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= document.getElementById("e2e9be02-034b-45ee-93ec-94fb4d422bcc"); + const element = document.getElementById("eb6b1ca7-5697-40c2-9a7e-62f958faa5b2"); if (element == null) { - console.warn("Bokeh: autoload.js configured with elementid 'e2e9be02-034b-45ee-93ec-94fb4d422bcc' but no matching script tag was found.") + console.warn("Bokeh: autoload.js configured with elementid 'eb6b1ca7-5697-40c2-9a7e-62f958faa5b2' but no matching script tag was found.") } function run_callbacks() { try { @@ -100,8 +100,8 @@ Bokeh.safely(function() { (function(root) { function embed_document(root) { - const docs_json = '{"21138b15-b3f7-4ce1-8b51-f8ccf069ecda":{"version":"3.6.0","title":"Bokeh Application","roots":[{"type":"object","name":"panel.models.layout.Column","id":"p1386","attributes":{"name":"Column00331","stylesheets":["\\n:host(.pn-loading):before, .pn-loading:before {\\n background-color: #c3c3c3;\\n mask-size: auto calc(min(50%, 400px));\\n -webkit-mask-size: auto calc(min(50%, 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a/pr-preview/pr-276/.doctrees/bokeh_plot/bokeh-content-17f42cf4af004c0db5a67b9da92838dd-auto_examples-01_model_comparison-plot_simple_model_comparison.js +++ b/pr-preview/pr-276/.doctrees/bokeh_plot/bokeh-content-2c51fce2dc92480e9c18c29dcc0f2af3-auto_examples-01_model_comparison-plot_simple_model_comparison.js @@ -14,9 +14,9 @@ } - const element = document.getElementById("b1b02290-04dd-4f69-bbd9-3da83d0fb160"); + const element = document.getElementById("ea98f70f-be76-461f-8c08-1bf7aa9d4c63"); if (element == null) { - console.warn("Bokeh: autoload.js configured with elementid 'b1b02290-04dd-4f69-bbd9-3da83d0fb160' but no matching script tag was found.") + console.warn("Bokeh: autoload.js configured with elementid 'ea98f70f-be76-461f-8c08-1bf7aa9d4c63' but no matching script tag was found.") } function run_callbacks() { try { @@ -100,8 +100,8 @@ Bokeh.safely(function() { (function(root) { function embed_document(root) { - const docs_json = 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zAR6gGDPLv8NnQ8uzDqLNLCt0RlrYMRheP8PLfs3IcrYxh87//aPE0Yda7f2PpT144uIiIiIiEh9sqFLJE3fnL2oc5a1f3o//EhSfj5uZNt2CLc/X4H7O7l59eVpZpAmcNSi0bQX7gTb95mlPYCPQNup7oUxlu7Lyn3AZ4E1vkOzdnsPM2LYi7n2widTDSKNb94pL6Fc/hnQ/szSW53oE3jbSyzdGzk4/X/Arzk1+FduwB6nbA9zW+/v0nxMERERERERaRyarMxTV1fhrbRvmRSHj/5r2U6+1/1HwWo7eetuA7ca644A5vECDVsAs3NYsHxMLR9fmlB720e2b9gCbAQ+4oQDsYFPQPwAXvPJ2yecPy2FjTOAXp63YQswkc1bP1zrx5cmVI7PZruGLcA3cX8ZVu7C4/90r/mNPACP/xq++fdu/3xw4Is3YI8D0OYTOWrR6Bo/nIiIiIiIiDQoTdrm6cjSdAo+ZfulV1o06kwLs/8Gjp4Q2SsKzsid/bYBtjwKv74T+8mnnBtvjoPTZl8D9kzw5WfR16uGl1Q3b9k0BuJfYAzbYd3d4LnJxAjoNv/WB7Cxk+DwEbDv4G+VgG/A/nSv87Ove/mGszz6I8YCIvvXob+Sftxexi2r7tmFx5VWUOw+DuzbFeu+45uZM3D/N7MfHuk+Ywx2sNnOv9nZ7/7XPxP9/OYQbjqd6GePEqof/GiFJ7ll5S929vuLiIiIiIhI81HTNi9HnD6C9idfg0fP+xzsaYXCRyw+5HCi6ftZOGC8M2UE7BmZjWhz7whmA2XYvAWeeMJ93Trjvl+5/els87vWxQzs8M0iezPG6UPmcvpp85dz4+q7d/+HlKbTWfoOzrE7rA1q2D7tHsqcRuQB4KQomvQmCy890DlwAjZlpLF3G4xodx/pwAC2ccB800Z4+GHnvv917rvE/c5npxCfESyinRXAIUNmdb7Lzb0n7M6PK02qq6uD9eN/Dv6iHdar3st+LYHzAF5t0ajlES9/UWDqxMj3H+3s1wF7tBsjzT0qW7S57GzebDz2F/f7H3DuuyHid+fHfm/ibP3+O3665uHd/hlFRERERESkoalpm5ejSy/HfXxmjxdZhLEamD5krfPv3Nz75vRDSUOZt7hIObquYt0xdnwtcSy8l9h+nUqOiEMxu4hEr19+DH2rbxi6TlrK7CXvxfzMivVBU7YYm/DoJEL816yiEXwrk564nauuijN7TBEREREREak72tM2DzOXjcu0YQsQPBBzATD0Fo3GmygueWP6oaRhdHUViAvnVqy7D27YgrE2tYYtQOA3OH3Jiu1curoKqWWRxrNg+QTwyi02tt3LOwp2RaYNW4DIhrF+r/0zfUwRERERERGpO2raZs/wMPS0ayr8TvCbE9aex+Gl9qHrpCU8Mq6E+8sqL9jghu1Wgl+aep6Yi4EtCSoP45Gxi9KOIw1k85ZPYdX29x50LzsPEYXvZZRqR17en+LC4bk8toiIiIiIiNQFNW2zVlw8mSiMyu3xy7aCZM2ulzCaU9OOIw1g5rJx4B+rWK82mYh/Eyf9/TjNH8H9qoTFn2FeKckhfNLsOhcdhts7K9a9ylYb7isZvDd4VoJFxMOm5fLYIiIiIiIiUhfUtM1SsaeN2A/MNcO2Ztc3EtU6n6R4yr4pJ5J61xafgdlelRcqDh9bT0zCRmoNBK7AeGTIOmMfYj6aQSKpd952LsaO22VU2+IDfgn8Z1axqgsTKC4cm28GERERERERyYuatlkqPzgNora8YzBgVwJ/HrLOGIPFn0o/kNStYukQYHHFetXJRFaTbIq7VrYQ7JJElYF3M7f7xSnnkXo2q/sEYFblhYotPmJCtCKbUEMot01HB4aKiIiIiIi0JDVtszL/xFFEUX1MrRZ8K7AqUW3wk5nd/Zp0A0kdOxvYcW/japOJEXcRQl9mqZ4Rx9fj/tsh64wOYs7OIJHUo+LC4UR2ZsV61cPHuAbi/80i1pDM9mBWqT5+b4iIiIiIiEim1LTNyuZh0/FQPxNTsd8E/ssh64wI7AI07dV6ZpWOBeZXXqiYTAyEsAIzzybYDlEc7EIgwWPbm5lTqvLzSPPreA9Gla1pBm/x4Rsw+0o2mRIyP4hiT/6f0BAREREREZFMqWmbhVknTQAbl3eMCsEuAI+HrDOOZPaSf8ogkdSLrq4OCnZWxXq1yUTneoL9PotYVQW/C+fGRLUx56sB1mLmvnsi7u+tWK92L1t0OSE8mUWsxJx2+tfluxe6iIiIiIiIZE5N29T1RBAdlHeKqtzvxbkmUa35Ocw/cVTKiaRePDLuVPAXVV6omLLdBNGlGaV6fsFXkmQ/XeOl+Lru9ANJ3QibP4fZmMoLg+7lyO4j5uqMUu2cNpvM4aWReccQERERERGR7Khpm7aZ66ZANCLvGM8vrMFJMlk2mf7hH0o9juRvwfIJQOVzXXXKNvo6If5rBqmG8hfcv5Gw9jO84eTxqaaR+jDnpFcRqPyUQNWD9MJKCOUsYu00D8boaFreMURERERERCQ7atqm6dCuDuCAvGO8oBA9CVyWrNjez9GL63NqWGpny9ZPYuxZeWHQZKLzEBZ/N6NUQxuwK4GHh6wzxtNf+Fj6gSRnRmg/d9u+3NupdpCe8VNibs8w286L4704Qm82iIiIiIiItAo1bdM0Ydw0zAt5x0jg+xh/SlA3nCg6J/U0kp/ORYcR+JeK9eqTiauIGcgiViIF3wq+OlGtcxqdyw5OOZHkads+3EdWXqjY4qMMtiqbULupI5qODoUUERERERFpCWrapuWoRaOJfWLeMRIJHghclLD6BOaU5qeaR/Ljbedi7PhGQ7XJRPgl2E8yy5VU2W8E7kxQ2Y7HX0g7juTkiNNHgH+qYr3aFh/4D4jD/Rmk2n2BkcxZOinvGCIiIiIiIpI+NW3T0l6YnneEnRL8DrBbE9XGnE+xpy3lRJK1Wd0nALMqL1RMJsZgF2cTaieZOe4X4oQE1cdSXPLG1DNJ9oZv+gDG/pUXbHDT9kk8fD2TTLUyMDCVw0vteccQERERERGRdKlpm4a5iycSV9sTtM45F+P0D1lnvBTWLckgkWSluHA4kZ1ZsV5tMjFwDSEk2U4jH4G7gRuSFft5aoA1mTlLJxP8PRXrVQ/Ss0uf3te7gURtjLKpeacQERERERGRdKlpW2s9PRFbaMzDukJYB3wrYfWnKZb2TjOOZMiHnY5xYOWFwZOJvgGzr2QTajcE7wU2J6h8CaPRGxDNJISzMEbusOZulRPjdi/YtRkmq6X9mP/+UXmHEBERERERkfSoaVtrax84kMiG5R1jlwX/GvBwgspxuH0y7TiSgWNOm4T5+yrWq00mGpcTQiNMJv4FLOnH3j/F3FP3SjWNZGPe4tcD/1B5odpWtn4xHsepZ0qDB2PThhl5xxAREREREZH0qGlbSwuWDyNmSt4xdtMWCGsSVZovoXPJYSnnkbSV+z8D7FF5YdBkYmT3EUdXZxOqBjz+JvDnBJXjKA+ckXYcSVtPRFw4l8Ed2moH6Rk/3raPdwOzMFafdhAREREREWleatrW0sb+6UTW+H+mZW7A+J8ElQXcL6LqGJs0hDknvYrAP1Wse5Xn1MNKCOUsYtVEsH7cexPVGqdQXPqylBNJmorrTsT91ZUXKg4fGyCQ7I2peldmOvQ0/u8cERERERERqaB/7NXKvNKeECbkHaMmzJwyF+KEBNUzKS45IfVMkgYjtH0BG/Q6UH0y8afE3J5httoIrAV+laCyDcL5aceRlBy1aDR45XYt1bb4gO/g/mD6oTJgPpyZ6xr90x0iIiIiIiJShZq2tbKV6XlHqC2/ByPhIT1+HseXRg5dJ3Xl6NI/gx1ReWHwgU2UwVZllKr2Yi5K+AbEPDqX/G3qeaT22to+BLZv5YWKKdvHcPtGJpmycwBdPR15hxAREREREZHaUtO2Fo5aNAl8dN4xUrAGbGOCuv3Z6O9PPY3UzvGlkRjJJhPdv08c7s8iVjr8HowfJiv18zi81J5yIKml+acdBJxWsV5ti49gl+DhqQxSZce8wAPrpuUdQ0RERERERGpLTdvd1dVVoK39wLxjpCL2x3C+mqjW7UPMObk5/xya0QY+gLF/5YWKycQnIVyZSaZ09YInadYdzGhOTT2N1M5A/+cxhu2wVnWLD/8DwX+UYbLstPnEbVtEiIiIiIiISLNQ03Z3PThuKiE070dT3b4D3JugcgSh8PmU00gtFJdPwXl3xXr1/T+/TIieTD9Uyra9AfH1hNVnUCztnWoeqY15i4s4b6q8UPHmA+BfIvIk22Q0pkLHjLwjiIiIiIiISO2oabs7jjh9BG3x5LxjpMrjGPcLE1a/lc7u2anmkd1nWz+PseMexO5WuZet3YtH12UZLVUWfwssyQFU43Cr3DpC6ktXV4Fy4ZyK9epvPtxEbL9OP1SOPB7D6xZPzDuGiIiIiIiI1Iaatruj/akZeFStQdBcAj/HuS1RrdsFdHUVUk4ku2rukiNw3lJ5odpgol+Mx3H6oTISFwZwT3agmvkSji69POVEsjseGX8yeJXnqOIgva24X5JRqny1cZBef0VERERERJqDmra7auaycbiPzztGduxCnIEEha9k/bjFqceRXdATEfxcBndoq+3/id1K8Dsyi5aV4LcAv0pQWcDsgrTjyC4qLhwL4RMV61WnbO2bOA9nkCp/kQ1j/V5V9qoWERERERGRRqOm7a4xPEzPO0SmQlgH9u1Etc6ZzD11r5QTyc46+sGFOIdXXqjY/3MAWJNFpFyEcCHO0Hubms+hs7vKfqmSv/aPYlblNabiXv4Lcbgqk0j1wsv7U1w4PO8YIiIiIiIisnvUtN0VxcWTicKovGNkrhwuB/vLkHXGeMoDZ2SQSJI6atFojKSTid/e1qRvUm5/wLg2Ye15LFg+LOVEsjM6lx0MtrRi3avu8bEa2JJ+qDoSLCIeNi3vGCIiIiIiIrJ71LTdWcWeNmI/MO8YuYjYRPBkE5jGKdoTtI60t30EsyqHFFVMJj6G881MMuVrDfhTCeqms3nraamnkZ0Qnw2077BUbYuPiN8S+03Z5aonYcK2LSRERERERESkUalpu7PKD06DqC3vGLkJ4Ye4/zZBZRsR56eeR4Y2b9k0nFMq1qtNJhqr8ZCkmdnYYn+MEF2esPoTFE/ZN9U8ksycxXNw3lB5oeLNB8dZgZlnkqseldumU/WEQREREREREWkEatrujPknjiKKWrt5s60Jcn6iPUFhLp1L3px2JBlCHJ+DseNH/KsePsY9DHBjZrnyFg18B3ggQeVoKPeknEaGUuxpIxTOrVivtsWH8SNi/10WseqW2R7MKrX27ysREREREZEGpqbtztg8bDoeNLkUuBvj+mS1fp4OxcnR3FInzrGVF6pMJlpYQeRJmvHNIS4MEFiZqNbppnNxlUPcJDP+0DLwl1ZeqLiXN2P25Uwy1Tvzgyj2tO4nQ0RERERERBqYmrZJzTppAti4vGPUkVWJ9gQ1puHtp2eQRwbr6ioQ7JyK9eqTiWuJ7ddZxKor7j/GuX3IOiPCo8o/S8nGG04eD+HDFetVD9LjG5TDo+mHagBOO/3rWnMPdhERERERkQanpm0iPRFEB+Wdoq7E/hhuVyQrto8xr3RAuoGkwiPjl+D+ssoLgyYTja0EvzSjVPXH+BJ4nKCyk9ndf596HqnUH/VgNr7ywuApW/szhG9nE6pBtNlkDi+NzDuGiIiIiIiI7Bw1bZOYuW4KRCPyjlF3rHwVbvcPXcdIynw2g0TyjJnLxkH4WMV61clE/ybOwxmkqk+x34tzTbJiO1fbfWRs3ikvwVlUsV7tID0PvQTrzyJWw/BgjI6m5R1DREREREREdo6atkM5tKsD0JRoNXFhgBAuTlj9dmYvnpVqHnlOW3xGsslE1hNzVSaZ6lpYg9uTQ5YZ02DYuzMIJM8ol88B23Ff1moH6Zn/huA/zjBZ44jjvTji5CqvByIiIiIiIlKv1LQdyoRx0zAv5B2jjv0n8NMEdQbRBdu2mpBUFUuHAIsr1qtOJtILbEk/VJ0L0ZPA15IV+0eZVdov1TyyzdHdbwLmVV6o2OIj4PEKzDybYA2oI5rO4Ea3iIiI6DLiVQAAIABJREFUiIiI1C010F7IUYtGE/vEvGPUveAX4jYwZJ3xKjofXJh+oFZn5wDtOyxVm0yMuIsQbs4uV50LfBdn6O0+YDQF/3TqeVpdV1cHkZ1ZsV5tiw/3awmFu7OI1bACI5mzdFLeMURERERERCQZNW1fSHthet4RGoLzAJF/P1FtsM8xr7RnyolaV7H7OPBjKi9UmUwMQZOJOwhljBWJSt1Oorjk1SkHam3rx78LmFF5oeJe3oS1fSWbUA1uYGAqh5fahy4UERERERGRvKlp+3zmLp5IjJqLScV2Ge5/HbLO2IcyH88gUevp6uqA6HMV61UnE7meYL/PIlZDif02nNuHrDMi3L+IPm6ejgXLJ+D+gYr1avdy4HLi8mNZxGp8URujbGreKURERERERGRoatpW09MTsYWD8o7RUDw8BXZJwup30bns4FTztKJHxp0K/qLKC1UmE4kuzShV4/FwEVAess44ktmlf0g/UAvatPUz2KA3zdytykF664j8B9kFawr7Mf/9o/IOISIiIiIiIi9MTdtq1j5wIJENyztGwylzLfC7BJXtePnCtOO0lAXLJwAfqlivPmX7dUI89FR0q3L7P9yvTlRrfIHjSyNTTtRait2vBE6svFBlqNn9YmKG3k9bnuPB2LShyrYTIiIiIiIiUk/UtB1swfJhxEzJO0ZDijzgfiGQYJ9UO4bO0rGpZ2oVm7d8qmIyEaiYTHQewvy7GaVqXDFfxnkyQeX+bOQ9qedpKXYuNuh3U7WD9PBf4PxXdrmaiIWxFEt75x1DREREREREnp+atoNt7J9OZPpz2VWB3+DcmKjW+SILlmuieXd1LjoMt3dWrHuV0UQPqzSZmICxAfevJqp1PswxiyalnKg1zC51ATMrL1Rs8RETChdnE6pJlZkOPfpdJyIiIiIiUqf0D7btzSvtCWFC3jEaXvCVwOYEldPZtPVdacdpet52LkZhx7Vqk4n8EuwnmeVqeIXvAfcmKNyDgfbPphym+R1x+gjgMxXr1bb4ML8a4v/NIFXzMh/OzHX6VImIiIiIiEidUtN2e1uZnneEJvEX8K8nqjQ+zqzSfinnaV6zuk8AZlVeqDKZiGkycWd4HD+93UeCWn8nc5a+NuVEza3jqdMxDqy8MPjwMd9AiC7PJlTTO4Cuno68Q4iIiIiIiEglNW2fcdSiSeCj847RRL4BPJCgbjRtaEpxVxQXDieyMyvWq00mBq4hhD9lEaupBH4O/HTIOiMihAuoelqWDOmY0yYB76tYr7bFB/YVQkiy37AMxbzAA+um5R1DREREREREKqlpC9DVVaCtvcqEl+yymAGCr0pUG/gXTSnuAh+WfDLR7CvZhGpC7l8Cygkqj6DY/da04zSlgYHPYozaYa3aFh+R3UewazJM1vzafCJHLdIbliIiIiIiInVGTVuAB8dNJQR9RLTWnFtxbh+yzogI/kU0pZjcMadNwrzKZGK1/T+5XJOJuyFwH9j3kxXbOcw/cdTQdfKsOSe/FvyfKi8MfvMB8LASQpIGuuyMQseMvCOIiIiIiIjIjtS0PeL0EbTFk/OO0bSMhFOK/npmd78j9TzNYmDrZ4E9dlhzt4pGV2T3EUdXZ5isOZXDV4AnElROYWB4ZTNdno8RCl9g8Bs21d58iLiNOMGbQLLzPB7D6xZPzDuGiIiIiIiIPEdN2/anZuCRJjzTEvu9OP+eqNbsHBYsH5NyosY3t/s1YNUmEyuXNJlYG8YGsMsS1bp9iHmlA9IN1CSKS98GvKbyQsWU7QDuvVlEalltHERXVyHvGCIiIiIiIrJNazdtZy4bh/v4vGM0vdgvJdmU4kQ2b/1w2nEanBFzNtUnE21Q5U81mVhD7j/ASHKY2wjKOlxvSMeXRkL4ZMV6tSlb+D5xooMNZVdFNoz1e+2fdwwRERERERHZppWbtoaH6XmHaAnGBpwvJ6p13svc7hennKhxHV36Z7AjKi/Y4IZtGSzZQXCSTPBA4KKE1W+nWJqZap5GtyH6IDCl8kLFQXqPE+KvZ5Kp1Xl5f4oLh+cdQ0RERERERFq5aVtcPJko6MCg7FwN/HHIKqODsp2TfpwGdHxpJEayyUT37xOH+7OI1VKC34FzW4JKA85Fh+tVV1w+BQ/vqlj3Kn9egUsh2phFrJYXLCIeNi3vGCIiIiIiItKqTdtiTxuxH5h3jJYSPBBzAeBD1hpvorjkjemHajAbog9iVPn4csWU7ROEcEVGqVqQXYgzkKDwdRSXvD31OI3Itn4eY+QOa9W3+PgjkV2XYTIhTKC4cGzeKURERERERFpdazZtyw9Og6gt7xitx+8Evzlh7XkcXmpPN08D2ZnJRNdkYqpCWAf+vWTFfhbzT9RE//bmLjkC5y2VFyoOHwPnYoKHDFLJ9spt09GUuIiIiIiISK5ar2k7/8RRRNG+ecdoWWVbAWxJUPkSRnNq2nEahm05O9lkot2LR5pMTFsIXwUeT1A5ma0jP5h2nMbRExG8ctuIalt8GLcQ/FcZBZPtme3BrJJ+T4qIiIiIiOSo9Zq2m4dNx4MmiPJi/gju30hY/QlmnTQh1TyNYO6SI3D7+8oL1SYT/WI8jjNI1eKijbglO1zP/AMUl05NN0+DmP3Qv+AcXnlh8BYf3o/bJRmlkmrMD6LYo0+kiIiIiIiI5KS1mrazTpoANi7vGC1vwK4E/pygciyF9k+nHae+7cRkInYrwe/IJpeA/wfY0IfrwQgIZ6Uep94dtWg0Fj5RsV7tXg727W3bUEhunHb612nvdxERERERkZy0UNO2J4LooLxTCFDwrcCqRLVON52Lq0zmtYijH1yYaDIRBoA1WUSSpwUPxH5Rwuq30tk9O9U89a697SNgVT5yX3EvPwb2rUwyyQtrs8kcXho5dKGIiIiIiIjUWus0bWeumwLRiLxjyNNivwn8l0PWGREeXUArHopz1KLRGMkmE52rNJmYB/8F8ONkpXb+tjePWtC8ZdNwTqlYr3aQnrEaD09lEUuG4MEYHU3LO4aIiIiIiEgrao0GwqFdHcABeceQQYJdAJ5k/9WZdHZXOW2+ybUXPorZxMoLmkysK24rcPoTVP4Nsx88OfU89agcn40xbIe1agfpwT0McGNmuWRocbwXR5w8Pu8YIiIiIiIiraY1mrYTxk3DvJB3DBnE/V6ca5LV2jkUFw5POVH9mLdsGm7LKtY1mVh/tk04fztZsX2G4sKxqeapN/MWF4HjKi9UvPngWFhB5CGDVLIzOqLptOKnHURERERERHLU/E3boxaNJvYq04pSH8Ia3J5MUHgQ1vHe1OPUizg+R5OJDSS2y4FHh6wz9oGOj6UfqE50dRWIC+dWrFfb4sNYS2y/ziKW7KTASOYsnZR3DBERERERkVbS/E3b9sL0vCPICwjRk7hfnqjW+TDHLGr+xsHcUifOsZUXNJlYtyxsJnjSg+DeReeyg1PNUy/Wj+/G/WWVFwbdy8ZWgl+aUSrZFQMDUzm81J53DBERERERkVbR3E3buYsnErNn3jFkKNF3gXsTFO7BQNuZKYfJV7GnjWDnVKxrMrH+xfZD3H+boLIdj7+Qep68zVw2Dg8fr1ivdi/j38R5OINUssuiNkbZ1LxTiIiIiIiItIrmbdr29ERs4aC8Y0gCHscELkpY/U7mLH1tqnny5A+VEk8mOl/OKJUksW3i+SLAE1Qfy+zuBSknyleh/DHM9tphzd2qTIyvJ+aq7ILJbtiP+e8flXcIERERERGRVtC8Tdu1DxxIZMOGLpS64P7fwE8TVBrBv0gzHoozc9k4CJX7nT7fZGLwRzJIJTsj8BvcbkhUa3Ze037cvFg6BLNS5YVqtzKrgS2pZ5Ld58HYtGFG3jFERERERERaQXM2bRcsH0bMlLxjyE5y/xJQTlD4eordb0s9T9ba4jMwG195QZOJDSWEVSRrQh7CaE5NO05OzgZ2bEhXO0gv4i5C6Msslew+C2MplvbOO4aIiIiIiEiza86m7cb+6UTWnD9bMwvcB/79ZMX2eeaf2Dwf0y2WDgEWV6x7ldFEpxdNJtazv+B8PWHtJ5h10oRU02Rt27YP8ysvVGzxEQhhBWZJtpOQelJmOvTod6yIiIiIiEiKmu8fXfNKe0JoriZIK4nDZcATCSons3XkB1NOkyE7hySTicZdhHBzdrlk14RvAH9OUDiWQtun0k6TmcNL7Zh9vmK9+hYfPyLY7zNIJbVmPpyZ6/RpFhERERERkRQ1X9N2K9PzjiC7I9qY+IAt8w9QXDo13TwZKHYfB35M5YUqk4muycSGEKyfwMpkxdZN55LD0g2UkdF2CvDiygsVW3xsxgs6SK+xHUBXT0feIURERERERJpVczVtZ5X2Ax+ddwzZbVeD/TFB3QgIZ6WeJk1dXR0Qfa5ivdpkovsPNZnYQNz7gF8lqCzgfhGNfrhesbQ3+Icr1qtu8WFfJ8R/zSKWpMS8wAPrpuUdQ0REREREpFk1T9O2q6tAZFPzjiE1EDwQ+0UJq9/KnCVHp5onTY+MPw38RZUXKqZsN0HhsmxCSc2EcCFOSFA5k+KSE1LPk64eYOwOK9W2+HAewuLvZpZK0tPmEzlqkd4oFRERERERSUHzNG0fHDeVEPRRzabhvwC7NVFp8Asa8lCcBcsngP9rxXrVKVs0mdiI3P4AXJOw+DyOL41MNU9a5i55KfjCygsV2yKAh1UE608/lGSi0DEj7wgiIiIiIiLNqPEaXdUccfoI2uLJeceQGnMuxknS3Pkbig+dlHqeWtu85VMYe+6w5m4VjS7nIcw1mdioQrgEbGOCyv3ZwOmp50lDCOeAte2wVu3NB+OXYD/JKpZkwOMxHLl0n7xjiIiIiIiINJvmaNq2PzUDjxp7P0ipFMI64NuJat3PZF5pz6EL60TnosNwe2flhSq3sftKYgbSDyXpsMdxvpqo1PkI80oHpByotoqlv8NtbuWFii0+YkKc8HA2aShWnkZXVyHvGCIiIiIiIs2k8Zu2M5eNw3183jEkJbFdDjw6ZJ2xDwN8JP1ANeJt52Ls2OSotv8n/BL4z6xiSUoC38W5f8g6YyQxn8kgUW10dXVAlbzVD9K7Fo+SHDAojSayYazfa/+8Y4iIiIiIiDSTRm/aGh6m5x1CUmRhM8HXJKx+D3O7X5xqnlooLv4HYFblhWqTidGKbEJJukIZ54uJSp13UCzNTDlQbTwy7j1AlT1NB+9l609hlmzaWBqTl/enuHB43jFERERERESaRWM3bYuLJxOFUXnHkJTF9kPcfztkndFBzNkZJNp1xYXD8eizFevVJhMD10D8v1nEkgy4/zfu/5Wg0sDq/3C9+Uv3AX9/xbpX2+ODy4n9sfRDSW6CRcTDpuUdQ0REREREpFnUd1PghRR72oj9wLxjSAYiD8BFgA9dbG+ms/sNaUfaZd7xXowq923FZOIGzL6STSjJjIcL8ST7E/vhFNedmH6g3dAfPovZmB3Wqm7xYQ9i/Ht2wSQ/YQLFhWPzTiEiIiIiItIMGrdpW35wGkRtQxdKUwj8BlibrNbOo9hTf/fGMadNAt5XsV5tMtGiywnhyQxSSZY8epCI7yes/jwLlo8ZuiwHxe5X4ry98sLgNx8AfIUO0msh5bbpVD1RUURERERERHZGYzZt5584iijaN+8YkrE4rAS2DFlnvBTWLUk/0E4a2PpZjB2386g2mRjZfcRcnWEyyVJsl+H+1wSVE9nS/6HU8+w8w6IvYIN+f1Tb4sO4g+A/yyqY1AGzPZhV0u9nERERERGR3dSYTdvNw6bjQZM8LcfW4/6NhMWfZu6pe6UaZ2fMOfm1YP9UeaHKZKKHlRDKGaSSPHh4CrgsUW3w93H0khelmmdnHb3kH3E/qvLC4IP0PCYULs4oldQT84Pq8tMOIiIiIiIiDaTx/lE166QJYOPyjiE5GbAr6WABMHGIynGUB84A3p1BqqEYofAFBk/Uulvl9p/8lJjbs4smuSjbf9DGcRgvfsE6owP8LOCEbIIN4YjTR2CbPl2xXu1exn6Al+/NJJfUF6ed/nUHAn/MO4qINKj5J45iYOQbcH8Nfb0fzjvOrvKurg7Wj/sC2DgGBpbZT768Ie9MItLafP6Jo+gfcRbmezHQdqr9+GIdFixSxxqsadsTwf0H5Z1CclTwrbj1Ynx8yFrjFIpLV9O38tcZJHt+xaVvg/CayguDJxMpg61KdN6aNLbIA2b/BlzIUPt/Gm9hTmk+a3uvzyTbCxm26X3gB1ReqJgYfxK3K3Qvt7A2m8zhpYf4ee+mvKOISIOYe+pehPKx4MfRzwLwPXD+BDRk09ZnLhvH+vKlwJvBob1wO/DFvHOJSOvy4skz2Fr4CsaRuEFbuAv4TKYZZi85BrwHmIHxWyL/iK1d/Z9ZZpB8+JzSfGI+wTPPvduH7eZVt+Wdq941VtN25ropEI3IO4bkLA5raY/ejPOKISrbIFwAzMsiVlXHl0ayIXyyYr3aZKL79wl+f0bJJG+x/5oCN4MVh67lfIo9h9HXk9+2GXOWTiYOp1e0mKsdpId/heA6SK+VeTBGF6YB+b5pJiL17ejFBxHZm8COIx4o0mj/NtmOF0szwWeATQVeDXERbLuzDCINnohIqnxO95HENhmAyA2isQQfBUzF7CjwV2Hb/d3dPdPXJZ+zeA7Bf8hz/xDeh2B9Xux+rfWt/mWWWSRb3rlkLsGv2+7+2wfzm/XcD61x/mJ0aFcHUGXCS1qOmVPmQiJWVRyGVGkuxe7j6Fv9H5lkG2xD9EEIUyovVEzZPkEcrmjUbaZlF5VtBW28Hhj+gnXGS7EHS8CKTHJV4/GZmFU5SK/iXv4/QuEaiLNMJ/UojvfiiJPHc9slSQ7eE5FWcHxpJBt9Fm7HAH8LvCTvSDW0EuzQ57/sheyiiEhLCtaF8R4A3ADfbk6o2ifgPNt+UIjOoPJThu1gHwKqnP8iTSP4GTu8YbCNnvsEGqdDNGHcNEx/2ZFn+D0YP0xWaxewYPmwdPNUUVw+BQ/vqlivNpnoXArRxixiSR0xfwT3byWqDZbf4Xqdpdfh9o+VF6ocpEdYicfq2Mo2HdF0htoCRESaW2f3oRSX/CvF0o/YwKO4XQe8j+Zq2ILbb/KOICKtzur9E04HV131Jvt9IJVMz/2uaoxJ26MWjSb2oQ6ektbTC370jh89q2o6W/pPBc7LItSzbMvZYCN3WKs6mWj3Euw6TSa2qAG7gg7ewFCH6xnjczpcz3DOJdFBev6fxPbf2UWTuhcYyZylk1i78sG8o4hIRoqn7IuXZ7Fte6rjcCa1xh7n4Rtgd4H/AfyPEP0BuAw4NudgItIqgv+MiBvAHweLgceBx8AfxbgPsz8Q+Djwd7nkc+7G2Lfygv0u+zCSLbsbqvX09NwPpTGatu2F6epnSYXYHyOyKzBKQ9YG/wTFU75O34o/Z5AM5i45gtj/vvJClclE94vxoDu8VRV8K/gaiD46ZK1xCkeX1nBL750ZJNtmdvc7gKEP0oMBYHUGiaTRDAxM5fDSI/y8dyDvKFJb3rnkvThvAz5mfauuyzuP5KR4yr4QzwQv4hwD5Re34ny93bz6e8D3tl/z4tB/RRURqRXb9m+EY16oxmeXNuX2Gl0InyREN7Dj5EeZKD47p0SSlSju0XO/a+q/aTt38UT62TPvGFKnrHwVtP0tUGXf2O3rGIOXPwksST9UT0T80BdIMpmI3UoId6SfSepamRto403Ay4eobCPifLI6XK94yh54+VMV61XvZb5LzAOZ5JIGE7UxyqYC9+QcRGrEiwvHYh1n4r7s6aVPAdd5ceFYvOMksOMw3x/YhLGWMufYrb0PPfv1naXXASfgfiTYPjgbMP8ZkZ1ra3v/uNv55iydjIfZwEzcZ+A2EdgDAONR4Pe4X88Y+45d3bsp8fc9+pT9KfTvk6i4ve1+u37lI5W5ylUmjJJ9fd2Yt2wacZhJ8KOAmVB+Cc/8UqjzZq0XS3uDnfzswoiOi+3aC3VwpohIBmztmrXeueQYnE+Avwi3u4j8I3bTmp/nna0ZebH0d2DbtiXw8D928+pr88pS8dzDbzA+qud+aPXdtO3pifjRAwc10M67krW4MID5KiL7dILqxXQu7iXtF4biQyeBv6rywuBtEbwftzWpZpHGYOa4Xwi2MtHherOXHM/Nq65OP1f4ILBflQuD/lnuj+PRla3x8VfZRfsx//3ruP7cp/IOIrvGi93HYdGRuL8CmIMzYrvL+3ux9B6gB2PPHV4LnMOIeLt3Ljsa93aIL8Qpbrto2/2PvYqYd3px6bHWt7Jvp/PNP3EU/cO7gMWEcNRzVyreY5oGvAazd7CBM7xYOt76epN9NK8w8AE8Wp6otj+cDlyww1oIp0H0oURfPxDeDfxboto0FUt7Y/Za3F+D+2sxew3leAJQ9w3aqswn4pz17P8f2HoloKatiEhG7KZVNwI35p2jRbwD/AQAzNYAuTVtQc/9rqrvpu3aBw4ksuwPkJLG4tyKcztW7SPc2zEi3L4IzCKt7tJRi0bj4YzKnlaVycRg38bDulRySOMJ3E2B64E3Dllrfj4Lll/PtRduTS1PcenU5AfpFS7BYzXj5Pl5MDZtmAH8Ku8osquiebg/357a+wLnP++XGvvg8Q+BiWDDX6BuJISv+4LlB1nC1zfv6upg/dhl9NvHgL2TfM12ZgDf9a6ul9tVV2mbomMWTWKg/RWYvwLnb3BeC0zDn/4rU7WzJ0VEREQkNfXbtF2wfBhPbp6iKVtJxPgS+CVghSEKj6Kz+y3ctPo7qeRoL3wUrNoG24P/pfMY2Lc0mSiDrAKflehwvU1b3wWck16U8Dlgx+ZK1YP0+AOE63Ury5AsjKVY2pu+3r/kHUV2gYffbPefv2/b0oAxVSp/AVyCcxfGXOCZ/boP3P674fwI4+uY34tHfw/PNoT3Y3P/8cC3E+VaP+4W4HVVrvwfZtcT/BdE9n9gT0DcRoimYb6M5/bqfgl/GfdG4JohH8vtUZw/AWAMAyZv9xM9Amx8rtieqPx6/gpPf/227zEamLBdxf/iz76aVn59rRyzaBJx+wyCvxjsZTgvx/wwBtgL/Lm/mqhHKyIiIpKrTJq2vmD5MDaURzLS2/H+Nrb4AMPa+5nGJut9noNJNvZPJzK1bCWZ2O8l4hqMNw1ZG+w8ji9dy/PsY+fFhcPpGD4GomH0bx1GW6GfKNpC/75PWl/Pluf9vvOWTWMgXlbxj5xqk4nGakLQZKLsaGcO1zM+zqzS19hun8jtebHYxrDpY9hUGEYhHkXZA23DN9FW3mw39L5wM2BO95GEaqfKPs9BeoEwZF4RgDLToeev0FP1nvHiKXsQ+idQsJHgw4kKm/HyU7T1/8Wuv1yvmXkyvxaLTiD2exgY9QeGbXwr2KXbVWzG/P3ctPpie67td5MXS28AXv1slXMfkS2yvlXbfzzuZi+W5vDMvt7Oy0natIUd3yh1W0uBT7J21a1W/Z3Rm7248ErouAs4aNvX+CwSNG2tr/eTwCfh6b8reMcfMSYBEHGN3dS76AW//ubec9juzTbvLH0Z56Sn/+9t1td75FAZdkmx+/3A68BeBMxggFHP/dG4mrMiIiIidarmTVuff+IoBoZPBZsKPhFsbzZvHUkb0P/0QxaAMnA3eOeSJ4G/YP5nzO+lY/j/2cDW4WwNE57vMUSqC2vwQvF5Jn+eYxzARt4DnOnFU/bAyocS7DCiMBVsCm7j6HcgBtq23as48BBeLD0OPIDZ/xIG7sSG32l9K7ZN1pTDuU9P3jyn2mQi3MMAN2qKXKpKergejKbgnwYWe1dXgcf2fDGxvfzpzean4D6RrVGBAkDb06/2MZQNLy7ZAjwIfj/Yb4jsTlu78sFt37YnIjx0LniSg/T6CP4/u/0zS+swH87MdVP4Mff53FP3IvQXcSvivAI4BMr7EEVP95MMYgcK0D8CL5YeAn4H/Aq3tbT7LUO+ASE1Y31rHoDnDhv0Yvf2l58CXm03ra7cG9a4HX+6aeusY1j0muc5YOtXPHsYo8/YtZD2Oetb9ZEhy/ou2+LF0vU8ezip7fTjWd9lW7yz9HmcLwLgnOjzln3Gbrj4T0N86bbyWSdNwPnn574hF+1shuTsZOCQ9L6/iNQb7yy9DucK8F8z4fETWnkLGJ9V2o8CVwIDdGz+O70JnD+fs3QyIVwB7Euw4+2WVTqstkV4cfEUPLoCY2IzPfc+66QJFNq+B7Yn7eU32I++XLNtMGvStPUjTh/BsE2HAofR71PY8WSJIb7YxwBjcKaBHcmWLeFGt/g6Cg9+Gb//UUK5FhmlBYToSYyvYpw2VGnB+dgdnaWJhPJUsAgDPEkX1cYCY3FehrUfj5fdO0t3vifir1+M/W+r1FeZuw0riEyTiVJdXBjAWEnEZ4asdTvpy0eXHmc9Uyq3VHjB19/hwHSw6UCR4HjnkoeIve//s3fm8XGVVR//njuT7vsGtEU22VFk0ZcCNpO2LBUQWSr4CsjSTApYdgREsLjxAiqylSYti/AqQhVEdmibKYso24uyKsjafS8tbZPM3N/7x0ySmWQyWyaZJH2+n0+VufdZzuTeuct5zvmdXQJLt/1AORbS8z1XSM+RFwea1/8XAc6ZVF51MNGGQ7BERk1ukX7bJf5VYLqAKDGVh+djdi+9Nj3kXsJKysa2i3lpWdJj4Yo2HLaArW6K/jT6FWSF+CSPtqubzztlXuxtiy39Z9P788uJn5dBotEfAlNz6hssq0RNEjTLGLE218hiRxZUUTURX8PS77XtUwKwGzhK5eHcJVu8wD+t9vZ/tdNEIPHiSuBo8I8E2w0l5DaMxcALeP59tmDOgmLMlTLvxHOHE41+E+MY0G6I7YA+GCsRH4NFMB6xSPUrxZ67lOjwU/tT1/c0sBMx7QkMBVYDixB/B70C1nZGndU/Y5G717XLhtDpfVDvw/B0HOIrxK8dQxLSKcvgwnmiAAAgAElEQVTw9ALYU4xY+2ShTtZEFsAZiOuBAWC7sGLY14AXm9pUVO0LOgoxCbEDxmjEZxiLEU8QtN/Z/Oq32/Nd09t21hexwPH4dgRoLDAWowGxHOM90DN4/p9twR0fF23O8qrDMNUAOwJQ1+d44N407cZh/mnIxmN8Ib6R1zC7xSLV7b4+67AzR9MQqEA2HvQVsG0xRhK/QW4EPkL2Evh328LZL7d3vq6KQjOCsPhIfP9WGmWTPFUBlxRn/KljkTeuGGMBMGrtwzZ3bn17hshw7CG+6F3wsddhZ46mPnhIhibNQUCynVUenpLz4EaDRWr+nI89mVBoRhAtnQy6BSv+sU8/Z3gEpm8iOwaxWyI7Kkg8COEj4DHw/5wITChsDjAqKg9HdjuNGVwNZacC1zXbkbjfS5Mxdk2535s9j8X+kOl+3y6nrSaFB9Ogg7HP9wfK2jNWIytkg4ZIw0/2Yl88ARpeh3evk735odRxBXccPQjvIfCPpvHG3AYx6HuFOP4xs9fbNZ2Z1Yt9H/d1aJko8w0/Fg/RTR+ZaCwgZm+2a05Hz0d6HllOxfV+ZZx5OvZCu7NbxXarAnbqRt8vDxrBmIjJTIl9aYb35qLY8vZO69g6ODxgQ6+Vjt3X/CkBrH+R0rEDGIeDDqe+7waVh+/CgtdaZOayoozuKA4+DTkdb8ma2km9OtIkADyzpgJbKqzorb1442aVh2/A+HViy2kaP/Xn9uycDzP1i7+4LDm7eYPVtPel0JGEdG2b90+1UMwwZuY3dvRSoF1OW02YOgHfmw4cAwqkiXUZBOyJ701VqPIZYtHv2nN3rWzPnJCQn1H0CmINF2FJmvXN8+4Qf5HWeOBqhcLz8LjMFtS81t65S43KKydTb9UYqU77uC71GIz/yr56WLYfUNB7g5jhUb70e6CfY9qulXBL3JEwGtn+wHRWDn1XFZU/shxrcGhCeBek85HtChyEMSR1fG0TL9o49DvAuUjNvw9r+v8+wCiM/YjqcpVX/gYbc5lFZrQ7gEqhaTti/vWIKQiwVr/DwcBuYEfhB25QRdXdeLGrbP6cvJ8zdciZAykLXoyxM+Jg0C6pc3mjU9pXVO6N792MaQK0eHUzxoPGqyJ8g9XW/CBvWw4IlzHQjgWmUq/DMOKBQq3PtT7ACEwHgp2tUPgnFqmZke98pUbjLuxLr89fAdZhtgbTWmRrwN+CvIHg74iWHIRZ6qJafOGoSEZ4x+R9Xc/EmlEjiC/u5GdG7se+L43HXjYtceyvyXmi+rIDMD2QU1vTBGBCzmOLz4DBOTXN5dizZBzG0BZGbZuzPXmQuA5chrgQLB4IkPqn3yPx70jwblR5VQ1+/Yxc77WaMiXAiiHXxxd47CDUIkPWtA0kFpHlTwc7GhRIut42MghpT+RVKlT1FOgUS1P7o6AEbU2e3luhqiNp4DzM/osiOWx9yZahpptMGZR9Fb50n+nEXwVsnwFO49aRDcViSLfk0vQJGPsULR5qCuAXYoePfAYCeMIrE2UBCKQp2FSHuLO98zm2EozbSIhzZOItGHqLkorhtINL5e+6FuttMgtiwSAKmmS0fsJYRZT7izGno2ezB/T5p+dVPSEe2N/s9ED2InuFMhDjPIi+p1DVjzR5ekFOOEcXwdLoZ7eF7HGMuRhzEf/pQKvSM4hqoNGxUEYgcEXWPrb4eJqjXxro1VDdQdY5uhS2r0JVL+J784FvEReMy9bnMAJlT2nchX3bM7MqKveG6JsYP6RlkdG2mYTPCyqvPLU9c5caharOwuwRYPvSzH/6EMqXPoPpTsjZObUHsj+qPHxTPDIxC15AyKYDR5L+3eZkVgx9D7gbsgQEAHEHk10Ei3N6p8qEKiq/Df7biFwj/MqQKol5L2tCOE3mV7beX9gMXI44Fdil1X5TAOKOdFVUXYTslYQzq23EpQpVfjdXEwSmisoTGMhboLmgI7Cc/S4G/FgVlUfkOl9XwV68cTNGX4yDQUfHj4HOB7sM0zmYfQOjdRZE7n+bXNixiGPlTbuOfbzdDE0IH96xVhafxLHvl/HY09JhC5hfdP+eKs7enWDwdeDKHDO3gpjOIVD2lsqrcorStrlzY5h9G+xE0kka+hygUPhvSPPAjiWn+72OQDyl0Omt7tF5R9pqYtVebK6bTPwFqagswoZEpVZfqAzKQtKBD5u3a43HC/f7aiO1zuEAfF4lwN9JX026CQEXwd5vQsFRisug7Fb8XVv6tDzhGRAzfDVVgtb9+Lhz15Eb8eJ6j2B2XLamPzPt+T1YNhgrWK/sn0a/B3xLru5O3HlLMGaSrxRJjzmYv7nQuRxbB/fAwVOCdkEfaVQnTjsA9FM2152m0LSwRWZFOnFuRwmwhdXn5tO+qdhofWxoMSpw2SM1mxQK3wD8EgBf39OEs36eMb1XnJf06cFi6p45AONd1Eagh9QXY6+kLW+A5RHlbO2I5NfXUz/iY/wV+DPS23gJeQ/f+zLoEoxGh9V+9Np4AXBtQbOWV41DeozUF+aNwO8wewLZfwjE6vHZDlkIOJ1mx0cfzO5RRWWd1c7OLZqrC6HycAVoFs0vzAL9BeP3eN6b+F4MYl9EnAKcROpF4WOw5oingNIWMM44/+HTRlHvz8P0peaN+Bh/Af6Mxxt4gXXEYiMRXwX+G2h2GhjnwZIAZJF+m7fNR4SWfA70BxqAjxC7JDmLTmpxudsM9iTocfDex2wpNAwA7xBEo+QLYFUqr7rHFla/SAEoFD4Xn5tTnFbiE0z3IFuAF1iC5wfwtRPiSOB7EA+EAbbHZ75CZ33VIne8n+ucFpkRVXn4XxhfTmxaC/SD5rojce3JJb9HTGrRfR6m2Xjem0RVjnETjcFpsl/ogPAD9mobRdSTCVVejOyGNHuWIx7GeBn8N/FZT6+yOqINozDvG4gfNNkpOxd4Ktfv3YV4k8YU8baI/wYagOIvsHvs1CKSfTmwlJzekbRPik3ibXp5G/OavxjHPsa5wNO5TRhbB4FX296vnWm+9q8Cy0N6RHnKjtmboB0zD9mBxx7QhPD+xGLPtFgcWInpD+A9Cf7HxOTj2RiwicB3aV7QG4lpvkJVx1uk+snss9lbCZkXAYuJX7vikcnG+FTDEvd7s4cQb2P+p0Dr+72xP/Q+nyRpBcjDaRtP5Vp8GDFldIQVyhYIrsqiKTZE/uBLPJt8qLx/XOjbP6IWS1cV2OGgD/6t9XgH+llWNd5GQ38DYy7EFhcyz8X4u32GpY00NyAoPN+QL1YoxtxC5nBsxcS4MxDksBgMyNRsJfS5Quwy0/h3oVOdJ/ZqaCP7IiAzDwIxIybs3zSo1hXSc7TFYAsEnjP/rC8Z30kvrdEp7Ar+AoUqr2fkuiu35gIsWxuaPH0QdZsPxA98FbQncefTGMQwjIFAGfU+xXDYNtFr8yzq+v4AYxRGL/zAFcC0tPZNCO+PT7L+XAcWINs6sdqa09rap4rKvVGSTFWQo21ede5ayMVhM3AHgdgv23Duv6Vjwg/zGc81v8jZGRTgtFXonG0h+iApDlu7m0DwEpt/W8uU338DC3VA+BcM0CWY/YTGd0XZHQqd9Vo+zrNSE5cD4A6a33c3YnaS1dY83qLpv4DHFKr8HdgfaXIm6N8WqSk44i2ePqv7ML6UtPnvyD/DFs55p0XzD4C/A7eqovKbyO4Ehif2nauKymczOc2NGb4IHwmxZbD9RwnH5efQKspsKejXNMSq7YU7N6QZ6lVNCD+Gz+vEnz0NjzNI0sPNlXhasG5KctjWAVcyau0taeRg3gYe08RzZxCNzsT07cT2ISjwJx0QPjAnZ2kjAZuOVIdX9r7Nv221QuHXgP3ihmk/AmWvkhp5/TGm86x29l+SbVIoPAa4EohnTA7RTpDLs7a1fG5/F7gCRj9qC9PKTXwA/E2hcB001bQ4OPs8XZAg3yfK9QnJi8GgIcjzEXV4rCdmHzLY/xcbmEncSV9clOww1iyLzD677cZJLUOV54P9JmnTf+gVPcyeqMlTorMIx95yP/a2cM5zkCj2mgaFwn8ETkh8/LNFqivbattugjqXKNeV6thr4rnDiTU82MJhWw31l1ttKz3yd4B5GnfhT+i98YfIfpi4VvUF3acJ4QNtQU3m7K1A7Ac0eJfS0P99e/HGzQqFHwcmt2i1GdMcgvZLm1eT7lkjfr/fwPM0XqPQGRTitFXonAGw5LuYFU9vpAWfKk2ofBo8X3aw6StzAzZ8quctXB11hcocqewBff6AHfk/sOIPOaQh/QL2PBPlHaX4d2PAg7KsqVaesEuw/zwqs7dc/TFHHsz17ICXxcrrLbPTFuBOY5fpxqd7irwjYP/oMfyvsczRkIlFiMAh5j8U8XAnsiMtFcbgP5l/7dDUSLZSYWCXsWLofjrkzBPbeEF19AAUOmdbFJsCfIvNdePBC7bQrSyqj7Yl9vS9n6si/CuUeMgWZ2hS+BdpH9D9lCjb1y1S83zHWebocsgeIGAX2YJZGYMF7JGaTSqv/BnYg4lNu2pS+AttvPRloOGeJM1AYVxgtdU3Z5w77hy7VuXh94E/JF5kB2CBq4E2HeJdjlVDTiE54s/se1Zb3dJh27w7MvtRlYevwrg+seUwlVeNKzTKlJVDL2+Rdv8HNnCavTono/PRamf/RRVnH4JiL9DouPXtWoVmPJhJXzbLtWQLxg1s6X+tvXhjxudEW1DzH5WH74xH+QLKIh2QBh1x1jDq9Dsag2fEZwQ02RbM/mvGueMLCScpFF5BY3Sx8WUG2CnAXbnObwuqn82w98QWG6oZyEX2yOw0kdTBWyH6ZbCZRLZ72phRyPPvW2xgv5ycztLTmDU6bYdrUniwzatZX8CcJSNxjcp6nVIo3FEm7Jj037lpQpdXVoLdmLTpU7zYxCJkwbzJBvYv4NiP0OTpg+yJWz5r5/ydSsmPfTQ6s6nAWZwfWaTm55m6JK6HV6mi6j183ZW43w3Bt/8VHGytHiaT+s6f888sFv0Bz7skp/t9ReXPkTUWPNxdoaljk4ujZY2V0qFnD8WLnUHuGjx5sx712YDy0mr6Atr+9zH/yC/mrsvk2AoYJwY+FLDKbWQ7/0haNCgHTdBV0Ocy+GK+c10k7RlT9t/QXtjG81DgQc+fVmG5iXk7tm6Ckj3u8Y1DjSOmo+U7GVlT8uogMN3XnvnOVQ/2QyknB9thHqt+B+W/Ctg++c7j6Pl8i9iIhwN2Yxdx2DZjHE5ZWa0On9aZMg2OTkCh0/uoPHwdRD/AlCgkkzUgYR3wIVDcgnUKzgTiqdRGL2Jc3qpJ/Bw8uXmLdVKUrWYBvwXmE48qdPI2pUDcaQurT8r2AteENdSmfG6w3fOarjxcAXZY83j8zGprMjpsU6ZfWDMX1Bx5Jr6j8eeURBe2IGTJGqovWm31g222baS+/63E0+kbOaWgqUOnDwEuTdpUC6NPzTVa1Gpv/xemM5s3sDMsbulszMMgha225upsDtukDpGkDzvmrRO/JXg+sE18KHw8Oz6bwzaFkWsvAF5p+mzKuwhYDmzBdJZFaqbZIzVpn7MtMnOZRWq+aZHqJwt02AJszDlK2Py1KZ+j/sA2WjrSoEPOHAiMaNoQDH6QtU+o6hSwWTQv7S7Dt4kZJY5yxnI/9vJSI0HrGtyxzwNNnPplTMnX/D9lc9gmY7XV92AkOe51EKHKowo2yGy2RWq+k/P9vpefer+3QMr9PqPDSYeePZRg7Ex8Dc/Urr0syjHKtiUjjBEzPQ4fHShOITRH9+ZA8/rfE7DKIWI0wHAs+n1ZThEJdxk7v2PkvHBwN9rmZTEyWzsDrsL/2AMGmY2q8bzKccJdhB0ZeSroHbsf9nWIK6Nf7fNRLv3mG6P/2JxOlxPJhfQy0Rv8q2SferLgyeg7/+NZ13LMOUrKCTDi94HgbQPFzqW2JT06gDq/VhPP7dDnGUfnEX8567UA4wfQ6v79EXA/6FJMx2Lah0DZCCKjAxapGWqRmp0xyzlqKxcsMnMj0q+bDeRMhaamFqeoV5hmHbfV1PX7fTFtaJPI7JuI1JxOpGYSkZo9iNT0A0bicQDYqYjrgSdQ9ggZRzvIYQE2pXnk7nVAUpSd8iuea2ouiif+iUb/JK/+ANbwU5qd/EG8WHcqkJMsQ/JYLh3iTk01R2mavlbY1L3Opbnq+maCnJ4pSjatLbWz/wL2t+YN9o3CbAG8/M49Aim6lwE2bswoYZiMQucMwNScUWDcbrXV8/OZPi5pZFclbdpDE8Kti4oVzko8r9xqZ3etAtF+MDUnJFjWq0SWdE/6eMlaujGGrlrUZltAFVXHQ1N0JcBq8A6zZ6vf6zgj26DMTz320ag79vkQtYtpdrzXQTCzDng66vpfRXNhWZBd2nbjLCjP+/1Td6wBkjIC/ZT7fZvRCDr81P7Ux74LHetgWioNqqNwp+twGDbbt4n/bd4z6xVzmnVbKTvFrPdve3HGgBaO1LPxV9wn2/bDLJUD60XgYrT749jr2ebaYr73E9keudg12bTyEFmTgPkAafidAe/M43y/+l3YkssYjq2LPwfssL2UWkTvSFh/iLTmBbOsC1yXSPscY/Zs7wzpHI20VUgvHd/Dluwo1QN4Mu9Uj5NX+4G7biD2YdbOjh7Ngeb1v9PTdX0bo2q6KsZeROsf1+GnTrCn782zuIKjyxEM3EpywZ54BO1NBPR7mz+7YH3vdhGN3UpZ8GLii2e9wbsMmA6gA8JloGadW+PO3KPeOoBIzSrikcGvpWyfeO5wYtFJmI5CnEhrh7ijc9lEU2ETyznaUYeePRRiyWntP8vXaQhxx7HKw89gfDOxqQK4I99xOptExF3yO2xG501qZ/u46bFIFJahIY5PerS6M39ZiwTGXMRBiTErChqjEPwWDodAIPdIW2uYgKzR4VCH5xVUQA+2m4eWrGnSp/QtBGTWmMyND/HtCIvM6kDHnJ6nSZPSPs25W5lvxEpVCqAHEAuMBsWjlcWnabSTm1BF1TfwdR/W5A9bj/lHWG3Nm231yY2kY2/K/XfvBw2cK6sQxAwPW5KkJWv3WWRm3tlUCV3a24EZ8Q0crEPPHmrP3742Y8fisYmm+1bq/T6t01ZTpgRY2e9k0Ih0+4tFVHgraH+6+LambWfiH/Rd8UIx7HJ0L4KSPdDL+/YwaUyrfZiuho/OIHu67pMw9hn00WFYS6HqFGb43o6LUP9s4/U1/B/J+7Sl72wI2vZ/A97JB0Vjv42auWJ6jiZu9mzf/xJptcN+jn18mBjSYJkzJD42Bv4UdvgZ2aNzL0FtFtJLZqRRfwFamnwqez5l3zed8g/Pv/XpmNdZNzNHF6OPb97jQf10EBQzAqbjMPsadf3uJF4l3NFN0fipO2F2avMWexV0pMUdkSXDXrhzg0LhG2kuJDNVE6b9jy2YtZiBOgGs8TklhryZJTIzM3FNyfuB+zn07PMJxs4DLsM5b0tFkqNVuUvClUUnIGssxvs5/Xr/JWP7zLwJjU5bf8d2jNN5lPkNxB/A4x4wy/7c3oQnQwnHmZFnEaKEDEqd/5Wk8QqPqDe9mfTsNUYHhMvyKshVMLFoSi1nz8vdaStrjsYWC3NOD25BopjauzQV5CrSuWfM6ehISovMngfMy6VtvHhmw0Ci0UH43p45xFw42sAi1U9C9gxuTZg6AV9/xGiMZv0c846y2ppX229DgcceP2+JO0eCikX7Ia85cM/8Rwsey/xHkTcj8SlIWXQCOWojF4Hm+72fg9OWNcMmgDpcs+hTaWgsiwMiV/ZAu/7QWPYLFWUFztGNuMfzDhmbQZPzSFj/dWPNc1lkOARcJPb+p/FCW2ucnxq9ZqFdcrmfnokWj4W0D1ZjpN3vNe/r30lOwXJs1XzTvOHH4X+rrajX3WHLd4yl90CrxYmW3IR2rxJLtjdrc4X5ZRjwp9TKuW1ysfTJAFpX0etl6nejvO8cbKper/wK+Tl6Bs+X2Wkj0f6ltiMvTN9WqKrWItWzSm2Ko0ACNhklXSwDsTNt/pySOmyb6Nv7FjbXXQwMBfoQ838AnA+WVIBMj1pk1kelMTAP4tEl1xA663cQeBTIS1PVURSa763KIzNRtl9Sv8/ZVHezygss/mL25SZHkiyrNFhXwCJ3b1F5eCXWGCnrfSVzjyR8dkm6uizP0DI9ddqX5PfbmJ2l8nBhldJ9G5nkxDP6MQJYWtBY+c0bS3lD9/M599iv6e9njFJ5uLodljRLzMi6rS593DlXdxC+fQ38fcHbCfQFYCSbE+sCngdyDtuORhMqD8a3h2leiNyC2bFWO6tDgv/cse8EFNgpZbHDAn9ru3EWRqx/nZVD62iUsvKt82TfRKzp2umlXnNbOW11+NSdqNfBHW3TJlG2NoeK6PlwrGfjaqNa8aLhKkRvJZwO25V7HJnNifoTn48PJ3uU4pvG0Nlo2zCWNqT+Avm7b1L2yMRtoe68NsZoZHyAw6fKe3+O77e3MqWjm9PHM+864ztBWcYomiulxY/ByNVmGXWONkLZBbDHn6DNqpYXkHshvZNhdVv7h2Hb32s28ZvS09nGcvQsrvPYe3/ptBzUNbogulGhcMQiNe+W2hJHAciSF5xW5lDBt9OwJ275TBWVv0F2TXwDYZVXzQMlSzl0UgGyIhG5431C4UOBl0mtzO3oqpiNanICxB2X7SjXnfwyTDfSWbQ3QfHsJek4HXLmxfbCnRnfETUpPJgo45M2/T3/af1RKQvwxpltN85Gixec3t2gjoulSEp8BSN3h3kmvG7w3ZOIa/vG/hvpODbVTYj/dhqDv52DrhSoYuoB+PY4NPmgGkBTrLYmL83lrPO4Y9/JKFmeTaz3VxQ6ks2dG1MovILGwCbrGtJvKS/smjIlQL13FLkIHLaTT6WCio9lIigFrwjaQcUe19E1CUp2scexnpLzd9KzB2yZkmOl6Blir01pnLvPmgY9ho1N16cll8HHfUXGKqOeCFzo6bigAt3S5eEoHnfAuCFp5D1aMgiLXURuhWIegu2faaNoyW/FqFwL6f0Y/6Nsnt0DjPEnye+2ERCO/BngmTfN7EIrUrZMCeiDqFYnPO84Ohjl/wakydMHIXLSpi8INdxMXGMXoA/oD0l73yEyu6gviJ1CpGYVxslkebZxdBE6roh0/pGnpcL8e5M+jSAY/E3WPlFmQFJQkWeFyEp0nLxgtL5gZ0Qn0jHnnrrPuaeKyjMh+hFSNXBklsWOdYgPgFcQf+0kE7c6FJq2D/KeolmaM4b4rkVmF55Kn24ed+w7H6X8jX1eHd3e7M/kTNXcpWE6kNRI2xWDD8Y68EaTYI3o97mRuy5THoyRxnw/4G1/a8zPXfTb0S25NWD7j8B2yLX9D9HiR7FRn2UowAew1Oj3U187Xmv2QfL2i8VevrK/4H/F+OwEkZPG5zCfsbOCsQOnxng5l/aOnsfX8AeMx5uUq+/odGzlA2LUP4yMlXxl2IWw9xukyn1sMd+7BtszFzfHZNPKcbKsBZs8EbgsEDjmfl9dvjiJozg86um4QbIvltqOdmGMJ1R5EpHZf8je2NG1sI+bolWMURo/bT97dtb/ZeulirN3R/5UNtdVUoSaCm1aF7l7nULhm4AfJ2xMLoZ6m3XXUJvamr8TqnwQ7MRSm+LIgiUVkhKfYFYcDWXpg+yNugpj/hcWTwU7BADjTIWqhuLpUltQkyKnp9A5A1DDNcAFSZsX2oLqAmTMWjw3GVdSFAkpRS1yd3coYryJuDwMQC3YU0UZ1RQpyjgdjEJVNyBdkmbXR2AvYHoJ6W0IvM/I1Z/a3LlN54YmVu5GzP7VedZuHcT/rv4zNC4oCB/jLFtYM7eo84TCv0RcnGbXh8ALGC+DvYXsP62OfcXZu6OYy/4qBNOKpPfoABNXDmV+21miOZAciJR3QbOOoMl5pSlTerHKG9fRj5G+ZEvQ0I4MbvmW2G+WtMgVeeq59PHMm2hWkY/+y3AsWiU+vcHYKVvbW41dpxuLRiu+0nI7/nb/kGWNDveAGfBxzkYBE9DEwcZrThN062RGwPt6NlmEZDzgJ/DRcfAlP8uF9C0Y+ms09mKsqWryNb7tuAgKLqTXFtvBFy8lsNMNxD7MqYOj2zI6QNlB8r7TXf1OqdiPxYwHjBkuejAHFJp6EBb4cvwD45p1Lumriqp4+rXq7knoSVbg2a6IryadK8NVURVG8i1SMyc+ZuXRmDcan32S2m3fPJ7/uUVm/y7VktijyLutSTfS/D+ofOpUWzjnuRR7p0wJsGLI/hjl4J2AYumzsYztmuZr6lz3gEXuzliYNCPRwE0EYxeQ7BwWnxGN3lPwmF0B6WbMOW27AclRiUGLVF9XMktKhEVmRDVx6glE7SWML8S36jh8vqlQ1euY3kbUIY2G6NcxG9jUWawhqMIkJWTLSX4FVewBi9zxfnu+S7dCLMca6y/Ykq3p3FNF5TdTHLaiHtOdGLda7ey3SmjaVotC03Yk5s8Dtm3chKfpVjv7t0Wdp6LqWKRmh62ox+MO0G3u2Hc0gcUkJwFFG74M1BYyksZP3QkYmLSloEKKxaY54nDV8K8iv1+GtkVhqWxQvWWOdGwvQ/GHTQ3amFkxFmVv7eiO/NJn3wFe/qlf07Hl90vbfmKZqyBvhLJLpV1/h721QQr8wiynNMpj0fL9ZZuyt2ymr7zB13v+flUxXsmnn6P78yWPfl+C/8q339fg82NgxcNk19n5Gez5PWPZCBH91Oh1O+RUSO8s2aKxKK8Kxd+xWMUNwjltezj3Ykf1Rh2eldNJ7EH5khNYSFGjLXou3okpLyWNGIMSqYAQs4eALRhnIp3SouX2iXZRYA4A8i4ETWixBLVP03jxRacUp61F5ixSKHw7cG5i/t3Ae1ah8CLgo0SzbVjJWJru9xkvfLs2z9c4Sd+FNEsc5I09f/tahSpvAftR00ZPv82mqdnlWTj2BUJLltH8AuzoktjLSdHo2yk0bcduUemZbcwAACAASURBVPyuyNj8Ocv19fBBBLgPKE9sDoAOQBwQb9Rq/XsR5k+x+XP+XdikgZeTi4BjwYOBrcdpa3oJLFGgVAcp/hfuCau82ZFdnvTpc1CFRWa7bMoSoQnTxhDz52NJhZfFZRaZnTHzQEecNYy6wP2Jj1EiNd/Ieg6L1GNvFrLaavdu3xkM9F9iA1sgkcnv2WEU6LTF8w5P+exrYTutKwoegJCB/7WOnqzeV2CldVw6WjLfMNurM+ZxlIaKQCLVKU/KkC613CJhH0A7vor6/wjbabkyO3kBBphiP0qKaMyH8QoU9H0c3Zsf+nZgUFaQVs5P0ScDU94K0rMOev9A2hXgIrT7phyqT28LddNNeWuHbWe260le963u68iNg2QnlNqGInNB9iaOrkf9JYiWepNjgUMT/3aFtPfudxAXIWo62kKMN5I+CYK3dficHc4MH4iU2oqCaZnV5Md6qK51YCHQ+F0N/O+V0ppSYs/VLMX3LgSyLURvBH4F7GeROQVXH7fIzGXAO00b5J9R6FjdEktxluzChKqvl8yWTkSTwoOB5GyO39hC57AtFTp82ih8fx7Gzkmbr7GFNTdk7bwluDswKfFvz2wO2/ixV1IQjm60SBdx2FpyCKp65P3OHqnZBNbsXJXOVOj0vKVYBYY4J2nTu/bsnC4RjBSPeJ10zk5EO96Zusizob4652QZLRv9FbP+r8vPqsfo6F6c5NmoYTkUbWqLE8Xau03rX8UynvNRzM41b++35Q/N1K6RsGzRyBycaOkYgrY9Qxp9l9mSQvo7uidf8Wy/QoMPRsqiZ8On1+cg93GvbKdDTGsfEUUrpNcWp4j974cnC+nr6Ppc57F3P9P22Vt2I4yDNbFyN5s/u7Coqq2L94B5GVv0Lmss4PBW222THGfGa5DxerMq7QiRu7cIvkUofBriIowvt9F/M9g/QE+CHrbI7NcBFKo6C7RzG31ADe1/fpQ1P/yLZyxye0/RKnwFOLnURhSE523ATzrd/EDWRfnuiEVmLlMo/AjwrcSmizUpfJfNq8mpmGlPQhOr9iLqPwmJRWuxBuOnYL1BfYFV4L1J37IX7Ylb6oozKbMxfh3/YCGVVx5nC2c/VJSxuzp9+jzMproVWEIX0ufXCs04yCIzCnpH6jbU+6PxvGY/h/RCCa3ZqtERZw1ji/8MllJw9FcWqZmR0wAeX2p6PVMOwV6yMakOUa/rHHufDU2ZTLIeeb8DwLgdcUTi0zbQ+xLgZ3mNUV55SsqzpHF78QxsH3GnbTTW1oNu0fhc6rUuBx3FYmHyOdWznV6P8WZnzenoHE4R+7dXEvnH4uNvWXZN0L8rNiqI+dmm296zzWf7/vL2aDV/O8B+d/k4p+1WwhnS6CGoXeml09HyP2KjPshybY2avAuNL/t+cQvppWNvz/9KMMpTTlO8Z/It7LBS29AhxOy/gRmlNqOrY5GaaqA6a8N42/8B/id7u+pLC7YHRKTmt8BvFZo6FvP2xdcwzKtHrEGxRXhj30vnLLBI9R1AhxVPVEXVvkjlzVvs1o6aq9PxeKOwZb0ugL9lNfSK0vgOZLYz0DOLv5j3S+QfS/zhdCBR/qyJ5x5m828ruECLxk/dk7LeK9ozRmeiSeHBRPVUkwMR/kWAo1oWIis6ZdxJlB9CY4Fvm6MJ4Y9tQc1rhQ6pr58xkmCfYVbbtRd/7Ilb6hQK30yTw0QHYEtqxIypherHC4yKaQdb7ayu4wxriddCUszLntmWjKZM6cVKJhfVpq0QTZ4+iM11T7ZyvtXW5P6s4bNv0xuTkX2hq4GGhMJ+wohCjn2sY469Z8ubagDF73c9k9rqv1Aefg2jUZrlaoWmPW+RWZFcumti1V7EdEvzBpZAfcdnZOWIJzDo+ArQnxpZizgVm9383KLKHN2LL5rt3t4xvop9fqSxMnMrAUbMUi7DablC/ie9aZ+Tamez3drT39G9+EbQa/d5HMT0M/FhVk+sYIPo5WdJiymkkF5L+sob/N9eIKvWrqN7sr2RvpBTd0dyL0rdHIvMWWS1NY/Zwtn3WqT6fltY/Yw9O+edkkV3+Tov6dPHjFrzeEns6Aj8HF5iuygWuXsLyanrvo7NpZ9C5wxQqPJohap+lL111yDh4EqW5NiPaMNfFQofms84GndhX5WHpygUnofnvUU0+t/FtbQDaeA8aHof3IgXO6LDHbaAzatZD3Z28waG4bNAoaqzlEeEh5jhaULVeJVX/pZA2aco9vMOMbjYjFx7A/B/TZ/FGYSWPKpJ4S/kM4wOnzZK5VXTCYXfRv7zCk3bp9imFo26QYuBpEht+1abbZNQaOpYVYR/yMqhH4D9poOs2yrQ4af2Z3PdY8BXmzfqHmpHfz9XXWWBYckLrjm8FzX0W0TKsVdux378OdsrFL6SFUM/BG7MpU/e+Gr+HaIDFJqak39ME6v2UqjyfFVM6xYSjgYiQCWwJbGpDPmPqaLq+Gx9NaFqPDEtpLFwrPDxrDLxvNAlMB0+bRT1/jnZmxbOKl/9PzE6vWiJb17syBj3rcbv2ekYWxEHmtf/LwG70vyswa9ZWWKUHQr7bYZAtrYBSV4bTtmDPNY94FOUVe+TsWsX+v5nxRjL0bV53bOztoGiLJh919htAWQozGeAMCCYQfbgW7D85uYiPgWzAB79ru/Swnoap8N2dwXsvlLb0UHEoH6ERe4uuPCUw9FIPDUz8ClGY4HfSyxS86uSGlVMjgn3YwOdIz8mPmBhzS5FHTIU/hlwZeJjDDiXkWvn2Ny5seY2U8dCYB/M/xryJoEOojG9PuDva/Pn/DPt2OOrdsUj6SVRp0NTiu6LYA837YrV32nP3ZUSQKDyqnEY45O2XAY0ynT9GaxZazVSfX1WrcX4sXqKuM5z02ZgPthvCerZlpIJYoZHxeI9kXcQ5k9EdjQp1bR53iI13UKnVOXhP2FNx2M9pnvA1iHL4fzVRkzLUODVQou4KRS+kZa66eJtjNngzWPk6neSzzuIO3IINIwDDkV2PJAsCbcZgqMsMnNjq7kmTJ2AH/hq0pafQlPE3/+CxbNPTZuttubmVv0rKr+NvLjkljQM4wdJu38FtjLR/1Orrfl9Dt99D+LFgJKzyrYgPQD8gVjwb/b87SmZXZo8vTd1Ww5A3kHIPxJsAsnvacZPrbbm6ozzjp+2H55SiwlJ52GMTgzyFLkUKerb6zf5ymUoFH6IZkkSga6nLHazPXPnkuY2M4Jo8X6YjQcmIypoK0BIXI/ZmqbP5v/Name3WRip9TnQZsuTga8k5ngNswcyN+dZW1j9YtZRQ+ERYGdlnx+Qvt0cFck/INvzpR+zyOxfZpn/L8AxSZvWYdzQSsu87REMY3/ElKZNZlVWW5014lKhyj+DNS4CCriOsugt6Y895WBHFvPYt23X6UOg1xKaNP71AgROSb6madyFfemzYS9kXwILIQ5r/r3wJ4vUnJh9ntIe+6ahy6vOwHQHqYtjj4DNZIPm26s1DZC4z5UvOgTPq0Sc0qL9NblIaWhi5W7EvOOStpwBJIKy9AJ4jyTtu8MiNSlyX5pQeTC+l3Qv1eXAkPh/8iBmLzXuCRKL7dCelO5s+JItaX7Y6FQ8+YGjzBtxj1hWivkdxefbXmwn872inLCjRcOpZktqSKfPGHdyNeKbmafWD8YBQz+WfVyoLmlLjotpp4XGP4oymKPLMsAzb6THDsVKL/05+qhCNnRLm1HhavpfH1m6BYgBpthVKqyQXku+GNfZdU7bHsYJZvuW2oYOJID1OgR4rNSGOHoAdYFwksN2I9HAnSW1p9g8UrOJUPhzOlH2rLj4s8A7j7gjMgDMYuXQ61QeXowxEDEc6Bf3uaQ+D8a7e6dAikOrmQB7IbUlCzIONK7pk5U9Di2yvjxVINqKpvxWagTXjBtgRsYHUHukZpOOCR/BBv4ITanXBkwCTSIKCoU3IFZibAJGwJIRyILN3z+FKLBYzPAKTXXvXOxNUKPTdjCy6fH/zPG5XQb4qDz8Gp6usdrZLYsfZp49UnOhKsIbEFc1b2Qv4EbwYeXQzQqFVwHrib+sj4BonwyJUa+DP4J40bQWtnpHgy5so98pTd9ZrAZaOW3x7Sws4exsPf3FTf19ngWyOm0tUvOuJoQPxecZaKq/0Aez04DTCMakUHgVYm2iWNJINtcNb34Ha2GE+AyUPbAlEBuHLPU3mDKUjoAm7cu2aaibRUr0ZA4E7Epimgz0js9ql9EQvETl4eXAOuK/r5GYNVuU6a3W+EHquepdC7TtuPO9b4AuzstmY3/Q/hnbeFwJZHXaErBRxNq8/rWcN5l9QVmeMa0OyOa426/F5yHx62ke7+mtmvq5ZSAaVyKOpOnYczkNwUs77di3NUzk7nUqD8/GSGT/2CHgv69QeBHxqNTh8Plw5LVl11EKnT4ka1CDaRuUXRIrzfjFOvbxlgur71KoqgHpDoxeic3HgI5hAPUKhZcAUVgyFrw+LY63MC632prrc/oeUfbG2jrf7RBQc5Sy6VFa1mjwvQmJxbU03Tk+6d6Fh++NzMmoAlliDI5a9kjGjmJXT0NKNbej+HxRxa1MfylaMlyqb7VDLX/BELXWT1AnSsv3looWOr+jR1G/n6NrMqlBwzw/P72jTOwoqz8dW5x2p+IRtp6kgCRrI2K8sh2F9FoyTHLncQ9kB7MdSm1Dh+KzV6lNcHR/dPip/UmOrDPd1TKarIfQbaPSLTJnEbIqUu95gxPOtO2THO4tWYRxL76yOy+6EPZIzSZGrj0GuBCxJk2TgYkK6/sQj4oMtmohPsG4AbxdLVJzcvdw2AKDdB2i/UUmjf2RPaxQeEbeXWtrrkb6BqTNyusLbE/8bz8WSFfxfD3wv+CPs0jNwYVG/ZaCuBRF/f6gWUBDy93ASIzdiEejp88YE68BF9Kv9/a5RtuVCptf/XZCFmFD0uYAxmiMvRLayq1ddeIT4BrMJnWSqY5ciSknOSCrnf0W0nEUduxngDquZoQFryReQLTZLtiBeFToiDbsqgd7Dtn10C/ddanLYpHq/8VjPNJLqTvoBexIPNO15Xd6F9PknB22nUwQOk62oB4FVskGFSsKsRDGKKFN4egRjMKKusgwQPjnm31ydUqaupHOryXMZMIU3zkIRS+H9I6yAhkFHbqI4ugaHBC0ol93f2Ba8ggauRhLugkJEgu6gQyay2M9tpzjq12F9JLpZwwfbAqszzkdydEdGGHkpUXX7TC1W2fa4aC+34WguK638PG9W7L06K6sIzVtu1thC6vvU2jaUtC1oP+i5Q1Q1GO8A7yB9CzyIvZs9Xs5DP0RIrfiJUGvtQPV1/+B5Vj8JHOUbTKJFPzfaPL0O9m05STMOyERBTSgjS6rwV7B9BK+HmHh7Fdy1YTsKogZHhsWX43ZrkmblyIeabNTEzYE/AGYjSM1Y/RqlYcX2sKa7On1yaMtnP2Epkx5mlVDjgJORlZBqmxAsuGbgNeBV/DsKUasmWdz57YOMGmJz4vkEv3utSFtYnoc2UdZ+5ty+R00N49H6J2tSeFraeB7GEcBB5BucSDOR6BXwF4A78+2MG8n9Vs5/wYz8Xn/7H/zNFik+klNmLYnvn85cDLpfS0bgdcxew7ff6jx96WJU7ch6rVtu/yXs8z+N6TiF09Ssi5qBuq1Fq8If/u0WA6BJfodsuJmeA/2cq71YQtnP5HzsRfPgv6ceuwzXPuzHvsMdkVmbtQhZ06grOxqUCWk9Y8tJf7beQlTLYPsr/ZI9aacJwnG1lIfLOGxb9GjtubvgoOoqDwecTLYZFpfH+uABaAH2GC/s1dnt1xYyjIJH+Z8rSmLtV68N17Dz62/qSJ8HuqYImHvSyM/o83V6k7hE88+PS6q+aW0wVE83vCYNoLiRnv5QMj05fewrA86yZqgVxgfni1WFNOWNR6L9o7qtuwtHd2ZewI27jDxzWKP+4Ax7PxGLR0T+F7TAkRZBi3b29C/jsGKGjVViXfDo34sXUSPo5vyWcC7YyAqqrZkl0K2wBZWTyy1GY6ujSqqLqJsU7U9fW8rx4cqqvZFepFG7Tjxe1tY893OtrFTCFW9Ajqgw+fpAE3bVlN8PbwdAXYB9cG0FtMq/O0Xl6yIXSchZnhM+HR78Mbg0w+jAQVWE/NX23M1S0ttX3tRefgqjJ8kbXmGWPS7LXWEM44xZUqAlcMqQTNpduznpPGYdeyvnzESL7gjxmDME/JXE7Q1REcv6fHn3pQpvVg1YifM3wbf74UFNiN/NdQv60na8gJjQnhnpLGIMvDWEdQK5o1e1G2i1R0FITBCZ+2CeWO60rFXaEYQW7o38keCbSFgawhGl9lTd/TodzZNmdKL5YPH4Hlj8MzD/GXEGj7pSsXGMmGqCF+Kiq9JtQF6vyelX0HsRJYpsOwoxZ4stR2O4vCO550/hOKfV08bg74He+fSNiDpi2afz5PeDGaIXiyEjWjlrj6/LuaYjq7H3IBVHCoOz94yf4417fkSlpCFUUIWzAiKtNX7vobW/RErSiG9ZK7wdfPd8VVbRw9hU8D+0LetyKCewSsWqcmhgIdja0UVUw9A3ivAp8D1BPy5Nn/O8vi+yiPw7Z5ECiTA5/j+l+zZOR+WzOCOJFT1IvHiXB1LJzhtHT0PlVd+FexvSUV+7mcDpzYWocl7vOQCR2KJLazptlHmDofD4eheeMh6d8TAn3ZQ9G6+lFmsV/ZWju5CL/wOOV8PF58dIuW0whQz7EJYVGyHLUDQvA75fo6uRR+fDjvOP5N91FxozOLyCAZKU9UiYGiGWU5aTfkyNNAx9xZH6QiqtJkzncDA7E0cWzUKfD/xX9sDtxDzlikUXqby8GpkTyY5bAG7pMc6bAFQj44EdHRzzC5Lcti+R6/NZxXqsAVANKcmWxvaqw6Hw+FwdABBzPcyVKgsmL2MpcXSR2wXphUsnJ13pT1H12TnUNXp0Haad3u4wnj3aHFw9pbGGyavCm910Y2QPuM5d772dI6uqNwFeft0xNgbUJ8+KLYZgk2rCoIYnpknWdKv5yTjo0myRR1xqf51IPZ/N0bm/LP4IztKRVko3K30DAvALfI6MmP6FNEAKYUkt2mlhgrXWKR6VmeaVgIKd4A5HB2IwBCTk36Xv0onZ5IXlrTYLta3ayyHw+FwOPLAQ1aQyHa3QV5dqU1wFBO/w47nUbI1+8OqXNrOxnZ5qyOizkybiz6mo+sRsw7Tzzkf9mwQXlD4Hmp2spmI+ngxZHgwCBquxd7vKDtoCLhzuaehNoqX9BSUUvHX4WiF1dZcDbG9wG4iLpGQjBDPIjvCIjXXlMK+zsVF2jq6KKGpY7CkZ3TZB+0ZTlOmBICjmzaY/tGe8RwOh8PhyIcgZvVIfbI37abI79lO6a0Or0MdQX/F/r6jVLHMMjtk60XgfLTHPOy14lpgztG1NWDa3BGZCA+h4fPEdo2fAzIZImZmEFdK8DGTzM423ttOHRgp5cXcudzz2FhqAzoUk3PaOrJikTveBy4ALtCEaWPwo9uDbaG3/0lPL+SRgmjoCgl1DkcrAjQQS96gfYBnCh5v5dCrgH2bPpv3x4LHcjgcDocjT4LEX8IGldqQDsPzenZk0FaH1nWk7EZv0NnGf34MX8rWdr4x+kH4+HgookyC9ZiKqY5MFP8414Ndge0Vz8xtxiPhuMUsgCSgn9FwlezjYtuQwiDfpQ/2NMyWg3YrtRkdh7ei1BY4uhe2YNZiYHGp7SgN5iJtHV0Smz9nucrDn2B8Ib6BK1VRtcBqq/OKkNWE8C74/Bw4qXkj/6Zvr98W1WCHw+FwODIQBH812OhSG9JhWG7p7o5uw2LgwI6c4Cr45AnY8W85FKW5GO1zNPZsr5aesoLRouKM4+jSBLWYWHEXH36CdvxA6c5ZmQHBhMO2l0zzsL8X75xNy3r7y50uarHHoX8BXy+1FR2G8W6pTXA4ug1msY69jTgc7cCzm5B+lfg0HF8vqSJ8P9LjmPcOI9a8Y3PnNmVjalJ4MH5sCL63K3gHgCbhUwEEmsYUaxAn2hO3OOk9h8PhcHQaQXLU8Oy2yC9+sShHCbEOd2quEEOvMFadiAY0YBk9ax/BwF+gHWZgHxVndm2lETtbGYEti4kVTxL5E+h9K3yx9R5ZcmC6ASeZrR1l6o+s4yJh5c7jHkrPdmpK/yq1CQ6Hw+EoAiPW3MSKIftidhoARi/EqWCnIsHKoSgUBlgHDCEKzf7ZtIsRLyP/e/bsnHc6wXqHw+FwOJrwCHpLSm1Eh6L+znnQk/D0XkcOH0WBVcawnYyGKWZrc+nzK9h9kVScquMW69Dv5+gaxKsYF8+xOV3aY5NSqpknJkr9OFxetMr8tRtgwIYsus3twvx/d9jYjtJheqXUJnQoAXu51CY4HA6Ho/3Y3LkxFs4+Hdl5iEzSN0MyDiTeBjubkWvHOYetw+FwOEqBx5DtPsZS5dp7DMYai/zGaYT2JBZUf4D0WUcNvxRG+IYHMF2sGZaDZttGKLvYaL/Oo2kzIza83+5xHN0D0z+LMcwT5g99Esa03qNWUeLno5X9MR9giRilNG2Kgqwo383RxRix7kWgp+rEL2d+tXshdzgcjh6CgWxh9S0MYidMJyLuBP4PaEveIIb4BHgS+BGe91+2sGZvi1TPsrlze+a7ssPhcDi6PEGbO6NeFZWLwb5QamOKjm8fltoER3ExTKLyTbCDiz32Jui9zhjc+Lm/4X9fWvUT2DZb3z/CDs8an4wXhTuUZf90D4VbE4F/gCa3Z4QYsh/g7al0qXwt3LH7YpuO8tjY+Lne1GuVGDwynhpYRBRlkL1V3DEdXQGbO7de5eEXMA4vtS3Fx+aZE+h0OByOHoc9UrMJ+FPiHwCaPL03m+sGEgzEi3Gr4XPWeWvs1ZqGEpnpcDgcDkdagvH/s7eAnue07R17s9QmODoAs+eAojttl8ColttOMD57SDb4DdQ3U18f7Pton39gfy04dNH85wrt6uiG9Cl7hS11m5FlPLcy8Qv4wr98pUntS42gDch0iddav3wlDB+KNgSxIi4W6FV7ZPam4o3n6FKYPQDqeU5bcX+pTXA4HA5H55AoJlZHT6/t4nA4HI5ujwfAAN5APUwiwewzDh77canNcHQAK9e+BCpqiu5aNHCztXbMGnCZaYXlEH/1hhg20zS6MAv8Lajh74X1dXRH7Ilb6hB/LbT/UqPsJim9LEeLlYPj0Lq90qQDxozAcmxYoTakxWNBUcdzdC369pqL6GlO+ZVs1JOlNsLhcDgcDofD4XA4kvEgkTYS6GFVoWWv2YwZfqnNcBQfe2tuPWZFcwwJbDmMaGv/l6HuSGN9LmNdI/ZajwLZW7bEIha5e0v+/RzdmmDwqUK7nof2+AxrXXysRZTtYFnsXM/WtDXOWhiyCXoXakcKprWsd8WcejL2xC2fxaNtexL2W5cS63A4HA6Hw+FwOLoaXtN/+cHn6Cl6bkY9A/2XSm2GowORHgRlLRKWC8vRsAYjjfOrmUuNVf1F1kWAldDnCrFLXgZIPjEezKuPo0dg82a+A8pbxmUB/uC/iLGt98haRtmea7ZqEG2fuzLZMjQyXxvS4ush5/zaGtB15HA97CbUUdZwY6mNcDgcDofD4XA4HI6WNDltLTJzGahnVK737eWE6Lyjh2KRmlWI2vaOUw/B1djQbO1GQOwMY3UuY97hsctbol8eZkTsuZqlebR39CiCeWlpxpBdaLa31NI9SytZhD2xLcebshbH+9zotx71z8eOVkifYWOeaNcYjm6BRWrexWsu6NLNmWPP3Lmk1EY4HA6Hw+FwOBwOR0u8lE+BXk+CFSV6sXTYRqzOFXTaGogF7m2vtu1SGOmbvOwt4Uxs3Y5pdEFbUi8C00175maB6ui9+Xe5tXX0RCwy83VMOWcG/Aob+45P1uJjJrjYtDLXwnhLYZRajJEXZvdaZIaT+NhasNilQFG1xTsdsYZYwzWlNsPhcDgcDofD4XA40pHirLL5t60G/2+lMqYoBGJPO23QrQN7/va1mN1XaP/PUZ/PTANzbR806WJYmUvbWtjuPssh5Vz6nT1974pcbXD0UPo0zAKyXreWQdkNaPe0O1u4W4/2WL8flvO1sMEoW4mlcQbngOk9IrMK1ud1dD9swR0fI64rtR3twrMr7Lm7crqmOxwOh8PhcDgcDkdn0zrCcDdbiFn3dCJ59i/mz3mj1GY4OpERax5FeruQrkvEqHz7lBubDoaNubS9wmevulautGT8dxm17i/52uDoedgTd63E/HuztbsE7bZe9Gq9JzVCdqC82Hl4Ocl5JLMSDa9Hwfx6qQ4ru8mwnqGJ7sidfr2vR7xWajMKZB61280ptREOh8PhcDgcDofD0RatnLZWU9NAmf0R6F7FZIz19NfD1lOKqTlywubOjWHcgLLrdiazCgZv8ehTyJyXGyt75XCefWwM/BnskH6vNtIrcIPNnRsrxAZHD6R29iOYXmxr94vYwD+hL6Td2WJpIGxaPQzlfW75hrcchufVKeBX24LbPs53Lkf3x564pY4A3wbWl9qWPFlOjNOMGT2lmJrD4XA4HA6Hw+HogaTV8rSnZ60A72G6jwO0AfMecMXHtk4sUrMK0w2gnPSYY4a30vJ0TCWxIzScJNbm0vY3aPdFUovISEUx/5fx35nDEcdAKHgzpk9b7hPwfdPeMVmayO3UKNtdsLqTrXAn2jpj8OcoxwUNe8rmz3mm0Lkc3R9bUPMfzL4HdJcFqC149m1X/NHhcDgcDofD4XB0ddoswGSRWW8S0OOdaUxBmPnAn2zBrMWlNsVROiwy+3V8uxEp60LDcjEsmncKeCrfD9jqkSKrk3gjlF1gKRqkAt1mtXNebc/8jp6JRWZuxCu7CinFoX+LNOYNX8Na95AlR9kaxiWwMqfKehlYimXXYzZeZuTq29s5laMHYLXVDyOdXWo7siJ8xGm2oPrZUpviyhXvAQAAIABJREFUcDgcDofj/9m78/i46nr/46/PmSTdCy1toS2LpeybQNlql0zSHQVFb0DUCrTJJK1UrVe5KqBxRdm1LM2EzYt60Xq9CvfHUtpmwq4sIiIginCRFmgohRaadc7n90cCpEnaTJJzzndm8nk+Hmg7c3q+77bp5MxnvufzMcYY05vdvreX9bWPIloXVZg+E9Kk9X8klXzOdRTjntxXcx/o9bsr3DZD4ZswZqBrDVPV5cIbmRz7W5X918EeqCpKraRq1w10fZO/ZP21W9DWatAtANvQ2I/hkJ4P3vmnc4VtJwmNA83QKDpsK7sZ0qf6JzZP/JG19zDvkfraWuCrZO8dOmk8rZD65BrXQYwxxhhjjDEmE71uyJK62np87/eIZlfvN6GFtNwm9yVt8Jh5n9TX3gVtlyDa0tPzr4qOV9HdDAfL3CeFbcdo7wUyFeTL6BFt4l8p9TV3BLG2yW9y383/oqjxAvA3XoAc1ADDuh+189fxcPBXIn0ePrYrrynjtcdBen498vx35a/VPf4bM4OXpJJXoHoO2dcTvxnk01JXe5PrIMYYY4wxxhiTqYzuopX7Vv8Jj9sQsqNnrPAmbdws99X83XUUk32k/qaHQb/13k7F92xXhm+HkYGtA/yHsDmTf0R/hb0KkX2CWtvkP1l76+aSomHX/CzD4WPnqbdl7wz7OmeizaPgdTq1ZFD1wf8FqdorJJUKbB2TX6S+9lbQU4HXXWfp8H8IxZKq+Y3rIMYYY4wxxhjTFxm3PpT1tc8TowbRl8MMlEGSpxk6pMaGiJjdkbrav1LAl0Afe++xV4Xe+3T20dFC86KMhz7JZcxfPCLoDCZ/pVoav90EpKXrkKedd9nuj7QsFu338LFdeQMZ2wIFoFtQLpJU7W2Svbe/mywhqdp1pDkOlQ2Oo/yOIenjpS75B8c5jDHGGGOMMabP+nybuIJQUnkM6AKU4WGE6pHINjzulvU1z0S2pskLOqvqpFdi6Yu3ivTcF3SA3kRip6n/oe1IrNeDhe9Rl/xWGDlMniktL8X33h8GKaoSE2KieF1fua8UNhYjgd8JoYr/dyT1qVH673JHMjvutDA5o+N6YTG+XoYwIcKFN+HJN6Su5j8jW9OYwSZe+VvQM0JfR/kn9cmpoa9jjDHGGJOF+jxkXEClrubPjOQaYvIgIs1hBPuAvoOna9mmq6xga/pDYk1PHS/y8O3KX5r84L9ex6Lp8+DNjA5WLiC+9KCgM5g8My1RiHpXdX5IRbRNSadFfFV5f7frTOSdwAu2ij6pvFyF3P1v6GvSyLhAz28GhY7rhf8kHTsM+AFkeldCvzUgXIgUHGoFW2OMMcYYY0yuK+jvL+zYdXWvlq18gDebTqDNPxbRvQJL5nkbSaef5B15Uh5PZttQE5NLtLCkAQovgr9cIfpclTK1ROTAfdA9g1riVJWXfiYUvQ179HLoEDT2Y+BTQa1t8tBIzkc5tNvjIp4P6osiiA4B/TJsDmrZJp/mR4WXb1Z9/jGRt9/vhJCWhUAt1hrB9IM8cP1W4CKdm7iMNJX4nINwRIBL/AmVmxmy4yZZe+u7AZ7XGGOMMcYYY5zpc3uE3dF4+b5I7FBUpyA6CZU+7OSVNtB/4cmL+PqMpJJvBJnNDFLxxDiUZUj3XeUfQcd8FNn3aGTv/dFxXg/H7Iqv+C8jW55WXr9T/H89gGxFmIJyTkYnEF1IXe09ffidmMFiftUEmv2/IF0+AFAVpMtrqsp9kz3+cJb6+x+P7H2QsvdwjyF9WW6LyrZnRV9/QHn1t6qbmkT8no/U35OqfbJvvxljeqbxyhPA/zgqpQgn0bcPkZuBRxDdgMZ+K6nVT4cU0xizK9YewRhjjDEmdIEWbTvTRSuG0Nw6DtVxeDoWX4ahWoRHIarNqNeC+I1obAtpfwuxSQ2SqraJ5CZY8crPgfZ6sT8aKThOdfQhoqM/hIwapVI0FC0YKhQ2Q1sj0vqO0vqi+Nv+od72P4n/9ltIT1+vZwGHZ5DsWbbzYWwXuekqnqgFFvfwzM49k5VteKxGaXnvIQ9PjoaRR6iOniqMHiv+0GF4hSN8v7DN8/xmSG9Xml/D3/EPkW1PqWx7CW3MLJi+y7Chq7hrVcgtccxgo/HlI9HWwxHvUJSD8fwxICOBkfhsB95BeBOV54np32gc8aw8fFWGX7fGmFBY0dYYY4wxJnT9bo/QG2l/Y7+x4z9joley7FA0ndGF/ja0rV54sx5p700rXe8C146POKTjuV193qFrwTsYtLd/W4czSpcDP8kknxkkSs87njSf7fbl1cPwMdB1qLR0fsTH1z/D9j8L24GNqND+tSugnb+mpaPRQV+6HcgImlpmAuv78IuM6ZWkrnsHeLTjP2OMMcYYY4wx9GMQmTE5oawshqbnRb+wbEX0oQyPrWbWeePDzWNyiOAX/qSnVh5It5LtvxB5NppYnSjTWbB0bOTrGmOMMcYYY4wxg4wVbU1+emOvU4DgBuP1hXI/8FYGR+6JV/j9sOOYHDG7cjFwYg/PdOljiyLcjZOhYBqjucDBhyHGGGOMMcYYY8zgYkVbk3/mLx6B+rMcJmgl81vIyymu6KlQZwaTGUtG4fnf7fa4qtC1F4fo4yivR5SsB3oYpQnrL2iMMcYYY4wxxoTIirYm/7QOnQMMcZziLyAv9XqU4IFcTYhDAU0OKIxdCLJPt8dFuu6ybcTz6qOKtUsqC6iutu8fxhhjjDHGGGNMSOxNt8kv8eX7gHes6xjt/LtR8Xs9TPgIJYmzIwhkslF8+UGoLOv2uPZUyJcUvjZGkGr3VMdTt3Ga6xjGGGOMMcYYY0y+sqKtyTP+wo5byrOAvIbwREaHKpcSXz4y5EAmG0nbFUgPO8O7DiQTeR3P/1NUsXrlSQnTVw5zHcMYY4wxxhhjjMlHVrQ1+SNedRT4B7iOsRPPX4+yI4MjJ6Nt3wg9j8kuxZWnoSzo9rj29NqsazPauR0VZRhDdhS7jmGMMcYYY4wxxuQjK9qa/BCvLgB/rusY3fjSCGTag/SrzK48OMw4JouUlRUhekmPz0m31ghPo/xf+KH6SPVE5ldNcB3DGGOMMcYYY4zJN1a0NflBNs4A9nAdo0eePgq83utxQhGef1n4gUxWaBjzFeCgHp7pOnysFaEukkx9JXi06kLXMYwxxhhjjDHGmHxjRVuT+xatGA0yw3WMXVLxEe7O7GD5OPFKK4Llu3nnTwK+2u3x9n7MO++yFR5EeTuaYP2gOoU5FYe4jmGMMcYYY4wxxuQTK9qa3NfUNA+l0HWM3VJeROWZDA++kmmJ7P79mIFpbfkh0H3wnEjXXbZvgT4SUar+82UhZWUx1zGMMcYYY4wxxph8YUVbk9vi5fuicpTrGJnRe1BaMzjwcEZyfuhxjBtzy08Bzur2uHbrYwvIvSBt4YcaIGUMW/Y82XUMY4wxxhhjjDEmX1jR1uQyAS93WgkIbyM8lOHR1cSX7xNqHuNAtUfau4KuLRCgvT/szg+8iOjfoskVgDTFxJd33z1sjDHGGGOMMcaYPrOirclds6uOBSa7jtFHD4C+1etRwmho+14EeUyUil9bijKt2+PadfiY+gj3RhUrECJFaLrEdQxjjDHGGGOMMSYfWNHW5Kay6iI8v9R1jH5oRSSzYpyyhNKqk0LOY6Iyc9kYSFf3+Jx0HT4mf0R1cwSpguVxHPOWTHIdwxji5w6lZNmhrmMYY4wxxuSkeGIcJYkLKE7Ypox8U1I+jXii1nUMk5kC1wGM6ZeGjbNBcvNWbOWvwInAh3Z7nODh+1cDMwANP5gJVUH6WyB79fBM1+FjOxAeiCZUwFSFtthC4CbXUcwgFS8/BeQckE+j6Z8DK1xHMsaYnDa/agLN/pF4sh/ovigTQUYiOgKfIQitCNvw2Y7oNlS24ek/oeBpxr3xD9asSbv+LZgAlSamojIDmImve+GRRtkB2gjyOvAqKpsQeZHt/t94PJnJPA+TTYorpyO6HChDGYLo2a4jMad8b3w5GZXjESagjAUpQrURYTPK83jeU4zb8ihr1rS4jpuVTksM5x39NCpVKCd2PFrhNNPuzF88gqYRh+DpIcDBoPsgDAdGdgyhb0LZhrAdYQfoSxD7G23Nz3P/zQ1uwwfLirYm98xcNgb8U3K6jil6J75Ude9j2s10iis+R33trZHkMuGYU3kEab+iWytbVUGky4OyHrQpunABU9mPkoojqav9q+soZpCYm9ifNjkb9FzgMNdxctKCpWMZve1tK64EJH7uUEYVedyR3OE6ijF9Mn/xCFqHn4LqLNo3DXyYFn88Amjn625tvwyXD37a/mNp/38VIA0NY5qJJ55B9S/AOvy2u/PtzXTem5vYgzaZB/5CkIX4TH7/PZjQ6e1Yp8tZ0fYnRtJCceI5PP4MmkJjG0itfinS/CYzcxN70MangSrQY13HAWD28v3w0p8B/Sxpjn7/ce30g/e+7ARQHzaP2UG88h6En6MTbydVnf0DncMWrzwB8T/PdhaD7Ok6zi5NSxQyWkvxpRShmBam4fk71yu7ln86fw9CAB9ihRBPbAV9DGQDquuZ8NYTuXyNa0Vbk3sK2haAxFzHGBCVzcDj8P6nXLsmchmLVvyeu1ZtCz2XCYevV4F0f70V6Tp87FVEnsrpDyQAfJnPtMTztrvChGZWYiIFlKF6Jm18pKNCYHozKzGRAjkC1cMQjkQ5DDiCZvamYfwo4B3XEXNOvOpDqB6Np0fjy4cRPRo4mHf0U8DtruMZ06vZy/cj1noa6p1Oi8ZBhwR49iHAcYgcB3wer9AnnngcuBO835Ba/XSAa5mgzF88gtZhH0flbNp0AWhhTzN0eyUUAcegHAOyGHyIJ15A+W8K/F+w/oanAs9u+qDao3TTXNKcQxtnAMNcJwIgnjgM5WKk7Sygb+/5heGgZ6CcAZteoqTyEsa9eWMuF+z6JV6+L8Q+i+rnQY/I4stkoaRiNr58Bvg3VMb256WmB2NA5gHzEIGGMW9RXHE7Me9GNtTcT4692bairckts8ungORHn0KRDYgeiTK8lyP3prH5G8A3oohlAjar4lMoxd0eV7ydvykJiH83Kjn1TaRHwmhG60eAetdRTB6Zt2QSrYWng38Wyuz2AX5ZexHqzqIVQ2hpmYrPwah/MHAYyBHA4cCe7++W2/mVJk3qunejjpozpiUKGSEfQvyD8KT9z1TlKNBjwN/j/d1mefDybQaJGUtGURj7FHAOtBWjIpG8h22/w+zE9v/8bxOveBC81dD8G1K35O5dRvmgrKyIzWMX4Pln0yKnAyNC+pqYinABae8C4omngdWM4ma7MyFCs8sPJxZbjG5ajM++WXMpFU+MQ7gUn3MyuBs1Ex9CtYaGMVXMKT837z8kmPOFvfBb2j9sUUoR9bLm77ar6SuHUfTO5xD5EsqREeTcE5HP4+vniSf+DtwELatJ3dL7gPgsYEVbk0NUkMqFrlMERxtBUqCn9n4oX2FOxc2sr30+/FwmMNNXDsN794c9Ptdt+Jg+hcrGKGJFQmUmcxNPsi75tusoJofNKT+GtHca8HFaOaF9R61YrbasrIg3xk1B2w5GvENAD0I5GDiIHc37f/BmJ+M/qG3k2K6DwJSVFbFl9ETwJqPePvhMRnRvkMmoTkKYChwAWgCy822ZxuSaORWHkJYvAucAWTAbQmaAzoCiq4lX3ECs6DLWX7vFdapBJV51FOIvZzNnITo24h15RwHXsJ1vU1JxDdr601wpouSckmWHon4Z6JnA0Tu3O8kC8cQ5wOUo40K4xjuOtPcHiiu+SH1tfg3fmnXeeAqKzsDXT5FuLX3/zs5svU4+LTGcdzgfffdrIOMcpTgYuASKLiCeuJSixlWsvTWrNy5Y0dbkjtJlJ+IzwXWMYPmPgRwP7LPbw4Qi0nI5cHoksUwwhuz4d+CAHp7Z+dNjoQWVDXlWBCggLXOA37oOYnJIez/FYvAXovIx0kxxHcmJGUtGMbRwP9L+JJDJIPshOgmVA1E9mAYOgHSsvYjY5XWjfxfq+dt+J15dAK/sA96HEDkQ1SnAFJQpCFPYzOQPityd+uPt9GNjclxp1Un4ejFtempAO9iCthfIf5BurSKeuIyixquz/U10TotXFyAbP47KF8Av2ak/sRvjUfkOWvQF4pXfJDXxZqj2nSbKB7PLDyfmnYFyJpr+sOs4PZqxZBSFBbXAWSGvNBSRJCWJ/ahLfivktcIVX3oQxD6GymmIFqMay/rrlfbXnATbuQiY6DpOhzHAJbQM+xLxxMWkkjeSpW/GrWhrckP83KGk/eKsf0HqKxUf5U6E8+j9cuk0iisWUV97VxTRzADFV+yLNn+l299qT8PHVO8nH3tJqh7N3MRjrEu+7DqKyVrC7Kpj8XQ+ynxadEZ7P8V8e7HvEK8uoPDlCbR5E1GZCN6+7cVY9gedhMpkhP2AUaS7TPnRjv8P548m94q2pyWG08g42mQC6o/H88a/P9le2R9hIrAvumlvxGsvUnUucEuX/zcmH5UmjseX7+D7HwNy4et9D+D7tAw7n+LEd6mfVGPFuwDNOm88XkE5bFqGyn6u43QjTAC9gfimSqTiPBtq20fTEoXs4c/C904DTgOmZmcJqsPsxNEIvwEOiWxN5WKKE63UJ78X2ZoD9d7fa1o+CvIx3vvzypWWTMWJEti0CpUjXUfZhX2AWuKJT1LYVs69N21yHagrK9qa3KCFJe2NxfOQ8DLCMyi9v5CJ/JRFK47irlXNESQzA9J8WY9fs12HjylbEe/RLP1gb+DSshCoJW9/g6Zvqj3iG49BZDY+xQizwB8P5EIxoXezyw9H5BMd03n3xGMMyhhgb2ACbJpAa0Gn36l2+pfhtO1DdrQxOS0xnO0sQxmO6AhUxiAMb/85o1HGIowHxrH9vYEpCiI9F2S7/tiYwaK0ajLqX4LP53J0UOM+CNcR3/QZ/Mol3Ffzd9eBctrcZQfSlv4GsJj24XDZ7kR8+SPFFV/Ou9vZg1a69AD8grmg84AF+N6eriNlpLhyHuhvEEZHvrbwHeKVz5Kq+U3ka2dGmFN+NGmvFGQOaDG+NyrnrmdmJSYS40rg066jZGgRLQV/oThRRX1yjeswnVnR1mS/eGIcygmuY4TKZy1wcMeU1d05iMbm84ErIkhl+mtuebxjCuvO2nfZ7vyYcA9oW0TJoqc6kXjFh0nVPuk6inFg/uIRtA2fhuopqMyCTTNB9syCWzHDIbFTEP2gj3WufFShmh07bZva9oSCy9u/NqT7bth8/JoxJkhlZUU0jL0A3/86MMJ1nADMRPRJ4okLSU36qe267aOSZYfit32TtvRnyLX3/cJwkCQlFSUMHXqebVjp0D5sKo4yF2QOPgfnzsVGh3jFuaBJoNBRAkH1RuLlj5C64RVHGToTSiqOQGUmEEcpJf1eS8gc+7t9T3uP4qtob0GQO4SxwK+IJ44klfwOWfIXkFsv3maQkoWIZmP/reAIb4M8BBrP4OhvMyvxS+5Pvhp2LNMP8eoC0q9e3uNrfLddtvI8ov+IKJlLc1m04lm74M5zZWUx3tjzMHzvJERPBk6hhSPbBzhBllz3mJ54WbLT1hjTf8WV02nQWtBsvQW1f9rvWrqK+MaPkz7vTO6/ucF1pKwXrzoK/Avx02d2u/bMNSpn09gyltMSn+SO5A7XcSLXvpN2FvgzQGaRbj2CbjtAcki8sgr0Olx/DCuMRr2fAJ+KfO3pK4cxtPF4VGcCM1H9CMrYTtlyV2nVZNLpJND7oPXsJcC3iVcewHZN8Hiy1XUgK9qa7Fay7FA0PdV1jGjog6DHdtxWuzujKOAHwJIoUpm+2lSFclS3hxVv52/CksZj/eCoY8kImlpmAutdJzEBiS8fidd6DMqx4B2L6rE0cBQwLGd6bJlOvPzrqW3MYBE/dygUXYLqF+k66DSvSJxY4aPEKz5hd+/swuzyw/G876H+GUjX685cpgvYzp1MX7mIh69qdJ0mNPOrJtCixyN6PKrHgZyMz37ky+1JJRXLUb2GbPnNCGdQWnUSG1b/MbQ1piUK2YOj8TkROAE4Ed49Eu1Uh8uOP42BK6n8OL5/IyJ7uY4SDD2XUezDohWfcL3xyIq2JnuVlcVoSM9zHSNCrai3FtEzez3S5xxKEjXUJf8QQS6TqQUrx9L0zjd7/ABcunxLFnkE9bdElMw9ZToLlv6Je25803UU0wcLlo6lpfBw/PQRiBwKHAkcBm0H4L/3hW5F2pynarvgjclFc8qPIe39Ajgqb974794BIA8Qrzw3i/tRRm/ekkm0FVajeh5QkKdfC8UMeXcN0xJnZMPOtwErrZpM+r0CLccjHE+Lvy/QcVmVZ3+JJRVLUMmegm07wfdXAmcHcrZZiYkUcjRpPozH0ShHoxyB32v7w9w2feUwinZcjupy11FCsJCm5luh+tMu2/NY0dZkry17ngzkySc1GRJ9BngB2P3uYsFD5VqoPsn6e2WRlne/j8jYHp7psvNF3wV9KJJMWUNjNBfMA37lOonpg+bY8+Dvlct34pkMCLn/BtiYwSaeOJ80lwFDAzyrAv8EeQrRl1C2gTTS3ntyD1THInooyDHAqADX7YsRoL8mnvgaqeTgnvGwaMVoGpsvoIWViObnwOadfZRR1ALnug7SL+1tKy4Fjsf39x40/dpLKk5HpYbs/J2ewaIVo7lr1cB6+8erC2DTJnzaf5fv7WfIxt9xkEoqjkTfvQ16uMs0XyhlxDdtJsX5riJY0dZkp/mLR9Ais13HcELkbnxdhvR2i5tOI75pMSl+Fk0ws1vximPxOafbN+f24WNdH103OHe26WGUJqayIfmC6yTGmE5UW1xHMMZk6LTEcLZTA3wuoDM2Ar9HuIPW2F08cP3WDH6NEE8cinA6yieBk4i2PCHA5cQThaSSP4pw3ewwLVHIaJbR2HwxMC7vC0M7O4d44klSyatdB+kz8Q9AWeQ6RqRKqmag/m1kb91pCE0tc4Hfug6Sc+KVVaheCQxzHSUCX6Ak8TJ1yUtdLJ7HfY9MTmsdOgcY4jqGE6oNePJYhkf/mLmJPULNYzIhIJcjxLo/03UAhLwC8nREubKPygKqq+17jzHZRMSKtsbkgnjVh9jOQwRTsH0N4UJgf1LJs6lL/jLDgi2Akko+R13yUlLJU/D9IxFuRon6teQS4pUXRbymW6XlpYzkSZSfAONcx3HkMkoqil2HML2IV30I9f+HbC/qqZ7oOkJOWbB0LPHK34JeT7b/3QZJ+QHx8lNcLG1vnE32iS/fB7xjXcdwSqlDyGRC6t608s3Q85jdK678NDCz2+PaZe+DoqiuZTA3AVUdT93Gaa5jGBOKmN4HcjnwJLn071yzpGj7zh5bgfNRksAfgHcdJzIme5QkTgb/EeDDAzxTI/ADKDiYuuQPSSXfGHC2+254lrrkEgo5GOR/Bny+PtHvEU9cGO2aDsTL96W48lf43nqEIwZ8vvYCez1QDVKG7x1PQWwqnrcvvhyC+CegcjrI14GfA68OeM3gFODLfxI/t7fhzcaVGUtGgX87MN51lF4ph7iOkDNKK2fTHHsS9AzXURwoAO+XLjbMZes2dTOo+QtRHVw3+nSjjahsAP1YBgd/mZJlN1F3/d9Cj2W6iy8fibb9oMfnurW40D8hsimCVNnNkxKmr3w6rycAm8GpvfXH1wCYdd54YgVzgLkgc4APOUy2e5Il7RHaXxOu/eCBao/4vw6EgmNRjkb0GJRjEKaQ/53ijPlAScWnUG5l4LuaHqYg9jnWXf/PIGJ1sy75MvBJiitPQ7QW2DuUdbr7HsWV/6S+5r8iWi86ZWVFNIz5CnARoiMCOOMLCD9lSPrnGQyHfRy4o+PHQmniOJRPo5QDYwLI0n/C/kjRT4HPO81heiIUFv4M9GjXQTIijr+Wc0G8ugB99WJ8vRB6uLN08JhCK9cBn41yUSvamuzS3qD9ANcxsoM8jvjHozJp94dRhJ/+KbAgmlxmZ+n/QOj+d6R4XUoKTSCpiEJlN2UYQ3YUA3e7jmJMaO6/uQG4reM/iC89CArmAvNRnYMw2mW8nUR/S3OGqn1S/AP4B/DBpPhFK0bT3HQUaW8a+CeAnAAc1nsveGNyULyyCl+vHeDXt6Jcgkz6Nuuq2wLLtiv1NXdQWjUN3/9v4OTQ1wNB9CaKK1+ivubhCNaLRmnFR2iQG4DDAzjbW8DX2c5NPJ7sz/BJZUPyCeAJTktUs02WglYj9DSANxrKYkoqf0ZdzXpnGUx3JYkVaC7txMySu42y1dzE/rRt/CUiM1xHyQrCZyip+E/qau+Jakm7uDXZI15dAP5c1zGyh68gd5PJLbbCfEoSHw0/k9nJ3GUHovrFHp+TrrvApD7DlheDg+qJzK+a4DqGMZFJ3fgPUjWrSdV8Epm0F3glqP4n0OQ6WtbstM3UXau2saH2IeprVlFfew71ySMR9kY1AXIPEH5RypgoFCe+BnrdAAu2zaCfoT55IakICrbv2bB6I8OGFKPy64hWHIro/xCv+lBE64Vn/uIRxCuvJi33E0zBth7PO4pUsqafBdud3ZHcQX3NKgoKD+loZ+OO6irKyoqcZjAfKE0cj+JkWFO/id/bjvPBq7hiEW08AVaw3YkvV7bXrqJhRVuTPWTjDMCGanWmvAzy1wyP/QmLVgzO4W2utPmXIz0OzOs6fOwNVJ+IJFOuEDxadaHrGKY3+hfa+x+aIKWq20itTlFfew6xwn0RXYXLHrjZ0tN2IFLJN6ivrSVVs5CC2KGgv+n9FxmTxYorv4NwKQNpBaJsw/NPJVV7W3DB+uCuVc3IxM8iGlXbgr1Bf5PTRbySyjk0D3sK9EuB3D0g3Mx25rFh9cYA0u1s/bVbqE9WInoWsD3w82fmcN7Ys9zR2n2j/hbgBRTfdZRQzFgyCp/byLVh4r40uI6QdcrKYpQkvgvyv8BeruNkHeGR+lcrAAAgAElEQVQI2FQV1XJWtDXZYdGK0fYJzi6ovzbDW1en0tT8pdDzmHYllXNAT+32eHs/5p3fYIm/FiEdUbLcoTqFORXW/D+bpWpLSE0aicdBiHwC1W+i/BLlGXJp0FY2W3/tFupqv4hyFq52iEoeFG07W3f9P0nVliH6hbx9c2zyW3HiYkS/NcCzNOLp6Wy4YUMgmforVd3GuLcWo9wezYI6jTfGVkezVoCmrxxGSeI6VO9FODCYk8ot1E0qD2R37e7U1f4a0enAa6Gusysq32T6yuyfYp+64RFSyYMYzSjEPwGRc0B/DNwFvO063oAVFKwGDnYdo888/fOAz5GqTiO6ELgI+B3wyoDP6cqs88bTMOZulIsDbDuVRnkG5bfA1R0DDs9HpBKRf0e4EOXSjg/4HiM3/j1URzUM0XramuzQ1DQPlULXMbKSyDaUB4GSXo9VLmLekp9z70027CpMZWVFNOhVPT4n0vWb27OohDPwIx/4spCyshdYs8aK2lmr2mcDLwAvAL9//+FZ543HK5iJyGyU2cCx1lN0AOqTayhO7ItwZeRrq5+f//7qaq8jnhgNXOI6ijEZi1f+B+h3B3iWNlTOIpWsDyTTQK1Zk2bGks9RWPAgEP5wItULiCfuJJV8IPS1ghCvOgrevQ3lyADPug4mVkB1NB9c1dX+lZJlcTS9HpgcyZofmMyQd5cC10S8bv/ckdxB+5C3x99/rKwsxut7HYPnFwPFwFxgpJuA/VBSsQTlM65j9EtaHg3gLNrR4/SDPqfx5ftA2ymgC0AWks0Dad9TWvERfPkVsO+Az6U8geidCOsYKY92fN1nbk7FIbTJHERLQRYBQQxiDNJeSFECwm8HYpN3jXvx8n3BW+o6RnaTAkSXoxk1+/8ZqeS5YSca1Iorv4LoD7s9rsjORSttA2816FsRpss9nt7LhtqHXMcwA1RaNRn1P4uyjOy4ML2GVHKF6xB9U+0R3/QE8OFIlxU+S13yl5GuGR0hnvgDcGK0q+rHqauNaGehA/HK30IEg2aUf1KfnBr6OtmipGI5KtcGcKbzSSWDOE+wZpdPQbwnIxrE+CLDhhzLXau2RbBW/7X/nV8OBLdTVPkn6dgJPHD91sDOmamSiiNReRgYFfHKfyc16bDIitRhiy8fCa3/BnIBwfQ1HgA9e7ctVuJLD4LYk2RfUa13ymYmbJ0UyeaReOIw4KPAeRDoBzT9l0p+UA+MJ75MewGy/xvplJfx5Bd4/Jz1Nc8MPGCHRStGs6P5swgJ4NjAzjtwr7CdA8O+m8F2xBjXBDzra9krbQO5N8ODP088MTPUOIPZ/KoJoP/R43Nddxmq95AVbDOQprj94tTktA2rN1KXvJTtHAKyDCW73yhnpWof1SsiX9aXfG51oYhUuw5hTK+KK85A5acBnOnnWVmwBbjvhhcR+UpEq02hsfk7Ea3Vd/HlIymu/FVHkT7IW/sVKHdSsIX2HbfweaJvoXQwJZsWRbxmeFLXvUOq9hbGbz0aWEHWzheo9iB2A7lYsG13W2R3+6WSz5FKXkEqeRQeC4CBt2UIwqIVoylJ/Bq4in4XbPVeROZSP2kKdTXfDLRgC+0DaOuT15NKHt/RQ/uFQM/ff/sySs8MexEr2hq3ZlcdS/S30OQm1WeBf2RwpIBc3f5N1ASuJf1DpIeBedq1YMs2PH0kqlg5TaQITffe/sPkhseTraRqVqP+scDTruPknLb074CmSNcUP5+LtjDuzXvI5f5yJv+VVM1A5BdAbIBneppRVAYRKTSpmhtp7+EZhS907G7LLrMrD4a2h5EQ3uyL3EB9si7w8/ZFKvk7hKsjX9dnSeRrhm3NmjSp5DWoFpONfT5LNlbR3s4hN4nc6mTdDcm1jN86DfgBLmdExKuOorH5jyhl/fjVCvK/CKeQqp1PXc36CHa6K3W1v2b81iMQvgdZMDNGw/8g0oo6xp2y6iI8v9R1jJwick9mA610GsWvnhN+oEGmpHwaKj33a5Kuw8fk3gwHyBkAj+OYt2SS6xgmQPfd8CJF3hyU511HySkP3rQd5YloF/Xyu2i7Zk0a5U7XMYzpUWliKur/noHvtmxD/HP73DfQhZh+GQh3OFa7Qid3L+xOvOJjePpH4KgQzv4OGhvoALtgFDZejBLtTAfhoyxYmkkrudxTX/so6L+RTUNgS5cegMqPXMfoN2UtqZrHnK3fXpC/CPiiswzq/wE4tO+/jvvwOIFUzWnUJf8QfLBerFnTQl3yW+DNRXE7y0c4njnlx4S5hBVtjTsNG2eTSw3Ws4FqA0qG31z0R8xNdN8Ravqp2kO9q3cxaKnrYy+BPhtBqPyhKrTFrFVKvlm7ejPo54A211FyiqeP935QgPJ9py2AR3YMZDKms0UrRpPmdmCvAZ9LuZK6G6J97eiv9bXPA8lI1hI5lXhldlxfxBP/jsrvgXAmjgtXkbrutVDO3Vdrb30XT5dHvOoQWrxPRLxmdFK161Budh2jg5COJRlY7+K3gb+hPNFR4H8nmGgZUdCLIlxv11LJa0BvdLK2MLyPv2IjKp+hPhlnQzLiDQY9SK1OId4MXLdL8GOfDvP0VrQ1bsxcNga8U1zHyE2SQni398OYQBsXRxBocIi/9nl6GmSjKnQe6qgoQqb9h01nKvtRUpEdjflNcOprHwVucR0jp6jkRuEll6T9P7mOYMzOqj12NP0XwhEBnOwftIyoDuA80SnyvktkfTp7GB4btXji+8Dlu/jwf+CUHXiFPwnl3P1VV3sPaCrSNVXmR7pe1Ar0xyjuh62VVJyH0J8/67+h+k1iciSpSWNJJQ+jPjmN+uRUUsnR4E0BPQ/094R567vIDR3Xp9mhreBrgJs+1JlpA/0xFBxGfc1/kU07vlOrX6KwbTbwnLMMvp4FXe66DZAVbY0bBW0LQAfat2uQ0kZUNmR48BezspdXrlm0YjSkq3t8TqTr6+hjKK+Hnilf+TKfaYn+Ty012cn3rySbLvCynWqwAxx6k9+DyNp5+/6dbOh9Zsx7SjZehMipgZxL9Os8fFWWDirahbWrN6NE1U/yOIor50W0VncliW8CF4a6hsfPWH/tllDX6BeJegPJnLye69G+S/0BpxlmJSai0te2Iy8gehapSUdQX3tJ+6Cqbv1PldTql0jV3kKq9hN46akoSYL/3v0chTtWBnzOgXng+q0gbnbb9u5veN4MUrVfJ3VdlLuhM3fvTZsoiH0UcPMaKBxIaVX3zV0Byd8XNJO9ZpdPAel77xTTiTyByMYMDiwEVoWdJu81NV8Esk+3x7sPH2skJvdFFSsvCaMZrR9xHcME7L4bngWech0jZxRKtB/8yCAo2qaq24A3XMcwBoDiRAkqQfUefZi62t8GdK5oqX81UX2gJ3pBJOt0VZL4DMr3Q19H+Wnoa/RHKvkASpTXxuOIv3p8hOs5oHc7XT4ml5Fpiw/FB/0xzSOOpq72130aVLXhxv+jPlmJx0nAX/sXtput+JzJ2lt7v2s1cvIz1wm6UERX0TziODas/qPrML1ad/0/OwaqRdEvvTvfPyOsU1vR1kRMBfGyo69UTvMV/LvJ7EJ3LsWVp4WdKG/FE4ehLOvxua7Dxzzq8DW3drpkI5WZ1o85Dyn/z3WEnPHuiIZI1xsMPW3bveo6gDHMr5qA8AsgqDvOLiBX72S474ZnUe6PaLW5zK46LqK12pUmpqKsJsTbZjs8Sirp7tbg3ohG07/4Peq721UdhZj3oLO1i8tngfY8lLkr5U08PZVU7cDuBNiQfILmESei3NTvc7TbjrCI+5J/GeB5wpFa/TTwL9cxAFA2o7KAutov5tRdHPXJOuBSR6uXhHViK9qaaJUuOxFhgusYeUHlXyhPZ3Ss6FUsWjEk5ET56lLadyx3tfPrp/Aa6JORJMp/BaRljusQJmjyiOsEOaP9Anl7dAt6uVnw6bvsGNBjBrcWXQ1MDOhsdaSSbm+VHiiR/4psrZgf7S3RvlzLwAY1ZUh/Ef4aA9H630R527JIaWRrueA3P4WLD2ri1QXgXUNmH0K8hDCjva9xAB6+qpH65FJEv01/fu/KJvDnU5f8QyB5wuOuIP8+fZCYdzz1NTk6o6Xl+8DfHCw8jRlLQnm9t6KtiU783KGk/WLXMfKKJ/ci2pLBkVNpbM6u3j25YHbF6dBDk/2uw8faf7gWFfeDAfKF6tHMTezvOoYJUKH+2XWEHLM5spX8wbLTVqxoa9wqrlgMGtwtlEJf+0pmn1jBGqK6nVU5g/mLR0SyVknVDNAFEazURkxvi2Cd/kvd0gS6JsIVPxzhWtFL3fIWLtr9yKblCMdkcOQ/8LyZoez+rqv9LipL6dNrhtyPFEwjdUMubB5wfa18NdulhA2rM2nDmJ1StzQh8gUHKxdQEJsZxomtaGuio4UlCMNdx8grqtvIvBn9hcxbMinMOHll0YoheNLztOFuw8f0aZSXI0g1uKRlIeHfUmiisi75MsoO1zFyhhJdv7XB0NMWAM3OARpmcJi3ZBIiwfUdVZ6hLnlnYOdzZf21WyK8E2MkzcNPj2YpPSeadVjH+huyfwCucnuEq41nflW+39kZbdF2TvneKN/p9TjlZQqYE2rRr77mZlQ+Cr0Oft4CVJGaGCd1XW58aKvqYocoQCPo2aSSK3k86aYnbJDqatYD9dEvLPEwzmpFWxONeGIcyAmuY+QneRDJ6JajkbQV/Cj0OPliR8tXgIO6Pd6+y7bTz2lFqIso1eCiOpF4RX7vlhh8XnEdIGeIRHfRPFiKtprRnSnGhKOl8CoyHd6TEY1uiFfYxF8X3Vpk1o9zoFQjmuEhWd4aocPwoRuIsu1PW9tRka3lhERbtE3HfkTvr1/vIvpx1iXD38hSX3MvRd4xHX1uO39dtQGPAd9gSPoQUsmaPg0/c82LRXeX1QdeQyghVZvdO/b7zKt2sOgpYZzUirYmIrIQsa+3UChplMx6ziifa28gb3artGoy6Fd7fK77LtsHUN6OINVgNdf6MecRyZIBC7khwp0Og6Q9gogVbY0bpYn5iJ4Z2PmUHQwf+qvAzudcbH10a+kC5nxhr1CXmJYoRJkc6hrtmiD2uwjWGbi7VjWjEW5yUC+/i7aqEQ4slR9msHNcEV1Cqja6+R5rV2+mPrmU8VvHgDcF3z8QWkaRSp5IKvkj7rnxzciyBMVLRzuIVnmKAk7OgV6/fZdanaK9gB+lUF53rIhmwley7FDQqa5j5LnnQP+RwXEC3tVQbf/2d0f9SxC69zzTLq+ZylaE/Psml1VkBE0tofQHMg6oRDeIJNdFuSvUHySDyNSKtsaBRSuG4HNNoOf0+G/uWrUt0HO61DTsCdp3yEWhkHTLvFBXGJceFc1mFakndV3utH0ReSiytXyOjGwtN6LcaTuF3tqVKZdQV/vraOJ0sWZNmtTql7jvhhfb+yfnsHRrtDtt29pmRrIz2pX2ndjREcYyKxHUoNH3WeHGhKusLIamw70wMh3kHoR074dxPPFNSyIIlJvmVE5HKevxOel2wXIvSFRvMgYvZToLlo51HcMEQd9ynSBnSIQ7bWWw7LS19gjGgcbm84GDAz2n+LcEej7XHr6qkWinfYc7GLl5v20oEdySrXeHv0aAPB6ObC0hvzcMCVm0i1TuoX7Sxa5T5IXULW+jRHet8uBN0bUscUFa/gtojHRNTwLfbWtFWxOuLXueDIR7C5J5TwMqf8zoSOUHxM8NsK9anigri5HWn9Dzp8ldXy9fRHg+glQGjdFcYB/+5APPWon0gfW0DVqUb4SMATpuw78w4LO+woZ9UwGf0z2Ncmp6OMNi3peqbgMyuQNuoHKraDtCHyO6HdX7RLSOG1F+sLt72ynQRE71jc1uStRD5vJZ6pa3gHsiXVM08F3+VrQ14Zm/eAS+zHYdY1ARUkDvt0kJE5Cib4WeJ9dsHrsUOKbb4+3Dxz4o5Ir6iOTWhXLO08MoTeT3ronBwJdoP+3OZaoRvgEaJEVb0cHx+zTZI916ETAm0HOq3J6XBRKPFyJc7bAwbmHt4oGQz/8SqeRzIa8RrDuSO1D+GdFqe0e0jiOaLXf6fSOvb693QWh2HSGvqN4Z8YKHBn1GK9qa8LQOnQPYAKEoqTajbMjsWFYQr8rvJv19MXPZGPC/3eNzXYePqfwRVevNGTWVBVRbP+bcljVvMkxn/iBpj2BMlEqrJgPLAj+v598e+DmzgrwS6XIeIffL19+EfP7c3DwgEs1daspY4tUFkazlRFa0Z3uA1KTrXYfIO5o1u6jzg+hdtO9gjor1tDU5Ir58H/COdR1jUBLvTyibMjiyAPWvCD1Priho+zYi3Vt5aLdWCe8S/u4J0xPV8dRtnOY6hhkIK9pmJRkkg8iMiVLav4CgNy8o2xj3Vl2g58wWvv+vSNfz9PBQz18/+R40xDZa4uVm0RaNpnex4FH48oRI1nLD9fVUE1CRl7v+3XP9d5tfUje8AkR3V4Ja0dbkDH9hxy3lJnK+IpLZJ0rCfEoqTg8/U5abU3kEUN7jc12n/6puoP1CxbjgSQnTVw5zHcP0U/b0YDPGmPDMSkxEqAj8vKJrWbMmP3szi26NdD1fAr+FdWfVPh5fCenkzbS0ZnZnXdaRKHr9tmsuyuMWCa532upPcq49R66wa+UwZDb3JwgiVrQ1OSBedRT4B7iOMbjpvxD9S2aHypUsWjG421ik/atBut9CpV1fI+VVJPZURKlMT5RhDNkR7tRnEyLXbzJMj8TaIxgTKI8vA8F/wCjk5y5bgBg7Il1POCT0NeqS/y+kNgm/y92p7/paZEt57BHZWpFzeufSWwzxL3W4fp6za+UQPBrZSsre9DzUvN+saGuCFa8uAH+u6xgGUG8tZNTIfCpNzV8KO07WKk6UwS4G5kmnF1xFEf9usOKGc6onMr8qn295y2e2eyAbySAZRGZMFOYvHhHKLlsAjd0XynmzQWss2kGVGkHRFoDCFcBLgZ5SpDbQ80XJ082RrSXp/N2UoqQdrn4599z4psP1851dKwdNeCzCtYqIJ7q3XBwAK9qaYMnGGZDPn2rmEn0HuD+zQ7kogim62Wf6ymEgP9jFszu/Poo+hcrG8EOZXgkerbrQdQzTL7Z7IBulrWhrTGBah54DjAnhzG+QWv3XEM47OAmj24fQhix13WtIbCHwRjAnlPupq1kfzLlckNcjXCx/i7buvE5R49WuQ+Q3m/8QOG2Jppf2e3x/VJCns6KtCc6iFaNBZriOYTqTR1Ay+SR0FB7fDz1Othm642uI7t/t8fZ+zB/sshVawMvfWxJzkeoU5lREtEvGBMjlzhCzK9YewZigCCorQjr1/UQ7ATtawrjI1yxqGR3JOnXX/w1kETCw1gBKC5JeGUwoR/yWLZGtpV5RZGsNFsIPWXvru65j5Df7ID1wqVveIrAPzjIyNMiTWdHWBKepaR5KoesYpjNtQ1ib4cHnUlp1Uqhxssn85fvh65d7fE6ky/Axua9j57LJJr4spKws5jqGMTlPPHuDYEwQistnAoeFcm7RR0I5b/Y4IfIV/cJAd0PtVqrmMTzvBFT7PxBH+Ap1NzweYKroDRsVYRsM33baBmsjQ4fUuA5hTD+9EN1SBVa0NVkoXr4vKke5jmF69BzQ+6RWwcP3rybgxtlZq6XtMoTh3R7XLr9/YQuq0TUvN5lTxrBlz5NdxzAm50nairbGBEFiS8I7N0+Gdm7nqj3Q8siXVY2uaAuwYfVGpLUYuAjoy2aANLCSVPLacIJF6K5VLUS1Y1ysPUKwZDV3rcpkXoox2eilyFYStaKtyToCnvWXzGYi9yAZ3ZY8nZLE2aHncW1ueRz4RI/PSZfXReXeDP/sjAtpiokvH+k6hjHGmEFuxpJRoGWhnb/Ay8+ibVlZjOJNVyEc42D16K8fUrc0kUr+gMK2Q0F+grK7wVwK1CH+yaSS+dJHVIGWaFYSuwM0OM0USdJ1CGMGoCG6pYIt2hYEeTIzSM2uOhb8ya5jmN1QbUDkcdDe2x8olzJ/8e/ztl9RvLqA9KuX9/ghv+LttM9WeB7NYJeycUekCE2XAHe4jmKMMWYQKyo8A9URIZ39Vdau3l1xL/fMTexBmyykQVciuLlrxvPdbWC696ZNwJeJV38Vb1Mpvh6DelPwdBi+bsXjr4jUsyEZ4S29kWkhkiFhMjjuHoyC6q/y7jXIDDbR9dO2oq3JKmXVRTRsKnUdw2RAqUM4Cu2hJcDOJtM8/ALg21HEipy+ugy051Ye0rlkK2l8WYf4EQUz/eZxHPOWPN7xBsgYY4yJnuqZIZ79g12201cOI9YUzBvCAhlCgXa/LvTTgvp77vSYsAd+557/MhKlENEY4nUM9NI9URHwRwKF4A1FdRhQgMcoVApAxwMTaeNAULdFNRX3GxRS1W3A2o7/Bgu7uM4917gOYMwARTeIzJNAPxC0oq0ZmIaNs0Hs1uScoI34mkLk1F4PFf0a8aqbSa1+KfxcEVq0YjyNzRfu4tmuL64PI/6bYUcyAVAV2mILgZtcRzHGGDMIzVw2Bk3PC3EqwCLiiY5bhAKuM7bt6okul0U9dSGVjv9R7XKgfPDjTj+MqpVpxnw/wp1XxuQqeYT6pM33MDlO3sq670EZsp62pv9mLhsD3imuY5g+8HgM4fUMjhyG6o9DzxO1xqbvAnt2e1xV6DyATWU7Kg9FF8wMmMp+lFQc6TqGMcaYQaig7eMIRa5jmD5QfEYM+6frGMZkP73FdQJjBkw1Z4foWdHW9F9B2wLQmOsYpg9UfODujI4VPZOSiuJwA0Vo7tLjUDmnx+ek2y0M6xE/miEJJji+zGdawoZOGGOMiZh8zHUC00cif+KuVTn7Jt6Y6OjbrhMYM2Ce5Ox7eyvamv6ZXT4F5FDXMUw/KC8Cz2V2rFxNWVk+FOaFdMHlSA+vedrtsVcQ/Ws0sUyghNHsIdNdxzDGGDOItH9YONd1DNNH4t/uOoIxxpiIKDn7IZ0VbU0/qCDeQtcpzAB4cg/ILruYdXIsb4xdGnqesM1OnI3qjB6f6zx8TFGEteRqwxsDvs5ibmIP1zGMMcYMEqP1I4B938klyja8omtdxxiUZlcdhzLMdQxjzCDjpW2nrRlEihMnIExwHcMMgK9bQR/J6FjV7xM/t3sf2FwRXz4S4Qe7eNbr8rMnUDaFH8qEqIC0zHEdwhhjzCChMs91BNMHig+Us/5aG0IWtXjFuXj+g9b/2RhjMmdFW9M38XOHgsRdxzABUO8+lO0ZHDkeLbw49Dyhafs6wsRuD3cdPgZNQH1UqUyIVI9mbmJ/1zGMMcYMBjLbdQKTsbeBT1OfXOM6yKAyp3xvihP/DXIz2C5bY4zpCyvamr7RwhKE4a5jmACI34KwPrNjZQUly3Kvh/HcZQeirOjxue7Dx+pRdoQfykQiLQvZuShvjDHGBCt+7lDQE13HML16A+VS0hxuBdsIlZUVEa/4Km3e8wifdB3HGGNyUYHrACaHxBPjUE5wHcME6kngRGByL8cVom2rgPnhRwpQW/oKhCHdHlcVpHM9TxpQHkeslW3eUJ1IvOLDpGqfdB3FGGNMnvKGnISvQ13HMNDxwfsriG4CeQV0IyKbwHuccVseYc2atOuIg0pJ5ak06JXAofYRujHG9J8VbU0fyEJEbXd2/rkLWEqvuxJlHiWVp1JXc2cUoQZsbsVc2ljU43Pddtn69yDiR5DKRGsui1Y8y12rcnZaqDHGmCzm+yfZTR2hagTeANkMuhnhDeANfG1AeBXhVeAVtHUj9be85TirAYiXnwKxalQXuI5ijDH5wIq2JjMlyw5F01NdxzCheAXRv6ByTK9Hqv6ERSvWZ30RrKysiAa5ssfnFG+n91fKM4i8FEkuEzEZQVPLTMiwDYgxxhjTFyrHWc22T94BGoDNIA3QUYTFfx2k4f2ibNrfzNDmzay99V2naU3mSis+gu99C3QB2J1rxhgTFCvamt6VlcVoSNtk3LzmrUP8w1DpbZrrQTS1fAHouSCaLRrGfhH0kB6fk85vr7QN8TbYxWUeU6azYOmfuOfGN11HMcYYk2eE411HcOAdYCvKVoSt0PH/vm5FvK2gnX6uW5HCrRTqVrb4W3k82eo6vAlYPDET9Fv4Ms+up40xJnhWtDW927LnycBermOYEKluQ70HEC3t9Vhfv018+S9JXfdaBMn6bn7VBJrTF+zcs/Z9O7dFEB5C1W6ny2sao7lgHvAr10mMMcbkkfmLR9DMITm101bxEd4G3qa9+LodZTseb6PvP74VeMsKr2aXyspiNIw9A9WVwEesRYgxxoTHirZm9+YvHkGLzHYdw0RAeAjhWJSxvRw3Gm37DlAZTbA+avV/hMjobo+3Dx/74KpS2YYnD9umgMFAD6M0MZUNyRdcJzHGGJMnmkYcgudHMevhFeBtkCbQrSBNiDaCvAV+E8qO9oKrNOHJu6i/rf0Y2Q7pt0jHtuHFtlO0fbu1GzADMmPJKApjS2iQL4FOsVqtMcaEz4q2Zvdah84BhriOYaKgbfiyDtEzMzi4nHhlLamax0KP1RcliZNRzu7xua7Dx4S1+NhukcFCZQHV1auprraBc8YYYwbOSx8azQ5DOSPrrrfM4DK7fApebDloBbCH6zjGGDOYWNHW7Fp8+T6QPtb6Ew0ios8gvICy+6Fzggf+1cAssuYLpNpDN11OT++gdOdOtsBLwHORxDLZQXU8dRunAY+6jmKMMSYfyKERrTMymnWM6aSsLMYbe34UlSqUBaBB7Sp/jfa2e4UBnc8YY/JaFLf0mJzlL0TVbnwZdPQelAx2I8oMSirKws+ToeJXzwFO7PE56fRap+qjsjaiVCabeFLC9JXDXMcwxhiTFw6KZBXVUZGsYwzArMRE4pUX0TDmRVR+Dyza6Tq6/xpAv8YopgI7AjifMcYMCrbT1vQsXnUU+Ae4jmEcUNmM8ARwQgbHXs5pif/ljqTbi42FqtYAACAASURBVK9FK0azo6m6x9sUFW/nh+UxRDdHlMxkE2UYQ3YUA3e7jmKMMSbX6b4RDWCynbYmXGVlMTbvOR+RpcDpoMHtglXexOMKtOCnpK57B4B4IrDTG2NMvrOirekuXl0Am+a6jmEc8nQ9aTkCYXgvR+7HO/pV4LtRxNqlxqaLEdm7x+c6N0ZQbSTm3Y+fJR0dTPRU/z97dx4fZ1nuf/xzPZOk+wa07Mq+CCJYQCrQTNpSKIIbxKNIsdJm0haLsnjwKEeioqKigCg0SUEUlyNFRdEfUtpmArII4oILCAgoUJYCLZSuyTzX748UaEvSZpl57lm+79eL1zlOZu7rmyaZZK65n+s+gqmz/8ii+Wrci4jIANjuydSJ1bSVwpg4d3ei3Gks99kYe+R59VfBv4t1XELbdSvzvLaISMVQ01bezJ4+GjcNma9ksa3F7HbwE7Z5X7fPMGnm91l6zb8TSPZm6cwBwOwePrrl4WNLiX1twTNJ8TIiOvwE4Aeho4iISEnbJZEqUaTxCJI/6RmD8ZqTMTLQOZn8bxdfjXMNg6Iv6w1yEZGBU9NWNjdt3kjWrT86dAwpAs69RLwTZ9w27jmEXOorwEeTiPVm9o1uL+NyN8ze+EPUeBb4S7EcmyYBue/J5Ib9WNL6cOgoIiJSgqZkRtFJMjPSNdNWBqq+PsXz203COA33D2KMzHsNZw1mV5Hb8HXu+N7yvK8vIlKhdBCZbG7duuNwneYpgHlM7L2b/Wl8hNpZxxY40ZulM+8HP67bj5lt8vxmgC/CrRcHrElFiO0E6utToWOIiEgJinOjE6tl6Oo36Z9Js48k3Xg5y8c8hfki8BkFaNiuw/k2VrU32eZPq2ErIpJf2mkrb0jP2g23g0PHkCJi9hjGwzj7beueWHQFNB0OTck0RqfNG8SaDV/Gutk665tNsgX8r7j9J5FcUhqcMTy/3ZHA3aGjiIhIifGq0SR16Y7zlmQKSVmom7M/nvsIcBpxvG8BK63FaSUVfZ2l858uYB0RkYqmpq28xiDa9vxSqUS/BfZi288Xh1H39AzauDaBTLBm/XkYe3f7MdvkKgJnAymWoj228iZxmvTcv75+mrGIiEhvxL5d3ieB9uytiVWS0nTcmbvQkaoH6vHcu8n/nNpNrcf5Pqnoi2rWiogUnpq20mXi7EMh3jV0DClCzksY9+K8e5v3je2rTMn8jMUtL/d+eYyp04faoutX9zrTpNm7kovP62HBaPM/Ve13xL6q12tL5TCrwXN1wM2ho4iISCmx4YnttIU9kiokJSQ9YzRW/UHcTqODNFDokU+vYlxNdXSpDhgTEUmOmrYC9U01LF82KXQMKWKxt2P2dmDrh2EY4+jgs8AFm97s9fUplm93GHgtxkE4B+DsjTECGMIG8HQGYCXwIvAw7g8S2f100mZ3tDyzeZ74EoxhPWR4o2XrrMC4t8+fr1SOiMM47sz7ue3aZaGjiIhIiYioTvBg0x1Izx2uq0KE+voalo85EWc6cCLO4ILXdF4BvoNxGW0tLxS8noiIbEZNW4HlT0/s2jEg0gOz9ThtGO/txb0/xeSGa3y7lf/i+dFTiZjOcpsG3nVox2svcrq/cGv0xv/2xmwaDqTAazP/wLiBiB8aviMxp/ZQe/PDFc0XAZ29yCyVyt3oTJ0ACY31EBGR0pf0ob3W8Vbg74nWlOJR13AQbnNYzoeB7RMazbESt28zuPMKbr3mpUQqiojIm6hpW+mOmTMG4qMSvMRLSpVFf8J8PO5bHaMx0qj5QqfdxPIxozF2zsu3lvE2oImYi35v0ar/dU8tsi2m1LobZpv+Gfs42CN5qC7lzm136hoOoq1VL4hFRGTbPK7BkhtqC7YHatpWGqO28STMP4kzicLOqX2D8xKRX4F3fJv261YmUlNERHqkpm2lq+o8HqzQM5CkLMQO/luwM+nmD8ca4Dzg08AY48AChbAj3UfeanCfE59jnrsT62remm2yy9ZyGL/F9WaE9FJsUxmfeZj7WzpCRxERkSIXRZbw3xjdH7wq5ciobXg/ZheBvyPBui9ifIshg77DLVe+kmBdERHZCjVtK9nEWXuC7R86hpQQtyfB/g5+8KY31wFXAQckGOUII7oDi74PufPd4hdtkxdPHt8L9mKCcaTUGSMZZROA20NHERGRIuesSbjeOxOtJ2GkZx0F0beBIxKs+gLu36Iz9x3uvFYH94qIFJlo23eR8uSGRSeETiElyONFOBug612fJmCxJ9uwfY0BMyD1Z/PqY/HXdv+uxuzOAHGk1MV+LFMyo0LHEBGRIme+NuGKhydcT5J0/MztqG34PkR3kVzDdi3mF0HVnrS3flUNWxGR4qSmbaWqzRyOMS50DClBZq9gftf2wFLgIiBKcqxbN3YDlmKpTxoRZkuAdWETSYmqImeTQ4cQEZEiFye+0/ZAjj5zRKI1JRnpxhNYn3oAszNIam4t9mvi+CDaWr9I9qpXk6kpIiL9oaZtJUrPGAyWDh1DStchZg/cbuSODR1kE1XA5U50q/uuemKTfnN/O1MybwkdQ0REiliUcNPWiKiKDk20phRWuqmK2oZvgt8CbPWQ3zx6Grf3km0+mdsXPJ5QTRERGQD1NiqRV9dhDA0dQ0rT4c6odudjb3OK8gC7qfCuRc57QueQEpazE0hst4uIiJScXMJNWwAijUgoF+kZo2HZbzE7N8Gqi6mJ3kl7880J1hQRkQFS07bSpDM7gOmPPumXvY2hvzFOHw1FPfdzsnHEQjwdOoeUKPedSTckeWKziIiUkqoQTVvNtS0LU2ePg5o2IJlxTE6M2xcZu+IEFs1/PpGaIiKSN2raVpzoeExfd+m7oU7qtpgPj4OxobP0xqlY+uuYGm/SX1OYNm9Q6BAiIlKEBg1annhNYxK6CqS0HfvxsWyI24GkRl3kiJhOe/NFLFyYS6imiIjkkZp3laRuzv4Q7xM6hpSmGyIm72mU1KzPT7m/5wSzkmgyS7GxYazbcEzoFCIiUoRuufIV4OWEq+5E3ax3JlxT8mXCOUOIqm8CDkiknhNjdiZtLT9OpJ6IiBSEmraVor4+heeOCx1DStPZxt7vcSaEztFX1UbN9/BTh1txzt+VIudM4PiZ24WOISIiRcj5d/I1o5MSryn5MXj19zHenVA1J/IG2pp/kFA9EREpEDVtK8WLo98FbB86hpSekVjVhW4nUqKX5O3k7DjfE/sjWcqKp1hfpTe7RETkzYwnA1TVQaulqDYzB6c+uYL2Tdpar02unoiIFIqatpVg6vRhxDYxdAwpTdcQHzMWL+mG/4ecie+C0aFzSCnyA5iU2Tt0ChERKTJuAXbaMp703J0Sryv9l565D8Y3E6vn/BF2/p/E6omISEGpaVsJOgZPBnSgjvTZXpEPea9FJTcWYUvVRvXXzY4NnUNKlNvxNDXp96WUr9j0/S3SV+bJ77Q1Ijw3LfG60n8eXQEMSahajijOkG3qTKieiIgUmP5IL3fpuTtBlNQJpVJmvhbbUTXuZdHwPwY/VLttpV/cx5J9Roe/SDkbHDqASOmxx8OU9elB6krf1TZMw+zE5Ara/9G24P7k6omISKGpaVv24hNwL8lZpBLWSKzqPW5Hhs6RL5GT+l/zI0LnkBJlPokJ5yS1U0YkWaarcUT6zKI/B6qcJj1zn0C1pU/swgSLOfjFCdYTEZEEqGlbztKzD4b4raFjSGk6133/IeZl1aQ6NrZDasz1vCd95wxh0Jra0DFECsK101akz9p2fATnlQCVDVIzA9SVvqhrqMUSPQj3drItDyVYT0REEqDmRblKN1VBPCV0DCldHzA7JHSGfBtpjJjjtlfoHFKi3I9g6uxxoWOI5F+kpq1InzXFGH8KVHwG4zPVgWpLr1iyjXWzHydaT0REEqGmbbmyp48GRoWOIaVpNF71Nrwsm5vvMdsvdAYpUUbE+tzxoWOI5J9r3rdIf7iHmh+6E8P9pEC1ZVvSc4fjfDDBio7n/l+C9UREJCFq2pajafNGgh0dOoaUro+Y7V4FZbmD45A43jN0BilhZnsxuUGNfyk3O4cOIFKSjD+Gq22fDFZbts5yU4FhidVzHie74KnE6omISGLUtC1H69Ydh5dnw02SMcnZI3SGQtnRbOzB2PDQOaSExXYC9fWp0DFE8sd2Cp1ApDRZqJ22ALVMmjUpYH3pifvUROtFhPw+FBGRAlLTttykZ+2G28GhY0hp2wfKem7nRCvvz08KzBnD89sdGTqGlDkzT6yWa6etSL9kW/4JPBOsfhx9IVht2ZqEm+n2aLL1REQkKWralheD6ITQIaT07ey+Q+gMhfR2KOvPT5IQp0nP1Y5tKaR1yZXy3ZKrJVJWHOO3AesfQ23jcQHry5amzRsJ7JNozdifTLSeiIgkRk3bcpJueAewa+gYUvp2MBsTOkMh7eHxdqEzSIkzq8FzdaFjSDmz5Jq2xt5MOGdIYvVEyknMLUHrm2u3bTFZs/YdgCVa0+ylROuJiEhi1LQtF/VNNWCTQ8eQ0jcGqlNQFTpHIY3ABoXOIGUg4jCOO3OX0DGkXMUJ7rQlRfXaAxKsJ1I+bMNtQGfABBOobfxIwPqyKYv2T75mkldmiIhIktS0LRfLn54I6FJdGbBxTk3oDIU2xFDTVgbO3ehMaSSNFEiCO20BLKd5+CL9kb1uJc5dYUP45Uw+a/uwGWSjAFc9xsnNQBcRkUSpaVsOjpkzBqKjQseQ8jCizHfZAgyKy/9zlIS47U5dw0GhY0hZSnrn1KEJ1xMpHxZ6RALjyHV8M2gG6WIWoGlr2rgjIlKm1LQtB1Wdx4OnQseQ8rAioiN0hkJba2wInUHKSGxTGZ+pDh1Dyo2vTbScmWY0i/RXyn8eOgJwhg4lKwIeJ38uROzDEq8pIiKJUNO21E2ctSdY8rOTpGw9B+tDZyi0NWraSj4ZIxllE0LHKAnuyR7OUtLsuUTLOe/Q5dUi/bSk9WHwOwOnMMybu67Ak3CiwYmXNNNzd3csVq9DREqenshKmhsWaZ6i5NWrTm6DWVk3bl921oTOIGUm9mOZkhkVOkbRiyI1bXvLbFmy9YjIdWq3bd7oe73y2HWhEwB7kur8ITTpNV4wPiRA0b0D1Cx+sWkHsoiUPP1CL2W1mcMxxoWOIeXn+dhfDJ2hkB618v78JIgqcjY5dIii5wn+3eHEidUqBM8l27QFMH9v4jXLlaORKZVmyKAbgNWhY2B2InXLmkLHKBp1mf9mcsN+idVzconVeoOatt3SrF8R2Si2ku196jCeUtbeeh9wX+gYUn52q83sB5wWOkehnGN8/dy2lsAnPYtUIHcjqf2HUZAXzvlTFS2jM+GazgeYOn0Yi64P33gqdeY1oSMUVkKjTpJ7xhi4W658hbrMz3Gmh46CcyF1jffT1vzL0FHCaYqoe/pynHnk7MNMmzeBW64s/JVkZqvBC15mC4cARoDCRS3yEaEjiPSdRokVRlRVqk+RJdttFpECMv4YOkIBdTJo8N9ChxCpSJbkTlsr7abtCp4JsFt4OBuGfiDhmuXJy71pm9hO4tLaYBLzvdARNjJi/wHpxsNDBwmivr6G9LIf4TZv4y2HsWb91xOpbf5qInU2tz3p2QcFqFvkynqnbXKNPde4n4QleMB8BY3SSfIKqDzv6q2cL5KI9F5EW+gIBXSf3XLlK6FDiFSoBP/wj0u7aXt/SwfwbPKFfVbyNctQuY9HSO7zK63md3tLFvhr6BhA1yGZ+CImzj4sdJREpecOZ/noXwMf3ux2Yx51DUmMgAnRtAXi2jB1i5j7dqEjFIxZgs+NcWm9eVb6kvv3Hr8swQZxaEl+H+e3lpq2IvJmS3f5M85LoWMUhJd1Q1qkuGmnbd+YhWj+1DKp4d0B6hZWesZosA8mVi/RF9QBJDX+ofSa3w7+ldAhNjGGKL6NybMOCR0kERMb98U77wQ7rpuPGm7Xkp61W0EzxP5kQdfv2SmB6hYv58DQEQomyas5LMmdn0JyV7JAzbAKashbcp+rRXn9mVHTVkTexGiKMf9F6BwF4dGNoSOIVCxPcE5Xqc+0BTB/IEjdHJ8LUrdQjs3sjNe0AxOSK2pjkqsVRDIvKq3kmrYwduVCnIdDx9jE9uSixaQbDg0dpKDSmfcT+X0YW2tQb49HP6K+vnBNqIh/FWztrXFqmTR71yC1i5WxL/X15fkGWqJvaCXY7BJIcqdt9HLlfG2jJH9mtNNWRJJgXB86Qt45/7Db5/8pdAyRiuUJntya/DzY/HML07Q1m0btrGOD1M639Mx9iPjdNho5+We+S6L1EpfYTuLSa7gsXJjD/KuhY2xhLNjvqGtMbrd5UtJNVdRmvgb8HBi1zfsbE3l+u88XLI/ZowVbe6t1iYhjjbfZXDUvjN43dIiCSPSwSzVtE5bcv3d1XEG7qJP8Po7UtBWRBLS13g6E+cOzUCK7JnQEkYoWWZKHWXQkWKswUrkwTVswiOaX/A6l2oYjIPU7jL0Sr+3l3rRNaMdK6Y1H6LLKfgQ8ETrGFobhfiN1DZ8n0fniBZSeuQ++bAnGf9Onz8lP6tv9+2DQoEcDvmk4j6nThwWqXZyc8txh7kmO4HE1bZOV4L93TeV8bRM9IFY7bUUkAQaOezIn7SbBeYkNHa2hY4hUNI+T+7vDbG1itQplu5cfAtYHqW28jefHXBCkdj6kG2djdgewY5gAVt5NW09oB6wRkW4qvReVXQcJXhI6RjcMty+Qzizk+Jmle0jT+Ew16cxnIPUAxsQ+PdZ5iepUPeAFyXbLla9g/KMga2/b9qwffHag2sXJoyQOn0teopd6J9lEFJKcaZuqoJ22ToJjq/K7q1dNWxHp2biV3weeCh0jLyK/wu68dlXoGCIVzRIcjxB76TdtFy7cANwVrL7xeSbNmhSsfn9MnT6MdOZ68KuBQcFyOG8NVjsZoxOrNOjJkYnVyqtdrsEJtVt+W05hfervJTkuobbhCEZwH/BVYEifHuvEmH2UxVc/VpBsbxT6XWHX3wqzzzEl85Zg9bcl3XA+kNzPtPmJpGcMTqxeUpyhyRWzcL9LK9PwxCq5l9/PRk8ixiZYLa//rmraikiPbOHCDbh/NnSOPHiGwYMvDx1CpOJ5guMRjDWJ1Sok99sCVq8ijn7KpJml0YCcPOsQNgz5PXB66CgY4wp+Sn1IxvaJ1VpblVytfMo2dWKcRaF2dA7cTrj/jLrMDUyeFWhHeh9MnLUn6cbvYXY38I5+rWF2Ednm3+Y3WDc84JttMIwOv5piHIGRbvgS2DdINttwbNDxCdZLhpHcTnnrxaxoyRejN7O58yZVmr9f+2eHUq2lpq2IbJW1t14PtIXOMTB2tt1y5SuhU4hUvETHI5TBTlsAi0I2bQF2IJdaVNQNyAnnDCGd+Sq56A/AQaHjvM5T5XGY25aOPnMESe5ijnKl+6Iy2/I74AehY2yVU08ueoS6zBdJz0huB3VvTcm8hbrGFqLon+AzgH5ezuu/JNv85XxG61FNbknQwzDNTiTd+N/B6m/p5MxQ0pnrwC4MUt+9dEf99MQTbEDFatom5ugzh5PkOIqcle7v175K8mfGLa+7etW0FZHemAusDh2if/yXlm2+MXQKESHZg8i8THbaZnf+I/Bi0AzGfhBli/KS20mZqdSs/hvwGZKcA9cb5u8LHaEgalLJfh94tHui9fItFV8ArAwdYxtG4Pwv1DxGuvFCps0LP5KituEI6hpb6OAR3BsY2M/3n+jITSepXc+3XbsM87sTqdUjv5i6hvDzXNMz9+EV7gY+FjDFBNKZ9wesXwg7JVYp8iQvK69sg1M7J1wxyd2ngeW3kbpVef6ZUdNWRLbJsi0P4dYQOkc/PEmqZmboECKykSc409ai8thpS1MMhN5tC7A3HdxHXePk0EEASDceTrrxZmJuxdgrdJwevJ/03OReWCcljpL99zYr1q9v7yxZ8Bzwv6Fj9NIY8C+xdv0y0o3fo3bWsSR5KXs6swPphk+SzjyA2b24N2ADPvTu31R3nkTy5xosTLjelqpw+2m45+ymiLqGuXjqfoxDwmTYzCVMnT4sdIg8MWCPxKrFXhojksqBe7JXNZlX0E5bH5dcLY1HEJEArL35JzgtoXP0wVqMelvy3bA71ETkDe4J7rTNlejVAd0wfhg6AtA1p9X9VtINX2LCOX07AChf6maNJ535Ffi94CcFydB7g7DcF0OHyDvz/RKt5wnXK4SxK64Gwh1O1XfDwGdg0e2kM/8knbmYuoZa6usH2kDdQlPEpMw7STdeQDpzG87TYJcDb89TgRWk7ERuu3ZZntbrvbj650FHJHQZjPtvSDd8ONGqdQ0HUbvsDty+iyV46NjW7c+GIddAU//7H/X1NdRlzqYuE3b0xHFn7kxfD+EbENsjuVoVLk7tmXDFythpe3JmKJboAbF53Wmb3LwMESl941bM5fkxO2AU+2nDOcxOt7bm34cOIiKbsATfLLbqFYnVKjTf5VZY9ixJXg7ZsxTYhdSsPo3ahvNpb72JQl9yPHX6MDYMORX4OM5E+rfz7z6wu8A/med0W+c+i7qGxbS13pBo3UIy3pXo0VrGEQlWK4yFC3NMnHUGFv25iJpYvbUv8DncPsfyMaupbWjH7C7MHwIeYoeVj7Bw4YZtrnJyZigvR/sT5fbHowMxPwiW1RKzw+tPIfl9W289RB9kyfx/5HXV3rr9qidJN/6/InhzaRDYj6nLvJMdVlzYq69Vf02cuztR5+dxZmBF2Wf4L2qX5bAZM8let67Xjzr6zBFUV3+U5X4BXTtcn4KmSzdeCZO8zqrDEq1njOPYzM7c0fJMonUrkif7tU1yx3ZIr8YHQtTPeej94OzV9QZRfp4jivHJVESKlC1cmPMJ55xOzZpfYz4pdJ4e5MBnWVvLz0MHEZE3SW6nbee6lxKrVWjZpk7SmR8B54WO8jpjL7Cfk848iPtldOb+L6+XH0+dPoyOobXEfgobqAdG9Hst9x9gHY1Q/YkAB6obsV1PXcMOtLVelXTxAjCcdydc821MmzeSUj9Q9PYFj1Pb8Amw4j6YbOuGYXYicCKvXTixfEwn6cwzOKvAX8XsFbAcMBR8OM4ojJGsYnui2MDACt7178SZTvv8bKELbV18BVjopi10/dx+muVjppCeNZfsgnvyuvqUOXvRGZ8NnY3A4LyunW/GaXjNoaQbLmDsyltYuDDX7f0mnDOEQa8ejUUfwv0j4MM3+ehu1D1TRxtLkgm9BfejEv9dFtnhwM3JFq1A7keQ4PEPYAcnWCwcjyYkWs8YyuSn92EJD+dnORGRPvL6+hqe3+56zD8UOssW1uNMt/aW0HPERKQ7dZkvdh12U3AOu9SQbepMoFYyJmbeTsQDoWNsxTqcReC/xlJ3k93pH33aYTBx7u5EHfvjNgGYAhyVh1mWneDnk229AoB0w/lg3xjgmgNg94B/jVX8hvtbOsLlGIDahiMwuzf5wv4Rsq3/l3zdAkhn5gONoWOUsRxu02lv/knoIIBRm/kbxttCB9mEAzeAX0K29c/9XiXdVIU9MxX3uTjTEr2SJl+cZcCvMR7H7CXcx9J1RctBwAS22oC268g2fzyRnFuqbfg9ZkcmXPU7ZFvmJVyzskydPY4N8TIguR2hsI71w7bj7svK5ByIHtRmfomR7MGMZh+jrTkvb9KqaSsi/eL19SmWb3cJ+HkUw3OJswzjvyzbUkoz40QqS23myxifTaDSSrItYxKok6x0ZjFQHAeBbYvzCsa/wJ7A/FncX8FYA9FgiIfj0QjwEcBbgf0ZyE7a7r1AFP8XSxcsff2W4E3b172M83vM78Wje4nix/COp8letzJ0sG1KZy4DPpV8Yf8l2dbyOP192rxBrFl3e4CmSyXIgX+MbOuPQgd5XV3jGbh/P3SMbjm3E9mP8NSvyF717Dbvf8ycMVR3HotHH8D9vRjbJZCyODmvsGHYTok3uybNfCtx6nGSf+31HGNX7NrjzmQZuHSmEZifeF3zE2hrvTXxukmZkhlFJ08DSR9E+FOyLXmZJ67xCCLSL9b1S/vTnm5oB7sOCHf6pNtSLPVR680fnCISjllNocefAuCUz2iETUX2RWIvjaZt19zOw8AP6/qS28YvvXf9/4X9PvgzRB9gacsThSwyAKMwpoJNxZyuy8xroDazBngKfGXXJea8DLYKi1cR26rXb+vo+FFeR1H01rEfHwvMSrwuAHYSkxvfxpLmMPNJ8+mWK9dz3JkfoKPqHmD30HHKhhMT2Zm0tRRPwxagbecfkl52LvCO0FHexJiI+0TobCadeQj4M24PAy8QsZ7YR2E2Dnw3YDzk9u16vvKBtQydlzBSwKh8fBpBGCMZ9Or7gGSvAIhT8wizWWZHlo85HSjONyBKXX19iuc5N8hX1u1UoHybtp2cTvINW4D3cMycMfzu6gGfsVF6lzGISFGxbOuvSVXvj9NCIt2YTTgvgX+K9p2PU8NWpAS45/nk8R5YmTZtlzbfDp4NHaPI/ZQRHE12/hOhg/SZMRRjv407MKcAp4DPwG1e1w51vwT8aqprwrxJGlVfAgzf5v0KI0XOv04xXNmTD7ddu4xUfBJOac/pLRbOBiKm5+tS1PxqijG/IHSKXjgA+DDmn8f827g3Y3wd/Hzgw3QdSDdwziJS0SEYZXD2hJ2eaLn0rN1w5iRac1POF5k2r9QOUiwNy8fMwdgvSG3nNCafFW7zVSGl5w4H/idQ9eFU5/Ly86qmrYgMmC357ovW3tKIeR3YHQmUXI1xGYNz+1q29QoLdXqriPRNRHUiddyfT6ROCG5fDB2hSOXAPkO25SPc3LImdJiyU9d4BsaZgVO8h9pMgNEMBbJkwQOYnwKsDx2lxL1KipNpa/lx6CA96rr0+LehYwS2FrezaW85gaXznwZ+FjpQHkxl6uxxiVSqr09B9EOMoYnU647xFtasvx6a1EPKp3Tj4cDXICvVRwAAIABJREFUg9U3hpLb8LVg9Quq8xJg12DlYz7HxMYBv+GlHzgRyRtra2034i/WAr8E8n3KytPAl4H9Yb61tZxrt15TnrvpRMpVUjttsaeSqRNAe0sbXg47lPJqBdhJZJu/RtJXfFSCusYM7teGjrHRpaQznwgdIm+yrYs3HupamgfThbcc90ksbVkUOsg2VdEIvBw6RiB/AN5Je/OVvPYcPXjQ4jLYaV7NhtykglcZn6lm+ZgfArUFr7UtxntJP3Pjxh2MMlC1mTrwxcCQsEHsTOoazwibIc/SmfOAs4JmMIYS+S9Jz91pIMuoaSsi+ZNuqgK7/Hbg/cAuwDy6thas7ueSjwELgKl0nVZzIfAwzCU9e4+BBxaRRHlCO23NnkykTig1nfOo3Bf/W/oL5I4k21zpu9jyb9LsXUlnbsS9mWRPs+5Z1wn1V5Ju/GFiO9wKra31V5ifjhq3ffUvUn4M7a33hQ7SK4tb/gN2XugYCesEv5hVvJtsy0ObfeSWK9cTlfRu22fAPky2tbAzbevm7M8Ib6NrREWR8A/gnfdT23hy6CQl6/iZ21GbuQJYTHHMdjbcryXd8ElKfQxResZgahu/C1waOspGB+Kdd1I3++j+LlDaXxARKS61mXMwvtXdh6qBw4GDgf2Avek6Kvy1493XA68CzwIPA/+k6235J3qq5XYD7c3/la/oIpKAdOYnJPLCwz9OtvW6wtcJKNQpw8XEuZYNwz7R69O70w3ng32jwKkSEO1Z0Jm9k2YfSZzLbJzXOKhgdQbuZdyvobrquyy++rHQYQastvFkzG8ABoeOUvzsVjqjj+TjgJeEGenGX4GfFDpIAh6FeDrZBff0eI90w6Fgf0ow08A5MeYt0PE/ZK9bWbA6kxv2I2fnAh+juJ8T/gAsgA0/Lei/R3kwJs0+gjieDnycMIdj9UY7RE1k52dDB+mTafMGsXb9h3G+iPGW0HHexIkxFmLxN2hbcH9fHqqmrYjkx/Ezt2Nd6hGM7RKraZ6mrbU9sXoiMjC1mZ9hfLDgdcym0Na8pOB1gmqKSC+7la4DqyqLswbzs/rcmFfTtjtGOrM/ZkcS8y7MTwT2yNPayXH+iHEzZnewoeNe7rx2VehI/VKbqcP4BcWx86oYOc43GLfisyxcmAsdpl/SM0ZDzT3A/qGjFIgDLdSsPY9F12/7Qrt0QxtYuuCp8uOvuDXS3nx33leur6/hxdGHk6MWsw/StdellHTi3Au2mMjvxaI/b5xdXNmmzNmLjs6jMZsIHEfXhaOl4u+Y34DbUmrW/qlXP89JOzkzlFU2EYtPxO0jwA6hI/XSQ5j9Ao/vItf5e+743vKt3VlNWxHJj3TD1WCzE676Z8auOLxk/3AXqTTpzK+Awl9OZ6kDaLv6nwWvE9rks7Yn13EfsGfoKAn6JzH13N7y1z4/stKbtseduQudqX3xaB/wfcAOA38XMDrvEcPKAQ+D/x2zB8Eexe0/RPGTDBr0FLdcWdwHf01ufBs5/zWV9XPdG6vAGsg2/zR0kAGrm7M/nruH8vvZexZjFm0tv+n1I+oa3ovbLwuYaeCcNRhfYhXf5P6WgY0xSc8YjQ16K11vju1N7AdhHELXxYjFvKO2P17AeRjjEbBHwZ/E/Fk89TS59c9tq1FVMsZnqhlme5BiX9z3A/ah6+t5KOXzBlwOeBDsDzh/hPhR3J8g6nyc7HXrCl69vj7Fc6PegqX2wdgb5+2YH4HzDoyEzssoIOcx4B7MHsDjh4l4mMGDH33t7xU1bUVk4OoaDsLtz0BV4rXdM7S3tiZeV0T6Lt34W/DjC1zFGcFwbm5ZU+A6xaGu8R2430nxXmaXP8b1bOg8q9+7KCuxaZvOfIaukST7UAnfI9vmdE1i+g9wE9mWSwLn6d6xHx9LVH0jxsTQUYqD3UNV9NGyGIPxmtrG4zC/meIeQdIXPwNmk215oY+PM9KNd4EfVYhQA2e3UhXNHdD3XrrxG+BT6GrUllujvv+cDRjPAU8BD5BtSXrzz8CkM7+gqzm7ByFeAxePZ3AeJ/IraGu9Ia8rpxtuwu1AYI+yaM72Rdc4hX/jPKKDyERk4JzLCPbLyi7uutRMRIpeMgeR/adiGrYAbc1/wewMunZBlCfnedw/SFvLGSV72Xv/vYz7vRjXAxeC1bN+yHO9f7iPB96BGravMWBn4F1gR4YO06M7vrcc22UycAldjeZKlQO/GHY+tqwatgDtzbdhnELXsQ6l7GXczyDbcmo/GrYADrlzcOK8JxuYZ3E7jWzzCXn43juGrl2Xer2yqa4m3O7ABGBy4DT9cRxdb4hWcsMWYGeMd29sruaZTcXYr+IatvDawat7Ykyt9G8wERmouoZTcDsuWH1jHNRcCJwfLIOI9I55An902UPbvk+ZaWv+OXUNp+H2Q0ikMZ4g+wWDbDaL5j8fOkkCngTux/kjEX+kqvNP3HbtstChJJBsUyfwP9Q23I7ZNXQ1myuH8w+iKEPb/DtDRymYtpbfUNtYD35jiTYlllDFmSxu/c+AVskuuIe6zBU45+QpV/91NY8XkEt9pgQPuhORMqSmrYj0X9cpjeEvLXTmUTentSJmWIqUtgQaiv5g4WsUobbWG6ht6AD7vxJ98b+llbifTXvL9aGDFMirwO9xuxPie4g7/1A28/0kv9pbb+H4mQezvurb4B8NHScB63C+wrgVX2Phwg2hwxRce/PN1DWeiPtCYEzoOL20Cvg02ZYW8rUTfN2wzzFodR1dO1JD+TtR1FjWbxSISMlR01ZE+m/d+vPouiwkLKMGjy8liQOORGQgCt9MNKvMpi1Ae+svqG08CfzHlM4Jut25CeJ5tC94KnSQPHoapx3jLvA7GbvyrzpEU3rt1mteAk6nrvHHuF8O7Bs6UmHYraTis1nS+nDoJIlqa17CxMZ3EfmvgANCx9kqZxHVNLC4ZWC7a7d092VrmTLnFDpz9wLb53XtbVuLcTGv8A3unz+wg8ZERPJMTVsR6Z/Js3akkwuK5zhDP4l04wlkm38bOomI9KjwTds4V7lNW+iakzglM54OX4gV8czO7t0N/DfZlt+FDjJgzjIgC57Foyy3Nz8SOpKUgbbm/8e0eUtYt/48Yi7AGBk6Ul44D5Di0yxtXhQ6SjC3Nz/ClMxRdHAtxgdDx+nGSrDzaW++lkLNWV589WOkZ50E0WKSmsPtLCLFXJa2/CuReiIifaSmrYj0T6d9vfheLPi3GJ9Zwv0tepdcpBg51QV/o6dq0D8KXKH4LW75D/X1x/LCmEtwzgZSoSNtwz8x+yxtzb+gVA9dcl7BWIqziCi1lKzG9UiB3HLleuArpDMtYBeAnwUMCR2rn/6F25dp3/n70FRsB1Elb3HLy8Ap1DZ+BPMrSX7HaXcc7PvU2AWJzBbPLriHdMP7wW6isI3b5zDOJdvy4wLWEBEZsCh0ABEpQXWzxoOdHjpGNw5khM8NHUJEelD4WauPs+S7Lxa4RmlYuHADbS3nYjYeKNadq8+CzYFdDqat+eeUUsPWiXG/F/xiPJ6I7bI92ZYP0N5ytearSyKyLS+Qbf40VO0FfBkopee+v2J8lLEr9qe9+Xtq2G6hvfknUHUw8H+EfV78E5EfQ7b544keBpltXQzxFODZAqzuwAI6UwfSpoatiBQ/7bQVkb4yPLocK9Y3fewi0pkfkW15IXQSEXmTwQVd3e2+gq5fitqa/wJMJN34UfDPURzzEv+N8S2q117DoutXhw7TB0/hLMJsEVVVi0vjDQJbDegE9G55KX3v9Sx71bPAhZyc+Qqv2um4nwm8K3SsbuRwfoPZ1WSbb6WU3qQJoevr+hEmZb5BzMXAtASrP4v5l9hhZXOw2dvZBfcwedah5KLvka/P3fkHRmPyI3jiVWB6Ht4a55XQEfphJVD+hyX2lrG2AKuuBNYVYN2SUjTTKEWkRNQ2TMfsB6FjbJXbVbQ3nxU6hohsIZ15BRhRwArnk235ZgHXL3FNEXVPn4RH54Mfm3h55y7Mr4Jdf0q2qTPx+umG88G+0YdHrAW7HfdbiXwRba1/L1g2kXyqazgIZzrYBwl/aNkjuP2Ear8m74dXVZJ05hiMs4l5X8GuWnGeJ7KvMdznc3PLmoLU6I+6xg8S+zcw9urnCuvAvszYl77OwoVqsolISVHTVkR67+TMUF7hQYy3hI6yDTlS8TtZsuCB0EFE5HVGbaazoLv0PZ5I+4I7CrZ+OalrOAi3U4F64KACVnoS+BnmC4I3PdOZ84BLt3of5wHMFnXtqF1/B9nrKn6Hh5S49OyDsfi9xDYZ8wkkMf/W+QcRv4H4p7QtuL/g9SrJ1NnjWB9/DOMM4OA8rfoMzmUMWntV0V79MD5TzUj7CO7nA2/vwyMXQ24O2WseLVQ0EZFCUtNWRHovnbkY+FzoGL3URrZlUugQIrLRyZmhrKKQLwZz1KwdVbQvOItZOnMAWBqPJ4AdhbHfAFZbhdt9mN+BxTfTtuCPFMtl0MfMGUMqvhHzTX83LMd8MW63kmMRd7Q8EyyfSKGlZwzGqt8F0Xicd4K/E2fvAe7cdOBh4B7c7yQVL2LpNf/OU2LZmvSs3TCbittU4Bhg1z48el3XwYn+A16xX5XUIb6TMu8k5x/F7ER6GvnjPI/ZeWSbf5hsOBGR/FLTVkR6Z+Lc3bHOhzCGho7Sa+4fpL31F6FjiAhw7MfHkqou5EEmfyHbcmgB168c6RmjiWr2wn1PnD3BdsUZSuQjiG0QxhCMV4FXwV+F6AncH4PoUca++GCwGYi9MT5TzQj/Es7LXSMPdvuTDkGSilZfn+K5UW8hsr2B3XAbi9k48NEYowBwhoOtg/glzF4i9hWYLSOKHmRQ9UPccmUpzqMsP1Myo8hxAPhBYHvgjAAfgdlInKG4v4jZ45jdi6duJ3vVq6EjD9ik2buSyx1JZAfi7IkxitiWM7jzf7n1mpdCxxMRGSg1bUWkd9INC8FODR2jT5zHsA0H6fJWkSIwcdaeRNFjBVvfuIy2lnMLtr6IiIiIiEiCivT0dxEpKnWzjwY7JXSMPjP2wqvPCR1DRACi4QUusKTA64uIiIiIiCRGTVsR2YamCPcrKNWd+Waf5bgzdwkdQ6TiRfGwAq7eyeBBOoBMRERERETKhpq2IrJ16WUN4ONDxxiA4XRUfzl0CBGxQu60vUczFUVEREREpJyoaSsiPZs2byTQFDrGwPnHmDT7yNApRCqaRYXcaavRCCIiIiIiUlbUtBWRnq1ZdxGwU+gYeWDEJTziQaQseCGbtosLuLaIiIiIiEji1LQVke6lZ+6D2VmhY+SPH0W64bTQKUQqV4HGIzjPM3bF3QVZW0REREREJBA1bUWkex5dAQwKHSO/7GtMnV7I3X4i0rPCNG3Nb2bhwlxB1hYREREREQlETVsRebN0wxTMTgwdowB2pWPIBaFDiFSkuGDjEW4q0LoiIiIiIiLBqGkrIptLN1WBXR46RsE455OevUfoGCIVqBA7bVezfrgOIRMRERERkbKjpq2IbGHZJ4CDQqcooCG4fy10CJEKtEP+l/RbuPuytflfV0REREREJCw1bUXkDcfP3A64MHSMgjP/EHUNtaFjiFQU8+0LsKpGI4iIiIiISFlS01ZE3rA++jJQiMZK8XG7nPr6VOgYIhXDbWyeV1wN1b/M85oiIiIiIiJFQU1bEelS13AQ2KzQMRJ0KM+PPjN0CJEKku/xCDeSverVPK8pIiIiIiJSFNS0FZEuzmVAVegYybKLmZIZFTqFSIXIb9M2in+Q1/VERERERESKiJq2IgJ1DaeAHRc6RuKMcXRa+c/wFQmtaxTJ6Dyu+BTbv9yex/VERERERESKipq2IpWuvr4Gt0tCxwjG/Wzq5uwfOoZIWXt5++2xvP7N8X0WLszlcT0REREREZGioqatSKV7Ycz5wD6hYwRj1ODxpaFjiJS1dZ35PODQsdT1eVxPRERERESk6KhpK1LJJs/akZgLQscIz08i3XhC6BQiZasqNTZva7m10Xb1P/O2noiIiIiISBFS01akknXa1zFGho5RHPxbjM9Uh04hUpY8zl/T1rg6b2uJiIiIiIgUqQo7KV5EXlc3azyxnR46RhE5kBE+F7gidBCRsuO2Q55WepZV/ss8rSUiItIjn3DOEAat+SrOzlTxBVvS/I/QmURkcz4+U80I/xLYPlj8VWtbcH/oTCL5pKatSGUyPLo8zwcDlQG7iHTmR2RbXgidRKSsOOOwPKxjtHJ/S0ceVhIREemRTzhnCDWv3gh2IgbkfBxQFzqXiB995giqqmZj1IFF4Fk6Or9rd167KnS2pHU1bPkB2Ie7boj2AQ4Nm0okv9S0FalEtQ2nA8eEjlGExuD2BeCs0EFEyoqxdx5WyWG5a/KwjoiISLd8YuO+RD4VVn8C7IA3PmKHhUsl0sXTc3eCziyw/8ZbAI6nqurjfmwmbXe0PBMuXXI8PXsPzKfiPofNm7QH+/hMtekNfikjatqKVJqTM0N5hYtDxyha5o1MntXMkgUPhI4iUj58Hwa+1fZmll7z73ykERERAfC6huOJORtsD4w9wYf0cNfhCcYS6UHHfLD933SzsR8prgI+kHymwvO62Ufj/hnwPYE9IB7W1a9+kxTjBg0B1LSVsqFLo0UqzSr/H4y3hI5RxFLkostDhxApK277DngNiy7NQxIREZE3uA3B7ESMtwE9NWxBr5slMD9+5na4nbyVu5zsx8/cLrFAicpVgZ8EHAQM2+pd167Sz6qUFX1Di1SSiXN3x+3c0DFKQB21DWX5TrVI4qZkRmGMG+Aqv6dt/p15ySMiIvKa2P4eOoJIr3Tabts4jyTF2tSuieVJkuvnVCqXmrYilSTq+BbG0NAxSoNdSnrG4NApREpeR3zIwBcx7bIVEZH82/Glx4C/ADdjXIb5WZifQCp+R+hoIpsb9DROvJU75KiuXpZYnARZtuUFnLuA3+B8G7ezcT+R2PZDoxCkzGmmrUilqJt9NB6fEjpGyTD2gppPAZeEjiJS0iwaaNP2cdj5prxkERER2YQtXJijm9PmPT13OHQGSCTSPVvy3Rc93XAz2Pt6uMvNtuS7LyYaKkHW3nJ0d7d7OpN0FJFEaaetSEVoinC/gjycBFRhPsdxZ+4SOoRIiXv7gB5tXE62Sa+cRUREpLKlvBF4sJuPPARVc5KOIyKFp522IpWg7plZuI8PHaMEDaej+svAx0MHESlhb9rB1AfLqV57Td6SiIiIiJQoW7LgOZ86/Qg2DGkAJoEZeJaatfNt0fWrk8zi6cbvsekb8x7PsfbW+5LMIFIJ1LQVKXfT5o1k7fovhI5RuvxjTJp9NUvn3xs6iUjJmXDOEHz1YQPY4/8tEn4RIiIiIlKsNjZnL9/4X0C+P/DGpqAoGhkui0j50ngEkXK3Zt1FwE6hY5QwI44vR6MlRPpuyJojMGr6+eiVVHF1XvOIiIiIiIiUCDVtRcpZeuY+mJ0VOkYZmEC64bTQIURKTsyEfj/W/DIWt7ycxzQiIiIiIiIlQ01bkbKWuhwYFDpFebCvMXX6sNApREqLT+nfw3iFjqor8xxGRERERESkZKhpK1Ku0g1TgPeEjlFGdqVjyAWhQ4iUjK43OY7t12MjruB3V6/IbyARERGR8uGTG/bz9IzRoXOIVBKvr095OnOAp2cMzst6E+fu7lNnj+vp42raipSjdFMVWODh9GXIOZ/07D1CxxApCR1D0vRvp/+LDB50aZ7TiIiIiJQFTzdVeTrzE3L2T7zmaa+bNX7bjxKRgfIJ5wxh+ZhbgQeh5m+enjt8QOulM5/AOp9gQ/yUpxtP6O4+VQMpICLFatkngINCpyhDQ3D/GvBfoYOIFD23U8H7/jizr3DLla/kP5CIiMjA+YRzhjBozXtwPw7jSOCtwJiNH34ZeBjnNjy60W6f/6dEs9XX1/D86PdgdjzOERj7AiOAHLAceAHsDziLML/Nsi0vJJmv0LzrPI9RfXvQbn+xbFNnnx4y4ZwhDF71tl4/ILJVtqT14T7l6qn2lDl70bHsaoypABhD8dR04P68rJ+eMRir2vbrSGf4Fv97X6+btbJfRSOesiULnuvXY3vBJ2X2JuYE4HicvTF2BUYBL+AsI7LfE/tPaG/JWr/+eC1eTlNE3VOH9fmBVfEzdtu1y9603pTMKHLxPn1eb3j0oN3csqbPj9u0dn19iufHTATej9mR4LvhdO1QNZ4DnsL9bvCbGPfyXbZwYW4g9d5Uv67xHfjq64BDN960N9ZZC/ymz2tNmzeSNRu+Cj4Hw4AIi88AfrvlfdW0FSk3x8/cjvVcGDpG2TL/EHUNV9HW2h46ikjRmjZvEGvXv78fj3yadUOvznseERGRAfKJjftifApWnw6MxLq92yjgCIwjsPizns78BfNPWoH/bvTxmWpGMoflXIgxFmCLfClgp67//GCMGcA6T2fmk4ovKWTDLFlVl+F+Up8eMujJ7YGX+vSYoav2JRf9odf3z9EGTOruQz5p5lvJpT6P2UuYr8DtJWAFzktY7EQWEbMz+D5gx9OZO3xjk2dTb+lT/q2Jq/Yk6sPn9jq/Gu/nhdw5zge+2b8H95Bm2rxBrF13KthZmx2Mu/m/3A4YO+B+CEYD6YY73apmWtvV/8xnlqBOXjaYVf34enbYxcD/vun2HMfg0a/7vN5qxgN/7PPj6NpZjj2dYbl9HmPHjbd2/Z83vp67A7tjNgHsXJaPedrTmSbGrvhef5u3np51FB7tDnYw5pNwP+ZNd4p797Pn6dl7YPFBOPtifiRr178XY/Pzctx27+6xatqKlJv10ZeB7UPHKGtul1Nffzh5fvdOpGys2/AeoD8z1pq4+7K1+Y4jIiIyQIb5Q1ifxwu+A7c2T2d+wJBBjXbLlevzHczTmWOAVpwD+vjQwcCnyEUzPZ05w7ItN+U7m/RC1YYXyA2ZAR519aE2bUYZxK/dsft3CTaqLli+UhMNHubpzEzWrj8fbKe+PdiOxnO/99qG46y99b7CBJS+8NqGI2DZD3Dr6/PbrkArz4+Z55NnTbclCx7oe/HoGoy3bX3ztfWypxp/FOfirnV7/Fnu9udYM21Fykldw0Fgs0LHqACH8vzoM0OHEClasTf241EPwS7X5TuKiIhIXnTfsO0EngX+BjwCrOv2kfAx1q7/zUDnH27J6xo+BCyGHhu2LwMPbsy3vIf7jAB+7ulGHbgbgC26fjXG46FzlI04/htwKV07y7vzKvAQ8Gegux3mozD7pQ54C8/TmfeDZen5+W0Vzj/oeo57tdt7GIfQGd3hdQ3H9zmA+T+2eZ+o5w7sFv7e5/obaaetSDlxLkM/1wmxi5mSuYHFLS+HTiJSVCY27ov5cX1+nNl5tPVtppyIiEjinA0YP8HtZ3R2ZO3Oa1dt9uH0rKMgmgnMYPO/yydD5y+cpuONppgB8nTjTGJv6aaZvBzz7xD5TVvuLvOJs/YksveCnUPXLN7XGPglnm58wrLNPx1otnD8Vpw35nAauwPT3vi43Y/75rNffUPfdz971Yt43LLZbcbewOQ37sMi4AkAItvG5fb+N7C9e1l9Bc4KjL16nbcvPFqJ07LN+xnvg9cuVQfgNzhP96+o9X0XZM+6a7b+AfefUxX9kiXND246t9YnN+xHZ/QlzD+0yf13xmrOBT6fx1xhDF7RyStjtvxe/RCb/zv9CWfzncU9jcjI8R9si+8P8x3B3vf6/3Y2ANdtvh59mp/tdQ0fwvkxRmqLDz2OMZ9cvNBuX7DZmx0+sXFfUl6PMwfY7Y18jMTt155u+IBlW/sw2sH+DpyKE2M8CTxK1/Nm32f6xvZ3ote/7Vbg/gjGGrD0NlP0uZiIFKe6hlNwuzF0jMpil5Jt/nToFCJFJZ1pBfq4499vI9s6tSB5RERE+qhrR2znqjd/gB/jVZ+x2696cttrzDoKop8Ae2zxkU9btvXSAeWrbTgCszuAQZulw75JzZomW3T96q0+vr6+hue3+xz4hVs0fV8l5ePzdWhWaH7cmbvQUbVpI/Emy7Z8oCC16hq/gvv/vH5DVWpvW3z1Y716bHrGaGzIjpAbjTEK99EQdTXVnAh4lSi3jI7oQbuj5ZmuhpZt0ly3X1u2+eS8fkLbzJy5CzadFWtTrK15SZIZNubYQHeXlXc12n5BxFdsactWZ6k6GLWN/7dF4/ZfZFv2LbeDyQC8ruHzuH1hk5sehV0O7OuBfG+st8X3vvNja2/5aL/zTW58Gzn/PWxy2J0TY/4V1g//im1jlJqn5w7HO76A2Tls3vNcQcQRtrTlX73KMWn2rnhuNN7xL8tet65r7S1e5xiftLaWb29zrfr6FMtHHUFq0CO25LsvAnhtpg5j6SZ3+71lW47a8rHakSdSDurra1hul4SOUXHcz6ZuzgLKaVi9yECkZ+4DfKyPj8oR23mFiCMiIpI37mdYe+v1vb27ZRfc4+nZdRDfzWaXatvFPmXOz3vb0HtTjCmZUXRyA5s3bNfh9iFrb765V9kWLtwAXOR1DX8ntp9s0rgdTqddBPS74VJM7LZrl3k68yBw4Mab6jzdVNXf5tRWuU/Z5H890Zevr2WvWwmszHumytWGRWdbdv7fenNnA3ezC3A/dZOfhb2Z3LAvZfIGxmZSdgWdnMMbu233wZ45DfhBX5fyCecMwVc3bH6rfae/0Xx8ppqc38imDVvoxHyGZVt/1Js1LHvVq8B5ns48jHPVJl/TMcT2U6fpyN5c7WBL5z8N/d09vsVaXWfh3NOfx2qmrUg5eGHM+fRnm74MjFGDxwPaKSFSViz1Ffp6GIZzDbe3/LUwgURERPLC+9KwfY1l5z8B1LP5br1B5Do/1e8knXyKzXfv5jBO7W3DdrN8ba03gF+4+Y38l09s3Lff+YrPprs/R2HPvivfBfyYOWOAd75+g9GW7xrSazMs2zKptw3b11h2/hMYm/89GkcH9nCXjD0lAAAcZElEQVT3kmZd4/2u2OzG2D/n9fVbjiLYtkFrTgN22GT1+629+e5+hxthZ/DGmyyv+XRvG7absmxLM9C0+a0+nvQz9f2NF4KatiKlbvKsHf9/e/ceH2dZ533887snk5LSAy3n8oDKchBFFqmr1mIz04ZiVQRdKyvaR2ybSctBRVdXHmSJ+1p8gEVblwWatGJ32X0e1+r64IEiPU0B5ZFFcPEExQOgtLYFCpQSmszcv/0jpZ2ZJM0cc88k3/frBa/ONdd93d9CJm1+c83vIkQHB0TG30ui411RpxCJXLL9XJxS/xK0i3HB1TXJIyIiUgcs3X0fkF9wcPv4vkJfSXze5ZNwPpF/A7vBNnX/oOyAL9mN9B/M9KoYgY+KnbYAmK3Pe+zhsH33PbEk4YmOBz3R8aAnOz4w7D3iYRJyem+GrqJtZHrvKP9aKyj0+msGnzcKZGJfpf+gwn7GKeyc+uHSF/LL8x+Hle2yxa8qGF5Puvurg15QjKN2fQksf4er+9+WVaCOiIq2Io0uYzdgTIo6xtjmX2F6qrTdhSKjydwlR+HWVcaVV3L3ih1VzyMiIlJPQm4oGJlAU6b008x7Xvk4xtSckSc54rnOSqLZT7v7MFtWMDxn0MmNyPduBrI5I0X00M9eCD4dfDqhF9H2Ka81ApiKto3J/1QwcEgkMUaA3XfrLvCb8kdL223ryfZW4M9zhp6Bvm+UHWqCtwGvyx8Mr66kr7CtWZMl8GvyB3kDz06dWe6aI01FW5FGllw8HeyjUccQTmMSS6MOIRKJ+fOb6Q2/Sf4p1MNzf4D0tJW1CSUiIlI/rL8N0MMFw60lL+T27oKRG/f1p61MkL2D/MLI2/y81PiK160D+/rFPpgz9BeeuPiwoeY7nQHY+QcWYK7PXDjxoDcJ84rcj1t61R/LjCtRcvYWDIzaoi0A48JlOC/mjLyeZ6ZcWPT1bgW7bFn16oFd5bHCT6/+xNKryuoDm2dj9zrgl3ljTsN8UlZFW5HGZXiwvODEV4mK00kidcTwE0VGkRlXtLBjyhpK/8EzQ5O3U8QhACIiIqOCsb5g4J2lXO4zrmjBmHVggBAof1dbbpINq7bj7MwZirOHY6uxdl3wvL62TXg8OeTcxB/fCnm/90OIx94z5NJtqRMwTsm5l3bZNqogb0c2hDZuiJmjgv3wa88B+e0MnC/0v3FxcN6WOgE4P2coSxO3Vhgpv5DqlN/2JYeBY9yZv7Y3TNG2KeoAIlKmRMdHwM+OOobsNwW3LwKXRh1EZEQkLj4MXv4uUNIPnQAYy9iw6pHqhxIREalX/iBY7sD/KOnycXumk/txbeMp8DM90T70NaXZk/coDI8EflutxSMVCzcQBv9r/2NjLvCdwSfbBYMM/iVDFciz3pb3/9VsY9k5JWKeKfh/2RxdlhFiLAM+AUzYN3Iaiac/RHqYN4QyfglYTj3Rvmvru54qN4YnOpvwrQUHq4fpctcbyNLgn80ZeH311q4t7dATaUTnpcbjfm3UMaSAeQdzFp8RdQyRmkum3gbND4CXXrB1nsKb/q4GqUREROqXe2FBY7LPn198UcgH7Hx9Ldi66v1T0EvSgsPL+W3WpTDzY6DnwIAd5DAyO3+QsXlDtosILbc1ghPLpsuJKBIFS3c/A35zwehBd9v2vxZscd5gkC37ADIAsluPHPAJ4njwZEVr5gkL12rxttTk6q1fOyraijSi3X4lxglRx5ABYmSD5VGHEKmZGVe0kEj9Pc6PgJPLW8QuI33LS1XNJSIiUu8svmvA2HOTpxR/PUdXM86w3LLDT2oM+/ps/ihn6M+8bemJhfM8kXo9g+/AO5SXBvTbxMGwvH62v7INq7ZXHFhkJGUzXyZ/p/0bST79wSHnv+gfAXLf1PklG1dV1hYk5gO/v7XwTEVr5orHdg4Y80HuWYdUtBVpNLMuOR63T0cdQ4aUpLX9/VGHEKkyozU1n3F7fglcBRR9smzBMt9hc9f3qhlMRESkMWQGtiZ8eeKLg0wcnFN8gbcaLDuwyNHQLL+ncCacO8ikwVoj9Av9LweMzVn8JsgrpqufrTQcu/frO8Hy+9G6XT3kblsrOIDMudnyDzIsQywzYCgzrnrtXHvCgT+7WNPAe9Yh9bQVaTSxzJdxRsVprqOX3Uji4rVUdHqmSB2Yd/k4evb+Fc6nMSpt/bGbMPbJquQSERFpNBYcjufVNV6y+5f1DDV94PXsLhj5I/i/VSPaoHrDLTVbOxob8h65nwOsKJiTW7TN0N9SYeK+x+/1eZePs7U37T0wI5iT16bYTEVbaUyx7I1kgkuw/XWG00ls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- julearn - INFO - = Data Information = - 2024-10-17 13:53:25,250 - julearn - INFO - Problem type: classification - 2024-10-17 13:53:25,250 - julearn - INFO - Number of samples: 120 - 2024-10-17 13:53:25,250 - julearn - INFO - Number of features: 3 - 2024-10-17 13:53:25,250 - julearn - INFO - ==================== - 2024-10-17 13:53:25,250 - julearn - INFO - - 2024-10-17 13:53:25,250 - julearn - INFO - Number of classes: 3 - 2024-10-17 13:53:25,250 - julearn - INFO - Target type: object - 2024-10-17 13:53:25,250 - julearn - INFO - Class distributions: species + 2024-10-17 14:01:40,175 - julearn - INFO - ==================== + 2024-10-17 14:01:40,175 - julearn - INFO - + 2024-10-17 14:01:40,176 - julearn - INFO - Adding step zscore that applies to ColumnTypes + 2024-10-17 14:01:40,176 - julearn - INFO - Step added + 2024-10-17 14:01:40,176 - julearn - INFO - Adding step svm that applies to ColumnTypes + 2024-10-17 14:01:40,176 - julearn - INFO - Step added + 2024-10-17 14:01:40,176 - julearn - INFO - = Model Parameters = + 2024-10-17 14:01:40,176 - julearn - INFO - ==================== + 2024-10-17 14:01:40,176 - julearn - INFO - + 2024-10-17 14:01:40,177 - julearn - INFO - = Data Information = + 2024-10-17 14:01:40,177 - julearn - INFO - Problem type: classification + 2024-10-17 14:01:40,177 - julearn - INFO - Number of samples: 120 + 2024-10-17 14:01:40,177 - julearn - INFO - Number of features: 3 + 2024-10-17 14:01:40,177 - julearn - INFO - ==================== + 2024-10-17 14:01:40,177 - julearn - INFO - + 2024-10-17 14:01:40,177 - julearn - INFO - Number of classes: 3 + 2024-10-17 14:01:40,177 - julearn - INFO - Target type: object + 2024-10-17 14:01:40,177 - julearn - INFO - Class distributions: species versicolor 40 virginica 40 setosa 40 Name: count, dtype: int64 - 2024-10-17 13:53:25,251 - julearn - INFO - Using outer CV scheme RepeatedKFold(n_repeats=5, n_splits=5, random_state=200) (incl. final model) - 2024-10-17 13:53:25,251 - julearn - INFO - Multi-class classification problem detected #classes = 3. + 2024-10-17 14:01:40,178 - julearn - INFO - Using outer CV scheme RepeatedKFold(n_repeats=5, n_splits=5, random_state=200) (incl. final model) + 2024-10-17 14:01:40,178 - julearn - INFO - Multi-class classification problem detected #classes = 3. @@ -240,8 +240,8 @@ The scores dataframe has all the values for each CV split. 0 - 0.004849 - 0.002696 + 0.004489 + 0.002588 0.916667 96 24 @@ -251,8 +251,8 @@ The scores dataframe has all the values for each CV split. 1 - 0.004468 - 0.002540 + 0.004421 + 0.002629 0.833333 96 24 @@ -262,8 +262,8 @@ The scores dataframe has all the values for each CV split. 2 - 0.004422 - 0.002557 + 0.004480 + 0.002599 0.958333 96 24 @@ -273,8 +273,8 @@ The scores dataframe has all the values for each CV split. 3 - 0.004423 - 0.002543 + 0.004435 + 0.002597 0.916667 96 24 @@ -284,8 +284,8 @@ The scores dataframe has all the values for each CV split. 4 - 0.004364 - 0.002551 + 0.004440 + 0.002539 0.833333 96 24 @@ -538,7 +538,7 @@ the heatmap with annotations. .. rst-class:: sphx-glr-timing - **Total running time of the script:** (0 minutes 0.534 seconds) + **Total running time of the script:** (0 minutes 0.529 seconds) .. _sphx_glr_download_auto_examples_00_starting_plot_cm_acc_multiclass.py: diff --git a/pr-preview/pr-276/_sources/auto_examples/00_starting/plot_example_regression.rst.txt b/pr-preview/pr-276/_sources/auto_examples/00_starting/plot_example_regression.rst.txt index 035f685fa..684e178b2 100644 --- a/pr-preview/pr-276/_sources/auto_examples/00_starting/plot_example_regression.rst.txt +++ b/pr-preview/pr-276/_sources/auto_examples/00_starting/plot_example_regression.rst.txt @@ -70,13 +70,13 @@ Set the logging level to info to see extra information. /home/runner/work/julearn/julearn/julearn/utils/logging.py:66: UserWarning: The '__version__' attribute is deprecated and will be removed in MarkupSafe 3.1. Use feature detection, or `importlib.metadata.version("markupsafe")`, instead. vstring = str(getattr(module, "__version__", None)) - 2024-10-17 13:53:26,944 - julearn - INFO - ===== Lib Versions ===== - 2024-10-17 13:53:26,944 - julearn - INFO - numpy: 1.26.4 - 2024-10-17 13:53:26,944 - julearn - INFO - scipy: 1.14.1 - 2024-10-17 13:53:26,945 - julearn - INFO - sklearn: 1.5.2 - 2024-10-17 13:53:26,945 - julearn - INFO - pandas: 2.2.3 - 2024-10-17 13:53:26,945 - julearn - INFO - julearn: 0.3.4.dev37 - 2024-10-17 13:53:26,945 - julearn - INFO - ======================== + 2024-10-17 14:01:41,842 - julearn - INFO - ===== Lib Versions ===== + 2024-10-17 14:01:41,842 - julearn - INFO - numpy: 1.26.4 + 2024-10-17 14:01:41,842 - julearn - INFO - scipy: 1.14.1 + 2024-10-17 14:01:41,842 - julearn - INFO - sklearn: 1.5.2 + 2024-10-17 14:01:41,842 - julearn - INFO - pandas: 2.2.3 + 2024-10-17 14:01:41,842 - julearn - INFO - julearn: 0.3.4.dev43 + 2024-10-17 14:01:41,842 - julearn - INFO - ======================== @@ -248,32 +248,32 @@ for scoring. .. code-block:: none - 2024-10-17 13:53:27,143 - julearn - INFO - ==== Input Data ==== - 2024-10-17 13:53:27,143 - julearn - INFO - Using dataframe as input - 2024-10-17 13:53:27,143 - julearn - INFO - Features: ['age', 'sex', 'bmi', 'bp', 's1', 's2', 's3', 's4', 's5', 's6'] - 2024-10-17 13:53:27,143 - julearn - INFO - Target: target - 2024-10-17 13:53:27,143 - julearn - INFO - Expanded features: ['age', 'sex', 'bmi', 'bp', 's1', 's2', 's3', 's4', 's5', 's6'] - 2024-10-17 13:53:27,143 - julearn - INFO - X_types:{} - 2024-10-17 13:53:27,144 - julearn - WARNING - The following columns are not defined in X_types: ['age', 'sex', 'bmi', 'bp', 's1', 's2', 's3', 's4', 's5', 's6']. They will be treated as continuous. + 2024-10-17 14:01:42,039 - julearn - INFO - ==== Input Data ==== + 2024-10-17 14:01:42,039 - julearn - INFO - Using dataframe as input + 2024-10-17 14:01:42,039 - julearn - INFO - Features: ['age', 'sex', 'bmi', 'bp', 's1', 's2', 's3', 's4', 's5', 's6'] + 2024-10-17 14:01:42,039 - julearn - INFO - Target: target + 2024-10-17 14:01:42,039 - julearn - INFO - Expanded features: ['age', 'sex', 'bmi', 'bp', 's1', 's2', 's3', 's4', 's5', 's6'] + 2024-10-17 14:01:42,039 - julearn - INFO - X_types:{} + 2024-10-17 14:01:42,039 - julearn - WARNING - The following columns are not defined in X_types: ['age', 'sex', 'bmi', 'bp', 's1', 's2', 's3', 's4', 's5', 's6']. They will be treated as continuous. /home/runner/work/julearn/julearn/julearn/prepare.py:509: RuntimeWarning: The following columns are not defined in X_types: ['age', 'sex', 'bmi', 'bp', 's1', 's2', 's3', 's4', 's5', 's6']. They will be treated as continuous. warn_with_log( - 2024-10-17 13:53:27,144 - julearn - INFO - ==================== - 2024-10-17 13:53:27,144 - julearn - INFO - - 2024-10-17 13:53:27,144 - julearn - INFO - Adding step zscore that applies to ColumnTypes - 2024-10-17 13:53:27,145 - julearn - INFO - Step added - 2024-10-17 13:53:27,145 - julearn - INFO - Adding step ridge that applies to ColumnTypes - 2024-10-17 13:53:27,145 - julearn - INFO - Step added - 2024-10-17 13:53:27,145 - julearn - INFO - = Model Parameters = - 2024-10-17 13:53:27,145 - julearn - INFO - ==================== - 2024-10-17 13:53:27,145 - julearn - INFO - - 2024-10-17 13:53:27,145 - julearn - INFO - = Data Information = - 2024-10-17 13:53:27,145 - julearn - INFO - Problem type: regression - 2024-10-17 13:53:27,146 - julearn - INFO - Number of samples: 309 - 2024-10-17 13:53:27,146 - julearn - INFO - Number of features: 10 - 2024-10-17 13:53:27,146 - julearn - INFO - ==================== - 2024-10-17 13:53:27,146 - julearn - INFO - - 2024-10-17 13:53:27,146 - julearn - INFO - Target type: float64 - 2024-10-17 13:53:27,146 - julearn - INFO - Using outer CV scheme KFold(n_splits=5, random_state=None, shuffle=False) (incl. final model) + 2024-10-17 14:01:42,040 - julearn - INFO - ==================== + 2024-10-17 14:01:42,040 - julearn - INFO - + 2024-10-17 14:01:42,040 - julearn - INFO - Adding step zscore that applies to ColumnTypes + 2024-10-17 14:01:42,040 - julearn - INFO - Step added + 2024-10-17 14:01:42,040 - julearn - INFO - Adding step ridge that applies to ColumnTypes + 2024-10-17 14:01:42,040 - julearn - INFO - Step added + 2024-10-17 14:01:42,041 - julearn - INFO - = Model Parameters = + 2024-10-17 14:01:42,041 - julearn - INFO - ==================== + 2024-10-17 14:01:42,041 - julearn - INFO - + 2024-10-17 14:01:42,041 - julearn - INFO - = Data Information = + 2024-10-17 14:01:42,041 - julearn - INFO - Problem type: regression + 2024-10-17 14:01:42,041 - julearn - INFO - Number of samples: 309 + 2024-10-17 14:01:42,041 - julearn - INFO - Number of features: 10 + 2024-10-17 14:01:42,041 - julearn - INFO - ==================== + 2024-10-17 14:01:42,041 - julearn - INFO - + 2024-10-17 14:01:42,041 - julearn - INFO - Target type: float64 + 2024-10-17 14:01:42,041 - julearn - INFO - Using outer CV scheme KFold(n_splits=5, random_state=None, shuffle=False) (incl. final model) @@ -328,8 +328,8 @@ The scores dataframe has all the values for each CV split. 0 - 0.005148 - 0.002323 + 0.004470 + 0.002319 -48.783874 247 62 @@ -339,8 +339,8 @@ The scores dataframe has all the values for each CV split. 1 - 0.004456 - 0.002264 + 0.004408 + 0.002301 -47.573568 247 62 @@ -350,8 +350,8 @@ The scores dataframe has all the values for each CV split. 2 - 0.004519 - 0.002297 + 0.004419 + 0.002285 -37.617474 247 62 @@ -361,8 +361,8 @@ The scores dataframe has all the values for each CV split. 3 - 0.004482 - 0.002329 + 0.004452 + 0.002309 -47.686852 247 62 @@ -372,8 +372,8 @@ The scores dataframe has all the values for each CV split. 4 - 0.004398 - 0.002251 + 0.004408 + 0.002306 -45.558655 248 61 @@ -604,7 +604,7 @@ of true values vs predicted values. .. rst-class:: sphx-glr-timing - **Total running time of the script:** (0 minutes 0.669 seconds) + **Total running time of the script:** (0 minutes 0.663 seconds) .. _sphx_glr_download_auto_examples_00_starting_plot_example_regression.py: diff --git a/pr-preview/pr-276/_sources/auto_examples/00_starting/plot_stratified_kfold_reg.rst.txt b/pr-preview/pr-276/_sources/auto_examples/00_starting/plot_stratified_kfold_reg.rst.txt index fe9f5c54b..d277ebfef 100644 --- a/pr-preview/pr-276/_sources/auto_examples/00_starting/plot_stratified_kfold_reg.rst.txt +++ b/pr-preview/pr-276/_sources/auto_examples/00_starting/plot_stratified_kfold_reg.rst.txt @@ -72,13 +72,13 @@ Set the logging level to info to see extra information. /home/runner/work/julearn/julearn/julearn/utils/logging.py:66: UserWarning: The '__version__' attribute is deprecated and will be removed in MarkupSafe 3.1. Use feature detection, or `importlib.metadata.version("markupsafe")`, instead. vstring = str(getattr(module, "__version__", None)) - 2024-10-17 13:53:25,966 - julearn - INFO - ===== Lib Versions ===== - 2024-10-17 13:53:25,966 - julearn - INFO - numpy: 1.26.4 - 2024-10-17 13:53:25,966 - julearn - INFO - scipy: 1.14.1 - 2024-10-17 13:53:25,966 - julearn - INFO - sklearn: 1.5.2 - 2024-10-17 13:53:25,966 - julearn - INFO - pandas: 2.2.3 - 2024-10-17 13:53:25,966 - julearn - INFO - julearn: 0.3.4.dev37 - 2024-10-17 13:53:25,966 - julearn - INFO - ======================== + 2024-10-17 14:01:40,865 - julearn - INFO - ===== Lib Versions ===== + 2024-10-17 14:01:40,865 - julearn - INFO - numpy: 1.26.4 + 2024-10-17 14:01:40,865 - julearn - INFO - scipy: 1.14.1 + 2024-10-17 14:01:40,865 - julearn - INFO - sklearn: 1.5.2 + 2024-10-17 14:01:40,865 - julearn - INFO - pandas: 2.2.3 + 2024-10-17 14:01:40,865 - julearn - INFO - julearn: 0.3.4.dev43 + 2024-10-17 14:01:40,865 - julearn - INFO - ======================== @@ -246,7 +246,7 @@ Let's see a couple of histrograms with different number of bins. /opt/hostedtoolcache/Python/3.10.15/x64/lib/python3.10/site-packages/seaborn/_oldcore.py:1119: FutureWarning: use_inf_as_na option is deprecated and will be removed in a future version. Convert inf values to NaN before operating instead. with pd.option_context('mode.use_inf_as_na', True): - + @@ -302,32 +302,32 @@ Train a linear regression model with stratification on target. .. code-block:: none - 2024-10-17 13:53:26,560 - julearn - INFO - ==== Input Data ==== - 2024-10-17 13:53:26,560 - julearn - INFO - Using dataframe as input - 2024-10-17 13:53:26,561 - julearn - INFO - Features: ['age', 'sex', 'bmi', 'bp', 's1', 's2', 's3', 's4', 's5', 's6'] - 2024-10-17 13:53:26,561 - julearn - INFO - Target: target - 2024-10-17 13:53:26,561 - julearn - INFO - Expanded features: ['age', 'sex', 'bmi', 'bp', 's1', 's2', 's3', 's4', 's5', 's6'] - 2024-10-17 13:53:26,561 - julearn - INFO - X_types:{} - 2024-10-17 13:53:26,561 - julearn - WARNING - The following columns are not defined in X_types: ['age', 'sex', 'bmi', 'bp', 's1', 's2', 's3', 's4', 's5', 's6']. They will be treated as continuous. + 2024-10-17 14:01:41,471 - julearn - INFO - ==== Input Data ==== + 2024-10-17 14:01:41,471 - julearn - INFO - Using dataframe as input + 2024-10-17 14:01:41,471 - julearn - INFO - Features: ['age', 'sex', 'bmi', 'bp', 's1', 's2', 's3', 's4', 's5', 's6'] + 2024-10-17 14:01:41,471 - julearn - INFO - Target: target + 2024-10-17 14:01:41,471 - julearn - INFO - Expanded features: ['age', 'sex', 'bmi', 'bp', 's1', 's2', 's3', 's4', 's5', 's6'] + 2024-10-17 14:01:41,472 - julearn - INFO - X_types:{} + 2024-10-17 14:01:41,472 - julearn - WARNING - The following columns are not defined in X_types: ['age', 'sex', 'bmi', 'bp', 's1', 's2', 's3', 's4', 's5', 's6']. They will be treated as continuous. /home/runner/work/julearn/julearn/julearn/prepare.py:509: RuntimeWarning: The following columns are not defined in X_types: ['age', 'sex', 'bmi', 'bp', 's1', 's2', 's3', 's4', 's5', 's6']. They will be treated as continuous. warn_with_log( - 2024-10-17 13:53:26,562 - julearn - INFO - ==================== - 2024-10-17 13:53:26,562 - julearn - INFO - - 2024-10-17 13:53:26,562 - julearn - INFO - Adding step zscore that applies to ColumnTypes - 2024-10-17 13:53:26,562 - julearn - INFO - Step added - 2024-10-17 13:53:26,562 - julearn - INFO - Adding step linreg that applies to ColumnTypes - 2024-10-17 13:53:26,563 - julearn - INFO - Step added - 2024-10-17 13:53:26,563 - julearn - INFO - = Model Parameters = - 2024-10-17 13:53:26,563 - julearn - INFO - ==================== - 2024-10-17 13:53:26,563 - julearn - INFO - - 2024-10-17 13:53:26,563 - julearn - INFO - = Data Information = - 2024-10-17 13:53:26,563 - julearn - INFO - Problem type: regression - 2024-10-17 13:53:26,563 - julearn - INFO - Number of samples: 449 - 2024-10-17 13:53:26,563 - julearn - INFO - Number of features: 10 - 2024-10-17 13:53:26,563 - julearn - INFO - ==================== - 2024-10-17 13:53:26,563 - julearn - INFO - - 2024-10-17 13:53:26,563 - julearn - INFO - Target type: float64 - 2024-10-17 13:53:26,564 - julearn - INFO - Using outer CV scheme ContinuousStratifiedKFold(method='binning', n_bins=40, n_splits=5, + 2024-10-17 14:01:41,472 - julearn - INFO - ==================== + 2024-10-17 14:01:41,473 - julearn - INFO - + 2024-10-17 14:01:41,473 - julearn - INFO - Adding step zscore that applies to ColumnTypes + 2024-10-17 14:01:41,473 - julearn - INFO - Step added + 2024-10-17 14:01:41,473 - julearn - INFO - Adding step linreg that applies to ColumnTypes + 2024-10-17 14:01:41,473 - julearn - INFO - Step added + 2024-10-17 14:01:41,473 - julearn - INFO - = Model Parameters = + 2024-10-17 14:01:41,473 - julearn - INFO - ==================== + 2024-10-17 14:01:41,473 - julearn - INFO - + 2024-10-17 14:01:41,474 - julearn - INFO - = Data Information = + 2024-10-17 14:01:41,474 - julearn - INFO - Problem type: regression + 2024-10-17 14:01:41,474 - julearn - INFO - Number of samples: 449 + 2024-10-17 14:01:41,474 - julearn - INFO - Number of features: 10 + 2024-10-17 14:01:41,474 - julearn - INFO - ==================== + 2024-10-17 14:01:41,474 - julearn - INFO - + 2024-10-17 14:01:41,474 - julearn - INFO - Target type: float64 + 2024-10-17 14:01:41,474 - julearn - INFO - Using outer CV scheme ContinuousStratifiedKFold(method='binning', n_bins=40, n_splits=5, random_state=None, shuffle=False) (incl. final model) /opt/hostedtoolcache/Python/3.10.15/x64/lib/python3.10/site-packages/sklearn/model_selection/_split.py:776: UserWarning: The least populated class in y has only 1 members, which is less than n_splits=5. warnings.warn( @@ -367,32 +367,32 @@ Train a linear regression model without stratification on target. .. code-block:: none - 2024-10-17 13:53:26,611 - julearn - INFO - ==== Input Data ==== - 2024-10-17 13:53:26,611 - julearn - INFO - Using dataframe as input - 2024-10-17 13:53:26,611 - julearn - INFO - Features: ['age', 'sex', 'bmi', 'bp', 's1', 's2', 's3', 's4', 's5', 's6'] - 2024-10-17 13:53:26,611 - julearn - INFO - Target: target - 2024-10-17 13:53:26,611 - julearn - INFO - Expanded features: ['age', 'sex', 'bmi', 'bp', 's1', 's2', 's3', 's4', 's5', 's6'] - 2024-10-17 13:53:26,611 - julearn - INFO - X_types:{} - 2024-10-17 13:53:26,611 - julearn - WARNING - The following columns are not defined in X_types: ['age', 'sex', 'bmi', 'bp', 's1', 's2', 's3', 's4', 's5', 's6']. They will be treated as continuous. + 2024-10-17 14:01:41,521 - julearn - INFO - ==== Input Data ==== + 2024-10-17 14:01:41,521 - julearn - INFO - Using dataframe as input + 2024-10-17 14:01:41,521 - julearn - INFO - Features: ['age', 'sex', 'bmi', 'bp', 's1', 's2', 's3', 's4', 's5', 's6'] + 2024-10-17 14:01:41,521 - julearn - INFO - Target: target + 2024-10-17 14:01:41,521 - julearn - INFO - Expanded features: ['age', 'sex', 'bmi', 'bp', 's1', 's2', 's3', 's4', 's5', 's6'] + 2024-10-17 14:01:41,521 - julearn - INFO - X_types:{} + 2024-10-17 14:01:41,521 - julearn - WARNING - The following columns are not defined in X_types: ['age', 'sex', 'bmi', 'bp', 's1', 's2', 's3', 's4', 's5', 's6']. They will be treated as continuous. /home/runner/work/julearn/julearn/julearn/prepare.py:509: RuntimeWarning: The following columns are not defined in X_types: ['age', 'sex', 'bmi', 'bp', 's1', 's2', 's3', 's4', 's5', 's6']. They will be treated as continuous. warn_with_log( - 2024-10-17 13:53:26,612 - julearn - INFO - ==================== - 2024-10-17 13:53:26,612 - julearn - INFO - - 2024-10-17 13:53:26,612 - julearn - INFO - Adding step zscore that applies to ColumnTypes - 2024-10-17 13:53:26,612 - julearn - INFO - Step added - 2024-10-17 13:53:26,612 - julearn - INFO - Adding step linreg that applies to ColumnTypes - 2024-10-17 13:53:26,612 - julearn - INFO - Step added - 2024-10-17 13:53:26,612 - julearn - INFO - = Model Parameters = - 2024-10-17 13:53:26,612 - julearn - INFO - ==================== - 2024-10-17 13:53:26,612 - julearn - INFO - - 2024-10-17 13:53:26,612 - julearn - INFO - = Data Information = - 2024-10-17 13:53:26,613 - julearn - INFO - Problem type: regression - 2024-10-17 13:53:26,613 - julearn - INFO - Number of samples: 449 - 2024-10-17 13:53:26,613 - julearn - INFO - Number of features: 10 - 2024-10-17 13:53:26,613 - julearn - INFO - ==================== - 2024-10-17 13:53:26,613 - julearn - INFO - - 2024-10-17 13:53:26,613 - julearn - INFO - Target type: float64 - 2024-10-17 13:53:26,613 - julearn - INFO - Using outer CV scheme KFold(n_splits=5, random_state=None, shuffle=False) (incl. final model) + 2024-10-17 14:01:41,522 - julearn - INFO - ==================== + 2024-10-17 14:01:41,522 - julearn - INFO - + 2024-10-17 14:01:41,522 - julearn - INFO - Adding step zscore that applies to ColumnTypes + 2024-10-17 14:01:41,522 - julearn - INFO - Step added + 2024-10-17 14:01:41,522 - julearn - INFO - Adding step linreg that applies to ColumnTypes + 2024-10-17 14:01:41,522 - julearn - INFO - Step added + 2024-10-17 14:01:41,522 - julearn - INFO - = Model Parameters = + 2024-10-17 14:01:41,522 - julearn - INFO - ==================== + 2024-10-17 14:01:41,522 - julearn - INFO - + 2024-10-17 14:01:41,522 - julearn - INFO - = Data Information = + 2024-10-17 14:01:41,522 - julearn - INFO - Problem type: regression + 2024-10-17 14:01:41,523 - julearn - INFO - Number of samples: 449 + 2024-10-17 14:01:41,523 - julearn - INFO - Number of features: 10 + 2024-10-17 14:01:41,523 - julearn - INFO - ==================== + 2024-10-17 14:01:41,523 - julearn - INFO - + 2024-10-17 14:01:41,523 - julearn - INFO - Target type: float64 + 2024-10-17 14:01:41,523 - julearn - INFO - Using outer CV scheme KFold(n_splits=5, random_state=None, shuffle=False) (incl. final model) @@ -459,7 +459,7 @@ the test score is higher when CV splits were not stratified. .. rst-class:: sphx-glr-timing - **Total running time of the script:** (0 minutes 0.855 seconds) + **Total running time of the script:** (0 minutes 0.862 seconds) .. _sphx_glr_download_auto_examples_00_starting_plot_stratified_kfold_reg.py: diff --git a/pr-preview/pr-276/_sources/auto_examples/00_starting/run_combine_pandas.rst.txt b/pr-preview/pr-276/_sources/auto_examples/00_starting/run_combine_pandas.rst.txt index 11e422d36..6d0804deb 100644 --- a/pr-preview/pr-276/_sources/auto_examples/00_starting/run_combine_pandas.rst.txt +++ b/pr-preview/pr-276/_sources/auto_examples/00_starting/run_combine_pandas.rst.txt @@ -1137,7 +1137,7 @@ We have finally the information we want. We can now reset the index. .. rst-class:: sphx-glr-timing - **Total running time of the script:** (0 minutes 0.413 seconds) + **Total running time of the script:** (0 minutes 0.426 seconds) .. _sphx_glr_download_auto_examples_00_starting_run_combine_pandas.py: diff --git a/pr-preview/pr-276/_sources/auto_examples/00_starting/run_grouped_cv.rst.txt b/pr-preview/pr-276/_sources/auto_examples/00_starting/run_grouped_cv.rst.txt index 287366110..6da039d85 100644 --- a/pr-preview/pr-276/_sources/auto_examples/00_starting/run_grouped_cv.rst.txt +++ b/pr-preview/pr-276/_sources/auto_examples/00_starting/run_grouped_cv.rst.txt @@ -78,13 +78,13 @@ Set the logging level to info to see extra information /home/runner/work/julearn/julearn/julearn/utils/logging.py:66: UserWarning: The '__version__' attribute is deprecated and will be removed in MarkupSafe 3.1. Use feature detection, or `importlib.metadata.version("markupsafe")`, instead. vstring = str(getattr(module, "__version__", None)) - 2024-10-17 13:53:23,756 - julearn - INFO - ===== Lib Versions ===== - 2024-10-17 13:53:23,756 - julearn - INFO - numpy: 1.26.4 - 2024-10-17 13:53:23,756 - julearn - INFO - scipy: 1.14.1 - 2024-10-17 13:53:23,756 - julearn - INFO - sklearn: 1.5.2 - 2024-10-17 13:53:23,756 - julearn - INFO - pandas: 2.2.3 - 2024-10-17 13:53:23,756 - julearn - INFO - julearn: 0.3.4.dev37 - 2024-10-17 13:53:23,756 - julearn - INFO - ======================== + 2024-10-17 14:01:38,681 - julearn - INFO - ===== Lib Versions ===== + 2024-10-17 14:01:38,681 - julearn - INFO - numpy: 1.26.4 + 2024-10-17 14:01:38,681 - julearn - INFO - scipy: 1.14.1 + 2024-10-17 14:01:38,681 - julearn - INFO - sklearn: 1.5.2 + 2024-10-17 14:01:38,681 - julearn - INFO - pandas: 2.2.3 + 2024-10-17 14:01:38,681 - julearn - INFO - julearn: 0.3.4.dev43 + 2024-10-17 14:01:38,681 - julearn - INFO - ======================== @@ -295,38 +295,38 @@ Machine classifier. .. code-block:: none - 2024-10-17 13:53:23,775 - julearn - INFO - ==== Input Data ==== - 2024-10-17 13:53:23,775 - julearn - INFO - Using dataframe as input - 2024-10-17 13:53:23,775 - julearn - INFO - Features: ['parietal', 'frontal'] - 2024-10-17 13:53:23,775 - julearn - INFO - Target: event - 2024-10-17 13:53:23,775 - julearn - INFO - Expanded features: ['parietal', 'frontal'] - 2024-10-17 13:53:23,775 - julearn - INFO - X_types:{} - 2024-10-17 13:53:23,775 - julearn - WARNING - The following columns are not defined in X_types: ['parietal', 'frontal']. They will be treated as continuous. + 2024-10-17 14:01:38,700 - julearn - INFO - ==== Input Data ==== + 2024-10-17 14:01:38,700 - julearn - INFO - Using dataframe as input + 2024-10-17 14:01:38,700 - julearn - INFO - Features: ['parietal', 'frontal'] + 2024-10-17 14:01:38,700 - julearn - INFO - Target: event + 2024-10-17 14:01:38,700 - julearn - INFO - Expanded features: ['parietal', 'frontal'] + 2024-10-17 14:01:38,700 - julearn - INFO - X_types:{} + 2024-10-17 14:01:38,700 - julearn - WARNING - The following columns are not defined in X_types: ['parietal', 'frontal']. They will be treated as continuous. /home/runner/work/julearn/julearn/julearn/prepare.py:509: RuntimeWarning: The following columns are not defined in X_types: ['parietal', 'frontal']. They will be treated as continuous. warn_with_log( - 2024-10-17 13:53:23,776 - julearn - INFO - ==================== - 2024-10-17 13:53:23,776 - julearn - INFO - - 2024-10-17 13:53:23,776 - julearn - INFO - Adding step zscore that applies to ColumnTypes - 2024-10-17 13:53:23,776 - julearn - INFO - Step added - 2024-10-17 13:53:23,776 - julearn - INFO - Adding step rf that applies to ColumnTypes - 2024-10-17 13:53:23,777 - julearn - INFO - Step added - 2024-10-17 13:53:23,777 - julearn - INFO - = Model Parameters = - 2024-10-17 13:53:23,777 - julearn - INFO - ==================== - 2024-10-17 13:53:23,777 - julearn - INFO - - 2024-10-17 13:53:23,777 - julearn - INFO - = Data Information = - 2024-10-17 13:53:23,777 - julearn - INFO - Problem type: classification - 2024-10-17 13:53:23,777 - julearn - INFO - Number of samples: 532 - 2024-10-17 13:53:23,777 - julearn - INFO - Number of features: 2 - 2024-10-17 13:53:23,777 - julearn - INFO - ==================== - 2024-10-17 13:53:23,777 - julearn - INFO - - 2024-10-17 13:53:23,778 - julearn - INFO - Number of classes: 2 - 2024-10-17 13:53:23,778 - julearn - INFO - Target type: object - 2024-10-17 13:53:23,778 - julearn - INFO - Class distributions: event + 2024-10-17 14:01:38,701 - julearn - INFO - ==================== + 2024-10-17 14:01:38,701 - julearn - INFO - + 2024-10-17 14:01:38,701 - julearn - INFO - Adding step zscore that applies to ColumnTypes + 2024-10-17 14:01:38,701 - julearn - INFO - Step added + 2024-10-17 14:01:38,701 - julearn - INFO - Adding step rf that applies to ColumnTypes + 2024-10-17 14:01:38,701 - julearn - INFO - Step added + 2024-10-17 14:01:38,702 - julearn - INFO - = Model Parameters = + 2024-10-17 14:01:38,702 - julearn - INFO - ==================== + 2024-10-17 14:01:38,702 - julearn - INFO - + 2024-10-17 14:01:38,702 - julearn - INFO - = Data Information = + 2024-10-17 14:01:38,702 - julearn - INFO - Problem type: classification + 2024-10-17 14:01:38,702 - julearn - INFO - Number of samples: 532 + 2024-10-17 14:01:38,702 - julearn - INFO - Number of features: 2 + 2024-10-17 14:01:38,702 - julearn - INFO - ==================== + 2024-10-17 14:01:38,702 - julearn - INFO - + 2024-10-17 14:01:38,703 - julearn - INFO - Number of classes: 2 + 2024-10-17 14:01:38,703 - julearn - INFO - Target type: object + 2024-10-17 14:01:38,703 - julearn - INFO - Class distributions: event cue 266 stim 266 Name: count, dtype: int64 - 2024-10-17 13:53:23,778 - julearn - INFO - Using outer CV scheme KFold(n_splits=5, random_state=None, shuffle=False) - 2024-10-17 13:53:23,779 - julearn - INFO - Binary classification problem detected. + 2024-10-17 14:01:38,703 - julearn - INFO - Using outer CV scheme KFold(n_splits=5, random_state=None, shuffle=False) + 2024-10-17 14:01:38,704 - julearn - INFO - Binary classification problem detected. 0.6841826838300122 @@ -362,38 +362,38 @@ Train classification model with stratification on data .. code-block:: none - 2024-10-17 13:53:24,399 - julearn - INFO - ==== Input Data ==== - 2024-10-17 13:53:24,399 - julearn - INFO - Using dataframe as input - 2024-10-17 13:53:24,399 - julearn - INFO - Features: ['parietal', 'frontal'] - 2024-10-17 13:53:24,399 - julearn - INFO - Target: event - 2024-10-17 13:53:24,399 - julearn - INFO - Expanded features: ['parietal', 'frontal'] - 2024-10-17 13:53:24,399 - julearn - INFO - X_types:{} - 2024-10-17 13:53:24,399 - julearn - WARNING - The following columns are not defined in X_types: ['parietal', 'frontal']. They will be treated as continuous. + 2024-10-17 14:01:39,328 - julearn - INFO - ==== Input Data ==== + 2024-10-17 14:01:39,328 - julearn - INFO - Using dataframe as input + 2024-10-17 14:01:39,328 - julearn - INFO - Features: ['parietal', 'frontal'] + 2024-10-17 14:01:39,328 - julearn - INFO - Target: event + 2024-10-17 14:01:39,328 - julearn - INFO - Expanded features: ['parietal', 'frontal'] + 2024-10-17 14:01:39,328 - julearn - INFO - X_types:{} + 2024-10-17 14:01:39,328 - julearn - WARNING - The following columns are not defined in X_types: ['parietal', 'frontal']. They will be treated as continuous. /home/runner/work/julearn/julearn/julearn/prepare.py:509: RuntimeWarning: The following columns are not defined in X_types: ['parietal', 'frontal']. They will be treated as continuous. warn_with_log( - 2024-10-17 13:53:24,400 - julearn - INFO - Using subject as groups - 2024-10-17 13:53:24,400 - julearn - INFO - ==================== - 2024-10-17 13:53:24,400 - julearn - INFO - - 2024-10-17 13:53:24,400 - julearn - INFO - Adding step rf that applies to ColumnTypes - 2024-10-17 13:53:24,400 - julearn - INFO - Step added - 2024-10-17 13:53:24,401 - julearn - INFO - = Model Parameters = - 2024-10-17 13:53:24,401 - julearn - INFO - ==================== - 2024-10-17 13:53:24,401 - julearn - INFO - - 2024-10-17 13:53:24,401 - julearn - INFO - = Data Information = - 2024-10-17 13:53:24,401 - julearn - INFO - Problem type: classification - 2024-10-17 13:53:24,401 - julearn - INFO - Number of samples: 532 - 2024-10-17 13:53:24,401 - julearn - INFO - Number of features: 2 - 2024-10-17 13:53:24,401 - julearn - INFO - ==================== - 2024-10-17 13:53:24,401 - julearn - INFO - - 2024-10-17 13:53:24,401 - julearn - INFO - Number of classes: 2 - 2024-10-17 13:53:24,401 - julearn - INFO - Target type: object - 2024-10-17 13:53:24,402 - julearn - INFO - Class distributions: event + 2024-10-17 14:01:39,329 - julearn - INFO - Using subject as groups + 2024-10-17 14:01:39,329 - julearn - INFO - ==================== + 2024-10-17 14:01:39,329 - julearn - INFO - + 2024-10-17 14:01:39,329 - julearn - INFO - Adding step rf that applies to ColumnTypes + 2024-10-17 14:01:39,329 - julearn - INFO - Step added + 2024-10-17 14:01:39,329 - julearn - INFO - = Model Parameters = + 2024-10-17 14:01:39,329 - julearn - INFO - ==================== + 2024-10-17 14:01:39,330 - julearn - INFO - + 2024-10-17 14:01:39,330 - julearn - INFO - = Data Information = + 2024-10-17 14:01:39,330 - julearn - INFO - Problem type: classification + 2024-10-17 14:01:39,330 - julearn - INFO - Number of samples: 532 + 2024-10-17 14:01:39,330 - julearn - INFO - Number of features: 2 + 2024-10-17 14:01:39,330 - julearn - INFO - ==================== + 2024-10-17 14:01:39,330 - julearn - INFO - + 2024-10-17 14:01:39,330 - julearn - INFO - Number of classes: 2 + 2024-10-17 14:01:39,330 - julearn - INFO - Target type: object + 2024-10-17 14:01:39,331 - julearn - INFO - Class distributions: event cue 266 stim 266 Name: count, dtype: int64 - 2024-10-17 13:53:24,402 - julearn - INFO - Using outer CV scheme StratifiedGroupKFold(n_splits=2, random_state=None, shuffle=False) (incl. final model) - 2024-10-17 13:53:24,402 - julearn - INFO - Binary classification problem detected. - 0.6898496240601504 + 2024-10-17 14:01:39,331 - julearn - INFO - Using outer CV scheme StratifiedGroupKFold(n_splits=2, random_state=None, shuffle=False) (incl. final model) + 2024-10-17 14:01:39,331 - julearn - INFO - Binary classification problem detected. + 0.6710526315789473 @@ -427,38 +427,38 @@ Train classification model without stratification on data .. code-block:: none - 2024-10-17 13:53:24,761 - julearn - INFO - ==== Input Data ==== - 2024-10-17 13:53:24,761 - julearn - INFO - Using dataframe as input - 2024-10-17 13:53:24,761 - julearn - INFO - Features: ['parietal', 'frontal'] - 2024-10-17 13:53:24,761 - julearn - INFO - Target: event - 2024-10-17 13:53:24,762 - julearn - INFO - Expanded features: ['parietal', 'frontal'] - 2024-10-17 13:53:24,762 - julearn - INFO - X_types:{} - 2024-10-17 13:53:24,762 - julearn - WARNING - The following columns are not defined in X_types: ['parietal', 'frontal']. They will be treated as continuous. + 2024-10-17 14:01:39,693 - julearn - INFO - ==== Input Data ==== + 2024-10-17 14:01:39,693 - julearn - INFO - Using dataframe as input + 2024-10-17 14:01:39,693 - julearn - INFO - Features: ['parietal', 'frontal'] + 2024-10-17 14:01:39,693 - julearn - INFO - Target: event + 2024-10-17 14:01:39,693 - julearn - INFO - Expanded features: ['parietal', 'frontal'] + 2024-10-17 14:01:39,693 - julearn - INFO - X_types:{} + 2024-10-17 14:01:39,693 - julearn - WARNING - The following columns are not defined in X_types: ['parietal', 'frontal']. They will be treated as continuous. /home/runner/work/julearn/julearn/julearn/prepare.py:509: RuntimeWarning: The following columns are not defined in X_types: ['parietal', 'frontal']. They will be treated as continuous. warn_with_log( - 2024-10-17 13:53:24,762 - julearn - INFO - Using subject as groups - 2024-10-17 13:53:24,762 - julearn - INFO - ==================== - 2024-10-17 13:53:24,762 - julearn - INFO - - 2024-10-17 13:53:24,763 - julearn - INFO - Adding step rf that applies to ColumnTypes - 2024-10-17 13:53:24,763 - julearn - INFO - Step added - 2024-10-17 13:53:24,763 - julearn - INFO - = Model Parameters = - 2024-10-17 13:53:24,763 - julearn - INFO - ==================== - 2024-10-17 13:53:24,763 - julearn - INFO - - 2024-10-17 13:53:24,763 - julearn - INFO - = Data Information = - 2024-10-17 13:53:24,763 - julearn - INFO - Problem type: classification - 2024-10-17 13:53:24,763 - julearn - INFO - Number of samples: 532 - 2024-10-17 13:53:24,763 - julearn - INFO - Number of features: 2 - 2024-10-17 13:53:24,763 - julearn - INFO - ==================== - 2024-10-17 13:53:24,763 - julearn - INFO - - 2024-10-17 13:53:24,764 - julearn - INFO - Number of classes: 2 - 2024-10-17 13:53:24,764 - julearn - INFO - Target type: object - 2024-10-17 13:53:24,764 - julearn - INFO - Class distributions: event + 2024-10-17 14:01:39,694 - julearn - INFO - Using subject as groups + 2024-10-17 14:01:39,694 - julearn - INFO - ==================== + 2024-10-17 14:01:39,694 - julearn - INFO - + 2024-10-17 14:01:39,694 - julearn - INFO - Adding step rf that applies to ColumnTypes + 2024-10-17 14:01:39,694 - julearn - INFO - Step added + 2024-10-17 14:01:39,695 - julearn - INFO - = Model Parameters = + 2024-10-17 14:01:39,695 - julearn - INFO - ==================== + 2024-10-17 14:01:39,695 - julearn - INFO - + 2024-10-17 14:01:39,695 - julearn - INFO - = Data Information = + 2024-10-17 14:01:39,695 - julearn - INFO - Problem type: classification + 2024-10-17 14:01:39,695 - julearn - INFO - Number of samples: 532 + 2024-10-17 14:01:39,695 - julearn - INFO - Number of features: 2 + 2024-10-17 14:01:39,695 - julearn - INFO - ==================== + 2024-10-17 14:01:39,695 - julearn - INFO - + 2024-10-17 14:01:39,695 - julearn - INFO - Number of classes: 2 + 2024-10-17 14:01:39,695 - julearn - INFO - Target type: object + 2024-10-17 14:01:39,696 - julearn - INFO - Class distributions: event cue 266 stim 266 Name: count, dtype: int64 - 2024-10-17 13:53:24,764 - julearn - INFO - Using outer CV scheme GroupKFold(n_splits=2) (incl. final model) - 2024-10-17 13:53:24,765 - julearn - INFO - Binary classification problem detected. - 0.6879699248120301 + 2024-10-17 14:01:39,696 - julearn - INFO - Using outer CV scheme GroupKFold(n_splits=2) (incl. final model) + 2024-10-17 14:01:39,696 - julearn - INFO - Binary classification problem detected. + 0.6672932330827068 @@ -466,7 +466,7 @@ Train classification model without stratification on data .. rst-class:: sphx-glr-timing - **Total running time of the script:** (0 minutes 1.366 seconds) + **Total running time of the script:** (0 minutes 1.376 seconds) .. _sphx_glr_download_auto_examples_00_starting_run_grouped_cv.py: diff --git a/pr-preview/pr-276/_sources/auto_examples/00_starting/run_simple_binary_classification.rst.txt b/pr-preview/pr-276/_sources/auto_examples/00_starting/run_simple_binary_classification.rst.txt index 1aa9d73e0..aab28d5ea 100644 --- a/pr-preview/pr-276/_sources/auto_examples/00_starting/run_simple_binary_classification.rst.txt +++ b/pr-preview/pr-276/_sources/auto_examples/00_starting/run_simple_binary_classification.rst.txt @@ -65,13 +65,13 @@ Set the logging level to info to see extra information /home/runner/work/julearn/julearn/julearn/utils/logging.py:66: UserWarning: The '__version__' attribute is deprecated and will be removed in MarkupSafe 3.1. Use feature detection, or `importlib.metadata.version("markupsafe")`, instead. vstring = str(getattr(module, "__version__", None)) - 2024-10-17 13:53:23,429 - julearn - INFO - ===== Lib Versions ===== - 2024-10-17 13:53:23,429 - julearn - INFO - numpy: 1.26.4 - 2024-10-17 13:53:23,429 - julearn - INFO - scipy: 1.14.1 - 2024-10-17 13:53:23,429 - julearn - INFO - sklearn: 1.5.2 - 2024-10-17 13:53:23,429 - julearn - INFO - pandas: 2.2.3 - 2024-10-17 13:53:23,429 - julearn - INFO - julearn: 0.3.4.dev37 - 2024-10-17 13:53:23,429 - julearn - INFO - ======================== + 2024-10-17 14:01:38,396 - julearn - INFO - ===== Lib Versions ===== + 2024-10-17 14:01:38,396 - julearn - INFO - numpy: 1.26.4 + 2024-10-17 14:01:38,396 - julearn - INFO - scipy: 1.14.1 + 2024-10-17 14:01:38,396 - julearn - INFO - sklearn: 1.5.2 + 2024-10-17 14:01:38,396 - julearn - INFO - pandas: 2.2.3 + 2024-10-17 14:01:38,396 - julearn - INFO - julearn: 0.3.4.dev43 + 2024-10-17 14:01:38,396 - julearn - INFO - ======================== @@ -138,38 +138,38 @@ We will try to predict the species. .. code-block:: none - 2024-10-17 13:53:23,503 - julearn - INFO - ==== Input Data ==== - 2024-10-17 13:53:23,503 - julearn - INFO - Using dataframe as input - 2024-10-17 13:53:23,503 - julearn - INFO - Features: ['sepal_length', 'sepal_width', 'petal_length'] - 2024-10-17 13:53:23,503 - julearn - INFO - Target: species - 2024-10-17 13:53:23,503 - julearn - INFO - Expanded features: ['sepal_length', 'sepal_width', 'petal_length'] - 2024-10-17 13:53:23,503 - julearn - INFO - X_types:{} - 2024-10-17 13:53:23,503 - julearn - WARNING - The following columns are not defined in X_types: ['sepal_length', 'sepal_width', 'petal_length']. They will be treated as continuous. + 2024-10-17 14:01:38,430 - julearn - INFO - ==== Input Data ==== + 2024-10-17 14:01:38,430 - julearn - INFO - Using dataframe as input + 2024-10-17 14:01:38,430 - julearn - INFO - Features: ['sepal_length', 'sepal_width', 'petal_length'] + 2024-10-17 14:01:38,430 - julearn - INFO - Target: species + 2024-10-17 14:01:38,430 - julearn - INFO - Expanded features: ['sepal_length', 'sepal_width', 'petal_length'] + 2024-10-17 14:01:38,430 - julearn - INFO - X_types:{} + 2024-10-17 14:01:38,430 - julearn - WARNING - The following columns are not defined in X_types: ['sepal_length', 'sepal_width', 'petal_length']. They will be treated as continuous. /home/runner/work/julearn/julearn/julearn/prepare.py:509: RuntimeWarning: The following columns are not defined in X_types: ['sepal_length', 'sepal_width', 'petal_length']. They will be treated as continuous. warn_with_log( - 2024-10-17 13:53:23,504 - julearn - INFO - ==================== - 2024-10-17 13:53:23,504 - julearn - INFO - - 2024-10-17 13:53:23,504 - julearn - INFO - Adding step zscore that applies to ColumnTypes - 2024-10-17 13:53:23,505 - julearn - INFO - Step added - 2024-10-17 13:53:23,505 - julearn - INFO - Adding step svm that applies to ColumnTypes - 2024-10-17 13:53:23,505 - julearn - INFO - Step added - 2024-10-17 13:53:23,506 - julearn - INFO - = Model Parameters = - 2024-10-17 13:53:23,506 - julearn - INFO - ==================== - 2024-10-17 13:53:23,506 - julearn - INFO - - 2024-10-17 13:53:23,506 - julearn - INFO - = Data Information = - 2024-10-17 13:53:23,506 - julearn - INFO - Problem type: classification - 2024-10-17 13:53:23,506 - julearn - INFO - Number of samples: 100 - 2024-10-17 13:53:23,506 - julearn - INFO - Number of features: 3 - 2024-10-17 13:53:23,506 - julearn - INFO - ==================== - 2024-10-17 13:53:23,506 - julearn - INFO - - 2024-10-17 13:53:23,506 - julearn - INFO - Number of classes: 2 - 2024-10-17 13:53:23,506 - julearn - INFO - Target type: object - 2024-10-17 13:53:23,507 - julearn - INFO - Class distributions: species + 2024-10-17 14:01:38,431 - julearn - INFO - ==================== + 2024-10-17 14:01:38,431 - julearn - INFO - + 2024-10-17 14:01:38,431 - julearn - INFO - Adding step zscore that applies to ColumnTypes + 2024-10-17 14:01:38,431 - julearn - INFO - Step added + 2024-10-17 14:01:38,431 - julearn - INFO - Adding step svm that applies to ColumnTypes + 2024-10-17 14:01:38,432 - julearn - INFO - Step added + 2024-10-17 14:01:38,433 - julearn - INFO - = Model Parameters = + 2024-10-17 14:01:38,433 - julearn - INFO - ==================== + 2024-10-17 14:01:38,433 - julearn - INFO - + 2024-10-17 14:01:38,433 - julearn - INFO - = Data Information = + 2024-10-17 14:01:38,433 - julearn - INFO - Problem type: classification + 2024-10-17 14:01:38,433 - julearn - INFO - Number of samples: 100 + 2024-10-17 14:01:38,433 - julearn - INFO - Number of features: 3 + 2024-10-17 14:01:38,433 - julearn - INFO - ==================== + 2024-10-17 14:01:38,433 - julearn - INFO - + 2024-10-17 14:01:38,433 - julearn - INFO - Number of classes: 2 + 2024-10-17 14:01:38,433 - julearn - INFO - Target type: object + 2024-10-17 14:01:38,434 - julearn - INFO - Class distributions: species versicolor 50 virginica 50 Name: count, dtype: int64 - 2024-10-17 13:53:23,507 - julearn - INFO - Using outer CV scheme KFold(n_splits=5, random_state=None, shuffle=False) - 2024-10-17 13:53:23,507 - julearn - INFO - Binary classification problem detected. + 2024-10-17 14:01:38,434 - julearn - INFO - Using outer CV scheme KFold(n_splits=5, random_state=None, shuffle=False) + 2024-10-17 14:01:38,434 - julearn - INFO - Binary classification problem detected. 0 0.90 1 0.75 2 0.95 @@ -245,39 +245,39 @@ We will also set the random seed so we always split the data in the same way. .. code-block:: none - 2024-10-17 13:53:23,549 - julearn - INFO - Setting random seed to 42 - 2024-10-17 13:53:23,549 - julearn - INFO - ==== Input Data ==== - 2024-10-17 13:53:23,549 - julearn - INFO - Using dataframe as input - 2024-10-17 13:53:23,549 - julearn - INFO - Features: ['sepal_length', 'sepal_width', 'petal_length'] - 2024-10-17 13:53:23,549 - julearn - INFO - Target: species - 2024-10-17 13:53:23,549 - julearn - INFO - Expanded features: ['sepal_length', 'sepal_width', 'petal_length'] - 2024-10-17 13:53:23,549 - julearn - INFO - X_types:{} - 2024-10-17 13:53:23,549 - julearn - WARNING - The following columns are not defined in X_types: ['sepal_length', 'sepal_width', 'petal_length']. They will be treated as continuous. + 2024-10-17 14:01:38,476 - julearn - INFO - Setting random seed to 42 + 2024-10-17 14:01:38,476 - julearn - INFO - ==== Input Data ==== + 2024-10-17 14:01:38,476 - julearn - INFO - Using dataframe as input + 2024-10-17 14:01:38,476 - julearn - INFO - Features: ['sepal_length', 'sepal_width', 'petal_length'] + 2024-10-17 14:01:38,476 - julearn - INFO - Target: species + 2024-10-17 14:01:38,476 - julearn - INFO - Expanded features: ['sepal_length', 'sepal_width', 'petal_length'] + 2024-10-17 14:01:38,476 - julearn - INFO - X_types:{} + 2024-10-17 14:01:38,476 - julearn - WARNING - The following columns are not defined in X_types: ['sepal_length', 'sepal_width', 'petal_length']. They will be treated as continuous. /home/runner/work/julearn/julearn/julearn/prepare.py:509: RuntimeWarning: The following columns are not defined in X_types: ['sepal_length', 'sepal_width', 'petal_length']. They will be treated as continuous. warn_with_log( - 2024-10-17 13:53:23,550 - julearn - INFO - ==================== - 2024-10-17 13:53:23,550 - julearn - INFO - - 2024-10-17 13:53:23,550 - julearn - INFO - Adding step zscore that applies to ColumnTypes - 2024-10-17 13:53:23,550 - julearn - INFO - Step added - 2024-10-17 13:53:23,550 - julearn - INFO - Adding step svm that applies to ColumnTypes - 2024-10-17 13:53:23,550 - julearn - INFO - Step added - 2024-10-17 13:53:23,551 - julearn - INFO - = Model Parameters = - 2024-10-17 13:53:23,551 - julearn - INFO - ==================== - 2024-10-17 13:53:23,551 - julearn - INFO - - 2024-10-17 13:53:23,551 - julearn - INFO - = Data Information = - 2024-10-17 13:53:23,551 - julearn - INFO - Problem type: classification - 2024-10-17 13:53:23,551 - julearn - INFO - Number of samples: 80 - 2024-10-17 13:53:23,551 - julearn - INFO - Number of features: 3 - 2024-10-17 13:53:23,551 - julearn - INFO - ==================== - 2024-10-17 13:53:23,551 - julearn - INFO - - 2024-10-17 13:53:23,551 - julearn - INFO - Number of classes: 2 - 2024-10-17 13:53:23,551 - julearn - INFO - Target type: object - 2024-10-17 13:53:23,552 - julearn - INFO - Class distributions: species + 2024-10-17 14:01:38,477 - julearn - INFO - ==================== + 2024-10-17 14:01:38,477 - julearn - INFO - + 2024-10-17 14:01:38,477 - julearn - INFO - Adding step zscore that applies to ColumnTypes + 2024-10-17 14:01:38,477 - julearn - INFO - Step added + 2024-10-17 14:01:38,477 - julearn - INFO - Adding step svm that applies to ColumnTypes + 2024-10-17 14:01:38,477 - julearn - INFO - Step added + 2024-10-17 14:01:38,478 - julearn - INFO - = Model Parameters = + 2024-10-17 14:01:38,478 - julearn - INFO - ==================== + 2024-10-17 14:01:38,478 - julearn - INFO - + 2024-10-17 14:01:38,478 - julearn - INFO - = Data Information = + 2024-10-17 14:01:38,478 - julearn - INFO - Problem type: classification + 2024-10-17 14:01:38,478 - julearn - INFO - Number of samples: 80 + 2024-10-17 14:01:38,478 - julearn - INFO - Number of features: 3 + 2024-10-17 14:01:38,478 - julearn - INFO - ==================== + 2024-10-17 14:01:38,478 - julearn - INFO - + 2024-10-17 14:01:38,478 - julearn - INFO - Number of classes: 2 + 2024-10-17 14:01:38,478 - julearn - INFO - Target type: object + 2024-10-17 14:01:38,479 - julearn - INFO - Class distributions: species virginica 50 versicolor 30 Name: count, dtype: int64 - 2024-10-17 13:53:23,552 - julearn - INFO - Using outer CV scheme KFold(n_splits=5, random_state=None, shuffle=False) - 2024-10-17 13:53:23,552 - julearn - INFO - Binary classification problem detected. + 2024-10-17 14:01:38,479 - julearn - INFO - Using outer CV scheme KFold(n_splits=5, random_state=None, shuffle=False) + 2024-10-17 14:01:38,479 - julearn - INFO - Binary classification problem detected. /opt/hostedtoolcache/Python/3.10.15/x64/lib/python3.10/site-packages/sklearn/metrics/_classification.py:2480: UserWarning: y_pred contains classes not in y_true warnings.warn("y_pred contains classes not in y_true") /opt/hostedtoolcache/Python/3.10.15/x64/lib/python3.10/site-packages/sklearn/metrics/_classification.py:2480: UserWarning: y_pred contains classes not in y_true @@ -329,40 +329,40 @@ In this example, we are interested in detecting `versicolor`. .. code-block:: none - 2024-10-17 13:53:23,597 - julearn - INFO - Setting random seed to 42 - 2024-10-17 13:53:23,597 - julearn - INFO - ==== Input Data ==== - 2024-10-17 13:53:23,597 - julearn - INFO - Using dataframe as input - 2024-10-17 13:53:23,597 - julearn - INFO - Features: ['sepal_length', 'sepal_width', 'petal_length'] - 2024-10-17 13:53:23,597 - julearn - INFO - Target: species - 2024-10-17 13:53:23,597 - julearn - INFO - Expanded features: ['sepal_length', 'sepal_width', 'petal_length'] - 2024-10-17 13:53:23,597 - julearn - INFO - X_types:{} - 2024-10-17 13:53:23,597 - julearn - WARNING - The following columns are not defined in X_types: ['sepal_length', 'sepal_width', 'petal_length']. They will be treated as continuous. + 2024-10-17 14:01:38,524 - julearn - INFO - Setting random seed to 42 + 2024-10-17 14:01:38,525 - julearn - INFO - ==== Input Data ==== + 2024-10-17 14:01:38,525 - julearn - INFO - Using dataframe as input + 2024-10-17 14:01:38,525 - julearn - INFO - Features: ['sepal_length', 'sepal_width', 'petal_length'] + 2024-10-17 14:01:38,525 - julearn - INFO - Target: species + 2024-10-17 14:01:38,525 - julearn - INFO - Expanded features: ['sepal_length', 'sepal_width', 'petal_length'] + 2024-10-17 14:01:38,525 - julearn - INFO - X_types:{} + 2024-10-17 14:01:38,525 - julearn - WARNING - The following columns are not defined in X_types: ['sepal_length', 'sepal_width', 'petal_length']. They will be treated as continuous. /home/runner/work/julearn/julearn/julearn/prepare.py:509: RuntimeWarning: The following columns are not defined in X_types: ['sepal_length', 'sepal_width', 'petal_length']. They will be treated as continuous. warn_with_log( - 2024-10-17 13:53:23,597 - julearn - INFO - Setting the following as positive labels ['versicolor'] - 2024-10-17 13:53:23,598 - julearn - INFO - ==================== - 2024-10-17 13:53:23,598 - julearn - INFO - - 2024-10-17 13:53:23,598 - julearn - INFO - Adding step zscore that applies to ColumnTypes - 2024-10-17 13:53:23,598 - julearn - INFO - Step added - 2024-10-17 13:53:23,598 - julearn - INFO - Adding step svm that applies to ColumnTypes - 2024-10-17 13:53:23,598 - julearn - INFO - Step added - 2024-10-17 13:53:23,599 - julearn - INFO - = Model Parameters = - 2024-10-17 13:53:23,599 - julearn - INFO - ==================== - 2024-10-17 13:53:23,599 - julearn - INFO - - 2024-10-17 13:53:23,599 - julearn - INFO - = Data Information = - 2024-10-17 13:53:23,599 - julearn - INFO - Problem type: classification - 2024-10-17 13:53:23,599 - julearn - INFO - Number of samples: 80 - 2024-10-17 13:53:23,599 - julearn - INFO - Number of features: 3 - 2024-10-17 13:53:23,599 - julearn - INFO - ==================== - 2024-10-17 13:53:23,599 - julearn - INFO - - 2024-10-17 13:53:23,599 - julearn - INFO - Number of classes: 2 - 2024-10-17 13:53:23,599 - julearn - INFO - Target type: int64 - 2024-10-17 13:53:23,600 - julearn - INFO - Class distributions: species + 2024-10-17 14:01:38,525 - julearn - INFO - Setting the following as positive labels ['versicolor'] + 2024-10-17 14:01:38,526 - julearn - INFO - ==================== + 2024-10-17 14:01:38,526 - julearn - INFO - + 2024-10-17 14:01:38,526 - julearn - INFO - Adding step zscore that applies to ColumnTypes + 2024-10-17 14:01:38,526 - julearn - INFO - Step added + 2024-10-17 14:01:38,526 - julearn - INFO - Adding step svm that applies to ColumnTypes + 2024-10-17 14:01:38,526 - julearn - INFO - Step added + 2024-10-17 14:01:38,527 - julearn - INFO - = Model Parameters = + 2024-10-17 14:01:38,527 - julearn - INFO - ==================== + 2024-10-17 14:01:38,527 - julearn - INFO - + 2024-10-17 14:01:38,527 - julearn - INFO - = Data Information = + 2024-10-17 14:01:38,527 - julearn - INFO - Problem type: classification + 2024-10-17 14:01:38,527 - julearn - INFO - Number of samples: 80 + 2024-10-17 14:01:38,527 - julearn - INFO - Number of features: 3 + 2024-10-17 14:01:38,527 - julearn - INFO - ==================== + 2024-10-17 14:01:38,527 - julearn - INFO - + 2024-10-17 14:01:38,527 - julearn - INFO - Number of classes: 2 + 2024-10-17 14:01:38,527 - julearn - INFO - Target type: int64 + 2024-10-17 14:01:38,528 - julearn - INFO - Class distributions: species 0 50 1 30 Name: count, dtype: int64 - 2024-10-17 13:53:23,600 - julearn - INFO - Using outer CV scheme KFold(n_splits=5, random_state=None, shuffle=False) - 2024-10-17 13:53:23,600 - julearn - INFO - Binary classification problem detected. + 2024-10-17 14:01:38,528 - julearn - INFO - Using outer CV scheme KFold(n_splits=5, random_state=None, shuffle=False) + 2024-10-17 14:01:38,528 - julearn - INFO - Binary classification problem detected. 0.4 @@ -371,7 +371,7 @@ In this example, we are interested in detecting `versicolor`. .. rst-class:: sphx-glr-timing - **Total running time of the script:** (0 minutes 0.221 seconds) + **Total running time of the script:** (0 minutes 0.183 seconds) .. _sphx_glr_download_auto_examples_00_starting_run_simple_binary_classification.py: diff --git a/pr-preview/pr-276/_sources/auto_examples/00_starting/sg_execution_times.rst.txt b/pr-preview/pr-276/_sources/auto_examples/00_starting/sg_execution_times.rst.txt index 5d27172ad..33e158569 100644 --- a/pr-preview/pr-276/_sources/auto_examples/00_starting/sg_execution_times.rst.txt +++ b/pr-preview/pr-276/_sources/auto_examples/00_starting/sg_execution_times.rst.txt @@ -6,18 +6,18 @@ Computation times ================= -**00:04.057** total execution time for **auto_examples_00_starting** files: +**00:04.039** total execution time for **auto_examples_00_starting** files: +-------------------------------------------------------------------------------------------------------------------------+-----------+--------+ -| :ref:`sphx_glr_auto_examples_00_starting_run_grouped_cv.py` (``run_grouped_cv.py``) | 00:01.366 | 0.0 MB | +| :ref:`sphx_glr_auto_examples_00_starting_run_grouped_cv.py` (``run_grouped_cv.py``) | 00:01.376 | 0.0 MB | +-------------------------------------------------------------------------------------------------------------------------+-----------+--------+ -| :ref:`sphx_glr_auto_examples_00_starting_plot_stratified_kfold_reg.py` (``plot_stratified_kfold_reg.py``) | 00:00.855 | 0.0 MB | +| :ref:`sphx_glr_auto_examples_00_starting_plot_stratified_kfold_reg.py` (``plot_stratified_kfold_reg.py``) | 00:00.862 | 0.0 MB | +-------------------------------------------------------------------------------------------------------------------------+-----------+--------+ -| :ref:`sphx_glr_auto_examples_00_starting_plot_example_regression.py` (``plot_example_regression.py``) | 00:00.669 | 0.0 MB | +| :ref:`sphx_glr_auto_examples_00_starting_plot_example_regression.py` (``plot_example_regression.py``) | 00:00.663 | 0.0 MB | +-------------------------------------------------------------------------------------------------------------------------+-----------+--------+ -| :ref:`sphx_glr_auto_examples_00_starting_plot_cm_acc_multiclass.py` (``plot_cm_acc_multiclass.py``) | 00:00.534 | 0.0 MB | +| :ref:`sphx_glr_auto_examples_00_starting_plot_cm_acc_multiclass.py` (``plot_cm_acc_multiclass.py``) | 00:00.529 | 0.0 MB | +-------------------------------------------------------------------------------------------------------------------------+-----------+--------+ -| :ref:`sphx_glr_auto_examples_00_starting_run_combine_pandas.py` (``run_combine_pandas.py``) | 00:00.413 | 0.0 MB | +| :ref:`sphx_glr_auto_examples_00_starting_run_combine_pandas.py` (``run_combine_pandas.py``) | 00:00.426 | 0.0 MB | +-------------------------------------------------------------------------------------------------------------------------+-----------+--------+ -| :ref:`sphx_glr_auto_examples_00_starting_run_simple_binary_classification.py` (``run_simple_binary_classification.py``) | 00:00.221 | 0.0 MB | +| :ref:`sphx_glr_auto_examples_00_starting_run_simple_binary_classification.py` (``run_simple_binary_classification.py``) | 00:00.183 | 0.0 MB | +-------------------------------------------------------------------------------------------------------------------------+-----------+--------+ diff --git a/pr-preview/pr-276/_sources/auto_examples/01_model_comparison/plot_simple_model_comparison.rst.txt b/pr-preview/pr-276/_sources/auto_examples/01_model_comparison/plot_simple_model_comparison.rst.txt index 19c926cee..e462ff97e 100644 --- a/pr-preview/pr-276/_sources/auto_examples/01_model_comparison/plot_simple_model_comparison.rst.txt +++ b/pr-preview/pr-276/_sources/auto_examples/01_model_comparison/plot_simple_model_comparison.rst.txt @@ -68,13 +68,13 @@ Set the logging level to info to see extra information. /home/runner/work/julearn/julearn/julearn/utils/logging.py:66: UserWarning: The '__version__' attribute is deprecated and will be removed in MarkupSafe 3.1. Use feature detection, or `importlib.metadata.version("markupsafe")`, instead. vstring = str(getattr(module, "__version__", None)) - 2024-10-17 13:53:27,787 - julearn - INFO - ===== Lib Versions ===== - 2024-10-17 13:53:27,787 - julearn - INFO - numpy: 1.26.4 - 2024-10-17 13:53:27,787 - julearn - INFO - scipy: 1.14.1 - 2024-10-17 13:53:27,787 - julearn - INFO - sklearn: 1.5.2 - 2024-10-17 13:53:27,787 - julearn - INFO - pandas: 2.2.3 - 2024-10-17 13:53:27,787 - julearn - INFO - julearn: 0.3.4.dev37 - 2024-10-17 13:53:27,787 - julearn - INFO - ======================== + 2024-10-17 14:01:42,675 - julearn - INFO - ===== Lib Versions ===== + 2024-10-17 14:01:42,675 - julearn - INFO - numpy: 1.26.4 + 2024-10-17 14:01:42,675 - julearn - INFO - scipy: 1.14.1 + 2024-10-17 14:01:42,675 - julearn - INFO - sklearn: 1.5.2 + 2024-10-17 14:01:42,676 - julearn - INFO - pandas: 2.2.3 + 2024-10-17 14:01:42,676 - julearn - INFO - julearn: 0.3.4.dev43 + 2024-10-17 14:01:42,676 - julearn - INFO - ======================== @@ -141,38 +141,38 @@ We will try to predict the species. .. code-block:: none - 2024-10-17 13:53:27,789 - julearn - INFO - ==== Input Data ==== - 2024-10-17 13:53:27,789 - julearn - INFO - Using dataframe as input - 2024-10-17 13:53:27,790 - julearn - INFO - Features: ['sepal_length', 'sepal_width', 'petal_length'] - 2024-10-17 13:53:27,790 - julearn - INFO - Target: species - 2024-10-17 13:53:27,790 - julearn - INFO - Expanded features: ['sepal_length', 'sepal_width', 'petal_length'] - 2024-10-17 13:53:27,790 - julearn - INFO - X_types:{} - 2024-10-17 13:53:27,790 - julearn - WARNING - The following columns are not defined in X_types: ['sepal_length', 'sepal_width', 'petal_length']. They will be treated as continuous. + 2024-10-17 14:01:42,678 - julearn - INFO - ==== Input Data ==== + 2024-10-17 14:01:42,678 - julearn - INFO - Using dataframe as input + 2024-10-17 14:01:42,678 - julearn - INFO - Features: ['sepal_length', 'sepal_width', 'petal_length'] + 2024-10-17 14:01:42,678 - julearn - INFO - Target: species + 2024-10-17 14:01:42,678 - julearn - INFO - Expanded features: ['sepal_length', 'sepal_width', 'petal_length'] + 2024-10-17 14:01:42,678 - julearn - INFO - X_types:{} + 2024-10-17 14:01:42,678 - julearn - WARNING - The following columns are not defined in X_types: ['sepal_length', 'sepal_width', 'petal_length']. They will be treated as continuous. /home/runner/work/julearn/julearn/julearn/prepare.py:509: RuntimeWarning: The following columns are not defined in X_types: ['sepal_length', 'sepal_width', 'petal_length']. They will be treated as continuous. warn_with_log( - 2024-10-17 13:53:27,790 - julearn - INFO - ==================== - 2024-10-17 13:53:27,790 - julearn - INFO - - 2024-10-17 13:53:27,791 - julearn - INFO - Adding step zscore that applies to ColumnTypes - 2024-10-17 13:53:27,791 - julearn - INFO - Step added - 2024-10-17 13:53:27,791 - julearn - INFO - Adding step svm that applies to ColumnTypes - 2024-10-17 13:53:27,791 - julearn - INFO - Step added - 2024-10-17 13:53:27,791 - julearn - INFO - = Model Parameters = - 2024-10-17 13:53:27,791 - julearn - INFO - ==================== - 2024-10-17 13:53:27,792 - julearn - INFO - - 2024-10-17 13:53:27,792 - julearn - INFO - = Data Information = - 2024-10-17 13:53:27,792 - julearn - INFO - Problem type: classification - 2024-10-17 13:53:27,792 - julearn - INFO - Number of samples: 100 - 2024-10-17 13:53:27,792 - julearn - INFO - Number of features: 3 - 2024-10-17 13:53:27,792 - julearn - INFO - ==================== - 2024-10-17 13:53:27,792 - julearn - INFO - - 2024-10-17 13:53:27,792 - julearn - INFO - Number of classes: 2 - 2024-10-17 13:53:27,792 - julearn - INFO - Target type: object - 2024-10-17 13:53:27,793 - julearn - INFO - Class distributions: species + 2024-10-17 14:01:42,679 - julearn - INFO - ==================== + 2024-10-17 14:01:42,679 - julearn - INFO - + 2024-10-17 14:01:42,679 - julearn - INFO - Adding step zscore that applies to ColumnTypes + 2024-10-17 14:01:42,679 - julearn - INFO - Step added + 2024-10-17 14:01:42,679 - julearn - INFO - Adding step svm that applies to ColumnTypes + 2024-10-17 14:01:42,680 - julearn - INFO - Step added + 2024-10-17 14:01:42,680 - julearn - INFO - = Model Parameters = + 2024-10-17 14:01:42,680 - julearn - INFO - ==================== + 2024-10-17 14:01:42,680 - julearn - INFO - + 2024-10-17 14:01:42,680 - julearn - INFO - = Data Information = + 2024-10-17 14:01:42,680 - julearn - INFO - Problem type: classification + 2024-10-17 14:01:42,680 - julearn - INFO - Number of samples: 100 + 2024-10-17 14:01:42,680 - julearn - INFO - Number of features: 3 + 2024-10-17 14:01:42,680 - julearn - INFO - ==================== + 2024-10-17 14:01:42,681 - julearn - INFO - + 2024-10-17 14:01:42,681 - julearn - INFO - Number of classes: 2 + 2024-10-17 14:01:42,681 - julearn - INFO - Target type: object + 2024-10-17 14:01:42,681 - julearn - INFO - Class distributions: species versicolor 50 virginica 50 Name: count, dtype: int64 - 2024-10-17 13:53:27,793 - julearn - INFO - Using outer CV scheme KFold(n_splits=5, random_state=None, shuffle=False) - 2024-10-17 13:53:27,793 - julearn - INFO - Binary classification problem detected. + 2024-10-17 14:01:42,681 - julearn - INFO - Using outer CV scheme KFold(n_splits=5, random_state=None, shuffle=False) + 2024-10-17 14:01:42,682 - julearn - INFO - Binary classification problem detected. 0 0.90 1 0.75 2 0.95 @@ -280,38 +280,38 @@ First we will use a default SVM model. .. code-block:: none - 2024-10-17 13:53:27,833 - julearn - INFO - ==== Input Data ==== - 2024-10-17 13:53:27,833 - julearn - INFO - Using dataframe as input - 2024-10-17 13:53:27,833 - julearn - INFO - Features: ['sepal_length', 'sepal_width', 'petal_length'] - 2024-10-17 13:53:27,833 - julearn - INFO - Target: species - 2024-10-17 13:53:27,833 - julearn - INFO - Expanded features: ['sepal_length', 'sepal_width', 'petal_length'] - 2024-10-17 13:53:27,833 - julearn - INFO - X_types:{} - 2024-10-17 13:53:27,833 - julearn - WARNING - The following columns are not defined in X_types: ['sepal_length', 'sepal_width', 'petal_length']. They will be treated as continuous. + 2024-10-17 14:01:42,722 - julearn - INFO - ==== Input Data ==== + 2024-10-17 14:01:42,722 - julearn - INFO - Using dataframe as input + 2024-10-17 14:01:42,722 - julearn - INFO - Features: ['sepal_length', 'sepal_width', 'petal_length'] + 2024-10-17 14:01:42,722 - julearn - INFO - Target: species + 2024-10-17 14:01:42,722 - julearn - INFO - Expanded features: ['sepal_length', 'sepal_width', 'petal_length'] + 2024-10-17 14:01:42,722 - julearn - INFO - X_types:{} + 2024-10-17 14:01:42,722 - julearn - WARNING - The following columns are not defined in X_types: ['sepal_length', 'sepal_width', 'petal_length']. They will be treated as continuous. /home/runner/work/julearn/julearn/julearn/prepare.py:509: RuntimeWarning: The following columns are not defined in X_types: ['sepal_length', 'sepal_width', 'petal_length']. They will be treated as continuous. warn_with_log( - 2024-10-17 13:53:27,834 - julearn - INFO - ==================== - 2024-10-17 13:53:27,834 - julearn - INFO - - 2024-10-17 13:53:27,834 - julearn - INFO - Adding step zscore that applies to ColumnTypes - 2024-10-17 13:53:27,834 - julearn - INFO - Step added - 2024-10-17 13:53:27,834 - julearn - INFO - Adding step svm that applies to ColumnTypes - 2024-10-17 13:53:27,834 - julearn - INFO - Step added - 2024-10-17 13:53:27,835 - julearn - INFO - = Model Parameters = - 2024-10-17 13:53:27,835 - julearn - INFO - ==================== - 2024-10-17 13:53:27,835 - julearn - INFO - - 2024-10-17 13:53:27,835 - julearn - INFO - = Data Information = - 2024-10-17 13:53:27,835 - julearn - INFO - Problem type: classification - 2024-10-17 13:53:27,835 - julearn - INFO - Number of samples: 80 - 2024-10-17 13:53:27,835 - julearn - INFO - Number of features: 3 - 2024-10-17 13:53:27,835 - julearn - INFO - ==================== - 2024-10-17 13:53:27,835 - julearn - INFO - - 2024-10-17 13:53:27,835 - julearn - INFO - Number of classes: 2 - 2024-10-17 13:53:27,835 - julearn - INFO - Target type: object - 2024-10-17 13:53:27,836 - julearn - INFO - Class distributions: species + 2024-10-17 14:01:42,722 - julearn - INFO - ==================== + 2024-10-17 14:01:42,723 - julearn - INFO - + 2024-10-17 14:01:42,723 - julearn - INFO - Adding step zscore that applies to ColumnTypes + 2024-10-17 14:01:42,723 - julearn - INFO - Step added + 2024-10-17 14:01:42,723 - julearn - INFO - Adding step svm that applies to ColumnTypes + 2024-10-17 14:01:42,723 - julearn - INFO - Step added + 2024-10-17 14:01:42,723 - julearn - INFO - = Model Parameters = + 2024-10-17 14:01:42,723 - julearn - INFO - ==================== + 2024-10-17 14:01:42,723 - julearn - INFO - + 2024-10-17 14:01:42,723 - julearn - INFO - = Data Information = + 2024-10-17 14:01:42,723 - julearn - INFO - Problem type: classification + 2024-10-17 14:01:42,724 - julearn - INFO - Number of samples: 80 + 2024-10-17 14:01:42,724 - julearn - INFO - Number of features: 3 + 2024-10-17 14:01:42,724 - julearn - INFO - ==================== + 2024-10-17 14:01:42,724 - julearn - INFO - + 2024-10-17 14:01:42,724 - julearn - INFO - Number of classes: 2 + 2024-10-17 14:01:42,724 - julearn - INFO - Target type: object + 2024-10-17 14:01:42,724 - julearn - INFO - Class distributions: species virginica 50 versicolor 30 Name: count, dtype: int64 - 2024-10-17 13:53:27,836 - julearn - INFO - Using outer CV scheme RepeatedStratifiedKFold(n_repeats=5, n_splits=5, random_state=42) - 2024-10-17 13:53:27,836 - julearn - INFO - Binary classification problem detected. + 2024-10-17 14:01:42,725 - julearn - INFO - Using outer CV scheme RepeatedStratifiedKFold(n_repeats=5, n_splits=5, random_state=42) + 2024-10-17 14:01:42,725 - julearn - INFO - Binary classification problem detected. @@ -345,38 +345,38 @@ Second we will use a default Random Forest model. .. code-block:: none - 2024-10-17 13:53:28,116 - julearn - INFO - ==== Input Data ==== - 2024-10-17 13:53:28,116 - julearn - INFO - Using dataframe as input - 2024-10-17 13:53:28,116 - julearn - INFO - Features: ['sepal_length', 'sepal_width', 'petal_length'] - 2024-10-17 13:53:28,116 - julearn - INFO - Target: species - 2024-10-17 13:53:28,116 - julearn - INFO - Expanded features: ['sepal_length', 'sepal_width', 'petal_length'] - 2024-10-17 13:53:28,116 - julearn - INFO - X_types:{} - 2024-10-17 13:53:28,116 - julearn - WARNING - The following columns are not defined in X_types: ['sepal_length', 'sepal_width', 'petal_length']. They will be treated as continuous. + 2024-10-17 14:01:43,003 - julearn - INFO - ==== Input Data ==== + 2024-10-17 14:01:43,003 - julearn - INFO - Using dataframe as input + 2024-10-17 14:01:43,003 - julearn - INFO - Features: ['sepal_length', 'sepal_width', 'petal_length'] + 2024-10-17 14:01:43,003 - julearn - INFO - Target: species + 2024-10-17 14:01:43,003 - julearn - INFO - Expanded features: ['sepal_length', 'sepal_width', 'petal_length'] + 2024-10-17 14:01:43,003 - julearn - INFO - X_types:{} + 2024-10-17 14:01:43,004 - julearn - WARNING - The following columns are not defined in X_types: ['sepal_length', 'sepal_width', 'petal_length']. They will be treated as continuous. /home/runner/work/julearn/julearn/julearn/prepare.py:509: RuntimeWarning: The following columns are not defined in X_types: ['sepal_length', 'sepal_width', 'petal_length']. They will be treated as continuous. warn_with_log( - 2024-10-17 13:53:28,117 - julearn - INFO - ==================== - 2024-10-17 13:53:28,117 - julearn - INFO - - 2024-10-17 13:53:28,117 - julearn - INFO - Adding step zscore that applies to ColumnTypes - 2024-10-17 13:53:28,117 - julearn - INFO - Step added - 2024-10-17 13:53:28,117 - julearn - INFO - Adding step rf that applies to ColumnTypes - 2024-10-17 13:53:28,117 - julearn - INFO - Step added - 2024-10-17 13:53:28,118 - julearn - INFO - = Model Parameters = - 2024-10-17 13:53:28,118 - julearn - INFO - ==================== - 2024-10-17 13:53:28,118 - julearn - INFO - - 2024-10-17 13:53:28,118 - julearn - INFO - = Data Information = - 2024-10-17 13:53:28,118 - julearn - INFO - Problem type: classification - 2024-10-17 13:53:28,118 - julearn - INFO - Number of samples: 80 - 2024-10-17 13:53:28,118 - julearn - INFO - Number of features: 3 - 2024-10-17 13:53:28,118 - julearn - INFO - ==================== - 2024-10-17 13:53:28,118 - julearn - INFO - - 2024-10-17 13:53:28,118 - julearn - INFO - Number of classes: 2 - 2024-10-17 13:53:28,118 - julearn - INFO - Target type: object - 2024-10-17 13:53:28,119 - julearn - INFO - Class distributions: species + 2024-10-17 14:01:43,004 - julearn - INFO - ==================== + 2024-10-17 14:01:43,004 - julearn - INFO - + 2024-10-17 14:01:43,004 - julearn - INFO - Adding step zscore that applies to ColumnTypes + 2024-10-17 14:01:43,004 - julearn - INFO - Step added + 2024-10-17 14:01:43,004 - julearn - INFO - Adding step rf that applies to ColumnTypes + 2024-10-17 14:01:43,005 - julearn - INFO - Step added + 2024-10-17 14:01:43,005 - julearn - INFO - = Model Parameters = + 2024-10-17 14:01:43,005 - julearn - INFO - ==================== + 2024-10-17 14:01:43,005 - julearn - INFO - + 2024-10-17 14:01:43,005 - julearn - INFO - = Data Information = + 2024-10-17 14:01:43,005 - julearn - INFO - Problem type: classification + 2024-10-17 14:01:43,005 - julearn - INFO - Number of samples: 80 + 2024-10-17 14:01:43,005 - julearn - INFO - Number of features: 3 + 2024-10-17 14:01:43,005 - julearn - INFO - ==================== + 2024-10-17 14:01:43,005 - julearn - INFO - + 2024-10-17 14:01:43,005 - julearn - INFO - Number of classes: 2 + 2024-10-17 14:01:43,006 - julearn - INFO - Target type: object + 2024-10-17 14:01:43,006 - julearn - INFO - Class distributions: species virginica 50 versicolor 30 Name: count, dtype: int64 - 2024-10-17 13:53:28,119 - julearn - INFO - Using outer CV scheme RepeatedStratifiedKFold(n_repeats=5, n_splits=5, random_state=42) - 2024-10-17 13:53:28,119 - julearn - INFO - Binary classification problem detected. + 2024-10-17 14:01:43,006 - julearn - INFO - Using outer CV scheme RepeatedStratifiedKFold(n_repeats=5, n_splits=5, random_state=42) + 2024-10-17 14:01:43,006 - julearn - INFO - Binary classification problem detected. @@ -411,39 +411,39 @@ The third model will be a SVM with a linear kernel. .. code-block:: none - 2024-10-17 13:53:30,734 - julearn - INFO - ==== Input Data ==== - 2024-10-17 13:53:30,734 - julearn - INFO - Using dataframe as input - 2024-10-17 13:53:30,735 - julearn - INFO - Features: ['sepal_length', 'sepal_width', 'petal_length'] - 2024-10-17 13:53:30,735 - julearn - INFO - Target: species - 2024-10-17 13:53:30,735 - julearn - INFO - Expanded features: ['sepal_length', 'sepal_width', 'petal_length'] - 2024-10-17 13:53:30,735 - julearn - INFO - X_types:{} - 2024-10-17 13:53:30,735 - julearn - WARNING - The following columns are not defined in X_types: ['sepal_length', 'sepal_width', 'petal_length']. They will be treated as continuous. + 2024-10-17 14:01:45,636 - julearn - INFO - ==== Input Data ==== + 2024-10-17 14:01:45,636 - julearn - INFO - Using dataframe as input + 2024-10-17 14:01:45,636 - julearn - INFO - Features: ['sepal_length', 'sepal_width', 'petal_length'] + 2024-10-17 14:01:45,636 - julearn - INFO - Target: species + 2024-10-17 14:01:45,636 - julearn - INFO - Expanded features: ['sepal_length', 'sepal_width', 'petal_length'] + 2024-10-17 14:01:45,636 - julearn - INFO - X_types:{} + 2024-10-17 14:01:45,636 - julearn - WARNING - The following columns are not defined in X_types: ['sepal_length', 'sepal_width', 'petal_length']. They will be treated as continuous. /home/runner/work/julearn/julearn/julearn/prepare.py:509: RuntimeWarning: The following columns are not defined in X_types: ['sepal_length', 'sepal_width', 'petal_length']. They will be treated as continuous. warn_with_log( - 2024-10-17 13:53:30,735 - julearn - INFO - ==================== - 2024-10-17 13:53:30,735 - julearn - INFO - - 2024-10-17 13:53:30,736 - julearn - INFO - Adding step zscore that applies to ColumnTypes - 2024-10-17 13:53:30,736 - julearn - INFO - Step added - 2024-10-17 13:53:30,736 - julearn - INFO - Adding step svm that applies to ColumnTypes - 2024-10-17 13:53:30,736 - julearn - INFO - Setting hyperparameter kernel = linear - 2024-10-17 13:53:30,736 - julearn - INFO - Step added - 2024-10-17 13:53:30,736 - julearn - INFO - = Model Parameters = - 2024-10-17 13:53:30,736 - julearn - INFO - ==================== - 2024-10-17 13:53:30,737 - julearn - INFO - - 2024-10-17 13:53:30,737 - julearn - INFO - = Data Information = - 2024-10-17 13:53:30,737 - julearn - INFO - Problem type: classification - 2024-10-17 13:53:30,737 - julearn - INFO - Number of samples: 80 - 2024-10-17 13:53:30,737 - julearn - INFO - Number of features: 3 - 2024-10-17 13:53:30,737 - julearn - INFO - ==================== - 2024-10-17 13:53:30,737 - julearn - INFO - - 2024-10-17 13:53:30,737 - julearn - INFO - Number of classes: 2 - 2024-10-17 13:53:30,737 - julearn - INFO - Target type: object - 2024-10-17 13:53:30,738 - julearn - INFO - Class distributions: species + 2024-10-17 14:01:45,637 - julearn - INFO - ==================== + 2024-10-17 14:01:45,637 - julearn - INFO - + 2024-10-17 14:01:45,637 - julearn - INFO - Adding step zscore that applies to ColumnTypes + 2024-10-17 14:01:45,637 - julearn - INFO - Step added + 2024-10-17 14:01:45,637 - julearn - INFO - Adding step svm that applies to ColumnTypes + 2024-10-17 14:01:45,637 - julearn - INFO - Setting hyperparameter kernel = linear + 2024-10-17 14:01:45,637 - julearn - INFO - Step added + 2024-10-17 14:01:45,638 - julearn - INFO - = Model Parameters = + 2024-10-17 14:01:45,638 - julearn - INFO - ==================== + 2024-10-17 14:01:45,638 - julearn - INFO - + 2024-10-17 14:01:45,638 - julearn - INFO - = Data Information = + 2024-10-17 14:01:45,638 - julearn - INFO - Problem type: classification + 2024-10-17 14:01:45,638 - julearn - INFO - Number of samples: 80 + 2024-10-17 14:01:45,638 - julearn - INFO - Number of features: 3 + 2024-10-17 14:01:45,638 - julearn - INFO - ==================== + 2024-10-17 14:01:45,638 - julearn - INFO - + 2024-10-17 14:01:45,638 - julearn - INFO - Number of classes: 2 + 2024-10-17 14:01:45,638 - julearn - INFO - Target type: object + 2024-10-17 14:01:45,639 - julearn - INFO - Class distributions: species virginica 50 versicolor 30 Name: count, dtype: int64 - 2024-10-17 13:53:30,738 - julearn - INFO - Using outer CV scheme RepeatedStratifiedKFold(n_repeats=5, n_splits=5, random_state=42) - 2024-10-17 13:53:30,738 - julearn - INFO - Binary classification problem detected. + 2024-10-17 14:01:45,639 - julearn - INFO - Using outer CV scheme RepeatedStratifiedKFold(n_repeats=5, n_splits=5, random_state=42) + 2024-10-17 14:01:45,639 - julearn - INFO - Binary classification problem detected. @@ -549,7 +549,7 @@ This is how the plot looks like. .. rst-class:: sphx-glr-timing - **Total running time of the script:** (0 minutes 3.353 seconds) + **Total running time of the script:** (0 minutes 3.364 seconds) .. _sphx_glr_download_auto_examples_01_model_comparison_plot_simple_model_comparison.py: diff --git a/pr-preview/pr-276/_sources/auto_examples/01_model_comparison/sg_execution_times.rst.txt b/pr-preview/pr-276/_sources/auto_examples/01_model_comparison/sg_execution_times.rst.txt index 67c970b52..f214b15fa 100644 --- a/pr-preview/pr-276/_sources/auto_examples/01_model_comparison/sg_execution_times.rst.txt +++ b/pr-preview/pr-276/_sources/auto_examples/01_model_comparison/sg_execution_times.rst.txt @@ -6,8 +6,8 @@ Computation times ================= -**00:03.353** total execution time for **auto_examples_01_model_comparison** files: +**00:03.364** total execution time for **auto_examples_01_model_comparison** files: +-------------------------------------------------------------------------------------------------------------------------+-----------+--------+ -| :ref:`sphx_glr_auto_examples_01_model_comparison_plot_simple_model_comparison.py` (``plot_simple_model_comparison.py``) | 00:03.353 | 0.0 MB | +| :ref:`sphx_glr_auto_examples_01_model_comparison_plot_simple_model_comparison.py` (``plot_simple_model_comparison.py``) | 00:03.364 | 0.0 MB | +-------------------------------------------------------------------------------------------------------------------------+-----------+--------+ diff --git a/pr-preview/pr-276/_sources/auto_examples/02_inspection/plot_groupcv_inspect_svm.rst.txt b/pr-preview/pr-276/_sources/auto_examples/02_inspection/plot_groupcv_inspect_svm.rst.txt index 844be5754..3c3094e3c 100644 --- a/pr-preview/pr-276/_sources/auto_examples/02_inspection/plot_groupcv_inspect_svm.rst.txt +++ b/pr-preview/pr-276/_sources/auto_examples/02_inspection/plot_groupcv_inspect_svm.rst.txt @@ -81,13 +81,13 @@ Set the logging level to info to see extra information. /home/runner/work/julearn/julearn/julearn/utils/logging.py:66: UserWarning: The '__version__' attribute is deprecated and will be removed in MarkupSafe 3.1. Use feature detection, or `importlib.metadata.version("markupsafe")`, instead. vstring = str(getattr(module, "__version__", None)) - 2024-10-17 13:53:33,207 - julearn - INFO - ===== Lib Versions ===== - 2024-10-17 13:53:33,207 - julearn - INFO - numpy: 1.26.4 - 2024-10-17 13:53:33,207 - julearn - INFO - scipy: 1.14.1 - 2024-10-17 13:53:33,207 - julearn - INFO - sklearn: 1.5.2 - 2024-10-17 13:53:33,207 - julearn - INFO - pandas: 2.2.3 - 2024-10-17 13:53:33,207 - julearn - INFO - julearn: 0.3.4.dev37 - 2024-10-17 13:53:33,207 - julearn - INFO - ======================== + 2024-10-17 14:01:48,121 - julearn - INFO - ===== Lib Versions ===== + 2024-10-17 14:01:48,121 - julearn - INFO - numpy: 1.26.4 + 2024-10-17 14:01:48,121 - julearn - INFO - scipy: 1.14.1 + 2024-10-17 14:01:48,121 - julearn - INFO - sklearn: 1.5.2 + 2024-10-17 14:01:48,121 - julearn - INFO - pandas: 2.2.3 + 2024-10-17 14:01:48,121 - julearn - INFO - julearn: 0.3.4.dev43 + 2024-10-17 14:01:48,121 - julearn - INFO - ======================== @@ -299,38 +299,38 @@ Machine classifier. .. code-block:: none - 2024-10-17 13:53:33,225 - julearn - INFO - ==== Input Data ==== - 2024-10-17 13:53:33,225 - julearn - INFO - Using dataframe as input - 2024-10-17 13:53:33,225 - julearn - INFO - Features: ['parietal', 'frontal'] - 2024-10-17 13:53:33,225 - julearn - INFO - Target: event - 2024-10-17 13:53:33,225 - julearn - INFO - Expanded features: ['parietal', 'frontal'] - 2024-10-17 13:53:33,225 - julearn - INFO - X_types:{} - 2024-10-17 13:53:33,226 - julearn - WARNING - The following columns are not defined in X_types: ['parietal', 'frontal']. They will be treated as continuous. + 2024-10-17 14:01:48,139 - julearn - INFO - ==== Input Data ==== + 2024-10-17 14:01:48,139 - julearn - INFO - Using dataframe as input + 2024-10-17 14:01:48,140 - julearn - INFO - Features: ['parietal', 'frontal'] + 2024-10-17 14:01:48,140 - julearn - INFO - Target: event + 2024-10-17 14:01:48,140 - julearn - INFO - Expanded features: ['parietal', 'frontal'] + 2024-10-17 14:01:48,140 - julearn - INFO - X_types:{} + 2024-10-17 14:01:48,140 - julearn - WARNING - The following columns are not defined in X_types: ['parietal', 'frontal']. They will be treated as continuous. /home/runner/work/julearn/julearn/julearn/prepare.py:509: RuntimeWarning: The following columns are not defined in X_types: ['parietal', 'frontal']. They will be treated as continuous. warn_with_log( - 2024-10-17 13:53:33,226 - julearn - INFO - ==================== - 2024-10-17 13:53:33,226 - julearn - INFO - - 2024-10-17 13:53:33,226 - julearn - INFO - Adding step zscore that applies to ColumnTypes - 2024-10-17 13:53:33,226 - julearn - INFO - Step added - 2024-10-17 13:53:33,226 - julearn - INFO - Adding step svm that applies to ColumnTypes - 2024-10-17 13:53:33,227 - julearn - INFO - Step added - 2024-10-17 13:53:33,227 - julearn - INFO - = Model Parameters = - 2024-10-17 13:53:33,227 - julearn - INFO - ==================== - 2024-10-17 13:53:33,227 - julearn - INFO - - 2024-10-17 13:53:33,227 - julearn - INFO - = Data Information = - 2024-10-17 13:53:33,227 - julearn - INFO - Problem type: classification - 2024-10-17 13:53:33,227 - julearn - INFO - Number of samples: 532 - 2024-10-17 13:53:33,227 - julearn - INFO - Number of features: 2 - 2024-10-17 13:53:33,227 - julearn - INFO - ==================== - 2024-10-17 13:53:33,227 - julearn - INFO - - 2024-10-17 13:53:33,228 - julearn - INFO - Number of classes: 2 - 2024-10-17 13:53:33,228 - julearn - INFO - Target type: object - 2024-10-17 13:53:33,228 - julearn - INFO - Class distributions: event + 2024-10-17 14:01:48,140 - julearn - INFO - ==================== + 2024-10-17 14:01:48,140 - julearn - INFO - + 2024-10-17 14:01:48,141 - julearn - INFO - Adding step zscore that applies to ColumnTypes + 2024-10-17 14:01:48,141 - julearn - INFO - Step added + 2024-10-17 14:01:48,141 - julearn - INFO - Adding step svm that applies to ColumnTypes + 2024-10-17 14:01:48,141 - julearn - INFO - Step added + 2024-10-17 14:01:48,141 - julearn - INFO - = Model Parameters = + 2024-10-17 14:01:48,141 - julearn - INFO - ==================== + 2024-10-17 14:01:48,141 - julearn - INFO - + 2024-10-17 14:01:48,142 - julearn - INFO - = Data Information = + 2024-10-17 14:01:48,142 - julearn - INFO - Problem type: classification + 2024-10-17 14:01:48,142 - julearn - INFO - Number of samples: 532 + 2024-10-17 14:01:48,142 - julearn - INFO - Number of features: 2 + 2024-10-17 14:01:48,142 - julearn - INFO - ==================== + 2024-10-17 14:01:48,142 - julearn - INFO - + 2024-10-17 14:01:48,142 - julearn - INFO - Number of classes: 2 + 2024-10-17 14:01:48,142 - julearn - INFO - Target type: object + 2024-10-17 14:01:48,142 - julearn - INFO - Class distributions: event cue 266 stim 266 Name: count, dtype: int64 - 2024-10-17 13:53:33,228 - julearn - INFO - Using outer CV scheme KFold(n_splits=5, random_state=None, shuffle=False) - 2024-10-17 13:53:33,229 - julearn - INFO - Binary classification problem detected. + 2024-10-17 14:01:48,143 - julearn - INFO - Using outer CV scheme KFold(n_splits=5, random_state=None, shuffle=False) + 2024-10-17 14:01:48,143 - julearn - INFO - Binary classification problem detected. 0.7218303650149884 @@ -388,39 +388,39 @@ later to do some analyses. .. code-block:: none - 2024-10-17 13:53:33,285 - julearn - INFO - ==== Input Data ==== - 2024-10-17 13:53:33,285 - julearn - INFO - Using dataframe as input - 2024-10-17 13:53:33,285 - julearn - INFO - Features: ['parietal', 'frontal'] - 2024-10-17 13:53:33,285 - julearn - INFO - Target: event - 2024-10-17 13:53:33,285 - julearn - INFO - Expanded features: ['parietal', 'frontal'] - 2024-10-17 13:53:33,285 - julearn - INFO - X_types:{} - 2024-10-17 13:53:33,285 - julearn - WARNING - The following columns are not defined in X_types: ['parietal', 'frontal']. They will be treated as continuous. + 2024-10-17 14:01:48,201 - julearn - INFO - ==== Input Data ==== + 2024-10-17 14:01:48,201 - julearn - INFO - Using dataframe as input + 2024-10-17 14:01:48,201 - julearn - INFO - Features: ['parietal', 'frontal'] + 2024-10-17 14:01:48,201 - julearn - INFO - Target: event + 2024-10-17 14:01:48,201 - julearn - INFO - Expanded features: ['parietal', 'frontal'] + 2024-10-17 14:01:48,201 - julearn - INFO - X_types:{} + 2024-10-17 14:01:48,201 - julearn - WARNING - The following columns are not defined in X_types: ['parietal', 'frontal']. They will be treated as continuous. /home/runner/work/julearn/julearn/julearn/prepare.py:509: RuntimeWarning: The following columns are not defined in X_types: ['parietal', 'frontal']. They will be treated as continuous. warn_with_log( - 2024-10-17 13:53:33,285 - julearn - INFO - Using subject as groups - 2024-10-17 13:53:33,286 - julearn - INFO - ==================== - 2024-10-17 13:53:33,286 - julearn - INFO - - 2024-10-17 13:53:33,286 - julearn - INFO - Adding step zscore that applies to ColumnTypes - 2024-10-17 13:53:33,286 - julearn - INFO - Step added - 2024-10-17 13:53:33,286 - julearn - INFO - Adding step svm that applies to ColumnTypes - 2024-10-17 13:53:33,286 - julearn - INFO - Step added - 2024-10-17 13:53:33,286 - julearn - INFO - = Model Parameters = - 2024-10-17 13:53:33,287 - julearn - INFO - ==================== - 2024-10-17 13:53:33,287 - julearn - INFO - - 2024-10-17 13:53:33,287 - julearn - INFO - = Data Information = - 2024-10-17 13:53:33,287 - julearn - INFO - Problem type: classification - 2024-10-17 13:53:33,287 - julearn - INFO - Number of samples: 532 - 2024-10-17 13:53:33,287 - julearn - INFO - Number of features: 2 - 2024-10-17 13:53:33,287 - julearn - INFO - ==================== - 2024-10-17 13:53:33,287 - julearn - INFO - - 2024-10-17 13:53:33,287 - julearn - INFO - Number of classes: 2 - 2024-10-17 13:53:33,287 - julearn - INFO - Target type: object - 2024-10-17 13:53:33,288 - julearn - INFO - Class distributions: event + 2024-10-17 14:01:48,202 - julearn - INFO - Using subject as groups + 2024-10-17 14:01:48,202 - julearn - INFO - ==================== + 2024-10-17 14:01:48,202 - julearn - INFO - + 2024-10-17 14:01:48,202 - julearn - INFO - Adding step zscore that applies to ColumnTypes + 2024-10-17 14:01:48,202 - julearn - INFO - Step added + 2024-10-17 14:01:48,202 - julearn - INFO - Adding step svm that applies to ColumnTypes + 2024-10-17 14:01:48,202 - julearn - INFO - Step added + 2024-10-17 14:01:48,203 - julearn - INFO - = Model Parameters = + 2024-10-17 14:01:48,203 - julearn - INFO - ==================== + 2024-10-17 14:01:48,203 - julearn - INFO - + 2024-10-17 14:01:48,203 - julearn - INFO - = Data Information = + 2024-10-17 14:01:48,203 - julearn - INFO - Problem type: classification + 2024-10-17 14:01:48,203 - julearn - INFO - Number of samples: 532 + 2024-10-17 14:01:48,203 - julearn - INFO - Number of features: 2 + 2024-10-17 14:01:48,203 - julearn - INFO - ==================== + 2024-10-17 14:01:48,203 - julearn - INFO - + 2024-10-17 14:01:48,203 - julearn - INFO - Number of classes: 2 + 2024-10-17 14:01:48,203 - julearn - INFO - Target type: object + 2024-10-17 14:01:48,204 - julearn - INFO - Class distributions: event cue 266 stim 266 Name: count, dtype: int64 - 2024-10-17 13:53:33,288 - julearn - INFO - Using outer CV scheme GroupShuffleSplit(n_splits=5, random_state=42, test_size=0.5, train_size=None) (incl. final model) - 2024-10-17 13:53:33,288 - julearn - INFO - Binary classification problem detected. + 2024-10-17 14:01:48,204 - julearn - INFO - Using outer CV scheme GroupShuffleSplit(n_splits=5, random_state=42, test_size=0.5, train_size=None) (incl. final model) + 2024-10-17 14:01:48,204 - julearn - INFO - Binary classification problem detected. 0.7210526315789474 @@ -545,7 +545,7 @@ how the SVM does this complex task. .. rst-class:: sphx-glr-timing - **Total running time of the script:** (0 minutes 0.495 seconds) + **Total running time of the script:** (0 minutes 0.491 seconds) .. _sphx_glr_download_auto_examples_02_inspection_plot_groupcv_inspect_svm.py: diff --git a/pr-preview/pr-276/_sources/auto_examples/02_inspection/plot_inspect_random_forest.rst.txt b/pr-preview/pr-276/_sources/auto_examples/02_inspection/plot_inspect_random_forest.rst.txt index d669f2ac0..0240b4ad0 100644 --- a/pr-preview/pr-276/_sources/auto_examples/02_inspection/plot_inspect_random_forest.rst.txt +++ b/pr-preview/pr-276/_sources/auto_examples/02_inspection/plot_inspect_random_forest.rst.txt @@ -69,13 +69,13 @@ Set the logging level to info to see extra information. /home/runner/work/julearn/julearn/julearn/utils/logging.py:66: UserWarning: The '__version__' attribute is deprecated and will be removed in MarkupSafe 3.1. Use feature detection, or `importlib.metadata.version("markupsafe")`, instead. vstring = str(getattr(module, "__version__", None)) - 2024-10-17 13:53:31,629 - julearn - INFO - ===== Lib Versions ===== - 2024-10-17 13:53:31,629 - julearn - INFO - numpy: 1.26.4 - 2024-10-17 13:53:31,629 - julearn - INFO - scipy: 1.14.1 - 2024-10-17 13:53:31,629 - julearn - INFO - sklearn: 1.5.2 - 2024-10-17 13:53:31,629 - julearn - INFO - pandas: 2.2.3 - 2024-10-17 13:53:31,629 - julearn - INFO - julearn: 0.3.4.dev37 - 2024-10-17 13:53:31,629 - julearn - INFO - ======================== + 2024-10-17 14:01:46,526 - julearn - INFO - ===== Lib Versions ===== + 2024-10-17 14:01:46,526 - julearn - INFO - numpy: 1.26.4 + 2024-10-17 14:01:46,526 - julearn - INFO - scipy: 1.14.1 + 2024-10-17 14:01:46,526 - julearn - INFO - sklearn: 1.5.2 + 2024-10-17 14:01:46,526 - julearn - INFO - pandas: 2.2.3 + 2024-10-17 14:01:46,526 - julearn - INFO - julearn: 0.3.4.dev43 + 2024-10-17 14:01:46,526 - julearn - INFO - ======================== @@ -150,38 +150,38 @@ returns the estimator fitted with all the data. .. code-block:: none - 2024-10-17 13:53:31,631 - julearn - INFO - ==== Input Data ==== - 2024-10-17 13:53:31,632 - julearn - INFO - Using dataframe as input - 2024-10-17 13:53:31,632 - julearn - INFO - Features: ['sepal_length', 'sepal_width', 'petal_length'] - 2024-10-17 13:53:31,632 - julearn - INFO - Target: species - 2024-10-17 13:53:31,632 - julearn - INFO - Expanded features: ['sepal_length', 'sepal_width', 'petal_length'] - 2024-10-17 13:53:31,632 - julearn - INFO - X_types:{} - 2024-10-17 13:53:31,632 - julearn - WARNING - The following columns are not defined in X_types: ['sepal_length', 'sepal_width', 'petal_length']. They will be treated as continuous. + 2024-10-17 14:01:46,528 - julearn - INFO - ==== Input Data ==== + 2024-10-17 14:01:46,529 - julearn - INFO - Using dataframe as input + 2024-10-17 14:01:46,529 - julearn - INFO - Features: ['sepal_length', 'sepal_width', 'petal_length'] + 2024-10-17 14:01:46,529 - julearn - INFO - Target: species + 2024-10-17 14:01:46,529 - julearn - INFO - Expanded features: ['sepal_length', 'sepal_width', 'petal_length'] + 2024-10-17 14:01:46,529 - julearn - INFO - X_types:{} + 2024-10-17 14:01:46,529 - julearn - WARNING - The following columns are not defined in X_types: ['sepal_length', 'sepal_width', 'petal_length']. They will be treated as continuous. /home/runner/work/julearn/julearn/julearn/prepare.py:509: RuntimeWarning: The following columns are not defined in X_types: ['sepal_length', 'sepal_width', 'petal_length']. They will be treated as continuous. warn_with_log( - 2024-10-17 13:53:31,633 - julearn - INFO - ==================== - 2024-10-17 13:53:31,633 - julearn - INFO - - 2024-10-17 13:53:31,633 - julearn - INFO - Adding step zscore that applies to ColumnTypes - 2024-10-17 13:53:31,633 - julearn - INFO - Step added - 2024-10-17 13:53:31,633 - julearn - INFO - Adding step rf that applies to ColumnTypes - 2024-10-17 13:53:31,633 - julearn - INFO - Step added - 2024-10-17 13:53:31,634 - julearn - INFO - = Model Parameters = - 2024-10-17 13:53:31,634 - julearn - INFO - ==================== - 2024-10-17 13:53:31,634 - julearn - INFO - - 2024-10-17 13:53:31,634 - julearn - INFO - = Data Information = - 2024-10-17 13:53:31,634 - julearn - INFO - Problem type: classification - 2024-10-17 13:53:31,634 - julearn - INFO - Number of samples: 100 - 2024-10-17 13:53:31,634 - julearn - INFO - Number of features: 3 - 2024-10-17 13:53:31,634 - julearn - INFO - ==================== - 2024-10-17 13:53:31,634 - julearn - INFO - - 2024-10-17 13:53:31,634 - julearn - INFO - Number of classes: 2 - 2024-10-17 13:53:31,634 - julearn - INFO - Target type: object - 2024-10-17 13:53:31,635 - julearn - INFO - Class distributions: species + 2024-10-17 14:01:46,529 - julearn - INFO - ==================== + 2024-10-17 14:01:46,530 - julearn - INFO - + 2024-10-17 14:01:46,530 - julearn - INFO - Adding step zscore that applies to ColumnTypes + 2024-10-17 14:01:46,530 - julearn - INFO - Step added + 2024-10-17 14:01:46,530 - julearn - INFO - Adding step rf that applies to ColumnTypes + 2024-10-17 14:01:46,530 - julearn - INFO - Step added + 2024-10-17 14:01:46,530 - julearn - INFO - = Model Parameters = + 2024-10-17 14:01:46,531 - julearn - INFO - ==================== + 2024-10-17 14:01:46,531 - julearn - INFO - + 2024-10-17 14:01:46,531 - julearn - INFO - = Data Information = + 2024-10-17 14:01:46,531 - julearn - INFO - Problem type: classification + 2024-10-17 14:01:46,531 - julearn - INFO - Number of samples: 100 + 2024-10-17 14:01:46,531 - julearn - INFO - Number of features: 3 + 2024-10-17 14:01:46,531 - julearn - INFO - ==================== + 2024-10-17 14:01:46,531 - julearn - INFO - + 2024-10-17 14:01:46,531 - julearn - INFO - Number of classes: 2 + 2024-10-17 14:01:46,531 - julearn - INFO - Target type: object + 2024-10-17 14:01:46,532 - julearn - INFO - Class distributions: species versicolor 50 virginica 50 Name: count, dtype: int64 - 2024-10-17 13:53:31,635 - julearn - INFO - Using outer CV scheme KFold(n_splits=5, random_state=None, shuffle=False) (incl. final model) - 2024-10-17 13:53:31,635 - julearn - INFO - Binary classification problem detected. + 2024-10-17 14:01:46,532 - julearn - INFO - Using outer CV scheme KFold(n_splits=5, random_state=None, shuffle=False) (incl. final model) + 2024-10-17 14:01:46,532 - julearn - INFO - Binary classification problem detected. @@ -256,38 +256,38 @@ estimator. .. code-block:: none - 2024-10-17 13:53:32,347 - julearn - INFO - ==== Input Data ==== - 2024-10-17 13:53:32,348 - julearn - INFO - Using dataframe as input - 2024-10-17 13:53:32,348 - julearn - INFO - Features: ['sepal_length', 'sepal_width', 'petal_length'] - 2024-10-17 13:53:32,348 - julearn - INFO - Target: species - 2024-10-17 13:53:32,348 - julearn - INFO - Expanded features: ['sepal_length', 'sepal_width', 'petal_length'] - 2024-10-17 13:53:32,348 - julearn - INFO - X_types:{} - 2024-10-17 13:53:32,348 - julearn - WARNING - The following columns are not defined in X_types: ['sepal_length', 'sepal_width', 'petal_length']. They will be treated as continuous. + 2024-10-17 14:01:47,278 - julearn - INFO - ==== Input Data ==== + 2024-10-17 14:01:47,278 - julearn - INFO - Using dataframe as input + 2024-10-17 14:01:47,278 - julearn - INFO - Features: ['sepal_length', 'sepal_width', 'petal_length'] + 2024-10-17 14:01:47,278 - julearn - INFO - Target: species + 2024-10-17 14:01:47,278 - julearn - INFO - Expanded features: ['sepal_length', 'sepal_width', 'petal_length'] + 2024-10-17 14:01:47,278 - julearn - INFO - X_types:{} + 2024-10-17 14:01:47,278 - julearn - WARNING - The following columns are not defined in X_types: ['sepal_length', 'sepal_width', 'petal_length']. They will be treated as continuous. /home/runner/work/julearn/julearn/julearn/prepare.py:509: RuntimeWarning: The following columns are not defined in X_types: ['sepal_length', 'sepal_width', 'petal_length']. They will be treated as continuous. warn_with_log( - 2024-10-17 13:53:32,349 - julearn - INFO - ==================== - 2024-10-17 13:53:32,349 - julearn - INFO - - 2024-10-17 13:53:32,349 - julearn - INFO - Adding step zscore that applies to ColumnTypes - 2024-10-17 13:53:32,349 - julearn - INFO - Step added - 2024-10-17 13:53:32,350 - julearn - INFO - Adding step rf that applies to ColumnTypes - 2024-10-17 13:53:32,350 - julearn - INFO - Step added - 2024-10-17 13:53:32,351 - julearn - INFO - = Model Parameters = - 2024-10-17 13:53:32,351 - julearn - INFO - ==================== - 2024-10-17 13:53:32,351 - julearn - INFO - - 2024-10-17 13:53:32,351 - julearn - INFO - = Data Information = - 2024-10-17 13:53:32,351 - julearn - INFO - Problem type: classification - 2024-10-17 13:53:32,351 - julearn - INFO - Number of samples: 100 - 2024-10-17 13:53:32,351 - julearn - INFO - Number of features: 3 - 2024-10-17 13:53:32,351 - julearn - INFO - ==================== - 2024-10-17 13:53:32,351 - julearn - INFO - - 2024-10-17 13:53:32,351 - julearn - INFO - Number of classes: 2 - 2024-10-17 13:53:32,351 - julearn - INFO - Target type: object - 2024-10-17 13:53:32,352 - julearn - INFO - Class distributions: species + 2024-10-17 14:01:47,279 - julearn - INFO - ==================== + 2024-10-17 14:01:47,279 - julearn - INFO - + 2024-10-17 14:01:47,279 - julearn - INFO - Adding step zscore that applies to ColumnTypes + 2024-10-17 14:01:47,279 - julearn - INFO - Step added + 2024-10-17 14:01:47,279 - julearn - INFO - Adding step rf that applies to ColumnTypes + 2024-10-17 14:01:47,280 - julearn - INFO - Step added + 2024-10-17 14:01:47,280 - julearn - INFO - = Model Parameters = + 2024-10-17 14:01:47,280 - julearn - INFO - ==================== + 2024-10-17 14:01:47,281 - julearn - INFO - + 2024-10-17 14:01:47,281 - julearn - INFO - = Data Information = + 2024-10-17 14:01:47,281 - julearn - INFO - Problem type: classification + 2024-10-17 14:01:47,281 - julearn - INFO - Number of samples: 100 + 2024-10-17 14:01:47,281 - julearn - INFO - Number of features: 3 + 2024-10-17 14:01:47,281 - julearn - INFO - ==================== + 2024-10-17 14:01:47,281 - julearn - INFO - + 2024-10-17 14:01:47,281 - julearn - INFO - Number of classes: 2 + 2024-10-17 14:01:47,281 - julearn - INFO - Target type: object + 2024-10-17 14:01:47,282 - julearn - INFO - Class distributions: species versicolor 50 virginica 50 Name: count, dtype: int64 - 2024-10-17 13:53:32,352 - julearn - INFO - Using outer CV scheme KFold(n_splits=5, random_state=None, shuffle=False) - 2024-10-17 13:53:32,353 - julearn - INFO - Binary classification problem detected. + 2024-10-17 14:01:47,282 - julearn - INFO - Using outer CV scheme KFold(n_splits=5, random_state=None, shuffle=False) + 2024-10-17 14:01:47,282 - julearn - INFO - Binary classification problem detected. @@ -362,7 +362,7 @@ Finally, we can plot the variable importances for each fold. .. rst-class:: sphx-glr-timing - **Total running time of the script:** (0 minutes 1.431 seconds) + **Total running time of the script:** (0 minutes 1.461 seconds) .. _sphx_glr_download_auto_examples_02_inspection_plot_inspect_random_forest.py: diff --git a/pr-preview/pr-276/_sources/auto_examples/02_inspection/plot_preprocess.rst.txt b/pr-preview/pr-276/_sources/auto_examples/02_inspection/plot_preprocess.rst.txt index 13e87caf6..7e0938364 100644 --- a/pr-preview/pr-276/_sources/auto_examples/02_inspection/plot_preprocess.rst.txt +++ b/pr-preview/pr-276/_sources/auto_examples/02_inspection/plot_preprocess.rst.txt @@ -72,13 +72,13 @@ Set the logging level to info to see extra information. /home/runner/work/julearn/julearn/julearn/utils/logging.py:66: UserWarning: The '__version__' attribute is deprecated and will be removed in MarkupSafe 3.1. Use feature detection, or `importlib.metadata.version("markupsafe")`, instead. vstring = str(getattr(module, "__version__", None)) - 2024-10-17 13:53:33,850 - julearn - INFO - ===== Lib Versions ===== - 2024-10-17 13:53:33,850 - julearn - INFO - numpy: 1.26.4 - 2024-10-17 13:53:33,850 - julearn - INFO - scipy: 1.14.1 - 2024-10-17 13:53:33,850 - julearn - INFO - sklearn: 1.5.2 - 2024-10-17 13:53:33,850 - julearn - INFO - pandas: 2.2.3 - 2024-10-17 13:53:33,850 - julearn - INFO - julearn: 0.3.4.dev37 - 2024-10-17 13:53:33,850 - julearn - INFO - ======================== + 2024-10-17 14:01:48,752 - julearn - INFO - ===== Lib Versions ===== + 2024-10-17 14:01:48,752 - julearn - INFO - numpy: 1.26.4 + 2024-10-17 14:01:48,752 - julearn - INFO - scipy: 1.14.1 + 2024-10-17 14:01:48,753 - julearn - INFO - sklearn: 1.5.2 + 2024-10-17 14:01:48,753 - julearn - INFO - pandas: 2.2.3 + 2024-10-17 14:01:48,753 - julearn - INFO - julearn: 0.3.4.dev43 + 2024-10-17 14:01:48,753 - julearn - INFO - ======================== @@ -241,40 +241,40 @@ to be used in the regression to the ``PipelineCreator``. .. code-block:: none - 2024-10-17 13:53:33,865 - julearn - INFO - Adding step select_variance that applies to ColumnTypes - 2024-10-17 13:53:33,865 - julearn - INFO - Setting hyperparameter threshold = 0.15 - 2024-10-17 13:53:33,866 - julearn - INFO - Step added - 2024-10-17 13:53:33,866 - julearn - INFO - Adding step zscore that applies to ColumnTypes - 2024-10-17 13:53:33,866 - julearn - INFO - Step added - 2024-10-17 13:53:33,866 - julearn - INFO - Adding step pca that applies to ColumnTypes - 2024-10-17 13:53:33,866 - julearn - INFO - Setting hyperparameter n_components = 2 - 2024-10-17 13:53:33,866 - julearn - INFO - Step added - 2024-10-17 13:53:33,866 - julearn - INFO - Adding step rf that applies to ColumnTypes - 2024-10-17 13:53:33,866 - julearn - INFO - Setting hyperparameter n_estimators = 200 - 2024-10-17 13:53:33,866 - julearn - INFO - Step added - 2024-10-17 13:53:33,866 - julearn - INFO - Setting random seed to 200 - 2024-10-17 13:53:33,866 - julearn - INFO - ==== Input Data ==== - 2024-10-17 13:53:33,866 - julearn - INFO - Using dataframe as input - 2024-10-17 13:53:33,866 - julearn - INFO - Features: ['Feature 1', 'Feature 2', 'Feature 3', 'Feature 4'] - 2024-10-17 13:53:33,866 - julearn - INFO - Target: y - 2024-10-17 13:53:33,867 - julearn - INFO - Expanded features: ['Feature 1', 'Feature 2', 'Feature 3', 'Feature 4'] - 2024-10-17 13:53:33,867 - julearn - INFO - X_types:{'X_to_zscore': ['Feature 1', 'Feature 2']} - 2024-10-17 13:53:33,867 - julearn - WARNING - The following columns are not defined in X_types: ['Feature 3', 'Feature 4']. They will be treated as continuous. + 2024-10-17 14:01:48,768 - julearn - INFO - Adding step select_variance that applies to ColumnTypes + 2024-10-17 14:01:48,768 - julearn - INFO - Setting hyperparameter threshold = 0.15 + 2024-10-17 14:01:48,768 - julearn - INFO - Step added + 2024-10-17 14:01:48,768 - julearn - INFO - Adding step zscore that applies to ColumnTypes + 2024-10-17 14:01:48,768 - julearn - INFO - Step added + 2024-10-17 14:01:48,768 - julearn - INFO - Adding step pca that applies to ColumnTypes + 2024-10-17 14:01:48,768 - julearn - INFO - Setting hyperparameter n_components = 2 + 2024-10-17 14:01:48,768 - julearn - INFO - Step added + 2024-10-17 14:01:48,768 - julearn - INFO - Adding step rf that applies to ColumnTypes + 2024-10-17 14:01:48,768 - julearn - INFO - Setting hyperparameter n_estimators = 200 + 2024-10-17 14:01:48,768 - julearn - INFO - Step added + 2024-10-17 14:01:48,769 - julearn - INFO - Setting random seed to 200 + 2024-10-17 14:01:48,769 - julearn - INFO - ==== Input Data ==== + 2024-10-17 14:01:48,769 - julearn - INFO - Using dataframe as input + 2024-10-17 14:01:48,769 - julearn - INFO - Features: ['Feature 1', 'Feature 2', 'Feature 3', 'Feature 4'] + 2024-10-17 14:01:48,769 - julearn - INFO - Target: y + 2024-10-17 14:01:48,769 - julearn - INFO - Expanded features: ['Feature 1', 'Feature 2', 'Feature 3', 'Feature 4'] + 2024-10-17 14:01:48,769 - julearn - INFO - X_types:{'X_to_zscore': ['Feature 1', 'Feature 2']} + 2024-10-17 14:01:48,769 - julearn - WARNING - The following columns are not defined in X_types: ['Feature 3', 'Feature 4']. They will be treated as continuous. /home/runner/work/julearn/julearn/julearn/prepare.py:509: RuntimeWarning: The following columns are not defined in X_types: ['Feature 3', 'Feature 4']. They will be treated as continuous. warn_with_log( - 2024-10-17 13:53:33,868 - julearn - INFO - ==================== - 2024-10-17 13:53:33,868 - julearn - INFO - - 2024-10-17 13:53:33,869 - julearn - INFO - = Model Parameters = - 2024-10-17 13:53:33,869 - julearn - INFO - ==================== - 2024-10-17 13:53:33,869 - julearn - INFO - - 2024-10-17 13:53:33,869 - julearn - INFO - = Data Information = - 2024-10-17 13:53:33,869 - julearn - INFO - Problem type: regression - 2024-10-17 13:53:33,869 - julearn - INFO - Number of samples: 100 - 2024-10-17 13:53:33,869 - julearn - INFO - Number of features: 4 - 2024-10-17 13:53:33,869 - julearn - INFO - ==================== - 2024-10-17 13:53:33,869 - julearn - INFO - - 2024-10-17 13:53:33,869 - julearn - INFO - Target type: float64 - 2024-10-17 13:53:33,869 - julearn - INFO - Using outer CV scheme KFold(n_splits=5, random_state=None, shuffle=False) (incl. final model) + 2024-10-17 14:01:48,770 - julearn - INFO - ==================== + 2024-10-17 14:01:48,770 - julearn - INFO - + 2024-10-17 14:01:48,771 - julearn - INFO - = Model Parameters = + 2024-10-17 14:01:48,771 - julearn - INFO - ==================== + 2024-10-17 14:01:48,771 - julearn - INFO - + 2024-10-17 14:01:48,771 - julearn - INFO - = Data Information = + 2024-10-17 14:01:48,771 - julearn - INFO - Problem type: regression + 2024-10-17 14:01:48,771 - julearn - INFO - Number of samples: 100 + 2024-10-17 14:01:48,771 - julearn - INFO - Number of features: 4 + 2024-10-17 14:01:48,771 - julearn - INFO - ==================== + 2024-10-17 14:01:48,771 - julearn - INFO - + 2024-10-17 14:01:48,771 - julearn - INFO - Target type: float64 + 2024-10-17 14:01:48,771 - julearn - INFO - Using outer CV scheme KFold(n_splits=5, random_state=None, shuffle=False) (incl. final model) X after PCA: =============================================================================== pca0 pca1 @@ -350,7 +350,7 @@ that the mean of all the features is zero and standard deviation is one. .. rst-class:: sphx-glr-timing - **Total running time of the script:** (0 minutes 1.255 seconds) + **Total running time of the script:** (0 minutes 1.260 seconds) .. _sphx_glr_download_auto_examples_02_inspection_plot_preprocess.py: diff --git a/pr-preview/pr-276/_sources/auto_examples/02_inspection/run_binary_inspect_folds.rst.txt b/pr-preview/pr-276/_sources/auto_examples/02_inspection/run_binary_inspect_folds.rst.txt index 21be83479..224b5337d 100644 --- a/pr-preview/pr-276/_sources/auto_examples/02_inspection/run_binary_inspect_folds.rst.txt +++ b/pr-preview/pr-276/_sources/auto_examples/02_inspection/run_binary_inspect_folds.rst.txt @@ -70,13 +70,13 @@ Set the logging level to info to see extra information. /home/runner/work/julearn/julearn/julearn/utils/logging.py:66: UserWarning: The '__version__' attribute is deprecated and will be removed in MarkupSafe 3.1. Use feature detection, or `importlib.metadata.version("markupsafe")`, instead. vstring = str(getattr(module, "__version__", None)) - 2024-10-17 13:53:31,267 - julearn - INFO - ===== Lib Versions ===== - 2024-10-17 13:53:31,267 - julearn - INFO - numpy: 1.26.4 - 2024-10-17 13:53:31,267 - julearn - INFO - scipy: 1.14.1 - 2024-10-17 13:53:31,267 - julearn - INFO - sklearn: 1.5.2 - 2024-10-17 13:53:31,267 - julearn - INFO - pandas: 2.2.3 - 2024-10-17 13:53:31,267 - julearn - INFO - julearn: 0.3.4.dev37 - 2024-10-17 13:53:31,267 - julearn - INFO - ======================== + 2024-10-17 14:01:46,161 - julearn - INFO - ===== Lib Versions ===== + 2024-10-17 14:01:46,161 - julearn - INFO - numpy: 1.26.4 + 2024-10-17 14:01:46,161 - julearn - INFO - scipy: 1.14.1 + 2024-10-17 14:01:46,161 - julearn - INFO - sklearn: 1.5.2 + 2024-10-17 14:01:46,161 - julearn - INFO - pandas: 2.2.3 + 2024-10-17 14:01:46,161 - julearn - INFO - julearn: 0.3.4.dev43 + 2024-10-17 14:01:46,161 - julearn - INFO - ======================== @@ -151,59 +151,59 @@ We will try to predict the species. .. code-block:: none - 2024-10-17 13:53:31,270 - julearn - INFO - Adding step zscore that applies to ColumnTypes - 2024-10-17 13:53:31,270 - julearn - INFO - Step added - 2024-10-17 13:53:31,270 - julearn - INFO - Adding step svm that applies to ColumnTypes - 2024-10-17 13:53:31,270 - julearn - INFO - Step added - 2024-10-17 13:53:31,270 - julearn - INFO - ==== Input Data ==== - 2024-10-17 13:53:31,270 - julearn - INFO - Using dataframe as input - 2024-10-17 13:53:31,270 - julearn - INFO - Features: ['sepal_length', 'sepal_width', 'petal_length'] - 2024-10-17 13:53:31,270 - julearn - INFO - Target: species - 2024-10-17 13:53:31,271 - julearn - INFO - Expanded features: ['sepal_length', 'sepal_width', 'petal_length'] - 2024-10-17 13:53:31,271 - julearn - INFO - X_types:{} - 2024-10-17 13:53:31,271 - julearn - WARNING - The following columns are not defined in X_types: ['sepal_length', 'sepal_width', 'petal_length']. They will be treated as continuous. + 2024-10-17 14:01:46,164 - julearn - INFO - Adding step zscore that applies to ColumnTypes + 2024-10-17 14:01:46,164 - julearn - INFO - Step added + 2024-10-17 14:01:46,164 - julearn - INFO - Adding step svm that applies to ColumnTypes + 2024-10-17 14:01:46,164 - julearn - INFO - Step added + 2024-10-17 14:01:46,164 - julearn - INFO - ==== Input Data ==== + 2024-10-17 14:01:46,164 - julearn - INFO - Using dataframe as input + 2024-10-17 14:01:46,164 - julearn - INFO - Features: ['sepal_length', 'sepal_width', 'petal_length'] + 2024-10-17 14:01:46,164 - julearn - INFO - Target: species + 2024-10-17 14:01:46,164 - julearn - INFO - Expanded features: ['sepal_length', 'sepal_width', 'petal_length'] + 2024-10-17 14:01:46,164 - julearn - INFO - X_types:{} + 2024-10-17 14:01:46,164 - julearn - WARNING - The following columns are not defined in X_types: ['sepal_length', 'sepal_width', 'petal_length']. They will be treated as continuous. /home/runner/work/julearn/julearn/julearn/prepare.py:509: RuntimeWarning: The following columns are not defined in X_types: ['sepal_length', 'sepal_width', 'petal_length']. They will be treated as continuous. warn_with_log( - 2024-10-17 13:53:31,271 - julearn - INFO - ==================== - 2024-10-17 13:53:31,271 - julearn - INFO - - 2024-10-17 13:53:31,272 - julearn - INFO - = Model Parameters = - 2024-10-17 13:53:31,272 - julearn - INFO - ==================== - 2024-10-17 13:53:31,272 - julearn - INFO - - 2024-10-17 13:53:31,272 - julearn - INFO - = Data Information = - 2024-10-17 13:53:31,272 - julearn - INFO - Problem type: classification - 2024-10-17 13:53:31,272 - julearn - INFO - Number of samples: 100 - 2024-10-17 13:53:31,272 - julearn - INFO - Number of features: 3 - 2024-10-17 13:53:31,272 - julearn - INFO - ==================== - 2024-10-17 13:53:31,272 - julearn - INFO - - 2024-10-17 13:53:31,272 - julearn - INFO - Number of classes: 2 - 2024-10-17 13:53:31,272 - julearn - INFO - Target type: object - 2024-10-17 13:53:31,273 - julearn - INFO - Class distributions: species + 2024-10-17 14:01:46,165 - julearn - INFO - ==================== + 2024-10-17 14:01:46,165 - julearn - INFO - + 2024-10-17 14:01:46,166 - julearn - INFO - = Model Parameters = + 2024-10-17 14:01:46,166 - julearn - INFO - ==================== + 2024-10-17 14:01:46,166 - julearn - INFO - + 2024-10-17 14:01:46,166 - julearn - INFO - = Data Information = + 2024-10-17 14:01:46,166 - julearn - INFO - Problem type: classification + 2024-10-17 14:01:46,166 - julearn - INFO - Number of samples: 100 + 2024-10-17 14:01:46,166 - julearn - INFO - Number of features: 3 + 2024-10-17 14:01:46,166 - julearn - INFO - ==================== + 2024-10-17 14:01:46,166 - julearn - INFO - + 2024-10-17 14:01:46,166 - julearn - INFO - Number of classes: 2 + 2024-10-17 14:01:46,166 - julearn - INFO - Target type: object + 2024-10-17 14:01:46,167 - julearn - INFO - Class distributions: species versicolor 50 virginica 50 Name: count, dtype: int64 - 2024-10-17 13:53:31,273 - julearn - INFO - Using outer CV scheme RepeatedStratifiedKFold(n_repeats=4, n_splits=5, random_state=200) (incl. final model) - 2024-10-17 13:53:31,273 - julearn - INFO - Binary classification problem detected. + 2024-10-17 14:01:46,167 - julearn - INFO - Using outer CV scheme RepeatedStratifiedKFold(n_repeats=4, n_splits=5, random_state=200) (incl. final model) + 2024-10-17 14:01:46,167 - julearn - INFO - Binary classification problem detected. fit_time score_time ... fold cv_mdsum - 0 0.004670 0.002430 ... 0 42489ff0163b2f12752440a6b7ef74c7 - 1 0.004345 0.002430 ... 1 42489ff0163b2f12752440a6b7ef74c7 - 2 0.004302 0.002423 ... 2 42489ff0163b2f12752440a6b7ef74c7 - 3 0.004303 0.002407 ... 3 42489ff0163b2f12752440a6b7ef74c7 - 4 0.004287 0.002420 ... 4 42489ff0163b2f12752440a6b7ef74c7 - 5 0.004317 0.002411 ... 0 42489ff0163b2f12752440a6b7ef74c7 - 6 0.004313 0.002491 ... 1 42489ff0163b2f12752440a6b7ef74c7 - 7 0.004341 0.002408 ... 2 42489ff0163b2f12752440a6b7ef74c7 - 8 0.004254 0.002415 ... 3 42489ff0163b2f12752440a6b7ef74c7 - 9 0.004286 0.002385 ... 4 42489ff0163b2f12752440a6b7ef74c7 - 10 0.004308 0.002391 ... 0 42489ff0163b2f12752440a6b7ef74c7 - 11 0.004325 0.002448 ... 1 42489ff0163b2f12752440a6b7ef74c7 - 12 0.004293 0.002457 ... 2 42489ff0163b2f12752440a6b7ef74c7 - 13 0.004297 0.002409 ... 3 42489ff0163b2f12752440a6b7ef74c7 - 14 0.004302 0.002368 ... 4 42489ff0163b2f12752440a6b7ef74c7 - 15 0.004325 0.002393 ... 0 42489ff0163b2f12752440a6b7ef74c7 - 16 0.004235 0.002387 ... 1 42489ff0163b2f12752440a6b7ef74c7 - 17 0.004372 0.002430 ... 2 42489ff0163b2f12752440a6b7ef74c7 - 18 0.004253 0.002423 ... 3 42489ff0163b2f12752440a6b7ef74c7 - 19 0.004312 0.002387 ... 4 42489ff0163b2f12752440a6b7ef74c7 + 0 0.004359 0.002398 ... 0 42489ff0163b2f12752440a6b7ef74c7 + 1 0.004312 0.002434 ... 1 42489ff0163b2f12752440a6b7ef74c7 + 2 0.004292 0.002444 ... 2 42489ff0163b2f12752440a6b7ef74c7 + 3 0.004311 0.002432 ... 3 42489ff0163b2f12752440a6b7ef74c7 + 4 0.004314 0.002455 ... 4 42489ff0163b2f12752440a6b7ef74c7 + 5 0.004353 0.002403 ... 0 42489ff0163b2f12752440a6b7ef74c7 + 6 0.004286 0.002410 ... 1 42489ff0163b2f12752440a6b7ef74c7 + 7 0.004295 0.002435 ... 2 42489ff0163b2f12752440a6b7ef74c7 + 8 0.004280 0.002417 ... 3 42489ff0163b2f12752440a6b7ef74c7 + 9 0.004313 0.002430 ... 4 42489ff0163b2f12752440a6b7ef74c7 + 10 0.004326 0.002405 ... 0 42489ff0163b2f12752440a6b7ef74c7 + 11 0.004322 0.002410 ... 1 42489ff0163b2f12752440a6b7ef74c7 + 12 0.004292 0.002418 ... 2 42489ff0163b2f12752440a6b7ef74c7 + 13 0.004283 0.002441 ... 3 42489ff0163b2f12752440a6b7ef74c7 + 14 0.004341 0.002399 ... 4 42489ff0163b2f12752440a6b7ef74c7 + 15 0.004330 0.002415 ... 0 42489ff0163b2f12752440a6b7ef74c7 + 16 0.004423 0.002392 ... 1 42489ff0163b2f12752440a6b7ef74c7 + 17 0.004294 0.002426 ... 2 42489ff0163b2f12752440a6b7ef74c7 + 18 0.004317 0.002451 ... 3 42489ff0163b2f12752440a6b7ef74c7 + 19 0.004290 0.002419 ... 4 42489ff0163b2f12752440a6b7ef74c7 [20 rows x 9 columns] @@ -263,14 +263,14 @@ We can now inspect the predictions of the model for each fold. .. code-block:: none - + .. rst-class:: sphx-glr-timing - **Total running time of the script:** (0 minutes 0.235 seconds) + **Total running time of the script:** (0 minutes 0.241 seconds) .. _sphx_glr_download_auto_examples_02_inspection_run_binary_inspect_folds.py: diff --git a/pr-preview/pr-276/_sources/auto_examples/02_inspection/sg_execution_times.rst.txt b/pr-preview/pr-276/_sources/auto_examples/02_inspection/sg_execution_times.rst.txt index e2eb50a48..428e113a7 100644 --- a/pr-preview/pr-276/_sources/auto_examples/02_inspection/sg_execution_times.rst.txt +++ b/pr-preview/pr-276/_sources/auto_examples/02_inspection/sg_execution_times.rst.txt @@ -6,14 +6,14 @@ Computation times ================= -**00:03.415** total execution time for **auto_examples_02_inspection** files: +**00:03.454** total execution time for **auto_examples_02_inspection** files: +---------------------------------------------------------------------------------------------------------------+-----------+--------+ -| :ref:`sphx_glr_auto_examples_02_inspection_plot_inspect_random_forest.py` (``plot_inspect_random_forest.py``) | 00:01.431 | 0.0 MB | +| :ref:`sphx_glr_auto_examples_02_inspection_plot_inspect_random_forest.py` (``plot_inspect_random_forest.py``) | 00:01.461 | 0.0 MB | +---------------------------------------------------------------------------------------------------------------+-----------+--------+ -| :ref:`sphx_glr_auto_examples_02_inspection_plot_preprocess.py` (``plot_preprocess.py``) | 00:01.255 | 0.0 MB | +| :ref:`sphx_glr_auto_examples_02_inspection_plot_preprocess.py` (``plot_preprocess.py``) | 00:01.260 | 0.0 MB | +---------------------------------------------------------------------------------------------------------------+-----------+--------+ -| :ref:`sphx_glr_auto_examples_02_inspection_plot_groupcv_inspect_svm.py` (``plot_groupcv_inspect_svm.py``) | 00:00.495 | 0.0 MB | +| :ref:`sphx_glr_auto_examples_02_inspection_plot_groupcv_inspect_svm.py` (``plot_groupcv_inspect_svm.py``) | 00:00.491 | 0.0 MB | +---------------------------------------------------------------------------------------------------------------+-----------+--------+ -| :ref:`sphx_glr_auto_examples_02_inspection_run_binary_inspect_folds.py` (``run_binary_inspect_folds.py``) | 00:00.235 | 0.0 MB | +| :ref:`sphx_glr_auto_examples_02_inspection_run_binary_inspect_folds.py` (``run_binary_inspect_folds.py``) | 00:00.241 | 0.0 MB | +---------------------------------------------------------------------------------------------------------------+-----------+--------+ diff --git a/pr-preview/pr-276/_sources/auto_examples/03_complex_models/run_apply_to_target.rst.txt b/pr-preview/pr-276/_sources/auto_examples/03_complex_models/run_apply_to_target.rst.txt index 7d548e2b5..0ed0e8aa5 100644 --- a/pr-preview/pr-276/_sources/auto_examples/03_complex_models/run_apply_to_target.rst.txt +++ b/pr-preview/pr-276/_sources/auto_examples/03_complex_models/run_apply_to_target.rst.txt @@ -70,13 +70,13 @@ Set the logging level to info to see extra information. /home/runner/work/julearn/julearn/julearn/utils/logging.py:66: UserWarning: The '__version__' attribute is deprecated and will be removed in MarkupSafe 3.1. Use feature detection, or `importlib.metadata.version("markupsafe")`, instead. vstring = str(getattr(module, "__version__", None)) - 2024-10-17 13:53:35,273 - julearn - INFO - ===== Lib Versions ===== - 2024-10-17 13:53:35,273 - julearn - INFO - numpy: 1.26.4 - 2024-10-17 13:53:35,273 - julearn - INFO - scipy: 1.14.1 - 2024-10-17 13:53:35,274 - julearn - INFO - sklearn: 1.5.2 - 2024-10-17 13:53:35,274 - julearn - INFO - pandas: 2.2.3 - 2024-10-17 13:53:35,274 - julearn - INFO - julearn: 0.3.4.dev37 - 2024-10-17 13:53:35,274 - julearn - INFO - ======================== + 2024-10-17 14:01:50,161 - julearn - INFO - ===== Lib Versions ===== + 2024-10-17 14:01:50,161 - julearn - INFO - numpy: 1.26.4 + 2024-10-17 14:01:50,161 - julearn - INFO - scipy: 1.14.1 + 2024-10-17 14:01:50,161 - julearn - INFO - sklearn: 1.5.2 + 2024-10-17 14:01:50,162 - julearn - INFO - pandas: 2.2.3 + 2024-10-17 14:01:50,162 - julearn - INFO - julearn: 0.3.4.dev43 + 2024-10-17 14:01:50,162 - julearn - INFO - ======================== @@ -203,7 +203,7 @@ we will first need to create a TargetPipelineCreator for this. .. code-block:: none - + @@ -238,38 +238,38 @@ Now we can create the pipeline using a PipelineCreator. .. code-block:: none - 2024-10-17 13:53:35,289 - julearn - INFO - Adding step jutargetpipeline that applies to ColumnTypes - 2024-10-17 13:53:35,290 - julearn - INFO - Step added - 2024-10-17 13:53:35,290 - julearn - INFO - Adding step ridge that applies to ColumnTypes - 2024-10-17 13:53:35,290 - julearn - INFO - Step added - 2024-10-17 13:53:35,290 - julearn - INFO - ==== Input Data ==== - 2024-10-17 13:53:35,290 - julearn - INFO - Using dataframe as input - 2024-10-17 13:53:35,290 - julearn - INFO - Features: ['age', 'sex', 'bmi', 'bp', 's1', 's2', 's3', 's4', 's5', 's6'] - 2024-10-17 13:53:35,290 - julearn - INFO - Target: target - 2024-10-17 13:53:35,290 - julearn - INFO - Expanded features: ['age', 'sex', 'bmi', 'bp', 's1', 's2', 's3', 's4', 's5', 's6'] - 2024-10-17 13:53:35,290 - julearn - INFO - X_types:{} - 2024-10-17 13:53:35,290 - julearn - WARNING - The following columns are not defined in X_types: ['age', 'sex', 'bmi', 'bp', 's1', 's2', 's3', 's4', 's5', 's6']. They will be treated as continuous. + 2024-10-17 14:01:50,177 - julearn - INFO - Adding step jutargetpipeline that applies to ColumnTypes + 2024-10-17 14:01:50,177 - julearn - INFO - Step added + 2024-10-17 14:01:50,177 - julearn - INFO - Adding step ridge that applies to ColumnTypes + 2024-10-17 14:01:50,178 - julearn - INFO - Step added + 2024-10-17 14:01:50,178 - julearn - INFO - ==== Input Data ==== + 2024-10-17 14:01:50,178 - julearn - INFO - Using dataframe as input + 2024-10-17 14:01:50,178 - julearn - INFO - Features: ['age', 'sex', 'bmi', 'bp', 's1', 's2', 's3', 's4', 's5', 's6'] + 2024-10-17 14:01:50,178 - julearn - INFO - Target: target + 2024-10-17 14:01:50,178 - julearn - INFO - Expanded features: ['age', 'sex', 'bmi', 'bp', 's1', 's2', 's3', 's4', 's5', 's6'] + 2024-10-17 14:01:50,178 - julearn - INFO - X_types:{} + 2024-10-17 14:01:50,178 - julearn - WARNING - The following columns are not defined in X_types: ['age', 'sex', 'bmi', 'bp', 's1', 's2', 's3', 's4', 's5', 's6']. They will be treated as continuous. /home/runner/work/julearn/julearn/julearn/prepare.py:509: RuntimeWarning: The following columns are not defined in X_types: ['age', 'sex', 'bmi', 'bp', 's1', 's2', 's3', 's4', 's5', 's6']. They will be treated as continuous. warn_with_log( - 2024-10-17 13:53:35,291 - julearn - INFO - ==================== - 2024-10-17 13:53:35,291 - julearn - INFO - - 2024-10-17 13:53:35,291 - julearn - INFO - = Model Parameters = - 2024-10-17 13:53:35,291 - julearn - INFO - ==================== - 2024-10-17 13:53:35,291 - julearn - INFO - - 2024-10-17 13:53:35,291 - julearn - INFO - = Data Information = - 2024-10-17 13:53:35,291 - julearn - INFO - Problem type: regression - 2024-10-17 13:53:35,292 - julearn - INFO - Number of samples: 309 - 2024-10-17 13:53:35,292 - julearn - INFO - Number of features: 10 - 2024-10-17 13:53:35,292 - julearn - INFO - ==================== - 2024-10-17 13:53:35,292 - julearn - INFO - - 2024-10-17 13:53:35,292 - julearn - INFO - Target type: float64 - 2024-10-17 13:53:35,292 - julearn - INFO - Using outer CV scheme KFold(n_splits=5, random_state=None, shuffle=False) (incl. final model) + 2024-10-17 14:01:50,179 - julearn - INFO - ==================== + 2024-10-17 14:01:50,179 - julearn - INFO - + 2024-10-17 14:01:50,179 - julearn - INFO - = Model Parameters = + 2024-10-17 14:01:50,179 - julearn - INFO - ==================== + 2024-10-17 14:01:50,179 - julearn - INFO - + 2024-10-17 14:01:50,179 - julearn - INFO - = Data Information = + 2024-10-17 14:01:50,179 - julearn - INFO - Problem type: regression + 2024-10-17 14:01:50,179 - julearn - INFO - Number of samples: 309 + 2024-10-17 14:01:50,179 - julearn - INFO - Number of features: 10 + 2024-10-17 14:01:50,179 - julearn - INFO - ==================== + 2024-10-17 14:01:50,179 - julearn - INFO - + 2024-10-17 14:01:50,179 - julearn - INFO - Target type: float64 + 2024-10-17 14:01:50,180 - julearn - INFO - Using outer CV scheme KFold(n_splits=5, random_state=None, shuffle=False) (incl. final model) fit_time score_time ... fold cv_mdsum - 0 0.003451 0.001575 ... 0 b10eef89b4192178d482d7a1587a248a - 1 0.003157 0.001528 ... 1 b10eef89b4192178d482d7a1587a248a - 2 0.003109 0.001582 ... 2 b10eef89b4192178d482d7a1587a248a - 3 0.003158 0.001519 ... 3 b10eef89b4192178d482d7a1587a248a - 4 0.003211 0.001525 ... 4 b10eef89b4192178d482d7a1587a248a + 0 0.003246 0.001584 ... 0 b10eef89b4192178d482d7a1587a248a + 1 0.003198 0.001589 ... 1 b10eef89b4192178d482d7a1587a248a + 2 0.003202 0.001590 ... 2 b10eef89b4192178d482d7a1587a248a + 3 0.003302 0.001643 ... 3 b10eef89b4192178d482d7a1587a248a + 4 0.003197 0.001559 ... 4 b10eef89b4192178d482d7a1587a248a [5 rows x 8 columns] @@ -301,7 +301,7 @@ Mean value of mean absolute error across CV .. rst-class:: sphx-glr-timing - **Total running time of the script:** (0 minutes 0.063 seconds) + **Total running time of the script:** (0 minutes 0.064 seconds) .. _sphx_glr_download_auto_examples_03_complex_models_run_apply_to_target.py: diff --git a/pr-preview/pr-276/_sources/auto_examples/03_complex_models/run_example_pca_featsets.rst.txt b/pr-preview/pr-276/_sources/auto_examples/03_complex_models/run_example_pca_featsets.rst.txt index 4f4ebca1a..b718373c4 100644 --- a/pr-preview/pr-276/_sources/auto_examples/03_complex_models/run_example_pca_featsets.rst.txt +++ b/pr-preview/pr-276/_sources/auto_examples/03_complex_models/run_example_pca_featsets.rst.txt @@ -77,13 +77,13 @@ Set the logging level to info to see extra information. /home/runner/work/julearn/julearn/julearn/utils/logging.py:66: UserWarning: The '__version__' attribute is deprecated and will be removed in MarkupSafe 3.1. Use feature detection, or `importlib.metadata.version("markupsafe")`, instead. vstring = str(getattr(module, "__version__", None)) - 2024-10-17 13:53:47,899 - julearn - INFO - ===== Lib Versions ===== - 2024-10-17 13:53:47,899 - julearn - INFO - numpy: 1.26.4 - 2024-10-17 13:53:47,899 - julearn - INFO - scipy: 1.14.1 - 2024-10-17 13:53:47,899 - julearn - INFO - sklearn: 1.5.2 - 2024-10-17 13:53:47,899 - julearn - INFO - pandas: 2.2.3 - 2024-10-17 13:53:47,899 - julearn - INFO - julearn: 0.3.4.dev37 - 2024-10-17 13:53:47,899 - julearn - INFO - ======================== + 2024-10-17 14:02:02,306 - julearn - INFO - ===== Lib Versions ===== + 2024-10-17 14:02:02,306 - julearn - INFO - numpy: 1.26.4 + 2024-10-17 14:02:02,306 - julearn - INFO - scipy: 1.14.1 + 2024-10-17 14:02:02,306 - julearn - INFO - sklearn: 1.5.2 + 2024-10-17 14:02:02,307 - julearn - INFO - pandas: 2.2.3 + 2024-10-17 14:02:02,307 - julearn - INFO - julearn: 0.3.4.dev43 + 2024-10-17 14:02:02,307 - julearn - INFO - ======================== @@ -218,16 +218,16 @@ know what to do with the categorical features. .. code-block:: none - 2024-10-17 13:53:47,914 - julearn - INFO - Adding step pca_feats1 that applies to ColumnTypes - 2024-10-17 13:53:47,914 - julearn - INFO - Setting hyperparameter n_components = 1 - 2024-10-17 13:53:47,914 - julearn - INFO - Step added - 2024-10-17 13:53:47,914 - julearn - INFO - Adding step pca_feats2 that applies to ColumnTypes - 2024-10-17 13:53:47,914 - julearn - INFO - Setting hyperparameter n_components = 1 - 2024-10-17 13:53:47,914 - julearn - INFO - Step added - 2024-10-17 13:53:47,914 - julearn - INFO - Adding step ridge that applies to ColumnTypes - 2024-10-17 13:53:47,914 - julearn - INFO - Step added + 2024-10-17 14:02:02,321 - julearn - INFO - Adding step pca_feats1 that applies to ColumnTypes + 2024-10-17 14:02:02,321 - julearn - INFO - Setting hyperparameter n_components = 1 + 2024-10-17 14:02:02,322 - julearn - INFO - Step added + 2024-10-17 14:02:02,322 - julearn - INFO - Adding step pca_feats2 that applies to ColumnTypes + 2024-10-17 14:02:02,322 - julearn - INFO - Setting hyperparameter n_components = 1 + 2024-10-17 14:02:02,322 - julearn - INFO - Step added + 2024-10-17 14:02:02,322 - julearn - INFO - Adding step ridge that applies to ColumnTypes + 2024-10-17 14:02:02,322 - julearn - INFO - Step added - + @@ -275,25 +275,25 @@ for scoring. .. code-block:: none - 2024-10-17 13:53:47,915 - julearn - INFO - ==== Input Data ==== - 2024-10-17 13:53:47,915 - julearn - INFO - Using dataframe as input - 2024-10-17 13:53:47,916 - julearn - INFO - Features: ['age', 'sex', 'bmi', 'bp', 's1', 's2', 's3', 's4', 's5', 's6'] - 2024-10-17 13:53:47,916 - julearn - INFO - Target: target - 2024-10-17 13:53:47,916 - julearn - INFO - Expanded features: ['age', 'sex', 'bmi', 'bp', 's1', 's2', 's3', 's4', 's5', 's6'] - 2024-10-17 13:53:47,916 - julearn - INFO - X_types:{'pca1': ['age', 'bmi', 'bp'], 'pca2': ['s1', 's2', 's3', 's4', 's5', 's6'], 'categorical': ['sex']} - 2024-10-17 13:53:47,916 - julearn - INFO - ==================== - 2024-10-17 13:53:47,916 - julearn - INFO - - 2024-10-17 13:53:47,918 - julearn - INFO - = Model Parameters = - 2024-10-17 13:53:47,918 - julearn - INFO - ==================== - 2024-10-17 13:53:47,918 - julearn - INFO - - 2024-10-17 13:53:47,918 - julearn - INFO - = Data Information = - 2024-10-17 13:53:47,918 - julearn - INFO - Problem type: regression - 2024-10-17 13:53:47,918 - julearn - INFO - Number of samples: 309 - 2024-10-17 13:53:47,918 - julearn - INFO - Number of features: 10 - 2024-10-17 13:53:47,918 - julearn - INFO - ==================== - 2024-10-17 13:53:47,918 - julearn - INFO - - 2024-10-17 13:53:47,918 - julearn - INFO - Target type: float64 - 2024-10-17 13:53:47,918 - julearn - INFO - Using outer CV scheme KFold(n_splits=5, random_state=None, shuffle=False) (incl. final model) + 2024-10-17 14:02:02,323 - julearn - INFO - ==== Input Data ==== + 2024-10-17 14:02:02,323 - julearn - INFO - Using dataframe as input + 2024-10-17 14:02:02,323 - julearn - INFO - Features: ['age', 'sex', 'bmi', 'bp', 's1', 's2', 's3', 's4', 's5', 's6'] + 2024-10-17 14:02:02,323 - julearn - INFO - Target: target + 2024-10-17 14:02:02,323 - julearn - INFO - Expanded features: ['age', 'sex', 'bmi', 'bp', 's1', 's2', 's3', 's4', 's5', 's6'] + 2024-10-17 14:02:02,323 - julearn - INFO - X_types:{'pca1': ['age', 'bmi', 'bp'], 'pca2': ['s1', 's2', 's3', 's4', 's5', 's6'], 'categorical': ['sex']} + 2024-10-17 14:02:02,324 - julearn - INFO - ==================== + 2024-10-17 14:02:02,324 - julearn - INFO - + 2024-10-17 14:02:02,325 - julearn - INFO - = Model Parameters = + 2024-10-17 14:02:02,325 - julearn - INFO - ==================== + 2024-10-17 14:02:02,325 - julearn - INFO - + 2024-10-17 14:02:02,325 - julearn - INFO - = Data Information = + 2024-10-17 14:02:02,325 - julearn - INFO - Problem type: regression + 2024-10-17 14:02:02,326 - julearn - INFO - Number of samples: 309 + 2024-10-17 14:02:02,326 - julearn - INFO - Number of features: 10 + 2024-10-17 14:02:02,326 - julearn - INFO - ==================== + 2024-10-17 14:02:02,326 - julearn - INFO - + 2024-10-17 14:02:02,326 - julearn - INFO - Target type: float64 + 2024-10-17 14:02:02,326 - julearn - INFO - Using outer CV scheme KFold(n_splits=5, random_state=None, shuffle=False) (incl. final model) @@ -317,11 +317,11 @@ The scores dataframe has all the values for each CV split. .. code-block:: none fit_time score_time ... fold cv_mdsum - 0 0.013706 0.006040 ... 0 b10eef89b4192178d482d7a1587a248a - 1 0.012865 0.005929 ... 1 b10eef89b4192178d482d7a1587a248a - 2 0.013188 0.005961 ... 2 b10eef89b4192178d482d7a1587a248a - 3 0.012801 0.005950 ... 3 b10eef89b4192178d482d7a1587a248a - 4 0.013081 0.006739 ... 4 b10eef89b4192178d482d7a1587a248a + 0 0.013009 0.006012 ... 0 b10eef89b4192178d482d7a1587a248a + 1 0.013259 0.006122 ... 1 b10eef89b4192178d482d7a1587a248a + 2 0.013024 0.005974 ... 2 b10eef89b4192178d482d7a1587a248a + 3 0.013157 0.005964 ... 3 b10eef89b4192178d482d7a1587a248a + 4 0.013014 0.005947 ... 4 b10eef89b4192178d482d7a1587a248a [5 rows x 8 columns] @@ -670,7 +670,7 @@ of true values vs predicted values. .. rst-class:: sphx-glr-timing - **Total running time of the script:** (0 minutes 0.427 seconds) + **Total running time of the script:** (0 minutes 0.430 seconds) .. _sphx_glr_download_auto_examples_03_complex_models_run_example_pca_featsets.py: diff --git a/pr-preview/pr-276/_sources/auto_examples/03_complex_models/run_hyperparameter_multiple_grids.rst.txt b/pr-preview/pr-276/_sources/auto_examples/03_complex_models/run_hyperparameter_multiple_grids.rst.txt index 6b6d19be2..c0d78770b 100644 --- a/pr-preview/pr-276/_sources/auto_examples/03_complex_models/run_hyperparameter_multiple_grids.rst.txt +++ b/pr-preview/pr-276/_sources/auto_examples/03_complex_models/run_hyperparameter_multiple_grids.rst.txt @@ -74,13 +74,13 @@ Set the logging level to info to see extra information. /home/runner/work/julearn/julearn/julearn/utils/logging.py:66: UserWarning: The '__version__' attribute is deprecated and will be removed in MarkupSafe 3.1. Use feature detection, or `importlib.metadata.version("markupsafe")`, instead. vstring = str(getattr(module, "__version__", None)) - 2024-10-17 13:53:40,096 - julearn - INFO - ===== Lib Versions ===== - 2024-10-17 13:53:40,096 - julearn - INFO - numpy: 1.26.4 - 2024-10-17 13:53:40,096 - julearn - INFO - scipy: 1.14.1 - 2024-10-17 13:53:40,096 - julearn - INFO - sklearn: 1.5.2 - 2024-10-17 13:53:40,096 - julearn - INFO - pandas: 2.2.3 - 2024-10-17 13:53:40,096 - julearn - INFO - julearn: 0.3.4.dev37 - 2024-10-17 13:53:40,096 - julearn - INFO - ======================== + 2024-10-17 14:01:54,449 - julearn - INFO - ===== Lib Versions ===== + 2024-10-17 14:01:54,450 - julearn - INFO - numpy: 1.26.4 + 2024-10-17 14:01:54,450 - julearn - INFO - scipy: 1.14.1 + 2024-10-17 14:01:54,450 - julearn - INFO - sklearn: 1.5.2 + 2024-10-17 14:01:54,450 - julearn - INFO - pandas: 2.2.3 + 2024-10-17 14:01:54,450 - julearn - INFO - julearn: 0.3.4.dev43 + 2024-10-17 14:01:54,450 - julearn - INFO - ======================== @@ -318,39 +318,39 @@ Lets do a first attempt and use a linear SVM with the default parameters. .. code-block:: none - 2024-10-17 13:53:40,105 - julearn - INFO - Adding step zscore that applies to ColumnTypes - 2024-10-17 13:53:40,105 - julearn - INFO - Step added - 2024-10-17 13:53:40,105 - julearn - INFO - Adding step svm that applies to ColumnTypes - 2024-10-17 13:53:40,105 - julearn - INFO - Setting hyperparameter kernel = linear - 2024-10-17 13:53:40,105 - julearn - INFO - Step added - 2024-10-17 13:53:40,105 - julearn - INFO - ==== Input Data ==== - 2024-10-17 13:53:40,105 - julearn - INFO - Using dataframe as input - 2024-10-17 13:53:40,106 - julearn - INFO - Features: ['frontal', 'parietal'] - 2024-10-17 13:53:40,106 - julearn - INFO - Target: event - 2024-10-17 13:53:40,106 - julearn - INFO - Expanded features: ['frontal', 'parietal'] - 2024-10-17 13:53:40,106 - julearn - INFO - X_types:{} - 2024-10-17 13:53:40,106 - julearn - WARNING - The following columns are not defined in X_types: ['frontal', 'parietal']. They will be treated as continuous. + 2024-10-17 14:01:54,458 - julearn - INFO - Adding step zscore that applies to ColumnTypes + 2024-10-17 14:01:54,459 - julearn - INFO - Step added + 2024-10-17 14:01:54,459 - julearn - INFO - Adding step svm that applies to ColumnTypes + 2024-10-17 14:01:54,459 - julearn - INFO - Setting hyperparameter kernel = linear + 2024-10-17 14:01:54,459 - julearn - INFO - Step added + 2024-10-17 14:01:54,459 - julearn - INFO - ==== Input Data ==== + 2024-10-17 14:01:54,459 - julearn - INFO - Using dataframe as input + 2024-10-17 14:01:54,459 - julearn - INFO - Features: ['frontal', 'parietal'] + 2024-10-17 14:01:54,459 - julearn - INFO - Target: event + 2024-10-17 14:01:54,459 - julearn - INFO - Expanded features: ['frontal', 'parietal'] + 2024-10-17 14:01:54,459 - julearn - INFO - X_types:{} + 2024-10-17 14:01:54,459 - julearn - WARNING - The following columns are not defined in X_types: ['frontal', 'parietal']. They will be treated as continuous. /home/runner/work/julearn/julearn/julearn/prepare.py:509: RuntimeWarning: The following columns are not defined in X_types: ['frontal', 'parietal']. They will be treated as continuous. warn_with_log( - 2024-10-17 13:53:40,106 - julearn - INFO - ==================== - 2024-10-17 13:53:40,106 - julearn - INFO - - 2024-10-17 13:53:40,107 - julearn - INFO - = Model Parameters = - 2024-10-17 13:53:40,107 - julearn - INFO - ==================== - 2024-10-17 13:53:40,107 - julearn - INFO - - 2024-10-17 13:53:40,107 - julearn - INFO - = Data Information = - 2024-10-17 13:53:40,107 - julearn - INFO - Problem type: classification - 2024-10-17 13:53:40,107 - julearn - INFO - Number of samples: 532 - 2024-10-17 13:53:40,107 - julearn - INFO - Number of features: 2 - 2024-10-17 13:53:40,107 - julearn - INFO - ==================== - 2024-10-17 13:53:40,107 - julearn - INFO - - 2024-10-17 13:53:40,108 - julearn - INFO - Number of classes: 2 - 2024-10-17 13:53:40,108 - julearn - INFO - Target type: object - 2024-10-17 13:53:40,108 - julearn - INFO - Class distributions: event + 2024-10-17 14:01:54,460 - julearn - INFO - ==================== + 2024-10-17 14:01:54,460 - julearn - INFO - + 2024-10-17 14:01:54,460 - julearn - INFO - = Model Parameters = + 2024-10-17 14:01:54,461 - julearn - INFO - ==================== + 2024-10-17 14:01:54,461 - julearn - INFO - + 2024-10-17 14:01:54,461 - julearn - INFO - = Data Information = + 2024-10-17 14:01:54,461 - julearn - INFO - Problem type: classification + 2024-10-17 14:01:54,461 - julearn - INFO - Number of samples: 532 + 2024-10-17 14:01:54,461 - julearn - INFO - Number of features: 2 + 2024-10-17 14:01:54,461 - julearn - INFO - ==================== + 2024-10-17 14:01:54,461 - julearn - INFO - + 2024-10-17 14:01:54,461 - julearn - INFO - Number of classes: 2 + 2024-10-17 14:01:54,461 - julearn - INFO - Target type: object + 2024-10-17 14:01:54,462 - julearn - INFO - Class distributions: event cue 266 stim 266 Name: count, dtype: int64 - 2024-10-17 13:53:40,108 - julearn - INFO - Using outer CV scheme KFold(n_splits=5, random_state=None, shuffle=False) - 2024-10-17 13:53:40,109 - julearn - INFO - Binary classification problem detected. + 2024-10-17 14:01:54,462 - julearn - INFO - Using outer CV scheme KFold(n_splits=5, random_state=None, shuffle=False) + 2024-10-17 14:01:54,462 - julearn - INFO - Binary classification problem detected. 0.5939164168576971 @@ -406,78 +406,78 @@ explicitly specify the name of the step. This is done by passing the .. code-block:: none - 2024-10-17 13:53:40,164 - julearn - INFO - Adding step zscore that applies to ColumnTypes - 2024-10-17 13:53:40,164 - julearn - INFO - Step added - 2024-10-17 13:53:40,164 - julearn - INFO - Adding step svm that applies to ColumnTypes - 2024-10-17 13:53:40,164 - julearn - INFO - Setting hyperparameter kernel = linear - 2024-10-17 13:53:40,164 - julearn - INFO - Tuning hyperparameter C = [0.01, 0.1] - 2024-10-17 13:53:40,164 - julearn - INFO - Step added - 2024-10-17 13:53:40,164 - julearn - INFO - Adding step svm that applies to ColumnTypes - 2024-10-17 13:53:40,164 - julearn - INFO - Setting hyperparameter kernel = rbf - 2024-10-17 13:53:40,164 - julearn - INFO - Tuning hyperparameter C = [0.01, 0.1] - 2024-10-17 13:53:40,164 - julearn - INFO - Tuning hyperparameter gamma = ['scale', 'auto', 0.01, 0.001] - 2024-10-17 13:53:40,165 - julearn - INFO - Step added - 2024-10-17 13:53:40,165 - julearn - INFO - ==== Input Data ==== - 2024-10-17 13:53:40,165 - julearn - INFO - Using dataframe as input - 2024-10-17 13:53:40,165 - julearn - INFO - Features: ['frontal', 'parietal'] - 2024-10-17 13:53:40,165 - julearn - INFO - Target: event - 2024-10-17 13:53:40,165 - julearn - INFO - Expanded features: ['frontal', 'parietal'] - 2024-10-17 13:53:40,165 - julearn - INFO - X_types:{} - 2024-10-17 13:53:40,165 - julearn - WARNING - The following columns are not defined in X_types: ['frontal', 'parietal']. They will be treated as continuous. + 2024-10-17 14:01:54,518 - julearn - INFO - Adding step zscore that applies to ColumnTypes + 2024-10-17 14:01:54,518 - julearn - INFO - Step added + 2024-10-17 14:01:54,518 - julearn - INFO - Adding step svm that applies to ColumnTypes + 2024-10-17 14:01:54,518 - julearn - INFO - Setting hyperparameter kernel = linear + 2024-10-17 14:01:54,518 - julearn - INFO - Tuning hyperparameter C = [0.01, 0.1] + 2024-10-17 14:01:54,518 - julearn - INFO - Step added + 2024-10-17 14:01:54,518 - julearn - INFO - Adding step svm that applies to ColumnTypes + 2024-10-17 14:01:54,518 - julearn - INFO - Setting hyperparameter kernel = rbf + 2024-10-17 14:01:54,518 - julearn - INFO - Tuning hyperparameter C = [0.01, 0.1] + 2024-10-17 14:01:54,518 - julearn - INFO - Tuning hyperparameter gamma = ['scale', 'auto', 0.01, 0.001] + 2024-10-17 14:01:54,518 - julearn - INFO - Step added + 2024-10-17 14:01:54,518 - julearn - INFO - ==== Input Data ==== + 2024-10-17 14:01:54,519 - julearn - INFO - Using dataframe as input + 2024-10-17 14:01:54,519 - julearn - INFO - Features: ['frontal', 'parietal'] + 2024-10-17 14:01:54,519 - julearn - INFO - Target: event + 2024-10-17 14:01:54,519 - julearn - INFO - Expanded features: ['frontal', 'parietal'] + 2024-10-17 14:01:54,519 - julearn - INFO - X_types:{} + 2024-10-17 14:01:54,519 - julearn - WARNING - The following columns are not defined in X_types: ['frontal', 'parietal']. They will be treated as continuous. /home/runner/work/julearn/julearn/julearn/prepare.py:509: RuntimeWarning: The following columns are not defined in X_types: ['frontal', 'parietal']. They will be treated as continuous. warn_with_log( - 2024-10-17 13:53:40,165 - julearn - INFO - ==================== - 2024-10-17 13:53:40,166 - julearn - INFO - - 2024-10-17 13:53:40,166 - julearn - INFO - = Model Parameters = - 2024-10-17 13:53:40,166 - julearn - INFO - Tuning hyperparameters using grid - 2024-10-17 13:53:40,166 - julearn - INFO - Hyperparameters: - 2024-10-17 13:53:40,166 - julearn - INFO - svm__C: [0.01, 0.1] - 2024-10-17 13:53:40,166 - julearn - INFO - Using inner CV scheme KFold(n_splits=2, random_state=None, shuffle=False) - 2024-10-17 13:53:40,166 - julearn - INFO - Search Parameters: - 2024-10-17 13:53:40,166 - julearn - INFO - cv: KFold(n_splits=2, random_state=None, shuffle=False) - 2024-10-17 13:53:40,167 - julearn - INFO - ==================== - 2024-10-17 13:53:40,167 - julearn - INFO - - 2024-10-17 13:53:40,167 - julearn - INFO - = Model Parameters = - 2024-10-17 13:53:40,167 - julearn - INFO - Tuning hyperparameters using grid - 2024-10-17 13:53:40,167 - julearn - INFO - Hyperparameters: - 2024-10-17 13:53:40,167 - julearn - INFO - svm__C: [0.01, 0.1] - 2024-10-17 13:53:40,167 - julearn - INFO - svm__gamma: ['scale', 'auto', 0.01, 0.001] - 2024-10-17 13:53:40,167 - julearn - INFO - Using inner CV scheme KFold(n_splits=2, random_state=None, shuffle=False) - 2024-10-17 13:53:40,167 - julearn - INFO - Search Parameters: - 2024-10-17 13:53:40,167 - julearn - INFO - cv: KFold(n_splits=2, random_state=None, shuffle=False) - 2024-10-17 13:53:40,168 - julearn - INFO - ==================== - 2024-10-17 13:53:40,168 - julearn - INFO - - 2024-10-17 13:53:40,168 - julearn - INFO - = Model Parameters = - 2024-10-17 13:53:40,168 - julearn - INFO - Tuning hyperparameters using grid - 2024-10-17 13:53:40,168 - julearn - INFO - Hyperparameters list: - 2024-10-17 13:53:40,168 - julearn - INFO - Set 0 - 2024-10-17 13:53:40,168 - julearn - INFO - svm__C: [0.01, 0.1] - 2024-10-17 13:53:40,168 - julearn - INFO - set_column_types: [SetColumnTypes(X_types={})] - 2024-10-17 13:53:40,168 - julearn - INFO - svm: [SVC(kernel='linear')] - 2024-10-17 13:53:40,168 - julearn - INFO - Set 1 - 2024-10-17 13:53:40,168 - julearn - INFO - svm__C: [0.01, 0.1] - 2024-10-17 13:53:40,168 - julearn - INFO - svm__gamma: ['scale', 'auto', 0.01, 0.001] - 2024-10-17 13:53:40,168 - julearn - INFO - set_column_types: [SetColumnTypes(X_types={})] - 2024-10-17 13:53:40,169 - julearn - INFO - svm: [SVC()] - 2024-10-17 13:53:40,169 - julearn - INFO - Using inner CV scheme KFold(n_splits=2, random_state=None, shuffle=False) - 2024-10-17 13:53:40,169 - julearn - INFO - Search Parameters: - 2024-10-17 13:53:40,169 - julearn - INFO - cv: KFold(n_splits=2, random_state=None, shuffle=False) - 2024-10-17 13:53:40,169 - julearn - INFO - ==================== - 2024-10-17 13:53:40,169 - julearn - INFO - - 2024-10-17 13:53:40,169 - julearn - INFO - = Data Information = - 2024-10-17 13:53:40,169 - julearn - INFO - Problem type: classification - 2024-10-17 13:53:40,169 - julearn - INFO - Number of samples: 532 - 2024-10-17 13:53:40,169 - julearn - INFO - Number of features: 2 - 2024-10-17 13:53:40,169 - julearn - INFO - ==================== - 2024-10-17 13:53:40,169 - julearn - INFO - - 2024-10-17 13:53:40,169 - julearn - INFO - Number of classes: 2 - 2024-10-17 13:53:40,169 - julearn - INFO - Target type: object - 2024-10-17 13:53:40,170 - julearn - INFO - Class distributions: event + 2024-10-17 14:01:54,519 - julearn - INFO - ==================== + 2024-10-17 14:01:54,519 - julearn - INFO - + 2024-10-17 14:01:54,520 - julearn - INFO - = Model Parameters = + 2024-10-17 14:01:54,520 - julearn - INFO - Tuning hyperparameters using grid + 2024-10-17 14:01:54,520 - julearn - INFO - Hyperparameters: + 2024-10-17 14:01:54,520 - julearn - INFO - svm__C: [0.01, 0.1] + 2024-10-17 14:01:54,520 - julearn - INFO - Using inner CV scheme KFold(n_splits=2, random_state=None, shuffle=False) + 2024-10-17 14:01:54,520 - julearn - INFO - Search Parameters: + 2024-10-17 14:01:54,520 - julearn - INFO - cv: KFold(n_splits=2, random_state=None, shuffle=False) + 2024-10-17 14:01:54,520 - julearn - INFO - ==================== + 2024-10-17 14:01:54,520 - julearn - INFO - + 2024-10-17 14:01:54,521 - julearn - INFO - = Model Parameters = + 2024-10-17 14:01:54,521 - julearn - INFO - Tuning hyperparameters using grid + 2024-10-17 14:01:54,521 - julearn - INFO - Hyperparameters: + 2024-10-17 14:01:54,521 - julearn - INFO - svm__C: [0.01, 0.1] + 2024-10-17 14:01:54,521 - julearn - INFO - svm__gamma: ['scale', 'auto', 0.01, 0.001] + 2024-10-17 14:01:54,521 - julearn - INFO - Using inner CV scheme KFold(n_splits=2, random_state=None, shuffle=False) + 2024-10-17 14:01:54,521 - julearn - INFO - Search Parameters: + 2024-10-17 14:01:54,521 - julearn - INFO - cv: KFold(n_splits=2, random_state=None, shuffle=False) + 2024-10-17 14:01:54,521 - julearn - INFO - ==================== + 2024-10-17 14:01:54,521 - julearn - INFO - + 2024-10-17 14:01:54,522 - julearn - INFO - = Model Parameters = + 2024-10-17 14:01:54,522 - julearn - INFO - Tuning hyperparameters using grid + 2024-10-17 14:01:54,522 - julearn - INFO - Hyperparameters list: + 2024-10-17 14:01:54,522 - julearn - INFO - Set 0 + 2024-10-17 14:01:54,522 - julearn - INFO - svm__C: [0.01, 0.1] + 2024-10-17 14:01:54,522 - julearn - INFO - set_column_types: [SetColumnTypes(X_types={})] + 2024-10-17 14:01:54,522 - julearn - INFO - svm: [SVC(kernel='linear')] + 2024-10-17 14:01:54,522 - julearn - INFO - Set 1 + 2024-10-17 14:01:54,522 - julearn - INFO - svm__C: [0.01, 0.1] + 2024-10-17 14:01:54,522 - julearn - INFO - svm__gamma: ['scale', 'auto', 0.01, 0.001] + 2024-10-17 14:01:54,522 - julearn - INFO - set_column_types: [SetColumnTypes(X_types={})] + 2024-10-17 14:01:54,523 - julearn - INFO - svm: [SVC()] + 2024-10-17 14:01:54,523 - julearn - INFO - Using inner CV scheme KFold(n_splits=2, random_state=None, shuffle=False) + 2024-10-17 14:01:54,523 - julearn - INFO - Search Parameters: + 2024-10-17 14:01:54,523 - julearn - INFO - cv: KFold(n_splits=2, random_state=None, shuffle=False) + 2024-10-17 14:01:54,523 - julearn - INFO - ==================== + 2024-10-17 14:01:54,523 - julearn - INFO - + 2024-10-17 14:01:54,523 - julearn - INFO - = Data Information = + 2024-10-17 14:01:54,523 - julearn - INFO - Problem type: classification + 2024-10-17 14:01:54,523 - julearn - INFO - Number of samples: 532 + 2024-10-17 14:01:54,523 - julearn - INFO - Number of features: 2 + 2024-10-17 14:01:54,523 - julearn - INFO - ==================== + 2024-10-17 14:01:54,523 - julearn - INFO - + 2024-10-17 14:01:54,523 - julearn - INFO - Number of classes: 2 + 2024-10-17 14:01:54,524 - julearn - INFO - Target type: object + 2024-10-17 14:01:54,524 - julearn - INFO - Class distributions: event cue 266 stim 266 Name: count, dtype: int64 - 2024-10-17 13:53:40,170 - julearn - INFO - Using outer CV scheme KFold(n_splits=5, random_state=None, shuffle=False) (incl. final model) - 2024-10-17 13:53:40,170 - julearn - INFO - Binary classification problem detected. + 2024-10-17 14:01:54,524 - julearn - INFO - Using outer CV scheme KFold(n_splits=5, random_state=None, shuffle=False) (incl. final model) + 2024-10-17 14:01:54,524 - julearn - INFO - Binary classification problem detected. 0.7087109857168048 @@ -510,7 +510,7 @@ It seems that we might have found a better model, but which one is it? .. rst-class:: sphx-glr-timing - **Total running time of the script:** (0 minutes 1.317 seconds) + **Total running time of the script:** (0 minutes 1.328 seconds) .. _sphx_glr_download_auto_examples_03_complex_models_run_hyperparameter_multiple_grids.py: diff --git a/pr-preview/pr-276/_sources/auto_examples/03_complex_models/run_hyperparameter_tuning.rst.txt b/pr-preview/pr-276/_sources/auto_examples/03_complex_models/run_hyperparameter_tuning.rst.txt index 513783a54..ed0e437cc 100644 --- a/pr-preview/pr-276/_sources/auto_examples/03_complex_models/run_hyperparameter_tuning.rst.txt +++ b/pr-preview/pr-276/_sources/auto_examples/03_complex_models/run_hyperparameter_tuning.rst.txt @@ -73,13 +73,13 @@ Set the logging level to info to see extra information. /home/runner/work/julearn/julearn/julearn/utils/logging.py:66: UserWarning: The '__version__' attribute is deprecated and will be removed in MarkupSafe 3.1. Use feature detection, or `importlib.metadata.version("markupsafe")`, instead. vstring = str(getattr(module, "__version__", None)) - 2024-10-17 13:53:45,335 - julearn - INFO - ===== Lib Versions ===== - 2024-10-17 13:53:45,335 - julearn - INFO - numpy: 1.26.4 - 2024-10-17 13:53:45,335 - julearn - INFO - scipy: 1.14.1 - 2024-10-17 13:53:45,335 - julearn - INFO - sklearn: 1.5.2 - 2024-10-17 13:53:45,335 - julearn - INFO - pandas: 2.2.3 - 2024-10-17 13:53:45,335 - julearn - INFO - julearn: 0.3.4.dev37 - 2024-10-17 13:53:45,335 - julearn - INFO - ======================== + 2024-10-17 14:01:59,739 - julearn - INFO - ===== Lib Versions ===== + 2024-10-17 14:01:59,739 - julearn - INFO - numpy: 1.26.4 + 2024-10-17 14:01:59,739 - julearn - INFO - scipy: 1.14.1 + 2024-10-17 14:01:59,739 - julearn - INFO - sklearn: 1.5.2 + 2024-10-17 14:01:59,739 - julearn - INFO - pandas: 2.2.3 + 2024-10-17 14:01:59,739 - julearn - INFO - julearn: 0.3.4.dev43 + 2024-10-17 14:01:59,739 - julearn - INFO - ======================== @@ -316,39 +316,39 @@ Let's do a first attempt and use a linear SVM with the default parameters. .. code-block:: none - 2024-10-17 13:53:45,343 - julearn - INFO - Adding step zscore that applies to ColumnTypes - 2024-10-17 13:53:45,343 - julearn - INFO - Step added - 2024-10-17 13:53:45,344 - julearn - INFO - Adding step svm that applies to ColumnTypes - 2024-10-17 13:53:45,344 - julearn - INFO - Setting hyperparameter kernel = linear - 2024-10-17 13:53:45,344 - julearn - INFO - Step added - 2024-10-17 13:53:45,344 - julearn - INFO - ==== Input Data ==== - 2024-10-17 13:53:45,344 - julearn - INFO - Using dataframe as input - 2024-10-17 13:53:45,344 - julearn - INFO - Features: ['frontal', 'parietal'] - 2024-10-17 13:53:45,344 - julearn - INFO - Target: event - 2024-10-17 13:53:45,344 - julearn - INFO - Expanded features: ['frontal', 'parietal'] - 2024-10-17 13:53:45,344 - julearn - INFO - X_types:{} - 2024-10-17 13:53:45,344 - julearn - WARNING - The following columns are not defined in X_types: ['frontal', 'parietal']. They will be treated as continuous. + 2024-10-17 14:01:59,748 - julearn - INFO - Adding step zscore that applies to ColumnTypes + 2024-10-17 14:01:59,748 - julearn - INFO - Step added + 2024-10-17 14:01:59,748 - julearn - INFO - Adding step svm that applies to ColumnTypes + 2024-10-17 14:01:59,748 - julearn - INFO - Setting hyperparameter kernel = linear + 2024-10-17 14:01:59,748 - julearn - INFO - Step added + 2024-10-17 14:01:59,748 - julearn - INFO - ==== Input Data ==== + 2024-10-17 14:01:59,748 - julearn - INFO - Using dataframe as input + 2024-10-17 14:01:59,748 - julearn - INFO - Features: ['frontal', 'parietal'] + 2024-10-17 14:01:59,748 - julearn - INFO - Target: event + 2024-10-17 14:01:59,749 - julearn - INFO - Expanded features: ['frontal', 'parietal'] + 2024-10-17 14:01:59,749 - julearn - INFO - X_types:{} + 2024-10-17 14:01:59,749 - julearn - WARNING - The following columns are not defined in X_types: ['frontal', 'parietal']. They will be treated as continuous. /home/runner/work/julearn/julearn/julearn/prepare.py:509: RuntimeWarning: The following columns are not defined in X_types: ['frontal', 'parietal']. They will be treated as continuous. warn_with_log( - 2024-10-17 13:53:45,345 - julearn - INFO - ==================== - 2024-10-17 13:53:45,345 - julearn - INFO - - 2024-10-17 13:53:45,345 - julearn - INFO - = Model Parameters = - 2024-10-17 13:53:45,345 - julearn - INFO - ==================== - 2024-10-17 13:53:45,345 - julearn - INFO - - 2024-10-17 13:53:45,345 - julearn - INFO - = Data Information = - 2024-10-17 13:53:45,345 - julearn - INFO - Problem type: classification - 2024-10-17 13:53:45,346 - julearn - INFO - Number of samples: 532 - 2024-10-17 13:53:45,346 - julearn - INFO - Number of features: 2 - 2024-10-17 13:53:45,346 - julearn - INFO - ==================== - 2024-10-17 13:53:45,346 - julearn - INFO - - 2024-10-17 13:53:45,346 - julearn - INFO - Number of classes: 2 - 2024-10-17 13:53:45,346 - julearn - INFO - Target type: object - 2024-10-17 13:53:45,346 - julearn - INFO - Class distributions: event + 2024-10-17 14:01:59,749 - julearn - INFO - ==================== + 2024-10-17 14:01:59,749 - julearn - INFO - + 2024-10-17 14:01:59,750 - julearn - INFO - = Model Parameters = + 2024-10-17 14:01:59,750 - julearn - INFO - ==================== + 2024-10-17 14:01:59,750 - julearn - INFO - + 2024-10-17 14:01:59,750 - julearn - INFO - = Data Information = + 2024-10-17 14:01:59,750 - julearn - INFO - Problem type: classification + 2024-10-17 14:01:59,750 - julearn - INFO - Number of samples: 532 + 2024-10-17 14:01:59,750 - julearn - INFO - Number of features: 2 + 2024-10-17 14:01:59,750 - julearn - INFO - ==================== + 2024-10-17 14:01:59,750 - julearn - INFO - + 2024-10-17 14:01:59,750 - julearn - INFO - Number of classes: 2 + 2024-10-17 14:01:59,751 - julearn - INFO - Target type: object + 2024-10-17 14:01:59,751 - julearn - INFO - Class distributions: event cue 266 stim 266 Name: count, dtype: int64 - 2024-10-17 13:53:45,347 - julearn - INFO - Using outer CV scheme KFold(n_splits=5, random_state=None, shuffle=False) - 2024-10-17 13:53:45,347 - julearn - INFO - Binary classification problem detected. + 2024-10-17 14:01:59,751 - julearn - INFO - Using outer CV scheme KFold(n_splits=5, random_state=None, shuffle=False) + 2024-10-17 14:01:59,751 - julearn - INFO - Binary classification problem detected. 0.5939164168576971 @@ -393,46 +393,46 @@ We will use a grid search to find the best ``C``. .. code-block:: none - 2024-10-17 13:53:45,402 - julearn - INFO - Adding step zscore that applies to ColumnTypes - 2024-10-17 13:53:45,402 - julearn - INFO - Step added - 2024-10-17 13:53:45,402 - julearn - INFO - Adding step svm that applies to ColumnTypes - 2024-10-17 13:53:45,402 - julearn - INFO - Setting hyperparameter kernel = linear - 2024-10-17 13:53:45,402 - julearn - INFO - Tuning hyperparameter C = [0.01, 0.1] - 2024-10-17 13:53:45,402 - julearn - INFO - Step added - 2024-10-17 13:53:45,403 - julearn - INFO - ==== Input Data ==== - 2024-10-17 13:53:45,403 - julearn - INFO - Using dataframe as input - 2024-10-17 13:53:45,403 - julearn - INFO - Features: ['frontal', 'parietal'] - 2024-10-17 13:53:45,403 - julearn - INFO - Target: event - 2024-10-17 13:53:45,403 - julearn - INFO - Expanded features: ['frontal', 'parietal'] - 2024-10-17 13:53:45,403 - julearn - INFO - X_types:{} - 2024-10-17 13:53:45,403 - julearn - WARNING - The following columns are not defined in X_types: ['frontal', 'parietal']. They will be treated as continuous. + 2024-10-17 14:01:59,810 - julearn - INFO - Adding step zscore that applies to ColumnTypes + 2024-10-17 14:01:59,810 - julearn - INFO - Step added + 2024-10-17 14:01:59,810 - julearn - INFO - Adding step svm that applies to ColumnTypes + 2024-10-17 14:01:59,810 - julearn - INFO - Setting hyperparameter kernel = linear + 2024-10-17 14:01:59,810 - julearn - INFO - Tuning hyperparameter C = [0.01, 0.1] + 2024-10-17 14:01:59,811 - julearn - INFO - Step added + 2024-10-17 14:01:59,811 - julearn - INFO - ==== Input Data ==== + 2024-10-17 14:01:59,811 - julearn - INFO - Using dataframe as input + 2024-10-17 14:01:59,811 - julearn - INFO - Features: ['frontal', 'parietal'] + 2024-10-17 14:01:59,811 - julearn - INFO - Target: event + 2024-10-17 14:01:59,811 - julearn - INFO - Expanded features: ['frontal', 'parietal'] + 2024-10-17 14:01:59,811 - julearn - INFO - X_types:{} + 2024-10-17 14:01:59,811 - julearn - WARNING - The following columns are not defined in X_types: ['frontal', 'parietal']. They will be treated as continuous. /home/runner/work/julearn/julearn/julearn/prepare.py:509: RuntimeWarning: The following columns are not defined in X_types: ['frontal', 'parietal']. They will be treated as continuous. warn_with_log( - 2024-10-17 13:53:45,403 - julearn - INFO - ==================== - 2024-10-17 13:53:45,403 - julearn - INFO - - 2024-10-17 13:53:45,404 - julearn - INFO - = Model Parameters = - 2024-10-17 13:53:45,404 - julearn - INFO - Tuning hyperparameters using grid - 2024-10-17 13:53:45,404 - julearn - INFO - Hyperparameters: - 2024-10-17 13:53:45,404 - julearn - INFO - svm__C: [0.01, 0.1] - 2024-10-17 13:53:45,404 - julearn - INFO - Using inner CV scheme KFold(n_splits=2, random_state=None, shuffle=False) - 2024-10-17 13:53:45,404 - julearn - INFO - Search Parameters: - 2024-10-17 13:53:45,404 - julearn - INFO - cv: KFold(n_splits=2, random_state=None, shuffle=False) - 2024-10-17 13:53:45,404 - julearn - INFO - ==================== - 2024-10-17 13:53:45,405 - julearn - INFO - - 2024-10-17 13:53:45,405 - julearn - INFO - = Data Information = - 2024-10-17 13:53:45,405 - julearn - INFO - Problem type: classification - 2024-10-17 13:53:45,405 - julearn - INFO - Number of samples: 532 - 2024-10-17 13:53:45,405 - julearn - INFO - Number of features: 2 - 2024-10-17 13:53:45,405 - julearn - INFO - ==================== - 2024-10-17 13:53:45,405 - julearn - INFO - - 2024-10-17 13:53:45,405 - julearn - INFO - Number of classes: 2 - 2024-10-17 13:53:45,405 - julearn - INFO - Target type: object - 2024-10-17 13:53:45,405 - julearn - INFO - Class distributions: event + 2024-10-17 14:01:59,811 - julearn - INFO - ==================== + 2024-10-17 14:01:59,812 - julearn - INFO - + 2024-10-17 14:01:59,812 - julearn - INFO - = Model Parameters = + 2024-10-17 14:01:59,812 - julearn - INFO - Tuning hyperparameters using grid + 2024-10-17 14:01:59,812 - julearn - INFO - Hyperparameters: + 2024-10-17 14:01:59,812 - julearn - INFO - svm__C: [0.01, 0.1] + 2024-10-17 14:01:59,812 - julearn - INFO - Using inner CV scheme KFold(n_splits=2, random_state=None, shuffle=False) + 2024-10-17 14:01:59,812 - julearn - INFO - Search Parameters: + 2024-10-17 14:01:59,813 - julearn - INFO - cv: KFold(n_splits=2, random_state=None, shuffle=False) + 2024-10-17 14:01:59,813 - julearn - INFO - ==================== + 2024-10-17 14:01:59,813 - julearn - INFO - + 2024-10-17 14:01:59,813 - julearn - INFO - = Data Information = + 2024-10-17 14:01:59,813 - julearn - INFO - Problem type: classification + 2024-10-17 14:01:59,813 - julearn - INFO - Number of samples: 532 + 2024-10-17 14:01:59,813 - julearn - INFO - Number of features: 2 + 2024-10-17 14:01:59,813 - julearn - INFO - ==================== + 2024-10-17 14:01:59,813 - julearn - INFO - + 2024-10-17 14:01:59,813 - julearn - INFO - Number of classes: 2 + 2024-10-17 14:01:59,813 - julearn - INFO - Target type: object + 2024-10-17 14:01:59,814 - julearn - INFO - Class distributions: event cue 266 stim 266 Name: count, dtype: int64 - 2024-10-17 13:53:45,406 - julearn - INFO - Using outer CV scheme KFold(n_splits=5, random_state=None, shuffle=False) (incl. final model) - 2024-10-17 13:53:45,406 - julearn - INFO - Binary classification problem detected. + 2024-10-17 14:01:59,814 - julearn - INFO - Using outer CV scheme KFold(n_splits=5, random_state=None, shuffle=False) (incl. final model) + 2024-10-17 14:01:59,814 - julearn - INFO - Binary classification problem detected. 0.588308940222183 @@ -469,47 +469,47 @@ This did not change much, lets explore other kernels too. .. code-block:: none - 2024-10-17 13:53:45,688 - julearn - INFO - Adding step zscore that applies to ColumnTypes - 2024-10-17 13:53:45,688 - julearn - INFO - Step added - 2024-10-17 13:53:45,688 - julearn - INFO - Adding step svm that applies to ColumnTypes - 2024-10-17 13:53:45,688 - julearn - INFO - Tuning hyperparameter kernel = ['linear', 'rbf', 'poly'] - 2024-10-17 13:53:45,688 - julearn - INFO - Tuning hyperparameter C = [0.01, 0.1] - 2024-10-17 13:53:45,688 - julearn - INFO - Step added - 2024-10-17 13:53:45,688 - julearn - INFO - ==== Input Data ==== - 2024-10-17 13:53:45,688 - julearn - INFO - Using dataframe as input - 2024-10-17 13:53:45,688 - julearn - INFO - Features: ['frontal', 'parietal'] - 2024-10-17 13:53:45,688 - julearn - INFO - Target: event - 2024-10-17 13:53:45,688 - julearn - INFO - Expanded features: ['frontal', 'parietal'] - 2024-10-17 13:53:45,689 - julearn - INFO - X_types:{} - 2024-10-17 13:53:45,689 - julearn - WARNING - The following columns are not defined in X_types: ['frontal', 'parietal']. They will be treated as continuous. + 2024-10-17 14:02:00,097 - julearn - INFO - Adding step zscore that applies to ColumnTypes + 2024-10-17 14:02:00,098 - julearn - INFO - Step added + 2024-10-17 14:02:00,098 - julearn - INFO - Adding step svm that applies to ColumnTypes + 2024-10-17 14:02:00,098 - julearn - INFO - Tuning hyperparameter kernel = ['linear', 'rbf', 'poly'] + 2024-10-17 14:02:00,098 - julearn - INFO - Tuning hyperparameter C = [0.01, 0.1] + 2024-10-17 14:02:00,098 - julearn - INFO - Step added + 2024-10-17 14:02:00,098 - julearn - INFO - ==== Input Data ==== + 2024-10-17 14:02:00,098 - julearn - INFO - Using dataframe as input + 2024-10-17 14:02:00,098 - julearn - INFO - Features: ['frontal', 'parietal'] + 2024-10-17 14:02:00,098 - julearn - INFO - Target: event + 2024-10-17 14:02:00,098 - julearn - INFO - Expanded features: ['frontal', 'parietal'] + 2024-10-17 14:02:00,098 - julearn - INFO - X_types:{} + 2024-10-17 14:02:00,098 - julearn - WARNING - The following columns are not defined in X_types: ['frontal', 'parietal']. They will be treated as continuous. /home/runner/work/julearn/julearn/julearn/prepare.py:509: RuntimeWarning: The following columns are not defined in X_types: ['frontal', 'parietal']. They will be treated as continuous. warn_with_log( - 2024-10-17 13:53:45,689 - julearn - INFO - ==================== - 2024-10-17 13:53:45,689 - julearn - INFO - - 2024-10-17 13:53:45,690 - julearn - INFO - = Model Parameters = - 2024-10-17 13:53:45,690 - julearn - INFO - Tuning hyperparameters using grid - 2024-10-17 13:53:45,690 - julearn - INFO - Hyperparameters: - 2024-10-17 13:53:45,690 - julearn - INFO - svm__kernel: ['linear', 'rbf', 'poly'] - 2024-10-17 13:53:45,690 - julearn - INFO - svm__C: [0.01, 0.1] - 2024-10-17 13:53:45,690 - julearn - INFO - Using inner CV scheme KFold(n_splits=2, random_state=None, shuffle=False) - 2024-10-17 13:53:45,690 - julearn - INFO - Search Parameters: - 2024-10-17 13:53:45,690 - julearn - INFO - cv: KFold(n_splits=2, random_state=None, shuffle=False) - 2024-10-17 13:53:45,690 - julearn - INFO - ==================== - 2024-10-17 13:53:45,690 - julearn - INFO - - 2024-10-17 13:53:45,690 - julearn - INFO - = Data Information = - 2024-10-17 13:53:45,690 - julearn - INFO - Problem type: classification - 2024-10-17 13:53:45,691 - julearn - INFO - Number of samples: 532 - 2024-10-17 13:53:45,691 - julearn - INFO - Number of features: 2 - 2024-10-17 13:53:45,691 - julearn - INFO - ==================== - 2024-10-17 13:53:45,691 - julearn - INFO - - 2024-10-17 13:53:45,691 - julearn - INFO - Number of classes: 2 - 2024-10-17 13:53:45,691 - julearn - INFO - Target type: object - 2024-10-17 13:53:45,691 - julearn - INFO - Class distributions: event + 2024-10-17 14:02:00,099 - julearn - INFO - ==================== + 2024-10-17 14:02:00,099 - julearn - INFO - + 2024-10-17 14:02:00,099 - julearn - INFO - = Model Parameters = + 2024-10-17 14:02:00,099 - julearn - INFO - Tuning hyperparameters using grid + 2024-10-17 14:02:00,099 - julearn - INFO - Hyperparameters: + 2024-10-17 14:02:00,100 - julearn - INFO - svm__kernel: ['linear', 'rbf', 'poly'] + 2024-10-17 14:02:00,100 - julearn - INFO - svm__C: [0.01, 0.1] + 2024-10-17 14:02:00,100 - julearn - INFO - Using inner CV scheme KFold(n_splits=2, random_state=None, shuffle=False) + 2024-10-17 14:02:00,100 - julearn - INFO - Search Parameters: + 2024-10-17 14:02:00,100 - julearn - INFO - cv: KFold(n_splits=2, random_state=None, shuffle=False) + 2024-10-17 14:02:00,100 - julearn - INFO - ==================== + 2024-10-17 14:02:00,100 - julearn - INFO - + 2024-10-17 14:02:00,100 - julearn - INFO - = Data Information = + 2024-10-17 14:02:00,100 - julearn - INFO - Problem type: classification + 2024-10-17 14:02:00,100 - julearn - INFO - Number of samples: 532 + 2024-10-17 14:02:00,100 - julearn - INFO - Number of features: 2 + 2024-10-17 14:02:00,100 - julearn - INFO - ==================== + 2024-10-17 14:02:00,100 - julearn - INFO - + 2024-10-17 14:02:00,100 - julearn - INFO - Number of classes: 2 + 2024-10-17 14:02:00,100 - julearn - INFO - Target type: object + 2024-10-17 14:02:00,101 - julearn - INFO - Class distributions: event cue 266 stim 266 Name: count, dtype: int64 - 2024-10-17 13:53:45,692 - julearn - INFO - Using outer CV scheme KFold(n_splits=5, random_state=None, shuffle=False) (incl. final model) - 2024-10-17 13:53:45,692 - julearn - INFO - Binary classification problem detected. + 2024-10-17 14:02:00,101 - julearn - INFO - Using outer CV scheme KFold(n_splits=5, random_state=None, shuffle=False) (incl. final model) + 2024-10-17 14:02:00,101 - julearn - INFO - Binary classification problem detected. 0.7087109857168048 @@ -572,48 +572,48 @@ parameters. .. code-block:: none - 2024-10-17 13:53:46,416 - julearn - INFO - Adding step zscore that applies to ColumnTypes - 2024-10-17 13:53:46,416 - julearn - INFO - Step added - 2024-10-17 13:53:46,416 - julearn - INFO - Adding step svm that applies to ColumnTypes - 2024-10-17 13:53:46,416 - julearn - INFO - Setting hyperparameter kernel = rbf - 2024-10-17 13:53:46,416 - julearn - INFO - Tuning hyperparameter C = [0.01, 0.1] - 2024-10-17 13:53:46,417 - julearn - INFO - Tuning hyperparameter gamma = [0.01, 0.001] - 2024-10-17 13:53:46,417 - julearn - INFO - Step added - 2024-10-17 13:53:46,417 - julearn - INFO - ==== Input Data ==== - 2024-10-17 13:53:46,417 - julearn - INFO - Using dataframe as input - 2024-10-17 13:53:46,417 - julearn - INFO - Features: ['frontal', 'parietal'] - 2024-10-17 13:53:46,417 - julearn - INFO - Target: event - 2024-10-17 13:53:46,417 - julearn - INFO - Expanded features: ['frontal', 'parietal'] - 2024-10-17 13:53:46,417 - julearn - INFO - X_types:{} - 2024-10-17 13:53:46,417 - julearn - WARNING - The following columns are not defined in X_types: ['frontal', 'parietal']. They will be treated as continuous. + 2024-10-17 14:02:00,834 - julearn - INFO - Adding step zscore that applies to ColumnTypes + 2024-10-17 14:02:00,834 - julearn - INFO - Step added + 2024-10-17 14:02:00,834 - julearn - INFO - Adding step svm that applies to ColumnTypes + 2024-10-17 14:02:00,834 - julearn - INFO - Setting hyperparameter kernel = rbf + 2024-10-17 14:02:00,834 - julearn - INFO - Tuning hyperparameter C = [0.01, 0.1] + 2024-10-17 14:02:00,834 - julearn - INFO - Tuning hyperparameter gamma = [0.01, 0.001] + 2024-10-17 14:02:00,835 - julearn - INFO - Step added + 2024-10-17 14:02:00,835 - julearn - INFO - ==== Input Data ==== + 2024-10-17 14:02:00,835 - julearn - INFO - Using dataframe as input + 2024-10-17 14:02:00,835 - julearn - INFO - Features: ['frontal', 'parietal'] + 2024-10-17 14:02:00,835 - julearn - INFO - Target: event + 2024-10-17 14:02:00,835 - julearn - INFO - Expanded features: ['frontal', 'parietal'] + 2024-10-17 14:02:00,835 - julearn - INFO - X_types:{} + 2024-10-17 14:02:00,835 - julearn - WARNING - The following columns are not defined in X_types: ['frontal', 'parietal']. They will be treated as continuous. /home/runner/work/julearn/julearn/julearn/prepare.py:509: RuntimeWarning: The following columns are not defined in X_types: ['frontal', 'parietal']. They will be treated as continuous. warn_with_log( - 2024-10-17 13:53:46,418 - julearn - INFO - ==================== - 2024-10-17 13:53:46,418 - julearn - INFO - - 2024-10-17 13:53:46,418 - julearn - INFO - = Model Parameters = - 2024-10-17 13:53:46,418 - julearn - INFO - Tuning hyperparameters using grid - 2024-10-17 13:53:46,418 - julearn - INFO - Hyperparameters: - 2024-10-17 13:53:46,418 - julearn - INFO - svm__C: [0.01, 0.1] - 2024-10-17 13:53:46,418 - julearn - INFO - svm__gamma: [0.01, 0.001] - 2024-10-17 13:53:46,418 - julearn - INFO - Using inner CV scheme KFold(n_splits=2, random_state=None, shuffle=False) - 2024-10-17 13:53:46,419 - julearn - INFO - Search Parameters: - 2024-10-17 13:53:46,419 - julearn - INFO - cv: KFold(n_splits=2, random_state=None, shuffle=False) - 2024-10-17 13:53:46,419 - julearn - INFO - ==================== - 2024-10-17 13:53:46,419 - julearn - INFO - - 2024-10-17 13:53:46,419 - julearn - INFO - = Data Information = - 2024-10-17 13:53:46,419 - julearn - INFO - Problem type: classification - 2024-10-17 13:53:46,419 - julearn - INFO - Number of samples: 532 - 2024-10-17 13:53:46,419 - julearn - INFO - Number of features: 2 - 2024-10-17 13:53:46,419 - julearn - INFO - ==================== - 2024-10-17 13:53:46,419 - julearn - INFO - - 2024-10-17 13:53:46,419 - julearn - INFO - Number of classes: 2 - 2024-10-17 13:53:46,419 - julearn - INFO - Target type: object - 2024-10-17 13:53:46,420 - julearn - INFO - Class distributions: event + 2024-10-17 14:02:00,836 - julearn - INFO - ==================== + 2024-10-17 14:02:00,836 - julearn - INFO - + 2024-10-17 14:02:00,836 - julearn - INFO - = Model Parameters = + 2024-10-17 14:02:00,836 - julearn - INFO - Tuning hyperparameters using grid + 2024-10-17 14:02:00,836 - julearn - INFO - Hyperparameters: + 2024-10-17 14:02:00,836 - julearn - INFO - svm__C: [0.01, 0.1] + 2024-10-17 14:02:00,836 - julearn - INFO - svm__gamma: [0.01, 0.001] + 2024-10-17 14:02:00,836 - julearn - INFO - Using inner CV scheme KFold(n_splits=2, random_state=None, shuffle=False) + 2024-10-17 14:02:00,837 - julearn - INFO - Search Parameters: + 2024-10-17 14:02:00,837 - julearn - INFO - cv: KFold(n_splits=2, random_state=None, shuffle=False) + 2024-10-17 14:02:00,837 - julearn - INFO - ==================== + 2024-10-17 14:02:00,837 - julearn - INFO - + 2024-10-17 14:02:00,837 - julearn - INFO - = Data Information = + 2024-10-17 14:02:00,837 - julearn - INFO - Problem type: classification + 2024-10-17 14:02:00,837 - julearn - INFO - Number of samples: 532 + 2024-10-17 14:02:00,837 - julearn - INFO - Number of features: 2 + 2024-10-17 14:02:00,837 - julearn - INFO - ==================== + 2024-10-17 14:02:00,837 - julearn - INFO - + 2024-10-17 14:02:00,837 - julearn - INFO - Number of classes: 2 + 2024-10-17 14:02:00,837 - julearn - INFO - Target type: object + 2024-10-17 14:02:00,838 - julearn - INFO - Class distributions: event cue 266 stim 266 Name: count, dtype: int64 - 2024-10-17 13:53:46,420 - julearn - INFO - Using outer CV scheme KFold(n_splits=5, random_state=None, shuffle=False) (incl. final model) - 2024-10-17 13:53:46,420 - julearn - INFO - Binary classification problem detected. + 2024-10-17 14:02:00,838 - julearn - INFO - Using outer CV scheme KFold(n_splits=5, random_state=None, shuffle=False) (incl. final model) + 2024-10-17 14:02:00,838 - julearn - INFO - Binary classification problem detected. 0.5188855581026275 {'svm__C': 0.01, 'svm__gamma': 0.001} @@ -658,48 +658,48 @@ Let's add the default value and see what happens. .. code-block:: none - 2024-10-17 13:53:46,972 - julearn - INFO - Adding step zscore that applies to ColumnTypes - 2024-10-17 13:53:46,973 - julearn - INFO - Step added - 2024-10-17 13:53:46,973 - julearn - INFO - Adding step svm that applies to ColumnTypes - 2024-10-17 13:53:46,973 - julearn - INFO - Setting hyperparameter kernel = rbf - 2024-10-17 13:53:46,973 - julearn - INFO - Tuning hyperparameter C = [0.01, 0.1] - 2024-10-17 13:53:46,973 - julearn - INFO - Tuning hyperparameter gamma = [0.01, 0.001, 'scale'] - 2024-10-17 13:53:46,973 - julearn - INFO - Step added - 2024-10-17 13:53:46,973 - julearn - INFO - ==== Input Data ==== - 2024-10-17 13:53:46,973 - julearn - INFO - Using dataframe as input - 2024-10-17 13:53:46,973 - julearn - INFO - Features: ['frontal', 'parietal'] - 2024-10-17 13:53:46,973 - julearn - INFO - Target: event - 2024-10-17 13:53:46,973 - julearn - INFO - Expanded features: ['frontal', 'parietal'] - 2024-10-17 13:53:46,973 - julearn - INFO - X_types:{} - 2024-10-17 13:53:46,973 - julearn - WARNING - The following columns are not defined in X_types: ['frontal', 'parietal']. They will be treated as continuous. + 2024-10-17 14:02:01,392 - julearn - INFO - Adding step zscore that applies to ColumnTypes + 2024-10-17 14:02:01,392 - julearn - INFO - Step added + 2024-10-17 14:02:01,393 - julearn - INFO - Adding step svm that applies to ColumnTypes + 2024-10-17 14:02:01,393 - julearn - INFO - Setting hyperparameter kernel = rbf + 2024-10-17 14:02:01,393 - julearn - INFO - Tuning hyperparameter C = [0.01, 0.1] + 2024-10-17 14:02:01,393 - julearn - INFO - Tuning hyperparameter gamma = [0.01, 0.001, 'scale'] + 2024-10-17 14:02:01,393 - julearn - INFO - Step added + 2024-10-17 14:02:01,393 - julearn - INFO - ==== Input Data ==== + 2024-10-17 14:02:01,393 - julearn - INFO - Using dataframe as input + 2024-10-17 14:02:01,393 - julearn - INFO - Features: ['frontal', 'parietal'] + 2024-10-17 14:02:01,393 - julearn - INFO - Target: event + 2024-10-17 14:02:01,393 - julearn - INFO - Expanded features: ['frontal', 'parietal'] + 2024-10-17 14:02:01,393 - julearn - INFO - X_types:{} + 2024-10-17 14:02:01,393 - julearn - WARNING - The following columns are not defined in X_types: ['frontal', 'parietal']. They will be treated as continuous. /home/runner/work/julearn/julearn/julearn/prepare.py:509: RuntimeWarning: The following columns are not defined in X_types: ['frontal', 'parietal']. They will be treated as continuous. warn_with_log( - 2024-10-17 13:53:46,974 - julearn - INFO - ==================== - 2024-10-17 13:53:46,974 - julearn - INFO - - 2024-10-17 13:53:46,974 - julearn - INFO - = Model Parameters = - 2024-10-17 13:53:46,975 - julearn - INFO - Tuning hyperparameters using grid - 2024-10-17 13:53:46,975 - julearn - INFO - Hyperparameters: - 2024-10-17 13:53:46,975 - julearn - INFO - svm__C: [0.01, 0.1] - 2024-10-17 13:53:46,975 - julearn - INFO - svm__gamma: [0.01, 0.001, 'scale'] - 2024-10-17 13:53:46,975 - julearn - INFO - Using inner CV scheme KFold(n_splits=2, random_state=None, shuffle=False) - 2024-10-17 13:53:46,975 - julearn - INFO - Search Parameters: - 2024-10-17 13:53:46,975 - julearn - INFO - cv: KFold(n_splits=2, random_state=None, shuffle=False) - 2024-10-17 13:53:46,975 - julearn - INFO - ==================== - 2024-10-17 13:53:46,975 - julearn - INFO - - 2024-10-17 13:53:46,975 - julearn - INFO - = Data Information = - 2024-10-17 13:53:46,975 - julearn - INFO - Problem type: classification - 2024-10-17 13:53:46,975 - julearn - INFO - Number of samples: 532 - 2024-10-17 13:53:46,975 - julearn - INFO - Number of features: 2 - 2024-10-17 13:53:46,975 - julearn - INFO - ==================== - 2024-10-17 13:53:46,975 - julearn - INFO - - 2024-10-17 13:53:46,976 - julearn - INFO - Number of classes: 2 - 2024-10-17 13:53:46,976 - julearn - INFO - Target type: object - 2024-10-17 13:53:46,976 - julearn - INFO - Class distributions: event + 2024-10-17 14:02:01,394 - julearn - INFO - ==================== + 2024-10-17 14:02:01,394 - julearn - INFO - + 2024-10-17 14:02:01,394 - julearn - INFO - = Model Parameters = + 2024-10-17 14:02:01,394 - julearn - INFO - Tuning hyperparameters using grid + 2024-10-17 14:02:01,394 - julearn - INFO - Hyperparameters: + 2024-10-17 14:02:01,394 - julearn - INFO - svm__C: [0.01, 0.1] + 2024-10-17 14:02:01,394 - julearn - INFO - svm__gamma: [0.01, 0.001, 'scale'] + 2024-10-17 14:02:01,395 - julearn - INFO - Using inner CV scheme KFold(n_splits=2, random_state=None, shuffle=False) + 2024-10-17 14:02:01,395 - julearn - INFO - Search Parameters: + 2024-10-17 14:02:01,395 - julearn - INFO - cv: KFold(n_splits=2, random_state=None, shuffle=False) + 2024-10-17 14:02:01,395 - julearn - INFO - ==================== + 2024-10-17 14:02:01,395 - julearn - INFO - + 2024-10-17 14:02:01,395 - julearn - INFO - = Data Information = + 2024-10-17 14:02:01,395 - julearn - INFO - Problem type: classification + 2024-10-17 14:02:01,395 - julearn - INFO - Number of samples: 532 + 2024-10-17 14:02:01,395 - julearn - INFO - Number of features: 2 + 2024-10-17 14:02:01,395 - julearn - INFO - ==================== + 2024-10-17 14:02:01,395 - julearn - INFO - + 2024-10-17 14:02:01,395 - julearn - INFO - Number of classes: 2 + 2024-10-17 14:02:01,395 - julearn - INFO - Target type: object + 2024-10-17 14:02:01,396 - julearn - INFO - Class distributions: event cue 266 stim 266 Name: count, dtype: int64 - 2024-10-17 13:53:46,976 - julearn - INFO - Using outer CV scheme KFold(n_splits=5, random_state=None, shuffle=False) (incl. final model) - 2024-10-17 13:53:46,977 - julearn - INFO - Binary classification problem detected. + 2024-10-17 14:02:01,396 - julearn - INFO - Using outer CV scheme KFold(n_splits=5, random_state=None, shuffle=False) (incl. final model) + 2024-10-17 14:02:01,396 - julearn - INFO - Binary classification problem detected. 0.7087109857168048 {'svm__C': 0.1, 'svm__gamma': 'scale'} @@ -727,7 +727,7 @@ Let's add the default value and see what happens. .. rst-class:: sphx-glr-timing - **Total running time of the script:** (0 minutes 2.442 seconds) + **Total running time of the script:** (0 minutes 2.447 seconds) .. _sphx_glr_download_auto_examples_03_complex_models_run_hyperparameter_tuning.py: diff --git a/pr-preview/pr-276/_sources/auto_examples/03_complex_models/run_hyperparameter_tuning_bayessearch.rst.txt b/pr-preview/pr-276/_sources/auto_examples/03_complex_models/run_hyperparameter_tuning_bayessearch.rst.txt index 4a905e6fb..ebb3f4d39 100644 --- a/pr-preview/pr-276/_sources/auto_examples/03_complex_models/run_hyperparameter_tuning_bayessearch.rst.txt +++ b/pr-preview/pr-276/_sources/auto_examples/03_complex_models/run_hyperparameter_tuning_bayessearch.rst.txt @@ -75,13 +75,13 @@ Set the logging level to info to see extra information. /home/runner/work/julearn/julearn/julearn/utils/logging.py:66: UserWarning: The '__version__' attribute is deprecated and will be removed in MarkupSafe 3.1. Use feature detection, or `importlib.metadata.version("markupsafe")`, instead. vstring = str(getattr(module, "__version__", None)) - 2024-10-17 13:53:35,465 - julearn - INFO - ===== Lib Versions ===== - 2024-10-17 13:53:35,466 - julearn - INFO - numpy: 1.26.4 - 2024-10-17 13:53:35,466 - julearn - INFO - scipy: 1.14.1 - 2024-10-17 13:53:35,466 - julearn - INFO - sklearn: 1.5.2 - 2024-10-17 13:53:35,466 - julearn - INFO - pandas: 2.2.3 - 2024-10-17 13:53:35,466 - julearn - INFO - julearn: 0.3.4.dev37 - 2024-10-17 13:53:35,466 - julearn - INFO - ======================== + 2024-10-17 14:01:50,343 - julearn - INFO - ===== Lib Versions ===== + 2024-10-17 14:01:50,343 - julearn - INFO - numpy: 1.26.4 + 2024-10-17 14:01:50,343 - julearn - INFO - scipy: 1.14.1 + 2024-10-17 14:01:50,343 - julearn - INFO - sklearn: 1.5.2 + 2024-10-17 14:01:50,343 - julearn - INFO - pandas: 2.2.3 + 2024-10-17 14:01:50,343 - julearn - INFO - julearn: 0.3.4.dev43 + 2024-10-17 14:01:50,343 - julearn - INFO - ======================== @@ -348,98 +348,98 @@ search to find the best hyperparameters for the SVM model. .. code-block:: none - 2024-10-17 13:53:35,475 - julearn - INFO - Adding step zscore that applies to ColumnTypes - 2024-10-17 13:53:35,475 - julearn - INFO - Step added - 2024-10-17 13:53:35,475 - julearn - INFO - Adding step svm that applies to ColumnTypes - 2024-10-17 13:53:35,475 - julearn - INFO - Setting hyperparameter kernel = linear - 2024-10-17 13:53:35,475 - julearn - INFO - Tuning hyperparameter C = (1e-06, 1000.0, 'log-uniform') - 2024-10-17 13:53:35,475 - julearn - INFO - Step added - 2024-10-17 13:53:35,475 - julearn - INFO - Adding step zscore that applies to ColumnTypes - 2024-10-17 13:53:35,476 - julearn - INFO - Step added - 2024-10-17 13:53:35,476 - julearn - INFO - Adding step svm that applies to ColumnTypes - 2024-10-17 13:53:35,476 - julearn - INFO - Setting hyperparameter kernel = rbf - 2024-10-17 13:53:35,476 - julearn - INFO - Tuning hyperparameter C = (1e-06, 1000.0, 'log-uniform') - 2024-10-17 13:53:35,476 - julearn - INFO - Tuning hyperparameter gamma = (1e-06, 10.0, 'log-uniform') - 2024-10-17 13:53:35,476 - julearn - INFO - Step added - 2024-10-17 13:53:35,476 - julearn - INFO - ==== Input Data ==== - 2024-10-17 13:53:35,476 - julearn - INFO - Using dataframe as input - 2024-10-17 13:53:35,476 - julearn - INFO - Features: ['frontal', 'parietal'] - 2024-10-17 13:53:35,476 - julearn - INFO - Target: event - 2024-10-17 13:53:35,476 - julearn - INFO - Expanded features: ['frontal', 'parietal'] - 2024-10-17 13:53:35,476 - julearn - INFO - X_types:{} - 2024-10-17 13:53:35,476 - julearn - WARNING - The following columns are not defined in X_types: ['frontal', 'parietal']. They will be treated as continuous. + 2024-10-17 14:01:50,352 - julearn - INFO - Adding step zscore that applies to ColumnTypes + 2024-10-17 14:01:50,352 - julearn - INFO - Step added + 2024-10-17 14:01:50,352 - julearn - INFO - Adding step svm that applies to ColumnTypes + 2024-10-17 14:01:50,353 - julearn - INFO - Setting hyperparameter kernel = linear + 2024-10-17 14:01:50,353 - julearn - INFO - Tuning hyperparameter C = (1e-06, 1000.0, 'log-uniform') + 2024-10-17 14:01:50,353 - julearn - INFO - Step added + 2024-10-17 14:01:50,353 - julearn - INFO - Adding step zscore that applies to ColumnTypes + 2024-10-17 14:01:50,353 - julearn - INFO - Step added + 2024-10-17 14:01:50,353 - julearn - INFO - Adding step svm that applies to ColumnTypes + 2024-10-17 14:01:50,353 - julearn - INFO - Setting hyperparameter kernel = rbf + 2024-10-17 14:01:50,353 - julearn - INFO - Tuning hyperparameter C = (1e-06, 1000.0, 'log-uniform') + 2024-10-17 14:01:50,353 - julearn - INFO - Tuning hyperparameter gamma = (1e-06, 10.0, 'log-uniform') + 2024-10-17 14:01:50,353 - julearn - INFO - Step added + 2024-10-17 14:01:50,353 - julearn - INFO - ==== Input Data ==== + 2024-10-17 14:01:50,353 - julearn - INFO - Using dataframe as input + 2024-10-17 14:01:50,353 - julearn - INFO - Features: ['frontal', 'parietal'] + 2024-10-17 14:01:50,353 - julearn - INFO - Target: event + 2024-10-17 14:01:50,353 - julearn - INFO - Expanded features: ['frontal', 'parietal'] + 2024-10-17 14:01:50,353 - julearn - INFO - X_types:{} + 2024-10-17 14:01:50,354 - julearn - WARNING - The following columns are not defined in X_types: ['frontal', 'parietal']. They will be treated as continuous. /home/runner/work/julearn/julearn/julearn/prepare.py:509: RuntimeWarning: The following columns are not defined in X_types: ['frontal', 'parietal']. They will be treated as continuous. warn_with_log( - 2024-10-17 13:53:35,477 - julearn - INFO - ==================== - 2024-10-17 13:53:35,477 - julearn - INFO - - 2024-10-17 13:53:35,478 - julearn - INFO - = Model Parameters = - 2024-10-17 13:53:35,478 - julearn - INFO - Tuning hyperparameters using bayes - 2024-10-17 13:53:35,478 - julearn - INFO - Hyperparameters: - 2024-10-17 13:53:35,478 - julearn - INFO - svm__C: (1e-06, 1000.0, 'log-uniform') - 2024-10-17 13:53:35,478 - julearn - INFO - Hyperparameter svm__C is log-uniform float [1e-06, 1000.0] - 2024-10-17 13:53:35,479 - julearn - INFO - Using inner CV scheme KFold(n_splits=2, random_state=None, shuffle=False) - 2024-10-17 13:53:35,479 - julearn - INFO - Search Parameters: - 2024-10-17 13:53:35,479 - julearn - INFO - cv: KFold(n_splits=2, random_state=None, shuffle=False) - 2024-10-17 13:53:35,479 - julearn - INFO - n_iter: 10 - 2024-10-17 13:53:35,479 - julearn - INFO - ==================== - 2024-10-17 13:53:35,479 - julearn - INFO - - 2024-10-17 13:53:35,480 - julearn - INFO - = Model Parameters = - 2024-10-17 13:53:35,480 - julearn - INFO - Tuning hyperparameters using bayes - 2024-10-17 13:53:35,480 - julearn - INFO - Hyperparameters: - 2024-10-17 13:53:35,480 - julearn - INFO - svm__C: (1e-06, 1000.0, 'log-uniform') - 2024-10-17 13:53:35,480 - julearn - INFO - svm__gamma: (1e-06, 10.0, 'log-uniform') - 2024-10-17 13:53:35,480 - julearn - INFO - Hyperparameter svm__C is log-uniform float [1e-06, 1000.0] - 2024-10-17 13:53:35,481 - julearn - INFO - Hyperparameter svm__gamma is log-uniform float [1e-06, 10.0] - 2024-10-17 13:53:35,481 - julearn - INFO - Using inner CV scheme KFold(n_splits=2, random_state=None, shuffle=False) - 2024-10-17 13:53:35,481 - julearn - INFO - Search Parameters: - 2024-10-17 13:53:35,481 - julearn - INFO - cv: KFold(n_splits=2, random_state=None, shuffle=False) - 2024-10-17 13:53:35,481 - julearn - INFO - n_iter: 10 - 2024-10-17 13:53:35,482 - julearn - INFO - ==================== - 2024-10-17 13:53:35,482 - julearn - INFO - - 2024-10-17 13:53:35,482 - julearn - INFO - = Model Parameters = - 2024-10-17 13:53:35,482 - julearn - INFO - Tuning hyperparameters using bayes - 2024-10-17 13:53:35,482 - julearn - INFO - Hyperparameters list: - 2024-10-17 13:53:35,482 - julearn - INFO - Set 0 - 2024-10-17 13:53:35,482 - julearn - INFO - svm__C: Real(low=1e-06, high=1000.0, prior='log-uniform', transform='identity') - 2024-10-17 13:53:35,482 - julearn - INFO - set_column_types: [SetColumnTypes(X_types={})] - 2024-10-17 13:53:35,482 - julearn - INFO - zscore: [StandardScaler()] - 2024-10-17 13:53:35,483 - julearn - INFO - svm: [SVC(kernel='linear')] - 2024-10-17 13:53:35,483 - julearn - INFO - Set 1 - 2024-10-17 13:53:35,483 - julearn - INFO - svm__C: Real(low=1e-06, high=1000.0, prior='log-uniform', transform='identity') - 2024-10-17 13:53:35,483 - julearn - INFO - svm__gamma: Real(low=1e-06, high=10.0, prior='log-uniform', transform='identity') - 2024-10-17 13:53:35,483 - julearn - INFO - set_column_types: [SetColumnTypes(X_types={})] - 2024-10-17 13:53:35,483 - julearn - INFO - zscore: [StandardScaler()] - 2024-10-17 13:53:35,483 - julearn - INFO - svm: [SVC()] - 2024-10-17 13:53:35,483 - julearn - INFO - Hyperparameter svm__C as is Real(low=1e-06, high=1000.0, prior='log-uniform', transform='identity') - 2024-10-17 13:53:35,483 - julearn - INFO - Hyperparameter set_column_types as is [SetColumnTypes(X_types={})] - 2024-10-17 13:53:35,483 - julearn - INFO - Hyperparameter zscore as is [StandardScaler()] - 2024-10-17 13:53:35,484 - julearn - INFO - Hyperparameter svm as is [SVC(kernel='linear')] - 2024-10-17 13:53:35,484 - julearn - INFO - Hyperparameter svm__C as is Real(low=1e-06, high=1000.0, prior='log-uniform', transform='identity') - 2024-10-17 13:53:35,484 - julearn - INFO - Hyperparameter svm__gamma as is Real(low=1e-06, high=10.0, prior='log-uniform', transform='identity') - 2024-10-17 13:53:35,484 - julearn - INFO - Hyperparameter set_column_types as is [SetColumnTypes(X_types={})] - 2024-10-17 13:53:35,484 - julearn - INFO - Hyperparameter zscore as is [StandardScaler()] - 2024-10-17 13:53:35,484 - julearn - INFO - Hyperparameter svm as is [SVC()] - 2024-10-17 13:53:35,484 - julearn - INFO - Using inner CV scheme KFold(n_splits=2, random_state=None, shuffle=False) - 2024-10-17 13:53:35,484 - julearn - INFO - Search Parameters: - 2024-10-17 13:53:35,484 - julearn - INFO - cv: KFold(n_splits=2, random_state=None, shuffle=False) - 2024-10-17 13:53:35,485 - julearn - INFO - n_iter: 10 - 2024-10-17 13:53:35,493 - julearn - INFO - ==================== - 2024-10-17 13:53:35,493 - julearn - INFO - - 2024-10-17 13:53:35,493 - julearn - INFO - = Data Information = - 2024-10-17 13:53:35,494 - julearn - INFO - Problem type: classification - 2024-10-17 13:53:35,494 - julearn - INFO - Number of samples: 532 - 2024-10-17 13:53:35,494 - julearn - INFO - Number of features: 2 - 2024-10-17 13:53:35,494 - julearn - INFO - ==================== - 2024-10-17 13:53:35,494 - julearn - INFO - - 2024-10-17 13:53:35,494 - julearn - INFO - Number of classes: 2 - 2024-10-17 13:53:35,494 - julearn - INFO - Target type: object - 2024-10-17 13:53:35,495 - julearn - INFO - Class distributions: event + 2024-10-17 14:01:50,354 - julearn - INFO - ==================== + 2024-10-17 14:01:50,354 - julearn - INFO - + 2024-10-17 14:01:50,355 - julearn - INFO - = Model Parameters = + 2024-10-17 14:01:50,355 - julearn - INFO - Tuning hyperparameters using bayes + 2024-10-17 14:01:50,355 - julearn - INFO - Hyperparameters: + 2024-10-17 14:01:50,355 - julearn - INFO - svm__C: (1e-06, 1000.0, 'log-uniform') + 2024-10-17 14:01:50,355 - julearn - INFO - Hyperparameter svm__C is log-uniform float [1e-06, 1000.0] + 2024-10-17 14:01:50,356 - julearn - INFO - Using inner CV scheme KFold(n_splits=2, random_state=None, shuffle=False) + 2024-10-17 14:01:50,356 - julearn - INFO - Search Parameters: + 2024-10-17 14:01:50,356 - julearn - INFO - cv: KFold(n_splits=2, random_state=None, shuffle=False) + 2024-10-17 14:01:50,356 - julearn - INFO - n_iter: 10 + 2024-10-17 14:01:50,356 - julearn - INFO - ==================== + 2024-10-17 14:01:50,356 - julearn - INFO - + 2024-10-17 14:01:50,357 - julearn - INFO - = Model Parameters = + 2024-10-17 14:01:50,357 - julearn - INFO - Tuning hyperparameters using bayes + 2024-10-17 14:01:50,357 - julearn - INFO - Hyperparameters: + 2024-10-17 14:01:50,357 - julearn - INFO - svm__C: (1e-06, 1000.0, 'log-uniform') + 2024-10-17 14:01:50,357 - julearn - INFO - svm__gamma: (1e-06, 10.0, 'log-uniform') + 2024-10-17 14:01:50,357 - julearn - INFO - Hyperparameter svm__C is log-uniform float [1e-06, 1000.0] + 2024-10-17 14:01:50,358 - julearn - INFO - Hyperparameter svm__gamma is log-uniform float [1e-06, 10.0] + 2024-10-17 14:01:50,358 - julearn - INFO - Using inner CV scheme KFold(n_splits=2, random_state=None, shuffle=False) + 2024-10-17 14:01:50,358 - julearn - INFO - Search Parameters: + 2024-10-17 14:01:50,358 - julearn - INFO - cv: KFold(n_splits=2, random_state=None, shuffle=False) + 2024-10-17 14:01:50,358 - julearn - INFO - n_iter: 10 + 2024-10-17 14:01:50,359 - julearn - INFO - ==================== + 2024-10-17 14:01:50,359 - julearn - INFO - + 2024-10-17 14:01:50,359 - julearn - INFO - = Model Parameters = + 2024-10-17 14:01:50,359 - julearn - INFO - Tuning hyperparameters using bayes + 2024-10-17 14:01:50,359 - julearn - INFO - Hyperparameters list: + 2024-10-17 14:01:50,359 - julearn - INFO - Set 0 + 2024-10-17 14:01:50,359 - julearn - INFO - svm__C: Real(low=1e-06, high=1000.0, prior='log-uniform', transform='identity') + 2024-10-17 14:01:50,359 - julearn - INFO - set_column_types: [SetColumnTypes(X_types={})] + 2024-10-17 14:01:50,359 - julearn - INFO - zscore: [StandardScaler()] + 2024-10-17 14:01:50,359 - julearn - INFO - svm: [SVC(kernel='linear')] + 2024-10-17 14:01:50,359 - julearn - INFO - Set 1 + 2024-10-17 14:01:50,360 - julearn - INFO - svm__C: Real(low=1e-06, high=1000.0, prior='log-uniform', transform='identity') + 2024-10-17 14:01:50,360 - julearn - INFO - svm__gamma: Real(low=1e-06, high=10.0, prior='log-uniform', transform='identity') + 2024-10-17 14:01:50,360 - julearn - INFO - set_column_types: [SetColumnTypes(X_types={})] + 2024-10-17 14:01:50,360 - julearn - INFO - zscore: [StandardScaler()] + 2024-10-17 14:01:50,360 - julearn - INFO - svm: [SVC()] + 2024-10-17 14:01:50,360 - julearn - INFO - Hyperparameter svm__C as is Real(low=1e-06, high=1000.0, prior='log-uniform', transform='identity') + 2024-10-17 14:01:50,360 - julearn - INFO - Hyperparameter set_column_types as is [SetColumnTypes(X_types={})] + 2024-10-17 14:01:50,360 - julearn - INFO - Hyperparameter zscore as is [StandardScaler()] + 2024-10-17 14:01:50,361 - julearn - INFO - Hyperparameter svm as is [SVC(kernel='linear')] + 2024-10-17 14:01:50,361 - julearn - INFO - Hyperparameter svm__C as is Real(low=1e-06, high=1000.0, prior='log-uniform', transform='identity') + 2024-10-17 14:01:50,361 - julearn - INFO - Hyperparameter svm__gamma as is Real(low=1e-06, high=10.0, prior='log-uniform', transform='identity') + 2024-10-17 14:01:50,361 - julearn - INFO - Hyperparameter set_column_types as is [SetColumnTypes(X_types={})] + 2024-10-17 14:01:50,361 - julearn - INFO - Hyperparameter zscore as is [StandardScaler()] + 2024-10-17 14:01:50,361 - julearn - INFO - Hyperparameter svm as is [SVC()] + 2024-10-17 14:01:50,361 - julearn - INFO - Using inner CV scheme KFold(n_splits=2, random_state=None, shuffle=False) + 2024-10-17 14:01:50,361 - julearn - INFO - Search Parameters: + 2024-10-17 14:01:50,361 - julearn - INFO - cv: KFold(n_splits=2, random_state=None, shuffle=False) + 2024-10-17 14:01:50,361 - julearn - INFO - n_iter: 10 + 2024-10-17 14:01:50,370 - julearn - INFO - ==================== + 2024-10-17 14:01:50,370 - julearn - INFO - + 2024-10-17 14:01:50,370 - julearn - INFO - = Data Information = + 2024-10-17 14:01:50,370 - julearn - INFO - Problem type: classification + 2024-10-17 14:01:50,370 - julearn - INFO - Number of samples: 532 + 2024-10-17 14:01:50,370 - julearn - INFO - Number of features: 2 + 2024-10-17 14:01:50,370 - julearn - INFO - ==================== + 2024-10-17 14:01:50,370 - julearn - INFO - + 2024-10-17 14:01:50,370 - julearn - INFO - Number of classes: 2 + 2024-10-17 14:01:50,371 - julearn - INFO - Target type: object + 2024-10-17 14:01:50,371 - julearn - INFO - Class distributions: event cue 266 stim 266 Name: count, dtype: int64 - 2024-10-17 13:53:35,495 - julearn - INFO - Using outer CV scheme KFold(n_splits=2, random_state=None, shuffle=False) (incl. final model) - 2024-10-17 13:53:35,495 - julearn - INFO - Binary classification problem detected. - 0.6203007518796992 + 2024-10-17 14:01:50,371 - julearn - INFO - Using outer CV scheme KFold(n_splits=2, random_state=None, shuffle=False) (incl. final model) + 2024-10-17 14:01:50,372 - julearn - INFO - Binary classification problem detected. + 0.656015037593985 @@ -461,7 +461,7 @@ It seems that we might have found a better model, but which one is it? .. code-block:: none - OrderedDict([('set_column_types', SetColumnTypes(X_types={})), ('svm', SVC()), ('svm__C', 193.62585277239563), ('svm__gamma', 4.909675645518994), ('zscore', StandardScaler())]) + OrderedDict([('set_column_types', SetColumnTypes(X_types={})), ('svm', SVC()), ('svm__C', 0.0018082604408073564), ('svm__gamma', 1.6437581151471767), ('zscore', StandardScaler())]) @@ -469,7 +469,7 @@ It seems that we might have found a better model, but which one is it? .. rst-class:: sphx-glr-timing - **Total running time of the script:** (0 minutes 4.504 seconds) + **Total running time of the script:** (0 minutes 3.976 seconds) .. _sphx_glr_download_auto_examples_03_complex_models_run_hyperparameter_tuning_bayessearch.py: diff --git a/pr-preview/pr-276/_sources/auto_examples/03_complex_models/run_stacked_models.rst.txt b/pr-preview/pr-276/_sources/auto_examples/03_complex_models/run_stacked_models.rst.txt index c8aca239c..379297f47 100644 --- a/pr-preview/pr-276/_sources/auto_examples/03_complex_models/run_stacked_models.rst.txt +++ b/pr-preview/pr-276/_sources/auto_examples/03_complex_models/run_stacked_models.rst.txt @@ -67,13 +67,13 @@ Set the logging level to info to see extra information. /home/runner/work/julearn/julearn/julearn/utils/logging.py:66: UserWarning: The '__version__' attribute is deprecated and will be removed in MarkupSafe 3.1. Use feature detection, or `importlib.metadata.version("markupsafe")`, instead. vstring = str(getattr(module, "__version__", None)) - 2024-10-17 13:53:41,534 - julearn - INFO - ===== Lib Versions ===== - 2024-10-17 13:53:41,534 - julearn - INFO - numpy: 1.26.4 - 2024-10-17 13:53:41,534 - julearn - INFO - scipy: 1.14.1 - 2024-10-17 13:53:41,534 - julearn - INFO - sklearn: 1.5.2 - 2024-10-17 13:53:41,534 - julearn - INFO - pandas: 2.2.3 - 2024-10-17 13:53:41,534 - julearn - INFO - julearn: 0.3.4.dev37 - 2024-10-17 13:53:41,534 - julearn - INFO - ======================== + 2024-10-17 14:01:55,891 - julearn - INFO - ===== Lib Versions ===== + 2024-10-17 14:01:55,891 - julearn - INFO - numpy: 1.26.4 + 2024-10-17 14:01:55,891 - julearn - INFO - scipy: 1.14.1 + 2024-10-17 14:01:55,891 - julearn - INFO - sklearn: 1.5.2 + 2024-10-17 14:01:55,891 - julearn - INFO - pandas: 2.2.3 + 2024-10-17 14:01:55,891 - julearn - INFO - julearn: 0.3.4.dev43 + 2024-10-17 14:01:55,891 - julearn - INFO - ======================== @@ -161,54 +161,54 @@ We will try to predict the species. .. code-block:: none - 2024-10-17 13:53:41,537 - julearn - INFO - Adding step filter_columns that applies to ColumnTypes - 2024-10-17 13:53:41,537 - julearn - INFO - Setting hyperparameter keep = sepal - 2024-10-17 13:53:41,537 - julearn - INFO - Step added - 2024-10-17 13:53:41,537 - julearn - INFO - Adding step zscore that applies to ColumnTypes - 2024-10-17 13:53:41,537 - julearn - INFO - Step added - 2024-10-17 13:53:41,537 - julearn - INFO - Adding step svm that applies to ColumnTypes - 2024-10-17 13:53:41,537 - julearn - INFO - Step added - 2024-10-17 13:53:41,537 - julearn - INFO - Adding step filter_columns that applies to ColumnTypes - 2024-10-17 13:53:41,537 - julearn - INFO - Setting hyperparameter keep = petal - 2024-10-17 13:53:41,538 - julearn - INFO - Step added - 2024-10-17 13:53:41,538 - julearn - INFO - Adding step zscore that applies to ColumnTypes - 2024-10-17 13:53:41,538 - julearn - INFO - Step added - 2024-10-17 13:53:41,538 - julearn - INFO - Adding step rf that applies to ColumnTypes - 2024-10-17 13:53:41,538 - julearn - INFO - Step added - 2024-10-17 13:53:41,538 - julearn - INFO - Adding step stacking that applies to ColumnTypes - 2024-10-17 13:53:41,538 - julearn - INFO - Setting hyperparameter estimators = [('model_sepal', ), ('model_petal', )] - 2024-10-17 13:53:41,538 - julearn - INFO - Step added - 2024-10-17 13:53:41,538 - julearn - INFO - ==== Input Data ==== - 2024-10-17 13:53:41,538 - julearn - INFO - Using dataframe as input - 2024-10-17 13:53:41,538 - julearn - INFO - Features: ['sepal_length', 'sepal_width', 'petal_length', 'petal_width'] - 2024-10-17 13:53:41,538 - julearn - INFO - Target: species - 2024-10-17 13:53:41,538 - julearn - INFO - Expanded features: ['sepal_length', 'sepal_width', 'petal_length', 'petal_width'] - 2024-10-17 13:53:41,539 - julearn - INFO - X_types:{'sepal': ['sepal_length', 'sepal_width'], 'petal': ['petal_length', 'petal_width']} - 2024-10-17 13:53:41,539 - julearn - INFO - ==================== - 2024-10-17 13:53:41,539 - julearn - INFO - - 2024-10-17 13:53:41,541 - julearn - INFO - = Model Parameters = - 2024-10-17 13:53:41,541 - julearn - INFO - ==================== - 2024-10-17 13:53:41,541 - julearn - INFO - - 2024-10-17 13:53:41,542 - julearn - INFO - = Model Parameters = - 2024-10-17 13:53:41,542 - julearn - INFO - ==================== - 2024-10-17 13:53:41,542 - julearn - INFO - - 2024-10-17 13:53:41,576 - julearn - INFO - = Model Parameters = - 2024-10-17 13:53:41,576 - julearn - INFO - ==================== - 2024-10-17 13:53:41,576 - julearn - INFO - - 2024-10-17 13:53:41,576 - julearn - INFO - = Data Information = - 2024-10-17 13:53:41,577 - julearn - INFO - Problem type: classification - 2024-10-17 13:53:41,577 - julearn - INFO - Number of samples: 100 - 2024-10-17 13:53:41,577 - julearn - INFO - Number of features: 4 - 2024-10-17 13:53:41,577 - julearn - INFO - ==================== - 2024-10-17 13:53:41,577 - julearn - INFO - - 2024-10-17 13:53:41,577 - julearn - INFO - Number of classes: 2 - 2024-10-17 13:53:41,577 - julearn - INFO - Target type: object - 2024-10-17 13:53:41,577 - julearn - INFO - Class distributions: species + 2024-10-17 14:01:55,894 - julearn - INFO - Adding step filter_columns that applies to ColumnTypes + 2024-10-17 14:01:55,894 - julearn - INFO - Setting hyperparameter keep = sepal + 2024-10-17 14:01:55,894 - julearn - INFO - Step added + 2024-10-17 14:01:55,894 - julearn - INFO - Adding step zscore that applies to ColumnTypes + 2024-10-17 14:01:55,894 - julearn - INFO - Step added + 2024-10-17 14:01:55,895 - julearn - INFO - Adding step svm that applies to ColumnTypes + 2024-10-17 14:01:55,895 - julearn - INFO - Step added + 2024-10-17 14:01:55,895 - julearn - INFO - Adding step filter_columns that applies to ColumnTypes + 2024-10-17 14:01:55,895 - julearn - INFO - Setting hyperparameter keep = petal + 2024-10-17 14:01:55,895 - julearn - INFO - Step added + 2024-10-17 14:01:55,895 - julearn - INFO - Adding step zscore that applies to ColumnTypes + 2024-10-17 14:01:55,895 - julearn - INFO - Step added + 2024-10-17 14:01:55,895 - julearn - INFO - Adding step rf that applies to ColumnTypes + 2024-10-17 14:01:55,895 - julearn - INFO - Step added + 2024-10-17 14:01:55,895 - julearn - INFO - Adding step stacking that applies to ColumnTypes + 2024-10-17 14:01:55,895 - julearn - INFO - Setting hyperparameter estimators = [('model_sepal', ), ('model_petal', )] + 2024-10-17 14:01:55,896 - julearn - INFO - Step added + 2024-10-17 14:01:55,896 - julearn - INFO - ==== Input Data ==== + 2024-10-17 14:01:55,896 - julearn - INFO - Using dataframe as input + 2024-10-17 14:01:55,896 - julearn - INFO - Features: ['sepal_length', 'sepal_width', 'petal_length', 'petal_width'] + 2024-10-17 14:01:55,896 - julearn - INFO - Target: species + 2024-10-17 14:01:55,896 - julearn - INFO - Expanded features: ['sepal_length', 'sepal_width', 'petal_length', 'petal_width'] + 2024-10-17 14:01:55,896 - julearn - INFO - X_types:{'sepal': ['sepal_length', 'sepal_width'], 'petal': ['petal_length', 'petal_width']} + 2024-10-17 14:01:55,897 - julearn - INFO - ==================== + 2024-10-17 14:01:55,897 - julearn - INFO - + 2024-10-17 14:01:55,898 - julearn - INFO - = Model Parameters = + 2024-10-17 14:01:55,898 - julearn - INFO - ==================== + 2024-10-17 14:01:55,898 - julearn - INFO - + 2024-10-17 14:01:55,899 - julearn - INFO - = Model Parameters = + 2024-10-17 14:01:55,899 - julearn - INFO - ==================== + 2024-10-17 14:01:55,899 - julearn - INFO - + 2024-10-17 14:01:55,934 - julearn - INFO - = Model Parameters = + 2024-10-17 14:01:55,934 - julearn - INFO - ==================== + 2024-10-17 14:01:55,934 - julearn - INFO - + 2024-10-17 14:01:55,934 - julearn - INFO - = Data Information = + 2024-10-17 14:01:55,934 - julearn - INFO - Problem type: classification + 2024-10-17 14:01:55,934 - julearn - INFO - Number of samples: 100 + 2024-10-17 14:01:55,934 - julearn - INFO - Number of features: 4 + 2024-10-17 14:01:55,934 - julearn - INFO - ==================== + 2024-10-17 14:01:55,934 - julearn - INFO - + 2024-10-17 14:01:55,935 - julearn - INFO - Number of classes: 2 + 2024-10-17 14:01:55,935 - julearn - INFO - Target type: object + 2024-10-17 14:01:55,935 - julearn - INFO - Class distributions: species versicolor 50 virginica 50 Name: count, dtype: int64 - 2024-10-17 13:53:41,578 - julearn - INFO - Using outer CV scheme KFold(n_splits=5, random_state=None, shuffle=False) - 2024-10-17 13:53:41,578 - julearn - INFO - Binary classification problem detected. + 2024-10-17 14:01:55,935 - julearn - INFO - Using outer CV scheme KFold(n_splits=5, random_state=None, shuffle=False) + 2024-10-17 14:01:55,935 - julearn - INFO - Binary classification problem detected. 0 1.00 1 0.85 2 0.95 @@ -222,7 +222,7 @@ We will try to predict the species. .. rst-class:: sphx-glr-timing - **Total running time of the script:** (0 minutes 3.682 seconds) + **Total running time of the script:** (0 minutes 3.722 seconds) .. _sphx_glr_download_auto_examples_03_complex_models_run_stacked_models.py: diff --git a/pr-preview/pr-276/_sources/auto_examples/03_complex_models/sg_execution_times.rst.txt b/pr-preview/pr-276/_sources/auto_examples/03_complex_models/sg_execution_times.rst.txt index f7d891bb4..bcd3547d7 100644 --- a/pr-preview/pr-276/_sources/auto_examples/03_complex_models/sg_execution_times.rst.txt +++ b/pr-preview/pr-276/_sources/auto_examples/03_complex_models/sg_execution_times.rst.txt @@ -6,18 +6,18 @@ Computation times ================= -**00:12.435** total execution time for **auto_examples_03_complex_models** files: +**00:11.967** total execution time for **auto_examples_03_complex_models** files: +-----------------------------------------------------------------------------------------------------------------------------------------+-----------+--------+ -| :ref:`sphx_glr_auto_examples_03_complex_models_run_hyperparameter_tuning_bayessearch.py` (``run_hyperparameter_tuning_bayessearch.py``) | 00:04.504 | 0.0 MB | +| :ref:`sphx_glr_auto_examples_03_complex_models_run_hyperparameter_tuning_bayessearch.py` (``run_hyperparameter_tuning_bayessearch.py``) | 00:03.976 | 0.0 MB | +-----------------------------------------------------------------------------------------------------------------------------------------+-----------+--------+ -| :ref:`sphx_glr_auto_examples_03_complex_models_run_stacked_models.py` (``run_stacked_models.py``) | 00:03.682 | 0.0 MB | +| :ref:`sphx_glr_auto_examples_03_complex_models_run_stacked_models.py` (``run_stacked_models.py``) | 00:03.722 | 0.0 MB | +-----------------------------------------------------------------------------------------------------------------------------------------+-----------+--------+ -| :ref:`sphx_glr_auto_examples_03_complex_models_run_hyperparameter_tuning.py` (``run_hyperparameter_tuning.py``) | 00:02.442 | 0.0 MB | +| :ref:`sphx_glr_auto_examples_03_complex_models_run_hyperparameter_tuning.py` (``run_hyperparameter_tuning.py``) | 00:02.447 | 0.0 MB | +-----------------------------------------------------------------------------------------------------------------------------------------+-----------+--------+ -| :ref:`sphx_glr_auto_examples_03_complex_models_run_hyperparameter_multiple_grids.py` (``run_hyperparameter_multiple_grids.py``) | 00:01.317 | 0.0 MB | +| :ref:`sphx_glr_auto_examples_03_complex_models_run_hyperparameter_multiple_grids.py` (``run_hyperparameter_multiple_grids.py``) | 00:01.328 | 0.0 MB | +-----------------------------------------------------------------------------------------------------------------------------------------+-----------+--------+ -| :ref:`sphx_glr_auto_examples_03_complex_models_run_example_pca_featsets.py` (``run_example_pca_featsets.py``) | 00:00.427 | 0.0 MB | +| :ref:`sphx_glr_auto_examples_03_complex_models_run_example_pca_featsets.py` (``run_example_pca_featsets.py``) | 00:00.430 | 0.0 MB | +-----------------------------------------------------------------------------------------------------------------------------------------+-----------+--------+ -| :ref:`sphx_glr_auto_examples_03_complex_models_run_apply_to_target.py` (``run_apply_to_target.py``) | 00:00.063 | 0.0 MB | +| :ref:`sphx_glr_auto_examples_03_complex_models_run_apply_to_target.py` (``run_apply_to_target.py``) | 00:00.064 | 0.0 MB | +-----------------------------------------------------------------------------------------------------------------------------------------+-----------+--------+ diff --git a/pr-preview/pr-276/_sources/auto_examples/04_confounds/plot_confound_removal_classification.rst.txt b/pr-preview/pr-276/_sources/auto_examples/04_confounds/plot_confound_removal_classification.rst.txt index 5b1536ce0..f076aa6bf 100644 --- a/pr-preview/pr-276/_sources/auto_examples/04_confounds/plot_confound_removal_classification.rst.txt +++ b/pr-preview/pr-276/_sources/auto_examples/04_confounds/plot_confound_removal_classification.rst.txt @@ -70,13 +70,13 @@ Set the logging level to info to see extra information. /home/runner/work/julearn/julearn/julearn/utils/logging.py:66: UserWarning: The '__version__' attribute is deprecated and will be removed in MarkupSafe 3.1. Use feature detection, or `importlib.metadata.version("markupsafe")`, instead. vstring = str(getattr(module, "__version__", None)) - 2024-10-17 13:53:49,256 - julearn - INFO - ===== Lib Versions ===== - 2024-10-17 13:53:49,256 - julearn - INFO - numpy: 1.26.4 - 2024-10-17 13:53:49,256 - julearn - INFO - scipy: 1.14.1 - 2024-10-17 13:53:49,256 - julearn - INFO - sklearn: 1.5.2 - 2024-10-17 13:53:49,256 - julearn - INFO - pandas: 2.2.3 - 2024-10-17 13:53:49,256 - julearn - INFO - julearn: 0.3.4.dev37 - 2024-10-17 13:53:49,256 - julearn - INFO - ======================== + 2024-10-17 14:02:03,690 - julearn - INFO - ===== Lib Versions ===== + 2024-10-17 14:02:03,690 - julearn - INFO - numpy: 1.26.4 + 2024-10-17 14:02:03,690 - julearn - INFO - scipy: 1.14.1 + 2024-10-17 14:02:03,690 - julearn - INFO - sklearn: 1.5.2 + 2024-10-17 14:02:03,690 - julearn - INFO - pandas: 2.2.3 + 2024-10-17 14:02:03,691 - julearn - INFO - julearn: 0.3.4.dev43 + 2024-10-17 14:02:03,691 - julearn - INFO - ======================== @@ -198,40 +198,40 @@ Note: confounds by default. .. code-block:: none - 2024-10-17 13:53:49,259 - julearn - INFO - Setting random seed to 200 - 2024-10-17 13:53:49,259 - julearn - INFO - ==== Input Data ==== - 2024-10-17 13:53:49,259 - julearn - INFO - Using dataframe as input - 2024-10-17 13:53:49,259 - julearn - INFO - Features: ['sepal_length', 'sepal_width', 'petal_length'] - 2024-10-17 13:53:49,259 - julearn - INFO - Target: species - 2024-10-17 13:53:49,259 - julearn - INFO - Expanded features: ['sepal_length', 'sepal_width', 'petal_length'] - 2024-10-17 13:53:49,259 - julearn - INFO - X_types:{} - 2024-10-17 13:53:49,259 - julearn - WARNING - The following columns are not defined in X_types: ['sepal_length', 'sepal_width', 'petal_length']. They will be treated as continuous. + 2024-10-17 14:02:03,693 - julearn - INFO - Setting random seed to 200 + 2024-10-17 14:02:03,693 - julearn - INFO - ==== Input Data ==== + 2024-10-17 14:02:03,694 - julearn - INFO - Using dataframe as input + 2024-10-17 14:02:03,694 - julearn - INFO - Features: ['sepal_length', 'sepal_width', 'petal_length'] + 2024-10-17 14:02:03,694 - julearn - INFO - Target: species + 2024-10-17 14:02:03,694 - julearn - INFO - Expanded features: ['sepal_length', 'sepal_width', 'petal_length'] + 2024-10-17 14:02:03,694 - julearn - INFO - X_types:{} + 2024-10-17 14:02:03,694 - julearn - WARNING - The following columns are not defined in X_types: ['sepal_length', 'sepal_width', 'petal_length']. They will be treated as continuous. /home/runner/work/julearn/julearn/julearn/prepare.py:509: RuntimeWarning: The following columns are not defined in X_types: ['sepal_length', 'sepal_width', 'petal_length']. They will be treated as continuous. warn_with_log( - 2024-10-17 13:53:49,260 - julearn - INFO - ==================== - 2024-10-17 13:53:49,260 - julearn - INFO - - 2024-10-17 13:53:49,260 - julearn - INFO - Adding step zscore that applies to ColumnTypes - 2024-10-17 13:53:49,260 - julearn - INFO - Step added - 2024-10-17 13:53:49,260 - julearn - INFO - Adding step rf that applies to ColumnTypes - 2024-10-17 13:53:49,261 - julearn - INFO - Step added - 2024-10-17 13:53:49,261 - julearn - INFO - = Model Parameters = - 2024-10-17 13:53:49,261 - julearn - INFO - ==================== - 2024-10-17 13:53:49,261 - julearn - INFO - - 2024-10-17 13:53:49,261 - julearn - INFO - = Data Information = - 2024-10-17 13:53:49,261 - julearn - INFO - Problem type: classification - 2024-10-17 13:53:49,261 - julearn - INFO - Number of samples: 100 - 2024-10-17 13:53:49,261 - julearn - INFO - Number of features: 3 - 2024-10-17 13:53:49,261 - julearn - INFO - ==================== - 2024-10-17 13:53:49,261 - julearn - INFO - - 2024-10-17 13:53:49,262 - julearn - INFO - Number of classes: 2 - 2024-10-17 13:53:49,262 - julearn - INFO - Target type: object - 2024-10-17 13:53:49,262 - julearn - INFO - Class distributions: species + 2024-10-17 14:02:03,695 - julearn - INFO - ==================== + 2024-10-17 14:02:03,695 - julearn - INFO - + 2024-10-17 14:02:03,695 - julearn - INFO - Adding step zscore that applies to ColumnTypes + 2024-10-17 14:02:03,695 - julearn - INFO - Step added + 2024-10-17 14:02:03,695 - julearn - INFO - Adding step rf that applies to ColumnTypes + 2024-10-17 14:02:03,695 - julearn - INFO - Step added + 2024-10-17 14:02:03,696 - julearn - INFO - = Model Parameters = + 2024-10-17 14:02:03,696 - julearn - INFO - ==================== + 2024-10-17 14:02:03,696 - julearn - INFO - + 2024-10-17 14:02:03,696 - julearn - INFO - = Data Information = + 2024-10-17 14:02:03,696 - julearn - INFO - Problem type: classification + 2024-10-17 14:02:03,696 - julearn - INFO - Number of samples: 100 + 2024-10-17 14:02:03,696 - julearn - INFO - Number of features: 3 + 2024-10-17 14:02:03,696 - julearn - INFO - ==================== + 2024-10-17 14:02:03,696 - julearn - INFO - + 2024-10-17 14:02:03,696 - julearn - INFO - Number of classes: 2 + 2024-10-17 14:02:03,696 - julearn - INFO - Target type: object + 2024-10-17 14:02:03,697 - julearn - INFO - Class distributions: species versicolor 50 virginica 50 Name: count, dtype: int64 - 2024-10-17 13:53:49,262 - julearn - INFO - Using outer CV scheme StratifiedBootstrap(n_splits=20, random_state=42, test_size=0.3, + 2024-10-17 14:02:03,697 - julearn - INFO - Using outer CV scheme StratifiedBootstrap(n_splits=20, random_state=42, test_size=0.3, train_size=None) - 2024-10-17 13:53:49,263 - julearn - INFO - Binary classification problem detected. + 2024-10-17 14:02:03,697 - julearn - INFO - Binary classification problem detected. @@ -302,40 +302,40 @@ the random forest will only be trained using "features". .. code-block:: none - 2024-10-17 13:53:51,369 - julearn - INFO - Adding step zscore that applies to ColumnTypes - 2024-10-17 13:53:51,369 - julearn - INFO - Step added - 2024-10-17 13:53:51,369 - julearn - INFO - Adding step confound_removal that applies to ColumnTypes - 2024-10-17 13:53:51,369 - julearn - INFO - Setting hyperparameter confounds = confound - 2024-10-17 13:53:51,369 - julearn - INFO - Step added - 2024-10-17 13:53:51,369 - julearn - INFO - Adding step rf that applies to ColumnTypes - 2024-10-17 13:53:51,369 - julearn - INFO - Step added - 2024-10-17 13:53:51,370 - julearn - INFO - Setting random seed to 200 - 2024-10-17 13:53:51,370 - julearn - INFO - ==== Input Data ==== - 2024-10-17 13:53:51,370 - julearn - INFO - Using dataframe as input - 2024-10-17 13:53:51,370 - julearn - INFO - Features: ['sepal_length', 'sepal_width', 'petal_length', 'petal_width'] - 2024-10-17 13:53:51,370 - julearn - INFO - Target: species - 2024-10-17 13:53:51,370 - julearn - INFO - Expanded features: ['sepal_length', 'sepal_width', 'petal_length', 'petal_width'] - 2024-10-17 13:53:51,370 - julearn - INFO - X_types:{'features': ['sepal_length', 'sepal_width', 'petal_length'], 'confound': ['petal_width']} - 2024-10-17 13:53:51,370 - julearn - INFO - ==================== - 2024-10-17 13:53:51,371 - julearn - INFO - - 2024-10-17 13:53:51,372 - julearn - INFO - = Model Parameters = - 2024-10-17 13:53:51,372 - julearn - INFO - ==================== - 2024-10-17 13:53:51,372 - julearn - INFO - - 2024-10-17 13:53:51,372 - julearn - INFO - = Data Information = - 2024-10-17 13:53:51,372 - julearn - INFO - Problem type: classification - 2024-10-17 13:53:51,372 - julearn - INFO - Number of samples: 100 - 2024-10-17 13:53:51,372 - julearn - INFO - Number of features: 4 - 2024-10-17 13:53:51,372 - julearn - INFO - ==================== - 2024-10-17 13:53:51,372 - julearn - INFO - - 2024-10-17 13:53:51,373 - julearn - INFO - Number of classes: 2 - 2024-10-17 13:53:51,373 - julearn - INFO - Target type: object - 2024-10-17 13:53:51,373 - julearn - INFO - Class distributions: species + 2024-10-17 14:02:05,830 - julearn - INFO - Adding step zscore that applies to ColumnTypes + 2024-10-17 14:02:05,830 - julearn - INFO - Step added + 2024-10-17 14:02:05,830 - julearn - INFO - Adding step confound_removal that applies to ColumnTypes + 2024-10-17 14:02:05,830 - julearn - INFO - Setting hyperparameter confounds = confound + 2024-10-17 14:02:05,830 - julearn - INFO - Step added + 2024-10-17 14:02:05,830 - julearn - INFO - Adding step rf that applies to ColumnTypes + 2024-10-17 14:02:05,830 - julearn - INFO - Step added + 2024-10-17 14:02:05,830 - julearn - INFO - Setting random seed to 200 + 2024-10-17 14:02:05,830 - julearn - INFO - ==== Input Data ==== + 2024-10-17 14:02:05,831 - julearn - INFO - Using dataframe as input + 2024-10-17 14:02:05,831 - julearn - INFO - Features: ['sepal_length', 'sepal_width', 'petal_length', 'petal_width'] + 2024-10-17 14:02:05,831 - julearn - INFO - Target: species + 2024-10-17 14:02:05,831 - julearn - INFO - Expanded features: ['sepal_length', 'sepal_width', 'petal_length', 'petal_width'] + 2024-10-17 14:02:05,831 - julearn - INFO - X_types:{'features': ['sepal_length', 'sepal_width', 'petal_length'], 'confound': ['petal_width']} + 2024-10-17 14:02:05,831 - julearn - INFO - ==================== + 2024-10-17 14:02:05,831 - julearn - INFO - + 2024-10-17 14:02:05,833 - julearn - INFO - = Model Parameters = + 2024-10-17 14:02:05,833 - julearn - INFO - ==================== + 2024-10-17 14:02:05,833 - julearn - INFO - + 2024-10-17 14:02:05,833 - julearn - INFO - = Data Information = + 2024-10-17 14:02:05,833 - julearn - INFO - Problem type: classification + 2024-10-17 14:02:05,833 - julearn - INFO - Number of samples: 100 + 2024-10-17 14:02:05,833 - julearn - INFO - Number of features: 4 + 2024-10-17 14:02:05,833 - julearn - INFO - ==================== + 2024-10-17 14:02:05,833 - julearn - INFO - + 2024-10-17 14:02:05,833 - julearn - INFO - Number of classes: 2 + 2024-10-17 14:02:05,834 - julearn - INFO - Target type: object + 2024-10-17 14:02:05,834 - julearn - INFO - Class distributions: species versicolor 50 virginica 50 Name: count, dtype: int64 - 2024-10-17 13:53:51,373 - julearn - INFO - Using outer CV scheme StratifiedBootstrap(n_splits=20, random_state=42, test_size=0.3, + 2024-10-17 14:02:05,834 - julearn - INFO - Using outer CV scheme StratifiedBootstrap(n_splits=20, random_state=42, test_size=0.3, train_size=None) - 2024-10-17 13:53:51,373 - julearn - INFO - Binary classification problem detected. + 2024-10-17 14:02:05,834 - julearn - INFO - Binary classification problem detected. @@ -676,7 +676,7 @@ a difference in importances. .. rst-class:: sphx-glr-timing - **Total running time of the script:** (0 minutes 5.618 seconds) + **Total running time of the script:** (0 minutes 5.913 seconds) .. _sphx_glr_download_auto_examples_04_confounds_plot_confound_removal_classification.py: diff --git a/pr-preview/pr-276/_sources/auto_examples/04_confounds/run_return_confounds.rst.txt b/pr-preview/pr-276/_sources/auto_examples/04_confounds/run_return_confounds.rst.txt index f97d97d5d..62f3a6353 100644 --- a/pr-preview/pr-276/_sources/auto_examples/04_confounds/run_return_confounds.rst.txt +++ b/pr-preview/pr-276/_sources/auto_examples/04_confounds/run_return_confounds.rst.txt @@ -164,14 +164,14 @@ Finally, we will fit a linear regression model. .. code-block:: none - 2024-10-17 13:53:48,497 - julearn - INFO - Adding step confound_removal that applies to ColumnTypes - 2024-10-17 13:53:48,497 - julearn - INFO - Step added - 2024-10-17 13:53:48,497 - julearn - INFO - Adding step pca that applies to ColumnTypes - 2024-10-17 13:53:48,497 - julearn - INFO - Step added - 2024-10-17 13:53:48,497 - julearn - INFO - Adding step linreg that applies to ColumnTypes - 2024-10-17 13:53:48,498 - julearn - INFO - Step added + 2024-10-17 14:02:02,911 - julearn - INFO - Adding step confound_removal that applies to ColumnTypes + 2024-10-17 14:02:02,911 - julearn - INFO - Step added + 2024-10-17 14:02:02,911 - julearn - INFO - Adding step pca that applies to ColumnTypes + 2024-10-17 14:02:02,911 - julearn - INFO - Step added + 2024-10-17 14:02:02,912 - julearn - INFO - Adding step linreg that applies to ColumnTypes + 2024-10-17 14:02:02,912 - julearn - INFO - Step added - + @@ -226,25 +226,25 @@ Now we can run the cross validation and get the scores. .. code-block:: none - 2024-10-17 13:53:48,498 - julearn - INFO - ==== Input Data ==== - 2024-10-17 13:53:48,498 - julearn - INFO - Using dataframe as input - 2024-10-17 13:53:48,498 - julearn - INFO - Features: ['age', 'bmi', 'bp', 's1', 's2', 's3', 's4', 's5', 's6', 'sex'] - 2024-10-17 13:53:48,498 - julearn - INFO - Target: target - 2024-10-17 13:53:48,499 - julearn - INFO - Expanded features: ['age', 'bmi', 'bp', 's1', 's2', 's3', 's4', 's5', 's6', 'sex'] - 2024-10-17 13:53:48,499 - julearn - INFO - X_types:{'continuous': ['age', 'bmi', 'bp', 's1', 's2', 's3', 's4', 's5', 's6'], 'confound': ['sex']} - 2024-10-17 13:53:48,499 - julearn - INFO - ==================== - 2024-10-17 13:53:48,499 - julearn - INFO - - 2024-10-17 13:53:48,501 - julearn - INFO - = Model Parameters = - 2024-10-17 13:53:48,501 - julearn - INFO - ==================== - 2024-10-17 13:53:48,501 - julearn - INFO - - 2024-10-17 13:53:48,501 - julearn - INFO - = Data Information = - 2024-10-17 13:53:48,501 - julearn - INFO - Problem type: regression - 2024-10-17 13:53:48,501 - julearn - INFO - Number of samples: 442 - 2024-10-17 13:53:48,501 - julearn - INFO - Number of features: 10 - 2024-10-17 13:53:48,501 - julearn - INFO - ==================== - 2024-10-17 13:53:48,501 - julearn - INFO - - 2024-10-17 13:53:48,501 - julearn - INFO - Target type: float64 - 2024-10-17 13:53:48,501 - julearn - INFO - Using outer CV scheme KFold(n_splits=5, random_state=None, shuffle=False) (incl. final model) + 2024-10-17 14:02:02,912 - julearn - INFO - ==== Input Data ==== + 2024-10-17 14:02:02,913 - julearn - INFO - Using dataframe as input + 2024-10-17 14:02:02,913 - julearn - INFO - Features: ['age', 'bmi', 'bp', 's1', 's2', 's3', 's4', 's5', 's6', 'sex'] + 2024-10-17 14:02:02,913 - julearn - INFO - Target: target + 2024-10-17 14:02:02,913 - julearn - INFO - Expanded features: ['age', 'bmi', 'bp', 's1', 's2', 's3', 's4', 's5', 's6', 'sex'] + 2024-10-17 14:02:02,913 - julearn - INFO - X_types:{'continuous': ['age', 'bmi', 'bp', 's1', 's2', 's3', 's4', 's5', 's6'], 'confound': ['sex']} + 2024-10-17 14:02:02,913 - julearn - INFO - ==================== + 2024-10-17 14:02:02,914 - julearn - INFO - + 2024-10-17 14:02:02,915 - julearn - INFO - = Model Parameters = + 2024-10-17 14:02:02,915 - julearn - INFO - ==================== + 2024-10-17 14:02:02,915 - julearn - INFO - + 2024-10-17 14:02:02,915 - julearn - INFO - = Data Information = + 2024-10-17 14:02:02,915 - julearn - INFO - Problem type: regression + 2024-10-17 14:02:02,915 - julearn - INFO - Number of samples: 442 + 2024-10-17 14:02:02,915 - julearn - INFO - Number of features: 10 + 2024-10-17 14:02:02,915 - julearn - INFO - ==================== + 2024-10-17 14:02:02,915 - julearn - INFO - + 2024-10-17 14:02:02,915 - julearn - INFO - Target type: float64 + 2024-10-17 14:02:02,915 - julearn - INFO - Using outer CV scheme KFold(n_splits=5, random_state=None, shuffle=False) (incl. final model) @@ -411,15 +411,15 @@ This will keep the confounds after confound removal. .. code-block:: none - 2024-10-17 13:53:48,707 - julearn - INFO - Adding step confound_removal that applies to ColumnTypes - 2024-10-17 13:53:48,707 - julearn - INFO - Setting hyperparameter keep_confounds = True - 2024-10-17 13:53:48,707 - julearn - INFO - Step added - 2024-10-17 13:53:48,707 - julearn - INFO - Adding step pca that applies to ColumnTypes - 2024-10-17 13:53:48,707 - julearn - INFO - Step added - 2024-10-17 13:53:48,707 - julearn - INFO - Adding step linreg that applies to ColumnTypes - 2024-10-17 13:53:48,707 - julearn - INFO - Step added + 2024-10-17 14:02:03,123 - julearn - INFO - Adding step confound_removal that applies to ColumnTypes + 2024-10-17 14:02:03,124 - julearn - INFO - Setting hyperparameter keep_confounds = True + 2024-10-17 14:02:03,124 - julearn - INFO - Step added + 2024-10-17 14:02:03,124 - julearn - INFO - Adding step pca that applies to ColumnTypes + 2024-10-17 14:02:03,124 - julearn - INFO - Step added + 2024-10-17 14:02:03,124 - julearn - INFO - Adding step linreg that applies to ColumnTypes + 2024-10-17 14:02:03,124 - julearn - INFO - Step added - + @@ -448,25 +448,25 @@ Now we can run the cross validation and get the scores. .. code-block:: none - 2024-10-17 13:53:48,708 - julearn - INFO - ==== Input Data ==== - 2024-10-17 13:53:48,708 - julearn - INFO - Using dataframe as input - 2024-10-17 13:53:48,708 - julearn - INFO - Features: ['age', 'bmi', 'bp', 's1', 's2', 's3', 's4', 's5', 's6', 'sex'] - 2024-10-17 13:53:48,708 - julearn - INFO - Target: target - 2024-10-17 13:53:48,708 - julearn - INFO - Expanded features: ['age', 'bmi', 'bp', 's1', 's2', 's3', 's4', 's5', 's6', 'sex'] - 2024-10-17 13:53:48,708 - julearn - INFO - X_types:{'continuous': ['age', 'bmi', 'bp', 's1', 's2', 's3', 's4', 's5', 's6'], 'confound': ['sex']} - 2024-10-17 13:53:48,709 - julearn - INFO - ==================== - 2024-10-17 13:53:48,709 - julearn - INFO - - 2024-10-17 13:53:48,710 - julearn - INFO - = Model Parameters = - 2024-10-17 13:53:48,710 - julearn - INFO - ==================== - 2024-10-17 13:53:48,710 - julearn - INFO - - 2024-10-17 13:53:48,710 - julearn - INFO - = Data Information = - 2024-10-17 13:53:48,710 - julearn - INFO - Problem type: regression - 2024-10-17 13:53:48,710 - julearn - INFO - Number of samples: 442 - 2024-10-17 13:53:48,710 - julearn - INFO - Number of features: 10 - 2024-10-17 13:53:48,710 - julearn - INFO - ==================== - 2024-10-17 13:53:48,710 - julearn - INFO - - 2024-10-17 13:53:48,710 - julearn - INFO - Target type: float64 - 2024-10-17 13:53:48,710 - julearn - INFO - Using outer CV scheme KFold(n_splits=5, random_state=None, shuffle=False) (incl. final model) + 2024-10-17 14:02:03,125 - julearn - INFO - ==== Input Data ==== + 2024-10-17 14:02:03,125 - julearn - INFO - Using dataframe as input + 2024-10-17 14:02:03,125 - julearn - INFO - Features: ['age', 'bmi', 'bp', 's1', 's2', 's3', 's4', 's5', 's6', 'sex'] + 2024-10-17 14:02:03,125 - julearn - INFO - Target: target + 2024-10-17 14:02:03,125 - julearn - INFO - Expanded features: ['age', 'bmi', 'bp', 's1', 's2', 's3', 's4', 's5', 's6', 'sex'] + 2024-10-17 14:02:03,125 - julearn - INFO - X_types:{'continuous': ['age', 'bmi', 'bp', 's1', 's2', 's3', 's4', 's5', 's6'], 'confound': ['sex']} + 2024-10-17 14:02:03,126 - julearn - INFO - ==================== + 2024-10-17 14:02:03,126 - julearn - INFO - + 2024-10-17 14:02:03,127 - julearn - INFO - = Model Parameters = + 2024-10-17 14:02:03,127 - julearn - INFO - ==================== + 2024-10-17 14:02:03,127 - julearn - INFO - + 2024-10-17 14:02:03,127 - julearn - INFO - = Data Information = + 2024-10-17 14:02:03,127 - julearn - INFO - Problem type: regression + 2024-10-17 14:02:03,127 - julearn - INFO - Number of samples: 442 + 2024-10-17 14:02:03,127 - julearn - INFO - Number of features: 10 + 2024-10-17 14:02:03,127 - julearn - INFO - ==================== + 2024-10-17 14:02:03,127 - julearn - INFO - + 2024-10-17 14:02:03,127 - julearn - INFO - Target type: float64 + 2024-10-17 14:02:03,127 - julearn - INFO - Using outer CV scheme KFold(n_splits=5, random_state=None, shuffle=False) (incl. final model) @@ -778,32 +778,32 @@ removal. .. code-block:: none - 2024-10-17 13:53:48,931 - julearn - INFO - Adding step confound_removal that applies to ColumnTypes - 2024-10-17 13:53:48,931 - julearn - INFO - Setting hyperparameter keep_confounds = True - 2024-10-17 13:53:48,932 - julearn - INFO - Step added - 2024-10-17 13:53:48,932 - julearn - INFO - Adding step pca that applies to ColumnTypes - 2024-10-17 13:53:48,932 - julearn - INFO - Step added - 2024-10-17 13:53:48,932 - julearn - INFO - Adding step linreg that applies to ColumnTypes - 2024-10-17 13:53:48,932 - julearn - INFO - Step added - 2024-10-17 13:53:48,932 - julearn - INFO - ==== Input Data ==== - 2024-10-17 13:53:48,932 - julearn - INFO - Using dataframe as input - 2024-10-17 13:53:48,932 - julearn - INFO - Features: ['age', 'bmi', 'bp', 's1', 's2', 's3', 's4', 's5', 's6', 'sex'] - 2024-10-17 13:53:48,932 - julearn - INFO - Target: target - 2024-10-17 13:53:48,932 - julearn - INFO - Expanded features: ['age', 'bmi', 'bp', 's1', 's2', 's3', 's4', 's5', 's6', 'sex'] - 2024-10-17 13:53:48,932 - julearn - INFO - X_types:{'continuous': ['age', 'bmi', 'bp', 's1', 's2', 's3', 's4', 's5', 's6'], 'confound': ['sex']} - 2024-10-17 13:53:48,933 - julearn - INFO - ==================== - 2024-10-17 13:53:48,933 - julearn - INFO - - 2024-10-17 13:53:48,934 - julearn - INFO - = Model Parameters = - 2024-10-17 13:53:48,934 - julearn - INFO - ==================== - 2024-10-17 13:53:48,934 - julearn - INFO - - 2024-10-17 13:53:48,934 - julearn - INFO - = Data Information = - 2024-10-17 13:53:48,934 - julearn - INFO - Problem type: regression - 2024-10-17 13:53:48,934 - julearn - INFO - Number of samples: 442 - 2024-10-17 13:53:48,934 - julearn - INFO - Number of features: 10 - 2024-10-17 13:53:48,934 - julearn - INFO - ==================== - 2024-10-17 13:53:48,934 - julearn - INFO - - 2024-10-17 13:53:48,934 - julearn - INFO - Target type: float64 - 2024-10-17 13:53:48,934 - julearn - INFO - Using outer CV scheme KFold(n_splits=5, random_state=None, shuffle=False) (incl. final model) + 2024-10-17 14:02:03,351 - julearn - INFO - Adding step confound_removal that applies to ColumnTypes + 2024-10-17 14:02:03,351 - julearn - INFO - Setting hyperparameter keep_confounds = True + 2024-10-17 14:02:03,352 - julearn - INFO - Step added + 2024-10-17 14:02:03,352 - julearn - INFO - Adding step pca that applies to ColumnTypes + 2024-10-17 14:02:03,352 - julearn - INFO - Step added + 2024-10-17 14:02:03,352 - julearn - INFO - Adding step linreg that applies to ColumnTypes + 2024-10-17 14:02:03,352 - julearn - INFO - Step added + 2024-10-17 14:02:03,352 - julearn - INFO - ==== Input Data ==== + 2024-10-17 14:02:03,352 - julearn - INFO - Using dataframe as input + 2024-10-17 14:02:03,352 - julearn - INFO - Features: ['age', 'bmi', 'bp', 's1', 's2', 's3', 's4', 's5', 's6', 'sex'] + 2024-10-17 14:02:03,352 - julearn - INFO - Target: target + 2024-10-17 14:02:03,352 - julearn - INFO - Expanded features: ['age', 'bmi', 'bp', 's1', 's2', 's3', 's4', 's5', 's6', 'sex'] + 2024-10-17 14:02:03,352 - julearn - INFO - X_types:{'continuous': ['age', 'bmi', 'bp', 's1', 's2', 's3', 's4', 's5', 's6'], 'confound': ['sex']} + 2024-10-17 14:02:03,353 - julearn - INFO - ==================== + 2024-10-17 14:02:03,353 - julearn - INFO - + 2024-10-17 14:02:03,354 - julearn - INFO - = Model Parameters = + 2024-10-17 14:02:03,354 - julearn - INFO - ==================== + 2024-10-17 14:02:03,354 - julearn - INFO - + 2024-10-17 14:02:03,354 - julearn - INFO - = Data Information = + 2024-10-17 14:02:03,354 - julearn - INFO - Problem type: regression + 2024-10-17 14:02:03,354 - julearn - INFO - Number of samples: 442 + 2024-10-17 14:02:03,354 - julearn - INFO - Number of features: 10 + 2024-10-17 14:02:03,354 - julearn - INFO - ==================== + 2024-10-17 14:02:03,354 - julearn - INFO - + 2024-10-17 14:02:03,354 - julearn - INFO - Target type: float64 + 2024-10-17 14:02:03,354 - julearn - INFO - Using outer CV scheme KFold(n_splits=5, random_state=None, shuffle=False) (incl. final model) .. raw:: html @@ -840,8 +840,8 @@ removal. 0 - 0.022144 - 0.007766 + 0.022143 + 0.007843 0.429556 353 89 @@ -851,8 +851,8 @@ removal. 1 - 0.022078 - 0.007774 + 0.023038 + 0.007818 0.522599 353 89 @@ -862,8 +862,8 @@ removal. 2 - 0.021953 - 0.007796 + 0.022169 + 0.007903 0.482681 354 88 @@ -873,8 +873,8 @@ removal. 3 - 0.022259 - 0.007771 + 0.027648 + 0.010719 0.426498 354 88 @@ -884,8 +884,8 @@ removal. 4 - 0.022041 - 0.007809 + 0.024092 + 0.007805 0.550248 354 88 @@ -930,7 +930,7 @@ and the confound). .. rst-class:: sphx-glr-timing - **Total running time of the script:** (0 minutes 0.642 seconds) + **Total running time of the script:** (0 minutes 0.661 seconds) .. _sphx_glr_download_auto_examples_04_confounds_run_return_confounds.py: diff --git a/pr-preview/pr-276/_sources/auto_examples/04_confounds/sg_execution_times.rst.txt b/pr-preview/pr-276/_sources/auto_examples/04_confounds/sg_execution_times.rst.txt index 3ff3319f4..a0e1ba169 100644 --- a/pr-preview/pr-276/_sources/auto_examples/04_confounds/sg_execution_times.rst.txt +++ b/pr-preview/pr-276/_sources/auto_examples/04_confounds/sg_execution_times.rst.txt @@ -6,10 +6,10 @@ Computation times ================= -**00:06.260** total execution time for **auto_examples_04_confounds** files: +**00:06.575** total execution time for **auto_examples_04_confounds** files: +----------------------------------------------------------------------------------------------------------------------------------+-----------+--------+ -| :ref:`sphx_glr_auto_examples_04_confounds_plot_confound_removal_classification.py` (``plot_confound_removal_classification.py``) | 00:05.618 | 0.0 MB | +| :ref:`sphx_glr_auto_examples_04_confounds_plot_confound_removal_classification.py` (``plot_confound_removal_classification.py``) | 00:05.913 | 0.0 MB | +----------------------------------------------------------------------------------------------------------------------------------+-----------+--------+ -| :ref:`sphx_glr_auto_examples_04_confounds_run_return_confounds.py` (``run_return_confounds.py``) | 00:00.642 | 0.0 MB | +| :ref:`sphx_glr_auto_examples_04_confounds_run_return_confounds.py` (``run_return_confounds.py``) | 00:00.661 | 0.0 MB | +----------------------------------------------------------------------------------------------------------------------------------+-----------+--------+ diff --git a/pr-preview/pr-276/_sources/auto_examples/05_customization/run_custom_scorers_regression.rst.txt b/pr-preview/pr-276/_sources/auto_examples/05_customization/run_custom_scorers_regression.rst.txt index b434c08cc..188da01c9 100644 --- a/pr-preview/pr-276/_sources/auto_examples/05_customization/run_custom_scorers_regression.rst.txt +++ b/pr-preview/pr-276/_sources/auto_examples/05_customization/run_custom_scorers_regression.rst.txt @@ -70,13 +70,13 @@ Set the logging level to info to see extra information. /home/runner/work/julearn/julearn/julearn/utils/logging.py:66: UserWarning: The '__version__' attribute is deprecated and will be removed in MarkupSafe 3.1. Use feature detection, or `importlib.metadata.version("markupsafe")`, instead. vstring = str(getattr(module, "__version__", None)) - 2024-10-17 13:53:55,037 - julearn - INFO - ===== Lib Versions ===== - 2024-10-17 13:53:55,037 - julearn - INFO - numpy: 1.26.4 - 2024-10-17 13:53:55,037 - julearn - INFO - scipy: 1.14.1 - 2024-10-17 13:53:55,037 - julearn - INFO - sklearn: 1.5.2 - 2024-10-17 13:53:55,037 - julearn - INFO - pandas: 2.2.3 - 2024-10-17 13:53:55,037 - julearn - INFO - julearn: 0.3.4.dev37 - 2024-10-17 13:53:55,037 - julearn - INFO - ======================== + 2024-10-17 14:02:09,776 - julearn - INFO - ===== Lib Versions ===== + 2024-10-17 14:02:09,776 - julearn - INFO - numpy: 1.26.4 + 2024-10-17 14:02:09,776 - julearn - INFO - scipy: 1.14.1 + 2024-10-17 14:02:09,776 - julearn - INFO - sklearn: 1.5.2 + 2024-10-17 14:02:09,776 - julearn - INFO - pandas: 2.2.3 + 2024-10-17 14:02:09,776 - julearn - INFO - julearn: 0.3.4.dev43 + 2024-10-17 14:02:09,776 - julearn - INFO - ======================== @@ -192,32 +192,32 @@ for scoring. .. code-block:: none - 2024-10-17 13:53:55,051 - julearn - INFO - ==== Input Data ==== - 2024-10-17 13:53:55,051 - julearn - INFO - Using dataframe as input - 2024-10-17 13:53:55,051 - julearn - INFO - Features: ['age', 'sex', 'bmi', 'bp', 's1', 's2', 's3', 's4', 's5', 's6'] - 2024-10-17 13:53:55,051 - julearn - INFO - Target: target - 2024-10-17 13:53:55,051 - julearn - INFO - Expanded features: ['age', 'sex', 'bmi', 'bp', 's1', 's2', 's3', 's4', 's5', 's6'] - 2024-10-17 13:53:55,051 - julearn - INFO - X_types:{} - 2024-10-17 13:53:55,052 - julearn - WARNING - The following columns are not defined in X_types: ['age', 'sex', 'bmi', 'bp', 's1', 's2', 's3', 's4', 's5', 's6']. They will be treated as continuous. + 2024-10-17 14:02:09,791 - julearn - INFO - ==== Input Data ==== + 2024-10-17 14:02:09,791 - julearn - INFO - Using dataframe as input + 2024-10-17 14:02:09,791 - julearn - INFO - Features: ['age', 'sex', 'bmi', 'bp', 's1', 's2', 's3', 's4', 's5', 's6'] + 2024-10-17 14:02:09,791 - julearn - INFO - Target: target + 2024-10-17 14:02:09,791 - julearn - INFO - Expanded features: ['age', 'sex', 'bmi', 'bp', 's1', 's2', 's3', 's4', 's5', 's6'] + 2024-10-17 14:02:09,791 - julearn - INFO - X_types:{} + 2024-10-17 14:02:09,791 - julearn - WARNING - The following columns are not defined in X_types: ['age', 'sex', 'bmi', 'bp', 's1', 's2', 's3', 's4', 's5', 's6']. They will be treated as continuous. /home/runner/work/julearn/julearn/julearn/prepare.py:509: RuntimeWarning: The following columns are not defined in X_types: ['age', 'sex', 'bmi', 'bp', 's1', 's2', 's3', 's4', 's5', 's6']. They will be treated as continuous. warn_with_log( - 2024-10-17 13:53:55,052 - julearn - INFO - ==================== - 2024-10-17 13:53:55,052 - julearn - INFO - - 2024-10-17 13:53:55,052 - julearn - INFO - Adding step zscore that applies to ColumnTypes - 2024-10-17 13:53:55,052 - julearn - INFO - Step added - 2024-10-17 13:53:55,052 - julearn - INFO - Adding step ridge that applies to ColumnTypes - 2024-10-17 13:53:55,053 - julearn - INFO - Step added - 2024-10-17 13:53:55,053 - julearn - INFO - = Model Parameters = - 2024-10-17 13:53:55,053 - julearn - INFO - ==================== - 2024-10-17 13:53:55,053 - julearn - INFO - - 2024-10-17 13:53:55,053 - julearn - INFO - = Data Information = - 2024-10-17 13:53:55,053 - julearn - INFO - Problem type: regression - 2024-10-17 13:53:55,053 - julearn - INFO - Number of samples: 442 - 2024-10-17 13:53:55,053 - julearn - INFO - Number of features: 10 - 2024-10-17 13:53:55,053 - julearn - INFO - ==================== - 2024-10-17 13:53:55,053 - julearn - INFO - - 2024-10-17 13:53:55,053 - julearn - INFO - Target type: float64 - 2024-10-17 13:53:55,054 - julearn - INFO - Using outer CV scheme KFold(n_splits=5, random_state=None, shuffle=False) (incl. final model) + 2024-10-17 14:02:09,792 - julearn - INFO - ==================== + 2024-10-17 14:02:09,792 - julearn - INFO - + 2024-10-17 14:02:09,792 - julearn - INFO - Adding step zscore that applies to ColumnTypes + 2024-10-17 14:02:09,792 - julearn - INFO - Step added + 2024-10-17 14:02:09,792 - julearn - INFO - Adding step ridge that applies to ColumnTypes + 2024-10-17 14:02:09,792 - julearn - INFO - Step added + 2024-10-17 14:02:09,793 - julearn - INFO - = Model Parameters = + 2024-10-17 14:02:09,793 - julearn - INFO - ==================== + 2024-10-17 14:02:09,793 - julearn - INFO - + 2024-10-17 14:02:09,793 - julearn - INFO - = Data Information = + 2024-10-17 14:02:09,793 - julearn - INFO - Problem type: regression + 2024-10-17 14:02:09,793 - julearn - INFO - Number of samples: 442 + 2024-10-17 14:02:09,793 - julearn - INFO - Number of features: 10 + 2024-10-17 14:02:09,793 - julearn - INFO - ==================== + 2024-10-17 14:02:09,793 - julearn - INFO - + 2024-10-17 14:02:09,793 - julearn - INFO - Target type: float64 + 2024-10-17 14:02:09,793 - julearn - INFO - Using outer CV scheme KFold(n_splits=5, random_state=None, shuffle=False) (incl. final model) @@ -271,8 +271,8 @@ The scores dataframe has all the values for each CV split. 0 - 0.004690 - 0.002261 + 0.004514 + 0.002321 -43.104359 353 89 @@ -282,8 +282,8 @@ The scores dataframe has all the values for each CV split. 1 - 0.004464 - 0.002251 + 0.004463 + 0.002284 -44.861364 353 89 @@ -293,8 +293,8 @@ The scores dataframe has all the values for each CV split. 2 - 0.004432 - 0.002243 + 0.004455 + 0.002260 -47.981407 354 88 @@ -304,8 +304,8 @@ The scores dataframe has all the values for each CV split. 3 - 0.005400 - 0.002886 + 0.004469 + 0.002284 -42.956254 354 88 @@ -315,8 +315,8 @@ The scores dataframe has all the values for each CV split. 4 - 0.004939 - 0.002305 + 0.004425 + 0.002272 -42.419886 354 88 @@ -382,32 +382,32 @@ correlation coefficient (squared) as scoring functions. .. code-block:: none - 2024-10-17 13:53:55,103 - julearn - INFO - ==== Input Data ==== - 2024-10-17 13:53:55,103 - julearn - INFO - Using dataframe as input - 2024-10-17 13:53:55,103 - julearn - INFO - Features: ['age', 'sex', 'bmi', 'bp', 's1', 's2', 's3', 's4', 's5', 's6'] - 2024-10-17 13:53:55,103 - julearn - INFO - Target: target - 2024-10-17 13:53:55,103 - julearn - INFO - Expanded features: ['age', 'sex', 'bmi', 'bp', 's1', 's2', 's3', 's4', 's5', 's6'] - 2024-10-17 13:53:55,103 - julearn - INFO - X_types:{} - 2024-10-17 13:53:55,103 - julearn - WARNING - The following columns are not defined in X_types: ['age', 'sex', 'bmi', 'bp', 's1', 's2', 's3', 's4', 's5', 's6']. They will be treated as continuous. + 2024-10-17 14:02:09,841 - julearn - INFO - ==== Input Data ==== + 2024-10-17 14:02:09,841 - julearn - INFO - Using dataframe as input + 2024-10-17 14:02:09,841 - julearn - INFO - Features: ['age', 'sex', 'bmi', 'bp', 's1', 's2', 's3', 's4', 's5', 's6'] + 2024-10-17 14:02:09,841 - julearn - INFO - Target: target + 2024-10-17 14:02:09,841 - julearn - INFO - Expanded features: ['age', 'sex', 'bmi', 'bp', 's1', 's2', 's3', 's4', 's5', 's6'] + 2024-10-17 14:02:09,841 - julearn - INFO - X_types:{} + 2024-10-17 14:02:09,841 - julearn - WARNING - The following columns are not defined in X_types: ['age', 'sex', 'bmi', 'bp', 's1', 's2', 's3', 's4', 's5', 's6']. They will be treated as continuous. /home/runner/work/julearn/julearn/julearn/prepare.py:509: RuntimeWarning: The following columns are not defined in X_types: ['age', 'sex', 'bmi', 'bp', 's1', 's2', 's3', 's4', 's5', 's6']. They will be treated as continuous. warn_with_log( - 2024-10-17 13:53:55,104 - julearn - INFO - ==================== - 2024-10-17 13:53:55,104 - julearn - INFO - - 2024-10-17 13:53:55,104 - julearn - INFO - Adding step zscore that applies to ColumnTypes - 2024-10-17 13:53:55,104 - julearn - INFO - Step added - 2024-10-17 13:53:55,104 - julearn - INFO - Adding step ridge that applies to ColumnTypes - 2024-10-17 13:53:55,104 - julearn - INFO - Step added - 2024-10-17 13:53:55,104 - julearn - INFO - = Model Parameters = - 2024-10-17 13:53:55,105 - julearn - INFO - ==================== - 2024-10-17 13:53:55,105 - julearn - INFO - - 2024-10-17 13:53:55,105 - julearn - INFO - = Data Information = - 2024-10-17 13:53:55,105 - julearn - INFO - Problem type: regression - 2024-10-17 13:53:55,105 - julearn - INFO - Number of samples: 442 - 2024-10-17 13:53:55,105 - julearn - INFO - Number of features: 10 - 2024-10-17 13:53:55,105 - julearn - INFO - ==================== - 2024-10-17 13:53:55,105 - julearn - INFO - - 2024-10-17 13:53:55,105 - julearn - INFO - Target type: float64 - 2024-10-17 13:53:55,105 - julearn - INFO - Using outer CV scheme KFold(n_splits=5, random_state=None, shuffle=False) (incl. final model) + 2024-10-17 14:02:09,842 - julearn - INFO - ==================== + 2024-10-17 14:02:09,842 - julearn - INFO - + 2024-10-17 14:02:09,842 - julearn - INFO - Adding step zscore that applies to ColumnTypes + 2024-10-17 14:02:09,842 - julearn - INFO - Step added + 2024-10-17 14:02:09,842 - julearn - INFO - Adding step ridge that applies to ColumnTypes + 2024-10-17 14:02:09,842 - julearn - INFO - Step added + 2024-10-17 14:02:09,842 - julearn - INFO - = Model Parameters = + 2024-10-17 14:02:09,843 - julearn - INFO - ==================== + 2024-10-17 14:02:09,843 - julearn - INFO - + 2024-10-17 14:02:09,843 - julearn - INFO - = Data Information = + 2024-10-17 14:02:09,843 - julearn - INFO - Problem type: regression + 2024-10-17 14:02:09,843 - julearn - INFO - Number of samples: 442 + 2024-10-17 14:02:09,843 - julearn - INFO - Number of features: 10 + 2024-10-17 14:02:09,843 - julearn - INFO - ==================== + 2024-10-17 14:02:09,843 - julearn - INFO - + 2024-10-17 14:02:09,843 - julearn - INFO - Target type: float64 + 2024-10-17 14:02:09,843 - julearn - INFO - Using outer CV scheme KFold(n_splits=5, random_state=None, shuffle=False) (incl. final model) @@ -482,7 +482,7 @@ with ``julearn``. .. code-block:: none - 2024-10-17 13:53:55,151 - julearn - INFO - registering scorer named pearsonr + 2024-10-17 14:02:09,890 - julearn - INFO - registering scorer named pearsonr @@ -513,32 +513,32 @@ Now we can use it as another scoring metric. .. code-block:: none - 2024-10-17 13:53:55,152 - julearn - INFO - ==== Input Data ==== - 2024-10-17 13:53:55,152 - julearn - INFO - Using dataframe as input - 2024-10-17 13:53:55,152 - julearn - INFO - Features: ['age', 'sex', 'bmi', 'bp', 's1', 's2', 's3', 's4', 's5', 's6'] - 2024-10-17 13:53:55,152 - julearn - INFO - Target: target - 2024-10-17 13:53:55,152 - julearn - INFO - Expanded features: ['age', 'sex', 'bmi', 'bp', 's1', 's2', 's3', 's4', 's5', 's6'] - 2024-10-17 13:53:55,152 - julearn - INFO - X_types:{} - 2024-10-17 13:53:55,152 - julearn - WARNING - The following columns are not defined in X_types: ['age', 'sex', 'bmi', 'bp', 's1', 's2', 's3', 's4', 's5', 's6']. They will be treated as continuous. + 2024-10-17 14:02:09,891 - julearn - INFO - ==== Input Data ==== + 2024-10-17 14:02:09,891 - julearn - INFO - Using dataframe as input + 2024-10-17 14:02:09,891 - julearn - INFO - Features: ['age', 'sex', 'bmi', 'bp', 's1', 's2', 's3', 's4', 's5', 's6'] + 2024-10-17 14:02:09,891 - julearn - INFO - Target: target + 2024-10-17 14:02:09,891 - julearn - INFO - Expanded features: ['age', 'sex', 'bmi', 'bp', 's1', 's2', 's3', 's4', 's5', 's6'] + 2024-10-17 14:02:09,891 - julearn - INFO - X_types:{} + 2024-10-17 14:02:09,891 - julearn - WARNING - The following columns are not defined in X_types: ['age', 'sex', 'bmi', 'bp', 's1', 's2', 's3', 's4', 's5', 's6']. They will be treated as continuous. /home/runner/work/julearn/julearn/julearn/prepare.py:509: RuntimeWarning: The following columns are not defined in X_types: ['age', 'sex', 'bmi', 'bp', 's1', 's2', 's3', 's4', 's5', 's6']. They will be treated as continuous. warn_with_log( - 2024-10-17 13:53:55,153 - julearn - INFO - ==================== - 2024-10-17 13:53:55,153 - julearn - INFO - - 2024-10-17 13:53:55,153 - julearn - INFO - Adding step zscore that applies to ColumnTypes - 2024-10-17 13:53:55,153 - julearn - INFO - Step added - 2024-10-17 13:53:55,153 - julearn - INFO - Adding step ridge that applies to ColumnTypes - 2024-10-17 13:53:55,153 - julearn - INFO - Step added - 2024-10-17 13:53:55,153 - julearn - INFO - = Model Parameters = - 2024-10-17 13:53:55,154 - julearn - INFO - ==================== - 2024-10-17 13:53:55,154 - julearn - INFO - - 2024-10-17 13:53:55,154 - julearn - INFO - = Data Information = - 2024-10-17 13:53:55,154 - julearn - INFO - Problem type: regression - 2024-10-17 13:53:55,154 - julearn - INFO - Number of samples: 442 - 2024-10-17 13:53:55,154 - julearn - INFO - Number of features: 10 - 2024-10-17 13:53:55,154 - julearn - INFO - ==================== - 2024-10-17 13:53:55,154 - julearn - INFO - - 2024-10-17 13:53:55,154 - julearn - INFO - Target type: float64 - 2024-10-17 13:53:55,154 - julearn - INFO - Using outer CV scheme KFold(n_splits=5, random_state=None, shuffle=False) (incl. final model) + 2024-10-17 14:02:09,892 - julearn - INFO - ==================== + 2024-10-17 14:02:09,892 - julearn - INFO - + 2024-10-17 14:02:09,892 - julearn - INFO - Adding step zscore that applies to ColumnTypes + 2024-10-17 14:02:09,892 - julearn - INFO - Step added + 2024-10-17 14:02:09,892 - julearn - INFO - Adding step ridge that applies to ColumnTypes + 2024-10-17 14:02:09,892 - julearn - INFO - Step added + 2024-10-17 14:02:09,892 - julearn - INFO - = Model Parameters = + 2024-10-17 14:02:09,892 - julearn - INFO - ==================== + 2024-10-17 14:02:09,892 - julearn - INFO - + 2024-10-17 14:02:09,892 - julearn - INFO - = Data Information = + 2024-10-17 14:02:09,893 - julearn - INFO - Problem type: regression + 2024-10-17 14:02:09,893 - julearn - INFO - Number of samples: 442 + 2024-10-17 14:02:09,893 - julearn - INFO - Number of features: 10 + 2024-10-17 14:02:09,893 - julearn - INFO - ==================== + 2024-10-17 14:02:09,893 - julearn - INFO - + 2024-10-17 14:02:09,893 - julearn - INFO - Target type: float64 + 2024-10-17 14:02:09,893 - julearn - INFO - Using outer CV scheme KFold(n_splits=5, random_state=None, shuffle=False) (incl. final model) @@ -546,7 +546,7 @@ Now we can use it as another scoring metric. .. rst-class:: sphx-glr-timing - **Total running time of the script:** (0 minutes 0.173 seconds) + **Total running time of the script:** (0 minutes 0.174 seconds) .. _sphx_glr_download_auto_examples_05_customization_run_custom_scorers_regression.py: diff --git a/pr-preview/pr-276/_sources/auto_examples/05_customization/sg_execution_times.rst.txt b/pr-preview/pr-276/_sources/auto_examples/05_customization/sg_execution_times.rst.txt index d5604d5bc..b54f8bdf2 100644 --- a/pr-preview/pr-276/_sources/auto_examples/05_customization/sg_execution_times.rst.txt +++ b/pr-preview/pr-276/_sources/auto_examples/05_customization/sg_execution_times.rst.txt @@ -6,8 +6,8 @@ Computation times ================= -**00:00.173** total execution time for **auto_examples_05_customization** files: +**00:00.174** total execution time for **auto_examples_05_customization** files: +------------------------------------------------------------------------------------------------------------------------+-----------+--------+ -| :ref:`sphx_glr_auto_examples_05_customization_run_custom_scorers_regression.py` (``run_custom_scorers_regression.py``) | 00:00.173 | 0.0 MB | +| :ref:`sphx_glr_auto_examples_05_customization_run_custom_scorers_regression.py` (``run_custom_scorers_regression.py``) | 00:00.174 | 0.0 MB | +------------------------------------------------------------------------------------------------------------------------+-----------+--------+ diff --git a/pr-preview/pr-276/_sources/whats_new.rst.txt b/pr-preview/pr-276/_sources/whats_new.rst.txt index 7a99c53dc..8ef257e4b 100644 --- a/pr-preview/pr-276/_sources/whats_new.rst.txt +++ b/pr-preview/pr-276/_sources/whats_new.rst.txt @@ -8,7 +8,7 @@ What's new .. towncrier release notes start -Julearn 0.3.4.dev37 (2024-10-17) +Julearn 0.3.4.dev43 (2024-10-17) -------------------------------- No significant changes. @@ -33,6 +33,8 @@ Enhancements API as :func:`.run_cross_validation` by `Fede Raimondo`_. (:gh:`271`) - Optimise wrapping of steps and models in the pipeline only when a subset of features is being used, by `Fede Raimondo`_ (:gh:`274`) +- Place the final model CV split at the beginning instead of the end of the CV + iterator wrapper by `Fede Raimondo`_ (:gh:`275`) - Change the internal logic of :func:`.run_cross_validation` to optimise joblib calls by `Fede Raimondo`_ (:gh:`293`) diff --git a/pr-preview/pr-276/auto_examples/00_starting/plot_cm_acc_multiclass.html b/pr-preview/pr-276/auto_examples/00_starting/plot_cm_acc_multiclass.html index dedef3545..31bce57bf 100644 --- a/pr-preview/pr-276/auto_examples/00_starting/plot_cm_acc_multiclass.html +++ b/pr-preview/pr-276/auto_examples/00_starting/plot_cm_acc_multiclass.html @@ -421,13 +421,13 @@ 

/home/runner/work/julearn/julearn/julearn/utils/logging.py:66: UserWarning: The '__version__' attribute is deprecated and will be removed in MarkupSafe 3.1. Use feature detection, or `importlib.metadata.version("markupsafe")`, instead.
   vstring = str(getattr(module, "__version__", None))
-2024-10-17 13:53:25,244 - julearn - INFO - ===== Lib Versions =====
-2024-10-17 13:53:25,244 - julearn - INFO - numpy: 1.26.4
-2024-10-17 13:53:25,244 - julearn - INFO - scipy: 1.14.1
-2024-10-17 13:53:25,244 - julearn - INFO - sklearn: 1.5.2
-2024-10-17 13:53:25,244 - julearn - INFO - pandas: 2.2.3
-2024-10-17 13:53:25,244 - julearn - INFO - julearn: 0.3.4.dev37
-2024-10-17 13:53:25,244 - julearn - INFO - ========================
+2024-10-17 14:01:40,171 - julearn - INFO - ===== Lib Versions =====
+2024-10-17 14:01:40,171 - julearn - INFO - numpy: 1.26.4
+2024-10-17 14:01:40,171 - julearn - INFO - scipy: 1.14.1
+2024-10-17 14:01:40,171 - julearn - INFO - sklearn: 1.5.2
+2024-10-17 14:01:40,171 - julearn - INFO - pandas: 2.2.3
+2024-10-17 14:01:40,171 - julearn - INFO - julearn: 0.3.4.dev43
+2024-10-17 14:01:40,171 - julearn - INFO - ========================

load the iris data from seaborn

@@ -459,39 +459,39 @@ ) -
2024-10-17 13:53:25,247 - julearn - INFO - ==== Input Data ====
-2024-10-17 13:53:25,247 - julearn - INFO - Using dataframe as input
-2024-10-17 13:53:25,247 - julearn - INFO -      Features: ['sepal_length', 'sepal_width', 'petal_length']
-2024-10-17 13:53:25,248 - julearn - INFO -      Target: species
-2024-10-17 13:53:25,248 - julearn - INFO -      Expanded features: ['sepal_length', 'sepal_width', 'petal_length']
-2024-10-17 13:53:25,248 - julearn - INFO -      X_types:{}
-2024-10-17 13:53:25,248 - julearn - WARNING - The following columns are not defined in X_types: ['sepal_length', 'sepal_width', 'petal_length']. They will be treated as continuous.
+
2024-10-17 14:01:40,174 - julearn - INFO - ==== Input Data ====
+2024-10-17 14:01:40,174 - julearn - INFO - Using dataframe as input
+2024-10-17 14:01:40,174 - julearn - INFO -      Features: ['sepal_length', 'sepal_width', 'petal_length']
+2024-10-17 14:01:40,175 - julearn - INFO -      Target: species
+2024-10-17 14:01:40,175 - julearn - INFO -      Expanded features: ['sepal_length', 'sepal_width', 'petal_length']
+2024-10-17 14:01:40,175 - julearn - INFO -      X_types:{}
+2024-10-17 14:01:40,175 - julearn - WARNING - The following columns are not defined in X_types: ['sepal_length', 'sepal_width', 'petal_length']. They will be treated as continuous.
 /home/runner/work/julearn/julearn/julearn/prepare.py:509: RuntimeWarning: The following columns are not defined in X_types: ['sepal_length', 'sepal_width', 'petal_length']. They will be treated as continuous.
   warn_with_log(
-2024-10-17 13:53:25,248 - julearn - INFO - ====================
-2024-10-17 13:53:25,249 - julearn - INFO -
-2024-10-17 13:53:25,249 - julearn - INFO - Adding step zscore that applies to ColumnTypes<types={'continuous'}; pattern=(?:__:type:__continuous)>
-2024-10-17 13:53:25,249 - julearn - INFO - Step added
-2024-10-17 13:53:25,249 - julearn - INFO - Adding step svm that applies to ColumnTypes<types={'continuous'}; pattern=(?:__:type:__continuous)>
-2024-10-17 13:53:25,249 - julearn - INFO - Step added
-2024-10-17 13:53:25,249 - julearn - INFO - = Model Parameters =
-2024-10-17 13:53:25,249 - julearn - INFO - ====================
-2024-10-17 13:53:25,250 - julearn - INFO -
-2024-10-17 13:53:25,250 - julearn - INFO - = Data Information =
-2024-10-17 13:53:25,250 - julearn - INFO -      Problem type: classification
-2024-10-17 13:53:25,250 - julearn - INFO -      Number of samples: 120
-2024-10-17 13:53:25,250 - julearn - INFO -      Number of features: 3
-2024-10-17 13:53:25,250 - julearn - INFO - ====================
-2024-10-17 13:53:25,250 - julearn - INFO -
-2024-10-17 13:53:25,250 - julearn - INFO -      Number of classes: 3
-2024-10-17 13:53:25,250 - julearn - INFO -      Target type: object
-2024-10-17 13:53:25,250 - julearn - INFO -      Class distributions: species
+2024-10-17 14:01:40,175 - julearn - INFO - ====================
+2024-10-17 14:01:40,175 - julearn - INFO -
+2024-10-17 14:01:40,176 - julearn - INFO - Adding step zscore that applies to ColumnTypes<types={'continuous'}; pattern=(?:__:type:__continuous)>
+2024-10-17 14:01:40,176 - julearn - INFO - Step added
+2024-10-17 14:01:40,176 - julearn - INFO - Adding step svm that applies to ColumnTypes<types={'continuous'}; pattern=(?:__:type:__continuous)>
+2024-10-17 14:01:40,176 - julearn - INFO - Step added
+2024-10-17 14:01:40,176 - julearn - INFO - = Model Parameters =
+2024-10-17 14:01:40,176 - julearn - INFO - ====================
+2024-10-17 14:01:40,176 - julearn - INFO -
+2024-10-17 14:01:40,177 - julearn - INFO - = Data Information =
+2024-10-17 14:01:40,177 - julearn - INFO -      Problem type: classification
+2024-10-17 14:01:40,177 - julearn - INFO -      Number of samples: 120
+2024-10-17 14:01:40,177 - julearn - INFO -      Number of features: 3
+2024-10-17 14:01:40,177 - julearn - INFO - ====================
+2024-10-17 14:01:40,177 - julearn - INFO -
+2024-10-17 14:01:40,177 - julearn - INFO -      Number of classes: 3
+2024-10-17 14:01:40,177 - julearn - INFO -      Target type: object
+2024-10-17 14:01:40,177 - julearn - INFO -      Class distributions: species
 versicolor    40
 virginica     40
 setosa        40
 Name: count, dtype: int64
-2024-10-17 13:53:25,251 - julearn - INFO - Using outer CV scheme RepeatedKFold(n_repeats=5, n_splits=5, random_state=200) (incl. final model)
-2024-10-17 13:53:25,251 - julearn - INFO - Multi-class classification problem detected #classes = 3.
+2024-10-17 14:01:40,178 - julearn - INFO - Using outer CV scheme RepeatedKFold(n_repeats=5, n_splits=5, random_state=200) (incl. final model)
+2024-10-17 14:01:40,178 - julearn - INFO - Multi-class classification problem detected #classes = 3.
 

 

 
The scores dataframe has all the values for each CV split.

@@ -530,8 +530,8 @@
   
     
       0
-      0.004849
-      0.002696
+      0.004489
+      0.002588
       0.916667
       96
       24
@@ -541,8 +541,8 @@
     
     
       1
-      0.004468
-      0.002540
+      0.004421
+      0.002629
       0.833333
       96
       24
@@ -552,8 +552,8 @@
     
     
       2
-      0.004422
-      0.002557
+      0.004480
+      0.002599
       0.958333
       96
       24
@@ -563,8 +563,8 @@
     
     
       3
-      0.004423
-      0.002543
+      0.004435
+      0.002597
       0.916667
       96
       24
@@ -574,8 +574,8 @@
     
     
       4
-      0.004364
-      0.002551
+      0.004440
+      0.002539
       0.833333
       96
       24
@@ -734,7 +734,7 @@
 Confusion matrix
Text(0.5, 1.0, 'Confusion matrix')
 

 

-
Total running time of the script: (0 minutes 0.534 seconds)

+
Total running time of the script: (0 minutes 0.529 seconds)
-
2024-10-17 13:53:27,143 - julearn - INFO - ==== Input Data ====
-2024-10-17 13:53:27,143 - julearn - INFO - Using dataframe as input
-2024-10-17 13:53:27,143 - julearn - INFO -      Features: ['age', 'sex', 'bmi', 'bp', 's1', 's2', 's3', 's4', 's5', 's6']
-2024-10-17 13:53:27,143 - julearn - INFO -      Target: target
-2024-10-17 13:53:27,143 - julearn - INFO -      Expanded features: ['age', 'sex', 'bmi', 'bp', 's1', 's2', 's3', 's4', 's5', 's6']
-2024-10-17 13:53:27,143 - julearn - INFO -      X_types:{}
-2024-10-17 13:53:27,144 - julearn - WARNING - The following columns are not defined in X_types: ['age', 'sex', 'bmi', 'bp', 's1', 's2', 's3', 's4', 's5', 's6']. They will be treated as continuous.
+
2024-10-17 14:01:42,039 - julearn - INFO - ==== Input Data ====
+2024-10-17 14:01:42,039 - julearn - INFO - Using dataframe as input
+2024-10-17 14:01:42,039 - julearn - INFO -      Features: ['age', 'sex', 'bmi', 'bp', 's1', 's2', 's3', 's4', 's5', 's6']
+2024-10-17 14:01:42,039 - julearn - INFO -      Target: target
+2024-10-17 14:01:42,039 - julearn - INFO -      Expanded features: ['age', 'sex', 'bmi', 'bp', 's1', 's2', 's3', 's4', 's5', 's6']
+2024-10-17 14:01:42,039 - julearn - INFO -      X_types:{}
+2024-10-17 14:01:42,039 - julearn - WARNING - The following columns are not defined in X_types: ['age', 'sex', 'bmi', 'bp', 's1', 's2', 's3', 's4', 's5', 's6']. They will be treated as continuous.
 /home/runner/work/julearn/julearn/julearn/prepare.py:509: RuntimeWarning: The following columns are not defined in X_types: ['age', 'sex', 'bmi', 'bp', 's1', 's2', 's3', 's4', 's5', 's6']. They will be treated as continuous.
   warn_with_log(
-2024-10-17 13:53:27,144 - julearn - INFO - ====================
-2024-10-17 13:53:27,144 - julearn - INFO -
-2024-10-17 13:53:27,144 - julearn - INFO - Adding step zscore that applies to ColumnTypes<types={'continuous'}; pattern=(?:__:type:__continuous)>
-2024-10-17 13:53:27,145 - julearn - INFO - Step added
-2024-10-17 13:53:27,145 - julearn - INFO - Adding step ridge that applies to ColumnTypes<types={'continuous'}; pattern=(?:__:type:__continuous)>
-2024-10-17 13:53:27,145 - julearn - INFO - Step added
-2024-10-17 13:53:27,145 - julearn - INFO - = Model Parameters =
-2024-10-17 13:53:27,145 - julearn - INFO - ====================
-2024-10-17 13:53:27,145 - julearn - INFO -
-2024-10-17 13:53:27,145 - julearn - INFO - = Data Information =
-2024-10-17 13:53:27,145 - julearn - INFO -      Problem type: regression
-2024-10-17 13:53:27,146 - julearn - INFO -      Number of samples: 309
-2024-10-17 13:53:27,146 - julearn - INFO -      Number of features: 10
-2024-10-17 13:53:27,146 - julearn - INFO - ====================
-2024-10-17 13:53:27,146 - julearn - INFO -
-2024-10-17 13:53:27,146 - julearn - INFO -      Target type: float64
-2024-10-17 13:53:27,146 - julearn - INFO - Using outer CV scheme KFold(n_splits=5, random_state=None, shuffle=False) (incl. final model)
+2024-10-17 14:01:42,040 - julearn - INFO - ====================
+2024-10-17 14:01:42,040 - julearn - INFO -
+2024-10-17 14:01:42,040 - julearn - INFO - Adding step zscore that applies to ColumnTypes<types={'continuous'}; pattern=(?:__:type:__continuous)>
+2024-10-17 14:01:42,040 - julearn - INFO - Step added
+2024-10-17 14:01:42,040 - julearn - INFO - Adding step ridge that applies to ColumnTypes<types={'continuous'}; pattern=(?:__:type:__continuous)>
+2024-10-17 14:01:42,040 - julearn - INFO - Step added
+2024-10-17 14:01:42,041 - julearn - INFO - = Model Parameters =
+2024-10-17 14:01:42,041 - julearn - INFO - ====================
+2024-10-17 14:01:42,041 - julearn - INFO -
+2024-10-17 14:01:42,041 - julearn - INFO - = Data Information =
+2024-10-17 14:01:42,041 - julearn - INFO -      Problem type: regression
+2024-10-17 14:01:42,041 - julearn - INFO -      Number of samples: 309
+2024-10-17 14:01:42,041 - julearn - INFO -      Number of features: 10
+2024-10-17 14:01:42,041 - julearn - INFO - ====================
+2024-10-17 14:01:42,041 - julearn - INFO -
+2024-10-17 14:01:42,041 - julearn - INFO -      Target type: float64
+2024-10-17 14:01:42,041 - julearn - INFO - Using outer CV scheme KFold(n_splits=5, random_state=None, shuffle=False) (incl. final model)
 

 

 
The scores dataframe has all the values for each CV split.

@@ -568,8 +568,8 @@
   
     
       0
-      0.005148
-      0.002323
+      0.004470
+      0.002319
       -48.783874
       247
       62
@@ -579,8 +579,8 @@
     
     
       1
-      0.004456
-      0.002264
+      0.004408
+      0.002301
       -47.573568
       247
       62
@@ -590,8 +590,8 @@
     
     
       2
-      0.004519
-      0.002297
+      0.004419
+      0.002285
       -37.617474
       247
       62
@@ -601,8 +601,8 @@
     
     
       3
-      0.004482
-      0.002329
+      0.004452
+      0.002309
       -47.686852
       247
       62
@@ -612,8 +612,8 @@
     
     
       4
-      0.004398
-      0.002251
+      0.004408
+      0.002306
       -45.558655
       248
       61
@@ -744,7 +744,7 @@
 Actual vs Predicted
(9.649999999999999, 347.35, 9.649999999999999, 347.35)
 

 

-
Total running time of the script: (0 minutes 0.669 seconds)

+
Total running time of the script: (0 minutes 0.663 seconds)

From the histogram above, we can see that the data is not uniformly @@ -535,32 +535,32 @@ ) -

2024-10-17 13:53:26,560 - julearn - INFO - ==== Input Data ====
-2024-10-17 13:53:26,560 - julearn - INFO - Using dataframe as input
-2024-10-17 13:53:26,561 - julearn - INFO -      Features: ['age', 'sex', 'bmi', 'bp', 's1', 's2', 's3', 's4', 's5', 's6']
-2024-10-17 13:53:26,561 - julearn - INFO -      Target: target
-2024-10-17 13:53:26,561 - julearn - INFO -      Expanded features: ['age', 'sex', 'bmi', 'bp', 's1', 's2', 's3', 's4', 's5', 's6']
-2024-10-17 13:53:26,561 - julearn - INFO -      X_types:{}
-2024-10-17 13:53:26,561 - julearn - WARNING - The following columns are not defined in X_types: ['age', 'sex', 'bmi', 'bp', 's1', 's2', 's3', 's4', 's5', 's6']. They will be treated as continuous.
+
2024-10-17 14:01:41,471 - julearn - INFO - ==== Input Data ====
+2024-10-17 14:01:41,471 - julearn - INFO - Using dataframe as input
+2024-10-17 14:01:41,471 - julearn - INFO -      Features: ['age', 'sex', 'bmi', 'bp', 's1', 's2', 's3', 's4', 's5', 's6']
+2024-10-17 14:01:41,471 - julearn - INFO -      Target: target
+2024-10-17 14:01:41,471 - julearn - INFO -      Expanded features: ['age', 'sex', 'bmi', 'bp', 's1', 's2', 's3', 's4', 's5', 's6']
+2024-10-17 14:01:41,472 - julearn - INFO -      X_types:{}
+2024-10-17 14:01:41,472 - julearn - WARNING - The following columns are not defined in X_types: ['age', 'sex', 'bmi', 'bp', 's1', 's2', 's3', 's4', 's5', 's6']. They will be treated as continuous.
 /home/runner/work/julearn/julearn/julearn/prepare.py:509: RuntimeWarning: The following columns are not defined in X_types: ['age', 'sex', 'bmi', 'bp', 's1', 's2', 's3', 's4', 's5', 's6']. They will be treated as continuous.
   warn_with_log(
-2024-10-17 13:53:26,562 - julearn - INFO - ====================
-2024-10-17 13:53:26,562 - julearn - INFO -
-2024-10-17 13:53:26,562 - julearn - INFO - Adding step zscore that applies to ColumnTypes<types={'continuous'}; pattern=(?:__:type:__continuous)>
-2024-10-17 13:53:26,562 - julearn - INFO - Step added
-2024-10-17 13:53:26,562 - julearn - INFO - Adding step linreg that applies to ColumnTypes<types={'continuous'}; pattern=(?:__:type:__continuous)>
-2024-10-17 13:53:26,563 - julearn - INFO - Step added
-2024-10-17 13:53:26,563 - julearn - INFO - = Model Parameters =
-2024-10-17 13:53:26,563 - julearn - INFO - ====================
-2024-10-17 13:53:26,563 - julearn - INFO -
-2024-10-17 13:53:26,563 - julearn - INFO - = Data Information =
-2024-10-17 13:53:26,563 - julearn - INFO -      Problem type: regression
-2024-10-17 13:53:26,563 - julearn - INFO -      Number of samples: 449
-2024-10-17 13:53:26,563 - julearn - INFO -      Number of features: 10
-2024-10-17 13:53:26,563 - julearn - INFO - ====================
-2024-10-17 13:53:26,563 - julearn - INFO -
-2024-10-17 13:53:26,563 - julearn - INFO -      Target type: float64
-2024-10-17 13:53:26,564 - julearn - INFO - Using outer CV scheme ContinuousStratifiedKFold(method='binning', n_bins=40, n_splits=5,
+2024-10-17 14:01:41,472 - julearn - INFO - ====================
+2024-10-17 14:01:41,473 - julearn - INFO -
+2024-10-17 14:01:41,473 - julearn - INFO - Adding step zscore that applies to ColumnTypes<types={'continuous'}; pattern=(?:__:type:__continuous)>
+2024-10-17 14:01:41,473 - julearn - INFO - Step added
+2024-10-17 14:01:41,473 - julearn - INFO - Adding step linreg that applies to ColumnTypes<types={'continuous'}; pattern=(?:__:type:__continuous)>
+2024-10-17 14:01:41,473 - julearn - INFO - Step added
+2024-10-17 14:01:41,473 - julearn - INFO - = Model Parameters =
+2024-10-17 14:01:41,473 - julearn - INFO - ====================
+2024-10-17 14:01:41,473 - julearn - INFO -
+2024-10-17 14:01:41,474 - julearn - INFO - = Data Information =
+2024-10-17 14:01:41,474 - julearn - INFO -      Problem type: regression
+2024-10-17 14:01:41,474 - julearn - INFO -      Number of samples: 449
+2024-10-17 14:01:41,474 - julearn - INFO -      Number of features: 10
+2024-10-17 14:01:41,474 - julearn - INFO - ====================
+2024-10-17 14:01:41,474 - julearn - INFO -
+2024-10-17 14:01:41,474 - julearn - INFO -      Target type: float64
+2024-10-17 14:01:41,474 - julearn - INFO - Using outer CV scheme ContinuousStratifiedKFold(method='binning', n_bins=40, n_splits=5,
              random_state=None, shuffle=False) (incl. final model)
 /opt/hostedtoolcache/Python/3.10.15/x64/lib/python3.10/site-packages/sklearn/model_selection/_split.py:776: UserWarning: The least populated class in y has only 1 members, which is less than n_splits=5.
   warnings.warn(
@@ -583,32 +583,32 @@
 )
 

 

-
2024-10-17 13:53:26,611 - julearn - INFO - ==== Input Data ====
-2024-10-17 13:53:26,611 - julearn - INFO - Using dataframe as input
-2024-10-17 13:53:26,611 - julearn - INFO -      Features: ['age', 'sex', 'bmi', 'bp', 's1', 's2', 's3', 's4', 's5', 's6']
-2024-10-17 13:53:26,611 - julearn - INFO -      Target: target
-2024-10-17 13:53:26,611 - julearn - INFO -      Expanded features: ['age', 'sex', 'bmi', 'bp', 's1', 's2', 's3', 's4', 's5', 's6']
-2024-10-17 13:53:26,611 - julearn - INFO -      X_types:{}
-2024-10-17 13:53:26,611 - julearn - WARNING - The following columns are not defined in X_types: ['age', 'sex', 'bmi', 'bp', 's1', 's2', 's3', 's4', 's5', 's6']. They will be treated as continuous.
+
2024-10-17 14:01:41,521 - julearn - INFO - ==== Input Data ====
+2024-10-17 14:01:41,521 - julearn - INFO - Using dataframe as input
+2024-10-17 14:01:41,521 - julearn - INFO -      Features: ['age', 'sex', 'bmi', 'bp', 's1', 's2', 's3', 's4', 's5', 's6']
+2024-10-17 14:01:41,521 - julearn - INFO -      Target: target
+2024-10-17 14:01:41,521 - julearn - INFO -      Expanded features: ['age', 'sex', 'bmi', 'bp', 's1', 's2', 's3', 's4', 's5', 's6']
+2024-10-17 14:01:41,521 - julearn - INFO -      X_types:{}
+2024-10-17 14:01:41,521 - julearn - WARNING - The following columns are not defined in X_types: ['age', 'sex', 'bmi', 'bp', 's1', 's2', 's3', 's4', 's5', 's6']. They will be treated as continuous.
 /home/runner/work/julearn/julearn/julearn/prepare.py:509: RuntimeWarning: The following columns are not defined in X_types: ['age', 'sex', 'bmi', 'bp', 's1', 's2', 's3', 's4', 's5', 's6']. They will be treated as continuous.
   warn_with_log(
-2024-10-17 13:53:26,612 - julearn - INFO - ====================
-2024-10-17 13:53:26,612 - julearn - INFO -
-2024-10-17 13:53:26,612 - julearn - INFO - Adding step zscore that applies to ColumnTypes<types={'continuous'}; pattern=(?:__:type:__continuous)>
-2024-10-17 13:53:26,612 - julearn - INFO - Step added
-2024-10-17 13:53:26,612 - julearn - INFO - Adding step linreg that applies to ColumnTypes<types={'continuous'}; pattern=(?:__:type:__continuous)>
-2024-10-17 13:53:26,612 - julearn - INFO - Step added
-2024-10-17 13:53:26,612 - julearn - INFO - = Model Parameters =
-2024-10-17 13:53:26,612 - julearn - INFO - ====================
-2024-10-17 13:53:26,612 - julearn - INFO -
-2024-10-17 13:53:26,612 - julearn - INFO - = Data Information =
-2024-10-17 13:53:26,613 - julearn - INFO -      Problem type: regression
-2024-10-17 13:53:26,613 - julearn - INFO -      Number of samples: 449
-2024-10-17 13:53:26,613 - julearn - INFO -      Number of features: 10
-2024-10-17 13:53:26,613 - julearn - INFO - ====================
-2024-10-17 13:53:26,613 - julearn - INFO -
-2024-10-17 13:53:26,613 - julearn - INFO -      Target type: float64
-2024-10-17 13:53:26,613 - julearn - INFO - Using outer CV scheme KFold(n_splits=5, random_state=None, shuffle=False) (incl. final model)
+2024-10-17 14:01:41,522 - julearn - INFO - ====================
+2024-10-17 14:01:41,522 - julearn - INFO -
+2024-10-17 14:01:41,522 - julearn - INFO - Adding step zscore that applies to ColumnTypes<types={'continuous'}; pattern=(?:__:type:__continuous)>
+2024-10-17 14:01:41,522 - julearn - INFO - Step added
+2024-10-17 14:01:41,522 - julearn - INFO - Adding step linreg that applies to ColumnTypes<types={'continuous'}; pattern=(?:__:type:__continuous)>
+2024-10-17 14:01:41,522 - julearn - INFO - Step added
+2024-10-17 14:01:41,522 - julearn - INFO - = Model Parameters =
+2024-10-17 14:01:41,522 - julearn - INFO - ====================
+2024-10-17 14:01:41,522 - julearn - INFO -
+2024-10-17 14:01:41,522 - julearn - INFO - = Data Information =
+2024-10-17 14:01:41,522 - julearn - INFO -      Problem type: regression
+2024-10-17 14:01:41,523 - julearn - INFO -      Number of samples: 449
+2024-10-17 14:01:41,523 - julearn - INFO -      Number of features: 10
+2024-10-17 14:01:41,523 - julearn - INFO - ====================
+2024-10-17 14:01:41,523 - julearn - INFO -
+2024-10-17 14:01:41,523 - julearn - INFO -      Target type: float64
+2024-10-17 14:01:41,523 - julearn - INFO - Using outer CV scheme KFold(n_splits=5, random_state=None, shuffle=False) (incl. final model)
 

 

 
Now we can compare the test score for model trained with and without
@@ -635,7 +635,7 @@
   data_subset = grouped_data.get_group(pd_key)
 

 

-
Total running time of the script: (0 minutes 0.855 seconds)

+
Total running time of the script: (0 minutes 0.862 seconds)
-
2024-10-17 13:53:23,775 - julearn - INFO - ==== Input Data ====
-2024-10-17 13:53:23,775 - julearn - INFO - Using dataframe as input
-2024-10-17 13:53:23,775 - julearn - INFO -      Features: ['parietal', 'frontal']
-2024-10-17 13:53:23,775 - julearn - INFO -      Target: event
-2024-10-17 13:53:23,775 - julearn - INFO -      Expanded features: ['parietal', 'frontal']
-2024-10-17 13:53:23,775 - julearn - INFO -      X_types:{}
-2024-10-17 13:53:23,775 - julearn - WARNING - The following columns are not defined in X_types: ['parietal', 'frontal']. They will be treated as continuous.
+
2024-10-17 14:01:38,700 - julearn - INFO - ==== Input Data ====
+2024-10-17 14:01:38,700 - julearn - INFO - Using dataframe as input
+2024-10-17 14:01:38,700 - julearn - INFO -      Features: ['parietal', 'frontal']
+2024-10-17 14:01:38,700 - julearn - INFO -      Target: event
+2024-10-17 14:01:38,700 - julearn - INFO -      Expanded features: ['parietal', 'frontal']
+2024-10-17 14:01:38,700 - julearn - INFO -      X_types:{}
+2024-10-17 14:01:38,700 - julearn - WARNING - The following columns are not defined in X_types: ['parietal', 'frontal']. They will be treated as continuous.
 /home/runner/work/julearn/julearn/julearn/prepare.py:509: RuntimeWarning: The following columns are not defined in X_types: ['parietal', 'frontal']. They will be treated as continuous.
   warn_with_log(
-2024-10-17 13:53:23,776 - julearn - INFO - ====================
-2024-10-17 13:53:23,776 - julearn - INFO -
-2024-10-17 13:53:23,776 - julearn - INFO - Adding step zscore that applies to ColumnTypes<types={'continuous'}; pattern=(?:__:type:__continuous)>
-2024-10-17 13:53:23,776 - julearn - INFO - Step added
-2024-10-17 13:53:23,776 - julearn - INFO - Adding step rf that applies to ColumnTypes<types={'continuous'}; pattern=(?:__:type:__continuous)>
-2024-10-17 13:53:23,777 - julearn - INFO - Step added
-2024-10-17 13:53:23,777 - julearn - INFO - = Model Parameters =
-2024-10-17 13:53:23,777 - julearn - INFO - ====================
-2024-10-17 13:53:23,777 - julearn - INFO -
-2024-10-17 13:53:23,777 - julearn - INFO - = Data Information =
-2024-10-17 13:53:23,777 - julearn - INFO -      Problem type: classification
-2024-10-17 13:53:23,777 - julearn - INFO -      Number of samples: 532
-2024-10-17 13:53:23,777 - julearn - INFO -      Number of features: 2
-2024-10-17 13:53:23,777 - julearn - INFO - ====================
-2024-10-17 13:53:23,777 - julearn - INFO -
-2024-10-17 13:53:23,778 - julearn - INFO -      Number of classes: 2
-2024-10-17 13:53:23,778 - julearn - INFO -      Target type: object
-2024-10-17 13:53:23,778 - julearn - INFO -      Class distributions: event
+2024-10-17 14:01:38,701 - julearn - INFO - ====================
+2024-10-17 14:01:38,701 - julearn - INFO -
+2024-10-17 14:01:38,701 - julearn - INFO - Adding step zscore that applies to ColumnTypes<types={'continuous'}; pattern=(?:__:type:__continuous)>
+2024-10-17 14:01:38,701 - julearn - INFO - Step added
+2024-10-17 14:01:38,701 - julearn - INFO - Adding step rf that applies to ColumnTypes<types={'continuous'}; pattern=(?:__:type:__continuous)>
+2024-10-17 14:01:38,701 - julearn - INFO - Step added
+2024-10-17 14:01:38,702 - julearn - INFO - = Model Parameters =
+2024-10-17 14:01:38,702 - julearn - INFO - ====================
+2024-10-17 14:01:38,702 - julearn - INFO -
+2024-10-17 14:01:38,702 - julearn - INFO - = Data Information =
+2024-10-17 14:01:38,702 - julearn - INFO -      Problem type: classification
+2024-10-17 14:01:38,702 - julearn - INFO -      Number of samples: 532
+2024-10-17 14:01:38,702 - julearn - INFO -      Number of features: 2
+2024-10-17 14:01:38,702 - julearn - INFO - ====================
+2024-10-17 14:01:38,702 - julearn - INFO -
+2024-10-17 14:01:38,703 - julearn - INFO -      Number of classes: 2
+2024-10-17 14:01:38,703 - julearn - INFO -      Target type: object
+2024-10-17 14:01:38,703 - julearn - INFO -      Class distributions: event
 cue     266
 stim    266
 Name: count, dtype: int64
-2024-10-17 13:53:23,778 - julearn - INFO - Using outer CV scheme KFold(n_splits=5, random_state=None, shuffle=False)
-2024-10-17 13:53:23,779 - julearn - INFO - Binary classification problem detected.
+2024-10-17 14:01:38,703 - julearn - INFO - Using outer CV scheme KFold(n_splits=5, random_state=None, shuffle=False)
+2024-10-17 14:01:38,704 - julearn - INFO - Binary classification problem detected.
 0.6841826838300122
 

 

@@ -581,38 +581,38 @@ 
Dealing with Cross-Validation techniquesprint(scores["test_score"].mean())
-
2024-10-17 13:53:24,399 - julearn - INFO - ==== Input Data ====
-2024-10-17 13:53:24,399 - julearn - INFO - Using dataframe as input
-2024-10-17 13:53:24,399 - julearn - INFO -      Features: ['parietal', 'frontal']
-2024-10-17 13:53:24,399 - julearn - INFO -      Target: event
-2024-10-17 13:53:24,399 - julearn - INFO -      Expanded features: ['parietal', 'frontal']
-2024-10-17 13:53:24,399 - julearn - INFO -      X_types:{}
-2024-10-17 13:53:24,399 - julearn - WARNING - The following columns are not defined in X_types: ['parietal', 'frontal']. They will be treated as continuous.
+
2024-10-17 14:01:39,328 - julearn - INFO - ==== Input Data ====
+2024-10-17 14:01:39,328 - julearn - INFO - Using dataframe as input
+2024-10-17 14:01:39,328 - julearn - INFO -      Features: ['parietal', 'frontal']
+2024-10-17 14:01:39,328 - julearn - INFO -      Target: event
+2024-10-17 14:01:39,328 - julearn - INFO -      Expanded features: ['parietal', 'frontal']
+2024-10-17 14:01:39,328 - julearn - INFO -      X_types:{}
+2024-10-17 14:01:39,328 - julearn - WARNING - The following columns are not defined in X_types: ['parietal', 'frontal']. They will be treated as continuous.
 /home/runner/work/julearn/julearn/julearn/prepare.py:509: RuntimeWarning: The following columns are not defined in X_types: ['parietal', 'frontal']. They will be treated as continuous.
   warn_with_log(
-2024-10-17 13:53:24,400 - julearn - INFO - Using subject as groups
-2024-10-17 13:53:24,400 - julearn - INFO - ====================
-2024-10-17 13:53:24,400 - julearn - INFO -
-2024-10-17 13:53:24,400 - julearn - INFO - Adding step rf that applies to ColumnTypes<types={'continuous'}; pattern=(?:__:type:__continuous)>
-2024-10-17 13:53:24,400 - julearn - INFO - Step added
-2024-10-17 13:53:24,401 - julearn - INFO - = Model Parameters =
-2024-10-17 13:53:24,401 - julearn - INFO - ====================
-2024-10-17 13:53:24,401 - julearn - INFO -
-2024-10-17 13:53:24,401 - julearn - INFO - = Data Information =
-2024-10-17 13:53:24,401 - julearn - INFO -      Problem type: classification
-2024-10-17 13:53:24,401 - julearn - INFO -      Number of samples: 532
-2024-10-17 13:53:24,401 - julearn - INFO -      Number of features: 2
-2024-10-17 13:53:24,401 - julearn - INFO - ====================
-2024-10-17 13:53:24,401 - julearn - INFO -
-2024-10-17 13:53:24,401 - julearn - INFO -      Number of classes: 2
-2024-10-17 13:53:24,401 - julearn - INFO -      Target type: object
-2024-10-17 13:53:24,402 - julearn - INFO -      Class distributions: event
+2024-10-17 14:01:39,329 - julearn - INFO - Using subject as groups
+2024-10-17 14:01:39,329 - julearn - INFO - ====================
+2024-10-17 14:01:39,329 - julearn - INFO -
+2024-10-17 14:01:39,329 - julearn - INFO - Adding step rf that applies to ColumnTypes<types={'continuous'}; pattern=(?:__:type:__continuous)>
+2024-10-17 14:01:39,329 - julearn - INFO - Step added
+2024-10-17 14:01:39,329 - julearn - INFO - = Model Parameters =
+2024-10-17 14:01:39,329 - julearn - INFO - ====================
+2024-10-17 14:01:39,330 - julearn - INFO -
+2024-10-17 14:01:39,330 - julearn - INFO - = Data Information =
+2024-10-17 14:01:39,330 - julearn - INFO -      Problem type: classification
+2024-10-17 14:01:39,330 - julearn - INFO -      Number of samples: 532
+2024-10-17 14:01:39,330 - julearn - INFO -      Number of features: 2
+2024-10-17 14:01:39,330 - julearn - INFO - ====================
+2024-10-17 14:01:39,330 - julearn - INFO -
+2024-10-17 14:01:39,330 - julearn - INFO -      Number of classes: 2
+2024-10-17 14:01:39,330 - julearn - INFO -      Target type: object
+2024-10-17 14:01:39,331 - julearn - INFO -      Class distributions: event
 cue     266
 stim    266
 Name: count, dtype: int64
-2024-10-17 13:53:24,402 - julearn - INFO - Using outer CV scheme StratifiedGroupKFold(n_splits=2, random_state=None, shuffle=False) (incl. final model)
-2024-10-17 13:53:24,402 - julearn - INFO - Binary classification problem detected.
-0.6898496240601504
+2024-10-17 14:01:39,331 - julearn - INFO - Using outer CV scheme StratifiedGroupKFold(n_splits=2, random_state=None, shuffle=False) (incl. final model)
+2024-10-17 14:01:39,331 - julearn - INFO - Binary classification problem detected.
+0.6710526315789473
 

 

 
Train classification model without stratification on data

@@ -631,41 +631,41 @@ 
Dealing with Cross-Validation techniquesprint(scores["test_score"].mean())
-
2024-10-17 13:53:24,761 - julearn - INFO - ==== Input Data ====
-2024-10-17 13:53:24,761 - julearn - INFO - Using dataframe as input
-2024-10-17 13:53:24,761 - julearn - INFO -      Features: ['parietal', 'frontal']
-2024-10-17 13:53:24,761 - julearn - INFO -      Target: event
-2024-10-17 13:53:24,762 - julearn - INFO -      Expanded features: ['parietal', 'frontal']
-2024-10-17 13:53:24,762 - julearn - INFO -      X_types:{}
-2024-10-17 13:53:24,762 - julearn - WARNING - The following columns are not defined in X_types: ['parietal', 'frontal']. They will be treated as continuous.
+
2024-10-17 14:01:39,693 - julearn - INFO - ==== Input Data ====
+2024-10-17 14:01:39,693 - julearn - INFO - Using dataframe as input
+2024-10-17 14:01:39,693 - julearn - INFO -      Features: ['parietal', 'frontal']
+2024-10-17 14:01:39,693 - julearn - INFO -      Target: event
+2024-10-17 14:01:39,693 - julearn - INFO -      Expanded features: ['parietal', 'frontal']
+2024-10-17 14:01:39,693 - julearn - INFO -      X_types:{}
+2024-10-17 14:01:39,693 - julearn - WARNING - The following columns are not defined in X_types: ['parietal', 'frontal']. They will be treated as continuous.
 /home/runner/work/julearn/julearn/julearn/prepare.py:509: RuntimeWarning: The following columns are not defined in X_types: ['parietal', 'frontal']. They will be treated as continuous.
   warn_with_log(
-2024-10-17 13:53:24,762 - julearn - INFO - Using subject as groups
-2024-10-17 13:53:24,762 - julearn - INFO - ====================
-2024-10-17 13:53:24,762 - julearn - INFO -
-2024-10-17 13:53:24,763 - julearn - INFO - Adding step rf that applies to ColumnTypes<types={'continuous'}; pattern=(?:__:type:__continuous)>
-2024-10-17 13:53:24,763 - julearn - INFO - Step added
-2024-10-17 13:53:24,763 - julearn - INFO - = Model Parameters =
-2024-10-17 13:53:24,763 - julearn - INFO - ====================
-2024-10-17 13:53:24,763 - julearn - INFO -
-2024-10-17 13:53:24,763 - julearn - INFO - = Data Information =
-2024-10-17 13:53:24,763 - julearn - INFO -      Problem type: classification
-2024-10-17 13:53:24,763 - julearn - INFO -      Number of samples: 532
-2024-10-17 13:53:24,763 - julearn - INFO -      Number of features: 2
-2024-10-17 13:53:24,763 - julearn - INFO - ====================
-2024-10-17 13:53:24,763 - julearn - INFO -
-2024-10-17 13:53:24,764 - julearn - INFO -      Number of classes: 2
-2024-10-17 13:53:24,764 - julearn - INFO -      Target type: object
-2024-10-17 13:53:24,764 - julearn - INFO -      Class distributions: event
+2024-10-17 14:01:39,694 - julearn - INFO - Using subject as groups
+2024-10-17 14:01:39,694 - julearn - INFO - ====================
+2024-10-17 14:01:39,694 - julearn - INFO -
+2024-10-17 14:01:39,694 - julearn - INFO - Adding step rf that applies to ColumnTypes<types={'continuous'}; pattern=(?:__:type:__continuous)>
+2024-10-17 14:01:39,694 - julearn - INFO - Step added
+2024-10-17 14:01:39,695 - julearn - INFO - = Model Parameters =
+2024-10-17 14:01:39,695 - julearn - INFO - ====================
+2024-10-17 14:01:39,695 - julearn - INFO -
+2024-10-17 14:01:39,695 - julearn - INFO - = Data Information =
+2024-10-17 14:01:39,695 - julearn - INFO -      Problem type: classification
+2024-10-17 14:01:39,695 - julearn - INFO -      Number of samples: 532
+2024-10-17 14:01:39,695 - julearn - INFO -      Number of features: 2
+2024-10-17 14:01:39,695 - julearn - INFO - ====================
+2024-10-17 14:01:39,695 - julearn - INFO -
+2024-10-17 14:01:39,695 - julearn - INFO -      Number of classes: 2
+2024-10-17 14:01:39,695 - julearn - INFO -      Target type: object
+2024-10-17 14:01:39,696 - julearn - INFO -      Class distributions: event
 cue     266
 stim    266
 Name: count, dtype: int64
-2024-10-17 13:53:24,764 - julearn - INFO - Using outer CV scheme GroupKFold(n_splits=2) (incl. final model)
-2024-10-17 13:53:24,765 - julearn - INFO - Binary classification problem detected.
-0.6879699248120301
+2024-10-17 14:01:39,696 - julearn - INFO - Using outer CV scheme GroupKFold(n_splits=2) (incl. final model)
+2024-10-17 14:01:39,696 - julearn - INFO - Binary classification problem detected.
+0.6672932330827068
 

 

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Total running time of the script: (0 minutes 1.366 seconds)

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Total running time of the script: (0 minutes 1.376 seconds)