AttributeError: 'SimpleImputer' object has no attribute '_fit_dtype'

[nikkisingh111333]

hi i m new into this FLAML. i m getting this error while predicting my banknote problem

while using colab i m not getting this error but on pycharm using fastapi i m getting this error AttributeError: 'SimpleImputer' object has no attribute '_fit_dtype'

heres my code:

@app.post('/predict') async def predict(data:person): per=data.dict() df = pd.DataFrame([[per['variance'],per['skewness'],per['curtosis'],per['entropy']]],columns=['variance','skewness','curtosis','entropy']) print(df.head(1)) prediction=automl.predict(df.head(1)) print(prediction)

this is my data

variance skewness curtosis entropy 0 1.22 0.333 0.44 -3.22

what should i do?

[sonichi]

It looks to be caused by sklearn version mismatch. Could you check the sklearn version in these two different environments? Also, having the full traceback error message helps.

[nikkisingh111333]

which version of sklearn is needed for flaml

ERROR:Exception in ASGI application Traceback (most recent call last): File "C:\Users\ACER\anaconda3\envs\pythonForAI\lib\site-packages\uvicorn\protocols\http\h11_impl.py", line 404, in run_asgi result = await app( # type: ignore[func-returns-value] File "C:\Users\ACER\anaconda3\envs\pythonForAI\lib\site-packages\uvicorn\middleware\proxy_headers.py", line 78, in call return await self.app(scope, receive, send) File "C:\Users\ACER\AppData\Roaming\Python\Python39\site-packages\fastapi\applications.py", line 270, in call await super().call(scope, receive, send) File "C:\Users\ACER\AppData\Roaming\Python\Python39\site-packages\starlette\applications.py", line 124, in call await self.middleware_stack(scope, receive, send) File "C:\Users\ACER\AppData\Roaming\Python\Python39\site-packages\starlette\middleware\errors.py", line 184, in call raise exc File "C:\Users\ACER\AppData\Roaming\Python\Python39\site-packages\starlette\middleware\errors.py", line 162, in call await self.app(scope, receive, _send) File "C:\Users\ACER\AppData\Roaming\Python\Python39\site-packages\starlette\middleware\exceptions.py", line 75, in call raise exc File "C:\Users\ACER\AppData\Roaming\Python\Python39\site-packages\starlette\middleware\exceptions.py", line 64, in call await self.app(scope, receive, sender) File "C:\Users\ACER\AppData\Roaming\Python\Python39\site-packages\fastapi\middleware\asyncexitstack.py", line 21, in call raise e File "C:\Users\ACER\AppData\Roaming\Python\Python39\site-packages\fastapi\middleware\asyncexitstack.py", line 18, in call await self.app(scope, receive, send) File "C:\Users\ACER\AppData\Roaming\Python\Python39\site-packages\starlette\routing.py", line 680, in call await route.handle(scope, receive, send) File "C:\Users\ACER\AppData\Roaming\Python\Python39\site-packages\starlette\routing.py", line 275, in handle await self.app(scope, receive, send) File "C:\Users\ACER\AppData\Roaming\Python\Python39\site-packages\starlette\routing.py", line 65, in app response = await func(request) File "C:\Users\ACER\AppData\Roaming\Python\Python39\site-packages\fastapi\routing.py", line 231, in app raw_response = await run_endpoint_function( File "C:\Users\ACER\AppData\Roaming\Python\Python39\site-packages\fastapi\routing.py", line 160, in run_endpoint_function return await dependant.call(values) File "H:\pythonForAI\FastAPIIntro.py", line 51, in predict prediction=automl.predict(df.head(1)) File "C:\Users\ACER\anaconda3\envs\pythonForAI\lib\site-packages\flaml\automl.py", line 936, in predict X = self._preprocess(X) File "C:\Users\ACER\anaconda3\envs\pythonForAI\lib\site-packages\flaml\automl.py", line 1000, in _preprocess X = self._transformer.transform(X) File "C:\Users\ACER\anaconda3\envs\pythonForAI\lib\site-packages\flaml\data.py", line 479, in transform X[num_columns] = self.transformer.transform(X_num) File "C:\Users\ACER\anaconda3\envs\pythonForAI\lib\site-packages\sklearn\compose_column_transformer.py", line 763, in transform Xs = self._fit_transform( File "C:\Users\ACER\anaconda3\envs\pythonForAI\lib\site-packages\sklearn\compose_column_transformer.py", line 621, in _fit_transform return Parallel(n_jobs=self.n_jobs)( File "C:\Users\ACER\anaconda3\envs\pythonForAI\lib\site-packages\joblib\parallel.py", line 1085, in call if self.dispatch_one_batch(iterator): File "C:\Users\ACER\anaconda3\envs\pythonForAI\lib\site-packages\joblib\parallel.py", line 901, in dispatch_one_batch self._dispatch(tasks) File "C:\Users\ACER\anaconda3\envs\pythonForAI\lib\site-packages\joblib\parallel.py", line 819, in _dispatch job = self._backend.apply_async(batch, callback=cb) File "C:\Users\ACER\anaconda3\envs\pythonForAI\lib\site-packages\joblib_parallel_backends.py", line 208, in apply_async result = ImmediateResult(func) File "C:\Users\ACER\anaconda3\envs\pythonForAI\lib\site-packages\joblib_parallel_backends.py", line 597, in init self.results = batch() File "C:\Users\ACER\anaconda3\envs\pythonForAI\lib\site-packages\joblib\parallel.py", line 288, in call return [func(*args, kwargs) File "C:\Users\ACER\anaconda3\envs\pythonForAI\lib\site-packages\joblib\parallel.py", line 288, in return [func(*args, *kwargs) File "C:\Users\ACER\anaconda3\envs\pythonForAI\lib\site-packages\sklearn\utils\fixes.py", line 117, in call return self.function(args, kwargs) File "C:\Users\ACER\anaconda3\envs\pythonForAI\lib\site-packages\sklearn\pipeline.py", line 853, in _transform_one res = transformer.transform(X) File "C:\Users\ACER\anaconda3\envs\pythonForAI\lib\site-packages\sklearn\impute_base.py", line 515, in transform X = self._validate_input(X, in_fit=False) File "C:\Users\ACER\anaconda3\envs\pythonForAI\lib\site-packages\sklearn\impute_base.py", line 292, in _validate_input if not in_fit and self._fit_dtype.kind == "O": AttributeError: 'SimpleImputer' object has no attribute '_fit_dtype**

heres the full stacktrace

[sonichi]

flaml requires scikit-learn>=0.24. But _fit_dtype is not available before scikit-learn 1.1. I guess the automl object was created under scikit-learn 1.1 but then loaded with a lower version.

[nikkisingh111333]

so suggest me a quick solution for his please.?

[sonichi]

so suggest me a quick solution for his please.?

Use the same version of sklearn in your notebook which creates the automl object as the environment in which you load the automl object.