notebooks/dichotomic_pattern_mining.ipynb

{
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  {
   "cell_type": "markdown",
   "metadata": {
    "pycharm": {
     "name": "#%% md\n"
    }
   },
   "source": [
    "# Dichotomic Pattern Mining (DPM)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "from ast import literal_eval\n",
    "from time import time\n",
    "from IPython.display import display\n",
    "\n",
    "from sequential.seq2pat import Seq2Pat, Attribute\n",
    "from sequential.pat2feat import Pat2Feat\n",
    "from sequential.dpm import dichotomic_pattern_mining, DichotomicAggregation"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "pycharm": {
     "name": "#%% md\n"
    }
   },
   "source": [
    "## Arguments"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "args = {}\n",
    "args['data'] = '../tests/data/sample_data.csv'\n",
    "args['min_frequency_pos'] = 0.3\n",
    "args['min_frequency_neg'] = 0.3\n",
    "args['max_span'] = 10"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "pycharm": {
     "name": "#%% md\n"
    }
   },
   "source": [
    "## Sample Data\n",
    "- This notebook is going to run DPM on a sample sequences dataset, which is extracted from the published dataset in E-commerce Shopper Intent Prediction (Requena et al., 2020). The sequences are associated with positive or negative labels, e.g. purchase vs. non-purchase."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>event_sequence</th>\n",
       "      <th>event_time</th>\n",
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       "                                      event_sequence  \\\n",
       "0  [1, 1, 1, 2, 3, 1, 4, 1, 2, 3, 1, 4, 1, 2, 1, ...   \n",
       "1  [2, 1, 2, 1, 2, 1, 2, 1, 2, 3, 1, 2, 3, 1, 2, ...   \n",
       "2                     [1, 1, 1, 2, 1, 2, 1, 1, 1, 1]   \n",
       "3  [1, 1, 2, 1, 2, 1, 2, 3, 1, 1, 1, 1, 1, 1, 1, ...   \n",
       "4                                    [1, 2, 1, 1, 1]   \n",
       "\n",
       "                                          event_time  label  \n",
       "0  [13118, 17085, 11839, 41749, 35195, 3348, 3309...      1  \n",
       "1  [50205, 32403, 51377, 4256, 52139, 15020, 6999...      1  \n",
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       "3  [355031, 50126, 26262, 44512, 39795, 49730, 14...      1  \n",
       "4               [19173, 159782, 12811, 88544, 53858]      1  "
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     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
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   ],
   "source": [
    "sequence_df = pd.read_csv(args['data'])\n",
    "sequence_df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "pycharm": {
     "name": "#%% md\n"
    }
   },
   "source": [
    "### Transform sequence from string to list"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "literal_columns = ['event_sequence', 'event_time']\n",
    "\n",
    "for column in literal_columns:\n",
    "    sequence_df[column] = sequence_df[column].apply(literal_eval)\n",
    "\n",
    "# Input lists\n",
    "sequences = sequence_df['event_sequence'].values.tolist()\n",
    "times = sequence_df['event_time'].values.tolist()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "pycharm": {
     "name": "#%% md\n"
    }
   },
   "source": [
    "## Data Exploration"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of sequences: 2000\n",
      "Maximum length: 155\n",
      "Average length: 28.3755\n",
      "Number of positives: 1000; Number of negatives: 1000\n"
     ]
    }
   ],
   "source": [
    "# EDA for items, max length, average length, number of positive and negative\n",
    "num_sequences = len(sequence_df)\n",
    "max_len = sequence_df['event_sequence'].apply(len).max()\n",
    "avg_len = sequence_df['event_sequence'].apply(len).mean()\n",
    "num_pos = len(sequence_df[sequence_df['label']==1])\n",
    "\n",
    "print(f'Number of sequences: {num_sequences}')\n",
    "print(f'Maximum length: {max_len}')\n",
    "print(f'Average length: {avg_len}')\n",
    "print(f'Number of positives: {num_pos}; Number of negatives: {num_sequences - num_pos}')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "pycharm": {
     "name": "#%% md\n"
    }
   },
   "source": [
    "## Seq2Pat for Positive Labels\n",
    "- There is one attribute for each event: `event_time`\n",
    "- Constraint 1: This constraint is to enforce the average event time greater than 20 sec\n",
    "- Constraint 2: The built-in constraint in Seq2Pat, which can be configured by max_span parameter. This is to enforce the pattern mining to be within a span of `max_span` items (max_span=10 by default). "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "# Get sequences having positive labels, and associated attributes.\n",
    "sequences_pos = sequence_df[sequence_df['label']==1]['event_sequence'].values.tolist()\n",
    "times_pos = sequence_df[sequence_df['label']==1]['event_time'].values.tolist()\n",
    "\n",
    "seq2pat_pos = Seq2Pat(sequences_pos)\n",
    "\n",
    "# Define a constraint on event time, average time >= 20 sec\n",
    "time_attr_pos = Attribute(times_pos)\n",
    "time_ct_pos = 20000 <= time_attr_pos.average()\n",
    "\n",
    "# Add constraints to seq2pat\n",
    "cs_pos = seq2pat_pos.add_constraint(time_ct_pos)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "pycharm": {
     "name": "#%% md\n"
    }
   },
   "source": [
    "## Seq2Pat for Negative Labels\n",
    "- Here we apply the same constraint models to sequences with negative labels"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "# Get sequences having positive labels, and associated attributes.\n",
    "sequences_neg = sequence_df[sequence_df['label']==0]['event_sequence'].values.tolist()\n",
    "times_neg = sequence_df[sequence_df['label']==0]['event_time'].values.tolist()\n",
    "\n",
    "seq2pat_neg = Seq2Pat(sequences_neg)\n",
    "\n",
    "# Define a constraint on event time, average time >= 20 sec\n",
    "time_attr_neg = Attribute(times_neg)\n",
    "time_ct_neg = 20000 <= time_attr_neg.average()\n",
    "\n",
    "# Add constraints to seq2pat\n",
    "cs_neg = seq2pat_neg.add_constraint(time_ct_neg)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "pycharm": {
     "name": "#%% md\n"
    }
   },
   "source": [
    "## Dichotomic Pattern Mining: From Sequences to Patterns\n",
    "- In DPM, we utilize the two Seq2Pat models for positive and negative sequences, mine the patterns that are frequent in each outcome and return different aggregations of mined patterns from the two cohorts."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "DPM finished! Runtime: 7.4062 sec\n",
      "Aggregation:  intersection  with number of patterns:  215\n",
      "Aggregation:  union  with number of patterns:  498\n",
      "Aggregation:  unique_negative  with number of patterns:  29\n",
      "Aggregation:  unique_positive  with number of patterns:  254\n"
     ]
    }
   ],
   "source": [
    "t = time()\n",
    "\n",
    "# Run DPM on positive and negative patterns and return a dict of pattern aggregations\n",
    "aggregation_to_patterns = dichotomic_pattern_mining(seq2pat_pos, seq2pat_neg,\n",
    "                                                    args['min_frequency_pos'],\n",
    "                                                    args['min_frequency_neg'])\n",
    "\n",
    "print(f'DPM finished! Runtime: {time()-t:.4f} sec')\n",
    "for aggregation, patterns in aggregation_to_patterns.items():\n",
    "    print(\"Aggregation: \", aggregation, \" with number of patterns: \", len(patterns))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "pycharm": {
     "name": "#%% md\n"
    }
   },
   "source": [
    "## From Patterns to Encodings\n",
    "- Finally, we generate encodings of all sequences based on an aggregation of patterns found by DPM."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
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    {
     "name": "stdout",
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      "Aggregation:  intersection\n",
      "Encoding finished! Runtime: 210.1841 sec\n"
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       "                                            sequence  feature_0  feature_1  \\\n",
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       "1  [2, 1, 2, 1, 2, 1, 2, 1, 2, 3, 1, 2, 3, 1, 2, ...          1          1   \n",
       "2                     [1, 1, 1, 2, 1, 2, 1, 1, 1, 1]          1          1   \n",
       "3  [1, 1, 2, 1, 2, 1, 2, 3, 1, 1, 1, 1, 1, 1, 1, ...          1          1   \n",
       "4                                    [1, 2, 1, 1, 1]          1          1   \n",
       "\n",
       "   feature_2  feature_3  feature_4  feature_5  feature_6  feature_7  \\\n",
       "0          0          0          0          0          0          0   \n",
       "1          1          1          1          1          1          1   \n",
       "2          1          1          1          1          0          0   \n",
       "3          1          1          1          1          0          0   \n",
       "4          1          0          0          0          0          0   \n",
       "\n",
       "   feature_8  ...  feature_205  feature_206  feature_207  feature_208  \\\n",
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       "\n",
       "   feature_214  \n",
       "0            0  \n",
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       "3            0  \n",
       "4            0  \n",
       "\n",
       "[5 rows x 216 columns]"
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     "text": [
      "Aggregation:  union\n",
      "Encoding finished! Runtime: 405.0462 sec\n"
     ]
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      "Aggregation:  unique_negative\n",
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      "Aggregation:  unique_positive\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 255 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                            sequence  feature_0  feature_1  \\\n",
       "0  [1, 1, 1, 2, 3, 1, 4, 1, 2, 3, 1, 4, 1, 2, 1, ...          0          0   \n",
       "1  [2, 1, 2, 1, 2, 1, 2, 1, 2, 3, 1, 2, 3, 1, 2, ...          1          1   \n",
       "2                     [1, 1, 1, 2, 1, 2, 1, 1, 1, 1]          1          0   \n",
       "3  [1, 1, 2, 1, 2, 1, 2, 3, 1, 1, 1, 1, 1, 1, 1, ...          1          0   \n",
       "4                                    [1, 2, 1, 1, 1]          0          0   \n",
       "\n",
       "   feature_2  feature_3  feature_4  feature_5  feature_6  feature_7  \\\n",
       "0          0          0          0          0          0          0   \n",
       "1          1          1          1          1          1          1   \n",
       "2          0          0          0          1          0          0   \n",
       "3          0          0          0          0          1          1   \n",
       "4          0          0          0          0          0          0   \n",
       "\n",
       "   feature_8  ...  feature_244  feature_245  feature_246  feature_247  \\\n",
       "0          0  ...            1            0            0            1   \n",
       "1          1  ...            1            1            0            1   \n",
       "2          0  ...            0            0            0            0   \n",
       "3          1  ...            0            0            0            0   \n",
       "4          0  ...            0            0            0            0   \n",
       "\n",
       "   feature_248  feature_249  feature_250  feature_251  feature_252  \\\n",
       "0            1            1            0            0            0   \n",
       "1            1            1            1            1            1   \n",
       "2            0            0            0            0            0   \n",
       "3            0            0            0            0            0   \n",
       "4            0            0            0            0            0   \n",
       "\n",
       "   feature_253  \n",
       "0            0  \n",
       "1            1  \n",
       "2            0  \n",
       "3            0  \n",
       "4            0  \n",
       "\n",
       "[5 rows x 255 columns]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Notice that constraints are optional to the generation of encodings\n",
    "# In the following, we define a constraint on event time for all sequences, average time >= 20 sec\n",
    "# The Seq2Pat built-in span constraint can be enforced in encodings generation by setting `max_span=10`.\n",
    "time_attr = Attribute(times)\n",
    "time_ct = 20000 <= time_attr.average()\n",
    "\n",
    "# List of constraints \n",
    "constraints = [time_ct]\n",
    "\n",
    "for aggregation, patterns in aggregation_to_patterns.items():\n",
    "    print(\"Aggregation: \", aggregation)\n",
    "    \n",
    "    t = time()\n",
    "    # find one hot encoding of each sequence for each pattern subject to constraints\n",
    "    pat2feat = Pat2Feat()\n",
    "    encodings = pat2feat.get_features(sequences, patterns, constraints, args['max_span'],\n",
    "                                      drop_pattern_frequency=False)\n",
    "    \n",
    "    print(f'Encoding finished! Runtime: {time()-t:.4f} sec')\n",
    "    display(encodings.head())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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