diff --git a/distCalc.ipynb b/distCalc.ipynb
index e5eeb87d5ea5ae1841e55407a499858901cdcff4..67cac9e6667f08eefb024c93b7d041f1dd2d4bdc 100644
--- a/distCalc.ipynb
+++ b/distCalc.ipynb
@@ -2,7 +2,7 @@
"cells": [
{
"cell_type": "code",
- "execution_count": 7,
+ "execution_count": 1,
"id": "2c81bc78-04e0-4bad-83ef-380cf3be1610",
"metadata": {
"tags": []
@@ -15,7 +15,7 @@
},
{
"cell_type": "code",
- "execution_count": 8,
+ "execution_count": 23,
"id": "af419e44-d6ef-41f7-970c-78c316aeb712",
"metadata": {
"tags": []
@@ -28,10 +28,36 @@
" self.stat_ks = []\n",
" self.stat_t = []\n",
" self.stat_cohend = []\n",
+ " self.stat_f_matches = []\n",
+ " self.stat_ks_matches = []\n",
+ " self.stat_t_matches = []\n",
+ " self.stat_cohend_matches = []\n",
+ " self.stat_f_new = []\n",
+ " self.stat_ks_new = []\n",
+ " self.stat_t_new = []\n",
+ " self.stat_cohend_new = []\n",
+ " self.stat_f_empty = []\n",
+ " self.stat_ks_empty = []\n",
+ " self.stat_t_empty = []\n",
+ " self.stat_cohend_empty = []\n",
+ " \n",
" self.stat_f_top3 = []\n",
" self.stat_ks_top3 = []\n",
" self.stat_t_top3 = []\n",
" self.stat_cohend_top3 = []\n",
+ " self.stat_f_matches_top3 = []\n",
+ " self.stat_ks_matches_top3 = []\n",
+ " self.stat_t_matches_top3 = []\n",
+ " self.stat_cohend_matches_top3 = []\n",
+ " self.stat_f_new_top3 = []\n",
+ " self.stat_ks_new_top3 = []\n",
+ " self.stat_t_new_top3 = []\n",
+ " self.stat_cohend_new_top3 = []\n",
+ " self.stat_f_empty_top3 = []\n",
+ " self.stat_ks_empty_top3 = []\n",
+ " self.stat_t_empty_top3 = []\n",
+ " self.stat_cohend_empty_top3 = []\n",
+ " \n",
" self.years = []\n",
" \n",
" @property\n",
@@ -104,35 +130,61 @@
"\n",
" all_match_columns = np.union1d(prev_col, next_col)\n",
" not_match_columns = np.setdiff1d(all_columns, all_match_columns)\n",
+ " 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",
- " acertos = 0\n",
- " acuracia = 0\n",
- " # Passeia pelos matches\n",
+ " acertos_p = 0\n",
" for i in range(len(prev_col)):\n",
" if prev_col[i] == next_col[i]: \n",
- " acertos += 1\n",
- "\n",
- " for col in not_match_columns:\n",
+ " acertos_p += 1\n",
+ " acuracia_matches = acertos_p / len(prev_col)\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",
- " acertos += 1\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",
+ " \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",
- " acertos += 1\n",
- "\n",
- " if len(all_columns) == 0:\n",
- " acuracia = 0\n",
+ " acertos_p += 1\n",
+ " if(len(unionEmptyColumns) > 0):\n",
+ " acuracia_empty_columns = acertos_p / len(unionEmptyColumns)\n",
" else:\n",
- " acuracia = acertos / len(all_columns)\n",
+ " acuracia_empty_columns = 1.0\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",
" \n",
" # Adiciona acuracia\n",
" if(stat_column == 'estatistica_f'):\n",
- " self.stat_f.append([ano, acuracia])\n",
+ " self.stat_f.append([ano, acuracia_total])\n",
+ " self.stat_f_matches.append([ano, acuracia_matches])\n",
+ " self.stat_f_new.append([ano, acuracia_new_columns])\n",
+ " self.stat_f_empty.append([ano, acuracia_empty_columns])\n",
" elif(stat_column == 'estatistica_t'):\n",
- " self.stat_t.append([ano, acuracia])\n",
+ " self.stat_t.append([ano, acuracia_total])\n",
+ " self.stat_t_matches.append([ano, acuracia_matches])\n",
+ " self.stat_t_new.append([ano, acuracia_new_columns])\n",
+ " self.stat_t_empty.append([ano, acuracia_empty_columns])\n",
" elif(stat_column == 'estatistica_ks'):\n",
- " self.stat_ks.append([ano, acuracia])\n",
+ " self.stat_ks.append([ano, acuracia_total])\n",
+ " self.stat_ks_matches.append([ano, acuracia_matches])\n",
+ " self.stat_ks_new.append([ano, acuracia_new_columns])\n",
+ " self.stat_ks_empty.append([ano, acuracia_empty_columns])\n",
" elif(stat_column == 'estatistica_cohend'):\n",
- " self.stat_cohend.append([ano, acuracia])\n",
+ " self.stat_cohend.append([ano, acuracia_total])\n",
+ " self.stat_cohend_matches.append([ano, acuracia_matches])\n",
+ " self.stat_cohend_new.append([ano, acuracia_new_columns])\n",
+ " self.stat_cohend_empty.append([ano, acuracia_empty_columns])\n",
"\n",
" \n",
" def calcTop3(self, df, stat_column, threshold):\n",
@@ -175,6 +227,7 @@
" acuracia_matches = 0\n",
" acuracia_novas_colunas = 0\n",
" acuracia_colunas_vazias = 0\n",
+ " \n",
"\n",
" # Acurácia matches\n",
" acertos = 0\n",
@@ -182,43 +235,96 @@
" if(len(res) == 0):\n",
" continue\n",
" for i in res:\n",
- " if i[0] == i[2]:\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",
" acertos += 1\n",
" break\n",
- " acuracia_matches = acertos / len(intersection_columns)\n",
- "\n",
+ " \n",
" # Acurácia novas colunas\n",
- " acertos = 0\n",
" for new in found_new_columns:\n",
- " if new in true_new_columns:\n",
+ " if new in true_new_columns and new not in no_data_columns and new not in all_match_columns:\n",
" acertos += 1\n",
- " if(len(true_new_columns) == 0 and len(found_new_columns) == 0):\n",
- " acuracia_novas_colunas = 1.0\n",
- " else:\n",
- " acuracia_novas_colunas = acertos / len(found_new_columns)\n",
"\n",
" # Acurácia colunas vazias\n",
- " acertos = 0\n",
" for no_data in no_data_columns:\n",
- " if no_data in true_empty_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",
" acertos += 1\n",
- " if(len(true_empty_columns) == 0 and len(no_data_columns) == 0):\n",
- " acuracia_colunas_vazias = 1.0\n",
- " else:\n",
- " acuracia_colunas_vazias = acertos / len(no_data_columns)\n",
"\n",
" # Acurácia total\n",
- " acuracia_total = (acuracia_matches + acuracia_colunas_vazias + acuracia_novas_colunas) / 3 \n",
+ " acuracia_total = acertos / len(all_columns)\n",
+ " \n",
+ " \n",
+ " # =========================\n",
+ " acertos_p = 0\n",
+ " unionNewColumns = np.union1d(found_new_columns, true_new_columns)\n",
+ " if len(unionNewColumns) > 0:\n",
+ " for col in unionNewColumns:\n",
+ " if col in found_new_columns and col in true_new_columns:\n",
+ " acertos_p += 1\n",
+ " acuracia_new_columns = acertos_p / len(unionNewColumns) \n",
+ " else:\n",
+ " acuracia_new_columns = 1.0\n",
+ "\n",
+ " acertos_p = 0\n",
+ " unionEmptyColumns = np.union1d(no_data_columns, true_empty_columns)\n",
+ " if len(unionEmptyColumns) > 0:\n",
+ " for col in unionEmptyColumns:\n",
+ " if col in no_data_columns and col in true_empty_columns:\n",
+ " acertos_p += 1\n",
+ " acuracia_empty_columns = acertos_p / len(unionEmptyColumns) \n",
+ " else:\n",
+ " acuracia_empty_columns = 1.0\n",
+ " \n",
+ " acertos_p = 0\n",
+ " results_len = 0\n",
+ " for res in resultados:\n",
+ " if(len(res) == 0):\n",
+ " continue\n",
+ " results_len += 1\n",
+ " for i in res:\n",
+ " if i[0] == i[2]:\n",
+ " acertos_p += 1\n",
+ " 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",
+ " \n",
+ " # print(ano)\n",
+ " # print(f'{acuracia_matches} matches')\n",
+ " # print(f'{acuracia_new_columns} new')\n",
+ " # print(f'{acuracia_empty_columns} empty')\n",
+ " # print(f'{acuracia_total} total')\n",
+ " \n",
+ " # =========================\n",
+ " \n",
+ " \n",
+ " \n",
+ " \n",
+ " \n",
+ " \n",
" \n",
+ " \n",
" # Adiciona acuracia\n",
" if(stat_column == 'estatistica_f'):\n",
" self.stat_f_top3.append([ano, acuracia_total])\n",
+ " self.stat_f_matches_top3.append([ano, acuracia_matches])\n",
+ " self.stat_f_new_top3.append([ano, acuracia_new_columns])\n",
+ " self.stat_f_empty_top3.append([ano, acuracia_empty_columns])\n",
" elif(stat_column == 'estatistica_t'):\n",
" self.stat_t_top3.append([ano, acuracia_total])\n",
+ " self.stat_t_matches_top3.append([ano, acuracia_matches])\n",
+ " self.stat_t_new_top3.append([ano, acuracia_new_columns])\n",
+ " self.stat_t_empty_top3.append([ano, acuracia_empty_columns])\n",
" elif(stat_column == 'estatistica_ks'):\n",
" self.stat_ks_top3.append([ano, acuracia_total])\n",
+ " self.stat_ks_matches_top3.append([ano, acuracia_matches])\n",
+ " self.stat_ks_new_top3.append([ano, acuracia_new_columns])\n",
+ " self.stat_ks_empty_top3.append([ano, acuracia_empty_columns])\n",
" elif(stat_column == 'estatistica_cohend'):\n",
- " self.stat_cohend_top3.append([ano, acuracia_total])"
+ " self.stat_cohend_top3.append([ano, acuracia_total])\n",
+ " self.stat_cohend_matches_top3.append([ano, acuracia_matches])\n",
+ " self.stat_cohend_new_top3.append([ano, acuracia_new_columns])\n",
+ " self.stat_cohend_empty_top3.append([ano, acuracia_empty_columns])"
]
},
{
@@ -231,7 +337,7 @@
},
{
"cell_type": "code",
- "execution_count": 9,
+ "execution_count": 3,
"id": "26287a6f-5537-4509-a09d-52dd59b3a76d",
"metadata": {
"tags": []
@@ -277,7 +383,7 @@
},
{
"cell_type": "code",
- "execution_count": 10,
+ "execution_count": 24,
"id": "f9541a11-c1bf-4318-847a-100917e13204",
"metadata": {
"tags": []
@@ -286,7 +392,7 @@
"source": [
"dist = DistCalc()\n",
"dist.calc(df_f, 'estatistica_f', 0.7)\n",
- "dist.calc(df_t, 'estatistica_t', 50)\n",
+ "dist.calc(df_t, 'estatistica_t', 40)\n",
"dist.calc(df_c, 'estatistica_cohend', 0.15)\n",
"dist.calc(df_ks, 'estatistica_ks', 0.10)\n",
"\n",
@@ -306,43 +412,104 @@
},
{
"cell_type": "code",
- "execution_count": 11,
- "id": "01ba08fd-63ce-4618-b3b2-434227604dcd",
+ "execution_count": 25,
+ "id": "527ff27d-f321-4749-a94d-dd7d824ef682",
"metadata": {
"tags": []
},
"outputs": [],
"source": [
- "result = pd.DataFrame(columns=['ano_base', 'estatistica_ks', 'estatistica_f', 'estatistica_t', 'estatistica_cohend'])\n",
- "resultTop3 = pd.DataFrame(columns=['ano_base', 'estatistica_ks', 'estatistica_f', 'estatistica_t', 'estatistica_cohend'])\n",
+ "# ================= KS =================\n",
+ "result_ks = pd.DataFrame(columns=['ano_base', 'match', 'new', 'empty', 'total'])\n",
+ "resultTop3_ks = pd.DataFrame(columns=['ano_base', 'match', 'new', 'empty', 'total'])\n",
"for i, ano in enumerate(dist.get_years):\n",
- " new_row = [ano, dist.stat_ks[i][1], dist.stat_f[i][1], dist.stat_t[i][1], dist.stat_cohend[i][1]]\n",
- " result.loc[len(result)] = new_row\n",
- " new_row = [ano, dist.stat_ks_top3[i][1], dist.stat_f_top3[i][1], dist.stat_t_top3[i][1], dist.stat_cohend_top3[i][1]]\n",
- " resultTop3.loc[len(resultTop3)] = new_row\n",
- "result.loc[len(result)] = result.mean()\n",
- "result.loc[len(result)] = result.std()\n",
- "resultTop3.loc[len(resultTop3)] = resultTop3.mean()\n",
- "resultTop3.loc[len(resultTop3)] = resultTop3.std()"
+ " new_row = [ano, dist.stat_ks_matches[i][1], dist.stat_ks_new[i][1], dist.stat_ks_empty[i][1], dist.stat_ks[i][1]]\n",
+ " result_ks.loc[len(result_ks)] = new_row\n",
+ " new_row = [ano, dist.stat_ks_matches_top3[i][1], dist.stat_ks_new_top3[i][1], dist.stat_ks_empty_top3[i][1], dist.stat_ks_top3[i][1]]\n",
+ " resultTop3_ks.loc[len(resultTop3_ks)] = new_row\n",
+ " \n",
+ "result_ks.loc[len(result_ks)] = result_ks.mean()\n",
+ "result_ks.loc[len(result_ks)] = result_ks.std()\n",
+ "resultTop3_ks.loc[len(resultTop3_ks)] = resultTop3_ks.mean()\n",
+ "resultTop3_ks.loc[len(resultTop3_ks)] = resultTop3_ks.std()\n",
+ "result_ks = result_ks.round(3)\n",
+ "resultTop3_ks = resultTop3_ks.round(3)\n",
+ "\n",
+ "# ================= F =================\n",
+ "result_f = pd.DataFrame(columns=['ano_base', 'match', 'new', 'empty', 'total'])\n",
+ "resultTop3_f = pd.DataFrame(columns=['ano_base', 'match', 'new', 'empty', 'total'])\n",
+ "for i, ano in enumerate(dist.get_years):\n",
+ " new_row = [ano, dist.stat_f_matches[i][1], dist.stat_f_new[i][1], dist.stat_f_empty[i][1], dist.stat_f[i][1]]\n",
+ " result_f.loc[len(result_f)] = new_row\n",
+ " new_row = [ano, dist.stat_f_matches_top3[i][1], dist.stat_f_new_top3[i][1], dist.stat_f_empty_top3[i][1], dist.stat_f_top3[i][1]]\n",
+ " resultTop3_f.loc[len(resultTop3_f)] = new_row\n",
+ " \n",
+ "result_f.loc[len(result_f)] = result_f.mean()\n",
+ "result_f.loc[len(result_f)] = result_f.std()\n",
+ "resultTop3_f.loc[len(resultTop3_f)] = resultTop3_f.mean()\n",
+ "resultTop3_f.loc[len(resultTop3_f)] = resultTop3_f.std()\n",
+ "result_f = result_f.round(3)\n",
+ "resultTop3_f = resultTop3_f.round(3)\n",
+ "\n",
+ "# ================= COHEN =================\n",
+ "result_cohend = pd.DataFrame(columns=['ano_base', 'match', 'new', 'empty', 'total'])\n",
+ "resultTop3_cohend = pd.DataFrame(columns=['ano_base', 'match', 'new', 'empty', 'total'])\n",
+ "for i, ano in enumerate(dist.get_years):\n",
+ " new_row = [ano, dist.stat_cohend_matches[i][1], dist.stat_cohend_new[i][1], dist.stat_cohend_empty[i][1], dist.stat_cohend[i][1]]\n",
+ " result_cohend.loc[len(result_cohend)] = new_row\n",
+ " new_row = [ano, dist.stat_cohend_matches_top3[i][1], dist.stat_cohend_new_top3[i][1], dist.stat_cohend_empty_top3[i][1], dist.stat_cohend_top3[i][1]]\n",
+ " resultTop3_cohend.loc[len(resultTop3_cohend)] = new_row\n",
+ " \n",
+ "result_cohend.loc[len(result_cohend)] = result_cohend.mean()\n",
+ "result_cohend.loc[len(result_cohend)] = result_cohend.std()\n",
+ "resultTop3_cohend.loc[len(resultTop3_cohend)] = resultTop3_cohend.mean()\n",
+ "resultTop3_cohend.loc[len(resultTop3_cohend)] = resultTop3_cohend.std()\n",
+ "result_cohend = result_cohend.round(3)\n",
+ "resultTop3_cohend = resultTop3_cohend.round(3)\n",
+ "\n",
+ "# ================= T =================\n",
+ "result_t = pd.DataFrame(columns=['ano_base', 'match', 'new', 'empty', 'total'])\n",
+ "resultTop3_t = pd.DataFrame(columns=['ano_base', 'match', 'new', 'empty', 'total'])\n",
+ "for i, ano in enumerate(dist.get_years):\n",
+ " new_row = [ano, dist.stat_t_matches[i][1], dist.stat_t_new[i][1], dist.stat_t_empty[i][1], dist.stat_t[i][1]]\n",
+ " result_t.loc[len(result_t)] = new_row\n",
+ " new_row = [ano, dist.stat_t_matches_top3[i][1], dist.stat_t_new_top3[i][1], dist.stat_t_empty_top3[i][1], dist.stat_t_top3[i][1]]\n",
+ " resultTop3_t.loc[len(resultTop3_t)] = new_row\n",
+ " \n",
+ "result_t.loc[len(result_t)] = result_t.mean()\n",
+ "result_t.loc[len(result_t)] = result_t.std()\n",
+ "resultTop3_t.loc[len(resultTop3_t)] = resultTop3_t.mean()\n",
+ "resultTop3_t.loc[len(resultTop3_t)] = resultTop3_t.std()\n",
+ "result_t = result_t.round(3)\n",
+ "resultTop3_t = resultTop3_t.round(3)"
]
},
{
"cell_type": "code",
- "execution_count": 12,
+ "execution_count": 26,
"id": "4cb4afc8-6149-40a7-8f77-af06183d4d23",
"metadata": {
"tags": []
},
"outputs": [],
"source": [
- "result.to_csv(f'./result.csv', index=False)\n",
- "resultTop3.to_csv(f'./resultTop3.csv', index=False)"
+ "result_ks.to_csv(f'./result_ks.csv', index=False)\n",
+ "resultTop3_ks.to_csv(f'./resultTop3_ks.csv', index=False)\n",
+ "\n",
+ "result_f.to_csv(f'./result_f.csv', index=False)\n",
+ "resultTop3_f.to_csv(f'./resultTop3_f.csv', index=False)\n",
+ "\n",
+ "result_t.to_csv(f'./result_t.csv', index=False)\n",
+ "resultTop3_t.to_csv(f'./resultTop3_t.csv', index=False)\n",
+ "\n",
+ "result_cohend.to_csv(f'./result_cohend.csv', index=False)\n",
+ "resultTop3_cohend.to_csv(f'./resultTop3_cohend.csv', index=False)"
]
},
{
"cell_type": "code",
"execution_count": null,
- "id": "f88a6745-c669-49f3-85b7-12c53b35d28a",
+ "id": "d0d2606e-2ddb-4752-a101-823af86fec45",
"metadata": {},
"outputs": [],
"source": []
@@ -364,7 +531,7 @@
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
- "version": "3.12.3"
+ "version": "3.11.4"
}
},
"nbformat": 4,
diff --git a/resultTop3_cohend.csv b/resultTop3_cohend.csv
new file mode 100644
index 0000000000000000000000000000000000000000..07b04721122be5a1b3af82f6adac45fdaf1ed1f1
--- /dev/null
+++ b/resultTop3_cohend.csv
@@ -0,0 +1,17 @@
+ano_base,match,new,empty,total
+2007.0,1.0,1.0,1.0,1.0
+2008.0,1.0,0.0,0.0,0.714
+2009.0,1.0,1.0,1.0,1.0
+2010.0,1.0,1.0,1.0,1.0
+2011.0,1.0,1.0,1.0,1.0
+2012.0,0.833,0.875,1.0,0.857
+2013.0,1.0,0.0,0.0,0.857
+2014.0,0.818,0.333,0.0,0.625
+2015.0,1.0,0.0,0.0,0.867
+2016.0,1.0,0.0,0.0,0.733
+2017.0,1.0,1.0,1.0,1.0
+2018.0,0.5,0.909,0.5,0.8
+2019.0,1.0,1.0,1.0,1.0
+2020.0,0.857,0.0,0.0,0.632
+2013.5,0.929,0.58,0.536,0.863
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new file mode 100644
index 0000000000000000000000000000000000000000..dabc1945ef4311199c485ed64e310e2f9d376f8e
--- /dev/null
+++ b/resultTop3_f.csv
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new file mode 100644
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