Develop Reference

BrokenProcessPool error in google colab when trying to run cell with SequentialFeatureSelector

When I try to run a cell in colab I get a BrokenProcessPool error, which I believe is due to the SequentialFeatureSelector. The error occurs at the last line, which is sfs.fit(X,Y). I imported joblib and I am still having issues.
nfeatures = len(X.columns)
clf = RandomForestClassifier(n_estimators=5)
#clf = LGBMClassifier(n_estimators=20,num_leaves=3)
sfs=SFS(clf,k_features=num_wrapper,
forward=True,verbose=0,scoring=fdr,cv=3,n_jobs=-1)
sfs=SFS(clf,k_features=1,forward=
False,verbose=0,scoring=fdr,cv=3,n_jobs=-1)
sfs.fit(X,Y)
I tried running the cell and expect sfs.fit(X,Y) to work but I get a BrokenProcessPool error. I have tried importing several packages and joblib and still won't get output.
BrokenProcessPool: A task has failed to un-serialize. Please ensure that the arguments of the function are all picklable.

FB Prophet ".fit()" gives weird output message

I am training a FB Prophet model and I am wondering about the output.
Here is my code:
model_multivariate = Prophet(changepoint_prior_scale=0.5, seasonality_prior_scale = 0.01, holidays = holi)
model_multivariate.add_regressor("wk-1_nvf_fcst",standardize=False)
model_multivariate.add_regressor("wk-2_nvf_fcst",standardize=False)
model_multivariate.add_regressor("wk-3_nvf_fcst",standardize=False)
model_multivariate.add_regressor("wk-1_vr_fcst",standardize=False)
model_multivariate.add_regressor("wk-2_vr_fcst",standardize=False)
model_multivariate.add_regressor("wk-3_vr_fcst",standardize=False)
model_multivariate.add_regressor("year",standardize=False)
model_multivariate.add_regressor("ib_units_trend",standardize=False)
model_multivariate.add_regressor("autocorr_ib_units_lag8",standardize=False)
model_multivariate.add_regressor("autocorr_ib_units_lag7",standardize=False)
model_multivariate.add_regressor("autocorr_ib_units_lag2",standardize=False)
model_multivariate.add_regressor("month_cos",standardize=False)
model_multivariate.add_regressor("week_of_year_cos",standardize=False)
model_multivariate.add_regressor("day_of_year_cos",standardize=False)
model_multivariate.add_regressor("autocorr_order_count_lag7",standardize=False)
model_multivariate.fit(train)
When executing this cell I get the following:
For me this looks weird, because before adding some of these additional regressors I dind't get these messages with: "Out[300]: <prophet.forecaster.Prophet at 0x2...>"
So does anyone know what this means?
Also I am wondering what the error message: "16:03:40 - cmdstanpy - ERROR - Chain 1 error: error during processing Unknown error
Optimization terminated abnormally. Falling back to Newton" means.
The model prediction and everything else works, I just want to get sure that every additional regressor gets used by the model, as these messages were not here before.
Thanks!

Cannot load a SolverResults object with bad status: error

Does anyone know what this error message means?
I tried looking in old conversations, but they had used different solvers so it seems like I need to use another method.
I run an optimization problem with pyomo in python with the solver gurobi.
My full error message:
File "C:\Users\frida.spyder-py3\26 january\optimization.py", line 183, in
solver.solve(m, tee=True)
File "C:\Users\frida\anaconda3\lib\site-packages\pyomo\solvers\plugins\solvers\direct_solver.py", line 183, in solve
default_variable_value=self._default_variable_value)
File "C:\Users\frida\anaconda3\lib\site-packages\pyomo\core\base\PyomoModel.py", line 226, in load_from
% str(results.solver.status))
ValueError: Cannot load a SolverResults object with bad status: error

ValueError: Cannot load a SolverResults object with bad status: error. This means that it is not possible to access the solution.
Your problem is either infeasible or unbounded, or a solution does not exist.
According to pyomo documentation, you can see the output of your solver with the option tee=True to the solver
SolverFactory('glpk').solve(model, tee=True)
Also you can use pprintto see your model or variables
model.pprint()
model.x.pprint()

Azure ML Studio: How to change input value with Python before it goes through data process

I am currently attempting to change the value of input as it goes through data process in Azure ML. However, I cannot find a clue about how to access to the input data with python.
For example, if you were to use python, you can access to the column of data with
print(dataframe1["Hello World"])
I tried to change the name of Web Service Input and tried to do it like how I did for other dataframe (e.g. sample)
print(dataframe["sample"])
But it returns an error with no luck, and from what I read from an error, it's not compatible to dataframe:
object of type 'NoneType' has no len()
I tried to look up a solution with Nonetype error, but there is no good solution.
The whole error message:
requestId = 1f0f621f1d8841baa7862d5c05154942 errorComponent=Module. taskStatusCode=400. {"Exception":{"ErrorId":"FailedToEvaluateScript","ErrorCode":"0085","ExceptionType":"ModuleException","Message":"Error 0085: The following error occurred during script evaluation, please view the output log for more information:\r\n---------- Start of error message from Python interpreter ----------\r\nCaught exception while executing function: Traceback (most recent call last):\r\n File \"C:\\server\\invokepy.py\", line 211, in batch\r\n xdrutils.XDRUtils.DataFrameToRFile(outlist[i], outfiles[i], True)\r\n File \"C:\\server\\XDRReader\\xdrutils.py\", line 51, in DataFrameToRFile\r\n attributes = XDRBridge.DataFrameToRObject(dataframe)\r\n File \"C:\\server\\XDRReader\\xdrbridge.py\", line 40, in DataFrameToRObject\r\n if (len(dataframe) == 1 and type(dataframe[0]) is pd.DataFrame):\r\nTypeError: object of type 'NoneType' has no len()\r\nProcess returned with non-zero exit code 1\r\n\r\n---------- End of error message from Python interpreter ----------"}}Error: Error 0085: The following error occurred during script evaluation, please view the output log for more information:---------- Start of error message from Python interpreter ----------Caught exception while executing function: Traceback (most recent call last): File "C:\server\invokepy.py", line 211, in batch xdrutils.XDRUtils.DataFrameToRFile(outlist[i], outfiles[i], True) File "C:\server\XDRReader\xdrutils.py", line 51, in DataFrameToRFile attributes = XDRBridge.DataFrameToRObject(dataframe) File "C:\server\XDRReader\xdrbridge.py", line 40, in DataFrameToRObject if (len(dataframe) == 1 and type(dataframe[0]) is pd.DataFrame):TypeError: object of type 'NoneType' has no len()Process returned with non-zero exit code 1---------- End of error message from Python interpreter ---------- Process exited with error code -2
I have also tried to a way to pass python script in data, but it is not able to make any change to Web Service Input value as I want it to be.
I have tried to look on forums like msdn or SO, but it's been difficult to find any information about it. Please let me know if you need any more information if needed. I would greatly appreciate your help!

tl;dr; You need to also link the dataset you used for training to the same port you link the Web service input, so that the Execute Python Script has something to work on - see the image below for how this should look.
You need to keep in mind that the Predictive experiment has some conventions that need to be followed (or learned the hard way :) ). One of them is that in order to use the Web service input, you need to pair it with an actual dataset, which Azure ML Studio can then use to infer structure and to provide you with some data while testing your predictive experiment. You can see it as some sort of 'ghost' module that doesn't do anything by itself.
Hope this helps.

Blender error "Invalid clnors in this fan!"

When applying a modifier via the python API to an object the log message "Invalid clnors in this fan!" is shown multiple times.
What are clnors?
What is the meaning of the log message?
How can the error be fixed?

'clnors' is 'custom loop normals'. As I found here this error is shown, well, when there are invalid normals, but it's trying to fix it (code here). If everything is working seems like you can ignore the error.