Upload dual lifecycle next-activity model

.gitattributes

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 *.zip filter=lfs diff=lfs merge=lfs -text
 *.zst filter=lfs diff=lfs merge=lfs -text
 *tfevents* filter=lfs diff=lfs merge=lfs -text
+best_model.keras filter=lfs diff=lfs merge=lfs -text
+model.keras filter=lfs diff=lfs merge=lfs -text

README.md

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+---
+library_name: tf-keras
+tags:
+  - process-mining
+  - next-activity-prediction
+  - sequence-modeling
+  - tensorflow
+---
+
+# next_activity_prediction_lifecycle_dual
+
+Dual-head next-event model that predicts:
+- next activity (`concept:name`)
+- next lifecycle transition (`lifecycle:transition`)
+
+This repository was exported from:
+`next_activity_prediction_lifecycle_dual\next_activity_prediction_lifecycle_dual\models\start_complete\baseline`
+
+## Included files
+
+- `model.keras`
+- `metadata.json`
+- `metrics.json` (if available)
+- `history.json` (if available)
+
+## Metrics
+
+- Activity accuracy: `0.8443847963680949`
+- Activity macro-F1: `0.6984303181387901`
+- Lifecycle accuracy: `0.8857407959704411`
+- Lifecycle macro-F1: `0.8672618282920227`
+- Joint accuracy: `0.8327037147496438`
+- Balanced score: `0.7994652870601522`
+
+## Usage (Python, platform-independent)
+
+```python
+import json
+import numpy as np
+from huggingface_hub import hf_hub_download
+from tensorflow import keras
+
+repo_id = "Nixion/next_activity_prediction_lifecycle_dual"
+model_path = hf_hub_download(repo_id=repo_id, filename="model.keras")
+metadata_path = hf_hub_download(repo_id=repo_id, filename="metadata.json")
+
+with open(metadata_path, "r", encoding="utf-8") as f:
+    metadata = json.load(f)
+
+model = keras.models.load_model(model_path)
+sequence_length = int(metadata["sequence_length"])
+activity_to_idx = metadata["activity_to_idx"]
+lifecycle_to_idx = metadata["lifecycle_to_idx"]
+
+def pad(xs, n):
+    return ([0] * (n - len(xs)) + xs)[-n:]
+
+# Example history:
+activity_hist = ["A_Create Application", "A_Submitted"]
+lifecycle_hist = ["complete", "complete"]
+
+X_act = np.array([pad([activity_to_idx.get(a, 0) for a in activity_hist], sequence_length)], dtype=np.int32)
+X_life = np.array([pad([lifecycle_to_idx.get(l, 0) for l in lifecycle_hist], sequence_length)], dtype=np.int32)
+
+pred_activity_probs, pred_lifecycle_probs = model.predict([X_act, X_life], verbose=0)
+next_activity_idx = int(np.argmax(pred_activity_probs[0]))
+next_lifecycle_idx = int(np.argmax(pred_lifecycle_probs[0]))
+
+idx_to_activity = {int(k): v for k, v in metadata["idx_to_activity"].items()}
+idx_to_lifecycle = {int(k): v for k, v in metadata["idx_to_lifecycle"].items()}
+
+print("next_activity:", idx_to_activity.get(next_activity_idx))
+print("next_lifecycle:", idx_to_lifecycle.get(next_lifecycle_idx))
+```

best_model.keras

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+version https://git-lfs.github.com/spec/v1
+oid sha256:8fc0d73e4a141e87cd01718251b66963e055a00c5f460361b6b2703a5ea364ab
+size 6822677

history.json

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+{
+  "activity_output_loss": [
+    0.46802908182144165
+  ],
+  "activity_output_sparse_categorical_accuracy": [
+    0.8246452808380127
+  ],
+  "lifecycle_output_loss": [
+    0.2494550198316574
+  ],
+  "lifecycle_output_sparse_categorical_accuracy": [
+    0.8745477795600891
+  ],
+  "loss": [
+    0.7174617648124695
+  ],
+  "val_activity_output_loss": [
+    0.38813483715057373
+  ],
+  "val_activity_output_sparse_categorical_accuracy": [
+    0.8443847894668579
+  ],
+  "val_lifecycle_output_loss": [
+    0.2222922146320343
+  ],
+  "val_lifecycle_output_sparse_categorical_accuracy": [
+    0.8857408165931702
+  ],
+  "val_loss": [
+    0.610468327999115
+  ],
+  "learning_rate": [
+    0.0010000000474974513
+  ]
+}

metadata.json

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+{
+  "mode": "start_complete",
+  "methodology": "baseline",
+  "sequence_length": 50,
+  "activity_to_idx": {
+    "<PAD>": 0,
+    "A_Accepted": 1,
+    "A_Cancelled": 2,
+    "A_Complete": 3,
+    "A_Concept": 4,
+    "A_Create Application": 5,
+    "A_Denied": 6,
+    "A_Incomplete": 7,
+    "A_Pending": 8,
+    "A_Submitted": 9,
+    "A_Validating": 10,
+    "O_Accepted": 11,
+    "O_Cancelled": 12,
+    "O_Create Offer": 13,
+    "O_Created": 14,
+    "O_Refused": 15,
+    "O_Returned": 16,
+    "O_Sent (mail and online)": 17,
+    "O_Sent (online only)": 18,
+    "W_Assess potential fraud": 19,
+    "W_Call after offers": 20,
+    "W_Call incomplete files": 21,
+    "W_Complete application": 22,
+    "W_Handle leads": 23,
+    "W_Personal Loan collection": 24,
+    "W_Shortened completion ": 25,
+    "W_Validate application": 26,
+    "END_ACTIVITY": 27
+  },
+  "lifecycle_to_idx": {
+    "<PAD>": 0,
+    "complete": 1,
+    "start": 2,
+    "END_LIFECYCLE": 3
+  },
+  "idx_to_activity": {
+    "0": "<PAD>",
+    "1": "A_Accepted",
+    "2": "A_Cancelled",
+    "3": "A_Complete",
+    "4": "A_Concept",
+    "5": "A_Create Application",
+    "6": "A_Denied",
+    "7": "A_Incomplete",
+    "8": "A_Pending",
+    "9": "A_Submitted",
+    "10": "A_Validating",
+    "11": "O_Accepted",
+    "12": "O_Cancelled",
+    "13": "O_Create Offer",
+    "14": "O_Created",
+    "15": "O_Refused",
+    "16": "O_Returned",
+    "17": "O_Sent (mail and online)",
+    "18": "O_Sent (online only)",
+    "19": "W_Assess potential fraud",
+    "20": "W_Call after offers",
+    "21": "W_Call incomplete files",
+    "22": "W_Complete application",
+    "23": "W_Handle leads",
+    "24": "W_Personal Loan collection",
+    "25": "W_Shortened completion ",
+    "26": "W_Validate application",
+    "27": "END_ACTIVITY"
+  },
+  "idx_to_lifecycle": {
+    "0": "<PAD>",
+    "1": "complete",
+    "2": "start",
+    "3": "END_LIFECYCLE"
+  },
+  "end_activity_token": "END_ACTIVITY",
+  "end_lifecycle_token": "END_LIFECYCLE"
+}

metrics.json

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+{
+  "mode": "start_complete",
+  "methodology": "baseline",
+  "metrics": {
+    "activity_accuracy": 0.8443847963680949,
+    "activity_macro_f1": 0.6984303181387901,
+    "lifecycle_accuracy": 0.8857407959704411,
+    "lifecycle_macro_f1": 0.8672618282920227,
+    "joint_accuracy": 0.8327037147496438,
+    "balanced_score": 0.7994652870601522
+  },
+  "model_dir": "next_activity_prediction_lifecycle_dual\\models\\start_complete\\baseline"
+}

model.keras

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+version https://git-lfs.github.com/spec/v1
+oid sha256:8fc0d73e4a141e87cd01718251b66963e055a00c5f460361b6b2703a5ea364ab
+size 6822677