Make use of ABC abstract class in unsequa module

climada/engine/unsequa/calc_base.py

@@ -23,6 +23,7 @@
 import datetime as dt
 import itertools
 import logging
+from abc import ABC, abstractmethod
 
 import numpy as np
 import pandas as pd
@@ -33,7 +34,7 @@
 LOGGER = logging.getLogger(__name__)
 
 
-class Calc:
+class Calc(ABC):
     """
     Base class for uncertainty quantification
 
@@ -80,6 +81,7 @@
     _metric_names = ()
     """Names of the output metrics"""
 
+    @abstractmethod
     def __init__(self):
         """
         Empty constructor to be overwritten by subclasses
@@ -423,6 +425,16 @@
 
         return sens_output
 
+    @abstractmethod
+    def uncertainty(self):
+        """Compute the uncertainty of the output values"""
+        pass
+
+    @abstractmethod
+    def _compute_metrics(self):
+        """Compute the uncertainty of the output values"""
+        pass
+
 
 def _multiprocess_chunksize(samples_df, processes):
     """Divides the samples into chunks for multiprocesses computing

climada/engine/unsequa/calc_cost_benefit.py

@@ -139,7 +139,7 @@
         self.value_unit = self.ent_input_var.evaluate().exposures.value_unit
         self.check_distr()
 
     def uncertainty(
         self, unc_sample, processes=1, chunksize=None, **cost_benefit_kwargs
     ):
         """
@@ -218,9 +218,7 @@
 
         one_sample = samples_df.iloc[0:1]
         start = time.time()
-        self._compute_cb_metrics(
-            one_sample, cost_benefit_kwargs, chunksize=1, processes=1
-        )
+        self._compute_metrics(one_sample, cost_benefit_kwargs, chunksize=1, processes=1)
         elapsed_time = time.time() - start
         self.est_comp_time(unc_sample.n_samples, elapsed_time, processes)
 
@@ -231,9 +229,7 @@
             tot_climate_risk,
             benefit,
             cost_ben_ratio,
-        ] = self._compute_cb_metrics(
-            samples_df, cost_benefit_kwargs, chunksize, processes
-        )
+        ] = self._compute_metrics(samples_df, cost_benefit_kwargs, chunksize, processes)
 
         # Assign computed impact distribution data to self
         tot_climate_risk_unc_df = pd.DataFrame(
@@ -293,9 +289,7 @@
             cost_benefit_kwargs=cost_benefit_kwargs,
         )
 
-    def _compute_cb_metrics(
-        self, samples_df, cost_benefit_kwargs, chunksize, processes
-    ):
+    def _compute_metrics(self, samples_df, cost_benefit_kwargs, chunksize, processes):
         """Compute the uncertainty metrics
 
         Parameters

climada/engine/unsequa/calc_delta_climate.py

@@ -150,7 +150,7 @@
         self.value_unit = self.exp_initial_input_var.evaluate().value_unit
         self.check_distr()
 
     def uncertainty(
         self,
         unc_sample,
         rp=None,
@@ -258,7 +258,7 @@
 
         one_sample = samples_df.iloc[0:1]
         start = time.time()
-        self._compute_imp_metrics(one_sample, chunksize=1, processes=1)
+        self._compute_metrics(one_sample, chunksize=1, processes=1)
         elapsed_time = time.time() - start
         self.est_comp_time(unc_sample.n_samples, elapsed_time, processes)
 
@@ -267,9 +267,7 @@
             freq_curve_list,
             eai_exp_list,
             at_event_list,
-        ] = self._compute_imp_metrics(
-            samples_df, chunksize=chunksize, processes=processes
-        )
+        ] = self._compute_metrics(samples_df, chunksize=chunksize, processes=processes)
 
         # Assign computed impact distribution data to self
         aai_agg_unc_df = pd.DataFrame(aai_agg_list, columns=["aai_agg"])
@@ -295,7 +293,7 @@
             coord_df=coord_df,
         )
 
-    def _compute_imp_metrics(self, samples_df, chunksize, processes):
+    def _compute_metrics(self, samples_df, chunksize, processes):
         """Compute the uncertainty metrics
 
         Parameters
@@ -428,7 +426,7 @@
             at_event_final = np.array([])
 
         if relative_delta:
             delta_func = lambda x, y: safe_divide(x - y, y)
         else:
             delta_func = lambda x, y: x - y
 

climada/engine/unsequa/calc_impact.py

@@ -121,7 +121,7 @@
         self.value_unit = self.exp_input_var.evaluate().value_unit
         self.check_distr()
 
     def uncertainty(
         self,
         unc_sample,
         rp=None,
@@ -219,14 +219,12 @@
 
         one_sample = samples_df.iloc[0:1]
         start = time.time()
-        self._compute_imp_metrics(one_sample, chunksize=1, processes=1)
+        self._compute_metrics(one_sample, chunksize=1, processes=1)
         elapsed_time = time.time() - start
         self.est_comp_time(unc_sample.n_samples, elapsed_time, processes)
 
         [aai_agg_list, freq_curve_list, eai_exp_list, at_event_list] = (
-            self._compute_imp_metrics(
-                samples_df, chunksize=chunksize, processes=processes
-            )
+            self._compute_metrics(samples_df, chunksize=chunksize, processes=processes)
         )
 
         # Assign computed impact distribution data to self
@@ -256,7 +254,7 @@
             coord_df=coord_df,
         )
 
-    def _compute_imp_metrics(self, samples_df, chunksize, processes):
+    def _compute_metrics(self, samples_df, chunksize, processes):
         """Compute the uncertainty metrics
 
         Parameters

climada/engine/unsequa/unc_output.py

@@ -28,6 +28,7 @@
 
 import datetime as dt
 import logging
+from abc import ABC, abstractmethod
 from itertools import zip_longest
 from pathlib import Path
 
@@ -71,7 +72,7 @@
 }
 
 
-class UncOutput:
+class UncOutput(ABC):
     """
     Class to store and plot uncertainty and sensitivity analysis output data
 
@@ -107,16 +108,20 @@
         "sensitivity_kwargs",
     ]
 
+    # @abstractmethod
     def __init__(self, samples_df, unit=None):
+        # """
+        # Empty constructor to be overwritten by subclasses
+        # """
         """
-        Initialize Uncertainty Data object.
+        Initialize Uncertainty Data object
 
         Parameters
         ----------
         samples_df : pandas.DataFrame
-            input parameters samples
+           input parameters samples
         unit : str, optional
-            value unit
+           value unit
         """
         # Data
         self.samples_df = samples_df
@@ -1232,6 +1237,58 @@
         return unc_data
 
 
+# class UncBaseOutput(UncOutput):
+#    """
+#    Class to store and plot uncertainty and sensitivity analysis output data
+#
+#    This is the base class to store uncertainty and sensitivity outputs of an
+#    analysis  done on climada.engine.impact.Impact() or
+#    climada.engine.costbenefit.CostBenefit() object.
+#
+#    Attributes
+#    ----------
+#    samples_df : pandas.DataFrame
+#        Values of the sampled uncertainty parameters. It has n_samples rows
+#        and one column per uncertainty parameter.
+#    sampling_method : str
+#        Name of the sampling method from SAlib.
+#        https://salib.readthedocs.io/en/latest/api.html#
+#    n_samples : int
+#        Effective number of samples (number of rows of samples_df)
+#    param_labels : list
+#        Name of all the uncertainty parameters
+#    distr_dict : dict
+#        Comon flattened dictionary of all the distr_dict of all input variables.
+#        It represents the distribution of all the uncertainty parameters.
+#    problem_sa : dict
+#        The description of the uncertainty variables and their
+#        distribution as used in SALib.
+#        https://salib.readthedocs.io/en/latest/basics.html.
+#    """
+#
+#    _metadata = [
+#        "sampling_method",
+#        "sampling_kwargs",
+#        "sensitivity_method",
+#        "sensitivity_kwargs",
+#    ]
+#
+#    def __init__(self, samples_df, unit=None):
+#        """
+#        Initialize Uncertainty Data object.
+#
+#        Parameters
+#        ----------
+#        samples_df : pandas.DataFrame
+#            input parameters samples
+#        unit : str, optional
+#            value unit
+#        """
+#        # Data
+#        self.samples_df = samples_df
+#        self.unit = unit
+
+
 class UncImpactOutput(UncOutput):
     """Extension of UncOutput specific for CalcImpact, returned by the
     uncertainty() method.