diff --git a/.ipynb_checkpoints/distCalc-checkpoint.ipynb b/.ipynb_checkpoints/distCalc-checkpoint.ipynb
index 9d3908717cae5e192e84bbb7a994a32899f54e7b..8ecafaf9ed9832954cf1c059ba30ca351c90d2db 100644
--- a/.ipynb_checkpoints/distCalc-checkpoint.ipynb
+++ b/.ipynb_checkpoints/distCalc-checkpoint.ipynb
@@ -1,8 +1,26 @@
{
"cells": [
+ {
+ "cell_type": "markdown",
+ "id": "ef5cb5f9-4c96-4c02-b8f9-6e233af4437b",
+ "metadata": {},
+ "source": [
+ "# Schema Evolution analysis\n",
+ "\n",
+ "The goal of this notebook is to analyze the results of the statistical methods used on the PDE Dataset columns and verify if it is possible to detect Schema Evolution from them. Three situations will be analyzed: columns that received data (matched columns), columns that didn't receive data (empty columns), and new information added (new columns)."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "655a12e4-babc-4a42-a3d3-a93d84269684",
+ "metadata": {},
+ "source": [
+ "## 1. Define the DistCalc class to help store the analyzed data"
+ ]
+ },
{
"cell_type": "code",
- "execution_count": 6,
+ "execution_count": 1,
"id": "2c81bc78-04e0-4bad-83ef-380cf3be1610",
"metadata": {
"tags": []
@@ -15,7 +33,7 @@
},
{
"cell_type": "code",
- "execution_count": 7,
+ "execution_count": 134,
"id": "af419e44-d6ef-41f7-970c-78c316aeb712",
"metadata": {
"tags": []
@@ -40,7 +58,6 @@
" 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",
@@ -57,7 +74,6 @@
" 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",
@@ -95,45 +111,51 @@
" @property\n",
" def get_years(self):\n",
" return self.years\n",
- " \n",
- " def calc(self, df, stat_column, threshold):\n",
+ "\n",
+ " # Calculates the most likely new column (Top1) to match each existing column in PDE Dataset along the years\n",
+ " def calc(self, df, stat_column, stat_method, threshold, threshGreater=True):\n",
" anos = df.ano_coluna1.unique()\n",
" self.years = np.union1d(self.years, anos)\n",
"\n",
- " # Itera sobre todos os anos\n",
+ " # Iterate over the years\n",
" for ano in anos:\n",
- " # Constroi dataframe do ano\n",
+ " # Build the year dataframe\n",
" ano_df = df[df.ano_coluna1 == ano]\n",
"\n",
- " # Estruturas\n",
- " base_columns = ano_df.coluna1.unique() # Colunas que ja existiam na base\n",
- " new_columns = ano_df.coluna2.unique() # Colunas do próximo ano\n",
- " true_new_columns = np.setdiff1d(new_columns, base_columns) # Colunas que nao existiam na base\n",
- " base_empty_columns = np.setdiff1d(base_columns, new_columns) # Colunas da base que nao receberam dados\n",
- " all_columns = np.union1d(base_columns, new_columns) # Todas as colunas possiveis\n",
- " # Alterar para um dicionario\n",
- " prev_col = [] # Colunas da base para match\n",
- " next_col = [] # Colunas do proximo ano para match\n",
+ " # Structure\n",
+ " base_columns = ano_df.coluna1.unique() # Existing PDE columns (base columns) \n",
+ " new_columns = ano_df.coluna2.unique() # New columns information\n",
+ " true_new_columns = np.setdiff1d(new_columns, base_columns) # Columns that didn't exist in PDE\n",
+ " base_empty_columns = np.setdiff1d(base_columns, new_columns) # Columns that didn't receive data\n",
+ " all_columns = np.union1d(base_columns, new_columns) # All the columns\n",
+ " prev_col = [] # Support array for columns match (base columns)\n",
+ " next_col = [] # Support array for columns match (new columns)\n",
"\n",
- " # Itera sobre o dataframe\n",
+ " # Iterate over the year dataframe\n",
" for index, row in ano_df.iterrows():\n",
- " # Ignora colunas ja selecionadas\n",
+ " # Ignore already selected columns\n",
" if row['coluna1'] in prev_col or row['coluna2'] in next_col:\n",
" continue\n",
- " # Testa treshold\n",
- " if row[stat_column] > threshold:\n",
- " break\n",
+ " # Test threshold\n",
+ " if(threshGreater):\n",
+ " if row[stat_column] < threshold:\n",
+ " break\n",
+ " else:\n",
+ " if row[stat_column] > threshold:\n",
+ " break\n",
"\n",
- " # Adiciona nas listas\n",
+ " # Append the matched columns in the support arrays\n",
" prev_col.append(row['coluna1'])\n",
" next_col.append(row['coluna2'])\n",
"\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",
+ " # Define other important structure subsets\n",
+ " all_match_columns = np.union1d(prev_col, next_col) # All the matched columns the algorithm found\n",
+ " not_match_columns = np.setdiff1d(all_columns, all_match_columns) # All the matched columns the algorithm didn't find\n",
+ " found_new_columns = np.setdiff1d(new_columns, next_col) # All the new columns the algorithm found\n",
+ " no_data_columns = np.setdiff1d(base_columns, prev_col) # All the empty columns the algorithm didn't find\n",
"\n",
- " # ========== CALCULA ACURACIAS ========== \n",
+ " # ========== ACCURACY ========== \n",
+ " # Match columns accuracy\n",
" acertos_p = 0\n",
" acertos = 0\n",
" for i in range(len(prev_col)):\n",
@@ -141,7 +163,8 @@
" acertos_p += 1\n",
" acuracia_matches = acertos_p / len(prev_col)\n",
" acertos += acertos_p\n",
- " \n",
+ "\n",
+ " # New columns accuracy\n",
" acertos_p = 0\n",
" unionNewColumns = np.union1d(found_new_columns, true_new_columns)\n",
" for col in unionNewColumns:\n",
@@ -152,7 +175,8 @@
" else:\n",
" acuracia_new_columns = 1.0\n",
" acertos += acertos_p \n",
- " \n",
+ "\n",
+ " # Empty columns accuracy\n",
" acertos_p = 0\n",
" unionEmptyColumns = np.union1d(no_data_columns, base_empty_columns)\n",
" for col in unionEmptyColumns:\n",
@@ -163,77 +187,81 @@
" 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",
+ "\n",
+ " # Total accuracy\n",
" acuracia_total = acertos / len(all_columns)\n",
" \n",
- " # ========== ADICIONA ACURACIAS ==========\n",
- " if(stat_column == 'estatistica_f'):\n",
+ " # ========== ADDING RESULTS ON CLASS VARIABLES ==========\n",
+ " if(stat_method == 'estatistica_f'):\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",
+ " elif(stat_method == 'estatistica_t'):\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",
+ " elif(stat_method == 'estatistica_ks'):\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",
+ " elif(stat_method == 'estatistica_cohend'):\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",
+ "\n",
+ " # Calculates the three most likely new columns (Top3) to match each existing column in PDE Dataset along the years\n",
+ " def calcTop3(self, df, stat_column, stat_method, threshold, threshGreater=True):\n",
" anos = df.ano_coluna1.unique()\n",
" \n",
- " # Itera sobre todos os anos\n",
+ " # Iterate over the years\n",
" for ano in anos:\n",
- " # Constroi dataframe do ano\n",
+ " # Build the year dataframe\n",
" ano_df = df[df.ano_coluna1 == ano]\n",
"\n",
- " # Estruturas\n",
- " base_columns = ano_df.coluna1.unique() # Colunas que ja existiam na base\n",
- " new_columns = ano_df.coluna2.unique() # Colunas do próximo ano\n",
- " intersection_columns = np.intersect1d(base_columns, new_columns) # Colunas que possuem match\n",
- " true_new_columns = np.setdiff1d(new_columns, base_columns) # Colunas que nao existiam na base\n",
- " true_empty_columns = np.setdiff1d(base_columns, new_columns) # Colunas da base que nao receberam dados\n",
- " all_columns = np.union1d(base_columns, new_columns) # Todas as colunas possiveis\n",
- " resultados = [] # Resultados dos matches\n",
- " prev_col = [] # Colunas da base que tiveram match\n",
- " next_col = [] # Colunas do proximo ano que tiveram match\n",
+ " # Structure\n",
+ " base_columns = ano_df.coluna1.unique() # Existing PDE columns (base columns) \n",
+ " new_columns = ano_df.coluna2.unique() # New columns information\n",
+ " intersection_columns = np.intersect1d(base_columns, new_columns) # Columns that received data\n",
+ " true_new_columns = np.setdiff1d(new_columns, base_columns) # Columns that didn't exist in PDE\n",
+ " true_empty_columns = np.setdiff1d(base_columns, new_columns) # Columns that didn't receive data\n",
+ " all_columns = np.union1d(base_columns, new_columns) # All the columns\n",
+ " resultados = [] # Matches results\n",
+ " prev_col = [] # Support array for columns match (base columns)\n",
+ " next_col = [] # Support array for columns match (new columns)\n",
"\n",
- " # Encontra as top3 novas colunas que mais se encaixam com as colunas base\n",
+ " # Find the Top3 columns for each base column\n",
" for col in base_columns:\n",
- " top3 = ano_df[(ano_df.coluna1 == col) & (ano_df[stat_column] < threshold)].iloc[:3,:]\n",
+ " if(threshGreater):\n",
+ " top3 = ano_df[(ano_df.coluna1 == col) & (ano_df[stat_column] > threshold)].iloc[:3,:]\n",
+ " else:\n",
+ " top3 = ano_df[(ano_df.coluna1 == col) & (ano_df[stat_column] < threshold)].iloc[:3,:]\n",
" resultados.append(top3.values)\n",
"\n",
- " # Preenche prev_col e next_col\n",
+ " # Fill the support arrays\n",
" for res in resultados:\n",
+ " if(len(res) == 0):\n",
+ " continue\n",
" for i in res:\n",
" prev_col = np.union1d(prev_col, i[0])\n",
" next_col = np.union1d(next_col, i[2])\n",
+ " \n",
+ " # Define other important structure subsets\n",
+ " all_match_columns = np.union1d(prev_col, next_col) # All the matched columns the algorithm found\n",
+ " not_match_columns = np.setdiff1d(all_columns, all_match_columns) # All the matched columns the algorithm didn't find\n",
+ " found_new_columns = np.setdiff1d(new_columns, next_col) # All the new columns the algorithm found\n",
+ " no_data_columns = np.setdiff1d(base_columns, prev_col) # All the empty columns the algorithm didn't find\n",
"\n",
- " # Determina alguns c\n",
- " all_match_columns = np.union1d(next_col, prev_col) # Colunas que tiveram algum match\n",
- " not_match_columns = np.setdiff1d(all_columns, all_match_columns) # Colunas que não tiveram nenhum match\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 acurácia\n",
+ " # ========== ACCURACY ========== \n",
" acuracia_matches = 0\n",
" acuracia_novas_colunas = 0\n",
" acuracia_colunas_vazias = 0\n",
" \n",
- " # ========== CALCULA ACURACIA TOTAL ==========\n",
- " # Acurácia matches\n",
+ " # Total accuracy\n",
" acertos = 0\n",
" for res in resultados:\n",
" if(len(res) == 0):\n",
@@ -243,21 +271,18 @@
" acertos += 1\n",
" break\n",
" \n",
- " # Acurácia novas colunas\n",
" for new in found_new_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:\n",
" acertos += 1\n",
"\n",
- " # Acurácia total\n",
" acuracia_total = acertos / len(all_columns)\n",
" \n",
" \n",
- " # ========== CALCULA ACURACIA PARCIAL ==========\n",
+ " # New columns accuracy\n",
" acertos_p = 0\n",
" unionNewColumns = np.union1d(found_new_columns, true_new_columns)\n",
" if len(unionNewColumns) > 0:\n",
@@ -268,6 +293,7 @@
" else:\n",
" acuracia_new_columns = 1.0\n",
"\n",
+ " # Empty columns accuracy\n",
" acertos_p = 0\n",
" unionEmptyColumns = np.union1d(no_data_columns, true_empty_columns)\n",
" if len(unionEmptyColumns) > 0:\n",
@@ -277,7 +303,8 @@
" acuracia_empty_columns = acertos_p / len(unionEmptyColumns) \n",
" else:\n",
" acuracia_empty_columns = 1.0\n",
- " \n",
+ "\n",
+ " # Match columns accuracy\n",
" acertos_p = 0\n",
" results_len = 0\n",
" for res in resultados:\n",
@@ -289,42 +316,24 @@
" 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",
+ " acuracia_matches = acertos_p / results_len \n",
+ " # ========== ADDING RESULTS ON CLASS VARIABLES ==========\n",
+ " if(stat_method == '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",
+ " elif(stat_method == '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",
+ " elif(stat_method == '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",
+ " elif(stat_method == 'estatistica_cohend'):\n",
" 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",
@@ -336,12 +345,12 @@
"id": "9eaff904-7ee7-45a0-9768-0f21989c65bd",
"metadata": {},
"source": [
- "## Import the results for each statistical method"
+ "## 2. Import the R results for each statistical method and prepare the data"
]
},
{
"cell_type": "code",
- "execution_count": 8,
+ "execution_count": 135,
"id": "26287a6f-5537-4509-a09d-52dd59b3a76d",
"metadata": {
"tags": []
@@ -349,31 +358,31 @@
"outputs": [],
"source": [
"# Import F results\n",
- "df_f = pd.read_csv('Testes_hist/Result_F/F_subsequente.csv', sep=',')\n",
- "stat_column = 'estatistica_f'\n",
- "df_f[stat_column] = (df_f[stat_column] - 1).abs()\n",
- "df_f = df_f.sort_values(by=['ano_coluna1', stat_column])\n",
+ "df_f = pd.read_csv('R_resultados/Histograma_10/F_subsequente.csv', sep=',')\n",
+ "stat_column = 'p_valor'\n",
+ "df_f[stat_column] = df_f[stat_column].abs()\n",
+ "df_f = df_f.sort_values(by=['ano_coluna1', stat_column], ascending=[True, False])\n",
"df_f = df_f[~df_f['coluna1'].str.contains('ANO_CENSO') & ~df_f['coluna2'].str.contains('ANO_CENSO')]\n",
"\n",
"# Import T results\n",
- "df_t = pd.read_csv('Testes_hist/Result_T/T_subsequente.csv', sep=',')\n",
- "stat_column = 'estatistica_t'\n",
+ "df_t = pd.read_csv('R_resultados/Histograma_10/T_subsequente.csv', sep=',')\n",
+ "stat_column = 'p_valor'\n",
"df_t[stat_column] = df_t[stat_column].abs()\n",
- "df_t = df_t.sort_values(by=['ano_coluna1', stat_column])\n",
+ "df_t = df_t.sort_values(by=['ano_coluna1', stat_column], ascending=[True, False])\n",
"df_t = df_t[~df_t['coluna1'].str.contains('ANO_CENSO') & ~df_t['coluna2'].str.contains('ANO_CENSO')]\n",
"\n",
"# Import COHEND results\n",
- "df_c = pd.read_csv('Testes_hist/Result_COHEND/COHEND_subsequente.csv', sep=',')\n",
+ "df_c = pd.read_csv('R_resultados/Histograma_10/COHEND_subsequente.csv', sep=',')\n",
"stat_column = 'estatistica_cohend'\n",
"df_c[stat_column] = df_c[stat_column].abs()\n",
"df_c = df_c.sort_values(by=['ano_coluna1', stat_column])\n",
"df_c = df_c[~df_c['coluna1'].str.contains('ANO_CENSO') & ~df_c['coluna2'].str.contains('ANO_CENSO')]\n",
"\n",
"# Import KS results\n",
- "df_ks = pd.read_csv('Testes_hist/Result_KS/KS_subsequente.csv', sep=',')\n",
- "stat_column = 'estatistica_ks'\n",
+ "df_ks = pd.read_csv('R_resultados/Histograma_10/KS_subsequente.csv', sep=',')\n",
+ "stat_column = 'p_valor'\n",
"df_ks[stat_column] = (df_ks[stat_column]).abs()\n",
- "df_ks = df_ks.sort_values(by=['ano_coluna1', stat_column])\n",
+ "df_ks = df_ks.sort_values(by=['ano_coluna1', stat_column], ascending=[True, False])\n",
"df_ks = df_ks[~df_ks['coluna1'].str.contains('ANO_CENSO') & ~df_ks['coluna2'].str.contains('ANO_CENSO')]"
]
},
@@ -382,41 +391,28 @@
"id": "e25f4f2d-3fb9-4cfc-8a92-c2e8b887262c",
"metadata": {},
"source": [
- "## Calcule the columns matches"
+ "## 3. Calculates the matches"
]
},
{
"cell_type": "code",
- "execution_count": 9,
+ "execution_count": 136,
"id": "f9541a11-c1bf-4318-847a-100917e13204",
"metadata": {
"tags": []
},
- "outputs": [
- {
- "ename": "ZeroDivisionError",
- "evalue": "division by zero",
- "output_type": "error",
- "traceback": [
- "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
- "\u001b[0;31mZeroDivisionError\u001b[0m Traceback (most recent call last)",
- "Cell \u001b[0;32mIn[9], line 2\u001b[0m\n\u001b[1;32m 1\u001b[0m dist \u001b[38;5;241m=\u001b[39m DistCalc()\n\u001b[0;32m----> 2\u001b[0m \u001b[43mdist\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mcalc\u001b[49m\u001b[43m(\u001b[49m\u001b[43mdf_f\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43mestatistica_f\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m0.7\u001b[39;49m\u001b[43m)\u001b[49m\n\u001b[1;32m 3\u001b[0m dist\u001b[38;5;241m.\u001b[39mcalc(df_t, \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mestatistica_t\u001b[39m\u001b[38;5;124m'\u001b[39m, \u001b[38;5;241m40\u001b[39m)\n\u001b[1;32m 4\u001b[0m dist\u001b[38;5;241m.\u001b[39mcalc(df_c, \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mestatistica_cohend\u001b[39m\u001b[38;5;124m'\u001b[39m, \u001b[38;5;241m0.15\u001b[39m)\n",
- "Cell \u001b[0;32mIn[7], line 118\u001b[0m, in \u001b[0;36mDistCalc.calc\u001b[0;34m(self, df, stat_column, threshold)\u001b[0m\n\u001b[1;32m 116\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m prev_col[i] \u001b[38;5;241m==\u001b[39m next_col[i]: \n\u001b[1;32m 117\u001b[0m acertos_p \u001b[38;5;241m+\u001b[39m\u001b[38;5;241m=\u001b[39m \u001b[38;5;241m1\u001b[39m\n\u001b[0;32m--> 118\u001b[0m acuracia_matches \u001b[38;5;241m=\u001b[39m \u001b[43macertos_p\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m/\u001b[39;49m\u001b[43m \u001b[49m\u001b[38;5;28;43mlen\u001b[39;49m\u001b[43m(\u001b[49m\u001b[43mprev_col\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 119\u001b[0m acertos \u001b[38;5;241m+\u001b[39m\u001b[38;5;241m=\u001b[39m acertos_p\n\u001b[1;32m 121\u001b[0m acertos_p \u001b[38;5;241m=\u001b[39m \u001b[38;5;241m0\u001b[39m\n",
- "\u001b[0;31mZeroDivisionError\u001b[0m: division by zero"
- ]
- }
- ],
+ "outputs": [],
"source": [
"dist = DistCalc()\n",
- "dist.calc(df_f, 'estatistica_f', 0.7)\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",
+ "dist.calc(df_f, 'p_valor', 'estatistica_f', 0.05)\n",
+ "dist.calc(df_t, 'p_valor', 'estatistica_t', 0.05)\n",
+ "dist.calc(df_c, 'estatistica_cohend', 'estatistica_cohend', 0.15, threshGreater=False)\n",
+ "dist.calc(df_ks, 'p_valor', 'estatistica_ks', 0.05)\n",
"\n",
- "dist.calcTop3(df_f, 'estatistica_f', 0.7)\n",
- "dist.calcTop3(df_t, 'estatistica_t', 40)\n",
- "dist.calcTop3(df_c, 'estatistica_cohend', 0.15)\n",
- "dist.calcTop3(df_ks, 'estatistica_ks', 0.10)"
+ "dist.calcTop3(df_f, 'p_valor', 'estatistica_f', 0.05)\n",
+ "dist.calcTop3(df_t, 'p_valor', 'estatistica_t', 0.05)\n",
+ "dist.calcTop3(df_c, 'estatistica_cohend', 'estatistica_cohend', 0.15, threshGreater=False)\n",
+ "dist.calcTop3(df_ks, 'p_valor', 'estatistica_ks', 0.05)"
]
},
{
@@ -424,12 +420,12 @@
"id": "47bcb19b-6aba-4d4a-9de0-4633bfa0eb20",
"metadata": {},
"source": [
- "## Create the result dataframes"
+ "## 4. Create the result dataframes and store all the data on it"
]
},
{
"cell_type": "code",
- "execution_count": null,
+ "execution_count": 137,
"id": "527ff27d-f321-4749-a94d-dd7d824ef682",
"metadata": {
"tags": []
@@ -501,9 +497,17 @@
"resultTop3_t = resultTop3_t.round(3)"
]
},
+ {
+ "cell_type": "markdown",
+ "id": "5c9338be-a29e-4fd4-9aa2-905eadf59cf7",
+ "metadata": {},
+ "source": [
+ "## 5. Export the results into CSVs"
+ ]
+ },
{
"cell_type": "code",
- "execution_count": null,
+ "execution_count": 138,
"id": "4cb4afc8-6149-40a7-8f77-af06183d4d23",
"metadata": {
"tags": []
diff --git a/Testes_hist/Result_COHEND/COHEND_acumulado.csv b/R_resultados/Histograma_10/COHEND_acumulado.csv
similarity index 100%
rename from Testes_hist/Result_COHEND/COHEND_acumulado.csv
rename to R_resultados/Histograma_10/COHEND_acumulado.csv
diff --git a/Testes_hist/Result_COHEND/COHEND_subsequente.csv b/R_resultados/Histograma_10/COHEND_subsequente.csv
similarity index 100%
rename from Testes_hist/Result_COHEND/COHEND_subsequente.csv
rename to R_resultados/Histograma_10/COHEND_subsequente.csv
diff --git a/Testes_hist/Result_F/F_acumulado.csv b/R_resultados/Histograma_10/F_acumulado.csv
similarity index 100%
rename from Testes_hist/Result_F/F_acumulado.csv
rename to R_resultados/Histograma_10/F_acumulado.csv
diff --git a/Testes_hist/Result_F/F_subsequente.csv b/R_resultados/Histograma_10/F_subsequente.csv
similarity index 100%
rename from Testes_hist/Result_F/F_subsequente.csv
rename to R_resultados/Histograma_10/F_subsequente.csv
diff --git a/Testes_hist/Result_KS/KS_acumulado.csv b/R_resultados/Histograma_10/KS_acumulado.csv
similarity index 100%
rename from Testes_hist/Result_KS/KS_acumulado.csv
rename to R_resultados/Histograma_10/KS_acumulado.csv
diff --git a/Testes_hist/Result_KS/KS_subsequente.csv b/R_resultados/Histograma_10/KS_subsequente.csv
similarity index 100%
rename from Testes_hist/Result_KS/KS_subsequente.csv
rename to R_resultados/Histograma_10/KS_subsequente.csv
diff --git a/Testes_hist/Result_T/T_acumulado.csv b/R_resultados/Histograma_10/T_acumulado.csv
similarity index 100%
rename from Testes_hist/Result_T/T_acumulado.csv
rename to R_resultados/Histograma_10/T_acumulado.csv
diff --git a/Testes_hist/Result_T/T_subsequente.csv b/R_resultados/Histograma_10/T_subsequente.csv
similarity index 100%
rename from Testes_hist/Result_T/T_subsequente.csv
rename to R_resultados/Histograma_10/T_subsequente.csv
diff --git a/Testes_R/Result_COHEND/COHEND_acumulado.csv b/R_resultados/Sem_histograma/COHEND_acumulado.csv
similarity index 100%
rename from Testes_R/Result_COHEND/COHEND_acumulado.csv
rename to R_resultados/Sem_histograma/COHEND_acumulado.csv
diff --git a/Testes_R/Result_COHEND/COHEND_subsequente.csv b/R_resultados/Sem_histograma/COHEND_subsequente.csv
similarity index 100%
rename from Testes_R/Result_COHEND/COHEND_subsequente.csv
rename to R_resultados/Sem_histograma/COHEND_subsequente.csv
diff --git a/Testes_R/Result_F/F_acumulado.csv b/R_resultados/Sem_histograma/F_acumulado.csv
similarity index 100%
rename from Testes_R/Result_F/F_acumulado.csv
rename to R_resultados/Sem_histograma/F_acumulado.csv
diff --git a/Testes_R/Result_F/F_subsequente.csv b/R_resultados/Sem_histograma/F_subsequente.csv
similarity index 100%
rename from Testes_R/Result_F/F_subsequente.csv
rename to R_resultados/Sem_histograma/F_subsequente.csv
diff --git a/Testes_R/Result_KS/KS_acumulado.csv b/R_resultados/Sem_histograma/KS_acumulado.csv
similarity index 100%
rename from Testes_R/Result_KS/KS_acumulado.csv
rename to R_resultados/Sem_histograma/KS_acumulado.csv
diff --git a/Testes_R/Result_KS/KS_subsequente.csv b/R_resultados/Sem_histograma/KS_subsequente.csv
similarity index 100%
rename from Testes_R/Result_KS/KS_subsequente.csv
rename to R_resultados/Sem_histograma/KS_subsequente.csv
diff --git a/Testes_R/Result_T/T_acumulado.csv b/R_resultados/Sem_histograma/T_acumulado.csv
similarity index 100%
rename from Testes_R/Result_T/T_acumulado.csv
rename to R_resultados/Sem_histograma/T_acumulado.csv
diff --git a/Testes_R/Result_T/T_subsequente.csv b/R_resultados/Sem_histograma/T_subsequente.csv
similarity index 100%
rename from Testes_R/Result_T/T_subsequente.csv
rename to R_resultados/Sem_histograma/T_subsequente.csv
diff --git a/Testes_R/raw_tests/.ipynb_checkpoints/run-checkpoint.sh b/Testes_R/raw_tests/.ipynb_checkpoints/run-checkpoint.sh
new file mode 100644
index 0000000000000000000000000000000000000000..aa197aa4e8cb3cb757f94ae49db98015bb0b7290
--- /dev/null
+++ b/Testes_R/raw_tests/.ipynb_checkpoints/run-checkpoint.sh
@@ -0,0 +1,6 @@
+#!/bin/bash
+
+#Rscript ./teste_ks.r
+#Rscript ./teste_f.r
+#Rscript ./teste_t.r
+Rscript ./teste_cohend.r
diff --git a/Testes_R/raw_tests/run.sh b/Testes_R/raw_tests/run.sh
index aa197aa4e8cb3cb757f94ae49db98015bb0b7290..4c43edad38d7afc62dfd117cbfd35e213dbfb5e4 100644
--- a/Testes_R/raw_tests/run.sh
+++ b/Testes_R/raw_tests/run.sh
@@ -1,6 +1,6 @@
#!/bin/bash
-#Rscript ./teste_ks.r
-#Rscript ./teste_f.r
-#Rscript ./teste_t.r
+Rscript ./teste_ks.r
+Rscript ./teste_f.r
+Rscript ./teste_t.r
Rscript ./teste_cohend.r
diff --git a/Testes_hist/raw_tests/.ipynb_checkpoints/Rplots-checkpoint.pdf b/Testes_hist/raw_tests/.ipynb_checkpoints/Rplots-checkpoint.pdf
deleted file mode 100644
index 07ef8c186be21d6eb311769c3539a1f993883226..0000000000000000000000000000000000000000
Binary files a/Testes_hist/raw_tests/.ipynb_checkpoints/Rplots-checkpoint.pdf and /dev/null differ
diff --git a/Testes_hist/raw_tests/.ipynb_checkpoints/run-checkpoint.sh b/Testes_hist/raw_tests/.ipynb_checkpoints/run-checkpoint.sh
new file mode 100644
index 0000000000000000000000000000000000000000..4c43edad38d7afc62dfd117cbfd35e213dbfb5e4
--- /dev/null
+++ b/Testes_hist/raw_tests/.ipynb_checkpoints/run-checkpoint.sh
@@ -0,0 +1,6 @@
+#!/bin/bash
+
+Rscript ./teste_ks.r
+Rscript ./teste_f.r
+Rscript ./teste_t.r
+Rscript ./teste_cohend.r
diff --git a/Testes_hist/raw_tests/Rplots.pdf b/Testes_hist/raw_tests/Rplots.pdf
deleted file mode 100644
index 07ef8c186be21d6eb311769c3539a1f993883226..0000000000000000000000000000000000000000
Binary files a/Testes_hist/raw_tests/Rplots.pdf and /dev/null differ
diff --git a/Trashcan/.ipynb_checkpoints/distCalcSemHist-checkpoint.ipynb b/Trashcan/.ipynb_checkpoints/distCalcSemHist-checkpoint.ipynb
new file mode 100644
index 0000000000000000000000000000000000000000..f151d0c97d9d86e583c3836593262e1e16462403
--- /dev/null
+++ b/Trashcan/.ipynb_checkpoints/distCalcSemHist-checkpoint.ipynb
@@ -0,0 +1,535 @@
+{
+ "cells": [
+ {
+ "cell_type": "code",
+ "execution_count": 2,
+ "id": "2c81bc78-04e0-4bad-83ef-380cf3be1610",
+ "metadata": {
+ "tags": []
+ },
+ "outputs": [],
+ "source": [
+ "import numpy as np\n",
+ "import pandas as pd"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 3,
+ "id": "af419e44-d6ef-41f7-970c-78c316aeb712",
+ "metadata": {
+ "tags": []
+ },
+ "outputs": [],
+ "source": [
+ "class DistCalc:\n",
+ " def __init__(self):\n",
+ " self.stat_f = []\n",
+ " 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",
+ " def get_stat_f(self):\n",
+ " return self.stat_f\n",
+ " \n",
+ " @property\n",
+ " def get_stat_ks(self):\n",
+ " return self.stat_ks\n",
+ " \n",
+ " @property\n",
+ " def get_stat_t(self):\n",
+ " return self.stat_t\n",
+ " \n",
+ " @property\n",
+ " def get_stat_cohend(self):\n",
+ " return self.stat_cohend\n",
+ " \n",
+ " @property\n",
+ " def get_stat_f_top3(self):\n",
+ " return self.stat_f\n",
+ " \n",
+ " @property\n",
+ " def get_stat_ks_top3(self):\n",
+ " return self.stat_ks\n",
+ " \n",
+ " @property\n",
+ " def get_stat_t_top3(self):\n",
+ " return self.stat_t\n",
+ " \n",
+ " @property\n",
+ " def get_stat_cohend_top3(self):\n",
+ " return self.stat_cohend\n",
+ " \n",
+ " @property\n",
+ " def get_years(self):\n",
+ " return self.years\n",
+ " \n",
+ " def calc(self, df, stat_column, threshold):\n",
+ " anos = df.ano_coluna1.unique()\n",
+ " self.years = np.union1d(self.years, anos)\n",
+ "\n",
+ " # Itera sobre todos os anos\n",
+ " for ano in anos:\n",
+ " # Constroi dataframe do ano\n",
+ " ano_df = df[df.ano_coluna1 == ano]\n",
+ "\n",
+ " # Estruturas\n",
+ " base_columns = ano_df.coluna1.unique() # Colunas que ja existiam na base\n",
+ " new_columns = ano_df.coluna2.unique() # Colunas do próximo ano\n",
+ " true_new_columns = np.setdiff1d(new_columns, base_columns) # Colunas que nao existiam na base\n",
+ " base_empty_columns = np.setdiff1d(base_columns, new_columns) # Colunas da base que nao receberam dados\n",
+ " all_columns = np.union1d(base_columns, new_columns) # Todas as colunas possiveis\n",
+ " # Alterar para um dicionario\n",
+ " prev_col = [] # Colunas da base para match\n",
+ " next_col = [] # Colunas do proximo ano para match\n",
+ "\n",
+ " # Itera sobre o dataframe\n",
+ " for index, row in ano_df.iterrows():\n",
+ " # Ignora colunas ja selecionadas\n",
+ " if row['coluna1'] in prev_col or row['coluna2'] in next_col:\n",
+ " continue\n",
+ " # Testa treshold\n",
+ " if row[stat_column] > threshold:\n",
+ " break\n",
+ "\n",
+ " # Adiciona nas listas\n",
+ " prev_col.append(row['coluna1'])\n",
+ " next_col.append(row['coluna2'])\n",
+ "\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 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 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",
+ " 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 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 = acertos / len(all_columns)\n",
+ " \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",
+ " 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_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_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_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",
+ " anos = df.ano_coluna1.unique()\n",
+ " \n",
+ " # Itera sobre todos os anos\n",
+ " for ano in anos:\n",
+ " # Constroi dataframe do ano\n",
+ " ano_df = df[df.ano_coluna1 == ano]\n",
+ "\n",
+ " # Estruturas\n",
+ " base_columns = ano_df.coluna1.unique() # Colunas que ja existiam na base\n",
+ " new_columns = ano_df.coluna2.unique() # Colunas do próximo ano\n",
+ " intersection_columns = np.intersect1d(base_columns, new_columns) # Colunas que possuem match\n",
+ " true_new_columns = np.setdiff1d(new_columns, base_columns) # Colunas que nao existiam na base\n",
+ " true_empty_columns = np.setdiff1d(base_columns, new_columns) # Colunas da base que nao receberam dados\n",
+ " all_columns = np.union1d(base_columns, new_columns) # Todas as colunas possiveis\n",
+ " resultados = [] # Resultados dos matches\n",
+ " prev_col = [] # Colunas da base que tiveram match\n",
+ " next_col = [] # Colunas do proximo ano que tiveram match\n",
+ "\n",
+ " # Encontra as top3 novas colunas que mais se encaixam com as colunas base\n",
+ " for col in base_columns:\n",
+ " top3 = ano_df[(ano_df.coluna1 == col) & (ano_df[stat_column] < threshold)].iloc[:3,:]\n",
+ " resultados.append(top3.values)\n",
+ "\n",
+ " # Preenche prev_col e next_col\n",
+ " for res in resultados:\n",
+ " for i in res:\n",
+ " prev_col = np.union1d(prev_col, i[0])\n",
+ " next_col = np.union1d(next_col, i[2])\n",
+ "\n",
+ " # Determina alguns c\n",
+ " all_match_columns = np.union1d(next_col, prev_col) # Colunas que tiveram algum match\n",
+ " not_match_columns = np.setdiff1d(all_columns, all_match_columns) # Colunas que não tiveram nenhum match\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 acurácia\n",
+ " acuracia_matches = 0\n",
+ " acuracia_novas_colunas = 0\n",
+ " acuracia_colunas_vazias = 0\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]:\n",
+ " acertos += 1\n",
+ " break\n",
+ " \n",
+ " # Acurácia novas colunas\n",
+ " for new in found_new_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:\n",
+ " acertos += 1\n",
+ "\n",
+ " # Acurácia total\n",
+ " acuracia_total = acertos / len(all_columns)\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",
+ " 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])\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])"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "9eaff904-7ee7-45a0-9768-0f21989c65bd",
+ "metadata": {},
+ "source": [
+ "## Import the results for each statistical method"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 4,
+ "id": "26287a6f-5537-4509-a09d-52dd59b3a76d",
+ "metadata": {
+ "tags": []
+ },
+ "outputs": [],
+ "source": [
+ "# Import F results\n",
+ "df_f = pd.read_csv('Testes_hist/Result_F/F_subsequente.csv', sep=',')\n",
+ "stat_column = 'estatistica_f'\n",
+ "df_f[stat_column] = (df_f[stat_column] - 1).abs()\n",
+ "df_f = df_f.sort_values(by=['ano_coluna1', stat_column])\n",
+ "df_f = df_f[~df_f['coluna1'].str.contains('ANO_CENSO') & ~df_f['coluna2'].str.contains('ANO_CENSO')]\n",
+ "\n",
+ "# Import T results\n",
+ "df_t = pd.read_csv('Testes_hist/Result_T/T_subsequente.csv', sep=',')\n",
+ "stat_column = 'estatistica_t'\n",
+ "df_t[stat_column] = df_t[stat_column].abs()\n",
+ "df_t = df_t.sort_values(by=['ano_coluna1', stat_column])\n",
+ "df_t = df_t[~df_t['coluna1'].str.contains('ANO_CENSO') & ~df_t['coluna2'].str.contains('ANO_CENSO')]\n",
+ "\n",
+ "# Import COHEND results\n",
+ "df_c = pd.read_csv('Testes_hist/Result_COHEND/COHEND_subsequente.csv', sep=',')\n",
+ "stat_column = 'estatistica_cohend'\n",
+ "df_c[stat_column] = df_c[stat_column].abs()\n",
+ "df_c = df_c.sort_values(by=['ano_coluna1', stat_column])\n",
+ "df_c = df_c[~df_c['coluna1'].str.contains('ANO_CENSO') & ~df_c['coluna2'].str.contains('ANO_CENSO')]\n",
+ "\n",
+ "# Import KS results\n",
+ "df_ks = pd.read_csv('Testes_hist/Result_KS/KS_subsequente.csv', sep=',')\n",
+ "stat_column = 'estatistica_ks'\n",
+ "df_ks[stat_column] = (df_ks[stat_column]).abs()\n",
+ "df_ks = df_ks.sort_values(by=['ano_coluna1', stat_column])\n",
+ "df_ks = df_ks[~df_ks['coluna1'].str.contains('ANO_CENSO') & ~df_ks['coluna2'].str.contains('ANO_CENSO')]"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "e25f4f2d-3fb9-4cfc-8a92-c2e8b887262c",
+ "metadata": {},
+ "source": [
+ "## Calcule the columns matches"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 5,
+ "id": "f9541a11-c1bf-4318-847a-100917e13204",
+ "metadata": {
+ "tags": []
+ },
+ "outputs": [],
+ "source": [
+ "dist = DistCalc()\n",
+ "dist.calc(df_f, 'estatistica_f', 0.7)\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",
+ "dist.calcTop3(df_f, 'estatistica_f', 0.7)\n",
+ "dist.calcTop3(df_t, 'estatistica_t', 40)\n",
+ "dist.calcTop3(df_c, 'estatistica_cohend', 0.15)\n",
+ "dist.calcTop3(df_ks, 'estatistica_ks', 0.10)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "47bcb19b-6aba-4d4a-9de0-4633bfa0eb20",
+ "metadata": {},
+ "source": [
+ "## Create the result dataframes"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 6,
+ "id": "527ff27d-f321-4749-a94d-dd7d824ef682",
+ "metadata": {
+ "tags": []
+ },
+ "outputs": [],
+ "source": [
+ "# ================= 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_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": 7,
+ "id": "4cb4afc8-6149-40a7-8f77-af06183d4d23",
+ "metadata": {
+ "tags": []
+ },
+ "outputs": [],
+ "source": [
+ "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)"
+ ]
+ }
+ ],
+ "metadata": {
+ "kernelspec": {
+ "display_name": "Python 3 (ipykernel)",
+ "language": "python",
+ "name": "python3"
+ },
+ "language_info": {
+ "codemirror_mode": {
+ "name": "ipython",
+ "version": 3
+ },
+ "file_extension": ".py",
+ "mimetype": "text/x-python",
+ "name": "python",
+ "nbconvert_exporter": "python",
+ "pygments_lexer": "ipython3",
+ "version": "3.12.3"
+ }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/Resultados/.ipynb_checkpoints/COHEND_resuoltados-checkpoint.ipynb b/Trashcan/Resultados/.ipynb_checkpoints/COHEND_resuoltados-checkpoint.ipynb
similarity index 100%
rename from Resultados/.ipynb_checkpoints/COHEND_resuoltados-checkpoint.ipynb
rename to Trashcan/Resultados/.ipynb_checkpoints/COHEND_resuoltados-checkpoint.ipynb
diff --git a/Resultados/.ipynb_checkpoints/F_resultados-checkpoint.ipynb b/Trashcan/Resultados/.ipynb_checkpoints/F_resultados-checkpoint.ipynb
similarity index 100%
rename from Resultados/.ipynb_checkpoints/F_resultados-checkpoint.ipynb
rename to Trashcan/Resultados/.ipynb_checkpoints/F_resultados-checkpoint.ipynb
diff --git a/Resultados/.ipynb_checkpoints/KS_resultados-checkpoint.ipynb b/Trashcan/Resultados/.ipynb_checkpoints/KS_resultados-checkpoint.ipynb
similarity index 100%
rename from Resultados/.ipynb_checkpoints/KS_resultados-checkpoint.ipynb
rename to Trashcan/Resultados/.ipynb_checkpoints/KS_resultados-checkpoint.ipynb
diff --git a/Resultados/.ipynb_checkpoints/T_resultados-checkpoint.ipynb b/Trashcan/Resultados/.ipynb_checkpoints/T_resultados-checkpoint.ipynb
similarity index 100%
rename from Resultados/.ipynb_checkpoints/T_resultados-checkpoint.ipynb
rename to Trashcan/Resultados/.ipynb_checkpoints/T_resultados-checkpoint.ipynb
diff --git a/Resultados/COHEND_resuoltados.ipynb b/Trashcan/Resultados/COHEND_resuoltados.ipynb
similarity index 100%
rename from Resultados/COHEND_resuoltados.ipynb
rename to Trashcan/Resultados/COHEND_resuoltados.ipynb
diff --git a/Resultados/F_resultados.ipynb b/Trashcan/Resultados/F_resultados.ipynb
similarity index 100%
rename from Resultados/F_resultados.ipynb
rename to Trashcan/Resultados/F_resultados.ipynb
diff --git a/Resultados/KS_resultados.ipynb b/Trashcan/Resultados/KS_resultados.ipynb
similarity index 100%
rename from Resultados/KS_resultados.ipynb
rename to Trashcan/Resultados/KS_resultados.ipynb
diff --git a/Resultados/T_resultados.ipynb b/Trashcan/Resultados/T_resultados.ipynb
similarity index 100%
rename from Resultados/T_resultados.ipynb
rename to Trashcan/Resultados/T_resultados.ipynb
diff --git a/algorithm results/.ipynb_checkpoints/resultTop3-checkpoint.csv b/Trashcan/algorithm results/.ipynb_checkpoints/resultTop3-checkpoint.csv
similarity index 100%
rename from algorithm results/.ipynb_checkpoints/resultTop3-checkpoint.csv
rename to Trashcan/algorithm results/.ipynb_checkpoints/resultTop3-checkpoint.csv
diff --git a/algorithm results/.ipynb_checkpoints/resultTop3_ks-checkpoint.csv b/Trashcan/algorithm results/.ipynb_checkpoints/resultTop3_ks-checkpoint.csv
similarity index 100%
rename from algorithm results/.ipynb_checkpoints/resultTop3_ks-checkpoint.csv
rename to Trashcan/algorithm results/.ipynb_checkpoints/resultTop3_ks-checkpoint.csv
diff --git a/algorithm results/.ipynb_checkpoints/result_ks-checkpoint.csv b/Trashcan/algorithm results/.ipynb_checkpoints/result_ks-checkpoint.csv
similarity index 100%
rename from algorithm results/.ipynb_checkpoints/result_ks-checkpoint.csv
rename to Trashcan/algorithm results/.ipynb_checkpoints/result_ks-checkpoint.csv
diff --git a/algorithm results/result.csv b/Trashcan/algorithm results/result.csv
similarity index 100%
rename from algorithm results/result.csv
rename to Trashcan/algorithm results/result.csv
diff --git a/algorithm results/resultTop3.csv b/Trashcan/algorithm results/resultTop3.csv
similarity index 100%
rename from algorithm results/resultTop3.csv
rename to Trashcan/algorithm results/resultTop3.csv
diff --git a/algorithm results/resultTop3_cohend.csv b/Trashcan/algorithm results/resultTop3_cohend.csv
similarity index 100%
rename from algorithm results/resultTop3_cohend.csv
rename to Trashcan/algorithm results/resultTop3_cohend.csv
diff --git a/algorithm results/resultTop3_f.csv b/Trashcan/algorithm results/resultTop3_f.csv
similarity index 100%
rename from algorithm results/resultTop3_f.csv
rename to Trashcan/algorithm results/resultTop3_f.csv
diff --git a/algorithm results/resultTop3_ks.csv b/Trashcan/algorithm results/resultTop3_ks.csv
similarity index 100%
rename from algorithm results/resultTop3_ks.csv
rename to Trashcan/algorithm results/resultTop3_ks.csv
diff --git a/algorithm results/resultTop3_t.csv b/Trashcan/algorithm results/resultTop3_t.csv
similarity index 100%
rename from algorithm results/resultTop3_t.csv
rename to Trashcan/algorithm results/resultTop3_t.csv
diff --git a/algorithm results/result_cohend.csv b/Trashcan/algorithm results/result_cohend.csv
similarity index 100%
rename from algorithm results/result_cohend.csv
rename to Trashcan/algorithm results/result_cohend.csv
diff --git a/algorithm results/result_f.csv b/Trashcan/algorithm results/result_f.csv
similarity index 100%
rename from algorithm results/result_f.csv
rename to Trashcan/algorithm results/result_f.csv
diff --git a/algorithm results/result_ks.csv b/Trashcan/algorithm results/result_ks.csv
similarity index 100%
rename from algorithm results/result_ks.csv
rename to Trashcan/algorithm results/result_ks.csv
diff --git a/algorithm results/result_t.csv b/Trashcan/algorithm results/result_t.csv
similarity index 100%
rename from algorithm results/result_t.csv
rename to Trashcan/algorithm results/result_t.csv
diff --git a/Trashcan/distCalcSemHist.ipynb b/Trashcan/distCalcSemHist.ipynb
new file mode 100644
index 0000000000000000000000000000000000000000..f151d0c97d9d86e583c3836593262e1e16462403
--- /dev/null
+++ b/Trashcan/distCalcSemHist.ipynb
@@ -0,0 +1,535 @@
+{
+ "cells": [
+ {
+ "cell_type": "code",
+ "execution_count": 2,
+ "id": "2c81bc78-04e0-4bad-83ef-380cf3be1610",
+ "metadata": {
+ "tags": []
+ },
+ "outputs": [],
+ "source": [
+ "import numpy as np\n",
+ "import pandas as pd"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 3,
+ "id": "af419e44-d6ef-41f7-970c-78c316aeb712",
+ "metadata": {
+ "tags": []
+ },
+ "outputs": [],
+ "source": [
+ "class DistCalc:\n",
+ " def __init__(self):\n",
+ " self.stat_f = []\n",
+ " 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",
+ " def get_stat_f(self):\n",
+ " return self.stat_f\n",
+ " \n",
+ " @property\n",
+ " def get_stat_ks(self):\n",
+ " return self.stat_ks\n",
+ " \n",
+ " @property\n",
+ " def get_stat_t(self):\n",
+ " return self.stat_t\n",
+ " \n",
+ " @property\n",
+ " def get_stat_cohend(self):\n",
+ " return self.stat_cohend\n",
+ " \n",
+ " @property\n",
+ " def get_stat_f_top3(self):\n",
+ " return self.stat_f\n",
+ " \n",
+ " @property\n",
+ " def get_stat_ks_top3(self):\n",
+ " return self.stat_ks\n",
+ " \n",
+ " @property\n",
+ " def get_stat_t_top3(self):\n",
+ " return self.stat_t\n",
+ " \n",
+ " @property\n",
+ " def get_stat_cohend_top3(self):\n",
+ " return self.stat_cohend\n",
+ " \n",
+ " @property\n",
+ " def get_years(self):\n",
+ " return self.years\n",
+ " \n",
+ " def calc(self, df, stat_column, threshold):\n",
+ " anos = df.ano_coluna1.unique()\n",
+ " self.years = np.union1d(self.years, anos)\n",
+ "\n",
+ " # Itera sobre todos os anos\n",
+ " for ano in anos:\n",
+ " # Constroi dataframe do ano\n",
+ " ano_df = df[df.ano_coluna1 == ano]\n",
+ "\n",
+ " # Estruturas\n",
+ " base_columns = ano_df.coluna1.unique() # Colunas que ja existiam na base\n",
+ " new_columns = ano_df.coluna2.unique() # Colunas do próximo ano\n",
+ " true_new_columns = np.setdiff1d(new_columns, base_columns) # Colunas que nao existiam na base\n",
+ " base_empty_columns = np.setdiff1d(base_columns, new_columns) # Colunas da base que nao receberam dados\n",
+ " all_columns = np.union1d(base_columns, new_columns) # Todas as colunas possiveis\n",
+ " # Alterar para um dicionario\n",
+ " prev_col = [] # Colunas da base para match\n",
+ " next_col = [] # Colunas do proximo ano para match\n",
+ "\n",
+ " # Itera sobre o dataframe\n",
+ " for index, row in ano_df.iterrows():\n",
+ " # Ignora colunas ja selecionadas\n",
+ " if row['coluna1'] in prev_col or row['coluna2'] in next_col:\n",
+ " continue\n",
+ " # Testa treshold\n",
+ " if row[stat_column] > threshold:\n",
+ " break\n",
+ "\n",
+ " # Adiciona nas listas\n",
+ " prev_col.append(row['coluna1'])\n",
+ " next_col.append(row['coluna2'])\n",
+ "\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 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 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",
+ " 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 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 = acertos / len(all_columns)\n",
+ " \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",
+ " 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_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_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_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",
+ " anos = df.ano_coluna1.unique()\n",
+ " \n",
+ " # Itera sobre todos os anos\n",
+ " for ano in anos:\n",
+ " # Constroi dataframe do ano\n",
+ " ano_df = df[df.ano_coluna1 == ano]\n",
+ "\n",
+ " # Estruturas\n",
+ " base_columns = ano_df.coluna1.unique() # Colunas que ja existiam na base\n",
+ " new_columns = ano_df.coluna2.unique() # Colunas do próximo ano\n",
+ " intersection_columns = np.intersect1d(base_columns, new_columns) # Colunas que possuem match\n",
+ " true_new_columns = np.setdiff1d(new_columns, base_columns) # Colunas que nao existiam na base\n",
+ " true_empty_columns = np.setdiff1d(base_columns, new_columns) # Colunas da base que nao receberam dados\n",
+ " all_columns = np.union1d(base_columns, new_columns) # Todas as colunas possiveis\n",
+ " resultados = [] # Resultados dos matches\n",
+ " prev_col = [] # Colunas da base que tiveram match\n",
+ " next_col = [] # Colunas do proximo ano que tiveram match\n",
+ "\n",
+ " # Encontra as top3 novas colunas que mais se encaixam com as colunas base\n",
+ " for col in base_columns:\n",
+ " top3 = ano_df[(ano_df.coluna1 == col) & (ano_df[stat_column] < threshold)].iloc[:3,:]\n",
+ " resultados.append(top3.values)\n",
+ "\n",
+ " # Preenche prev_col e next_col\n",
+ " for res in resultados:\n",
+ " for i in res:\n",
+ " prev_col = np.union1d(prev_col, i[0])\n",
+ " next_col = np.union1d(next_col, i[2])\n",
+ "\n",
+ " # Determina alguns c\n",
+ " all_match_columns = np.union1d(next_col, prev_col) # Colunas que tiveram algum match\n",
+ " not_match_columns = np.setdiff1d(all_columns, all_match_columns) # Colunas que não tiveram nenhum match\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 acurácia\n",
+ " acuracia_matches = 0\n",
+ " acuracia_novas_colunas = 0\n",
+ " acuracia_colunas_vazias = 0\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]:\n",
+ " acertos += 1\n",
+ " break\n",
+ " \n",
+ " # Acurácia novas colunas\n",
+ " for new in found_new_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:\n",
+ " acertos += 1\n",
+ "\n",
+ " # Acurácia total\n",
+ " acuracia_total = acertos / len(all_columns)\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",
+ " 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])\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])"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "9eaff904-7ee7-45a0-9768-0f21989c65bd",
+ "metadata": {},
+ "source": [
+ "## Import the results for each statistical method"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 4,
+ "id": "26287a6f-5537-4509-a09d-52dd59b3a76d",
+ "metadata": {
+ "tags": []
+ },
+ "outputs": [],
+ "source": [
+ "# Import F results\n",
+ "df_f = pd.read_csv('Testes_hist/Result_F/F_subsequente.csv', sep=',')\n",
+ "stat_column = 'estatistica_f'\n",
+ "df_f[stat_column] = (df_f[stat_column] - 1).abs()\n",
+ "df_f = df_f.sort_values(by=['ano_coluna1', stat_column])\n",
+ "df_f = df_f[~df_f['coluna1'].str.contains('ANO_CENSO') & ~df_f['coluna2'].str.contains('ANO_CENSO')]\n",
+ "\n",
+ "# Import T results\n",
+ "df_t = pd.read_csv('Testes_hist/Result_T/T_subsequente.csv', sep=',')\n",
+ "stat_column = 'estatistica_t'\n",
+ "df_t[stat_column] = df_t[stat_column].abs()\n",
+ "df_t = df_t.sort_values(by=['ano_coluna1', stat_column])\n",
+ "df_t = df_t[~df_t['coluna1'].str.contains('ANO_CENSO') & ~df_t['coluna2'].str.contains('ANO_CENSO')]\n",
+ "\n",
+ "# Import COHEND results\n",
+ "df_c = pd.read_csv('Testes_hist/Result_COHEND/COHEND_subsequente.csv', sep=',')\n",
+ "stat_column = 'estatistica_cohend'\n",
+ "df_c[stat_column] = df_c[stat_column].abs()\n",
+ "df_c = df_c.sort_values(by=['ano_coluna1', stat_column])\n",
+ "df_c = df_c[~df_c['coluna1'].str.contains('ANO_CENSO') & ~df_c['coluna2'].str.contains('ANO_CENSO')]\n",
+ "\n",
+ "# Import KS results\n",
+ "df_ks = pd.read_csv('Testes_hist/Result_KS/KS_subsequente.csv', sep=',')\n",
+ "stat_column = 'estatistica_ks'\n",
+ "df_ks[stat_column] = (df_ks[stat_column]).abs()\n",
+ "df_ks = df_ks.sort_values(by=['ano_coluna1', stat_column])\n",
+ "df_ks = df_ks[~df_ks['coluna1'].str.contains('ANO_CENSO') & ~df_ks['coluna2'].str.contains('ANO_CENSO')]"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "e25f4f2d-3fb9-4cfc-8a92-c2e8b887262c",
+ "metadata": {},
+ "source": [
+ "## Calcule the columns matches"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 5,
+ "id": "f9541a11-c1bf-4318-847a-100917e13204",
+ "metadata": {
+ "tags": []
+ },
+ "outputs": [],
+ "source": [
+ "dist = DistCalc()\n",
+ "dist.calc(df_f, 'estatistica_f', 0.7)\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",
+ "dist.calcTop3(df_f, 'estatistica_f', 0.7)\n",
+ "dist.calcTop3(df_t, 'estatistica_t', 40)\n",
+ "dist.calcTop3(df_c, 'estatistica_cohend', 0.15)\n",
+ "dist.calcTop3(df_ks, 'estatistica_ks', 0.10)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "47bcb19b-6aba-4d4a-9de0-4633bfa0eb20",
+ "metadata": {},
+ "source": [
+ "## Create the result dataframes"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 6,
+ "id": "527ff27d-f321-4749-a94d-dd7d824ef682",
+ "metadata": {
+ "tags": []
+ },
+ "outputs": [],
+ "source": [
+ "# ================= 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_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": 7,
+ "id": "4cb4afc8-6149-40a7-8f77-af06183d4d23",
+ "metadata": {
+ "tags": []
+ },
+ "outputs": [],
+ "source": [
+ "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)"
+ ]
+ }
+ ],
+ "metadata": {
+ "kernelspec": {
+ "display_name": "Python 3 (ipykernel)",
+ "language": "python",
+ "name": "python3"
+ },
+ "language_info": {
+ "codemirror_mode": {
+ "name": "ipython",
+ "version": 3
+ },
+ "file_extension": ".py",
+ "mimetype": "text/x-python",
+ "name": "python",
+ "nbconvert_exporter": "python",
+ "pygments_lexer": "ipython3",
+ "version": "3.12.3"
+ }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/dados/colunas/.ipynb_checkpoints/num_computador_adm-checkpoint.csv b/dados/colunas/.ipynb_checkpoints/num_computador_adm-checkpoint.csv
new file mode 100644
index 0000000000000000000000000000000000000000..5911145eee871d4dbbccf0d8e3a41ad3e09537c8
--- /dev/null
+++ b/dados/colunas/.ipynb_checkpoints/num_computador_adm-checkpoint.csv
@@ -0,0 +1,2223 @@
+| 2007 | 0 | 0.046 |
+| 2007 | 1 | 14.853 |
+| 2007 | 2 | 7.510 |
+| 2007 | 3 | 4.120 |
+| 2007 | 4 | 2.350 |
+| 2007 | 5 | 1.476 |
+| 2007 | 6 | 0.880 |
+| 2007 | 7 | 0.582 |
+| 2007 | 8 | 0.416 |
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+| 2007 | 134 | 0.000 |
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+| 2007 | 574 | 0.000 |
+| 2007 | 647 | 0.000 |
+| 2007 | 999 | 0.000 |
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+| 2008 | 1 | 17.185 |
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+| 2008 | 13 | 0.121 |
+| 2008 | 14 | 0.102 |
+| 2008 | 15 | 0.117 |
+| 2008 | 16 | 0.066 |
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+| 2008 | 19 | 0.035 |
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+| 2017 | 75 | 0.004 |
+| 2017 | 76 | 0.001 |
+| 2017 | 77 | 0.001 |
+| 2017 | 78 | 0.004 |
+| 2017 | 79 | 0.001 |
+| 2017 | 80 | 0.008 |
+| 2017 | 81 | 0.002 |
+| 2017 | 82 | 0.001 |
+| 2017 | 83 | 0.002 |
+| 2017 | 84 | 0.001 |
+| 2017 | 85 | 0.001 |
+| 2017 | 86 | 0.001 |
+| 2017 | 88 | 0.000 |
+| 2017 | 89 | 0.002 |
+| 2017 | 90 | 0.002 |
+| 2017 | 91 | 0.000 |
+| 2017 | 93 | 0.001 |
+| 2017 | 94 | 0.000 |
+| 2017 | 95 | 0.002 |
+| 2017 | 96 | 0.000 |
+| 2017 | 97 | 0.002 |
+| 2017 | 98 | 0.001 |
+| 2017 | 99 | 0.001 |
+| 2017 | 100 | 0.004 |
+| 2017 | 101 | 0.001 |
+| 2017 | 102 | 0.001 |
+| 2017 | 103 | 0.000 |
+| 2017 | 104 | 0.001 |
+| 2017 | 105 | 0.000 |
+| 2017 | 106 | 0.001 |
+| 2017 | 107 | 0.001 |
+| 2017 | 108 | 0.000 |
+| 2017 | 109 | 0.001 |
+| 2017 | 110 | 0.001 |
+| 2017 | 112 | 0.001 |
+| 2017 | 113 | 0.000 |
+| 2017 | 114 | 0.002 |
+| 2017 | 115 | 0.000 |
+| 2017 | 118 | 0.001 |
+| 2017 | 119 | 0.000 |
+| 2017 | 120 | 0.002 |
+| 2017 | 121 | 0.001 |
+| 2017 | 122 | 0.000 |
+| 2017 | 123 | 0.000 |
+| 2017 | 124 | 0.000 |
+| 2017 | 125 | 0.001 |
+| 2017 | 126 | 0.000 |
+| 2017 | 127 | 0.000 |
+| 2017 | 128 | 0.000 |
+| 2017 | 130 | 0.001 |
+| 2017 | 131 | 0.001 |
+| 2017 | 132 | 0.001 |
+| 2017 | 134 | 0.000 |
+| 2017 | 135 | 0.000 |
+| 2017 | 136 | 0.001 |
+| 2017 | 137 | 0.001 |
+| 2017 | 138 | 0.000 |
+| 2017 | 139 | 0.000 |
+| 2017 | 140 | 0.001 |
+| 2017 | 141 | 0.000 |
+| 2017 | 142 | 0.000 |
+| 2017 | 144 | 0.000 |
+| 2017 | 146 | 0.000 |
+| 2017 | 147 | 0.000 |
+| 2017 | 148 | 0.000 |
+| 2017 | 150 | 0.002 |
+| 2017 | 151 | 0.000 |
+| 2017 | 152 | 0.000 |
+| 2017 | 154 | 0.000 |
+| 2017 | 155 | 0.000 |
+| 2017 | 159 | 0.000 |
+| 2017 | 160 | 0.001 |
+| 2017 | 161 | 0.000 |
+| 2017 | 162 | 0.000 |
+| 2017 | 164 | 0.001 |
+| 2017 | 166 | 0.000 |
+| 2017 | 168 | 0.000 |
+| 2017 | 169 | 0.000 |
+| 2017 | 170 | 0.001 |
+| 2017 | 173 | 0.000 |
+| 2017 | 174 | 0.000 |
+| 2017 | 175 | 0.001 |
+| 2017 | 176 | 0.000 |
+| 2017 | 177 | 0.000 |
+| 2017 | 180 | 0.000 |
+| 2017 | 181 | 0.000 |
+| 2017 | 184 | 0.000 |
+| 2017 | 185 | 0.000 |
+| 2017 | 186 | 0.001 |
+| 2017 | 187 | 0.000 |
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+| 2017 | 191 | 0.000 |
+| 2017 | 194 | 0.001 |
+| 2017 | 196 | 0.000 |
+| 2017 | 197 | 0.000 |
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+| 2017 | 211 | 0.000 |
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+| 2017 | 270 | 0.000 |
+| 2017 | 271 | 0.000 |
+| 2017 | 274 | 0.000 |
+| 2017 | 280 | 0.000 |
+| 2017 | 282 | 0.000 |
+| 2017 | 286 | 0.000 |
+| 2017 | 287 | 0.000 |
+| 2017 | 289 | 0.000 |
+| 2017 | 297 | 0.000 |
+| 2017 | 300 | 0.002 |
+| 2017 | 301 | 0.000 |
+| 2017 | 309 | 0.000 |
+| 2017 | 335 | 0.000 |
+| 2017 | 350 | 0.000 |
+| 2017 | 359 | 0.000 |
+| 2017 | 388 | 0.000 |
+| 2017 | 390 | 0.000 |
+| 2017 | 400 | 0.000 |
+| 2017 | 413 | 0.000 |
+| 2017 | 422 | 0.000 |
+| 2017 | 432 | 0.000 |
+| 2017 | 462 | 0.000 |
+| 2017 | 513 | 0.000 |
+| 2017 | 537 | 0.000 |
+| 2017 | 554 | 0.000 |
+| 2017 | 600 | 0.000 |
+| 2017 | 670 | 0.000 |
+| 2017 | 956 | 0.000 |
+| 2017 | 981 | 0.000 |
+| 2017 | 1000 | 0.000 |
+| 2017 | 1200 | 0.000 |
diff --git a/dados/colunas/.ipynb_checkpoints/num_computador_adm-checkpoint.out b/dados/colunas/.ipynb_checkpoints/num_computador_adm-checkpoint.out
new file mode 100644
index 0000000000000000000000000000000000000000..3a365b1f8079638a51be3b6edaf154657a350255
--- /dev/null
+++ b/dados/colunas/.ipynb_checkpoints/num_computador_adm-checkpoint.out
@@ -0,0 +1,2228 @@
++-----------+--------------------+-----------------------------+
+| ano_censo | num_computador_adm | percentage |
++===========+====================+=============================+
+| 2007 | 0 | 0.046 |
+| 2007 | 1 | 14.853 |
+| 2007 | 2 | 7.510 |
+| 2007 | 3 | 4.120 |
+| 2007 | 4 | 2.350 |
+| 2007 | 5 | 1.476 |
+| 2007 | 6 | 0.880 |
+| 2007 | 7 | 0.582 |
+| 2007 | 8 | 0.416 |
+| 2007 | 9 | 0.255 |
+| 2007 | 10 | 0.275 |
+| 2007 | 11 | 0.132 |
+| 2007 | 12 | 0.150 |
+| 2007 | 13 | 0.100 |
+| 2007 | 14 | 0.075 |
+| 2007 | 15 | 0.077 |
+| 2007 | 16 | 0.053 |
+| 2007 | 17 | 0.042 |
+| 2007 | 18 | 0.040 |
+| 2007 | 19 | 0.028 |
+| 2007 | 20 | 0.055 |
+| 2007 | 21 | 0.020 |
+| 2007 | 22 | 0.026 |
+| 2007 | 23 | 0.013 |
+| 2007 | 24 | 0.015 |
+| 2007 | 25 | 0.025 |
+| 2007 | 26 | 0.016 |
+| 2007 | 27 | 0.013 |
+| 2007 | 28 | 0.016 |
+| 2007 | 29 | 0.007 |
+| 2007 | 30 | 0.023 |
+| 2007 | 31 | 0.013 |
+| 2007 | 32 | 0.008 |
+| 2007 | 33 | 0.005 |
+| 2007 | 34 | 0.002 |
+| 2007 | 35 | 0.013 |
+| 2007 | 36 | 0.005 |
+| 2007 | 37 | 0.005 |
+| 2007 | 38 | 0.007 |
+| 2007 | 39 | 0.005 |
+| 2007 | 40 | 0.013 |
+| 2007 | 41 | 0.005 |
+| 2007 | 42 | 0.001 |
+| 2007 | 43 | 0.003 |
+| 2007 | 44 | 0.005 |
+| 2007 | 45 | 0.005 |
+| 2007 | 46 | 0.005 |
+| 2007 | 47 | 0.002 |
+| 2007 | 48 | 0.003 |
+| 2007 | 49 | 0.002 |
+| 2007 | 50 | 0.005 |
+| 2007 | 51 | 0.001 |
+| 2007 | 52 | 0.004 |
+| 2007 | 53 | 0.002 |
+| 2007 | 54 | 0.003 |
+| 2007 | 55 | 0.004 |
+| 2007 | 56 | 0.001 |
+| 2007 | 57 | 0.002 |
+| 2007 | 58 | 0.002 |
+| 2007 | 59 | 0.002 |
+| 2007 | 60 | 0.003 |
+| 2007 | 61 | 0.000 |
+| 2007 | 62 | 0.000 |
+| 2007 | 63 | 0.002 |
+| 2007 | 64 | 0.002 |
+| 2007 | 65 | 0.002 |
+| 2007 | 66 | 0.000 |
+| 2007 | 68 | 0.001 |
+| 2007 | 69 | 0.001 |
+| 2007 | 70 | 0.003 |
+| 2007 | 71 | 0.001 |
+| 2007 | 72 | 0.001 |
+| 2007 | 73 | 0.001 |
+| 2007 | 75 | 0.000 |
+| 2007 | 77 | 0.001 |
+| 2007 | 78 | 0.001 |
+| 2007 | 79 | 0.000 |
+| 2007 | 80 | 0.003 |
+| 2007 | 81 | 0.000 |
+| 2007 | 82 | 0.000 |
+| 2007 | 84 | 0.000 |
+| 2007 | 85 | 0.000 |
+| 2007 | 88 | 0.000 |
+| 2007 | 89 | 0.000 |
+| 2007 | 90 | 0.001 |
+| 2007 | 91 | 0.000 |
+| 2007 | 92 | 0.000 |
+| 2007 | 93 | 0.000 |
+| 2007 | 94 | 0.000 |
+| 2007 | 95 | 0.001 |
+| 2007 | 96 | 0.000 |
+| 2007 | 97 | 0.001 |
+| 2007 | 98 | 0.000 |
+| 2007 | 100 | 0.001 |
+| 2007 | 103 | 0.000 |
+| 2007 | 104 | 0.000 |
+| 2007 | 105 | 0.002 |
+| 2007 | 108 | 0.000 |
+| 2007 | 110 | 0.001 |
+| 2007 | 114 | 0.000 |
+| 2007 | 115 | 0.000 |
+| 2007 | 117 | 0.000 |
+| 2007 | 120 | 0.001 |
+| 2007 | 122 | 0.000 |
+| 2007 | 125 | 0.000 |
+| 2007 | 127 | 0.000 |
+| 2007 | 130 | 0.000 |
+| 2007 | 133 | 0.000 |
+| 2007 | 134 | 0.000 |
+| 2007 | 135 | 0.000 |
+| 2007 | 139 | 0.000 |
+| 2007 | 140 | 0.000 |
+| 2007 | 141 | 0.000 |
+| 2007 | 142 | 0.000 |
+| 2007 | 149 | 0.001 |
+| 2007 | 150 | 0.000 |
+| 2007 | 178 | 0.000 |
+| 2007 | 180 | 0.000 |
+| 2007 | 188 | 0.000 |
+| 2007 | 189 | 0.000 |
+| 2007 | 200 | 0.000 |
+| 2007 | 215 | 0.000 |
+| 2007 | 222 | 0.000 |
+| 2007 | 229 | 0.000 |
+| 2007 | 232 | 0.000 |
+| 2007 | 244 | 0.000 |
+| 2007 | 250 | 0.000 |
+| 2007 | 253 | 0.000 |
+| 2007 | 294 | 0.000 |
+| 2007 | 300 | 0.000 |
+| 2007 | 346 | 0.000 |
+| 2007 | 549 | 0.000 |
+| 2007 | 574 | 0.000 |
+| 2007 | 647 | 0.000 |
+| 2007 | 999 | 0.000 |
+| 2007 | 1370 | 0.000 |
+| 2007 | 4000 | 0.000 |
+| 2008 | 1 | 17.185 |
+| 2008 | 2 | 8.744 |
+| 2008 | 3 | 4.787 |
+| 2008 | 4 | 2.942 |
+| 2008 | 5 | 1.954 |
+| 2008 | 6 | 1.230 |
+| 2008 | 7 | 0.768 |
+| 2008 | 8 | 0.536 |
+| 2008 | 9 | 0.360 |
+| 2008 | 10 | 0.361 |
+| 2008 | 11 | 0.182 |
+| 2008 | 12 | 0.180 |
+| 2008 | 13 | 0.121 |
+| 2008 | 14 | 0.102 |
+| 2008 | 15 | 0.117 |
+| 2008 | 16 | 0.066 |
+| 2008 | 17 | 0.055 |
+| 2008 | 18 | 0.048 |
+| 2008 | 19 | 0.035 |
+| 2008 | 20 | 0.066 |
+| 2008 | 21 | 0.021 |
+| 2008 | 22 | 0.033 |
+| 2008 | 23 | 0.020 |
+| 2008 | 24 | 0.017 |
+| 2008 | 25 | 0.033 |
+| 2008 | 26 | 0.019 |
+| 2008 | 27 | 0.011 |
+| 2008 | 28 | 0.019 |
+| 2008 | 29 | 0.012 |
+| 2008 | 30 | 0.030 |
+| 2008 | 31 | 0.012 |
+| 2008 | 32 | 0.008 |
+| 2008 | 33 | 0.008 |
+| 2008 | 34 | 0.008 |
+| 2008 | 35 | 0.013 |
+| 2008 | 36 | 0.008 |
+| 2008 | 37 | 0.010 |
+| 2008 | 38 | 0.006 |
+| 2008 | 39 | 0.005 |
+| 2008 | 40 | 0.012 |
+| 2008 | 41 | 0.006 |
+| 2008 | 42 | 0.004 |
+| 2008 | 43 | 0.007 |
+| 2008 | 44 | 0.003 |
+| 2008 | 45 | 0.009 |
+| 2008 | 46 | 0.005 |
+| 2008 | 47 | 0.003 |
+| 2008 | 48 | 0.003 |
+| 2008 | 49 | 0.003 |
+| 2008 | 50 | 0.009 |
+| 2008 | 51 | 0.001 |
+| 2008 | 52 | 0.003 |
+| 2008 | 53 | 0.003 |
+| 2008 | 54 | 0.003 |
+| 2008 | 55 | 0.003 |
+| 2008 | 56 | 0.004 |
+| 2008 | 57 | 0.002 |
+| 2008 | 58 | 0.001 |
+| 2008 | 59 | 0.002 |
+| 2008 | 60 | 0.007 |
+| 2008 | 61 | 0.002 |
+| 2008 | 62 | 0.001 |
+| 2008 | 63 | 0.003 |
+| 2008 | 64 | 0.001 |
+| 2008 | 65 | 0.003 |
+| 2008 | 66 | 0.000 |
+| 2008 | 67 | 0.001 |
+| 2008 | 68 | 0.002 |
+| 2008 | 69 | 0.002 |
+| 2008 | 70 | 0.004 |
+| 2008 | 71 | 0.001 |
+| 2008 | 72 | 0.000 |
+| 2008 | 73 | 0.002 |
+| 2008 | 74 | 0.001 |
+| 2008 | 75 | 0.002 |
+| 2008 | 76 | 0.001 |
+| 2008 | 77 | 0.001 |
+| 2008 | 78 | 0.001 |
+| 2008 | 79 | 0.001 |
+| 2008 | 80 | 0.003 |
+| 2008 | 81 | 0.000 |
+| 2008 | 82 | 0.000 |
+| 2008 | 83 | 0.001 |
+| 2008 | 85 | 0.001 |
+| 2008 | 86 | 0.000 |
+| 2008 | 88 | 0.000 |
+| 2008 | 89 | 0.000 |
+| 2008 | 90 | 0.001 |
+| 2008 | 91 | 0.001 |
+| 2008 | 93 | 0.000 |
+| 2008 | 94 | 0.001 |
+| 2008 | 95 | 0.001 |
+| 2008 | 97 | 0.000 |
+| 2008 | 98 | 0.000 |
+| 2008 | 99 | 0.000 |
+| 2008 | 100 | 0.001 |
+| 2008 | 103 | 0.000 |
+| 2008 | 104 | 0.000 |
+| 2008 | 105 | 0.001 |
+| 2008 | 106 | 0.000 |
+| 2008 | 108 | 0.002 |
+| 2008 | 110 | 0.001 |
+| 2008 | 112 | 0.000 |
+| 2008 | 113 | 0.000 |
+| 2008 | 114 | 0.000 |
+| 2008 | 115 | 0.000 |
+| 2008 | 116 | 0.000 |
+| 2008 | 117 | 0.000 |
+| 2008 | 118 | 0.000 |
+| 2008 | 119 | 0.000 |
+| 2008 | 120 | 0.002 |
+| 2008 | 121 | 0.000 |
+| 2008 | 122 | 0.000 |
+| 2008 | 123 | 0.000 |
+| 2008 | 126 | 0.000 |
+| 2008 | 130 | 0.001 |
+| 2008 | 132 | 0.000 |
+| 2008 | 133 | 0.000 |
+| 2008 | 137 | 0.000 |
+| 2008 | 139 | 0.000 |
+| 2008 | 140 | 0.000 |
+| 2008 | 141 | 0.000 |
+| 2008 | 142 | 0.001 |
+| 2008 | 145 | 0.002 |
+| 2008 | 146 | 0.000 |
+| 2008 | 149 | 0.000 |
+| 2008 | 150 | 0.000 |
+| 2008 | 155 | 0.000 |
+| 2008 | 163 | 0.000 |
+| 2008 | 165 | 0.000 |
+| 2008 | 171 | 0.000 |
+| 2008 | 179 | 0.000 |
+| 2008 | 180 | 0.000 |
+| 2008 | 182 | 0.000 |
+| 2008 | 189 | 0.000 |
+| 2008 | 191 | 0.000 |
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+| 2008 | 205 | 0.000 |
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+| 2008 | 574 | 0.000 |
+| 2008 | 595 | 0.000 |
+| 2008 | 600 | 0.000 |
+| 2008 | 954 | 0.000 |
+| 2008 | 1370 | 0.000 |
+| 2009 | 1 | 17.715 |
+| 2009 | 2 | 9.232 |
+| 2009 | 3 | 5.167 |
+| 2009 | 4 | 3.304 |
+| 2009 | 5 | 2.423 |
+| 2009 | 6 | 1.489 |
+| 2009 | 7 | 0.936 |
+| 2009 | 8 | 0.687 |
+| 2009 | 9 | 0.419 |
+| 2009 | 10 | 0.416 |
+| 2009 | 11 | 0.205 |
+| 2009 | 12 | 0.216 |
+| 2009 | 13 | 0.139 |
+| 2009 | 14 | 0.112 |
+| 2009 | 15 | 0.124 |
+| 2009 | 16 | 0.077 |
+| 2009 | 17 | 0.070 |
+| 2009 | 18 | 0.054 |
+| 2009 | 19 | 0.040 |
+| 2009 | 20 | 0.079 |
+| 2009 | 21 | 0.032 |
+| 2009 | 22 | 0.032 |
+| 2009 | 23 | 0.028 |
+| 2009 | 24 | 0.030 |
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+| 2009 | 26 | 0.031 |
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+| 2009 | 28 | 0.021 |
+| 2009 | 29 | 0.011 |
+| 2009 | 30 | 0.036 |
+| 2009 | 31 | 0.014 |
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+| 2010 | 87 | 0.001 |
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+| 2010 | 89 | 0.000 |
+| 2010 | 90 | 0.002 |
+| 2010 | 91 | 0.001 |
+| 2010 | 92 | 0.000 |
+| 2010 | 93 | 0.001 |
+| 2010 | 94 | 0.000 |
+| 2010 | 95 | 0.001 |
+| 2010 | 96 | 0.001 |
+| 2010 | 97 | 0.000 |
+| 2010 | 98 | 0.000 |
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+| 2017 | 58 | 0.007 |
+| 2017 | 59 | 0.005 |
+| 2017 | 60 | 0.011 |
+| 2017 | 61 | 0.003 |
+| 2017 | 62 | 0.003 |
+| 2017 | 63 | 0.002 |
+| 2017 | 64 | 0.004 |
+| 2017 | 65 | 0.006 |
+| 2017 | 66 | 0.003 |
+| 2017 | 67 | 0.002 |
+| 2017 | 68 | 0.001 |
+| 2017 | 69 | 0.002 |
+| 2017 | 70 | 0.007 |
+| 2017 | 71 | 0.002 |
+| 2017 | 72 | 0.002 |
+| 2017 | 73 | 0.000 |
+| 2017 | 74 | 0.002 |
+| 2017 | 75 | 0.004 |
+| 2017 | 76 | 0.001 |
+| 2017 | 77 | 0.001 |
+| 2017 | 78 | 0.004 |
+| 2017 | 79 | 0.001 |
+| 2017 | 80 | 0.008 |
+| 2017 | 81 | 0.002 |
+| 2017 | 82 | 0.001 |
+| 2017 | 83 | 0.002 |
+| 2017 | 84 | 0.001 |
+| 2017 | 85 | 0.001 |
+| 2017 | 86 | 0.001 |
+| 2017 | 88 | 0.000 |
+| 2017 | 89 | 0.002 |
+| 2017 | 90 | 0.002 |
+| 2017 | 91 | 0.000 |
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+| 2017 | 95 | 0.002 |
+| 2017 | 96 | 0.000 |
+| 2017 | 97 | 0.002 |
+| 2017 | 98 | 0.001 |
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+| 2017 | 100 | 0.004 |
+| 2017 | 101 | 0.001 |
+| 2017 | 102 | 0.001 |
+| 2017 | 103 | 0.000 |
+| 2017 | 104 | 0.001 |
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+| 2017 | 106 | 0.001 |
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+| 2017 | 194 | 0.001 |
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+| 2017 | 200 | 0.001 |
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+| 2017 | 260 | 0.001 |
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+| 2017 | 297 | 0.000 |
+| 2017 | 300 | 0.002 |
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+| 2017 | 600 | 0.000 |
+| 2017 | 670 | 0.000 |
+| 2017 | 956 | 0.000 |
+| 2017 | 981 | 0.000 |
+| 2017 | 1000 | 0.000 |
+| 2017 | 1200 | 0.000 |
++-----------+--------------------+-----------------------------+
+2238 tuples
diff --git a/dados/estatisticas_null/.ipynb_checkpoints/adh_idh_null_statistics-checkpoint.csv b/dados/estatisticas_null/.ipynb_checkpoints/adh_idh_null_statistics-checkpoint.csv
new file mode 100644
index 0000000000000000000000000000000000000000..367c8fe411d272a3c103d5fa32e862e8af6b0d6c
--- /dev/null
+++ b/dados/estatisticas_null/.ipynb_checkpoints/adh_idh_null_statistics-checkpoint.csv
@@ -0,0 +1,10 @@
+colName;total;1991;2000;2010
+ano_censo;0.000000000000000000000000;0.00000000000000000000;0.00000000000000000000;0.00000000000000000000
+estado_id;0.000000000000000000000000;0.00000000000000000000;0.00000000000000000000;0.00000000000000000000
+idhm;0.000000000000000000000000;0.00000000000000000000;0.00000000000000000000;0.00000000000000000000
+idhm_e;0.000000000000000000000000;0.00000000000000000000;0.00000000000000000000;0.00000000000000000000
+idhm_l;0.000000000000000000000000;0.00000000000000000000;0.00000000000000000000;0.00000000000000000000
+idhm_nivel;0.000000000000000000000000;0.00000000000000000000;0.00000000000000000000;0.00000000000000000000
+idhm_r;0.000000000000000000000000;0.00000000000000000000;0.00000000000000000000;0.00000000000000000000
+municipio_id;0.000000000000000000000000;0.00000000000000000000;0.00000000000000000000;0.00000000000000000000
+regiao_id;0.000000000000000000000000;0.00000000000000000000;0.00000000000000000000;0.00000000000000000000
diff --git a/distCalc.ipynb b/distCalc.ipynb
index 1eed85cf7a6e705bc3df70089d678a80f4fabca1..28137071cbd3ebcc8aad0d6c64f143dea288a37d 100644
--- a/distCalc.ipynb
+++ b/distCalc.ipynb
@@ -20,7 +20,7 @@
},
{
"cell_type": "code",
- "execution_count": 1,
+ "execution_count": 2,
"id": "2c81bc78-04e0-4bad-83ef-380cf3be1610",
"metadata": {
"tags": []
@@ -33,7 +33,7 @@
},
{
"cell_type": "code",
- "execution_count": 134,
+ "execution_count": 3,
"id": "af419e44-d6ef-41f7-970c-78c316aeb712",
"metadata": {
"tags": []
@@ -350,7 +350,7 @@
},
{
"cell_type": "code",
- "execution_count": 135,
+ "execution_count": 4,
"id": "26287a6f-5537-4509-a09d-52dd59b3a76d",
"metadata": {
"tags": []
@@ -358,28 +358,28 @@
"outputs": [],
"source": [
"# Import F results\n",
- "df_f = pd.read_csv('Testes_hist/Result_F/F_subsequente.csv', sep=',')\n",
+ "df_f = pd.read_csv('R_resultados/Histograma_10/F_subsequente.csv', sep=',')\n",
"stat_column = 'p_valor'\n",
"df_f[stat_column] = df_f[stat_column].abs()\n",
"df_f = df_f.sort_values(by=['ano_coluna1', stat_column], ascending=[True, False])\n",
"df_f = df_f[~df_f['coluna1'].str.contains('ANO_CENSO') & ~df_f['coluna2'].str.contains('ANO_CENSO')]\n",
"\n",
"# Import T results\n",
- "df_t = pd.read_csv('Testes_hist/Result_T/T_subsequente.csv', sep=',')\n",
+ "df_t = pd.read_csv('R_resultados/Histograma_10/T_subsequente.csv', sep=',')\n",
"stat_column = 'p_valor'\n",
"df_t[stat_column] = df_t[stat_column].abs()\n",
"df_t = df_t.sort_values(by=['ano_coluna1', stat_column], ascending=[True, False])\n",
"df_t = df_t[~df_t['coluna1'].str.contains('ANO_CENSO') & ~df_t['coluna2'].str.contains('ANO_CENSO')]\n",
"\n",
"# Import COHEND results\n",
- "df_c = pd.read_csv('Testes_hist/Result_COHEND/COHEND_subsequente.csv', sep=',')\n",
+ "df_c = pd.read_csv('R_resultados/Histograma_10/COHEND_subsequente.csv', sep=',')\n",
"stat_column = 'estatistica_cohend'\n",
"df_c[stat_column] = df_c[stat_column].abs()\n",
"df_c = df_c.sort_values(by=['ano_coluna1', stat_column])\n",
"df_c = df_c[~df_c['coluna1'].str.contains('ANO_CENSO') & ~df_c['coluna2'].str.contains('ANO_CENSO')]\n",
"\n",
"# Import KS results\n",
- "df_ks = pd.read_csv('Testes_hist/Result_KS/KS_subsequente.csv', sep=',')\n",
+ "df_ks = pd.read_csv('R_resultados/Histograma_10/KS_subsequente.csv', sep=',')\n",
"stat_column = 'p_valor'\n",
"df_ks[stat_column] = (df_ks[stat_column]).abs()\n",
"df_ks = df_ks.sort_values(by=['ano_coluna1', stat_column], ascending=[True, False])\n",
@@ -396,7 +396,7 @@
},
{
"cell_type": "code",
- "execution_count": 136,
+ "execution_count": 5,
"id": "f9541a11-c1bf-4318-847a-100917e13204",
"metadata": {
"tags": []
@@ -425,7 +425,7 @@
},
{
"cell_type": "code",
- "execution_count": 137,
+ "execution_count": 6,
"id": "527ff27d-f321-4749-a94d-dd7d824ef682",
"metadata": {
"tags": []
@@ -507,7 +507,7 @@
},
{
"cell_type": "code",
- "execution_count": 138,
+ "execution_count": 7,
"id": "4cb4afc8-6149-40a7-8f77-af06183d4d23",
"metadata": {
"tags": []
@@ -544,7 +544,7 @@
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
- "version": "3.11.9"
+ "version": "3.12.3"
}
},
"nbformat": 4,
diff --git a/resultTop3_cohend.csv b/resultTop3_cohend.csv
deleted file mode 100644
index ac5e0a7cf7daf22296c6fa4d21be18596fc8804b..0000000000000000000000000000000000000000
--- a/resultTop3_cohend.csv
+++ /dev/null
@@ -1,17 +0,0 @@
-ano_base,match,new,empty,total
-2007.0,1.0,0.0,0.0,0.833
-2008.0,1.0,1.0,0.0,0.833
-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
-2012.0,1.0,0.286,1.0,0.615
-2013.0,0.846,1.0,1.0,0.846
-2014.0,0.846,1.0,1.0,0.846
-2015.0,0.846,1.0,1.0,0.846
-2016.0,0.846,1.0,1.0,0.846
-2017.0,0.6,1.0,0.8,0.846
-2018.0,0.333,0.857,0.0,0.765
-2019.0,0.867,0.125,1.0,0.636
-2020.0,0.818,1.0,1.0,0.818
-2013.5,0.857,0.805,0.771,0.838
-4.031,0.182,0.355,0.406,0.112
diff --git a/resultTop3_f.csv b/resultTop3_f.csv
deleted file mode 100644
index 6ed42214e687a43c802c6df4710cc37618c3cd67..0000000000000000000000000000000000000000
--- a/resultTop3_f.csv
+++ /dev/null
@@ -1,17 +0,0 @@
-ano_base,match,new,empty,total
-2007.0,1.0,1.0,1.0,1.0
-2008.0,1.0,1.0,1.0,1.0
-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
-2012.0,0.833,0.125,1.0,0.462
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-2016.0,0.769,1.0,1.0,0.769
-2017.0,0.333,1.0,0.4,0.538
-2018.0,0.333,0.643,0.0,0.588
-2019.0,0.867,0.125,1.0,0.636
-2020.0,0.909,1.0,1.0,0.909
-2013.5,0.805,0.849,0.886,0.795
-4.031,0.22,0.31,0.29,0.188
diff --git a/resultTop3_ks.csv b/resultTop3_ks.csv
deleted file mode 100644
index 8617b49726351fcd2172e0b85db05837543d6e25..0000000000000000000000000000000000000000
--- a/resultTop3_ks.csv
+++ /dev/null
@@ -1,17 +0,0 @@
-ano_base,match,new,empty,total
-2007.0,1.0,1.0,1.0,1.0
-2008.0,1.0,1.0,1.0,1.0
-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
-2012.0,1.0,0.714,1.0,0.846
-2013.0,0.846,0.0,1.0,0.846
-2014.0,0.923,0.0,1.0,0.923
-2015.0,0.923,0.0,1.0,0.923
-2016.0,0.769,0.0,1.0,0.769
-2017.0,0.333,1.0,0.4,0.538
-2018.0,0.333,0.857,0.0,0.765
-2019.0,0.8,0.667,1.0,0.818
-2020.0,0.864,0.0,1.0,0.864
-2013.5,0.842,0.588,0.886,0.878
-4.031,0.222,0.451,0.29,0.127
diff --git a/resultTop3_t.csv b/resultTop3_t.csv
deleted file mode 100644
index 75e39ead8da2d8667ae0eced71641e14e1c623e2..0000000000000000000000000000000000000000
--- a/resultTop3_t.csv
+++ /dev/null
@@ -1,17 +0,0 @@
-ano_base,match,new,empty,total
-2007.0,1.0,1.0,1.0,1.0
-2008.0,1.0,1.0,1.0,1.0
-2009.0,1.0,1.0,1.0,1.0
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-2011.0,1.0,1.0,1.0,1.0
-2012.0,1.0,0.286,1.0,0.615
-2013.0,0.846,1.0,1.0,0.846
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-2016.0,0.769,1.0,1.0,0.769
-2017.0,0.231,1.0,0.0,0.231
-2018.0,0.333,0.857,0.0,0.765
-2019.0,0.867,0.125,1.0,0.636
-2020.0,0.818,1.0,1.0,0.818
-2013.5,0.825,0.876,0.857,0.812
-4.031,0.237,0.278,0.35,0.206
diff --git a/result_cohend.csv b/result_cohend.csv
deleted file mode 100644
index 21c32e59a93515a79b18f6200ff0a203bc96832c..0000000000000000000000000000000000000000
--- a/result_cohend.csv
+++ /dev/null
@@ -1,17 +0,0 @@
-ano_base,match,new,empty,total
-2007.0,1.0,0.0,0.0,0.833
-2008.0,0.6,0.0,0.0,0.5
-2009.0,0.667,1.0,1.0,0.667
-2010.0,0.667,1.0,1.0,0.667
-2011.0,1.0,1.0,1.0,1.0
-2012.0,0.5,0.75,1.0,0.692
-2013.0,0.5,0.0,0.0,0.462
-2014.0,0.538,1.0,1.0,0.538
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-2016.0,0.583,0.0,0.0,0.538
-2017.0,0.333,1.0,0.818,0.769
-2018.0,0.0,0.857,0.0,0.706
-2019.0,0.733,0.4,1.0,0.682
-2020.0,0.45,0.0,0.0,0.409
-2013.5,0.574,0.572,0.558,0.637
-4.031,0.244,0.453,0.486,0.158
diff --git a/result_f.csv b/result_f.csv
deleted file mode 100644
index 835c4f769af554fb7baad9caba652c2587420196..0000000000000000000000000000000000000000
--- a/result_f.csv
+++ /dev/null
@@ -1,17 +0,0 @@
-ano_base,match,new,empty,total
-2007.0,0.667,1.0,1.0,0.667
-2008.0,0.667,1.0,1.0,0.667
-2009.0,0.667,1.0,1.0,0.667
-2010.0,0.667,1.0,1.0,0.667
-2011.0,0.667,1.0,1.0,0.667
-2012.0,0.5,0.556,1.0,0.615
-2013.0,0.231,1.0,1.0,0.231
-2014.0,0.462,1.0,1.0,0.462
-2015.0,0.615,1.0,1.0,0.615
-2016.0,0.333,0.0,0.0,0.308
-2017.0,0.333,1.0,0.818,0.769
-2018.0,0.333,0.857,0.0,0.765
-2019.0,0.533,0.75,1.0,0.636
-2020.0,0.381,0.0,0.0,0.364
-2013.5,0.504,0.797,0.773,0.578
-4.031,0.152,0.349,0.406,0.163
diff --git a/result_ks.csv b/result_ks.csv
deleted file mode 100644
index af1360f15c471bf1f80a6b8e6b289e006df8758e..0000000000000000000000000000000000000000
--- a/result_ks.csv
+++ /dev/null
@@ -1,17 +0,0 @@
-ano_base,match,new,empty,total
-2007.0,0.667,1.0,1.0,0.667
-2008.0,1.0,1.0,1.0,1.0
-2009.0,1.0,1.0,1.0,1.0
-2010.0,0.333,1.0,1.0,0.333
-2011.0,1.0,1.0,1.0,1.0
-2012.0,0.667,1.0,1.0,0.846
-2013.0,1.0,1.0,1.0,1.0
-2014.0,1.0,1.0,1.0,1.0
-2015.0,1.0,1.0,1.0,1.0
-2016.0,0.615,1.0,1.0,0.615
-2017.0,1.0,1.0,1.0,1.0
-2018.0,0.0,0.857,0.0,0.706
-2019.0,1.0,1.0,1.0,1.0
-2020.0,0.909,1.0,1.0,0.909
-2013.5,0.799,0.99,0.929,0.863
-4.031,0.3,0.037,0.258,0.2
diff --git a/result_t.csv b/result_t.csv
deleted file mode 100644
index af54dbdc67fe69e6b6b048867c37121de6812c78..0000000000000000000000000000000000000000
--- a/result_t.csv
+++ /dev/null
@@ -1,17 +0,0 @@
-ano_base,match,new,empty,total
-2007.0,1.0,1.0,1.0,1.0
-2008.0,0.333,1.0,1.0,0.333
-2009.0,0.667,1.0,1.0,0.667
-2010.0,0.667,1.0,1.0,0.667
-2011.0,1.0,1.0,1.0,1.0
-2012.0,0.5,0.75,1.0,0.692
-2013.0,0.462,1.0,1.0,0.462
-2014.0,0.538,1.0,1.0,0.538
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-2017.0,0.0,1.0,0.818,0.692
-2018.0,0.0,0.857,0.0,0.706
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-2013.5,0.522,0.929,0.916,0.632
-4.031,0.286,0.163,0.258,0.19