[krige] update kriging package, config

configs/kriging.yaml

@@ -1,25 +1,25 @@
 # basic kriging setup
 features:
-  - name: my pickle files
-    type: pickle
-    files:
-      covariates: features.pk
-      targets: targets.pk
+#  - name: my pickle files
+#    type: pickle
+#    files:
+#      covariates: features.pk
+#      targets: targets.pk
   - name: my features 1
     type: ordinal
     files:
-      - path: /path/to/GA_data/GA-cover2/LATITUDE_GRID1.tif
-      - path: /path/to/GA_data/GA-cover2/LONGITUDE_GRID1.tif
+      - path: configs/data/sirsam/LATITUDE_GRID1.tif
+      - path: configs/data/sirsam/LONGITUDE_GRID1.tif
     # transforms are performed in order
     transforms:
-      - centre
-      - standardise
+#      - centre
+#      - standardise
     imputation: nn
 
 # only used during prediction
-mask:
-  file: /path/to/GA_data/mask/old_mask_test.tif
-  retain: 1
+#mask:
+#  file: /path/to/GA_data/mask/old_mask_test.tif
+#  retain: 1
 
 
 # resample: choice (value, spatial)
@@ -29,7 +29,7 @@ mask:
 # rows and cols: for spatial resampling, sample from rows X cols tiles
 # resampling is performed in sequence. The order of spatial/value is important.
 targets:
-  file: /path/to/GA_data/GA-cover2/geochem_sites.shp
+  file: configs/data/sirsam/out_resampled/geochem_sites.shp
   property: K_ppm_imp_
 #  resample:
 #    - spatial:
@@ -50,6 +50,7 @@ learning:
   arguments:
     method: ordinary  # ordinary or universal
     variogram_model: spherical  # linear, power, gaussian, spherical, exponential
+    variogram_parameters: [0, 1, 0.1]   # [sill, range, nugget] for gaussian, spherical, exponential
     verbose: False
 
 
@@ -74,4 +75,4 @@ validation:
       random_seed: 1
 
 output:
-  directory: .
+  directory: krige

setup.py

@@ -99,7 +99,7 @@ def run(self):
         'PyYAML >= 3.11',
         'pandas >= 1.2.5',
         'matplotlib >= 3.4.0',
-        'PyKrige==1.3.0',
+        'PyKrige==1.7.0',
         'xgboost>=1.4.2',
         'setuptools>=30.0.0',
         'eli5>=0.8.2',

uncoverml/krige.py

@@ -93,6 +93,7 @@ class Krige(TagsMixin, RegressorMixin, BaseEstimator, KrigePredictDistMixin):
     def __init__(self,
                  method='ordinary',
                  variogram_model='linear',
+                 variogram_parameters=None,
                  nlags=6,
                  weight=False,
                  n_closest_points=10,
@@ -102,6 +103,7 @@ def __init__(self,
             raise ConfigException('Kirging method must be '
                                   'one of {}'.format(krige_methods.keys()))
         self.variogram_model = variogram_model
+        self.variogram_parameters = variogram_parameters
         self.verbose = verbose
         self.nlags = nlags
         self.weight = weight
@@ -126,6 +128,7 @@ def fit(self, x, y, *args, **kwargs):
             y=x[:, 1],
             z=y,
             variogram_model=self.variogram_model,
+            variogram_parameters=self.variogram_parameters,
             verbose=self.verbose,
             nlags=self.nlags,
             weight=self.weight,