2-analysis_I_II.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pylab as plt\n",
    "\n",
    "import pickle\n",
    "import warnings\n",
    "warnings.filterwarnings('ignore')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "RANDOM_STATE=0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "dat_cits = pd.read_csv(\"data/ml_data.csv\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# STASTICAL ANALYSIS"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "from scipy.stats import (shapiro, chi2_contingency, mannwhitneyu, kruskal, ttest_ind, spearmanr)\n",
    "from statsmodels.graphics.gofplots import ProbPlot\n",
    "from statsmodels.stats.multitest import multipletests\n",
    "from functions import (bootci, delta)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " Shapiro-wilk test W = 0.479 and p = 0.000000 for variable GM-CSF\n",
      " Shapiro-wilk test W = 0.662 and p = 0.000000 for variable IL-4\n",
      " Shapiro-wilk test W = 0.542 and p = 0.000000 for variable IL-6\n",
      " Shapiro-wilk test W = 0.676 and p = 0.000000 for variable IL-10\n",
      " Shapiro-wilk test W = 0.691 and p = 0.000000 for variable IL-17A\n",
      " Shapiro-wilk test W = 0.826 and p = 0.000000 for variable IFNg\n",
      " Shapiro-wilk test W = 0.844 and p = 0.000000 for variable TGFb\n",
      " Shapiro-wilk test W = 0.837 and p = 0.000000 for variable Dermicidina\n",
      " Shapiro-wilk test W = 0.970 and p = 0.000003 for variable AGE\n"
     ]
    }
   ],
   "source": [
    "# Check normality of variables\n",
    "for var in ['GM-CSF', 'IL-4', u'IL-6', u'IL-10', 'IL-17A', 'IFNg',  'TGFb', 'Dermicidina', 'AGE']:\n",
    "    W, p = shapiro(dat_cits.loc[:, var].dropna())\n",
    "    print(\" Shapiro-wilk test W = %.3f and p = %f for variable %s\" % (W, p, var))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x720 with 9 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, axs = plt.subplots(ncols=3, nrows=3, figsize= (10, 10))\n",
    "axs = axs.flatten()\n",
    "for ii, var in enumerate(['GM-CSF', 'IL-4', u'IL-6', \n",
    "                          u'IL-10', 'IL-17A', 'IFNg',  \n",
    "                          'TGFb', 'Dermicidina', 'AGE']): \n",
    "    foo=ProbPlot(data=dat_cits.loc[:,var].dropna().values.astype(float), fit=True)\n",
    "    foo.ppplot(line='45', ax=axs[ii], fmt='o', linestyle = '-', markersize=1, \n",
    "               ylabel=\"GAUSSIAN\", xlabel = \"observed\")\n",
    "    axs[ii].set_title(var)\n",
    "plt.tight_layout()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Metastasis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "median disease-free group = 121.387 and median metastasis group = 131.550\n",
      "median disease-free group = 31.960 and median metastasis group = 62.270\n",
      "median disease-free group = 3.290 and median metastasis group = 4.710\n",
      "median disease-free group = 11.230 and median metastasis group = 10.034\n",
      "median disease-free group = 17.880 and median metastasis group = 20.092\n",
      "median disease-free group = 17.230 and median metastasis group = 22.400\n",
      "median disease-free group = 49.713 and median metastasis group = 48.189\n",
      "median disease-free group = 4786.926 and median metastasis group = 4383.755\n",
      "median disease-free group = 53.000 and median metastasis group = 60.000\n"
     ]
    }
   ],
   "source": [
    "for var in ['GM-CSF', 'IL-4', u'IL-6', u'IL-10', 'IL-17A', 'IFNg',  'TGFb', 'Dermicidina', 'AGE']:\n",
    "    free = dat_cits[dat_cits.loc[:,'EVOL_METASTASIS']==0].loc[:,var].dropna().values\n",
    "    met  = dat_cits[dat_cits.loc[:,'EVOL_METASTASIS']==1].loc[:,var].dropna().values\n",
    "    print('median disease-free group = %.3f'\n",
    "          ' and median metastasis group = %.3f' %(np.median(free), np.median(met)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "median of GM-CSF in disease-free group = 121.387, 95% CI [103.170, 138.630]\n",
      "median of GM-CSF in metastasis group = 131.550, 95% CI [101.010, 153.240]\n",
      " \n",
      "median of IL-4 in disease-free group = 31.960, 95% CI [27.760, 38.060]\n",
      "median of IL-4 in metastasis group = 62.270, 95% CI [39.060, 92.210]\n",
      " \n",
      "median of IL-6 in disease-free group = 3.290, 95% CI [2.820, 3.870]\n",
      "median of IL-6 in metastasis group = 4.710, 95% CI [3.310, 5.900]\n",
      " \n",
      "median of IL-10 in disease-free group = 11.230, 95% CI [7.890, 14.828]\n",
      "median of IL-10 in metastasis group = 10.034, 95% CI [7.784, 15.310]\n",
      " \n",
      "median of IL-17A in disease-free group = 17.880, 95% CI [16.330, 20.060]\n",
      "median of IL-17A in metastasis group = 20.092, 95% CI [14.890, 24.330]\n",
      " \n",
      "median of IFNg in disease-free group = 17.230, 95% CI [14.840, 19.340]\n",
      "median of IFNg in metastasis group = 22.400, 95% CI [16.990, 26.910]\n",
      " \n",
      "median of TGFb in disease-free group = 49.713, 95% CI [45.319, 53.680]\n",
      "median of TGFb in metastasis group = 48.189, 95% CI [40.135, 60.439]\n",
      " \n",
      "median of Dermicidina in disease-free group = 4786.926, 95% CI [4395.193, 5018.089]\n",
      "median of Dermicidina in metastasis group = 4383.755, 95% CI [3922.372, 4848.096]\n",
      " \n"
     ]
    }
   ],
   "source": [
    "np.random.seed(RANDOM_STATE)\n",
    "for var in ['GM-CSF', 'IL-4', u'IL-6', u'IL-10', 'IL-17A', 'IFNg', 'TGFb', 'Dermicidina']:\n",
    "\n",
    "    case = dat_cits[dat_cits['EVOL_METASTASIS']==0].loc[:,var].dropna().values\n",
    "    l, u = bootci(case, stat=np.median)\n",
    "    print(('median of %s in disease-free group = %.3f, 95%% CI [%.3f, %.3f]') % (var,\n",
    "                                                                                   np.median(case),\n",
    "                                                                                   l, u))\n",
    "\n",
    "    case = dat_cits[dat_cits['EVOL_METASTASIS']==1].loc[:,var].dropna().values\n",
    "    l, u = bootci(case, stat=np.median)\n",
    "    print(('median of %s in metastasis group = %.3f, 95%% CI [%.3f, %.3f]') % (var,\n",
    "                                                                                 np.median(case),\n",
    "                                                                                 l, u))\n",
    "    \n",
    "    print(\" \")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "GM-CSF  FDR p-val corrected = 0.720 and Cliff delta = 0.028\n",
      "IL-4  FDR p-val corrected = 0.001 and Cliff delta = 0.300\n",
      "IL-6  FDR p-val corrected = 0.044 and Cliff delta = 0.201\n",
      "IL-10  FDR p-val corrected = 0.672 and Cliff delta = 0.045\n",
      "IL-17A  FDR p-val corrected = 0.672 and Cliff delta = 0.045\n",
      "IFNg  FDR p-val corrected = 0.377 and Cliff delta = 0.116\n",
      "TGFb  FDR p-val corrected = 0.672 and Cliff delta = 0.042\n",
      "Dermicidina  FDR p-val corrected = 0.672 and Cliff delta = 0.071\n"
     ]
    }
   ],
   "source": [
    "pvals = []\n",
    "cliffs = []\n",
    "for var in ['GM-CSF', 'IL-4', u'IL-6', u'IL-10', 'IL-17A', 'IFNg',  'TGFb', 'Dermicidina']:\n",
    "    free = dat_cits[dat_cits.loc[:,'EVOL_METASTASIS']==0].loc[:,var].dropna().values\n",
    "    met  = dat_cits[dat_cits.loc[:,'EVOL_METASTASIS']==1].loc[:,var].dropna().values\n",
    "    u, p = mannwhitneyu(free, met, alternative='two-sided')\n",
    "    pvals.append(p)\n",
    "    N1N2=float(len(free)*len(met))\n",
    "    delta = 1.0 - 2*u/N1N2\n",
    "    cliffs.append(delta)\n",
    "\n",
    "_, pvals_fdr, _, _ = multipletests(pvals, method=\"fdr_bh\")    \n",
    "for ii, var in enumerate(['GM-CSF', 'IL-4', u'IL-6', u'IL-10', 'IL-17A', 'IFNg',  'TGFb', 'Dermicidina']):\n",
    "    print( '%s  FDR p-val corrected = %.3f and Cliff delta = %.3f' % (var, pvals_fdr[ii], abs(cliffs[ii])))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Mean age for disease group = 54.142 and metastasis  = 58.893 (t = -2.272, p = 0.024)\n",
      "Metastasis vs Sex (chi2 = 0.011 and p = 0.915)\n"
     ]
    }
   ],
   "source": [
    "# AGE and SEX\n",
    "free_age = dat_cits[dat_cits.loc[:,'EVOL_METASTASIS']==0].loc[:,'AGE'].dropna().values\n",
    "met_age = dat_cits[dat_cits.loc[:,'EVOL_METASTASIS']==1].loc[:,'AGE'].dropna().values\n",
    "t, p = ttest_ind(free_age,met_age)\n",
    "print('Mean age for disease group = %.3f and metastasis  = %.3f'\n",
    "      ' (t = %.3f, p = %.3f)' % (np.mean(free_age), np.mean(met_age), t, p))\n",
    "\n",
    "sex_free_counts = dat_cits[dat_cits.loc[:,'EVOL_METASTASIS']==0].loc[:,'SEX'].dropna().value_counts().values\n",
    "sex_met_counts = dat_cits[dat_cits.loc[:,'EVOL_METASTASIS']==1].loc[:,'SEX'].dropna().value_counts().values\n",
    "chi2, p, _,_ = chi2_contingency([sex_free_counts,\n",
    "                                 sex_met_counts])\n",
    "\n",
    "print((\"Metastasis vs Sex (chi2 = %.3f and p = %.3f)\") % (chi2, p))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Age"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Sperman correlation between Age and GM-CSF r = -0.257, p = 0.000\n",
      "Sperman correlation between Age and IL-4 r = -0.119, p = 0.037\n",
      "Sperman correlation between Age and IL-6 r = -0.049, p = 0.390\n",
      "Sperman correlation between Age and IL-10 r = -0.122, p = 0.033\n",
      "Sperman correlation between Age and IL-17A r = -0.132, p = 0.022\n",
      "Sperman correlation between Age and IFNg r = -0.128, p = 0.025\n",
      "Sperman correlation between Age and TGFb r = -0.121, p = 0.033\n",
      "Sperman correlation between Age and Dermicidina r = -0.026, p = 0.669\n"
     ]
    }
   ],
   "source": [
    "# Spearman correlation with age\n",
    "for var in ['GM-CSF', 'IL-4', u'IL-6', u'IL-10', 'IL-17A', 'IFNg',  'TGFb', 'Dermicidina']:\n",
    "    temp_df = dat_cits.loc[:, [var, 'AGE']].dropna()\n",
    "    a = temp_df.loc[:,var].values\n",
    "    b = temp_df.loc[:,'AGE'].values\n",
    "    r, p = spearmanr(a,b)\n",
    "    print((\"Sperman correlation between Age and %s r = %.3f, p = %.3f\") % (var, r, p))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Sex"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Male median = 106.855 and Female median = 137.865 between sexs for GM-CSF (p = 0.005)\n",
      "Male median = 29.290 and Female median = 39.700 between sexs for IL-4 (p = 0.010)\n",
      "Male median = 2.895 and Female median = 4.110 between sexs for IL-6 (p = 0.010)\n",
      "Male median = 9.025 and Female median = 13.136 between sexs for IL-10 (p = 0.036)\n",
      "Male median = 15.925 and Female median = 19.671 between sexs for IL-17A (p = 0.025)\n",
      "Male median = 14.025 and Female median = 19.760 between sexs for IFNg (p = 0.001)\n",
      "Male median = 46.042 and Female median = 51.434 between sexs for TGFb (p = 0.101)\n",
      "Male median = 4953.067 and Female median = 4263.560 between sexs for Dermicidina (p = 0.001)\n"
     ]
    }
   ],
   "source": [
    "# SEX\n",
    "for var in ['GM-CSF', 'IL-4', u'IL-6', u'IL-10', 'IL-17A', 'IFNg',  'TGFb', 'Dermicidina']:\n",
    "    temp_df = dat_cits.loc[:, [var, 'SEX']].dropna()\n",
    "    a = temp_df[temp_df['SEX'] ==0][var].values\n",
    "    b = temp_df[temp_df['SEX'] ==1][var].values\n",
    "    u, p = mannwhitneyu(a, b)\n",
    "    print('Male median = %.3f and Female median = %.3f between sexs for'\n",
    "          ' %s (p = %.3f)' % (np.median(a), np.median(b), var, p))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# MACHINE LEARNING ANALYSIS"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Import classifiers\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.svm import SVC\n",
    "from sklearn.tree import DecisionTreeClassifier\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from sklearn.naive_bayes import GaussianNB\n",
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "\n",
    "from imblearn import pipeline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Import preprocessing modules\n",
    "from sklearn.preprocessing import RobustScaler\n",
    "from imblearn.over_sampling import RandomOverSampler\n",
    "from sklearn.feature_selection import SelectFromModel"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Import model selection and validation modules\n",
    "from sklearn.model_selection import (StratifiedKFold, cross_validate, \n",
    "                                     GridSearchCV, RepeatedStratifiedKFold)\n",
    "\n",
    "# Import metrics modules for measuring the performance\n",
    "from sklearn import metrics"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "scoring = {'acc': 'accuracy',\n",
    "           'bac': 'balanced_accuracy',\n",
    "           'rec': 'recall',\n",
    "           'prec': 'precision',\n",
    "          'roc':'roc_auc',\n",
    "          'avg': 'average_precision',\n",
    "          'f1w':'f1_weighted'}\n",
    "\n",
    "clf_names = ['LogReg', 'SVM', 'Tree', 'NBayes', 'KNN']\n",
    "\n",
    "# 3 List of classifiers and parameter grids\n",
    "classifiers =  [\n",
    "        (LogisticRegression(class_weight='balanced', random_state=RANDOM_STATE), \n",
    "         {'clf__C':np.logspace(-3, 2, 10)}),\n",
    "         (SVC(class_weight='balanced', random_state=RANDOM_STATE),\n",
    "          {'clf__C':np.logspace(-3, 2, 10)}),\n",
    "          (DecisionTreeClassifier(class_weight='balanced', random_state=RANDOM_STATE),\n",
    "           {'clf__max_leaf_nodes':np.arange(2,12)}),\n",
    "           (GaussianNB(),\n",
    "            {}),\n",
    "             (KNeighborsClassifier(),\n",
    "              {'clf__n_neighbors': np.arange(1,20,2)})\n",
    "]\n",
    "    \n",
    "feat = SelectFromModel(LogisticRegression(C=0.1, class_weight='balanced', penalty='l2', random_state=RANDOM_STATE),\n",
    "                       threshold=-np.inf)\n",
    "scaler = RobustScaler()\n",
    "ros = RandomOverSampler(random_state=RANDOM_STATE)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "cyt_dcd = ['GM-CSF', 'IL-4', 'IL-6', 'IL-10', 'IL-17A', 'IFNg', 'TGFb', 'Dermicidina']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "scores= ['train_bac', 'test_bac', \n",
    "         'train_rec', 'test_rec', \n",
    "         'train_prec', 'test_prec',\n",
    "         'train_roc', 'test_roc']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## CLINICAL CLASSIFICATION (Only Breslow)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_baseline = dat_cits.loc[:,'HIST_BRESLOW_mm'].values.reshape(-1,1)\n",
    "y_baseline = dat_cits.loc[:,'EVOL_METASTASIS'].values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=6)]: Using backend LokyBackend with 6 concurrent workers.\n",
      "[Parallel(n_jobs=6)]: Done  38 tasks      | elapsed:    9.1s\n",
      "[Parallel(n_jobs=6)]: Done  50 out of  50 | elapsed:   11.4s finished\n",
      "[Parallel(n_jobs=6)]: Using backend LokyBackend with 6 concurrent workers.\n",
      "[Parallel(n_jobs=6)]: Done  38 tasks      | elapsed:   14.7s\n",
      "[Parallel(n_jobs=6)]: Done  50 out of  50 | elapsed:   18.8s finished\n",
      "[Parallel(n_jobs=6)]: Using backend LokyBackend with 6 concurrent workers.\n",
      "[Parallel(n_jobs=6)]: Done  38 tasks      | elapsed:    6.9s\n",
      "[Parallel(n_jobs=6)]: Done  50 out of  50 | elapsed:    9.1s finished\n",
      "[Parallel(n_jobs=6)]: Using backend LokyBackend with 6 concurrent workers.\n",
      "[Parallel(n_jobs=6)]: Done  50 out of  50 | elapsed:    1.3s finished\n",
      "[Parallel(n_jobs=6)]: Using backend LokyBackend with 6 concurrent workers.\n",
      "[Parallel(n_jobs=6)]: Done  38 tasks      | elapsed:   10.3s\n",
      "[Parallel(n_jobs=6)]: Done  50 out of  50 | elapsed:   12.7s finished\n"
     ]
    }
   ],
   "source": [
    "list_res_clinical = dict()\n",
    "for clf_name, (clf, param_grid) in zip(clf_names, classifiers):\n",
    "    \n",
    "    pip = pipeline.Pipeline([('scaler', scaler), ('ros', ros), ('clf', clf)])\n",
    "    param_grid_serum = param_grid.copy()\n",
    "    \n",
    "    grid = GridSearchCV(estimator=pip, \n",
    "                        param_grid=param_grid_serum, \n",
    "                        scoring='balanced_accuracy', \n",
    "                        iid=False,\n",
    "                        cv= StratifiedKFold(n_splits=10, random_state=RANDOM_STATE))\n",
    "\n",
    "    res_cv = cross_validate(grid, X_baseline, y_baseline, \n",
    "                         scoring=scoring, \n",
    "                         cv = RepeatedStratifiedKFold(n_splits=10, n_repeats=5, random_state=RANDOM_STATE),\n",
    "                         return_estimator=True,\n",
    "                         return_train_score=True,\n",
    "                         verbose=1,\n",
    "                         n_jobs=6)\n",
    "    \n",
    "    list_res_clinical[clf_name] = res_cv"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>LogReg</th>\n",
       "      <th>SVM</th>\n",
       "      <th>Tree</th>\n",
       "      <th>NBayes</th>\n",
       "      <th>KNN</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>train_bac</th>\n",
       "      <td>0.749188</td>\n",
       "      <td>0.740251</td>\n",
       "      <td>0.779294</td>\n",
       "      <td>0.726583</td>\n",
       "      <td>0.786886</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>test_bac</th>\n",
       "      <td>0.730167</td>\n",
       "      <td>0.735706</td>\n",
       "      <td>0.725611</td>\n",
       "      <td>0.725550</td>\n",
       "      <td>0.732606</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>train_rec</th>\n",
       "      <td>0.726526</td>\n",
       "      <td>0.656481</td>\n",
       "      <td>0.768091</td>\n",
       "      <td>0.547607</td>\n",
       "      <td>0.803182</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>test_rec</th>\n",
       "      <td>0.695000</td>\n",
       "      <td>0.649444</td>\n",
       "      <td>0.690556</td>\n",
       "      <td>0.546667</td>\n",
       "      <td>0.729444</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>train_prec</th>\n",
       "      <td>0.529778</td>\n",
       "      <td>0.563737</td>\n",
       "      <td>0.570666</td>\n",
       "      <td>0.666506</td>\n",
       "      <td>0.549913</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>test_prec</th>\n",
       "      <td>0.516929</td>\n",
       "      <td>0.584004</td>\n",
       "      <td>0.535002</td>\n",
       "      <td>0.691692</td>\n",
       "      <td>0.496681</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>train_roc</th>\n",
       "      <td>0.834462</td>\n",
       "      <td>0.822780</td>\n",
       "      <td>0.840743</td>\n",
       "      <td>0.830751</td>\n",
       "      <td>0.876466</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>test_roc</th>\n",
       "      <td>0.837185</td>\n",
       "      <td>0.815058</td>\n",
       "      <td>0.787187</td>\n",
       "      <td>0.832778</td>\n",
       "      <td>0.791731</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              LogReg       SVM      Tree    NBayes       KNN\n",
       "train_bac   0.749188  0.740251  0.779294  0.726583  0.786886\n",
       "test_bac    0.730167  0.735706  0.725611  0.725550  0.732606\n",
       "train_rec   0.726526  0.656481  0.768091  0.547607  0.803182\n",
       "test_rec    0.695000  0.649444  0.690556  0.546667  0.729444\n",
       "train_prec  0.529778  0.563737  0.570666  0.666506  0.549913\n",
       "test_prec   0.516929  0.584004  0.535002  0.691692  0.496681\n",
       "train_roc   0.834462  0.822780  0.840743  0.830751  0.876466\n",
       "test_roc    0.837185  0.815058  0.787187  0.832778  0.791731"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "res_df = pd.DataFrame({})\n",
    "for name, estim in list_res_clinical.items():\n",
    "    res_df[name] = pd.DataFrame(estim).loc[:, scores].mean()\n",
    "                                                                    \n",
    "res_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "f = open(\"results/res_breslow_dict.pkl\", \"wb\")\n",
    "pickle.dump(list_res_clinical, f)\n",
    "f.close()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## SERUM CLASSIFICATION"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_cits = dat_cits.loc[:, ['EVOL_METASTASIS'] +  cyt_dcd].dropna().loc[:, cyt_dcd].values\n",
    "y_cits = dat_cits.loc[:, ['EVOL_METASTASIS'] +  cyt_dcd].dropna().loc[:, 'EVOL_METASTASIS'].values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=6)]: Using backend LokyBackend with 6 concurrent workers.\n",
      "[Parallel(n_jobs=6)]: Done  38 tasks      | elapsed:  1.7min\n",
      "[Parallel(n_jobs=6)]: Done  50 out of  50 | elapsed:  2.2min finished\n",
      "[Parallel(n_jobs=6)]: Using backend LokyBackend with 6 concurrent workers.\n",
      "[Parallel(n_jobs=6)]: Done  38 tasks      | elapsed:  2.6min\n",
      "[Parallel(n_jobs=6)]: Done  50 out of  50 | elapsed:  3.2min finished\n",
      "[Parallel(n_jobs=6)]: Using backend LokyBackend with 6 concurrent workers.\n",
      "[Parallel(n_jobs=6)]: Done  38 tasks      | elapsed:  1.8min\n",
      "[Parallel(n_jobs=6)]: Done  50 out of  50 | elapsed:  2.2min finished\n",
      "[Parallel(n_jobs=6)]: Using backend LokyBackend with 6 concurrent workers.\n",
      "[Parallel(n_jobs=6)]: Done  38 tasks      | elapsed:    8.6s\n",
      "[Parallel(n_jobs=6)]: Done  50 out of  50 | elapsed:   11.1s finished\n",
      "[Parallel(n_jobs=6)]: Using backend LokyBackend with 6 concurrent workers.\n",
      "[Parallel(n_jobs=6)]: Done  38 tasks      | elapsed:  2.0min\n",
      "[Parallel(n_jobs=6)]: Done  50 out of  50 | elapsed:  2.4min finished\n"
     ]
    }
   ],
   "source": [
    "list_res_serum = dict()\n",
    "for clf_name, (clf, param_grid) in zip(clf_names, classifiers):\n",
    "    \n",
    "    pip = pipeline.Pipeline([('scaler', scaler), ('ros', ros), ('feat', feat), ('clf', clf)])\n",
    "    param_grid_serum = param_grid.copy()\n",
    "\n",
    "    param_grid_serum['feat__max_features'] = range(1, X_cits.shape[1] + 1)\n",
    "    \n",
    "    grid = GridSearchCV(estimator=pip, param_grid=param_grid_serum, scoring='balanced_accuracy', iid=False,\n",
    "                        cv= StratifiedKFold(n_splits=10, random_state=RANDOM_STATE))\n",
    "\n",
    "    res_cv = cross_validate(grid, X_cits, y_cits, \n",
    "                         scoring=scoring, \n",
    "                         cv = RepeatedStratifiedKFold(n_splits=10, n_repeats=5, random_state=RANDOM_STATE),\n",
    "                         return_estimator=True,\n",
    "                         return_train_score=True,\n",
    "                         verbose=1,\n",
    "                         n_jobs=6)\n",
    "    \n",
    "    list_res_serum[clf_name] = res_cv"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>LogReg</th>\n",
       "      <th>SVM</th>\n",
       "      <th>Tree</th>\n",
       "      <th>NBayes</th>\n",
       "      <th>KNN</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>train_bac</th>\n",
       "      <td>0.657485</td>\n",
       "      <td>0.690426</td>\n",
       "      <td>0.735731</td>\n",
       "      <td>0.606189</td>\n",
       "      <td>0.722054</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>test_bac</th>\n",
       "      <td>0.599792</td>\n",
       "      <td>0.581913</td>\n",
       "      <td>0.577931</td>\n",
       "      <td>0.555203</td>\n",
       "      <td>0.567844</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>train_rec</th>\n",
       "      <td>0.626925</td>\n",
       "      <td>0.537780</td>\n",
       "      <td>0.708839</td>\n",
       "      <td>0.430055</td>\n",
       "      <td>0.749647</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>test_rec</th>\n",
       "      <td>0.527333</td>\n",
       "      <td>0.354000</td>\n",
       "      <td>0.477333</td>\n",
       "      <td>0.354000</td>\n",
       "      <td>0.520667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>train_prec</th>\n",
       "      <td>0.350344</td>\n",
       "      <td>0.479563</td>\n",
       "      <td>0.465760</td>\n",
       "      <td>0.455982</td>\n",
       "      <td>0.396820</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>test_prec</th>\n",
       "      <td>0.298257</td>\n",
       "      <td>0.332845</td>\n",
       "      <td>0.301155</td>\n",
       "      <td>0.314379</td>\n",
       "      <td>0.267980</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>train_roc</th>\n",
       "      <td>0.719791</td>\n",
       "      <td>0.752545</td>\n",
       "      <td>0.787368</td>\n",
       "      <td>0.658982</td>\n",
       "      <td>0.796883</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>test_roc</th>\n",
       "      <td>0.654242</td>\n",
       "      <td>0.629765</td>\n",
       "      <td>0.583815</td>\n",
       "      <td>0.585032</td>\n",
       "      <td>0.584555</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              LogReg       SVM      Tree    NBayes       KNN\n",
       "train_bac   0.657485  0.690426  0.735731  0.606189  0.722054\n",
       "test_bac    0.599792  0.581913  0.577931  0.555203  0.567844\n",
       "train_rec   0.626925  0.537780  0.708839  0.430055  0.749647\n",
       "test_rec    0.527333  0.354000  0.477333  0.354000  0.520667\n",
       "train_prec  0.350344  0.479563  0.465760  0.455982  0.396820\n",
       "test_prec   0.298257  0.332845  0.301155  0.314379  0.267980\n",
       "train_roc   0.719791  0.752545  0.787368  0.658982  0.796883\n",
       "test_roc    0.654242  0.629765  0.583815  0.585032  0.584555"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "res_df = pd.DataFrame({})\n",
    "for name, estim in list_res_serum.items():\n",
    "    res_df[name] = pd.DataFrame(estim).loc[:, scores].mean()\n",
    "                                                                    \n",
    "res_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "f = open(\"results/res_serum_dict.pkl\", \"wb\")\n",
    "pickle.dump(list_res_serum, f)\n",
    "f.close()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## BRESLOW + SERUM CLASSIFICATION"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_all = dat_cits.dropna().loc[:, ['HIST_BRESLOW_mm'] + cyt_dcd].values\n",
    "y_all = dat_cits.dropna().loc[:,'EVOL_METASTASIS'].values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The size of this matrix is =  (267, 9)\n"
     ]
    }
   ],
   "source": [
    "print(\"The size of this matrix is = \", X_all.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=6)]: Using backend LokyBackend with 6 concurrent workers.\n",
      "[Parallel(n_jobs=6)]: Done  38 tasks      | elapsed:  1.9min\n",
      "[Parallel(n_jobs=6)]: Done  50 out of  50 | elapsed:  2.4min finished\n",
      "[Parallel(n_jobs=6)]: Using backend LokyBackend with 6 concurrent workers.\n",
      "[Parallel(n_jobs=6)]: Done  38 tasks      | elapsed:  2.6min\n",
      "[Parallel(n_jobs=6)]: Done  50 out of  50 | elapsed:  3.3min finished\n",
      "[Parallel(n_jobs=6)]: Using backend LokyBackend with 6 concurrent workers.\n",
      "[Parallel(n_jobs=6)]: Done  38 tasks      | elapsed:  1.9min\n",
      "[Parallel(n_jobs=6)]: Done  50 out of  50 | elapsed:  2.4min finished\n",
      "[Parallel(n_jobs=6)]: Using backend LokyBackend with 6 concurrent workers.\n",
      "[Parallel(n_jobs=6)]: Done  38 tasks      | elapsed:   11.3s\n",
      "[Parallel(n_jobs=6)]: Done  50 out of  50 | elapsed:   14.1s finished\n",
      "[Parallel(n_jobs=6)]: Using backend LokyBackend with 6 concurrent workers.\n",
      "[Parallel(n_jobs=6)]: Done  38 tasks      | elapsed:  2.4min\n",
      "[Parallel(n_jobs=6)]: Done  50 out of  50 | elapsed:  2.9min finished\n"
     ]
    }
   ],
   "source": [
    "res_all_dict = dict()\n",
    "for clf_name, (clf, param_grid) in zip(clf_names, classifiers):\n",
    "    \n",
    "    pip = pipeline.Pipeline([('scaler', scaler), ('ros', ros), ('feat', feat), ('clf', clf)])    \n",
    "    \n",
    "    param_grid_all = param_grid.copy()\n",
    "    param_grid_all['feat__max_features'] = range(1, X_all.shape[1] + 1)\n",
    "    \n",
    "    grid = GridSearchCV(estimator=pip, param_grid = param_grid_all, \n",
    "                        scoring='balanced_accuracy', iid=False,\n",
    "                        cv= StratifiedKFold(n_splits=10, random_state=RANDOM_STATE))\n",
    "\n",
    "    res_cv = cross_validate(grid, X_all, y_all, \n",
    "                         scoring=scoring, \n",
    "                         cv = RepeatedStratifiedKFold(n_splits=10, n_repeats=5, random_state=RANDOM_STATE),\n",
    "                         return_estimator=True,\n",
    "                         return_train_score=True,\n",
    "                         verbose=1,\n",
    "                         n_jobs=6)\n",
    "    \n",
    "    res_all_dict[clf_name] = res_cv"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>LogReg</th>\n",
       "      <th>SVM</th>\n",
       "      <th>Tree</th>\n",
       "      <th>NBayes</th>\n",
       "      <th>KNN</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>train_bac</th>\n",
       "      <td>0.823814</td>\n",
       "      <td>0.801495</td>\n",
       "      <td>0.865363</td>\n",
       "      <td>0.797285</td>\n",
       "      <td>0.850183</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>test_bac</th>\n",
       "      <td>0.803706</td>\n",
       "      <td>0.775597</td>\n",
       "      <td>0.760277</td>\n",
       "      <td>0.777926</td>\n",
       "      <td>0.745160</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>train_rec</th>\n",
       "      <td>0.794125</td>\n",
       "      <td>0.731788</td>\n",
       "      <td>0.879027</td>\n",
       "      <td>0.690933</td>\n",
       "      <td>0.862557</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>test_rec</th>\n",
       "      <td>0.775333</td>\n",
       "      <td>0.686000</td>\n",
       "      <td>0.708000</td>\n",
       "      <td>0.662000</td>\n",
       "      <td>0.707333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>train_prec</th>\n",
       "      <td>0.593963</td>\n",
       "      <td>0.604365</td>\n",
       "      <td>0.626551</td>\n",
       "      <td>0.656769</td>\n",
       "      <td>0.588830</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>test_prec</th>\n",
       "      <td>0.573773</td>\n",
       "      <td>0.600193</td>\n",
       "      <td>0.538696</td>\n",
       "      <td>0.634567</td>\n",
       "      <td>0.469746</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>train_roc</th>\n",
       "      <td>0.909471</td>\n",
       "      <td>0.879378</td>\n",
       "      <td>0.926753</td>\n",
       "      <td>0.884525</td>\n",
       "      <td>0.926597</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>test_roc</th>\n",
       "      <td>0.892224</td>\n",
       "      <td>0.859915</td>\n",
       "      <td>0.854579</td>\n",
       "      <td>0.856176</td>\n",
       "      <td>0.819926</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              LogReg       SVM      Tree    NBayes       KNN\n",
       "train_bac   0.823814  0.801495  0.865363  0.797285  0.850183\n",
       "test_bac    0.803706  0.775597  0.760277  0.777926  0.745160\n",
       "train_rec   0.794125  0.731788  0.879027  0.690933  0.862557\n",
       "test_rec    0.775333  0.686000  0.708000  0.662000  0.707333\n",
       "train_prec  0.593963  0.604365  0.626551  0.656769  0.588830\n",
       "test_prec   0.573773  0.600193  0.538696  0.634567  0.469746\n",
       "train_roc   0.909471  0.879378  0.926753  0.884525  0.926597\n",
       "test_roc    0.892224  0.859915  0.854579  0.856176  0.819926"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "res_df = pd.DataFrame({})\n",
    "for name, estim in res_all_dict.items():\n",
    "    res_df[name] = pd.DataFrame(estim).loc[:, scores].mean()\n",
    "                                                                    \n",
    "res_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "f = open(\"results/res_all_dict.pkl\", \"wb\")\n",
    "pickle.dump(res_all_dict, f)\n",
    "f.close()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Is there a significant improve between breslow and combination?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "WilcoxonResult(statistic=346.0, pvalue=0.004893727738598007)\n"
     ]
    }
   ],
   "source": [
    "from scipy.stats import wilcoxon, ttest_rel\n",
    "print(wilcoxon(list_res_clinical['LogReg']['test_bac'], res_all_dict['LogReg']['test_bac']))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "One of the reviewers asked to include the classification of DCD, IL-4 and GM-CSF, since they are the serum variables that contribute most to classification along with Breslow. Here is the result for LogisticRegression:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=6)]: Using backend LokyBackend with 6 concurrent workers.\n",
      "[Parallel(n_jobs=6)]: Done  38 tasks      | elapsed:   10.3s\n",
      "[Parallel(n_jobs=6)]: Done  50 out of  50 | elapsed:   12.5s finished\n"
     ]
    }
   ],
   "source": [
    "X_cits_sub = dat_cits.loc[:, ['EVOL_METASTASIS'] +  cyt_dcd].dropna().loc[:, ['GM-CSF', \n",
    "                                                                              'IL-4', \n",
    "                                                                              'Dermicidina']].values\n",
    "y_cits_sub = dat_cits.loc[:, ['EVOL_METASTASIS'] +  cyt_dcd].dropna().loc[:, 'EVOL_METASTASIS'].values\n",
    "\n",
    "# We don't want know the feature selection, since, we are going to use the three of them together\n",
    "clf = LogisticRegression(class_weight='balanced', random_state=RANDOM_STATE)        \n",
    "pip = pipeline.Pipeline([('scaler', scaler), ('ros', ros), ('clf', clf)])\n",
    "grid = GridSearchCV(estimator=pip, \n",
    "                    param_grid={'clf__C':np.logspace(-3, 2, 10)}, \n",
    "                    scoring='balanced_accuracy', \n",
    "                    iid=False,\n",
    "                    cv= StratifiedKFold(n_splits=10, random_state=RANDOM_STATE))\n",
    "\n",
    "res_cv = cross_validate(grid, X_cits_sub, y_cits_sub, \n",
    "                         scoring=scoring, \n",
    "                         cv = RepeatedStratifiedKFold(n_splits=10, n_repeats=5, random_state=RANDOM_STATE),\n",
    "                         return_estimator=True,\n",
    "                         return_train_score=True,\n",
    "                         verbose=1,\n",
    "                         n_jobs=6)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "acc=0.5982, rec = 0.5647, prec = 0.2920, roc= 0.6697 \n"
     ]
    }
   ],
   "source": [
    "print(\"acc=%.4f, rec = %.4f, prec = %.4f, roc= %.4f \" % (pd.DataFrame(res_cv)['test_bac'].mean(),\n",
    "                                               pd.DataFrame(res_cv)['test_rec'].mean(),\n",
    "                                               pd.DataFrame(res_cv)['test_prec'].mean(),\n",
    "                                             pd.DataFrame(res_cv)['test_roc'].mean()))"
   ]
  }
 ],
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