notebooks/test-nursery.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "00cac1f9",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "# ACRO Demonstration"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "e33fd4fb",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "outputs": [],
   "source": [
    "import os\n",
    "\n",
    "import numpy as np\n",
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "c01cfe12",
   "metadata": {
    "slideshow": {
     "slide_type": "skip"
    }
   },
   "outputs": [],
   "source": [
    "# uncomment this line if acro is not installed\n",
    "# ie you are in development mode\n",
    "# sys.path.insert(0, os.path.abspath(\"..\"))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "cc8d993a",
   "metadata": {
    "scrolled": true,
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [],
   "source": [
    "from acro import ACRO, add_constant, add_to_acro"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "530efcfe",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "### Instantiate ACRO"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "4b8a77e2",
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO:acro:version: 0.4.5\n",
      "INFO:acro:config: {'safe_threshold': 10, 'safe_dof_threshold': 10, 'safe_nk_n': 2, 'safe_nk_k': 0.9, 'safe_pratio_p': 0.1, 'check_missing_values': False, 'survival_safe_threshold': 10, 'zeros_are_disclosive': True, 'unsupported_filetypes': ['.gph', '.svg']}\n",
      "INFO:acro:automatic suppression: True\n"
     ]
    }
   ],
   "source": [
    "acro = ACRO(suppress=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "27a2baaa",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "### Load test data\n",
    "The dataset used in this notebook is the nursery dataset from OpenML.  \n",
    "- The dataset can be read directly from OpenML using the code commented in the next cell.\n",
    "- In this version, it can be read directly from the local machine if it has been downloaded. \n",
    "- The code below reads the data from a folder called \"data\" which we assume is at the same level as the folder where you are working.\n",
    "- The path might need to be changed if the data has been downloaded and stored elsewhere.\n",
    " - for example use:  \n",
    "    path = os.path.join(\"data\", \"nursery.arff\")  \n",
    "    if the data is in a sub-folder of your work folder"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "ac790b2b-b02f-49f7-8237-a033abed6e87",
   "metadata": {
    "scrolled": true,
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>parents</th>\n",
       "      <th>has_nurs</th>\n",
       "      <th>form</th>\n",
       "      <th>children</th>\n",
       "      <th>housing</th>\n",
       "      <th>finance</th>\n",
       "      <th>social</th>\n",
       "      <th>health</th>\n",
       "      <th>recommend</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>usual</td>\n",
       "      <td>proper</td>\n",
       "      <td>complete</td>\n",
       "      <td>1</td>\n",
       "      <td>convenient</td>\n",
       "      <td>convenient</td>\n",
       "      <td>nonprob</td>\n",
       "      <td>recommended</td>\n",
       "      <td>recommend</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>usual</td>\n",
       "      <td>proper</td>\n",
       "      <td>complete</td>\n",
       "      <td>1</td>\n",
       "      <td>convenient</td>\n",
       "      <td>convenient</td>\n",
       "      <td>nonprob</td>\n",
       "      <td>priority</td>\n",
       "      <td>priority</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>usual</td>\n",
       "      <td>proper</td>\n",
       "      <td>complete</td>\n",
       "      <td>1</td>\n",
       "      <td>convenient</td>\n",
       "      <td>convenient</td>\n",
       "      <td>nonprob</td>\n",
       "      <td>not_recom</td>\n",
       "      <td>not_recom</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>usual</td>\n",
       "      <td>proper</td>\n",
       "      <td>complete</td>\n",
       "      <td>1</td>\n",
       "      <td>convenient</td>\n",
       "      <td>convenient</td>\n",
       "      <td>slightly_prob</td>\n",
       "      <td>recommended</td>\n",
       "      <td>recommend</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>usual</td>\n",
       "      <td>proper</td>\n",
       "      <td>complete</td>\n",
       "      <td>1</td>\n",
       "      <td>convenient</td>\n",
       "      <td>convenient</td>\n",
       "      <td>slightly_prob</td>\n",
       "      <td>priority</td>\n",
       "      <td>priority</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  parents has_nurs      form children     housing     finance         social  \\\n",
       "0   usual   proper  complete        1  convenient  convenient        nonprob   \n",
       "1   usual   proper  complete        1  convenient  convenient        nonprob   \n",
       "2   usual   proper  complete        1  convenient  convenient        nonprob   \n",
       "3   usual   proper  complete        1  convenient  convenient  slightly_prob   \n",
       "4   usual   proper  complete        1  convenient  convenient  slightly_prob   \n",
       "\n",
       "        health  recommend  \n",
       "0  recommended  recommend  \n",
       "1     priority   priority  \n",
       "2    not_recom  not_recom  \n",
       "3  recommended  recommend  \n",
       "4     priority   priority  "
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from scipy.io.arff import loadarff\n",
    "\n",
    "path = os.path.join(\"../data\", \"nursery.arff\")\n",
    "data = loadarff(path)\n",
    "df = pd.DataFrame(data[0])\n",
    "df = df.select_dtypes([object])\n",
    "df = df.stack().str.decode(\"utf-8\").unstack()\n",
    "df.rename(columns={\"class\": \"recommend\"}, inplace=True)\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ea2a0d76-ba68-4a74-93c3-bdaa88c48929",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "### Convert 'more than 3' children to random between 4 and 10\n",
    "Change the children column from categorical to numeric in order to be able to test some of the ACRO functions that require a numeric feature"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "b43810a8-4da9-4cec-a613-e2562ed95601",
   "metadata": {
    "scrolled": true,
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " children column entries in raw file ['1' '2' '3' 'more']\n"
     ]
    }
   ],
   "source": [
    "print(f\" children column entries in raw file {df.children.unique()}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "042f8e9d-b33b-4daf-851e-9f70b5d4859a",
   "metadata": {
    "scrolled": true,
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [],
   "source": [
    "df[\"children\"].replace(to_replace={\"more\": \"4\"}, inplace=True)\n",
    "df[\"children\"] = pd.to_numeric(df[\"children\"])\n",
    "\n",
    "df[\"children\"] = df.apply(\n",
    "    lambda row: (\n",
    "        row[\"children\"] if row[\"children\"] in (1, 2, 3) else np.random.randint(4, 10)\n",
    "    ),\n",
    "    axis=1,\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d098c704",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "# Examples of producing tabular output\n",
    "We rely on the industry-standard package **pandas** for tabulating data.  \n",
    "In the next few examples we show:\n",
    "- first, how a researcher would normally make a call in pandas, saving the results in a variable that they can view on screen (or save to file?)\n",
    "- then how the call is identical in SACRO, except that:\n",
    "  - \"pd\" is replaced by \"acro\"\n",
    "  - the researcher immediately sees a copy of what the TRE output checker will see.\n",
    "  "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4ae844a0",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "### Pandas crosstab\n",
    "This is an example of crosstab using pandas.  \n",
    "We first make the call, then the second line print the outputs to screen."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "961684cb",
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "parents     great_pret  pretentious  usual\n",
      "recommend                                 \n",
      "not_recom         1440         1440   1440\n",
      "priority           858         1484   1924\n",
      "recommend            0            0      2\n",
      "spec_prior        2022         1264    758\n",
      "very_recom           0          132    196\n"
     ]
    }
   ],
   "source": [
    "table = pd.crosstab(df.recommend, df.parents)\n",
    "print(table)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d642ed00",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "### ACRO crosstab\n",
    "This is an example of crosstab using ACRO.  \n",
    "The INFO lines show the researcher what will be reported to the output checkers.\n",
    "Then the (suppressed as necessary)  table is shown via. the print command as before."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "bb4b2677",
   "metadata": {
    "scrolled": true,
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO:acro:get_summary(): fail; threshold: 4 cells suppressed; \n",
      "INFO:acro:outcome_df:\n",
      "----------------------------------------------------|\n",
      "parents    |great_pret   |pretentious  |usual       |\n",
      "recommend  |             |             |            |\n",
      "----------------------------------------------------|\n",
      "not_recom  |          ok |          ok |          ok|\n",
      "priority   |          ok |          ok |          ok|\n",
      "recommend  | threshold;  | threshold;  | threshold; |\n",
      "spec_prior |          ok |          ok |          ok|\n",
      "very_recom | threshold;  |          ok |          ok|\n",
      "----------------------------------------------------|\n",
      "\n",
      "INFO:acro:records:add(): output_0\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "parents     great_pret  pretentious   usual\n",
      "recommend                                  \n",
      "not_recom       1440.0       1440.0  1440.0\n",
      "priority         858.0       1484.0  1924.0\n",
      "recommend          NaN          NaN     NaN\n",
      "spec_prior      2022.0       1264.0   758.0\n",
      "very_recom         NaN        132.0   196.0\n"
     ]
    }
   ],
   "source": [
    "safe_table = acro.crosstab(\n",
    "    df.recommend,\n",
    "    df.parents,\n",
    ")\n",
    "print(safe_table)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b3d09450",
   "metadata": {},
   "source": [
    "### ACRO crosstab with totals\n",
    "This is an example of crosstab with totals columns and suppression.  \n",
    "Note that when margins is true any row or column where all the cells are discolsive is deleted. If you wish to see such row or column set show_suppressed to True.  \n",
    "show_suppressed parameter does not work with herichical tables and when the aggregation function is the standard deviation."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "42825f24",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO:acro:get_summary(): fail; threshold: 5 cells suppressed; \n",
      "INFO:acro:outcome_df:\n",
      "------------------------------------------------------------------|\n",
      "parents    |great_pret   |pretentious  |usual        |All         |\n",
      "recommend  |             |             |             |            |\n",
      "------------------------------------------------------------------|\n",
      "not_recom  |          ok |          ok |          ok |          ok|\n",
      "priority   |          ok |          ok |          ok |          ok|\n",
      "recommend  | threshold;  | threshold;  | threshold;  | threshold; |\n",
      "spec_prior |          ok |          ok |          ok |          ok|\n",
      "very_recom | threshold;  |          ok |          ok |          ok|\n",
      "All        |          ok |          ok |          ok |          ok|\n",
      "------------------------------------------------------------------|\n",
      "\n",
      "INFO:acro:records:add(): output_1\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "parents     great_pret  pretentious  usual    All\n",
      "recommend                                        \n",
      "not_recom       1440.0         1440   1440   4320\n",
      "priority         858.0         1484   1924   4266\n",
      "spec_prior      2022.0         1264    758   4044\n",
      "very_recom         NaN          132    196    328\n",
      "All             4320.0         4320   4318  12958\n"
     ]
    }
   ],
   "source": [
    "safe_table = acro.crosstab(df.recommend, df.parents, margins=True)\n",
    "print(safe_table)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b3e48dec",
   "metadata": {},
   "source": [
    "### ACRO crosstab without suppression\n",
    "This is an example of crosstab without suppressing the cells that violate the disclosure tests.  \n",
    "Note that you need to change the value of the suppress variable in the acro object to False. Then run the crosstab command.  \n",
    "If you wish to continue the research while suppressing the outputs, turn on the suppress variable otherwise leave it as it is."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "27329e57",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO:acro:get_summary(): fail; threshold: 4 cells may need suppressing; \n",
      "INFO:acro:outcome_df:\n",
      "----------------------------------------------------|\n",
      "parents    |great_pret   |pretentious  |usual       |\n",
      "recommend  |             |             |            |\n",
      "----------------------------------------------------|\n",
      "not_recom  |          ok |          ok |          ok|\n",
      "priority   |          ok |          ok |          ok|\n",
      "recommend  | threshold;  | threshold;  | threshold; |\n",
      "spec_prior |          ok |          ok |          ok|\n",
      "very_recom | threshold;  |          ok |          ok|\n",
      "----------------------------------------------------|\n",
      "\n",
      "INFO:acro:records:add(): output_2\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "parents     great_pret  pretentious  usual\n",
      "recommend                                 \n",
      "not_recom         1440         1440   1440\n",
      "priority           858         1484   1924\n",
      "recommend            0            0      2\n",
      "spec_prior        2022         1264    758\n",
      "very_recom           0          132    196\n"
     ]
    }
   ],
   "source": [
    "acro.suppress = False\n",
    "\n",
    "safe_table = acro.crosstab(df.recommend, df.parents)\n",
    "print(safe_table)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "ed5134ab",
   "metadata": {},
   "outputs": [],
   "source": [
    "acro.suppress = True"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8b603548",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "### ACRO crosstab with aggregation function"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "298d2b40",
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO:acro:get_summary(): fail; threshold: 1 cells suppressed; p-ratio: 4 cells suppressed; nk-rule: 4 cells suppressed; \n",
      "INFO:acro:outcome_df:\n",
      "------------------------------------------------------------------------------------|\n",
      "parents    |great_pret          |pretentious         |usual                         |\n",
      "recommend  |                    |                    |                              |\n",
      "------------------------------------------------------------------------------------|\n",
      "not_recom  |                 ok |                 ok |                            ok|\n",
      "priority   |                 ok |                 ok |                            ok|\n",
      "recommend  | p-ratio; nk-rule;  | p-ratio; nk-rule;  | threshold; p-ratio; nk-rule; |\n",
      "spec_prior |                 ok |                 ok |                            ok|\n",
      "very_recom | p-ratio; nk-rule;  |                 ok |                            ok|\n",
      "------------------------------------------------------------------------------------|\n",
      "\n",
      "INFO:acro:records:add(): output_3\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "parents     great_pret  pretentious   usual\n",
      "recommend                                  \n",
      "not_recom       1440.0       1440.0  1440.0\n",
      "priority         858.0       1484.0  1924.0\n",
      "recommend          NaN          NaN     NaN\n",
      "spec_prior      2022.0       1264.0   758.0\n",
      "very_recom         NaN        132.0   196.0\n"
     ]
    }
   ],
   "source": [
    "safe_table = acro.crosstab(\n",
    "    df.recommend, df.parents, values=df.children, aggfunc=\"count\"\n",
    ")\n",
    "print(safe_table)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "b4aea046",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO:acro:get_summary(): fail; threshold: 2 cells suppressed; p-ratio: 8 cells suppressed; nk-rule: 8 cells suppressed; \n",
      "INFO:acro:outcome_df:\n",
      "---------------------------------------------------------------------------------------------------------------------------------------------------------|\n",
      "           mode_aggfunc                                                           |mean                                                                  |\n",
      "parents    great_pret          pretentious         usual                          |great_pret          pretentious         usual                         |\n",
      "recommend                                                                         |                                                                      |\n",
      "---------------------------------------------------------------------------------------------------------------------------------------------------------|\n",
      "not_recom                   ok                  ok                             ok |                 ok                  ok                             ok|\n",
      "priority                    ok                  ok                             ok |                 ok                  ok                             ok|\n",
      "recommend   p-ratio; nk-rule;   p-ratio; nk-rule;   threshold; p-ratio; nk-rule;  | p-ratio; nk-rule;   p-ratio; nk-rule;   threshold; p-ratio; nk-rule; |\n",
      "spec_prior                  ok                  ok                             ok |                 ok                  ok                             ok|\n",
      "very_recom  p-ratio; nk-rule;                   ok                             ok | p-ratio; nk-rule;                   ok                             ok|\n",
      "---------------------------------------------------------------------------------------------------------------------------------------------------------|\n",
      "\n",
      "INFO:acro:records:add(): output_4\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "           mode_aggfunc                         mean                      \n",
      "parents      great_pret pretentious usual great_pret pretentious     usual\n",
      "recommend                                                                 \n",
      "not_recom           3.0         2.0   1.0   3.100694    3.143750  3.111806\n",
      "priority            1.0         1.0   1.0   2.627040    3.022237  3.120062\n",
      "recommend           NaN         NaN   NaN        NaN         NaN       NaN\n",
      "spec_prior          3.0         3.0   3.0   3.307122    3.336234  3.323219\n",
      "very_recom          NaN         1.0   1.0        NaN    2.227273  2.178571\n"
     ]
    }
   ],
   "source": [
    "safe_table = acro.crosstab(\n",
    "    df.recommend, df.parents, values=df.children, aggfunc=[\"mode\", \"mean\"]\n",
    ")\n",
    "print(safe_table)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d66e565b",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "### ACRO pivot_table\n",
    "This is an example of pivot table using ACRO.  \n",
    "- Some researchers may prefer this to using crosstab.   \n",
    "- Again the call syntax is identical to the pandas \"pd.pivot_table\"\n",
    "- in this case the output is non-disclosive"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "966c1a9b",
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO:acro:get_summary(): pass\n",
      "INFO:acro:outcome_df:\n",
      "------------------------------|\n",
      "            mean     |std     |\n",
      "            children |children|\n",
      "parents              |        |\n",
      "------------------------------|\n",
      "great_pret   ok      | ok     |\n",
      "pretentious  ok      | ok     |\n",
      "usual        ok      | ok     |\n",
      "------------------------------|\n",
      "\n",
      "INFO:acro:records:add(): output_5\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                 mean       std\n",
      "             children  children\n",
      "parents                        \n",
      "great_pret   3.103241  2.213116\n",
      "pretentious  3.130324  2.250447\n",
      "usual        3.109259  2.216799\n"
     ]
    }
   ],
   "source": [
    "table = acro.pivot_table(\n",
    "    df, index=[\"parents\"], values=[\"children\"], aggfunc=[\"mean\", \"std\"]\n",
    ")\n",
    "print(table)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4adc374b",
   "metadata": {},
   "source": [
    "### ACRO pivot_table with margins"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "9c024b65",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO:acro:get_summary(): fail; threshold: 5 cells suppressed; p-ratio: 5 cells suppressed; nk-rule: 5 cells suppressed; \n",
      "INFO:acro:outcome_df:\n",
      "-----------------------------------------------------------------------------------------------------------|\n",
      "            children                                                                                       |\n",
      "recommend   not_recom priority recommend                      spec_prior very_recom                     All|\n",
      "parents                                                                                                    |\n",
      "-----------------------------------------------------------------------------------------------------------|\n",
      "great_pret   ok        ok       threshold; p-ratio; nk-rule;   ok         threshold; p-ratio; nk-rule;   ok|\n",
      "pretentious  ok        ok       threshold; p-ratio; nk-rule;   ok                                    ok  ok|\n",
      "usual        ok        ok       threshold; p-ratio; nk-rule;   ok                                    ok  ok|\n",
      "All          ok        ok       threshold; p-ratio; nk-rule;   ok                                    ok  ok|\n",
      "-----------------------------------------------------------------------------------------------------------|\n",
      "\n",
      "INFO:acro:Disclosive cells were deleted from the dataframe before calculating the pivot table\n",
      "INFO:acro:records:add(): output_6\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "             children                                          \n",
      "recommend   not_recom  priority spec_prior very_recom       All\n",
      "parents                                                        \n",
      "great_pret   3.100694  2.627040   3.307122        NaN  3.103241\n",
      "pretentious  3.143750  3.022237   3.336234   2.227273  3.130324\n",
      "usual        3.111806  3.120062   3.323219   2.178571  3.110236\n",
      "All          3.118750  2.986873   3.319238   2.198171  3.114601\n"
     ]
    }
   ],
   "source": [
    "safe_table = acro.pivot_table(\n",
    "    df, columns=[\"recommend\"], index=[\"parents\"], values=[\"children\"], margins=True\n",
    ")\n",
    "print(safe_table)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8446fa99-c073-48b8-875e-700dfa17ea0c",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "# Regression examples using ACRO\n",
    "\n",
    "Again there is an industry-standard package in python, this time called **statsmodels**.\n",
    "- The examples below illustrate the use of the ACRO wrapper standard statsmodel functions\n",
    "- Note that statsmodels can be called using an 'R-like' format (using an 'r' suffix on the command names)\n",
    "- most statsmodels functiobns return a \"results object\" which has a \"summary\" function that produces printable/saveable outputs \n",
    "\n",
    "### Start by manipulating the nursery data to get two numeric variables\n",
    "- The 'recommend' column is converted to an integer scale"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "72aefb22",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "outputs": [],
   "source": [
    "df[\"recommend\"].replace(\n",
    "    to_replace={\n",
    "        \"not_recom\": \"0\",\n",
    "        \"recommend\": \"1\",\n",
    "        \"very_recom\": \"2\",\n",
    "        \"priority\": \"3\",\n",
    "        \"spec_prior\": \"4\",\n",
    "    },\n",
    "    inplace=True,\n",
    ")\n",
    "df[\"recommend\"] = pd.to_numeric(df[\"recommend\"])\n",
    "\n",
    "new_df = df[[\"recommend\", \"children\"]]\n",
    "new_df = new_df.dropna()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3ef880e6-726f-4a0b-9bcd-da8861dbd5a7",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "### ACRO OLS \n",
    "This is an example of ordinary least square regression using ACRO.  \n",
    "- Above recommend column was converted form categorical to numeric. \n",
    "- Now we perform a the linear regression between recommend and children. \n",
    "- This version includes a constant (intercept)\n",
    "- This is just to show how the regression is done using ACRO.  \n",
    "- **No correlation is expected to be seen by using these variables**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "2f462e42",
   "metadata": {
    "scrolled": true,
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO:acro:ols() outcome: pass; dof=12958.0 >= 10\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO:acro:records:add(): output_7\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<table class=\"simpletable\">\n",
       "<caption>OLS Regression Results</caption>\n",
       "<tr>\n",
       "  <th>Dep. Variable:</th>        <td>recommend</td>    <th>  R-squared:         </th> <td>   0.000</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Model:</th>                   <td>OLS</td>       <th>  Adj. R-squared:    </th> <td>   0.000</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Method:</th>             <td>Least Squares</td>  <th>  F-statistic:       </th> <td>   6.417</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Date:</th>             <td>Mon, 04 Mar 2024</td> <th>  Prob (F-statistic):</th>  <td>0.0113</td>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Time:</th>                 <td>21:21:09</td>     <th>  Log-Likelihood:    </th> <td> -25124.</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>No. Observations:</th>      <td> 12960</td>      <th>  AIC:               </th> <td>5.025e+04</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Df Residuals:</th>          <td> 12958</td>      <th>  BIC:               </th> <td>5.027e+04</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Df Model:</th>              <td>     1</td>      <th>                     </th>     <td> </td>    \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Covariance Type:</th>      <td>nonrobust</td>    <th>                     </th>     <td> </td>    \n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "      <td></td>        <th>coef</th>     <th>std err</th>      <th>t</th>      <th>P>|t|</th>  <th>[0.025</th>    <th>0.975]</th>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>const</th>    <td>    2.2341</td> <td>    0.025</td> <td>   87.965</td> <td> 0.000</td> <td>    2.184</td> <td>    2.284</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>children</th> <td>    0.0168</td> <td>    0.007</td> <td>    2.533</td> <td> 0.011</td> <td>    0.004</td> <td>    0.030</td>\n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "  <th>Omnibus:</th>       <td>76735.931</td> <th>  Durbin-Watson:     </th> <td>   2.883</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Prob(Omnibus):</th>  <td> 0.000</td>   <th>  Jarque-Bera (JB):  </th> <td>1742.843</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Skew:</th>           <td>-0.485</td>   <th>  Prob(JB):          </th> <td>    0.00</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Kurtosis:</th>       <td> 1.487</td>   <th>  Cond. No.          </th> <td>    6.89</td>\n",
       "</tr>\n",
       "</table><br/><br/>Notes:<br/>[1] Standard Errors assume that the covariance matrix of the errors is correctly specified."
      ],
      "text/plain": [
       "<class 'statsmodels.iolib.summary.Summary'>\n",
       "\"\"\"\n",
       "                            OLS Regression Results                            \n",
       "==============================================================================\n",
       "Dep. Variable:              recommend   R-squared:                       0.000\n",
       "Model:                            OLS   Adj. R-squared:                  0.000\n",
       "Method:                 Least Squares   F-statistic:                     6.417\n",
       "Date:                Mon, 04 Mar 2024   Prob (F-statistic):             0.0113\n",
       "Time:                        21:21:09   Log-Likelihood:                -25124.\n",
       "No. Observations:               12960   AIC:                         5.025e+04\n",
       "Df Residuals:                   12958   BIC:                         5.027e+04\n",
       "Df Model:                           1                                         \n",
       "Covariance Type:            nonrobust                                         \n",
       "==============================================================================\n",
       "                 coef    std err          t      P>|t|      [0.025      0.975]\n",
       "------------------------------------------------------------------------------\n",
       "const          2.2341      0.025     87.965      0.000       2.184       2.284\n",
       "children       0.0168      0.007      2.533      0.011       0.004       0.030\n",
       "==============================================================================\n",
       "Omnibus:                    76735.931   Durbin-Watson:                   2.883\n",
       "Prob(Omnibus):                  0.000   Jarque-Bera (JB):             1742.843\n",
       "Skew:                          -0.485   Prob(JB):                         0.00\n",
       "Kurtosis:                       1.487   Cond. No.                         6.89\n",
       "==============================================================================\n",
       "\n",
       "Notes:\n",
       "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n",
       "\"\"\""
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y = new_df[\"recommend\"]\n",
    "x = new_df[\"children\"]\n",
    "x = add_constant(x)\n",
    "\n",
    "results = acro.ols(y, x)\n",
    "results.summary()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0c826271",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "### ACRO OLSR\n",
    "This is an example of ordinary least squares regression using the 'R-like' statsmodels api, i.e. from a formula and dataframe using ACRO "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "cc90f7c9",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO:acro:olsr() outcome: pass; dof=12958.0 >= 10\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO:acro:records:add(): output_8\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                            OLS Regression Results                            \n",
      "==============================================================================\n",
      "Dep. Variable:              recommend   R-squared:                       0.000\n",
      "Model:                            OLS   Adj. R-squared:                  0.000\n",
      "Method:                 Least Squares   F-statistic:                     6.417\n",
      "Date:                Mon, 04 Mar 2024   Prob (F-statistic):             0.0113\n",
      "Time:                        21:21:09   Log-Likelihood:                -25124.\n",
      "No. Observations:               12960   AIC:                         5.025e+04\n",
      "Df Residuals:                   12958   BIC:                         5.027e+04\n",
      "Df Model:                           1                                         \n",
      "Covariance Type:            nonrobust                                         \n",
      "==============================================================================\n",
      "                 coef    std err          t      P>|t|      [0.025      0.975]\n",
      "------------------------------------------------------------------------------\n",
      "Intercept      2.2341      0.025     87.965      0.000       2.184       2.284\n",
      "children       0.0168      0.007      2.533      0.011       0.004       0.030\n",
      "==============================================================================\n",
      "Omnibus:                    76735.931   Durbin-Watson:                   2.883\n",
      "Prob(Omnibus):                  0.000   Jarque-Bera (JB):             1742.843\n",
      "Skew:                          -0.485   Prob(JB):                         0.00\n",
      "Kurtosis:                       1.487   Cond. No.                         6.89\n",
      "==============================================================================\n",
      "\n",
      "Notes:\n",
      "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n"
     ]
    }
   ],
   "source": [
    "results = acro.olsr(formula=\"recommend ~ children\", data=new_df)\n",
    "print(results.summary())"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2816eac7",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "### ACRO Probit\n",
    "This is an example of probit regression using ACRO  \n",
    "We use a different combination of variables from the original dataset.\n",
    "\n",
    "Again, we support the use of R-like formulas - because we support R "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "5b1a1611",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO:acro:probit() outcome: pass; dof=12958.0 >= 10\n",
      "INFO:acro:records:add(): output_9\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Optimization terminated successfully.\n",
      "         Current function value: 0.693146\n",
      "         Iterations 2\n",
      "                          Probit Regression Results                           \n",
      "==============================================================================\n",
      "Dep. Variable:                finance   No. Observations:                12960\n",
      "Model:                         Probit   Df Residuals:                    12958\n",
      "Method:                           MLE   Df Model:                            1\n",
      "Date:                Mon, 04 Mar 2024   Pseudo R-squ.:               1.186e-06\n",
      "Time:                        21:21:09   Log-Likelihood:                -8983.2\n",
      "converged:                       True   LL-Null:                       -8983.2\n",
      "Covariance Type:            nonrobust   LLR p-value:                    0.8839\n",
      "==============================================================================\n",
      "                 coef    std err          z      P>|z|      [0.025      0.975]\n",
      "------------------------------------------------------------------------------\n",
      "const         -0.0022      0.019     -0.119      0.905      -0.039       0.035\n",
      "children       0.0007      0.005      0.146      0.884      -0.009       0.010\n",
      "==============================================================================\n"
     ]
    }
   ],
   "source": [
    "new_df = df[[\"finance\", \"children\"]]\n",
    "new_df = new_df.dropna()\n",
    "\n",
    "y = new_df[\"finance\"].astype(\"category\").cat.codes  # numeric\n",
    "y.name = \"finance\"\n",
    "x = new_df[\"children\"]\n",
    "x = add_constant(x)\n",
    "\n",
    "results = acro.probit(y, x)\n",
    "print(results.summary())"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f38b4334",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "### ACRO Logit\n",
    "This is an example of logistic regression using ACRO using the statmodels function"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "dcf30f8f",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Optimization terminated successfully.\n",
      "         Current function value: 0.693146\n",
      "         Iterations 2\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO:acro:logit() outcome: pass; dof=12958.0 >= 10\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO:acro:records:add(): output_10\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<table class=\"simpletable\">\n",
       "<caption>Logit Regression Results</caption>\n",
       "<tr>\n",
       "  <th>Dep. Variable:</th>        <td>finance</td>     <th>  No. Observations:  </th>  <td> 12960</td>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Model:</th>                 <td>Logit</td>      <th>  Df Residuals:      </th>  <td> 12958</td>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Method:</th>                 <td>MLE</td>       <th>  Df Model:          </th>  <td>     1</td>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Date:</th>            <td>Mon, 04 Mar 2024</td> <th>  Pseudo R-squ.:     </th> <td>1.186e-06</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Time:</th>                <td>21:21:09</td>     <th>  Log-Likelihood:    </th> <td> -8983.2</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>converged:</th>             <td>True</td>       <th>  LL-Null:           </th> <td> -8983.2</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Covariance Type:</th>     <td>nonrobust</td>    <th>  LLR p-value:       </th>  <td>0.8839</td>  \n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "      <td></td>        <th>coef</th>     <th>std err</th>      <th>z</th>      <th>P>|z|</th>  <th>[0.025</th>    <th>0.975]</th>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>const</th>    <td>   -0.0036</td> <td>    0.030</td> <td>   -0.119</td> <td> 0.905</td> <td>   -0.063</td> <td>    0.056</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>children</th> <td>    0.0012</td> <td>    0.008</td> <td>    0.146</td> <td> 0.884</td> <td>   -0.014</td> <td>    0.017</td>\n",
       "</tr>\n",
       "</table>"
      ],
      "text/plain": [
       "<class 'statsmodels.iolib.summary.Summary'>\n",
       "\"\"\"\n",
       "                           Logit Regression Results                           \n",
       "==============================================================================\n",
       "Dep. Variable:                finance   No. Observations:                12960\n",
       "Model:                          Logit   Df Residuals:                    12958\n",
       "Method:                           MLE   Df Model:                            1\n",
       "Date:                Mon, 04 Mar 2024   Pseudo R-squ.:               1.186e-06\n",
       "Time:                        21:21:09   Log-Likelihood:                -8983.2\n",
       "converged:                       True   LL-Null:                       -8983.2\n",
       "Covariance Type:            nonrobust   LLR p-value:                    0.8839\n",
       "==============================================================================\n",
       "                 coef    std err          z      P>|z|      [0.025      0.975]\n",
       "------------------------------------------------------------------------------\n",
       "const         -0.0036      0.030     -0.119      0.905      -0.063       0.056\n",
       "children       0.0012      0.008      0.146      0.884      -0.014       0.017\n",
       "==============================================================================\n",
       "\"\"\""
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "results = acro.logit(y, x)\n",
    "results.summary()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a962ff17",
   "metadata": {},
   "source": [
    "### ACRO survival analysis\n",
    "This is an example of survival tables and plots using ACRO.  \n",
    "- A dataset from statsmodels is used for the survival analysis.\n",
    "- A subset of tha dataset is used in this example to demostrate the survival analysis.\n",
    "- The output parameter in the surv_func define the type of output (table or plot)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "dcf2c86d",
   "metadata": {},
   "outputs": [],
   "source": [
    "import statsmodels.api as sm\n",
    "\n",
    "data = sm.datasets.get_rdataset(\"flchain\", \"survival\").data\n",
    "data = data.loc[data.sex == \"F\", :]\n",
    "data = data.iloc[:20, :]\n",
    "# data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "df1cbb9e",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO:acro:get_summary(): fail; threshold: 76 cells suppressed; \n",
      "INFO:acro:outcome_df:\n",
      "-----------------------------------------------------------------|\n",
      "     |Surv| |prob    Surv prob |SE ||num| at| risk  num |events  |\n",
      "Time |    | |                  |   ||   |   |           |        |\n",
      "-----------------------------------------------------------------|\n",
      "51   |    | |     ok           |ok ||   |   |    ok     |      ok|\n",
      "69   | thr|e|shold;   threshold|;  || th|res|hold;   thr|eshold; |\n",
      "85   | thr|e|shold;   threshold|;  || th|res|hold;   thr|eshold; |\n",
      "91   | thr|e|shold;   threshold|;  || th|res|hold;   thr|eshold; |\n",
      "115  | thr|e|shold;   threshold|;  || th|res|hold;   thr|eshold; |\n",
      "372  | thr|e|shold;   threshold|;  || th|res|hold;   thr|eshold; |\n",
      "667  | thr|e|shold;   threshold|;  || th|res|hold;   thr|eshold; |\n",
      "874  | thr|e|shold;   threshold|;  || th|res|hold;   thr|eshold; |\n",
      "1039 | thr|e|shold;   threshold|;  || th|res|hold;   thr|eshold; |\n",
      "1046 | thr|e|shold;   threshold|;  || th|res|hold;   thr|eshold; |\n",
      "1281 | thr|e|shold;   threshold|;  || th|res|hold;   thr|eshold; |\n",
      "1286 | thr|e|shold;   threshold|;  || th|res|hold;   thr|eshold; |\n",
      "1326 | thr|e|shold;   threshold|;  || th|res|hold;   thr|eshold; |\n",
      "1355 | thr|e|shold;   threshold|;  || th|res|hold;   thr|eshold; |\n",
      "1626 | thr|e|shold;   threshold|;  || th|res|hold;   thr|eshold; |\n",
      "1903 | thr|e|shold;   threshold|;  || th|res|hold;   thr|eshold; |\n",
      "1914 | thr|e|shold;   threshold|;  || th|res|hold;   thr|eshold; |\n",
      "2776 | thr|e|shold;   threshold|;  || th|res|hold;   thr|eshold; |\n",
      "2851 | thr|e|shold;   threshold|;  || th|res|hold;   thr|eshold; |\n",
      "3309 | thr|e|shold;   threshold|;  || th|res|hold;   thr|eshold; |\n",
      "-----------------------------------------------------------------|\n",
      "\n",
      "INFO:acro:records:add(): output_11\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "      Surv prob  Surv prob SE  num at risk  num events\n",
      "Time                                                  \n",
      "51         0.95      0.048734         20.0         1.0\n",
      "69          NaN           NaN          NaN         NaN\n",
      "85          NaN           NaN          NaN         NaN\n",
      "91          NaN           NaN          NaN         NaN\n",
      "115         NaN           NaN          NaN         NaN\n",
      "372         NaN           NaN          NaN         NaN\n",
      "667         NaN           NaN          NaN         NaN\n",
      "874         NaN           NaN          NaN         NaN\n",
      "1039        NaN           NaN          NaN         NaN\n",
      "1046        NaN           NaN          NaN         NaN\n",
      "1281        NaN           NaN          NaN         NaN\n",
      "1286        NaN           NaN          NaN         NaN\n",
      "1326        NaN           NaN          NaN         NaN\n",
      "1355        NaN           NaN          NaN         NaN\n",
      "1626        NaN           NaN          NaN         NaN\n",
      "1903        NaN           NaN          NaN         NaN\n",
      "1914        NaN           NaN          NaN         NaN\n",
      "2776        NaN           NaN          NaN         NaN\n",
      "2851        NaN           NaN          NaN         NaN\n",
      "3309        NaN           NaN          NaN         NaN\n"
     ]
    }
   ],
   "source": [
    "safe_table = acro.surv_func(data.futime, data.death, output=\"table\")\n",
    "print(safe_table)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "8762ab29",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO:acro:get_summary(): fail; threshold: 76 cells suppressed; \n",
      "INFO:acro:outcome_df:\n",
      "-----------------------------------------------------------------|\n",
      "     |Surv| |prob    Surv prob |SE ||num| at| risk  num |events  |\n",
      "Time |    | |                  |   ||   |   |           |        |\n",
      "-----------------------------------------------------------------|\n",
      "51   |    | |     ok           |ok ||   |   |    ok     |      ok|\n",
      "69   | thr|e|shold;   threshold|;  || th|res|hold;   thr|eshold; |\n",
      "85   | thr|e|shold;   threshold|;  || th|res|hold;   thr|eshold; |\n",
      "91   | thr|e|shold;   threshold|;  || th|res|hold;   thr|eshold; |\n",
      "115  | thr|e|shold;   threshold|;  || th|res|hold;   thr|eshold; |\n",
      "372  | thr|e|shold;   threshold|;  || th|res|hold;   thr|eshold; |\n",
      "667  | thr|e|shold;   threshold|;  || th|res|hold;   thr|eshold; |\n",
      "874  | thr|e|shold;   threshold|;  || th|res|hold;   thr|eshold; |\n",
      "1039 | thr|e|shold;   threshold|;  || th|res|hold;   thr|eshold; |\n",
      "1046 | thr|e|shold;   threshold|;  || th|res|hold;   thr|eshold; |\n",
      "1281 | thr|e|shold;   threshold|;  || th|res|hold;   thr|eshold; |\n",
      "1286 | thr|e|shold;   threshold|;  || th|res|hold;   thr|eshold; |\n",
      "1326 | thr|e|shold;   threshold|;  || th|res|hold;   thr|eshold; |\n",
      "1355 | thr|e|shold;   threshold|;  || th|res|hold;   thr|eshold; |\n",
      "1626 | thr|e|shold;   threshold|;  || th|res|hold;   thr|eshold; |\n",
      "1903 | thr|e|shold;   threshold|;  || th|res|hold;   thr|eshold; |\n",
      "1914 | thr|e|shold;   threshold|;  || th|res|hold;   thr|eshold; |\n",
      "2776 | thr|e|shold;   threshold|;  || th|res|hold;   thr|eshold; |\n",
      "2851 | thr|e|shold;   threshold|;  || th|res|hold;   thr|eshold; |\n",
      "3309 | thr|e|shold;   threshold|;  || th|res|hold;   thr|eshold; |\n",
      "-----------------------------------------------------------------|\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO:acro:records:add(): output_12\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(<Axes: xlabel='Time'>, 'acro_artifacts/kaplan-mier_0.png')\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "safe_plot = acro.surv_func(\n",
    "    data.futime, data.death, output=\"plot\", filename=\"kaplan-mier.png\"\n",
    ")\n",
    "print(safe_plot)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9e554eea",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "# ACRO functionality to let users manage their outputs\n",
    "\n",
    "### 1: List current ACRO outputs\n",
    "This is an example of using the print_output function to list all the outputs created so far"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "ec960039",
   "metadata": {
    "scrolled": true,
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "uid: output_0\n",
      "status: fail\n",
      "type: table\n",
      "properties: {'method': 'crosstab'}\n",
      "sdc: {'summary': {'suppressed': True, 'negative': 0, 'missing': 0, 'threshold': 4, 'p-ratio': 0, 'nk-rule': 0, 'all-values-are-same': 0}, 'cells': {'negative': [], 'missing': [], 'threshold': [[2, 0], [2, 1], [2, 2], [4, 0]], 'p-ratio': [], 'nk-rule': [], 'all-values-are-same': []}}\n",
      "command: safe_table = acro.crosstab(\n",
      "summary: fail; threshold: 4 cells suppressed; \n",
      "outcome: parents      great_pret  pretentious        usual\n",
      "recommend                                        \n",
      "not_recom            ok           ok           ok\n",
      "priority             ok           ok           ok\n",
      "recommend   threshold;   threshold;   threshold; \n",
      "spec_prior           ok           ok           ok\n",
      "very_recom  threshold;            ok           ok\n",
      "output: [parents     great_pret  pretentious   usual\n",
      "recommend                                  \n",
      "not_recom       1440.0       1440.0  1440.0\n",
      "priority         858.0       1484.0  1924.0\n",
      "recommend          NaN          NaN     NaN\n",
      "spec_prior      2022.0       1264.0   758.0\n",
      "very_recom         NaN        132.0   196.0]\n",
      "timestamp: 2024-03-04T21:21:07.616714\n",
      "comments: []\n",
      "exception: \n",
      "\n",
      "uid: output_1\n",
      "status: fail\n",
      "type: table\n",
      "properties: {'method': 'crosstab'}\n",
      "sdc: {'summary': {'suppressed': True, 'negative': 0, 'missing': 0, 'threshold': 5, 'p-ratio': 0, 'nk-rule': 0, 'all-values-are-same': 0}, 'cells': {'negative': [], 'missing': [], 'threshold': [[2, 0], [2, 1], [2, 2], [2, 3], [4, 0]], 'p-ratio': [], 'nk-rule': [], 'all-values-are-same': []}}\n",
      "command: safe_table = acro.crosstab(\n",
      "summary: fail; threshold: 5 cells suppressed; \n",
      "outcome: parents      great_pret  pretentious        usual          All\n",
      "recommend                                                     \n",
      "not_recom            ok           ok           ok           ok\n",
      "priority             ok           ok           ok           ok\n",
      "recommend   threshold;   threshold;   threshold;   threshold; \n",
      "spec_prior           ok           ok           ok           ok\n",
      "very_recom  threshold;            ok           ok           ok\n",
      "All                  ok           ok           ok           ok\n",
      "output: [parents     great_pret  pretentious  usual    All\n",
      "recommend                                        \n",
      "not_recom       1440.0         1440   1440   4320\n",
      "priority         858.0         1484   1924   4266\n",
      "spec_prior      2022.0         1264    758   4044\n",
      "very_recom         NaN          132    196    328\n",
      "All             4320.0         4320   4318  12958]\n",
      "timestamp: 2024-03-04T21:21:07.845655\n",
      "comments: []\n",
      "exception: \n",
      "\n",
      "uid: output_2\n",
      "status: fail\n",
      "type: table\n",
      "properties: {'method': 'crosstab'}\n",
      "sdc: {'summary': {'suppressed': False, 'negative': 0, 'missing': 0, 'threshold': 4, 'p-ratio': 0, 'nk-rule': 0, 'all-values-are-same': 0}, 'cells': {'negative': [], 'missing': [], 'threshold': [[2, 0], [2, 1], [2, 2], [4, 0]], 'p-ratio': [], 'nk-rule': [], 'all-values-are-same': []}}\n",
      "command: safe_table = acro.crosstab(df.recommend, df.parents)\n",
      "summary: fail; threshold: 4 cells may need suppressing; \n",
      "outcome: parents      great_pret  pretentious        usual\n",
      "recommend                                        \n",
      "not_recom            ok           ok           ok\n",
      "priority             ok           ok           ok\n",
      "recommend   threshold;   threshold;   threshold; \n",
      "spec_prior           ok           ok           ok\n",
      "very_recom  threshold;            ok           ok\n",
      "output: [parents     great_pret  pretentious  usual\n",
      "recommend                                 \n",
      "not_recom         1440         1440   1440\n",
      "priority           858         1484   1924\n",
      "recommend            0            0      2\n",
      "spec_prior        2022         1264    758\n",
      "very_recom           0          132    196]\n",
      "timestamp: 2024-03-04T21:21:07.967630\n",
      "comments: []\n",
      "exception: \n",
      "\n",
      "uid: output_3\n",
      "status: fail\n",
      "type: table\n",
      "properties: {'method': 'crosstab'}\n",
      "sdc: {'summary': {'suppressed': True, 'negative': 0, 'missing': 0, 'threshold': 1, 'p-ratio': 4, 'nk-rule': 4, 'all-values-are-same': 0}, 'cells': {'negative': [], 'missing': [], 'threshold': [[2, 2]], 'p-ratio': [[2, 0], [2, 1], [2, 2], [4, 0]], 'nk-rule': [[2, 0], [2, 1], [2, 2], [4, 0]], 'all-values-are-same': []}}\n",
      "command: safe_table = acro.crosstab(\n",
      "summary: fail; threshold: 1 cells suppressed; p-ratio: 4 cells suppressed; nk-rule: 4 cells suppressed; \n",
      "outcome: parents             great_pret         pretentious  \\\n",
      "recommend                                            \n",
      "not_recom                   ok                  ok   \n",
      "priority                    ok                  ok   \n",
      "recommend   p-ratio; nk-rule;   p-ratio; nk-rule;    \n",
      "spec_prior                  ok                  ok   \n",
      "very_recom  p-ratio; nk-rule;                   ok   \n",
      "\n",
      "parents                             usual  \n",
      "recommend                                  \n",
      "not_recom                              ok  \n",
      "priority                               ok  \n",
      "recommend   threshold; p-ratio; nk-rule;   \n",
      "spec_prior                             ok  \n",
      "very_recom                             ok  \n",
      "output: [parents     great_pret  pretentious   usual\n",
      "recommend                                  \n",
      "not_recom       1440.0       1440.0  1440.0\n",
      "priority         858.0       1484.0  1924.0\n",
      "recommend          NaN          NaN     NaN\n",
      "spec_prior      2022.0       1264.0   758.0\n",
      "very_recom         NaN        132.0   196.0]\n",
      "timestamp: 2024-03-04T21:21:08.142362\n",
      "comments: []\n",
      "exception: \n",
      "\n",
      "uid: output_4\n",
      "status: fail\n",
      "type: table\n",
      "properties: {'method': 'crosstab'}\n",
      "sdc: {'summary': {'suppressed': True, 'negative': 0, 'missing': 0, 'threshold': 2, 'p-ratio': 8, 'nk-rule': 8, 'all-values-are-same': 0}, 'cells': {'negative': [], 'missing': [], 'threshold': [[2, 2], [2, 5]], 'p-ratio': [[2, 0], [2, 1], [2, 2], [2, 3], [2, 4], [2, 5], [4, 0], [4, 3]], 'nk-rule': [[2, 0], [2, 1], [2, 2], [2, 3], [2, 4], [2, 5], [4, 0], [4, 3]], 'all-values-are-same': []}}\n",
      "command: safe_table = acro.crosstab(\n",
      "summary: fail; threshold: 2 cells suppressed; p-ratio: 8 cells suppressed; nk-rule: 8 cells suppressed; \n",
      "outcome:                   mode_aggfunc                      \\\n",
      "parents             great_pret         pretentious   \n",
      "recommend                                            \n",
      "not_recom                   ok                  ok   \n",
      "priority                    ok                  ok   \n",
      "recommend   p-ratio; nk-rule;   p-ratio; nk-rule;    \n",
      "spec_prior                  ok                  ok   \n",
      "very_recom  p-ratio; nk-rule;                   ok   \n",
      "\n",
      "                                                         mean  \\\n",
      "parents                             usual          great_pret   \n",
      "recommend                                                       \n",
      "not_recom                              ok                  ok   \n",
      "priority                               ok                  ok   \n",
      "recommend   threshold; p-ratio; nk-rule;   p-ratio; nk-rule;    \n",
      "spec_prior                             ok                  ok   \n",
      "very_recom                             ok  p-ratio; nk-rule;    \n",
      "\n",
      "                                                               \n",
      "parents            pretentious                          usual  \n",
      "recommend                                                      \n",
      "not_recom                   ok                             ok  \n",
      "priority                    ok                             ok  \n",
      "recommend   p-ratio; nk-rule;   threshold; p-ratio; nk-rule;   \n",
      "spec_prior                  ok                             ok  \n",
      "very_recom                  ok                             ok  \n",
      "output: [           mode_aggfunc                         mean                      \n",
      "parents      great_pret pretentious usual great_pret pretentious     usual\n",
      "recommend                                                                 \n",
      "not_recom           3.0         2.0   1.0   3.100694    3.143750  3.111806\n",
      "priority            1.0         1.0   1.0   2.627040    3.022237  3.120062\n",
      "recommend           NaN         NaN   NaN        NaN         NaN       NaN\n",
      "spec_prior          3.0         3.0   3.0   3.307122    3.336234  3.323219\n",
      "very_recom          NaN         1.0   1.0        NaN    2.227273  2.178571]\n",
      "timestamp: 2024-03-04T21:21:08.334715\n",
      "comments: []\n",
      "exception: \n",
      "\n",
      "uid: output_5\n",
      "status: pass\n",
      "type: table\n",
      "properties: {'method': 'pivot_table'}\n",
      "sdc: {'summary': {'suppressed': True, 'negative': 0, 'missing': 0, 'threshold': 0, 'p-ratio': 0, 'nk-rule': 0, 'all-values-are-same': 0}, 'cells': {'negative': [], 'missing': [], 'threshold': [], 'p-ratio': [], 'nk-rule': [], 'all-values-are-same': []}}\n",
      "command: table = acro.pivot_table(\n",
      "summary: pass\n",
      "outcome:                 mean      std\n",
      "            children children\n",
      "parents                      \n",
      "great_pret        ok       ok\n",
      "pretentious       ok       ok\n",
      "usual             ok       ok\n",
      "output: [                 mean       std\n",
      "             children  children\n",
      "parents                        \n",
      "great_pret   3.103241  2.213116\n",
      "pretentious  3.130324  2.250447\n",
      "usual        3.109259  2.216799]\n",
      "timestamp: 2024-03-04T21:21:08.485335\n",
      "comments: []\n",
      "exception: \n",
      "\n",
      "uid: output_6\n",
      "status: fail\n",
      "type: table\n",
      "properties: {'method': 'pivot_table'}\n",
      "sdc: {'summary': {'suppressed': True, 'negative': 0, 'missing': 0, 'threshold': 5, 'p-ratio': 5, 'nk-rule': 5, 'all-values-are-same': 0}, 'cells': {'negative': [], 'missing': [], 'threshold': [[0, 2], [0, 4], [1, 2], [2, 2], [3, 2]], 'p-ratio': [[0, 2], [0, 4], [1, 2], [2, 2], [3, 2]], 'nk-rule': [[0, 2], [0, 4], [1, 2], [2, 2], [3, 2]], 'all-values-are-same': []}}\n",
      "command: safe_table = acro.pivot_table(\n",
      "summary: fail; threshold: 5 cells suppressed; p-ratio: 5 cells suppressed; nk-rule: 5 cells suppressed; \n",
      "outcome:              children                                                     \\\n",
      "recommend   not_recom priority                      recommend spec_prior   \n",
      "parents                                                                    \n",
      "great_pret         ok       ok  threshold; p-ratio; nk-rule;          ok   \n",
      "pretentious        ok       ok  threshold; p-ratio; nk-rule;          ok   \n",
      "usual              ok       ok  threshold; p-ratio; nk-rule;          ok   \n",
      "All                ok       ok  threshold; p-ratio; nk-rule;          ok   \n",
      "\n",
      "                                                \n",
      "recommend                       very_recom All  \n",
      "parents                                         \n",
      "great_pret   threshold; p-ratio; nk-rule;   ok  \n",
      "pretentious                             ok  ok  \n",
      "usual                                   ok  ok  \n",
      "All                                     ok  ok  \n",
      "output: [             children                                          \n",
      "recommend   not_recom  priority spec_prior very_recom       All\n",
      "parents                                                        \n",
      "great_pret   3.100694  2.627040   3.307122        NaN  3.103241\n",
      "pretentious  3.143750  3.022237   3.336234   2.227273  3.130324\n",
      "usual        3.111806  3.120062   3.323219   2.178571  3.110236\n",
      "All          3.118750  2.986873   3.319238   2.198171  3.114601]\n",
      "timestamp: 2024-03-04T21:21:08.834120\n",
      "comments: []\n",
      "exception: \n",
      "\n",
      "uid: output_7\n",
      "status: pass\n",
      "type: regression\n",
      "properties: {'method': 'ols', 'dof': 12958.0}\n",
      "sdc: {}\n",
      "command: results = acro.ols(y, x)\n",
      "summary: pass; dof=12958.0 >= 10\n",
      "outcome: Empty DataFrame\n",
      "Columns: []\n",
      "Index: []\n",
      "output: [                          recommend           R-squared:       0.000\n",
      "Dep. Variable:                                                      \n",
      "Model:                          OLS      Adj. R-squared:      0.0000\n",
      "Method:               Least Squares         F-statistic:      6.4170\n",
      "Date:              Mon, 04 Mar 2024  Prob (F-statistic):      0.0113\n",
      "Time:                      21:21:09      Log-Likelihood: -25124.0000\n",
      "No. Observations:             12960                 AIC:  50250.0000\n",
      "Df Residuals:                 12958                 BIC:  50270.0000\n",
      "Df Model:                         1                  NaN         NaN\n",
      "Covariance Type:          nonrobust                  NaN         NaN,             coef  std err       t  P>|t|  [0.025  0.975]\n",
      "const     2.2341    0.025  87.965  0.000   2.184   2.284\n",
      "children  0.0168    0.007   2.533  0.011   0.004   0.030,                 76735.931     Durbin-Watson:     2.883\n",
      "Omnibus:                                              \n",
      "Prob(Omnibus):      0.000  Jarque-Bera (JB):  1742.843\n",
      "Skew:              -0.485          Prob(JB):     0.000\n",
      "Kurtosis:           1.487          Cond. No.     6.890]\n",
      "timestamp: 2024-03-04T21:21:09.117190\n",
      "comments: []\n",
      "exception: \n",
      "\n",
      "uid: output_8\n",
      "status: pass\n",
      "type: regression\n",
      "properties: {'method': 'olsr', 'dof': 12958.0}\n",
      "sdc: {}\n",
      "command: results = acro.olsr(formula=\"recommend ~ children\", data=new_df)\n",
      "summary: pass; dof=12958.0 >= 10\n",
      "outcome: Empty DataFrame\n",
      "Columns: []\n",
      "Index: []\n",
      "output: [                          recommend           R-squared:       0.000\n",
      "Dep. Variable:                                                      \n",
      "Model:                          OLS      Adj. R-squared:      0.0000\n",
      "Method:               Least Squares         F-statistic:      6.4170\n",
      "Date:              Mon, 04 Mar 2024  Prob (F-statistic):      0.0113\n",
      "Time:                      21:21:09      Log-Likelihood: -25124.0000\n",
      "No. Observations:             12960                 AIC:  50250.0000\n",
      "Df Residuals:                 12958                 BIC:  50270.0000\n",
      "Df Model:                         1                  NaN         NaN\n",
      "Covariance Type:          nonrobust                  NaN         NaN,              coef  std err       t  P>|t|  [0.025  0.975]\n",
      "Intercept  2.2341    0.025  87.965  0.000   2.184   2.284\n",
      "children   0.0168    0.007   2.533  0.011   0.004   0.030,                 76735.931     Durbin-Watson:     2.883\n",
      "Omnibus:                                              \n",
      "Prob(Omnibus):      0.000  Jarque-Bera (JB):  1742.843\n",
      "Skew:              -0.485          Prob(JB):     0.000\n",
      "Kurtosis:           1.487          Cond. No.     6.890]\n",
      "timestamp: 2024-03-04T21:21:09.224540\n",
      "comments: []\n",
      "exception: \n",
      "\n",
      "uid: output_9\n",
      "status: pass\n",
      "type: regression\n",
      "properties: {'method': 'probit', 'dof': 12958.0}\n",
      "sdc: {}\n",
      "command: results = acro.probit(y, x)\n",
      "summary: pass; dof=12958.0 >= 10\n",
      "outcome: Empty DataFrame\n",
      "Columns: []\n",
      "Index: []\n",
      "output: [                           finance No. Observations:         12960\n",
      "Dep. Variable:                                                    \n",
      "Model:                      Probit     Df Residuals:  12958.000000\n",
      "Method:                        MLE         Df Model:      1.000000\n",
      "Date:             Mon, 04 Mar 2024    Pseudo R-squ.:      0.000001\n",
      "Time:                     21:21:09   Log-Likelihood:  -8983.200000\n",
      "converged:                    True          LL-Null:  -8983.200000\n",
      "Covariance Type:         nonrobust      LLR p-value:      0.883900,             coef  std err      z  P>|z|  [0.025  0.975]\n",
      "const    -0.0022    0.019 -0.119  0.905  -0.039   0.035\n",
      "children  0.0007    0.005  0.146  0.884  -0.009   0.010]\n",
      "timestamp: 2024-03-04T21:21:09.358683\n",
      "comments: []\n",
      "exception: \n",
      "\n",
      "uid: output_10\n",
      "status: pass\n",
      "type: regression\n",
      "properties: {'method': 'logit', 'dof': 12958.0}\n",
      "sdc: {}\n",
      "command: results = acro.logit(y, x)\n",
      "summary: pass; dof=12958.0 >= 10\n",
      "outcome: Empty DataFrame\n",
      "Columns: []\n",
      "Index: []\n",
      "output: [                           finance No. Observations:         12960\n",
      "Dep. Variable:                                                    \n",
      "Model:                       Logit     Df Residuals:  12958.000000\n",
      "Method:                        MLE         Df Model:      1.000000\n",
      "Date:             Mon, 04 Mar 2024    Pseudo R-squ.:      0.000001\n",
      "Time:                     21:21:09   Log-Likelihood:  -8983.200000\n",
      "converged:                    True          LL-Null:  -8983.200000\n",
      "Covariance Type:         nonrobust      LLR p-value:      0.883900,             coef  std err      z  P>|z|  [0.025  0.975]\n",
      "const    -0.0036    0.030 -0.119  0.905  -0.063   0.056\n",
      "children  0.0012    0.008  0.146  0.884  -0.014   0.017]\n",
      "timestamp: 2024-03-04T21:21:09.456005\n",
      "comments: []\n",
      "exception: \n",
      "\n",
      "uid: output_11\n",
      "status: fail\n",
      "type: table\n",
      "properties: {'method': 'surv_func'}\n",
      "sdc: {'summary': {'suppressed': True, 'negative': 0, 'missing': 0, 'threshold': 76, 'p-ratio': 0, 'nk-rule': 0, 'all-values-are-same': 0}, 'cells': {'negative': [], 'missing': [], 'threshold': [[1, 0], [1, 1], [1, 2], [1, 3], [2, 0], [2, 1], [2, 2], [2, 3], [3, 0], [3, 1], [3, 2], [3, 3], [4, 0], [4, 1], [4, 2], [4, 3], [5, 0], [5, 1], [5, 2], [5, 3], [6, 0], [6, 1], [6, 2], [6, 3], [7, 0], [7, 1], [7, 2], [7, 3], [8, 0], [8, 1], [8, 2], [8, 3], [9, 0], [9, 1], [9, 2], [9, 3], [10, 0], [10, 1], [10, 2], [10, 3], [11, 0], [11, 1], [11, 2], [11, 3], [12, 0], [12, 1], [12, 2], [12, 3], [13, 0], [13, 1], [13, 2], [13, 3], [14, 0], [14, 1], [14, 2], [14, 3], [15, 0], [15, 1], [15, 2], [15, 3], [16, 0], [16, 1], [16, 2], [16, 3], [17, 0], [17, 1], [17, 2], [17, 3], [18, 0], [18, 1], [18, 2], [18, 3], [19, 0], [19, 1], [19, 2], [19, 3]], 'p-ratio': [], 'nk-rule': [], 'all-values-are-same': []}}\n",
      "command: safe_table = acro.surv_func(data.futime, data.death, output=\"table\")\n",
      "summary: fail; threshold: 76 cells suppressed; \n",
      "outcome:         Surv prob Surv prob SE  num at risk   num events\n",
      "Time                                                    \n",
      "51             ok           ok           ok           ok\n",
      "69    threshold;   threshold;   threshold;   threshold; \n",
      "85    threshold;   threshold;   threshold;   threshold; \n",
      "91    threshold;   threshold;   threshold;   threshold; \n",
      "115   threshold;   threshold;   threshold;   threshold; \n",
      "372   threshold;   threshold;   threshold;   threshold; \n",
      "667   threshold;   threshold;   threshold;   threshold; \n",
      "874   threshold;   threshold;   threshold;   threshold; \n",
      "1039  threshold;   threshold;   threshold;   threshold; \n",
      "1046  threshold;   threshold;   threshold;   threshold; \n",
      "1281  threshold;   threshold;   threshold;   threshold; \n",
      "1286  threshold;   threshold;   threshold;   threshold; \n",
      "1326  threshold;   threshold;   threshold;   threshold; \n",
      "1355  threshold;   threshold;   threshold;   threshold; \n",
      "1626  threshold;   threshold;   threshold;   threshold; \n",
      "1903  threshold;   threshold;   threshold;   threshold; \n",
      "1914  threshold;   threshold;   threshold;   threshold; \n",
      "2776  threshold;   threshold;   threshold;   threshold; \n",
      "2851  threshold;   threshold;   threshold;   threshold; \n",
      "3309  threshold;   threshold;   threshold;   threshold; \n",
      "output: [      Surv prob  Surv prob SE  num at risk  num events\n",
      "Time                                                  \n",
      "51         0.95      0.048734         20.0         1.0\n",
      "69          NaN           NaN          NaN         NaN\n",
      "85          NaN           NaN          NaN         NaN\n",
      "91          NaN           NaN          NaN         NaN\n",
      "115         NaN           NaN          NaN         NaN\n",
      "372         NaN           NaN          NaN         NaN\n",
      "667         NaN           NaN          NaN         NaN\n",
      "874         NaN           NaN          NaN         NaN\n",
      "1039        NaN           NaN          NaN         NaN\n",
      "1046        NaN           NaN          NaN         NaN\n",
      "1281        NaN           NaN          NaN         NaN\n",
      "1286        NaN           NaN          NaN         NaN\n",
      "1326        NaN           NaN          NaN         NaN\n",
      "1355        NaN           NaN          NaN         NaN\n",
      "1626        NaN           NaN          NaN         NaN\n",
      "1903        NaN           NaN          NaN         NaN\n",
      "1914        NaN           NaN          NaN         NaN\n",
      "2776        NaN           NaN          NaN         NaN\n",
      "2851        NaN           NaN          NaN         NaN\n",
      "3309        NaN           NaN          NaN         NaN]\n",
      "timestamp: 2024-03-04T21:21:10.943849\n",
      "comments: []\n",
      "exception: \n",
      "\n",
      "uid: output_12\n",
      "status: fail\n",
      "type: survival plot\n",
      "properties: {'method': 'surv_func'}\n",
      "sdc: {'summary': {'suppressed': True, 'negative': 0, 'missing': 0, 'threshold': 76, 'p-ratio': 0, 'nk-rule': 0, 'all-values-are-same': 0}, 'cells': {'negative': [], 'missing': [], 'threshold': [[1, 0], [1, 1], [1, 2], [1, 3], [2, 0], [2, 1], [2, 2], [2, 3], [3, 0], [3, 1], [3, 2], [3, 3], [4, 0], [4, 1], [4, 2], [4, 3], [5, 0], [5, 1], [5, 2], [5, 3], [6, 0], [6, 1], [6, 2], [6, 3], [7, 0], [7, 1], [7, 2], [7, 3], [8, 0], [8, 1], [8, 2], [8, 3], [9, 0], [9, 1], [9, 2], [9, 3], [10, 0], [10, 1], [10, 2], [10, 3], [11, 0], [11, 1], [11, 2], [11, 3], [12, 0], [12, 1], [12, 2], [12, 3], [13, 0], [13, 1], [13, 2], [13, 3], [14, 0], [14, 1], [14, 2], [14, 3], [15, 0], [15, 1], [15, 2], [15, 3], [16, 0], [16, 1], [16, 2], [16, 3], [17, 0], [17, 1], [17, 2], [17, 3], [18, 0], [18, 1], [18, 2], [18, 3], [19, 0], [19, 1], [19, 2], [19, 3]], 'p-ratio': [], 'nk-rule': [], 'all-values-are-same': []}}\n",
      "command: safe_plot = acro.surv_func(\n",
      "summary: fail; threshold: 76 cells suppressed; \n",
      "outcome: Empty DataFrame\n",
      "Columns: []\n",
      "Index: []\n",
      "output: ['acro_artifacts\\\\kaplan-mier_0.png']\n",
      "timestamp: 2024-03-04T21:21:11.236799\n",
      "comments: []\n",
      "exception: \n",
      "\n",
      "\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "'uid: output_0\\nstatus: fail\\ntype: table\\nproperties: {\\'method\\': \\'crosstab\\'}\\nsdc: {\\'summary\\': {\\'suppressed\\': True, \\'negative\\': 0, \\'missing\\': 0, \\'threshold\\': 4, \\'p-ratio\\': 0, \\'nk-rule\\': 0, \\'all-values-are-same\\': 0}, \\'cells\\': {\\'negative\\': [], \\'missing\\': [], \\'threshold\\': [[2, 0], [2, 1], [2, 2], [4, 0]], \\'p-ratio\\': [], \\'nk-rule\\': [], \\'all-values-are-same\\': []}}\\ncommand: safe_table = acro.crosstab(\\nsummary: fail; threshold: 4 cells suppressed; \\noutcome: parents      great_pret  pretentious        usual\\nrecommend                                        \\nnot_recom            ok           ok           ok\\npriority             ok           ok           ok\\nrecommend   threshold;   threshold;   threshold; \\nspec_prior           ok           ok           ok\\nvery_recom  threshold;            ok           ok\\noutput: [parents     great_pret  pretentious   usual\\nrecommend                                  \\nnot_recom       1440.0       1440.0  1440.0\\npriority         858.0       1484.0  1924.0\\nrecommend          NaN          NaN     NaN\\nspec_prior      2022.0       1264.0   758.0\\nvery_recom         NaN        132.0   196.0]\\ntimestamp: 2024-03-04T21:21:07.616714\\ncomments: []\\nexception: \\n\\nuid: output_1\\nstatus: fail\\ntype: table\\nproperties: {\\'method\\': \\'crosstab\\'}\\nsdc: {\\'summary\\': {\\'suppressed\\': True, \\'negative\\': 0, \\'missing\\': 0, \\'threshold\\': 5, \\'p-ratio\\': 0, \\'nk-rule\\': 0, \\'all-values-are-same\\': 0}, \\'cells\\': {\\'negative\\': [], \\'missing\\': [], \\'threshold\\': [[2, 0], [2, 1], [2, 2], [2, 3], [4, 0]], \\'p-ratio\\': [], \\'nk-rule\\': [], \\'all-values-are-same\\': []}}\\ncommand: safe_table = acro.crosstab(\\nsummary: fail; threshold: 5 cells suppressed; \\noutcome: parents      great_pret  pretentious        usual          All\\nrecommend                                                     \\nnot_recom            ok           ok           ok           ok\\npriority             ok           ok           ok           ok\\nrecommend   threshold;   threshold;   threshold;   threshold; \\nspec_prior           ok           ok           ok           ok\\nvery_recom  threshold;            ok           ok           ok\\nAll                  ok           ok           ok           ok\\noutput: [parents     great_pret  pretentious  usual    All\\nrecommend                                        \\nnot_recom       1440.0         1440   1440   4320\\npriority         858.0         1484   1924   4266\\nspec_prior      2022.0         1264    758   4044\\nvery_recom         NaN          132    196    328\\nAll             4320.0         4320   4318  12958]\\ntimestamp: 2024-03-04T21:21:07.845655\\ncomments: []\\nexception: \\n\\nuid: output_2\\nstatus: fail\\ntype: table\\nproperties: {\\'method\\': \\'crosstab\\'}\\nsdc: {\\'summary\\': {\\'suppressed\\': False, \\'negative\\': 0, \\'missing\\': 0, \\'threshold\\': 4, \\'p-ratio\\': 0, \\'nk-rule\\': 0, \\'all-values-are-same\\': 0}, \\'cells\\': {\\'negative\\': [], \\'missing\\': [], \\'threshold\\': [[2, 0], [2, 1], [2, 2], [4, 0]], \\'p-ratio\\': [], \\'nk-rule\\': [], \\'all-values-are-same\\': []}}\\ncommand: safe_table = acro.crosstab(df.recommend, df.parents)\\nsummary: fail; threshold: 4 cells may need suppressing; \\noutcome: parents      great_pret  pretentious        usual\\nrecommend                                        \\nnot_recom            ok           ok           ok\\npriority             ok           ok           ok\\nrecommend   threshold;   threshold;   threshold; \\nspec_prior           ok           ok           ok\\nvery_recom  threshold;            ok           ok\\noutput: [parents     great_pret  pretentious  usual\\nrecommend                                 \\nnot_recom         1440         1440   1440\\npriority           858         1484   1924\\nrecommend            0            0      2\\nspec_prior        2022         1264    758\\nvery_recom           0          132    196]\\ntimestamp: 2024-03-04T21:21:07.967630\\ncomments: []\\nexception: \\n\\nuid: output_3\\nstatus: fail\\ntype: table\\nproperties: {\\'method\\': \\'crosstab\\'}\\nsdc: {\\'summary\\': {\\'suppressed\\': True, \\'negative\\': 0, \\'missing\\': 0, \\'threshold\\': 1, \\'p-ratio\\': 4, \\'nk-rule\\': 4, \\'all-values-are-same\\': 0}, \\'cells\\': {\\'negative\\': [], \\'missing\\': [], \\'threshold\\': [[2, 2]], \\'p-ratio\\': [[2, 0], [2, 1], [2, 2], [4, 0]], \\'nk-rule\\': [[2, 0], [2, 1], [2, 2], [4, 0]], \\'all-values-are-same\\': []}}\\ncommand: safe_table = acro.crosstab(\\nsummary: fail; threshold: 1 cells suppressed; p-ratio: 4 cells suppressed; nk-rule: 4 cells suppressed; \\noutcome: parents             great_pret         pretentious  \\\\\\nrecommend                                            \\nnot_recom                   ok                  ok   \\npriority                    ok                  ok   \\nrecommend   p-ratio; nk-rule;   p-ratio; nk-rule;    \\nspec_prior                  ok                  ok   \\nvery_recom  p-ratio; nk-rule;                   ok   \\n\\nparents                             usual  \\nrecommend                                  \\nnot_recom                              ok  \\npriority                               ok  \\nrecommend   threshold; p-ratio; nk-rule;   \\nspec_prior                             ok  \\nvery_recom                             ok  \\noutput: [parents     great_pret  pretentious   usual\\nrecommend                                  \\nnot_recom       1440.0       1440.0  1440.0\\npriority         858.0       1484.0  1924.0\\nrecommend          NaN          NaN     NaN\\nspec_prior      2022.0       1264.0   758.0\\nvery_recom         NaN        132.0   196.0]\\ntimestamp: 2024-03-04T21:21:08.142362\\ncomments: []\\nexception: \\n\\nuid: output_4\\nstatus: fail\\ntype: table\\nproperties: {\\'method\\': \\'crosstab\\'}\\nsdc: {\\'summary\\': {\\'suppressed\\': True, \\'negative\\': 0, \\'missing\\': 0, \\'threshold\\': 2, \\'p-ratio\\': 8, \\'nk-rule\\': 8, \\'all-values-are-same\\': 0}, \\'cells\\': {\\'negative\\': [], \\'missing\\': [], \\'threshold\\': [[2, 2], [2, 5]], \\'p-ratio\\': [[2, 0], [2, 1], [2, 2], [2, 3], [2, 4], [2, 5], [4, 0], [4, 3]], \\'nk-rule\\': [[2, 0], [2, 1], [2, 2], [2, 3], [2, 4], [2, 5], [4, 0], [4, 3]], \\'all-values-are-same\\': []}}\\ncommand: safe_table = acro.crosstab(\\nsummary: fail; threshold: 2 cells suppressed; p-ratio: 8 cells suppressed; nk-rule: 8 cells suppressed; \\noutcome:                   mode_aggfunc                      \\\\\\nparents             great_pret         pretentious   \\nrecommend                                            \\nnot_recom                   ok                  ok   \\npriority                    ok                  ok   \\nrecommend   p-ratio; nk-rule;   p-ratio; nk-rule;    \\nspec_prior                  ok                  ok   \\nvery_recom  p-ratio; nk-rule;                   ok   \\n\\n                                                         mean  \\\\\\nparents                             usual          great_pret   \\nrecommend                                                       \\nnot_recom                              ok                  ok   \\npriority                               ok                  ok   \\nrecommend   threshold; p-ratio; nk-rule;   p-ratio; nk-rule;    \\nspec_prior                             ok                  ok   \\nvery_recom                             ok  p-ratio; nk-rule;    \\n\\n                                                               \\nparents            pretentious                          usual  \\nrecommend                                                      \\nnot_recom                   ok                             ok  \\npriority                    ok                             ok  \\nrecommend   p-ratio; nk-rule;   threshold; p-ratio; nk-rule;   \\nspec_prior                  ok                             ok  \\nvery_recom                  ok                             ok  \\noutput: [           mode_aggfunc                         mean                      \\nparents      great_pret pretentious usual great_pret pretentious     usual\\nrecommend                                                                 \\nnot_recom           3.0         2.0   1.0   3.100694    3.143750  3.111806\\npriority            1.0         1.0   1.0   2.627040    3.022237  3.120062\\nrecommend           NaN         NaN   NaN        NaN         NaN       NaN\\nspec_prior          3.0         3.0   3.0   3.307122    3.336234  3.323219\\nvery_recom          NaN         1.0   1.0        NaN    2.227273  2.178571]\\ntimestamp: 2024-03-04T21:21:08.334715\\ncomments: []\\nexception: \\n\\nuid: output_5\\nstatus: pass\\ntype: table\\nproperties: {\\'method\\': \\'pivot_table\\'}\\nsdc: {\\'summary\\': {\\'suppressed\\': True, \\'negative\\': 0, \\'missing\\': 0, \\'threshold\\': 0, \\'p-ratio\\': 0, \\'nk-rule\\': 0, \\'all-values-are-same\\': 0}, \\'cells\\': {\\'negative\\': [], \\'missing\\': [], \\'threshold\\': [], \\'p-ratio\\': [], \\'nk-rule\\': [], \\'all-values-are-same\\': []}}\\ncommand: table = acro.pivot_table(\\nsummary: pass\\noutcome:                 mean      std\\n            children children\\nparents                      \\ngreat_pret        ok       ok\\npretentious       ok       ok\\nusual             ok       ok\\noutput: [                 mean       std\\n             children  children\\nparents                        \\ngreat_pret   3.103241  2.213116\\npretentious  3.130324  2.250447\\nusual        3.109259  2.216799]\\ntimestamp: 2024-03-04T21:21:08.485335\\ncomments: []\\nexception: \\n\\nuid: output_6\\nstatus: fail\\ntype: table\\nproperties: {\\'method\\': \\'pivot_table\\'}\\nsdc: {\\'summary\\': {\\'suppressed\\': True, \\'negative\\': 0, \\'missing\\': 0, \\'threshold\\': 5, \\'p-ratio\\': 5, \\'nk-rule\\': 5, \\'all-values-are-same\\': 0}, \\'cells\\': {\\'negative\\': [], \\'missing\\': [], \\'threshold\\': [[0, 2], [0, 4], [1, 2], [2, 2], [3, 2]], \\'p-ratio\\': [[0, 2], [0, 4], [1, 2], [2, 2], [3, 2]], \\'nk-rule\\': [[0, 2], [0, 4], [1, 2], [2, 2], [3, 2]], \\'all-values-are-same\\': []}}\\ncommand: safe_table = acro.pivot_table(\\nsummary: fail; threshold: 5 cells suppressed; p-ratio: 5 cells suppressed; nk-rule: 5 cells suppressed; \\noutcome:              children                                                     \\\\\\nrecommend   not_recom priority                      recommend spec_prior   \\nparents                                                                    \\ngreat_pret         ok       ok  threshold; p-ratio; nk-rule;          ok   \\npretentious        ok       ok  threshold; p-ratio; nk-rule;          ok   \\nusual              ok       ok  threshold; p-ratio; nk-rule;          ok   \\nAll                ok       ok  threshold; p-ratio; nk-rule;          ok   \\n\\n                                                \\nrecommend                       very_recom All  \\nparents                                         \\ngreat_pret   threshold; p-ratio; nk-rule;   ok  \\npretentious                             ok  ok  \\nusual                                   ok  ok  \\nAll                                     ok  ok  \\noutput: [             children                                          \\nrecommend   not_recom  priority spec_prior very_recom       All\\nparents                                                        \\ngreat_pret   3.100694  2.627040   3.307122        NaN  3.103241\\npretentious  3.143750  3.022237   3.336234   2.227273  3.130324\\nusual        3.111806  3.120062   3.323219   2.178571  3.110236\\nAll          3.118750  2.986873   3.319238   2.198171  3.114601]\\ntimestamp: 2024-03-04T21:21:08.834120\\ncomments: []\\nexception: \\n\\nuid: output_7\\nstatus: pass\\ntype: regression\\nproperties: {\\'method\\': \\'ols\\', \\'dof\\': 12958.0}\\nsdc: {}\\ncommand: results = acro.ols(y, x)\\nsummary: pass; dof=12958.0 >= 10\\noutcome: Empty DataFrame\\nColumns: []\\nIndex: []\\noutput: [                          recommend           R-squared:       0.000\\nDep. Variable:                                                      \\nModel:                          OLS      Adj. R-squared:      0.0000\\nMethod:               Least Squares         F-statistic:      6.4170\\nDate:              Mon, 04 Mar 2024  Prob (F-statistic):      0.0113\\nTime:                      21:21:09      Log-Likelihood: -25124.0000\\nNo. Observations:             12960                 AIC:  50250.0000\\nDf Residuals:                 12958                 BIC:  50270.0000\\nDf Model:                         1                  NaN         NaN\\nCovariance Type:          nonrobust                  NaN         NaN,             coef  std err       t  P>|t|  [0.025  0.975]\\nconst     2.2341    0.025  87.965  0.000   2.184   2.284\\nchildren  0.0168    0.007   2.533  0.011   0.004   0.030,                 76735.931     Durbin-Watson:     2.883\\nOmnibus:                                              \\nProb(Omnibus):      0.000  Jarque-Bera (JB):  1742.843\\nSkew:              -0.485          Prob(JB):     0.000\\nKurtosis:           1.487          Cond. No.     6.890]\\ntimestamp: 2024-03-04T21:21:09.117190\\ncomments: []\\nexception: \\n\\nuid: output_8\\nstatus: pass\\ntype: regression\\nproperties: {\\'method\\': \\'olsr\\', \\'dof\\': 12958.0}\\nsdc: {}\\ncommand: results = acro.olsr(formula=\"recommend ~ children\", data=new_df)\\nsummary: pass; dof=12958.0 >= 10\\noutcome: Empty DataFrame\\nColumns: []\\nIndex: []\\noutput: [                          recommend           R-squared:       0.000\\nDep. Variable:                                                      \\nModel:                          OLS      Adj. R-squared:      0.0000\\nMethod:               Least Squares         F-statistic:      6.4170\\nDate:              Mon, 04 Mar 2024  Prob (F-statistic):      0.0113\\nTime:                      21:21:09      Log-Likelihood: -25124.0000\\nNo. Observations:             12960                 AIC:  50250.0000\\nDf Residuals:                 12958                 BIC:  50270.0000\\nDf Model:                         1                  NaN         NaN\\nCovariance Type:          nonrobust                  NaN         NaN,              coef  std err       t  P>|t|  [0.025  0.975]\\nIntercept  2.2341    0.025  87.965  0.000   2.184   2.284\\nchildren   0.0168    0.007   2.533  0.011   0.004   0.030,                 76735.931     Durbin-Watson:     2.883\\nOmnibus:                                              \\nProb(Omnibus):      0.000  Jarque-Bera (JB):  1742.843\\nSkew:              -0.485          Prob(JB):     0.000\\nKurtosis:           1.487          Cond. No.     6.890]\\ntimestamp: 2024-03-04T21:21:09.224540\\ncomments: []\\nexception: \\n\\nuid: output_9\\nstatus: pass\\ntype: regression\\nproperties: {\\'method\\': \\'probit\\', \\'dof\\': 12958.0}\\nsdc: {}\\ncommand: results = acro.probit(y, x)\\nsummary: pass; dof=12958.0 >= 10\\noutcome: Empty DataFrame\\nColumns: []\\nIndex: []\\noutput: [                           finance No. Observations:         12960\\nDep. Variable:                                                    \\nModel:                      Probit     Df Residuals:  12958.000000\\nMethod:                        MLE         Df Model:      1.000000\\nDate:             Mon, 04 Mar 2024    Pseudo R-squ.:      0.000001\\nTime:                     21:21:09   Log-Likelihood:  -8983.200000\\nconverged:                    True          LL-Null:  -8983.200000\\nCovariance Type:         nonrobust      LLR p-value:      0.883900,             coef  std err      z  P>|z|  [0.025  0.975]\\nconst    -0.0022    0.019 -0.119  0.905  -0.039   0.035\\nchildren  0.0007    0.005  0.146  0.884  -0.009   0.010]\\ntimestamp: 2024-03-04T21:21:09.358683\\ncomments: []\\nexception: \\n\\nuid: output_10\\nstatus: pass\\ntype: regression\\nproperties: {\\'method\\': \\'logit\\', \\'dof\\': 12958.0}\\nsdc: {}\\ncommand: results = acro.logit(y, x)\\nsummary: pass; dof=12958.0 >= 10\\noutcome: Empty DataFrame\\nColumns: []\\nIndex: []\\noutput: [                           finance No. Observations:         12960\\nDep. Variable:                                                    \\nModel:                       Logit     Df Residuals:  12958.000000\\nMethod:                        MLE         Df Model:      1.000000\\nDate:             Mon, 04 Mar 2024    Pseudo R-squ.:      0.000001\\nTime:                     21:21:09   Log-Likelihood:  -8983.200000\\nconverged:                    True          LL-Null:  -8983.200000\\nCovariance Type:         nonrobust      LLR p-value:      0.883900,             coef  std err      z  P>|z|  [0.025  0.975]\\nconst    -0.0036    0.030 -0.119  0.905  -0.063   0.056\\nchildren  0.0012    0.008  0.146  0.884  -0.014   0.017]\\ntimestamp: 2024-03-04T21:21:09.456005\\ncomments: []\\nexception: \\n\\nuid: output_11\\nstatus: fail\\ntype: table\\nproperties: {\\'method\\': \\'surv_func\\'}\\nsdc: {\\'summary\\': {\\'suppressed\\': True, \\'negative\\': 0, \\'missing\\': 0, \\'threshold\\': 76, \\'p-ratio\\': 0, \\'nk-rule\\': 0, \\'all-values-are-same\\': 0}, \\'cells\\': {\\'negative\\': [], \\'missing\\': [], \\'threshold\\': [[1, 0], [1, 1], [1, 2], [1, 3], [2, 0], [2, 1], [2, 2], [2, 3], [3, 0], [3, 1], [3, 2], [3, 3], [4, 0], [4, 1], [4, 2], [4, 3], [5, 0], [5, 1], [5, 2], [5, 3], [6, 0], [6, 1], [6, 2], [6, 3], [7, 0], [7, 1], [7, 2], [7, 3], [8, 0], [8, 1], [8, 2], [8, 3], [9, 0], [9, 1], [9, 2], [9, 3], [10, 0], [10, 1], [10, 2], [10, 3], [11, 0], [11, 1], [11, 2], [11, 3], [12, 0], [12, 1], [12, 2], [12, 3], [13, 0], [13, 1], [13, 2], [13, 3], [14, 0], [14, 1], [14, 2], [14, 3], [15, 0], [15, 1], [15, 2], [15, 3], [16, 0], [16, 1], [16, 2], [16, 3], [17, 0], [17, 1], [17, 2], [17, 3], [18, 0], [18, 1], [18, 2], [18, 3], [19, 0], [19, 1], [19, 2], [19, 3]], \\'p-ratio\\': [], \\'nk-rule\\': [], \\'all-values-are-same\\': []}}\\ncommand: safe_table = acro.surv_func(data.futime, data.death, output=\"table\")\\nsummary: fail; threshold: 76 cells suppressed; \\noutcome:         Surv prob Surv prob SE  num at risk   num events\\nTime                                                    \\n51             ok           ok           ok           ok\\n69    threshold;   threshold;   threshold;   threshold; \\n85    threshold;   threshold;   threshold;   threshold; \\n91    threshold;   threshold;   threshold;   threshold; \\n115   threshold;   threshold;   threshold;   threshold; \\n372   threshold;   threshold;   threshold;   threshold; \\n667   threshold;   threshold;   threshold;   threshold; \\n874   threshold;   threshold;   threshold;   threshold; \\n1039  threshold;   threshold;   threshold;   threshold; \\n1046  threshold;   threshold;   threshold;   threshold; \\n1281  threshold;   threshold;   threshold;   threshold; \\n1286  threshold;   threshold;   threshold;   threshold; \\n1326  threshold;   threshold;   threshold;   threshold; \\n1355  threshold;   threshold;   threshold;   threshold; \\n1626  threshold;   threshold;   threshold;   threshold; \\n1903  threshold;   threshold;   threshold;   threshold; \\n1914  threshold;   threshold;   threshold;   threshold; \\n2776  threshold;   threshold;   threshold;   threshold; \\n2851  threshold;   threshold;   threshold;   threshold; \\n3309  threshold;   threshold;   threshold;   threshold; \\noutput: [      Surv prob  Surv prob SE  num at risk  num events\\nTime                                                  \\n51         0.95      0.048734         20.0         1.0\\n69          NaN           NaN          NaN         NaN\\n85          NaN           NaN          NaN         NaN\\n91          NaN           NaN          NaN         NaN\\n115         NaN           NaN          NaN         NaN\\n372         NaN           NaN          NaN         NaN\\n667         NaN           NaN          NaN         NaN\\n874         NaN           NaN          NaN         NaN\\n1039        NaN           NaN          NaN         NaN\\n1046        NaN           NaN          NaN         NaN\\n1281        NaN           NaN          NaN         NaN\\n1286        NaN           NaN          NaN         NaN\\n1326        NaN           NaN          NaN         NaN\\n1355        NaN           NaN          NaN         NaN\\n1626        NaN           NaN          NaN         NaN\\n1903        NaN           NaN          NaN         NaN\\n1914        NaN           NaN          NaN         NaN\\n2776        NaN           NaN          NaN         NaN\\n2851        NaN           NaN          NaN         NaN\\n3309        NaN           NaN          NaN         NaN]\\ntimestamp: 2024-03-04T21:21:10.943849\\ncomments: []\\nexception: \\n\\nuid: output_12\\nstatus: fail\\ntype: survival plot\\nproperties: {\\'method\\': \\'surv_func\\'}\\nsdc: {\\'summary\\': {\\'suppressed\\': True, \\'negative\\': 0, \\'missing\\': 0, \\'threshold\\': 76, \\'p-ratio\\': 0, \\'nk-rule\\': 0, \\'all-values-are-same\\': 0}, \\'cells\\': {\\'negative\\': [], \\'missing\\': [], \\'threshold\\': [[1, 0], [1, 1], [1, 2], [1, 3], [2, 0], [2, 1], [2, 2], [2, 3], [3, 0], [3, 1], [3, 2], [3, 3], [4, 0], [4, 1], [4, 2], [4, 3], [5, 0], [5, 1], [5, 2], [5, 3], [6, 0], [6, 1], [6, 2], [6, 3], [7, 0], [7, 1], [7, 2], [7, 3], [8, 0], [8, 1], [8, 2], [8, 3], [9, 0], [9, 1], [9, 2], [9, 3], [10, 0], [10, 1], [10, 2], [10, 3], [11, 0], [11, 1], [11, 2], [11, 3], [12, 0], [12, 1], [12, 2], [12, 3], [13, 0], [13, 1], [13, 2], [13, 3], [14, 0], [14, 1], [14, 2], [14, 3], [15, 0], [15, 1], [15, 2], [15, 3], [16, 0], [16, 1], [16, 2], [16, 3], [17, 0], [17, 1], [17, 2], [17, 3], [18, 0], [18, 1], [18, 2], [18, 3], [19, 0], [19, 1], [19, 2], [19, 3]], \\'p-ratio\\': [], \\'nk-rule\\': [], \\'all-values-are-same\\': []}}\\ncommand: safe_plot = acro.surv_func(\\nsummary: fail; threshold: 76 cells suppressed; \\noutcome: Empty DataFrame\\nColumns: []\\nIndex: []\\noutput: [\\'acro_artifacts\\\\\\\\kaplan-mier_0.png\\']\\ntimestamp: 2024-03-04T21:21:11.236799\\ncomments: []\\nexception: \\n\\n'"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "acro.print_outputs()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3136bc78",
   "metadata": {},
   "source": [
    "### 2: Remove some ACRO outputs before finalising \n",
    "This is an example of deleting some of the ACRO outputs.  \n",
    "The name of the output that needs to be removed should be passed to the function remove_output.  \n",
    "- Currently, all outputs names contain timestamp; that is the time when the output was created.  \n",
    "- The output name can be taken from the outputs listed by the print_outputs function, \n",
    "- or by listing the results and choosing the specific output that needs to be removed"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "e4ee985e",
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO:acro:records:remove(): output_0 removed\n"
     ]
    }
   ],
   "source": [
    "acro.remove_output(\"output_0\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "df2a02e0",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "### 3: Rename ACRO outputs before finalising\n",
    "This is an example of renaming the outputs to provide a more descriptive name.  \n",
    "The timestamp associated with the output name will not get overwritten"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "b9d0b9ac",
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO:acro:records:rename_output(): output_2 renamed to pivot_table\n"
     ]
    }
   ],
   "source": [
    "acro.rename_output(\"output_2\", \"pivot_table\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "56d2b6a1",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "### 4: Add a comment to output\n",
    "This is an example to add a comment to outputs.  \n",
    "It can be used to provide a description or to pass additional information to the output checkers."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "8e21f7b0",
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO:acro:records:a comment was added to output_1\n",
      "INFO:acro:records:a comment was added to output_1\n"
     ]
    }
   ],
   "source": [
    "acro.add_comments(\"output_1\", \"Please let me have this data.\")\n",
    "acro.add_comments(\"output_1\", \"6 cells were suppressed in this table\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8496fed4",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "### 5: Add an unsupported output to the list of outputs\n",
    "This is an example to add an unsupported outputs (such as images) to the list of outputs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "1e8000a1",
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO:acro:records:add_custom(): output_13\n"
     ]
    }
   ],
   "source": [
    "acro.custom_output(\n",
    "    \"XandY.jpeg\", \"This output is an image showing the relationship between X and Y\"\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5a586694",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "## 6: (the big one) Finalise ACRO\n",
    "This is an example of the function _finalise()_ which the users must call at the end of each session.  \n",
    "- It takes each output and saves it to a CSV file.    \n",
    "- It also saves the SDC analysis for each output to a json file or Excel file  \n",
    "  (depending on the extension of the name of the file provided as an input to the function)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "f941aca2",
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING:acro:Results file can not be created. Directory NURSERY already exists. Please choose a different directory name.\n"
     ]
    }
   ],
   "source": [
    "output = acro.finalise(\"NURSERY\", \"json\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "113d84ec",
   "metadata": {},
   "source": [
    "### 7: Add a directory of outputs to an acro object \n",
    "This is an example of adding a list of files (produced by the researcher without using ACRO) to an acro object and creates a results file for checking."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "fdea993a",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO:acro:version: 0.4.5\n",
      "INFO:acro:config: {'safe_threshold': 10, 'safe_dof_threshold': 10, 'safe_nk_n': 2, 'safe_nk_k': 0.9, 'safe_pratio_p': 0.1, 'check_missing_values': False, 'survival_safe_threshold': 10, 'zeros_are_disclosive': True, 'unsupported_filetypes': ['.gph', '.svg']}\n",
      "INFO:acro:automatic suppression: False\n",
      "INFO:acro:records:add_custom(): output_0\n",
      "INFO:acro:records:rename_output(): output_0 renamed to crosstab.pkl\n",
      "WARNING:acro:Results file can not be created. Directory SDC_results already exists. Please choose a different directory name.\n"
     ]
    }
   ],
   "source": [
    "import shutil\n",
    "\n",
    "table = pd.crosstab(df.recommend, df.parents)\n",
    "# save the output table to a file and add this file to a directory\n",
    "src_path = \"test_add_to_acro\"\n",
    "file_path = \"crosstab.pkl\"\n",
    "dest_path = \"SDC_results\"\n",
    "if not os.path.exists(src_path):\n",
    "    table.to_pickle(file_path)\n",
    "    os.mkdir(src_path)\n",
    "    shutil.move(file_path, src_path, copy_function=shutil.copytree)\n",
    "\n",
    "# add the output to acro\n",
    "add_to_acro(src_path, dest_path)"
   ]
  }
 ],
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