init

.gitattributes

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+*.csv filter=lfs diff=lfs merge=lfs -text
+
+*.7z filter=lfs diff=lfs merge=lfs -text
+*.arrow filter=lfs diff=lfs merge=lfs -text
+*.bin filter=lfs diff=lfs merge=lfs -text
+*.bz2 filter=lfs diff=lfs merge=lfs -text
+*.ckpt filter=lfs diff=lfs merge=lfs -text
+*.ftz filter=lfs diff=lfs merge=lfs -text
+*.gz filter=lfs diff=lfs merge=lfs -text
+*.h5 filter=lfs diff=lfs merge=lfs -text
+*.joblib filter=lfs diff=lfs merge=lfs -text
+*.lfs.* filter=lfs diff=lfs merge=lfs -text
+*.lz4 filter=lfs diff=lfs merge=lfs -text
+*.mds filter=lfs diff=lfs merge=lfs -text
+*.mlmodel filter=lfs diff=lfs merge=lfs -text
+*.model filter=lfs diff=lfs merge=lfs -text
+*.msgpack filter=lfs diff=lfs merge=lfs -text
+*.npy filter=lfs diff=lfs merge=lfs -text
+*.npz filter=lfs diff=lfs merge=lfs -text
+*.onnx filter=lfs diff=lfs merge=lfs -text
+*.ot filter=lfs diff=lfs merge=lfs -text
+*.parquet filter=lfs diff=lfs merge=lfs -text
+*.pb filter=lfs diff=lfs merge=lfs -text
+*.pickle filter=lfs diff=lfs merge=lfs -text
+*.pkl filter=lfs diff=lfs merge=lfs -text
+*.pt filter=lfs diff=lfs merge=lfs -text
+*.pth filter=lfs diff=lfs merge=lfs -text
+*.rar filter=lfs diff=lfs merge=lfs -text
+*.safetensors filter=lfs diff=lfs merge=lfs -text
+saved_model/**/* filter=lfs diff=lfs merge=lfs -text
+*.tar.* filter=lfs diff=lfs merge=lfs -text
+*.tar filter=lfs diff=lfs merge=lfs -text
+*.tflite filter=lfs diff=lfs merge=lfs -text
+*.tgz filter=lfs diff=lfs merge=lfs -text
+*.wasm filter=lfs diff=lfs merge=lfs -text
+*.xz filter=lfs diff=lfs merge=lfs -text
+*.zip filter=lfs diff=lfs merge=lfs -text
+*.zst filter=lfs diff=lfs merge=lfs -text
+*tfevents* filter=lfs diff=lfs merge=lfs -text
+# Audio files - uncompressed
+*.pcm filter=lfs diff=lfs merge=lfs -text
+*.sam filter=lfs diff=lfs merge=lfs -text
+*.raw filter=lfs diff=lfs merge=lfs -text
+# Audio files - compressed
+*.aac filter=lfs diff=lfs merge=lfs -text
+*.flac filter=lfs diff=lfs merge=lfs -text
+*.mp3 filter=lfs diff=lfs merge=lfs -text
+*.ogg filter=lfs diff=lfs merge=lfs -text
+*.wav filter=lfs diff=lfs merge=lfs -text
+# Image files - uncompressed
+*.bmp filter=lfs diff=lfs merge=lfs -text
+*.gif filter=lfs diff=lfs merge=lfs -text
+*.png filter=lfs diff=lfs merge=lfs -text
+*.tiff filter=lfs diff=lfs merge=lfs -text
+# Image files - compressed
+*.jpg filter=lfs diff=lfs merge=lfs -text
+*.jpeg filter=lfs diff=lfs merge=lfs -text
+*.webp filter=lfs diff=lfs merge=lfs -text
+# Video files - compressed
+*.mp4 filter=lfs diff=lfs merge=lfs -text
+*.webm filter=lfs diff=lfs merge=lfs -text

dataset/game_logs_bt_vs_fsm.csv

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dataset/game_logs_bt_vs_fsm_2.csv

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dataset/game_logs_fsm_vs_bt.csv

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dataset/game_logs_fsm_vs_fsm.csv

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dataset/game_logs_mcts_vs_bt.csv

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dataset/game_logs_mcts_vs_primitive.csv

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model/ml.onnx

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requirements.txt

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+joblib==1.5.1
+keras==2.13.1
+matplotlib==3.10.5
+numpy==1.24.3
+onnx==1.17.0
+pandas==2.3.1
+scikit_learn==1.7.1
+tensorflow==2.13.0
+tensorflow_macos==2.13.0
+tf2onnx==1.16.1

train_ml.ipynb

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+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "8bb0209c-63c4-4601-894c-0ded8f4db2e6",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# PREPARE DATASET\n",
+    "\n",
+    "import numpy as np\n",
+    "import pandas as pd\n",
+    "import glob\n",
+    "\n",
+    "def filter_inside_arena(df, radius=4.73485, margin=0.95, apply=True):\n",
+    "    if not apply:\n",
+    "        return df.copy()\n",
+    "    \n",
+    "    bot_dist = np.sqrt(df[\"BotPosX\"]**2 + df[\"BotPosY\"]**2)\n",
+    "    df[\"IsOutOfArena\"] = bot_dist > (radius * margin)\n",
+    "    return df[~df[\"IsOutOfArena\"]].copy()\n",
+    "\n",
+    "\n",
+    "# Gather all battle logs from multiple CSV files\n",
+    "csv_files = glob.glob(\"dataset/game_logs_*.csv\")\n",
+    "\n",
+    "dfs = []\n",
+    "for file in csv_files:\n",
+    "    df = pd.read_csv(file)\n",
+    "    df = df.dropna(subset=[\"Name\", \"Duration\"])\n",
+    "\n",
+    "    # Optional: Filter for winner actions only\n",
+    "    # df = df[df[\"Actor\"] == \"RoundWinner\"]\n",
+    "\n",
+    "    dfs.append(df)\n",
+    "\n",
+    "df_combined = pd.concat(dfs, ignore_index=True).drop_duplicates()\n",
+    "\n",
+    "# pply arena filter\n",
+    "df_safe = filter_inside_arena(df_combined, apply=True)\n",
+    "\n",
+    "# Save result\n",
+    "df_safe.to_csv(\"dataset/cleaned_log.csv\", index=False)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "817107a1-86f4-49de-a366-f1e80536ecef",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# TRAIN SETUP\n",
+    "\n",
+    "import json\n",
+    "import pandas as pd\n",
+    "import numpy as np\n",
+    "import tf2onnx\n",
+    "\n",
+    "from sklearn.preprocessing import LabelEncoder\n",
+    "from sklearn.model_selection import train_test_split\n",
+    "from sklearn.impute import SimpleImputer\n",
+    "from sklearn.metrics import classification_report\n",
+    "\n",
+    "import tensorflow as tf\n",
+    "from tensorflow.keras.models import Model\n",
+    "from tensorflow.keras.layers import Input, Dense, BatchNormalization\n",
+    "from tensorflow.keras.utils import to_categorical\n",
+    "from tensorflow.keras.optimizers import Adam\n",
+    "from tensorflow.keras.callbacks import EarlyStopping\n",
+    "from tensorflow.keras.losses import CategoricalCrossentropy\n",
+    "\n",
+    "\n",
+    "# Load & proses data\n",
+    "df = pd.read_csv(\"dataset/cleaned_log.csv\")\n",
+    "\n",
+    "features = [\n",
+    "    \"BotPosX\", \n",
+    "    \"BotPosY\", \n",
+    "    \"BotRot\", \n",
+    "    # \"BotLinv\",\n",
+    "    # \"BotAngv\", \n",
+    "    # \"BotIsDashActive\",\n",
+    "    # \"BotIsSkillActive\", \n",
+    "    # \"BotIsOutFromArena\",\n",
+    "    # enemy\n",
+    "    \"EnemyBotPosX\", \n",
+    "    \"EnemyBotPosY\", \n",
+    "    \"EnemyBotRot\",\n",
+    "    # \"EnemyBotLinv\",\n",
+    "    # \"EnemyBotAngv\", \n",
+    "    # \"EnemyBotIsDashActive\",\n",
+    "    # \"EnemyBotIsSkillActive\", \n",
+    "    # \"EnemyBotIsOutFromArena\",\n",
+    "]\n",
+    "\n",
+    "X = df[features]\n",
+    "imputer = SimpleImputer(strategy=\"mean\")\n",
+    "X = imputer.fit_transform(X)\n",
+    "\n",
+    "# Encode label\n",
+    "le = LabelEncoder()\n",
+    "y_action = le.fit_transform(df[\"Name\"])\n",
+    "y_duration = df[\"Duration\"].values.astype(\"float32\")\n",
+    "\n",
+    "# One-hot encoding for action\n",
+    "y_action_cat = to_categorical(y_action)\n",
+    "\n",
+    "# Split\n",
+    "X_train, X_test, y_action_train, y_action_test, y_duration_train, y_duration_test, df_train, df_val = train_test_split(\n",
+    "    X, y_action_cat, y_duration, df, test_size=0.2, random_state=42\n",
+    ")\n",
+    "\n",
+    "# Build model\n",
+    "inputs = Input(shape=(X.shape[1], ))\n",
+    "x = Dense(256, activation='relu')(inputs)\n",
+    "x = BatchNormalization()(x)\n",
+    "x = Dense(128, activation='relu')(x)\n",
+    "x = Dense(64, activation='relu')(x)\n",
+    "x = Dense(32, activation='relu')(x)\n",
+    "\n",
+    "output_action = Dense(y_action_cat.shape[1], activation='softmax', name=\"action\")(x)\n",
+    "output_duration = Dense(1, activation='linear', name=\"duration\")(x)\n",
+    "\n",
+    "loss_action = CategoricalCrossentropy(label_smoothing=0.1)\n",
+    "\n",
+    "# Compile model\n",
+    "model = Model(inputs=inputs, outputs=[output_action, output_duration])\n",
+    "model.compile(\n",
+    "    optimizer=Adam(learning_rate=0.0001),\n",
+    "    loss={\"action\": loss_action, \"duration\": \"mae\"},\n",
+    "    metrics={'action': 'accuracy', 'duration': 'mae'},\n",
+    "    weighted_metrics={'action': 'accuracy', 'duration': 'mae'}\n",
+    ")\n",
+    "\n",
+    "# Early stopping\n",
+    "early_stop = EarlyStopping(\n",
+    "    monitor='val_loss',\n",
+    "    patience=10,\n",
+    "    min_delta=0.001,\n",
+    "    mode='min',\n",
+    "    restore_best_weights=True,\n",
+    "    verbose=1\n",
+    ")\n",
+    "\n",
+    "# Train\n",
+    "model.fit(X_train, {\"action\": y_action_train, \"duration\": y_duration_train},\n",
+    "          validation_data=(X_test, {'action': y_action_test, 'duration': y_duration_test}),\n",
+    "          epochs=100,\n",
+    "          batch_size=512,\n",
+    "          callbacks=[early_stop],\n",
+    "          )\n",
+    "\n",
+    "# Predict\n",
+    "pred_action_prob, pred_duration = model.predict(X_test)\n",
+    "pred_action = np.argmax(pred_action_prob, axis=1)\n",
+    "true_action = np.argmax(y_action_test, axis=1)\n",
+    "\n",
+    "# Evaluation\n",
+    "print(classification_report(true_action, pred_action, target_names=le.classes_))\n",
+    "\n",
+    "# Convert the model\n",
+    "spec = (tf.TensorSpec((None, X.shape[1]), tf.float32, name=\"input\"),)\n",
+    "onnx_model, _ = tf2onnx.convert.from_keras(model, input_signature=spec, opset=13)\n",
+    "\n",
+    "# Save to file\n",
+    "with open(\"model/ml.onnx\", \"wb\") as f:\n",
+    "    f.write(onnx_model.SerializeToString())\n",
+    "\n",
+    "print(\"Model saved to model/ml.onnx\")\n",
+    "\n",
+    "class_labels = le.classes_.tolist()\n",
+    "\n",
+    "# Optional: Save labels to JSON\n",
+    "with open(\"model/action_labels.json\", \"w\") as f:\n",
+    "    json.dump(class_labels, f)\n",
+    "\n",
+    "print(\"Exported label encoder classes to action_labels.json:\")\n",
+    "print(class_labels)"
+   ]
+  }
+ ],
+ "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": "3.10.16"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}