{ "cells": [ { "metadata": { "ExecuteTime": { "end_time": "2026-05-29T14:53:00.201326Z", "start_time": "2026-05-29T14:53:00.170821500Z" } }, "cell_type": "code", "source": [ "import warnings\n", "warnings.filterwarnings(\"ignore\", category=UserWarning)" ], "outputs": [], "execution_count": 15 }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Log Encodings\n", "\n", "Declare4Py provides several among the main encoding techniques for vectorizing a traces log. These are useful for applying Machine Learning techniques. The encoding classes provided by Declare4Py (see the `Declare4Py.Encodings` package) take as an input a log in a Pandas dataframe format and return a Pandas dataframe whose rows represent a single trace and the columns the extracted features. The Declare4Py encodings are implemented as scikit-learn transformers so it is straightfoward to use them in a Machine Learning pipeline.\n", "\n", "The tutorial will cover the following points:\n", "\n", "1. Encodings families:\n", " 1. The boolean encoding;\n", " 2. The frequency-based encoding;\n", " 3. Aggregated encodings;\n", " 4. Indexed encodings:\n", " 1. The simple-index encoding;\n", " 2. The complex-Index encoding;\n", " 5. Static Encodings:\n", " 1. The first-state encoding;\n", " 2. The second-to-last-state encoding;\n", " 3. The last-state encoding;\n", " 6. The Ngram encoding;\n", " 7. The Declare encoding;\n", "2. Encoding combinations:\n", " 1. The index-latest-payload encoding;\n", "3. A Machine Learning pipeline.\n", "\n", "Before starting how to use the encodings the necessary packages need to be imported.\n", "\n", "[1]\n", "[2]\n", "[3]\n", "[4]" ] }, { "cell_type": "code", "metadata": { "ExecuteTime": { "end_time": "2026-05-29T14:53:00.226325200Z", "start_time": "2026-05-29T14:53:00.207325600Z" } }, "source": [ "import os\n", "import pandas as pd\n", "\n", "\n", "from Declare4Py.Encodings.Aggregate import Aggregate\n", "from Declare4Py.Encodings.IndexBased import IndexBased\n", "from Declare4Py.Encodings.Static import Static\n", "from Declare4Py.Encodings.PreviousState import PreviousState\n", "from Declare4Py.Encodings.LastState import LastState\n", "from Declare4Py.Encodings.Ngram import Ngram\n", "from Declare4Py.Encodings.Declare import Declare" ], "outputs": [], "execution_count": 16 }, { "cell_type": "markdown", "metadata": {}, "source": [ "The input format for the `Encodings` classes are logs as Pandas dataframe. Therefore, we import the event log and convert it in a Pandas dataframe." ] }, { "cell_type": "code", "metadata": { "ExecuteTime": { "end_time": "2026-05-29T14:53:01.741917Z", "start_time": "2026-05-29T14:53:00.227326900Z" } }, "source": [ "from Declare4Py.D4PyEventLog import D4PyEventLog\n", "\n", "log_path = os.path.join(\"../../../\", \"tests\", \"test_logs\", \"Sepsis Cases.xes.gz\")\n", "event_log = D4PyEventLog(case_name=\"case:concept:name\")\n", "event_log.parse_xes_log(log_path)\n", "case_id_key = event_log.get_case_name()\n", "event_log.to_dataframe()\n", "df = event_log.log\n", "df.head()" ], "outputs": [ { "data": { "text/plain": [ "parsing log, completed traces :: 0%| | 0/1050 [00:00\n", "\n", "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
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\n", "" ] }, "execution_count": 17, "metadata": {}, "output_type": "execute_result" } ], "execution_count": 17 }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Encodings families\n", "\n", "A Declare4Py encoding is implemented as a scikit-learn transformer class, you just need to instantiate the corresponding `encoder` object and call the function `fit_transform(df)` on the input dataframe. The name of the features can be retrieved with the `get_feature_names()` function.\n", "\n", "### The Boolean Encoding\n", "\n", "In the __boolean encoding__ sequences of events are represented as feature vectors, in such a way that each feature corresponds to an event class (an activity) from the log. This is achieved with the `Declare4Py.Encodings.Aggregate.Aggregate` class by setting the categorical attributes and the `boolean` parameter to `True`." ] }, { "cell_type": "code", "metadata": { "ExecuteTime": { "end_time": "2026-05-29T14:53:01.851642200Z", "start_time": "2026-05-29T14:53:01.822640200Z" } }, "source": [ "encoder = Aggregate(case_id_col=case_id_key, cat_cols=['concept:name', 'org:group'], boolean=True)\n", "enc_df = encoder.fit_transform(df)\n", "\n", "print(f\"Log features:\\n {encoder.get_feature_names()}\")\n", "enc_df.head()" ], "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Log features:\n", " Index(['concept:name_Admission IC', 'concept:name_Admission NC',\n", " 'concept:name_CRP', 'concept:name_ER Registration',\n", " 'concept:name_ER Sepsis Triage', 'concept:name_ER Triage',\n", " 'concept:name_IV Antibiotics', 'concept:name_IV Liquid',\n", " 'concept:name_LacticAcid', 'concept:name_Leucocytes',\n", " 'concept:name_Release A', 'concept:name_Release B',\n", " 'concept:name_Release C', 'concept:name_Release D',\n", " 'concept:name_Release E', 'concept:name_Return ER', 'org:group_?',\n", " 'org:group_A', 'org:group_B', 'org:group_C', 'org:group_D',\n", " 'org:group_E', 'org:group_F', 'org:group_G', 'org:group_H',\n", " 'org:group_I', 'org:group_J', 'org:group_K', 'org:group_L',\n", " 'org:group_M', 'org:group_N', 'org:group_O', 'org:group_P',\n", " 'org:group_Q', 'org:group_R', 'org:group_S', 'org:group_T',\n", " 'org:group_U', 'org:group_V', 'org:group_W', 'org:group_X',\n", " 'org:group_Y'],\n", " dtype='object')\n" ] }, { "data": { "text/plain": [ " concept:name_Admission IC concept:name_Admission NC \\\n", "case:concept:name \n", "A False True \n", "AA False False \n", "AAA False True \n", "AB False False \n", "ABA False True \n", "\n", " concept:name_CRP concept:name_ER Registration \\\n", "case:concept:name \n", "A True True \n", "AA True True \n", "AAA True True \n", "AB True True \n", "ABA True True \n", "\n", " concept:name_ER Sepsis Triage concept:name_ER Triage \\\n", "case:concept:name \n", "A True True \n", "AA True True \n", "AAA True True \n", "AB True True \n", "ABA True True \n", "\n", " concept:name_IV Antibiotics concept:name_IV Liquid \\\n", "case:concept:name \n", "A True True \n", "AA True True \n", "AAA True True \n", "AB True True \n", "ABA True True \n", "\n", " concept:name_LacticAcid concept:name_Leucocytes ... \\\n", "case:concept:name ... \n", "A True True ... \n", "AA True True ... \n", "AAA True True ... \n", "AB True True ... \n", "ABA True True ... \n", "\n", " org:group_P org:group_Q org:group_R org:group_S \\\n", "case:concept:name \n", "A False False False False \n", "AA False False False False \n", "AAA False False False False \n", "AB False False False False \n", "ABA False False False False \n", "\n", " org:group_T org:group_U org:group_V org:group_W \\\n", "case:concept:name \n", "A False False False False \n", "AA False False False False \n", "AAA False False False False \n", "AB False False False False \n", "ABA False False False False \n", "\n", " org:group_X org:group_Y \n", "case:concept:name \n", "A False False \n", "AA False False \n", "AAA False False \n", "AB False False \n", "ABA False False \n", "\n", "[5 rows x 42 columns]" ], "text/html": [ "
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" ] }, "execution_count": 18, "metadata": {}, "output_type": "execute_result" } ], "execution_count": 18 }, { "cell_type": "markdown", "metadata": {}, "source": [ "### The Frequency-Based Encoding\n", "\n", "The __frequency-based encoding__, instead of boolean values, represents the control flow in a case with the frequency of each event class in the case. This is achieved with the `Declare4Py.Encodings.Aggregate.Aggregate` class by setting the categorical attributes and the `boolean` parameter to `False`." ] }, { "cell_type": "code", "metadata": { "ExecuteTime": { "end_time": "2026-05-29T14:53:02.769238Z", "start_time": "2026-05-29T14:53:02.735721900Z" } }, "source": [ "encoder = Aggregate(case_id_col=case_id_key, cat_cols=['concept:name', 'org:group'], boolean=False)\n", "enc_df = encoder.fit_transform(df)\n", "enc_df.head()" ], "outputs": [ { "data": { "text/plain": [ " concept:name_Admission IC concept:name_Admission NC \\\n", "case:concept:name \n", "A 0 1 \n", "AA 0 0 \n", "AAA 0 1 \n", "AB 0 0 \n", "ABA 0 1 \n", "\n", " concept:name_CRP concept:name_ER Registration \\\n", "case:concept:name \n", "A 7 1 \n", "AA 1 1 \n", "AAA 1 1 \n", "AB 1 1 \n", "ABA 4 1 \n", "\n", " concept:name_ER Sepsis Triage concept:name_ER Triage \\\n", "case:concept:name \n", "A 1 1 \n", "AA 1 1 \n", "AAA 1 1 \n", "AB 1 1 \n", "ABA 1 1 \n", "\n", " concept:name_IV Antibiotics concept:name_IV Liquid \\\n", "case:concept:name \n", "A 1 1 \n", "AA 1 1 \n", "AAA 1 1 \n", "AB 1 1 \n", "ABA 1 1 \n", "\n", " concept:name_LacticAcid concept:name_Leucocytes ... \\\n", "case:concept:name ... \n", "A 1 7 ... \n", "AA 1 1 ... \n", "AAA 1 1 ... \n", "AB 1 1 ... \n", "ABA 1 5 ... \n", "\n", " org:group_P org:group_Q org:group_R org:group_S \\\n", "case:concept:name \n", "A 0 0 0 0 \n", "AA 0 0 0 0 \n", "AAA 0 0 0 0 \n", "AB 0 0 0 0 \n", "ABA 0 0 0 0 \n", "\n", " org:group_T org:group_U org:group_V org:group_W \\\n", "case:concept:name \n", "A 0 0 0 0 \n", "AA 0 0 0 0 \n", "AAA 0 0 0 0 \n", "AB 0 0 0 0 \n", "ABA 0 0 0 0 \n", "\n", " org:group_X org:group_Y \n", "case:concept:name \n", "A 0 0 \n", "AA 0 0 \n", "AAA 0 0 \n", "AB 0 0 \n", "ABA 0 0 \n", "\n", "[5 rows x 42 columns]" ], "text/html": [ "
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" ] }, "execution_count": 19, "metadata": {}, "output_type": "execute_result" } ], "execution_count": 19 }, { "cell_type": "markdown", "metadata": {}, "source": [ "### The Aggregated Encoding\n", "\n", "The __aggregated encoding__ considers all events since the beginning of the case, but ignore the order of the events. In this case, several aggregation functions can be applied to the values that an event attribute has taken throughout the case. This is achieved with the `Declare4Py.Encodings.Aggregate.Aggregate` class by setting the categorical attributes, the numeric attributes, the `boolean` parameter to `False` and a list of functions to aggregate the numeric attributes, e.g., 'mean', 'max', 'min', 'sum', 'std'." ] }, { "cell_type": "code", "metadata": { "ExecuteTime": { "end_time": "2026-05-29T14:53:02.848751700Z", "start_time": "2026-05-29T14:53:02.822747500Z" } }, "source": [ "encoder = Aggregate(case_id_col=case_id_key, cat_cols=['concept:name', 'org:group'], num_cols=['CRP'], boolean=False, aggregation_functions=['min', 'mean', 'max'])\n", "enc_df = encoder.fit_transform(df)\n", "enc_df.head()" ], "outputs": [ { "data": { "text/plain": [ " concept:name_Admission IC concept:name_Admission NC \\\n", "case:concept:name \n", "A 0 1 \n", "AA 0 0 \n", "AAA 0 1 \n", "AB 0 0 \n", "ABA 0 1 \n", "\n", " concept:name_CRP concept:name_ER Registration \\\n", "case:concept:name \n", "A 7 1 \n", "AA 1 1 \n", "AAA 1 1 \n", "AB 1 1 \n", "ABA 4 1 \n", "\n", " concept:name_ER Sepsis Triage concept:name_ER Triage \\\n", "case:concept:name \n", "A 1 1 \n", "AA 1 1 \n", "AAA 1 1 \n", "AB 1 1 \n", "ABA 1 1 \n", "\n", " concept:name_IV Antibiotics concept:name_IV Liquid \\\n", "case:concept:name \n", "A 1 1 \n", "AA 1 1 \n", "AAA 1 1 \n", "AB 1 1 \n", "ABA 1 1 \n", "\n", " concept:name_LacticAcid concept:name_Leucocytes ... \\\n", "case:concept:name ... \n", "A 1 7 ... \n", "AA 1 1 ... \n", "AAA 1 1 ... \n", "AB 1 1 ... \n", "ABA 1 5 ... \n", "\n", " org:group_S org:group_T org:group_U org:group_V \\\n", "case:concept:name \n", "A 0 0 0 0 \n", "AA 0 0 0 0 \n", "AAA 0 0 0 0 \n", "AB 0 0 0 0 \n", "ABA 0 0 0 0 \n", "\n", " org:group_W org:group_X org:group_Y CRP_min CRP_mean \\\n", "case:concept:name \n", "A 0 0 0 6.0 30.857143 \n", "AA 0 0 0 23.0 23.000000 \n", "AAA 0 0 0 68.0 68.000000 \n", "AB 0 0 0 48.0 48.000000 \n", "ABA 0 0 0 78.0 105.000000 \n", "\n", " CRP_max \n", "case:concept:name \n", "A 109.0 \n", "AA 23.0 \n", "AAA 68.0 \n", "AB 48.0 \n", "ABA 140.0 \n", "\n", "[5 rows x 45 columns]" ], "text/html": [ "
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" ] }, "execution_count": 20, "metadata": {}, "output_type": "execute_result" } ], "execution_count": 20 }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Indexed Encodings\n", "\n", "#### The Simple-Index Encoding\n", "\n", "Another way of encoding a sequence is by taking into account also information about the order in which events occur in the sequence, as in the __simple-index encoding__. Here, each feature corresponds to a position in the sequence and the possible values for each feature are the presence of that event classes. This is achieved with the `Declare4Py.Encodings.IndexBased.IndexBased` class by setting the categorical attributes, the `create_dummies` parameter to `True` and the `max_events` to an integer value lower or equal than the maximum number of events in a trace in the log. If None, the parameter will set to the maximum number of events in a trace in the log. Such parameter sets the first events in the log to be use for indexing." ] }, { "cell_type": "code", "metadata": { "ExecuteTime": { "end_time": "2026-05-29T14:53:03.990518100Z", "start_time": "2026-05-29T14:53:03.626122500Z" } }, "source": [ "# with max_events the maximum number of events in a trace in the log.\n", "encoder = IndexBased(case_id_col=case_id_key, cat_cols=['concept:name'], create_dummies=True)\n", "enc_df = encoder.fit_transform(df)\n", "enc_df.head()" ], "outputs": [ { "data": { "text/plain": [ " concept:name_0_CRP concept:name_0_ER Registration \\\n", "case:concept:name \n", "A False True \n", "AA False True \n", "AAA False True \n", "AB False True \n", "ABA False True \n", "\n", " concept:name_0_ER Sepsis Triage concept:name_0_ER Triage \\\n", "case:concept:name \n", "A False False \n", "AA False False \n", "AAA False False \n", "AB False False \n", "ABA False False \n", "\n", " concept:name_0_IV Liquid concept:name_0_Leucocytes \\\n", "case:concept:name \n", "A False False \n", "AA False False \n", "AAA False False \n", "AB False False \n", "ABA False False \n", "\n", " concept:name_1_CRP concept:name_1_ER Registration \\\n", "case:concept:name \n", "A False False \n", "AA False False \n", "AAA False False \n", "AB False False \n", "ABA False False \n", "\n", " concept:name_1_ER Sepsis Triage concept:name_1_ER Triage \\\n", "case:concept:name \n", "A False False \n", "AA False True \n", "AAA False True \n", "AB False True \n", "ABA False True \n", "\n", " ... concept:name_175_Leucocytes concept:name_176_CRP \\\n", "case:concept:name ... \n", "A ... False False \n", "AA ... False False \n", "AAA ... False False \n", "AB ... False False \n", "ABA ... False False \n", "\n", " concept:name_177_CRP concept:name_178_Leucocytes \\\n", "case:concept:name \n", "A False False \n", "AA False False \n", "AAA False False \n", "AB False False \n", "ABA False False \n", "\n", " concept:name_179_Leucocytes concept:name_180_CRP \\\n", "case:concept:name \n", "A False False \n", "AA False False \n", "AAA False False \n", "AB False False \n", "ABA False False \n", "\n", " concept:name_181_Leucocytes concept:name_182_CRP \\\n", "case:concept:name \n", "A False False \n", "AA False False \n", "AAA False False \n", "AB False False \n", "ABA False False \n", "\n", " concept:name_183_Leucocytes concept:name_184_Release C \n", "case:concept:name \n", "A False False \n", "AA False False \n", "AAA False False \n", "AB False False \n", "ABA False False \n", "\n", "[5 rows x 656 columns]" ], "text/html": [ "
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" ] }, "execution_count": 21, "metadata": {}, "output_type": "execute_result" } ], "execution_count": 21 }, { "cell_type": "code", "metadata": { "ExecuteTime": { "end_time": "2026-05-29T14:53:04.135688400Z", "start_time": "2026-05-29T14:53:04.086159400Z" } }, "source": [ "# with max_events equal to 2.\n", "encoder = IndexBased(case_id_col=case_id_key, cat_cols=['concept:name'], max_events=2, create_dummies=True)\n", "enc_df = encoder.fit_transform(df)\n", "enc_df.head()" ], "outputs": [ { "data": { "text/plain": [ " concept:name_0_CRP concept:name_0_ER Registration \\\n", "case:concept:name \n", "A False True \n", "AA False True \n", "AAA False True \n", "AB False True \n", "ABA False True \n", "\n", " concept:name_0_ER Sepsis Triage concept:name_0_ER Triage \\\n", "case:concept:name \n", "A False False \n", "AA False False \n", "AAA False False \n", "AB False False \n", "ABA False False \n", "\n", " concept:name_0_IV Liquid concept:name_0_Leucocytes \\\n", "case:concept:name \n", "A False False \n", "AA False False \n", "AAA False False \n", "AB False False \n", "ABA False False \n", "\n", " concept:name_1_CRP concept:name_1_ER Registration \\\n", "case:concept:name \n", "A False False \n", "AA False False \n", "AAA False False \n", "AB False False \n", "ABA False False \n", "\n", " concept:name_1_ER Sepsis Triage concept:name_1_ER Triage \\\n", "case:concept:name \n", "A False False \n", "AA False True \n", "AAA False True \n", "AB False True \n", "ABA False True \n", "\n", " concept:name_1_IV Antibiotics concept:name_1_IV Liquid \\\n", "case:concept:name \n", "A False False \n", "AA False False \n", "AAA False False \n", "AB False False \n", "ABA False False \n", "\n", " concept:name_1_LacticAcid concept:name_1_Leucocytes \n", "case:concept:name \n", "A False True \n", "AA False False \n", "AAA False False \n", "AB False False \n", "ABA False False " ], "text/html": [ "
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" ] }, "execution_count": 22, "metadata": {}, "output_type": "execute_result" } ], "execution_count": 22 }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### The Complex-Index Encoding\n", "\n", "The __complex-based encoding__ takes into account also payload columns in the `cat_cols` or `num_cols` parameters." ] }, { "cell_type": "code", "metadata": { "ExecuteTime": { "end_time": "2026-05-29T14:53:04.980319Z", "start_time": "2026-05-29T14:53:04.399230500Z" } }, "source": [ "encoder = IndexBased(case_id_col=case_id_key, cat_cols = ['concept:name', 'org:group'], num_cols=['CRP'], create_dummies=True)\n", "enc_df = encoder.fit_transform(df)\n", "enc_df.head()" ], "outputs": [ { "data": { "text/plain": [ " CRP_0 CRP_1 CRP_2 CRP_3 CRP_4 CRP_5 CRP_6 CRP_7 \\\n", "case:concept:name \n", "A 0.0 0.0 21.0 0.0 0.0 0.0 0.0 0.0 \n", "AA 0.0 0.0 0.0 0.0 0.0 23.0 0.0 0.0 \n", "AAA 0.0 0.0 0.0 0.0 0.0 68.0 0.0 0.0 \n", "AB 0.0 0.0 0.0 48.0 0.0 0.0 0.0 0.0 \n", "ABA 0.0 0.0 0.0 0.0 0.0 0.0 78.0 0.0 \n", "\n", " CRP_8 CRP_9 ... org:group_175_B org:group_176_B \\\n", "case:concept:name ... \n", "A 0.0 109.0 ... False False \n", "AA 0.0 0.0 ... False False \n", "AAA 0.0 0.0 ... False False \n", "AB 0.0 0.0 ... False False \n", "ABA 0.0 0.0 ... False False \n", "\n", " org:group_177_B org:group_178_B org:group_179_B \\\n", "case:concept:name \n", "A False False False \n", "AA False False False \n", "AAA False False False \n", "AB False False False \n", "ABA False False False \n", "\n", " org:group_180_B org:group_181_B org:group_182_B \\\n", "case:concept:name \n", "A False False False \n", "AA False False False \n", "AAA False False False \n", "AB False False False \n", "ABA False False False \n", "\n", " org:group_183_B org:group_184_E \n", "case:concept:name \n", "A False False \n", "AA False False \n", "AAA False False \n", "AB False False \n", "ABA False False \n", "\n", "[5 rows x 1400 columns]" ], "text/html": [ "
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" ] }, "execution_count": 23, "metadata": {}, "output_type": "execute_result" } ], "execution_count": 23 }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Static Encodings\n", "\n", "In a static encoding, only an available snapshot of the data is used. Therefore, the size of the feature vector is proportional to the number of event attributes and is fixed throughout the execution of a case.\n", "\n", "Using the last state abstraction, only one value (e.g., the last snapshot) of each data attribute is available. Here, the numeric attributes are added to the feature vector \"as is\" while one hot encoding is applied to each categorical attribute.\n", "\n", "#### The First-State Encoding\n", "In the __first-state encoding__ only the information (control flow and payload) of the first event is retained. This is achieved with the `Declare4Py.Encodings.Static.Static` class by setting the categorical and numeric attributes." ] }, { "cell_type": "code", "metadata": { "ExecuteTime": { "end_time": "2026-05-29T14:53:05.770878200Z", "start_time": "2026-05-29T14:53:05.740878100Z" } }, "source": [ "encoder = Static(case_id_col=case_id_key, cat_cols = ['concept:name', 'org:group'], num_cols=['CRP'])\n", "enc_df = encoder.fit_transform(df)\n", "enc_df.head()" ], "outputs": [ { "data": { "text/plain": [ " CRP concept:name_CRP concept:name_ER Registration \\\n", "case:concept:name \n", "A 21.0 False True \n", "AA 23.0 False True \n", "AAA 68.0 False True \n", "AB 48.0 False True \n", "ABA 78.0 False True \n", "\n", " concept:name_ER Sepsis Triage concept:name_ER Triage \\\n", "case:concept:name \n", "A False False \n", "AA False False \n", "AAA False False \n", "AB False False \n", "ABA False False \n", "\n", " concept:name_IV Liquid concept:name_Leucocytes \\\n", "case:concept:name \n", "A False False \n", "AA False False \n", "AAA False False \n", "AB False False \n", "ABA False False \n", "\n", " org:group_A org:group_B org:group_C org:group_L \n", "case:concept:name \n", "A True False False False \n", "AA True False False False \n", "AAA True False False False \n", "AB True False False False \n", "ABA True False False False " ], "text/html": [ "
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" ] }, "execution_count": 24, "metadata": {}, "output_type": "execute_result" } ], "execution_count": 24 }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### The Second-to-Last-State Encoding\n", "\n", "In the __second-to-last-state encoding__ only the information (control flow and payload) of the second-to-last event is retained. This is achieved with the `Declare4Py.Encodings.PreviousState.PreviousState` class by setting the categorical and numeric attributes." ] }, { "cell_type": "code", "metadata": { "ExecuteTime": { "end_time": "2026-05-29T14:53:05.849821Z", "start_time": "2026-05-29T14:53:05.818819800Z" } }, "source": [ "encoder = PreviousState(case_id_col=case_id_key, cat_cols = ['concept:name', 'org:group'], num_cols=['CRP'])\n", "enc_df = encoder.fit_transform(df)\n", "enc_df.head()" ], "outputs": [ { "data": { "text/plain": [ " CRP concept:name_Admission NC concept:name_CRP \\\n", "case:concept:name \n", "A 0.0 0 0 \n", "AA 0.0 0 0 \n", "AAA 0.0 0 0 \n", "AB 0.0 0 0 \n", "ABA 0.0 0 0 \n", "\n", " concept:name_ER Sepsis Triage concept:name_ER Triage \\\n", "case:concept:name \n", "A 0 0 \n", "AA 0 0 \n", "AAA 0 0 \n", "AB 0 0 \n", "ABA 0 0 \n", "\n", " concept:name_IV Antibiotics concept:name_IV Liquid \\\n", "case:concept:name \n", "A 0 0 \n", "AA 0 0 \n", "AAA 0 0 \n", "AB 0 0 \n", "ABA 0 0 \n", "\n", " concept:name_LacticAcid concept:name_Leucocytes \\\n", "case:concept:name \n", "A 0 0 \n", "AA 0 0 \n", "AAA 0 0 \n", "AB 0 0 \n", "ABA 0 0 \n", "\n", " concept:name_Release A ... org:group_M org:group_N \\\n", "case:concept:name ... \n", "A 0 ... 0 0 \n", "AA 0 ... 0 0 \n", "AAA 0 ... 0 0 \n", "AB 0 ... 0 0 \n", "ABA 0 ... 0 0 \n", "\n", " org:group_O org:group_P org:group_Q org:group_R \\\n", "case:concept:name \n", "A 0 0 0 0 \n", "AA 0 0 0 0 \n", "AAA 0 0 0 0 \n", "AB 0 0 0 0 \n", "ABA 0 0 0 0 \n", "\n", " org:group_S org:group_T org:group_U org:group_V \n", "case:concept:name \n", "A 0 0 0 0 \n", "AA 0 0 0 0 \n", "AAA 0 0 0 0 \n", "AB 0 0 0 0 \n", "ABA 0 0 0 0 \n", "\n", "[5 rows x 36 columns]" ], "text/html": [ "
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" ] }, "execution_count": 25, "metadata": {}, "output_type": "execute_result" } ], "execution_count": 25 }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### The Last-State Encoding\n", "\n", "In the __last-state encoding__ only the information (control flow and payload) of the last event is retained. This is achieved with the `Declare4Py.Encodings.LastState.LastState` class by setting the categorical and numeric attributes." ] }, { "cell_type": "code", "metadata": { "ExecuteTime": { "end_time": "2026-05-29T14:53:06.640346900Z", "start_time": "2026-05-29T14:53:06.609347100Z" } }, "source": [ "encoder = LastState(case_id_col=case_id_key, cat_cols = ['concept:name', 'org:group'], num_cols=['CRP'])\n", "enc_df = encoder.fit_transform(df)\n", "enc_df.head()" ], "outputs": [ { "data": { "text/plain": [ " CRP concept:name_Admission NC concept:name_CRP \\\n", "case:concept:name \n", "A 6.0 False False \n", "AA 23.0 False False \n", "AAA 68.0 False False \n", "AB 48.0 False False \n", "ABA 140.0 False False \n", "\n", " concept:name_ER Sepsis Triage concept:name_ER Triage \\\n", "case:concept:name \n", "A False False \n", "AA False False \n", "AAA False False \n", "AB False False \n", "ABA False False \n", "\n", " concept:name_IV Antibiotics concept:name_IV Liquid \\\n", "case:concept:name \n", "A False False \n", "AA True False \n", "AAA False False \n", "AB True False \n", "ABA False False \n", "\n", " concept:name_LacticAcid concept:name_Leucocytes \\\n", "case:concept:name \n", "A False False \n", "AA False False \n", "AAA False False \n", "AB False False \n", "ABA False False \n", "\n", " concept:name_Release A ... org:group_B org:group_C \\\n", "case:concept:name ... \n", "A True ... False False \n", "AA False ... False False \n", "AAA False ... False False \n", "AB False ... False False \n", "ABA True ... False False \n", "\n", " org:group_D org:group_E org:group_F org:group_G \\\n", "case:concept:name \n", "A False True False False \n", "AA False False False False \n", "AAA False False False False \n", "AB False False False False \n", "ABA False True False False \n", "\n", " org:group_I org:group_L org:group_R org:group_V \n", "case:concept:name \n", "A False False False False \n", "AA False False False False \n", "AAA False False False False \n", "AB False False False False \n", "ABA False False False False \n", "\n", "[5 rows x 27 columns]" ], "text/html": [ "
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" ] }, "execution_count": 26, "metadata": {}, "output_type": "execute_result" } ], "execution_count": 26 }, { "cell_type": "markdown", "metadata": {}, "source": [ "### The Ngram encoding" ] }, { "cell_type": "code", "metadata": { "ExecuteTime": { "end_time": "2026-05-29T14:53:08.628313900Z", "start_time": "2026-05-29T14:53:06.788141400Z" } }, "source": [ "encoder = Ngram(case_id_col=case_id_key, n=2 , v=0.7, act_col='concept:name')\n", "enc_df = encoder.fit_transform(df)\n", "enc_df.head()" ], "outputs": [ { "data": { "text/plain": [ " Admission NC|CRP ER Triage|IV Liquid \\\n", "case:concept:name \n", "A 1.188124 0.49000 \n", "B 1.190000 0.16807 \n", "C 1.241170 0.24010 \n", "D 0.490000 0.16807 \n", "E 0.000000 0.49000 \n", "\n", " ER Sepsis Triage|CRP CRP|IV Liquid IV Liquid|LacticAcid \\\n", "case:concept:name \n", "A 0.407527 0.2401 0.000 \n", "B 0.408170 0.2401 0.000 \n", "C 0.575896 0.7000 0.000 \n", "D 0.757648 0.3430 0.000 \n", "E 0.490000 0.0000 0.343 \n", "\n", " CRP|Release B Admission NC|Release A \\\n", "case:concept:name \n", "A 0.0 0.009689 \n", "B 0.0 0.343000 \n", "C 0.0 0.285719 \n", "D 0.0 0.343000 \n", "E 0.0 0.000000 \n", "\n", " ER Triage|ER Registration LacticAcid|CRP \\\n", "case:concept:name \n", "A 0.0 0.199688 \n", "B 0.0 0.200003 \n", "C 0.0 0.000000 \n", "D 0.0 0.117649 \n", "E 0.0 0.000000 \n", "\n", " Admission NC|LacticAcid ... Release D|Return ER \\\n", "case:concept:name ... \n", "A 0.0 ... 0.0 \n", "B 0.0 ... 0.0 \n", "C 0.0 ... 0.0 \n", "D 0.0 ... 0.0 \n", "E 0.0 ... 0.0 \n", "\n", " IV Liquid|Admission IC Admission NC|IV Antibiotics \\\n", "case:concept:name \n", "A 0.0 0.0 \n", "B 0.0 0.0 \n", "C 0.0 0.0 \n", "D 0.0 0.0 \n", "E 0.0 0.0 \n", "\n", " CRP|Release D LacticAcid|Release B ER Triage|LacticAcid \\\n", "case:concept:name \n", "A 0.0 0.0 0.00000 \n", "B 0.0 0.0 0.49000 \n", "C 0.0 0.0 0.00000 \n", "D 0.0 0.0 0.34300 \n", "E 0.0 0.0 0.16807 \n", "\n", " Admission IC|LacticAcid Leucocytes|Release E \\\n", "case:concept:name \n", "A 0.0 0.0 \n", "B 0.0 0.0 \n", "C 0.0 0.0 \n", "D 0.0 0.0 \n", "E 0.0 0.0 \n", "\n", " ER Sepsis Triage|Leucocytes CRP|LacticAcid \n", "case:concept:name \n", "A 0.381729 0.70 \n", "B 0.000000 0.70 \n", "C 0.822708 0.00 \n", "D 0.425354 0.70 \n", "E 0.343000 0.49 \n", "\n", "[5 rows x 115 columns]" ], "text/html": [ "
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