Coverage for biobb_ml/clustering/spectral_clustering.py: 83%
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« prev ^ index » next coverage.py v7.6.1, created at 2024-10-03 14:57 +0000
1#!/usr/bin/env python3
3"""Module containing the SpecClustering class and the command line interface."""
4import argparse
5import pandas as pd
6from biobb_common.generic.biobb_object import BiobbObject
7from sklearn.preprocessing import StandardScaler
8from sklearn.cluster import SpectralClustering
9from biobb_common.configuration import settings
10from biobb_common.tools import file_utils as fu
11from biobb_common.tools.file_utils import launchlogger
12from biobb_ml.clustering.common import check_input_path, check_output_path, getHeader, getIndependentVars, getIndependentVarsList, hopkins, plotCluster
15class SpecClustering(BiobbObject):
16 """
17 | biobb_ml SpecClustering
18 | Wrapper of the scikit-learn SpectralClustering method.
19 | Clusters a given dataset. Visit the `SpectralClustering documentation page <https://scikit-learn.org/stable/modules/generated/sklearn.cluster.SpectralClustering.html>`_ in the sklearn official website for further information.
21 Args:
22 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_spectral_clustering.csv>`_. Accepted formats: csv (edam:format_3752).
23 output_results_path (str): Path to the clustered dataset. File type: output. `Sample file <https://github.com/bioexcel/biobb_ml/raw/master/biobb_ml/test/reference/clustering/ref_output_results_spectral_clustering.csv>`_. Accepted formats: csv (edam:format_3752).
24 output_plot_path (str) (Optional): Path to the clustering plot. File type: output. `Sample file <https://github.com/bioexcel/biobb_ml/raw/master/biobb_ml/test/reference/clustering/ref_output_plot_spectral_clustering.png>`_. Accepted formats: png (edam:format_3603).
25 properties (dic - Python dictionary object containing the tool parameters, not input/output files):
26 * **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.
27 * **clusters** (*int*) - (3) [1~100|1] The number of clusters to form as well as the number of centroids to generate.
28 * **affinity** (*string*) - ("rbf") How to construct the affinity matrix. Values: nearest_neighbors (construct the affinity matrix by computing a graph of nearest neighbors), rbf (construct the affinity matrix using a radial basis function -RBF- kernel), precomputed (interpret X as a precomputed affinity matrix), precomputed_nearest_neighbors (interpret X as a sparse graph of precomputed nearest neighbors and constructs the affinity matrix by selecting the n_neighbors nearest neighbors).
29 * **plots** (*list*) - (None) List of dictionaries with all plots you want to generate. Only 2D or 3D plots accepted. Format: [ { 'title': 'Plot 1', 'features': ['feat1', 'feat2'] } ].
30 * **random_state_method** (*int*) - (5) [1~1000|1] A pseudo random number generator used for the initialization of the lobpcg eigen vectors decomposition when *eigen_solver='amg'* and by the K-Means initialization.
31 * **scale** (*bool*) - (False) Whether or not to scale the input dataset.
32 * **remove_tmp** (*bool*) - (True) [WF property] Remove temporal files.
33 * **restart** (*bool*) - (False) [WF property] Do not execute if output files exist.
34 * **sandbox_path** (*str*) - ("./") [WF property] Parent path to the sandbox directory.
36 Examples:
37 This is a use example of how to use the building block from Python::
39 from biobb_ml.clustering.spectral_clustering import spectral_clustering
40 prop = {
41 'predictors': {
42 'columns': [ 'column1', 'column2', 'column3' ]
43 },
44 'clusters': 3,
45 'affinity': 'rbf',
46 'plots': [
47 {
48 'title': 'Plot 1',
49 'features': ['feat1', 'feat2']
50 }
51 ]
52 }
53 spectral_clustering(input_dataset_path='/path/to/myDataset.csv',
54 output_results_path='/path/to/newTable.csv',
55 output_plot_path='/path/to/newPlot.png',
56 properties=prop)
58 Info:
59 * wrapped_software:
60 * name: scikit-learn SpectralClustering
61 * version: >=0.24.2
62 * license: BSD 3-Clause
63 * ontology:
64 * name: EDAM
65 * schema: http://edamontology.org/EDAM.owl
67 """
69 def __init__(self, input_dataset_path, output_results_path,
70 output_plot_path=None, properties=None, **kwargs) -> None:
71 properties = properties or {}
73 # Call parent class constructor
74 super().__init__(properties)
75 self.locals_var_dict = locals().copy()
77 # Input/Output files
78 self.io_dict = {
79 "in": {"input_dataset_path": input_dataset_path},
80 "out": {"output_results_path": output_results_path, "output_plot_path": output_plot_path}
81 }
83 # Properties specific for BB
84 self.predictors = properties.get('predictors', {})
85 self.clusters = properties.get('clusters', 3)
86 self.affinity = properties.get('affinity', 'rbf')
87 self.plots = properties.get('plots', [])
88 self.random_state_method = properties.get('random_state_method', 5)
89 self.scale = properties.get('scale', False)
90 self.properties = properties
92 # Check the properties
93 self.check_properties(properties)
94 self.check_arguments()
96 def check_data_params(self, out_log, err_log):
97 """ Checks all the input/output paths and parameters """
98 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__)
99 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__)
100 if self.io_dict["out"]["output_plot_path"]:
101 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__)
103 @launchlogger
104 def launch(self) -> int:
105 """Execute the :class:`SpecClustering <clustering.spectral_clustering.SpecClustering>` clustering.spectral_clustering.SpecClustering object."""
107 # check input/output paths and parameters
108 self.check_data_params(self.out_log, self.err_log)
110 # Setup Biobb
111 if self.check_restart():
112 return 0
113 self.stage_files()
115 # load dataset
116 fu.log('Getting dataset from %s' % self.io_dict["in"]["input_dataset_path"], self.out_log, self.global_log)
117 if 'columns' in self.predictors:
118 labels = getHeader(self.io_dict["in"]["input_dataset_path"])
119 skiprows = 1
120 else:
121 labels = None
122 skiprows = None
123 data = pd.read_csv(self.io_dict["in"]["input_dataset_path"], header=None, sep="\\s+|;|:|,|\t", engine="python", skiprows=skiprows, names=labels)
125 # the features are the predictors
126 predictors = getIndependentVars(self.predictors, data, self.out_log, self.__class__.__name__)
127 fu.log('Predictors: [%s]' % (getIndependentVarsList(self.predictors)), self.out_log, self.global_log)
129 # Hopkins test
130 H = hopkins(predictors)
131 fu.log('Performing Hopkins test over dataset. H = %f' % H, self.out_log, self.global_log)
133 # scale dataset
134 if self.scale:
135 fu.log('Scaling dataset', self.out_log, self.global_log)
136 scaler = StandardScaler()
137 predictors = scaler.fit_transform(predictors)
139 # create a spectral clustering object with self.clusters clusters
140 model = SpectralClustering(n_clusters=self.clusters, affinity=self.affinity, random_state=self.random_state_method)
141 # fit the data
142 model.fit(predictors)
144 # create a copy of data, so we can see the clusters next to the original data
145 clusters = data.copy()
146 # predict the cluster for each observation
147 clusters['cluster'] = model.fit_predict(predictors)
149 fu.log('Calculating results\n\nCLUSTERING TABLE\n\n%s\n' % clusters, self.out_log, self.global_log)
151 # save results
152 fu.log('Saving results to %s' % self.io_dict["out"]["output_results_path"], self.out_log, self.global_log)
153 clusters.to_csv(self.io_dict["out"]["output_results_path"], index=False, header=True, float_format='%.3f')
155 if self.io_dict["out"]["output_plot_path"] and self.plots:
156 new_plots = []
157 i = 0
158 for plot in self.plots:
159 if len(plot['features']) == 2 or len(plot['features']) == 3:
160 new_plots.append(plot)
161 i += 1
162 if i == 6:
163 break
165 plot = plotCluster(new_plots, clusters)
166 fu.log('Saving output plot to %s' % self.io_dict["out"]["output_plot_path"], self.out_log, self.global_log)
167 plot.savefig(self.io_dict["out"]["output_plot_path"], dpi=150)
169 # Copy files to host
170 self.copy_to_host()
172 self.tmp_files.extend([
173 self.stage_io_dict.get("unique_dir")
174 ])
175 self.remove_tmp_files()
177 self.check_arguments(output_files_created=True, raise_exception=False)
179 return 0
182def spectral_clustering(input_dataset_path: str, output_results_path: str, output_plot_path: str = None, properties: dict = None, **kwargs) -> int:
183 """Execute the :class:`SpecClustering <clustering.spectral_clustering.SpecClustering>` class and
184 execute the :meth:`launch() <clustering.spectral_clustering.SpecClustering.launch>` method."""
186 return SpecClustering(input_dataset_path=input_dataset_path,
187 output_results_path=output_results_path,
188 output_plot_path=output_plot_path,
189 properties=properties, **kwargs).launch()
192def main():
193 """Command line execution of this building block. Please check the command line documentation."""
194 parser = argparse.ArgumentParser(description="Wrapper of the scikit-learn SpectralClustering method.", formatter_class=lambda prog: argparse.RawTextHelpFormatter(prog, width=99999))
195 parser.add_argument('--config', required=False, help='Configuration file')
197 # Specific args of each building block
198 required_args = parser.add_argument_group('required arguments')
199 required_args.add_argument('--input_dataset_path', required=True, help='Path to the input dataset. Accepted formats: csv.')
200 required_args.add_argument('--output_results_path', required=True, help='Path to the clustered dataset. Accepted formats: csv.')
201 parser.add_argument('--output_plot_path', required=False, help='Path to the clustering plot. Accepted formats: png.')
203 args = parser.parse_args()
204 args.config = args.config or "{}"
205 properties = settings.ConfReader(config=args.config).get_prop_dic()
207 # Specific call of each building block
208 spectral_clustering(input_dataset_path=args.input_dataset_path,
209 output_results_path=args.output_results_path,
210 output_plot_path=args.output_plot_path,
211 properties=properties)
214if __name__ == '__main__':
215 main()