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[Snyk] Security upgrade requests from 2.31.0 to 2.32.0 #600

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fix: doc/requirements.txt to reduce vulnerabilities

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[Snyk] Security upgrade requests from 2.31.0 to 2.32.0 #600

fix: doc/requirements.txt to reduce vulnerabilities
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Azure Pipelines / bashtage.linearmodels succeeded May 23, 2024 in 25m 12s

Build #20240523.1 had test failures

1 errors / 0 warnings

Details

Tests

  • Failed: 4 (0.00%)
  • Passed: 162,305 (83.26%)
  • Other: 32,619 (16.73%)
  • Total: 194,928
Code coverage

  • 17217 of 17269 line covered (99.70%)

Annotations

Check failure on line 953 in Build log

See this annotation in the file changed.

@azure-pipelines azure-pipelines / bashtage.linearmodels

Build log #L953 

Bash exited with code '1'.

Check failure on line 1 in test_notebook[iv_advanced-examples]

See this annotation in the file changed.

@azure-pipelines azure-pipelines / bashtage.linearmodels

test_notebook[iv_advanced-examples] 

nbclient.exceptions.CellExecutionError: An error occurred while executing the following cell:
------------------
from statsmodels.api import OLS, add_constant

data["const"] = 1
controls = ["const"] + controls
------------------


#x1B[0;31m---------------------------------------------------------------------------#x1B[0m
#x1B[0;31mValueError#x1B[0m                                Traceback (most recent call last)
Cell #x1B[0;32mIn[6], line 1#x1B[0m
#x1B[0;32m----> 1#x1B[0m #x1B[38;5;28;01mfrom#x1B[39;00m #x1B[38;5;21;01mstatsmodels#x1B[39;00m#x1B[38;5;21;01m.#x1B[39;00m#x1B[38;5;21;01mapi#x1B[39;00m #x1B[38;5;28;01mimport#x1B[39;00m OLS, add_constant
#x1B[1;32m      3#x1B[0m data[#x1B[38;5;124m"#x1B[39m#x1B[38;5;124mconst#x1B[39m#x1B[38;5;124m"#x1B[39m] #x1B[38;5;241m=#x1B[39m #x1B[38;5;241m1#x1B[39m
#x1B[1;32m      4#x1B[0m controls #x1B[38;5;241m=#x1B[39m [#x1B[38;5;124m"#x1B[39m#x1B[38;5;124mconst#x1B[39m#x1B[38;5;124m"#x1B[39m] #x1B[38;5;241m+#x1B[39m controls

File #x1B[0;32m/opt/hostedtoolcache/Python/3.12.3/x64/lib/python3.12/site-packages/statsmodels/api.py:123#x1B[0m
#x1B[1;32m    112#x1B[0m #x1B[38;5;28;01mfrom#x1B[39;00m #x1B[38;5;21;01m.#x1B[39;00m#x1B[38;5;21;01mgenmod#x1B[39;00m #x1B[38;5;28;01mimport#x1B[39;00m api #x1B[38;5;28;01mas#x1B[39;00m genmod
#x1B[1;32m    113#x1B[0m #x1B[38;5;28;01mfrom#x1B[39;00m #x1B[38;5;21;01m.#x1B[39;00m#x1B[38;5;21;01mgenmod#x1B[39;00m#x1B[38;5;21;01m.#x1B[39;00m#x1B[38;5;21;01mapi#x1B[39;00m #x1B[38;5;28;01mimport#x1B[39;00m (
#x1B[1;32m    114#x1B[0m     GEE,
#x1B[1;32m    115#x1B[0m     GLM,
#x1B[0;32m   (...)#x1B[0m
#x1B[1;32m    121#x1B[0m     families,
#x1B[1;32m    122#x1B[0m )
#x1B[0;32m--> 123#x1B[0m #x1B[38;5;28;01mfrom#x1B[39;00m #x1B[38;5;21;01m.#x1B[39;00m#x1B[38;5;21;01mgraphics#x1B[39;00m #x1B[38;5;28;01mimport#x1B[39;00m api #x1B[38;5;28;01mas#x1B[39;00m graphics
#x1B[1;32m    124#x1B[0m #x1B[38;5;28;01mfrom#x1B[39;00m #x1B[38;5;21;01m.#x1B[39;00m#x1B[38;5;21;01mgraphics#x1B[39;00m#x1B[38;5;21;01m.#x1B[39;00m#x1B[38;5;21;01mgofplots#x1B[39;00m #x1B[38;5;28;01mimport#x1B[39;00m ProbPlot, qqline, qqplot, qqplot_2samples
#x1B[1;32m    125#x1B[0m #x1B[38;5;28;01mfrom#x1B[39;00m #x1B[38;5;21;01m.#x1B[39;00m#x1B[38;5;21;01mimputation#x1B[39;00m#x1B[38;5;21;01m.#x1B[39;00m#x1B[38;5;21;01mbayes_mi#x1B[39;00m #x1B[38;5;28;01mimport#x1B[39;00m MI, BayesGaussMI

File #x1B[0;32m/opt/hostedtoolcache/Python/3.12.3/x64/lib/python3.12/site-packages/statsmodels/graphics/api.py:9#x1B[0m
#x1B[1;32m      7#x1B[0m #x1B[38;5;28;01mfrom#x1B[39;00m #x1B[38;5;21;01m.#x1B[39;00m#x1B[38;5;21;01mgofplots#x1B[39;00m #x1B[38;5;28;01mimport#x1B[39;00m qqplot
#x1B[1;32m      8#x1B[0m #x1B[38;5;28;01mfrom#x1B[39;00m #x1B[38;5;21;01m.#x1B[39;00m#x1B[38;5;21;01mplottools#x1B[39;00m #x1B[38;5;28;01mimport#x1B[39;00m rainbow
#x1B[0;32m----> 9#x1B[0m #x1B[38;5;28;01mfrom#x1B[39;00m #x1B[38;5;21;01m.#x1B[39;00m#x1B[38;5;21;01mregressionplots#x1B[39;00m #x1B[38;5;28;01mimport#x1B[39;00m (
#x1B[1;32m     10#x1B[0m     abline_plot,
#x1B[1;32m     11#x1B[0m     influence_plot,
#x1B[1;32m     12#x1B[0m     plot_ccpr,
#x1B[1;32m     13#x1B[0m     plot_ccpr_grid,
#x1B[1;32m     14#x1B[0m     plot_fit,
#x1B[1;32m     15#x1B[0m     plot_leverage_resid2,
#x1B[1;32m     16#x1B[0m     plot_partregress,
#x1B[1;32m     17#x1B[0m     plot_partregress_grid,
#x1B[1;32m     18#x1B[0m     plot_regress_exog,
#x1B[1;32m     19#x1B[0m )
#x1B[1;32m     21#x1B[0m __all__ #x1B[38;5;241m=#x1B[39m [
#x1B[1;32m     22#x1B[0m     #x1B[38;5;124m"#x1B[39m#x1B[38;5;124mabline_plot#x1B[39m#x1B[38;5;124m"#x1B[39m,
#x1B[1;32m     23#x1B[0m     #x1B[38;5;124m"#x1B[39m#x1B[38;5;124mbeanplot#x1B[39m#x1B[38;5;124m"#x1B[39m,
#x1B[0;32m   (...)#x1B[0m
#x1B[1;32m     42#x1B[0m     #x1B[38;5;124m"#x1B[39m#x1B[38;5;124mviolinplot#x1B[39m#x1B[38;5;124m"#x1B[39m,
#x1B[1;32m     43#x1B[0m ]

File #x1B[0;32m/opt/hostedtoolcache/Python/3.12.3/x64/lib/python3.12/site-packages/statsmodels/graphics/regressionplots.py:23#x1B[0m
#x1B[1;32m     21#x1B[0m #
Raw output 
notebook = '/home/vsts/work/1/s/examples/iv_advanced-examples.ipynb'

    @pytest.mark.slow
    @pytest.mark.example
    @pytest.mark.skipif(SKIP, reason="Required packages not available")
    def test_notebook(notebook):
        nb_name = os.path.split(notebook)[-1]
        if MISSING_XARRAY and nb_name in NOTEBOOKS_USING_XARRAY:
            pytest.skip(f"xarray is required to test {notebook}")
    
        nb = nbformat.read(notebook, as_version=4)
        ep = ExecutePreprocessor(allow_errors=False, timeout=120, kernel_name=kernel_name)
>       ep.preprocess(nb, {"metadata": {"path": NOTEBOOK_DIR}})

linearmodels/tests/test_examples.py:68: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
/opt/hostedtoolcache/Python/3.12.3/x64/lib/python3.12/site-packages/nbconvert/preprocessors/execute.py:103: in preprocess
    self.preprocess_cell(cell, resources, index)
/opt/hostedtoolcache/Python/3.12.3/x64/lib/python3.12/site-packages/nbconvert/preprocessors/execute.py:124: in preprocess_cell
    cell = self.execute_cell(cell, index, store_history=True)
/opt/hostedtoolcache/Python/3.12.3/x64/lib/python3.12/site-packages/jupyter_core/utils/__init__.py:165: in wrapped
    return loop.run_until_complete(inner)
/opt/hostedtoolcache/Python/3.12.3/x64/lib/python3.12/asyncio/base_events.py:687: in run_until_complete
    return future.result()
/opt/hostedtoolcache/Python/3.12.3/x64/lib/python3.12/site-packages/nbclient/client.py:1062: in async_execute_cell
    await self._check_raise_for_error(cell, cell_index, exec_reply)
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self = <nbconvert.preprocessors.execute.ExecutePreprocessor object at 0x7fb1b9e87b90>
cell = {'cell_type': 'code', 'execution_count': 6, 'metadata': {'execution': {'iopub.status.busy': '2024-05-23T00:42:08.61322...t']}], 'source': 'from statsmodels.api import OLS, add_constant\n\ndata["const"] = 1\ncontrols = ["const"] + controls'}
cell_index = 12
exec_reply = {'buffers': [], 'content': {'ename': 'ValueError', 'engine_info': {'engine_id': -1, 'engine_uuid': '2ce97d22-7460-4690...e, 'engine': '2ce97d22-7460-4690-a29d-8fba8cc58b7a', 'started': '2024-05-23T00:42:08.613975Z', 'status': 'error'}, ...}

    async def _check_raise_for_error(
        self, cell: NotebookNode, cell_index: int, exec_reply: dict[str, t.Any] | None
    ) -> None:
        if exec_reply is None:
            return None
    
        exec_reply_content = exec_reply["content"]
        if exec_reply_content["status"] != "error":
            return None
    
        cell_allows_errors = (not self.force_raise_errors) and (
            self.allow_errors
            or exec_reply_content.get("ename") in self.allow_error_names
            or "raises-exception" in cell.metadata.get("tags", [])
        )
        await run_hook(
            self.on_cell_error, cell=cell, cell_index=cell_index, execute_reply=exec_reply
        )
        if not cell_allows_errors:
>           raise CellExecutionError.from_cell_and_msg(cell, exec_reply_content)
E           nbclient.exceptions.CellExecutionError: An error occurred while executing the following cell:
E           ------------------
E           from statsmodels.api import OLS, add_constant
E           
E           data["const"] = 1
E           controls = ["const"] + controls
E           ------------------
E           
E           
E           #x1B[0;31m---------------------------------------------------------------------------#x1B[0m
E           #x1B[0;31mValueError#x1B[0m                                Traceback (most recent call last)
E           Cell #x1B[0;32mIn[6], line 1#x1B[0m
E           #x1B[0;32m----> 1#x1B[0m #x1B[38;5;28;01mfrom#x1B[39;00m #x1B[38;5;21;01mstatsmodels#x1B[39;00m#x1B[38;5;21;01m.#x1B[39;00m#x1B[38;5;21;01mapi#x1B[39;00m #x1B[38;5;28;01mimport#x1B[39;00m OLS, add_constant
E           #x1B[1;32m      3#x1B[0m data[#x1B[38;5;124m"#x1B[39m#x1B[38;5;124mconst#x1B[39m#x1B[38;5;124m"#x1B[39m]

Check failure on line 1 in test_notebook[iv_basic-examples]

See this annotation in the file changed.

@azure-pipelines azure-pipelines / bashtage.linearmodels

test_notebook[iv_basic-examples] 

nbclient.exceptions.CellExecutionError: An error occurred while executing the following cell:
------------------
import numpy as np
from linearmodels.datasets import mroz
from statsmodels.api import add_constant

print(mroz.DESCR)
data = mroz.load()
data = data.dropna()
data = add_constant(data, has_constant="add")
------------------


#x1B[0;31m---------------------------------------------------------------------------#x1B[0m
#x1B[0;31mValueError#x1B[0m                                Traceback (most recent call last)
Cell #x1B[0;32mIn[1], line 3#x1B[0m
#x1B[1;32m      1#x1B[0m #x1B[38;5;28;01mimport#x1B[39;00m #x1B[38;5;21;01mnumpy#x1B[39;00m #x1B[38;5;28;01mas#x1B[39;00m #x1B[38;5;21;01mnp#x1B[39;00m
#x1B[1;32m      2#x1B[0m #x1B[38;5;28;01mfrom#x1B[39;00m #x1B[38;5;21;01mlinearmodels#x1B[39;00m#x1B[38;5;21;01m.#x1B[39;00m#x1B[38;5;21;01mdatasets#x1B[39;00m #x1B[38;5;28;01mimport#x1B[39;00m mroz
#x1B[0;32m----> 3#x1B[0m #x1B[38;5;28;01mfrom#x1B[39;00m #x1B[38;5;21;01mstatsmodels#x1B[39;00m#x1B[38;5;21;01m.#x1B[39;00m#x1B[38;5;21;01mapi#x1B[39;00m #x1B[38;5;28;01mimport#x1B[39;00m add_constant
#x1B[1;32m      5#x1B[0m #x1B[38;5;28mprint#x1B[39m(mroz#x1B[38;5;241m.#x1B[39mDESCR)
#x1B[1;32m      6#x1B[0m data #x1B[38;5;241m=#x1B[39m mroz#x1B[38;5;241m.#x1B[39mload()

File #x1B[0;32m/opt/hostedtoolcache/Python/3.12.3/x64/lib/python3.12/site-packages/statsmodels/api.py:123#x1B[0m
#x1B[1;32m    112#x1B[0m #x1B[38;5;28;01mfrom#x1B[39;00m #x1B[38;5;21;01m.#x1B[39;00m#x1B[38;5;21;01mgenmod#x1B[39;00m #x1B[38;5;28;01mimport#x1B[39;00m api #x1B[38;5;28;01mas#x1B[39;00m genmod
#x1B[1;32m    113#x1B[0m #x1B[38;5;28;01mfrom#x1B[39;00m #x1B[38;5;21;01m.#x1B[39;00m#x1B[38;5;21;01mgenmod#x1B[39;00m#x1B[38;5;21;01m.#x1B[39;00m#x1B[38;5;21;01mapi#x1B[39;00m #x1B[38;5;28;01mimport#x1B[39;00m (
#x1B[1;32m    114#x1B[0m     GEE,
#x1B[1;32m    115#x1B[0m     GLM,
#x1B[0;32m   (...)#x1B[0m
#x1B[1;32m    121#x1B[0m     families,
#x1B[1;32m    122#x1B[0m )
#x1B[0;32m--> 123#x1B[0m #x1B[38;5;28;01mfrom#x1B[39;00m #x1B[38;5;21;01m.#x1B[39;00m#x1B[38;5;21;01mgraphics#x1B[39;00m #x1B[38;5;28;01mimport#x1B[39;00m api #x1B[38;5;28;01mas#x1B[39;00m graphics
#x1B[1;32m    124#x1B[0m #x1B[38;5;28;01mfrom#x1B[39;00m #x1B[38;5;21;01m.#x1B[39;00m#x1B[38;5;21;01mgraphics#x1B[39;00m#x1B[38;5;21;01m.#x1B[39;00m#x1B[38;5;21;01mgofplots#x1B[39;00m #x1B[38;5;28;01mimport#x1B[39;00m ProbPlot, qqline, qqplot, qqplot_2samples
#x1B[1;32m    125#x1B[0m #x1B[38;5;28;01mfrom#x1B[39;00m #x1B[38;5;21;01m.#x1B[39;00m#x1B[38;5;21;01mimputation#x1B[39;00m#x1B[38;5;21;01m.#x1B[39;00m#x1B[38;5;21;01mbayes_mi#x1B[39;00m #x1B[38;5;28;01mimport#x1B[39;00m MI, BayesGaussMI

File #x1B[0;32m/opt/hostedtoolcache/Python/3.12.3/x64/lib/python3.12/site-packages/statsmodels/graphics/api.py:9#x1B[0m
#x1B[1;32m      7#x1B[0m #x1B[38;5;28;01mfrom#x1B[39;00m #x1B[38;5;21;01m.#x1B[39;00m#x1B[38;5;21;01mgofplots#x1B[39;00m #x1B[38;5;28;01mimport#x1B[39;00m qqplot
#x1B[1;32m      8#x1B[0m #x1B[38;5;28;01mfrom#x1B[39;00m #x1B[38;5;21;01m.#x1B[39;00m#x1B[38;5;21;01mplottools#x1B[39;00m #x1B[38;5;28;01mimport#x1B[39;00m rainbow
#x1B[0;32m----> 9#x1B[0m #x1B[38;5;28;01mfrom#x1B[39;00m #x1B[38;5;21;01m.#x1B[39;00m#x1B[38;5;21;01mregressionplots#x1B[39;00m #x1B[38;5;28;01mimport#x1B[39;00m (
#x1B[1;32m     10#x1B[0m     abline_plot,
#x1B[1;32m     11#x1B[0m     influence_plot,
#x1B[1;32m     12#x1B[0m     plot_ccpr,
#x1B[1;32m     13#x1B[0m     plot_ccpr_grid,
#x1B[1;32m     14#x1B[0m     plot_fit,
#x1B[1;32m     15#x1B[0m     plot_leverage_resid2,
#x1B[1;32m     16#x1B[0m     plot_partregress,
#x1B[1;32m     17#x1B[0m     plot_partregress_grid,
#x1B[1;32m     18#x1B[0m     plot_regress_exog,
#x1B[1;32m     19#x1B[0m )
#x1B[1;32m     21#x1B[0m __all__ #x1B[38;5;241m=#x1B[39m [
#x1B[1;32m     22#x1B[0m     #x1B[38;5;124m"#x1B[39m#x1B[38;5;124mabline_plot#x1B[39m#x1B[38;5;124m"#x1B[39m,
#x1B[1;32m     23#x1B[0m     #x1B[38;5;124m"#x1B[39m#x1B[38;5;124mbeanplot#x1B[39m#x1B[38;5;124m"#x1B[39m,
#x1B[0;32m   (.
Raw output 
notebook = '/home/vsts/work/1/s/examples/iv_basic-examples.ipynb'

    @pytest.mark.slow
    @pytest.mark.example
    @pytest.mark.skipif(SKIP, reason="Required packages not available")
    def test_notebook(notebook):
        nb_name = os.path.split(notebook)[-1]
        if MISSING_XARRAY and nb_name in NOTEBOOKS_USING_XARRAY:
            pytest.skip(f"xarray is required to test {notebook}")
    
        nb = nbformat.read(notebook, as_version=4)
        ep = ExecutePreprocessor(allow_errors=False, timeout=120, kernel_name=kernel_name)
>       ep.preprocess(nb, {"metadata": {"path": NOTEBOOK_DIR}})

linearmodels/tests/test_examples.py:68: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
/opt/hostedtoolcache/Python/3.12.3/x64/lib/python3.12/site-packages/nbconvert/preprocessors/execute.py:103: in preprocess
    self.preprocess_cell(cell, resources, index)
/opt/hostedtoolcache/Python/3.12.3/x64/lib/python3.12/site-packages/nbconvert/preprocessors/execute.py:124: in preprocess_cell
    cell = self.execute_cell(cell, index, store_history=True)
/opt/hostedtoolcache/Python/3.12.3/x64/lib/python3.12/site-packages/jupyter_core/utils/__init__.py:165: in wrapped
    return loop.run_until_complete(inner)
/opt/hostedtoolcache/Python/3.12.3/x64/lib/python3.12/asyncio/base_events.py:687: in run_until_complete
    return future.result()
/opt/hostedtoolcache/Python/3.12.3/x64/lib/python3.12/site-packages/nbclient/client.py:1062: in async_execute_cell
    await self._check_raise_for_error(cell, cell_index, exec_reply)
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self = <nbconvert.preprocessors.execute.ExecutePreprocessor object at 0x7fb1b9923080>
cell = {'cell_type': 'code', 'execution_count': 1, 'metadata': {'execution': {'iopub.status.busy': '2024-05-23T00:42:12.94646...onstant\n\nprint(mroz.DESCR)\ndata = mroz.load()\ndata = data.dropna()\ndata = add_constant(data, has_constant="add")'}
cell_index = 3
exec_reply = {'buffers': [], 'content': {'ename': 'ValueError', 'engine_info': {'engine_id': -1, 'engine_uuid': 'aa3aa8c8-a01c-43b9...e, 'engine': 'aa3aa8c8-a01c-43b9-bdb3-193c56cce434', 'started': '2024-05-23T00:42:12.947326Z', 'status': 'error'}, ...}

    async def _check_raise_for_error(
        self, cell: NotebookNode, cell_index: int, exec_reply: dict[str, t.Any] | None
    ) -> None:
        if exec_reply is None:
            return None
    
        exec_reply_content = exec_reply["content"]
        if exec_reply_content["status"] != "error":
            return None
    
        cell_allows_errors = (not self.force_raise_errors) and (
            self.allow_errors
            or exec_reply_content.get("ename") in self.allow_error_names
            or "raises-exception" in cell.metadata.get("tags", [])
        )
        await run_hook(
            self.on_cell_error, cell=cell, cell_index=cell_index, execute_reply=exec_reply
        )
        if not cell_allows_errors:
>           raise CellExecutionError.from_cell_and_msg(cell, exec_reply_content)
E           nbclient.exceptions.CellExecutionError: An error occurred while executing the following cell:
E           ------------------
E           import numpy as np
E           from linearmodels.datasets import mroz
E           from statsmodels.api import add_constant
E           
E           print(mroz.DESCR)
E           data = mroz.load()
E           data = data.dropna()
E           data = add_constant(data, has_constant="add")
E           ------------------
E           
E           
E           #x1B[0;31m---------------------------------------------------------------------------#x1B[0m
E           #x1B[0;31mValueError#x1B[0m                                Traceback (most recent call last)
E           Cell #x1B[0;32mIn[1], line 3#x1B[0m
E           #x1B[1;32m      1#x1B[0m #x1B[38;5;28;01mimport#x1B[39;00m #x1B[38;5;21;01mnumpy#x1B[39;00m #x1B[38;5;28;01mas#x1B[39;00m #x1B[38;5;21;01mnp#x1B[39;00m
E           #x1B[1;

Check failure on line 1 in test_notebook[panel_examples]

See this annotation in the file changed.

@azure-pipelines azure-pipelines / bashtage.linearmodels

test_notebook[panel_examples] 

nbclient.exceptions.CellExecutionError: An error occurred while executing the following cell:
------------------
import statsmodels.api as sm
from linearmodels.panel import PooledOLS

exog_vars = ["black", "hisp", "exper", "expersq", "married", "educ", "union", "year"]
exog = sm.add_constant(data[exog_vars])
mod = PooledOLS(data.lwage, exog)
pooled_res = mod.fit()
print(pooled_res)
------------------


#x1B[0;31m---------------------------------------------------------------------------#x1B[0m
#x1B[0;31mValueError#x1B[0m                                Traceback (most recent call last)
Cell #x1B[0;32mIn[2], line 1#x1B[0m
#x1B[0;32m----> 1#x1B[0m #x1B[38;5;28;01mimport#x1B[39;00m #x1B[38;5;21;01mstatsmodels#x1B[39;00m#x1B[38;5;21;01m.#x1B[39;00m#x1B[38;5;21;01mapi#x1B[39;00m #x1B[38;5;28;01mas#x1B[39;00m #x1B[38;5;21;01msm#x1B[39;00m
#x1B[1;32m      2#x1B[0m #x1B[38;5;28;01mfrom#x1B[39;00m #x1B[38;5;21;01mlinearmodels#x1B[39;00m#x1B[38;5;21;01m.#x1B[39;00m#x1B[38;5;21;01mpanel#x1B[39;00m #x1B[38;5;28;01mimport#x1B[39;00m PooledOLS
#x1B[1;32m      4#x1B[0m exog_vars #x1B[38;5;241m=#x1B[39m [#x1B[38;5;124m"#x1B[39m#x1B[38;5;124mblack#x1B[39m#x1B[38;5;124m"#x1B[39m, #x1B[38;5;124m"#x1B[39m#x1B[38;5;124mhisp#x1B[39m#x1B[38;5;124m"#x1B[39m, #x1B[38;5;124m"#x1B[39m#x1B[38;5;124mexper#x1B[39m#x1B[38;5;124m"#x1B[39m, #x1B[38;5;124m"#x1B[39m#x1B[38;5;124mexpersq#x1B[39m#x1B[38;5;124m"#x1B[39m, #x1B[38;5;124m"#x1B[39m#x1B[38;5;124mmarried#x1B[39m#x1B[38;5;124m"#x1B[39m, #x1B[38;5;124m"#x1B[39m#x1B[38;5;124meduc#x1B[39m#x1B[38;5;124m"#x1B[39m, #x1B[38;5;124m"#x1B[39m#x1B[38;5;124munion#x1B[39m#x1B[38;5;124m"#x1B[39m, #x1B[38;5;124m"#x1B[39m#x1B[38;5;124myear#x1B[39m#x1B[38;5;124m"#x1B[39m]

File #x1B[0;32m/opt/hostedtoolcache/Python/3.12.3/x64/lib/python3.12/site-packages/statsmodels/api.py:123#x1B[0m
#x1B[1;32m    112#x1B[0m #x1B[38;5;28;01mfrom#x1B[39;00m #x1B[38;5;21;01m.#x1B[39;00m#x1B[38;5;21;01mgenmod#x1B[39;00m #x1B[38;5;28;01mimport#x1B[39;00m api #x1B[38;5;28;01mas#x1B[39;00m genmod
#x1B[1;32m    113#x1B[0m #x1B[38;5;28;01mfrom#x1B[39;00m #x1B[38;5;21;01m.#x1B[39;00m#x1B[38;5;21;01mgenmod#x1B[39;00m#x1B[38;5;21;01m.#x1B[39;00m#x1B[38;5;21;01mapi#x1B[39;00m #x1B[38;5;28;01mimport#x1B[39;00m (
#x1B[1;32m    114#x1B[0m     GEE,
#x1B[1;32m    115#x1B[0m     GLM,
#x1B[0;32m   (...)#x1B[0m
#x1B[1;32m    121#x1B[0m     families,
#x1B[1;32m    122#x1B[0m )
#x1B[0;32m--> 123#x1B[0m #x1B[38;5;28;01mfrom#x1B[39;00m #x1B[38;5;21;01m.#x1B[39;00m#x1B[38;5;21;01mgraphics#x1B[39;00m #x1B[38;5;28;01mimport#x1B[39;00m api #x1B[38;5;28;01mas#x1B[39;00m graphics
#x1B[1;32m    124#x1B[0m #x1B[38;5;28;01mfrom#x1B[39;00m #x1B[38;5;21;01m.#x1B[39;00m#x1B[38;5;21;01mgraphics#x1B[39;00m#x1B[38;5;21;01m.#x1B[39;00m#x1B[38;5;21;01mgofplots#x1B[39;00m #x1B[38;5;28;01mimport#x1B[39;00m ProbPlot, qqline, qqplot, qqplot_2samples
#x1B[1;32m    125#x1B[0m #x1B[38;5;28;01mfrom#x1B[39;00m #x1B[38;5;21;01m.#x1B[39;00m#x1B[38;5;21;01mimputation#x1B[39;00m#x1B[38;5;21;01m.#x1B[39;00m#x1B[38;5;21;01mbayes_mi#x1B[39;00m #x1B[38;5;28;01mimport#x1B[39;00m MI, BayesGaussMI

File #x1B[0;32m/opt/hostedtoolcache/Python/3.12.3/x64/lib/python3.12/site-packages/statsmodels/graphics/api.py:9#x1B[0m
#x1B[1;32m      7#x1B[0m #x1B[38;5;28;01mfrom#x1B[39;00m #x1B[38;5;21;01m.#x1B[39;00m#x1B[38;5;21;01mgofplots#x1B[39;00m #x1B[38;5;28;01mimport#x1B[39;00m qqplot
#x1B[1;32m      8#x1B[0m #x1B[38;5;28;01mfrom#x1B[39;00m #x1B[38;5;21;01m.#x1B[39;00m#x1B[38;5;21;01mplottools#x1B[39;00m #x1B[38;5;28;01mimport#x1B[39;00m rainbow
#x1B[0;32m----> 9#x1B[0m #x1B[38;5;28;01mfrom#x1B[39;00m #x1B[38;5;21;01m.#x1B[39;00m#x1B[38;5;21;01mregressionplots#x1B[39;00m #x1B[38;5;28;01mimport#x1B[39;00m (
#x1B[1;32m     10#x1B[0m     abline_plot,
#x1B[1;32m     11#x1B[0m     influence_plot,
#x1B[1;32m     12#x1B[0m     plot_ccpr,
#x1B[1;32m     13#x1B[0m     plot_ccpr_grid,
#x1B[1;32m     14#x1B[0m     plot_fit,
#x1B[1;32m     15#x1B[0m     plot_leverage_resid2,
#x1B[1;32m     16#x1B[0m     plot_partregress
Raw output 
notebook = '/home/vsts/work/1/s/examples/panel_examples.ipynb'

    @pytest.mark.slow
    @pytest.mark.example
    @pytest.mark.skipif(SKIP, reason="Required packages not available")
    def test_notebook(notebook):
        nb_name = os.path.split(notebook)[-1]
        if MISSING_XARRAY and nb_name in NOTEBOOKS_USING_XARRAY:
            pytest.skip(f"xarray is required to test {notebook}")
    
        nb = nbformat.read(notebook, as_version=4)
        ep = ExecutePreprocessor(allow_errors=False, timeout=120, kernel_name=kernel_name)
>       ep.preprocess(nb, {"metadata": {"path": NOTEBOOK_DIR}})

linearmodels/tests/test_examples.py:68: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
/opt/hostedtoolcache/Python/3.12.3/x64/lib/python3.12/site-packages/nbconvert/preprocessors/execute.py:103: in preprocess
    self.preprocess_cell(cell, resources, index)
/opt/hostedtoolcache/Python/3.12.3/x64/lib/python3.12/site-packages/nbconvert/preprocessors/execute.py:124: in preprocess_cell
    cell = self.execute_cell(cell, index, store_history=True)
/opt/hostedtoolcache/Python/3.12.3/x64/lib/python3.12/site-packages/jupyter_core/utils/__init__.py:165: in wrapped
    return loop.run_until_complete(inner)
/opt/hostedtoolcache/Python/3.12.3/x64/lib/python3.12/asyncio/base_events.py:687: in run_until_complete
    return future.result()
/opt/hostedtoolcache/Python/3.12.3/x64/lib/python3.12/site-packages/nbclient/client.py:1062: in async_execute_cell
    await self._check_raise_for_error(cell, cell_index, exec_reply)
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self = <nbconvert.preprocessors.execute.ExecutePreprocessor object at 0x7fb1b9efa6c0>
cell = {'cell_type': 'code', 'execution_count': 2, 'metadata': {'execution': {'iopub.status.busy': '2024-05-23T00:42:31.25640...exog = sm.add_constant(data[exog_vars])\nmod = PooledOLS(data.lwage, exog)\npooled_res = mod.fit()\nprint(pooled_res)'}
cell_index = 4
exec_reply = {'buffers': [], 'content': {'ename': 'ValueError', 'engine_info': {'engine_id': -1, 'engine_uuid': 'be76d54a-d048-42d6...e, 'engine': 'be76d54a-d048-42d6-9e84-785cbf15c7a1', 'started': '2024-05-23T00:42:31.258327Z', 'status': 'error'}, ...}

    async def _check_raise_for_error(
        self, cell: NotebookNode, cell_index: int, exec_reply: dict[str, t.Any] | None
    ) -> None:
        if exec_reply is None:
            return None
    
        exec_reply_content = exec_reply["content"]
        if exec_reply_content["status"] != "error":
            return None
    
        cell_allows_errors = (not self.force_raise_errors) and (
            self.allow_errors
            or exec_reply_content.get("ename") in self.allow_error_names
            or "raises-exception" in cell.metadata.get("tags", [])
        )
        await run_hook(
            self.on_cell_error, cell=cell, cell_index=cell_index, execute_reply=exec_reply
        )
        if not cell_allows_errors:
>           raise CellExecutionError.from_cell_and_msg(cell, exec_reply_content)
E           nbclient.exceptions.CellExecutionError: An error occurred while executing the following cell:
E           ------------------
E           import statsmodels.api as sm
E           from linearmodels.panel import PooledOLS
E           
E           exog_vars = ["black", "hisp", "exper", "expersq", "married", "educ", "union", "year"]
E           exog = sm.add_constant(data[exog_vars])
E           mod = PooledOLS(data.lwage, exog)
E           pooled_res = mod.fit()
E           print(pooled_res)
E           ------------------
E           
E           
E           #x1B[0;31m---------------------------------------------------------------------------#x1B[0m
E           #x1B[0;31mValueError#x1B[0m                                Traceback (most recent call last)
E           Cell #x1B[0;32mIn[2], line 1#x1B[0m
E           #x1B[0;32m----> 1#x1B[0m #x1B[38;5;28;01mimport#x1B[39;00m #x1B[38;5;21;01mstatsmodels#x1B[39;00m#x1B[38;5

Check failure on line 1 in test_notebook[system_examples]

See this annotation in the file changed.

@azure-pipelines azure-pipelines / bashtage.linearmodels

test_notebook[system_examples] 

nbclient.exceptions.CellExecutionError: An error occurred while executing the following cell:
------------------
# Common libraries
%matplotlib inline
import numpy as np
import pandas as pd
import statsmodels.api as sm
------------------


#x1B[0;31m---------------------------------------------------------------------------#x1B[0m
#x1B[0;31mValueError#x1B[0m                                Traceback (most recent call last)
Cell #x1B[0;32mIn[1], line 5#x1B[0m
#x1B[1;32m      3#x1B[0m #x1B[38;5;28;01mimport#x1B[39;00m #x1B[38;5;21;01mnumpy#x1B[39;00m #x1B[38;5;28;01mas#x1B[39;00m #x1B[38;5;21;01mnp#x1B[39;00m
#x1B[1;32m      4#x1B[0m #x1B[38;5;28;01mimport#x1B[39;00m #x1B[38;5;21;01mpandas#x1B[39;00m #x1B[38;5;28;01mas#x1B[39;00m #x1B[38;5;21;01mpd#x1B[39;00m
#x1B[0;32m----> 5#x1B[0m #x1B[38;5;28;01mimport#x1B[39;00m #x1B[38;5;21;01mstatsmodels#x1B[39;00m#x1B[38;5;21;01m.#x1B[39;00m#x1B[38;5;21;01mapi#x1B[39;00m #x1B[38;5;28;01mas#x1B[39;00m #x1B[38;5;21;01msm#x1B[39;00m

File #x1B[0;32m/opt/hostedtoolcache/Python/3.12.3/x64/lib/python3.12/site-packages/statsmodels/api.py:123#x1B[0m
#x1B[1;32m    112#x1B[0m #x1B[38;5;28;01mfrom#x1B[39;00m #x1B[38;5;21;01m.#x1B[39;00m#x1B[38;5;21;01mgenmod#x1B[39;00m #x1B[38;5;28;01mimport#x1B[39;00m api #x1B[38;5;28;01mas#x1B[39;00m genmod
#x1B[1;32m    113#x1B[0m #x1B[38;5;28;01mfrom#x1B[39;00m #x1B[38;5;21;01m.#x1B[39;00m#x1B[38;5;21;01mgenmod#x1B[39;00m#x1B[38;5;21;01m.#x1B[39;00m#x1B[38;5;21;01mapi#x1B[39;00m #x1B[38;5;28;01mimport#x1B[39;00m (
#x1B[1;32m    114#x1B[0m     GEE,
#x1B[1;32m    115#x1B[0m     GLM,
#x1B[0;32m   (...)#x1B[0m
#x1B[1;32m    121#x1B[0m     families,
#x1B[1;32m    122#x1B[0m )
#x1B[0;32m--> 123#x1B[0m #x1B[38;5;28;01mfrom#x1B[39;00m #x1B[38;5;21;01m.#x1B[39;00m#x1B[38;5;21;01mgraphics#x1B[39;00m #x1B[38;5;28;01mimport#x1B[39;00m api #x1B[38;5;28;01mas#x1B[39;00m graphics
#x1B[1;32m    124#x1B[0m #x1B[38;5;28;01mfrom#x1B[39;00m #x1B[38;5;21;01m.#x1B[39;00m#x1B[38;5;21;01mgraphics#x1B[39;00m#x1B[38;5;21;01m.#x1B[39;00m#x1B[38;5;21;01mgofplots#x1B[39;00m #x1B[38;5;28;01mimport#x1B[39;00m ProbPlot, qqline, qqplot, qqplot_2samples
#x1B[1;32m    125#x1B[0m #x1B[38;5;28;01mfrom#x1B[39;00m #x1B[38;5;21;01m.#x1B[39;00m#x1B[38;5;21;01mimputation#x1B[39;00m#x1B[38;5;21;01m.#x1B[39;00m#x1B[38;5;21;01mbayes_mi#x1B[39;00m #x1B[38;5;28;01mimport#x1B[39;00m MI, BayesGaussMI

File #x1B[0;32m/opt/hostedtoolcache/Python/3.12.3/x64/lib/python3.12/site-packages/statsmodels/graphics/api.py:9#x1B[0m
#x1B[1;32m      7#x1B[0m #x1B[38;5;28;01mfrom#x1B[39;00m #x1B[38;5;21;01m.#x1B[39;00m#x1B[38;5;21;01mgofplots#x1B[39;00m #x1B[38;5;28;01mimport#x1B[39;00m qqplot
#x1B[1;32m      8#x1B[0m #x1B[38;5;28;01mfrom#x1B[39;00m #x1B[38;5;21;01m.#x1B[39;00m#x1B[38;5;21;01mplottools#x1B[39;00m #x1B[38;5;28;01mimport#x1B[39;00m rainbow
#x1B[0;32m----> 9#x1B[0m #x1B[38;5;28;01mfrom#x1B[39;00m #x1B[38;5;21;01m.#x1B[39;00m#x1B[38;5;21;01mregressionplots#x1B[39;00m #x1B[38;5;28;01mimport#x1B[39;00m (
#x1B[1;32m     10#x1B[0m     abline_plot,
#x1B[1;32m     11#x1B[0m     influence_plot,
#x1B[1;32m     12#x1B[0m     plot_ccpr,
#x1B[1;32m     13#x1B[0m     plot_ccpr_grid,
#x1B[1;32m     14#x1B[0m     plot_fit,
#x1B[1;32m     15#x1B[0m     plot_leverage_resid2,
#x1B[1;32m     16#x1B[0m     plot_partregress,
#x1B[1;32m     17#x1B[0m     plot_partregress_grid,
#x1B[1;32m     18#x1B[0m     plot_regress_exog,
#x1B[1;32m     19#x1B[0m )
#x1B[1;32m     21#x1B[0m __all__ #x1B[38;5;241m=#x1B[39m [
#x1B[1;32m     22#x1B[0m     #x1B[38;5;124m"#x1B[39m#x1B[38;5;124mabline_plot#x1B[39m#x1B[38;5;124m"#x1B[39m,
#x1B[1;32m     23#x1B[0m     #x1B[38;5;124m"#x1B[39m#x1B[38;5;124mbeanplot#x1B[39m#x1B[38;5;124m"#x1B[39m,
#x1B[0;32m   (...)#x1B[0m
#x1B[1;32m     42#x1B[0m     #x1B[38;5;124m"#x1B[39m#x1B[38;5;124mviolinplot#x1B[39m#x1B[38;5;124m"#x1B[39m,
#x1B[1;32m     43#x1B[0m ]

File #x1B[0;32m/opt/hostedtoolcache/Python/3.12.3/x64/lib/python3.12/site-packages/statsmodels/graphics/regressionplots.py:23#x1B[0m
#x1B[1;32m     21#x1B[
Raw output 
notebook = '/home/vsts/work/1/s/examples/system_examples.ipynb'

    @pytest.mark.slow
    @pytest.mark.example
    @pytest.mark.skipif(SKIP, reason="Required packages not available")
    def test_notebook(notebook):
        nb_name = os.path.split(notebook)[-1]
        if MISSING_XARRAY and nb_name in NOTEBOOKS_USING_XARRAY:
            pytest.skip(f"xarray is required to test {notebook}")
    
        nb = nbformat.read(notebook, as_version=4)
        ep = ExecutePreprocessor(allow_errors=False, timeout=120, kernel_name=kernel_name)
>       ep.preprocess(nb, {"metadata": {"path": NOTEBOOK_DIR}})

linearmodels/tests/test_examples.py:68: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
/opt/hostedtoolcache/Python/3.12.3/x64/lib/python3.12/site-packages/nbconvert/preprocessors/execute.py:103: in preprocess
    self.preprocess_cell(cell, resources, index)
/opt/hostedtoolcache/Python/3.12.3/x64/lib/python3.12/site-packages/nbconvert/preprocessors/execute.py:124: in preprocess_cell
    cell = self.execute_cell(cell, index, store_history=True)
/opt/hostedtoolcache/Python/3.12.3/x64/lib/python3.12/site-packages/jupyter_core/utils/__init__.py:165: in wrapped
    return loop.run_until_complete(inner)
/opt/hostedtoolcache/Python/3.12.3/x64/lib/python3.12/asyncio/base_events.py:687: in run_until_complete
    return future.result()
/opt/hostedtoolcache/Python/3.12.3/x64/lib/python3.12/site-packages/nbclient/client.py:1062: in async_execute_cell
    await self._check_raise_for_error(cell, cell_index, exec_reply)
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self = <nbconvert.preprocessors.execute.ExecutePreprocessor object at 0x7fb1b9923ad0>
cell = {'cell_type': 'code', 'execution_count': 1, 'metadata': {'execution': {'iopub.status.busy': '2024-05-23T00:42:40.42557...urce': '# Common libraries\n%matplotlib inline\nimport numpy as np\nimport pandas as pd\nimport statsmodels.api as sm'}
cell_index = 1
exec_reply = {'buffers': [], 'content': {'ename': 'ValueError', 'engine_info': {'engine_id': -1, 'engine_uuid': '78df8f61-cffd-421f...e, 'engine': '78df8f61-cffd-421f-a383-67851f7c0beb', 'started': '2024-05-23T00:42:40.426287Z', 'status': 'error'}, ...}

    async def _check_raise_for_error(
        self, cell: NotebookNode, cell_index: int, exec_reply: dict[str, t.Any] | None
    ) -> None:
        if exec_reply is None:
            return None
    
        exec_reply_content = exec_reply["content"]
        if exec_reply_content["status"] != "error":
            return None
    
        cell_allows_errors = (not self.force_raise_errors) and (
            self.allow_errors
            or exec_reply_content.get("ename") in self.allow_error_names
            or "raises-exception" in cell.metadata.get("tags", [])
        )
        await run_hook(
            self.on_cell_error, cell=cell, cell_index=cell_index, execute_reply=exec_reply
        )
        if not cell_allows_errors:
>           raise CellExecutionError.from_cell_and_msg(cell, exec_reply_content)
E           nbclient.exceptions.CellExecutionError: An error occurred while executing the following cell:
E           ------------------
E           # Common libraries
E           %matplotlib inline
E           import numpy as np
E           import pandas as pd
E           import statsmodels.api as sm
E           ------------------
E           
E           
E           #x1B[0;31m---------------------------------------------------------------------------#x1B[0m
E           #x1B[0;31mValueError#x1B[0m                                Traceback (most recent call last)
E           Cell #x1B[0;32mIn[1], line 5#x1B[0m
E           #x1B[1;32m      3#x1B[0m #x1B[38;5;28;01mimport#x1B[39;00m #x1B[38;5;21;01mnumpy#x1B[39;00m #x1B[38;5;28;01mas#x1B[39;00m #x1B[38;5;21;01mnp#x1B[39;00m
E           #x1B[1;32m      4#x1B[0m #x1B[38;5;28;01mimport#x1B[39;00m #x1B[38;5;21;01mpandas#x1B[39;00m #x1B[38;5;28;01mas#x1B[39;00m #x1B[38;5;21;01mpd#x
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