autoit.control_click("Digital Oscilloscope", "", "[CLASS:ScrollToolbar; INSTANCE:1]", "Left", "1", 295, 11)
When I execute this I get the following error:
Traceback (most recent call last): File "", line 1, in
autoit.control_click("Digital Oscilloscope", "", "[CLASS:ScrollToolbar; INSTANCE:1]", "Left", "1", 295, 11) File
"C:\Python34\lib\site-packages\pyautoit-0.4-py3.4.egg\autoit\autoit.py",
line 73, in wrapper
ret = fn(*args, **kwargs)
TypeError: control_click() takes 2 positional arguments but 7 were given
What's wrong with the code?
Working after defining the **Kwargs explicitly
autoit.control_click("Digital Oscilloscope", "", "[CLASS:ScrollToolbar; INSTANCE:1]",click=1,x=95,y=11)
You have given too many arguments to the control_click() function, you only need 2 parameters: ClassID and ControlID.
Look here for an example: https://pypi.python.org/pypi/PyAutoIt/0.3
Related
While using PyCaret's ML capabilities I am facing the following error.
The ML Code I am using:
if choice == "ML":
st.title("Your Machine Learning Process Starts Here!")
target = st.selectbox("Select Your Target", df.columns)
setup(df, target=target, silent=True)
setup_df = pull()
st.info("This is the ML Experiment Settings")
st.dataframe(setup_df)
best_model = compare_models()
compare_df = pull()
st.info("This is the ML Model")
st.dataframe(compare_df)
best_model
Now while running the code I am getting the error:
TypeError: setup() got an unexpected keyword argument 'silent'
Traceback:
File "D:\Python Projects\Machine-Learning-App\mlapp\lib\site-packages\streamlit\runtime\scriptrunner\script_runner.py", line 563, in _run_script
exec(code, module.__dict__)
File "D:\Python Projects\Machine-Learning-App\app.py", line 37, in <module>
setup(df, target=target, silent=True)
Hence, I removed the silent=True and now am getting the following error:
ValueError: Cannot cast object dtype to float64
Traceback:
File "D:\Python Projects\Machine-Learning-App\mlapp\lib\site-packages\streamlit\runtime\scriptrunner\script_runner.py", line 563, in _run_script
exec(code, module.__dict__)
File "D:\Python Projects\Machine-Learning-App\app.py", line 37, in <module>
setup(df, target=target)
File "C:\Users\aviparna.biswas\AppData\Roaming\Python\Python39\site-packages\pycaret\classification\functional.py", line 596, in setup
return exp.setup(
File "C:\Users\aviparna.biswas\AppData\Roaming\Python\Python39\site-packages\pycaret\classification\oop.py", line 885, in setup
self.pipeline.fit(self.X_train, self.y_train)
File "C:\Users\aviparna.biswas\AppData\Roaming\Python\Python39\site-packages\pycaret\internal\pipeline.py", line 211, in fit
X, y, _ = self._fit(X, y, **fit_params_steps)
File "C:\Users\aviparna.biswas\AppData\Roaming\Python\Python39\site-packages\pycaret\internal\pipeline.py", line 192, in _fit
X, y, fitted_transformer = self._memory_fit(
File "D:\Python Projects\Machine-Learning-App\mlapp\lib\site-packages\joblib\memory.py", line 594, in __call__
return self._cached_call(args, kwargs)[0]
File "D:\Python Projects\Machine-Learning-App\mlapp\lib\site-packages\joblib\memory.py", line 537, in _cached_call
out, metadata = self.call(*args, **kwargs)
File "D:\Python Projects\Machine-Learning-App\mlapp\lib\site-packages\joblib\memory.py", line 779, in call
output = self.func(*args, **kwargs)
File "C:\Users\aviparna.biswas\AppData\Roaming\Python\Python39\site-packages\pycaret\internal\pipeline.py", line 87, in _fit_transform_one
_fit_one(transformer, X, y, message, **fit_params)
File "C:\Users\aviparna.biswas\AppData\Roaming\Python\Python39\site-packages\pycaret\internal\pipeline.py", line 54, in _fit_one
transformer.fit(*args, **fit_params)
File "C:\Users\aviparna.biswas\AppData\Roaming\Python\Python39\site-packages\pycaret\internal\preprocess\transformers.py", line 216, in fit
self.transformer.fit(*args, **fit_params)
File "D:\Python Projects\Machine-Learning-App\mlapp\lib\site-packages\sklearn\impute\_base.py", line 364, in fit
X = self._validate_input(X, in_fit=True)
File "D:\Python Projects\Machine-Learning-App\mlapp\lib\site-packages\sklearn\impute\_base.py", line 319, in _validate_input
raise ve
File "D:\Python Projects\Machine-Learning-App\mlapp\lib\site-packages\sklearn\impute\_base.py", line 302, in _validate_input
X = self._validate_data(
File "D:\Python Projects\Machine-Learning-App\mlapp\lib\site-packages\sklearn\base.py", line 577, in _validate_data
X = check_array(X, input_name="X", **check_params)
File "D:\Python Projects\Machine-Learning-App\mlapp\lib\site-packages\sklearn\utils\validation.py", line 791, in check_array
array = array.astype(new_dtype)
File "D:\Python Projects\Machine-Learning-App\mlapp\lib\site-packages\pandas\core\generic.py", line 5912, in astype
new_data = self._mgr.astype(dtype=dtype, copy=copy, errors=errors)
File "D:\Python Projects\Machine-Learning-App\mlapp\lib\site-packages\pandas\core\internals\managers.py", line 419, in astype
return self.apply("astype", dtype=dtype, copy=copy, errors=errors)
File "D:\Python Projects\Machine-Learning-App\mlapp\lib\site-packages\pandas\core\internals\managers.py", line 304, in apply
applied = getattr(b, f)(**kwargs)
File "D:\Python Projects\Machine-Learning-App\mlapp\lib\site-packages\pandas\core\internals\blocks.py", line 580, in astype
new_values = astype_array_safe(values, dtype, copy=copy, errors=errors)
File "D:\Python Projects\Machine-Learning-App\mlapp\lib\site-packages\pandas\core\dtypes\cast.py", line 1292, in astype_array_safe
new_values = astype_array(values, dtype, copy=copy)
File "D:\Python Projects\Machine-Learning-App\mlapp\lib\site-packages\pandas\core\dtypes\cast.py", line 1234, in astype_array
values = values.astype(dtype, copy=copy)
File "D:\Python Projects\Machine-Learning-App\mlapp\lib\site-packages\pandas\core\arrays\categorical.py", line 556, in astype
raise ValueError(msg)
Is there any workaround this error?
For installing pycaret I have used the command pip install -U --pre pycaret the latest release 3.0.0rc2 which works in python 3.9.
I had the same issue. It is working for me when I use python 3.7.9, but not 3.8.x or 3.9.x (as of now).
The 3.0.0rc2 version does not support silent parameter in setup() anymore. Hence the first error.
About this line:
setup(df, target=target)
The error would indicate that your labels are not of numerical type but an object. I'd look into that.
This is due to Pycaret not being compatible with scikit-learn version.
As the documentation says (https://pycaret.gitbook.io/docs/get-started/installation)
"PyCaret is not yet compatible with sklearn>=0.23.2"
Try downgrading scikit-learn:
pip install scikit-learn==0.23.2
I am trying to use writeOGR using R called from Python 3.8.
import rpy2.robjects as ro
.....
ro.r('ttops <- .....')
ro.r('writeOGR(obj=ttops, dsn="T:/Internal/segmentation", layer="test", driver="ESRI Shapefile")')
errors with:
R[write to console]: Error in writeOGR(obj = ttops, dsn = "T:/Internal/LiDAR/crown_segmentation", :
could not find function "writeOGR"
Traceback (most recent call last):
File "C:/Users/david/PycharmProjects/main.py", line 7, in <module>
main()
File "C:/Users/david/PycharmProjects/main.py", line 4, in main
R_Packages().process()
File "C:\Users\david\PycharmProjects\model_testing\r_methods.py", line 17, in process
ro.r('writeOGR(obj=ttops, dsn="T:/Internal/segmentation", layer="test", driver="ESRI Shapefile")')
File "C:\Users\david\AppData\Local\Programs\Python\Python38\lib\site-packages\rpy2\robjects\__init__.py", line 416, in __call__
res = self.eval(p)
File "C:\Users\david\AppData\Local\Programs\Python\Python38\lib\site-packages\rpy2\robjects\functions.py", line 197, in __call__
return (super(SignatureTranslatedFunction, self)
File "C:\Users\david\AppData\Local\Programs\Python\Python38\lib\site-packages\rpy2\robjects\functions.py", line 125, in __call__
res = super(Function, self).__call__(*new_args, **new_kwargs)
File "C:\Users\david\AppData\Local\Programs\Python\Python38\lib\site-packages\rpy2\rinterface_lib\conversion.py", line 44, in _
cdata = function(*args, **kwargs)
File "C:\Users\david\AppData\Local\Programs\Python\Python38\lib\site-packages\rpy2\rinterface.py", line 624, in __call__
raise embedded.RRuntimeError(_rinterface._geterrmessage())
rpy2.rinterface_lib.embedded.RRuntimeError: Error in writeOGR(obj = ttops, dsn = "T:/Internal/segmentation", :
could not find function "writeOGR"
Am I missing something or is this a limit of rpy2? If it is a limit, what is an alternative to write shapefiles of R data using Python?
There was a library that I did not need to define in R that was needed in Python:
ro.r('library(rgdal)')
I'm calling Rserve from python and it runs for basic operations, but not if I call basic functions as min
import pyRserve
conn = pyRserve.connect()
cars = [1, 2, 3]
conn.r.x = cars
print(conn.eval('x'))
print(conn.eval('min(x)'))
The result is:
[1, 2, 3]
Traceback (most recent call last):
File "test3.py", line 9, in <module>
print(conn.eval('min(x)'))
File "C:\Users\acastro\.windows-build-tools\python27\lib\site-packages\pyRserve\rconn.py", line 78, in decoCheckIfClosed
return func(self, *args, **kw)
File "C:\Users\acastro\.windows-build-tools\python27\lib\site-packages\pyRserve\rconn.py", line 191, in eval
raise REvalError(errorMsg)
pyRserve.rexceptions.REvalError: Error in min(x) : invalid 'type' (list) of argument
Do you know where is the problem?
Thanks
You should try min(unlist(x)).
If the list is simple, you may just try as.data.frame(x).
For some more complicate list, StackOverFlow has many other answers.
I am learning the Word2Vec Model to process my data.
I using Spark 1.6.0.
Using the example of the official documentation explain my problem:
import pyspark.mllib.feature import Word2Vec
sentence = "a b " * 100 + "a c " * 10
localDoc = [sentence, sentence]
doc = sc.parallelize(localDoc).map(lambda line: line.split(" "))
model = Word2Vec().setVectorSize(10).setSeed(42).fit(doc)
The vectors are as follows:
>>> model.getVectors()
{'a': [0.26699373, -0.26908076, 0.0579859, -0.080141746, 0.18208595, 0.4162335, 0.0258975, -0.2162928, 0.17868409, 0.07642203], 'b': [-0.29602322, -0.67824656, -0.9063686, -0.49016926, 0.14347662, -0.23329848, -0.44695938, -0.69160634, 0.7037, 0.28236762], 'c': [-0.08954003, 0.24668643, 0.16183868, 0.10982372, -0.099240996, -0.1358507, 0.09996107, 0.30981666, -0.2477713, -0.063234895]}
When I use the getVectors() to get the map of representation of the words. How to convert it into RDD, so I can pass it to KMeans Model?
EDIT:
I did what #user9590153 said.
>>> v = sc.parallelize(model.getVectors()).values()
# the above code is successful.
>>> v.collect()
The Spark-Shell shows another problem:
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
File "D:\spark-1.6.3-bin-hadoop2.6\python\pyspark\rdd.py", line 771, in collect
port = self.ctx._jvm.PythonRDD.collectAndServe(self._jrdd.rdd())
File "D:\spark-1.6.3-bin-hadoop2.6\python\lib\py4j-0.9-src.zip\py4j\java_gateway.py", line 813, in __call__
File "D:\spark-1.6.3-bin-hadoop2.6\python\pyspark\sql\utils.py", line 45, in deco
return f(*a, **kw)
File "D:\spark-1.6.3-bin-hadoop2.6\python\lib\py4j-0.9-src.zip\py4j\protocol.py", line 308, in get_return_value
py4j.protocol.Py4JJavaError: An error occurred while calling z:org.apache.spark.api.python.PythonRDD.collectAndServe.
: org.apache.spark.SparkException: Job aborted due to stage failure: Task 3 in stage 8.0 failed 1 times, most recent failure: Lost task 3.0 in stage 8.0 (TID 29, localhost): org.apache.spark.api.python.PythonException: Traceback (most recent call last):
File "D:\spark-1.6.3-bin-hadoop2.6\python\lib\pyspark.zip\pyspark\worker.py", line 111, in main
File "D:\spark-1.6.3-bin-hadoop2.6\python\lib\pyspark.zip\pyspark\worker.py", line 106, in process
File "D:\spark-1.6.3-bin-hadoop2.6\python\lib\pyspark.zip\pyspark\serializers.py", line 263, in dump_stream
vs = list(itertools.islice(iterator, batch))
File "D:\spark-1.6.3-bin-hadoop2.6\python\pyspark\rdd.py", line 1540, in <lambda>
return self.map(lambda x: x[1])
IndexError: string index out of range
at org.apache.spark.api.python.PythonRunner$$anon$1.read(PythonRDD.scala:166)
at org.apache.spark.api.python.PythonRunner$$anon$1.<init>(PythonRDD.scala:207)
at org.apache.spark.api.python.PythonRunner.compute(PythonRDD.scala:125)
at org.apache.spark.api.python.PythonRDD.compute(PythonRDD.scala:70)
at org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:306)
at org.apache.spark.rdd.RDD.iterator(RDD.scala:270)
at org.apache.spark.scheduler.ResultTask.runTask(ResultTask.scala:66)
at org.apache.spark.scheduler.Task.run(Task.scala:89)
at org.apache.spark.executor.Executor$TaskRunner.run(Executor.scala:227)
at java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1149)
at java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:624)
at java.lang.Thread.run(Thread.java:748)
Just parallelize:
sc.parallelize(model.getVectors()).values()
Parallelized Collections will help you over here.
val data = Array(1, 2, 3, 4, 5) # data here is the collection
val distData = sc.parallelize(data) # converted into rdd
For your case:
sc.parallelize(model.getVectors()).values()
For your doubt:
The action collect() is the common and simplest operation that returns our entire RDDs content to driver program.
The application of collect() is unit testing where the entire RDD is expected to fit in memory. As a result, it makes easy to compare the result of RDD with the expected result.
Action Collect() had a constraint that all the data should fit in the machine, and copies to the driver.
So, you can not perform collect on RDD
So I've got a program that should take a function as input and graph it on a Tkinter canvas.
def draw(self):
self.canvas.delete(ALL)
for n, i in enumerate(self.sav):
self.function, colour = self.sav_func[n]
i = self.p1(i)
i = self.p2(i, self.function, colour)
if i != [0]:
try:
self.canvas.create_line(i, fill = colour)
except TclError as err:
tkMessageBox.showerror(TclError, err)
self.sav.remove(self.sav[len(self.sav)-1])
self.sav_func.remove(self.sav_func[len(self.sav_func)-1])
This section is giving me the following error:
Exception in Tkinter callback
Traceback (most recent call last):
File "C:\Python27\lib\lib-tk\Tkinter.py", line 1410, in __call__
return self.func(*args)
File "D:/Google Drive/assign2_2-1.py", line 113, in add_func
self.redraw_all()
File "D:/Google Drive/assign2_2-1.py", line 132, in redraw_all
self.draw()
File "D:/Google Drive/assign2_2-1.py", line 145, in draw
self.canvas.create_line(i, fill = colour)
File "C:\Python27\lib\lib-tk\Tkinter.py", line 2201, in create_line
return self._create('line', args, kw)
File "C:\Python27\lib\lib-tk\Tkinter.py", line 2182, in _create
cnf = args[-1]
IndexError: tuple index out of range
From what I can gather it's something to do with the number of inputs not matching the number of outputs, but I'm still a little lost. Help would be great!
it looks like i doesn't have enough values. To create a line it needs four values: x1,y1,x2,y2.