i have a column date in the format 1/1/15 (month / day / year) without leading zeros and 15 instead of 2015.
i tried
data = data.withColumn('date' , to_date(unix_timestamp(data['date'], 'MM-dd-yyyy').cast("timestamp")))
data.orderBy('date').show()
it gives NULL in the date column (maybe because of the year format; i tried with MM-dd-yy , M-d-yy too)
so i tried
data = data.withColumn('date' , regexp_replace('date', '15', '2015'))
data = data.withColumn('date' , regexp_replace('date', '/2015/', '-15-'))
data = data.withColumn('date' , regexp_replace('date' , '/' , '-'))
now I have the date as 1-1-2015 and then when i tried the code from above , it shows the following error:
---------------------------------------------------------------------------
Py4JJavaError Traceback (most recent call last)
<ipython-input-39-470368900f3b> in <module>
----> 1 data.orderBy('date').show()
C:\Users\Admin\Anaconda3\lib\site-packages\pyspark\sql\dataframe.py in show(self, n, truncate, vertical)
438 """
439 if isinstance(truncate, bool) and truncate:
--> 440 print(self._jdf.showString(n, 20, vertical))
441 else:
442 print(self._jdf.showString(n, int(truncate), vertical))
C:\Users\Admin\Anaconda3\lib\site-packages\py4j\java_gateway.py in __call__(self, *args)
1303 answer = self.gateway_client.send_command(command)
1304 return_value = get_return_value(
-> 1305 answer, self.gateway_client, self.target_id, self.name)
1306
1307 for temp_arg in temp_args:
C:\Users\Admin\Anaconda3\lib\site-packages\pyspark\sql\utils.py in deco(*a, **kw)
126 def deco(*a, **kw):
127 try:
--> 128 return f(*a, **kw)
129 except py4j.protocol.Py4JJavaError as e:
130 converted = convert_exception(e.java_exception)
C:\Users\Admin\Anaconda3\lib\site-packages\py4j\protocol.py in get_return_value(answer, gateway_client, target_id, name)
326 raise Py4JJavaError(
327 "An error occurred while calling {0}{1}{2}.\n".
--> 328 format(target_id, ".", name), value)
329 else:
330 raise Py4JError(
Py4JJavaError: An error occurred while calling o140.showString.
: org.apache.spark.SparkException: Job aborted due to stage failure: Task 1 in stage 34.0 failed 1 times, most recent failure: Lost task 1.0 in stage 34.0 (TID 54, DESKTOP-IQ36PJF, executor driver): org.apache.spark.SparkUpgradeException: You may get a different result due to the upgrading of Spark 3.0: Fail to parse '5-17-2015' in the new parser. You can set spark.sql.legacy.timeParserPolicy to LEGACY to restore the behavior before Spark 3.0, or set to CORRECTED and treat it as an invalid datetime string.
at org.apache.spark.sql.catalyst.util.DateTimeFormatterHelper$$anonfun$checkParsedDiff$1.applyOrElse(DateTimeFormatterHelper.scala:150)
at org.apache.spark.sql.catalyst.util.DateTimeFormatterHelper$$anonfun$checkParsedDiff$1.applyOrElse(DateTimeFormatterHelper.scala:141)
at scala.runtime.AbstractPartialFunction.apply(AbstractPartialFunction.scala:38)
at org.apache.spark.sql.catalyst.util.Iso8601TimestampFormatter.$anonfun$parse$1(TimestampFormatter.scala:86)
at scala.runtime.java8.JFunction0$mcJ$sp.apply(JFunction0$mcJ$sp.java:23)
at scala.Option.getOrElse(Option.scala:189)
at org.apache.spark.sql.catalyst.util.Iso8601TimestampFormatter.parse(TimestampFormatter.scala:77)
at org.apache.spark.sql.catalyst.expressions.GeneratedClass$GeneratedIteratorForCodegenStage1.processNext(Unknown Source)
at org.apache.spark.sql.execution.BufferedRowIterator.hasNext(BufferedRowIterator.java:43)
at org.apache.spark.sql.execution.WholeStageCodegenExec$$anon$1.hasNext(WholeStageCodegenExec.scala:729)
at scala.collection.Iterator$$anon$10.hasNext(Iterator.scala:458)
at scala.collection.convert.Wrappers$IteratorWrapper.hasNext(Wrappers.scala:31)
at org.sparkproject.guava.collect.Ordering.leastOf(Ordering.java:628)
at org.apache.spark.util.collection.Utils$.takeOrdered(Utils.scala:37)
at org.apache.spark.rdd.RDD.$anonfun$takeOrdered$2(RDD.scala:1492)
at org.apache.spark.rdd.RDD.$anonfun$mapPartitions$2(RDD.scala:837)
at org.apache.spark.rdd.RDD.$anonfun$mapPartitions$2$adapted(RDD.scala:837)
at org.apache.spark.rdd.MapPartitionsRDD.compute(MapPartitionsRDD.scala:52)
at org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:349)
at org.apache.spark.rdd.RDD.iterator(RDD.scala:313)
at org.apache.spark.scheduler.ResultTask.runTask(ResultTask.scala:90)
at org.apache.spark.scheduler.Task.run(Task.scala:127)
at org.apache.spark.executor.Executor$TaskRunner.$anonfun$run$3(Executor.scala:446)
at org.apache.spark.util.Utils$.tryWithSafeFinally(Utils.scala:1377)
at org.apache.spark.executor.Executor$TaskRunner.run(Executor.scala:449)
at java.base/java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1135)
at java.base/java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:635)
at java.base/java.lang.Thread.run(Thread.java:844)
Caused by: java.time.format.DateTimeParseException: Text '5-17-2015' could not be parsed at index 0
at java.base/java.time.format.DateTimeFormatter.parseResolved0(DateTimeFormatter.java:2046)
at java.base/java.time.format.DateTimeFormatter.parse(DateTimeFormatter.java:1874)
at org.apache.spark.sql.catalyst.util.Iso8601TimestampFormatter.$anonfun$parse$1(TimestampFormatter.scala:78)
... 24 more
Driver stacktrace:
at org.apache.spark.scheduler.DAGScheduler.failJobAndIndependentStages(DAGScheduler.scala:2059)
at org.apache.spark.scheduler.DAGScheduler.$anonfun$abortStage$2(DAGScheduler.scala:2008)
at org.apache.spark.scheduler.DAGScheduler.$anonfun$abortStage$2$adapted(DAGScheduler.scala:2007)
at scala.collection.mutable.ResizableArray.foreach(ResizableArray.scala:62)
at scala.collection.mutable.ResizableArray.foreach$(ResizableArray.scala:55)
at scala.collection.mutable.ArrayBuffer.foreach(ArrayBuffer.scala:49)
at org.apache.spark.scheduler.DAGScheduler.abortStage(DAGScheduler.scala:2007)
at org.apache.spark.scheduler.DAGScheduler.$anonfun$handleTaskSetFailed$1(DAGScheduler.scala:973)
at org.apache.spark.scheduler.DAGScheduler.$anonfun$handleTaskSetFailed$1$adapted(DAGScheduler.scala:973)
at scala.Option.foreach(Option.scala:407)
at org.apache.spark.scheduler.DAGScheduler.handleTaskSetFailed(DAGScheduler.scala:973)
at org.apache.spark.scheduler.DAGSchedulerEventProcessLoop.doOnReceive(DAGScheduler.scala:2239)
at org.apache.spark.scheduler.DAGSchedulerEventProcessLoop.onReceive(DAGScheduler.scala:2188)
at org.apache.spark.scheduler.DAGSchedulerEventProcessLoop.onReceive(DAGScheduler.scala:2177)
at org.apache.spark.util.EventLoop$$anon$1.run(EventLoop.scala:49)
at org.apache.spark.scheduler.DAGScheduler.runJob(DAGScheduler.scala:775)
at org.apache.spark.SparkContext.runJob(SparkContext.scala:2099)
at org.apache.spark.SparkContext.runJob(SparkContext.scala:2194)
at org.apache.spark.rdd.RDD.$anonfun$reduce$1(RDD.scala:1094)
at org.apache.spark.rdd.RDDOperationScope$.withScope(RDDOperationScope.scala:151)
at org.apache.spark.rdd.RDDOperationScope$.withScope(RDDOperationScope.scala:112)
at org.apache.spark.rdd.RDD.withScope(RDD.scala:388)
at org.apache.spark.rdd.RDD.reduce(RDD.scala:1076)
at org.apache.spark.rdd.RDD.$anonfun$takeOrdered$1(RDD.scala:1498)
at org.apache.spark.rdd.RDDOperationScope$.withScope(RDDOperationScope.scala:151)
at org.apache.spark.rdd.RDDOperationScope$.withScope(RDDOperationScope.scala:112)
at org.apache.spark.rdd.RDD.withScope(RDD.scala:388)
at org.apache.spark.rdd.RDD.takeOrdered(RDD.scala:1486)
at org.apache.spark.sql.execution.TakeOrderedAndProjectExec.executeCollect(limit.scala:183)
at org.apache.spark.sql.Dataset.collectFromPlan(Dataset.scala:3627)
at org.apache.spark.sql.Dataset.$anonfun$head$1(Dataset.scala:2697)
at org.apache.spark.sql.Dataset.$anonfun$withAction$1(Dataset.scala:3618)
at org.apache.spark.sql.execution.SQLExecution$.$anonfun$withNewExecutionId$5(SQLExecution.scala:100)
at org.apache.spark.sql.execution.SQLExecution$.withSQLConfPropagated(SQLExecution.scala:160)
at org.apache.spark.sql.execution.SQLExecution$.$anonfun$withNewExecutionId$1(SQLExecution.scala:87)
at org.apache.spark.sql.SparkSession.withActive(SparkSession.scala:764)
at org.apache.spark.sql.execution.SQLExecution$.withNewExecutionId(SQLExecution.scala:64)
at org.apache.spark.sql.Dataset.withAction(Dataset.scala:3616)
at org.apache.spark.sql.Dataset.head(Dataset.scala:2697)
at org.apache.spark.sql.Dataset.take(Dataset.scala:2904)
at org.apache.spark.sql.Dataset.getRows(Dataset.scala:300)
at org.apache.spark.sql.Dataset.showString(Dataset.scala:337)
at java.base/jdk.internal.reflect.NativeMethodAccessorImpl.invoke0(Native Method)
at java.base/jdk.internal.reflect.NativeMethodAccessorImpl.invoke(NativeMethodAccessorImpl.java:62)
at java.base/jdk.internal.reflect.DelegatingMethodAccessorImpl.invoke(DelegatingMethodAccessorImpl.java:43)
at java.base/java.lang.reflect.Method.invoke(Method.java:564)
at py4j.reflection.MethodInvoker.invoke(MethodInvoker.java:244)
at py4j.reflection.ReflectionEngine.invoke(ReflectionEngine.java:357)
at py4j.Gateway.invoke(Gateway.java:282)
at py4j.commands.AbstractCommand.invokeMethod(AbstractCommand.java:132)
at py4j.commands.CallCommand.execute(CallCommand.java:79)
at py4j.GatewayConnection.run(GatewayConnection.java:238)
at java.base/java.lang.Thread.run(Thread.java:844)
Caused by: org.apache.spark.SparkUpgradeException: You may get a different result due to the upgrading of Spark 3.0: Fail to parse '5-17-2015' in the new parser. You can set spark.sql.legacy.timeParserPolicy to LEGACY to restore the behavior before Spark 3.0, or set to CORRECTED and treat it as an invalid datetime string.
at org.apache.spark.sql.catalyst.util.DateTimeFormatterHelper$$anonfun$checkParsedDiff$1.applyOrElse(DateTimeFormatterHelper.scala:150)
at org.apache.spark.sql.catalyst.util.DateTimeFormatterHelper$$anonfun$checkParsedDiff$1.applyOrElse(DateTimeFormatterHelper.scala:141)
at scala.runtime.AbstractPartialFunction.apply(AbstractPartialFunction.scala:38)
at org.apache.spark.sql.catalyst.util.Iso8601TimestampFormatter.$anonfun$parse$1(TimestampFormatter.scala:86)
at scala.runtime.java8.JFunction0$mcJ$sp.apply(JFunction0$mcJ$sp.java:23)
at scala.Option.getOrElse(Option.scala:189)
at org.apache.spark.sql.catalyst.util.Iso8601TimestampFormatter.parse(TimestampFormatter.scala:77)
at org.apache.spark.sql.catalyst.expressions.GeneratedClass$GeneratedIteratorForCodegenStage1.processNext(Unknown Source)
at org.apache.spark.sql.execution.BufferedRowIterator.hasNext(BufferedRowIterator.java:43)
at org.apache.spark.sql.execution.WholeStageCodegenExec$$anon$1.hasNext(WholeStageCodegenExec.scala:729)
at scala.collection.Iterator$$anon$10.hasNext(Iterator.scala:458)
at scala.collection.convert.Wrappers$IteratorWrapper.hasNext(Wrappers.scala:31)
at org.sparkproject.guava.collect.Ordering.leastOf(Ordering.java:628)
at org.apache.spark.util.collection.Utils$.takeOrdered(Utils.scala:37)
at org.apache.spark.rdd.RDD.$anonfun$takeOrdered$2(RDD.scala:1492)
at org.apache.spark.rdd.RDD.$anonfun$mapPartitions$2(RDD.scala:837)
at org.apache.spark.rdd.RDD.$anonfun$mapPartitions$2$adapted(RDD.scala:837)
at org.apache.spark.rdd.MapPartitionsRDD.compute(MapPartitionsRDD.scala:52)
at org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:349)
at org.apache.spark.rdd.RDD.iterator(RDD.scala:313)
at org.apache.spark.scheduler.ResultTask.runTask(ResultTask.scala:90)
at org.apache.spark.scheduler.Task.run(Task.scala:127)
at org.apache.spark.executor.Executor$TaskRunner.$anonfun$run$3(Executor.scala:446)
at org.apache.spark.util.Utils$.tryWithSafeFinally(Utils.scala:1377)
at org.apache.spark.executor.Executor$TaskRunner.run(Executor.scala:449)
at java.base/java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1135)
at java.base/java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:635)
... 1 more
Caused by: java.time.format.DateTimeParseException: Text '5-17-2015' could not be parsed at index 0
at java.base/java.time.format.DateTimeFormatter.parseResolved0(DateTimeFormatter.java:2046)
at java.base/java.time.format.DateTimeFormatter.parse(DateTimeFormatter.java:1874)
at org.apache.spark.sql.catalyst.util.Iso8601TimestampFormatter.$anonfun$parse$1(TimestampFormatter.scala:78)
... 24 more
any help regarding this would be helpful!!!
thanks !
Related
TypeError Traceback (most recent call last)
File D:\Anaconda3\lib\site-packages\pandas\core\indexes\base.py:3621, in Index.get_loc(self, key, method, tolerance)
3620 try:
-> 3621 return self._engine.get_loc(casted_key)
3622 except KeyError as err:
File D:\Anaconda3\lib\site-packages\pandas\_libs\index.pyx:136, in pandas._libs.index.IndexEngine.get_loc()
File D:\Anaconda3\lib\site-packages\pandas\_libs\index.pyx:142, in pandas._libs.index.IndexEngine.get_loc()
TypeError: '(slice(None, None, None), None)' is an invalid key
During handling of the above exception, another exception occurred:
InvalidIndexError Traceback (most recent call last)
Input In [18], in <cell line: 4>()
1 plt.figure(figsize=(20,10))
2 ax = plt.subplot()
----> 4 plt.plot(x,y_all)
5 ax.set_xticks(x_ticks)
6 ax.set_xticklabels(x_ticklabels)
File D:\Anaconda3\lib\site-packages\matplotlib\pyplot.py:2757, in plot(scalex, scaley, data, *args, **kwargs)
2755 #_copy_docstring_and_deprecators(Axes.plot)
2756 def plot(*args, scalex=True, scaley=True, data=None, **kwargs):
-> 2757 return gca().plot(
2758 *args, scalex=scalex, scaley=scaley,
2759 **({"data": data} if data is not None else {}), **kwargs)
File D:\Anaconda3\lib\site-packages\matplotlib\axes\_axes.py:1632, in Axes.plot(self, scalex, scaley, data, *args, **kwargs)
1390 """
1391 Plot y versus x as lines and/or markers.
1392
(...)
1629 (``'green'``) or hex strings (``'#008000'``).
1630 """
1631 kwargs = cbook.normalize_kwargs(kwargs, mlines.Line2D)
-> 1632 lines = [*self._get_lines(*args, data=data, **kwargs)]
1633 for line in lines:
1634 self.add_line(line)
File D:\Anaconda3\lib\site-packages\matplotlib\axes\_base.py:312, in _process_plot_var_args.__call__(self, data, *args, **kwargs)
310 this += args[0],
311 args = args[1:]
--> 312 yield from self._plot_args(this, kwargs)
File D:\Anaconda3\lib\site-packages\matplotlib\axes\_base.py:488, in _process_plot_var_args._plot_args(self, tup, kwargs, return_kwargs)
486 if len(xy) == 2:
487 x = _check_1d(xy[0])
--> 488 y = _check_1d(xy[1])
489 else:
490 x, y = index_of(xy[-1])
File D:\Anaconda3\lib\site-packages\matplotlib\cbook\__init__.py:1327, in _check_1d(x)
1321 with warnings.catch_warnings(record=True) as w:
1322 warnings.filterwarnings(
1323 "always",
1324 category=Warning,
1325 message='Support for multi-dimensional indexing')
-> 1327 ndim = x[:, None].ndim
1328 # we have definitely hit a pandas index or series object
1329 # cast to a numpy array.
1330 if len(w) > 0:
File D:\Anaconda3\lib\site-packages\pandas\core\frame.py:3505, in DataFrame.__getitem__(self, key)
3503 if self.columns.nlevels > 1:
3504 return self._getitem_multilevel(key)
-> 3505 indexer = self.columns.get_loc(key)
3506 if is_integer(indexer):
3507 indexer = [indexer]
File D:\Anaconda3\lib\site-packages\pandas\core\indexes\base.py:3628, in Index.get_loc(self, key, method, tolerance)
3623 raise KeyError(key) from err
3624 except TypeError:
3625 # If we have a listlike key, _check_indexing_error will raise
3626 # InvalidIndexError. Otherwise we fall through and re-raise
3627 # the TypeError.
-> 3628 self._check_indexing_error(key)
3629 raise
3631 # GH#42269
File D:\Anaconda3\lib\site-packages\pandas\core\indexes\base.py:5637, in Index._check_indexing_error(self, key)
5633 def _check_indexing_error(self, key):
5634 if not is_scalar(key):
5635 # if key is not a scalar, directly raise an error (the code below
5636 # would convert to numpy arrays and raise later any way) - GH29926
-> 5637 raise InvalidIndexError(key)
InvalidIndexError: (slice(None, None, None), None)
from bokeh.m
y_all = groupby_all[['AUD_mean', 'EUR_mean', 'NZD_mean', 'SGD_mean', 'GBP_mean', 'CHF_mean','USD_mean']]
labels = ["AUD_mean", "EUR_mean", "NZD_mean", "SGD_mean", "GBP_mean", "CHF_mean", "USD_mean"]
x_ticks = list(range(1, 240, 12))
x_ticklabels = [x for x in range(2000, 2021)]
plt.figure(figsize=(20,10))
ax = plt.subplot()
plt.plot(x, y_all)
ax.set_xticks(x_ticks)
ax.set_xticklabels(x_ticklabels)
plt.legend(labels)
plt.title("Exchange Rate: Top Countries/USD")
plt.xlabel("Year")
plt.ylabel("Exchange Rate")
plt.show()
I am trying to run the below notebook through databricks but getting the below error. I have tried to update the notebook timeout and the retry mechanism but still no luck yet.
NotebookData("/Users/mynotebook",9900, retry=3)
]
res = parallelNotebooks(notebooks, 2)
result = [f.result(timeout=9900) for f in res] # This is a blocking call.
print(result)
Can someone please help me to sort out this issue? Thanks
%python
from concurrent.futures import ThreadPoolExecutor
class NotebookData:
def __init__(self, path, timeout, parameters=None, retry=0):
self.path = path
self.timeout = timeout
self.parameters = parameters
self.retry = retry
def submitNotebook(notebook):
print("Running notebook %s" % notebook.path)
try:
if (notebook.parameters):
return dbutils.notebook.run(notebook.path, notebook.timeout, notebook.parameters)
else:
return dbutils.notebook.run(notebook.path, notebook.timeout)
except Exception:
if notebook.retry < 1:
raise
print("Retrying notebook %s" % notebook.path)
notebook.retry = notebook.retry - 1
submitNotebook(notebook)
def parallelNotebooks(notebooks, numInParallel):
# This code limits the number of parallel notebooks.
with ThreadPoolExecutor(max_workers=numInParallel) as ec:
return [ec.submit(submitNotebook, notebook) for notebook in notebooks]
notebooks = [
NotebookData("/Users/mynotebook",1200000, retry=0)
]
res = parallelNotebooks(notebooks, 2)
result = [f.result(timeout=1200000) for f in res] # This is a blocking call.
print(result)
Error:
Py4JJavaError Traceback (most recent call last)
<command-1143841910698378> in <module>
32 ]
33 res = parallelNotebooks(notebooks, 2)
---> 34 result = [f.result(timeout=1200000) for f in res] # This is a blocking call.
35 print(result)
<command-1143841910698378> in <listcomp>(.0)
32 ]
33 res = parallelNotebooks(notebooks, 2)
---> 34 result = [f.result(timeout=1200000) for f in res] # This is a blocking call.
35 print(result)
/usr/lib/python3.7/concurrent/futures/_base.py in result(self, timeout)
426 raise CancelledError()
427 elif self._state == FINISHED:
--> 428 return self.__get_result()
429
430 self._condition.wait(timeout)
/usr/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
/usr/lib/python3.7/concurrent/futures/thread.py in run(self)
55
56 try:
---> 57 result = self.fn(*self.args, **self.kwargs)
58 except BaseException as exc:
59 self.future.set_exception(exc)
<command-1143841910698378> in submitNotebook(notebook)
12 return dbutils.notebook.run(notebook.path, notebook.timeout, notebook.parameters)
13 else:
---> 14 return dbutils.notebook.run(notebook.path, notebook.timeout)
15 except Exception:
16 if notebook.retry < 1:
/local_disk0/tmp/1664351986642-0/dbutils.py in run(self, path, timeout_seconds, arguments, _NotebookHandler__databricks_internal_cluster_spec)
136 arguments,
137 __databricks_internal_cluster_spec,
--> 138 self.shell.currentJobGroup)
139
140 def __repr__(self):
/databricks/spark/python/lib/py4j-0.10.9-src.zip/py4j/java_gateway.py in __call__(self, *args)
1303 answer = self.gateway_client.send_command(command)
1304 return_value = get_return_value(
-> 1305 answer, self.gateway_client, self.target_id, self.name)
1306
1307 for temp_arg in temp_args:
/databricks/spark/python/pyspark/sql/utils.py in deco(*a, **kw)
125 def deco(*a, **kw):
126 try:
--> 127 return f(*a, **kw)
128 except py4j.protocol.Py4JJavaError as e:
129 converted = convert_exception(e.java_exception)
/databricks/spark/python/lib/py4j-0.10.9-src.zip/py4j/protocol.py in get_return_value(answer, gateway_client, target_id, name)
326 raise Py4JJavaError(
327 "An error occurred while calling {0}{1}{2}.\n".
--> 328 format(target_id, ".", name), value)
329 else:
330 raise Py4JError(
Py4JJavaError: An error occurred while calling o1741._run.
: com.databricks.WorkflowException: com.databricks.NotebookExecutionException: FAILED
at com.databricks.workflow.WorkflowDriver.run(WorkflowDriver.scala:95)
at com.databricks.dbutils_v1.impl.NotebookUtilsImpl.run(NotebookUtilsImpl.scala:122)
at com.databricks.dbutils_v1.impl.NotebookUtilsImpl._run(NotebookUtilsImpl.scala:89)
at sun.reflect.GeneratedMethodAccessor820.invoke(Unknown Source)
at sun.reflect.DelegatingMethodAccessorImpl.invoke(DelegatingMethodAccessorImpl.java:43)
at java.lang.reflect.Method.invoke(Method.java:498)
at py4j.reflection.MethodInvoker.invoke(MethodInvoker.java:244)
at py4j.reflection.ReflectionEngine.invoke(ReflectionEngine.java:380)
at py4j.Gateway.invoke(Gateway.java:295)
at py4j.commands.AbstractCommand.invokeMethod(AbstractCommand.java:132)
at py4j.commands.CallCommand.execute(CallCommand.java:79)
at py4j.GatewayConnection.run(GatewayConnection.java:251)
at java.lang.Thread.run(Thread.java:748)
Caused by: com.databricks.NotebookExecutionException: FAILED
at com.databricks.workflow.WorkflowDriver.run0(WorkflowDriver.scala:141)
at com.databricks.workflow.WorkflowDriver.run(WorkflowDriver.scala:90)
... 12 more
I am just a beginner in NLP and was trying to learn the Semantic role labeling concept through implementation.
I was trying to load the bert-base-srl model from the public storage of allennlp.
But was facing the following error:
from allennlp.predictors.predictor import Predictor
predictor = Predictor.from_path("https://storage.googleapis.com/allennlp-public-models/bert-base-srl-2020.03.24.tar.gz")
---------------------------------------------------------------------------
RuntimeError Traceback (most recent call last)
~\AppData\Local\Temp/ipykernel_11672/96061884.py in <module>
1 from allennlp.predictors.predictor import Predictor
----> 2 predictor = Predictor.from_path("https://storage.googleapis.com/allennlp-public-models/bert-base-srl-2020.03.24.tar.gz")
~\anaconda3\lib\site-packages\allennlp\predictors\predictor.py in from_path(cls, archive_path, predictor_name, cuda_device, dataset_reader_to_load, frozen, import_plugins, overrides, **kwargs)
364 plugins.import_plugins()
365 return Predictor.from_archive(
--> 366 load_archive(archive_path, cuda_device=cuda_device, overrides=overrides),
367 predictor_name,
368 dataset_reader_to_load=dataset_reader_to_load,
~\anaconda3\lib\site-packages\allennlp\models\archival.py in load_archive(archive_file, cuda_device, overrides, weights_file)
233 config.duplicate(), serialization_dir
234 )
--> 235 model = _load_model(config.duplicate(), weights_path, serialization_dir, cuda_device)
236
237 # Load meta.
~\anaconda3\lib\site-packages\allennlp\models\archival.py in _load_model(config, weights_path, serialization_dir, cuda_device)
277
278 def _load_model(config, weights_path, serialization_dir, cuda_device):
--> 279 return Model.load(
280 config,
281 weights_file=weights_path,
~\anaconda3\lib\site-packages\allennlp\models\model.py in load(cls, config, serialization_dir, weights_file, cuda_device)
436 # get_model_class method, that recurses whenever it finds a from_archive model type.
437 model_class = Model
--> 438 return model_class._load(config, serialization_dir, weights_file, cuda_device)
439
440 def extend_embedder_vocab(self, embedding_sources_mapping: Dict[str, str] = None) -> None:
~\anaconda3\lib\site-packages\allennlp\models\model.py in _load(cls, config, serialization_dir, weights_file, cuda_device)
378
379 if unexpected_keys or missing_keys:
--> 380 raise RuntimeError(
381 f"Error loading state dict for {model.__class__.__name__}\n\t"
382 f"Missing keys: {missing_keys}\n\t"
RuntimeError: Error loading state dict for SrlBert
Missing keys: ['bert_model.embeddings.position_ids']
Unexpected keys: []
Does someone know a fix for this?
If you are on the later versions of allennlp-models, you can use this archive_file instead: https://storage.googleapis.com/allennlp-public-models/structured-prediction-srl-bert.2020.12.15.tar.gz.
The latest versions of the model archive files can be found on the demo page in the Model Card tab: https://demo.allennlp.org/semantic-role-labeling
I have a pyspark dataframe contains rows of data seperated by comma. I want to split each row and apply LabeledPoints method to it. Then covnert it to dataframe.
Here is my code
import os.path
from pyspark.mllib.regression import LabeledPoint
import numpy as np
file_name = os.path.join('databricks-datasets', 'cs190', 'data-001', 'millionsong.txt')
raw_data_df = sqlContext.read.load(file_name, 'text')
rdd = raw_data_df.rdd.map(lambda line: line.split(',')).map(lambda seq:LabeledPoints(seq[0],seq[1:])).toDF()
It gives the following error message after apply .DF().
---------------------------------------------------------------------------
org.apache.spark.SparkException: Job aborted due to stage failure: Task 0 in stage 38.0 failed 1 times, most recent failure: Lost task 0.0 in stage 38.0 (TID 44, localhost): org.apache.spark.api.python.PythonException: Traceback (most recent call last):
Py4JJavaError Traceback (most recent call last)
<ipython-input-65-dc4d86a8ee45> in <module>()
----> 1 rdd = raw_data_df.rdd.map(lambda line: line.split(',')).map(lambda seq:LabeledPoints(seq[0],seq[1:])).toDF()
2 print(type(rdd))
3 #print(rdd.take(5))
/databricks/spark/python/pyspark/sql/context.py in toDF(self, schema, sampleRatio)
62 [Row(name=u'Alice', age=1)]
63 """
---> 64 return sqlContext.createDataFrame(self, schema, sampleRatio)
65
66 RDD.toDF = toDF
/databricks/spark/python/pyspark/sql/context.py in createDataFrame(self, data, schema, samplingRatio)
421
422 if isinstance(data, RDD):
--> 423 rdd, schema = self._createFromRDD(data, schema, samplingRatio)
424 else:
425 rdd, schema = self._createFromLocal(data, schema)
/databricks/spark/python/pyspark/sql/context.py in _createFromRDD(self, rdd, schema, samplingRatio)
Answer found:
rdd = raw_data_df.map(lambda row: row['value'].split(',')).map(lambda seq:LabeledPoint(float(seq[0]),seq[1:])).toDF()
Here, I need to specifically reference each line of text using row['value'], even though there is only one feature in the row.
I just installed HTS-2.2 and HTK-3.4.1 in my 64bit Ubuntu with gcc 3.4 compiler. After that, typing HInit and HCopy etc. seemed work. So I wondered how the HTKDemo works.
When I run the demo, HTK caused a problem:
HMM Def Error: <Mean> symbol expected in GetMean at line 6/col 11/char 120 in proto/L
ERROR [+7050] HMError:
HMM Def Error: GetMean Failed at line 6/col 12/char 121 in proto/L
ERROR [+7050] HMError:
HMM Def Error: Regression Class Number expected at line 7/col 0/char 122 in proto/L
ERROR [+7050] HMError:
HMM Def Error: GetMixtures failed at line 7/col 1/char 123 in proto/L
ERROR [+7050] HMError:
HMM Def Error: Get Stream Information failed at line 7/col 2/char 124 in proto/L
ERROR [+7050] HMError:
HMM Def Error: GetStream failed at line 7/col 3/char 125 in proto/L
ERROR [+7050] HMError:
HMM Def Error: GetStateInfo failed at line 7/col 4/char 126 in proto/L
ERROR [+7050] HMError:
ERROR [+7032] LoadHMMSet: GetHMMDef failed
ERROR [+2128] Initialise: LoadHMMSet failed
FATAL ERROR - Terminating program HInit
Source Directory Empty hmms/hmm.0
The same to the proto S, V, N, C.
I really want to know the reason and how to fix it? Thx!
I got the same +7050 error, in my case was a mispelled hmm definition file, probably you have the same problem since the error reported: " symbol expected".
Just check your definition file had the right format as follows:
~h (phoneme name)
<BEGINHMM>
<NUMSTATES> (NStates)
<STATE> 2 (number starting from 2, ending NStates-1 )
<MEAN> 13 (or any number defined)
-4.717658e+000 ...
<VARIANCE> 13 (the same as mean possibly)
4.735534e+001 ...
<STATE> 3 ....
...
<GCONST> 1.269744e+002
<TRANSP> 3 (again not important)
0.0 1.0 0.0
0.0 0.9 0.1
0.0 0.0 0.0
<ENDHMM>
~h (next phoneme )
...