20190923_error_detection_with_smoothness.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import great_expectations as ge\n",
    "import matplotlib.pyplot as plt\n",
    "import scipy.stats as stats\n",
    "import pandas as pd\n",
    "import json\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "## In the given problem, we expect that we have discrete values in the range 1-100 with a mean that is usually\n",
    "## somewhat less than 50 and very fat tails.\n",
    "\n",
    "# We choose the beta distribution with scale to provide a sample of data for this notebook, but real data\n",
    "# would of coure be preferable and would allow the same procedure\n",
    "\n",
    "a = np.random.uniform(1, 1.5, size=10)\n",
    "b = np.random.uniform(1, 1.5, size=10)\n",
    "\n",
    "beta = []\n",
    "for k in range(len(a)):\n",
    "    beta.append(np.ceil(np.random.beta(a[k], b[k], size=10000)*100))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Review the data manually\n",
    "\n",
    "for k in range(10):\n",
    "    plt.hist(beta[k], alpha=0.05, color='blue')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x1a2306db38>"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Compute our \"smoothness\" statistic\n",
    "mean_hist = np.histogram(np.array(beta), bins=range(0, 101))\n",
    "grad = np.gradient(mean_hist[0] / 100000 )\n",
    "\n",
    "# Note that we (based on this model) essentially expect large gradients around the 1st and last few elements.\n",
    "# We might want to handle that by excluding them, we might want to add a mostly parameter, or we might want\n",
    "# to create separate expectations for them.\n",
    "\n",
    "plt.scatter(range(100), grad)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "check_asset = ge.dataset.PandasDataset({\"gradient\": grad})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'success': True,\n",
       " 'result': {'element_count': 100,\n",
       "  'missing_count': 0,\n",
       "  'missing_percent': 0.0,\n",
       "  'unexpected_count': 0,\n",
       "  'unexpected_percent': 0.0,\n",
       "  'unexpected_percent_nonmissing': 0.0,\n",
       "  'partial_unexpected_list': []}}"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "check_asset.expect_column_values_to_be_between('gradient', -.01, .01)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "# We'll now repeat the sampling process to demonstrate data that passes our expectation,\n",
    "# and as a mechanism to simply create a collection of (in this case identical) expectations\n",
    "\n",
    "a_prime = np.random.uniform(1, 1.5, size=10)\n",
    "b_prime = np.random.uniform(1, 1.5, size=10)\n",
    "\n",
    "check_asset_prime = {}\n",
    "for k in range(len(a_prime)):\n",
    "    raw = np.ceil(np.random.beta(a_prime[k], b_prime[k], size=1000)*100)\n",
    "    hist = np.histogram(raw, bins=range(0,101))\n",
    "    grad = np.gradient(hist[0] / len(raw))\n",
    "    check_asset_prime[str(k)] = grad"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = ge.dataset.PandasDataset(check_asset_prime)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "res = ge.dataset.util.create_multiple_expectations(\n",
    "    df,\n",
    "    [str(k) for k in range(10)],\n",
    "    'expect_column_values_to_be_between',\n",
    "    min_value= -0.01,\n",
    "    max_value= 0.01\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Finally, we repeat the process but adjust the data generating process to include an error (double counting)\n",
    "# a subset of the data.\n",
    "\n",
    "a_prime = np.random.uniform(1, 1.5, size=10)\n",
    "b_prime = np.random.uniform(1, 1.5, size=10)\n",
    "\n",
    "check_asset_broken  = {}\n",
    "for k in range(len(a_prime)):\n",
    "    beta_vals = np.ceil(np.random.beta(a_prime[k], b_prime[k], size=1000)*100)\n",
    "    # In the estimated error condition, we observed extremely high counts for some value\n",
    "    # (because it was being multiply counted)\n",
    "    miscount_vals = np.array([np.random.choice(range(30,40), size=1)] * 100)\n",
    "    raw = np.append(beta_vals, miscount_vals)\n",
    "    hist = np.histogram(raw, bins=range(0, 101))\n",
    "    grad = np.gradient(hist[0] / len(raw))\n",
    "    check_asset_broken[str(k)] = grad"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "check_asset_broken = ge.dataset.PandasDataset(check_asset_broken)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "res = check_asset_broken.validate(expectation_suite=df.get_expectation_suite(discard_failed_expectations=False))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{\n",
      "  \"results\": [\n",
      "    {\n",
      "      \"success\": false,\n",
      "      \"result\": {\n",
      "        \"element_count\": 100,\n",
      "        \"missing_count\": 0,\n",
      "        \"missing_percent\": 0.0,\n",
      "        \"unexpected_count\": 2,\n",
      "        \"unexpected_percent\": 0.02,\n",
      "        \"unexpected_percent_nonmissing\": 0.02,\n",
      "        \"partial_unexpected_list\": [\n",
      "          0.04727272727272727,\n",
      "          -0.046818181818181814\n",
      "        ]\n",
      "      },\n",
      "      \"exception_info\": {\n",
      "        \"raised_exception\": false,\n",
      "        \"exception_message\": null,\n",
      "        \"exception_traceback\": null\n",
      "      },\n",
      "      \"expectation_config\": {\n",
      "        \"expectation_type\": \"expect_column_values_to_be_between\",\n",
      "        \"kwargs\": {\n",
      "          \"column\": \"0\",\n",
      "          \"min_value\": -0.01,\n",
      "          \"max_value\": 0.01\n",
      "        }\n",
      "      }\n",
      "    },\n",
      "    {\n",
      "      \"success\": false,\n",
      "      \"result\": {\n",
      "        \"element_count\": 100,\n",
      "        \"missing_count\": 0,\n",
      "        \"missing_percent\": 0.0,\n",
      "        \"unexpected_count\": 2,\n",
      "        \"unexpected_percent\": 0.02,\n",
      "        \"unexpected_percent_nonmissing\": 0.02,\n",
      "        \"partial_unexpected_list\": [\n",
      "          0.04363636363636364,\n",
      "          -0.045\n",
      "        ]\n",
      "      },\n",
      "      \"exception_info\": {\n",
      "        \"raised_exception\": false,\n",
      "        \"exception_message\": null,\n",
      "        \"exception_traceback\": null\n",
      "      },\n",
      "      \"expectation_config\": {\n",
      "        \"expectation_type\": \"expect_column_values_to_be_between\",\n",
      "        \"kwargs\": {\n",
      "          \"column\": \"1\",\n",
      "          \"min_value\": -0.01,\n",
      "          \"max_value\": 0.01\n",
      "        }\n",
      "      }\n",
      "    },\n",
      "    {\n",
      "      \"success\": false,\n",
      "      \"result\": {\n",
      "        \"element_count\": 100,\n",
      "        \"missing_count\": 0,\n",
      "        \"missing_percent\": 0.0,\n",
      "        \"unexpected_count\": 2,\n",
      "        \"unexpected_percent\": 0.02,\n",
      "        \"unexpected_percent_nonmissing\": 0.02,\n",
      "        \"partial_unexpected_list\": [\n",
      "          0.04590909090909091,\n",
      "          -0.04454545454545455\n",
      "        ]\n",
      "      },\n",
      "      \"exception_info\": {\n",
      "        \"raised_exception\": false,\n",
      "        \"exception_message\": null,\n",
      "        \"exception_traceback\": null\n",
      "      },\n",
      "      \"expectation_config\": {\n",
      "        \"expectation_type\": \"expect_column_values_to_be_between\",\n",
      "        \"kwargs\": {\n",
      "          \"column\": \"2\",\n",
      "          \"min_value\": -0.01,\n",
      "          \"max_value\": 0.01\n",
      "        }\n",
      "      }\n",
      "    },\n",
      "    {\n",
      "      \"success\": false,\n",
      "      \"result\": {\n",
      "        \"element_count\": 100,\n",
      "        \"missing_count\": 0,\n",
      "        \"missing_percent\": 0.0,\n",
      "        \"unexpected_count\": 2,\n",
      "        \"unexpected_percent\": 0.02,\n",
      "        \"unexpected_percent_nonmissing\": 0.02,\n",
      "        \"partial_unexpected_list\": [\n",
      "          0.04409090909090909,\n",
      "          -0.046363636363636364\n",
      "        ]\n",
      "      },\n",
      "      \"exception_info\": {\n",
      "        \"raised_exception\": false,\n",
      "        \"exception_message\": null,\n",
      "        \"exception_traceback\": null\n",
      "      },\n",
      "      \"expectation_config\": {\n",
      "        \"expectation_type\": \"expect_column_values_to_be_between\",\n",
      "        \"kwargs\": {\n",
      "          \"column\": \"3\",\n",
      "          \"min_value\": -0.01,\n",
      "          \"max_value\": 0.01\n",
      "        }\n",
      "      }\n",
      "    },\n",
      "    {\n",
      "      \"success\": false,\n",
      "      \"result\": {\n",
      "        \"element_count\": 100,\n",
      "        \"missing_count\": 0,\n",
      "        \"missing_percent\": 0.0,\n",
      "        \"unexpected_count\": 2,\n",
      "        \"unexpected_percent\": 0.02,\n",
      "        \"unexpected_percent_nonmissing\": 0.02,\n",
      "        \"partial_unexpected_list\": [\n",
      "          0.04772727272727273,\n",
      "          -0.04863636363636364\n",
      "        ]\n",
      "      },\n",
      "      \"exception_info\": {\n",
      "        \"raised_exception\": false,\n",
      "        \"exception_message\": null,\n",
      "        \"exception_traceback\": null\n",
      "      },\n",
      "      \"expectation_config\": {\n",
      "        \"expectation_type\": \"expect_column_values_to_be_between\",\n",
      "        \"kwargs\": {\n",
      "          \"column\": \"4\",\n",
      "          \"min_value\": -0.01,\n",
      "          \"max_value\": 0.01\n",
      "        }\n",
      "      }\n",
      "    },\n",
      "    {\n",
      "      \"success\": false,\n",
      "      \"result\": {\n",
      "        \"element_count\": 100,\n",
      "        \"missing_count\": 0,\n",
      "        \"missing_percent\": 0.0,\n",
      "        \"unexpected_count\": 3,\n",
      "        \"unexpected_percent\": 0.03,\n",
      "        \"unexpected_percent_nonmissing\": 0.03,\n",
      "        \"partial_unexpected_list\": [\n",
      "          0.042727272727272725,\n",
      "          -0.04363636363636363,\n",
      "          0.010000000000000002\n",
      "        ]\n",
      "      },\n",
      "      \"exception_info\": {\n",
      "        \"raised_exception\": false,\n",
      "        \"exception_message\": null,\n",
      "        \"exception_traceback\": null\n",
      "      },\n",
      "      \"expectation_config\": {\n",
      "        \"expectation_type\": \"expect_column_values_to_be_between\",\n",
      "        \"kwargs\": {\n",
      "          \"column\": \"5\",\n",
      "          \"min_value\": -0.01,\n",
      "          \"max_value\": 0.01\n",
      "        }\n",
      "      }\n",
      "    },\n",
      "    {\n",
      "      \"success\": false,\n",
      "      \"result\": {\n",
      "        \"element_count\": 100,\n",
      "        \"missing_count\": 0,\n",
      "        \"missing_percent\": 0.0,\n",
      "        \"unexpected_count\": 3,\n",
      "        \"unexpected_percent\": 0.03,\n",
      "        \"unexpected_percent_nonmissing\": 0.03,\n",
      "        \"partial_unexpected_list\": [\n",
      "          0.046363636363636364,\n",
      "          -0.046363636363636364,\n",
      "          0.010000000000000002\n",
      "        ]\n",
      "      },\n",
      "      \"exception_info\": {\n",
      "        \"raised_exception\": false,\n",
      "        \"exception_message\": null,\n",
      "        \"exception_traceback\": null\n",
      "      },\n",
      "      \"expectation_config\": {\n",
      "        \"expectation_type\": \"expect_column_values_to_be_between\",\n",
      "        \"kwargs\": {\n",
      "          \"column\": \"6\",\n",
      "          \"min_value\": -0.01,\n",
      "          \"max_value\": 0.01\n",
      "        }\n",
      "      }\n",
      "    },\n",
      "    {\n",
      "      \"success\": false,\n",
      "      \"result\": {\n",
      "        \"element_count\": 100,\n",
      "        \"missing_count\": 0,\n",
      "        \"missing_percent\": 0.0,\n",
      "        \"unexpected_count\": 2,\n",
      "        \"unexpected_percent\": 0.02,\n",
      "        \"unexpected_percent_nonmissing\": 0.02,\n",
      "        \"partial_unexpected_list\": [\n",
      "          0.04818181818181818,\n",
      "          -0.05136363636363636\n",
      "        ]\n",
      "      },\n",
      "      \"exception_info\": {\n",
      "        \"raised_exception\": false,\n",
      "        \"exception_message\": null,\n",
      "        \"exception_traceback\": null\n",
      "      },\n",
      "      \"expectation_config\": {\n",
      "        \"expectation_type\": \"expect_column_values_to_be_between\",\n",
      "        \"kwargs\": {\n",
      "          \"column\": \"7\",\n",
      "          \"min_value\": -0.01,\n",
      "          \"max_value\": 0.01\n",
      "        }\n",
      "      }\n",
      "    },\n",
      "    {\n",
      "      \"success\": false,\n",
      "      \"result\": {\n",
      "        \"element_count\": 100,\n",
      "        \"missing_count\": 0,\n",
      "        \"missing_percent\": 0.0,\n",
      "        \"unexpected_count\": 2,\n",
      "        \"unexpected_percent\": 0.02,\n",
      "        \"unexpected_percent_nonmissing\": 0.02,\n",
      "        \"partial_unexpected_list\": [\n",
      "          0.045,\n",
      "          -0.042727272727272725\n",
      "        ]\n",
      "      },\n",
      "      \"exception_info\": {\n",
      "        \"raised_exception\": false,\n",
      "        \"exception_message\": null,\n",
      "        \"exception_traceback\": null\n",
      "      },\n",
      "      \"expectation_config\": {\n",
      "        \"expectation_type\": \"expect_column_values_to_be_between\",\n",
      "        \"kwargs\": {\n",
      "          \"column\": \"8\",\n",
      "          \"min_value\": -0.01,\n",
      "          \"max_value\": 0.01\n",
      "        }\n",
      "      }\n",
      "    },\n",
      "    {\n",
      "      \"success\": false,\n",
      "      \"result\": {\n",
      "        \"element_count\": 100,\n",
      "        \"missing_count\": 0,\n",
      "        \"missing_percent\": 0.0,\n",
      "        \"unexpected_count\": 2,\n",
      "        \"unexpected_percent\": 0.02,\n",
      "        \"unexpected_percent_nonmissing\": 0.02,\n",
      "        \"partial_unexpected_list\": [\n",
      "          0.04363636363636364,\n",
      "          -0.046363636363636364\n",
      "        ]\n",
      "      },\n",
      "      \"exception_info\": {\n",
      "        \"raised_exception\": false,\n",
      "        \"exception_message\": null,\n",
      "        \"exception_traceback\": null\n",
      "      },\n",
      "      \"expectation_config\": {\n",
      "        \"expectation_type\": \"expect_column_values_to_be_between\",\n",
      "        \"kwargs\": {\n",
      "          \"column\": \"9\",\n",
      "          \"min_value\": -0.01,\n",
      "          \"max_value\": 0.01\n",
      "        }\n",
      "      }\n",
      "    }\n",
      "  ],\n",
      "  \"success\": false,\n",
      "  \"statistics\": {\n",
      "    \"evaluated_expectations\": 10,\n",
      "    \"successful_expectations\": 0,\n",
      "    \"unsuccessful_expectations\": 10,\n",
      "    \"success_percent\": 0.0\n",
      "  },\n",
      "  \"meta\": {\n",
      "    \"great_expectations.__version__\": \"0.7.11__develop\",\n",
      "    \"data_asset_name\": null,\n",
      "    \"expectation_suite_name\": \"default\",\n",
      "    \"run_id\": \"2019-09-23T225015.937561Z\"\n",
      "  }\n",
      "}\n"
     ]
    }
   ],
   "source": [
    "print(json.dumps(res,indent=2))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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