esempio.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "outputs": [],
   "source": [
    "import numpy\n",
    "import torch\n",
    "\n",
    "import mobster as mb\n",
    "\n",
    "import numpy as np\n"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "outputs": [],
   "source": [
    "data = mb.load_example_data(\"./\")"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Running MOBSTER on 1 karyotypes with 1 subclones.\n",
      "Fitting a model with tail\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "ELBO: 2438.316337149  :   1%|          | 11/1000 [00:00<00:09, 99.52it/s]"
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      "ELBO: 1899.965920916  :   2%|▏         | 22/1000 [00:00<00:09, 105.24it/s]"
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      " 1.37761352e-03 7.64516437e-05]\n",
      "[7.09224678e-04 1.49216965e-03 6.00677708e-04 3.07112036e-03\n",
      " 4.13931686e-04 8.29184600e-04 2.99106023e-04 7.52505683e-05\n",
      " 2.34594098e-04 2.66533774e-04]\n",
      "[1.29895948e-03 2.00588860e-03 1.19994305e-03 3.42182251e-03\n",
      " 1.02601534e-05 2.26587078e-04 8.19380341e-05 2.53361247e-04\n",
      " 7.90390657e-04 5.74100349e-04]\n",
      "[0.00050575 0.00113647 0.00173611 0.00241065 0.00035121 0.00117173\n",
      " 0.00042288 0.00054826 0.00170903 0.00084995]\n",
      "[1.11313484e-03 3.57748735e-04 9.01713541e-04 2.82606984e-03\n",
      " 4.41468658e-05 8.61718870e-05 3.10558729e-05 1.72824001e-04\n",
      " 5.37085181e-04 1.09739626e-03]\n",
      "[0.0016573  0.00033924 0.00015356 0.00187788 0.00039855 0.00088756\n",
      " 0.00031979 0.0001645  0.00051091 0.00065318]\n",
      "[0.00214459 0.00035432 0.00051701 0.00234493 0.00071577 0.00175949\n",
      " 0.00063476 0.00046609 0.00144917 0.00025437]\n",
      "[0.0012705  0.00097612 0.00111837 0.00276253 0.00028554 0.00061894\n",
      " 0.00022384 0.00010054 0.00031345 0.00010364]\n",
      "[0.00179411 0.00022008 0.0016569  0.00182557 0.00061224 0.00151287\n",
      " 0.00054771 0.00040678 0.00126787 0.00042403]\n",
      "[9.59137600e-04 4.56722997e-04 8.36255931e-04 2.29342238e-03\n",
      " 1.94346308e-04 4.03202645e-04 1.46120944e-04 4.93419072e-05\n",
      " 1.54239637e-04 7.10973581e-04]\n",
      "[0.00151142 0.00024273 0.00010157 0.00140684 0.00017954 0.00058973\n",
      " 0.00021397 0.00027046 0.00084467 0.00030859]\n",
      "[0.00070835 0.00086992 0.00073889 0.00061163 0.00051462 0.00147884\n",
      " 0.0005362  0.00055776 0.0017396  0.00060556]\n",
      "[1.28308917e-03 1.30650386e-04 1.76906276e-05 1.01386312e-04\n",
      " 8.14889064e-04 2.27587785e-03 8.23595383e-04 8.16154231e-04\n",
      " 2.54009942e-03 8.70748938e-04]\n",
      "[0.00050664 0.0007661  0.00066015 0.00074053 0.00038033 0.0010995\n",
      " 0.00039662 0.00042012 0.00130299 0.00045481]\n",
      "[1.88739178e-04 4.07257458e-05 5.00368765e-05 1.69686872e-05\n",
      " 6.92744454e-04 1.93168008e-03 6.95951547e-04 6.91865151e-04\n",
      " 2.14250689e-03 8.26834818e-05]\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "ELBO: 1847.910509886  :  89%|████████▉ | 889/1000 [00:06<00:00, 137.02it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0.00047271 0.00068217 0.00068642 0.00066156 0.0009734  0.00267802\n",
      " 0.00096202 0.00093688 0.0028931  0.00025051]\n",
      "[2.16211877e-04 1.25696519e-03 2.33782818e-05 1.23940649e-03\n",
      " 1.22481639e-03 3.34749141e-03 1.19825329e-03 1.15835895e-03\n",
      " 3.56287201e-03 5.49040412e-04]\n",
      "[0.00044505 0.0004858  0.00061738 0.00046901 0.00145091 0.00394872\n",
      " 0.0014071  0.00135889 0.00416014 0.00016984]\n",
      "[2.38070786e-04 1.07754688e-03 8.25048406e-05 1.06342047e-03\n",
      " 9.54829465e-04 2.61277218e-03 9.26062047e-04 9.09403029e-04\n",
      " 2.76864883e-03 4.75322924e-04]\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "ELBO: 2040.178069719  :  90%|█████████ | 903/1000 [00:06<00:00, 132.44it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0.00042243 0.00160799 0.00056105 0.00031406 0.00050924 0.00141052\n",
      " 0.00049829 0.00050318 0.00152637 0.00010469]\n",
      "[0.0002554  0.00208281 0.00113759 0.00092122 0.00010908 0.00220367\n",
      " 0.00077681 0.0007678  0.00232424 0.00041566]\n",
      "[8.62097463e-04 1.23246355e-03 1.65324202e-03 1.46520631e-03\n",
      " 4.45517020e-04 2.91457453e-03 1.02439604e-03 1.00637639e-03\n",
      " 3.03731115e-03 5.24023640e-05]\n",
      "[0.00140521 0.00174241 0.00211489 0.00195244 0.00074701 0.00355281\n",
      " 0.00124368 0.0012221  0.00367367 0.0002731 ]\n",
      "[0.00189115 0.00093008 0.00252737 0.0023887  0.00101742 0.00412593\n",
      " 0.00143743 0.00141773 0.00424026 0.00056456]\n",
      "[0.00232581 0.00146803 0.00164282 0.00151055 0.00056851 0.0027797\n",
      " 0.00096315 0.00096123 0.0028592  0.00018816]\n",
      "[0.00271448 0.00068663 0.00210111 0.00072409 0.00016573 0.00342844\n",
      " 0.00118335 0.00118259 0.00350369 0.00014877]\n",
      "[0.00181316 0.00124656 0.00126233 0.00128069 0.00019555 0.00215349\n",
      " 0.00073997 0.00074811 0.00220638 0.00045049]\n",
      "[1.00464734e-03 4.90774314e-04 5.10117327e-04 1.77942543e-03\n",
      " 5.19056003e-04 1.00712074e-03 3.44984848e-04 3.56244885e-04\n",
      " 1.04726970e-03 8.83732552e-05]\n",
      "[2.79405547e-04 1.85728254e-04 1.64519108e-04 9.69685773e-04\n",
      " 8.08548735e-04 2.24471904e-05 7.67968544e-06 3.67558401e-06\n",
      " 1.10271768e-05 2.36408765e-04]\n",
      "Computing cluster assignements.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Computing information criteria.\n",
      "Done!\n",
      "\n"
     ]
    }
   ],
   "source": [
    "import pyro\n",
    "\n",
    "pyro.enable_validation(True)\n",
    "fitted = mb.fit_mobster(data,1,1,purity = 0.8,  truncated_pareto=False, max_it = 1000, lr = 0.01,  e = 0.001, lrd_gamma = 0.1,\n",
    "        number_of_trials_k= 1000, prior_lims_k=[100,100000], prior_lims_clonal=[100,100000.], number_of_trials_clonal_mean= 1000)\n"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "outputs": [],
   "source": [
    "\n",
    "\n",
    "mb.plot_results(data, fitted, output= \"cane.png\", drivers=None)"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "outputs": [
    {
     "data": {
      "text/plain": "{'cluster_probs': array([[0.03497262, 0.07140464, 0.03612739, ..., 0.03704995, 0.03174392,\n         0.03550312],\n        [0.80902725, 0.599062  , 0.8021555 , ..., 0.800182  , 0.82919   ,\n         0.80891657],\n        [0.10563455, 0.30697507, 0.11295464, ..., 0.1146294 , 0.08134615,\n         0.10511845],\n        [0.019469  , 0.00662082, 0.01864247, ..., 0.01829935, 0.02353621,\n         0.01952583],\n        [0.03089677, 0.01593747, 0.03011987, ..., 0.02983923, 0.03418358,\n         0.03093594]], dtype=float32),\n 'cluster_assignments': array(['C1', 'C1', 'C1', ..., 'C1', 'C1', 'C1'], dtype=object),\n 'cluster_types': array(['Tail', 'C1', 'C2', 'S1', 'S2'], dtype='<U4'),\n 'mixture_probs': array([0.04556274, 0.7288686 , 0.15989874, 0.03283497, 0.03283497],\n       dtype=float32),\n 'beta_concentration1': array([ 793.2617 , 1581.5739 ,  345.21948,  585.0272 ], dtype=float32),\n 'beta_concentration2': array([1809.1179, 1020.8057, 2257.1602, 2017.3524], dtype=float32),\n 'ccf_subclones': array([0.49745739, 0.84301764], dtype=float32),\n 'dispersion_noise': tensor([0.0530, 0.0551, 0.0485, 0.0473], grad_fn=<MulBackward0>),\n 'tail_shape': array(0.6459293, dtype=float32),\n 'tail_scale': 0.05,\n 'tail_noise': 0.017747394800188804}"
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fitted[\"model_parameters\"][\"2:1\"]"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "outputs": [],
   "source": [
    "import tkinter\n",
    "import matplotlib\n",
    "import matplotlib.pyplot as plt\n",
    "matplotlib.use('TkAgg')\n",
    "losses = fitted[\"loss\"]\n",
    "\n",
    "plt.plot(losses)\n",
    "plt.title(\"ELBO\")\n",
    "plt.xlabel(\"step\")\n",
    "plt.ylabel(\"loss\")\n",
    "plt.show()"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "outputs": [
    {
     "data": {
      "text/plain": "(array(['C1', 'S1', 'S2', 'Tail'], dtype=object),\n array([36110,   397,   333,   217]))"
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import numpy\n",
    "numpy.unique(fitted[\"model_parameters\"][\"1:1\"][\"cluster_assignments\"],return_counts=True)\n"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "outputs": [
    {
     "ename": "KeyError",
     "evalue": "'param_weights_2'",
     "output_type": "error",
     "traceback": [
      "\u001B[0;31m---------------------------------------------------------------------------\u001B[0m",
      "\u001B[0;31mKeyError\u001B[0m                                  Traceback (most recent call last)",
      "\u001B[0;32m/var/folders/36/hkmr20bj0b73djsjg63qpp4h0000gp/T/ipykernel_41390/1601396709.py\u001B[0m in \u001B[0;36m<module>\u001B[0;34m\u001B[0m\n\u001B[0;32m----> 1\u001B[0;31m \u001B[0mpyro\u001B[0m\u001B[0;34m.\u001B[0m\u001B[0mparam\u001B[0m\u001B[0;34m(\u001B[0m\u001B[0;34m\"param_weights_2\"\u001B[0m\u001B[0;34m)\u001B[0m\u001B[0;34m.\u001B[0m\u001B[0mdetach\u001B[0m\u001B[0;34m(\u001B[0m\u001B[0;34m)\u001B[0m\u001B[0;34m.\u001B[0m\u001B[0mtolist\u001B[0m\u001B[0;34m(\u001B[0m\u001B[0;34m)\u001B[0m\u001B[0;34m\u001B[0m\u001B[0;34m\u001B[0m\u001B[0m\n\u001B[0m\u001B[1;32m      2\u001B[0m \u001B[0;34m\u001B[0m\u001B[0m\n\u001B[1;32m      3\u001B[0m \u001B[0;34m\u001B[0m\u001B[0m\n",
      "\u001B[0;32m~/miniforge3/envs/HMOBSTER/lib/python3.9/site-packages/pyro/primitives.py\u001B[0m in \u001B[0;36mparam\u001B[0;34m(name, init_tensor, constraint, event_dim)\u001B[0m\n\u001B[1;32m     76\u001B[0m     \u001B[0;31m# Note effectful(-) requires the double passing of name below.\u001B[0m\u001B[0;34m\u001B[0m\u001B[0;34m\u001B[0m\u001B[0m\n\u001B[1;32m     77\u001B[0m     \u001B[0margs\u001B[0m \u001B[0;34m=\u001B[0m \u001B[0;34m(\u001B[0m\u001B[0mname\u001B[0m\u001B[0;34m,\u001B[0m\u001B[0;34m)\u001B[0m \u001B[0;32mif\u001B[0m \u001B[0minit_tensor\u001B[0m \u001B[0;32mis\u001B[0m \u001B[0;32mNone\u001B[0m \u001B[0;32melse\u001B[0m \u001B[0;34m(\u001B[0m\u001B[0mname\u001B[0m\u001B[0;34m,\u001B[0m \u001B[0minit_tensor\u001B[0m\u001B[0;34m)\u001B[0m\u001B[0;34m\u001B[0m\u001B[0;34m\u001B[0m\u001B[0m\n\u001B[0;32m---> 78\u001B[0;31m     \u001B[0;32mreturn\u001B[0m \u001B[0m_param\u001B[0m\u001B[0;34m(\u001B[0m\u001B[0;34m*\u001B[0m\u001B[0margs\u001B[0m\u001B[0;34m,\u001B[0m \u001B[0mconstraint\u001B[0m\u001B[0;34m=\u001B[0m\u001B[0mconstraint\u001B[0m\u001B[0;34m,\u001B[0m \u001B[0mevent_dim\u001B[0m\u001B[0;34m=\u001B[0m\u001B[0mevent_dim\u001B[0m\u001B[0;34m,\u001B[0m \u001B[0mname\u001B[0m\u001B[0;34m=\u001B[0m\u001B[0mname\u001B[0m\u001B[0;34m)\u001B[0m\u001B[0;34m\u001B[0m\u001B[0;34m\u001B[0m\u001B[0m\n\u001B[0m\u001B[1;32m     79\u001B[0m \u001B[0;34m\u001B[0m\u001B[0m\n\u001B[1;32m     80\u001B[0m \u001B[0;34m\u001B[0m\u001B[0m\n",
      "\u001B[0;32m~/miniforge3/envs/HMOBSTER/lib/python3.9/site-packages/pyro/poutine/runtime.py\u001B[0m in \u001B[0;36m_fn\u001B[0;34m(*args, **kwargs)\u001B[0m\n\u001B[1;32m    261\u001B[0m \u001B[0;34m\u001B[0m\u001B[0m\n\u001B[1;32m    262\u001B[0m         \u001B[0;32mif\u001B[0m \u001B[0;32mnot\u001B[0m \u001B[0mam_i_wrapped\u001B[0m\u001B[0;34m(\u001B[0m\u001B[0;34m)\u001B[0m\u001B[0;34m:\u001B[0m\u001B[0;34m\u001B[0m\u001B[0;34m\u001B[0m\u001B[0m\n\u001B[0;32m--> 263\u001B[0;31m             \u001B[0;32mreturn\u001B[0m \u001B[0mfn\u001B[0m\u001B[0;34m(\u001B[0m\u001B[0;34m*\u001B[0m\u001B[0margs\u001B[0m\u001B[0;34m,\u001B[0m \u001B[0;34m**\u001B[0m\u001B[0mkwargs\u001B[0m\u001B[0;34m)\u001B[0m\u001B[0;34m\u001B[0m\u001B[0;34m\u001B[0m\u001B[0m\n\u001B[0m\u001B[1;32m    264\u001B[0m         \u001B[0;32melse\u001B[0m\u001B[0;34m:\u001B[0m\u001B[0;34m\u001B[0m\u001B[0;34m\u001B[0m\u001B[0m\n\u001B[1;32m    265\u001B[0m             msg = {\n",
      "\u001B[0;32m~/miniforge3/envs/HMOBSTER/lib/python3.9/site-packages/pyro/params/param_store.py\u001B[0m in \u001B[0;36mget_param\u001B[0;34m(self, name, init_tensor, constraint, event_dim)\u001B[0m\n\u001B[1;32m    212\u001B[0m         \"\"\"\n\u001B[1;32m    213\u001B[0m         \u001B[0;32mif\u001B[0m \u001B[0minit_tensor\u001B[0m \u001B[0;32mis\u001B[0m \u001B[0;32mNone\u001B[0m\u001B[0;34m:\u001B[0m\u001B[0;34m\u001B[0m\u001B[0;34m\u001B[0m\u001B[0m\n\u001B[0;32m--> 214\u001B[0;31m             \u001B[0;32mreturn\u001B[0m \u001B[0mself\u001B[0m\u001B[0;34m[\u001B[0m\u001B[0mname\u001B[0m\u001B[0;34m]\u001B[0m\u001B[0;34m\u001B[0m\u001B[0;34m\u001B[0m\u001B[0m\n\u001B[0m\u001B[1;32m    215\u001B[0m         \u001B[0;32melse\u001B[0m\u001B[0;34m:\u001B[0m\u001B[0;34m\u001B[0m\u001B[0;34m\u001B[0m\u001B[0m\n\u001B[1;32m    216\u001B[0m             \u001B[0;32mreturn\u001B[0m \u001B[0mself\u001B[0m\u001B[0;34m.\u001B[0m\u001B[0msetdefault\u001B[0m\u001B[0;34m(\u001B[0m\u001B[0mname\u001B[0m\u001B[0;34m,\u001B[0m \u001B[0minit_tensor\u001B[0m\u001B[0;34m,\u001B[0m \u001B[0mconstraint\u001B[0m\u001B[0;34m)\u001B[0m\u001B[0;34m\u001B[0m\u001B[0;34m\u001B[0m\u001B[0m\n",
      "\u001B[0;32m~/miniforge3/envs/HMOBSTER/lib/python3.9/site-packages/pyro/params/param_store.py\u001B[0m in \u001B[0;36m__getitem__\u001B[0;34m(self, name)\u001B[0m\n\u001B[1;32m    103\u001B[0m         \u001B[0mGet\u001B[0m \u001B[0mthe\u001B[0m \u001B[0;34m*\u001B[0m\u001B[0mconstrained\u001B[0m\u001B[0;34m*\u001B[0m \u001B[0mvalue\u001B[0m \u001B[0mof\u001B[0m \u001B[0ma\u001B[0m \u001B[0mnamed\u001B[0m \u001B[0mparameter\u001B[0m\u001B[0;34m.\u001B[0m\u001B[0;34m\u001B[0m\u001B[0;34m\u001B[0m\u001B[0m\n\u001B[1;32m    104\u001B[0m         \"\"\"\n\u001B[0;32m--> 105\u001B[0;31m         \u001B[0munconstrained_value\u001B[0m \u001B[0;34m=\u001B[0m \u001B[0mself\u001B[0m\u001B[0;34m.\u001B[0m\u001B[0m_params\u001B[0m\u001B[0;34m[\u001B[0m\u001B[0mname\u001B[0m\u001B[0;34m]\u001B[0m\u001B[0;34m\u001B[0m\u001B[0;34m\u001B[0m\u001B[0m\n\u001B[0m\u001B[1;32m    106\u001B[0m \u001B[0;34m\u001B[0m\u001B[0m\n\u001B[1;32m    107\u001B[0m         \u001B[0;31m# compute the constrained value\u001B[0m\u001B[0;34m\u001B[0m\u001B[0;34m\u001B[0m\u001B[0m\n",
      "\u001B[0;31mKeyError\u001B[0m: 'param_weights_2'"
     ]
    }
   ],
   "source": [
    "pyro.param(\"param_weights_2\").detach().tolist()\n"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "outputs": [],
   "source": [
    "pars = retrieve_params()\n",
    "\n",
    "pars = list(flatten([value.detach().tolist() for key, value in pars.items() if key.find(\"weights\") >= 0]))"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "outputs": [],
   "source": [
    "\n",
    "def retrieve_params():\n",
    "    param_names = pyro.get_param_store()\n",
    "    res = {nms: pyro.param(nms) for nms in param_names}\n",
    "    return res"
   ],
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   "cell_type": "code",
   "execution_count": 47,
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    {
     "data": {
      "text/plain": "[0.480760395526886, 0.5192395448684692, 1.0]"
     },
     "execution_count": 47,
     "metadata": {},
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   "execution_count": 42,
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