Python papermill.execute_notebook方法代碼示例

# 需要導入模塊: import papermill [as 別名]
# 或者: from papermill import execute_notebook [as 別名]
def test_text_classification_unified_information(notebooks, tmp):
    notebook_path = notebooks["tc_unified_information"]
    pm.execute_notebook(
        notebook_path,
        OUTPUT_NOTEBOOK,
        kernel_name=KERNEL_NAME,
        parameters=dict(
            DATA_FOLDER=tmp,
            BERT_CACHE_DIR=tmp,
            BATCH_SIZE=32,
            BATCH_SIZE_PRED=512,
            NUM_EPOCHS=1,
            TEST=True,
            QUICK_RUN=True,
        ),
    )
    result = sb.read_notebook(OUTPUT_NOTEBOOK).scraps.data_dict
    assert pytest.approx(result["accuracy"], 0.93, abs=ABS_TOL)
    assert pytest.approx(result["precision"], 0.93, abs=ABS_TOL)
    assert pytest.approx(result["recall"], 0.93, abs=ABS_TOL)
    assert pytest.approx(result["f1"], 0.93, abs=ABS_TOL) 

# 需要導入模塊: import papermill [as 別名]
# 或者: from papermill import execute_notebook [as 別名]
def test_text_classification_introspective_rationale(notebooks, tmp):
    notebook_path = notebooks["tc_introspective_rationale"]
    pm.execute_notebook(
        notebook_path,
        OUTPUT_NOTEBOOK,
        kernel_name=KERNEL_NAME,
        parameters=dict(
            DATA_FOLDER=tmp,
            CUDA=torch.cuda.is_available(),
            QUICK_RUN=False,
            MODEL_SAVE_DIR=tmp
        ),
    )
    result = sb.read_notebook(OUTPUT_NOTEBOOK).scraps.data_dict
    print(result)
    assert pytest.approx(result["accuracy"], 0.72, abs=ABS_TOL)
    assert pytest.approx(result["anti_accuracy"], 0.69, abs=ABS_TOL)
    assert pytest.approx(result["sparsity"], 0.17, abs=ABS_TOL) 

# 需要導入模塊: import papermill [as 別名]
# 或者: from papermill import execute_notebook [as 別名]
def test_notebooks_basic_translations_diff(
    isolated_filesystem, translated_notebook
):  # pragma: no cover
    """
    Test Notebooks in the tutorial translations folder if they have been
    modified in the current pull request. This test should not consider any
    notebooks locally. It should be used on Github Actions.
    """
    notebook = "/".join(translated_notebook.split("/")[-2:])
    notebook = f"translations/{notebook}"
    list_name = Path(f"examples/tutorials/{notebook}")
    tested_notebooks.append(str(list_name))
    res = pm.execute_notebook(
        notebook,
        "/dev/null",
        parameters={"epochs": 1, "n_test_batches": 5, "n_train_items": 64, "n_test_items": 64},
        timeout=300,
    )
    assert isinstance(res, nbformat.notebooknode.NotebookNode) 

# 需要導入模塊: import papermill [as 別名]
# 或者: from papermill import execute_notebook [as 別名]
def test_fl_sms(isolated_filesystem):  # pragma: no cover
    sys.path.append("advanced/federated_sms_spam_prediction/")
    import preprocess

    os.chdir("advanced/federated_sms_spam_prediction/")

    notebook = "Federated SMS Spam prediction.ipynb"
    p_name = Path("examples/tutorials/advanced/federated_sms_spam_prediction/")
    tested_notebooks.append(str(p_name / notebook))
    Path("data").mkdir(parents=True, exist_ok=True)
    url = "https://archive.ics.uci.edu/ml/machine-learning-databases/00228/smsspamcollection.zip"
    urllib.request.urlretrieve(url, "data.zip")
    with ZipFile("data.zip", "r") as zipObj:
        # Extract all the contents of the zip file in current directory
        zipObj.extractall()
    preprocess.main()
    res = pm.execute_notebook(notebook, "/dev/null", parameters={"epochs": 1}, timeout=300)
    assert isinstance(res, nbformat.notebooknode.NotebookNode) 

# 需要導入模塊: import papermill [as 別名]
# 或者: from papermill import execute_notebook [as 別名]
def assay_one_notebook(notebook_name, test_values):
    """Test a single notebook.

    This uses nbformat to append `nteract-scrapbook` commands to the
    specified notebook. The content of the commands and their expected
    values are stored in the `test_values` dictionary. The keys of this
    dictionary are strings to be used as scrapbook keys. They corresponding
    value is a `ScrapSpec` tuple. The `code` member of this tuple is
    the code (as a string) to be run to generate the scrapbook value. The
    `expected` member is a Python object which is checked for equality with
    the scrapbook value

    Makes certain assumptions about directory layout.
    """
    input_notebook = "notebooks/" + notebook_name + ".ipynb"
    processed_notebook = "./test/notebooks/" + notebook_name + ".processed.ipynb"
    output_notebook = "./test/notebooks/" + notebook_name + ".output.ipynb"

    append_scrapbook_commands(input_notebook, processed_notebook, test_values)
    pm.execute_notebook(processed_notebook, output_notebook)
    nb = sb.read_notebook(output_notebook)

    for k, v in test_values.items():
        assert nb.scraps[k].data == v.expected 

# 需要導入模塊: import papermill [as 別名]
# 或者: from papermill import execute_notebook [as 別名]
def test_no_raise(self):

        nbs = self.list_notebooks()

        here = os.path.dirname(__file__)
        out_dir = "{}/out".format(here)
        if not os.path.exists(out_dir):
            os.mkdir(out_dir)

        for nb_input in nbs:
            basename = os.path.basename(nb_input)

            nb_output = "{}/{}".format(out_dir, basename)

            try:
                pm.execute_notebook(nb_input, nb_output)
            except Exception as e:
                with open(nb_output) as f:
                    print(f.read())

                raise e

        self.assertEqual(1, 1) 

# 需要導入模塊: import papermill [as 別名]
# 或者: from papermill import execute_notebook [as 別名]
def test_unilm_abstractive_summarization(notebooks, tmp):
    notebook_path = notebooks["unilm_abstractive_summarization"]
    pm.execute_notebook(
        notebook_path,
        OUTPUT_NOTEBOOK,
        kernel_name=KERNEL_NAME,
        parameters=dict(
            QUICK_RUN=True,
            NUM_GPUS=torch.cuda.device_count(),
            TOP_N=100,
            WARMUP_STEPS=5,
            MAX_STEPS=50,
            GRADIENT_ACCUMULATION_STEPS=1,
            TEST_PER_GPU_BATCH_SIZE=2,
            BEAM_SIZE=3,
            MODEL_DIR=tmp,
            RESULT_DIR=tmp,
        ),
    )
    result = sb.read_notebook(OUTPUT_NOTEBOOK).scraps.data_dict
    assert pytest.approx(result["rouge_1_f_score"], 0.2, abs=ABS_TOL)
    assert pytest.approx(result["rouge_2_f_score"], 0.07, abs=ABS_TOL)
    assert pytest.approx(result["rouge_l_f_score"], 0.16, abs=ABS_TOL) 

# 需要導入模塊: import papermill [as 別名]
# 或者: from papermill import execute_notebook [as 別名]
def test_entailment_multinli_bert(notebooks, tmp):
    notebook_path = notebooks["entailment_multinli_transformers"]
    pm.execute_notebook(
        notebook_path,
        OUTPUT_NOTEBOOK,
        parameters={
            "MODEL_NAME": "bert-base-uncased",
            "TO_LOWER": True,
            "TRAIN_DATA_USED_FRACTION": 0.05,
            "DEV_DATA_USED_FRACTION": 0.05,
            "NUM_EPOCHS": 1,
            "CACHE_DIR": tmp
        },
        kernel_name=KERNEL_NAME,
    )
    result = sb.read_notebook(OUTPUT_NOTEBOOK).scraps.data_dict
    assert pytest.approx(result["matched_precision"], 0.76, abs=ABS_TOL)
    assert pytest.approx(result["matched_recall"], 0.76, abs=ABS_TOL)
    assert pytest.approx(result["matched_f1"], 0.76, abs=ABS_TOL)
    assert pytest.approx(result["mismatched_precision"], 0.76, abs=ABS_TOL)
    assert pytest.approx(result["mismatched_recall"], 0.76, abs=ABS_TOL)
    assert pytest.approx(result["mismatched_f1"], 0.76, abs=ABS_TOL) 

# 需要導入模塊: import papermill [as 別名]
# 或者: from papermill import execute_notebook [as 別名]
def test_entailment_xnli_bert_azureml(
    notebooks, subscription_id, resource_group, workspace_name, workspace_region, cluster_name
):
    notebook_path = notebooks["entailment_xnli_bert_azureml"]
    pm.execute_notebook(
        notebook_path,
        OUTPUT_NOTEBOOK,
        parameters={
            "DATA_PERCENT_USED": 0.0025,
            "subscription_id": subscription_id,
            "resource_group": resource_group,
            "workspace_name": workspace_name,
            "workspace_region": workspace_region,
            "cluster_name": cluster_name,
        },
        kernel_name=KERNEL_NAME,
    )

    with open("outputs/results.json", "r") as handle:
        result_dict = json.load(handle)
        assert result_dict["weighted avg"]["f1-score"] == pytest.approx(0.2, abs=ABS_TOL)

    if os.path.exists("outputs"):
        shutil.rmtree("outputs") 

# 需要導入模塊: import papermill [as 別名]
# 或者: from papermill import execute_notebook [as 別名]
def test_minilm_abstractive_summarization(notebooks, tmp):
    notebook_path = notebooks["minilm_abstractive_summarization"]
    pm.execute_notebook(
        notebook_path,
        OUTPUT_NOTEBOOK,
        kernel_name=KERNEL_NAME,
        parameters=dict(
            QUICK_RUN=True,
            NUM_GPUS=torch.cuda.device_count(),
            TOP_N=100,
            WARMUP_STEPS=5,
            MAX_STEPS=50,
            GRADIENT_ACCUMULATION_STEPS=1,
            TEST_PER_GPU_BATCH_SIZE=2,
            BEAM_SIZE=3,
            CLEANUP_RESULTS=True,
        ),
    )
    result = sb.read_notebook(OUTPUT_NOTEBOOK).scraps.data_dict
    assert pytest.approx(result["rouge_1_f_score"], 0.2, abs=ABS_TOL)
    assert pytest.approx(result["rouge_2_f_score"], 0.07, abs=ABS_TOL)
    assert pytest.approx(result["rouge_l_f_score"], 0.16, abs=ABS_TOL) 

# 需要導入模塊: import papermill [as 別名]
# 或者: from papermill import execute_notebook [as 別名]
def test_question_answering_squad_transformers(notebooks, tmp):
    notebook_path = notebooks["question_answering_squad_transformers"]
    pm.execute_notebook(
        notebook_path,
        OUTPUT_NOTEBOOK,
        parameters={
            "TRAIN_DATA_USED_PERCENT": 0.15,
            "DEV_DATA_USED_PERCENT": 0.15,
            "NUM_EPOCHS": 1,
            "MAX_SEQ_LENGTH": 384,
            "DOC_STRIDE": 128,
            "PER_GPU_BATCH_SIZE": 4,
            "MODEL_NAME": "distilbert-base-uncased",
            "DO_LOWER_CASE": True,
            "CACHE_DIR": tmp
        },
        kernel_name=KERNEL_NAME,
    )
    result = sb.read_notebook(OUTPUT_NOTEBOOK).scraps.data_dict
    assert pytest.approx(result["exact"], 0.55, abs=ABS_TOL)
    assert pytest.approx(result["f1"], 0.70, abs=ABS_TOL) 

# 需要導入模塊: import papermill [as 別名]
# 或者: from papermill import execute_notebook [as 別名]
def test_bidaf_deep_dive(
    notebooks, subscription_id, resource_group, workspace_name, workspace_region
):
    notebook_path = notebooks["bidaf_deep_dive"]
    pm.execute_notebook(
        notebook_path,
        OUTPUT_NOTEBOOK,
        parameters={
            "NUM_EPOCHS": 1,
            "config_path": None,
            "PROJECT_FOLDER": "examples/question_answering/bidaf-question-answering",
            "SQUAD_FOLDER": "examples/question_answering/squad",
            "LOGS_FOLDER": "examples/question_answering/",
            "BIDAF_CONFIG_PATH": "examples/question_answering/",
            "subscription_id": subscription_id,
            "resource_group": resource_group,
            "workspace_name": workspace_name,
            "workspace_region": workspace_region,
        },
    )
    result = sb.read_notebook(OUTPUT_NOTEBOOK).scraps.data_dict["validation_EM"]
    assert result == pytest.approx(0.5, abs=ABS_TOL) 

# 需要導入模塊: import papermill [as 別名]
# 或者: from papermill import execute_notebook [as 別名]
def test_bidaf_quickstart(
    notebooks, subscription_id, resource_group, workspace_name, workspace_region
):
    notebook_path = notebooks["bidaf_quickstart"]
    pm.execute_notebook(
        notebook_path,
        OUTPUT_NOTEBOOK,
        parameters={
            "config_path": None,
            "subscription_id": subscription_id,
            "resource_group": resource_group,
            "workspace_name": workspace_name,
            "workspace_region": workspace_region,
            "webservice_name": "aci-test-service",
        },
    )
    result = sb.read_notebook(OUTPUT_NOTEBOOK).scraps.data_dict["answer"]
    assert result == "Bi-Directional Attention Flow" 

# 需要導入模塊: import papermill [as 別名]
# 或者: from papermill import execute_notebook [as 別名]
def test_extractive_summarization_cnndm_transformers(notebooks, tmp):
    notebook_path = notebooks["extractive_summarization_cnndm_transformer"]
    pm.execute_notebook(
        notebook_path,
        OUTPUT_NOTEBOOK,
        kernel_name=KERNEL_NAME,
        parameters=dict(
            QUICK_RUN=True,
            TOP_N=100,
            CHUNK_SIZE=200,
            USE_PREPROCESSED_DATA=False,
            DATA_PATH=tmp,
            CACHE_DIR=tmp,
            BATCH_SIZE=3000,
            REPORT_EVERY=50,
            MAX_STEPS=100,
            WARMUP_STEPS=5e2,
            MODEL_NAME="distilbert-base-uncased",
        ),
    )
    result = sb.read_notebook(OUTPUT_NOTEBOOK).scraps.data_dict
    assert pytest.approx(result["rouge_2_f_score"], 0.1, abs=ABS_TOL) 

# 需要導入模塊: import papermill [as 別名]
# 或者: from papermill import execute_notebook [as 別名]
def test_abstractive_summarization_bertsumabs_cnndm(notebooks, tmp):
    notebook_path = notebooks["abstractive_summarization_bertsumabs_cnndm"]
    pm.execute_notebook(
        notebook_path,
        OUTPUT_NOTEBOOK,
        kernel_name=KERNEL_NAME,
        parameters=dict(
            QUICK_RUN=True,
            TOP_N=1000,
            MAX_POS=512,
            DATA_FOLDER=tmp,
            CACHE_DIR=tmp,
            BATCH_SIZE_PER_GPU=3,
            REPORT_EVERY=50,
            MAX_STEPS=100,
            MODEL_NAME="bert-base-uncased",
        ),
    )
    result = sb.read_notebook(OUTPUT_NOTEBOOK).scraps.data_dict
    assert pytest.approx(result["rouge_2_f_score"], 0.01, abs=ABS_TOL)