Classification algos on Http CSIC  2010 dataset.ipynb

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       "Number of unpacked features : 1"
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     },
     "metadata": {},
     "output_type": "display_data"
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     "data": {
      "text/html": [
       "<pre>+-----------+--------------+-------------------+---------------------+-------------------+---------------------+</pre>"
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      "text/plain": [
       "+-----------+--------------+-------------------+---------------------+-------------------+---------------------+"
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     "metadata": {},
     "output_type": "display_data"
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     "data": {
      "text/html": [
       "<pre>| Iteration | Elapsed Time | Training-accuracy | Validation-accuracy | Training-log_loss | Validation-log_loss |</pre>"
      ],
      "text/plain": [
       "| Iteration | Elapsed Time | Training-accuracy | Validation-accuracy | Training-log_loss | Validation-log_loss |"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>+-----------+--------------+-------------------+---------------------+-------------------+---------------------+</pre>"
      ],
      "text/plain": [
       "+-----------+--------------+-------------------+---------------------+-------------------+---------------------+"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>| 1         | 0.187500     | 0.625424          | 0.624316            | 0.670271          | 0.670537            |</pre>"
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      "text/plain": [
       "| 1         | 0.187500     | 0.625424          | 0.624316            | 0.670271          | 0.670537            |"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>+-----------+--------------+-------------------+---------------------+-------------------+---------------------+</pre>"
      ],
      "text/plain": [
       "+-----------+--------------+-------------------+---------------------+-------------------+---------------------+"
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     },
     "metadata": {},
     "output_type": "display_data"
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   ],
   "source": [
    "dt_classifier_model = gl.classifier.decision_tree_classifier.create(train_data,target = 'label',features = ['payload'], validation_set=test_data)"
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  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<pre>Boosted trees classifier:</pre>"
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       "Boosted trees classifier:"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>--------------------------------------------------------</pre>"
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      "text/plain": [
       "--------------------------------------------------------"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>Number of examples          : 179021</pre>"
      ],
      "text/plain": [
       "Number of examples          : 179021"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>Number of classes           : 2</pre>"
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      "text/plain": [
       "Number of classes           : 2"
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     },
     "metadata": {},
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    {
     "data": {
      "text/html": [
       "<pre>Number of feature columns   : 1</pre>"
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    {
     "data": {
      "text/html": [
       "<pre>Number of unpacked features : 1</pre>"
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      "text/plain": [
       "Number of unpacked features : 1"
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     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>+-----------+--------------+-------------------+---------------------+-------------------+---------------------+</pre>"
      ],
      "text/plain": [
       "+-----------+--------------+-------------------+---------------------+-------------------+---------------------+"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>| Iteration | Elapsed Time | Training-accuracy | Validation-accuracy | Training-log_loss | Validation-log_loss |</pre>"
      ],
      "text/plain": [
       "| Iteration | Elapsed Time | Training-accuracy | Validation-accuracy | Training-log_loss | Validation-log_loss |"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>+-----------+--------------+-------------------+---------------------+-------------------+---------------------+</pre>"
      ],
      "text/plain": [
       "+-----------+--------------+-------------------+---------------------+-------------------+---------------------+"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>| 1         | 0.156250     | 0.625424          | 0.624316            | 0.670271          | 0.670537            |</pre>"
      ],
      "text/plain": [
       "| 1         | 0.156250     | 0.625424          | 0.624316            | 0.670271          | 0.670537            |"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>| 2         | 0.312501     | 0.625424          | 0.624316            | 0.658265          | 0.658559            |</pre>"
      ],
      "text/plain": [
       "| 2         | 0.312501     | 0.625424          | 0.624316            | 0.658265          | 0.658559            |"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>| 3         | 0.468753     | 0.625424          | 0.624316            | 0.651420          | 0.651587            |</pre>"
      ],
      "text/plain": [
       "| 3         | 0.468753     | 0.625424          | 0.624316            | 0.651420          | 0.651587            |"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>| 4         | 0.609379     | 0.625424          | 0.624316            | 0.647211          | 0.647137            |</pre>"
      ],
      "text/plain": [
       "| 4         | 0.609379     | 0.625424          | 0.624316            | 0.647211          | 0.647137            |"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>| 5         | 0.750006     | 0.625424          | 0.624316            | 0.644536          | 0.644332            |</pre>"
      ],
      "text/plain": [
       "| 5         | 0.750006     | 0.625424          | 0.624316            | 0.644536          | 0.644332            |"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>| 6         | 0.906258     | 0.625424          | 0.624316            | 0.642784          | 0.642451            |</pre>"
      ],
      "text/plain": [
       "| 6         | 0.906258     | 0.625424          | 0.624316            | 0.642784          | 0.642451            |"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>| 10        | 1.453143     | 0.629010          | 0.628489            | 0.638705          | 0.638337            |</pre>"
      ],
      "text/plain": [
       "| 10        | 1.453143     | 0.629010          | 0.628489            | 0.638705          | 0.638337            |"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>+-----------+--------------+-------------------+---------------------+-------------------+---------------------+</pre>"
      ],
      "text/plain": [
       "+-----------+--------------+-------------------+---------------------+-------------------+---------------------+"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "bt_classifier_model = gl.classifier.boosted_trees_classifier.create(train_data,target = 'label',features = ['payload'], validation_set=test_data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<pre>Logistic regression:</pre>"
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      "text/plain": [
       "Logistic regression:"
      ]
     },
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    },
    {
     "data": {
      "text/html": [
       "<pre>--------------------------------------------------------</pre>"
      ],
      "text/plain": [
       "--------------------------------------------------------"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>Number of examples          : 179021</pre>"
      ],
      "text/plain": [
       "Number of examples          : 179021"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>Number of classes           : 2</pre>"
      ],
      "text/plain": [
       "Number of classes           : 2"
      ]
     },
     "metadata": {},
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    },
    {
     "data": {
      "text/html": [
       "<pre>Number of feature columns   : 1</pre>"
      ],
      "text/plain": [
       "Number of feature columns   : 1"
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     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>Number of unpacked features : 1</pre>"
      ],
      "text/plain": [
       "Number of unpacked features : 1"
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     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>Number of coefficients    : 32901</pre>"
      ],
      "text/plain": [
       "Number of coefficients    : 32901"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>Starting L-BFGS</pre>"
      ],
      "text/plain": [
       "Starting L-BFGS"
      ]
     },
     "metadata": {},
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    },
    {
     "data": {
      "text/html": [
       "<pre>--------------------------------------------------------</pre>"
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      "text/plain": [
       "--------------------------------------------------------"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>+-----------+----------+-----------+--------------+-------------------+---------------------+</pre>"
      ],
      "text/plain": [
       "+-----------+----------+-----------+--------------+-------------------+---------------------+"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>| Iteration | Passes   | Step size | Elapsed Time | Training-accuracy | Validation-accuracy |</pre>"
      ],
      "text/plain": [
       "| Iteration | Passes   | Step size | Elapsed Time | Training-accuracy | Validation-accuracy |"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>+-----------+----------+-----------+--------------+-------------------+---------------------+</pre>"
      ],
      "text/plain": [
       "+-----------+----------+-----------+--------------+-------------------+---------------------+"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>| 1         | 4        | 0.000028  | 0.281252     | 0.830763          | 0.798829            |</pre>"
      ],
      "text/plain": [
       "| 1         | 4        | 0.000028  | 0.281252     | 0.830763          | 0.798829            |"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>| 2         | 6        | 1.000000  | 0.421878     | 0.821730          | 0.788058            |</pre>"
      ],
      "text/plain": [
       "| 2         | 6        | 1.000000  | 0.421878     | 0.821730          | 0.788058            |"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>| 3         | 7        | 1.000000  | 0.531254     | 0.829925          | 0.797819            |</pre>"
      ],
      "text/plain": [
       "| 3         | 7        | 1.000000  | 0.531254     | 0.829925          | 0.797819            |"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>| 4         | 8        | 1.000000  | 0.640631     | 0.829925          | 0.797819            |</pre>"
      ],
      "text/plain": [
       "| 4         | 8        | 1.000000  | 0.640631     | 0.829925          | 0.797819            |"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>| 5         | 9        | 1.000000  | 0.734382     | 0.821932          | 0.791177            |</pre>"
      ],
      "text/plain": [
       "| 5         | 9        | 1.000000  | 0.734382     | 0.821932          | 0.791177            |"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>| 6         | 11       | 1.000000  | 0.890634     | 0.830763          | 0.798829            |</pre>"
      ],
      "text/plain": [
       "| 6         | 11       | 1.000000  | 0.890634     | 0.830763          | 0.798829            |"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>| 10        | 15       | 1.000000  | 1.281263     | 0.835209          | 0.823849            |</pre>"
      ],
      "text/plain": [
       "| 10        | 15       | 1.000000  | 1.281263     | 0.835209          | 0.823849            |"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>+-----------+----------+-----------+--------------+-------------------+---------------------+</pre>"
      ],
      "text/plain": [
       "+-----------+----------+-----------+--------------+-------------------+---------------------+"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>TERMINATED: Iteration limit reached.</pre>"
      ],
      "text/plain": [
       "TERMINATED: Iteration limit reached."
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>This model may not be optimal. To improve it, consider increasing `max_iterations`.</pre>"
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      "text/plain": [
       "This model may not be optimal. To improve it, consider increasing `max_iterations`."
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     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "lr_classifier_model = gl.classifier.logistic_classifier.create(train_data,target = 'label',features = ['payload'], validation_set=test_data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Using network:\n",
      "\n",
      "### network layers ###\n",
      "layer[0]: FullConnectionLayer\n",
      "  init_sigma = 0.01\n",
      "  init_random = gaussian\n",
      "  init_bias = 0\n",
      "  num_hidden_units = 10\n",
      "layer[1]: SigmoidLayer\n",
      "layer[2]: FullConnectionLayer\n",
      "  init_sigma = 0.01\n",
      "  init_random = gaussian\n",
      "  init_bias = 0\n",
      "  num_hidden_units = 2\n",
      "layer[3]: SoftmaxLayer\n",
      "### end network layers ###\n",
      "\n",
      "### network parameters ###\n",
      "learning_rate = 0.001\n",
      "momentum = 0.9\n",
      "### end network parameters ###\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[ERROR] graphlab.toolkits._main: Toolkit error: Neuralnet supports only one image typed column, or multiple int, float or array typed columns.\n"
     ]
    },
    {
     "ename": "ToolkitError",
     "evalue": "Neuralnet supports only one image typed column, or multiple int, float or array typed columns.",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mToolkitError\u001b[0m                              Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-11-3f5a4a600e10>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m()\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0mnn_classifier_model\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mgl\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mclassifier\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mneuralnet_classifier\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mcreate\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mtrain_data\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mtarget\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;34m'label'\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mfeatures\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;33m[\u001b[0m\u001b[1;34m'payload'\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mvalidation_set\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mtest_data\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[1;32mC:\\Users\\Smitha\\Anaconda2\\envs\\gl-env\\lib\\site-packages\\graphlab\\toolkits\\classifier\\neuralnet_classifier.pyc\u001b[0m in \u001b[0;36mcreate\u001b[1;34m(dataset, target, features, max_iterations, network, validation_set, verbose, class_weights, **kwargs)\u001b[0m\n\u001b[0;32m    971\u001b[0m                        \u001b[0mmax_iterations\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mmax_iterations\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    972\u001b[0m                        \u001b[0mclass_weights\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mclass_weights\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 973\u001b[1;33m                        **(_network.params))\n\u001b[0m\u001b[0;32m    974\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    975\u001b[0m     \u001b[1;32mreturn\u001b[0m \u001b[0mNeuralNetClassifier\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mmodel\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m__proxy__\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mC:\\Users\\Smitha\\Anaconda2\\envs\\gl-env\\lib\\site-packages\\graphlab\\toolkits\\_supervised_learning.pyc\u001b[0m in \u001b[0;36mcreate\u001b[1;34m(dataset, target, model_name, features, validation_set, verbose, distributed, **kwargs)\u001b[0m\n\u001b[0;32m    434\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    435\u001b[0m     ret = _graphlab.toolkits._main.run(\"supervised_learning_train\",\n\u001b[1;32m--> 436\u001b[1;33m                                        options, verbose)\n\u001b[0m\u001b[0;32m    437\u001b[0m     \u001b[0mmodel\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mSupervisedLearningModel\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mret\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m'model'\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mmodel_name\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    438\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mC:\\Users\\Smitha\\Anaconda2\\envs\\gl-env\\lib\\site-packages\\graphlab\\toolkits\\_main.pyc\u001b[0m in \u001b[0;36mrun\u001b[1;34m(toolkit_name, options, verbose, show_progress)\u001b[0m\n\u001b[0;32m     87\u001b[0m         \u001b[0m_get_metric_tracker\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mtrack\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mmetric_name\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mvalue\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;36m1\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mproperties\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mtrack_props\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0msend_sys_info\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mFalse\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     88\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 89\u001b[1;33m         \u001b[1;32mraise\u001b[0m \u001b[0mToolkitError\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mstr\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mmessage\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[1;31mToolkitError\u001b[0m: Neuralnet supports only one image typed column, or multiple int, float or array typed columns."
     ]
    }
   ],
   "source": [
    "nn_classifier_model = gl.classifier.neuralnet_classifier.create(train_data,target = 'label',features = ['payload'], validation_set=test_data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "ename": "TypeError",
     "evalue": "create() got an unexpected keyword argument 'validation_set'",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-12-23fa20f4193d>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m()\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0mnn_classifier_model\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mgl\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mclassifier\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mnearest_neighbor_classifier\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mcreate\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mtrain_data\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mtarget\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;34m'label'\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mfeatures\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;33m[\u001b[0m\u001b[1;34m'payload'\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mvalidation_set\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mtest_data\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[1;31mTypeError\u001b[0m: create() got an unexpected keyword argument 'validation_set'"
     ]
    }
   ],
   "source": [
    "nn_classifier_model = gl.classifier.nearest_neighbor_classifier.create(train_data,target = 'label',features = ['payload'], validation_set=test_data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "ename": "TypeError",
     "evalue": "create() got an unexpected keyword argument 'validation_set'",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-13-23fa20f4193d>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m()\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0mnn_classifier_model\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mgl\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mclassifier\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mnearest_neighbor_classifier\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mcreate\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mtrain_data\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mtarget\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;34m'label'\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mfeatures\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;33m[\u001b[0m\u001b[1;34m'payload'\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mvalidation_set\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mtest_data\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[1;31mTypeError\u001b[0m: create() got an unexpected keyword argument 'validation_set'"
     ]
    }
   ],
   "source": [
    "nn_classifier_model = gl.classifier.nearest_neighbor_classifier.create(train_data,target = 'label',features = ['payload'], validation_set=test_data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'gl' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-1-82b355e4480a>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m()\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0msvm_classifier_model\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mgl\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mclassifier\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0msvm_classifier\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mcreate\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mtrain_data\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mtarget\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;34m'label'\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mfeatures\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;33m[\u001b[0m\u001b[1;34m'method'\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;34m'host'\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;34m'contentLength'\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;34m'contentType'\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;34m'payload'\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;34m'cookie'\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mvalidation_set\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mtest_data\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[1;31mNameError\u001b[0m: name 'gl' is not defined"
     ]
    }
   ],
   "source": [
    "svm_classifier_model = gl.classifier.svm_classifier.create(train_data,target = 'label',features = ['method','host','contentLength','contentType','payload','cookie'], validation_set=test_data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import graphlab as gl"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "This non-commercial license of GraphLab Create for academic use is assigned to smitha.r@learner.manipal.edu and will expire on February 06, 2018.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[INFO] graphlab.cython.cy_server: GraphLab Create v2.1 started. Logging: C:\\Users\\Smitha\\AppData\\Local\\Temp\\graphlab_server_1488558104.log.0\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<pre>Finished parsing file C:\\Users\\Smitha\\Desktop\\output_http_csic_2010_weka_with_duplications_RAW-RFC2616_escd_v02_full.csv</pre>"
      ],
      "text/plain": [
       "Finished parsing file C:\\Users\\Smitha\\Desktop\\output_http_csic_2010_weka_with_duplications_RAW-RFC2616_escd_v02_full.csv"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>Parsing completed. Parsed 100 lines in 1.45417 secs.</pre>"
      ],
      "text/plain": [
       "Parsing completed. Parsed 100 lines in 1.45417 secs."
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "------------------------------------------------------\n",
      "Inferred types from first 100 line(s) of file as \n",
      "column_type_hints=[long,str,str,str,str,str,str,str,str,str,str,str,str,str,str,str,str,str]\n",
      "If parsing fails due to incorrect types, you can correct\n",
      "the inferred type list above and pass it to read_csv in\n",
      "the column_type_hints argument\n",
      "------------------------------------------------------\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<pre>Read 118011 lines. Lines per second: 89912.1</pre>"
      ],
      "text/plain": [
       "Read 118011 lines. Lines per second: 89912.1"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>Finished parsing file C:\\Users\\Smitha\\Desktop\\output_http_csic_2010_weka_with_duplications_RAW-RFC2616_escd_v02_full.csv</pre>"
      ],
      "text/plain": [
       "Finished parsing file C:\\Users\\Smitha\\Desktop\\output_http_csic_2010_weka_with_duplications_RAW-RFC2616_escd_v02_full.csv"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>Parsing completed. Parsed 223585 lines in 1.46879 secs.</pre>"
      ],
      "text/plain": [
       "Parsing completed. Parsed 223585 lines in 1.46879 secs."
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "data=gl.SFrame(\"C:/Users/Smitha/Desktop/output_http_csic_2010_weka_with_duplications_RAW-RFC2616_escd_v02_full.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'train_data' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-4-82b355e4480a>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m()\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0msvm_classifier_model\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mgl\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mclassifier\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0msvm_classifier\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mcreate\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mtrain_data\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mtarget\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;34m'label'\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mfeatures\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;33m[\u001b[0m\u001b[1;34m'method'\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;34m'host'\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;34m'contentLength'\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;34m'contentType'\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;34m'payload'\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;34m'cookie'\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mvalidation_set\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mtest_data\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[1;31mNameError\u001b[0m: name 'train_data' is not defined"
     ]
    }
   ],
   "source": [
    "svm_classifier_model = gl.classifier.svm_classifier.create(train_data,target = 'label',features = ['method','host','contentLength','contentType','payload','cookie'], validation_set=test_data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "train_data,test_data=data.random_split(0.8)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<pre>SVM:</pre>"
      ],
      "text/plain": [
       "SVM:"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>--------------------------------------------------------</pre>"
      ],
      "text/plain": [
       "--------------------------------------------------------"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>Number of examples          : 178916</pre>"
      ],
      "text/plain": [
       "Number of examples          : 178916"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>Number of classes           : 2</pre>"
      ],
      "text/plain": [
       "Number of classes           : 2"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>Number of feature columns   : 6</pre>"
      ],
      "text/plain": [
       "Number of feature columns   : 6"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>Number of unpacked features : 6</pre>"
      ],
      "text/plain": [
       "Number of unpacked features : 6"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>Number of coefficients    : 86978</pre>"
      ],
      "text/plain": [
       "Number of coefficients    : 86978"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>Starting L-BFGS</pre>"
      ],
      "text/plain": [
       "Starting L-BFGS"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>--------------------------------------------------------</pre>"
      ],
      "text/plain": [
       "--------------------------------------------------------"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>+-----------+----------+-----------+--------------+-------------------+---------------------+</pre>"
      ],
      "text/plain": [
       "+-----------+----------+-----------+--------------+-------------------+---------------------+"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>| Iteration | Passes   | Step size | Elapsed Time | Training-accuracy | Validation-accuracy |</pre>"
      ],
      "text/plain": [
       "| Iteration | Passes   | Step size | Elapsed Time | Training-accuracy | Validation-accuracy |"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>+-----------+----------+-----------+--------------+-------------------+---------------------+</pre>"
      ],
      "text/plain": [
       "+-----------+----------+-----------+--------------+-------------------+---------------------+"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>| 1         | 3        | 0.000006  | 1.250968     | 0.998580          | 0.970337            |</pre>"
      ],
      "text/plain": [
       "| 1         | 3        | 0.000006  | 1.250968     | 0.998580          | 0.970337            |"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>| 2         | 5        | 1.000000  | 1.454097     | 0.999899          | 0.974636            |</pre>"
      ],
      "text/plain": [
       "| 2         | 5        | 1.000000  | 1.454097     | 0.999899          | 0.974636            |"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>| 3         | 6        | 1.000000  | 1.579097     | 0.999989          | 0.972352            |</pre>"
      ],
      "text/plain": [
       "| 3         | 6        | 1.000000  | 1.579097     | 0.999989          | 0.972352            |"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>| 4         | 7        | 1.000000  | 1.704098     | 0.999989          | 0.972352            |</pre>"
      ],
      "text/plain": [
       "| 4         | 7        | 1.000000  | 1.704098     | 0.999989          | 0.972352            |"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>| 5         | 8        | 1.000000  | 1.829099     | 0.999989          | 0.972352            |</pre>"
      ],
      "text/plain": [
       "| 5         | 8        | 1.000000  | 1.829099     | 0.999989          | 0.972352            |"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>| 6         | 9        | 1.000000  | 1.954102     | 0.999989          | 0.972352            |</pre>"
      ],
      "text/plain": [
       "| 6         | 9        | 1.000000  | 1.954102     | 0.999989          | 0.972352            |"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>| 10        | 13       | 1.000000  | 2.439493     | 0.999989          | 0.972419            |</pre>"
      ],
      "text/plain": [
       "| 10        | 13       | 1.000000  | 2.439493     | 0.999989          | 0.972419            |"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>+-----------+----------+-----------+--------------+-------------------+---------------------+</pre>"
      ],
      "text/plain": [
       "+-----------+----------+-----------+--------------+-------------------+---------------------+"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>TERMINATED: Iteration limit reached.</pre>"
      ],
      "text/plain": [
       "TERMINATED: Iteration limit reached."
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>This model may not be optimal. To improve it, consider increasing `max_iterations`.</pre>"
      ],
      "text/plain": [
       "This model may not be optimal. To improve it, consider increasing `max_iterations`."
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "svm_classifier_model = gl.classifier.svm_classifier.create(train_data,target = 'label',features = ['method','host','contentLength','contentType','payload','cookie'], validation_set=test_data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'accuracy': 0.9724193512279209, 'confusion_matrix': Columns:\n",
       " \ttarget_label\tstr\n",
       " \tpredicted_label\tstr\n",
       " \tcount\tint\n",
       " \n",
       " Rows: 4\n",
       " \n",
       " Data:\n",
       " +--------------+-----------------+-------+\n",
       " | target_label | predicted_label | count |\n",
       " +--------------+-----------------+-------+\n",
       " |     anom     |       anom      | 22786 |\n",
       " |     norm     |       anom      |  111  |\n",
       " |     anom     |       norm      |  1121 |\n",
       " |     norm     |       norm      | 20651 |\n",
       " +--------------+-----------------+-------+\n",
       " [4 rows x 3 columns], 'f1_score': 0.9710349367564772, 'precision': 0.948511850082675, 'recall': 0.994653694249109}"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "svm_classifier_model.evaluate(test_data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<pre>Decision tree classifier:</pre>"
      ],
      "text/plain": [
       "Decision tree classifier:"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>--------------------------------------------------------</pre>"
      ],
      "text/plain": [
       "--------------------------------------------------------"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>Number of examples          : 178916</pre>"
      ],
      "text/plain": [
       "Number of examples          : 178916"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>Number of classes           : 2</pre>"
      ],
      "text/plain": [
       "Number of classes           : 2"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>Number of feature columns   : 6</pre>"
      ],
      "text/plain": [
       "Number of feature columns   : 6"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>Number of unpacked features : 6</pre>"
      ],
      "text/plain": [
       "Number of unpacked features : 6"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>+-----------+--------------+-------------------+---------------------+-------------------+---------------------+</pre>"
      ],
      "text/plain": [
       "+-----------+--------------+-------------------+---------------------+-------------------+---------------------+"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>| Iteration | Elapsed Time | Training-accuracy | Validation-accuracy | Training-log_loss | Validation-log_loss |</pre>"
      ],
      "text/plain": [
       "| Iteration | Elapsed Time | Training-accuracy | Validation-accuracy | Training-log_loss | Validation-log_loss |"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>+-----------+--------------+-------------------+---------------------+-------------------+---------------------+</pre>"
      ],
      "text/plain": [
       "+-----------+--------------+-------------------+---------------------+-------------------+---------------------+"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>| 1         | 0.229163     | 0.628798          | 0.628310            | 0.667151          | 0.667315            |</pre>"
      ],
      "text/plain": [
       "| 1         | 0.229163     | 0.628798          | 0.628310            | 0.667151          | 0.667315            |"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>+-----------+--------------+-------------------+---------------------+-------------------+---------------------+</pre>"
      ],
      "text/plain": [
       "+-----------+--------------+-------------------+---------------------+-------------------+---------------------+"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "dt_classifier_model = gl.classifier.decision_tree_classifier.create(train_data,target = 'label',features = ['method','host','contentLength','contentType','payload','cookie'], validation_set=test_data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'accuracy': 0.6283104613938078,\n",
       " 'auc': 0.6119970192671795,\n",
       " 'confusion_matrix': Columns:\n",
       " \ttarget_label\tstr\n",
       " \tpredicted_label\tstr\n",
       " \tcount\tint\n",
       " \n",
       " Rows: 4\n",
       " \n",
       " Data:\n",
       " +--------------+-----------------+-------+\n",
       " | target_label | predicted_label | count |\n",
       " +--------------+-----------------+-------+\n",
       " |     anom     |       anom      | 22758 |\n",
       " |     anom     |       norm      |  1149 |\n",
       " |     norm     |       norm      |  5308 |\n",
       " |     norm     |       anom      | 15454 |\n",
       " +--------------+-----------------+-------+\n",
       " [4 rows x 3 columns],\n",
       " 'f1_score': 0.3900216760351225,\n",
       " 'log_loss': 0.6673152536123284,\n",
       " 'precision': 0.822053585256311,\n",
       " 'recall': 0.25565937770927655,\n",
       " 'roc_curve': Columns:\n",
       " \tthreshold\tfloat\n",
       " \tfpr\tfloat\n",
       " \ttpr\tfloat\n",
       " \tp\tint\n",
       " \tn\tint\n",
       " \n",
       " Rows: 100001\n",
       " \n",
       " Data:\n",
       " +-----------+-----+-----+-------+-------+\n",
       " | threshold | fpr | tpr |   p   |   n   |\n",
       " +-----------+-----+-----+-------+-------+\n",
       " |    0.0    | 1.0 | 1.0 | 20762 | 23907 |\n",
       " |   1e-05   | 1.0 | 1.0 | 20762 | 23907 |\n",
       " |   2e-05   | 1.0 | 1.0 | 20762 | 23907 |\n",
       " |   3e-05   | 1.0 | 1.0 | 20762 | 23907 |\n",
       " |   4e-05   | 1.0 | 1.0 | 20762 | 23907 |\n",
       " |   5e-05   | 1.0 | 1.0 | 20762 | 23907 |\n",
       " |   6e-05   | 1.0 | 1.0 | 20762 | 23907 |\n",
       " |   7e-05   | 1.0 | 1.0 | 20762 | 23907 |\n",
       " |   8e-05   | 1.0 | 1.0 | 20762 | 23907 |\n",
       " |   9e-05   | 1.0 | 1.0 | 20762 | 23907 |\n",
       " +-----------+-----+-----+-------+-------+\n",
       " [100001 rows x 5 columns]\n",
       " Note: Only the head of the SFrame is printed.\n",
       " You can use print_rows(num_rows=m, num_columns=n) to print more rows and columns.}"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dt_classifier_model.evaluate(test_data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<pre>Random forest classifier:</pre>"
      ],
      "text/plain": [
       "Random forest classifier:"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>--------------------------------------------------------</pre>"
      ],
      "text/plain": [
       "--------------------------------------------------------"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>Number of examples          : 178916</pre>"
      ],
      "text/plain": [
       "Number of examples          : 178916"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>Number of classes           : 2</pre>"
      ],
      "text/plain": [
       "Number of classes           : 2"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>Number of feature columns   : 6</pre>"
      ],
      "text/plain": [
       "Number of feature columns   : 6"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>Number of unpacked features : 6</pre>"
      ],
      "text/plain": [
       "Number of unpacked features : 6"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>+-----------+--------------+-------------------+---------------------+-------------------+---------------------+</pre>"
      ],
      "text/plain": [
       "+-----------+--------------+-------------------+---------------------+-------------------+---------------------+"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>| Iteration | Elapsed Time | Training-accuracy | Validation-accuracy | Training-log_loss | Validation-log_loss |</pre>"
      ],
      "text/plain": [
       "| Iteration | Elapsed Time | Training-accuracy | Validation-accuracy | Training-log_loss | Validation-log_loss |"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>+-----------+--------------+-------------------+---------------------+-------------------+---------------------+</pre>"
      ],
      "text/plain": [
       "+-----------+--------------+-------------------+---------------------+-------------------+---------------------+"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>| 1         | 0.218153     | 0.563773          | 0.563836            | 0.662071          | 0.661735            |</pre>"
      ],
      "text/plain": [
       "| 1         | 0.218153     | 0.563773          | 0.563836            | 0.662071          | 0.661735            |"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>| 2         | 0.415293     | 0.633269          | 0.632609            | 0.643882          | 0.644163            |</pre>"
      ],
      "text/plain": [
       "| 2         | 0.415293     | 0.633269          | 0.632609            | 0.643882          | 0.644163            |"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>| 3         | 0.615434     | 0.633275          | 0.632609            | 0.640522          | 0.640848            |</pre>"
      ],
      "text/plain": [
       "| 3         | 0.615434     | 0.633275          | 0.632609            | 0.640522          | 0.640848            |"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>| 4         | 0.798564     | 0.633247          | 0.632609            | 0.639898          | 0.640233            |</pre>"
      ],
      "text/plain": [
       "| 4         | 0.798564     | 0.633247          | 0.632609            | 0.639898          | 0.640233            |"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>| 5         | 0.985696     | 0.633331          | 0.632698            | 0.641712          | 0.641990            |</pre>"
      ],
      "text/plain": [
       "| 5         | 0.985696     | 0.633331          | 0.632698            | 0.641712          | 0.641990            |"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>| 6         | 1.172829     | 0.633342          | 0.632698            | 0.640293          | 0.640647            |</pre>"
      ],
      "text/plain": [
       "| 6         | 1.172829     | 0.633342          | 0.632698            | 0.640293          | 0.640647            |"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>| 10        | 1.889337     | 0.633208          | 0.632564            | 0.639522          | 0.640046            |</pre>"
      ],
      "text/plain": [
       "| 10        | 1.889337     | 0.633208          | 0.632564            | 0.639522          | 0.640046            |"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>+-----------+--------------+-------------------+---------------------+-------------------+---------------------+</pre>"
      ],
      "text/plain": [
       "+-----------+--------------+-------------------+---------------------+-------------------+---------------------+"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "rf_classifier_model = gl.classifier.random_forest_classifier.create(train_data,target = 'label',features = ['method','host','contentLength','contentType','payload','cookie'], validation_set=test_data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'accuracy': 0.6325639705388525,\n",
       " 'auc': 0.6356517160484692,\n",
       " 'confusion_matrix': Columns:\n",
       " \ttarget_label\tstr\n",
       " \tpredicted_label\tstr\n",
       " \tcount\tint\n",
       " \n",
       " Rows: 4\n",
       " \n",
       " Data:\n",
       " +--------------+-----------------+-------+\n",
       " | target_label | predicted_label | count |\n",
       " +--------------+-----------------+-------+\n",
       " |     anom     |       anom      | 22754 |\n",
       " |     anom     |       norm      |  1153 |\n",
       " |     norm     |       norm      |  5502 |\n",
       " |     norm     |       anom      | 15260 |\n",
       " +--------------+-----------------+-------+\n",
       " [4 rows x 3 columns],\n",
       " 'f1_score': 0.40135682240945403,\n",
       " 'log_loss': 0.6400455170054594,\n",
       " 'precision': 0.8267468069120961,\n",
       " 'recall': 0.26500337154416725,\n",
       " 'roc_curve': Columns:\n",
       " \tthreshold\tfloat\n",
       " \tfpr\tfloat\n",
       " \ttpr\tfloat\n",
       " \tp\tint\n",
       " \tn\tint\n",
       " \n",
       " Rows: 100001\n",
       " \n",
       " Data:\n",
       " +-----------+-----+-----+-------+-------+\n",
       " | threshold | fpr | tpr |   p   |   n   |\n",
       " +-----------+-----+-----+-------+-------+\n",
       " |    0.0    | 1.0 | 1.0 | 20762 | 23907 |\n",
       " |   1e-05   | 1.0 | 1.0 | 20762 | 23907 |\n",
       " |   2e-05   | 1.0 | 1.0 | 20762 | 23907 |\n",
       " |   3e-05   | 1.0 | 1.0 | 20762 | 23907 |\n",
       " |   4e-05   | 1.0 | 1.0 | 20762 | 23907 |\n",
       " |   5e-05   | 1.0 | 1.0 | 20762 | 23907 |\n",
       " |   6e-05   | 1.0 | 1.0 | 20762 | 23907 |\n",
       " |   7e-05   | 1.0 | 1.0 | 20762 | 23907 |\n",
       " |   8e-05   | 1.0 | 1.0 | 20762 | 23907 |\n",
       " |   9e-05   | 1.0 | 1.0 | 20762 | 23907 |\n",
       " +-----------+-----+-----+-------+-------+\n",
       " [100001 rows x 5 columns]\n",
       " Note: Only the head of the SFrame is printed.\n",
       " You can use print_rows(num_rows=m, num_columns=n) to print more rows and columns.}"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rf_classifier_model.evaluate(test_data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<pre>Boosted trees classifier:</pre>"
      ],
      "text/plain": [
       "Boosted trees classifier:"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>--------------------------------------------------------</pre>"
      ],
      "text/plain": [
       "--------------------------------------------------------"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>Number of examples          : 178916</pre>"
      ],
      "text/plain": [
       "Number of examples          : 178916"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>Number of classes           : 2</pre>"
      ],
      "text/plain": [
       "Number of classes           : 2"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>Number of feature columns   : 6</pre>"
      ],
      "text/plain": [
       "Number of feature columns   : 6"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>Number of unpacked features : 6</pre>"
      ],
      "text/plain": [
       "Number of unpacked features : 6"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>+-----------+--------------+-------------------+---------------------+-------------------+---------------------+</pre>"
      ],
      "text/plain": [
       "+-----------+--------------+-------------------+---------------------+-------------------+---------------------+"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>| Iteration | Elapsed Time | Training-accuracy | Validation-accuracy | Training-log_loss | Validation-log_loss |</pre>"
      ],
      "text/plain": [
       "| Iteration | Elapsed Time | Training-accuracy | Validation-accuracy | Training-log_loss | Validation-log_loss |"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>+-----------+--------------+-------------------+---------------------+-------------------+---------------------+</pre>"
      ],
      "text/plain": [
       "+-----------+--------------+-------------------+---------------------+-------------------+---------------------+"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>| 1         | 0.230164     | 0.628798          | 0.628310            | 0.667151          | 0.667315            |</pre>"
      ],
      "text/plain": [
       "| 1         | 0.230164     | 0.628798          | 0.628310            | 0.667151          | 0.667315            |"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>| 2         | 0.450319     | 0.637394          | 0.636303            | 0.652798          | 0.653212            |</pre>"
      ],
      "text/plain": [
       "| 2         | 0.450319     | 0.637394          | 0.636303            | 0.652798          | 0.653212            |"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>| 3         | 0.659468     | 0.637444          | 0.636347            | 0.644271          | 0.644780            |</pre>"
      ],
      "text/plain": [
       "| 3         | 0.659468     | 0.637444          | 0.636347            | 0.644271          | 0.644780            |"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>| 4         | 0.863613     | 0.640502          | 0.639302            | 0.638831          | 0.639407            |</pre>"
      ],
      "text/plain": [
       "| 4         | 0.863613     | 0.640502          | 0.639302            | 0.638831          | 0.639407            |"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>| 5         | 1.073761     | 0.644872          | 0.642481            | 0.634566          | 0.635375            |</pre>"
      ],
      "text/plain": [
       "| 5         | 1.073761     | 0.644872          | 0.642481            | 0.634566          | 0.635375            |"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>| 6         | 1.279907     | 0.644912          | 0.642481            | 0.631665          | 0.632304            |</pre>"
      ],
      "text/plain": [
       "| 6         | 1.279907     | 0.644912          | 0.642481            | 0.631665          | 0.632304            |"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>| 10        | 2.085478     | 0.645364          | 0.642683            | 0.623218          | 0.623956            |</pre>"
      ],
      "text/plain": [
       "| 10        | 2.085478     | 0.645364          | 0.642683            | 0.623218          | 0.623956            |"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>+-----------+--------------+-------------------+---------------------+-------------------+---------------------+</pre>"
      ],
      "text/plain": [
       "+-----------+--------------+-------------------+---------------------+-------------------+---------------------+"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "bt_classifier_model = gl.classifier.boosted_trees_classifier.create(train_data,target = 'label',features = ['method','host','contentLength','contentType','payload','cookie'], validation_set=test_data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'accuracy': 0.6426828449260114,\n",
       " 'auc': 0.6510032522268533,\n",
       " 'confusion_matrix': Columns:\n",
       " \ttarget_label\tstr\n",
       " \tpredicted_label\tstr\n",
       " \tcount\tint\n",
       " \n",
       " Rows: 4\n",
       " \n",
       " Data:\n",
       " +--------------+-----------------+-------+\n",
       " | target_label | predicted_label | count |\n",
       " +--------------+-----------------+-------+\n",
       " |     anom     |       anom      | 22458 |\n",
       " |     anom     |       norm      |  1449 |\n",
       " |     norm     |       norm      |  6250 |\n",
       " |     norm     |       anom      | 14512 |\n",
       " +--------------+-----------------+-------+\n",
       " [4 rows x 3 columns],\n",
       " 'f1_score': 0.4391974983310495,\n",
       " 'log_loss': 0.6239557226317366,\n",
       " 'precision': 0.8117937394466814,\n",
       " 'recall': 0.30103072921683843,\n",
       " 'roc_curve': Columns:\n",
       " \tthreshold\tfloat\n",
       " \tfpr\tfloat\n",
       " \ttpr\tfloat\n",
       " \tp\tint\n",
       " \tn\tint\n",
       " \n",
       " Rows: 100001\n",
       " \n",
       " Data:\n",
       " +-----------+-----+-----+-------+-------+\n",
       " | threshold | fpr | tpr |   p   |   n   |\n",
       " +-----------+-----+-----+-------+-------+\n",
       " |    0.0    | 1.0 | 1.0 | 20762 | 23907 |\n",
       " |   1e-05   | 1.0 | 1.0 | 20762 | 23907 |\n",
       " |   2e-05   | 1.0 | 1.0 | 20762 | 23907 |\n",
       " |   3e-05   | 1.0 | 1.0 | 20762 | 23907 |\n",
       " |   4e-05   | 1.0 | 1.0 | 20762 | 23907 |\n",
       " |   5e-05   | 1.0 | 1.0 | 20762 | 23907 |\n",
       " |   6e-05   | 1.0 | 1.0 | 20762 | 23907 |\n",
       " |   7e-05   | 1.0 | 1.0 | 20762 | 23907 |\n",
       " |   8e-05   | 1.0 | 1.0 | 20762 | 23907 |\n",
       " |   9e-05   | 1.0 | 1.0 | 20762 | 23907 |\n",
       " +-----------+-----+-----+-------+-------+\n",
       " [100001 rows x 5 columns]\n",
       " Note: Only the head of the SFrame is printed.\n",
       " You can use print_rows(num_rows=m, num_columns=n) to print more rows and columns.}"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bt_classifier_model.evaluate(test_data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<pre>Logistic regression:</pre>"
      ],
      "text/plain": [
       "Logistic regression:"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>--------------------------------------------------------</pre>"
      ],
      "text/plain": [
       "--------------------------------------------------------"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>Number of examples          : 178916</pre>"
      ],
      "text/plain": [
       "Number of examples          : 178916"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>Number of classes           : 2</pre>"
      ],
      "text/plain": [
       "Number of classes           : 2"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>Number of feature columns   : 6</pre>"
      ],
      "text/plain": [
       "Number of feature columns   : 6"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>Number of unpacked features : 6</pre>"
      ],
      "text/plain": [
       "Number of unpacked features : 6"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>Number of coefficients    : 86978</pre>"
      ],
      "text/plain": [
       "Number of coefficients    : 86978"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>Starting L-BFGS</pre>"
      ],
      "text/plain": [
       "Starting L-BFGS"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>--------------------------------------------------------</pre>"
      ],
      "text/plain": [
       "--------------------------------------------------------"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>+-----------+----------+-----------+--------------+-------------------+---------------------+</pre>"
      ],
      "text/plain": [
       "+-----------+----------+-----------+--------------+-------------------+---------------------+"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>| Iteration | Passes   | Step size | Elapsed Time | Training-accuracy | Validation-accuracy |</pre>"
      ],
      "text/plain": [
       "| Iteration | Passes   | Step size | Elapsed Time | Training-accuracy | Validation-accuracy |"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>+-----------+----------+-----------+--------------+-------------------+---------------------+</pre>"
      ],
      "text/plain": [
       "+-----------+----------+-----------+--------------+-------------------+---------------------+"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>| 1         | 3        | 0.000006  | 0.255180     | 0.998580          | 0.970337            |</pre>"
      ],
      "text/plain": [
       "| 1         | 3        | 0.000006  | 0.255180     | 0.998580          | 0.970337            |"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>| 2         | 5        | 1.000000  | 0.456320     | 0.999978          | 0.974031            |</pre>"
      ],
      "text/plain": [
       "| 2         | 5        | 1.000000  | 0.456320     | 0.999978          | 0.974031            |"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>| 3         | 6        | 1.000000  | 0.587414     | 1.000000          | 0.972218            |</pre>"
      ],
      "text/plain": [
       "| 3         | 6        | 1.000000  | 0.587414     | 1.000000          | 0.972218            |"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>| 4         | 7        | 1.000000  | 0.715512     | 1.000000          | 0.972375            |</pre>"
      ],
      "text/plain": [
       "| 4         | 7        | 1.000000  | 0.715512     | 1.000000          | 0.972375            |"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>| 5         | 8        | 1.000000  | 0.838596     | 1.000000          | 0.972375            |</pre>"
      ],
      "text/plain": [
       "| 5         | 8        | 1.000000  | 0.838596     | 1.000000          | 0.972375            |"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>| 6         | 9        | 1.000000  | 0.968685     | 1.000000          | 0.972375            |</pre>"
      ],
      "text/plain": [
       "| 6         | 9        | 1.000000  | 0.968685     | 1.000000          | 0.972375            |"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>| 10        | 13       | 1.000000  | 1.486050     | 1.000000          | 0.972263            |</pre>"
      ],
      "text/plain": [
       "| 10        | 13       | 1.000000  | 1.486050     | 1.000000          | 0.972263            |"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>+-----------+----------+-----------+--------------+-------------------+---------------------+</pre>"
      ],
      "text/plain": [
       "+-----------+----------+-----------+--------------+-------------------+---------------------+"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>TERMINATED: Iteration limit reached.</pre>"
      ],
      "text/plain": [
       "TERMINATED: Iteration limit reached."
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>This model may not be optimal. To improve it, consider increasing `max_iterations`.</pre>"
      ],
      "text/plain": [
       "This model may not be optimal. To improve it, consider increasing `max_iterations`."
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "lr_classifier_model = gl.classifier.logistic_classifier.create(train_data,target = 'label',features = ['method','host','contentLength','contentType','payload','cookie'], validation_set=test_data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'accuracy': 0.9722626429962614,\n",
       " 'auc': 0.9949717968594868,\n",
       " 'confusion_matrix': Columns:\n",
       " \ttarget_label\tstr\n",
       " \tpredicted_label\tstr\n",
       " \tcount\tint\n",
       " \n",
       " Rows: 4\n",
       " \n",
       " Data:\n",
       " +--------------+-----------------+-------+\n",
       " | target_label | predicted_label | count |\n",
       " +--------------+-----------------+-------+\n",
       " |     anom     |       anom      | 22796 |\n",
       " |     norm     |       anom      |  128  |\n",
       " |     anom     |       norm      |  1111 |\n",
       " |     norm     |       norm      | 20634 |\n",
       " +--------------+-----------------+-------+\n",
       " [4 rows x 3 columns],\n",
       " 'f1_score': 0.9708518596936976,\n",
       " 'log_loss': 0.07149136939933483,\n",
       " 'precision': 0.9489077948953782,\n",
       " 'recall': 0.9938348906656391,\n",
       " 'roc_curve': Columns:\n",
       " \tthreshold\tfloat\n",
       " \tfpr\tfloat\n",
       " \ttpr\tfloat\n",
       " \tp\tint\n",
       " \tn\tint\n",
       " \n",
       " Rows: 100001\n",
       " \n",
       " Data:\n",
       " +-----------+----------------+-----+-------+-------+\n",
       " | threshold |      fpr       | tpr |   p   |   n   |\n",
       " +-----------+----------------+-----+-------+-------+\n",
       " |    0.0    |      1.0       | 1.0 | 20762 | 23907 |\n",
       " |   1e-05   | 0.997448446062 | 1.0 | 20762 | 23907 |\n",
       " |   2e-05   | 0.987284059062 | 1.0 | 20762 | 23907 |\n",
       " |   3e-05   | 0.965072991174 | 1.0 | 20762 | 23907 |\n",
       " |   4e-05   | 0.92027439662  | 1.0 | 20762 | 23907 |\n",
       " |   5e-05   | 0.839628560673 | 1.0 | 20762 | 23907 |\n",
       " |   6e-05   | 0.744468147405 | 1.0 | 20762 | 23907 |\n",
       " |   7e-05   | 0.66210733258  | 1.0 | 20762 | 23907 |\n",
       " |   8e-05   | 0.59647801899  | 1.0 | 20762 | 23907 |\n",
       " |   9e-05   | 0.55050821935  | 1.0 | 20762 | 23907 |\n",
       " +-----------+----------------+-----+-------+-------+\n",
       " [100001 rows x 5 columns]\n",
       " Note: Only the head of the SFrame is printed.\n",
       " You can use print_rows(num_rows=m, num_columns=n) to print more rows and columns.}"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lr_classifier_model.evaluate(test_data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[ERROR] graphlab.toolkits._main: Toolkit error: Neuralnet supports only one image typed column, or multiple int, float or array typed columns.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Using network:\n",
      "\n",
      "### network layers ###\n",
      "layer[0]: FullConnectionLayer\n",
      "  init_sigma = 0.01\n",
      "  init_random = gaussian\n",
      "  init_bias = 0\n",
      "  num_hidden_units = 10\n",
      "layer[1]: SigmoidLayer\n",
      "layer[2]: FullConnectionLayer\n",
      "  init_sigma = 0.01\n",
      "  init_random = gaussian\n",
      "  init_bias = 0\n",
      "  num_hidden_units = 2\n",
      "layer[3]: SoftmaxLayer\n",
      "### end network layers ###\n",
      "\n",
      "### network parameters ###\n",
      "learning_rate = 0.001\n",
      "momentum = 0.9\n",
      "### end network parameters ###\n",
      "\n"
     ]
    },
    {
     "ename": "ToolkitError",
     "evalue": "Neuralnet supports only one image typed column, or multiple int, float or array typed columns.",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mToolkitError\u001b[0m                              Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-17-f3395e53e361>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m()\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0mnn_classifier_model\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mgl\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mclassifier\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mneuralnet_classifier\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mcreate\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mtrain_data\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mtarget\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;34m'label'\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mfeatures\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;33m[\u001b[0m\u001b[1;34m'method'\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;34m'host'\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;34m'contentLength'\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;34m'contentType'\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;34m'payload'\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;34m'cookie'\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mvalidation_set\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mtest_data\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[1;32mC:\\Users\\Smitha\\Anaconda2\\envs\\gl-env\\lib\\site-packages\\graphlab\\toolkits\\classifier\\neuralnet_classifier.pyc\u001b[0m in \u001b[0;36mcreate\u001b[1;34m(dataset, target, features, max_iterations, network, validation_set, verbose, class_weights, **kwargs)\u001b[0m\n\u001b[0;32m    971\u001b[0m                        \u001b[0mmax_iterations\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mmax_iterations\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    972\u001b[0m                        \u001b[0mclass_weights\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mclass_weights\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 973\u001b[1;33m                        **(_network.params))\n\u001b[0m\u001b[0;32m    974\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    975\u001b[0m     \u001b[1;32mreturn\u001b[0m \u001b[0mNeuralNetClassifier\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mmodel\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m__proxy__\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mC:\\Users\\Smitha\\Anaconda2\\envs\\gl-env\\lib\\site-packages\\graphlab\\toolkits\\_supervised_learning.pyc\u001b[0m in \u001b[0;36mcreate\u001b[1;34m(dataset, target, model_name, features, validation_set, verbose, distributed, **kwargs)\u001b[0m\n\u001b[0;32m    434\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    435\u001b[0m     ret = _graphlab.toolkits._main.run(\"supervised_learning_train\",\n\u001b[1;32m--> 436\u001b[1;33m                                        options, verbose)\n\u001b[0m\u001b[0;32m    437\u001b[0m     \u001b[0mmodel\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mSupervisedLearningModel\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mret\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m'model'\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mmodel_name\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    438\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mC:\\Users\\Smitha\\Anaconda2\\envs\\gl-env\\lib\\site-packages\\graphlab\\toolkits\\_main.pyc\u001b[0m in \u001b[0;36mrun\u001b[1;34m(toolkit_name, options, verbose, show_progress)\u001b[0m\n\u001b[0;32m     87\u001b[0m         \u001b[0m_get_metric_tracker\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mtrack\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mmetric_name\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mvalue\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;36m1\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mproperties\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mtrack_props\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0msend_sys_info\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mFalse\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     88\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 89\u001b[1;33m         \u001b[1;32mraise\u001b[0m \u001b[0mToolkitError\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mstr\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mmessage\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[1;31mToolkitError\u001b[0m: Neuralnet supports only one image typed column, or multiple int, float or array typed columns."
     ]
    }
   ],
   "source": [
    "nn_classifier_model = gl.classifier.neuralnet_classifier.create(train_data,target = 'label',features = ['method','host','contentLength','contentType','payload','cookie'], validation_set=test_data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "ename": "TypeError",
     "evalue": "create() got an unexpected keyword argument 'validation_set'",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-18-c7fbec3faef5>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m()\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0mnnb_classifier_model\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mgl\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mclassifier\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mnearest_neighbor_classifier\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mcreate\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mtrain_data\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mtarget\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;34m'label'\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mfeatures\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;33m[\u001b[0m\u001b[1;34m'method'\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;34m'host'\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;34m'contentLength'\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;34m'contentType'\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;34m'payload'\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;34m'cookie'\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mvalidation_set\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mtest_data\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[1;31mTypeError\u001b[0m: create() got an unexpected keyword argument 'validation_set'"
     ]
    }
   ],
   "source": [
    "nnb_classifier_model = gl.classifier.nearest_neighbor_classifier.create(train_data,target = 'label',features = ['method','host','contentLength','contentType','payload','cookie'], validation_set=test_data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
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