code: #!/usr/bin/env python3
  
  """Module containing the ClassificationPredict 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
  from sklearn import linear_model
  from sklearn.neighbors import KNeighborsClassifier
  from sklearn.tree import DecisionTreeClassifier
  from sklearn import ensemble
  from sklearn import svm
  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 *
  
  class ClassificationPredict(BiobbObject):
      """
      | biobb_ml ClassificationPredict
      | Makes predictions from an input dataset and a given classification model.
      | Makes predictions from an input dataset (provided either as a file or as a dictionary property) and a given classification model trained with `DecisionTreeClassifier <https://scikit-learn.org/stable/modules/generated/sklearn.tree.DecisionTreeClassifier.html>`_, `KNeighborsClassifier <https://scikit-learn.org/stable/modules/generated/sklearn.neighbors.KNeighborsClassifier.html>`_, `LogisticRegression <https://scikit-learn.org/stable/modules/generated/sklearn.linear_model.LogisticRegression.html>`_, `RandomForestClassifier <https://scikit-learn.org/stable/modules/generated/sklearn.ensemble.RandomForestClassifier.html>`_, `Support Vector Machine <https://scikit-learn.org/stable/modules/generated/sklearn.svm.SVC.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/classification/model_classification_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/classification/input_classification_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/classification/ref_output_classification_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.classification.classification_predict import classification_predict
              prop = { 
                  'predictions': [
                      { 
                          'var1': 1.0, 
                          'var2': 2.0 
                      }, 
                      { 
                          'var1': 4.0, 
                          'var2': 2.7 
                      }
                  ] 
              }
              classification_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)
  
          # 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)
  
      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__)
  
      @launchlogger
      def launch(self) -> int:
          """Execute the :class:`ClassificationPredict <classification.classification_predict.ClassificationPredict>` classification.classification_predict.ClassificationPredict 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.LogisticRegression)
                          or isinstance(m, KNeighborsClassifier)
                          or isinstance(m, DecisionTreeClassifier)
                          or isinstance(m, ensemble.RandomForestClassifier)
                          or isinstance(m, svm.SVC)):
                          new_model = m
                      if isinstance(m, StandardScaler):
                          scaler = 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
  
          p = new_model.predict_proba(new_data)
  
          # if headers, create target column with proper label
          if self.io_dict["in"]["input_dataset_path"] or 'columns' in variables['independent_vars']:
              clss = ' (' + ', '.join(str(x) for x in variables['target_values']) + ')'
              new_data_table[variables['target']['column'] + ' ' + clss] = tuple(map(tuple, p))
          else:
              new_data_table[len(new_data_table.columns)] = tuple(map(tuple, 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')
  
          return 0
  
  def classification_predict(input_model_path: str, output_results_path: str, input_dataset_path: str = None, properties: dict = None, **kwargs) -> int:
      """Execute the :class:`ClassificationPredict <classification.classification_predict.ClassificationPredict>` class and
      execute the :meth:`launch() <classification.classification_predict.ClassificationPredict.launch>` method."""
  
      return ClassificationPredict(input_model_path=input_model_path, 
                      output_results_path=output_results_path, 
                      input_dataset_path=input_dataset_path,
                      properties=properties, **kwargs).launch()
  
  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 classification 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
      classification_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()
  
  
apis: pandas.read_csv pandas.DataFrame