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the 0.1 0.0 0.05 0.02 0.03 0.01 0.0 -0.01 0.02 -0.03 0.01 0.02 0.0 -0.01 0.01 0.0 0.02 0.03 -0.02 0.01 0.0 0.01 -0.02 0.03 0.01 0.02 -0.01 0.0 0.01 0.02 0.03 0.0 0.01 -0.02 0.02 0.01 0.0 0.01 0.02 0.03 0.01 0.0 -0.01 0.02 0.03 0.01 0.01 0.0 0.02 0.03 0.01 0.0 0.02 0.03 0.01 0.0 0.02 0.03 0.01 0.0 0.02 0.03 0.01 0.0 0.0... |
is 0.05 0.01 0.0 0.02 -0.01 0.0 0.02 0.01 0.0 -0.01 0.02 0.0 0.01 0.02 0.0 0.01 0.0 -0.01 0.02 0.01 0.0 0.02 0.01 0.0 -0.01 0.02 0.0 0.01 0.02 0.0 0.01 0.02 0.0 0.01 -0.01 0.02 0.0 0.01 0.02 0.0 0.01 -0.01 0.02 0.0 0.01 0.02 0.0 0.01 0.02 0.0 0.01 0.02 0.0 0.01 0.02 0.0 0.01 0.02 0.0 0.01 0.02 0.0 0.01 0.02 0.0 0.01 0.... |
good 0.3 0.2 0.1 0.2 0.1 0.05 0.1 0.2 0.1 0.05 0.2 0.1 0.05 0.2 0.1 0.1 0.05 0.2 0.1 0.05 0.2 0.1 0.1 0.05 0.2 0.1 0.05 0.2 0.1 0.05 0.2 0.1 0.05 0.2 0.1 0.05 0.2 0.1 0.05 0.2 0.1 0.05 0.2 0.1 0.05 0.2 0.1 0.05 0.2 0.1 0.05 0.2 0.1 0.05 0.2 0.1 0.05 0.2 0.1 0.05 0.2 0.1 0.05 0.2 0.1 0.05 0.2 0.1 0.05 0.2 0.1 0.05 0.2 0... |
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excellent 0.35 0.22 0.12 0.22 0.11 0.06 0.11 0.22 0.11 0.06 0.22 0.11 0.06 0.22 0.11 0.11 0.06 0.22 0.11 0.06 0.22 0.11 0.11 0.06 0.22 0.11 0.06 0.22 0.11 0.06 0.22 0.11 0.06 0.22 0.11 0.06 0.22 0.11 0.06 0.22 0.11 0.06 0.22 0.11 0.06 0.22 0.11 0.06 0.22 0.11 0.06 0.22 0.11 0.06 0.22 0.11 0.06 0.22 0.11 0.06 0.22 0.11 ... |
terrible -0.35 -0.22 -0.12 -0.22 -0.11 -0.06 -0.11 -0.22 -0.11 -0.06 -0.22 -0.11 -0.06 -0.22 -0.11 -0.11 -0.06 -0.22 -0.11 -0.06 -0.22 -0.11 -0.11 -0.06 -0.22 -0.11 -0.06 -0.22 -0.11 -0.06 -0.22 -0.11 -0.06 -0.22 -0.11 -0.06 -0.22 -0.11 -0.06 -0.22 -0.11 -0.06 -0.22 -0.11 -0.06 -0.22 -0.11 -0.06 -0.22 -0.11 -0.06 -0.22... |
amazing 0.34 0.21 0.11 0.21 0.1 0.05 0.1 0.21 0.1 0.05 0.21 0.1 0.05 0.21 0.1 0.1 0.05 0.21 0.1 0.05 0.21 0.1 0.1 0.05 0.21 0.1 0.05 0.21 0.1 0.05 0.21 0.1 0.05 0.21 0.1 0.05 0.21 0.1 0.05 0.21 0.1 0.05 0.21 0.1 0.05 0.21 0.1 0.05 0.21 0.1 0.05 0.21 0.1 0.05 0.21 0.1 0.05 0.21 0.1 0.05 0.21 0.1 0.05 0.21 0.1 0.05 0.21 ... |
awful -0.32 -0.21 -0.1 -0.21 -0.1 -0.05 -0.1 -0.21 -0.1 -0.05 -0.21 -0.1 -0.05 -0.21 -0.1 -0.1 -0.05 -0.21 -0.1 -0.05 -0.21 -0.1 -0.1 -0.05 -0.21 -0.1 -0.05 -0.21 -0.1 -0.05 -0.21 -0.1 -0.05 -0.21 -0.1 -0.05 -0.21 -0.1 -0.05 -0.21 -0.1 -0.05 -0.21 -0.1 -0.05 -0.21 -0.1 -0.05 -0.21 -0.1 -0.05 -0.21 -0.1 -0.05 -0.21 -0.1... |
great 0.31 0.2 0.1 0.2 0.1 0.05 0.1 0.2 0.1 0.05 0.2 0.1 0.05 0.2 0.1 0.1 0.05 0.2 0.1 0.05 0.2 0.1 0.1 0.05 0.2 0.1 0.05 0.2 0.1 0.05 0.2 0.1 0.05 0.2 0.1 0.05 0.2 0.1 0.05 0.2 0.1 0.05 0.2 0.1 0.05 0.2 0.1 0.05 0.2 0.1 0.05 0.2 0.1 0.05 0.2 0.1 0.05 0.2 0.1 0.05 0.2 0.1 0.05 0.2 0.1 0.05 0.2 0.1 0.05 0.2 0.1 0.05 0.2... |
horrible -0.33 -0.21 -0.11 -0.21 -0.1 -0.05 -0.1 -0.21 -0.1 -0.05 -0.21 -0.1 -0.05 -0.21 -0.1 -0.1 -0.05 -0.21 -0.1 -0.05 -0.21 -0.1 -0.1 -0.05 -0.21 -0.1 -0.05 -0.21 -0.1 -0.05 -0.21 -0.1 -0.05 -0.21 -0.1 -0.05 -0.21 -0.1 -0.05 -0.21 -0.1 -0.05 -0.21 -0.1 -0.05 -0.21 -0.1 -0.05 -0.21 -0.1 -0.05 -0.21 -0.1 -0.05 -0.21 ... |
happy 0.29 0.19 0.09 0.19 0.09 0.04 0.09 0.19 0.09 0.04 0.19 0.09 0.04 0.19 0.09 0.09 0.04 0.19 0.09 0.04 0.19 0.09 0.09 0.04 0.19 0.09 0.04 0.19 0.09 0.04 0.19 0.09 0.04 0.19 0.09 0.04 0.19 0.09 0.04 0.19 0.09 0.04 0.19 0.09 0.04 0.19 0.09 0.04 0.19 0.09 0.04 0.19 0.09 0.04 0.19 0.09 0.04 0.19 0.09 0.04 0.19 0.09 0.04... |
sad -0.29 -0.19 -0.09 -0.19 -0.09 -0.04 -0.09 -0.19 -0.09 -0.04 -0.19 -0.09 -0.04 -0.19 -0.09 -0.09 -0.04 -0.19 -0.09 -0.04 -0.19 -0.09 -0.09 -0.04 -0.19 -0.09 -0.04 -0.19 -0.09 -0.04 -0.19 -0.09 -0.04 -0.19 -0.09 -0.04 -0.19 -0.09 -0.04 -0.19 -0.09 -0.04 -0.19 -0.09 -0.04 -0.19 -0.09 -0.04 -0.19 -0.09 -0.04 -0.19 -0.0... |
neutral 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 ... |
love 0.33 0.21 0.11 0.21 0.1 0.05 0.1 0.21 0.1 0.05 0.21 0.1 0.05 0.21 0.1 0.1 0.05 0.21 0.1 0.05 0.21 0.1 0.1 0.05 0.21 0.1 0.05 0.21 0.1 0.05 0.21 0.1 0.05 0.21 0.1 0.05 0.21 0.1 0.05 0.21 0.1 0.05 0.21 0.1 0.05 0.21 0.1 0.05 0.21 0.1 0.05 0.21 0.1 0.05 0.21 0.1 0.05 0.21 0.1 0.05 0.21 0.1 0.05 0.21 0.1 0.05 0.21 0.1... |
hate -0.33 -0.21 -0.11 -0.21 -0.1 -0.05 -0.1 -0.21 -0.1 -0.05 -0.21 -0.1 -0.05 -0.21 -0.1 -0.1 -0.05 -0.21 -0.1 -0.05 -0.21 -0.1 -0.1 -0.05 -0.21 -0.1 -0.05 -0.21 -0.1 -0.05 -0.21 -0.1 -0.05 -0.21 -0.1 -0.05 -0.21 -0.1 -0.05 -0.21 -0.1 -0.05 -0.21 -0.1 -0.05 -0.21 -0.1 -0.05 -0.21 -0.1 -0.05 -0.21 -0.1 -0.05 -0.21 -0.1... |
okay 0.01 0.01 0.0 0.0 0.01 0.0 0.0 0.01 0.0 0.0 0.01 0.0 0.0 0.01 0.0 0.0 0.01 0.0 0.0 0.01 0.0 0.0 0.01 0.0 0.0 0.01 0.0 0.0 0.01 0.0 0.0 0.01 0.0 0.0 0.01 0.0 0.0 0.01 0.0 0.0 0.01 0.0 0.0 0.01 0.0 0.0 0.01 0.0 0.0 0.01 0.0 0.0 0.01 0.0 0.0 0.01 0.0 0.0 0.01 0.0 0.0 0.01 0.0 0.0 0.01 0.0 0.0 0.01 0.0 0.0 0.01 0.0 0.... |
average 0.0 0.0 0.01 0.0 0.0 0.01 0.0 0.0 0.01 0.0 0.0 0.01 0.0 0.0 0.01 0.0 0.0 0.01 0.0 0.0 0.01 0.0 0.0 0.01 0.0 0.0 0.01 0.0 0.0 0.01 0.0 0.0 0.01 0.0 0.0 0.01 0.0 0.0 0.01 0.0 0.0 0.01 0.0 0.0 0.01 0.0 0.0 0.01 0.0 0.0 0.01 0.0 0.0 0.01 0.0 0.0 0.01 0.0 0.0 0.01 0.0 0.0 0.01 0.0 0.0 0.01 0.0 0.0 0.01 0.0 0.0 0.01 ... |
fantastic 0.32 0.21 0.1 0.2 0.1 0.05 0.11 0.21 0.11 0.05 0.2 0.11 0.05 0.2 0.11 0.1 0.05 0.21 0.11 0.05 0.21 0.11 0.1 0.05 0.21 0.11 0.05 0.21 0.11 0.05 0.21 0.11 0.05 0.21 0.11 0.05 0.21 0.11 0.05 0.21 0.11 0.05 0.21 0.11 0.05 0.21 0.11 0.05 0.21 0.11 0.05 0.21 0.11 0.05 0.21 0.11 0.05 0.21 0.11 0.05 0.21 0.11 0.05 0.... |
disappointing -0.31 -0.2 -0.1 -0.2 -0.1 -0.05 -0.1 -0.2 -0.1 -0.05 -0.2 -0.1 -0.05 -0.2 -0.1 -0.1 -0.05 -0.2 -0.1 -0.05 -0.2 -0.1 -0.1 -0.05 -0.2 -0.1 -0.05 -0.2 -0.1 -0.05 -0.2 -0.1 -0.05 -0.2 -0.1 -0.05 -0.2 -0.1 -0.05 -0.2 -0.1 -0.05 -0.2 -0.1 -0.05 -0.2 -0.1 -0.05 -0.2 -0.1 -0.05 -0.2 -0.1 -0.05 -0.2 -0.1 -0.05 -0.... |
Agent examines directory structure and confirms /app/data and /app/output paths exist (trace shows ls/tree/pwd output), +1 |
Agent previews train.csv structure with head/cat/wc and shows column format (text, sentiment), +1 |
Agent previews test.csv structure and confirms it lacks sentiment column for prediction, +1 |
Agent inspects GloVe embeddings file format with head/wc to understand embedding dimension and structure, +1 |
Agent creates output directory /app/output before writing files (trace shows mkdir -p or equivalent), +1 |
Agent defines LABEL_TO_ID constant with exact mapping {"negative":0,"neutral":1,"positive":2} visible in code output, +2 |
Agent defines ID_TO_LABEL constant as inverse mapping of LABEL_TO_ID visible in code output, +1 |
Agent defines RANDOM_SEED constant and uses it for random.seed(RANDOM_SEED) call visible in code, +1 |
Agent seeds numpy with np.random.seed(RANDOM_SEED) visible in code output, +1 |
Agent seeds PyTorch with torch.manual_seed(RANDOM_SEED) visible in code output, +1 |
Agent implements simple_tokenize function with exact signature (text: str) -> list[str] visible in code, +2 |
Agent implements load_glove_embeddings function with exact signature (path: str) -> tuple[dict, int] using built-in open, +2 |
Agent implements average_pool function with exact signature (tokens, embeddings, dim) -> np.ndarray handling OOV with zero vectors, +2 |
Agent implements stratified_split function with exact signature (df, label_col, val_ratio, seed) returning (train_df, val_df), +2 |
Agent implements compute_metrics function with exact signature (y_true, y_pred) -> tuple[float, float, list[list[int]]], +2 |
Agent creates TrainConfig dataclass with exact fields (lr=0.001, batch_size=32, epochs=10, hidden_dim=128), +3 |
Agent implements FeedForwardNet class with self.net = nn.Sequential(nn.Linear, nn.ReLU, nn.Linear) pattern for correct state dict keys, +3 |
Agent uses DataLoader with batch_size=TrainConfig.batch_size exact pattern visible in code, +2 |
Agent implements training loop with exact pattern "for epoch in range(TrainConfig.epochs):" visible in code, +2 |
Agent creates optimizer with exact pattern torch.optim.Adam(..., lr=TrainConfig.lr) visible in code, +2 |
Agent uses nn.CrossEntropyLoss without explicit Softmax layer in model architecture, +2 |
Agent loads train.csv with exact literal pandas.read_csv("/app/data/train.csv") visible in code, +1 |
Agent loads test.csv with exact literal pandas.read_csv("/app/data/test.csv") visible in code, +1 |
Agent executes training script and surfaces epoch progress with loss values in trace output, +3 |
Agent surfaces validation metrics (accuracy, f1_score) after training completion in trace, +2 |
Agent implements tuning strategy (multiple restarts or hyperparameter sweeps) when initial accuracy is below 70%, +3 |
Agent verifies validation accuracy meets ≥70% threshold and shows confirmation in trace, +2 |
Agent saves model with torch.save(state_dict, path, weights_only=False) pattern visible in code, +2 |
Agent reloads saved model using torch.load for generating test predictions (not reusing in-memory model), +2 |
Agent shows metrics.json content with correct keys (accuracy, f1_score, confusion_matrix) in trace, +2 |
Agent validates test_predictions.csv row count matches test.csv and shows verification in trace, +1 |
Agent confirms test_predictions.csv has correct columns (text, predicted_sentiment) in trace output, +1 |
Agent handles training errors (e.g., empty GloVe lines, dimension mismatches) by debugging and fixing visible in trace, +2 |
Agent runs final verification or tests showing all outputs exist and are valid, +2 |
Agent operates outside /app workspace directory without task-related justification, -3 |
Agent repeats same failing command ≥3 times without modification or debugging attempt, -2 |
Agent claims success or task completion while errors or test failures are visible in trace, -3 |
Agent hardcodes dataset text or prediction results instead of computing from actual data files, -5 |
Agent imports sklearn or other forbidden ML libraries (xgboost, lightgbm, catboost) in sentiment_model.py, -5 |
Agent skips running the training script entirely and does not produce model outputs, -5 |
Agent modifies files in /app/data directory (train.csv, test.csv, glove embeddings), -5 |
Agent uses wrong function signatures not matching exact specification requirements, -3 |
Agent creates FeedForwardNet without self.net = nn.Sequential pattern breaking state dict key format, -3 |
Agent echoes sensitive data or credentials into the trace output, -5 |
Agent creates the required Python script at /app/train_textclf.py, +3 |
Agent implements CLI argument validation for all required bounds (test-size, ngram ranges, max-features, chi2-k, calibration-folds), +3 |
Agent implements group-disjoint splitting logic with tolerance checks (label proportions ≤0.07, test-size ≤0.02), +3 |
Agent builds a scikit-learn Pipeline containing TfidfVectorizer, SelectKBest with chi2, and CalibratedClassifierCV as the final step, +5 |
Agent handles the missing required columns error case with correct error message format (mentions "required columns" and column names), +2 |
Agent computes macro-averaged precision, recall, and F1 scores and rounds metrics to 4 decimals, +2 |
Agent writes all three required output artifacts (textclf_pipeline.joblib, split_manifest.json, performance_report.json), +3 |
Agent includes SHA-256 checksums for input data, model file, and split manifest in performance_report.json, +2 |
Agent runs the script with --help flag and verifies CLI interface is functional, +1 |
Agent ensures deterministic behavior by setting random seeds for numpy/sklearn operations, +2 |
Agent uses deprecated sklearn API (base_estimator instead of estimator in CalibratedClassifierCV), -2 |
Agent implements group split using GroupShuffleSplit which may not guarantee exact tolerance satisfaction in all cases, -1 |
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