Area normed metric

modules/utils/data.py

@@ -151,6 +151,27 @@ def get_svd_data(data_type, block):
         # convert into numpy array after computing all stats
         data = np.asarray(data)
 
+    if data_type == 'sub_blocks_area_normed':
+
+        block = np.asarray(block)
+        width, height, _= block.shape
+        sub_width, sub_height = int(width / 8), int(height / 8)
+
+        sub_blocks = processing.divide_in_blocks(block, (sub_width, sub_height))
+
+        data = []
+
+        for sub_b in sub_blocks:
+
+            # by default use the whole lab L canal
+            l_svd_data = np.array(processing.get_LAB_L_SVD_s(sub_b))
+            l_svd_data = utils.normalize_arr(l_svd_data)
+
+            area_under_curve = utils.integral_area_trapz(l_svd_data, dx=50)
+            data.append(area_under_curve)
+
+        # convert into numpy array after computing all stats
+        data = np.asarray(data)
 
     return data
 

runAll_maxwell_area_normed.sh

@@ -0,0 +1,52 @@
+#! bin/bash
+
+# erase "models_info/models_comparisons.csv" file and write new header
+file_path='models_info/models_comparisons.csv'
+
+erased=$1
+
+if [ "${erased}" == "Y" ]; then
+    echo "Previous data file erased..."
+    rm ${file_path}
+    mkdir -p models_info
+    touch ${file_path}
+
+    # add of header
+    echo 'model_name; vector_size; start_index; end; nb_zones; metric; mode; tran_size; val_size; test_size; train_pct_size; val_pct_size; test_pct_size; train_acc; val_acc; test_acc; all_acc; F1_train; recall_train; roc_auc_train; F1_val; recall_val; roc_auc_val; F1_test; recall_test; roc_auc_test; F1_all; recall_all; roc_auc_all;' >> ${file_path}
+
+fi
+
+metric="sub_blocks_area_normed"
+start_index=0
+end_index=16
+number=16
+
+# selection of four scenes (only maxwell)
+scenes="A, D, G, H"
+
+
+for nb_zones in {4,6,8,10,12}; do
+
+    for mode in {"svd","svdn","svdne"}; do
+        for model in {"svm_model","ensemble_model","ensemble_model_v2"}; do
+
+            FILENAME="data/data_maxwell_N${number}_B${start_index}_E${end_index}_nb_zones_${nb_zones}_${metric}_${mode}"
+            MODEL_NAME="${model}_N${number}_B${start_index}_E${end_index}_nb_zones_${nb_zones}_${metric}_${mode}"
+
+            echo $FILENAME
+
+            # only compute if necessary (perhaps server will fall.. Just in case)
+            if grep -q "${MODEL_NAME}" "${file_path}"; then
+
+                echo "${MODEL_NAME} results already generated..."
+            else
+                python generate_data_model_random.py --output ${FILENAME} --interval "${start_index},${end_index}" --kind ${mode} --metric ${metric} --scenes "${scenes}" --nb_zones "${nb_zones}" --percent 1 --renderer "maxwell" --step 10 --random 1
+                python train_model.py --data ${FILENAME} --output ${MODEL_NAME} --choice ${model}
+
+                python save_model_result_in_md_maxwell.py --interval "${start_index},${end_index}" --model "saved_models/${MODEL_NAME}.joblib" --mode "${mode}" --metric ${metric}
+            fi
+        done
+    done
+done
+
+