text: """Creates an SVM classifier from the time course expression data."""
  
  import add_path
  from common import constants as cn
  from common.trinary_data import TrinaryData
  from common_python.classifier import classifier_ensemble
  from common_python.util import persister
  from common import transform_data
  
  import argparse
  import os
  import numpy as np
  import pandas as pd
  
  import numpy as np
  import pandas as pd
  
  NUM_FEATURES = 15
  NUM_CLASSIFIERS = 50
  
  def make(num_features=NUM_FEATURES,
      num_classifiers=NUM_CLASSIFIERS, 
      file_path=cn.ENSEMBLE_PATH, is_force=True):
    """
    Creates a classifier ensemble from the time course data,
    and fits the classifier.
    :param int num_features: number of features in the ensemble
    :param int num_classifiers: number of classifiers
    :param str file_path: path where classifier is exported
    :param bool is_force: Create new classifier even
        if one exists already.
    :return  ClassifierEnsemble:
    """
    # See if there's a classifier already
    if not is_force:
      try:
        return classifier_ensemble.ClassifierEnsemble.deserialize(
            file_path)
      except ValueError:
        pass
    # Construct a new classifier
    svm_ensemble = classifier_ensemble.ClassifierEnsemble(
            classifier_ensemble.ClassifierDescriptorSVM(), 
            filter_high_rank=num_features, size=num_classifiers)
    data = TrinaryData()
    data.df_X.columns = data.features
    svm_ensemble.fit(data.df_X, data.ser_y)
    svm_ensemble.serialize(file_path)
    return svm_ensemble
  
  if __name__ == '__main__':
    # Do arg parse with errors
    desc = 'Create classifier ensemble in a destination file'
    parser = argparse.ArgumentParser(description=desc)
    parser.add_argument('--dest', '-d', help='destination file',
        default=cn.ENSEMBLE_PATH)
    parser.add_argument('--numf', '-f', 
        help='number of features in classifiers',
        type=int, default=NUM_FEATURES)
    parser.add_argument('--numc', '-c', 
        help='number of classifiers in ensemble',
        type=int, default=NUM_CLASSIFIERS)
    args = parser.parse_args()
    make(num_features=args.numf, num_classifiers=args.numc,
        file_path=args.dest)
    print("Success!")
  