Coverage for biobb_ml/dimensionality_reduction/pls_components.py: 72%
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« prev ^ index » next coverage.py v7.5.1, created at 2024-05-07 09:39 +0000
1#!/usr/bin/env python3
3"""Module containing the PLSComponents class and the command line interface."""
4import argparse
5import warnings
6import pandas as pd
7import numpy as np
8import matplotlib.pyplot as plt
9from biobb_common.generic.biobb_object import BiobbObject
10from scipy.signal import savgol_filter
11from sys import stdout
12from sklearn.cross_decomposition import PLSRegression
13from sklearn.model_selection import cross_val_predict
14from sklearn.metrics import mean_squared_error, r2_score
15from biobb_common.configuration import settings
16from biobb_common.tools import file_utils as fu
17from biobb_common.tools.file_utils import launchlogger
18from biobb_ml.dimensionality_reduction.common import check_input_path, check_output_path, getHeader, getIndependentVars, getIndependentVarsList, getTarget, getTargetValue, getWindowLength
21class PLSComponents(BiobbObject):
22 """
23 | biobb_ml PLSComponents
24 | Wrapper of the scikit-learn PLSRegression method.
25 | Calculates best components number for a Partial Least Square (PLS) Regression. Visit the `PLSRegression documentation page <https://scikit-learn.org/stable/modules/generated/sklearn.cross_decomposition.PLSRegression.html>`_ in the sklearn official website for further information.
27 Args:
28 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/dimensionality_reduction/dataset_pls_components.csv>`_. Accepted formats: csv (edam:format_3752).
29 output_results_path (str): Table with R2 and MSE for calibration and cross-validation data for the best number of components. File type: output. `Sample file <https://github.com/bioexcel/biobb_ml/raw/master/biobb_ml/test/reference/dimensionality_reduction/ref_output_results_pls_components.csv>`_. Accepted formats: csv (edam:format_3752).
30 output_plot_path (str) (Optional): Path to the Mean Square Error plot. File type: output. `Sample file <https://github.com/bioexcel/biobb_ml/raw/master/biobb_ml/test/reference/dimensionality_reduction/ref_output_plot_pls_components.png>`_. Accepted formats: png (edam:format_3603).
31 properties (dic - Python dictionary object containing the tool parameters, not input/output files):
32 * **features** (*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.
33 * **target** (*dict*) - ({}) Dependent variable you want to predict from your dataset. You can specify either a column name or a column index. Formats: { "column": "column3" } or { "index": 21 }. In case of mulitple formats, the first one will be picked.
34 * **optimise** (*boolean*) - (False) Whether or not optimise the process of MSE calculation. Beware, if True selected, the process can take a long time depending on the **max_components** value.
35 * **max_components** (*int*) - (10) [1~1000|1] Maximum number of components to use by default for PLS queries.
36 * **cv** (*int*) - (10) [1~10000|1] Specify the number of folds in the cross-validation splitting strategy. Value must be between 2 and number of samples in the dataset.
37 * **scale** (*bool*) - (False) Whether or not to scale the input dataset.
38 * **remove_tmp** (*bool*) - (True) [WF property] Remove temporal files.
39 * **restart** (*bool*) - (False) [WF property] Do not execute if output files exist.
41 Examples:
42 This is a use example of how to use the building block from Python::
44 from biobb_ml.dimensionality_reduction.pls_components import pls_components
45 prop = {
46 'features': {
47 'columns': [ 'column1', 'column2', 'column3' ]
48 },
49 'target': {
50 'column': 'target'
51 },
52 'max_components': 10,
53 'cv': 10
54 }
55 pls_components(input_dataset_path='/path/to/myDataset.csv',
56 output_results_path='/path/to/newTable.csv',
57 output_plot_path='/path/to/newPlot.png',
58 properties=prop)
60 Info:
61 * wrapped_software:
62 * name: scikit-learn PLSRegression
63 * version: >=0.24.2
64 * license: BSD 3-Clause
65 * ontology:
66 * name: EDAM
67 * schema: http://edamontology.org/EDAM.owl
69 """
71 def __init__(self, input_dataset_path, output_results_path,
72 output_plot_path=None, properties=None, **kwargs) -> None:
73 properties = properties or {}
75 # Call parent class constructor
76 super().__init__(properties)
77 self.locals_var_dict = locals().copy()
79 # Input/Output files
80 self.io_dict = {
81 "in": {"input_dataset_path": input_dataset_path},
82 "out": {"output_results_path": output_results_path, "output_plot_path": output_plot_path}
83 }
85 # Properties specific for BB
86 self.features = properties.get('features', [])
87 self.target = properties.get('target', '')
88 self.optimise = properties.get('optimise', False)
89 self.max_components = properties.get('max_components', 10)
90 self.cv = properties.get('cv', 10)
91 self.scale = properties.get('scale', False)
92 self.properties = properties
94 # Check the properties
95 self.check_properties(properties)
96 self.check_arguments()
98 def check_data_params(self, out_log, err_log):
99 """ Checks all the input/output paths and parameters """
100 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__)
101 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__)
102 if self.io_dict["out"]["output_plot_path"]:
103 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__)
105 def warn(*args, **kwargs):
106 pass
108 @launchlogger
109 def launch(self) -> int:
110 """Execute the :class:`PLSComponents <dimensionality_reduction.pls_components.PLSComponents>` dimensionality_reduction.pls_components.PLSComponents object."""
112 # trick for disable warnings in interations
113 warnings.warn = self.warn
115 # check input/output paths and parameters
116 self.check_data_params(self.out_log, self.err_log)
118 # Setup Biobb
119 if self.check_restart():
120 return 0
121 self.stage_files()
123 # load dataset
124 fu.log('Getting dataset from %s' % self.io_dict["in"]["input_dataset_path"], self.out_log, self.global_log)
125 if 'columns' in self.features:
126 labels = getHeader(self.io_dict["in"]["input_dataset_path"])
127 skiprows = 1
128 else:
129 labels = None
130 skiprows = None
131 data = pd.read_csv(self.io_dict["in"]["input_dataset_path"], header=None, sep="\\s+|;|:|,|\t", engine="python", skiprows=skiprows, names=labels)
133 # declare inputs, targets and weights
134 # the inputs are all the features
135 features = getIndependentVars(self.features, data, self.out_log, self.__class__.__name__)
136 fu.log('Features: [%s]' % (getIndependentVarsList(self.features)), self.out_log, self.global_log)
137 # target
138 y = getTarget(self.target, data, self.out_log, self.__class__.__name__)
139 fu.log('Target: %s' % (getTargetValue(self.target)), self.out_log, self.global_log)
141 if self.scale:
142 fu.log('Scaling selected', self.out_log, self.global_log)
144 if self.optimise:
146 # get rid of baseline and linear variations calculating second derivative
147 fu.log('Performing second derivative on the data', self.out_log, self.global_log)
148 self.window_length = getWindowLength(17, features.shape[1])
149 X = savgol_filter(features, window_length=self.window_length, polyorder=2, deriv=2)
151 # run PLS from 1 to max_components
152 fu.log('Calculating MSE for each %d components' % self.max_components, self.out_log, self.global_log)
154 mse = []
155 # Define MSE array to be populated
156 msep = np.zeros((self.max_components, X.shape[1]))
157 # Loop over the number of PLS components
158 stdout.write("\r0% completed")
159 for i in range(self.max_components):
161 # Regression with specified number of components, using full spectrum
162 pls1 = PLSRegression(n_components=i+1, scale=self.scale)
163 pls1.fit(X, y)
165 # Indices of sort spectra according to ascending absolute value of PLS coefficients
166 sorted_ind = np.argsort(np.abs(pls1.coef_[:, 0]))
167 # Sort spectra accordingly
168 Xc = X[:, sorted_ind]
169 # Discard one wavelength at a time of the sorted spectra,
170 # regress, and calculate the MSE cross-validation
171 for j in range(Xc.shape[1]-(i+1)):
172 pls2 = PLSRegression(n_components=i+1)
173 pls2.fit(Xc[:, j:], y)
175 y_cv = cross_val_predict(pls2, Xc[:, j:], y, cv=self.cv)
176 msep[i, j] = mean_squared_error(y, y_cv)
178 # TO BE REVIEWED:
179 # https://nirpyresearch.com/variable-selection-method-pls-python/
180 mx, my = np.where(msep == np.min(msep[np.nonzero(msep)]))
181 mse.append(my[0])
183 comp = 100*(i+1)/(self.max_components)
184 if comp > 100:
185 comp = 100
186 stdout.write("\r%d%% completed" % comp)
187 stdout.flush()
188 print()
190 # Calculate the position of minimum in MSE
191 mseminx, mseminy = np.where(msep == np.min(msep[np.nonzero(msep)]))
192 best_c = mseminx[0] + 1
194 else:
196 # run PLS from 1 to max_components
197 fu.log('Calculating MSE for each %d components' % self.max_components, self.out_log, self.global_log)
199 X = features
201 mse = []
202 stdout.write("\r0% completed")
203 for i in np.arange(1, self.max_components + 1):
204 pls = PLSRegression(n_components=i, scale=self.scale)
205 # Cross-validation
206 y_cv = cross_val_predict(pls, X, y, cv=self.cv)
207 mse.append(mean_squared_error(y, y_cv))
208 # Trick to update status on the same line
209 comp = 100*(i+1)/self.max_components
210 if comp > 100:
211 comp = 100
212 stdout.write("\r%d%% completed" % comp)
213 stdout.flush()
214 print()
215 # calculate the position of minimum in MSE
216 best_c = np.argmin(mse) + 1
218 # mse table
219 results_table = pd.DataFrame(data={'component': np.arange(1, self.max_components + 1), 'MSE': mse})
220 fu.log('Gathering results\n\nMSE TABLE\n\n%s\n' % results_table.to_string(index=False), self.out_log, self.global_log)
222 fu.log('Calculating scores and coefficients for best number of components = %d according to the MSE Method' % best_c, self.out_log, self.global_log)
224 # define PLS object with optimal number of components
225 model = PLSRegression(n_components=best_c)
226 # fit to the entire dataset
227 model.fit(X, y)
228 y_c = model.predict(X)
229 # cross-validation
230 y_cv = cross_val_predict(model, X, y, cv=self.cv)
231 # calculate scores for calibration and cross-validation
232 score_c = r2_score(y, y_c)
233 score_cv = r2_score(y, y_cv)
234 # calculate mean squared error for calibration and cross validation
235 mse_c = mean_squared_error(y, y_c)
236 mse_cv = mean_squared_error(y, y_cv)
237 # create scores table
238 r2_table = pd.DataFrame()
239 r2_table["feature"] = ['R2 calib', 'R2 CV', 'MSE calib', 'MSE CV']
240 r2_table['coefficient'] = [score_c, score_cv, mse_c, mse_cv]
242 fu.log('Generating scores table\n\nR2 & MSE TABLE\n\n%s\n' % r2_table, self.out_log, self.global_log)
244 # save results table
245 fu.log('Saving R2 & MSE table to %s' % self.io_dict["out"]["output_results_path"], self.out_log, self.global_log)
246 r2_table.to_csv(self.io_dict["out"]["output_results_path"], index=False, header=True, float_format='%.3f')
248 # mse plot
249 if self.io_dict["out"]["output_plot_path"]:
250 fu.log('Saving MSE plot to %s' % self.io_dict["out"]["output_plot_path"], self.out_log, self.global_log)
251 number_clusters = range(1, self.max_components + 1)
252 plt.figure()
253 plt.title('PLS', size=15)
254 plt.plot(number_clusters, mse, '-o')
255 plt.ylabel('MSE')
256 plt.xlabel('Number of PLS Components')
257 plt.axvline(x=best_c, c='red')
258 plt.tight_layout()
260 plt.savefig(self.io_dict["out"]["output_plot_path"], dpi=150)
262 # Copy files to host
263 self.copy_to_host()
265 self.tmp_files.extend([
266 self.stage_io_dict.get("unique_dir")
267 ])
268 self.remove_tmp_files()
270 self.check_arguments(output_files_created=True, raise_exception=False)
272 return 0
275def pls_components(input_dataset_path: str, output_results_path: str, output_plot_path: str = None, properties: dict = None, **kwargs) -> int:
276 """Execute the :class:`PLSComponents <dimensionality_reduction.pls_components.PLSComponents>` class and
277 execute the :meth:`launch() <dimensionality_reduction.pls_components.PLSComponents.launch>` method."""
279 return PLSComponents(input_dataset_path=input_dataset_path,
280 output_results_path=output_results_path,
281 output_plot_path=output_plot_path,
282 properties=properties, **kwargs).launch()
285def main():
286 """Command line execution of this building block. Please check the command line documentation."""
287 parser = argparse.ArgumentParser(description="Wrapper of the scikit-learn PLSRegression method.", formatter_class=lambda prog: argparse.RawTextHelpFormatter(prog, width=99999))
288 parser.add_argument('--config', required=False, help='Configuration file')
290 # Specific args of each building block
291 required_args = parser.add_argument_group('required arguments')
292 required_args.add_argument('--input_dataset_path', required=True, help='Path to the input dataset. Accepted formats: csv.')
293 required_args.add_argument('--output_results_path', required=True, help='Table with R2 and MSE for calibration and cross-validation data for the best number of components. Accepted formats: csv.')
294 parser.add_argument('--output_plot_path', required=False, help='Path to the Mean Square Error plot. Accepted formats: png.')
296 args = parser.parse_args()
297 args.config = args.config or "{}"
298 properties = settings.ConfReader(config=args.config).get_prop_dic()
300 # Specific call of each building block
301 pls_components(input_dataset_path=args.input_dataset_path,
302 output_results_path=args.output_results_path,
303 output_plot_path=args.output_plot_path,
304 properties=properties)
307if __name__ == '__main__':
308 main()