#!/usr/bin/env python3
"""Module containing the CorrelationMatrix class and the command line interface."""
import argparse
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
from biobb_common.generic.biobb_object import BiobbObject
from biobb_common.configuration import settings
from biobb_common.tools import file_utils as fu
from biobb_common.tools.file_utils import launchlogger
from biobb_ml.utils.common import check_input_path, check_output_path, getHeader, getIndependentVars
[docs]class CorrelationMatrix(BiobbObject):
"""
| biobb_ml CorrelationMatrix
| Generates a correlation matrix from a given dataset.
Args:
input_dataset_path (str): Path to the input dataset. File type: input. `Sample file <https://github.com/bioexcel/biobb_ml/raw/master/biobb_ml/test/data/utils/dataset_correlation_matrix.csv>`_. Accepted formats: csv (edam:format_3752).
output_plot_path (str): Path to the correlation matrix plot. File type: output. `Sample file <https://github.com/bioexcel/biobb_ml/raw/master/biobb_ml/test/reference/utils/ref_output_plot_correlation_matrix.png>`_. Accepted formats: png (edam:format_3603).
properties (dic):
* **features** (*dict*) - ({}) Independent variables or columns from your dataset you want to compare. You can specify either a list of columns names from your input dataset, a list of columns indexes or a range of columns indexes. Formats: { "columns": ["column1", "column2"] } or { "indexes": [0, 2, 3, 10, 11, 17] } or { "range": [[0, 20], [50, 102]] }. In case of mulitple formats, the first one will be picked.
* **remove_tmp** (*bool*) - (True) [WF property] Remove temporal files.
* **restart** (*bool*) - (False) [WF property] Do not execute if output files exist.
* **sandbox_path** (*str*) - ("./") [WF property] Parent path to the sandbox directory.
Examples:
This is a use example of how to use the building block from Python::
from biobb_ml.utils.correlation_matrix import correlation_matrix
prop = {
'features': {
'columns': [ 'column1', 'column2', 'column3' ]
}
}
correlation_matrix(input_dataset_path='/path/to/myDataset.csv',
output_plot_path='/path/to/newPlot.png',
properties=prop)
Info:
* wrapped_software:
* name: In house
* license: Apache-2.0
* ontology:
* name: EDAM
* schema: http://edamontology.org/EDAM.owl
"""
def __init__(self, input_dataset_path, output_plot_path,
properties=None, **kwargs) -> None:
properties = properties or {}
# Call parent class constructor
super().__init__(properties)
self.locals_var_dict = locals().copy()
# Input/Output files
self.io_dict = {
"in": {"input_dataset_path": input_dataset_path},
"out": {"output_plot_path": output_plot_path}
}
# Properties specific for BB
self.features = properties.get('features', {})
self.properties = properties
# Check the properties
self.check_properties(properties)
self.check_arguments()
[docs] def check_data_params(self, out_log, err_log):
""" Checks all the input/output paths and parameters """
self.io_dict["in"]["input_dataset_path"] = check_input_path(self.io_dict["in"]["input_dataset_path"], "input_dataset_path", out_log, self.__class__.__name__)
self.io_dict["out"]["output_plot_path"] = check_output_path(self.io_dict["out"]["output_plot_path"], "output_plot_path", False, out_log, self.__class__.__name__)
[docs] @launchlogger
def launch(self) -> int:
"""Execute the :class:`CorrelationMatrix <utils.correlation_matrix.CorrelationMatrix>` utils.correlation_matrix.CorrelationMatrix object."""
# check input/output paths and parameters
self.check_data_params(self.out_log, self.err_log)
# Setup Biobb
if self.check_restart():
return 0
self.stage_files()
# load dataset
fu.log('Getting dataset from %s' % self.io_dict["in"]["input_dataset_path"], self.out_log, self.global_log)
if 'columns' in self.features:
labels = getHeader(self.io_dict["in"]["input_dataset_path"])
skiprows = 1
else:
labels = None
skiprows = None
data = pd.read_csv(self.io_dict["in"]["input_dataset_path"], header=None, sep="\\s+|;|:|,|\t", engine="python", skiprows=skiprows, names=labels)
if self.features:
data = getIndependentVars(self.features, data, self.out_log, self.__class__.__name__)
fu.log('Parsing dataset', self.out_log, self.global_log)
if data.shape[1] < 10:
s = None
fs = 12
elif data.shape[1] >= 10 and data.shape[1] < 20:
s = (12, 12)
fs = 9
elif data.shape[1] >= 20:
s = (16, 16)
fs = 7
f, ax = plt.subplots(figsize=s)
corr = data.corr()
sns.heatmap(round(corr, 2), annot=True, ax=ax, cmap='Blues', fmt='.2f', square=True, annot_kws={"fontsize": fs})
f.subplots_adjust(top=0.93)
f.suptitle('Attributes Correlation Matrix', fontsize=14)
plt.tight_layout(rect=[0, 0.03, 1, 0.95])
plt.savefig(self.io_dict["out"]["output_plot_path"], dpi=150)
fu.log('Saving Correlation Matrix Plot to %s' % self.io_dict["out"]["output_plot_path"], self.out_log, self.global_log)
# Copy files to host
self.copy_to_host()
self.tmp_files.extend([
self.stage_io_dict.get("unique_dir")
])
self.remove_tmp_files()
self.check_arguments(output_files_created=True, raise_exception=False)
return 0
[docs]def correlation_matrix(input_dataset_path: str, output_plot_path: str, properties: dict = None, **kwargs) -> int:
"""Execute the :class:`CorrelationMatrix <utils.correlation_matrix.CorrelationMatrix>` class and
execute the :meth:`launch() <utils.correlation_matrix.CorrelationMatrix.launch>` method."""
return CorrelationMatrix(input_dataset_path=input_dataset_path,
output_plot_path=output_plot_path,
properties=properties, **kwargs).launch()
[docs]def main():
"""Command line execution of this building block. Please check the command line documentation."""
parser = argparse.ArgumentParser(description="Generates a correlation matrix from a given dataset", formatter_class=lambda prog: argparse.RawTextHelpFormatter(prog, width=99999))
parser.add_argument('--config', required=False, help='Configuration file')
# Specific args of each building block
required_args = parser.add_argument_group('required arguments')
required_args.add_argument('--input_dataset_path', required=True, help='Path to the input dataset. Accepted formats: csv.')
required_args.add_argument('--output_plot_path', required=True, help='Path to the correlation matrix plot. Accepted formats: png.')
args = parser.parse_args()
args.config = args.config or "{}"
properties = settings.ConfReader(config=args.config).get_prop_dic()
# Specific call of each building block
correlation_matrix(input_dataset_path=args.input_dataset_path,
output_plot_path=args.output_plot_path,
properties=properties)
if __name__ == '__main__':
main()