Trying to move towards better testing

accuracy_tests.ipynb

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   "source": [
    "import dask.array as da\n",
    "import inspect\n",
    "import numpy as np\n",
    "\n",
    "from dask_glm.algorithms import admm, gradient_descent, newton, proximal_grad\n",
    "from dask_glm.families import Logistic, Normal\n",
    "from dask_glm.utils import sigmoid"
   ]
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   "cell_type": "code",
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   "source": [
    "## Normal family check\n",
    "true_beta = (np.random.random(15) - 0.5) * 3\n",
    "X = da.random.random((10000, 15), chunks=(2000, 15))\n",
    "y = X.dot(true_beta)"
   ]
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   "execution_count": 3,
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     "text": [
      "Converged! 2\n",
      "admm error: 0.007000568\n",
      "newton error: 0.000000000\n",
      "gradient_descent error: 0.125664047\n",
      "proximal_grad error: 0.146943416\n"
     ]
    }
   ],
   "source": [
    "for algo in [admm, newton, gradient_descent, proximal_grad]:\n",
    "    sig = inspect.signature(newton)\n",
    "    if 'lamduh' in sig.parameters.keys():\n",
    "        out = algo(X, y, family=Normal, lamduh=0)\n",
    "    else:\n",
    "        out = algo(X, y, family=Normal)\n",
    "    perc_diff = (out - true_beta) / true_beta\n",
    "    print('{0} error: {1:.9f}'.format(algo.__name__, abs(perc_diff).max()))"
   ]
  },
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   "cell_type": "code",
   "execution_count": 4,
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     "text": [
      "//anaconda/lib/python3.5/site-packages/dask/array/core.py:476: RuntimeWarning: overflow encountered in true_divide\n",
      "  o = func(*args, **kwargs)\n"
     ]
    }
   ],
   "source": [
    "## Logistic family check\n",
    "y = sigmoid(X.dot(true_beta))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
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     "name": "stderr",
     "output_type": "stream",
     "text": [
      "//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": [
      "Converged! 3\n",
      "admm error: 0.083115191\n",
      "newton error: 0.000000000\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "//anaconda/lib/python3.5/site-packages/dask/async.py:247: RuntimeWarning: overflow encountered in exp\n",
      "  return func(*args2)\n",
      "//anaconda/lib/python3.5/site-packages/dask/array/core.py:476: RuntimeWarning: invalid value encountered in subtract\n",
      "  o = func(*args, **kwargs)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "gradient_descent error: 0.196703062\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "//anaconda/lib/python3.5/site-packages/dask/array/core.py:476: RuntimeWarning: overflow encountered in exp\n",
      "  o = func(*args, **kwargs)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "proximal_grad error: 0.288486711\n"
     ]
    }
   ],
   "source": [
    "for algo in [admm, newton, gradient_descent, proximal_grad]:\n",
    "    sig = inspect.signature(newton)\n",
    "    if 'lamduh' in sig.parameters.keys():\n",
    "        out = algo(X, y, family=Logistic, lamduh=0)\n",
    "    else:\n",
    "        out = algo(X, y, family=Logistic)\n",
    "    perc_diff = (out - true_beta) / true_beta\n",
    "    print('{0} error: {1:.9f}'.format(algo.__name__, abs(perc_diff).max()))"
   ]
  }
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