MCCS_HGG_vs_LGG (1).ipynb

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     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import sys\n",
    "from multiprocessing import Manager, Lock, Pool, cpu_count\n",
    "import time\n",
    "\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import umap\n",
    "\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from sklearn.svm import LinearSVC\n",
    "from sklearn.metrics import matthews_corrcoef\n",
    "from sklearn.model_selection import train_test_split, GridSearchCV\n",
    "\n",
    "from bokeh.io import show\n",
    "from bokeh.palettes import Category20\n",
    "from bokeh.plotting import figure\n",
    "from bokeh.io import export_svgs\n",
    "from bokeh.io import output_notebook\n",
    "from bokeh.resources import CDN\n",
    "output_notebook()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [],
   "source": [
    "def progress(count, total, status=''):\n",
    "    \n",
    "    bar_len = 40\n",
    "    filled_len = int(round(bar_len * count / float(total)))\n",
    "\n",
    "    percents = round(100.0 * count / float(total), 1)\n",
    "    bar = '█' * filled_len + '░' * (bar_len - filled_len)\n",
    "\n",
    "    sys.stdout.write(f'\\r|{bar}| {percents}% ... {status}')\n",
    "    sys.stdout.flush()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [],
   "source": [
    "INDEX_COLUMN_NAME = 'sample'\n",
    "TUMOR_COLUMN_NAME = 'tumor'\n",
    "HGG_ROW_VALUE = 'hgg'\n",
    "LGG_ROW_VALUE = 'lgg'\n",
    "HGG_CSV = '/home/group2/marco/HGG_even_more_ficiurz.csv'\n",
    "LGG_CSV = '/home/group2/marco/LGG_lot_of_nasty_ficiurz.csv'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [],
   "source": [
    "hgg = pd.read_csv(filepath_or_buffer=HGG_CSV)\n",
    "lgg = pd.read_csv(filepath_or_buffer=LGG_CSV)\n",
    "hgg = hgg.set_index(['sample'])\n",
    "lgg = lgg.set_index(['sample'])\n",
    "surv_hgg = hgg.loc[:, 'surv']\n",
    "surv_lgg = lgg.loc[:, 'surv']\n",
    "tot_cols = len(list(hgg))\n",
    "hgg = hgg.iloc[:, 1:(tot_cols-2)]\n",
    "lgg = lgg.iloc[:, 1:(tot_cols-2)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [],
   "source": [
    "tumor_hgg = ['hgg' for _ in range(hgg.shape[0])]\n",
    "tumor_lgg = ['lgg' for _ in range(lgg.shape[0])]\n",
    "hgg = hgg.assign(tumor=tumor_hgg)\n",
    "lgg = lgg.assign(tumor=tumor_lgg)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(267, 5521)\n"
     ]
    }
   ],
   "source": [
    "alldata = pd.concat([hgg,lgg])\n",
    "alldata = alldata.replace(np.inf, np.nan)\n",
    "alldata = alldata.dropna(axis=1)\n",
    "print(alldata.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 127,
   "metadata": {},
   "outputs": [],
   "source": [
    "def calc_mcc(exps, test_size, estimator_cls, **kwargs):\n",
    "    for exp in exps:\n",
    "        x_train, x_test, y_train, y_test = train_test_split(alldata.iloc[:, :alldata.shape[1]-1], \n",
    "                                                            alldata['tumor'],\n",
    "                                                            test_size=test_size,\n",
    "                                                            random_state=exp,\n",
    "                                                            stratify=alldata['tumor'])\n",
    "        x_train, x_test = (x_train - x_train.mean(axis=0)) / x_train.var(axis=0), (x_test - x_train.mean(axis=0)) / x_test.var(axis=0)\n",
    "        classifier = estimator_cls(**kwargs)\n",
    "        classifier.fit(x_train, y_train)\n",
    "        y_pred = classifier.predict(x_test)\n",
    "        with Lock():\n",
    "            mccs_man.append(matthews_corrcoef(y_test, y_pred))\n",
    "#             features_man.append(classifier.feature_importances_)\n",
    "            features_man.append(classifier.coef_.ravel())\n",
    "            count.value += 1\n",
    "            progress(count.value, num_exps, status='({}) Calculating MCCs'.format(estimator_cls.__name__))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 128,
   "metadata": {},
   "outputs": [],
   "source": [
    "def segment(array, parts):\n",
    "    '''\n",
    "    Function to segment the array in order to utilize multiprocessing\n",
    "    '''\n",
    "    \n",
    "    avg = len(array) / parts\n",
    "    last = 0.0\n",
    "\n",
    "    while last < len(array):\n",
    "        yield array[int(last):int(last + avg)]\n",
    "        last += avg"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 129,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.svm import LinearSVC\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from sklearn.svm import SVC"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 130,
   "metadata": {},
   "outputs": [],
   "source": [
    "ESTIMATOR_CLASS = LinearSVC"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 132,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "|████████████████████████████████████████| 99.8% ... (LinearSVC) Calculating MCCs ... 87.05s\n"
     ]
    },
    {
     "data": {
      "text/html": [
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       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>0</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>1000.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>0.732013</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>0.209166</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>-0.030083</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>0.610218</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>0.808608</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>0.877058</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 0\n",
       "count  1000.000000\n",
       "mean      0.732013\n",
       "std       0.209166\n",
       "min      -0.030083\n",
       "25%       0.610218\n",
       "50%       0.808608\n",
       "75%       0.877058\n",
       "max       1.000000"
      ]
     },
     "execution_count": 132,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "num_exps = 1000\n",
    "test_size = 0.2\n",
    "\n",
    "with Manager() as manager:\n",
    "    # List of all features\n",
    "    mccs_man = manager.list()\n",
    "    features_man = manager.list()\n",
    "    \n",
    "    count = manager.Value('i', 0)\n",
    "    \n",
    "    t0 = time.time()\n",
    "\n",
    "    with Pool(cpu_count()) as pool:\n",
    "        tasks = [pool.apply_async(calc_mcc, args=(part, test_size, ESTIMATOR_CLASS), kwds={'C' : 1e-3}) \n",
    "                 for part in segment([*range(num_exps)], cpu_count())]\n",
    "        [task.get() for task in tasks]\n",
    "        mccs = [x for x in mccs_man]\n",
    "        top_features = [x for x in features_man]\n",
    "\n",
    "    print(f' ... {round(time.time() - t0, 2)}s')\n",
    "\n",
    "pd.DataFrame(mccs).describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [],
   "source": [
    "from bokeh.models import ColumnDataSource\n",
    "from bokeh.io import output_file, show\n",
    "from bokeh.layouts import widgetbox\n",
    "from bokeh.models.widgets import Slider\n",
    "from bokeh.models.callbacks import CustomJS"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 121,
   "metadata": {},
   "outputs": [
    {
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},\"y_range\":{\"id\":\"ad706182-df88-4bde-963b-cdd0064d581a\",\"type\":\"DataRange1d\"},\"y_scale\":{\"id\":\"5846e843-73da-493e-8f50-bf9ec7370f41\",\"type\":\"LinearScale\"}},\"id\":\"8279a349-4daa-4326-9f21-9858fccc2f3c\",\"subtype\":\"Figure\",\"type\":\"Plot\"},{\"attributes\":{},\"id\":\"0a9d13bb-1aa9-4a6e-b2a8-bfdcd64d4eee\",\"type\":\"BasicTickFormatter\"},{\"attributes\":{},\"id\":\"bb641bbc-8433-477c-ac62-d83442e38c4a\",\"type\":\"BasicTicker\"}],\"root_ids\":[\"8279a349-4daa-4326-9f21-9858fccc2f3c\"]},\"title\":\"Bokeh Application\",\"version\":\"0.12.16\"}};\n",
       "  var render_items = [{\"docid\":\"525868ab-10f8-4ac5-8af3-a6b9988d9df6\",\"elementid\":\"c537d9bb-9849-49dc-88bf-03b567119c61\",\"modelid\":\"8279a349-4daa-4326-9f21-9858fccc2f3c\"}];\n",
       "  root.Bokeh.embed.embed_items_notebook(docs_json, render_items);\n",
       "\n",
       "  }\n",
       "  if (root.Bokeh !== undefined) {\n",
       "    embed_document(root);\n",
       "  } else {\n",
       "    var attempts = 0;\n",
       "    var timer = setInterval(function(root) {\n",
       "      if (root.Bokeh !== undefined) {\n",
       "        embed_document(root);\n",
       "        clearInterval(timer);\n",
       "      }\n",
       "      attempts++;\n",
       "      if (attempts > 100) {\n",
       "        console.log(\"Bokeh: ERROR: Unable to run BokehJS code because BokehJS library is missing\")\n",
       "        clearInterval(timer);\n",
       "      }\n",
       "    }, 10, root)\n",
       "  }\n",
       "})(window);"
      ],
      "application/vnd.bokehjs_exec.v0+json": ""
     },
     "metadata": {
      "application/vnd.bokehjs_exec.v0+json": {
       "id": "8279a349-4daa-4326-9f21-9858fccc2f3c"
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#params\n",
    "n_neighbors = 15\n",
    "min_dist = 0.001\n",
    "n_components = 2\n",
    "metric = 'manhattan'\n",
    "mapping = umap.UMAP(n_neighbors=n_neighbors, min_dist=min_dist, n_components=n_components, metric=metric, random_state=12345)\n",
    "\n",
    "embedding = mapping.fit_transform(alldata.iloc[:, 1:5520])\n",
    "\n",
    "thecols = alldata['tumor']\n",
    "\n",
    "thecols = pd.DataFrame(thecols).replace('hgg', 'blue')\n",
    "thecols = pd.DataFrame(thecols).replace('lgg', 'yellow')\n",
    "source = ColumnDataSource(dict(x=embedding[:, 0], y= embedding[:, 1], colors= list(thecols.tumor)))\n",
    "\n",
    "\n",
    "# thecols = [choose_cols(s) for s in surv_hgg]\n",
    "p = figure(plot_width=800, plot_height=600, toolbar_location='left')\n",
    "p.circle(x='x', y='y', size=10, color='colors', source=source)\n",
    "show(p)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 143,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "5520\n"
     ]
    }
   ],
   "source": [
    "importances = top_features\n",
    "print(len(importances[0]))\n",
    "tops = [[0, i] for i in range(5520)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 144,
   "metadata": {},
   "outputs": [],
   "source": [
    "for importance in importances:\n",
    "    indices = np.argsort(importance)[::-1]\n",
    "    for f in range(5520):\n",
    "        tops[indices[f]][0] += 5520 - f"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 145,
   "metadata": {},
   "outputs": [],
   "source": [
    "tops = [*sorted(tops, key=lambda el: el[0], reverse=True)][:10]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 125,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "wavelet-HLL_gldm_DependenceNonUniformityNormalized_flair\n",
      "wavelet-LLL_gldm_LargeDependenceEmphasis_flair\n",
      "wavelet-HLL_gldm_LargeDependenceEmphasis_flair\n",
      "wavelet-HLL_gldm_DependenceNonUniformityNormalized_t2\n",
      "wavelet-HLL_glrlm_RunPercentage_flair\n",
      "wavelet-HLL_glrlm_RunLengthNonUniformityNormalized_flair\n",
      "wavelet-HLL_gldm_DependenceVariance_flair\n",
      "wavelet-LLL_glrlm_LongRunEmphasis_flair\n",
      "wavelet-LLL_glrlm_ShortRunEmphasis_flair\n",
      "wavelet-LLL_glrlm_RunPercentage_flair\n"
     ]
    }
   ],
   "source": [
    "for couple in tops:\n",
    "    print(alldata.columns[couple[1]])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 146,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "wavelet-LLL_glszm_GrayLevelNonUniformityNormalized_t2\n",
      "wavelet-LLL_glszm_GrayLevelNonUniformityNormalized_flair\n",
      "wavelet-LLL_glszm_SmallAreaLowGrayLevelEmphasis_t1\n",
      "wavelet-LLL_glszm_SmallAreaLowGrayLevelEmphasis_t1ce\n",
      "wavelet-LLL_glszm_SmallAreaLowGrayLevelEmphasis_flair\n",
      "log-sigma-1-0-mm-3D_glcm_Idmn_flair\n",
      "wavelet-LLL_glrlm_ShortRunLowGrayLevelEmphasis_t1\n",
      "wavelet-LLL_glszm_LowGrayLevelZoneEmphasis_flair\n",
      "wavelet-HLL_glrlm_ShortRunLowGrayLevelEmphasis_flair\n",
      "wavelet-HLL_glszm_SmallAreaLowGrayLevelEmphasis_flair\n"
     ]
    }
   ],
   "source": [
    "for couple in tops:\n",
    "    print(alldata.columns[couple[1]])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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