M06-A01 - Regression .ipynb

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    "## Assignment 5 - Helper Functions"
   ]
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      "Populating the interactive namespace from numpy and matplotlib\n"
     ]
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    "%pylab inline\n",
    "\n",
    "# (c) 2014 Reid Johnson and Everaldo Aguiar\n",
    "#\n",
    "# Functions to work with continuous data and linear regression models.\n",
    "\n",
    "import matplotlib.pyplot as pl\n",
    "\n",
    "def pairs(data):\n",
    "    \"\"\"Generates and shows a pairwise scatterplot of the dataset features.\n",
    "\n",
    "    A figure with nxn scatterplots is generated, where n is the number of features. The features are\n",
    "    defined as the all columns excluding the final column, which is defined as the class.\n",
    "\n",
    "    Args:\n",
    "      data (array): A dataset.\n",
    "\n",
    "    \"\"\"\n",
    "    i = 1\n",
    "\n",
    "    # Divide columns into features and class.\n",
    "    features = list(data.columns)\n",
    "    classes = features[-1] # create class column\n",
    "    del features[-1] # delete class column from feature vector\n",
    "\n",
    "    # Generate an nxn subplot figure, where n is the number of features.\n",
    "    figure = pl.figure(figsize=(5*(len(data.columns)-1), 4*(len(data.columns)-1)))\n",
    "    for col1 in data[features]:\n",
    "        for col2 in data[features]:\n",
    "            ax = pl.subplot(len(data.columns)-1, len(data.columns)-1, i)\n",
    "            if col1 == col2:\n",
    "                ax.text(2.5, 4.5, col1, style='normal', fontsize=20)\n",
    "                ax.axis([0, 10, 0, 10])\n",
    "                pl.xticks([]), pl.yticks([])\n",
    "            else:\n",
    "                for name in data[classes]:\n",
    "                    cond = data[classes] == name\n",
    "                    ax.plot(data[col2][cond], data[col1][cond], linestyle='none', marker='o', label=name)\n",
    "                #t = plt.title(name)\n",
    "            i += 1\n",
    "\n",
    "    pl.show()"
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