Upload folder using huggingface_hub (part 3)

full_22model/run.log

@@ -0,0 +1,266 @@
+[NbConvertApp] Converting notebook run_full_21model.ipynb to notebook
+[NbConvertApp] ERROR | Notebook JSON is invalid: Additional properties are not allowed ('execution_count', 'outputs' were unexpected)
+
+Failed validating 'additionalProperties' in markdown_cell:
+
+On instance['cells'][0]:
+{'cell_type': 'markdown',
+ 'execution_count': None,
+ 'id': 'p0c000',
+ 'metadata': {},
+ 'outputs': ['...0 outputs...'],
+ 'source': '# Structural aliasing in Chronos-Bolt, Bayesian analysis\n'
+           '\n'
+           '**PATC...'}
+C:\Users\feder\AppData\Local\Programs\Python\Python312\Lib\site-packages\zmq\_future.py:718: RuntimeWarning: Proactor event loop does not implement add_reader family of methods required for zmq. Registering an additional selector thread for add_reader support via tornado. Use `asyncio.set_event_loop_policy(WindowsSelectorEventLoopPolicy())` to avoid this warning.
+  self._get_loop()
+[IPKernelApp] WARNING | Kernel is running over TCP without encryption. All communication (including code and outputs) is sent in plain text and is susceptible to eavesdropping. Use IPC transport or launch with kernel manager-provisioned CurveZMQ keys to enable transport encryption.
+Traceback (most recent call last):
+  File "<frozen runpy>", line 198, in _run_module_as_main
+  File "<frozen runpy>", line 88, in _run_code
+  File "C:\Users\feder\AppData\Local\Programs\Python\Python312\Scripts\jupyter-nbconvert.EXE\__main__.py", line 7, in <module>
+  File "C:\Users\feder\AppData\Local\Programs\Python\Python312\Lib\site-packages\jupyter_core\application.py", line 284, in launch_instance
+    super().launch_instance(argv=argv, **kwargs)
+  File "C:\Users\feder\AppData\Local\Programs\Python\Python312\Lib\site-packages\traitlets\config\application.py", line 1080, in launch_instance
+    app.start()
+  File "C:\Users\feder\AppData\Local\Programs\Python\Python312\Lib\site-packages\nbconvert\nbconvertapp.py", line 420, in start
+    self.convert_notebooks()
+  File "C:\Users\feder\AppData\Local\Programs\Python\Python312\Lib\site-packages\nbconvert\nbconvertapp.py", line 597, in convert_notebooks
+    self.convert_single_notebook(notebook_filename)
+  File "C:\Users\feder\AppData\Local\Programs\Python\Python312\Lib\site-packages\nbconvert\nbconvertapp.py", line 563, in convert_single_notebook
+    output, resources = self.export_single_notebook(
+                        ^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+  File "C:\Users\feder\AppData\Local\Programs\Python\Python312\Lib\site-packages\nbconvert\nbconvertapp.py", line 487, in export_single_notebook
+    output, resources = self.exporter.from_filename(
+                        ^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+  File "C:\Users\feder\AppData\Local\Programs\Python\Python312\Lib\site-packages\nbconvert\exporters\exporter.py", line 201, in from_filename
+    return self.from_file(f, resources=resources, **kw)
+           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+  File "C:\Users\feder\AppData\Local\Programs\Python\Python312\Lib\site-packages\nbconvert\exporters\exporter.py", line 220, in from_file
+    return self.from_notebook_node(
+           ^^^^^^^^^^^^^^^^^^^^^^^^
+  File "C:\Users\feder\AppData\Local\Programs\Python\Python312\Lib\site-packages\nbconvert\exporters\notebook.py", line 36, in from_notebook_node
+    nb_copy, resources = super().from_notebook_node(nb, resources, **kw)
+                         ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+  File "C:\Users\feder\AppData\Local\Programs\Python\Python312\Lib\site-packages\nbconvert\exporters\exporter.py", line 154, in from_notebook_node
+    nb_copy, resources = self._preprocess(nb_copy, resources)
+                         ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+  File "C:\Users\feder\AppData\Local\Programs\Python\Python312\Lib\site-packages\nbconvert\exporters\exporter.py", line 353, in _preprocess
+    nbc, resc = preprocessor(nbc, resc)
+                ^^^^^^^^^^^^^^^^^^^^^^^
+  File "C:\Users\feder\AppData\Local\Programs\Python\Python312\Lib\site-packages\nbconvert\preprocessors\base.py", line 48, in __call__
+    return self.preprocess(nb, resources)
+           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+  File "C:\Users\feder\AppData\Local\Programs\Python\Python312\Lib\site-packages\nbconvert\preprocessors\execute.py", line 103, in preprocess
+    self.preprocess_cell(cell, resources, index)
+  File "C:\Users\feder\AppData\Local\Programs\Python\Python312\Lib\site-packages\nbconvert\preprocessors\execute.py", line 124, in preprocess_cell
+    cell = self.execute_cell(cell, index, store_history=True)
+           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+  File "C:\Users\feder\AppData\Local\Programs\Python\Python312\Lib\site-packages\jupyter_core\utils\__init__.py", line 165, in wrapped
+    return loop.run_until_complete(inner)
+           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+  File "C:\Users\feder\AppData\Local\Programs\Python\Python312\Lib\asyncio\base_events.py", line 691, in run_until_complete
+    return future.result()
+           ^^^^^^^^^^^^^^^
+  File "C:\Users\feder\AppData\Local\Programs\Python\Python312\Lib\site-packages\nbclient\client.py", line 1062, in async_execute_cell
+    await self._check_raise_for_error(cell, cell_index, exec_reply)
+  File "C:\Users\feder\AppData\Local\Programs\Python\Python312\Lib\site-packages\nbclient\client.py", line 918, in _check_raise_for_error
+    raise CellExecutionError.from_cell_and_msg(cell, exec_reply_content)
+nbclient.exceptions.CellExecutionError: An error occurred while executing the following cell:
+------------------
+idata_A = cached_fit("04_A.nc", lambda: sample(model_A_contrast(live), "A"))
+
+print("\nModel A, H1 behavioural")
+rowsA = [report(idata_A, "beta_bar", label="beta_bar (log ratio)"),
+         report(idata_A, "beta_bar", np.exp, label="recovery ratio exp(beta_bar)"),
+         report(idata_A, "delta_O", label="delta_O (overlap slope, M1)"),
+         report(idata_A, "delta_P", label="delta_P (log patch-size slope)")]
+pA_att = prob(idata_A, "beta_bar", lambda x: x < ATTENUATION_20)
+pA_rope = prob(idata_A, "beta_bar", lambda x: np.abs(x) < ROPE_LOG)
+pA_neg = prob(idata_A, "beta_bar", lambda x: x < 0)
+print(f"\n  P(at least 20% attenuation | D) = {pA_att:.3f}   (prior: "
+      f"{prior_summary['p_attenuation20_prior']:.3f})")
+print(f"  P(practically no effect  | D)   = {pA_rope:.3f}   (ROPE |beta| < log 1.1)")
+print(f"  P(beta_bar < 0 | D)             = {pA_neg:.3f}   (any attenuation at all)")
+
+pA_mit = prob(idata_A, "delta_O", lambda x: x < 0)
+print(f"  P(delta_O < 0 | D)              = {pA_mit:.3f}   (M1: overlap reduces the deficit)")
+
+display(az.summary(idata_A.posterior, var_names=["beta_bar", "delta_O", "delta_P", "tau", "sigma",
+                                       "sigma_harm", "sigma_bg"], ci_prob=0.95).round(3))
+
+# M1, second half: which description of the configuration level does the data prefer?
+# deliverable2.tex, Mitigation: "the overlap ratio, the absolute stride, or the patch size".
+# idata_A IS the config_level="both" fit (it is the default), so it is reused rather than
+# sampled a second time: on the full design that is one avoidable fit of the largest model.
+fitsM1 = {"overlap + patch size": idata_A}
+fitsM1.update({lbl: cached_fit(f"04_A_{lvl}.nc",
+                               lambda l=lvl, n=lbl: sample(model_A_contrast(live, config_level=l), n))
+               for lvl, lbl in (("overlap", "overlap only"), ("patch", "patch size only"),
+                                ("none", "neither"))})
+cmpM1 = az.compare(fitsM1)
+display(cmpM1)
+
+# plot_forest: ArviZ 1.x changed the API; draw manually
+post_beta = idata_A.posterior["beta"].values.reshape(-1, idata_A.posterior.sizes["config"])
+cfg_names = list(idata_A.posterior.coords["config"].values)
+fig, ax = plt.subplots(figsize=(8, 3.4))
+for j, name in enumerate(cfg_names):
+    lo, med, hi = np.quantile(post_beta[:, j], [0.025, 0.5, 0.975])
+    ax.plot([lo, hi], [j, j], color="steelblue", lw=2)
+    ax.plot(med, j, "o", color="steelblue", ms=5)
+ax.axvline(0, color="k", lw=.9)
+ax.axvline(ATTENUATION_20, color="crimson", ls="--", lw=1)
+ax.set_yticks(range(len(cfg_names))); ax.set_yticklabels(cfg_names, fontsize=8)
+ax.set_title("Model A: per-configuration phase-lock effect $\\beta_c$\n"
+             "(left of the dashed line = at least 20% attenuation)")
+plt.tight_layout(); plt.savefig(FIG_DIR / "P4_A_forest.png", dpi=140, bbox_inches="tight"); plt.show()
+
+------------------
+
+----- stderr -----
+NUTS[nutpie]: [beta_bar, delta_O, delta_P, tau, z_cfg, sigma_harm, z_harm, sigma_bg, z_bg, sigma]
+----- stdout -----
+  checkpoint -> 04_A.nc
+
+Model A, H1 behavioural
+  beta_bar (log ratio)     median +0.0185   95% CrI [-0.4954, +0.5329]
+  recovery ratio exp(beta_bar) median +1.0187   95% CrI [+0.6094, +1.7039]
+  delta_O (overlap slope, M1) median +0.3791   95% CrI [+0.0091, +0.7166]
+  delta_P (log patch-size slope) median -0.7530   95% CrI [-1.1491, -0.2461]
+
+  P(at least 20% attenuation | D) = 0.212   (prior: 0.336)
+  P(practically no effect  | D)   = 0.244   (ROPE |beta| < log 1.1)
+  P(beta_bar < 0 | D)             = 0.478   (any attenuation at all)
+  P(delta_O < 0 | D)              = 0.023   (M1: overlap reduces the deficit)
+----- stderr -----
+NUTS[nutpie]: [beta_bar, delta_O, tau, z_cfg, sigma_harm, z_harm, sigma_bg, z_bg, sigma]
+----- stdout -----
+  checkpoint -> 04_A_overlap.nc
+----- stderr -----
+NUTS[nutpie]: [beta_bar, delta_P, tau, z_cfg, sigma_harm, z_harm, sigma_bg, z_bg, sigma]
+----- stdout -----
+  checkpoint -> 04_A_patch.nc
+----- stderr -----
+NUTS[nutpie]: [beta_bar, tau, z_cfg, sigma_harm, z_harm, sigma_bg, z_bg, sigma]
+----- stdout -----
+  checkpoint -> 04_A_none.nc
+------------------
+
+---------------------------------------------------------------------------
+MemoryError                               Traceback (most recent call last)
+File ~\AppData\Local\Programs\Python\Python312\Lib\site-packages\arviz_stats\loo\compare.py:636, in _calculate_ics(compare_dict, var_name)
+    635 try:
+--> 636     new_compare_dict[name] = loo(
+    637         dataset,
+    638         pointwise=True,
+    639         var_name=var_name,
+    640     )
+    641 except Exception as e:
+
+File ~\AppData\Local\Programs\Python\Python312\Lib\site-packages\arviz_stats\loo\loo.py:227, in loo(data, pointwise, var_name, reff, log_lik_fn, log_weights, pareto_k, log_jacobian, mixture, moment_match, model)
+    226 if log_weights is None and pareto_k is None:
+--> 227     log_weights, pareto_k = loo_inputs.log_likelihood.azstats.psislw(
+    228         r_eff=reff, dim=loo_inputs.sample_dims
+    229     )
+    231 if mixture:
+
+File ~\AppData\Local\Programs\Python\Python312\Lib\site-packages\arviz_stats\accessors.py:170, in _BaseAccessor.psislw(self, dim, **kwargs)
+    169 """Pareto smoothed importance sampling."""
+--> 170 return self._apply("psislw", dim=dim, **kwargs)
+
+File ~\AppData\Local\Programs\Python\Python312\Lib\site-packages\arviz_stats\accessors.py:398, in AzStatsDaAccessor._apply(self, func, **kwargs)
+    397     func = get_function(func)
+--> 398 return func(self._obj, **kwargs)
+
+File ~\AppData\Local\Programs\Python\Python312\Lib\site-packages\arviz_stats\base\dataarray.py:546, in BaseDataArray.psislw(self, da, r_eff, dim)
+    545 dims = validate_dims(dim)
+--> 546 return apply_ufunc(
+    547     self.array_class.psislw,
+    548     da,
+    549     r_eff,
+    550     input_core_dims=[dims, []],
+    551     output_core_dims=[dims, []],
+    552     kwargs={"axis": np.arange(-len(dims), 0, 1)},
+    553 )
+
+File ~\AppData\Local\Programs\Python\Python312\Lib\site-packages\xarray\computation\apply_ufunc.py:1267, in apply_ufunc(func, input_core_dims, output_core_dims, exclude_dims, vectorize, join, dataset_join, dataset_fill_value, keep_attrs, kwargs, dask, output_dtypes, output_sizes, meta, dask_gufunc_kwargs, on_missing_core_dim, *args)
+   1266 elif any(isinstance(a, DataArray) for a in args):
+-> 1267     return apply_dataarray_vfunc(
+   1268         variables_vfunc,
+   1269         *args,
+   1270         signature=signature,
+   1271         join=join,
+   1272         exclude_dims=exclude_dims,
+   1273         keep_attrs=keep_attrs,
+   1274     )
+   1275 # feed Variables directly through apply_variable_ufunc
+
+File ~\AppData\Local\Programs\Python\Python312\Lib\site-packages\xarray\computation\apply_ufunc.py:312, in apply_dataarray_vfunc(func, signature, join, exclude_dims, keep_attrs, *args)
+    311 data_vars = [getattr(a, "variable", a) for a in args]
+--> 312 result_var = func(*data_vars)
+    314 out: tuple[DataArray, ...] | DataArray
+
+File ~\AppData\Local\Programs\Python\Python312\Lib\site-packages\xarray\computation\apply_ufunc.py:820, in apply_variable_ufunc(func, signature, exclude_dims, dask, output_dtypes, vectorize, keep_attrs, dask_gufunc_kwargs, *args)
+    816     func = _vectorize(
+    817         func, signature, output_dtypes=output_dtypes, exclude_dims=exclude_dims
+    818     )
+--> 820 result_data = func(*input_data)
+    822 if signature.num_outputs == 1:
+
+File ~\AppData\Local\Programs\Python\Python312\Lib\site-packages\arviz_stats\base\array.py:306, in BaseArray.psislw(self, ary, r_eff, axis)
+    299 psl_ufunc = make_ufunc(
+    300     self._psislw,
+    301     n_output=2,
+   (...)    304     ravel=False,
+    305 )
+--> 306 return psl_ufunc(ary, out_shape=[(ary.shape[i] for i in axes), []], r_eff=r_eff)
+
+File ~\AppData\Local\Programs\Python\Python312\Lib\site-packages\arviz_stats\base\stats_utils.py:89, in make_ufunc.<locals>._multi_ufunc(out, out_shape, shape_from_1st, *args, **kwargs)
+     88     else:
+---> 89         out = tuple(np.empty((*element_shape, *out_shape[i])) for i in range(n_output))
+     91 elif check_shape:
+
+File ~\AppData\Local\Programs\Python\Python312\Lib\site-packages\arviz_stats\base\stats_utils.py:89, in <genexpr>(.0)
+     88     else:
+---> 89         out = tuple(np.empty((*element_shape, *out_shape[i])) for i in range(n_output))
+     91 elif check_shape:
+
+MemoryError: Unable to allocate 10.2 GiB for an array with shape (171259, 4, 2000) and data type float64
+
+During handling of the above exception, another exception occurred:
+
+TypeError                                 Traceback (most recent call last)
+Cell In[22], line 31
+     27 fitsM1.update({lbl: cached_fit(f"04_A_{lvl}.nc",
+     28                                lambda l=lvl, n=lbl: sample(model_A_contrast(live, config_level=l), n))
+     29                for lvl, lbl in (("overlap", "overlap only"), ("patch", "patch size only"),
+     30                                 ("none", "neither"))})
+---> 31 cmpM1 = az.compare(fitsM1)
+     32 display(cmpM1)
+     33 
+     34 # plot_forest: ArviZ 1.x changed the API; draw manually
+
+File ~\AppData\Local\Programs\Python\Python312\Lib\site-packages\arviz_stats\loo\compare.py:202, in compare(compare_dict, method, var_name, reference, round_to)
+    199 else:
+    200     round_val = round_to
+--> 202 ics_dict = _calculate_ics(compare_dict, var_name=var_name)
+    203 names = list(ics_dict.keys())
+    205 has_subsampling = any(
+    206     getattr(elpd, "subsample_size", None) is not None for elpd in ics_dict.values()
+    207 )
+
+File ~\AppData\Local\Programs\Python\Python312\Lib\site-packages\arviz_stats\loo\compare.py:642, in _calculate_ics(compare_dict, var_name)
+    636             new_compare_dict[name] = loo(
+    637                 dataset,
+    638                 pointwise=True,
+    639                 var_name=var_name,
+    640             )
+    641         except Exception as e:
+--> 642             raise e.__class__(
+    643                 f"Encountered error trying to compute ELPD from model {name}."
+    644             ) from e
+    645 return new_compare_dict
+
+TypeError: _ArrayMemoryError.__init__() missing 1 required positional argument: 'dtype'
+

ontology_run.log

@@ -0,0 +1,30 @@
+[NbConvertApp] Converting notebook chronos/bayesian/reconstruction_figures_ontology.ipynb to notebook
+C:\Users\feder\AppData\Local\Programs\Python\Python312\Lib\site-packages\nbformat\validator.py:434: MissingIDFieldWarning: Cell is missing an id field, this will become a hard error in future nbformat versions. You may want to use `normalize()` on your notebooks before validations (available since nbformat 5.1.4). Previous versions of nbformat are fixing this issue transparently, and will stop doing so in the future.
+  _validate(nbdict, ref, version, version_minor, relax_add_props)
+C:\Users\feder\AppData\Local\Programs\Python\Python312\Lib\site-packages\zmq\_future.py:718: RuntimeWarning: Proactor event loop does not implement add_reader family of methods required for zmq. Registering an additional selector thread for add_reader support via tornado. Use `asyncio.set_event_loop_policy(WindowsSelectorEventLoopPolicy())` to avoid this warning.
+  self._get_loop()
+[IPKernelApp] WARNING | Kernel is running over TCP without encryption. All communication (including code and outputs) is sent in plain text and is susceptible to eavesdropping. Use IPC transport or launch with kernel manager-provisioned CurveZMQ keys to enable transport encryption.
+[NbConvertApp] Writing 4217454 bytes to chronos\bayesian\chronos\bayesian\reconstruction_figures_ontology_executed.ipynb
+Traceback (most recent call last):
+  File "<frozen runpy>", line 198, in _run_module_as_main
+  File "<frozen runpy>", line 88, in _run_code
+  File "C:\Users\feder\AppData\Local\Programs\Python\Python312\Lib\site-packages\nbconvert\__main__.py", line 5, in <module>
+    main()
+  File "C:\Users\feder\AppData\Local\Programs\Python\Python312\Lib\site-packages\jupyter_core\application.py", line 284, in launch_instance
+    super().launch_instance(argv=argv, **kwargs)
+  File "C:\Users\feder\AppData\Local\Programs\Python\Python312\Lib\site-packages\traitlets\config\application.py", line 1080, in launch_instance
+    app.start()
+  File "C:\Users\feder\AppData\Local\Programs\Python\Python312\Lib\site-packages\nbconvert\nbconvertapp.py", line 420, in start
+    self.convert_notebooks()
+  File "C:\Users\feder\AppData\Local\Programs\Python\Python312\Lib\site-packages\nbconvert\nbconvertapp.py", line 597, in convert_notebooks
+    self.convert_single_notebook(notebook_filename)
+  File "C:\Users\feder\AppData\Local\Programs\Python\Python312\Lib\site-packages\nbconvert\nbconvertapp.py", line 566, in convert_single_notebook
+    write_results = self.write_single_notebook(output, resources)
+                    ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+  File "C:\Users\feder\AppData\Local\Programs\Python\Python312\Lib\site-packages\nbconvert\nbconvertapp.py", line 526, in write_single_notebook
+    return self.writer.write(output, resources, notebook_name=notebook_name)
+           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+  File "C:\Users\feder\AppData\Local\Programs\Python\Python312\Lib\site-packages\nbconvert\writers\files.py", line 152, in write
+    with open(dest_path, "w", encoding="utf-8") as f:
+         ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+FileNotFoundError: [Errno 2] No such file or directory: 'chronos\\bayesian\\chronos\\bayesian\\reconstruction_figures_ontology_executed.ipynb'