def _encode_by_model(self, dataset, params: EncoderParams, vectors):
examples = CacheHandler.memo_by_params(
self._prepare_caching_params(
dataset, params, vectors,
Word2VecEncoder.DESCRIPTION_REPERTOIRES),
lambda: self._encode_examples(dataset, vectors, params))
if params.encode_labels:
labels = CacheHandler.memo_by_params(
self._prepare_caching_params(
dataset, params, vectors,
Word2VecEncoder.DESCRIPTION_LABELS),
lambda: self._encode_labels(dataset, params))
else:
labels = None
if params.learn_model:
self.scaler = StandardScaler(with_std=True, with_mean=True)
scaled_examples = FeatureScaler.standard_scale_fit(
self.scaler, examples)
else:
scaled_examples = FeatureScaler.standard_scale(
self.scaler, examples)
encoded_dataset = self._build_encoded_dataset(dataset, scaled_examples,
labels, params)
return encoded_dataset
def _encode_data(self, dataset, params: EncoderParams) -> EncodedData:
encoded_example_list, example_ids, encoded_labels, feature_annotation_names = CacheHandler.memo_by_params(
self._prepare_caching_params(dataset, params,
KmerFrequencyEncoder.STEP_ENCODED),
lambda: self._encode_examples(dataset, params))
vectorized_examples, feature_names = CacheHandler.memo_by_params(
self._prepare_caching_params(dataset, params,
KmerFrequencyEncoder.STEP_VECTORIZED),
lambda: self._vectorize_encoded(examples=encoded_example_list,
params=params))
normalized_examples = CacheHandler.memo_by_params(
self._prepare_caching_params(dataset, params,
KmerFrequencyEncoder.STEP_NORMALIZED),
lambda: FeatureScaler.normalize(vectorized_examples, self.
normalization_type))
if self.scale_to_unit_variance:
examples = self.scale_normalized(params, dataset,
normalized_examples)
else:
examples = normalized_examples
feature_annotations = self._get_feature_annotations(
feature_names, feature_annotation_names)
encoded_data = EncodedData(examples=examples,
labels=encoded_labels,
feature_names=feature_names,
example_ids=example_ids,
feature_annotations=feature_annotations,
encoding=KmerFrequencyEncoder.__name__)
return encoded_data
def encode(self, dataset, params: EncoderParams):
encoded_dataset = CacheHandler.memo_by_params(
self._prepare_caching_params(dataset, params),
lambda: self._encode_new_dataset(dataset, params))
return encoded_dataset
def compute_tcr_dist(dataset: ReceptorDataset,
label_names: list,
cores: int = 1):
return CacheHandler.memo_by_params(
(('dataset_identifier', dataset.identifier), ("type", "TCRrep")),
lambda: TCRdistHelper._compute_tcr_dist(dataset, label_names, cores
))
def get_encoded_repertoire(self, repertoire, params: EncoderParams):
params.model = vars(self)
return CacheHandler.memo_by_params((("encoding_model", params.model), ("type", "kmer_encoding"),
("labels", params.label_config.get_labels_by_name()),
("repertoire_id", repertoire.identifier)),
lambda: self.encode_repertoire(repertoire, params), CacheObjectType.ENCODING_STEP)
def _encode_by_model(self, dataset, params: EncoderParams, vectors):
examples = CacheHandler.memo_by_params(self._prepare_caching_params(dataset, params, vectors,
Word2VecEncoder.DESCRIPTION_REPERTOIRES),
lambda: self._encode_examples(dataset, vectors, params))
if params.encode_labels:
labels = CacheHandler.memo_by_params(self._prepare_caching_params(dataset, params, vectors, Word2VecEncoder.DESCRIPTION_LABELS),
lambda: self._encode_labels(dataset, params))
else:
labels = None
scaler_filename = params.result_path / FilenameHandler.get_filename("standard_scaling", "pkl")
scaled_examples = FeatureScaler.standard_scale(scaler_filename, examples)
encoded_dataset = self._build_encoded_dataset(dataset, scaled_examples, labels, params)
return encoded_dataset
def fit(self,
encoded_data: EncodedData,
label_name: str,
cores_for_training: int = 2):
self.feature_names = encoded_data.feature_names
self.models = CacheHandler.memo_by_params(
self._prepare_caching_params(encoded_data, "fit", label_name),
lambda: self._fit(encoded_data, label_name, cores_for_training))
def scale_normalized(self, params, dataset, normalized_examples):
self.scaler_path = params.result_path / 'scaler.pickle' if self.scaler_path is None else self.scaler_path
examples = CacheHandler.memo_by_params(
self._prepare_caching_params(dataset, params, step=KmerFrequencyEncoder.STEP_SCALED),
lambda: FeatureScaler.standard_scale(self.scaler_path, normalized_examples, with_mean=self.scale_to_zero_mean))
return examples
def _get_encoded_repertoire(self, repertoire, params: EncoderParams):
params.model = vars(self)
return CacheHandler.memo_by_params((("encoding_model", params.model),
("labels", params.label_config.get_labels_by_name()),
("repertoire_id", repertoire.identifier),
("repertoire_data", hashlib.sha256(np.ascontiguousarray(repertoire.get_attribute(self.sequence_type.value))).hexdigest())),
lambda: self._encode_repertoire(repertoire, params), CacheObjectType.ENCODING)
def _get_sequence_presence(self, full_dataset, full_sequence_set, params):
sequence_presence_matrix, matrix_repertoire_ids = CacheHandler.memo_by_params(
self._build_sequence_presence_params(full_dataset,
self.compairr_params),
lambda: self._compute_sequence_presence_with_compairr(
full_dataset, full_sequence_set, params))
return sequence_presence_matrix, matrix_repertoire_ids
def _process_repertoire_cached(self, repertoire, index, example_count):
return CacheHandler.memo_by_params(
(('repertoire', repertoire.identifier),
('encoder', AtchleyKmerEncoder.__name__),
(self.abundance, self.skip_last_n_aa, self.skip_first_n_aa,
self.k)),
lambda: self._process_repertoire(repertoire, index, example_count),
CacheObjectType.ENCODING_STEP)
def fit(self,
encoded_data: EncodedData,
label_name: str,
cores_for_training: int = 2):
assert encoded_data.encoding == "DeepRCEncoder", f"DeepRC: ML method DeepRC is only compatible with the DeepRC encoder, found {encoded_data.encoding.replace('Encoder','')} encoder"
self.feature_names = encoded_data.feature_names
self._set_label_classes({label_name: encoded_data.labels[label_name]})
self.model = CacheHandler.memo_by_params(
self._prepare_caching_params(encoded_data, "fit", label_name),
lambda: self._fit(encoded_data, label_name, cores_for_training))
def fit(self, encoded_data: EncodedData, label_name: str, cores_for_training: int = 2):
self.class_mapping = Util.make_class_mapping(encoded_data.labels[label_name])
self.feature_names = encoded_data.feature_names
self.label_name = label_name
mapped_y = Util.map_to_new_class_values(encoded_data.labels[label_name], self.class_mapping)
self.model = CacheHandler.memo_by_params(self._prepare_caching_params(encoded_data, encoded_data.labels[label_name], self.FIT, label_name),
lambda: self._fit(encoded_data.examples, mapped_y, cores_for_training))
def encode(self, dataset, params: EncoderParams):
cache_params = self._prepare_caching_params(dataset, params)
encoded_dataset = CacheHandler.memo_by_params(
cache_params, lambda: self._encode_new_dataset(dataset, params))
EncoderHelper.sync_encoder_with_cache(
cache_params, lambda: {
'model_path': self.model_path,
'scaler': self.scaler
}, self, ['model_path', 'scaler'])
return encoded_dataset
def fit_by_cross_validation(self, encoded_data: EncodedData, number_of_splits: int = 5, label_name: str = None, cores_for_training: int = -1,
optimization_metric='balanced_accuracy'):
self.class_mapping = Util.make_class_mapping(encoded_data.labels[label_name])
self.feature_names = encoded_data.feature_names
self.label_name = label_name
mapped_y = Util.map_to_new_class_values(encoded_data.labels[label_name], self.class_mapping)
self.model = CacheHandler.memo_by_params(
self._prepare_caching_params(encoded_data, mapped_y, self.FIT_CV, label_name, number_of_splits),
lambda: self._fit_by_cross_validation(encoded_data.examples, mapped_y, number_of_splits, label_name, cores_for_training,
optimization_metric))
def scale_normalized(self, params, dataset, normalized_examples):
if params.learn_model:
self.scaler = StandardScaler(with_mean=self.scale_to_zero_mean)
examples = CacheHandler.memo_by_params(
self._prepare_caching_params(
dataset, params, step=KmerFrequencyEncoder.STEP_SCALED),
lambda: FeatureScaler.standard_scale_fit(self.scaler,
normalized_examples,
with_mean=self.
scale_to_zero_mean))
else:
examples = CacheHandler.memo_by_params(
self._prepare_caching_params(
dataset, params, step=KmerFrequencyEncoder.STEP_SCALED),
lambda: FeatureScaler.standard_scale(self.scaler,
normalized_examples,
with_mean=self.
scale_to_zero_mean))
return examples
def build_comparison_data(dataset: RepertoireDataset, context: dict,
comparison_attributes: list,
params: EncoderParams, sequence_batch_size: int):
current_dataset = EncoderHelper.get_current_dataset(dataset, context)
comparison_data = CacheHandler.memo_by_params(
EncoderHelper.build_comparison_params(current_dataset,
comparison_attributes),
lambda: EncoderHelper.build_comparison_data(
current_dataset, params, comparison_attributes,
sequence_batch_size))
return comparison_data
def sync_encoder_with_cache(cache_params: tuple, encoder_memo_func,
encoder, param_names):
encoder_cache_params = tuple(
(key, val) for key, val in dict(cache_params).items()
if key != 'learn_model')
encoder_cache_params = (encoder_cache_params, "encoder")
encoder_from_cache = CacheHandler.memo_by_params(
encoder_cache_params, encoder_memo_func)
for param in param_names:
setattr(encoder, param, copy.deepcopy(encoder_from_cache[param]))
return encoder
def _encode_data(self, dataset, params: EncoderParams) -> EncodedData:
encoded_example_list, example_ids, encoded_labels = CacheHandler.memo_by_params(
self._prepare_caching_params(dataset, params, EvennessProfileEncoder.STEP_ENCODED),
lambda: self._encode_examples(dataset, params))
vectorized_examples = CacheHandler.memo_by_params(
self._prepare_caching_params(dataset, params, EvennessProfileEncoder.STEP_VECTORIZED),
lambda: self._vectorize_encoded(examples=encoded_example_list))
feature_names = list(range(self.dimension))
feature_annotations = pd.DataFrame({"feature": feature_names})
encoded_data = EncodedData(examples=vectorized_examples,
labels=encoded_labels,
feature_names=feature_names,
example_ids=example_ids,
feature_annotations=feature_annotations,
encoding=EvennessProfileEncoder.__name__)
return encoded_data
def memo_by_params(self, dataset: RepertoireDataset):
comparison_data = CacheHandler.memo_by_params(
self.prepare_caching_params(dataset),
lambda: self.create_comparison_data(dataset))
return comparison_data
def compare(self, dataset: RepertoireDataset, comparison_fn,
comparison_fn_name):
return CacheHandler.memo_by_params(
(("dataset_identifier", dataset.identifier), "pairwise_comparison",
("comparison_fn", comparison_fn_name)),
lambda: self.compare_repertoires(dataset, comparison_fn))
def _get_full_sequence_set(self, full_dataset):
full_sequence_set = CacheHandler.memo_by_params(
self._build_dataset_params(full_dataset),
lambda: self.get_sequence_set(full_dataset))
return full_sequence_set
