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Created November 25, 2014 10:40
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Bias and Variance in Python
{
"metadata": {
"name": "manu.ipynb"
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"nbformat": 3,
"nbformat_minor": 0,
"worksheets": [
{
"cells": [
{
"cell_type": "code",
"collapsed": false,
"input": [
"%matplotlib inline\n",
"#Importing libraries and intial set up. \n",
"import numpy as np\n",
"import matplotlib.pyplot as plt\n",
"import pandas as pd\n",
"import brewer2mpl\n",
"from matplotlib import rcParams\n",
"\n",
"#colorbrewer2 Dark2 qualitative color table\n",
"dark2_cmap = brewer2mpl.get_map('Dark2', 'Qualitative', 7)\n",
"dark2_colors = dark2_cmap.mpl_colors\n",
"\n",
"rcParams['figure.figsize'] = (10, 6)\n",
"rcParams['figure.dpi'] = 150\n",
"rcParams['axes.color_cycle'] = dark2_colors\n",
"rcParams['lines.linewidth'] = 2\n",
"rcParams['axes.facecolor'] = 'white'\n",
"rcParams['font.size'] = 14\n",
"rcParams['patch.edgecolor'] = 'white'\n",
"rcParams['patch.facecolor'] = dark2_colors[0]\n",
"rcParams['font.family'] = 'StixGeneral'\n",
"\n",
"\n",
"def remove_border(axes=None, top=False, right=False, left=True, bottom=True):\n",
" \"\"\"\n",
" Minimize chartjunk by stripping out unnecesasry plot borders and axis ticks\n",
" \n",
" The top/right/left/bottom keywords toggle whether the corresponding plot border is drawn\n",
" \"\"\"\n",
" ax = axes or plt.gca()\n",
" ax.spines['top'].set_visible(top)\n",
" ax.spines['right'].set_visible(right)\n",
" ax.spines['left'].set_visible(left)\n",
" ax.spines['bottom'].set_visible(bottom)\n",
" \n",
" #turn off all ticks\n",
" ax.yaxis.set_ticks_position('none')\n",
" ax.xaxis.set_ticks_position('none')\n",
" \n",
" #now re-enable visibles\n",
" if top:\n",
" ax.xaxis.tick_top()\n",
" if bottom:\n",
" ax.xaxis.tick_bottom()\n",
" if left:\n",
" ax.yaxis.tick_left()\n",
" if right:\n",
" ax.yaxis.tick_right()\n",
" \n",
"pd.set_option('display.width', 500)\n",
"pd.set_option('display.max_columns', 100)\n",
"import warnings\n",
"warnings.filterwarnings('ignore', message='Polyfit*')"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stderr",
"text": [
"C:\\Users\\Manu\\AppData\\Local\\Enthought\\Canopy\\User\\lib\\site-packages\\pandas\\io\\excel.py:626: UserWarning: Installed openpyxl is not supported at this time. Use >=1.6.1 and <2.0.0.\n",
" .format(openpyxl_compat.start_ver, openpyxl_compat.stop_ver))\n"
]
}
],
"prompt_number": 1
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"import random\n",
"import copy\n",
"def scatter_by(df, scatterx, scattery, by=None, figure=None, axes=None, colorscale=dark2_cmap, labeler={}, mfunc=None, setupfunc=None, mms=8):\n",
" cs=copy.deepcopy(colorscale.mpl_colors)\n",
" if not figure:\n",
" figure=plt.figure(figsize=(8,8))\n",
" if not axes:\n",
" axes=figure.gca()\n",
" x=df[scatterx]\n",
" y=df[scattery]\n",
" if not by:\n",
" col=random.choice(cs)\n",
" axes.scatter(x, y, cmap=colorscale, c=col)\n",
" if setupfunc:\n",
" axeslist=setupfunc(axes, figure)\n",
" else:\n",
" axeslist=[axes]\n",
" if mfunc:\n",
" mfunc(axeslist,x,y,color=col, mms=mms)\n",
" else:\n",
" cs=list(np.linspace(0,1,len(df.groupby(by))))\n",
" xlimsd={}\n",
" ylimsd={}\n",
" xs={}\n",
" ys={}\n",
" cold={}\n",
" for k,g in df.groupby(by):\n",
" col=cs.pop()\n",
" x=g[scatterx]\n",
" y=g[scattery]\n",
" xs[k]=x\n",
" ys[k]=y\n",
" c=colorscale.mpl_colormap(col)\n",
" cold[k]=c\n",
" axes.scatter(x, y, c=c, label=labeler.get(k,k), s=40, alpha=0.3);\n",
" xlimsd[k]=axes.get_xlim()\n",
" ylimsd[k]=axes.get_ylim()\n",
" xlims=[min([xlimsd[k][0] for k in xlimsd.keys()]), max([xlimsd[k][1] for k in xlimsd.keys()])]\n",
" ylims=[min([ylimsd[k][0] for k in ylimsd.keys()]), max([ylimsd[k][1] for k in ylimsd.keys()])]\n",
" axes.set_xlim(xlims)\n",
" axes.set_ylim(ylims)\n",
" if setupfunc:\n",
" axeslist=setupfunc(axes, figure)\n",
" else:\n",
" axeslist=[axes]\n",
" if mfunc:\n",
" for k in xs.keys():\n",
" mfunc(axeslist,xs[k],ys[k],color=cold[k], mms=mms);\n",
" axes.set_xlabel(scatterx);\n",
" axes.set_ylabel(scattery);\n",
" \n",
" return axes\n",
"\n",
"def make_rug(axeslist, x, y, color='b', mms=8):\n",
" axes=axeslist[0]\n",
" zerosx1=np.zeros(len(x))\n",
" zerosx2=np.zeros(len(x))\n",
" xlims=axes.get_xlim()\n",
" ylims=axes.get_ylim()\n",
" zerosx1.fill(ylims[1])\n",
" zerosx2.fill(xlims[1])\n",
" axes.plot(x, zerosx1, marker='|', color=color, ms=mms)\n",
" axes.plot(zerosx2, y, marker='_', color=color, ms=mms)\n",
" axes.set_xlim(xlims)\n",
" axes.set_ylim(ylims)\n",
" return axes"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 2
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"np.random.seed(42) #random number seed"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 3
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"def rmse(p,x,y):\n",
" yfit = np.polyval(p, x)\n",
" return np.sqrt(np.mean((y - yfit) ** 2))\n",
"\n",
"def generate_curve(x, sigma):\n",
" return np.random.normal(10 - 1. / (x + 0.1), sigma)\n",
"x = 10 ** np.linspace(-2, 0, 8)\n",
"intrinsic_error=1.\n",
"y=generate_curve(x, intrinsic_error)\n",
"plt.scatter(x,y)"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 4,
"text": [
"<matplotlib.collections.PathCollection at 0x7e50e48>"
]
},
{
"metadata": {},
"output_type": "display_data",
"png": 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"text": [
"<matplotlib.figure.Figure at 0x748f9b0>"
]
}
],
"prompt_number": 4
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"x_new=np.linspace(-0.2, 1.2, 1000)\n",
"plt.scatter(x,y, s=50)\n",
"f1=np.polyfit(x,y,1)\n",
"plt.plot(x_new,np.polyval(f1,x_new))\n",
"print \"d=1, rmse=\",rmse(f1,x,y)\n",
"f2=np.polyfit(x,y,2)\n",
"plt.plot(x_new,np.polyval(f2,x_new))\n",
"print \"d=2, rmse=\",rmse(f2,x,y)\n",
"f4=np.polyfit(x,y,4)\n",
"plt.plot(x_new,np.polyval(f4,x_new))\n",
"print \"d=4, rmse=\",rmse(f4,x,y)\n",
"f6=np.polyfit(x,y,6)\n",
"plt.plot(x_new,np.polyval(f6,x_new))\n",
"print \"d=6, rmse=\",rmse(f6,x,y)\n",
"plt.xlim(-0.2, 1.2)\n",
"plt.ylim(-1, 12)"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
"d=1, rmse= 1.70524942832\n",
"d=2, rmse= 0.89214139212\n",
"d=4, rmse= 0.496552676012\n",
"d=6, rmse= 0.132980700607\n"
]
},
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 5,
"text": [
"(-1, 12)"
]
},
{
"metadata": {},
"output_type": "display_data",
"png": 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KK65o9CKaEylMg1JlEXVUIU6RFdGHqgEsx7dTNPdRzHuWA6BN6UTipOeJHPDn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tf3D1MRNukRt96rppqzU0Sq0LBtApGrQgzslK4UEyoH2qsr0HTgT4AkIS5zg6rn9d3KpUtdZ/gN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\nTkSuQmCC\n",
"text": [
"<matplotlib.figure.Figure at 0x7e602e8>"
]
}
],
"prompt_number": 5
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"N = 200\n",
"x = np.random.random(N)\n",
"y = generate_curve(x, intrinsic_error)\n",
"plt.scatter(x,y)"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 6,
"text": [
"<matplotlib.collections.PathCollection at 0xa788c50>"
]
},
{
"metadata": {},
"output_type": "display_data",
"png": 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R3r3++wcPRqYN4apYpqFtW+MM7VL0+SbYmM3yuj62N5uMhT1XRNUo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\n",
"text": [
"<matplotlib.figure.Figure at 0x7ea4198>"
]
}
],
"prompt_number": 6
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"from sklearn.cross_validation import train_test_split\n",
"xtrain, xtest, ytrain, ytest = train_test_split(x, y, train_size=0.6)\n",
"plt.scatter(xtrain, ytrain, color='red')\n",
"plt.scatter(xtest, ytest, color='blue')"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 7,
"text": [
"<matplotlib.collections.PathCollection at 0xb16a940>"
]
},
{
"metadata": {},
"output_type": "display_data",
"png": 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KJK0lJspmgvXrgzyIEBs4UClALUJHl9eZUnHIUqnRnSQhjEodf+MWVmgnCpbq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\nDwCq960pOgAAAABJRU5ErkJggg==\n",
"text": [
"<matplotlib.figure.Figure at 0xa778470>"
]
}
],
"prompt_number": 7
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"ds = np.arange(21)\n",
"train_err = np.zeros(len(ds))\n",
"test_err = np.zeros(len(ds))\n",
"\n",
"for i,d in enumerate(ds):\n",
" p = np.polyfit(xtrain, ytrain, d)\n",
"\n",
" train_err[i] = rmse(p, xtrain, ytrain)\n",
" test_err[i] = rmse(p, xtest, ytest)"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 8
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"fig, ax = plt.subplots()\n",
"ax.plot(ds, test_err, lw=2, label = 'test error')\n",
"ax.plot(ds, train_err, lw=2, label = 'training error')\n",
"ax.legend(loc=0)\n",
"ax.set_xlabel('degree of fit')\n",
"ax.set_ylabel('rms error')"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 9,
"text": [
"<matplotlib.text.Text at 0xb1874e0>"
]
},
{
"metadata": {},
"output_type": "display_data",
"png": 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rr2PRokUNjl24cKH5dUJCAhISEhocMza6N1Yf240fcn9H0k13NRjE4BneG5fB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\n",
"text": [
"<matplotlib.figure.Figure at 0xa79d780>"
]
}
],
"prompt_number": 9
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"#taken lock stock and barrel from Vanderplas.\n",
"def plot_learning_curve(d):\n",
" sizes = np.linspace(2, N, 50).astype(int)\n",
" train_err = np.zeros(sizes.shape)\n",
" crossval_err = np.zeros(sizes.shape)\n",
"\n",
" for i, size in enumerate(sizes):\n",
" # Train on only the first `size` points\n",
" p = np.polyfit(xtrain[:size], ytrain[:size], d)\n",
" \n",
" # Validation error is on the *entire* validation set\n",
" crossval_err[i] = rmse(p, xtest, ytest)\n",
" \n",
" # Training error is on only the points used for training\n",
" train_err[i] = rmse(p, xtrain[:size], ytrain[:size])\n",
"\n",
" fig, ax = plt.subplots()\n",
" ax.plot(sizes, crossval_err, lw=2, label='validation error')\n",
" ax.plot(sizes, train_err, lw=2, label='training error')\n",
" ax.plot([0, N], [intrinsic_error, intrinsic_error], '--k', label='intrinsic error')\n",
"\n",
" ax.set_xlabel('training set size')\n",
" ax.set_ylabel('rms error')\n",
" \n",
" ax.legend(loc=0)\n",
" \n",
" ax.set_xlim(0, 99)\n",
"\n",
" ax.set_title('d = %i' % d)"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 10
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"plot_learning_curve(d=1)\n",
"plt.ylim(0, 10)"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 11,
"text": [
"(0, 10)"
]
},
{
"metadata": {},
"output_type": "display_data",
"png": 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yWbNmzWHvOyMjw8TExJh7773XzJo1y9x8883G6/WarVu3mltvvdU4HA4zevRo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\nAAAAAElFTkSuQmCC\n",
"text": [
"<matplotlib.figure.Figure at 0xb17e1d0>"
]
}
],
"prompt_number": 11
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"plot_learning_curve(d=20)\n",
"plt.ylim(0, 10)"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 12,
"text": [
"(0, 10)"
]
},
{
"metadata": {},
"output_type": "display_data",
"png": 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RRx6RVDS1SPPmzXX48GHFxcUpMjJS8fHx6tKli7p06SJJ2rVrlyTp1ltvVUBA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\nxUHKGRYtZ/iJ136lXpc8mPgTAHAmCF7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1lJeXf/d3EEop9aNp4aWUUkoplSS61KiUUkop\nlSRaeCmllFJKJYkWXkoppZRSSaKFl1JKKaVUkmjhpZRSSimVJH8CnEyMcvNZWcwAAAAASUVORK5C\nYII=\n",
"text": [
"<matplotlib.figure.Figure at 0xb17b320>"
]
}
],
"prompt_number": 12
}
],
"metadata": {}
}
]
}
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