Week_6_2  Deep features for image retrieval.ipynb

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   "source": [
    "import graphlab"
   ]
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  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Load the CIFAR-10 dataset"
   ]
  },
  {
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   "execution_count": 2,
   "metadata": {
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     "text": [
      "This non-commercial license of GraphLab Create for academic use is assigned to amitha353@gmail.com and will expire on May 07, 2019.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[INFO] graphlab.cython.cy_server: GraphLab Create v2.1 started. Logging: C:\\Users\\Amitha\\AppData\\Local\\Temp\\graphlab_server_1526884336.log.0\n"
     ]
    }
   ],
   "source": [
    "image_train = graphlab.SFrame('image_train_data/')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
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       "Columns:\n",
       "\tid\tint\n",
       "\timage\tImage\n",
       "\tlabel\tstr\n",
       "\tdeep_features\tarray\n",
       "\timage_array\tarray\n",
       "\n",
       "Rows: 10\n",
       "\n",
       "Data:\n",
       "+-----+----------------------+------------+-------------------------------+\n",
       "|  id |        image         |   label    |         deep_features         |\n",
       "+-----+----------------------+------------+-------------------------------+\n",
       "|  24 | Height: 32 Width: 32 |    bird    | [0.242871761322, 1.0954537... |\n",
       "|  33 | Height: 32 Width: 32 |    cat     | [0.525087952614, 0.0, 0.0,... |\n",
       "|  36 | Height: 32 Width: 32 |    cat     | [0.566015958786, 0.0, 0.0,... |\n",
       "|  70 | Height: 32 Width: 32 |    dog     | [1.12979578972, 0.0, 0.0, ... |\n",
       "|  90 | Height: 32 Width: 32 |    bird    | [1.71786928177, 0.0, 0.0, ... |\n",
       "|  97 | Height: 32 Width: 32 | automobile | [1.57818555832, 0.0, 0.0, ... |\n",
       "| 107 | Height: 32 Width: 32 |    dog     | [0.0, 0.0, 0.220677852631,... |\n",
       "| 121 | Height: 32 Width: 32 |    bird    | [0.0, 0.23753464222, 0.0, ... |\n",
       "| 136 | Height: 32 Width: 32 | automobile | [0.0, 0.0, 0.0, 0.0, 0.0, ... |\n",
       "| 138 | Height: 32 Width: 32 |    bird    | [0.658935725689, 0.0, 0.0,... |\n",
       "+-----+----------------------+------------+-------------------------------+\n",
       "+-------------------------------+\n",
       "|          image_array          |\n",
       "+-------------------------------+\n",
       "| [73.0, 77.0, 58.0, 71.0, 6... |\n",
       "| [7.0, 5.0, 8.0, 7.0, 5.0, ... |\n",
       "| [169.0, 122.0, 65.0, 131.0... |\n",
       "| [154.0, 179.0, 152.0, 159.... |\n",
       "| [216.0, 195.0, 180.0, 201.... |\n",
       "| [33.0, 44.0, 27.0, 29.0, 4... |\n",
       "| [97.0, 51.0, 31.0, 104.0, ... |\n",
       "| [93.0, 96.0, 88.0, 102.0, ... |\n",
       "| [35.0, 59.0, 53.0, 36.0, 5... |\n",
       "| [205.0, 193.0, 195.0, 200.... |\n",
       "+-------------------------------+\n",
       "[10 rows x 5 columns]"
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     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
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   ],
   "source": [
    "image_train.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Creating a nearest neighbors model for image retrieval - using Deep Features - KNN model"
   ]
  },
  {
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   "execution_count": 4,
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      "text/html": [
       "<pre>Starting brute force nearest neighbors model training.</pre>"
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       "Starting brute force nearest neighbors model training."
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "knn_model = graphlab.nearest_neighbors.create(image_train, features = ['deep_features'],\n",
    "                                             label='id')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Use image retrieval model with Deep features to find similar images"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false
   },
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205, 187, 160, 209, 189, 162, 209, 187, 160, 209, 187, 160, 211, 188, 160, 209, 184, 157, 204, 178, 151, 198, 172, 143, 195, 168, 138, 186, 159, 130, 55, 51, 56, 62, 53, 50, 78, 63, 51, 98, 76, 61, 108, 83, 66, 110, 82, 69, 106, 80, 66, 102, 79, 64, 108, 87, 71, 116, 97, 82, 119, 103, 88, 124, 111, 96, 132, 121, 106, 152, 142, 128, 169, 162, 147, 172, 165, 149, 187, 176, 158, 185, 172, 151, 194, 179, 156, 213, 200, 178, 215, 203, 184, 222, 212, 192, 229, 217, 194, 229, 216, 192, 224, 209, 184, 221, 203, 179, 219, 199, 173, 211, 188, 162, 203, 180, 154, 196, 171, 145, 193, 167, 140, 187, 160, 133, 54, 50, 53, 60, 52, 46, 68, 53, 39, 84, 63, 45, 102, 77, 58, 111, 84, 68, 110, 82, 66, 110, 84, 65, 120, 94, 74, 124, 101, 82, 126, 106, 87, 127, 110, 91, 122, 105, 87, 122, 106, 89, 122, 108, 90, 139, 124, 106, 148, 132, 111, 138, 121, 98, 160, 143, 121, 175, 161, 141, 182, 170, 153, 208, 198, 180, 222, 209, 187, 221, 206, 182, 210, 194, 170, 201, 182, 158, 200, 179, 153, 200, 178, 151, 200, 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93, 66, 46, 93, 64, 46, 103, 75, 55, 113, 88, 68, 113, 92, 75, 122, 102, 85, 138, 119, 99, 154, 137, 115, 179, 163, 141, 187, 171, 151, 177, 162, 145, 164, 148, 133, 174, 158, 142, 190, 176, 160, 190, 177, 158, 194, 180, 158, 179, 163, 136, 174, 156, 125, 169, 148, 117, 177, 154, 126, 178, 155, 127, 182, 157, 129, 189, 163, 135, 194, 167, 137, 190, 162, 132, 180, 151, 120, 191, 158, 124, 202, 166, 130, 196, 159, 122, 50, 45, 41, 77, 64, 46, 87, 65, 42, 97, 67, 47, 107, 75, 57, 113, 84, 62, 115, 87, 63, 120, 92, 70, 137, 110, 87, 159, 135, 110, 177, 157, 131, 193, 176, 152, 202, 187, 167, 196, 181, 165, 185, 172, 154, 184, 170, 151, 188, 175, 156, 208, 195, 175, 193, 179, 156, 196, 177, 150, 199, 175, 144, 198, 171, 137, 192, 162, 129, 186, 155, 122, 181, 149, 116, 177, 144, 110, 174, 140, 105, 180, 145, 110, 192, 157, 121, 202, 163, 126, 201, 160, 122, 196, 154, 116, 47, 40, 35, 87, 73, 54, 104, 80, 56, 113, 82, 61, 119, 86, 66, 122, 91, 67, 129, 97, 70, 138, 103, 75, 142, 108, 80, 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68, 55, 95, 77, 64, 87, 69, 59, 87, 69, 58], \"height\": 32, \"channels\": 3, \"width\": 32, \"type\": \"image\", \"id\": 919997408}], \"selected_variable\": {\"name\": [\"<SArray>\"], \"dtype\": \"Image\", \"view_component\": \"Images\", \"view_file\": \"sarray\", \"descriptives\": {\"rows\": 1}, \"type\": \"SArray\", \"view_components\": [\"Images\"]}}, e);\n",
       "                });\n",
       "            })();\n",
       "        "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "graphlab.canvas.set_target('ipynb')\n",
    "cat = image_train[18:19]\n",
    "cat['image'].show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Find the nearest neighbours for the cat image"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
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    "collapsed": false
   },
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       "<pre>Starting pairwise querying.</pre>"
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       "Starting pairwise querying."
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     },
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    {
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       "<pre>+--------------+---------+-------------+--------------+</pre>"
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      "text/plain": [
       "+--------------+---------+-------------+--------------+"
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       "<pre>| Query points | # Pairs | % Complete. | Elapsed Time |</pre>"
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       "| Query points | # Pairs | % Complete. | Elapsed Time |"
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     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>+--------------+---------+-------------+--------------+</pre>"
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      "text/plain": [
       "+--------------+---------+-------------+--------------+"
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       "<pre>| 0            | 1       | 0.0498753   | 46.848ms     |</pre>"
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      "text/html": [
       "<pre>| Done         |         | 100         | 390.605ms    |</pre>"
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       "| Done         |         | 100         | 390.605ms    |"
      ]
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       "    <tr>\n",
       "        <th style=\"padding-left: 1em; padding-right: 1em; text-align: center\">query_label</th>\n",
       "        <th style=\"padding-left: 1em; padding-right: 1em; text-align: center\">reference_label</th>\n",
       "        <th style=\"padding-left: 1em; padding-right: 1em; text-align: center\">distance</th>\n",
       "        <th style=\"padding-left: 1em; padding-right: 1em; text-align: center\">rank</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">0</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">384</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">0.0</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">0</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">6910</td>\n",
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       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">2</td>\n",
       "    </tr>\n",
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       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">0</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">39777</td>\n",
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       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">3</td>\n",
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       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">0</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">36870</td>\n",
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       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">4</td>\n",
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       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">41734</td>\n",
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       "    </tr>\n",
       "</table>\n",
       "[5 rows x 4 columns]<br/>\n",
       "</div>"
      ],
      "text/plain": [
       "Columns:\n",
       "\tquery_label\tint\n",
       "\treference_label\tint\n",
       "\tdistance\tfloat\n",
       "\trank\tint\n",
       "\n",
       "Rows: 5\n",
       "\n",
       "Data:\n",
       "+-------------+-----------------+---------------+------+\n",
       "| query_label | reference_label |    distance   | rank |\n",
       "+-------------+-----------------+---------------+------+\n",
       "|      0      |       384       |      0.0      |  1   |\n",
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       "|      0      |      39777      | 38.4634888975 |  3   |\n",
       "|      0      |      36870      | 39.7559623119 |  4   |\n",
       "|      0      |      41734      | 39.7866014148 |  5   |\n",
       "+-------------+-----------------+---------------+------+\n",
       "[5 rows x 4 columns]"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "knn_model.query(cat)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def get_images_from_ids(query_result):\n",
    "    return image_train.filter_by(query_result['reference_label'], 'id')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
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    {
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       "<pre>| Query points | # Pairs | % Complete. | Elapsed Time |</pre>"
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     "output_type": "display_data"
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     "data": {
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   "source": [
    "cat_neighbors = get_images_from_ids(knn_model.query(cat))"
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       "\tid\tint\n",
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       "Data:\n",
       "+-------+----------------------+-------+-------------------------------+\n",
       "|   id  |        image         | label |         deep_features         |\n",
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       "|  384  | Height: 32 Width: 32 |  cat  | [1.04403531551, 0.0, 0.0, ... |\n",
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       "+-------+----------------------+-------+-------------------------------+\n",
       "+-------------------------------+\n",
       "|          image_array          |\n",
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     "execution_count": 9,
     "metadata": {},
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   "source": [
    "cat_neighbors.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
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       "                });\n",
       "            })();\n",
       "        "
      ]
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     "metadata": {},
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   ],
   "source": [
    "get_images_from_ids(knn_model.query(car))['image'].show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Create a lambda to find and show the nearest neighbor images"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "show_neighbors = lambda i : get_images_from_ids(knn_model.query(image_train[i:i+1]))['image'].show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
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       "<pre>Starting pairwise querying.</pre>"
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       "Starting pairwise querying."
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       "<pre>+--------------+---------+-------------+--------------+</pre>"
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       "<pre>| Query points | # Pairs | % Complete. | Elapsed Time |</pre>"
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       "| Query points | # Pairs | % Complete. | Elapsed Time |"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>+--------------+---------+-------------+--------------+</pre>"
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      "text/plain": [
       "+--------------+---------+-------------+--------------+"
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    {
     "data": {
      "text/html": [
       "<pre>| 0            | 1       | 0.0498753   | 46.877ms     |</pre>"
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       "| 0            | 1       | 0.0498753   | 46.877ms     |"
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     "data": {
      "text/html": [
       "<pre>| Done         |         | 100         | 390.634ms    |</pre>"
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      "text/plain": [
       "| Done         |         | 100         | 390.634ms    |"
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     "data": {
      "text/html": [
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       "                });\n",
       "            })();\n",
       "        "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "show_neighbors(2000)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Scenario 1 : Computing summary statistics of the data:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "\n",
       "+------------------+-------+----------+\n",
       "|       item       | value | is exact |\n",
       "+------------------+-------+----------+\n",
       "|      Length      |  2005 |   Yes    |\n",
       "| # Missing Values |   0   |   Yes    |\n",
       "| # unique values  |   4   |    No    |\n",
       "+------------------+-------+----------+\n",
       "\n",
       "Most frequent items:\n",
       "+-------+------------+-----+-----+------+\n",
       "| value | automobile | cat | dog | bird |\n",
       "+-------+------------+-----+-----+------+\n",
       "| count |    509     | 509 | 509 | 478  |\n",
       "+-------+------------+-----+-----+------+\n"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "image_train['label'].sketch_summary()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Q1 : What’s the least common category in the training data?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Bird (478)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Scenario 2: Creating category-specific image retrieval models"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "image_train_automobile = image_train[image_train['label'] =='automobile']\n",
    "image_train_dog = image_train[image_train['label'] =='dog']\n",
    "image_train_cat = image_train[image_train['label'] == 'cat']\n",
    "image_train_bird = image_train[image_train['label']=='bird']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<pre>Starting brute force nearest neighbors model training.</pre>"
      ],
      "text/plain": [
       "Starting brute force nearest neighbors model training."
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>Starting brute force nearest neighbors model training.</pre>"
      ],
      "text/plain": [
       "Starting brute force nearest neighbors model training."
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>Starting brute force nearest neighbors model training.</pre>"
      ],
      "text/plain": [
       "Starting brute force nearest neighbors model training."
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>Starting brute force nearest neighbors model training.</pre>"
      ],
      "text/plain": [
       "Starting brute force nearest neighbors model training."
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "knn_model_automobile = graphlab.nearest_neighbors.create(image_train_automobile,\n",
    "                                                        features=['deep_features'],\n",
    "                                                        label='id')\n",
    "knn_model_dog = graphlab.nearest_neighbors.create(image_train_dog,\n",
    "                                                 features=['deep_features'],\n",
    "                                                 label='id')\n",
    "knn_model_cat = graphlab.nearest_neighbors.create(image_train_cat,\n",
    "                                                  features=['deep_features'],\n",
    "                                                  label='id')\n",
    "knn_model_bird = graphlab.nearest_neighbors.create(image_train_bird,\n",
    "                                                   features=['deep_features'],\n",
    "                                                   label='id')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "image_test = graphlab.SFrame('image_test_data/')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "show_neighbors_automobile = lambda i:get_images_from_ids(knn_model_automobile.query(image_test[i:i+1]))['image'].show()\n",
    "show_neighbors_dog = lambda i:get_images_from_ids(knn_model_dog.query(image_test[i:i+1]))['image'].show()\n",
    "show_neighbors_cats = lambda i:get_images_from_ids(knn_model_cat.query(image_test[i:i+1]))['image'].show()\n",
    "show_neighbors_bird = lambda i:get_images_from_ids(knn_model_bird.query(image_test[i:i+1]))['image'].show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Q2 : Of the images below, which is the nearest ‘cat’ labeled image in the training data to the the first image in the test data (image_test[0:1])?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<pre>Starting pairwise querying.</pre>"
      ],
      "text/plain": [
       "Starting pairwise querying."
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>+--------------+---------+-------------+--------------+</pre>"
      ],
      "text/plain": [
       "+--------------+---------+-------------+--------------+"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>| Query points | # Pairs | % Complete. | Elapsed Time |</pre>"
      ],
      "text/plain": [
       "| Query points | # Pairs | % Complete. | Elapsed Time |"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>+--------------+---------+-------------+--------------+</pre>"
      ],
      "text/plain": [
       "+--------------+---------+-------------+--------------+"
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     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>| 0            | 1       | 0.196464    | 31.25ms      |</pre>"
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      "text/plain": [
       "| 0            | 1       | 0.196464    | 31.25ms      |"
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     },
     "metadata": {},
     "output_type": "display_data"
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    {
     "data": {
      "text/html": [
       "<pre>| Done         |         | 100         | 125.002ms    |</pre>"
      ],
      "text/plain": [
       "| Done         |         | 100         | 125.002ms    |"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
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     "data": {
      "text/html": [
       "<pre>+--------------+---------+-------------+--------------+</pre>"
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      "text/plain": [
       "+--------------+---------+-------------+--------------+"
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       "                require(['https://static.turi.com/products/graphlab-create/2.1/canvas/js/ipython_app.js'], function(IPythonApp){\n",
       "                    var app = new IPythonApp();\n",
       "                    app.attachView('sarray','Images', {\"ipython\": true, \"rows\": [{\"format\": \"raw\", \"value\": [91, 98, 71, 138, 123, 63, 135, 115, 50, 116, 99, 54, 118, 101, 54, 114, 99, 56, 101, 86, 55, 100, 88, 62, 95, 86, 58, 93, 84, 56, 93, 84, 56, 102, 91, 67, 114, 99, 80, 121, 101, 71, 124, 96, 59, 122, 88, 49, 123, 88, 47, 124, 94, 56, 117, 91, 55, 125, 95, 57, 122, 90, 49, 114, 86, 49, 110, 86, 51, 109, 79, 42, 100, 70, 27, 99, 74, 29, 104, 73, 29, 117, 79, 29, 138, 96, 37, 139, 94, 41, 131, 92, 43, 105, 74, 29, 96, 102, 70, 126, 115, 61, 137, 120, 63, 101, 93, 55, 91, 86, 49, 91, 87, 52, 79, 77, 53, 73, 69, 48, 86, 79, 53, 99, 92, 65, 103, 96, 69, 114, 107, 82, 143, 132, 113, 143, 128, 101, 129, 106, 78, 119, 91, 65, 121, 94, 61, 125, 101, 68, 126, 102, 68, 127, 97, 58, 120, 87, 46, 125, 97, 58, 119, 94, 57, 103, 76, 39, 96, 67, 26, 101, 72, 26, 102, 72, 27, 115, 83, 35, 141, 107, 54, 148, 106, 55, 141, 104, 54, 121, 91, 43, 111, 103, 64, 108, 100, 66, 97, 96, 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115, 143, 143, 122, 161, 159, 138, 179, 177, 157, 180, 182, 160, 177, 181, 158, 187, 191, 167, 185, 188, 161, 180, 183, 154, 176, 179, 150, 177, 180, 151, 179, 182, 153, 181, 184, 155, 180, 183, 155, 174, 177, 148, 169, 172, 143, 169, 172, 144, 174, 175, 147, 190, 184, 162, 199, 192, 170, 190, 184, 162, 203, 196, 175, 221, 215, 193, 226, 218, 197, 216, 208, 187, 200, 194, 173, 183, 179, 157, 166, 165, 142, 152, 153, 129], \"height\": 32, \"channels\": 3, \"width\": 32, \"type\": \"image\", \"id\": 896564640}], \"selected_variable\": {\"name\": [\"<SArray>\"], \"dtype\": \"Image\", \"view_component\": \"Images\", \"view_file\": \"sarray\", \"descriptives\": {\"rows\": 5}, \"type\": \"SArray\", \"view_components\": [\"Images\"]}}, e);\n",
       "                });\n",
       "            })();\n",
       "        "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "show_neighbors_dog(0)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Q5: For the first image in the test data, in what range is the mean distance between this image and its 5 nearest neighbors that were labeled ‘cat’ in the training data?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<pre>Starting pairwise querying.</pre>"
      ],
      "text/plain": [
       "Starting pairwise querying."
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>+--------------+---------+-------------+--------------+</pre>"
      ],
      "text/plain": [
       "+--------------+---------+-------------+--------------+"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>| Query points | # Pairs | % Complete. | Elapsed Time |</pre>"
      ],
      "text/plain": [
       "| Query points | # Pairs | % Complete. | Elapsed Time |"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>+--------------+---------+-------------+--------------+</pre>"
      ],
      "text/plain": [
       "+--------------+---------+-------------+--------------+"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>| 0            | 1       | 0.196464    | 31.251ms     |</pre>"
      ],
      "text/plain": [
       "| 0            | 1       | 0.196464    | 31.251ms     |"
      ]
     },
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     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>| Done         |         | 100         | 125.003ms    |</pre>"
      ],
      "text/plain": [
       "| Done         |         | 100         | 125.003ms    |"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>+--------------+---------+-------------+--------------+</pre>"
      ],
      "text/plain": [
       "+--------------+---------+-------------+--------------+"
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     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\"><table frame=\"box\" rules=\"cols\">\n",
       "    <tr>\n",
       "        <th style=\"padding-left: 1em; padding-right: 1em; text-align: center\">query_label</th>\n",
       "        <th style=\"padding-left: 1em; padding-right: 1em; text-align: center\">reference_label</th>\n",
       "        <th style=\"padding-left: 1em; padding-right: 1em; text-align: center\">distance</th>\n",
       "        <th style=\"padding-left: 1em; padding-right: 1em; text-align: center\">rank</th>\n",
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       "    <tr>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">0</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">16289</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">34.623719208</td>\n",
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       "    </tr>\n",
       "    <tr>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">0</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">45646</td>\n",
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       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">0</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">32139</td>\n",
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       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">3</td>\n",
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       "    <tr>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">0</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">25713</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">36.7548502521</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">4</td>\n",
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       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">0</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">331</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">36.8731228168</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">5</td>\n",
       "    </tr>\n",
       "</table>\n",
       "[5 rows x 4 columns]<br/>\n",
       "</div>"
      ],
      "text/plain": [
       "Columns:\n",
       "\tquery_label\tint\n",
       "\treference_label\tint\n",
       "\tdistance\tfloat\n",
       "\trank\tint\n",
       "\n",
       "Rows: 5\n",
       "\n",
       "Data:\n",
       "+-------------+-----------------+---------------+------+\n",
       "| query_label | reference_label |    distance   | rank |\n",
       "+-------------+-----------------+---------------+------+\n",
       "|      0      |      16289      |  34.623719208 |  1   |\n",
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       "|      0      |       331       | 36.8731228168 |  5   |\n",
       "+-------------+-----------------+---------------+------+\n",
       "[5 rows x 4 columns]"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "knn_model_cat.query(image_test[0:1])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Result = 36.87 (35 - 37)\n",
    "### Q5: For the first image in the test data, in what range is the mean distance between this image and its 5 nearest neighbors that were labeled ‘dog’ in the training data?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<pre>Starting pairwise querying.</pre>"
      ],
      "text/plain": [
       "Starting pairwise querying."
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>+--------------+---------+-------------+--------------+</pre>"
      ],
      "text/plain": [
       "+--------------+---------+-------------+--------------+"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>| Query points | # Pairs | % Complete. | Elapsed Time |</pre>"
      ],
      "text/plain": [
       "| Query points | # Pairs | % Complete. | Elapsed Time |"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>+--------------+---------+-------------+--------------+</pre>"
      ],
      "text/plain": [
       "+--------------+---------+-------------+--------------+"
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     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>| 0            | 1       | 0.196464    | 15.626ms     |</pre>"
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       "| 0            | 1       | 0.196464    | 15.626ms     |"
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    },
    {
     "data": {
      "text/html": [
       "<pre>| Done         |         | 100         | 125.002ms    |</pre>"
      ],
      "text/plain": [
       "| Done         |         | 100         | 125.002ms    |"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>+--------------+---------+-------------+--------------+</pre>"
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      "text/plain": [
       "+--------------+---------+-------------+--------------+"
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     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\"><table frame=\"box\" rules=\"cols\">\n",
       "    <tr>\n",
       "        <th style=\"padding-left: 1em; padding-right: 1em; text-align: center\">query_label</th>\n",
       "        <th style=\"padding-left: 1em; padding-right: 1em; text-align: center\">reference_label</th>\n",
       "        <th style=\"padding-left: 1em; padding-right: 1em; text-align: center\">distance</th>\n",
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       "    <tr>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">0</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">16976</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">37.4642628784</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">0</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">13387</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">37.5666832169</td>\n",
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       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">35867</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">37.6047267079</td>\n",
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       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">6094</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">38.5113254907</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">5</td>\n",
       "    </tr>\n",
       "</table>\n",
       "[5 rows x 4 columns]<br/>\n",
       "</div>"
      ],
      "text/plain": [
       "Columns:\n",
       "\tquery_label\tint\n",
       "\treference_label\tint\n",
       "\tdistance\tfloat\n",
       "\trank\tint\n",
       "\n",
       "Rows: 5\n",
       "\n",
       "Data:\n",
       "+-------------+-----------------+---------------+------+\n",
       "| query_label | reference_label |    distance   | rank |\n",
       "+-------------+-----------------+---------------+------+\n",
       "|      0      |      16976      | 37.4642628784 |  1   |\n",
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       "+-------------+-----------------+---------------+------+\n",
       "[5 rows x 4 columns]"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "knn_model_dog.query(image_test[0:1])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Result : (38.51) 37 - 39\n",
    "### Q6 : On average, is the first image in the test data closer to its 5 nearest neighbors in the ‘cat’ data or in the ‘dog’ data?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Result : Cat (35 - 37) < Dog(37-39); Therefore Cat;\n",
    "### Q7 : In what range is the accuracy of the 1-nearest neighbor classifier at classifying ‘dog’ images from the test set? "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "image_test_automobile = image_test[image_test['label'] == 'auomobile']\n",
    "image_test_dog = image_test[image_test['label'] == 'dog']\n",
    "image_test_cat = image_test[image_test['label'] == 'cat']\n",
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   "source": [
    "dog_automobile_neighbors = knn_model_automobile.query(image_test_dog, k =1)\n",
    "dog_dog_neighbors = knn_model_dog.query(image_test_dog, k =1)\n",
    "dog_cat_neighbors = knn_model_cat.query(image_test_dog, k =1)\n",
    "dog_bird_neighbors = knn_model_bird.query(image_test_dog, k =1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
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   },
   "outputs": [],
   "source": [
    "dog_distance = graphlab.SFrame({'dog-automobile-distance': dog_automobile_neighbors['distance'],\n",
    "                               'dog-dog-distance': dog_dog_neighbors['distance'],\n",
    "                               'dog-cat-distance':dog_cat_neighbors['distance'],\n",
    "                               'dog-bird-distance':dog_bird_neighbors['distance']})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
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       "Columns:\n",
       "\tdog-automobile-distance\tfloat\n",
       "\tdog-bird-distance\tfloat\n",
       "\tdog-cat-distance\tfloat\n",
       "\tdog-dog-distance\tfloat\n",
       "\n",
       "Rows: 10\n",
       "\n",
       "Data:\n",
       "+-------------------------+-------------------+------------------+------------------+\n",
       "| dog-automobile-distance | dog-bird-distance | dog-cat-distance | dog-dog-distance |\n",
       "+-------------------------+-------------------+------------------+------------------+\n",
       "|      41.9579761457      |   41.7538647304   |  36.4196077068   |  33.4773590373   |\n",
       "|      46.0021331807      |   41.3382958925   |  38.8353268874   |  32.8458495684   |\n",
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       "|      41.6866060048      |   37.0892269954   |  34.5750072914   |  33.9010327697   |\n",
       "|      39.2269664935      |    38.272288694   |   34.778824791   |  37.4849250909   |\n",
       "|      40.5845117698      |   39.1462089236   |  35.1171578292   |   34.945165344   |\n",
       "|      45.1067352961      |    40.523040106   |  40.6095830913   |  39.0957278345   |\n",
       "|      41.3221140974      |   38.1947918393   |  39.9036867306   |  37.7696131032   |\n",
       "|      41.8244654995      |   40.1567131661   |  38.0674700168   |  35.1089144603   |\n",
       "|      45.4976929401      |   45.5597962603   |  42.7258732951   |  43.2422832585   |\n",
       "+-------------------------+-------------------+------------------+------------------+\n",
       "[10 rows x 4 columns]"
      ]
     },
     "execution_count": 31,
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   ],
   "source": [
    "dog_distance.head()"
   ]
  },
  {
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   "execution_count": 32,
   "metadata": {
    "collapsed": false
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   "outputs": [],
   "source": [
    "def is_dog_correct(row):\n",
    "    if (row['dog-dog-distance'] < row['dog-automobile-distance'] and \n",
    "        row['dog-dog-distance'] < row['dog-cat-distance'] and row['dog-dog-distance'] < row['dog-bird-distance']):\n",
    "        return 1\n",
    "    else:\n",
    "        return 0"
   ]
  },
  {
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    {
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       "1"
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     },
     "execution_count": 33,
     "metadata": {},
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   ],
   "source": [
    "is_dog_correct(dog_distance[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
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   },
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    {
     "data": {
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       "678L"
      ]
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     "execution_count": 34,
     "metadata": {},
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   ],
   "source": [
    "dog_distance.apply(is_dog_correct).sum()"
   ]
  },
  {
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   "execution_count": 35,
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    {
     "data": {
      "text/plain": [
       "1000"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dog_distance.num_rows()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "67.8 %\n"
     ]
    }
   ],
   "source": [
    "total = float(dog_distance.num_rows())\n",
    "dog_correct = float(dog_distance.apply(is_dog_correct).sum())\n",
    "Result = float(dog_correct / total)\n",
    "print Result * 100,'%'"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Result : 67.8 % (60 - 70)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
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