Source code for dimensionality_reduction.pls_regression

#!/usr/bin/env python3

"""Module containing the PLS_Regression class and the command line interface."""
import argparse
import warnings
import pandas as pd
from biobb_common.generic.biobb_object import BiobbObject
from sklearn.cross_decomposition import PLSRegression
from sklearn.model_selection import cross_val_predict
from sklearn.metrics import mean_squared_error, r2_score
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.dimensionality_reduction.common import check_input_path, check_output_path, getHeader, getIndependentVars, getIndependentVarsList, getTarget, getTargetValue, PLSRegPlot


[docs]class PLS_Regression(BiobbObject): """ | biobb_ml PLS_Regression | Wrapper of the scikit-learn PLSRegression method. | 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. 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/dimensionality_reduction/dataset_pls_regression.csv>`_. Accepted formats: csv (edam:format_3752). 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). 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). properties (dic - Python dictionary object containing the tool parameters, not input/output files): * **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. * **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. * **n_components** (*int*) - (5) [1~1000|1] Maximum number of components to use by default for PLS queries. * **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. * **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.dimensionality_reduction.pls_regression import pls_regression prop = { 'features': { 'columns': [ 'column1', 'column2', 'column3' ] }, 'target': { 'column': 'target' }, 'n_components': 12, 'cv': 10 } pls_regression(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 PLSRegression * 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.features = properties.get('features', []) self.target = properties.get('target', '') self.n_components = properties.get('n_components', 5) self.cv = properties.get('cv', 10) 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] def warn(*args, **kwargs): pass
[docs] @launchlogger def launch(self) -> int: """Execute the :class:`PLS_Regression <dimensionality_reduction.pls_regression.PLS_Regression>` dimensionality_reduction.pls_regression.PLS_Regression object.""" # trick for disable warnings in interations warnings.warn = self.warn # 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) # declare inputs, targets and weights # the inputs are all the features features = getIndependentVars(self.features, data, self.out_log, self.__class__.__name__) fu.log('Features: [%s]' % (getIndependentVarsList(self.features)), self.out_log, self.global_log) # target y = getTarget(self.target, data, self.out_log, self.__class__.__name__) fu.log('Target: %s' % (getTargetValue(self.target)), self.out_log, self.global_log) # get rid of baseline and linear variations calculating second derivative # fu.log('Performing second derivative on the data', self.out_log, self.global_log) # self.window_length = getWindowLength(17, features.shape[1]) # X = savgol_filter(features, window_length = self.window_length, polyorder = 2, deriv = 2) X = features # define PLS object with optimal number of components model = PLSRegression(n_components=self.n_components, scale=self.scale) # fit to the entire dataset model.fit(X, y) y_c = model.predict(X) # cross-validation y_cv = cross_val_predict(model, X, y, cv=self.cv) # calculate scores for calibration and cross-validation score_c = r2_score(y, y_c) score_cv = r2_score(y, y_cv) # calculate mean squared error for calibration and cross validation mse_c = mean_squared_error(y, y_c) mse_cv = mean_squared_error(y, y_cv) # create scores table r2_table = pd.DataFrame() r2_table["feature"] = ['R2 calib', 'R2 CV', 'MSE calib', 'MSE CV'] r2_table['coefficient'] = [score_c, score_cv, mse_c, mse_cv] fu.log('Generating scores table\n\nR2 & MSE TABLE\n\n%s\n' % r2_table, self.out_log, self.global_log) # save results table fu.log('Saving R2 & MSE table to %s' % self.io_dict["out"]["output_results_path"], self.out_log, self.global_log) r2_table.to_csv(self.io_dict["out"]["output_results_path"], index=False, header=True, float_format='%.3f') # mse plot if self.io_dict["out"]["output_plot_path"]: fu.log('Saving MSE plot to %s' % self.io_dict["out"]["output_plot_path"], self.out_log, self.global_log) plot = PLSRegPlot(y, y_c, y_cv) 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 pls_regression(input_dataset_path: str, output_results_path: str, output_plot_path: str = None, properties: dict = None, **kwargs) -> int: """Execute the :class:`PLS_Regression <dimensionality_reduction.pls_regression.PLS_Regression>` class and execute the :meth:`launch() <dimensionality_reduction.pls_regression.PLS_Regression.launch>` method.""" return PLS_Regression(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 PLSRegression 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 R2 and MSE for calibration and cross-validation data. Accepted formats: csv.') parser.add_argument('--output_plot_path', required=False, help='Path to the R2 cross-validation 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 pls_regression(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()