add spatial and temporal scale warnings #43 #44

stemflow/model/AdaSTEM.py

@@ -45,9 +45,11 @@
     check_prediciton_aggregation,
     check_prediction_return,
     check_random_state,
+    check_spatial_scale,
     check_spatio_bin_jitter_magnitude,
     check_task,
     check_temporal_bin_start_jitter,
+    check_temporal_scale,
     check_transform_njobs,
     check_transform_spatio_bin_jitter_magnitude,
     check_verbosity,
@@ -288,10 +290,32 @@ def split(
         save_path = os.path.join(self.save_dir, "ensemble_quadtree_df.csv") if self.save_tmp else ""
 
         if "grid_len" not in self.__dir__():
-            # We har using AdaSTEM
+            # We are using AdaSTEM
             self.grid_len = None
+            check_spatial_scale(
+                X_train[self.Spatio1].min(),
+                X_train[self.Spatio1].max(),
+                X_train[self.Spatio2].min(),
+                X_train[self.Spatio2].max(),
+                self.grid_len_upper_threshold,
+                self.grid_len_lower_threshold,
+            )
+            check_temporal_scale(
+                X_train[self.Temporal1].min(), X_train[self.Temporal1].min(), self.temporal_bin_interval
+            )
         else:
-            # We har using STEM
+            # We are using STEM
+            check_spatial_scale(
+                X_train[self.Spatio1].min(),
+                X_train[self.Spatio1].max(),
+                X_train[self.Spatio2].min(),
+                X_train[self.Spatio2].max(),
+                self.grid_len,
+                self.grid_len,
+            )
+            check_temporal_scale(
+                X_train[self.Temporal1].min(), X_train[self.Temporal1].min(), self.temporal_bin_interval
+            )
             pass
 
         spatio_bin_jitter_magnitude = check_transform_spatio_bin_jitter_magnitude(

stemflow/model/SphereAdaSTEM.py

@@ -22,9 +22,11 @@
 from ..utils.validation import (
     check_base_model,
     check_prediciton_aggregation,
+    check_spatial_scale,
     check_spatio_bin_jitter_magnitude,
     check_task,
     check_temporal_bin_start_jitter,
+    check_temporal_scale,
     check_transform_njobs,
     check_verbosity,
 )
@@ -244,10 +246,32 @@ def split(
         save_path = os.path.join(self.save_dir, "ensemble_quadtree_df.csv") if self.save_tmp else ""
 
         if "grid_len" not in self.__dir__():
-            # We har using AdaSTEM
+            # We are using AdaSTEM
             self.grid_len = None
+            check_spatial_scale(
+                X_train[self.Spatio1].min(),
+                X_train[self.Spatio1].max(),
+                X_train[self.Spatio2].min(),
+                X_train[self.Spatio2].max(),
+                self.grid_len_upper_threshold,
+                self.grid_len_lower_threshold,
+            )
+            check_temporal_scale(
+                X_train[self.Temporal1].min(), X_train[self.Temporal1].min(), self.temporal_bin_interval
+            )
         else:
-            # We har using STEM
+            # We are using STEM
+            check_spatial_scale(
+                X_train[self.Spatio1].min(),
+                X_train[self.Spatio1].max(),
+                X_train[self.Spatio2].min(),
+                X_train[self.Spatio2].max(),
+                self.grid_len,
+                self.grid_len,
+            )
+            check_temporal_scale(
+                X_train[self.Temporal1].min(), X_train[self.Temporal1].min(), self.temporal_bin_interval
+            )
             pass
 
         partial_get_one_ensemble_sphere_quadtree = partial(

stemflow/utils/validation.py

@@ -158,3 +158,33 @@ def check_X_y_shape_match(X, y):
     X_size = X.shape[0]
     if not y_size == X_size:
         raise ValueError(f"The shape of X and y should match. Got X: {X_size}, y: {y_size}")
+
+
+def check_spatial_scale(x_min, x_max, y_min, y_max, grid_length_upper, grid_length_lower):
+    if (grid_length_upper <= (x_max - x_min) / 100) or (grid_length_upper <= (y_max - y_min) / 100):
+        warnings(
+            "The grid_len_upper_threshold is significantly smaller than the scale of longitude and latitude (x and y). Be sure if this is desired."
+        )
+    if (grid_length_upper >= (x_max - x_min) * 100) or (grid_length_upper >= (y_max - y_min) * 100):
+        warnings(
+            "The grid_len_upper_threshold is significantly larger than the scale of longitude and latitude (x and y). Be sure if this is desired."
+        )
+    if (grid_length_lower <= (x_max - x_min) / 100) or (grid_length_lower <= (y_max - y_min) / 100):
+        warnings(
+            "The grid_len_lower_threshold is significantly smaller than the scale of longitude and latitude (x and y). Be sure if this is desired."
+        )
+    if (grid_length_lower >= (x_max - x_min) * 100) or (grid_length_lower >= (y_max - y_min) * 100):
+        warnings(
+            "The grid_len_lower_threshold is significantly larger than the scale of longitude and latitude (x and y). Be sure if this is desired."
+        )
+
+
+def check_temporal_scale(t_min, t_max, temporal_bin_interval):
+    if temporal_bin_interval <= (t_max - t_min) / 100:
+        warnings(
+            "The temporal_bin_interval is significantly smaller than the scale of temporal parameters in provided data. Be sure if this is desired."
+        )
+    if temporal_bin_interval >= (t_max - t_min) * 100:
+        warnings(
+            "The temporal_bin_interval is significantly larger than the scale of temporal parameters in provided data. Be sure if this is desired."
+        )