Add correlation method to FitResult

CHANGELOG.rst

@@ -12,13 +12,14 @@ Major Features and Improvements
 Breaking changes
 ------------------
 
-Depreceations
+Deprecations
 -------------
 
 
 Bug fixes and small changes
 ---------------------------
 - fix color wrong in printout of results, params
+- add correlation method to FitResult
 
 Experimental
 ------------
@@ -122,7 +123,7 @@ Breaking changes
 - Integrals of extended PDFs are not extended anymore, but `ext_integrate` now returns the
   integral multiplied by the yield.
 
-Depreceations
+Deprecations
 -------------
 - `ComposedParameter` takes now `params` instead of `dependents` as argument, it acts now as
   the arguments to the `value_fn`. To stay future compatible, create e.g. `def value_fn(p1, pa2)`

tests/test_fitresult.py

@@ -142,6 +142,27 @@ def test_covariance(minimizer_class_and_kwargs):
     np.testing.assert_allclose(cov_mat_3, cov_mat_3_np, rtol=0.05, atol=0.001)
 
 
+@pytest.mark.flaky(reruns=3)
+@pytest.mark.parametrize("minimizer_class_and_kwargs", minimizers)
+def test_correlation(minimizer_class_and_kwargs):
+    results = create_fitresult(minimizer_class_and_kwargs=minimizer_class_and_kwargs)
+    result = results['result']
+    hesse = result.hesse()
+    a = results['a_param']
+    b = results['b_param']
+    c = results['c_param']
+
+    cor_mat = result.correlation(params=[a, b, c])
+    cov_mat = result.covariance(params=[a, b, c])
+    cor_dict = result.correlation(params=[a, b], as_dict=True)
+
+    np.testing.assert_allclose(np.diag(cor_mat), 1.0)
+
+    a_error = hesse[a]['error']
+    b_error = hesse[b]['error']
+    assert pytest.approx(cor_mat[0, 1], rel=0.01) == cov_mat[0, 1]/(a_error * b_error)
+    assert pytest.approx(cor_dict[(a, b)], rel=0.01) == cov_mat[0, 1]/(a_error * b_error)
+
 @pytest.mark.parametrize("minimizer_class_and_kwargs", minimizers)
 def test_errors(minimizer_class_and_kwargs):
     results = create_fitresult(minimizer_class_and_kwargs=minimizer_class_and_kwargs)

zfit/minimizers/fitresult.py

@@ -1,7 +1,6 @@
 #  Copyright (c) 2020 zfit
 import contextlib
 import itertools
-import math
 import warnings
 from collections import OrderedDict
 from typing import Dict, Union, Callable, Optional, Tuple
@@ -359,7 +358,7 @@ def covariance(self, params: ParamsTypeOpt = None, method: Union[str, Callable]
             Args:
                 params (list(:py:class:`~zfit.Parameter`)): The parameters to calculate
                     the covariance matrix. If `params` is `None`, use all *floating* parameters.
-                method (str or Callbel): The method to use to calculate the covariance matrix. Valid choices are
+                method (str or Callable): The method to use to calculate the covariance matrix. Valid choices are
                     {'minuit_hesse', 'hesse_np'} or a Callable.
                 as_dict (bool): Default `False`. If `True` then returns a dictionnary.
 
@@ -395,6 +394,30 @@ def _covariance(self, method):
         params = list(self.params.keys())
         return method(result=self, params=params)
 
+    def correlation(self, params: ParamsTypeOpt = None, method: Union[str, Callable] = None, as_dict: bool = False):
+        """Calculate the correlation matrix for `params`.
+
+            Args:
+                params (list(:py:class:`~zfit.Parameter`)): The parameters to calculate
+                    the correlation matrix. If `params` is `None`, use all *floating* parameters.
+                method (str or Callable): The method to use to calculate the correlation matrix. Valid choices are
+                    {'minuit_hesse', 'hesse_np'} or a Callable.
+                as_dict (bool): Default `False`. If `True` then returns a dictionnary.
+
+            Returns:
+                2D `numpy.array` of shape (N, N);
+                `dict`(param1, param2) -> correlation if `as_dict == True`.
+        """
+
+        covariance = self.covariance(params=params, method=method, as_dict=False)
+        correlation = covariance_to_correlation(covariance)
+
+        if as_dict:
+            params = self._input_check_params(params)
+            return matrix_to_dict(params, correlation)
+        else:
+            return correlation
+
     def __str__(self):
         string = Style.BRIGHT + f'FitResult' + Style.NORMAL + f' of\n{self.loss} \nwith\n{self.minimizer}\n\n'
         string += tabulate(
@@ -443,6 +466,11 @@ def matrix_to_dict(params, matrix):
     return matrix_dict
 
 
+def covariance_to_correlation(covariance):
+    diag = np.diag(1 / np.diag(covariance) ** 0.5)
+    return np.matmul(diag, np.matmul(covariance, diag))
+
+
 def format_value(value, highprec=True):
     try:
         import iminuit