When I try to import a meta graph using saver = tf.train.import_meta_graph(meta_graph_path, clear_devices=True) I get KeyError: 'MaxBytesInUse' from within the importer.
Tensorflow version: 1.7-gpu-python3
OS: Ubuntu 16.04
Here is the stack trace of the error:
/usr/local/lib/python3.5/dist-packages/tensorflow/python/training/saver.py in import_meta_graph(meta_graph_or_file, clear_devices, import_scope, **kwargs)
1953 clear_devices=clear_devices,
1954 import_scope=import_scope,
-> 1955 **kwargs)
1956
1957 if meta_graph_def.HasField("saver_def"):
/usr/local/lib/python3.5/dist-packages/tensorflow/python/framework/meta_graph.py in import_scoped_meta_graph(meta_graph_or_file, clear_devices, graph, import_scope, input_map, unbound_inputs_col_name, restore_collections_predicate)
741 name=(import_scope or scope_to_prepend_to_names),
742 input_map=input_map,
--> 743 producer_op_list=producer_op_list)
744
745 # Restores all the other collections.
/usr/local/lib/python3.5/dist-packages/tensorflow/python/util/deprecation.py in new_func(*args, **kwargs)
430 'in a future version' if date is None else ('after %s' % date),
431 instructions)
--> 432 return func(*args, **kwargs)
433 return tf_decorator.make_decorator(func, new_func, 'deprecated',
434 _add_deprecated_arg_notice_to_docstring(
/usr/local/lib/python3.5/dist-packages/tensorflow/python/framework/importer.py in import_graph_def(graph_def, input_map, return_elements, name, op_dict, producer_op_list)
458 if producer_op_list is not None:
459 # TODO(skyewm): make a copy of graph_def so we're not mutating the argument?
--> 460 _RemoveDefaultAttrs(op_dict, producer_op_list, graph_def)
461
462 graph = ops.get_default_graph()
/usr/local/lib/python3.5/dist-packages/tensorflow/python/framework/importer.py in _RemoveDefaultAttrs(op_dict, producer_op_list, graph_def)
225 # Remove any default attr values that aren't in op_def.
226 if node.op in producer_op_dict:
--> 227 op_def = op_dict[node.op]
228 producer_op_def = producer_op_dict[node.op]
229 # We make a copy of node.attr to iterate through since we may modify
KeyError: 'MaxBytesInUse'
Add dir(tf.contrib)
See the link: https://github.com/tensorflow/tensorflow/issues/10130
Related
I'm trying to import some public data from the web but can't understand the error.
My code:
import pandas as pd
df2022 = pd.read_excel("https://ofslivefs.blob.core.windows.net/files/NSS%20data%202022/September/NSS2022_summary_data.xlsx")
It returns this:
---------------------------------------------------------------------------
IndexError Traceback (most recent call last)
/var/folders/v_/yq26pm194xj5ckqy8p_njwc00000gn/T/ipykernel_89117/2424267382.py in <module>
----> 1 df2022 = pd.read_excel("https://ofslivefs.blob.core.windows.net/files/NSS%20data%202022/September/NSS2022_summary_data.xlsx")
~/opt/anaconda3/lib/python3.9/site-packages/pandas/util/_decorators.py in wrapper(*args, **kwargs)
209 else:
210 kwargs[new_arg_name] = new_arg_value
--> 211 return func(*args, **kwargs)
212
213 return cast(F, wrapper)
~/opt/anaconda3/lib/python3.9/site-packages/pandas/util/_decorators.py in wrapper(*args, **kwargs)
329 stacklevel=find_stack_level(),
330 )
--> 331 return func(*args, **kwargs)
332
333 # error: "Callable[[VarArg(Any), KwArg(Any)], Any]" has no
~/opt/anaconda3/lib/python3.9/site-packages/pandas/io/excel/_base.py in read_excel(io, sheet_name, header, names, index_col, usecols, squeeze, dtype, engine, converters, true_values, false_values, skiprows, nrows, na_values, keep_default_na, na_filter, verbose, parse_dates, date_parser, thousands, decimal, comment, skipfooter, convert_float, mangle_dupe_cols, storage_options)
480 if not isinstance(io, ExcelFile):
481 should_close = True
--> 482 io = ExcelFile(io, storage_options=storage_options, engine=engine)
483 elif engine and engine != io.engine:
484 raise ValueError(
~/opt/anaconda3/lib/python3.9/site-packages/pandas/io/excel/_base.py in __init__(self, path_or_buffer, engine, storage_options)
1693 self.storage_options = storage_options
1694
-> 1695 self._reader = self._engines[engine](self._io, storage_options=storage_options)
1696
1697 def __fspath__(self):
~/opt/anaconda3/lib/python3.9/site-packages/pandas/io/excel/_openpyxl.py in __init__(self, filepath_or_buffer, storage_options)
555 """
556 import_optional_dependency("openpyxl")
--> 557 super().__init__(filepath_or_buffer, storage_options=storage_options)
558
559 #property
~/opt/anaconda3/lib/python3.9/site-packages/pandas/io/excel/_base.py in __init__(self, filepath_or_buffer, storage_options)
543 self.handles.handle.seek(0)
544 try:
--> 545 self.book = self.load_workbook(self.handles.handle)
546 except Exception:
547 self.close()
~/opt/anaconda3/lib/python3.9/site-packages/pandas/io/excel/_openpyxl.py in load_workbook(self, filepath_or_buffer)
566 from openpyxl import load_workbook
567
--> 568 return load_workbook(
569 filepath_or_buffer, read_only=True, data_only=True, keep_links=False
570 )
~/opt/anaconda3/lib/python3.9/site-packages/openpyxl/reader/excel.py in load_workbook(filename, read_only, keep_vba, data_only, keep_links)
315 reader = ExcelReader(filename, read_only, keep_vba,
316 data_only, keep_links)
--> 317 reader.read()
318 return reader.wb
~/opt/anaconda3/lib/python3.9/site-packages/openpyxl/reader/excel.py in read(self)
281 apply_stylesheet(self.archive, self.wb)
282 self.read_worksheets()
--> 283 self.parser.assign_names()
284 if not self.read_only:
285 self.archive.close()
~/opt/anaconda3/lib/python3.9/site-packages/openpyxl/reader/workbook.py in assign_names(self)
100 reserved = defn.is_reserved
101 if reserved in ("Print_Titles", "Print_Area"):
--> 102 sheet = self.wb._sheets[defn.localSheetId]
103 if reserved == "Print_Titles":
104 rows, cols = _unpack_print_titles(defn)
IndexError: list index out of range
At this point I would traditonally download and convert to CSV but I want to access straight from web.
The sheet (which I guess I could access as sheetname="Q27 Providers (benchmarked)") doesn't work.
It looks like xlsx file is broken, therefore u can't download it. Did u try to open that xlsx file?
Using jupyterlab, i receive a KeyError: 'Default' when using plt.tight_layout() in combination with %matplotlib widget. The following code reproduces the issue:
import matplotlib.pyplot as plt
import numpy as np
%matplotlib widget
x=np.linspace(0,10)
y=x**2
plt.plot(x,y)
plt.tight_layout()
The complete error message is the following:
---------------------------------------------------------------------------
KeyError Traceback (most recent call last)
~/anaconda3/lib/python3.8/site-packages/matplotlib/backend_bases.py in _wait_cursor_for_draw_cm(self)
3024 try:
-> 3025 self.canvas.set_cursor(tools.Cursors.WAIT)
3026 yield
~/anaconda3/lib/python3.8/site-packages/matplotlib/backends/backend_webagg_core.py in set_cursor(self, cursor)
209 }, cursor=cursor)
--> 210 self.send_event('cursor', cursor=cursor)
211
~/anaconda3/lib/python3.8/site-packages/matplotlib/backends/backend_webagg_core.py in send_event(self, event_type, **kwargs)
391 if self.manager:
--> 392 self.manager._send_event(event_type, **kwargs)
393
~/anaconda3/lib/python3.8/site-packages/matplotlib/backends/backend_webagg_core.py in _send_event(self, event_type, **kwargs)
540 for s in self.web_sockets:
--> 541 s.send_json(payload)
542
~/anaconda3/lib/python3.8/site-packages/ipympl/backend_nbagg.py in send_json(self, content)
180 if content['type'] == 'cursor':
--> 181 self._cursor = cursors_str[content['cursor']]
182
KeyError: 'wait'
During handling of the above exception, another exception occurred:
KeyError Traceback (most recent call last)
/tmp/ipykernel_119035/3466922198.py in <module>
7 y=x**2
8 plt.plot(x,y)
----> 9 plt.tight_layout()
~/anaconda3/lib/python3.8/site-packages/matplotlib/pyplot.py in tight_layout(pad, h_pad, w_pad, rect)
2300 #_copy_docstring_and_deprecators(Figure.tight_layout)
2301 def tight_layout(*, pad=1.08, h_pad=None, w_pad=None, rect=None):
-> 2302 return gcf().tight_layout(pad=pad, h_pad=h_pad, w_pad=w_pad, rect=rect)
2303
2304
~/anaconda3/lib/python3.8/site-packages/matplotlib/figure.py in tight_layout(self, pad, h_pad, w_pad, rect)
3186 "compatible with tight_layout, so results "
3187 "might be incorrect.")
-> 3188 renderer = _get_renderer(self)
3189 with getattr(renderer, "_draw_disabled", nullcontext)():
3190 kwargs = get_tight_layout_figure(
~/anaconda3/lib/python3.8/site-packages/matplotlib/backend_bases.py in _get_renderer(figure, print_method)
1542 figure.canvas._get_output_canvas(None, fmt), f"print_{fmt}")
1543 try:
-> 1544 print_method(io.BytesIO())
1545 except Done as exc:
1546 renderer, = figure._cachedRenderer, = exc.args
~/anaconda3/lib/python3.8/site-packages/matplotlib/backend_bases.py in wrapper(*args, **kwargs)
1641 kwargs.pop(arg)
1642
-> 1643 return func(*args, **kwargs)
1644
1645 return wrapper
~/anaconda3/lib/python3.8/site-packages/matplotlib/_api/deprecation.py in wrapper(*inner_args, **inner_kwargs)
410 else deprecation_addendum,
411 **kwargs)
--> 412 return func(*inner_args, **inner_kwargs)
413
414 DECORATORS[wrapper] = decorator
~/anaconda3/lib/python3.8/site-packages/matplotlib/backends/backend_agg.py in print_png(self, filename_or_obj, metadata, pil_kwargs, *args)
538 *metadata*, including the default 'Software' key.
539 """
--> 540 FigureCanvasAgg.draw(self)
541 mpl.image.imsave(
542 filename_or_obj, self.buffer_rgba(), format="png", origin="upper",
~/anaconda3/lib/python3.8/site-packages/matplotlib/backends/backend_agg.py in draw(self)
431 self.renderer = self.get_renderer(cleared=True)
432 # Acquire a lock on the shared font cache.
--> 433 with RendererAgg.lock, \
434 (self.toolbar._wait_cursor_for_draw_cm() if self.toolbar
435 else nullcontext()):
~/anaconda3/lib/python3.8/contextlib.py in __enter__(self)
111 del self.args, self.kwds, self.func
112 try:
--> 113 return next(self.gen)
114 except StopIteration:
115 raise RuntimeError("generator didn't yield") from None
~/anaconda3/lib/python3.8/site-packages/matplotlib/backend_bases.py in _wait_cursor_for_draw_cm(self)
3026 yield
3027 finally:
-> 3028 self.canvas.set_cursor(self._lastCursor)
3029 else:
3030 yield
~/anaconda3/lib/python3.8/site-packages/matplotlib/backends/backend_webagg_core.py in set_cursor(self, cursor)
208 backend_tools.Cursors.RESIZE_VERTICAL: 'ns-resize',
209 }, cursor=cursor)
--> 210 self.send_event('cursor', cursor=cursor)
211
212 def set_image_mode(self, mode):
~/anaconda3/lib/python3.8/site-packages/matplotlib/backends/backend_webagg_core.py in send_event(self, event_type, **kwargs)
390 def send_event(self, event_type, **kwargs):
391 if self.manager:
--> 392 self.manager._send_event(event_type, **kwargs)
393
394
~/anaconda3/lib/python3.8/site-packages/matplotlib/backends/backend_webagg_core.py in _send_event(self, event_type, **kwargs)
539 payload = {'type': event_type, **kwargs}
540 for s in self.web_sockets:
--> 541 s.send_json(payload)
542
543
~/anaconda3/lib/python3.8/site-packages/ipympl/backend_nbagg.py in send_json(self, content)
179 # Change in the widget state?
180 if content['type'] == 'cursor':
--> 181 self._cursor = cursors_str[content['cursor']]
182
183 elif content['type'] == 'message':
KeyError: 'default'
I am using rapids UMAP in conjunction with HDBSCAN inside a rapidsai docker container : rapidsai/rapidsai-core:0.18-cuda11.0-runtime-ubuntu18.04-py3.7
import cudf
import cupy
from cuml.manifold import UMAP
import hdbscan
from sklearn.datasets import make_blobs
from cuml.experimental.preprocessing import StandardScaler
blobs, labels = make_blobs(n_samples=100000, n_features=10)
df_gpu=cudf.DataFrame(blobs)
scaler= StandardScaler()
cupy_scaled=scaler.fit_transform(df_gpu.values)
projector= UMAP(n_components=3, n_neighbors=2000)
cupy_projected=projector.fit_transform(cupy_scaled)
numpy_projected=cupy.asnumpy(cupy_projected)
clusterer= hdbscan.HDBSCAN(min_cluster_size=1000, prediction_data=True, gen_min_span_tree=True)#, core_dist_n_jobs=1)
clusterer.fit(numpy_projected)
I get an error which is fixed if I use core_dist_n_jobs=1 but makes the code slower:
--------------------------------------------------------------------------- TerminatedWorkerError Traceback (most recent call
last) in
1 clusterer= hdbscan.HDBSCAN(min_cluster_size=1000, prediction_data=True, gen_min_span_tree=True)
----> 2 clusterer.fit(numpy_projected)
/opt/conda/envs/rapids/lib/python3.7/site-packages/hdbscan/hdbscan_.py
in fit(self, X, y)
917 self._condensed_tree,
918 self._single_linkage_tree,
--> 919 self._min_spanning_tree) = hdbscan(X, **kwargs)
920
921 if self.prediction_data:
/opt/conda/envs/rapids/lib/python3.7/site-packages/hdbscan/hdbscan_.py
in hdbscan(X, min_cluster_size, min_samples, alpha,
cluster_selection_epsilon, metric, p, leaf_size, algorithm, memory,
approx_min_span_tree, gen_min_span_tree, core_dist_n_jobs,
cluster_selection_method, allow_single_cluster,
match_reference_implementation, **kwargs)
613 approx_min_span_tree,
614 gen_min_span_tree,
--> 615 core_dist_n_jobs, **kwargs)
616 else: # Metric is a valid BallTree metric
617 # TO DO: Need heuristic to decide when to go to boruvka;
/opt/conda/envs/rapids/lib/python3.7/site-packages/joblib/memory.py in
call(self, *args, **kwargs)
350
351 def call(self, *args, **kwargs):
--> 352 return self.func(*args, **kwargs)
353
354 def call_and_shelve(self, *args, **kwargs):
/opt/conda/envs/rapids/lib/python3.7/site-packages/hdbscan/hdbscan_.py
in _hdbscan_boruvka_kdtree(X, min_samples, alpha, metric, p,
leaf_size, approx_min_span_tree, gen_min_span_tree, core_dist_n_jobs,
**kwargs)
276 leaf_size=leaf_size // 3,
277 approx_min_span_tree=approx_min_span_tree,
--> 278 n_jobs=core_dist_n_jobs, **kwargs)
279 min_spanning_tree = alg.spanning_tree()
280 # Sort edges of the min_spanning_tree by weight
hdbscan/_hdbscan_boruvka.pyx in
hdbscan._hdbscan_boruvka.KDTreeBoruvkaAlgorithm.init()
hdbscan/_hdbscan_boruvka.pyx in
hdbscan._hdbscan_boruvka.KDTreeBoruvkaAlgorithm._compute_bounds()
/opt/conda/envs/rapids/lib/python3.7/site-packages/joblib/parallel.py
in call(self, iterable) 1052 1053 with
self._backend.retrieval_context():
-> 1054 self.retrieve() 1055 # Make sure that we get a last message telling us we are done 1056
elapsed_time = time.time() - self._start_time
/opt/conda/envs/rapids/lib/python3.7/site-packages/joblib/parallel.py
in retrieve(self)
931 try:
932 if getattr(self._backend, 'supports_timeout', False):
--> 933 self._output.extend(job.get(timeout=self.timeout))
934 else:
935 self._output.extend(job.get())
/opt/conda/envs/rapids/lib/python3.7/site-packages/joblib/_parallel_backends.py
in wrap_future_result(future, timeout)
540 AsyncResults.get from multiprocessing."""
541 try:
--> 542 return future.result(timeout=timeout)
543 except CfTimeoutError as e:
544 raise TimeoutError from e
/opt/conda/envs/rapids/lib/python3.7/concurrent/futures/_base.py in
result(self, timeout)
433 raise CancelledError()
434 elif self._state == FINISHED:
--> 435 return self.__get_result()
436 else:
437 raise TimeoutError()
/opt/conda/envs/rapids/lib/python3.7/concurrent/futures/_base.py in
__get_result(self)
382 def __get_result(self):
383 if self._exception:
--> 384 raise self._exception
385 else:
386 return self._result
TerminatedWorkerError: A worker process managed by the executor was
unexpectedly terminated. This could be caused by a segmentation fault
while calling the function or by an excessive memory usage causing the
Operating System to kill the worker.
The exit codes of the workers are {EXIT(1)}
Is there a way to solve this issue but still keep HDBSCAN to be fast?
Try setting min_samples to a value
In https://github.com/scikit-learn-contrib/hdbscan/issues/345#issuecomment-628749332 , lmcinnes says that you "may have issues if your min_cluster_size is large and your min_samples is not set. You could try setting min_samples to something smallish and see if that helps." I noticed that you do not have a min_samples set in your code.
I am trying to implement a InceptionV3 to classify the images.
(python 3.65 / tensorflow 1.14.0)
when I run the code, I am getting these key error : "logtostderr".
I don't know why this Key error happens.
How can I solve this problem?
Last week, I ran this code smoothly.
but, the problem was happened yesterday suddenly.
with tf.train.MonitoredSession(session_creator=session_creator) as sess:
for filenames, images in load_images(FLAGS.input_dir, batch_shape):
labels = sess.run(predicted_labels, feed_dict={x_input: images})
for filename, label in zip(filenames, labels):
true_label = image_labels.merge(pd.DataFrame({"ImageId":[filename[:-4]]}), on="ImageId")["TrueLabel"][0]
predictions.append([filename[:-4], true_label, label])
KeyError Traceback (most recent call last)
<ipython-input-19-307b2daa19e5> in <module>
88
89
---> 90 with tf.train.MonitoredSession(session_creator=session_creator) as sess:
91 for filenames, images in load_images(FLAGS.input_dir, batch_shape):
92 labels = sess.run(predicted_labels, feed_dict={x_input: images})
C:\ProgramData\Anaconda3\lib\site-packages\tensorflow\python\training\monitored_session.py in __init__(self, session_creator, hooks, stop_grace_period_secs)
1005 hooks,
1006 should_recover=True,
-> 1007 stop_grace_period_secs=stop_grace_period_secs)
1008
1009
C:\ProgramData\Anaconda3\lib\site-packages\tensorflow\python\training\monitored_session.py in __init__(self, session_creator, hooks, should_recover, stop_grace_period_secs)
723 stop_grace_period_secs=stop_grace_period_secs)
724 if should_recover:
--> 725 self._sess = _RecoverableSession(self._coordinated_creator)
726 else:
727 self._sess = self._coordinated_creator.create_session()
C:\ProgramData\Anaconda3\lib\site-packages\tensorflow\python\training\monitored_session.py in __init__(self, sess_creator)
1198 """
1199 self._sess_creator = sess_creator
-> 1200 _WrappedSession.__init__(self, self._create_session())
1201
1202 def _create_session(self):
C:\ProgramData\Anaconda3\lib\site-packages\tensorflow\python\training\monitored_session.py in _create_session(self)
1203 while True:
1204 try:
-> 1205 return self._sess_creator.create_session()
1206 except _PREEMPTION_ERRORS as e:
1207 logging.info(
C:\ProgramData\Anaconda3\lib\site-packages\tensorflow\python\training\monitored_session.py in create_session(self)
869 """Creates a coordinated session."""
870 # Keep the tf_sess for unit testing.
--> 871 self.tf_sess = self._session_creator.create_session()
872 # We don't want coordinator to suppress any exception.
873 self.coord = coordinator.Coordinator(clean_stop_exception_types=[])
C:\ProgramData\Anaconda3\lib\site-packages\tensorflow\python\training\monitored_session.py in create_session(self)
636
637 def create_session(self):
--> 638 self._scaffold.finalize()
639 return self._get_session_manager().prepare_session(
640 self._master,
C:\ProgramData\Anaconda3\lib\site-packages\tensorflow\python\training\monitored_session.py in finalize(self)
238
239 ops.get_default_graph().finalize()
--> 240 logging.info('Graph was finalized.')
241 return self
242
C:\ProgramData\Anaconda3\lib\site-packages\tensorflow\python\platform\tf_logging.py in info(msg, *args, **kwargs)
154 #tf_export(v1=['logging.info'])
155 def info(msg, *args, **kwargs):
--> 156 get_logger().info(msg, *args, **kwargs)
157
158
C:\ProgramData\Anaconda3\lib\logging\__init__.py in info(self, msg, *args, **kwargs)
1306 """
1307 if self.isEnabledFor(INFO):
-> 1308 self._log(INFO, msg, args, **kwargs)
1309
1310 def warning(self, msg, *args, **kwargs):
C:\ProgramData\Anaconda3\lib\logging\__init__.py in _log(self, level, msg, args, exc_info, extra, stack_info)
1442 record = self.makeRecord(self.name, level, fn, lno, msg, args,
1443 exc_info, func, extra, sinfo)
-> 1444 self.handle(record)
1445
1446 def handle(self, record):
C:\ProgramData\Anaconda3\lib\logging\__init__.py in handle(self, record)
1452 """
1453 if (not self.disabled) and self.filter(record):
-> 1454 self.callHandlers(record)
1455
1456 def addHandler(self, hdlr):
C:\ProgramData\Anaconda3\lib\logging\__init__.py in callHandlers(self, record)
1514 found = found + 1
1515 if record.levelno >= hdlr.level:
-> 1516 hdlr.handle(record)
1517 if not c.propagate:
1518 c = None #break out
C:\ProgramData\Anaconda3\lib\site-packages\absl\logging\__init__.py in handle(self, record)
889 rv = self.filter(record)
890 if rv:
--> 891 return self._current_handler.handle(record)
892 return rv
893
C:\ProgramData\Anaconda3\lib\logging\__init__.py in handle(self, record)
863 self.acquire()
864 try:
--> 865 self.emit(record)
866 finally:
867 self.release()
C:\ProgramData\Anaconda3\lib\site-packages\absl\logging\__init__.py in emit(self, record)
824 _warn_preinit_stderr = False
825 self._log_to_stderr(record)
--> 826 elif FLAGS['logtostderr'].value:
827 self._log_to_stderr(record)
828 else:
C:\ProgramData\Anaconda3\lib\site-packages\absl\flags\_flagvalues.py in __getitem__(self, name)
461 def __getitem__(self, name):
462 """Returns the Flag object for the flag --name."""
--> 463 return self._flags()[name]
464
465 def _hide_flag(self, name):
KeyError: 'logtostderr'
I'm trying to do a choropleth using folium which offers a great link between GeoJSON, Pandas and leaflet.
GeoJSON format is like below :
{
"type":"FeatureCollection",
"features":[
{
"type":"Feature",
"geometry":
{
"type":"Polygon",
"coordinates":[[[-1.6704591323124895,49.62681486270549], .....
{
"insee":"50173",
"nom":"Équeurdreville-Hainneville",
"wikipedia":"fr:Équeurdreville-Hainneville",
"surf_m2":12940306}},
Pandas DataFrame :
postal_count.head(5)
Out[98]:
Code_commune_INSEE CP_count
0 75120 723
1 75115 698
2 75112 671
3 75118 627
4 75111 622
"Code_communes_INSEE" corresponds to the attribute "insee" in the GeoJSON. I'd like to do a choropleth using the variable "CP_count" in the above DataFrame.
Here is my code (snippet from this notebook)
map_france = folium.Map(location=[47.000000, 2.000000], zoom_start=6)
map_france.choropleth(
geo_str=open(geo_path + 'simplified_communes100m.json').read(),
data=postal_count,
columns=['Code_commune_INSEE', 'CP_count'],
key_on='feature.geometry.properties.insee',
fill_color='YlGn',
)
map_france.save(table_path + 'choro_test1.html')
I'm still getting this error again and again :
---------------------------------------------------------------------------
AttributeError Traceback (most recent call last)
<ipython-input-83-ea0fd2c1c207> in <module>()
8 fill_color='YlGn',
9 )
---> 10 map_france.save('/media/flo/Stockage/Data/MesAides/map/choro_test1.html')
/home/flo/.virtualenvs/mesaides/lib/python3.4/site-packages/folium/element.py in save(self, outfile, close_file, **kwargs)
151
152 root = self.get_root()
--> 153 html = root.render(**kwargs)
154 fid.write(html.encode('utf8'))
155 if close_file:
/home/flo/.virtualenvs/mesaides/lib/python3.4/site-packages/folium/element.py in render(self, **kwargs)
357 """Renders the HTML representation of the element."""
358 for name, child in self._children.items():
--> 359 child.render(**kwargs)
360 return self._template.render(this=self, kwargs=kwargs)
361
/home/flo/.virtualenvs/mesaides/lib/python3.4/site-packages/folium/element.py in render(self, **kwargs)
665
666 for name, element in self._children.items():
--> 667 element.render(**kwargs)
/home/flo/.virtualenvs/mesaides/lib/python3.4/site-packages/folium/element.py in render(self, **kwargs)
661 script = self._template.module.__dict__.get('script', None)
662 if script is not None:
--> 663 figure.script.add_children(Element(script(self, kwargs)),
664 name=self.get_name())
665
/home/flo/.virtualenvs/mesaides/lib/python3.4/site-packages/jinja2/runtime.py in __call__(self, *args, **kwargs)
434 raise TypeError('macro %r takes not more than %d argument(s)' %
435 (self.name, len(self.arguments)))
--> 436 return self._func(*arguments)
437
438 def __repr__(self):
<template> in macro(l_this, l_kwargs)
/home/flo/.virtualenvs/mesaides/lib/python3.4/site-packages/jinja2/runtime.py in call(_Context__self, _Context__obj, *args, **kwargs)
194 args = (__self.environment,) + args
195 try:
--> 196 return __obj(*args, **kwargs)
197 except StopIteration:
198 return __self.environment.undefined('value was undefined because '
/home/flo/.virtualenvs/mesaides/lib/python3.4/site-packages/folium/features.py in style_data(self)
352
353 for feature in self.data['features']:
--> 354 feature.setdefault('properties', {}).setdefault('style', {}).update(self.style_function(feature)) # noqa
355 return json.dumps(self.data, sort_keys=True)
356
/home/flo/.virtualenvs/mesaides/lib/python3.4/site-packages/folium/folium.py in style_function(x)
671 "color": line_color,
672 "fillOpacity": fill_opacity,
--> 673 "fillColor": color_scale_fun(x)
674 }
675
/home/flo/.virtualenvs/mesaides/lib/python3.4/site-packages/folium/folium.py in color_scale_fun(x)
659 def color_scale_fun(x):
660 return color_range[len(
--> 661 [u for u in color_domain if
662 u <= color_data[get_by_key(x, key_on)]])]
663 else:
/home/flo/.virtualenvs/mesaides/lib/python3.4/site-packages/folium/folium.py in <listcomp>(.0)
660 return color_range[len(
661 [u for u in color_domain if
--> 662 u <= color_data[get_by_key(x, key_on)]])]
663 else:
664 def color_scale_fun(x):
/home/flo/.virtualenvs/mesaides/lib/python3.4/site-packages/folium/folium.py in get_by_key(obj, key)
655 return (obj.get(key, None) if len(key.split('.')) <= 1 else
656 get_by_key(obj.get(key.split('.')[0], None),
--> 657 '.'.join(key.split('.')[1:])))
658
659 def color_scale_fun(x):
/home/flo/.virtualenvs/mesaides/lib/python3.4/site-packages/folium/folium.py in get_by_key(obj, key)
655 return (obj.get(key, None) if len(key.split('.')) <= 1 else
656 get_by_key(obj.get(key.split('.')[0], None),
--> 657 '.'.join(key.split('.')[1:])))
658
659 def color_scale_fun(x):
/home/flo/.virtualenvs/mesaides/lib/python3.4/site-packages/folium/folium.py in get_by_key(obj, key)
653
654 def get_by_key(obj, key):
--> 655 return (obj.get(key, None) if len(key.split('.')) <= 1 else
656 get_by_key(obj.get(key.split('.')[0], None),
657 '.'.join(key.split('.')[1:])))
AttributeError: 'NoneType' object has no attribute 'get'
I tried playing with key_on='feature.geometry.properties.insee' without any success.
There were 2 problems :
1 - The correct access to 'insee' parameters is : key_on='feature.properties.insee'
The best way to find the right key_on is to play with the geoJSON dict to make sure you are calling the right properties.
2- Once you have the right key_on parameters, you need to make sure that all the available keys in the geoJSON are contained in your Pandas DataFrame (otherwise it will raise a KeyError)
In this case, I used the following command line to get all the insee keys contained by my geoJSON:
ogrinfo -ro -al communes-20150101-100m.shp -geom=NO | grep insee > list_code_insee.txt
If you are experiencing the same issue, this should solve your problem.
I had the same problem on JupyterLab (on labs.cognitiveclass.ai) using Folium 0.5.0. Then I copied my code and ran it in PyCharm, and it worked! I don't understand why, perhaps there is some backend issue (?)
If you want to display a folium map outside of a Jupyter notebook, you have to save the map to html:
map_france.save('map_france.html')
and open the html in your browser.