def _encode_sequence_count(self, dataset: RepertoireDataset, comparison_data: ComparisonData, label: str, params: EncoderParams) -> EncodedData:
sequence_p_values_indices, indices_path, relevant_sequences_path = SequenceFilterHelper.get_relevant_sequences(dataset, params, comparison_data, label, self.p_value_threshold,
self.comparison_attributes, self.relevant_indices_path)
if self.relevant_indices_path is None:
self.relevant_indices_path = indices_path
if self.relevant_sequence_csv_path is None:
self.relevant_sequence_csv_path = relevant_sequences_path
count_matrix = self._build_count_matrix(comparison_data, dataset.get_repertoire_ids(), sequence_p_values_indices)
feature_names = comparison_data.get_item_names()[sequence_p_values_indices]
encoded_data = EncodedData(count_matrix, dataset.get_metadata([label]) if params.encode_labels else None,
dataset.get_repertoire_ids(),
feature_names,
encoding=SequenceCountEncoder.__name__, info={'relevant_sequence_path': self.relevant_sequence_csv_path})
return encoded_data
def create_comparison_data(self,
dataset: RepertoireDataset) -> ComparisonData:
comparison_data = ComparisonData(dataset.get_repertoire_ids(),
self.matching_columns,
self.sequence_batch_size, self.path)
comparison_data.process_dataset(dataset)
return comparison_data
def build_comparison_data(dataset: RepertoireDataset, params: EncoderParams,
comparison_attributes, sequence_batch_size):
comp_data = ComparisonData(dataset.get_repertoire_ids(), comparison_attributes,
sequence_batch_size, params.result_path)
comp_data.process_dataset(dataset)
return comp_data
def build_labels(self, dataset: RepertoireDataset,
params: EncoderParams) -> dict:
lbl = ["repertoire_identifier"]
lbl.extend(params.label_config.get_labels_by_name())
tmp_labels = dataset.get_metadata(lbl, return_df=True)
tmp_labels = tmp_labels.iloc[pd.Index(
tmp_labels['repertoire_identifier']).get_indexer(
dataset.get_repertoire_ids())]
tmp_labels = tmp_labels.to_dict("list")
del tmp_labels["repertoire_identifier"]
return tmp_labels
def _calculate_sequence_abundance(self, dataset: RepertoireDataset, comparison_data: ComparisonData, label: str, params: EncoderParams):
sequence_p_values_indices, indices_path, sequence_csv_path = SequenceFilterHelper.get_relevant_sequences(dataset=dataset, params=params,
comparison_data=comparison_data,
label=label, p_value_threshold=self.p_value_threshold,
comparison_attributes=self.comparison_attributes,
sequence_indices_path=self.relevant_indices_path)
if self.relevant_indices_path is None:
self.relevant_indices_path = indices_path
if self.relevant_sequence_csv_path is None:
self.relevant_sequence_csv_path = sequence_csv_path
abundance_matrix = self._build_abundance_matrix(comparison_data, dataset.get_repertoire_ids(), sequence_p_values_indices)
return abundance_matrix
def build_distance_matrix(self, dataset: RepertoireDataset,
params: EncoderParams,
train_repertoire_ids: list):
self.comparison = PairwiseRepertoireComparison(
self.attributes_to_match,
self.attributes_to_match,
params.result_path,
sequence_batch_size=self.sequence_batch_size)
current_dataset = dataset if self.context is None or "dataset" not in self.context else self.context[
"dataset"]
distance_matrix = self.comparison.compare(current_dataset,
self.distance_fn,
self.distance_metric.value)
repertoire_ids = dataset.get_repertoire_ids()
distance_matrix = distance_matrix.loc[repertoire_ids,
train_repertoire_ids]
return distance_matrix
def compare_repertoires(self, dataset: RepertoireDataset, comparison_fn):
self.comparison_data = self.memo_by_params(dataset)
repertoire_count = dataset.get_example_count()
comparison_result = np.zeros([repertoire_count, repertoire_count])
repertoire_identifiers = dataset.get_repertoire_ids()
for index1 in range(repertoire_count):
repertoire_vector_1 = self.comparison_data.get_repertoire_vector(
repertoire_identifiers[index1])
for index2 in range(index1, repertoire_count):
repertoire_vector_2 = self.comparison_data.get_repertoire_vector(
repertoire_identifiers[index2])
comparison_result[index1, index2] = comparison_fn(
repertoire_vector_1, repertoire_vector_2)
comparison_result[index2, index1] = comparison_result[index1,
index2]
comparison_df = pd.DataFrame(comparison_result,
columns=repertoire_identifiers,
index=repertoire_identifiers)
return comparison_df
def _encode_data(self, dataset: RepertoireDataset, params: EncoderParams):
labels = params.label_config.get_labels_by_name()
assert len(labels) == 1, \
"SequenceAbundanceEncoder: this encoding works only for single label."
examples = self._calculate_sequence_abundance(dataset, self.comparison_data, labels[0], params)
encoded_data = EncodedData(examples, dataset.get_metadata([labels[0]]) if params.encode_labels else None, dataset.get_repertoire_ids(),
[SequenceAbundanceEncoder.RELEVANT_SEQUENCE_ABUNDANCE, SequenceAbundanceEncoder.TOTAL_SEQUENCE_ABUNDANCE],
encoding=SequenceAbundanceEncoder.__name__, info={'relevant_sequence_path': self.relevant_sequence_csv_path})
encoded_dataset = RepertoireDataset(params=dataset.params, encoded_data=encoded_data, repertoires=dataset.repertoires)
return encoded_dataset