Set parameters for execution on HPC. Commit only to ETHZ gitlab

.idea/statistic.xml

+<?xml version="1.0" encoding="UTF-8"?>
+<project version="4">
+  <component name="Statistic">
+    <option name="excludedDirectories">
+      <list>
+        <option value="$PROJECT_DIR$/results" />
+        <option value="$PROJECT_DIR$/results_old" />
+        <option value="$PROJECT_DIR$/notebooks" />
+      </list>
+    </option>
+  </component>
+</project>
\ No newline at end of file

main.py

@@ -20,8 +20,8 @@ import pygimli.meshtools as mt
 import pygimli.physics.ert as ert
 
 # Config
-create_new_data = False
-create_new_data_only = False
+create_new_data = True
+create_new_data_only = True
 
 # Prepare folder structure for output
 is_success = slopestabilitytools.folder_structure.create_folder_structure()
@@ -35,25 +35,25 @@ else:
     # TODO Put this part into a function
 
     # Settings
-    number_of_tests = 5
+    number_of_tests = 100
     rho_spread_factor = 1.5
-    rho_max = 10
+    rho_max = 25
     layers_min = 1
-    layers_max = 1
+    layers_max = 2
     min_depth = 4
-    max_depth = 8
+    max_depth = 15
 
     # Generate parameters for tests
-    # tests_horizontal = slopestabilitytools.model_params(number_of_tests,
-    #                                                     rho_spread_factor, rho_max,
-    #                                                     layers_min, layers_max,
-    #                                                     min_depth, max_depth)
-
-    tests_horizontal = {'hor_1': {'layer_n': 1, 'rho_values': [[1, 5], [2, 15]], 'layers_pos': np.array([-5])},
-                        'hor_2': {'layer_n': 1, 'rho_values': [[1, 5], [2, 50]], 'layers_pos': np.array([-5])},
-                        'hor_3': {'layer_n': 1, 'rho_values': [[1, 15], [2, 20]], 'layers_pos': np.array([-8])},
-                        'hor_4': {'layer_n': 1, 'rho_values': [[1, 5], [2, 10]], 'layers_pos': np.array([-3])},
-                        'hor_5': {'layer_n': 1, 'rho_values': [[1, 5], [2, 25]], 'layers_pos': np.array([-3])}}
+    tests_horizontal = slopestabilitytools.model_params(number_of_tests,
+                                                        rho_spread_factor, rho_max,
+                                                        layers_min, layers_max,
+                                                        min_depth, max_depth)
+
+    # tests_horizontal = {'hor_1': {'layer_n': 1, 'rho_values': [[1, 5], [2, 15]], 'layers_pos': np.array([-5])},
+    #                     'hor_2': {'layer_n': 1, 'rho_values': [[1, 5], [2, 50]], 'layers_pos': np.array([-5])},
+    #                     'hor_3': {'layer_n': 1, 'rho_values': [[1, 15], [2, 20]], 'layers_pos': np.array([-8])},
+    #                     'hor_4': {'layer_n': 1, 'rho_values': [[1, 5], [2, 10]], 'layers_pos': np.array([-3])},
+    #                     'hor_5': {'layer_n': 1, 'rho_values': [[1, 5], [2, 25]], 'layers_pos': np.array([-3])}}
 
     #  Create models and invert them
     test_results = {}

slopestabilityML/run_classification.py

@@ -56,6 +56,7 @@ def run_classification(test_training, test_prediction, test_results, clf):
         score = clf_pipeline_UM.score(x_question, y_answer)
         # print('score: '+str(score))
 
+        # TODO: Move plotting to a function for plotting a, b and a-b
         x = test_results[test_name_pred]['X']
         y = test_results[test_name_pred]['Y']
         class_in = test_results[test_name]['CLASS']
@@ -114,7 +115,8 @@ def run_classification(test_training, test_prediction, test_results, clf):
         fig.savefig('results/figures/pdf/{}_ML_class_res.pdf'.format(test_name_pred))
 
         # Evaluate result
-        accuracy_score.append(len(np.where(y_pred == y_answer.to_numpy())) / len(y_answer.to_numpy()) * 100)
+        #accuracy_score.append(len(np.where(y_pred == y_answer.to_numpy())) / len(y_answer.to_numpy()) * 100)
+        accuracy_score.append(score)
         accuracy_labels.append(test_name_pred)
 
     return accuracy_labels, accuracy_score

slopestabilitytools/plot_and_save.py

@@ -42,6 +42,7 @@ def plot_and_save(test_name, test_result, plot_title):
     print('plot_and_save')
 
     # Plot data input, inversion result and difference
+    # TODO: Move plotting to a function for plotting a, b and a-b
     cb = []
 
     fig, _ax = plt.subplots(nrows=3, ncols=1)