text: import numpy as np
  import matplotlib.pyplot as plt
  from hyperopt import hp, tpe, fmin
  from pyodds.algo.iforest import IFOREST
  from pyodds.algo.ocsvm import OCSVM
  from pyodds.algo.lof import LOF
  from pyodds.algo.robustcovariance import RCOV
  from pyodds.algo.staticautoencoder import StaticAutoEncoder
  from pyodds.algo.luminolFunc import luminolDet
  from pyodds.algo.cblof import CBLOF
  from pyodds.algo.knn import KNN
  from pyodds.algo.hbos import HBOS
  from pyodds.algo.sod import SOD
  from pyodds.algo.pca import PCA
  from pyodds.algo.dagmm import DAGMM
  from pyodds.algo.lstmad import LSTMAD
  from pyodds.algo.lstmencdec import LSTMED
  from pyodds.algo.autoencoder import AUTOENCODER
  
  
  def construct_search_space():
      """ Search space constructed for each of the 13 baseline algorithms.
  
      Each algorithm is set with its own parameter space, consisting of both
      static parameters and hp.choice()
  
      Returns
      -------
      space_config :  hyperopt choice containing each baseline algorithm's
                      parameter dictionary
          The cumulative search space for pyodds autoML.
      """
      activation = hp.choice('activation', ['sigmoid', 'relu', 'hard_sigmoid'])
      random_state = np.random.randint(500)
      contamination = hp.choice('contamination', [0.5, 0.4, 0.3])
  
      space_config = hp.choice('classifier_type', [
          {
              'type': 'iforest', 'contamination': contamination, 'n_estimators': 100,
              'max_samples': "auto",
              'max_features': 1.,
              'bootstrap': False, 'n_jobs': None,
              'behaviour': 'old',
              'random_state': random_state
          },
          {
              'type': 'ocsvm',
              'gamma': 'auto', 'kernel': 'rbf',
              'degree': 3,
              'coef0': 0.0,
              'tol': 1e-3,
              'nu': 0.5,
              'shrinking': True, 'cache_size': 200, 'verbose': False, 'max_iter': -1,
          },
          {
              'type': 'lof',
              'contamination': contamination, 'n_neighbors': 20, 'algorithm': 'auto',
              'leaf_size': 30, 'metric': 'minkowski', 'p': 2, 'metric_params': None,
              'novelty': True,
          },
          {
              'type': 'robustcovariance',
              'random_state': random_state, 'store_precision': True,
              'assume_centered': False, 'support_fraction': None,
              'contamination': contamination,
          },
          {
              'type': 'staticautoencoder',
              'contamination': contamination, 'epoch': 100, 'dropout_rate': 0.2,
              'regularizer_weight': 0.1, 'activation': activation,
              'kernel_regularizer': 0.01,
              'loss_function': 'mse', 'optimizer': 'adam'
          },
          {
              'type': 'cblof',
              'contamination': contamination, 'n_clusters': 8,
              'clustering_estimator': None,
              'alpha': 0.9, 'beta': 5, 'use_weights': False,
              'random_state': random_state, 'n_jobs': 1,
          },
          {
              'type': 'knn',
              'contamination': contamination, 'n_neighbors': 5, 'method': 'largest',
              'radius': 1.0,
              'algorithm': 'auto', 'leaf_size': 30, 'metric': 'minkowski', 'p': 2,
              'metric_params': None, 'n_jobs': 1,
          },
          {
              'type': 'hbos',
              'contamination': contamination, 'n_bins': 10, 'alpha': 0.1, 'tol': 0.5,
          },
          {
              'type': 'sod',
              'contamination': contamination, 'n_neighbors': 20, 'ref_set': 10,
              'alpha': 0.8,
          },
          {
              'type': 'pca',
              'contamination': contamination, 'n_components': None,
              'n_selected_components': None,
              'copy': True, 'whiten': False, 'svd_solver': 'auto', 'tol': 0.0,
              'iterated_power': 'auto', 'random_state': random_state, 'weighted': True,
              'standardization': True,
          },
          {
              'type': 'dagmm',
              'contamination': contamination, 'num_epochs': 10, 'lambda_energy': 0.1,
              'lambda_cov_diag': 0.005, 'lr': 1e-3, 'batch_size': 50, 'gmm_k': 3,
              'normal_percentile': 80, 'sequence_length': 30, 'autoencoder_args': None,
          },
          # {
          # 	'type':'luminol','contamination':contamination
          # },
          {
              'type': 'autoencoder',
              'contamination': contamination, 'num_epochs': 10, 'batch_size': 20,
              'lr': 1e-3,
              'hidden_size': 5, 'sequence_length': 30,
              'train_gaussian_percentage': 0.25,
          },
          {
              'type': 'lstm_ad',
              'contamination': contamination, 'len_in': 1, 'len_out': 10,
              'num_epochs': 10,
              'lr': 1e-3, 'batch_size': 1,
          },
          {
              'type': 'lstm_ed',
              'contamination': contamination, 'num_epochs': 10, 'batch_size': 20,
              'lr': 1e-3,
              'hidden_size': 5, 'sequence_length': 30,
              'train_gaussian_percentage': 0.25,
          }])
  
      return space_config
  
  
  CUMULATIVE_SEARCH_SPACE = construct_search_space()
  
  
  def construct_classifier(x):
      clf = None
      if x['type'] == 'iforest':
          clf = IFOREST(contamination=x['contamination'],
                        n_estimators=x['n_estimators'],
                        max_samples=x['max_samples'], max_features=x['max_features'],
                        bootstrap=x['bootstrap'],
                        n_jobs=x['n_jobs'],
                        random_state=x['random_state'])
      elif x['type'] == 'ocsvm':
          clf = OCSVM(gamma=x['gamma'], kernel=x['kernel'], degree=x['degree'],
                      coef0=x['coef0'], tol=x['tol'], nu=x['nu'],
                      shrinking=x['shrinking'], cache_size=x['cache_size'],
                      verbose=x['verbose'], max_iter=x['max_iter'])
      elif x['type'] == 'lof':
          clf = LOF(contamination=x['contamination'], n_neighbors=x['n_neighbors'],
                    algorithm=x['algorithm'],
                    leaf_size=x['leaf_size'], metric=x['metric'], p=x['p'],
                    metric_params=x['metric_params'],
                    novelty=True)
      elif x['type'] == 'robustcovariance':
          clf = RCOV(random_state=x['random_state'],
                     store_precision=x['store_precision'],
                     assume_centered=x['assume_centered'],
                     support_fraction=x['support_fraction'],
                     contamination=x['contamination'])
      elif x['type'] == 'staticautoencoder':
          clf = StaticAutoEncoder(contamination=x['contamination'], epoch=x['epoch'],
                                  dropout_rate=x['dropout_rate'],
                                  regularizer_weight=x['regularizer_weight'],
                                  activation=x['activation'],
                                  kernel_regularizer=x['kernel_regularizer'],
                                  loss_function=x['loss_function'],
                                  optimizer=x['optimizer'])
      elif x['type'] == 'cblof':
          clf = CBLOF(contamination=x['contamination'], n_clusters=x['n_clusters'],
                      clustering_estimator=x['clustering_estimator'], alpha=x['alpha'],
                      beta=x['beta'], use_weights=x['use_weights'],
                      random_state=x['random_state'], n_jobs=x['n_jobs'])
      elif x['type'] == 'knn':
          clf = KNN(contamination=x['contamination'], n_neighbors=x['n_neighbors'],
                    method=x['method'],
                    radius=x['radius'], algorithm=x['algorithm'],
                    leaf_size=x['leaf_size'], metric=x['metric'], p=x['p'],
                    metric_params=x['metric_params'], n_jobs=x['n_jobs'])
      elif x['type'] == 'hbos':
          clf = HBOS(contamination=x['contamination'], n_bins=x['n_bins'],
                     alpha=x['alpha'], tol=x['tol'])
      elif x['type'] == 'sod':
          clf = SOD(contamination=x['contamination'], n_neighbors=x['n_neighbors'],
                    ref_set=x['ref_set'], alpha=x['alpha'])
      elif x['type'] == 'pca':
          clf = PCA(contamination=x['contamination'], n_components=x['n_components'],
                    n_selected_components=x['n_selected_components'], copy=x['copy'],
                    whiten=x['whiten'],
                    svd_solver=x['svd_solver'], tol=x['tol'],
                    iterated_power=x['iterated_power'],
                    random_state=x['random_state'], weighted=x['weighted'],
                    standardization=x['standardization'])
      elif x['type'] == 'dagmm':
          clf = DAGMM(contamination=x['contamination'], num_epochs=x['num_epochs'],
                      lambda_energy=x['lambda_energy'],
                      lambda_cov_diag=x['lambda_cov_diag'],
                      lr=x['lr'], batch_size=x['batch_size'], gmm_k=x['gmm_k'],
                      normal_percentile=x['normal_percentile'],
                      sequence_length=x['sequence_length'],
                      autoencoder_args=x['autoencoder_args'])
      elif x['type'] == 'luminol':
          clf = luminolDet(contamination=x['contamination'])
      elif x['type'] == 'autoencoder':
          clf = AUTOENCODER(contamination=x['contamination'],
                            num_epochs=x['num_epochs'],
                            batch_size=x['batch_size'], lr=x['lr'],
                            hidden_size=x['hidden_size'],
                            sequence_length=x['sequence_length'],
                            train_gaussian_percentage=x['train_gaussian_percentage'])
      elif x['type'] == 'lstm_ad':
          clf = LSTMAD(contamination=x['contamination'], len_in=x['len_in'],
                       len_out=x['len_out'],
                       num_epochs=x['num_epochs'], lr=x['lr'],
                       batch_size=x['batch_size'])
      elif x['type'] == 'lstm_ed':
          clf = LSTMED(contamination=x['contamination'], num_epochs=x['num_epochs'],
                       batch_size=x['batch_size'],
                       lr=x['lr'], hidden_size=x['hidden_size'],
                       sequence_length=x['sequence_length'],
                       train_gaussian_percentage=x['train_gaussian_percentage'])
      else:
          clf = DAGMM(contamination=0.5, num_epochs=10, lambda_energy=0.1,
                      lambda_cov_diag=0.005, lr=1e-3, batch_size=50, gmm_k=3,
                      normal_percentile=80, sequence_length=30, autoencoder_args=None)
  
      print("Selected classifier is ", clf, " for ", x)
      return clf
  
  
  def plot_predictions(predictions, ground_truth, filename):
      x_ax = list(range(len(predictions)))
      plt.plot(x_ax, ground_truth, 'b-')
      plt.plot(x_ax, predictions, 'r-')
      plt.savefig(filename)
      return
  