Coverage for biobb_ml/dimensionality_reduction/pls_regression.py: 84%
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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 PLS_Regression class and the command line interface."""
4import argparse
5import warnings
6import pandas as pd
7from biobb_common.generic.biobb_object import BiobbObject
8from sklearn.cross_decomposition import PLSRegression
9from sklearn.model_selection import cross_val_predict
10from sklearn.metrics import mean_squared_error, r2_score
11from biobb_common.configuration import settings
12from biobb_common.tools import file_utils as fu
13from biobb_common.tools.file_utils import launchlogger
14from biobb_ml.dimensionality_reduction.common import check_input_path, check_output_path, getHeader, getIndependentVars, getIndependentVarsList, getTarget, getTargetValue, PLSRegPlot
17class PLS_Regression(BiobbObject):
18 """
19 | biobb_ml PLS_Regression
20 | Wrapper of the scikit-learn PLSRegression method.
21 | Gives results 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.
23 Args:
24 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_regression.csv>`_. Accepted formats: csv (edam:format_3752).
25 output_results_path (str): Table with R2 and MSE for calibration and cross-validation data. File type: output. `Sample file <https://github.com/bioexcel/biobb_ml/raw/master/biobb_ml/test/reference/dimensionality_reduction/ref_output_results_pls_regression.csv>`_. Accepted formats: csv (edam:format_3752).
26 output_plot_path (str) (Optional): Path to the R2 cross-validation plot. File type: output. `Sample file <https://github.com/bioexcel/biobb_ml/raw/master/biobb_ml/test/reference/dimensionality_reduction/ref_output_plot_pls_regression.png>`_. Accepted formats: png (edam:format_3603).
27 properties (dic - Python dictionary object containing the tool parameters, not input/output files):
28 * **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.
29 * **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.
30 * **n_components** (*int*) - (5) [1~1000|1] Maximum number of components to use by default for PLS queries.
31 * **cv** (*int*) - (10) [1~10000|1] Specify the number of folds in the cross-validation splitting strategy. Value must be betwwen 2 and number of samples in the dataset.
32 * **scale** (*bool*) - (False) Whether or not to scale the input dataset.
33 * **remove_tmp** (*bool*) - (True) [WF property] Remove temporal files.
34 * **restart** (*bool*) - (False) [WF property] Do not execute if output files exist.
36 Examples:
37 This is a use example of how to use the building block from Python::
39 from biobb_ml.dimensionality_reduction.pls_regression import pls_regression
40 prop = {
41 'features': {
42 'columns': [ 'column1', 'column2', 'column3' ]
43 },
44 'target': {
45 'column': 'target'
46 },
47 'n_components': 12,
48 'cv': 10
49 }
50 pls_regression(input_dataset_path='/path/to/myDataset.csv',
51 output_results_path='/path/to/newTable.csv',
52 output_plot_path='/path/to/newPlot.png',
53 properties=prop)
55 Info:
56 * wrapped_software:
57 * name: scikit-learn PLSRegression
58 * version: >=0.24.2
59 * license: BSD 3-Clause
60 * ontology:
61 * name: EDAM
62 * schema: http://edamontology.org/EDAM.owl
64 """
66 def __init__(self, input_dataset_path, output_results_path,
67 output_plot_path=None, properties=None, **kwargs) -> None:
68 properties = properties or {}
70 # Call parent class constructor
71 super().__init__(properties)
72 self.locals_var_dict = locals().copy()
74 # Input/Output files
75 self.io_dict = {
76 "in": {"input_dataset_path": input_dataset_path},
77 "out": {"output_results_path": output_results_path, "output_plot_path": output_plot_path}
78 }
80 # Properties specific for BB
81 self.features = properties.get('features', [])
82 self.target = properties.get('target', '')
83 self.n_components = properties.get('n_components', 5)
84 self.cv = properties.get('cv', 10)
85 self.scale = properties.get('scale', False)
86 self.properties = properties
88 # Check the properties
89 self.check_properties(properties)
90 self.check_arguments()
92 def check_data_params(self, out_log, err_log):
93 """ Checks all the input/output paths and parameters """
94 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__)
95 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__)
96 if self.io_dict["out"]["output_plot_path"]:
97 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__)
99 def warn(*args, **kwargs):
100 pass
102 @launchlogger
103 def launch(self) -> int:
104 """Execute the :class:`PLS_Regression <dimensionality_reduction.pls_regression.PLS_Regression>` dimensionality_reduction.pls_regression.PLS_Regression object."""
106 # trick for disable warnings in interations
107 warnings.warn = self.warn
109 # check input/output paths and parameters
110 self.check_data_params(self.out_log, self.err_log)
112 # Setup Biobb
113 if self.check_restart():
114 return 0
115 self.stage_files()
117 # load dataset
118 fu.log('Getting dataset from %s' % self.io_dict["in"]["input_dataset_path"], self.out_log, self.global_log)
119 if 'columns' in self.features:
120 labels = getHeader(self.io_dict["in"]["input_dataset_path"])
121 skiprows = 1
122 else:
123 labels = None
124 skiprows = None
125 data = pd.read_csv(self.io_dict["in"]["input_dataset_path"], header=None, sep="\\s+|;|:|,|\t", engine="python", skiprows=skiprows, names=labels)
127 # declare inputs, targets and weights
128 # the inputs are all the features
129 features = getIndependentVars(self.features, data, self.out_log, self.__class__.__name__)
130 fu.log('Features: [%s]' % (getIndependentVarsList(self.features)), self.out_log, self.global_log)
131 # target
132 y = getTarget(self.target, data, self.out_log, self.__class__.__name__)
133 fu.log('Target: %s' % (getTargetValue(self.target)), self.out_log, self.global_log)
135 # get rid of baseline and linear variations calculating second derivative
136 # fu.log('Performing second derivative on the data', self.out_log, self.global_log)
137 # self.window_length = getWindowLength(17, features.shape[1])
138 # X = savgol_filter(features, window_length = self.window_length, polyorder = 2, deriv = 2)
139 X = features
141 # define PLS object with optimal number of components
142 model = PLSRegression(n_components=self.n_components, scale=self.scale)
143 # fit to the entire dataset
144 model.fit(X, y)
145 y_c = model.predict(X)
146 # cross-validation
147 y_cv = cross_val_predict(model, X, y, cv=self.cv)
148 # calculate scores for calibration and cross-validation
149 score_c = r2_score(y, y_c)
150 score_cv = r2_score(y, y_cv)
151 # calculate mean squared error for calibration and cross validation
152 mse_c = mean_squared_error(y, y_c)
153 mse_cv = mean_squared_error(y, y_cv)
154 # create scores table
155 r2_table = pd.DataFrame()
156 r2_table["feature"] = ['R2 calib', 'R2 CV', 'MSE calib', 'MSE CV']
157 r2_table['coefficient'] = [score_c, score_cv, mse_c, mse_cv]
159 fu.log('Generating scores table\n\nR2 & MSE TABLE\n\n%s\n' % r2_table, self.out_log, self.global_log)
161 # save results table
162 fu.log('Saving R2 & MSE table to %s' % self.io_dict["out"]["output_results_path"], self.out_log, self.global_log)
163 r2_table.to_csv(self.io_dict["out"]["output_results_path"], index=False, header=True, float_format='%.3f')
165 # mse plot
166 if self.io_dict["out"]["output_plot_path"]:
167 fu.log('Saving MSE plot to %s' % self.io_dict["out"]["output_plot_path"], self.out_log, self.global_log)
168 plot = PLSRegPlot(y, y_c, y_cv)
169 plot.savefig(self.io_dict["out"]["output_plot_path"], dpi=150)
171 # Copy files to host
172 self.copy_to_host()
174 self.tmp_files.extend([
175 self.stage_io_dict.get("unique_dir")
176 ])
177 self.remove_tmp_files()
179 self.check_arguments(output_files_created=True, raise_exception=False)
181 return 0
184def pls_regression(input_dataset_path: str, output_results_path: str, output_plot_path: str = None, properties: dict = None, **kwargs) -> int:
185 """Execute the :class:`PLS_Regression <dimensionality_reduction.pls_regression.PLS_Regression>` class and
186 execute the :meth:`launch() <dimensionality_reduction.pls_regression.PLS_Regression.launch>` method."""
188 return PLS_Regression(input_dataset_path=input_dataset_path,
189 output_results_path=output_results_path,
190 output_plot_path=output_plot_path,
191 properties=properties, **kwargs).launch()
194def main():
195 """Command line execution of this building block. Please check the command line documentation."""
196 parser = argparse.ArgumentParser(description="Wrapper of the scikit-learn PLSRegression method.", formatter_class=lambda prog: argparse.RawTextHelpFormatter(prog, width=99999))
197 parser.add_argument('--config', required=False, help='Configuration file')
199 # Specific args of each building block
200 required_args = parser.add_argument_group('required arguments')
201 required_args.add_argument('--input_dataset_path', required=True, help='Path to the input dataset. Accepted formats: csv.')
202 required_args.add_argument('--output_results_path', required=True, help='Table with R2 and MSE for calibration and cross-validation data. Accepted formats: csv.')
203 parser.add_argument('--output_plot_path', required=False, help='Path to the R2 cross-validation plot. Accepted formats: png.')
205 args = parser.parse_args()
206 args.config = args.config or "{}"
207 properties = settings.ConfReader(config=args.config).get_prop_dic()
209 # Specific call of each building block
210 pls_regression(input_dataset_path=args.input_dataset_path,
211 output_results_path=args.output_results_path,
212 output_plot_path=args.output_plot_path,
213 properties=properties)
216if __name__ == '__main__':
217 main()