l_bfgs_b-dask-array (1).ipynb

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    {
     "data": {
      "text/plain": [
       "<Client: scheduler='tcp://localhost:8786' processes=7 cores=56>"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from dask import persist\n",
    "from dask.distributed import Client, progress\n",
    "client = Client('localhost:8786')\n",
    "client"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/anaconda/lib/python3.5/site-packages/dask/array/core.py:476: RuntimeWarning: overflow encountered in exp\n",
      "  o = func(*args, **kwargs)\n"
     ]
    }
   ],
   "source": [
    "import dask\n",
    "import dask.array as da\n",
    "import numpy as np\n",
    "from scipy.optimize import fmin_l_bfgs_b\n",
    "\n",
    "def make_classification(n_samples=1000, n_features=100, n_informative=2, scale=1.0, chunksize=100):\n",
    "    X = da.random.normal(0, 1, size=(n_samples, n_features), \n",
    "                         chunks=(chunksize, n_features))\n",
    "    informative_idx = np.random.choice(n_features, n_informative)\n",
    "    beta = (np.random.random(n_features) - 1) * scale\n",
    "    z0 = X[:, informative_idx].dot(beta[informative_idx])\n",
    "    y = da.random.random(z0.shape, chunks=(chunksize,)) < 1 / (1 + da.exp(-z0))\n",
    "    return X, y\n",
    "\n",
    "\n",
    "# make dataset\n",
    "X, y = make_classification(n_samples=100000000, n_features=50, \n",
    "                           n_informative=2, chunksize=500000)\n",
    "\n",
    "X, y = dask.persist(X, y)\n",
    "\n",
    "# logistic\n",
    "def sigmoid(x):\n",
    "    '''Sigmoid function of x.'''\n",
    "    return 1 / (1 + da.exp(-x))\n",
    "\n",
    "def compute_logistic_loss_grad(beta, X, y):\n",
    "    Xbeta = X.dot(beta)\n",
    "    # loss\n",
    "    eXbeta = da.exp(Xbeta)\n",
    "    loss_fn = (da.log1p(eXbeta)).sum() - da.dot(y, Xbeta)\n",
    "    # gradient\n",
    "    p = sigmoid(Xbeta)\n",
    "    gradient_fn = da.dot(X.T, p - y)\n",
    "    loss, gradient = dask.compute(loss_fn, gradient_fn)\n",
    "    return loss, gradient.copy()\n",
    "\n",
    "n, p = X.shape\n",
    "beta = np.zeros(p)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "dask.array<da.random.normal, shape=(100000000, 50), dtype=float64, chunksize=(500000, 50)>"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
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   "source": [
    "X"
   ]
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  {
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   "execution_count": 5,
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    {
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      "text/plain": [
       "40.0"
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     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
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   "source": [
    "X.nbytes / 1e9"
   ]
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  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/anaconda/lib/python3.5/site-packages/dask/array/core.py:476: RuntimeWarning: overflow encountered in true_divide\n",
      "  o = func(*args, **kwargs)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 1.86 s, sys: 35.1 ms, total: 1.89 s\n",
      "Wall time: 8.82 s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "with dask.set_options(fuse_ave_width=0):  # optimizations slows this down\n",
    "    new_beta_dask, loss_dask, info_dask = fmin_l_bfgs_b(\n",
    "        compute_logistic_loss_grad, beta, fprime=None,\n",
    "        args=(X, y),\n",
    "        iprint=0, pgtol=1e-14, maxiter=10)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false
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   "outputs": [
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       "         5.20665150e-05,   1.68109365e-04,   3.71687228e-04,\n",
       "         1.14815910e-04,   3.12092343e-04,   3.11045287e-04,\n",
       "         3.12777296e-04,  -4.30556434e-01,  -4.10634311e-05,\n",
       "        -2.95176733e-04,  -7.64938745e-06,  -9.16969603e-01,\n",
       "        -1.57075385e-04,   1.16032906e-04,  -2.60510424e-04,\n",
       "         2.29526165e-05,  -3.78676990e-04,   7.99983434e-05,\n",
       "         1.29415052e-04,   4.31178181e-04,   3.28448348e-04,\n",
       "         2.06186917e-04,  -1.37779101e-04,  -1.74945595e-04,\n",
       "        -1.36139608e-04,  -3.76378757e-05,  -2.63300072e-04,\n",
       "        -1.76055108e-04,  -5.29483078e-05,  -1.50231453e-04,\n",
       "         2.56707029e-04,  -1.89528783e-04,   3.83597204e-04,\n",
       "         1.27296870e-04,  -1.28490118e-04])"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "new_beta_dask"
   ]
  }
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