w2.ipynb

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   "source": [
    "import graphlab\n",
    "graphlab.canvas.set_target('ipynb')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
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    {
     "data": {
      "text/html": [
       "<pre>Finished parsing file /Users/tolo/Dropbox/Data/Studies/Informatics/ML Foundations/data/home_data.csv.gz</pre>"
      ],
      "text/plain": [
       "Finished parsing file /Users/tolo/Dropbox/Data/Studies/Informatics/ML Foundations/data/home_data.csv.gz"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
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      "text/html": [
       "<pre>Parsing completed. Parsed 100 lines in 0.173667 secs.</pre>"
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      "text/plain": [
       "Parsing completed. Parsed 100 lines in 0.173667 secs."
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      "text/html": [
       "<pre>Finished parsing file /Users/tolo/Dropbox/Data/Studies/Informatics/ML Foundations/data/home_data.csv.gz</pre>"
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      "text/plain": [
       "Finished parsing file /Users/tolo/Dropbox/Data/Studies/Informatics/ML Foundations/data/home_data.csv.gz"
      ]
     },
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     "output_type": "display_data"
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      "text/html": [
       "<pre>Parsing completed. Parsed 21613 lines in 0.130752 secs.</pre>"
      ],
      "text/plain": [
       "Parsing completed. Parsed 21613 lines in 0.130752 secs."
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     "output_type": "display_data"
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    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "------------------------------------------------------\n",
      "Inferred types from first line of file as \n",
      "column_type_hints=[int,str,int,int,float,int,int,float,int,int,int,int,int,int,int,int,int,float,float,int,int]\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"
     ]
    }
   ],
   "source": [
    "sales = graphlab.SFrame('../data/home_data.csv.gz')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Filter Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2160606.6\n"
     ]
    },
    {
     "data": {
      "application/javascript": [
       "$(\"head\").append($(\"<link/>\").attr({\n",
       "  rel:  \"stylesheet\",\n",
       "  type: \"text/css\",\n",
       "  href: \"//cdnjs.cloudflare.com/ajax/libs/font-awesome/4.1.0/css/font-awesome.min.css\"\n",
       "}));\n",
       "$(\"head\").append($(\"<link/>\").attr({\n",
       "  rel:  \"stylesheet\",\n",
       "  type: \"text/css\",\n",
       "  href: \"//dato.com/files/canvas/1.8.4/css/canvas.css\"\n",
       "}));\n",
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       "\n",
       "                if (typeof requirejs !== 'undefined') {\n",
       "                    // disable load timeout; ipython_app.js is large and can take a while to load.\n",
       "                    requirejs.config({waitSeconds: 0});\n",
       "                }\n",
       "\n",
       "                require(['//dato.com/files/canvas/1.8.4/js/ipython_app.js'], function(IPythonApp){\n",
       "                    var app = new IPythonApp();\n",
       "                    app.attachView('sframe','Plots', {\"selected_variable\": {\"name\": [\"sales_zip\"], \"descriptives\": {\"rows\": 50, \"columns\": 21}, \"view_component\": \"Plots\", \"view_file\": \"sframe\", \"view_params\": {\"y\": \"price\", \"x\": \"sqft_living\", \"columns\": [\"id\", \"date\", \"price\", \"bedrooms\", \"bathrooms\", \"sqft_living\", \"sqft_lot\", \"floors\", \"waterfront\", \"view\", \"condition\", \"grade\", \"sqft_above\", \"sqft_basement\", \"yr_built\", \"yr_renovated\", \"zipcode\", \"lat\", \"long\", \"sqft_living15\", \"sqft_lot15\"], \"view\": \"Scatter Plot\"}, \"view_components\": [\"Summary\", \"Table\", \"Bar Chart\", \"BoxWhisker Plot\", \"Line Chart\", \"Scatter Plot\", \"Heat Map\", \"Plots\"], \"type\": \"SFrame\", \"columns\": [{\"dtype\": \"str\", \"name\": \"id\"}, {\"dtype\": \"datetime\", \"name\": \"date\"}, {\"dtype\": \"int\", \"name\": \"price\"}, {\"dtype\": \"str\", \"name\": \"bedrooms\"}, {\"dtype\": \"str\", \"name\": \"bathrooms\"}, {\"dtype\": \"int\", \"name\": \"sqft_living\"}, {\"dtype\": \"int\", \"name\": \"sqft_lot\"}, {\"dtype\": \"str\", \"name\": \"floors\"}, {\"dtype\": \"int\", \"name\": \"waterfront\"}, {\"dtype\": \"int\", \"name\": \"view\"}, {\"dtype\": \"int\", \"name\": \"condition\"}, {\"dtype\": \"int\", \"name\": \"grade\"}, {\"dtype\": \"int\", \"name\": \"sqft_above\"}, {\"dtype\": \"int\", \"name\": \"sqft_basement\"}, {\"dtype\": \"int\", \"name\": \"yr_built\"}, {\"dtype\": \"int\", \"name\": \"yr_renovated\"}, {\"dtype\": \"str\", \"name\": \"zipcode\"}, {\"dtype\": \"float\", \"name\": \"lat\"}, {\"dtype\": \"float\", \"name\": \"long\"}, {\"dtype\": \"float\", \"name\": \"sqft_living15\"}, {\"dtype\": \"float\", \"name\": \"sqft_lot15\"}], \"column_identifiers\": [\"bathrooms\", \"sqft_living15\", \"sqft_above\", \"grade\", \"yr_built\", \"price\", \"bedrooms\", \"zipcode\", \"long\", \"id\", \"sqft_lot15\", \"sqft_living\", \"floors\", \"sqft_lot\", \"date\", \"waterfront\", \"sqft_basement\", \"yr_renovated\", \"lat\", \"condition\", \"view\"]}, \"complete\": 1, \"ipython\": true, \"progress\": 1.0, \"data\": [[4860, 2950000], [4210, 1905000], [1220, 875000], [1220, 940000], [7080, 4000000], [9200, 5570000], [1320, 937500], [2680, 1160000], [2620, 1880000], [5450, 3418800], [2870, 1555000], [2640, 1398000], [2680, 1320000], [9890, 6885000], [1530, 865000], [2300, 1000000], [3550, 2100000], [4780, 2575000], [2660, 1712750], [1580, 787500], [3640, 1651000], [4460, 1800000], [2950, 1550000], [3060, 1762000], [2740, 1225000], [4430, 2750000], [4150, 1950000], [3570, 2200000], [3110, 1260000], [4670, 2450000], [4830, 3640900], [4040, 2271150], [2350, 1620000], [5550, 3710000], [2150, 1200000], [4300, 2500000], [4240, 2187730], [3090, 1450000], [3260, 1880000], [6500, 2458000], [2070, 1350000], [4290, 2680000], [3250, 1410000], [3070, 1920000], [5490, 3635000], [3990, 2720000], [4065, 1950000], [3410, 2250000], [5190, 2900000], [3790, 1865000]], \"columns\": [{\"dtype\": \"str\", \"name\": \"id\"}, {\"dtype\": \"datetime\", \"name\": \"date\"}, {\"dtype\": \"int\", \"name\": \"price\"}, {\"dtype\": \"str\", \"name\": \"bedrooms\"}, {\"dtype\": \"str\", \"name\": \"bathrooms\"}, {\"dtype\": \"int\", \"name\": \"sqft_living\"}, {\"dtype\": \"int\", \"name\": \"sqft_lot\"}, {\"dtype\": \"str\", \"name\": \"floors\"}, {\"dtype\": \"int\", \"name\": \"waterfront\"}, {\"dtype\": \"int\", \"name\": \"view\"}, {\"dtype\": \"int\", \"name\": \"condition\"}, {\"dtype\": \"int\", \"name\": \"grade\"}, {\"dtype\": \"int\", \"name\": \"sqft_above\"}, {\"dtype\": \"int\", \"name\": \"sqft_basement\"}, {\"dtype\": \"int\", \"name\": \"yr_built\"}, {\"dtype\": \"int\", \"name\": \"yr_renovated\"}, {\"dtype\": \"str\", \"name\": \"zipcode\"}, {\"dtype\": \"float\", \"name\": \"lat\"}, {\"dtype\": \"float\", \"name\": \"long\"}, {\"dtype\": \"float\", \"name\": \"sqft_living15\"}, {\"dtype\": \"float\", \"name\": \"sqft_lot15\"}]}, e);\n",
       "                });\n",
       "            })();\n",
       "        "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sales_zip = sales[sales[\"zipcode\"] == '98039']\n",
    "print sales_zip[\"price\"].mean()\n",
    "sales_zip.show(view=\"Scatter Plot\", x=\"sqft_living\", y=\"price\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
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     "text": [
      "0.421875722945\n"
     ]
    }
   ],
   "source": [
    "sales_large = sales[(sales[\"sqft_living\"] > 2000) & (sales[\"sqft_living\"] <= 4000)]\n",
    "print float(len(sales_large))/len(sales)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Advanced Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "train_data, test_data = sales.random_split(.8, seed=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {
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      "text/html": [
       "<pre>Linear regression:</pre>"
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       "Linear regression:"
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     "data": {
      "text/html": [
       "<pre>--------------------------------------------------------</pre>"
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      "text/plain": [
       "--------------------------------------------------------"
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     "data": {
      "text/html": [
       "<pre>Number of examples          : 17384</pre>"
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       "Number of examples          : 17384"
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      "text/html": [
       "<pre>Number of features          : 1</pre>"
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       "Number of features          : 1"
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       "<pre>Number of unpacked features : 1</pre>"
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       "Number of unpacked features : 1"
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      "text/html": [
       "<pre>Number of coefficients    : 2</pre>"
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       "Number of coefficients    : 2"
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     "data": {
      "text/html": [
       "<pre>Starting Newton Method</pre>"
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       "Starting Newton Method"
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       "<pre>--------------------------------------------------------</pre>"
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       "--------------------------------------------------------"
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       "<pre>+-----------+----------+--------------+--------------------+---------------+</pre>"
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       "<pre>| Iteration | Passes   | Elapsed Time | Training-max_error | Training-rmse |</pre>"
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       "| Iteration | Passes   | Elapsed Time | Training-max_error | Training-rmse |"
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      "text/html": [
       "<pre>| 1         | 2        | 0.011351     | 4349521.926170     | 262943.613754 |</pre>"
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       "| 1         | 2        | 0.011351     | 4349521.926170     | 262943.613754 |"
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       "<pre>SUCCESS: Optimal solution found.</pre>"
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       "SUCCESS: Optimal solution found."
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   "source": [
    "sqft_model = graphlab.linear_regression.create(\n",
    "    train_data, target='price', features=['sqft_living'], validation_set=None)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {
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   "source": [
    "my_features = [\n",
    "    'bedrooms', 'bathrooms', 'sqft_living', 'sqft_lot', 'floors', 'zipcode']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
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       "<pre>Linear regression:</pre>"
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       "Linear regression:"
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     "data": {
      "text/html": [
       "<pre>--------------------------------------------------------</pre>"
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       "--------------------------------------------------------"
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       "<pre>Number of examples          : 17384</pre>"
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       "Number of examples          : 17384"
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       "<pre>Number of features          : 6</pre>"
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       "Number of features          : 6"
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       "<pre>Number of coefficients    : 115</pre>"
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       "Number of coefficients    : 115"
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       "<pre>Starting Newton Method</pre>"
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       "| Iteration | Passes   | Elapsed Time | Training-max_error | Training-rmse |"
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       "| 1         | 2        | 0.040231     | 3763208.270523     | 181908.848367 |"
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       "<pre>SUCCESS: Optimal solution found.</pre>"
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       "SUCCESS: Optimal solution found."
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   "source": [
    "my_features_model = graphlab.linear_regression.create(\n",
    "    train_data, target='price', features=my_features, validation_set=None)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {
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   "source": [
    "advanced_features = [\n",
    "    'bedrooms', 'bathrooms', 'sqft_living', 'sqft_lot', 'floors', \n",
    "    'zipcode', 'condition', 'grade', 'waterfront', 'view', 'sqft_above', \n",
    "    'sqft_basement', 'yr_built', 'yr_renovated', 'lat', 'long', \n",
    "    'sqft_living15', 'sqft_lot15',]"
   ]
  },
  {
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   "execution_count": 58,
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       "Linear regression:"
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       "<pre>Number of coefficients    : 127</pre>"
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       "Number of coefficients    : 127"
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       "| Iteration | Passes   | Elapsed Time | Training-max_error | Training-rmse |"
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       "+-----------+----------+--------------+--------------------+---------------+"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>SUCCESS: Optimal solution found.</pre>"
      ],
      "text/plain": [
       "SUCCESS: Optimal solution found."
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre></pre>"
      ],
      "text/plain": []
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "advanced_features_model = graphlab.linear_regression.create(\n",
    "    train_data, target='price', features=advanced_features, validation_set=None)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'max_error': 4143550.8825285938, 'rmse': 255191.02870527358}\n",
      "{'max_error': 3486584.509381705, 'rmse': 179542.4333126903}\n",
      "{'max_error': 3556849.413858208, 'rmse': 156831.1168021901}\n"
     ]
    }
   ],
   "source": [
    "print sqft_model.evaluate(test_data)\n",
    "print my_features_model.evaluate(test_data)\n",
    "print advanced_features_model.evaluate(test_data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "22711.316510500183"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "my_features_model.evaluate(test_data)['rmse']-advanced_features_model.evaluate(test_data)['rmse']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## MAPEs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "def mape(model, target=\"price\"):\n",
    "    return (abs(model.predict(test_data)-test_data[target])/test_data[\"price\"]).mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.355860750112\n",
      "0.207181620509\n",
      "0.191556062218\n"
     ]
    }
   ],
   "source": [
    "print mape(sqft_model)\n",
    "print mape(my_features_model)\n",
    "print mape(advanced_features_model)"
   ]
  }
 ],
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