{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# NeSy4PPM: Suffix prediction Tutorial\n",
    "This notebook demonstrates how to use the NeSy4PPM framework for suffix prediction, specifically focused on activity and resource or activity-only prediction using neural architectures like LSTM and Transformer models. NeSy4PPM combines neural learning with symbolic background knowledge (BK) to produce accurate and compliant predictions under various conditions, including concept drift.\n",
    "\n",
    "This notebook walks through the entire NeSy4PPM pipeline, including:\n",
    "\n",
    "    1. Data preprocessing\n",
    "    2. Training\n",
    "    3. Prediction\n",
    "    4. Evaluation"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 1 Data preprocessing\n",
    "The __Data preprocessing__ is responsible for loading and transforming event log into neural-compatible inputs. This phase involves:\n",
    "\n",
    "   - `Data loading`, which involves reading the event log from the input file,\n",
    "   - `Prefixes extraction`,which refers to extracting prefixes (i.e., partial trace executions) from the training log.\n",
    "   - `Prefixes encoding`,which handles the conversion of input prefixes and their corresponding labels into numerical representations suitable for neural models."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 1.1 Data loading\n",
    "The first step in the __data preprocessing__ pipeline is to load the event log (in a .xes, .csv or .xes.gz files) using the `LogData` class. The input of this step can be:\n",
    "- A __single event log__, which will be automatically split into training and test sets based on the chronological order of case start timestamps and a defined train ratio.\n",
    "- A pair of __separate training and test logs__."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### A. Single event log:"
   ]
  },
  {
   "cell_type": "code",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-06-28T11:03:28.422201Z",
     "start_time": "2025-06-28T11:03:24.919846Z"
    }
   },
   "source": [
    "from pathlib import Path\n",
    "from NeSy4PPM.Data_preprocessing import log_utils\n",
    "\n",
    "log_path = Path.cwd().parent/'data'/'input'/'logs'\n",
    "log_name = \"helpdesk.xes\"\n",
    "train_ratio = 0.8\n",
    "case_name_key = 'case:concept:name'\n",
    "act_name_key = 'concept:name'\n",
    "res_name_key = 'org:resource'\n",
    "timestamp_key = 'time:timestamp'\n",
    "\n",
    "log_data = log_utils.LogData(log_path=log_path,log_name=log_name,train_ratio=train_ratio,\n",
    "                             case_name_key=case_name_key,act_name_key=act_name_key,\n",
    "                             res_name_key=res_name_key,timestamp_key=timestamp_key)\n",
    "print(f\"Loaded log: {log_data.log_name}\")\n",
    "print(f\"Trace max size: {log_data.max_len}\")"
   ],
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "parsing log, completed traces :: 100%|██████████| 4580/4580 [00:01<00:00, 2715.77it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Loaded log: helpdesk\n",
      "Trace max size: 15\n"
     ]
    }
   ],
   "execution_count": 2
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### B. Separate training and test logs:"
   ]
  },
  {
   "cell_type": "code",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-06-28T13:03:21.614995Z",
     "start_time": "2025-06-28T13:03:20.414310Z"
    }
   },
   "source": [
    "from pathlib import Path\n",
    "from NeSy4PPM.Data_preprocessing import log_utils\n",
    "\n",
    "log_path = Path.cwd().parent/'data'/'input'/'logs'\n",
    "train_log = \"helpdesk_train.xes\"\n",
    "test_log = \"helpdesk_test.xes\"\n",
    "\n",
    "log_data = log_utils.LogData(log_path=log_path,train_log=train_log,test_log=test_log)\n",
    "print(f\"Loaded log: {log_data.log_name}\")\n",
    "print(f\"Trace max size: {log_data.max_len}\")"
   ],
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "parsing log, completed traces :: 100%|██████████| 3664/3664 [00:00<00:00, 5390.36it/s]\n",
      "parsing log, completed traces :: 100%|██████████| 820/820 [00:00<00:00, 10806.80it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Loaded log: helpdesk_train\n",
      "Trace max size: 15\n"
     ]
    }
   ],
   "execution_count": 2
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 1.2 Prefixes extraction\n",
    "The `extract_trace_prefixes` function extracts all possible prefixes from each trace in the training log, up to a predefined maximum length. These prefixes represent partial executions of cases and are used as inputs to the neural model."
   ]
  },
  {
   "cell_type": "code",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-06-28T13:03:28.323294Z",
     "start_time": "2025-06-28T13:03:28.051152Z"
    }
   },
   "source": [
    "from NeSy4PPM.Data_preprocessing.data_preprocessing import extract_trace_prefixes\n",
    "\n",
    "extracted_prefixes = extract_trace_prefixes(log_data=log_data, resource=True)"
   ],
   "outputs": [],
   "execution_count": 3
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 1.3 Prefixes encodings\n",
    "Before training a neural model, extracted prefixes must be converted into vectorized formats. NeSy4PPM supports four encoding techniques for multi-attribute: \n",
    "- `One-hot encoding`,\n",
    "- `Index-based encoding`,\n",
    "- `Shrinked index-based encoding`, \n",
    "- `Multi-encoders encoding`.\n",
    "\n",
    "Each encoding is implemented via the function `encode_prefixes` and prepares both input features (`x`) and targets labels: `y_a` for activity prediction and `y_g` for resource prediction when `resource = True`. Note that only `One-hot` and `Index-based` encodings are applicable when performing activity-only prediction."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### One-hot encoding\n",
    "In the __One-hot encoding__, sequences of events are converted into high-dimensional binary feature vectors. Each feature corresponds to a concatenation of one-hot encoded activity and resource values derived from the log. To apply index-based encoding, set the `encoder` parameter to `Encodings.One_hot` when calling the `encode_prefixes` function:"
   ]
  },
  {
   "cell_type": "code",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-06-28T13:04:00.430360Z",
     "start_time": "2025-06-28T13:04:00.217942Z"
    }
   },
   "source": [
    "from NeSy4PPM.Data_preprocessing.data_preprocessing import encode_prefixes\n",
    "from NeSy4PPM.Data_preprocessing.utils import Encodings\n",
    "encoder=Encodings.One_hot\n",
    "x, y_a, y_g= encode_prefixes(log_data,prefixes=extracted_prefixes,encoder=encoder,resource=True)"
   ],
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Total resources: 22 - Target resources: 23\n",
      "\t ['Value 2', 'Value 5', 'Value 16', 'Value 15', 'Value 21', 'Value 10', 'Value 11', 'Value 12', 'Value 6', 'Value 7', 'Value 9', 'Value 14', 'Value 19', 'Value 17', 'Value 8', 'Value 13', 'Value 22', 'Value 1', 'Value 4', 'Value 3', 'Value 18', 'Value 20']\n",
      "Total activities: 14 - Target activities: 15\n",
      "\t ['Assign seriousness', 'Take in charge ticket', 'Resolve ticket', 'Closed', 'Wait', 'Create SW anomaly', 'Insert ticket', 'Schedule intervention', 'INVALID', 'RESOLVED', 'VERIFIED', 'Resolve SW anomaly', 'Require upgrade', 'DUPLICATE']\n",
      "Num. of Training sequences: 16937\n",
      "Encoding...\n",
      "Num. of features: 36\n"
     ]
    }
   ],
   "execution_count": 5
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Index-based encoding\n",
    "In the __Index-based encoding__, sequences of events are transformed into numerical feature vectors, where each event is represented by a pair of indices: one for the activity and one for the resource. These indices correspond to the positions of the activity and resource in their respective predefined sets. To apply index-based encoding, set the `encoder` parameter to `Encodings.Index_based` when calling the `encode_prefixes` function:"
   ]
  },
  {
   "cell_type": "code",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-06-28T13:04:19.497561Z",
     "start_time": "2025-06-28T13:04:19.287455Z"
    }
   },
   "source": [
    "from NeSy4PPM.Data_preprocessing.data_preprocessing import encode_prefixes\n",
    "from NeSy4PPM.Data_preprocessing.utils import Encodings\n",
    "encoder=Encodings.Index_based\n",
    "x, y_a, y_g = encode_prefixes(log_data,prefixes=extracted_prefixes, encoder=encoder,resource=True)"
   ],
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Total resources: 22 - Target resources: 23\n",
      "\t ['Value 2', 'Value 5', 'Value 16', 'Value 15', 'Value 21', 'Value 10', 'Value 11', 'Value 12', 'Value 6', 'Value 7', 'Value 9', 'Value 14', 'Value 19', 'Value 17', 'Value 8', 'Value 13', 'Value 22', 'Value 1', 'Value 4', 'Value 3', 'Value 18', 'Value 20']\n",
      "Total activities: 14 - Target activities: 15\n",
      "\t ['Assign seriousness', 'Take in charge ticket', 'Resolve ticket', 'Closed', 'Wait', 'Create SW anomaly', 'Insert ticket', 'Schedule intervention', 'INVALID', 'RESOLVED', 'VERIFIED', 'Resolve SW anomaly', 'Require upgrade', 'DUPLICATE']\n",
      "Num. of Training sequences: 16937\n",
      "Encoding...\n",
      "Num. of features: 30\n"
     ]
    }
   ],
   "execution_count": 6
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Shrinked index-based encoding\n",
    "In the __Shrinked index-based encoding__, sequences of events are transformed into numerical feature vectors by assigning a unique integer index to each activity–resource pair. To apply shrinked index-based encoding, set the encoder parameter to `Encodings.Shrinked_based` when calling the `encode_prefixes` function:"
   ]
  },
  {
   "cell_type": "code",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-06-28T13:04:39.141789Z",
     "start_time": "2025-06-28T13:04:38.944126Z"
    }
   },
   "source": [
    "from NeSy4PPM.Data_preprocessing.data_preprocessing import encode_prefixes\n",
    "from NeSy4PPM.Data_preprocessing.utils import Encodings\n",
    "\n",
    "encoder=Encodings.Shrinked_based\n",
    "x, y_a, y_g = encode_prefixes(log_data,prefixes=extracted_prefixes, encoder=encoder, resource=True)"
   ],
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Total resources: 22 - Target resources: 23\n",
      "\t ['Value 2', 'Value 5', 'Value 16', 'Value 15', 'Value 21', 'Value 10', 'Value 11', 'Value 12', 'Value 6', 'Value 7', 'Value 9', 'Value 14', 'Value 19', 'Value 17', 'Value 8', 'Value 13', 'Value 22', 'Value 1', 'Value 4', 'Value 3', 'Value 18', 'Value 20']\n",
      "Total activities: 14 - Target activities: 15\n",
      "\t ['Assign seriousness', 'Take in charge ticket', 'Resolve ticket', 'Closed', 'Wait', 'Create SW anomaly', 'Insert ticket', 'Schedule intervention', 'INVALID', 'RESOLVED', 'VERIFIED', 'Resolve SW anomaly', 'Require upgrade', 'DUPLICATE']\n",
      "Num. of Training sequences: 16937\n",
      "Encoding...\n",
      "Num. of features: 15\n"
     ]
    }
   ],
   "execution_count": 7
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Multi-encoders encoding\n",
    "In the __Multi-encoders encoding__, sequences of events are represented using separate embedding spaces for activities and resources. Each activity and resource is first embedded independently, and then enriched with cross-information using a modulation mechanism that captures their interactions. The final representation combines the modulated embeddings using learned alignment weights. To apply multi-encoders encoding, set the encoder parameter to `Encodings.Multi_encoders` when calling the `encode_prefixes` function:"
   ]
  },
  {
   "cell_type": "code",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-06-28T13:05:12.433914Z",
     "start_time": "2025-06-28T13:05:12.234572Z"
    }
   },
   "source": [
    "from NeSy4PPM.Data_preprocessing.data_preprocessing import encode_prefixes\n",
    "from NeSy4PPM.Data_preprocessing.utils import Encodings\n",
    "\n",
    "encoder=Encodings.Multi_encoders\n",
    "x, y_a, y_g = encode_prefixes(log_data,prefixes=extracted_prefixes, encoder=encoder, resource=True)"
   ],
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Total resources: 22 - Target resources: 23\n",
      "\t ['Value 2', 'Value 5', 'Value 16', 'Value 15', 'Value 21', 'Value 10', 'Value 11', 'Value 12', 'Value 6', 'Value 7', 'Value 9', 'Value 14', 'Value 19', 'Value 17', 'Value 8', 'Value 13', 'Value 22', 'Value 1', 'Value 4', 'Value 3', 'Value 18', 'Value 20']\n",
      "Total activities: 14 - Target activities: 15\n",
      "\t ['Assign seriousness', 'Take in charge ticket', 'Resolve ticket', 'Closed', 'Wait', 'Create SW anomaly', 'Insert ticket', 'Schedule intervention', 'INVALID', 'RESOLVED', 'VERIFIED', 'Resolve SW anomaly', 'Require upgrade', 'DUPLICATE']\n",
      "Num. of Training sequences: 16937\n",
      "Encoding...\n",
      "Num. of features: 15\n"
     ]
    }
   ],
   "execution_count": 9
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## End-to-end data preprocessing"
   ]
  },
  {
   "cell_type": "code",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-06-28T12:45:09.132835Z",
     "start_time": "2025-06-28T12:45:07.718810Z"
    }
   },
   "source": [
    "from NeSy4PPM.Data_preprocessing.data_preprocessing import end_to_end_process\n",
    "from NeSy4PPM.Data_preprocessing.utils import Encodings\n",
    "from pathlib import Path\n",
    "\n",
    "log_path = Path.cwd().parent/'data'/'input'/'logs'\n",
    "train_log = \"helpdesk_train.xes\"\n",
    "test_log = \"helpdesk_test.xes\"\n",
    "encoder = Encodings.Index_based\n",
    "log_data, x, y_a, y_g = end_to_end_process(log_path=log_path,train_log=train_log,test_log=test_log, encoder=encoder, resource=True)"
   ],
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "parsing log, completed traces :: 100%|██████████| 3664/3664 [00:00<00:00, 8690.64it/s]\n",
      "parsing log, completed traces :: 100%|██████████| 820/820 [00:00<00:00, 8723.79it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Total resources: 22 - Target resources: 23\n",
      "\t ['Value 2', 'Value 5', 'Value 16', 'Value 15', 'Value 21', 'Value 10', 'Value 11', 'Value 12', 'Value 6', 'Value 7', 'Value 9', 'Value 14', 'Value 19', 'Value 17', 'Value 8', 'Value 13', 'Value 22', 'Value 1', 'Value 4', 'Value 3', 'Value 18', 'Value 20']\n",
      "Total activities: 14 - Target activities: 15\n",
      "\t ['Assign seriousness', 'Take in charge ticket', 'Resolve ticket', 'Closed', 'Wait', 'Create SW anomaly', 'Insert ticket', 'Schedule intervention', 'INVALID', 'RESOLVED', 'VERIFIED', 'Resolve SW anomaly', 'Require upgrade', 'DUPLICATE']\n",
      "Num. of Training sequences: 16937\n",
      "Encoding...\n",
      "Num. of features: 30\n"
     ]
    }
   ],
   "execution_count": 37
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 2 Training\n",
    "Once the prefixes are encoded, NeSy4PPM proceeds to train a neural network that learns to predict the next activity and resource given a partial trace. The training is handled via the `train` function, which takes the encoded prefix data (`x`, `y_a`, `y_g`) and builds a model according to the chosen architecture. NeSy4PPM supports two neural architectures:\n",
    "\n",
    "- __LSTM (Long Short-Term Memory)__ networks, which are recurrent neural networks designed to handle sequential data with long-range dependencies. To use LSTM, set the `model_arch` parameter to `NN_model.LSTM`.\n",
    "- __Transformer__ architectures, which use attention mechanisms to model relationships across all positions in the prefix sequence simultaneously. To use a Transformer, set the `model_arch` parameter to `NN_model.Transformer`."
   ]
  },
  {
   "cell_type": "code",
   "metadata": {},
   "source": [
    "from NeSy4PPM.Training.train_model import train\n",
    "from NeSy4PPM.Data_preprocessing.utils import NN_model\n",
    "\n",
    "model = NN_model.Transformer\n",
    "model_folder= Path.cwd().parent/'data'/'output'\n",
    "train(log_data, encoder, model_arch=model, output_folder=model_folder, X=x, y_a=y_a, y_g=y_g)"
   ],
   "outputs": [],
   "execution_count": null
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 3 Prediction\n",
    "The __Prediction__ in NeSy4PPM is responsible for generating suffix predictions from a prefix (i.e., an incomplete trace) using a trained neural model. To improve both accuracy and compliance, particularly under concept drift, NeSy4PPM supports three main prediction modes:\n",
    "\n",
    "- `Purely Neural predictions`: The suffix is predicted mainly based on the output of the neural model, without considering background knowledge (BK).\n",
    "- `BK-Contextualized predictions`: BK is integrated during either the greedy or beam search process. It guides the search by pruning branches that violate compliance constraints, ensuring that only valid continuations are explored.\n",
    "- `BK-filtering`: BK is applied after generating candidate suffixes. Predicted suffixes that do not satisfy the compliance constraints are filtered out."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 3.1 Set prediction parameters\n",
    "The prediction process begins by specifying the following parameters that control how the prediction algorithm operates:\n",
    "- `log_data.evaluation_prefix_start`: the minimum prefix length (in events) for prediction.\n",
    "- `log_data.evaluation_prefix_end`: the maximum prefix length for prediction.\n",
    "- `model_arch`: the trained model architecture (`NN_model.LSTM` or `NN_model.Transformer`).\n",
    "- `output_folder`: the path where the trained model and prediction results are saved.\n",
    "- `encoder`: the encoding method used during training (`Encodings.One_hot`, `Encodings.Index_based`, `Encodings.Shrinked_index_based` or `Encodings.Multi_Encoders` ).\n",
    "- `beam_size`: the number of alternative suffixes explored in parallel by the beam search. A `simple autoregressive prediction` can be performed by setting `beam_size` to `1` (greedy search).\n",
    "- `bk_file_path`: the path to the `BK` (background knowledge) file. `BK` can represent domain constraints or business rules, and can be encoded in various formats:\n",
    "    - __Procedural models__: `.bpmn` (Business Process Model and Notation), `.pnml` (Petri Nets),\n",
    "    - __Declare models__: `.decl` (Crisp or Multi-perspective Declare constraints)\n",
    "    - __Probabilistic Declare models__: `.txt` (Declare constraints annotated with probabilities).\n",
    "- `weight`: a float value in [0, 1] that balances the importance of neural predictions and BK compliance. A value of 0 uses only the neural model, while higher values increase the importance of BK during the search.\n",
    "- `BK_end`: a boolean parameter indicating whether BK is applied at the end (i.e., filtering) instead of during the search.\n",
    "- `resource`: a boolean parameter indicating whether the resource prediction is performed onlongside activty prediction.\n",
    "- `fitness_method`: the method used to compute compliance scores, i.e., the alignment or replay fitness between the predicted trace and the procedural model. This parameter is only applicable when the BK model is procedural, and must be set to one of the following: : `conformance_diagnostics_alignments`, `fitness_alignments`, `conformance_diagnostics_token_based_replay` or `fitness_token_based_replay`.\n"
   ]
  },
  {
   "cell_type": "code",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-06-28T11:10:56.147680Z",
     "start_time": "2025-06-28T11:10:56.131592Z"
    }
   },
   "source": [
    "from NeSy4PPM.Data_preprocessing.utils import NN_model\n",
    "from NeSy4PPM.Data_preprocessing.utils import Encodings\n",
    "\n",
    "(log_data.evaluation_prefix_start, log_data.evaluation_prefix_end) = (1,4)\n",
    "model_arch = NN_model.Transformer\n",
    "encoder = Encodings.Index_based\n",
    "output_folder= Path.cwd().parent/'data'/'output'\n",
    "bk_file_path = Path.cwd().parent/'data'/'input'/'declare_models'/'BK_helpdesk.decl'\n",
    "beam_size = 3\n",
    "weight = 0.9\n",
    "BK_end = False\n",
    "resource = True"
   ],
   "outputs": [],
   "execution_count": 11
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 2.2 Load the Background Knowledge (BK)\n",
    "After setting the parameters, a background knowledge (BK) model must be loaded using the `load_bk` function."
   ]
  },
  {
   "cell_type": "code",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-06-28T11:34:40.605733Z",
     "start_time": "2025-06-28T11:34:40.543425Z"
    }
   },
   "source": [
    "from NeSy4PPM.Data_preprocessing.utils import load_bk\n",
    "\n",
    "bk_model = load_bk(bk_file_path)"
   ],
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0 Existence1[Closed] |A.org:resource is Value 3 |\n",
      "1 Chain Precedence[Resolve ticket, Closed] |A.org:resource is Value 3 | |\n"
     ]
    }
   ],
   "execution_count": 21
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 2.3 Perform Prediction\n",
    "NeSy4PPM implements the `predict_evaluate` function, which generates activity-resource suffixes using the proposed neuro-symbolic beam search algorithm and computes two evaluation metrics:\n",
    "   - __Damerau-Levenshtein Similarity__, measuring the similarity between the predicted and actual suffixes based on edit distance,\n",
    "   - __Jaccard Similarity__, measuring the overlap between the sets of predicted and actual activities. suffix prediction using a trained neural model and loaded `BK` model.\n",
    "\n",
    "By default, this function operates on the __entire test log__, predicting suffixes for all traces defined in the test set."
   ]
  },
  {
   "cell_type": "code",
   "metadata": {},
   "source": [
    "## Entire test log Prediction\n",
    "from NeSy4PPM.Prediction import predict_suffix\n",
    "prediction_filename = 'helpdesk_test_prediction.csv'\n",
    "predict_suffix.predict_evaluate(log_data, model_arch=model_arch, encoder=encoder,output_filename=prediction_filename,\n",
    "                            output_folder=output_folder, bk_model=bk_model, beam_size=beam_size, resource=resource, weight=weight, bk_end=BK_end)"
   ],
   "outputs": [],
   "execution_count": null
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "However, `predict_evaluate` function can also be used to predict suffixes for a specific __subset of traces__ by providing a list of case IDs from the test log."
   ]
  },
  {
   "cell_type": "code",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-06-28T11:36:55.384275Z",
     "start_time": "2025-06-28T11:36:14.374552Z"
    }
   },
   "source": [
    "## A subset of test log Prediction Beam search with KB-contextualized\n",
    "from NeSy4PPM.Prediction import predict_suffix\n",
    "traces_ids = ['Case 1327']\n",
    "prediction_filename = 'helpdesk_case1327_prediction.csv'\n",
    "predict_suffix.predict_evaluate(log_data, model_arch=model_arch, encoder=encoder,output_filename=prediction_filename,\n",
    "                                output_folder=output_folder, evaluation_trace_ids= traces_ids, bk_model=bk_model, beam_size=beam_size,\n",
    "                                resource=resource, weight=weight, bk_end=BK_end)"
   ],
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Activity & Resource Prediction ...\n",
      "Model filepath: C:\\Users\\JOukharijane\\Desktop\\PostDoc\\NeSy4PPM\\docs\\source\\data\\output\\keras_trans_index-based\\models\\CFR\\helpdesk_train\n",
      "Latest checkpoint file: C:\\Users\\JOukharijane\\Desktop\\PostDoc\\NeSy4PPM\\docs\\source\\data\\output\\keras_trans_index-based\\models\\CFR\\helpdesk_train\\model_015-1.198.keras\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\JOukharijane\\AppData\\Local\\Programs\\Python\\Python310\\lib\\random.py:370: DeprecationWarning: non-integer arguments to randrange() have been deprecated since Python 3.10 and will be removed in a subsequent version\n",
      "  return self.randrange(a, b+1)\n",
      "C:\\Users\\JOukharijane\\AppData\\Local\\Programs\\Python\\Python310\\lib\\random.py:370: DeprecationWarning: non-integer arguments to randrange() have been deprecated since Python 3.10 and will be removed in a subsequent version\n",
      "  return self.randrange(a, b+1)\n",
      "C:\\Users\\JOukharijane\\AppData\\Local\\Programs\\Python\\Python310\\lib\\random.py:370: DeprecationWarning: non-integer arguments to randrange() have been deprecated since Python 3.10 and will be removed in a subsequent version\n",
      "  return self.randrange(a, b+1)\n",
      "C:\\Users\\JOukharijane\\AppData\\Local\\Programs\\Python\\Python310\\lib\\random.py:370: DeprecationWarning: non-integer arguments to randrange() have been deprecated since Python 3.10 and will be removed in a subsequent version\n",
      "  return self.randrange(a, b+1)\n",
      "C:\\Users\\JOukharijane\\AppData\\Local\\Programs\\Python\\Python310\\lib\\random.py:370: DeprecationWarning: non-integer arguments to randrange() have been deprecated since Python 3.10 and will be removed in a subsequent version\n",
      "  return self.randrange(a, b+1)\n",
      "C:\\Users\\JOukharijane\\AppData\\Local\\Programs\\Python\\Python310\\lib\\random.py:370: DeprecationWarning: non-integer arguments to randrange() have been deprecated since Python 3.10 and will be removed in a subsequent version\n",
      "  return self.randrange(a, b+1)\n",
      "C:\\Users\\JOukharijane\\AppData\\Local\\Programs\\Python\\Python310\\lib\\random.py:370: DeprecationWarning: non-integer arguments to randrange() have been deprecated since Python 3.10 and will be removed in a subsequent version\n",
      "  return self.randrange(a, b+1)\n",
      "C:\\Users\\JOukharijane\\AppData\\Local\\Programs\\Python\\Python310\\lib\\random.py:370: DeprecationWarning: non-integer arguments to randrange() have been deprecated since Python 3.10 and will be removed in a subsequent version\n",
      "  return self.randrange(a, b+1)\n",
      "C:\\Users\\JOukharijane\\AppData\\Local\\Programs\\Python\\Python310\\lib\\random.py:370: DeprecationWarning: non-integer arguments to randrange() have been deprecated since Python 3.10 and will be removed in a subsequent version\n",
      "  return self.randrange(a, b+1)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['Case ID', 'Prefix length', 'Trace Prefix Act', 'Ground truth', 'Predicted Acts', 'Damerau-Levenshtein Acts', 'Jaccard Acts', 'Trace Prefix Res', 'Ground Truth Resources', 'Predicted Resources', 'Damerau-Levenshtein Resources', 'Jaccard Resources', 'Weight', 'Time']\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "  0%|          | 0/1 [00:00<?, ?it/s]C:\\Users\\JOukharijane\\Desktop\\PostDoc\\NeSy4PPM\\venv\\lib\\site-packages\\tqdm\\std.py:917: FutureWarning: DataFrameGroupBy.apply operated on the grouping columns. This behavior is deprecated, and in a future version of pandas the grouping columns will be excluded from the operation. Either pass `include_groups=False` to exclude the groupings or explicitly select the grouping columns after groupby to silence this warning.\n",
      "  return getattr(df, df_function)(wrapper, **kwargs)\n",
      "100%|██████████| 1/1 [00:20<00:00, 20.76s/it]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['Case 1327', 1, 'Assign seriousness', 'Wait>>Resolve ticket>>Closed', 'Take in charge ticket>>Resolve ticket>>Closed', 0.6666666666666667, 0.5, 'Value 13', 'Value 1>>Value 13>>Value 3', 'Value 13>>Value 13>>Value 3', 0.6666666666666667, 0.6666666666666666, 0.9, 20.74420714378357]\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "  0%|          | 0/1 [00:00<?, ?it/s]C:\\Users\\JOukharijane\\Desktop\\PostDoc\\NeSy4PPM\\venv\\lib\\site-packages\\tqdm\\std.py:917: FutureWarning: DataFrameGroupBy.apply operated on the grouping columns. This behavior is deprecated, and in a future version of pandas the grouping columns will be excluded from the operation. Either pass `include_groups=False` to exclude the groupings or explicitly select the grouping columns after groupby to silence this warning.\n",
      "  return getattr(df, df_function)(wrapper, **kwargs)\n",
      "100%|██████████| 1/1 [00:12<00:00, 12.49s/it]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['Case 1327', 2, 'Assign seriousness>>Wait', 'Resolve ticket>>Closed', 'Resolve ticket>>Closed', 1.0, 1.0, 'Value 13>>Value 1', 'Value 13>>Value 3', 'Value 1>>Value 3', 0.5, 0.33333333333333326, 0.9, 12.488699197769165]\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "  0%|          | 0/1 [00:00<?, ?it/s]C:\\Users\\JOukharijane\\Desktop\\PostDoc\\NeSy4PPM\\venv\\lib\\site-packages\\tqdm\\std.py:917: FutureWarning: DataFrameGroupBy.apply operated on the grouping columns. This behavior is deprecated, and in a future version of pandas the grouping columns will be excluded from the operation. Either pass `include_groups=False` to exclude the groupings or explicitly select the grouping columns after groupby to silence this warning.\n",
      "  return getattr(df, df_function)(wrapper, **kwargs)\n",
      "100%|██████████| 1/1 [00:07<00:00,  7.09s/it]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['Case 1327', 3, 'Assign seriousness>>Wait>>Resolve ticket', 'Closed', 'Closed', 1.0, 1.0, 'Value 13>>Value 1>>Value 13', 'Value 3', 'Value 3', 1.0, 1.0, 0.9, 7.0855488777160645]\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "  0%|          | 0/1 [00:00<?, ?it/s]C:\\Users\\JOukharijane\\Desktop\\PostDoc\\NeSy4PPM\\venv\\lib\\site-packages\\tqdm\\std.py:917: FutureWarning: DataFrameGroupBy.apply operated on the grouping columns. This behavior is deprecated, and in a future version of pandas the grouping columns will be excluded from the operation. Either pass `include_groups=False` to exclude the groupings or explicitly select the grouping columns after groupby to silence this warning.\n",
      "  return getattr(df, df_function)(wrapper, **kwargs)\n",
      "100%|██████████| 1/1 [00:00<?, ?it/s]\n"
     ]
    }
   ],
   "execution_count": 23
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 4 Evaluation\n",
    "The __evaluation__ assesses the performance of the prediction by computing a variety of metrics, including:\n",
    "\n",
    "- `Time`: Computes the average and standard deviation of prediction times.\n",
    "- `Jaccard similarity`: Measures the jaccard similarity between the predicted and ground-truth sequences of activities, and resources if `resource = True`.\n",
    "- `Damerau-Levenshtein similarity`: Computes the Damerau-Levenshtein similarity between predicted and ground-truth sequences of activities, and resources if `resource = True`.\n",
    "- `Compliance`: Measures the proportion of predicted traces (i.e., the prefix concatenated with the predicted suffix) that satisfy the (MP)-Declare background knowledge.\n",
    "- `Fitness`: Evaluates how well the predicted traces can be replayed in the discovered Petri net, using the specified `fitness_method` (e.g., `token-based replay`).\n"
   ]
  },
  {
   "cell_type": "code",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-06-28T11:37:24.543983Z",
     "start_time": "2025-06-28T11:37:22.298909Z"
    }
   },
   "source": [
    "from NeSy4PPM.Evaluation import evaluate_all, discover_petri_net\n",
    "\n",
    "# discover a petri nets model from test log:\n",
    "petri_net_model = discover_petri_net(log_path/test_log)\n",
    "\n",
    "metrics = [\"Time\", \"Jaccard similarity\", \"Damerau-Levenshtien similarity\", \"Compliance\", \"Fitness\"]\n",
    "prediction_filename = 'helpdesk_case1327_prediction.csv'\n",
    "\n",
    "metrics_results = evaluate_all(log_data=log_data, model_arch=model_arch, encoder=encoder, output_folder=output_folder,\n",
    "                               filename=prediction_filename, metrics=metrics, resource=resource, declare_model=bk_model,petri_net_model=petri_net_model, fitness_method=\"fitness_token_based_replay\")\n",
    "for metric in metrics:\n",
    "    print(metric,\":\",metrics_results[metric],\"\\t\")"
   ],
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "parsing log, completed traces :: 100%|██████████| 820/820 [00:00<00:00, 2480.57it/s]\n",
      "DEBUG:graphviz._tools:os.makedirs('C:\\\\Users\\\\JOUKHA~1\\\\AppData\\\\Local\\\\Temp')\n",
      "DEBUG:graphviz.saving:write lines to 'C:\\\\Users\\\\JOUKHA~1\\\\AppData\\\\Local\\\\Temp\\\\tmpyzof20bn.gv'\n",
      "DEBUG:graphviz.backend.execute:run [WindowsPath('dot'), '-Kdot', '-Tpng', '-O', 'tmpyzof20bn.gv']\n"
     ]
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "replaying log with TBR, completed traces :: 100%|██████████| 2/2 [00:00<00:00, 249.25it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Time : {'Average time': 13.439, 'Standard deviation time': 6.879} \t\n",
      "Jaccard similarity : {'Activities': 0.833, 'Resources': 0.667} \t\n",
      "Damerau-Levenshtien similarity : {'Activities': 0.889, 'Resources': 0.722} \t\n",
      "Compliance : 1.0 \t\n",
      "Fitness : 1.0 \t\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    }
   ],
   "execution_count": 24
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.10.0"
  },
  "vscode": {
   "interpreter": {
    "hash": "9b13726099ff4a9270d97cd5a303046c40236cea9d4b3d3acf7f22861afad882"
   }
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}
