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+2015.0,0.923,0.0,0.0,0.923
+2016.0,0.615,0.0,0.0,0.615
+2017.0,0.5,1.0,0.818,0.846
+2018.0,0.0,0.857,0.0,0.706
+2019.0,0.533,0.4,1.0,0.545
+2020.0,0.545,0.0,0.0,0.545
+2013.5,0.729,0.59,0.558,0.805
+4.031,0.289,0.465,0.486,0.191
diff --git a/Resultados_finais/Normalizado/95/result_t.csv b/Resultados_finais/Normalizado/95/result_t.csv
new file mode 100644
index 0000000000000000000000000000000000000000..3651d0baf70ac268ed787e108e840618de2ac741
--- /dev/null
+++ b/Resultados_finais/Normalizado/95/result_t.csv
@@ -0,0 +1,17 @@
+ano_base,match,new,empty,total
+2007.0,1.0,1.0,1.0,1.0
+2008.0,0.333,0.0,0.0,0.333
+2009.0,0.667,0.0,0.0,0.667
+2010.0,0.333,0.0,0.0,0.333
+2011.0,0.833,0.0,0.0,0.833
+2012.0,0.333,0.7,0.0,0.692
+2013.0,0.231,0.0,0.0,0.231
+2014.0,0.154,1.0,1.0,0.154
+2015.0,0.077,0.0,0.0,0.077
+2016.0,0.154,0.0,0.0,0.154
+2017.0,0.5,1.0,0.818,0.846
+2018.0,0.0,0.857,0.0,0.706
+2019.0,0.067,0.077,1.0,0.091
+2020.0,0.273,1.0,1.0,0.273
+2013.5,0.354,0.402,0.344,0.456
+4.031,0.287,0.46,0.464,0.308
diff --git a/distCalc.ipynb b/distCalc.ipynb
index 28137071cbd3ebcc8aad0d6c64f143dea288a37d..56599f05c6d15b08705924f14ecdd010f46acd1c 100644
--- a/distCalc.ipynb
+++ b/distCalc.ipynb
@@ -20,7 +20,7 @@
},
{
"cell_type": "code",
- "execution_count": 2,
+ "execution_count": 1,
"id": "2c81bc78-04e0-4bad-83ef-380cf3be1610",
"metadata": {
"tags": []
@@ -33,7 +33,7 @@
},
{
"cell_type": "code",
- "execution_count": 3,
+ "execution_count": 95,
"id": "af419e44-d6ef-41f7-970c-78c316aeb712",
"metadata": {
"tags": []
@@ -125,6 +125,7 @@
" # 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_match_columns = np.intersect1d(base_columns, new_columns)# All the columns that has a match\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",
@@ -161,7 +162,7 @@
" 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",
+ " acuracia_matches = acertos_p / len(np.union1d(true_match_columns, prev_col))\n",
" acertos += acertos_p\n",
"\n",
" # New columns accuracy\n",
@@ -237,7 +238,7 @@
" # Find the Top3 columns for each base column\n",
" for col in base_columns:\n",
" if(threshGreater):\n",
- " top3 = ano_df[(ano_df.coluna1 == col) & (ano_df[stat_column] > threshold)].iloc[:3,:]\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",
@@ -308,6 +309,7 @@
" acertos_p = 0\n",
" results_len = 0\n",
" for res in resultados:\n",
+ " \n",
" if(len(res) == 0):\n",
" continue\n",
" results_len += 1\n",
@@ -315,8 +317,7 @@
" if i[0] == i[2]:\n",
" acertos_p += 1\n",
" break\n",
- " \n",
- " acuracia_matches = acertos_p / results_len \n",
+ " acuracia_matches = acertos_p / len(np.union1d(intersection_columns, prev_col)) \n",
" # ========== ADDING RESULTS ON CLASS VARIABLES ==========\n",
" if(stat_method == 'estatistica_f'):\n",
" self.stat_f_top3.append([ano, acuracia_total])\n",
@@ -350,7 +351,7 @@
},
{
"cell_type": "code",
- "execution_count": 4,
+ "execution_count": 108,
"id": "26287a6f-5537-4509-a09d-52dd59b3a76d",
"metadata": {
"tags": []
@@ -358,31 +359,31 @@
"outputs": [],
"source": [
"# Import F results\n",
- "df_f = pd.read_csv('R_resultados/Histograma_10/F_subsequente.csv', sep=',')\n",
+ "df_f = pd.read_csv('R_resultados/Histograma_norm/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.sort_values(by=['ano_coluna1', stat_column, 'estatistica_f'], ascending=[True, False, True])\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('R_resultados/Histograma_10/T_subsequente.csv', sep=',')\n",
+ "df_t = pd.read_csv('R_resultados/Histograma_norm/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.sort_values(by=['ano_coluna1', stat_column, 'estatistica_t'], ascending=[True, False, True])\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('R_resultados/Histograma_10/COHEND_subsequente.csv', sep=',')\n",
+ "df_c = pd.read_csv('R_resultados/Histograma_norm/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('R_resultados/Histograma_10/KS_subsequente.csv', sep=',')\n",
+ "df_ks = pd.read_csv('R_resultados/Histograma_norm/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",
+ "df_ks = df_ks.sort_values(by=['ano_coluna1', stat_column, 'estatistica_ks'], ascending=[True, False, True])\n",
"df_ks = df_ks[~df_ks['coluna1'].str.contains('ANO_CENSO') & ~df_ks['coluna2'].str.contains('ANO_CENSO')]"
]
},
@@ -396,7 +397,7 @@
},
{
"cell_type": "code",
- "execution_count": 5,
+ "execution_count": 109,
"id": "f9541a11-c1bf-4318-847a-100917e13204",
"metadata": {
"tags": []
@@ -404,15 +405,15 @@
"outputs": [],
"source": [
"dist = DistCalc()\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_f, 'p_valor', 'estatistica_f', 0.95)\n",
+ "dist.calc(df_t, 'p_valor', 'estatistica_t', 0.95)\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",
+ "dist.calc(df_ks, 'p_valor', 'estatistica_ks', 0.95)\n",
"\n",
- "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_f, 'p_valor', 'estatistica_f', 0.95)\n",
+ "dist.calcTop3(df_t, 'p_valor', 'estatistica_t', 0.95)\n",
"dist.calcTop3(df_c, 'estatistica_cohend', 'estatistica_cohend', 0.15, threshGreater=False)\n",
- "dist.calcTop3(df_ks, 'p_valor', 'estatistica_ks', 0.05)"
+ "dist.calcTop3(df_ks, 'p_valor', 'estatistica_ks', 0.95)"
]
},
{
@@ -425,7 +426,7 @@
},
{
"cell_type": "code",
- "execution_count": 6,
+ "execution_count": 111,
"id": "527ff27d-f321-4749-a94d-dd7d824ef682",
"metadata": {
"tags": []
@@ -507,7 +508,7 @@
},
{
"cell_type": "code",
- "execution_count": 7,
+ "execution_count": 112,
"id": "4cb4afc8-6149-40a7-8f77-af06183d4d23",
"metadata": {
"tags": []
@@ -526,6 +527,14 @@
"result_cohend.to_csv(f'./result_cohend.csv', index=False)\n",
"resultTop3_cohend.to_csv(f'./resultTop3_cohend.csv', index=False)"
]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "7605d234-6edf-4513-939f-73e0f49034aa",
+ "metadata": {},
+ "outputs": [],
+ "source": []
}
],
"metadata": {
@@ -544,7 +553,7 @@
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
- "version": "3.12.3"
+ "version": "3.11.4"
}
},
"nbformat": 4,