Access_Google_Drive_File_From_Colab.ipynb

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     "text": [
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      "  Found existing installation: scikit-learn 0.19.2\n",
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      "Collecting pydrive\n",
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      "Installing collected packages: mlxtend\n",
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   ],
   "source": [
    "!pip install imblearn\n",
    "!pip install pydrive\n",
    "!pip install mlxtend"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "13CJbjMyX2Wr"
   },
   "outputs": [],
   "source": [
    "#Accessing Google drive File\n",
    "from pydrive.auth import GoogleAuth\n",
    "from pydrive.drive import GoogleDrive\n",
    "from google.colab import auth\n",
    "from oauth2client.client import GoogleCredentials"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 33
    },
    "colab_type": "code",
    "id": "fvybMLYqYDVF",
    "outputId": "bba8da87-df64-48e9-80ff-fa6db8c1d381"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Drive already mounted at /content/drive/; to attempt to forcibly remount, call drive.mount(\"/content/drive/\", force_remount=True).\n"
     ]
    }
   ],
   "source": [
    "# Mount drive\n",
    "from google.colab import drive\n",
    "drive.mount('/content/drive/')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "v_xDFBFMYLN7"
   },
   "outputs": [],
   "source": [
    "#Import Libraries\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "from sklearn.metrics import classification_report\n",
    "from sklearn.model_selection import GridSearchCV\n",
    "import matplotlib.pyplot as plt\n",
    "#Store Model to Local Disk\n",
    "from sklearn.externals import joblib\n",
    "#confusion matrix\n",
    "from mlxtend.evaluate import confusion_matrix \n",
    "from mlxtend.plotting import plot_confusion_matrix\n",
    "\n",
    "%matplotlib inline\n",
    "\n",
    "# Authenticate and create the PyDrive client.\n",
    "auth.authenticate_user()\n",
    "gauth = GoogleAuth()\n",
    "gauth.credentials = GoogleCredentials.get_application_default()\n",
    "drive = GoogleDrive(gauth)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "QjrmD6tqYYlh"
   },
   "outputs": [],
   "source": [
    "#Get the file\n",
    "#Replace the id with id of file you want to access\n",
    "normal_1 = drive.CreateFile({'id':'13AR0sS1pndF0fcsssxmdjQRv_1Bv5aBNpkT'}) \n",
    "normal_1.GetContentFile('normal_1.csv')\n",
    "\n",
    "normal_2 = drive.CreateFile({'id':'1Z0DO8M1Qco07kyVoxooogxXBx6XYGBzJd'}) \n",
    "normal_2.GetContentFile('normal_2.csv')\n",
    "\n",
    "abnormal = drive.CreateFile({'id':'12zFHDXVcccccorRrHHhYrA1n82VQLuawsl'}) \n",
    "abnormal.GetContentFile('abnormal.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "3TWr3NcHaEfQ"
   },
   "outputs": [],
   "source": [
    "# Read file as panda dataframe\n",
    "normal_1 = pd.read_csv('normal_1.csv', header=None) \n",
    "normal_2 = pd.read_csv('normal_2.csv', header=None)\n",
    "abnormal = pd.read_csv('abnormal.csv', header=None)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "hJBz18WYat0J"
   },
   "outputs": [],
   "source": [
    "# Read some rows for smaller computations\n",
    "normal_1 = pd.read_csv('normal_1.csv', skiprows = np.arange(100, normal_1.shape[0])) \n",
    "normal_2 = pd.read_csv('normal_2.csv', skiprows = np.arange(100, normal_2.shape[0])) \n",
    "abnormal = pd.read_csv('abnormal.csv', skiprows = np.arange(50, abnormal.shape[0])) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "XjEod1xnbzao"
   },
   "outputs": [],
   "source": [
    "#Merge Normal 2 and abnormal\n",
    "anomaly = pd.DataFrame(np.concatenate([normal_1.values, abnormal.values]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "U2PdY-KEcK0K"
   },
   "outputs": [],
   "source": [
    "#Get train data\n",
    "X_train=normal_1.iloc[:, 1:normal_1.shape[1]].values\n",
    "y_train = np.repeat(0, len(X_train))\n",
    "\n",
    "X_test=anomaly.iloc[:, 1:anomaly.shape[1]].values\n",
    "y_test = np.concatenate([np.repeat(0, len(normal_2)), np.repeat(1, len(abnormal))])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "0sZe9Kh5erWK"
   },
   "outputs": [],
   "source": [
    "# Apply the random under-sampling for test data\n",
    "from imblearn.under_sampling import RandomUnderSampler\n",
    "rus = RandomUnderSampler(return_indices=True, random_state = 42)\n",
    "X_test, y_test, idx_resampled = rus.fit_sample(X_test, y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "PC3Gr0rafbNS"
   },
   "outputs": [],
   "source": [
    "#Normalize the train dataset\n",
    "from sklearn import preprocessing\n",
    "min_max_scaler = preprocessing.MinMaxScaler()\n",
    "X_train = min_max_scaler.fit_transform(X_train)\n",
    "\n",
    "#Normalize the test dataset\n",
    "X_test = min_max_scaler.transform(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 67
    },
    "colab_type": "code",
    "id": "VyAVbhMvgcUx",
    "outputId": "66226828-7959-47f0-a3ff-e3f4c208f915"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "OneClassSVM(cache_size=200, coef0=0.0, degree=3, gamma='auto_deprecated',\n",
       "      kernel='linear', max_iter=-1, nu=0.49, random_state=None,\n",
       "      shrinking=True, tol=0.001, verbose=False)"
      ]
     },
     "execution_count": 37,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Apply One Class SVM\n",
    "from sklearn import svm\n",
    "clf = svm.OneClassSVM(kernel = 'linear',\n",
    "                      nu=(len(y_test[y_test==1])/X_train.shape[0]))  \n",
    "clf.fit(X_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "R7F2-zOhgeqN"
   },
   "outputs": [],
   "source": [
    "#Predict on test data\n",
    "y_pred=clf.predict(X_test)\n",
    "\n",
    "y_pred[y_pred==1]=0\n",
    "y_pred[y_pred==-1]=1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 33
    },
    "colab_type": "code",
    "id": "4vCnGa_qghRL",
    "outputId": "37baa394-0c27-4ab8-c14d-ccfb94c0bcc8"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy 26.53\n"
     ]
    }
   ],
   "source": [
    "#Calculate accuracy\n",
    "from sklearn import metrics\n",
    "print(\"Accuracy %0.2f\" % (100*metrics.accuracy_score(y_test, y_pred)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 33
    },
    "colab_type": "code",
    "id": "6Q6KjUWegj3k",
    "outputId": "2a559984-6b4c-40ba-d64d-cd4a7e6296e4"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['model.sav']"
      ]
     },
     "execution_count": 40,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# save the model to disk\n",
    "filename = 'model.sav'\n",
    "joblib.dump(clf, filename)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "QF2N04vng75D"
   },
   "outputs": [],
   "source": [
    "model_file = drive.CreateFile({'title' : 'model.sav'})\n",
    "model_file.SetContentFile('model.sav')\n",
    "model_file.Upload()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "Ku4gvrjwhKKa"
   },
   "outputs": [],
   "source": []
  }
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