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@RMDK
Created May 8, 2014 01:16
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{
"worksheets": [
{
"cells": [
{
"metadata": {},
"cell_type": "heading",
"source": "Machine Learning: Classification",
"level": 1
},
{
"metadata": {},
"cell_type": "markdown",
"source": "This is a series of notebooks (in progress) to document my learning, and hopefully to help others learn machine learning. I would love suggestions / corrections / feedback for these notebooks.\n\n<a target=\"_parent\" href=\"http://rmdk.ca\">Visit my webpage for more</a>. \n\nEmail me: <a href=\"mailto:email.ryan.kelly@gmail.com?Subject=Hey\" target=\"_top\">email.ryan.kelly@gmail.com</a>\n\nShare this post"
},
{
"metadata": {},
"cell_type": "code",
"input": "social()",
"prompt_number": 4,
"outputs": [
{
"output_type": "pyout",
"html": "\n <a style='float:left; margin-right:5px;' href=\"https://twitter.com/share\" class=\"twitter-share-button\" data-text=\"Check this out\" data-via=\"Ryanmdk\">Tweet</a>\n<script>!function(d,s,id){var js,fjs=d.getElementsByTagName(s)[0],p=/^http:/.test(d.location)?'http':'https';if(!d.getElementById(id)){js=d.createElement(s);js.id=id;js.src=p+'://platform.twitter.com/widgets.js';fjs.parentNode.insertBefore(js,fjs);}}(document, 'script', 'twitter-wjs');</script>\n <a style='float:left; margin-right:5px;' href=\"https://twitter.com/Ryanmdk\" class=\"twitter-follow-button\" data-show-count=\"false\">Follow @Ryanmdk</a>\n<script>!function(d,s,id){var js,fjs=d.getElementsByTagName(s)[0],p=/^http:/.test(d.location)?'http':'https';if(!d.getElementById(id)){js=d.createElement(s);js.id=id;js.src=p+'://platform.twitter.com/widgets.js';fjs.parentNode.insertBefore(js,fjs);}}(document, 'script', 'twitter-wjs');</script>\n <a style='float:left; margin-right:5px;'target='_parent' href=\"http://www.reddit.com/submit\" onclick=\"window.location = 'http://www.reddit.com/submit?url=' + encodeURIComponent(window.location); return false\"> <img src=\"http://www.reddit.com/static/spreddit7.gif\" alt=\"submit to reddit\" border=\"0\" /> </a>\n<script src=\"//platform.linkedin.com/in.js\" type=\"text/javascript\">\n lang: en_US\n</script>\n<script type=\"IN/Share\"></script>\n",
"metadata": {},
"prompt_number": 4,
"text": "<IPython.core.display.HTML at 0x103494690>"
}
],
"language": "python",
"trusted": true,
"collapsed": false
},
{
"metadata": {},
"cell_type": "heading",
"source": "This notebook covers or includes:",
"level": 2
},
{
"metadata": {},
"cell_type": "markdown",
"source": "- Importing testing datasets \n- ggplot plotting \n- Classification Concepts\n- K-Nearest Neighbour Classification\n- Training and Testing data\n- Standardization / Normalization"
},
{
"metadata": {},
"cell_type": "heading",
"source": "Data Sources:",
"level": 2
},
{
"metadata": {},
"cell_type": "markdown",
"source": "- <a target=\"_parent\" href=\"http://scikit-learn.org/stable/\">Scikit-learn Docs</a>\n- <a target=\"_parent\" href=\"http://blog.yhathq.com/posts/classification-using-knn-and-python.html\">Yhat Blog</a>\n- <a target=\"_parent\" href=\"http://en.wikipedia.org/wiki/K-nearest_neighbors_algorithm\">Wikipedia</a>"
},
{
"metadata": {},
"cell_type": "heading",
"source": "TO DO:",
"level": 6
},
{
"metadata": {},
"cell_type": "markdown",
"source": "> - Lots.\n- Other suggestions? Email me: <u>ryan@rmdk.ca</u>"
},
{
"metadata": {},
"cell_type": "markdown",
"source": "<hr>"
},
{
"metadata": {},
"cell_type": "heading",
"source": "Getting Started: Iris dataset\n",
"level": 1
},
{
"metadata": {},
"cell_type": "markdown",
"source": "Thankfully, we can easily import several testing datasets from <a href = \"http://scikit-learn.org/stable/index.html\">scikit-learn</a>\n\nThe datasets to choose from are:\n\n- `load_boston`: regression\n- `load_iris`: classification\n- `load_diabetes`: regression\n- `load_digits`: classification\n- `load_linnerud`: multivariate regression\n\nWe also have several more options, which are documented on the <a href = \"http://scikit-learn.org/stable/datasets/\">scikit-learn website</a>."
},
{
"metadata": {},
"cell_type": "code",
"input": "from ggplot import *\n\n# Enable inline plotting\n%matplotlib inline\n\nfrom sklearn.datasets import load_iris\nimport numpy as np\nimport pandas as pd",
"prompt_number": 1,
"outputs": [],
"language": "python",
"trusted": true,
"collapsed": false
},
{
"metadata": {},
"cell_type": "code",
"input": "data = load_iris()\n# The data has some meta data appended at the top of the file\n# The data is stored in the array \"data\"\n# The field names are stored in \"feature_names\"\n# Acutal flower names are stored in \"target\"\ncol_names = data['feature_names']\n\ndf = pd.DataFrame(data['data'], columns = col_names)\n# Add in the Iris Id data\ndf['Iris Flower'] = data['target']\ndf.info()",
"prompt_number": 431,
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": "<class 'pandas.core.frame.DataFrame'>\nInt64Index: 150 entries, 0 to 149\nData columns (total 5 columns):\nsepal length (cm) 150 non-null float64\nsepal width (cm) 150 non-null float64\npetal length (cm) 150 non-null float64\npetal width (cm) 150 non-null float64\nIris Flower 150 non-null int64\ndtypes: float64(4), int64(1)"
}
],
"language": "python",
"trusted": true,
"collapsed": false
},
{
"metadata": {},
"cell_type": "markdown",
"source": "Let's quickly re-name the **Iris flower** from an integer to the actual names as strings."
},
{
"metadata": {},
"cell_type": "code",
"input": "df['Iris Flower'][df['Iris Flower']== 0] = 'Setosa' \ndf['Iris Flower'][df['Iris Flower']== 1] = 'Versicolor' \ndf['Iris Flower'][df['Iris Flower']== 2] = 'Virginica' \n# Print unique names to test if it worked properly\nprint pd.unique(df[\"Iris Flower\"])",
"prompt_number": 432,
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": "['Setosa' 'Versicolor' 'Virginica']\n"
}
],
"language": "python",
"trusted": true,
"collapsed": false
},
{
"metadata": {},
"cell_type": "markdown",
"source": "Let's get a quick look at our data so far."
},
{
"metadata": {},
"cell_type": "code",
"input": "df.describe()",
"prompt_number": 4,
"outputs": [
{
"output_type": "pyout",
"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>sepal length (cm)</th>\n <th>sepal width (cm)</th>\n <th>petal length (cm)</th>\n <th>petal width (cm)</th>\n </tr>\n </thead>\n <tbody>\n <tr>\n <th>count</th>\n <td> 150.000000</td>\n <td> 150.000000</td>\n <td> 150.000000</td>\n <td> 150.000000</td>\n </tr>\n <tr>\n <th>mean</th>\n <td> 5.843333</td>\n <td> 3.054000</td>\n <td> 3.758667</td>\n <td> 1.198667</td>\n </tr>\n <tr>\n <th>std</th>\n <td> 0.828066</td>\n <td> 0.433594</td>\n <td> 1.764420</td>\n <td> 0.763161</td>\n </tr>\n <tr>\n <th>min</th>\n <td> 4.300000</td>\n <td> 2.000000</td>\n <td> 1.000000</td>\n <td> 0.100000</td>\n </tr>\n <tr>\n <th>25%</th>\n <td> 5.100000</td>\n <td> 2.800000</td>\n <td> 1.600000</td>\n <td> 0.300000</td>\n </tr>\n <tr>\n <th>50%</th>\n <td> 5.800000</td>\n <td> 3.000000</td>\n <td> 4.350000</td>\n <td> 1.300000</td>\n </tr>\n <tr>\n <th>75%</th>\n <td> 6.400000</td>\n <td> 3.300000</td>\n <td> 5.100000</td>\n <td> 1.800000</td>\n </tr>\n <tr>\n <th>max</th>\n <td> 7.900000</td>\n <td> 4.400000</td>\n <td> 6.900000</td>\n <td> 2.500000</td>\n </tr>\n </tbody>\n</table>\n<p>8 rows × 4 columns</p>\n</div>",
"metadata": {},
"prompt_number": 4,
"text": " sepal length (cm) sepal width (cm) petal length (cm) \\\ncount 150.000000 150.000000 150.000000 \nmean 5.843333 3.054000 3.758667 \nstd 0.828066 0.433594 1.764420 \nmin 4.300000 2.000000 1.000000 \n25% 5.100000 2.800000 1.600000 \n50% 5.800000 3.000000 4.350000 \n75% 6.400000 3.300000 5.100000 \nmax 7.900000 4.400000 6.900000 \n\n petal width (cm) \ncount 150.000000 \nmean 1.198667 \nstd 0.763161 \nmin 0.100000 \n25% 0.300000 \n50% 1.300000 \n75% 1.800000 \nmax 2.500000 \n\n[8 rows x 4 columns]"
}
],
"language": "python",
"trusted": true,
"collapsed": false
},
{
"metadata": {},
"cell_type": "markdown",
"source": "It's always a good idea to start plotting early to get a deeper sense of the data."
},
{
"metadata": {},
"cell_type": "code",
"input": "from pandas.tools.plotting import scatter_matrix\n\nscatter_matrix(df, figsize=[12,8])",
"prompt_number": 5,
"outputs": [
{
"output_type": "pyout",
"prompt_number": 5,
"metadata": {},
"text": "array([[<matplotlib.axes.AxesSubplot object at 0x10b8cd790>,\n <matplotlib.axes.AxesSubplot object at 0x10b8f0550>,\n <matplotlib.axes.AxesSubplot object at 0x10b940b90>,\n <matplotlib.axes.AxesSubplot object at 0x10b9667d0>],\n [<matplotlib.axes.AxesSubplot object at 0x10ccdc810>,\n <matplotlib.axes.AxesSubplot object at 0x10c12bc90>,\n <matplotlib.axes.AxesSubplot object at 0x10ccc1650>,\n <matplotlib.axes.AxesSubplot object at 0x102f42dd0>],\n [<matplotlib.axes.AxesSubplot object at 0x102f6a350>,\n <matplotlib.axes.AxesSubplot object at 0x10c92fd90>,\n <matplotlib.axes.AxesSubplot object at 0x10c959810>,\n <matplotlib.axes.AxesSubplot object at 0x10c940890>],\n [<matplotlib.axes.AxesSubplot object at 0x10c99bd10>,\n <matplotlib.axes.AxesSubplot object at 0x10c9c3110>,\n <matplotlib.axes.AxesSubplot object at 0x10c9ddcd0>,\n <matplotlib.axes.AxesSubplot object at 0x10cc1e410>]], dtype=object)"
},
{
"output_type": "display_data",
"metadata": {},
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PPjuvXo/ehLq15/rZmun1ejIzM/H09KweuJeXl8eaNZ+h0+n5+98XWbwQyI4d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\nyj8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mLs6N55+f4/AIMr+NfS8vIwsWPCbu9ojcktjloKTRaEhLS+PixYtoNJqWlkmklUlJ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"text": "<matplotlib.figure.Figure at 0x10b8cd290>"
}
],
"language": "python",
"trusted": true,
"collapsed": false
},
{
"metadata": {},
"cell_type": "markdown",
"source": "This looks alright, but really we want to see the different flowers as separate colors, which is not easily possible right now. I did however find a keen <a href = \"\"http://stackoverflow.com/users/1586229/bgschiller>\"Stack Overflow user</a> who developed a custom version for now."
},
{
"metadata": {},
"cell_type": "code",
"input": "def factor_scatter_matrix(df, factor, palette=None):\n\n import matplotlib.colors\n from scipy.stats import gaussian_kde\n\n if isinstance(factor, basestring):\n factor_name = factor #save off the name\n factor = df[factor] #extract column\n df = df.drop(factor_name,axis=1) # remove from df, so it \n # doesn't get a row and col in the plot.\n\n classes = list(set(factor))\n\n if palette is None:\n palette = ['#e41a1c', '#377eb8', '#4eae4b', \n '#994fa1', '#ff8101', '#fdfc33', \n '#a8572c', '#f482be', '#999999']\n\n color_map = dict(zip(classes,palette))\n\n if len(classes) > len(palette):\n raise ValueError('''Too many groups for the number of colors provided.\nWe only have {} colors in the palette, but you have {}\ngroups.'''.format(len(palette), len(classes)))\n\n colors = factor.apply(lambda group: color_map[group])\n axarr = scatter_matrix(df,figsize=(10,10),marker='o',c=colors,diagonal=None)\n\n\n for rc in xrange(len(df.columns)):\n for group in classes:\n y = df[factor == group].icol(rc).values\n gkde = gaussian_kde(y)\n ind = np.linspace(y.min(), y.max(), 1000)\n axarr[rc][rc].plot(ind, gkde.evaluate(ind),c=color_map[group])\n\n return axarr, color_map",
"prompt_number": 6,
"outputs": [],
"language": "python",
"trusted": true,
"collapsed": false
},
{
"metadata": {},
"cell_type": "code",
"input": "axarr, color_map = factor_scatter_matrix(df,'Iris Flower')",
"prompt_number": 7,
"outputs": [
{
"output_type": "display_data",
"metadata": {},
"png": 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FnpIszuMSXTSsVituiRv/UO8Qt1QuwzdETU1NDeFJoc1J+HyUPhzec6Q7QxVd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DAAAg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oTRG4XB6MYRHYPU5MEUbcHjdqvQq1RY0QAK3ZSFD0Yo6OIiD40UfoUZtV+Fx+TAlGquqqaGpq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ZTnf/8nnnn+aTpz2vFFzfO//yOJiYlMGj2J/cf2gQDDE0acdhyWIJWiUgVQl1eNIJUgF/2Q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\nDm63G3+FlWVvPAOCiqhgb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1NcXIzBYDiv9XGyMwsODuaJxx5pS/jau3dvVCoV18ycfkri\n06CgIAoKCnjhjXdRB/RGEt2s2bKDO26eRWhEBBGpo2m2NWIwDaVg3wYsFguP//phcnJyAIiNjaWi\nogJJdCN63ChUGkSPB4/biclk4jcPLyAnJwePx0NMTAyGM+yKk3U/yXbm4tUAgtEgZ5uX9SgDBvQn\nLCyU8vLyttRNK1etZu+2TfjEDUSl1VPz3Q4G9w4gICCA/ulXYbc1otUZKD9xGJPJxLfrv2bRokXU\n19czadJvSU1NZdfBLFxOBzp964iwx+084zXy567DAdddd93Fyy+/zIABAy76Sd66dWtHu9El/P39\nGZjUiwPbvsE7JIqGsgIGJvU6pbbhD+x2O19v3smgq66jxW5DpdGy6qM32LP+CwIjY6nNz+TmqeOB\n1m8b08aP5KsdO/GNjKei4DiRPlpiYmLa2tNqtadsRtDr9fIQbScym82nJISF1i8KP9QI/cFXazdi\njk7DYPJGoVRSXaok+8RJUmPDyTxxGO+gCIqP7mN4vwS8vb0RBOGUv1tERARDkmPY/+UH+EQn01By\ngsQwH1JTU1GpVCQmJnbHw5VdINFuR3GWAFgwmuQpRVmPo9Vq0ev16PX677+0S6jM/hgDolCo1OgC\nahGEZmZOHsPnG3fgF51IdXEuISbo06cPGo2Gu+6665Q2Z04ezyer1+IbnUxLUz0WyXrKZ6DsRx0O\nuCwWC1OmTOmMvlx2BEFg7uxb6b1jJ2XllYQkpTNi+LB2R5ecTie1tbWc/GIpKpMfLmstAT4Whsf5\nY/SCxBHXnHKBnzZ1CsFBgeTkFeIXG8fIkSPkenaXobq6OvYc2ofC6IcouhGa6+kXMpK775jD9h07\nKa+oIrzvEIYOHdLu60KhUPDqi8+x8N33yD6ZR/ToBO6/956fdVLgy53kcoHbDWeZvlUYDfKUoqxH\nycrK4s0PP0Vh8MVlq+eaSSNRqzX0798fjbcet8dD74H9UVcfZdLECQT4+3H8ZB4+vaIZNWrkGWuz\njho5Ah+LN1nZJzBHhzJq5A3yaP0ZdPhTf9y4cTz66KPMmjWrrfI3wIABV0aeKKVSyehRI895nF6v\np6K8AnPaNAIjY2morqD428VMnDiB4ODg04IpQRAYOHDgKVniZZef5mY7dsFAXNpYPB4Px7d8hsvR\nWp9yzOhR59WGRqNh/v33XdB5nU4narVanjq+BFpTQhjP+tzLi+ZlPYnH4+HtRUuIGDgZi18gjpZm\nVqxbzp3XT8VVvYugsEi0eiO5B75lxqg0BEGgf//+9O/f/7S2RFHE4/Gcck3r27evXCLuPHQ44Nq1\naxeCILB3795Tbt+0aVNHm+5RbDYb8UnJqIJ8qK3Iw9doQBHTmz89/Sx6kzcJsZHcdftt8u7CHsbs\n7UO/tBjqaopQCAr6pw9Co+u6b29VVVW89d5HFJZX4m3Uc/ecm0+rfSnrWqLt7DsUAQSTSV7DJesx\n7HY7Do+ExS8QAK1Oj9bsh8lk4sE7bmTFV+tocDiYPrIfV02ccMZ2duzcxeLPV+NweUhNiOWO2be0\nmyZJ1r4OB1ybN2/uhG70fF5eXvgYtZh8TfRLS6G4IJd9Xx3lurt/jX9IOMcP7mLRf5ew4N67zt2Y\n7LLROzqCkqxSRo8Yj+hxk7Hta3pFdc26K0mSeP2d93H7xpE+cDp1VeX8+91PeOrxR7BY2s/RJet8\nktV61gXzAAqDAdEqTynKegaj0YiPSUdp/glCo+NorKvBba0mKCgIX19fkpKSztlGbm4uH32xlvhR\ns9AZTBzfv51Pl33OvLmzu+ERXBk6HHCVl5fzxz/+kZKSEr755huysrLYuXPnaQvrLmc5OTl8tXYT\nDpeLYQP7XVRFc5VKxQN3386/3/mQsqyd1FaU0HfAEAJCIwCI7ZvOpkUv0OJo3cFx1bhR8oLpTiSK\nIps2b2H/kaOYDHpmTJlEeHh4h9udNmUyldWfcOibj5BEkbFD+zNs6JAzHn/48GE2bN0JwITRw9pq\njJ4Pq9VKRZ2V/kNbh+Z9A0MoN/pRXl4uB1zd6FwpIeD7KcXq6m7qkUzWMQqFgjk3XstdD/6GqoZm\n9GqBZ5949II2YhUVFWEIiMZgai3nE508gMztX3RVl69Iio42cMcdd3DVVVdRWloKQFxcHC+99FKH\nO9ZdioqKeOmtRdRqw3D5J/Dx6s1s277jotqKiIjgmSce55nHH+axh+/HbNC25dbK3LuNohobzT59\naDRG86/3PuXEiROd+VB+1r5es5blm/bhCUymDD+ef30h1Z1wQdRqtdx31x3884lHeeHpP3DTDdeh\nULT/tsnMzOTNj1dg847F5h3Lmx+vIDMz87zPpdfrUSFibagDwO1y4rTVYzrHaIusc4k2K4qzZJkH\nEEzfZ5qXyXoAURR55rmXkPziSJp0G77JY3nulf9QX19/3m2YzWZaGquRJAmA+qoK/Hy8u6rLV6QO\nB1zV1dXcdNNNbSkh1Gp1j9qBdfDQYQxhCYT2isM/JIKotJFs+27fRbenVCqxWCykpaXRLyaIwxs/\n5+iu9WTuWMeIKdcTGBZFcGQMvrH9+G7vgU58JD9vW3fuI27QOPyCw4jonYTCEsHRo0c7pW1BEDCb\nzefcebNjz34C4wcSFB5NUHg0gfED2bFn/3mf54fkhCd3fsmxXRvI2LScCUPTCAsL6+hDkF0AyWpD\nMJ1jDZfBiCgvmpf1ELW1tRzJKSZl4k2E9E4hYdhk7Fo/vvvuu/NuIy0tjaQwbw5v/Jysneuoyd7J\nbTf8ogt7feXpcGRkMpmoqalp+3nXrl14e/ecqFetUiG6nG0/u11ONJ0QMCoUCubNnU1OTg52u50w\no4dGw49lYjwuF2p1zwlML3cqlRL3T/6OotvV7cn3NCoVbtv/vJaMF/Y3Tk8fQHh4GGVlZVgsFqKi\nouSdit2sdZfiOdZwGY1IdnnRvKxnUKlUIIm4XQ40SgOiJCK5nBeUikipVHL/3Xdy4sQJWlpaiIqK\nkpc6XKAOX/FfeOEFZsyYQW5uLsOHD6eqqoply5Z1Rt+6VHV1NXV1dfTpE8e6b3exd3MdCoUKsaGM\nh++6pVPOoVAoiIuLA1qHY1/8z4c4W+yIHg+OsmxGX3f2Ui6y8zf9qnF8+PlafHv1pcXWiNFde0Hr\np35QWVlJQ0MDgYGBbV8cnE4nxcXFKJVKwsPDzxjIjR8zkn2vvUOOszXoshdnMv7Buy+4D0FBQQQF\nBV3w/WSdQ7RZUZxrhMtkRLLKAZesZ7BYLEwakc7aFf9B7x+Bs6mWSJPI8OHDL6gdhUJBfHx8F/Xy\nytfhgCs9PZ0tW7aQnZ0NQHx8/GWfwHPz5q0s/WojapMPrqZqtIKbsqJsUKiI8jd2SaXzmJgYHntg\nHrv37kepeVPVBwAAIABJREFU0DLsxvtOq8cou3jDhg7By2wiIysbY2Qgo0dee8Frn9Zt2MiKtd+i\nNloQ7XXcf/uNhIeH8/Lrb1HdDKLHTe8QCwvumdduEsCIiAh+9/C97NrdmiJl6Kx75enAHqh1SvEc\na7iMRjkthKxHGTl0EJ99tZ6Ghgak5nquuXGaXJu1mwnSDyvgLtBnn32GIAhttQJ/aOaH6Y9Zs2Z1\nrGM/abMzVVdX85fnXiNh7LXo9EYO7tjA/j27mbPgt6jVanIz9hNrcnD3nXM6/dyyrtEZr5Xy8nKe\nfuk/JI6dhVanp766gtL96+jfN56MKg9xaUORJInMHeuYMTSeiWfJVSO7vJ3r9dLw7D8RdDq8Hvnl\nGY9x5+VRfdtsgnds74ouyi4TXXUd6m5Op5ORk39Br4l34BsSTYu9iUOfv8b7Lz11UTMBsvad6/Vy\n0SNcq1atOuvako4GXF2lvr4etdHSVmhTQkDjE4Lb7UatVuMXGkHpyV2XuJey7lZfX4/G5ItW17rO\nzuIfRJ5HoqC4DN+o1moAgiDgHRROeVXN2ZqS9XCSzYaynXqpPyWYzUiNTd3UI5msY6qrq3Ghwjck\nGgCdwYzOJ4TCwkI54OpGFx1wvf/++53Yje4TEBCA2FxHQ20V3r4BSG4XzppCFN9HpqU5WQyM7nj+\nJlnPEhgYiNtWQ1N9DWaLH2UFJ/Ex6UiMi2HbsaP4BAQjih5qik4QPXnYpe6urAv9UNrnbBRmM2JT\nU9sIv0x2OQsMDMSkESg+fpDwPv1oqCrFUVskr8fqZj+7bXLe3t7cN+dG3l60hHyUeGkU3DlzDAc2\nLEZQKEmIDuXamdMvdTdl3czX15e7br6W9/77GW5BhZdOxYP3zMXf35+qmo85+M1HIEmMHpzG8GFD\nL3V3ZV1ItJ474BK0WlApkZqbEeRCvbLLnEql4qVn/swjf/grJXu/RuFq4bEFd7Zt6pJ1j4tew9VR\nmZmZ3HvvvSiVSpKTk3njjTdO7VgXz527XC6sViteXl4olUrsdjtutxuz2Sx/Y+1hOvO14nQ6sdvt\nmM3mtt2IkiTR1NSEUqmU64ZdAc71eqm+bTamu+5CN37cWdsp6zeAwG++QilvfrliXSlruH7gdDop\nKyvDz89PTqjcBbpsDVdHxcfHs31764LTefPmceDAgXYrk3cVtVqNj49P288/TWpZWFjIsezj6HVa\n0tPTz5nwUnbl0Gg0p+1AFAQBLy+vU24TRZGDBw9SVV1DSHAQKSkpcqB+hTifxKcACi8vxKYmOeCS\n9RgajYaoqKhTbrNarezfvx+H00VSYoK8s7oLXXTA9dNdiv9LEIRzLpr/aTb65ubmyyaBWmZmJq9/\nsBRDSBzO5iY2btvFo798QA66ZG0kSeKTxUvYebQYg18ots37mJiTx6xrr7nUXZN1gvMp7QMgeJkR\nGxq7oUcyWdewWq3886XXaFL5odLq+WLdt/zy7tvkqcYuckl3Ka5cuZI//vGPDBw4kF69el1sVzrV\n8tVrCO8/Fv/g1oXzGTvXc+DAAUaMGHGJeya7XFRWVrLz8HFSJt6IUqnC5Uxj/brFTJww7rSRMFnP\nc94jXGYzUpMccMl6rt2792DVBJA8eAwAFUVBrPhyLY8+IgdcXeGS7lKcOXMmM2fO5OGHH2bdunVM\nmjTplN8/+eSTbf8fO3YsY8eO7fA5z8Xe3EKQ4cdvt2qdkZYWR5efV9ZzOBwOVBo9SmXr20el1qBQ\na3E6nee4p6wnkGznTnwKoPDyRpRTQ8h6sOaWFtT6H79c6I1mrKXy9a6rdMoartWrV5OVlUVLS0vb\nbU888cRZ7+N0OtvWynh5ebV7sfppwNVdhvRPYf2BbcT2G4Hd2khzxUni48++eFb28xIcHIy3RiTv\n6AECw2Moy8sm1Nd4yppAWc8l2mwozmNzhOBlRmqSAy5Zz5WclMhXWz6gOiAYrd5A7qHtTBve91J3\n64ql6GgD9913H0uWLOHVV19FkiSWLFlCQUHBOe/3zTffMHbsWMaMGUNxcTFTpkzpaFc6xfRpU5g0\noDfVhzdA2REenncr4eFyXi7ZjzQaDb+cfzfhykYq9q8h1uzigXvndXuxbFnnk1wucLtBqz3nsQqz\nGbFRnlKU9VzR0dHcP3sWnqID1B7ZxNRhyUyeNPFSd+uK1eG0ECkpKRw5coTU1FQOHz6M1Wrl6quv\nZtu2bR3r2BW2HVfWdeTXiuxCnO31ItbVUT5yFKGZGedsp/Gll5EcDrwf/11nd/Gy5XKLbM2uJL/K\nSt8IC4Nj/K7o3bnyZ4vsQpzr9dLhES69vrUUisFgoKSkBJVKRXl5eUeblclksm4nNjai8PI+r2MV\n3t4/qynFE+WNzP3PTpbtLsTpFnnxq2P8fWUmoigHJDLZ+ejwGq7p06dTV1fHo48+Snp6OgD33HNP\nhzsmk8lk3a014Dq/naY/pynFfXk1/GnpYR66qg9T0kIRBIE7Rsfw8If7+HhHPnNGXh67zGWyy1mH\nA67HHnsMnU7Hddddx7Rp02hpaUGn03VG32QymaxbSQ2NCOcZcAleP48C1luOVvDsqiz+dkMa6b18\n227Xa1Q8dV0qd/xnJxOSgwj1kXMVymRn0+EpxeHDh7f9X6fTYbFYTrlNJpPJegqxsRGF93mOcHl5\nIV7hebi2Hqvk2VVZvDQ7/ZRg6wehPnpuGBzJ25tyLkHvZLKe5aJHuMrKyigtLcVut7N//34kSUIQ\nBBobG7Hb7Z3ZR5lMJusWYmPDeU8pCmavK3pKcdfJav6xMpMXbxtAQuiZn5Nbh0dz7ctbqGhoJshb\n3409lMl6losOuNauXcv7779PSUkJv/nNb9puN5vN/P3vf++UzslkMll3Ei9gSlFxBU8pZhbX89Ty\nI/zz5n4khp19E4FRp2Jyaiif7y3m/glyhnKZ7EwuOuCaO3cuc+fOZdmyZVx//fWd2SeZTCa7JKTG\nRhTe57lL8fvi1VeayoYWHv/0IL+fmUxq5Pkl871uUAQL3t/D3WNjUSk7vFJFJrsidfidMXLkSO66\n6y6uvvpqALKysli4cGGHOyaTyWTd7UJ2KQpmM5LViiSKXdyr7iOKEk98dpjrBkUyOiHwvO8XHWAi\nxKJnT25NF/ZOJuvZOrxL8Y477uDOO+/kmWeeASAuLo4bb7yRu+66q8Odk8lksu4kNlxAwKVUIuj1\nSFbreU9DXu4+3VWAJEkXleZhckoIaw6XMSwu4JzHukQXZdYyquyV2Fw2bG4bNqcNu9uGw+NAp9QT\n6RVJetBATJpz17WUyXqCDgdc1dXV3HTTTTz77LMAqNVqVKpOKdEok8lk3UpqbEA4z12K0Jr8VGw4\n/4X2l7PqJgfvbc1l4T1DUCouPHv8hL7BvLXpJC1ODzpN+2WuGh0N/PfYJ2wt2oKXxotAYxAmtQmj\n2ohRbcSgNuKttdDsbmZ7yTbeOvQmv4ibxaw+16EU5NJZsp6tw5GRyWSipubHYeRdu3bhfZ5rIGQy\nmexyciFTigAKX1/EujqIiOjCXnWPtzedZEb/MCL8zl24uz1+Ji3JYd7sOFHF+OTg035f2FjAUzue\nZGjoUF6d8Bp+ev9ztllpr+SVfS+RXXuM3w3+PWql+qL6JpNdDjq8huuFF15gxowZ5ObmMnz4cObM\nmcOrr77aGX2TyWSybiXW1593aR8AhY8PYm1tF/aoe+RXWdl6rJK5o2M61M6YxCC2HKs87fa6ljqe\n3PEXZifN4Z7U+84r2AIINATy5Ii/olKoeGHv/yFKV856OdnPT4cDrvT0dLZu3cqOHTt46623yMrK\nIi0trTP6JpPJZN1KrKlF4Xd6gs8zUfj6tI5w9XCf7ipg1qAIvPQdG0EaFR/AzhNVuD0/BkaSJPHy\nvheZGDWJcZHjL7hNtULNbwY+Sm1LLcuyl3SofzLZpdThgKu5uZlXXnmFP/3pTzzxxBO89tprtLS0\ndEbfZDKZrNtIotg6wuV7IQGXL2Jtzw64GuxO1meUM2tgx6dFA7x0RPgZ2Z//43OyreRb6lvquCn+\n5otuV61U8/iQP7A6dzUn6o53uJ8y2aXQ4YDr9ttvJysri4cffpgHH3yQzMxM5syZ0xl9k8lksm4j\n1jcgGI0I6vMf5bkSphS/2FfMqIRA/MzaTmlvTEIgW49VAK27ET/IeI/70uajVHRs0buvzpe7U+7h\nlX0v4/K4OqOrMlm36nDAlZmZycKFCxk3bhzjx4/nnXfeITMz85z3++677xgxYgSjRo3i17/+dUe7\nIZPJZB0i1tZc0OgW/GTRfA/l9ogs3V3ITUOjOq3N0QmBbDlWiSRJbC7cRJg5nCT/5E5pe1T4aEKM\nIaw4+XmntCeTdacOB1wDBgxg586dbT/v2rWL9PT0c94vOjqaTZs28e2331JZWUlGRkZHuyKTyWQX\nTaypQel/fou5f6Dw7dkjXJuyKgj3NRAf0nlpLaIDTBg0KjJK6lh2fCk3xt/UaW0LgsBdqffwxckV\n1DRXd1q7Mll36HDAtXfvXkaMGEFUVBTR0dEMHz6cvXv3kpKSQmpq6hnvFxQUhEajAeTcXTKZ7NK7\n0AXz8MOUYs8d4Vq8q4CbO3F06wejEwJZdmQTPlofkv37dmrbwcZgJkdfzQeZ73dquzJZV+twlPPN\nN9906P6HDx+mqqqKhISEjnbljERRZOvGjZw8dAijxcKEGTMIDGy/bIUkSezcto2je/agM5kYO20a\nYWFhXdY3mexSqqqqYsOqVVjr6ohJSWHMhAkolR1PMGm1WlmzciW1JSWExsUxccoUtNrOWSPUVcTq\nahR+fhd0n548pZhRVE+dzcnI+PMv4XO+xiQE8udtm/ll31md3jbA9fE38MD6+RytOUqiX2KXnONc\namtrWbdyJU01NUQnJTF20qQzDhw0NjayduVK6srKiIiPZ8KUKagvYK2g7MrQ4YArOjr6ou9bW1vL\nQw89xNKlS9v9/ZNPPtn2/7FjxzJ27NiLOs83K1dSuHo1wwODqC4sYmFmJgv+8pd2E7Ru2bCBjP8u\nZmRgII1FxXxw5Aj3PPEEAQHnLlchk/UkTU1NLPznPxnkcpNkNLJryRKsTU3MvO66DrXrcrlY+OKL\n9CqvYKiPD0e+/ob/lpYyd/58BOHCM5h3F0919YVPKfr44umhU4qLdxVw45DIi8oqfy5+lmY86nIi\ntP06vW0AvUrP7UlzWXjkLZ4b8wIKoXsLZttsNhY+9xxpdjuJJjN7PlvOF3V1XHfrracd63A4WPj8\nC8TV1DDUYuHg6tV8WlHBbXfffVm/H2Sd75KVdXe73cyePZvnn3/+jKNNTz75ZNu/iw22APavX8/M\nmFii/fwYGBlJL3sz2dnZ7R67b/16pkZGEuPvT7/wcBJdrvPaBCCT9TTHjx8nwmplUGQk0X5+XNM7\njv3r1iFJUofaLSkpQVVSytjYWKJ8fZnauzelBw7Q1NTUST3vGp7SUpShoRd0H+X3ebg6+px1t/L6\nZnbn1DC9f9eM3m8oWk+oOp3t2V03+jc6YgwgsKVoc5ed40xycnIIrG9gaFR063snLo7Dmzbj8XhO\nO7awsBBDRQWjY2KI8vVlelwf8r7bTXNzc7f3W3ZpXbKAa+nSpezdu5fHHnuMcePGsWvXri47l0Kp\nxC3+mIjPLUkoFO0/dIVSiesnbxqPxBmPlcl6MoVCgfsngYLb40Gp7PhaSoVCgUeS2oIQUZIQufzf\nR57SUpQhIRd0H0GvR9Drety04tLvCpnWLxSTrvOntTyihw0F65geO6XdrPOdRSEouDvlHhZlfkCL\nu3tzP7a+d35yTRFFBIXQ7oiVQqHAzY/vB48oIgmX//tB1vku2Ur1W265hVtuuaVbzjV85kyWf/IJ\ngyw+VNvtlPv7cW1i+/P+w6dPZ8V/3iJVrcbm8XDS14dJZ8mcb7VaOXToEFqtln79+p118b/b7aa0\ntBSFQkFoaKj8hpN1q+bmZioqKtDr9QQGBpKQkMDGgEC+zMjAR6XiqNPJ8Ftv6fA0R1hYGLqEeD4/\nfJgQtYZcp5M+EydgMplOeQ+EhIR0ynqxzuIpLUMZemEBF4AyJKT1vheYUuJSsTncrDpQwgf3D+uS\n9veW7yHIEMzkhBReXb2JysYWAr10XXKuBL9Ekv37svzEZ9yaeFuXnKM9cXFxrA8NZdWRI/ip1WQ7\nHQy9/noUCgVOp5OysjLUajUhISFERUWhiInli4wMgtRqTjqd9J1yNTqdDpfLRVlZmXxN+Jn4WWwN\nHDN+PF4WCzkZGRi9vbl3/HiMxvYLtMb07s1SpYKVOTm4lUqGpg/AbDa3e2xBQQG/u30u/rW12EUP\nun79eHHhQnS60z9cbDYb773yClJBIR5JxJSUxO0LFrTt1JTJulJZWRkfvPAC3jYbTW4PfSZNZOb1\n1xORlMjaPbtRu9wog4O47gxfRC6EUqkkvl8/Ply/Hk1LC5KXFwvS0rDb7bz7yiuIefmISBgTE7l9\nwYLLYjG9JEkXNaUI3wdcZWXQt3NyTXW1lfuKGRzrR4hF3yXtr8n/hqt6TUatUjAsLoCtxyq5fnBk\nl5wL4Pbkufxq4y+ZFDWJAEPnbwBoj1arJSY5mS927EDjcqEIDGRm377U1dXx3gsvoquuxu5xEzJk\nCDffeSe901L5dPMm1A4H+PoyoV8/mpqaeO/ll1EUl+CSRLxTUphz//3yYvor2BUdcP0whCsIAgPS\n00lNS0OhULT7LcLj8eDxeFj58cdMM3vRf8oUPKLIkgMHOHToEP379z/tPi/+5S9MsFr5RZ94PKLI\nqwcP8/5773H//PmnHbt21SrCiooZHxcHwOqso2zduJGJV1/dyY9adqWSvp+m+9/XryiKp932v8d+\n9u67jEEguXccTrebj9euZa2XFyUbN/HM2HFolEpyaqr57J2F/OqvT7W1KwjtT5OcTU1NDdsXf8pT\nI0Zi0miotFpZ+vY7JAwfRmhhERP69AHgy2PZbN2wgUlTp17sU9JppPp6EAQErwvPR6UMCUUsK+uC\nXnW+Zqebj3fk88JtA7qk/Sp7Jcdrs/ndkN8DMCYxkBV7i7s04AowBDIlZhofZn7AbwY92mXn+an8\n/HyOr1nDP8aNR3K7KbXZWPbWWwRHRdG3sYGhcXF4RJFlu3axNiyMQ59/zl9HjcagVlPW2MjyN9+k\n94ABRJeVM7ZPHyRJ4ovDR9i2dSvjJkwA2n9fy3q2KzLgkiSJ9V9/zY5Vq5FEDzGDBrFj7VpKMjJQ\nGwzc8thj3HDjjW3H//rBB9n16RIQRVqMBm5JSWXdd9+hVqvxCQigprr9BHt1BQX082mdRlAqFCQb\nDGTn5LR7bE1JCYN9fNouXjFeXhT2kA9p2aW3fetWNi1ZisvpIGnECK695RZaWlpYsnAhhZlZGC0W\nZt41j8TERPbu2cOajz7C2dxM3MCBXDdnDjXFJfT+fkexRqUiUq2hqKiI+uIintiwAYfTSXBgAK6k\nJFpaWlj+0Udk796NWqtl0q23MmTY+U8/1dXVobDb+XDzZhqbmgjw9cXp70dJbi6jLZYf3wNmM/mX\nyXvAdfIkqt6xFzWdqgwJxl1a2gW96nxLvyskLdLSqYlOf2pdwTpGR4xBq2wdtRwa68/fVmTQ2Ozq\ncGHss7muz/UsWHcfx2qOktANaSJqa2vZvH07H374IUrAo1LRf/RoFILACL/Wna5KhYJeegO5eXkI\nNhvvbdhAk81GkL8fdh9fKvLyGP/9NLQgCMSYzZSXllJcXMzSt96itrSUgKgobrr3XoKCgrr8Mcm6\n3hUZPu/ds4djny3nnogIFvSKYcVzzxF88BD/iU/k975+fPbUX9m7dy8A//rXvyha/Cmvh4ayKCqa\n8No6ju3ezYPBwdxoNnHoaBYut7vd8wTFx7OtugpREml2u9lrtxKT3P60QnBMDJk11YiiiNvj4Wh9\nPcEdSKkh+/k4duwYuz78kNuDg3m4dxzO7dv5+osv+PSddwjJyeHX8fHM1Ov5/NVX2bdvHxveeotb\nfP34Ze84NPv388XixYTExnDk++DG7nSS63KiUCjYtW8fc40mng4NI7C6hkOHj/Dl8uUIe/bwy95x\nzA4MYss7Czl58uR591ev17Mn4wjD3R5+FRpGr4ZGMk+cJKpPH7JqaxFFEY8ocrTh8nkPuE/moOod\nd1H3VYWH4ykq6uQedb4aq4P/7izgnnG9u6R9j+hhfcE6ror+cdTeoFWRHu3LtuyuWzwPoFPpmJM8\nl3eOvI34k8XsXWX79u1U5eTynMWHFf6B3KLSsH3zZsJ69+ZIRTmSJOF0uzlutxMSEcHuI0cYI8Cv\nQsMIq63jeH4+4QkJZFZXI0kSbo+HY42N+AQH89FLLzHK4eA38QmkNzSw6JVXcJ/hGiTrWa6YEa76\n+nrWrVxJQ1UVRSUlTPL2wvT92hBNk5XBwcGoFQqizV70EYt46Pa5eCsECuvqWKDRYnK6kCQnEVoN\nfi43S3fuRFQoiAkP45N33+XzN94guE8fbrrzTg7t2EGLzcboKVN4Ydculm3aiAvoM2ECs89QuHvS\n1Kn83+7d/HbFCiRBIGX6NEaOHt2Nz5Csp8o7cYJUvR6LvnXNzfDQMFYcOEBVURF79+7ljYpKtHod\n/QcO4sCBAySq1PibTACMiIjkg0OHuO/Pf+bDV19lf3Y2zUiMuOEGCkpLidTpeb4wH7co4afTIWg1\n5Bw4wCgEjuzZg0qtJkajIS8nh969T79Qezwe/vH00+xcsQKlRsOcRx9lwIAB9I+NpSAnl/9n77wD\no6qyx/95U5Mpmcyk94SQBAiQQgkBAgjSEQsWLGADO9Yt7v52V3d116+rrm3tDUUpupZVigiIlNAh\nlFCSQAqkl0mdPvPe749oFiSBBAgQmM9fyZt77zv3zbl3zrv33HNKKiuRazUkxceTkZXFiupq3t69\nGxGIychg5OjRHfY7Pz+fLT/8gCRKpF8xhgEDBnTpubndbtb+8AMlBw9iCApi/PTp+Pv7t1+2oABl\nwpkZIoo+SbjeeeeM6p5PXv3+EFNTw4kN0nVL+7uqdhLoE0CsIfaE6xMGhPFdThlTUrs3gPToqDH8\nUPw93x35lqt7X3PO2nW73dxx880cyd6EoNHw8LPPkJ2dTX+lgg02Gz+IVsLkMvRuDxlXXMHbO3fy\nn6++RJTLueKWW+jduzdJ4RF8mpOD0+nET6cjKiODkWPH8t+KCt4+cAC3KNE7ayRx8fHkW60k/mz8\nDwgLZ2t+HmazucPwSV56DpeEwWW32/nghRfo29BIksHAwdxctgMDIyIBEOVyal2tbwjNDgdLS4q5\nSqcnw2jkvaYmtooSUzW+yAUZdXY7OrmCDJUaq8fNG/sPoK+oZGZ8PBtWrOSRr7/mT9Ouwl+jYf5X\nX6M31zMnMQmLR+TrvHx2797N4MGDT5IxLy8PZVU1twwehChKbDtSSElJCb169Tqfj8pLD0RrMHDU\n7kCSJARBoKq5GUNEOB+9/wGjbXbu12opsjt4/4eV3HHFGAS3u61sdXMzepOJgIAAHv7LX2hoaMDH\nxwetVsubb77Jrpoa7tXriJQrWWa1UGo2U9fYyIFdOWQFBWN3ufi6pppxN93YrmzPPP00uW+9zRyj\nEUtjM28/+CC3Pf88h4+VMsbPjwC1D0etFlZWVGAwGLjjgQdoaGhAEAQMBkOHW3hHjhzhyxdeZKy/\nP3KZwIpXX4VHHumS0fXVokVY169naFAwpYVFfJCXxwN/+lO7ZZ1796F/4GTfy86gTEjAXVSE5HQi\nXKSHYFbnVnKgrJE/Tj+3aXaOZ2Xx90yInXjS9aw+wbyw7EC3nlaE1jARD6c/xu/WPUFacDrRfufG\nb2zGlCnotmzh93o/KpqbeXHuXDRpadidLkZp1USpFaxx2DGLIkeLiwm12bgmIwOby82W3FxKe/fm\nxy2buUPtQ4JOz0arlaWbN/F//v7c/cgjNDQ0IJPJ8PPzo66ujga3G7vLhY9SSYvDgQU6POTlpWfR\nowwus9lMbm4uMpmMAQMGtEWKLywsRF9VTbnNyp5jRxkUHsHne/dgOHQQlVyBYdgwPt6xgxVbNlFk\ntxMpk3FXWDiSJPHnkDBmlxTxUEU5epmM7Q4HCh8Zux0O6jxubKJIpkKBnyAQ46umxVxHhFpNSGAg\nCWYzyGUMj4wCwFp4hAUffEBTUxMBAQEMGDCgzekx56d1jAsKJDG4dS9ecewYOZs2eQ0uLyeRn59P\naWkpRqORlJQUMjIyWL98OQ989hmCx40+IYHfPHA/b/y/PzHTaMIolxOmULDH5aK4uJikpESe27gB\nlSjhMPpz71NPAa0BSY8cOYJWqyU1NZWamhqG+PiQqFAiR2CSRsMWmxWFXM4ep5OGokKsCFj99MgE\nAY/Hw549e2hsbCQqKorevXuz6fPPeSwgkNSfx2Kj28XSJUsINfqzqamFCOwUiSI6kxGXy4Wvry9G\no/G0z2BXdjYjdDr6hoYCIEmQs24d/fv3Jy8vj/Ly8pPG2PG4XC4OrFvHw4lJKORyYgMCqCwooKio\n6KSyksuFa+9eVIPOzJFc8PFBEdcL5959qAcPOqM2upMjVc28uOwAr84ejI+qe8Jw1NpqOVh3gN8M\n+d1Jn/ko5YzuG8IPeyu4bWRct9z/F8J0YczqN5uXtv+T/xv9Ar6Ksz+JWbZlK68ZTYQplKQKAhUu\nF+/k5ZElk3HY4ybP40YNaGQCW1asIMkjkr0rBx+1iriwML5fvpxoQU6Mjy8qQWCgRsPK5kYqKiqI\njo4+YTwEBgaSPn06C779liilkhKni9G33Ow1uC4ReozBVVVVxYfPPUeizY4IbNTrmPuHPxAQEIAo\nivxn3U8MsNnppVDyvdOBMyqKAffcg0KhwLNnD/W7d9NXJsNHlDhgsdDU2IBWocBisyMXBGJ8fNHL\nZeQ5HKQplARLEjoEimQyrC0tiDU11DQ2YXG5kP38Vi7CCRGm9zc10bx9O1ZBIM/p5ODIkdx0++2t\nJ71B6Kf/AAAgAElEQVRkAm7P/3wLPKKIcBHFIPJycfDT6tXsXLyYJJWKfKeT/RkZDB83jhWLFjPE\nZkUvyNlQUcG6deuQyQT0ajUquQxfQUBqaUYQBFw2GxEqNTqghNb4W3v27GHFG2/QT66gxO1iR3xv\n3G43HknERxBQINAkSiAINNTXE2o24ysIqIBqhw27w8Gn776LY+cuwlQqvnE6GDZrFoJcjsv1v0DB\nLglkSiWJoaGMCgikwWZjoFbLf2uqu+SQLpPL8Yi/DiwpY/WKFeR++RUJatVJY+x4BEEAoTUA6y+T\nnFtq/9SXc/du5NFRyNpJ9dVZfCeMx/b11xedwXW0zsKjn+7kscl9us1RHmBF4TJGRY3GR9H+Ctbk\nlHBeWn6w2w0ugPGxE8mrz+PlHS/xZMYfzzrtjyiJNDuchErgAeweEUkhUSGK9BUgWiZnh9OJQ5LY\nn5fHrnXrGaNW0yyKLN++g6gbrketVBDtb8AlSfghoLJaOgwJNPGqq0hITqauro6M4GBiYs59cnEv\nF4YeY3D9tHw5aS436p9jXKktFlYtXcqw0aM5cOAAQRYrd5hMaBRKkm02/lpZQWBgIAqFgu/efJPf\nxsbRy2CgxWLhsQ3r+Ke5jkk6PxY1NtBfqWJmUDAKmQyrzcFXNgv3qJRUejxscjoI9NVQ63JjlUR2\nSCJ7amsJdrk4HBpKfkEzy4sLaXC6yGlp5p9TpuDr50c/pZKvsrMpHz+eiIgIMidO5D8vvIjd7cYj\nimz1uLnd68N10WE2m2lqaiIwMBCdrnt8XTrC6XSy7osvmNsrHp1ajSiKzN++nb+uW8c4p5PZkdGI\nksTApkbefeFFgtPTeWHPHsb7ail2udghk/H06NE0LFvGyLg4HHY7KSoVqxYvxuVwcm1oGGEGA5Ik\n8WV+HlJwMDscThLkdqIUClZYbVjUKoqPHAFRRK9WIyFwrKWJLVu2EFZby+ykJGQyGWl2O+8vWsTE\nu+7inf97HrPLSYso8pXLyd+eeIL8nTs5mJdPnJ8fW6urScgaiV6vx+12twV6DAsL6/DY+9BRo1iw\nMRuppASZTMZmm5VpWVl88/rrzO3dG41Khdvj4cPjxtjxKBQK0idN5D/LV5Di70+ZpQV7VFS7K8rW\nL75EM336WX132jvvoHrSZBTx8ejuuvOs2jpXFFQ285uFu7h7dDwTB3Y9vlhnsbvt/FC8kn+OfqnD\nMqnRRmxODwfKGukXceaGbWcQBIH7Uh/g6ey/8PquV3ko7WHksjN/uVVHRfFWcQnXaHVUe9ysdNrx\nMejxlzWTpVSjl8tQCwIbHHbyc3K4XqUkXtXqP1zS3ESTxUKpycjC6moSVCo22WwEDx1C6M+rt+3R\nq1cv7+7HJUiPMbjqqqtZtPJ7gux2RAnKlEoCDx2ifssWth05gr+vD6rAIBwuF+EmI8LBgyz+61/R\nqtRUlZYSkNbqcKiQy+jr58d6lYrPFQrMgoEBDidldhtyQSBIqUApqfgK8KgU9NfrUUWEs1ujITIp\nkWl+fjQPy8CjUnHjjOv47K23+Hb3buyShC4wkGU5uwhAwCyK2IODsNtbU04kJCRw05O/J2fTJgS5\nnNtHjTrpR8LLhWXD2rVsWLQYo1xGvULBDQ8/TELCmZ1cOxOcTidyj4j25zdfmUyGn1xBk9mM4PFw\nqK4WBQKNHjdOp5MV+/Zyz5138tH27fgGBvHZO29TU1PDzh07ERob8ZHJqJHJKcvMwKDX43/cEXTD\nz6ur42NjKLRY2ed0khAZQZ0AktPJIauVJquFFknColBgs1gwKBRtBpJerUZwu3lw3jwMRiPLPluI\nwkfN3373O8aMGUNmZibrf/yRo5WVJMXFMTwrC6vVykevvYa7sAgRCX2/fsy6//52A59GRUUx+//9\nkW0bNiB5PMwcMQKj0YgK8P05MKRCLkevULSNsV8z7dpr2RoayrGCAvwCA5k6duxJqwqSzYZt2VJC\nVq06q+9OHhRE0H+/wXzPvWiuvQZZJ7ZNu5NN+TU8800uj03uw4QBXY+e3xXWlKwiObA/YbqO7yOT\nCVw7OIr/bDvKX67t2uGHM0EpU/LnzKf4x5Zn+ceWZ3lk0GP4qc9shW/qlVdSt2Mni4oKkWt03Jo1\nkndycwmRy6iTJMxuDz6AShAQHU6cHpFyuw03oBYEBJeLR//+d1588kmyrVbk4WG89uKL57S/XnoG\nPcbg2r13LwMsNuaGhSFJEs8WFWJqbuHW3gmM8jfy8J497LZYSA0O4suCAlS+vjzQv3VgrzMa+eDA\nQWb37cux5ma2SRL3jrmCcYmJ/HX5cg7k5nK1TodWoWBNQwNuuZznBg+mwmLh6UMHuS45mazkZPJq\namjU6bjljjtQKpUs+uADrjKaGHHnXVjtdm5949+kqH3IiIrmaFMTr5UcPeHHpL23ll+cm71cWKqq\nqshetIg7YuPQqdWUNtTzxRtv8PuXXjpv6We0Wi2BSYmsLyokPTyCo/X1VGt8SU5PZ+eGDVzpq8Gg\nkLO8qQFV794oFAo+XLDghDbee+89dpceY0poOCalkiXVVezLzeWuRx5h9cZsRsfEUGexkAdMnjyZ\nl7/9jnuCQ4jS6fmm9Bi909PYvXkLVyuVzDAasbg9/L2uBl+tlnKbjUOVlUT4+7OttJTIAQNQq9Xc\nfffd3H333SfIoVarGT958gnXVi1bRmhJCVf+Evj04CHW//hjW7lfj4XIyEgij0v/JYoi2thYsouL\nSQkLo8hsplGvJ7yD6PAymYzMESPIHDGiw2duW7kSVWrqGaX0+TWKyEiCli29oONZkiQ+33qUBRuL\n+OfNaQyIav9U5rnCJbr45vA3PDH4N6cte1VaBDe8toEGixN/bfcfLvBR+PDn4U/x6f5PePTHedza\nbxZjoq9ALnRtPCcPH06TzcbjU6bQYLPxXW0NfT0e9hw9xlRfSJAr+MpmpckjofL1odps5ip/f5rc\nIv+1WDD6+HBw5UqW3HlX29zyzXvv0ec8zi1eLg56jMElt9tJDwulVhIRJYl4jQanrHViiwoMZHxW\nFovKy/m8sgJ3UCDzUlLaJr4nJk7i/7Zu4c81Vfjo9dz3+uuYi0v4pLwMfb++GJ1O3i4uxiOKBIeF\nEaDX8VRtDUofH+a+8AIWs5lPSkoIjo3h9lmz2lIv1B47xuCfVw3sokg/UwCinx97rBZ8jUYyY6M7\nfPtuaGjgiw8+4OjBg/gFBnLtnDntHrv3cn6or68nRC5vCyUS6W+EyiqsVmuHqZ3ONYIgcOt99/HN\nZ5/xSV4e/qGhzJ41iz07diCkpPBhQQEej0hoVBQTxo5ttw2r1UqCTs+nNdU4PR5CdTpUHg9X33QT\nSxUKPt25E43ejxsef5yEhARcr73K+888g72mithhGfz1n/9k7vjx9FMoKLZaEWQyhoSE4NDrueWB\nB/ju009ZU11NdHo6M2/pWi7UurIy0v2NJwY+LS+noqKCz999l9pjxwiMiuLGe+4hrJ0k0jKZjFkP\nPtj6fAoKMIVHcPvsWfj6nrljtHXJ52hm3nTG9X/NhTS23B6Rf604xO6Set69O4NwY/ek7jmeH4q+\nJ0of1algo/5aFf+6bRAa9fn72VHKlNw54G4yw4fz8f6PWHxoIRNiJ3FlzHiMPp1bhZw2YwZLgc+2\nb8dXp+O6xx6j9LXXUCsVLLC04JQgQBAwKOXEhoTg7xF5r7kFuSCQHBSETaMhRBAu6Nzi5eKgxxhc\nkX37sj//MJm9eyMChdXVRPhqAGiy2xFMJt587TXCw8NZt3YtRz5b2OqYDhy1WLjnN79h2rXXntTu\nksWLyd60ib8MG4avXMGCoiP4Zmby7CuvnFam0F69yN28hWC9Hl+FglqZDEN8PBn9+lHb0sKmilY/\nsvZY+M479Cot5fqkPpQ1NPD5yy9z/7PPduoEl5dzT2BgIBWiiNlqxaTRcKSmBrm/4byfDtLr9cy6\n774TrlVGRhKW1IdHrpqOUi5nXWEh1qSkduvrdDqOtrTwdEQERqWKJZUVeFQq1Go1M265BW655YTy\nY8aMYcyYMSdcC0lMpOTQIa5JSsLm9rDkSAFjk5KIiorigT/84Yz7FhIby4EDB4k1mZCAQ40NRERE\nsODll8kSJfom9eFgVRULXn6ZR599tl2nYoPBwO0PPHDGMhyPu6wc5959BHz04Tlp70LSbHPx/77Y\ng0wQeO/uDLQ+3T+1W11Wvsj7nKdG/LXTdbp7xa0j+gT05blR/+Rw/WG+L1rOg6vvY2BQChNjJ5ES\nnHpKx3qlUsm1M2fCzJlt12JiYjgoSvzTaCRMoWSV1cI2i4WY5GQCnS7uHzIEh0fkpYJ8+qamUpGT\nc8HnFi8Xnova4Kqrq2PN0qW01NeTNnQo3+Tn80TufkQBfIdnooyN5f38fCzAuFmz2rYWRmRlUVZU\nxFubNyMHAgcM4JpfbW/8go9CQWBUFJ/U1aIApJAQ/Ds5EKbMmMGCqireycvDhUTWXXeyo6SEffn5\nWBVypt17T7uBFu12O7UFBdzapy+CIBBtMhFZb24LBeDl/BMYGMjEOXP49MMP0YgSTr2OWx5++KLI\nZZaWlkbO4EH8btEiFKJIQEoKf7j++nbL9urVi8i4OF4rL0cjCDh9fUlO7lrspd899xx/nDOHTXl5\nWCWJftOmcm07Lytd5crJk/ns2DHe2bcPUZKIGjqUpH79OPjNNyQntG4zJoeFsb0gn9ra2g63Cs8V\ngo8a4ysvI7STbL4ncbTOwu8W5TA4LoBHJyWhkJ8fnf30wALSQwYRZ+g5zt29jb15yPgwdw2Yw7rS\nn/h4/3yce53MSp7NsLDMdlcoJUli88aNHNq5E1+9niumTiU6OhqXjw+P1ZsxymRUixJ+ASbm/fnP\nPPXAA2zLy8MmiSROnMg999xDzs6dF+Xc4uX8ckENroqKCqZOncrBgwexWCwnKeAHzz9Pmt1BolbL\n1txcpt54IwOfbz12HRMTgyiK1NfXo9Vq0Wg0bfUUCgU333knjdddhyiKGI/LYfhr/IxGEqNjGBkT\ng9vj4WhDA0c6mbdKq9VyzxNPUF9fj0KhwGAw4HQ6aWxsRK/X49PBRK5SqRB8fKi3WjFptbg9Hswu\nt/eN5wIzaMgQ+vXvT0tLC/7+/m1bxxeaqqoqqnJzuXHAAFQKJTnNTezft69d3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FpMeHA4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\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dB/UomhUs+eBzXnzrBdSZakQSEboSHffdfH+fMh2DnBgX+tpyruB2u3n+1efpkHZQWldK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\nYDAYDIaGYRMuBoPBYDAYDA3zf2AOQHb1zUpFAAAAAElFTkSuQmCC\n",
"text": "<matplotlib.figure.Figure at 0x10c1cdc50>"
}
],
"language": "python",
"trusted": true,
"collapsed": false
},
{
"metadata": {},
"cell_type": "markdown",
"source": "**ITS BEAUTIFUL**\n\nOn the subject of useful plotting methods, <a href = \"http://stanford.edu/~mwaskom/software/seaborn/tutorial/axis_grids.html#plotting-bivariate-data-with-jointgrid\"> Seaborn</a> is producing some really cool stuff. I find the scatterplot + histograms really useful."
},
{
"metadata": {},
"cell_type": "code",
"input": "import seaborn as sns\n\nsns.jointplot(\"sepal length (cm)\", \"sepal width (cm)\", df, space=0.1);",
"prompt_number": 8,
"outputs": [
{
"output_type": "display_data",
"metadata": {},
"png": 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lKUqJRSlxgMSi5DjAH0tjKiut7RxJx2jq/UOIb6Y4nc/nY+XKlVitVmbNmsX999/PLbfc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\nFioiIpIaCxUREUnt/wGzGZecFyFMDwAAAABJRU5ErkJggg==\n",
"text": "<matplotlib.figure.Figure at 0x10e240490>"
}
],
"language": "python",
"trusted": true,
"collapsed": false
},
{
"metadata": {},
"cell_type": "markdown",
"source": "Anyways, ggplot can at least take care of the rest of out plotting needs."
},
{
"metadata": {},
"cell_type": "code",
"input": "print(ggplot(df, aes(x=\"petal length (cm)\",y=\"petal width (cm)\", color = \"Iris Flower\"))+geom_point())\nprint(ggplot(df, aes(x=\"sepal length (cm)\",y=\"sepal width (cm)\", color = \"Iris Flower\"))+geom_point())",
"prompt_number": 21,
"outputs": [
{
"output_type": "display_data",
"metadata": {},
"png": 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7Ut4d2flOLjtW+SDRWiw7vHFGz63F6UtvbsD+kHNv2Jt3ul4Kvn3Yp7bP9c9I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\nJCb/H3Xblten6C7EAAAAAElFTkSuQmCC\n",
"text": "<matplotlib.figure.Figure at 0x10f89b150>"
},
{
"output_type": "stream",
"stream": "stdout",
"text": "<ggplot: (284728301)>\n"
},
{
"output_type": "display_data",
"metadata": {},
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x2+04nU7y8vIAGDVqFHl5eZimSU5ODtdcc02LlezaSwsxihwk7Z+/JWHX53z1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\nahRFRERExNb/A3ImQH5BRQLJAAAAAElFTkSuQmCC\n",
"text": "<matplotlib.figure.Figure at 0x10f7d69d0>"
},
{
"output_type": "stream",
"stream": "stdout",
"text": "<ggplot: (284666025)>\n"
}
],
"language": "python",
"trusted": true,
"collapsed": false
},
{
"metadata": {},
"cell_type": "heading",
"source": "The simplest way to classify",
"level": 1
},
{
"metadata": {},
"cell_type": "markdown",
"source": "Our goal is to develop a model to classify the three different types of flowers. \n\nBy inspecting the graphs above, we can see that there is actually a very clear separation in petal length and width between Iris Setosa and the other two flowers. \n\nWe could write some quick code to classify that."
},
{
"metadata": {},
"cell_type": "code",
"input": "# Determine the cutoffs for Iris Setosa petal length\nsetosa = df['Iris Flower'] == \"Setosa\"\n\nmax_setosa = df['petal length (cm)'][setosa].max()\nprint \"Setosa max petal length: {} cm\".format(max_setosa)\n\n# Find the minimum petal length for the other plants\nmin_not_setosa = df['petal length (cm)'][-setosa].min()\nprint \"Not Setosa min petal length: {} cm\".format(min_not_setosa)",
"prompt_number": 433,
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": "Setosa max petal length: 1.9 cm\nNot Setosa min petal length: 3.0 cm\n"
}
],
"language": "python",
"trusted": true,
"collapsed": false
},
{
"metadata": {},
"cell_type": "markdown",
"source": "Once we have this classification scheme, we can create a very simply model to identify the type of flower."
},
{
"metadata": {},
"cell_type": "code",
"input": "petal_length = df['petal length (cm)']\n#Incoming wall of text\nfor flower in petal_length:\n if flower <= 1.9:\n print(\"Iris Setosa\")\n else:\n print(\"Iris Virginica or Iris Versicolour\")",
"prompt_number": 434,
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": "Iris Setosa\nIris Setosa\nIris Setosa\nIris Setosa\nIris Setosa\nIris Setosa\nIris Setosa\nIris Setosa\nIris Setosa\nIris Setosa\nIris Setosa\nIris Setosa\nIris Setosa\nIris Setosa\nIris Setosa\nIris Setosa\nIris Setosa\nIris Setosa\nIris Setosa\nIris Setosa\nIris Setosa\nIris Setosa\nIris Setosa\nIris Setosa\nIris Setosa\nIris Setosa\nIris Setosa\nIris Setosa\nIris Setosa\nIris Setosa\nIris Setosa\nIris Setosa\nIris Setosa\nIris Setosa\nIris Setosa\nIris Setosa\nIris Setosa\nIris Setosa\nIris Setosa\nIris Setosa\nIris Setosa\nIris Setosa\nIris Setosa\nIris Setosa\nIris Setosa\nIris Setosa\nIris Setosa\nIris Setosa\nIris Setosa\nIris Setosa\nIris Virginica or Iris Versicolour\nIris Virginica or Iris Versicolour\nIris Virginica or Iris Versicolour\nIris Virginica or Iris Versicolour\nIris Virginica or Iris Versicolour\nIris Virginica or Iris Versicolour\nIris Virginica or Iris Versicolour\nIris Virginica or Iris Versicolour\nIris Virginica or Iris Versicolour\nIris Virginica or Iris Versicolour\nIris Virginica or Iris Versicolour\nIris Virginica or Iris Versicolour\nIris Virginica or Iris Versicolour\nIris Virginica or Iris Versicolour\nIris Virginica or Iris Versicolour\nIris Virginica or Iris Versicolour\nIris Virginica or Iris Versicolour\nIris Virginica or Iris Versicolour\nIris Virginica or Iris Versicolour\nIris Virginica or Iris Versicolour\nIris Virginica or Iris Versicolour\nIris Virginica or Iris Versicolour\nIris Virginica or Iris Versicolour\nIris Virginica or Iris Versicolour\nIris Virginica or Iris Versicolour\nIris Virginica or Iris Versicolour\nIris Virginica or Iris Versicolour\nIris Virginica or Iris Versicolour\nIris Virginica or Iris Versicolour\nIris Virginica or Iris Versicolour\nIris Virginica or Iris Versicolour\nIris Virginica or Iris Versicolour\nIris Virginica or Iris Versicolour\nIris Virginica or Iris Versicolour\nIris Virginica or Iris Versicolour\nIris Virginica or Iris Versicolour\nIris Virginica or Iris Versicolour\nIris Virginica or Iris Versicolour\nIris Virginica or Iris Versicolour\nIris Virginica or Iris Versicolour\nIris Virginica or Iris Versicolour\nIris Virginica or Iris Versicolour\nIris Virginica or Iris Versicolour\nIris Virginica or Iris Versicolour\nIris Virginica or Iris Versicolour\nIris Virginica or Iris Versicolour\nIris Virginica or Iris Versicolour\nIris Virginica or Iris Versicolour\nIris Virginica or Iris Versicolour\nIris Virginica or Iris Versicolour\nIris Virginica or Iris Versicolour\nIris Virginica or Iris Versicolour\nIris Virginica or Iris Versicolour\nIris Virginica or Iris Versicolour\nIris Virginica or Iris Versicolour\nIris Virginica or Iris Versicolour\nIris Virginica or Iris Versicolour\nIris Virginica or Iris Versicolour\nIris Virginica or Iris Versicolour\nIris Virginica or Iris Versicolour\nIris Virginica or Iris Versicolour\nIris Virginica or Iris Versicolour\nIris Virginica or Iris Versicolour\nIris Virginica or Iris Versicolour\nIris Virginica or Iris Versicolour\nIris Virginica or Iris Versicolour\nIris Virginica or Iris Versicolour\nIris Virginica or Iris Versicolour\nIris Virginica or Iris Versicolour\nIris Virginica or Iris Versicolour\nIris Virginica or Iris Versicolour\nIris Virginica or Iris Versicolour\nIris Virginica or Iris Versicolour\nIris Virginica or Iris Versicolour\nIris Virginica or Iris Versicolour\nIris Virginica or Iris Versicolour\nIris Virginica or Iris Versicolour\nIris Virginica or Iris Versicolour\nIris Virginica or Iris Versicolour\nIris Virginica or Iris Versicolour\nIris Virginica or Iris Versicolour\nIris Virginica or Iris Versicolour\nIris Virginica or Iris Versicolour\nIris Virginica or Iris Versicolour\nIris Virginica or Iris Versicolour\nIris Virginica or Iris Versicolour\nIris Virginica or Iris Versicolour\nIris Virginica or Iris Versicolour\nIris Virginica or Iris Versicolour\nIris Virginica or Iris Versicolour\nIris Virginica or Iris Versicolour\nIris Virginica or Iris Versicolour\nIris Virginica or Iris Versicolour\nIris Virginica or Iris Versicolour\nIris Virginica or Iris Versicolour\nIris Virginica or Iris Versicolour\nIris Virginica or Iris Versicolour\nIris Virginica or Iris Versicolour\nIris Virginica or Iris Versicolour\nIris Virginica or Iris Versicolour\n"
}
],
"language": "python",
"trusted": true,
"collapsed": false
},
{
"metadata": {},
"cell_type": "markdown",
"source": "<hr>\nThat was less than exciting. This was just a simple example of a **manually implemented dimension threshold**. Eventually, we will get some **machine learning** to do this for us."
},
{
"metadata": {},
"cell_type": "markdown",
"source": "We can see that we will never attain perfect separation between Iris Virginica and Iris Versicolour, since there is some degree of overlap between them.\n\nHowever, we can still try to implement another threshold."
},
{
"metadata": {},
"cell_type": "code",
"input": "# First we select non-Setosa flowers\nflowers = df[-setosa]\n\n# Create a boolean mask of Iris Virginica to test prediction against\nis_virginica = flowers['Iris Flower'] == 'Virginica'\n\n# Convert to plain matrix for this simple looping operation\nflowers = flowers.as_matrix()",
"prompt_number": 191,
"outputs": [],
"language": "python",
"trusted": true,
"collapsed": false
},
{
"metadata": {},
"cell_type": "markdown",
"source": "Now we are going to loop through each of the petal and sepal features in the data and test each value as a potential threshold. At the same time we will keep track of the threshold with the best prediction accuracy."
},
{
"metadata": {},
"cell_type": "code",
"input": "best_accuracy = [0, 0, 0] # accuracy, feature, threshold\n# for each flower feature in the data (columns 1 - 4)\n# sepal length (cm) = 0 \t\n# sepal width (cm) = 1\t\n# petal length (cm)\t= 2\n# petal width (cm) = 3\nfor feature in xrange(4):\n \n threshold = flowers[:,feature].copy()\n threshold.sort()\n \n # Now test all these values as thresholds\n for thresh in threshold:\n prediction = flowers[:,feature] > thresh\n accuracy = (prediction == is_virginica).mean()\n if accuracy > best_accuracy[0]:\n best_accuracy = [accuracy, feature, thresh]\nprint best_accuracy",
"prompt_number": 192,
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": "[0.93999999999999995, 3, 1.6]\n"
}
],
"language": "python",
"trusted": true,
"collapsed": false
},
{
"metadata": {},
"cell_type": "markdown",
"source": "Great, we can see that we found our best flower classifying accuracy was 94% using petal width at 1.6 cm.\n\nLets see how this looks on a plot."
},
{
"metadata": {},
"cell_type": "code",
"input": "# Select non-Setosa flowers\nflowers = df[-setosa]\nggplot(flowers, aes(x=\"petal width (cm)\", y=\"petal length (cm)\", \\\n color = \"Iris Flower\")) + \\\ngeom_point() + \\\ngeom_vline(xintercept = 1.6, color = \"black\")",
"prompt_number": 210,
"outputs": [
{
"output_type": "display_data",
"metadata": {},
"png": 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UdDnDMvh+rL69w2oTtyWLeD4Tpw6Tfmac22LV9ArZ7R2WdALB+3NIRMRjv0sV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\nJdk3m1W5JPtms2suydpspmnadpuh+FlWAjdv3qy33npLw4Y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ddNNN+6z1RRoaGvTrX/86\n+9bv9OnT+9ze27nnnqtnnnlGK1euVE9Pj2699dZsYZN6j/g1NTXts26uZyiPHDlSY8aM0SuvvCJJ\ncrlcuvLKK/XDH/5Q27ZtUzqd1ksvvaSenp6cXm9vjY2NOv3007/SzwLAYEYJBIqAYRi6+OKLdfLJ\nJ2v06NEaO3asfvSjH0mSJk+erPvuu0833HCDKisrNXbsWP3ud7/L/uzs2bM1d+5cVVRU6M4779Rl\nl12muro6jRgxQocddpiOPfbYPkfj+vuOvoaGBkWjUU2bNu0zb+/9GuPHj9fdd9+tiy++WLW1taqs\nrOzz1vb5558vSRo6dKiOOuqoPq+R60zXXnutFi1alL19xx136PDDD9eUKVM0dOhQzZ49O1sqczny\nuOdzHnnkEV177bX9/gwAFBvDLLYvBAMc6IADDtADDzygE044odCjDEo9PT2aOHGili9fPqBfGP30\n00/rwQcf1COPPDJgrwkAgwUlECgClEAAwEDj7WAAAAAH4kggAACAA3EkEAAAwIEogQAAAA5ECQQA\nAHAgSiAAAIADUQIBAAAc6P8DuH+Uwek43OMAAAAASUVORK5CYII=\n",
"text": "<matplotlib.figure.Figure at 0x110b1c090>"
},
{
"output_type": "pyout",
"prompt_number": 210,
"metadata": {},
"text": "<ggplot: (285940765)>"
}
],
"language": "python",
"trusted": true,
"collapsed": false
},
{
"metadata": {},
"cell_type": "markdown",
"source": "Hey! Not bad. **BUT**"
},
{
"metadata": {},
"cell_type": "heading",
"source": "Testing / Training data and Cross-Validation",
"level": 1
},
{
"metadata": {},
"cell_type": "markdown",
"source": "Our previous model achieved 94% accuracy, however, this is likely over-estimated. We used the same data to both define the threshold, and then evaluate the model.\n\nWhat we should be doing is generalize the power of our model on new instances of data. In other words, we will define the threshold on a **training set** of data, and evaluate the model on the **test set** of data that the model did not see in training.\n\nIt is therefore only appropriate to report training error, not testing error, when communicating your results.\n\nFor now, we will split the data in half to achieve this, though more consideration should be taken later, in more complex models. Sometimes the models can be sensitive to how much data we include in either set. For example: sometimes too little data for testing could skew the result.\n\nTo avoid this problem, it would be nice to somehow use all the data for both testing and training.\n\nThis is where **cross-validation** comes in. There are many types of cross validation, but the most extreme case is **leave-one-out**. Under this framework, if we have 100 points of data in our training set, we:\n\n- remove *i* th element of the training set\n- train model using the training set\n- predict model with testing set, where testing = training - *i* th element\n- record the error\n- repeat until all *i* elements of training set have been omitted (one at a time)\n- sum error\n\nGenerally, we don't use leave-one-out unless we need particularly high precision, as other techniques are sufficient for most cases. The biggest caveat of \"leave-one-out\" cross-validation is dramatic increases in computation speed, particularly as dataset become large.\n\n\nYou can read up on the other types of cross validation <a href = \"http://en.wikipedia.org/wiki/Cross-validation_(statistics)\">here</a>."
},
{
"metadata": {},
"cell_type": "heading",
"source": "Getting a feel for testing and training data",
"level": 2
},
{
"metadata": {},
"cell_type": "markdown",
"source": "Using pandas, we can efficiently split the data into testing and training sets.\n\nNow let's modify our model definition from before and create a more formal function."
},
{
"metadata": {},
"cell_type": "code",
"input": "def model(input_data):\n \n # Determine the row index's for the testing set\n test_rows = np.random.choice(input_data.index, size=50, replace = False)\n # Use this as a mask on the full dataset\n\n # Use .ix to specify array mask\n testing_data = input_data.ix[test_rows]\n training_data = input_data.drop(test_rows)\n \n # Create a boolean mask of Iris Virginica to test prediction against\n is_virginica = testing_data['Iris Flower'] == 'Virginica'\n\n # Convert to plain matrix for this simple looping operation\n training_data = training_data.as_matrix()\n \n best_accuracy = [0, 0, 0] # accuracy, feature, threshold\n # for each flower feature in the data (columns 1 - 4)\n # sepal length (cm) = 0 \t\n # sepal width (cm) = 1\t\n # petal length (cm)\t= 2\n # petal width (cm) = 3\n for feature in xrange(4):\n\n threshold = training_data[:,feature].copy()\n threshold.sort()\n\n # Now test all these values as thresholds\n for thresh in threshold:\n prediction = training_data[:,feature] > thresh\n accuracy = (prediction == is_virginica).mean()\n \n if accuracy > best_accuracy[0]:\n best_accuracy = [accuracy, feature, thresh]\n \n return best_accuracy\n\nprint \"Best model accuracy = {}\".format(model(flowers)[0])",
"prompt_number": 229,
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": "Best model accuracy = 0.6\n"
}
],
"language": "python",
"trusted": true,
"collapsed": false
},
{
"metadata": {},
"cell_type": "markdown",
"source": "As you can see, our results are much worse than before, but that's ok. We will be moving on to more sophisticated techniques soon.\n\nBefore that, let's check how sensitive our model is to the random sampling of our training and testing data."
},
{
"metadata": {},
"cell_type": "code",
"input": "results_l = []\ni = 0\n\nwhile i < 1000:\n results = model(flowers)[0]\n results_l.append(results)\n i+=1\n\nqplot(results_l)",
"prompt_number": 240,
"outputs": [
{
"output_type": "display_data",
"metadata": {},
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5etiqVauUyWQkSYODg9q8efPcK5jLly/X8PCwT2MDAACgkSz69veNnDp1SnfddZckKZvN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\n",
"text": "<matplotlib.figure.Figure at 0x10f7c4f90>"
},
{
"output_type": "pyout",
"prompt_number": 240,
"metadata": {},
"text": "<ggplot: (283946089)>"
}
],
"language": "python",
"trusted": true,
"collapsed": false
},
{
"metadata": {},
"cell_type": "markdown",
"source": "After 1000 runs we can start to get a pretty good picture of the potential variation. Generally, we will let our machine learning package handle this type of thing for us though."
},
{
"metadata": {},
"cell_type": "heading",
"source": "K-Nearest Neighbor Classifier",
"level": 1
},
{
"metadata": {},
"cell_type": "markdown",
"source": "Let's get into more complex classifiers. We will also be using a new agriculturally based dataset called <a href='http://archive.ics.uci.edu/ml/datasets/seeds#'>Seeds</a>, which consists of measurements of wheat seeds.\n\nPandas can load the data right from a url. Note that we had to exclude come rows due to data errors."
},
{
"metadata": {},
"cell_type": "code",
"input": "url = \"http://archive.ics.uci.edu/ml/machine-learning-databases/00236/seeds_dataset.txt\"\n\ndf = pd.read_csv(url, sep = \"\\t\", header=False, error_bad_lines=False,\nnames = ['area','perim','compact','length','width','asymc','length_groove', 'wheat_type'])",
"prompt_number": 301,
"outputs": [
{
"output_type": "stream",
"stream": "stderr",
"text": "Skipping line 8: expected 8 fields, saw 10\nSkipping line 36: expected 8 fields, saw 10\nSkipping line 61: expected 8 fields, saw 9\nSkipping line 69: expected 8 fields, saw 9\nSkipping line 107: expected 8 fields, saw 9\nSkipping line 136: expected 8 fields, saw 9\nSkipping line 170: expected 8 fields, saw 9\nSkipping line 171: expected 8 fields, saw 9\nSkipping line 173: expected 8 fields, saw 9\nSkipping line 202: expected 8 fields, saw 9\nSkipping line 204: expected 8 fields, saw 9\n\n"
}
],
"language": "python",
"trusted": true,
"collapsed": false
},
{
"metadata": {},
"cell_type": "code",
"input": "df.describe()",
"prompt_number": 302,
"outputs": [
{
"output_type": "pyout",
"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>area</th>\n <th>perim</th>\n <th>compact</th>\n <th>length</th>\n <th>width</th>\n <th>asymc</th>\n <th>length_groove</th>\n <th>wheat_type</th>\n </tr>\n </thead>\n <tbody>\n <tr>\n <th>count</th>\n <td> 198.000000</td>\n <td> 198.000000</td>\n <td> 198.000000</td>\n <td> 198.000000</td>\n <td> 198.000000</td>\n <td> 198.000000</td>\n <td> 198.000000</td>\n <td> 198.000000</td>\n </tr>\n <tr>\n <th>mean</th>\n <td> 14.917020</td>\n <td> 14.594596</td>\n <td> 0.870810</td>\n <td> 5.642545</td>\n <td> 3.265298</td>\n <td> 3.706683</td>\n <td> 5.421667</td>\n <td> 2.000000</td>\n </tr>\n <tr>\n <th>std</th>\n <td> 2.927276</td>\n <td> 1.313651</td>\n <td> 0.023379</td>\n <td> 0.444635</td>\n <td> 0.379266</td>\n <td> 1.471047</td>\n <td> 0.493759</td>\n <td> 0.812341</td>\n </tr>\n <tr>\n <th>min</th>\n <td> 10.590000</td>\n <td> 12.410000</td>\n <td> 0.808100</td>\n <td> 4.899000</td>\n <td> 2.630000</td>\n <td> 0.765100</td>\n <td> 4.519000</td>\n <td> 1.000000</td>\n </tr>\n <tr>\n <th>25%</th>\n <td> 12.315000</td>\n <td> 13.470000</td>\n <td> 0.856900</td>\n <td> 5.267000</td>\n <td> 2.953750</td>\n <td> 2.600250</td>\n <td> 5.046000</td>\n <td> 1.000000</td>\n </tr>\n <tr>\n <th>50%</th>\n <td> 14.405000</td>\n <td> 14.360000</td>\n <td> 0.873450</td>\n <td> 5.541000</td>\n <td> 3.243500</td>\n <td> 3.634500</td>\n <td> 5.229500</td>\n <td> 2.000000</td>\n </tr>\n <tr>\n <th>75%</th>\n <td> 17.502500</td>\n <td> 15.827500</td>\n <td> 0.886900</td>\n <td> 6.004000</td>\n <td> 3.565250</td>\n <td> 4.812000</td>\n <td> 5.879000</td>\n <td> 3.000000</td>\n </tr>\n <tr>\n <th>max</th>\n <td> 21.180000</td>\n <td> 17.250000</td>\n <td> 0.918300</td>\n <td> 6.675000</td>\n <td> 4.033000</td>\n <td> 8.315000</td>\n <td> 6.550000</td>\n <td> 3.000000</td>\n </tr>\n </tbody>\n</table>\n<p>8 rows × 8 columns</p>\n</div>",
"metadata": {},
"prompt_number": 302,
"text": " area perim compact length width asymc \\\ncount 198.000000 198.000000 198.000000 198.000000 198.000000 198.000000 \nmean 14.917020 14.594596 0.870810 5.642545 3.265298 3.706683 \nstd 2.927276 1.313651 0.023379 0.444635 0.379266 1.471047 \nmin 10.590000 12.410000 0.808100 4.899000 2.630000 0.765100 \n25% 12.315000 13.470000 0.856900 5.267000 2.953750 2.600250 \n50% 14.405000 14.360000 0.873450 5.541000 3.243500 3.634500 \n75% 17.502500 15.827500 0.886900 6.004000 3.565250 4.812000 \nmax 21.180000 17.250000 0.918300 6.675000 4.033000 8.315000 \n\n length_groove wheat_type \ncount 198.000000 198.000000 \nmean 5.421667 2.000000 \nstd 0.493759 0.812341 \nmin 4.519000 1.000000 \n25% 5.046000 1.000000 \n50% 5.229500 2.000000 \n75% 5.879000 3.000000 \nmax 6.550000 3.000000 \n\n[8 rows x 8 columns]"
}
],
"language": "python",
"trusted": true,
"collapsed": false
},
{
"metadata": {},
"cell_type": "markdown",
"source": "As we can see, this dataset is a bit more complex than `iris`. We now have 7 features and three types of wheat: `Canadian`, `Koma`, and `Rosa`."
},
{
"metadata": {},
"cell_type": "markdown",
"source": "Of these 7 features, one of them, **compactness**, is not technically a new variable, but a function of two features, `perimeter` and `area`. It is common practise, and often very useful to derive new combined features like this. It usually termed **feature engineering**. \n\nThe target for a good feature in any type of machine learning is that it is both varies with what matters and is invariate with what does not. Compactness in wheat varies with shape, and not size, which is a better predictor of wheat type. The best tool for creating good features is strong intuition and knowledge of the data. Generally, for most common sources of data, there are already a set of preferred and potential features generated by previous literature.\n\n**All in all, simpler is better.**"
},
{
"metadata": {},
"cell_type": "heading",
"source": "Definition:",
"level": 2
},
{
"metadata": {},
"cell_type": "markdown",
"source": "K-nearest neighbour (KNN) is a supervised learning algorithm for classification or regression. One of the most common usages is for personalization tasks. To make an accurate personalized offer to a customer, you could use KNN to find similar customers and base your offer from their previous purchase behaviour."
},
{
"metadata": {},
"cell_type": "markdown",
"source": "<hr>\nKNN is utilized when class labels are known for a dataset and you want to predict the class of a new data point. The predicted class will be based on the known classes NN. We are simply trying to find the class that is most similar to the new data point you are trying to predict.\n\nFor example: what class should the green circle belong to? Blue or Red?"
},
{
"metadata": {},
"cell_type": "code",
"input": "from IPython.core.display import Image\nImage(\"http://upload.wikimedia.org/wikipedia/commons/thumb/e/e7/KnnClassification.svg/200px-KnnClassification.svg.png\")",
"prompt_number": 347,
"outputs": [
{
"output_type": "pyout",
"prompt_number": 347,
"metadata": {},
"png": 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6ajTawIT4hX9FFvGA2vPNUIgzFutD7DCQXDBdRDW+QuBkmpBVvxllnH0i0Cfv6z5I35XQ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\n4XBszf8DdLjnpSJ7PUwAAAAASUVORK5CYII=\n",
"text": "<IPython.core.display.Image at 0x111e52290>"
}
],
"language": "python",
"trusted": true,
"collapsed": false
},
{
"metadata": {},
"cell_type": "heading",
"source": "Choosing the right value of k",
"level": 2
},
{
"metadata": {},
"cell_type": "markdown",
"source": "It turns out that the correct value for `k` is very important. Generally, it is best to start out with an odd number, since this avoids situations where your classifier \"ties\" as a result of having the same number of votes for two or more classes. Like before in the IPython notebook for <a href = \"\">Curve Fitting (add link)</a>, we are vulnerable to <a href=\"http://en.wikipedia.org/wiki/Overfitting\">overfitting</a>. To remedy this we can examine a plot of accuracy over increasing `k`. The optimal `k` is not often the highest `k` values, but rather the `k` at which improvements in accuracy begin to slow down dramatically.\n\nLet's see what I mean here, and grab a dataset with many more values. Here we will use a few features of the wine ingredients to predict if it is high quality or not."
},
{
"metadata": {},
"cell_type": "code",
"input": "from sklearn.neighbors import KNeighborsClassifier\n\ndf = pd.read_csv(\"https://s3.amazonaws.com/demo-datasets/wine.csv\")",
"prompt_number": 2,
"outputs": [],
"language": "python",
"trusted": true,
"collapsed": false
},
{
"metadata": {},
"cell_type": "code",
"input": "# Take a look at the data\ndf.head()",
"prompt_number": 351,
"outputs": [
{
"output_type": "pyout",
"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>fixed_acidity</th>\n <th>volatile_acidity</th>\n <th>citric_acid</th>\n <th>residual_sugar</th>\n <th>chlorides</th>\n <th>free_sulfur_dioxide</th>\n <th>total_sulfur_dioxide</th>\n <th>density</th>\n <th>pH</th>\n <th>sulphates</th>\n <th>alcohol</th>\n <th>quality</th>\n <th>color</th>\n <th>is_red</th>\n <th>high_quality</th>\n </tr>\n </thead>\n <tbody>\n <tr>\n <th>0</th>\n <td> 7.4</td>\n <td> 0.70</td>\n <td> 0.00</td>\n <td> 1.9</td>\n <td> 0.076</td>\n <td> 11</td>\n <td> 34</td>\n <td> 0.9978</td>\n <td> 3.51</td>\n <td> 0.56</td>\n <td> 9.4</td>\n <td> 5</td>\n <td> red</td>\n <td> 1</td>\n <td> 0</td>\n </tr>\n <tr>\n <th>1</th>\n <td> 7.8</td>\n <td> 0.88</td>\n <td> 0.00</td>\n <td> 2.6</td>\n <td> 0.098</td>\n <td> 25</td>\n <td> 67</td>\n <td> 0.9968</td>\n <td> 3.20</td>\n <td> 0.68</td>\n <td> 9.8</td>\n <td> 5</td>\n <td> red</td>\n <td> 1</td>\n <td> 0</td>\n </tr>\n <tr>\n <th>2</th>\n <td> 7.8</td>\n <td> 0.76</td>\n <td> 0.04</td>\n <td> 2.3</td>\n <td> 0.092</td>\n <td> 15</td>\n <td> 54</td>\n <td> 0.9970</td>\n <td> 3.26</td>\n <td> 0.65</td>\n <td> 9.8</td>\n <td> 5</td>\n <td> red</td>\n <td> 1</td>\n <td> 0</td>\n </tr>\n <tr>\n <th>3</th>\n <td> 11.2</td>\n <td> 0.28</td>\n <td> 0.56</td>\n <td> 1.9</td>\n <td> 0.075</td>\n <td> 17</td>\n <td> 60</td>\n <td> 0.9980</td>\n <td> 3.16</td>\n <td> 0.58</td>\n <td> 9.8</td>\n <td> 6</td>\n <td> red</td>\n <td> 1</td>\n <td> 0</td>\n </tr>\n <tr>\n <th>4</th>\n <td> 7.4</td>\n <td> 0.70</td>\n <td> 0.00</td>\n <td> 1.9</td>\n <td> 0.076</td>\n <td> 11</td>\n <td> 34</td>\n <td> 0.9978</td>\n <td> 3.51</td>\n <td> 0.56</td>\n <td> 9.4</td>\n <td> 5</td>\n <td> red</td>\n <td> 1</td>\n <td> 0</td>\n </tr>\n </tbody>\n</table>\n<p>5 rows × 15 columns</p>\n</div>",
"metadata": {},
"prompt_number": 351,
"text": " fixed_acidity volatile_acidity citric_acid residual_sugar chlorides \\\n0 7.4 0.70 0.00 1.9 0.076 \n1 7.8 0.88 0.00 2.6 0.098 \n2 7.8 0.76 0.04 2.3 0.092 \n3 11.2 0.28 0.56 1.9 0.075 \n4 7.4 0.70 0.00 1.9 0.076 \n\n free_sulfur_dioxide total_sulfur_dioxide density pH sulphates \\\n0 11 34 0.9978 3.51 0.56 \n1 25 67 0.9968 3.20 0.68 \n2 15 54 0.9970 3.26 0.65 \n3 17 60 0.9980 3.16 0.58 \n4 11 34 0.9978 3.51 0.56 \n\n alcohol quality color is_red high_quality \n0 9.4 5 red 1 0 \n1 9.8 5 red 1 0 \n2 9.8 5 red 1 0 \n3 9.8 6 red 1 0 \n4 9.4 5 red 1 0 \n\n[5 rows x 15 columns]"
}
],
"language": "python",
"trusted": true,
"collapsed": false
},
{
"metadata": {},
"cell_type": "code",
"input": "def best_k(df, features):\n # Randomly sample 70% training data, 30% testing data\n test_idx = np.random.uniform(0,1, len(df)) <= 0.3\n train = df[test_idx==True]\n test = df[test_idx==False]\n\n # List of features we want to test\n features = ['density', 'sulphates', 'residual_sugar']\n results = []\n for n in range(1,51,2):\n clf = KNeighborsClassifier(n_neighbors=n)\n clf.fit(train[features], train['high_quality'])\n preds = clf.predict(test[features])\n # If True = 1, if False = 0\n accuracy = np.where(preds==test['high_quality'], 1, 0).sum() / float(len(test))\n\n results.append([n, accuracy])\n\n results = pd.DataFrame(results, columns = ['n', 'accuracy'])\n \n plot = ggplot(results, aes(\"n\", \"accuracy\"))+geom_line()\n return [results, test, train, plot]\n\n# Features we want to use\nfeatures = ['density', 'sulphates', 'residual_sugar']\n\noptimal_k_model = best_k(df, features)\noptimal_k_model[3]",
"prompt_number": 4,
"outputs": [
{
"output_type": "display_data",
"metadata": {},
"png": 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W4uLijI4DAAB8GCf6wWN27Nih++67T//+97/Vt29fo+MAAAAfRzGFR3z11Vf6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\nTkSuQmCC\n",
"text": "<matplotlib.figure.Figure at 0x10bb26050>"
},
{
"output_type": "pyout",
"prompt_number": 4,
"metadata": {},
"text": "<ggplot: (280700665)>"
}
],
"language": "python",
"trusted": true,
"collapsed": false
},
{
"metadata": {},
"cell_type": "markdown",
"source": "As we can see from this plot, the `k` with the best accuracy can be found at around **37**, however, accuracy doesn't really improve much after `k = ~9`. Most of the trend is caught at this `k` level, and beyond that we risk overfitting. Again, there are more sophisticated ways of doing this, but that will be discussed later.\n\nIn general, a larger `k` suppresses the effects of the noise, but makes the classification boundaries less distinct, which is really our actual goal.\n<hr>"
},
{
"metadata": {},
"cell_type": "heading",
"source": "Distance between points",
"level": 2
},
{
"metadata": {},
"cell_type": "markdown",
"source": "Remember, KNN classification utilizes the idea that each observation is represented by it's features in N-dimensional space. Therefore we can compute the distance between the observations (in several ways). The second most important parameter in KNN models is how you choose to calculate the distance between the points.\n\nThe most common distance functions are `uniform`, which weights everything equally, and `distance`, which weights the points based on the inverse distance between them. There are many other ways to do this as well. \n\nLet's compare the results of all three of these types of weights:"
},
{
"metadata": {},
"cell_type": "code",
"input": "def KNN(train_data, test_data, k):\n results = []\n\n for w in ['uniform','distance', lambda x: np.log(x)]:\n # Run with 9 neighbors\n clf = KNeighborsClassifier(k, weights=w)\n w = str(w)\n clf.fit(train_data[features], train_data['high_quality'])\n prediction = clf.predict(test_data[features])\n accuracy = np.where(prediction==test_data['high_quality'], 1, 0).sum() / float(len(test_data))\n\n results.append([w, accuracy])\n\n results = pd.DataFrame(results, columns = ['weight_method', 'accuracy'])\n\n return results\n\ntrain = optimal_k_model[2]\ntest = optimal_k_model[1]\n\nKNN(train, test, 9)",
"prompt_number": 5,
"outputs": [
{
"output_type": "pyout",
"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>weight_method</th>\n <th>accuracy</th>\n </tr>\n </thead>\n <tbody>\n <tr>\n <th>0</th>\n <td> uniform</td>\n <td> 0.794598</td>\n </tr>\n <tr>\n <th>1</th>\n <td> distance</td>\n <td> 0.798301</td>\n </tr>\n <tr>\n <th>2</th>\n <td> &lt;function &lt;lambda&gt; at 0x10bee7500&gt;</td>\n <td> 0.809192</td>\n </tr>\n </tbody>\n</table>\n<p>3 rows × 2 columns</p>\n</div>",
"metadata": {},
"prompt_number": 5,
"text": " weight_method accuracy\n0 uniform 0.794598\n1 distance 0.798301\n2 <function <lambda> at 0x10bee7500> 0.809192\n\n[3 rows x 2 columns]"
}
],
"language": "python",
"trusted": true,
"collapsed": false
},
{
"metadata": {},
"cell_type": "markdown",
"source": "Pretty slick. Though ideally, we would select the weighting scheme based on some intuition about our data. Should there be more feature influence at shorter distances? When it doubt, inverse distance is a good place to start, because it helps avoid problems of \"majority voting\" where one class dominates the data."
},
{
"metadata": {},
"cell_type": "heading",
"source": "Standardization",
"level": 2
},
{
"metadata": {},
"cell_type": "markdown",
"source": "You may have noticed something about these above examples. We have been mixing up our units (this is bad) by summing up volumes, weights, densities and concentrations.\n\nLet's take a look at our data again to understand whats happening here."
},
{
"metadata": {},
"cell_type": "code",
"input": "df[features].describe()",
"prompt_number": 359,
"outputs": [
{
"output_type": "pyout",
"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>density</th>\n <th>sulphates</th>\n <th>residual_sugar</th>\n </tr>\n </thead>\n <tbody>\n <tr>\n <th>count</th>\n <td> 6497.000000</td>\n <td> 6497.000000</td>\n <td> 6497.000000</td>\n </tr>\n <tr>\n <th>mean</th>\n <td> 0.994697</td>\n <td> 0.531268</td>\n <td> 5.443235</td>\n </tr>\n <tr>\n <th>std</th>\n <td> 0.002999</td>\n <td> 0.148806</td>\n <td> 4.757804</td>\n </tr>\n <tr>\n <th>min</th>\n <td> 0.987110</td>\n <td> 0.220000</td>\n <td> 0.600000</td>\n </tr>\n <tr>\n <th>25%</th>\n <td> 0.992340</td>\n <td> 0.430000</td>\n <td> 1.800000</td>\n </tr>\n <tr>\n <th>50%</th>\n <td> 0.994890</td>\n <td> 0.510000</td>\n <td> 3.000000</td>\n </tr>\n <tr>\n <th>75%</th>\n <td> 0.996990</td>\n <td> 0.600000</td>\n <td> 8.100000</td>\n </tr>\n <tr>\n <th>max</th>\n <td> 1.038980</td>\n <td> 2.000000</td>\n <td> 65.800000</td>\n </tr>\n </tbody>\n</table>\n<p>8 rows × 3 columns</p>\n</div>",
"metadata": {},
"prompt_number": 359,
"text": " density sulphates residual_sugar\ncount 6497.000000 6497.000000 6497.000000\nmean 0.994697 0.531268 5.443235\nstd 0.002999 0.148806 4.757804\nmin 0.987110 0.220000 0.600000\n25% 0.992340 0.430000 1.800000\n50% 0.994890 0.510000 3.000000\n75% 0.996990 0.600000 8.100000\nmax 1.038980 2.000000 65.800000\n\n[8 rows x 3 columns]"
}
],
"language": "python",
"trusted": true,
"collapsed": false
},
{
"metadata": {},
"cell_type": "markdown",
"source": "If we are computing distance between these features in 3D space, we can see that small changes in density and sulphates, will equate to large changes in residual sugar content. This, in essence, makes residual sugar seem more important if we are treating the units equally, since the numbers are simply larger.\n\nThe solution is **standardization**, commonly referred to as **normalization** in statistics, whereby we adjust features on different scales to a single common scale. There are also various types of normalization, <a href=\"http://en.wikipedia.org/wiki/Normalization_(statistics)\">see here</a>.\n\nA common technique for normalizing **Z-score** (standard-score) values, which measures how far away from the mean a value is in terms of units of standard deviation. To do this we subtract the mean of each feature, then scale it by dividing it by their standard deviation. This can be implemented quite easily by hand, or also by using **`scale`** from `sklearns's` **`preprocessing`** library."
},
{
"metadata": {},
"cell_type": "code",
"input": "from sklearn import preprocessing\n\n# Let's standardize our data from before we put in into our functions\n# Remove color column because it is text and we arnt using it anyways.\n#del df['color']\ntmp = preprocessing.scale(df[features])\n# Re add the column names back\ndf_norm = pd.DataFrame(tmp, columns = list(df[features].columns.values))\n# Re-join the \"High Quality\" feature we are trying to predict\ndf_norm['high_quality'] = df['high_quality'] \n\n# Ignore some pesky warning about color not being an integer, thanks anyways pandas.\nimport warnings\nwarnings.simplefilter(action = \"ignore\", category = DeprecationWarning)\n\ndf_norm.describe()",
"prompt_number": 54,
"outputs": [
{
"output_type": "pyout",
"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>density</th>\n <th>sulphates</th>\n <th>residual_sugar</th>\n <th>high_quality</th>\n </tr>\n </thead>\n <tbody>\n <tr>\n <th>count</th>\n <td> 6.497000e+03</td>\n <td> 6.497000e+03</td>\n <td> 6.497000e+03</td>\n <td> 6497.000000</td>\n </tr>\n <tr>\n <th>mean</th>\n <td> 2.261421e-12</td>\n <td>-5.293322e-15</td>\n <td>-2.203802e-15</td>\n <td> 0.196552</td>\n </tr>\n <tr>\n <th>std</th>\n <td> 1.000077e+00</td>\n <td> 1.000077e+00</td>\n <td> 1.000077e+00</td>\n <td> 0.397421</td>\n </tr>\n <tr>\n <th>min</th>\n <td>-2.530192e+00</td>\n <td>-2.091935e+00</td>\n <td>-1.018034e+00</td>\n <td> 0.000000</td>\n </tr>\n <tr>\n <th>25%</th>\n <td>-7.859527e-01</td>\n <td>-6.805919e-01</td>\n <td>-7.657978e-01</td>\n <td> 0.000000</td>\n </tr>\n <tr>\n <th>50%</th>\n <td> 6.448888e-02</td>\n <td>-1.429373e-01</td>\n <td>-5.135612e-01</td>\n <td> 0.000000</td>\n </tr>\n <tr>\n <th>75%</th>\n <td> 7.648525e-01</td>\n <td> 4.619241e-01</td>\n <td> 5.584445e-01</td>\n <td> 0.000000</td>\n </tr>\n <tr>\n <th>max</th>\n <td> 1.476879e+01</td>\n <td> 9.870879e+00</td>\n <td> 1.268682e+01</td>\n <td> 1.000000</td>\n </tr>\n </tbody>\n</table>\n<p>8 rows × 4 columns</p>\n</div>",
"metadata": {},
"prompt_number": 54,
"text": " density sulphates residual_sugar high_quality\ncount 6.497000e+03 6.497000e+03 6.497000e+03 6497.000000\nmean 2.261421e-12 -5.293322e-15 -2.203802e-15 0.196552\nstd 1.000077e+00 1.000077e+00 1.000077e+00 0.397421\nmin -2.530192e+00 -2.091935e+00 -1.018034e+00 0.000000\n25% -7.859527e-01 -6.805919e-01 -7.657978e-01 0.000000\n50% 6.448888e-02 -1.429373e-01 -5.135612e-01 0.000000\n75% 7.648525e-01 4.619241e-01 5.584445e-01 0.000000\nmax 1.476879e+01 9.870879e+00 1.268682e+01 1.000000\n\n[8 rows x 4 columns]"
}
],
"language": "python",
"trusted": true,
"collapsed": false
},
{
"metadata": {},
"cell_type": "markdown",
"source": "Now lets repeat our KNN classification we did above with our newly standardized data and see what happens."
},
{
"metadata": {},
"cell_type": "code",
"input": "# Find optimal k value\noptimal_k_model = best_k(df_norm, features)\n# Display the plot\noptimal_k_model[3]",
"prompt_number": 58,
"outputs": [
{
"output_type": "display_data",
"metadata": {},
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aiYh0RF5eHr755husXLkS1atXL/f548aNg6GhIXbu3KmBdKQNJSUlmDlzJrKzs3Hu3DnE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\nAAAASUVORK5CYII=\n",
"text": "<matplotlib.figure.Figure at 0x10bb26290>"
},
{
"output_type": "pyout",
"prompt_number": 58,
"metadata": {},
"text": "<ggplot: (280696437)>"
}
],
"language": "python",
"trusted": true,
"collapsed": false
},
{
"metadata": {},
"cell_type": "markdown",
"source": "Looks like our `k` is still pretty close to before standardization, where a `k = ~9` appears to capture most of the trend."
},
{
"metadata": {},
"cell_type": "code",
"input": "# Fit KNN function with different weights\n# Extract the data from optimal_k_model\ntrain = optimal_k_model[2]\ntest = optimal_k_model[1]\nKNN(train, test, 9)",
"prompt_number": 60,
"outputs": [
{
"output_type": "pyout",
"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>weight_method</th>\n <th>accuracy</th>\n </tr>\n </thead>\n <tbody>\n <tr>\n <th>0</th>\n <td> uniform</td>\n <td> 0.807540</td>\n </tr>\n <tr>\n <th>1</th>\n <td> distance</td>\n <td> 0.826499</td>\n </tr>\n <tr>\n <th>2</th>\n <td> &lt;function &lt;lambda&gt; at 0x10c4dc7d0&gt;</td>\n <td> 0.804674</td>\n </tr>\n </tbody>\n</table>\n<p>3 rows × 2 columns</p>\n</div>",
"metadata": {},
"prompt_number": 60,
"text": " weight_method accuracy\n0 uniform 0.807540\n1 distance 0.826499\n2 <function <lambda> at 0x10c4dc7d0> 0.804674\n\n[3 rows x 2 columns]"
}
],
"language": "python",
"trusted": true,
"collapsed": false
},
{
"metadata": {},
"cell_type": "markdown",
"source": "Great! We can see that our model improves after standardization, this is because the distances are more accurately abstracted in 3D space.\n\nLets go full circle now and predict if a **new** wine will be high quality or not using our model."
},
{
"metadata": {},
"cell_type": "code",
"input": "# Sulphates, Residual sugar, and density of two new unseen wine to pass to our model\nnew_wine_features = [[0.66, 0.6, 0.92], [2.66, 2.1, 0.8]]\n\n# Going through the motions again for repitition, \n# normally we would just take our model computed from above\n\ntrain = optimal_k_model[2]\ntest = optimal_k_model[1]\n\nclf = KNeighborsClassifier(9, \"distance\")\nclf.fit(train[features], train['high_quality'])\n\nprint clf.get_params()\nprint clf.predict(new_wine_features)",
"prompt_number": 98,
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": "{'n_neighbors': 9, 'algorithm': 'auto', 'metric': 'minkowski', 'p': 2, 'weights': 'distance', 'leaf_size': 30}\n[ 0. 1.]\n"
}
],
"language": "python",
"trusted": true,
"collapsed": false
},
{
"metadata": {},
"cell_type": "markdown",
"source": "Where 0 is not high quality and 1 is high quality."
},
{
"metadata": {},
"cell_type": "code",
"input": "from IPython.core.display import HTML\n\n\ndef social():\n code = \"\"\"\n <a style='float:left; margin-right:5px;' href=\"https://twitter.com/share\" class=\"twitter-share-button\" data-text=\"Check this out\" data-via=\"Ryanmdk\">Tweet</a>\n<script>!function(d,s,id){var js,fjs=d.getElementsByTagName(s)[0],p=/^http:/.test(d.location)?'http':'https';if(!d.getElementById(id)){js=d.createElement(s);js.id=id;js.src=p+'://platform.twitter.com/widgets.js';fjs.parentNode.insertBefore(js,fjs);}}(document, 'script', 'twitter-wjs');</script>\n <a style='float:left; margin-right:5px;' href=\"https://twitter.com/Ryanmdk\" class=\"twitter-follow-button\" data-show-count=\"false\">Follow @Ryanmdk</a>\n<script>!function(d,s,id){var js,fjs=d.getElementsByTagName(s)[0],p=/^http:/.test(d.location)?'http':'https';if(!d.getElementById(id)){js=d.createElement(s);js.id=id;js.src=p+'://platform.twitter.com/widgets.js';fjs.parentNode.insertBefore(js,fjs);}}(document, 'script', 'twitter-wjs');</script>\n <a style='float:left; margin-right:5px;'target='_parent' href=\"http://www.reddit.com/submit\" onclick=\"window.location = 'http://www.reddit.com/submit?url=' + encodeURIComponent(window.location); return false\"> <img src=\"http://www.reddit.com/static/spreddit7.gif\" alt=\"submit to reddit\" border=\"0\" /> </a>\n<script src=\"//platform.linkedin.com/in.js\" type=\"text/javascript\">\n lang: en_US\n</script>\n<script type=\"IN/Share\"></script>\n\"\"\"\n return HTML(code)",
"prompt_number": 2,
"outputs": [],
"language": "python",
"trusted": true,
"collapsed": false
},
{
"metadata": {},
"cell_type": "code",
"input": "from IPython.core.display import HTML\n\n\ndef css_styling():\n styles = open(\"/users/ryankelly/desktop/custom_notebook.css\", \"r\").read()\n\n return HTML(styles)\ncss_styling()\n",
"prompt_number": 3,
"outputs": [
{
"output_type": "pyout",
"html": "\n<style>\nbody {\n font-family: Century Gothic, sans;\n\n}\n\n\ndiv.text_cell_render h1 { /* Main titles bigger, centered */\nfont-size: 2.2em;\nline-height:1.4em;\ntext-align:center;\n}\n\n/*Input and output cells formatting*/\ndiv.prompt.input_prompt, div.prompt.output_prompt {\n visibility: hidden;\n /*font-family: Consolas;*/\n color: #575748;\n /*background-color: #CCCCCC;*/\n border: 0px;\n width: 6.5em;\n float:left;\n}\n\n\ndiv.output_subarea.output_text.output_stream.output_stdout,div.output_subarea.output_text {\n margin-left: 1.5em;\n padding-top: 1em;\n padding-bottom: 0.5em;\n margin-top: 8px; /*This is for getting the box-shadow property of the parent to display properly;*/\n}\n\ndiv.cell { /* Tunes the space between cells */\nmargin-top:1em;\nmargin-bottom:1em;\nwidth:100%;\nmargin-right:auto;\noverflow-x:hidden;\n}\n\ndiv.text_cell_render{\n overflow-x:hidden;\n \n}\n\n\ndiv.input{\nmargin-right:1%;\n}\n\n</style>\n \n\n\n\n",
"metadata": {},
"prompt_number": 3,
"text": "<IPython.core.display.HTML at 0x103494490>"
}
],
"language": "python",
"trusted": true,
"collapsed": false
},
{
"metadata": {},
"cell_type": "code",
"input": "",
"outputs": [],
"language": "python",
"trusted": true,
"collapsed": false
}
],
"metadata": {}
}
],
"metadata": {
"gist_id": "cbf1005e75cd04ad6921",
"name": "",
"signature": "sha256:e1aafb79027758ba534ee846a120693df82f28e4da8fefaade050b57c1a811c1"
},
"nbformat": 3
}
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