#!/usr/bin/env python3
"""Module containing the KMeansCoefficient class and the command line interface."""
import argparse
import pandas as pd
import numpy as np
from biobb_common.generic.biobb_object import BiobbObject
from sklearn.preprocessing import StandardScaler
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.clustering.common import check_input_path, check_output_path, getHeader, getIndependentVars, getIndependentVarsList, hopkins, getWCSS, get_best_K, getGap, getSilhouetthe, plotKmeansTrain
[docs]class KMeansCoefficient(BiobbObject):
"""
| biobb_ml KMeansCoefficient
| Wrapper of the scikit-learn KMeans method.
| Clusters a given dataset and calculates best K coefficient. Visit the `KMeans documentation page <https://scikit-learn.org/stable/modules/generated/sklearn.cluster.KMeans.html>`_ in the sklearn official website for further information.
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/clustering/dataset_k_means_coefficient.csv>`_. Accepted formats: csv (edam:format_3752).
output_results_path (str): Table with WCSS (elbow method), Gap and Silhouette coefficients for each cluster. File type: output. `Sample file <https://github.com/bioexcel/biobb_ml/raw/master/biobb_ml/test/reference/clustering/ref_output_results_k_means_coefficient.csv>`_. Accepted formats: csv (edam:format_3752).
output_plot_path (str) (Optional): Path to the elbow method and gap statistics plot. File type: output. `Sample file <https://github.com/bioexcel/biobb_ml/raw/master/biobb_ml/test/reference/clustering/ref_output_plot_k_means_coefficient.png>`_. Accepted formats: png (edam:format_3603).
properties (dic - Python dictionary object containing the tool parameters, not input/output files):
* **predictors** (*dict*) - ({}) Features or columns from your dataset you want to use for fitting. 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.
* **max_clusters** (*int*) - (6) [1~100|1] Maximum number of clusters to use by default for kmeans queries.
* **random_state_method** (*int*) - (5) [1~1000|1] Determines random number generation for centroid initialization.
* **scale** (*bool*) - (False) Whether or not to scale the input dataset.
* **remove_tmp** (*bool*) - (True) [WF property] Remove temporal files.
* **restart** (*bool*) - (False) [WF property] Do not execute if output files exist.
Examples:
This is a use example of how to use the building block from Python::
from biobb_ml.clustering.k_means_coefficient import k_means_coefficient
prop = {
'predictors': {
'columns': [ 'column1', 'column2', 'column3' ]
},
'max_clusters': 3
}
k_means_coefficient(input_dataset_path='/path/to/myDataset.csv',
output_results_path='/path/to/newTable.csv',
output_plot_path='/path/to/newPlot.png',
properties=prop)
Info:
* wrapped_software:
* name: scikit-learn KMeans
* version: >=0.24.2
* license: BSD 3-Clause
* ontology:
* name: EDAM
* schema: http://edamontology.org/EDAM.owl
"""
def __init__(self, input_dataset_path, output_results_path,
output_plot_path=None, 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_results_path": output_results_path, "output_plot_path": output_plot_path}
}
# Properties specific for BB
self.predictors = properties.get('predictors', {})
self.max_clusters = properties.get('max_clusters', 6)
self.random_state_method = properties.get('random_state_method', 5)
self.scale = properties.get('scale', False)
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_results_path"] = check_output_path(self.io_dict["out"]["output_results_path"], "output_results_path", False, out_log, self.__class__.__name__)
if self.io_dict["out"]["output_plot_path"]:
self.io_dict["out"]["output_plot_path"] = check_output_path(self.io_dict["out"]["output_plot_path"], "output_plot_path", True, out_log, self.__class__.__name__)
[docs] @launchlogger
def launch(self) -> int:
"""Execute the :class:`KMeansCoefficient <clustering.k_means_coefficient.KMeansCoefficient>` clustering.k_means_coefficient.KMeansCoefficient 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.predictors:
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)
# the features are the predictors
predictors = getIndependentVars(self.predictors, data, self.out_log, self.__class__.__name__)
fu.log('Predictors: [%s]' % (getIndependentVarsList(self.predictors)), self.out_log, self.global_log)
# Hopkins test
H = hopkins(predictors)
fu.log('Performing Hopkins test over dataset. H = %f' % H, self.out_log, self.global_log)
# scale dataset
if self.scale:
fu.log('Scaling dataset', self.out_log, self.global_log)
scaler = StandardScaler()
predictors = scaler.fit_transform(predictors)
# calculate wcss for each cluster
fu.log('Calculating Within-Clusters Sum of Squares (WCSS) for each %d clusters' % self.max_clusters, self.out_log, self.global_log)
wcss = getWCSS('kmeans', self.max_clusters, predictors)
# wcss table
wcss_table = pd.DataFrame(data={'cluster': np.arange(1, self.max_clusters + 1), 'WCSS': wcss})
fu.log('Calculating WCSS for each cluster\n\nWCSS TABLE\n\n%s\n' % wcss_table.to_string(index=False), self.out_log, self.global_log)
# get best cluster elbow method
best_k, elbow_index = get_best_K(wcss)
fu.log('Optimal number of clusters according to the Elbow Method is %d' % best_k, self.out_log, self.global_log)
# calculate gap
best_g, gap = getGap('kmeans', predictors, nrefs=5, maxClusters=(self.max_clusters + 1))
# gap table
gap_table = pd.DataFrame(data={'cluster': np.arange(1, self.max_clusters + 1), 'GAP': gap['gap']})
fu.log('Calculating Gap for each cluster\n\nGAP TABLE\n\n%s\n' % gap_table.to_string(index=False), self.out_log, self.global_log)
# log best cluster gap method
fu.log('Optimal number of clusters according to the Gap Statistics Method is %d' % best_g, self.out_log, self.global_log)
# calculate silhouette
silhouette_list, s_list = getSilhouetthe(method='kmeans', X=predictors, max_clusters=self.max_clusters, random_state=self.random_state_method)
# silhouette table
silhouette_table = pd.DataFrame(data={'cluster': np.arange(1, self.max_clusters + 1), 'SILHOUETTE': silhouette_list})
fu.log('Calculating Silhouette for each cluster\n\nSILHOUETTE TABLE\n\n%s\n' % silhouette_table.to_string(index=False), self.out_log, self.global_log)
# get best cluster silhouette method
key = silhouette_list.index(max(silhouette_list))
best_s = s_list.__getitem__(key)
fu.log('Optimal number of clusters according to the Silhouette Method is %d' % best_s, self.out_log, self.global_log)
# save results table
results_table = pd.DataFrame(data={'method': ['elbow', 'gap', 'silhouette'], 'coefficient': [wcss[elbow_index], max(gap['gap']), max(silhouette_list)], 'clusters': [best_k, best_g, best_s]})
fu.log('Gathering results\n\nRESULTS TABLE\n\n%s\n' % results_table.to_string(index=False), self.out_log, self.global_log)
fu.log('Saving results to %s' % self.io_dict["out"]["output_results_path"], self.out_log, self.global_log)
results_table.to_csv(self.io_dict["out"]["output_results_path"], index=False, header=True, float_format='%.3f')
# wcss plot
if self.io_dict["out"]["output_plot_path"]:
fu.log('Saving methods plot to %s' % self.io_dict["out"]["output_plot_path"], self.out_log, self.global_log)
plot = plotKmeansTrain(self.max_clusters, wcss, gap['gap'], silhouette_list, best_k, best_g, best_s)
plot.savefig(self.io_dict["out"]["output_plot_path"], dpi=150)
# 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 k_means_coefficient(input_dataset_path: str, output_results_path: str, output_plot_path: str = None, properties: dict = None, **kwargs) -> int:
"""Execute the :class:`KMeansCoefficient <clustering.k_means_coefficient.KMeansCoefficient>` class and
execute the :meth:`launch() <clustering.k_means_coefficient.KMeansCoefficient.launch>` method."""
return KMeansCoefficient(input_dataset_path=input_dataset_path,
output_results_path=output_results_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="Wrapper of the scikit-learn KMeans method.", 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_results_path', required=True, help='Table with WCSS (elbow method), Gap and Silhouette coefficients for each cluster. Accepted formats: csv.')
parser.add_argument('--output_plot_path', required=False, help='Path to the elbow and gap methods 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
k_means_coefficient(input_dataset_path=args.input_dataset_path,
output_results_path=args.output_results_path,
output_plot_path=args.output_plot_path,
properties=properties)
if __name__ == '__main__':
main()