From b60ee1adaccde01a265ae1f9152f72401888c4a7 Mon Sep 17 00:00:00 2001 From: Simon <31246246+SimonMolinsky@users.noreply.github.com> Date: Thu, 19 Mar 2026 09:57:21 +0200 Subject: [PATCH] Z-value added to cross validation --- CHANGELOG.rst | 1 + .../doctrees/api/evaluate/evaluate.doctree | Bin 140660 -> 140866 bytes docs/build/doctrees/environment.pickle | Bin 654918 -> 650751 bytes .../pyinterpolate/distance/block.html | 135 ------------------ .../evaluate/cross_validation.html | 23 ++- docs/build/html/api/evaluate/evaluate.html | 2 +- docs/build/html/searchindex.js | 2 +- .../evaluate/cross_validation.py | 17 ++- tests/test_evaluate/test_cross_validation.py | 17 +++ 9 files changed, 49 insertions(+), 148 deletions(-) diff --git a/CHANGELOG.rst b/CHANGELOG.rst index 47ce3e38..86d887a2 100644 --- a/CHANGELOG.rst +++ b/CHANGELOG.rst @@ -9,6 +9,7 @@ Changes - from version >= 1.x * [experimental] LSA-method Ordinary Kriging tests and experiments * [enhancement] Users can set negative predictions to zero in Area-to-Area, Area-to-Point, and Centroid-based Poisson Kriging * [docs] Added block-to-block distance equation, and corrected semivariance equation in functions docstrings +* [enhancement] Z-value added to cross validation 2025-12-26 ---------- diff --git a/docs/build/doctrees/api/evaluate/evaluate.doctree b/docs/build/doctrees/api/evaluate/evaluate.doctree index 245bf666d5e8f78ffeaf41c4c870c6f6c06ba09f..e665c578613a724c65a5c40fad99f44c11ccef28 100644 GIT binary patch delta 375 zcmexzisR53jtzT1F*(GO+rqdlEEH3B}1$ydGduXuJ~==eC~GvGv`!BkjH>vvfcl4 vlh>Tp-|E81@}G06vKm;FVfsaBM)~P{4;k5~=ly5gFx|S2QF^NrlaVR_Uy_Dw delta 209 zcmX?fhU3dAjtzT1F{*Cf_eqA4Gd3ZC3k;?|^k9^pes>EaFH?lzX00zAjGO(xdNVTh zJf8gVljP(b-}dps)dDq4nXYKasJ8jo_XA9f@ssa=31$Y$P7jP>lwk}5aVNL`c4G9J ztpDAe2PiQmgFSXihFFi=WW(>So9Fx~V4gn7lu>A^I^!PBshkXq3?MLlq8_8h|pcLRC5;kOQ)$kphUofen!pf*Tn{5wHL% zQUnRTi&#*w{_Og*cMuzj@_%pUZugSgyUW~>{QmiTGUd(eo0;#tdGn_1-q~yIuYb_q 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Source code for pyinterpolate.distance.block

)
 
 
-# def _calc_b2b_dist_from_dataframe(
-#         ps_blocks: Union[pd.DataFrame, gpd.GeoDataFrame],
-#         lon_col_name: Union[str, Hashable],
-#         lat_col_name: Union[str, Hashable],
-#         val_col_name: Union[str, Hashable],
-#         block_id_col_name: Union[str, Hashable],
-#         verbose=False
-# ) -> pd.DataFrame:
-#     r"""
-#     Function calculates distances between the blocks' point supports.
-#
-#     Parameters
-#     ----------
-#     ps_blocks : Union[pd.DataFrame, gpd.GeoDataFrame]
-#         DataFrame with point supports and block indexes.
-#
-#     lon_col_name : Union[str, Hashable]
-#         Longitude or x coordinate.
-#
-#     lat_col_name : Union[str, Hashable]
-#         Latitude or y coordinate.
-#
-#     val_col_name : Union[str, Hashable]
-#         The point support values column.
-#
-#     block_id_col_name : Union[str, Hashable]
-#         Column with block names / indexes.
-#
-#     verbose : bool, default = False
-#         Show progress bar.
-#
-#     Returns
-#     -------
-#     block_distances : DataFrame
-#         Indexes and columns are block indexes, cells are distances.
-#
-#     """
-#     calculated_pairs = set()
-#     unique_blocks = list(ps_blocks[block_id_col_name].unique())
-#
-#     col_set = [lon_col_name, lat_col_name, val_col_name]
-#
-#     results = []
-#
-#     for block_i in tqdm(unique_blocks, disable=not verbose):
-#         for block_j in unique_blocks:
-#             # Check if it was estimated
-#             if not (block_i, block_j) in calculated_pairs:
-#                 if block_i == block_j:
-#                     results.append([block_i, block_j, 0])
-#                 else:
-#                     i_value = ps_blocks[
-#                         ps_blocks[block_id_col_name] == block_i
-#                     ]
-#                     j_value = ps_blocks[
-#                         ps_blocks[block_id_col_name] == block_j
-#                     ]
-#                     value = _calculate_block_to_block_distance(
-#                         i_value[col_set].to_numpy(),
-#                         j_value[col_set].to_numpy()
-#                     )
-#                     results.append([block_i, block_j, value])
-#                     results.append([block_j, block_i, value])
-#                     calculated_pairs.add((block_i, block_j))
-#                     calculated_pairs.add((block_j, block_i))
-#
-#     # Create output dataframe
-#     df = pd.DataFrame(data=results, columns=['block_i', 'block_j', 'z'])
-#     df = df.pivot_table(
-#         values='z',
-#         index='block_i',
-#         columns='block_j'
-#     )
-#
-#     # sort
-#     df = df.reindex(columns=unique_blocks)
-#     df = df.reindex(index=unique_blocks)
-#
-#     return df
-
-
 # noinspection PyUnresolvedReferences
 def _calc_b2b_dist_from_ps(ps_blocks: 'PointSupport') -> Dict:
     r"""
@@ -739,60 +658,6 @@ 
Source code for pyinterpolate.distance.block
# def _calculate_block_to_block_distance(ps_block_1: np.ndarray,
-#                                        ps_block_2: np.ndarray) -> float:
-#     r"""
-#     Function calculates distance between two blocks' point supports.
-#
-#     Parameters
-#     ----------
-#     ps_block_1 : numpy array
-#         Point support of the first block.
-#
-#     ps_block_2 : numpy array
-#         Point support of the second block.
-#
-#     Returns
-#     -------
-#     weighted_distances : float
-#         Weighted point-support distance between blocks.
-#
-#     Notes
-#     -----
-#     The weighted distance between blocks is derived from the equation given
-#     in publication [1] from References. This distance is weighted by
-#
-#     References
-#     ----------
-#     .. [1] Goovaerts, P. Kriging and Semivariogram Deconvolution in the
-#            Presence of Irregular Geographical Units.
-#            Math Geosci 40, 101–128 (2008).
-#            https://doi.org/10.1007/s11004-007-9129-1
-#
-#     TODO
-#     ----
-#     * Add reference equation to the special part of the documentation.
-#     """
-#
-#     a_shape = ps_block_1.shape[0]
-#     b_shape = ps_block_2.shape[0]
-#     ax = ps_block_1[:, 0].reshape(1, a_shape)
-#     bx = ps_block_2[:, 0].reshape(b_shape, 1)
-#     dx = ax - bx
-#     ay = ps_block_1[:, 1].reshape(1, a_shape)
-#     by = ps_block_2[:, 1].reshape(b_shape, 1)
-#     dy = ay - by
-#     aval = ps_block_1[:, -1].reshape(1, a_shape)
-#     bval = ps_block_2[:, -1].reshape(b_shape, 1)
-#     w = aval * bval
-#
-#     dist = np.sqrt(dx ** 2 + dy ** 2, dtype=float, casting='unsafe')
-#
-#     wdist = dist * w
-#     distances_sum = np.sum(wdist) / np.sum(w)
-#     return distances_sum
-
-
 def select_neighbors_in_range(data: pd.DataFrame,
                              current_lag: float,
                              previous_lag: float):
diff --git a/docs/build/html/_modules/pyinterpolate/evaluate/cross_validation.html b/docs/build/html/_modules/pyinterpolate/evaluate/cross_validation.html
index 7873a076..c4257240 100644
--- a/docs/build/html/_modules/pyinterpolate/evaluate/cross_validation.html
+++ b/docs/build/html/_modules/pyinterpolate/evaluate/cross_validation.html
@@ -7,7 +7,7 @@
   
     
     
-    pyinterpolate.evaluate.cross_validation — pyinterpolate 1.1.0 documentation
+    pyinterpolate.evaluate.cross_validation — pyinterpolate 1.2.0 documentation
   
   
   
@@ -38,7 +38,7 @@
   
 
 
-    
+    
     
     
     
@@ -111,7 +111,7 @@
   
   
   
-    
pyinterpolate 1.1.0 documentation

+    
pyinterpolate 1.2.0 documentation

   
 

     
@@ -514,7 +514,7 @@ 
Source code for pyinterpolate.evaluate.cross_validation
          * Mean Prediction Error,
           * Mean Kriging Error: ratio of variance of prediction errors to
             the average variance error of kriging,
-          * array with: ``[coordinate x, coordinate y, prediction error, kriging estimate error]``
+          * array with: ``[coordinate x, coordinate y, prediction error, kriging estimate error, z-value, z-ci-min, z-ci-max]``
 
     References
     ----------
@@ -552,8 +552,6 @@ 
Source code for pyinterpolate.evaluate.cross_validation
    >>> print(validation_results[1])  # mean kriging error
     1.6386630811210166
     """
-    # TODO:
-    # Use (2) to calc Z-score
     # TODO:
     # Validation tutorials
     # TODO:
@@ -606,9 +604,20 @@ 
Source code for pyinterpolate.evaluate.cross_validation
preds = preds[0]
 
         prediction_error = row[-1] - preds[0]
+        p1 = np.sqrt(preds[1])
+
+        z = prediction_error / (p1 * preds[0])
+        z_ci_min = z - 2*p1
+        z_ci_max = z + 2*p1
 
         coordinates_and_errors.append(
-            [preds[2], preds[3], prediction_error, preds[1]]
+            [preds[2],
+             preds[3],
+             prediction_error,
+             preds[1],
+             z,
+             z_ci_min,
+             z_ci_max]
         )
 
     output_arr = np.array(coordinates_and_errors)
diff --git a/docs/build/html/api/evaluate/evaluate.html b/docs/build/html/api/evaluate/evaluate.html
index 3d619b43..556dc6ea 100644
--- a/docs/build/html/api/evaluate/evaluate.html
+++ b/docs/build/html/api/evaluate/evaluate.html
@@ -516,7 +516,7 @@ 
Cross-validation[coordinate x, coordinate y, prediction error, kriging estimate error]

+
  • array with: [coordinate x, coordinate y, prediction error, kriging estimate error, z-value, z-ci-min, z-ci-max]
    • 
 
 
    
 
diff --git a/docs/build/html/searchindex.js b/docs/build/html/searchindex.js
index 1d415e5e..a2942faa 100644
--- a/docs/build/html/searchindex.js
+++ b/docs/build/html/searchindex.js
@@ -1 +1 @@
-Search.setIndex({"alltitles": {"1. Create Variogram Point Cloud": [[35, "1.-Create-Variogram-Point-Cloud"]], "1. Directional process": [[34, "1.-Directional-process"]], "1. Introduction - IDW as bechmarking tool": [[37, "1.-Introduction---IDW-as-bechmarking-tool"]], "1. Prepare data": [[38, "1.-Prepare-data"], [40, "1.-Prepare-data"], [41, "1.-Prepare-data"], [42, "1.-Prepare-data"], [43, "1.-Prepare-data"], [44, "1.-Prepare-data"]], "1. Set semivariogram model (fit)": [[36, "1.-Set-semivariogram-model-(fit)"]], "2. Analyze Variogram Point Cloud": [[35, "2.-Analyze-Variogram-Point-Cloud"]], "2. Analyze data distribution and remove potential outliers": [[38, "2.-Analyze-data-distribution-and-remove-potential-outliers"]], "2. Create Ordinary and Simple Kriging models": [[36, "2.-Create-Ordinary-and-Simple-Kriging-models"]], "2. Create directional and isotropic semivariograms": [[34, "2.-Create-directional-and-isotropic-semivariograms"]], "2. Create directional semivariograms": [[39, "2.-Create-directional-semivariograms"]], "2. Detect and remove outliers": [[40, "2.-Detect-and-remove-outliers"]], "2. Load regularized semivariogram model": [[42, "2.-Load-regularized-semivariogram-model"], [43, "2.-Load-regularized-semivariogram-model"], [44, "2.-Load-regularized-semivariogram-model"]], "2. Perform IDW and validate outputs": [[37, "2.-Perform-IDW-and-validate-outputs"]], "2. Set semivariogram parameters": [[41, "2.-Set-semivariogram-parameters"]], "2. Why do we use Spatial Dependency Index?": [[33, "2.-Why-do-we-use-Spatial-Dependency-Index?"]], "3. Compare semivariograms": [[34, "3.-Compare-semivariograms"]], "3. Create Variogram Clouds": [[38, "3.-Create-Variogram-Clouds"]], "3. Detect and remove outliers": [[35, "3.-Detect-and-remove-outliers"]], "3. Example: Spatial Dependence over the same study extent but for different elements": [[33, "3.-Example:-Spatial-Dependence-over-the-same-study-extent-but-for-different-elements"]], "3. Fit semivariogram model": [[40, "3.-Fit-semivariogram-model"]], "3. Interpolate with directional Kriging": [[39, "3.-Interpolate-with-directional-Kriging"]], "3. Perform Kriging and validate outputs": [[37, "3.-Perform-Kriging-and-validate-outputs"]], "3. Predict values at unknown locations and evaluate output": [[36, "3.-Predict-values-at-unknown-locations-and-evaluate-output"]], "3. Prepare data for Poisson Kriging": [[42, "3.-Prepare-data-for-Poisson-Kriging"], [43, "3.-Prepare-data-for-Poisson-Kriging"]], "3. Regularize semivariogram": [[41, "3.-Regularize-semivariogram"]], "3. Smooth blocks": [[44, "3.-Smooth-blocks"]], "4. API": [[33, "4.-API"]], "4. Compare models": [[39, "4.-Compare-models"]], "4. Experimental variogram from the point cloud": [[35, "4.-Experimental-variogram-from-the-point-cloud"]], "4. Export results": [[44, "4.-Export-results"]], "4. Filtering areas": [[42, "4.-Filtering-areas"], [43, "4.-Filtering-areas"]], "4. Prepare canvas": [[40, "4.-Prepare-canvas"]], "4. Remove outliers from the point cloud": [[38, "4.-Remove-outliers-from-the-point-cloud"]], "4. Visualize process": [[41, "4.-Visualize-process"]], "5. Evaluate": [[42, "5.-Evaluate"], [43, "5.-Evaluate"]], "5. Export semivariogram": [[41, "5.-Export-semivariogram"]], "5. Interpolate": [[40, "5.-Interpolate"]], "5. Is variogram point cloud a scatter plot?": [[35, "5.-Is-variogram-point-cloud-a-scatter-plot?"]], "5. 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\ No newline at end of file
+Search.setIndex({"alltitles": {"1. Create Variogram Point Cloud": [[35, "1.-Create-Variogram-Point-Cloud"]], "1. Directional process": [[34, "1.-Directional-process"]], "1. Introduction - IDW as bechmarking tool": [[37, "1.-Introduction---IDW-as-bechmarking-tool"]], "1. Prepare data": [[38, "1.-Prepare-data"], [40, "1.-Prepare-data"], [41, "1.-Prepare-data"], [42, "1.-Prepare-data"], [43, "1.-Prepare-data"], [44, "1.-Prepare-data"]], "1. Set semivariogram model (fit)": [[36, "1.-Set-semivariogram-model-(fit)"]], "2. Analyze Variogram Point Cloud": [[35, "2.-Analyze-Variogram-Point-Cloud"]], "2. Analyze data distribution and remove potential outliers": [[38, "2.-Analyze-data-distribution-and-remove-potential-outliers"]], "2. Create Ordinary and Simple Kriging models": [[36, "2.-Create-Ordinary-and-Simple-Kriging-models"]], "2. Create directional and isotropic semivariograms": [[34, "2.-Create-directional-and-isotropic-semivariograms"]], "2. Create directional semivariograms": [[39, "2.-Create-directional-semivariograms"]], "2. Detect and remove outliers": [[40, "2.-Detect-and-remove-outliers"]], "2. Load regularized semivariogram model": [[42, "2.-Load-regularized-semivariogram-model"], [43, "2.-Load-regularized-semivariogram-model"], [44, "2.-Load-regularized-semivariogram-model"]], "2. Perform IDW and validate outputs": [[37, "2.-Perform-IDW-and-validate-outputs"]], "2. Set semivariogram parameters": [[41, "2.-Set-semivariogram-parameters"]], "2. Why do we use Spatial Dependency Index?": [[33, "2.-Why-do-we-use-Spatial-Dependency-Index?"]], "3. Compare semivariograms": [[34, "3.-Compare-semivariograms"]], "3. Create Variogram Clouds": [[38, "3.-Create-Variogram-Clouds"]], "3. Detect and remove outliers": [[35, "3.-Detect-and-remove-outliers"]], "3. Example: Spatial Dependence over the same study extent but for different elements": [[33, "3.-Example:-Spatial-Dependence-over-the-same-study-extent-but-for-different-elements"]], "3. Fit semivariogram model": [[40, "3.-Fit-semivariogram-model"]], "3. Interpolate with directional Kriging": [[39, "3.-Interpolate-with-directional-Kriging"]], "3. Perform Kriging and validate outputs": [[37, "3.-Perform-Kriging-and-validate-outputs"]], "3. Predict values at unknown locations and evaluate output": [[36, "3.-Predict-values-at-unknown-locations-and-evaluate-output"]], "3. Prepare data for Poisson Kriging": [[42, "3.-Prepare-data-for-Poisson-Kriging"], [43, "3.-Prepare-data-for-Poisson-Kriging"]], "3. Regularize semivariogram": [[41, "3.-Regularize-semivariogram"]], "3. Smooth blocks": [[44, "3.-Smooth-blocks"]], "4. API": [[33, "4.-API"]], "4. Compare models": [[39, "4.-Compare-models"]], "4. Experimental variogram from the point cloud": [[35, "4.-Experimental-variogram-from-the-point-cloud"]], "4. Export results": [[44, "4.-Export-results"]], "4. Filtering areas": [[42, "4.-Filtering-areas"], [43, "4.-Filtering-areas"]], "4. Prepare canvas": [[40, "4.-Prepare-canvas"]], "4. Remove outliers from the point cloud": [[38, "4.-Remove-outliers-from-the-point-cloud"]], "4. Visualize process": [[41, "4.-Visualize-process"]], "5. Evaluate": [[42, "5.-Evaluate"], [43, "5.-Evaluate"]], "5. Export semivariogram": [[41, "5.-Export-semivariogram"]], "5. Interpolate": [[40, "5.-Interpolate"]], "5. Is variogram point cloud a scatter plot?": [[35, "5.-Is-variogram-point-cloud-a-scatter-plot?"]], "5. 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\ No newline at end of file
diff --git a/src/pyinterpolate/evaluate/cross_validation.py b/src/pyinterpolate/evaluate/cross_validation.py
index a64ef2ed..70b420a7 100644
--- a/src/pyinterpolate/evaluate/cross_validation.py
+++ b/src/pyinterpolate/evaluate/cross_validation.py
@@ -88,7 +88,7 @@ def validate_kriging(
           * Mean Prediction Error,
           * Mean Kriging Error: ratio of variance of prediction errors to
             the average variance error of kriging,
-          * array with: ``[coordinate x, coordinate y, prediction error, kriging estimate error]``
+          * array with: ``[coordinate x, coordinate y, prediction error, kriging estimate error, z-value, z-ci-min, z-ci-max]``
 
     References
     ----------
@@ -127,8 +127,6 @@ def validate_kriging(
     1.6386630811210166
     """
     # TODO:
-    # Use (2) to calc Z-score
-    # TODO:
     # Validation tutorials
     # TODO:
     # Areal kriging validation
@@ -180,9 +178,20 @@ def validate_kriging(
             preds = preds[0]
 
         prediction_error = row[-1] - preds[0]
+        p1 = np.sqrt(preds[1])
+
+        z = prediction_error / (p1 * preds[0])
+        z_ci_min = z - 2*p1
+        z_ci_max = z + 2*p1
 
         coordinates_and_errors.append(
-            [preds[2], preds[3], prediction_error, preds[1]]
+            [preds[2],
+             preds[3],
+             prediction_error,
+             preds[1],
+             z,
+             z_ci_min,
+             z_ci_max]
         )
 
     output_arr = np.array(coordinates_and_errors)
diff --git a/tests/test_evaluate/test_cross_validation.py b/tests/test_evaluate/test_cross_validation.py
index 5c1ea3df..a12c7b73 100644
--- a/tests/test_evaluate/test_cross_validation.py
+++ b/tests/test_evaluate/test_cross_validation.py
@@ -1,3 +1,5 @@
+import math
+
 import numpy as np
 
 from pyinterpolate.semivariogram.experimental.classes.experimental_variogram import ExperimentalVariogram
@@ -76,3 +78,18 @@ def test_with_separate_geometry():
 
     assert validation_results_sep[0] == validation_results[0]
     assert validation_results_sep[1] == validation_results[1]
+
+
+def test_z_scores():
+    validation_results = validate_kriging(
+        theoretical_model=THEORETICAL_MODEL,
+        points=ARMSTRONG_DATA,
+        no_neighbors=4,
+        progress_bar=False
+    )
+    validation_arr = validation_results[-1]
+
+    m = np.mean(validation_arr[:, 4])
+
+    assert math.isclose(m, 0, abs_tol=0.01)
+