From e85961f369dac0750275197e64c8fd104ca29623 Mon Sep 17 00:00:00 2001 From: rasbt Date: Fri, 10 May 2019 19:31:36 -0500 Subject: [PATCH 1/2] improved compat of seq feature selector and nested gridsearch --- .../SequentialFeatureSelector.ipynb | 394 +++++++++++------- .../sequential_feature_selector.py | 32 +- 2 files changed, 264 insertions(+), 162 deletions(-) diff --git a/docs/sources/user_guide/feature_selection/SequentialFeatureSelector.ipynb b/docs/sources/user_guide/feature_selection/SequentialFeatureSelector.ipynb index 498c3a7d4..db157a748 100644 --- a/docs/sources/user_guide/feature_selection/SequentialFeatureSelector.ipynb +++ b/docs/sources/user_guide/feature_selection/SequentialFeatureSelector.ipynb @@ -21,15 +21,15 @@ "output_type": "stream", "text": [ "Sebastian Raschka \n", - "last updated: 2018-05-06 \n", + "last updated: 2019-05-10 \n", "\n", - "CPython 3.6.4\n", - "IPython 6.2.1\n", + "CPython 3.7.1\n", + "IPython 7.4.0\n", "\n", - "matplotlib 2.1.2\n", - "numpy 1.12.1\n", - "scipy 1.0.0\n", - "mlxtend 0.13.0dev0\n" + "matplotlib 3.0.3\n", + "numpy 1.16.2\n", + "scipy 1.2.1\n", + "mlxtend 0.16.0dev0\n" ] } ], @@ -320,16 +320,19 @@ "name": "stderr", "output_type": "stream", "text": [ + "[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.\n", "[Parallel(n_jobs=1)]: Done 1 out of 1 | elapsed: 0.0s remaining: 0.0s\n", "[Parallel(n_jobs=1)]: Done 4 out of 4 | elapsed: 0.0s finished\n", "\n", - "[2018-05-06 12:49:16] Features: 1/3 -- score: 0.96[Parallel(n_jobs=1)]: Done 1 out of 1 | elapsed: 0.0s remaining: 0.0s\n", + "[2019-05-10 19:29:31] Features: 1/3 -- score: 0.96[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.\n", + "[Parallel(n_jobs=1)]: Done 1 out of 1 | elapsed: 0.0s remaining: 0.0s\n", "[Parallel(n_jobs=1)]: Done 3 out of 3 | elapsed: 0.0s finished\n", "\n", - "[2018-05-06 12:49:16] Features: 2/3 -- score: 0.973333333333[Parallel(n_jobs=1)]: Done 1 out of 1 | elapsed: 0.0s remaining: 0.0s\n", + "[2019-05-10 19:29:31] Features: 2/3 -- score: 0.9733333333333334[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.\n", + "[Parallel(n_jobs=1)]: Done 1 out of 1 | elapsed: 0.0s remaining: 0.0s\n", "[Parallel(n_jobs=1)]: Done 2 out of 2 | elapsed: 0.0s finished\n", "\n", - "[2018-05-06 12:49:16] Features: 3/3 -- score: 0.973333333333" + "[2019-05-10 19:29:31] Features: 3/3 -- score: 0.9733333333333334" ] } ], @@ -362,17 +365,17 @@ { "data": { "text/plain": [ - "{1: {'avg_score': 0.95999999999999996,\n", - " 'cv_scores': array([ 0.96]),\n", - " 'feature_idx': (3,),\n", + "{1: {'feature_idx': (3,),\n", + " 'cv_scores': array([0.96]),\n", + " 'avg_score': 0.96,\n", " 'feature_names': ('3',)},\n", - " 2: {'avg_score': 0.97333333333333338,\n", - " 'cv_scores': array([ 0.97333333]),\n", - " 'feature_idx': (2, 3),\n", + " 2: {'feature_idx': (2, 3),\n", + " 'cv_scores': array([0.97333333]),\n", + " 'avg_score': 0.9733333333333334,\n", " 'feature_names': ('2', '3')},\n", - " 3: {'avg_score': 0.97333333333333338,\n", - " 'cv_scores': array([ 0.97333333]),\n", - " 'feature_idx': (1, 2, 3),\n", + " 3: {'feature_idx': (1, 2, 3),\n", + " 'cv_scores': array([0.97333333]),\n", + " 'avg_score': 0.9733333333333334,\n", " 'feature_names': ('1', '2', '3')}}" ] }, @@ -401,32 +404,35 @@ "name": "stderr", "output_type": "stream", "text": [ + "[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.\n", "[Parallel(n_jobs=1)]: Done 1 out of 1 | elapsed: 0.0s remaining: 0.0s\n", "[Parallel(n_jobs=1)]: Done 4 out of 4 | elapsed: 0.0s finished\n", "\n", - "[2018-05-06 12:49:16] Features: 1/3 -- score: 0.96[Parallel(n_jobs=1)]: Done 1 out of 1 | elapsed: 0.0s remaining: 0.0s\n", + "[2019-05-10 19:29:31] Features: 1/3 -- score: 0.96[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.\n", + "[Parallel(n_jobs=1)]: Done 1 out of 1 | elapsed: 0.0s remaining: 0.0s\n", "[Parallel(n_jobs=1)]: Done 3 out of 3 | elapsed: 0.0s finished\n", "\n", - "[2018-05-06 12:49:16] Features: 2/3 -- score: 0.973333333333[Parallel(n_jobs=1)]: Done 1 out of 1 | elapsed: 0.0s remaining: 0.0s\n", + "[2019-05-10 19:29:31] Features: 2/3 -- score: 0.9733333333333334[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.\n", + "[Parallel(n_jobs=1)]: Done 1 out of 1 | elapsed: 0.0s remaining: 0.0s\n", "[Parallel(n_jobs=1)]: Done 2 out of 2 | elapsed: 0.0s finished\n", "\n", - "[2018-05-06 12:49:16] Features: 3/3 -- score: 0.973333333333" + "[2019-05-10 19:29:31] Features: 3/3 -- score: 0.9733333333333334" ] }, { "data": { "text/plain": [ - "{1: {'avg_score': 0.95999999999999996,\n", - " 'cv_scores': array([ 0.96]),\n", - " 'feature_idx': (3,),\n", + "{1: {'feature_idx': (3,),\n", + " 'cv_scores': array([0.96]),\n", + " 'avg_score': 0.96,\n", " 'feature_names': ('petal width',)},\n", - " 2: {'avg_score': 0.97333333333333338,\n", - " 'cv_scores': array([ 0.97333333]),\n", - " 'feature_idx': (2, 3),\n", + " 2: {'feature_idx': (2, 3),\n", + " 'cv_scores': array([0.97333333]),\n", + " 'avg_score': 0.9733333333333334,\n", " 'feature_names': ('petal length', 'petal width')},\n", - " 3: {'avg_score': 0.97333333333333338,\n", - " 'cv_scores': array([ 0.97333333]),\n", - " 'feature_idx': (1, 2, 3),\n", + " 3: {'feature_idx': (1, 2, 3),\n", + " 'cv_scores': array([0.97333333]),\n", + " 'avg_score': 0.9733333333333334,\n", " 'feature_names': ('sepal width', 'petal length', 'petal width')}}" ] }, @@ -510,7 +516,7 @@ { "data": { "text/plain": [ - "0.97333333333333338" + "0.9733333333333334" ] }, "execution_count": 9, @@ -549,22 +555,22 @@ "Sequential Forward Selection (k=3):\n", "(1, 2, 3)\n", "CV Score:\n", - "0.972756410256\n", + "0.9727564102564104\n", "\n", "Sequential Backward Selection (k=3):\n", "(1, 2, 3)\n", "CV Score:\n", - "0.972756410256\n", + "0.9727564102564104\n", "\n", "Sequential Forward Floating Selection (k=3):\n", "(1, 2, 3)\n", "CV Score:\n", - "0.972756410256\n", + "0.9727564102564104\n", "\n", "Sequential Backward Floating Selection (k=3):\n", "(1, 2, 3)\n", "CV Score:\n", - "0.972756410256\n" + "0.9727564102564104\n" ] } ], @@ -704,7 +710,7 @@ " 1\n", " 0.952991\n", " 0.0660624\n", - " [0.974358974359, 0.948717948718, 0.88888888888...\n", + " [0.9743589743589743, 0.9487179487179487, 0.888...\n", " (3,)\n", " (3,)\n", " 0.0412122\n", @@ -714,7 +720,7 @@ " 2\n", " 0.959936\n", " 0.0494801\n", - " [0.974358974359, 0.948717948718, 0.91666666666...\n", + " [0.9743589743589743, 0.9487179487179487, 0.916...\n", " (2, 3)\n", " (2, 3)\n", " 0.0308676\n", @@ -724,7 +730,7 @@ " 3\n", " 0.972756\n", " 0.0315204\n", - " [0.974358974359, 1.0, 0.944444444444, 0.972222...\n", + " [0.9743589743589743, 1.0, 0.9444444444444444, ...\n", " (1, 2, 3)\n", " (1, 2, 3)\n", " 0.0196636\n", @@ -736,9 +742,9 @@ ], "text/plain": [ " avg_score ci_bound cv_scores \\\n", - "1 0.952991 0.0660624 [0.974358974359, 0.948717948718, 0.88888888888... \n", - "2 0.959936 0.0494801 [0.974358974359, 0.948717948718, 0.91666666666... \n", - "3 0.972756 0.0315204 [0.974358974359, 1.0, 0.944444444444, 0.972222... \n", + "1 0.952991 0.0660624 [0.9743589743589743, 0.9487179487179487, 0.888... \n", + "2 0.959936 0.0494801 [0.9743589743589743, 0.9487179487179487, 0.916... \n", + "3 0.972756 0.0315204 [0.9743589743589743, 1.0, 0.9444444444444444, ... \n", "\n", " feature_idx feature_names std_dev std_err \n", "1 (3,) (3,) 0.0412122 0.0237939 \n", @@ -802,37 +808,37 @@ " \n", " \n", " \n", - " 3\n", - " 0.972756\n", - " 0.0315204\n", - " [0.974358974359, 1.0, 0.944444444444, 0.972222...\n", - " (1, 2, 3)\n", - " (1, 2, 3)\n", - " 0.0196636\n", - " 0.0113528\n", - " \n", - " \n", " 4\n", " 0.952991\n", " 0.0372857\n", - " [0.974358974359, 0.948717948718, 0.91666666666...\n", + " [0.9743589743589743, 0.9487179487179487, 0.916...\n", " (0, 1, 2, 3)\n", " (0, 1, 2, 3)\n", " 0.0232602\n", " 0.0134293\n", " \n", + " \n", + " 3\n", + " 0.972756\n", + " 0.0315204\n", + " [0.9743589743589743, 1.0, 0.9444444444444444, ...\n", + " (1, 2, 3)\n", + " (1, 2, 3)\n", + " 0.0196636\n", + " 0.0113528\n", + " \n", " \n", "\n", "" ], "text/plain": [ " avg_score ci_bound cv_scores \\\n", - "3 0.972756 0.0315204 [0.974358974359, 1.0, 0.944444444444, 0.972222... \n", - "4 0.952991 0.0372857 [0.974358974359, 0.948717948718, 0.91666666666... \n", + "4 0.952991 0.0372857 [0.9743589743589743, 0.9487179487179487, 0.916... \n", + "3 0.972756 0.0315204 [0.9743589743589743, 1.0, 0.9444444444444444, ... \n", "\n", " feature_idx feature_names std_dev std_err \n", - "3 (1, 2, 3) (1, 2, 3) 0.0196636 0.0113528 \n", - "4 (0, 1, 2, 3) (0, 1, 2, 3) 0.0232602 0.0134293 " + "4 (0, 1, 2, 3) (0, 1, 2, 3) 0.0232602 0.0134293 \n", + "3 (1, 2, 3) (1, 2, 3) 0.0196636 0.0113528 " ] }, "execution_count": 12, @@ -895,37 +901,37 @@ " \n", " \n", " \n", - " 3\n", - " 0.972756\n", - " 0.0242024\n", - " [0.974358974359, 1.0, 0.944444444444, 0.972222...\n", - " (1, 2, 3)\n", - " (1, 2, 3)\n", - " 0.0196636\n", - " 0.0113528\n", - " \n", - " \n", " 4\n", " 0.952991\n", " 0.0286292\n", - " [0.974358974359, 0.948717948718, 0.91666666666...\n", + " [0.9743589743589743, 0.9487179487179487, 0.916...\n", " (0, 1, 2, 3)\n", " (0, 1, 2, 3)\n", " 0.0232602\n", " 0.0134293\n", " \n", + " \n", + " 3\n", + " 0.972756\n", + " 0.0242024\n", + " [0.9743589743589743, 1.0, 0.9444444444444444, ...\n", + " (1, 2, 3)\n", + " (1, 2, 3)\n", + " 0.0196636\n", + " 0.0113528\n", + " \n", " \n", "\n", "" ], "text/plain": [ " avg_score ci_bound cv_scores \\\n", - "3 0.972756 0.0242024 [0.974358974359, 1.0, 0.944444444444, 0.972222... \n", - "4 0.952991 0.0286292 [0.974358974359, 0.948717948718, 0.91666666666... \n", + "4 0.952991 0.0286292 [0.9743589743589743, 0.9487179487179487, 0.916... \n", + "3 0.972756 0.0242024 [0.9743589743589743, 1.0, 0.9444444444444444, ... \n", "\n", " feature_idx feature_names std_dev std_err \n", - "3 (1, 2, 3) (1, 2, 3) 0.0196636 0.0113528 \n", - "4 (0, 1, 2, 3) (0, 1, 2, 3) 0.0232602 0.0134293 " + "4 (0, 1, 2, 3) (0, 1, 2, 3) 0.0232602 0.0134293 \n", + "3 (1, 2, 3) (1, 2, 3) 0.0196636 0.0113528 " ] }, "execution_count": 13, @@ -960,29 +966,35 @@ "name": "stderr", "output_type": "stream", "text": [ + "[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.\n", "[Parallel(n_jobs=1)]: Done 1 out of 1 | elapsed: 0.0s remaining: 0.0s\n", "[Parallel(n_jobs=1)]: Done 4 out of 4 | elapsed: 0.0s finished\n", "\n", - "[2018-05-06 12:49:18] Features: 1/4 -- score: 0.96[Parallel(n_jobs=1)]: Done 1 out of 1 | elapsed: 0.0s remaining: 0.0s\n", + "[2019-05-10 19:29:33] Features: 1/4 -- score: 0.96[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.\n", + "[Parallel(n_jobs=1)]: Done 1 out of 1 | elapsed: 0.0s remaining: 0.0s\n", "[Parallel(n_jobs=1)]: Done 3 out of 3 | elapsed: 0.0s finished\n", "\n", - "[2018-05-06 12:49:18] Features: 2/4 -- score: 0.966666666667[Parallel(n_jobs=1)]: Done 1 out of 1 | elapsed: 0.0s remaining: 0.0s\n", + "[2019-05-10 19:29:33] Features: 2/4 -- score: 0.9666666666666668[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.\n", + "[Parallel(n_jobs=1)]: Done 1 out of 1 | elapsed: 0.0s remaining: 0.0s\n", "[Parallel(n_jobs=1)]: Done 2 out of 2 | elapsed: 0.0s finished\n", "\n", - "[2018-05-06 12:49:18] Features: 3/4 -- score: 0.953333333333[Parallel(n_jobs=1)]: Done 1 out of 1 | elapsed: 0.0s remaining: 0.0s\n", + "[2019-05-10 19:29:33] Features: 3/4 -- score: 0.9533333333333334[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.\n", + "[Parallel(n_jobs=1)]: Done 1 out of 1 | elapsed: 0.0s remaining: 0.0s\n", "[Parallel(n_jobs=1)]: Done 1 out of 1 | elapsed: 0.0s finished\n", "\n", - "[2018-05-06 12:49:18] Features: 4/4 -- score: 0.973333333333" + "[2019-05-10 19:29:33] Features: 4/4 -- score: 0.9733333333333334" ] }, { "data": { - "image/png": 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cnGVKU5bmxgyNmTQNmZSfD1cX+pQyOrpp7+imszsfr0VGJp3u+R6nxvB764FkEAZMndQwpifJVS+TTvWp3soXjJ2dObbu6Or51ZZKiclNWaY2Z2lpDO0ujR5cXI0VSxlduRAwdnR0096Z66mxSJYymhvq77JdfzlybpSkU6K5IUNzovq4UDC6cwWefnEn+fhHnJJCg35TlklNMbhk0/7jwg1ZsV2vozuUMnZ0drN9V7KUAelUCBiTm8a2lDEUHkicS0ilRGMqBIqigoXg8szWDnKbd/ZUR7Y0ZpjcnGFKU0NPcPHuyK4oWcrYEaul2jtzWCG0kSoVShnZOi1lDMX4zr1zoyAl9QSKop4eY9s7eWbLLmI/BxqzaaY0eY+xiaT4XejsLrCrK8eOzm7ad3XTUa6U0ZgltQf+2PBA4tww9NtjLFdgy47OXj3GGrNppsRG/aaGDA0Z7zE2XuULRmd3nq5cPnat7epVykChh2A2k2L6OC9lDMXEOVLnRkE2E6oqknL5Att2dvP8tg4EFICGYo+x5izNDRmasqH3jTfq14dkKaOjK0d7opRRtKeXMoai1s9sXwx8E0gD3zezy0rWH0B4nO6+wGbgg2bWJumvga8nNn0lcJqZ3SjpauDNwNa47iwzW13L43CuGsUeYy2J7sj5QqFXjzER2memNDUwuTlLS2PGe4yNknzB6Mrl6eyOpYzYnmEF63Uf0kQrZQxFzT4VSWngCuBtQBuwUtJyM3s4sdlXgWvM7IeS3gJcCpxpZrcDh8d09gLWAbck9vusmd1Qq7w7V2vpVIrmhlSfHmMd3Tm27erq6fYZuiNnmNLcQEtjKLk0eI+xYUmWMjq7Y8BIlDLMQtD3UsbQ1TK8Hg2sM7P1AJKuA04CkoFkAXBunL4duLFMOu8Bfm1mO2uYV+fGXColmhoyNCWDS+wx9pctu8gXCmE7iebGDFOas0xuzHqPsTLKlTJ2dHSTz1tx/J89upSx/OdZ/u3SJp7edByzZsNll8IZZ9Tu/Wr5Cc4CNiTm24BjSrZ5ADiVUP31N8AUSXub2QuJbU4D/q1kv0skXQTcBnzOzDpHNOfO1YmBeow9v62Dv+R39vQMam7IMLU53OvS1JCmMTMxeoyFgBFKGe0doQG82GOqkChlTJogpYzlP89y4Web6dgVjrVtAyxZEtbVKpjUMpCUO2OlI4ycD1wu6Szgd8BGINeTgLQf8GpgRWKfC4C/AA3AMuCfgKV93lxaAiwBmDlzJq2trcM6iFzHLp5Ye++w9nW10dmxk/UPrRrrbNSd5DhwRZJIp8L4aintHtKnFtrb24f9d1ap4hh3uwcmtV5XleT4chPVvyx9XU8QKdq5E847r4NZs+6uyXvWMpC0AXMS87OBTckNzGwTcAqApMnAqWa2NbHJ+4BfmFl3Yp+n42SnpKsIwagPM1tGCDQsXLjQFi1aNKyDuPmW3zLv4KO8TrqOrH9oFQcdsnCsszEu5PIFunLhprhiySWbSTG1KcukRI+xkeiO3NraynD/zkr1KWV0dNHRlbwvI3S/zqZTE6KUMZBCAZ5Yn+KB+9M8cF+aZ58tP9Lvs882jdj5KVXLQLISmC/pQEJJ4zTgA8kNJO0DbDazAqGkcWVJGqfH5cl99jOzpxW6spwMPFSj/Ds37vXXY6y9Mw5gGRv10+nQY2xKS5aWeCPlaAxgmWzL2NWVY9uu2JaRGBW7GDCmtaS9Bxuw+QXxwH3pnsDx4AMZtm0Nn8ukSUZDI3SVqeyfO7d2eapZIDGznKRPEqql0sCVZrZW0lJglZktBxYBl0oyQtXWJ4r7S5pHKNHcUZL0tZL2JZReVwMfq9UxOLcnKtdjLF/sMfZCcXRkoRShx1jTyPQY68rl6crF+zI6crR3dLOrK9fzfIR0HDJkorRlVKKzAx5+aHfQeOD+DG1PhXavVMp4xSsLLH5XN4cdkeOwI/O8bH6BX93Uu40EoKUFLrmkdvmsaXcFM7sZuLlk2UWJ6RuAst14zewJQoN96fK3jGwunXPplEj322Os9wCWLcUeY3EAy9JHHpeWMrbvCvdl5Au7H5K2u5TR4KWMqLSKas39af7n4TS5XPh8XrpfgcOOzPOBD3Vy2JF5Dj40T0tL33ROPCW0BIReW4q9tjRue20558axgXqMPbetg6df7N1jrCuX56GnNtPRlQ+lmkQpo6Ux692TSxSrqNbcH0oca1b3rqJ69eF5/u5jnRx2RJ5Dj8gz86WVP6DqxFO6OfGUbv60ZiWve8MbmT6psVaHAXggcePI7r7xb2G//Y3PXNDR8+vLjY5yY4yZGbm8kS+E4DO1JeuljBLDqaJKj6Ph2DyQuHGhtG/8po3iws82A3gwGWOSyGZC9+LSccYmopGqohpPPJC4UZfLwbat4sXNYuuW8NqyRWx5MU6/uHs6rEuxcYMw6/0rt2OX+Px5zdy3Ms3ceQXmHVjggAMLzJlboKG2JXnnemx+QaFq6r6Rr6IaLzyQuGEbTkDY8qJo395/tUcqZUydZkyfYUybbuy1j3HQ/BxtT2XLbt/ZCb+8saHnD7eYxv6zjLnz8sw7qOBBxo2Ywaqo5o/zKqrh8kDiRiUg7L1vCAjTp4f56TMsTM9ITE8vMGUqpMrUjqy6O8OmjX3fb/9ZRuvK7by4WTz1RIonHk/xxPpUz7QHGTdcySqqYoP4ow+n6e7ec6uohssDyR5kPASE4frMBR19+sY3NYcGd4AZexkz9spz2JH5Pvt6kHGVqKSK6m//fs+uohouDyR1aE8OCMNV2jd+KL22ahFkDjiwwAHzPMiMV6VVVGtWp9nwZKiDmshVVMPlgaQf114Ln/88PPXUcey3f4HPXNA55N5Box0QisvqOSBUo9g3fiTH2hosyDz5eIonn/AgM54VCvDk46lEu0b5KqrTz+ya8FVUw+WBpIxrrw3DLu/cCSA2bUxz4fnNPPMXcdRr8h4QJohikDn8KA8y40mlVVSHHpHnMK+iGhEeSMr4/OeLQWS3jg7xr19u7rNtMiBMn9F/QJi+VyI4eEAY9zzI1IdkFVWxQdyrqEafB5IynnqqvzXGD36y0wOCG9BIBpkDDsyHAONBBrPeN/p5FVVf+YLR2Z2nK5fDCM9mGY2bRD2QlDF3Ljz5ZN/l+88y3rgo13eFcxXqL8iYwZYXPcgklVZRPfhAmq1bwkXRq6iCfKFAR1ee7nwBLASNaZMamNY8iZbGDHe3ZZjUWP4erJHkgaSMSy5JtpEEya6mzo00aWIHmc4OeGRtuldpo7SK6h3vzE34KqpcvhBLHIWe0sb0SY1Ma2mguTFD4yg8Q6YcDyRlFIdbDr22bNi9tpwbCXtakPEqqsp1x8CRyxcQkMmk2GtyI1NbGmiODyCrBx5I+nHGGeF18y238bJDFvqjdl1dqocgM9iozMkqqjWrw6u0iuqsJeHu8IlaRVWUDBwYNDak2TsGjpbGzIg8ErkWPJA4t4caapApTg8lyDxwX5qL/3fvUZn/93nN/P72DPk8XkU1iO5cgY7uPIVCgQLQnE2z95RGprU00tyQrtvAUcoDiXMT0EgFmZ7n5CZ0dYqbftbgVVRlhCdHFsgXCgA0ZdO8ZFoTU5obxlXgKOWBxDnXy1CCzGfP6XtvVUjD+N2920cju3Wt+MjhfD48MbK5IcPMYuBozJBN7xn3DtQ0kEhaDHwTSAPfN7PLStYfAFwJ7AtsBj5oZm1xXR54MG76lJmdGJcfCFwH7AXcB5xpZl21PA7nXFAaZL5+WVPZUZn323/itXMUH0Pc0ZWnYOH4JzVmeen0FiY3Z2lu2HMCR6maHZWkNHAFcDywADhd0oKSzb4KXGNmhwJLgUsT63aZ2eHxdWJi+VeAr5vZfOBF4OxaHYNzbmCfuaCDpubeQWOidJU3Czf/bdvZxZYdnWzd2YUk9t+rhVfOmsERB+7Dgjkz2H+vSUxtbthjgwjUtkRyNLDOzNYDSLoOOAl4OLHNAuDcOH07cONACSp0kH4L8IG46IfAxcB3RizXzrmKVTMq83hjZnTlQq+qQsFQKpQ4Zu09iUmNWVoa06Qn6DAXtQwks4ANifk24JiSbR4ATiVUf/0NMEXS3mb2AtAkaRWQAy4zsxuBvYEtZpZLpDmr3JtLWgIsAZg5cyatra3DOohcxy6eWHvvsPZ1tdHZsZP1D60a62y46JBXwJVXhfPS2BRa09c/NMaZGiFm9FRTAaRTIp0SqZRISbQDz4xd9gbV3t4+7GvfUNQykJS78aK04vR84HJJZwG/AzYSAgfAXDPbJOkg4L8kPQhsqyDNsNBsGbAMYOHChbZo0aIhHwDAzbf8lnkHH+X3kdSRkRxG3o2c8X5eCmZ0defp6A4dDISY0pJleksDk5uyNDVkSKfG13WgtbWV4V77hqKWgaQNmJOYnw1sSm5gZpuAUwAkTQZONbOtiXWY2XpJrcARwM+A6ZIysVTSJ03nnKtEIbZxdHbnwSCVElOas8yc3sKkxsy4DBxjpZaBZCUwP/ay2gicxu62DQAk7QNsNrMCcAGhBxeSZgA7zawzbvN64F/MzCTdDryH0HPrw8BNNTwG59weolAwOnN5urrzGJCSmNrcwH4zWmhpzNDckPGah2GqWSAxs5ykTwIrCN1/rzSztZKWAqvMbDmwCLhUkhGqtj4Rd38V8B+SCoSeZZeZWbGR/p+A6yR9Gbgf+EGtjsHVj3yhQL5g5PJG3oztu7pISbHOOkUq1l07V1QMHMkSx7RJDew/o4WWxixNDWkPHCOkpveRmNnNwM0lyy5KTN8A3FBmvzuBV/eT5npCjzC3h8gXrHegKIQB6szCfQtGGOW0OZumpTHLs+kU++81ia7ucJdw6LufC0NpR2J341kx2KRjA2mxsdTtWXqexRHbOFJpMb0lBI5JTVkasx44asXvbHc1VSgYuRgkQqAo9Nkmm07RmE3T0pyhMZOmMZsmm06RSafIpEUmnep1AXgyneKl0/uOtVGw8B75fIFc4v26cmHY7a7uAp25PB2dOQoFMKxXwAF6Ak4x2BSDj6s/ux/iFKqqsqkU01qyTNs7PIujKZsekyHVJyIPJG7YCmbk870DhcWukkIUzMikRVM2w+TGNE0NGRoyKbKZNJm0eoLFSF2oUxKpmG4lec/lCzHQhekQdMKrM5cPA+p15cgXLHZBFBbDjqRE1ZoHndEw2EOcGj1wjBkPJK6scr/uzXr/gk+lRGMm3fPrrymbDkEipViaSNVtu0VKqniAvHJVb8WgExpvQ6lnV2eegu3u9178nJQMOIlpv+gNLPkQp5REJq26eIiT68sDyQRkFn6FF+Iv8XzBKFiB5K0/KUFjNkNTNkNjQ4qmbChNFANENq0JcxdvuPAPLeiEgBOmu3MFuopVbN15OnMFdnTmQxtQopQDsVSV2h1sisFnIlwwc/kwpHqx+jObSTFjcgwcdfQQJ9eXB5I9jBVLEok2goIZJBquJXraIqY2Z2lsSNOYSYcgkShNuKErBp2GCv6ykqWcXKFAPh8G/SsGnK6eqrYQdIqKpcLxHnRKH+LUkN39ECcPHOOLB5JxpvTXbr7Q+8Z+CRpikJiSDf8Xg0Q2LdIxWIyXi82eLDTsM2jQMbPYppNoj8obXfnQtbU7tusUf81bmbEekr3Vkr3YRlN3rArM5QqYwrM49p7SyNTm+n76nxucB5I60rtapJBo5A1dYdHubrBhyIYUDT1BItVTovAgsWeRRFoinYJGBr7YFkukueIPjRh8ihfxYu+1ju5c76CTeD5V77ac4d+jU+4hTvtOHf8PcXJ9eSAZJclusMVAUVSsqih2g53aEhqvG7NpMqn+u8E6V0qxUTpcowcPOsWOFLs7VcTqtNiW05XL7+4pVXwPyt+jYwbbd3WFhzgBzY0ZXjKtianNDTR54NijeSAZAYVie0RPoEj+0YXG1EwqBIlkN9iGTJp0DbrBOlcJSWTTIpsGBmmPqOQeHcSEeIiT68sDSQWS9xvkY28nQa+qgMZsmW6wafWUKOq1G6xzlajkHp2Nj6XZf69Jo5grVy88kAwirVBkb2rI0JhN0ZzNkM2ENon0BOsG65xz5XggGURjNs0hc/ca62w451zd8p/SzjnnquKBxDnnXFU8kDjnnKsS1DDSAAAWtUlEQVSKBxLnnHNV8UDinHOuKh5InHPOVaWmgUTSYkmPSlon6XNl1h8g6TZJayS1Spodlx8u6S5Ja+O69yf2uVrS45JWx9fhtTwG55xzA6s4kEhqlvRXQ9g+DVwBHA8sAE6XtKBks68C15jZocBS4NK4fCfwITM7GFgMfEPS9MR+nzWzw+NrdaV5cs45N/IqCiSS3g2sBn4T5w+XtHyQ3Y4G1pnZejPrAq4DTirZZgFwW5y+vbjezB4zsz/F6U3As8C+leTVOefc6Kr0zvaLCYGhFcDMVkuaN8g+s4ANifk24JiSbR4ATgW+CfwNMEXS3mb2QnEDSUcDDcCfE/tdIukiQhD6nJl1lr65pCXAEoCZM2fS2to6SHbLa29vH/a+rjb8nNQnPy/1Z7TOSaWBJGdmW4f4nItyG5c+cud84HJJZwG/AzYCuZ4EpP2AHwEfNrPikLoXAH8hBJdlwD8RqsV6v5HZsriehQsX2qJFi4aS9x6tra0Md19XG35O6pOfl/ozWuek0kDykKQPAGlJ84FPAXcOsk8bMCcxPxvYlNwgVludAiBpMnCqmW2N81OBXwEXmtndiX2ejpOdkq4iBCPnnHNjpNLG9nOAg4FO4CfAVuAfBtlnJTBf0oGSGoDTgF7tKpL2kVTMwwXAlXF5A/ALQkP8f5bss1/8X8DJwEMVHoNzzrkaqKhEYmY7gc/HV0XMLCfpk8AKwqParjSztZKWAqvMbDmwCLhUkhGqtj4Rd38f8CZg71jtBXBW7KF1raR9CVVnq4GPVZon55xzI6+iQCLpVuC9ZrYlzs8ArjOzdwy0n5ndDNxcsuyixPQNwA1l9vsx8ON+0nxLJXl2zjk3Oiqt2tqnGEQAzOxF4CW1yZJzzrnxpNJAUpA0tzgj6QD69sByzjk3AVXaa+vzwB8k3RHn30S8R8M559zEVmlj+28kHQkcS2jkPtfMnq9pzpxzzo0LQ3lmeyOwOe6zQBJm9rvaZMs559x4UWmvra8A7wfWAsU7zItddp1zzk1glZZITgb+qtyYVs455ya2SnttrQeytcyIc8658anSEslOYLWk2wjDpABgZp+qSa6cc86NG5UGkuWUjJPlnHPOQeXdf39Y64w455wbnyrttTWf8BjcBUBTcbmZHVSjfDnnnBsnKm1svwr4DuGhU38NXEN44JRzzrkJrtJA0mxmtwEysyfN7GLAR+F1zjlXcWN7R3wA1Z/iM0Y24qP/Oueco/ISyT8ALYRH7B4FnAl8uFaZcs45N35U2mtrZZxsB/62dtlxzjk33lTaa2shYSj5A5L7mNmhNcqXc865caLSqq1rCT23TgXenXgNSNJiSY9KWifpc2XWHyDpNklrJLVKmp1Y92FJf4qvDyeWHyXpwZjmtySpwmNwzjlXA5UGkufMbLmZPR57bT1pZk8OtIOkNHAFcDzh/pPTJS0o2eyrwDWxZLOUcK8KkvYCvggcAxwNfDE+Jx5CN+QlwPz4WlzhMTjnnKuBSnttfVHS94HSsbZ+PsA+RwPrzGw9gKTrgJOAhxPbLADOjdO3AzfG6XcAt5rZ5rjvrcBiSa3AVDO7Ky6/hjAy8a8rPA7nnHMjrNJA8rfAKwkjACefRzJQIJkFbEjMtxFKGEkPEKrLvgn8DTBF0t797DsrvtrKLO9D0hLi44BnzpxJa2vrAFntX3t7+7D3dbXh56Q++XmpP6N1TioNJIeZ2auHmHa5tgsrmT8fuFzSWYSHZG0k3D3f376VpBkWmi0DlgEsXLjQFi1aVFGmS7W2tjLcfV1t+DmpT35e6s9onZNK20juLtO+MZg2YE5ifjawKbmBmW0ys1PM7AhCrzDMbOsA+7bF6X7TdM45N7oqDSRvIDyP5NHYw+pBSWsG2WclMF/SgZIagNMoGYpe0j7xjnmAC4Ar4/QK4O2SZsRG9rcDK8zsaWC7pGNjb60PATdVeAzOOedqoNKqrSH3jDKzXBxOZQWQBq40s7WSlgKrzGw5sAi4VFLx+e+fiPtulvTPhGAEsLTY8A58HLgaaCY0sntDu3POjaFBA0ksMfzKzA4ZauJmdjNwc8myixLTNwA39LPvlewuoSSXrwKGnBfnnHO1MWjVlpkVgAckzR2F/DjnnBtnKq3a2g9YK+keYEdxoZmdWJNcOeecGzcqDSRfqmkunHPOjVuVjv57h6SZwGvionvM7NnaZcs559x4UVH3X0nvA+4B3gu8D/ijpPfUMmPOOefGh0qrtj4PvKZYCpG0L/Bb+ulx5ZxzbuKo9IbEVElV1gtD2Nc559werNISyW8krQB+GuffT8n9Ic455yamAQOJpEYz6zSzz0o6hTBUioBlZvaLUcmhc865ujZYieQu4EhJPzKzMxl42HjnnHMT0GCBpCE+5vZ1sUTSyyAPtnLOOTcBDBZIPgacAUyn7zPaB3uwlXPOuQlgwEBiZn+QdCfQZmaXjFKenHPOjSOVDtr4rlHIi3POuXGo0ntBbpF0anyYlHPOOdej0vtIPgNMAvKSdhG6AJuZTa1Zzpxzzo0LlQ7aOKXWGXHOOTc+VTpooyR9UNIX4vwcSUfXNmvOOefGg0rbSL4NvBb4QJxvB64YbCdJiyU9KmmdpM+VWT9X0u2S7pe0RtIJcfkZklYnXgVJh8d1rTHN4rqXVHgMzjnnaqDSNpJjzOxISfcDmNmLkhoG2kFSmhBs3ga0ASslLTezhxObXQhcb2bfkbSAMH7XPDO7Frg2pvNq4CYzW53Y74z47HbnnHNjrNISSXcMDAY9w8gXBtnnaGCdma03sy7gOuCkkm0MKDbYTwM2lUnndHYPFumcc67OVFoi+RbwC+Alki4B3kMoTQxkFrAhMd8GHFOyzcWErsXnEHqFvbVMOu+nbwC6SlIe+BnwZTOz0p0kLQGWAMycOZPW1tZBsltee3v7sPd1teHnpD75eak/o3VOKu21da2ke4HjCF1/TzazRwbZrdw9J6UX/NOBq83sa5JeC/xI0iHxJkgkHQPsNLOHEvucYWYbJU0hBJIzgWvK5HkZsAxg4cKFtmjRokGPs5zW1laGu6+rDT8n9cnPS/0ZrXMy2DDyTYTxtl4OPAj8h5nlKky7DZiTmJ9N36qrs4HFAGZ2V3y/fYDiQ7ROo6Ray8w2xv+3S/oJoQqtTyBxzjk3OgZrI/khsJAQRI4HvjqEtFcC8yUdGBvmTwOWl2zzFKGUg6RXAU3Ac3E+RXhG/HXFjSVlJO0Tp7OEoVsewjnn3JgZrGprgZm9GkDSD4B7Kk3YzHKSPgmsANLAlWa2VtJSYJWZLQfOA74n6VxCtddZifaONxEGi1yfSLYRWBGDSJrw3PjvVZon55xzI2+wQNJdnIiBYUiJm9nNlDyS18wuSkw/DLy+n31bgWNLlu0AjhpSJpxzztXUYIHkMEnb4rSA5jjvY20555wDBn8eSXq0MuKcc258qvSGROecc64sDyTOOeeq4oHEOedcVTyQOOecq4oHEuecc1XxQOKcc64qHkicc85VxQOJc865qnggcc45VxUPJM4556rigcQ551xVPJA455yrigcS55xzVfFA4pxzrioeSJxzzlXFA4lzzrmq1DSQSFos6VFJ6yR9rsz6uZJul3S/pDWSTojL50naJWl1fH03sc9Rkh6MaX5LQ33+r3POuRFVs0AiKQ1cARwPLABOl7SgZLMLgevN7AjgNODbiXV/NrPD4+tjieXfAZYA8+Nrca2OwTnn3OBqWSI5GlhnZuvNrAu4DjipZBsDis99nwZsGihBSfsBU83sLjMz4Brg5JHNtnPOuaEY8JntVZoFbEjMtwHHlGxzMXCLpHOAScBbE+sOlHQ/sA240Mx+H9NsK0lzVrk3l7SEUHJh5syZtLa2Dusg2tvbh72vqw0/J/XJz0v9Ga1zUstAUq7twkrmTweuNrOvSXot8CNJhwBPA3PN7AVJRwE3Sjq4wjTDQrNlwDKAhQsX2qJFi4Z1EK2trQx3X1cbfk7qk5+X+jNa56SWgaQNmJOYn03fqquziW0cZnaXpCZgHzN7FuiMy++V9GfgFTHN2YOk6ZxzbhTVso1kJTBf0oGSGgiN6ctLtnkKOA5A0quAJuA5SfvGxnokHURoVF9vZk8D2yUdG3trfQi4qYbH4JxzbhA1K5GYWU7SJ4EVQBq40szWSloKrDKz5cB5wPcknUuoojrLzEzSm4ClknJAHviYmW2OSX8cuBpoBn4dX84558ZILau2MLObgZtLll2UmH4YeH2Z/X4G/KyfNFcBh4xsTp1zzg2X39nunHOuKh5InHPOVcUDiXPOuap4IHHOOVcVDyTOOeeq4oHEOedcVTyQOOecq4oHEuecc1XxQOKcc64qHkicc85VxQOJc865qnggcc45VxUPJM4556rigcQ551xVPJA455yrigcS55xzVfFA4pxzrioeSJxzzlWlpoFE0mJJj0paJ+lzZdbPlXS7pPslrZF0Qlz+Nkn3Snow/v+WxD6tMc3V8fWSWh6Dc865gdXsme2S0sAVwNuANmClpOXxOe1FFwLXm9l3JC0gPN99HvA88G4z2yTpEGAFMCux3xnx2e3OOefGWC1LJEcD68xsvZl1AdcBJ5VsY8DUOD0N2ARgZveb2aa4fC3QJKmxhnl1zjk3TLUMJLOADYn5NnqXKgAuBj4oqY1QGjmnTDqnAvebWWdi2VWxWusLkjSCeXbOOTdENavaAspd4K1k/nTgajP7mqTXAj+SdIiZFQAkHQx8BXh7Yp8zzGyjpCnAz4AzgWv6vLm0BFgCMHPmTFpbW4d1EO3t7cPe19WGn5P65Oel/ozWOallIGkD5iTmZxOrrhLOBhYDmNldkpqAfYBnJc0GfgF8yMz+XNzBzDbG/7dL+gmhCq1PIDGzZcAygIULF9qiRYuGdRCtra0Md19XG35O6pOfl/ozWuekllVbK4H5kg6U1ACcBiwv2eYp4DgASa8CmoDnJE0HfgVcYGb/XdxYUkbSPnE6C7wLeKiGx+Ccc24QNQskZpYDPknocfUIoXfWWklLJZ0YNzsP+KikB4CfAmeZmcX9Xg58oaSbbyOwQtIaYDWwEfherY7BOefc4GpZtYWZ3UxoRE8uuygx/TDw+jL7fRn4cj/JHjWSeXTOOVcdv7PdOedcVTyQOOecq4oHEuecc1XxQOKcc64qHkicc85VxQOJc865qnggcc45VxUPJM4556rigcQ551xVPJA455yrigcS55xzVfFA4pxzrioeSJxzzlXFA4lzzrmqeCBxzjlXFQ8kzjnnquKBxDnnXFU8kDjnnKuKBxLnnHNVqWkgkbRY0qOS1kn6XJn1cyXdLul+SWsknZBYd0Hc71FJ76g0Teecc6OrZoFEUhq4AjgeWACcLmlByWYXAteb2RHAacC3474L4vzBwGLg25LSFabpnHNuFNWyRHI0sM7M1ptZF3AdcFLJNgZMjdPTgE1x+iTgOjPrNLPHgXUxvUrSdM45N4oyNUx7FrAhMd8GHFOyzcXALZLOASYBb03se3fJvrPi9GBpAiBpCbAkzrZLenSI+S/aB3h+mPu62vBzUp/8vNSfas/JAZVsVMtAojLLrGT+dOBqM/uapNcCP5J0yAD7litBlaYZFpotA5YNIb9lSVplZgurTceNHD8n9cnPS/0ZrXNSy0DSBsxJzM9md9VV0dmENhDM7C5JTYQIOtC+g6XpnHNuFNWyjWQlMF/SgZIaCI3ny0u2eQo4DkDSq4Am4Lm43WmSGiUdCMwH7qkwTeecc6OoZiUSM8tJ+iSwAkgDV5rZWklLgVVmthw4D/iepHMJVVRnmZkBayVdDzwM5IBPmFkeoFyatTqGqOrqMTfi/JzUJz8v9WdUzonCdds555wbHr+z3TnnXFU8kDjnnKuKB5J+SLpS0rOSHhrrvLhA0pw4pM4jktZK+vRY58mBpCZJ90h6IJ6XL411nlwQRwS5X9Iva/k+Hkj6dzWxa7KrGzngPDN7FXAs8AkfIqcudAJvMbPDgMOBxZKOHeM8ueDTwCO1fhMPJP0ws98Bm8c6H243M3vazO6L09sJfyCzBt7L1ZoF7XE2G1/ei2eMSZoNvBP4fq3fywOJG5ckzQOOAP44tjlx0FOFshp4FrjVzPy8jL1vAP8IFGr9Rh5I3LgjaTLwM+AfzGzbWOfHgZnlzexwwmgTR8ehjtwYkfQu4Fkzu3c03s8DiRtXJGUJQeRaM/v5WOfH9WZmW4BWvH1xrL0eOFHSE4RR0t8i6ce1ejMPJG7ckCTgB8AjZvZvY50fF0jaV9L0ON1MGMX7f8Y2VxObmV1gZrPNbB5hKKn/MrMP1ur9PJD0Q9JPgbuAv5LUJunssc6T4/XAmYRfV6vj64TBdnI1tx9wu6Q1hPHwbjWzmnY3dfXFh0hxzjlXFS+ROOecq4oHEuecc1XxQOKcc64qHkicc85VxQOJc865qnggceOWJJP0tcT8+ZIuHqG0r5b0npFIa5D3eW8czfj2kuXzJO1KdHNeHR8vPdT050n6wMjl2Lm+PJC48awTOEXSPmOdkSRJ6SFsfjbwv8zsr8us+7OZHZ54dQ0jO/OAIQeSIR6Dm+A8kLjxLEd4JvW5pStKSxSS2uP/iyTdIel6SY9JukzSGfF5Gg9KelkimbdK+n3c7l1x/7Skf5W0UtIaSX+fSPd2ST8BHiyTn9Nj+g9J+kpcdhHwBuC7kv61kgOWNCk+K2dlfM7ESXH5vJjX++LrdXGXy4A3xhLNuZLOknR5Ir1fSlpU/IwkLZX0R+C1ko6Kn9W9klZI2i9u9ylJD8fjv66SfLs9nJn5y1/j8gW0A1OBJ4BpwPnAxXHd1cB7ktvG/xcBWwh3YzcCG4EvxXWfBr6R2P83hB9b84E2oAlYAlwYt2kEVgEHxnR3AAeWyef+wFPAvkAG+C/g5LiuFVhYZp95wC5gdXxdEZf/H+CDcXo68BgwCWgBmuLy+cCqxPH+MpHuWcDliflfAovitAHvi9NZ4E5g3zj/fuDKOL0JaCzmYay/B/4a+1dmkDjjXF0zs22SrgE+RbjwVmKlmT0NIOnPwC1x+YNAsorpejMrAH+StB54JfB24NBEaWca4cLdBdxjZo+Xeb/XAK1m9lx8z2uBNwE3DpLPP1sYUTfp7YTB+M6P803AXMLF/XJJhwN54BWDpF1OnjAgJsBfAYcAt4YhzkgDT8d1a4BrJd1YwTG4CcADidsTfAO4D7gqsSxHrLqNgz0mG6o7E9OFxHyB3n8TpeMHGSDgHDNbkVwRq4d29JM/DXoElRNwqpk9WvL+FwPPAIcRjrujn/17PpeoKTHdYWb5xPusNbPXlknjnYRAeCLwBUkHm1luqAfi9hzeRuLGPTPbDFxPaLguegI4Kk6fRKiqGar3SkrFdpODgEeBFcDH43D2SHqFpEmDpPNH4M2S9omN2KcDdwwjP8T3PycGRyQdEZdPA56OJagzCSUIgO3AlMT+TwCHx+OaAxzdz/s8Cuwr6bXxfbKSDpaUAuaY2e2EhyZNByYP81jcHsJLJG5P8TXgk4n57wE3SboHuI3+SwsDeZRwwZ8JfMzMOiR9n9B+cV+8mD8HnDxQImb2tKQLgNsJv/RvNrObhpEfgH8mlMDWxPd/AngX8G3gZ5LeG9+neLxrgJykBwjtPt8AHidU4z1EKMmVy3NXrL77lqRphGvFNwhtMj+OywR83cIzSNwE5qP/Ouecq4pXbTnnnKuKBxLnnHNV8UDinHOuKh5InHPOVcUDiXPOuap4IHHOOVcVDyTOOeeq8v8BC2XO3gqCyzsAAAAASUVORK5CYII=\n", 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\n", 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\n", 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\n", "text/plain": [ - "" + "
" ] }, - "metadata": {}, + "metadata": { + "needs_background": "light" + }, "output_type": "display_data" } ], @@ -1079,7 +1093,7 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 16, "metadata": {}, "outputs": [ { @@ -1108,23 +1122,26 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": 17, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ + "[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.\n", "[Parallel(n_jobs=1)]: Done 1 out of 1 | elapsed: 0.0s remaining: 0.0s\n", "[Parallel(n_jobs=1)]: Done 4 out of 4 | elapsed: 0.0s finished\n", "\n", - "[2018-09-24 02:31:21] Features: 1/3 -- score: 0.9666666666666667[Parallel(n_jobs=1)]: Done 1 out of 1 | elapsed: 0.0s remaining: 0.0s\n", + "[2019-05-10 19:29:34] Features: 1/3 -- score: 0.9666666666666667[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.\n", + "[Parallel(n_jobs=1)]: Done 1 out of 1 | elapsed: 0.0s remaining: 0.0s\n", "[Parallel(n_jobs=1)]: Done 3 out of 3 | elapsed: 0.0s finished\n", "\n", - "[2018-09-24 02:31:21] Features: 2/3 -- score: 0.9666666666666667[Parallel(n_jobs=1)]: Done 1 out of 1 | elapsed: 0.0s remaining: 0.0s\n", + "[2019-05-10 19:29:34] Features: 2/3 -- score: 0.9666666666666667[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.\n", + "[Parallel(n_jobs=1)]: Done 1 out of 1 | elapsed: 0.0s remaining: 0.0s\n", "[Parallel(n_jobs=1)]: Done 2 out of 2 | elapsed: 0.0s finished\n", "\n", - "[2018-09-24 02:31:21] Features: 3/3 -- score: 0.9666666666666667" + "[2019-05-10 19:29:34] Features: 3/3 -- score: 0.9666666666666667" ] } ], @@ -1159,7 +1176,7 @@ }, { "cell_type": "code", - "execution_count": 21, + "execution_count": 18, "metadata": {}, "outputs": [], "source": [ @@ -1179,7 +1196,7 @@ }, { "cell_type": "code", - "execution_count": 22, + "execution_count": 19, "metadata": {}, "outputs": [], "source": [ @@ -1199,7 +1216,7 @@ }, { "cell_type": "code", - "execution_count": 23, + "execution_count": 20, "metadata": {}, "outputs": [ { @@ -1216,7 +1233,7 @@ }, { "cell_type": "code", - "execution_count": 24, + "execution_count": 21, "metadata": {}, "outputs": [ { @@ -1254,7 +1271,7 @@ }, { "cell_type": "code", - "execution_count": 25, + "execution_count": 22, "metadata": {}, "outputs": [], "source": [ @@ -1267,7 +1284,7 @@ "iris = load_iris()\n", "X, y = iris.data, iris.target\n", "X_train, X_test, y_train, y_test = train_test_split(\n", - " X, y, test_size=0.33, random_state=1)" + " X, y, test_size=0.2, random_state=123)" ] }, { @@ -1279,7 +1296,7 @@ }, { "cell_type": "code", - "execution_count": 26, + "execution_count": 23, "metadata": {}, "outputs": [], "source": [ @@ -1301,15 +1318,16 @@ " ('knn', knn)])\n", "\n", "param_grid = [\n", - " {'sfs__k_features': [1, 2, 3, 4],\n", - " 'sfs__estimator__n_neighbors': [1, 2, 3, 4]}\n", + " {'sfs__k_features': [1, 4],\n", + " 'sfs__estimator__n_neighbors': [1, 5, 10]}\n", " ]\n", " \n", "gs = GridSearchCV(estimator=pipe, \n", " param_grid=param_grid, \n", " scoring='accuracy', \n", " n_jobs=1, \n", - " cv=5, \n", + " cv=5,\n", + " iid=True,\n", " refit=False)\n", "\n", "# run gridearch\n", @@ -1325,14 +1343,37 @@ }, { "cell_type": "code", - "execution_count": 27, + "execution_count": 24, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "Best parameters via GridSearch {'sfs__estimator__n_neighbors': 1, 'sfs__k_features': 3}\n" + "{'sfs__estimator__n_neighbors': 1, 'sfs__k_features': 1} test acc.: 0.9166666666666666\n", + "{'sfs__estimator__n_neighbors': 1, 'sfs__k_features': 4} test acc.: 0.9416666666666667\n", + "{'sfs__estimator__n_neighbors': 5, 'sfs__k_features': 1} test acc.: 0.9166666666666666\n", + "{'sfs__estimator__n_neighbors': 5, 'sfs__k_features': 4} test acc.: 0.9416666666666667\n", + "{'sfs__estimator__n_neighbors': 10, 'sfs__k_features': 1} test acc.: 0.9166666666666666\n", + "{'sfs__estimator__n_neighbors': 10, 'sfs__k_features': 4} test acc.: 0.9416666666666667\n" + ] + } + ], + "source": [ + "for i in range(len(gs.cv_results_['params'])):\n", + " print(gs.cv_results_['params'][i], 'test acc.:', gs.cv_results_['mean_test_score'][i])" + ] + }, + { + "cell_type": "code", + "execution_count": 25, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Best parameters via GridSearch {'sfs__estimator__n_neighbors': 1, 'sfs__k_features': 4}\n" ] } ], @@ -1356,7 +1397,7 @@ }, { "cell_type": "code", - "execution_count": 28, + "execution_count": 26, "metadata": {}, "outputs": [], "source": [ @@ -1365,6 +1406,7 @@ " scoring='accuracy', \n", " n_jobs=1, \n", " cv=5, \n", + " iid=True,\n", " refit=True)\n", "gs = gs.fit(X_train, y_train)" ] @@ -1378,7 +1420,7 @@ }, { "cell_type": "code", - "execution_count": 29, + "execution_count": 27, "metadata": {}, "outputs": [ { @@ -1386,17 +1428,17 @@ "text/plain": [ "[('sfs', SequentialFeatureSelector(clone_estimator=True, cv=5,\n", " estimator=KNeighborsClassifier(algorithm='auto', leaf_size=30, metric='minkowski',\n", - " metric_params=None, n_jobs=1, n_neighbors=1, p=2,\n", + " metric_params=None, n_jobs=None, n_neighbors=1, p=2,\n", " weights='uniform'),\n", - " floating=False, forward=True, k_features=3, n_jobs=1,\n", + " floating=False, forward=True, k_features=4, n_jobs=1,\n", " pre_dispatch='2*n_jobs', scoring='accuracy', verbose=0)),\n", " ('knn',\n", " KNeighborsClassifier(algorithm='auto', leaf_size=30, metric='minkowski',\n", - " metric_params=None, n_jobs=1, n_neighbors=2, p=2,\n", + " metric_params=None, n_jobs=None, n_neighbors=2, p=2,\n", " weights='uniform'))]" ] }, - "execution_count": 29, + "execution_count": 27, "metadata": {}, "output_type": "execute_result" } @@ -1414,14 +1456,14 @@ }, { "cell_type": "code", - "execution_count": 30, + "execution_count": 28, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "Best features: (0, 1, 3)\n" + "Best features: (0, 1, 2, 3)\n" ] } ], @@ -1438,14 +1480,14 @@ }, { "cell_type": "code", - "execution_count": 31, + "execution_count": 29, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "Best score: 0.94\n" + "Best score: 0.9416666666666667\n" ] } ], @@ -1455,16 +1497,16 @@ }, { "cell_type": "code", - "execution_count": 32, + "execution_count": 30, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "{'sfs__estimator__n_neighbors': 1, 'sfs__k_features': 3}" + "{'sfs__estimator__n_neighbors': 1, 'sfs__k_features': 4}" ] }, - "execution_count": 32, + "execution_count": 30, "metadata": {}, "output_type": "execute_result" } @@ -1473,6 +1515,33 @@ "gs.best_params_" ] }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Just to ensure that the values were indeed assigned correctly, the following number of neighbors should match the one above:" + ] + }, + { + "cell_type": "code", + "execution_count": 31, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "2" + ] + }, + "execution_count": 31, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "gs.estimator.named_steps['sfs'].estimator.n_neighbors" + ] + }, { "cell_type": "markdown", "metadata": {}, @@ -1482,14 +1551,14 @@ }, { "cell_type": "code", - "execution_count": 33, + "execution_count": 32, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "Best features: (0, 1, 3)\n" + "Best features: (0, 1, 2, 3)\n" ] } ], @@ -1515,7 +1584,7 @@ }, { "cell_type": "code", - "execution_count": 34, + "execution_count": 33, "metadata": {}, "outputs": [ { @@ -1524,7 +1593,7 @@ "(150, 4)" ] }, - "execution_count": 34, + "execution_count": 33, "metadata": {}, "output_type": "execute_result" } @@ -1535,7 +1604,7 @@ }, { "cell_type": "code", - "execution_count": 35, + "execution_count": 34, "metadata": {}, "outputs": [ { @@ -1545,17 +1614,19 @@ "best combination (ACC: 0.992): (0, 1, 2, 3, 6, 8, 9, 10, 11, 12)\n", "\n", "all subsets:\n", - " {1: {'feature_idx': (6,), 'cv_scores': array([ 0.84615385, 0.6 , 0.88 , 0.79166667, 0.875 ]), 'avg_score': 0.7985641025641026, 'feature_names': ('6',)}, 2: {'feature_idx': (6, 9), 'cv_scores': array([ 0.92307692, 0.88 , 1. , 0.95833333, 0.91666667]), 'avg_score': 0.93561538461538463, 'feature_names': ('6', '9')}, 3: {'feature_idx': (6, 9, 12), 'cv_scores': array([ 0.92307692, 0.92 , 0.96 , 1. , 0.95833333]), 'avg_score': 0.95228205128205123, 'feature_names': ('6', '9', '12')}, 4: {'feature_idx': (3, 6, 9, 12), 'cv_scores': array([ 0.96153846, 0.96 , 0.96 , 1. , 0.95833333]), 'avg_score': 0.96797435897435891, 'feature_names': ('3', '6', '9', '12')}, 5: {'feature_idx': (3, 6, 9, 10, 12), 'cv_scores': array([ 0.92307692, 0.96 , 1. , 1. , 1. ]), 'avg_score': 0.97661538461538466, 'feature_names': ('3', '6', '9', '10', '12')}, 6: {'feature_idx': (2, 3, 6, 9, 10, 12), 'cv_scores': array([ 0.92307692, 0.96 , 1. , 0.95833333, 1. ]), 'avg_score': 0.96828205128205125, 'feature_names': ('2', '3', '6', '9', '10', '12')}, 7: {'feature_idx': (0, 2, 3, 6, 9, 10, 12), 'cv_scores': array([ 0.92307692, 0.92 , 1. , 1. , 1. ]), 'avg_score': 0.96861538461538466, 'feature_names': ('0', '2', '3', '6', '9', '10', '12')}, 8: {'feature_idx': (0, 2, 3, 6, 8, 9, 10, 12), 'cv_scores': array([ 1. , 0.92, 1. , 1. , 1. ]), 'avg_score': 0.98399999999999999, 'feature_names': ('0', '2', '3', '6', '8', '9', '10', '12')}, 9: {'feature_idx': (0, 2, 3, 6, 8, 9, 10, 11, 12), 'cv_scores': array([ 1. , 0.92, 1. , 1. , 1. ]), 'avg_score': 0.98399999999999999, 'feature_names': ('0', '2', '3', '6', '8', '9', '10', '11', '12')}, 10: {'feature_idx': (0, 1, 2, 3, 6, 8, 9, 10, 11, 12), 'cv_scores': array([ 1. , 0.96, 1. , 1. , 1. ]), 'avg_score': 0.99199999999999999, 'feature_names': ('0', '1', '2', '3', '6', '8', '9', '10', '11', '12')}}\n" + " {1: {'feature_idx': (6,), 'cv_scores': array([0.84615385, 0.6 , 0.88 , 0.79166667, 0.875 ]), 'avg_score': 0.7985641025641026, 'feature_names': ('6',)}, 2: {'feature_idx': (6, 9), 'cv_scores': array([0.92307692, 0.88 , 1. , 0.95833333, 0.91666667]), 'avg_score': 0.9356153846153846, 'feature_names': ('6', '9')}, 3: {'feature_idx': (6, 9, 12), 'cv_scores': array([0.92307692, 0.92 , 0.96 , 1. , 0.95833333]), 'avg_score': 0.9522820512820512, 'feature_names': ('6', '9', '12')}, 4: {'feature_idx': (3, 6, 9, 12), 'cv_scores': array([0.96153846, 0.96 , 0.96 , 1. , 0.95833333]), 'avg_score': 0.9679743589743589, 'feature_names': ('3', '6', '9', '12')}, 5: {'feature_idx': (3, 6, 9, 10, 12), 'cv_scores': array([0.92307692, 0.96 , 1. , 1. , 1. ]), 'avg_score': 0.9766153846153847, 'feature_names': ('3', '6', '9', '10', '12')}, 6: {'feature_idx': (2, 3, 6, 9, 10, 12), 'cv_scores': array([0.92307692, 0.96 , 1. , 0.95833333, 1. ]), 'avg_score': 0.9682820512820512, 'feature_names': ('2', '3', '6', '9', '10', '12')}, 7: {'feature_idx': (0, 2, 3, 6, 9, 10, 12), 'cv_scores': array([0.92307692, 0.92 , 1. , 1. , 1. ]), 'avg_score': 0.9686153846153847, 'feature_names': ('0', '2', '3', '6', '9', '10', '12')}, 8: {'feature_idx': (0, 2, 3, 6, 8, 9, 10, 12), 'cv_scores': array([1. , 0.92, 1. , 1. , 1. ]), 'avg_score': 0.984, 'feature_names': ('0', '2', '3', '6', '8', '9', '10', '12')}, 9: {'feature_idx': (0, 2, 3, 6, 8, 9, 10, 11, 12), 'cv_scores': array([1. , 0.92, 1. , 1. , 1. ]), 'avg_score': 0.984, 'feature_names': ('0', '2', '3', '6', '8', '9', '10', '11', '12')}, 10: {'feature_idx': (0, 1, 2, 3, 6, 8, 9, 10, 11, 12), 'cv_scores': array([1. , 0.96, 1. , 1. , 1. ]), 'avg_score': 0.992, 'feature_names': ('0', '1', '2', '3', '6', '8', '9', '10', '11', '12')}}\n" ] }, { "data": { - "image/png": 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\n", + "image/png": 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\n", 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" ] }, - "metadata": {}, + "metadata": { + "needs_background": "light" + }, "output_type": "display_data" } ], @@ -1614,19 +1685,20 @@ }, { "cell_type": "code", - "execution_count": 36, + "execution_count": 35, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "groups: [ 0 0 0 0 0 0 0 0 0 0 1 1 1 1 1 1 1 1 1 1 2 2 2 2 2\n", - " 2 2 2 2 2 3 3 3 3 3 3 3 3 3 3 4 4 4 4 4 4 4 4 4 4\n", - " 5 5 5 5 5 5 5 5 5 5 6 6 6 6 6 6 6 6 6 6 7 7 7 7 7\n", - " 7 7 7 7 7 8 8 8 8 8 8 8 8 8 8 9 9 9 9 9 9 9 9 9 9\n", - " 10 10 10 10 10 10 10 10 10 10 11 11 11 11 11 11 11 11 11 11 12 12 12 12 12\n", - " 12 12 12 12 12 13 13 13 13 13 13 13 13 13 13 14 14 14 14 14 14 14 14 14 14]\n" + "groups: [ 0 0 0 0 0 0 0 0 0 0 1 1 1 1 1 1 1 1 1 1 2 2 2 2\n", + " 2 2 2 2 2 2 3 3 3 3 3 3 3 3 3 3 4 4 4 4 4 4 4 4\n", + " 4 4 5 5 5 5 5 5 5 5 5 5 6 6 6 6 6 6 6 6 6 6 7 7\n", + " 7 7 7 7 7 7 7 7 8 8 8 8 8 8 8 8 8 8 9 9 9 9 9 9\n", + " 9 9 9 9 10 10 10 10 10 10 10 10 10 10 11 11 11 11 11 11 11 11 11 11\n", + " 12 12 12 12 12 12 12 12 12 12 13 13 13 13 13 13 13 13 13 13 14 14 14 14\n", + " 14 14 14 14 14 14]\n" ] } ], @@ -1651,16 +1723,16 @@ }, { "cell_type": "code", - "execution_count": 37, + "execution_count": 36, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "" + "" ] }, - "execution_count": 37, + "execution_count": 36, "metadata": {}, "output_type": "execute_result" } @@ -1679,7 +1751,7 @@ }, { "cell_type": "code", - "execution_count": 38, + "execution_count": 37, "metadata": {}, "outputs": [], "source": [ @@ -1688,7 +1760,7 @@ }, { "cell_type": "code", - "execution_count": 39, + "execution_count": 38, "metadata": {}, "outputs": [ { @@ -1735,7 +1807,7 @@ }, { "cell_type": "code", - "execution_count": 15, + "execution_count": 39, "metadata": {}, "outputs": [], "source": [ @@ -1760,7 +1832,7 @@ }, { "cell_type": "code", - "execution_count": 16, + "execution_count": 40, "metadata": {}, "outputs": [ { @@ -1839,7 +1911,7 @@ "4 5.0 3.6 1.4 0.2" ] }, - "execution_count": 16, + "execution_count": 40, "metadata": {}, "output_type": "execute_result" } @@ -1859,7 +1931,7 @@ }, { "cell_type": "code", - "execution_count": 17, + "execution_count": 41, "metadata": {}, "outputs": [ { @@ -1873,7 +1945,7 @@ "dtype: int64" ] }, - "execution_count": 17, + "execution_count": 41, "metadata": {}, "output_type": "execute_result" } @@ -1885,7 +1957,7 @@ }, { "cell_type": "code", - "execution_count": 18, + "execution_count": 42, "metadata": {}, "outputs": [], "source": [ @@ -1901,27 +1973,27 @@ }, { "cell_type": "code", - "execution_count": 19, + "execution_count": 43, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "{1: {'avg_score': 0.95999999999999996,\n", - " 'cv_scores': array([ 0.96]),\n", - " 'feature_idx': (3,),\n", + "{1: {'feature_idx': (3,),\n", + " 'cv_scores': array([0.96]),\n", + " 'avg_score': 0.96,\n", " 'feature_names': ('petal width',)},\n", - " 2: {'avg_score': 0.97333333333333338,\n", - " 'cv_scores': array([ 0.97333333]),\n", - " 'feature_idx': (2, 3),\n", + " 2: {'feature_idx': (2, 3),\n", + " 'cv_scores': array([0.97333333]),\n", + " 'avg_score': 0.9733333333333334,\n", " 'feature_names': ('sepal width', 'petal width')},\n", - " 3: {'avg_score': 0.97333333333333338,\n", - " 'cv_scores': array([ 0.97333333]),\n", - " 'feature_idx': (1, 2, 3),\n", + " 3: {'feature_idx': (1, 2, 3),\n", + " 'cv_scores': array([0.97333333]),\n", + " 'avg_score': 0.9733333333333334,\n", " 'feature_names': ('petal len', 'sepal width', 'petal width')}}" ] }, - "execution_count": 19, + "execution_count": 43, "metadata": {}, "output_type": "execute_result" } @@ -1946,7 +2018,7 @@ }, { "cell_type": "code", - "execution_count": 43, + "execution_count": 44, "metadata": {}, "outputs": [ { @@ -2197,11 +2269,7 @@ "*set_params(**params)*\n", "\n", "Set the parameters of this estimator.\n", - "\n", - "The method works on simple estimators as well as on nested objects\n", - "(such as pipelines). The latter have parameters of the form\n", - "``__`` so that it's possible to update each\n", - "component of a nested object.\n", + "Valid parameter keys can be listed with ``get_params()``.\n", "\n", "**Returns**\n", "\n", @@ -2226,6 +2294,16 @@ "\n", "Reduced feature subset of X, shape={n_samples, k_features}\n", "\n", + "### Properties\n", + "\n", + "
\n", + "\n", + "*named_estimators*\n", + "\n", + "**Returns**\n", + "\n", + "List of named estimator tuples, like [('svc', SVC(...))]\n", + "\n", "\n" ] } @@ -2254,7 +2332,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.6.6" + "version": "3.7.1" }, "toc": { "nav_menu": {}, diff --git a/mlxtend/feature_selection/sequential_feature_selector.py b/mlxtend/feature_selection/sequential_feature_selector.py index 4b0ffffb8..199263280 100644 --- a/mlxtend/feature_selection/sequential_feature_selector.py +++ b/mlxtend/feature_selection/sequential_feature_selector.py @@ -16,9 +16,9 @@ from itertools import combinations from sklearn.metrics import get_scorer from sklearn.base import clone -from sklearn.base import BaseEstimator from sklearn.base import MetaEstimatorMixin from ..externals.name_estimators import _name_estimators +from ..utils.base_compostion import _BaseXComposition from sklearn.model_selection import cross_val_score from sklearn.externals.joblib import Parallel, delayed @@ -64,7 +64,7 @@ def _get_featurenames(subsets_dict, feature_idx, custom_feature_names, X): return subsets_dict_, feature_names -class SequentialFeatureSelector(BaseEstimator, MetaEstimatorMixin): +class SequentialFeatureSelector(_BaseXComposition, MetaEstimatorMixin): """Sequential Feature Selection for Classification and Regression. @@ -188,8 +188,6 @@ def __init__(self, estimator, k_features=1, self.cv = cv self.n_jobs = n_jobs self.verbose = verbose - self.named_est = {key: value for key, value in - _name_estimators([self.estimator])} self.clone_estimator = clone_estimator if self.clone_estimator: @@ -218,6 +216,32 @@ def __init__(self, estimator, k_features=1, # don't mess with this unless testing self._TESTING_INTERRUPT_MODE = False + @property + def named_estimators(self): + """ + Returns + ------- + List of named estimator tuples, like [('svc', SVC(...))] + """ + return _name_estimators([self.estimator]) + + def get_params(self, deep=True): + # + # Return estimator parameter names for GridSearch support. + # + return self._get_params('named_estimators', deep=deep) + + def set_params(self, **params): + """Set the parameters of this estimator. + Valid parameter keys can be listed with ``get_params()``. + + Returns + ------- + self + """ + self._set_params('estimator', 'named_estimators', **params) + return self + def fit(self, X, y, custom_feature_names=None, **fit_params): """Perform feature selection and learn model from training data. From 486a0cc2413d249f5cf892698aa3e3343b2d5420 Mon Sep 17 00:00:00 2001 From: rasbt Date: Fri, 10 May 2019 19:34:04 -0500 Subject: [PATCH 2/2] pep8 fix --- mlxtend/feature_selection/sequential_feature_selector.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/mlxtend/feature_selection/sequential_feature_selector.py b/mlxtend/feature_selection/sequential_feature_selector.py index 199263280..08636d157 100644 --- a/mlxtend/feature_selection/sequential_feature_selector.py +++ b/mlxtend/feature_selection/sequential_feature_selector.py @@ -234,7 +234,7 @@ def get_params(self, deep=True): def set_params(self, **params): """Set the parameters of this estimator. Valid parameter keys can be listed with ``get_params()``. - + Returns ------- self