Macrobase_python_usage_example.ipynb

{
 "cells": [
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Usage"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Data preprocess:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
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       "      <th>Outlier</th>\n",
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       "      <td>ColorGreen</td>\n",
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       "      <td>StatusUnsure</td>\n",
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       "      <td>6</td>\n",
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       "      <th>40263</th>\n",
       "      <td>ColorGreen</td>\n",
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       "      <td>False</td>\n",
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       "              state        relevance  truncation               inlay  \\\n",
       "0        ColorGreen  RelevanceUnsure         0.0  InlayArrowStraight   \n",
       "1        ColorGreen  RelevanceUnsure         0.0         InlayUnsure   \n",
       "2        ColorGreen  RelevanceUnsure         0.0  InlayArrowStraight   \n",
       "3        ColorGreen  RelevanceUnsure         0.0         InlayCircle   \n",
       "4              none             none        -1.0                none   \n",
       "...             ...              ...         ...                 ...   \n",
       "40260          none             none        -1.0                none   \n",
       "40261    ColorGreen  RelevanceUnsure         0.0         InlayCircle   \n",
       "40262  StatusUnsure  RelevanceUnsure         0.0                 NaN   \n",
       "40263    ColorGreen  RelevanceUnsure         0.0         InlayCircle   \n",
       "40264    ColorGreen  RelevanceUnsure         0.0         InlayCircle   \n",
       "\n",
       "       height_binned  occlusion  width_binned       direction  \\\n",
       "0                  9        0.0             9  DirectionFront   \n",
       "1                  9        0.0             9  DirectionFront   \n",
       "2                  9        0.0             8  DirectionFront   \n",
       "3                  9        0.0             8  DirectionFront   \n",
       "4                  8       -1.0             7            none   \n",
       "...              ...        ...           ...             ...   \n",
       "40260              6       -1.0             3            none   \n",
       "40261              8        0.0             8  DirectionFront   \n",
       "40262              7        0.0             9  DirectionRight   \n",
       "40263              8        0.0             8  DirectionFront   \n",
       "40264              8        0.0             8  DirectionFront   \n",
       "\n",
       "       aspect_ratio_binned         status          stacking  top_binned  \\\n",
       "0                        5  StatusDeduced  StackingVertical           0   \n",
       "1                        4  StatusDeduced  StackingVertical           5   \n",
       "2                        4  StatusDeduced  StackingVertical           5   \n",
       "3                        6  StatusDeduced  StackingVertical           5   \n",
       "4                        0           none              none           1   \n",
       "...                    ...            ...               ...         ...   \n",
       "40260                    0           none              none           2   \n",
       "40261                    6  StatusDeduced  StackingVertical           5   \n",
       "40262                    9   StatusUnsure  StackingVertical           6   \n",
       "40263                    4  StatusDeduced  StackingVertical           0   \n",
       "40264                    4  StatusDeduced  StackingVertical           0   \n",
       "\n",
       "             bulbNumber         type  size_binned  left_binned  Outlier  \n",
       "0      BulbNumberUnsure  TypeVehicle          9.0          3.0     True  \n",
       "1      BulbNumberUnsure  TypeVehicle          9.0          1.0    False  \n",
       "2      BulbNumberUnsure  TypeVehicle          8.0          6.0    False  \n",
       "3      BulbNumberUnsure  TypeVehicle          9.0          9.0    False  \n",
       "4                  none         none          8.0          4.0     True  \n",
       "...                 ...          ...          ...          ...      ...  \n",
       "40260              none         none          4.0          4.0    False  \n",
       "40261   BulbNumberThree  TypeVehicle          8.0          1.0    False  \n",
       "40262     BulbNumberTwo   TypeUnsure          8.0          1.0    False  \n",
       "40263   BulbNumberThree  TypeVehicle          8.0          5.0    False  \n",
       "40264   BulbNumberThree          NaN          NaN          NaN    False  \n",
       "\n",
       "[40265 rows x 17 columns]"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "df = pd.read_csv(\"model1.csv\", header=0)\n",
    "# Add an \"Outlier\" column. The outlier here is defined as true_value != predicted_value.\n",
    "df[\"Outlier\"] = df['true_value'] != df[\"predicted_value\"]\n",
    "# Drop true class and predicted class columns.\n",
    "df = df.drop(columns=[\"true_value\", \"predicted_value\"])\n",
    "df\n"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Import Macrobase_python and use it:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import Macrobase_python\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/hannah/GT/Research/macrobase/Macrobase_python.py:154: RuntimeWarning: invalid value encountered in divide\n",
      "  return np.array(1-ao/all).reshape(-1)\n",
      "/Users/hannah/GT/Research/macrobase/Macrobase_python.py:138: RuntimeWarning: invalid value encountered in divide\n",
      "  return np.array((ao/aoi)/(bo/boi)).reshape(-1)\n"
     ]
    },
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>support</th>\n",
       "      <th>accuracy</th>\n",
       "      <th>risk ratio</th>\n",
       "      <th>itemsets</th>\n",
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       "      <th>0</th>\n",
       "      <td>0.186111</td>\n",
       "      <td>0.536131</td>\n",
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       "      <td>(aspect_ratio_binned=0)</td>\n",
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       "      <th>1</th>\n",
       "      <td>0.205241</td>\n",
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       "      <td>2.324503</td>\n",
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       "      <th>2</th>\n",
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       "      <td>0.184218</td>\n",
       "      <td>0.522469</td>\n",
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       "      <td>0.203746</td>\n",
       "      <td>0.474428</td>\n",
       "      <td>2.392036</td>\n",
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       "    <tr>\n",
       "      <th>6138</th>\n",
       "      <td>0.182923</td>\n",
       "      <td>0.460476</td>\n",
       "      <td>2.425062</td>\n",
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       "      <th>6140</th>\n",
       "      <td>0.100827</td>\n",
       "      <td>0.489662</td>\n",
       "      <td>2.164737</td>\n",
       "      <td>(truncation=-1.0, type=none, state=none, occlu...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>6141 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       support  accuracy  risk ratio  \\\n",
       "0     0.186111  0.536131    2.057740   \n",
       "1     0.205241  0.488326    2.324503   \n",
       "2     0.187307  0.533151    2.074006   \n",
       "3     0.794859  0.469795    6.493792   \n",
       "4     0.794859  0.469795    6.493792   \n",
       "...        ...       ...         ...   \n",
       "6136  0.184218  0.522469    2.122470   \n",
       "6137  0.203746  0.474428    2.392036   \n",
       "6138  0.182923  0.460476    2.425062   \n",
       "6139  0.102023  0.478880    2.214455   \n",
       "6140  0.100827  0.489662    2.164737   \n",
       "\n",
       "                                               itemsets  \n",
       "0                               (aspect_ratio_binned=0)  \n",
       "1                               (aspect_ratio_binned=1)  \n",
       "2                               (aspect_ratio_binned=2)  \n",
       "3                                     (bulbNumber=none)  \n",
       "4                                      (direction=none)  \n",
       "...                                                 ...  \n",
       "6136  (truncation=-1.0, type=none, state=none, occlu...  \n",
       "6137  (truncation=-1.0, type=none, state=none, occlu...  \n",
       "6138  (truncation=-1.0, type=none, state=none, aspec...  \n",
       "6139  (truncation=-1.0, type=none, state=none, heigh...  \n",
       "6140  (truncation=-1.0, type=none, state=none, occlu...  \n",
       "\n",
       "[6141 rows x 4 columns]"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Input: a discretized dataframe with an outlier column, and without true class and predicted class.\n",
    "# min_support is the minimum support, and min_rr is the minimum risk ratio.\n",
    "macrobase = Macrobase_python.Macrobase(df, min_support=0.1, min_rr=1.5)\n",
    "macrobase.fit()\n"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The output is a pandas dataframe. Each row indicates a frequent itemset with its support, accuracy, and risk ratio."
   ]
  }
 ],
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