machine-learning.ipynb

{
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   "cell_type": "markdown",
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   "source": [
    "# Dask for Machine Learning\n",
    "\n",
    "This is a high-level overview demonstrating some the components of Dask-ML.\n",
    "Visit the main [Dask-ML](http://ml.dask.org) documentation, see the [dask tutorial](https://github.com/dask/dask-tutorial) notebook 08, or explore some of the other machine-learning examples."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from dask.distributed import Client, progress\n",
    "client = Client(processes=False, threads_per_worker=4,\n",
    "                n_workers=1, memory_limit='2GB')\n",
    "client"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Distributed Training\n",
    "\n",
    "<img src=\"images/scikit-learn-logo-notext.png\"/> <img src=\"images/joblib_logo.svg\" width=\"20%\"/> \n",
    "\n",
    "Scikit-learn uses [joblib](http://joblib.readthedocs.io/) for single-machine parallelism. This lets you train most estimators (anything that accepts an `n_jobs` parameter) using all the cores of your laptop or workstation.\n",
    "\n",
    "Alternatively, Scikit-Learn can use Dask for parallelism.  This lets you train those estimators using all the cores of your *cluster* without significantly changing your code.\n",
    "\n",
    "This is most useful for training large models on medium-sized datasets. You may have a large model when searching over many hyper-parameters, or when using an ensemble method with many individual estimators. For too small datasets, training times will typically be small enough that cluster-wide parallelism isn't helpful. For too large datasets (larger than a single machine's memory), the scikit-learn estimators may not be able to cope (see below)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Create Scikit-Learn Estimator"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.datasets import make_classification\n",
    "from sklearn.svm import SVC\n",
    "from sklearn.model_selection import GridSearchCV\n",
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We'll use scikit-learn to create a pair of small random arrays, one for the features `X`, and one for the target `y`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "X, y = make_classification(n_samples=1000, random_state=0)\n",
    "X[:5]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We'll fit a [Support Vector Classifier](http://scikit-learn.org/stable/modules/generated/sklearn.svm.SVC.html), using [grid search](http://scikit-learn.org/stable/modules/generated/sklearn.model_selection.GridSearchCV.html) to find the best value of the $C$ hyperparameter."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "param_grid = {\"C\": [0.001, 0.01, 0.1, 0.5, 1.0, 2.0, 5.0, 10.0],\n",
    "              \"kernel\": ['rbf', 'poly', 'sigmoid'],\n",
    "              \"shrinking\": [True, False]}\n",
    "\n",
    "grid_search = GridSearchCV(SVC(gamma='auto', random_state=0, probability=True),\n",
    "                           param_grid=param_grid,\n",
    "                           return_train_score=False,\n",
    "                           cv=3,\n",
    "                           n_jobs=-1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To fit that normally, we would call\n",
    "\n",
    "```python\n",
    "grid_search.fit(X, y)\n",
    "```\n",
    "\n",
    "To fit it using the cluster, we just need to use a context manager provided by joblib."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import joblib\n",
    "\n",
    "with joblib.parallel_backend('dask'):\n",
    "    grid_search.fit(X, y)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We fit 48 different models, one for each hyper-parameter combination in `param_grid`, distributed across the cluster. At this point, we have a regular scikit-learn model, which can be used for prediction, scoring, etc."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "pd.DataFrame(grid_search.cv_results_).head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "grid_search.predict(X)[:5]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "grid_search.score(X, y)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "For more on training scikit-learn models with distributed joblib, see the [dask-ml documentation](http://dask-ml.readthedocs.io/en/latest/joblib.html)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Training on Large Datasets\n",
    "\n",
    "Most estimators in scikit-learn are designed to work on in-memory arrays. Training with larger datasets may require different algorithms.\n",
    "\n",
    "All of the algorithms implemented in Dask-ML work well on larger than memory datasets, which you might store in a [dask array](http://dask.pydata.org/en/latest/array.html) or [dataframe](http://dask.pydata.org/en/latest/dataframe.html)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import dask_ml.datasets\n",
    "import dask_ml.cluster\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In this example, we'll use `dask_ml.datasets.make_blobs` to generate some random *dask* arrays."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "X, y = dask_ml.datasets.make_blobs(n_samples=10000000,\n",
    "                                   chunks=1000000,\n",
    "                                   random_state=0,\n",
    "                                   centers=3)\n",
    "X = X.persist()\n",
    "X"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We'll use the k-means implemented in Dask-ML to cluster the points. It uses the `k-means||` (read: \"k-means parallel\") initialization algorithm, which scales better than `k-means++`. All of the computation, both during and after initialization, can be done in parallel."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "km = dask_ml.cluster.KMeans(n_clusters=3, init_max_iter=2, oversampling_factor=10)\n",
    "km.fit(X)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We'll plot a sample of points, colored by the cluster each falls into."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "fig, ax = plt.subplots()\n",
    "ax.scatter(X[::10000, 0], X[::10000, 1], marker='.', c=km.labels_[::10000],\n",
    "           cmap='viridis', alpha=0.25);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "For all the estimators implemented in Dask-ML, see the [API documentation](http://dask-ml.readthedocs.io/en/latest/modules/api.html)."
   ]
  }
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