{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "# The GDSCTools library \n",
    "Nore that in this notebook, we need the following code but this may not be required in a script or a standard Python shell if pylab is already loaded.\n",
    "\n",
    "## Regression methods\n",
    "\n",
    "Currently (v0.15), we have 3 classes available that implements 3 regression methods namely:\n",
    "\n",
    "- GDSCElasticNet\n",
    "- GDSCRidge\n",
    "- GDSCLasso\n",
    "\n",
    "In the following, we will only use the GDSCElasticNet class but one can use other regression methods with the same usage.\n",
    "\n",
    "## Features\n",
    "\n",
    "- Train a model for a given alpha with a cross validation (CV)\n",
    "- Scan alpha parameter, repeating the model training with CV\n",
    "- Find the best alpha parameter automatically using CV\n",
    "- Check significance of the Pearson correlation with random models\n",
    "- Plot importances and weights of model coefficients\n",
    "- Identify drugs of interest automatically\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Populating the interactive namespace from numpy and matplotlib\n"
     ]
    }
   ],
   "source": [
    "# For ipython notebooks only\n",
    "%pylab inline\n",
    "rcParams['figure.figsize'] = 10,5\n",
    "#import warnings\n",
    "#warnings.simplefilter('ignore', 'DeprecationWarning')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Then, we can import some functionalities from GDSCtools library"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from gdsctools import gdsctools_data\n",
    "from gdsctools import regression\n",
    "from gdsctools import GenomicFeatures, IC50     # optional"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "skip"
    }
   },
   "source": [
    "You may import everything in one go as follows::\n",
    "        \n",
    "    from gdsctools import *"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "## The Data\n",
    "Let us get some data to play with. First, we get the drug responses from the v17 data set (*ic50* variable here below). Second, we read the genomic features for the same version (*gf* here below). "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "An IC50 data set with 988 cell lines and Nd=265 drugs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "IC50 object <Nd=265, Nc=988>"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ic50 = gdsctools_data(\"test_IC50.csv\")\n",
    "ic50 = gdsctools_data(\"IC50_v17.csv.gz\")\n",
    "IC50(ic50)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Genomic feature data with 988 cell lines and Nf features. Yet, this data set is quite large hence the selection of only a subset of the features."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "GenomicFeatures <Nc=988, Nf=52, Nt=27>"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gf = gdsctools_data(\"genomic_features_v17.csv.gz\")\n",
    "# to speed up computation, we keep only 50 features:\n",
    "gf = GenomicFeatures(gf)\n",
    "gf.df = gf.df[[\"TISSUE_FACTOR\", \"MSI_FACTOR\"] + list(gf.df.columns[219:271])]\n",
    "gf"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "## The regression"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "From the regression module, we can use the **GDSCElasticNet** class. It takes as input a \n",
    "drug response matrix and a genomic features matrix. See the ANOVA analysis\n",
    "or http://gdsctools.readthedocs.org documentation for details about the data formats."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "gd = regression.GDSCElasticNet(ic50, gf)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Data are not normalised. However, one can force the X data (features) to be standarised (mean is substracted and result divided by the standard deviation). To do so, uncomment the following statement."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# gd.scale = True"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "-"
    }
   },
   "source": [
    "**Note:** The ElasticNet like Lasso and Ridge methods have a parameter alpha, which will be tuned and of paramount importance in the next sections. The Elastic Net has also a l1_ratio parameter (0.5 by default). Let us emphasize the different terminology use in scikit-learn and the R package glmnet:\n",
    "\n",
    "| sklearn  |  glmnet |\n",
    "|-----------------------\n",
    "|l1_ratio  | alpha|\n",
    "|alpha     | lambda|\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "## Get the best model"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Our first objective is to get the best model that is the best set of parameters (alpha parameter). This can be achieved using a cross validation strategy. In GDSCTools, this can be achieved in one go using  the runCV function. Let us pick a drug identifier known to have a large pearson correlation. Its identifier being 1047 in the present data set"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "drugid = 1047"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Just call this method (**runCV**)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Best alpha on 10 folds: 0.013334336995 (-4.32 in log scale); Rp=0.565318466278\n"
     ]
    }
   ],
   "source": [
    "res = gd.runCV(drugid, kfolds=10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This is the fastest way in GDSCTools to get the best alpha parameter (highest pearson correlation). For performance reasons, there is no plot being shown. The returned objects contains information such as the best alpha parameter"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(0.013334336994956059, -4.3174128417475979)"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "res.alpha, res.ln_alpha"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "Note that in general we will use a log scale alpha value (*ln alpha*):"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "## Importance features"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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Zts0wDMMVK9qxY4caNWqky5cvO6ZNmzZN69at09dff51t2TFjxuiNN96Qt7e3QkJCNGTI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\nxjS586TXS5J69erl7gjZeNr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xZaIFCxaYHcsjUIAlDR06VNWqVdPLL7+sxYsXq2PHjipa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\nRbEAAwBQFAswAABFsQADAFCUT63Em1EBcsSjAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fe28bdc9cf8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "alpha = 0.01159\n",
    "best_model = gd.get_model(alpha=alpha)\n",
    "res = gd.plot_weight(drugid, best_model)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Here we see that TP53_mut feature is predominent."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "scrolled": false,
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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Tw/Tp0/nTn/5ETU0NNTU17LDDDp0+ps3MzMw6l+sCLwt//etf+epXv8pjjz3G4MGDuemm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\nBngRERERlckALyIiIqIy/wE6qdGTb+fxDgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fe28ba5f828>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "res = gd.plot_importance(drugid, model=gd.get_model(alpha=alpha))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>weight</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>name</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>SETD2_mut</th>\n",
       "      <td>-0.037432</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SIN3A_mut</th>\n",
       "      <td>-0.054333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TET2_mut</th>\n",
       "      <td>-0.305070</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TP53_mut</th>\n",
       "      <td>1.506210</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TP53BP1_mut</th>\n",
       "      <td>-0.253850</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>USP6_mut</th>\n",
       "      <td>-0.040929</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>XRN1_mut</th>\n",
       "      <td>-0.541888</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ZFP36L2_mut</th>\n",
       "      <td>-0.052753</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ZNF292_mut</th>\n",
       "      <td>-0.013491</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               weight\n",
       "name                 \n",
       "SETD2_mut   -0.037432\n",
       "SIN3A_mut   -0.054333\n",
       "TET2_mut    -0.305070\n",
       "TP53_mut     1.506210\n",
       "TP53BP1_mut -0.253850\n",
       "USP6_mut    -0.040929\n",
       "XRN1_mut    -0.541888\n",
       "ZFP36L2_mut -0.052753\n",
       "ZNF292_mut  -0.013491"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "res[res.weight !=0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "scrolled": false,
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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HnaVQsrM54cPK41bUzz7beZPC1Kl96OnJQW8vLXvWCuWWa/p0usXsjDy/n9Yk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\nrkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fe287b06ef0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# One CV for a given alpha\n",
    "# plots the prediction of the model for all data\n",
    "scores = gd.fit(drugid, alpha=alpha)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": true,
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "outputs": [],
   "source": [
    "all_scores = []\n",
    "for this in range(100):\n",
    "    scores = gd.fit(drugid, alpha=0.08, show=False, shuffle=True, randomize_Y=False)\n",
    "    all_scores.extend(scores)\n",
    "random_scores = []\n",
    "for this in range(100):\n",
    "    scores = gd.fit(drugid, alpha=0.08, show=False, shuffle=True, randomize_Y=True)\n",
    "    random_scores.extend(scores)\n",
    "    "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The scores contains the pearson correlation for each K-fold"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.text.Text at 0x7f6e26688400>"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f6e26695588>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# scores returned corresponds to the N=10 KFold scores\n",
    "_ = hist(all_scores,bins=20); xlabel('scores for the N folds ')\n",
    "_ = hist(random_scores,bins=20, color=\"r\"); xlabel('scores for the N folds ')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Tuning alpha manually (and plotting)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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fjwGd1q9n9MSJ3DlxIh02b+aj88/nz/ffz1vXX8/y3r3ZHxPjDY+G+i5VpNLO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\nBxV/b7aSx0T5AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fe287987240>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "results = gd.tune_alpha(drugid, alpha_range=(-3.5,-1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'alpha_best': 0.015038869469554102,\n",
       " 'ln_alpha': -4.1971171315334503,\n",
       " 'maximum_Rp': 0.55257352697582762}"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "-4.1971171315334503"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "results[\"ln_alpha\"]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This is not and runCV is recommended. \n",
    "- shuffle\n",
    "- runCV gives stochastic answer while fitCV does not"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " [                  0%                  ] 0 of 265 complete in 0.1 sec[                  0%                  ] 2 of 265 complete in 0.4 sec[                  1%                  ] 4 of 265 complete in 0.7 sec[                  2%                  ] 6 of 265 complete in 1.0 sec[-                 3%                  ] 8 of 265 complete in 1.2 sec[-                 3%                  ] 10 of 265 complete in 1.5 sec[-                 4%                  ] 12 of 265 complete in 1.8 sec[--                5%                  ] 14 of 265 complete in 2.1 sec[--                6%                  ] 16 of 265 complete in 2.3 sec[--                6%                  ] 18 of 265 complete in 2.6 sec[--                7%                  ] 20 of 265 complete in 3.0 sec[---               8%                  ] 22 of 265 complete in 3.2 sec[---               9%                  ] 24 of 265 complete in 3.5 sec[---               9%                  ] 26 of 265 complete in 3.9 sec[----             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complete in 11.1 sec[------------     32%                  ] 86 of 265 complete in 11.4 sec[------------     33%                  ] 88 of 265 complete in 11.7 sec[------------     33%                  ] 90 of 265 complete in 11.9 sec[-------------    34%                  ] 92 of 265 complete in 12.1 sec[-------------    35%                  ] 94 of 265 complete in 12.3 sec[-------------    36%                  ] 96 of 265 complete in 12.5 sec[--------------   36%                  ] 98 of 265 complete in 12.6 sec[--------------   37%                  ] 100 of 265 complete in 12.8 sec[--------------   38%                  ] 102 of 265 complete in 13.0 sec[--------------   39%                  ] 104 of 265 complete in 13.2 sec[---------------  40%                  ] 106 of 265 complete in 13.4 sec[---------------  40%                  ] 108 of 265 complete in 13.6 sec[---------------  41%                  ] 110 of 265 complete in 13.8 sec[---------------- 42%                  ] 112 of 265 complete in 14.0 sec[---------------- 43%                  ] 114 of 265 complete in 14.2 sec[---------------- 43%                  ] 116 of 265 complete in 14.4 sec[---------------- 44%                  ] 118 of 265 complete in 14.6 sec[-----------------45%                  ] 120 of 265 complete in 14.8 sec[-----------------46%                  ] 122 of 265 complete in 15.0 sec[-----------------46%                  ] 124 of 265 complete in 15.2 sec[-----------------47%                  ] 126 of 265 complete in 15.4 sec[-----------------48%                  ] 128 of 265 complete in 15.6 sec[-----------------49%                  ] 130 of 265 complete in 15.8 sec[-----------------49%                  ] 132 of 265 complete in 16.0 sec[-----------------50%                  ] 134 of 265 complete in 16.2 sec[-----------------51%                  ] 136 of 265 complete in 16.3 sec[-----------------52%                  ] 138 of 265 complete in 16.5 sec[-----------------52%                  ] 140 of 265 complete in 16.7 sec[-----------------53%                  ] 142 of 265 complete in 16.9 sec[-----------------54%                  ] 144 of 265 complete in 17.1 sec[-----------------55%                  ] 146 of 265 complete in 17.3 sec[-----------------55%-                 ] 148 of 265 complete in 17.5 sec[-----------------56%-                 ] 150 of 265 complete in 17.7 sec[-----------------57%-                 ] 152 of 265 complete in 17.9 sec[-----------------58%--                ] 154 of 265 complete in 18.1 sec[-----------------58%--                ] 156 of 265 complete in 18.3 sec[-----------------59%--                ] 158 of 265 complete in 18.5 sec[-----------------60%--                ] 160 of 265 complete in 18.7 sec[-----------------61%---               ] 162 of 265 complete in 18.9 sec[-----------------61%---               ] 164 of 265 complete in 19.1 sec[-----------------62%---               ] 166 of 265 complete in 19.3 sec[-----------------63%----             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        ] 196 of 265 complete in 22.3 sec[-----------------74%--------          ] 198 of 265 complete in 22.5 sec[-----------------75%--------          ] 200 of 265 complete in 22.7 sec[-----------------76%--------          ] 202 of 265 complete in 22.9 sec[-----------------76%---------         ] 204 of 265 complete in 23.0 sec[-----------------77%---------         ] 206 of 265 complete in 23.2 sec[-----------------78%---------         ] 208 of 265 complete in 23.4 sec[-----------------79%----------        ] 210 of 265 complete in 23.6 sec[-----------------80%----------        ] 212 of 265 complete in 23.8 sec[-----------------80%----------        ] 214 of 265 complete in 24.0 sec[-----------------81%----------        ] 216 of 265 complete in 24.2 sec[-----------------82%-----------       ] 218 of 265 complete in 24.4 sec[-----------------83%-----------       ] 220 of 265 complete in 24.6 sec[-----------------83%-----------       ] 222 of 265 complete in 24.8 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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " [-----------------87%-------------     ] 232 of 265 complete in 25.9 sec[-----------------88%-------------     ] 234 of 265 complete in 26.2 sec[-----------------89%-------------     ] 236 of 265 complete in 26.4 sec[-----------------89%--------------    ] 238 of 265 complete in 26.6 sec[-----------------90%--------------    ] 240 of 265 complete in 26.9 sec[-----------------91%--------------    ] 242 of 265 complete in 27.2 sec[-----------------92%--------------    ] 244 of 265 complete in 27.4 sec[-----------------92%---------------   ] 246 of 265 complete in 27.7 sec[-----------------93%---------------   ] 248 of 265 complete in 27.9 sec[-----------------94%---------------   ] 250 of 265 complete in 28.1 sec[-----------------95%----------------  ] 252 of 265 complete in 28.3 sec[-----------------95%----------------  ] 254 of 265 complete in 28.5 sec[-----------------96%----------------  ] 256 of 265 complete in 28.7 sec[-----------------97%----------------  ] 258 of 265 complete in 28.8 sec[-----------------98%----------------- ] 260 of 265 complete in 29.0 sec[-----------------98%----------------- ] 262 of 265 complete in 29.2 sec[-----------------99%----------------- ] 264 of 265 complete in 29.4 sec"
     ]
    }
   ],
   "source": [
    "alphas = []\n",
    "Rps = []\n",
    "from easydev import Progress\n",
    "pb = Progress(len(gd.drugIds))\n",
    "count = 0\n",
    "for drug in gd.drugIds:\n",
    "    res = gd.runCV(drug, kfolds=10, verbose=False)\n",
    "    alphas.append(res.alpha)\n",
    "    Rps.append(res.Rp)\n",
    "    pb.animate(count)\n",
    "    count += 1\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.text.Text at 0x7fe28788c908>"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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SAIDelTRk+SPuvk6SzOz3JX1b0ip3/+fcW9bjxiendM+OffO1xLohGKjiesGyDENSHBYA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\nJ6dHH3oAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fe287a48208>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot(alphas, Rps, \"o\")\n",
    "xlabel(\"alpha\")\n",
    "ylabel(\"Rps\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "best_alpha = 0.085"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Rps</th>\n",
       "      <th>drug</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>0.383882</td>\n",
       "      <td>32</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>0.382437</td>\n",
       "      <td>86</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>205</th>\n",
       "      <td>0.566228</td>\n",
       "      <td>1047</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          Rps  drug\n",
       "9    0.383882    32\n",
       "29   0.382437    86\n",
       "205  0.566228  1047"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "df = pd.DataFrame({\"Rps\":Rps, \"drug\": gd.drugIds})\n",
    "df[df.Rps > 0.38]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "scrolled": true,
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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bZ/rWarPrU6efA15dVbcAVNWNjce4Xiz2PbUN2LWcDjd66PJWRf30qZMGFlur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\nrkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fe297c3b940>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Checking the validity of runCV on N instances\n",
    "res = gd.check_randomness(1047, N=20)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can also now look at the weights for that model:\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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qeplHwDmKz6UxvB76XtmHxVukMgbx9m6sqvfiLK7m5hP70Ik4ReluXg3fwZnM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\nAnxGnowPjV9oAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fe28ba3b0f0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "res = gd.boxplot(drugid, model=gd.get_model(alpha=best_alpha), n=10, bx_vert=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " [                  0%                  ] 2 of 265 complete in 1.0 sec[                  1%                  ] 4 of 265 complete in 1.7 sec[                  2%                  ] 6 of 265 complete in 2.7 sec[-                 3%                  ] 8 of 265 complete in 3.7 sec[-                 3%                  ] 10 of 265 complete in 4.8 sec[-                 4%                  ] 12 of 265 complete in 5.9 sec[--                5%                  ] 14 of 265 complete in 6.6 sec[--                6%                  ] 16 of 265 complete in 7.3 sec[--                6%                  ] 18 of 265 complete in 8.1 sec[--                7%                  ] 20 of 265 complete in 8.6 sec[---               8%                  ] 22 of 265 complete in 9.2 sec[---               9%                  ] 24 of 265 complete in 9.9 sec[---               9%                  ] 26 of 265 complete in 10.7 sec[----             10%                  ] 28 of 265 complete in 11.3 sec[----          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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " [-----------------87%-------------     ] 232 of 265 complete in 68.5 sec[-----------------88%-------------     ] 234 of 265 complete in 68.8 sec[-----------------89%-------------     ] 236 of 265 complete in 69.1 sec[-----------------89%--------------    ] 238 of 265 complete in 69.5 sec[-----------------90%--------------    ] 240 of 265 complete in 69.8 sec[-----------------91%--------------    ] 242 of 265 complete in 70.1 sec[-----------------92%--------------    ] 244 of 265 complete in 70.4 sec[-----------------92%---------------   ] 246 of 265 complete in 70.8 sec[-----------------93%---------------   ] 248 of 265 complete in 71.1 sec[-----------------94%---------------   ] 250 of 265 complete in 71.4 sec[-----------------95%----------------  ] 252 of 265 complete in 71.8 sec[-----------------95%----------------  ] 254 of 265 complete in 72.1 sec[-----------------96%----------------  ] 256 of 265 complete in 72.4 sec[-----------------97%----------------  ] 258 of 265 complete in 72.8 sec[-----------------98%----------------- ] 260 of 265 complete in 73.2 sec[-----------------98%----------------- ] 262 of 265 complete in 73.5 sec[-----------------99%----------------- ] 264 of 265 complete in 73.9 sec[-----------------100%-----------------] 265 of 265 complete in 74.1 sec"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/cokelaer/miniconda3/envs/py3/lib/python3.5/site-packages/biokit/viz/linkage.py:41: ClusterWarning: scipy.cluster: The symmetric non-negative hollow observation matrix looks suspiciously like an uncondensed distance matrix\n",
      "  Y = hierarchy.linkage(D, method=method, metric=metric)\n"
     ]
    },
    {
     "data": {
      "image/png": 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Wy1TMJfudaUR0qP0b6UhmsmhLFmwNJxsyvga+8Etub9vz26i8vpIb33jjkKOdG87bwDX3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\nLkBzWsLRI/UpJaNMnzuSHc+Xtbl+1Lmjef3J/SnXqbPCPXa4sjb6zkL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57mefAOD8i6e2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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fe2879db080>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "res = gd.dendogram_coefficients(cmap=\"terrain\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " [                  0%                  ] 2 of 265 complete in 1.9 sec[                  1%                  ] 4 of 265 complete in 3.8 sec[                  2%                  ] 6 of 265 complete in 5.6 sec[-                 3%                  ] 8 of 265 complete in 7.5 sec[-                 3%                  ] 10 of 265 complete in 10.3 sec[-                 4%                  ] 12 of 265 complete in 12.3 sec[--                5%                  ] 14 of 265 complete in 14.7 sec[--                6%                  ] 16 of 265 complete in 17.5 sec[--                6%                  ] 18 of 265 complete in 20.3 sec[--                7%                  ] 20 of 265 complete in 23.1 sec[---               8%                  ] 22 of 265 complete in 25.9 sec[---               9%                  ] 24 of 265 complete in 28.5 sec[---               9%                  ] 26 of 265 complete in 31.3 sec[----             10%                  ] 28 of 265 complete in 34.3 sec[----  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sec[---------------- 43%                  ] 114 of 265 complete in 132.9 sec[---------------- 43%                  ] 116 of 265 complete in 134.8 sec[---------------- 44%                  ] 118 of 265 complete in 136.8 sec[-----------------45%                  ] 120 of 265 complete in 138.7 sec[-----------------46%                  ] 122 of 265 complete in 140.7 sec[-----------------46%                  ] 124 of 265 complete in 142.6 sec[-----------------47%                  ] 126 of 265 complete in 144.6 sec[-----------------48%                  ] 128 of 265 complete in 146.6 sec[-----------------49%                  ] 130 of 265 complete in 148.5 sec[-----------------49%                  ] 132 of 265 complete in 150.5 sec[-----------------50%                  ] 134 of 265 complete in 152.4 sec[-----------------51%                  ] 136 of 265 complete in 154.3 sec[-----------------52%                  ] 138 of 265 complete in 156.2 sec[-----------------52%                  ] 140 of 265 complete in 158.1 sec[-----------------53%                  ] 142 of 265 complete in 160.0 sec[-----------------54%                  ] 144 of 265 complete in 161.9 sec[-----------------55%                  ] 146 of 265 complete in 163.8 sec[-----------------55%-                 ] 148 of 265 complete in 165.7 sec[-----------------56%-                 ] 150 of 265 complete in 167.6 sec[-----------------57%-                 ] 152 of 265 complete in 169.6 sec[-----------------58%--                ] 154 of 265 complete in 171.6 sec[-----------------58%--                ] 156 of 265 complete in 173.5 sec[-----------------59%--                ] 158 of 265 complete in 175.5 sec[-----------------60%--                ] 160 of 265 complete in 177.5 sec[-----------------61%---               ] 162 of 265 complete in 179.5 sec[-----------------61%---               ] 164 of 265 complete in 181.5 sec[-----------------62%---               ] 166 of 265 complete in 183.5 sec[-----------------63%----              ] 168 of 265 complete in 185.5 sec[-----------------64%----              ] 170 of 265 complete in 187.4 sec[-----------------64%----              ] 172 of 265 complete in 189.3 sec[-----------------65%----              ] 174 of 265 complete in 191.2 sec[-----------------66%-----             ] 176 of 265 complete in 193.1 sec[-----------------67%-----             ] 178 of 265 complete in 195.1 sec[-----------------67%-----             ] 180 of 265 complete in 197.0 sec[-----------------68%------            ] 182 of 265 complete in 198.9 sec[-----------------69%------            ] 184 of 265 complete in 200.8 sec[-----------------70%------            ] 186 of 265 complete in 202.8 sec[-----------------70%------            ] 188 of 265 complete in 204.7 sec[-----------------71%-------           ] 190 of 265 complete in 206.6 sec[-----------------72%-------           ] 192 of 265 complete in 208.6 sec[-----------------73%-------           ] 194 of 265 complete in 211.1 sec[-----------------73%--------          ] 196 of 265 complete in 213.7 sec[-----------------74%--------          ] 198 of 265 complete in 215.5 sec[-----------------75%--------          ] 200 of 265 complete in 217.4 sec[-----------------76%--------          ] 202 of 265 complete in 219.4 sec[-----------------76%---------         ] 204 of 265 complete in 221.4 sec[-----------------77%---------         ] 206 of 265 complete in 223.3 sec[-----------------78%---------         ] 208 of 265 complete in 225.2 sec[-----------------79%----------        ] 210 of 265 complete in 227.1 sec[-----------------80%----------        ] 212 of 265 complete in 229.0 sec[-----------------80%----------        ] 214 of 265 complete in 231.0 sec[-----------------81%----------        ] 216 of 265 complete in 232.9 sec[-----------------82%-----------       ] 218 of 265 complete in 234.9 sec[-----------------83%-----------       ] 220 of 265 complete in 236.9 sec[-----------------83%-----------       ] 222 of 265 complete in 239.4 sec[-----------------84%------------      ] 224 of 265 complete in 241.7 sec[-----------------85%------------      ] 226 of 265 complete in 244.0 sec[-----------------86%------------      ] 228 of 265 complete in 246.3 sec"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " [-----------------86%------------      ] 230 of 265 complete in 248.2 sec[-----------------87%-------------     ] 232 of 265 complete in 250.2 sec[-----------------88%-------------     ] 234 of 265 complete in 252.1 sec[-----------------89%-------------     ] 236 of 265 complete in 254.0 sec[-----------------89%--------------    ] 238 of 265 complete in 255.9 sec[-----------------90%--------------    ] 240 of 265 complete in 257.8 sec[-----------------91%--------------    ] 242 of 265 complete in 259.7 sec[-----------------92%--------------    ] 244 of 265 complete in 261.6 sec[-----------------92%---------------   ] 246 of 265 complete in 263.5 sec[-----------------93%---------------   ] 248 of 265 complete in 265.4 sec[-----------------94%---------------   ] 250 of 265 complete in 267.3 sec[-----------------95%----------------  ] 252 of 265 complete in 269.3 sec[-----------------95%----------------  ] 254 of 265 complete in 271.4 sec[-----------------96%----------------  ] 256 of 265 complete in 273.3 sec[-----------------97%----------------  ] 258 of 265 complete in 275.2 sec[-----------------98%----------------- ] 260 of 265 complete in 277.2 sec[-----------------98%----------------- ] 262 of 265 complete in 279.1 sec[-----------------99%----------------- ] 264 of 265 complete in 281.1 sec[-----------------100%-----------------] 265 of 265 complete in 282.0 sec"
     ]
    }
   ],
   "source": [
    "tt = []\n",
    "bayes = []\n",
    "Rp = []\n",
    "from easydev import Progress\n",
    "pb = Progress(len(gd.drugIds))\n",
    "for i, this in enumerate(gd.drugIds):\n",
    "    res = gd.check_randomness(this, 4, show=False)\n",
    "    tt.append(res['ttest_pval'])\n",
    "    bayes.append(res['bayes_factor'])\n",
    "    Rp.append(mean(res['scores']))\n",
    "    pb.animate(i+1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.text.Text at 0x7f6e0facc048>"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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w4sEEVAmvQeNjruwVSb8S8BcjhXuOu5wyHk16ofJ9l8uRJsWC78pw48No5QdI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UlEhBBCiPlGEhEhhBBClIwkIkIIIYQoDa9K+GwhiYgQQggx30giIoQQQoiS0MyqtWYkERFCCCHm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\nQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f6e0fac74e0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "bayes = [x if not np.isinf(x) else 25 for x in bayes]\n",
    "scatter(-log(tt), Rp, c=[1+x for x in bayes], s=60, alpha=0.5)\n",
    "colorbar()\n",
    "ylabel(\"Rp\"); \n",
    "xlabel(\"-log tt\")\n",
    "#xlim([0,40])\n",
    "#ylim([0,25])\n",
    "#plot(-log(tt), Rp, \"ro\", alpha=0.5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "df = pd.DataFrame({\"drugid\": gd.drugIds, \"Rp\":Rp, \"logtt\":-log(tt), \"bayes\":bayes})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Rp</th>\n",
       "      <th>bayes</th>\n",
       "      <th>drugid</th>\n",
       "      <th>logtt</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.346599</td>\n",
       "      <td>10.0</td>\n",
       "      <td>5</td>\n",
       "      <td>10.620522</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>0.381472</td>\n",
       "      <td>10.0</td>\n",
       "      <td>32</td>\n",
       "      <td>15.685800</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>0.349923</td>\n",
       "      <td>10.0</td>\n",
       "      <td>60</td>\n",
       "      <td>13.567814</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>0.377621</td>\n",
       "      <td>10.0</td>\n",
       "      <td>86</td>\n",
       "      <td>19.123810</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>0.323437</td>\n",
       "      <td>10.0</td>\n",
       "      <td>104</td>\n",
       "      <td>15.412550</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>205</th>\n",
       "      <td>0.567041</td>\n",
       "      <td>10.0</td>\n",
       "      <td>1047</td>\n",
       "      <td>27.650776</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>230</th>\n",
       "      <td>0.308370</td>\n",
       "      <td>10.0</td>\n",
       "      <td>1164</td>\n",
       "      <td>20.969969</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>237</th>\n",
       "      <td>0.346942</td>\n",
       "      <td>10.0</td>\n",
       "      <td>1203</td>\n",
       "      <td>20.218602</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           Rp  bayes  drugid      logtt\n",
       "2    0.346599   10.0       5  10.620522\n",
       "9    0.381472   10.0      32  15.685800\n",
       "23   0.349923   10.0      60  13.567814\n",
       "29   0.377621   10.0      86  19.123810\n",
       "35   0.323437   10.0     104  15.412550\n",
       "205  0.567041   10.0    1047  27.650776\n",
       "230  0.308370   10.0    1164  20.969969\n",
       "237  0.346942   10.0    1203  20.218602"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.query(\"Rp>0.3 and logtt>10 and bayes>=10\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "celltoolbar": "Slideshow",
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
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  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.5.3"
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