(task_type)
| 2374 | |
| 2375 | |
| 2376 | def test_fit_data(task_type): |
| 2377 | pool = Pool(CLOUDNESS_TRAIN_FILE, column_description=CLOUDNESS_CD_FILE) |
| 2378 | eval_pool = Pool(CLOUDNESS_TEST_FILE, column_description=CLOUDNESS_CD_FILE) |
| 2379 | base_model = CatBoostClassifier(iterations=10, learning_rate=0.05, loss_function="MultiClass", task_type=task_type, gpu_ram_part=TEST_GPU_RAM_PART, devices='0') |
| 2380 | base_model.fit(pool) |
| 2381 | baseline = np.array(base_model.predict(pool, prediction_type='RawFormulaVal')) |
| 2382 | eval_baseline = np.array(base_model.predict(eval_pool, prediction_type='RawFormulaVal')) |
| 2383 | eval_pool.set_baseline(eval_baseline) |
| 2384 | model = CatBoostClassifier(iterations=90, learning_rate=0.05, loss_function="MultiClass") |
| 2385 | data = get_features_data_from_file( |
| 2386 | CLOUDNESS_TRAIN_FILE, |
| 2387 | drop_columns=[0], |
| 2388 | cat_feature_indices=pool.get_cat_feature_indices() |
| 2389 | ) |
| 2390 | model.fit(data, pool.get_label(), sample_weight=np.arange(1, pool.num_row() + 1), baseline=baseline, use_best_model=True, eval_set=eval_pool) |
| 2391 | pred = model.predict_proba(eval_pool) |
| 2392 | preds_path = test_output_path(PREDS_PATH) |
| 2393 | np.save(preds_path, np.array(pred)) |
| 2394 | return local_canonical_file(preds_path, diff_tool=get_limited_precision_numpy_diff_tool()) |
| 2395 | |
| 2396 | |
| 2397 | def test_fit_predict_baseline(task_type): |
nothing calls this directly
no test coverage detected