text: """
  Extract TF-IDF features from the corpus
  Train a Linear SVM model with hyperparameter search
  Train a model for each class (info / event)
  because the business logic has different cost imposed on false positives and false negatives
  """
  
  from sklearn.svm import SVC
  from sklearn.model_selection import GridSearchCV
  from sklearn.pipeline import Pipeline
  
  pipeline = Pipeline([
  	('clf', SVC(probability=True))
  ])
  
  parameters = {
  	'clf__kernel': ('rbf','sigmoid'),
  	'clf__C': (1, 10, 100, 1000)
  }
  
  
  def train_ensemble(_data):
  	X, y = _data
  	grid_search = GridSearchCV(pipeline, parameters, n_jobs=-1, verbose=1)
  	grid_search.fit(X, y)
  	return grid_search
  