diff --git a/distCalc.ipynb b/distCalc.ipynb
index 67cac9e6667f08eefb024c93b7d041f1dd2d4bdc..6066c1061263539ee650e82f2979e08d25ff1f27 100644
--- a/distCalc.ipynb
+++ b/distCalc.ipynb
@@ -15,7 +15,7 @@
},
{
"cell_type": "code",
- "execution_count": 23,
+ "execution_count": 14,
"id": "af419e44-d6ef-41f7-970c-78c316aeb712",
"metadata": {
"tags": []
@@ -133,38 +133,42 @@
" found_new_columns = np.setdiff1d(new_columns, next_col) # Colunas novas encontradas pelo algoritmo\n",
" no_data_columns = np.setdiff1d(base_columns, prev_col) # Colunas que não receram dados encontradas pelo algoritmo\n",
"\n",
- " # Calcula resultados ========================\n",
+ " # ========== CALCULA ACURACIAS ========== \n",
" acertos_p = 0\n",
+ " acertos = 0\n",
" for i in range(len(prev_col)):\n",
" if prev_col[i] == next_col[i]: \n",
" acertos_p += 1\n",
" acuracia_matches = acertos_p / len(prev_col)\n",
+ " acertos += acertos_p\n",
" \n",
" acertos_p = 0\n",
" unionNewColumns = np.union1d(found_new_columns, true_new_columns)\n",
" for col in unionNewColumns:\n",
- " if col in true_new_columns:\n",
+ " if col in true_new_columns and col in found_new_columns:\n",
" acertos_p += 1\n",
" if(len(unionNewColumns) > 0):\n",
" acuracia_new_columns = acertos_p / len(unionNewColumns)\n",
" else:\n",
" acuracia_new_columns = 1.0\n",
- " \n",
+ " acertos += acertos_p \n",
" \n",
" acertos_p = 0\n",
" unionEmptyColumns = np.union1d(no_data_columns, base_empty_columns)\n",
" for col in unionEmptyColumns:\n",
- " if col in base_empty_columns:\n",
+ " if col in base_empty_columns and col in no_data_columns:\n",
" acertos_p += 1\n",
" if(len(unionEmptyColumns) > 0):\n",
" acuracia_empty_columns = acertos_p / len(unionEmptyColumns)\n",
" else:\n",
" acuracia_empty_columns = 1.0\n",
+ " acertos += acertos_p\n",
" \n",
" soma_acuracia = acuracia_matches * len(prev_col) + acuracia_new_columns * len(unionNewColumns) + acuracia_empty_columns * len(unionEmptyColumns)\n",
- " acuracia_total = soma_acuracia / (len(prev_col) + len(unionNewColumns) + len(unionEmptyColumns))\n",
+ " # acuracia_total = soma_acuracia / (len(prev_col) + len(unionNewColumns) + len(unionEmptyColumns))\n",
+ " acuracia_total = acertos / len(all_columns)\n",
" \n",
- " # Adiciona acuracia\n",
+ " # ========== ADICIONA ACURACIAS ==========\n",
" if(stat_column == 'estatistica_f'):\n",
" self.stat_f.append([ano, acuracia_total])\n",
" self.stat_f_matches.append([ano, acuracia_matches])\n",
@@ -228,32 +232,32 @@
" acuracia_novas_colunas = 0\n",
" acuracia_colunas_vazias = 0\n",
" \n",
- "\n",
+ " # ========== CALCULA ACURACIA TOTAL ==========\n",
" # Acurácia matches\n",
" acertos = 0\n",
" for res in resultados:\n",
" if(len(res) == 0):\n",
" continue\n",
" for i in res:\n",
- " if i[0] == i[2] and i[0] not in no_data_columns and i[0] not in found_new_columns and i[2] not in no_data_columns and i[2] not in found_new_columns:\n",
+ " if i[0] == i[2]:\n",
" acertos += 1\n",
" break\n",
" \n",
" # Acurácia novas colunas\n",
" for new in found_new_columns:\n",
- " if new in true_new_columns and new not in no_data_columns and new not in all_match_columns:\n",
+ " if new in true_new_columns:\n",
" acertos += 1\n",
"\n",
" # Acurácia colunas vazias\n",
" for no_data in no_data_columns:\n",
- " if no_data in true_empty_columns and no_data not in found_new_columns and no_data not in all_match_columns:\n",
+ " if no_data in true_empty_columns:\n",
" acertos += 1\n",
"\n",
" # Acurácia total\n",
" acuracia_total = acertos / len(all_columns)\n",
" \n",
" \n",
- " # =========================\n",
+ " # ========== CALCULA ACURACIA PARCIAL ==========\n",
" acertos_p = 0\n",
" unionNewColumns = np.union1d(found_new_columns, true_new_columns)\n",
" if len(unionNewColumns) > 0:\n",
@@ -286,8 +290,8 @@
" break\n",
" \n",
" acuracia_matches = acertos_p / len(prev_col)\n",
- " soma_acuracia = acuracia_matches * results_len + acuracia_new_columns * len(unionNewColumns) + acuracia_empty_columns * len(unionEmptyColumns)\n",
- " acuracia_total = soma_acuracia / (results_len + len(unionNewColumns) + len(unionEmptyColumns))\n",
+ " # soma_acuracia = acuracia_matches * results_len + acuracia_new_columns * len(unionNewColumns) + acuracia_empty_columns * len(unionEmptyColumns)\n",
+ " # acuracia_total = soma_acuracia / (results_len + len(unionNewColumns) + len(unionEmptyColumns))\n",
" \n",
" # print(ano)\n",
" # print(f'{acuracia_matches} matches')\n",
@@ -337,7 +341,7 @@
},
{
"cell_type": "code",
- "execution_count": 3,
+ "execution_count": 15,
"id": "26287a6f-5537-4509-a09d-52dd59b3a76d",
"metadata": {
"tags": []
@@ -383,7 +387,7 @@
},
{
"cell_type": "code",
- "execution_count": 24,
+ "execution_count": 16,
"id": "f9541a11-c1bf-4318-847a-100917e13204",
"metadata": {
"tags": []
@@ -412,7 +416,7 @@
},
{
"cell_type": "code",
- "execution_count": 25,
+ "execution_count": 17,
"id": "527ff27d-f321-4749-a94d-dd7d824ef682",
"metadata": {
"tags": []
@@ -486,7 +490,7 @@
},
{
"cell_type": "code",
- "execution_count": 26,
+ "execution_count": 18,
"id": "4cb4afc8-6149-40a7-8f77-af06183d4d23",
"metadata": {
"tags": []
@@ -531,7 +535,7 @@
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
- "version": "3.11.4"
+ "version": "3.11.9"
}
},
"nbformat": 4,