Ensemble_Learning.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "uVNsRgE_SB5n"
   },
   "source": [
    "#1.Imports"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "executionInfo": {
     "elapsed": 1378,
     "status": "ok",
     "timestamp": 1618605374247,
     "user": {
      "displayName": "sadra barikbin",
      "photoUrl": "https://lh3.googleusercontent.com/a-/AOh14GiCq0YqNHWfcZyly3I_AMTkvS8_tO8PZOQpGtkK=s64",
      "userId": "07513140462530453530"
     },
     "user_tz": -270
    },
    "id": "hfDt_en2tAxA"
   },
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "from numpy.random import choice,randint\n",
    "from collections import Counter\n",
    "from sklearn.base import BaseEstimator,ClassifierMixin,clone\n",
    "from sklearn.utils import resample\n",
    "from sklearn.datasets import load_breast_cancer\n",
    "from sklearn.model_selection import train_test_split,GridSearchCV,cross_val_score,ShuffleSplit,validation_curve\n",
    "from sklearn.svm import SVC\n",
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "from sklearn.tree import DecisionTreeClassifier\n",
    "from sklearn.ensemble import AdaBoostClassifier,RandomForestClassifier\n",
    "from sklearn.discriminant_analysis import LinearDiscriminantAnalysis as LDA\n",
    "from sklearn.metrics import balanced_accuracy_score,make_scorer,classification_report\n",
    "from sklearn.pipeline import Pipeline\n",
    "from sklearn.decomposition import PCA\n",
    "from sklearn.feature_selection import SelectKBest,mutual_info_classif"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "ZLAHLZhmSGfC"
   },
   "source": [
    "#2.Two simple Bagging implementation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "executionInfo": {
     "elapsed": 2067,
     "status": "ok",
     "timestamp": 1618558198854,
     "user": {
      "displayName": "sadra barikbin",
      "photoUrl": "https://lh3.googleusercontent.com/a-/AOh14GiCq0YqNHWfcZyly3I_AMTkvS8_tO8PZOQpGtkK=s64",
      "userId": "07513140462530453530"
     },
     "user_tz": -270
    },
    "id": "VTcy3Mv3s3G1"
   },
   "outputs": [],
   "source": [
    "class SimpleBagger(BaseEstimator,ClassifierMixin):\n",
    "  def __init__(self,base_estimator=SVC(),n_estimators=5):\n",
    "    self.base_estimator=base_estimator\n",
    "    self.n_estimators=n_estimators\n",
    "  def get_params(self,deep=True):\n",
    "    return {'base_estimator':self.base_estimator,'n_estimators':self.n_estimators}\n",
    "  def set_params(self,**params):\n",
    "    self.base_estimator=params['base_estimator']\n",
    "    self.n_estimators=params['n_estimators']\n",
    "    return self\n",
    "  def fit(self,X,y):\n",
    "    self.members=[clone(self.base_estimator) for _ in range(self.n_estimators)]\n",
    "    for member in self.members:\n",
    "      member.fit(*resample(X,y))\n",
    "    return self\n",
    "  def predict(self,X):\n",
    "    preds=np.array([member.predict(X) for member in self.members])\n",
    "    return np.array([Counter(votes).most_common(1)[0][0] for votes in preds.T])\n",
    "  def score(self,X,y):\n",
    "    return sum(self.predict(X)==y)/X.shape[0]\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "executionInfo": {
     "elapsed": 1676,
     "status": "ok",
     "timestamp": 1618345563427,
     "user": {
      "displayName": "sadra barikbin",
      "photoUrl": "https://lh3.googleusercontent.com/a-/AOh14GiCq0YqNHWfcZyly3I_AMTkvS8_tO8PZOQpGtkK=s64",
      "userId": "07513140462530453530"
     },
     "user_tz": -270
    },
    "id": "Vb5r-Lnmo8qG"
   },
   "outputs": [],
   "source": [
    "data = load_breast_cancer()\n",
    "X_train, X_test, y_train, y_test = train_test_split(data.data,data.target,test_size=0.33, random_state=42)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 978,
     "status": "ok",
     "timestamp": 1618350304476,
     "user": {
      "displayName": "sadra barikbin",
      "photoUrl": "https://lh3.googleusercontent.com/a-/AOh14GiCq0YqNHWfcZyly3I_AMTkvS8_tO8PZOQpGtkK=s64",
      "userId": "07513140462530453530"
     },
     "user_tz": -270
    },
    "id": "-PkSLk6A5_7e",
    "outputId": "5b8ff95c-481d-4f00-bc6e-51beee450412"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9414893617021277"
      ]
     },
     "execution_count": 61,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bagger=SimpleBagger(SVC(),10)\n",
    "bagger.fit(X_train,y_train)\n",
    "bagger.score(X_test,y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 9936,
     "status": "ok",
     "timestamp": 1618350525292,
     "user": {
      "displayName": "sadra barikbin",
      "photoUrl": "https://lh3.googleusercontent.com/a-/AOh14GiCq0YqNHWfcZyly3I_AMTkvS8_tO8PZOQpGtkK=s64",
      "userId": "07513140462530453530"
     },
     "user_tz": -270
    },
    "id": "pVnN9PHm667x",
    "outputId": "e646f3e1-c3e7-4c61-ca87-c4888a5c91cb"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Best Ensemble:{'base_estimator': DecisionTreeClassifier(ccp_alpha=0.0, class_weight=None, criterion='gini',\n",
      "                       max_depth=None, max_features=None, max_leaf_nodes=None,\n",
      "                       min_impurity_decrease=0.0, min_impurity_split=None,\n",
      "                       min_samples_leaf=1, min_samples_split=2,\n",
      "                       min_weight_fraction_leaf=0.0, presort='deprecated',\n",
      "                       random_state=None, splitter='best'), 'n_estimators': 50}\n",
      "Its score:0.9561092997981679\n"
     ]
    }
   ],
   "source": [
    "baggerGrid=GridSearchCV(SimpleBagger(),{'n_estimators':[2,5,10,20,50],'base_estimator':(SVC(),KNeighborsClassifier(),DecisionTreeClassifier())})\n",
    "baggerGrid.fit(data.data,data.target)\n",
    "print(f\"Best Ensemble:{baggerGrid.best_params_}\\nIts score:{baggerGrid.best_score_}\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "executionInfo": {
     "elapsed": 1851,
     "status": "ok",
     "timestamp": 1618558208495,
     "user": {
      "displayName": "sadra barikbin",
      "photoUrl": "https://lh3.googleusercontent.com/a-/AOh14GiCq0YqNHWfcZyly3I_AMTkvS8_tO8PZOQpGtkK=s64",
      "userId": "07513140462530453530"
     },
     "user_tz": -270
    },
    "id": "S61H_mBcGRMt"
   },
   "outputs": [],
   "source": [
    "class LessSimpleBagger(ClassifierMixin):\n",
    "  def __init__(self,base_estimator=SVC(),n_estimators=5,max_features=0.5):\n",
    "    self.base_estimator=base_estimator\n",
    "    self.n_estimators=n_estimators\n",
    "    self.max_features=max_features\n",
    "  def get_params(self,deep=True):\n",
    "    return {'base_estimator':self.base_estimator,'n_estimators':self.n_estimators,'max_features':self.max_features}\n",
    "  def set_params(self,**params):\n",
    "    self.base_estimator=params['base_estimator']\n",
    "    self.n_estimators=params['n_estimators']\n",
    "    self.max_features=params['max_features']\n",
    "    return self\n",
    "  def fit(self,X,y):\n",
    "    self.members=[(clone(self.base_estimator),choice(X.shape[1],size=(randint(self.max_features*X.shape[1])+1,))) for _ in range(self.n_estimators)]\n",
    "    for member,features in self.members:\n",
    "      member.fit(*resample(X[:,features],y))\n",
    "  def predict(self,X):\n",
    "    preds=np.array([member.predict(X[:,features]) for member,features in self.members])\n",
    "    return np.array([Counter(votes).most_common(1)[0][0] for votes in preds.T])\n",
    "  def score(self,X,y):\n",
    "    return sum(self.predict(X)==y)/X.shape[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 21465,
     "status": "ok",
     "timestamp": 1618350514036,
     "user": {
      "displayName": "sadra barikbin",
      "photoUrl": "https://lh3.googleusercontent.com/a-/AOh14GiCq0YqNHWfcZyly3I_AMTkvS8_tO8PZOQpGtkK=s64",
      "userId": "07513140462530453530"
     },
     "user_tz": -270
    },
    "id": "bcd9vK9wLgWp",
    "outputId": "57ea5a07-6732-4f98-da72-a5cd5bcbc584"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Best Ensemble:{'base_estimator': DecisionTreeClassifier(ccp_alpha=0.0, class_weight=None, criterion='gini',\n",
      "                       max_depth=None, max_features=None, max_leaf_nodes=None,\n",
      "                       min_impurity_decrease=0.0, min_impurity_split=None,\n",
      "                       min_samples_leaf=1, min_samples_split=2,\n",
      "                       min_weight_fraction_leaf=0.0, presort='deprecated',\n",
      "                       random_state=None, splitter='best'), 'max_features': 1.0, 'n_estimators': 20}\n",
      "Its score:0.9648657040832168\n"
     ]
    }
   ],
   "source": [
    "baggerGrid=GridSearchCV(LessSimpleBagger(),{'n_estimators':[2,5,10,20,50],'base_estimator':(SVC(),KNeighborsClassifier(),DecisionTreeClassifier()),'max_features':[0.5,0.8,1.]})\n",
    "baggerGrid.fit(data.data,data.target)\n",
    "print(f\"Best Ensemble:{baggerGrid.best_params_}\\nIts score:{baggerGrid.best_score_}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "WzqA1cIZSWii"
   },
   "source": [
    "#3.OCD high-dimensional dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 7577,
     "status": "ok",
     "timestamp": 1618605208226,
     "user": {
      "displayName": "sadra barikbin",
      "photoUrl": "https://lh3.googleusercontent.com/a-/AOh14GiCq0YqNHWfcZyly3I_AMTkvS8_tO8PZOQpGtkK=s64",
      "userId": "07513140462530453530"
     },
     "user_tz": -270
    },
    "id": "sG5vjPiKV7uw",
    "outputId": "7c912cab-6b96-4c35-a73c-0976f091f303"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Collecting GEOparse\n",
      "\u001b[?25l  Downloading https://files.pythonhosted.org/packages/8b/28/e2e4e3bb168b6d6494c3cee264e351d9b9f8cb3b27a70f4f475a76d90275/GEOparse-2.0.3.tar.gz (278kB)\n",
      "\r",
      "\u001b[K     |█▏                              | 10kB 15.3MB/s eta 0:00:01\r",
      "\u001b[K     |██▍                             | 20kB 20.4MB/s eta 0:00:01\r",
      "\u001b[K     |███▌                            | 30kB 10.1MB/s eta 0:00:01\r",
      "\u001b[K     |████▊                           | 40kB 7.6MB/s eta 0:00:01\r",
      "\u001b[K     |█████▉                          | 51kB 4.6MB/s eta 0:00:01\r",
      "\u001b[K     |███████                         | 61kB 5.0MB/s eta 0:00:01\r",
      "\u001b[K     |████████▎                       | 71kB 5.5MB/s eta 0:00:01\r",
      "\u001b[K     |█████████▍                      | 81kB 5.5MB/s eta 0:00:01\r",
      "\u001b[K     |██████████▋                     | 92kB 5.1MB/s eta 0:00:01\r",
      "\u001b[K     |███████████▊                    | 102kB 4.4MB/s eta 0:00:01\r",
      "\u001b[K     |█████████████                   | 112kB 4.4MB/s eta 0:00:01\r",
      "\u001b[K     |██████████████▏                 | 122kB 4.4MB/s eta 0:00:01\r",
      "\u001b[K     |███████████████▎                | 133kB 4.4MB/s eta 0:00:01\r",
      "\u001b[K     |████████████████▌               | 143kB 4.4MB/s eta 0:00:01\r",
      "\u001b[K     |█████████████████▋              | 153kB 4.4MB/s eta 0:00:01\r",
      "\u001b[K     |██████████████████▉             | 163kB 4.4MB/s eta 0:00:01\r",
      "\u001b[K     |████████████████████            | 174kB 4.4MB/s eta 0:00:01\r",
      "\u001b[K     |█████████████████████▏          | 184kB 4.4MB/s eta 0:00:01\r",
      "\u001b[K     |██████████████████████▍         | 194kB 4.4MB/s eta 0:00:01\r",
      "\u001b[K     |███████████████████████▌        | 204kB 4.4MB/s eta 0:00:01\r",
      "\u001b[K     |████████████████████████▊       | 215kB 4.4MB/s eta 0:00:01\r",
      "\u001b[K     |█████████████████████████▉      | 225kB 4.4MB/s eta 0:00:01\r",
      "\u001b[K     |███████████████████████████     | 235kB 4.4MB/s eta 0:00:01\r",
      "\u001b[K     |████████████████████████████▎   | 245kB 4.4MB/s eta 0:00:01\r",
      "\u001b[K     |█████████████████████████████▍  | 256kB 4.4MB/s eta 0:00:01\r",
      "\u001b[K     |██████████████████████████████▋ | 266kB 4.4MB/s eta 0:00:01\r",
      "\u001b[K     |███████████████████████████████▊| 276kB 4.4MB/s eta 0:00:01\r",
      "\u001b[K     |████████████████████████████████| 286kB 4.4MB/s \n",
      "\u001b[?25hRequirement already satisfied: numpy>=1.7 in /usr/local/lib/python3.7/dist-packages (from GEOparse) (1.19.5)\n",
      "Requirement already satisfied: pandas>=0.17 in /usr/local/lib/python3.7/dist-packages (from GEOparse) (1.1.5)\n",
      "Requirement already satisfied: requests>=2.21.0 in /usr/local/lib/python3.7/dist-packages (from GEOparse) (2.23.0)\n",
      "Requirement already satisfied: tqdm>=4.31.1 in /usr/local/lib/python3.7/dist-packages (from GEOparse) (4.41.1)\n",
      "Requirement already satisfied: pytz>=2017.2 in /usr/local/lib/python3.7/dist-packages (from pandas>=0.17->GEOparse) (2018.9)\n",
      "Requirement already satisfied: python-dateutil>=2.7.3 in /usr/local/lib/python3.7/dist-packages (from pandas>=0.17->GEOparse) (2.8.1)\n",
      "Requirement already satisfied: urllib3!=1.25.0,!=1.25.1,<1.26,>=1.21.1 in /usr/local/lib/python3.7/dist-packages (from requests>=2.21.0->GEOparse) (1.24.3)\n",
      "Requirement already satisfied: chardet<4,>=3.0.2 in /usr/local/lib/python3.7/dist-packages (from requests>=2.21.0->GEOparse) (3.0.4)\n",
      "Requirement already satisfied: idna<3,>=2.5 in /usr/local/lib/python3.7/dist-packages (from requests>=2.21.0->GEOparse) (2.10)\n",
      "Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.7/dist-packages (from requests>=2.21.0->GEOparse) (2020.12.5)\n",
      "Requirement already satisfied: six>=1.5 in /usr/local/lib/python3.7/dist-packages (from python-dateutil>=2.7.3->pandas>=0.17->GEOparse) (1.15.0)\n",
      "Building wheels for collected packages: GEOparse\n",
      "  Building wheel for GEOparse (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
      "  Created wheel for GEOparse: filename=GEOparse-2.0.3-cp37-none-any.whl size=29034 sha256=baa473adc8e0526bc12ea6e5c5c5d8fe7548335c53a3372e9c6309fbc9d64a71\n",
      "  Stored in directory: /root/.cache/pip/wheels/c6/5e/b4/7109a7b8e8046bd8586349a6ac1a76ef8190d12a61a7b4cf0d\n",
      "Successfully built GEOparse\n",
      "Installing collected packages: GEOparse\n",
      "Successfully installed GEOparse-2.0.3\n"
     ]
    }
   ],
   "source": [
    "!pip install GEOparse"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 26786,
     "status": "ok",
     "timestamp": 1618605229753,
     "user": {
      "displayName": "sadra barikbin",
      "photoUrl": "https://lh3.googleusercontent.com/a-/AOh14GiCq0YqNHWfcZyly3I_AMTkvS8_tO8PZOQpGtkK=s64",
      "userId": "07513140462530453530"
     },
     "user_tz": -270
    },
    "id": "Fo76pjASa7Wk",
    "outputId": "1dc6e954-2244-4603-9897-b43873dfdd97"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "16-Apr-2021 20:33:27 DEBUG utils - Directory ./ already exists. Skipping.\n",
      "16-Apr-2021 20:33:27 INFO GEOparse - Downloading ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE60nnn/GSE60190/soft/GSE60190_family.soft.gz to ./GSE60190_family.soft.gz\n",
      "100%|██████████| 65.6M/65.6M [00:00<00:00, 111MB/s]\n",
      "16-Apr-2021 20:33:28 DEBUG downloader - Size validation passed\n",
      "16-Apr-2021 20:33:28 DEBUG downloader - Moving /tmp/tmphdlrt7y6 to /content/GSE60190_family.soft.gz\n",
      "16-Apr-2021 20:33:28 DEBUG downloader - Successfully downloaded ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE60nnn/GSE60190/soft/GSE60190_family.soft.gz\n",
      "16-Apr-2021 20:33:28 INFO GEOparse - Parsing ./GSE60190_family.soft.gz: \n",
      "16-Apr-2021 20:33:28 DEBUG GEOparse - DATABASE: GeoMiame\n",
      "16-Apr-2021 20:33:28 DEBUG GEOparse - SERIES: GSE60190\n",
      "16-Apr-2021 20:33:28 DEBUG GEOparse - PLATFORM: GPL6947\n",
      "16-Apr-2021 20:33:30 DEBUG GEOparse - SAMPLE: GSM1467273\n",
      "16-Apr-2021 20:33:30 DEBUG GEOparse - SAMPLE: GSM1467274\n",
      "16-Apr-2021 20:33:30 DEBUG GEOparse - SAMPLE: GSM1467275\n",
      "16-Apr-2021 20:33:30 DEBUG GEOparse - SAMPLE: GSM1467276\n",
      "16-Apr-2021 20:33:30 DEBUG GEOparse - SAMPLE: GSM1467277\n",
      "16-Apr-2021 20:33:30 DEBUG GEOparse - SAMPLE: GSM1467278\n",
      "16-Apr-2021 20:33:30 DEBUG GEOparse - SAMPLE: GSM1467279\n",
      "16-Apr-2021 20:33:31 DEBUG GEOparse - SAMPLE: GSM1467280\n",
      "16-Apr-2021 20:33:31 DEBUG GEOparse - SAMPLE: GSM1467281\n",
      "16-Apr-2021 20:33:31 DEBUG GEOparse - SAMPLE: GSM1467282\n",
      "16-Apr-2021 20:33:31 DEBUG GEOparse - SAMPLE: GSM1467283\n",
      "16-Apr-2021 20:33:31 DEBUG GEOparse - SAMPLE: GSM1467284\n",
      "16-Apr-2021 20:33:31 DEBUG GEOparse - SAMPLE: GSM1467285\n",
      "16-Apr-2021 20:33:31 DEBUG GEOparse - SAMPLE: GSM1467286\n",
      "16-Apr-2021 20:33:32 DEBUG GEOparse - SAMPLE: GSM1467287\n",
      "16-Apr-2021 20:33:32 DEBUG GEOparse - SAMPLE: GSM1467288\n",
      "16-Apr-2021 20:33:32 DEBUG GEOparse - SAMPLE: GSM1467289\n",
      "16-Apr-2021 20:33:32 DEBUG GEOparse - SAMPLE: GSM1467290\n",
      "16-Apr-2021 20:33:32 DEBUG GEOparse - SAMPLE: GSM1467291\n",
      "16-Apr-2021 20:33:32 DEBUG GEOparse - SAMPLE: GSM1467292\n",
      "16-Apr-2021 20:33:32 DEBUG GEOparse - SAMPLE: GSM1467293\n",
      "16-Apr-2021 20:33:33 DEBUG GEOparse - SAMPLE: GSM1467294\n",
      "16-Apr-2021 20:33:33 DEBUG GEOparse - SAMPLE: GSM1467295\n",
      "16-Apr-2021 20:33:33 DEBUG GEOparse - SAMPLE: GSM1467296\n",
      "16-Apr-2021 20:33:33 DEBUG GEOparse - SAMPLE: GSM1467297\n",
      "16-Apr-2021 20:33:33 DEBUG GEOparse - SAMPLE: GSM1467298\n",
      "16-Apr-2021 20:33:33 DEBUG GEOparse - SAMPLE: GSM1467299\n",
      "16-Apr-2021 20:33:33 DEBUG GEOparse - SAMPLE: GSM1467300\n",
      "16-Apr-2021 20:33:34 DEBUG GEOparse - SAMPLE: GSM1467301\n",
      "16-Apr-2021 20:33:34 DEBUG GEOparse - SAMPLE: GSM1467302\n",
      "16-Apr-2021 20:33:34 DEBUG GEOparse - SAMPLE: GSM1467303\n",
      "16-Apr-2021 20:33:34 DEBUG GEOparse - SAMPLE: GSM1467304\n",
      "16-Apr-2021 20:33:34 DEBUG GEOparse - SAMPLE: GSM1467305\n",
      "16-Apr-2021 20:33:34 DEBUG GEOparse - SAMPLE: GSM1467306\n",
      "16-Apr-2021 20:33:34 DEBUG GEOparse - SAMPLE: GSM1467307\n",
      "16-Apr-2021 20:33:35 DEBUG GEOparse - SAMPLE: GSM1467308\n",
      "16-Apr-2021 20:33:35 DEBUG GEOparse - SAMPLE: GSM1467309\n",
      "16-Apr-2021 20:33:35 DEBUG GEOparse - SAMPLE: GSM1467310\n",
      "16-Apr-2021 20:33:35 DEBUG GEOparse - SAMPLE: GSM1467311\n",
      "16-Apr-2021 20:33:35 DEBUG GEOparse - SAMPLE: GSM1467312\n",
      "16-Apr-2021 20:33:35 DEBUG GEOparse - SAMPLE: GSM1467313\n",
      "16-Apr-2021 20:33:35 DEBUG GEOparse - SAMPLE: GSM1467314\n",
      "16-Apr-2021 20:33:36 DEBUG GEOparse - SAMPLE: GSM1467315\n",
      "16-Apr-2021 20:33:36 DEBUG GEOparse - SAMPLE: GSM1467316\n",
      "16-Apr-2021 20:33:36 DEBUG GEOparse - SAMPLE: GSM1467317\n",
      "16-Apr-2021 20:33:36 DEBUG GEOparse - SAMPLE: GSM1467318\n",
      "16-Apr-2021 20:33:36 DEBUG GEOparse - SAMPLE: GSM1467319\n",
      "16-Apr-2021 20:33:36 DEBUG GEOparse - SAMPLE: GSM1467320\n",
      "16-Apr-2021 20:33:37 DEBUG GEOparse - SAMPLE: GSM1467321\n",
      "16-Apr-2021 20:33:37 DEBUG GEOparse - SAMPLE: GSM1467322\n",
      "16-Apr-2021 20:33:37 DEBUG GEOparse - SAMPLE: GSM1467323\n",
      "16-Apr-2021 20:33:37 DEBUG GEOparse - SAMPLE: GSM1467324\n",
      "16-Apr-2021 20:33:37 DEBUG GEOparse - SAMPLE: GSM1467325\n",
      "16-Apr-2021 20:33:37 DEBUG GEOparse - SAMPLE: GSM1467326\n",
      "16-Apr-2021 20:33:37 DEBUG GEOparse - SAMPLE: GSM1467327\n",
      "16-Apr-2021 20:33:38 DEBUG GEOparse - SAMPLE: GSM1467328\n",
      "16-Apr-2021 20:33:38 DEBUG GEOparse - SAMPLE: GSM1467329\n",
      "16-Apr-2021 20:33:38 DEBUG GEOparse - SAMPLE: GSM1467330\n",
      "16-Apr-2021 20:33:38 DEBUG GEOparse - SAMPLE: GSM1467331\n",
      "16-Apr-2021 20:33:38 DEBUG GEOparse - SAMPLE: GSM1467332\n",
      "16-Apr-2021 20:33:38 DEBUG GEOparse - SAMPLE: GSM1467333\n",
      "16-Apr-2021 20:33:38 DEBUG GEOparse - SAMPLE: GSM1467334\n",
      "16-Apr-2021 20:33:39 DEBUG GEOparse - SAMPLE: GSM1467335\n",
      "16-Apr-2021 20:33:39 DEBUG GEOparse - SAMPLE: GSM1467336\n",
      "16-Apr-2021 20:33:39 DEBUG GEOparse - SAMPLE: GSM1467337\n",
      "16-Apr-2021 20:33:39 DEBUG GEOparse - SAMPLE: GSM1467338\n",
      "16-Apr-2021 20:33:39 DEBUG GEOparse - SAMPLE: GSM1467339\n",
      "16-Apr-2021 20:33:39 DEBUG GEOparse - SAMPLE: GSM1467340\n",
      "16-Apr-2021 20:33:39 DEBUG GEOparse - SAMPLE: GSM1467341\n",
      "16-Apr-2021 20:33:40 DEBUG GEOparse - SAMPLE: GSM1467342\n",
      "16-Apr-2021 20:33:40 DEBUG GEOparse - SAMPLE: GSM1467343\n",
      "16-Apr-2021 20:33:40 DEBUG GEOparse - SAMPLE: GSM1467344\n",
      "16-Apr-2021 20:33:40 DEBUG GEOparse - SAMPLE: GSM1467345\n",
      "16-Apr-2021 20:33:40 DEBUG GEOparse - SAMPLE: GSM1467346\n",
      "16-Apr-2021 20:33:40 DEBUG GEOparse - SAMPLE: GSM1467347\n",
      "16-Apr-2021 20:33:40 DEBUG GEOparse - SAMPLE: GSM1467348\n",
      "16-Apr-2021 20:33:41 DEBUG GEOparse - SAMPLE: GSM1467349\n",
      "16-Apr-2021 20:33:41 DEBUG GEOparse - SAMPLE: GSM1467350\n",
      "16-Apr-2021 20:33:41 DEBUG GEOparse - SAMPLE: GSM1467351\n",
      "16-Apr-2021 20:33:41 DEBUG GEOparse - SAMPLE: GSM1467352\n",
      "16-Apr-2021 20:33:41 DEBUG GEOparse - SAMPLE: GSM1467353\n",
      "16-Apr-2021 20:33:41 DEBUG GEOparse - SAMPLE: GSM1467354\n",
      "16-Apr-2021 20:33:41 DEBUG GEOparse - SAMPLE: GSM1467355\n",
      "16-Apr-2021 20:33:42 DEBUG GEOparse - SAMPLE: GSM1467356\n",
      "16-Apr-2021 20:33:42 DEBUG GEOparse - SAMPLE: GSM1467357\n",
      "16-Apr-2021 20:33:42 DEBUG GEOparse - SAMPLE: GSM1467358\n",
      "16-Apr-2021 20:33:42 DEBUG GEOparse - SAMPLE: GSM1467359\n",
      "16-Apr-2021 20:33:42 DEBUG GEOparse - SAMPLE: GSM1467360\n",
      "16-Apr-2021 20:33:42 DEBUG GEOparse - SAMPLE: GSM1467361\n",
      "16-Apr-2021 20:33:42 DEBUG GEOparse - SAMPLE: GSM1467362\n",
      "16-Apr-2021 20:33:43 DEBUG GEOparse - SAMPLE: GSM1467363\n",
      "16-Apr-2021 20:33:43 DEBUG GEOparse - SAMPLE: GSM1467364\n",
      "16-Apr-2021 20:33:43 DEBUG GEOparse - SAMPLE: GSM1467365\n",
      "16-Apr-2021 20:33:43 DEBUG GEOparse - SAMPLE: GSM1467366\n",
      "16-Apr-2021 20:33:43 DEBUG GEOparse - SAMPLE: GSM1467367\n",
      "16-Apr-2021 20:33:43 DEBUG GEOparse - SAMPLE: GSM1467368\n",
      "16-Apr-2021 20:33:43 DEBUG GEOparse - SAMPLE: GSM1467369\n",
      "16-Apr-2021 20:33:44 DEBUG GEOparse - SAMPLE: GSM1467370\n",
      "16-Apr-2021 20:33:44 DEBUG GEOparse - SAMPLE: GSM1467371\n",
      "16-Apr-2021 20:33:44 DEBUG GEOparse - SAMPLE: GSM1467372\n",
      "16-Apr-2021 20:33:44 DEBUG GEOparse - SAMPLE: GSM1467373\n",
      "16-Apr-2021 20:33:44 DEBUG GEOparse - SAMPLE: GSM1467374\n",
      "16-Apr-2021 20:33:44 DEBUG GEOparse - SAMPLE: GSM1467375\n",
      "16-Apr-2021 20:33:44 DEBUG GEOparse - SAMPLE: GSM1467376\n",
      "16-Apr-2021 20:33:45 DEBUG GEOparse - SAMPLE: GSM1467377\n",
      "16-Apr-2021 20:33:45 DEBUG GEOparse - SAMPLE: GSM1467378\n",
      "16-Apr-2021 20:33:45 DEBUG GEOparse - SAMPLE: GSM1467379\n",
      "16-Apr-2021 20:33:45 DEBUG GEOparse - SAMPLE: GSM1467380\n",
      "16-Apr-2021 20:33:45 DEBUG GEOparse - SAMPLE: GSM1467381\n",
      "16-Apr-2021 20:33:45 DEBUG GEOparse - SAMPLE: GSM1467382\n",
      "16-Apr-2021 20:33:46 DEBUG GEOparse - SAMPLE: GSM1467383\n",
      "16-Apr-2021 20:33:46 DEBUG GEOparse - SAMPLE: GSM1467384\n",
      "16-Apr-2021 20:33:46 DEBUG GEOparse - SAMPLE: GSM1467385\n",
      "16-Apr-2021 20:33:46 DEBUG GEOparse - SAMPLE: GSM1467386\n",
      "16-Apr-2021 20:33:46 DEBUG GEOparse - SAMPLE: GSM1467387\n",
      "16-Apr-2021 20:33:46 DEBUG GEOparse - SAMPLE: GSM1467388\n",
      "16-Apr-2021 20:33:46 DEBUG GEOparse - SAMPLE: GSM1467389\n",
      "16-Apr-2021 20:33:47 DEBUG GEOparse - SAMPLE: GSM1467390\n",
      "16-Apr-2021 20:33:47 DEBUG GEOparse - SAMPLE: GSM1467391\n",
      "16-Apr-2021 20:33:47 DEBUG GEOparse - SAMPLE: GSM1467392\n",
      "16-Apr-2021 20:33:47 DEBUG GEOparse - SAMPLE: GSM1467393\n",
      "16-Apr-2021 20:33:47 DEBUG GEOparse - SAMPLE: GSM1467394\n",
      "16-Apr-2021 20:33:47 DEBUG GEOparse - SAMPLE: GSM1467395\n",
      "16-Apr-2021 20:33:47 DEBUG GEOparse - SAMPLE: GSM1467396\n",
      "16-Apr-2021 20:33:48 DEBUG GEOparse - SAMPLE: GSM1467397\n",
      "16-Apr-2021 20:33:48 DEBUG GEOparse - SAMPLE: GSM1467398\n",
      "16-Apr-2021 20:33:48 DEBUG GEOparse - SAMPLE: GSM1467399\n",
      "16-Apr-2021 20:33:48 DEBUG GEOparse - SAMPLE: GSM1467400\n",
      "16-Apr-2021 20:33:48 DEBUG GEOparse - SAMPLE: GSM1467401\n",
      "16-Apr-2021 20:33:48 DEBUG GEOparse - SAMPLE: GSM1467402\n",
      "16-Apr-2021 20:33:48 DEBUG GEOparse - SAMPLE: GSM1467403\n",
      "16-Apr-2021 20:33:49 DEBUG GEOparse - SAMPLE: GSM1467404\n",
      "16-Apr-2021 20:33:49 DEBUG GEOparse - SAMPLE: GSM1467405\n"
     ]
    }
   ],
   "source": [
    "import GEOparse as G\n",
    "gse = G.get_GEO(geo=\"GSE60190\", destdir=\"./\")\n",
    "#https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE60190"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 284
    },
    "executionInfo": {
     "elapsed": 25371,
     "status": "ok",
     "timestamp": 1618605231339,
     "user": {
      "displayName": "sadra barikbin",
      "photoUrl": "https://lh3.googleusercontent.com/a-/AOh14GiCq0YqNHWfcZyly3I_AMTkvS8_tO8PZOQpGtkK=s64",
      "userId": "07513140462530453530"
     },
     "user_tz": -270
    },
    "id": "LV4dMo1sWDfV",
    "outputId": "d8d54e60-300b-4843-840c-6356aebbf671"
   },
   "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>GSM1467273</th>\n",
       "      <th>GSM1467274</th>\n",
       "      <th>GSM1467275</th>\n",
       "      <th>GSM1467276</th>\n",
       "      <th>GSM1467277</th>\n",
       "      <th>GSM1467278</th>\n",
       "      <th>GSM1467279</th>\n",
       "      <th>GSM1467280</th>\n",
       "      <th>GSM1467281</th>\n",
       "      <th>GSM1467282</th>\n",
       "      <th>GSM1467283</th>\n",
       "      <th>GSM1467284</th>\n",
       "      <th>GSM1467285</th>\n",
       "      <th>GSM1467286</th>\n",
       "      <th>GSM1467287</th>\n",
       "      <th>GSM1467288</th>\n",
       "      <th>GSM1467289</th>\n",
       "      <th>GSM1467290</th>\n",
       "      <th>GSM1467291</th>\n",
       "      <th>GSM1467292</th>\n",
       "      <th>GSM1467293</th>\n",
       "      <th>GSM1467294</th>\n",
       "      <th>GSM1467295</th>\n",
       "      <th>GSM1467296</th>\n",
       "      <th>GSM1467297</th>\n",
       "      <th>GSM1467298</th>\n",
       "      <th>GSM1467299</th>\n",
       "      <th>GSM1467300</th>\n",
       "      <th>GSM1467301</th>\n",
       "      <th>GSM1467302</th>\n",
       "      <th>GSM1467303</th>\n",
       "      <th>GSM1467304</th>\n",
       "      <th>GSM1467305</th>\n",
       "      <th>GSM1467306</th>\n",
       "      <th>GSM1467307</th>\n",
       "      <th>GSM1467308</th>\n",
       "      <th>GSM1467309</th>\n",
       "      <th>GSM1467310</th>\n",
       "      <th>GSM1467311</th>\n",
       "      <th>GSM1467312</th>\n",
       "      <th>...</th>\n",
       "      <th>GSM1467366</th>\n",
       "      <th>GSM1467367</th>\n",
       "      <th>GSM1467368</th>\n",
       "      <th>GSM1467369</th>\n",
       "      <th>GSM1467370</th>\n",
       "      <th>GSM1467371</th>\n",
       "      <th>GSM1467372</th>\n",
       "      <th>GSM1467373</th>\n",
       "      <th>GSM1467374</th>\n",
       "      <th>GSM1467375</th>\n",
       "      <th>GSM1467376</th>\n",
       "      <th>GSM1467377</th>\n",
       "      <th>GSM1467378</th>\n",
       "      <th>GSM1467379</th>\n",
       "      <th>GSM1467380</th>\n",
       "      <th>GSM1467381</th>\n",
       "      <th>GSM1467382</th>\n",
       "      <th>GSM1467383</th>\n",
       "      <th>GSM1467384</th>\n",
       "      <th>GSM1467385</th>\n",
       "      <th>GSM1467386</th>\n",
       "      <th>GSM1467387</th>\n",
       "      <th>GSM1467388</th>\n",
       "      <th>GSM1467389</th>\n",
       "      <th>GSM1467390</th>\n",
       "      <th>GSM1467391</th>\n",
       "      <th>GSM1467392</th>\n",
       "      <th>GSM1467393</th>\n",
       "      <th>GSM1467394</th>\n",
       "      <th>GSM1467395</th>\n",
       "      <th>GSM1467396</th>\n",
       "      <th>GSM1467397</th>\n",
       "      <th>GSM1467398</th>\n",
       "      <th>GSM1467399</th>\n",
       "      <th>GSM1467400</th>\n",
       "      <th>GSM1467401</th>\n",
       "      <th>GSM1467402</th>\n",
       "      <th>GSM1467403</th>\n",
       "      <th>GSM1467404</th>\n",
       "      <th>GSM1467405</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>GENE</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>ILMN_1762337</th>\n",
       "      <td>7.386233</td>\n",
       "      <td>7.383662</td>\n",
       "      <td>7.391908</td>\n",
       "      <td>7.394525</td>\n",
       "      <td>7.380174</td>\n",
       "      <td>7.392893</td>\n",
       "      <td>7.395035</td>\n",
       "      <td>7.385740</td>\n",
       "      <td>7.392392</td>\n",
       "      <td>7.381276</td>\n",
       "      <td>7.400817</td>\n",
       "      <td>7.400051</td>\n",
       "      <td>7.451436</td>\n",
       "      <td>7.392118</td>\n",
       "      <td>7.395321</td>\n",
       "      <td>7.385121</td>\n",
       "      <td>7.385313</td>\n",
       "      <td>7.375387</td>\n",
       "      <td>7.390471</td>\n",
       "      <td>7.419163</td>\n",
       "      <td>7.380511</td>\n",
       "      <td>7.403535</td>\n",
       "      <td>7.398820</td>\n",
       "      <td>7.393632</td>\n",
       "      <td>7.394032</td>\n",
       "      <td>7.385716</td>\n",
       "      <td>7.387610</td>\n",
       "      <td>7.392589</td>\n",
       "      <td>7.387535</td>\n",
       "      <td>7.393915</td>\n",
       "      <td>7.379798</td>\n",
       "      <td>7.394142</td>\n",
       "      <td>7.419568</td>\n",
       "      <td>7.385260</td>\n",
       "      <td>7.382448</td>\n",
       "      <td>7.390901</td>\n",
       "      <td>7.386295</td>\n",
       "      <td>7.399189</td>\n",
       "      <td>7.397037</td>\n",
       "      <td>7.390348</td>\n",
       "      <td>...</td>\n",
       "      <td>7.388379</td>\n",
       "      <td>7.389425</td>\n",
       "      <td>7.385863</td>\n",
       "      <td>7.382194</td>\n",
       "      <td>7.410882</td>\n",
       "      <td>7.405704</td>\n",
       "      <td>7.404767</td>\n",
       "      <td>7.395976</td>\n",
       "      <td>7.378603</td>\n",
       "      <td>7.398384</td>\n",
       "      <td>7.384679</td>\n",
       "      <td>7.404515</td>\n",
       "      <td>7.389422</td>\n",
       "      <td>7.385648</td>\n",
       "      <td>7.389029</td>\n",
       "      <td>7.385783</td>\n",
       "      <td>7.395879</td>\n",
       "      <td>7.398518</td>\n",
       "      <td>7.384185</td>\n",
       "      <td>7.402536</td>\n",
       "      <td>7.380916</td>\n",
       "      <td>7.386598</td>\n",
       "      <td>7.429825</td>\n",
       "      <td>7.395899</td>\n",
       "      <td>7.384458</td>\n",
       "      <td>7.383185</td>\n",
       "      <td>7.404278</td>\n",
       "      <td>7.384904</td>\n",
       "      <td>7.390146</td>\n",
       "      <td>7.384375</td>\n",
       "      <td>7.388642</td>\n",
       "      <td>7.391313</td>\n",
       "      <td>7.383116</td>\n",
       "      <td>7.378260</td>\n",
       "      <td>7.386379</td>\n",
       "      <td>7.394498</td>\n",
       "      <td>7.382933</td>\n",
       "      <td>7.386308</td>\n",
       "      <td>7.383719</td>\n",
       "      <td>7.397961</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ILMN_2055271</th>\n",
       "      <td>7.566603</td>\n",
       "      <td>7.503766</td>\n",
       "      <td>7.514338</td>\n",
       "      <td>7.454698</td>\n",
       "      <td>7.461963</td>\n",
       "      <td>7.469844</td>\n",
       "      <td>7.529504</td>\n",
       "      <td>7.440339</td>\n",
       "      <td>7.432822</td>\n",
       "      <td>7.459135</td>\n",
       "      <td>7.543113</td>\n",
       "      <td>7.452501</td>\n",
       "      <td>7.705146</td>\n",
       "      <td>7.596125</td>\n",
       "      <td>7.498154</td>\n",
       "      <td>7.545299</td>\n",
       "      <td>7.406923</td>\n",
       "      <td>7.401228</td>\n",
       "      <td>7.421056</td>\n",
       "      <td>7.462629</td>\n",
       "      <td>7.400502</td>\n",
       "      <td>7.397459</td>\n",
       "      <td>7.424542</td>\n",
       "      <td>7.500946</td>\n",
       "      <td>7.544474</td>\n",
       "      <td>7.500917</td>\n",
       "      <td>7.466203</td>\n",
       "      <td>7.422578</td>\n",
       "      <td>7.429444</td>\n",
       "      <td>7.405371</td>\n",
       "      <td>7.445335</td>\n",
       "      <td>7.679057</td>\n",
       "      <td>7.536705</td>\n",
       "      <td>7.440260</td>\n",
       "      <td>7.393190</td>\n",
       "      <td>7.617611</td>\n",
       "      <td>7.590569</td>\n",
       "      <td>7.435465</td>\n",
       "      <td>7.400972</td>\n",
       "      <td>7.390185</td>\n",
       "      <td>...</td>\n",
       "      <td>7.456618</td>\n",
       "      <td>7.420314</td>\n",
       "      <td>7.467558</td>\n",
       "      <td>7.464614</td>\n",
       "      <td>7.425586</td>\n",
       "      <td>7.488138</td>\n",
       "      <td>7.437731</td>\n",
       "      <td>7.435836</td>\n",
       "      <td>7.514324</td>\n",
       "      <td>7.528472</td>\n",
       "      <td>7.449622</td>\n",
       "      <td>7.520484</td>\n",
       "      <td>7.460191</td>\n",
       "      <td>7.443415</td>\n",
       "      <td>7.463826</td>\n",
       "      <td>7.424417</td>\n",
       "      <td>7.430239</td>\n",
       "      <td>7.525707</td>\n",
       "      <td>7.486611</td>\n",
       "      <td>7.451858</td>\n",
       "      <td>7.491203</td>\n",
       "      <td>7.541226</td>\n",
       "      <td>7.456086</td>\n",
       "      <td>7.427137</td>\n",
       "      <td>7.536929</td>\n",
       "      <td>7.504397</td>\n",
       "      <td>7.417781</td>\n",
       "      <td>7.450517</td>\n",
       "      <td>7.488699</td>\n",
       "      <td>7.401609</td>\n",
       "      <td>7.578161</td>\n",
       "      <td>7.503312</td>\n",
       "      <td>7.467789</td>\n",
       "      <td>7.500765</td>\n",
       "      <td>7.470310</td>\n",
       "      <td>7.502963</td>\n",
       "      <td>7.446026</td>\n",
       "      <td>7.490703</td>\n",
       "      <td>7.434865</td>\n",
       "      <td>7.446862</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ILMN_1736007</th>\n",
       "      <td>7.395490</td>\n",
       "      <td>7.415111</td>\n",
       "      <td>7.411259</td>\n",
       "      <td>7.452471</td>\n",
       "      <td>7.406781</td>\n",
       "      <td>7.400772</td>\n",
       "      <td>7.464517</td>\n",
       "      <td>7.428403</td>\n",
       "      <td>7.393926</td>\n",
       "      <td>7.419256</td>\n",
       "      <td>7.430893</td>\n",
       "      <td>7.463886</td>\n",
       "      <td>7.441676</td>\n",
       "      <td>7.399954</td>\n",
       "      <td>7.416419</td>\n",
       "      <td>7.394407</td>\n",
       "      <td>7.394149</td>\n",
       "      <td>7.471065</td>\n",
       "      <td>7.413662</td>\n",
       "      <td>7.414325</td>\n",
       "      <td>7.396228</td>\n",
       "      <td>7.408074</td>\n",
       "      <td>7.457374</td>\n",
       "      <td>7.426909</td>\n",
       "      <td>7.408528</td>\n",
       "      <td>7.446387</td>\n",
       "      <td>7.398156</td>\n",
       "      <td>7.400009</td>\n",
       "      <td>7.429735</td>\n",
       "      <td>7.457548</td>\n",
       "      <td>7.397040</td>\n",
       "      <td>7.392384</td>\n",
       "      <td>7.418896</td>\n",
       "      <td>7.426542</td>\n",
       "      <td>7.427151</td>\n",
       "      <td>7.426393</td>\n",
       "      <td>7.530829</td>\n",
       "      <td>7.408560</td>\n",
       "      <td>7.458966</td>\n",
       "      <td>7.410572</td>\n",
       "      <td>...</td>\n",
       "      <td>7.392883</td>\n",
       "      <td>7.427061</td>\n",
       "      <td>7.426749</td>\n",
       "      <td>7.421202</td>\n",
       "      <td>7.409734</td>\n",
       "      <td>7.415489</td>\n",
       "      <td>7.396247</td>\n",
       "      <td>7.386492</td>\n",
       "      <td>7.395270</td>\n",
       "      <td>7.392601</td>\n",
       "      <td>7.405761</td>\n",
       "      <td>7.444102</td>\n",
       "      <td>7.398172</td>\n",
       "      <td>7.406047</td>\n",
       "      <td>7.405839</td>\n",
       "      <td>7.411998</td>\n",
       "      <td>7.409239</td>\n",
       "      <td>7.414095</td>\n",
       "      <td>7.398139</td>\n",
       "      <td>7.407446</td>\n",
       "      <td>7.422754</td>\n",
       "      <td>7.393836</td>\n",
       "      <td>7.399502</td>\n",
       "      <td>7.428688</td>\n",
       "      <td>7.413477</td>\n",
       "      <td>7.421273</td>\n",
       "      <td>7.439048</td>\n",
       "      <td>7.400521</td>\n",
       "      <td>7.406692</td>\n",
       "      <td>7.400641</td>\n",
       "      <td>7.417159</td>\n",
       "      <td>7.412115</td>\n",
       "      <td>7.388127</td>\n",
       "      <td>7.387117</td>\n",
       "      <td>7.407231</td>\n",
       "      <td>7.407277</td>\n",
       "      <td>7.393548</td>\n",
       "      <td>7.394282</td>\n",
       "      <td>7.420361</td>\n",
       "      <td>7.401746</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ILMN_2383229</th>\n",
       "      <td>7.396522</td>\n",
       "      <td>7.380184</td>\n",
       "      <td>7.396145</td>\n",
       "      <td>7.437754</td>\n",
       "      <td>7.389166</td>\n",
       "      <td>7.386301</td>\n",
       "      <td>7.381726</td>\n",
       "      <td>7.411211</td>\n",
       "      <td>7.393087</td>\n",
       "      <td>7.388776</td>\n",
       "      <td>7.410690</td>\n",
       "      <td>7.394225</td>\n",
       "      <td>7.380928</td>\n",
       "      <td>7.394825</td>\n",
       "      <td>7.389272</td>\n",
       "      <td>7.381265</td>\n",
       "      <td>7.384548</td>\n",
       "      <td>7.389634</td>\n",
       "      <td>7.385577</td>\n",
       "      <td>7.390940</td>\n",
       "      <td>7.391638</td>\n",
       "      <td>7.398699</td>\n",
       "      <td>7.386617</td>\n",
       "      <td>7.392951</td>\n",
       "      <td>7.390627</td>\n",
       "      <td>7.385841</td>\n",
       "      <td>7.383043</td>\n",
       "      <td>7.387548</td>\n",
       "      <td>7.379674</td>\n",
       "      <td>7.414392</td>\n",
       "      <td>7.387134</td>\n",
       "      <td>7.391204</td>\n",
       "      <td>7.396711</td>\n",
       "      <td>7.383633</td>\n",
       "      <td>7.388312</td>\n",
       "      <td>7.422167</td>\n",
       "      <td>7.391642</td>\n",
       "      <td>7.394340</td>\n",
       "      <td>7.405836</td>\n",
       "      <td>7.386327</td>\n",
       "      <td>...</td>\n",
       "      <td>7.390146</td>\n",
       "      <td>7.389623</td>\n",
       "      <td>7.390705</td>\n",
       "      <td>7.398594</td>\n",
       "      <td>7.398851</td>\n",
       "      <td>7.414568</td>\n",
       "      <td>7.396422</td>\n",
       "      <td>7.394282</td>\n",
       "      <td>7.392053</td>\n",
       "      <td>7.387050</td>\n",
       "      <td>7.383961</td>\n",
       "      <td>7.381736</td>\n",
       "      <td>7.406727</td>\n",
       "      <td>7.385173</td>\n",
       "      <td>7.377312</td>\n",
       "      <td>7.400326</td>\n",
       "      <td>7.415997</td>\n",
       "      <td>7.379473</td>\n",
       "      <td>7.389521</td>\n",
       "      <td>7.391817</td>\n",
       "      <td>7.401613</td>\n",
       "      <td>7.400046</td>\n",
       "      <td>7.396275</td>\n",
       "      <td>7.400992</td>\n",
       "      <td>7.394005</td>\n",
       "      <td>7.410140</td>\n",
       "      <td>7.401575</td>\n",
       "      <td>7.390314</td>\n",
       "      <td>7.388930</td>\n",
       "      <td>7.403501</td>\n",
       "      <td>7.395504</td>\n",
       "      <td>7.383655</td>\n",
       "      <td>7.387001</td>\n",
       "      <td>7.387021</td>\n",
       "      <td>7.385719</td>\n",
       "      <td>7.393925</td>\n",
       "      <td>7.389303</td>\n",
       "      <td>7.390593</td>\n",
       "      <td>7.415308</td>\n",
       "      <td>7.388519</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ILMN_1806310</th>\n",
       "      <td>7.406622</td>\n",
       "      <td>7.390529</td>\n",
       "      <td>7.390354</td>\n",
       "      <td>7.399255</td>\n",
       "      <td>7.428401</td>\n",
       "      <td>7.395842</td>\n",
       "      <td>7.405722</td>\n",
       "      <td>7.389512</td>\n",
       "      <td>7.405325</td>\n",
       "      <td>7.379728</td>\n",
       "      <td>7.398370</td>\n",
       "      <td>7.400199</td>\n",
       "      <td>7.397150</td>\n",
       "      <td>7.400205</td>\n",
       "      <td>7.411000</td>\n",
       "      <td>7.397182</td>\n",
       "      <td>7.407737</td>\n",
       "      <td>7.403152</td>\n",
       "      <td>7.405225</td>\n",
       "      <td>7.426077</td>\n",
       "      <td>7.392762</td>\n",
       "      <td>7.412809</td>\n",
       "      <td>7.386735</td>\n",
       "      <td>7.421235</td>\n",
       "      <td>7.441504</td>\n",
       "      <td>7.399473</td>\n",
       "      <td>7.391785</td>\n",
       "      <td>7.403696</td>\n",
       "      <td>7.416192</td>\n",
       "      <td>7.385968</td>\n",
       "      <td>7.421323</td>\n",
       "      <td>7.401927</td>\n",
       "      <td>7.392627</td>\n",
       "      <td>7.397413</td>\n",
       "      <td>7.441277</td>\n",
       "      <td>7.404173</td>\n",
       "      <td>7.389825</td>\n",
       "      <td>7.382910</td>\n",
       "      <td>7.402599</td>\n",
       "      <td>7.391979</td>\n",
       "      <td>...</td>\n",
       "      <td>7.402744</td>\n",
       "      <td>7.392958</td>\n",
       "      <td>7.439407</td>\n",
       "      <td>7.441851</td>\n",
       "      <td>7.418915</td>\n",
       "      <td>7.414568</td>\n",
       "      <td>7.394683</td>\n",
       "      <td>7.408915</td>\n",
       "      <td>7.446330</td>\n",
       "      <td>7.393242</td>\n",
       "      <td>7.397649</td>\n",
       "      <td>7.394347</td>\n",
       "      <td>7.405761</td>\n",
       "      <td>7.405052</td>\n",
       "      <td>7.398851</td>\n",
       "      <td>7.415227</td>\n",
       "      <td>7.417366</td>\n",
       "      <td>7.406236</td>\n",
       "      <td>7.385113</td>\n",
       "      <td>7.402284</td>\n",
       "      <td>7.406308</td>\n",
       "      <td>7.414682</td>\n",
       "      <td>7.408162</td>\n",
       "      <td>7.414487</td>\n",
       "      <td>7.401781</td>\n",
       "      <td>7.406691</td>\n",
       "      <td>7.415308</td>\n",
       "      <td>7.404915</td>\n",
       "      <td>7.437486</td>\n",
       "      <td>7.387104</td>\n",
       "      <td>7.431948</td>\n",
       "      <td>7.434326</td>\n",
       "      <td>7.421676</td>\n",
       "      <td>7.400124</td>\n",
       "      <td>7.422484</td>\n",
       "      <td>7.413941</td>\n",
       "      <td>7.404666</td>\n",
       "      <td>7.436115</td>\n",
       "      <td>7.406383</td>\n",
       "      <td>7.394833</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 133 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "              GSM1467273  GSM1467274  ...  GSM1467404  GSM1467405\n",
       "GENE                                  ...                        \n",
       "ILMN_1762337    7.386233    7.383662  ...    7.383719    7.397961\n",
       "ILMN_2055271    7.566603    7.503766  ...    7.434865    7.446862\n",
       "ILMN_1736007    7.395490    7.415111  ...    7.420361    7.401746\n",
       "ILMN_2383229    7.396522    7.380184  ...    7.415308    7.388519\n",
       "ILMN_1806310    7.406622    7.390529  ...    7.406383    7.394833\n",
       "\n",
       "[5 rows x 133 columns]"
      ]
     },
     "execution_count": 4,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y=[]\n",
    "X={}\n",
    "for sample in gse.gsms:\n",
    "  X_sample=gse.gsms[sample].table.set_index('ID_REF')['VALUE']\n",
    "  X_sample.index.rename('GENE',inplace=True)\n",
    "  X[sample]=X_sample\n",
    "  if gse.gsms[sample].metadata['title'][0].startswith('DLPFC_Control'):\n",
    "    y+=[0]\n",
    "  else:\n",
    "    y+=[1]\n",
    "X=pd.DataFrame(X)\n",
    "y=np.array(y)\n",
    "X.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "LMBlmVgcSzex"
   },
   "source": [
    "##3.1.LessSimpleBagger"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 19466,
     "status": "ok",
     "timestamp": 1618411918274,
     "user": {
      "displayName": "sadra barikbin",
      "photoUrl": "https://lh3.googleusercontent.com/a-/AOh14GiCq0YqNHWfcZyly3I_AMTkvS8_tO8PZOQpGtkK=s64",
      "userId": "07513140462530453530"
     },
     "user_tz": -270
    },
    "id": "JbUMq24GbAR_",
    "outputId": "0766ac6a-6ef2-41c3-cd61-530eef1ea01c"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Balanced Accuracies:[0.5        0.46153846 0.50961538 0.75       0.625      0.625\n",
      " 0.625      0.62820513]\n",
      "Average Balanced Accuracies:0.5905448717948718\n"
     ]
    }
   ],
   "source": [
    "bagger=LessSimpleBagger(DecisionTreeClassifier(max_depth=10),n_estimators=5,max_features=0.5)\n",
    "scores = cross_val_score(bagger, X.T.to_numpy(), y, cv=8,scoring=make_scorer(balanced_accuracy_score))\n",
    "print(f\"Balanced Accuracies:{scores}\\nAverage Balanced Accuracies:{scores.mean()}\")\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "PuEj3YCf-EXG"
   },
   "source": [
    "##3.2.AdaBoost"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 541165,
     "status": "ok",
     "timestamp": 1618412463915,
     "user": {
      "displayName": "sadra barikbin",
      "photoUrl": "https://lh3.googleusercontent.com/a-/AOh14GiCq0YqNHWfcZyly3I_AMTkvS8_tO8PZOQpGtkK=s64",
      "userId": "07513140462530453530"
     },
     "user_tz": -270
    },
    "id": "SNmGzCmvlxsF",
    "outputId": "b3e2d063-22fc-4554-afd6-7982f5540b13"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[CV]  ................................................................\n",
      "[CV] ................................................. , total= 1.2min\n",
      "[CV]  ................................................................\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=1)]: Done   1 out of   1 | elapsed:  1.2min remaining:    0.0s\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[CV] ................................................. , total= 1.1min\n",
      "[CV]  ................................................................\n",
      "[CV] ................................................. , total= 1.1min\n",
      "[CV]  ................................................................\n",
      "[CV] ................................................. , total= 1.1min\n",
      "[CV]  ................................................................\n",
      "[CV] ................................................. , total= 1.1min\n",
      "[CV]  ................................................................\n",
      "[CV] ................................................. , total= 1.1min\n",
      "[CV]  ................................................................\n",
      "[CV] ................................................. , total= 1.1min\n",
      "[CV]  ................................................................\n",
      "[CV] ................................................. , total= 1.1min\n",
      "AdaBoost average balanced accuracy:0.5893429487179487\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=1)]: Done   8 out of   8 | elapsed:  9.0min finished\n"
     ]
    }
   ],
   "source": [
    "clf = AdaBoostClassifier(n_estimators=100)\n",
    "scores = cross_val_score(clf, X.T, y, cv=8,verbose=2,scoring=make_scorer(balanced_accuracy_score))\n",
    "print(f\"AdaBoost average balanced accuracy:{scores.mean()}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "9zCsRA0B-UF3"
   },
   "source": [
    "##3.3.Random Forest"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "Z56S8jF1_PkL"
   },
   "source": [
    "Each tree is built on at most square root of data features."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 296
    },
    "executionInfo": {
     "elapsed": 16849,
     "status": "ok",
     "timestamp": 1618413610063,
     "user": {
      "displayName": "sadra barikbin",
      "photoUrl": "https://lh3.googleusercontent.com/a-/AOh14GiCq0YqNHWfcZyly3I_AMTkvS8_tO8PZOQpGtkK=s64",
      "userId": "07513140462530453530"
     },
     "user_tz": -270
    },
    "id": "S7ZIKVr9pkNs",
    "outputId": "3550c77e-dcf4-476b-8977-b04fd9e620e8"
   },
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light",
      "tags": []
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "n_estimators_param=[2,5,10,20,50,100,200,500,1000]\n",
    "train_scores,test_scores=validation_curve(RandomForestClassifier(max_features='sqrt'),X.T,y,'n_estimators',n_estimators_param,cv=ShuffleSplit(n_splits=1,test_size=0.2),scoring=make_scorer(balanced_accuracy_score))\n",
    "plt.plot(n_estimators_param,train_scores)\n",
    "plt.plot(n_estimators_param,test_scores)\n",
    "plt.title(\"Validation Curve with RandomForest\")\n",
    "plt.xlabel(\"n_estimator\")\n",
    "plt.ylabel(\"Balanced Accuracy\")\n",
    "plt.ylim(0.0, 1.1)\n",
    "plt.legend(['Train','Test'],loc=\"best\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "Fd7xtAeI_Byb"
   },
   "source": [
    "Each tree is built on at most log2 of data features."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 296
    },
    "executionInfo": {
     "elapsed": 7786,
     "status": "ok",
     "timestamp": 1618413827547,
     "user": {
      "displayName": "sadra barikbin",
      "photoUrl": "https://lh3.googleusercontent.com/a-/AOh14GiCq0YqNHWfcZyly3I_AMTkvS8_tO8PZOQpGtkK=s64",
      "userId": "07513140462530453530"
     },
     "user_tz": -270
    },
    "id": "5lMhD6y_7_r2",
    "outputId": "69abd912-0856-4a86-a749-09aff545f09d"
   },
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light",
      "tags": []
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "n_estimators_param=[2,5,10,20,50,100,200,500,1000]\n",
    "train_scores,test_scores=validation_curve(RandomForestClassifier(max_features='log2'),X.T,y,'n_estimators',n_estimators_param,cv=ShuffleSplit(n_splits=1,test_size=0.2),scoring=make_scorer(balanced_accuracy_score))\n",
    "plt.plot(n_estimators_param,train_scores)\n",
    "plt.plot(n_estimators_param,test_scores)\n",
    "plt.title(\"Validation Curve with RandomForest\")\n",
    "plt.xlabel(\"n_estimator\")\n",
    "plt.ylabel(\"Balanced Accuracy\")\n",
    "plt.ylim(0.0, 1.1)\n",
    "plt.legend(['Train','Test'],loc=\"best\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "kihrkouWAWuH"
   },
   "source": [
    "##3.4.LDA"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 21301,
     "status": "ok",
     "timestamp": 1618413892278,
     "user": {
      "displayName": "sadra barikbin",
      "photoUrl": "https://lh3.googleusercontent.com/a-/AOh14GiCq0YqNHWfcZyly3I_AMTkvS8_tO8PZOQpGtkK=s64",
      "userId": "07513140462530453530"
     },
     "user_tz": -270
    },
    "id": "Bi1HttqK9Yo1",
    "outputId": "5dad89a5-8983-44f0-a4c4-179aff9c1477"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[CV]  ................................................................\n",
      "[CV] ................................................. , total=   2.5s\n",
      "[CV]  ................................................................\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=1)]: Done   1 out of   1 | elapsed:    2.5s remaining:    0.0s\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[CV] ................................................. , total=   2.5s\n",
      "[CV]  ................................................................\n",
      "[CV] ................................................. , total=   2.5s\n",
      "[CV]  ................................................................\n",
      "[CV] ................................................. , total=   2.4s\n",
      "[CV]  ................................................................\n",
      "[CV] ................................................. , total=   2.4s\n",
      "[CV]  ................................................................\n",
      "[CV] ................................................. , total=   2.4s\n",
      "[CV]  ................................................................\n",
      "[CV] ................................................. , total=   2.5s\n",
      "[CV]  ................................................................\n",
      "[CV] ................................................. , total=   2.5s\n",
      "LDA average balanced accuracy:0.5729166666666666\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=1)]: Done   8 out of   8 | elapsed:   19.8s finished\n"
     ]
    }
   ],
   "source": [
    "scores = cross_val_score(LDA(), X.T, y, cv=8,verbose=2,scoring=make_scorer(balanced_accuracy_score))\n",
    "print(f\"LDA average balanced accuracy:{scores.mean()}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "OrCeHd5CXNEM"
   },
   "source": [
    "##3.5.With PCA & a simple classifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 296
    },
    "executionInfo": {
     "elapsed": 2005,
     "status": "ok",
     "timestamp": 1618562011326,
     "user": {
      "displayName": "sadra barikbin",
      "photoUrl": "https://lh3.googleusercontent.com/a-/AOh14GiCq0YqNHWfcZyly3I_AMTkvS8_tO8PZOQpGtkK=s64",
      "userId": "07513140462530453530"
     },
     "user_tz": -270
    },
    "id": "f2P0gomPncVM",
    "outputId": "8424f900-b1dd-4117-d8cc-1f70eb055225"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Explained variance:[0.2150034  0.14759176]\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light",
      "tags": []
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "pca=PCA(n_components=2)\n",
    "Xprime=pca.fit_transform(X.T)\n",
    "print(f\"Explained variance:{pca.explained_variance_ratio_}\")\n",
    "plt.scatter(Xprime[y==0,0],Xprime[y==0,1],c='blue',label='Control')\n",
    "plt.scatter(Xprime[y==1,0],Xprime[y==1,1],c='red',label='Disorder')\n",
    "plt.xlabel('PC#1')\n",
    "plt.ylabel('PC#2')\n",
    "plt.legend(loc='best')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 3371,
     "status": "ok",
     "timestamp": 1618561240808,
     "user": {
      "displayName": "sadra barikbin",
      "photoUrl": "https://lh3.googleusercontent.com/a-/AOh14GiCq0YqNHWfcZyly3I_AMTkvS8_tO8PZOQpGtkK=s64",
      "userId": "07513140462530453530"
     },
     "user_tz": -270
    },
    "id": "s2ccyrOguEEq",
    "outputId": "aea861da-1447-4daf-a5fb-89a48b361e37"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "49\n"
     ]
    }
   ],
   "source": [
    "pca=PCA()\n",
    "pca.fit_transform(X.T)\n",
    "var=0.\n",
    "n_pc=0\n",
    "for pc_var in pca.explained_variance_ratio_:\n",
    "  var+=pc_var\n",
    "  n_pc+=1\n",
    "  if var>=0.9:\n",
    "    break\n",
    "print(n_pc)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 14033,
     "status": "ok",
     "timestamp": 1618562145869,
     "user": {
      "displayName": "sadra barikbin",
      "photoUrl": "https://lh3.googleusercontent.com/a-/AOh14GiCq0YqNHWfcZyly3I_AMTkvS8_tO8PZOQpGtkK=s64",
      "userId": "07513140462530453530"
     },
     "user_tz": -270
    },
    "id": "iYPaluO6XUY1",
    "outputId": "4cdb0bd2-8be2-4d8d-f6d7-20b239f4ecb7"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[CV]  ................................................................\n",
      "[CV] ................................................. , total=   1.6s\n",
      "[CV]  ................................................................\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=1)]: Done   1 out of   1 | elapsed:    1.6s remaining:    0.0s\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[CV] ................................................. , total=   1.6s\n",
      "[CV]  ................................................................\n",
      "[CV] ................................................. , total=   1.7s\n",
      "[CV]  ................................................................\n",
      "[CV] ................................................. , total=   1.6s\n",
      "[CV]  ................................................................\n",
      "[CV] ................................................. , total=   1.6s\n",
      "[CV]  ................................................................\n",
      "[CV] ................................................. , total=   1.5s\n",
      "[CV]  ................................................................\n",
      "[CV] ................................................. , total=   1.6s\n",
      "[CV]  ................................................................\n",
      "[CV] ................................................. , total=   1.6s\n",
      "PCA+Tree average balanced accuracy:0.530849358974359\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=1)]: Done   8 out of   8 | elapsed:   12.8s finished\n"
     ]
    }
   ],
   "source": [
    "pipe=Pipeline([('pca',PCA(n_components=49)),('tree',DecisionTreeClassifier())])\n",
    "scores = cross_val_score(pipe, X.T, y, cv=8,verbose=2,scoring=make_scorer(balanced_accuracy_score))\n",
    "print(f\"PCA+Tree average balanced accuracy:{scores.mean()}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "WQ4EK4Hc0qL4"
   },
   "source": [
    "##3.6.With PCA & an Ensemble"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 9912,
     "status": "ok",
     "timestamp": 1618562360838,
     "user": {
      "displayName": "sadra barikbin",
      "photoUrl": "https://lh3.googleusercontent.com/a-/AOh14GiCq0YqNHWfcZyly3I_AMTkvS8_tO8PZOQpGtkK=s64",
      "userId": "07513140462530453530"
     },
     "user_tz": -270
    },
    "id": "hDrYg9tG1DrS",
    "outputId": "bfc22ec1-ff0a-40bb-8c8e-1464cd47da10"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[CV]  ................................................................\n",
      "[CV] ................................................. , total=   1.1s\n",
      "[CV]  ................................................................\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=1)]: Done   1 out of   1 | elapsed:    1.1s remaining:    0.0s\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[CV] ................................................. , total=   1.1s\n",
      "[CV]  ................................................................\n",
      "[CV] ................................................. , total=   1.1s\n",
      "[CV]  ................................................................\n",
      "[CV] ................................................. , total=   1.1s\n",
      "[CV]  ................................................................\n",
      "[CV] ................................................. , total=   1.1s\n",
      "[CV]  ................................................................\n",
      "[CV] ................................................. , total=   1.1s\n",
      "[CV]  ................................................................\n",
      "[CV] ................................................. , total=   1.1s\n",
      "[CV]  ................................................................\n",
      "[CV] ................................................. , total=   1.1s\n",
      "PCA+AB average balanced accuracy:0.5673076923076923\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=1)]: Done   8 out of   8 | elapsed:    8.9s finished\n"
     ]
    }
   ],
   "source": [
    "pipe=Pipeline([('pca',PCA(n_components=2)),('ab',AdaBoostClassifier(n_estimators=100))])\n",
    "scores = cross_val_score(pipe, X.T, y, cv=8,verbose=2,scoring=make_scorer(balanced_accuracy_score))\n",
    "print(f\"PCA+AB average balanced accuracy:{scores.mean()}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 14897,
     "status": "ok",
     "timestamp": 1618562257537,
     "user": {
      "displayName": "sadra barikbin",
      "photoUrl": "https://lh3.googleusercontent.com/a-/AOh14GiCq0YqNHWfcZyly3I_AMTkvS8_tO8PZOQpGtkK=s64",
      "userId": "07513140462530453530"
     },
     "user_tz": -270
    },
    "id": "-OOEZFln0oad",
    "outputId": "7d02d371-89cc-4e77-d715-c914c60749a8"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[CV]  ................................................................\n",
      "[CV] ................................................. , total=   1.6s\n",
      "[CV]  ................................................................\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=1)]: Done   1 out of   1 | elapsed:    1.6s remaining:    0.0s\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[CV] ................................................. , total=   1.8s\n",
      "[CV]  ................................................................\n",
      "[CV] ................................................. , total=   1.8s\n",
      "[CV]  ................................................................\n",
      "[CV] ................................................. , total=   1.8s\n",
      "[CV]  ................................................................\n",
      "[CV] ................................................. , total=   1.7s\n",
      "[CV]  ................................................................\n",
      "[CV] ................................................. , total=   1.7s\n",
      "[CV]  ................................................................\n",
      "[CV] ................................................. , total=   1.7s\n",
      "[CV]  ................................................................\n",
      "[CV] ................................................. , total=   1.8s\n",
      "PCA+RF average balanced accuracy:0.5416666666666666\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=1)]: Done   8 out of   8 | elapsed:   13.9s finished\n"
     ]
    }
   ],
   "source": [
    "pipe=Pipeline([('pca',PCA(n_components=49)),('tree',RandomForestClassifier())])\n",
    "scores = cross_val_score(pipe, X.T, y, cv=8,verbose=2,scoring=make_scorer(balanced_accuracy_score))\n",
    "print(f\"PCA+RF average balanced accuracy:{scores.mean()}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "7gW98mCM1k9v"
   },
   "source": [
    "##3.7.With Univariate Feature Selection & a simple classifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 146197,
     "status": "ok",
     "timestamp": 1618584231591,
     "user": {
      "displayName": "sadra barikbin",
      "photoUrl": "https://lh3.googleusercontent.com/a-/AOh14GiCq0YqNHWfcZyly3I_AMTkvS8_tO8PZOQpGtkK=s64",
      "userId": "07513140462530453530"
     },
     "user_tz": -270
    },
    "id": "DOMSQ_pVIMoi",
    "outputId": "3d491939-c92e-4313-8735-d8f01864cbc9"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "SelectKBest(k=2, score_func=<function mutual_info_classif at 0x7efc7626a200>)"
      ]
     },
     "execution_count": 6,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "kbest=SelectKBest(score_func=mutual_info_classif,k=2)\n",
    "kbest.fit(X.T,y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 280
    },
    "executionInfo": {
     "elapsed": 1431,
     "status": "ok",
     "timestamp": 1618584872231,
     "user": {
      "displayName": "sadra barikbin",
      "photoUrl": "https://lh3.googleusercontent.com/a-/AOh14GiCq0YqNHWfcZyly3I_AMTkvS8_tO8PZOQpGtkK=s64",
      "userId": "07513140462530453530"
     },
     "user_tz": -270
    },
    "id": "rRoAM0Tn1yN6",
    "outputId": "abdf43f5-a461-4bb5-cd2e-f39e0f7d19b0"
   },
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light",
      "tags": []
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "Xprime=kbest.transform(X.T)\n",
    "plt.scatter(Xprime[y==0,0],Xprime[y==0,1],c='blue',label='Control')\n",
    "plt.scatter(Xprime[y==1,0],Xprime[y==1,1],c='red',label='Disorder')\n",
    "plt.xlabel(X.index[kbest.get_support()][0])\n",
    "plt.ylabel(X.index[kbest.get_support()][1])\n",
    "plt.legend(loc='best')\n",
    "plt.show()\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 147521,
     "status": "ok",
     "timestamp": 1618585421909,
     "user": {
      "displayName": "sadra barikbin",
      "photoUrl": "https://lh3.googleusercontent.com/a-/AOh14GiCq0YqNHWfcZyly3I_AMTkvS8_tO8PZOQpGtkK=s64",
      "userId": "07513140462530453530"
     },
     "user_tz": -270
    },
    "id": "ZH3Xq0zPHyXx",
    "outputId": "f287ad4b-b2f2-4d2d-c290-539c5aab7508"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[CV]  ................................................................\n",
      "[CV] ................................................. , total= 2.4min\n",
      "5best+SVM average balanced accuracy:0.5\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=1)]: Done   1 out of   1 | elapsed:  2.4min remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done   1 out of   1 | elapsed:  2.4min finished\n"
     ]
    }
   ],
   "source": [
    "pipe=Pipeline([('kbest',SelectKBest(score_func=mutual_info_classif,k=5)),('svm',SVC())])\n",
    "scores = cross_val_score(pipe, X.T, y, cv=ShuffleSplit(n_splits=1,test_size=0.2),verbose=2,scoring=make_scorer(balanced_accuracy_score))\n",
    "print(f\"5best+SVM average balanced accuracy:{scores.mean()}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "09tL4Ht1NiK1"
   },
   "source": [
    "##3.8.With Univariate Feature Selection & an Ensemble"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 141493,
     "status": "ok",
     "timestamp": 1618605372850,
     "user": {
      "displayName": "sadra barikbin",
      "photoUrl": "https://lh3.googleusercontent.com/a-/AOh14GiCq0YqNHWfcZyly3I_AMTkvS8_tO8PZOQpGtkK=s64",
      "userId": "07513140462530453530"
     },
     "user_tz": -270
    },
    "id": "4l_VGshENhNS",
    "outputId": "015fe11d-acf6-4a08-a572-2851e7550e8e"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[CV]  ................................................................\n",
      "[CV] ................................................. , total= 2.3min\n",
      "3best+ average balanced accuracy:0.6863636363636363\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=1)]: Done   1 out of   1 | elapsed:  2.3min remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done   1 out of   1 | elapsed:  2.3min finished\n"
     ]
    }
   ],
   "source": [
    "pipe=Pipeline([('kbest',SelectKBest(score_func=mutual_info_classif,k=3)),('ab',AdaBoostClassifier(n_estimators=100))])\n",
    "scores = cross_val_score(pipe, X.T, y, cv=ShuffleSplit(n_splits=1,test_size=0.2),verbose=2,scoring=make_scorer(balanced_accuracy_score))\n",
    "print(f\"3best+ average balanced accuracy:{scores.mean()}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "xFsla0mcYOf9"
   },
   "source": [
    "Classification report for the best classifier:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 137641,
     "status": "ok",
     "timestamp": 1618605577491,
     "user": {
      "displayName": "sadra barikbin",
      "photoUrl": "https://lh3.googleusercontent.com/a-/AOh14GiCq0YqNHWfcZyly3I_AMTkvS8_tO8PZOQpGtkK=s64",
      "userId": "07513140462530453530"
     },
     "user_tz": -270
    },
    "id": "Fvqt3wfNYN5I",
    "outputId": "42b30626-9c7c-46b4-82dd-528ccfad5879"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "     Control       0.71      0.84      0.77        32\n",
      "    Disorder       0.17      0.08      0.11        12\n",
      "\n",
      "    accuracy                           0.64        44\n",
      "   macro avg       0.44      0.46      0.44        44\n",
      "weighted avg       0.56      0.64      0.59        44\n",
      "\n"
     ]
    }
   ],
   "source": [
    "X_train, X_test, y_train, y_test=train_test_split(X.T,y,test_size=0.33)\n",
    "pipe.fit(X_train,y_train)\n",
    "print(classification_report(y_test,pipe.predict(X_test),target_names=['Control','Disorder']))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 136714,
     "status": "ok",
     "timestamp": 1618585902544,
     "user": {
      "displayName": "sadra barikbin",
      "photoUrl": "https://lh3.googleusercontent.com/a-/AOh14GiCq0YqNHWfcZyly3I_AMTkvS8_tO8PZOQpGtkK=s64",
      "userId": "07513140462530453530"
     },
     "user_tz": -270
    },
    "id": "UlkCn7T6OQph",
    "outputId": "e2948545-5d5b-4201-e8f6-9704e13cb2f5"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[CV]  ................................................................\n",
      "[CV] ................................................. , total= 2.3min\n",
      "20Best+RF average balanced accuracy:0.5597826086956521\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=1)]: Done   1 out of   1 | elapsed:  2.3min remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done   1 out of   1 | elapsed:  2.3min finished\n"
     ]
    }
   ],
   "source": [
    "pipe=Pipeline([('kbest',SelectKBest(score_func=mutual_info_classif,k=20)),('tree',RandomForestClassifier())])\n",
    "scores = cross_val_score(pipe, X.T, y, cv=ShuffleSplit(n_splits=1,test_size=0.2),verbose=2,scoring=make_scorer(balanced_accuracy_score))\n",
    "print(f\"20Best+RF average balanced accuracy:{scores.mean()}\")"
   ]
  }
 ],
 "metadata": {
  "colab": {
   "authorship_tag": "ABX9TyP1oQxOpwbch+oJvLUt7bGH",
   "name": "Ensemble.ipynb",
   "provenance": []
  },
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.10"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 1
}