Version

• Permetrics version >= 1.2.0:

  from permetrics import RegressionMetric, ClassificationMetric, ClusteringMetric
  

• Permetrics version >= 1.2.0:

  from numpy import array
  from permetrics.regression import RegressionMetric
  
  ## For 1-D array
  y_true = array([3, -0.5, 2, 7])
  y_pred = array([2.5, 0.0, 2, 8])
  
  evaluator = RegressionMetric(y_true, y_pred)
  print(evaluator.RMSE())
  print(evaluator.MSE())
  
  ## For > 1-D array
  y_true = array([[0.5, 1], [-1, 1], [7, -6]])
  y_pred = array([[0, 2], [-1, 2], [8, -5]])
  
  evaluator = RegressionMetric(y_true, y_pred)
  print(evaluator.RMSE(multi_output="raw_values"))
  print(evaluator.MAE(multi_output="raw_values"))
  
  
  ## All metrics
  
  EVS = evs = explained_variance_score
  ME = me = max_error
  MBE = mbe = mean_bias_error
  MAE = mae = mean_absolute_error
  MSE = mse = mean_squared_error
  RMSE = rmse = root_mean_squared_error
  MSLE = msle = mean_squared_log_error
  MedAE = medae = median_absolute_error
  MRE = mre = MRB = mrb = mean_relative_bias = mean_relative_error
  MPE = mpe = mean_percentage_error
  MAPE = mape = mean_absolute_percentage_error
  SMAPE = smape = symmetric_mean_absolute_percentage_error
  MAAPE = maape = mean_arctangent_absolute_percentage_error
  MASE = mase = mean_absolute_scaled_error
  NSE = nse = nash_sutcliffe_efficiency
  NNSE = nnse = normalized_nash_sutcliffe_efficiency
  WI = wi = willmott_index
  R = r = PCC = pcc = pearson_correlation_coefficient
  AR = ar = APCC = apcc = absolute_pearson_correlation_coefficient
  R2s = r2s = pearson_correlation_coefficient_square
  CI = ci = confidence_index
  COD = cod = R2 = r2 = coefficient_of_determination
  ACOD = acod = AR2 = ar2 = adjusted_coefficient_of_determination
  DRV = drv = deviation_of_runoff_volume
  KGE = kge = kling_gupta_efficiency
  GINI = gini = normalized_gini_coefficient
  GINI_WIKI = gini_wiki = residual_gini_index
  PCD = pcd = prediction_of_change_in_direction
  CE = ce = cross_entropy
  KLD = kld = kullback_leibler_divergence
  JSD = jsd = jensen_shannon_divergence
  VAF = vaf = variance_accounted_for
  RAE = rae = relative_absolute_error
  A10 = a10 = a10_index
  A20 = a20 = a20_index
  A30 = a30 = a30_index
  NRMSE = nrmse = normalized_root_mean_square_error
  RSE = rse = residual_standard_error
  
  RE = re = RB = rb = single_relative_bias = single_relative_error
  AE = ae = single_absolute_error
  SE = se = single_squared_error
  SLE = sle = single_squared_log_error
  

• Permetrics version <= 1.1.3:

  ##  All you need to do is: (Make sure your y_true and y_pred is a numpy array)
  ## For example with RMSE:
  
  from numpy import array
  from permetrics.regression import Metrics
  
  ## 1-D array
  y_true = array([3, -0.5, 2, 7])
  y_pred = array([2.5, 0.0, 2, 8])
  
  y_true2 = array([3, -0.5, 2, 7])
  y_pred2 = array([2.5, 0.0, 2, 9])
  
  ### C1. Using OOP style - very powerful when calculating multiple metrics
  obj1 = Metrics(y_true, y_pred)  # Pass the data here
  result = obj1.root_mean_squared_error(clean=True)
  print(f"1-D array, OOP style: {result}")
  
  ### C2. Using functional style
  obj2 = Metrics()
  result = obj2.root_mean_squared_error(clean=True, y_true=y_true2, y_pred=y_pred2)
  # Pass the data here, remember the keywords (y_true, y_pred)
  print(f"1-D array, Functional style: {result}")
  
  ## > 1-D array - Multi-dimensional Array
  y_true = array([[0.5, 1], [-1, 1], [7, -6]])
  y_pred = array([[0, 2], [-1, 2], [8, -5]])
  
  multi_outputs = [None, "raw_values", [0.3, 1.2], array([0.5, 0.2]), (0.1, 0.9)]
  obj3 = Metrics(y_true, y_pred)
  for multi_output in multi_outputs:
      result = obj3.root_mean_squared_error(clean=False, multi_output=multi_output)
      print(f"n-D array, OOP style: {result}")
  
  # Or run the simple:
  python examples/RMSE.py
  

# The more complicated tests in the folder: examples