when i try to run this code,
ftr_mtrx_custmr, features_defs = ft.dfs(entities=entities,
relationships=relationship,
target_entity="transactions")
i get such error，
490 featuretools.entityset - WARNING index session_id not found in dataframe, creating new integer column
KeyError Traceback (most recent call last)
<ipython-input-82-d467a36d5254> in <module>()
1 ftr_mtrx_custmr, features_defs = ft.dfs(entities=entities,
2 relationships=relationshp,
----> 3 target_entity="transactions")
4 frames
/usr/local/lib/python3.6/dist-packages/featuretools/utils/entry_point.py
in function_wrapper(*args, **kwargs)
38 ep.on_error(error=e,
39 runtime=runtime)
---> 40 raise e
41
42 # send return value
/usr/local/lib/python3.6/dist-packages/featuretools/utils/entry_point.py
in function_wrapper(*args, **kwargs)
30 # call function
31 start = time.time()
---> 32 return_value = func(*args, **kwargs)
33 runtime = time.time() - start
34 except Exception as e:
/usr/local/lib/python3.6/dist-packages/featuretools/synthesis/dfs.py
in dfs(entities, relationships, entityset, target_entity, cutoff_time,
instance_ids, agg_primitives, trans_primitives,
groupby_trans_primitives, allowed_paths, max_depth, ignore_entities,
ignore_variables, primitive_options, seed_features, drop_contains,
drop_exact, where_primitives, max_features, cutoff_time_in_index,
save_progress, features_only, training_window, approximate,
chunk_size, n_jobs, dask_kwargs, verbose, return_variable_types,
progress_callback)
225 '''
226 if not isinstance(entityset, EntitySet):
--> 227 entityset = EntitySet("dfs", entities, relationships)
228
229 dfs_object = DeepFeatureSynthesis(target_entity, entityset,
/usr/local/lib/python3.6/dist-packages/featuretools/entityset/entityset.py
in init(self, id, entities, relationships)
83
84 for relationship in relationships:
---> 85 parent_variable = self[relationship[0]][relationship[1]]
86 child_variable = self[relationship[2]][relationship[3]]
87 self.add_relationship(Relationship(parent_variable,
/usr/local/lib/python3.6/dist-packages/featuretools/entityset/entityset.py
in getitem(self, entity_id)
124 return self.entity_dict[entity_id]
125 name = self.id or "entity set"
--> 126 raise KeyError('Entity %s does not exist in %s' % (entity_id, name))
127
128 #property
however, this returned KeyError : 'Entity c does not exist in dfs'
any idea what's wrong with my code?
Related
The code below is triggering a AttributeError: can't set attribute. I'm still new to programming so am having a difficult time figuring out why this error is occurring. Any help is appreciated.
import cimcb_lite as cb
cv = cb.cross_val.kfold(model=cb.model.PLS_SIMPLS,X=XTknn,
Y=Ytrain,
param_dict={'n_components': [1,2,3,4,5]},
folds=5,
bootnum=100)
cv.run()
seeing this error
---------------------------------------------------------------------------
AttributeError Traceback (most recent call last)
/var/folders/rs/f6nsd1894354_821jj157jnr0000gn/T/ipykernel_30013/1292624611.py in <module>
8
9 # run the cross validation
---> 10 cv.run()
11
/opt/anaconda3/lib/python3.9/site-packages/cimcb_lite/cross_val/kfold.py in run(self)
82 def run(self):
83 """Runs all functions prior to plot."""
---> 84 self.calc_ypred()
85 self.calc_stats()
86 if self.bootnum > 1:
/opt/anaconda3/lib/python3.9/site-packages/cimcb_lite/cross_val/kfold.py in calc_ypred(self)
55 model_i = self.model(**params_i)
56 # Full
---> 57 model_i.train(self.X, self.Y)
58 ypred_full_i = model_i.test(self.X)
59 self.ypred_full.append(ypred_full_i)
/opt/anaconda3/lib/python3.9/site-packages/cimcb_lite/model/PLS_SIMPLS.py in train(self, X, Y)
77 # Calculates and store attributes of PLS SIMPLS
78 Xscores, Yscores, Xloadings, Yloadings, Weights, Beta = self.pls_simpls(X, Y, ncomp=self.n_component)
---> 79 self.model.x_scores_ = Xscores
80 self.model.y_scores_ = Yscores
81 self.model.x_loadings_ = Xloadings
AttributeError: can't set attribute
I have this model:
poss_in = layers.Input((1,))
poss_lr = layers.Dense(8, activation='relu')(poss_in)
hist_in = layers.Input((100,))
hist_lr = layers.Reshape((100, 1))(hist_in)
hist_lr = layers.LSTM(32)(hist_lr)
hist_lr = layers.Dense(32, activation='relu')(hist_lr)
sent_in = layers.Input((10,))
sent_lr = layers.Reshape((10, 1))(sent_in)
sent_lr = layers.Conv1D(4, 3)(sent_lr)
sent_lr = layers.GRU(4)(sent_lr)
root_lr = layers.concatenate([poss_lr, hist_lr, sent_lr])
root_lr = layers.Reshape((44, 1))(root_lr)
root_lr = Attention(16)(root_lr)
root_lr = layers.Dense(16)(root_lr)
root_lr = layers.Dense(1)(root_lr)
model = Model([poss_in, hist_in, sent_in], root_lr)
and I'm trying to create a DQN agent with:
dqn = agents.DQNAgent(
model=model,
memory=memory.SequentialMemory(limit=50000, window_length=1),
policy=policy.BoltzmannQPolicy(),
nb_actions=1,
nb_steps_warmup=64,
target_model_update=1e-2
)
dqn.compile('Adam', metrics=['mae'])
but I receive this error:
/usr/local/lib/python3.7/dist-packages/keras/optimizer_v2/adam.py:105: UserWarning: The `lr` argument is deprecated, use `learning_rate` instead.
super(Adam, self).__init__(name, **kwargs)
---------------------------------------------------------------------------
TypeError Traceback (most recent call last)
<ipython-input-18-3d71fb800af2> in <module>
7 target_model_update=1e-2
8 )
----> 9 dqn.compile(opt.Adam(lr=1e-3), metrics=['mae'])
17 frames
/usr/local/lib/python3.7/dist-packages/rl/agents/dqn.py in compile(self, optimizer, metrics)
165
166 # We never train the target model, hence we can set the optimizer and loss arbitrarily.
--> 167 self.target_model = clone_model(self.model, self.custom_model_objects)
168 self.target_model.compile(optimizer='sgd', loss='mse')
169 self.model.compile(optimizer='sgd', loss='mse')
/usr/local/lib/python3.7/dist-packages/rl/util.py in clone_model(model, custom_objects)
13 'config': model.get_config(),
14 }
---> 15 clone = model_from_config(config, custom_objects=custom_objects)
16 clone.set_weights(model.get_weights())
17 return clone
/usr/local/lib/python3.7/dist-packages/keras/saving/model_config.py in model_from_config(config, custom_objects)
50 '`Sequential.from_config(config)`?')
51 from keras.layers import deserialize # pylint: disable=g-import-not-at-top
---> 52 return deserialize(config, custom_objects=custom_objects)
53
54
/usr/local/lib/python3.7/dist-packages/keras/layers/serialization.py in deserialize(config, custom_objects)
209 module_objects=LOCAL.ALL_OBJECTS,
210 custom_objects=custom_objects,
--> 211 printable_module_name='layer')
212
213
/usr/local/lib/python3.7/dist-packages/keras/utils/generic_utils.py in deserialize_keras_object(identifier, module_objects, custom_objects, printable_module_name)
681 custom_objects=dict(
682 list(_GLOBAL_CUSTOM_OBJECTS.items()) +
--> 683 list(custom_objects.items())))
684 else:
685 with CustomObjectScope(custom_objects):
/usr/local/lib/python3.7/dist-packages/keras/engine/functional.py in from_config(cls, config, custom_objects)
707 'name', 'layers', 'input_layers', 'output_layers']):
708 input_tensors, output_tensors, created_layers = reconstruct_from_config(
--> 709 config, custom_objects)
710 model = cls(
711 inputs=input_tensors,
/usr/local/lib/python3.7/dist-packages/keras/engine/functional.py in reconstruct_from_config(config, custom_objects, created_layers)
1324 # First, we create all layers and enqueue nodes to be processed
1325 for layer_data in config['layers']:
-> 1326 process_layer(layer_data)
1327 # Then we process nodes in order of layer depth.
1328 # Nodes that cannot yet be processed (if the inbound node
/usr/local/lib/python3.7/dist-packages/keras/engine/functional.py in process_layer(layer_data)
1306 from keras.layers import deserialize as deserialize_layer # pylint: disable=g-import-not-at-top
1307
-> 1308 layer = deserialize_layer(layer_data, custom_objects=custom_objects)
1309 created_layers[layer_name] = layer
1310
/usr/local/lib/python3.7/dist-packages/keras/layers/serialization.py in deserialize(config, custom_objects)
209 module_objects=LOCAL.ALL_OBJECTS,
210 custom_objects=custom_objects,
--> 211 printable_module_name='layer')
212
213
/usr/local/lib/python3.7/dist-packages/keras/utils/generic_utils.py in deserialize_keras_object(identifier, module_objects, custom_objects, printable_module_name)
684 else:
685 with CustomObjectScope(custom_objects):
--> 686 deserialized_obj = cls.from_config(cls_config)
687 else:
688 # Then `cls` may be a function returning a class.
/usr/local/lib/python3.7/dist-packages/keras/engine/base_layer_v1.py in from_config(cls, config)
515 A layer instance.
516 """
--> 517 return cls(**config)
518
519 def compute_output_shape(self, input_shape):
/usr/local/lib/python3.7/dist-packages/keras/layers/dense_attention.py in __init__(self, use_scale, **kwargs)
321
322 def __init__(self, use_scale=False, **kwargs):
--> 323 super(Attention, self).__init__(**kwargs)
324 self.use_scale = use_scale
325
/usr/local/lib/python3.7/dist-packages/keras/layers/dense_attention.py in __init__(self, causal, dropout, **kwargs)
70
71 def __init__(self, causal=False, dropout=0.0, **kwargs):
---> 72 super(BaseDenseAttention, self).__init__(**kwargs)
73 self.causal = causal
74 self.dropout = dropout
/usr/local/lib/python3.7/dist-packages/tensorflow/python/training/tracking/base.py in _method_wrapper(self, *args, **kwargs)
627 self._self_setattr_tracking = False # pylint: disable=protected-access
628 try:
--> 629 result = method(self, *args, **kwargs)
630 finally:
631 self._self_setattr_tracking = previous_value # pylint: disable=protected-access
/usr/local/lib/python3.7/dist-packages/keras/engine/base_layer.py in __init__(self, seed, force_generator, **kwargs)
3436 **kwargs: other keyword arguments that will be passed to the parent class
3437 """
-> 3438 super().__init__(**kwargs)
3439 self._random_generator = backend.RandomGenerator(
3440 seed, force_generator=force_generator)
/usr/local/lib/python3.7/dist-packages/tensorflow/python/training/tracking/base.py in _method_wrapper(self, *args, **kwargs)
627 self._self_setattr_tracking = False # pylint: disable=protected-access
628 try:
--> 629 result = method(self, *args, **kwargs)
630 finally:
631 self._self_setattr_tracking = previous_value # pylint: disable=protected-access
/usr/local/lib/python3.7/dist-packages/keras/engine/base_layer_v1.py in __init__(self, trainable, name, dtype, dynamic, **kwargs)
138 }
139 # Validate optional keyword arguments.
--> 140 generic_utils.validate_kwargs(kwargs, allowed_kwargs)
141
142 # Mutable properties
/usr/local/lib/python3.7/dist-packages/keras/utils/generic_utils.py in validate_kwargs(kwargs, allowed_kwargs, error_message)
1172 for kwarg in kwargs:
1173 if kwarg not in allowed_kwargs:
-> 1174 raise TypeError(error_message, kwarg)
1175
1176
TypeError: ('Keyword argument not understood:', 'units')
I have tryied to replace the DQN with SARSA and DDPG agents but they all generated the same error.
I looked up the problem in internet for a while and I've asked on r/tensorflow but I haven't resolved anything yet.
For additional information, I'm using Google Colab.
Thanks for every reply!
UPDATE:
I tryied to simplify the model in order to check if the problem was in a layer, so I created this model:
poss_in = layers.Input((1,))
poss_lr = layers.Dense(1)(poss_in)
hist_in = layers.Input((100,))
hist_lr = layers.Dense(1)(hist_in)
sent_in = layers.Input((10,))
sent_lr = layers.Dense(1)(sent_in)
root_lr = layers.concatenate([poss_lr, hist_lr, sent_lr])
root_lr = layers.Dense(1)(root_lr)
model = Model([poss_in, hist_in, sent_in], root_lr)
Using this model the DQN agent was compiled with no errors.
I am trying to get the shap values for the masked language modeling task using transformer. I get the error KeyError: 'label' for the code where I input a single data sample to get the explanation. My complete code and error trace are as follows:
import transformers
import shap
from transformers import RobertaTokenizer, RobertaForMaskedLM, pipeline
import torch
model = RobertaForMaskedLM.from_pretrained('microsoft/codebert-base-mlm')
tokenizer = RobertaTokenizer.from_pretrained('microsoft/codebert-base-mlm')
code_example = "if (x <mask> 10)"
fill_mask = pipeline('fill-mask', model=model, tokenizer=tokenizer)
explainer = shap.Explainer(fill_mask)
shap_values = explainer(['x {tokenizer.mask_token} 10'])
Following is the error trace
KeyError Traceback (most recent call last)
[<ipython-input-12-bb3832d1772d>](https://localhost:8080/#) in <module>
6 # explain the model on two sample inputs
7 explainer = shap.Explainer(fill_mask)
----> 8 shap_values = explainer(['x {tokenizer.mask_token} 10'])
9 print(shap_values)
10 # visualize the first prediction's explanation for the POSITIVE output class
5 frames
[/usr/local/lib/python3.7/dist-packages/shap/explainers/_partition.py](https://localhost:8080/#) in __call__(self, max_evals, fixed_context, main_effects, error_bounds, batch_size, outputs, silent, *args)
136 return super().__call__(
137 *args, max_evals=max_evals, fixed_context=fixed_context, main_effects=main_effects, error_bounds=error_bounds, batch_size=batch_size,
--> 138 outputs=outputs, silent=silent
139 )
140
[/usr/local/lib/python3.7/dist-packages/shap/explainers/_explainer.py](https://localhost:8080/#) in __call__(self, max_evals, main_effects, error_bounds, batch_size, outputs, silent, *args, **kwargs)
266 row_result = self.explain_row(
267 *row_args, max_evals=max_evals, main_effects=main_effects, error_bounds=error_bounds,
--> 268 batch_size=batch_size, outputs=outputs, silent=silent, **kwargs
269 )
270 values.append(row_result.get("values", None))
[/usr/local/lib/python3.7/dist-packages/shap/explainers/_partition.py](https://localhost:8080/#) in explain_row(self, max_evals, main_effects, error_bounds, batch_size, outputs, silent, fixed_context, *row_args)
159 # if not fixed background or no base value assigned then compute base value for a row
160 if self._curr_base_value is None or not getattr(self.masker, "fixed_background", False):
--> 161 self._curr_base_value = fm(m00.reshape(1, -1), zero_index=0)[0] # the zero index param tells the masked model what the baseline is
162 f11 = fm(~m00.reshape(1, -1))[0]
163
[/usr/local/lib/python3.7/dist-packages/shap/utils/_masked_model.py](https://localhost:8080/#) in __call__(self, masks, zero_index, batch_size)
65
66 else:
---> 67 return self._full_masking_call(masks, batch_size=batch_size)
68
69 def _full_masking_call(self, masks, zero_index=None, batch_size=None):
[/usr/local/lib/python3.7/dist-packages/shap/utils/_masked_model.py](https://localhost:8080/#) in _full_masking_call(self, masks, zero_index, batch_size)
142
143 joined_masked_inputs = tuple([np.concatenate(v) for v in all_masked_inputs])
--> 144 outputs = self.model(*joined_masked_inputs)
145 _assert_output_input_match(joined_masked_inputs, outputs)
146 all_outputs.append(outputs)
[/usr/local/lib/python3.7/dist-packages/shap/models/_transformers_pipeline.py](https://localhost:8080/#) in __call__(self, strings)
33 val = [val]
34 for obj in val:
---> 35 output[i, self.label2id[obj["label"]]] = sp.special.logit(obj["score"]) if self.rescale_to_logits else obj["score"]
36 return output
KeyError: 'label'
I am trying to perform inference with a finetuned GPT2HeadWithValueModel from the Transformers library. I'm using the model.generate() method from generation_utils.py
I am using this function to call the generate() method:
def top_p_sampling(text, model, tokenizer):
encoding = tokenizer(text, return_tensors="pt")['input_ids']
output_tensor = model.generate(
encoding,
do_sample=True,
max_length=max_len,
top_k=50,
top_p= .92,
temperature= .9,
early_stopping=False)
return tokenizer.decode(output_tensor[0], skip_special_tokens=True).strip()
But when i try:
text = "this is an example of input text"
comp = top_p_sampling(text, model_name, tokenizer_name)
I get the following error:
TypeError: forward() got an unexpected keyword argument 'return_dict'
Full traceback:
TypeError Traceback (most recent call last)
<ipython-input-24-cc7c3f8aa367> in <module>()
1 text = "this is an example of input text"
----> 2 comp = top_p_sampling(text, model_name, tokenizer_name)
4 frames
<ipython-input-23-a5241487f309> in top_p_sampling(text, model, tokenizer)
9 temperature=temp,
10 early_stopping=False,
---> 11 return_dict=False)
12
13 return tokenizer.decode(output_tensor[0], skip_special_tokens=True).strip()
/usr/local/lib/python3.7/dist-packages/torch/autograd/grad_mode.py in decorate_context(*args, **kwargs)
26 def decorate_context(*args, **kwargs):
27 with self.__class__():
---> 28 return func(*args, **kwargs)
29 return cast(F, decorate_context)
30
/usr/local/lib/python3.7/dist-packages/transformers/generation_utils.py in generate(self, input_ids, max_length, min_length, do_sample, early_stopping, num_beams, temperature, top_k, top_p, repetition_penalty, bad_words_ids, bos_token_id, pad_token_id, eos_token_id, length_penalty, no_repeat_ngram_size, encoder_no_repeat_ngram_size, num_return_sequences, decoder_start_token_id, use_cache, num_beam_groups, diversity_penalty, prefix_allowed_tokens_fn, output_attentions, output_hidden_states, output_scores, return_dict_in_generate, **model_kwargs)
938 output_scores=output_scores,
939 return_dict_in_generate=return_dict_in_generate,
--> 940 **model_kwargs,
941 )
942
/usr/local/lib/python3.7/dist-packages/transformers/generation_utils.py in sample(self, input_ids, logits_processor, logits_warper, max_length, pad_token_id, eos_token_id, output_attentions, output_hidden_states, output_scores, return_dict_in_generate, **model_kwargs)
1383 return_dict=True,
1384 output_attentions=output_attentions,
-> 1385 output_hidden_states=output_hidden_states,
1386 )
1387 next_token_logits = outputs.logits[:, -1, :]
/usr/local/lib/python3.7/dist-packages/torch/nn/modules/module.py in _call_impl(self, *input, **kwargs)
1100 if not (self._backward_hooks or self._forward_hooks or self._forward_pre_hooks or _global_backward_hooks
1101 or _global_forward_hooks or _global_forward_pre_hooks):
-> 1102 return forward_call(*input, **kwargs)
1103 # Do not call functions when jit is used
1104 full_backward_hooks, non_full_backward_hooks = [], []
TypeError: forward() got an unexpected keyword argument 'return_dict'
I'm a bit of a rookie, so I hope someone can point out what I'm doing wrong. Thanks a lot
I have the following code for ingesting data into Azure Data Explore using Python in Databricks:
df=pd.DataFrame({"StringCol": ["123ABC", 'B123', 'C123','D123'],"NumberCol": [1,2,3,4],"DecimalCol": [1,2.2,3.3,4.4],"DateCol": ['1/1/20','2/2/20','3/3/30','4/4/20']})
ingestion_props = IngestionProperties(database=db, table='TestTable_DeleteMe')
connWrite.ingest_from_dataframe(df, ingestion_properties=ingestion_props)
This gives me the error:
BadRequest_SyntaxError', 'message': 'Request is invalid and cannot be executed
Earlier in my code I created a table using the same data types as this dummy pandas dataframe. Now I'm trying to load the data into the table. Full stack trace:
KustoServiceError Traceback (most recent call last)
<command-3953651275234016> in <module>
1 df=pd.DataFrame({"StringCol": ["123ABC", 'B123', 'C123','D123'],"NumberCol": [1,2,3,4],"DecimalCol": [1,2.2,3.3,4.4],"DateCol": ['1/1/20','2/2/20','3/3/30','4/4/20']})
2 ingestion_props = IngestionProperties(database=db, table='TestTable_DeleteMe')
----> 3 connWrite.ingest_from_dataframe(df, ingestion_properties=ingestion_props)
4
5 #adx_loadIntoTable(connWrite,db,df,'TestTable_DeleteMe')
/databricks/python/lib/python3.7/site-packages/azure/kusto/ingest/ingest_client.py in ingest_from_dataframe(self, df, ingestion_properties)
52 ingestion_properties.format = DataFormat.CSV
53
---> 54 self.ingest_from_file(temp_file_path, ingestion_properties)
55
56 os.unlink(temp_file_path)
/databricks/python/lib/python3.7/site-packages/azure/kusto/ingest/ingest_client.py in ingest_from_file(self, file_descriptor, ingestion_properties)
64 :param azure.kusto.ingest.IngestionProperties ingestion_properties: Ingestion properties.
65 """
---> 66 containers = self._resource_manager.get_containers()
67
68 if isinstance(file_descriptor, FileDescriptor):
/databricks/python/lib/python3.7/site-packages/azure/kusto/ingest/_resource_manager.py in get_containers(self)
121
122 def get_containers(self) -> List[_ResourceUri]:
--> 123 self._refresh_ingest_client_resources()
124 return self._ingest_client_resources.containers
125
/databricks/python/lib/python3.7/site-packages/azure/kusto/ingest/_resource_manager.py in _refresh_ingest_client_resources(self)
79 or not self._ingest_client_resources.is_applicable()
80 ):
---> 81 self._ingest_client_resources = self._get_ingest_client_resources_from_service()
82 self._ingest_client_resources_last_update = datetime.utcnow()
83
/databricks/python/lib/python3.7/site-packages/azure/kusto/ingest/_resource_manager.py in _get_ingest_client_resources_from_service(self)
86
87 def _get_ingest_client_resources_from_service(self):
---> 88 table = self._kusto_client.execute("NetDefaultDB", ".get ingestion resources").primary_results[0]
89
90 secured_ready_for_aggregation_queues = self._get_resource_by_name(table, "SecuredReadyForAggregationQueue")
/databricks/python/lib/python3.7/site-packages/azure/kusto/data/client.py in execute(self, database, query, properties)
553 query = query.strip()
554 if query.startswith("."):
--> 555 return self.execute_mgmt(database, query, properties)
556 return self.execute_query(database, query, properties)
557
/databricks/python/lib/python3.7/site-packages/azure/kusto/data/client.py in execute_mgmt(self, database, query, properties)
578 :rtype: azure.kusto.data.response.KustoResponseDataSet
579 """
--> 580 return self._execute(self._mgmt_endpoint, database, query, None, KustoClient._mgmt_default_timeout, properties)
581
582 def execute_streaming_ingest(
/databricks/python/lib/python3.7/site-packages/azure/kusto/data/client.py in _execute(self, endpoint, database, query, payload, timeout, properties)
654 )
655
--> 656 raise KustoServiceError([response.json()], response)
KustoServiceError: (KustoServiceError(...), [{'error': {'code': 'BadRequest_SyntaxError', 'message': 'Request is invalid and cannot be executed.', '#type': 'Kusto.Data.Exceptions.SyntaxException', '#message': "Syntax error: Query could not be parsed: . Query: '.get ingestion resources'", '#context': {'timestamp': '2020-06-27T21:44:48.0697658Z', 'serviceAlias': 'USCPIRSTASADE01', 'machineName': 'KEngine000000', 'processName': 'Kusto.WinSvc.Svc', 'processId': 7124, 'threadId': 7240, 'appDomainName': 'Kusto.WinSvc.Svc.exe', 'clientRequestId': 'KPC.execute;0c2173bf-ea69-4253-bbaf-0203f3aa298c', 'activityId': 'cf41c806-8e15-458e-b388-386613f63952', 'subActivityId': 'df366667-ca8d-487b-a281-723f696a8f68', 'activityType': 'DN.FE.ExecuteControlCommand', 'parentActivityId': 'f8cd0bb8-04e9-48cf-8a84-8b16e1e24197', 'activityStack': '(Activity stack: CRID=KPC.execute;0c2173bf-ea69-4253-bbaf-0203f3aa298c ARID=cf41c806-8e15-458e-b388-386613f63952 > DN.Admin.Client.ExecuteControlCommand/7271d9ec-2adf-4714-b19e-69495ad80d65 > P.WCF.Service.ExecuteControlCommandInternal..IAdminClientServiceCommunicationContract/f8cd0bb8-04e9-48cf-8a84-8b16e1e24197 > DN.FE.ExecuteControlCommand/df366667-ca8d-487b-a281-723f696a8f68)'}, '#permanent': True}}])
It is likely that your connection has the engine endpoint instead of the data management endpoint. Can you check that the connection to the cluster starts with "ingest-"? See here an example:
client = KustoIngestClient("https://ingest-{cluster_name}.kusto.windows.net")