LINCS_MOA_FullCurated_decisionTree_Gimm4.ipynb

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  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## LINCS MOA prediciton for LINCS curated using decision trees"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "from sklearn.preprocessing  import StandardScaler\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.tree import DecisionTreeClassifier\n",
    "from sklearn.metrics import confusion_matrix,classification_report,accuracy_score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "file_prostate=pd.read_hdf(\"/opt/raid10/genomics/rashid/GCN/data/GSE92742_fully_restricted.hdf\")\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_prostate=file_prostate.reset_index(level=[0,1,2,3,4,5,6]) #change from multi index to single index"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "prostate_y=df_prostate[\"moa\"].values #change moa column to numpy array\n",
    "df_prostate= df_prostate.drop(columns=[\"pert_id\",\"pert_name\",\"cell_id\",\"primary_site\",\"subtype\",\"moa\",\"Fold\"])\n",
    "prostate_X=df_prostate.values #get the matrix of feature values for 978 genes\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "labels,uniques=pd.factorize(prostate_y) #labels are the encodings\n",
    "prostate_y=labels"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "sc=StandardScaler()\n",
    "prostate_X=sc.fit_transform(prostate_X) #normalize feature data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#SPLIT DATA\n",
    "X_train,X_test,y_train,y_test=train_test_split(prostate_X,prostate_y,test_size=0.2,random_state=42)#split into train and test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "dct = DecisionTreeClassifier()\n",
    "dct.fit(X_train,y_train)\n",
    "pred_dct=dct.predict(X_test)  #1 min!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#METRICS\n",
    "print(confusion_matrix(y_test,pred_dct))\n",
    "print(classification_report(y_test,pred_dct))\n",
    "acc_dct=accuracy_score(y_test,pred_dct) #result for svm\n",
    "print(acc_dct) #print precision score \n",
    "#results doesn't quite matches in the paper where accuracy is 53.2 ± 1.16, macro F1 is 32.6 ± 0.91. Took 4-5 minutes on macbook pro 2.7 GHz inter core i5 processor, 16 GB 1867 MHz DDR3 Memomry, Intel Iris Graphics 6100 1536 MB Graphics"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.externals import joblib\n",
    "joblib.dump(dct,'decisionTree_MOA_FullCurated_notTuned')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "dct_benchmark=DecisionTreeClassifier(min_impurity_decrease=0.0012257673197162205, min_samples_leaf= 2, min_weight_fraction_leaf=0.0020780796100258966, max_depth=10, max_features=None, criterion= 'entropy', max_leaf_nodes=None, min_samples_split=2, splitter='best')\n",
    "dct_benchmark.fit(X_train,y_train)\n",
    "pred_dct_benchmark=dct_benchmark.predict(X_test)  #1 min!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#METRICS\n",
    "print(confusion_matrix(y_test,pred_dct_benchmark))\n",
    "print(classification_report(y_test,pred_dct_benchmark))\n",
    "acc_dct_benchmark=accuracy_score(y_test,pred_dct_benchmark) #result for svm\n",
    "print(acc_dct_benchmark) #print precision score \n",
    "#results almost matches in the paper where accuracy is 53.2 ± 1.16, macro F1 is 32.6 ± 0.91. Took 4-5 minutes on macbook pro 2.7 GHz inter core i5 processor, 16 GB 1867 MHz DDR3 Memomry, Intel Iris Graphics 6100 1536 MB Graphics"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "joblib.dump(dct_benchmark,'decisionTree_MOA_FullCurated_Tuned')"
   ]
  }
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