From 062891dd70918f016893cc489414e3e233420ad0 Mon Sep 17 00:00:00 2001
From: Zack Cooper <zackjcooper@gmail.com>
Date: Tue, 10 Feb 2015 13:56:44 -0500
Subject: [PATCH 1/3] built first model and produced a 80 percent score

---
 spambase.ipynb | 271 +++++++++++++++++++++++++++++++++++++++++++++++++
 1 file changed, 271 insertions(+)
 create mode 100644 spambase.ipynb

diff --git a/spambase.ipynb b/spambase.ipynb
new file mode 100644
index 0000000..b6130de
--- /dev/null
+++ b/spambase.ipynb
@@ -0,0 +1,271 @@
+{
+ "metadata": {
+  "name": "",
+  "signature": "sha256:87fcd5dbdd57238417355308bed338c47620bb8996a75c2e0ddbf2aef11d092a"
+ },
+ "nbformat": 3,
+ "nbformat_minor": 0,
+ "worksheets": [
+  {
+   "cells": [
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "import pandas as pd\n",
+      "import matplotlib.pyplot as plt\n",
+      "from sklearn.cross_validation import train_test_split\n",
+      "from sklearn.naive_bayes import GaussianNB\n",
+      "from sklearn import metrics\n",
+      "from sklearn.cross_validation import cross_val_score\n",
+      "\n",
+      "orig_spam_file = pd.read_csv('spambase/spambase.data')\n",
+      "orig_train = orig_spam_file.values[0::,:-1:]\n",
+      "orig_answ = orig_spam_file.values[0::,-1]\n",
+      "\n",
+      "orig_spam_file.head()"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [
+      {
+       "html": [
+        "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
+        "<table border=\"1\" class=\"dataframe\">\n",
+        "  <thead>\n",
+        "    <tr style=\"text-align: right;\">\n",
+        "      <th></th>\n",
+        "      <th>0</th>\n",
+        "      <th>0.64</th>\n",
+        "      <th>0.64.1</th>\n",
+        "      <th>0.1</th>\n",
+        "      <th>0.32</th>\n",
+        "      <th>0.2</th>\n",
+        "      <th>0.3</th>\n",
+        "      <th>0.4</th>\n",
+        "      <th>0.5</th>\n",
+        "      <th>0.6</th>\n",
+        "      <th>...</th>\n",
+        "      <th>0.40</th>\n",
+        "      <th>0.41</th>\n",
+        "      <th>0.42</th>\n",
+        "      <th>0.778</th>\n",
+        "      <th>0.43</th>\n",
+        "      <th>0.44</th>\n",
+        "      <th>3.756</th>\n",
+        "      <th>61</th>\n",
+        "      <th>278</th>\n",
+        "      <th>1</th>\n",
+        "    </tr>\n",
+        "  </thead>\n",
+        "  <tbody>\n",
+        "    <tr>\n",
+        "      <th>0</th>\n",
+        "      <td> 0.21</td>\n",
+        "      <td> 0.28</td>\n",
+        "      <td> 0.50</td>\n",
+        "      <td> 0</td>\n",
+        "      <td> 0</td>\n",
+        "      <td> 0.28</td>\n",
+        "      <td> 0.21</td>\n",
+        "      <td> 0.07</td>\n",
+        "      <td> 0.00</td>\n",
+        "      <td> 0.94</td>\n",
+        "      <td>...</td>\n",
+        "      <td> 0.00</td>\n",
+        "      <td> 0.132</td>\n",
+        "      <td> 0</td>\n",
+        "      <td> 0.372</td>\n",
+        "      <td> 0.180</td>\n",
+        "      <td> 0.048</td>\n",
+        "      <td> 5.114</td>\n",
+        "      <td> 101</td>\n",
+        "      <td> 1028</td>\n",
+        "      <td> 1</td>\n",
+        "    </tr>\n",
+        "    <tr>\n",
+        "      <th>1</th>\n",
+        "      <td> 0.06</td>\n",
+        "      <td> 0.00</td>\n",
+        "      <td> 0.71</td>\n",
+        "      <td> 0</td>\n",
+        "      <td> 0</td>\n",
+        "      <td> 0.19</td>\n",
+        "      <td> 0.19</td>\n",
+        "      <td> 0.12</td>\n",
+        "      <td> 0.64</td>\n",
+        "      <td> 0.25</td>\n",
+        "      <td>...</td>\n",
+        "      <td> 0.01</td>\n",
+        "      <td> 0.143</td>\n",
+        "      <td> 0</td>\n",
+        "      <td> 0.276</td>\n",
+        "      <td> 0.184</td>\n",
+        "      <td> 0.010</td>\n",
+        "      <td> 9.821</td>\n",
+        "      <td> 485</td>\n",
+        "      <td> 2259</td>\n",
+        "      <td> 1</td>\n",
+        "    </tr>\n",
+        "    <tr>\n",
+        "      <th>2</th>\n",
+        "      <td> 0.00</td>\n",
+        "      <td> 0.00</td>\n",
+        "      <td> 0.00</td>\n",
+        "      <td> 0</td>\n",
+        "      <td> 0</td>\n",
+        "      <td> 0.00</td>\n",
+        "      <td> 0.31</td>\n",
+        "      <td> 0.63</td>\n",
+        "      <td> 0.31</td>\n",
+        "      <td> 0.63</td>\n",
+        "      <td>...</td>\n",
+        "      <td> 0.00</td>\n",
+        "      <td> 0.137</td>\n",
+        "      <td> 0</td>\n",
+        "      <td> 0.137</td>\n",
+        "      <td> 0.000</td>\n",
+        "      <td> 0.000</td>\n",
+        "      <td> 3.537</td>\n",
+        "      <td>  40</td>\n",
+        "      <td>  191</td>\n",
+        "      <td> 1</td>\n",
+        "    </tr>\n",
+        "    <tr>\n",
+        "      <th>3</th>\n",
+        "      <td> 0.00</td>\n",
+        "      <td> 0.00</td>\n",
+        "      <td> 0.00</td>\n",
+        "      <td> 0</td>\n",
+        "      <td> 0</td>\n",
+        "      <td> 0.00</td>\n",
+        "      <td> 0.31</td>\n",
+        "      <td> 0.63</td>\n",
+        "      <td> 0.31</td>\n",
+        "      <td> 0.63</td>\n",
+        "      <td>...</td>\n",
+        "      <td> 0.00</td>\n",
+        "      <td> 0.135</td>\n",
+        "      <td> 0</td>\n",
+        "      <td> 0.135</td>\n",
+        "      <td> 0.000</td>\n",
+        "      <td> 0.000</td>\n",
+        "      <td> 3.537</td>\n",
+        "      <td>  40</td>\n",
+        "      <td>  191</td>\n",
+        "      <td> 1</td>\n",
+        "    </tr>\n",
+        "    <tr>\n",
+        "      <th>4</th>\n",
+        "      <td> 0.00</td>\n",
+        "      <td> 0.00</td>\n",
+        "      <td> 0.00</td>\n",
+        "      <td> 0</td>\n",
+        "      <td> 0</td>\n",
+        "      <td> 0.00</td>\n",
+        "      <td> 0.00</td>\n",
+        "      <td> 1.85</td>\n",
+        "      <td> 0.00</td>\n",
+        "      <td> 0.00</td>\n",
+        "      <td>...</td>\n",
+        "      <td> 0.00</td>\n",
+        "      <td> 0.223</td>\n",
+        "      <td> 0</td>\n",
+        "      <td> 0.000</td>\n",
+        "      <td> 0.000</td>\n",
+        "      <td> 0.000</td>\n",
+        "      <td> 3.000</td>\n",
+        "      <td>  15</td>\n",
+        "      <td>   54</td>\n",
+        "      <td> 1</td>\n",
+        "    </tr>\n",
+        "  </tbody>\n",
+        "</table>\n",
+        "<p>5 rows \u00d7 58 columns</p>\n",
+        "</div>"
+       ],
+       "metadata": {},
+       "output_type": "pyout",
+       "prompt_number": 31,
+       "text": [
+        "      0  0.64  0.64.1  0.1  0.32   0.2   0.3   0.4   0.5   0.6 ...  0.40  \\\n",
+        "0  0.21  0.28    0.50    0     0  0.28  0.21  0.07  0.00  0.94 ...  0.00   \n",
+        "1  0.06  0.00    0.71    0     0  0.19  0.19  0.12  0.64  0.25 ...  0.01   \n",
+        "2  0.00  0.00    0.00    0     0  0.00  0.31  0.63  0.31  0.63 ...  0.00   \n",
+        "3  0.00  0.00    0.00    0     0  0.00  0.31  0.63  0.31  0.63 ...  0.00   \n",
+        "4  0.00  0.00    0.00    0     0  0.00  0.00  1.85  0.00  0.00 ...  0.00   \n",
+        "\n",
+        "    0.41  0.42  0.778   0.43   0.44  3.756   61   278  1  \n",
+        "0  0.132     0  0.372  0.180  0.048  5.114  101  1028  1  \n",
+        "1  0.143     0  0.276  0.184  0.010  9.821  485  2259  1  \n",
+        "2  0.137     0  0.137  0.000  0.000  3.537   40   191  1  \n",
+        "3  0.135     0  0.135  0.000  0.000  3.537   40   191  1  \n",
+        "4  0.223     0  0.000  0.000  0.000  3.000   15    54  1  \n",
+        "\n",
+        "[5 rows x 58 columns]"
+       ]
+      }
+     ],
+     "prompt_number": 31
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "x_train, x_test, y_train, y_test = train_test_split(orig_train, orig_answ, test_size=.4, random_state=0)"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [],
+     "prompt_number": 30
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "gau_bayesian = GaussianNB()\n",
+      "classifier = gau_bayesian\n",
+      "\n",
+      "classifier.fit(x_train, y_train)\n",
+      "predicted = classifier.predict(x_test)\n",
+      "\n",
+      "print(metrics.classification_report(y_test, predicted))\n",
+      "print(metrics.confusion_matrix(y_test, predicted))\n",
+      "print(metrics.f1_score(y_test, predicted))\n",
+      "\n",
+      "scores = cross_val_score(classifier, orig_train, orig_answ)"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [],
+     "prompt_number": 28
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [],
+     "language": "python",
+     "metadata": {},
+     "outputs": [],
+     "prompt_number": 25
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "print(metrics.classification_report(y_test, predicted))\n",
+      "print(metrics.confusion_matrix(y_test, predicted))\n",
+      "print(metrics.f1_score(y_test, predicted))\n",
+      "\n",
+      "\n",
+      "scores = cross_val_score(classifier, iris.data, iris.target, cv=5)"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": []
+    }
+   ],
+   "metadata": {}
+  }
+ ]
+}
\ No newline at end of file

From e11fd9905b6751620b53d96d01c3a30e22a20e26 Mon Sep 17 00:00:00 2001
From: Zack Cooper <zackjcooper@gmail.com>
Date: Tue, 10 Feb 2015 16:36:17 -0500
Subject: [PATCH 2/3] found the best algorithm. Still trying to remove bad
 columns though

---
 spambase.ipynb | 183 ++++++++++++++++++++++++++++++++++++++++++-------
 1 file changed, 160 insertions(+), 23 deletions(-)

diff --git a/spambase.ipynb b/spambase.ipynb
index b6130de..487319c 100644
--- a/spambase.ipynb
+++ b/spambase.ipynb
@@ -1,7 +1,7 @@
 {
  "metadata": {
   "name": "",
-  "signature": "sha256:87fcd5dbdd57238417355308bed338c47620bb8996a75c2e0ddbf2aef11d092a"
+  "signature": "sha256:223c70de0d6130674a0f9f67aaa7a6530bb115820d246b808c697231b97b0a31"
  },
  "nbformat": 3,
  "nbformat_minor": 0,
@@ -13,16 +13,31 @@
      "collapsed": false,
      "input": [
       "import pandas as pd\n",
+      "import numpy as np\n",
+      "import seaborn as sbn\n",
       "import matplotlib.pyplot as plt\n",
       "from sklearn.cross_validation import train_test_split\n",
-      "from sklearn.naive_bayes import GaussianNB\n",
       "from sklearn import metrics\n",
       "from sklearn.cross_validation import cross_val_score\n",
       "\n",
+      "#import models below\n",
+      "from sklearn.naive_bayes import GaussianNB, MultinomialNB, BernoulliNB\n",
+      "from sklearn.linear_model import SGDClassifier\n",
+      "from sklearn.neighbors import KNeighborsClassifier\n",
+      "from sklearn.svm import LinearSVC\n",
+      "from sklearn.tree import DecisionTreeClassifier\n",
+      "from sklearn.ensemble import RandomForestClassifier, AdaBoostClassifier\n",
+      "from sklearn.lda import LDA\n",
+      "from sklearn.qda import QDA\n",
+      "\n",
+      "%matplotlib inline\n",
+      "\n",
       "orig_spam_file = pd.read_csv('spambase/spambase.data')\n",
       "orig_train = orig_spam_file.values[0::,:-1:]\n",
       "orig_answ = orig_spam_file.values[0::,-1]\n",
       "\n",
+      "x_train, x_test, y_train, y_test = train_test_split(orig_train, orig_answ, test_size=.4, random_state=0)\n",
+      "\n",
       "orig_spam_file.head()"
      ],
      "language": "python",
@@ -186,7 +201,7 @@
        ],
        "metadata": {},
        "output_type": "pyout",
-       "prompt_number": 31,
+       "prompt_number": 297,
        "text": [
         "      0  0.64  0.64.1  0.1  0.32   0.2   0.3   0.4   0.5   0.6 ...  0.40  \\\n",
         "0  0.21  0.28    0.50    0     0  0.28  0.21  0.07  0.00  0.94 ...  0.00   \n",
@@ -206,39 +221,110 @@
        ]
       }
      ],
-     "prompt_number": 31
+     "prompt_number": 297
     },
     {
      "cell_type": "code",
      "collapsed": false,
      "input": [
-      "x_train, x_test, y_train, y_test = train_test_split(orig_train, orig_answ, test_size=.4, random_state=0)"
+      "def run_model(classifier, x_train, y_train, x_test, y_test, orig_train, orig_answ):\n",
+      "\n",
+      "    #print(classifier)\n",
+      "    classifier.fit(x_train, y_train)\n",
+      "    predicted = classifier.predict(x_test)\n",
+      "\n",
+      "    #print(metrics.classification_report(y_test, predicted))\n",
+      "    #print(metrics.confusion_matrix(y_test, predicted))\n",
+      "    #scores = cross_val_score(classifier, orig_train, orig_answ, cv=5)\n",
+      "    #print(metrics.f1_score(y_test, predicted))\n",
+      "    \n",
+      "    return metrics.f1_score(y_test, predicted)\n",
+      "\n",
+      "def run_rank_multiple_models(list_of_models):\n",
+      "    return [(model, run_model(model, x_train, y_train, x_test, y_test, orig_train, orig_answ)) \n",
+      "            for model \n",
+      "            in list_of_models]\n",
+      "\n",
+      "model_list = [GaussianNB(), SGDClassifier(), KNeighborsClassifier(), LinearSVC(), DecisionTreeClassifier(max_depth=10), \n",
+      "              RandomForestClassifier(n_estimators=136, max_features=4), AdaBoostClassifier(n_estimators=64), LDA(), \n",
+      "              QDA(), MultinomialNB(), BernoulliNB()]"
      ],
      "language": "python",
      "metadata": {},
      "outputs": [],
-     "prompt_number": 30
+     "prompt_number": 298
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [],
+     "language": "python",
+     "metadata": {},
+     "outputs": [],
+     "prompt_number": 298
     },
     {
      "cell_type": "code",
      "collapsed": false,
      "input": [
-      "gau_bayesian = GaussianNB()\n",
-      "classifier = gau_bayesian\n",
-      "\n",
-      "classifier.fit(x_train, y_train)\n",
-      "predicted = classifier.predict(x_test)\n",
+      "model_results = run_rank_multiple_models(model_list)\n",
       "\n",
-      "print(metrics.classification_report(y_test, predicted))\n",
-      "print(metrics.confusion_matrix(y_test, predicted))\n",
-      "print(metrics.f1_score(y_test, predicted))\n",
+      "model_results.sort(key=lambda x: x[1])\n",
+      "x_labels = [group[0] for group in model_results]\n",
+      "x_values = [group[1] for group in model_results]  \n",
+      "width = .75\n",
+      "height = np.arange(len(model_results))\n",
+      "mean = np.array(x_values).mean()\n",
       "\n",
-      "scores = cross_val_score(classifier, orig_train, orig_answ)"
+      "plt.yticks(height+width/2., x_labels)\n",
+      "plt.barh(height, x_values, width, color = sbn.color_palette())\n",
+      "plt.axvline(mean, c='r')\n",
+      "plt.rc('figure', figsize=(14, 10))\n",
+      "plt.show()"
      ],
      "language": "python",
      "metadata": {},
-     "outputs": [],
-     "prompt_number": 28
+     "outputs": [
+      {
+       "output_type": "stream",
+       "stream": "stderr",
+       "text": [
+        "/Users/zackjcooper/.pyenv/versions/sandbox/lib/python3.4/site-packages/sklearn/lda.py:161: UserWarning: Variables are collinear\n",
+        "  warnings.warn(\"Variables are collinear\")\n",
+        "/Users/zackjcooper/.pyenv/versions/sandbox/lib/python3.4/site-packages/sklearn/qda.py:124: UserWarning: Variables are collinear\n",
+        "  warnings.warn(\"Variables are collinear\")\n"
+       ]
+      },
+      {
+       "metadata": {},
+       "output_type": "display_data",
+       "png": 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I8vnIKez/Q+Bm2/2Jpb/Pm4F1a2YVYAsA2yfbbm3FrilZHNizXWrVgGJZ+ROZ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GAAAg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\nXBtwyOu+MgAAAABJRU5ErkJggg==\n",
+       "text": [
+        "<matplotlib.figure.Figure at 0x1131c4668>"
+       ]
+      }
+     ],
+     "prompt_number": 300
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "run_model(AdaBoostClassifier(n_estimators=64), x_train, y_train, x_test, y_test, orig_train, orig_answ)"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [
+      {
+       "metadata": {},
+       "output_type": "pyout",
+       "prompt_number": 301,
+       "text": [
+        "0.93054619015509099"
+       ]
+      }
+     ],
+     "prompt_number": 301
     },
     {
      "cell_type": "code",
@@ -246,22 +332,73 @@
      "input": [],
      "language": "python",
      "metadata": {},
-     "outputs": [],
-     "prompt_number": 25
+     "outputs": []
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [],
+     "language": "python",
+     "metadata": {},
+     "outputs": []
     },
     {
      "cell_type": "code",
      "collapsed": false,
      "input": [
-      "print(metrics.classification_report(y_test, predicted))\n",
-      "print(metrics.confusion_matrix(y_test, predicted))\n",
-      "print(metrics.f1_score(y_test, predicted))\n",
+      "answer_list = []\n",
+      "orig_train_deleting = np.delete(orig_train, [5,47], axis=1)\n",
       "\n",
+      "for num in range(0, len(orig_train_deleting[0])):\n",
+      "    orig_train_changing = np.delete(orig_train_deleting, num, axis=1)\n",
+      "    x_train, x_test, y_train, y_test = train_test_split(orig_train_changing, orig_answ, test_size=.4, random_state=0)\n",
       "\n",
-      "scores = cross_val_score(classifier, iris.data, iris.target, cv=5)"
+      "    result = run_model(AdaBoostClassifier(n_estimators=64), x_train, y_train, x_test, y_test, orig_train_changing, orig_answ)\n",
+      "    answer_list.append((num, result))\n",
+      "answer_list.sort(key=lambda x: x[1], reverse=True)\n",
+      "answer_list"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": []
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "run_model(AdaBoostClassifier(n_estimators=64), x_train, y_train, x_test, y_test, orig_train_deleting, orig_answ)"
      ],
      "language": "python",
      "metadata": {},
+     "outputs": [
+      {
+       "metadata": {},
+       "output_type": "pyout",
+       "prompt_number": 336,
+       "text": [
+        "0.93180283592167457"
+       ]
+      }
+     ],
+     "prompt_number": 336
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "[(48, 0.93207800941492935),"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [],
+     "prompt_number": 328
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [],
+     "language": "python",
+     "metadata": {},
      "outputs": []
     }
    ],

From 5763113e6db4fab6c9686c90558b0066b6d6150a Mon Sep 17 00:00:00 2001
From: Zack Cooper <zackjcooper@gmail.com>
Date: Wed, 11 Feb 2015 08:54:26 -0500
Subject: [PATCH 3/3] finished

---
 spambase.ipynb | 274 +++++++++++++++++++++++++++++++++++++++++++++++--
 1 file changed, 263 insertions(+), 11 deletions(-)

diff --git a/spambase.ipynb b/spambase.ipynb
index 487319c..4212cbf 100644
--- a/spambase.ipynb
+++ b/spambase.ipynb
@@ -1,7 +1,7 @@
 {
  "metadata": {
   "name": "",
-  "signature": "sha256:223c70de0d6130674a0f9f67aaa7a6530bb115820d246b808c697231b97b0a31"
+  "signature": "sha256:db682d346cdc9e6e8dcaac10740b6388f29f9f2322e883a0575f5dcfa4b16fc5"
  },
  "nbformat": 3,
  "nbformat_minor": 0,
@@ -332,7 +332,8 @@
      "input": [],
      "language": "python",
      "metadata": {},
-     "outputs": []
+     "outputs": [],
+     "prompt_number": 346
     },
     {
      "cell_type": "code",
@@ -340,14 +341,15 @@
      "input": [],
      "language": "python",
      "metadata": {},
-     "outputs": []
+     "outputs": [],
+     "prompt_number": 346
     },
     {
      "cell_type": "code",
      "collapsed": false,
      "input": [
       "answer_list = []\n",
-      "orig_train_deleting = np.delete(orig_train, [5,47], axis=1)\n",
+      "orig_train_deleting = np.delete(orig_train, [5, 17, 17, 47], axis=1)\n",
       "\n",
       "for num in range(0, len(orig_train_deleting[0])):\n",
       "    orig_train_changing = np.delete(orig_train_deleting, num, axis=1)\n",
@@ -360,7 +362,70 @@
      ],
      "language": "python",
      "metadata": {},
-     "outputs": []
+     "outputs": [
+      {
+       "metadata": {},
+       "output_type": "pyout",
+       "prompt_number": 353,
+       "text": [
+        "[(17, 0.93396226415094341),\n",
+        " (12, 0.93288590604026855),\n",
+        " (48, 0.9323410013531801),\n",
+        " (18, 0.93226022803487596),\n",
+        " (53, 0.93226022803487596),\n",
+        " (41, 0.93207800941492935),\n",
+        " (3, 0.93198653198653192),\n",
+        " (10, 0.93063973063973071),\n",
+        " (34, 0.92976588628762535),\n",
+        " (28, 0.92957746478873249),\n",
+        " (46, 0.92957746478873249),\n",
+        " (0, 0.92938802958977795),\n",
+        " (39, 0.92919757248819979),\n",
+        " (11, 0.92876344086021501),\n",
+        " (26, 0.92857142857142849),\n",
+        " (13, 0.92837837837837833),\n",
+        " (36, 0.92818428184281843),\n",
+        " (1, 0.92804303967720236),\n",
+        " (2, 0.92804303967720236),\n",
+        " (4, 0.92804303967720236),\n",
+        " (7, 0.92804303967720236),\n",
+        " (19, 0.92804303967720236),\n",
+        " (23, 0.92804303967720236),\n",
+        " (27, 0.92804303967720236),\n",
+        " (29, 0.92804303967720236),\n",
+        " (30, 0.92804303967720236),\n",
+        " (31, 0.92804303967720236),\n",
+        " (33, 0.92804303967720236),\n",
+        " (35, 0.92804303967720236),\n",
+        " (37, 0.92804303967720236),\n",
+        " (38, 0.92804303967720236),\n",
+        " (40, 0.92804303967720236),\n",
+        " (44, 0.92804303967720236),\n",
+        " (45, 0.92804303967720236),\n",
+        " (47, 0.92804303967720236),\n",
+        " (50, 0.92804303967720236),\n",
+        " (6, 0.92741935483870963),\n",
+        " (16, 0.92669804976462666),\n",
+        " (42, 0.92669804976462666),\n",
+        " (9, 0.92659932659932653),\n",
+        " (51, 0.92659932659932653),\n",
+        " (21, 0.92607526881720437),\n",
+        " (25, 0.92597577388963659),\n",
+        " (8, 0.92525252525252522),\n",
+        " (43, 0.92464358452138495),\n",
+        " (49, 0.9246164109406273),\n",
+        " (15, 0.92421193829644532),\n",
+        " (32, 0.92400806993947537),\n",
+        " (20, 0.92297387809778964),\n",
+        " (14, 0.9226630800268999),\n",
+        " (52, 0.92255892255892258),\n",
+        " (5, 0.92225201072386065),\n",
+        " (24, 0.92142377434519784),\n",
+        " (22, 0.91532258064516137)]"
+       ]
+      }
+     ],
+     "prompt_number": 353
     },
     {
      "cell_type": "code",
@@ -374,24 +439,211 @@
       {
        "metadata": {},
        "output_type": "pyout",
-       "prompt_number": 336,
+       "prompt_number": 351,
        "text": [
-        "0.93180283592167457"
+        "0.92861907938625743"
        ]
       }
      ],
-     "prompt_number": 336
+     "prompt_number": 351
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [],
+     "language": "python",
+     "metadata": {},
+     "outputs": [],
+     "prompt_number": 351
     },
     {
      "cell_type": "code",
      "collapsed": false,
      "input": [
-      "[(48, 0.93207800941492935),"
+      "orig_spam_file.head()"
      ],
      "language": "python",
      "metadata": {},
-     "outputs": [],
-     "prompt_number": 328
+     "outputs": [
+      {
+       "html": [
+        "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
+        "<table border=\"1\" class=\"dataframe\">\n",
+        "  <thead>\n",
+        "    <tr style=\"text-align: right;\">\n",
+        "      <th></th>\n",
+        "      <th>0</th>\n",
+        "      <th>0.64</th>\n",
+        "      <th>0.64.1</th>\n",
+        "      <th>0.1</th>\n",
+        "      <th>0.32</th>\n",
+        "      <th>0.2</th>\n",
+        "      <th>0.3</th>\n",
+        "      <th>0.4</th>\n",
+        "      <th>0.5</th>\n",
+        "      <th>0.6</th>\n",
+        "      <th>...</th>\n",
+        "      <th>0.40</th>\n",
+        "      <th>0.41</th>\n",
+        "      <th>0.42</th>\n",
+        "      <th>0.778</th>\n",
+        "      <th>0.43</th>\n",
+        "      <th>0.44</th>\n",
+        "      <th>3.756</th>\n",
+        "      <th>61</th>\n",
+        "      <th>278</th>\n",
+        "      <th>1</th>\n",
+        "    </tr>\n",
+        "  </thead>\n",
+        "  <tbody>\n",
+        "    <tr>\n",
+        "      <th>0</th>\n",
+        "      <td> 0.21</td>\n",
+        "      <td> 0.28</td>\n",
+        "      <td> 0.50</td>\n",
+        "      <td> 0</td>\n",
+        "      <td> 0</td>\n",
+        "      <td> 0.28</td>\n",
+        "      <td> 0.21</td>\n",
+        "      <td> 0.07</td>\n",
+        "      <td> 0.00</td>\n",
+        "      <td> 0.94</td>\n",
+        "      <td>...</td>\n",
+        "      <td> 0.00</td>\n",
+        "      <td> 0.132</td>\n",
+        "      <td> 0</td>\n",
+        "      <td> 0.372</td>\n",
+        "      <td> 0.180</td>\n",
+        "      <td> 0.048</td>\n",
+        "      <td> 5.114</td>\n",
+        "      <td> 101</td>\n",
+        "      <td> 1028</td>\n",
+        "      <td> 1</td>\n",
+        "    </tr>\n",
+        "    <tr>\n",
+        "      <th>1</th>\n",
+        "      <td> 0.06</td>\n",
+        "      <td> 0.00</td>\n",
+        "      <td> 0.71</td>\n",
+        "      <td> 0</td>\n",
+        "      <td> 0</td>\n",
+        "      <td> 0.19</td>\n",
+        "      <td> 0.19</td>\n",
+        "      <td> 0.12</td>\n",
+        "      <td> 0.64</td>\n",
+        "      <td> 0.25</td>\n",
+        "      <td>...</td>\n",
+        "      <td> 0.01</td>\n",
+        "      <td> 0.143</td>\n",
+        "      <td> 0</td>\n",
+        "      <td> 0.276</td>\n",
+        "      <td> 0.184</td>\n",
+        "      <td> 0.010</td>\n",
+        "      <td> 9.821</td>\n",
+        "      <td> 485</td>\n",
+        "      <td> 2259</td>\n",
+        "      <td> 1</td>\n",
+        "    </tr>\n",
+        "    <tr>\n",
+        "      <th>2</th>\n",
+        "      <td> 0.00</td>\n",
+        "      <td> 0.00</td>\n",
+        "      <td> 0.00</td>\n",
+        "      <td> 0</td>\n",
+        "      <td> 0</td>\n",
+        "      <td> 0.00</td>\n",
+        "      <td> 0.31</td>\n",
+        "      <td> 0.63</td>\n",
+        "      <td> 0.31</td>\n",
+        "      <td> 0.63</td>\n",
+        "      <td>...</td>\n",
+        "      <td> 0.00</td>\n",
+        "      <td> 0.137</td>\n",
+        "      <td> 0</td>\n",
+        "      <td> 0.137</td>\n",
+        "      <td> 0.000</td>\n",
+        "      <td> 0.000</td>\n",
+        "      <td> 3.537</td>\n",
+        "      <td>  40</td>\n",
+        "      <td>  191</td>\n",
+        "      <td> 1</td>\n",
+        "    </tr>\n",
+        "    <tr>\n",
+        "      <th>3</th>\n",
+        "      <td> 0.00</td>\n",
+        "      <td> 0.00</td>\n",
+        "      <td> 0.00</td>\n",
+        "      <td> 0</td>\n",
+        "      <td> 0</td>\n",
+        "      <td> 0.00</td>\n",
+        "      <td> 0.31</td>\n",
+        "      <td> 0.63</td>\n",
+        "      <td> 0.31</td>\n",
+        "      <td> 0.63</td>\n",
+        "      <td>...</td>\n",
+        "      <td> 0.00</td>\n",
+        "      <td> 0.135</td>\n",
+        "      <td> 0</td>\n",
+        "      <td> 0.135</td>\n",
+        "      <td> 0.000</td>\n",
+        "      <td> 0.000</td>\n",
+        "      <td> 3.537</td>\n",
+        "      <td>  40</td>\n",
+        "      <td>  191</td>\n",
+        "      <td> 1</td>\n",
+        "    </tr>\n",
+        "    <tr>\n",
+        "      <th>4</th>\n",
+        "      <td> 0.00</td>\n",
+        "      <td> 0.00</td>\n",
+        "      <td> 0.00</td>\n",
+        "      <td> 0</td>\n",
+        "      <td> 0</td>\n",
+        "      <td> 0.00</td>\n",
+        "      <td> 0.00</td>\n",
+        "      <td> 1.85</td>\n",
+        "      <td> 0.00</td>\n",
+        "      <td> 0.00</td>\n",
+        "      <td>...</td>\n",
+        "      <td> 0.00</td>\n",
+        "      <td> 0.223</td>\n",
+        "      <td> 0</td>\n",
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+        "      <td> 0.000</td>\n",
+        "      <td> 3.000</td>\n",
+        "      <td>  15</td>\n",
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+        "      <td> 1</td>\n",
+        "    </tr>\n",
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+        "</div>"
+       ],
+       "metadata": {},
+       "output_type": "pyout",
+       "prompt_number": 349,
+       "text": [
+        "      0  0.64  0.64.1  0.1  0.32   0.2   0.3   0.4   0.5   0.6 ...  0.40  \\\n",
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+        "\n",
+        "[5 rows x 58 columns]"
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+     "prompt_number": 349
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     {
      "cell_type": "code",