def _generate(self) -> ReportResult:
self.result_path = PathBuilder.build(self.result_path +
f'{self.name}/')
assert all(self.instruction_states[0].label_configuration.get_labels_by_name() == state.label_configuration.get_labels_by_name() and
self.instruction_states[0].label_configuration.get_label_values(
self.instruction_states[0].label_configuration.get_labels_by_name()[0]) ==
state.label_configuration.get_label_values(state.label_configuration.get_labels_by_name()[0])
for state in self.instruction_states), \
"PerformanceOverview: there is a difference in labels between instructions, the plots cannot be created."
assert len(self.instruction_states[0].label_configuration.get_labels_by_name()) == 1, \
'PerformanceOverview: multiple labels were provided, but only one can be used in this report.'
assert all(state.refit_optimal_model is False for state in self.instruction_states), \
f"{PerformanceOverview.__name__}: no test datasets were available to assess the performance of optimal models as they were refitted on " \
f"the full datasets. No reports will be generated."
label = self.instruction_states[
0].label_configuration.get_label_objects()[0]
optimal_hp_items = [
list(state.optimal_hp_items.values())[0]
for state in self.instruction_states
]
colors = px.colors.sequential.Viridis[::2][::-1]
figure_auc, table_aucs = self.plot_roc(optimal_hp_items, label, colors)
figure_pr, table_pr = self.plot_precision_recall(
optimal_hp_items, label, colors)
return ReportResult(output_figures=[figure_auc, figure_pr],
output_tables=table_aucs + table_pr)
def _generate(self) -> ReportResult:
PathBuilder.build(self.result_path)
data_long_format = DataReshaper.reshape(self.dataset)
table_result = self._write_results_table(data_long_format)
report_output_fig = self._safe_plot(data_long_format=data_long_format)
output_figures = None if report_output_fig is None else [report_output_fig]
return ReportResult(self.name, output_figures, [table_result])
def _generate(self) -> ReportResult:
PathBuilder.build(self.result_path)
test_metadata_filepath = self.test_dataset.encoded_data.info[
'metadata_filepath']
label_names = [self.label]
hdf5_filepath = self.method._metadata_to_hdf5(test_metadata_filepath,
label_names)
n_examples_test = len(self.test_dataset.encoded_data.example_ids)
indices = np.array(range(n_examples_test))
dataloader = self.method.make_data_loader(hdf5_filepath,
pre_loaded_hdf5_file=None,
indices=indices,
label=self.label,
eval_only=True,
is_train=False)
model = self.method.get_model(self.label)[self.label]
compute_contributions(intgrds_set_loader=dataloader,
deeprc_model=model,
n_steps=self.n_steps,
threshold=self.threshold,
resdir=self.result_path,
filename_inputs=self.filename_inputs,
filename_kernels=self.filename_kernels)
return ReportResult(self.name,
output_figures=[
ReportOutput(self.filename_inputs),
ReportOutput(self.filename_kernels)
])
def _generate(self) -> ReportResult:
figures, tables = [], []
PathBuilder.build(self.result_path)
if ReferenceSequenceOverlap._check_encoder_class(
self.state.optimal_hp_items[self.label].encoder):
figure, data = self._compute_optimal_model_overlap()
figures.append(figure)
tables.append(data)
for assessment_state in self.state.assessment_states:
encoder = assessment_state.label_states[
self.label].optimal_assessment_item.encoder
if ReferenceSequenceOverlap._check_encoder_class(encoder):
figure_filename = f"{self.result_path}assessment_split_{assessment_state.split_index + 1}_model_vs_reference_overlap_{self.label}.pdf"
df_filename = f"{self.result_path}assessment_split_{assessment_state.split_index + 1}_overlap_sequences_{self.label}"
figure, data = self._compute_model_overlap(
figure_filename, df_filename, encoder,
f"overlap sequences between the model for assessment split "
f"{assessment_state.split_index + 1} and reference list")
figures.append(figure)
tables.append(data)
return ReportResult(self.name,
output_figures=figures,
output_tables=tables)
def _generate(self) -> ReportResult:
data = self._generate_data()
report_output_fig = self._safe_plot(data=data, output_written=False)
output_figures = [report_output_fig
] if report_output_fig is not None else []
return ReportResult(self.name, output_figures=output_figures)
def _generate(self) -> ReportResult:
PathBuilder.build(self.result_path)
matrix_result = self._export_matrix()
details_result = self._export_details()
label_result = self._export_labels()
return ReportResult(self.name,
output_tables=[matrix_result],
output_text=[details_result, label_result])
def _generate(self) -> ReportResult:
PathBuilder.build(self.result_path)
plotting_data = self._retrieve_plotting_data()
result_table = self._write_results_table(plotting_data)
report_output_fig = self._safe_plot(plotting_data=plotting_data)
output_figures = [report_output_fig
] if report_output_fig is not None else []
return ReportResult(self.name,
output_tables=[result_table],
output_figures=output_figures)
def _generate(self) -> ReportResult:
PathBuilder.build(self.result_path)
upper_limit, lower_limit = self.get_distribution_limits()
self.result_name = "beta_distribution"
report_output_fig = self._safe_plot(upper_limit=upper_limit, lower_limit=lower_limit, output_written=False)
output_figures = [] if report_output_fig is None else [report_output_fig]
return ReportResult(name="Beta distribution priors - probability that a sequence is disease-associated",
output_figures=output_figures)
def _generate(self) -> ReportResult:
self.result_path = PathBuilder.build(f"{self.result_path}/{self.name}/")
self._extract_label()
hp_items = [state.optimal_hp_items[self.label] for state in self.instruction_states]
overlap_matrix = SequenceAnalysisHelper.compute_overlap_matrix(hp_items)
labels = [state.dataset.name for state in self.instruction_states]
figure_path = self._make_figure(overlap_matrix, labels)
data_path = self._export_matrix(overlap_matrix, labels)
return ReportResult(output_figures=[ReportOutput(figure_path, 'sequence overlap across datasets')],
output_tables=[ReportOutput(data_path, 'sequence overlap across datasets (csv)')])
def _generate(self) -> ReportResult:
df = pd.read_csv(
self.dataset.encoded_data.info["relevant_sequence_path"])
column_mapping = self._compute_column_mapping(df)
df.rename(columns=column_mapping, inplace=True)
PathBuilder.build(self.result_path)
filename = f"{self.result_path}relevant_sequences.csv"
df.to_csv(filename, index=False)
return ReportResult(
self.name,
output_tables=[ReportOutput(filename, "relevant sequences")])
def _generate(self) -> ReportResult:
PathBuilder.build(self.result_path)
report_result = ReportResult()
sequence_alphabet = EnvironmentSettings.get_sequence_alphabet(self.method.sequence_type)
for kernel_name in self.method.CNN.conv_chain_1 + self.method.CNN.conv_chain_2:
figure_outputs, table_outputs = self._plot_kernels(kernel_name, sequence_alphabet)
report_result.output_figures.extend(figure_outputs)
report_result.output_tables.extend(table_outputs)
figure_output, table_output = self._plot_fc_layer()
report_result.output_figures.append(figure_output)
report_result.output_tables.append(table_output)
return report_result
def _generate(self) -> ReportResult:
PathBuilder.build(self.result_path)
alpha_chains, beta_chains, trbv, trbj, subject_condition, count = [], [], [], [], [], []
for index, receptor in enumerate(self.dataset.get_data()):
alpha_chains.append(receptor.get_chain("alpha").amino_acid_sequence)
beta_chains.append(receptor.get_chain("beta").amino_acid_sequence)
trbv.append(receptor.get_chain("beta").metadata.v_gene)
trbj.append(receptor.get_chain("beta").metadata.j_gene)
subject_condition.append(f"{getattr(receptor.metadata, 'subject_id', str(index))}:{receptor.metadata[self.condition]}")
count.append(receptor.get_chain("beta").metadata.count if receptor.get_chain('beta').metadata is not None else 1)
df = pd.DataFrame({"CDR3b": beta_chains, "TRBV": trbv, "TRBJ": trbj, "CDR3a": alpha_chains, "subject:condition": subject_condition,
"count": count})
file_path = self.result_path + "exported_data.tsv"
df.to_csv(file_path, sep="\t", index=False)
return ReportResult(self.name, output_tables=[ReportOutput(file_path, "exported data in GLIPH2 format")])
def _generate(self) -> ReportResult:
PathBuilder.build(self.result_path)
tables, figures = [], []
for label in self.state.label_configuration.get_labels_by_name():
if self.compare_in_assessment:
table, figure = self._generate_for_assessment(label)
tables.append(table)
figures.append(figure)
if self.compare_in_selection:
tmp_tables, tmp_figures = self._generate_for_selection(label)
tables += tmp_tables
figures += tmp_figures
return ReportResult(self.name,
[fig for fig in figures if fig is not None],
[tab for tab in tables if tab is not None])
def _generate(self) -> ReportResult:
PathBuilder.build(self.result_path)
self.result_name = f"{self.feature}_performance"
training_dataframe, test_dataframe = self._make_plot_dataframes()
table_results = self._store_dataframes(training_dataframe,
test_dataframe)
report_output_fig = self._plot(training_dataframe=training_dataframe,
test_dataframe=test_dataframe)
output_figures = None if report_output_fig is None else [
report_output_fig
]
return ReportResult(output_tables=table_results,
output_figures=output_figures)
def _generate(self):
report_output_tables = []
if isinstance(self.dataset, RepertoireDataset):
for repertoire in self.dataset.get_data():
result_path = f"{self.result_path}/{repertoire.identifier}/"
PathBuilder.build(result_path)
report_output_tables = self.export_receptorlist(
repertoire.receptors, result_path)
elif isinstance(self.dataset, ReceptorDataset):
receptors = self.dataset.get_data()
result_path = f"{self.result_path}/{self.dataset.identifier}/"
PathBuilder.build(result_path)
report_output_tables = self.export_receptorlist(
receptors, result_path=result_path)
return ReportResult(output_tables=report_output_tables)
def _generate(self) -> ReportResult:
self.label = list(self.train_dataset.encoded_data.labels.keys())[0]
from source.util.TCRdistHelper import TCRdistHelper
from tcrdist.rep_diff import hcluster_diff
PathBuilder.build(self.result_path)
subsampled_dataset = self._extract_positive_example_dataset()
reference_sequences = self._extract_reference_sequences()
tcr_rep = TCRdistHelper.compute_tcr_dist(subsampled_dataset,
[self.label], self.cores)
tcr_rep.hcluster_df, tcr_rep.Z = hcluster_diff(
clone_df=tcr_rep.clone_df,
pwmat=tcr_rep.pw_alpha + tcr_rep.pw_beta,
x_cols=["epitope"],
count_col='count')
figures, tables = [], []
logging.info(
f'{TCRdistMotifDiscovery.__name__}: created {tcr_rep.hcluster_df.shape[0]} clusters, now discovering motifs in clusters.'
)
for index, row in tcr_rep.hcluster_df.iterrows():
if len(row['neighbors_i']) >= self.min_cluster_size:
figure_outputs, table_outputs = self._discover_motif_in_cluster(
tcr_rep, index, row, reference_sequences)
figures.extend(figure_outputs)
tables.extend(table_outputs)
res_summary = member_summ(res_df=tcr_rep.hcluster_df,
clone_df=tcr_rep.clone_df,
addl_cols=['epitope'])
res_summary.to_csv(self.result_path + "tcrdist_summary.csv")
tables.append(
ReportOutput(path=self.result_path + "tcrdist_summary.csv",
name="TCRdist summary (csv)"))
return ReportResult("TCRdist motif discovery", figures, tables)
def _write_reports(self) -> ReportResult:
all_matches_table = self._write_match_table()
repertoire_sizes = self._write_repertoire_sizes()
output_tables = [all_matches_table, repertoire_sizes]
if self.dataset.encoded_data.encoding == "MatchedSequencesEncoder":
output_tables += self._write_sequence_info(self.result_path +
"/sequence_info")
else:
if len(self.dataset.encoded_data.feature_annotations["chain"].
unique()) == 2:
output_tables += self._write_paired_matches(self.result_path +
"/paired_matches")
if self.dataset.encoded_data.encoding == "MatchedReceptorsEncoder":
output_tables += self._write_receptor_info(self.result_path +
"/receptor_info")
return ReportResult(self.name, output_tables=output_tables)
def _generate(self):
PathBuilder.build(self.result_path)
paths = []
self._set_plotting_parameters()
plot_data = self._retrieve_plot_data()
plot_data["abs_coefficients"] = abs(plot_data["coefficients"])
plot_data.sort_values(by="abs_coefficients", inplace=True, ascending=False)
result_table_path = self._write_results_table(plot_data[["features", "coefficients"]])
self._write_settings()
if CoefficientPlottingSetting.ALL in self._coefs_to_plot:
report_output_fig = self._plot(plotting_data=plot_data, output_name="all_coefficients")
paths.append(report_output_fig)
if CoefficientPlottingSetting.NONZERO in self._coefs_to_plot:
nonzero_data = plot_data[plot_data["coefficients"] != 0]
report_output_fig = self._plot(plotting_data=nonzero_data, output_name="nonzero_coefficients")
paths.append(report_output_fig)
if CoefficientPlottingSetting.CUTOFF in self._coefs_to_plot:
for cutoff_val in self._cutoff:
cutoff_data = plot_data[plot_data["abs_coefficients"] >= cutoff_val]
report_output_fig = self._plot(plotting_data=cutoff_data, output_name="cutoff_{}_coefficients".format(cutoff_val))
paths.append(report_output_fig)
if CoefficientPlottingSetting.N_LARGEST in self._coefs_to_plot:
for n_val in self._n_largest:
n_largest_data = plot_data.nlargest(n=n_val, columns=["abs_coefficients"])
report_output_fig = self._plot(plotting_data=n_largest_data, output_name="largest_{}_coefficients".format(n_val))
paths.append(report_output_fig)
return ReportResult(self.name, output_tables=[ReportOutput(result_table_path, "features and coefficients csv")],
output_figures=[p for p in paths if p is not None])
def _generate(self) -> ReportResult:
sequence_lengths = self._get_sequence_lengths()
report_output_fig = self._plot(sequence_lengths=sequence_lengths)
output_figures = None if report_output_fig is None else [report_output_fig]
return ReportResult(type(self).__name__, output_figures=output_figures)
def _generate(self) -> ReportResult:
PathBuilder.build(self.result_path)
figure, tables = self._generate_sequence_length_distribution_plots()
return ReportResult(name=self.name,
output_figures=[figure],
output_tables=tables)
