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Logistic Regression in Dask
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dominion.ipynb
{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Logistic Regression with Dask"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"This notebook is a RFC on some work I've been doing implementing machine learning things in dask. The library in reference lives here: https://github.com/jcrist/dask-learn. A few points to keep in mind:\n",
"\n",
"- This will not be as performant/full-featured as scikit-learn. The goal here is functioning out-of-core/parallel/distributed learning, not necessarily faster learning.\n",
"- I am not a machine learning expert. The work done here is naive, and potentially wrong.\n",
"- This is less of a demo and more of a request for comment. At the bottom of the notebook are some questions I have.\n",
"\n",
"---\n",
"\n",
"In this notebook we walk through a simple example to test out an experimental implementation of gradient descent for fitting a logistic regression model. Our example dataset is taken from [here](http://mlcomp.org/datasets/872), and contains 270k intermediate game stats from a [dominion game server](https://dominion.isotropic.org/). The goal is to predict the game outcome from these stats.\n",
"\n",
"The data is separated into a train and test dataset, stored in a sparse format. Each row first contains the game outcome (either `-1` or `1`), and is followed by a number of column indices (1 indexed) mapping to the value. There doesn't seem to be a standard reader for this format, so I rolled my own."
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"-1 18:10 24:45 29:10 32:8 35:9 36:8 46:9 50:29 52:9 77:10 85:10 99:8 100:9 104:9 112:36 130:10 145:9 148:2 149:11 171:8 183:3 199:1 247:1 259:3 295:5 296:5 297:-1 298:16 320:7 331:1 332:1 342:1 346:1 400:1 408:1 441:1 444:1 445:8 446:-1 447:14 469:1 480:-1 481:2 491:-1 495:-1 497:1 545:1 549:-1 557:2 590:-1 593:4 594:-3 596:2 \r\n"
]
}
],
"source": [
"!head -n 1 ../dominion_stats/train"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"import numpy as np\n",
"\n",
"def read(fp, n, p):\n",
" \"\"\"Read the sparse matrix format\"\"\"\n",
" x = np.zeros((n, p), dtype='f8')\n",
" y = np.zeros(n, dtype='i1')\n",
" with open(fp) as f:\n",
" for i, line in enumerate(f):\n",
" chunks = line.split()\n",
" if chunks[0] == '1':\n",
" y[i] = 1\n",
" for c in chunks[1:]:\n",
" j, v = map(int, c.split(':'))\n",
" x[i, j - 1] = v\n",
" return x, y\n",
"\n",
"\n",
"X_train, y_train = read('../dominion_stats/train', 193657, 596)\n",
"X_test, y_test = read('../dominion_stats/test', 82996, 596)"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Training Data: 193657 samples\n",
"Testing Data: 82996 samples\n"
]
}
],
"source": [
"print(\"Training Data: {0} samples\".format(X_train.shape[0]))\n",
"print(\"Testing Data: {0} samples\".format(X_test.shape[0]))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## In memory with Scikit-Learn"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"For comparison purposes, we'll first fit using scikit-learn, using the [LogisticRegression model](http://scikit-learn.org/stable/modules/generated/sklearn.linear_model.LogisticRegression.html#sklearn.linear_model.LogisticRegression). The `C` parameter is set high to minimize the effect of regularization (which isn't yet implemented in the dask version)."
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"CPU times: user 26 s, sys: 142 ms, total: 26.1 s\n",
"Wall time: 26.2 s\n"
]
},
{
"data": {
"text/plain": [
"LogisticRegression(C=100000.0, class_weight=None, dual=False,\n",
" fit_intercept=True, intercept_scaling=1, max_iter=100,\n",
" multi_class='ovr', n_jobs=1, penalty='l2', random_state=None,\n",
" solver='liblinear', tol=0.0001, verbose=0, warm_start=False)"
]
},
"execution_count": 4,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"from sklearn import linear_model\n",
"\n",
"logreg = linear_model.LogisticRegression(C=1e5)\n",
"\n",
"%time logreg.fit(X_train, y_train)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Note that the timings here are just to provide a sense of the order of magnitude. This example is *not* to compete with scikit-learn.\n",
"\n",
"---\n",
"\n",
"After fitting, we can predict outcomes, and score our model on how well it fits using the test dataset. Scikit-learn has a nice standard interface for this, with the `predict` and `score` methods."
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"array([0, 1, 0, 0, 0], dtype=int8)"
]
},
"execution_count": 5,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"logreg.predict(X_test[:5])"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"68.8189792279% accurate\n"
]
}
],
"source": [
"s = logreg.score(X_test, y_test)\n",
"print(\"{0}% accurate\".format(s * 100))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"So ~69% of the game outcomes in the test dataset were predicted correctly. There may be some settings for this model that could be tuned to achieve better performance, but that's not the point of this example."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## In memory with dask-learn"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"As a proof-of-concept, an api compatible version of the `LogisticRegression` model has been implemented using dask. Fitting is done using gradient descent. The goal here is not to necessarily be speedy, but rather to handle larger datasets by working out-of-core.\n",
"\n",
"Currently regularization isn't implemented, and only binomial problems are supported. Still, a few of the keywords avaialable to the scikit-learn version are also supported here, as well as the common `fit`, `predict`, and `score` methods.\n",
"\n",
"Note that the code is generic between numpy and dask, and works fine with either. We'll start with numpy arrays."
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"CPU times: user 49.9 s, sys: 285 ms, total: 50.1 s\n",
"Wall time: 12.6 s\n"
]
},
{
"data": {
"text/plain": [
"LogisticRegression(fit_intercept=True, max_iter=100, tol=0.0001,\n",
" verbose=False)"
]
},
"execution_count": 7,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"from dask_learn.linear_model import LogisticRegression\n",
"\n",
"dlogreg = LogisticRegression()\n",
"\n",
"%time dlogreg.fit(X_train, y_train)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"So the fitting happened quicker (on this problem). Again, it should be noted that this is not the point of this example. \n",
"\n",
"---\n",
"\n",
"The interface of our `LogisticRegression` class is similar to that of the equivalent scikit-learn class."
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"['coef_',\n",
" 'decision_function',\n",
" 'densify',\n",
" 'fit',\n",
" 'fit_intercept',\n",
" 'get_params',\n",
" 'intercept_',\n",
" 'max_iter',\n",
" 'predict',\n",
" 'predict_log_proba',\n",
" 'predict_proba',\n",
" 'score',\n",
" 'set_params',\n",
" 'sparsify',\n",
" 'tol',\n",
" 'verbose']"
]
},
"execution_count": 8,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"sorted(m for m in dir(dlogreg) if not m.startswith('_'))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"After fitting, we'll run the same `predict` and `score` methods:"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"array([0, 1, 1, 0, 0])"
]
},
"execution_count": 9,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"dlogreg.predict(X_test[:5])"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"62.5885584847% accurate\n"
]
}
],
"source": [
"ds = dlogreg.score(X_test, y_test)\n",
"print(\"{0}% accurate\".format(ds * 100))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The accuracy here is worse than that provided scikit-learn. I'm not sure why that is - there may be some things that could be done to improve the performance of our algorithm. Still, we're doing better than random guessing (50%)."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## In Memory Dask Arrays"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Next, we'll run the same model using dask arrays. Note that this is still in memory."
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"import dask.array as da\n",
"\n",
"# Convert the numpy arrays to in memory dask arrays\n",
"dX_train = da.from_array(X_train, chunks=(10000, 596))\n",
"dy_train = da.from_array(y_train, chunks=10000)"
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {
"collapsed": false
},
"outputs": [
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" }\n",
"}(this));"
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},
"metadata": {},
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}
],
"source": [
"# Imports for profiling\n",
"from dask.diagnostics import Profiler, ResourceProfiler, visualize\n",
"from bokeh.io import output_notebook\n",
"output_notebook(hide_banner=True)"
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"# Fit, with profiling\n",
"dlogreg = LogisticRegression()\n",
"\n",
"with Profiler() as prof, ResourceProfiler(0.1) as rprof:\n",
" dlogreg.fit(dX_train, dy_train)\n",
"rprof.close()"
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {
"collapsed": false
},
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