, write_mem_limit=write_mem_limit, write_labels=write_labels) self.needs_base_pred = True self._results = [] self._samples = [] self._features = features self._columns_for_ids = columns_for_ids self._output_path_prefix = output_path_prefix self._output_format = output_format self._write_mem_limit = write_mem_limit self._write_labels = write_labels self._create_write_handler("logits")
[docs] def handle_batch_predictions(self, batch_predictions, batch_ids, baseline_predictions): """ Handles the model predications for a batch of sequences. Parameters ---------- batch_predictions : arraylike The predictions for a batch of sequences. This should have dimensions of :math:B \\times N (where :math:B is the size of the mini-batch and :math:N is the number of features). batch_ids : list(arraylike) Batch of sequence identifiers. Each element is arraylike because it may contain more than one column (written to file) that together make up a unique identifier for a sequence. base_predictions : arraylike The baseline prediction(s) used to compute the logit scores. This must either be a vector of :math:N values, or a matrix of shape :math:B \\times N (where :math:B is the size of the mini-batch, and :math:N is the number of features). """ baseline_predictions[baseline_predictions == 0] = 1e-24 baseline_predictions[baseline_predictions >= 1] = 0.999999 batch_predictions[batch_predictions == 0] = 1e-24 batch_predictions[batch_predictions >= 1] = 0.999999 logits = logit(batch_predictions) - logit(baseline_predictions) self._results.append(logits) self._samples.append(batch_ids) if self._reached_mem_limit(): self.write_to_file()
[docs] def write_to_file(self): """ Write the stored scores to file. """ super().write_to_file()