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@rmcgibbo
Created January 17, 2013 02:25
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{
"metadata": {
"name": "parallel_stats"
},
"nbformat": 3,
"nbformat_minor": 0,
"worksheets": [
{
"cells": [
{
"cell_type": "code",
"collapsed": false,
"input": [
"import itertools\n",
"from sklearn import datasets\n",
"from sklearn.cross_validation import train_test_split\n",
"\n",
"# import some of the parallel machinery\n",
"from IPython import parallel\n",
"from IPython.parallel import require"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 1
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"# let's connect to our parallel cluster\n",
"# I started the cluster from the IPython dashboard under\n",
"# the \"Clusters\" tab\n",
"client = parallel.Client() # you might need to supply some connection info\n",
"\n",
"# this is a \"view\" of the cluster that will automatically perform load balancing,\n",
"# which is a good thing\n",
"lbview = client.load_balanced_view()\n",
"\n",
"# this \"direct view\" gives us direct access to each engine.\n",
"dview = client[:]"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 2
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"# load up a dataset and split it into a test and training set\n",
"digits = datasets.load_digits()\n",
"X_train, X_test, y_train, y_test = train_test_split(digits.data, digits.target, test_size=0.3)\n",
"\n",
"# send (\"push\") the test and training set to all of the engines via the direct vbiew\n",
"dview.push(dict(X_train=X_train, X_test=X_test, y_train=y_train, y_test=y_test))"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "pyout",
"prompt_number": 3,
"text": [
"<AsyncResult: _push>"
]
}
],
"prompt_number": 3
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"# this is the function that's going to run in parallel remotely under\n",
"# load balancing\n",
"\n",
"# it's going to be an expensive calculation, and I want to explore its behavior over\n",
"# lots of possible values of the two parameters, so doing that exploration in parallel\n",
"# is a big win\n",
"@lbview.parallel()\n",
"def fit_model(n_estimators, max_features):\n",
" # by doingh the imports here, we make sure that these modules\n",
" # are imported on the engine where they're actually run\n",
" from sklearn.ensemble import RandomForestClassifier\n",
" from sklearn.metrics import zero_one_score\n",
" \n",
" clf = RandomForestClassifier(n_estimators=n_estimators, max_features=max_features)\n",
" clf = clf.fit(X_train, y_train)\n",
" accuracy = zero_one_score(y_test, clf.predict(X_test))\n",
" \n",
" return accuracy"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 4
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"n_estimator_range = range(5, 50, 5)\n",
"max_features_range = range(2, 10)\n",
"\n",
"# constuct all pairs\n",
"matrix = zip(*(itertools.product(n_estimator_range, max_features_range)))\n",
"n_estimators = matrix[0]\n",
"max_features = matrix[1]\n",
"\n",
"# run the fitting in parallel by mapping the pairs\n",
"# of (n_est, max_feat) against the fit_model function\n",
"result = fit_model.map(n_estimators, max_features)\n",
"\n",
"# wait for the results to be ready\n",
"result.wait()\n",
"accuracy = result.get()"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 5
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"# Let's take a look at our results\n",
"\n",
"title('Accuracy vs. n_estimators')\n",
"scatter(n_estimators, accuracy, c=max_features, edgecolor='none', s=30)\n",
"xlabel('n_estimators')\n",
"ylabel('Accuracy')\n",
"cbar = colorbar()\n",
"cbar.set_label('max_features')\n",
"\n",
"figure()\n",
"title('Accuracy vs max_features')\n",
"accuracy = result.get()\n",
"scatter(max_features, accuracy, c=n_estimators, edgecolor='none', s=30)\n",
"xlabel('max_features')\n",
"ylabel('Accuracy')\n",
"cbar = colorbar()\n",
"cbar.set_label('n_estimators')"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "display_data",
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ZUs2zqPwnTdpK48bz6ej7HU2azOfYMfXnoAymiJfCFzge8ddff+Hp6ak9rKys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\n",
"text": [
"<matplotlib.figure.Figure at 0x10c6dfc90>"
]
},
{
"output_type": "display_data",
"png": 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3IyOxtLLCpV49o8Trx23CHzoL1QC7qYI36s9QmJAGf93MwtetDBWMMGIhg6PE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\n1VOPl6IvhBCF8KSinxF2gMywp09vrNV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QRX9/f3R1dQEAurq64O/vb7PsPw0V/R/EGENC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\n",
"text": [
"<matplotlib.figure.Figure at 0x10ce1af50>"
]
}
],
"prompt_number": 6
}
],
"metadata": {}
}
]
}
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