seqgra.learner.bayes.bayeshelper module¶

MIT - CSAIL - Gifford Lab - seqgra

TensorFlow Keras learner helper class

@author: Konstantin Krismer

class BayesOptimalHelper[source]¶

Bases: object

static create_model(learner: seqgra.learner.learner.Learner) → None[source]¶

static create_se_pwm_dict(sequence_elements: List[seqgra.model.data.sequenceelement.SequenceElement]) → Dict[str, Any][source]¶

static evaluate_model(learner: seqgra.learner.learner.Learner, x: List[str], y: List[str])[source]¶

static get_pwm_max_score(pwm) → float[source]¶

static get_pwm_min_score(pwm) → float[source]¶

static get_pwms_for_label(label: str, conditions: List[seqgra.model.data.condition.Condition], pwm_dict: Dict[str, Any]) → Dict[str, Any][source]¶

static normalize_pwm_score(score: float, pwm) → float[source]¶

static ppm_to_pwm(ppm, alphabet_size) → Any[source]¶

static predict(learner: seqgra.learner.learner.Learner, x: Any, silent: bool = False)[source]¶: This is the forward calculation from x to y :returns: Output tensor with the computed logits. :rtype: softmax_linear

static print_model_summary(learner: seqgra.learner.learner.Learner)[source]¶

static score_example(example, pwm) → Any[source]¶

static se_to_pwm(sequence_element: seqgra.model.data.sequenceelement.SequenceElement) → Any[source]¶

static set_seed(learner: seqgra.learner.learner.Learner) → None[source]¶

static train_model(learner: seqgra.learner.learner.Learner) → None[source]¶

static write_session_info(learner: seqgra.learner.learner.Learner) → None[source]¶