#!/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.
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()