From 3a340d27feb3107c51aae3320400d881213465db Mon Sep 17 00:00:00 2001 From: SvenKlaassen Date: Mon, 11 Aug 2025 08:46:54 +0200 Subject: [PATCH 1/5] add example nb --- doc/examples/learners/py_tabpfn.ipynb | 1430 +++++++++++++++++++++++++ 1 file changed, 1430 insertions(+) create mode 100644 doc/examples/learners/py_tabpfn.ipynb diff --git a/doc/examples/learners/py_tabpfn.ipynb b/doc/examples/learners/py_tabpfn.ipynb new file mode 100644 index 00000000..1cc62808 --- /dev/null +++ b/doc/examples/learners/py_tabpfn.ipynb @@ -0,0 +1,1430 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "id": "b9131b27", + "metadata": {}, + "outputs": [], + "source": [ + "import numpy as np\n", + "import pandas as pd\n", + "\n", + "import matplotlib.pyplot as plt\n", + "import seaborn as sns\n", + "\n", + "from sklearn.linear_model import LogisticRegression, LinearRegression\n", + "from sklearn.ensemble import RandomForestClassifier, RandomForestRegressor\n", + "\n", + "import doubleml as dml\n", + "from doubleml.datasets import make_irm_data_discrete_treatments" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "97feabd8", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Average treatment effects in each group:\n", + "[ 0. 1.46 6.67 9.31 10.36 10.47]\n", + "\n", + "Average potential outcomes in each group:\n", + "[209.9 211.36 216.56 219.2 220.26 220.37]\n", + "\n", + "Levels and their counts:\n", + "(array([0., 1., 2., 3., 4., 5.]), array([183, 165, 154, 162, 175, 161]))\n" + ] + } + ], + "source": [ + "# Parameters\n", + "n_obs = 1000\n", + "n_levels = 5\n", + "linear = False\n", + "n_rep = 1\n", + "\n", + "np.random.seed(42)\n", + "data_apo = make_irm_data_discrete_treatments(n_obs=n_obs,n_levels=n_levels, linear=linear)\n", + "\n", + "y0 = data_apo['oracle_values']['y0']\n", + "cont_d = data_apo['oracle_values']['cont_d']\n", + "ite = data_apo['oracle_values']['ite']\n", + "d = data_apo['d']\n", + "potential_level = data_apo['oracle_values']['potential_level']\n", + "level_bounds = data_apo['oracle_values']['level_bounds']\n", + "\n", + "average_ites = np.full(n_levels + 1, np.nan)\n", + "apos = np.full(n_levels + 1, np.nan)\n", + "mid_points = np.full(n_levels, np.nan)\n", + "\n", + "for i in range(n_levels + 1):\n", + " average_ites[i] = np.mean(ite[d == i]) * (i > 0)\n", + " apos[i] = np.mean(y0) + average_ites[i]\n", + "\n", + "print(f\"Average treatment effects in each group:\\n{np.round(average_ites,2)}\\n\")\n", + "print(f\"Average potential outcomes in each group:\\n{np.round(apos,2)}\\n\")\n", + "print(f\"Levels and their counts:\\n{np.unique(d, return_counts=True)}\")" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "906c6c36", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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LfYioMkziSIu9vT08PT1x/vz5cs+VHgwrG4vJ3Ny81j/6lbUYqdXqWq9Do9FAoVBgz549FfafelBfJrmVtkZ8/fXXWn3OSunqCkxLS0scOnQI+/fvx+7du7F3715s2bIFjz76KH777bcq+56NHDkSM2fOxNatWzF16lT873//g729vVaC5ubmhjNnzuDXX3/Fnj17sGfPHmzYsAGjR4/Gpk2bahRr69atAQDnzp1DWFhYrba3KpVt6/3Jg67WeT9d7P+1XWddPv/quL81Dfh3v168eDGCgoIqfE3pd/P111/Hhg0bMHXqVPTo0QP29vZQKBR49tlna9RqWNk2POjzLq3jrbfeQmhoaIVlyw4JVB/7EFFlmMRROYMGDcKXX36J48ePSx2Ma8vPzw+///47srOztVrjLl26JD0P/PvfatmBaKtqqbty5YrWf/kxMTHQaDRSx+5mzZpBCIGAgAC0bNmyyhhL13d/q1dRURFiY2PRsWPHamyp7jVr1gxASRJUVQtfaWthRYn3/ao6tapUKvHYY4/hsccew7Jly/Dhhx/inXfewf79+6usOyAgAN27d8eWLVswadIkbN++HWFhYTA3N9cqZ2ZmhsGDB2Pw4MHQaDR47bXXsG7dOsyePbtG4+L17t0bjo6O+O677zBr1qwHJhh+fn6Ijo4ut7zs/lcT1TlFXVPV3f9L461sm1xcXKRWOEdHxwoHdq7oO1Xbz782SvdrOzu7B677hx9+wJgxY7B06VJpWX5+frntqo/PBPj3u2VqaqrT96G+4qXGh33iqJy3334bVlZWeOGFF5CcnFzu+Zr8Rzlw4ECo1WqsWrVKa/knn3wChUKBAQMGACj5QXdxccGhQ4e0yn322WeVrrtsn7yVK1cCgLTOYcOGQaVSYf78+eViFkLgzp07AICuXbvC1dUVa9euRWFhoVRm48aNss5uEBoaCjs7O3z44YcoKioq93xqaiqAkitMH3nkEaxfvx5xcXFaZe7f7tKDe9ltSktLK7fu0haSgoKCB8Y5cuRIHD16FOvXr8ft27e1TqUCkN7nUkqlUrr6tnT9RUVFuHTpEhITE6usy8rKCtOnT8fFixcxffr0CvfFb775BsePHwdQsv8dP34cR44ckZ7Pzc3F559/Dn9/fwQGBj5w+8qytrau0am86vDz84NKpXrg/u/p6YmgoCBs2rRJ63M8f/48fvvtNwwcOFBa1qxZM2RmZmrNrlI6IPL96vr511SXLl3QrFkzLFmypMJuG6X7NVDSqlX2M165cmW51sTK9u26cnNzQ9++fbFu3boK9837Y60Ja2vrBjNzCsmLLXFUTosWLbB582aMGjUKrVq1kmZsEEIgNjYWmzdvhlKpLNf/rSKDBw9Gv3798M477+D69evo2LEjfvvtN/z444+YOnWq9F85ALz00kv46KOP8NJLL6Fr1644dOgQLl++XOm6Y2NjMWTIEPTv3x9HjhzBN998g//85z9Sy1mzZs2wYMECzJw5E9evX0dYWBhsbW0RGxuLHTt2YMKECXjrrbdgamqKBQsW4OWXX8ajjz6KkSNHIjY2Fhs2bKhRn7iEhAR888035Zbb2NjU6tSfnZ0d1qxZg+effx6dO3fGs88+C1dXV8TFxWH37t3o1auXlBx/+umn6N27Nzp37owJEyYgICAA169fx+7du3HmzBkAJQdPoGSYjGeffRampqYYPHgw3nvvPRw6dAiDBg2Cn58fUlJS8Nlnn8HHx0frgoDKjBgxAm+99RbeeustODk5lWuxeOmll5CWloZHH30UPj4+uHHjBlauXImgoCCpb1pCQgLatGmDMWPGPHA6omnTpuHChQtYunQp9u/fj2eeeQYeHh5ISkrCzp07cfz4cRw+fBgAMGPGDHz33XcYMGAAJk+eDCcnJ2zatAmxsbHYtm1brU7/d+nSBVu2bEF4eDi6desGGxsbDB48uMbruZ+9vT2GDx+OlStXQqFQoFmzZti1a5c0CPT9Fi9ejAEDBqBHjx548cUXpSFG7O3tMW/ePKncs88+i+nTp+Opp57C5MmTpeFpWrZsqXVRQF0//5pSKpX48ssvMWDAALRt2xbjxo2Dt7c3EhISsH//ftjZ2eHnn38GADz55JP4+uuvYW9vj8DAQBw5cgS///47nJ2dtdYZFBQElUqFRYsWITMzE+bm5nj00Ufh5uZW53hXr16N3r17o3379hg/fjyaNm2K5ORkHDlyBPHx8Th79myN19mlSxesWbMGCxYsQPPmzeHm5lZp31eiKslwRSwZiZiYGPHqq6+K5s2bCwsLC2FpaSlat24tXnnlFXHmzBmtsqWDmlYkOztbvPHGG8LLy0uYmpqKFi1alBvsV4iS4QRefPFFYW9vL2xtbcWIESNESkpKpUOMREVFiWeeeUbY2toKR0dHMWnSpAoH+922bZvo3bu3sLa2FtbW1qJ169Zi4sSJIjo6WqvcZ599JgICAoS5ubno2rVrjQf7RSVDjNw/zENNhhgptX//fhEaGirs7e2FhYWFaNasmRg7dqw4efKkVrnz58+Lp556Sjg4OAgLCwvRqlUrMXv2bK0y77//vvD29hZKpVIabiQiIkIMHTpUeHl5CTMzM+Hl5SVGjRolLl++/MDtLtWrVy8BQLz00kvlnvvhhx/EE088Idzc3ISZmZnw9fUVL7/8skhMTJTKlA6lMWbMmGrXWbpeJycnYWJiIjw9PcXIkSPFgQMHtMqVDvZb+r5079690sF+t27dqrW8oiE+cnJyxH/+8x/h4OBQ7cF+y6powN3U1FTx9NNPCysrK+Ho6Chefvllcf78+QqH3vn9999Fr169hKWlpbCzsxODBw8uN9ivECWD+LZr106YmZmJVq1aiW+++aZc3br4/KsaYqTse1rq9OnTYtiwYcLZ2VmYm5sLPz8/MWLECBERESGVSU9PF+PGjRMuLi7CxsZGhIaGikuXLgk/P79y+8oXX3whmjZtKlQqVYWD/ZYFQEycOFFrWelnuHjxYq3lV69eFaNHjxYeHh7C1NRUeHt7iyeffFL88MMPUpnKvtsVDR+TlJQkBg0aJGxtbTnYL9WJQgj2tiTjMm/ePMyfPx+pqanSoLVERESNDfvEERERERkhJnFERERERohJHBEREZERYp84IiIiIiPEljgiIiIiI8QkjoiIiMgINarBfouLi3H69Gm4u7vrZGJnIiIiqn8ajQbJycno1KmTzuaNbgga1Ttx+vTpOs8FSkRERPI4fvw4unXrJncYBqNRJXHu7u4ASnYCT09PnayzuLgYu3btAlAyRQz/QyDSPzm/h3LUbUi/O4YUC8mnvveDxMREdO/eXTqOU4lG9W0rPYXq6elZrXk/q6O4uFiax8/Hx4c/YEQykPN7KEfdhvS7Y0ixkHz0tR+wK5Q2vhtERERERohJHBEREZERMpgk7tChQxg8eDC8vLygUCiwc+dO6bmioiJMnz4d7du3h7W1Nby8vDB69GjcunVLvoDvUSgUCAgIQEBAABQKhdzhEDVKcn4P5ajbkH53DCkWkg/3A3kYTOeF3NxcdOzYES+88AKGDRum9VxeXh4iIyMxe/ZsdOzYEenp6ZgyZQqGDBmCkydP6jQOjUaDwsLCGr0mKCgIQEmyWVRUpNN4iKg8MzMzrb4xKpUKDz30kCyxyFG3nNtbliHFQvLhfiAPg0niBgwYgAEDBlT4nL29Pfbt26e1bNWqVejevTvi4uLg6+urkxgKCwsRGxsLjUajk/URUf1QKpUICAiAmZmZ3KEQEcnGYJK4msrMzIRCoYCDg0OlZQoKClBQUCA9zs7OrrSsEAKJiYlQqVRo0qRJta+AKTv1LJuRieqXRqPBrVu3kJiYCF9fXygUCgghoFarAZS0COjzeyhH3XJuryHHQvLhfiAPo0zi8vPzMX36dIwaNQp2dnaVllu4cCHmz59frXUWFxcjLy8PXl5esLKyqnYsQgikpaUBAJycnLjjEumBq6srbt26heLiYpiamkKtVmPr1q0AgOHDh+t1mAs56pZzew05FpIP9wN5GMyFDdVVVFSEESNGQAiBNWvWVFl25syZyMzMlG5RUVGVli39D4KnZ4gMX+n3tPR7S0TUGBlVqlyawN24cQN//PFHla1wAGBubg5zc3PpcVZW1gPrYEsakeHj95SIyIiSuNIE7sqVK9i/f780MjQRERFRY2QwSVxOTg5iYmKkx7GxsThz5gycnJzg6emJZ555BpGRkdi1axfUajWSkpIAlPRD4ylQIiIiamwMpk/cyZMn0alTJ3Tq1AkAEB4ejk6dOmHOnDlISEjATz/9hPj4eAQFBcHT01O6HT58WObI5TV27FiEhYVBoVBUeZs3bx6uX79e6fNHjx6Ve1OIiIioBgymJa5v377lhuu4X1XPEZCYmCjd37JlC+bMmYPo6GhpmY2NDW7fvg0A+P3339G2bVut1/P0NBER1UWhWoOcgmK5w2hUDCaJM2aGcDrXw8NDum9vbw+FQqG1DICUxDk7O5d7jsiYKRQKNGnSRLrf0OuWc3sNORaSz6WUHOy4XoScAjUeSc5Be28HuUNqFJjEVUIIgbzC6g1foDSzBIBql38QKzMOlEhUEyqVCr179240dcu5vWUZUiwkjz0XkzHq20hk5TvD39ESSpVK7pAaDSZxlcgrVMP2nT2y1J39wQBYm9ffR9OzZ89yM1Lk5OTUW31EDd3PcRdq/drBvm0fXIjIAAkhsPzPa5j2cxQ0AnikqRO2ju4KVxvzB7+YdIJJXCO0ZcsWtGnTRu4wiIjISBUUq/Ha9nPYcPwmAOCF7r74bFh7mJkYzPWSjQKTuEpYmamQ/cGAB5YTEEhLSwcAODk5QoG6nwa1MqvfpugmTZqgefPm9VoHkT4VFxfLNuVPcXExDh06BADo1asXVHo4lSTn9hpyLKQfKdkFeOark/grNg1KBbB0SFu89lAT/LB1CwDuB/rEd7kSCoWiWqc0hRAouJd0WZuZsC8bERE1WOcSszBk/XHcSL8LewsTfP98F4S2ckNxMa9KlQOTuEbozp070mDJpRwcHGBhYSFTREREZOh+upCE/9sciZwCNZq7WOOnF7qhtZut3GE1akziGqGQkJByy7777js8++yzMkRDRESGTAiBRftj8M6eSxACeLS5C/43ugucrOQfXquxYxJn5DZu3Fhu2dixYzF27Nhyy/39/TloMhERVVt+kRoTtp7FN5EJAIBXe/pj+dC2MFXxAgZDwCSOiIiIyrmZcRdPbzyBk/GZUCkVWDG0HV7r5S93WHQfJnFERESk5eDV2xjx1Smk5hbCycoUW57vgsdauModFpXBJE4HDGHaLaLGTKFQwNPTU7qv77odnZz0Wrec22vIsVDdCSHw6V+xeOvnKKg1AkFedtg2phsCnK2qfB33A3kwiasjhUIBW1tenUMkJ5VKhb59+8pWd/t27fRep1zbW5YhxUJ1c7dIjVd++Adfn4oHADzX2RvrnukAK7MHpwrcD+TBJI6IiKiRu56Wh6c3ncDphCyolAosfjIQUx4OYKuagWMSR0RE1IhFXEnFs1+fwp28IrhYm+F/z3dB3+YucodF1cAkro6EEEhPL5l2y9HRkf+1EMmguLgY27dvBwAMGzZM79Nu/fXXXwCAHj166G3aLbm215BjoZoRQmDZwWuYvrtkAvsuPvbYNqYrfB2r7v9WEe4H8uC7rAMce41Ifmq1Wra6NRqN3uuUc3vLMqRYqHpyC4oxfutZfH/mFgBgTFcffPZ0B1ia1v6fEO4H+sckjoiIqBG5dicXwzaexD+JWTBRKvDJ0LZ4rac/zyQZISZxZND8/f0xdepUTJ06Ve5QGo158+ZhzZo1SElJwY4dOxAWFlbhMtKNn+Mu1Pg1arUakeoMdFY56D4gatB+i07BqG8ikX63CG42Ztg6uisebuosd1hUS5w3owFISkrClClT0Lx5c1hYWMDd3R29evXCmjVrkJeXJ3d49cbfv+Q/x8puFU09pgvz5s1DUFBQvay7Ng4cOACFQoGMjIxqlavolpSUBAC4ePEi5s+fj3Xr1iExMREDBgyocFldGdp7SNTQFRSr8fauKAz48hjS7xYh2NcBp954hAmckWNLnJG7du0aevXqBQcHB3z44Ydo3749zM3Nce7cOXz++efw9vbGkCFDKnxtUVERTE1N9Ryx7pw4cULqg3H48GE8/fTTiI6Ohp2dHQDA0tJSq7yxb6+u3P8elXJzcwMAXL16FQAwdOhQ6dRKRcuIyHicScjE6O9O43xSNgBg/EO++DSsHcxN6v8iHKpfbInTMXV+OgrTopGfeByFadFQ56fXa32vvfYaTExMcPLkSYwYMQJt2rRB06ZNMXToUOzevRuDBw+WyioUCqxZswZDhgyBtbU1PvjgAwDAmjVr0KxZM5iZmaFVq1b4+uuvpddcv34dCoUCZ86ckZZlZGRAoVDgwIEDAP5t4dm9ezc6dOgACwsLPPTQQzh//rxWrH/99RcefvhhWFpaokmTJpg8eTJyc3Ol51NSUjB48GBYWloiICAA3377bZXb7urqCg8PD3h4eMDp3oj5bm5u8PDwQH5+PhwcHLBlyxb06dMHFhYW0vq+/PJLtGnTBhYWFmjdujU+++wzrfVOnz4dLVu2hJWVFZo2bYrZs2ejqKgIALBx40bMnz8fZ8+elVqxNm7cKL2/69atw5NPPgkrKyu0adMGR44cQUxMDPr27Qtra2v07NlTSopK/fjjj+jcuTMsLCzQtGlTzJ8/H8XFxVqf25dffomnnnoKVlZWaNGiBX766Sfp8+nXrx+Af6+OflALZOl7dP9NqVRi3rx50v6iVCqhUCgqXFbqQe9jfHw8Ro0aBScnJ1hbW6Nr1644duxYpe+hEALz5s2Dr68vzM3N4eXlhcmTJ1e5LURUObVGYGHEFQR/+ifOJ2XDzcYMO8d1w7pnOjKBayDYEqcDpa076px4pO57Gflxv0vPWfg9DteQtTCxbaLzeu/cuYPffvsNH374IaytrSssU7blZN68efjoo4+wfPlymJiYYMeOHZgyZQqWL1+OkJAQ7Nq1C+PGjYOPj4+UHFTXtGnTsGLFCnh4eGDWrFkYPHgwLl++DFNTU1y9ehX9+/fHggULsH79eqSmpmLSpEmYNGkSNmzYAAAYO3Ysbt26hf3798PU1BSTJ09GSkpK7d6ce2bMmIGlS5eiU6dOUiI3Z84crFq1Cp06dcLp06cxfvx4WFtbY8yYMQAAW1tbbNy4EV5eXjh37hzGjx8PW1tbvP322xg5ciTOnz+PvXv34vffSz5ne3t7qb73338fy5Ytw7JlyzB9+nT85z//QdOmTTFz5kz4+vrihRdewKRJk7Bnzx4AwJ9//onRo0fj008/xcMPP4yrV69iwoQJAIC5c+dK650/fz4+/vhjLF68GCtXrsRzzz2HGzduoEmTJti2bZtWK2TZFsjqeuutt+Dv749x48YhMTERAGBjY1NuGYAHvo85OTno06cPvL298dNPP8HDwwORkZHQaDSVvofbtm3DJ598gu+//x5t27ZFUlISzp49W63YFQoFXF1dpfv6pFAopH1An9Nu2dvbw9XSVfbWUTnfe6pczO1cjP3+NA5fL2lICGvngXXPdICrjXm91Mf9QCaiEbl586YAIG7evFnuubt374qoqChx9+7dWq27+G6auLVtgLj2iWm5263tA0Xx3bS6hl/O0aNHBQCxfft2reXOzs7C2tpaWFtbi7fffltaDkBMnTpVq2zPnj3F+PHjtZYNHz5cDBw4UAghRGxsrAAgTp8+LT2fnp4uAIj9+/cLIYTYv3+/ACC+//57qcydO3eEpaWl2LJlixBCiBdffFFMmDBBq54///xTKJVKcffuXREdHS0AiOPHj0vPX7x4UQAQn3zyyQPfi9IY0tPTteJevny5VrlmzZqJzZs3ay17//33RY8ePSpd9+LFi0WXLl2kx3PnzhUdO3YsVw6AePfdd6XHR44cEQDEf//7X2nZd999JywsLKTHjz32mPjwww+11vP1118LT0/PStebk5MjAIg9e/ZUuO2VKS1Xum+U3gIDA6UyO3bsEGV/Fipa9qD3cd26dcLW1lbcuXOnwlgqeg+XLl0qWrZsKQoLC6vcDiHq/n3VtZ9unJflRlSWRqMRa/6OFdYzdgvFmz8J+3d+ERuPxwmNRiN3aHVS1fG7MWNLnI6o81K0WuDul39jH9R5KVBZOOolluPHj0Oj0eC5555DQUGB1nNdu3bVenzx4kWp5adUr169sGLFihrX26NHD+m+k5MTWrVqhYsXLwIAzp49i3/++UfrFKkQAhqNBrGxsbh8+TJMTEzQpUsX6fnWrVvDwcGhxnHc7/7tzc3NxdWrV/Hiiy9i/Pjx0vLi4mKt1rQtW7bg008/xdWrV5GTk4Pi4uJyfcgq06FDB+m+u7s7AKB9+/Zay/Lz85GVlQU7OzucPXsWf//9t3RqGyi58jA/Px95eXmwsrIqt15ra2vY2dnVupXyzz//1Jrvt6b9BKvzPp45cwadOnWSTnNXx/Dhw7F8+XI0bdoU/fv3x8CBAzF48GAOGkpUTTfS8vDa9nPYc6nkt6FfM2esHxkEP6eaD95LxoG/jjqiKcis+vnCqp+vjebNm0OhUCA6OlpredOmTQGU79gPoNLTrpVRKku6TYr7BjQu7R9WEzk5OXj55Zcr7OPk6+uLy5cv13id1XH/9ubk5AAAvvjiCwQHB2uVKx1l/8iRI3juuecwf/58hIaGwt7eHt9//z2WLl1arfruT4hKTylUtKx0cNicnBzMnz8fw4YNK7cuCwuLCtdbup7aDjAbEBBQp+S4Ou9jbU7pNmnSBNHR0fj999+xb98+vPbaa1i8eDEOHjzIC1KIqpBXWIxF+69i8f4Y5BdrYG6ixMKBbTC5dwCUSp7abMiYxNWREAIZGRmwNLWpspzSzL7K52vD2dkZjz/+OFatWoXXX3+9xgkaALRp0wZ///231B8MAP7++28EBgYCgNTHITExEZ06dQIArYsc7nf06FH4+voCANLT03H58mW0adMGANC5c2dERUWhefPmFb62devWKC4uxqlTp9CtWzcAJVdRPmjYjJpwd3eHl5cXrl27hueee67CMocPH4afnx/eeecdadmNGze0ypiZmelsZPLOnTsjOjq60velOszMzADob7T06ryPHTp0wJdffom0tLQKW+Mqew8tLS0xePBgDB48GBMnTkTr1q1x7tw5dO7cucqYiouLpYs9hgwZovdpt44cOQIA6N69u16m3VKr1Th+/DiKTkbrfXvLkvO9b+yEEPjf2Vt4e1cUbmbkAwD6NHXG6mHtEehh+4BX6xb3A3nwXdYBjUYDtcoBFn6PI//GvnLPW/g9DpWVW73U/dlnn6FXr17o2rUr5s2bhw4dOkCpVOLEiRO4dOmS1unJikybNg0jRoxAp06dEBISgp9//hnbt2+XOpxbWlrioYcewkcffYSAgACkpKTg3XffrXBd7733HpydneHu7o533nkHLi4u0qCw06dPx0MPPYRJkybhpZdegrW1NaKiorBv3z6sWrUKrVq1Qv/+/fHyyy9jzZo1MDExwdSpU2vdSb8y8+fPx+TJk2Fvb4/+/fujoKAAJ0+eRHp6OsLDw9GiRQvExcXh+++/R7du3bB7927s2LFDax3+/v6IjY3FmTNn4OPjA1tbW5ib166z8Jw5c/Dkk0/C19cXzzzzDJRKJc6ePYvz589jwYIF1VqHn58fFAoFdu3ahYEDB8LS0hI2NpX/U5GSkoL8/HytZc7OzjVq7XrQ+zhq1Ch8+OGHCAsLw8KFC+Hp6YnTp0/Dy8sLPXr0qPA9/O6776BWqxEcHAwrKyt88803sLS0hJ+fX7ViKtt1QJ9q0zqtizoLNPrbZiEEMu4W4Ub6XVxPz8PNjHyk5RUiIzcfZtdSUKwR2LjxONRQQXOv5d7SVAUbMxWszExgbaaCvYUpPOzM4WFbcnO3MYeXvQVMVcY9UII6Px3qvBRoCjKhNLeHysqt3rvPnE7IxNSd5/FnbBoAwM/REoufDMTTHTxlu7BAzu9gY8UkTkfyNeZwDVmL1N9f0UrkSq5OXVdvX+hmzZrh9OnT+PDDDzFz5kzEx8fD3NwcgYGBeOutt/Daa69V+fqwsDCsWLECS5YswZQpUxAQEIANGzagb9++Upn169fjxRdfRJcuXdCqVSt8/PHHeOKJJ8qt66OPPsKUKVNw5coVBAUF4eeff5ZaiTp06ICDBw/inXfewcMPPwwhBJo1a4aRI0dKr9+wYQNeeukl9OnTB+7u7liwYAFmz56tmzfqnpdeeglWVlZYvHgxpk2bBmtra7Rv316aEWLIkCF44403MGnSJBQUFGDQoEGYPXs25s2bJ63j6aefxvbt29GvXz9kZGRgw4YNtR5YODQ0FLt27cJ7772HRYsWwdTUFK1bt8ZLL71U7XV4e3tj/vz5mDFjBsaNG4fRo0dLw55UpFWrVuWWHTlyBA899FC163zQ+2hmZobffvsNb775JgYOHIji4mIEBgZi9erVACp+Dx0cHPDRRx8hPDwcarUa7du3x88//wxnZw5Gqm9CCFxKycGxuAycuZWJf25l4eytLKTfLZ+sqqDBcMu7AIA9KalQ13DkKpVSAV8HSzR1tkKAkxWau1ijrbst2nnYwtfR0uCvdCzOvqnXUQmikrKx4PfL2HL2FoQALE2VmPFoC7zVt1md5j0l46QQovHM3h4fH48mTZrg5s2b8PHx0XouPz8fsbGxCAgI0OqL9CBCCKSllfwn5OTkBE1BRsl/ZIWZUJrp5z8yuR04cAD9+vVDenp6nS9EIKqOst/X4uJibN26FUDJBRL6Pp068/svAJRcFKSv06l///03OqscdLa9iVn5+DkqGftjbuPA1TtIzq64VcXV2gz+TlbwdbCEi40Z7M2UsL5+BCZKJdw694PKxBRKBSAEcLdIjZxCNXILi5FToEZGfhFSsguQlF2ApOx8JGUXoEhd+SHI1twEge42aOthh3YetujoZYfO3vawtzSMPpLq/HSk/PJchRe1Wfg9DrcB3+js9//srUws+iNGSt4A4NkgLyx6MhBNHHR7xqI26vs7WNXxuzKrV6/G4sWLkZSUhI4dO2LlypXo3r17hWUvXLiAOXPm4NSpU7hx4wY++eSTCqd7rMk69YEtcTqmsnBs8EkbERk/dX464pIT8MO5VOyMKcbRhHzc/y+9hYkS3X0d0MnbHh297NHRyw4tXaxhba592Cg5eJ8DAAzv5lujg7dGI5CUXYBrabm4dicP1+7k4XJqDi4kZ+NSSg6yC4pxLC4Dx+IytF7XwsUaXZs4oIuPPbr6lMRoa6H/w1l9j0pQUKzGD/8kYs3h69J4bwAwrL0HZj/eEh29dN/XuqHYsmULwsPDsXbtWgQHB2P58uUIDQ1FdHS0NEPN/fLy8tC0aVMMHz4cb7zxhk7WqQ9M4oiIGhG1RmDX6Qv4bO9+RKQ3gQb/thx297ZC/0Af9GvujIf8HOt9VH+lUgEvewt42Vugd4D2afMitQZXUnNxITkb55OycT4xC6cTMnE9/S6u3M7Fldu5+O50AgBAoQBaudqgq489OvuUJHedvO1hY16/h7j6GpXgeloe1h29gfXH4pCaWwgAMFEqMKy9J2Y+1pzJWzUsW7YM48ePx7hx4wAAa9euxe7du7F+/XrMmDGjXPlu3bpJF9VV9Hxt1qkPTOKozvr27YtGdFaeyCjdLVLjv8fisGT/FcRlFgDwBwB0NYvCAMsjeMLyGJr6d4ZbH92dAqwLU5USgR62CPSwxfCO/y6/nVuAyPhMnIzPxKmbGTgZn4GbGfm4lJKDSyk5+Cby38SupYs1uvg4oLOPPTp7lyR2ujwVqzSvOpmqyagEsXfysON8InacS8ThG+lSq6iPvQUm9PDDi9194WlX/a4+jVlhYSFOnTqFmTNnSsuUSiVCQkKkK8kNYZ26wCROB3gpNZG8FAqFNJSJHNNulQ6erM9pt2xtbeFk4fTAOnMKirHm8HUsO3RN6ufmqMzCMKv9GGkdgWamCVLZ2pwC1Pd772JtjidaueGJVv+evkrJLsCp+IySxC4+A6fiM5GQmY/o1FxEp+Zi8+l/t7G5i7XUUtfJ2x6B7jbwsrOoVewqK7daj0qQlleIw9fT8VdsGvZdTsHphCyt5x9v6YJXe/rjyTbuMDGCq3f1tR9kZ2cjK+vf98rc3Lzc6AC3b9+GWq2WBlwv5e7ujkuXLtWq3vpYpy4w+yijpi1K98+bSET6UfZ7qlKpEBoaWqd1/hx3odavLR1DUV+USiU6deqEUN+2lZYpVmuw/sRNzP01Wkre/Bwt8UZnEzxx6VmYKyoeFqWmpwB18d7XlZutOQa0cceANv8eYJOzCxAZn4HIhExExmciMiETN9LvIuZ2LmJu52LLmVtSWTsLE7R2tUGguy1aulnD39EKfo6W8HO0goeteaUD5qosHB84KkFBsRqxaXmIuZ2HmNu5iErOxpHr6biQnK21LqWiZIy3p9p7IqydB3wM4GKFmtDXflA6hmmpuXPnao0e0Ngwibun9IqywsJCnY9NRkS6VVhY0k9IH1eCGqMj19PwyrZ/cC6xJFFo7mKNdx5rgf909obIvIKE6MrHtauPgcnl4F5BYnc7twCnE7JwKj4DkfGZ+CcxC1fv5CErvxjHb2bg+M2McusxUynhYWsOF2sz6WZjbgJzEyUsTJQwVSmRr3gfee7TkVdQgHyNKQrSTJHxdRyup19CXMZdVNY20MrVGr0CnPFwgBMGtnGrt8npG5KoqCh4e3tLjysao9PFxQUqlQrJyclay5OTk+Hh4VGreutjnbrAJO4eExMTWFlZITU1FaamptJ0U0RkWDQaDVJTU2FlZcWuDGVk5Rdh5i+XsPbIdQgBOFmZYs7jLfFKD3+YmZT8pqnrcArQ2LlYm+Pxlq54vKWrtKygWI2Y23m4mJyNiyk5uJKagxvpd3Ej/S7iM++iUK1BXMZdxGXcrWYt+eWW2Jir0NzZGs1drNHMxRrBvg7o5e/EpK0WbG1tHziXtZmZGbp06YKIiAhpwHmNRoOIiAhMmjSpVvXWxzp1gb+A9ygUCnh6eiI2NrbcNEtVEULg7t2SL7elpeEPTEnUECiVSvj6+krft+LiYuzevRsAMGjQIL0md2q1GidPngQAdO3aVW/jxJ08eRKa0zHS9v5x5TbGfH8aCZklScTYbk2w+MlAOFubab22OqcAa0LO914XzE1UaOthi7YVTFNVrNYgIbNkPLs7eYW4nVuIO3mFyClQI79YjfwiDQrVJXOVWpqq7t2UsDRRwcHSFL6OlmjubA1XG7MGf2wwtP0gPDwcY8aMQdeuXdG9e3csX74cubm50pWlo0ePhre3NxYuXAigpHU/KipKup+QkIAzZ87AxsZGmhbxQeuUg8F82w4dOoTFixfj1KlTSExMxI4dO6RsFyhJlubOnYsvvvgCGRkZ6NWrF9asWYMWLVroLAYzMzO0aNFCOlVTHcXFxdi7dy8AoH///rLvuESNgZmZWbnW8ry8PJmikWe6oYKCAuQV56FIrcHcfRfx0R8xEAJo5myFdc90xKMtXCp9rYltE7gN+EZnA5PL+d7XJxOVEn5OVvBzspI7FKNgSPvByJEjkZqaijlz5iApKQlBQUHYu3evdGFCXFyc1m/IrVu3tPq2LlmyBEuWLEGfPn1w4MCBaq1TDgaTceTm5qJjx4544YUXMGzYsHLPf/zxx/j000+xadMmBAQEYPbs2QgNDUVUVFSNZlh4EKVSWaP1FRcXS/MmWlhYMIkjIr25W6TGE+uO4O+4kqv1Xgr2xSdD2pYbkLciHJicGrpJkyZVeqqzNDEr5e/vX60LG6tapxwMJuMYMGAABgwYUOFzQggsX74c7777LoYOHQoA+Oqrr+Du7o6dO3fi2Wef1WeoRESyyy4oxp/xaTiabQt7CzOse6YjRgR5yR0WEemRUfTej42NRVJSEkJCQqRl9vb2CA4OlnWQPSIiOeyPuY3olBwUFKvRzsMWkW/0YQJH1AgZTEtcVZKSkgCgwkH2Sp+rSEFBgVZflezs7ErLEhEZOgFg29lb+PbUTfQyF/C0tcBnr/aEgzVH8idqjIwiiauthQsXYv78+XKHQURUZwLAhuNx+PF8EpQAPG0toLAXOJByuVZXxA6uYqBgIjIORnE6tXQgvZoOsjdz5kxkZmZKt9LLh3XNzs7ugePWEFH9kvN7aGVlBSur+ruCUa0RWPlnLH48X3LmYUy3Jmjl5Qxra+t6q7Mm+BtIAPcDORhFS1xAQAA8PDwQERGBoKAgAEBWVhaOHTuGV199tdLXlZ1T7f751nTFxMQEgwYN0vl6iaj65PweqlQqdO3atd7WLwSw8q9Y7I+5DaVSgUm9AvBYCxcAhtEHjr+BBHA/kIvBJHE5OTmIiYmRHsfGxuLMmTNwcnKCr68vpk6digULFqBFixbSECNeXl5aY8kRETUkAsCaI9elBO7tfs3Rw4/DghBRCYNJ4k6ePIl+/fpJj8PDwwEAY8aMwcaNG/H2228jNzcXEyZMQEZGBnr37o29e/fqdIw4IiJDIQCsPx6HXy+lQKEAwh9pygSOiLQYTBLXt2/fKgfaUygUeO+99/Dee+/pMaoHKy4uxq+//goACA0N5WC/RDKQ83uoVqtx+vRpAECnTp10Nu3W9n8S8dO9PnCTegfg4abO9V5nbfA3kADuB3Lhu6wD9dHXjohqRs7voa6nG/orNg1fnbwJAHjpIT+EtHAtV8aQpjjibyAB3A/kYBRXpxIRNRaXUnKw/NA1AMDgth4YHCjfvIxEZNiYxBERGYjk7AJ88PsVFKk1CPZzxAvdfeUOiYgMGJM4IiIDUKjWYOEfV5CVX4RmLtZ485GmUCrkjoqIDBmTOCIimQkAaw5fR+ydPNhZmOKdx1rA3FS+ixWIyDgwiSMiktmvl1Lwx5XbUCiAaX2bwdnaTO6QiMgI8OpUHajP6XaIqHrk/B7ePzNMTcWm5eGLY3EAgNFdm6CDV/WmLapLnbrG30ACuB/IQSGqGpytgYmPj0eTJk1w8+ZN+Pj4yB0OERmQn+Mu6LU+TVEO8nMzMO33ZMRnqdHVxwbvPhEIfXWDG+zbVk81EdUdj98VY0scEZGeqe/eRtaZ1dgU64b4/C6wU+ZitFUENHfdoLJ0kTs8IjIS7BNHRKRHmqIcZJ1ZjVMJOdif3wUAMNZmFyzSTiDr7GfQFOXIHCERGQu2xNWRWq3G77//DgAICQmRdfobosZKzu+hRqPB2bNnAQAdO3aEUln1/8aagkxkpFzCppwJAIDHLE+gnVksAKAw5Qw0BZlQmtrotM76xN9AArgfyIVJXB0JIZCWlibdJyL9k/N7KIRAdnZ2tesWRXnYmvMYsjTW8FClYZjVgXLP67rO+sTfQAK4H8iFp1OJiPTonzQl/i7oAAUERtvshqlCrfW8wpRX+BFR9TCJIyLSk4IiNdaeKpkkvK9FJJqbJmg9b+YWBKW5vRyhEZERYhJHRKQn35xOQEpOEVytTTDCL03rOTO3INh1nPjA/nBERKXYJ46ISA9upOdhV1QyAODVXk3h4T4FmoJMiKI8KEytoDS3ZwJHRDXCJI6IqJ4JAJ8fjYNGI/CQnyO6+DgAAJM2IqoTJnE6YEjT3xA1VnJ+D01NTat8/u/YNJxPzIKpSokXg331Uqc+8TeQAO4HcuC0W0REqL9pt/KL1Hht+zncyS3EqE4+eLaTV73UU1OcdouMCY/fFeOFDURE9eiHfxJxJ7cQ7rbmGNbBQ+5wiKgB4elUImow9D2J/YPcySvEjxeSAAAvBPvCTMX/m4lId5jE1ZFarcb+/fsBAP369eNUI0QyKP0enr2bgvbt2+t1GiqNRoNz584BQLm6N0cmoLBYg0APWwT7OuqlTn3jbyAB3A/kwiSujoQQSE1Nle4Tkf6Vfg8z1ZmyTLuVmZkp3S91M/0uIq6U/DaM6doECj3UKQf+BhLA/UAubNsnIqoHX0XGQwjgIT9HtHbjUCJEpHtM4oiIdCwqORvHb6RDqVTg+a68ko6I6geTOCIiHRIAvj4VDwAIaekCH3tLeQMiogaLSRwRkQ6du5WFqKRsmKiUeDbIW+5wiKgBYxJHRKRD359JAACEtnKFs5WZzNEQUUPGq1N1gJdSE8lPpVJBKeT5v7R0iI/zSdm4cK8V7un2nnqp0xDwN5AA7gdy4LRbRNRgyD3Y7zu/XML5pCwMaOOOV3r4yRrLg3DaLTImPH5XzHD+lSMiMmLnk7JwPikLJiolnulQv61wREQAkzgiIp344WwiACCkhQtcrNkXjojqH/vE1ZFarcaff/4JAHj44YfZJ4BIBqXfw3N5yWgbGKj3abf+PnkWRSkpUCkcMKye+8KV1nkhKgoA9L69ZfE3kADuB3JhEldHQggkJiZK94lI/0q/h+nqDFmm3bqakAxHZQF6+TvB3dZcL3Wmp6VJ9+XE30ACuB/IhadTiYjqIDWnAHfyCgEAQ9q6yxwNETUmTOKIiOpg18VkCAjYm5uiqbO13OEQUSPCJI6IqJZyCooRceU2AMDDrv5PoxIR3Y9JHBFRLe25lIL8Ig2sTE1gZ84uxkSkX0ziiIhqQa0R2H0xGQDgYWsOhUIhc0RE1NgYTRKnVqsxe/ZsBAQEwNLSEs2aNcP777/Pq2CISBZHbqQjPa8IDlamcLYylTscImqEjKb9f9GiRVizZg02bdqEtm3b4uTJkxg3bhzs7e0xefJk2eIyMTHBqFGjZKufiP79Htrocdqt0la4kJZu6NO5s97qBUrmqHzkkUf0Wmdl+BtIAPcDuRhNEnf48GEMHToUgwYNAgD4+/vju+++w/Hjx2WOjIgam+tpeYhKyoZSqUD/1m5yh0NEjZTRnE7t2bMnIiIicPnyZQDA2bNn8ddff2HAgAGVvqagoABZWVnSLTs7W1/hElEDtvtSSStcDz9HOFtxii0ikofRtMTNmDEDWVlZaN26NVQqFdRqNT744AM899xzlb5m4cKFmD9/fr3GpVarceTIEQBAjx49ONUIkQxKv4dROYlo3bp1vU5DlVNQjIMxJbMlDGrjDo1Gg0uXLgFAvdddSo46K8PfQAK4H8jFaFri/ve//+Hbb7/F5s2bERkZiU2bNmHJkiXYtGlTpa+ZOXMmMjMzpVvUvbkGdUkIgZs3b+LmzZu8yIJIJqXfw9u3b9f79zAi5jYKitXwc7JCoIcthBC4ffu2XuouJUedVcXC30AyxP1g9erV8Pf3h4WFBYKDgx/Y/Wrr1q1o3bo1LCws0L59e/zyyy9az+fk5GDSpEnw8fGBpaUlAgMDsXbt2vrchAcymiRu2rRpmDFjBp599lm0b98ezz//PN544w0sXLiw0teYm5vDzs5Outna2uoxYiJqaIQAfrl3QcOgNu7goCJEhmnLli0IDw/H3LlzERkZiY4dOyI0NBQpKSkVlj98+DBGjRqFF198EadPn0ZYWBjCwsJw/vx5qUx4eDj27t2Lb775BhcvXsTUqVMxadIk/PTTT/rarHKMJonLy8srd8pApVJBo9HIFBERNTZnbmUiKasAVmYq9G3qJHc4RFSJZcuWYfz48Rg3bpzUYmZlZYX169dXWH7FihXo378/pk2bhjZt2uD9999H586dsWrVKqnM4cOHMWbMGPTt2xf+/v6YMGECOnbsKOsFlkaTxA0ePBgffPABdu/ejevXr2PHjh1YtmwZnnrqKblDI6JG4rfLqQCAfs1dYG7KPj9EhqiwsBCnTp1CSEiItEypVCIkJETqt1fWkSNHtMoDQGhoqFb5nj174qeffkJCQgKEENi/fz8uX76MJ554on42pBqM5sKGlStXYvbs2XjttdeQkpICLy8vvPzyy5gzZ47coRFRI5B5twjH4tIBAE+0dJU5GqLGKTs7G1lZWdJjc3NzmJtrz1t8+/ZtqNVquLu7ay13d3eXLggqKykpqcLySUlJ0uOVK1diwoQJ8PHxgYmJCZRKJb744gtZx2w0miTO1tYWy5cvx/Lly+UOhYgaof1X70CtFmjhagN/Jyu5wyFqlAIDA7Uez507F/PmzdNL3StXrsTRo0fx008/wc/PD4cOHcLEiRPh5eVVrhVPX4wmiSMikosAsO/eqdTH2QpHJJuoqCh4e3tLj8u2wgGAi4sLVCoVkpOTtZYnJyfDw8OjwvV6eHhUWf7u3buYNWsWduzYIU060KFDB5w5cwZLlixhEmesVCoVhg8fLt0nIv0r/R5a3oyCsh6+h5eScxCfcRfmpio8UuaCBqVKhV69ekn39UGOOivD30AC9Lcf2Nraws7OrsoyZmZm6NKlCyIiIhAWFgagZGzFiIgITJo0qcLX9OjRAxEREZg6daq0bN++fejRowcAoKioCEVFRQZ3gSWTuDpSKBQwMeHbSCSn0u9hfR08SlvhHg5wgmWZCxoU0H/yIkedleFvIAGGtx+Eh4djzJgx6Nq1K7p3747ly5cjNzcX48aNAwCMHj0a3t7e0jBlU6ZMQZ8+fbB06VIMGjQI33//PU6ePInPP/8cAGBnZ4c+ffpg2rRpsLS0hJ+fHw4ePIivvvoKy5Ytk207DecdJyIyQHlFavwVWzJDA0+lEhmHkSNHIjU1FXPmzEFSUhKCgoKwd+9e6eKFuLg4rVa1nj17YvPmzXj33Xcxa9YstGjRAjt37kS7du2kMt9//z1mzpyJ5557DmlpafDz88MHH3yAV155Re/bV0ohDGVoZT2Ij49HkyZNcPPmTfj4+OhknWq1GidOnAAAdOvWzWD+OyZqTEq/h39l30KLFi10Og3VvsupWPVXLHwcLLFqWPtyA/xqNBpcuXIFAHRed2V0Uedg37Y6iYW/gQTU/35QH8fvhsBoxokzVEIIxMbGIjY21mCmGiFqbEq/h8nJyTr/Hh6IuQMA6NfcucIZGoQQSE5Orpe6KyNHnVXFwt9A4n4gDyZxRESVSM0pwPmkLCgUQN9mLnKHQ0SkhUkcEVEl9l8taYVr72kHF2szmaMhItLGCxuIiCogAByIuQ2gYbbC/Rx3odav1VV/OiKqG7bEERFVICY1FwmZ+TAzUaKnv6Pc4RARlcMkjoioAvuvlrTCPeTnWG5sOCIiQ8AkjoioDLVG4M9rJWPD9WuAp1KJqGFgn7g6UqlUeOqpp6T7RKR/pd9D84RLOpmG6lR8JrLyi+BgZYqOXlVP8aNUqaSpefQ57Za+66wMfwMJ4H4gFyZxdaRQKGBhYSF3GESNWun30NTUVCfrO3jvqtRHmjpDpaxodLj76gZ0Vm91yVFnZfgbSAD3A7nwdCoR0X3yi9Q4fjMdANC3mbPM0RARVY4tcXWkVqsRGRkJAOjcuTObkYlkUPo9vJKVgGbNmtVp6qsTNzNQWKyBh505mjpbP7C8RqPB1atXAaDOdVeXHHVWhr+BBHA/kAtb4upICIGYmBjExMRwqhEimZR+DxMTE+v8PSyd7L53QMXTbFVUd2Jiok7qri456qwqFv4GEvcDeTCJIyK6J69IjVPxmQCAhwOcZI6GiKhqTOKIiO45HpeBIrUGPg6W8HOykjscIqIqMYkjIrrnr2ulp1KdqnUqlYhITkziiIgA5BQUI/JWBgCgtz9PpRKR4WMSR0QE4GhcOtRqAT9HKzRxtJQ7HCKiB+IQI0TUqGmKcqApyMShyykAgF6+Dx5WhIjIEDCJqyOVSoUhQ4ZI94lI/0q/h6a3oms0DZX67m1knVmNtOTLOJf2OgAlOuRsg/ruWKgsqzdnqlKlQnBwsHRfH+SoszL8DSSA+4FcmMTVkUKhgLU1/3MnklPp99Dc3Lzar9EU5SDrzGoUpp5FZGEQNFDC1yQZTpmHkXU2D/ZdwqE0tXlw3UCN6tUFOeqsDH8DCeB+IBf2iSOiRklTkInC1LMAgMiCVgCArmYXAQCFKWegKciULTYioupgEldHGo0Gp0+fxunTp6HRaOQOh6hRKv0eXrt2rdqjxYuiPABAnjBHdJEfAKCTeXS55x+4HiFw7dq1GtVdV3LUWRn+BhLA/UAuTOLqSKPR4NKlS7h06RJ3XCKZlH4P4+Pjq/09VJiWDOb7T0FzqKGElyoV7qr0cs9Xp+74+Pga1V1XctRZVSz8DSTuB/JgEkdEjZLS3B5mbkE4XdgSANDJ/LL0nJlbEJTm9nKFRkRULUziiKhRUprawLztKzhf3BwA0Nms5FSqmVsQ7DpOrNZFDUREcuLVqUTUaJ1NM0WRRgVXaxU69AuH0tQaSnN7JnBEZBSYxBFRo3X0eslcqT0DXGHm6CtzNERENcPTqUTUKBWrBY7fzAAA9PDjXKlEZHyYxBFRo3QuKQt5hWo4WJmitRtPnxKR8eHp1DpSqVQYOHCgdJ+I6ubnuAs1fo0AoOzUHF1R/WmojtwoGU4k2NcRCkWNq5QoVSp07dpVuq8PctRZGf4GEsD9QC5M4upIoVDA3p5DERDJSQHAyqp647oBgEYAx+JKkrgefo56rVsX5KizMvwNJID7gVx4OpWIGp3olBxk5BXBykyF9h52codDRFQrbImrI41GgwsXSk7/tG3bFkol82IifRNCIC4uDgDg6+sLxQPOjx6/1wrXtYkDTFR1OJdai7p1QY46K8PfQAK4H8iFSVwdaTQanD9/HgDQpk0b7rhEMtBoNLhx4wYAwMfH54F9ckqvSu3exEHvdeuCHHVWFQt/A4n7gTyM6l1OSEjA//3f/8HZ2RmWlpZo3749Tp48KXdYRGREErMKEJ9xFyqlAp19HOQOh4io1oymJS49PR29evVCv379sGfPHri6uuLKlStwdKxbp2QialyO3yw5lRrobgtrM15FR0TGy2iSuEWLFqFJkybYsGGDtCwgIEDGiIjIGJ2IywAAdPflP4BEZNyM5nTqTz/9hK5du2L48OFwc3NDp06d8MUXX1T5moKCAmRlZUm37OxsPUVLRIYop6AYF5JLfgd00R+OiEhORpPEXbt2DWvWrEGLFi3w66+/4tVXX8XkyZOxadOmSl+zcOFC2NvbS7fAwEA9RkxEhiYyIRMajYCPgyU87MzlDoeIqE6MJonTaDTo3LkzPvzwQ3Tq1AkTJkzA+PHjsXbt2kpfM3PmTGRmZkq3qKgoPUZMRIbmROlVqTyVSkQNgNH0ifP09CzXktamTRts27at0teYm5vD3Pzf/7azsrJ0HpdKpcITTzwh3Sci/VOqVOjUqZN0vyJqjcCp+EwAuj2VWp26dU2OOivD30ACuB/IxWiSuF69eiE6Olpr2eXLl+Hn5ydTRCUUCgWcnZ1ljYGosVMAsLW1rbJMVHI2cguKYWdhilY6nPC+OnXrmhx1Voa/gQRwP5CL0ZxOfeONN3D06FF8+OGHiImJwebNm/H5559j4sSJcodGREagdIDfrk3soZRvggMiasSuXbum0/UZTRLXrVs37NixA9999x3atWuH999/H8uXL8dzzz0na1wajQYXL17ExYsXodFoZI2FqLESQiA+Ph7x8fEQQpR/Hv8OLdJNx1elPqju+iBHnZXhbyAB3A+qq3nz5ujXrx+++eYb5Ofn13l9RpPEAcCTTz6Jc+fOIT8/HxcvXsT48ePlDgkajQZnzpzBmTNnuOMSyUSj0eDatWu4du1ahd/DhMy7SMzKh4lKiU7e9nqtuz7IUWdVsfA3kLgfVE9kZCQ6dOiA8PBweHh44OWXX8bx48drvb5aJXF79+7FX3/9JT1evXo1goKC8J///Afp6em1DoaIqD4cv9cK197TFpam7HRNRPIICgrCihUrcOvWLaxfvx6JiYno3bs32rVrh2XLliE1NbVG66tVEjdt2jTpSs9z587hzTffxMCBAxEbG4vw8PDarJKIqN6UDi2i61OpRES1YWJigmHDhmHr1q1YtGgRYmJi8NZbb6FJkyYYPXo0EhMTq7WeWiVxsbGx0nAf27Ztw5NPPokPP/wQq1evxp49e2qzSiKiepFTUIyLnKWBiAzIyZMn8dprr8HT0xPLli3DW2+9hatXr2Lfvn24desWhg4dWq311GqIETMzM+Tl5QEAfv/9d4wePRoA4OTkVC9jsRER1dbpW5kQAmjiaAlXG87SoAs/x12Q7qvVakSqMwAAljejHjhG2GDftvUZGpFBW7ZsGTZs2IDo6GgMHDgQX331FQYOHAilsqRNLSAgABs3boS/v3+11lerJK53794IDw9Hr169cPz4cWzZsgVAybhtPj4+tVklEVG9iLw3wG8Xbwd5AyGiRm/NmjV44YUXMHbsWHh6elZYxs3NDf/973+rtb5anU5dtWoVTExM8MMPP2DNmjXw9vYGAOzZswf9+/evzSqJiHROCEizNHTx0e1VqURENbVv3z5Mnz69XAInhEBcXByAkrOdY8aMqdb6atUS5+vri127dpVb/sknn9RmdUZNpVLh0Ucfle4Tkf4pVSp06NBBul/qWlouMu8WwcJUhUD3+pnhoLK665McdRpDLCQfHgurp1mzZkhMTISbm5vW8rS0NAQEBECtVtdofbVK4lQqVYVB3LlzB25ubjUOwpgpFAq4u7vLHQZRo6YA4ODgUG75yZslrXBBXnYwUdXPNA2V1V2f5KizMoYUC8mHx8LqqWxw7pycHFhYWNR4fbVK4ioLoqCgAGZmZrVZJRGRzkXGZwAAuvg4yBoHEenf6tWrsXjxYiQlJaFjx45YuXIlunfvXmn5rVu3Yvbs2bh+/TpatGiBRYsWYeDAgVplLl68iOnTp+PgwYMoLi5GYGAgtm3bBl9f3ypjKR1+TaFQYM6cObCyspKeU6vVOHbsGIKCgmq8jTVK4j799FMpiC+//BI2Nv9OIq1Wq3Ho0CG0bt26xkEYM41Gg5iYGAAl02mUXmFCRPojhJDGVfL09IRCoUB2QTGiU3MA1G9/uIrqrm9y1GkMsZB8DO1YuGXLFoSHh2Pt2rUIDg7G8uXLERoaiujo6HJnEQHg8OHDGDVqFBYuXIgnn3wSmzdvRlhYGCIjI9GuXTsAwNWrV9G7d2+8+OKLmD9/Puzs7HDhwoVqtaCdPn0aQMn35dy5c1oNXmZmZujYsSPeeuutGm9njZK40j5vQgisXbtW67y3mZkZ/P39sXbt2hoHYcw0Gg1OnToFAGjatKnsOy5RY3T/AcTd3R0qlQqnE0qGFvFzsoKzdf2dIaio7vomR53GEAvJx9COhcuWLcP48eMxbtw4AMDatWuxe/durF+/HjNmzChXfsWKFejfvz+mTZsGAHj//fexb98+rFq1Sspr3nnnHQwcOBAff/yx9LpmzZpVK579+/cDAMaNG4cVK1bAzs6uTttXqkbvcmxsLGJjY9GnTx+cPXtWehwbG4vo6Gj8+uuvCA4O1klgRER1cereLA1ddDxXKhEZtsLCQpw6dQohISHSMqVSiZCQEBw5cqTC1xw5ckSrPACEhoZK5TUaDXbv3o2WLVsiNDQUbm5uCA4Oxs6dO2sU24YNG3SWwAG17BNXmlESERkiIYDIhJKBx7s0YRJH1FBkZ2drTSpgbm4Oc3PtQbxv374NtVpd7kILd3d3XLp0qcL1JiUlVVg+KSkJAJCSkoKcnBx89NFHWLBgARYtWoS9e/di2LBh2L9/P/r06VNpzMOGDcPGjRthZ2eHYcOGVbl927dvr/L5smqVxD399NPo3r07pk+frrX8448/xokTJ7B169barJaISCeu3M5BVn4RrMxUaONWP0OLUO3cP9tDTXG2Byqd8rPU3LlzMW/evHqvV6PRAACGDh2KN954A0DJZPaHDx/G2rVrq0zi7O3tpb6i9va6/aeyVkncoUOHKnzTBgwYgKVLl9Y1JiKiOikd4DfIyx4qJTvaEzUUUVFR0gQDAMq1wgGAi4sLVCoVkpOTtZYnJyfDw8OjwvV6eHhUWd7FxQUmJiblksg2bdrgr7/+qjLmDRs2VHhfF2rV8zAnJ6fCoURMTU05dyoRyU6aaounUokaFFtbW9jZ2Um3ipI4MzMzdOnSBREREdIyjUaDiIgI9OjRo8L19ujRQ6s8UDK7Qml5MzMzdOvWDdHR0VplLl++DD8/v2rHf/fuXWnueQC4ceMGli9fjt9++63a67hfrZK49u3bS/Ol3u/7778vl6USEelTVn4xrtwuGVqkM+dLJWqUwsPD8cUXX2DTpk24ePEiXn31VeTm5kpXq44ePRozZ86Uyk+ZMgV79+7F0qVLcenSJcybNw8nT57EpEmTpDLTpk3Dli1b8MUXXyAmJgarVq3Czz//jNdee63acQ0dOhRfffUVACAjIwPdu3fH0qVLMXToUKxZs6bG21mr06mzZ8/GsGHDcPXqVWmajYiICHz33XeNrj+cUqnEI488It0nIv1TKpVo27akv9SZxCwIAQQ4W8HJylSvdevrN0COOo0hFpKPoR0LR44cidTUVMyZMwdJSUkICgrC3r17pYsX4uLitOLs2bMnNm/ejHfffRezZs1CixYtsHPnTmmMOAB46qmnsHbtWixcuBCTJ09Gq1atsG3bNvTu3bvacUVGRkrDtf3www/w8PDA6dOnsW3bNsyZMwevvvpqjbZTISqbfuEBdu/ejQ8//BBnzpyBpaUlOnTogLlz51bZuU9u8fHxaNKkCW7evAkfHx+5wyGiCtSl4zsALD14FYeu3sEzHb3wfBd+zxsSXtjQeDWU47eVlRUuXboEX19fjBgxAm3btsXcuXNx8+ZNtGrVSutUa3XUqiUOAAYNGoRBgwbV9uVERDqnEff1h6vHWRqIiGqjefPm2LlzJ5566in8+uuv0pWuKSkptRo/rtZtnhkZGfjyyy8xa9YspKWlAShpJkxISKjtKo2SRqPBtWvXcO3aNekSZCLSLyEEkpKScSo6FrkFxbA2N0ErV5sHv1CHdSclJVc6r3RDqNMYYiH58FhYPXPmzMFbb70Ff39/BAcHSxdO/Pbbb+jUqVON11erlrh//vkHISEhsLe3x/Xr1/HSSy/ByckJ27dvR1xcnNRprzHQaDQ4duwYAMDX19cg+gIQNTYajQaXL0cjITMfCtijkx6HFimtGwBcXV30Nu2Wvus0hlhIPjwWVs8zzzyD3r17IzExER07dpSWP/bYY3jqqadqvL5avcvh4eEYO3Ysrly5ojXx68CBA3Ho0KHarJKIqM4y7hYBADrzVCoRGSgPDw906tRJK9Ht3r07WrduXeN11aol7sSJE1i3bl255d7e3tIUFURE+lSk1iC3qBgA0JnzpRKRAcrNzcVHH32EiIgIpKSklDv1fO3atRqtr1ZJnLm5eYWD+l6+fBmurq61WSURUZ1k5pckcM2creCoh6FFiIhq6qWXXsLBgwfx/PPPw9PTU5qOq7ZqlcQNGTIE7733Hv73v/8BABQKBeLi4jB9+nQ8/fTTdQqIiKg2MvNLTqV24qlUIjJQe/bswe7du9GrVy+drK9WfeKWLl2KnJwcuLm54e7du+jTpw+aN28OW1tbfPDBBzoJjIioujQagcy7JS1xQV5M4ojIMDk6OsLJyUln66tVS5y9vT327duHv//+G2fPnkVOTg46d+6MkJAQnQVGRFRdV+7kolhoYKJQoqWLtdzhEBFV6P3338ecOXOwadMmWFlZ1Xl91U7inJyccPnyZbi4uOCFF17AihUr0KtXL501CRorpVIpvQe8pJpIHmduZeNisSM6eNrBxES/Q1wolUq0adNGut9Q6zSGWEg+PBZWz9KlS3H16lW4u7vD398fpqba/XcjIyNrtL5qJ3GFhYXIysqCi4sLNm3ahEWLFsHW1rZGlTVESqUSvr6+codB1KhFJmThttoCbQO869xRuKYUCoXeL+iSo87KGFIsJB8eC6snLCxMp+urdhLXo0cPhIWFoUuXLhBCYPLkybC0tKyw7Pr163UWIBFRVbLyixBzOwcA0MXbQd5giIiqMHfuXJ2ur9ptnt988w0GDhyInJySH8vMzEykp6dXeGtMNBoN4uLiEBcXx6lGiGRwOiELEEB7B4Hi3Ay9T/0khEBqaipSU1P1Ou2Wvus0hlhIPjwWVl/ptKUzZ86s87Sl1W6Jc3d3x0cffQQACAgIwNdffw1nZ+caV9jQaDQa/P333wCA4cOHsy8AkZ5FxmdAAYE2Jhm4ePEievXqpdepnzQaDS5evAgAeqtbjjqNIRaSD4+F1VN22tLx48fXadrSar/LTk5OuH37NgCgX79+MDMzq1nkREQ6JkRJfzgAcLDgAL9EZNh0PW1ptZO40gsbAGDTpk3Iz8+vcWVERLp0LS0XWflFsDRVwcacLUBEZNhOnDiBl19+udzy2k5bygsbiMhonYrPBAC097KFQpEnczRERFXT9bSltbqwQaFQ8MIGIpJdZHwGAKATZ2kgIiNQOm1pUVHJNIF1nbaUFzYQkVHKKSjGpZSSq+WDvOxwOVXmgIiIHmDp0qV45pln4OrqKk1bmpSUhB49etRq2tIaXT4ycOBAZGZmIjY2Fs7Ozvjoo4+QkZEhPX/nzh0EBgbWOIja+Oijj6BQKDB16lS91EdEhuXsrSwIAfg4WMLVxlzucIiIHqh02tLdu3fj008/xaRJk/DLL7/g4MGDsLau+ZSBNZo7de/evSgoKJAef/jhhxgxYgQcHBwAAMXFxYiOjq5xEDV14sQJrFu3Dh06dKj3uh5EqVQiODhYuk9E+hGZUNIfrouPPZRKJVq2bAVA/99DOeqWc3sNORaSD4+FD6bRaLBx40Zs374d169fh0KhQEBAADw8PCCEqNVsMzVK4sqSY2DHnJwcPPfcc/jiiy+wYMECvddfllKpRNOmTeUOg6hREQAi713U0NnHAQqFAh4e7rLEIkfdcm5vWYYUC8mHx8KqCSEwZMgQ/PLLL+jYsSPat28PIQQuXryIsWPHYvv27di5c2eN11unJE4OEydOxKBBgxASEvLAJK6goECr5TA7O7u+wyMiPbiRloe0vEKYm6jQ1t1G7nCIiKq0ceNGHDp0CBEREejXr5/Wc3/88QfCwsLw1VdfYfTo0TVab42SOIVCUa65T5+TTX///feIjIzEiRMnqlV+4cKFmD9/fr3GpNFokJiYCADw9PRkMzIRgJ/jLtTr+ktPpbb3tIWpSgkhhDR9jZOTk15/l+SoW87tNeRYSD48Flbtu+++w6xZs8olcADw6KOPYsaMGfj222/rN4kTQmDs2LEwNy/pRJyfn49XXnlF6ox3f6uXrt28eRNTpkzBvn37tEY5rsrMmTMRHh4uPU5ISND5hRcajUYaZZlTjRDph3Qq1btkaBGNRoMLF0oSRzmm3dJ33XJuryHHQvLhsbBq//zzDz7++ONKnx8wYAA+/fTTGq+3RkncmDFjtB7/3//9X7kyNc0iq+vUqVNISUlB586dpWVqtRqHDh3CqlWrUFBQUO7Hw9zcXEo4AVQ4wB4RGZe7RWpEpZR0jejs4yBvMERE1ZCWlgZ398r7jrq7u9dqnN0aJXEbNmyocQW68thjj+HcuXNay8aNG4fWrVtj+vTp/O+PqJH4JzEbarWAp50FPO04tAgRGT61Wg0Tk8pTLpVKheLi4hqv12gubLC1tUW7du20lllbW8PZ2bncciJquEpnaejsw1kaiMg4lO2OVlZtu6MZTRJHRKQ1tIg3kzgiMg5lu6NVpDbd0Yw6iTtw4IDcIRCRHiVk3kVKTgFMVUq097CVOxwiomqpr+5ovHyEiIxGZHzJxUltPWxgbsp+sETUuBl1S5whUCqV6NKli3SfiOpPZEIGAKCzt4PWcqVSiebNm0v39UmOuuXcXkOOheTDY6E8mMTVUcm8gS3lDoOowSsoVuN8YsnQIl3KXNSgUCjg5eUlR1iy1C3n9pZlSLGQfHgslAfTZSIyCueTslGk1sDVxhzeDpZyh0NEJDu2xNWREAIpKSkAADc3N045Q1RPSvvDdfKxQ9lvmQCQmZEBALB3cCj3fH2So245t9eQYyH58FgoD7bE1ZFarcYff/yBP/74A2q1Wu5wiBqs0vHhulQwS4NGrcY///yDf/75Bxo9fw/lqFvO7TXkWEg+PBbKg0kcERm8pKwC3MrKh0qpQAdPO7nDISIyCEziiMjgRSaUDPDbxt0WVhxahIgIAJM4IjICJ29mAOAsDURE92MSR0QGraBYjX8SSy5q6NqESRwRUSkmcURk0EqHFnGxMYOvo5Xc4RARGQwmcURk0E7cLOkP19WHw1cQEd2P48TVkVKpRFBQkHSfiHRH4N/+cF2bOFRaTqlUomnTptJ9fZKjbjm315BjIfnwWCgPJnF1pFQq0aZNG7nDIGqQbqbfRWpOAUxVSnTwtK20nEKhgI+Pjx4jk7duObe3LEOKheTDY6E8mC4TkcE6eW+A3w6edjA34dAiRET3Y0tcHQkhkJaWBgBwcnLiVCNEOlR6KrXLA65KFQBysrMBADa2tnqfdkvfdcu5vYYcC8mHx0J5sCWujtRqNX777Tf89ttvnGqESIdyCopxMSUHQMlFDVXRqNU4ffo0Tp8+Lcu0W/quW87tNeRYSD48FsqDSRwRGaTTtzKh0Qj4OFjC3dZc7nCIiAwOkzgiMkin7htahIiIymMSR0QGRwjgVPy9JI6zNBARVYhJHBEZnCu3c5CVXwQrMxUC3SsfWoSIqDFjEkdEBufEvatSO3nbQ6XkVW5ERBVhEkdEBof94YiIHoxJXB0plUq0a9cO7dq141QjRDqQnleEq3dyAQCdfarXH06pVMLPzw9+fn6yTLul77rl3F5DjoXkY4jHwtWrV8Pf3x8WFhYIDg7G8ePHqyy/detWtG7dGhYWFmjfvj1++eWXSsu+8sorUCgUWL58uY6jrhkO9ltHSqUS7du3lzsMogajdJaGFq42cLA0rdZrFAoF/Pz86jEqw6pbzu0ty5BiIfkY2rFwy5YtCA8Px9q1axEcHIzly5cjNDQU0dHRcHNzK1f+8OHDGDVqFBYuXIgnn3wSmzdvRlhYGCIjI9GuXTutsjt27MDRo0fh5eWlr82plGGky0RE95T2h+tSzVY4IqKyli1bhvHjx2PcuHEIDAzE2rVrYWVlhfXr11dYfsWKFejfvz+mTZuGNm3a4P3330fnzp2xatUqrXIJCQl4/fXX8e2338LUtHr/ZNYnJnF1JIRAZmYmMjMzIYSQOxwio1ao1uB0Qkl/uId8Hav9OgEgLy8PeXl50Pe3UI665dxeQ46F5GNIx8LCwkKcOnUKISEh0jKlUomQkBAcOXKkwtccOXJEqzwAhIaGapXXaDR4/vnnMW3aNLRt27Z+gq8hJnF1pFar8csvv+CXX37hVCNEdXQmIQuFxRq42pjD39mq2q/TqNU4efIkTp48Kcu0W/quW87tNeRYSD76OhZmZ2cjKytLuhUUFJQrc/v2bajVari7u2std3d3R1JSUoXrTUpKemD5RYsWwcTEBJMnT9bBlugGkzgiMhjHb6YDALr7OnAidSIqJzAwEPb29tJt4cKFeqn31KlTWLFiBTZu3AiFwnB+nXhhAxEZBCH+7Q/XvQanUomo8YiKioK3t7f02Ny8/LzKLi4uUKlUSE5O1lqenJwMDw+PCtfr4eFRZfk///wTKSkp8PX1lZ5Xq9V48803sXz5cly/fr22m1QnbIkjIoNw+XYOMvJKZmlox1kaiKgCtra2sLOzk24VJXFmZmbo0qULIiIipGUajQYRERHo0aNHhevt0aOHVnkA2Ldvn1T++eefxz///IMzZ85INy8vL0ybNg2//vqrDrewZtgSR0QG4XhcBgCgi48DTFSGc7qCDMfPcRdq/drBvobREZ30Izw8HGPGjEHXrl3RvXt3LF++HLm5uRg3bhwAYPTo0fD29pZOx06ZMgV9+vTB0qVLMWjQIHz//fc4efIkPv/8cwCAs7MznJ2dteowNTWFh4cHWrVqpd+Nuw+TOCIyCMfiSvrDdWviIG8gRGT0Ro4cidTUVMyZMwdJSUkICgrC3r17pYsX4uLitAYl7tmzJzZv3ox3330Xs2bNQosWLbBz585yY8QZGiZxRCS7xKwC3Ey/C5VSwfHhiEgnJk2ahEmTJlX43IEDB8otGz58OIYPH17t9cvVD+5+TOLqSKlUonXr1tJ9ooaiLqeuaur4vVa4th62sDGv+c+SUqmEj4+PdF+f5Khbzu015FhIPjwWyoNJXB0plUp06tRJ7jCIjFppf7jaXpWqUCjQtGlTHUZk2HXLub1lGVIsJB8eC+XBdJmIZJVdUIwLyVkAgO7sD0dEVG1siasjIQTy8vIAAFZWVgY1CCCRMTgWlw4hAH8nK7jblh8uoDoEgMJ7I7ebmZvrdaBgOeqWc3sNORaSD4+F8mBLXB2p1Wr89NNP+OmnnzjtFlEtHLle0h+uh59TrdehUatx7NgxHDt2TJZpt/Rdt5zba8ixkHx4LJSH0SRxCxcuRLdu3WBraws3NzeEhYUhOjpa7rCIqA7yCtU4fatkwvte/pylgYioJowmiTt48CAmTpyIo0ePYt++fSgqKsITTzyB3NxcuUMjolo6fjMDarWAj4Mlmjhayh0OEZFRMZo+cXv37tV6vHHjRri5ueHUqVN45JFHZIqKiOri8PU0AEBPtsIREdWY0SRxZWVmlpyCcXKqvB9NQUEBCu51uAWA7Ozseo+LiKrnbpEakfEl3+Oe/rXvD0dE1FgZzenU+2k0GkydOhW9evWqckqMhQsXwt7eXroFBgbqMUoiqsrJ+AwUqTXwtLOAv5OV3OEQERkdo0ziJk6ciPPnz+P777+vstzMmTORmZkp3aKiovQUIRE9yOF7V6X2DHDisBRERLVgdKdTJ02ahF27duHQoUPSVC+VMTc3h7n5v+NOZWVl6TwehUKB5s2bS/eJ6MEKitU4dfPeqVS/uveHUygU8PT0lO7rkxx1y7m9hhwLyYfHQnkYTRInhMDrr7+OHTt24MCBAwgICJA7JACASqVCt27d5A6DyKhEJmShoFgNVxtzNHOxrvP6lEolWrRooYPIjKNuObe3LEOKheTDY6E8jCaJmzhxIjZv3owff/wRtra2SEpKAgDY29vD0pJDExAZk/uvSuX/7EREtWM0feLWrFmDzMxM9O3bF56entJty5YtssYlhEB+fj7y8/MhhJA1FiJjUKTWSBPe9wrQzVWpAkBRURGKioqg72+hHHXLub2GHAvJh8dCeRhNS5yh7hRqtRo7duwAAAwfPhwmJkbzlhLJ4nRCFvKL1HC2NkNLFxudrFOjVuPIkSMAgF69ekGlUulkvYZat5zba8ixkHx4LJSH0bTEEVHDUHoqtYefE9j/mYio9pjEEZHeFKk1OBZ3b2gRztJARFQnbO+kRk+dnw51Xgo0BZlQmttDZeUGlQUTjPpwKiETeYUlp1LbuNvKHQ4RkVFjEkeNWnH2TaTuexn5cb9Lyyz8HodryFqY2DaRMbKG6c+rJadSewc4QclTqUREdcLTqdRoqfPTyyVwAJB/Yx9Sf38F6vx0mSJrmO4WqXH8Zsl7+khTZ5mjISIyfkziqNFS56WUS+BK5d/YB3Veip4jatiOxWWgsFgDLzsLnQzwS0TU2PF0ah0pFApp9ghONWJcNAWZVT9fWPXzVDOHrt4BADzczFnnA/wqFAq4u7tL9/VJjrrl3F5DjoXkw2OhPJjE1ZFKpcJDDz0kdxhUC0pz+6qfN6v6eaq+rPwinLlVkhQ/0lQ3A/zeT6lUolWrVjpfr6HWLef2lmVIsVTl57gLtX7tYN+2OoykYeKxUB48nUqNlsrKDRZ+j1f4nIXf41BZuek5oobr4LU0qDUCzV2s4WPPafKIiHSBSVwdCSFQXFyM4uJig51VgiqmsnCEa8jacolcydWp6zjMiA79cSUVAPBoc5d6Wb9AyYjxarValmm39F23nNtryLGQfHgslAdPp9aRWq3G1q1bAXCqEWNkYtsEbgO+KRknrjATSjOOE6drN9LzcO1OHlQqBR5pVj9XpWrUavz9998A5Jl2S991y7m9hhwLyYfHQnnwXaZGT2XhyKStHv1xpeSChm4+DrA1508OEZGu8HQqEdUbtUbg4LXbAIB+LernVCoRUWPFJI6I6s2ZW5lIzyuCrbkJuno7yB0OEVGDwiSOiOrN/piSU6mPNHOBiYpjRxER6RKTOCKqF7mFahy9UTLN1mMtOM0WEZGuMYkjonrx57U7KFJr4OtohabOnGaLiEjXeKlYHSkUCjRp0kS6T0QlfosuGRsupIWLzqfZKkuhUMDFxUW6r09y1C3n9hpyLCQfHgvlwSSujlQqFXr37i13GEQGJeZ2Lq7eyYWJSolH9XBVqlKpRGBgYL3XYyh1y7m9ZRlSLCQfHgvlwdOpRKRze6NTAAC9/B05NhwRUT1hEkdEOpVXpMaha2kAgNBWnH+WiKi+8F/kOiouLuZUI0T3OXT1DgqK1PBxsESgh61e6lTLOPWTHHXLub2GHAvJh8dCebAljoh0RuDfU6lPtHKt9wsaiIgaMyZxRKQzMam5iL2TB1OVEo815zRbRET1iUkcEenM7ovJAEouaLDhBQ1ERPWKSRwR6UR6XhH+jC25oGFQoLvM0RARNXxM4ohIJ/ZGp6BYrUErNxu0dLWROxwiogaPSRwR1VmRWoM9l0ouaBjMVjgiIr1gp5U6UigU8PT0lO4TNUZ/xaYh824RnK3N0NPfSe/1KxQKODo5Sfcbet1ybq8hx0Ly4bFQHkzi6kilUqFv375yh0EkGwHgpwtJAICBrd2gUur/B1ypVKJ9u3Z6r1euuuXc3rIMKRaSD4+F8uDpVCKqk6ikbFy7N6xIaGvO0EBEpC9M4oioTrb9kwgA6NfcmfOkEhHpEX9x66i4uBjbt28HAAwbNoxTjVCjEnsnD6fiM6BQAE+195QtDrVajSNHjgAAevToofdpt/Rdt5zba8ixkHx4LJQH32UdUKvVcodAJItt50pa4XoHOMPLzkLWWDQaTaOqW87tLcuQYiH58FiofzydSkS1cisrH3/F3gEAPN1BvlY4IqLGikkcEdXK9nOJEALo4uOAACcrucMhImp0mMQRUY3dySvEHzG3AQDDO3rJHA0RUePEJI6IauyHs4lQqwUCPWzRxp1TbBGR4Vm9ejX8/f1hYWGB4OBgHD9+vMryW7duRevWrWFhYYH27dvjl19+kZ4rKirC9OnT0b59e1hbW8PLywujR4/GrVu36nszqmR0SVxNPxQi0q2k7AL8Gl0yxdaoIG+ZoyEiKm/Lli0IDw/H3LlzERkZiY4dOyI0NBQpKSkVlj98+DBGjRqFF198EadPn0ZYWBjCwsJw/vx5AEBeXh4iIyMxe/ZsREZGYvv27YiOjsaQIUP0uVnlGFUSV9MPRR8UCgVcXV3h6urKqUaoUdgcGQ+1RqCTtz06eNnJHQ6Aku+hvb097O3tZZl2S991y7m9hhwLycfQjoXLli3D+PHjMW7cOAQGBmLt2rWwsrLC+vXrKyy/YsUK9O/fH9OmTUObNm3w/vvvo3Pnzli1ahUAwN7eHvv27cOIESPQqlUrPPTQQ1i1ahVOnTqFuLg4fW6aFqMaYuT+DwUA1q5di927d2P9+vWYMWOGLDGpVCqEhITIUjeRvsXeycPBqyVXpD7f1UfmaP6lVCrRsWPHRlO3nNtbliHFQvIxpGNhYWEhTp06hZkzZ0rLlEolQkJCpDENyzpy5AjCw8O1loWGhmLnzp2V1pOZmQmFQgEHBwddhF0rRtMSV/qh3L+TPOhDISLd+vpUPADg4abOaOZsLXM0RNTYZGdnIysrS7oVFBSUK3P79m2o1Wq4u7trLXd3d0dSUlKF601KSqpR+fz8fEyfPh2jRo2CnZ18ZySMJomrzYdSUFCg9WFnZ2frI1SiBulCUjZOxWdApVTgP53ZF46I9C8wMFA6fW9vb4+FCxfqPYaioiKMGDECQgisWbNG7/Xfz6hOp9bUwoULMX/+/Hqto7i4GD/99BMAYMiQIZxqhBokAeCrkzcBAI+3dJV9doay1Gq1dJFT9+7d9T7tlr7rlnN7DTkWko++joVRUVHw9v73n0hzc/NyZVxcXKBSqZCcnKy1PDk5GR4eHhWu18PDo1rlSxO4Gzdu4I8//pC1FQ4wopa42nwoM2fORGZmpnSLioqql9gKCgoqbNIlaihOxGXgUkoOzEyUGGmgV6QWFRWhqKio0dQt5/aWZUixkHz0cSy0tbWFnZ2ddKsoiTMzM0OXLl0QEREhLdNoNIiIiECPHj0qXG+PHj20ygPAvn37tMqXJnBXrlzB77//DmdnZx1tVe0ZTRJXmw/F3Nxc68O2tbXVV7hEDYZaI7DpXivc4LYecLIylTkiIqKqhYeH44svvsCmTZtw8eJFvPrqq8jNzZUujBw9erTWhQ9TpkzB3r17sXTpUly6dAnz5s3DyZMnMWnSJAAlCdwzzzyDkydP4ttvv4VarUZSUhKSkpJQWFgoyzYCRnY6NTw8HGPGjEHXrl3RvXt3LF++XOtDISLd+ykqGfEZd2FnYYqn23OOVCIyfCNHjkRqairmzJmDpKQkBAUFYe/evVK/+ri4OCiV/7Zj9ezZE5s3b8a7776LWbNmoUWLFti5cyfatWsHAEhISJBOFwcFBWnVtX//fvTt21cv21WWUSVxD/pQiEi3UnMK8N3pBADAmG4+sDZjfyciMg6TJk2SWtLKOnDgQLllw4cPx/Dhwyss7+/vDyGELsPTCaNK4oCqPxQi0q0vj8WhoEiNQA9bPNbcVe5wiIjoPkbTJ46I9Ot4XAaO3kiHSqnAKz38YACDsBMR0X2MriXO0CgUCjg5OUn3iRqC3IJifH70BgBgaDtP+DlayRxR1RQKhXThkhzTbum7bjm315BjIfnwWCgPJnF1pFKpEBoaKncYRDr17t5LSM0pgKuNOZ7taPgXMyiVSnTq1KnR1C3n9pZlSLGQfHgslAdPpxKRlogrqVjxZywA4NWefjA35cUMRESGiEkcEUky7hZh3PdnAAChrd3QxcdB1niIiKhyPJ1aR8XFxdi9ezcAYNCgQZx2i4za5B3nEJ+Zj2bOVnihm/FcjapWq3Hy5EkAQNeuXfU+7Za+65Zzew05FpIPj4Xy4LusA3l5eXKHQFRnW8/ewjeRCVAqgK9GdcJtZaLcIdWInFPfyVG3IU31Z0ixkHx4LNQ/nk4lIly9nYsJW88CAGY82gI9/J1kjoiIiB6ESRxRI3e3SI3hX51EZn4xevg5Ys7jLeUOiYiIqoGnU4kasJ/jLjywzMq/YnEmKxV2LqYYH+KDX29d1ENkRERUV2yJI2rEdkcl4/fLqVAogLf6NoOztZncIRERUTUxiSNqpI7HZeCLYyWzMjzfpQk6etnJHBEREdUET6fqgJ0dD35kXK7eycWSA1chBPB4K1cM62D4szI8iJWVfFODyVG3nNtbliHFUh+q0y2hMoN92+owEsPGY6H+KYQQQu4g9CU+Ph5NmjTBzZs34ePjI3c4RPWuooNPak4Bpu2KQnpeETp522P24y2hUnKuQ6L60JiSuPrE43fFeDqVqBFJzyvC3F+jkZ5XBH8nK7z9aHMmcERERoqnU4kaiTt5hXj3l0u4lZUPFxszzHm8Jaw4LyoRkdFiEldHxcXF+PXXXwEAoaGhnGqEDNLt3EK8s+cikrIK4Gpjjg8GtG5QV6Kq1WqcPn0aANCpUye9T7ul77rl3F5DjoXkw2OhPPgu60BWVpbcIRBVKjWnAO/suYTk7AK425YkcK425nKHpXNyTvkjR92GNMWRIcVC8uGxUP+YxBE1YLey8jF3bzRScgrgYWeODwa0gUsDaoEjImrMmMQRNVCHrt7BtJ+jkFNQDC87CywY2BrOVkzgiIgaCiZxRA3QxhM38fIPZ1FkWYyWrjZ4J6QFHCxN5Q6LiIh0iEkcUQOSV1iMSTvOY+OJmwCAXgFOmPpIU5ipOJoQEVFDwySOqIGISsrGiK9PIio5B0oFMPeJVujYsggKDgNHRNQgMYnTgYY+5QwZNo1G4MvjcQj/8QLyitTwsDXHt891Rr/mLnWaLsjYmJvLd8WtHHXLub1lGVIsJB8eC/WP024RGbGLydl45Yd/8GdsGgAgpIULvv5PZ7jblhxUG1MSR2SIOO2WbvD4XTG2xBEZofwiNT6MuIJF+2NQpBawMlXhvf6tMOXhppxGi4iokWASR2RENBqB7ecS8c6eS7hyOxcAMKiNG1Y91R5+TjyVQUTUmDCJqyO1Wo3ff/8dABASEsIpZ6heCCGw+2IK5uy9hDO3SkZF97Qzx4qh7fB0B08oGvnVCxqNBmfPngUAdOzYEUql/q7GlaNuObfXkGMh+fBYKA8mcXUkhEBaWpp0n6isuvRLEwDOJGRic2QCLqfmAAAsHVUY0s4DYW09YG6Wjl0303UUqfESQiA7O1u639DrlnN7DTkWkg+PhfJgEkdkgPKL1PgzNg27LyYj9k7JvJRmJko8GeiBYe09YGvOry4RUWPHIwGRgRAArt7Oxa+XU3Doahryi9QAAHMTFZ5o5Yqn23vC0YqzLhARUQkmcUQyS84uwF+xafgz9o7U6gYAnnYWeKKVK55o6QobtrwRGaW6dKfg8CT0IDwyEOlZkVqDi8k5iEzIxOmETFxP+zdxM1Ep0dPfEU+0dEU7Tzs07ssViIioKkziiOqZAJCcVSAlbWcTs1Bw71QpACgUQHtPO/QOcEZPf0f2dyMiomrh0UIHOOUMldJoBK6l5eF0acJ2KwtH064gPa9Iq5yDlSk6e9ujs7c9grztmbjpgKmpfP0F5ahbzu0ty5BiIfnwWKh/nHaLqJYKitWISs7RStjO3spCdkGxdkGb21ApFWjtZlOSuPk4IMDJihPTE1GV2CfuXzx+V4z//hNVQgiBO3mFiL1zF7FpeYhNy8O1tFxcT8tDbNpdXE/LQ7Gm/P9A5iZKdPC0Q0cvO3TytkeOWTICnCxhbsLBL4mISHeYxFGDJITA3SI1MvOLkZlfhIy7JX8z75Y+LpKey7z/fn6x9FxWfhEqyNG0OFqaIsjLDkHe9ujkbY8gLzu0drOBierfUet/jsut560lIqLGiElcHanVaqz73y4UFKvh0LIzlMqS1haF4t7t3vWFJfchTY+kwL/Pl55Wk5ZVUKbqddx7vUJR/vG9MiXlFfeto8yyCspUuQ6tmBX3la9om8qso4oyZZ8vUmvuS6yKyiVlWRUlZffKV9RKVhueduYIcLIqd2vmYg0fe4tGP+WVIdBoNDh37hwAoH379nqfdkvfdcu5vYYcC8lHrVZj//79AIB+/fpx2i09MYok7vr163j//ffxxx9/ICkpCV5eXvi///s/vPPOOzAzM5M1NiEEzsTEIaewGFvPqqAGf8AMiUIB2FuYwt7CBPYWpnCwNJEe21mYwsHy3nP3/pY8/re8k5UpLEz5Y2TohBDIzMyU7jf0uuXcXkOOheQjhEBqaqp0n/TDKJK4S5cuQaPRYN26dWjevDnOnz+P8ePHIzc3F0uWLJE7PDhbmcLaTIVQX1cIhQpCCAgAQgAl90rvl/wteSy0H0PcV+bea+57nfSaso8fVE/pemtaTyWxPrCeCmKtdj33xVpaj0qpgIPFvcTKsjQRM4Wdxb/JmJSI3SvjYGEqJWU2ZiZQKtlSRkREDY9RJHH9+/dH//79pcdNmzZFdHQ01qxZYxBJXAcvewDA4uHdYWJiFG8p1VBdRl0nIiKqD0abcWRmZsLJyanKMgUFBSgoKJAeZ2dn13dYRERERHphlB24YmJisHLlSrz88stVllu4cCHs7e2lW2BgoJ4iJCIiIqpfsrbEzZgxA4sWLaqyzMWLF9G6dWvpcUJCAvr374/hw4dj/PjxVb525syZCA8P13otEzkiIjIGdenGwYGCGwdZk7g333wTY8eOrbJM06ZNpfu3bt1Cv3790LNnT3z++ecPXL+5ubnWNCBZWVm1jrUqvJSaSH5yDm0hR92GNJSHIcVC8uGxUP+MZtqthIQE9OvXD126dME333xTq52F03ZQbfHCBiIyJg2tJY7H74oZxYUNCQkJ6Nu3L/z8/LBkyRJpLBoA8PDwkDEyIiIiInkYRRK3b98+xMTEICYmplwGbiQNiUREREQ6ZRQdGcaOHXtv8NjyN7mp1WocOHAABw4cgFqtljscokZJo9Hg3PnzOHf+PDQaTYOvW87tNeRYSD48FsrDKFriDJkQAomJidJ9ItI/IQTS09Kk+w29bjm315BjoX/pux+vWq3G3/EX0VnlwP1Aj4yiJY6IiIiItDGJIyIiogZn9erV8Pf3h4WFBYKDg3H8+PEqy2/duhWtW7eGhYUF2rdvj19++UXreSEE5syZA09PT1haWiIkJARXrlypz014ICZxRERE1KBs2bIF4eHhmDt3LiIjI9GxY0eEhoYiJSWlwvKHDx/GqFGj8OKLL+L06dMICwtDWFgYzp8/L5X5+OOP8emnn2Lt2rU4duwYrK2tERoaivz8fH1tVjnsE6cjkeoMWN6MqtX4dQ1tPJ8HkWsUco71RkTUOCxbtgzjx4/HuHHjAABr167F7t27sX79esyYMaNc+RUrVqB///6YNm0aAOD999/Hvn37sGrVKqxduxZCCCxfvhzvvvsuhg4dCgD46quv4O7ujp07d+LZZ5/V38bdhy1xRERE1GAUFhbi1KlTCAkJkZYplUqEhITgyJEjFb7myJEjWuUBIDQ0VCofGxuLpKQkrTL29vYIDg6udJ360Kha4kovfy+9mlQXiouLcefOHWQjB3dSb0OpVNR4HfFKe53FYwxuV9KcXR11ea/qUi8ZNo1GIDsjEwBq/T00prrl3F5DjoXkU7of3IEa8fHxMDHRbXpRetzOzMyEnZ2dtLzs9JoAcPv2bajVari7u2std3d3x6VLlypcf1JSUoXlk5KSpOdLl1VWRg6NKolLTk4GAHTv3r1e1r+uXtZKRDUh5/dQjroN6XfHkGIh+bz++uv1tu527dppPZ47dy7mzZtXb/UZukaVxHXq1AnHjx+Hu7s7J2zWg+zsbAQGBiIqKgq2trZyh0P38HMxTPxcDBM/F8Og0WgQFxeHwMBArVa+sq1wAODi4gKVSiU13JRKTk6udKpODw+PKsuX/k1OToanp6dWmaCgoFptky40qiTOxMQE3bp1kzuMRiMrKwsA4O3trdX8TfLi52KY+LkYJn4uhsPX17da5czMzNClSxdEREQgLCwMQEkSGBERgUmTJlX4mh49eiAiIgJTp06Vlu3btw89evQAAAQEBMDDwwMRERFS0paVlYVjx47h1VdfrfU21VWjSuKIiIio4QsPD8eYMWPQtWtXdO/eHcuXL0dubq50tero0aPh7e2NhQsXAgCmTJmCPn36YOnSpRg0aBC+//57nDx5Ep9//jkAQKFQYOrUqViwYAFatGiBgIAAzJ49G15eXlKiKAcmcURERNSgjBw5EqmpqZgzZw6SkpIQFBSEvXv3ShcmxMXFaXWr6tmzJzZv3ox3330Xs2bNQosWLbBz506tPnhvv/02cnNzMWHCBGRkZKB3797Yu3cvLCws9L59pRSCk5xRPSkoKMDChQsxc+bMCvstkDz4uRgmfi6GiZ8LGTImcURERERGiJdoEhERERkhJnFERERERohJHBEREZERYhJHREREZISYxJFeFRQUICgoCAqFAmfOnJE7nEbt+vXrePHFFxEQEABLS0s0a9YMc+fORWFhodyhNTqrV6+Gv78/LCwsEBwcjOPHj8sdUqO3cOFCdOvWDba2tnBzc0NYWBiio6PlDotIC5M40qu3334bXl5ecodBAC5dugSNRoN169bhwoUL+OSTT7B27VrMmjVL7tAalS1btiA8PBxz585FZGQkOnbsiNDQUKSkpMgdWqN28OBBTJw4EUePHsW+fftQVFSEJ554Arm5uXKHRiThECOkN3v27EF4eDi2bduGtm3b4vTp07LOOUflLV68GGvWrMG1a9fkDqXRCA4ORrdu3bBq1SoAJdMDNWnSBK+//jpmzJghc3RUKjU1FW5ubjh48CAeeeQRucMhAsCWONKT5ORkjB8/Hl9//TWsrKzkDocqkZmZCScnJ7nDaDQKCwtx6tQphISESMuUSiVCQkJw5MgRGSOjsjIzMwGA3w8yKEziqN4JITB27Fi88sor6Nq1q9zhUCViYmKwcuVKvPzyy3KH0mjcvn0barVamgqolLu7O5KSkmSKisrSaDSYOnUqevXqpTUNE5HcmMRRrc2YMQMKhaLK26VLl7By5UpkZ2dj5syZcofcKFT3c7lfQkIC+vfvj+HDh2P8+PEyRU5kmCZOnIjz58/j+++/lzsUIi3sE0e1lpqaijt37lRZpmnTphgxYgR+/vlnKBQKablarYZKpcJzzz2HTZs21XeojUp1PxczMzMAwK1bt9C3b1889NBD2Lhxo9ak0FS/CgsLYWVlhR9++AFhYWHS8jFjxiAjIwM//vijfMERAGDSpEn48ccfcejQIQQEBMgdDpEWJnFU7+Li4pCVlSU9vnXrFkJDQ/HDDz8gODgYPj4+MkbXuCUkJKBfv37o0qULvvnmG6hUKrlDanSCg4PRvXt3rFy5EkDJqTtfX19MmjSJFzbISAiB119/HTt27MCBAwfQokULuUMiKsdE7gCo4fP19dV6bGNjAwBo1qwZEzgZJSQkoG/fvvDz88OSJUuQmpoqPefh4SFjZI1LeHg4xowZg65du6J79+5Yvnw5cnNzMW7cOLlDa9QmTpyIzZs348cff4Stra3UR9He3h6WlpYyR0dUgkkcUSO1b98+xMTEICYmplwyzQZ6/Rk5ciRSU1MxZ84cJCUlISgoCHv37i13sQPp15o1awAAffv21Vq+YcMGjB07Vv8BEVWAp1OJiIiIjBB7MBMREREZISZxREREREaISRwRERGREWISR0RERGSEmMQRERERGSEmcURERERGiEkcERERkRFiEkdElTpw4AAUCgUyMjLkDoWIiMpgEkdk4JKSkvD666+jadOmMDc3R5MmTTB48GBERETotJ6+ffti6tSpWst69uyJxMRE2Nvb67Qufdq4cSMUCkWVt+vXr9dL3QqFAjt37qyXdddGRZ8xERkvTrtFZMCuX7+OXr16wcHBAYsXL0b79u1RVFSEX3/9FRMnTsSlS5fqtX4zMzOjn0d15MiR6N+/v/R42LBhaNeuHd577z1pmaurq3S/sLAQZmZmeo2RiKhWBBEZrAEDBghvb2+Rk5NT7rn09HTp/o0bN8SQIUOEtbW1sLW1FcOHDxdJSUnS83PnzhUdO3YUX331lfDz8xN2dnZi5MiRIisrSwghxJgxYwQArVtsbKzYv3+/ACDVtWHDBmFvby/27t0rWrduLaytrUVoaKi4deuWVFefPn3ElClTtGIdOnSoGDNmjPQ4LS1NPP/888LBwUFYWlqK/v37i8uXL5eL936ffPKJ8PPzkx7v379fdOvWTVhZWQl7e3vRs2dPcf369Qe+p2XjGzNmjBg6dKhYsGCB8PT0FP7+/kIIIeLi4sTw4cOFvb29cHR0FEOGDBGxsbHS644fPy5CQkKEs7OzsLOzE4888og4deqU9Lyfn5/W+1kae+m2/fe//xVNmjQR1tbW4tVXXxXFxcVi0aJFwt3dXbi6uooFCxZoxZ2eni5efPFF4eLiImxtbUW/fv3EmTNnyr1nNf2Mich48XQqkYFKS0vD3r17MXHiRFhbW5d73sHBAQCg0WgwdOhQpKWl4eDBg9i3bx+uXbuGkSNHapW/evUqdu7ciV27dmHXrl04ePAgPvroIwDAihUr0KNHD4wfPx6JiYlITExEkyZNKowrLy8PS5Yswddff41Dhw4hLi4Ob731Vo22bezYsTh58iR++uknHDlyBEIIDBw4EEVFRdV6fXFxMcLCwtCnTx/8888/OHLkCCZMmACFQlGjOEpFREQgOjoa+/btw65du1BUVITQ0FDY2trizz//xN9//w0bGxv0798fhYWFAIDs7GyMGTMGf/31F44ePYoWLVpg4MCByM7OBgCcOHECQMmE6YmJidJjoOSz2LNnD/bu3YvvvvsO//3vfzFo0CDEx8fj4MGDWLRoEd59910cO3ZMes3w4cORkpKCPXv24NSpU+jcuTMee+wxpKWlaa1XF58xERkHnk4lMlAxMTEQQqB169ZVlouIiMC5c+cQGxsrHZS/+uortG3bFidOnEC3bt0AlCR7GzduhK2tLQDg+eefR0REBD744APY29vDzMwMVlZWDzx9WlRUhLVr16JZs2YAgEmTJmmdmnyQK1eu4KeffsLff/+Nnj17AgC+/fZbNGnSBDt37sTw4cMfuI6srCxkZmbiySeflOJo06ZNtWMoy9raGl9++aV0GvWbb76BRqPBl19+KSWGGzZsgIODAw4cOIAnnngCjz76qNY6Pv/8czg4OODgwYN48sknpVO0Dg4O5d5TjUaD9evXw9bWFoGBgejXrx+io6Pxyy+/QKlUolWrVli0aBH279+P4OBg/PXXXzh+/DhSUlJgbm4OAFiyZAl27tyJH374ARMmTJDWq4vPmIiMA5M4IgMlhKhWuYsXL6JJkyZarSqBgYFwcHDAxYsXpSTO399fOrgDgKenJ1JSUmocl5WVlZQ41WY9Fy9ehImJCYKDg6Vlzs7OaNWqFS5evFitdTg5OWHs2LEIDQ3F448/jpCQEIwYMQKenp7V35D7tG/fXqsf3NmzZxETE6P1fgFAfn4+rl69CgBITk7Gu+++iwMHDiAlJQVqtRp5eXmIi4t7YH1lPwt3d3eoVCoolf/fzv29svfHcQB/DvuIC4WNJD9aqDFpIzk3NE1KauJGzY8Qkjo33Cz+AuWCGldyIcqFCxcbTUp+ZmlZyY+W0JSVaS4mLozPxSfrs+98Pw272Knno3Zxdtbr/dp53zw77/c5CWHffVxXl8uFQCCAzMzMsDrPz8+hfj6r+905JiJpYIgjilPFxcWQyWQxe3hBLpeHHctkMry9vcWkzt+BMyEhISKARrtM+pUa8/PzEEUR6+vrWF5exvj4ODY2NlBTU/OlsQBELFcHAgFUVlZicXEx4rcfd9i6u7vx8PCAqakpFBQUIDk5GYIghJZb/+Wza/iv+QkEAsjJycHW1lZErY9l9f+r+505JiJp4J44ojiVkZGBxsZGWCwWPD09RZz/eHebWq2Gx+OBx+MJnTs9PcXj4yNKS0ujHu/Xr18IBoM/7lupVOLu7i50HAwGcXJyEjpWq9V4fX0N2+/18PCAi4uLUL9KpRJerzcsyB0fH0eMpdVqYTabsb+/D41Gg6WlpR/3DwA6nQ5utxtZWVkoKioK+3y8bmVvbw+iKKKpqQllZWVITk6Gz+cLqyOXy2NyTXU6HbxeL5KSkiL6USgUUdeJ1RwTUXxgiCOKYxaLBcFgENXV1VhZWYHb7cbZ2Rmmp6chCAIAwGAwoLy8HCaTCU6nEw6HA11dXairq0NVVVXUYxUWFuLw8BDX19fw+XzfvoNTX18Pq9UKq9WK8/NzDA0Nhb0suLi4GEajEf39/djd3YXL5UJHRwdyc3NhNBoB/Hmf2f39PSYmJnB5eQmLxYK1tbVQjaurK5jNZhwcHODm5gZ2ux1ut/tH++L+ZjKZoFAoYDQasbOzg6urK2xtbUEURdze3ob+x8LCAs7OznB4eAiTyYSUlJSwOoWFhdjc3ITX64Xf7/92PwaDAYIgoKWlBXa7HdfX19jf38fY2BiOjo6irhOrOSai+MAQRxTHVCoVnE4n9Ho9RkZGoNFo0NDQgM3NTczOzgL4s2S2urqK9PR01NbWwmAwQKVSYXl5+UtjjY6OIjExEaWlpVAqlVHt7fpMb28vuru7Q0FSpVJBr9eH/WZ+fh6VlZVobm6GIAh4f3+HzWYLLQeq1WrMzMzAYrGgoqICDocj7AnY1NRUnJ+fo62tDSUlJRgYGMDw8DAGBwe/1fN/paamYnt7G/n5+WhtbYVarUZfXx9eXl6QlpYGAJibm4Pf74dOp0NnZydEUURWVlZYncnJSWxsbCAvLw9arfbb/chkMthsNtTW1qKnpwclJSVob2/Hzc0NsrOzo64Tqzkmovgge4929zQRERERxQ3eiSMiIiKSIIY4IiIiIgliiCMiIiKSIIY4IiIiIgliiCMiIiKSIIY4IiIiIgliiCMiIiKSIIY4IiIiIgliiCMiIiKSIIY4IiIiIgliiCMiIiKSIIY4IiIiIgn6DT9hFqHHmhnHAAAAAElFTkSuQmCC", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Get a colorblind-friendly palette\n", + "palette = sns.color_palette(\"colorblind\")\n", + "\n", + "df = pd.DataFrame({'cont_d': cont_d, 'ite': ite})\n", + "df_sorted = df.sort_values('cont_d')\n", + "\n", + "mid_points = np.full(n_levels, np.nan)\n", + "for i in range(n_levels):\n", + " mid_points[i] = (level_bounds[i] + level_bounds[i + 1]) / 2\n", + "\n", + "df_apos = pd.DataFrame({'mid_points': mid_points, 'treatment effects': apos[1:] - apos[0]})\n", + "\n", + "# Create the primary plot with scatter and line plots\n", + "fig, ax1 = plt.subplots()\n", + "\n", + "sns.lineplot(data=df_sorted, x='cont_d', y='ite', color=palette[0], label='ITE', ax=ax1)\n", + "sns.scatterplot(data=df_apos, x='mid_points', y='treatment effects', color=palette[1], label='Grouped Treatment Effects', ax=ax1)\n", + "\n", + "# Add vertical dashed lines at level_bounds\n", + "for bound in level_bounds:\n", + " ax1.axvline(x=bound, color='grey', linestyle='--', alpha=0.7)\n", + "\n", + "ax1.set_title('Grouped Effects vs. Continuous Treatment')\n", + "ax1.set_xlabel('Continuous Treatment')\n", + "ax1.set_ylabel('Effects')\n", + "\n", + "# Create a secondary y-axis for the histogram\n", + "ax2 = ax1.twinx()\n", + "\n", + "# Plot the histogram on the secondary y-axis\n", + "ax2.hist(df_sorted['cont_d'], bins=30, alpha=0.3, weights=np.ones_like(df_sorted['cont_d']) / len(df_sorted['cont_d']), color=palette[2])\n", + "ax2.set_ylabel('Density')\n", + "\n", + "# Make sure the legend includes all plots\n", + "lines, labels = ax1.get_legend_handles_labels()\n", + "ax1.legend(lines, labels, loc='upper left')\n", + "\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "d827dfab", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "================== DoubleMLData Object ==================\n", + "\n", + "------------------ Data summary ------------------\n", + "Outcome variable: y\n", + "Treatment variable(s): ['d']\n", + "Covariates: ['x0', 'x1', 'x2', 'x3', 'x4']\n", + "Instrument variable(s): None\n", + "No. Observations: 1000\n", + "\n", + "------------------ DataFrame info ------------------\n", + "\n", + "RangeIndex: 1000 entries, 0 to 999\n", + "Columns: 7 entries, y to x4\n", + "dtypes: float64(7)\n", + "memory usage: 54.8 KB\n", + "\n" + ] + } + ], + "source": [ + "y = data_apo['y']\n", + "x = data_apo['x']\n", + "d = data_apo['d']\n", + "df_apo = pd.DataFrame(\n", + " np.column_stack((y, d, x)),\n", + " columns=['y', 'd'] + ['x' + str(i) for i in range(data_apo['x'].shape[1])]\n", + ")\n", + "\n", + "dml_data = dml.DoubleMLData(df_apo, 'y', 'd')\n", + "print(dml_data)" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "ea1c0ce4", + "metadata": {}, + "outputs": [], + "source": [ + "# Define a dictionary of learner combinations\n", + "from sklearn.ensemble import RandomForestRegressor, RandomForestClassifier\n", + "from sklearn.linear_model import LinearRegression, LogisticRegression\n", + "import lightgbm as lgbm\n", + "from tabpfn import TabPFNRegressor, TabPFNClassifier\n", + "\n", + "device = 'cuda'\n", + "learner_dict = {\n", + " 'RandomForest': {\n", + " 'ml_g': RandomForestRegressor(),\n", + " 'ml_m': RandomForestClassifier()\n", + " },\n", + " 'Linear': {\n", + " 'ml_g': LinearRegression(),\n", + " 'ml_m': LogisticRegression(max_iter=1000)\n", + " },\n", + " 'LightGBM': {\n", + " 'ml_g': lgbm.LGBMRegressor(n_estimators=50, verbose=-1),\n", + " 'ml_m': lgbm.LGBMClassifier(n_estimators=50, verbose=-1)\n", + " },\n", + " 'TabPFN': {\n", + " 'ml_g': TabPFNRegressor(device=device),\n", + " 'ml_m': TabPFNClassifier(device=device)\n", + " }\n", + "}" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "id": "db8b5c59", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "c:\\Users\\svenk\\anaconda3\\envs\\dml_tabpfn\\Lib\\site-packages\\sklearn\\utils\\deprecation.py:151: FutureWarning: 'force_all_finite' was renamed to 'ensure_all_finite' in 1.6 and will be removed in 1.8.\n", + " warnings.warn(\n", + "c:\\Users\\svenk\\anaconda3\\envs\\dml_tabpfn\\Lib\\site-packages\\sklearn\\utils\\deprecation.py:151: FutureWarning: 'force_all_finite' was renamed to 'ensure_all_finite' in 1.6 and will be removed in 1.8.\n", + " warnings.warn(\n", + "c:\\Users\\svenk\\anaconda3\\envs\\dml_tabpfn\\Lib\\site-packages\\doubleml\\double_ml.py:1479: UserWarning: The estimated nu2 for d is not positive. Re-estimation based on riesz representer (non-orthogonal).\n", + " warnings.warn(msg, UserWarning)\n", + "c:\\Users\\svenk\\anaconda3\\envs\\dml_tabpfn\\Lib\\site-packages\\sklearn\\utils\\deprecation.py:151: FutureWarning: 'force_all_finite' was renamed to 'ensure_all_finite' in 1.6 and will be removed in 1.8.\n", + " warnings.warn(\n", + "c:\\Users\\svenk\\anaconda3\\envs\\dml_tabpfn\\Lib\\site-packages\\sklearn\\utils\\deprecation.py:151: FutureWarning: 'force_all_finite' was renamed to 'ensure_all_finite' in 1.6 and will be removed in 1.8.\n", + " warnings.warn(\n", + "c:\\Users\\svenk\\anaconda3\\envs\\dml_tabpfn\\Lib\\site-packages\\doubleml\\double_ml.py:1479: UserWarning: The estimated nu2 for d is not positive. Re-estimation based on riesz representer (non-orthogonal).\n", + " warnings.warn(msg, UserWarning)\n", + "c:\\Users\\svenk\\anaconda3\\envs\\dml_tabpfn\\Lib\\site-packages\\sklearn\\utils\\deprecation.py:151: FutureWarning: 'force_all_finite' was renamed to 'ensure_all_finite' in 1.6 and will be removed in 1.8.\n", + " warnings.warn(\n", + "c:\\Users\\svenk\\anaconda3\\envs\\dml_tabpfn\\Lib\\site-packages\\sklearn\\utils\\deprecation.py:151: FutureWarning: 'force_all_finite' was renamed to 'ensure_all_finite' in 1.6 and will be removed in 1.8.\n", + " warnings.warn(\n", + "c:\\Users\\svenk\\anaconda3\\envs\\dml_tabpfn\\Lib\\site-packages\\doubleml\\utils\\_checks.py:194: UserWarning: Propensity predictions from learner RandomForestClassifier() for ml_m are close to zero or one (eps=1e-12).\n", + " warnings.warn(\n", + "c:\\Users\\svenk\\anaconda3\\envs\\dml_tabpfn\\Lib\\site-packages\\doubleml\\double_ml.py:1479: UserWarning: The estimated nu2 for d is not positive. Re-estimation based on riesz representer (non-orthogonal).\n", + " warnings.warn(msg, UserWarning)\n", + "c:\\Users\\svenk\\anaconda3\\envs\\dml_tabpfn\\Lib\\site-packages\\sklearn\\utils\\deprecation.py:151: FutureWarning: 'force_all_finite' was renamed to 'ensure_all_finite' in 1.6 and will be removed in 1.8.\n", + " warnings.warn(\n", + "c:\\Users\\svenk\\anaconda3\\envs\\dml_tabpfn\\Lib\\site-packages\\sklearn\\utils\\deprecation.py:151: FutureWarning: 'force_all_finite' was renamed to 'ensure_all_finite' in 1.6 and will be removed in 1.8.\n", + " warnings.warn(\n", + "c:\\Users\\svenk\\anaconda3\\envs\\dml_tabpfn\\Lib\\site-packages\\doubleml\\utils\\_checks.py:194: UserWarning: Propensity predictions from learner RandomForestClassifier() for ml_m are close to zero or one (eps=1e-12).\n", + " warnings.warn(\n", + "c:\\Users\\svenk\\anaconda3\\envs\\dml_tabpfn\\Lib\\site-packages\\doubleml\\double_ml.py:1479: UserWarning: The estimated nu2 for d is not positive. Re-estimation based on riesz representer (non-orthogonal).\n", + " warnings.warn(msg, UserWarning)\n", + "c:\\Users\\svenk\\anaconda3\\envs\\dml_tabpfn\\Lib\\site-packages\\sklearn\\utils\\deprecation.py:151: FutureWarning: 'force_all_finite' was renamed to 'ensure_all_finite' in 1.6 and will be removed in 1.8.\n", + " warnings.warn(\n", + "c:\\Users\\svenk\\anaconda3\\envs\\dml_tabpfn\\Lib\\site-packages\\sklearn\\utils\\deprecation.py:151: FutureWarning: 'force_all_finite' was renamed to 'ensure_all_finite' in 1.6 and will be removed in 1.8.\n", + " warnings.warn(\n", + "c:\\Users\\svenk\\anaconda3\\envs\\dml_tabpfn\\Lib\\site-packages\\doubleml\\utils\\_checks.py:194: UserWarning: Propensity predictions from learner RandomForestClassifier() for ml_m are close to zero or one (eps=1e-12).\n", + " warnings.warn(\n", + "c:\\Users\\svenk\\anaconda3\\envs\\dml_tabpfn\\Lib\\site-packages\\doubleml\\double_ml.py:1479: UserWarning: The estimated nu2 for d is not positive. Re-estimation based on riesz representer (non-orthogonal).\n", + " warnings.warn(msg, UserWarning)\n", + "c:\\Users\\svenk\\anaconda3\\envs\\dml_tabpfn\\Lib\\site-packages\\sklearn\\utils\\deprecation.py:151: FutureWarning: 'force_all_finite' was renamed to 'ensure_all_finite' in 1.6 and will be removed in 1.8.\n", + " warnings.warn(\n", + "c:\\Users\\svenk\\anaconda3\\envs\\dml_tabpfn\\Lib\\site-packages\\sklearn\\utils\\deprecation.py:151: FutureWarning: 'force_all_finite' was renamed to 'ensure_all_finite' in 1.6 and will be removed in 1.8.\n", + " warnings.warn(\n", + "c:\\Users\\svenk\\anaconda3\\envs\\dml_tabpfn\\Lib\\site-packages\\doubleml\\utils\\_checks.py:194: UserWarning: Propensity predictions from learner RandomForestClassifier() for ml_m are close to zero or one (eps=1e-12).\n", + " warnings.warn(\n", + "c:\\Users\\svenk\\anaconda3\\envs\\dml_tabpfn\\Lib\\site-packages\\doubleml\\double_ml.py:1479: UserWarning: The estimated nu2 for d is not positive. Re-estimation based on riesz representer (non-orthogonal).\n", + " warnings.warn(msg, UserWarning)\n", + "c:\\Users\\svenk\\anaconda3\\envs\\dml_tabpfn\\Lib\\site-packages\\sklearn\\utils\\deprecation.py:151: FutureWarning: 'force_all_finite' was renamed to 'ensure_all_finite' in 1.6 and will be removed in 1.8.\n", + " warnings.warn(\n", + "c:\\Users\\svenk\\anaconda3\\envs\\dml_tabpfn\\Lib\\site-packages\\sklearn\\utils\\deprecation.py:151: FutureWarning: 'force_all_finite' was renamed to 'ensure_all_finite' in 1.6 and will be removed in 1.8.\n", + " warnings.warn(\n", + "c:\\Users\\svenk\\anaconda3\\envs\\dml_tabpfn\\Lib\\site-packages\\doubleml\\utils\\_checks.py:194: UserWarning: Propensity predictions from learner RandomForestClassifier() for ml_m are close to zero or one (eps=1e-12).\n", + " warnings.warn(\n", + "c:\\Users\\svenk\\anaconda3\\envs\\dml_tabpfn\\Lib\\site-packages\\doubleml\\double_ml.py:1479: UserWarning: The estimated nu2 for d is not positive. Re-estimation based on riesz representer (non-orthogonal).\n", + " warnings.warn(msg, UserWarning)\n", + "c:\\Users\\svenk\\anaconda3\\envs\\dml_tabpfn\\Lib\\site-packages\\sklearn\\utils\\deprecation.py:151: FutureWarning: 'force_all_finite' was renamed to 'ensure_all_finite' in 1.6 and will be removed in 1.8.\n", + " warnings.warn(\n", + "c:\\Users\\svenk\\anaconda3\\envs\\dml_tabpfn\\Lib\\site-packages\\sklearn\\utils\\deprecation.py:151: FutureWarning: 'force_all_finite' was renamed to 'ensure_all_finite' in 1.6 and will be removed in 1.8.\n", + " warnings.warn(\n", + "c:\\Users\\svenk\\anaconda3\\envs\\dml_tabpfn\\Lib\\site-packages\\doubleml\\utils\\_checks.py:194: UserWarning: Propensity predictions from learner RandomForestClassifier() for ml_m are close to zero or one (eps=1e-12).\n", + " warnings.warn(\n", + "c:\\Users\\svenk\\anaconda3\\envs\\dml_tabpfn\\Lib\\site-packages\\doubleml\\double_ml.py:1479: UserWarning: The estimated nu2 for d is not positive. Re-estimation based on riesz representer (non-orthogonal).\n", + " warnings.warn(msg, UserWarning)\n", + "c:\\Users\\svenk\\anaconda3\\envs\\dml_tabpfn\\Lib\\site-packages\\sklearn\\utils\\deprecation.py:151: FutureWarning: 'force_all_finite' was renamed to 'ensure_all_finite' in 1.6 and will be removed in 1.8.\n", + " warnings.warn(\n", + "c:\\Users\\svenk\\anaconda3\\envs\\dml_tabpfn\\Lib\\site-packages\\sklearn\\utils\\deprecation.py:151: FutureWarning: 'force_all_finite' was renamed to 'ensure_all_finite' in 1.6 and will be removed in 1.8.\n", + " warnings.warn(\n", + "c:\\Users\\svenk\\anaconda3\\envs\\dml_tabpfn\\Lib\\site-packages\\doubleml\\utils\\_checks.py:194: UserWarning: Propensity predictions from learner RandomForestClassifier() for ml_m are close to zero or one (eps=1e-12).\n", + " warnings.warn(\n", + "c:\\Users\\svenk\\anaconda3\\envs\\dml_tabpfn\\Lib\\site-packages\\doubleml\\double_ml.py:1479: UserWarning: The estimated nu2 for d is not positive. Re-estimation based on riesz representer (non-orthogonal).\n", + " warnings.warn(msg, UserWarning)\n", + "c:\\Users\\svenk\\anaconda3\\envs\\dml_tabpfn\\Lib\\site-packages\\sklearn\\utils\\deprecation.py:151: FutureWarning: 'force_all_finite' was renamed to 'ensure_all_finite' in 1.6 and will be removed in 1.8.\n", + " warnings.warn(\n", + "c:\\Users\\svenk\\anaconda3\\envs\\dml_tabpfn\\Lib\\site-packages\\sklearn\\utils\\deprecation.py:151: FutureWarning: 'force_all_finite' was renamed to 'ensure_all_finite' in 1.6 and will be removed in 1.8.\n", + " warnings.warn(\n", + "c:\\Users\\svenk\\anaconda3\\envs\\dml_tabpfn\\Lib\\site-packages\\doubleml\\utils\\_checks.py:194: UserWarning: Propensity predictions from learner RandomForestClassifier() for ml_m are close to zero or one (eps=1e-12).\n", + " warnings.warn(\n", + "c:\\Users\\svenk\\anaconda3\\envs\\dml_tabpfn\\Lib\\site-packages\\doubleml\\double_ml.py:1479: UserWarning: The estimated nu2 for d is not positive. Re-estimation based on riesz representer (non-orthogonal).\n", + " warnings.warn(msg, UserWarning)\n", + "c:\\Users\\svenk\\anaconda3\\envs\\dml_tabpfn\\Lib\\site-packages\\sklearn\\utils\\deprecation.py:151: FutureWarning: 'force_all_finite' was renamed to 'ensure_all_finite' in 1.6 and will be removed in 1.8.\n", + " warnings.warn(\n", + "c:\\Users\\svenk\\anaconda3\\envs\\dml_tabpfn\\Lib\\site-packages\\sklearn\\utils\\deprecation.py:151: FutureWarning: 'force_all_finite' was renamed to 'ensure_all_finite' in 1.6 and will be removed in 1.8.\n", + " warnings.warn(\n", + "c:\\Users\\svenk\\anaconda3\\envs\\dml_tabpfn\\Lib\\site-packages\\doubleml\\utils\\_checks.py:194: UserWarning: Propensity predictions from learner RandomForestClassifier() for ml_m are close to zero or one (eps=1e-12).\n", + " warnings.warn(\n", + "c:\\Users\\svenk\\anaconda3\\envs\\dml_tabpfn\\Lib\\site-packages\\doubleml\\double_ml.py:1479: UserWarning: The estimated nu2 for d is not positive. Re-estimation based on riesz representer (non-orthogonal).\n", + " warnings.warn(msg, UserWarning)\n", + "c:\\Users\\svenk\\anaconda3\\envs\\dml_tabpfn\\Lib\\site-packages\\sklearn\\utils\\deprecation.py:151: FutureWarning: 'force_all_finite' was renamed to 'ensure_all_finite' in 1.6 and will be removed in 1.8.\n", + " warnings.warn(\n", + "c:\\Users\\svenk\\anaconda3\\envs\\dml_tabpfn\\Lib\\site-packages\\sklearn\\utils\\deprecation.py:151: FutureWarning: 'force_all_finite' was renamed to 'ensure_all_finite' in 1.6 and will be removed in 1.8.\n", + " warnings.warn(\n", + "c:\\Users\\svenk\\anaconda3\\envs\\dml_tabpfn\\Lib\\site-packages\\sklearn\\utils\\deprecation.py:151: FutureWarning: 'force_all_finite' was renamed to 'ensure_all_finite' in 1.6 and will be removed in 1.8.\n", + " warnings.warn(\n", + "c:\\Users\\svenk\\anaconda3\\envs\\dml_tabpfn\\Lib\\site-packages\\sklearn\\utils\\deprecation.py:151: FutureWarning: 'force_all_finite' was renamed to 'ensure_all_finite' in 1.6 and will be removed in 1.8.\n", + " warnings.warn(\n", + "c:\\Users\\svenk\\anaconda3\\envs\\dml_tabpfn\\Lib\\site-packages\\sklearn\\utils\\deprecation.py:151: FutureWarning: 'force_all_finite' was renamed to 'ensure_all_finite' in 1.6 and will be removed in 1.8.\n", + " warnings.warn(\n", + "c:\\Users\\svenk\\anaconda3\\envs\\dml_tabpfn\\Lib\\site-packages\\sklearn\\utils\\deprecation.py:151: FutureWarning: 'force_all_finite' was renamed to 'ensure_all_finite' in 1.6 and will be removed in 1.8.\n", + " warnings.warn(\n", + "c:\\Users\\svenk\\anaconda3\\envs\\dml_tabpfn\\Lib\\site-packages\\sklearn\\utils\\deprecation.py:151: FutureWarning: 'force_all_finite' was renamed to 'ensure_all_finite' in 1.6 and will be removed in 1.8.\n", + " warnings.warn(\n", + "c:\\Users\\svenk\\anaconda3\\envs\\dml_tabpfn\\Lib\\site-packages\\sklearn\\utils\\deprecation.py:151: FutureWarning: 'force_all_finite' was renamed to 'ensure_all_finite' in 1.6 and will be removed in 1.8.\n", + " warnings.warn(\n", + "c:\\Users\\svenk\\anaconda3\\envs\\dml_tabpfn\\Lib\\site-packages\\sklearn\\utils\\deprecation.py:151: FutureWarning: 'force_all_finite' was renamed to 'ensure_all_finite' in 1.6 and will be removed in 1.8.\n", + " warnings.warn(\n", + "c:\\Users\\svenk\\anaconda3\\envs\\dml_tabpfn\\Lib\\site-packages\\sklearn\\utils\\deprecation.py:151: FutureWarning: 'force_all_finite' was renamed to 'ensure_all_finite' in 1.6 and will be removed in 1.8.\n", + " warnings.warn(\n", + "c:\\Users\\svenk\\anaconda3\\envs\\dml_tabpfn\\Lib\\site-packages\\sklearn\\utils\\deprecation.py:151: FutureWarning: 'force_all_finite' was renamed to 'ensure_all_finite' in 1.6 and will be removed in 1.8.\n", + " warnings.warn(\n", + "c:\\Users\\svenk\\anaconda3\\envs\\dml_tabpfn\\Lib\\site-packages\\sklearn\\utils\\deprecation.py:151: FutureWarning: 'force_all_finite' was renamed to 'ensure_all_finite' in 1.6 and will be removed in 1.8.\n", + " warnings.warn(\n", + "c:\\Users\\svenk\\anaconda3\\envs\\dml_tabpfn\\Lib\\site-packages\\sklearn\\utils\\deprecation.py:151: FutureWarning: 'force_all_finite' was renamed to 'ensure_all_finite' in 1.6 and will be removed in 1.8.\n", + " warnings.warn(\n", + "c:\\Users\\svenk\\anaconda3\\envs\\dml_tabpfn\\Lib\\site-packages\\sklearn\\utils\\deprecation.py:151: FutureWarning: 'force_all_finite' was renamed to 'ensure_all_finite' in 1.6 and will be removed in 1.8.\n", + " warnings.warn(\n", + "c:\\Users\\svenk\\anaconda3\\envs\\dml_tabpfn\\Lib\\site-packages\\doubleml\\utils\\_checks.py:194: UserWarning: Propensity predictions from learner RandomForestClassifier() for ml_m are close to zero or one (eps=1e-12).\n", + " warnings.warn(\n", + "c:\\Users\\svenk\\anaconda3\\envs\\dml_tabpfn\\Lib\\site-packages\\doubleml\\double_ml.py:1479: UserWarning: The estimated nu2 for d is not positive. Re-estimation based on riesz representer (non-orthogonal).\n", + " warnings.warn(msg, UserWarning)\n", + "c:\\Users\\svenk\\anaconda3\\envs\\dml_tabpfn\\Lib\\site-packages\\sklearn\\utils\\deprecation.py:151: FutureWarning: 'force_all_finite' was renamed to 'ensure_all_finite' in 1.6 and will be removed in 1.8.\n", + " warnings.warn(\n", + "c:\\Users\\svenk\\anaconda3\\envs\\dml_tabpfn\\Lib\\site-packages\\sklearn\\utils\\deprecation.py:151: FutureWarning: 'force_all_finite' was renamed to 'ensure_all_finite' in 1.6 and will be removed in 1.8.\n", + " warnings.warn(\n", + "c:\\Users\\svenk\\anaconda3\\envs\\dml_tabpfn\\Lib\\site-packages\\sklearn\\utils\\deprecation.py:151: FutureWarning: 'force_all_finite' was renamed to 'ensure_all_finite' in 1.6 and will be removed in 1.8.\n", + " warnings.warn(\n", + "c:\\Users\\svenk\\anaconda3\\envs\\dml_tabpfn\\Lib\\site-packages\\sklearn\\utils\\deprecation.py:151: FutureWarning: 'force_all_finite' was renamed to 'ensure_all_finite' in 1.6 and will be removed in 1.8.\n", + " warnings.warn(\n", + "c:\\Users\\svenk\\anaconda3\\envs\\dml_tabpfn\\Lib\\site-packages\\sklearn\\utils\\deprecation.py:151: FutureWarning: 'force_all_finite' was renamed to 'ensure_all_finite' in 1.6 and will be removed in 1.8.\n", + " warnings.warn(\n", + "c:\\Users\\svenk\\anaconda3\\envs\\dml_tabpfn\\Lib\\site-packages\\sklearn\\utils\\deprecation.py:151: FutureWarning: 'force_all_finite' was renamed to 'ensure_all_finite' in 1.6 and will be removed in 1.8.\n", + " warnings.warn(\n", + "c:\\Users\\svenk\\anaconda3\\envs\\dml_tabpfn\\Lib\\site-packages\\sklearn\\utils\\deprecation.py:151: FutureWarning: 'force_all_finite' was renamed to 'ensure_all_finite' in 1.6 and will be removed in 1.8.\n", + " warnings.warn(\n", + "c:\\Users\\svenk\\anaconda3\\envs\\dml_tabpfn\\Lib\\site-packages\\sklearn\\utils\\deprecation.py:151: FutureWarning: 'force_all_finite' was renamed to 'ensure_all_finite' in 1.6 and will be removed in 1.8.\n", + " warnings.warn(\n", + "c:\\Users\\svenk\\anaconda3\\envs\\dml_tabpfn\\Lib\\site-packages\\sklearn\\utils\\deprecation.py:151: FutureWarning: 'force_all_finite' was renamed to 'ensure_all_finite' in 1.6 and will be removed in 1.8.\n", + " warnings.warn(\n", + "c:\\Users\\svenk\\anaconda3\\envs\\dml_tabpfn\\Lib\\site-packages\\sklearn\\utils\\deprecation.py:151: FutureWarning: 'force_all_finite' was renamed to 'ensure_all_finite' in 1.6 and will be removed in 1.8.\n", + " warnings.warn(\n", + "c:\\Users\\svenk\\anaconda3\\envs\\dml_tabpfn\\Lib\\site-packages\\sklearn\\utils\\deprecation.py:151: FutureWarning: 'force_all_finite' was renamed to 'ensure_all_finite' in 1.6 and will be removed in 1.8.\n", + " warnings.warn(\n", + "c:\\Users\\svenk\\anaconda3\\envs\\dml_tabpfn\\Lib\\site-packages\\sklearn\\utils\\deprecation.py:151: FutureWarning: 'force_all_finite' was renamed to 'ensure_all_finite' in 1.6 and will be removed in 1.8.\n", + " warnings.warn(\n", + "c:\\Users\\svenk\\anaconda3\\envs\\dml_tabpfn\\Lib\\site-packages\\sklearn\\utils\\deprecation.py:151: FutureWarning: 'force_all_finite' was renamed to 'ensure_all_finite' in 1.6 and will be removed in 1.8.\n", + " warnings.warn(\n", + "c:\\Users\\svenk\\anaconda3\\envs\\dml_tabpfn\\Lib\\site-packages\\sklearn\\utils\\deprecation.py:151: FutureWarning: 'force_all_finite' was renamed to 'ensure_all_finite' in 1.6 and will be removed in 1.8.\n", + " warnings.warn(\n", + "Exception in thread Thread-6 (_readerthread):\n", + "Traceback (most recent call last):\n", + " File \"c:\\Users\\svenk\\anaconda3\\envs\\dml_tabpfn\\Lib\\threading.py\", line 1045, in _bootstrap_inner\n", + " self.run()\n", + " File \"c:\\Users\\svenk\\anaconda3\\envs\\dml_tabpfn\\Lib\\threading.py\", line 982, in run\n", + " self._target(*self._args, **self._kwargs)\n", + " File \"c:\\Users\\svenk\\anaconda3\\envs\\dml_tabpfn\\Lib\\subprocess.py\", line 1599, in _readerthread\n", + " buffer.append(fh.read())\n", + " ^^^^^^^^^\n", + " File \"\", line 322, in decode\n", + "UnicodeDecodeError: 'utf-8' codec can't decode byte 0x81 in position 109: invalid start byte\n", + "Exception in thread Thread-6 (_readerthread):\n", + "Traceback (most recent call last):\n", + " File \"c:\\Users\\svenk\\anaconda3\\envs\\dml_tabpfn\\Lib\\threading.py\", line 1045, in _bootstrap_inner\n", + " self.run()\n", + " File \"c:\\Users\\svenk\\anaconda3\\envs\\dml_tabpfn\\Lib\\threading.py\", line 982, in run\n", + " self._target(*self._args, **self._kwargs)\n", + " File \"c:\\Users\\svenk\\anaconda3\\envs\\dml_tabpfn\\Lib\\subprocess.py\", line 1599, in _readerthread\n", + " buffer.append(fh.read())\n", + " ^^^^^^^^^\n", + " File \"\", line 322, in decode\n", + "UnicodeDecodeError: 'utf-8' codec can't decode byte 0x81 in position 109: invalid start byte\n", + "c:\\Users\\svenk\\anaconda3\\envs\\dml_tabpfn\\Lib\\site-packages\\sklearn\\utils\\validation.py:2739: UserWarning: X does not have valid feature names, but LGBMRegressor was fitted with feature names\n", + " warnings.warn(\n", + "c:\\Users\\svenk\\anaconda3\\envs\\dml_tabpfn\\Lib\\site-packages\\sklearn\\utils\\validation.py:2739: UserWarning: X does not have valid feature names, but LGBMRegressor was fitted with feature names\n", + " warnings.warn(\n", + "c:\\Users\\svenk\\anaconda3\\envs\\dml_tabpfn\\Lib\\site-packages\\sklearn\\utils\\validation.py:2739: UserWarning: X does not have valid feature names, but LGBMRegressor was fitted with feature names\n", + " warnings.warn(\n", + "c:\\Users\\svenk\\anaconda3\\envs\\dml_tabpfn\\Lib\\site-packages\\sklearn\\utils\\validation.py:2739: UserWarning: X does not have valid feature names, but LGBMRegressor was fitted with feature names\n", + " warnings.warn(\n", + "c:\\Users\\svenk\\anaconda3\\envs\\dml_tabpfn\\Lib\\site-packages\\sklearn\\utils\\validation.py:2739: UserWarning: X does not have valid feature names, but LGBMRegressor was fitted with feature names\n", + " warnings.warn(\n", + "c:\\Users\\svenk\\anaconda3\\envs\\dml_tabpfn\\Lib\\site-packages\\sklearn\\utils\\validation.py:2739: UserWarning: X does not have valid feature names, but LGBMRegressor was fitted with feature names\n", + " warnings.warn(\n", + "c:\\Users\\svenk\\anaconda3\\envs\\dml_tabpfn\\Lib\\site-packages\\sklearn\\utils\\validation.py:2739: UserWarning: X does not have valid feature names, but LGBMRegressor was fitted with feature names\n", + " warnings.warn(\n", + "c:\\Users\\svenk\\anaconda3\\envs\\dml_tabpfn\\Lib\\site-packages\\sklearn\\utils\\validation.py:2739: UserWarning: X does not have valid feature names, but LGBMRegressor was fitted with feature names\n", + " warnings.warn(\n", + "c:\\Users\\svenk\\anaconda3\\envs\\dml_tabpfn\\Lib\\site-packages\\sklearn\\utils\\validation.py:2739: UserWarning: X does not have valid feature names, but LGBMRegressor was fitted with feature names\n", + " warnings.warn(\n", + "c:\\Users\\svenk\\anaconda3\\envs\\dml_tabpfn\\Lib\\site-packages\\sklearn\\utils\\validation.py:2739: UserWarning: X does not have valid feature names, but LGBMRegressor was fitted with feature names\n", + " warnings.warn(\n", + "c:\\Users\\svenk\\anaconda3\\envs\\dml_tabpfn\\Lib\\site-packages\\sklearn\\utils\\validation.py:2739: UserWarning: X does not have valid feature names, but LGBMClassifier was fitted with feature names\n", + " warnings.warn(\n", + "c:\\Users\\svenk\\anaconda3\\envs\\dml_tabpfn\\Lib\\site-packages\\sklearn\\utils\\validation.py:2739: UserWarning: X does not have valid feature names, but LGBMClassifier was fitted with feature names\n", + " warnings.warn(\n", + "c:\\Users\\svenk\\anaconda3\\envs\\dml_tabpfn\\Lib\\site-packages\\sklearn\\utils\\validation.py:2739: UserWarning: X does not have valid feature names, but LGBMClassifier was fitted with feature names\n", + " warnings.warn(\n", + "c:\\Users\\svenk\\anaconda3\\envs\\dml_tabpfn\\Lib\\site-packages\\sklearn\\utils\\validation.py:2739: UserWarning: X does not have valid feature names, but LGBMClassifier was fitted with feature names\n", + " warnings.warn(\n", + "c:\\Users\\svenk\\anaconda3\\envs\\dml_tabpfn\\Lib\\site-packages\\sklearn\\utils\\validation.py:2739: UserWarning: X does not have valid feature names, but LGBMClassifier was fitted with feature names\n", + " warnings.warn(\n", + "c:\\Users\\svenk\\anaconda3\\envs\\dml_tabpfn\\Lib\\site-packages\\doubleml\\double_ml.py:1479: UserWarning: The estimated nu2 for d is not positive. Re-estimation based on riesz representer (non-orthogonal).\n", + " warnings.warn(msg, UserWarning)\n", + "c:\\Users\\svenk\\anaconda3\\envs\\dml_tabpfn\\Lib\\site-packages\\sklearn\\utils\\deprecation.py:151: FutureWarning: 'force_all_finite' was renamed to 'ensure_all_finite' in 1.6 and will be removed in 1.8.\n", + " warnings.warn(\n", + "c:\\Users\\svenk\\anaconda3\\envs\\dml_tabpfn\\Lib\\site-packages\\sklearn\\utils\\deprecation.py:151: FutureWarning: 'force_all_finite' was renamed to 'ensure_all_finite' in 1.6 and will be removed in 1.8.\n", + " warnings.warn(\n", + "c:\\Users\\svenk\\anaconda3\\envs\\dml_tabpfn\\Lib\\site-packages\\sklearn\\utils\\validation.py:2739: UserWarning: X does not have valid feature names, but LGBMRegressor was fitted with feature names\n", + " warnings.warn(\n", + "c:\\Users\\svenk\\anaconda3\\envs\\dml_tabpfn\\Lib\\site-packages\\sklearn\\utils\\validation.py:2739: UserWarning: X does not have valid feature names, but LGBMRegressor was fitted with feature names\n", + " warnings.warn(\n", + "c:\\Users\\svenk\\anaconda3\\envs\\dml_tabpfn\\Lib\\site-packages\\sklearn\\utils\\validation.py:2739: UserWarning: X does not have valid feature names, but LGBMRegressor was fitted with feature names\n", + " warnings.warn(\n", + "c:\\Users\\svenk\\anaconda3\\envs\\dml_tabpfn\\Lib\\site-packages\\sklearn\\utils\\validation.py:2739: UserWarning: X does not have valid feature names, but LGBMRegressor was fitted with feature names\n", + " warnings.warn(\n", + "c:\\Users\\svenk\\anaconda3\\envs\\dml_tabpfn\\Lib\\site-packages\\sklearn\\utils\\validation.py:2739: UserWarning: X does not have valid feature names, but LGBMRegressor was fitted with feature names\n", + " warnings.warn(\n", + "c:\\Users\\svenk\\anaconda3\\envs\\dml_tabpfn\\Lib\\site-packages\\sklearn\\utils\\validation.py:2739: UserWarning: X does not have valid feature names, but LGBMRegressor was fitted with feature names\n", + " warnings.warn(\n", + "c:\\Users\\svenk\\anaconda3\\envs\\dml_tabpfn\\Lib\\site-packages\\sklearn\\utils\\validation.py:2739: UserWarning: X does not have valid feature names, but LGBMRegressor was fitted with feature names\n", + " warnings.warn(\n", + "c:\\Users\\svenk\\anaconda3\\envs\\dml_tabpfn\\Lib\\site-packages\\sklearn\\utils\\validation.py:2739: UserWarning: X does not have valid feature names, but LGBMRegressor was fitted with feature names\n", + " warnings.warn(\n", + "c:\\Users\\svenk\\anaconda3\\envs\\dml_tabpfn\\Lib\\site-packages\\sklearn\\utils\\validation.py:2739: UserWarning: X does not have valid feature names, but LGBMRegressor was fitted with feature names\n", + " warnings.warn(\n", + "c:\\Users\\svenk\\anaconda3\\envs\\dml_tabpfn\\Lib\\site-packages\\sklearn\\utils\\validation.py:2739: UserWarning: X does not have valid feature names, but LGBMRegressor was fitted with feature names\n", + " warnings.warn(\n", + "c:\\Users\\svenk\\anaconda3\\envs\\dml_tabpfn\\Lib\\site-packages\\sklearn\\utils\\validation.py:2739: UserWarning: X does not have valid feature names, but LGBMRegressor was fitted with feature names\n", + " warnings.warn(\n", + "c:\\Users\\svenk\\anaconda3\\envs\\dml_tabpfn\\Lib\\site-packages\\sklearn\\utils\\validation.py:2739: UserWarning: X does not have valid feature names, but LGBMRegressor was fitted with feature names\n", + " warnings.warn(\n", + "c:\\Users\\svenk\\anaconda3\\envs\\dml_tabpfn\\Lib\\site-packages\\sklearn\\utils\\validation.py:2739: UserWarning: X does not have valid feature names, but LGBMRegressor was fitted with feature names\n", + " warnings.warn(\n", + "c:\\Users\\svenk\\anaconda3\\envs\\dml_tabpfn\\Lib\\site-packages\\sklearn\\utils\\validation.py:2739: UserWarning: X does not have valid feature names, but LGBMRegressor was fitted with feature names\n", + " warnings.warn(\n", + "c:\\Users\\svenk\\anaconda3\\envs\\dml_tabpfn\\Lib\\site-packages\\sklearn\\utils\\validation.py:2739: UserWarning: X does not have valid feature names, but LGBMRegressor was fitted with feature names\n", + " warnings.warn(\n", + "c:\\Users\\svenk\\anaconda3\\envs\\dml_tabpfn\\Lib\\site-packages\\sklearn\\utils\\validation.py:2739: UserWarning: X does not have valid feature names, but LGBMClassifier was fitted with feature names\n", + " warnings.warn(\n", + "c:\\Users\\svenk\\anaconda3\\envs\\dml_tabpfn\\Lib\\site-packages\\sklearn\\utils\\validation.py:2739: UserWarning: X does not have valid feature names, but LGBMClassifier was fitted with feature names\n", + " warnings.warn(\n", + "c:\\Users\\svenk\\anaconda3\\envs\\dml_tabpfn\\Lib\\site-packages\\sklearn\\utils\\validation.py:2739: UserWarning: X does not have valid feature names, but LGBMClassifier was fitted with feature names\n", + " warnings.warn(\n", + "c:\\Users\\svenk\\anaconda3\\envs\\dml_tabpfn\\Lib\\site-packages\\sklearn\\utils\\validation.py:2739: UserWarning: X does not have valid feature names, but LGBMClassifier was fitted with feature names\n", + " warnings.warn(\n", + "c:\\Users\\svenk\\anaconda3\\envs\\dml_tabpfn\\Lib\\site-packages\\sklearn\\utils\\validation.py:2739: UserWarning: X does not have valid feature names, but LGBMClassifier was fitted with feature names\n", + " warnings.warn(\n", + "c:\\Users\\svenk\\anaconda3\\envs\\dml_tabpfn\\Lib\\site-packages\\doubleml\\double_ml.py:1479: UserWarning: The estimated nu2 for d is not positive. Re-estimation based on riesz representer (non-orthogonal).\n", + " warnings.warn(msg, UserWarning)\n", + "c:\\Users\\svenk\\anaconda3\\envs\\dml_tabpfn\\Lib\\site-packages\\sklearn\\utils\\deprecation.py:151: FutureWarning: 'force_all_finite' was renamed to 'ensure_all_finite' in 1.6 and will be removed in 1.8.\n", + " warnings.warn(\n", + "c:\\Users\\svenk\\anaconda3\\envs\\dml_tabpfn\\Lib\\site-packages\\sklearn\\utils\\deprecation.py:151: FutureWarning: 'force_all_finite' was renamed to 'ensure_all_finite' in 1.6 and will be removed in 1.8.\n", + " warnings.warn(\n", + "c:\\Users\\svenk\\anaconda3\\envs\\dml_tabpfn\\Lib\\site-packages\\sklearn\\utils\\validation.py:2739: UserWarning: X does not have valid feature names, but LGBMRegressor was fitted with feature names\n", + " warnings.warn(\n", + "c:\\Users\\svenk\\anaconda3\\envs\\dml_tabpfn\\Lib\\site-packages\\sklearn\\utils\\validation.py:2739: UserWarning: X does not have valid feature names, but LGBMRegressor was fitted with feature names\n", + " warnings.warn(\n", + "c:\\Users\\svenk\\anaconda3\\envs\\dml_tabpfn\\Lib\\site-packages\\sklearn\\utils\\validation.py:2739: UserWarning: X does not have valid feature names, but LGBMRegressor was fitted with feature names\n", + " warnings.warn(\n", + "c:\\Users\\svenk\\anaconda3\\envs\\dml_tabpfn\\Lib\\site-packages\\sklearn\\utils\\validation.py:2739: UserWarning: X does not have valid feature names, but LGBMRegressor was fitted with feature names\n", + " warnings.warn(\n", + "c:\\Users\\svenk\\anaconda3\\envs\\dml_tabpfn\\Lib\\site-packages\\sklearn\\utils\\validation.py:2739: UserWarning: X does not have valid feature names, but LGBMRegressor was fitted with feature names\n", + " warnings.warn(\n", + "c:\\Users\\svenk\\anaconda3\\envs\\dml_tabpfn\\Lib\\site-packages\\sklearn\\utils\\validation.py:2739: UserWarning: X does not have valid feature names, but LGBMRegressor was fitted with feature names\n", + " warnings.warn(\n", + "c:\\Users\\svenk\\anaconda3\\envs\\dml_tabpfn\\Lib\\site-packages\\sklearn\\utils\\validation.py:2739: UserWarning: X does not have valid feature names, but LGBMRegressor was fitted with feature names\n", + " warnings.warn(\n", + "c:\\Users\\svenk\\anaconda3\\envs\\dml_tabpfn\\Lib\\site-packages\\sklearn\\utils\\validation.py:2739: UserWarning: X does not have valid feature names, but LGBMRegressor was fitted with feature names\n", + " warnings.warn(\n", + "c:\\Users\\svenk\\anaconda3\\envs\\dml_tabpfn\\Lib\\site-packages\\sklearn\\utils\\validation.py:2739: UserWarning: X does not have valid feature names, but LGBMRegressor was fitted with feature names\n", + " warnings.warn(\n", + "c:\\Users\\svenk\\anaconda3\\envs\\dml_tabpfn\\Lib\\site-packages\\sklearn\\utils\\validation.py:2739: UserWarning: X does not have valid feature names, but LGBMRegressor was fitted with feature names\n", + " warnings.warn(\n", + "c:\\Users\\svenk\\anaconda3\\envs\\dml_tabpfn\\Lib\\site-packages\\sklearn\\utils\\validation.py:2739: UserWarning: X does not have valid feature names, but LGBMClassifier was fitted with feature names\n", + " warnings.warn(\n", + "c:\\Users\\svenk\\anaconda3\\envs\\dml_tabpfn\\Lib\\site-packages\\sklearn\\utils\\validation.py:2739: UserWarning: X does not have valid feature names, but LGBMClassifier was fitted with feature names\n", + " warnings.warn(\n", + "c:\\Users\\svenk\\anaconda3\\envs\\dml_tabpfn\\Lib\\site-packages\\sklearn\\utils\\validation.py:2739: UserWarning: X does not have valid feature names, but LGBMClassifier was fitted with feature names\n", + " warnings.warn(\n", + "c:\\Users\\svenk\\anaconda3\\envs\\dml_tabpfn\\Lib\\site-packages\\sklearn\\utils\\validation.py:2739: UserWarning: X does not have valid feature names, but LGBMClassifier was fitted with feature names\n", + " warnings.warn(\n", + "c:\\Users\\svenk\\anaconda3\\envs\\dml_tabpfn\\Lib\\site-packages\\sklearn\\utils\\validation.py:2739: UserWarning: X does not have valid feature names, but LGBMClassifier was fitted with feature names\n", + " warnings.warn(\n", + "c:\\Users\\svenk\\anaconda3\\envs\\dml_tabpfn\\Lib\\site-packages\\doubleml\\double_ml.py:1479: UserWarning: The estimated nu2 for d is not positive. Re-estimation based on riesz representer (non-orthogonal).\n", + " warnings.warn(msg, UserWarning)\n", + "c:\\Users\\svenk\\anaconda3\\envs\\dml_tabpfn\\Lib\\site-packages\\sklearn\\utils\\deprecation.py:151: FutureWarning: 'force_all_finite' was renamed to 'ensure_all_finite' in 1.6 and will be removed in 1.8.\n", + " warnings.warn(\n", + "c:\\Users\\svenk\\anaconda3\\envs\\dml_tabpfn\\Lib\\site-packages\\sklearn\\utils\\deprecation.py:151: FutureWarning: 'force_all_finite' was renamed to 'ensure_all_finite' in 1.6 and will be removed in 1.8.\n", + " warnings.warn(\n", + "c:\\Users\\svenk\\anaconda3\\envs\\dml_tabpfn\\Lib\\site-packages\\sklearn\\utils\\validation.py:2739: UserWarning: X does not have valid feature names, but LGBMRegressor was fitted with feature names\n", + " warnings.warn(\n", + "c:\\Users\\svenk\\anaconda3\\envs\\dml_tabpfn\\Lib\\site-packages\\sklearn\\utils\\validation.py:2739: UserWarning: X does not have valid feature names, but LGBMRegressor was fitted with feature names\n", + " warnings.warn(\n", + "c:\\Users\\svenk\\anaconda3\\envs\\dml_tabpfn\\Lib\\site-packages\\sklearn\\utils\\validation.py:2739: UserWarning: X does not have valid feature names, but LGBMRegressor was fitted with feature names\n", + " warnings.warn(\n", + "c:\\Users\\svenk\\anaconda3\\envs\\dml_tabpfn\\Lib\\site-packages\\sklearn\\utils\\validation.py:2739: UserWarning: X does not have valid feature names, but LGBMRegressor was fitted with feature names\n", + " warnings.warn(\n", + "c:\\Users\\svenk\\anaconda3\\envs\\dml_tabpfn\\Lib\\site-packages\\sklearn\\utils\\validation.py:2739: UserWarning: X does not have valid feature names, but LGBMRegressor was fitted with feature names\n", + " warnings.warn(\n", + "c:\\Users\\svenk\\anaconda3\\envs\\dml_tabpfn\\Lib\\site-packages\\sklearn\\utils\\validation.py:2739: UserWarning: X does not have valid feature names, but LGBMRegressor was fitted with feature names\n", + " warnings.warn(\n", + "c:\\Users\\svenk\\anaconda3\\envs\\dml_tabpfn\\Lib\\site-packages\\sklearn\\utils\\validation.py:2739: UserWarning: X does not have valid feature names, but LGBMRegressor was fitted with feature names\n", + " warnings.warn(\n", + "c:\\Users\\svenk\\anaconda3\\envs\\dml_tabpfn\\Lib\\site-packages\\sklearn\\utils\\validation.py:2739: UserWarning: X does not have valid feature names, but LGBMRegressor was fitted with feature names\n", + " warnings.warn(\n", + "c:\\Users\\svenk\\anaconda3\\envs\\dml_tabpfn\\Lib\\site-packages\\sklearn\\utils\\validation.py:2739: UserWarning: X does not have valid feature names, but LGBMClassifier was fitted with feature names\n", + " warnings.warn(\n", + "c:\\Users\\svenk\\anaconda3\\envs\\dml_tabpfn\\Lib\\site-packages\\sklearn\\utils\\validation.py:2739: UserWarning: X does not have valid feature names, but LGBMClassifier was fitted with feature names\n", + " warnings.warn(\n", + "c:\\Users\\svenk\\anaconda3\\envs\\dml_tabpfn\\Lib\\site-packages\\sklearn\\utils\\validation.py:2739: UserWarning: X does not have valid feature names, but LGBMClassifier was fitted with feature names\n", + " warnings.warn(\n", + "c:\\Users\\svenk\\anaconda3\\envs\\dml_tabpfn\\Lib\\site-packages\\sklearn\\utils\\validation.py:2739: UserWarning: X does not have valid feature names, but LGBMClassifier was fitted with feature names\n", + " warnings.warn(\n", + "c:\\Users\\svenk\\anaconda3\\envs\\dml_tabpfn\\Lib\\site-packages\\sklearn\\utils\\validation.py:2739: UserWarning: X does not have valid feature names, but LGBMClassifier was fitted with feature names\n", + " warnings.warn(\n", + "c:\\Users\\svenk\\anaconda3\\envs\\dml_tabpfn\\Lib\\site-packages\\doubleml\\double_ml.py:1479: UserWarning: The estimated nu2 for d is not positive. Re-estimation based on riesz representer (non-orthogonal).\n", + " warnings.warn(msg, UserWarning)\n", + "c:\\Users\\svenk\\anaconda3\\envs\\dml_tabpfn\\Lib\\site-packages\\sklearn\\utils\\deprecation.py:151: FutureWarning: 'force_all_finite' was renamed to 'ensure_all_finite' in 1.6 and will be removed in 1.8.\n", + " warnings.warn(\n", + "c:\\Users\\svenk\\anaconda3\\envs\\dml_tabpfn\\Lib\\site-packages\\sklearn\\utils\\deprecation.py:151: FutureWarning: 'force_all_finite' was renamed to 'ensure_all_finite' in 1.6 and will be removed in 1.8.\n", + " warnings.warn(\n", + "c:\\Users\\svenk\\anaconda3\\envs\\dml_tabpfn\\Lib\\site-packages\\sklearn\\utils\\validation.py:2739: UserWarning: X does not have valid feature names, but LGBMRegressor was fitted with feature names\n", + " warnings.warn(\n", + "c:\\Users\\svenk\\anaconda3\\envs\\dml_tabpfn\\Lib\\site-packages\\sklearn\\utils\\validation.py:2739: UserWarning: X does not have valid feature names, but LGBMRegressor was fitted with feature names\n", + " warnings.warn(\n", + "c:\\Users\\svenk\\anaconda3\\envs\\dml_tabpfn\\Lib\\site-packages\\sklearn\\utils\\validation.py:2739: UserWarning: X does not have valid feature names, but LGBMRegressor was fitted with feature names\n", + " warnings.warn(\n", + "c:\\Users\\svenk\\anaconda3\\envs\\dml_tabpfn\\Lib\\site-packages\\sklearn\\utils\\validation.py:2739: UserWarning: X does not have valid feature names, but LGBMRegressor was fitted with feature names\n", + " warnings.warn(\n", + "c:\\Users\\svenk\\anaconda3\\envs\\dml_tabpfn\\Lib\\site-packages\\sklearn\\utils\\validation.py:2739: UserWarning: X does not have valid feature names, but LGBMRegressor was fitted with feature names\n", + " warnings.warn(\n", + "c:\\Users\\svenk\\anaconda3\\envs\\dml_tabpfn\\Lib\\site-packages\\sklearn\\utils\\validation.py:2739: UserWarning: X does not have valid feature names, but LGBMRegressor was fitted with feature names\n", + " warnings.warn(\n", + "c:\\Users\\svenk\\anaconda3\\envs\\dml_tabpfn\\Lib\\site-packages\\sklearn\\utils\\validation.py:2739: UserWarning: X does not have valid feature names, but LGBMRegressor was fitted with feature names\n", + " warnings.warn(\n", + "c:\\Users\\svenk\\anaconda3\\envs\\dml_tabpfn\\Lib\\site-packages\\sklearn\\utils\\validation.py:2739: UserWarning: X does not have valid feature names, but LGBMRegressor was fitted with feature names\n", + " warnings.warn(\n", + "c:\\Users\\svenk\\anaconda3\\envs\\dml_tabpfn\\Lib\\site-packages\\sklearn\\utils\\validation.py:2739: UserWarning: X does not have valid feature names, but LGBMRegressor was fitted with feature names\n", + " warnings.warn(\n", + "c:\\Users\\svenk\\anaconda3\\envs\\dml_tabpfn\\Lib\\site-packages\\sklearn\\utils\\validation.py:2739: UserWarning: X does not have valid feature names, but LGBMRegressor was fitted with feature names\n", + " warnings.warn(\n", + "c:\\Users\\svenk\\anaconda3\\envs\\dml_tabpfn\\Lib\\site-packages\\sklearn\\utils\\validation.py:2739: UserWarning: X does not have valid feature names, but LGBMRegressor was fitted with feature names\n", + " warnings.warn(\n", + "c:\\Users\\svenk\\anaconda3\\envs\\dml_tabpfn\\Lib\\site-packages\\sklearn\\utils\\validation.py:2739: UserWarning: X does not have valid feature names, but LGBMRegressor was fitted with feature names\n", + " warnings.warn(\n", + "c:\\Users\\svenk\\anaconda3\\envs\\dml_tabpfn\\Lib\\site-packages\\sklearn\\utils\\validation.py:2739: UserWarning: X does not have valid feature names, but LGBMRegressor was fitted with feature names\n", + " warnings.warn(\n", + "c:\\Users\\svenk\\anaconda3\\envs\\dml_tabpfn\\Lib\\site-packages\\sklearn\\utils\\validation.py:2739: UserWarning: X does not have valid feature names, but LGBMClassifier was fitted with feature names\n", + " warnings.warn(\n", + "c:\\Users\\svenk\\anaconda3\\envs\\dml_tabpfn\\Lib\\site-packages\\sklearn\\utils\\validation.py:2739: UserWarning: X does not have valid feature names, but LGBMClassifier was fitted with feature names\n", + " warnings.warn(\n", + "c:\\Users\\svenk\\anaconda3\\envs\\dml_tabpfn\\Lib\\site-packages\\sklearn\\utils\\validation.py:2739: UserWarning: X does not have valid feature names, but LGBMClassifier was fitted with feature names\n", + " warnings.warn(\n", + "c:\\Users\\svenk\\anaconda3\\envs\\dml_tabpfn\\Lib\\site-packages\\sklearn\\utils\\validation.py:2739: UserWarning: X does not have valid feature names, but LGBMClassifier was fitted with feature names\n", + " warnings.warn(\n", + "c:\\Users\\svenk\\anaconda3\\envs\\dml_tabpfn\\Lib\\site-packages\\sklearn\\utils\\validation.py:2739: UserWarning: X does not have valid feature names, but LGBMClassifier was fitted with feature names\n", + " warnings.warn(\n", + "c:\\Users\\svenk\\anaconda3\\envs\\dml_tabpfn\\Lib\\site-packages\\doubleml\\double_ml.py:1479: UserWarning: The estimated nu2 for d is not positive. Re-estimation based on riesz representer (non-orthogonal).\n", + " warnings.warn(msg, UserWarning)\n", + "c:\\Users\\svenk\\anaconda3\\envs\\dml_tabpfn\\Lib\\site-packages\\sklearn\\utils\\deprecation.py:151: FutureWarning: 'force_all_finite' was renamed to 'ensure_all_finite' in 1.6 and will be removed in 1.8.\n", + " warnings.warn(\n", + "c:\\Users\\svenk\\anaconda3\\envs\\dml_tabpfn\\Lib\\site-packages\\sklearn\\utils\\deprecation.py:151: FutureWarning: 'force_all_finite' was renamed to 'ensure_all_finite' in 1.6 and will be removed in 1.8.\n", + " warnings.warn(\n", + "c:\\Users\\svenk\\anaconda3\\envs\\dml_tabpfn\\Lib\\site-packages\\sklearn\\utils\\validation.py:2739: UserWarning: X does not have valid feature names, but LGBMRegressor was fitted with feature names\n", + " warnings.warn(\n", + "c:\\Users\\svenk\\anaconda3\\envs\\dml_tabpfn\\Lib\\site-packages\\sklearn\\utils\\validation.py:2739: UserWarning: X does not have valid feature names, but LGBMRegressor was fitted with feature names\n", + " warnings.warn(\n", + "c:\\Users\\svenk\\anaconda3\\envs\\dml_tabpfn\\Lib\\site-packages\\sklearn\\utils\\validation.py:2739: UserWarning: X does not have valid feature names, but LGBMRegressor was fitted with feature names\n", + " warnings.warn(\n", + "c:\\Users\\svenk\\anaconda3\\envs\\dml_tabpfn\\Lib\\site-packages\\sklearn\\utils\\validation.py:2739: UserWarning: X does not have valid feature names, but LGBMRegressor was fitted with feature names\n", + " warnings.warn(\n", + "c:\\Users\\svenk\\anaconda3\\envs\\dml_tabpfn\\Lib\\site-packages\\sklearn\\utils\\validation.py:2739: UserWarning: X does not have valid feature names, but LGBMRegressor was fitted with feature names\n", + " warnings.warn(\n", + "c:\\Users\\svenk\\anaconda3\\envs\\dml_tabpfn\\Lib\\site-packages\\sklearn\\utils\\validation.py:2739: UserWarning: X does not have valid feature names, but LGBMRegressor was fitted with feature names\n", + " warnings.warn(\n", + "c:\\Users\\svenk\\anaconda3\\envs\\dml_tabpfn\\Lib\\site-packages\\sklearn\\utils\\validation.py:2739: UserWarning: X does not have valid feature names, but LGBMRegressor was fitted with feature names\n", + " warnings.warn(\n", + "c:\\Users\\svenk\\anaconda3\\envs\\dml_tabpfn\\Lib\\site-packages\\sklearn\\utils\\validation.py:2739: UserWarning: X does not have valid feature names, but LGBMRegressor was fitted with feature names\n", + " warnings.warn(\n", + "c:\\Users\\svenk\\anaconda3\\envs\\dml_tabpfn\\Lib\\site-packages\\sklearn\\utils\\validation.py:2739: UserWarning: X does not have valid feature names, but LGBMClassifier was fitted with feature names\n", + " warnings.warn(\n", + "c:\\Users\\svenk\\anaconda3\\envs\\dml_tabpfn\\Lib\\site-packages\\sklearn\\utils\\validation.py:2739: UserWarning: X does not have valid feature names, but LGBMClassifier was fitted with feature names\n", + " warnings.warn(\n", + "c:\\Users\\svenk\\anaconda3\\envs\\dml_tabpfn\\Lib\\site-packages\\sklearn\\utils\\validation.py:2739: UserWarning: X does not have valid feature names, but LGBMClassifier was fitted with feature names\n", + " warnings.warn(\n", + "c:\\Users\\svenk\\anaconda3\\envs\\dml_tabpfn\\Lib\\site-packages\\sklearn\\utils\\validation.py:2739: UserWarning: X does not have valid feature names, but LGBMClassifier was fitted with feature names\n", + " warnings.warn(\n", + "c:\\Users\\svenk\\anaconda3\\envs\\dml_tabpfn\\Lib\\site-packages\\sklearn\\utils\\validation.py:2739: UserWarning: X does not have valid feature names, but LGBMClassifier was fitted with feature names\n", + " warnings.warn(\n", + "c:\\Users\\svenk\\anaconda3\\envs\\dml_tabpfn\\Lib\\site-packages\\doubleml\\double_ml.py:1479: UserWarning: The estimated nu2 for d is not positive. Re-estimation based on riesz representer (non-orthogonal).\n", + " warnings.warn(msg, UserWarning)\n", + "c:\\Users\\svenk\\anaconda3\\envs\\dml_tabpfn\\Lib\\site-packages\\sklearn\\utils\\deprecation.py:151: FutureWarning: 'force_all_finite' was renamed to 'ensure_all_finite' in 1.6 and will be removed in 1.8.\n", + " warnings.warn(\n", + "c:\\Users\\svenk\\anaconda3\\envs\\dml_tabpfn\\Lib\\site-packages\\sklearn\\utils\\deprecation.py:151: FutureWarning: 'force_all_finite' was renamed to 'ensure_all_finite' in 1.6 and will be removed in 1.8.\n", + " warnings.warn(\n", + "c:\\Users\\svenk\\anaconda3\\envs\\dml_tabpfn\\Lib\\site-packages\\sklearn\\utils\\validation.py:2739: UserWarning: X does not have valid feature names, but LGBMRegressor was fitted with feature names\n", + " warnings.warn(\n", + "c:\\Users\\svenk\\anaconda3\\envs\\dml_tabpfn\\Lib\\site-packages\\sklearn\\utils\\validation.py:2739: UserWarning: X does not have valid feature names, but LGBMRegressor was fitted with feature names\n", + " warnings.warn(\n", + "c:\\Users\\svenk\\anaconda3\\envs\\dml_tabpfn\\Lib\\site-packages\\sklearn\\utils\\validation.py:2739: UserWarning: X does not have valid feature names, but LGBMRegressor was fitted with feature names\n", + " warnings.warn(\n", + "c:\\Users\\svenk\\anaconda3\\envs\\dml_tabpfn\\Lib\\site-packages\\sklearn\\utils\\validation.py:2739: UserWarning: X does not have valid feature names, but LGBMRegressor was fitted with feature names\n", + " warnings.warn(\n", + "c:\\Users\\svenk\\anaconda3\\envs\\dml_tabpfn\\Lib\\site-packages\\sklearn\\utils\\validation.py:2739: UserWarning: X does not have valid feature names, but LGBMRegressor was fitted with feature names\n", + " warnings.warn(\n", + "c:\\Users\\svenk\\anaconda3\\envs\\dml_tabpfn\\Lib\\site-packages\\sklearn\\utils\\validation.py:2739: UserWarning: X does not have valid feature names, but LGBMRegressor was fitted with feature names\n", + " warnings.warn(\n", + "c:\\Users\\svenk\\anaconda3\\envs\\dml_tabpfn\\Lib\\site-packages\\sklearn\\utils\\validation.py:2739: UserWarning: X does not have valid feature names, but LGBMRegressor was fitted with feature names\n", + " warnings.warn(\n", + "c:\\Users\\svenk\\anaconda3\\envs\\dml_tabpfn\\Lib\\site-packages\\sklearn\\utils\\validation.py:2739: UserWarning: X does not have valid feature names, but LGBMRegressor was fitted with feature names\n", + " warnings.warn(\n", + "c:\\Users\\svenk\\anaconda3\\envs\\dml_tabpfn\\Lib\\site-packages\\sklearn\\utils\\validation.py:2739: UserWarning: X does not have valid feature names, but LGBMRegressor was fitted with feature names\n", + " warnings.warn(\n", + "c:\\Users\\svenk\\anaconda3\\envs\\dml_tabpfn\\Lib\\site-packages\\sklearn\\utils\\validation.py:2739: UserWarning: X does not have valid feature names, but LGBMRegressor was fitted with feature names\n", + " warnings.warn(\n", + "c:\\Users\\svenk\\anaconda3\\envs\\dml_tabpfn\\Lib\\site-packages\\sklearn\\utils\\validation.py:2739: UserWarning: X does not have valid feature names, but LGBMRegressor was fitted with feature names\n", + " warnings.warn(\n", + "c:\\Users\\svenk\\anaconda3\\envs\\dml_tabpfn\\Lib\\site-packages\\sklearn\\utils\\validation.py:2739: UserWarning: X does not have valid feature names, but LGBMRegressor was fitted with feature names\n", + " warnings.warn(\n", + "c:\\Users\\svenk\\anaconda3\\envs\\dml_tabpfn\\Lib\\site-packages\\sklearn\\utils\\validation.py:2739: UserWarning: X does not have valid feature names, but LGBMRegressor was fitted with feature names\n", + " warnings.warn(\n", + "c:\\Users\\svenk\\anaconda3\\envs\\dml_tabpfn\\Lib\\site-packages\\sklearn\\utils\\validation.py:2739: UserWarning: X does not have valid feature names, but LGBMRegressor was fitted with feature names\n", + " warnings.warn(\n", + "c:\\Users\\svenk\\anaconda3\\envs\\dml_tabpfn\\Lib\\site-packages\\sklearn\\utils\\validation.py:2739: UserWarning: X does not have valid feature names, but LGBMClassifier was fitted with feature names\n", + " warnings.warn(\n", + "c:\\Users\\svenk\\anaconda3\\envs\\dml_tabpfn\\Lib\\site-packages\\sklearn\\utils\\validation.py:2739: UserWarning: X does not have valid feature names, but LGBMClassifier was fitted with feature names\n", + " warnings.warn(\n", + "c:\\Users\\svenk\\anaconda3\\envs\\dml_tabpfn\\Lib\\site-packages\\sklearn\\utils\\validation.py:2739: UserWarning: X does not have valid feature names, but LGBMClassifier was fitted with feature names\n", + " warnings.warn(\n", + "c:\\Users\\svenk\\anaconda3\\envs\\dml_tabpfn\\Lib\\site-packages\\sklearn\\utils\\validation.py:2739: UserWarning: X does not have valid feature names, but LGBMClassifier was fitted with feature names\n", + " warnings.warn(\n", + "c:\\Users\\svenk\\anaconda3\\envs\\dml_tabpfn\\Lib\\site-packages\\sklearn\\utils\\validation.py:2739: UserWarning: X does not have valid feature names, but LGBMClassifier was fitted with feature names\n", + " warnings.warn(\n", + "c:\\Users\\svenk\\anaconda3\\envs\\dml_tabpfn\\Lib\\site-packages\\doubleml\\double_ml.py:1479: UserWarning: The estimated nu2 for d is not positive. Re-estimation based on riesz representer (non-orthogonal).\n", + " warnings.warn(msg, UserWarning)\n", + "c:\\Users\\svenk\\anaconda3\\envs\\dml_tabpfn\\Lib\\site-packages\\sklearn\\utils\\deprecation.py:151: FutureWarning: 'force_all_finite' was renamed to 'ensure_all_finite' in 1.6 and will be removed in 1.8.\n", + " warnings.warn(\n", + "c:\\Users\\svenk\\anaconda3\\envs\\dml_tabpfn\\Lib\\site-packages\\sklearn\\utils\\deprecation.py:151: FutureWarning: 'force_all_finite' was renamed to 'ensure_all_finite' in 1.6 and will be removed in 1.8.\n", + " warnings.warn(\n", + "c:\\Users\\svenk\\anaconda3\\envs\\dml_tabpfn\\Lib\\site-packages\\sklearn\\utils\\validation.py:2739: UserWarning: X does not have valid feature names, but LGBMRegressor was fitted with feature names\n", + " warnings.warn(\n", + "c:\\Users\\svenk\\anaconda3\\envs\\dml_tabpfn\\Lib\\site-packages\\sklearn\\utils\\validation.py:2739: UserWarning: X does not have valid feature names, but LGBMRegressor was fitted with feature names\n", + " warnings.warn(\n", + "c:\\Users\\svenk\\anaconda3\\envs\\dml_tabpfn\\Lib\\site-packages\\sklearn\\utils\\validation.py:2739: UserWarning: X does not have valid feature names, but LGBMRegressor was fitted with feature names\n", + " warnings.warn(\n", + "c:\\Users\\svenk\\anaconda3\\envs\\dml_tabpfn\\Lib\\site-packages\\sklearn\\utils\\validation.py:2739: UserWarning: X does not have valid feature names, but LGBMRegressor was fitted with feature names\n", + " warnings.warn(\n", + "c:\\Users\\svenk\\anaconda3\\envs\\dml_tabpfn\\Lib\\site-packages\\sklearn\\utils\\validation.py:2739: UserWarning: X does not have valid feature names, but LGBMRegressor was fitted with feature names\n", + " warnings.warn(\n", + "c:\\Users\\svenk\\anaconda3\\envs\\dml_tabpfn\\Lib\\site-packages\\sklearn\\utils\\validation.py:2739: UserWarning: X does not have valid feature names, but LGBMRegressor was fitted with feature names\n", + " warnings.warn(\n", + "c:\\Users\\svenk\\anaconda3\\envs\\dml_tabpfn\\Lib\\site-packages\\sklearn\\utils\\validation.py:2739: UserWarning: X does not have valid feature names, but LGBMRegressor was fitted with feature names\n", + " warnings.warn(\n", + "c:\\Users\\svenk\\anaconda3\\envs\\dml_tabpfn\\Lib\\site-packages\\sklearn\\utils\\validation.py:2739: UserWarning: X does not have valid feature names, but LGBMRegressor was fitted with feature names\n", + " warnings.warn(\n", + "c:\\Users\\svenk\\anaconda3\\envs\\dml_tabpfn\\Lib\\site-packages\\sklearn\\utils\\validation.py:2739: UserWarning: X does not have valid feature names, but LGBMClassifier was fitted with feature names\n", + " warnings.warn(\n", + "c:\\Users\\svenk\\anaconda3\\envs\\dml_tabpfn\\Lib\\site-packages\\sklearn\\utils\\validation.py:2739: UserWarning: X does not have valid feature names, but LGBMClassifier was fitted with feature names\n", + " warnings.warn(\n", + "c:\\Users\\svenk\\anaconda3\\envs\\dml_tabpfn\\Lib\\site-packages\\sklearn\\utils\\validation.py:2739: UserWarning: X does not have valid feature names, but LGBMClassifier was fitted with feature names\n", + " warnings.warn(\n", + "c:\\Users\\svenk\\anaconda3\\envs\\dml_tabpfn\\Lib\\site-packages\\sklearn\\utils\\validation.py:2739: UserWarning: X does not have valid feature names, but LGBMClassifier was fitted with feature names\n", + " warnings.warn(\n", + "c:\\Users\\svenk\\anaconda3\\envs\\dml_tabpfn\\Lib\\site-packages\\sklearn\\utils\\validation.py:2739: UserWarning: X does not have valid feature names, but LGBMClassifier was fitted with feature names\n", + " warnings.warn(\n", + "c:\\Users\\svenk\\anaconda3\\envs\\dml_tabpfn\\Lib\\site-packages\\doubleml\\double_ml.py:1479: UserWarning: The estimated nu2 for d is not positive. Re-estimation based on riesz representer (non-orthogonal).\n", + " warnings.warn(msg, UserWarning)\n", + "c:\\Users\\svenk\\anaconda3\\envs\\dml_tabpfn\\Lib\\site-packages\\sklearn\\utils\\deprecation.py:151: FutureWarning: 'force_all_finite' was renamed to 'ensure_all_finite' in 1.6 and will be removed in 1.8.\n", + " warnings.warn(\n", + "c:\\Users\\svenk\\anaconda3\\envs\\dml_tabpfn\\Lib\\site-packages\\sklearn\\utils\\deprecation.py:151: FutureWarning: 'force_all_finite' was renamed to 'ensure_all_finite' in 1.6 and will be removed in 1.8.\n", + " warnings.warn(\n", + "c:\\Users\\svenk\\anaconda3\\envs\\dml_tabpfn\\Lib\\site-packages\\sklearn\\utils\\validation.py:2739: UserWarning: X does not have valid feature names, but LGBMRegressor was fitted with feature names\n", + " warnings.warn(\n", + "c:\\Users\\svenk\\anaconda3\\envs\\dml_tabpfn\\Lib\\site-packages\\sklearn\\utils\\validation.py:2739: UserWarning: X does not have valid feature names, but LGBMRegressor was fitted with feature names\n", + " warnings.warn(\n", + "c:\\Users\\svenk\\anaconda3\\envs\\dml_tabpfn\\Lib\\site-packages\\sklearn\\utils\\validation.py:2739: UserWarning: X does not have valid feature names, but LGBMRegressor was fitted with feature names\n", + " warnings.warn(\n", + "c:\\Users\\svenk\\anaconda3\\envs\\dml_tabpfn\\Lib\\site-packages\\sklearn\\utils\\validation.py:2739: UserWarning: X does not have valid feature names, but LGBMRegressor was fitted with feature names\n", + " warnings.warn(\n", + "c:\\Users\\svenk\\anaconda3\\envs\\dml_tabpfn\\Lib\\site-packages\\sklearn\\utils\\validation.py:2739: UserWarning: X does not have valid feature names, but LGBMRegressor was fitted with feature names\n", + " warnings.warn(\n", + "c:\\Users\\svenk\\anaconda3\\envs\\dml_tabpfn\\Lib\\site-packages\\sklearn\\utils\\validation.py:2739: UserWarning: X does not have valid feature names, but LGBMRegressor was fitted with feature names\n", + " warnings.warn(\n", + "c:\\Users\\svenk\\anaconda3\\envs\\dml_tabpfn\\Lib\\site-packages\\sklearn\\utils\\validation.py:2739: UserWarning: X does not have valid feature names, but LGBMRegressor was fitted with feature names\n", + " warnings.warn(\n", + "c:\\Users\\svenk\\anaconda3\\envs\\dml_tabpfn\\Lib\\site-packages\\sklearn\\utils\\validation.py:2739: UserWarning: X does not have valid feature names, but LGBMRegressor was fitted with feature names\n", + " warnings.warn(\n", + "c:\\Users\\svenk\\anaconda3\\envs\\dml_tabpfn\\Lib\\site-packages\\sklearn\\utils\\validation.py:2739: UserWarning: X does not have valid feature names, but LGBMRegressor was fitted with feature names\n", + " warnings.warn(\n", + "c:\\Users\\svenk\\anaconda3\\envs\\dml_tabpfn\\Lib\\site-packages\\sklearn\\utils\\validation.py:2739: UserWarning: X does not have valid feature names, but LGBMRegressor was fitted with feature names\n", + " warnings.warn(\n", + "c:\\Users\\svenk\\anaconda3\\envs\\dml_tabpfn\\Lib\\site-packages\\sklearn\\utils\\validation.py:2739: UserWarning: X does not have valid feature names, but LGBMRegressor was fitted with feature names\n", + " warnings.warn(\n", + "c:\\Users\\svenk\\anaconda3\\envs\\dml_tabpfn\\Lib\\site-packages\\sklearn\\utils\\validation.py:2739: UserWarning: X does not have valid feature names, but LGBMRegressor was fitted with feature names\n", + " warnings.warn(\n", + "c:\\Users\\svenk\\anaconda3\\envs\\dml_tabpfn\\Lib\\site-packages\\sklearn\\utils\\validation.py:2739: UserWarning: X does not have valid feature names, but LGBMRegressor was fitted with feature names\n", + " warnings.warn(\n", + "c:\\Users\\svenk\\anaconda3\\envs\\dml_tabpfn\\Lib\\site-packages\\sklearn\\utils\\validation.py:2739: UserWarning: X does not have valid feature names, but LGBMClassifier was fitted with feature names\n", + " warnings.warn(\n", + "c:\\Users\\svenk\\anaconda3\\envs\\dml_tabpfn\\Lib\\site-packages\\sklearn\\utils\\validation.py:2739: UserWarning: X does not have valid feature names, but LGBMClassifier was fitted with feature names\n", + " warnings.warn(\n", + "c:\\Users\\svenk\\anaconda3\\envs\\dml_tabpfn\\Lib\\site-packages\\sklearn\\utils\\validation.py:2739: UserWarning: X does not have valid feature names, but LGBMClassifier was fitted with feature names\n", + " warnings.warn(\n", + "c:\\Users\\svenk\\anaconda3\\envs\\dml_tabpfn\\Lib\\site-packages\\sklearn\\utils\\validation.py:2739: UserWarning: X does not have valid feature names, but LGBMClassifier was fitted with feature names\n", + " warnings.warn(\n", + "c:\\Users\\svenk\\anaconda3\\envs\\dml_tabpfn\\Lib\\site-packages\\sklearn\\utils\\validation.py:2739: UserWarning: X does not have valid feature names, but LGBMClassifier was fitted with feature names\n", + " warnings.warn(\n", + "c:\\Users\\svenk\\anaconda3\\envs\\dml_tabpfn\\Lib\\site-packages\\doubleml\\double_ml.py:1479: UserWarning: The estimated nu2 for d is not positive. Re-estimation based on riesz representer (non-orthogonal).\n", + " warnings.warn(msg, UserWarning)\n", + "c:\\Users\\svenk\\anaconda3\\envs\\dml_tabpfn\\Lib\\site-packages\\sklearn\\utils\\deprecation.py:151: FutureWarning: 'force_all_finite' was renamed to 'ensure_all_finite' in 1.6 and will be removed in 1.8.\n", + " warnings.warn(\n", + "c:\\Users\\svenk\\anaconda3\\envs\\dml_tabpfn\\Lib\\site-packages\\sklearn\\utils\\deprecation.py:151: FutureWarning: 'force_all_finite' was renamed to 'ensure_all_finite' in 1.6 and will be removed in 1.8.\n", + " warnings.warn(\n", + "c:\\Users\\svenk\\anaconda3\\envs\\dml_tabpfn\\Lib\\site-packages\\sklearn\\utils\\validation.py:2739: UserWarning: X does not have valid feature names, but LGBMRegressor was fitted with feature names\n", + " warnings.warn(\n", + "c:\\Users\\svenk\\anaconda3\\envs\\dml_tabpfn\\Lib\\site-packages\\sklearn\\utils\\validation.py:2739: UserWarning: X does not have valid feature names, but LGBMRegressor was fitted with feature names\n", + " warnings.warn(\n", + "c:\\Users\\svenk\\anaconda3\\envs\\dml_tabpfn\\Lib\\site-packages\\sklearn\\utils\\validation.py:2739: UserWarning: X does not have valid feature names, but LGBMRegressor was fitted with feature names\n", + " warnings.warn(\n", + "c:\\Users\\svenk\\anaconda3\\envs\\dml_tabpfn\\Lib\\site-packages\\sklearn\\utils\\validation.py:2739: UserWarning: X does not have valid feature names, but LGBMRegressor was fitted with feature names\n", + " warnings.warn(\n", + "c:\\Users\\svenk\\anaconda3\\envs\\dml_tabpfn\\Lib\\site-packages\\sklearn\\utils\\validation.py:2739: UserWarning: X does not have valid feature names, but LGBMRegressor was fitted with feature names\n", + " warnings.warn(\n", + "c:\\Users\\svenk\\anaconda3\\envs\\dml_tabpfn\\Lib\\site-packages\\sklearn\\utils\\validation.py:2739: UserWarning: X does not have valid feature names, but LGBMRegressor was fitted with feature names\n", + " warnings.warn(\n", + "c:\\Users\\svenk\\anaconda3\\envs\\dml_tabpfn\\Lib\\site-packages\\sklearn\\utils\\validation.py:2739: UserWarning: X does not have valid feature names, but LGBMRegressor was fitted with feature names\n", + " warnings.warn(\n", + "c:\\Users\\svenk\\anaconda3\\envs\\dml_tabpfn\\Lib\\site-packages\\sklearn\\utils\\validation.py:2739: UserWarning: X does not have valid feature names, but LGBMRegressor was fitted with feature names\n", + " warnings.warn(\n", + "c:\\Users\\svenk\\anaconda3\\envs\\dml_tabpfn\\Lib\\site-packages\\sklearn\\utils\\validation.py:2739: UserWarning: X does not have valid feature names, but LGBMRegressor was fitted with feature names\n", + " warnings.warn(\n", + "c:\\Users\\svenk\\anaconda3\\envs\\dml_tabpfn\\Lib\\site-packages\\sklearn\\utils\\validation.py:2739: UserWarning: X does not have valid feature names, but LGBMRegressor was fitted with feature names\n", + " warnings.warn(\n", + "c:\\Users\\svenk\\anaconda3\\envs\\dml_tabpfn\\Lib\\site-packages\\sklearn\\utils\\validation.py:2739: UserWarning: X does not have valid feature names, but LGBMClassifier was fitted with feature names\n", + " warnings.warn(\n", + "c:\\Users\\svenk\\anaconda3\\envs\\dml_tabpfn\\Lib\\site-packages\\sklearn\\utils\\validation.py:2739: UserWarning: X does not have valid feature names, but LGBMClassifier was fitted with feature names\n", + " warnings.warn(\n", + "c:\\Users\\svenk\\anaconda3\\envs\\dml_tabpfn\\Lib\\site-packages\\sklearn\\utils\\validation.py:2739: UserWarning: X does not have valid feature names, but LGBMClassifier was fitted with feature names\n", + " warnings.warn(\n", + "c:\\Users\\svenk\\anaconda3\\envs\\dml_tabpfn\\Lib\\site-packages\\sklearn\\utils\\validation.py:2739: UserWarning: X does not have valid feature names, but LGBMClassifier was fitted with feature names\n", + " warnings.warn(\n", + "c:\\Users\\svenk\\anaconda3\\envs\\dml_tabpfn\\Lib\\site-packages\\sklearn\\utils\\validation.py:2739: UserWarning: X does not have valid feature names, but LGBMClassifier was fitted with feature names\n", + " warnings.warn(\n", + "c:\\Users\\svenk\\anaconda3\\envs\\dml_tabpfn\\Lib\\site-packages\\doubleml\\double_ml.py:1479: UserWarning: The estimated nu2 for d is not positive. Re-estimation based on riesz representer (non-orthogonal).\n", + " warnings.warn(msg, UserWarning)\n", + "c:\\Users\\svenk\\anaconda3\\envs\\dml_tabpfn\\Lib\\site-packages\\sklearn\\utils\\deprecation.py:151: FutureWarning: 'force_all_finite' was renamed to 'ensure_all_finite' in 1.6 and will be removed in 1.8.\n", + " warnings.warn(\n", + "c:\\Users\\svenk\\anaconda3\\envs\\dml_tabpfn\\Lib\\site-packages\\sklearn\\utils\\deprecation.py:151: FutureWarning: 'force_all_finite' was renamed to 'ensure_all_finite' in 1.6 and will be removed in 1.8.\n", + " warnings.warn(\n", + "c:\\Users\\svenk\\anaconda3\\envs\\dml_tabpfn\\Lib\\site-packages\\sklearn\\utils\\validation.py:2739: UserWarning: X does not have valid feature names, but LGBMRegressor was fitted with feature names\n", + " warnings.warn(\n", + "c:\\Users\\svenk\\anaconda3\\envs\\dml_tabpfn\\Lib\\site-packages\\sklearn\\utils\\validation.py:2739: UserWarning: X does not have valid feature names, but LGBMRegressor was fitted with feature names\n", + " warnings.warn(\n", + "c:\\Users\\svenk\\anaconda3\\envs\\dml_tabpfn\\Lib\\site-packages\\sklearn\\utils\\validation.py:2739: UserWarning: X does not have valid feature names, but LGBMRegressor was fitted with feature names\n", + " warnings.warn(\n", + "c:\\Users\\svenk\\anaconda3\\envs\\dml_tabpfn\\Lib\\site-packages\\sklearn\\utils\\validation.py:2739: UserWarning: X does not have valid feature names, but LGBMRegressor was fitted with feature names\n", + " warnings.warn(\n", + "c:\\Users\\svenk\\anaconda3\\envs\\dml_tabpfn\\Lib\\site-packages\\sklearn\\utils\\validation.py:2739: UserWarning: X does not have valid feature names, but LGBMRegressor was fitted with feature names\n", + " warnings.warn(\n", + "c:\\Users\\svenk\\anaconda3\\envs\\dml_tabpfn\\Lib\\site-packages\\sklearn\\utils\\validation.py:2739: UserWarning: X does not have valid feature names, but LGBMRegressor was fitted with feature names\n", + " warnings.warn(\n", + "c:\\Users\\svenk\\anaconda3\\envs\\dml_tabpfn\\Lib\\site-packages\\sklearn\\utils\\validation.py:2739: UserWarning: X does not have valid feature names, but LGBMRegressor was fitted with feature names\n", + " warnings.warn(\n", + "c:\\Users\\svenk\\anaconda3\\envs\\dml_tabpfn\\Lib\\site-packages\\sklearn\\utils\\validation.py:2739: UserWarning: X does not have valid feature names, but LGBMRegressor was fitted with feature names\n", + " warnings.warn(\n", + "c:\\Users\\svenk\\anaconda3\\envs\\dml_tabpfn\\Lib\\site-packages\\sklearn\\utils\\validation.py:2739: UserWarning: X does not have valid feature names, but LGBMRegressor was fitted with feature names\n", + " warnings.warn(\n", + "c:\\Users\\svenk\\anaconda3\\envs\\dml_tabpfn\\Lib\\site-packages\\sklearn\\utils\\validation.py:2739: UserWarning: X does not have valid feature names, but LGBMRegressor was fitted with feature names\n", + " warnings.warn(\n", + "c:\\Users\\svenk\\anaconda3\\envs\\dml_tabpfn\\Lib\\site-packages\\sklearn\\utils\\validation.py:2739: UserWarning: X does not have valid feature names, but LGBMClassifier was fitted with feature names\n", + " warnings.warn(\n", + "c:\\Users\\svenk\\anaconda3\\envs\\dml_tabpfn\\Lib\\site-packages\\sklearn\\utils\\validation.py:2739: UserWarning: X does not have valid feature names, but LGBMRegressor was fitted with feature names\n", + " warnings.warn(\n", + "c:\\Users\\svenk\\anaconda3\\envs\\dml_tabpfn\\Lib\\site-packages\\sklearn\\utils\\validation.py:2739: UserWarning: X does not have valid feature names, but LGBMClassifier was fitted with feature names\n", + " warnings.warn(\n", + "c:\\Users\\svenk\\anaconda3\\envs\\dml_tabpfn\\Lib\\site-packages\\sklearn\\utils\\validation.py:2739: UserWarning: X does not have valid feature names, but LGBMClassifier was fitted with feature names\n", + " warnings.warn(\n", + "c:\\Users\\svenk\\anaconda3\\envs\\dml_tabpfn\\Lib\\site-packages\\sklearn\\utils\\validation.py:2739: UserWarning: X does not have valid feature names, but LGBMClassifier was fitted with feature names\n", + " warnings.warn(\n", + "c:\\Users\\svenk\\anaconda3\\envs\\dml_tabpfn\\Lib\\site-packages\\sklearn\\utils\\validation.py:2739: UserWarning: X does not have valid feature names, but LGBMClassifier was fitted with feature names\n", + " warnings.warn(\n", + "c:\\Users\\svenk\\anaconda3\\envs\\dml_tabpfn\\Lib\\site-packages\\sklearn\\utils\\validation.py:2739: UserWarning: X does not have valid feature names, but LGBMClassifier was fitted with feature names\n", + " warnings.warn(\n", + "c:\\Users\\svenk\\anaconda3\\envs\\dml_tabpfn\\Lib\\site-packages\\doubleml\\double_ml.py:1479: UserWarning: The estimated nu2 for d is not positive. Re-estimation based on riesz representer (non-orthogonal).\n", + " warnings.warn(msg, UserWarning)\n", + "c:\\Users\\svenk\\anaconda3\\envs\\dml_tabpfn\\Lib\\site-packages\\sklearn\\utils\\deprecation.py:151: FutureWarning: 'force_all_finite' was renamed to 'ensure_all_finite' in 1.6 and will be removed in 1.8.\n", + " warnings.warn(\n", + "c:\\Users\\svenk\\anaconda3\\envs\\dml_tabpfn\\Lib\\site-packages\\sklearn\\utils\\deprecation.py:151: FutureWarning: 'force_all_finite' was renamed to 'ensure_all_finite' in 1.6 and will be removed in 1.8.\n", + " warnings.warn(\n", + "c:\\Users\\svenk\\anaconda3\\envs\\dml_tabpfn\\Lib\\site-packages\\sklearn\\utils\\validation.py:2739: UserWarning: X does not have valid feature names, but LGBMClassifier was fitted with feature names\n", + " warnings.warn(\n", + "c:\\Users\\svenk\\anaconda3\\envs\\dml_tabpfn\\Lib\\site-packages\\sklearn\\utils\\validation.py:2739: UserWarning: X does not have valid feature names, but LGBMClassifier was fitted with feature names\n", + " warnings.warn(\n", + "c:\\Users\\svenk\\anaconda3\\envs\\dml_tabpfn\\Lib\\site-packages\\sklearn\\utils\\validation.py:2739: UserWarning: X does not have valid feature names, but LGBMClassifier was fitted with feature names\n", + " warnings.warn(\n", + "c:\\Users\\svenk\\anaconda3\\envs\\dml_tabpfn\\Lib\\site-packages\\sklearn\\utils\\validation.py:2739: UserWarning: X does not have valid feature names, but LGBMClassifier was fitted with feature names\n", + " warnings.warn(\n", + "c:\\Users\\svenk\\anaconda3\\envs\\dml_tabpfn\\Lib\\site-packages\\doubleml\\double_ml.py:1479: UserWarning: The estimated nu2 for d is not positive. Re-estimation based on riesz representer (non-orthogonal).\n", + " warnings.warn(msg, UserWarning)\n", + "c:\\Users\\svenk\\anaconda3\\envs\\dml_tabpfn\\Lib\\site-packages\\sklearn\\utils\\deprecation.py:151: FutureWarning: 'force_all_finite' was renamed to 'ensure_all_finite' in 1.6 and will be removed in 1.8.\n", + " warnings.warn(\n", + "c:\\Users\\svenk\\anaconda3\\envs\\dml_tabpfn\\Lib\\site-packages\\sklearn\\utils\\deprecation.py:151: FutureWarning: 'force_all_finite' was renamed to 'ensure_all_finite' in 1.6 and will be removed in 1.8.\n", + " warnings.warn(\n", + "c:\\Users\\svenk\\anaconda3\\envs\\dml_tabpfn\\Lib\\site-packages\\sklearn\\utils\\deprecation.py:151: FutureWarning: 'force_all_finite' was renamed to 'ensure_all_finite' in 1.6 and will be removed in 1.8.\n", + " warnings.warn(\n", + "c:\\Users\\svenk\\anaconda3\\envs\\dml_tabpfn\\Lib\\site-packages\\sklearn\\utils\\deprecation.py:151: FutureWarning: 'force_all_finite' was renamed to 'ensure_all_finite' in 1.6 and will be removed in 1.8.\n", + " warnings.warn(\n", + "c:\\Users\\svenk\\anaconda3\\envs\\dml_tabpfn\\Lib\\site-packages\\sklearn\\utils\\deprecation.py:151: FutureWarning: 'force_all_finite' was renamed to 'ensure_all_finite' in 1.6 and will be removed in 1.8.\n", + " warnings.warn(\n", + "c:\\Users\\svenk\\anaconda3\\envs\\dml_tabpfn\\Lib\\site-packages\\sklearn\\utils\\deprecation.py:151: FutureWarning: 'force_all_finite' was renamed to 'ensure_all_finite' in 1.6 and will be removed in 1.8.\n", + " warnings.warn(\n", + "c:\\Users\\svenk\\anaconda3\\envs\\dml_tabpfn\\Lib\\site-packages\\sklearn\\utils\\deprecation.py:151: FutureWarning: 'force_all_finite' was renamed to 'ensure_all_finite' in 1.6 and will be removed in 1.8.\n", + " warnings.warn(\n", + "c:\\Users\\svenk\\anaconda3\\envs\\dml_tabpfn\\Lib\\site-packages\\sklearn\\utils\\deprecation.py:151: FutureWarning: 'force_all_finite' was renamed to 'ensure_all_finite' in 1.6 and will be removed in 1.8.\n", + " warnings.warn(\n", + "c:\\Users\\svenk\\anaconda3\\envs\\dml_tabpfn\\Lib\\site-packages\\sklearn\\utils\\deprecation.py:151: FutureWarning: 'force_all_finite' was renamed to 'ensure_all_finite' in 1.6 and will be removed in 1.8.\n", + " warnings.warn(\n", + "c:\\Users\\svenk\\anaconda3\\envs\\dml_tabpfn\\Lib\\site-packages\\sklearn\\utils\\deprecation.py:151: FutureWarning: 'force_all_finite' was renamed to 'ensure_all_finite' in 1.6 and will be removed in 1.8.\n", + " warnings.warn(\n", + "c:\\Users\\svenk\\anaconda3\\envs\\dml_tabpfn\\Lib\\site-packages\\sklearn\\utils\\deprecation.py:151: FutureWarning: 'force_all_finite' was renamed to 'ensure_all_finite' in 1.6 and will be removed in 1.8.\n", + " warnings.warn(\n", + "c:\\Users\\svenk\\anaconda3\\envs\\dml_tabpfn\\Lib\\site-packages\\sklearn\\utils\\deprecation.py:151: FutureWarning: 'force_all_finite' was renamed to 'ensure_all_finite' in 1.6 and will be removed in 1.8.\n", + " warnings.warn(\n", + "c:\\Users\\svenk\\anaconda3\\envs\\dml_tabpfn\\Lib\\site-packages\\sklearn\\utils\\deprecation.py:151: FutureWarning: 'force_all_finite' was renamed to 'ensure_all_finite' in 1.6 and will be removed in 1.8.\n", + " warnings.warn(\n", + "c:\\Users\\svenk\\anaconda3\\envs\\dml_tabpfn\\Lib\\site-packages\\sklearn\\utils\\deprecation.py:151: FutureWarning: 'force_all_finite' was renamed to 'ensure_all_finite' in 1.6 and will be removed in 1.8.\n", + " warnings.warn(\n", + "c:\\Users\\svenk\\anaconda3\\envs\\dml_tabpfn\\Lib\\site-packages\\sklearn\\utils\\deprecation.py:151: FutureWarning: 'force_all_finite' was renamed to 'ensure_all_finite' in 1.6 and will be removed in 1.8.\n", + " warnings.warn(\n", + "c:\\Users\\svenk\\anaconda3\\envs\\dml_tabpfn\\Lib\\site-packages\\sklearn\\utils\\deprecation.py:151: FutureWarning: 'force_all_finite' was renamed to 'ensure_all_finite' in 1.6 and will be removed in 1.8.\n", + " warnings.warn(\n", + "c:\\Users\\svenk\\anaconda3\\envs\\dml_tabpfn\\Lib\\site-packages\\sklearn\\utils\\deprecation.py:151: FutureWarning: 'force_all_finite' was renamed to 'ensure_all_finite' in 1.6 and will be removed in 1.8.\n", + " warnings.warn(\n", + "c:\\Users\\svenk\\anaconda3\\envs\\dml_tabpfn\\Lib\\site-packages\\sklearn\\utils\\deprecation.py:151: FutureWarning: 'force_all_finite' was renamed to 'ensure_all_finite' in 1.6 and will be removed in 1.8.\n", + " warnings.warn(\n", + "c:\\Users\\svenk\\anaconda3\\envs\\dml_tabpfn\\Lib\\site-packages\\sklearn\\utils\\deprecation.py:151: FutureWarning: 'force_all_finite' was renamed to 'ensure_all_finite' in 1.6 and will be removed in 1.8.\n", + " warnings.warn(\n", + "c:\\Users\\svenk\\anaconda3\\envs\\dml_tabpfn\\Lib\\site-packages\\sklearn\\utils\\deprecation.py:151: FutureWarning: 'force_all_finite' was renamed to 'ensure_all_finite' in 1.6 and will be removed in 1.8.\n", + " warnings.warn(\n", + 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learnertreatment_levelateci_lowerci_upper
0RandomForest1.02.744951-5.65741611.147319
1RandomForest2.04.550421-1.79427610.895118
2RandomForest3.010.6405744.32040216.960747
3RandomForest4.09.3151723.21219815.418145
4RandomForest5.011.8577862.75928420.956289
5Linear1.03.709369-1.7845839.203320
6Linear2.08.2318203.26275313.200887
7Linear3.010.3324606.49816114.166758
8Linear4.011.4060497.86179914.950299
9Linear5.07.0501113.43456110.665662
10LightGBM1.06.471167-9.93629022.878624
11LightGBM2.016.217765-0.00886232.444392
12LightGBM3.07.282430-8.06698522.631844
13LightGBM4.014.8250652.02650827.623622
14LightGBM5.019.5159320.61826838.413596
15TabPFN1.02.0378870.6063133.469461
16TabPFN2.07.1664846.1824008.150568
17TabPFN3.010.0813068.97431911.188293
18TabPFN4.010.3537079.35598111.351433
19TabPFN5.09.7943968.69807110.890721
\n", + "
" + ], + "text/plain": [ + " learner treatment_level ate ci_lower ci_upper\n", + "0 RandomForest 1.0 2.744951 -5.657416 11.147319\n", + "1 RandomForest 2.0 4.550421 -1.794276 10.895118\n", + "2 RandomForest 3.0 10.640574 4.320402 16.960747\n", + "3 RandomForest 4.0 9.315172 3.212198 15.418145\n", + "4 RandomForest 5.0 11.857786 2.759284 20.956289\n", + "5 Linear 1.0 3.709369 -1.784583 9.203320\n", + "6 Linear 2.0 8.231820 3.262753 13.200887\n", + "7 Linear 3.0 10.332460 6.498161 14.166758\n", + "8 Linear 4.0 11.406049 7.861799 14.950299\n", + "9 Linear 5.0 7.050111 3.434561 10.665662\n", + "10 LightGBM 1.0 6.471167 -9.936290 22.878624\n", + "11 LightGBM 2.0 16.217765 -0.008862 32.444392\n", + "12 LightGBM 3.0 7.282430 -8.066985 22.631844\n", + "13 LightGBM 4.0 14.825065 2.026508 27.623622\n", + "14 LightGBM 5.0 19.515932 0.618268 38.413596\n", + "15 TabPFN 1.0 2.037887 0.606313 3.469461\n", + "16 TabPFN 2.0 7.166484 6.182400 8.150568\n", + "17 TabPFN 3.0 10.081306 8.974319 11.188293\n", + "18 TabPFN 4.0 10.353707 9.355981 11.351433\n", + "19 TabPFN 5.0 9.794396 8.698071 10.890721" + ] + }, + "execution_count": 6, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Estimate causal contrasts (ATEs) for all models (difference to reference level 0)\n", + "treatment_levels = np.unique(dml_data.d)\n", + "reference_level = 0\n", + "\n", + "apo_results = []\n", + "causal_contrast_results = []\n", + "model_list = []\n", + "\n", + "for learner_name, learner_pair in learner_dict.items():\n", + " # Recreate dml_obj for each learner (as in the main loop)\n", + " dml_obj = dml.DoubleMLAPOS(\n", + " dml_data,\n", + " learner_pair['ml_g'],\n", + " learner_pair['ml_m'],\n", + " treatment_levels=treatment_levels,\n", + " n_rep=n_rep,\n", + " )\n", + " dml_obj.fit()\n", + " model_list.append(dml_obj)\n", + "\n", + " # APO confidence intervals\n", + " ci_pointwise = dml_obj.confint(level=0.95)\n", + " df_apos = pd.DataFrame({\n", + " 'learner': learner_name,\n", + " 'treatment_level': treatment_levels,\n", + " 'apo': dml_obj.coef,\n", + " 'ci_lower': ci_pointwise.values[:, 0],\n", + " 'ci_upper': ci_pointwise.values[:, 1]}\n", + " )\n", + " apo_results.append(df_apos)\n", + "\n", + " # ATE confidence intervals\n", + " causal_contrast_model = dml_obj.causal_contrast(reference_levels=reference_level)\n", + " ates = causal_contrast_model.thetas\n", + " ci_ates = causal_contrast_model.confint(level=0.95)\n", + " df_ates = pd.DataFrame({\n", + " 'learner': learner_name,\n", + " 'treatment_level': treatment_levels[1:],\n", + " 'ate': ates,\n", + " 'ci_lower': ci_ates.iloc[:, 0].values,\n", + " 'ci_upper': ci_ates.iloc[:, 1].values\n", + " })\n", + " causal_contrast_results.append(df_ates)\n", + "\n", + "# Combine all results\n", + "df_all_apos = pd.concat(apo_results, ignore_index=True)\n", + "df_all_ates = pd.concat(causal_contrast_results, ignore_index=True)\n", + "df_all_ates" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "id": "6076a90e", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Plot APOs and 95% CIs for all models\n", + "plt.figure(figsize=(12, 7))\n", + "palette = sns.color_palette(\"colorblind\")\n", + "learners = df_all_apos['learner'].unique()\n", + "n_learners = len(learners)\n", + "jitter_strength = 0.12\n", + "\n", + "for i, learner in enumerate(learners):\n", + " df = df_all_apos[df_all_apos['learner'] == learner]\n", + " # Jitter x positions for each learner\n", + " jitter = (i - (n_learners - 1) / 2) * jitter_strength\n", + " x_jittered = df['treatment_level'] + jitter\n", + " plt.errorbar(\n", + " x_jittered,\n", + " df['apo'],\n", + " yerr=[df['apo'] - df['ci_lower'], df['ci_upper'] - df['apo']],\n", + " fmt='o',\n", + " capsize=5,\n", + " capthick=2,\n", + " ecolor=palette[i % len(palette)],\n", + " color=palette[i % len(palette)],\n", + " label=f\"{learner} APO ±95% CI\",\n", + " zorder=2\n", + " )\n", + "\n", + "# Get treatment levels for proper line positioning\n", + "treatment_levels = sorted(df_all_apos['treatment_level'].unique())\n", + "x_range = plt.xlim()\n", + "total_width = x_range[1] - x_range[0]\n", + "\n", + "# Add true APOs as red horizontal lines\n", + "for i, level in enumerate(treatment_levels):\n", + " # Center each line around its treatment level with a reasonable width\n", + " line_width = 0.6 # Width of each horizontal line relative to treatment level spacing\n", + " x_center = level\n", + " x_start = x_center - line_width/2\n", + " x_end = x_center + line_width/2\n", + " \n", + " # Convert to relative coordinates (0-1) for xmin/xmax\n", + " xmin_rel = max(0, (x_start - x_range[0]) / total_width)\n", + " xmax_rel = min(1, (x_end - x_range[0]) / total_width)\n", + " \n", + " plt.axhline(y=apos[int(level)], color='red', linestyle='-', alpha=0.7, \n", + " xmin=xmin_rel, xmax=xmax_rel,\n", + " linewidth=3, label='True APO' if i == 0 else \"\")\n", + "\n", + "plt.title('Estimated APO and 95% Confidence Interval by Treatment Level (All Learners)')\n", + "plt.xlabel('Treatment Level')\n", + "plt.ylabel('Average Potential Outcome (APO)')\n", + "plt.xticks(sorted(df_all_apos['treatment_level'].unique()))\n", + "plt.legend()\n", + "plt.grid(True)\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "id": "bd512fd4", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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+ "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Plot ATEs and 95% CIs for all models\n", + "plt.figure(figsize=(12, 7))\n", + "palette = sns.color_palette(\"colorblind\")\n", + "learners = df_all_ates['learner'].unique()\n", + "n_learners = len(learners)\n", + "jitter_strength = 0.12\n", + "\n", + "for i, learner in enumerate(learners):\n", + " df = df_all_ates[df_all_ates['learner'] == learner]\n", + " # Jitter x positions for each learner\n", + " jitter = (i - (n_learners - 1) / 2) * jitter_strength\n", + " x_jittered = df['treatment_level'] + jitter\n", + " plt.errorbar(\n", + " x_jittered,\n", + " df['ate'],\n", + " yerr=[df['ate'] - df['ci_lower'], df['ci_upper'] - df['ate']],\n", + " fmt='o',\n", + " capsize=5,\n", + " capthick=2,\n", + " ecolor=palette[i % len(palette)],\n", + " color=palette[i % len(palette)],\n", + " label=f\"{learner} ATE ±95% CI\",\n", + " zorder=2\n", + " )\n", + "\n", + "# Get treatment levels for proper line positioning\n", + "treatment_levels = sorted(df_all_ates['treatment_level'].unique())\n", + "x_range = plt.xlim()\n", + "total_width = x_range[1] - x_range[0]\n", + "\n", + "# Add true ATEs as red horizontal lines\n", + "for i, level in enumerate(treatment_levels):\n", + " # Center each line around its treatment level with a reasonable width\n", + " line_width = 0.6 # Width of each horizontal line relative to treatment level spacing\n", + " x_center = level\n", + " x_start = x_center - line_width/2\n", + " x_end = x_center + line_width/2\n", + " \n", + " # Convert to relative coordinates (0-1) for xmin/xmax\n", + " xmin_rel = max(0, (x_start - x_range[0]) / total_width)\n", + " xmax_rel = min(1, (x_end - x_range[0]) / total_width)\n", + " \n", + " # Use average_ites[level] for the true ATE (treatment levels start from 1 for ATEs)\n", + " plt.axhline(y=average_ites[int(level)], color='red', linestyle='-', alpha=0.7, \n", + " xmin=xmin_rel, xmax=xmax_rel,\n", + " linewidth=3, label='True ATE' if i == 0 else \"\")\n", + "\n", + "plt.title('Estimated ATE and 95% Confidence Interval by Treatment Level (All Learners)')\n", + "plt.xlabel('Treatment Level (vs. 0)')\n", + "plt.ylabel('ATE')\n", + "plt.xticks(sorted(df_all_ates['treatment_level'].unique()))\n", + "plt.legend()\n", + "plt.grid(True)\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "id": "9d683935", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "\n", + "RMSE g0 by Learner and Treatment Level:\n", + "================================================================================\n", + "Treatment_Level 1.0 2.0 3.0 4.0 5.0\n", + "Learner \n", + "LightGBM 15.0193 11.2384 15.1283 14.7045 14.3642\n", + "Linear 21.3597 17.3510 20.5465 21.0926 21.1883\n", + "RandomForest 14.7957 11.0708 15.0855 14.6947 14.9856\n", + "TabPFN 9.6678 3.0042 9.9569 9.8757 9.7987\n", + "\n", + "\n", + "RMSE g1 by Learner and Treatment Level:\n", + "================================================================================\n", + "Treatment_Level 1.0 2.0 3.0 4.0 5.0\n", + "Learner \n", + "LightGBM 16.5259 31.5894 20.1407 18.7611 16.3981\n", + "Linear 16.9486 29.5049 20.8541 17.9796 16.7760\n", + "RandomForest 14.7852 26.5536 19.5189 16.7581 16.0231\n", + "TabPFN 3.3231 19.0293 4.9100 5.7262 5.2918\n", + "\n", + "\n", + "LogLoss m by Learner and Treatment Level:\n", + "================================================================================\n", + "Treatment_Level 1.0 2.0 3.0 4.0 5.0\n", + "Learner \n", + "LightGBM 0.5898 0.5076 0.5115 0.5599 0.5599\n", + "Linear 0.4791 0.4333 0.4240 0.4423 0.4652\n", + "RandomForest 0.5216 0.4704 0.4458 0.4867 0.5053\n", + "TabPFN 0.4770 0.4363 0.4310 0.4437 0.4652\n" + ] + } + ], + "source": [ + "# Create a comprehensive table with RMSE for g0, g1 and log loss for all learners and treatment levels\n", + "performance_results = []\n", + "\n", + "for idx_learner, learner_name in enumerate(learner_dict.keys()):\n", + " for idx_treat, treatment_level in enumerate(treatment_levels):\n", + " # Get the specific model for this learner and treatment level\n", + " model = model_list[idx_learner].modellist[idx_treat]\n", + " \n", + " # Extract performance metrics from nuisance_loss\n", + " if model.nuisance_loss is not None:\n", + " # RMSE for g0 (outcome model for treatment level != d)\n", + " rmse_g0 = model.nuisance_loss['ml_g_d_lvl0'][0][0]\n", + " \n", + " # RMSE for g1 (outcome model for treatment level = d)\n", + " rmse_g1 = model.nuisance_loss['ml_g_d_lvl1'][0][0]\n", + " \n", + " # Log loss for propensity score model\n", + " logloss_m = model.nuisance_loss['ml_m'][0][0]\n", + " else:\n", + " rmse_g0 = rmse_g1 = logloss_m = None\n", + " \n", + " # Store results\n", + " performance_results.append({\n", + " 'Learner': learner_name,\n", + " 'Treatment_Level': treatment_level,\n", + " 'RMSE_g0': rmse_g0,\n", + " 'RMSE_g1': rmse_g1,\n", + " 'LogLoss_m': logloss_m\n", + " })\n", + "\n", + "# Create DataFrame and display as a nicely formatted table\n", + "df_performance = pd.DataFrame(performance_results)\n", + "\n", + "# Round values for better readability\n", + "df_performance['RMSE_g0'] = df_performance['RMSE_g0'].round(4)\n", + "df_performance['RMSE_g1'] = df_performance['RMSE_g1'].round(4)\n", + "df_performance['LogLoss_m'] = df_performance['LogLoss_m'].round(4)\n", + "\n", + "print(\"\\n\\nRMSE g0 by Learner and Treatment Level:\")\n", + "print(\"=\" * 80)\n", + "pivot_rmse_g0 = df_performance.pivot(index='Learner', columns='Treatment_Level', values='RMSE_g0')\n", + "print(pivot_rmse_g0.to_string())\n", + "\n", + "print(\"\\n\\nRMSE g1 by Learner and Treatment Level:\")\n", + "print(\"=\" * 80)\n", + "pivot_rmse_g1 = df_performance.pivot(index='Learner', columns='Treatment_Level', values='RMSE_g1')\n", + "print(pivot_rmse_g1.to_string())\n", + "\n", + "print(\"\\n\\nLogLoss m by Learner and Treatment Level:\")\n", + "print(\"=\" * 80)\n", + "pivot_logloss = df_performance.pivot(index='Learner', columns='Treatment_Level', values='LogLoss_m')\n", + "print(pivot_logloss.to_string())" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "id": "3fb531ef", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "Best performing learners (averaged across treatment levels):\n", + "------------------------------------------------------------\n", + " RMSE_g0 RMSE_g1 LogLoss_m\n", + "Learner \n", + "LightGBM 14.0909 20.6830 0.5457\n", + "Linear 20.3076 20.4126 0.4488\n", + "RandomForest 14.1265 18.7278 0.4860\n", + "TabPFN 8.4607 7.6561 0.4506\n" + ] + } + ], + "source": [ + "# Best performing learners for each metric\n", + "print(\"\\nBest performing learners (averaged across treatment levels):\")\n", + "print(\"-\" * 60)\n", + "\n", + "# Calculate average metrics across treatment levels for each learner\n", + "summary_stats = df_performance.groupby('Learner')[['RMSE_g0', 'RMSE_g1', 'LogLoss_m']].mean().round(4)\n", + "print(summary_stats)" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "dml_tabpfn", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.11.13" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} From 9b0c701196ce33affbae12934d296f800182861c Mon Sep 17 00:00:00 2001 From: SvenKlaassen Date: Mon, 11 Aug 2025 09:48:32 +0200 Subject: [PATCH 2/5] update example nb --- doc/examples/learners/py_tabpfn.ipynb | 1415 +++++++++++++++++++++---- 1 file changed, 1217 insertions(+), 198 deletions(-) diff --git a/doc/examples/learners/py_tabpfn.ipynb b/doc/examples/learners/py_tabpfn.ipynb index 1cc62808..e3a9b33a 100644 --- a/doc/examples/learners/py_tabpfn.ipynb +++ b/doc/examples/learners/py_tabpfn.ipynb @@ -1,9 +1,40 @@ { "cells": [ + { + "cell_type": "markdown", + "id": "3364570e", + "metadata": {}, + "source": [ + "# Python: Causal Machine Learning with TabPFN\n", + "\n", + "In this example, we demonstrate how to use [TabPFN](https://github.com/automl/TabPFN) (Tabular Prior-data Fitted Network) as a machine learning estimator within the [DoubleML](https://docs.doubleml.org/stable/index.html) framework for causal inference. We compare TabPFN's performance against (untuned) traditional machine learning methods including Random Forest, Linear models, and LightGBM.\n", + "\n", + "TabPFN is a foundation model specifically designed for tabular data that can perform inference without traditional training. It leverages a transformer architecture trained on a vast collection of synthetic tabular datasets, making it particularly effective for small to medium-sized datasets commonly encountered in causal inference applications.\n", + "\n", + "We will estimate **Average Potential Outcomes (APOs)** using the [DoubleMLAPOS](https://docs.doubleml.org/stable/api/generated/doubleml.irm.DoubleMLAPOS.html) model, which allows us to estimate:\n", + "\n", + "$$\\theta_d = \\mathbb{E}[Y(d)]$$\n", + "\n", + "for different treatment levels $d$ in a discrete treatment setting." + ] + }, + { + "cell_type": "markdown", + "id": "8dc2e533", + "metadata": {}, + "source": [ + "## Imports and Setup\n", + "\n", + "We start by importing the necessary libraries. Note that TabPFN requires a separate installation, see [installation instructions](https://priorlabs.ai/getting_started/install/).\n", + "\n", + "For GPU acceleration (recommended), ensure you have CUDA-enabled PyTorch installed.\n", + "Instead you can also use the [TabPFN API Client](https://github.com/PriorLabs/tabpfn-client?tab=readme-ov-file#-quick-start)." + ] + }, { "cell_type": "code", "execution_count": 1, - "id": "b9131b27", + "id": "49c76183", "metadata": {}, "outputs": [], "source": [ @@ -13,17 +44,35 @@ "import matplotlib.pyplot as plt\n", "import seaborn as sns\n", "\n", - "from sklearn.linear_model import LogisticRegression, LinearRegression\n", - "from sklearn.ensemble import RandomForestClassifier, RandomForestRegressor\n", + "from sklearn.ensemble import RandomForestRegressor, RandomForestClassifier\n", + "from sklearn.linear_model import LinearRegression, LogisticRegression\n", + "import lightgbm as lgbm\n", + "from tabpfn import TabPFNRegressor, TabPFNClassifier\n", "\n", "import doubleml as dml\n", "from doubleml.datasets import make_irm_data_discrete_treatments" ] }, + { + "cell_type": "markdown", + "id": "4a04c896", + "metadata": {}, + "source": [ + "## Data Generating Process (DGP)\n", + "\n", + "We generate synthetic data using DoubleML's discrete treatment data generating process. This creates:\n", + "- A continuous treatment variable that is subsequently discretized into multiple levels $D$\n", + "- True individual treatment effects (ITEs) for comparison with our estimates\n", + "- Covariates $X$ that affect both treatment assignment $D$ and outcomes $Y$\n", + "\n", + "The discretization allows us to compare estimated Average Potential Outcomes (APOs) and Average Treatment Effects (ATEs) against their true values, providing a clear benchmark for evaluating different machine learning methods.\n", + "For more details on the data generating process and the APO model, we refer to the [APO Model Example Notebook](https://docs.doubleml.org/stable/examples/py_double_ml_apo.html)." + ] + }, { "cell_type": "code", "execution_count": 2, - "id": "97feabd8", + "id": "746d6b11", "metadata": {}, "outputs": [ { @@ -71,6 +120,16 @@ "print(f\"Levels and their counts:\\n{np.unique(d, return_counts=True)}\")" ] }, + { + "cell_type": "markdown", + "id": "230ae06b", + "metadata": {}, + "source": [ + "### Visualizing the Treatment Effect Structure\n", + "\n", + "To better understand our data, let's visualize the relationship between the continuous treatment variable and the individual treatment effects, along with how the treatment is discretized into levels." + ] + }, { "cell_type": "code", "execution_count": 3, @@ -129,6 +188,16 @@ "plt.show()" ] }, + { + "cell_type": "markdown", + "id": "2a3ef4e2", + "metadata": {}, + "source": [ + "### Creating the DoubleMLData Object\n", + "\n", + "As with all DoubleML models, we need to create a [DoubleMLData](https://docs.doubleml.org/stable/api/generated/doubleml.data.DoubleMLData.html) object to properly structure our data for causal inference. This object handles the separation of outcome variables, treatment variables, and covariates." + ] + }, { "cell_type": "code", "execution_count": 4, @@ -171,20 +240,527 @@ "print(dml_data)" ] }, + { + "cell_type": "markdown", + "id": "70beea16", + "metadata": {}, + "source": [ + "## DoubleML with TabPFN\n", + "\n", + "The [TabPFN package](https://github.com/PriorLabs/tabpfn) integrates seamlessly with the [DoubleML](https://docs.doubleml.org/stable/index.html) framework for causal inference tasks.\n", + "\n", + "For fitting [average potential outcome models](https://docs.doubleml.org/stable/guide/models.html#average-potential-outcomes-apos), the `DoubleML` interface requires to specify the `ml_g` and `ml_m` learners:\n", + "- `ml_g`: A regressor for the outcome model $g_0(D,X) = \\mathbb{E}[Y|X,D]$\n", + "- `ml_m`: A classifier for the propensity score model $m_{0,d}(X) = \\mathbb{E}[1\\{D=d\\}|X]$\n", + "\n", + "**Note**: TabPFN works best with CUDA acceleration. If CUDA is not available, it will fall back to CPU computation. Instead you can use [TabPFN API Client](https://github.com/PriorLabs/tabpfn-client?tab=readme-ov-file#-quick-start)." + ] + }, { "cell_type": "code", "execution_count": 5, - "id": "ea1c0ce4", + "id": "15aa7b39", "metadata": {}, "outputs": [], "source": [ - "# Define a dictionary of learner combinations\n", - "from sklearn.ensemble import RandomForestRegressor, RandomForestClassifier\n", - "from sklearn.linear_model import LinearRegression, LogisticRegression\n", - "import lightgbm as lgbm\n", - "from tabpfn import TabPFNRegressor, TabPFNClassifier\n", + "device = 'cpu'\n", + "ml_g = TabPFNRegressor(device=device)\n", + "ml_m = TabPFNClassifier(device=device)" + ] + }, + { + "cell_type": "markdown", + "id": "23c720e8", + "metadata": {}, + "source": [ + "To model average potential outcomes, we initialize the [DoubleMLAPOS](https://docs.doubleml.org/stable/api/generated/doubleml.irm.DoubleMLAPOS.html#doubleml.irm.DoubleMLAPOS) object with the specified machine learning methods and treatment levels." + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "id": "a85a3301", + "metadata": {}, + "outputs": [], + "source": [ + "treatment_levels = np.unique(dml_data.d)\n", + "dml_obj = dml.DoubleMLAPOS(\n", + " dml_data,\n", + " ml_g=ml_g,\n", + " ml_m=ml_m,\n", + " treatment_levels=treatment_levels,\n", + " n_rep=n_rep,\n", + ")" + ] + }, + { + "cell_type": "markdown", + "id": "941f3c8e", + "metadata": {}, + "source": [ + "As usual, you can estimate the parameters by calling the `fit` method on the `dml_obj` instance." + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "id": "dbd90a29", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "c:\\Users\\svenk\\anaconda3\\envs\\dml_tabpfn\\Lib\\site-packages\\sklearn\\utils\\deprecation.py:151: FutureWarning: 'force_all_finite' was renamed to 'ensure_all_finite' in 1.6 and will be removed in 1.8.\n", + " warnings.warn(\n", + "c:\\Users\\svenk\\anaconda3\\envs\\dml_tabpfn\\Lib\\site-packages\\sklearn\\utils\\deprecation.py:151: FutureWarning: 'force_all_finite' was renamed to 'ensure_all_finite' in 1.6 and will be removed in 1.8.\n", + " warnings.warn(\n", + "c:\\Users\\svenk\\anaconda3\\envs\\dml_tabpfn\\Lib\\site-packages\\tabpfn\\regressor.py:490: UserWarning: Running on CPU with more than 200 samples may be slow.\n", + "Consider using a GPU or the tabpfn-client API: https://github.com/PriorLabs/tabpfn-client\n", + " check_cpu_warning(\n", + "c:\\Users\\svenk\\anaconda3\\envs\\dml_tabpfn\\Lib\\site-packages\\tabpfn\\regressor.py:490: UserWarning: Running on CPU with more than 200 samples may be slow.\n", + "Consider using a GPU or the tabpfn-client API: https://github.com/PriorLabs/tabpfn-client\n", + " check_cpu_warning(\n", + "c:\\Users\\svenk\\anaconda3\\envs\\dml_tabpfn\\Lib\\site-packages\\tabpfn\\regressor.py:490: UserWarning: Running on CPU with more than 200 samples may be slow.\n", + "Consider using a GPU or the tabpfn-client API: https://github.com/PriorLabs/tabpfn-client\n", + " check_cpu_warning(\n", + "c:\\Users\\svenk\\anaconda3\\envs\\dml_tabpfn\\Lib\\site-packages\\tabpfn\\regressor.py:490: UserWarning: Running on CPU with more than 200 samples may be slow.\n", + "Consider using a GPU or the tabpfn-client API: https://github.com/PriorLabs/tabpfn-client\n", + " check_cpu_warning(\n", + "c:\\Users\\svenk\\anaconda3\\envs\\dml_tabpfn\\Lib\\site-packages\\tabpfn\\regressor.py:490: UserWarning: Running on CPU with more than 200 samples may be slow.\n", + "Consider using a GPU or the tabpfn-client API: https://github.com/PriorLabs/tabpfn-client\n", + " check_cpu_warning(\n", + "c:\\Users\\svenk\\anaconda3\\envs\\dml_tabpfn\\Lib\\site-packages\\tabpfn\\regressor.py:490: UserWarning: Running on CPU with more than 200 samples may be slow.\n", + "Consider using a GPU or the tabpfn-client API: https://github.com/PriorLabs/tabpfn-client\n", + " check_cpu_warning(\n", + "c:\\Users\\svenk\\anaconda3\\envs\\dml_tabpfn\\Lib\\site-packages\\tabpfn\\regressor.py:490: UserWarning: Running on CPU with more than 200 samples may be slow.\n", + "Consider using a GPU or the tabpfn-client API: https://github.com/PriorLabs/tabpfn-client\n", + " check_cpu_warning(\n", + "c:\\Users\\svenk\\anaconda3\\envs\\dml_tabpfn\\Lib\\site-packages\\tabpfn\\regressor.py:490: UserWarning: Running on CPU with more than 200 samples may be slow.\n", + "Consider using a GPU or the tabpfn-client API: https://github.com/PriorLabs/tabpfn-client\n", + " check_cpu_warning(\n", + "c:\\Users\\svenk\\anaconda3\\envs\\dml_tabpfn\\Lib\\site-packages\\tabpfn\\regressor.py:490: UserWarning: Running on CPU with more than 200 samples may be slow.\n", + "Consider using a GPU or the tabpfn-client API: https://github.com/PriorLabs/tabpfn-client\n", + " check_cpu_warning(\n", + "c:\\Users\\svenk\\anaconda3\\envs\\dml_tabpfn\\Lib\\site-packages\\tabpfn\\regressor.py:490: UserWarning: Running on CPU with more than 200 samples may be slow.\n", + "Consider using a GPU or the tabpfn-client API: https://github.com/PriorLabs/tabpfn-client\n", + " check_cpu_warning(\n", + "c:\\Users\\svenk\\anaconda3\\envs\\dml_tabpfn\\Lib\\site-packages\\tabpfn\\classifier.py:459: UserWarning: Running on CPU with more than 200 samples may be slow.\n", + "Consider using a GPU or the tabpfn-client API: https://github.com/PriorLabs/tabpfn-client\n", + " check_cpu_warning(\n", + "c:\\Users\\svenk\\anaconda3\\envs\\dml_tabpfn\\Lib\\site-packages\\tabpfn\\classifier.py:459: UserWarning: Running on CPU with more than 200 samples may be slow.\n", + "Consider using a GPU or the tabpfn-client API: https://github.com/PriorLabs/tabpfn-client\n", + " check_cpu_warning(\n", + "c:\\Users\\svenk\\anaconda3\\envs\\dml_tabpfn\\Lib\\site-packages\\tabpfn\\classifier.py:459: UserWarning: Running on CPU with more than 200 samples may be slow.\n", + "Consider using a GPU or the tabpfn-client API: https://github.com/PriorLabs/tabpfn-client\n", + " check_cpu_warning(\n", + "c:\\Users\\svenk\\anaconda3\\envs\\dml_tabpfn\\Lib\\site-packages\\tabpfn\\classifier.py:459: UserWarning: Running on CPU with more than 200 samples may be slow.\n", + "Consider using a GPU or the tabpfn-client API: https://github.com/PriorLabs/tabpfn-client\n", + " check_cpu_warning(\n", + "c:\\Users\\svenk\\anaconda3\\envs\\dml_tabpfn\\Lib\\site-packages\\tabpfn\\classifier.py:459: UserWarning: Running on CPU with more than 200 samples may be slow.\n", + "Consider using a GPU or the tabpfn-client API: https://github.com/PriorLabs/tabpfn-client\n", + " check_cpu_warning(\n", + "c:\\Users\\svenk\\anaconda3\\envs\\dml_tabpfn\\Lib\\site-packages\\tabpfn\\classifier.py:459: UserWarning: Running on CPU with more than 200 samples may be slow.\n", + "Consider using a GPU or the tabpfn-client API: https://github.com/PriorLabs/tabpfn-client\n", + " check_cpu_warning(\n", + "c:\\Users\\svenk\\anaconda3\\envs\\dml_tabpfn\\Lib\\site-packages\\tabpfn\\classifier.py:459: UserWarning: Running on CPU with more than 200 samples may be slow.\n", + "Consider using a GPU or the tabpfn-client API: https://github.com/PriorLabs/tabpfn-client\n", + " check_cpu_warning(\n", + "c:\\Users\\svenk\\anaconda3\\envs\\dml_tabpfn\\Lib\\site-packages\\tabpfn\\classifier.py:459: UserWarning: Running on CPU with more than 200 samples may be slow.\n", + "Consider using a GPU or the tabpfn-client API: https://github.com/PriorLabs/tabpfn-client\n", + " check_cpu_warning(\n", + "c:\\Users\\svenk\\anaconda3\\envs\\dml_tabpfn\\Lib\\site-packages\\tabpfn\\classifier.py:459: UserWarning: Running on CPU with more than 200 samples may be slow.\n", + "Consider using a GPU or the tabpfn-client API: https://github.com/PriorLabs/tabpfn-client\n", + " check_cpu_warning(\n", + "c:\\Users\\svenk\\anaconda3\\envs\\dml_tabpfn\\Lib\\site-packages\\tabpfn\\classifier.py:459: UserWarning: Running on CPU with more than 200 samples may be slow.\n", + "Consider using a GPU or the tabpfn-client API: https://github.com/PriorLabs/tabpfn-client\n", + " check_cpu_warning(\n", + "c:\\Users\\svenk\\anaconda3\\envs\\dml_tabpfn\\Lib\\site-packages\\sklearn\\utils\\deprecation.py:151: FutureWarning: 'force_all_finite' was renamed to 'ensure_all_finite' in 1.6 and will be removed in 1.8.\n", + " warnings.warn(\n", + "c:\\Users\\svenk\\anaconda3\\envs\\dml_tabpfn\\Lib\\site-packages\\sklearn\\utils\\deprecation.py:151: FutureWarning: 'force_all_finite' was renamed to 'ensure_all_finite' in 1.6 and will be removed in 1.8.\n", + " warnings.warn(\n", + "c:\\Users\\svenk\\anaconda3\\envs\\dml_tabpfn\\Lib\\site-packages\\sklearn\\utils\\deprecation.py:151: FutureWarning: 'force_all_finite' was renamed to 'ensure_all_finite' in 1.6 and will be removed in 1.8.\n", + " warnings.warn(\n", + "c:\\Users\\svenk\\anaconda3\\envs\\dml_tabpfn\\Lib\\site-packages\\sklearn\\utils\\deprecation.py:151: FutureWarning: 'force_all_finite' was renamed to 'ensure_all_finite' in 1.6 and will be removed in 1.8.\n", + " warnings.warn(\n", + "c:\\Users\\svenk\\anaconda3\\envs\\dml_tabpfn\\Lib\\site-packages\\tabpfn\\regressor.py:490: UserWarning: Running on CPU with more than 200 samples may be slow.\n", + "Consider using a GPU or the tabpfn-client API: https://github.com/PriorLabs/tabpfn-client\n", + " check_cpu_warning(\n", + "c:\\Users\\svenk\\anaconda3\\envs\\dml_tabpfn\\Lib\\site-packages\\tabpfn\\regressor.py:490: UserWarning: Running on CPU with more than 200 samples may be slow.\n", + "Consider using a GPU or the tabpfn-client API: https://github.com/PriorLabs/tabpfn-client\n", + " check_cpu_warning(\n", + "c:\\Users\\svenk\\anaconda3\\envs\\dml_tabpfn\\Lib\\site-packages\\tabpfn\\regressor.py:490: UserWarning: Running on CPU with more than 200 samples may be slow.\n", + "Consider using a GPU or the tabpfn-client API: https://github.com/PriorLabs/tabpfn-client\n", + " check_cpu_warning(\n", + "c:\\Users\\svenk\\anaconda3\\envs\\dml_tabpfn\\Lib\\site-packages\\tabpfn\\regressor.py:490: UserWarning: Running on CPU with more than 200 samples may be slow.\n", + "Consider using a GPU or the tabpfn-client API: https://github.com/PriorLabs/tabpfn-client\n", + " check_cpu_warning(\n", + "c:\\Users\\svenk\\anaconda3\\envs\\dml_tabpfn\\Lib\\site-packages\\tabpfn\\regressor.py:490: UserWarning: Running on CPU with more than 200 samples may be slow.\n", + "Consider using a GPU or the tabpfn-client API: https://github.com/PriorLabs/tabpfn-client\n", + " check_cpu_warning(\n", + "c:\\Users\\svenk\\anaconda3\\envs\\dml_tabpfn\\Lib\\site-packages\\tabpfn\\regressor.py:490: UserWarning: Running on CPU with more than 200 samples may be slow.\n", + "Consider using a GPU or the tabpfn-client API: https://github.com/PriorLabs/tabpfn-client\n", + " check_cpu_warning(\n", + "c:\\Users\\svenk\\anaconda3\\envs\\dml_tabpfn\\Lib\\site-packages\\tabpfn\\regressor.py:490: UserWarning: Running on CPU with more than 200 samples may be slow.\n", + "Consider using a GPU or the tabpfn-client API: https://github.com/PriorLabs/tabpfn-client\n", + " check_cpu_warning(\n", + "c:\\Users\\svenk\\anaconda3\\envs\\dml_tabpfn\\Lib\\site-packages\\tabpfn\\regressor.py:490: UserWarning: Running on CPU with more than 200 samples may be slow.\n", + "Consider using a GPU or the tabpfn-client API: https://github.com/PriorLabs/tabpfn-client\n", + " check_cpu_warning(\n", + "c:\\Users\\svenk\\anaconda3\\envs\\dml_tabpfn\\Lib\\site-packages\\tabpfn\\regressor.py:490: UserWarning: Running on CPU with more than 200 samples may be slow.\n", + "Consider using a GPU or the tabpfn-client API: https://github.com/PriorLabs/tabpfn-client\n", + " check_cpu_warning(\n", + "c:\\Users\\svenk\\anaconda3\\envs\\dml_tabpfn\\Lib\\site-packages\\tabpfn\\regressor.py:490: UserWarning: Running on CPU with more than 200 samples may be slow.\n", + "Consider using a GPU or the tabpfn-client API: https://github.com/PriorLabs/tabpfn-client\n", + " check_cpu_warning(\n", + "c:\\Users\\svenk\\anaconda3\\envs\\dml_tabpfn\\Lib\\site-packages\\tabpfn\\classifier.py:459: UserWarning: Running on CPU with more than 200 samples may be slow.\n", + "Consider using a GPU or the tabpfn-client API: https://github.com/PriorLabs/tabpfn-client\n", + " check_cpu_warning(\n", + "c:\\Users\\svenk\\anaconda3\\envs\\dml_tabpfn\\Lib\\site-packages\\tabpfn\\classifier.py:459: UserWarning: Running on CPU with more than 200 samples may be slow.\n", + "Consider using a GPU or the tabpfn-client API: https://github.com/PriorLabs/tabpfn-client\n", + " check_cpu_warning(\n", + "c:\\Users\\svenk\\anaconda3\\envs\\dml_tabpfn\\Lib\\site-packages\\tabpfn\\classifier.py:459: UserWarning: Running on CPU with more than 200 samples may be slow.\n", + "Consider using a GPU or the tabpfn-client API: https://github.com/PriorLabs/tabpfn-client\n", + " check_cpu_warning(\n", + "c:\\Users\\svenk\\anaconda3\\envs\\dml_tabpfn\\Lib\\site-packages\\tabpfn\\classifier.py:459: UserWarning: Running on CPU with more than 200 samples may be slow.\n", + "Consider using a GPU or the tabpfn-client API: https://github.com/PriorLabs/tabpfn-client\n", + " check_cpu_warning(\n", + "c:\\Users\\svenk\\anaconda3\\envs\\dml_tabpfn\\Lib\\site-packages\\tabpfn\\classifier.py:459: UserWarning: Running on CPU with more than 200 samples may be slow.\n", + "Consider using a GPU or the tabpfn-client API: https://github.com/PriorLabs/tabpfn-client\n", + " check_cpu_warning(\n", + "c:\\Users\\svenk\\anaconda3\\envs\\dml_tabpfn\\Lib\\site-packages\\tabpfn\\classifier.py:459: UserWarning: Running on CPU with more than 200 samples may be slow.\n", + "Consider using a GPU or the tabpfn-client API: https://github.com/PriorLabs/tabpfn-client\n", + " check_cpu_warning(\n", + "c:\\Users\\svenk\\anaconda3\\envs\\dml_tabpfn\\Lib\\site-packages\\tabpfn\\classifier.py:459: UserWarning: Running on CPU with more than 200 samples may be slow.\n", + "Consider using a GPU or the tabpfn-client API: https://github.com/PriorLabs/tabpfn-client\n", + " check_cpu_warning(\n", + "c:\\Users\\svenk\\anaconda3\\envs\\dml_tabpfn\\Lib\\site-packages\\tabpfn\\classifier.py:459: UserWarning: Running on CPU with more than 200 samples may be slow.\n", + "Consider using a GPU or the tabpfn-client API: https://github.com/PriorLabs/tabpfn-client\n", + " check_cpu_warning(\n", + "c:\\Users\\svenk\\anaconda3\\envs\\dml_tabpfn\\Lib\\site-packages\\tabpfn\\classifier.py:459: UserWarning: Running on CPU with more than 200 samples may be slow.\n", + "Consider using a GPU or the tabpfn-client API: https://github.com/PriorLabs/tabpfn-client\n", + " check_cpu_warning(\n", + "c:\\Users\\svenk\\anaconda3\\envs\\dml_tabpfn\\Lib\\site-packages\\tabpfn\\classifier.py:459: UserWarning: Running on CPU with more than 200 samples may be slow.\n", + "Consider using a GPU or the tabpfn-client API: https://github.com/PriorLabs/tabpfn-client\n", + " check_cpu_warning(\n", + "c:\\Users\\svenk\\anaconda3\\envs\\dml_tabpfn\\Lib\\site-packages\\sklearn\\utils\\deprecation.py:151: FutureWarning: 'force_all_finite' was renamed to 'ensure_all_finite' in 1.6 and will be removed in 1.8.\n", + " warnings.warn(\n", + "c:\\Users\\svenk\\anaconda3\\envs\\dml_tabpfn\\Lib\\site-packages\\sklearn\\utils\\deprecation.py:151: FutureWarning: 'force_all_finite' was renamed to 'ensure_all_finite' in 1.6 and will be removed in 1.8.\n", + " warnings.warn(\n", + "c:\\Users\\svenk\\anaconda3\\envs\\dml_tabpfn\\Lib\\site-packages\\sklearn\\utils\\deprecation.py:151: FutureWarning: 'force_all_finite' was renamed to 'ensure_all_finite' in 1.6 and will be removed in 1.8.\n", + " warnings.warn(\n", + "c:\\Users\\svenk\\anaconda3\\envs\\dml_tabpfn\\Lib\\site-packages\\sklearn\\utils\\deprecation.py:151: FutureWarning: 'force_all_finite' was renamed to 'ensure_all_finite' in 1.6 and will be removed in 1.8.\n", + " warnings.warn(\n", + "c:\\Users\\svenk\\anaconda3\\envs\\dml_tabpfn\\Lib\\site-packages\\tabpfn\\regressor.py:490: UserWarning: Running on CPU with more than 200 samples may be slow.\n", + "Consider using a GPU or the tabpfn-client API: https://github.com/PriorLabs/tabpfn-client\n", + " check_cpu_warning(\n", + "c:\\Users\\svenk\\anaconda3\\envs\\dml_tabpfn\\Lib\\site-packages\\tabpfn\\regressor.py:490: UserWarning: Running on CPU with more than 200 samples may be slow.\n", + "Consider using a GPU or the tabpfn-client API: https://github.com/PriorLabs/tabpfn-client\n", + " check_cpu_warning(\n", + "c:\\Users\\svenk\\anaconda3\\envs\\dml_tabpfn\\Lib\\site-packages\\tabpfn\\regressor.py:490: UserWarning: Running on CPU with more than 200 samples may be slow.\n", + "Consider using a GPU or the tabpfn-client API: https://github.com/PriorLabs/tabpfn-client\n", + " check_cpu_warning(\n", + "c:\\Users\\svenk\\anaconda3\\envs\\dml_tabpfn\\Lib\\site-packages\\tabpfn\\regressor.py:490: UserWarning: Running on CPU with more than 200 samples may be slow.\n", + "Consider using a GPU or the tabpfn-client API: https://github.com/PriorLabs/tabpfn-client\n", + " check_cpu_warning(\n", + "c:\\Users\\svenk\\anaconda3\\envs\\dml_tabpfn\\Lib\\site-packages\\tabpfn\\regressor.py:490: UserWarning: Running on CPU with more than 200 samples may be slow.\n", + "Consider using a GPU or the tabpfn-client API: https://github.com/PriorLabs/tabpfn-client\n", + " check_cpu_warning(\n", + "c:\\Users\\svenk\\anaconda3\\envs\\dml_tabpfn\\Lib\\site-packages\\tabpfn\\regressor.py:490: UserWarning: Running on CPU with more than 200 samples may be slow.\n", + "Consider using a GPU or the tabpfn-client API: https://github.com/PriorLabs/tabpfn-client\n", + " check_cpu_warning(\n", + "c:\\Users\\svenk\\anaconda3\\envs\\dml_tabpfn\\Lib\\site-packages\\tabpfn\\regressor.py:490: UserWarning: Running on CPU with more than 200 samples may be slow.\n", + "Consider using a GPU or the tabpfn-client API: https://github.com/PriorLabs/tabpfn-client\n", + " check_cpu_warning(\n", + "c:\\Users\\svenk\\anaconda3\\envs\\dml_tabpfn\\Lib\\site-packages\\tabpfn\\regressor.py:490: UserWarning: Running on CPU with more than 200 samples may be slow.\n", + "Consider using a GPU or the tabpfn-client API: https://github.com/PriorLabs/tabpfn-client\n", + " check_cpu_warning(\n", + "c:\\Users\\svenk\\anaconda3\\envs\\dml_tabpfn\\Lib\\site-packages\\tabpfn\\regressor.py:490: UserWarning: Running on CPU with more than 200 samples may be slow.\n", + "Consider using a GPU or the tabpfn-client API: https://github.com/PriorLabs/tabpfn-client\n", + " check_cpu_warning(\n", + "c:\\Users\\svenk\\anaconda3\\envs\\dml_tabpfn\\Lib\\site-packages\\tabpfn\\regressor.py:490: UserWarning: Running on CPU with more than 200 samples may be slow.\n", + "Consider using a GPU or the tabpfn-client API: https://github.com/PriorLabs/tabpfn-client\n", + " check_cpu_warning(\n", + "c:\\Users\\svenk\\anaconda3\\envs\\dml_tabpfn\\Lib\\site-packages\\tabpfn\\classifier.py:459: UserWarning: Running on CPU with more than 200 samples may be slow.\n", + "Consider using a GPU or the tabpfn-client API: https://github.com/PriorLabs/tabpfn-client\n", + " check_cpu_warning(\n", + "c:\\Users\\svenk\\anaconda3\\envs\\dml_tabpfn\\Lib\\site-packages\\tabpfn\\classifier.py:459: UserWarning: Running on CPU with more than 200 samples may be slow.\n", + "Consider using a GPU or the tabpfn-client API: https://github.com/PriorLabs/tabpfn-client\n", + " check_cpu_warning(\n", + "c:\\Users\\svenk\\anaconda3\\envs\\dml_tabpfn\\Lib\\site-packages\\tabpfn\\classifier.py:459: UserWarning: Running on CPU with more than 200 samples may be slow.\n", + "Consider using a GPU or the tabpfn-client API: https://github.com/PriorLabs/tabpfn-client\n", + " check_cpu_warning(\n", + "c:\\Users\\svenk\\anaconda3\\envs\\dml_tabpfn\\Lib\\site-packages\\tabpfn\\classifier.py:459: UserWarning: Running on CPU with more than 200 samples may be slow.\n", + "Consider using a GPU or the tabpfn-client API: https://github.com/PriorLabs/tabpfn-client\n", + " check_cpu_warning(\n", + "c:\\Users\\svenk\\anaconda3\\envs\\dml_tabpfn\\Lib\\site-packages\\tabpfn\\classifier.py:459: UserWarning: Running on CPU with more than 200 samples may be slow.\n", + "Consider using a GPU or the tabpfn-client API: https://github.com/PriorLabs/tabpfn-client\n", + " check_cpu_warning(\n", + "c:\\Users\\svenk\\anaconda3\\envs\\dml_tabpfn\\Lib\\site-packages\\tabpfn\\classifier.py:459: UserWarning: Running on CPU with more than 200 samples may be slow.\n", + "Consider using a GPU or the tabpfn-client API: https://github.com/PriorLabs/tabpfn-client\n", + " check_cpu_warning(\n", + "c:\\Users\\svenk\\anaconda3\\envs\\dml_tabpfn\\Lib\\site-packages\\tabpfn\\classifier.py:459: UserWarning: Running on CPU with more than 200 samples may be slow.\n", + "Consider using a GPU or the tabpfn-client API: https://github.com/PriorLabs/tabpfn-client\n", + " check_cpu_warning(\n", + "c:\\Users\\svenk\\anaconda3\\envs\\dml_tabpfn\\Lib\\site-packages\\tabpfn\\classifier.py:459: UserWarning: Running on CPU with more than 200 samples may be slow.\n", + "Consider using a GPU or the tabpfn-client API: https://github.com/PriorLabs/tabpfn-client\n", + " check_cpu_warning(\n", + "c:\\Users\\svenk\\anaconda3\\envs\\dml_tabpfn\\Lib\\site-packages\\tabpfn\\classifier.py:459: UserWarning: Running on CPU with more than 200 samples may be slow.\n", + "Consider using a GPU or the tabpfn-client API: https://github.com/PriorLabs/tabpfn-client\n", + " check_cpu_warning(\n", + "c:\\Users\\svenk\\anaconda3\\envs\\dml_tabpfn\\Lib\\site-packages\\tabpfn\\classifier.py:459: UserWarning: Running on CPU with more than 200 samples may be slow.\n", + "Consider using a GPU or the tabpfn-client API: https://github.com/PriorLabs/tabpfn-client\n", + " check_cpu_warning(\n", + "c:\\Users\\svenk\\anaconda3\\envs\\dml_tabpfn\\Lib\\site-packages\\sklearn\\utils\\deprecation.py:151: FutureWarning: 'force_all_finite' was renamed to 'ensure_all_finite' in 1.6 and will be removed in 1.8.\n", + " warnings.warn(\n", + "c:\\Users\\svenk\\anaconda3\\envs\\dml_tabpfn\\Lib\\site-packages\\sklearn\\utils\\deprecation.py:151: FutureWarning: 'force_all_finite' was renamed to 'ensure_all_finite' in 1.6 and will be removed in 1.8.\n", + " warnings.warn(\n", + "c:\\Users\\svenk\\anaconda3\\envs\\dml_tabpfn\\Lib\\site-packages\\sklearn\\utils\\deprecation.py:151: FutureWarning: 'force_all_finite' was renamed to 'ensure_all_finite' in 1.6 and will be removed in 1.8.\n", + " warnings.warn(\n", + "c:\\Users\\svenk\\anaconda3\\envs\\dml_tabpfn\\Lib\\site-packages\\sklearn\\utils\\deprecation.py:151: FutureWarning: 'force_all_finite' was renamed to 'ensure_all_finite' in 1.6 and will be removed in 1.8.\n", + " warnings.warn(\n", + "c:\\Users\\svenk\\anaconda3\\envs\\dml_tabpfn\\Lib\\site-packages\\tabpfn\\regressor.py:490: UserWarning: Running on CPU with more than 200 samples may be slow.\n", + "Consider using a GPU or the tabpfn-client API: https://github.com/PriorLabs/tabpfn-client\n", + " check_cpu_warning(\n", + "c:\\Users\\svenk\\anaconda3\\envs\\dml_tabpfn\\Lib\\site-packages\\tabpfn\\regressor.py:490: UserWarning: Running on CPU with more than 200 samples may be slow.\n", + "Consider using a GPU or the tabpfn-client API: https://github.com/PriorLabs/tabpfn-client\n", + " check_cpu_warning(\n", + "c:\\Users\\svenk\\anaconda3\\envs\\dml_tabpfn\\Lib\\site-packages\\tabpfn\\regressor.py:490: UserWarning: Running on CPU with more than 200 samples may be slow.\n", + "Consider using a GPU or the tabpfn-client API: https://github.com/PriorLabs/tabpfn-client\n", + " check_cpu_warning(\n", + "c:\\Users\\svenk\\anaconda3\\envs\\dml_tabpfn\\Lib\\site-packages\\tabpfn\\regressor.py:490: UserWarning: Running on CPU with more than 200 samples may be slow.\n", + "Consider using a GPU or the tabpfn-client API: https://github.com/PriorLabs/tabpfn-client\n", + " check_cpu_warning(\n", + "c:\\Users\\svenk\\anaconda3\\envs\\dml_tabpfn\\Lib\\site-packages\\tabpfn\\regressor.py:490: UserWarning: Running on CPU with more than 200 samples may be slow.\n", + "Consider using a GPU or the tabpfn-client API: https://github.com/PriorLabs/tabpfn-client\n", + " check_cpu_warning(\n", + "c:\\Users\\svenk\\anaconda3\\envs\\dml_tabpfn\\Lib\\site-packages\\tabpfn\\regressor.py:490: UserWarning: Running on CPU with more than 200 samples may be slow.\n", + "Consider using a GPU or the tabpfn-client API: https://github.com/PriorLabs/tabpfn-client\n", + " check_cpu_warning(\n", + "c:\\Users\\svenk\\anaconda3\\envs\\dml_tabpfn\\Lib\\site-packages\\tabpfn\\regressor.py:490: UserWarning: Running on CPU with more than 200 samples may be slow.\n", + "Consider using a GPU or the tabpfn-client API: https://github.com/PriorLabs/tabpfn-client\n", + " check_cpu_warning(\n", + "c:\\Users\\svenk\\anaconda3\\envs\\dml_tabpfn\\Lib\\site-packages\\tabpfn\\regressor.py:490: UserWarning: Running on CPU with more than 200 samples may be slow.\n", + "Consider using a GPU or the tabpfn-client API: https://github.com/PriorLabs/tabpfn-client\n", + " check_cpu_warning(\n", + "c:\\Users\\svenk\\anaconda3\\envs\\dml_tabpfn\\Lib\\site-packages\\tabpfn\\regressor.py:490: UserWarning: Running on CPU with more than 200 samples may be slow.\n", + "Consider using a GPU or the tabpfn-client API: https://github.com/PriorLabs/tabpfn-client\n", + " check_cpu_warning(\n", + "c:\\Users\\svenk\\anaconda3\\envs\\dml_tabpfn\\Lib\\site-packages\\tabpfn\\regressor.py:490: UserWarning: Running on CPU with more than 200 samples may be slow.\n", + "Consider using a GPU or the tabpfn-client API: https://github.com/PriorLabs/tabpfn-client\n", + " check_cpu_warning(\n", + "c:\\Users\\svenk\\anaconda3\\envs\\dml_tabpfn\\Lib\\site-packages\\tabpfn\\classifier.py:459: UserWarning: Running on CPU with more than 200 samples may be slow.\n", + "Consider using a GPU or the tabpfn-client API: https://github.com/PriorLabs/tabpfn-client\n", + " check_cpu_warning(\n", + "c:\\Users\\svenk\\anaconda3\\envs\\dml_tabpfn\\Lib\\site-packages\\tabpfn\\classifier.py:459: UserWarning: Running on CPU with more than 200 samples may be slow.\n", + "Consider using a GPU or the tabpfn-client API: https://github.com/PriorLabs/tabpfn-client\n", + " check_cpu_warning(\n", + "c:\\Users\\svenk\\anaconda3\\envs\\dml_tabpfn\\Lib\\site-packages\\tabpfn\\classifier.py:459: UserWarning: Running on CPU with more than 200 samples may be slow.\n", + "Consider using a GPU or the tabpfn-client API: https://github.com/PriorLabs/tabpfn-client\n", + " check_cpu_warning(\n", + "c:\\Users\\svenk\\anaconda3\\envs\\dml_tabpfn\\Lib\\site-packages\\tabpfn\\classifier.py:459: UserWarning: Running on CPU with more than 200 samples may be slow.\n", + "Consider using a GPU or the tabpfn-client API: https://github.com/PriorLabs/tabpfn-client\n", + " check_cpu_warning(\n", + "c:\\Users\\svenk\\anaconda3\\envs\\dml_tabpfn\\Lib\\site-packages\\tabpfn\\classifier.py:459: UserWarning: Running on CPU with more than 200 samples may be slow.\n", + "Consider using a GPU or the tabpfn-client API: https://github.com/PriorLabs/tabpfn-client\n", + " check_cpu_warning(\n", + "c:\\Users\\svenk\\anaconda3\\envs\\dml_tabpfn\\Lib\\site-packages\\tabpfn\\classifier.py:459: UserWarning: Running on CPU with more than 200 samples may be slow.\n", + "Consider using a GPU or the tabpfn-client API: https://github.com/PriorLabs/tabpfn-client\n", + " check_cpu_warning(\n", + "c:\\Users\\svenk\\anaconda3\\envs\\dml_tabpfn\\Lib\\site-packages\\tabpfn\\classifier.py:459: UserWarning: Running on CPU with more than 200 samples may be slow.\n", + "Consider using a GPU or the tabpfn-client API: https://github.com/PriorLabs/tabpfn-client\n", + " check_cpu_warning(\n", + "c:\\Users\\svenk\\anaconda3\\envs\\dml_tabpfn\\Lib\\site-packages\\tabpfn\\classifier.py:459: UserWarning: Running on CPU with more than 200 samples may be slow.\n", + "Consider using a GPU or the tabpfn-client API: https://github.com/PriorLabs/tabpfn-client\n", + " check_cpu_warning(\n", + "c:\\Users\\svenk\\anaconda3\\envs\\dml_tabpfn\\Lib\\site-packages\\tabpfn\\classifier.py:459: UserWarning: Running on CPU with more than 200 samples may be slow.\n", + "Consider using a GPU or the tabpfn-client API: https://github.com/PriorLabs/tabpfn-client\n", + " check_cpu_warning(\n", + "c:\\Users\\svenk\\anaconda3\\envs\\dml_tabpfn\\Lib\\site-packages\\tabpfn\\classifier.py:459: UserWarning: Running on CPU with more than 200 samples may be slow.\n", + "Consider using a GPU or the tabpfn-client API: https://github.com/PriorLabs/tabpfn-client\n", + " check_cpu_warning(\n", + "c:\\Users\\svenk\\anaconda3\\envs\\dml_tabpfn\\Lib\\site-packages\\sklearn\\utils\\deprecation.py:151: FutureWarning: 'force_all_finite' was renamed to 'ensure_all_finite' in 1.6 and will be removed in 1.8.\n", + " warnings.warn(\n", + "c:\\Users\\svenk\\anaconda3\\envs\\dml_tabpfn\\Lib\\site-packages\\sklearn\\utils\\deprecation.py:151: FutureWarning: 'force_all_finite' was renamed to 'ensure_all_finite' in 1.6 and will be removed in 1.8.\n", + " warnings.warn(\n", + "c:\\Users\\svenk\\anaconda3\\envs\\dml_tabpfn\\Lib\\site-packages\\sklearn\\utils\\deprecation.py:151: FutureWarning: 'force_all_finite' was renamed to 'ensure_all_finite' in 1.6 and will be removed in 1.8.\n", + " warnings.warn(\n", + "c:\\Users\\svenk\\anaconda3\\envs\\dml_tabpfn\\Lib\\site-packages\\sklearn\\utils\\deprecation.py:151: FutureWarning: 'force_all_finite' was renamed to 'ensure_all_finite' in 1.6 and will be removed in 1.8.\n", + " warnings.warn(\n", + "c:\\Users\\svenk\\anaconda3\\envs\\dml_tabpfn\\Lib\\site-packages\\tabpfn\\regressor.py:490: UserWarning: Running on CPU with more than 200 samples may be slow.\n", + "Consider using a GPU or the tabpfn-client API: https://github.com/PriorLabs/tabpfn-client\n", + " check_cpu_warning(\n", + "c:\\Users\\svenk\\anaconda3\\envs\\dml_tabpfn\\Lib\\site-packages\\tabpfn\\regressor.py:490: UserWarning: Running on CPU with more than 200 samples may be slow.\n", + "Consider using a GPU or the tabpfn-client API: https://github.com/PriorLabs/tabpfn-client\n", + " check_cpu_warning(\n", + "c:\\Users\\svenk\\anaconda3\\envs\\dml_tabpfn\\Lib\\site-packages\\tabpfn\\regressor.py:490: UserWarning: Running on CPU with more than 200 samples may be slow.\n", + "Consider using a GPU or the tabpfn-client API: https://github.com/PriorLabs/tabpfn-client\n", + " check_cpu_warning(\n", + "c:\\Users\\svenk\\anaconda3\\envs\\dml_tabpfn\\Lib\\site-packages\\tabpfn\\regressor.py:490: UserWarning: Running on CPU with more than 200 samples may be slow.\n", + "Consider using a GPU or the tabpfn-client API: https://github.com/PriorLabs/tabpfn-client\n", + " check_cpu_warning(\n", + "c:\\Users\\svenk\\anaconda3\\envs\\dml_tabpfn\\Lib\\site-packages\\tabpfn\\regressor.py:490: UserWarning: Running on CPU with more than 200 samples may be slow.\n", + "Consider using a GPU or the tabpfn-client API: https://github.com/PriorLabs/tabpfn-client\n", + " check_cpu_warning(\n", + "c:\\Users\\svenk\\anaconda3\\envs\\dml_tabpfn\\Lib\\site-packages\\tabpfn\\regressor.py:490: UserWarning: Running on CPU with more than 200 samples may be slow.\n", + "Consider using a GPU or the tabpfn-client API: https://github.com/PriorLabs/tabpfn-client\n", + " check_cpu_warning(\n", + "c:\\Users\\svenk\\anaconda3\\envs\\dml_tabpfn\\Lib\\site-packages\\tabpfn\\regressor.py:490: UserWarning: Running on CPU with more than 200 samples may be slow.\n", + "Consider using a GPU or the tabpfn-client API: https://github.com/PriorLabs/tabpfn-client\n", + " check_cpu_warning(\n", + "c:\\Users\\svenk\\anaconda3\\envs\\dml_tabpfn\\Lib\\site-packages\\tabpfn\\regressor.py:490: UserWarning: Running on CPU with more than 200 samples may be slow.\n", + "Consider using a GPU or the tabpfn-client API: https://github.com/PriorLabs/tabpfn-client\n", + " check_cpu_warning(\n", + "c:\\Users\\svenk\\anaconda3\\envs\\dml_tabpfn\\Lib\\site-packages\\tabpfn\\regressor.py:490: UserWarning: Running on CPU with more than 200 samples may be slow.\n", + "Consider using a GPU or the tabpfn-client API: https://github.com/PriorLabs/tabpfn-client\n", + " check_cpu_warning(\n", + "c:\\Users\\svenk\\anaconda3\\envs\\dml_tabpfn\\Lib\\site-packages\\tabpfn\\regressor.py:490: UserWarning: Running on CPU with more than 200 samples may be slow.\n", + "Consider using a GPU or the tabpfn-client API: https://github.com/PriorLabs/tabpfn-client\n", + " check_cpu_warning(\n", + "c:\\Users\\svenk\\anaconda3\\envs\\dml_tabpfn\\Lib\\site-packages\\tabpfn\\classifier.py:459: UserWarning: Running on CPU with more than 200 samples may be slow.\n", + "Consider using a GPU or the tabpfn-client API: https://github.com/PriorLabs/tabpfn-client\n", + " check_cpu_warning(\n", + "c:\\Users\\svenk\\anaconda3\\envs\\dml_tabpfn\\Lib\\site-packages\\tabpfn\\classifier.py:459: UserWarning: Running on CPU with more than 200 samples may be slow.\n", + "Consider using a GPU or the tabpfn-client API: https://github.com/PriorLabs/tabpfn-client\n", + " check_cpu_warning(\n", + "c:\\Users\\svenk\\anaconda3\\envs\\dml_tabpfn\\Lib\\site-packages\\tabpfn\\classifier.py:459: UserWarning: Running on CPU with more than 200 samples may be slow.\n", + "Consider using a GPU or the tabpfn-client API: https://github.com/PriorLabs/tabpfn-client\n", + " check_cpu_warning(\n", + "c:\\Users\\svenk\\anaconda3\\envs\\dml_tabpfn\\Lib\\site-packages\\tabpfn\\classifier.py:459: UserWarning: Running on CPU with more than 200 samples may be slow.\n", + "Consider using a GPU or the tabpfn-client API: https://github.com/PriorLabs/tabpfn-client\n", + " check_cpu_warning(\n", + "c:\\Users\\svenk\\anaconda3\\envs\\dml_tabpfn\\Lib\\site-packages\\tabpfn\\classifier.py:459: UserWarning: Running on CPU with more than 200 samples may be slow.\n", + "Consider using a GPU or the tabpfn-client API: https://github.com/PriorLabs/tabpfn-client\n", + " check_cpu_warning(\n", + "c:\\Users\\svenk\\anaconda3\\envs\\dml_tabpfn\\Lib\\site-packages\\tabpfn\\classifier.py:459: UserWarning: Running on CPU with more than 200 samples may be slow.\n", + "Consider using a GPU or the tabpfn-client API: https://github.com/PriorLabs/tabpfn-client\n", + " check_cpu_warning(\n", + "c:\\Users\\svenk\\anaconda3\\envs\\dml_tabpfn\\Lib\\site-packages\\tabpfn\\classifier.py:459: UserWarning: Running on CPU with more than 200 samples may be slow.\n", + "Consider using a GPU or the tabpfn-client API: https://github.com/PriorLabs/tabpfn-client\n", + " check_cpu_warning(\n", + "c:\\Users\\svenk\\anaconda3\\envs\\dml_tabpfn\\Lib\\site-packages\\tabpfn\\classifier.py:459: UserWarning: Running on CPU with more than 200 samples may be slow.\n", + "Consider using a GPU or the tabpfn-client API: https://github.com/PriorLabs/tabpfn-client\n", + " check_cpu_warning(\n", + "c:\\Users\\svenk\\anaconda3\\envs\\dml_tabpfn\\Lib\\site-packages\\tabpfn\\classifier.py:459: UserWarning: Running on CPU with more than 200 samples may be slow.\n", + "Consider using a GPU or the tabpfn-client API: https://github.com/PriorLabs/tabpfn-client\n", + " check_cpu_warning(\n", + "c:\\Users\\svenk\\anaconda3\\envs\\dml_tabpfn\\Lib\\site-packages\\tabpfn\\classifier.py:459: UserWarning: Running on CPU with more than 200 samples may be slow.\n", + "Consider using a GPU or the tabpfn-client API: https://github.com/PriorLabs/tabpfn-client\n", + " check_cpu_warning(\n", + "c:\\Users\\svenk\\anaconda3\\envs\\dml_tabpfn\\Lib\\site-packages\\sklearn\\utils\\deprecation.py:151: FutureWarning: 'force_all_finite' was renamed to 'ensure_all_finite' in 1.6 and will be removed in 1.8.\n", + " warnings.warn(\n", + "c:\\Users\\svenk\\anaconda3\\envs\\dml_tabpfn\\Lib\\site-packages\\sklearn\\utils\\deprecation.py:151: FutureWarning: 'force_all_finite' was renamed to 'ensure_all_finite' in 1.6 and will be removed in 1.8.\n", + " warnings.warn(\n", + "c:\\Users\\svenk\\anaconda3\\envs\\dml_tabpfn\\Lib\\site-packages\\sklearn\\utils\\deprecation.py:151: FutureWarning: 'force_all_finite' was renamed to 'ensure_all_finite' in 1.6 and will be removed in 1.8.\n", + " warnings.warn(\n", + "c:\\Users\\svenk\\anaconda3\\envs\\dml_tabpfn\\Lib\\site-packages\\sklearn\\utils\\deprecation.py:151: FutureWarning: 'force_all_finite' was renamed to 'ensure_all_finite' in 1.6 and will be removed in 1.8.\n", + " warnings.warn(\n", + "c:\\Users\\svenk\\anaconda3\\envs\\dml_tabpfn\\Lib\\site-packages\\tabpfn\\regressor.py:490: UserWarning: Running on CPU with more than 200 samples may be slow.\n", + "Consider using a GPU or the tabpfn-client API: https://github.com/PriorLabs/tabpfn-client\n", + " check_cpu_warning(\n", + "c:\\Users\\svenk\\anaconda3\\envs\\dml_tabpfn\\Lib\\site-packages\\tabpfn\\regressor.py:490: UserWarning: Running on CPU with more than 200 samples may be slow.\n", + "Consider using a GPU or the tabpfn-client API: https://github.com/PriorLabs/tabpfn-client\n", + " check_cpu_warning(\n", + "c:\\Users\\svenk\\anaconda3\\envs\\dml_tabpfn\\Lib\\site-packages\\tabpfn\\regressor.py:490: UserWarning: Running on CPU with more than 200 samples may be slow.\n", + "Consider using a GPU or the tabpfn-client API: https://github.com/PriorLabs/tabpfn-client\n", + " check_cpu_warning(\n", + "c:\\Users\\svenk\\anaconda3\\envs\\dml_tabpfn\\Lib\\site-packages\\tabpfn\\regressor.py:490: UserWarning: Running on CPU with more than 200 samples may be slow.\n", + "Consider using a GPU or the tabpfn-client API: https://github.com/PriorLabs/tabpfn-client\n", + " check_cpu_warning(\n", + "c:\\Users\\svenk\\anaconda3\\envs\\dml_tabpfn\\Lib\\site-packages\\tabpfn\\regressor.py:490: UserWarning: Running on CPU with more than 200 samples may be slow.\n", + "Consider using a GPU or the tabpfn-client API: https://github.com/PriorLabs/tabpfn-client\n", + " check_cpu_warning(\n", + "c:\\Users\\svenk\\anaconda3\\envs\\dml_tabpfn\\Lib\\site-packages\\tabpfn\\regressor.py:490: UserWarning: Running on CPU with more than 200 samples may be slow.\n", + "Consider using a GPU or the tabpfn-client API: https://github.com/PriorLabs/tabpfn-client\n", + " check_cpu_warning(\n", + "c:\\Users\\svenk\\anaconda3\\envs\\dml_tabpfn\\Lib\\site-packages\\tabpfn\\regressor.py:490: UserWarning: Running on CPU with more than 200 samples may be slow.\n", + "Consider using a GPU or the tabpfn-client API: https://github.com/PriorLabs/tabpfn-client\n", + " check_cpu_warning(\n", + "c:\\Users\\svenk\\anaconda3\\envs\\dml_tabpfn\\Lib\\site-packages\\tabpfn\\regressor.py:490: UserWarning: Running on CPU with more than 200 samples may be slow.\n", + "Consider using a GPU or the tabpfn-client API: https://github.com/PriorLabs/tabpfn-client\n", + " check_cpu_warning(\n", + "c:\\Users\\svenk\\anaconda3\\envs\\dml_tabpfn\\Lib\\site-packages\\tabpfn\\regressor.py:490: UserWarning: Running on CPU with more than 200 samples may be slow.\n", + "Consider using a GPU or the tabpfn-client API: https://github.com/PriorLabs/tabpfn-client\n", + " check_cpu_warning(\n", + "c:\\Users\\svenk\\anaconda3\\envs\\dml_tabpfn\\Lib\\site-packages\\tabpfn\\regressor.py:490: UserWarning: Running on CPU with more than 200 samples may be slow.\n", + "Consider using a GPU or the tabpfn-client API: https://github.com/PriorLabs/tabpfn-client\n", + " check_cpu_warning(\n", + "c:\\Users\\svenk\\anaconda3\\envs\\dml_tabpfn\\Lib\\site-packages\\tabpfn\\classifier.py:459: UserWarning: Running on CPU with more than 200 samples may be slow.\n", + "Consider using a GPU or the tabpfn-client API: https://github.com/PriorLabs/tabpfn-client\n", + " check_cpu_warning(\n", + "c:\\Users\\svenk\\anaconda3\\envs\\dml_tabpfn\\Lib\\site-packages\\tabpfn\\classifier.py:459: UserWarning: Running on CPU with more than 200 samples may be slow.\n", + "Consider using a GPU or the tabpfn-client API: https://github.com/PriorLabs/tabpfn-client\n", + " check_cpu_warning(\n", + "c:\\Users\\svenk\\anaconda3\\envs\\dml_tabpfn\\Lib\\site-packages\\tabpfn\\classifier.py:459: UserWarning: Running on CPU with more than 200 samples may be slow.\n", + "Consider using a GPU or the tabpfn-client API: https://github.com/PriorLabs/tabpfn-client\n", + " check_cpu_warning(\n", + "c:\\Users\\svenk\\anaconda3\\envs\\dml_tabpfn\\Lib\\site-packages\\tabpfn\\classifier.py:459: UserWarning: Running on CPU with more than 200 samples may be slow.\n", + "Consider using a GPU or the tabpfn-client API: https://github.com/PriorLabs/tabpfn-client\n", + " check_cpu_warning(\n", + "c:\\Users\\svenk\\anaconda3\\envs\\dml_tabpfn\\Lib\\site-packages\\tabpfn\\classifier.py:459: UserWarning: Running on CPU with more than 200 samples may be slow.\n", + "Consider using a GPU or the tabpfn-client API: https://github.com/PriorLabs/tabpfn-client\n", + " check_cpu_warning(\n", + "c:\\Users\\svenk\\anaconda3\\envs\\dml_tabpfn\\Lib\\site-packages\\tabpfn\\classifier.py:459: UserWarning: Running on CPU with more than 200 samples may be slow.\n", + "Consider using a GPU or the tabpfn-client API: https://github.com/PriorLabs/tabpfn-client\n", + " check_cpu_warning(\n", + "c:\\Users\\svenk\\anaconda3\\envs\\dml_tabpfn\\Lib\\site-packages\\tabpfn\\classifier.py:459: UserWarning: Running on CPU with more than 200 samples may be slow.\n", + "Consider using a GPU or the tabpfn-client API: https://github.com/PriorLabs/tabpfn-client\n", + " check_cpu_warning(\n", + "c:\\Users\\svenk\\anaconda3\\envs\\dml_tabpfn\\Lib\\site-packages\\tabpfn\\classifier.py:459: UserWarning: Running on CPU with more than 200 samples may be slow.\n", + "Consider using a GPU or the tabpfn-client API: https://github.com/PriorLabs/tabpfn-client\n", + " check_cpu_warning(\n", + "c:\\Users\\svenk\\anaconda3\\envs\\dml_tabpfn\\Lib\\site-packages\\tabpfn\\classifier.py:459: UserWarning: Running on CPU with more than 200 samples may be slow.\n", + "Consider using a GPU or the tabpfn-client API: https://github.com/PriorLabs/tabpfn-client\n", + " check_cpu_warning(\n", + "c:\\Users\\svenk\\anaconda3\\envs\\dml_tabpfn\\Lib\\site-packages\\tabpfn\\classifier.py:459: UserWarning: Running on CPU with more than 200 samples may be slow.\n", + "Consider using a GPU or the tabpfn-client API: https://github.com/PriorLabs/tabpfn-client\n", + " check_cpu_warning(\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "================== DoubleMLAPOS Object ==================\n", + "\n", + "------------------ Fit summary ------------------\n", + " coef std err t P>|t| 2.5 % 97.5 %\n", + "0.0 209.381897 1.211443 172.836726 0.0 207.007512 211.756282\n", + "1.0 210.959765 1.380311 152.834917 0.0 208.254404 213.665125\n", + "2.0 216.595908 1.244418 174.054026 0.0 214.156894 219.034922\n", + "3.0 219.330138 1.331450 164.730250 0.0 216.720543 221.939733\n", + "4.0 219.854115 1.272405 172.786257 0.0 217.360247 222.347983\n", + "5.0 219.355109 1.190624 184.235377 0.0 217.021529 221.688690\n" + ] + } + ], + "source": [ + "dml_obj.fit()\n", + "print(dml_obj)" + ] + }, + { + "cell_type": "markdown", + "id": "bd66a2f9", + "metadata": {}, + "source": [ + "## Machine Learning Methods Comparison\n", "\n", - "device = 'cuda'\n", + "We compare four different machine learning approaches for estimating the nuisance functions in our causal model:\n", + "\n", + "1. **Random Forest**: Ensemble method with bagging and random feature selection\n", + "2. **Linear Models**: Linear/Logistic regression\n", + "3. **LightGBM**: Gradient boosting framework\n", + "4. **TabPFN**: A foundation model for tabular data" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "id": "ea1c0ce4", + "metadata": {}, + "outputs": [], + "source": [ "learner_dict = {\n", " 'RandomForest': {\n", " 'ml_g': RandomForestRegressor(),\n", @@ -205,9 +781,25 @@ "}" ] }, + { + "cell_type": "markdown", + "id": "7ffd5a74", + "metadata": {}, + "source": [ + "### Estimation of Average Potential Outcomes\n", + "\n", + "Now we estimate the Average Potential Outcomes (APOs) for each treatment level using all four machine learning methods. We use the [DoubleMLAPOS](https://docs.doubleml.org/dev/api/generated/doubleml.irm.DoubleMLAPOS.html) class, which:\n", + "\n", + "1. **Estimates nuisance functions**: Uses cross-fitting to estimate $g_0(D,X)$ and $m_{0,d}(X)$ \n", + "2. **Computes APO estimates**: Uses the efficient influence function to estimate $\\theta_d = \\mathbb{E}[Y(d)]$\n", + "3. **Provides confidence intervals**: Based on the asymptotic distribution of the estimator\n", + "\n", + "We also compute **causal contrasts** (Average Treatment Effects) as differences between treatment levels and the reference level (no treatment)." + ] + }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 9, "id": "db8b5c59", "metadata": {}, "outputs": [ @@ -249,30 +841,22 @@ " warnings.warn(\n", "c:\\Users\\svenk\\anaconda3\\envs\\dml_tabpfn\\Lib\\site-packages\\doubleml\\utils\\_checks.py:194: UserWarning: Propensity predictions from learner RandomForestClassifier() for ml_m are close to zero or one (eps=1e-12).\n", " warnings.warn(\n", - "c:\\Users\\svenk\\anaconda3\\envs\\dml_tabpfn\\Lib\\site-packages\\doubleml\\double_ml.py:1479: UserWarning: The estimated nu2 for d is not positive. Re-estimation based on riesz representer (non-orthogonal).\n", - " warnings.warn(msg, UserWarning)\n", "c:\\Users\\svenk\\anaconda3\\envs\\dml_tabpfn\\Lib\\site-packages\\sklearn\\utils\\deprecation.py:151: FutureWarning: 'force_all_finite' was renamed to 'ensure_all_finite' in 1.6 and will be removed in 1.8.\n", " warnings.warn(\n", "c:\\Users\\svenk\\anaconda3\\envs\\dml_tabpfn\\Lib\\site-packages\\sklearn\\utils\\deprecation.py:151: FutureWarning: 'force_all_finite' was renamed to 'ensure_all_finite' in 1.6 and will be removed in 1.8.\n", " warnings.warn(\n", "c:\\Users\\svenk\\anaconda3\\envs\\dml_tabpfn\\Lib\\site-packages\\doubleml\\utils\\_checks.py:194: UserWarning: Propensity predictions from learner RandomForestClassifier() for ml_m are close to zero or one (eps=1e-12).\n", " warnings.warn(\n", - "c:\\Users\\svenk\\anaconda3\\envs\\dml_tabpfn\\Lib\\site-packages\\doubleml\\double_ml.py:1479: UserWarning: The estimated nu2 for d is not positive. Re-estimation based on riesz representer (non-orthogonal).\n", - " warnings.warn(msg, UserWarning)\n", "c:\\Users\\svenk\\anaconda3\\envs\\dml_tabpfn\\Lib\\site-packages\\sklearn\\utils\\deprecation.py:151: FutureWarning: 'force_all_finite' was renamed to 'ensure_all_finite' in 1.6 and will be removed in 1.8.\n", " warnings.warn(\n", "c:\\Users\\svenk\\anaconda3\\envs\\dml_tabpfn\\Lib\\site-packages\\sklearn\\utils\\deprecation.py:151: FutureWarning: 'force_all_finite' was renamed to 'ensure_all_finite' in 1.6 and will be removed in 1.8.\n", " warnings.warn(\n", - "c:\\Users\\svenk\\anaconda3\\envs\\dml_tabpfn\\Lib\\site-packages\\doubleml\\utils\\_checks.py:194: UserWarning: Propensity predictions from learner RandomForestClassifier() for ml_m are close to zero or one (eps=1e-12).\n", - " warnings.warn(\n", "c:\\Users\\svenk\\anaconda3\\envs\\dml_tabpfn\\Lib\\site-packages\\doubleml\\double_ml.py:1479: UserWarning: The estimated nu2 for d is not positive. Re-estimation based on riesz representer (non-orthogonal).\n", " warnings.warn(msg, UserWarning)\n", "c:\\Users\\svenk\\anaconda3\\envs\\dml_tabpfn\\Lib\\site-packages\\sklearn\\utils\\deprecation.py:151: FutureWarning: 'force_all_finite' was renamed to 'ensure_all_finite' in 1.6 and will be removed in 1.8.\n", " warnings.warn(\n", "c:\\Users\\svenk\\anaconda3\\envs\\dml_tabpfn\\Lib\\site-packages\\sklearn\\utils\\deprecation.py:151: FutureWarning: 'force_all_finite' was renamed to 'ensure_all_finite' in 1.6 and will be removed in 1.8.\n", " warnings.warn(\n", - "c:\\Users\\svenk\\anaconda3\\envs\\dml_tabpfn\\Lib\\site-packages\\doubleml\\utils\\_checks.py:194: UserWarning: Propensity predictions from learner RandomForestClassifier() for ml_m are close to zero or one (eps=1e-12).\n", - " warnings.warn(\n", "c:\\Users\\svenk\\anaconda3\\envs\\dml_tabpfn\\Lib\\site-packages\\doubleml\\double_ml.py:1479: UserWarning: The estimated nu2 for d is not positive. Re-estimation based on riesz representer (non-orthogonal).\n", " warnings.warn(msg, UserWarning)\n", "c:\\Users\\svenk\\anaconda3\\envs\\dml_tabpfn\\Lib\\site-packages\\sklearn\\utils\\deprecation.py:151: FutureWarning: 'force_all_finite' was renamed to 'ensure_all_finite' in 1.6 and will be removed in 1.8.\n", @@ -443,10 +1027,6 @@ " warnings.warn(\n", "c:\\Users\\svenk\\anaconda3\\envs\\dml_tabpfn\\Lib\\site-packages\\sklearn\\utils\\validation.py:2739: UserWarning: X does not have valid feature names, but LGBMRegressor was fitted with feature names\n", " warnings.warn(\n", - "c:\\Users\\svenk\\anaconda3\\envs\\dml_tabpfn\\Lib\\site-packages\\sklearn\\utils\\validation.py:2739: UserWarning: X does not have valid feature names, but LGBMRegressor was fitted with feature names\n", - " warnings.warn(\n", - "c:\\Users\\svenk\\anaconda3\\envs\\dml_tabpfn\\Lib\\site-packages\\sklearn\\utils\\validation.py:2739: UserWarning: X does not have valid feature names, but LGBMRegressor was fitted with feature names\n", - " warnings.warn(\n", "c:\\Users\\svenk\\anaconda3\\envs\\dml_tabpfn\\Lib\\site-packages\\sklearn\\utils\\validation.py:2739: UserWarning: X does not have valid feature names, but LGBMClassifier was fitted with feature names\n", " warnings.warn(\n", "c:\\Users\\svenk\\anaconda3\\envs\\dml_tabpfn\\Lib\\site-packages\\sklearn\\utils\\validation.py:2739: UserWarning: X does not have valid feature names, but LGBMClassifier was fitted with feature names\n", @@ -515,6 +1095,14 @@ " warnings.warn(\n", "c:\\Users\\svenk\\anaconda3\\envs\\dml_tabpfn\\Lib\\site-packages\\sklearn\\utils\\validation.py:2739: UserWarning: X does not have valid feature names, but LGBMRegressor was fitted with feature names\n", " warnings.warn(\n", + "c:\\Users\\svenk\\anaconda3\\envs\\dml_tabpfn\\Lib\\site-packages\\sklearn\\utils\\validation.py:2739: UserWarning: X does not have valid feature names, but LGBMRegressor was fitted with feature names\n", + " warnings.warn(\n", + "c:\\Users\\svenk\\anaconda3\\envs\\dml_tabpfn\\Lib\\site-packages\\sklearn\\utils\\validation.py:2739: UserWarning: X does not have valid feature names, but LGBMRegressor was fitted with feature names\n", + " warnings.warn(\n", + "c:\\Users\\svenk\\anaconda3\\envs\\dml_tabpfn\\Lib\\site-packages\\sklearn\\utils\\validation.py:2739: UserWarning: X does not have valid feature names, but LGBMRegressor was fitted with feature names\n", + " warnings.warn(\n", + "c:\\Users\\svenk\\anaconda3\\envs\\dml_tabpfn\\Lib\\site-packages\\sklearn\\utils\\validation.py:2739: UserWarning: X does not have valid feature names, but LGBMRegressor was fitted with feature names\n", + " warnings.warn(\n", "c:\\Users\\svenk\\anaconda3\\envs\\dml_tabpfn\\Lib\\site-packages\\sklearn\\utils\\validation.py:2739: UserWarning: X does not have valid feature names, but LGBMClassifier was fitted with feature names\n", " warnings.warn(\n", "c:\\Users\\svenk\\anaconda3\\envs\\dml_tabpfn\\Lib\\site-packages\\sklearn\\utils\\validation.py:2739: UserWarning: X does not have valid feature names, but LGBMClassifier was fitted with feature names\n", @@ -551,12 +1139,6 @@ " warnings.warn(\n", "c:\\Users\\svenk\\anaconda3\\envs\\dml_tabpfn\\Lib\\site-packages\\sklearn\\utils\\validation.py:2739: UserWarning: X does not have valid feature names, but LGBMRegressor was fitted with feature names\n", " warnings.warn(\n", - "c:\\Users\\svenk\\anaconda3\\envs\\dml_tabpfn\\Lib\\site-packages\\sklearn\\utils\\validation.py:2739: UserWarning: X does not have valid feature names, but LGBMRegressor was fitted with feature names\n", - " warnings.warn(\n", - "c:\\Users\\svenk\\anaconda3\\envs\\dml_tabpfn\\Lib\\site-packages\\sklearn\\utils\\validation.py:2739: UserWarning: X does not have valid feature names, but LGBMRegressor was fitted with feature names\n", - " warnings.warn(\n", - "c:\\Users\\svenk\\anaconda3\\envs\\dml_tabpfn\\Lib\\site-packages\\sklearn\\utils\\validation.py:2739: UserWarning: X does not have valid feature names, but LGBMRegressor was fitted with feature names\n", - " warnings.warn(\n", "c:\\Users\\svenk\\anaconda3\\envs\\dml_tabpfn\\Lib\\site-packages\\sklearn\\utils\\validation.py:2739: UserWarning: X does not have valid feature names, but LGBMClassifier was fitted with feature names\n", " warnings.warn(\n", "c:\\Users\\svenk\\anaconda3\\envs\\dml_tabpfn\\Lib\\site-packages\\sklearn\\utils\\validation.py:2739: UserWarning: X does not have valid feature names, but LGBMClassifier was fitted with feature names\n", @@ -589,6 +1171,12 @@ " warnings.warn(\n", "c:\\Users\\svenk\\anaconda3\\envs\\dml_tabpfn\\Lib\\site-packages\\sklearn\\utils\\validation.py:2739: UserWarning: X does not have valid feature names, but LGBMRegressor was fitted with feature names\n", " warnings.warn(\n", + "c:\\Users\\svenk\\anaconda3\\envs\\dml_tabpfn\\Lib\\site-packages\\sklearn\\utils\\validation.py:2739: UserWarning: X does not have valid feature names, but LGBMRegressor was fitted with feature names\n", + " warnings.warn(\n", + "c:\\Users\\svenk\\anaconda3\\envs\\dml_tabpfn\\Lib\\site-packages\\sklearn\\utils\\validation.py:2739: UserWarning: X does not have valid feature names, but LGBMRegressor was fitted with feature names\n", + " warnings.warn(\n", + "c:\\Users\\svenk\\anaconda3\\envs\\dml_tabpfn\\Lib\\site-packages\\sklearn\\utils\\validation.py:2739: UserWarning: X does not have valid feature names, but LGBMRegressor was fitted with feature names\n", + " warnings.warn(\n", "c:\\Users\\svenk\\anaconda3\\envs\\dml_tabpfn\\Lib\\site-packages\\sklearn\\utils\\validation.py:2739: UserWarning: X does not have valid feature names, but LGBMClassifier was fitted with feature names\n", " warnings.warn(\n", "c:\\Users\\svenk\\anaconda3\\envs\\dml_tabpfn\\Lib\\site-packages\\sklearn\\utils\\validation.py:2739: UserWarning: X does not have valid feature names, but LGBMClassifier was fitted with feature names\n", @@ -625,14 +1213,6 @@ " warnings.warn(\n", "c:\\Users\\svenk\\anaconda3\\envs\\dml_tabpfn\\Lib\\site-packages\\sklearn\\utils\\validation.py:2739: UserWarning: X does not have valid feature names, but LGBMRegressor was fitted with feature names\n", " warnings.warn(\n", - "c:\\Users\\svenk\\anaconda3\\envs\\dml_tabpfn\\Lib\\site-packages\\sklearn\\utils\\validation.py:2739: UserWarning: X does not have valid feature names, but LGBMRegressor was fitted with feature names\n", - " warnings.warn(\n", - "c:\\Users\\svenk\\anaconda3\\envs\\dml_tabpfn\\Lib\\site-packages\\sklearn\\utils\\validation.py:2739: UserWarning: X does not have valid feature names, but LGBMRegressor was fitted with feature names\n", - " warnings.warn(\n", - "c:\\Users\\svenk\\anaconda3\\envs\\dml_tabpfn\\Lib\\site-packages\\sklearn\\utils\\validation.py:2739: UserWarning: X does not have valid feature names, but LGBMRegressor was fitted with feature names\n", - " warnings.warn(\n", - "c:\\Users\\svenk\\anaconda3\\envs\\dml_tabpfn\\Lib\\site-packages\\sklearn\\utils\\validation.py:2739: UserWarning: X does not have valid feature names, but LGBMRegressor was fitted with feature names\n", - " warnings.warn(\n", "c:\\Users\\svenk\\anaconda3\\envs\\dml_tabpfn\\Lib\\site-packages\\sklearn\\utils\\validation.py:2739: UserWarning: X does not have valid feature names, but LGBMClassifier was fitted with feature names\n", " warnings.warn(\n", "c:\\Users\\svenk\\anaconda3\\envs\\dml_tabpfn\\Lib\\site-packages\\sklearn\\utils\\validation.py:2739: UserWarning: X does not have valid feature names, but LGBMClassifier was fitted with feature names\n", @@ -665,6 +1245,12 @@ " warnings.warn(\n", "c:\\Users\\svenk\\anaconda3\\envs\\dml_tabpfn\\Lib\\site-packages\\sklearn\\utils\\validation.py:2739: UserWarning: X does not have valid feature names, but LGBMRegressor was fitted with feature names\n", " warnings.warn(\n", + "c:\\Users\\svenk\\anaconda3\\envs\\dml_tabpfn\\Lib\\site-packages\\sklearn\\utils\\validation.py:2739: UserWarning: X does not have valid feature names, but LGBMRegressor was fitted with feature names\n", + " warnings.warn(\n", + "c:\\Users\\svenk\\anaconda3\\envs\\dml_tabpfn\\Lib\\site-packages\\sklearn\\utils\\validation.py:2739: UserWarning: X does not have valid feature names, but LGBMRegressor was fitted with feature names\n", + " warnings.warn(\n", + "c:\\Users\\svenk\\anaconda3\\envs\\dml_tabpfn\\Lib\\site-packages\\sklearn\\utils\\validation.py:2739: UserWarning: X does not have valid feature names, but LGBMRegressor was fitted with feature names\n", + " warnings.warn(\n", "c:\\Users\\svenk\\anaconda3\\envs\\dml_tabpfn\\Lib\\site-packages\\sklearn\\utils\\validation.py:2739: UserWarning: X does not have valid feature names, but LGBMClassifier was fitted with feature names\n", " warnings.warn(\n", "c:\\Users\\svenk\\anaconda3\\envs\\dml_tabpfn\\Lib\\site-packages\\sklearn\\utils\\validation.py:2739: UserWarning: X does not have valid feature names, but LGBMClassifier was fitted with feature names\n", @@ -701,12 +1287,6 @@ " warnings.warn(\n", "c:\\Users\\svenk\\anaconda3\\envs\\dml_tabpfn\\Lib\\site-packages\\sklearn\\utils\\validation.py:2739: UserWarning: X does not have valid feature names, but LGBMRegressor was fitted with feature names\n", " warnings.warn(\n", - "c:\\Users\\svenk\\anaconda3\\envs\\dml_tabpfn\\Lib\\site-packages\\sklearn\\utils\\validation.py:2739: UserWarning: X does not have valid feature names, but LGBMRegressor was fitted with feature names\n", - " warnings.warn(\n", - "c:\\Users\\svenk\\anaconda3\\envs\\dml_tabpfn\\Lib\\site-packages\\sklearn\\utils\\validation.py:2739: UserWarning: X does not have valid feature names, but LGBMRegressor was fitted with feature names\n", - " warnings.warn(\n", - "c:\\Users\\svenk\\anaconda3\\envs\\dml_tabpfn\\Lib\\site-packages\\sklearn\\utils\\validation.py:2739: UserWarning: X does not have valid feature names, but LGBMRegressor was fitted with feature names\n", - " warnings.warn(\n", "c:\\Users\\svenk\\anaconda3\\envs\\dml_tabpfn\\Lib\\site-packages\\sklearn\\utils\\validation.py:2739: UserWarning: X does not have valid feature names, but LGBMClassifier was fitted with feature names\n", " warnings.warn(\n", "c:\\Users\\svenk\\anaconda3\\envs\\dml_tabpfn\\Lib\\site-packages\\sklearn\\utils\\validation.py:2739: UserWarning: X does not have valid feature names, but LGBMClassifier was fitted with feature names\n", @@ -743,6 +1323,8 @@ " warnings.warn(\n", "c:\\Users\\svenk\\anaconda3\\envs\\dml_tabpfn\\Lib\\site-packages\\sklearn\\utils\\validation.py:2739: UserWarning: X does not have valid feature names, but LGBMRegressor was fitted with feature names\n", " warnings.warn(\n", + "c:\\Users\\svenk\\anaconda3\\envs\\dml_tabpfn\\Lib\\site-packages\\sklearn\\utils\\validation.py:2739: UserWarning: X does not have valid feature names, but LGBMRegressor was fitted with feature names\n", + " warnings.warn(\n", "c:\\Users\\svenk\\anaconda3\\envs\\dml_tabpfn\\Lib\\site-packages\\sklearn\\utils\\validation.py:2739: UserWarning: X does not have valid feature names, but LGBMClassifier was fitted with feature names\n", " warnings.warn(\n", "c:\\Users\\svenk\\anaconda3\\envs\\dml_tabpfn\\Lib\\site-packages\\sklearn\\utils\\validation.py:2739: UserWarning: X does not have valid feature names, but LGBMClassifier was fitted with feature names\n", @@ -781,10 +1363,6 @@ " warnings.warn(\n", "c:\\Users\\svenk\\anaconda3\\envs\\dml_tabpfn\\Lib\\site-packages\\sklearn\\utils\\validation.py:2739: UserWarning: X does not have valid feature names, but LGBMClassifier was fitted with feature names\n", " warnings.warn(\n", - "c:\\Users\\svenk\\anaconda3\\envs\\dml_tabpfn\\Lib\\site-packages\\sklearn\\utils\\validation.py:2739: UserWarning: X does not have valid feature names, but LGBMRegressor was fitted with feature names\n", - " warnings.warn(\n", - "c:\\Users\\svenk\\anaconda3\\envs\\dml_tabpfn\\Lib\\site-packages\\sklearn\\utils\\validation.py:2739: UserWarning: X does not have valid feature names, but LGBMClassifier was fitted with feature names\n", - " warnings.warn(\n", "c:\\Users\\svenk\\anaconda3\\envs\\dml_tabpfn\\Lib\\site-packages\\sklearn\\utils\\validation.py:2739: UserWarning: X does not have valid feature names, but LGBMClassifier was fitted with feature names\n", " warnings.warn(\n", "c:\\Users\\svenk\\anaconda3\\envs\\dml_tabpfn\\Lib\\site-packages\\sklearn\\utils\\validation.py:2739: UserWarning: X does not have valid feature names, but LGBMClassifier was fitted with feature names\n", @@ -799,6 +1377,12 @@ " warnings.warn(\n", "c:\\Users\\svenk\\anaconda3\\envs\\dml_tabpfn\\Lib\\site-packages\\sklearn\\utils\\deprecation.py:151: FutureWarning: 'force_all_finite' was renamed to 'ensure_all_finite' in 1.6 and will be removed in 1.8.\n", " warnings.warn(\n", + "c:\\Users\\svenk\\anaconda3\\envs\\dml_tabpfn\\Lib\\site-packages\\sklearn\\utils\\validation.py:2739: UserWarning: X does not have valid feature names, but LGBMRegressor was fitted with feature names\n", + " warnings.warn(\n", + "c:\\Users\\svenk\\anaconda3\\envs\\dml_tabpfn\\Lib\\site-packages\\sklearn\\utils\\validation.py:2739: UserWarning: X does not have valid feature names, but LGBMRegressor was fitted with feature names\n", + " warnings.warn(\n", + "c:\\Users\\svenk\\anaconda3\\envs\\dml_tabpfn\\Lib\\site-packages\\sklearn\\utils\\validation.py:2739: UserWarning: X does not have valid feature names, but LGBMClassifier was fitted with feature names\n", + " warnings.warn(\n", "c:\\Users\\svenk\\anaconda3\\envs\\dml_tabpfn\\Lib\\site-packages\\sklearn\\utils\\validation.py:2739: UserWarning: X does not have valid feature names, but LGBMClassifier was fitted with feature names\n", " warnings.warn(\n", "c:\\Users\\svenk\\anaconda3\\envs\\dml_tabpfn\\Lib\\site-packages\\sklearn\\utils\\validation.py:2739: UserWarning: X does not have valid feature names, but LGBMClassifier was fitted with feature names\n", @@ -813,46 +1397,406 @@ " warnings.warn(\n", "c:\\Users\\svenk\\anaconda3\\envs\\dml_tabpfn\\Lib\\site-packages\\sklearn\\utils\\deprecation.py:151: FutureWarning: 'force_all_finite' was renamed to 'ensure_all_finite' in 1.6 and will be removed in 1.8.\n", " warnings.warn(\n", - "c:\\Users\\svenk\\anaconda3\\envs\\dml_tabpfn\\Lib\\site-packages\\sklearn\\utils\\deprecation.py:151: FutureWarning: 'force_all_finite' was renamed to 'ensure_all_finite' in 1.6 and will be removed in 1.8.\n", - " warnings.warn(\n", - "c:\\Users\\svenk\\anaconda3\\envs\\dml_tabpfn\\Lib\\site-packages\\sklearn\\utils\\deprecation.py:151: FutureWarning: 'force_all_finite' was renamed to 'ensure_all_finite' in 1.6 and will be removed in 1.8.\n", - " warnings.warn(\n", - "c:\\Users\\svenk\\anaconda3\\envs\\dml_tabpfn\\Lib\\site-packages\\sklearn\\utils\\deprecation.py:151: FutureWarning: 'force_all_finite' was renamed to 'ensure_all_finite' in 1.6 and will be removed in 1.8.\n", - " warnings.warn(\n", - "c:\\Users\\svenk\\anaconda3\\envs\\dml_tabpfn\\Lib\\site-packages\\sklearn\\utils\\deprecation.py:151: FutureWarning: 'force_all_finite' was renamed to 'ensure_all_finite' in 1.6 and will be removed in 1.8.\n", - " warnings.warn(\n", - "c:\\Users\\svenk\\anaconda3\\envs\\dml_tabpfn\\Lib\\site-packages\\sklearn\\utils\\deprecation.py:151: FutureWarning: 'force_all_finite' was renamed to 'ensure_all_finite' in 1.6 and will be removed in 1.8.\n", - " warnings.warn(\n", - "c:\\Users\\svenk\\anaconda3\\envs\\dml_tabpfn\\Lib\\site-packages\\sklearn\\utils\\deprecation.py:151: FutureWarning: 'force_all_finite' was renamed to 'ensure_all_finite' in 1.6 and will be removed in 1.8.\n", - " warnings.warn(\n", - "c:\\Users\\svenk\\anaconda3\\envs\\dml_tabpfn\\Lib\\site-packages\\sklearn\\utils\\deprecation.py:151: FutureWarning: 'force_all_finite' was renamed to 'ensure_all_finite' in 1.6 and will be removed in 1.8.\n", - " warnings.warn(\n", - "c:\\Users\\svenk\\anaconda3\\envs\\dml_tabpfn\\Lib\\site-packages\\sklearn\\utils\\deprecation.py:151: FutureWarning: 'force_all_finite' was renamed to 'ensure_all_finite' in 1.6 and will be removed in 1.8.\n", - " warnings.warn(\n", - "c:\\Users\\svenk\\anaconda3\\envs\\dml_tabpfn\\Lib\\site-packages\\sklearn\\utils\\deprecation.py:151: FutureWarning: 'force_all_finite' was renamed to 'ensure_all_finite' in 1.6 and will be removed in 1.8.\n", - " warnings.warn(\n", - "c:\\Users\\svenk\\anaconda3\\envs\\dml_tabpfn\\Lib\\site-packages\\sklearn\\utils\\deprecation.py:151: FutureWarning: 'force_all_finite' was renamed to 'ensure_all_finite' in 1.6 and will be removed in 1.8.\n", - " warnings.warn(\n", - "c:\\Users\\svenk\\anaconda3\\envs\\dml_tabpfn\\Lib\\site-packages\\sklearn\\utils\\deprecation.py:151: FutureWarning: 'force_all_finite' was renamed to 'ensure_all_finite' in 1.6 and will be removed in 1.8.\n", - " warnings.warn(\n", - "c:\\Users\\svenk\\anaconda3\\envs\\dml_tabpfn\\Lib\\site-packages\\sklearn\\utils\\deprecation.py:151: FutureWarning: 'force_all_finite' was renamed to 'ensure_all_finite' in 1.6 and will be removed in 1.8.\n", - " warnings.warn(\n", - "c:\\Users\\svenk\\anaconda3\\envs\\dml_tabpfn\\Lib\\site-packages\\sklearn\\utils\\deprecation.py:151: FutureWarning: 'force_all_finite' was renamed to 'ensure_all_finite' in 1.6 and will be removed in 1.8.\n", - " warnings.warn(\n", - "c:\\Users\\svenk\\anaconda3\\envs\\dml_tabpfn\\Lib\\site-packages\\sklearn\\utils\\deprecation.py:151: FutureWarning: 'force_all_finite' was renamed to 'ensure_all_finite' in 1.6 and will be removed in 1.8.\n", - " warnings.warn(\n", - "c:\\Users\\svenk\\anaconda3\\envs\\dml_tabpfn\\Lib\\site-packages\\sklearn\\utils\\deprecation.py:151: FutureWarning: 'force_all_finite' was renamed to 'ensure_all_finite' in 1.6 and will be removed in 1.8.\n", - " warnings.warn(\n", - "c:\\Users\\svenk\\anaconda3\\envs\\dml_tabpfn\\Lib\\site-packages\\sklearn\\utils\\deprecation.py:151: FutureWarning: 'force_all_finite' was renamed to 'ensure_all_finite' in 1.6 and will be removed in 1.8.\n", - " warnings.warn(\n", - "c:\\Users\\svenk\\anaconda3\\envs\\dml_tabpfn\\Lib\\site-packages\\sklearn\\utils\\deprecation.py:151: FutureWarning: 'force_all_finite' was renamed to 'ensure_all_finite' in 1.6 and will be removed in 1.8.\n", - " warnings.warn(\n", - "c:\\Users\\svenk\\anaconda3\\envs\\dml_tabpfn\\Lib\\site-packages\\sklearn\\utils\\deprecation.py:151: FutureWarning: 'force_all_finite' was renamed to 'ensure_all_finite' in 1.6 and will be removed in 1.8.\n", - " warnings.warn(\n", - "c:\\Users\\svenk\\anaconda3\\envs\\dml_tabpfn\\Lib\\site-packages\\sklearn\\utils\\deprecation.py:151: FutureWarning: 'force_all_finite' was renamed to 'ensure_all_finite' in 1.6 and will be removed in 1.8.\n", - " warnings.warn(\n", - "c:\\Users\\svenk\\anaconda3\\envs\\dml_tabpfn\\Lib\\site-packages\\sklearn\\utils\\deprecation.py:151: FutureWarning: 'force_all_finite' was renamed to 'ensure_all_finite' in 1.6 and will be removed in 1.8.\n", - " warnings.warn(\n" + "c:\\Users\\svenk\\anaconda3\\envs\\dml_tabpfn\\Lib\\site-packages\\tabpfn\\regressor.py:490: UserWarning: Running on CPU with more than 200 samples may be slow.\n", + "Consider using a GPU or the tabpfn-client API: https://github.com/PriorLabs/tabpfn-client\n", + " check_cpu_warning(\n", + "c:\\Users\\svenk\\anaconda3\\envs\\dml_tabpfn\\Lib\\site-packages\\tabpfn\\regressor.py:490: UserWarning: Running on CPU with more than 200 samples may be slow.\n", + "Consider using a GPU or the tabpfn-client API: https://github.com/PriorLabs/tabpfn-client\n", + " check_cpu_warning(\n", + "c:\\Users\\svenk\\anaconda3\\envs\\dml_tabpfn\\Lib\\site-packages\\tabpfn\\regressor.py:490: UserWarning: Running on CPU with more than 200 samples may be slow.\n", + "Consider using a GPU or the tabpfn-client API: https://github.com/PriorLabs/tabpfn-client\n", + " check_cpu_warning(\n", + "c:\\Users\\svenk\\anaconda3\\envs\\dml_tabpfn\\Lib\\site-packages\\tabpfn\\regressor.py:490: UserWarning: Running on CPU with more than 200 samples may be slow.\n", + "Consider using a GPU or the tabpfn-client API: https://github.com/PriorLabs/tabpfn-client\n", + " check_cpu_warning(\n", + "c:\\Users\\svenk\\anaconda3\\envs\\dml_tabpfn\\Lib\\site-packages\\tabpfn\\regressor.py:490: UserWarning: Running on CPU with more than 200 samples may be slow.\n", + "Consider using a GPU or the tabpfn-client API: https://github.com/PriorLabs/tabpfn-client\n", + " check_cpu_warning(\n", + "c:\\Users\\svenk\\anaconda3\\envs\\dml_tabpfn\\Lib\\site-packages\\tabpfn\\regressor.py:490: UserWarning: Running on CPU with more than 200 samples may be slow.\n", + "Consider using a GPU or the tabpfn-client API: https://github.com/PriorLabs/tabpfn-client\n", + " check_cpu_warning(\n", + "c:\\Users\\svenk\\anaconda3\\envs\\dml_tabpfn\\Lib\\site-packages\\tabpfn\\regressor.py:490: UserWarning: Running on CPU with more than 200 samples may be slow.\n", + "Consider using a GPU or the tabpfn-client API: https://github.com/PriorLabs/tabpfn-client\n", + " check_cpu_warning(\n", + "c:\\Users\\svenk\\anaconda3\\envs\\dml_tabpfn\\Lib\\site-packages\\tabpfn\\regressor.py:490: UserWarning: Running on CPU with more than 200 samples may be slow.\n", + "Consider using a GPU or the tabpfn-client API: https://github.com/PriorLabs/tabpfn-client\n", + " check_cpu_warning(\n", + "c:\\Users\\svenk\\anaconda3\\envs\\dml_tabpfn\\Lib\\site-packages\\tabpfn\\regressor.py:490: UserWarning: Running on CPU with more than 200 samples may be slow.\n", + "Consider using a GPU or the tabpfn-client API: https://github.com/PriorLabs/tabpfn-client\n", + " check_cpu_warning(\n", + "c:\\Users\\svenk\\anaconda3\\envs\\dml_tabpfn\\Lib\\site-packages\\tabpfn\\regressor.py:490: UserWarning: Running on CPU with more than 200 samples may be slow.\n", + "Consider using a GPU or the tabpfn-client API: https://github.com/PriorLabs/tabpfn-client\n", + " check_cpu_warning(\n", + "c:\\Users\\svenk\\anaconda3\\envs\\dml_tabpfn\\Lib\\site-packages\\tabpfn\\classifier.py:459: UserWarning: Running on CPU with more than 200 samples may be slow.\n", + "Consider using a GPU or the tabpfn-client API: https://github.com/PriorLabs/tabpfn-client\n", + " check_cpu_warning(\n", + "c:\\Users\\svenk\\anaconda3\\envs\\dml_tabpfn\\Lib\\site-packages\\tabpfn\\classifier.py:459: UserWarning: Running on CPU with more than 200 samples may be slow.\n", + "Consider using a GPU or the tabpfn-client API: https://github.com/PriorLabs/tabpfn-client\n", + " check_cpu_warning(\n", + "c:\\Users\\svenk\\anaconda3\\envs\\dml_tabpfn\\Lib\\site-packages\\tabpfn\\classifier.py:459: UserWarning: Running on CPU with more than 200 samples may be slow.\n", + "Consider using a GPU or the tabpfn-client API: https://github.com/PriorLabs/tabpfn-client\n", + " check_cpu_warning(\n", + "c:\\Users\\svenk\\anaconda3\\envs\\dml_tabpfn\\Lib\\site-packages\\tabpfn\\classifier.py:459: UserWarning: Running on CPU with more than 200 samples may be slow.\n", + "Consider using a GPU or the tabpfn-client API: https://github.com/PriorLabs/tabpfn-client\n", + " check_cpu_warning(\n", + "c:\\Users\\svenk\\anaconda3\\envs\\dml_tabpfn\\Lib\\site-packages\\tabpfn\\classifier.py:459: UserWarning: Running on CPU with more than 200 samples may be slow.\n", + "Consider using a GPU or the tabpfn-client API: https://github.com/PriorLabs/tabpfn-client\n", + " check_cpu_warning(\n", + "c:\\Users\\svenk\\anaconda3\\envs\\dml_tabpfn\\Lib\\site-packages\\tabpfn\\classifier.py:459: UserWarning: Running on CPU with more than 200 samples may be slow.\n", + "Consider using a GPU or the tabpfn-client API: https://github.com/PriorLabs/tabpfn-client\n", + " check_cpu_warning(\n", + "c:\\Users\\svenk\\anaconda3\\envs\\dml_tabpfn\\Lib\\site-packages\\tabpfn\\classifier.py:459: UserWarning: Running on CPU with more than 200 samples may be slow.\n", + "Consider using a GPU or the tabpfn-client API: https://github.com/PriorLabs/tabpfn-client\n", + " check_cpu_warning(\n", + "c:\\Users\\svenk\\anaconda3\\envs\\dml_tabpfn\\Lib\\site-packages\\tabpfn\\classifier.py:459: UserWarning: Running on CPU with more than 200 samples may be slow.\n", + "Consider using a GPU or the tabpfn-client API: https://github.com/PriorLabs/tabpfn-client\n", + " check_cpu_warning(\n", + "c:\\Users\\svenk\\anaconda3\\envs\\dml_tabpfn\\Lib\\site-packages\\tabpfn\\classifier.py:459: UserWarning: Running on CPU with more than 200 samples may be slow.\n", + "Consider using a GPU or the tabpfn-client API: https://github.com/PriorLabs/tabpfn-client\n", + " check_cpu_warning(\n", + "c:\\Users\\svenk\\anaconda3\\envs\\dml_tabpfn\\Lib\\site-packages\\tabpfn\\classifier.py:459: UserWarning: Running on CPU with more than 200 samples may be slow.\n", + "Consider using a GPU or the tabpfn-client API: https://github.com/PriorLabs/tabpfn-client\n", + " check_cpu_warning(\n", + "c:\\Users\\svenk\\anaconda3\\envs\\dml_tabpfn\\Lib\\site-packages\\sklearn\\utils\\deprecation.py:151: FutureWarning: 'force_all_finite' was renamed to 'ensure_all_finite' in 1.6 and will be removed in 1.8.\n", + " warnings.warn(\n", + "c:\\Users\\svenk\\anaconda3\\envs\\dml_tabpfn\\Lib\\site-packages\\sklearn\\utils\\deprecation.py:151: FutureWarning: 'force_all_finite' was renamed to 'ensure_all_finite' in 1.6 and will be removed in 1.8.\n", + " warnings.warn(\n", + "c:\\Users\\svenk\\anaconda3\\envs\\dml_tabpfn\\Lib\\site-packages\\sklearn\\utils\\deprecation.py:151: FutureWarning: 'force_all_finite' was renamed to 'ensure_all_finite' in 1.6 and will be removed in 1.8.\n", + " warnings.warn(\n", + "c:\\Users\\svenk\\anaconda3\\envs\\dml_tabpfn\\Lib\\site-packages\\sklearn\\utils\\deprecation.py:151: FutureWarning: 'force_all_finite' was renamed to 'ensure_all_finite' in 1.6 and will be removed in 1.8.\n", + " warnings.warn(\n", + "c:\\Users\\svenk\\anaconda3\\envs\\dml_tabpfn\\Lib\\site-packages\\tabpfn\\regressor.py:490: UserWarning: Running on CPU with more than 200 samples may be slow.\n", + "Consider using a GPU or the tabpfn-client API: https://github.com/PriorLabs/tabpfn-client\n", + " check_cpu_warning(\n", + "c:\\Users\\svenk\\anaconda3\\envs\\dml_tabpfn\\Lib\\site-packages\\tabpfn\\regressor.py:490: UserWarning: Running on CPU with more than 200 samples may be slow.\n", + "Consider using a GPU or the tabpfn-client API: https://github.com/PriorLabs/tabpfn-client\n", + " check_cpu_warning(\n", + "c:\\Users\\svenk\\anaconda3\\envs\\dml_tabpfn\\Lib\\site-packages\\tabpfn\\regressor.py:490: UserWarning: Running on CPU with more than 200 samples may be slow.\n", + "Consider using a GPU or the tabpfn-client API: https://github.com/PriorLabs/tabpfn-client\n", + " check_cpu_warning(\n", + "c:\\Users\\svenk\\anaconda3\\envs\\dml_tabpfn\\Lib\\site-packages\\tabpfn\\regressor.py:490: UserWarning: Running on CPU with more than 200 samples may be slow.\n", + "Consider using a GPU or the tabpfn-client API: https://github.com/PriorLabs/tabpfn-client\n", + " check_cpu_warning(\n", + "c:\\Users\\svenk\\anaconda3\\envs\\dml_tabpfn\\Lib\\site-packages\\tabpfn\\regressor.py:490: UserWarning: Running on CPU with more than 200 samples may be slow.\n", + "Consider using a GPU or the tabpfn-client API: https://github.com/PriorLabs/tabpfn-client\n", + " check_cpu_warning(\n", + "c:\\Users\\svenk\\anaconda3\\envs\\dml_tabpfn\\Lib\\site-packages\\tabpfn\\regressor.py:490: UserWarning: Running on CPU with more than 200 samples may be slow.\n", + "Consider using a GPU or the tabpfn-client API: https://github.com/PriorLabs/tabpfn-client\n", + " check_cpu_warning(\n", + "c:\\Users\\svenk\\anaconda3\\envs\\dml_tabpfn\\Lib\\site-packages\\tabpfn\\regressor.py:490: UserWarning: Running on CPU with more than 200 samples may be slow.\n", + "Consider using a GPU or the tabpfn-client API: https://github.com/PriorLabs/tabpfn-client\n", + " check_cpu_warning(\n", + "c:\\Users\\svenk\\anaconda3\\envs\\dml_tabpfn\\Lib\\site-packages\\tabpfn\\regressor.py:490: UserWarning: Running on CPU with more than 200 samples may be slow.\n", + "Consider using a GPU or the tabpfn-client API: https://github.com/PriorLabs/tabpfn-client\n", + " check_cpu_warning(\n", + "c:\\Users\\svenk\\anaconda3\\envs\\dml_tabpfn\\Lib\\site-packages\\tabpfn\\regressor.py:490: UserWarning: Running on CPU with more than 200 samples may be slow.\n", + "Consider using a GPU or the tabpfn-client API: https://github.com/PriorLabs/tabpfn-client\n", + " check_cpu_warning(\n", + "c:\\Users\\svenk\\anaconda3\\envs\\dml_tabpfn\\Lib\\site-packages\\tabpfn\\regressor.py:490: UserWarning: Running on CPU with more than 200 samples may be slow.\n", + "Consider using a GPU or the tabpfn-client API: https://github.com/PriorLabs/tabpfn-client\n", + " check_cpu_warning(\n", + "c:\\Users\\svenk\\anaconda3\\envs\\dml_tabpfn\\Lib\\site-packages\\tabpfn\\classifier.py:459: UserWarning: Running on CPU with more than 200 samples may be slow.\n", + "Consider using a GPU or the tabpfn-client API: https://github.com/PriorLabs/tabpfn-client\n", + " check_cpu_warning(\n", + "c:\\Users\\svenk\\anaconda3\\envs\\dml_tabpfn\\Lib\\site-packages\\tabpfn\\classifier.py:459: UserWarning: Running on CPU with more than 200 samples may be slow.\n", + "Consider using a GPU or the tabpfn-client API: https://github.com/PriorLabs/tabpfn-client\n", + " check_cpu_warning(\n", + "c:\\Users\\svenk\\anaconda3\\envs\\dml_tabpfn\\Lib\\site-packages\\tabpfn\\classifier.py:459: UserWarning: Running on CPU with more than 200 samples may be slow.\n", + "Consider using a GPU or the tabpfn-client API: https://github.com/PriorLabs/tabpfn-client\n", + " check_cpu_warning(\n", + "c:\\Users\\svenk\\anaconda3\\envs\\dml_tabpfn\\Lib\\site-packages\\tabpfn\\classifier.py:459: UserWarning: Running on CPU with more than 200 samples may be slow.\n", + "Consider using a GPU or the tabpfn-client API: https://github.com/PriorLabs/tabpfn-client\n", + " check_cpu_warning(\n", + "c:\\Users\\svenk\\anaconda3\\envs\\dml_tabpfn\\Lib\\site-packages\\tabpfn\\classifier.py:459: UserWarning: Running on CPU with more than 200 samples may be slow.\n", + "Consider using a GPU or the tabpfn-client API: https://github.com/PriorLabs/tabpfn-client\n", + " check_cpu_warning(\n", + "c:\\Users\\svenk\\anaconda3\\envs\\dml_tabpfn\\Lib\\site-packages\\tabpfn\\classifier.py:459: UserWarning: Running on CPU with more than 200 samples may be slow.\n", + "Consider using a GPU or the tabpfn-client API: https://github.com/PriorLabs/tabpfn-client\n", + " check_cpu_warning(\n", + "c:\\Users\\svenk\\anaconda3\\envs\\dml_tabpfn\\Lib\\site-packages\\tabpfn\\classifier.py:459: UserWarning: Running on CPU with more than 200 samples may be slow.\n", + "Consider using a GPU or the tabpfn-client API: https://github.com/PriorLabs/tabpfn-client\n", + " check_cpu_warning(\n", + "c:\\Users\\svenk\\anaconda3\\envs\\dml_tabpfn\\Lib\\site-packages\\tabpfn\\classifier.py:459: UserWarning: Running on CPU with more than 200 samples may be slow.\n", + "Consider using a GPU or the tabpfn-client API: https://github.com/PriorLabs/tabpfn-client\n", + " check_cpu_warning(\n", + "c:\\Users\\svenk\\anaconda3\\envs\\dml_tabpfn\\Lib\\site-packages\\tabpfn\\classifier.py:459: UserWarning: Running on CPU with more than 200 samples may be slow.\n", + "Consider using a GPU or the tabpfn-client API: https://github.com/PriorLabs/tabpfn-client\n", + " check_cpu_warning(\n", + "c:\\Users\\svenk\\anaconda3\\envs\\dml_tabpfn\\Lib\\site-packages\\tabpfn\\classifier.py:459: UserWarning: Running on CPU with more than 200 samples may be slow.\n", + "Consider using a GPU or the tabpfn-client API: https://github.com/PriorLabs/tabpfn-client\n", + " check_cpu_warning(\n", + "c:\\Users\\svenk\\anaconda3\\envs\\dml_tabpfn\\Lib\\site-packages\\sklearn\\utils\\deprecation.py:151: FutureWarning: 'force_all_finite' was renamed to 'ensure_all_finite' in 1.6 and will be removed in 1.8.\n", + " warnings.warn(\n", + "c:\\Users\\svenk\\anaconda3\\envs\\dml_tabpfn\\Lib\\site-packages\\sklearn\\utils\\deprecation.py:151: FutureWarning: 'force_all_finite' was renamed to 'ensure_all_finite' in 1.6 and will be removed in 1.8.\n", + " warnings.warn(\n", + "c:\\Users\\svenk\\anaconda3\\envs\\dml_tabpfn\\Lib\\site-packages\\sklearn\\utils\\deprecation.py:151: FutureWarning: 'force_all_finite' was renamed to 'ensure_all_finite' in 1.6 and will be removed in 1.8.\n", + " warnings.warn(\n", + "c:\\Users\\svenk\\anaconda3\\envs\\dml_tabpfn\\Lib\\site-packages\\sklearn\\utils\\deprecation.py:151: FutureWarning: 'force_all_finite' was renamed to 'ensure_all_finite' in 1.6 and will be removed in 1.8.\n", + " warnings.warn(\n", + "c:\\Users\\svenk\\anaconda3\\envs\\dml_tabpfn\\Lib\\site-packages\\tabpfn\\regressor.py:490: UserWarning: Running on CPU with more than 200 samples may be slow.\n", + "Consider using a GPU or the tabpfn-client API: https://github.com/PriorLabs/tabpfn-client\n", + " check_cpu_warning(\n", + "c:\\Users\\svenk\\anaconda3\\envs\\dml_tabpfn\\Lib\\site-packages\\tabpfn\\regressor.py:490: UserWarning: Running on CPU with more than 200 samples may be slow.\n", + "Consider using a GPU or the tabpfn-client API: https://github.com/PriorLabs/tabpfn-client\n", + " check_cpu_warning(\n", + "c:\\Users\\svenk\\anaconda3\\envs\\dml_tabpfn\\Lib\\site-packages\\tabpfn\\regressor.py:490: UserWarning: Running on CPU with more than 200 samples may be slow.\n", + "Consider using a GPU or the tabpfn-client API: https://github.com/PriorLabs/tabpfn-client\n", + " check_cpu_warning(\n", + "c:\\Users\\svenk\\anaconda3\\envs\\dml_tabpfn\\Lib\\site-packages\\tabpfn\\regressor.py:490: UserWarning: Running on CPU with more than 200 samples may be slow.\n", + "Consider using a GPU or the tabpfn-client API: https://github.com/PriorLabs/tabpfn-client\n", + " check_cpu_warning(\n", + "c:\\Users\\svenk\\anaconda3\\envs\\dml_tabpfn\\Lib\\site-packages\\tabpfn\\regressor.py:490: UserWarning: Running on CPU with more than 200 samples may be slow.\n", + "Consider using a GPU or the tabpfn-client API: https://github.com/PriorLabs/tabpfn-client\n", + " check_cpu_warning(\n", + "c:\\Users\\svenk\\anaconda3\\envs\\dml_tabpfn\\Lib\\site-packages\\tabpfn\\regressor.py:490: UserWarning: Running on CPU with more than 200 samples may be slow.\n", + "Consider using a GPU or the tabpfn-client API: https://github.com/PriorLabs/tabpfn-client\n", + " check_cpu_warning(\n", + "c:\\Users\\svenk\\anaconda3\\envs\\dml_tabpfn\\Lib\\site-packages\\tabpfn\\regressor.py:490: UserWarning: Running on CPU with more than 200 samples may be slow.\n", + "Consider using a GPU or the tabpfn-client API: https://github.com/PriorLabs/tabpfn-client\n", + " check_cpu_warning(\n", + "c:\\Users\\svenk\\anaconda3\\envs\\dml_tabpfn\\Lib\\site-packages\\tabpfn\\regressor.py:490: UserWarning: Running on CPU with more than 200 samples may be slow.\n", + "Consider using a GPU or the tabpfn-client API: https://github.com/PriorLabs/tabpfn-client\n", + " check_cpu_warning(\n", + "c:\\Users\\svenk\\anaconda3\\envs\\dml_tabpfn\\Lib\\site-packages\\tabpfn\\regressor.py:490: UserWarning: Running on CPU with more than 200 samples may be slow.\n", + "Consider using a GPU or the tabpfn-client API: https://github.com/PriorLabs/tabpfn-client\n", + " check_cpu_warning(\n", + "c:\\Users\\svenk\\anaconda3\\envs\\dml_tabpfn\\Lib\\site-packages\\tabpfn\\regressor.py:490: UserWarning: Running on CPU with more than 200 samples may be slow.\n", + "Consider using a GPU or the tabpfn-client API: https://github.com/PriorLabs/tabpfn-client\n", + " check_cpu_warning(\n", + "c:\\Users\\svenk\\anaconda3\\envs\\dml_tabpfn\\Lib\\site-packages\\tabpfn\\classifier.py:459: UserWarning: Running on CPU with more than 200 samples may be slow.\n", + "Consider using a GPU or the tabpfn-client API: https://github.com/PriorLabs/tabpfn-client\n", + " check_cpu_warning(\n", + "c:\\Users\\svenk\\anaconda3\\envs\\dml_tabpfn\\Lib\\site-packages\\tabpfn\\classifier.py:459: UserWarning: Running on CPU with more than 200 samples may be slow.\n", + "Consider using a GPU or the tabpfn-client API: https://github.com/PriorLabs/tabpfn-client\n", + " check_cpu_warning(\n", + "c:\\Users\\svenk\\anaconda3\\envs\\dml_tabpfn\\Lib\\site-packages\\tabpfn\\classifier.py:459: UserWarning: Running on CPU with more than 200 samples may be slow.\n", + "Consider using a GPU or the tabpfn-client API: https://github.com/PriorLabs/tabpfn-client\n", + " check_cpu_warning(\n", + "c:\\Users\\svenk\\anaconda3\\envs\\dml_tabpfn\\Lib\\site-packages\\tabpfn\\classifier.py:459: UserWarning: Running on CPU with more than 200 samples may be slow.\n", + "Consider using a GPU or the tabpfn-client API: https://github.com/PriorLabs/tabpfn-client\n", + " check_cpu_warning(\n", + "c:\\Users\\svenk\\anaconda3\\envs\\dml_tabpfn\\Lib\\site-packages\\tabpfn\\classifier.py:459: UserWarning: Running on CPU with more than 200 samples may be slow.\n", + "Consider using a GPU or the tabpfn-client API: https://github.com/PriorLabs/tabpfn-client\n", + " check_cpu_warning(\n", + "c:\\Users\\svenk\\anaconda3\\envs\\dml_tabpfn\\Lib\\site-packages\\tabpfn\\classifier.py:459: UserWarning: Running on CPU with more than 200 samples may be slow.\n", + "Consider using a GPU or the tabpfn-client API: https://github.com/PriorLabs/tabpfn-client\n", + " check_cpu_warning(\n", + "c:\\Users\\svenk\\anaconda3\\envs\\dml_tabpfn\\Lib\\site-packages\\tabpfn\\classifier.py:459: UserWarning: Running on CPU with more than 200 samples may be slow.\n", + "Consider using a GPU or the tabpfn-client API: https://github.com/PriorLabs/tabpfn-client\n", + " check_cpu_warning(\n", + "c:\\Users\\svenk\\anaconda3\\envs\\dml_tabpfn\\Lib\\site-packages\\tabpfn\\classifier.py:459: UserWarning: Running on CPU with more than 200 samples may be slow.\n", + "Consider using a GPU or the tabpfn-client API: https://github.com/PriorLabs/tabpfn-client\n", + " check_cpu_warning(\n", + "c:\\Users\\svenk\\anaconda3\\envs\\dml_tabpfn\\Lib\\site-packages\\tabpfn\\classifier.py:459: UserWarning: Running on CPU with more than 200 samples may be slow.\n", + "Consider using a GPU or the tabpfn-client API: https://github.com/PriorLabs/tabpfn-client\n", + " check_cpu_warning(\n", + "c:\\Users\\svenk\\anaconda3\\envs\\dml_tabpfn\\Lib\\site-packages\\tabpfn\\classifier.py:459: UserWarning: Running on CPU with more than 200 samples may be slow.\n", + "Consider using a GPU or the tabpfn-client API: https://github.com/PriorLabs/tabpfn-client\n", + " check_cpu_warning(\n", + "c:\\Users\\svenk\\anaconda3\\envs\\dml_tabpfn\\Lib\\site-packages\\sklearn\\utils\\deprecation.py:151: FutureWarning: 'force_all_finite' was renamed to 'ensure_all_finite' in 1.6 and will be removed in 1.8.\n", + " warnings.warn(\n", + "c:\\Users\\svenk\\anaconda3\\envs\\dml_tabpfn\\Lib\\site-packages\\sklearn\\utils\\deprecation.py:151: FutureWarning: 'force_all_finite' was renamed to 'ensure_all_finite' in 1.6 and will be removed in 1.8.\n", + " warnings.warn(\n", + "c:\\Users\\svenk\\anaconda3\\envs\\dml_tabpfn\\Lib\\site-packages\\sklearn\\utils\\deprecation.py:151: FutureWarning: 'force_all_finite' was renamed to 'ensure_all_finite' in 1.6 and will be removed in 1.8.\n", + " warnings.warn(\n", + "c:\\Users\\svenk\\anaconda3\\envs\\dml_tabpfn\\Lib\\site-packages\\sklearn\\utils\\deprecation.py:151: FutureWarning: 'force_all_finite' was renamed to 'ensure_all_finite' in 1.6 and will be removed in 1.8.\n", + " warnings.warn(\n", + "c:\\Users\\svenk\\anaconda3\\envs\\dml_tabpfn\\Lib\\site-packages\\tabpfn\\regressor.py:490: UserWarning: Running on CPU with more than 200 samples may be slow.\n", + "Consider using a GPU or the tabpfn-client API: https://github.com/PriorLabs/tabpfn-client\n", + " check_cpu_warning(\n", + "c:\\Users\\svenk\\anaconda3\\envs\\dml_tabpfn\\Lib\\site-packages\\tabpfn\\regressor.py:490: UserWarning: Running on CPU with more than 200 samples may be slow.\n", + "Consider using a GPU or the tabpfn-client API: https://github.com/PriorLabs/tabpfn-client\n", + " check_cpu_warning(\n", + "c:\\Users\\svenk\\anaconda3\\envs\\dml_tabpfn\\Lib\\site-packages\\tabpfn\\regressor.py:490: UserWarning: Running on CPU with more than 200 samples may be slow.\n", + "Consider using a GPU or the tabpfn-client API: https://github.com/PriorLabs/tabpfn-client\n", + " check_cpu_warning(\n", + "c:\\Users\\svenk\\anaconda3\\envs\\dml_tabpfn\\Lib\\site-packages\\tabpfn\\regressor.py:490: UserWarning: Running on CPU with more than 200 samples may be slow.\n", + "Consider using a GPU or the tabpfn-client API: https://github.com/PriorLabs/tabpfn-client\n", + " check_cpu_warning(\n", + "c:\\Users\\svenk\\anaconda3\\envs\\dml_tabpfn\\Lib\\site-packages\\tabpfn\\regressor.py:490: UserWarning: Running on CPU with more than 200 samples may be slow.\n", + "Consider using a GPU or the tabpfn-client API: https://github.com/PriorLabs/tabpfn-client\n", + " check_cpu_warning(\n", + "c:\\Users\\svenk\\anaconda3\\envs\\dml_tabpfn\\Lib\\site-packages\\tabpfn\\regressor.py:490: UserWarning: Running on CPU with more than 200 samples may be slow.\n", + "Consider using a GPU or the tabpfn-client API: https://github.com/PriorLabs/tabpfn-client\n", + " check_cpu_warning(\n", + "c:\\Users\\svenk\\anaconda3\\envs\\dml_tabpfn\\Lib\\site-packages\\tabpfn\\regressor.py:490: UserWarning: Running on CPU with more than 200 samples may be slow.\n", + "Consider using a GPU or the tabpfn-client API: https://github.com/PriorLabs/tabpfn-client\n", + " check_cpu_warning(\n", + "c:\\Users\\svenk\\anaconda3\\envs\\dml_tabpfn\\Lib\\site-packages\\tabpfn\\regressor.py:490: UserWarning: Running on CPU with more than 200 samples may be slow.\n", + "Consider using a GPU or the tabpfn-client API: https://github.com/PriorLabs/tabpfn-client\n", + " check_cpu_warning(\n", + "c:\\Users\\svenk\\anaconda3\\envs\\dml_tabpfn\\Lib\\site-packages\\tabpfn\\regressor.py:490: UserWarning: Running on CPU with more than 200 samples may be slow.\n", + "Consider using a GPU or the tabpfn-client API: https://github.com/PriorLabs/tabpfn-client\n", + " check_cpu_warning(\n", + "c:\\Users\\svenk\\anaconda3\\envs\\dml_tabpfn\\Lib\\site-packages\\tabpfn\\regressor.py:490: UserWarning: Running on CPU with more than 200 samples may be slow.\n", + "Consider using a GPU or the tabpfn-client API: https://github.com/PriorLabs/tabpfn-client\n", + " check_cpu_warning(\n", + "c:\\Users\\svenk\\anaconda3\\envs\\dml_tabpfn\\Lib\\site-packages\\tabpfn\\classifier.py:459: UserWarning: Running on CPU with more than 200 samples may be slow.\n", + "Consider using a GPU or the tabpfn-client API: https://github.com/PriorLabs/tabpfn-client\n", + " check_cpu_warning(\n", + "c:\\Users\\svenk\\anaconda3\\envs\\dml_tabpfn\\Lib\\site-packages\\tabpfn\\classifier.py:459: UserWarning: Running on CPU with more than 200 samples may be slow.\n", + "Consider using a GPU or the tabpfn-client API: https://github.com/PriorLabs/tabpfn-client\n", + " check_cpu_warning(\n", + "c:\\Users\\svenk\\anaconda3\\envs\\dml_tabpfn\\Lib\\site-packages\\tabpfn\\classifier.py:459: UserWarning: Running on CPU with more than 200 samples may be slow.\n", + "Consider using a GPU or the tabpfn-client API: https://github.com/PriorLabs/tabpfn-client\n", + " check_cpu_warning(\n", + "c:\\Users\\svenk\\anaconda3\\envs\\dml_tabpfn\\Lib\\site-packages\\tabpfn\\classifier.py:459: UserWarning: Running on CPU with more than 200 samples may be slow.\n", + "Consider using a GPU or the tabpfn-client API: https://github.com/PriorLabs/tabpfn-client\n", + " check_cpu_warning(\n", + "c:\\Users\\svenk\\anaconda3\\envs\\dml_tabpfn\\Lib\\site-packages\\tabpfn\\classifier.py:459: UserWarning: Running on CPU with more than 200 samples may be slow.\n", + "Consider using a GPU or the tabpfn-client API: https://github.com/PriorLabs/tabpfn-client\n", + " check_cpu_warning(\n", + "c:\\Users\\svenk\\anaconda3\\envs\\dml_tabpfn\\Lib\\site-packages\\tabpfn\\classifier.py:459: UserWarning: Running on CPU with more than 200 samples may be slow.\n", + "Consider using a GPU or the tabpfn-client API: https://github.com/PriorLabs/tabpfn-client\n", + " check_cpu_warning(\n", + "c:\\Users\\svenk\\anaconda3\\envs\\dml_tabpfn\\Lib\\site-packages\\tabpfn\\classifier.py:459: UserWarning: Running on CPU with more than 200 samples may be slow.\n", + "Consider using a GPU or the tabpfn-client API: https://github.com/PriorLabs/tabpfn-client\n", + " check_cpu_warning(\n", + "c:\\Users\\svenk\\anaconda3\\envs\\dml_tabpfn\\Lib\\site-packages\\tabpfn\\classifier.py:459: UserWarning: Running on CPU with more than 200 samples may be slow.\n", + "Consider using a GPU or the tabpfn-client API: https://github.com/PriorLabs/tabpfn-client\n", + " check_cpu_warning(\n", + "c:\\Users\\svenk\\anaconda3\\envs\\dml_tabpfn\\Lib\\site-packages\\tabpfn\\classifier.py:459: UserWarning: Running on CPU with more than 200 samples may be slow.\n", + "Consider using a GPU or the tabpfn-client API: https://github.com/PriorLabs/tabpfn-client\n", + " check_cpu_warning(\n", + "c:\\Users\\svenk\\anaconda3\\envs\\dml_tabpfn\\Lib\\site-packages\\tabpfn\\classifier.py:459: UserWarning: Running on CPU with more than 200 samples may be slow.\n", + "Consider using a GPU or the tabpfn-client API: https://github.com/PriorLabs/tabpfn-client\n", + " check_cpu_warning(\n", + "c:\\Users\\svenk\\anaconda3\\envs\\dml_tabpfn\\Lib\\site-packages\\sklearn\\utils\\deprecation.py:151: FutureWarning: 'force_all_finite' was renamed to 'ensure_all_finite' in 1.6 and will be removed in 1.8.\n", + " warnings.warn(\n", + "c:\\Users\\svenk\\anaconda3\\envs\\dml_tabpfn\\Lib\\site-packages\\sklearn\\utils\\deprecation.py:151: FutureWarning: 'force_all_finite' was renamed to 'ensure_all_finite' in 1.6 and will be removed in 1.8.\n", + " warnings.warn(\n", + "c:\\Users\\svenk\\anaconda3\\envs\\dml_tabpfn\\Lib\\site-packages\\sklearn\\utils\\deprecation.py:151: FutureWarning: 'force_all_finite' was renamed to 'ensure_all_finite' in 1.6 and will be removed in 1.8.\n", + " warnings.warn(\n", + "c:\\Users\\svenk\\anaconda3\\envs\\dml_tabpfn\\Lib\\site-packages\\sklearn\\utils\\deprecation.py:151: FutureWarning: 'force_all_finite' was renamed to 'ensure_all_finite' in 1.6 and will be removed in 1.8.\n", + " warnings.warn(\n", + "c:\\Users\\svenk\\anaconda3\\envs\\dml_tabpfn\\Lib\\site-packages\\tabpfn\\regressor.py:490: UserWarning: Running on CPU with more than 200 samples may be slow.\n", + "Consider using a GPU or the tabpfn-client API: https://github.com/PriorLabs/tabpfn-client\n", + " check_cpu_warning(\n", + "c:\\Users\\svenk\\anaconda3\\envs\\dml_tabpfn\\Lib\\site-packages\\tabpfn\\regressor.py:490: UserWarning: Running on CPU with more than 200 samples may be slow.\n", + "Consider using a GPU or the tabpfn-client API: https://github.com/PriorLabs/tabpfn-client\n", + " check_cpu_warning(\n", + "c:\\Users\\svenk\\anaconda3\\envs\\dml_tabpfn\\Lib\\site-packages\\tabpfn\\regressor.py:490: UserWarning: Running on CPU with more than 200 samples may be slow.\n", + "Consider using a GPU or the tabpfn-client API: https://github.com/PriorLabs/tabpfn-client\n", + " check_cpu_warning(\n", + "c:\\Users\\svenk\\anaconda3\\envs\\dml_tabpfn\\Lib\\site-packages\\tabpfn\\regressor.py:490: UserWarning: Running on CPU with more than 200 samples may be slow.\n", + "Consider using a GPU or the tabpfn-client API: https://github.com/PriorLabs/tabpfn-client\n", + " check_cpu_warning(\n", + "c:\\Users\\svenk\\anaconda3\\envs\\dml_tabpfn\\Lib\\site-packages\\tabpfn\\regressor.py:490: UserWarning: Running on CPU with more than 200 samples may be slow.\n", + "Consider using a GPU or the tabpfn-client API: https://github.com/PriorLabs/tabpfn-client\n", + " check_cpu_warning(\n", + "c:\\Users\\svenk\\anaconda3\\envs\\dml_tabpfn\\Lib\\site-packages\\tabpfn\\regressor.py:490: UserWarning: Running on CPU with more than 200 samples may be slow.\n", + "Consider using a GPU or the tabpfn-client API: https://github.com/PriorLabs/tabpfn-client\n", + " check_cpu_warning(\n", + "c:\\Users\\svenk\\anaconda3\\envs\\dml_tabpfn\\Lib\\site-packages\\tabpfn\\regressor.py:490: UserWarning: Running on CPU with more than 200 samples may be slow.\n", + "Consider using a GPU or the tabpfn-client API: https://github.com/PriorLabs/tabpfn-client\n", + " check_cpu_warning(\n", + "c:\\Users\\svenk\\anaconda3\\envs\\dml_tabpfn\\Lib\\site-packages\\tabpfn\\regressor.py:490: UserWarning: Running on CPU with more than 200 samples may be slow.\n", + "Consider using a GPU or the tabpfn-client API: https://github.com/PriorLabs/tabpfn-client\n", + " check_cpu_warning(\n", + "c:\\Users\\svenk\\anaconda3\\envs\\dml_tabpfn\\Lib\\site-packages\\tabpfn\\regressor.py:490: UserWarning: Running on CPU with more than 200 samples may be slow.\n", + "Consider using a GPU or the tabpfn-client API: https://github.com/PriorLabs/tabpfn-client\n", + " check_cpu_warning(\n", + "c:\\Users\\svenk\\anaconda3\\envs\\dml_tabpfn\\Lib\\site-packages\\tabpfn\\regressor.py:490: UserWarning: Running on CPU with more than 200 samples may be slow.\n", + "Consider using a GPU or the tabpfn-client API: https://github.com/PriorLabs/tabpfn-client\n", + " check_cpu_warning(\n", + "c:\\Users\\svenk\\anaconda3\\envs\\dml_tabpfn\\Lib\\site-packages\\tabpfn\\classifier.py:459: UserWarning: Running on CPU with more than 200 samples may be slow.\n", + "Consider using a GPU or the tabpfn-client API: https://github.com/PriorLabs/tabpfn-client\n", + " check_cpu_warning(\n", + "c:\\Users\\svenk\\anaconda3\\envs\\dml_tabpfn\\Lib\\site-packages\\tabpfn\\classifier.py:459: UserWarning: Running on CPU with more than 200 samples may be slow.\n", + "Consider using a GPU or the tabpfn-client API: https://github.com/PriorLabs/tabpfn-client\n", + " check_cpu_warning(\n", + "c:\\Users\\svenk\\anaconda3\\envs\\dml_tabpfn\\Lib\\site-packages\\tabpfn\\classifier.py:459: UserWarning: Running on CPU with more than 200 samples may be slow.\n", + "Consider using a GPU or the tabpfn-client API: https://github.com/PriorLabs/tabpfn-client\n", + " check_cpu_warning(\n", + "c:\\Users\\svenk\\anaconda3\\envs\\dml_tabpfn\\Lib\\site-packages\\tabpfn\\classifier.py:459: UserWarning: Running on CPU with more than 200 samples may be slow.\n", + "Consider using a GPU or the tabpfn-client API: https://github.com/PriorLabs/tabpfn-client\n", + " check_cpu_warning(\n", + "c:\\Users\\svenk\\anaconda3\\envs\\dml_tabpfn\\Lib\\site-packages\\tabpfn\\classifier.py:459: UserWarning: Running on CPU with more than 200 samples may be slow.\n", + "Consider using a GPU or the tabpfn-client API: https://github.com/PriorLabs/tabpfn-client\n", + " check_cpu_warning(\n", + "c:\\Users\\svenk\\anaconda3\\envs\\dml_tabpfn\\Lib\\site-packages\\tabpfn\\classifier.py:459: UserWarning: Running on CPU with more than 200 samples may be slow.\n", + "Consider using a GPU or the tabpfn-client API: https://github.com/PriorLabs/tabpfn-client\n", + " check_cpu_warning(\n", + "c:\\Users\\svenk\\anaconda3\\envs\\dml_tabpfn\\Lib\\site-packages\\tabpfn\\classifier.py:459: UserWarning: Running on CPU with more than 200 samples may be slow.\n", + "Consider using a GPU or the tabpfn-client API: https://github.com/PriorLabs/tabpfn-client\n", + " check_cpu_warning(\n", + "c:\\Users\\svenk\\anaconda3\\envs\\dml_tabpfn\\Lib\\site-packages\\tabpfn\\classifier.py:459: UserWarning: Running on CPU with more than 200 samples may be slow.\n", + "Consider using a GPU or the tabpfn-client API: https://github.com/PriorLabs/tabpfn-client\n", + " check_cpu_warning(\n", + "c:\\Users\\svenk\\anaconda3\\envs\\dml_tabpfn\\Lib\\site-packages\\tabpfn\\classifier.py:459: UserWarning: Running on CPU with more than 200 samples may be slow.\n", + "Consider using a GPU or the tabpfn-client API: https://github.com/PriorLabs/tabpfn-client\n", + " check_cpu_warning(\n", + "c:\\Users\\svenk\\anaconda3\\envs\\dml_tabpfn\\Lib\\site-packages\\tabpfn\\classifier.py:459: UserWarning: Running on CPU with more than 200 samples may be slow.\n", + "Consider using a GPU or the tabpfn-client API: https://github.com/PriorLabs/tabpfn-client\n", + " check_cpu_warning(\n", + "c:\\Users\\svenk\\anaconda3\\envs\\dml_tabpfn\\Lib\\site-packages\\sklearn\\utils\\deprecation.py:151: FutureWarning: 'force_all_finite' was renamed to 'ensure_all_finite' in 1.6 and will be removed in 1.8.\n", + " warnings.warn(\n", + "c:\\Users\\svenk\\anaconda3\\envs\\dml_tabpfn\\Lib\\site-packages\\sklearn\\utils\\deprecation.py:151: FutureWarning: 'force_all_finite' was renamed to 'ensure_all_finite' in 1.6 and will be removed in 1.8.\n", + " warnings.warn(\n", + "c:\\Users\\svenk\\anaconda3\\envs\\dml_tabpfn\\Lib\\site-packages\\sklearn\\utils\\deprecation.py:151: FutureWarning: 'force_all_finite' was renamed to 'ensure_all_finite' in 1.6 and will be removed in 1.8.\n", + " warnings.warn(\n", + "c:\\Users\\svenk\\anaconda3\\envs\\dml_tabpfn\\Lib\\site-packages\\sklearn\\utils\\deprecation.py:151: FutureWarning: 'force_all_finite' was renamed to 'ensure_all_finite' in 1.6 and will be removed in 1.8.\n", + " warnings.warn(\n", + "c:\\Users\\svenk\\anaconda3\\envs\\dml_tabpfn\\Lib\\site-packages\\tabpfn\\regressor.py:490: UserWarning: Running on CPU with more than 200 samples may be slow.\n", + "Consider using a GPU or the tabpfn-client API: https://github.com/PriorLabs/tabpfn-client\n", + " check_cpu_warning(\n", + "c:\\Users\\svenk\\anaconda3\\envs\\dml_tabpfn\\Lib\\site-packages\\tabpfn\\regressor.py:490: UserWarning: Running on CPU with more than 200 samples may be slow.\n", + "Consider using a GPU or the tabpfn-client API: https://github.com/PriorLabs/tabpfn-client\n", + " check_cpu_warning(\n", + "c:\\Users\\svenk\\anaconda3\\envs\\dml_tabpfn\\Lib\\site-packages\\tabpfn\\regressor.py:490: UserWarning: Running on CPU with more than 200 samples may be slow.\n", + "Consider using a GPU or the tabpfn-client API: https://github.com/PriorLabs/tabpfn-client\n", + " check_cpu_warning(\n", + "c:\\Users\\svenk\\anaconda3\\envs\\dml_tabpfn\\Lib\\site-packages\\tabpfn\\regressor.py:490: UserWarning: Running on CPU with more than 200 samples may be slow.\n", + "Consider using a GPU or the tabpfn-client API: https://github.com/PriorLabs/tabpfn-client\n", + " check_cpu_warning(\n", + "c:\\Users\\svenk\\anaconda3\\envs\\dml_tabpfn\\Lib\\site-packages\\tabpfn\\regressor.py:490: UserWarning: Running on CPU with more than 200 samples may be slow.\n", + "Consider using a GPU or the tabpfn-client API: https://github.com/PriorLabs/tabpfn-client\n", + " check_cpu_warning(\n", + "c:\\Users\\svenk\\anaconda3\\envs\\dml_tabpfn\\Lib\\site-packages\\tabpfn\\regressor.py:490: UserWarning: Running on CPU with more than 200 samples may be slow.\n", + "Consider using a GPU or the tabpfn-client API: https://github.com/PriorLabs/tabpfn-client\n", + " check_cpu_warning(\n", + "c:\\Users\\svenk\\anaconda3\\envs\\dml_tabpfn\\Lib\\site-packages\\tabpfn\\regressor.py:490: UserWarning: Running on CPU with more than 200 samples may be slow.\n", + "Consider using a GPU or the tabpfn-client API: https://github.com/PriorLabs/tabpfn-client\n", + " check_cpu_warning(\n", + "c:\\Users\\svenk\\anaconda3\\envs\\dml_tabpfn\\Lib\\site-packages\\tabpfn\\regressor.py:490: UserWarning: Running on CPU with more than 200 samples may be slow.\n", + "Consider using a GPU or the tabpfn-client API: https://github.com/PriorLabs/tabpfn-client\n", + " check_cpu_warning(\n", + "c:\\Users\\svenk\\anaconda3\\envs\\dml_tabpfn\\Lib\\site-packages\\tabpfn\\regressor.py:490: UserWarning: Running on CPU with more than 200 samples may be slow.\n", + "Consider using a GPU or the tabpfn-client API: https://github.com/PriorLabs/tabpfn-client\n", + " check_cpu_warning(\n", + "c:\\Users\\svenk\\anaconda3\\envs\\dml_tabpfn\\Lib\\site-packages\\tabpfn\\regressor.py:490: UserWarning: Running on CPU with more than 200 samples may be slow.\n", + "Consider using a GPU or the tabpfn-client API: https://github.com/PriorLabs/tabpfn-client\n", + " check_cpu_warning(\n", + "c:\\Users\\svenk\\anaconda3\\envs\\dml_tabpfn\\Lib\\site-packages\\tabpfn\\classifier.py:459: UserWarning: Running on CPU with more than 200 samples may be slow.\n", + "Consider using a GPU or the tabpfn-client API: https://github.com/PriorLabs/tabpfn-client\n", + " check_cpu_warning(\n", + "c:\\Users\\svenk\\anaconda3\\envs\\dml_tabpfn\\Lib\\site-packages\\tabpfn\\classifier.py:459: UserWarning: Running on CPU with more than 200 samples may be slow.\n", + "Consider using a GPU or the tabpfn-client API: https://github.com/PriorLabs/tabpfn-client\n", + " check_cpu_warning(\n", + "c:\\Users\\svenk\\anaconda3\\envs\\dml_tabpfn\\Lib\\site-packages\\tabpfn\\classifier.py:459: UserWarning: Running on CPU with more than 200 samples may be slow.\n", + "Consider using a GPU or the tabpfn-client API: https://github.com/PriorLabs/tabpfn-client\n", + " check_cpu_warning(\n", + "c:\\Users\\svenk\\anaconda3\\envs\\dml_tabpfn\\Lib\\site-packages\\tabpfn\\classifier.py:459: UserWarning: Running on CPU with more than 200 samples may be slow.\n", + "Consider using a GPU or the tabpfn-client API: https://github.com/PriorLabs/tabpfn-client\n", + " check_cpu_warning(\n", + "c:\\Users\\svenk\\anaconda3\\envs\\dml_tabpfn\\Lib\\site-packages\\tabpfn\\classifier.py:459: UserWarning: Running on CPU with more than 200 samples may be slow.\n", + "Consider using a GPU or the tabpfn-client API: https://github.com/PriorLabs/tabpfn-client\n", + " check_cpu_warning(\n", + "c:\\Users\\svenk\\anaconda3\\envs\\dml_tabpfn\\Lib\\site-packages\\tabpfn\\classifier.py:459: UserWarning: Running on CPU with more than 200 samples may be slow.\n", + "Consider using a GPU or the tabpfn-client API: https://github.com/PriorLabs/tabpfn-client\n", + " check_cpu_warning(\n", + "c:\\Users\\svenk\\anaconda3\\envs\\dml_tabpfn\\Lib\\site-packages\\tabpfn\\classifier.py:459: UserWarning: Running on CPU with more than 200 samples may be slow.\n", + "Consider using a GPU or the tabpfn-client API: https://github.com/PriorLabs/tabpfn-client\n", + " check_cpu_warning(\n", + "c:\\Users\\svenk\\anaconda3\\envs\\dml_tabpfn\\Lib\\site-packages\\tabpfn\\classifier.py:459: UserWarning: Running on CPU with more than 200 samples may be slow.\n", + "Consider using a GPU or the tabpfn-client API: https://github.com/PriorLabs/tabpfn-client\n", + " check_cpu_warning(\n", + "c:\\Users\\svenk\\anaconda3\\envs\\dml_tabpfn\\Lib\\site-packages\\tabpfn\\classifier.py:459: UserWarning: Running on CPU with more than 200 samples may be slow.\n", + "Consider using a GPU or the tabpfn-client API: https://github.com/PriorLabs/tabpfn-client\n", + " check_cpu_warning(\n", + "c:\\Users\\svenk\\anaconda3\\envs\\dml_tabpfn\\Lib\\site-packages\\tabpfn\\classifier.py:459: UserWarning: Running on CPU with more than 200 samples may be slow.\n", + "Consider using a GPU or the tabpfn-client API: https://github.com/PriorLabs/tabpfn-client\n", + " check_cpu_warning(\n" ] }, { @@ -888,199 +1832,197 @@ " 0\n", " RandomForest\n", " 1.0\n", - " 2.744951\n", - " -5.657416\n", - " 11.147319\n", + " 1.325370\n", + " -4.218919\n", + " 6.869658\n", " \n", " \n", " 1\n", " RandomForest\n", " 2.0\n", - " 4.550421\n", - " -1.794276\n", - " 10.895118\n", + " 5.702248\n", + " 1.094595\n", + " 10.309901\n", " \n", " \n", " 2\n", " RandomForest\n", " 3.0\n", - " 10.640574\n", - " 4.320402\n", - " 16.960747\n", + " 8.738424\n", + " 4.077025\n", + " 13.399824\n", " \n", " \n", " 3\n", " RandomForest\n", " 4.0\n", - " 9.315172\n", - " 3.212198\n", - " 15.418145\n", + " 6.632407\n", + " 1.979031\n", + " 11.285783\n", " \n", " \n", " 4\n", " RandomForest\n", " 5.0\n", - " 11.857786\n", - " 2.759284\n", - " 20.956289\n", + " 7.967206\n", + " 2.359083\n", + " 13.575329\n", " \n", " \n", " 5\n", " Linear\n", " 1.0\n", - " 3.709369\n", - " -1.784583\n", - " 9.203320\n", + " 4.971059\n", + " -1.231281\n", + " 11.173400\n", " \n", " \n", " 6\n", " Linear\n", " 2.0\n", - " 8.231820\n", - " 3.262753\n", - " 13.200887\n", + " 7.963367\n", + " 2.882965\n", + " 13.043769\n", " \n", " \n", " 7\n", " Linear\n", " 3.0\n", - " 10.332460\n", - " 6.498161\n", - " 14.166758\n", + " 10.747538\n", + " 6.745536\n", + " 14.749540\n", " \n", " \n", " 8\n", " Linear\n", " 4.0\n", - " 11.406049\n", - " 7.861799\n", - " 14.950299\n", + " 11.594317\n", + " 7.889236\n", + " 15.299398\n", " \n", " \n", " 9\n", " Linear\n", " 5.0\n", - " 7.050111\n", - " 3.434561\n", - " 10.665662\n", + " 6.626998\n", + " 3.014525\n", + " 10.239471\n", " \n", " \n", " 10\n", " LightGBM\n", " 1.0\n", - " 6.471167\n", - " -9.936290\n", - " 22.878624\n", + " 2.156988\n", + " -16.411264\n", + " 20.725240\n", " \n", " \n", " 11\n", " LightGBM\n", " 2.0\n", - " 16.217765\n", - " -0.008862\n", - " 32.444392\n", + " 5.973140\n", + " -7.915225\n", + " 19.861505\n", " \n", " \n", " 12\n", " LightGBM\n", " 3.0\n", - " 7.282430\n", - " -8.066985\n", - " 22.631844\n", + " 10.824140\n", + " -4.925410\n", + " 26.573689\n", " \n", " \n", " 13\n", " LightGBM\n", " 4.0\n", - " 14.825065\n", - " 2.026508\n", - " 27.623622\n", + " 11.547482\n", + " -2.465476\n", + " 25.560440\n", " \n", " \n", " 14\n", " LightGBM\n", " 5.0\n", - " 19.515932\n", - " 0.618268\n", - " 38.413596\n", + " 12.528573\n", + " -2.627754\n", + " 27.684899\n", " \n", " \n", " 15\n", " TabPFN\n", " 1.0\n", - " 2.037887\n", - " 0.606313\n", - " 3.469461\n", + " 1.554511\n", + " 0.216895\n", + " 2.892126\n", " \n", " \n", " 16\n", " TabPFN\n", " 2.0\n", - " 7.166484\n", - " 6.182400\n", - " 8.150568\n", + " 6.998593\n", + " 6.048500\n", + " 7.948686\n", " \n", " \n", " 17\n", " TabPFN\n", " 3.0\n", - " 10.081306\n", - " 8.974319\n", - " 11.188293\n", + " 10.345530\n", + " 9.048832\n", + " 11.642228\n", " \n", " \n", " 18\n", " TabPFN\n", " 4.0\n", - " 10.353707\n", - " 9.355981\n", - " 11.351433\n", + " 10.416777\n", + " 9.453538\n", + " 11.380015\n", " \n", " \n", " 19\n", " TabPFN\n", " 5.0\n", - " 9.794396\n", - " 8.698071\n", - " 10.890721\n", + " 9.827502\n", + " 8.873946\n", + " 10.781058\n", " \n", " \n", "\n", "" ], "text/plain": [ - " learner treatment_level ate ci_lower ci_upper\n", - "0 RandomForest 1.0 2.744951 -5.657416 11.147319\n", - "1 RandomForest 2.0 4.550421 -1.794276 10.895118\n", - "2 RandomForest 3.0 10.640574 4.320402 16.960747\n", - "3 RandomForest 4.0 9.315172 3.212198 15.418145\n", - "4 RandomForest 5.0 11.857786 2.759284 20.956289\n", - "5 Linear 1.0 3.709369 -1.784583 9.203320\n", - "6 Linear 2.0 8.231820 3.262753 13.200887\n", - "7 Linear 3.0 10.332460 6.498161 14.166758\n", - "8 Linear 4.0 11.406049 7.861799 14.950299\n", - "9 Linear 5.0 7.050111 3.434561 10.665662\n", - "10 LightGBM 1.0 6.471167 -9.936290 22.878624\n", - "11 LightGBM 2.0 16.217765 -0.008862 32.444392\n", - "12 LightGBM 3.0 7.282430 -8.066985 22.631844\n", - "13 LightGBM 4.0 14.825065 2.026508 27.623622\n", - "14 LightGBM 5.0 19.515932 0.618268 38.413596\n", - "15 TabPFN 1.0 2.037887 0.606313 3.469461\n", - "16 TabPFN 2.0 7.166484 6.182400 8.150568\n", - "17 TabPFN 3.0 10.081306 8.974319 11.188293\n", - "18 TabPFN 4.0 10.353707 9.355981 11.351433\n", - "19 TabPFN 5.0 9.794396 8.698071 10.890721" + " learner treatment_level ate ci_lower ci_upper\n", + "0 RandomForest 1.0 1.325370 -4.218919 6.869658\n", + "1 RandomForest 2.0 5.702248 1.094595 10.309901\n", + "2 RandomForest 3.0 8.738424 4.077025 13.399824\n", + "3 RandomForest 4.0 6.632407 1.979031 11.285783\n", + "4 RandomForest 5.0 7.967206 2.359083 13.575329\n", + "5 Linear 1.0 4.971059 -1.231281 11.173400\n", + "6 Linear 2.0 7.963367 2.882965 13.043769\n", + "7 Linear 3.0 10.747538 6.745536 14.749540\n", + "8 Linear 4.0 11.594317 7.889236 15.299398\n", + "9 Linear 5.0 6.626998 3.014525 10.239471\n", + "10 LightGBM 1.0 2.156988 -16.411264 20.725240\n", + "11 LightGBM 2.0 5.973140 -7.915225 19.861505\n", + "12 LightGBM 3.0 10.824140 -4.925410 26.573689\n", + "13 LightGBM 4.0 11.547482 -2.465476 25.560440\n", + "14 LightGBM 5.0 12.528573 -2.627754 27.684899\n", + "15 TabPFN 1.0 1.554511 0.216895 2.892126\n", + "16 TabPFN 2.0 6.998593 6.048500 7.948686\n", + "17 TabPFN 3.0 10.345530 9.048832 11.642228\n", + "18 TabPFN 4.0 10.416777 9.453538 11.380015\n", + "19 TabPFN 5.0 9.827502 8.873946 10.781058" ] }, - "execution_count": 6, + "execution_count": 9, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "# Estimate causal contrasts (ATEs) for all models (difference to reference level 0)\n", - "treatment_levels = np.unique(dml_data.d)\n", - "reference_level = 0\n", + "reference_level = 0 \n", "\n", "apo_results = []\n", "causal_contrast_results = []\n", @@ -1128,15 +2070,28 @@ "df_all_ates" ] }, + { + "cell_type": "markdown", + "id": "9c4a4e31", + "metadata": {}, + "source": [ + "### Visualizing Average Potential Outcomes\n", + "\n", + "Let's compare the estimated APOs across all methods with their true values. The plot shows:\n", + "- **Estimated APOs**: Point estimates with 95% confidence intervals for each method\n", + "- **True APOs**: Red horizontal lines showing the oracle values\n", + "- **Treatment levels**: Different dosage levels of the treatment (0 = no treatment)" + ] + }, { "cell_type": "code", - "execution_count": 7, + "execution_count": 10, "id": "6076a90e", "metadata": {}, "outputs": [ { "data": { - "image/png": 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", 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", 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" ] @@ -1192,7 +2147,7 @@ " xmin=xmin_rel, xmax=xmax_rel,\n", " linewidth=3, label='True APO' if i == 0 else \"\")\n", "\n", - "plt.title('Estimated APO and 95% Confidence Interval by Treatment Level (All Learners)')\n", + "plt.title('Estimated APO and 95% Confidence Interval by Treatment Level')\n", "plt.xlabel('Treatment Level')\n", "plt.ylabel('Average Potential Outcome (APO)')\n", "plt.xticks(sorted(df_all_apos['treatment_level'].unique()))\n", @@ -1201,15 +2156,39 @@ "plt.show()" ] }, + { + "cell_type": "markdown", + "id": "95e25c9e", + "metadata": {}, + "source": [ + "It is quite clear to see, that without tuning the hyperparameters of the models, the TabPFN model achieves the best performance (smallest confidence intervals) across all treatment levels." + ] + }, + { + "cell_type": "markdown", + "id": "aee3fc20", + "metadata": {}, + "source": [ + "### Visualizing Average Treatment Effects\n", + "\n", + "Now let's examine the Average Treatment Effects (ATEs), which represent the causal effect of each treatment level compared to the reference level (no treatment). The ATE for treatment level $d$ is defined as:\n", + "\n", + "$$\\text{ATE}_d = \\mathbb{E}[Y(d)] - \\mathbb{E}[Y(0)]$$" + ] + }, { "cell_type": "code", - "execution_count": 8, + "execution_count": 11, "id": "bd512fd4", - "metadata": {}, + "metadata": { + "tags": [ + "nbsphinx-gallery" + ] + }, "outputs": [ { "data": { - "image/png": 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", "text/plain": [ "
" ] @@ -1266,7 +2245,7 @@ " xmin=xmin_rel, xmax=xmax_rel,\n", " linewidth=3, label='True ATE' if i == 0 else \"\")\n", "\n", - "plt.title('Estimated ATE and 95% Confidence Interval by Treatment Level (All Learners)')\n", + "plt.title('Estimated ATE and 95% Confidence Interval by Treatment Level')\n", "plt.xlabel('Treatment Level (vs. 0)')\n", "plt.ylabel('ATE')\n", "plt.xticks(sorted(df_all_ates['treatment_level'].unique()))\n", @@ -1275,9 +2254,25 @@ "plt.show()" ] }, + { + "cell_type": "markdown", + "id": "391d7fb2", + "metadata": {}, + "source": [ + "### Model Performance Evaluation\n", + "\n", + "To understand why different methods perform differently, let's examine the performance of the underlying machine learning models used for the nuisance functions. DoubleML provides access to performance metrics for each component:\n", + "\n", + "- **RMSE g0**: Root Mean Square Error for the outcome model when treatment $D \\neq d$\n", + "- **RMSE g1**: Root Mean Square Error for the outcome model when treatment $D = d$\n", + "- **LogLoss m**: Logarithmic loss for the propensity score model (treatment assignment prediction)\n", + "\n", + "Better performance on these nuisance functions typically translates to more accurate causal estimates." + ] + }, { "cell_type": "code", - "execution_count": 9, + "execution_count": 12, "id": "9d683935", "metadata": {}, "outputs": [ @@ -1291,30 +2286,30 @@ "================================================================================\n", "Treatment_Level 1.0 2.0 3.0 4.0 5.0\n", "Learner \n", - "LightGBM 15.0193 11.2384 15.1283 14.7045 14.3642\n", - "Linear 21.3597 17.3510 20.5465 21.0926 21.1883\n", - "RandomForest 14.7957 11.0708 15.0855 14.6947 14.9856\n", - "TabPFN 9.6678 3.0042 9.9569 9.8757 9.7987\n", + "LightGBM 15.2287 11.1751 15.2422 15.1509 14.8526\n", + "Linear 21.1795 17.4716 20.3264 20.9783 21.0718\n", + "RandomForest 14.3689 11.4111 14.5989 14.5166 14.3319\n", + "TabPFN 10.0397 2.8823 10.5563 9.9802 10.2092\n", "\n", "\n", "RMSE g1 by Learner and Treatment Level:\n", "================================================================================\n", "Treatment_Level 1.0 2.0 3.0 4.0 5.0\n", "Learner \n", - "LightGBM 16.5259 31.5894 20.1407 18.7611 16.3981\n", - "Linear 16.9486 29.5049 20.8541 17.9796 16.7760\n", - "RandomForest 14.7852 26.5536 19.5189 16.7581 16.0231\n", - "TabPFN 3.3231 19.0293 4.9100 5.7262 5.2918\n", + "LightGBM 17.2116 31.2223 20.1003 18.2962 15.7370\n", + "Linear 16.6144 30.1755 21.2086 18.4788 16.9636\n", + "RandomForest 16.0477 25.8403 18.6678 19.0742 15.5213\n", + "TabPFN 3.3651 16.5897 4.8409 6.4998 4.9453\n", "\n", "\n", "LogLoss m by Learner and Treatment Level:\n", "================================================================================\n", "Treatment_Level 1.0 2.0 3.0 4.0 5.0\n", "Learner \n", - "LightGBM 0.5898 0.5076 0.5115 0.5599 0.5599\n", - "Linear 0.4791 0.4333 0.4240 0.4423 0.4652\n", - "RandomForest 0.5216 0.4704 0.4458 0.4867 0.5053\n", - "TabPFN 0.4770 0.4363 0.4310 0.4437 0.4652\n" + "LightGBM 0.5732 0.5118 0.4976 0.5644 0.5650\n", + "Linear 0.4822 0.4352 0.4252 0.4460 0.4660\n", + "RandomForest 0.5277 0.4642 0.4364 0.4860 0.5101\n", + "TabPFN 0.4777 0.4351 0.4319 0.4461 0.4649\n" ] } ], @@ -1373,9 +2368,19 @@ "print(pivot_logloss.to_string())" ] }, + { + "cell_type": "markdown", + "id": "96d1cb08", + "metadata": {}, + "source": [ + "### Performance Summary and Insights\n", + "\n", + "Let's summarize the average performance across all treatment levels to identify the best-performing methods:" + ] + }, { "cell_type": "code", - "execution_count": 10, + "execution_count": 13, "id": "3fb531ef", "metadata": {}, "outputs": [ @@ -1388,10 +2393,10 @@ "------------------------------------------------------------\n", " RMSE_g0 RMSE_g1 LogLoss_m\n", "Learner \n", - "LightGBM 14.0909 20.6830 0.5457\n", - "Linear 20.3076 20.4126 0.4488\n", - "RandomForest 14.1265 18.7278 0.4860\n", - "TabPFN 8.4607 7.6561 0.4506\n" + "LightGBM 14.3299 20.5135 0.5424\n", + "Linear 20.2055 20.6882 0.4509\n", + "RandomForest 13.8455 19.0303 0.4849\n", + "TabPFN 8.7335 7.2482 0.4511\n" ] } ], @@ -1404,6 +2409,20 @@ "summary_stats = df_performance.groupby('Learner')[['RMSE_g0', 'RMSE_g1', 'LogLoss_m']].mean().round(4)\n", "print(summary_stats)" ] + }, + { + "cell_type": "markdown", + "id": "99cefd29", + "metadata": {}, + "source": [ + "## Key Takeaways\n", + "\n", + "This example demonstrates several important findings about using TabPFN for causal inference.\n", + "\n", + "- **Outcome modeling**: TabPFN significantly outperforms traditional methods for both g0 and g1 functions, with much lower RMSE values\n", + "- **Causal estimates**: The superior nuisance function performance translates to more accurate APO and ATE estimates\n", + "- **No hyperparameter tuning**: TabPFN achieves these results without any model-specific tuning\n" + ] } ], "metadata": { From f4a9a56a10b74a03caa99ab871a78dcff5a73a28 Mon Sep 17 00:00:00 2001 From: SvenKlaassen Date: Mon, 11 Aug 2025 09:48:44 +0200 Subject: [PATCH 3/5] update requirements and index --- doc/examples/index.rst | 1 + requirements.txt | 3 ++- 2 files changed, 3 insertions(+), 1 deletion(-) diff --git a/doc/examples/index.rst b/doc/examples/index.rst index b3e60803..94896f76 100644 --- a/doc/examples/index.rst +++ b/doc/examples/index.rst @@ -27,6 +27,7 @@ General Examples py_double_ml_multiway_cluster.ipynb py_double_ml_ssm.ipynb py_double_ml_sensitivity_booking.ipynb + learners/py_tabpfn.ipynb py_double_ml_basic_iv.ipynb py_double_ml_robust_iv.ipynb py_double_ml_plm_irm_hetfx.ipynb diff --git a/requirements.txt b/requirements.txt index 543443a5..3204d519 100644 --- a/requirements.txt +++ b/requirements.txt @@ -25,4 +25,5 @@ flaml # notebooks ipykernel -pyreadr \ No newline at end of file +pyreadr +tabpfn \ No newline at end of file From 2c19e01ba5a15ceb538e720e5fcfa9927b42798d Mon Sep 17 00:00:00 2001 From: SvenKlaassen Date: Mon, 11 Aug 2025 11:08:45 +0200 Subject: [PATCH 4/5] fix broken links --- doc/conf.py | 4 ++++ doc/examples/learners/py_tabpfn.ipynb | 4 ++-- 2 files changed, 6 insertions(+), 2 deletions(-) diff --git a/doc/conf.py b/doc/conf.py index ce2016e6..cb82342a 100644 --- a/doc/conf.py +++ b/doc/conf.py @@ -269,6 +269,10 @@ "https://doi.org/10.1146/annurev-economics-051520-021409", # Valdi DOI; Causes 504 Server Error: Gateway Time-out for ... "https://doi.org/10.1017/CBO9781139060035.008", + # Valid DOI; Causes 403 Client Error: Forbidden for url:... + "https://doi.org/10.1097%2FEDE.0b013e3181f74493", + # Valid DOI; Causes 403 Client Error: Forbidden for url:... + "https://doi.org/10.3982/ECTA15732", ] # To execute R code via jupyter-execute one needs to install the R kernel for jupyter diff --git a/doc/examples/learners/py_tabpfn.ipynb b/doc/examples/learners/py_tabpfn.ipynb index e3a9b33a..bd46f3c5 100644 --- a/doc/examples/learners/py_tabpfn.ipynb +++ b/doc/examples/learners/py_tabpfn.ipynb @@ -28,7 +28,7 @@ "We start by importing the necessary libraries. Note that TabPFN requires a separate installation, see [installation instructions](https://priorlabs.ai/getting_started/install/).\n", "\n", "For GPU acceleration (recommended), ensure you have CUDA-enabled PyTorch installed.\n", - "Instead you can also use the [TabPFN API Client](https://github.com/PriorLabs/tabpfn-client?tab=readme-ov-file#-quick-start)." + "Instead you can also use the [TabPFN API Client](https://github.com/PriorLabs/tabpfn-client)." ] }, { @@ -253,7 +253,7 @@ "- `ml_g`: A regressor for the outcome model $g_0(D,X) = \\mathbb{E}[Y|X,D]$\n", "- `ml_m`: A classifier for the propensity score model $m_{0,d}(X) = \\mathbb{E}[1\\{D=d\\}|X]$\n", "\n", - "**Note**: TabPFN works best with CUDA acceleration. If CUDA is not available, it will fall back to CPU computation. Instead you can use [TabPFN API Client](https://github.com/PriorLabs/tabpfn-client?tab=readme-ov-file#-quick-start)." + "**Note**: TabPFN works best with CUDA acceleration. If CUDA is not available, it will fall back to CPU computation. Instead you can use [TabPFN API Client](https://github.com/PriorLabs/tabpfn-client)." ] }, { From 732ac83da6e5e252b5e8bd3f744747edaeac77a8 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Klaa=C3=9Fen?= <47529404+SvenKlaassen@users.noreply.github.com> Date: Mon, 11 Aug 2025 17:55:18 +0200 Subject: [PATCH 5/5] remove warninings --- doc/examples/learners/py_tabpfn.ipynb | 1517 ++----------------------- 1 file changed, 90 insertions(+), 1427 deletions(-) diff --git a/doc/examples/learners/py_tabpfn.ipynb b/doc/examples/learners/py_tabpfn.ipynb index bd46f3c5..6e96b02b 100644 --- a/doc/examples/learners/py_tabpfn.ipynb +++ b/doc/examples/learners/py_tabpfn.ipynb @@ -50,7 +50,12 @@ "from tabpfn import TabPFNRegressor, TabPFNClassifier\n", "\n", "import doubleml as dml\n", - "from doubleml.datasets import make_irm_data_discrete_treatments" + "from doubleml.datasets import make_irm_data_discrete_treatments\n", + "\n", + "import warnings\n", + "warnings.filterwarnings(\"ignore\", message=\"Running on CPU*\", category=UserWarning, module=\"tabpfn\")\n", + "warnings.filterwarnings(\"ignore\", message=\".*does not have valid feature names.*\", category=UserWarning, module=\"lgbm\")\n", + "warnings.filterwarnings(\"ignore\", category=FutureWarning, module=\"sklearn\")" ] }, { @@ -138,7 +143,7 @@ "outputs": [ { "data": { - "image/png": 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", + "image/png": 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", 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" ] @@ -308,414 +313,32 @@ "metadata": {}, "outputs": [ { - "name": "stderr", - "output_type": "stream", - "text": [ - "c:\\Users\\svenk\\anaconda3\\envs\\dml_tabpfn\\Lib\\site-packages\\sklearn\\utils\\deprecation.py:151: FutureWarning: 'force_all_finite' was renamed to 'ensure_all_finite' in 1.6 and will be removed in 1.8.\n", - " warnings.warn(\n", - "c:\\Users\\svenk\\anaconda3\\envs\\dml_tabpfn\\Lib\\site-packages\\sklearn\\utils\\deprecation.py:151: FutureWarning: 'force_all_finite' was renamed to 'ensure_all_finite' in 1.6 and will be removed in 1.8.\n", - " warnings.warn(\n", - "c:\\Users\\svenk\\anaconda3\\envs\\dml_tabpfn\\Lib\\site-packages\\tabpfn\\regressor.py:490: UserWarning: Running on CPU with more than 200 samples may be slow.\n", - "Consider using a GPU or the tabpfn-client API: https://github.com/PriorLabs/tabpfn-client\n", - " check_cpu_warning(\n", - "c:\\Users\\svenk\\anaconda3\\envs\\dml_tabpfn\\Lib\\site-packages\\tabpfn\\regressor.py:490: UserWarning: Running on CPU with more than 200 samples may be slow.\n", - "Consider using a GPU or the tabpfn-client API: https://github.com/PriorLabs/tabpfn-client\n", - " check_cpu_warning(\n", - "c:\\Users\\svenk\\anaconda3\\envs\\dml_tabpfn\\Lib\\site-packages\\tabpfn\\regressor.py:490: UserWarning: Running on CPU with more than 200 samples may be slow.\n", - "Consider using a GPU or the tabpfn-client API: https://github.com/PriorLabs/tabpfn-client\n", - " check_cpu_warning(\n", - "c:\\Users\\svenk\\anaconda3\\envs\\dml_tabpfn\\Lib\\site-packages\\tabpfn\\regressor.py:490: UserWarning: Running on CPU with more than 200 samples may be slow.\n", - "Consider using a GPU or the tabpfn-client API: https://github.com/PriorLabs/tabpfn-client\n", - " check_cpu_warning(\n", - "c:\\Users\\svenk\\anaconda3\\envs\\dml_tabpfn\\Lib\\site-packages\\tabpfn\\regressor.py:490: UserWarning: Running on CPU with more than 200 samples may be slow.\n", - "Consider using a GPU or the tabpfn-client API: https://github.com/PriorLabs/tabpfn-client\n", - " check_cpu_warning(\n", - "c:\\Users\\svenk\\anaconda3\\envs\\dml_tabpfn\\Lib\\site-packages\\tabpfn\\regressor.py:490: UserWarning: Running on CPU with more than 200 samples may be slow.\n", - "Consider using a GPU or the tabpfn-client API: https://github.com/PriorLabs/tabpfn-client\n", - " check_cpu_warning(\n", - "c:\\Users\\svenk\\anaconda3\\envs\\dml_tabpfn\\Lib\\site-packages\\tabpfn\\regressor.py:490: UserWarning: Running on CPU with more than 200 samples may be slow.\n", - "Consider using a GPU or the tabpfn-client API: https://github.com/PriorLabs/tabpfn-client\n", - " check_cpu_warning(\n", - "c:\\Users\\svenk\\anaconda3\\envs\\dml_tabpfn\\Lib\\site-packages\\tabpfn\\regressor.py:490: UserWarning: Running on CPU with more than 200 samples may be slow.\n", - "Consider using a GPU or the tabpfn-client API: https://github.com/PriorLabs/tabpfn-client\n", - " check_cpu_warning(\n", - "c:\\Users\\svenk\\anaconda3\\envs\\dml_tabpfn\\Lib\\site-packages\\tabpfn\\regressor.py:490: UserWarning: Running on CPU with more than 200 samples may be slow.\n", - "Consider using a GPU or the tabpfn-client API: https://github.com/PriorLabs/tabpfn-client\n", - " check_cpu_warning(\n", - "c:\\Users\\svenk\\anaconda3\\envs\\dml_tabpfn\\Lib\\site-packages\\tabpfn\\regressor.py:490: UserWarning: Running on CPU with more than 200 samples may be slow.\n", - "Consider using a GPU or the tabpfn-client API: https://github.com/PriorLabs/tabpfn-client\n", - " check_cpu_warning(\n", - "c:\\Users\\svenk\\anaconda3\\envs\\dml_tabpfn\\Lib\\site-packages\\tabpfn\\classifier.py:459: UserWarning: Running on CPU with more than 200 samples may be slow.\n", - "Consider using a GPU or the tabpfn-client API: https://github.com/PriorLabs/tabpfn-client\n", - " check_cpu_warning(\n", - "c:\\Users\\svenk\\anaconda3\\envs\\dml_tabpfn\\Lib\\site-packages\\tabpfn\\classifier.py:459: UserWarning: Running on CPU with more than 200 samples may be slow.\n", - "Consider using a GPU or the tabpfn-client API: https://github.com/PriorLabs/tabpfn-client\n", - " check_cpu_warning(\n", - "c:\\Users\\svenk\\anaconda3\\envs\\dml_tabpfn\\Lib\\site-packages\\tabpfn\\classifier.py:459: UserWarning: Running on CPU with more than 200 samples may be slow.\n", - "Consider using a GPU or the tabpfn-client API: https://github.com/PriorLabs/tabpfn-client\n", - " check_cpu_warning(\n", - "c:\\Users\\svenk\\anaconda3\\envs\\dml_tabpfn\\Lib\\site-packages\\tabpfn\\classifier.py:459: UserWarning: Running on CPU with more than 200 samples may be slow.\n", - "Consider using a GPU or the tabpfn-client API: https://github.com/PriorLabs/tabpfn-client\n", - " check_cpu_warning(\n", - "c:\\Users\\svenk\\anaconda3\\envs\\dml_tabpfn\\Lib\\site-packages\\tabpfn\\classifier.py:459: UserWarning: Running on CPU with more than 200 samples may be slow.\n", - "Consider using a GPU or the tabpfn-client API: https://github.com/PriorLabs/tabpfn-client\n", - " check_cpu_warning(\n", - "c:\\Users\\svenk\\anaconda3\\envs\\dml_tabpfn\\Lib\\site-packages\\tabpfn\\classifier.py:459: UserWarning: Running on CPU with more than 200 samples may be slow.\n", - "Consider using a GPU or the tabpfn-client API: https://github.com/PriorLabs/tabpfn-client\n", - " check_cpu_warning(\n", - "c:\\Users\\svenk\\anaconda3\\envs\\dml_tabpfn\\Lib\\site-packages\\tabpfn\\classifier.py:459: UserWarning: Running on CPU with more than 200 samples may be slow.\n", - "Consider using a GPU or the tabpfn-client API: https://github.com/PriorLabs/tabpfn-client\n", - " check_cpu_warning(\n", - "c:\\Users\\svenk\\anaconda3\\envs\\dml_tabpfn\\Lib\\site-packages\\tabpfn\\classifier.py:459: UserWarning: Running on CPU with more than 200 samples may be slow.\n", - "Consider using a GPU or the tabpfn-client API: https://github.com/PriorLabs/tabpfn-client\n", - " check_cpu_warning(\n", - "c:\\Users\\svenk\\anaconda3\\envs\\dml_tabpfn\\Lib\\site-packages\\tabpfn\\classifier.py:459: UserWarning: Running on CPU with more than 200 samples may be slow.\n", - "Consider using a GPU or the tabpfn-client API: https://github.com/PriorLabs/tabpfn-client\n", - " check_cpu_warning(\n", - "c:\\Users\\svenk\\anaconda3\\envs\\dml_tabpfn\\Lib\\site-packages\\tabpfn\\classifier.py:459: UserWarning: Running on CPU with more than 200 samples may be slow.\n", - "Consider using a GPU or the tabpfn-client API: https://github.com/PriorLabs/tabpfn-client\n", - " check_cpu_warning(\n", - "c:\\Users\\svenk\\anaconda3\\envs\\dml_tabpfn\\Lib\\site-packages\\sklearn\\utils\\deprecation.py:151: FutureWarning: 'force_all_finite' was renamed to 'ensure_all_finite' in 1.6 and will be removed in 1.8.\n", - " warnings.warn(\n", - "c:\\Users\\svenk\\anaconda3\\envs\\dml_tabpfn\\Lib\\site-packages\\sklearn\\utils\\deprecation.py:151: FutureWarning: 'force_all_finite' was renamed to 'ensure_all_finite' in 1.6 and will be removed in 1.8.\n", - " warnings.warn(\n", - "c:\\Users\\svenk\\anaconda3\\envs\\dml_tabpfn\\Lib\\site-packages\\sklearn\\utils\\deprecation.py:151: FutureWarning: 'force_all_finite' was renamed to 'ensure_all_finite' in 1.6 and will be removed in 1.8.\n", - " warnings.warn(\n", - "c:\\Users\\svenk\\anaconda3\\envs\\dml_tabpfn\\Lib\\site-packages\\sklearn\\utils\\deprecation.py:151: FutureWarning: 'force_all_finite' was renamed to 'ensure_all_finite' in 1.6 and will be removed in 1.8.\n", - " warnings.warn(\n", - "c:\\Users\\svenk\\anaconda3\\envs\\dml_tabpfn\\Lib\\site-packages\\tabpfn\\regressor.py:490: UserWarning: Running on CPU with more than 200 samples may be slow.\n", - "Consider using a GPU or the tabpfn-client API: https://github.com/PriorLabs/tabpfn-client\n", - " check_cpu_warning(\n", - "c:\\Users\\svenk\\anaconda3\\envs\\dml_tabpfn\\Lib\\site-packages\\tabpfn\\regressor.py:490: UserWarning: Running on CPU with more than 200 samples may be slow.\n", - "Consider using a GPU or the tabpfn-client API: https://github.com/PriorLabs/tabpfn-client\n", - " check_cpu_warning(\n", - "c:\\Users\\svenk\\anaconda3\\envs\\dml_tabpfn\\Lib\\site-packages\\tabpfn\\regressor.py:490: UserWarning: Running on CPU with more than 200 samples may be slow.\n", - "Consider using a GPU or the tabpfn-client API: https://github.com/PriorLabs/tabpfn-client\n", - " check_cpu_warning(\n", - "c:\\Users\\svenk\\anaconda3\\envs\\dml_tabpfn\\Lib\\site-packages\\tabpfn\\regressor.py:490: UserWarning: Running on CPU with more than 200 samples may be slow.\n", - "Consider using a GPU or the tabpfn-client API: https://github.com/PriorLabs/tabpfn-client\n", - " check_cpu_warning(\n", - "c:\\Users\\svenk\\anaconda3\\envs\\dml_tabpfn\\Lib\\site-packages\\tabpfn\\regressor.py:490: UserWarning: Running on CPU with more than 200 samples may be slow.\n", - "Consider using a GPU or the tabpfn-client API: https://github.com/PriorLabs/tabpfn-client\n", - " check_cpu_warning(\n", - "c:\\Users\\svenk\\anaconda3\\envs\\dml_tabpfn\\Lib\\site-packages\\tabpfn\\regressor.py:490: UserWarning: Running on CPU with more than 200 samples may be slow.\n", - "Consider using a GPU or the tabpfn-client API: https://github.com/PriorLabs/tabpfn-client\n", - " check_cpu_warning(\n", - "c:\\Users\\svenk\\anaconda3\\envs\\dml_tabpfn\\Lib\\site-packages\\tabpfn\\regressor.py:490: UserWarning: Running on CPU with more than 200 samples may be slow.\n", - "Consider using a GPU or the tabpfn-client API: https://github.com/PriorLabs/tabpfn-client\n", - " check_cpu_warning(\n", - "c:\\Users\\svenk\\anaconda3\\envs\\dml_tabpfn\\Lib\\site-packages\\tabpfn\\regressor.py:490: UserWarning: Running on CPU with more than 200 samples may be slow.\n", - "Consider using a GPU or the tabpfn-client API: https://github.com/PriorLabs/tabpfn-client\n", - " check_cpu_warning(\n", - "c:\\Users\\svenk\\anaconda3\\envs\\dml_tabpfn\\Lib\\site-packages\\tabpfn\\regressor.py:490: UserWarning: Running on CPU with more than 200 samples may be slow.\n", - "Consider using a GPU or the tabpfn-client API: https://github.com/PriorLabs/tabpfn-client\n", - " check_cpu_warning(\n", - "c:\\Users\\svenk\\anaconda3\\envs\\dml_tabpfn\\Lib\\site-packages\\tabpfn\\regressor.py:490: UserWarning: Running on CPU with more than 200 samples may be slow.\n", - "Consider using a GPU or the tabpfn-client API: https://github.com/PriorLabs/tabpfn-client\n", - " check_cpu_warning(\n", - "c:\\Users\\svenk\\anaconda3\\envs\\dml_tabpfn\\Lib\\site-packages\\tabpfn\\classifier.py:459: UserWarning: Running on CPU with more than 200 samples may be slow.\n", - "Consider using a GPU or the tabpfn-client API: https://github.com/PriorLabs/tabpfn-client\n", - " check_cpu_warning(\n", - "c:\\Users\\svenk\\anaconda3\\envs\\dml_tabpfn\\Lib\\site-packages\\tabpfn\\classifier.py:459: UserWarning: Running on CPU with more than 200 samples may be slow.\n", - "Consider using a GPU or the tabpfn-client API: https://github.com/PriorLabs/tabpfn-client\n", - " check_cpu_warning(\n", - "c:\\Users\\svenk\\anaconda3\\envs\\dml_tabpfn\\Lib\\site-packages\\tabpfn\\classifier.py:459: UserWarning: Running on CPU with more than 200 samples may be slow.\n", - "Consider using a GPU or the tabpfn-client API: https://github.com/PriorLabs/tabpfn-client\n", - " check_cpu_warning(\n", - "c:\\Users\\svenk\\anaconda3\\envs\\dml_tabpfn\\Lib\\site-packages\\tabpfn\\classifier.py:459: UserWarning: Running on CPU with more than 200 samples may be slow.\n", - "Consider using a GPU or the tabpfn-client API: https://github.com/PriorLabs/tabpfn-client\n", - " check_cpu_warning(\n", - "c:\\Users\\svenk\\anaconda3\\envs\\dml_tabpfn\\Lib\\site-packages\\tabpfn\\classifier.py:459: UserWarning: Running on CPU with more than 200 samples may be slow.\n", - "Consider using a GPU or the tabpfn-client API: https://github.com/PriorLabs/tabpfn-client\n", - " check_cpu_warning(\n", - "c:\\Users\\svenk\\anaconda3\\envs\\dml_tabpfn\\Lib\\site-packages\\tabpfn\\classifier.py:459: UserWarning: Running on CPU with more than 200 samples may be slow.\n", - "Consider using a GPU or the tabpfn-client API: https://github.com/PriorLabs/tabpfn-client\n", - " check_cpu_warning(\n", - "c:\\Users\\svenk\\anaconda3\\envs\\dml_tabpfn\\Lib\\site-packages\\tabpfn\\classifier.py:459: UserWarning: Running on CPU with more than 200 samples may be slow.\n", - "Consider using a GPU or the tabpfn-client API: https://github.com/PriorLabs/tabpfn-client\n", - " check_cpu_warning(\n", - "c:\\Users\\svenk\\anaconda3\\envs\\dml_tabpfn\\Lib\\site-packages\\tabpfn\\classifier.py:459: UserWarning: Running on CPU with more than 200 samples may be slow.\n", - "Consider using a GPU or the tabpfn-client API: https://github.com/PriorLabs/tabpfn-client\n", - " check_cpu_warning(\n", - "c:\\Users\\svenk\\anaconda3\\envs\\dml_tabpfn\\Lib\\site-packages\\tabpfn\\classifier.py:459: UserWarning: Running on CPU with more than 200 samples may be slow.\n", - "Consider using a GPU or the tabpfn-client API: https://github.com/PriorLabs/tabpfn-client\n", - " check_cpu_warning(\n", - "c:\\Users\\svenk\\anaconda3\\envs\\dml_tabpfn\\Lib\\site-packages\\tabpfn\\classifier.py:459: UserWarning: Running on CPU with more than 200 samples may be slow.\n", - "Consider using a GPU or the tabpfn-client API: https://github.com/PriorLabs/tabpfn-client\n", - " check_cpu_warning(\n", - "c:\\Users\\svenk\\anaconda3\\envs\\dml_tabpfn\\Lib\\site-packages\\sklearn\\utils\\deprecation.py:151: FutureWarning: 'force_all_finite' was renamed to 'ensure_all_finite' in 1.6 and will be removed in 1.8.\n", - " warnings.warn(\n", - "c:\\Users\\svenk\\anaconda3\\envs\\dml_tabpfn\\Lib\\site-packages\\sklearn\\utils\\deprecation.py:151: FutureWarning: 'force_all_finite' was renamed to 'ensure_all_finite' in 1.6 and will be removed in 1.8.\n", - " warnings.warn(\n", - "c:\\Users\\svenk\\anaconda3\\envs\\dml_tabpfn\\Lib\\site-packages\\sklearn\\utils\\deprecation.py:151: FutureWarning: 'force_all_finite' was renamed to 'ensure_all_finite' in 1.6 and will be removed in 1.8.\n", - " warnings.warn(\n", - "c:\\Users\\svenk\\anaconda3\\envs\\dml_tabpfn\\Lib\\site-packages\\sklearn\\utils\\deprecation.py:151: FutureWarning: 'force_all_finite' was renamed to 'ensure_all_finite' in 1.6 and will be removed in 1.8.\n", - " warnings.warn(\n", - "c:\\Users\\svenk\\anaconda3\\envs\\dml_tabpfn\\Lib\\site-packages\\tabpfn\\regressor.py:490: UserWarning: Running on CPU with more than 200 samples may be slow.\n", - "Consider using a GPU or the tabpfn-client API: https://github.com/PriorLabs/tabpfn-client\n", - " check_cpu_warning(\n", - "c:\\Users\\svenk\\anaconda3\\envs\\dml_tabpfn\\Lib\\site-packages\\tabpfn\\regressor.py:490: UserWarning: Running on CPU with more than 200 samples may be slow.\n", - "Consider using a GPU or the tabpfn-client API: https://github.com/PriorLabs/tabpfn-client\n", - " check_cpu_warning(\n", - "c:\\Users\\svenk\\anaconda3\\envs\\dml_tabpfn\\Lib\\site-packages\\tabpfn\\regressor.py:490: UserWarning: Running on CPU with more than 200 samples may be slow.\n", - "Consider using a GPU or the tabpfn-client API: https://github.com/PriorLabs/tabpfn-client\n", - " check_cpu_warning(\n", - "c:\\Users\\svenk\\anaconda3\\envs\\dml_tabpfn\\Lib\\site-packages\\tabpfn\\regressor.py:490: UserWarning: Running on CPU with more than 200 samples may be slow.\n", - "Consider using a GPU or the tabpfn-client API: https://github.com/PriorLabs/tabpfn-client\n", - " check_cpu_warning(\n", - "c:\\Users\\svenk\\anaconda3\\envs\\dml_tabpfn\\Lib\\site-packages\\tabpfn\\regressor.py:490: UserWarning: Running on CPU with more than 200 samples may be slow.\n", - "Consider using a GPU or the tabpfn-client API: https://github.com/PriorLabs/tabpfn-client\n", - " check_cpu_warning(\n", - "c:\\Users\\svenk\\anaconda3\\envs\\dml_tabpfn\\Lib\\site-packages\\tabpfn\\regressor.py:490: UserWarning: Running on CPU with more than 200 samples may be slow.\n", - "Consider using a GPU or the tabpfn-client API: https://github.com/PriorLabs/tabpfn-client\n", - " check_cpu_warning(\n", - "c:\\Users\\svenk\\anaconda3\\envs\\dml_tabpfn\\Lib\\site-packages\\tabpfn\\regressor.py:490: UserWarning: Running on CPU with more than 200 samples may be slow.\n", - "Consider using a GPU or the tabpfn-client API: https://github.com/PriorLabs/tabpfn-client\n", - " check_cpu_warning(\n", - "c:\\Users\\svenk\\anaconda3\\envs\\dml_tabpfn\\Lib\\site-packages\\tabpfn\\regressor.py:490: UserWarning: Running on CPU with more than 200 samples may be slow.\n", - "Consider using a GPU or the tabpfn-client API: https://github.com/PriorLabs/tabpfn-client\n", - " check_cpu_warning(\n", - "c:\\Users\\svenk\\anaconda3\\envs\\dml_tabpfn\\Lib\\site-packages\\tabpfn\\regressor.py:490: UserWarning: Running on CPU with more than 200 samples may be slow.\n", - "Consider using a GPU or the tabpfn-client API: https://github.com/PriorLabs/tabpfn-client\n", - " check_cpu_warning(\n", - "c:\\Users\\svenk\\anaconda3\\envs\\dml_tabpfn\\Lib\\site-packages\\tabpfn\\regressor.py:490: UserWarning: Running on CPU with more than 200 samples may be slow.\n", - "Consider using a GPU or the tabpfn-client API: https://github.com/PriorLabs/tabpfn-client\n", - " check_cpu_warning(\n", - "c:\\Users\\svenk\\anaconda3\\envs\\dml_tabpfn\\Lib\\site-packages\\tabpfn\\classifier.py:459: UserWarning: Running on CPU with more than 200 samples may be slow.\n", - "Consider using a GPU or the tabpfn-client API: https://github.com/PriorLabs/tabpfn-client\n", - " check_cpu_warning(\n", - "c:\\Users\\svenk\\anaconda3\\envs\\dml_tabpfn\\Lib\\site-packages\\tabpfn\\classifier.py:459: UserWarning: Running on CPU with more than 200 samples may be slow.\n", - "Consider using a GPU or the tabpfn-client API: https://github.com/PriorLabs/tabpfn-client\n", - " check_cpu_warning(\n", - "c:\\Users\\svenk\\anaconda3\\envs\\dml_tabpfn\\Lib\\site-packages\\tabpfn\\classifier.py:459: UserWarning: Running on CPU with more than 200 samples may be slow.\n", - "Consider using a GPU or the tabpfn-client API: https://github.com/PriorLabs/tabpfn-client\n", - " check_cpu_warning(\n", - "c:\\Users\\svenk\\anaconda3\\envs\\dml_tabpfn\\Lib\\site-packages\\tabpfn\\classifier.py:459: UserWarning: Running on CPU with more than 200 samples may be slow.\n", - "Consider using a GPU or the tabpfn-client API: https://github.com/PriorLabs/tabpfn-client\n", - " check_cpu_warning(\n", - "c:\\Users\\svenk\\anaconda3\\envs\\dml_tabpfn\\Lib\\site-packages\\tabpfn\\classifier.py:459: UserWarning: Running on CPU with more than 200 samples may be slow.\n", - "Consider using a GPU or the tabpfn-client API: https://github.com/PriorLabs/tabpfn-client\n", - " check_cpu_warning(\n", - "c:\\Users\\svenk\\anaconda3\\envs\\dml_tabpfn\\Lib\\site-packages\\tabpfn\\classifier.py:459: UserWarning: Running on CPU with more than 200 samples may be slow.\n", - "Consider using a GPU or the tabpfn-client API: https://github.com/PriorLabs/tabpfn-client\n", - " check_cpu_warning(\n", - "c:\\Users\\svenk\\anaconda3\\envs\\dml_tabpfn\\Lib\\site-packages\\tabpfn\\classifier.py:459: UserWarning: Running on CPU with more than 200 samples may be slow.\n", - "Consider using a GPU or the tabpfn-client API: https://github.com/PriorLabs/tabpfn-client\n", - " check_cpu_warning(\n", - "c:\\Users\\svenk\\anaconda3\\envs\\dml_tabpfn\\Lib\\site-packages\\tabpfn\\classifier.py:459: UserWarning: Running on CPU with more than 200 samples may be slow.\n", - "Consider using a GPU or the tabpfn-client API: https://github.com/PriorLabs/tabpfn-client\n", - " check_cpu_warning(\n", - "c:\\Users\\svenk\\anaconda3\\envs\\dml_tabpfn\\Lib\\site-packages\\tabpfn\\classifier.py:459: UserWarning: Running on CPU with more than 200 samples may be slow.\n", - "Consider using a GPU or the tabpfn-client API: https://github.com/PriorLabs/tabpfn-client\n", - " check_cpu_warning(\n", - "c:\\Users\\svenk\\anaconda3\\envs\\dml_tabpfn\\Lib\\site-packages\\tabpfn\\classifier.py:459: UserWarning: Running on CPU with more than 200 samples may be slow.\n", - "Consider using a GPU or the tabpfn-client API: https://github.com/PriorLabs/tabpfn-client\n", - " check_cpu_warning(\n", - "c:\\Users\\svenk\\anaconda3\\envs\\dml_tabpfn\\Lib\\site-packages\\sklearn\\utils\\deprecation.py:151: FutureWarning: 'force_all_finite' was renamed to 'ensure_all_finite' in 1.6 and will be removed in 1.8.\n", - " warnings.warn(\n", - "c:\\Users\\svenk\\anaconda3\\envs\\dml_tabpfn\\Lib\\site-packages\\sklearn\\utils\\deprecation.py:151: FutureWarning: 'force_all_finite' was renamed to 'ensure_all_finite' in 1.6 and will be removed in 1.8.\n", - " warnings.warn(\n", - "c:\\Users\\svenk\\anaconda3\\envs\\dml_tabpfn\\Lib\\site-packages\\sklearn\\utils\\deprecation.py:151: FutureWarning: 'force_all_finite' was renamed to 'ensure_all_finite' in 1.6 and will be removed in 1.8.\n", - " warnings.warn(\n", - "c:\\Users\\svenk\\anaconda3\\envs\\dml_tabpfn\\Lib\\site-packages\\sklearn\\utils\\deprecation.py:151: FutureWarning: 'force_all_finite' was renamed to 'ensure_all_finite' in 1.6 and will be removed in 1.8.\n", - " warnings.warn(\n", - "c:\\Users\\svenk\\anaconda3\\envs\\dml_tabpfn\\Lib\\site-packages\\tabpfn\\regressor.py:490: UserWarning: Running on CPU with more than 200 samples may be slow.\n", - "Consider using a GPU or the tabpfn-client API: https://github.com/PriorLabs/tabpfn-client\n", - " check_cpu_warning(\n", - "c:\\Users\\svenk\\anaconda3\\envs\\dml_tabpfn\\Lib\\site-packages\\tabpfn\\regressor.py:490: UserWarning: Running on CPU with more than 200 samples may be slow.\n", - "Consider using a GPU or the tabpfn-client API: https://github.com/PriorLabs/tabpfn-client\n", - " check_cpu_warning(\n", - "c:\\Users\\svenk\\anaconda3\\envs\\dml_tabpfn\\Lib\\site-packages\\tabpfn\\regressor.py:490: UserWarning: Running on CPU with more than 200 samples may be slow.\n", - "Consider using a GPU or the tabpfn-client API: https://github.com/PriorLabs/tabpfn-client\n", - " check_cpu_warning(\n", - "c:\\Users\\svenk\\anaconda3\\envs\\dml_tabpfn\\Lib\\site-packages\\tabpfn\\regressor.py:490: UserWarning: Running on CPU with more than 200 samples may be slow.\n", - "Consider using a GPU or the tabpfn-client API: https://github.com/PriorLabs/tabpfn-client\n", - " check_cpu_warning(\n", - "c:\\Users\\svenk\\anaconda3\\envs\\dml_tabpfn\\Lib\\site-packages\\tabpfn\\regressor.py:490: UserWarning: Running on CPU with more than 200 samples may be slow.\n", - "Consider using a GPU or the tabpfn-client API: https://github.com/PriorLabs/tabpfn-client\n", - " check_cpu_warning(\n", - "c:\\Users\\svenk\\anaconda3\\envs\\dml_tabpfn\\Lib\\site-packages\\tabpfn\\regressor.py:490: UserWarning: Running on CPU with more than 200 samples may be slow.\n", - "Consider using a GPU or the tabpfn-client API: https://github.com/PriorLabs/tabpfn-client\n", - " check_cpu_warning(\n", - "c:\\Users\\svenk\\anaconda3\\envs\\dml_tabpfn\\Lib\\site-packages\\tabpfn\\regressor.py:490: UserWarning: Running on CPU with more than 200 samples may be slow.\n", - "Consider using a GPU or the tabpfn-client API: https://github.com/PriorLabs/tabpfn-client\n", - " check_cpu_warning(\n", - "c:\\Users\\svenk\\anaconda3\\envs\\dml_tabpfn\\Lib\\site-packages\\tabpfn\\regressor.py:490: UserWarning: Running on CPU with more than 200 samples may be slow.\n", - "Consider using a GPU or the tabpfn-client API: https://github.com/PriorLabs/tabpfn-client\n", - " check_cpu_warning(\n", - "c:\\Users\\svenk\\anaconda3\\envs\\dml_tabpfn\\Lib\\site-packages\\tabpfn\\regressor.py:490: UserWarning: Running on CPU with more than 200 samples may be slow.\n", - "Consider using a GPU or the tabpfn-client API: https://github.com/PriorLabs/tabpfn-client\n", - " check_cpu_warning(\n", - "c:\\Users\\svenk\\anaconda3\\envs\\dml_tabpfn\\Lib\\site-packages\\tabpfn\\regressor.py:490: UserWarning: Running on CPU with more than 200 samples may be slow.\n", - "Consider using a GPU or the tabpfn-client API: https://github.com/PriorLabs/tabpfn-client\n", - " check_cpu_warning(\n", - "c:\\Users\\svenk\\anaconda3\\envs\\dml_tabpfn\\Lib\\site-packages\\tabpfn\\classifier.py:459: UserWarning: Running on CPU with more than 200 samples may be slow.\n", - "Consider using a GPU or the tabpfn-client API: https://github.com/PriorLabs/tabpfn-client\n", - " check_cpu_warning(\n", - "c:\\Users\\svenk\\anaconda3\\envs\\dml_tabpfn\\Lib\\site-packages\\tabpfn\\classifier.py:459: UserWarning: Running on CPU with more than 200 samples may be slow.\n", - "Consider using a GPU or the tabpfn-client API: https://github.com/PriorLabs/tabpfn-client\n", - " check_cpu_warning(\n", - "c:\\Users\\svenk\\anaconda3\\envs\\dml_tabpfn\\Lib\\site-packages\\tabpfn\\classifier.py:459: UserWarning: Running on CPU with more than 200 samples may be slow.\n", - "Consider using a GPU or the tabpfn-client API: https://github.com/PriorLabs/tabpfn-client\n", - " check_cpu_warning(\n", - "c:\\Users\\svenk\\anaconda3\\envs\\dml_tabpfn\\Lib\\site-packages\\tabpfn\\classifier.py:459: UserWarning: Running on CPU with more than 200 samples may be slow.\n", - "Consider using a GPU or the tabpfn-client API: https://github.com/PriorLabs/tabpfn-client\n", - " check_cpu_warning(\n", - "c:\\Users\\svenk\\anaconda3\\envs\\dml_tabpfn\\Lib\\site-packages\\tabpfn\\classifier.py:459: UserWarning: Running on CPU with more than 200 samples may be slow.\n", - "Consider using a GPU or the tabpfn-client API: https://github.com/PriorLabs/tabpfn-client\n", - " check_cpu_warning(\n", - "c:\\Users\\svenk\\anaconda3\\envs\\dml_tabpfn\\Lib\\site-packages\\tabpfn\\classifier.py:459: UserWarning: Running on CPU with more than 200 samples may be slow.\n", - "Consider using a GPU or the tabpfn-client API: https://github.com/PriorLabs/tabpfn-client\n", - " check_cpu_warning(\n", - "c:\\Users\\svenk\\anaconda3\\envs\\dml_tabpfn\\Lib\\site-packages\\tabpfn\\classifier.py:459: UserWarning: Running on CPU with more than 200 samples may be slow.\n", - "Consider using a GPU or the tabpfn-client API: https://github.com/PriorLabs/tabpfn-client\n", - " check_cpu_warning(\n", - "c:\\Users\\svenk\\anaconda3\\envs\\dml_tabpfn\\Lib\\site-packages\\tabpfn\\classifier.py:459: UserWarning: Running on CPU with more than 200 samples may be slow.\n", - "Consider using a GPU or the tabpfn-client API: https://github.com/PriorLabs/tabpfn-client\n", - " check_cpu_warning(\n", - "c:\\Users\\svenk\\anaconda3\\envs\\dml_tabpfn\\Lib\\site-packages\\tabpfn\\classifier.py:459: UserWarning: Running on CPU with more than 200 samples may be slow.\n", - "Consider using a GPU or the tabpfn-client API: https://github.com/PriorLabs/tabpfn-client\n", - " check_cpu_warning(\n", - "c:\\Users\\svenk\\anaconda3\\envs\\dml_tabpfn\\Lib\\site-packages\\tabpfn\\classifier.py:459: UserWarning: Running on CPU with more than 200 samples may be slow.\n", - "Consider using a GPU or the tabpfn-client API: https://github.com/PriorLabs/tabpfn-client\n", - " check_cpu_warning(\n", - "c:\\Users\\svenk\\anaconda3\\envs\\dml_tabpfn\\Lib\\site-packages\\sklearn\\utils\\deprecation.py:151: FutureWarning: 'force_all_finite' was renamed to 'ensure_all_finite' in 1.6 and will be removed in 1.8.\n", - " warnings.warn(\n", - "c:\\Users\\svenk\\anaconda3\\envs\\dml_tabpfn\\Lib\\site-packages\\sklearn\\utils\\deprecation.py:151: FutureWarning: 'force_all_finite' was renamed to 'ensure_all_finite' in 1.6 and will be removed in 1.8.\n", - " warnings.warn(\n", - "c:\\Users\\svenk\\anaconda3\\envs\\dml_tabpfn\\Lib\\site-packages\\sklearn\\utils\\deprecation.py:151: FutureWarning: 'force_all_finite' was renamed to 'ensure_all_finite' in 1.6 and will be removed in 1.8.\n", - " warnings.warn(\n", - "c:\\Users\\svenk\\anaconda3\\envs\\dml_tabpfn\\Lib\\site-packages\\sklearn\\utils\\deprecation.py:151: FutureWarning: 'force_all_finite' was renamed to 'ensure_all_finite' in 1.6 and will be removed in 1.8.\n", - " warnings.warn(\n", - "c:\\Users\\svenk\\anaconda3\\envs\\dml_tabpfn\\Lib\\site-packages\\tabpfn\\regressor.py:490: UserWarning: Running on CPU with more than 200 samples may be slow.\n", - "Consider using a GPU or the tabpfn-client API: https://github.com/PriorLabs/tabpfn-client\n", - " check_cpu_warning(\n", - "c:\\Users\\svenk\\anaconda3\\envs\\dml_tabpfn\\Lib\\site-packages\\tabpfn\\regressor.py:490: UserWarning: Running on CPU with more than 200 samples may be slow.\n", - "Consider using a GPU or the tabpfn-client API: https://github.com/PriorLabs/tabpfn-client\n", - " check_cpu_warning(\n", - "c:\\Users\\svenk\\anaconda3\\envs\\dml_tabpfn\\Lib\\site-packages\\tabpfn\\regressor.py:490: UserWarning: Running on CPU with more than 200 samples may be slow.\n", - "Consider using a GPU or the tabpfn-client API: https://github.com/PriorLabs/tabpfn-client\n", - " check_cpu_warning(\n", - "c:\\Users\\svenk\\anaconda3\\envs\\dml_tabpfn\\Lib\\site-packages\\tabpfn\\regressor.py:490: UserWarning: Running on CPU with more than 200 samples may be slow.\n", - "Consider using a GPU or the tabpfn-client API: https://github.com/PriorLabs/tabpfn-client\n", - " check_cpu_warning(\n", - "c:\\Users\\svenk\\anaconda3\\envs\\dml_tabpfn\\Lib\\site-packages\\tabpfn\\regressor.py:490: UserWarning: Running on CPU with more than 200 samples may be slow.\n", - "Consider using a GPU or the tabpfn-client API: https://github.com/PriorLabs/tabpfn-client\n", - " check_cpu_warning(\n", - "c:\\Users\\svenk\\anaconda3\\envs\\dml_tabpfn\\Lib\\site-packages\\tabpfn\\regressor.py:490: UserWarning: Running on CPU with more than 200 samples may be slow.\n", - "Consider using a GPU or the tabpfn-client API: https://github.com/PriorLabs/tabpfn-client\n", - " check_cpu_warning(\n", - "c:\\Users\\svenk\\anaconda3\\envs\\dml_tabpfn\\Lib\\site-packages\\tabpfn\\regressor.py:490: UserWarning: Running on CPU with more than 200 samples may be slow.\n", - "Consider using a GPU or the tabpfn-client API: https://github.com/PriorLabs/tabpfn-client\n", - " check_cpu_warning(\n", - "c:\\Users\\svenk\\anaconda3\\envs\\dml_tabpfn\\Lib\\site-packages\\tabpfn\\regressor.py:490: UserWarning: Running on CPU with more than 200 samples may be slow.\n", - "Consider using a GPU or the tabpfn-client API: https://github.com/PriorLabs/tabpfn-client\n", - " check_cpu_warning(\n", - "c:\\Users\\svenk\\anaconda3\\envs\\dml_tabpfn\\Lib\\site-packages\\tabpfn\\regressor.py:490: UserWarning: Running on CPU with more than 200 samples may be slow.\n", - "Consider using a GPU or the tabpfn-client API: https://github.com/PriorLabs/tabpfn-client\n", - " check_cpu_warning(\n", - "c:\\Users\\svenk\\anaconda3\\envs\\dml_tabpfn\\Lib\\site-packages\\tabpfn\\regressor.py:490: UserWarning: Running on CPU with more than 200 samples may be slow.\n", - "Consider using a GPU or the tabpfn-client API: https://github.com/PriorLabs/tabpfn-client\n", - " check_cpu_warning(\n", - "c:\\Users\\svenk\\anaconda3\\envs\\dml_tabpfn\\Lib\\site-packages\\tabpfn\\classifier.py:459: UserWarning: Running on CPU with more than 200 samples may be slow.\n", - "Consider using a GPU or the tabpfn-client API: https://github.com/PriorLabs/tabpfn-client\n", - " check_cpu_warning(\n", - "c:\\Users\\svenk\\anaconda3\\envs\\dml_tabpfn\\Lib\\site-packages\\tabpfn\\classifier.py:459: UserWarning: Running on CPU with more than 200 samples may be slow.\n", - "Consider using a GPU or the tabpfn-client API: https://github.com/PriorLabs/tabpfn-client\n", - " check_cpu_warning(\n", - "c:\\Users\\svenk\\anaconda3\\envs\\dml_tabpfn\\Lib\\site-packages\\tabpfn\\classifier.py:459: UserWarning: Running on CPU with more than 200 samples may be slow.\n", - "Consider using a GPU or the tabpfn-client API: https://github.com/PriorLabs/tabpfn-client\n", - " check_cpu_warning(\n", - "c:\\Users\\svenk\\anaconda3\\envs\\dml_tabpfn\\Lib\\site-packages\\tabpfn\\classifier.py:459: UserWarning: Running on CPU with more than 200 samples may be slow.\n", - "Consider using a GPU or the tabpfn-client API: https://github.com/PriorLabs/tabpfn-client\n", - " check_cpu_warning(\n", - "c:\\Users\\svenk\\anaconda3\\envs\\dml_tabpfn\\Lib\\site-packages\\tabpfn\\classifier.py:459: UserWarning: Running on CPU with more than 200 samples may be slow.\n", - "Consider using a GPU or the tabpfn-client API: https://github.com/PriorLabs/tabpfn-client\n", - " check_cpu_warning(\n", - "c:\\Users\\svenk\\anaconda3\\envs\\dml_tabpfn\\Lib\\site-packages\\tabpfn\\classifier.py:459: UserWarning: Running on CPU with more than 200 samples may be slow.\n", - "Consider using a GPU or the tabpfn-client API: https://github.com/PriorLabs/tabpfn-client\n", - " check_cpu_warning(\n", - "c:\\Users\\svenk\\anaconda3\\envs\\dml_tabpfn\\Lib\\site-packages\\tabpfn\\classifier.py:459: UserWarning: Running on CPU with more than 200 samples may be slow.\n", - "Consider using a GPU or the tabpfn-client API: https://github.com/PriorLabs/tabpfn-client\n", - " check_cpu_warning(\n", - "c:\\Users\\svenk\\anaconda3\\envs\\dml_tabpfn\\Lib\\site-packages\\tabpfn\\classifier.py:459: UserWarning: Running on CPU with more than 200 samples may be slow.\n", - "Consider using a GPU or the tabpfn-client API: https://github.com/PriorLabs/tabpfn-client\n", - " check_cpu_warning(\n", - "c:\\Users\\svenk\\anaconda3\\envs\\dml_tabpfn\\Lib\\site-packages\\tabpfn\\classifier.py:459: UserWarning: Running on CPU with more than 200 samples may be slow.\n", - "Consider using a GPU or the tabpfn-client API: https://github.com/PriorLabs/tabpfn-client\n", - " check_cpu_warning(\n", - "c:\\Users\\svenk\\anaconda3\\envs\\dml_tabpfn\\Lib\\site-packages\\tabpfn\\classifier.py:459: UserWarning: Running on CPU with more than 200 samples may be slow.\n", - "Consider using a GPU or the tabpfn-client API: https://github.com/PriorLabs/tabpfn-client\n", - " check_cpu_warning(\n", - "c:\\Users\\svenk\\anaconda3\\envs\\dml_tabpfn\\Lib\\site-packages\\sklearn\\utils\\deprecation.py:151: FutureWarning: 'force_all_finite' was renamed to 'ensure_all_finite' in 1.6 and will be removed in 1.8.\n", - " warnings.warn(\n", - "c:\\Users\\svenk\\anaconda3\\envs\\dml_tabpfn\\Lib\\site-packages\\sklearn\\utils\\deprecation.py:151: FutureWarning: 'force_all_finite' was renamed to 'ensure_all_finite' in 1.6 and will be removed in 1.8.\n", - " warnings.warn(\n", - "c:\\Users\\svenk\\anaconda3\\envs\\dml_tabpfn\\Lib\\site-packages\\sklearn\\utils\\deprecation.py:151: FutureWarning: 'force_all_finite' was renamed to 'ensure_all_finite' in 1.6 and will be removed in 1.8.\n", - " warnings.warn(\n", - "c:\\Users\\svenk\\anaconda3\\envs\\dml_tabpfn\\Lib\\site-packages\\sklearn\\utils\\deprecation.py:151: FutureWarning: 'force_all_finite' was renamed to 'ensure_all_finite' in 1.6 and will be removed in 1.8.\n", - " warnings.warn(\n", - "c:\\Users\\svenk\\anaconda3\\envs\\dml_tabpfn\\Lib\\site-packages\\tabpfn\\regressor.py:490: UserWarning: Running on CPU with more than 200 samples may be slow.\n", - "Consider using a GPU or the tabpfn-client API: https://github.com/PriorLabs/tabpfn-client\n", - " check_cpu_warning(\n", - "c:\\Users\\svenk\\anaconda3\\envs\\dml_tabpfn\\Lib\\site-packages\\tabpfn\\regressor.py:490: UserWarning: Running on CPU with more than 200 samples may be slow.\n", - "Consider using a GPU or the tabpfn-client API: https://github.com/PriorLabs/tabpfn-client\n", - " check_cpu_warning(\n", - "c:\\Users\\svenk\\anaconda3\\envs\\dml_tabpfn\\Lib\\site-packages\\tabpfn\\regressor.py:490: UserWarning: Running on CPU with more than 200 samples may be slow.\n", - "Consider using a GPU or the tabpfn-client API: https://github.com/PriorLabs/tabpfn-client\n", - " check_cpu_warning(\n", - "c:\\Users\\svenk\\anaconda3\\envs\\dml_tabpfn\\Lib\\site-packages\\tabpfn\\regressor.py:490: UserWarning: Running on CPU with more than 200 samples may be slow.\n", - "Consider using a GPU or the tabpfn-client API: https://github.com/PriorLabs/tabpfn-client\n", - " check_cpu_warning(\n", - "c:\\Users\\svenk\\anaconda3\\envs\\dml_tabpfn\\Lib\\site-packages\\tabpfn\\regressor.py:490: UserWarning: Running on CPU with more than 200 samples may be slow.\n", - "Consider using a GPU or the tabpfn-client API: https://github.com/PriorLabs/tabpfn-client\n", - " check_cpu_warning(\n", - "c:\\Users\\svenk\\anaconda3\\envs\\dml_tabpfn\\Lib\\site-packages\\tabpfn\\regressor.py:490: UserWarning: Running on CPU with more than 200 samples may be slow.\n", - "Consider using a GPU or the tabpfn-client API: https://github.com/PriorLabs/tabpfn-client\n", - " check_cpu_warning(\n", - "c:\\Users\\svenk\\anaconda3\\envs\\dml_tabpfn\\Lib\\site-packages\\tabpfn\\regressor.py:490: UserWarning: Running on CPU with more than 200 samples may be slow.\n", - "Consider using a GPU or the tabpfn-client API: https://github.com/PriorLabs/tabpfn-client\n", - " check_cpu_warning(\n", - "c:\\Users\\svenk\\anaconda3\\envs\\dml_tabpfn\\Lib\\site-packages\\tabpfn\\regressor.py:490: UserWarning: Running on CPU with more than 200 samples may be slow.\n", - "Consider using a GPU or the tabpfn-client API: https://github.com/PriorLabs/tabpfn-client\n", - " check_cpu_warning(\n", - "c:\\Users\\svenk\\anaconda3\\envs\\dml_tabpfn\\Lib\\site-packages\\tabpfn\\regressor.py:490: UserWarning: Running on CPU with more than 200 samples may be slow.\n", - "Consider using a GPU or the tabpfn-client API: https://github.com/PriorLabs/tabpfn-client\n", - " check_cpu_warning(\n", - "c:\\Users\\svenk\\anaconda3\\envs\\dml_tabpfn\\Lib\\site-packages\\tabpfn\\regressor.py:490: UserWarning: Running on CPU with more than 200 samples may be slow.\n", - "Consider using a GPU or the tabpfn-client API: https://github.com/PriorLabs/tabpfn-client\n", - " check_cpu_warning(\n", - "c:\\Users\\svenk\\anaconda3\\envs\\dml_tabpfn\\Lib\\site-packages\\tabpfn\\classifier.py:459: UserWarning: Running on CPU with more than 200 samples may be slow.\n", - "Consider using a GPU or the tabpfn-client API: https://github.com/PriorLabs/tabpfn-client\n", - " check_cpu_warning(\n", - "c:\\Users\\svenk\\anaconda3\\envs\\dml_tabpfn\\Lib\\site-packages\\tabpfn\\classifier.py:459: UserWarning: Running on CPU with more than 200 samples may be slow.\n", - "Consider using a GPU or the tabpfn-client API: https://github.com/PriorLabs/tabpfn-client\n", - " check_cpu_warning(\n", - "c:\\Users\\svenk\\anaconda3\\envs\\dml_tabpfn\\Lib\\site-packages\\tabpfn\\classifier.py:459: UserWarning: Running on CPU with more than 200 samples may be slow.\n", - "Consider using a GPU or the tabpfn-client API: https://github.com/PriorLabs/tabpfn-client\n", - " check_cpu_warning(\n", - "c:\\Users\\svenk\\anaconda3\\envs\\dml_tabpfn\\Lib\\site-packages\\tabpfn\\classifier.py:459: UserWarning: Running on CPU with more than 200 samples may be slow.\n", - "Consider using a GPU or the tabpfn-client API: https://github.com/PriorLabs/tabpfn-client\n", - " check_cpu_warning(\n", - "c:\\Users\\svenk\\anaconda3\\envs\\dml_tabpfn\\Lib\\site-packages\\tabpfn\\classifier.py:459: UserWarning: Running on CPU with more than 200 samples may be slow.\n", - "Consider using a GPU or the tabpfn-client API: https://github.com/PriorLabs/tabpfn-client\n", - " check_cpu_warning(\n", - "c:\\Users\\svenk\\anaconda3\\envs\\dml_tabpfn\\Lib\\site-packages\\tabpfn\\classifier.py:459: UserWarning: Running on CPU with more than 200 samples may be slow.\n", - "Consider using a GPU or the tabpfn-client API: https://github.com/PriorLabs/tabpfn-client\n", - " check_cpu_warning(\n", - "c:\\Users\\svenk\\anaconda3\\envs\\dml_tabpfn\\Lib\\site-packages\\tabpfn\\classifier.py:459: UserWarning: Running on CPU with more than 200 samples may be slow.\n", - "Consider using a GPU or the tabpfn-client API: https://github.com/PriorLabs/tabpfn-client\n", - " check_cpu_warning(\n", - "c:\\Users\\svenk\\anaconda3\\envs\\dml_tabpfn\\Lib\\site-packages\\tabpfn\\classifier.py:459: UserWarning: Running on CPU with more than 200 samples may be slow.\n", - "Consider using a GPU or the tabpfn-client API: https://github.com/PriorLabs/tabpfn-client\n", - " check_cpu_warning(\n", - "c:\\Users\\svenk\\anaconda3\\envs\\dml_tabpfn\\Lib\\site-packages\\tabpfn\\classifier.py:459: UserWarning: Running on CPU with more than 200 samples may be slow.\n", - "Consider using a GPU or the tabpfn-client API: https://github.com/PriorLabs/tabpfn-client\n", - " check_cpu_warning(\n", - "c:\\Users\\svenk\\anaconda3\\envs\\dml_tabpfn\\Lib\\site-packages\\tabpfn\\classifier.py:459: UserWarning: Running on CPU with more than 200 samples may be slow.\n", - "Consider using a GPU or the tabpfn-client API: https://github.com/PriorLabs/tabpfn-client\n", - " check_cpu_warning(\n" - ] + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "0b99ff64cd2644dfaa62c41b58bf02f9", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + "tabpfn-v2-classifier-finetuned-zk73skhh.(…): 0%| | 0.00/29.0M [00:00|t| 2.5 % 97.5 %\n", - "0.0 209.381897 1.211443 172.836726 0.0 207.007512 211.756282\n", - "1.0 210.959765 1.380311 152.834917 0.0 208.254404 213.665125\n", - "2.0 216.595908 1.244418 174.054026 0.0 214.156894 219.034922\n", - "3.0 219.330138 1.331450 164.730250 0.0 216.720543 221.939733\n", - "4.0 219.854115 1.272405 172.786257 0.0 217.360247 222.347983\n", - "5.0 219.355109 1.190624 184.235377 0.0 217.021529 221.688690\n" + "0.0 209.395480 1.211939 172.777199 0.0 207.020123 211.770838\n", + "1.0 210.966031 1.367425 154.279824 0.0 208.285928 213.646134\n", + "2.0 216.538410 1.245027 173.922656 0.0 214.098202 218.978618\n", + "3.0 219.333914 1.334717 164.329850 0.0 216.717916 221.949912\n", + "4.0 219.905724 1.278724 171.972735 0.0 217.399470 222.411977\n", + "5.0 219.265669 1.177094 186.277179 0.0 216.958608 221.572730\n" ] } ], @@ -807,996 +430,36 @@ "name": "stderr", "output_type": "stream", "text": [ - "c:\\Users\\svenk\\anaconda3\\envs\\dml_tabpfn\\Lib\\site-packages\\sklearn\\utils\\deprecation.py:151: FutureWarning: 'force_all_finite' was renamed to 'ensure_all_finite' in 1.6 and will be removed in 1.8.\n", - " warnings.warn(\n", - "c:\\Users\\svenk\\anaconda3\\envs\\dml_tabpfn\\Lib\\site-packages\\sklearn\\utils\\deprecation.py:151: FutureWarning: 'force_all_finite' was renamed to 'ensure_all_finite' in 1.6 and will be removed in 1.8.\n", - " warnings.warn(\n", - "c:\\Users\\svenk\\anaconda3\\envs\\dml_tabpfn\\Lib\\site-packages\\doubleml\\double_ml.py:1479: UserWarning: The estimated nu2 for d is not positive. Re-estimation based on riesz representer (non-orthogonal).\n", + "c:\\Users\\BAM5698\\AppData\\Local\\miniconda3\\envs\\dml_docs\\Lib\\site-packages\\doubleml\\double_ml.py:1479: UserWarning: The estimated nu2 for d is not positive. Re-estimation based on riesz representer (non-orthogonal).\n", " warnings.warn(msg, UserWarning)\n", - "c:\\Users\\svenk\\anaconda3\\envs\\dml_tabpfn\\Lib\\site-packages\\sklearn\\utils\\deprecation.py:151: FutureWarning: 'force_all_finite' was renamed to 'ensure_all_finite' in 1.6 and will be removed in 1.8.\n", - " warnings.warn(\n", - "c:\\Users\\svenk\\anaconda3\\envs\\dml_tabpfn\\Lib\\site-packages\\sklearn\\utils\\deprecation.py:151: FutureWarning: 'force_all_finite' was renamed to 'ensure_all_finite' in 1.6 and will be removed in 1.8.\n", + "c:\\Users\\BAM5698\\AppData\\Local\\miniconda3\\envs\\dml_docs\\Lib\\site-packages\\doubleml\\utils\\_checks.py:194: UserWarning: Propensity predictions from learner RandomForestClassifier() for ml_m are close to zero or one (eps=1e-12).\n", " warnings.warn(\n", - "c:\\Users\\svenk\\anaconda3\\envs\\dml_tabpfn\\Lib\\site-packages\\doubleml\\double_ml.py:1479: UserWarning: The estimated nu2 for d is not positive. Re-estimation based on riesz representer (non-orthogonal).\n", + "c:\\Users\\BAM5698\\AppData\\Local\\miniconda3\\envs\\dml_docs\\Lib\\site-packages\\doubleml\\double_ml.py:1479: UserWarning: The estimated nu2 for d is not positive. Re-estimation based on riesz representer (non-orthogonal).\n", " warnings.warn(msg, UserWarning)\n", - "c:\\Users\\svenk\\anaconda3\\envs\\dml_tabpfn\\Lib\\site-packages\\sklearn\\utils\\deprecation.py:151: FutureWarning: 'force_all_finite' was renamed to 'ensure_all_finite' in 1.6 and will be removed in 1.8.\n", - " warnings.warn(\n", - "c:\\Users\\svenk\\anaconda3\\envs\\dml_tabpfn\\Lib\\site-packages\\sklearn\\utils\\deprecation.py:151: FutureWarning: 'force_all_finite' was renamed to 'ensure_all_finite' in 1.6 and will be removed in 1.8.\n", + "c:\\Users\\BAM5698\\AppData\\Local\\miniconda3\\envs\\dml_docs\\Lib\\site-packages\\doubleml\\utils\\_checks.py:194: UserWarning: Propensity predictions from learner RandomForestClassifier() for ml_m are close to zero or one (eps=1e-12).\n", " warnings.warn(\n", - "c:\\Users\\svenk\\anaconda3\\envs\\dml_tabpfn\\Lib\\site-packages\\doubleml\\utils\\_checks.py:194: UserWarning: Propensity predictions from learner RandomForestClassifier() for ml_m are close to zero or one (eps=1e-12).\n", - " warnings.warn(\n", - "c:\\Users\\svenk\\anaconda3\\envs\\dml_tabpfn\\Lib\\site-packages\\doubleml\\double_ml.py:1479: UserWarning: The estimated nu2 for d is not positive. Re-estimation based on riesz representer (non-orthogonal).\n", + "c:\\Users\\BAM5698\\AppData\\Local\\miniconda3\\envs\\dml_docs\\Lib\\site-packages\\doubleml\\double_ml.py:1479: UserWarning: The estimated nu2 for d is not positive. Re-estimation based on riesz representer (non-orthogonal).\n", " warnings.warn(msg, UserWarning)\n", - "c:\\Users\\svenk\\anaconda3\\envs\\dml_tabpfn\\Lib\\site-packages\\sklearn\\utils\\deprecation.py:151: FutureWarning: 'force_all_finite' was renamed to 'ensure_all_finite' in 1.6 and will be removed in 1.8.\n", - " warnings.warn(\n", - "c:\\Users\\svenk\\anaconda3\\envs\\dml_tabpfn\\Lib\\site-packages\\sklearn\\utils\\deprecation.py:151: FutureWarning: 'force_all_finite' was renamed to 'ensure_all_finite' in 1.6 and will be removed in 1.8.\n", - " warnings.warn(\n", - "c:\\Users\\svenk\\anaconda3\\envs\\dml_tabpfn\\Lib\\site-packages\\doubleml\\utils\\_checks.py:194: UserWarning: Propensity predictions from learner RandomForestClassifier() for ml_m are close to zero or one (eps=1e-12).\n", + "c:\\Users\\BAM5698\\AppData\\Local\\miniconda3\\envs\\dml_docs\\Lib\\site-packages\\doubleml\\utils\\_checks.py:194: UserWarning: Propensity predictions from learner RandomForestClassifier() for ml_m are close to zero or one (eps=1e-12).\n", " warnings.warn(\n", - "c:\\Users\\svenk\\anaconda3\\envs\\dml_tabpfn\\Lib\\site-packages\\doubleml\\double_ml.py:1479: UserWarning: The estimated nu2 for d is not positive. Re-estimation based on riesz representer (non-orthogonal).\n", + "c:\\Users\\BAM5698\\AppData\\Local\\miniconda3\\envs\\dml_docs\\Lib\\site-packages\\doubleml\\double_ml.py:1479: UserWarning: The estimated nu2 for d is not positive. Re-estimation based on riesz representer (non-orthogonal).\n", " warnings.warn(msg, UserWarning)\n", - "c:\\Users\\svenk\\anaconda3\\envs\\dml_tabpfn\\Lib\\site-packages\\sklearn\\utils\\deprecation.py:151: FutureWarning: 'force_all_finite' was renamed to 'ensure_all_finite' in 1.6 and will be removed in 1.8.\n", - " warnings.warn(\n", - "c:\\Users\\svenk\\anaconda3\\envs\\dml_tabpfn\\Lib\\site-packages\\sklearn\\utils\\deprecation.py:151: FutureWarning: 'force_all_finite' was renamed to 'ensure_all_finite' in 1.6 and will be removed in 1.8.\n", - " warnings.warn(\n", - "c:\\Users\\svenk\\anaconda3\\envs\\dml_tabpfn\\Lib\\site-packages\\doubleml\\utils\\_checks.py:194: UserWarning: Propensity predictions from learner RandomForestClassifier() for ml_m are close to zero or one (eps=1e-12).\n", - " warnings.warn(\n", - "c:\\Users\\svenk\\anaconda3\\envs\\dml_tabpfn\\Lib\\site-packages\\sklearn\\utils\\deprecation.py:151: FutureWarning: 'force_all_finite' was renamed to 'ensure_all_finite' in 1.6 and will be removed in 1.8.\n", - " warnings.warn(\n", - "c:\\Users\\svenk\\anaconda3\\envs\\dml_tabpfn\\Lib\\site-packages\\sklearn\\utils\\deprecation.py:151: FutureWarning: 'force_all_finite' was renamed to 'ensure_all_finite' in 1.6 and will be removed in 1.8.\n", - " warnings.warn(\n", - "c:\\Users\\svenk\\anaconda3\\envs\\dml_tabpfn\\Lib\\site-packages\\doubleml\\utils\\_checks.py:194: UserWarning: Propensity predictions from learner RandomForestClassifier() for ml_m are close to zero or one (eps=1e-12).\n", + "c:\\Users\\BAM5698\\AppData\\Local\\miniconda3\\envs\\dml_docs\\Lib\\site-packages\\doubleml\\utils\\_checks.py:194: UserWarning: Propensity predictions from learner RandomForestClassifier() for ml_m are close to zero or one (eps=1e-12).\n", " warnings.warn(\n", - "c:\\Users\\svenk\\anaconda3\\envs\\dml_tabpfn\\Lib\\site-packages\\sklearn\\utils\\deprecation.py:151: FutureWarning: 'force_all_finite' was renamed to 'ensure_all_finite' in 1.6 and will be removed in 1.8.\n", - " warnings.warn(\n", - "c:\\Users\\svenk\\anaconda3\\envs\\dml_tabpfn\\Lib\\site-packages\\sklearn\\utils\\deprecation.py:151: FutureWarning: 'force_all_finite' was renamed to 'ensure_all_finite' in 1.6 and will be removed in 1.8.\n", - " warnings.warn(\n", - "c:\\Users\\svenk\\anaconda3\\envs\\dml_tabpfn\\Lib\\site-packages\\doubleml\\double_ml.py:1479: UserWarning: The estimated nu2 for d is not positive. Re-estimation based on riesz representer (non-orthogonal).\n", - " warnings.warn(msg, UserWarning)\n", - "c:\\Users\\svenk\\anaconda3\\envs\\dml_tabpfn\\Lib\\site-packages\\sklearn\\utils\\deprecation.py:151: FutureWarning: 'force_all_finite' was renamed to 'ensure_all_finite' in 1.6 and will be removed in 1.8.\n", - " warnings.warn(\n", - "c:\\Users\\svenk\\anaconda3\\envs\\dml_tabpfn\\Lib\\site-packages\\sklearn\\utils\\deprecation.py:151: FutureWarning: 'force_all_finite' was renamed to 'ensure_all_finite' in 1.6 and will be removed in 1.8.\n", - " warnings.warn(\n", - "c:\\Users\\svenk\\anaconda3\\envs\\dml_tabpfn\\Lib\\site-packages\\doubleml\\double_ml.py:1479: UserWarning: The estimated nu2 for d is not positive. Re-estimation based on riesz representer (non-orthogonal).\n", + "c:\\Users\\BAM5698\\AppData\\Local\\miniconda3\\envs\\dml_docs\\Lib\\site-packages\\doubleml\\double_ml.py:1479: UserWarning: The estimated nu2 for d is not positive. Re-estimation based on riesz representer (non-orthogonal).\n", " warnings.warn(msg, UserWarning)\n", - "c:\\Users\\svenk\\anaconda3\\envs\\dml_tabpfn\\Lib\\site-packages\\sklearn\\utils\\deprecation.py:151: FutureWarning: 'force_all_finite' was renamed to 'ensure_all_finite' in 1.6 and will be removed in 1.8.\n", - " warnings.warn(\n", - "c:\\Users\\svenk\\anaconda3\\envs\\dml_tabpfn\\Lib\\site-packages\\sklearn\\utils\\deprecation.py:151: FutureWarning: 'force_all_finite' was renamed to 'ensure_all_finite' in 1.6 and will be removed in 1.8.\n", - " warnings.warn(\n", - "c:\\Users\\svenk\\anaconda3\\envs\\dml_tabpfn\\Lib\\site-packages\\doubleml\\utils\\_checks.py:194: UserWarning: Propensity predictions from learner RandomForestClassifier() for ml_m are close to zero or one (eps=1e-12).\n", - " warnings.warn(\n", - "c:\\Users\\svenk\\anaconda3\\envs\\dml_tabpfn\\Lib\\site-packages\\doubleml\\double_ml.py:1479: UserWarning: The estimated nu2 for d is not positive. Re-estimation based on riesz representer (non-orthogonal).\n", + "c:\\Users\\BAM5698\\AppData\\Local\\miniconda3\\envs\\dml_docs\\Lib\\site-packages\\doubleml\\double_ml.py:1479: UserWarning: The estimated nu2 for d is not positive. Re-estimation based on riesz representer (non-orthogonal).\n", " warnings.warn(msg, UserWarning)\n", - "c:\\Users\\svenk\\anaconda3\\envs\\dml_tabpfn\\Lib\\site-packages\\sklearn\\utils\\deprecation.py:151: FutureWarning: 'force_all_finite' was renamed to 'ensure_all_finite' in 1.6 and will be removed in 1.8.\n", - " warnings.warn(\n", - "c:\\Users\\svenk\\anaconda3\\envs\\dml_tabpfn\\Lib\\site-packages\\sklearn\\utils\\deprecation.py:151: FutureWarning: 'force_all_finite' was renamed to 'ensure_all_finite' in 1.6 and will be removed in 1.8.\n", - " warnings.warn(\n", - "c:\\Users\\svenk\\anaconda3\\envs\\dml_tabpfn\\Lib\\site-packages\\doubleml\\utils\\_checks.py:194: UserWarning: Propensity predictions from learner RandomForestClassifier() for ml_m are close to zero or one (eps=1e-12).\n", - " warnings.warn(\n", - "c:\\Users\\svenk\\anaconda3\\envs\\dml_tabpfn\\Lib\\site-packages\\doubleml\\double_ml.py:1479: UserWarning: The estimated nu2 for d is not positive. Re-estimation based on riesz representer (non-orthogonal).\n", - " warnings.warn(msg, UserWarning)\n", - "c:\\Users\\svenk\\anaconda3\\envs\\dml_tabpfn\\Lib\\site-packages\\sklearn\\utils\\deprecation.py:151: FutureWarning: 'force_all_finite' was renamed to 'ensure_all_finite' in 1.6 and will be removed in 1.8.\n", - " warnings.warn(\n", - "c:\\Users\\svenk\\anaconda3\\envs\\dml_tabpfn\\Lib\\site-packages\\sklearn\\utils\\deprecation.py:151: FutureWarning: 'force_all_finite' was renamed to 'ensure_all_finite' in 1.6 and will be removed in 1.8.\n", - " warnings.warn(\n", - "c:\\Users\\svenk\\anaconda3\\envs\\dml_tabpfn\\Lib\\site-packages\\doubleml\\utils\\_checks.py:194: UserWarning: Propensity predictions from learner RandomForestClassifier() for ml_m are close to zero or one (eps=1e-12).\n", - " warnings.warn(\n", - "c:\\Users\\svenk\\anaconda3\\envs\\dml_tabpfn\\Lib\\site-packages\\doubleml\\double_ml.py:1479: UserWarning: The estimated nu2 for d is not positive. Re-estimation based on riesz representer (non-orthogonal).\n", - " warnings.warn(msg, UserWarning)\n", - "c:\\Users\\svenk\\anaconda3\\envs\\dml_tabpfn\\Lib\\site-packages\\sklearn\\utils\\deprecation.py:151: FutureWarning: 'force_all_finite' was renamed to 'ensure_all_finite' in 1.6 and will be removed in 1.8.\n", - " warnings.warn(\n", - "c:\\Users\\svenk\\anaconda3\\envs\\dml_tabpfn\\Lib\\site-packages\\sklearn\\utils\\deprecation.py:151: FutureWarning: 'force_all_finite' was renamed to 'ensure_all_finite' in 1.6 and will be removed in 1.8.\n", - " warnings.warn(\n", - "c:\\Users\\svenk\\anaconda3\\envs\\dml_tabpfn\\Lib\\site-packages\\sklearn\\utils\\deprecation.py:151: FutureWarning: 'force_all_finite' was renamed to 'ensure_all_finite' in 1.6 and will be removed in 1.8.\n", - " warnings.warn(\n", - "c:\\Users\\svenk\\anaconda3\\envs\\dml_tabpfn\\Lib\\site-packages\\sklearn\\utils\\deprecation.py:151: FutureWarning: 'force_all_finite' was renamed to 'ensure_all_finite' in 1.6 and will be removed in 1.8.\n", - " warnings.warn(\n", - "c:\\Users\\svenk\\anaconda3\\envs\\dml_tabpfn\\Lib\\site-packages\\sklearn\\utils\\deprecation.py:151: FutureWarning: 'force_all_finite' was renamed to 'ensure_all_finite' in 1.6 and will be removed in 1.8.\n", - " warnings.warn(\n", - "c:\\Users\\svenk\\anaconda3\\envs\\dml_tabpfn\\Lib\\site-packages\\sklearn\\utils\\deprecation.py:151: FutureWarning: 'force_all_finite' was renamed to 'ensure_all_finite' in 1.6 and will be removed in 1.8.\n", - " warnings.warn(\n", - "c:\\Users\\svenk\\anaconda3\\envs\\dml_tabpfn\\Lib\\site-packages\\sklearn\\utils\\deprecation.py:151: FutureWarning: 'force_all_finite' was renamed to 'ensure_all_finite' in 1.6 and will be removed in 1.8.\n", - " warnings.warn(\n", - "c:\\Users\\svenk\\anaconda3\\envs\\dml_tabpfn\\Lib\\site-packages\\sklearn\\utils\\deprecation.py:151: FutureWarning: 'force_all_finite' was renamed to 'ensure_all_finite' in 1.6 and will be removed in 1.8.\n", - " warnings.warn(\n", - "c:\\Users\\svenk\\anaconda3\\envs\\dml_tabpfn\\Lib\\site-packages\\sklearn\\utils\\deprecation.py:151: FutureWarning: 'force_all_finite' was renamed to 'ensure_all_finite' in 1.6 and will be removed in 1.8.\n", - " warnings.warn(\n", - "c:\\Users\\svenk\\anaconda3\\envs\\dml_tabpfn\\Lib\\site-packages\\sklearn\\utils\\deprecation.py:151: FutureWarning: 'force_all_finite' was renamed to 'ensure_all_finite' in 1.6 and will be removed in 1.8.\n", - " warnings.warn(\n", - "c:\\Users\\svenk\\anaconda3\\envs\\dml_tabpfn\\Lib\\site-packages\\sklearn\\utils\\deprecation.py:151: FutureWarning: 'force_all_finite' was renamed to 'ensure_all_finite' in 1.6 and will be removed in 1.8.\n", - " warnings.warn(\n", - "c:\\Users\\svenk\\anaconda3\\envs\\dml_tabpfn\\Lib\\site-packages\\sklearn\\utils\\deprecation.py:151: FutureWarning: 'force_all_finite' was renamed to 'ensure_all_finite' in 1.6 and will be removed in 1.8.\n", - " warnings.warn(\n", - "c:\\Users\\svenk\\anaconda3\\envs\\dml_tabpfn\\Lib\\site-packages\\sklearn\\utils\\deprecation.py:151: FutureWarning: 'force_all_finite' was renamed to 'ensure_all_finite' in 1.6 and will be removed in 1.8.\n", - " warnings.warn(\n", - "c:\\Users\\svenk\\anaconda3\\envs\\dml_tabpfn\\Lib\\site-packages\\sklearn\\utils\\deprecation.py:151: FutureWarning: 'force_all_finite' was renamed to 'ensure_all_finite' in 1.6 and will be removed in 1.8.\n", - " warnings.warn(\n", - "c:\\Users\\svenk\\anaconda3\\envs\\dml_tabpfn\\Lib\\site-packages\\doubleml\\utils\\_checks.py:194: UserWarning: Propensity predictions from learner RandomForestClassifier() for ml_m are close to zero or one (eps=1e-12).\n", - " warnings.warn(\n", - "c:\\Users\\svenk\\anaconda3\\envs\\dml_tabpfn\\Lib\\site-packages\\doubleml\\double_ml.py:1479: UserWarning: The estimated nu2 for d is not positive. Re-estimation based on riesz representer (non-orthogonal).\n", + "c:\\Users\\BAM5698\\AppData\\Local\\miniconda3\\envs\\dml_docs\\Lib\\site-packages\\doubleml\\double_ml.py:1479: UserWarning: The estimated nu2 for d is not positive. Re-estimation based on riesz representer (non-orthogonal).\n", " warnings.warn(msg, UserWarning)\n", - "c:\\Users\\svenk\\anaconda3\\envs\\dml_tabpfn\\Lib\\site-packages\\sklearn\\utils\\deprecation.py:151: FutureWarning: 'force_all_finite' was renamed to 'ensure_all_finite' in 1.6 and will be removed in 1.8.\n", - " warnings.warn(\n", - "c:\\Users\\svenk\\anaconda3\\envs\\dml_tabpfn\\Lib\\site-packages\\sklearn\\utils\\deprecation.py:151: FutureWarning: 'force_all_finite' was renamed to 'ensure_all_finite' in 1.6 and will be removed in 1.8.\n", - " warnings.warn(\n", - "c:\\Users\\svenk\\anaconda3\\envs\\dml_tabpfn\\Lib\\site-packages\\sklearn\\utils\\deprecation.py:151: FutureWarning: 'force_all_finite' was renamed to 'ensure_all_finite' in 1.6 and will be removed in 1.8.\n", - " warnings.warn(\n", - "c:\\Users\\svenk\\anaconda3\\envs\\dml_tabpfn\\Lib\\site-packages\\sklearn\\utils\\deprecation.py:151: FutureWarning: 'force_all_finite' was renamed to 'ensure_all_finite' in 1.6 and will be removed in 1.8.\n", - " warnings.warn(\n", - "c:\\Users\\svenk\\anaconda3\\envs\\dml_tabpfn\\Lib\\site-packages\\sklearn\\utils\\deprecation.py:151: FutureWarning: 'force_all_finite' was renamed to 'ensure_all_finite' in 1.6 and will be removed in 1.8.\n", - " warnings.warn(\n", - "c:\\Users\\svenk\\anaconda3\\envs\\dml_tabpfn\\Lib\\site-packages\\sklearn\\utils\\deprecation.py:151: FutureWarning: 'force_all_finite' was renamed to 'ensure_all_finite' in 1.6 and will be removed in 1.8.\n", - " warnings.warn(\n", - "c:\\Users\\svenk\\anaconda3\\envs\\dml_tabpfn\\Lib\\site-packages\\sklearn\\utils\\deprecation.py:151: FutureWarning: 'force_all_finite' was renamed to 'ensure_all_finite' in 1.6 and will be removed in 1.8.\n", - " warnings.warn(\n", - "c:\\Users\\svenk\\anaconda3\\envs\\dml_tabpfn\\Lib\\site-packages\\sklearn\\utils\\deprecation.py:151: FutureWarning: 'force_all_finite' was renamed to 'ensure_all_finite' in 1.6 and will be removed in 1.8.\n", - " warnings.warn(\n", - "c:\\Users\\svenk\\anaconda3\\envs\\dml_tabpfn\\Lib\\site-packages\\sklearn\\utils\\deprecation.py:151: FutureWarning: 'force_all_finite' was renamed to 'ensure_all_finite' in 1.6 and will be removed in 1.8.\n", - " warnings.warn(\n", - "c:\\Users\\svenk\\anaconda3\\envs\\dml_tabpfn\\Lib\\site-packages\\sklearn\\utils\\deprecation.py:151: FutureWarning: 'force_all_finite' was renamed to 'ensure_all_finite' in 1.6 and will be removed in 1.8.\n", - " warnings.warn(\n", - "c:\\Users\\svenk\\anaconda3\\envs\\dml_tabpfn\\Lib\\site-packages\\sklearn\\utils\\deprecation.py:151: FutureWarning: 'force_all_finite' was renamed to 'ensure_all_finite' in 1.6 and will be removed in 1.8.\n", - " warnings.warn(\n", - "c:\\Users\\svenk\\anaconda3\\envs\\dml_tabpfn\\Lib\\site-packages\\sklearn\\utils\\deprecation.py:151: FutureWarning: 'force_all_finite' was renamed to 'ensure_all_finite' in 1.6 and will be removed in 1.8.\n", - " warnings.warn(\n", - "c:\\Users\\svenk\\anaconda3\\envs\\dml_tabpfn\\Lib\\site-packages\\sklearn\\utils\\deprecation.py:151: FutureWarning: 'force_all_finite' was renamed to 'ensure_all_finite' in 1.6 and will be removed in 1.8.\n", - " warnings.warn(\n", - "c:\\Users\\svenk\\anaconda3\\envs\\dml_tabpfn\\Lib\\site-packages\\sklearn\\utils\\deprecation.py:151: FutureWarning: 'force_all_finite' was renamed to 'ensure_all_finite' in 1.6 and will be removed in 1.8.\n", - " warnings.warn(\n", - "Exception in thread Thread-6 (_readerthread):\n", - "Traceback (most recent call last):\n", - " File \"c:\\Users\\svenk\\anaconda3\\envs\\dml_tabpfn\\Lib\\threading.py\", line 1045, in _bootstrap_inner\n", - " self.run()\n", - " File \"c:\\Users\\svenk\\anaconda3\\envs\\dml_tabpfn\\Lib\\threading.py\", line 982, in run\n", - " self._target(*self._args, **self._kwargs)\n", - " File \"c:\\Users\\svenk\\anaconda3\\envs\\dml_tabpfn\\Lib\\subprocess.py\", line 1599, in _readerthread\n", - " buffer.append(fh.read())\n", - " ^^^^^^^^^\n", - " File \"\", line 322, in decode\n", - "UnicodeDecodeError: 'utf-8' codec can't decode byte 0x81 in position 109: invalid start byte\n", - "Exception in thread Thread-6 (_readerthread):\n", - "Traceback (most recent call last):\n", - " File \"c:\\Users\\svenk\\anaconda3\\envs\\dml_tabpfn\\Lib\\threading.py\", line 1045, in _bootstrap_inner\n", - " self.run()\n", - " File \"c:\\Users\\svenk\\anaconda3\\envs\\dml_tabpfn\\Lib\\threading.py\", line 982, in run\n", - " self._target(*self._args, **self._kwargs)\n", - " File \"c:\\Users\\svenk\\anaconda3\\envs\\dml_tabpfn\\Lib\\subprocess.py\", line 1599, in _readerthread\n", - " buffer.append(fh.read())\n", - " ^^^^^^^^^\n", - " File \"\", line 322, in decode\n", - "UnicodeDecodeError: 'utf-8' codec can't decode byte 0x81 in position 109: invalid start byte\n", - "c:\\Users\\svenk\\anaconda3\\envs\\dml_tabpfn\\Lib\\site-packages\\sklearn\\utils\\validation.py:2739: UserWarning: X does not have valid feature names, but LGBMRegressor was fitted with feature names\n", - " warnings.warn(\n", - "c:\\Users\\svenk\\anaconda3\\envs\\dml_tabpfn\\Lib\\site-packages\\sklearn\\utils\\validation.py:2739: UserWarning: X does not have valid feature names, but LGBMRegressor was fitted with feature names\n", - " warnings.warn(\n", - "c:\\Users\\svenk\\anaconda3\\envs\\dml_tabpfn\\Lib\\site-packages\\sklearn\\utils\\validation.py:2739: UserWarning: X does not have valid feature names, but LGBMRegressor was fitted with feature names\n", - " warnings.warn(\n", - "c:\\Users\\svenk\\anaconda3\\envs\\dml_tabpfn\\Lib\\site-packages\\sklearn\\utils\\validation.py:2739: UserWarning: X does not have valid feature names, but LGBMRegressor was fitted with feature names\n", - " warnings.warn(\n", - "c:\\Users\\svenk\\anaconda3\\envs\\dml_tabpfn\\Lib\\site-packages\\sklearn\\utils\\validation.py:2739: UserWarning: X does not have valid feature names, but LGBMRegressor was fitted with feature names\n", - " warnings.warn(\n", - "c:\\Users\\svenk\\anaconda3\\envs\\dml_tabpfn\\Lib\\site-packages\\sklearn\\utils\\validation.py:2739: UserWarning: X does not have valid feature names, but LGBMRegressor was fitted with feature names\n", - " warnings.warn(\n", - "c:\\Users\\svenk\\anaconda3\\envs\\dml_tabpfn\\Lib\\site-packages\\sklearn\\utils\\validation.py:2739: UserWarning: X does not have valid feature names, but LGBMRegressor was fitted with feature names\n", - " warnings.warn(\n", - "c:\\Users\\svenk\\anaconda3\\envs\\dml_tabpfn\\Lib\\site-packages\\sklearn\\utils\\validation.py:2739: UserWarning: X does not have valid feature names, but LGBMRegressor was fitted with feature names\n", - " warnings.warn(\n", - "c:\\Users\\svenk\\anaconda3\\envs\\dml_tabpfn\\Lib\\site-packages\\sklearn\\utils\\validation.py:2739: UserWarning: X does not have valid feature names, but LGBMRegressor was fitted with feature names\n", - " warnings.warn(\n", - "c:\\Users\\svenk\\anaconda3\\envs\\dml_tabpfn\\Lib\\site-packages\\sklearn\\utils\\validation.py:2739: UserWarning: X does not have valid feature names, but LGBMRegressor was fitted with feature names\n", - " warnings.warn(\n", - "c:\\Users\\svenk\\anaconda3\\envs\\dml_tabpfn\\Lib\\site-packages\\sklearn\\utils\\validation.py:2739: UserWarning: X does not have valid feature names, but LGBMClassifier was fitted with feature names\n", - " warnings.warn(\n", - "c:\\Users\\svenk\\anaconda3\\envs\\dml_tabpfn\\Lib\\site-packages\\sklearn\\utils\\validation.py:2739: UserWarning: X does not have valid feature names, but LGBMClassifier was fitted with feature names\n", - " warnings.warn(\n", - "c:\\Users\\svenk\\anaconda3\\envs\\dml_tabpfn\\Lib\\site-packages\\sklearn\\utils\\validation.py:2739: UserWarning: X does not have valid feature names, but LGBMClassifier was fitted with feature names\n", - " warnings.warn(\n", - "c:\\Users\\svenk\\anaconda3\\envs\\dml_tabpfn\\Lib\\site-packages\\sklearn\\utils\\validation.py:2739: UserWarning: X does not have valid feature names, but LGBMClassifier was fitted with feature names\n", - " warnings.warn(\n", - "c:\\Users\\svenk\\anaconda3\\envs\\dml_tabpfn\\Lib\\site-packages\\sklearn\\utils\\validation.py:2739: UserWarning: X does not have valid feature names, but LGBMClassifier was fitted with feature names\n", - " warnings.warn(\n", - "c:\\Users\\svenk\\anaconda3\\envs\\dml_tabpfn\\Lib\\site-packages\\doubleml\\double_ml.py:1479: UserWarning: The estimated nu2 for d is not positive. Re-estimation based on riesz representer (non-orthogonal).\n", + "c:\\Users\\BAM5698\\AppData\\Local\\miniconda3\\envs\\dml_docs\\Lib\\site-packages\\doubleml\\double_ml.py:1479: UserWarning: The estimated nu2 for d is not positive. Re-estimation based on riesz representer (non-orthogonal).\n", " warnings.warn(msg, UserWarning)\n", - "c:\\Users\\svenk\\anaconda3\\envs\\dml_tabpfn\\Lib\\site-packages\\sklearn\\utils\\deprecation.py:151: FutureWarning: 'force_all_finite' was renamed to 'ensure_all_finite' in 1.6 and will be removed in 1.8.\n", - " warnings.warn(\n", - "c:\\Users\\svenk\\anaconda3\\envs\\dml_tabpfn\\Lib\\site-packages\\sklearn\\utils\\deprecation.py:151: FutureWarning: 'force_all_finite' was renamed to 'ensure_all_finite' in 1.6 and will be removed in 1.8.\n", - " warnings.warn(\n", - "c:\\Users\\svenk\\anaconda3\\envs\\dml_tabpfn\\Lib\\site-packages\\sklearn\\utils\\validation.py:2739: UserWarning: X does not have valid feature names, but LGBMRegressor was fitted with feature names\n", - " warnings.warn(\n", - "c:\\Users\\svenk\\anaconda3\\envs\\dml_tabpfn\\Lib\\site-packages\\sklearn\\utils\\validation.py:2739: UserWarning: X does not have valid feature names, but LGBMRegressor was fitted with feature names\n", - " warnings.warn(\n", - "c:\\Users\\svenk\\anaconda3\\envs\\dml_tabpfn\\Lib\\site-packages\\sklearn\\utils\\validation.py:2739: UserWarning: X does not have valid feature names, but LGBMRegressor was fitted with feature names\n", - " warnings.warn(\n", - "c:\\Users\\svenk\\anaconda3\\envs\\dml_tabpfn\\Lib\\site-packages\\sklearn\\utils\\validation.py:2739: UserWarning: X does not have valid feature names, but LGBMRegressor was fitted with feature names\n", - " warnings.warn(\n", - "c:\\Users\\svenk\\anaconda3\\envs\\dml_tabpfn\\Lib\\site-packages\\sklearn\\utils\\validation.py:2739: UserWarning: X does not have valid feature names, but LGBMRegressor was fitted with feature names\n", - " warnings.warn(\n", - "c:\\Users\\svenk\\anaconda3\\envs\\dml_tabpfn\\Lib\\site-packages\\sklearn\\utils\\validation.py:2739: UserWarning: X does not have valid feature names, but LGBMRegressor was fitted with feature names\n", - " warnings.warn(\n", - "c:\\Users\\svenk\\anaconda3\\envs\\dml_tabpfn\\Lib\\site-packages\\sklearn\\utils\\validation.py:2739: UserWarning: X does not have valid feature names, but LGBMRegressor was fitted with feature names\n", - " warnings.warn(\n", - "c:\\Users\\svenk\\anaconda3\\envs\\dml_tabpfn\\Lib\\site-packages\\sklearn\\utils\\validation.py:2739: UserWarning: X does not have valid feature names, but LGBMRegressor was fitted with feature names\n", - " warnings.warn(\n", - "c:\\Users\\svenk\\anaconda3\\envs\\dml_tabpfn\\Lib\\site-packages\\sklearn\\utils\\validation.py:2739: UserWarning: X does not have valid feature names, but LGBMRegressor was fitted with feature names\n", - " warnings.warn(\n", - "c:\\Users\\svenk\\anaconda3\\envs\\dml_tabpfn\\Lib\\site-packages\\sklearn\\utils\\validation.py:2739: UserWarning: X does not have valid feature names, but LGBMRegressor was fitted with feature names\n", - " warnings.warn(\n", - "c:\\Users\\svenk\\anaconda3\\envs\\dml_tabpfn\\Lib\\site-packages\\sklearn\\utils\\validation.py:2739: UserWarning: X does not have valid feature names, but LGBMRegressor was fitted with feature names\n", - " warnings.warn(\n", - "c:\\Users\\svenk\\anaconda3\\envs\\dml_tabpfn\\Lib\\site-packages\\sklearn\\utils\\validation.py:2739: UserWarning: X does not have valid feature names, but LGBMRegressor was fitted with feature names\n", - " warnings.warn(\n", - "c:\\Users\\svenk\\anaconda3\\envs\\dml_tabpfn\\Lib\\site-packages\\sklearn\\utils\\validation.py:2739: UserWarning: X does not have valid feature names, but LGBMRegressor was fitted with feature names\n", - " warnings.warn(\n", - "c:\\Users\\svenk\\anaconda3\\envs\\dml_tabpfn\\Lib\\site-packages\\sklearn\\utils\\validation.py:2739: UserWarning: X does not have valid feature names, but LGBMClassifier was fitted with feature names\n", - " warnings.warn(\n", - "c:\\Users\\svenk\\anaconda3\\envs\\dml_tabpfn\\Lib\\site-packages\\sklearn\\utils\\validation.py:2739: UserWarning: X does not have valid feature names, but LGBMClassifier was fitted with feature names\n", - " warnings.warn(\n", - "c:\\Users\\svenk\\anaconda3\\envs\\dml_tabpfn\\Lib\\site-packages\\sklearn\\utils\\validation.py:2739: UserWarning: X does not have valid feature names, but LGBMClassifier was fitted with feature names\n", - " warnings.warn(\n", - "c:\\Users\\svenk\\anaconda3\\envs\\dml_tabpfn\\Lib\\site-packages\\sklearn\\utils\\validation.py:2739: UserWarning: X does not have valid feature names, but LGBMClassifier was fitted with feature names\n", - " warnings.warn(\n", - "c:\\Users\\svenk\\anaconda3\\envs\\dml_tabpfn\\Lib\\site-packages\\sklearn\\utils\\validation.py:2739: UserWarning: X does not have valid feature names, but LGBMClassifier was fitted with feature names\n", - " warnings.warn(\n", - "c:\\Users\\svenk\\anaconda3\\envs\\dml_tabpfn\\Lib\\site-packages\\doubleml\\double_ml.py:1479: UserWarning: The estimated nu2 for d is not positive. Re-estimation based on riesz representer (non-orthogonal).\n", + "c:\\Users\\BAM5698\\AppData\\Local\\miniconda3\\envs\\dml_docs\\Lib\\site-packages\\doubleml\\double_ml.py:1479: UserWarning: The estimated nu2 for d is not positive. Re-estimation based on riesz representer (non-orthogonal).\n", " warnings.warn(msg, UserWarning)\n", - "c:\\Users\\svenk\\anaconda3\\envs\\dml_tabpfn\\Lib\\site-packages\\sklearn\\utils\\deprecation.py:151: FutureWarning: 'force_all_finite' was renamed to 'ensure_all_finite' in 1.6 and will be removed in 1.8.\n", - " warnings.warn(\n", - "c:\\Users\\svenk\\anaconda3\\envs\\dml_tabpfn\\Lib\\site-packages\\sklearn\\utils\\deprecation.py:151: FutureWarning: 'force_all_finite' was renamed to 'ensure_all_finite' in 1.6 and will be removed in 1.8.\n", - " warnings.warn(\n", - "c:\\Users\\svenk\\anaconda3\\envs\\dml_tabpfn\\Lib\\site-packages\\sklearn\\utils\\validation.py:2739: UserWarning: X does not have valid feature names, but LGBMRegressor was fitted with feature names\n", - " warnings.warn(\n", - "c:\\Users\\svenk\\anaconda3\\envs\\dml_tabpfn\\Lib\\site-packages\\sklearn\\utils\\validation.py:2739: UserWarning: X does not have valid feature names, but LGBMRegressor was fitted with feature names\n", - " warnings.warn(\n", - "c:\\Users\\svenk\\anaconda3\\envs\\dml_tabpfn\\Lib\\site-packages\\sklearn\\utils\\validation.py:2739: UserWarning: X does not have valid feature names, but LGBMRegressor was fitted with feature names\n", - " warnings.warn(\n", - "c:\\Users\\svenk\\anaconda3\\envs\\dml_tabpfn\\Lib\\site-packages\\sklearn\\utils\\validation.py:2739: UserWarning: X does not have valid feature names, but LGBMRegressor was fitted with feature names\n", - " warnings.warn(\n", - "c:\\Users\\svenk\\anaconda3\\envs\\dml_tabpfn\\Lib\\site-packages\\sklearn\\utils\\validation.py:2739: UserWarning: X does not have valid feature names, but LGBMRegressor was fitted with feature names\n", - " warnings.warn(\n", - "c:\\Users\\svenk\\anaconda3\\envs\\dml_tabpfn\\Lib\\site-packages\\sklearn\\utils\\validation.py:2739: UserWarning: X does not have valid feature names, but LGBMRegressor was fitted with feature names\n", - " warnings.warn(\n", - "c:\\Users\\svenk\\anaconda3\\envs\\dml_tabpfn\\Lib\\site-packages\\sklearn\\utils\\validation.py:2739: UserWarning: X does not have valid feature names, but LGBMRegressor was fitted with feature names\n", - " warnings.warn(\n", - "c:\\Users\\svenk\\anaconda3\\envs\\dml_tabpfn\\Lib\\site-packages\\sklearn\\utils\\validation.py:2739: UserWarning: X does not have valid feature names, but LGBMRegressor was fitted with feature names\n", - " warnings.warn(\n", - "c:\\Users\\svenk\\anaconda3\\envs\\dml_tabpfn\\Lib\\site-packages\\sklearn\\utils\\validation.py:2739: UserWarning: X does not have valid feature names, but LGBMRegressor was fitted with feature names\n", - " warnings.warn(\n", - "c:\\Users\\svenk\\anaconda3\\envs\\dml_tabpfn\\Lib\\site-packages\\sklearn\\utils\\validation.py:2739: UserWarning: X does not have valid feature names, but LGBMRegressor was fitted with feature names\n", - " warnings.warn(\n", - "c:\\Users\\svenk\\anaconda3\\envs\\dml_tabpfn\\Lib\\site-packages\\sklearn\\utils\\validation.py:2739: UserWarning: X does not have valid feature names, but LGBMClassifier was fitted with feature names\n", - " warnings.warn(\n", - "c:\\Users\\svenk\\anaconda3\\envs\\dml_tabpfn\\Lib\\site-packages\\sklearn\\utils\\validation.py:2739: UserWarning: X does not have valid feature names, but LGBMClassifier was fitted with feature names\n", - " warnings.warn(\n", - "c:\\Users\\svenk\\anaconda3\\envs\\dml_tabpfn\\Lib\\site-packages\\sklearn\\utils\\validation.py:2739: UserWarning: X does not have valid feature names, but LGBMClassifier was fitted with feature names\n", - " warnings.warn(\n", - "c:\\Users\\svenk\\anaconda3\\envs\\dml_tabpfn\\Lib\\site-packages\\sklearn\\utils\\validation.py:2739: UserWarning: X does not have valid feature names, but LGBMClassifier was fitted with feature names\n", - " warnings.warn(\n", - "c:\\Users\\svenk\\anaconda3\\envs\\dml_tabpfn\\Lib\\site-packages\\sklearn\\utils\\validation.py:2739: UserWarning: X does not have valid feature names, but LGBMClassifier was fitted with feature names\n", - " warnings.warn(\n", - "c:\\Users\\svenk\\anaconda3\\envs\\dml_tabpfn\\Lib\\site-packages\\doubleml\\double_ml.py:1479: UserWarning: The estimated nu2 for d is not positive. Re-estimation based on riesz representer (non-orthogonal).\n", + "c:\\Users\\BAM5698\\AppData\\Local\\miniconda3\\envs\\dml_docs\\Lib\\site-packages\\doubleml\\double_ml.py:1479: UserWarning: The estimated nu2 for d is not positive. Re-estimation based on riesz representer (non-orthogonal).\n", " warnings.warn(msg, UserWarning)\n", - "c:\\Users\\svenk\\anaconda3\\envs\\dml_tabpfn\\Lib\\site-packages\\sklearn\\utils\\deprecation.py:151: FutureWarning: 'force_all_finite' was renamed to 'ensure_all_finite' in 1.6 and will be removed in 1.8.\n", - " warnings.warn(\n", - "c:\\Users\\svenk\\anaconda3\\envs\\dml_tabpfn\\Lib\\site-packages\\sklearn\\utils\\deprecation.py:151: FutureWarning: 'force_all_finite' was renamed to 'ensure_all_finite' in 1.6 and will be removed in 1.8.\n", - " warnings.warn(\n", - "c:\\Users\\svenk\\anaconda3\\envs\\dml_tabpfn\\Lib\\site-packages\\sklearn\\utils\\validation.py:2739: UserWarning: X does not have valid feature names, but LGBMRegressor was fitted with feature names\n", - " warnings.warn(\n", - "c:\\Users\\svenk\\anaconda3\\envs\\dml_tabpfn\\Lib\\site-packages\\sklearn\\utils\\validation.py:2739: UserWarning: X does not have valid feature names, but LGBMRegressor was fitted with feature names\n", - " warnings.warn(\n", - "c:\\Users\\svenk\\anaconda3\\envs\\dml_tabpfn\\Lib\\site-packages\\sklearn\\utils\\validation.py:2739: UserWarning: X does not have valid feature names, but LGBMRegressor was fitted with feature names\n", - " warnings.warn(\n", - "c:\\Users\\svenk\\anaconda3\\envs\\dml_tabpfn\\Lib\\site-packages\\sklearn\\utils\\validation.py:2739: UserWarning: X does not have valid feature names, but LGBMRegressor was fitted with feature names\n", - " warnings.warn(\n", - "c:\\Users\\svenk\\anaconda3\\envs\\dml_tabpfn\\Lib\\site-packages\\sklearn\\utils\\validation.py:2739: UserWarning: X does not have valid feature names, but LGBMRegressor was fitted with feature names\n", - " warnings.warn(\n", - "c:\\Users\\svenk\\anaconda3\\envs\\dml_tabpfn\\Lib\\site-packages\\sklearn\\utils\\validation.py:2739: UserWarning: X does not have valid feature names, but LGBMRegressor was fitted with feature names\n", - " warnings.warn(\n", - "c:\\Users\\svenk\\anaconda3\\envs\\dml_tabpfn\\Lib\\site-packages\\sklearn\\utils\\validation.py:2739: UserWarning: X does not have valid feature names, but LGBMRegressor was fitted with feature names\n", - " warnings.warn(\n", - "c:\\Users\\svenk\\anaconda3\\envs\\dml_tabpfn\\Lib\\site-packages\\sklearn\\utils\\validation.py:2739: UserWarning: X does not have valid feature names, but LGBMRegressor was fitted with feature names\n", - " warnings.warn(\n", - "c:\\Users\\svenk\\anaconda3\\envs\\dml_tabpfn\\Lib\\site-packages\\sklearn\\utils\\validation.py:2739: UserWarning: X does not have valid feature names, but LGBMRegressor was fitted with feature names\n", - " warnings.warn(\n", - "c:\\Users\\svenk\\anaconda3\\envs\\dml_tabpfn\\Lib\\site-packages\\sklearn\\utils\\validation.py:2739: UserWarning: X does not have valid feature names, but LGBMRegressor was fitted with feature names\n", - " warnings.warn(\n", - "c:\\Users\\svenk\\anaconda3\\envs\\dml_tabpfn\\Lib\\site-packages\\sklearn\\utils\\validation.py:2739: UserWarning: X does not have valid feature names, but LGBMRegressor was fitted with feature names\n", - " warnings.warn(\n", - "c:\\Users\\svenk\\anaconda3\\envs\\dml_tabpfn\\Lib\\site-packages\\sklearn\\utils\\validation.py:2739: UserWarning: X does not have valid feature names, but LGBMRegressor was fitted with feature names\n", - " warnings.warn(\n", - "c:\\Users\\svenk\\anaconda3\\envs\\dml_tabpfn\\Lib\\site-packages\\sklearn\\utils\\validation.py:2739: UserWarning: X does not have valid feature names, but LGBMClassifier was fitted with feature names\n", - " warnings.warn(\n", - "c:\\Users\\svenk\\anaconda3\\envs\\dml_tabpfn\\Lib\\site-packages\\sklearn\\utils\\validation.py:2739: UserWarning: X does not have valid feature names, but LGBMClassifier was fitted with feature names\n", - " warnings.warn(\n", - "c:\\Users\\svenk\\anaconda3\\envs\\dml_tabpfn\\Lib\\site-packages\\sklearn\\utils\\validation.py:2739: UserWarning: X does not have valid feature names, but LGBMClassifier was fitted with feature names\n", - " warnings.warn(\n", - "c:\\Users\\svenk\\anaconda3\\envs\\dml_tabpfn\\Lib\\site-packages\\sklearn\\utils\\validation.py:2739: UserWarning: X does not have valid feature names, but LGBMClassifier was fitted with feature names\n", - " warnings.warn(\n", - "c:\\Users\\svenk\\anaconda3\\envs\\dml_tabpfn\\Lib\\site-packages\\sklearn\\utils\\validation.py:2739: UserWarning: X does not have valid feature names, but LGBMClassifier was fitted with feature names\n", - " warnings.warn(\n", - "c:\\Users\\svenk\\anaconda3\\envs\\dml_tabpfn\\Lib\\site-packages\\doubleml\\double_ml.py:1479: UserWarning: The estimated nu2 for d is not positive. Re-estimation based on riesz representer (non-orthogonal).\n", - " warnings.warn(msg, UserWarning)\n", - "c:\\Users\\svenk\\anaconda3\\envs\\dml_tabpfn\\Lib\\site-packages\\sklearn\\utils\\deprecation.py:151: FutureWarning: 'force_all_finite' was renamed to 'ensure_all_finite' in 1.6 and will be removed in 1.8.\n", - " warnings.warn(\n", - "c:\\Users\\svenk\\anaconda3\\envs\\dml_tabpfn\\Lib\\site-packages\\sklearn\\utils\\deprecation.py:151: FutureWarning: 'force_all_finite' was renamed to 'ensure_all_finite' in 1.6 and will be removed in 1.8.\n", - " warnings.warn(\n", - "c:\\Users\\svenk\\anaconda3\\envs\\dml_tabpfn\\Lib\\site-packages\\sklearn\\utils\\validation.py:2739: UserWarning: X does not have valid feature names, but LGBMRegressor was fitted with feature names\n", - " warnings.warn(\n", - "c:\\Users\\svenk\\anaconda3\\envs\\dml_tabpfn\\Lib\\site-packages\\sklearn\\utils\\validation.py:2739: UserWarning: X does not have valid feature names, but LGBMRegressor was fitted with feature names\n", - " warnings.warn(\n", - "c:\\Users\\svenk\\anaconda3\\envs\\dml_tabpfn\\Lib\\site-packages\\sklearn\\utils\\validation.py:2739: UserWarning: X does not have valid feature names, but LGBMRegressor was fitted with feature names\n", - " warnings.warn(\n", - "c:\\Users\\svenk\\anaconda3\\envs\\dml_tabpfn\\Lib\\site-packages\\sklearn\\utils\\validation.py:2739: UserWarning: X does not have valid feature names, but LGBMRegressor was fitted with feature names\n", - " warnings.warn(\n", - "c:\\Users\\svenk\\anaconda3\\envs\\dml_tabpfn\\Lib\\site-packages\\sklearn\\utils\\validation.py:2739: UserWarning: X does not have valid feature names, but LGBMRegressor was fitted with feature names\n", - " warnings.warn(\n", - "c:\\Users\\svenk\\anaconda3\\envs\\dml_tabpfn\\Lib\\site-packages\\sklearn\\utils\\validation.py:2739: UserWarning: X does not have valid feature names, but LGBMRegressor was fitted with feature names\n", - " warnings.warn(\n", - "c:\\Users\\svenk\\anaconda3\\envs\\dml_tabpfn\\Lib\\site-packages\\sklearn\\utils\\validation.py:2739: UserWarning: X does not have valid feature names, but LGBMRegressor was fitted with feature names\n", - " warnings.warn(\n", - "c:\\Users\\svenk\\anaconda3\\envs\\dml_tabpfn\\Lib\\site-packages\\sklearn\\utils\\validation.py:2739: UserWarning: X does not have valid feature names, but LGBMRegressor was fitted with feature names\n", - " warnings.warn(\n", - "c:\\Users\\svenk\\anaconda3\\envs\\dml_tabpfn\\Lib\\site-packages\\sklearn\\utils\\validation.py:2739: UserWarning: X does not have valid feature names, but LGBMRegressor was fitted with feature names\n", - " warnings.warn(\n", - "c:\\Users\\svenk\\anaconda3\\envs\\dml_tabpfn\\Lib\\site-packages\\sklearn\\utils\\validation.py:2739: UserWarning: X does not have valid feature names, but LGBMRegressor was fitted with feature names\n", - " warnings.warn(\n", - "c:\\Users\\svenk\\anaconda3\\envs\\dml_tabpfn\\Lib\\site-packages\\sklearn\\utils\\validation.py:2739: UserWarning: X does not have valid feature names, but LGBMClassifier was fitted with feature names\n", - " warnings.warn(\n", - "c:\\Users\\svenk\\anaconda3\\envs\\dml_tabpfn\\Lib\\site-packages\\sklearn\\utils\\validation.py:2739: UserWarning: X does not have valid feature names, but LGBMClassifier was fitted with feature names\n", - " warnings.warn(\n", - "c:\\Users\\svenk\\anaconda3\\envs\\dml_tabpfn\\Lib\\site-packages\\sklearn\\utils\\validation.py:2739: UserWarning: X does not have valid feature names, but LGBMClassifier was fitted with feature names\n", - " warnings.warn(\n", - "c:\\Users\\svenk\\anaconda3\\envs\\dml_tabpfn\\Lib\\site-packages\\sklearn\\utils\\validation.py:2739: UserWarning: X does not have valid feature names, but LGBMClassifier was fitted with feature names\n", - " warnings.warn(\n", - "c:\\Users\\svenk\\anaconda3\\envs\\dml_tabpfn\\Lib\\site-packages\\sklearn\\utils\\validation.py:2739: UserWarning: X does not have valid feature names, but LGBMClassifier was fitted with feature names\n", - " warnings.warn(\n", - "c:\\Users\\svenk\\anaconda3\\envs\\dml_tabpfn\\Lib\\site-packages\\doubleml\\double_ml.py:1479: UserWarning: The estimated nu2 for d is not positive. Re-estimation based on riesz representer (non-orthogonal).\n", - " warnings.warn(msg, UserWarning)\n", - "c:\\Users\\svenk\\anaconda3\\envs\\dml_tabpfn\\Lib\\site-packages\\sklearn\\utils\\deprecation.py:151: FutureWarning: 'force_all_finite' was renamed to 'ensure_all_finite' in 1.6 and will be removed in 1.8.\n", - " warnings.warn(\n", - "c:\\Users\\svenk\\anaconda3\\envs\\dml_tabpfn\\Lib\\site-packages\\sklearn\\utils\\deprecation.py:151: FutureWarning: 'force_all_finite' was renamed to 'ensure_all_finite' in 1.6 and will be removed in 1.8.\n", - " warnings.warn(\n", - "c:\\Users\\svenk\\anaconda3\\envs\\dml_tabpfn\\Lib\\site-packages\\sklearn\\utils\\validation.py:2739: UserWarning: X does not have valid feature names, but LGBMRegressor was fitted with feature names\n", - " warnings.warn(\n", - "c:\\Users\\svenk\\anaconda3\\envs\\dml_tabpfn\\Lib\\site-packages\\sklearn\\utils\\validation.py:2739: UserWarning: X does not have valid feature names, but LGBMRegressor was fitted with feature names\n", - " warnings.warn(\n", - "c:\\Users\\svenk\\anaconda3\\envs\\dml_tabpfn\\Lib\\site-packages\\sklearn\\utils\\validation.py:2739: UserWarning: X does not have valid feature names, but LGBMRegressor was fitted with feature names\n", - " warnings.warn(\n", - "c:\\Users\\svenk\\anaconda3\\envs\\dml_tabpfn\\Lib\\site-packages\\sklearn\\utils\\validation.py:2739: UserWarning: X does not have valid feature names, but LGBMRegressor was fitted with feature names\n", - " warnings.warn(\n", - "c:\\Users\\svenk\\anaconda3\\envs\\dml_tabpfn\\Lib\\site-packages\\sklearn\\utils\\validation.py:2739: UserWarning: X does not have valid feature names, but LGBMRegressor was fitted with feature names\n", - " warnings.warn(\n", - "c:\\Users\\svenk\\anaconda3\\envs\\dml_tabpfn\\Lib\\site-packages\\sklearn\\utils\\validation.py:2739: UserWarning: X does not have valid feature names, but LGBMRegressor was fitted with feature names\n", - " warnings.warn(\n", - "c:\\Users\\svenk\\anaconda3\\envs\\dml_tabpfn\\Lib\\site-packages\\sklearn\\utils\\validation.py:2739: UserWarning: X does not have valid feature names, but LGBMRegressor was fitted with feature names\n", - " warnings.warn(\n", - "c:\\Users\\svenk\\anaconda3\\envs\\dml_tabpfn\\Lib\\site-packages\\sklearn\\utils\\validation.py:2739: UserWarning: X does not have valid feature names, but LGBMRegressor was fitted with feature names\n", - " warnings.warn(\n", - "c:\\Users\\svenk\\anaconda3\\envs\\dml_tabpfn\\Lib\\site-packages\\sklearn\\utils\\validation.py:2739: UserWarning: X does not have valid feature names, but LGBMRegressor was fitted with feature names\n", - " warnings.warn(\n", - "c:\\Users\\svenk\\anaconda3\\envs\\dml_tabpfn\\Lib\\site-packages\\sklearn\\utils\\validation.py:2739: UserWarning: X does not have valid feature names, but LGBMRegressor was fitted with feature names\n", - " warnings.warn(\n", - "c:\\Users\\svenk\\anaconda3\\envs\\dml_tabpfn\\Lib\\site-packages\\sklearn\\utils\\validation.py:2739: UserWarning: X does not have valid feature names, but LGBMRegressor was fitted with feature names\n", - " warnings.warn(\n", - "c:\\Users\\svenk\\anaconda3\\envs\\dml_tabpfn\\Lib\\site-packages\\sklearn\\utils\\validation.py:2739: UserWarning: X does not have valid feature names, but LGBMClassifier was fitted with feature names\n", - " warnings.warn(\n", - "c:\\Users\\svenk\\anaconda3\\envs\\dml_tabpfn\\Lib\\site-packages\\sklearn\\utils\\validation.py:2739: UserWarning: X does not have valid feature names, but LGBMClassifier was fitted with feature names\n", - " warnings.warn(\n", - "c:\\Users\\svenk\\anaconda3\\envs\\dml_tabpfn\\Lib\\site-packages\\sklearn\\utils\\validation.py:2739: UserWarning: X does not have valid feature names, but LGBMClassifier was fitted with feature names\n", - " warnings.warn(\n", - "c:\\Users\\svenk\\anaconda3\\envs\\dml_tabpfn\\Lib\\site-packages\\sklearn\\utils\\validation.py:2739: UserWarning: X does not have valid feature names, but LGBMClassifier was fitted with feature names\n", - " warnings.warn(\n", - "c:\\Users\\svenk\\anaconda3\\envs\\dml_tabpfn\\Lib\\site-packages\\sklearn\\utils\\validation.py:2739: UserWarning: X does not have valid feature names, but LGBMClassifier was fitted with feature names\n", - " warnings.warn(\n", - "c:\\Users\\svenk\\anaconda3\\envs\\dml_tabpfn\\Lib\\site-packages\\doubleml\\double_ml.py:1479: UserWarning: The estimated nu2 for d is not positive. Re-estimation based on riesz representer (non-orthogonal).\n", - " warnings.warn(msg, UserWarning)\n", - "c:\\Users\\svenk\\anaconda3\\envs\\dml_tabpfn\\Lib\\site-packages\\sklearn\\utils\\deprecation.py:151: FutureWarning: 'force_all_finite' was renamed to 'ensure_all_finite' in 1.6 and will be removed in 1.8.\n", - " warnings.warn(\n", - "c:\\Users\\svenk\\anaconda3\\envs\\dml_tabpfn\\Lib\\site-packages\\sklearn\\utils\\deprecation.py:151: FutureWarning: 'force_all_finite' was renamed to 'ensure_all_finite' in 1.6 and will be removed in 1.8.\n", - " warnings.warn(\n", - "c:\\Users\\svenk\\anaconda3\\envs\\dml_tabpfn\\Lib\\site-packages\\sklearn\\utils\\validation.py:2739: UserWarning: X does not have valid feature names, but LGBMRegressor was fitted with feature names\n", - " warnings.warn(\n", - "c:\\Users\\svenk\\anaconda3\\envs\\dml_tabpfn\\Lib\\site-packages\\sklearn\\utils\\validation.py:2739: UserWarning: X does not have valid feature names, but LGBMRegressor was fitted with feature names\n", - " warnings.warn(\n", - "c:\\Users\\svenk\\anaconda3\\envs\\dml_tabpfn\\Lib\\site-packages\\sklearn\\utils\\validation.py:2739: UserWarning: X does not have valid feature names, but LGBMRegressor was fitted with feature names\n", - " warnings.warn(\n", - "c:\\Users\\svenk\\anaconda3\\envs\\dml_tabpfn\\Lib\\site-packages\\sklearn\\utils\\validation.py:2739: UserWarning: X does not have valid feature names, but LGBMRegressor was fitted with feature names\n", - " warnings.warn(\n", - "c:\\Users\\svenk\\anaconda3\\envs\\dml_tabpfn\\Lib\\site-packages\\sklearn\\utils\\validation.py:2739: UserWarning: X does not have valid feature names, but LGBMRegressor was fitted with feature names\n", - " warnings.warn(\n", - "c:\\Users\\svenk\\anaconda3\\envs\\dml_tabpfn\\Lib\\site-packages\\sklearn\\utils\\validation.py:2739: UserWarning: X does not have valid feature names, but LGBMRegressor was fitted with feature names\n", - " warnings.warn(\n", - "c:\\Users\\svenk\\anaconda3\\envs\\dml_tabpfn\\Lib\\site-packages\\sklearn\\utils\\validation.py:2739: UserWarning: X does not have valid feature names, but LGBMRegressor was fitted with feature names\n", - " warnings.warn(\n", - "c:\\Users\\svenk\\anaconda3\\envs\\dml_tabpfn\\Lib\\site-packages\\sklearn\\utils\\validation.py:2739: UserWarning: X does not have valid feature names, but LGBMRegressor was fitted with feature names\n", - " warnings.warn(\n", - "c:\\Users\\svenk\\anaconda3\\envs\\dml_tabpfn\\Lib\\site-packages\\sklearn\\utils\\validation.py:2739: UserWarning: X does not have valid feature names, but LGBMRegressor was fitted with feature names\n", - " warnings.warn(\n", - "c:\\Users\\svenk\\anaconda3\\envs\\dml_tabpfn\\Lib\\site-packages\\sklearn\\utils\\validation.py:2739: UserWarning: X does not have valid feature names, but LGBMRegressor was fitted with feature names\n", - " warnings.warn(\n", - "c:\\Users\\svenk\\anaconda3\\envs\\dml_tabpfn\\Lib\\site-packages\\sklearn\\utils\\validation.py:2739: UserWarning: X does not have valid feature names, but LGBMClassifier was fitted with feature names\n", - " warnings.warn(\n", - "c:\\Users\\svenk\\anaconda3\\envs\\dml_tabpfn\\Lib\\site-packages\\sklearn\\utils\\validation.py:2739: UserWarning: X does not have valid feature names, but LGBMClassifier was fitted with feature names\n", - " warnings.warn(\n", - "c:\\Users\\svenk\\anaconda3\\envs\\dml_tabpfn\\Lib\\site-packages\\sklearn\\utils\\validation.py:2739: UserWarning: X does not have valid feature names, but LGBMClassifier was fitted with feature names\n", - " warnings.warn(\n", - "c:\\Users\\svenk\\anaconda3\\envs\\dml_tabpfn\\Lib\\site-packages\\sklearn\\utils\\validation.py:2739: UserWarning: X does not have valid feature names, but LGBMClassifier was fitted with feature names\n", - " warnings.warn(\n", - "c:\\Users\\svenk\\anaconda3\\envs\\dml_tabpfn\\Lib\\site-packages\\sklearn\\utils\\validation.py:2739: UserWarning: X does not have valid feature names, but LGBMClassifier was fitted with feature names\n", - " warnings.warn(\n", - "c:\\Users\\svenk\\anaconda3\\envs\\dml_tabpfn\\Lib\\site-packages\\doubleml\\double_ml.py:1479: UserWarning: The estimated nu2 for d is not positive. Re-estimation based on riesz representer (non-orthogonal).\n", - " warnings.warn(msg, UserWarning)\n", - "c:\\Users\\svenk\\anaconda3\\envs\\dml_tabpfn\\Lib\\site-packages\\sklearn\\utils\\deprecation.py:151: FutureWarning: 'force_all_finite' was renamed to 'ensure_all_finite' in 1.6 and will be removed in 1.8.\n", - " warnings.warn(\n", - "c:\\Users\\svenk\\anaconda3\\envs\\dml_tabpfn\\Lib\\site-packages\\sklearn\\utils\\deprecation.py:151: FutureWarning: 'force_all_finite' was renamed to 'ensure_all_finite' in 1.6 and will be removed in 1.8.\n", - " warnings.warn(\n", - "c:\\Users\\svenk\\anaconda3\\envs\\dml_tabpfn\\Lib\\site-packages\\sklearn\\utils\\validation.py:2739: UserWarning: X does not have valid feature names, but LGBMRegressor was fitted with feature names\n", - " warnings.warn(\n", - "c:\\Users\\svenk\\anaconda3\\envs\\dml_tabpfn\\Lib\\site-packages\\sklearn\\utils\\validation.py:2739: UserWarning: X does not have valid feature names, but LGBMRegressor was fitted with feature names\n", - " warnings.warn(\n", - "c:\\Users\\svenk\\anaconda3\\envs\\dml_tabpfn\\Lib\\site-packages\\sklearn\\utils\\validation.py:2739: UserWarning: X does not have valid feature names, but LGBMRegressor was fitted with feature names\n", - " warnings.warn(\n", - "c:\\Users\\svenk\\anaconda3\\envs\\dml_tabpfn\\Lib\\site-packages\\sklearn\\utils\\validation.py:2739: UserWarning: X does not have valid feature names, but LGBMRegressor was fitted with feature names\n", - " warnings.warn(\n", - "c:\\Users\\svenk\\anaconda3\\envs\\dml_tabpfn\\Lib\\site-packages\\sklearn\\utils\\validation.py:2739: UserWarning: X does not have valid feature names, but LGBMRegressor was fitted with feature names\n", - " warnings.warn(\n", - "c:\\Users\\svenk\\anaconda3\\envs\\dml_tabpfn\\Lib\\site-packages\\sklearn\\utils\\validation.py:2739: UserWarning: X does not have valid feature names, but LGBMRegressor was fitted with feature names\n", - " warnings.warn(\n", - "c:\\Users\\svenk\\anaconda3\\envs\\dml_tabpfn\\Lib\\site-packages\\sklearn\\utils\\validation.py:2739: UserWarning: X does not have valid feature names, but LGBMRegressor was fitted with feature names\n", - " warnings.warn(\n", - "c:\\Users\\svenk\\anaconda3\\envs\\dml_tabpfn\\Lib\\site-packages\\sklearn\\utils\\validation.py:2739: UserWarning: X does not have valid feature names, but LGBMRegressor was fitted with feature names\n", - " warnings.warn(\n", - "c:\\Users\\svenk\\anaconda3\\envs\\dml_tabpfn\\Lib\\site-packages\\sklearn\\utils\\validation.py:2739: UserWarning: X does not have valid feature names, but LGBMRegressor was fitted with feature names\n", - " warnings.warn(\n", - "c:\\Users\\svenk\\anaconda3\\envs\\dml_tabpfn\\Lib\\site-packages\\sklearn\\utils\\validation.py:2739: UserWarning: X does not have valid feature names, but LGBMRegressor was fitted with feature names\n", - " warnings.warn(\n", - "c:\\Users\\svenk\\anaconda3\\envs\\dml_tabpfn\\Lib\\site-packages\\sklearn\\utils\\validation.py:2739: UserWarning: X does not have valid feature names, but LGBMRegressor was fitted with feature names\n", - " warnings.warn(\n", - "c:\\Users\\svenk\\anaconda3\\envs\\dml_tabpfn\\Lib\\site-packages\\sklearn\\utils\\validation.py:2739: UserWarning: X does not have valid feature names, but LGBMClassifier was fitted with feature names\n", - " warnings.warn(\n", - "c:\\Users\\svenk\\anaconda3\\envs\\dml_tabpfn\\Lib\\site-packages\\sklearn\\utils\\validation.py:2739: UserWarning: X does not have valid feature names, but LGBMClassifier was fitted with feature names\n", - " warnings.warn(\n", - "c:\\Users\\svenk\\anaconda3\\envs\\dml_tabpfn\\Lib\\site-packages\\sklearn\\utils\\validation.py:2739: UserWarning: X does not have valid feature names, but LGBMClassifier was fitted with feature names\n", - " warnings.warn(\n", - "c:\\Users\\svenk\\anaconda3\\envs\\dml_tabpfn\\Lib\\site-packages\\sklearn\\utils\\validation.py:2739: UserWarning: X does not have valid feature names, but LGBMClassifier was fitted with feature names\n", - " warnings.warn(\n", - "c:\\Users\\svenk\\anaconda3\\envs\\dml_tabpfn\\Lib\\site-packages\\sklearn\\utils\\validation.py:2739: UserWarning: X does not have valid feature names, but LGBMClassifier was fitted with feature names\n", - " warnings.warn(\n", - "c:\\Users\\svenk\\anaconda3\\envs\\dml_tabpfn\\Lib\\site-packages\\doubleml\\double_ml.py:1479: UserWarning: The estimated nu2 for d is not positive. Re-estimation based on riesz representer (non-orthogonal).\n", - " warnings.warn(msg, UserWarning)\n", - "c:\\Users\\svenk\\anaconda3\\envs\\dml_tabpfn\\Lib\\site-packages\\sklearn\\utils\\deprecation.py:151: FutureWarning: 'force_all_finite' was renamed to 'ensure_all_finite' in 1.6 and will be removed in 1.8.\n", - " warnings.warn(\n", - "c:\\Users\\svenk\\anaconda3\\envs\\dml_tabpfn\\Lib\\site-packages\\sklearn\\utils\\deprecation.py:151: FutureWarning: 'force_all_finite' was renamed to 'ensure_all_finite' in 1.6 and will be removed in 1.8.\n", - " warnings.warn(\n", - "c:\\Users\\svenk\\anaconda3\\envs\\dml_tabpfn\\Lib\\site-packages\\sklearn\\utils\\validation.py:2739: UserWarning: X does not have valid feature names, but LGBMRegressor was fitted with feature names\n", - " warnings.warn(\n", - "c:\\Users\\svenk\\anaconda3\\envs\\dml_tabpfn\\Lib\\site-packages\\sklearn\\utils\\validation.py:2739: UserWarning: X does not have valid feature names, but LGBMRegressor was fitted with feature names\n", - " warnings.warn(\n", - "c:\\Users\\svenk\\anaconda3\\envs\\dml_tabpfn\\Lib\\site-packages\\sklearn\\utils\\validation.py:2739: UserWarning: X does not have valid feature names, but LGBMRegressor was fitted with feature names\n", - " warnings.warn(\n", - "c:\\Users\\svenk\\anaconda3\\envs\\dml_tabpfn\\Lib\\site-packages\\sklearn\\utils\\validation.py:2739: UserWarning: X does not have valid feature names, but LGBMRegressor was fitted with feature names\n", - " warnings.warn(\n", - "c:\\Users\\svenk\\anaconda3\\envs\\dml_tabpfn\\Lib\\site-packages\\sklearn\\utils\\validation.py:2739: UserWarning: X does not have valid feature names, but LGBMRegressor was fitted with feature names\n", - " warnings.warn(\n", - "c:\\Users\\svenk\\anaconda3\\envs\\dml_tabpfn\\Lib\\site-packages\\sklearn\\utils\\validation.py:2739: UserWarning: X does not have valid feature names, but LGBMRegressor was fitted with feature names\n", - " warnings.warn(\n", - "c:\\Users\\svenk\\anaconda3\\envs\\dml_tabpfn\\Lib\\site-packages\\sklearn\\utils\\validation.py:2739: UserWarning: X does not have valid feature names, but LGBMRegressor was fitted with feature names\n", - " warnings.warn(\n", - "c:\\Users\\svenk\\anaconda3\\envs\\dml_tabpfn\\Lib\\site-packages\\sklearn\\utils\\validation.py:2739: UserWarning: X does not have valid feature names, but LGBMRegressor was fitted with feature names\n", - " warnings.warn(\n", - "c:\\Users\\svenk\\anaconda3\\envs\\dml_tabpfn\\Lib\\site-packages\\sklearn\\utils\\validation.py:2739: UserWarning: X does not have valid feature names, but LGBMRegressor was fitted with feature names\n", - " warnings.warn(\n", - "c:\\Users\\svenk\\anaconda3\\envs\\dml_tabpfn\\Lib\\site-packages\\sklearn\\utils\\validation.py:2739: UserWarning: X does not have valid feature names, but LGBMRegressor was fitted with feature names\n", - " warnings.warn(\n", - "c:\\Users\\svenk\\anaconda3\\envs\\dml_tabpfn\\Lib\\site-packages\\sklearn\\utils\\validation.py:2739: UserWarning: X does not have valid feature names, but LGBMClassifier was fitted with feature names\n", - " warnings.warn(\n", - "c:\\Users\\svenk\\anaconda3\\envs\\dml_tabpfn\\Lib\\site-packages\\sklearn\\utils\\validation.py:2739: UserWarning: X does not have valid feature names, but LGBMClassifier was fitted with feature names\n", - " warnings.warn(\n", - "c:\\Users\\svenk\\anaconda3\\envs\\dml_tabpfn\\Lib\\site-packages\\sklearn\\utils\\validation.py:2739: UserWarning: X does not have valid feature names, but LGBMClassifier was fitted with feature names\n", - " warnings.warn(\n", - "c:\\Users\\svenk\\anaconda3\\envs\\dml_tabpfn\\Lib\\site-packages\\sklearn\\utils\\validation.py:2739: UserWarning: X does not have valid feature names, but LGBMClassifier was fitted with feature names\n", - " warnings.warn(\n", - "c:\\Users\\svenk\\anaconda3\\envs\\dml_tabpfn\\Lib\\site-packages\\sklearn\\utils\\validation.py:2739: UserWarning: X does not have valid feature names, but LGBMClassifier was fitted with feature names\n", - " warnings.warn(\n", - "c:\\Users\\svenk\\anaconda3\\envs\\dml_tabpfn\\Lib\\site-packages\\doubleml\\double_ml.py:1479: UserWarning: The estimated nu2 for d is not positive. Re-estimation based on riesz representer (non-orthogonal).\n", - " warnings.warn(msg, UserWarning)\n", - "c:\\Users\\svenk\\anaconda3\\envs\\dml_tabpfn\\Lib\\site-packages\\sklearn\\utils\\deprecation.py:151: FutureWarning: 'force_all_finite' was renamed to 'ensure_all_finite' in 1.6 and will be removed in 1.8.\n", - " warnings.warn(\n", - "c:\\Users\\svenk\\anaconda3\\envs\\dml_tabpfn\\Lib\\site-packages\\sklearn\\utils\\deprecation.py:151: FutureWarning: 'force_all_finite' was renamed to 'ensure_all_finite' in 1.6 and will be removed in 1.8.\n", - " warnings.warn(\n", - "c:\\Users\\svenk\\anaconda3\\envs\\dml_tabpfn\\Lib\\site-packages\\sklearn\\utils\\validation.py:2739: UserWarning: X does not have valid feature names, but LGBMRegressor was fitted with feature names\n", - " warnings.warn(\n", - "c:\\Users\\svenk\\anaconda3\\envs\\dml_tabpfn\\Lib\\site-packages\\sklearn\\utils\\validation.py:2739: UserWarning: X does not have valid feature names, but LGBMRegressor was fitted with feature names\n", - " warnings.warn(\n", - "c:\\Users\\svenk\\anaconda3\\envs\\dml_tabpfn\\Lib\\site-packages\\sklearn\\utils\\validation.py:2739: UserWarning: X does not have valid feature names, but LGBMRegressor was fitted with feature names\n", - " warnings.warn(\n", - "c:\\Users\\svenk\\anaconda3\\envs\\dml_tabpfn\\Lib\\site-packages\\sklearn\\utils\\validation.py:2739: UserWarning: X does not have valid feature names, but LGBMRegressor was fitted with feature names\n", - " warnings.warn(\n", - "c:\\Users\\svenk\\anaconda3\\envs\\dml_tabpfn\\Lib\\site-packages\\sklearn\\utils\\validation.py:2739: UserWarning: X does not have valid feature names, but LGBMRegressor was fitted with feature names\n", - " warnings.warn(\n", - "c:\\Users\\svenk\\anaconda3\\envs\\dml_tabpfn\\Lib\\site-packages\\sklearn\\utils\\validation.py:2739: UserWarning: X does not have valid feature names, but LGBMRegressor was fitted with feature names\n", - " warnings.warn(\n", - "c:\\Users\\svenk\\anaconda3\\envs\\dml_tabpfn\\Lib\\site-packages\\sklearn\\utils\\validation.py:2739: UserWarning: X does not have valid feature names, but LGBMRegressor was fitted with feature names\n", - " warnings.warn(\n", - "c:\\Users\\svenk\\anaconda3\\envs\\dml_tabpfn\\Lib\\site-packages\\sklearn\\utils\\validation.py:2739: UserWarning: X does not have valid feature names, but LGBMRegressor was fitted with feature names\n", - " warnings.warn(\n", - "c:\\Users\\svenk\\anaconda3\\envs\\dml_tabpfn\\Lib\\site-packages\\sklearn\\utils\\validation.py:2739: UserWarning: X does not have valid feature names, but LGBMRegressor was fitted with feature names\n", - " warnings.warn(\n", - "c:\\Users\\svenk\\anaconda3\\envs\\dml_tabpfn\\Lib\\site-packages\\sklearn\\utils\\validation.py:2739: UserWarning: X does not have valid feature names, but LGBMRegressor was fitted with feature names\n", - " warnings.warn(\n", - "c:\\Users\\svenk\\anaconda3\\envs\\dml_tabpfn\\Lib\\site-packages\\sklearn\\utils\\validation.py:2739: UserWarning: X does not have valid feature names, but LGBMRegressor was fitted with feature names\n", - " warnings.warn(\n", - "c:\\Users\\svenk\\anaconda3\\envs\\dml_tabpfn\\Lib\\site-packages\\sklearn\\utils\\validation.py:2739: UserWarning: X does not have valid feature names, but LGBMClassifier was fitted with feature names\n", - " warnings.warn(\n", - "c:\\Users\\svenk\\anaconda3\\envs\\dml_tabpfn\\Lib\\site-packages\\sklearn\\utils\\validation.py:2739: UserWarning: X does not have valid feature names, but LGBMClassifier was fitted with feature names\n", - " warnings.warn(\n", - "c:\\Users\\svenk\\anaconda3\\envs\\dml_tabpfn\\Lib\\site-packages\\sklearn\\utils\\validation.py:2739: UserWarning: X does not have valid feature names, but LGBMClassifier was fitted with feature names\n", - " warnings.warn(\n", - "c:\\Users\\svenk\\anaconda3\\envs\\dml_tabpfn\\Lib\\site-packages\\sklearn\\utils\\validation.py:2739: UserWarning: X does not have valid feature names, but LGBMClassifier was fitted with feature names\n", - " warnings.warn(\n", - "c:\\Users\\svenk\\anaconda3\\envs\\dml_tabpfn\\Lib\\site-packages\\sklearn\\utils\\validation.py:2739: UserWarning: X does not have valid feature names, but LGBMClassifier was fitted with feature names\n", - " warnings.warn(\n", - "c:\\Users\\svenk\\anaconda3\\envs\\dml_tabpfn\\Lib\\site-packages\\doubleml\\double_ml.py:1479: UserWarning: The estimated nu2 for d is not positive. Re-estimation based on riesz representer (non-orthogonal).\n", - " warnings.warn(msg, UserWarning)\n", - "c:\\Users\\svenk\\anaconda3\\envs\\dml_tabpfn\\Lib\\site-packages\\sklearn\\utils\\deprecation.py:151: FutureWarning: 'force_all_finite' was renamed to 'ensure_all_finite' in 1.6 and will be removed in 1.8.\n", - " warnings.warn(\n", - "c:\\Users\\svenk\\anaconda3\\envs\\dml_tabpfn\\Lib\\site-packages\\sklearn\\utils\\deprecation.py:151: FutureWarning: 'force_all_finite' was renamed to 'ensure_all_finite' in 1.6 and will be removed in 1.8.\n", - " warnings.warn(\n", - "c:\\Users\\svenk\\anaconda3\\envs\\dml_tabpfn\\Lib\\site-packages\\sklearn\\utils\\validation.py:2739: UserWarning: X does not have valid feature names, but LGBMRegressor was fitted with feature names\n", - " warnings.warn(\n", - "c:\\Users\\svenk\\anaconda3\\envs\\dml_tabpfn\\Lib\\site-packages\\sklearn\\utils\\validation.py:2739: UserWarning: X does not have valid feature names, but LGBMRegressor was fitted with feature names\n", - " warnings.warn(\n", - "c:\\Users\\svenk\\anaconda3\\envs\\dml_tabpfn\\Lib\\site-packages\\sklearn\\utils\\validation.py:2739: UserWarning: X does not have valid feature names, but LGBMRegressor was fitted with feature names\n", - " warnings.warn(\n", - "c:\\Users\\svenk\\anaconda3\\envs\\dml_tabpfn\\Lib\\site-packages\\sklearn\\utils\\validation.py:2739: UserWarning: X does not have valid feature names, but LGBMRegressor was fitted with feature names\n", - " warnings.warn(\n", - "c:\\Users\\svenk\\anaconda3\\envs\\dml_tabpfn\\Lib\\site-packages\\sklearn\\utils\\validation.py:2739: UserWarning: X does not have valid feature names, but LGBMRegressor was fitted with feature names\n", - " warnings.warn(\n", - "c:\\Users\\svenk\\anaconda3\\envs\\dml_tabpfn\\Lib\\site-packages\\sklearn\\utils\\validation.py:2739: UserWarning: X does not have valid feature names, but LGBMRegressor was fitted with feature names\n", - " warnings.warn(\n", - "c:\\Users\\svenk\\anaconda3\\envs\\dml_tabpfn\\Lib\\site-packages\\sklearn\\utils\\validation.py:2739: UserWarning: X does not have valid feature names, but LGBMRegressor was fitted with feature names\n", - " warnings.warn(\n", - "c:\\Users\\svenk\\anaconda3\\envs\\dml_tabpfn\\Lib\\site-packages\\sklearn\\utils\\validation.py:2739: UserWarning: X does not have valid feature names, but LGBMRegressor was fitted with feature names\n", - " warnings.warn(\n", - "c:\\Users\\svenk\\anaconda3\\envs\\dml_tabpfn\\Lib\\site-packages\\sklearn\\utils\\validation.py:2739: UserWarning: X does not have valid feature names, but LGBMRegressor was fitted with feature names\n", - " warnings.warn(\n", - "c:\\Users\\svenk\\anaconda3\\envs\\dml_tabpfn\\Lib\\site-packages\\sklearn\\utils\\validation.py:2739: UserWarning: X does not have valid feature names, but LGBMRegressor was fitted with feature names\n", - " warnings.warn(\n", - "c:\\Users\\svenk\\anaconda3\\envs\\dml_tabpfn\\Lib\\site-packages\\sklearn\\utils\\validation.py:2739: UserWarning: X does not have valid feature names, but LGBMClassifier was fitted with feature names\n", - " warnings.warn(\n", - "c:\\Users\\svenk\\anaconda3\\envs\\dml_tabpfn\\Lib\\site-packages\\sklearn\\utils\\validation.py:2739: UserWarning: X does not have valid feature names, but LGBMClassifier was fitted with feature names\n", - " warnings.warn(\n", - "c:\\Users\\svenk\\anaconda3\\envs\\dml_tabpfn\\Lib\\site-packages\\sklearn\\utils\\validation.py:2739: UserWarning: X does not have valid feature names, but LGBMClassifier was fitted with feature names\n", - " warnings.warn(\n", - "c:\\Users\\svenk\\anaconda3\\envs\\dml_tabpfn\\Lib\\site-packages\\sklearn\\utils\\validation.py:2739: UserWarning: X does not have valid feature names, but LGBMClassifier was fitted with feature names\n", - " warnings.warn(\n", - "c:\\Users\\svenk\\anaconda3\\envs\\dml_tabpfn\\Lib\\site-packages\\sklearn\\utils\\validation.py:2739: UserWarning: X does not have valid feature names, but LGBMClassifier was fitted with feature names\n", - " warnings.warn(\n", - "c:\\Users\\svenk\\anaconda3\\envs\\dml_tabpfn\\Lib\\site-packages\\doubleml\\double_ml.py:1479: UserWarning: The estimated nu2 for d is not positive. Re-estimation based on riesz representer (non-orthogonal).\n", - " warnings.warn(msg, UserWarning)\n", - "c:\\Users\\svenk\\anaconda3\\envs\\dml_tabpfn\\Lib\\site-packages\\sklearn\\utils\\deprecation.py:151: FutureWarning: 'force_all_finite' was renamed to 'ensure_all_finite' in 1.6 and will be removed in 1.8.\n", - " warnings.warn(\n", - "c:\\Users\\svenk\\anaconda3\\envs\\dml_tabpfn\\Lib\\site-packages\\sklearn\\utils\\deprecation.py:151: FutureWarning: 'force_all_finite' was renamed to 'ensure_all_finite' in 1.6 and will be removed in 1.8.\n", - " warnings.warn(\n", - "c:\\Users\\svenk\\anaconda3\\envs\\dml_tabpfn\\Lib\\site-packages\\sklearn\\utils\\validation.py:2739: UserWarning: X does not have valid feature names, but LGBMRegressor was fitted with feature names\n", - " warnings.warn(\n", - "c:\\Users\\svenk\\anaconda3\\envs\\dml_tabpfn\\Lib\\site-packages\\sklearn\\utils\\validation.py:2739: UserWarning: X does not have valid feature names, but LGBMRegressor was fitted with feature names\n", - " warnings.warn(\n", - "c:\\Users\\svenk\\anaconda3\\envs\\dml_tabpfn\\Lib\\site-packages\\sklearn\\utils\\validation.py:2739: UserWarning: X does not have valid feature names, but LGBMClassifier was fitted with feature names\n", - " warnings.warn(\n", - "c:\\Users\\svenk\\anaconda3\\envs\\dml_tabpfn\\Lib\\site-packages\\sklearn\\utils\\validation.py:2739: UserWarning: X does not have valid feature names, but LGBMClassifier was fitted with feature names\n", - " warnings.warn(\n", - "c:\\Users\\svenk\\anaconda3\\envs\\dml_tabpfn\\Lib\\site-packages\\sklearn\\utils\\validation.py:2739: UserWarning: X does not have valid feature names, but LGBMClassifier was fitted with feature names\n", - " warnings.warn(\n", - "c:\\Users\\svenk\\anaconda3\\envs\\dml_tabpfn\\Lib\\site-packages\\sklearn\\utils\\validation.py:2739: UserWarning: X does not have valid feature names, but LGBMClassifier was fitted with feature names\n", - " warnings.warn(\n", - "c:\\Users\\svenk\\anaconda3\\envs\\dml_tabpfn\\Lib\\site-packages\\sklearn\\utils\\validation.py:2739: UserWarning: X does not have valid feature names, but LGBMClassifier was fitted with feature names\n", - " warnings.warn(\n", - "c:\\Users\\svenk\\anaconda3\\envs\\dml_tabpfn\\Lib\\site-packages\\doubleml\\double_ml.py:1479: UserWarning: The estimated nu2 for d is not positive. Re-estimation based on riesz representer (non-orthogonal).\n", - " warnings.warn(msg, UserWarning)\n", - "c:\\Users\\svenk\\anaconda3\\envs\\dml_tabpfn\\Lib\\site-packages\\sklearn\\utils\\deprecation.py:151: FutureWarning: 'force_all_finite' was renamed to 'ensure_all_finite' in 1.6 and will be removed in 1.8.\n", - " warnings.warn(\n", - "c:\\Users\\svenk\\anaconda3\\envs\\dml_tabpfn\\Lib\\site-packages\\sklearn\\utils\\deprecation.py:151: FutureWarning: 'force_all_finite' was renamed to 'ensure_all_finite' in 1.6 and will be removed in 1.8.\n", - " warnings.warn(\n", - "c:\\Users\\svenk\\anaconda3\\envs\\dml_tabpfn\\Lib\\site-packages\\tabpfn\\regressor.py:490: UserWarning: Running on CPU with more than 200 samples may be slow.\n", - "Consider using a GPU or the tabpfn-client API: https://github.com/PriorLabs/tabpfn-client\n", - " check_cpu_warning(\n", - "c:\\Users\\svenk\\anaconda3\\envs\\dml_tabpfn\\Lib\\site-packages\\tabpfn\\regressor.py:490: UserWarning: Running on CPU with more than 200 samples may be slow.\n", - "Consider using a GPU or the tabpfn-client API: https://github.com/PriorLabs/tabpfn-client\n", - " check_cpu_warning(\n", - "c:\\Users\\svenk\\anaconda3\\envs\\dml_tabpfn\\Lib\\site-packages\\tabpfn\\regressor.py:490: UserWarning: Running on CPU with more than 200 samples may be slow.\n", - "Consider using a GPU or the tabpfn-client API: https://github.com/PriorLabs/tabpfn-client\n", - " check_cpu_warning(\n", - "c:\\Users\\svenk\\anaconda3\\envs\\dml_tabpfn\\Lib\\site-packages\\tabpfn\\regressor.py:490: UserWarning: Running on CPU with more than 200 samples may be slow.\n", - "Consider using a GPU or the tabpfn-client API: https://github.com/PriorLabs/tabpfn-client\n", - " check_cpu_warning(\n", - "c:\\Users\\svenk\\anaconda3\\envs\\dml_tabpfn\\Lib\\site-packages\\tabpfn\\regressor.py:490: UserWarning: Running on CPU with more than 200 samples may be slow.\n", - "Consider using a GPU or the tabpfn-client API: https://github.com/PriorLabs/tabpfn-client\n", - " check_cpu_warning(\n", - "c:\\Users\\svenk\\anaconda3\\envs\\dml_tabpfn\\Lib\\site-packages\\tabpfn\\regressor.py:490: UserWarning: Running on CPU with more than 200 samples may be slow.\n", - "Consider using a GPU or the tabpfn-client API: https://github.com/PriorLabs/tabpfn-client\n", - " check_cpu_warning(\n", - "c:\\Users\\svenk\\anaconda3\\envs\\dml_tabpfn\\Lib\\site-packages\\tabpfn\\regressor.py:490: UserWarning: Running on CPU with more than 200 samples may be slow.\n", - "Consider using a GPU or the tabpfn-client API: https://github.com/PriorLabs/tabpfn-client\n", - " check_cpu_warning(\n", - "c:\\Users\\svenk\\anaconda3\\envs\\dml_tabpfn\\Lib\\site-packages\\tabpfn\\regressor.py:490: UserWarning: Running on CPU with more than 200 samples may be slow.\n", - "Consider using a GPU or the tabpfn-client API: https://github.com/PriorLabs/tabpfn-client\n", - " check_cpu_warning(\n", - "c:\\Users\\svenk\\anaconda3\\envs\\dml_tabpfn\\Lib\\site-packages\\tabpfn\\regressor.py:490: UserWarning: Running on CPU with more than 200 samples may be slow.\n", - "Consider using a GPU or the tabpfn-client API: https://github.com/PriorLabs/tabpfn-client\n", - " check_cpu_warning(\n", - "c:\\Users\\svenk\\anaconda3\\envs\\dml_tabpfn\\Lib\\site-packages\\tabpfn\\regressor.py:490: UserWarning: Running on CPU with more than 200 samples may be slow.\n", - "Consider using a GPU or the tabpfn-client API: https://github.com/PriorLabs/tabpfn-client\n", - " check_cpu_warning(\n", - "c:\\Users\\svenk\\anaconda3\\envs\\dml_tabpfn\\Lib\\site-packages\\tabpfn\\classifier.py:459: UserWarning: Running on CPU with more than 200 samples may be slow.\n", - "Consider using a GPU or the tabpfn-client API: https://github.com/PriorLabs/tabpfn-client\n", - " check_cpu_warning(\n", - "c:\\Users\\svenk\\anaconda3\\envs\\dml_tabpfn\\Lib\\site-packages\\tabpfn\\classifier.py:459: UserWarning: Running on CPU with more than 200 samples may be slow.\n", - "Consider using a GPU or the tabpfn-client API: https://github.com/PriorLabs/tabpfn-client\n", - " check_cpu_warning(\n", - "c:\\Users\\svenk\\anaconda3\\envs\\dml_tabpfn\\Lib\\site-packages\\tabpfn\\classifier.py:459: UserWarning: Running on CPU with more than 200 samples may be slow.\n", - "Consider using a GPU or the tabpfn-client API: https://github.com/PriorLabs/tabpfn-client\n", - " check_cpu_warning(\n", - "c:\\Users\\svenk\\anaconda3\\envs\\dml_tabpfn\\Lib\\site-packages\\tabpfn\\classifier.py:459: UserWarning: Running on CPU with more than 200 samples may be slow.\n", - "Consider using a GPU or the tabpfn-client API: https://github.com/PriorLabs/tabpfn-client\n", - " check_cpu_warning(\n", - "c:\\Users\\svenk\\anaconda3\\envs\\dml_tabpfn\\Lib\\site-packages\\tabpfn\\classifier.py:459: UserWarning: Running on CPU with more than 200 samples may be slow.\n", - "Consider using a GPU or the tabpfn-client API: https://github.com/PriorLabs/tabpfn-client\n", - " check_cpu_warning(\n", - "c:\\Users\\svenk\\anaconda3\\envs\\dml_tabpfn\\Lib\\site-packages\\tabpfn\\classifier.py:459: UserWarning: Running on CPU with more than 200 samples may be slow.\n", - "Consider using a GPU or the tabpfn-client API: https://github.com/PriorLabs/tabpfn-client\n", - " check_cpu_warning(\n", - "c:\\Users\\svenk\\anaconda3\\envs\\dml_tabpfn\\Lib\\site-packages\\tabpfn\\classifier.py:459: UserWarning: Running on CPU with more than 200 samples may be slow.\n", - "Consider using a GPU or the tabpfn-client API: https://github.com/PriorLabs/tabpfn-client\n", - " check_cpu_warning(\n", - "c:\\Users\\svenk\\anaconda3\\envs\\dml_tabpfn\\Lib\\site-packages\\tabpfn\\classifier.py:459: UserWarning: Running on CPU with more than 200 samples may be slow.\n", - "Consider using a GPU or the tabpfn-client API: https://github.com/PriorLabs/tabpfn-client\n", - " check_cpu_warning(\n", - "c:\\Users\\svenk\\anaconda3\\envs\\dml_tabpfn\\Lib\\site-packages\\tabpfn\\classifier.py:459: UserWarning: Running on CPU with more than 200 samples may be slow.\n", - "Consider using a GPU or the tabpfn-client API: https://github.com/PriorLabs/tabpfn-client\n", - " check_cpu_warning(\n", - "c:\\Users\\svenk\\anaconda3\\envs\\dml_tabpfn\\Lib\\site-packages\\tabpfn\\classifier.py:459: UserWarning: Running on CPU with more than 200 samples may be slow.\n", - "Consider using a GPU or the tabpfn-client API: https://github.com/PriorLabs/tabpfn-client\n", - " check_cpu_warning(\n", - "c:\\Users\\svenk\\anaconda3\\envs\\dml_tabpfn\\Lib\\site-packages\\sklearn\\utils\\deprecation.py:151: FutureWarning: 'force_all_finite' was renamed to 'ensure_all_finite' in 1.6 and will be removed in 1.8.\n", - " warnings.warn(\n", - "c:\\Users\\svenk\\anaconda3\\envs\\dml_tabpfn\\Lib\\site-packages\\sklearn\\utils\\deprecation.py:151: FutureWarning: 'force_all_finite' was renamed to 'ensure_all_finite' in 1.6 and will be removed in 1.8.\n", - " warnings.warn(\n", - "c:\\Users\\svenk\\anaconda3\\envs\\dml_tabpfn\\Lib\\site-packages\\sklearn\\utils\\deprecation.py:151: FutureWarning: 'force_all_finite' was renamed to 'ensure_all_finite' in 1.6 and will be removed in 1.8.\n", - " warnings.warn(\n", - "c:\\Users\\svenk\\anaconda3\\envs\\dml_tabpfn\\Lib\\site-packages\\sklearn\\utils\\deprecation.py:151: FutureWarning: 'force_all_finite' was renamed to 'ensure_all_finite' in 1.6 and will be removed in 1.8.\n", - " warnings.warn(\n", - "c:\\Users\\svenk\\anaconda3\\envs\\dml_tabpfn\\Lib\\site-packages\\tabpfn\\regressor.py:490: UserWarning: Running on CPU with more than 200 samples may be slow.\n", - "Consider using a GPU or the tabpfn-client API: https://github.com/PriorLabs/tabpfn-client\n", - " check_cpu_warning(\n", - "c:\\Users\\svenk\\anaconda3\\envs\\dml_tabpfn\\Lib\\site-packages\\tabpfn\\regressor.py:490: UserWarning: Running on CPU with more than 200 samples may be slow.\n", - "Consider using a GPU or the tabpfn-client API: https://github.com/PriorLabs/tabpfn-client\n", - " check_cpu_warning(\n", - "c:\\Users\\svenk\\anaconda3\\envs\\dml_tabpfn\\Lib\\site-packages\\tabpfn\\regressor.py:490: UserWarning: Running on CPU with more than 200 samples may be slow.\n", - "Consider using a GPU or the tabpfn-client API: https://github.com/PriorLabs/tabpfn-client\n", - " check_cpu_warning(\n", - "c:\\Users\\svenk\\anaconda3\\envs\\dml_tabpfn\\Lib\\site-packages\\tabpfn\\regressor.py:490: UserWarning: Running on CPU with more than 200 samples may be slow.\n", - "Consider using a GPU or the tabpfn-client API: https://github.com/PriorLabs/tabpfn-client\n", - " check_cpu_warning(\n", - "c:\\Users\\svenk\\anaconda3\\envs\\dml_tabpfn\\Lib\\site-packages\\tabpfn\\regressor.py:490: UserWarning: Running on CPU with more than 200 samples may be slow.\n", - "Consider using a GPU or the tabpfn-client API: https://github.com/PriorLabs/tabpfn-client\n", - " check_cpu_warning(\n", - "c:\\Users\\svenk\\anaconda3\\envs\\dml_tabpfn\\Lib\\site-packages\\tabpfn\\regressor.py:490: UserWarning: Running on CPU with more than 200 samples may be slow.\n", - "Consider using a GPU or the tabpfn-client API: https://github.com/PriorLabs/tabpfn-client\n", - " check_cpu_warning(\n", - "c:\\Users\\svenk\\anaconda3\\envs\\dml_tabpfn\\Lib\\site-packages\\tabpfn\\regressor.py:490: UserWarning: Running on CPU with more than 200 samples may be slow.\n", - "Consider using a GPU or the tabpfn-client API: https://github.com/PriorLabs/tabpfn-client\n", - " check_cpu_warning(\n", - "c:\\Users\\svenk\\anaconda3\\envs\\dml_tabpfn\\Lib\\site-packages\\tabpfn\\regressor.py:490: UserWarning: Running on CPU with more than 200 samples may be slow.\n", - "Consider using a GPU or the tabpfn-client API: https://github.com/PriorLabs/tabpfn-client\n", - " check_cpu_warning(\n", - "c:\\Users\\svenk\\anaconda3\\envs\\dml_tabpfn\\Lib\\site-packages\\tabpfn\\regressor.py:490: UserWarning: Running on CPU with more than 200 samples may be slow.\n", - "Consider using a GPU or the tabpfn-client API: https://github.com/PriorLabs/tabpfn-client\n", - " check_cpu_warning(\n", - "c:\\Users\\svenk\\anaconda3\\envs\\dml_tabpfn\\Lib\\site-packages\\tabpfn\\regressor.py:490: UserWarning: Running on CPU with more than 200 samples may be slow.\n", - "Consider using a GPU or the tabpfn-client API: https://github.com/PriorLabs/tabpfn-client\n", - " check_cpu_warning(\n", - "c:\\Users\\svenk\\anaconda3\\envs\\dml_tabpfn\\Lib\\site-packages\\tabpfn\\classifier.py:459: UserWarning: Running on CPU with more than 200 samples may be slow.\n", - "Consider using a GPU or the tabpfn-client API: https://github.com/PriorLabs/tabpfn-client\n", - " check_cpu_warning(\n", - "c:\\Users\\svenk\\anaconda3\\envs\\dml_tabpfn\\Lib\\site-packages\\tabpfn\\classifier.py:459: UserWarning: Running on CPU with more than 200 samples may be slow.\n", - "Consider using a GPU or the tabpfn-client API: https://github.com/PriorLabs/tabpfn-client\n", - " check_cpu_warning(\n", - "c:\\Users\\svenk\\anaconda3\\envs\\dml_tabpfn\\Lib\\site-packages\\tabpfn\\classifier.py:459: UserWarning: Running on CPU with more than 200 samples may be slow.\n", - "Consider using a GPU or the tabpfn-client API: https://github.com/PriorLabs/tabpfn-client\n", - " check_cpu_warning(\n", - "c:\\Users\\svenk\\anaconda3\\envs\\dml_tabpfn\\Lib\\site-packages\\tabpfn\\classifier.py:459: UserWarning: Running on CPU with more than 200 samples may be slow.\n", - "Consider using a GPU or the tabpfn-client API: https://github.com/PriorLabs/tabpfn-client\n", - " check_cpu_warning(\n", - "c:\\Users\\svenk\\anaconda3\\envs\\dml_tabpfn\\Lib\\site-packages\\tabpfn\\classifier.py:459: UserWarning: Running on CPU with more than 200 samples may be slow.\n", - "Consider using a GPU or the tabpfn-client API: https://github.com/PriorLabs/tabpfn-client\n", - " check_cpu_warning(\n", - "c:\\Users\\svenk\\anaconda3\\envs\\dml_tabpfn\\Lib\\site-packages\\tabpfn\\classifier.py:459: UserWarning: Running on CPU with more than 200 samples may be slow.\n", - "Consider using a GPU or the tabpfn-client API: https://github.com/PriorLabs/tabpfn-client\n", - " check_cpu_warning(\n", - "c:\\Users\\svenk\\anaconda3\\envs\\dml_tabpfn\\Lib\\site-packages\\tabpfn\\classifier.py:459: UserWarning: Running on CPU with more than 200 samples may be slow.\n", - "Consider using a GPU or the tabpfn-client API: https://github.com/PriorLabs/tabpfn-client\n", - " check_cpu_warning(\n", - "c:\\Users\\svenk\\anaconda3\\envs\\dml_tabpfn\\Lib\\site-packages\\tabpfn\\classifier.py:459: UserWarning: Running on CPU with more than 200 samples may be slow.\n", - "Consider using a GPU or the tabpfn-client API: https://github.com/PriorLabs/tabpfn-client\n", - " check_cpu_warning(\n", - "c:\\Users\\svenk\\anaconda3\\envs\\dml_tabpfn\\Lib\\site-packages\\tabpfn\\classifier.py:459: UserWarning: Running on CPU with more than 200 samples may be slow.\n", - "Consider using a GPU or the tabpfn-client API: https://github.com/PriorLabs/tabpfn-client\n", - " check_cpu_warning(\n", - "c:\\Users\\svenk\\anaconda3\\envs\\dml_tabpfn\\Lib\\site-packages\\tabpfn\\classifier.py:459: UserWarning: Running on CPU with more than 200 samples may be slow.\n", - "Consider using a GPU or the tabpfn-client API: https://github.com/PriorLabs/tabpfn-client\n", - " check_cpu_warning(\n", - "c:\\Users\\svenk\\anaconda3\\envs\\dml_tabpfn\\Lib\\site-packages\\sklearn\\utils\\deprecation.py:151: FutureWarning: 'force_all_finite' was renamed to 'ensure_all_finite' in 1.6 and will be removed in 1.8.\n", - " warnings.warn(\n", - "c:\\Users\\svenk\\anaconda3\\envs\\dml_tabpfn\\Lib\\site-packages\\sklearn\\utils\\deprecation.py:151: FutureWarning: 'force_all_finite' was renamed to 'ensure_all_finite' in 1.6 and will be removed in 1.8.\n", - " warnings.warn(\n", - "c:\\Users\\svenk\\anaconda3\\envs\\dml_tabpfn\\Lib\\site-packages\\sklearn\\utils\\deprecation.py:151: FutureWarning: 'force_all_finite' was renamed to 'ensure_all_finite' in 1.6 and will be removed in 1.8.\n", - " warnings.warn(\n", - "c:\\Users\\svenk\\anaconda3\\envs\\dml_tabpfn\\Lib\\site-packages\\sklearn\\utils\\deprecation.py:151: FutureWarning: 'force_all_finite' was renamed to 'ensure_all_finite' in 1.6 and will be removed in 1.8.\n", - " warnings.warn(\n", - "c:\\Users\\svenk\\anaconda3\\envs\\dml_tabpfn\\Lib\\site-packages\\tabpfn\\regressor.py:490: UserWarning: Running on CPU with more than 200 samples may be slow.\n", - "Consider using a GPU or the tabpfn-client API: https://github.com/PriorLabs/tabpfn-client\n", - " check_cpu_warning(\n", - "c:\\Users\\svenk\\anaconda3\\envs\\dml_tabpfn\\Lib\\site-packages\\tabpfn\\regressor.py:490: UserWarning: Running on CPU with more than 200 samples may be slow.\n", - "Consider using a GPU or the tabpfn-client API: https://github.com/PriorLabs/tabpfn-client\n", - " check_cpu_warning(\n", - "c:\\Users\\svenk\\anaconda3\\envs\\dml_tabpfn\\Lib\\site-packages\\tabpfn\\regressor.py:490: UserWarning: Running on CPU with more than 200 samples may be slow.\n", - "Consider using a GPU or the tabpfn-client API: https://github.com/PriorLabs/tabpfn-client\n", - " check_cpu_warning(\n", - "c:\\Users\\svenk\\anaconda3\\envs\\dml_tabpfn\\Lib\\site-packages\\tabpfn\\regressor.py:490: UserWarning: Running on CPU with more than 200 samples may be slow.\n", - "Consider using a GPU or the tabpfn-client API: https://github.com/PriorLabs/tabpfn-client\n", - " check_cpu_warning(\n", - "c:\\Users\\svenk\\anaconda3\\envs\\dml_tabpfn\\Lib\\site-packages\\tabpfn\\regressor.py:490: UserWarning: Running on CPU with more than 200 samples may be slow.\n", - "Consider using a GPU or the tabpfn-client API: https://github.com/PriorLabs/tabpfn-client\n", - " check_cpu_warning(\n", - "c:\\Users\\svenk\\anaconda3\\envs\\dml_tabpfn\\Lib\\site-packages\\tabpfn\\regressor.py:490: UserWarning: Running on CPU with more than 200 samples may be slow.\n", - "Consider using a GPU or the tabpfn-client API: https://github.com/PriorLabs/tabpfn-client\n", - " check_cpu_warning(\n", - "c:\\Users\\svenk\\anaconda3\\envs\\dml_tabpfn\\Lib\\site-packages\\tabpfn\\regressor.py:490: UserWarning: Running on CPU with more than 200 samples may be slow.\n", - "Consider using a GPU or the tabpfn-client API: https://github.com/PriorLabs/tabpfn-client\n", - " check_cpu_warning(\n", - "c:\\Users\\svenk\\anaconda3\\envs\\dml_tabpfn\\Lib\\site-packages\\tabpfn\\regressor.py:490: UserWarning: Running on CPU with more than 200 samples may be slow.\n", - "Consider using a GPU or the tabpfn-client API: https://github.com/PriorLabs/tabpfn-client\n", - " check_cpu_warning(\n", - "c:\\Users\\svenk\\anaconda3\\envs\\dml_tabpfn\\Lib\\site-packages\\tabpfn\\regressor.py:490: UserWarning: Running on CPU with more than 200 samples may be slow.\n", - "Consider using a GPU or the tabpfn-client API: https://github.com/PriorLabs/tabpfn-client\n", - " check_cpu_warning(\n", - "c:\\Users\\svenk\\anaconda3\\envs\\dml_tabpfn\\Lib\\site-packages\\tabpfn\\regressor.py:490: UserWarning: Running on CPU with more than 200 samples may be slow.\n", - "Consider using a GPU or the tabpfn-client API: https://github.com/PriorLabs/tabpfn-client\n", - " check_cpu_warning(\n", - "c:\\Users\\svenk\\anaconda3\\envs\\dml_tabpfn\\Lib\\site-packages\\tabpfn\\classifier.py:459: UserWarning: Running on CPU with more than 200 samples may be slow.\n", - "Consider using a GPU or the tabpfn-client API: https://github.com/PriorLabs/tabpfn-client\n", - " check_cpu_warning(\n", - "c:\\Users\\svenk\\anaconda3\\envs\\dml_tabpfn\\Lib\\site-packages\\tabpfn\\classifier.py:459: UserWarning: Running on CPU with more than 200 samples may be slow.\n", - "Consider using a GPU or the tabpfn-client API: https://github.com/PriorLabs/tabpfn-client\n", - " check_cpu_warning(\n", - "c:\\Users\\svenk\\anaconda3\\envs\\dml_tabpfn\\Lib\\site-packages\\tabpfn\\classifier.py:459: UserWarning: Running on CPU with more than 200 samples may be slow.\n", - "Consider using a GPU or the tabpfn-client API: https://github.com/PriorLabs/tabpfn-client\n", - " check_cpu_warning(\n", - "c:\\Users\\svenk\\anaconda3\\envs\\dml_tabpfn\\Lib\\site-packages\\tabpfn\\classifier.py:459: UserWarning: Running on CPU with more than 200 samples may be slow.\n", - "Consider using a GPU or the tabpfn-client API: https://github.com/PriorLabs/tabpfn-client\n", - " check_cpu_warning(\n", - "c:\\Users\\svenk\\anaconda3\\envs\\dml_tabpfn\\Lib\\site-packages\\tabpfn\\classifier.py:459: UserWarning: Running on CPU with more than 200 samples may be slow.\n", - "Consider using a GPU or the tabpfn-client API: https://github.com/PriorLabs/tabpfn-client\n", - " check_cpu_warning(\n", - "c:\\Users\\svenk\\anaconda3\\envs\\dml_tabpfn\\Lib\\site-packages\\tabpfn\\classifier.py:459: UserWarning: Running on CPU with more than 200 samples may be slow.\n", - "Consider using a GPU or the tabpfn-client API: https://github.com/PriorLabs/tabpfn-client\n", - " check_cpu_warning(\n", - "c:\\Users\\svenk\\anaconda3\\envs\\dml_tabpfn\\Lib\\site-packages\\tabpfn\\classifier.py:459: UserWarning: Running on CPU with more than 200 samples may be slow.\n", - "Consider using a GPU or the tabpfn-client API: https://github.com/PriorLabs/tabpfn-client\n", - " check_cpu_warning(\n", - "c:\\Users\\svenk\\anaconda3\\envs\\dml_tabpfn\\Lib\\site-packages\\tabpfn\\classifier.py:459: UserWarning: Running on CPU with more than 200 samples may be slow.\n", - "Consider using a GPU or the tabpfn-client API: https://github.com/PriorLabs/tabpfn-client\n", - " check_cpu_warning(\n", - "c:\\Users\\svenk\\anaconda3\\envs\\dml_tabpfn\\Lib\\site-packages\\tabpfn\\classifier.py:459: UserWarning: Running on CPU with more than 200 samples may be slow.\n", - "Consider using a GPU or the tabpfn-client API: https://github.com/PriorLabs/tabpfn-client\n", - " check_cpu_warning(\n", - "c:\\Users\\svenk\\anaconda3\\envs\\dml_tabpfn\\Lib\\site-packages\\tabpfn\\classifier.py:459: UserWarning: Running on CPU with more than 200 samples may be slow.\n", - "Consider using a GPU or the tabpfn-client API: https://github.com/PriorLabs/tabpfn-client\n", - " check_cpu_warning(\n", - "c:\\Users\\svenk\\anaconda3\\envs\\dml_tabpfn\\Lib\\site-packages\\sklearn\\utils\\deprecation.py:151: FutureWarning: 'force_all_finite' was renamed to 'ensure_all_finite' in 1.6 and will be removed in 1.8.\n", - " warnings.warn(\n", - "c:\\Users\\svenk\\anaconda3\\envs\\dml_tabpfn\\Lib\\site-packages\\sklearn\\utils\\deprecation.py:151: FutureWarning: 'force_all_finite' was renamed to 'ensure_all_finite' in 1.6 and will be removed in 1.8.\n", - " warnings.warn(\n", - "c:\\Users\\svenk\\anaconda3\\envs\\dml_tabpfn\\Lib\\site-packages\\sklearn\\utils\\deprecation.py:151: FutureWarning: 'force_all_finite' was renamed to 'ensure_all_finite' in 1.6 and will be removed in 1.8.\n", - " warnings.warn(\n", - "c:\\Users\\svenk\\anaconda3\\envs\\dml_tabpfn\\Lib\\site-packages\\sklearn\\utils\\deprecation.py:151: FutureWarning: 'force_all_finite' was renamed to 'ensure_all_finite' in 1.6 and will be removed in 1.8.\n", - " warnings.warn(\n", - "c:\\Users\\svenk\\anaconda3\\envs\\dml_tabpfn\\Lib\\site-packages\\tabpfn\\regressor.py:490: UserWarning: Running on CPU with more than 200 samples may be slow.\n", - "Consider using a GPU or the tabpfn-client API: https://github.com/PriorLabs/tabpfn-client\n", - " check_cpu_warning(\n", - "c:\\Users\\svenk\\anaconda3\\envs\\dml_tabpfn\\Lib\\site-packages\\tabpfn\\regressor.py:490: UserWarning: Running on CPU with more than 200 samples may be slow.\n", - "Consider using a GPU or the tabpfn-client API: https://github.com/PriorLabs/tabpfn-client\n", - " check_cpu_warning(\n", - "c:\\Users\\svenk\\anaconda3\\envs\\dml_tabpfn\\Lib\\site-packages\\tabpfn\\regressor.py:490: UserWarning: Running on CPU with more than 200 samples may be slow.\n", - "Consider using a GPU or the tabpfn-client API: https://github.com/PriorLabs/tabpfn-client\n", - " check_cpu_warning(\n", - "c:\\Users\\svenk\\anaconda3\\envs\\dml_tabpfn\\Lib\\site-packages\\tabpfn\\regressor.py:490: UserWarning: Running on CPU with more than 200 samples may be slow.\n", - "Consider using a GPU or the tabpfn-client API: https://github.com/PriorLabs/tabpfn-client\n", - " check_cpu_warning(\n", - "c:\\Users\\svenk\\anaconda3\\envs\\dml_tabpfn\\Lib\\site-packages\\tabpfn\\regressor.py:490: UserWarning: Running on CPU with more than 200 samples may be slow.\n", - "Consider using a GPU or the tabpfn-client API: https://github.com/PriorLabs/tabpfn-client\n", - " check_cpu_warning(\n", - "c:\\Users\\svenk\\anaconda3\\envs\\dml_tabpfn\\Lib\\site-packages\\tabpfn\\regressor.py:490: UserWarning: Running on CPU with more than 200 samples may be slow.\n", - "Consider using a GPU or the tabpfn-client API: https://github.com/PriorLabs/tabpfn-client\n", - " check_cpu_warning(\n", - "c:\\Users\\svenk\\anaconda3\\envs\\dml_tabpfn\\Lib\\site-packages\\tabpfn\\regressor.py:490: UserWarning: Running on CPU with more than 200 samples may be slow.\n", - "Consider using a GPU or the tabpfn-client API: https://github.com/PriorLabs/tabpfn-client\n", - " check_cpu_warning(\n", - "c:\\Users\\svenk\\anaconda3\\envs\\dml_tabpfn\\Lib\\site-packages\\tabpfn\\regressor.py:490: UserWarning: Running on CPU with more than 200 samples may be slow.\n", - "Consider using a GPU or the tabpfn-client API: https://github.com/PriorLabs/tabpfn-client\n", - " check_cpu_warning(\n", - "c:\\Users\\svenk\\anaconda3\\envs\\dml_tabpfn\\Lib\\site-packages\\tabpfn\\regressor.py:490: UserWarning: Running on CPU with more than 200 samples may be slow.\n", - "Consider using a GPU or the tabpfn-client API: https://github.com/PriorLabs/tabpfn-client\n", - " check_cpu_warning(\n", - "c:\\Users\\svenk\\anaconda3\\envs\\dml_tabpfn\\Lib\\site-packages\\tabpfn\\regressor.py:490: UserWarning: Running on CPU with more than 200 samples may be slow.\n", - "Consider using a GPU or the tabpfn-client API: https://github.com/PriorLabs/tabpfn-client\n", - " check_cpu_warning(\n", - "c:\\Users\\svenk\\anaconda3\\envs\\dml_tabpfn\\Lib\\site-packages\\tabpfn\\classifier.py:459: UserWarning: Running on CPU with more than 200 samples may be slow.\n", - "Consider using a GPU or the tabpfn-client API: https://github.com/PriorLabs/tabpfn-client\n", - " check_cpu_warning(\n", - "c:\\Users\\svenk\\anaconda3\\envs\\dml_tabpfn\\Lib\\site-packages\\tabpfn\\classifier.py:459: UserWarning: Running on CPU with more than 200 samples may be slow.\n", - "Consider using a GPU or the tabpfn-client API: https://github.com/PriorLabs/tabpfn-client\n", - " check_cpu_warning(\n", - "c:\\Users\\svenk\\anaconda3\\envs\\dml_tabpfn\\Lib\\site-packages\\tabpfn\\classifier.py:459: UserWarning: Running on CPU with more than 200 samples may be slow.\n", - "Consider using a GPU or the tabpfn-client API: https://github.com/PriorLabs/tabpfn-client\n", - " check_cpu_warning(\n", - "c:\\Users\\svenk\\anaconda3\\envs\\dml_tabpfn\\Lib\\site-packages\\tabpfn\\classifier.py:459: UserWarning: Running on CPU with more than 200 samples may be slow.\n", - "Consider using a GPU or the tabpfn-client API: https://github.com/PriorLabs/tabpfn-client\n", - " check_cpu_warning(\n", - "c:\\Users\\svenk\\anaconda3\\envs\\dml_tabpfn\\Lib\\site-packages\\tabpfn\\classifier.py:459: UserWarning: Running on CPU with more than 200 samples may be slow.\n", - "Consider using a GPU or the tabpfn-client API: https://github.com/PriorLabs/tabpfn-client\n", - " check_cpu_warning(\n", - "c:\\Users\\svenk\\anaconda3\\envs\\dml_tabpfn\\Lib\\site-packages\\tabpfn\\classifier.py:459: UserWarning: Running on CPU with more than 200 samples may be slow.\n", - "Consider using a GPU or the tabpfn-client API: https://github.com/PriorLabs/tabpfn-client\n", - " check_cpu_warning(\n", - "c:\\Users\\svenk\\anaconda3\\envs\\dml_tabpfn\\Lib\\site-packages\\tabpfn\\classifier.py:459: UserWarning: Running on CPU with more than 200 samples may be slow.\n", - "Consider using a GPU or the tabpfn-client API: https://github.com/PriorLabs/tabpfn-client\n", - " check_cpu_warning(\n", - "c:\\Users\\svenk\\anaconda3\\envs\\dml_tabpfn\\Lib\\site-packages\\tabpfn\\classifier.py:459: UserWarning: Running on CPU with more than 200 samples may be slow.\n", - "Consider using a GPU or the tabpfn-client API: https://github.com/PriorLabs/tabpfn-client\n", - " check_cpu_warning(\n", - "c:\\Users\\svenk\\anaconda3\\envs\\dml_tabpfn\\Lib\\site-packages\\tabpfn\\classifier.py:459: UserWarning: Running on CPU with more than 200 samples may be slow.\n", - "Consider using a GPU or the tabpfn-client API: https://github.com/PriorLabs/tabpfn-client\n", - " check_cpu_warning(\n", - "c:\\Users\\svenk\\anaconda3\\envs\\dml_tabpfn\\Lib\\site-packages\\tabpfn\\classifier.py:459: UserWarning: Running on CPU with more than 200 samples may be slow.\n", - "Consider using a GPU or the tabpfn-client API: https://github.com/PriorLabs/tabpfn-client\n", - " check_cpu_warning(\n", - "c:\\Users\\svenk\\anaconda3\\envs\\dml_tabpfn\\Lib\\site-packages\\sklearn\\utils\\deprecation.py:151: FutureWarning: 'force_all_finite' was renamed to 'ensure_all_finite' in 1.6 and will be removed in 1.8.\n", - " warnings.warn(\n", - "c:\\Users\\svenk\\anaconda3\\envs\\dml_tabpfn\\Lib\\site-packages\\sklearn\\utils\\deprecation.py:151: FutureWarning: 'force_all_finite' was renamed to 'ensure_all_finite' in 1.6 and will be removed in 1.8.\n", - " warnings.warn(\n", - "c:\\Users\\svenk\\anaconda3\\envs\\dml_tabpfn\\Lib\\site-packages\\sklearn\\utils\\deprecation.py:151: FutureWarning: 'force_all_finite' was renamed to 'ensure_all_finite' in 1.6 and will be removed in 1.8.\n", - " warnings.warn(\n", - "c:\\Users\\svenk\\anaconda3\\envs\\dml_tabpfn\\Lib\\site-packages\\sklearn\\utils\\deprecation.py:151: FutureWarning: 'force_all_finite' was renamed to 'ensure_all_finite' in 1.6 and will be removed in 1.8.\n", - " warnings.warn(\n", - "c:\\Users\\svenk\\anaconda3\\envs\\dml_tabpfn\\Lib\\site-packages\\tabpfn\\regressor.py:490: UserWarning: Running on CPU with more than 200 samples may be slow.\n", - "Consider using a GPU or the tabpfn-client API: https://github.com/PriorLabs/tabpfn-client\n", - " check_cpu_warning(\n", - "c:\\Users\\svenk\\anaconda3\\envs\\dml_tabpfn\\Lib\\site-packages\\tabpfn\\regressor.py:490: UserWarning: Running on CPU with more than 200 samples may be slow.\n", - "Consider using a GPU or the tabpfn-client API: https://github.com/PriorLabs/tabpfn-client\n", - " check_cpu_warning(\n", - "c:\\Users\\svenk\\anaconda3\\envs\\dml_tabpfn\\Lib\\site-packages\\tabpfn\\regressor.py:490: UserWarning: Running on CPU with more than 200 samples may be slow.\n", - "Consider using a GPU or the tabpfn-client API: https://github.com/PriorLabs/tabpfn-client\n", - " check_cpu_warning(\n", - "c:\\Users\\svenk\\anaconda3\\envs\\dml_tabpfn\\Lib\\site-packages\\tabpfn\\regressor.py:490: UserWarning: Running on CPU with more than 200 samples may be slow.\n", - "Consider using a GPU or the tabpfn-client API: https://github.com/PriorLabs/tabpfn-client\n", - " check_cpu_warning(\n", - "c:\\Users\\svenk\\anaconda3\\envs\\dml_tabpfn\\Lib\\site-packages\\tabpfn\\regressor.py:490: UserWarning: Running on CPU with more than 200 samples may be slow.\n", - "Consider using a GPU or the tabpfn-client API: https://github.com/PriorLabs/tabpfn-client\n", - " check_cpu_warning(\n", - "c:\\Users\\svenk\\anaconda3\\envs\\dml_tabpfn\\Lib\\site-packages\\tabpfn\\regressor.py:490: UserWarning: Running on CPU with more than 200 samples may be slow.\n", - "Consider using a GPU or the tabpfn-client API: https://github.com/PriorLabs/tabpfn-client\n", - " check_cpu_warning(\n", - "c:\\Users\\svenk\\anaconda3\\envs\\dml_tabpfn\\Lib\\site-packages\\tabpfn\\regressor.py:490: UserWarning: Running on CPU with more than 200 samples may be slow.\n", - "Consider using a GPU or the tabpfn-client API: https://github.com/PriorLabs/tabpfn-client\n", - " check_cpu_warning(\n", - "c:\\Users\\svenk\\anaconda3\\envs\\dml_tabpfn\\Lib\\site-packages\\tabpfn\\regressor.py:490: UserWarning: Running on CPU with more than 200 samples may be slow.\n", - "Consider using a GPU or the tabpfn-client API: https://github.com/PriorLabs/tabpfn-client\n", - " check_cpu_warning(\n", - "c:\\Users\\svenk\\anaconda3\\envs\\dml_tabpfn\\Lib\\site-packages\\tabpfn\\regressor.py:490: UserWarning: Running on CPU with more than 200 samples may be slow.\n", - "Consider using a GPU or the tabpfn-client API: https://github.com/PriorLabs/tabpfn-client\n", - " check_cpu_warning(\n", - "c:\\Users\\svenk\\anaconda3\\envs\\dml_tabpfn\\Lib\\site-packages\\tabpfn\\regressor.py:490: UserWarning: Running on CPU with more than 200 samples may be slow.\n", - "Consider using a GPU or the tabpfn-client API: https://github.com/PriorLabs/tabpfn-client\n", - " check_cpu_warning(\n", - "c:\\Users\\svenk\\anaconda3\\envs\\dml_tabpfn\\Lib\\site-packages\\tabpfn\\classifier.py:459: UserWarning: Running on CPU with more than 200 samples may be slow.\n", - "Consider using a GPU or the tabpfn-client API: https://github.com/PriorLabs/tabpfn-client\n", - " check_cpu_warning(\n", - "c:\\Users\\svenk\\anaconda3\\envs\\dml_tabpfn\\Lib\\site-packages\\tabpfn\\classifier.py:459: UserWarning: Running on CPU with more than 200 samples may be slow.\n", - "Consider using a GPU or the tabpfn-client API: https://github.com/PriorLabs/tabpfn-client\n", - " check_cpu_warning(\n", - "c:\\Users\\svenk\\anaconda3\\envs\\dml_tabpfn\\Lib\\site-packages\\tabpfn\\classifier.py:459: UserWarning: Running on CPU with more than 200 samples may be slow.\n", - "Consider using a GPU or the tabpfn-client API: https://github.com/PriorLabs/tabpfn-client\n", - " check_cpu_warning(\n", - "c:\\Users\\svenk\\anaconda3\\envs\\dml_tabpfn\\Lib\\site-packages\\tabpfn\\classifier.py:459: UserWarning: Running on CPU with more than 200 samples may be slow.\n", - "Consider using a GPU or the tabpfn-client API: https://github.com/PriorLabs/tabpfn-client\n", - " check_cpu_warning(\n", - "c:\\Users\\svenk\\anaconda3\\envs\\dml_tabpfn\\Lib\\site-packages\\tabpfn\\classifier.py:459: UserWarning: Running on CPU with more than 200 samples may be slow.\n", - "Consider using a GPU or the tabpfn-client API: https://github.com/PriorLabs/tabpfn-client\n", - " check_cpu_warning(\n", - "c:\\Users\\svenk\\anaconda3\\envs\\dml_tabpfn\\Lib\\site-packages\\tabpfn\\classifier.py:459: UserWarning: Running on CPU with more than 200 samples may be slow.\n", - "Consider using a GPU or the tabpfn-client API: https://github.com/PriorLabs/tabpfn-client\n", - " check_cpu_warning(\n", - "c:\\Users\\svenk\\anaconda3\\envs\\dml_tabpfn\\Lib\\site-packages\\tabpfn\\classifier.py:459: UserWarning: Running on CPU with more than 200 samples may be slow.\n", - "Consider using a GPU or the tabpfn-client API: https://github.com/PriorLabs/tabpfn-client\n", - " check_cpu_warning(\n", - "c:\\Users\\svenk\\anaconda3\\envs\\dml_tabpfn\\Lib\\site-packages\\tabpfn\\classifier.py:459: UserWarning: Running on CPU with more than 200 samples may be slow.\n", - "Consider using a GPU or the tabpfn-client API: https://github.com/PriorLabs/tabpfn-client\n", - " check_cpu_warning(\n", - "c:\\Users\\svenk\\anaconda3\\envs\\dml_tabpfn\\Lib\\site-packages\\tabpfn\\classifier.py:459: UserWarning: Running on CPU with more than 200 samples may be slow.\n", - "Consider using a GPU or the tabpfn-client API: https://github.com/PriorLabs/tabpfn-client\n", - " check_cpu_warning(\n", - "c:\\Users\\svenk\\anaconda3\\envs\\dml_tabpfn\\Lib\\site-packages\\tabpfn\\classifier.py:459: UserWarning: Running on CPU with more than 200 samples may be slow.\n", - "Consider using a GPU or the tabpfn-client API: https://github.com/PriorLabs/tabpfn-client\n", - " check_cpu_warning(\n", - "c:\\Users\\svenk\\anaconda3\\envs\\dml_tabpfn\\Lib\\site-packages\\sklearn\\utils\\deprecation.py:151: FutureWarning: 'force_all_finite' was renamed to 'ensure_all_finite' in 1.6 and will be removed in 1.8.\n", - " warnings.warn(\n", - "c:\\Users\\svenk\\anaconda3\\envs\\dml_tabpfn\\Lib\\site-packages\\sklearn\\utils\\deprecation.py:151: FutureWarning: 'force_all_finite' was renamed to 'ensure_all_finite' in 1.6 and will be removed in 1.8.\n", - " warnings.warn(\n", - "c:\\Users\\svenk\\anaconda3\\envs\\dml_tabpfn\\Lib\\site-packages\\sklearn\\utils\\deprecation.py:151: FutureWarning: 'force_all_finite' was renamed to 'ensure_all_finite' in 1.6 and will be removed in 1.8.\n", - " warnings.warn(\n", - "c:\\Users\\svenk\\anaconda3\\envs\\dml_tabpfn\\Lib\\site-packages\\sklearn\\utils\\deprecation.py:151: FutureWarning: 'force_all_finite' was renamed to 'ensure_all_finite' in 1.6 and will be removed in 1.8.\n", - " warnings.warn(\n", - "c:\\Users\\svenk\\anaconda3\\envs\\dml_tabpfn\\Lib\\site-packages\\tabpfn\\regressor.py:490: UserWarning: Running on CPU with more than 200 samples may be slow.\n", - "Consider using a GPU or the tabpfn-client API: https://github.com/PriorLabs/tabpfn-client\n", - " check_cpu_warning(\n", - "c:\\Users\\svenk\\anaconda3\\envs\\dml_tabpfn\\Lib\\site-packages\\tabpfn\\regressor.py:490: UserWarning: Running on CPU with more than 200 samples may be slow.\n", - "Consider using a GPU or the tabpfn-client API: https://github.com/PriorLabs/tabpfn-client\n", - " check_cpu_warning(\n", - "c:\\Users\\svenk\\anaconda3\\envs\\dml_tabpfn\\Lib\\site-packages\\tabpfn\\regressor.py:490: UserWarning: Running on CPU with more than 200 samples may be slow.\n", - "Consider using a GPU or the tabpfn-client API: https://github.com/PriorLabs/tabpfn-client\n", - " check_cpu_warning(\n", - "c:\\Users\\svenk\\anaconda3\\envs\\dml_tabpfn\\Lib\\site-packages\\tabpfn\\regressor.py:490: UserWarning: Running on CPU with more than 200 samples may be slow.\n", - "Consider using a GPU or the tabpfn-client API: https://github.com/PriorLabs/tabpfn-client\n", - " check_cpu_warning(\n", - "c:\\Users\\svenk\\anaconda3\\envs\\dml_tabpfn\\Lib\\site-packages\\tabpfn\\regressor.py:490: UserWarning: Running on CPU with more than 200 samples may be slow.\n", - "Consider using a GPU or the tabpfn-client API: https://github.com/PriorLabs/tabpfn-client\n", - " check_cpu_warning(\n", - "c:\\Users\\svenk\\anaconda3\\envs\\dml_tabpfn\\Lib\\site-packages\\tabpfn\\regressor.py:490: UserWarning: Running on CPU with more than 200 samples may be slow.\n", - "Consider using a GPU or the tabpfn-client API: https://github.com/PriorLabs/tabpfn-client\n", - " check_cpu_warning(\n", - "c:\\Users\\svenk\\anaconda3\\envs\\dml_tabpfn\\Lib\\site-packages\\tabpfn\\regressor.py:490: UserWarning: Running on CPU with more than 200 samples may be slow.\n", - "Consider using a GPU or the tabpfn-client API: https://github.com/PriorLabs/tabpfn-client\n", - " check_cpu_warning(\n", - "c:\\Users\\svenk\\anaconda3\\envs\\dml_tabpfn\\Lib\\site-packages\\tabpfn\\regressor.py:490: UserWarning: Running on CPU with more than 200 samples may be slow.\n", - "Consider using a GPU or the tabpfn-client API: https://github.com/PriorLabs/tabpfn-client\n", - " check_cpu_warning(\n", - "c:\\Users\\svenk\\anaconda3\\envs\\dml_tabpfn\\Lib\\site-packages\\tabpfn\\regressor.py:490: UserWarning: Running on CPU with more than 200 samples may be slow.\n", - "Consider using a GPU or the tabpfn-client API: https://github.com/PriorLabs/tabpfn-client\n", - " check_cpu_warning(\n", - "c:\\Users\\svenk\\anaconda3\\envs\\dml_tabpfn\\Lib\\site-packages\\tabpfn\\regressor.py:490: UserWarning: Running on CPU with more than 200 samples may be slow.\n", - "Consider using a GPU or the tabpfn-client API: https://github.com/PriorLabs/tabpfn-client\n", - " check_cpu_warning(\n", - "c:\\Users\\svenk\\anaconda3\\envs\\dml_tabpfn\\Lib\\site-packages\\tabpfn\\classifier.py:459: UserWarning: Running on CPU with more than 200 samples may be slow.\n", - "Consider using a GPU or the tabpfn-client API: https://github.com/PriorLabs/tabpfn-client\n", - " check_cpu_warning(\n", - "c:\\Users\\svenk\\anaconda3\\envs\\dml_tabpfn\\Lib\\site-packages\\tabpfn\\classifier.py:459: UserWarning: Running on CPU with more than 200 samples may be slow.\n", - "Consider using a GPU or the tabpfn-client API: https://github.com/PriorLabs/tabpfn-client\n", - " check_cpu_warning(\n", - "c:\\Users\\svenk\\anaconda3\\envs\\dml_tabpfn\\Lib\\site-packages\\tabpfn\\classifier.py:459: UserWarning: Running on CPU with more than 200 samples may be slow.\n", - "Consider using a GPU or the tabpfn-client API: https://github.com/PriorLabs/tabpfn-client\n", - " check_cpu_warning(\n", - "c:\\Users\\svenk\\anaconda3\\envs\\dml_tabpfn\\Lib\\site-packages\\tabpfn\\classifier.py:459: UserWarning: Running on CPU with more than 200 samples may be slow.\n", - "Consider using a GPU or the tabpfn-client API: https://github.com/PriorLabs/tabpfn-client\n", - " check_cpu_warning(\n", - "c:\\Users\\svenk\\anaconda3\\envs\\dml_tabpfn\\Lib\\site-packages\\tabpfn\\classifier.py:459: UserWarning: Running on CPU with more than 200 samples may be slow.\n", - "Consider using a GPU or the tabpfn-client API: https://github.com/PriorLabs/tabpfn-client\n", - " check_cpu_warning(\n", - "c:\\Users\\svenk\\anaconda3\\envs\\dml_tabpfn\\Lib\\site-packages\\tabpfn\\classifier.py:459: UserWarning: Running on CPU with more than 200 samples may be slow.\n", - "Consider using a GPU or the tabpfn-client API: https://github.com/PriorLabs/tabpfn-client\n", - " check_cpu_warning(\n", - "c:\\Users\\svenk\\anaconda3\\envs\\dml_tabpfn\\Lib\\site-packages\\tabpfn\\classifier.py:459: UserWarning: Running on CPU with more than 200 samples may be slow.\n", - "Consider using a GPU or the tabpfn-client API: https://github.com/PriorLabs/tabpfn-client\n", - " check_cpu_warning(\n", - "c:\\Users\\svenk\\anaconda3\\envs\\dml_tabpfn\\Lib\\site-packages\\tabpfn\\classifier.py:459: UserWarning: Running on CPU with more than 200 samples may be slow.\n", - "Consider using a GPU or the tabpfn-client API: https://github.com/PriorLabs/tabpfn-client\n", - " check_cpu_warning(\n", - "c:\\Users\\svenk\\anaconda3\\envs\\dml_tabpfn\\Lib\\site-packages\\tabpfn\\classifier.py:459: UserWarning: Running on CPU with more than 200 samples may be slow.\n", - "Consider using a GPU or the tabpfn-client API: https://github.com/PriorLabs/tabpfn-client\n", - " check_cpu_warning(\n", - "c:\\Users\\svenk\\anaconda3\\envs\\dml_tabpfn\\Lib\\site-packages\\tabpfn\\classifier.py:459: UserWarning: Running on CPU with more than 200 samples may be slow.\n", - "Consider using a GPU or the tabpfn-client API: https://github.com/PriorLabs/tabpfn-client\n", - " check_cpu_warning(\n" + "c:\\Users\\BAM5698\\AppData\\Local\\miniconda3\\envs\\dml_docs\\Lib\\site-packages\\doubleml\\double_ml.py:1479: UserWarning: The estimated nu2 for d is not positive. Re-estimation based on riesz representer (non-orthogonal).\n", + " warnings.warn(msg, UserWarning)\n" ] }, { @@ -1848,9 +511,9 @@ " 2\n", " RandomForest\n", " 3.0\n", - " 8.738424\n", - " 4.077025\n", - " 13.399824\n", + " 8.743203\n", + " 4.080729\n", + " 13.405677\n", " \n", " \n", " 3\n", @@ -1952,41 +615,41 @@ " 15\n", " TabPFN\n", " 1.0\n", - " 1.554511\n", - " 0.216895\n", - " 2.892126\n", + " 1.531682\n", + " 0.208840\n", + " 2.854525\n", " \n", " \n", " 16\n", " TabPFN\n", " 2.0\n", - " 6.998593\n", - " 6.048500\n", - " 7.948686\n", + " 6.958612\n", + " 6.026292\n", + " 7.890933\n", " \n", " \n", " 17\n", " TabPFN\n", " 3.0\n", - " 10.345530\n", - " 9.048832\n", - " 11.642228\n", + " 10.317946\n", + " 9.048090\n", + " 11.587801\n", " \n", " \n", " 18\n", " TabPFN\n", " 4.0\n", - " 10.416777\n", - " 9.453538\n", - " 11.380015\n", + " 10.391389\n", + " 9.417502\n", + " 11.365275\n", " \n", " \n", " 19\n", " TabPFN\n", " 5.0\n", - " 9.827502\n", - " 8.873946\n", - " 10.781058\n", + " 9.816896\n", + " 8.873275\n", + " 10.760517\n", " \n", " \n", "\n", @@ -1996,7 +659,7 @@ " learner treatment_level ate ci_lower ci_upper\n", "0 RandomForest 1.0 1.325370 -4.218919 6.869658\n", "1 RandomForest 2.0 5.702248 1.094595 10.309901\n", - "2 RandomForest 3.0 8.738424 4.077025 13.399824\n", + "2 RandomForest 3.0 8.743203 4.080729 13.405677\n", "3 RandomForest 4.0 6.632407 1.979031 11.285783\n", "4 RandomForest 5.0 7.967206 2.359083 13.575329\n", "5 Linear 1.0 4.971059 -1.231281 11.173400\n", @@ -2009,11 +672,11 @@ "12 LightGBM 3.0 10.824140 -4.925410 26.573689\n", "13 LightGBM 4.0 11.547482 -2.465476 25.560440\n", "14 LightGBM 5.0 12.528573 -2.627754 27.684899\n", - "15 TabPFN 1.0 1.554511 0.216895 2.892126\n", - "16 TabPFN 2.0 6.998593 6.048500 7.948686\n", - "17 TabPFN 3.0 10.345530 9.048832 11.642228\n", - "18 TabPFN 4.0 10.416777 9.453538 11.380015\n", - "19 TabPFN 5.0 9.827502 8.873946 10.781058" + "15 TabPFN 1.0 1.531682 0.208840 2.854525\n", + "16 TabPFN 2.0 6.958612 6.026292 7.890933\n", + "17 TabPFN 3.0 10.317946 9.048090 11.587801\n", + "18 TabPFN 4.0 10.391389 9.417502 11.365275\n", + "19 TabPFN 5.0 9.816896 8.873275 10.760517" ] }, "execution_count": 9, @@ -2091,7 +754,7 @@ "outputs": [ { "data": { - "image/png": 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", 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", 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", 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", 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" ] @@ -2288,8 +951,8 @@ "Learner \n", "LightGBM 15.2287 11.1751 15.2422 15.1509 14.8526\n", "Linear 21.1795 17.4716 20.3264 20.9783 21.0718\n", - "RandomForest 14.3689 11.4111 14.5989 14.5166 14.3319\n", - "TabPFN 10.0397 2.8823 10.5563 9.9802 10.2092\n", + "RandomForest 14.3663 11.4107 14.5979 14.5166 14.3334\n", + "TabPFN 10.0373 2.8767 10.5702 9.9764 10.2091\n", "\n", "\n", "RMSE g1 by Learner and Treatment Level:\n", @@ -2298,8 +961,8 @@ "Learner \n", "LightGBM 17.2116 31.2223 20.1003 18.2962 15.7370\n", "Linear 16.6144 30.1755 21.2086 18.4788 16.9636\n", - "RandomForest 16.0477 25.8403 18.6678 19.0742 15.5213\n", - "TabPFN 3.3651 16.5897 4.8409 6.4998 4.9453\n", + "RandomForest 16.0477 25.8403 18.6678 19.0762 15.5213\n", + "TabPFN 3.3852 16.5840 4.8130 6.5550 4.9701\n", "\n", "\n", "LogLoss m by Learner and Treatment Level:\n", @@ -2309,7 +972,7 @@ "LightGBM 0.5732 0.5118 0.4976 0.5644 0.5650\n", "Linear 0.4822 0.4352 0.4252 0.4460 0.4660\n", "RandomForest 0.5277 0.4642 0.4364 0.4860 0.5101\n", - "TabPFN 0.4777 0.4351 0.4319 0.4461 0.4649\n" + "TabPFN 0.4776 0.4347 0.4320 0.4462 0.4645\n" ] } ], @@ -2395,8 +1058,8 @@ "Learner \n", "LightGBM 14.3299 20.5135 0.5424\n", "Linear 20.2055 20.6882 0.4509\n", - "RandomForest 13.8455 19.0303 0.4849\n", - "TabPFN 8.7335 7.2482 0.4511\n" + "RandomForest 13.8450 19.0307 0.4849\n", + "TabPFN 8.7339 7.2615 0.4510\n" ] } ], @@ -2427,7 +1090,7 @@ ], "metadata": { "kernelspec": { - "display_name": "dml_tabpfn", + "display_name": "dml_docs", "language": "python", "name": "python3" }, @@ -2441,7 +1104,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.11.13" + "version": "3.12.4" } }, "nbformat": 4,