Source code for classification.logistic_regression

#!/usr/bin/env python3

"""Module containing the LogisticRegression class and the command line interface."""
import argparse
import joblib
import pandas as pd
import numpy as np
from biobb_common.generic.biobb_object import BiobbObject
from sklearn.preprocessing import StandardScaler
from sklearn.model_selection import train_test_split
from sklearn.metrics import confusion_matrix, classification_report, log_loss
from sklearn import linear_model
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.classification.common import check_input_path, check_output_path, getHeader, getIndependentVars, getIndependentVarsList, getTarget, getTargetValue, getWeight, plotMultipleCM, plotBinaryClassifier


[docs]class LogisticRegression(BiobbObject): """ | biobb_ml LogisticRegression | Wrapper of the scikit-learn LogisticRegression method. | Trains and tests a given dataset and saves the model and scaler. Visit the `LogisticRegression documentation page <https://scikit-learn.org/stable/modules/generated/sklearn.linear_model.LogisticRegression.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/classification/dataset_logistic_regression.csv>`_. Accepted formats: csv (edam:format_3752). output_model_path (str): Path to the output model file. File type: output. `Sample file <https://github.com/bioexcel/biobb_ml/raw/master/biobb_ml/test/reference/classification/ref_output_model_logistic_regression.pkl>`_. Accepted formats: pkl (edam:format_3653). output_test_table_path (str) (Optional): Path to the test table file. File type: output. `Sample file <https://github.com/bioexcel/biobb_ml/raw/master/biobb_ml/test/reference/classification/ref_output_test_logistic_regression.csv>`_. Accepted formats: csv (edam:format_3752). output_plot_path (str) (Optional): Path to the statistics plot. If target is binary it shows confusion matrix, distributions of the predicted probabilities of both classes and ROC curve. If target is non-binary it shows confusion matrix. File type: output. `Sample file <https://github.com/bioexcel/biobb_ml/raw/master/biobb_ml/test/reference/classification/ref_output_plot_logistic_regression.png>`_. Accepted formats: png (edam:format_3603). properties (dic - Python dictionary object containing the tool parameters, not input/output files): * **independent_vars** (*dict*) - ({}) Independent variables you want to train from your dataset. 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. * **weight** (*dict*) - ({}) Weight variable 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. * **solver** (*string*) - ("liblinear") Numerical optimizer to find parameters. Values: newton-cg (Recall the motivation for gradient descent step at x: we minimize the quadratic function), lbfgs (It's analogue of the Newton's Method but here the Hessian matrix is approximated using updates specified by gradient evaluations), liblinear (It's a linear classification that supports logistic regression and linear support vector machines), sag (SAG method optimizes the sum of a finite number of smooth convex functions), saga (It's a variant of SAG that also supports the non-smooth penalty=l1 option). * **c_parameter** (*float*) - (0.01) [0~100|0.01] Inverse of regularization strength; must be a positive float. Smaller values specify stronger regularization. * **normalize_cm** (*bool*) - (False) Whether or not to normalize the confusion matrix. * **random_state_method** (*int*) - (5) [1~1000|1] Controls the randomness of the estimator. * **random_state_train_test** (*int*) - (5) [1~1000|1] Controls the shuffling applied to the data before applying the split. * **test_size** (*float*) - (0.2) [0~1|0.05] Represents the proportion of the dataset to include in the test split. It should be between 0.0 and 1.0. * **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. * **sandbox_path** (*str*) - ("./") [WF property] Parent path to the sandbox directory. Examples: This is a use example of how to use the building block from Python:: from biobb_ml.classification.logistic_regression import logistic_regression prop = { 'independent_vars': { 'columns': [ 'column1', 'column2', 'column3' ] }, 'target': { 'column': 'target' }, 'solver': 'liblinear', 'c_parameter': 0.01, 'test_size': 0.2 } logistic_regression(input_dataset_path='/path/to/myDataset.csv', output_model_path='/path/to/newModel.pkl', output_test_table_path='/path/to/newTable.csv', output_plot_path='/path/to/newPlot.png', properties=prop) Info: * wrapped_software: * name: scikit-learn LogisticRegression * version: >=0.24.2 * license: BSD 3-Clause * ontology: * name: EDAM * schema: http://edamontology.org/EDAM.owl """ def __init__(self, input_dataset_path, output_model_path, output_test_table_path=None, 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_model_path": output_model_path, "output_test_table_path": output_test_table_path, "output_plot_path": output_plot_path} } # Properties specific for BB self.independent_vars = properties.get('independent_vars', {}) self.target = properties.get('target', {}) self.weight = properties.get('weight', {}) self.solver = properties.get('solver', 'liblinear') self.c_parameter = properties.get('c_parameter', 0.01) self.normalize_cm = properties.get('normalize_cm', False) self.random_state_method = properties.get('random_state_method', 5) self.random_state_train_test = properties.get('random_state_train_test', 5) self.test_size = properties.get('test_size', 0.2) 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_model_path"] = check_output_path(self.io_dict["out"]["output_model_path"], "output_model_path", False, out_log, self.__class__.__name__) if self.io_dict["out"]["output_test_table_path"]: self.io_dict["out"]["output_test_table_path"] = check_output_path(self.io_dict["out"]["output_test_table_path"], "output_test_table_path", True, 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:`LogisticRegression <classification.logistic_regression.LogisticRegression>` classification.logistic_regression.LogisticRegression 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.independent_vars: 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 independent variables X = getIndependentVars(self.independent_vars, data, self.out_log, self.__class__.__name__) fu.log('Independent variables: [%s]' % (getIndependentVarsList(self.independent_vars)), 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) # weights if self.weight: w = getWeight(self.weight, data, self.out_log, self.__class__.__name__) fu.log('Weight column provided', self.out_log, self.global_log) # train / test split fu.log('Creating train and test sets', self.out_log, self.global_log) arrays_sets = (X, y) # if user provide weights if self.weight: arrays_sets = arrays_sets + (w,) X_train, X_test, y_train, y_test, w_train, w_test = train_test_split(*arrays_sets, test_size=self.test_size, random_state=self.random_state_train_test) else: X_train, X_test, y_train, y_test = train_test_split(*arrays_sets, test_size=self.test_size, random_state=self.random_state_train_test) # scale dataset if self.scale: fu.log('Scaling dataset', self.out_log, self.global_log) scaler = StandardScaler() X_train = scaler.fit_transform(X_train) # classification fu.log('Training dataset applying logistic regression', self.out_log, self.global_log) model = linear_model.LogisticRegression(C=self.c_parameter, solver=self.solver, random_state=self.random_state_method) arrays_fit = (X_train, y_train) # if user provide weights if self.weight: arrays_fit = arrays_fit + (w_train,) model.fit(*arrays_fit) y_hat_train = model.predict(X_train) # classification report cr_train = classification_report(y_train, y_hat_train) # log loss yhat_prob_train = model.predict_proba(X_train) l_loss_train = log_loss(y_train, yhat_prob_train) fu.log('Calculating scores and report for training dataset\n\nCLASSIFICATION REPORT\n\n%s\nLog loss: %.3f\n' % (cr_train, l_loss_train), self.out_log, self.global_log) # compute confusion matrix cnf_matrix_train = confusion_matrix(y_train, y_hat_train) np.set_printoptions(precision=2) if self.normalize_cm: cnf_matrix_train = cnf_matrix_train.astype('float') / cnf_matrix_train.sum(axis=1)[:, np.newaxis] cm_type = 'NORMALIZED CONFUSION MATRIX' else: cm_type = 'CONFUSION MATRIX, WITHOUT NORMALIZATION' fu.log('Calculating confusion matrix for training dataset\n\n%s\n\n%s\n' % (cm_type, cnf_matrix_train), self.out_log, self.global_log) # testing # predict data from x_test if self.scale: X_test = scaler.transform(X_test) y_hat_test = model.predict(X_test) test_table = pd.DataFrame() y_hat_prob = model.predict_proba(X_test) y_hat_prob = np.around(y_hat_prob, decimals=2) y_hat_prob = tuple(map(tuple, y_hat_prob)) test_table['P' + np.array2string(np.unique(y_test))] = y_hat_prob y_test = y_test.reset_index(drop=True) test_table['target'] = y_test fu.log('Testing\n\nTEST DATA\n\n%s\n' % test_table, self.out_log, self.global_log) # classification report cr = classification_report(y_test, y_hat_test) # log loss yhat_prob = model.predict_proba(X_test) l_loss = log_loss(y_test, yhat_prob) fu.log('Calculating scores and report for testing dataset\n\nCLASSIFICATION REPORT\n\n%s\nLog loss: %.3f\n' % (cr, l_loss), self.out_log, self.global_log) # compute confusion matrix cnf_matrix = confusion_matrix(y_test, y_hat_test) np.set_printoptions(precision=2) if self.normalize_cm: cnf_matrix = cnf_matrix.astype('float') / cnf_matrix.sum(axis=1)[:, np.newaxis] cm_type = 'NORMALIZED CONFUSION MATRIX' else: cm_type = 'CONFUSION MATRIX, WITHOUT NORMALIZATION' fu.log('Calculating confusion matrix for testing dataset\n\n%s\n\n%s\n' % (cm_type, cnf_matrix), self.out_log, self.global_log) if (self.io_dict["out"]["output_test_table_path"]): fu.log('Saving testing data to %s' % self.io_dict["out"]["output_test_table_path"], self.out_log, self.global_log) test_table.to_csv(self.io_dict["out"]["output_test_table_path"], index=False, header=True) # plot if self.io_dict["out"]["output_plot_path"]: vs = y.unique().tolist() vs.sort() if len(vs) > 2: plot = plotMultipleCM(cnf_matrix_train, cnf_matrix, self.normalize_cm, vs) fu.log('Saving confusion matrix plot to %s' % self.io_dict["out"]["output_plot_path"], self.out_log, self.global_log) else: plot = plotBinaryClassifier(model, yhat_prob_train, yhat_prob, cnf_matrix_train, cnf_matrix, y_train, y_test, normalize=self.normalize_cm) fu.log('Saving binary classifier evaluator plot to %s' % self.io_dict["out"]["output_plot_path"], self.out_log, self.global_log) plot.savefig(self.io_dict["out"]["output_plot_path"], dpi=150) # save model, scaler and parameters tv = y.unique().tolist() tv.sort() variables = { 'target': self.target, 'independent_vars': self.independent_vars, 'scale': self.scale, 'target_values': tv } fu.log('Saving model to %s' % self.io_dict["out"]["output_model_path"], self.out_log, self.global_log) with open(self.io_dict["out"]["output_model_path"], "wb") as f: joblib.dump(model, f) if self.scale: joblib.dump(scaler, f) joblib.dump(variables, f) # 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 logistic_regression(input_dataset_path: str, output_model_path: str, output_test_table_path: str = None, output_plot_path: str = None, properties: dict = None, **kwargs) -> int: """Execute the :class:`LogisticRegression <classification.logistic_regression.LogisticRegression>` class and execute the :meth:`launch() <classification.logistic_regression.LogisticRegression.launch>` method.""" return LogisticRegression(input_dataset_path=input_dataset_path, output_model_path=output_model_path, output_test_table_path=output_test_table_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 LogisticRegression 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_model_path', required=True, help='Path to the output model file. Accepted formats: pkl.') parser.add_argument('--output_test_table_path', required=False, help='Path to the test table file. Accepted formats: csv.') parser.add_argument('--output_plot_path', required=False, help='Path to the statistics plot. If target is binary it shows confusion matrix, distributions of the predicted probabilities of both classes and ROC curve. If target is non-binary it shows confusion matrix. 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 logistic_regression(input_dataset_path=args.input_dataset_path, output_model_path=args.output_model_path, output_test_table_path=args.output_test_table_path, output_plot_path=args.output_plot_path, properties=properties)
if __name__ == '__main__': main()