From 36c8749f6c3672a42f080419aad960715c6e30fb Mon Sep 17 00:00:00 2001 From: jeugregg Date: Wed, 11 Sep 2024 17:04:46 +0200 Subject: [PATCH] update UNRATE model features and results --- data/res_mlp_0.017.csv | 181 + estimate_employment_rate.ipynb | 6688 +++++++++++++++----------------- 2 files changed, 3240 insertions(+), 3629 deletions(-) create mode 100644 data/res_mlp_0.017.csv diff --git a/data/res_mlp_0.017.csv b/data/res_mlp_0.017.csv new file mode 100644 index 0000000..7a02336 --- /dev/null +++ b/data/res_mlp_0.017.csv @@ -0,0 +1,181 @@ +nb_neurons,nb_iter,random_state,accuracy,accuracy_test +8.0,1000.0,0.0,0.574726609963548,0.26666666666666666 +8.0,1000.0,1.0,0.5577156743620899,0.6666666666666666 +8.0,1000.0,2.0,0.5735115431348724,0.6 +8.0,1000.0,3.0,0.5820170109356014,0.6 +8.0,1000.0,4.0,0.5589307411907655,0.4 +8.0,2000.0,0.0,0.606318347509113,0.3333333333333333 +8.0,2000.0,1.0,0.5820170109356014,0.6 +8.0,2000.0,2.0,0.5710814094775213,0.6666666666666666 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+256.0,4000.0,4.0,1.0,0.6666666666666666 +256.0,8000.0,0.0,1.0,0.4 +256.0,8000.0,1.0,1.0,0.4666666666666667 +256.0,8000.0,2.0,1.0,0.6666666666666666 +256.0,8000.0,3.0,1.0,0.4666666666666667 +256.0,8000.0,4.0,1.0,0.5333333333333333 +256.0,16000.0,0.0,1.0,0.4 +256.0,16000.0,1.0,1.0,0.5333333333333333 +256.0,16000.0,2.0,1.0,0.6666666666666666 +256.0,16000.0,3.0,1.0,0.4666666666666667 +256.0,16000.0,4.0,1.0,0.4666666666666667 +256.0,32000.0,0.0,1.0,0.4 +256.0,32000.0,1.0,1.0,0.5333333333333333 +256.0,32000.0,2.0,1.0,0.6666666666666666 +256.0,32000.0,3.0,1.0,0.5333333333333333 +256.0,32000.0,4.0,1.0,0.4666666666666667 diff --git a/estimate_employment_rate.ipynb b/estimate_employment_rate.ipynb index 6f5d840..669dcfd 100644 --- a/estimate_employment_rate.ipynb +++ b/estimate_employment_rate.ipynb @@ -774,7 +774,7 @@ { "data": { "text/plain": [ - "[]" + "[]" ] }, "execution_count": 11, @@ -1563,7 +1563,7 @@ { "data": { "text/plain": [ - "[]" + "[]" ] }, "execution_count": 18, @@ -1900,7 +1900,7 @@ { "data": { "text/plain": [ - "[]" + "[]" ] }, "execution_count": 21, @@ -2786,18 +2786,7 @@ "cell_type": "code", "execution_count": 29, "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "'# same for \"Men, 20 years and over\"\\ndf[\"Men-1\"] = df[\"Men, 20 years and over\"].shift(1)\\ndf[\"Men-2\"] = df[\"Men, 20 years and over\"].shift(2)\\ndf[\"Men-3\"] = df[\"Men, 20 years and over\"].shift(3)\\n\\n#same for \"Women, 20 years and over\"\\ndf[\"Women-1\"] = df[\"Women, 20 years and over\"].shift(1)\\ndf[\"Women-2\"] = df[\"Women, 20 years and over\"].shift(2)\\ndf[\"Women-3\"] = df[\"Women, 20 years and over\"].shift(3)\\n\\n# same for \"16 to 19 years old\"\\t\\ndf[\"16-1\"] = df[\"16 to 19 years old\"].shift(1)\\ndf[\"16-2\"] = df[\"16 to 19 years old\"].shift(2)\\ndf[\"16-3\"] = df[\"16 to 19 years old\"].shift(3)\\n\\n# same for \\t\"White\"\\t\\ndf[\"White-1\"] = df[\"White\"].shift(1)\\ndf[\"White-2\"] = df[\"White\"].shift(2)\\ndf[\"White-3\"] = df[\"White\"].shift(3)\\n\\n# same for Black\\ndf[\"Black-1\"] = df[\"Black or African American\"].shift(1)\\ndf[\"Black-2\"] = df[\"Black or African American\"].shift(2)\\ndf[\"Black-3\"] = df[\"Black or African American\"].shift(3)\\n\\n# same for Asian\\ndf[\"Asian-1\"] = df[\"Asian\"].shift(1)\\ndf[\"Asian-2\"] = df[\"Asian\"].shift(2)\\ndf[\"Asian-3\"] = df[\"Asian\"].shift(3)\\n\\n# same for Hispanic or Latino\\ndf[\"Hispanic-1\"] = df[\"Hispanic or Latino\"].shift(1)\\ndf[\"Hispanic-2\"] = df[\"Hispanic or Latino\"].shift(2)\\ndf[\"Hispanic-3\"] = df[\"Hispanic or Latino\"].shift(3)'" - ] - }, - "execution_count": 29, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "# add last 3 month data about unemployment rate, inflation , fedfunds, SPX to df \n", "# as new columns (i.e: \"Total-1\", \"Total-2\", \"Total-3\")\n", @@ -2818,40 +2807,18 @@ "df[\"spx-2\"] = df[\"SPX_diff\"].shift(2)\n", "df[\"spx-3\"] = df[\"SPX_diff\"].shift(3)\n", "\n", - "\"\"\"# same for \"Men, 20 years and over\"\n", - "df[\"Men-1\"] = df[\"Men, 20 years and over\"].shift(1)\n", - "df[\"Men-2\"] = df[\"Men, 20 years and over\"].shift(2)\n", - "df[\"Men-3\"] = df[\"Men, 20 years and over\"].shift(3)\n", - "\n", - "#same for \"Women, 20 years and over\"\n", - "df[\"Women-1\"] = df[\"Women, 20 years and over\"].shift(1)\n", - "df[\"Women-2\"] = df[\"Women, 20 years and over\"].shift(2)\n", - "df[\"Women-3\"] = df[\"Women, 20 years and over\"].shift(3)\n", + "# diff : the difference between the current value and the previous value\n", + "df[\"Total-1_diff\"] = df[\"Total\"] - df[\"Total-1\"]\n", + "df[\"Total-2_diff\"] = df[\"Total-1\"] - df[\"Total-2\"]\n", + "df[\"Total-3_diff\"] = df[\"Total-2\"] - df[\"Total-3\"] \n", "\n", - "# same for \"16 to 19 years old\"\t\n", - "df[\"16-1\"] = df[\"16 to 19 years old\"].shift(1)\n", - "df[\"16-2\"] = df[\"16 to 19 years old\"].shift(2)\n", - "df[\"16-3\"] = df[\"16 to 19 years old\"].shift(3)\n", + "df[\"Inflation-1_diff\"] = df[\"Inflation\"] - df[\"Inflation-1\"]\n", + "df[\"Inflation-2_diff\"] = df[\"Inflation-1\"] - df[\"Inflation-2\"]\n", + "df[\"Inflation-3_diff\"] = df[\"Inflation-2\"] - df[\"Inflation-3\"]\n", "\n", - "# same for \t\"White\"\t\n", - "df[\"White-1\"] = df[\"White\"].shift(1)\n", - "df[\"White-2\"] = df[\"White\"].shift(2)\n", - "df[\"White-3\"] = df[\"White\"].shift(3)\n", - "\n", - "# same for Black\n", - "df[\"Black-1\"] = df[\"Black or African American\"].shift(1)\n", - "df[\"Black-2\"] = df[\"Black or African American\"].shift(2)\n", - "df[\"Black-3\"] = df[\"Black or African American\"].shift(3)\n", - "\n", - "# same for Asian\n", - "df[\"Asian-1\"] = df[\"Asian\"].shift(1)\n", - "df[\"Asian-2\"] = df[\"Asian\"].shift(2)\n", - "df[\"Asian-3\"] = df[\"Asian\"].shift(3)\n", - "\n", - "# same for Hispanic or Latino\n", - "df[\"Hispanic-1\"] = df[\"Hispanic or Latino\"].shift(1)\n", - "df[\"Hispanic-2\"] = df[\"Hispanic or Latino\"].shift(2)\n", - "df[\"Hispanic-3\"] = df[\"Hispanic or Latino\"].shift(3)\"\"\"" + "df[\"fedfunds-1_diff\"] = df[\"FEDFUNDS\"] - df[\"fedfunds-1\"]\n", + "df[\"fedfunds-2_diff\"] = df[\"fedfunds-1\"] - df[\"fedfunds-2\"]\n", + "df[\"fedfunds-3_diff\"] = df[\"fedfunds-2\"] - df[\"fedfunds-3\"]\n" ] }, { @@ -2891,16 +2858,16 @@ " ur_stable\n", " num_month\n", " ...\n", - " Total-3\n", - " Inflation-1\n", - " Inflation-2\n", - " Inflation-3\n", - " fedfunds-1\n", - " fedfunds-2\n", - " fedfunds-3\n", - " spx-1\n", - " spx-2\n", " spx-3\n", + " Total-1_diff\n", + " Total-2_diff\n", + " Total-3_diff\n", + " Inflation-1_diff\n", + " Inflation-2_diff\n", + " Inflation-3_diff\n", + " fedfunds-1_diff\n", + " fedfunds-2_diff\n", + " fedfunds-3_diff\n", " \n", " \n", " \n", @@ -2917,16 +2884,16 @@ " False\n", " 10\n", " ...\n", - " 5.8\n", - " -0.297508\n", - " 0.000000\n", - " 0.261292\n", - " 1.07\n", - " 1.22\n", - " 0.80\n", - " 0.351526\n", - " 0.259943\n", " 0.240428\n", + " -0.4\n", + " 0.1\n", + " 0.2\n", + " -0.555924\n", + " -0.297508\n", + " -0.261292\n", + " -0.22\n", + " -0.15\n", + " 0.42\n", " \n", " \n", " 82\n", @@ -2941,16 +2908,16 @@ " False\n", " 11\n", " ...\n", - " 6.0\n", - " -0.853432\n", - " -0.297508\n", - " 0.000000\n", - " 0.85\n", - " 1.07\n", - " 1.22\n", - " 0.342511\n", - " 0.351526\n", " 0.259943\n", + " -0.4\n", + " -0.4\n", + " 0.1\n", + " 0.592725\n", + " -0.555924\n", + " -0.297508\n", + " -0.02\n", + " -0.22\n", + " -0.15\n", " \n", " \n", " 83\n", @@ -2965,16 +2932,16 @@ " False\n", " 12\n", " ...\n", - " 6.1\n", - " -0.260708\n", - " -0.853432\n", - " -0.297508\n", - " 0.83\n", - " 0.85\n", - " 1.07\n", - " 0.364898\n", - " 0.342511\n", " 0.351526\n", + " -0.3\n", + " -0.4\n", + " -0.4\n", + " -0.111455\n", + " 0.592725\n", + " -0.555924\n", + " 0.45\n", + " -0.02\n", + " -0.22\n", " \n", " \n", " 84\n", @@ -2989,16 +2956,16 @@ " False\n", " 1\n", " ...\n", - " 5.7\n", - " -0.372162\n", - " -0.260708\n", - " -0.853432\n", - " 1.28\n", - " 0.83\n", - " 0.85\n", - " 0.408377\n", - " 0.364898\n", " 0.342511\n", + " -0.1\n", + " -0.3\n", + " -0.4\n", + " -0.258870\n", + " -0.111455\n", + " 0.592725\n", + " 0.11\n", + " 0.45\n", + " -0.02\n", " \n", " \n", " 85\n", @@ -3013,16 +2980,16 @@ " False\n", " 2\n", " ...\n", - " 5.3\n", - " -0.631032\n", - " -0.372162\n", - " -0.260708\n", - " 1.39\n", - " 1.28\n", - " 0.83\n", - " 0.398272\n", - " 0.408377\n", " 0.364898\n", + " -0.2\n", + " -0.1\n", + " -0.3\n", + " 0.001169\n", + " -0.258870\n", + " -0.111455\n", + " -0.10\n", + " 0.11\n", + " 0.45\n", " \n", " \n", " ...\n", @@ -3061,16 +3028,16 @@ " False\n", " 3\n", " ...\n", - " 3.7\n", - " 3.165743\n", - " 3.105981\n", - " 3.323160\n", - " 5.33\n", - " 5.33\n", - " 5.33\n", - " 0.228518\n", - " 0.213053\n", " 0.197494\n", + " -0.1\n", + " 0.2\n", + " 0.0\n", + " 0.309388\n", + " 0.059762\n", + " -0.217179\n", + " 0.00\n", + " 0.00\n", + " 0.00\n", " \n", " \n", " 915\n", @@ -3085,16 +3052,16 @@ " False\n", " 4\n", " ...\n", - " 3.7\n", - " 3.475131\n", - " 3.165743\n", - " 3.105981\n", - " 5.33\n", - " 5.33\n", - " 5.33\n", - " 0.302883\n", - " 0.228518\n", " 0.213053\n", + " 0.1\n", + " -0.1\n", + " 0.2\n", + " -0.117400\n", + " 0.309388\n", + " 0.059762\n", + " 0.00\n", + " 0.00\n", + " 0.00\n", " \n", " \n", " 916\n", @@ -3109,16 +3076,16 @@ " False\n", " 5\n", " ...\n", - " 3.9\n", - " 3.357731\n", - " 3.475131\n", - " 3.165743\n", - " 5.33\n", - " 5.33\n", - " 5.33\n", - " 0.240453\n", - " 0.302883\n", " 0.228518\n", + " 0.1\n", + " 0.1\n", + " -0.1\n", + " -0.107521\n", + " -0.117400\n", + " 0.309388\n", + " 0.00\n", + " 0.00\n", + " 0.00\n", " \n", " \n", " 917\n", @@ -3133,16 +3100,16 @@ " False\n", " 6\n", " ...\n", - " 3.8\n", - " 3.250210\n", - " 3.357731\n", - " 3.475131\n", - " 5.33\n", - " 5.33\n", - " 5.33\n", - " 0.262664\n", - " 0.240453\n", " 0.302883\n", + " 0.1\n", + " 0.1\n", + " 0.1\n", + " -0.274582\n", + " -0.107521\n", + " -0.117400\n", + " 0.00\n", + " 0.00\n", + " 0.00\n", " \n", " \n", " 918\n", @@ -3157,20 +3124,20 @@ " False\n", " 7\n", " ...\n", - " 3.9\n", - " 2.975629\n", - " 3.250210\n", - " 3.357731\n", - " 5.33\n", - " 5.33\n", - " 5.33\n", - " 0.246186\n", - " 0.262664\n", " 0.240453\n", + " 0.2\n", + " 0.1\n", + " 0.1\n", + " -0.052063\n", + " -0.274582\n", + " -0.107521\n", + " 0.00\n", + " 0.00\n", + " 0.00\n", " \n", " \n", "\n", - "

838 rows × 22 columns

\n", + "

838 rows × 31 columns

\n", "" ], "text/plain": [ @@ -3187,46 +3154,46 @@ "917 2024-06-01 4.1 2.975629 5.33 0.246186 0.2 False \n", "918 2024-07-01 4.3 2.923566 5.33 0.228461 -0.1 True \n", "\n", - " ur_higher ur_stable num_month ... Total-3 Inflation-1 Inflation-2 \\\n", - "81 False False 10 ... 5.8 -0.297508 0.000000 \n", - "82 False False 11 ... 6.0 -0.853432 -0.297508 \n", - "83 False False 12 ... 6.1 -0.260708 -0.853432 \n", - "84 False False 1 ... 5.7 -0.372162 -0.260708 \n", - "85 False False 2 ... 5.3 -0.631032 -0.372162 \n", - ".. ... ... ... ... ... ... ... \n", - "914 True False 3 ... 3.7 3.165743 3.105981 \n", - "915 True False 4 ... 3.7 3.475131 3.165743 \n", - "916 True False 5 ... 3.9 3.357731 3.475131 \n", - "917 True False 6 ... 3.8 3.250210 3.357731 \n", - "918 False False 7 ... 3.9 2.975629 3.250210 \n", + " ur_higher ur_stable num_month ... spx-3 Total-1_diff \\\n", + "81 False False 10 ... 0.240428 -0.4 \n", + "82 False False 11 ... 0.259943 -0.4 \n", + "83 False False 12 ... 0.351526 -0.3 \n", + "84 False False 1 ... 0.342511 -0.1 \n", + "85 False False 2 ... 0.364898 -0.2 \n", + ".. ... ... ... ... ... ... \n", + "914 True False 3 ... 0.197494 -0.1 \n", + "915 True False 4 ... 0.213053 0.1 \n", + "916 True False 5 ... 0.228518 0.1 \n", + "917 True False 6 ... 0.302883 0.1 \n", + "918 False False 7 ... 0.240453 0.2 \n", "\n", - " Inflation-3 fedfunds-1 fedfunds-2 fedfunds-3 spx-1 spx-2 \\\n", - "81 0.261292 1.07 1.22 0.80 0.351526 0.259943 \n", - "82 0.000000 0.85 1.07 1.22 0.342511 0.351526 \n", - "83 -0.297508 0.83 0.85 1.07 0.364898 0.342511 \n", - "84 -0.853432 1.28 0.83 0.85 0.408377 0.364898 \n", - "85 -0.260708 1.39 1.28 0.83 0.398272 0.408377 \n", - ".. ... ... ... ... ... ... \n", - "914 3.323160 5.33 5.33 5.33 0.228518 0.213053 \n", - "915 3.105981 5.33 5.33 5.33 0.302883 0.228518 \n", - "916 3.165743 5.33 5.33 5.33 0.240453 0.302883 \n", - "917 3.475131 5.33 5.33 5.33 0.262664 0.240453 \n", - "918 3.357731 5.33 5.33 5.33 0.246186 0.262664 \n", + " Total-2_diff Total-3_diff Inflation-1_diff Inflation-2_diff \\\n", + "81 0.1 0.2 -0.555924 -0.297508 \n", + "82 -0.4 0.1 0.592725 -0.555924 \n", + "83 -0.4 -0.4 -0.111455 0.592725 \n", + "84 -0.3 -0.4 -0.258870 -0.111455 \n", + "85 -0.1 -0.3 0.001169 -0.258870 \n", + ".. ... ... ... ... \n", + "914 0.2 0.0 0.309388 0.059762 \n", + "915 -0.1 0.2 -0.117400 0.309388 \n", + "916 0.1 -0.1 -0.107521 -0.117400 \n", + "917 0.1 0.1 -0.274582 -0.107521 \n", + "918 0.1 0.1 -0.052063 -0.274582 \n", "\n", - " spx-3 \n", - "81 0.240428 \n", - "82 0.259943 \n", - "83 0.351526 \n", - "84 0.342511 \n", - "85 0.364898 \n", - ".. ... \n", - "914 0.197494 \n", - "915 0.213053 \n", - "916 0.228518 \n", - "917 0.302883 \n", - "918 0.240453 \n", + " Inflation-3_diff fedfunds-1_diff fedfunds-2_diff fedfunds-3_diff \n", + "81 -0.261292 -0.22 -0.15 0.42 \n", + "82 -0.297508 -0.02 -0.22 -0.15 \n", + "83 -0.555924 0.45 -0.02 -0.22 \n", + "84 0.592725 0.11 0.45 -0.02 \n", + "85 -0.111455 -0.10 0.11 0.45 \n", + ".. ... ... ... ... \n", + "914 -0.217179 0.00 0.00 0.00 \n", + "915 0.059762 0.00 0.00 0.00 \n", + "916 0.309388 0.00 0.00 0.00 \n", + "917 -0.117400 0.00 0.00 0.00 \n", + "918 -0.107521 0.00 0.00 0.00 \n", "\n", - "[838 rows x 22 columns]" + "[838 rows x 31 columns]" ] }, "execution_count": 30, @@ -3250,7 +3217,10 @@ "Index(['DATE', 'Total', 'Inflation', 'FEDFUNDS', 'SPX_diff', 'Total_diff',\n", " 'ur_lower', 'ur_higher', 'ur_stable', 'num_month', 'Total-1', 'Total-2',\n", " 'Total-3', 'Inflation-1', 'Inflation-2', 'Inflation-3', 'fedfunds-1',\n", - " 'fedfunds-2', 'fedfunds-3', 'spx-1', 'spx-2', 'spx-3'],\n", + " 'fedfunds-2', 'fedfunds-3', 'spx-1', 'spx-2', 'spx-3', 'Total-1_diff',\n", + " 'Total-2_diff', 'Total-3_diff', 'Inflation-1_diff', 'Inflation-2_diff',\n", + " 'Inflation-3_diff', 'fedfunds-1_diff', 'fedfunds-2_diff',\n", + " 'fedfunds-3_diff'],\n", " dtype='object')" ] }, @@ -3267,6 +3237,39 @@ "cell_type": "code", "execution_count": 32, "metadata": {}, + "outputs": [], + "source": [ + "\"\"\"list_feat = ['Total', 'Inflation', 'FEDFUNDS', 'SPX_diff', 'num_month', 'Total-1',\n", + " 'Total-2', 'Total-3', 'Inflation-1', 'Inflation-2', 'Inflation-3',\n", + " 'fedfunds-1', 'fedfunds-2', 'fedfunds-3', 'spx-1', 'spx-2', 'spx-3']\"\"\"\n", + "\n", + "list_feat = ['Total', 'Inflation', 'FEDFUNDS', 'SPX_diff', 'num_month', \n", + " 'Total-1_diff', 'Total-2_diff', 'Total-3_diff', \n", + " 'Inflation-1_diff', 'Inflation-2_diff', 'Inflation-3_diff',\n", + " 'fedfunds-1_diff', 'fedfunds-2_diff', 'fedfunds-3_diff', \n", + " 'spx-1', 'spx-2', 'spx-3']\n", + "#df_x = df.filter(list_feat)\n", + "#df_x" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Target" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Creation of a multi-class target : df_y[\"class\"]" + ] + }, + { + "cell_type": "code", + "execution_count": 33, + "metadata": {}, "outputs": [ { "data": { @@ -3289,125 +3292,149 @@ " \n", " \n", " \n", + " DATE\n", " Total\n", " Inflation\n", " FEDFUNDS\n", " SPX_diff\n", + " Total_diff\n", + " ur_lower\n", + " ur_higher\n", + " ur_stable\n", " num_month\n", - " Total-1\n", - " Total-2\n", - " Total-3\n", - " Inflation-1\n", - " Inflation-2\n", - " Inflation-3\n", - " fedfunds-1\n", - " fedfunds-2\n", - " fedfunds-3\n", - " spx-1\n", - " spx-2\n", - " spx-3\n", + " ...\n", + " Total-1_diff\n", + " Total-2_diff\n", + " Total-3_diff\n", + " Inflation-1_diff\n", + " Inflation-2_diff\n", + " Inflation-3_diff\n", + " fedfunds-1_diff\n", + " fedfunds-2_diff\n", + " fedfunds-3_diff\n", + " class\n", " \n", " \n", " \n", " \n", " 81\n", + " 1954-10-01\n", " 5.7\n", " -0.853432\n", " 0.85\n", " 0.342511\n", + " -0.4\n", + " True\n", + " False\n", + " False\n", " 10\n", - " 6.1\n", - " 6.0\n", - " 5.8\n", + " ...\n", + " -0.4\n", + " 0.1\n", + " 0.2\n", + " -0.555924\n", " -0.297508\n", - " 0.000000\n", - " 0.261292\n", - " 1.07\n", - " 1.22\n", - " 0.80\n", - " 0.351526\n", - " 0.259943\n", - " 0.240428\n", + " -0.261292\n", + " -0.22\n", + " -0.15\n", + " 0.42\n", + " 0\n", " \n", " \n", " 82\n", + " 1954-11-01\n", " 5.3\n", " -0.260708\n", " 0.83\n", " 0.364898\n", + " -0.3\n", + " True\n", + " False\n", + " False\n", " 11\n", - " 5.7\n", - " 6.1\n", - " 6.0\n", - " -0.853432\n", + " ...\n", + " -0.4\n", + " -0.4\n", + " 0.1\n", + " 0.592725\n", + " -0.555924\n", " -0.297508\n", - " 0.000000\n", - " 0.85\n", - " 1.07\n", - " 1.22\n", - " 0.342511\n", - " 0.351526\n", - " 0.259943\n", + " -0.02\n", + " -0.22\n", + " -0.15\n", + " 0\n", " \n", " \n", " 83\n", + " 1954-12-01\n", " 5.0\n", " -0.372162\n", " 1.28\n", " 0.408377\n", + " -0.1\n", + " True\n", + " False\n", + " False\n", " 12\n", - " 5.3\n", - " 5.7\n", - " 6.1\n", - " -0.260708\n", - " -0.853432\n", - " -0.297508\n", - " 0.83\n", - " 0.85\n", - " 1.07\n", - " 0.364898\n", - " 0.342511\n", - " 0.351526\n", + " ...\n", + " -0.3\n", + " -0.4\n", + " -0.4\n", + " -0.111455\n", + " 0.592725\n", + " -0.555924\n", + " 0.45\n", + " -0.02\n", + " -0.22\n", + " 0\n", " \n", " \n", " 84\n", + " 1955-01-01\n", " 4.9\n", " -0.631032\n", " 1.39\n", " 0.398272\n", + " -0.2\n", + " True\n", + " False\n", + " False\n", " 1\n", - " 5.0\n", - " 5.3\n", - " 5.7\n", - " -0.372162\n", - " -0.260708\n", - " -0.853432\n", - " 1.28\n", - " 0.83\n", - " 0.85\n", - " 0.408377\n", - " 0.364898\n", - " 0.342511\n", - " \n", - " \n", + " ...\n", + " -0.1\n", + " -0.3\n", + " -0.4\n", + " -0.258870\n", + " -0.111455\n", + " 0.592725\n", + " 0.11\n", + " 0.45\n", + " -0.02\n", + " 0\n", + " \n", + " \n", " 85\n", + " 1955-02-01\n", " 4.7\n", " -0.629863\n", " 1.29\n", " 0.413912\n", + " -0.1\n", + " True\n", + " False\n", + " False\n", " 2\n", - " 4.9\n", - " 5.0\n", - " 5.3\n", - " -0.631032\n", - " -0.372162\n", - " -0.260708\n", - " 1.39\n", - " 1.28\n", - " 0.83\n", - " 0.398272\n", - " 0.408377\n", - " 0.364898\n", + " ...\n", + " -0.2\n", + " -0.1\n", + " -0.3\n", + " 0.001169\n", + " -0.258870\n", + " -0.111455\n", + " -0.10\n", + " 0.11\n", + " 0.45\n", + " 0\n", " \n", " \n", " ...\n", @@ -3428,172 +3455,205 @@ " ...\n", " ...\n", " ...\n", + " ...\n", + " ...\n", + " ...\n", + " ...\n", " \n", " \n", " 914\n", + " 2024-03-01\n", " 3.8\n", " 3.475131\n", " 5.33\n", " 0.302883\n", + " 0.1\n", + " False\n", + " True\n", + " False\n", " 3\n", - " 3.9\n", - " 3.7\n", - " 3.7\n", - " 3.165743\n", - " 3.105981\n", - " 3.323160\n", - " 5.33\n", - " 5.33\n", - " 5.33\n", - " 0.228518\n", - " 0.213053\n", - " 0.197494\n", + " ...\n", + " -0.1\n", + " 0.2\n", + " 0.0\n", + " 0.309388\n", + " 0.059762\n", + " -0.217179\n", + " 0.00\n", + " 0.00\n", + " 0.00\n", + " 2\n", " \n", " \n", " 915\n", + " 2024-04-01\n", " 3.9\n", " 3.357731\n", " 5.33\n", " 0.240453\n", + " 0.1\n", + " False\n", + " True\n", + " False\n", " 4\n", - " 3.8\n", - " 3.9\n", - " 3.7\n", - " 3.475131\n", - " 3.165743\n", - " 3.105981\n", - " 5.33\n", - " 5.33\n", - " 5.33\n", - " 0.302883\n", - " 0.228518\n", - " 0.213053\n", + " ...\n", + " 0.1\n", + " -0.1\n", + " 0.2\n", + " -0.117400\n", + " 0.309388\n", + " 0.059762\n", + " 0.00\n", + " 0.00\n", + " 0.00\n", + " 2\n", " \n", " \n", " 916\n", + " 2024-05-01\n", " 4.0\n", " 3.250210\n", " 5.33\n", " 0.262664\n", + " 0.1\n", + " False\n", + " True\n", + " False\n", " 5\n", - " 3.9\n", - " 3.8\n", - " 3.9\n", - " 3.357731\n", - " 3.475131\n", - " 3.165743\n", - " 5.33\n", - " 5.33\n", - " 5.33\n", - " 0.240453\n", - " 0.302883\n", - " 0.228518\n", + " ...\n", + " 0.1\n", + " 0.1\n", + " -0.1\n", + " -0.107521\n", + " -0.117400\n", + " 0.309388\n", + " 0.00\n", + " 0.00\n", + " 0.00\n", + " 2\n", " \n", " \n", " 917\n", + " 2024-06-01\n", " 4.1\n", " 2.975629\n", " 5.33\n", " 0.246186\n", + " 0.2\n", + " False\n", + " True\n", + " False\n", " 6\n", - " 4.0\n", - " 3.9\n", - " 3.8\n", - " 3.250210\n", - " 3.357731\n", - " 3.475131\n", - " 5.33\n", - " 5.33\n", - " 5.33\n", - " 0.262664\n", - " 0.240453\n", - " 0.302883\n", + " ...\n", + " 0.1\n", + " 0.1\n", + " 0.1\n", + " -0.274582\n", + " -0.107521\n", + " -0.117400\n", + " 0.00\n", + " 0.00\n", + " 0.00\n", + " 2\n", " \n", " \n", " 918\n", + " 2024-07-01\n", " 4.3\n", " 2.923566\n", " 5.33\n", " 0.228461\n", + " -0.1\n", + " True\n", + " False\n", + " False\n", " 7\n", - " 4.1\n", - " 4.0\n", - " 3.9\n", - " 2.975629\n", - " 3.250210\n", - " 3.357731\n", - " 5.33\n", - " 5.33\n", - " 5.33\n", - " 0.246186\n", - " 0.262664\n", - " 0.240453\n", + " ...\n", + " 0.2\n", + " 0.1\n", + " 0.1\n", + " -0.052063\n", + " -0.274582\n", + " -0.107521\n", + " 0.00\n", + " 0.00\n", + " 0.00\n", + " 0\n", " \n", " \n", "\n", - "

838 rows × 17 columns

\n", + "

838 rows × 32 columns

\n", "" ], "text/plain": [ - " Total Inflation FEDFUNDS SPX_diff num_month Total-1 Total-2 \\\n", - "81 5.7 -0.853432 0.85 0.342511 10 6.1 6.0 \n", - "82 5.3 -0.260708 0.83 0.364898 11 5.7 6.1 \n", - "83 5.0 -0.372162 1.28 0.408377 12 5.3 5.7 \n", - "84 4.9 -0.631032 1.39 0.398272 1 5.0 5.3 \n", - "85 4.7 -0.629863 1.29 0.413912 2 4.9 5.0 \n", - ".. ... ... ... ... ... ... ... \n", - "914 3.8 3.475131 5.33 0.302883 3 3.9 3.7 \n", - "915 3.9 3.357731 5.33 0.240453 4 3.8 3.9 \n", - "916 4.0 3.250210 5.33 0.262664 5 3.9 3.8 \n", - "917 4.1 2.975629 5.33 0.246186 6 4.0 3.9 \n", - "918 4.3 2.923566 5.33 0.228461 7 4.1 4.0 \n", + " DATE Total Inflation FEDFUNDS SPX_diff Total_diff ur_lower \\\n", + "81 1954-10-01 5.7 -0.853432 0.85 0.342511 -0.4 True \n", + "82 1954-11-01 5.3 -0.260708 0.83 0.364898 -0.3 True \n", + "83 1954-12-01 5.0 -0.372162 1.28 0.408377 -0.1 True \n", + "84 1955-01-01 4.9 -0.631032 1.39 0.398272 -0.2 True \n", + "85 1955-02-01 4.7 -0.629863 1.29 0.413912 -0.1 True \n", + ".. ... ... ... ... ... ... ... \n", + "914 2024-03-01 3.8 3.475131 5.33 0.302883 0.1 False \n", + "915 2024-04-01 3.9 3.357731 5.33 0.240453 0.1 False \n", + "916 2024-05-01 4.0 3.250210 5.33 0.262664 0.1 False \n", + "917 2024-06-01 4.1 2.975629 5.33 0.246186 0.2 False \n", + "918 2024-07-01 4.3 2.923566 5.33 0.228461 -0.1 True \n", + "\n", + " ur_higher ur_stable num_month ... Total-1_diff Total-2_diff \\\n", + "81 False False 10 ... -0.4 0.1 \n", + "82 False False 11 ... -0.4 -0.4 \n", + "83 False False 12 ... -0.3 -0.4 \n", + "84 False False 1 ... -0.1 -0.3 \n", + "85 False False 2 ... -0.2 -0.1 \n", + ".. ... ... ... ... ... ... \n", + "914 True False 3 ... -0.1 0.2 \n", + "915 True False 4 ... 0.1 -0.1 \n", + "916 True False 5 ... 0.1 0.1 \n", + "917 True False 6 ... 0.1 0.1 \n", + "918 False False 7 ... 0.2 0.1 \n", "\n", - " Total-3 Inflation-1 Inflation-2 Inflation-3 fedfunds-1 fedfunds-2 \\\n", - "81 5.8 -0.297508 0.000000 0.261292 1.07 1.22 \n", - "82 6.0 -0.853432 -0.297508 0.000000 0.85 1.07 \n", - "83 6.1 -0.260708 -0.853432 -0.297508 0.83 0.85 \n", - "84 5.7 -0.372162 -0.260708 -0.853432 1.28 0.83 \n", - "85 5.3 -0.631032 -0.372162 -0.260708 1.39 1.28 \n", - ".. ... ... ... ... ... ... \n", - "914 3.7 3.165743 3.105981 3.323160 5.33 5.33 \n", - "915 3.7 3.475131 3.165743 3.105981 5.33 5.33 \n", - "916 3.9 3.357731 3.475131 3.165743 5.33 5.33 \n", - "917 3.8 3.250210 3.357731 3.475131 5.33 5.33 \n", - "918 3.9 2.975629 3.250210 3.357731 5.33 5.33 \n", + " Total-3_diff Inflation-1_diff Inflation-2_diff Inflation-3_diff \\\n", + "81 0.2 -0.555924 -0.297508 -0.261292 \n", + "82 0.1 0.592725 -0.555924 -0.297508 \n", + "83 -0.4 -0.111455 0.592725 -0.555924 \n", + "84 -0.4 -0.258870 -0.111455 0.592725 \n", + "85 -0.3 0.001169 -0.258870 -0.111455 \n", + ".. ... ... ... ... \n", + "914 0.0 0.309388 0.059762 -0.217179 \n", + "915 0.2 -0.117400 0.309388 0.059762 \n", + "916 -0.1 -0.107521 -0.117400 0.309388 \n", + "917 0.1 -0.274582 -0.107521 -0.117400 \n", + "918 0.1 -0.052063 -0.274582 -0.107521 \n", "\n", - " fedfunds-3 spx-1 spx-2 spx-3 \n", - "81 0.80 0.351526 0.259943 0.240428 \n", - "82 1.22 0.342511 0.351526 0.259943 \n", - "83 1.07 0.364898 0.342511 0.351526 \n", - "84 0.85 0.408377 0.364898 0.342511 \n", - "85 0.83 0.398272 0.408377 0.364898 \n", - ".. ... ... ... ... \n", - "914 5.33 0.228518 0.213053 0.197494 \n", - "915 5.33 0.302883 0.228518 0.213053 \n", - "916 5.33 0.240453 0.302883 0.228518 \n", - "917 5.33 0.262664 0.240453 0.302883 \n", - "918 5.33 0.246186 0.262664 0.240453 \n", + " fedfunds-1_diff fedfunds-2_diff fedfunds-3_diff class \n", + "81 -0.22 -0.15 0.42 0 \n", + "82 -0.02 -0.22 -0.15 0 \n", + "83 0.45 -0.02 -0.22 0 \n", + "84 0.11 0.45 -0.02 0 \n", + "85 -0.10 0.11 0.45 0 \n", + ".. ... ... ... ... \n", + "914 0.00 0.00 0.00 2 \n", + "915 0.00 0.00 0.00 2 \n", + "916 0.00 0.00 0.00 2 \n", + "917 0.00 0.00 0.00 2 \n", + "918 0.00 0.00 0.00 0 \n", "\n", - "[838 rows x 17 columns]" + "[838 rows x 32 columns]" ] }, - "execution_count": 32, + "execution_count": 33, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "list_feat = ['Total', 'Inflation', 'FEDFUNDS', 'SPX_diff', 'num_month', 'Total-1',\n", - " 'Total-2', 'Total-3', 'Inflation-1', 'Inflation-2', 'Inflation-3',\n", - " 'fedfunds-1', 'fedfunds-2', 'fedfunds-3', 'spx-1', 'spx-2', 'spx-3']\n", - "\n", - "df_x = df.filter(list_feat)\n", - "df_x" + "df[\"class\"] = 0*df[\"ur_lower\"] + 1*df[\"ur_stable\"] + 2*df[\"ur_higher\"] \n", + "df" ] }, { "cell_type": "code", - "execution_count": 33, + "execution_count": 34, "metadata": {}, "outputs": [ { @@ -3621,6 +3681,7 @@ " ur_lower\n", " ur_higher\n", " ur_stable\n", + " class\n", " \n", " \n", " \n", @@ -3630,6 +3691,7 @@ " True\n", " False\n", " False\n", + " 0\n", " \n", " \n", " 82\n", @@ -3637,6 +3699,7 @@ " True\n", " False\n", " False\n", + " 0\n", " \n", " \n", " 83\n", @@ -3644,6 +3707,7 @@ " True\n", " False\n", " False\n", + " 0\n", " \n", " \n", " 84\n", @@ -3651,6 +3715,7 @@ " True\n", " False\n", " False\n", + " 0\n", " \n", " \n", " 85\n", @@ -3658,6 +3723,7 @@ " True\n", " False\n", " False\n", + " 0\n", " \n", " \n", " ...\n", @@ -3665,6 +3731,7 @@ " ...\n", " ...\n", " ...\n", + " ...\n", " \n", " \n", " 914\n", @@ -3672,6 +3739,7 @@ " False\n", " True\n", " False\n", + " 2\n", " \n", " \n", " 915\n", @@ -3679,6 +3747,7 @@ " False\n", " True\n", " False\n", + " 2\n", " \n", " \n", " 916\n", @@ -3686,6 +3755,7 @@ " False\n", " True\n", " False\n", + " 2\n", " \n", " \n", " 917\n", @@ -3693,6 +3763,7 @@ " False\n", " True\n", " False\n", + " 2\n", " \n", " \n", " 918\n", @@ -3700,90 +3771,81 @@ " True\n", " False\n", " False\n", + " 0\n", " \n", " \n", "\n", - "

838 rows × 4 columns

\n", + "

838 rows × 5 columns

\n", "" ], "text/plain": [ - " Total_diff ur_lower ur_higher ur_stable\n", - "81 -0.4 True False False\n", - "82 -0.3 True False False\n", - "83 -0.1 True False False\n", - "84 -0.2 True False False\n", - "85 -0.1 True False False\n", - ".. ... ... ... ...\n", - "914 0.1 False True False\n", - "915 0.1 False True False\n", - "916 0.1 False True False\n", - "917 0.2 False True False\n", - "918 -0.1 True False False\n", + " Total_diff ur_lower ur_higher ur_stable class\n", + "81 -0.4 True False False 0\n", + "82 -0.3 True False False 0\n", + "83 -0.1 True False False 0\n", + "84 -0.2 True False False 0\n", + "85 -0.1 True False False 0\n", + ".. ... ... ... ... ...\n", + "914 0.1 False True False 2\n", + "915 0.1 False True False 2\n", + "916 0.1 False True False 2\n", + "917 0.2 False True False 2\n", + "918 -0.1 True False False 0\n", "\n", - "[838 rows x 4 columns]" + "[838 rows x 5 columns]" ] }, - "execution_count": 33, + "execution_count": 34, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df_y = df.filter(['Total_diff',\n", - " 'ur_lower', 'ur_higher', 'ur_stable'])\n", + " 'ur_lower', 'ur_higher', 'ur_stable', \"class\"])\n", "df_y" ] }, { "cell_type": "code", - "execution_count": 34, + "execution_count": 35, "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "array([[ 5.7 , -0.85343228, 0.85 , ..., 0.35152557,\n", - " 0.2599426 , 0.24042816],\n", - " [ 5.3 , -0.26070764, 0.83 , ..., 0.34251147,\n", - " 0.35152557, 0.2599426 ],\n", - " [ 5. , -0.37216226, 1.28 , ..., 0.36489796,\n", - " 0.34251147, 0.35152557],\n", - " ...,\n", - " [ 4. , 3.25021014, 5.33 , ..., 0.24045304,\n", - " 0.30288316, 0.22851792],\n", - " [ 4.1 , 2.97562853, 5.33 , ..., 0.2626641 ,\n", - " 0.24045304, 0.30288316],\n", - " [ 4.3 , 2.92356579, 5.33 , ..., 0.24618617,\n", - " 0.2626641 , 0.24045304]])" - ] - }, - "execution_count": 34, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ - "data = df_x.values\n", - "data" + "list_targets = [\"ur_lower\", \"ur_stable\", \"ur_higher\"]" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "## Target" + "## Split Train / Test" ] }, { - "cell_type": "markdown", + "cell_type": "code", + "execution_count": 36, "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "nb_test : 823\n" + ] + } + ], "source": [ - "Creation of a multi-class target : df_y[\"class\"]" + "ratio_test = 0.017\n", + "nb_test = int(df.shape[0] * (1 - ratio_test))\n", + "print(\"nb_test : \", nb_test)\n", + "#from sklearn.model_selection import train_test_split\n", + "#xtrain, xtest, ytrain, ytest = train_test_split(data, target, train_size=0.8)\n" ] }, { "cell_type": "code", - "execution_count": 35, + "execution_count": 37, "metadata": {}, "outputs": [ { @@ -3807,52 +3869,148 @@ " \n", " \n", " \n", + " DATE\n", + " Total\n", + " Inflation\n", + " FEDFUNDS\n", + " SPX_diff\n", " Total_diff\n", " ur_lower\n", " ur_higher\n", " ur_stable\n", + " num_month\n", + " ...\n", + " Total-1_diff\n", + " Total-2_diff\n", + " Total-3_diff\n", + " Inflation-1_diff\n", + " Inflation-2_diff\n", + " Inflation-3_diff\n", + " fedfunds-1_diff\n", + " fedfunds-2_diff\n", + " fedfunds-3_diff\n", " class\n", " \n", " \n", " \n", " \n", " 81\n", + " 1954-10-01\n", + " 5.7\n", + " -0.853432\n", + " 0.85\n", + " 0.342511\n", " -0.4\n", " True\n", " False\n", " False\n", + " 10\n", + " ...\n", + " -0.4\n", + " 0.1\n", + " 0.2\n", + " -0.555924\n", + " -0.297508\n", + " -0.261292\n", + " -0.22\n", + " -0.15\n", + " 0.42\n", " 0\n", " \n", " \n", " 82\n", + " 1954-11-01\n", + " 5.3\n", + " -0.260708\n", + " 0.83\n", + " 0.364898\n", " -0.3\n", " True\n", " False\n", " False\n", + " 11\n", + " ...\n", + " -0.4\n", + " -0.4\n", + " 0.1\n", + " 0.592725\n", + " -0.555924\n", + " -0.297508\n", + " -0.02\n", + " -0.22\n", + " -0.15\n", " 0\n", " \n", " \n", " 83\n", + " 1954-12-01\n", + " 5.0\n", + " -0.372162\n", + " 1.28\n", + " 0.408377\n", " -0.1\n", " True\n", " False\n", " False\n", + " 12\n", + " ...\n", + " -0.3\n", + " -0.4\n", + " -0.4\n", + " -0.111455\n", + " 0.592725\n", + " -0.555924\n", + " 0.45\n", + " -0.02\n", + " -0.22\n", " 0\n", " \n", " \n", " 84\n", + " 1955-01-01\n", + " 4.9\n", + " -0.631032\n", + " 1.39\n", + " 0.398272\n", " -0.2\n", " True\n", " False\n", " False\n", + " 1\n", + " ...\n", + " -0.1\n", + " -0.3\n", + " -0.4\n", + " -0.258870\n", + " -0.111455\n", + " 0.592725\n", + " 0.11\n", + " 0.45\n", + " -0.02\n", " 0\n", " \n", " \n", " 85\n", + " 1955-02-01\n", + " 4.7\n", + " -0.629863\n", + " 1.29\n", + " 0.413912\n", " -0.1\n", " True\n", " False\n", " False\n", + " 2\n", + " ...\n", + " -0.2\n", + " -0.1\n", + " -0.3\n", + " 0.001169\n", + " -0.258870\n", + " -0.111455\n", + " -0.10\n", + " 0.11\n", + " 0.45\n", " 0\n", " \n", " \n", @@ -3862,190 +4020,202 @@ " ...\n", " ...\n", " ...\n", + " ...\n", + " ...\n", + " ...\n", + " ...\n", + " ...\n", + " ...\n", + " ...\n", + " ...\n", + " ...\n", + " ...\n", + " ...\n", + " ...\n", + " ...\n", + " ...\n", + " ...\n", + " ...\n", " \n", " \n", - " 914\n", - " 0.1\n", - " False\n", + " 899\n", + " 2022-12-01\n", + " 3.5\n", + " 6.411498\n", + " 4.10\n", + " -0.163086\n", + " -0.1\n", " True\n", " False\n", - " 2\n", - " \n", - " \n", - " 915\n", - " 0.1\n", - " False\n", - " True\n", " False\n", - " 2\n", + " 12\n", + " ...\n", + " -0.1\n", + " 0.0\n", + " 0.1\n", + " -0.707968\n", + " -0.632475\n", + " -0.446331\n", + " 0.32\n", + " 0.70\n", + " 0.52\n", + " 0\n", " \n", " \n", - " 916\n", - " 0.1\n", + " 900\n", + " 2023-01-01\n", + " 3.4\n", + " 6.362123\n", + " 4.33\n", + " -0.134059\n", + " 0.2\n", " False\n", " True\n", " False\n", + " 1\n", + " ...\n", + " -0.1\n", + " -0.1\n", + " 0.0\n", + " -0.049375\n", + " -0.707968\n", + " -0.632475\n", + " 0.23\n", + " 0.32\n", + " 0.70\n", " 2\n", " \n", " \n", - " 917\n", - " 0.2\n", - " False\n", + " 901\n", + " 2023-02-01\n", + " 3.6\n", + " 5.965523\n", + " 4.57\n", + " -0.080320\n", + " -0.1\n", " True\n", " False\n", + " False\n", " 2\n", + " ...\n", + " 0.2\n", + " -0.1\n", + " -0.1\n", + " -0.396601\n", + " -0.049375\n", + " -0.707968\n", + " 0.24\n", + " 0.23\n", + " 0.32\n", + " 0\n", " \n", " \n", - " 918\n", + " 902\n", + " 2023-03-01\n", + " 3.5\n", + " 4.935090\n", + " 4.65\n", + " -0.096261\n", " -0.1\n", " True\n", " False\n", " False\n", + " 3\n", + " ...\n", + " -0.1\n", + " 0.2\n", + " -0.1\n", + " -1.030432\n", + " -0.396601\n", + " -0.049375\n", + " 0.08\n", + " 0.24\n", + " 0.23\n", " 0\n", " \n", + " \n", + " 903\n", + " 2023-04-01\n", + " 3.4\n", + " 4.941059\n", + " 4.83\n", + " -0.061446\n", + " 0.3\n", + " False\n", + " True\n", + " False\n", + " 4\n", + " ...\n", + " -0.1\n", + " -0.1\n", + " 0.2\n", + " 0.005969\n", + " -1.030432\n", + " -0.396601\n", + " 0.18\n", + " 0.08\n", + " 0.24\n", + " 2\n", + " \n", " \n", "\n", - "

838 rows × 5 columns

\n", + "

823 rows × 32 columns

\n", "" ], "text/plain": [ - " Total_diff ur_lower ur_higher ur_stable class\n", - "81 -0.4 True False False 0\n", - "82 -0.3 True False False 0\n", - "83 -0.1 True False False 0\n", - "84 -0.2 True False False 0\n", - "85 -0.1 True False False 0\n", - ".. ... ... ... ... ...\n", - "914 0.1 False True False 2\n", - "915 0.1 False True False 2\n", - "916 0.1 False True False 2\n", - "917 0.2 False True False 2\n", - "918 -0.1 True False False 0\n", + " DATE Total Inflation FEDFUNDS SPX_diff Total_diff ur_lower \\\n", + "81 1954-10-01 5.7 -0.853432 0.85 0.342511 -0.4 True \n", + "82 1954-11-01 5.3 -0.260708 0.83 0.364898 -0.3 True \n", + "83 1954-12-01 5.0 -0.372162 1.28 0.408377 -0.1 True \n", + "84 1955-01-01 4.9 -0.631032 1.39 0.398272 -0.2 True \n", + "85 1955-02-01 4.7 -0.629863 1.29 0.413912 -0.1 True \n", + ".. ... ... ... ... ... ... ... \n", + "899 2022-12-01 3.5 6.411498 4.10 -0.163086 -0.1 True \n", + "900 2023-01-01 3.4 6.362123 4.33 -0.134059 0.2 False \n", + "901 2023-02-01 3.6 5.965523 4.57 -0.080320 -0.1 True \n", + "902 2023-03-01 3.5 4.935090 4.65 -0.096261 -0.1 True \n", + "903 2023-04-01 3.4 4.941059 4.83 -0.061446 0.3 False \n", "\n", - "[838 rows x 5 columns]" - ] - }, - "execution_count": 35, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "df_y[\"class\"] = 0*df_y[\"ur_lower\"] +1*df_y[\"ur_stable\"] + 2*df_y[\"ur_higher\"] \n", - "df_y" - ] - }, - { - "cell_type": "code", - "execution_count": 36, - "metadata": {}, - "outputs": [], - "source": [ - "list_targets = [\"ur_lower\", \"ur_stable\", \"ur_higher\"]" - ] - }, - { - "cell_type": "code", - "execution_count": 37, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "array([ True, True, True, True, True, False, True, True, True,\n", - " False, True, False, True, False, True, True, False, True,\n", - " False, False, False, True, True, False, False, True, False,\n", - " True, True, False, False, False, True, True, False, False,\n", - " False, False, False, False, False, False, False, True, False,\n", - " True, True, True, True, False, True, True, True, True,\n", - " True, True, False, False, False, False, False, True, True,\n", - " True, False, True, True, False, False, False, True, False,\n", - " False, False, False, False, False, False, False, True, False,\n", - " True, False, True, True, True, True, True, False, False,\n", - " True, False, True, False, True, True, False, True, False,\n", - " False, True, False, False, True, False, True, False, False,\n", - " False, True, False, True, False, True, True, False, True,\n", - " False, False, False, True, False, True, False, True, False,\n", - " True, False, True, False, True, True, True, True, False,\n", - " True, False, False, False, True, False, False, True, False,\n", - " True, False, False, True, False, False, False, False, True,\n", - " False, False, False, True, True, True, False, True, True,\n", - " False, False, False, True, True, False, False, False, False,\n", - " False, False, False, False, False, False, False, False, False,\n", - " True, False, False, False, False, False, False, False, False,\n", - " False, False, False, False, False, True, False, False, True,\n", - " False, False, False, False, True, True, False, False, True,\n", - " True, False, True, False, False, True, False, True, False,\n", - " True, True, True, False, True, False, True, False, True,\n", - " False, False, True, False, False, False, False, True, False,\n", - " False, False, False, False, False, False, False, False, False,\n", - " False, False, False, False, True, True, True, False, False,\n", - " True, True, True, True, True, False, True, False, False,\n", - " False, True, False, False, False, True, False, True, True,\n", - " True, False, True, False, True, False, False, True, False,\n", - " True, False, True, True, True, False, True, False, True,\n", - " False, False, True, False, True, False, True, False, False,\n", - " False, True, False, True, False, False, False, False, False,\n", - " False, False, False, True, True, False, False, True, False,\n", - " True, False, True, False, False, True, False, False, False,\n", - " False, False, False, False, False, False, False, False, False,\n", - " False, False, False, False, False, True, False, True, True,\n", - " True, False, True, False, True, True, True, True, True,\n", - " True, False, True, True, True, False, False, True, False,\n", - " True, False, False, True, False, False, True, False, False,\n", - " True, False, False, True, False, True, False, False, True,\n", - " False, False, True, True, False, False, True, True, False,\n", - " False, False, True, False, True, True, True, True, False,\n", - " True, True, False, False, False, True, False, True, False,\n", - " False, True, False, True, False, False, True, True, False,\n", - " False, False, True, False, False, False, False, False, False,\n", - " True, True, False, False, True, False, False, False, False,\n", - " False, False, False, False, False, True, False, False, True,\n", - " False, False, False, False, False, False, False, False, False,\n", - " False, False, True, True, False, True, False, False, True,\n", - " True, True, False, False, True, True, True, True, False,\n", - " True, True, False, False, True, True, True, False, False,\n", - " True, True, True, True, True, False, True, False, False,\n", - " True, False, False, False, True, True, False, False, False,\n", - " True, False, False, False, True, False, True, False, False,\n", - " False, False, True, True, False, True, True, False, True,\n", - " True, False, True, True, False, True, False, False, True,\n", - " False, False, False, False, False, True, True, False, True,\n", - " False, True, False, True, False, False, True, False, True,\n", - " False, True, False, False, True, True, False, False, False,\n", - " False, True, False, False, False, False, False, False, False,\n", - " True, False, False, False, False, False, False, False, False,\n", - " False, False, False, True, False, False, True, False, False,\n", - " False, False, True, False, False, False, False, False, True,\n", - " True, False, True, True, True, False, True, False, True,\n", - " False, False, True, True, False, False, True, False, True,\n", - " False, True, False, True, True, False, True, False, False,\n", - " False, True, True, False, True, False, True, False, False,\n", - " False, True, True, False, True, False, True, True, False,\n", - " True, False, False, True, False, False, False, False, False,\n", - " True, False, True, False, False, False, False, False, False,\n", - " False, False, False, False, False, False, False, False, False,\n", - " False, False, False, True, False, True, False, False, False,\n", - " True, True, False, False, False, True, False, True, True,\n", - " True, False, False, True, False, True, False, False, True,\n", - " True, True, True, False, True, False, False, False, False,\n", - " True, True, False, True, False, False, True, True, False,\n", - " True, False, True, True, False, False, True, True, True,\n", - " False, False, True, False, True, False, True, True, True,\n", - " False, True, False, True, True, False, False, True, True,\n", - " True, True, False, False, True, True, False, False, False,\n", - " True, False, True, False, False, True, True, False, False,\n", - " True, True, False, False, True, False, False, True, True,\n", - " False, True, True, False, True, False, True, False, True,\n", - " False, True, False, False, False, False, True, False, True,\n", - " True, False, False, True, True, False, False, False, False,\n", - " True, False, False, True, True, True, True, True, True,\n", - " True, False, True, True, True, False, True, False, True,\n", - " True, True, True, True, True, False, True, True, False,\n", - " True, False, True, False, True, False, False, True, True,\n", - " False, True, True, False, True, True, False, False, False,\n", - " True, False, False, False, True, False, False, False, False,\n", - " True])" + " ur_higher ur_stable num_month ... Total-1_diff Total-2_diff \\\n", + "81 False False 10 ... -0.4 0.1 \n", + "82 False False 11 ... -0.4 -0.4 \n", + "83 False False 12 ... -0.3 -0.4 \n", + "84 False False 1 ... -0.1 -0.3 \n", + "85 False False 2 ... -0.2 -0.1 \n", + ".. ... ... ... ... ... ... \n", + "899 False False 12 ... -0.1 0.0 \n", + "900 True False 1 ... -0.1 -0.1 \n", + "901 False False 2 ... 0.2 -0.1 \n", + "902 False False 3 ... -0.1 0.2 \n", + "903 True False 4 ... -0.1 -0.1 \n", + "\n", + " Total-3_diff Inflation-1_diff Inflation-2_diff Inflation-3_diff \\\n", + "81 0.2 -0.555924 -0.297508 -0.261292 \n", + "82 0.1 0.592725 -0.555924 -0.297508 \n", + "83 -0.4 -0.111455 0.592725 -0.555924 \n", + "84 -0.4 -0.258870 -0.111455 0.592725 \n", + "85 -0.3 0.001169 -0.258870 -0.111455 \n", + ".. ... ... ... ... \n", + "899 0.1 -0.707968 -0.632475 -0.446331 \n", + "900 0.0 -0.049375 -0.707968 -0.632475 \n", + "901 -0.1 -0.396601 -0.049375 -0.707968 \n", + "902 -0.1 -1.030432 -0.396601 -0.049375 \n", + "903 0.2 0.005969 -1.030432 -0.396601 \n", + "\n", + " fedfunds-1_diff fedfunds-2_diff fedfunds-3_diff class \n", + "81 -0.22 -0.15 0.42 0 \n", + "82 -0.02 -0.22 -0.15 0 \n", + "83 0.45 -0.02 -0.22 0 \n", + "84 0.11 0.45 -0.02 0 \n", + "85 -0.10 0.11 0.45 0 \n", + ".. ... ... ... ... \n", + "899 0.32 0.70 0.52 0 \n", + "900 0.23 0.32 0.70 2 \n", + "901 0.24 0.23 0.32 0 \n", + "902 0.08 0.24 0.23 0 \n", + "903 0.18 0.08 0.24 2 \n", + "\n", + "[823 rows x 32 columns]" ] }, "execution_count": 37, @@ -4054,106 +4224,15 @@ } ], "source": [ - "target = df_y[\"ur_lower\"].values\n", - "target" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## SPlit Train / Test" + "df_train = df.iloc[:nb_test].copy()\n", + "df_test = df.iloc[nb_test:].copy()\n", + "df_train" ] }, { "cell_type": "code", "execution_count": 38, "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "(838, 17)" - ] - }, - "execution_count": 38, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "data.shape" - ] - }, - { - "cell_type": "code", - "execution_count": 39, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "nb_test : 812\n" - ] - } - ], - "source": [ - "ratio_test = 0.03\n", - "nb_test = int(data.shape[0] * (1 - ratio_test))\n", - "print(\"nb_test : \", nb_test)\n", - "#from sklearn.model_selection import train_test_split\n", - "#xtrain, xtest, ytrain, ytest = train_test_split(data, target, train_size=0.8)\n", - "xtrain = data[:nb_test, :]\n", - "xtest = data[nb_test:, :]\n", - "ytrain = target[:nb_test]\n", - "ytest = target[nb_test:]" - ] - }, - { - "cell_type": "code", - "execution_count": 40, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "(812,)" - ] - }, - "execution_count": 40, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "ytrain.shape" - ] - }, - { - "cell_type": "code", - "execution_count": 41, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "(26,)" - ] - }, - "execution_count": 41, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "ytest.shape" - ] - }, - { - "cell_type": "code", - "execution_count": 42, - "metadata": {}, "outputs": [ { "data": { @@ -4187,284 +4266,20 @@ " ur_stable\n", " num_month\n", " ...\n", - " Total-3\n", - " Inflation-1\n", - " Inflation-2\n", - " Inflation-3\n", - " fedfunds-1\n", - " fedfunds-2\n", - " fedfunds-3\n", - " spx-1\n", - " spx-2\n", - " spx-3\n", + " Total-1_diff\n", + " Total-2_diff\n", + " Total-3_diff\n", + " Inflation-1_diff\n", + " Inflation-2_diff\n", + " Inflation-3_diff\n", + " fedfunds-1_diff\n", + " fedfunds-2_diff\n", + " fedfunds-3_diff\n", + " class\n", " \n", " \n", " \n", " \n", - " 893\n", - " 2022-06-01\n", - " 3.6\n", - " 8.989744\n", - " 1.21\n", - " -0.080109\n", - " -0.1\n", - " True\n", - " False\n", - " False\n", - " 6\n", - " ...\n", - " 3.6\n", - " 8.532997\n", - " 8.251859\n", - " 8.547431\n", - " 0.77\n", - " 0.33\n", - " 0.20\n", - " -0.030589\n", - " 0.060398\n", - " 0.122940\n", - " \n", - " \n", - " 894\n", - " 2022-07-01\n", - " 3.5\n", - " 8.449819\n", - " 1.68\n", - " -0.103577\n", - " 0.1\n", - " False\n", - " True\n", - " False\n", - " 7\n", - " ...\n", - " 3.7\n", - " 8.989744\n", - " 8.532997\n", - " 8.251859\n", - " 1.21\n", - " 0.77\n", - " 0.33\n", - " -0.080109\n", - " -0.030589\n", - " 0.060398\n", - " \n", - " \n", - " 895\n", - " 2022-08-01\n", - " 3.6\n", - " 8.218806\n", - " 2.33\n", - " -0.066375\n", - " -0.1\n", - " True\n", - " False\n", - " False\n", - " 8\n", - " ...\n", - " 3.6\n", - " 8.449819\n", - " 8.989744\n", - " 8.532997\n", - " 1.68\n", - " 1.21\n", - " 0.77\n", - " -0.103577\n", - " -0.080109\n", - " -0.030589\n", - " \n", - " \n", - " 896\n", - " 2022-09-01\n", - " 3.5\n", - " 8.198272\n", - " 2.56\n", - " -0.133847\n", - " 0.1\n", - " False\n", - " True\n", - " False\n", - " 9\n", - " ...\n", - " 3.6\n", - " 8.218806\n", - " 8.449819\n", - " 8.989744\n", - " 2.33\n", - " 1.68\n", - " 1.21\n", - " -0.066375\n", - " -0.103577\n", - " -0.080109\n", - " \n", - " \n", - " 897\n", - " 2022-10-01\n", - " 3.6\n", - " 7.751941\n", - " 3.08\n", - " -0.164696\n", - " 0.0\n", - " False\n", - " False\n", - " True\n", - " 10\n", - " ...\n", - " 3.5\n", - " 8.198272\n", - " 8.218806\n", - " 8.449819\n", - " 2.56\n", - " 2.33\n", - " 1.68\n", - " -0.133847\n", - " -0.066375\n", - " -0.103577\n", - " \n", - " \n", - " 898\n", - " 2022-11-01\n", - " 3.6\n", - " 7.119466\n", - " 3.78\n", - " -0.160668\n", - " -0.1\n", - " True\n", - " False\n", - " False\n", - " 11\n", - " ...\n", - " 3.6\n", - " 7.751941\n", - " 8.198272\n", - " 8.218806\n", - " 3.08\n", - " 2.56\n", - " 2.33\n", - " -0.164696\n", - " -0.133847\n", - " -0.066375\n", - " \n", - " \n", - " 899\n", - " 2022-12-01\n", - " 3.5\n", - " 6.411498\n", - " 4.10\n", - " -0.163086\n", - " -0.1\n", - " True\n", - " False\n", - " False\n", - " 12\n", - " ...\n", - " 3.5\n", - " 7.119466\n", - " 7.751941\n", - " 8.198272\n", - " 3.78\n", - " 3.08\n", - " 2.56\n", - " -0.160668\n", - " -0.164696\n", - " -0.133847\n", - " \n", - " \n", - " 900\n", - " 2023-01-01\n", - " 3.4\n", - " 6.362123\n", - " 4.33\n", - " -0.134059\n", - " 0.2\n", - " False\n", - " True\n", - " False\n", - " 1\n", - " ...\n", - " 3.6\n", - " 6.411498\n", - " 7.119466\n", - " 7.751941\n", - " 4.10\n", - " 3.78\n", - " 3.08\n", - " -0.163086\n", - " -0.160668\n", - " -0.164696\n", - " \n", - " \n", - " 901\n", - " 2023-02-01\n", - " 3.6\n", - " 5.965523\n", - " 4.57\n", - " -0.080320\n", - " -0.1\n", - " True\n", - " False\n", - " False\n", - " 2\n", - " ...\n", - " 3.6\n", - " 6.362123\n", - " 6.411498\n", - " 7.119466\n", - " 4.33\n", - " 4.10\n", - " 3.78\n", - " -0.134059\n", - " -0.163086\n", - " -0.160668\n", - " \n", - " \n", - " 902\n", - " 2023-03-01\n", - " 3.5\n", - " 4.935090\n", - " 4.65\n", - " -0.096261\n", - " -0.1\n", - " True\n", - " False\n", - " False\n", - " 3\n", - " ...\n", - " 3.5\n", - " 5.965523\n", - " 6.362123\n", - " 6.411498\n", - " 4.57\n", - " 4.33\n", - " 4.10\n", - " -0.080320\n", - " -0.134059\n", - " -0.163086\n", - " \n", - " \n", - " 903\n", - " 2023-04-01\n", - " 3.4\n", - " 4.941059\n", - " 4.83\n", - " -0.061446\n", - " 0.3\n", - " False\n", - " True\n", - " False\n", - " 4\n", - " ...\n", - " 3.4\n", - " 4.935090\n", - " 5.965523\n", - " 6.362123\n", - " 4.65\n", - " 4.57\n", - " 4.33\n", - " -0.096261\n", - " -0.080320\n", - " -0.134059\n", - " \n", - " \n", " 904\n", " 2023-05-01\n", " 3.7\n", @@ -4477,16 +4292,16 @@ " False\n", " 5\n", " ...\n", - " 3.6\n", - " 4.941059\n", - " 4.935090\n", - " 5.965523\n", - " 4.83\n", - " 4.65\n", - " 4.57\n", - " -0.061446\n", - " -0.096261\n", - " -0.080320\n", + " 0.3\n", + " -0.1\n", + " -0.1\n", + " -0.820370\n", + " 0.005969\n", + " -1.030432\n", + " 0.23\n", + " 0.18\n", + " 0.08\n", + " 0\n", " \n", " \n", " 905\n", @@ -4501,16 +4316,16 @@ " False\n", " 6\n", " ...\n", - " 3.5\n", - " 4.120690\n", - " 4.941059\n", - " 4.935090\n", - " 5.06\n", - " 4.83\n", - " 4.65\n", - " 0.026188\n", - " -0.061446\n", - " -0.096261\n", + " -0.1\n", + " 0.3\n", + " -0.1\n", + " -1.067428\n", + " -0.820370\n", + " 0.005969\n", + " 0.02\n", + " 0.23\n", + " 0.18\n", + " 0\n", " \n", " \n", " 906\n", @@ -4525,16 +4340,16 @@ " False\n", " 7\n", " ...\n", - " 3.4\n", - " 3.053262\n", - " 4.120690\n", - " 4.941059\n", - " 5.08\n", - " 5.06\n", - " 4.83\n", - " 0.114497\n", - " 0.026188\n", - " -0.061446\n", + " -0.1\n", + " -0.1\n", + " 0.3\n", + " 0.218519\n", + " -1.067428\n", + " -0.820370\n", + " 0.04\n", + " 0.02\n", + " 0.23\n", + " 2\n", " \n", " \n", " 907\n", @@ -4549,16 +4364,16 @@ " True\n", " 8\n", " ...\n", - " 3.7\n", - " 3.271781\n", - " 3.053262\n", - " 4.120690\n", - " 5.12\n", - " 5.08\n", - " 5.06\n", - " 0.152452\n", - " 0.114497\n", - " 0.026188\n", + " 0.3\n", + " -0.1\n", + " -0.1\n", + " 0.446941\n", + " 0.218519\n", + " -1.067428\n", + " 0.21\n", + " 0.04\n", + " 0.02\n", + " 1\n", " \n", " \n", " 908\n", @@ -4573,16 +4388,16 @@ " True\n", " 9\n", " ...\n", - " 3.6\n", - " 3.718721\n", - " 3.271781\n", - " 3.053262\n", - " 5.33\n", - " 5.12\n", - " 5.08\n", - " 0.064368\n", - " 0.152452\n", - " 0.114497\n", + " 0.0\n", + " 0.3\n", + " -0.1\n", + " -0.024666\n", + " 0.446941\n", + " 0.218519\n", + " 0.00\n", + " 0.21\n", + " 0.04\n", + " 1\n", " \n", " \n", " 909\n", @@ -4597,16 +4412,16 @@ " False\n", " 10\n", " ...\n", - " 3.5\n", - " 3.694055\n", - " 3.718721\n", - " 3.271781\n", - " 5.33\n", - " 5.33\n", - " 5.12\n", - " 0.145066\n", - " 0.064368\n", - " 0.152452\n", + " 0.0\n", + " 0.0\n", + " 0.3\n", + " -0.448268\n", + " -0.024666\n", + " 0.446941\n", + " 0.00\n", + " 0.00\n", + " 0.21\n", + " 0\n", " \n", " \n", " 910\n", @@ -4621,18 +4436,18 @@ " True\n", " 11\n", " ...\n", - " 3.8\n", - " 3.245787\n", - " 3.694055\n", - " 3.718721\n", - " 5.33\n", - " 5.33\n", - " 5.33\n", - " 0.143028\n", - " 0.145066\n", - " 0.064368\n", - " \n", - " \n", + " -0.1\n", + " 0.0\n", + " 0.0\n", + " -0.106306\n", + " -0.448268\n", + " -0.024666\n", + " 0.00\n", + " 0.00\n", + " 0.00\n", + " 1\n", + " \n", + " \n", " 911\n", " 2023-12-01\n", " 3.7\n", @@ -4645,16 +4460,16 @@ " True\n", " 12\n", " ...\n", - " 3.8\n", - " 3.139482\n", - " 3.245787\n", - " 3.694055\n", - " 5.33\n", - " 5.33\n", - " 5.33\n", - " 0.138499\n", - " 0.143028\n", - " 0.145066\n", + " 0.0\n", + " -0.1\n", + " 0.0\n", + " 0.183678\n", + " -0.106306\n", + " -0.448268\n", + " 0.00\n", + " 0.00\n", + " 0.00\n", + " 1\n", " \n", " \n", " 912\n", @@ -4669,16 +4484,16 @@ " False\n", " 1\n", " ...\n", - " 3.8\n", - " 3.323160\n", - " 3.139482\n", - " 3.245787\n", - " 5.33\n", - " 5.33\n", - " 5.33\n", - " 0.197494\n", - " 0.138499\n", - " 0.143028\n", + " 0.0\n", + " 0.0\n", + " -0.1\n", + " -0.217179\n", + " 0.183678\n", + " -0.106306\n", + " 0.00\n", + " 0.00\n", + " 0.00\n", + " 2\n", " \n", " \n", " 913\n", @@ -4693,16 +4508,16 @@ " False\n", " 2\n", " ...\n", - " 3.7\n", - " 3.105981\n", - " 3.323160\n", - " 3.139482\n", - " 5.33\n", - " 5.33\n", - " 5.33\n", - " 0.213053\n", - " 0.197494\n", - " 0.138499\n", + " 0.2\n", + " 0.0\n", + " 0.0\n", + " 0.059762\n", + " -0.217179\n", + " 0.183678\n", + " 0.00\n", + " 0.00\n", + " 0.00\n", + " 0\n", " \n", " \n", " 914\n", @@ -4717,16 +4532,16 @@ " False\n", " 3\n", " ...\n", - " 3.7\n", - " 3.165743\n", - " 3.105981\n", - " 3.323160\n", - " 5.33\n", - " 5.33\n", - " 5.33\n", - " 0.228518\n", - " 0.213053\n", - " 0.197494\n", + " -0.1\n", + " 0.2\n", + " 0.0\n", + " 0.309388\n", + " 0.059762\n", + " -0.217179\n", + " 0.00\n", + " 0.00\n", + " 0.00\n", + " 2\n", " \n", " \n", " 915\n", @@ -4741,16 +4556,16 @@ " False\n", " 4\n", " ...\n", - " 3.7\n", - " 3.475131\n", - " 3.165743\n", - " 3.105981\n", - " 5.33\n", - " 5.33\n", - " 5.33\n", - " 0.302883\n", - " 0.228518\n", - " 0.213053\n", + " 0.1\n", + " -0.1\n", + " 0.2\n", + " -0.117400\n", + " 0.309388\n", + " 0.059762\n", + " 0.00\n", + " 0.00\n", + " 0.00\n", + " 2\n", " \n", " \n", " 916\n", @@ -4765,16 +4580,16 @@ " False\n", " 5\n", " ...\n", - " 3.9\n", - " 3.357731\n", - " 3.475131\n", - " 3.165743\n", - " 5.33\n", - " 5.33\n", - " 5.33\n", - " 0.240453\n", - " 0.302883\n", - " 0.228518\n", + " 0.1\n", + " 0.1\n", + " -0.1\n", + " -0.107521\n", + " -0.117400\n", + " 0.309388\n", + " 0.00\n", + " 0.00\n", + " 0.00\n", + " 2\n", " \n", " \n", " 917\n", @@ -4789,16 +4604,16 @@ " False\n", " 6\n", " ...\n", - " 3.8\n", - " 3.250210\n", - " 3.357731\n", - " 3.475131\n", - " 5.33\n", - " 5.33\n", - " 5.33\n", - " 0.262664\n", - " 0.240453\n", - " 0.302883\n", + " 0.1\n", + " 0.1\n", + " 0.1\n", + " -0.274582\n", + " -0.107521\n", + " -0.117400\n", + " 0.00\n", + " 0.00\n", + " 0.00\n", + " 2\n", " \n", " \n", " 918\n", @@ -4813,35 +4628,24 @@ " False\n", " 7\n", " ...\n", - " 3.9\n", - " 2.975629\n", - " 3.250210\n", - " 3.357731\n", - " 5.33\n", - " 5.33\n", - " 5.33\n", - " 0.246186\n", - " 0.262664\n", - " 0.240453\n", + " 0.2\n", + " 0.1\n", + " 0.1\n", + " -0.052063\n", + " -0.274582\n", + " -0.107521\n", + " 0.00\n", + " 0.00\n", + " 0.00\n", + " 0\n", " \n", " \n", "\n", - "

26 rows × 22 columns

\n", + "

15 rows × 32 columns

\n", "" ], "text/plain": [ " DATE Total Inflation FEDFUNDS SPX_diff Total_diff ur_lower \\\n", - "893 2022-06-01 3.6 8.989744 1.21 -0.080109 -0.1 True \n", - "894 2022-07-01 3.5 8.449819 1.68 -0.103577 0.1 False \n", - "895 2022-08-01 3.6 8.218806 2.33 -0.066375 -0.1 True \n", - "896 2022-09-01 3.5 8.198272 2.56 -0.133847 0.1 False \n", - "897 2022-10-01 3.6 7.751941 3.08 -0.164696 0.0 False \n", - "898 2022-11-01 3.6 7.119466 3.78 -0.160668 -0.1 True \n", - "899 2022-12-01 3.5 6.411498 4.10 -0.163086 -0.1 True \n", - "900 2023-01-01 3.4 6.362123 4.33 -0.134059 0.2 False \n", - "901 2023-02-01 3.6 5.965523 4.57 -0.080320 -0.1 True \n", - "902 2023-03-01 3.5 4.935090 4.65 -0.096261 -0.1 True \n", - "903 2023-04-01 3.4 4.941059 4.83 -0.061446 0.3 False \n", "904 2023-05-01 3.7 4.120690 5.06 0.026188 -0.1 True \n", "905 2023-06-01 3.6 3.053262 5.08 0.114497 -0.1 True \n", "906 2023-07-01 3.5 3.271781 5.12 0.152452 0.3 False \n", @@ -4858,108 +4662,72 @@ "917 2024-06-01 4.1 2.975629 5.33 0.246186 0.2 False \n", "918 2024-07-01 4.3 2.923566 5.33 0.228461 -0.1 True \n", "\n", - " ur_higher ur_stable num_month ... Total-3 Inflation-1 Inflation-2 \\\n", - "893 False False 6 ... 3.6 8.532997 8.251859 \n", - "894 True False 7 ... 3.7 8.989744 8.532997 \n", - "895 False False 8 ... 3.6 8.449819 8.989744 \n", - "896 True False 9 ... 3.6 8.218806 8.449819 \n", - "897 False True 10 ... 3.5 8.198272 8.218806 \n", - "898 False False 11 ... 3.6 7.751941 8.198272 \n", - "899 False False 12 ... 3.5 7.119466 7.751941 \n", - "900 True False 1 ... 3.6 6.411498 7.119466 \n", - "901 False False 2 ... 3.6 6.362123 6.411498 \n", - "902 False False 3 ... 3.5 5.965523 6.362123 \n", - "903 True False 4 ... 3.4 4.935090 5.965523 \n", - "904 False False 5 ... 3.6 4.941059 4.935090 \n", - "905 False False 6 ... 3.5 4.120690 4.941059 \n", - "906 True False 7 ... 3.4 3.053262 4.120690 \n", - "907 False True 8 ... 3.7 3.271781 3.053262 \n", - "908 False True 9 ... 3.6 3.718721 3.271781 \n", - "909 False False 10 ... 3.5 3.694055 3.718721 \n", - "910 False True 11 ... 3.8 3.245787 3.694055 \n", - "911 False True 12 ... 3.8 3.139482 3.245787 \n", - "912 True False 1 ... 3.8 3.323160 3.139482 \n", - "913 False False 2 ... 3.7 3.105981 3.323160 \n", - "914 True False 3 ... 3.7 3.165743 3.105981 \n", - "915 True False 4 ... 3.7 3.475131 3.165743 \n", - "916 True False 5 ... 3.9 3.357731 3.475131 \n", - "917 True False 6 ... 3.8 3.250210 3.357731 \n", - "918 False False 7 ... 3.9 2.975629 3.250210 \n", + " ur_higher ur_stable num_month ... Total-1_diff Total-2_diff \\\n", + "904 False False 5 ... 0.3 -0.1 \n", + "905 False False 6 ... -0.1 0.3 \n", + "906 True False 7 ... -0.1 -0.1 \n", + "907 False True 8 ... 0.3 -0.1 \n", + "908 False True 9 ... 0.0 0.3 \n", + "909 False False 10 ... 0.0 0.0 \n", + "910 False True 11 ... -0.1 0.0 \n", + "911 False True 12 ... 0.0 -0.1 \n", + "912 True False 1 ... 0.0 0.0 \n", + "913 False False 2 ... 0.2 0.0 \n", + "914 True False 3 ... -0.1 0.2 \n", + "915 True False 4 ... 0.1 -0.1 \n", + "916 True False 5 ... 0.1 0.1 \n", + "917 True False 6 ... 0.1 0.1 \n", + "918 False False 7 ... 0.2 0.1 \n", "\n", - " Inflation-3 fedfunds-1 fedfunds-2 fedfunds-3 spx-1 spx-2 \\\n", - "893 8.547431 0.77 0.33 0.20 -0.030589 0.060398 \n", - "894 8.251859 1.21 0.77 0.33 -0.080109 -0.030589 \n", - "895 8.532997 1.68 1.21 0.77 -0.103577 -0.080109 \n", - "896 8.989744 2.33 1.68 1.21 -0.066375 -0.103577 \n", - "897 8.449819 2.56 2.33 1.68 -0.133847 -0.066375 \n", - "898 8.218806 3.08 2.56 2.33 -0.164696 -0.133847 \n", - "899 8.198272 3.78 3.08 2.56 -0.160668 -0.164696 \n", - "900 7.751941 4.10 3.78 3.08 -0.163086 -0.160668 \n", - "901 7.119466 4.33 4.10 3.78 -0.134059 -0.163086 \n", - "902 6.411498 4.57 4.33 4.10 -0.080320 -0.134059 \n", - "903 6.362123 4.65 4.57 4.33 -0.096261 -0.080320 \n", - "904 5.965523 4.83 4.65 4.57 -0.061446 -0.096261 \n", - "905 4.935090 5.06 4.83 4.65 0.026188 -0.061446 \n", - "906 4.941059 5.08 5.06 4.83 0.114497 0.026188 \n", - "907 4.120690 5.12 5.08 5.06 0.152452 0.114497 \n", - "908 3.053262 5.33 5.12 5.08 0.064368 0.152452 \n", - "909 3.271781 5.33 5.33 5.12 0.145066 0.064368 \n", - "910 3.718721 5.33 5.33 5.33 0.143028 0.145066 \n", - "911 3.694055 5.33 5.33 5.33 0.138499 0.143028 \n", - "912 3.245787 5.33 5.33 5.33 0.197494 0.138499 \n", - "913 3.139482 5.33 5.33 5.33 0.213053 0.197494 \n", - "914 3.323160 5.33 5.33 5.33 0.228518 0.213053 \n", - "915 3.105981 5.33 5.33 5.33 0.302883 0.228518 \n", - "916 3.165743 5.33 5.33 5.33 0.240453 0.302883 \n", - "917 3.475131 5.33 5.33 5.33 0.262664 0.240453 \n", - "918 3.357731 5.33 5.33 5.33 0.246186 0.262664 \n", + " Total-3_diff Inflation-1_diff Inflation-2_diff Inflation-3_diff \\\n", + "904 -0.1 -0.820370 0.005969 -1.030432 \n", + "905 -0.1 -1.067428 -0.820370 0.005969 \n", + "906 0.3 0.218519 -1.067428 -0.820370 \n", + "907 -0.1 0.446941 0.218519 -1.067428 \n", + "908 -0.1 -0.024666 0.446941 0.218519 \n", + "909 0.3 -0.448268 -0.024666 0.446941 \n", + "910 0.0 -0.106306 -0.448268 -0.024666 \n", + "911 0.0 0.183678 -0.106306 -0.448268 \n", + "912 -0.1 -0.217179 0.183678 -0.106306 \n", + "913 0.0 0.059762 -0.217179 0.183678 \n", + "914 0.0 0.309388 0.059762 -0.217179 \n", + "915 0.2 -0.117400 0.309388 0.059762 \n", + "916 -0.1 -0.107521 -0.117400 0.309388 \n", + "917 0.1 -0.274582 -0.107521 -0.117400 \n", + "918 0.1 -0.052063 -0.274582 -0.107521 \n", "\n", - " spx-3 \n", - "893 0.122940 \n", - "894 0.060398 \n", - "895 -0.030589 \n", - "896 -0.080109 \n", - "897 -0.103577 \n", - "898 -0.066375 \n", - "899 -0.133847 \n", - "900 -0.164696 \n", - "901 -0.160668 \n", - "902 -0.163086 \n", - "903 -0.134059 \n", - "904 -0.080320 \n", - "905 -0.096261 \n", - "906 -0.061446 \n", - "907 0.026188 \n", - "908 0.114497 \n", - "909 0.152452 \n", - "910 0.064368 \n", - "911 0.145066 \n", - "912 0.143028 \n", - "913 0.138499 \n", - "914 0.197494 \n", - "915 0.213053 \n", - "916 0.228518 \n", - "917 0.302883 \n", - "918 0.240453 \n", + " fedfunds-1_diff fedfunds-2_diff fedfunds-3_diff class \n", + "904 0.23 0.18 0.08 0 \n", + "905 0.02 0.23 0.18 0 \n", + "906 0.04 0.02 0.23 2 \n", + "907 0.21 0.04 0.02 1 \n", + "908 0.00 0.21 0.04 1 \n", + "909 0.00 0.00 0.21 0 \n", + "910 0.00 0.00 0.00 1 \n", + "911 0.00 0.00 0.00 1 \n", + "912 0.00 0.00 0.00 2 \n", + "913 0.00 0.00 0.00 0 \n", + "914 0.00 0.00 0.00 2 \n", + "915 0.00 0.00 0.00 2 \n", + "916 0.00 0.00 0.00 2 \n", + "917 0.00 0.00 0.00 2 \n", + "918 0.00 0.00 0.00 0 \n", "\n", - "[26 rows x 22 columns]" + "[15 rows x 32 columns]" ] }, - "execution_count": 42, + "execution_count": 38, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "df_train = df.iloc[:nb_test].copy()\n", - "df_train\n", - "df_test = df.iloc[nb_test:].copy()\n", "df_test" ] }, { "cell_type": "code", - "execution_count": 43, + "execution_count": 39, "metadata": {}, "outputs": [ { @@ -4994,783 +4762,1009 @@ " ur_stable\n", " num_month\n", " ...\n", - " Total-3\n", - " Inflation-1\n", - " Inflation-2\n", - " Inflation-3\n", - " fedfunds-1\n", - " fedfunds-2\n", - " fedfunds-3\n", - " spx-1\n", - " spx-2\n", - " spx-3\n", + " Total-2_diff\n", + " Total-3_diff\n", + " Inflation-1_diff\n", + " Inflation-2_diff\n", + " Inflation-3_diff\n", + " fedfunds-1_diff\n", + " fedfunds-2_diff\n", + " fedfunds-3_diff\n", + " class\n", + " TRAIN\n", " \n", " \n", " \n", " \n", - " 893\n", - " 2022-06-01\n", - " 3.6\n", - " 8.989744\n", - " 1.21\n", - " -0.080109\n", - " -0.1\n", - " True\n", - " False\n", - " False\n", - " 6\n", - " ...\n", - " 3.6\n", - " 8.532997\n", - " 8.251859\n", - " 8.547431\n", - " 0.77\n", - " 0.33\n", - " 0.20\n", - " -0.030589\n", - " 0.060398\n", - 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838 rows × 33 columns

\n", + "" + ], + "text/plain": [ + " DATE Total Inflation FEDFUNDS SPX_diff Total_diff ur_lower \\\n", + "81 1954-10-01 5.7 -0.853432 0.85 0.342511 -0.4 True \n", + "82 1954-11-01 5.3 -0.260708 0.83 0.364898 -0.3 True \n", + "83 1954-12-01 5.0 -0.372162 1.28 0.408377 -0.1 True \n", + "84 1955-01-01 4.9 -0.631032 1.39 0.398272 -0.2 True \n", + "85 1955-02-01 4.7 -0.629863 1.29 0.413912 -0.1 True \n", + ".. ... ... ... ... ... ... ... \n", + "914 2024-03-01 3.8 3.475131 5.33 0.302883 0.1 False \n", + "915 2024-04-01 3.9 3.357731 5.33 0.240453 0.1 False \n", + "916 2024-05-01 4.0 3.250210 5.33 0.262664 0.1 False \n", + "917 2024-06-01 4.1 2.975629 5.33 0.246186 0.2 False \n", + "918 2024-07-01 4.3 2.923566 5.33 0.228461 -0.1 True \n", + "\n", + " ur_higher ur_stable num_month ... Total-2_diff Total-3_diff \\\n", + "81 False False 10 ... 0.1 0.2 \n", + "82 False False 11 ... -0.4 0.1 \n", + "83 False False 12 ... -0.4 -0.4 \n", + "84 False False 1 ... -0.3 -0.4 \n", + "85 False False 2 ... -0.1 -0.3 \n", + ".. ... ... ... ... ... ... \n", + "914 True False 3 ... 0.2 0.0 \n", + "915 True False 4 ... -0.1 0.2 \n", + "916 True False 5 ... 0.1 -0.1 \n", + "917 True False 6 ... 0.1 0.1 \n", + "918 False False 7 ... 0.1 0.1 \n", + "\n", + " Inflation-1_diff Inflation-2_diff Inflation-3_diff fedfunds-1_diff \\\n", + "81 -0.555924 -0.297508 -0.261292 -0.22 \n", + "82 0.592725 -0.555924 -0.297508 -0.02 \n", + "83 -0.111455 0.592725 -0.555924 0.45 \n", + "84 -0.258870 -0.111455 0.592725 0.11 \n", + "85 0.001169 -0.258870 -0.111455 -0.10 \n", + ".. ... ... ... ... \n", + "914 0.309388 0.059762 -0.217179 0.00 \n", + "915 -0.117400 0.309388 0.059762 0.00 \n", + "916 -0.107521 -0.117400 0.309388 0.00 \n", + "917 -0.274582 -0.107521 -0.117400 0.00 \n", + "918 -0.052063 -0.274582 -0.107521 0.00 \n", + "\n", + " fedfunds-2_diff fedfunds-3_diff class TRAIN \n", + "81 -0.15 0.42 0 1 \n", + "82 -0.22 -0.15 0 1 \n", + "83 -0.02 -0.22 0 1 \n", + "84 0.45 -0.02 0 1 \n", + "85 0.11 0.45 0 1 \n", + ".. ... ... ... ... \n", + "914 0.00 0.00 2 0 \n", + "915 0.00 0.00 2 0 \n", + "916 0.00 0.00 2 0 \n", + "917 0.00 0.00 2 0 \n", + "918 0.00 0.00 0 0 \n", + "\n", + "[838 rows x 33 columns]" + ] + }, + "execution_count": 39, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df_train[\"TRAIN\"] = 1\n", + "df_test[\"TRAIN\"] = 0\n", + "df = pd.concat([df_train, df_test], axis=0)\n", + "df" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Check features repart" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Check Out of range\n", + "check if Test out of Train range for each features" + ] + }, + { + "cell_type": "code", + "execution_count": 40, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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TRAIN01featout_minpc_out_min
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TRAIN01featout_maxpc_out_max
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SPX_diff0.3028830.2285180.526508SPX_diffFalse0.0
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Total-2_diff0.30000010.400000Total-2_diffFalse0.0
Total-3_diff0.30000010.400000Total-3_diffFalse0.0
Inflation-1_diff0.4469412.138555Inflation-1_diffFalse0.0
Inflation-2_diff0.4469412.138555Inflation-2_diffFalse0.0
Inflation-3_diff0.4469412.138555Inflation-3_diffFalse0.0
fedfunds-1_diff0.2300003.060000fedfunds-1_diffFalse0.0
fedfunds-2_diff0.2300003.060000fedfunds-2_diffFalse0.0
fedfunds-3_diff0.2300003.060000fedfunds-3_diffFalse0.0
spx-10.3028830.526508spx-1False0.0
9182024-07-014.32.9235665.330.228461-0.1Truespx-20.3028830.526508spx-2False0.0
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-0.080320 -0.134059 \n", - "903 6.362123 4.65 4.57 4.33 -0.096261 -0.080320 \n", - "904 5.965523 4.83 4.65 4.57 -0.061446 -0.096261 \n", - "905 4.935090 5.06 4.83 4.65 0.026188 -0.061446 \n", - "906 4.941059 5.08 5.06 4.83 0.114497 0.026188 \n", - "907 4.120690 5.12 5.08 5.06 0.152452 0.114497 \n", - "908 3.053262 5.33 5.12 5.08 0.064368 0.152452 \n", - "909 3.271781 5.33 5.33 5.12 0.145066 0.064368 \n", - "910 3.718721 5.33 5.33 5.33 0.143028 0.145066 \n", - "911 3.694055 5.33 5.33 5.33 0.138499 0.143028 \n", - "912 3.245787 5.33 5.33 5.33 0.197494 0.138499 \n", - "913 3.139482 5.33 5.33 5.33 0.213053 0.197494 \n", - "914 3.323160 5.33 5.33 5.33 0.228518 0.213053 \n", - "915 3.105981 5.33 5.33 5.33 0.302883 0.228518 \n", - "916 3.165743 5.33 5.33 5.33 0.240453 0.302883 \n", - "917 3.475131 5.33 5.33 5.33 0.262664 0.240453 \n", - "918 3.357731 5.33 5.33 5.33 0.246186 0.262664 \n", - "\n", - " spx-3 \n", - "893 0.122940 \n", - "894 0.060398 \n", - "895 -0.030589 \n", - "896 -0.080109 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0.0\n", + "Inflation-1_diff 0.446941 2.138555 Inflation-1_diff False 0.0\n", + "Inflation-2_diff 0.446941 2.138555 Inflation-2_diff False 0.0\n", + "Inflation-3_diff 0.446941 2.138555 Inflation-3_diff False 0.0\n", + "fedfunds-1_diff 0.230000 3.060000 fedfunds-1_diff False 0.0\n", + "fedfunds-2_diff 0.230000 3.060000 fedfunds-2_diff False 0.0\n", + "fedfunds-3_diff 0.230000 3.060000 fedfunds-3_diff False 0.0\n", + "spx-1 0.302883 0.526508 spx-1 False 0.0\n", + "spx-2 0.302883 0.526508 spx-2 False 0.0\n", + "spx-3 0.302883 0.526508 spx-3 False 0.0" + ] + }, + "execution_count": 41, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df_max = df.filter(list_feat).groupby(df[\"TRAIN\"]).max().transpose()\n", + "df_max[\"feat\"] = df_max.index\n", + "df_max[\"out_max\"] = df_max[0] > df_max[1]\n", + "df_max[\"pc_out_max\"] = df_max[\"feat\"].apply(\n", + " lambda x: 100*sum(df[df[\"TRAIN\"] == 0 ][x] > df[df[\"TRAIN\"] == 1][x].max()) / df[df[\"TRAIN\"] == 0 ].shape[0]\n", + " )\n", + "df_max" + ] + }, + { + "cell_type": "code", + "execution_count": 42, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "3.4" + ] + }, + "execution_count": 42, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df_min.at[\"Total\", 1]" + ] + }, + { + "cell_type": "code", + "execution_count": 43, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# detect unemployment rate < in test set\n", + "markers = {0: \"X\", 1: \"o\"}\n", + "ax = sns.lineplot(df.index.values, \n", + " df[\"Total\"], hue=df[\"Total\"] > df_max.at[\"Total\", 1] , \n", + " style=df[\"TRAIN\"], \n", + " markers=markers)\n", + "sns.move_legend(ax, \"best\", facecolor=\"lightgrey\")" + ] + }, + { + "cell_type": "code", + "execution_count": 44, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "[Text(0.0, 1.0, 'Detect Inflation test > max TRAIN set')]" + ] + }, + "execution_count": 44, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# detect FEDFUNDS > in test set\n", + "\n", + "markers = {0: \"X\", 1: \"o\"}\n", + "ax = sns.scatterplot(df.index.values, \n", + " df[\"Inflation\"], \n", + " hue=df[\"Inflation\"] > df_max.at[\"Inflation\", 1], \n", + " style=df[\"TRAIN\"], \n", + " markers=markers)\n", + "sns.move_legend(ax, \"best\", facecolor=\"lightgrey\")\n", + "ax.set(title=\"Detect Inflation test > max TRAIN set\")" + ] + }, + { + "cell_type": "code", + "execution_count": 45, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "[Text(0.0, 1.0, 'Detect FEDFUNDS test > max TRAIN set')]" ] }, - "execution_count": 43, + "execution_count": 45, "metadata": {}, "output_type": "execute_result" + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" } ], "source": [ - "df_test" + "# detect FEDFUNDS > in test set\n", + "\n", + "markers = {0: \"s\", 1: \".\"}\n", + "ax = sns.lineplot(df.index.values, \n", + " df[\"FEDFUNDS\"], \n", + " hue=df[\"FEDFUNDS\"] < df_min.at[\"FEDFUNDS\", 1], \n", + " style=df[\"TRAIN\"], \n", + " markers=markers,\n", + " )\n", + "sns.move_legend(ax, \"best\", facecolor=\"lightgrey\")\n", + "ax.set(title=\"Detect FEDFUNDS test > max TRAIN set\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "### Check features repart" + "### Check repart target" ] }, { "cell_type": "code", - "execution_count": 44, + "execution_count": 46, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "Repart for ur_lower :\n", + "- on TRAIN :\n", + " - proba:\n", + " False 0.583232\n", + "True 0.416768\n", + "Name: ur_lower, dtype: float64 \n", + " - nb: [480. 343.]\n", + "- on TEST :\n", + " - proba:\n", + " False 0.666667\n", + "True 0.333333\n", + "Name: ur_lower, dtype: float64 \n", + " - nb: [10. 5.]\n", + "\n", + "Repart for ur_stable :\n", + "- on TRAIN :\n", + " - proba:\n", + " False 0.749696\n", + "True 0.250304\n", + "Name: ur_stable, dtype: float64 \n", + " - nb: [617. 206.]\n", + "- on TEST :\n", + " - proba:\n", + " False 0.733333\n", + "True 0.266667\n", + "Name: ur_stable, dtype: float64 \n", + " - nb: [11. 4.]\n", + "\n", + "Repart for ur_higher :\n", + "- on TRAIN :\n", + " - proba:\n", + " False 0.667072\n", + "True 0.332928\n", + "Name: ur_higher, dtype: float64 \n", + " - nb: [549. 274.]\n", + "- on TEST :\n", + " - proba:\n", + " False 0.6\n", + "True 0.4\n", + "Name: ur_higher, dtype: float64 \n", + " - nb: [9. 6.]\n", + "end\n" + ] + } + ], + "source": [ + "def print_repart(df_train, df_test, str_target):\n", + " print(\"\\nRepart for \", str_target, \":\")\n", + " print(\"- on TRAIN :\\n\",\n", + " \" - proba:\\n\",\n", + " df_train[str_target].value_counts() / df_train.shape[0],\n", + " \"\\n - nb: \", \n", + " df_train[str_target].shape[0]*(df_train[str_target].value_counts() / df_train.shape[0]).values,\n", + " )\n", + " print(\"- on TEST :\\n\", \n", + " \" - proba:\\n\",\n", + " df_test[str_target].value_counts() / df_test.shape[0],\n", + " \"\\n - nb: \", \n", + " df_test[str_target].shape[0]*(df_test[str_target].value_counts() / df_test.shape[0]).values,)\n", + "\n", + "for str_target in list_targets:\n", + " print_repart(df_train, df_test, str_target)\n", + "\n", + "print(\"end\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Scale" + ] + }, + { + "cell_type": "code", + "execution_count": 47, "metadata": {}, "outputs": [ { @@ -5805,15 +5799,15 @@ " ur_stable\n", " num_month\n", " ...\n", - " Inflation-1\n", - " Inflation-2\n", - " Inflation-3\n", - " fedfunds-1\n", - " fedfunds-2\n", - " fedfunds-3\n", - " spx-1\n", - " spx-2\n", - " spx-3\n", + " Total-2_diff\n", + " Total-3_diff\n", + " Inflation-1_diff\n", + " Inflation-2_diff\n", + " Inflation-3_diff\n", + " fedfunds-1_diff\n", + " fedfunds-2_diff\n", + " fedfunds-3_diff\n", + " class\n", " TRAIN\n", " \n", " \n", @@ -5831,15 +5825,15 @@ " False\n", " 10\n", " ...\n", + " 0.1\n", + " 0.2\n", + " -0.555924\n", " -0.297508\n", - " 0.000000\n", - " 0.261292\n", - " 1.07\n", - " 1.22\n", - " 0.80\n", - " 0.351526\n", - " 0.259943\n", - " 0.240428\n", + " -0.261292\n", + " -0.22\n", + " -0.15\n", + " 0.42\n", + " 0\n", " 1\n", " \n", " \n", @@ -5855,15 +5849,15 @@ " False\n", " 11\n", " ...\n", - " -0.853432\n", + " -0.4\n", + " 0.1\n", + " 0.592725\n", + " -0.555924\n", " -0.297508\n", - " 0.000000\n", - " 0.85\n", - " 1.07\n", - " 1.22\n", - " 0.342511\n", - " 0.351526\n", - " 0.259943\n", + " -0.02\n", + " -0.22\n", + " -0.15\n", + " 0\n", " 1\n", " \n", " \n", @@ -5879,15 +5873,15 @@ " False\n", " 12\n", " ...\n", - " -0.260708\n", - " -0.853432\n", - " -0.297508\n", - " 0.83\n", - " 0.85\n", - " 1.07\n", - " 0.364898\n", - " 0.342511\n", - " 0.351526\n", + " -0.4\n", + " -0.4\n", + " -0.111455\n", + " 0.592725\n", + " -0.555924\n", + " 0.45\n", + " -0.02\n", + " -0.22\n", + " 0\n", " 1\n", " \n", " \n", @@ -5903,15 +5897,15 @@ " False\n", " 1\n", " ...\n", - " -0.372162\n", - " -0.260708\n", - " -0.853432\n", - " 1.28\n", - " 0.83\n", - " 0.85\n", - " 0.408377\n", - " 0.364898\n", - " 0.342511\n", + " -0.3\n", + " -0.4\n", + " -0.258870\n", + " -0.111455\n", + " 0.592725\n", + " 0.11\n", + " 0.45\n", + " -0.02\n", + " 0\n", " 1\n", " \n", " \n", @@ -5927,15 +5921,15 @@ " False\n", " 2\n", " ...\n", - " -0.631032\n", - " -0.372162\n", - " -0.260708\n", - " 1.39\n", - " 1.28\n", - " 0.83\n", - " 0.398272\n", - " 0.408377\n", - " 0.364898\n", + " -0.1\n", + " -0.3\n", + " 0.001169\n", + " -0.258870\n", + " -0.111455\n", + " -0.10\n", + " 0.11\n", + " 0.45\n", + " 0\n", " 1\n", " \n", " \n", @@ -5975,15 +5969,15 @@ " False\n", " 3\n", " ...\n", - " 3.165743\n", - " 3.105981\n", - " 3.323160\n", - " 5.33\n", - " 5.33\n", - " 5.33\n", - " 0.228518\n", - " 0.213053\n", - " 0.197494\n", + " 0.2\n", + " 0.0\n", + " 0.309388\n", + " 0.059762\n", + " -0.217179\n", + " 0.00\n", + " 0.00\n", + " 0.00\n", + " 2\n", " 0\n", " \n", " \n", @@ -5999,15 +5993,15 @@ " False\n", " 4\n", " ...\n", - " 3.475131\n", - " 3.165743\n", - " 3.105981\n", - " 5.33\n", - " 5.33\n", - " 5.33\n", - " 0.302883\n", - " 0.228518\n", - " 0.213053\n", + " -0.1\n", + " 0.2\n", + " -0.117400\n", + " 0.309388\n", + " 0.059762\n", + " 0.00\n", + " 0.00\n", + " 0.00\n", + " 2\n", " 0\n", " \n", " \n", @@ -6023,15 +6017,15 @@ " False\n", " 5\n", " ...\n", - " 3.357731\n", - " 3.475131\n", - " 3.165743\n", - " 5.33\n", - " 5.33\n", - " 5.33\n", - " 0.240453\n", - " 0.302883\n", - " 0.228518\n", + " 0.1\n", + " -0.1\n", + " -0.107521\n", + " -0.117400\n", + " 0.309388\n", + " 0.00\n", + " 0.00\n", + " 0.00\n", + " 2\n", " 0\n", " \n", " \n", @@ -6047,15 +6041,15 @@ " False\n", " 6\n", " ...\n", - " 3.250210\n", - " 3.357731\n", - " 3.475131\n", - " 5.33\n", - " 5.33\n", - " 5.33\n", - " 0.262664\n", - " 0.240453\n", - " 0.302883\n", + " 0.1\n", + " 0.1\n", + " -0.274582\n", + " -0.107521\n", + " -0.117400\n", + " 0.00\n", + " 0.00\n", + " 0.00\n", + " 2\n", " 0\n", " \n", " \n", @@ -6071,20 +6065,20 @@ " False\n", " 7\n", " ...\n", - " 2.975629\n", - " 3.250210\n", - " 3.357731\n", - " 5.33\n", - " 5.33\n", - " 5.33\n", - " 0.246186\n", - " 0.262664\n", - " 0.240453\n", + " 0.1\n", + " 0.1\n", + " -0.052063\n", + " -0.274582\n", + " -0.107521\n", + " 0.00\n", + " 0.00\n", + " 0.00\n", + " 0\n", " 0\n", " \n", " \n", "\n", - "

838 rows × 23 columns

\n", + "

838 rows × 33 columns

\n", "" ], "text/plain": [ @@ -6101,281 +6095,253 @@ "917 2024-06-01 4.1 2.975629 5.33 0.246186 0.2 False \n", "918 2024-07-01 4.3 2.923566 5.33 0.228461 -0.1 True \n", "\n", - " ur_higher ur_stable num_month ... Inflation-1 Inflation-2 \\\n", - "81 False False 10 ... -0.297508 0.000000 \n", - "82 False False 11 ... -0.853432 -0.297508 \n", - "83 False False 12 ... -0.260708 -0.853432 \n", - "84 False False 1 ... -0.372162 -0.260708 \n", - "85 False False 2 ... -0.631032 -0.372162 \n", - ".. ... ... ... ... ... ... \n", - "914 True False 3 ... 3.165743 3.105981 \n", - "915 True False 4 ... 3.475131 3.165743 \n", - "916 True False 5 ... 3.357731 3.475131 \n", - "917 True False 6 ... 3.250210 3.357731 \n", - "918 False False 7 ... 2.975629 3.250210 \n", + " ur_higher ur_stable num_month ... Total-2_diff Total-3_diff \\\n", + "81 False False 10 ... 0.1 0.2 \n", + "82 False False 11 ... -0.4 0.1 \n", + "83 False False 12 ... -0.4 -0.4 \n", + "84 False False 1 ... -0.3 -0.4 \n", + "85 False False 2 ... -0.1 -0.3 \n", + ".. ... ... ... ... ... ... \n", + "914 True False 3 ... 0.2 0.0 \n", + "915 True False 4 ... -0.1 0.2 \n", + "916 True False 5 ... 0.1 -0.1 \n", + "917 True False 6 ... 0.1 0.1 \n", + "918 False False 7 ... 0.1 0.1 \n", "\n", - " Inflation-3 fedfunds-1 fedfunds-2 fedfunds-3 spx-1 spx-2 \\\n", - "81 0.261292 1.07 1.22 0.80 0.351526 0.259943 \n", - "82 0.000000 0.85 1.07 1.22 0.342511 0.351526 \n", - "83 -0.297508 0.83 0.85 1.07 0.364898 0.342511 \n", - "84 -0.853432 1.28 0.83 0.85 0.408377 0.364898 \n", - "85 -0.260708 1.39 1.28 0.83 0.398272 0.408377 \n", - ".. ... ... ... ... ... ... \n", - "914 3.323160 5.33 5.33 5.33 0.228518 0.213053 \n", - "915 3.105981 5.33 5.33 5.33 0.302883 0.228518 \n", - "916 3.165743 5.33 5.33 5.33 0.240453 0.302883 \n", - "917 3.475131 5.33 5.33 5.33 0.262664 0.240453 \n", - "918 3.357731 5.33 5.33 5.33 0.246186 0.262664 \n", + " Inflation-1_diff Inflation-2_diff Inflation-3_diff fedfunds-1_diff \\\n", + "81 -0.555924 -0.297508 -0.261292 -0.22 \n", + "82 0.592725 -0.555924 -0.297508 -0.02 \n", + "83 -0.111455 0.592725 -0.555924 0.45 \n", + "84 -0.258870 -0.111455 0.592725 0.11 \n", + "85 0.001169 -0.258870 -0.111455 -0.10 \n", + ".. ... ... ... ... \n", + "914 0.309388 0.059762 -0.217179 0.00 \n", + "915 -0.117400 0.309388 0.059762 0.00 \n", + "916 -0.107521 -0.117400 0.309388 0.00 \n", + "917 -0.274582 -0.107521 -0.117400 0.00 \n", + "918 -0.052063 -0.274582 -0.107521 0.00 \n", "\n", - " spx-3 TRAIN \n", - "81 0.240428 1 \n", - "82 0.259943 1 \n", - "83 0.351526 1 \n", - "84 0.342511 1 \n", - "85 0.364898 1 \n", - ".. ... ... \n", - "914 0.197494 0 \n", - "915 0.213053 0 \n", - "916 0.228518 0 \n", - "917 0.302883 0 \n", - "918 0.240453 0 \n", + " fedfunds-2_diff fedfunds-3_diff class TRAIN \n", + "81 -0.15 0.42 0 1 \n", + "82 -0.22 -0.15 0 1 \n", + "83 -0.02 -0.22 0 1 \n", + "84 0.45 -0.02 0 1 \n", + "85 0.11 0.45 0 1 \n", + ".. ... ... ... ... \n", + "914 0.00 0.00 2 0 \n", + "915 0.00 0.00 2 0 \n", + "916 0.00 0.00 2 0 \n", + "917 0.00 0.00 2 0 \n", + "918 0.00 0.00 0 0 \n", "\n", - "[838 rows x 23 columns]" + "[838 rows x 33 columns]" ] }, - "execution_count": 44, + "execution_count": 47, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "df_train[\"TRAIN\"] = 1\n", - "df_test[\"TRAIN\"] = 0\n", - "df = pd.concat([df_train, df_test], axis=0)\n", "df" ] }, + { + "cell_type": "code", + "execution_count": 48, + "metadata": {}, + "outputs": [], + "source": [ + "# feat on train\n", + "xtrain = df[df[\"TRAIN\"] == 1][list_feat].values\n", + "# feat on test\n", + "xtest = df[df[\"TRAIN\"] == 0][list_feat].values \n", + "# target on train\n", + "ytrain = df[df[\"TRAIN\"] == 1][\"class\"].values\n", + "# target on test\n", + "ytest = df[df[\"TRAIN\"] == 0][\"class\"].values" + ] + }, + { + "cell_type": "code", + "execution_count": 49, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([[-0.94285087, -0.24184417, 0.20002009, 0.88434092, 0.14536363,\n", + " 0.47606023, 0.24130148, 0.24042241, -0.15397388, -0.73911366,\n", + " -0.30277934, -0.00932664, -0.00850823, -0.00934766, 0.99139026,\n", + " 1.0921672 , 0.95049764]])" + ] + }, + "execution_count": 49, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "from sklearn.preprocessing import StandardScaler\n", + "\n", + "scaler = StandardScaler()\n", + "\n", + "X = scaler.fit_transform(xtrain)\n", + "X_test = scaler.transform(xtest)\n", + "\n", + "# for last pred : (to predict next month value)\n", + "x_for_pred = df.filter(list_feat).iloc[-1].values.reshape(1, -1)\n", + "X_for_pred = scaler.transform(x_for_pred)\n", + "X_for_pred" + ] + }, + { + "cell_type": "code", + "execution_count": 50, + "metadata": {}, + "outputs": [], + "source": [ + "#scaler_y = StandardScaler()\n", + "\n", + "#Y = scaler_y.fit_transform(ytrain.reshape(-1, 1))\n", + "Y = ytrain.reshape(-1, 1)\n", + "Y_test = ytest.reshape(-1, 1)" + ] + }, { "cell_type": "markdown", "metadata": {}, "source": [ - "#### Check Out of range\n", - "check if Test out of Train range for each features" + "## Correlations" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### On Target CLass" ] }, { "cell_type": "code", - "execution_count": 45, + "execution_count": 51, "metadata": {}, "outputs": [ { "data": { - "text/html": [ - "
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Total3.4000003.400000TotalFalse0.0
Inflation2.923566-1.958761InflationFalse0.0
FEDFUNDS1.2100000.050000FEDFUNDSFalse0.0
SPX_diff-0.164696-0.425084SPX_diffFalse0.0
num_month1.0000001.000000num_monthFalse0.0
Total-13.4000003.400000Total-1False0.0
Total-23.4000003.400000Total-2False0.0
Total-33.4000003.400000Total-3False0.0
Inflation-12.975629-1.958761Inflation-1False0.0
Inflation-23.053262-1.958761Inflation-2False0.0
Inflation-33.053262-1.958761Inflation-3False0.0
fedfunds-10.7700000.050000fedfunds-1False0.0
fedfunds-20.3300000.050000fedfunds-2False0.0
fedfunds-30.2000000.050000fedfunds-3False0.0
spx-1-0.164696-0.425084spx-1False0.0
spx-2-0.164696-0.425084spx-2False0.0
spx-3-0.164696-0.425084spx-3False0.0
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"text/plain": [ - "TRAIN 0 1 feat out_min pc_out_min\n", - "Total 3.400000 3.400000 Total False 0.0\n", - "Inflation 2.923566 -1.958761 Inflation False 0.0\n", - "FEDFUNDS 1.210000 0.050000 FEDFUNDS False 0.0\n", - "SPX_diff -0.164696 -0.425084 SPX_diff False 0.0\n", - "num_month 1.000000 1.000000 num_month False 0.0\n", - "Total-1 3.400000 3.400000 Total-1 False 0.0\n", - "Total-2 3.400000 3.400000 Total-2 False 0.0\n", - "Total-3 3.400000 3.400000 Total-3 False 0.0\n", - "Inflation-1 2.975629 -1.958761 Inflation-1 False 0.0\n", - "Inflation-2 3.053262 -1.958761 Inflation-2 False 0.0\n", - "Inflation-3 3.053262 -1.958761 Inflation-3 False 0.0\n", - "fedfunds-1 0.770000 0.050000 fedfunds-1 False 0.0\n", - "fedfunds-2 0.330000 0.050000 fedfunds-2 False 0.0\n", - "fedfunds-3 0.200000 0.050000 fedfunds-3 False 0.0\n", - "spx-1 -0.164696 -0.425084 spx-1 False 0.0\n", - "spx-2 -0.164696 -0.425084 spx-2 False 0.0\n", - "spx-3 -0.164696 -0.425084 spx-3 False 0.0" + "
" ] }, - "execution_count": 45, "metadata": {}, - "output_type": "execute_result" + "output_type": "display_data" } ], "source": [ - "df_min = df.filter(list_feat).groupby(df[\"TRAIN\"]).min().transpose()\n", - "df_min[\"feat\"] = df_min.index\n", - "df_min[\"out_min\"] = df_min[0] < df_min[1]\n", - "df_min[\"pc_out_min\"] = df_min[\"feat\"].apply(\n", - " lambda x: 100*sum(df[df[\"TRAIN\"] == 0 ][x] < df[df[\"TRAIN\"] == 1][x].min()) / df[df[\"TRAIN\"] == 0 ].shape[0]\n", - " )\n", - "df_min" + "def plot_corr(corr_matrix, title='Corrélation with 2 variables', aspect=None, size=25):\n", + " # Afficher la matrice de corrélation\n", + " if aspect is None:\n", + " aspect = 1\n", + " fig_size = (size, size*len(corr_matrix.index)/len(corr_matrix.columns))\n", + " fig, ax = plt.subplots(figsize=fig_size)\n", + " im = ax.matshow(corr_matrix, aspect=aspect)\n", + " plt.xticks(range(len(corr_matrix.columns)), corr_matrix.columns, rotation=90)\n", + " plt.yticks(range(len(corr_matrix.index)), corr_matrix.index)\n", + " plt.title(title)\n", + " fig.colorbar(im, aspect=1/aspect, orientation='vertical', location=\"left\")\n", + " plt.show()\n", + "\n", + "\n", + "nb_plot = len(list_feat)\n", + "list_col_targets = [ f\"target_{n_t}\" for n_t in range(len([\"class\"]))]\n", + "list_col_corr = list_feat[:nb_plot] + list_col_targets\n", + "\n", + "# Créer un DataFrame pandas à partir des données d'entrée X et de la variable à prédire y\n", + "\n", + "\n", + "df_for_corr = pd.DataFrame(np.hstack((X, Y)), \n", + " columns=list_col_corr)\n", + "# Calculer la matrice de corrélation\n", + "corr_matrix = df_for_corr.corr()\n", + "\n", + "fig = plot_corr(corr_matrix)\n" ] }, { "cell_type": "code", - "execution_count": 46, + "execution_count": 52, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "corr_matrix_targets = corr_matrix.copy().loc[list_feat, list_col_targets]\n", + "fig = plot_corr(corr_matrix_targets, aspect=0.1, size=5)" + ] + }, + { + "cell_type": "code", + "execution_count": 53, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "fig = plot_corr(\n", + " corr_matrix_targets.where(corr_matrix_targets>0.1, 0),\n", + " aspect=0.1, \n", + " size=5,\n", + " title=\"Positive Features Correlation with UNRATE\"\n", + " )" + ] + }, + { + "cell_type": "code", + "execution_count": 54, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
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TRAIN01featout_maxpc_out_maxtarget_0
Total4.30000014.800000TotalFalse0.00.000000
Inflation8.98974414.592275InflationFalse0.00.000000
FEDFUNDS5.33000019.100000FEDFUNDSFalse0.00.000000
SPX_diff0.3028830.526508SPX_diffFalse0.0-0.257393
num_month12.00000012.000000num_monthFalse0.00.000000
Total-14.10000014.800000Total-1False0.0Total-1_diff0.000000
Total-24.00000014.800000Total-2False0.0Total-2_diff0.000000
Total-33.90000014.800000Total-3False0.0Total-3_diff0.000000
Inflation-18.98974414.592275Inflation-1False0.0Inflation-1_diff0.000000
Inflation-28.98974414.592275Inflation-2False0.0Inflation-2_diff0.000000
Inflation-38.98974414.592275Inflation-3False0.0Inflation-3_diff0.000000
fedfunds-15.33000019.100000fedfunds-1False0.0fedfunds-1_diff0.000000
fedfunds-25.33000019.100000fedfunds-2False0.0fedfunds-2_diff0.000000
fedfunds-35.33000019.100000fedfunds-3False0.0fedfunds-3_diff0.000000
spx-10.3028830.526508spx-1False0.0-0.249575
spx-20.3028830.526508spx-2False0.0-0.237904
spx-30.3028830.526508spx-3False0.0-0.223658
\n", "" ], "text/plain": [ - "TRAIN 0 1 feat out_max pc_out_max\n", - "Total 4.300000 14.800000 Total False 0.0\n", - "Inflation 8.989744 14.592275 Inflation False 0.0\n", - "FEDFUNDS 5.330000 19.100000 FEDFUNDS False 0.0\n", - "SPX_diff 0.302883 0.526508 SPX_diff False 0.0\n", - "num_month 12.000000 12.000000 num_month False 0.0\n", - "Total-1 4.100000 14.800000 Total-1 False 0.0\n", - "Total-2 4.000000 14.800000 Total-2 False 0.0\n", - "Total-3 3.900000 14.800000 Total-3 False 0.0\n", - "Inflation-1 8.989744 14.592275 Inflation-1 False 0.0\n", - "Inflation-2 8.989744 14.592275 Inflation-2 False 0.0\n", - "Inflation-3 8.989744 14.592275 Inflation-3 False 0.0\n", - "fedfunds-1 5.330000 19.100000 fedfunds-1 False 0.0\n", - "fedfunds-2 5.330000 19.100000 fedfunds-2 False 0.0\n", - "fedfunds-3 5.330000 19.100000 fedfunds-3 False 0.0\n", - "spx-1 0.302883 0.526508 spx-1 False 0.0\n", - "spx-2 0.302883 0.526508 spx-2 False 0.0\n", - "spx-3 0.302883 0.526508 spx-3 False 0.0" - ] - }, - "execution_count": 46, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "df_max = df.filter(list_feat).groupby(df[\"TRAIN\"]).max().transpose()\n", - "df_max[\"feat\"] = df_max.index\n", - "df_max[\"out_max\"] = df_max[0] > df_max[1]\n", - "df_max[\"pc_out_max\"] = df_max[\"feat\"].apply(\n", - " lambda x: 100*sum(df[df[\"TRAIN\"] == 0 ][x] > df[df[\"TRAIN\"] == 1][x].max()) / df[df[\"TRAIN\"] == 0 ].shape[0]\n", - " )\n", - "df_max" - ] - }, - { - "cell_type": "code", - "execution_count": 47, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "3.4" + " target_0\n", + "Total 0.000000\n", + "Inflation 0.000000\n", + "FEDFUNDS 0.000000\n", + "SPX_diff -0.257393\n", + "num_month 0.000000\n", + "Total-1_diff 0.000000\n", + "Total-2_diff 0.000000\n", + "Total-3_diff 0.000000\n", + "Inflation-1_diff 0.000000\n", + "Inflation-2_diff 0.000000\n", + "Inflation-3_diff 0.000000\n", + "fedfunds-1_diff 0.000000\n", + "fedfunds-2_diff 0.000000\n", + "fedfunds-3_diff 0.000000\n", + "spx-1 -0.249575\n", + "spx-2 -0.237904\n", + "spx-3 -0.223658" ] }, - "execution_count": 47, + "execution_count": 55, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "df_min.at[\"Total\", 1]" - ] - }, - { - "cell_type": "code", - "execution_count": 48, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "# detect unemployment rate < in test set\n", - "markers = {0: \"X\", 1: \"o\"}\n", - "ax = sns.lineplot(df.index.values, \n", - " df[\"Total\"], hue=df[\"Total\"] > df_max.at[\"Total\", 1] , \n", - " style=df[\"TRAIN\"], \n", - " markers=markers)\n", - "sns.move_legend(ax, \"best\", facecolor=\"lightgrey\")" + "corr_matrix_targets.where(corr_matrix_targets < -0.15, 0)" ] }, { - "cell_type": "code", - "execution_count": 49, + "cell_type": "markdown", "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "[Text(0.0, 1.0, 'Detect Inflation test > max TRAIN set')]" - ] - }, - "execution_count": 49, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], "source": [ - "# detect FEDFUNDS > in test set\n", - "\n", - "markers = {0: \"X\", 1: \"o\"}\n", - "ax = sns.scatterplot(df.index.values, \n", - " df[\"Inflation\"], \n", - " hue=df[\"Inflation\"] > df_max.at[\"Inflation\", 1], \n", - " style=df[\"TRAIN\"], \n", - " markers=markers)\n", - "sns.move_legend(ax, \"best\", facecolor=\"lightgrey\")\n", - "ax.set(title=\"Detect Inflation test > max TRAIN set\")" + "### on Total_diff" ] }, { "cell_type": "code", - "execution_count": 50, + "execution_count": 56, "metadata": {}, "outputs": [ { "data": { + "image/png": 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vvPIKX3zxBbNmzeLy5ct89tlnzg5JRAqBeRMPEXEp//nPf1i0aBHr168nLS2NGTNmMH/+fGeHJSIFzMSJhw3DMO8XaHCpmEtsbCzvvfcevr6+lC5dmpUrVxITE+PssESkgJk48RARV+Lp6YmPj4/951KlSuHp6enEiESkMJh3cKnNZu6igM3MjZM7kb+/Pz/99JP9viwrV66kdOnSTo5KRAqaeRMPEXEpY8eOZciQIZw4cYJHH32UkiVLMm/ePGeHJSIFzLyJh2GYe5EtLSEtJnPvvfcSGxvLiRMnsFqtVK1aFQ8P8/6JErlT6f9qESkS1qxZk+Pnn3/+GV9fX2rWrElQUJBTYhKRgqfEQ0SKhLVr1/L999/TpEkT3Nzc2LFjB1WqVOH8+fMMHjyYp59+2tkhikgBMG/iYVjBZuZ7tairRczFYrGwcuVK7rvvPgBOnjzJlClTeP/99+nVq5cSDxGT0HRaESkSUlJS7EkHwD333ENSUhIlSpTAzU1/qkTMwrz/N18dXGraL1U8xFy8vb358ssv7T9/9dVX+Pj4kJSURHZ2thMjE5GCZDEMc72DpaWlcfDgQaqXNPA1b0cS6dnw63kLtWrVws/Pz9nhiNy2n3/+mcGDB3P58mVsNhslS5Zkzpw5rFmzhsqVK9OjRw9nhygiBcC8b82mX0AMwN3ZUYgUmPPnz7NhwwYOHz6Mu7s71apVw93dXXesFTEZ83a1iIhLmThxIm5ubtSqVYsaNWrg7q7EWsSMTFvxuHIzNWdHUXiutE1/mMU8ateuzerVq3nkkUdy3LMlMDDQiVGJSEEzbeIhIq5l3bp1rFu3Lsc2i8XCwYMHnRSRiBQGJR4iUiQcOnTI2SGIiAOYN/EwDLCZua/FxIujyR0pKyuLr7/+mkuXLgFgtVo5efIkL730kpMjE5GCZN7EQ0RcyvDhw/n11185e/YsNWrUYP/+/TRq1MjZYYlIATNv4mH2RbZU8RCT2b9/P1988QWvvvoqffv25fLly/zrX/9ydlgiUsA0nVZEioSAgAA8PT0JCgril19+4YEHHrB3u4iIeZi34mGzgcXEK4jZlDOKuXh6erJt2zaqV6/OV199xSOPPEJqaqqzwxKRAqZ3LxEpEl5++WU++eQTmjdvzvHjx2natCnVq1d3dlgiUsDMW/EQEZeQnJyMYRiMHTuWBQsWcPbsWd58803OnTvHkCFDnB2eiBQwEyce/7uLq4gUaWPGjGHbtm0AtGjRAsMwsFgsuLu706ZNGydHJyIFzcSJh4i4ggULFgAwYsQIpk2b5uRoRKSwmTfxsBmY+va0Nk2nFXNR0iFyZ9DgUhEREXEY8yYehs38XyImlZyczJw5c0hOTnZ2KCJSwMybeIiIy0pJSWHu3LmkpKQ4OxQRKWDmHeNh2K4sImZWWjJdRERckCoeIiIi4jBKPERERMRhTNvVYhg2DKzODqPQGOpqERERF2TaxEOcJzs7m/Pnz+Pt7Y2bm4pqknc2m42goCBsNhtpaWnODkdckM1mIyMjg5IlS+Lhobe6osRiGIbh7CAKUlpaGgcPHqSqWwq+lmxnh1No0g0PjtnKUatWLfz8/JwdTg5nz57lxIkTzg5DRISgoCDKlCnj7DDkT5QGSoHz9vYGYM2MVZw9dcbJ0Ygr8vD25B+Tn2PxmP+QnZHl7HDEBZWpVJanXnra/vdIig7zJh42Ayxmnk5bdAtVV7tXzp46Q+Kx006ORlyRl++VN4vkE0lkpmc4ORpxZeruLXr0GxERERGHMW/FA7MvK27mtomIiFmp4iEiIiIOo8RDREREHMa8XS02G5h4ATFwV9ooIiIuR29dIiIi4jDmrXgYNsw9ANPMbRMREbNSxUNEREQcxrwVD5uBuasCBrg7OwYREZG8MW/iISIikgejR4/m559/BuDo0aNUrFgRX19fAN5++20qVKiQY/+LFy/Sv39/li1bdsPzxsfH06ZNG/u573RKPERERIApU6bYvw8JCWHy5Mk0aNDguvufP3+eH3/80RGhmYqJEw+Tr1zqAveh8fD2tN9zQyQvvHy9cvwrklce3p4Fdq6PP/6YxYsXY7FYKF68OKNGjaJu3bqMGDECq9VKeHg4S5cuZe/evURHR5OdnU1KSgrNmzdn4sSJBRaHWZg48RBn+8fk55wdgri4iIXDnR2C3OF27tzJ3LlzWb58OYGBgWzbto0XX3yR9evXM23aNNq0aUNsbCyGYbBw4UImTJhAjRo1uHjxIqGhoXTt2hV/f39nN6NIKdDEY+vWrUybNo2MjAwqVKjAv/71LwICAnLsc+7cOUaPHs1vv/2G1Wrl5ZdfplWrVgCsWbOGKVOmUL58efv+S5YsoUSJEnkPxqaKh7MtHvMfkk8kOTsMcUFevl5ELBzOm89PJzM909nhiAsKCAoskA8/W7dupW3btgQGBgLQtGlTypYtyw8//EC1atXs+1ksFubNm8fXX3/NV199xfHjx8nIyCAtLU2Jx18UWOKRmprKiBEjeP/996lZsybvv/8+o0ePZuHChTn2e/3116lRowZvv/028fHxdO3aldq1a1OhQgX27NnDkCFDePbZZwsqLHGi7Iws3dJcbktmeqZeQ5Iv2RlZBXIemy33hzzDMLBac66MnZaWRseOHWnRogUNGzYkLCyM7777DsMwCiQOMymwxGPbtm3UrFmTmjVrAtCtWzfeeOMNUlJSKFeuHADZ2dls3ryZ9evXA1C5cmWaNm3KunXr6N+/P9999x2nT59m9erV+Pj4MHToUB599NFrXi85OZmUlJRc2+0vEpth8oqHXswiIoWtefPmjBo1ij59+ti7WhISEnj44Ye5fPkyhmFgs9k4efIkqampDBs2DB8fH7Zu3crp06evmbjc6Qos8UhMTMwx1cjLy4tSpUpx+vRpe+Jx7tw5Ll++nKMrpXz58pw+fZrMzEwqVqzI888/z2OPPcbu3bsZOHAgq1ev5u677851veXLlzN37txc24OCgpg8eXJBNUtERO5gTZo0YdCgQfTt2xfDMPD19eWdd97B398fq9VKvXr1aNeuHe+99x5t27alffv2+Pv7ExAQQL169Thx4sQ138PuZAWWeBiGgcViybXdzc0txz5Arv3c3Nzw8vLK0S3TsGFDHnnkEbZu3UqPHj1ynbdr166EhITk2m6z2cjMzMTAimGY9yZxhsW8bRMRcbZNmzbZv3/mmWd45plncu3j7u6eYw2PG81g0Roe/6/AEo+KFSuyc+dO+8+ZmZmcO3eOihUr2reVKVMGb29vkpOT7QN1kpKSqF69OklJSXz66ac899z/DwYyDANPz2tPiQoICMg1cBWu9LMdPHiwoJolIiIiBajA7tXyt7/9jZ9//pkjR44AsHLlSh566CFKly5t38fd3Z3Q0FA+/PBDAE6dOsXWrVtp1aoVfn5+zJs3jx07dgCwb98+fvjhB1q2bFlQIYqIiIiTFVjFo3Tp0sycOZORI0eSkZFBmTJleOONNwAIDw9n0qRJPPjgg4wbN47x48fz5JNPkp2dzahRowgKCgIgOjqaN954g4yMDDw8PJg1axZly5bNX0A248qUWtPS4FIREXE9BbqOx2OPPUZMTEyu7bGxsfbvS5cufc1BoQCNGjVi1apVBRmSiIiIFCHmXbnUMPkCYmZum4iImFaBjfEQERERuRlzVzzMPMbDBZZMFxER+SvzJh7idFOWDsfPSy8xyTurzeBA/HkW/Xcq7m651wcSuZm0zGx+TfzD2WHINZj3XcEw+ZLpWv9fRERckMZ4iIiIiMMo8RARERGHMW9Xi02DS0VERIoaVTxERETEYcxb8dDgUhERkSJHFQ8RERFxGBNXPEw+xsPNxG0TERHTUsVDREREHMa8FQ+zz2oxc9tERMS0VPEQERERh1HiISIiIg5j3sTj6nRa035pOq2ISEELCQlhz549N9wnJiaG4OBgevTowciRI5k3b95NzxsdHc1nn30GwLJly5g7d26BxOuKzJt4iIiIFIKYmBgGDx7Mhx9+iMVya3dP3rlzJ1lZWQB0796dwYMHF2aIRZp5B5eafTqtCyyOZrNdub25SF5dfd3o9SP5dbt//kNCQggPD+fbb78lISGB0NBQIiMjmTRpEvv27SMuLo4zZ87kOGbfvn3861//IjMzk+TkZOrUqcOMGTP48MMP2b9/P/Hx8VgsFk6ePEliYiJRUVEkJSUxYcIETp48icViITg4mIiICDw9Pa8bg6szb+IhTncs+Q9nhyAu7lDCBWeHIHewCxcusHTpUlJSUmjdujXdunUjMjKSw4cP07lzZ8LDwxk1apR9/yVLltC/f3+aN29OZmYm4eHhbN68md69e7Nx40Y6d+5MWFgYc+bMsR8zYsQIGjduTHR0NJcvX2bAgAG8++67DBw48LoxVK9e3eHPRUEyb+KhJdOdrmpAcXy93J0dhrggq83gUMIF7q9YAne3Wytli/xZeqb1tj/8hISEAFCuXDnKli1LamrqDfePioriv//9L/Pnz+f48eOcPXuWtLS06+6flpbGnj17eOuttwDw8fGhR48ezJ8/35545DUGV2DexEOczs0NvWnIbXF3s+g1JPniVgAjGL29ve3fWywWjJt84OvVqxdBQUEEBwcTGhpKXFzcDY8xDCPX4zabDavVmu8YXIEGl4qIiNymCxcusHfvXl566SXatWtHRkYG+/fvtycR7u7uORIKgGLFivHII4+wePFiAC5fvsxHH31E06ZNHR6/I5m34qGVS0VExEFKlCjBoEGD6NatG/7+/hQrVoxGjRpx8uRJ4EqXyYwZM8jIyMhx3LRp05g4cSIdOnQgMzOTZs2amX7Gi8UwQ93mT9LS0jh48CD3nt6Jb9ZFZ4dTaNI97+J4hcbUqlULPz8/Z4eTw9XfQfXyxfHzMm9uK4XHajM4EH+e2pVLqqtF8iUtM5tfE/8okn8j73QmflewmXtwKWZum4iImJXGeIiIiIjDmLfiYTPMPQ5CCyuJiIgLUsVDREREHMbEFQ/NahERESlqVPEQERERh1HiISIiIg5j3q4WDJe4n0n+Ff222c7EY3Wz3nxHkb+wGhagNNak42Ap+q91KXpsNnfA39lhyDWo4iEiIiIOY96Kh6bTioiIFDmqeIiIiIjDmLjioem0IiIiRY0qHiIiIuIw5q14YJj8JnEa4yEiIq5HFQ8RERFxGCUeIiIi4jDm7WrR4FIREZEiRxUPERERcRjzVjwMky+Zbua2iYiIaaniISIiIg5j7oqHmcdBqOIhIlLgatasSfXq1fHw+P+3xwoVKvD2228zatQotm3bRpkyZXIc06xZM0aMGMGcOXP44IMPKF++PABZWVkEBAQwfPhwHnzwQYDrnmPq1KlcuHCByMhIvvrqqxyPzZs3j99++42pU6cyZ84clixZwpo1a6hYsaJ9n969exMWFkanTp1yXSMjI4OqVasycuRI7rnnHgDOnDnDlClTOHz4MBaLBU9PT/7+97/z1FNPFcwTeQPmTTxERETyYeHChfbk4a969OjBwIEDr3tsq1atiIqKsv8cGxtLnz59WLt2rT1RuN45du3adUvxpaenM2LECJYsWYK7u/tN4zQMg/nz5/P3v/+dTz/9lOLFixMZGUm9evWYPn06AAkJCTzzzDMEBgbSpEmTW4ojv5R4SKGxYfnf7c1F8sZq/PlfvYYk72xF5HUTHh5ObGwsH374ISNGjCiQc7Zr145ff/2VuXPnEhERcdP9LRYLL7zwAqtXr2bdunV0796dpKQkMjIysFqtuLu7U7FiRebNm0epUqUKJMYbMW/ioem0Tnciu6SzQxAX90t2aWeHIHeg559/PkdXyxtvvEHNmjUB+PDDD/niiy9y7D9y5Egee+yx656vZs2aHDlyxP7zX8/Rtm1bBgwYcMvxeXp6MmPGDDp37kyTJk149NFHb+m4P8cxcuRIXnnlFT744APq16/Pww8/TPv27bn77rtvOY78Mm/iIU4X5HEeH4vV2WGIC7IaV5KO+zxScS8aH1zFxVw23PP94ed2ulquxWKx4OPjc9NzuLlde76HzWbL9VhQUBCRkZG8/PLLxMbG5jmOxo0bs3nzZn766Sd2797Njh07iI6OZu7cuQQHB99q0/LFvImHYTP3vVpcoG1uGLhbNAhW8uNKtuFuQa8hyRe3IjQAf9++fTRq1Oim+5UqVYrz58/n2n727Fn8/f1zbX/qqafYvn07Y8aMuem5DcPgwIEDtGzZkrNnz/Lmm28ybtw46tevT/369enfvz8zZ87kww8/LPTEQ9NpRURECsnHH3/M4cOH6dGjx033rVq1KnfddRcxMTH2bfHx8Xz55Ze0aNHimse8+uqr/Prrr3z33XfXPa/VaiU6OhqA9u3bU6JECXbu3MnChQux/a/bPjMzk7i4OOrUqZOH1uWPaSseVwoeRSfjLWguUPAQETGda43xCAgI4N133wVgw4YN7N+/H7jSRVKpUiU++OADSpe++XglNzc35s+fz9SpU1m4cCEAXl5evPLKK9cdx1G8eHFmzJhBt27drhtndnY2999/P0uWLMHLywuA//znP0yfPp1WrVrh6+uLYRg8/vjjee5Gyg+LYRShelQBSEtL4+DBgwT9/Bm+6anODqfQpPuW5sQD7alVqxZ+fn7ODieHq7+Dqh6/4+umMR6Sd1bDwqGs0tzvmaquFsmXdJs7x7L9i+TfyDudaSseGCaf1aKSh4iIuCCN8RARERGHUeIhIiIiDmPurhYzd0eYuW0iImJaqniIiIiIw5i34mEDTDydFhU8RETEBaniISIiIg5j3oqHptM63d96vsbhw0edHYa4oGJ+vmzetIIKzV7kUlq6s8MRF1SzZjWWLJ7l7DDkGlTxEBEREYcxb8XDZvKKh5nbJiIipqWKh4iIiDiMEg8RERFxGPN2tRjGlS+zMnPbRETEtFTxEBEREYcxd8XDzAMwVfEQEREXpIqHiIiIOIx5Kx42w+RLppu4bSIiYlqqeIiIiIjDKPEQERERhzFvV4thc4n7meSbmdsmIiKmpYqHiIiIOIx5Ew+D/x9gasYvjS0VESlwP/30E//4xz/o0KEDHTp04O9//zs//PADACEhIbRp04bw8HCeeuop2rZtS0REBOfOnSM7O5sePXowbty4HOd777336Ny5MxkZGXmKo3Xr1uzatQuA8PBwzp49C8Dbb79NixYtGDp0KNu3b6dly5Z06tSJ33///bbb7ijm7WoRp/P18aaYn6+zwxAX5Pe/142fXj+ST74+3nk+JjMzk379+jFv3jweeeQRALZv387zzz/Ppk2bAJg8eTINGjQAwGazMXToUF599VVmz57NrFmz6NSpEzExMXTs2JEtW7awaNEiVqxYgbd33uO5KjY21v79ypUrmTx5Mo899hijR48mLCyMYcOG5fvczmDexMNmwzDzAmIu0Lb57/zL2SGIi/v0k8XODkHuIOnp6Vy4cIH09HT7tr/97W/MmDEDd3f3XPu7ubnRqFEjli1bBkBAQACzZs3ihRde4K677mL8+PHMmzePwMDAm1577969TJgwAZvNRvXq1bl8+bL9sZo1a7JlyxYmT55MUlISr732Gk8//TQbN27Ey8uLCxcu8OqrrxbAM+AY5k08xOn6vzCSX3457uwwxAX5+fny6SeLeeLJf5CWln7zA0T+4r777s3zh5+SJUsycuRIBg8eTKlSpahfvz4NGjTgySefpHjx4rn2//333/nss89o0qSJfVuDBg0YOHAggwYNYvz48dSrV++m183KymLIkCFMnDiRli1bsmPHDj799NNc+82ePZuQkBB71eX48eNUqVKFgQMH5qmdzmbexMMw+QJiLrBkevrlDC7pTUNuQ1paul5Dki/pl/M2puKq3r1788wzz7B7926+//57li1bxrx581ixYgUAY8aMwdfXF8MwMAyDJk2a5OjqsNls7Nq1i4CAADZv3kz37t1xc7vxcMojR46QnZ1Ny5YtAWjSpAn33ntvvuJ3BeZNPERERPJgz549fP/99/Tv35/g4GCCg4MZOnQoPXv2ZP369UDOMR7XMmPGDC5cuMDatWvp1q0bs2fPZujQoTe9tvGXD5MeHuZ9ezbvrBYREZE8KF26NG+99RY7duywb0tNTeXcuXPUqVPnpsd/+umnrFmzhjfffJNSpUoxZ84cFi9ezObNm294XM2aNfHx8eHLL78ErsysOXr06O01pggzb0plGOZeZMsFulpERFxJ1apVeeedd5gzZw5jx47F19cXb29vBg0aRKNGjW547M8//8z48eN555137INJa9Soweuvv84rr7zCqlWrqFKlyjWP9fDwYN68ebz66qvMnTuXoKAgU3e1WIy/1ndcXFpaGgcPHiRo+1J8LiQ7O5xCc7lEACf+1pNatWrh5+fn7HByuPo76PWPoRw+bN6sXQpPMT9fNm9aQcuQZzTGQ/KlZs1qLFk8q0j+jbzTmbfiobvTiohIETF58mT7gmB/1adPH5566inHBuRE5k08REREiogxY8Y4O4Qiw7yJh2FziUW28s3M41dERMS0NKtFREREHMa8FQ+N8RARESlyVPEQERERhzFvxUOcbl21e/Hw9HJ2GOKCbD7epAI/PlATt3wufS13tuwqlfjd2UHINZk38dACYiIiIkWOulpERETEYcxd8TDzAExVPERExAWp4iEiIiIOY9qKh2GzYZh4ATEzt01ERMxLFQ8RERFxGNNWPLBh7jEeKniIiIgLUsVDREREHEaJh4iIiDiMebtaNJ1WRESkyFHFQ0RERBzGxBUPm8mXTDdx20RExLRU8RARERGHKdDEY+vWrYSHh9O2bVv69OlDcnJyrn3OnTvHiy++SPv27Xn88cfZsGGD/bG4uDh69erFE088QYcOHdi7d2/+g7EZ5v8SERG5BdHR0Xz22WcAjBo1innz5jktlgLraklNTWXEiBG8//771KxZk/fff5/Ro0ezcOHCHPu9/vrr1KhRg7fffpv4+Hi6du1K7dq1qVChAi+99BKdO3ema9eu7N+/nxdffJEvv/wSPz+/ggpTHMjm5YnNx9vZYYgLMv73ujF8vLVkjeSLzcvT2SEUKTt37qRy5crODgMowMRj27Zt1KxZk5o1awLQrVs33njjDVJSUihXrhwA2dnZbN68mfXr1wNQuXJlmjZtyrp16wgPD+fQoUN06tQJgDp16hAUFMTXX39N+/btc10vOTmZlJSUXNttWkq8yLgwaqCzQxAXd25apLNDkDvMrl27eOONN6hRowaHDh0iPT2dyMhI9u7dS2JiIlFRUQDMmzeP3377jalTp9KrVy/q1KnDzp07OXv2LE8++ST+/v5s3ryZxMRExowZQ+vWrW943Vs9xx9//MGkSZPYt28fFouFevXqMWrUKIoXL06vXr148MEH+emnn0hISOChhx7i3//+Nx988AH79+8nPj4ei8UCwL59++jevTupqamUK1eO6dOnExgYWOjPLxRg4pGYmEiFChXsP3t5eVGqVClOnz5tTzzOnTvH5cuXKV++vH2/8uXLc/r0aU6fPk3ZsmXx9Pz/LDUwMJCEhIRrXm/58uXMnTs31/agoCAmT56MYYBh4u4IV5hNW2LqPDziTzs7DHFBho8356ZFUmrEJCyXM5wdjrig7MoV8v3h58CBA7z66qvUrVuX999/n1mzZhEcHHzDY44ePcqqVas4ffo0ISEhjB49mmXLlrF27VrmzJlz08TjVs8RFRWFh4cH69atwzAMxowZw5QpU+wJ0fHjx1m8eDEZGRm0b9+e7du307t3bzZu3Ejnzp0JCwvjm2++4fTp03zwwQcUK1aMAQMGsGzZMoYOHZqv5yuvCizxMAzDnkn9mZubW459gFz7ubm53dLxf9a1a1dCQkJybbfZbGRmZuYpdikcbplZuOlNQ/Lhat3ScjlDryHJF7fMrHwfW65cOerWrQvAAw88wKJFi256TOvWrXFzc6NSpUq4u7vTokULAKpUqcLvv/9+S9e9lXNs2bKF9957z/7e+I9//IO+ffvaz9GyZUvc3d3x8/PjnnvuITU19ZrXCgkJoXjx4vY2XmtMZmEpsMSjYsWK7Ny50/5zZmYm586do2LFivZtZcqUwdvbm+TkZHtJJykpierVq1OxYkXOnDlDdnY2Hh5XwkpOTqZVq1bXvF5AQAABAQG5tqelpXHw4EHzD8A0c9tERJzIx8cn1zaLxWL/8AyQlZUzsfHy8srx89X3sby4lXP8dTiBzWbDarXaf/b2/v9xdX+N+XrnvtF+haHAZrX87W9/4+eff+bIkSMArFy5koceeojSpUvb93F3dyc0NJQPP/wQgFOnTrF161ZatWpFYGAg999/PzExMQD8/PPP/PLLLzz22GMFFaKIiEi+lCpVisOHD2Oz2UhPT2fbtm1OiaN58+YsWbLEnnAsWbKEpk2b3vQ4d3f3HAmKMxVYxaN06dLMnDmTkSNHkpGRQZkyZXjjjTcACA8PZ9KkSTz44IOMGzeO8ePH8+STT5Kdnc2oUaMICgoCYPr06YwbN473338fgBkzZlCyZMn8BWTYwMwDTbWAmIiIw4SFhbFp0ybatGlDYGAg9evX58KFCw6PY+zYsUyZMoWwsDCysrKoV68e48ePv+lxISEhzJgxg4wM53ddWgxH1lcc4GpXS5W18/A5a96BjZfLVOC3sIHUqlWryE03vvo78J/wJh6/nXJ2OOKCbD7epM6dSOnB4zTGQ/Ilu0olfh8fUST/Rt7pzLtkusZ4iIhIEbFgwQLWrVt3zceeeOIJ+vfv7+CInMe8iYeIiEgR0bdv3xyzT+5kuleLiIiIOIx5Kx42zN0dobGlIiLiglTxEBEREYcxbcXDMAyHLojiaGZum4iImJcqHiIiIuIwpq14YJh8Oq0LVDz8+zXCN+uis8MQF2S1uJMKlB3ZEnejaKy2KK4l3fMufnd2EHJNqniIiIiIw5i34qEFxERERIocVTxERETEYZR4iIiIiMOYtqvFMAwME3dHaDqtiIi4IlU8RERExGFMW/HQkukiIiJFjyoeIiIi4jAmr3g4O4hCZOa2iYiIaaniISIiIg6jxENEREQcxrxdLSafTusK92oRERH5K1U8REREgNGjRxMeHk54eDh16tShTZs29p9Pnz6da/+LFy/SvXv3m543Pj6eBx544Ib72Gw2Ro4cybx58/IV+9ixY5kzZ479+y1btgCwfft2WrZsSadOnTh16hQdOnQgLCyMb775Jl/XKQjmrXjoXi1OZ7O4YbW4OzsMcUFXXzd6/Uh+2Sx5/1w9ZcoU+/chISFMnjyZBg0aXHf/8+fP8+OPP+Yrvj87dOgQEydOZP/+/bzwwgu3fb6oqCj795988glhYWEMGzaMNWvWULx4cZYtW3bb17gd5k08xOl+K/+os0MQF3e0crCzQxDh448/ZvHixVgsFooXL86oUaOoW7cuI0aMwGq1Eh4eztKlS9m7dy/R0dFkZ2eTkpJC8+bNmThx4k3Pv2TJErp37862bdtuOaaUlBRGjx5NQkIC5cqVw2azUb58eQB69epF586dSUpKYuPGjXh5ebFhwwYuXbrE+fPn6dKlCytXrsz383G7zJt4GJh7ymnRL3hQJfFbfLL+cHYY4oKsFneOVg6mWvwW3A2rs8MRF3TZs3iBfPjZuXMnc+fOZfny5QQGBrJt2zZefPFF1q9fz7Rp02jTpg2xsbEYhsHChQuZMGECNWrU4OLFi4SGhtK1a1f8/f1veI2rFYq8JB4TJ07kvvvuY8GCBSQnJ9OpUycefTRne/v378+xY8eoUqUKAwcOZPXq1axdu5ZFixbl9WkoUOZNPMTp3Ayb3jTktrgbVr2GJF/cjIL55Ll161batm1LYGAgAE2bNqVs2bL88MMPVKtWzb6fxWJh3rx5fP3113z11VccP36cjIwM0tLSbpp45Me2bdtYvnw5AAEBAYSGhhb4NQqLaRMPw2buWS1mbpuISFFhs+VOYAzDwGrNmRCnpaXRsWNHWrRoQcOGDQkLC+O7777LdUPPsWPHsn//fgC6det2S4NTr+fP5/b09Mz3eRxNs1pERESuo3nz5qxfv56kpCTgSqUhISGBhx9+GA8PDwzDwGazcfLkSVJTUxk2bBitWrXixIkTnD59OlfiEhUVRWxsLLGxsbeVdLRo0YIVK1YAcO7cOTZt2pT/RjqYaSseIiIit6tJkyYMGjSIvn37YhgGvr6+vPPOO/j7+2O1WqlXrx7t2rXjvffeo23btrRv3x5/f38CAgKoV68eJ06c4O677y7wuMaNG0dkZCRt27albNmy1KxZs8CvUVgsxl/rQC4uLS2NgwcPUmnhDLyTTjk7nEKTEViJU8+/RK1atfDz83N2ODlc/R0End6Jb9ZFZ4cjLshqcefI3SHUiNukMR6SL+med3GiQuMi+TfyTqeKh4iISBGzdu1aFi5ceM3HGjZsSGRkpIMjKjimTTwMky+ZbrJClYiI/ElYWBhhYWHODqNQaHCpiIiIOIxpKx7YMPcCYmZum4iImJYqHiIiIuIwpq14GAYU0MJ1RZKGeIiIiCtSxUNEREQcxrQVD3G+vZMPYz2a5OwwxAVZfL0osyqEHUN/wkjPdHY44oLcqwVSak5jZ4ch12DexEODS0VERIocdbWIiIiIw5i24mHYTD641MRtExER81LFQ0RERBzGtBUPDMw9DkLTaUVExAWp4iEiIiIOo8RDREREHMa8XS0mH1xq6m4kERExLVU8RERExGFMW/HQvVpERESKHlU8RERExGHMW/Ew+RgPM7dNRETMSxUPERERYPTo0YSHhxMeHk6dOnVo06aN/efTp0/n2v/ixYt07979pueNj4/ngQceuO7jc+fO5YknnqBDhw707t2b48eP5zn23r17s3r1agD69evHwYMHAYiJiSE4OJgePXpw6NAhWrVqRceOHfnll1/yfI2CYtqKB4blypdZuUDbLN4eWHy9nB2GuKCrrxu9fiS/LN55f3ubMmWK/fuQkBAmT55MgwYNrrv/+fPn+fHHH/MV31Xr1q1j8+bNrFy5Ej8/Pz744ANeeeUVVq5cme9zvvvuu/bvY2JiGDx4MF26dGHu3LnUq1ePadOm3VbMt8u8iYc4nf+0ns4OQVxc6Q8GOjsEET7++GMWL16MxWKhePHijBo1irp16zJixAisVivh4eEsXbqUvXv3Eh0dTXZ2NikpKTRv3pyJEyfe8Nz33HMPkZGR+Pn5AVC3bl3mzZt305iOHTvGmDFjuHTpEpUqVeLcuXP2x0JCQnjjjTf4/PPP2bdvH3FxcSQlJbFs2TKys7MZNGgQ0dHRt/ek3AYlHlJofh+xFOvxFGeHIS7I4utF6Q8GkvrsPIz0TGeHIy7I/d5yBfLhZ+fOncydO5fly5cTGBjItm3bePHFF1m/fj3Tpk2jTZs2xMbGYhgGCxcuZMKECdSoUYOLFy8SGhpK165d8ff3v+7569ata/8+IyODf//737Rv3/6mcb388st06tSJnj178uuvv9KpU6dc+0RGRnL48GE6d+5MeHg4hmGQmJhIVFRUvp6LgmLaxEPTaZ3PyMjWm4bcFiM9U68hyRcjI7tAzrN161batm1LYGAgAE2bNqVs2bL88MMPVKtWzb6fxWJh3rx5fP3113z11VccP36cjIwM0tLSbph4XJWcnMw///lPSpcuzSuvvHLDfX///Xf279/PkiVLAKhevTqPPvpo/hvpYBpcKiIich02W+5PsIZhYLVac2xLS0ujY8eO/Pjjj9SsWZOIiAhKly6N8ZdPiWPHjrUPWF22bBkAP/74I08//TQNGjRg7ty5eHnd2timP5/bw8N16giuE2keGTYLhq3oD8DMLzO3TUSkqGjevDmjRo2iT58+9q6WhIQEHn74YS5fvoxhGNhsNk6ePElqairDhg3Dx8eHrVu3cvr06VyJy1+7OQ4cOMBzzz3HhAkTeOKJJ24pJn9/f+rVq8eKFSvo06cPcXFx7N69mzZt2hRYuwuTaRMPERGR29WkSRMGDRpE3759MQwDX19f3nnnHfz9/bFardSrV4927drx3nvv0bZtW9q3b4+/vz8BAQHUq1ePEydOcPfdd1/3/HPmzMFmszF//nzmz59v3x4bG3vDuKZNm8aYMWNYtWoVgYGB1KpVq8DaXNgsxl/rQC4uLS2NgwcPUvZfM/GMP+XscApNVuVKnBk5jFq1atlHQxcVV38H54Ysxno0ydnhiAuy+HpRZtVQzj49S2M8JF/cqwVSas4/iuTfyDudKh4iIiJFzIIFC1i3bt01H3viiSfo37+/gyMqOKZNPAzDguECi2zll5nbJiJyp+vbty99+/Z1dhiFQrNaRERExGGUeIiIiIjDmLirRQuIiYiIFDWqeIiIiIjDmLfioQXEREREihzTJh7ifPU6GvicV5+Q5J3Vw+BXoFFvA/dsvYYk7y6XNDjp7CDkmkybeBiGucdBmLltIiJiXhrjISIiIg6jxENEREQcxrRdLRgmH4CprhYREXFBqniIiIiIw5i24qHptCIiIkWPKh4iIiLiMOateGg6rYiISJGjioeIiIg4jIkrHiYf42GYt20iImJeqniIiIiIwyjxEBEREYcxbeJhGBbTf4mISMEZPXo04eHhhIeHU6dOHdq0aWP/+fTp07n2v3jxIt27d7/peePj43nggQeu+/j8+fNp3749TzzxBAMGDODMmTN5jr13796sXr0agH79+nHw4EEAYmJiCA4OpkePHhw6dIhWrVrRsWNHfvnllzxfo6CYdoyHOJ/VwxOrh5ezwxAXZPvf68am14/kk9XDM8/HTJkyxf59SEgIkydPpkGDBtfd//z58/z444/5iu+qbdu2sWbNGj7++GP8/PyYMmUK06ZNY+rUqfk+57vvvmv/PiYmhsGDB9OlSxfmzp1LvXr1mDZt2m3FfLvMm3jYwLA5O4hC5AJti2/e09khiIs71m6Qs0MQ4eOPP2bx4sVYLBaKFy/OqFGjqFu3LiNGjMBqtRIeHs7SpUvZu3cv0dHRZGdnk5KSQvPmzZk4ceINz920aVNiY2Px9PQkIyODlJQUAgMDbxrTsWPHGDNmDJcuXaJSpUqcO3fO/lhISAhvvPEGn3/+Ofv27SMuLo6kpCSWLVtGdnY2gwYNIjo6+rafl/wyb+IhTlf5v0vxOZ/s7DDEBdk8vDjWbhBV10fjlp3p7HDEBV0uGVAgH3527tzJ3LlzWb58OYGBgWzbto0XX3yR9evXM23aNNq0aUNsbCyGYbBw4UImTJhAjRo1uHjxIqGhoXTt2hV/f/8bXsPT05NPPvmEiRMn4uXlxZIlS24a18svv0ynTp3o2bMnv/76K506dcq1T2RkJIcPH6Zz586Eh4djGAaJiYlERUXl9+koEKZNPGxYsJh4HISNot829+ws3PWmIbfBLTtTryHJF/fsrAI5z9atW2nbtq29CtG0aVPKli3LDz/8QLVq1ez7WSwW5s2bx9dff81XX33F8ePHycjIIC0t7aaJB8CTTz7Jk08+yZIlS3j++ef56quvcHO79jDM33//nf3799sTlOrVq/Poo4/efmMdxLSDS0VERG6XzZa7X9swDKxWa45taWlpdOzYkR9//JGaNWsSERFB6dKlMf6yzPTYsWPtA1aXLVvGL7/8kmOcSJcuXYiPj+f8+fM3je3P5/bwcJ06gmkTjytLpjt/5knhfTn7GRYRMb/mzZuzfv16kpKSgCuDQRMSEnj44Yfx8PDAMAxsNhsnT54kNTWVYcOG0apVK06cOMHp06dzJS5RUVHExsYSGxtL9+7dOXHiBC+//DIXL14EYPXq1dSsWZNSpUpdNyZ/f3/q1avHihUrAIiLi2P37t2F9AwUPNdJkURERBysSZMmDBo0iL59+2IYBr6+vrzzzjv4+/tjtVqpV68e7dq147333qNt27a0b98ef39/AgICqFevHidOnODuu+++7vlbt27N8ePH6dq1Kx4eHlSoUOGWBn5OmzaNMWPGsGrVKgIDA6lVq1ZBNrtQWYy/1oFcXFpaGgcPHsR3bDTuJ3LPuzYLa1AF0qMGUatWLfz8/JwdTg5Xfwf3bFqEz/kkZ4cjLsjq4cWvHYZRfd1MjfGQfLlcMpCTIb2L5N/IO51pKx6GzeT3ajFx20RE7nQLFixg3bp113zsiSeeoH///g6OqOCYNvEQERFxVX379qVv377ODqNQmDfxMDD3AEwzt01EREzLtLNaREREpOgxbcXDMEw+xsPEi6OJiIh5qeIhIiIiDmPaiofNsICJqwI2E7dNRETMy7SJhzjfunWepB3Tbc0l79x9vWjYAZYt9cKa7uxoxBX5VfWkboizo5BrUVeLiIiIOIxpKx5X72liVmZum4iImJcqHiIiIuIwpq14aAExERGRokcVDxEREXEY01Y8bIDFxOMgVPAQERFXpIqHiIiIOIwSDxEREXEY03a1GCZfuVTTaUVExBUVWMVj//79dOnShfbt29OlSxeOHTt2zf0uX77MqFGjaNu2La1bt+ajjz6yP7Z7924efvhhwsPD7V+//vprQYUoIiIiTlYgFY/MzEwGDx7MpEmTaNq0KRs3bmTw4MF8+umnWCw5P5nPmTOH7Oxs1q9fz++//063bt24//77qVevHnv27KFr166MHDnytmMyDEw9AtPUU4VFRMS0CqTisW/fPtzd3WnatCkAoaGhpKen89NPP+Xa96uvvqJr165YLBZKlSrFE088QUxMDADfffcd+/fv5+mnn6Zz5858/vnnBRGeiIiIFBF5qnhs2bKFAQMG5No+cOBAKlSokGNbYGAgCQkJPPTQQzm2nz59mvLly9t/Ll++PPv37wfgrrvuokOHDvYull69ehEYGEj9+vVzXTM5OZmUlJRc2202G2D+JdMxLJi4dSIiThESEsIbb7xBgwYNrrtPTEwMs2bNolKlStx9993cc889DBw48IbnjY6O5t5776V9+/YsW7aMs2fPMnjw4IIOn5UrV/LZZ5/x3nvv5fnY2NhYPv74Y5YsWZIjxtOnT9O/f38sFgsvv/wyy5Yt45dffqFLly70798/z9fJU+IRHBzMzz//nGv7J598wq5du3Jtd3PLXVAxDCNX98vV/WbOnGnfVr16ddq3b8+GDRuumXgsX76cuXPn5toeFBTE5MmTb94YKXTu3h64++rutJJ3br6eOf4VySt378KbOxETE8PgwYPp0qULo0aNuqVjdu7cSeXKlQHo3r17gceUmprK9OnT+fTTT6lXr95tn+/PMe7atYvixYuzbNkyEhIS2LRpEz/99BNeXvn7+14gv5mKFSuSlJSUY1tycnKuKghApUqVSEpKsv8CkpKSqFChApcuXWLx4sX0798fD48rYRmGYf/+r7p27UpISO57HttsNjIzM00/q8UVKh61p3Z1dgji4h55L++fpkQKQkhICOHh4Xz77bckJCQQGhpKZGQkkyZNYt++fcTFxXHmzJkcx+zbt49//etfZGZmkpycTJ06dZgxYwYffvgh+/fvJz4+HovFwsmTJ0lMTCQqKoqkpCQmTJjAyZMnsVgsBAcHExERgaen53VjuJa1a9dSuXJlXnnlFb788stbamN2djZTp07l66+/pnTp0tx99932x+bMmUNiYiJPPPEEs2bN4vz58zz++OO4u7tjGAZdunThjTfeoGbNmnl+bgsk8ahbty4ZGRl88803PPbYY3z99ddYLBZq166da9/WrVuzfPly6tevz4ULF/jkk0947bXX8PPzY9WqVQQEBNC5c2fi4uL4/PPP+c9//nPNawYEBBAQEJBre1paGgcPHiyIZsltOjBqOWknztx8R5G/cPP15JH3+vNdn/nY0rOcHY64IL+gsrf94efChQssXbqUlJQUWrduTbdu3YiMjOTw4cN07tyZ8PDwHBWPJUuW0L9/f5o3b05mZibh4eFs3ryZ3r17s3HjRjp37kxYWBhz5syxHzNixAgaN25MdHQ0ly9fZsCAAbz77rv2rptrxVC9evVcsfbu3RuA1atX33L7li9fzs8//2yfCPLiiy/m2uexxx7jn//8J2vXrmXRokXEx8fTpk0bYmNjb/k6f1UgiYeHhwdvvfUWr7/+OlFRUfj6+jJnzhzc3d0B6NevH926dSM0NJRBgwYxadIkOnToQFZWFt27d6dJkybAlT6wiRMnsnjxYqxWK5GRkdx///0FEaI4gTUjG2t6prPDEBdmS8/Sa0jyxZqRfdvnuFpVL1euHGXLliU1NfWG+0dFRfHf//6X+fPnc/z4cc6ePUtaWtp1909LS2PPnj289dZbAPj4+NCjRw/mz59vTzzyGkNebN26lSeeeAJvb28AOnfuzLJlywrs/NdTYJ1gDzzwAMuXL7/mY++++679e19fX6Kioq653/3338/SpUsLJB4bJu9qwaJlZ0VECtHVN2QAi8WCcZN1DHr16kVQUBDBwcGEhoYSFxd3w2MMw8j1uM1mw2q13jCGZcuW2dfAqlOnznXfU2/Fn69/vaENBU3vXSIiIrfpwoUL7N27l5deeol27dqRkZHB/v377UmEu7t7joQCoFixYjzyyCMsXrwYuLLA5kcffWRfmuJ6unfvTmxsLLGxsbeVdLRo0YK1a9eSnp5Odnb2bXWf5IV5l0x3dgAiInLHKFGiBIMGDaJbt274+/tTrFgxGjVqxMmTJ4ErXSYzZswgIyMjx3HTpk1j4sSJdOjQgczMTJo1a1Yo02yv5ZlnniEuLo4OHTrg7+/P/fffz4ULFwr9uhbjZrUjF3N1cGnW0IVwNNHZ4RSeauXxnPU8tWrVws/Pz9nR5HD1d/DTsKWkHUt2djjigtx9vWj40SB2d4vWGA/JF7+qAdSd2bNI/o2805m34mGY+0ZqFlOliyIiUtCOHTvGsGHDrvmYr69vjnulOZJpEw8REZE7WdWqVR02biMvTJt43AlLpouIiLgazWoRERERh1HiISIiIg5j2q4WG+aeUquOFhERcUWqeIiIiIjDmLbiARYME9cFiv69aaFt7US8y5xydhjigmxe3pwEOj8cj1tmxk33F/mrjPI2EpwdhFyTKh4iIiLiMKateNiMK1+mZea2iYiIaaniISIiIg6jxENEREQcxrxdLViwucAAzPwzc9tERMSsVPEQERERhzFtxcPs02lV8RAREVekioeIiIg4jGkrHrb/fZmV6h0iIuKKVPEQERERhzFtxcMAU4/x0PphIiLiilTxEBEREYdR4iEiIvI/ISEh7Nmz54b7xMTEEBwcTI8ePRg5ciTz5s276Xmjo6P57LPPAFi2bBlz584tkHiv+vLLL+nYsSPh4eF07NiRLVu25Pkc8+bNY9SoUQC8+eabrFy5EoBDhw7RqlUrOnbsyKFDh+jRowft27dnzZo1+YrV1F0tZh5c6goZo83TC5uXt7PDEBd09XWj14/kl83Tq9DOHRMTw+DBg+nSpYv9jfpmdu7cSeXKlQHo3r17gcZz6tQpxo0bx4oVK7jnnns4ePAgPXv2ZNOmTfj7++frnBEREfbvN2zYQL169Zg2bRq7d+/m1KlT+UpsrjJt4iHOl/iPIc4OQVxc3NDXnB2C3KFCQkIIDw/n22+/JSEhgdDQUCIjI5k0aRL79u0jLi6OM2fO5Dhm3759/Otf/yIzM5Pk5GTq1KnDjBkz+PDDD9m/fz/x8fFYLBZOnjxJYmIiUVFRJCUlMWHCBE6ePInFYiE4OJiIiAg8PT2vG8Nfubm5MXHiRO655x4A7rvvPiwWC2fPnr1h4nHp0iXGjx/P/v37KV26NP7+/pQsWRKAUaNGUaVKFSpWrMiyZcvIzs5m0KBBHDlyhLNnzxIeHs6iRYsoVapUnp9b0yYeZp9O6wptK794Dt5JCc4OQ1yQzcubuKGvcfes13DLzHB2OOKCMgIr3vaHnwsXLrB06VJSUlJo3bo13bp1IzIyksOHD9O5c2fCw8NzVDyWLFlC//79ad68OZmZmYSHh7N582Z69+7Nxo0b6dy5M2FhYcyZM8d+zIgRI2jcuDHR0dFcvnyZAQMG8O677zJw4MDrxlC9evUccVaoUIEKFSrYf37zzTe5++67qVq16g3bN3fuXKxWK+vXryctLY3u3bvbE4+rnnrqKeLi4uyJ0q5du4iMjCQ2Njbfz6tpEw9xPresTL1pyG1xy8zQa0jyxS0r87bPERISAkC5cuUoW7YsqampN9w/KiqK//73v8yfP5/jx49z9uxZ0tLSrrt/Wloae/bs4a233gLAx8eHHj16MH/+fHvikZcYMjMzmTRpErt27eK9997DYrnxzM5t27YxfPhw3NzcKF68OB06dODYsWM3PKYgmDbxMEy+ZLqZ2yYiUhR4e///GCOLxYJh3Hghg169ehEUFERwcDChoaHExcXd8BjDMHI9brPZsFqtN4xh2bJlfPTRRwDUqVOHqKgozpw5w+DBgylWrBgrVqzIVbm4UQxXeXp63tIxt8sVxiiKiIgUaRcuXGDv3r289NJLtGvXjoyMDPbv329PItzd3XMkFADFihXjkUceYfHixQBcvnyZjz76iKZNm97wWt27dyc2NpbY2FiioqL4448/ePbZZ6lTpw7vvvvuLScdLVq0YNWqVWRnZ5Oens769evz0fK8M2/FwwI2ExcFDBO3TUTE1ZQoUYJBgwbRrVs3/P39KVasGI0aNeLkyZPAlS6TGTNmkJGRs+tw2rRpTJw4kQ4dOpCZmUmzZs0YPHhwnq69YsUKjh8/jpeXFx07drRvnzRpEg8++OB1jxs0aBATJkygXbt2lC5dmipVquTpuvllMW5WO3IxaWlpHDx4kJR/vk/W0WRnh1NoPKsFUG7236lVqxZ+fn7ODieHq7+Digum4514ytnhiAuyeXlz8pUp3PPGaI3xkHzJKF+JhL7Di+TfyDudaSseIiIid7KdO3cyZcqUaz52zz33MHv2bAdHdIVpEw8bFmwmHoBp5raJiMjta9y48W1Ney0sGlwqIiIiDmPaioeBue/gaua2iYiIeaniISIiIg5j6oqHKywrnl+qeIiIiCtSxUNEREQcxrQVD3E+r0b34ZNWxtlhiAuyul9Zutk7pA7u1iwnRyOuyPAr7ewQ5DpMm3jYLBZsN7lBjiszc9tERMS81NUiIiIiDmPaioem04qIiBQ9qniIiIiIw5i64qHptCIiIkWLKh4iIiLiMKateNgsV77MysxtExER81LFQ0RERBxGiYeIiIg4jHm7WrBgw7z9EWZum4iImJcqHiIiIuIwpq14gKacioiIFDWqeIiIiPxPSEgIe/bsueE+MTExBAcH06NHD0aOHMm8efNuet7o6Gg+++wzAJYtW8bcuXMLJN6rvv76a5566inCwsJ45pln+OGHH/J8jnnz5jFq1CgA3nzzTVauXAnAoUOHaNWqFR07duTQoUP06NGD9u3bs2bNmnzFatqKhw1zTzk18+JoIiJFWUxMDIMHD6ZLly72N+qb2blzJ5UrVwage/fuBRrPhQsXGDFiBCtWrKBq1aps2rSJl156iU2bNuX7nBEREfbvN2zYQL169Zg2bRq7d+/m1KlTbNmyJd/nNm3iIc5nc/ew395cJC+sbh45/hXJK5v77b12QkJCCA8P59tvvyUhIYHQ0FAiIyOZNGkS+/btIy4ujjNnzuQ4Zt++ffzrX/8iMzOT5ORk6tSpw4wZM/jwww/Zv38/8fHxWCwWTp48SWJiIlFRUSQlJTFhwgROnjyJxWIhODiYiIgIPD09rxvDX5UoUYIdO3bg6emJYRjEx8fj7+9/0zZeunSJ8ePHs3//fkqXLo2/vz8lS5YEYNSoUVSpUoWKFSuybNkysrOzGTRoEEeOHOHs2bOEh4ezaNEiSpUqlefn1rT/V9swd1XAFdr22wPtnR2CuLijD3d1dghyB7tw4QJLly4lJSWF1q1b061bNyIjIzl8+DCdO3cmPDw8R8VjyZIl9O/fn+bNm5OZmUl4eDibN2+md+/ebNy4kc6dOxMWFsacOXPsx4wYMYLGjRsTHR3N5cuXGTBgAO+++y4DBw68bgzVq1fPFaunpyfx8fF0796dc+fOMXPmzJu2b+7cuVitVtavX09aWhrdu3e3Jx5XPfXUU8TFxdkTpV27dhEZGUlsbGx+n1bzJh7ifFV+/gyftHPODkNckNXNg6MPd6Xa98txt2U7OxxxQZf9St32h5+QkBAAypUrR9myZUlNTb3h/lFRUfz3v/9l/vz5HD9+nLNnz5KWlnbd/dPS0tizZw9vvfUWAD4+PvTo0YP58+fbE4+8xFC5cmW2bt3Kjz/+SJ8+fahevTr33nvvdffftm0bw4cPx83NjeLFi9OhQweOHTt2wzYWBCUeUmjcrNm4W7OcHYa4MHebXkOSP27W209Yvb297d9bLBYM48ZzJXv16kVQUBDBwcGEhoYSFxd3w2MMw8j1uM1mw2q13jCGZcuW8dFHHwFQp04dXnrpJX766SdatmwJwEMPPUS1atU4cuTIDROPqzFc5enpmK5xU89qMUz8JSIiRceFCxfYu3cvL730Eu3atSMjI4P9+/fbkwh3d/ccCQVAsWLFeOSRR1i8eDEAly9f5qOPPqJp06Y3vFb37t2JjY0lNjaWqKgorFYrw4cP59ChQwAcOHCAuLg4HnrooRuep0WLFqxatYrs7GzS09NZv359fpufJ6p4iIiI3KYSJUowaNAgunXrhr+/P8WKFaNRo0acPHkSuNJlMmPGDDIyMnIcN23aNCZOnEiHDh3IzMykWbNmDB48OE/XDggIYMaMGYwePRqbzYaXlxczZ86kfPnyNzxu0KBBTJgwgXbt2lG6dGmqVKmSt0bnk8W4We3IxaSlpXHw4EEOD1tK+rFkZ4dTaHyrBlBzZk9q1aqFn5+fs8PJ4ervIGjfWnzTbtwnKnItVndPjjToSY09S9XVIvmS7leaEw+GFcm/kXc6VTxERERMaOfOnUyZMuWaj91zzz3Mnj3bwRFdYdrEQ9NpRUTkTta4cePbmvZaWEw9uFRERESKFtNWPAzMXRUw1cAcERG5Y6jiISIiIg6jxENEREQcxrxdLZYrX2Zl5raJiIh5mTbxEOebv/h3fj925uY7ivyFh68XnZbC1JlnyU7PdHY44oL8q7rRZpqzo5BrMW3ioem0IiIiRY/GeIiIiIjDmLbioem0IiIiRY8qHiIiIuIwSjxERETEYUzd1WLm7ggzt01ERMxLFQ8RERFxGNNWPGyWK19mZea2iYiIeaniISIiIg5j2oqHptOKiIgUPap4iIiIiMOYtuKhJdNFRESKHlU8RERE/uftt9+mRYsWDB069JaPad26Nbt27QIgJiaG4OBgevTogWHcfqd4YmIiNWvWzPfxs2bNYuzYsfk6dt68eYwaNQqAN998k5UrVwJw6NAhWrVqRceOHTl06BA9evSgffv2rFmz5pbOa9qKh4iISF6tXLmSyZMn89hjj+Xr+JiYGAYPHkyXLl0KOLK8iY+PZ8qUKWzbto0nn3zyts8XERFh/37Dhg3Uq1ePadOmsXv3bk6dOsWWLVtu+VymTTy0gJjzeXh74uHr5ewwxAVdfd3o9SP55eHtmedj/vnPf5KUlMRrr71Gz549OXLkCAcPHiQrK4u6desyevRoihcvzt69e5kwYQI2m43q1atz+fJlACZNmsS+ffuIi4vjzJkznDx5kipVqjBw4EAAxo4dS/ny5RkyZAghISGEh4fz7bffkpCQQGhoKJGRkQCsWLGChQsXUqxYMerUqWOPLz4+ntGjR3Pp0iUMw6B58+YMGzbsmm1Zvnw5jz32GPfddx8pKSm31P5Lly4xfvx49u/fT+nSpfH396dkyZIAjBo1iipVqlCxYkWWLVtGdnY2gwYN4siRI5w9e5bw8HAWLVpEqVKlbnod0yYe4nwhUd2dHYK4uLAFLzo7BLmDzJ49m5CQECZPnkxMTAxVq1YlKioKwzCYOnUq06dPZ8yYMQwZMoSJEyfSsmVLduzYwaeffgpAZGQkhw8fpnPnzoSHh9u7Ka7nwoULLF26lJSUFFq3bk23bt2wWq1Mnz6d2NhYypcvz+zZs+37f/DBB9StW5eXX36ZtLQ0xowZw7lz5675Zj98+HAA5syZc8vtnzt3LlarlfXr15OWlkb37t3ticdVTz31FHFxcSQmJhIVFcWuXbuIjIwkNjb2lq9j2sTDMPkCYoYLtG3T2GX8fuLWMm2RP/Pw9SJswYus7fs22emZzg5HXJB/ULnb+vCzadMm9u7dy9q1awHIysqiTJkyHDlyhOzsbFq2bAlAkyZNuPfee/N1jZCQEADKlStH2bJlSU1N5cCBAzRq1Ijy5csD0KNHD6KjowFo2bIlQ4YM4ejRozRu3Jjhw4ffUoXhVm3bto3hw4fj5uZG8eLF6dChA8eOHSuw819l2sRDnC87I0tvGnJbstMz9RqSfMnOyLqt4202G9OmTeOBBx4A4I8//iArK4uEhIRcg0Y9PK79VmqxWHLsm5WVMyZvb+/r7nutczdq1IiNGzeyY8cOdu3aRZcuXZg9ezY7duxg06ZNwJVk5s/jMfLqzzF4eua9u+pWmHZWi+0O+BIRkcLRrFkzFi9ejNVqxWq1MmrUKGbOnEnNmjXx8fHhyy+/BOCnn37i6NGj1zxHqVKlOHjwIADnzp1jz549t3TdnTt3EhcXB8Dq1avtj7366qssXryYNm3aEBkZyX333cfRo0eJiIggNjaW2NjY20o6WrRowapVq8jOziY9PZ3169fn+1w3YtrEQ0REJL+uDvQMCwvjiSeewNfXl5EjR+Lh4cG8efOYP38+YWFhLFiw4LpdLc8++yxnzpzh8ccfZ/jw4TRq1Oim161evTqRkZH069ePTp06kZSUZH+sX79+7Nq1iw4dOtCpUycqVqxIx44dC6bBwKBBgyhRogTt2rWjd+/eVKlSpcDO/WcWoyAmGhchaWlpHDx4kB0jlnLxWLKzwyk0d1UNoMm0ntSqVQs/Pz9nh5PD1d/BlyPe53cT/w6k8Hj4etFp6T9Z3XO2ulokX/yrBtBm2t+L5N/IO53GeIiIiNwBdu7cyZQpU6752D333JNjBk1hUuIhIiJyB2jcuHGepr0WFtMmHgYGNpdYZit/DBO3TUREzEuDS0VERMRhTFvxMPuUUzO3TUREzEsVDxEREXEY01Y8dJM4ERGRose0iYc4X49yZ/HMSHR2GOKCbD7eJAEDKibhdjnD2eGIC8oq584ZZwch16SuFhEREXEY01Y8DMw9AFNdLSIi4opU8RARERGHMW3FwwbYLM6OovCYuZojIiLmpYqHiIiIOIyJKx7mXjLdzG0TERHzUsVDREREHKbAEo/9+/fTpUsX2rdvT5cuXTh27NgN909ISKBZs2YkJv7/Og/nzp3jxRdfpH379jz++ONs2LDhtmIyTPwlIiLiigok8cjMzGTw4MFERETw2Wef8eKLLzJ48GAMI/dbpGEYrFy5kp49e5KcnJzjsddff50aNWrw2WefsXDhQl599VVOnz5dECGKiIhIEVAgice+fftwd3enadOmAISGhpKens5PP/2Ua9/ffvuN7du3s2DBghzbs7Oz2bx5M926dQOgcuXKNG3alHXr1l3zmsnJyRw4cCDX19GjRwuiSSIiIlII8jS4dMuWLQwYMCDX9oEDB1KhQoUc2wIDA0lISOChhx7Ksf2ee+5h1qxZuc5x7tw5Ll++TPny5e3bypcvf92Kx/Lly5k7d26u7UFBQUyePFl3pxURESmC8pR4BAcH8/PPP+fa/sknn7Br165c293cbr2gcrVbxmLJufjG9c7RtWtXQkJCcm232WxkZmbe8nVFRETEcQqkq6VixYokJSXl2JacnJyrCnIjZcqUwdvbO8e4j6SkpOueIyAggNq1a+f6qlatGvD/02nN/CUiIgXr7bffpkWLFgwdOvSWj2ndurX9w3dMTAzBwcH06NHjmuMc8yoxMZGaNWvm+bhly5bRoUMHwsLC6Nq16zWHPtzM2LFjmTNnjv37LVu2ALB9+3ZatmxJp06dOHXqlP0633zzzS2dt0ASj7p165KRkWG/6Ndff43FYqF27dq3fA53d3dCQ0P58MMPATh16hRbt26lVatWBRGiiIjITa1cuZLJkydfc0jArYiJiWHw4MF8+OGHuSr4jvL9998zf/58Fi9ezNq1a3nxxRcZOHDgbSVCUVFRBAcHA1d6OcLCwli9ejW7d++mePHirF27lscee+yWzlUgC4h5eHjw1ltv8frrrxMVFYWvry9z5szB3d0dgH79+tGtWzdCQ0NveJ5x48Yxfvx4nnzySbKzsxk1ahRBQUH5jks1AeeyeXth8/F2dhjigmze3jn+Fckrm7dXno/55z//SVJSEq+99ho9e/bkyJEjHDx4kKysLOrWrcvo0aMpXrw4e/fuZcKECdhsNqpXr87ly5cBmDRpEvv27SMuLo4zZ85w8uRJqlSpwsCBA4ErVYPy5cszZMgQQkJCCA8P59tvvyUhIYHQ0FAiIyMBWLFiBQsXLqRYsWLUqVPHHl98fDyjR4/m0qVLGIZB8+bNGTZsWK52lCxZkokTJ1K6dGngSnHg7NmzpKen4+fnd932p6SkMHr0aBISEihXrhw2m80+7rJXr1507tyZpKQkNm7ciJeXFxs2bODSpUucP3+eLl26sHLlylt6ngts5dIHHniA5cuXX/Oxd99995rbDx8+nOPn0qVLX3PAqLim1KGDnB2CuLiUSeOdHYLcQWbPnk1ISAiTJ08mJiaGqlWrEhUVhWEYTJ06lenTpzNmzBiGDBnCxIkTadmyJTt27ODTTz8FIDIyksOHD9O5c2fCw8MZNWrUDa934cIFli5dSkpKCq1bt6Zbt25YrVamT59ObGws5cuXZ/bs2fb9P/jgA+rWrcvLL79MWloaY8aM4dy5c5QqVSrHeatVq/b/ww5sNiZPnkyLFi1umHQATJw4kfvuu48FCxaQnJxMp06dePTRR3Ps079/f44dO2ZPqFavXs3atWtZtGjRrT7NZl4y3dwzP1yhbaVnReN5KsHZYYgLsnl7kzJpPOUiJ+CWkeHscMQFZVWqeFsffjZt2sTevXtZu3btlfNlZVGmTBmOHDlCdnY2LVu2BKBJkybce++9+brG1QkS5cqVo2zZsqSmpnLgwAEaNWpkrzT06NGD6OhoAFq2bMmQIUM4evQojRs3Zvjw4bmSjj/7448/eOWVV0hNTWX+/Pk3jWfbtm32AkJAQMBNeynyy7SJhzifW0Ymbpf1piH555aRodeQ5Itbxu3NbrTZbEybNo0HHngAuPImnpWVRUJCQq6xEh4e134rtVgsOfbNysrK8bj3n7oS/7rvtc7dqFEjNm7cyI4dO9i1axddunRh9uzZ7Nixg02bNgFXkpmIiAiOHz/OgAEDqFu3LjNnzsxxrRv5cwyenp63dExe6V4tIiIif9GsWTMWL16M1WrFarUyatQoZs6cSc2aNfHx8eHLL78E4KeffrruwpWlSpXi4MGDwJW1qvbs2XNL1925cydxcXEArF692v7Yq6++yuLFi2nTpg2RkZHcd999HD16lIiICGJjY4mNjSUiIoKEhAR69uxJly5deOONN2456WjRogUrVqywx3s1mSlopq14mH3KqZnbJiLibJGRkUyZMoWwsDCsVisPPvggI0eOxMPDg3nz5vHqq68yd+5cgoKCrtvV8uyzz/LSSy/x+OOPU6lSJRo1anTT61avXp3IyEj69euHn58fDRs2tD/Wr18/Ro8ezRdffIGHhwc1atSgY8eOuc6xcOFCLly4wNq1a+1dRQDz588nMDDwutceN24ckZGRtG3blrJly+ZrGu+tsBgFMdG4CElLS+PgwYN8NmIx544n3/wAF1Xq3gDaT/sHtWrVuumAIUe7+jso+6+ZeMafcnY44oJsPt4k/TuKwJfHqqtF8iWrciXOjBxWJP9G3ulMW/EATacVERG5au3atSxcuPCajzVs2NA+nbewmTrxEBERkSvCwsIICwtzdhjmTTw0nVZERKTo0awWERERcRglHiIiIuIwpu1qMQDDxMNLzdsyERExM1U8RERExGFMXfEw8wBMV6h4pF/0JPP3vN8hUsTwufK6SfvdC8tlV3i1S1FjLVk4y33L7VPFQ0RERBzGtBUPLZkuIiJS9KjiISIiIg5j2oqHgWuMg8gvM7dNRETMSxUPERERcRglHiIiIuIwpu1q0eBSERGRokcVDxEREXEY01Y8tICYiIhI0aOKh4iIiDiMiSsehslvEmfetomIiHmp4iEiIiIOY9rEw3YHfImISMF6++23adGiBUOHDr3lY1q3bs2uXbsAiImJITg4mB49emAYt1+ZTkxMpGbNmnk+btWqVXTo0IEOHTrw97//nePHj+f5HGPHjmXOnDn277ds2QLA9u3badmyJZ06deLUqVN06NCBsLAwvvnmm1s6r2m7WkRERPJq5cqVTJ48mcceeyxfx8fExDB48GC6dOlSwJHdumPHjjF9+nTWrVtHmTJlWLJkCePHj2fJkiX5PmdUVJT9+08++YSwsDCGDRvGmjVrKF68OMuWLbvlcynxkEJjeHti+Hg7OwxxQYaPV45/RfLK8PbM8zH//Oc/SUpK4rXXXqNnz54cOXKEgwcPkpWVRd26dRk9ejTFixdn7969TJgwAZvNRvXq1bl8+TIAkyZNYt++fcTFxXHmzBlOnjxJlSpVGDhwIHClalC+fHmGDBlCSEgI4eHhfPvttyQkJBAaGkpkZCQAK1asYOHChRQrVow6derY44uPj2f06NFcunQJwzBo3rw5w4YNy9WOqlWrsmXLFjw9PcnOziYhIYFSpUrdtP0pKSmMHj2ahIQEypUrh81mo3z58gD06tWLzp07k5SUxMaNG/Hy8mLDhg1cunSJ8+fP06VLF1auXHlLz7OJEw9zDy51hQm1aeNecHYI4uL+mDPa2SHIHWT27NmEhIQwefJkYmJiqFq1KlFRURiGwdSpU5k+fTpjxoxhyJAhTJw4kZYtW7Jjxw4+/fRTACIjIzl8+DCdO3cmPDycUaNG3fB6Fy5cYOnSpaSkpNC6dWu6deuG1Wpl+vTpxMbGUr58eWbPnm3f/4MPPqBu3bq8/PLLpKWlMWbMGM6dO3fNpMLT05M9e/YQERFBeno6CxcuvGn7J06cyH333ceCBQtITk6mU6dOPProozn26d+/P8eOHbMnVKtXr2bt2rUsWrToFp7hK0yceIiz+U18B/ffEp0dhrggw8eLP+aMpviQKVguZzo7HHFB1irlb+vDz6ZNm9i7dy9r164FICsrizJlynDkyBGys7Np2bIlAE2aNOHee+/N1zVCQkIAKFeuHGXLliU1NZUDBw7QqFEje6WhR48eREdHA9CyZUuGDBnC0aNHady4McOHD79hJaNBgwZs376dDRs20L9/fzZu3EiJEiWuu/+2bdtYvnw5AAEBAYSGhuarXTdj2sTD7AMwXaFtlowsLJcznB2GuDDL5Uy9hiRfLBlZt3W8zWZj2rRpPPDAAwD88ccfZGVlkZCQkGvQqIfHtd9KLRZLjn2zsnLG5O3tfd19r3XuRo0asXHjRnbs2MGuXbvo0qULs2fPZseOHWzatAm4ksw8/fTTxMXF0aRJEwBatWrF+PHj+e2333J03VzLn2Pw9Mx7d9WtMO2sFhERkfxq1qwZixcvxmq1YrVaGTVqFDNnzqRmzZr4+Pjw5ZdfAvDTTz9x9OjRa56jVKlSHDx4EIBz586xZ8+eW7ruzp07iYuLA2D16tX2x1599VUWL15MmzZtiIyM5L777uPo0aNEREQQGxtLbGwsERERnD9/nqFDh5KYeKXivGXLFtzc3KhWrdoNr92iRQtWrFhhj/dqMlPQzF3xKICpTEWVK1Q8RERcVWRkJFOmTCEsLAyr1cqDDz7IyJEj8fDwYN68ebz66qvMnTuXoKCg63a1PPvss7z00ks8/vjjVKpUiUaNGt30utWrVycyMpJ+/frh5+dHw4YN7Y/169eP0aNH88UXX+Dh4UGNGjXo2LFjrnPUrl2bkSNH0q9fP9zc3ChRogTvvvsuvr6+N7z2uHHjiIyMpG3btpQtWzZf03hvhcUoiInGRUhaWhoHDx5k6YiFpBwz7/iCclXL03Pa89SqVQs/Pz9nh5PD1d9Bsci5uJ9McHY44oIMH28uvvsqd/V7XV0tki/WeypyadLgIvk38k5n2ooHuMK8DxEREcdYu3btdWe3NGzY0D6dt7CZOvEQERGRK8LCwggLC3N2GBpcKiIiIo5j2oqHDQObiTtbzNw2ERExL1U8RERExGFMW/EwTL5kupnbJiIi5qWKh4iIiDiMiSse5l5kS/UOERFxRaZNPMT5ZqZ7kPhH4az1L+bmZfVkFDD2kieZ6Wb+CCGFpXy6B/2dHYRck7paRERExGFMW/HQdFoREZGiRxUPERERcRjTVjw0nVZERKToUcVDREREHMbEFQ9NpxURESlqVPEQERERhzFvxcMAwzBvXcDETRMRERNTxUNEREQcRomHiIiIOIxpu1q0gJiIiEjRo4qHiIiIg8yaNYuxY8c6OwynMm3icXU6rVm/VO8QEXEd8fHxDBo0iPfee8/ZoTidabtaxPk8vT3x8vV2dhjigrx8vXL8K5JXnt55vzP2+fPnGTlyJImJiVgsFmrXrs0LL7xA9+7dCQ4O5sCBA2RkZDBixAhCQ0OJjo5mw4YNrFixgoyMDDp27MjgwYMJDw/Pde7ly5fz2GOPcd9995GSklIQTXRZpk08tGS68/WZ/LyzQxAX99LCEc4OQe4ga9euxcfHhzVr1mC1Whk/fjyZmZmkpKRQv359oqKi+PHHH+nTpw8bNmxgwIAB7N69m+joaI4fP06zZs2umXQADB8+HIA5c+Y4sklFkmkTD3G+98YsJOlEkrPDEBfk5evFSwtHMOP5aWSmZzo7HHFBgUGBef7w06RJE95++2169+5N48aN6d27N97e3hQrVozOnTsD8NBDDxEUFMTu3bt5/PHHmTZtGmFhYZQvX55///vfhdEU0zFt4qFZLc6XlZFFZnqGs8MQF5aZnqnXkORLVkZWno+pXr06GzduZNeuXezatYs+ffrQq1cvPDxyv1W6u7sDcPr0adzc3EhOTubs2bNUqFCBN998k02bNgEQEhJCRETE7TXGZEybeIiIiOTF/PnzOXHiBJMnTyY4OJjU1FQOHTrE+fPn2bBhA61atWLv3r3Ex8fTsGFD/vjjD4YNG8b48eP57bffGDZsGB988AERERFKNm7AtLNaRERE8qJr166cO3eO9u3b06lTJy5cuMBzzz2Hm5sbGzZs4KmnnmLChAnMmTOHkiVLEhkZScOGDWnTpg3PPfccnp6ezJo1y9nNKPJMW/EwDMPk92oxb9tERJyhZMmSvPXWWzm2xcfHY7FYmDp1aq79/5xkuLm5sWTJkpteY8iQIbcdp6tTxUNEREQcxrwVD64stGVWqneIiBS+ypUr8/PPPzs7DFNRxUNEREQcxtQVD1dYZCu/zNsyERExM1U8RERExGGUeIiIiIjDmLarRSuXioiIFD2qeIiIiIjDmLbigckXEMMF2lbG4g0WX2eHIS7I0+IFQKDFhyyLPh9J3pWxeDs7BLkO/R8tIiIiDmPaiofGeIiIiBQ9qniIiIiIw5i24mH87z+zMnPbRETEvFTxEBEREYdR4iEiIiIOY96uFgNsLjDlNL9M3DQRETExVTxERETEYcxb8cDcd3A1c9tERMS8VPEQERERhzFtxUMLiImIiBQ9pk08REREioply5bx4YcfYrFY8PX1ZezYsdStW9fZYTmFaRMPVTxERKQo+P7775k/fz6rVq2idOnSbN68mYEDB7J161YsFouzw3M40yYe4nye3p54+no5OwxxQVdfN3r9SH55envm+Zjz588zcuRIEhMTsVgs1K5dmxdeeIHu3bsTHBzMgQMHyMjIYMSIEYSGhhIdHc2GDRtYsWIFGRkZdOzYkcGDBxMeHp7jvCVLlmTixImULl0agLp163L27FnS09Px8/MrkPa6EiUeUmjCop51dgji4p5dMNjZIcgdZO3atfj4+LBmzRqsVivjx48nMzOTlJQU6tevT1RUFD/++CN9+vRhw4YNDBgwgN27dxMdHc3x48dp1qxZrqQDoFq1alSrVg0Am83G5MmTadGixR2ZdICpEw8Dw9SrbBX9tq0d+wFnTyQ7OwxxQZ6+Xjy7YDAf9J1LVnqms8MRF1QmKCDPH36aNGnC22+/Te/evWncuDG9e/fG29ubYsWK0blzZwAeeughgoKC2L17N48//jjTpk0jLCyM8uXL8+9///uG5//jjz945ZVXSE1NZf78+flum6szceIhzpaVkaU3DbktWemZeg1JvmRlZOX5mOrVq7Nx40Z27drFrl276NOnD7169cLDI/dbpbu7OwCnT5/Gzc2N5ORkzp49S4UKFXjzzTfZtGkTACEhIURERHD8+HEGDBhA3bp1mTlzJt7e3rfXQBdm2sRDg0tFRCQv5s+fz4kTJ5g8eTLBwcGkpqZy6NAhzp8/z4YNG2jVqhV79+4lPj6ehg0b8scffzBs2DDGjx/Pb7/9xrBhw/jggw+IiIggIiLCft6EhAR69uzJ888/z/PPP+/EFhYNWkBMREQE6Nq1K+fOnaN9+/Z06tSJCxcu8Nxzz+Hm5saGDRt46qmnmDBhAnPmzKFkyZJERkbSsGFD2rRpw3PPPYenpyezZs3Kdd6FCxdy4cIF1q5dS3h4uP0rKSnJ8Y0sAkxb8biyZLp5qwLmbZmIiHOULFmSt956K8e2+Ph4LBYLU6dOzbX/n5MMNzc3lixZcs3zjhs3jnHjxhVorK5MFQ8RERFxGCUeIiIi11G5cmV+/vlnZ4dhKubtajHMPZ3WzG0TERHzUsVDREREHMa8FQ+TT6c188BZERExL1U8RERExGHMW/HQGA8REZEiRxUPERERcRjTVjy0ZLrzZWEjA5uzwxAXZPvf6yYDG1l6DUk+6HVTdKniISIiIg6jxENEREQcxrRdLVcmnBb97oj8M3PbRETErAqs4rF//366dOlC+/bt6dKlC8eOHbvh/gkJCTRr1ozExET7tt27d/Pwww/nuHvfr7/+WlAhioiIiJMVSMUjMzOTwYMHM2nSJJo2bcrGjRsZPHgwn376KRaLJce+hmHw8ccfM2/ePJKTk3M8tmfPHrp27crIkSNvOyabATYTTzm1mbdpIiJiYgWSeOzbtw93d3eaNm0KQGhoKJMmTeKnn37ioYceyrHvb7/9xvbt21mwYAHt27fP8dh3331HRkYGTz/9NBaLhb59+9K2bdtrXjM5OZmUlJRc2202jWQWEREpqvKUeGzZsoUBAwbk2j5w4EAqVKiQY1tgYCAJCQm5Eo977rmHWbNmXfP8d911Fx06dLB3sfTq1YvAwEDq16+fa9/ly5czd+7cXNuDgoKYPHny/0Z4mLcsYOa2iYiIeeUp8QgODr7m7YE/+eQTdu3alWu7m1vehpDMnDnT/n316tVp3749GzZsuGbi0bVrV0JCQnJtt9lsZGZm5um6IiIi4hgF0tVSsWJFkpKScmxLTk7OVQW5kUuXLrF48WL69++Ph8eVsAzDsH//VwEBAQQEBOTanpaWxsGDBzEMw9RjPLRkuoiIuKICmdVSt25dMjIy+OabbwD4+uuvsVgs1K5d+5bP4efnx6pVq1izZg0AcXFxfP7557Rr164gQhQREZEioEASDw8PD9566y3efPNNnnjiCebOncucOXNwd3cHoF+/fmzcuPGG57BYLERHRxMTE0OHDh144YUXiIyM5P777y+IEEVERKQIKLAFxB544AGWL19+zcfefffda24/fPhwjp/vv/9+li5dWiDxaHCpiIgUFatWrWLRokUAlCpVitdff517773XuUE5iZZMFxERKUTHjh1j+vTpLFq0iHXr1tG6dWvGjx/v7LCcxrRLptsw9+BSV7g7rYe3J56+Xs4OQ1zQ1deNXj+SXx7ennk+5vz584wcOZLExET7OMUXXniB7t27ExwczIEDB8jIyGDEiBGEhoYSHR3Nhg0bWLFiBRkZGXTs2JHBgwcTHh6e47xVq1Zly5YteHp6kp2dTUJCAqVKlSqoproc0yYe4nydo/7u7BDExT2/4J/ODkHuIGvXrsXHx4c1a9ZgtVoZP348mZmZpKSkUL9+faKiovjxxx/p06cPGzZsYMCAAezevZvo6GiOHz9Os2bNciUdV3l6erJnzx4iIiJIT09n4cKFDm5d0WHaxMPA3OMgXKFlH499nzMnkm++o8hfePp68fyCf7Kw72yy0rUuj+Rd2aCAPH/4adKkCW+//Ta9e/emcePG9O7dG29vb4oVK0bnzp0BeOihhwgKCmL37t08/vjjTJs2jbCwMMqXL8+///3vG56/QYMGbN++nQ0bNtC/f382btxIiRIl8t1GV2XaxEOcLzsjS28acluy0jP1GpJ8yc7IyvMx1atXZ+PGjezatYtdu3bRp08fevXqdc31pK7O2jx9+jRubm4kJydz9uxZKlSowJtvvsmmTZsACAkJ4emnnyYuLo4mTZoA0KpVK8aPH89vv/1GnTp1bqOVrkmJh4iICDB//nxOnDjB5MmTCQ4OJjU1lUOHDnH+/Hk2bNhAq1at2Lt3L/Hx8TRs2JA//viDYcOG2ZOIYcOG8cEHHxAREUFERIT9vAcOHGDo0KHExsZSvnx5tmzZgpubG9WqVXNia53HtImHVi4VEZG86Nq1K6NGjaJ9+/b4+PhQvnx5BgwYwOeff86GDRuYO3cuFouFOXPmULJkSYYOHUrDhg1p06YNNpuNLVu2MGvWLEaMGJHjvLVr12bkyJH069cPNzc3SpQowbvvvouvr6+TWupcpk08RERE8qJkyZK89dZbObbFx8djsViYOnVqrv3/fMNTNzc3lixZct1zd+rUiU6dOhVYrK7MtImHFhATEREperSAmIiIyHVUrlz5mndll/wzb8XDsGEYNmeHUWjM3DYRETEvVTxERETEYcxb8cA1lhXPL/O2TEREzEwVDxEREXEYJR4iIiLiMObtajEMUy+yVZTbZrNdGfhaqlIZJ0cirurqnUXLBgXka+lrkat/f67+PZKiw7SJhzhPRkYGAI8Pe8q5gYjL0x2O5XZlZGRQvHhxZ4chf2LaxMOGYerBpUW5bSVLliQoKAhvb2/c3NSbJ3l39OhRRowYwbRp0+7Y+1nI7bHZbGRkZFCyZElnhyJ/YdrEQ5zHw8ODMmXUzSL55+bmxokTJ3Bzc8PPz8/Z4YiLUqWjaDJt4qExHiIiIkWP6uAiIiLiMOateGBgM3FVQDeJExERV6SKh4iIiDiMEg8RKXLKlSvH4MGDKVeunLNDEZECZuKuFnN3R5i3ZSIQEBDAkCFDnB2GiBQCVTxERETEYcxb8dB0WhERkSJHFQ8RERFxGPNWPEy+ZLqZx6+IiIh5qeIhIiIiDmPeiofGeIiIiBQ5qniIiIiIwyjxEBEREYcxbVeLzTD3vVrM3DYRETEvVTxERETEYUxb8TAMcw/ANHHTRETExFTxEBEREYcxb8VDC4iJiIgUOap4iIiIiMMo8RARERGHMXVXi6kHl6qrRUREXJAqHiIiIuIwpq14aAExERGRokcVDxEREXEY01Y8/jfKw9lBFCIzt01ERMxKFQ8RERFxGNNWPGyGucdB2MzbNBERMTFVPERERMRhlHiIiIiIw5i2q8UwTL6AmInbJiIi5qWKh4iIiDiMeSseJp9Oa+a2iYiIeaniISIiIg5j3oqHxniIiIgUOap4iIiIiMOYtuKBySsemLltIiJiWqp4iIiIiMMo8RARERGHMW1Xi4G5799q5raJiIh5mTbxCAq629khFCqzt09ERMzJYphsBGZmZiYHDhzAZrM5O5RC5+bmRu3atfHy8nJ2KCIiIrfEdIkHXEk+srOznR1GofPw8FDSISIiLsWUiYeIiIgUTZrVIiIiIg6jxENEREQcRomHiIiIOIwSDxEREXEYJR4iIiLiMEo8RERExGGUeIiIiIjD/B9c7AUF4SNr5AAAAABJRU5ErkJggg==", "text/plain": [ - "[Text(0.0, 1.0, 'Detect FEDFUNDS test > max TRAIN set')]" - ] - }, - "execution_count": 50, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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", 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" + "
" ] }, "metadata": {}, @@ -6695,153 +6495,112 @@ } ], "source": [ - "# detect FEDFUNDS > in test set\n", - "\n", - "markers = {0: \"s\", 1: \".\"}\n", - "ax = sns.lineplot(df.index.values, \n", - " df[\"FEDFUNDS\"], \n", - " hue=df[\"FEDFUNDS\"] < df_min.at[\"FEDFUNDS\", 1], \n", - " style=df[\"TRAIN\"], \n", - " markers=markers,\n", - " )\n", - "sns.move_legend(ax, \"best\", facecolor=\"lightgrey\")\n", - "ax.set(title=\"Detect FEDFUNDS test > max TRAIN set\")" + "# Créer un DataFrame pandas à partir des données d'entrée X et de la variable à prédire y\n", + "df_for_corr = pd.DataFrame(np.hstack((X, df[df[\"TRAIN\"] == 1 ][\"Total_diff\"].values.reshape(-1, 1))), \n", + " columns=list_col_corr)\n", + "# Calculer la matrice de corrélation\n", + "corr_matrix = df_for_corr.corr()\n", + "corr_matrix_targets = corr_matrix.copy().loc[list_feat, list_col_targets]\n", + "fig = plot_corr(corr_matrix_targets, aspect=0.1, size=5)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "### Check repart target" + "### On ur_lower" ] }, { "cell_type": "code", - "execution_count": 51, + "execution_count": 57, "metadata": {}, "outputs": [ { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "Repart for ur_lower :\n", - "- on TRAIN :\n", - " - proba:\n", - " False 0.584975\n", - "True 0.415025\n", - "Name: ur_lower, dtype: float64 \n", - " - nb: [475. 337.]\n", - "- on TEST :\n", - " - proba:\n", - " False 0.576923\n", - "True 0.423077\n", - "Name: ur_lower, dtype: float64 \n", - " - nb: [15. 11.]\n", - "\n", - "Repart for ur_stable :\n", - "- on TRAIN :\n", - " - proba:\n", - " False 0.747537\n", - "True 0.252463\n", - "Name: ur_stable, dtype: float64 \n", - " - nb: [607. 205.]\n", - "- on TEST :\n", - " - proba:\n", - " False 0.807692\n", - "True 0.192308\n", - "Name: ur_stable, dtype: float64 \n", - " - nb: [21. 5.]\n", - "\n", - "Repart for ur_higher :\n", - "- on TRAIN :\n", - " - proba:\n", - " False 0.667488\n", - "True 0.332512\n", - "Name: ur_higher, dtype: float64 \n", - " - nb: [542. 270.]\n", - "- on TEST :\n", - " - proba:\n", - " False 0.615385\n", - "True 0.384615\n", - "Name: ur_higher, dtype: float64 \n", - " - nb: [16. 10.]\n", - "end\n" - ] + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" } ], "source": [ - "def print_repart(df_train, df_test, str_target):\n", - " print(\"\\nRepart for \", str_target, \":\")\n", - " print(\"- on TRAIN :\\n\",\n", - " \" - proba:\\n\",\n", - " df_train[str_target].value_counts() / df_train.shape[0],\n", - " \"\\n - nb: \", \n", - " df_train[str_target].shape[0]*(df_train[str_target].value_counts() / df_train.shape[0]).values,\n", - " )\n", - " print(\"- on TEST :\\n\", \n", - " \" - proba:\\n\",\n", - " df_test[str_target].value_counts() / df_test.shape[0],\n", - " \"\\n - nb: \", \n", - " df_test[str_target].shape[0]*(df_test[str_target].value_counts() / df_test.shape[0]).values,)\n", - "\n", - "for str_target in list_targets:\n", - " print_repart(df_train, df_test, str_target)\n", - "\n", - "print(\"end\")" + "# Créer un DataFrame pandas à partir des données d'entrée X et de la variable à prédire y\n", + "df_for_corr = pd.DataFrame(np.hstack((X, df[df[\"TRAIN\"] == 1 ][\"ur_lower\"].values.reshape(-1, 1))), \n", + " columns=list_col_corr)\n", + "# Calculer la matrice de corrélation\n", + "corr_matrix = df_for_corr.corr()\n", + "corr_matrix_targets = corr_matrix.copy().loc[list_feat, list_col_targets]\n", + "fig = plot_corr(corr_matrix_targets, aspect=0.1, size=5)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "## Scale" + "### On ur_stable" ] }, { "cell_type": "code", - "execution_count": 52, + "execution_count": 58, "metadata": {}, "outputs": [ { "data": { + "image/png": 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", "text/plain": [ - "array([[-0.96854595, -0.22650404, 0.19437695, 0.87120859, 0.14597715,\n", - " -1.09263245, -1.15615168, -1.2198648 , -0.20424194, -0.10280243,\n", - " -0.0608682 , 0.19429529, 0.1940553 , 0.19389644, 0.978788 ,\n", - " 1.08033351, 0.93908339]])" + "
" ] }, - "execution_count": 52, "metadata": {}, - "output_type": "execute_result" + "output_type": "display_data" } ], "source": [ - "from sklearn.preprocessing import StandardScaler\n", - "\n", - "scaler = StandardScaler()\n", - "\n", - "X = scaler.fit_transform(xtrain)\n", - "X_test = scaler.transform(xtest)\n", - "\n", - "# for last pred : (to predict next month value)\n", - "x_for_pred = df.filter(list_feat).iloc[-1].values.reshape(1, -1)\n", - "X_for_pred = scaler.transform(x_for_pred)\n", - "X_for_pred" + "# Créer un DataFrame pandas à partir des données d'entrée X et de la variable à prédire y\n", + "df_for_corr = pd.DataFrame(np.hstack((X, df[df[\"TRAIN\"] == 1 ][\"ur_stable\"].values.reshape(-1, 1))), \n", + " columns=list_col_corr)\n", + "# Calculer la matrice de corrélation\n", + "corr_matrix = df_for_corr.corr()\n", + "corr_matrix_targets = corr_matrix.copy().loc[list_feat, list_col_targets]\n", + "fig = plot_corr(corr_matrix_targets, aspect=0.1, size=5)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### On ur_higher" ] }, { "cell_type": "code", - "execution_count": 53, + "execution_count": 59, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ - "#scaler_y = StandardScaler()\n", - "\n", - "#Y = scaler_y.fit_transform(ytrain.reshape(-1, 1))\n", - "Y = ytrain.reshape(-1, 1)\n", - "Y_test = ytest.reshape(-1, 1)" + "# Créer un DataFrame pandas à partir des données d'entrée X et de la variable à prédire y\n", + "df_for_corr = pd.DataFrame(np.hstack((X, df[df[\"TRAIN\"] == 1 ][\"ur_higher\"].values.reshape(-1, 1))), \n", + " columns=list_col_corr)\n", + "# Calculer la matrice de corrélation\n", + "corr_matrix = df_for_corr.corr()\n", + "corr_matrix_targets = corr_matrix.copy().loc[list_feat, list_col_targets]\n", + "fig = plot_corr(corr_matrix_targets, aspect=0.1, size=5)" ] }, { @@ -6860,7 +6619,7 @@ }, { "cell_type": "code", - "execution_count": 54, + "execution_count": 60, "metadata": {}, "outputs": [], "source": [ @@ -6896,7 +6655,7 @@ }, { "cell_type": "code", - "execution_count": 55, + "execution_count": 61, "metadata": {}, "outputs": [ { @@ -6905,19 +6664,19 @@ "text": [ "\n", "Target : ur_lower\n", - "TRAIN score : 0.5972906403940886\n", - "TEST score : 0.5769230769230769\n", - "Next month : [False] 0.5401610930155866\n", + "TRAIN score : 0.6014580801944107\n", + "TEST score : 0.4\n", + "Next month : [False] 0.5403433809993854\n", "\n", "Target : ur_stable\n", - "TRAIN score : 0.7376847290640394\n", - "TEST score : 0.8076923076923077\n", - "Next month : [False] 0.5770437677209102\n", + "TRAIN score : 0.7496962332928311\n", + "TEST score : 0.7333333333333333\n", + "Next month : [False] 0.7832399748056454\n", "\n", "Target : ur_higher\n", - "TRAIN score : 0.6391625615763546\n", - "TEST score : 0.4230769230769231\n", - "Next month : [False] 0.8542771801856839\n" + "TRAIN score : 0.675577156743621\n", + "TEST score : 0.5333333333333333\n", + "Next month : [False] 0.7097900868620662\n" ] } ], @@ -6929,7 +6688,7 @@ }, { "cell_type": "code", - "execution_count": 56, + "execution_count": 62, "metadata": {}, "outputs": [ { @@ -6938,9 +6697,9 @@ "text": [ "\n", "Target : class\n", - "TRAIN score : 0.45320197044334976\n", - "TEST score : 0.3076923076923077\n", - "Next month : [1] 0.43576541091884663\n" + "TRAIN score : 0.46780072904009723\n", + "TEST score : 0.13333333333333333\n", + "Next month : [0] 0.47170050124148305\n" ] } ], @@ -6952,7 +6711,7 @@ }, { "cell_type": "code", - "execution_count": 57, + "execution_count": 63, "metadata": {}, "outputs": [ { @@ -6961,9 +6720,9 @@ "text": [ "\n", "Target : class\n", - "TRAIN score : 0.5024630541871922\n", - "TEST score : 0.34615384615384615\n", - "Next month : [0] 0.715263202727787\n" + "TRAIN score : 0.39003645200486026\n", + "TEST score : 0.3333333333333333\n", + "Next month : [1] 0.7315697364243728\n" ] }, { @@ -6972,7 +6731,7 @@ "'clf.fit(X, Y)\\nprint(\"TRAIN score :\", clf.score(X, Y))\\nprint(\"TEST score :\", clf.score(X_test, Y_test))\\nprint(\"Next month : \", clf.predict(X_for_pred))\\nprint(\"classes : \", clf.classes_)\\nprint(\"classes prob : \", clf.predict_proba(X_for_pred))'" ] }, - "execution_count": 57, + "execution_count": 63, "metadata": {}, "output_type": "execute_result" } @@ -6992,7 +6751,7 @@ }, { "cell_type": "code", - "execution_count": 58, + "execution_count": 64, "metadata": {}, "outputs": [ { @@ -7002,7 +6761,7 @@ "\n", "Target : class\n", "TRAIN score : 1.0\n", - "TEST score : 0.3076923076923077\n", + "TEST score : 0.26666666666666666\n", "Next month : [2] 1.0\n" ] } @@ -7015,7 +6774,7 @@ }, { "cell_type": "code", - "execution_count": 59, + "execution_count": 65, "metadata": {}, "outputs": [ { @@ -7024,9 +6783,9 @@ "text": [ "\n", "Target : class\n", - "TRAIN score : 0.5320197044334976\n", - "TEST score : 0.4230769230769231\n", - "Next month : [0] 0.46046752223170834\n" + "TRAIN score : 0.5650060753341434\n", + "TEST score : 0.3333333333333333\n", + "Next month : [0] 0.4685494576001107\n" ] } ], @@ -7038,7 +6797,7 @@ }, { "cell_type": "code", - "execution_count": 60, + "execution_count": 66, "metadata": {}, "outputs": [ { @@ -7047,9 +6806,9 @@ "text": [ "\n", "Target : class\n", - "TRAIN score : 0.3842364532019704\n", - "TEST score : 0.5\n", - "Next month : [2] 0.7324379965899719\n" + "TRAIN score : 0.4313487241798299\n", + "TEST score : 0.4666666666666667\n", + "Next month : [0] 0.5930376000082627\n" ] } ], @@ -7068,7 +6827,7 @@ }, { "cell_type": "code", - "execution_count": 61, + "execution_count": 67, "metadata": {}, "outputs": [ { @@ -7077,18 +6836,18 @@ "text": [ "\n", "Target : ur_lower\n", - "TRAIN score : 0.7327586206896551\n", - "TEST score : 0.5384615384615384\n", - "Next month : [False] 0.6666666666666666\n", + "TRAIN score : 0.7800729040097205\n", + "TEST score : 0.7333333333333333\n", + "Next month : [ True] 1.0\n", "\n", "Target : ur_stable\n", - "TRAIN score : 0.8041871921182266\n", - "TEST score : 0.7692307692307693\n", - "Next month : [ True] 0.6666666666666666\n", + "TRAIN score : 0.8080194410692588\n", + "TEST score : 0.7333333333333333\n", + "Next month : [False] 1.0\n", "\n", "Target : ur_higher\n", - "TRAIN score : 0.7684729064039408\n", - "TEST score : 0.5769230769230769\n", + "TRAIN score : 0.7910085054678008\n", + "TEST score : 0.8\n", "Next month : [False] 1.0\n" ] } @@ -7108,7 +6867,7 @@ }, { "cell_type": "code", - "execution_count": 62, + "execution_count": 68, "metadata": {}, "outputs": [ { @@ -7117,15 +6876,15 @@ "text": [ "\n", "Target : class\n", - "TRAIN score : 0.6194581280788177\n", - "TEST score : 0.4230769230769231\n", - "Next month : [1] 0.6666666666666666\n" + "TRAIN score : 0.5978128797083839\n", + "TEST score : 0.8\n", + "Next month : [0] 0.8\n" ] } ], "source": [ "from sklearn.neighbors import KNeighborsClassifier\n", - "clf = KNeighborsClassifier(n_neighbors=3)\n", + "clf = KNeighborsClassifier(n_neighbors=5)\n", "list_clf = multi_target_fit(clf, df_y, nb_test, list_targets=[\"class\"])" ] }, @@ -7145,7 +6904,7 @@ }, { "cell_type": "code", - "execution_count": 63, + "execution_count": 69, "metadata": {}, "outputs": [ { @@ -7154,19 +6913,19 @@ "text": [ "\n", "Target : ur_lower\n", - "TRAIN score : 0.8891625615763546\n", - "TEST score : 0.5769230769230769\n", - "Next month : [False] 0.5946965806866578\n", + "TRAIN score : 0.9027946537059538\n", + "TEST score : 0.8666666666666667\n", + "Next month : [False] 0.6797803760388993\n", "\n", "Target : ur_stable\n", - "TRAIN score : 0.8682266009852216\n", - "TEST score : 0.6538461538461539\n", - "Next month : [False] 0.7760022657936757\n", + "TRAIN score : 0.8712029161603888\n", + "TEST score : 0.8\n", + "Next month : [False] 0.7710096803260211\n", "\n", "Target : ur_higher\n", - "TRAIN score : 0.875615763546798\n", - "TEST score : 0.5769230769230769\n", - "Next month : [False] 0.6956914900983011\n" + "TRAIN score : 0.8784933171324423\n", + "TEST score : 0.4666666666666667\n", + "Next month : [False] 0.8281108329761698\n" ] } ], @@ -7185,7 +6944,7 @@ }, { "cell_type": "code", - "execution_count": 64, + "execution_count": 70, "metadata": {}, "outputs": [ { @@ -7198,8 +6957,8 @@ "Target : ur_stable\n", "\n", "Target : ur_higher\n", - "Accuracy train : 0.8669950738916257\n", - "Accuracy test : 0.34615384615384615\n" + "Accuracy train : 0.8821385176184691\n", + "Accuracy test : 0.3333333333333333\n" ] } ], @@ -7232,7 +6991,7 @@ }, { "cell_type": "code", - "execution_count": 65, + "execution_count": 71, "metadata": {}, "outputs": [ { @@ -7244,8 +7003,8 @@ "Target : ur_lower\n", "\n", "Target : ur_stable\n", - "Accuracy train : 0.7783251231527094\n", - "Accuracy test : 0.34615384615384615\n" + "Accuracy train : 0.7922235722964763\n", + "Accuracy test : 0.6666666666666666\n" ] } ], @@ -7284,7 +7043,7 @@ }, { "cell_type": "code", - "execution_count": 66, + "execution_count": 72, "metadata": {}, "outputs": [ { @@ -7293,8 +7052,8 @@ "text": [ "\n", "Target : class\n", - "TRAIN score : 0.8768472906403941\n", - "TEST score : 0.3076923076923077\n" + "TRAIN score : 0.905224787363305\n", + "TEST score : 0.4\n" ] } ], @@ -7308,7 +7067,7 @@ }, { "cell_type": "code", - "execution_count": 67, + "execution_count": 73, "metadata": {}, "outputs": [ { @@ -7317,9 +7076,9 @@ "text": [ "\n", "Target : class\n", - "TRAIN score : 0.8879310344827587\n", - "TEST score : 0.2692307692307692\n", - "Next month : [0] 0.5266316621634072\n" + "TRAIN score : 0.905224787363305\n", + "TEST score : 0.4\n", + "Next month : [0] 0.5175071400991427\n" ] } ], @@ -7336,7 +7095,7 @@ }, { "cell_type": "code", - "execution_count": 68, + "execution_count": 74, "metadata": {}, "outputs": [ { @@ -7345,9 +7104,9 @@ "text": [ "\n", "Target : class\n", - "TRAIN score : 0.8879310344827587\n", - "TEST score : 0.3076923076923077\n", - "Next month : [0] 0.4595640993995918\n" + "TRAIN score : 0.8979343863912516\n", + "TEST score : 0.4\n", + "Next month : [0] 0.5037598212369104\n" ] } ], @@ -7378,7 +7137,7 @@ }, { "cell_type": "code", - "execution_count": 69, + "execution_count": 75, "metadata": {}, "outputs": [ { @@ -7387,9 +7146,9 @@ "text": [ "\n", "Target : class\n", - "TRAIN score : 0.5480295566502463\n", - "TEST score : 0.3076923076923077\n", - "Next month : [1] 0.40055718621597264\n", + "TRAIN score : 0.6743620899149453\n", + "TEST score : 0.5333333333333333\n", + "Next month : [0] 0.37589442470806467\n", "nb iteration : 200\n" ] } @@ -7408,7 +7167,7 @@ }, { "cell_type": "code", - "execution_count": 70, + "execution_count": 76, "metadata": {}, "outputs": [ { @@ -7417,24 +7176,24 @@ "text": [ "\n", "Target : class\n", - "TRAIN score : 0.7992610837438424\n", - "TEST score : 0.38461538461538464\n", - "Next month : [1] 0.40467888854935397\n", - "nb iteration : 3000\n" + "TRAIN score : 0.9428918590522479\n", + "TEST score : 0.6666666666666666\n", + "Next month : [0] 0.6751355825017273\n", + "nb iteration : 1000\n" ] } ], "source": [ "clf = MLPClassifier(random_state=0, \n", - " max_iter=3000,\n", - " n_iter_no_change=3000)\n", + " max_iter=1000,\n", + " n_iter_no_change=1000)\n", "list_clf = multi_target_fit(clf, df_y, nb_test, [\"class\"])\n", "print(\"nb iteration : \", list_clf[-1].n_iter_)" ] }, { "cell_type": "code", - "execution_count": 71, + "execution_count": 77, "metadata": {}, "outputs": [ { @@ -7443,25 +7202,24 @@ "text": [ "\n", "Target : class\n", - "TRAIN score : 1.0\n", - "TEST score : 0.5\n", - "Next month : [0] 0.9999999998805851\n", - "nb iteration : 32000\n" + "TRAIN score : 0.9890643985419199\n", + "TEST score : 0.5333333333333333\n", + "Next month : [0] 0.8288396192507876\n", + "nb iteration : 1500\n" ] } ], "source": [ - "clf = MLPClassifier(hidden_layer_sizes=(256),\n", - " random_state=0, \n", - " max_iter=32000,\n", - " n_iter_no_change=32000)\n", + "clf = MLPClassifier(random_state=0, \n", + " max_iter=1500,\n", + " n_iter_no_change=1500)\n", "list_clf = multi_target_fit(clf, df_y, nb_test, [\"class\"])\n", "print(\"nb iteration : \", list_clf[-1].n_iter_)" ] }, { "cell_type": "code", - "execution_count": 72, + "execution_count": 78, "metadata": {}, "outputs": [ { @@ -7470,1479 +7228,1323 @@ "text": [ "\n", "Target : class\n", - "\n", - "nb_neurons: 4 , nb_iter: 1000, random_state: 0\n", - "\n", - "Target : class\n", - "TRAIN score : 0.520935960591133\n", - "TEST score : 0.4230769230769231\n", - "Next month : [0] 0.4270229163762406\n", - "\n", - "nb_neurons: 4 , nb_iter: 1000, random_state: 1\n", - "\n", - "Target : class\n", - "TRAIN score : 0.49261083743842365\n", - "TEST score : 0.3076923076923077\n", - "Next month : [0] 0.4679475719159728\n", - "\n", - "nb_neurons: 4 , nb_iter: 1000, random_state: 2\n", - "\n", - "Target : class\n", - "TRAIN score : 0.5283251231527094\n", - "TEST score : 0.46153846153846156\n", - "Next month : [2] 0.3805167016452497\n", - "\n", - "nb_neurons: 4 , nb_iter: 1000, random_state: 3\n", - "\n", - "Target : class\n", - "TRAIN score : 0.5147783251231527\n", - "TEST score : 0.34615384615384615\n", - "Next month : [0] 0.407878034166583\n", - "\n", - "nb_neurons: 4 , nb_iter: 1000, random_state: 4\n", - "\n", - "Target : class\n", - "TRAIN score : 0.5135467980295566\n", - "TEST score : 0.3076923076923077\n", - "Next month : [0] 0.4381825718623318\n", - "\n", - "nb_neurons: 4 , nb_iter: 2000, random_state: 0\n", - "\n", - "Target : class\n", - "TRAIN score : 0.5270935960591133\n", - "TEST score : 0.46153846153846156\n", - "Next month : [0] 0.40595575527155286\n", - "\n", - "nb_neurons: 4 , nb_iter: 2000, random_state: 1\n", - "\n", - "Target : class\n", - "TRAIN score : 0.49261083743842365\n", - "TEST score : 0.34615384615384615\n", - "Next month : [0] 0.3961225337904361\n", - "\n", - "nb_neurons: 4 , nb_iter: 2000, random_state: 2\n", - "\n", - "Target : class\n", - "TRAIN score : 0.5381773399014779\n", - "TEST score : 0.46153846153846156\n", - "Next month : [2] 0.418221948050499\n", - "\n", - "nb_neurons: 4 , nb_iter: 2000, random_state: 3\n", - "\n", - "Target : class\n", - "TRAIN score : 0.5172413793103449\n", - "TEST score : 0.38461538461538464\n", - "Next month : [0] 0.4075318178948882\n", - "\n", - "nb_neurons: 4 , nb_iter: 2000, random_state: 4\n", - "\n", - "Target : class\n", - "TRAIN score : 0.5172413793103449\n", - "TEST score : 0.34615384615384615\n", - "Next month : [0] 0.4479057757792367\n", - "\n", - "nb_neurons: 4 , nb_iter: 4000, random_state: 0\n", - "\n", - "Target : class\n", - "TRAIN score : 0.5307881773399015\n", - "TEST score : 0.38461538461538464\n", - "Next month : [1] 0.3951742924452216\n", - "\n", - "nb_neurons: 4 , nb_iter: 4000, random_state: 1\n", - "\n", - "Target : class\n", - "TRAIN score : 0.49507389162561577\n", - "TEST score : 0.34615384615384615\n", - "Next month : [0] 0.3875878401087891\n", - "\n", - "nb_neurons: 4 , nb_iter: 4000, random_state: 2\n", - "\n", - "Target : class\n", - "TRAIN score : 0.5431034482758621\n", - "TEST score : 0.46153846153846156\n", - "Next month : [2] 0.3835616215313614\n", - "\n", - "nb_neurons: 4 , nb_iter: 4000, random_state: 3\n", - "\n", - "Target : class\n", - "TRAIN score : 0.5246305418719212\n", - "TEST score : 0.38461538461538464\n", - "Next month : [0] 0.3976833779941396\n", - "\n", - "nb_neurons: 4 , nb_iter: 4000, random_state: 4\n", - "\n", - "Target : class\n", - "TRAIN score : 0.5221674876847291\n", - "TEST score : 0.2692307692307692\n", - "Next month : [0] 0.47659566786485225\n", - "\n", - "nb_neurons: 4 , nb_iter: 8000, random_state: 0\n", - "\n", - "Target : class\n", - "TRAIN score : 0.5332512315270936\n", - "TEST score : 0.34615384615384615\n", - "Next month : [1] 0.41397152991975233\n", - "\n", - "nb_neurons: 4 , nb_iter: 8000, random_state: 1\n", - "\n", - "Target : class\n", - "TRAIN score : 0.5049261083743842\n", - "TEST score : 0.38461538461538464\n", - "Next month : [0] 0.3768282510679473\n", - "\n", - "nb_neurons: 4 , nb_iter: 8000, random_state: 2\n", - "\n", - "Target : class\n", - "TRAIN score : 0.5492610837438424\n", - "TEST score : 0.46153846153846156\n", - "Next month : [2] 0.3672549701169297\n", - "\n", - "nb_neurons: 4 , nb_iter: 8000, random_state: 3\n", - "\n", - "Target : class\n", - "TRAIN score : 0.5381773399014779\n", - "TEST score : 0.38461538461538464\n", - "Next month : [0] 0.3970588636752335\n", - "\n", - "nb_neurons: 4 , nb_iter: 8000, random_state: 4\n", - "\n", - "Target : class\n", - "TRAIN score : 0.520935960591133\n", - "TEST score : 0.34615384615384615\n", - "Next month : [0] 0.4498781188736968\n", - "\n", - "nb_neurons: 4 , nb_iter: 16000, random_state: 0\n", - "\n", - "Target : class\n", - "TRAIN score : 0.5246305418719212\n", - "TEST score : 0.34615384615384615\n", - "Next month : [0] 0.3802785210439707\n", - "\n", - "nb_neurons: 4 , nb_iter: 16000, random_state: 1\n", - "\n", - "Target : class\n", - "TRAIN score : 0.5135467980295566\n", - "TEST score : 0.3076923076923077\n", - "Next month : [0] 0.41315620775356676\n", - "\n", - "nb_neurons: 4 , nb_iter: 16000, random_state: 2\n", - "\n", - "Target : class\n", - "TRAIN score : 0.562807881773399\n", - "TEST score : 0.5\n", - "Next month : [0] 0.3853604467146994\n", - "\n", - "nb_neurons: 4 , nb_iter: 16000, random_state: 3\n", - "\n", - "Target : class\n", - "TRAIN score : 0.5603448275862069\n", - "TEST score : 0.34615384615384615\n", - "Next month : [0] 0.4087999775791441\n", - "\n", - "nb_neurons: 4 , nb_iter: 16000, random_state: 4\n", - "\n", - "Target : class\n", - "TRAIN score : 0.5160098522167488\n", - "TEST score : 0.38461538461538464\n", - "Next month : [0] 0.46523150481174386\n", - "\n", - "nb_neurons: 4 , nb_iter: 32000, random_state: 0\n", - "\n", - "Target : class\n", - "TRAIN score : 0.5283251231527094\n", - "TEST score : 0.34615384615384615\n", - "Next month : [1] 0.3710218136736889\n", - "\n", - "nb_neurons: 4 , nb_iter: 32000, random_state: 1\n", - "\n", - "Target : class\n", - "TRAIN score : 0.5554187192118226\n", - "TEST score : 0.4230769230769231\n", - "Next month : [0] 0.39830966596214645\n", - "\n", - "nb_neurons: 4 , nb_iter: 32000, random_state: 2\n", - "\n", - "Target : class\n", - "TRAIN score : 0.5615763546798029\n", - "TEST score : 0.5769230769230769\n", - "Next month : [0] 0.4631961061969344\n", - "\n", - "nb_neurons: 4 , nb_iter: 32000, random_state: 3\n", + "TRAIN score : 0.5504252733900364\n", + "TEST score : 0.5333333333333333\n", + "Next month : [0] 0.4245317989010325\n", + "nb iteration : 48000\n" + ] + } + ], + "source": [ + "clf = MLPClassifier(hidden_layer_sizes=(4),\n", + " random_state=0, \n", + " max_iter=48000,\n", + " n_iter_no_change=48000)\n", + "list_clf = multi_target_fit(clf, df_y, nb_test, [\"class\"])\n", + "print(\"nb iteration : \", list_clf[-1].n_iter_)" + ] + }, + { + "cell_type": "code", + "execution_count": 79, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ "\n", "Target : class\n", - "TRAIN score : 0.5381773399014779\n", - "TEST score : 0.3076923076923077\n", - "Next month : [0] 0.41248395926479137\n", - "\n", - "nb_neurons: 4 , nb_iter: 32000, random_state: 4\n", + "TRAIN score : 0.6245443499392467\n", + "TEST score : 0.4\n", + "Next month : [0] 0.4008627948123794\n", + "nb iteration : 40000\n" + ] + } + ], + "source": [ + "clf = MLPClassifier(hidden_layer_sizes=(8),\n", + " random_state=4, \n", + " max_iter=40000,\n", + " n_iter_no_change=40000)\n", + "list_clf = multi_target_fit(clf, df_y, nb_test, [\"class\"])\n", + "print(\"nb iteration : \", list_clf[-1].n_iter_)" + ] + }, + { + "cell_type": "code", + "execution_count": 80, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ "\n", "Target : class\n", - "TRAIN score : 0.5246305418719212\n", - "TEST score : 0.3076923076923077\n", - "Next month : [0] 0.49133195541421176\n", "\n", "nb_neurons: 8 , nb_iter: 1000, random_state: 0\n", "\n", "Target : class\n", - "TRAIN score : 0.5283251231527094\n", - "TEST score : 0.5\n", - "Next month : [2] 0.4085299572423682\n", + "TRAIN score : 0.574726609963548\n", + "TEST score : 0.26666666666666666\n", + "Next month : [1] 0.36069847550587386\n", "\n", "nb_neurons: 8 , nb_iter: 1000, random_state: 1\n", "\n", "Target : class\n", - "TRAIN score : 0.5344827586206896\n", - "TEST score : 0.38461538461538464\n", - "Next month : [0] 0.4403949101245531\n", + "TRAIN score : 0.5577156743620899\n", + "TEST score : 0.6666666666666666\n", + "Next month : [0] 0.4498551463570214\n", "\n", "nb_neurons: 8 , nb_iter: 1000, random_state: 2\n", "\n", "Target : class\n", - "TRAIN score : 0.5357142857142857\n", - "TEST score : 0.3076923076923077\n", - "Next month : [1] 0.3889578647085896\n", + "TRAIN score : 0.5735115431348724\n", + "TEST score : 0.6\n", + "Next month : [2] 0.3743415354514351\n", "\n", "nb_neurons: 8 , nb_iter: 1000, random_state: 3\n", "\n", "Target : class\n", - "TRAIN score : 0.5554187192118226\n", - "TEST score : 0.4230769230769231\n", - "Next month : [2] 0.4421735284495356\n", + "TRAIN score : 0.5820170109356014\n", + "TEST score : 0.6\n", + "Next month : [2] 0.4136227737600784\n", "\n", "nb_neurons: 8 , nb_iter: 1000, random_state: 4\n", "\n", "Target : class\n", - "TRAIN score : 0.5554187192118226\n", - "TEST score : 0.3076923076923077\n", - "Next month : [0] 0.4131244461992789\n", + "TRAIN score : 0.5589307411907655\n", + "TEST score : 0.4\n", + "Next month : [0] 0.40245902871825323\n", "\n", "nb_neurons: 8 , nb_iter: 2000, random_state: 0\n", "\n", "Target : class\n", - "TRAIN score : 0.5517241379310345\n", - "TEST score : 0.5\n", - "Next month : [2] 0.46610694891964166\n", + "TRAIN score : 0.606318347509113\n", + "TEST score : 0.3333333333333333\n", + "Next month : [1] 0.39745490047535903\n", "\n", "nb_neurons: 8 , nb_iter: 2000, random_state: 1\n", "\n", "Target : class\n", - "TRAIN score : 0.5603448275862069\n", - "TEST score : 0.4230769230769231\n", - "Next month : [0] 0.3658135988455491\n", + "TRAIN score : 0.5820170109356014\n", + "TEST score : 0.6\n", + "Next month : [0] 0.49322816915928575\n", "\n", "nb_neurons: 8 , nb_iter: 2000, random_state: 2\n", "\n", "Target : class\n", - "TRAIN score : 0.541871921182266\n", - "TEST score : 0.46153846153846156\n", - "Next month : [1] 0.35719465478433593\n", + "TRAIN score : 0.5710814094775213\n", + "TEST score : 0.6666666666666666\n", + "Next month : [2] 0.42407640906945365\n", "\n", "nb_neurons: 8 , nb_iter: 2000, random_state: 3\n", "\n", "Target : class\n", - "TRAIN score : 0.5726600985221675\n", - "TEST score : 0.4230769230769231\n", - "Next month : [2] 0.4841383449977176\n", + "TRAIN score : 0.5735115431348724\n", + "TEST score : 0.6666666666666666\n", + "Next month : [2] 0.40614450147211617\n", "\n", "nb_neurons: 8 , nb_iter: 2000, random_state: 4\n", "\n", "Target : class\n", - "TRAIN score : 0.5554187192118226\n", - "TEST score : 0.34615384615384615\n", - "Next month : [0] 0.39675594810077935\n", + "TRAIN score : 0.5917375455650061\n", + "TEST score : 0.4666666666666667\n", + "Next month : [0] 0.3685096705915226\n", "\n", "nb_neurons: 8 , nb_iter: 4000, random_state: 0\n", "\n", "Target : class\n", - "TRAIN score : 0.5652709359605911\n", - "TEST score : 0.5384615384615384\n", - "Next month : [2] 0.41919611855114436\n", + "TRAIN score : 0.6245443499392467\n", + "TEST score : 0.4\n", + "Next month : [1] 0.4352693945309561\n", "\n", "nb_neurons: 8 , nb_iter: 4000, random_state: 1\n", "\n", "Target : class\n", - "TRAIN score : 0.583743842364532\n", - "TEST score : 0.4230769230769231\n", - "Next month : [0] 0.4178899154776283\n", + "TRAIN score : 0.6184690157958688\n", + "TEST score : 0.5333333333333333\n", + "Next month : [0] 0.5121118273296482\n", "\n", "nb_neurons: 8 , nb_iter: 4000, random_state: 2\n", "\n", "Target : class\n", - "TRAIN score : 0.562807881773399\n", - "TEST score : 0.38461538461538464\n", - "Next month : [1] 0.3554712518489608\n", + "TRAIN score : 0.583232077764277\n", + "TEST score : 0.6\n", + "Next month : [2] 0.4896542072527098\n", "\n", "nb_neurons: 8 , nb_iter: 4000, random_state: 3\n", "\n", "Target : class\n", - "TRAIN score : 0.5788177339901478\n", - "TEST score : 0.4230769230769231\n", - "Next month : [2] 0.49168398551137027\n", + "TRAIN score : 0.5953827460510328\n", + "TEST score : 0.6\n", + "Next month : [1] 0.4116265785316771\n", "\n", "nb_neurons: 8 , nb_iter: 4000, random_state: 4\n", "\n", "Target : class\n", - "TRAIN score : 0.5701970443349754\n", - "TEST score : 0.34615384615384615\n", - "Next month : [0] 0.4365767292466546\n", + "TRAIN score : 0.5978128797083839\n", + "TEST score : 0.5333333333333333\n", + "Next month : [0] 0.41331184373191565\n", "\n", "nb_neurons: 8 , nb_iter: 8000, random_state: 0\n", "\n", "Target : class\n", - "TRAIN score : 0.5652709359605911\n", - "TEST score : 0.5769230769230769\n", - "Next month : [0] 0.39930025686546805\n", + "TRAIN score : 0.6208991494532199\n", + "TEST score : 0.3333333333333333\n", + "Next month : [1] 0.47795955346400365\n", "\n", "nb_neurons: 8 , nb_iter: 8000, random_state: 1\n", "\n", "Target : class\n", - "TRAIN score : 0.5849753694581281\n", - "TEST score : 0.4230769230769231\n", - "Next month : [0] 0.5183768569610424\n", + "TRAIN score : 0.6123936816524909\n", + "TEST score : 0.4666666666666667\n", + "Next month : [0] 0.5100162789154349\n", "\n", "nb_neurons: 8 , nb_iter: 8000, random_state: 2\n", "\n", "Target : class\n", - "TRAIN score : 0.5738916256157636\n", - "TEST score : 0.5\n", - "Next month : [0] 0.5444535867920445\n", + "TRAIN score : 0.5856622114216282\n", + "TEST score : 0.6666666666666666\n", + "Next month : [2] 0.5201084464933095\n", "\n", "nb_neurons: 8 , nb_iter: 8000, random_state: 3\n", "\n", "Target : class\n", - "TRAIN score : 0.5886699507389163\n", - "TEST score : 0.4230769230769231\n", - "Next month : [2] 0.4447456398427559\n", + "TRAIN score : 0.5856622114216282\n", + "TEST score : 0.6666666666666666\n", + "Next month : [0] 0.36768764225865963\n", "\n", "nb_neurons: 8 , nb_iter: 8000, random_state: 4\n", "\n", "Target : class\n", - "TRAIN score : 0.5652709359605911\n", - "TEST score : 0.4230769230769231\n", - "Next month : [0] 0.4080243355109216\n", + "TRAIN score : 0.6123936816524909\n", + "TEST score : 0.4666666666666667\n", + "Next month : [0] 0.38609405640366307\n", "\n", "nb_neurons: 8 , nb_iter: 16000, random_state: 0\n", "\n", "Target : class\n", - "TRAIN score : 0.5886699507389163\n", - "TEST score : 0.5384615384615384\n", - "Next month : [0] 0.4057787545153102\n", + "TRAIN score : 0.6294046172539489\n", + "TEST score : 0.2\n", + "Next month : [1] 0.5305283053610422\n", "\n", "nb_neurons: 8 , nb_iter: 16000, random_state: 1\n", "\n", "Target : class\n", - "TRAIN score : 0.5985221674876847\n", - "TEST score : 0.4230769230769231\n", - "Next month : [0] 0.7164383393764239\n", + "TRAIN score : 0.6196840826245443\n", + "TEST score : 0.5333333333333333\n", + "Next month : [0] 0.5914428549665203\n", "\n", "nb_neurons: 8 , nb_iter: 16000, random_state: 2\n", "\n", "Target : class\n", - "TRAIN score : 0.5775862068965517\n", - "TEST score : 0.46153846153846156\n", - "Next month : [0] 0.5662795362798719\n", + "TRAIN score : 0.5893074119076549\n", + "TEST score : 0.6666666666666666\n", + "Next month : [2] 0.492114319151144\n", "\n", "nb_neurons: 8 , nb_iter: 16000, random_state: 3\n", "\n", "Target : class\n", - "TRAIN score : 0.5812807881773399\n", - "TEST score : 0.46153846153846156\n", - "Next month : [2] 0.4206565380428531\n", + "TRAIN score : 0.6148238153098421\n", + "TEST score : 0.6\n", + "Next month : [0] 0.4014096666699753\n", "\n", "nb_neurons: 8 , nb_iter: 16000, random_state: 4\n", "\n", "Target : class\n", - "TRAIN score : 0.5874384236453202\n", - "TEST score : 0.4230769230769231\n", - "Next month : [1] 0.4364301351545022\n", + "TRAIN score : 0.6184690157958688\n", + "TEST score : 0.4666666666666667\n", + "Next month : [0] 0.39336014590471263\n", "\n", "nb_neurons: 8 , nb_iter: 32000, random_state: 0\n", "\n", "Target : class\n", - "TRAIN score : 0.5997536945812808\n", - "TEST score : 0.5\n", - "Next month : [1] 0.3661067345457724\n", + "TRAIN score : 0.6257594167679222\n", + "TEST score : 0.2\n", + "Next month : [1] 0.506296983139\n", "\n", "nb_neurons: 8 , nb_iter: 32000, random_state: 1\n", "\n", "Target : class\n", - "TRAIN score : 0.6231527093596059\n", - "TEST score : 0.46153846153846156\n", - "Next month : [0] 0.7073735307799095\n", + "TRAIN score : 0.6281895504252734\n", + "TEST score : 0.4\n", + "Next month : [0] 0.6164598069675671\n", "\n", "nb_neurons: 8 , nb_iter: 32000, random_state: 2\n", "\n", "Target : class\n", - "TRAIN score : 0.5899014778325123\n", - "TEST score : 0.5\n", - "Next month : [0] 0.49904457482427195\n", + "TRAIN score : 0.6014580801944107\n", + "TEST score : 0.6666666666666666\n", + "Next month : [2] 0.47005577765179773\n", "\n", "nb_neurons: 8 , nb_iter: 32000, random_state: 3\n", "\n", "Target : class\n", - "TRAIN score : 0.5985221674876847\n", - "TEST score : 0.4230769230769231\n", - "Next month : [2] 0.48220498649480237\n", + "TRAIN score : 0.6123936816524909\n", + "TEST score : 0.6\n", + "Next month : [0] 0.3945987684653412\n", "\n", "nb_neurons: 8 , nb_iter: 32000, random_state: 4\n", "\n", "Target : class\n", - "TRAIN score : 0.5775862068965517\n", - "TEST score : 0.34615384615384615\n", - "Next month : [1] 0.5607910902465182\n", + "TRAIN score : 0.6245443499392467\n", + "TEST score : 0.4\n", + "Next month : [0] 0.3862455529103428\n", "\n", "nb_neurons: 16 , nb_iter: 1000, random_state: 0\n", "\n", "Target : class\n", - "TRAIN score : 0.5431034482758621\n", - "TEST score : 0.23076923076923078\n", - "Next month : [1] 0.3911370319224751\n", + "TRAIN score : 0.6196840826245443\n", + "TEST score : 0.3333333333333333\n", + "Next month : [1] 0.4794324745740478\n", "\n", "nb_neurons: 16 , nb_iter: 1000, random_state: 1\n", "\n", "Target : class\n", - "TRAIN score : 0.5615763546798029\n", - "TEST score : 0.3076923076923077\n", - "Next month : [2] 0.35939720170531564\n", + "TRAIN score : 0.6075334143377886\n", + "TEST score : 0.5333333333333333\n", + "Next month : [1] 0.5128052760713334\n", "\n", "nb_neurons: 16 , nb_iter: 1000, random_state: 2\n", "\n", "Target : class\n", - "TRAIN score : 0.541871921182266\n", - "TEST score : 0.34615384615384615\n", - "Next month : [1] 0.39420649520010026\n", + "TRAIN score : 0.6294046172539489\n", + "TEST score : 0.5333333333333333\n", + "Next month : [0] 0.4780979809763392\n", "\n", "nb_neurons: 16 , nb_iter: 1000, random_state: 3\n", "\n", "Target : class\n", - "TRAIN score : 0.5529556650246306\n", - "TEST score : 0.38461538461538464\n", - "Next month : [2] 0.3848487882732849\n", + "TRAIN score : 0.6427703523693803\n", + "TEST score : 0.6\n", + "Next month : [2] 0.3929715097407747\n", "\n", "nb_neurons: 16 , nb_iter: 1000, random_state: 4\n", "\n", "Target : class\n", - "TRAIN score : 0.5554187192118226\n", - "TEST score : 0.38461538461538464\n", - "Next month : [2] 0.4486873301842449\n", + "TRAIN score : 0.6269744835965978\n", + "TEST score : 0.6666666666666666\n", + "Next month : [2] 0.37883230429585574\n", "\n", "nb_neurons: 16 , nb_iter: 2000, random_state: 0\n", "\n", "Target : class\n", - "TRAIN score : 0.5874384236453202\n", - "TEST score : 0.23076923076923078\n", - "Next month : [1] 0.37765554221192715\n", + "TRAIN score : 0.6537059538274606\n", + "TEST score : 0.3333333333333333\n", + "Next month : [1] 0.43522854173398323\n", "\n", "nb_neurons: 16 , nb_iter: 2000, random_state: 1\n", "\n", "Target : class\n", - "TRAIN score : 0.5812807881773399\n", - "TEST score : 0.2692307692307692\n", - "Next month : [1] 0.3898066620007446\n", + "TRAIN score : 0.6452004860267315\n", + "TEST score : 0.6\n", + "Next month : [1] 0.6472851345048259\n", "\n", "nb_neurons: 16 , nb_iter: 2000, random_state: 2\n", "\n", "Target : class\n", - "TRAIN score : 0.5862068965517241\n", - "TEST score : 0.34615384615384615\n", - "Next month : [1] 0.4446961551592148\n", + "TRAIN score : 0.6439854191980559\n", + "TEST score : 0.4\n", + "Next month : [0] 0.5254153079159894\n", "\n", "nb_neurons: 16 , nb_iter: 2000, random_state: 3\n", "\n", "Target : class\n", - "TRAIN score : 0.5911330049261084\n", - "TEST score : 0.46153846153846156\n", - "Next month : [2] 0.4168155923551944\n", + "TRAIN score : 0.6707168894289186\n", + "TEST score : 0.6\n", + "Next month : [0] 0.4272407478665379\n", "\n", "nb_neurons: 16 , nb_iter: 2000, random_state: 4\n", "\n", "Target : class\n", - "TRAIN score : 0.5849753694581281\n", - "TEST score : 0.2692307692307692\n", - "Next month : [1] 0.3901447640784033\n", + "TRAIN score : 0.6488456865127582\n", + "TEST score : 0.6666666666666666\n", + "Next month : [2] 0.40057266629554195\n", "\n", "nb_neurons: 16 , nb_iter: 4000, random_state: 0\n", "\n", "Target : class\n", - "TRAIN score : 0.6120689655172413\n", - "TEST score : 0.3076923076923077\n", - "Next month : [1] 0.35270902493428674\n", + "TRAIN score : 0.6901579586877278\n", + "TEST score : 0.5333333333333333\n", + "Next month : [0] 0.4292846637333858\n", "\n", "nb_neurons: 16 , nb_iter: 4000, random_state: 1\n", "\n", "Target : class\n", - "TRAIN score : 0.6120689655172413\n", - "TEST score : 0.2692307692307692\n", - "Next month : [0] 0.3622630243810579\n", + "TRAIN score : 0.675577156743621\n", + "TEST score : 0.4666666666666667\n", + "Next month : [1] 0.7883938084201026\n", "\n", "nb_neurons: 16 , nb_iter: 4000, random_state: 2\n", "\n", "Target : class\n", - "TRAIN score : 0.6059113300492611\n", - "TEST score : 0.3076923076923077\n", - "Next month : [1] 0.4668848708806782\n", + "TRAIN score : 0.6622114216281896\n", + "TEST score : 0.6\n", + "Next month : [0] 0.5396187197130146\n", "\n", "nb_neurons: 16 , nb_iter: 4000, random_state: 3\n", "\n", "Target : class\n", - "TRAIN score : 0.6206896551724138\n", - "TEST score : 0.2692307692307692\n", - "Next month : [1] 0.3531041964228501\n", + "TRAIN score : 0.701093560145808\n", + "TEST score : 0.6\n", + "Next month : [0] 0.5340768057905964\n", "\n", "nb_neurons: 16 , nb_iter: 4000, random_state: 4\n", "\n", "Target : class\n", - "TRAIN score : 0.6071428571428571\n", - "TEST score : 0.19230769230769232\n", - "Next month : [1] 0.4311670821039637\n", + "TRAIN score : 0.6865127582017011\n", + "TEST score : 0.8666666666666667\n", + "Next month : [0] 0.3580796794794029\n", "\n", "nb_neurons: 16 , nb_iter: 8000, random_state: 0\n", "\n", "Target : class\n", - "TRAIN score : 0.6391625615763546\n", - "TEST score : 0.3076923076923077\n", - "Next month : [0] 0.5154270471501681\n", + "TRAIN score : 0.7108140947752126\n", + "TEST score : 0.5333333333333333\n", + "Next month : [0] 0.5467255407869765\n", "\n", "nb_neurons: 16 , nb_iter: 8000, random_state: 1\n", "\n", "Target : class\n", - "TRAIN score : 0.6305418719211823\n", - "TEST score : 0.5\n", - "Next month : [0] 0.45328240173697343\n", + "TRAIN score : 0.7035236938031592\n", + "TEST score : 0.4666666666666667\n", + "Next month : [1] 0.551395269342849\n", "\n", "nb_neurons: 16 , nb_iter: 8000, random_state: 2\n", "\n", "Target : class\n", - "TRAIN score : 0.6194581280788177\n", - "TEST score : 0.38461538461538464\n", - "Next month : [0] 0.5795424204454248\n", + "TRAIN score : 0.6865127582017011\n", + "TEST score : 0.6\n", + "Next month : [0] 0.6240900526627355\n", "\n", "nb_neurons: 16 , nb_iter: 8000, random_state: 3\n", "\n", "Target : class\n", - "TRAIN score : 0.6440886699507389\n", - "TEST score : 0.2692307692307692\n", - "Next month : [1] 0.45900169332060703\n", + "TRAIN score : 0.7168894289185905\n", + "TEST score : 0.6\n", + "Next month : [0] 0.44892500349726777\n", "\n", "nb_neurons: 16 , nb_iter: 8000, random_state: 4\n", "\n", "Target : class\n", - "TRAIN score : 0.6440886699507389\n", - "TEST score : 0.23076923076923078\n", - "Next month : [1] 0.5675566537518063\n", + "TRAIN score : 0.7120291616038882\n", + "TEST score : 0.8\n", + "Next month : [2] 0.6411197984365643\n", "\n", "nb_neurons: 16 , nb_iter: 16000, random_state: 0\n", "\n", "Target : class\n", - "TRAIN score : 0.6884236453201971\n", - "TEST score : 0.46153846153846156\n", - "Next month : [0] 0.6862627899989423\n", + "TRAIN score : 0.7351154313487241\n", + "TEST score : 0.6666666666666666\n", + "Next month : [0] 0.6084756081651406\n", "\n", "nb_neurons: 16 , nb_iter: 16000, random_state: 1\n", "\n", "Target : class\n", - "TRAIN score : 0.6625615763546798\n", - "TEST score : 0.5\n", - "Next month : [0] 0.6605465938457067\n", + "TRAIN score : 0.7205346294046172\n", + "TEST score : 0.4\n", + "Next month : [1] 0.4891111754707869\n", "\n", "nb_neurons: 16 , nb_iter: 16000, random_state: 2\n", "\n", "Target : class\n", - "TRAIN score : 0.6514778325123153\n", - "TEST score : 0.4230769230769231\n", - "Next month : [0] 0.6743390248372627\n", + "TRAIN score : 0.7217496962332929\n", + "TEST score : 0.4\n", + "Next month : [0] 0.5778515888425966\n", "\n", "nb_neurons: 16 , nb_iter: 16000, random_state: 3\n", "\n", "Target : class\n", - "TRAIN score : 0.6600985221674877\n", - "TEST score : 0.3076923076923077\n", - "Next month : [1] 0.5480030436138722\n", + "TRAIN score : 0.7193195625759417\n", + "TEST score : 0.6666666666666666\n", + "Next month : [0] 0.4050343925666878\n", "\n", "nb_neurons: 16 , nb_iter: 16000, random_state: 4\n", "\n", "Target : class\n", - "TRAIN score : 0.6625615763546798\n", - "TEST score : 0.23076923076923078\n", - "Next month : [1] 0.6089341997237244\n", + "TRAIN score : 0.7399756986634265\n", + "TEST score : 0.7333333333333333\n", + "Next month : [2] 0.576386400776731\n", "\n", "nb_neurons: 16 , nb_iter: 32000, random_state: 0\n", "\n", "Target : class\n", - "TRAIN score : 0.6908866995073891\n", - "TEST score : 0.38461538461538464\n", - "Next month : [0] 0.6617513469905341\n", + "TRAIN score : 0.7569866342648846\n", + "TEST score : 0.5333333333333333\n", + "Next month : [0] 0.7422496320883388\n", "\n", "nb_neurons: 16 , nb_iter: 32000, random_state: 1\n", "\n", "Target : class\n", - "TRAIN score : 0.6699507389162561\n", - "TEST score : 0.34615384615384615\n", - "Next month : [0] 0.7323086783416026\n", + "TRAIN score : 0.7339003645200486\n", + "TEST score : 0.4666666666666667\n", + "Next month : [0] 0.49771437400302093\n", "\n", "nb_neurons: 16 , nb_iter: 32000, random_state: 2\n", "\n", "Target : class\n", - "TRAIN score : 0.6761083743842364\n", - "TEST score : 0.4230769230769231\n", - "Next month : [0] 0.7150607965616256\n", + "TRAIN score : 0.7253948967193196\n", + "TEST score : 0.3333333333333333\n", + "Next month : [0] 0.5104668971809142\n", "\n", "nb_neurons: 16 , nb_iter: 32000, random_state: 3\n", "\n", "Target : class\n", - "TRAIN score : 0.6785714285714286\n", - "TEST score : 0.3076923076923077\n", - "Next month : [1] 0.5738450265301905\n", + "TRAIN score : 0.7302551640340219\n", + "TEST score : 0.6666666666666666\n", + "Next month : [0] 0.5256454760690437\n", "\n", "nb_neurons: 16 , nb_iter: 32000, random_state: 4\n", "\n", "Target : class\n", - "TRAIN score : 0.6810344827586207\n", - "TEST score : 0.23076923076923078\n", - "Next month : [0] 0.46421258313705754\n", + "TRAIN score : 0.7484811664641555\n", + "TEST score : 0.7333333333333333\n", + "Next month : [1] 0.597868193869663\n", "\n", "nb_neurons: 32 , nb_iter: 1000, random_state: 0\n", "\n", "Target : class\n", - "TRAIN score : 0.5849753694581281\n", - "TEST score : 0.23076923076923078\n", - "Next month : [0] 0.4516397512262423\n", + "TRAIN score : 0.7205346294046172\n", + "TEST score : 0.6666666666666666\n", + "Next month : [0] 0.6976801286578457\n", "\n", "nb_neurons: 32 , nb_iter: 1000, random_state: 1\n", "\n", "Target : class\n", - "TRAIN score : 0.604679802955665\n", - "TEST score : 0.34615384615384615\n", - "Next month : [2] 0.45599848865583903\n", + "TRAIN score : 0.7193195625759417\n", + "TEST score : 0.6\n", + "Next month : [1] 0.5908489077528808\n", "\n", "nb_neurons: 32 , nb_iter: 1000, random_state: 2\n", "\n", "Target : class\n", - "TRAIN score : 0.5935960591133005\n", - "TEST score : 0.4230769230769231\n", - "Next month : [1] 0.39475364939503244\n", + "TRAIN score : 0.7266099635479951\n", + "TEST score : 0.4666666666666667\n", + "Next month : [0] 0.5781678891137694\n", "\n", "nb_neurons: 32 , nb_iter: 1000, random_state: 3\n", "\n", "Target : class\n", - "TRAIN score : 0.5960591133004927\n", - "TEST score : 0.34615384615384615\n", - "Next month : [1] 0.35674763034446044\n", + "TRAIN score : 0.7217496962332929\n", + "TEST score : 0.6\n", + "Next month : [0] 0.36815752581339595\n", "\n", "nb_neurons: 32 , nb_iter: 1000, random_state: 4\n", "\n", "Target : class\n", - "TRAIN score : 0.5763546798029556\n", - "TEST score : 0.38461538461538464\n", - "Next month : [0] 0.37653929624945726\n", + "TRAIN score : 0.7108140947752126\n", + "TEST score : 0.6\n", + "Next month : [0] 0.5285698234180699\n", "\n", "nb_neurons: 32 , nb_iter: 2000, random_state: 0\n", "\n", "Target : class\n", - "TRAIN score : 0.625615763546798\n", - "TEST score : 0.2692307692307692\n", - "Next month : [0] 0.5457392233229127\n", + "TRAIN score : 0.7812879708383961\n", + "TEST score : 0.6\n", + "Next month : [0] 0.6592981329831659\n", "\n", "nb_neurons: 32 , nb_iter: 2000, random_state: 1\n", "\n", "Target : class\n", - "TRAIN score : 0.6391625615763546\n", - "TEST score : 0.3076923076923077\n", - "Next month : [1] 0.4007653072164003\n", + "TRAIN score : 0.8007290400972054\n", + "TEST score : 0.4\n", + "Next month : [1] 0.7029007018825774\n", "\n", "nb_neurons: 32 , nb_iter: 2000, random_state: 2\n", "\n", "Target : class\n", - "TRAIN score : 0.6305418719211823\n", - "TEST score : 0.4230769230769231\n", - "Next month : [2] 0.39206360544974656\n", + "TRAIN score : 0.7982989064398542\n", + "TEST score : 0.5333333333333333\n", + "Next month : [0] 0.7072983628233231\n", "\n", "nb_neurons: 32 , nb_iter: 2000, random_state: 3\n", "\n", "Target : class\n", - "TRAIN score : 0.6366995073891626\n", - "TEST score : 0.38461538461538464\n", - "Next month : [0] 0.7326479094617696\n", + "TRAIN score : 0.8031591737545565\n", + "TEST score : 0.6\n", + "Next month : [0] 0.525343186676991\n", "\n", "nb_neurons: 32 , nb_iter: 2000, random_state: 4\n", "\n", "Target : class\n", - "TRAIN score : 0.6305418719211823\n", - "TEST score : 0.4230769230769231\n", - "Next month : [0] 0.45120902999940143\n", + "TRAIN score : 0.7910085054678008\n", + "TEST score : 0.5333333333333333\n", + "Next month : [1] 0.6361951174285697\n", "\n", "nb_neurons: 32 , nb_iter: 4000, random_state: 0\n", "\n", "Target : class\n", - "TRAIN score : 0.6822660098522167\n", - "TEST score : 0.2692307692307692\n", - "Next month : [0] 0.53010382777886\n", + "TRAIN score : 0.8469015795868773\n", + "TEST score : 0.6\n", + "Next month : [0] 0.864786366520076\n", "\n", "nb_neurons: 32 , nb_iter: 4000, random_state: 1\n", "\n", "Target : class\n", - "TRAIN score : 0.7032019704433498\n", - "TEST score : 0.2692307692307692\n", - "Next month : [1] 0.6080292280339604\n", + "TRAIN score : 0.8481166464155528\n", + "TEST score : 0.4\n", + "Next month : [1] 0.7823653535097953\n", "\n", "nb_neurons: 32 , nb_iter: 4000, random_state: 2\n", "\n", "Target : class\n", - "TRAIN score : 0.6896551724137931\n", - "TEST score : 0.4230769230769231\n", - "Next month : [1] 0.5440239336679908\n", + "TRAIN score : 0.8869987849331713\n", + "TEST score : 0.4666666666666667\n", + "Next month : [0] 0.865153646310948\n", "\n", "nb_neurons: 32 , nb_iter: 4000, random_state: 3\n", "\n", "Target : class\n", - "TRAIN score : 0.7118226600985221\n", - "TEST score : 0.2692307692307692\n", - "Next month : [0] 0.8759358290790527\n", + "TRAIN score : 0.8541919805589308\n", + "TEST score : 0.8\n", + "Next month : [0] 0.8891149071962743\n", "\n", "nb_neurons: 32 , nb_iter: 4000, random_state: 4\n", "\n", "Target : class\n", - "TRAIN score : 0.6785714285714286\n", - "TEST score : 0.2692307692307692\n", - "Next month : [0] 0.6291454862610363\n", + "TRAIN score : 0.8760631834750912\n", + "TEST score : 0.4666666666666667\n", + "Next month : [1] 0.799033313083717\n", "\n", "nb_neurons: 32 , nb_iter: 8000, random_state: 0\n", "\n", "Target : class\n", - "TRAIN score : 0.7278325123152709\n", - "TEST score : 0.34615384615384615\n", - "Next month : [0] 0.7012784842780232\n", + "TRAIN score : 0.913730255164034\n", + "TEST score : 0.6\n", + "Next month : [0] 0.9843844092685771\n", "\n", "nb_neurons: 32 , nb_iter: 8000, random_state: 1\n", "\n", "Target : class\n", - "TRAIN score : 0.7586206896551724\n", - "TEST score : 0.34615384615384615\n", - "Next month : [1] 0.6899643067192809\n", + "TRAIN score : 0.9125151883353585\n", + "TEST score : 0.26666666666666666\n", + "Next month : [1] 0.6339150559444805\n", "\n", "nb_neurons: 32 , nb_iter: 8000, random_state: 2\n", "\n", "Target : class\n", - "TRAIN score : 0.7561576354679803\n", - "TEST score : 0.4230769230769231\n", - "Next month : [1] 0.5216532353424203\n", + "TRAIN score : 0.9416767922235723\n", + "TEST score : 0.4666666666666667\n", + "Next month : [0] 0.9342445665669447\n", "\n", "nb_neurons: 32 , nb_iter: 8000, random_state: 3\n", "\n", "Target : class\n", - "TRAIN score : 0.7635467980295566\n", - "TEST score : 0.3076923076923077\n", - "Next month : [0] 0.9235064997086572\n", + "TRAIN score : 0.9149453219927096\n", + "TEST score : 0.7333333333333333\n", + "Next month : [0] 0.9869995382946011\n", "\n", "nb_neurons: 32 , nb_iter: 8000, random_state: 4\n", "\n", "Target : class\n", - "TRAIN score : 0.7278325123152709\n", - "TEST score : 0.34615384615384615\n", - "Next month : [0] 0.8022742379870245\n", + "TRAIN score : 0.9222357229647631\n", + "TEST score : 0.4\n", + "Next month : [1] 0.9469570690404583\n", "\n", "nb_neurons: 32 , nb_iter: 16000, random_state: 0\n", "\n", "Target : class\n", - "TRAIN score : 0.7844827586206896\n", - "TEST score : 0.38461538461538464\n", - "Next month : [0] 0.794419202792577\n", + "TRAIN score : 0.9671931956257594\n", + "TEST score : 0.5333333333333333\n", + "Next month : [0] 0.9996622572071099\n", "\n", "nb_neurons: 32 , nb_iter: 16000, random_state: 1\n", "\n", "Target : class\n", - "TRAIN score : 0.8152709359605911\n", - "TEST score : 0.5\n", - "Next month : [1] 0.8192097329341368\n", + "TRAIN score : 0.9708383961117861\n", + "TEST score : 0.4666666666666667\n", + "Next month : [0] 0.760308582765463\n", "\n", "nb_neurons: 32 , nb_iter: 16000, random_state: 2\n", "\n", "Target : class\n", - "TRAIN score : 0.8004926108374384\n", - "TEST score : 0.46153846153846156\n", - "Next month : [1] 0.5088069166152105\n", + "TRAIN score : 0.9878493317132442\n", + "TEST score : 0.4\n", + "Next month : [0] 0.9605221452537142\n", "\n", "nb_neurons: 32 , nb_iter: 16000, random_state: 3\n", "\n", "Target : class\n", - "TRAIN score : 0.8103448275862069\n", - "TEST score : 0.34615384615384615\n", - "Next month : [0] 0.9287670525464117\n", + "TRAIN score : 0.9805589307411907\n", + "TEST score : 0.6666666666666666\n", + "Next month : [0] 0.9999877637552466\n", "\n", "nb_neurons: 32 , nb_iter: 16000, random_state: 4\n", "\n", "Target : class\n", - "TRAIN score : 0.7857142857142857\n", - "TEST score : 0.4230769230769231\n", - "Next month : [0] 0.9060109743734653\n", + "TRAIN score : 0.982989064398542\n", + "TEST score : 0.3333333333333333\n", + "Next month : [1] 0.9923114507605736\n", "\n", "nb_neurons: 32 , nb_iter: 32000, random_state: 0\n", "\n", "Target : class\n", - "TRAIN score : 0.8497536945812808\n", - "TEST score : 0.38461538461538464\n", - "Next month : [0] 0.7690853181635449\n", + "TRAIN score : 1.0\n", + "TEST score : 0.6\n", + "Next month : [0] 0.9999999599185567\n", "\n", "nb_neurons: 32 , nb_iter: 32000, random_state: 1\n", "\n", "Target : class\n", - "TRAIN score : 0.874384236453202\n", - "TEST score : 0.5\n", - "Next month : [1] 0.8323168274802398\n", + "TRAIN score : 1.0\n", + "TEST score : 0.4666666666666667\n", + "Next month : [0] 0.9481203398465841\n", "\n", "nb_neurons: 32 , nb_iter: 32000, random_state: 2\n", "\n", "Target : class\n", - "TRAIN score : 0.8583743842364532\n", - "TEST score : 0.4230769230769231\n", - "Next month : [1] 0.5538257195504793\n", + "TRAIN score : 1.0\n", + "TEST score : 0.4\n", + "Next month : [0] 0.5165372438060548\n", "\n", "nb_neurons: 32 , nb_iter: 32000, random_state: 3\n", "\n", "Target : class\n", - "TRAIN score : 0.8719211822660099\n", - "TEST score : 0.34615384615384615\n", - "Next month : [0] 0.6971750346338091\n", + "TRAIN score : 1.0\n", + "TEST score : 0.6\n", + "Next month : [0] 0.9999999998523601\n", "\n", "nb_neurons: 32 , nb_iter: 32000, random_state: 4\n", "\n", "Target : class\n", - "TRAIN score : 0.8399014778325123\n", - "TEST score : 0.5\n", - "Next month : [0] 0.9704200049699456\n", + "TRAIN score : 1.0\n", + "TEST score : 0.3333333333333333\n", + "Next month : [1] 0.999997149333499\n", "\n", "nb_neurons: 64 , nb_iter: 1000, random_state: 0\n", "\n", "Target : class\n", - "TRAIN score : 0.6576354679802956\n", - "TEST score : 0.38461538461538464\n", - "Next month : [0] 0.3764700929696033\n", + "TRAIN score : 0.8493317132442284\n", + "TEST score : 0.6\n", + "Next month : [0] 0.4405614396861298\n", "\n", "nb_neurons: 64 , nb_iter: 1000, random_state: 1\n", "\n", "Target : class\n", - "TRAIN score : 0.6366995073891626\n", - "TEST score : 0.38461538461538464\n", - "Next month : [0] 0.3910158772182619\n", + "TRAIN score : 0.8408262454434994\n", + "TEST score : 0.6\n", + "Next month : [0] 0.699480265955787\n", "\n", "nb_neurons: 64 , nb_iter: 1000, random_state: 2\n", "\n", "Target : class\n", - "TRAIN score : 0.6613300492610837\n", - "TEST score : 0.4230769230769231\n", - "Next month : [2] 0.42571239663077115\n", + "TRAIN score : 0.8663426488456865\n", + "TEST score : 0.6\n", + "Next month : [0] 0.48435374622717503\n", "\n", "nb_neurons: 64 , nb_iter: 1000, random_state: 3\n", "\n", "Target : class\n", - "TRAIN score : 0.6416256157635468\n", - "TEST score : 0.34615384615384615\n", - "Next month : [0] 0.5775104874633115\n", + "TRAIN score : 0.8566221142162819\n", + "TEST score : 0.6\n", + "Next month : [0] 0.5110083459093926\n", "\n", "nb_neurons: 64 , nb_iter: 1000, random_state: 4\n", "\n", "Target : class\n", - "TRAIN score : 0.6280788177339901\n", - "TEST score : 0.34615384615384615\n", - "Next month : [0] 0.4029155172692643\n", + "TRAIN score : 0.8554070473876063\n", + "TEST score : 0.5333333333333333\n", + "Next month : [0] 0.5613208800160615\n", "\n", "nb_neurons: 64 , nb_iter: 2000, random_state: 0\n", "\n", "Target : class\n", - "TRAIN score : 0.7179802955665024\n", - "TEST score : 0.38461538461538464\n", - "Next month : [0] 0.43651101862813724\n", + "TRAIN score : 0.9611178614823815\n", + "TEST score : 0.5333333333333333\n", + "Next month : [2] 0.4531884861931165\n", "\n", "nb_neurons: 64 , nb_iter: 2000, random_state: 1\n", "\n", "Target : class\n", - "TRAIN score : 0.6995073891625616\n", - "TEST score : 0.4230769230769231\n", - "Next month : [0] 0.5187231096504891\n", + "TRAIN score : 0.968408262454435\n", + "TEST score : 0.4666666666666667\n", + "Next month : [0] 0.7671170418650557\n", "\n", "nb_neurons: 64 , nb_iter: 2000, random_state: 2\n", "\n", "Target : class\n", - "TRAIN score : 0.7179802955665024\n", - "TEST score : 0.5769230769230769\n", - "Next month : [0] 0.37426038923257665\n", + "TRAIN score : 0.9635479951397327\n", + "TEST score : 0.4666666666666667\n", + "Next month : [1] 0.6069119683543176\n", "\n", "nb_neurons: 64 , nb_iter: 2000, random_state: 3\n", "\n", "Target : class\n", - "TRAIN score : 0.7019704433497537\n", - "TEST score : 0.34615384615384615\n", - "Next month : [0] 0.665307894083592\n", + "TRAIN score : 0.959902794653706\n", + "TEST score : 0.5333333333333333\n", + "Next month : [0] 0.8242311675257262\n", "\n", "nb_neurons: 64 , nb_iter: 2000, random_state: 4\n", "\n", "Target : class\n", - "TRAIN score : 0.7179802955665024\n", - "TEST score : 0.38461538461538464\n", - "Next month : [0] 0.5684546897994558\n", + "TRAIN score : 0.9635479951397327\n", + "TEST score : 0.6\n", + "Next month : [0] 0.6766626151586128\n", "\n", "nb_neurons: 64 , nb_iter: 4000, random_state: 0\n", "\n", "Target : class\n", - "TRAIN score : 0.7795566502463054\n", - "TEST score : 0.23076923076923078\n", - "Next month : [0] 0.7058733908368687\n", + "TRAIN score : 1.0\n", + "TEST score : 0.6\n", + "Next month : [2] 0.6676391854825793\n", "\n", "nb_neurons: 64 , nb_iter: 4000, random_state: 1\n", "\n", "Target : class\n", - "TRAIN score : 0.770935960591133\n", - "TEST score : 0.5\n", - "Next month : [0] 0.7849527946266569\n", + "TRAIN score : 1.0\n", + "TEST score : 0.5333333333333333\n", + "Next month : [0] 0.9154078162921835\n", "\n", "nb_neurons: 64 , nb_iter: 4000, random_state: 2\n", "\n", "Target : class\n", - "TRAIN score : 0.791871921182266\n", - "TEST score : 0.46153846153846156\n", - "Next month : [0] 0.4767154347612268\n", + "TRAIN score : 1.0\n", + "TEST score : 0.3333333333333333\n", + "Next month : [1] 0.6675140483919294\n", "\n", "nb_neurons: 64 , nb_iter: 4000, random_state: 3\n", "\n", "Target : class\n", - "TRAIN score : 0.770935960591133\n", - "TEST score : 0.3076923076923077\n", - "Next month : [0] 0.8188495422596622\n", + "TRAIN score : 1.0\n", + "TEST score : 0.4\n", + "Next month : [0] 0.9887541007055582\n", "\n", "nb_neurons: 64 , nb_iter: 4000, random_state: 4\n", "\n", "Target : class\n", - "TRAIN score : 0.7869458128078818\n", - "TEST score : 0.5\n", - "Next month : [0] 0.6096193943477758\n", + "TRAIN score : 1.0\n", + "TEST score : 0.5333333333333333\n", + "Next month : [0] 0.6137052614081657\n", "\n", "nb_neurons: 64 , nb_iter: 8000, random_state: 0\n", "\n", "Target : class\n", - "TRAIN score : 0.8793103448275862\n", - "TEST score : 0.19230769230769232\n", - "Next month : [0] 0.9391949511947917\n", + "TRAIN score : 1.0\n", + "TEST score : 0.5333333333333333\n", + "Next month : [2] 0.9851524670198869\n", "\n", "nb_neurons: 64 , nb_iter: 8000, random_state: 1\n", "\n", "Target : class\n", - "TRAIN score : 0.8780788177339901\n", - "TEST score : 0.5\n", - "Next month : [0] 0.96925091014899\n", + "TRAIN score : 1.0\n", + "TEST score : 0.6\n", + "Next month : [0] 0.9938801219535965\n", "\n", "nb_neurons: 64 , nb_iter: 8000, random_state: 2\n", "\n", "Target : class\n", - "TRAIN score : 0.8706896551724138\n", - "TEST score : 0.4230769230769231\n", - "Next month : [0] 0.7220299281244509\n", + "TRAIN score : 1.0\n", + "TEST score : 0.4666666666666667\n", + "Next month : [0] 0.6252704382681632\n", "\n", "nb_neurons: 64 , nb_iter: 8000, random_state: 3\n", "\n", "Target : class\n", - "TRAIN score : 0.8669950738916257\n", - "TEST score : 0.2692307692307692\n", - "Next month : [0] 0.8910947667806121\n", + "TRAIN score : 1.0\n", + "TEST score : 0.4666666666666667\n", + "Next month : [0] 0.9999844208686343\n", "\n", "nb_neurons: 64 , nb_iter: 8000, random_state: 4\n", "\n", "Target : class\n", - "TRAIN score : 0.8805418719211823\n", - "TEST score : 0.46153846153846156\n", - "Next month : [0] 0.9046246921944071\n", + "TRAIN score : 1.0\n", + "TEST score : 0.4666666666666667\n", + "Next month : [0] 0.5592573946883694\n", "\n", "nb_neurons: 64 , nb_iter: 16000, random_state: 0\n", "\n", "Target : class\n", - "TRAIN score : 0.9568965517241379\n", - "TEST score : 0.2692307692307692\n", - "Next month : [0] 0.9952951650621492\n", + "TRAIN score : 1.0\n", + "TEST score : 0.5333333333333333\n", + "Next month : [2] 0.9518880223576424\n", "\n", "nb_neurons: 64 , nb_iter: 16000, random_state: 1\n", "\n", "Target : class\n", - "TRAIN score : 0.9667487684729064\n", - "TEST score : 0.46153846153846156\n", - "Next month : [0] 0.9775702762862716\n", + "TRAIN score : 1.0\n", + "TEST score : 0.6666666666666666\n", + "Next month : [0] 0.9997939793638447\n", "\n", "nb_neurons: 64 , nb_iter: 16000, random_state: 2\n", "\n", "Target : class\n", - "TRAIN score : 0.9593596059113301\n", - "TEST score : 0.5\n", - "Next month : [0] 0.9333076923814627\n", + "TRAIN score : 1.0\n", + "TEST score : 0.4666666666666667\n", + "Next month : [0] 0.8739423952053312\n", "\n", "nb_neurons: 64 , nb_iter: 16000, random_state: 3\n", "\n", "Target : class\n", - "TRAIN score : 0.9248768472906403\n", - "TEST score : 0.3076923076923077\n", - "Next month : [0] 0.9848017972399858\n", + "TRAIN score : 1.0\n", + "TEST score : 0.4\n", + "Next month : [0] 0.9999996742181576\n", "\n", "nb_neurons: 64 , nb_iter: 16000, random_state: 4\n", "\n", "Target : class\n", - "TRAIN score : 0.9618226600985221\n", - "TEST score : 0.34615384615384615\n", - "Next month : [0] 0.9996572286765986\n", + "TRAIN score : 1.0\n", + "TEST score : 0.4666666666666667\n", + "Next month : [1] 0.9850431339083642\n", "\n", "nb_neurons: 64 , nb_iter: 32000, random_state: 0\n", "\n", "Target : class\n", - "TRAIN score : 0.9987684729064039\n", - "TEST score : 0.34615384615384615\n", - "Next month : [0] 0.9999776995688346\n", + "TRAIN score : 1.0\n", + "TEST score : 0.5333333333333333\n", + "Next month : [2] 0.832514667784912\n", "\n", "nb_neurons: 64 , nb_iter: 32000, random_state: 1\n", "\n", "Target : class\n", - "TRAIN score : 0.9975369458128078\n", - "TEST score : 0.5384615384615384\n", - "Next month : [0] 0.9601561804326351\n", + "TRAIN score : 1.0\n", + "TEST score : 0.7333333333333333\n", + "Next month : [0] 0.9998712975126092\n", "\n", "nb_neurons: 64 , nb_iter: 32000, random_state: 2\n", "\n", "Target : class\n", "TRAIN score : 1.0\n", - "TEST score : 0.38461538461538464\n", - "Next month : [0] 0.9988367262300395\n", + "TEST score : 0.4666666666666667\n", + "Next month : [0] 0.827274385705079\n", "\n", "nb_neurons: 64 , nb_iter: 32000, random_state: 3\n", "\n", "Target : class\n", - "TRAIN score : 0.9963054187192119\n", - "TEST score : 0.2692307692307692\n", - "Next month : [0] 0.9938800544958862\n", + "TRAIN score : 1.0\n", + "TEST score : 0.4\n", + "Next month : [0] 0.9999884373113082\n", "\n", "nb_neurons: 64 , nb_iter: 32000, random_state: 4\n", "\n", "Target : class\n", - "TRAIN score : 0.9987684729064039\n", - "TEST score : 0.2692307692307692\n", - "Next month : [0] 0.9999833134482689\n", + "TRAIN score : 1.0\n", + "TEST score : 0.4666666666666667\n", + "Next month : [1] 0.9978293303415594\n", "\n", "nb_neurons: 128 , nb_iter: 1000, random_state: 0\n", "\n", "Target : class\n", - "TRAIN score : 0.6859605911330049\n", - "TEST score : 0.4230769230769231\n", - "Next month : [0] 0.4793070977977214\n", + "TRAIN score : 0.9659781287970839\n", + "TEST score : 0.5333333333333333\n", + "Next month : [0] 0.9260478241060746\n", "\n", "nb_neurons: 128 , nb_iter: 1000, random_state: 1\n", "\n", "Target : class\n", - "TRAIN score : 0.6995073891625616\n", - "TEST score : 0.38461538461538464\n", - "Next month : [0] 0.5621736707650424\n", + "TRAIN score : 0.9756986634264885\n", + "TEST score : 0.6\n", + "Next month : [0] 0.5774281115655883\n", "\n", "nb_neurons: 128 , nb_iter: 1000, random_state: 2\n", "\n", "Target : class\n", - "TRAIN score : 0.6834975369458128\n", - "TEST score : 0.38461538461538464\n", - "Next month : [0] 0.6094083539923385\n", + "TRAIN score : 0.9635479951397327\n", + "TEST score : 0.6\n", + "Next month : [0] 0.8179000821423019\n", "\n", "nb_neurons: 128 , nb_iter: 1000, random_state: 3\n", "\n", "Target : class\n", - "TRAIN score : 0.7118226600985221\n", - "TEST score : 0.5\n", - "Next month : [0] 0.4059076146444846\n", + "TRAIN score : 0.9732685297691372\n", + "TEST score : 0.5333333333333333\n", + "Next month : [0] 0.6347646596131469\n", "\n", "nb_neurons: 128 , nb_iter: 1000, random_state: 4\n", "\n", "Target : class\n", - "TRAIN score : 0.708128078817734\n", - "TEST score : 0.38461538461538464\n", - "Next month : [2] 0.39113443323153\n", + "TRAIN score : 0.9635479951397327\n", + "TEST score : 0.4\n", + "Next month : [1] 0.590095582520521\n", "\n", "nb_neurons: 128 , nb_iter: 2000, random_state: 0\n", "\n", "Target : class\n", - "TRAIN score : 0.7931034482758621\n", - "TEST score : 0.38461538461538464\n", - "Next month : [0] 0.6976846204599405\n", + "TRAIN score : 1.0\n", + "TEST score : 0.6\n", + "Next month : [0] 0.9878686423740228\n", "\n", "nb_neurons: 128 , nb_iter: 2000, random_state: 1\n", "\n", "Target : class\n", - "TRAIN score : 0.8017241379310345\n", - "TEST score : 0.5\n", - "Next month : [0] 0.7409964103258359\n", + "TRAIN score : 1.0\n", + "TEST score : 0.6\n", + "Next month : [0] 0.8678534230645754\n", "\n", "nb_neurons: 128 , nb_iter: 2000, random_state: 2\n", "\n", "Target : class\n", - "TRAIN score : 0.7955665024630542\n", - "TEST score : 0.4230769230769231\n", - "Next month : [0] 0.7957139252346302\n", + "TRAIN score : 1.0\n", + "TEST score : 0.6\n", + "Next month : [0] 0.8056323364436209\n", "\n", "nb_neurons: 128 , nb_iter: 2000, random_state: 3\n", "\n", "Target : class\n", - "TRAIN score : 0.7758620689655172\n", - "TEST score : 0.5\n", - "Next month : [0] 0.6364649701180879\n", + "TRAIN score : 1.0\n", + "TEST score : 0.6\n", + "Next month : [0] 0.7500581123309931\n", "\n", "nb_neurons: 128 , nb_iter: 2000, random_state: 4\n", "\n", "Target : class\n", - "TRAIN score : 0.7906403940886699\n", - "TEST score : 0.38461538461538464\n", - "Next month : [0] 0.6164222295375738\n", + "TRAIN score : 1.0\n", + "TEST score : 0.3333333333333333\n", + "Next month : [1] 0.5906612156568948\n", "\n", "nb_neurons: 128 , nb_iter: 4000, random_state: 0\n", "\n", "Target : class\n", - "TRAIN score : 0.9076354679802956\n", - "TEST score : 0.34615384615384615\n", - "Next month : [0] 0.9513640760387971\n", + "TRAIN score : 1.0\n", + "TEST score : 0.6\n", + "Next month : [0] 0.9999158690636144\n", "\n", "nb_neurons: 128 , nb_iter: 4000, random_state: 1\n", "\n", "Target : class\n", - "TRAIN score : 0.8977832512315271\n", - "TEST score : 0.5\n", - "Next month : [0] 0.7905759603902233\n", + "TRAIN score : 1.0\n", + "TEST score : 0.5333333333333333\n", + "Next month : [0] 0.9596673682229667\n", "\n", "nb_neurons: 128 , nb_iter: 4000, random_state: 2\n", "\n", "Target : class\n", - "TRAIN score : 0.8903940886699507\n", - "TEST score : 0.38461538461538464\n", - "Next month : [0] 0.9555927368772301\n", + "TRAIN score : 1.0\n", + "TEST score : 0.4666666666666667\n", + "Next month : [0] 0.6084777551609848\n", "\n", "nb_neurons: 128 , nb_iter: 4000, random_state: 3\n", "\n", "Target : class\n", - "TRAIN score : 0.8805418719211823\n", - "TEST score : 0.5\n", - "Next month : [0] 0.8798088683921651\n", + "TRAIN score : 1.0\n", + "TEST score : 0.5333333333333333\n", + "Next month : [0] 0.8837746462070116\n", "\n", "nb_neurons: 128 , nb_iter: 4000, random_state: 4\n", "\n", "Target : class\n", - "TRAIN score : 0.8990147783251231\n", - "TEST score : 0.3076923076923077\n", - "Next month : [0] 0.8821469971688133\n", + "TRAIN score : 1.0\n", + "TEST score : 0.3333333333333333\n", + "Next month : [0] 0.8006653145794417\n", "\n", "nb_neurons: 128 , nb_iter: 8000, random_state: 0\n", "\n", "Target : class\n", - "TRAIN score : 0.9901477832512315\n", - "TEST score : 0.4230769230769231\n", - "Next month : [0] 0.9946641918341675\n", + "TRAIN score : 1.0\n", + "TEST score : 0.5333333333333333\n", + "Next month : [0] 0.9999585059457847\n", "\n", "nb_neurons: 128 , nb_iter: 8000, random_state: 1\n", "\n", "Target : class\n", - "TRAIN score : 0.9839901477832512\n", - "TEST score : 0.4230769230769231\n", - "Next month : [0] 0.9717632439779865\n", + "TRAIN score : 1.0\n", + "TEST score : 0.5333333333333333\n", + "Next month : [0] 0.9894826187280971\n", "\n", "nb_neurons: 128 , nb_iter: 8000, random_state: 2\n", "\n", "Target : class\n", - "TRAIN score : 0.9889162561576355\n", - "TEST score : 0.38461538461538464\n", - "Next month : [0] 0.9988858641760732\n", + "TRAIN score : 1.0\n", + "TEST score : 0.4666666666666667\n", + "Next month : [0] 0.6520141172120888\n", "\n", "nb_neurons: 128 , nb_iter: 8000, random_state: 3\n", "\n", "Target : class\n", - "TRAIN score : 0.9815270935960592\n", - "TEST score : 0.46153846153846156\n", - "Next month : [0] 0.9652990343009592\n", + "TRAIN score : 1.0\n", + "TEST score : 0.6\n", + "Next month : [0] 0.9589063155753204\n", "\n", "nb_neurons: 128 , nb_iter: 8000, random_state: 4\n", "\n", "Target : class\n", - "TRAIN score : 0.9876847290640394\n", - "TEST score : 0.34615384615384615\n", - "Next month : [0] 0.9994350222396853\n", + "TRAIN score : 1.0\n", + "TEST score : 0.3333333333333333\n", + "Next month : [0] 0.9206991184152762\n", "\n", "nb_neurons: 128 , nb_iter: 16000, random_state: 0\n", "\n", "Target : class\n", "TRAIN score : 1.0\n", - "TEST score : 0.4230769230769231\n", - "Next month : [0] 0.999993464167565\n", + "TEST score : 0.4666666666666667\n", + "Next month : [0] 0.999038442574224\n", "\n", "nb_neurons: 128 , nb_iter: 16000, random_state: 1\n", "\n", "Target : class\n", "TRAIN score : 1.0\n", - "TEST score : 0.46153846153846156\n", - "Next month : [0] 0.9992452415877549\n", + "TEST score : 0.4666666666666667\n", + "Next month : [0] 0.9971169334193938\n", "\n", "nb_neurons: 128 , nb_iter: 16000, random_state: 2\n", "\n", "Target : class\n", "TRAIN score : 1.0\n", - "TEST score : 0.3076923076923077\n", - "Next month : [0] 0.9999992143848931\n", + "TEST score : 0.4666666666666667\n", + "Next month : [0] 0.580237396408167\n", "\n", "nb_neurons: 128 , nb_iter: 16000, random_state: 3\n", "\n", "Target : class\n", "TRAIN score : 1.0\n", - "TEST score : 0.5384615384615384\n", - "Next month : [0] 0.9999328588535168\n", + "TEST score : 0.6\n", + "Next month : [0] 0.9906897747933944\n", "\n", "nb_neurons: 128 , nb_iter: 16000, random_state: 4\n", "\n", "Target : class\n", "TRAIN score : 1.0\n", - "TEST score : 0.38461538461538464\n", - "Next month : [0] 0.9999986923062006\n", + "TEST score : 0.3333333333333333\n", + "Next month : [0] 0.9697244724037415\n", "\n", "nb_neurons: 128 , nb_iter: 32000, random_state: 0\n", "\n", "Target : class\n", "TRAIN score : 1.0\n", - "TEST score : 0.46153846153846156\n", - "Next month : [0] 0.9999999957716177\n", + "TEST score : 0.4666666666666667\n", + "Next month : [0] 0.993156978670504\n", "\n", "nb_neurons: 128 , nb_iter: 32000, random_state: 1\n", "\n", "Target : class\n", "TRAIN score : 1.0\n", - "TEST score : 0.46153846153846156\n", - "Next month : [0] 0.9999989319742157\n", + "TEST score : 0.4666666666666667\n", + "Next month : [0] 0.9966857576608583\n", "\n", "nb_neurons: 128 , nb_iter: 32000, random_state: 2\n", "\n", "Target : class\n", "TRAIN score : 1.0\n", - "TEST score : 0.2692307692307692\n", - "Next month : [0] 0.999999999275281\n", + "TEST score : 0.4666666666666667\n", + "Next month : [0] 0.6875879480394463\n", "\n", "nb_neurons: 128 , nb_iter: 32000, random_state: 3\n", "\n", "Target : class\n", "TRAIN score : 1.0\n", - "TEST score : 0.5\n", - "Next month : [0] 0.9999999384434799\n", + "TEST score : 0.6666666666666666\n", + "Next month : [0] 0.9937265774930407\n", "\n", "nb_neurons: 128 , nb_iter: 32000, random_state: 4\n", "\n", "Target : class\n", "TRAIN score : 1.0\n", - "TEST score : 0.4230769230769231\n", - "Next month : [0] 0.9999999966545017\n", + "TEST score : 0.4666666666666667\n", + "Next month : [0] 0.9887421859378752\n", "\n", "nb_neurons: 256 , nb_iter: 1000, random_state: 0\n", "\n", "Target : class\n", - "TRAIN score : 0.7807881773399015\n", - "TEST score : 0.3076923076923077\n", - "Next month : [0] 0.6440140533151625\n", + "TRAIN score : 0.9987849331713244\n", + "TEST score : 0.4666666666666667\n", + "Next month : [0] 0.5152884794467056\n", "\n", "nb_neurons: 256 , nb_iter: 1000, random_state: 1\n", "\n", "Target : class\n", - "TRAIN score : 0.7795566502463054\n", - "TEST score : 0.38461538461538464\n", - "Next month : [0] 0.7958229376746254\n", + "TRAIN score : 1.0\n", + "TEST score : 0.5333333333333333\n", + "Next month : [0] 0.8860659533356497\n", "\n", "nb_neurons: 256 , nb_iter: 1000, random_state: 2\n", "\n", "Target : class\n", - "TRAIN score : 0.7623152709359606\n", - "TEST score : 0.4230769230769231\n", - "Next month : [0] 0.7801393479377035\n", + "TRAIN score : 1.0\n", + "TEST score : 0.6666666666666666\n", + "Next month : [0] 0.8986129111220336\n", "\n", "nb_neurons: 256 , nb_iter: 1000, random_state: 3\n", "\n", "Target : class\n", - "TRAIN score : 0.7820197044334976\n", - "TEST score : 0.4230769230769231\n", - "Next month : [0] 0.5078069402676045\n", + "TRAIN score : 1.0\n", + "TEST score : 0.4666666666666667\n", + "Next month : [0] 0.8500796984003661\n", "\n", "nb_neurons: 256 , nb_iter: 1000, random_state: 4\n", "\n", "Target : class\n", - "TRAIN score : 0.7389162561576355\n", - "TEST score : 0.46153846153846156\n", - "Next month : [0] 0.5874656108310718\n", + "TRAIN score : 1.0\n", + "TEST score : 0.6\n", + "Next month : [1] 0.5443420644026459\n", "\n", "nb_neurons: 256 , nb_iter: 2000, random_state: 0\n", "\n", "Target : class\n", - "TRAIN score : 0.8435960591133005\n", - "TEST score : 0.4230769230769231\n", - "Next month : [0] 0.8328008178203675\n", + "TRAIN score : 1.0\n", + "TEST score : 0.4666666666666667\n", + "Next month : [0] 0.6226737061792872\n", "\n", "nb_neurons: 256 , nb_iter: 2000, random_state: 1\n", "\n", "Target : class\n", - "TRAIN score : 0.8719211822660099\n", - "TEST score : 0.38461538461538464\n", - "Next month : [0] 0.8996010488586988\n", + "TRAIN score : 1.0\n", + "TEST score : 0.4666666666666667\n", + "Next month : [0] 0.9797863974734868\n", "\n", "nb_neurons: 256 , nb_iter: 2000, random_state: 2\n", "\n", "Target : class\n", - "TRAIN score : 0.874384236453202\n", - "TEST score : 0.34615384615384615\n", - "Next month : [0] 0.8709934674026609\n", + "TRAIN score : 1.0\n", + "TEST score : 0.6666666666666666\n", + "Next month : [0] 0.981809074005862\n", "\n", "nb_neurons: 256 , nb_iter: 2000, random_state: 3\n", "\n", "Target : class\n", - "TRAIN score : 0.8793103448275862\n", - "TEST score : 0.5\n", - "Next month : [0] 0.5134188293166884\n", + "TRAIN score : 1.0\n", + "TEST score : 0.4666666666666667\n", + "Next month : [0] 0.9520167038492259\n", "\n", "nb_neurons: 256 , nb_iter: 2000, random_state: 4\n", "\n", "Target : class\n", - "TRAIN score : 0.8719211822660099\n", - "TEST score : 0.4230769230769231\n", - "Next month : [0] 0.7889948786129917\n", + "TRAIN score : 1.0\n", + "TEST score : 0.5333333333333333\n", + "Next month : [1] 0.5153795821475315\n", "\n", "nb_neurons: 256 , nb_iter: 4000, random_state: 0\n", "\n", "Target : class\n", - "TRAIN score : 0.9655172413793104\n", - "TEST score : 0.5\n", - "Next month : [0] 0.9928054322406319\n", + "TRAIN score : 1.0\n", + "TEST score : 0.3333333333333333\n", + "Next month : [1] 0.5222742795236941\n", "\n", "nb_neurons: 256 , nb_iter: 4000, random_state: 1\n", "\n", "Target : class\n", - "TRAIN score : 0.9630541871921182\n", - "TEST score : 0.34615384615384615\n", - "Next month : [0] 0.9923269911487949\n", + "TRAIN score : 1.0\n", + "TEST score : 0.4\n", + "Next month : [0] 0.9896177189805744\n", "\n", "nb_neurons: 256 , nb_iter: 4000, random_state: 2\n", "\n", "Target : class\n", - "TRAIN score : 0.9876847290640394\n", - "TEST score : 0.5\n", - "Next month : [0] 0.981209907148314\n", + "TRAIN score : 1.0\n", + "TEST score : 0.6666666666666666\n", + "Next month : [0] 0.9950875642106493\n", "\n", "nb_neurons: 256 , nb_iter: 4000, random_state: 3\n", "\n", "Target : class\n", - "TRAIN score : 0.9741379310344828\n", - "TEST score : 0.46153846153846156\n", - "Next month : [1] 0.5700379276397263\n", + "TRAIN score : 1.0\n", + "TEST score : 0.4666666666666667\n", + "Next month : [0] 0.9876200842880793\n", "\n", "nb_neurons: 256 , nb_iter: 4000, random_state: 4\n", "\n", "Target : class\n", - "TRAIN score : 0.9692118226600985\n", - "TEST score : 0.4230769230769231\n", - "Next month : [0] 0.7286249576593468\n", + "TRAIN score : 1.0\n", + "TEST score : 0.6666666666666666\n", + "Next month : [0] 0.7231064276309687\n", "\n", "nb_neurons: 256 , nb_iter: 8000, random_state: 0\n", "\n", "Target : class\n", "TRAIN score : 1.0\n", - "TEST score : 0.46153846153846156\n", - "Next month : [0] 0.999961763942813\n", + "TEST score : 0.4\n", + "Next month : [0] 0.6808173139329894\n", "\n", "nb_neurons: 256 , nb_iter: 8000, random_state: 1\n", "\n", "Target : class\n", "TRAIN score : 1.0\n", - "TEST score : 0.2692307692307692\n", - "Next month : [0] 0.9999464794978801\n", + "TEST score : 0.4666666666666667\n", + "Next month : [0] 0.9963146966194365\n", "\n", "nb_neurons: 256 , nb_iter: 8000, random_state: 2\n", "\n", "Target : class\n", "TRAIN score : 1.0\n", - "TEST score : 0.4230769230769231\n", - "Next month : [0] 0.9998137207802289\n", + "TEST score : 0.6666666666666666\n", + "Next month : [0] 0.9986089141401293\n", "\n", "nb_neurons: 256 , nb_iter: 8000, random_state: 3\n", "\n", "Target : class\n", - "TRAIN score : 0.9950738916256158\n", - "TEST score : 0.38461538461538464\n", - "Next month : [0] 0.9844770450124212\n", + "TRAIN score : 1.0\n", + "TEST score : 0.4666666666666667\n", + "Next month : [0] 0.7973781472968413\n", "\n", "nb_neurons: 256 , nb_iter: 8000, random_state: 4\n", "\n", "Target : class\n", "TRAIN score : 1.0\n", - "TEST score : 0.34615384615384615\n", - "Next month : [0] 0.9801403190366346\n", + "TEST score : 0.5333333333333333\n", + "Next month : [0] 0.9245264400835022\n", "\n", "nb_neurons: 256 , nb_iter: 16000, random_state: 0\n", "\n", "Target : class\n", "TRAIN score : 1.0\n", - "TEST score : 0.5\n", - "Next month : [0] 0.9999999649356986\n", + "TEST score : 0.4\n", + "Next month : [0] 0.6284286417897778\n", "\n", "nb_neurons: 256 , nb_iter: 16000, random_state: 1\n", "\n", "Target : class\n", "TRAIN score : 1.0\n", - "TEST score : 0.3076923076923077\n", - "Next month : [0] 0.9999999647918646\n", + "TEST score : 0.5333333333333333\n", + "Next month : [0] 0.9964453810428616\n", "\n", "nb_neurons: 256 , nb_iter: 16000, random_state: 2\n", "\n", "Target : class\n", "TRAIN score : 1.0\n", - "TEST score : 0.38461538461538464\n", - "Next month : [0] 0.9999998509762895\n", + "TEST score : 0.6666666666666666\n", + "Next month : [0] 0.9992866545776247\n", "\n", "nb_neurons: 256 , nb_iter: 16000, random_state: 3\n", "\n", "Target : class\n", "TRAIN score : 1.0\n", - "TEST score : 0.4230769230769231\n", - "Next month : [0] 0.9938614783551208\n", + "TEST score : 0.4666666666666667\n", + "Next month : [0] 0.8400496414501077\n", "\n", "nb_neurons: 256 , nb_iter: 16000, random_state: 4\n", "\n", "Target : class\n", - "TRAIN score : 0.9987684729064039\n", - "TEST score : 0.3076923076923077\n", - "Next month : [0] 0.9990018743580656\n", + "TRAIN score : 1.0\n", + "TEST score : 0.4666666666666667\n", + "Next month : [0] 0.8275854482270136\n", "\n", "nb_neurons: 256 , nb_iter: 32000, random_state: 0\n", "\n", "Target : class\n", "TRAIN score : 1.0\n", - "TEST score : 0.5\n", - "Next month : [0] 0.9999999998805851\n", + "TEST score : 0.4\n", + "Next month : [0] 0.8381328081208612\n", "\n", "nb_neurons: 256 , nb_iter: 32000, random_state: 1\n", "\n", "Target : class\n", "TRAIN score : 1.0\n", - "TEST score : 0.3076923076923077\n", - "Next month : [0] 0.9999999991036161\n", + "TEST score : 0.5333333333333333\n", + "Next month : [0] 0.9971942110148015\n", "\n", "nb_neurons: 256 , nb_iter: 32000, random_state: 2\n", "\n", "Target : class\n", "TRAIN score : 1.0\n", - "TEST score : 0.4230769230769231\n", - "Next month : [0] 0.9999999959297743\n", + "TEST score : 0.6666666666666666\n", + "Next month : [0] 0.9997033897376052\n", "\n", "nb_neurons: 256 , nb_iter: 32000, random_state: 3\n", "\n", "Target : class\n", "TRAIN score : 1.0\n", - "TEST score : 0.4230769230769231\n", - "Next month : [0] 0.9990597489221474\n", + "TEST score : 0.5333333333333333\n", + "Next month : [0] 0.8921997658444726\n", "\n", "nb_neurons: 256 , nb_iter: 32000, random_state: 4\n", "\n", "Target : class\n", "TRAIN score : 1.0\n", - "TEST score : 0.3076923076923077\n", - "Next month : [0] 0.9999600032105562\n", - "new best param : (4, 32000, 2)\n", + "TEST score : 0.4666666666666667\n", + "Next month : [0] 0.9306788194255998\n", + "new best param : (16, 4000, 4)\n", "TRAIN score best : 1.0\n", - "TEST score best : 0.5769230769230769\n" + "TEST score best : 0.8666666666666667\n" ] }, { @@ -8976,43 +8578,43 @@ " \n", " \n", " 0\n", - " 4.0\n", + " 8.0\n", " 1000.0\n", " 0.0\n", - " 0.520936\n", - " 0.423077\n", + " 0.574727\n", + " 0.266667\n", " \n", " \n", " 1\n", - " 4.0\n", + " 8.0\n", " 1000.0\n", " 1.0\n", - " 0.492611\n", - " 0.307692\n", + " 0.557716\n", + " 0.666667\n", " \n", " \n", " 2\n", - " 4.0\n", + " 8.0\n", " 1000.0\n", " 2.0\n", - " 0.528325\n", - " 0.461538\n", + " 0.573512\n", + " 0.600000\n", " \n", " \n", " 3\n", - " 4.0\n", + " 8.0\n", " 1000.0\n", " 3.0\n", - " 0.514778\n", - " 0.346154\n", + " 0.582017\n", + " 0.600000\n", " \n", " \n", " 4\n", - " 4.0\n", + " 8.0\n", " 1000.0\n", " 4.0\n", - " 0.513547\n", - " 0.307692\n", + " 0.558931\n", + " 0.400000\n", " \n", " \n", " ...\n", @@ -9023,68 +8625,68 @@ " ...\n", " \n", " \n", - " 205\n", + " 175\n", " 256.0\n", " 32000.0\n", " 0.0\n", " 1.000000\n", - " 0.500000\n", + " 0.400000\n", " \n", " \n", - " 206\n", + " 176\n", " 256.0\n", " 32000.0\n", " 1.0\n", " 1.000000\n", - " 0.307692\n", + " 0.533333\n", " \n", " \n", - " 207\n", + " 177\n", " 256.0\n", " 32000.0\n", " 2.0\n", " 1.000000\n", - " 0.423077\n", + " 0.666667\n", " \n", " \n", - " 208\n", + " 178\n", " 256.0\n", " 32000.0\n", " 3.0\n", " 1.000000\n", - " 0.423077\n", + " 0.533333\n", " \n", " \n", - " 209\n", + " 179\n", " 256.0\n", " 32000.0\n", " 4.0\n", " 1.000000\n", - " 0.307692\n", + " 0.466667\n", " \n", " \n", "\n", - "

210 rows × 5 columns

\n", + "

180 rows × 5 columns

\n", "" ], "text/plain": [ " nb_neurons nb_iter random_state accuracy accuracy_test\n", - "0 4.0 1000.0 0.0 0.520936 0.423077\n", - "1 4.0 1000.0 1.0 0.492611 0.307692\n", - "2 4.0 1000.0 2.0 0.528325 0.461538\n", - "3 4.0 1000.0 3.0 0.514778 0.346154\n", - "4 4.0 1000.0 4.0 0.513547 0.307692\n", + "0 8.0 1000.0 0.0 0.574727 0.266667\n", + "1 8.0 1000.0 1.0 0.557716 0.666667\n", + "2 8.0 1000.0 2.0 0.573512 0.600000\n", + "3 8.0 1000.0 3.0 0.582017 0.600000\n", + "4 8.0 1000.0 4.0 0.558931 0.400000\n", ".. ... ... ... ... ...\n", - "205 256.0 32000.0 0.0 1.000000 0.500000\n", - "206 256.0 32000.0 1.0 1.000000 0.307692\n", - "207 256.0 32000.0 2.0 1.000000 0.423077\n", - "208 256.0 32000.0 3.0 1.000000 0.423077\n", - "209 256.0 32000.0 4.0 1.000000 0.307692\n", + "175 256.0 32000.0 0.0 1.000000 0.400000\n", + "176 256.0 32000.0 1.0 1.000000 0.533333\n", + "177 256.0 32000.0 2.0 1.000000 0.666667\n", + "178 256.0 32000.0 3.0 1.000000 0.533333\n", + "179 256.0 32000.0 4.0 1.000000 0.466667\n", "\n", - "[210 rows x 5 columns]" + "[180 rows x 5 columns]" ] }, - "execution_count": 72, + "execution_count": 80, "metadata": {}, "output_type": "execute_result" } @@ -9093,7 +8695,7 @@ "from sklearn.neural_network import MLPClassifier\n", "list_nb_iter = [1000, 2000, 4000, 8000, 16000, 32000]\n", "list_random_state = [0, 1, 2, 3, 4]\n", - "list_nb_cell = [4, 8, 16, 32, 64, 128, 256]\n", + "list_nb_cell = [8, 16, 32, 64, 128, 256]\n", "df_res = pd.DataFrame(columns=[\"nb_neurons\", \"nb_iter\", \"random_state\", \"accuracy\", \"accuracy_test\"])\n", "Y, Y_test = choose_target(df_y, \"class\", nb_test)\n", "acc_best = 0\n", @@ -9136,7 +8738,7 @@ }, { "cell_type": "code", - "execution_count": 73, + "execution_count": 81, "metadata": {}, "outputs": [], "source": [ @@ -9145,22 +8747,22 @@ }, { "cell_type": "code", - "execution_count": 74, + "execution_count": 82, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "" + "" ] }, - "execution_count": 74, + "execution_count": 82, "metadata": {}, "output_type": "execute_result" }, { "data": { - "image/png": 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", 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", 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" ] @@ -9175,22 +8777,22 @@ }, { "cell_type": "code", - "execution_count": 75, + "execution_count": 83, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "" + "" ] }, - "execution_count": 75, + "execution_count": 83, "metadata": {}, "output_type": "execute_result" }, { "data": { - "image/png": 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", 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", 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" ] @@ -9205,7 +8807,7 @@ }, { "cell_type": "code", - "execution_count": 76, + "execution_count": 92, "metadata": {}, "outputs": [ { @@ -9238,244 +8840,60 @@ " \n", " \n", " \n", - " 180\n", - " 256.0\n", - " 1000.0\n", - " 0.0\n", - " 0.780788\n", - " 0.307692\n", - " \n", - " \n", - " 181\n", - " 256.0\n", - " 1000.0\n", - " 1.0\n", - " 0.779557\n", - " 0.384615\n", - " \n", - " \n", - " 182\n", - " 256.0\n", - " 1000.0\n", - " 2.0\n", - " 0.762315\n", - " 0.423077\n", - " \n", - " \n", - " 183\n", - " 256.0\n", - " 1000.0\n", - " 3.0\n", - " 0.782020\n", - " 0.423077\n", - " \n", - " \n", - " 184\n", - " 256.0\n", - " 1000.0\n", - " 4.0\n", - " 0.738916\n", - " 0.461538\n", - " \n", - " \n", - " 185\n", - " 256.0\n", - " 2000.0\n", - " 0.0\n", - " 0.843596\n", - " 0.423077\n", - " \n", - " \n", - " 186\n", - " 256.0\n", - " 2000.0\n", - " 1.0\n", - " 0.871921\n", - " 0.384615\n", - " \n", - " \n", - " 187\n", - " 256.0\n", - " 2000.0\n", - " 2.0\n", - " 0.874384\n", - " 0.346154\n", - " \n", - " \n", - " 188\n", - " 256.0\n", - " 2000.0\n", - " 3.0\n", - " 0.879310\n", - " 0.500000\n", - " \n", - " \n", - " 189\n", - " 256.0\n", - " 2000.0\n", - " 4.0\n", - " 0.871921\n", - " 0.423077\n", - " \n", - " \n", - " 190\n", - " 256.0\n", - " 4000.0\n", - " 0.0\n", - " 0.965517\n", - " 0.500000\n", - " \n", - " \n", - " 191\n", - " 256.0\n", - " 4000.0\n", - " 1.0\n", - " 0.963054\n", - " 0.346154\n", - " \n", - " \n", - " 192\n", - " 256.0\n", - " 4000.0\n", - " 2.0\n", - " 0.987685\n", - " 0.500000\n", - " \n", - " \n", - " 193\n", - " 256.0\n", - " 4000.0\n", - " 3.0\n", - " 0.974138\n", - " 0.461538\n", - " \n", - " \n", - " 194\n", - " 256.0\n", + " 44\n", + " 16.0\n", " 4000.0\n", " 4.0\n", - " 0.969212\n", - " 0.423077\n", - " \n", - " \n", - " 195\n", - " 256.0\n", - " 8000.0\n", - " 0.0\n", - " 1.000000\n", - " 0.461538\n", - " \n", - " \n", - " 196\n", - " 256.0\n", - " 8000.0\n", - " 1.0\n", - " 1.000000\n", - " 0.269231\n", + " 0.686513\n", + " 0.866667\n", " 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" 0.500000\n", + " 0.739976\n", + " 0.733333\n", " \n", " \n", - " 206\n", - " 256.0\n", + " 59\n", + " 16.0\n", " 32000.0\n", - " 1.0\n", - " 1.000000\n", - " 0.307692\n", + " 4.0\n", + " 0.748481\n", + " 0.733333\n", " \n", " \n", - " 207\n", - " 256.0\n", - " 32000.0\n", - " 2.0\n", - " 1.000000\n", - " 0.423077\n", + " 73\n", + " 32.0\n", + " 4000.0\n", + " 3.0\n", + " 0.854192\n", + " 0.800000\n", " \n", " \n", - " 208\n", - " 256.0\n", - " 32000.0\n", + " 78\n", + " 32.0\n", + " 8000.0\n", " 3.0\n", - " 1.000000\n", - " 0.423077\n", + " 0.914945\n", + " 0.733333\n", " \n", " \n", - " 209\n", - " 256.0\n", + " 116\n", + " 64.0\n", " 32000.0\n", - " 4.0\n", + " 1.0\n", " 1.000000\n", - " 0.307692\n", + " 0.733333\n", " \n", " \n", "\n", @@ -9483,50 +8901,61 @@ ], "text/plain": [ " nb_neurons nb_iter random_state accuracy accuracy_test\n", - "180 256.0 1000.0 0.0 0.780788 0.307692\n", - "181 256.0 1000.0 1.0 0.779557 0.384615\n", - "182 256.0 1000.0 2.0 0.762315 0.423077\n", - "183 256.0 1000.0 3.0 0.782020 0.423077\n", - "184 256.0 1000.0 4.0 0.738916 0.461538\n", - "185 256.0 2000.0 0.0 0.843596 0.423077\n", - "186 256.0 2000.0 1.0 0.871921 0.384615\n", - "187 256.0 2000.0 2.0 0.874384 0.346154\n", - "188 256.0 2000.0 3.0 0.879310 0.500000\n", - "189 256.0 2000.0 4.0 0.871921 0.423077\n", - "190 256.0 4000.0 0.0 0.965517 0.500000\n", - "191 256.0 4000.0 1.0 0.963054 0.346154\n", - "192 256.0 4000.0 2.0 0.987685 0.500000\n", - "193 256.0 4000.0 3.0 0.974138 0.461538\n", - "194 256.0 4000.0 4.0 0.969212 0.423077\n", - "195 256.0 8000.0 0.0 1.000000 0.461538\n", - "196 256.0 8000.0 1.0 1.000000 0.269231\n", - "197 256.0 8000.0 2.0 1.000000 0.423077\n", - "198 256.0 8000.0 3.0 0.995074 0.384615\n", - "199 256.0 8000.0 4.0 1.000000 0.346154\n", - "200 256.0 16000.0 0.0 1.000000 0.500000\n", - "201 256.0 16000.0 1.0 1.000000 0.307692\n", - "202 256.0 16000.0 2.0 1.000000 0.384615\n", - "203 256.0 16000.0 3.0 1.000000 0.423077\n", - "204 256.0 16000.0 4.0 0.998768 0.307692\n", - "205 256.0 32000.0 0.0 1.000000 0.500000\n", - "206 256.0 32000.0 1.0 1.000000 0.307692\n", - "207 256.0 32000.0 2.0 1.000000 0.423077\n", - "208 256.0 32000.0 3.0 1.000000 0.423077\n", - "209 256.0 32000.0 4.0 1.000000 0.307692" + "44 16.0 4000.0 4.0 0.686513 0.866667\n", + "49 16.0 8000.0 4.0 0.712029 0.800000\n", + "54 16.0 16000.0 4.0 0.739976 0.733333\n", + "59 16.0 32000.0 4.0 0.748481 0.733333\n", + "73 32.0 4000.0 3.0 0.854192 0.800000\n", + "78 32.0 8000.0 3.0 0.914945 0.733333\n", + "116 64.0 32000.0 1.0 1.000000 0.733333" ] }, - "execution_count": 76, + "execution_count": 92, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "df_res[df_res[\"nb_neurons\"] == 256]" + "df_res[df_res[\"accuracy_test\"] > 0.7]" ] }, { "cell_type": "code", - "execution_count": 77, + "execution_count": 85, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "for param best : (16, 4000, 4)\n" + ] + }, + { + "data": { + 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "from sklearn.metrics import confusion_matrix, ConfusionMatrixDisplay\n", + "print(\"for param best : \", param_best)\n", + "predictions = clf_best.predict(X_test)\n", + "cm = confusion_matrix(Y_test, predictions, labels=clf_best.classes_)\n", + "disp = ConfusionMatrixDisplay(confusion_matrix=cm,\n", + " display_labels=clf_best.classes_)\n", + "disp.plot()\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 113, "metadata": {}, "outputs": [ { @@ -9535,30 +8964,31 @@ "text": [ "\n", "Target : class\n", - "TRAIN score : 0.7389162561576355\n", - "TEST score : 0.46153846153846156\n", - "Next month : [0] 0.5874656108310718\n" + "TRAIN score : 0.6865127582017011\n", + "TEST score : 0.8666666666666667\n", + "Next month : [0] 0.3580796794794029\n" ] } ], "source": [ "clf = MLPClassifier(\n", - " hidden_layer_sizes=(256),\n", - " random_state=4, \n", - " max_iter=1000,\n", - " n_iter_no_change=1000,\n", - " )\n", + " hidden_layer_sizes=(16),\n", + " random_state=4,\n", + " max_iter=4000,\n", + " n_iter_no_change=4000,\n", + " alpha=0.0001,\n", + ")\n", "list_clf = multi_target_fit(clf, df_y, nb_test, [\"class\"])" ] }, { "cell_type": "code", - "execution_count": 78, + "execution_count": 112, "metadata": {}, "outputs": [ { "data": { - "image/png": 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", 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" ] @@ -9580,7 +9010,7 @@ }, { "cell_type": "code", - "execution_count": 79, + "execution_count": 88, "metadata": {}, "outputs": [ { @@ -9589,9 +9019,9 @@ "text": [ "\n", "Target : class\n", - "TRAIN score : 0.5615763546798029\n", - "TEST score : 0.3076923076923077\n", - "Next month : [2] 0.35939720170531564\n", + "TRAIN score : 0.6075334143377886\n", + "TEST score : 0.5333333333333333\n", + "Next month : [1] 0.5128052760713334\n", "nb iteration : 1000\n" ] } @@ -9607,12 +9037,12 @@ }, { "cell_type": "code", - "execution_count": 80, + "execution_count": 89, "metadata": {}, "outputs": [ { "data": { - "image/png": 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", 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", 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" ] @@ -9634,7 +9064,7 @@ }, { "cell_type": "code", - "execution_count": 81, + "execution_count": 90, "metadata": {}, "outputs": [ { @@ -9643,19 +9073,19 @@ "text": [ "\n", "Target : ur_lower\n", - "TRAIN score : 0.6366995073891626\n", - "TEST score : 0.5769230769230769\n", - "Next month : [False] 0.5596370945820496\n", + "TRAIN score : 0.6561360874848117\n", + "TEST score : 0.6666666666666666\n", + "Next month : [False] 0.5762379465936085\n", "\n", "Target : ur_stable\n", - "TRAIN score : 0.7610837438423645\n", - "TEST score : 0.7692307692307693\n", - "Next month : [False] 0.5586839019165861\n", + "TRAIN score : 0.7484811664641555\n", + "TEST score : 0.7333333333333333\n", + "Next month : [False] 0.7056648651632249\n", "\n", "Target : ur_higher\n", - "TRAIN score : 0.7179802955665024\n", - "TEST score : 0.5\n", - "Next month : [False] 0.6898501237762087\n" + "TRAIN score : 0.7132442284325637\n", + "TEST score : 0.6\n", + "Next month : [False] 0.6706974193738885\n" ] } ],