#!/usr/bin/env python3
"""Module containing the RegressionPredict class and the command line interface."""
import argparse
import pandas as pd
import joblib
from biobb_common.generic.biobb_object import BiobbObject
from sklearn.preprocessing import StandardScaler, PolynomialFeatures
from sklearn import linear_model
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, get_list_of_predictors, get_keys_of_predictors
[docs]class RegressionPredict(BiobbObject):
"""
| biobb_ml RegressionPredict
| Makes predictions from an input dataset and a given regression model.
| Makes predictions from an input dataset (provided either as a file or as a dictionary property) and a given regression model trained with `LinearRegression <https://scikit-learn.org/stable/modules/generated/sklearn.linear_model.LinearRegression.html>`_, `RandomForestRegressor <https://scikit-learn.org/stable/modules/generated/sklearn.ensemble.RandomForestRegressor.html>`_ methods.
Args:
input_model_path (str): Path to the input model. File type: input. `Sample file <https://github.com/bioexcel/biobb_ml/raw/master/biobb_ml/test/data/regression/model_regression_predict.pkl>`_. Accepted formats: pkl (edam:format_3653).
input_dataset_path (str) (Optional): Path to the dataset to predict. File type: input. `Sample file <https://github.com/bioexcel/biobb_ml/raw/master/biobb_ml/test/data/regression/input_regression_predict.csv>`_. Accepted formats: csv (edam:format_3752).
output_results_path (str): Path to the output results file. File type: output. `Sample file <https://github.com/bioexcel/biobb_ml/raw/master/biobb_ml/test/reference/regression/ref_output_regression_predict.csv>`_. Accepted formats: csv (edam:format_3752).
properties (dic - Python dictionary object containing the tool parameters, not input/output files):
* **predictions** (*list*) - (None) List of dictionaries with all values you want to predict targets. It will be taken into account only in case **input_dataset_path** is not provided. Format: [{ 'var1': 1.0, 'var2': 2.0 }, { 'var1': 4.0, 'var2': 2.7 }] for datasets with headers and [[ 1.0, 2.0 ], [ 4.0, 2.7 ]] for datasets without headers.
* **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.regression_predict import regression_predict
prop = {
'predictions': [
{
'var1': 1.0,
'var2': 2.0
},
{
'var1': 4.0,
'var2': 2.7
}
]
}
regression_predict(input_model_path='/path/to/myModel.pkl',
output_results_path='/path/to/newPredictedResults.csv',
input_dataset_path='/path/to/myDataset.csv',
properties=prop)
Info:
* wrapped_software:
* name: scikit-learn
* version: >=0.24.2
* license: BSD 3-Clause
* ontology:
* name: EDAM
* schema: http://edamontology.org/EDAM.owl
"""
def __init__(self, input_model_path, output_results_path,
input_dataset_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_model_path": input_model_path, "input_dataset_path": input_dataset_path},
"out": {"output_results_path": output_results_path}
}
# Properties specific for BB
self.predictions = properties.get('predictions', [])
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_model_path"] = check_input_path(self.io_dict["in"]["input_model_path"], "input_model_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["in"]["input_dataset_path"]:
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__)
[docs] @launchlogger
def launch(self) -> int:
"""Execute the :class:`RegressionPredict <regression.regression_predict.RegressionPredict>` regression.regression_predict.RegressionPredict 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()
fu.log('Getting model from %s' % self.io_dict["in"]["input_model_path"], self.out_log, self.global_log)
with open(self.io_dict["in"]["input_model_path"], "rb") as f:
while True:
try:
m = joblib.load(f)
if (isinstance(m, linear_model.LinearRegression) or isinstance(m, ensemble.RandomForestRegressor)):
new_model = m
if isinstance(m, StandardScaler):
scaler = m
if isinstance(m, PolynomialFeatures):
poly_features = m
if isinstance(m, dict):
variables = m
except EOFError:
break
if self.io_dict["in"]["input_dataset_path"]:
# load dataset from input_dataset_path file
fu.log('Getting dataset from %s' % self.io_dict["in"]["input_dataset_path"], self.out_log, self.global_log)
if 'columns' in variables['independent_vars']:
labels = getHeader(self.io_dict["in"]["input_dataset_path"])
skiprows = 1
else:
labels = None
skiprows = None
new_data_table = pd.read_csv(self.io_dict["in"]["input_dataset_path"], header=None, sep="\\s+|;|:|,|\t", engine="python", skiprows=skiprows, names=labels)
else:
# load dataset from properties
if 'columns' in variables['independent_vars']:
# sorting self.properties in the correct order given by variables['independent_vars']['columns']
index_map = {v: i for i, v in enumerate(variables['independent_vars']['columns'])}
predictions = []
for i, pred in enumerate(self.predictions):
sorted_pred = sorted(pred.items(), key=lambda pair: index_map[pair[0]])
predictions.append(dict(sorted_pred))
new_data_table = pd.DataFrame(data=get_list_of_predictors(predictions), columns=get_keys_of_predictors(predictions))
else:
predictions = self.predictions
new_data_table = pd.DataFrame(data=predictions)
if variables['scale']:
fu.log('Scaling dataset', self.out_log, self.global_log)
new_data = scaler.transform(new_data_table)
else:
new_data = new_data_table
if 'poly_features' in locals():
new_data = poly_features.transform(new_data)
p = new_model.predict(new_data)
if self.io_dict["in"]["input_dataset_path"] or 'columns' in variables['independent_vars']:
new_data_table[variables['target']['column']] = p
else:
new_data_table[len(new_data_table.columns)] = p
fu.log('Predicting results\n\nPREDICTION RESULTS\n\n%s\n' % new_data_table, self.out_log, self.global_log)
fu.log('Saving results to %s' % self.io_dict["out"]["output_results_path"], self.out_log, self.global_log)
new_data_table.to_csv(self.io_dict["out"]["output_results_path"], index=False, header=True, float_format='%.3f')
# 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 regression_predict(input_model_path: str, output_results_path: str, input_dataset_path: str = None, properties: dict = None, **kwargs) -> int:
"""Execute the :class:`RegressionPredict <regression.regression_predict.RegressionPredict>` class and
execute the :meth:`launch() <regression.regression_predict.RegressionPredict.launch>` method."""
return RegressionPredict(input_model_path=input_model_path,
output_results_path=output_results_path,
input_dataset_path=input_dataset_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="Makes predictions from an input dataset and a given regression model.", 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_model_path', required=True, help='Path to the input model. Accepted formats: pkl.')
required_args.add_argument('--output_results_path', required=True, help='Path to the output results file. Accepted formats: csv.')
parser.add_argument('--input_dataset_path', required=False, help='Path to the dataset to predict. Accepted formats: csv.')
args = parser.parse_args()
args.config = args.config or "{}"
properties = settings.ConfReader(config=args.config).get_prop_dic()
# Specific call of each building block
regression_predict(input_model_path=args.input_model_path,
output_results_path=args.output_results_path,
input_dataset_path=args.input_dataset_path,
properties=properties)
if __name__ == '__main__':
main()