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