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@akelleh
Created June 26, 2015 18:49
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{
"worksheets": [
{
"cells": [
{
"metadata": {},
"cell_type": "code",
"input": "import nonparametric\nimport scipy\nimport numpy\nimport pandas as pd\nimport matplotlib.pyplot as pp\nimport sklearn.linear_model",
"prompt_number": 205,
"outputs": [],
"language": "python",
"trusted": true,
"collapsed": false
},
{
"metadata": {},
"cell_type": "heading",
"source": "RTP: The precision and recall of an estimator improves when you take advantage of factoring the joint distribution",
"level": 4
},
{
"metadata": {},
"cell_type": "code",
"input": "# Generate the data with the structure of a chain\nx1 = numpy.random.normal(size=1000)\nx2 = numpy.random.normal(loc=x1)\nx3 = numpy.random.normal(loc=x2)\nx4 = numpy.random.normal(loc=x3)\nX_train = pd.DataFrame({ 'x1' : x1, 'x2' : x2, 'x3' : x3, 'x4' : x4 })\n",
"prompt_number": 241,
"outputs": [],
"language": "python",
"trusted": true,
"collapsed": false
},
{
"metadata": {},
"cell_type": "code",
"input": "X_train.corr()",
"prompt_number": 230,
"outputs": [
{
"text": " x1 x2 x3 x4\nx1 1.000000 0.859751 0.821837 0.822629\nx2 0.859751 1.000000 0.954007 0.785530\nx3 0.821837 0.954007 1.000000 0.863840\nx4 0.822629 0.785530 0.863840 1.000000",
"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>x1</th>\n <th>x2</th>\n <th>x3</th>\n <th>x4</th>\n </tr>\n </thead>\n <tbody>\n <tr>\n <th>x1</th>\n <td> 1.000000</td>\n <td> 0.859751</td>\n <td> 0.821837</td>\n <td> 0.822629</td>\n </tr>\n <tr>\n <th>x2</th>\n <td> 0.859751</td>\n <td> 1.000000</td>\n <td> 0.954007</td>\n <td> 0.785530</td>\n </tr>\n <tr>\n <th>x3</th>\n <td> 0.821837</td>\n <td> 0.954007</td>\n <td> 1.000000</td>\n <td> 0.863840</td>\n </tr>\n <tr>\n <th>x4</th>\n <td> 0.822629</td>\n <td> 0.785530</td>\n <td> 0.863840</td>\n <td> 1.000000</td>\n </tr>\n </tbody>\n</table>\n</div>",
"output_type": "pyout",
"metadata": {},
"prompt_number": 230
}
],
"language": "python",
"trusted": true,
"collapsed": false
},
{
"metadata": {},
"cell_type": "code",
"input": "X_train.plot('x1','x2', style='bo', xlim=(-3,3), ylim=(-3,3)); pp.ylabel('x2')\nX_train.plot('x1','x3', style='bo', xlim=(-3,3), ylim=(-3,3)); pp.ylabel('x3')\nX_train.plot('x1','x4', style='bo', xlim=(-3,3), ylim=(-3,3));pp.ylabel('x4')",
"prompt_number": 245,
"outputs": [
{
"text": "<matplotlib.text.Text at 0x10afbb190>",
"output_type": "pyout",
"metadata": {},
"prompt_number": 245
},
{
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W6hICrrlGegZZpavoKbR7YFz1pKc/xFNPzY25naamc5bb7SblwarVx4RovFAi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\nDvTFXGIpeC8LuuvdYyuRjOp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pnwaPU6uNPR289mbMhegBmpsJK8dofAZrCI9S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"text": "<matplotlib.figure.Figure at 0x10993ea50>",
"output_type": "display_data",
"metadata": {}
},
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nECPF5p+qB80+iZTT1lae0tA46eh7gEDASHOwkXfe6aOtrQJpFtlsOUpF7yBD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"text": "<matplotlib.figure.Figure at 0x10af0eb90>",
"output_type": "display_data",
"metadata": {}
},
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udz0GB8+ir+8K+vsFDxan8xG4XGuRlTU7RCG8+WY3/vrXjQgE+Pq67A/+/Plr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\nRK5CYII=\n",
"text": "<matplotlib.figure.Figure at 0x10af294d0>",
"output_type": "display_data",
"metadata": {}
}
],
"language": "python",
"trusted": true,
"collapsed": false
},
{
"metadata": {},
"cell_type": "code",
"input": "regs = [{'x' : ['x1'], 'y' : 'x2'},\n {'x' : ['x2'], 'y' : 'x3'},\n {'x' : ['x3'], 'y' : 'x4'}]\nfor reg in regs:\n reg['reg'] = sklearn.linear_model.LinearRegression()\n reg['reg'].fit(X_train[reg['x']], y=X_train[reg['y']])",
"prompt_number": 232,
"outputs": [],
"language": "python",
"trusted": true,
"collapsed": false
},
{
"metadata": {},
"cell_type": "code",
"input": "regs",
"prompt_number": 233,
"outputs": [
{
"text": "[{'reg': LinearRegression(copy_X=True, fit_intercept=True, normalize=False),\n 'x': ['x1'],\n 'y': 'x2'},\n {'reg': LinearRegression(copy_X=True, fit_intercept=True, normalize=False),\n 'x': ['x2'],\n 'y': 'x3'},\n {'reg': LinearRegression(copy_X=True, fit_intercept=True, normalize=False),\n 'x': ['x3'],\n 'y': 'x4'}]",
"output_type": "pyout",
"metadata": {},
"prompt_number": 233
}
],
"language": "python",
"trusted": true,
"collapsed": false
},
{
"metadata": {},
"cell_type": "code",
"input": "# Generate the data with the structure of a chain\nx1 = numpy.random.normal(size=50)\nx2 = numpy.random.normal(loc=x1)\nx3 = numpy.random.normal(loc=x2)\nx4 = numpy.random.normal(loc=x3)\nX_test = pd.DataFrame({ 'x1' : x1, 'x2' : x2, 'x3' : x3, 'x4' : x4 })",
"prompt_number": 234,
"outputs": [],
"language": "python",
"trusted": true,
"collapsed": false
},
{
"metadata": {},
"cell_type": "code",
"input": "preds = []\nx2_pred = regs[0]['reg'].predict(X_test[['x1']])\nx3_pred = regs[1]['reg'].predict([[xi] for xi in x2_pred])\nx4_pred = regs[2]['reg'].predict([[xi] for xi in X_test['x3']])",
"prompt_number": 235,
"outputs": [],
"language": "python",
"trusted": true,
"collapsed": false
},
{
"metadata": {},
"cell_type": "code",
"input": "rms_error = numpy.sqrt(sum([(predi - actuali)**2. for predi, actuali in zip(x4_pred,X_test['x4'])]))\nrms_error",
"prompt_number": 236,
"outputs": [
{
"text": "9.7146923172892059",
"output_type": "pyout",
"metadata": {},
"prompt_number": 236
}
],
"language": "python",
"trusted": true,
"collapsed": false
},
{
"metadata": {},
"cell_type": "code",
"input": "total_reg = sklearn.linear_model.LinearRegression()\ntotal_reg.fit(X_train[['x1','x2','x3']], X_train['x4'])\nx4_tot_pred = total_reg.predict(X_test[['x1','x2','x3']])\nrms_error = numpy.sqrt(sum([(predi - actuali)**2. for predi, actuali in zip(x4_tot_pred,X_test['x4'])]))\nrms_error\ntotal_reg.coef_",
"prompt_number": 238,
"outputs": [
{
"text": "array([ 0.77359185, -0.95363371, 1.01610361])",
"output_type": "pyout",
"metadata": {},
"prompt_number": 238
}
],
"language": "python",
"trusted": true,
"collapsed": false
},
{
"metadata": {},
"cell_type": "code",
"input": "factored = []\nnot_factored = []\nfor i in range(5000):\n\n # Generate the data with the structure of a chain\n x1 = numpy.random.normal(size=100)\n x2 = numpy.random.normal(loc=x1)\n x3 = numpy.random.normal(loc=x2)\n x4 = numpy.random.normal(loc=x3)\n X_train = pd.DataFrame({ 'x1' : x1, 'x2' : x2, 'x3' : x3, 'x4' : x4 })\n\n regs = [{'x' : ['x1'], 'y' : 'x2'},\n {'x' : ['x2'], 'y' : 'x3'},\n {'x' : ['x3'], 'y' : 'x4'}]\n for reg in regs:\n reg['reg'] = sklearn.linear_model.LinearRegression()\n reg['reg'].fit(X_train[reg['x']], y=X_train[reg['y']])\n \n # Generate the data with the structure of a chain\n x1 = numpy.random.normal(size=50)\n x2 = numpy.random.normal(loc=x1)\n x3 = numpy.random.normal(loc=x2)\n x4 = numpy.random.normal(loc=x3)\n X_test = pd.DataFrame({ 'x1' : x1, 'x2' : x2, 'x3' : x3, 'x4' : x4 })\n\n preds = []\n x2_pred = regs[0]['reg'].predict(X_test[['x1']])\n x3_pred = regs[1]['reg'].predict([[xi] for xi in x2_pred])\n x4_pred = regs[2]['reg'].predict([[xi] for xi in X_test['x3']])\n\n rms_error1 = numpy.sqrt(sum([(predi - actuali)**2. for predi, actuali in zip(x4_pred,X_test['x4'])]))\n total_reg = sklearn.linear_model.LinearRegression()\n total_reg.fit(X_train[['x1','x2','x3']], X_train['x4'])\n \n x4_tot_pred = total_reg.predict(X_test[['x1','x2','x3']])\n rms_error2 = numpy.sqrt(sum([(predi - actuali)**2. for predi, actuali in zip(x4_tot_pred,X_test['x4'])]))\n\n factored.append(rms_error1)\n not_factored.append( rms_error2)\nZ = pd.DataFrame({'factored' : factored, 'not_factored' : not_factored})",
"prompt_number": 239,
"outputs": [],
"language": "python",
"trusted": true,
"collapsed": false
},
{
"metadata": {},
"cell_type": "code",
"input": "Z.mean()",
"prompt_number": 240,
"outputs": [
{
"text": "factored 7.116371\nnot_factored 7.190001\ndtype: float64",
"output_type": "pyout",
"metadata": {},
"prompt_number": 240
}
],
"language": "python",
"trusted": true,
"collapsed": false
},
{
"metadata": {},
"cell_type": "code",
"input": "Z.plot( 'factored', 'not_factored', style='bo')\nZ.mean()",
"prompt_number": 217,
"outputs": [
{
"text": "factored 7.841575\nnot_factored 9.139548\ndtype: float64",
"output_type": "pyout",
"metadata": {},
"prompt_number": 217
},
{
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1Ki3t8pD4ebXJnJybumzD1o0vPf0a5bfrBqUrWUP9eE0M7g2swofxkcqFDNcd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\nfoiYIX4f5T59bJRbeGgXwKAGOBvJe7e75C2I33I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"text": "<matplotlib.figure.Figure at 0x10bb7aa10>",
"output_type": "display_data",
"metadata": {}
}
],
"language": "python",
"trusted": true,
"collapsed": false
},
{
"metadata": {},
"cell_type": "code",
"input": "X_test.corr()",
"prompt_number": 125,
"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>x1</th>\n <th>x2</th>\n <th>x3</th>\n <th>x4</th>\n </tr>\n </thead>\n <tbody>\n <tr>\n <th>x1</th>\n <td> 1.000000</td>\n <td> 0.653274</td>\n <td> 0.484606</td>\n <td> 0.469604</td>\n </tr>\n <tr>\n <th>x2</th>\n <td> 0.653274</td>\n <td> 1.000000</td>\n <td> 0.799568</td>\n <td> 0.608126</td>\n </tr>\n <tr>\n <th>x3</th>\n <td> 0.484606</td>\n <td> 0.799568</td>\n <td> 1.000000</td>\n <td> 0.821549</td>\n </tr>\n <tr>\n <th>x4</th>\n <td> 0.469604</td>\n <td> 0.608126</td>\n <td> 0.821549</td>\n <td> 1.000000</td>\n </tr>\n </tbody>\n</table>\n</div>",
"metadata": {},
"prompt_number": 125,
"text": " x1 x2 x3 x4\nx1 1.000000 0.653274 0.484606 0.469604\nx2 0.653274 1.000000 0.799568 0.608126\nx3 0.484606 0.799568 1.000000 0.821549\nx4 0.469604 0.608126 0.821549 1.000000"
}
],
"language": "python",
"trusted": true,
"collapsed": false
},
{
"metadata": {},
"cell_type": "code",
"input": "total_reg.coef_",
"prompt_number": 126,
"outputs": [
{
"output_type": "pyout",
"prompt_number": 126,
"metadata": {},
"text": "array([-0.86332015, -0.00279217, 1.2444915 ])"
}
],
"language": "python",
"trusted": true,
"collapsed": false
},
{
"metadata": {},
"cell_type": "code",
"input": "total_reg = sklearn.linear_model.LinearRegression()\ntotal_reg.fit(X_test[['x1','x2','x3']], X_test['x4'])\ntotal_reg.coef_",
"prompt_number": 132,
"outputs": [
{
"output_type": "pyout",
"prompt_number": 132,
"metadata": {},
"text": "array([ 0.01802557, -0.04011796, 1.00937628])"
}
],
"language": "python",
"trusted": true,
"collapsed": false
},
{
"metadata": {},
"cell_type": "code",
"input": "import numpy\nimport statsmodels.api as sm\nimport pandas as pd\n\nsamples = 5000\nx1 = numpy.random.binomial(1,0.5,size=samples)\nx2 = numpy.random.binomial(1, 0.5, size=samples)\np = [ numpy.exp( 0.4 * x1i - 0.1 * x2i) / (1. + numpy.exp( 0.4 * x1i - 0.1 * x2i)) for (x1i, x2i) in zip(x1,x2) ]\ny_test = numpy.random.binomial(1, p = p)\ninter = [ 1. for i in range(samples)]\n\ntest_data = pd.DataFrame({ 'green' : x1, 'blue' : x2, 'is_good' : y })",
"prompt_number": 176,
"outputs": [],
"language": "python",
"trusted": true,
"collapsed": false
},
{
"metadata": {},
"cell_type": "code",
"input": "model = sm.Logit(data['is_good'], data[['green','blue']])\nresult = model.fit()",
"prompt_number": 167,
"outputs": [
{
"output_type": "stream",
"text": "Optimization terminated successfully.\n Current function value: 0.687257\n Iterations 4\n",
"stream": "stdout"
}
],
"language": "python",
"trusted": true,
"collapsed": false
},
{
"metadata": {},
"cell_type": "code",
"input": "result.summary()",
"prompt_number": 168,
"outputs": [
{
"text": "<class 'statsmodels.iolib.summary.Summary'>\n\"\"\"\n Logit Regression Results \n==============================================================================\nDep. Variable: is_good No. Observations: 5000\nModel: Logit Df Residuals: 4998\nMethod: MLE Df Model: 1\nDate: Tue, 23 Jun 2015 Pseudo R-squ.: 0.004257\nTime: 00:50:15 Log-Likelihood: -3436.3\nconverged: True LL-Null: -3451.0\n LLR p-value: 5.934e-08\n==============================================================================\n coef std err z P>|z| [95.0% Conf. Int.]\n------------------------------------------------------------------------------\ngreen 0.3287 0.047 7.064 0.000 0.237 0.420\nblue -0.0454 0.046 -0.983 0.325 -0.136 0.045\n==============================================================================\n\"\"\"",
"html": "<table class=\"simpletable\">\n<caption>Logit Regression Results</caption>\n<tr>\n <th>Dep. Variable:</th> <td>is_good</td> <th> No. Observations: </th> <td> 5000</td> \n</tr>\n<tr>\n <th>Model:</th> <td>Logit</td> <th> Df Residuals: </th> <td> 4998</td> \n</tr>\n<tr>\n <th>Method:</th> <td>MLE</td> <th> Df Model: </th> <td> 1</td> \n</tr>\n<tr>\n <th>Date:</th> <td>Tue, 23 Jun 2015</td> <th> Pseudo R-squ.: </th> <td>0.004257</td> \n</tr>\n<tr>\n <th>Time:</th> <td>00:50:15</td> <th> Log-Likelihood: </th> <td> -3436.3</td> \n</tr>\n<tr>\n <th>converged:</th> <td>True</td> <th> LL-Null: </th> <td> -3451.0</td> \n</tr>\n<tr>\n <th> </th> <td> </td> <th> LLR p-value: </th> <td>5.934e-08</td>\n</tr>\n</table>\n<table class=\"simpletable\">\n<tr>\n <td></td> <th>coef</th> <th>std err</th> <th>z</th> <th>P>|z|</th> <th>[95.0% Conf. Int.]</th> \n</tr>\n<tr>\n <th>green</th> <td> 0.3287</td> <td> 0.047</td> <td> 7.064</td> <td> 0.000</td> <td> 0.237 0.420</td>\n</tr>\n<tr>\n <th>blue</th> <td> -0.0454</td> <td> 0.046</td> <td> -0.983</td> <td> 0.325</td> <td> -0.136 0.045</td>\n</tr>\n</table>",
"output_type": "pyout",
"metadata": {},
"prompt_number": 168
}
],
"language": "python",
"trusted": true,
"collapsed": false
},
{
"metadata": {},
"cell_type": "code",
"input": "numpy.exp(result.params)",
"prompt_number": 169,
"outputs": [
{
"text": "green 1.389151\nblue 0.955631\ndtype: float64",
"output_type": "pyout",
"metadata": {},
"prompt_number": 169
}
],
"language": "python",
"trusted": true,
"collapsed": false
},
{
"metadata": {},
"cell_type": "code",
"input": "data.head()",
"prompt_number": 170,
"outputs": [
{
"text": " blue green is_good\n0 1 0 0\n1 0 0 0\n2 1 1 0\n3 1 1 0\n4 0 1 1",
"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>blue</th>\n <th>green</th>\n <th>is_good</th>\n </tr>\n </thead>\n <tbody>\n <tr>\n <th>0</th>\n <td> 1</td>\n <td> 0</td>\n <td> 0</td>\n </tr>\n <tr>\n <th>1</th>\n <td> 0</td>\n <td> 0</td>\n <td> 0</td>\n </tr>\n <tr>\n <th>2</th>\n <td> 1</td>\n <td> 1</td>\n <td> 0</td>\n </tr>\n <tr>\n <th>3</th>\n <td> 1</td>\n <td> 1</td>\n <td> 0</td>\n </tr>\n <tr>\n <th>4</th>\n <td> 0</td>\n <td> 1</td>\n <td> 1</td>\n </tr>\n </tbody>\n</table>\n</div>",
"output_type": "pyout",
"metadata": {},
"prompt_number": 170
}
],
"language": "python",
"trusted": true,
"collapsed": false
},
{
"metadata": {},
"cell_type": "code",
"input": "probs = model.predict(result.params,exog=test_data[['green','blue']])\npredictions = [ 1. if pi > 0.5 else 0. for pi in probs ]",
"prompt_number": 177,
"outputs": [],
"language": "python",
"trusted": true,
"collapsed": false
},
{
"metadata": {},
"cell_type": "code",
"input": "true_pos = sum([ 1. if xi_pred == xi_true else 0. for (xi_pred, xi_true) in zip(predictions, y_test)])\nfalse_pos = sum([ 1. if (xi_pred == 1. and xi_true != 1) else 0 for (xi_pred, xi_true) in zip(predictions, y_test)])\ntrue_neg = sum([1. if xi_pred == 0 and xi_true == 0 else 0for (xi_pred, xi_true) in zip(predictions, y_test)])\nfalse_neg = sum([1. if xi_pred == 0 and xi_true == 1 else 0 for (xi_pred, xi_true) in zip(predictions, y_test)])",
"prompt_number": 185,
"outputs": [],
"language": "python",
"trusted": true,
"collapsed": false
},
{
"metadata": {},
"cell_type": "code",
"input": "precision = true_pos / (true_pos + false_pos)\nprint \"precision %f\" % precision\nrecall = true_pos / ( true_pos + false_neg)\nprint \"recall %f\" % recall",
"prompt_number": 195,
"outputs": [
{
"output_type": "stream",
"text": "precision 0.738474\nrecall 0.694493\n",
"stream": "stdout"
}
],
"language": "python",
"trusted": true,
"collapsed": false
},
{
"metadata": {},
"cell_type": "code",
"input": "recall = true_pos / ( true_pos + false_neg)\nrecall",
"prompt_number": 193,
"outputs": [
{
"text": "0.6944928980812359",
"output_type": "pyout",
"metadata": {},
"prompt_number": 193
}
],
"language": "python",
"trusted": true,
"collapsed": false
},
{
"metadata": {},
"cell_type": "code",
"input": "sum(y_test) / float(len(y_test))",
"prompt_number": 197,
"outputs": [
{
"text": "0.54579999999999995",
"output_type": "pyout",
"metadata": {},
"prompt_number": 197
}
],
"language": "python",
"trusted": true,
"collapsed": false
},
{
"metadata": {},
"cell_type": "code",
"input": "X = pd.DataFrame( 'x1' : x1, 'x1^2' : [ xi*xi for xi in x1], ... )",
"outputs": [],
"language": "python",
"trusted": true,
"collapsed": false
}
],
"metadata": {}
}
],
"metadata": {
"name": "",
"signature": "sha256:df6f3cb02ab708c525fd99aaf4723a351315bdfab71236b51dcb94b9df8f55ef"
}
}
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