---------------------------------------------------------------------------
Empty Traceback (most recent call last)
File ~/lib/python3.10/site-packages/joblib/parallel.py:862, in Parallel.dispatch_one_batch(self, iterator)
861 try:
--> 862 tasks = self._ready_batches.get(block=False)
863 except queue.Empty:
864 # slice the iterator n_jobs * batchsize items at a time. If the
865 # slice returns less than that, then the current batchsize puts
(...)
868 # accordingly to distribute evenly the last items between all
869 # workers.
File ~/lib/python3.10/queue.py:168, in Queue.get(self, block, timeout)
167 if not self._qsize():
--> 168 raise Empty
169 elif timeout is None:
Empty:
During handling of the above exception, another exception occurred:
TypeError Traceback (most recent call last)
Cell In[4], line 1
----> 1 fig = plot_static_mapper_graph(pipe, data)
2 fig.show(config={'scrollZoom': True})
File ~/lib/python3.10/site-packages/gtda/mapper/visualization.py:172, in plot_static_mapper_graph(pipeline, data, color_data, color_features, node_color_statistic, layout, layout_dim, clone_pipeline, n_sig_figs, node_scale, plotly_params)
169 # Compute the graph and fetch the indices of points in each node
170 _pipeline = clone(pipeline) if clone_pipeline else pipeline
--> 172 graph = _pipeline.fit_transform(data)
173 (color_data_transformed, column_names_dropdown,
174 node_color_statistic) = \
175 _validate_color_kwargs(graph, data, color_data, color_features,
176 node_color_statistic, interactive=False)
177 edge_trace, node_trace, node_colors_color_features = \
178 _calculate_graph_data(
179 graph, color_data_transformed, node_color_statistic, layout,
180 layout_dim, n_sig_figs, node_scale
181 )
File ~/lib/python3.10/site-packages/sklearn/base.py:1473, in _fit_context.<locals>.decorator.<locals>.wrapper(estimator, *args, **kwargs)
1466 estimator._validate_params()
1468 with config_context(
1469 skip_parameter_validation=(
1470 prefer_skip_nested_validation or global_skip_validation
1471 )
1472 ):
-> 1473 return fit_method(estimator, *args, **kwargs)
File ~/lib/python3.10/site-packages/sklearn/pipeline.py:533, in Pipeline.fit_transform(self, X, y, **params)
490 """Fit the model and transform with the final estimator.
491
492 Fit all the transformers one after the other and sequentially transform
(...)
530 Transformed samples.
531 """
532 routed_params = self._check_method_params(method="fit_transform", props=params)
--> 533 Xt = self._fit(X, y, routed_params)
535 last_step = self._final_estimator
536 with _print_elapsed_time("Pipeline", self._log_message(len(self.steps) - 1)):
File ~/lib/python3.10/site-packages/sklearn/pipeline.py:406, in Pipeline._fit(self, X, y, routed_params)
404 cloned_transformer = clone(transformer)
405 # Fit or load from cache the current transformer
--> 406 X, fitted_transformer = fit_transform_one_cached(
407 cloned_transformer,
408 X,
409 y,
410 None,
411 message_clsname="Pipeline",
412 message=self._log_message(step_idx),
413 params=routed_params[name],
414 )
415 # Replace the transformer of the step with the fitted
416 # transformer. This is necessary when loading the transformer
417 # from the cache.
418 self.steps[step_idx] = (name, fitted_transformer)
File ~/lib/python3.10/site-packages/joblib/memory.py:349, in NotMemorizedFunc.__call__(self, *args, **kwargs)
348 def __call__(self, *args, **kwargs):
--> 349 return self.func(*args, **kwargs)
File ~/lib/python3.10/site-packages/sklearn/pipeline.py:1310, in _fit_transform_one(transformer, X, y, weight, message_clsname, message, params)
1308 with _print_elapsed_time(message_clsname, message):
1309 if hasattr(transformer, "fit_transform"):
-> 1310 res = transformer.fit_transform(X, y, **params.get("fit_transform", {}))
1311 else:
1312 res = transformer.fit(X, y, **params.get("fit", {})).transform(
1313 X, **params.get("transform", {})
1314 )
File ~/lib/python3.10/site-packages/sklearn/utils/_set_output.py:316, in _wrap_method_output.<locals>.wrapped(self, X, *args, **kwargs)
314 @wraps(f)
315 def wrapped(self, X, *args, **kwargs):
--> 316 data_to_wrap = f(self, X, *args, **kwargs)
317 if isinstance(data_to_wrap, tuple):
318 # only wrap the first output for cross decomposition
319 return_tuple = (
320 _wrap_data_with_container(method, data_to_wrap[0], X, self),
321 *data_to_wrap[1:],
322 )
File ~/lib/python3.10/site-packages/gtda/mapper/utils/_list_feature_union.py:21, in ListFeatureUnion.fit_transform(self, X, y, **fit_params)
7 def fit_transform(self, X, y=None, **fit_params):
8 """Fit all transformers, transform the data and concatenate results.
9 Parameters
10 ----------
(...)
19
20 """
---> 21 results = self._parallel_func(X, y, fit_params, _fit_transform_one)
22 if not results:
23 # All transformers are None
24 return np.zeros((X.shape[0], 0))
File ~/lib/python3.10/site-packages/sklearn/pipeline.py:1757, in FeatureUnion._parallel_func(self, X, y, func, routed_params)
1754 self._validate_transformer_weights()
1755 transformers = list(self._iter())
-> 1757 return Parallel(n_jobs=self.n_jobs)(
1758 delayed(func)(
1759 transformer,
1760 X,
1761 y,
1762 weight,
1763 message_clsname="FeatureUnion",
1764 message=self._log_message(name, idx, len(transformers)),
1765 params=routed_params[name],
1766 )
1767 for idx, (name, transformer, weight) in enumerate(transformers, 1)
1768 )
File ~/lib/python3.10/site-packages/sklearn/utils/parallel.py:74, in Parallel.__call__(self, iterable)
69 config = get_config()
70 iterable_with_config = (
71 (_with_config(delayed_func, config), args, kwargs)
72 for delayed_func, args, kwargs in iterable
73 )
---> 74 return super().__call__(iterable_with_config)
File ~/lib/python3.10/site-packages/joblib/parallel.py:1085, in Parallel.__call__(self, iterable)
1076 try:
1077 # Only set self._iterating to True if at least a batch
1078 # was dispatched. In particular this covers the edge
(...)
1082 # was very quick and its callback already dispatched all the
1083 # remaining jobs.
1084 self._iterating = False
-> 1085 if self.dispatch_one_batch(iterator):
1086 self._iterating = self._original_iterator is not None
1088 while self.dispatch_one_batch(iterator):
File ~/lib/python3.10/site-packages/joblib/parallel.py:873, in Parallel.dispatch_one_batch(self, iterator)
870 n_jobs = self._cached_effective_n_jobs
871 big_batch_size = batch_size * n_jobs
--> 873 islice = list(itertools.islice(iterator, big_batch_size))
874 if len(islice) == 0:
875 return False
File ~/lib/python3.10/site-packages/sklearn/utils/parallel.py:70, in <genexpr>(.0)
65 # Capture the thread-local scikit-learn configuration at the time
66 # Parallel.__call__ is issued since the tasks can be dispatched
67 # in a different thread depending on the backend and on the value of
68 # pre_dispatch and n_jobs.
69 config = get_config()
---> 70 iterable_with_config = (
71 (_with_config(delayed_func, config), args, kwargs)
72 for delayed_func, args, kwargs in iterable
73 )
74 return super().__call__(iterable_with_config)
File ~/lib/python3.10/site-packages/sklearn/pipeline.py:1765, in <genexpr>(.0)
1754 self._validate_transformer_weights()
1755 transformers = list(self._iter())
1757 return Parallel(n_jobs=self.n_jobs)(
1758 delayed(func)(
1759 transformer,
1760 X,
1761 y,
1762 weight,
1763 message_clsname="FeatureUnion",
1764 message=self._log_message(name, idx, len(transformers)),
-> 1765 params=routed_params[name],
1766 )
1767 for idx, (name, transformer, weight) in enumerate(transformers, 1)
1768 )
TypeError: 'function' object is not subscriptable
Versions
import platform; print(platform.platform())
Linux-6.8.0-45-generic-x86_64-with-glibc2.35
import sys; print("Python", sys.version)
Python 3.10.9 (main, Mar 31 2023, 07:34:00) [GCC 11.3.0]
Additional context
All other notebooks in the tutorial work and all the tests passed. The only error is this.
I think it is a conflict between make_mapper_pipeline and sklearn.pipeline because of the latter's change of API
I cannot downgrade scikit-learn as I need it for other packages.
I would appreciate it if you can point me to a workaround. I am thinking maybe it is possible to just change a few lines in make_mapper_module for this to work.
I am getting some errors running the mapper_quickstart.ipynb notebook in tutorial.
To reproduce just run the ipython notebook.
The cell
produces the following errors
Versions
import platform; print(platform.platform())
import sys; print("Python", sys.version)
import numpy; print("NumPy", numpy.version)
import scipy; print("SciPy", scipy.version)
import joblib; print("Joblib", joblib.version)
import sklearn; print("Scikit-learn", sklearn.version)
import gtda; print("Giotto-tda", gtda.version)
Additional context All other notebooks in the tutorial work and all the tests passed. The only error is this.
I think it is a conflict between make_mapper_pipeline and sklearn.pipeline because of the latter's change of API
I cannot downgrade scikit-learn as I need it for other packages.
I would appreciate it if you can point me to a workaround. I am thinking maybe it is possible to just change a few lines in make_mapper_module for this to work.
Thanks.