From 7d0c2e9efe58a425bf88d8d546a6e56ca89d2fb2 Mon Sep 17 00:00:00 2001 From: Aurelien Geron Date: Tue, 8 May 2018 19:41:47 +0200 Subject: [PATCH] Add code to compute a confidence interval --- 02_end_to_end_machine_learning_project.ipynb | 3437 +++++++++--------- 1 file changed, 1769 insertions(+), 1668 deletions(-) diff --git a/02_end_to_end_machine_learning_project.ipynb b/02_end_to_end_machine_learning_project.ipynb index 666e83293..5d583142d 100644 --- a/02_end_to_end_machine_learning_project.ipynb +++ b/02_end_to_end_machine_learning_project.ipynb @@ -955,7 +955,7 @@ { "data": { "text/plain": [ - "" + "" ] }, "execution_count": 21, @@ -1022,7 +1022,7 @@ { "data": { "text/plain": [ - "" + "" ] }, "execution_count": 24, @@ -3320,7 +3320,7 @@ "output_type": "stream", "text": [ "Predictions: [210644.60459286 317768.80697211 210956.43331178 59218.98886849\n", - " 189747.55849878]\n" + " 189747.55849879]\n" ] } ], @@ -3429,7 +3429,7 @@ { "data": { "text/plain": [ - "49439.89599001896" + "49439.89599001897" ] }, "execution_count": 81, @@ -3549,11 +3549,11 @@ "name": "stdout", "output_type": "stream", "text": [ - "Scores: [66782.73843989 66960.118071 70361.18285107 74742.02956966\n", - " 68022.09224176 71191.82593104 64969.63056405 68276.69992785\n", - " 71543.69797334 67665.10082067]\n", - "Mean: 69051.51163903397\n", - "Standard deviation: 2732.29492244669\n" + "Scores: [66782.73843989 66960.118071 70347.95244419 74739.57052552\n", + " 68031.13388938 71193.84183426 64969.63056405 68281.61137997\n", + " 71552.91566558 67665.10082067]\n", + "Mean: 69052.46136345083\n", + "Standard deviation: 2731.674001798348\n" ] } ], @@ -3650,13 +3650,13 @@ "data": { "text/plain": [ "count 10.000000\n", - "mean 69051.511639\n", - "std 2880.091731\n", + "mean 69052.461363\n", + "std 2879.437224\n", "min 64969.630564\n", "25% 67136.363758\n", - "50% 68149.396085\n", - "75% 70984.165161\n", - "max 74742.029570\n", + "50% 68156.372635\n", + "75% 70982.369487\n", + "max 74739.570526\n", "dtype: float64" ] }, @@ -3713,7 +3713,7 @@ " min_weight_fraction_leaf=0.0, n_estimators=10, n_jobs=1,\n", " oob_score=False, random_state=42, verbose=0, warm_start=False),\n", " fit_params=None, iid=True, n_jobs=1,\n", - " param_grid=[{'n_estimators': [3, 10, 30], 'max_features': [2, 4, 6, 8]}, {'bootstrap': [False], 'n_estimators': [3, 10], 'max_features': [2, 3, 4]}],\n", + " param_grid=[{'max_features': [2, 4, 6, 8], 'n_estimators': [3, 10, 30]}, {'bootstrap': [False], 'max_features': [2, 3, 4], 'n_estimators': [3, 10]}],\n", " pre_dispatch='2*n_jobs', refit=True, return_train_score=True,\n", " scoring='neg_mean_squared_error', verbose=0)" ] @@ -3887,8 +3887,8 @@ " \n", " \n", " 0\n", - " 0.063470\n", - " 0.004361\n", + " 0.062995\n", + " 0.002987\n", " -4.051049e+09\n", " -1.106013e+09\n", " NaN\n", @@ -3904,15 +3904,15 @@ " -1.112813e+09\n", " -4.169669e+09\n", " -1.129842e+09\n", - " 0.003057\n", - " 0.000455\n", + " 0.021348\n", + " 0.000740\n", " 1.431223e+08\n", " 2.173798e+07\n", " \n", " \n", " 1\n", - " 0.209347\n", - " 0.011809\n", + " 0.145794\n", + " 0.007416\n", " -3.092639e+09\n", " -5.819353e+08\n", " NaN\n", @@ -3928,15 +3928,15 @@ " -5.713421e+08\n", " -3.169914e+09\n", " -5.797944e+08\n", - " 0.007495\n", - " 0.001353\n", + " 0.003562\n", + " 0.000552\n", " 1.306954e+08\n", " 7.584886e+06\n", " \n", " \n", " 2\n", - " 0.626505\n", - " 0.031570\n", + " 0.428165\n", + " 0.022323\n", " -2.848364e+09\n", " -4.396234e+08\n", " NaN\n", @@ -3952,15 +3952,15 @@ " -4.374715e+08\n", " -2.968460e+09\n", " -4.451903e+08\n", - " 0.007113\n", - " 0.003467\n", + " 0.005923\n", + " 0.000406\n", " 1.604534e+08\n", " 2.883885e+06\n", " \n", " \n", " 3\n", - " 0.100939\n", - " 0.003802\n", + " 0.071661\n", + " 0.002170\n", " -3.716017e+09\n", " -9.850011e+08\n", " NaN\n", @@ -3976,15 +3976,15 @@ " -1.035901e+09\n", " -3.914186e+09\n", " -9.711998e+08\n", - " 0.001737\n", - " 0.000428\n", + " 0.001118\n", + " 0.000042\n", " 1.690029e+08\n", " 4.047487e+07\n", " \n", " \n", " 4\n", - " 0.334867\n", - " 0.010084\n", + " 0.238946\n", + " 0.007286\n", " -2.781569e+09\n", " -5.160154e+08\n", " NaN\n", @@ -4000,15 +4000,15 @@ " -5.422542e+08\n", " -2.949550e+09\n", " -5.158794e+08\n", - " 0.004043\n", - " 0.000630\n", + " 0.001779\n", + " 0.000247\n", " 1.278498e+08\n", " 1.498960e+07\n", " \n", " \n", " 5\n", - " 0.991426\n", - " 0.030009\n", + " 0.709232\n", + " 0.022390\n", " -2.537554e+09\n", " -3.878685e+08\n", " NaN\n", @@ -4024,15 +4024,15 @@ " -4.036920e+08\n", " -2.649850e+09\n", " -3.846171e+08\n", - " 0.014581\n", - " 0.002804\n", + " 0.006213\n", + " 0.000294\n", " 1.209935e+08\n", " 8.424973e+06\n", " \n", " \n", " 6\n", - " 0.136193\n", - " 0.003943\n", + " 0.097383\n", + " 0.002201\n", " -3.441147e+09\n", " -9.030212e+08\n", " NaN\n", @@ -4048,15 +4048,15 @@ " -9.025026e+08\n", " -3.577062e+09\n", " -8.612945e+08\n", - " 0.003806\n", - " 0.000518\n", + " 0.002897\n", + " 0.000047\n", " 1.884229e+08\n", " 2.639683e+07\n", " \n", " \n", " 7\n", - " 0.457367\n", - " 0.012050\n", + " 0.326697\n", + " 0.007389\n", " -2.705037e+09\n", " -5.014210e+08\n", " NaN\n", @@ -4072,15 +4072,15 @@ " -4.989516e+08\n", " -2.906270e+09\n", " -5.063617e+08\n", - " 0.009329\n", - " 0.001353\n", + " 0.003809\n", + " 0.000178\n", " 1.464963e+08\n", " 3.357661e+06\n", " \n", " \n", " 8\n", - " 1.367555\n", - " 0.028310\n", + " 1.019054\n", + " 0.022486\n", " -2.515436e+09\n", " -3.840197e+08\n", " NaN\n", @@ -4096,15 +4096,15 @@ " -3.856159e+08\n", " -2.662399e+09\n", " -3.904866e+08\n", - " 0.018900\n", - " 0.001768\n", + " 0.020982\n", + " 0.000343\n", " 1.283580e+08\n", " 3.796810e+06\n", " \n", " \n", " 9\n", - " 0.171814\n", - " 0.003423\n", + " 0.128580\n", + " 0.002374\n", " -3.348400e+09\n", " -8.884890e+08\n", " NaN\n", @@ -4120,15 +4120,15 @@ " -8.893698e+08\n", " -3.509451e+09\n", " -9.146734e+08\n", - " 0.001904\n", - " 0.000269\n", + " 0.002220\n", + " 0.000096\n", " 1.226683e+08\n", " 2.730057e+07\n", " \n", " \n", " 10\n", - " 0.577376\n", - " 0.010003\n", + " 0.425095\n", + " 0.007284\n", " -2.676001e+09\n", " -4.923247e+08\n", " NaN\n", @@ -4144,15 +4144,15 @@ " -5.154156e+08\n", " -2.777049e+09\n", " -4.979127e+08\n", - " 0.004503\n", - " 0.000661\n", + " 0.004683\n", + " 0.000094\n", " 1.393253e+08\n", " 1.446900e+07\n", " \n", " \n", " 11\n", - " 1.769046\n", - " 0.028638\n", + " 1.298401\n", + " 0.022745\n", " -2.469578e+09\n", " -3.809175e+08\n", " NaN\n", @@ -4168,15 +4168,15 @@ " -3.881153e+08\n", " -2.528200e+09\n", " -3.807496e+08\n", - " 0.017102\n", - " 0.002513\n", + " 0.018710\n", + " 0.000306\n", " 1.089395e+08\n", " 4.853344e+06\n", " \n", " \n", " 12\n", - " 0.099325\n", - " 0.005168\n", + " 0.066767\n", + " 0.002538\n", " -3.953191e+09\n", " 0.000000e+00\n", " False\n", @@ -4192,15 +4192,15 @@ " -0.000000e+00\n", " -4.087237e+09\n", " -0.000000e+00\n", - " 0.002609\n", - " 0.000743\n", + " 0.001541\n", + " 0.000050\n", " 1.898516e+08\n", " 0.000000e+00\n", " \n", " \n", " 13\n", - " 0.333156\n", - " 0.016455\n", + " 0.224886\n", + " 0.009243\n", " -2.985912e+09\n", " -6.056027e-01\n", " False\n", @@ -4216,15 +4216,15 @@ " -0.000000e+00\n", " -3.100391e+09\n", " -2.967449e+00\n", - " 0.006816\n", - " 0.002490\n", + " 0.001934\n", + " 0.000245\n", " 1.535103e+08\n", " 1.181156e+00\n", " \n", " \n", " 14\n", - " 0.131024\n", - " 0.005100\n", + " 0.092006\n", + " 0.002561\n", " -3.532863e+09\n", " -1.214568e+01\n", " False\n", @@ -4240,15 +4240,15 @@ " -0.000000e+00\n", " -3.455760e+09\n", " -6.072840e+01\n", - " 0.004370\n", - " 0.000348\n", + " 0.000956\n", + " 0.000075\n", " 7.251650e+07\n", " 2.429136e+01\n", " \n", " \n", " 15\n", - " 0.433759\n", - " 0.015153\n", + " 0.306362\n", + " 0.009182\n", " -2.781018e+09\n", " -5.272080e+00\n", " False\n", @@ -4264,15 +4264,15 @@ " -0.000000e+00\n", " -2.793018e+09\n", " -5.465556e+00\n", - " 0.010757\n", - " 0.000829\n", + " 0.002280\n", + " 0.000197\n", " 6.329307e+07\n", " 8.093117e+00\n", " \n", " \n", " 16\n", - " 0.162551\n", - " 0.005081\n", + " 0.113758\n", + " 0.002555\n", " -3.305102e+09\n", " 0.000000e+00\n", " False\n", @@ -4288,15 +4288,15 @@ " -0.000000e+00\n", " -3.337950e+09\n", " -0.000000e+00\n", - " 0.002787\n", - " 0.000378\n", + " 0.001864\n", + " 0.000054\n", " 1.867866e+08\n", " 0.000000e+00\n", " \n", " \n", " 17\n", - " 0.528412\n", - " 0.014856\n", + " 0.382453\n", + " 0.009275\n", " -2.601843e+09\n", " -3.028238e-03\n", " False\n", @@ -4312,8 +4312,8 @@ " -0.000000e+00\n", " -2.726341e+09\n", " -0.000000e+00\n", - " 0.005960\n", - " 0.001037\n", + " 0.006457\n", + " 0.000269\n", " 1.082086e+08\n", " 6.056477e-03\n", " \n", @@ -4324,24 +4324,24 @@ ], "text/plain": [ " mean_fit_time mean_score_time mean_test_score mean_train_score \\\n", - "0 0.063470 0.004361 -4.051049e+09 -1.106013e+09 \n", - "1 0.209347 0.011809 -3.092639e+09 -5.819353e+08 \n", - "2 0.626505 0.031570 -2.848364e+09 -4.396234e+08 \n", - "3 0.100939 0.003802 -3.716017e+09 -9.850011e+08 \n", - "4 0.334867 0.010084 -2.781569e+09 -5.160154e+08 \n", - "5 0.991426 0.030009 -2.537554e+09 -3.878685e+08 \n", - "6 0.136193 0.003943 -3.441147e+09 -9.030212e+08 \n", - "7 0.457367 0.012050 -2.705037e+09 -5.014210e+08 \n", - "8 1.367555 0.028310 -2.515436e+09 -3.840197e+08 \n", - "9 0.171814 0.003423 -3.348400e+09 -8.884890e+08 \n", - "10 0.577376 0.010003 -2.676001e+09 -4.923247e+08 \n", - "11 1.769046 0.028638 -2.469578e+09 -3.809175e+08 \n", - "12 0.099325 0.005168 -3.953191e+09 0.000000e+00 \n", - "13 0.333156 0.016455 -2.985912e+09 -6.056027e-01 \n", - "14 0.131024 0.005100 -3.532863e+09 -1.214568e+01 \n", - "15 0.433759 0.015153 -2.781018e+09 -5.272080e+00 \n", - "16 0.162551 0.005081 -3.305102e+09 0.000000e+00 \n", - "17 0.528412 0.014856 -2.601843e+09 -3.028238e-03 \n", + "0 0.062995 0.002987 -4.051049e+09 -1.106013e+09 \n", + "1 0.145794 0.007416 -3.092639e+09 -5.819353e+08 \n", + "2 0.428165 0.022323 -2.848364e+09 -4.396234e+08 \n", + "3 0.071661 0.002170 -3.716017e+09 -9.850011e+08 \n", + "4 0.238946 0.007286 -2.781569e+09 -5.160154e+08 \n", + "5 0.709232 0.022390 -2.537554e+09 -3.878685e+08 \n", + "6 0.097383 0.002201 -3.441147e+09 -9.030212e+08 \n", + "7 0.326697 0.007389 -2.705037e+09 -5.014210e+08 \n", + "8 1.019054 0.022486 -2.515436e+09 -3.840197e+08 \n", + "9 0.128580 0.002374 -3.348400e+09 -8.884890e+08 \n", + "10 0.425095 0.007284 -2.676001e+09 -4.923247e+08 \n", + "11 1.298401 0.022745 -2.469578e+09 -3.809175e+08 \n", + "12 0.066767 0.002538 -3.953191e+09 0.000000e+00 \n", + "13 0.224886 0.009243 -2.985912e+09 -6.056027e-01 \n", + "14 0.092006 0.002561 -3.532863e+09 -1.214568e+01 \n", + "15 0.306362 0.009182 -2.781018e+09 -5.272080e+00 \n", + "16 0.113758 0.002555 -3.305102e+09 0.000000e+00 \n", + "17 0.382453 0.009275 -2.601843e+09 -3.028238e-03 \n", "\n", " param_bootstrap param_max_features param_n_estimators \\\n", "0 NaN 2 3 \n", @@ -4424,24 +4424,24 @@ "17 -2.437626e+09 -0.000000e+00 -2.726341e+09 \n", "\n", " split4_train_score std_fit_time std_score_time std_test_score \\\n", - "0 -1.129842e+09 0.003057 0.000455 1.431223e+08 \n", - "1 -5.797944e+08 0.007495 0.001353 1.306954e+08 \n", - "2 -4.451903e+08 0.007113 0.003467 1.604534e+08 \n", - "3 -9.711998e+08 0.001737 0.000428 1.690029e+08 \n", - "4 -5.158794e+08 0.004043 0.000630 1.278498e+08 \n", - "5 -3.846171e+08 0.014581 0.002804 1.209935e+08 \n", - "6 -8.612945e+08 0.003806 0.000518 1.884229e+08 \n", - "7 -5.063617e+08 0.009329 0.001353 1.464963e+08 \n", - "8 -3.904866e+08 0.018900 0.001768 1.283580e+08 \n", - "9 -9.146734e+08 0.001904 0.000269 1.226683e+08 \n", - "10 -4.979127e+08 0.004503 0.000661 1.393253e+08 \n", - "11 -3.807496e+08 0.017102 0.002513 1.089395e+08 \n", - "12 -0.000000e+00 0.002609 0.000743 1.898516e+08 \n", - "13 -2.967449e+00 0.006816 0.002490 1.535103e+08 \n", - "14 -6.072840e+01 0.004370 0.000348 7.251650e+07 \n", - "15 -5.465556e+00 0.010757 0.000829 6.329307e+07 \n", - "16 -0.000000e+00 0.002787 0.000378 1.867866e+08 \n", - "17 -0.000000e+00 0.005960 0.001037 1.082086e+08 \n", + "0 -1.129842e+09 0.021348 0.000740 1.431223e+08 \n", + "1 -5.797944e+08 0.003562 0.000552 1.306954e+08 \n", + "2 -4.451903e+08 0.005923 0.000406 1.604534e+08 \n", + "3 -9.711998e+08 0.001118 0.000042 1.690029e+08 \n", + "4 -5.158794e+08 0.001779 0.000247 1.278498e+08 \n", + "5 -3.846171e+08 0.006213 0.000294 1.209935e+08 \n", + "6 -8.612945e+08 0.002897 0.000047 1.884229e+08 \n", + "7 -5.063617e+08 0.003809 0.000178 1.464963e+08 \n", + "8 -3.904866e+08 0.020982 0.000343 1.283580e+08 \n", + "9 -9.146734e+08 0.002220 0.000096 1.226683e+08 \n", + "10 -4.979127e+08 0.004683 0.000094 1.393253e+08 \n", + "11 -3.807496e+08 0.018710 0.000306 1.089395e+08 \n", + "12 -0.000000e+00 0.001541 0.000050 1.898516e+08 \n", + "13 -2.967449e+00 0.001934 0.000245 1.535103e+08 \n", + "14 -6.072840e+01 0.000956 0.000075 7.251650e+07 \n", + "15 -5.465556e+00 0.002280 0.000197 6.329307e+07 \n", + "16 -0.000000e+00 0.001864 0.000054 1.867866e+08 \n", + "17 -0.000000e+00 0.006457 0.000269 1.082086e+08 \n", "\n", " std_train_score \n", "0 2.173798e+07 \n", @@ -4491,7 +4491,7 @@ " min_weight_fraction_leaf=0.0, n_estimators=10, n_jobs=1,\n", " oob_score=False, random_state=42, verbose=0, warm_start=False),\n", " fit_params=None, iid=True, n_iter=10, n_jobs=1,\n", - " param_distributions={'n_estimators': , 'max_features': },\n", + " param_distributions={'max_features': , 'n_estimators': },\n", " pre_dispatch='2*n_jobs', random_state=42, refit=True,\n", " return_train_score='warn', scoring='neg_mean_squared_error',\n", " verbose=0)" @@ -4646,6 +4646,107 @@ "final_rmse" ] }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We can compute a 95% confidence interval for the test RMSE:" + ] + }, + { + "cell_type": "code", + "execution_count": 103, + "metadata": {}, + "outputs": [], + "source": [ + "from scipy import stats" + ] + }, + { + "cell_type": "code", + "execution_count": 104, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([45720.21311746, 49727.70236334])" + ] + }, + "execution_count": 104, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "confidence = 0.95\n", + "squared_errors = (final_predictions - y_test) ** 2\n", + "mean = squared_errors.mean()\n", + "m = len(squared_errors)\n", + "\n", + "np.sqrt(stats.t.interval(confidence, m - 1,\n", + " loc=np.mean(squared_errors),\n", + " scale=stats.sem(squared_errors)))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We could compute the interval manually like this:" + ] + }, + { + "cell_type": "code", + "execution_count": 105, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(45720.21311746037, 49727.70236333674)" + ] + }, + "execution_count": 105, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "tscore = stats.t.ppf((1 + confidence) / 2, df=m - 1)\n", + "tmargin = tscore * squared_errors.std(ddof=1) / np.sqrt(m)\n", + "np.sqrt(mean - tmargin), np.sqrt(mean + tmargin)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Alternatively, we could use a z-scores rather than t-scores:" + ] + }, + { + "cell_type": "code", + "execution_count": 106, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(45720.8265224029, 49727.13838489947)" + ] + }, + "execution_count": 106, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "zscore = stats.norm.ppf((1 + confidence) / 2)\n", + "zmargin = zscore * squared_errors.std(ddof=1) / np.sqrt(m)\n", + "np.sqrt(mean - zmargin), np.sqrt(mean + zmargin)" + ] + }, { "cell_type": "markdown", "metadata": {}, @@ -4662,17 +4763,17 @@ }, { "cell_type": "code", - "execution_count": 103, + "execution_count": 107, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "array([210644.60459286, 317768.80697211, 210956.43331178, 59218.98886849,\n", - " 189747.55849878])" + " 189747.55849879])" ] }, - "execution_count": 103, + "execution_count": 107, "metadata": {}, "output_type": "execute_result" } @@ -4696,7 +4797,7 @@ }, { "cell_type": "code", - "execution_count": 104, + "execution_count": 108, "metadata": {}, "outputs": [], "source": [ @@ -4705,7 +4806,7 @@ }, { "cell_type": "code", - "execution_count": 105, + "execution_count": 109, "metadata": {}, "outputs": [], "source": [ @@ -4724,14 +4825,14 @@ }, { "cell_type": "code", - "execution_count": 106, + "execution_count": 110, "metadata": {}, "outputs": [ { "data": { - "image/png": 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\n", + "image/png": 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\n", 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\n", 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" ] }, "metadata": {}, @@ -4783,7 +4884,7 @@ }, { "cell_type": "code", - "execution_count": 107, + "execution_count": 111, "metadata": {}, "outputs": [ { @@ -4791,557 +4892,557 @@ "output_type": "stream", "text": [ "Fitting 5 folds for each of 50 candidates, totalling 250 fits\n", - "[CV] C=10.0, kernel=linear ...........................................\n", - "[CV] C=10.0, kernel=linear ...........................................\n", - "[CV] C=10.0, kernel=linear ...........................................\n", - "[CV] C=10.0, kernel=linear ...........................................\n", - "[CV] ............................ C=10.0, kernel=linear, total= 7.0s\n", - "[CV] C=10.0, kernel=linear ...........................................\n", - "[CV] ............................ C=10.0, kernel=linear, total= 7.1s\n", - "[CV] ............................ C=10.0, kernel=linear, total= 7.0s\n", - "[CV] C=30.0, kernel=linear ...........................................\n", - "[CV] C=30.0, kernel=linear ...........................................\n", - "[CV] ............................ C=10.0, kernel=linear, total= 7.0s\n", - "[CV] C=30.0, kernel=linear ...........................................\n", - "[CV] ............................ C=30.0, kernel=linear, total= 6.3s\n", - "[CV] C=30.0, kernel=linear ...........................................\n", - "[CV] ............................ C=30.0, kernel=linear, total= 6.4s\n", - "[CV] ............................ C=10.0, kernel=linear, total= 6.4s\n", - "[CV] C=30.0, kernel=linear ...........................................\n", - "[CV] C=100.0, kernel=linear ..........................................\n", - "[CV] ............................ C=30.0, kernel=linear, total= 6.5s\n", - "[CV] C=100.0, kernel=linear ..........................................\n", - "[CV] ............................ C=30.0, kernel=linear, total= 6.6s\n", - "[CV] C=100.0, kernel=linear ..........................................\n", - "[CV] ........................... C=100.0, kernel=linear, total= 6.6s\n", - "[CV] C=100.0, kernel=linear ..........................................\n", - "[CV] ............................ C=30.0, kernel=linear, total= 6.8s\n", - "[CV] C=100.0, kernel=linear ..........................................\n", - "[CV] ........................... C=100.0, kernel=linear, total= 6.6s\n", - "[CV] C=300.0, kernel=linear ..........................................\n", - "[CV] ........................... C=100.0, kernel=linear, total= 7.0s\n", - "[CV] C=300.0, kernel=linear ..........................................\n", - "[CV] ........................... C=100.0, kernel=linear, total= 7.1s\n", - "[CV] C=300.0, kernel=linear ..........................................\n", - "[CV] ........................... C=100.0, kernel=linear, total= 7.2s\n", - "[CV] C=300.0, kernel=linear ..........................................\n", - "[CV] ........................... C=300.0, kernel=linear, total= 7.1s\n", - "[CV] C=300.0, kernel=linear ..........................................\n", - "[CV] ........................... C=300.0, kernel=linear, total= 7.0s\n", - "[CV] C=1000.0, kernel=linear .........................................\n", - "[CV] ........................... C=300.0, kernel=linear, total= 7.1s\n", - "[CV] ........................... C=300.0, kernel=linear, total= 7.2s\n", - "[CV] C=1000.0, kernel=linear .........................................\n", - "[CV] C=1000.0, kernel=linear .........................................\n", - "[CV] ........................... C=300.0, kernel=linear, total= 7.0s\n", - "[CV] C=1000.0, kernel=linear .........................................\n", - "[CV] .......................... C=1000.0, kernel=linear, total= 7.0s\n", - "[CV] C=1000.0, kernel=linear .........................................\n", - "[CV] .......................... C=1000.0, kernel=linear, total= 7.0s\n", - "[CV] C=3000.0, kernel=linear .........................................\n", - "[CV] .......................... C=1000.0, kernel=linear, total= 7.0s\n", - "[CV] C=3000.0, kernel=linear .........................................\n", - "[CV] .......................... C=1000.0, kernel=linear, total= 7.1s\n", - "[CV] C=3000.0, kernel=linear .........................................\n", - "[CV] .......................... C=1000.0, kernel=linear, total= 7.1s\n", - "[CV] C=3000.0, kernel=linear .........................................\n", - "[CV] .......................... C=3000.0, kernel=linear, total= 7.5s\n", - "[CV] C=3000.0, kernel=linear .........................................\n", - "[CV] .......................... C=3000.0, kernel=linear, total= 7.5s\n", - "[CV] C=10000.0, kernel=linear ........................................\n", - "[CV] .......................... C=3000.0, kernel=linear, total= 7.6s\n", - "[CV] C=10000.0, kernel=linear ........................................\n", - "[CV] .......................... C=3000.0, kernel=linear, total= 7.8s\n", - "[CV] C=10000.0, kernel=linear ........................................\n", - "[CV] .......................... C=3000.0, kernel=linear, total= 7.4s\n", - "[CV] C=10000.0, kernel=linear ........................................\n", - "[CV] ......................... C=10000.0, kernel=linear, total= 9.7s\n", - "[CV] C=10000.0, kernel=linear ........................................\n", - "[CV] ......................... 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gamma='auto',\n", " kernel='rbf', max_iter=-1, shrinking=True, tol=0.001, verbose=False),\n", " fit_params=None, iid=True, n_jobs=4,\n", - " param_grid=[{'kernel': ['linear'], 'C': [10.0, 30.0, 100.0, 300.0, 1000.0, 3000.0, 10000.0, 30000.0]}, {'kernel': ['rbf'], 'C': [1.0, 3.0, 10.0, 30.0, 100.0, 300.0, 1000.0], 'gamma': [0.01, 0.03, 0.1, 0.3, 1.0, 3.0]}],\n", + " param_grid=[{'kernel': ['linear'], 'C': [10.0, 30.0, 100.0, 300.0, 1000.0, 3000.0, 10000.0, 30000.0]}, {'kernel': ['rbf'], 'gamma': [0.01, 0.03, 0.1, 0.3, 1.0, 3.0], 'C': [1.0, 3.0, 10.0, 30.0, 100.0, 300.0, 1000.0]}],\n", " pre_dispatch='2*n_jobs', refit=True, return_train_score='warn',\n", " scoring='neg_mean_squared_error', verbose=2)" ] }, - "execution_count": 107, + "execution_count": 111, "metadata": {}, "output_type": "execute_result" } @@ -5384,7 +5485,7 @@ }, { "cell_type": "code", - "execution_count": 108, + "execution_count": 112, "metadata": {}, "outputs": [ { @@ -5393,7 +5494,7 @@ "70363.90313964167" ] }, - "execution_count": 108, + "execution_count": 112, "metadata": {}, "output_type": "execute_result" } @@ -5413,7 +5514,7 @@ }, { "cell_type": "code", - "execution_count": 109, + "execution_count": 113, "metadata": {}, "outputs": [ { @@ -5422,7 +5523,7 @@ "{'C': 30000.0, 'kernel': 'linear'}" ] }, - "execution_count": 109, + "execution_count": 113, "metadata": {}, "output_type": "execute_result" } @@ -5454,7 +5555,7 @@ }, { "cell_type": "code", - "execution_count": 110, + "execution_count": 114, "metadata": {}, "outputs": [ { @@ -5462,557 +5563,557 @@ "output_type": "stream", "text": [ "Fitting 5 folds for each of 50 candidates, totalling 250 fits\n", - "[CV] C=629.782329591372, gamma=3.010121430917521, kernel=linear ......\n", - "[CV] C=629.782329591372, gamma=3.010121430917521, kernel=linear ......\n", - "[CV] C=629.782329591372, gamma=3.010121430917521, kernel=linear ......\n", - "[CV] C=629.782329591372, gamma=3.010121430917521, kernel=linear ......\n", - "[CV] C=629.782329591372, 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gamma=3.503557475158312, C=24.17508294611391 ........\n", + "[CV] kernel=rbf, gamma=3.503557475158312, C=24.17508294611391, total= 12.3s\n", + "[CV] kernel=rbf, gamma=3.503557475158312, C=24.17508294611391 ........\n", + "[CV] kernel=rbf, gamma=3.503557475158312, C=24.17508294611391, total= 12.4s\n", + "[CV] kernel=rbf, gamma=0.0007790692366582295, C=113564.03940586245 ...\n", + "[CV] kernel=rbf, gamma=3.503557475158312, C=24.17508294611391, total= 12.5s\n", + "[CV] kernel=rbf, gamma=0.0007790692366582295, C=113564.03940586245 ...\n", + "[CV] kernel=rbf, gamma=3.503557475158312, C=24.17508294611391, total= 12.7s\n", + "[CV] kernel=rbf, gamma=0.0007790692366582295, C=113564.03940586245 ...\n", + "[CV] kernel=rbf, gamma=0.0007790692366582295, C=113564.03940586245, total= 11.2s\n", + "[CV] kernel=rbf, gamma=0.0007790692366582295, C=113564.03940586245 ...\n", + "[CV] kernel=rbf, gamma=3.503557475158312, C=24.17508294611391, total= 13.0s\n", + "[CV] kernel=rbf, gamma=0.0007790692366582295, C=113564.03940586245 ...\n", + "[CV] kernel=rbf, gamma=0.0007790692366582295, C=113564.03940586245, total= 11.3s\n", + "[CV] kernel=rbf, gamma=0.3627537294604771, C=108.30488238805073 ......\n", + "[CV] kernel=rbf, gamma=0.0007790692366582295, C=113564.03940586245, total= 11.3s\n", + "[CV] kernel=rbf, gamma=0.3627537294604771, C=108.30488238805073 ......\n", + "[CV] kernel=rbf, gamma=0.0007790692366582295, C=113564.03940586245, total= 10.7s\n", + "[CV] kernel=rbf, gamma=0.3627537294604771, C=108.30488238805073 ......\n", + "[CV] kernel=rbf, gamma=0.0007790692366582295, C=113564.03940586245, total= 10.8s\n", + "[CV] kernel=rbf, gamma=0.3627537294604771, C=108.30488238805073 ......\n", + "[CV] kernel=rbf, gamma=0.3627537294604771, C=108.30488238805073, total= 10.8s\n", + "[CV] kernel=rbf, gamma=0.3627537294604771, C=108.30488238805073 ......\n", + "[CV] kernel=rbf, gamma=0.3627537294604771, C=108.30488238805073, total= 10.7s\n", + "[CV] kernel=linear, gamma=0.023332523598323388, 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gamma=0.578634378499143, kernel=linear .....\n", - "[CV] C=1888.9148509967113, gamma=0.739678838777267, kernel=linear, total= 6.7s\n", - "[CV] C=55.53838911232773, gamma=0.578634378499143, kernel=linear .....\n", - "[CV] C=1888.9148509967113, gamma=0.739678838777267, kernel=linear, total= 6.7s\n", - "[CV] C=55.53838911232773, gamma=0.578634378499143, kernel=linear .....\n", - "[CV] C=1888.9148509967113, gamma=0.739678838777267, kernel=linear, total= 6.6s\n", - "[CV] C=55.53838911232773, gamma=0.578634378499143, kernel=linear .....\n", - "[CV] C=55.53838911232773, gamma=0.578634378499143, kernel=linear, total= 6.2s\n", - "[CV] C=55.53838911232773, gamma=0.578634378499143, kernel=linear .....\n", - "[CV] C=55.53838911232773, gamma=0.578634378499143, kernel=linear, total= 6.2s\n", - "[CV] C=26.714480823948186, gamma=1.0117295509275495, kernel=rbf ......\n", - "[CV] C=55.53838911232773, gamma=0.578634378499143, kernel=linear, total= 6.3s\n", - "[CV] C=26.714480823948186, 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gamma=0.3504567255332862, C=6287.039489427172 ....\n", + "[CV] kernel=linear, gamma=0.3504567255332862, C=6287.039489427172, total= 7.9s\n", + "[CV] kernel=linear, gamma=0.3504567255332862, C=6287.039489427172 ....\n", + "[CV] kernel=linear, gamma=0.3504567255332862, C=6287.039489427172, total= 8.2s\n", + "[CV] kernel=rbf, gamma=1.6279689407405564, C=61217.04421344494 .......\n", + "[CV] kernel=rbf, gamma=0.17580835850006285, C=288.4269299593897, total= 10.8s\n", + "[CV] kernel=rbf, gamma=1.6279689407405564, C=61217.04421344494 .......\n", + "[CV] kernel=linear, gamma=0.3504567255332862, C=6287.039489427172, total= 8.6s\n", + "[CV] kernel=rbf, gamma=1.6279689407405564, C=61217.04421344494 .......\n", + "[CV] kernel=linear, gamma=0.3504567255332862, C=6287.039489427172, total= 8.0s\n", + "[CV] kernel=rbf, gamma=1.6279689407405564, C=61217.04421344494 .......\n", + "[CV] kernel=rbf, gamma=1.6279689407405564, C=61217.04421344494, total= 38.7s\n", + "[CV] kernel=rbf, gamma=1.6279689407405564, C=61217.04421344494 .......\n", + "[CV] kernel=rbf, gamma=1.6279689407405564, C=61217.04421344494, total= 44.3s\n", + "[CV] kernel=rbf, gamma=2.147979593060577, C=926.9787684096649 ........\n", + "[CV] kernel=rbf, gamma=1.6279689407405564, C=61217.04421344494, total= 41.7s\n", + "[CV] kernel=rbf, gamma=2.147979593060577, C=926.9787684096649 ........\n", + "[CV] kernel=rbf, gamma=1.6279689407405564, C=61217.04421344494, total= 42.3s\n", + "[CV] kernel=rbf, gamma=2.147979593060577, C=926.9787684096649 ........\n", + "[CV] kernel=rbf, gamma=2.147979593060577, C=926.9787684096649, total= 11.2s\n", + "[CV] kernel=rbf, gamma=2.147979593060577, C=926.9787684096649 ........\n", + "[CV] kernel=rbf, gamma=2.147979593060577, C=926.9787684096649, total= 11.1s\n", + "[CV] kernel=rbf, gamma=2.147979593060577, C=926.9787684096649 ........\n", + "[CV] kernel=rbf, gamma=2.147979593060577, C=926.9787684096649, total= 11.1s\n", + "[CV] kernel=linear, gamma=2.2642426492862313, C=33946.157064934 ......\n", + "[CV] kernel=rbf, gamma=2.147979593060577, C=926.9787684096649, total= 11.1s\n", + "[CV] kernel=linear, gamma=2.2642426492862313, C=33946.157064934 ......\n", + "[CV] kernel=rbf, gamma=1.6279689407405564, C=61217.04421344494, total= 39.2s\n", + "[CV] kernel=linear, gamma=2.2642426492862313, C=33946.157064934 ......\n", + "[CV] kernel=rbf, gamma=2.147979593060577, C=926.9787684096649, total= 11.1s\n", + "[CV] kernel=linear, gamma=2.2642426492862313, C=33946.157064934 ......\n", + "[CV] kernel=linear, gamma=2.2642426492862313, C=33946.157064934, total= 16.3s\n", + "[CV] kernel=linear, gamma=2.2642426492862313, C=33946.157064934 ......\n", + "[CV] kernel=linear, gamma=2.2642426492862313, C=33946.157064934, total= 14.7s\n", + "[CV] kernel=linear, gamma=0.3176359085304841, C=84789.82947739525 ....\n", + "[CV] kernel=linear, gamma=2.2642426492862313, C=33946.157064934, total= 15.9s\n", + "[CV] kernel=linear, gamma=0.3176359085304841, C=84789.82947739525 ....\n", + "[CV] kernel=linear, gamma=2.2642426492862313, C=33946.157064934, total= 16.6s\n", + "[CV] kernel=linear, gamma=0.3176359085304841, C=84789.82947739525 ....\n", + "[CV] kernel=linear, gamma=2.2642426492862313, C=33946.157064934, total= 14.8s\n", + "[CV] kernel=linear, gamma=0.3176359085304841, C=84789.82947739525 ....\n", + "[CV] kernel=linear, gamma=0.3176359085304841, C=84789.82947739525, total= 30.1s\n", + "[CV] kernel=linear, gamma=0.3176359085304841, C=84789.82947739525 ....\n", + "[CV] kernel=linear, gamma=0.3176359085304841, C=84789.82947739525, total= 32.9s\n", + "[CV] kernel=linear, gamma=0.3176359085304841, C=84789.82947739525, total= 41.8s\n", + "[CV] kernel=linear, gamma=0.3176359085304841, C=84789.82947739525, total= 43.1s\n", + "[CV] kernel=linear, gamma=0.3176359085304841, C=84789.82947739525, total= 24.2s\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "[Parallel(n_jobs=4)]: Done 250 out of 250 | elapsed: 19.3min finished\n" + "[Parallel(n_jobs=4)]: Done 250 out of 250 | elapsed: 20.5min finished\n" ] }, { @@ -6022,13 +6123,13 @@ " estimator=SVR(C=1.0, cache_size=200, coef0=0.0, degree=3, epsilon=0.1, gamma='auto',\n", " kernel='rbf', max_iter=-1, shrinking=True, tol=0.001, verbose=False),\n", " fit_params=None, iid=True, n_iter=50, n_jobs=4,\n", - " param_distributions={'kernel': ['linear', 'rbf'], 'C': , 'gamma': },\n", + " param_distributions={'kernel': ['linear', 'rbf'], 'gamma': , 'C': },\n", " pre_dispatch='2*n_jobs', random_state=42, refit=True,\n", " return_train_score='warn', scoring='neg_mean_squared_error',\n", " verbose=2)" ] }, - "execution_count": 110, + "execution_count": 114, "metadata": {}, "output_type": "execute_result" } @@ -6037,7 +6138,7 @@ "from sklearn.model_selection import RandomizedSearchCV\n", "from scipy.stats import expon, reciprocal\n", "\n", - "# see https://docs.scipy.org/doc/scipy-0.19.0/reference/stats.html\n", + "# see https://docs.scipy.org/doc/scipy/reference/stats.html\n", "# for `expon()` and `reciprocal()` documentation and more probability distribution functions.\n", "\n", "# Note: gamma is ignored when kernel is \"linear\"\n", @@ -6063,16 +6164,16 @@ }, { "cell_type": "code", - "execution_count": 111, + "execution_count": 115, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "54767.99053704408" + "54767.99053704409" ] }, - "execution_count": 111, + "execution_count": 115, "metadata": {}, "output_type": "execute_result" } @@ -6092,7 +6193,7 @@ }, { "cell_type": "code", - "execution_count": 112, + "execution_count": 116, "metadata": {}, "outputs": [ { @@ -6101,7 +6202,7 @@ "{'C': 157055.10989448498, 'gamma': 0.26497040005002437, 'kernel': 'rbf'}" ] }, - "execution_count": 112, + "execution_count": 116, "metadata": {}, "output_type": "execute_result" } @@ -6126,14 +6227,14 @@ }, { "cell_type": "code", - "execution_count": 113, + "execution_count": 117, "metadata": {}, "outputs": [ { "data": { - "image/png": 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\n", 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\n", 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" ] }, "metadata": {}, @@ -6162,14 +6263,14 @@ }, { "cell_type": "code", - "execution_count": 114, + "execution_count": 118, "metadata": {}, "outputs": [ { "data": { - "image/png": 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\n", 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\n", 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" ] }, "metadata": {}, @@ -6212,7 +6313,7 @@ }, { "cell_type": "code", - "execution_count": 115, + "execution_count": 119, "metadata": {}, "outputs": [], "source": [ @@ -6248,7 +6349,7 @@ }, { "cell_type": "code", - "execution_count": 116, + "execution_count": 120, "metadata": {}, "outputs": [], "source": [ @@ -6264,7 +6365,7 @@ }, { "cell_type": "code", - "execution_count": 117, + "execution_count": 121, "metadata": {}, "outputs": [ { @@ -6273,7 +6374,7 @@ "array([ 0, 1, 7, 9, 12])" ] }, - "execution_count": 117, + "execution_count": 121, "metadata": {}, "output_type": "execute_result" } @@ -6285,7 +6386,7 @@ }, { "cell_type": "code", - "execution_count": 118, + "execution_count": 122, "metadata": {}, "outputs": [ { @@ -6295,7 +6396,7 @@ " 'INLAND'], dtype='