Source code for immuneML.analysis.data_manipulation.DataReshaper

import pandas as pd

from immuneML.data_model.dataset.Dataset import Dataset

[docs]class DataReshaper:
[docs] @staticmethod def reshape(dataset: Dataset, labels=None): """ Takes a 2D matrix of values from the encoded data and reshapes it to long format, retaining the column and row annotations. This is for ease of use in plotting the data. It is suggested that some sort of filtering is done first, otherwise the memory usage may explode, as the resulting data frame is of shape (matrix.shape[0] * matrix.shape[1], labels.shape[0] + feature_annotations.shape[1] + 1) """ if labels is None: row_annotations = pd.DataFrame(dataset.encoded_data.labels) else: row_annotations = pd.DataFrame(dataset.get_metadata(labels, return_df=True)) row_annotations["example_id"] = dataset.encoded_data.example_ids column_annotations = dataset.encoded_data.feature_annotations column_annotations["feature"] = dataset.encoded_data.feature_names matrix = dataset.encoded_data.examples matrix_1d = matrix.A.ravel() column_annotations = pd.concat([column_annotations]*matrix.shape[0], ignore_index=True) row_annotations = pd.DataFrame(row_annotations.values.repeat(matrix.shape[1], axis=0), columns=row_annotations.columns) data = pd.concat([row_annotations.reset_index(drop=True), column_annotations.reset_index(drop=True), pd.DataFrame({"value": matrix_1d})], axis=1) for column in data.columns: data[column] = pd.to_numeric(data[column], errors="ignore") return data