01_0_transform_data_to_wide_format.py

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# %% [markdown]
# # Transfer data for NAGuideR format
#

# %% tags=["hide-input"]
import pandas as pd

import pimmslearn
import pimmslearn.models
from pimmslearn.io import datasplits

# %% tags=["hide-input"]
# catch passed parameters
args = None
args = dict(globals()).keys()

# %% [markdown]
# Papermill script parameters:

# %% tags=["parameters"]
# files and folders
# Datasplit folder with data for experiment
folder_experiment: str = 'runs/example'
folder_data: str = ''  # specify data directory if needed
file_format_in: str = 'csv'  # file format of original splits, default pickle (pkl)
file_format_out: str = 'csv'  # file format of transformed splits, default csv

# %% tags=["hide-input"]
args = pimmslearn.nb.get_params(args, globals=globals())
args

# %% tags=["hide-input"]
params = pimmslearn.nb.args_from_dict(args)
# params = OmegaConf.create(args)
params

# %% tags=["hide-input"]
splits = datasplits.DataSplits.from_folder(params.data, file_format=params.file_format_in)

# %% tags=["hide-input"]
train_data = splits.train_X.unstack()
train_data


# %% [markdown]
# Save placeholder sample annotation for use in NAGuideR app which requires such a file


# %% tags=["hide-input"]
annotation = pd.Series('test', train_data.index).to_frame('group')
annotation.index.name = 'Samples'
annotation

# %% tags=["hide-input"]
fname = params.data / 'sample_annotation_placeholder.csv'
annotation.to_csv(fname)
fname

# %% [markdo]
# Save with samples in columns

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fname = params.data / 'data_wide_sample_cols.csv'
# fillna('Filtered')
train_data.T.to_csv(fname)
fname


# %% tags=["hide-input"]
# 'data_wide_sample_cols.csv'