{ "cells": [ { "cell_type": "markdown", "id": "cb5f8ca7", "metadata": { "papermill": { "duration": 0.003855, "end_time": "2026-02-15T15:41:19.443065", "exception": false, "start_time": "2026-02-15T15:41:19.439210", "status": "completed" }, "tags": [] }, "source": [ "# Paradigm Classification: XGBoost + CodeBERT Ensemble\n", "\n", "Binary ensemble: XGBoost (TF-IDF + features) + Fine-tuned CodeBERT" ] }, { "cell_type": "code", "execution_count": 1, "id": "316f6cff", "metadata": { "execution": { "iopub.execute_input": "2026-02-15T15:41:19.450156Z", "iopub.status.busy": "2026-02-15T15:41:19.449882Z", "iopub.status.idle": "2026-02-15T15:41:23.827179Z", "shell.execute_reply": "2026-02-15T15:41:23.826418Z" }, "papermill": { "duration": 4.382753, "end_time": "2026-02-15T15:41:23.828864", "exception": false, "start_time": "2026-02-15T15:41:19.446111", "status": "completed" }, "tags": [] }, "outputs": [], "source": [ "!pip install -q transformers datasets torch scikit-learn xgboost imbalanced-learn ipywidgets\n" ] }, { "cell_type": "code", "execution_count": 2, "id": "c3b6dc46", "metadata": { "execution": { "iopub.execute_input": "2026-02-15T15:41:23.836625Z", "iopub.status.busy": "2026-02-15T15:41:23.835978Z", "iopub.status.idle": "2026-02-15T15:41:57.132006Z", "shell.execute_reply": "2026-02-15T15:41:57.131323Z" }, "papermill": { "duration": 33.304703, "end_time": "2026-02-15T15:41:57.136659", "exception": false, "start_time": "2026-02-15T15:41:23.831956", "status": "completed" }, "tags": [] }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "2026-02-15 15:41:40.990357: E external/local_xla/xla/stream_executor/cuda/cuda_fft.cc:467] Unable to register cuFFT factory: Attempting to register factory for plugin cuFFT when one has already been registered\n", "WARNING: All log messages before absl::InitializeLog() is called are written to STDERR\n", "E0000 00:00:1771170101.187712 24 cuda_dnn.cc:8579] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered\n", "E0000 00:00:1771170101.239102 24 cuda_blas.cc:1407] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered\n", "W0000 00:00:1771170101.712079 24 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n", "W0000 00:00:1771170101.712121 24 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n", "W0000 00:00:1771170101.712124 24 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n", "W0000 00:00:1771170101.712126 24 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Device: cuda\n" ] } ], "source": [ "import pandas as pd\n", "import numpy as np\n", "import torch\n", "from sklearn.feature_extraction.text import TfidfVectorizer\n", "from sklearn.metrics import classification_report, accuracy_score\n", "from sklearn.utils.class_weight import compute_class_weight\n", "import xgboost as xgb\n", "from imblearn.over_sampling import SMOTE\n", "from transformers import AutoTokenizer, AutoModelForSequenceClassification, Trainer, TrainingArguments\n", "from datasets import Dataset\n", "from scipy.sparse import hstack\n", "import warnings\n", "warnings.filterwarnings('ignore')\n", "\n", "np.random.seed(42)\n", "torch.manual_seed(42)\n", "\n", "device = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")\n", "print(f\"Device: {device}\")" ] }, { "cell_type": "markdown", "id": "9e0b4233", "metadata": { "papermill": { "duration": 0.002984, "end_time": "2026-02-15T15:41:57.142722", "exception": false, "start_time": "2026-02-15T15:41:57.139738", "status": "completed" }, "tags": [] }, "source": [ "## 1. Load and Prepare Data" ] }, { "cell_type": "code", "execution_count": 3, "id": "968009e7", "metadata": { "execution": { "iopub.execute_input": "2026-02-15T15:41:57.149967Z", "iopub.status.busy": "2026-02-15T15:41:57.149477Z", "iopub.status.idle": "2026-02-15T15:41:59.659590Z", "shell.execute_reply": "2026-02-15T15:41:59.658708Z" }, "papermill": { "duration": 2.515456, "end_time": "2026-02-15T15:41:59.661083", "exception": false, "start_time": "2026-02-15T15:41:57.145627", "status": "completed" }, "tags": [] }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Train: 57,235 | Val: 12,265 | Test: 12,265\n", "\n", "After sampling: 19,643 train samples\n", "\n", "Class distribution:\n", "label\n", "Functional 5000\n", "Non-Paradigm 5000\n", "Oop 5000\n", "Procedural 4643\n", "Name: count, dtype: int64\n", "\n", "Labels: {'Functional': 0, 'Non-Paradigm': 1, 'Oop': 2, 'Procedural': 3}\n" ] } ], "source": [ "train_df = pd.read_csv('/kaggle/input/datasets/aryanprakhar/paradigm/data/train.csv')\n", "val_df = pd.read_csv('/kaggle/input/datasets/aryanprakhar/paradigm/data/val.csv')\n", "test_df = pd.read_csv('/kaggle/input/datasets/aryanprakhar/paradigm/data/test.csv')\n", "\n", "print(f\"Train: {len(train_df):,} | Val: {len(val_df):,} | Test: {len(test_df):,}\")\n", "\n", "# Combine title + body\n", "def combine_text(row):\n", " title = str(row.get('title', '')) if pd.notna(row.get('title')) else ''\n", " body = str(row.get('question_body', '')) if pd.notna(row.get('question_body')) else ''\n", " return f\"{title} {body}\".strip()\n", "\n", "for df in [train_df, val_df, test_df]:\n", " if 'text' not in df.columns:\n", " df['text'] = df.apply(combine_text, axis=1)\n", " if 'paradigm_label' in df.columns:\n", " df['label'] = df['paradigm_label']\n", "\n", "# Remove Mixed class if present\n", "for df in [train_df, val_df, test_df]:\n", " if 'Mixed' in df['label'].unique():\n", " df.drop(df[df['label'] == 'Mixed'].index, inplace=True)\n", "\n", "# Stratified sampling for balanced training\n", "SAMPLES_PER_CLASS = 5000 # Adjust based on needs\n", "\n", "train_df = train_df.groupby('label', group_keys=False).apply(\n", " lambda x: x.sample(min(len(x), SAMPLES_PER_CLASS), random_state=42)\n", ").reset_index(drop=True)\n", "\n", "print(f\"\\nAfter sampling: {len(train_df):,} train samples\")\n", "print(\"\\nClass distribution:\")\n", "print(train_df['label'].value_counts())\n", "\n", "label2id = {label: idx for idx, label in enumerate(sorted(train_df['label'].unique()))}\n", "id2label = {idx: label for label, idx in label2id.items()}\n", "print(f\"\\nLabels: {label2id}\")" ] }, { "cell_type": "markdown", "id": "2fc0bcd4", "metadata": { "papermill": { "duration": 0.003083, "end_time": "2026-02-15T15:41:59.667723", "exception": false, "start_time": "2026-02-15T15:41:59.664640", "status": "completed" }, "tags": [] }, "source": [ "## 2. Feature Engineering" ] }, { "cell_type": "code", "execution_count": 4, "id": "d695f09a", "metadata": { "execution": { "iopub.execute_input": "2026-02-15T15:41:59.675370Z", "iopub.status.busy": "2026-02-15T15:41:59.675061Z", "iopub.status.idle": "2026-02-15T15:42:02.334217Z", "shell.execute_reply": "2026-02-15T15:42:02.333391Z" }, "papermill": { "duration": 2.664696, "end_time": "2026-02-15T15:42:02.335706", "exception": false, "start_time": "2026-02-15T15:41:59.671010", "status": "completed" }, "tags": [] }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Features extracted: 10 features\n" ] } ], "source": [ "import re\n", "\n", "class FeatureExtractor:\n", " def __init__(self):\n", " self.oop_kw = ['class', 'object', 'this', 'self', 'extends', 'implements', 'interface',\n", " 'public', 'private', 'protected', 'static', 'virtual', 'override']\n", " self.fp_kw = ['map', 'filter', 'reduce', 'fold', 'lambda', 'closure', '=>',\n", " 'monad', 'functor', 'pure', 'immutable', 'const', 'let']\n", " self.proc_kw = ['void', 'int', 'char', 'float', 'struct', 'malloc', 'free',\n", " 'pointer', 'goto', 'scanf', 'printf']\n", " \n", " def extract(self, text):\n", " t = text.lower()\n", " return {\n", " 'oop_score': sum(t.count(k) for k in self.oop_kw),\n", " 'fp_score': sum(t.count(k) for k in self.fp_kw),\n", " 'proc_score': sum(t.count(k) for k in self.proc_kw),\n", " 'length': len(text),\n", " 'num_lines': text.count('\\n') + 1,\n", " 'has_class': 1 if re.search(r'\\bclass\\s+\\w+', t) else 0,\n", " 'has_lambda': 1 if 'lambda' in t or '=>' in text else 0,\n", " 'num_dots': text.count('.'),\n", " 'num_arrows': text.count('->') + text.count('=>'),\n", " 'num_braces': text.count('{') + text.count('}')\n", " }\n", " \n", " def extract_batch(self, texts):\n", " return pd.DataFrame([self.extract(t) for t in texts])\n", "\n", "feature_extractor = FeatureExtractor()\n", "\n", "train_features = feature_extractor.extract_batch(train_df['text'].values)\n", "val_features = feature_extractor.extract_batch(val_df['text'].values)\n", "test_features = feature_extractor.extract_batch(test_df['text'].values)\n", "\n", "print(f\"Features extracted: {train_features.shape[1]} features\")" ] }, { "cell_type": "markdown", "id": "d501d2b8", "metadata": { "papermill": { "duration": 0.003151, "end_time": "2026-02-15T15:42:02.342259", "exception": false, "start_time": "2026-02-15T15:42:02.339108", "status": "completed" }, "tags": [] }, "source": [ "## 3. XGBoost Model" ] }, { "cell_type": "code", "execution_count": 5, "id": "3f5a061d", "metadata": { "execution": { "iopub.execute_input": "2026-02-15T15:42:02.349520Z", "iopub.status.busy": "2026-02-15T15:42:02.349268Z", "iopub.status.idle": "2026-02-15T15:42:15.967743Z", "shell.execute_reply": "2026-02-15T15:42:15.966880Z" }, "papermill": { "duration": 13.6239, "end_time": "2026-02-15T15:42:15.969262", "exception": false, "start_time": "2026-02-15T15:42:02.345362", "status": "completed" }, "tags": [] }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "After SMOTE: 20,000 samples\n" ] } ], "source": [ "# TF-IDF\n", "tfidf = TfidfVectorizer(max_features=1000, ngram_range=(1, 2), min_df=2, max_df=0.95)\n", "\n", "tfidf_train = tfidf.fit_transform(train_df['text'])\n", "tfidf_val = tfidf.transform(val_df['text'])\n", "tfidf_test = tfidf.transform(test_df['text'])\n", "\n", "X_train = hstack([tfidf_train, train_features.values])\n", "X_val = hstack([tfidf_val, val_features.values])\n", "X_test = hstack([tfidf_test, test_features.values])\n", "\n", "y_train = train_df['label'].map(label2id).values\n", "y_val = val_df['label'].map(label2id).values\n", "y_test = test_df['label'].map(label2id).values\n", "\n", "# SMOTE\n", "smote = SMOTE(random_state=42, k_neighbors=3)\n", "X_train_balanced, y_train_balanced = smote.fit_resample(X_train, y_train)\n", "\n", "print(f\"After SMOTE: {X_train_balanced.shape[0]:,} samples\")" ] }, { "cell_type": "code", "execution_count": 6, "id": "7701b18d", "metadata": { "execution": { "iopub.execute_input": "2026-02-15T15:42:15.977275Z", "iopub.status.busy": "2026-02-15T15:42:15.976976Z", "iopub.status.idle": "2026-02-15T15:42:15.989126Z", "shell.execute_reply": "2026-02-15T15:42:15.988614Z" }, "papermill": { "duration": 0.018139, "end_time": "2026-02-15T15:42:15.990815", "exception": false, "start_time": "2026-02-15T15:42:15.972676", "status": "completed" }, "tags": [] }, "outputs": [], "source": [ "class_weights = compute_class_weight('balanced', classes=np.unique(y_train), y=y_train)\n", "sample_weights = np.array([class_weights[y] for y in y_train_balanced])" ] }, { "cell_type": "code", "execution_count": 7, "id": "7e9494ac", "metadata": { "execution": { "iopub.execute_input": "2026-02-15T15:42:15.999038Z", "iopub.status.busy": "2026-02-15T15:42:15.998799Z", "iopub.status.idle": "2026-02-15T15:44:27.427666Z", "shell.execute_reply": "2026-02-15T15:44:27.426660Z" }, "papermill": { "duration": 131.434731, "end_time": "2026-02-15T15:44:27.429146", "exception": false, "start_time": "2026-02-15T15:42:15.994415", "status": "completed" }, "tags": [] }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[0]\tvalidation_0-mlogloss:1.28819\n", "[50]\tvalidation_0-mlogloss:0.39114\n", "[100]\tvalidation_0-mlogloss:0.32716\n", "[150]\tvalidation_0-mlogloss:0.30578\n", "[199]\tvalidation_0-mlogloss:0.29511\n", "\n", "XGBoost Validation Accuracy: 0.9051\n", " precision recall f1-score support\n", "\n", " Functional 0.80 0.90 0.85 1352\n", "Non-Paradigm 0.90 0.91 0.90 4539\n", " Oop 0.94 0.89 0.92 5379\n", " Procedural 0.89 0.97 0.93 995\n", "\n", " accuracy 0.91 12265\n", " macro avg 0.88 0.92 0.90 12265\n", "weighted avg 0.91 0.91 0.91 12265\n", "\n" ] } ], "source": [ "# Train XGBoost\n", "class_weights = compute_class_weight('balanced', classes=np.unique(y_train), y=y_train)\n", "sample_weights = np.array([class_weights[y] for y in y_train_balanced])\n", "\n", "xgb_model = xgb.XGBClassifier(\n", " n_estimators=200,\n", " max_depth=6,\n", " learning_rate=0.1,\n", " subsample=0.8,\n", " colsample_bytree=0.8,\n", " objective='multi:softprob',\n", " num_class=len(label2id),\n", " random_state=42,\n", " tree_method='hist'\n", ")\n", "\n", "xgb_model.fit(X_train_balanced, y_train_balanced, sample_weight=sample_weights,\n", " eval_set=[(X_val, y_val)], verbose=50)\n", "\n", "xgb_val_preds = xgb_model.predict(X_val)\n", "xgb_val_proba = xgb_model.predict_proba(X_val)\n", "xgb_acc = accuracy_score(y_val, xgb_val_preds)\n", "\n", "print(f\"\\nXGBoost Validation Accuracy: {xgb_acc:.4f}\")\n", "print(classification_report(y_val, xgb_val_preds, target_names=list(label2id.keys())))" ] }, { "cell_type": "markdown", "id": "7a54b788", "metadata": { "papermill": { "duration": 0.00343, "end_time": "2026-02-15T15:44:27.436143", "exception": false, "start_time": "2026-02-15T15:44:27.432713", "status": "completed" }, "tags": [] }, "source": [ "## 4. CodeBERT Model" ] }, { "cell_type": "code", "execution_count": 8, "id": "50f1062f", "metadata": { "execution": { "iopub.execute_input": "2026-02-15T15:44:27.443936Z", "iopub.status.busy": "2026-02-15T15:44:27.443695Z", "iopub.status.idle": "2026-02-15T15:44:53.838922Z", "shell.execute_reply": "2026-02-15T15:44:53.838324Z" }, "papermill": { "duration": 26.400859, "end_time": "2026-02-15T15:44:53.840288", "exception": false, "start_time": "2026-02-15T15:44:27.439429", "status": "completed" }, "tags": [] }, "outputs": [ { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "cedf920aee4a495788cebf645ad3197e", "version_major": 2, "version_minor": 0 }, "text/plain": [ "tokenizer_config.json: 0%| | 0.00/25.0 [00:00\n", " \n", " \n", " [1842/1842 26:38, Epoch 3/3]\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", "
EpochTraining LossValidation LossAccuracyF1 MacroF1 WeightedPrecisionRecall
10.1882000.2289990.9263760.9219050.9276370.9041590.948625
20.0846000.2641080.9366490.9321760.9369220.9161460.951797
30.0605000.2315790.9471670.9452630.9473640.9363710.955111

" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/plain": [ "TrainOutput(global_step=1842, training_loss=0.13871806852689395, metrics={'train_runtime': 1600.323, 'train_samples_per_second': 36.823, 'train_steps_per_second': 1.151, 'total_flos': 7752574902638592.0, 'train_loss': 0.13871806852689395, 'epoch': 3.0})" ] }, "execution_count": 9, "metadata": {}, "output_type": "execute_result" } ], "source": [ "class_weights_tensor = torch.tensor(class_weights, dtype=torch.float32).to(device)\n", "from sklearn.metrics import f1_score, precision_score, recall_score\n", "\n", "def compute_metrics(eval_pred):\n", " logits, labels = eval_pred\n", " preds = np.argmax(logits, axis=-1)\n", " \n", " return {\n", " \"accuracy\": accuracy_score(labels, preds),\n", " \"f1_macro\": f1_score(labels, preds, average='macro'),\n", " \"f1_weighted\": f1_score(labels, preds, average='weighted'),\n", " \"precision\": precision_score(labels, preds, average='macro'),\n", " \"recall\": recall_score(labels, preds, average='macro')\n", " }\n", "\n", "class WeightedTrainer(Trainer):\n", " def compute_loss(self, model, inputs, return_outputs=False, num_items_in_batch=None):\n", " labels = inputs.pop(\"labels\")\n", " outputs = model(**inputs)\n", " logits = outputs.logits\n", " loss_fct = torch.nn.CrossEntropyLoss(weight=class_weights_tensor)\n", " loss = loss_fct(logits, labels)\n", " return (loss, outputs) if return_outputs else loss\n", "\n", "training_args = TrainingArguments(\n", " output_dir=\"./results\",\n", " eval_strategy=\"epoch\",\n", " save_strategy=\"epoch\",\n", " learning_rate=3e-5,\n", " per_device_train_batch_size=16,\n", " per_device_eval_batch_size=32,\n", " num_train_epochs=3,\n", " weight_decay=0.01,\n", " load_best_model_at_end=True,\n", " metric_for_best_model=\"f1_macro\",\n", " fp16=torch.cuda.is_available(),\n", " gradient_accumulation_steps=2,\n", "\n", " logging_strategy=\"steps\",\n", " logging_steps=10,\n", " disable_tqdm=False,\n", " report_to=\"none\" \n", ")\n", "\n", "\n", "\n", "trainer = WeightedTrainer(\n", " model=model,\n", " args=training_args,\n", " train_dataset=tokenized_train,\n", " eval_dataset=tokenized_val,\n", " compute_metrics=compute_metrics\n", ")\n", "\n", "trainer.train()" ] }, { "cell_type": "code", "execution_count": 10, "id": "e428092b", "metadata": { "execution": { "iopub.execute_input": "2026-02-15T16:11:34.833452Z", "iopub.status.busy": "2026-02-15T16:11:34.832900Z", "iopub.status.idle": "2026-02-15T16:13:05.520893Z", "shell.execute_reply": "2026-02-15T16:13:05.519939Z" }, "papermill": { "duration": 90.695098, "end_time": "2026-02-15T16:13:05.522419", "exception": false, "start_time": "2026-02-15T16:11:34.827321", "status": "completed" }, "tags": [] }, "outputs": [ { "data": { "text/html": [], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "\n", "Validation Accuracy: 0.9472\n", " precision recall f1-score support\n", "\n", " Functional 0.87 0.94 0.90 1352\n", "Non-Paradigm 0.95 0.95 0.95 4539\n", " Oop 0.96 0.94 0.95 5379\n", " Procedural 0.96 0.99 0.98 995\n", "\n", " accuracy 0.95 12265\n", " macro avg 0.94 0.96 0.95 12265\n", "weighted avg 0.95 0.95 0.95 12265\n", "\n" ] } ], "source": [ "codebert_val_results = trainer.predict(tokenized_val)\n", "codebert_val_preds = np.argmax(codebert_val_results.predictions, axis=-1)\n", "codebert_val_proba = torch.softmax(torch.tensor(codebert_val_results.predictions), dim=-1).numpy()\n", "codebert_acc = accuracy_score(y_val, codebert_val_preds)\n", "\n", "print(f\"\\nValidation Accuracy: {codebert_acc:.4f}\")\n", "print(classification_report(y_val, codebert_val_preds, target_names=list(label2id.keys())))" ] }, { "cell_type": "markdown", "id": "cb38ece9", "metadata": { "papermill": { "duration": 0.004888, "end_time": "2026-02-15T16:13:05.532005", "exception": false, "start_time": "2026-02-15T16:13:05.527117", "status": "completed" }, "tags": [] }, "source": [ "## 5. Ensemble" ] }, { "cell_type": "code", "execution_count": 11, "id": "0d1f3337", "metadata": { "execution": { "iopub.execute_input": "2026-02-15T16:13:05.542314Z", "iopub.status.busy": "2026-02-15T16:13:05.542033Z", "iopub.status.idle": "2026-02-15T16:13:05.556713Z", "shell.execute_reply": "2026-02-15T16:13:05.555968Z" }, "papermill": { "duration": 0.021499, "end_time": "2026-02-15T16:13:05.558128", "exception": false, "start_time": "2026-02-15T16:13:05.536629", "status": "completed" }, "tags": [] }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\n", "Ensemble Validation Accuracy: 0.9491\n", " precision recall f1-score support\n", "\n", " Functional 0.87 0.95 0.91 1352\n", "Non-Paradigm 0.95 0.95 0.95 4539\n", " Oop 0.96 0.94 0.95 5379\n", " Procedural 0.97 0.99 0.98 995\n", "\n", " accuracy 0.95 12265\n", " macro avg 0.94 0.96 0.95 12265\n", "weighted avg 0.95 0.95 0.95 12265\n", "\n", "\n", "Model Comparison:\n", "XGBoost: 0.9051\n", "CodeBERT: 0.9472\n", "Ensemble: 0.9491\n" ] } ], "source": [ "# Weighted average: CodeBERT (60%) + XGBoost (40%)\n", "ensemble_val_proba = 0.6 * codebert_val_proba + 0.4 * xgb_val_proba\n", "ensemble_val_preds = np.argmax(ensemble_val_proba, axis=1)\n", "ensemble_acc = accuracy_score(y_val, ensemble_val_preds)\n", "\n", "print(f\"\\nEnsemble Validation Accuracy: {ensemble_acc:.4f}\")\n", "print(classification_report(y_val, ensemble_val_preds, target_names=list(label2id.keys())))\n", "\n", "print(f\"\\nModel Comparison:\")\n", "print(f\"XGBoost: {xgb_acc:.4f}\")\n", "print(f\"CodeBERT: {codebert_acc:.4f}\")\n", "print(f\"Ensemble: {ensemble_acc:.4f}\")" ] }, { "cell_type": "markdown", "id": "d4d3b6c1", "metadata": { "papermill": { "duration": 0.004365, "end_time": "2026-02-15T16:13:05.567562", "exception": false, "start_time": "2026-02-15T16:13:05.563197", "status": "completed" }, "tags": [] }, "source": [ "## 6. Test Set Evaluation" ] }, { "cell_type": "code", "execution_count": 12, "id": "e2f5f2a0", "metadata": { "execution": { "iopub.execute_input": "2026-02-15T16:13:05.577419Z", "iopub.status.busy": "2026-02-15T16:13:05.577152Z", "iopub.status.idle": "2026-02-15T16:14:36.715691Z", "shell.execute_reply": "2026-02-15T16:14:36.714952Z" }, "papermill": { "duration": 91.145215, "end_time": "2026-02-15T16:14:36.717102", "exception": false, "start_time": "2026-02-15T16:13:05.571887", "status": "completed" }, "tags": [] }, "outputs": [ { "data": { "text/html": [], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "\n", "Test Set Accuracy: 0.9525\n", " precision recall f1-score support\n", "\n", " Functional 0.88 0.96 0.92 1352\n", "Non-Paradigm 0.96 0.95 0.96 4539\n", " Oop 0.97 0.94 0.95 5379\n", " Procedural 0.97 0.99 0.98 995\n", "\n", " accuracy 0.95 12265\n", " macro avg 0.94 0.96 0.95 12265\n", "weighted avg 0.95 0.95 0.95 12265\n", "\n" ] } ], "source": [ "xgb_test_proba = xgb_model.predict_proba(X_test)\n", "\n", "codebert_test_results = trainer.predict(tokenized_test)\n", "codebert_test_proba = torch.softmax(torch.tensor(codebert_test_results.predictions), dim=-1).numpy()\n", "\n", "ensemble_test_proba = 0.6 * codebert_test_proba + 0.4 * xgb_test_proba\n", "ensemble_test_preds = np.argmax(ensemble_test_proba, axis=1)\n", "test_acc = accuracy_score(y_test, ensemble_test_preds)\n", "\n", "print(f\"\\nTest Set Accuracy: {test_acc:.4f}\")\n", "print(classification_report(y_test, ensemble_test_preds, target_names=list(label2id.keys())))" ] }, { "cell_type": "markdown", "id": "6f89a26c", "metadata": { "papermill": { "duration": 0.004672, "end_time": "2026-02-15T16:14:36.726621", "exception": false, "start_time": "2026-02-15T16:14:36.721949", "status": "completed" }, "tags": [] }, "source": [ "## 7. Save Models" ] }, { "cell_type": "code", "execution_count": 13, "id": "e7af5486", "metadata": { "execution": { "iopub.execute_input": "2026-02-15T16:14:36.736589Z", "iopub.status.busy": "2026-02-15T16:14:36.736360Z", "iopub.status.idle": "2026-02-15T16:14:38.393837Z", "shell.execute_reply": "2026-02-15T16:14:38.392936Z" }, "papermill": { "duration": 1.664389, "end_time": "2026-02-15T16:14:38.395432", "exception": false, "start_time": "2026-02-15T16:14:36.731043", "status": "completed" }, "tags": [] }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Models saved\n" ] } ], "source": [ "import pickle\n", "\n", "trainer.save_model(\"./codebert_model\")\n", "tokenizer.save_pretrained(\"./codebert_model\")\n", "\n", "with open('xgboost_model.pkl', 'wb') as f:\n", " pickle.dump(xgb_model, f)\n", "with open('tfidf_vectorizer.pkl', 'wb') as f:\n", " pickle.dump(tfidf, f)\n", "\n", "results_df = test_df.copy()\n", "results_df['predicted'] = [id2label[p] for p in ensemble_test_preds]\n", "results_df['confidence'] = np.max(ensemble_test_proba, axis=1)\n", "results_df.to_csv('predictions.csv', index=False)\n", "\n", "print(\"Models saved\")" ] } ], "metadata": { "kaggle": { "accelerator": "gpu", "dataSources": [ { "datasetId": 9494748, "sourceId": 14845098, "sourceType": "datasetVersion" }, { "datasetId": 9495391, "sourceId": 14846001, "sourceType": "datasetVersion" } ], "dockerImageVersionId": 31260, "isGpuEnabled": true, "isInternetEnabled": true, "language": "python", "sourceType": "notebook" }, "kernelspec": { "display_name": "Python 3", "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": "3.12.12" }, "papermill": { "default_parameters": {}, "duration": 2227.752824, "end_time": "2026-02-15T16:18:24.820078", "environment_variables": {}, 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