From 449e5f176d13226e55bc23a8d10127710f9b496c Mon Sep 17 00:00:00 2001 From: Tom Schierenbeck Date: Fri, 22 Mar 2024 10:36:29 +0100 Subject: [PATCH] Fixed a sampling bug. Made sampling not reach the maximum recursion depth. Updated notebooks --- examples/joint_probability_trees.ipynb | 2128 +- examples/nyga_distribution.ipynb | 40252 +++++---- examples/probability_theory.ipynb | 72628 ++++++++-------- examples/template_modelling.ipynb | 176 +- examples/truncated_gaussians.ipynb | 16044 ++-- .../bayesian_network/distributions.py | 7 +- .../distributions/distributions.py | 8 +- .../distributions/gaussian.py | 34 +- .../probabilistic_circuit.py | 37 +- test/test_bayesian_network/__init__.py | 0 .../test_bayesian_network.py | 22 +- .../test_distributions.py | 7 +- test/test_distributions/test_gaussian.py | 5 + test/test_jpt/test_jpt.py | 35 +- 14 files changed, 65024 insertions(+), 66359 deletions(-) create mode 100644 test/test_bayesian_network/__init__.py diff --git a/examples/joint_probability_trees.ipynb b/examples/joint_probability_trees.ipynb index 47a5115..eab1fe9 100644 --- a/examples/joint_probability_trees.ipynb +++ b/examples/joint_probability_trees.ipynb @@ -119,15 +119,15 @@ "metadata": { "collapsed": false, "ExecuteTime": { - "end_time": "2024-02-18T10:17:34.670732227Z", - "start_time": "2024-02-18T10:17:34.542603321Z" + "end_time": "2024-03-22T09:34:01.293440Z", + "start_time": "2024-03-22T09:34:00.888768Z" } }, "id": "8d622e03f59d6da2", "outputs": [ { "data": { - "text/html": " \n " + "text/html": " \n " }, "metadata": {}, "output_type": "display_data" @@ -10989,13 +10989,13 @@ "plotlyServerURL": "https://plot.ly" } }, - "text/html": "
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oF6pugMolUAJ9TtUNUNlU3kCfU3UDVDYTSqBPqboBKp9ACfQZVTdAdVB5A31G1Q1QHUwogT6h6gaoHgIl0OtU3QDVReUN9DpVN0B1MaEEepWqG6D6CJRAr1F1A1QnlTfQa1TdANXJhBLoFapugOolUAJFU3UDVDeVN1A0VTdAdTOhBIqi6gZAoAR6TNUNQKLyBoqg6gYgMaEEekjVDcAWAiXQbapuALam8ga6TdUNwNZMKIFuUXUDsC2BEugyVTcA7VF5A12m6gagPSaUQJeougHoiEAJbJeqG4DOqLyB7VJ1A9AZE0qgU6puALZHoAQ6pOoGoCtU3kCHVN0AdIUJJdAuVTcAXSVQAm2ougHoDpU30IaqG4DuMKEEWlF1A9BdAiXQQtUNQE+ovIEWqm4AesKEEkii6gag5wRKQNUNQFFU3oCqG4CimFBClVN1A1AsgRKq2Jaq+7nnnsuTTz5pOglAj6i8oYptqbrvvvtuYRKAHjOhhCql6gagtwiUUIVU3QD0JpU3VCFVNwC9yYQSqoyqG4DeJlBCFVF1A9AXVN5QRVTdAPQFE0qoEqpuAPqKQAlVQNUNQF9SeUMVUHUD0JdMKKHCqboB6GsCJVQwVTcA/UHlDRVM1Q1AfzChhAql6gagvwiUUIFU3QD0J5U3VCBVNwD9yYQSKoyqG4D+JlBCBVF1AzAQVN5QQVTdAAwEE0qoEKpuAAaKQAkVQNUNwEBSeUMFUHUDMJBMKKHMqboBGGgCJZQxVTcApUDlDWVM1Q1AKTChhDKl6gagVAiUUIZU3QCUEpU3lCFVNwClxIQSyoyqG4BSI1BCGVF1A1CKVN5QRlTdAJQiE0ooE6puAEqVQAllQNUNQClTeUMZUHUDUMpMKKHEqboBKHUCJZQwVTcA5UDlDSVM1Q1AOTChhBKl6gagXAiUUIJU3QCUE5U3lCBVNwDlxIQSSoyqG4ByI1BCCVF1A1COVN5QQlTdAJQjE0ooEapuAMqVQAklQNUNQDlTeUMJUHUDUM5MKGGAqboBKHcCJQwgVTcAlUDlDQNI1Q1AJTChhAGi6gagUgiUMABU3QBUEpU3DABVNwCVxIQS+pmqG4BKI1BCP1J1A1CJVN7Qj1TdAFQiE0roJ6puACqVQAn9QNUNQCVTeUM/UHUDUMlMKKGPqboBqHQCJfQhVTcA1UDlDX1I1Q1ANTChhD6i6gagWgiU0AdU3QBUE5U39AFVNwDVxIQSepmqG4BqI1BCL1J1A1CNVN7Qi1TdAFQjE0roJapuAKqVQAm9QNUNQDVTeUMvUHUDUM1MKKFIqm4Aqp1ACUVQdQOAyhuKouoGABNK6DFVNwBsJlBCD6i6AeAtKm/oAVU3ALzFhBK6SdUNAK0JlNANqm4AaEvlDd2g6gaAtkwooYtU3QDQPoESukDVDQAdU3lDF6i6AaBjJpSwHapuAOicQAmdUHUDwPapvKETqm4A2D4TSuiAqhsAukaghHaougGg61Te0A5VNwB0nQklbEPVDQDdI1DCVlTdANB9Km/YiqobALrPhBL+18KFCzNlyhRVNwB0k0AJ2Vx1n3TSSZk/f76qGwC6SeUN2Vx1z507V9UNAD1gQknVU3UDQHEESqqaqhsAiqfypqqpugGgeCaUVC1VNwD0DoGSqqTqBoDeo/KmKqm6AaD3mFBSdVTdANC7BEqqiqobAHqfypuqouoGgN5nQknVUHUDQN8QKKkKqm4A6Dsqb6qCqhsA+o4JJRVP1Q0AfUugpKKpugGg76m8qWiqbgDoeyaUVCxVNwD0D4GSiqTqBoD+o/KmIqm6AaD/mFBScVTdANC/BEoqiqobAPqfypuKouoGgP5nQknFUHUDwMAQKKkIqm4AGDgqbyqCqhsABo4JJWVP1Q0AA0ugpKypugFg4Km8KWuqbgAYeCaUlC1VNwCUBoGSsqTqBoDSofKmLKm6AaB0mFBSdlTdAFBaBErKiqobAEqPypuyouoGgNJjQknZUHUDQGkSKCkLqm4AKF0qb8qCqhsASpcJJSVP1Q0ApU2gpKSpugGg9Km8KWmqbgAofSaUlCxVNwCUB4GSkqTqBoDyofKmJKm6AaB8mFBSclTdAFBeBEpKiqobAMqPypuSouoGgPJjQknJUHUDQHkSKCkJqm4AKF8qb0qCqhsAypcJJQNO1Q0A5U2gZECpugGg/Km8GVCqbgAofyaUDBhVNwBUBoGSAaHqBoDKofJmQKi6AaBymFDS71TdAFBZBEr6laobACqPypt+peoGgMpjQkm/UXUDQGUSKOkXqm4AqFwqb/qFqhsAKpcJJX1O1Q0AlU2gpE9tqbqfffbZPPXUU6aTAFCBVN70qS1V95w5c4RJAKhQJpT0GVU3AFQHgZI+oeoGgOqh8qZPqLoBoHqYUNLrVN0AUF0ESnqVqhsAqo/Km16l6gaA6mNCSa9RdQNAdRIo6RWqbgCoXipveoWqGwCqlwklRVN1A0B1EygpiqobAFB5UxRVNwBgQkmPqboBgESgpIdU3QDAFipvekTVDQBsYUJJt6m6AYCtCZR0i6obANiWyptuUXUDANsyoaTLVN0AQHsESrpE1Q0AdETlTZeougGAjphQsl2qbgCgMwIlnVJ1AwDbo/KmU6puAGB7TCjpkKobAOgKgZJ2qboBgK5SedMuVTcA0FUmlLSh6gYAukOgpBVVNwDQXSpvWlF1AwDdZUJJC1U3ANATAiVJVN0AQM+pvEmi6gYAes6EElU3AFAUgbLKqboBgGKpvKucqhsAKJYJZRVTdQMAvUGgrFKqbgCgt6i8q5SqGwDoLSaUVUjVDQD0JoGyyqi6AYDepvKuMqpuAKC3mVBWEVU3ANAXBMoqoeoGAPqKyrtKqLoBgL5iQlkFVN0AQF8SKCucqhsA6Gsq7wqn6gYA+poJZQVTdQMA/UGgrFCqbgCgv6i8K5SqGwDoLyaUFUjVDQD0J4Gywqi6AYD+pvKuMKpuAKC/mVBWEFU3ADAQBMoKoeoGAAaKyrtCqLoBgIFiQlkBVN0AwEASKMucqhsAGGgq7zKn6gYABpoJZRlTdQMApUCgLFOqbgCgVKi8y5SqGwAoFSaUZUjVDQCUEoGyzKi6AYBSo/IuM6puAKDUmFCWEVU3AFCKBMoyoeoGAEqVyrtMqLoBgFJlQlkGVN0AQCkTKEucqhsAKHUq7xKn6gYASp0JZQlTdQMA5UCgLFGqbgCgXKi8S5SqGwAoFyaUJUjVDQCUE4GyxKi6AYByo/IuMapuAKDcmFCWEFU3AFCOBMoSoeoGAMqVyrtEqLoBgHJlQlkCVN0AQDkTKAeYqhsAKHcq7wGm6gYAyp0J5QBSdQMAlUCgHCCqbgCgUqi8B4iqGwCoFCaUA0DVDQBUEoGyn6m6AYBKo/LuZ6puAKDSmFD2I1U3AFCJBMp+ouoGACqVyrufqLoBgEplQtkPVN0AQCUTKPuYqhsAqHQq7z6m6gYAKp0JZR9SdQMA1UCg7COqbgCgWqi8+4iqGwCoFiaUfUDVDQBUE4Gyl6m6AYBqo/LuZapuAKDamFD2IlU3AFCNBMpeouoGAKqVyruXqLoBgGplQtkLVN0AQDUTKIuk6gYAqp3Ku0iqbgCg2plQFkHVDQAgUPaYqhsAYDOVdw+pugEANjOh7AFVNwDAWwTKblJ1AwC0pvLuJlU3AEBrJpTdoOoGAGhLoOwiVTcAQPtU3l2k6gYAaJ8JZReougEAOlb1gXL1+sYsWL46GxqbUldbkwmjG9JQ/9bgVtUNAGxre/mh2lTlv/y5V97MjY8syv3PvppFK9Zk60RdSDJ+1NAcu+/YfPiw8bnv9n9XdQMA3coP++yy40Atc0BU1YRy8Yo1ufS2J/PQ88syqKaQTU0d/9O37N+w6Dc5bsdXc8N3v9l/CwUASkZP8sNRe++cGadPzbhRQ/txpQOnagLlzY8typfueDqNTc2d/iK00bQp9XU75PLT9suHDhnfdwsEAEpOT/PDoJpCamsKVZMfqiJQzrr/ucy8e37R5/ncSZNy/rH79MKKAIBSJz90Xc1AL6Cv3fzYol75ZUiSmXfPzy2PLeqVcwEApUt+6J6KDpSLV6zJl+54ulfPedkdT2fxijW9ek4AoGOPPvpo6urqsnDhwpZthUKh5WvmzJm9+n6VmB8uvPDClp/XsGHDWrYvX748DQ0Nueuuu4o6f0UHyktvezKN3blesgsam5pz6W1P9uo5AaBafPnLX06hUMiyZcva3T9lypQcc8wxrbZNnz49Z511Vt72tre12n766adn9uzZOfXUU7f7vqtWrcqXvvSlnHzyyRk1alQKhUKuv/76do9tLz+s+u3cLLzyfe1+bVr1p+2+f2f54fe//31OPvnkDBs2LKNGjcpHPvKRvPbaa9s9Z5JMmDChVbje8nXeeee1Ou4jH/lIZs+enaOOOqrV9tGjR+cTn/hEvvjFL3bp/TpSsY8Neu6VN/PQ8+3/shZjU1NzHnp+WZ5/9c3sPba6HgkAAP3tiSeeyNy5czNv3rw2+/bff/+cc845XTrPsmXLcsUVV2T8+PF517velQceeKDd47aXH0Yc9eHUjti11baawQ3bff+O8sOLL76Yo48+OiNGjMiMGTOyatWqzJw5M08++WTLZHZ7DjjggPzjP/5jq22TJk1q9f1BBx2Ugw46KHPnzs2vfvWrVvvOO++8fOtb38p9992X4447brvv156KDZQ3PrKo01v7m5ubs+Z3D+bNX9+VDa/+MUlSN3Zidnz3/8nQd74nhUKhw3MPqinkhv9ZlC+ftl+frB0A2OwHP/hBxo8fn8MPP7yo8+y22255+eWXs8suu+QXv/hF/vzP/7zd47aXH4bsdXDqd+vZDTbt5YcZM2Zk9erVefzxxzN+/Oa7wQ899NCceOKJuf766/PJT35yu+fdY489uhys2zN58uRMmTIl119/fY8DZcVW3vc/+2rHYbJpU5bd8S9ZMfe6DN3n8Ox82kUZM+2SDN3n8Ky49/9m2R1XpblpU4fn3tTUnPvnv9pXSwcA/tftt9+e4447rtNBT1fU19dn1113zZw5c1pq31dfbfv/8s7ywxZN69d0mhM60l5+uPXWW/O+972vJUwmyQknnJBJkyblxz/+cZfPvWHDhqxevbrba9rixBNPzJ133pmePvynIgPlqvWNWdTJha8rH7oh65c8m90+9s0MP+wDefOxn2X17x7I8MM+kN0++o2sX/L7rHzohk7fY9HyNVm9vrG3lw4A/K8lS5Zk0aJFOfDAA3vtnK+88kqampqSJJ///Odz7rnn5oUXXkiy/fyQJK/cdGkWf+Mvs+jqM/LqT67IxhVLuvX+W+eHJUuW5NVXX83BBx/c5rhDDz00v/71r7t0zvvuuy9Dhw7NsGHDMmHChFxzzTXdWlOyuRJfuXJlnn66ZzcjVWTlvXD56nSUrzeteT1vPHp7xn7wS6kdMbbN/toRYzP6/3wmr/7H5Rl+yF9k0NAR7Z6nOcl3b7w1uw9p6r2FA0CFe/LJzTem3Hrrre1+pPHrr7+epqam3HTTTXnqqaeSJIsWLcpNN93U7rna296Z//mf/2n57+bm5vzoRz/KD3/4wxx99NG5eMa3OswPhR3q0zD1hAwePzU19UOzYenzeeOx27N09kXZ7dxrUjt8TJfevznJguWrs9/uI/Lyyy8n2VzHb2u33XbLihUrsn79+tTX13d4vv333z9HHnlk9t133yxfvjzXX399Lrzwwrz00kv5+te/3qU1Jclee+2VJPnd736XKVOmdPl1W1RkoNzQ2HHIW7fgiQwaOjyDx01Jc+OGJJt/oZqbm1u+Hzx+agYNHZ51C55Iwzvf0+G5Lv3CZdnwcu88owoAqsm2dyFv6+yzz275744eC/TTn/40P/3pT4tax5Zp5YMPPpjh//f7ya7vbfe4hslHpWHyW3dID530Zxk88cC8cuPn8/q8WzL65PO7/J5bcsratWuTpN3AOHjw4JZjOguUd9xxR6vvzz333Jxyyin513/911xwwQXZc889u7SmkSNHJkmHd99vT0UGyrrajpv8TatWpHb42Cz/z/8vq5+6t9W+Rb97MEky/PAzUzt8zHYfA/DwQw9m8q7u9AaArvrnf/7nfO1rX8uCBQuy8847t9l/yCGHZOedd85//ud/5tZbb81HP/rR3H333TniiCNaHTds2LD80z/9U6ZPn96ybdOmTW0C0ciRI1vdKX3jjTfmb//2b5NsfpZlc3Nz3vnOd+aLX/xiph51Sk6d9f+6/G8ZPG6/1O0+KesW/KbT45o2rE3zhnUt3696fUWSkRkyZEiSZP369W1es27d5uO3HNNVhUIhn/3sZzNnzpw88MADXb5ZZ8u1kz29VrUiA+WE0Q0pJO2OrWsG75hNq1dmp2M+lsHjpyZJ3nj0p6mpH5ph7zo5SbLD2AlZ88zDqRnScVgsJJm8585pqK/IHyEA9IktNXdNTU0aGto+bmfdunVpaGhIQ0ND9thjj1bbtlVXV9dq+4IFC/L2t7+91TH3339/q+dabj3t23333TNr1qxMmzYthUIhq9c3dpgfOlI7fEzWbec6yjce+Wlef/itav6cO96WhQsXtFTdW6rvrb388ssZNWpUp9PJjowbNy5JsmLFii6/5k9/2jxEay/kd0VFpqGG+tqMHzU0C9u5sHbw+KlZftc1qdlhcIbtf0KSZPXT92fQsJEt329Y+kIaVy5tCZztGT96qDAJAN205eHkzz77bEvw2WLNmjVZvHhxTjrppCTJO97xjiTJH//4xy6de9ddd80999zTatu73vWuVt8fdNBBOeKIIzJv3rx85StfyV/8xV+07OssP3SkceXS1HRwv0XLeacen/pxmx8VtMuO9fnOR/8syebH/YwZMya//OUv27zm0UcfzQEHHNDldWztD3/4Q5JkzJiuXdeZvPUznjx5co/esyLv8k6SY/cdm0E1bce2tTvtkob9jsmyu65J0/q2vzBN69dk2V3XpGG/Y9q9aSfZ/BypYye1vw8A6Njxxx+furq6fOc732m5fnGL6667Lo2NjTnllFOSbA5c48aNazdwtWfw4ME54YQTWn1tuTZwiylTprTcBd1evXvsvmPTvOZP2bh8cZo3vfU0l01rXm9z7NoXHsuGpc9nyMTO70LfYaddM2TCARm217vzgfed0uoZmGeccUZ+/vOfZ/HixS3b7r333syfPz8f/OAHW7Zt3LgxzzzzTKtp5ooVK7JpU+vHF23cuDFXXnll6urqcuyxx3a6rq09/vjjGTFiRPbbr2fP2K7YEduHDxuf63+xoN19o977qbxy8xfy8o/+ISOPOTc7HnxaCrV1WfPcI1n5wPUp1A/NqPd+qsNzb2pqzjmHj+9wPwDQvrFjx+ayyy7LF77whRx99NE57bTTMnTo0MybNy833XRTTjrppLz//e9vOX7atGm57bbb0tzcXPSzKGfNmpWVK1fmpZdeSpLceeedefHFF5MkF1xwQUaMGJEPHzY+M6d/Jqufujd7nPdvqd1plyTJ0tmfS90ub0/drnunpr4hG155Iat+e08GDR+TEUf8Zav3Wfbzb7R5fdJ+frj00kvzH//xHzn22GPzmc98JqtWrcpVV12VqVOn5txzz205bsmSJZk8eXI++tGPtnxk5B133JGvfvWrOfPMMzNx4sSsWLEi//7v/56nnnoqM2bMyK67tv5En87cc889ef/73+8aym3ts8uOOWrvnTPvD8vbPKC0pm5IdjlrRl6fd0uW33VNmta+sXn7kOEZdsDJGXHEX6Vmh/avWRhUU8gRe432sYsA0EPTp0/PhAkTMmvWrFxxxRVpbGzMxIkTc/nll+eSSy5JTc1bBerHP/7xzJo1Kw8//HCOPPLIot535syZWbhwYcv3W98lfs4552TEiBHZZ5cds8vw+vxhm9cOnXx01j7/WNb+8ddp3rh+86Vy73pvdjryrAxqaD0Fbd64NoXa+lYfydhRfhg3blwefPDB/MM//EM+//nPp66uLqeeemquvvrq7V4/OXXq1Lzzne/MDTfckNdeey11dXU54IAD8uMf/7jVdHN7nnnmmTz11FP55je/2eXXbKvQ3NNHopeBxSvW5IRvPJj1nTxGqLm5KZve3HzR6qAdR6VQ6PwqgPramsz97HsybtTQXl0rANC+448/Prvvvntmz57dsq1QKOSiiy7KxRdfnIaGhm7fDd2ZruSHTl//rXMybMpxGXncx1u2DXR+WL16ddauXZsLLrggd955Z1atWtWy78ILL8x///d/5/HHH+/xhLJir6FMknGjhuby7XzedqFQk9rhO6d2+M7bDZNJcsVp+wmTANCPZsyYkVtuuaXVdDFJrrrqqowZMybXXnttr75fV/JDRza8tjDNjesz/PAzWm0f6Pwwffr0jBkzJjfffHOr7cuXL8/3v//9fPWrXy3qkoKKnlBuMev+5zLz7uIfQH7RSfvm08fu3QsrAgCKMXfu3Jb/njRpUqvPwu4tlZQf5s+fn0WLFiVJamtrWz1KqTdURaBMkpsfW5Qv3fF0Gpuat/uh71sbVFNIbU0hV5y2X/7qEDfiAEA1kR+6pmoCZbL5mohLb3syDz2/LINqCp3+YmzZf9TeO2fG6VPV3ABQpeSH7auqQLnFc6+8mRsfWZT757+aRcvXtHoifiGbH1p+7KSxOefw8e7mBgCSyA+dqcpAubXV6xuzYPnqbGhsSl1tTSaMbvAJOABAp+SH1qo+UAIAUJyKfmwQAAB9T6AEAKAoAiUAAEURKAEAKIpACQBAUQRKAACKIlACAFAUgRIAgKIIlAAAFEWgBACgKAIlAABFESgBACiKQAkAQFEESgAAiiJQAgBQFIESAICiCJQAABRFoAQAoCgCJQAARREoAQAoikAJAEBRBEoAAIoiUAIAUBSBEgCAogiUAAAURaAEAKAoAiUAAEURKAEAKIpACQBAUQRKAACKIlACAFAUgRIAgKIIlAAAFEWgBACgKAIlAABFESgBACiKQAkAQFH+fySM7hPMjbprAAAAAElFTkSuQmCC" }, "metadata": {}, @@ -1127,8 +1063,8 @@ "metadata": { "collapsed": false, "ExecuteTime": { - "end_time": "2024-02-15T14:09:03.284108Z", - "start_time": "2024-02-15T14:09:03.280594Z" + "end_time": "2024-03-22T09:35:29.699585Z", + "start_time": "2024-03-22T09:35:29.695913Z" } }, "id": "3360f87d35e776de", @@ -1138,7 +1074,7 @@ "output_type": "stream", "text": [ "╒════════════════════════╤════════════════════════╤═════╕\n", - "│ 140153060450512.latent │ 140154764220544.latent │ P │\n", + "│ 140266762506144.latent │ 140266764927616.latent │ P │\n", "├────────────────────────┼────────────────────────┼─────┤\n", "│ 0 │ 0 │ 0.0 │\n", "├────────────────────────┼────────────────────────┼─────┤\n", @@ -1209,8 +1145,8 @@ "metadata": { "collapsed": false, "ExecuteTime": { - "end_time": "2024-02-15T14:09:03.290645Z", - "start_time": "2024-02-15T14:09:03.284774Z" + "end_time": "2024-03-22T09:35:29.706685Z", + "start_time": "2024-03-22T09:35:29.700409Z" } }, "id": "94a46929f1b3a3c8", @@ -1251,8 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}, "metadata": {}, @@ -2227,8 +2135,8 @@ "metadata": { "collapsed": false, "ExecuteTime": { - "end_time": "2024-02-15T14:09:03.503394Z", - "start_time": "2024-02-15T14:09:03.463725Z" + "end_time": "2024-03-22T09:35:29.857856Z", + "start_time": "2024-03-22T09:35:29.813040Z" } }, "id": "47b6c0255810eb18", @@ -3202,33 +3110,7 @@ "plotlyServerURL": "https://plot.ly" } }, - "text/html": [ - "
" - ] + "text/html": "
" }, "metadata": {}, "output_type": "display_data" @@ -3244,8 +3126,8 @@ "metadata": { "collapsed": false, "ExecuteTime": { - "end_time": "2024-02-15T14:09:03.506600Z", - "start_time": "2024-02-15T14:09:03.504572Z" + "end_time": "2024-03-22T09:35:29.861113Z", + "start_time": "2024-03-22T09:35:29.858937Z" } }, "id": "1dcc91d8a48af213", @@ -3278,15 +3160,15 @@ "print(\"Expectation:\", expanded_model.expectation(expanded_model.variables))\n", "print(\"-\" * 80)\n", "modes, ml = expanded_model.mode()\n", - "print(\"First possible Mode:\", modes[0])\n", + "print(\"First possible Mode:\", modes.events[0])\n", "print(\"-\" * 80)\n", - "print(\"Second possible Mode:\", modes[1])" + "print(\"Second possible Mode:\", modes.events[1])" ], "metadata": { "collapsed": false, "ExecuteTime": { - "end_time": "2024-02-15T14:09:03.546514Z", - "start_time": "2024-02-15T14:09:03.528317Z" + "end_time": "2024-03-22T09:35:29.890805Z", + "start_time": "2024-03-22T09:35:29.861794Z" } }, "id": "3ca976322054b8cb", @@ -3299,9 +3181,9 @@ "--------------------------------------------------------------------------------\n", "Expectation: {Continuous(x0): 0.0, Continuous(x1): 0.0, Continuous(x10): 0.0, Continuous(x11): 0.0, Continuous(x12): 0.0, Continuous(x13): 0.0, Continuous(x14): 0.0, Continuous(x15): 0.0, Continuous(x16): 0.0, Continuous(x17): 0.0, Continuous(x18): 0.0, Continuous(x19): 0.0, Continuous(x2): 0.0, Continuous(x3): 0.0, Continuous(x4): 0.0, Continuous(x5): 0.0, Continuous(x6): 0.0, Continuous(x7): 0.0, Continuous(x8): 0.0, Continuous(x9): 0.0}\n", "--------------------------------------------------------------------------------\n", - "First possible Mode: {Continuous(x19): [-1.5,-0.5], Continuous(x0): [0.5,1.5], Continuous(x16): [0.5,1.5], Continuous(x15): [-1.5,-0.5], Continuous(x3): [-1.5,-0.5], Continuous(x7): [-1.5,-0.5], Continuous(x14): [0.5,1.5], Continuous(x11): [-1.5,-0.5], Continuous(x5): [-1.5,-0.5], Continuous(x6): [0.5,1.5], Continuous(x12): [0.5,1.5], Continuous(x2): [0.5,1.5], Continuous(x4): [0.5,1.5], Continuous(x17): [-1.5,-0.5], Continuous(x8): [0.5,1.5], Continuous(x13): [-1.5,-0.5], Continuous(x10): [0.5,1.5], Continuous(x1): [-1.5,-0.5], Continuous(x9): [-1.5,-0.5], Continuous(x18): [0.5,1.5]}\n", + "First possible Mode: {Continuous(x0): [0.5,1.5], Continuous(x2): [0.5,1.5], Continuous(x14): [0.5,1.5], Continuous(x15): [-1.5,-0.5], Continuous(x16): [0.5,1.5], Continuous(x19): [-1.5,-0.5], Continuous(x12): [0.5,1.5], Continuous(x17): [-1.5,-0.5], Continuous(x11): [-1.5,-0.5], Continuous(x18): [0.5,1.5], Continuous(x8): [0.5,1.5], Continuous(x5): [-1.5,-0.5], Continuous(x1): [-1.5,-0.5], Continuous(x9): [-1.5,-0.5], Continuous(x10): [0.5,1.5], Continuous(x6): [0.5,1.5], Continuous(x4): [0.5,1.5], Continuous(x7): [-1.5,-0.5], Continuous(x13): [-1.5,-0.5], Continuous(x3): [-1.5,-0.5]}\n", "--------------------------------------------------------------------------------\n", - "Second possible Mode: {Continuous(x19): [0.5,1.5], Continuous(x0): [-1.5,-0.5], Continuous(x16): [-1.5,-0.5], Continuous(x15): [0.5,1.5], Continuous(x3): [0.5,1.5], Continuous(x7): [0.5,1.5], Continuous(x14): [-1.5,-0.5], Continuous(x11): [0.5,1.5], Continuous(x5): [0.5,1.5], Continuous(x6): [-1.5,-0.5], Continuous(x12): [-1.5,-0.5], Continuous(x2): [-1.5,-0.5], Continuous(x4): [-1.5,-0.5], Continuous(x17): [0.5,1.5], Continuous(x8): [-1.5,-0.5], Continuous(x13): [0.5,1.5], Continuous(x10): [-1.5,-0.5], Continuous(x1): [0.5,1.5], Continuous(x9): [0.5,1.5], Continuous(x18): [-1.5,-0.5]}\n" + "Second possible Mode: {Continuous(x0): [-1.5,-0.5], Continuous(x2): [-1.5,-0.5], Continuous(x14): [-1.5,-0.5], Continuous(x15): [0.5,1.5], Continuous(x16): [-1.5,-0.5], Continuous(x19): [0.5,1.5], Continuous(x12): [-1.5,-0.5], Continuous(x17): [0.5,1.5], Continuous(x11): [0.5,1.5], Continuous(x18): [-1.5,-0.5], Continuous(x8): [-1.5,-0.5], Continuous(x5): [0.5,1.5], Continuous(x1): [0.5,1.5], Continuous(x9): [0.5,1.5], Continuous(x10): [-1.5,-0.5], Continuous(x6): [-1.5,-0.5], Continuous(x4): [-1.5,-0.5], Continuous(x7): [0.5,1.5], Continuous(x13): [0.5,1.5], Continuous(x3): [0.5,1.5]}\n" ] } ], diff --git a/examples/truncated_gaussians.ipynb b/examples/truncated_gaussians.ipynb index eb98af0..1c6f228 100644 --- a/examples/truncated_gaussians.ipynb +++ b/examples/truncated_gaussians.ipynb @@ -16,19 +16,19 @@ }, { "cell_type": "code", - "execution_count": 1, + "execution_count": 2, "id": "initial_id", "metadata": { "collapsed": true, "ExecuteTime": { - "end_time": "2024-01-31T07:10:43.299152754Z", - "start_time": "2024-01-31T07:10:42.815309893Z" + "end_time": "2024-03-22T09:34:47.790874Z", + "start_time": "2024-03-22T09:34:47.691864Z" } }, "outputs": [ { "data": { - "text/html": " \n " + "text/html": " \n " }, "metadata": {}, "output_type": "display_data" @@ -41,2014 +41,2014 @@ "mode": "lines", "name": "PDF", "x": [ - -4.089632693967386, - -3.7193194204437687, - -3.7193194204437687, - -3.2475455261206365, - 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" + "text/html": "
" }, "metadata": {}, "output_type": "display_data" @@ -4960,7 +4960,7 @@ "import plotly.graph_objects as go\n", "\n", "variable = Continuous(\"x\")\n", - "distribution = GaussianDistribution(variable, mean=0, variance=1)\n", + "distribution = GaussianDistribution(variable, mean=0, scale=1)\n", "fig = go.Figure(distribution.plot())\n", "fig.update_layout(title=\"Normal Distribution\", xaxis_title=distribution.variable.name)\n", "fig.show()" @@ -4978,7 +4978,7 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": 3, "outputs": [ { "data": { @@ -4989,2013 +4989,2013 @@ "name": "PDF", "x": [ 0.425, - 0.5019691519445912, - 0.5019691519445912, - 0.5023663582129047, - 0.5038790518304297, - 0.5044798515384353, - 0.50478584153749, - 0.5048190088830519, - 0.5049131501744814, - 0.5050298774213391, - 0.5053174423718022, - 0.5054226362955778, - 0.5054743817863026, - 0.5054903365981764, - 0.5059218209797093, - 0.505961042163273, - 0.506332989764053, - 0.5070036966808613, - 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" + "text/html": "
" }, "metadata": {}, "output_type": "display_data" @@ -9907,8 +9907,8 @@ "metadata": { "collapsed": false, "ExecuteTime": { - "end_time": "2024-01-31T07:10:43.302643395Z", - "start_time": "2024-01-31T07:10:43.297997987Z" + "end_time": "2024-03-22T09:34:47.821346Z", + "start_time": "2024-03-22T09:34:47.792054Z" } }, "id": "1dac512e89a546e5" diff --git a/src/probabilistic_model/bayesian_network/distributions.py b/src/probabilistic_model/bayesian_network/distributions.py index 3078de2..297f44e 100644 --- a/src/probabilistic_model/bayesian_network/distributions.py +++ b/src/probabilistic_model/bayesian_network/distributions.py @@ -94,12 +94,9 @@ def forward_pass(self, event: ComplexEvent): # calculate the probability of said state parent_state_probability = self.parent.forward_message.likelihood(parent_state) - event_for_parent = ComplexEvent([Event({self.parent.variable: parent_state})]) # construct the conditional distribution - # conditional, current_probability = (self.conditional_probability_distributions[parent_state] - # ._conditional(event)) - - conditional, current_probability = self.conditional_probability_distributions[parent_state].conditional(event_for_parent) + conditional, current_probability = (self.conditional_probability_distributions[parent_state] + ._conditional(event)) # if the conditional is None, skip if conditional is None: diff --git a/src/probabilistic_model/distributions/distributions.py b/src/probabilistic_model/distributions/distributions.py index 2b03a46..1c33d4f 100644 --- a/src/probabilistic_model/distributions/distributions.py +++ b/src/probabilistic_model/distributions/distributions.py @@ -453,8 +453,8 @@ def __init__(self, variable: Continuous, location: float, density_cap: float = f self.density_cap = density_cap @property - def domain(self) -> Event: - return Event({self.variable: portion.singleton(self.location)}) + def domain(self) -> ComplexEvent: + return ComplexEvent([Event({self.variable: portion.singleton(self.location)})]) def _pdf(self, value: float) -> float: if value == self.location: @@ -474,8 +474,8 @@ def _probability(self, event: EncodedEvent) -> float: else: return 0 - def _mode(self): - return ComplexEvent([self.domain.encode()]), self.density_cap + def _mode(self) -> Tuple[ComplexEvent, float]: + return self.domain.encode(), self.density_cap def sample(self, amount: int) -> List[List[float]]: return [[self.location] for _ in range(amount)] diff --git a/src/probabilistic_model/distributions/gaussian.py b/src/probabilistic_model/distributions/gaussian.py index cf4da32..bc9b1b0 100644 --- a/src/probabilistic_model/distributions/gaussian.py +++ b/src/probabilistic_model/distributions/gaussian.py @@ -330,11 +330,13 @@ def robert_rejection_sample(self, amount: int) -> np.ndarray: # enforce an upper bound if it is infinite if standard_distribution.interval.upper >= float("inf"): - standard_distribution.interval = standard_distribution.interval.replace(upper=10) + standard_distribution.interval = (standard_distribution.interval. + replace(upper=standard_distribution.interval.lower + 10)) # enforce a lower bound if it is infinite if standard_distribution.interval.lower <= -float("inf"): - standard_distribution.interval = standard_distribution.interval.replace(lower=-10) + standard_distribution.interval = (standard_distribution.interval. + replace(lower=standard_distribution.interval.upper - 10)) # sample from double truncated standard normal instead samples = standard_distribution.robert_rejection_sample_from_standard_normal_with_double_truncation(amount) @@ -348,13 +350,28 @@ def robert_rejection_sample(self, amount: int) -> np.ndarray: def robert_rejection_sample_from_standard_normal_with_double_truncation(self, amount: int) -> np.ndarray: """ Use robert rejection sampling to sample from the truncated standard normal distribution. + Resamples as long as the amount of samples is not reached. :param amount: The amount of samples to generate :return: The samples """ assert self.scale == 1 and self.mean == 0 # sample from uniform distribution over this distribution's interval + accepted_samples = np.array([]) + while len(accepted_samples) < amount: + accepted_samples = np.append( + accepted_samples, + self.robert_rejection_sample_from_standard_normal_with_double_truncation_helper(amount - len(accepted_samples))) + return accepted_samples + + def robert_rejection_sample_from_standard_normal_with_double_truncation_helper(self, amount: int) -> np.ndarray: + """ + Use robert rejection sampling to sample from the truncated standard normal distribution. + :param amount: The maximum number of samples to generate. The actual number of samples can be lower due to + rejection sampling. + :return: The samples + """ uniform_samples = np.random.uniform(self.interval.lower, self.interval.upper, amount) # if the mean in the interval @@ -371,18 +388,11 @@ def robert_rejection_sample_from_standard_normal_with_double_truncation(self, am else: raise ValueError("This should never happen") - # generate standard uniform samples - different_uniform_samples = np.random.uniform(0, 1, amount) + # generate standard uniform samples as acceptance probabilities + acceptance_probabilities = np.random.uniform(0, 1, amount) # accept samples that are below the limiting function - accepted_samples = uniform_samples[different_uniform_samples <= limiting_function] - - # if any got rejected - if len(accepted_samples) < amount: - # resample the rejected samples - accepted_samples = np.concatenate( - [accepted_samples, self.robert_rejection_sample(amount - len(accepted_samples))]) - + accepted_samples = uniform_samples[acceptance_probabilities <= limiting_function] return accepted_samples def sample(self, amount: int) -> List[List[float]]: diff --git a/src/probabilistic_model/probabilistic_circuit/probabilistic_circuit.py b/src/probabilistic_model/probabilistic_circuit/probabilistic_circuit.py index ce3effb..961c6a7 100644 --- a/src/probabilistic_model/probabilistic_circuit/probabilistic_circuit.py +++ b/src/probabilistic_model/probabilistic_circuit/probabilistic_circuit.py @@ -1,11 +1,9 @@ from __future__ import annotations -import itertools import random from typing import Tuple, Iterable, TYPE_CHECKING import networkx as nx -import numpy as np import portion from random_events.events import EncodedEvent, VariableMap, Event, ComplexEvent from random_events.variables import Variable, Symbolic, Continuous @@ -195,7 +193,6 @@ def filter_variable_map_by_self(self, variable_map: VariableMap): return variable_map.__class__( {variable: value for variable, value in variable_map.items() if variable in variables}) - # @cache_inference_result def _conditional(self, event: ComplexEvent) -> Tuple[Optional[Self], float]: # skip trivial case @@ -882,6 +879,7 @@ def merge_modes_if_one_dimensional(self, modes: List[EncodedEvent]) -> List[Enco @cache_inference_result def _mode(self) -> Tuple[ComplexEvent, float]: + modes = [] likelihoods = [] @@ -893,7 +891,6 @@ def _mode(self) -> Tuple[ComplexEvent, float]: # get the most likely result maximum_likelihood = max(likelihoods) - mode_events = [] # gather all results that are maximum likely @@ -979,6 +976,14 @@ def _probability(self, event: EncodedEvent) -> float: def is_deterministic(self) -> bool: return True + def is_decomposable(self): + for index, subcircuit in enumerate(self.subcircuits): + variables = subcircuit.variables + for subcircuit_ in self.subcircuits[index+1:]: + if len(set(subcircuit_.variables).intersection(set(variables))) > 0: + return False + return True + @cache_inference_result def _mode(self) -> Tuple[ComplexEvent, float]: @@ -987,8 +992,10 @@ def _mode(self) -> Tuple[ComplexEvent, float]: # gather all modes from the children for subcircuit in self.subcircuits[1:]: + subcircuit_mode, subcircuit_likelihood = subcircuit._mode() mode = mode & subcircuit_mode + likelihood *= subcircuit_likelihood return mode, likelihood @@ -1097,27 +1104,6 @@ def __copy__(self): result.probabilistic_circuit.add_edge(result, copied_subcircuit) return result - def is_decomposable(self) -> bool: - """ - Check if only this product unit is decomposable. - - A product mode is decomposable iff all children have disjoint scopes. - - :return: if this product unit is decomposable - """ - # for every child pair - for subcircuits_a, subcircuits_b in itertools.combinations(self.subcircuits, 2): - - # form the intersection of the scopes - scope_intersection = set(subcircuits_a.variables) & set(subcircuits_b.variables) - - # if this not empty, the product unit is not decomposable - if len(scope_intersection) > 0: - return False - - # if every pairwise intersection is empty, the product unit is decomposable - return True - @cache_inference_result def simplify(self) -> Self: @@ -1234,7 +1220,6 @@ def marginal(self, variables: Iterable[Variable]) -> Optional[Self]: root.reset_result_of_current_query() return result.probabilistic_circuit - # @graph_inference_caching_wrapper def sample(self, amount: int) -> Iterable: return self.root.sample(amount) diff --git a/test/test_bayesian_network/__init__.py b/test/test_bayesian_network/__init__.py new file mode 100644 index 0000000..e69de29 diff --git a/test/test_bayesian_network/test_bayesian_network.py b/test/test_bayesian_network/test_bayesian_network.py index 6dd62ac..95c115d 100644 --- a/test/test_bayesian_network/test_bayesian_network.py +++ b/test/test_bayesian_network/test_bayesian_network.py @@ -77,16 +77,6 @@ def test_likelihood(self): likelihood = self.model.likelihood(event) self.assertEqual(likelihood, 0.3 * 0.3) - def test_probability(self): - event = Event({self.x: [1], self.y: [0, 1]}) - probability = self.model.probability(event) - self.assertEqual(probability, self.bf_distribution.probability(event)) - - def test_probability_empty_x(self): - event = Event({self.y: [1]}) - probability = self.model.probability(event) - self.assertEqual(probability, self.bf_distribution.probability(event)) - def test_as_probabilistic_circuit(self): circuit = self.model.as_probabilistic_circuit() for event in itertools.product(self.x.domain, self.y.domain): @@ -141,11 +131,6 @@ def test_likelihood(self): for event in itertools.product(*[variable.domain for variable in self.model.variables]): self.assertEqual(self.model.likelihood(event), self.bf_distribution.likelihood(event)) - def test_probability(self): - event = Event({self.x: [1], self.y: [0, 1], self.z: [0]}) - probability = self.model.probability(event) - self.assertEqual(probability, self.bf_distribution.probability(event)) - def test_as_probabilistic_circuit(self): circuit = self.model.as_probabilistic_circuit().simplify() self.assertLess(len(circuit.weighted_edges), math.prod([len(v.domain) for v in circuit.variables])) @@ -193,16 +178,11 @@ def test_likelihood(self): likelihood = self.bayesian_network.likelihood(event) self.assertEqual(likelihood, 0.3 * 0.5 * 1 / 3) - def test_probability(self): - event = Event({self.x: [0, 1], self.y: portion.closed(1.5, 2)}) - probability = self.bayesian_network.probability(event) - self.assertEqual(probability, 0.3 * 0.25) - def test_as_probabilistic_circuit(self): circuit = self.bayesian_network.as_probabilistic_circuit().simplify() self.assertEqual(circuit.probability(Event()), 1.) event = Event({self.x: [0, 1], self.y: portion.closed(1.5, 2)}) - self.assertAlmostEqual(self.bayesian_network.probability(event), circuit.probability(event)) + self.assertAlmostEqual(0.075, circuit.probability(event)) class BayesianNetworkWrongOrderTestCase(unittest.TestCase): diff --git a/test/test_bayesian_network/test_distributions.py b/test/test_bayesian_network/test_distributions.py index b777b88..3073f52 100644 --- a/test/test_bayesian_network/test_distributions.py +++ b/test/test_bayesian_network/test_distributions.py @@ -141,18 +141,13 @@ def test_forward_pass(self): self.assertEqual(self.p_x.forward_probability, 1) self.assertEqual(self.p_yzx.forward_probability, 1) - def test_probability(self): - event = Event({self.x: 0, self.y: portion.closed(0, 0.5)}) - probability = self.bayesian_network.probability(event) - self.assertEqual(probability, 0.7 * 0.5) - def test_joint_distribution_with_parent(self): event = self.bayesian_network.preprocess_event(Event()) self.bayesian_network.forward_pass(event) joint_distribution = self.p_yzx.joint_distribution_with_parent() event = Event({self.x: 0, self.y: portion.closed(0, 0.5)}) - self.assertEqual(joint_distribution.probability(event), self.bayesian_network.probability(event)) + self.assertEqual(joint_distribution.probability(event),0.35) if __name__ == '__main__': diff --git a/test/test_distributions/test_gaussian.py b/test/test_distributions/test_gaussian.py index e0e710b..06b40e6 100644 --- a/test/test_distributions/test_gaussian.py +++ b/test/test_distributions/test_gaussian.py @@ -326,6 +326,11 @@ def test_with_zero_in_bound(self): expectation = model.expectation(model.variables)[model.variable] self.assertAlmostEqual(mean, expectation, delta=0.1) + def test_with_far_right_interval(self): + model = TruncatedGaussianDistribution(self.x, portion.closed(11, float("inf")), 0, 1) + samples = model.sample(1000) + self.assertEqual(len(samples), 1000) + if __name__ == '__main__': unittest.main() diff --git a/test/test_jpt/test_jpt.py b/test/test_jpt/test_jpt.py index 5412d86..520b91c 100644 --- a/test/test_jpt/test_jpt.py +++ b/test/test_jpt/test_jpt.py @@ -121,21 +121,6 @@ def setUp(self): self.real, self.integer, self.symbol = infer_variables_from_dataframe(self.data, scale_continuous_types=False) self.model = JPT([self.real, self.integer, self.symbol]) - @unittest.skip("Scaled variables are kinda buggy.") - def test_preprocess_data(self): - preprocessed_data = self.model.preprocess_data(self.data) - mean = preprocessed_data[:, 1].mean() - std = preprocessed_data[:, 1].std(ddof=1) - self.assertEqual(self.real.mean, mean) - self.assertEqual(self.real.std, std) - - # assert that this does not throw exceptions - for variable, column in zip(self.model.variables, preprocessed_data.T): - if isinstance(variable, random_events.variables.Discrete): - variable.decode_many(column.astype(int)) - else: - variable.decode_many(column) - def test_construct_impurity(self): impurity = self.model.construct_impurity() self.assertIsInstance(impurity, Impurity) @@ -291,7 +276,7 @@ def setUpClass(cls): variables = infer_variables_from_dataframe(cls.data, scale_continuous_types=False) - cls.model = JPT(variables, min_samples_leaf=0.1) + cls.model = JPT(variables, min_samples_leaf=0.4) cls.model.fit(cls.data) def test_serialization(self): @@ -344,6 +329,10 @@ def test_marginal_conditional_chain(self): x, y = self.model.variables[:2] conditional, probability = model.conditional(Event({x: portion.closed(0, 10)})) + def test_mode(self): + mode, likelihood = self.model.mode() + self.assertGreater(len(mode.events), 0) + class MNISTTestCase(unittest.TestCase): model: JPT @@ -525,21 +514,13 @@ class MaxProblemTestCase(unittest.TestCase): model: ProbabilisticCircuit relative_x: Continuous relative_y: Continuous - event: ComplexEvent @classmethod def setUpClass(cls): - with open(os.path.join(os.path.expanduser("~"), "Documents", "move_and_pick_up.pm"), "r") as file: + with open(os.path.join(os.path.expanduser("~"), "Documents", "boob_cancer_jpt.json"), "r") as file: loaded_json = json.load(file) cls.model = ProbabilisticCircuit.from_json(loaded_json) - with open(os.path.join(os.path.expanduser("~"), "Documents", "complex.event"), "r") as file: - loaded_json = json.load(file) - cls.event = ComplexEvent.from_json(loaded_json) - cls.relative_x = [var for var in cls.model.variables if var.name == "relative_x"][0] - cls.relative_y = [var for var in cls.model.variables if var.name == "relative_y"][0] - def test_inference(self): - p_xy = self.model.marginal([self.relative_x, self.relative_y]) - conditional, probability = self.model.conditional(self.event) - print(conditional) + mode, _ = self.model.mode() + print(mode.events) \ No newline at end of file