Source code for regression.random_forest_regressor

#!/usr/bin/env python3

"""Module containing the RandomForestRegressor class and the command line interface."""
import argparse
import joblib
import numpy as np
import pandas as pd
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 mean_squared_error, r2_score
from sklearn import ensemble
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.regression.common import check_input_path, check_output_path, getHeader, getIndependentVars, getIndependentVarsList, getTarget, getTargetValue, getWeight, plotResults


[docs]class RandomForestRegressor(BiobbObject): """ | biobb_ml RandomForestRegressor | Wrapper of the scikit-learn RandomForestRegressor method. | Trains and tests a given dataset and saves the model and scaler. Visit the `RandomForestRegressor documentation page <https://scikit-learn.org/stable/modules/generated/sklearn.ensemble.RandomForestRegressor.html>`_. 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/regression/dataset_random_forest_regressor.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/regression/ref_output_model_random_forest_regressor.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/regression/ref_output_test_random_forest_regressor.csv>`_. Accepted formats: csv (edam:format_3752). output_plot_path (str) (Optional): Residual plot checks the error between actual values and predicted values. File type: output. `Sample file <https://github.com/bioexcel/biobb_ml/raw/master/biobb_ml/test/reference/regression/ref_output_plot_random_forest_regressor.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. * **n_estimators** (*int*) - (10) The number of trees in the forest. * **max_depth** (*int*) - (None) The maximum depth of the tree. * **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) 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.regression.random_forest_regressor import random_forest_regressor prop = { 'independent_vars': { 'columns': [ 'column1', 'column2', 'column3' ] }, 'target': { 'column': 'target' }, 'n_estimators': 10, 'max_depth': 5, 'test_size': 0.2 } random_forest_regressor(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 RandomForestRegressor * 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.n_estimators = properties.get('n_estimators', 10) self.max_depth = properties.get('max_depth', None) 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:`RandomForestRegressor <regression.random_forest_regressor.RandomForestRegressor>` regression.random_forest_regressor.RandomForestRegressor 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) # regression fu.log('Training dataset applying random forest regressor', self.out_log, self.global_log) model = ensemble.RandomForestRegressor(max_depth=self.max_depth, n_estimators=self.n_estimators, 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) # scores and coefficients train y_hat_train = model.predict(X_train) rmse = (np.sqrt(mean_squared_error(y_train, y_hat_train))) rss = ((y_train - y_hat_train) ** 2).sum() score_train_inputs = (y_train, y_hat_train) if self.weight: score_train_inputs = score_train_inputs + (w_train,) score = r2_score(*score_train_inputs) # r-squared r2_table = pd.DataFrame() r2_table["feature"] = ['R2', 'RMSE', 'RSS'] r2_table['coefficient'] = [score, rmse, rss] fu.log('Calculating scores and coefficients for TRAINING dataset\n\nSCORES\n\n%s\n' % r2_table, 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_test, columns=['prediction']) # reset y_test (problem with old indexes column) y_test = y_test.reset_index(drop=True) # add real values to predicted ones in test_table table test_table['target'] = y_test # calculate difference between target and prediction (absolute and %) test_table['residual'] = test_table['target'] - test_table['prediction'] test_table['difference %'] = np.absolute(test_table['residual']/test_table['target']*100) # sort by difference in % test_table = test_table.sort_values(by=['difference %']) test_table = test_table.reset_index(drop=True) fu.log('Testing\n\nTEST DATA\n\n%s\n' % test_table, self.out_log, self.global_log) # scores and coefficients test score_test_inputs = (y_test, y_hat_test) if self.weight: score_test_inputs = score_test_inputs + (w_test,) r2_test = r2_score(*score_test_inputs) rmse_test = np.sqrt(mean_squared_error(y_test, y_hat_test)) rss_test = ((y_test - y_hat_test) ** 2).sum() # r-squared r2_table_test = pd.DataFrame() r2_table_test["feature"] = ['R2', 'RMSE', 'RSS'] r2_table_test['coefficient'] = [r2_test, rmse_test, rss_test] fu.log('Calculating scores and coefficients for TESTING dataset\n\nSCORES\n\n%s\n' % r2_table_test, 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) # create test plot if (self.io_dict["out"]["output_plot_path"]): fu.log('Saving residual plot to %s' % self.io_dict["out"]["output_plot_path"], self.out_log, self.global_log) y_hat_test = y_hat_test.flatten() y_hat_train = y_hat_train.flatten() plot = plotResults(y_train, y_hat_train, y_test, y_hat_test) plot.savefig(self.io_dict["out"]["output_plot_path"], dpi=150) # save model, scaler and parameters variables = { 'target': self.target, 'independent_vars': self.independent_vars, 'scale': self.scale } 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 random_forest_regressor(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:`RandomForestRegressor <regression.random_forest_regressor.RandomForestRegressor>` class and execute the :meth:`launch() <regression.random_forest_regressor.RandomForestRegressor.launch>` method.""" return RandomForestRegressor(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 RandomForestRegressor 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='Residual plot checks the error between actual values and predicted values. 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 random_forest_regressor(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()