Add of simulation scripts for filters_statistics

predict_seuil_expe_maxwell_curve.py

@@ -45,7 +45,8 @@ def main():
 
     args = parser.parse_args()
 
-    p_interval   = list(map(int, args.interval.split(',')))
+    # keep p_interval as it is
+    p_interval   = args.interval
     p_model_file = args.model
     p_mode       = args.mode
     p_metric     = args.metric
@@ -123,11 +124,8 @@ def main():
                         tmp_file_path = tmp_filename.replace('__model__',  p_model_file.split('/')[-1].replace('.joblib', '_'))
                         block.save(tmp_file_path)
 
-                        python_cmd = "python predict_noisy_image_svd.py --image " + tmp_file_path + \
-                                        " --interval '" + p_interval + \
-                                        "' --model " + p_model_file  + \
-                                        " --mode " + p_mode + \
-                                        " --metric " + p_metric
+                        python_cmd_line = "python predict_noisy_image_svd.py --image {0} --interval '{1}' --model {2} --mode {3} --metric {4}"
+                        python_cmd = python_cmd_line.format(tmp_file_path, p_interval, p_model_file, p_mode, p_metric) 
 
                         # specify use of custom file for min max normalization
                         if p_custom:
@@ -177,4 +175,4 @@ def main():
 
 
 if __name__== "__main__":
-    main()
+    main()

run_maxwell_simulation_filters_statistics.sh

@@ -0,0 +1,39 @@
+#! bin/bash
+
+# file which contains model names we want to use for simulation
+simulate_models="simulate_models.csv"
+
+# selection of four scenes (only maxwell)
+scenes="A, D, G, H"
+
+size="26"
+
+# for metric in {"lab","mscn","low_bits_2","low_bits_3","low_bits_4","low_bits_5","low_bits_6","low_bits_4_shifted_2","ica_diff","svd_trunc_diff","ipca_diff","svd_reconstruct"}; do
+metric="filters_statistics"
+
+for nb_zones in {4,6,8,10,12}; do
+    for mode in {"svd","svdn","svdne"}; do
+        for model in {"svm_model","ensemble_model","ensemble_model_v2"}; do
+
+            FILENAME="data/${model}_N${size}_B0_E${size}_nb_zones_${nb_zones}_${metric}_${mode}"
+            MODEL_NAME="${model}_N${size}_B0_E${size}_nb_zones_${nb_zones}_${metric}_${mode}"
+            CUSTOM_MIN_MAX_FILENAME="N${size}_B0_E${size}_nb_zones_${nb_zones}_${metric}_${mode}_min_max"
+
+            echo $MODEL_NAME
+
+            # only compute if necessary (perhaps server will fall.. Just in case)
+            if grep -q "${MODEL_NAME}" "${simulate_models}"; then
+
+                echo "${MODEL_NAME} results already generated..."
+            else
+                # Use of already generated model
+                # python generate_data_model_random.py --output ${FILENAME} --interval "0,${size}" --kind ${mode} --metric ${metric} --scenes "${scenes}" --nb_zones "${nb_zones}" --percent 1 --renderer "maxwell" --step 40 --random 1 --custom ${CUSTOM_MIN_MAX_FILENAME}
+                # python train_model.py --data ${FILENAME} --output ${MODEL_NAME} --choice ${model}
+
+                python predict_seuil_expe_maxwell_curve.py --interval "0,${size}" --model "saved_models/${MODEL_NAME}.joblib" --mode "${mode}" --metric ${metric} --custom ${CUSTOM_MIN_MAX_FILENAME}
+
+                python save_model_result_in_md_maxwell.py --interval "0,${size}" --model "saved_models/${MODEL_NAME}.joblib" --mode "${mode}" --metric ${metric}
+            fi
+        done
+    done
+done