Looks like you need to install pandas for this example--`fetch_openl` is trying to give you back a pandas DataFrame
https://scikit-learn.org/stable/modules/generated/sklearn.datasets.fetch_openml.html#sklearn.datasets.fetch_openml not sure if you could just run it with as_frame = False David Nicholson, Ph.D. https://nicholdav.info/ https://github.com/NickleDave Prinz lab <http://www.biology.emory.edu/research/Prinz/>, Emory University, Atlanta, GA, USA On Tue, Mar 23, 2021 at 10:57 PM James Bunn <leibni...@gmail.com> wrote: > Hi, > > I am a new user trying to run the Visualization of MLP weights on MNIST > example for neural networks. > > I am not able to get the example to run. I loaded the scikitlearn and > matplotlib packages called in the program, but still it will not work. > > Is there any more I need to do? > > My error text is below. > > Thank you, > > James > > "C:\Users\James\PycharmProjects\MATH541 Project\venv\Scripts\python.exe" > C:/Users/James/Documents/MATH541/plot_mnist_filters.py > C:\Users\James\PycharmProjects\MATH541 > Project\venv\lib\site-packages\sklearn\datasets\_openml.py:65: > RuntimeWarning: Invalid cache, redownloading file > warn("Invalid cache, redownloading file", RuntimeWarning) > > ===================================== > Visualization of MLP weights on MNIST > ===================================== > > Sometimes looking at the learned coefficients of a neural network can > provide > insight into the learning behavior. For example if weights look > unstructured, > maybe some were not used at all, or if very large coefficients exist, maybe > regularization was too low or the learning rate too high. > > This example shows how to plot some of the first layer weights in a > MLPClassifier trained on the MNIST dataset. > > The input data consists of 28x28 pixel handwritten digits, leading to 784 > features in the dataset. Therefore the first layer weight matrix have the > shape > (784, hidden_layer_sizes[0]). We can therefore visualize a single column > of > the weight matrix as a 28x28 pixel image. > > To make the example run faster, we use very few hidden units, and train > only > for a very short time. Training longer would result in weights with a much > smoother spatial appearance. The example will throw a warning because it > doesn't converge, in this case this is what we want because of CI's time > constraints. > > Traceback (most recent call last): > File "C:\Users\James\PycharmProjects\MATH541 > Project\venv\lib\site-packages\sklearn\utils\__init__.py", line 1081, in > check_pandas_support > import pandas # noqa > ModuleNotFoundError: No module named 'pandas' > > The above exception was the direct cause of the following exception: > > Traceback (most recent call last): > File "C:\Users\James\PycharmProjects\MATH541 > Project\venv\lib\site-packages\sklearn\datasets\_openml.py", line 61, in > wrapper > return f(*args, **kw) > File "C:\Users\James\PycharmProjects\MATH541 > Project\venv\lib\site-packages\sklearn\datasets\_openml.py", line 518, in > _load_arff_response > parsed_arff = parse_arff(arff) > File "C:\Users\James\PycharmProjects\MATH541 > Project\venv\lib\site-packages\sklearn\datasets\_openml.py", line 332, in > _convert_arff_data_dataframe > pd = check_pandas_support('fetch_openml with as_frame=True') > File "C:\Users\James\PycharmProjects\MATH541 > Project\venv\lib\site-packages\sklearn\utils\__init__.py", line 1084, in > check_pandas_support > raise ImportError( > ImportError: fetch_openml with as_frame=True requires pandas. > > During handling of the above exception, another exception occurred: > > Traceback (most recent call last): > File "C:\Users\James\PycharmProjects\MATH541 > Project\venv\lib\site-packages\sklearn\utils\__init__.py", line 1081, in > check_pandas_support > import pandas # noqa > ModuleNotFoundError: No module named 'pandas' > > The above exception was the direct cause of the following exception: > > Traceback (most recent call last): > File "C:\Users\James\Documents\MATH541\plot_mnist_filters.py", line 36, > in <module> > X, y = fetch_openml('mnist_784', version=1, return_X_y=True) > File "C:\Users\James\PycharmProjects\MATH541 > Project\venv\lib\site-packages\sklearn\utils\validation.py", line 63, in > inner_f > return f(*args, **kwargs) > File "C:\Users\James\PycharmProjects\MATH541 > Project\venv\lib\site-packages\sklearn\datasets\_openml.py", line 915, in > fetch_openml > bunch = _download_data_to_bunch(url, return_sparse, data_home, > File "C:\Users\James\PycharmProjects\MATH541 > Project\venv\lib\site-packages\sklearn\datasets\_openml.py", line 633, in > _download_data_to_bunch > out = _retry_with_clean_cache(url, data_home)( > File "C:\Users\James\PycharmProjects\MATH541 > Project\venv\lib\site-packages\sklearn\datasets\_openml.py", line 69, in > wrapper > return f(*args, **kw) > File "C:\Users\James\PycharmProjects\MATH541 > Project\venv\lib\site-packages\sklearn\datasets\_openml.py", line 518, in > _load_arff_response > parsed_arff = parse_arff(arff) > File "C:\Users\James\PycharmProjects\MATH541 > Project\venv\lib\site-packages\sklearn\datasets\_openml.py", line 332, in > _convert_arff_data_dataframe > pd = check_pandas_support('fetch_openml with as_frame=True') > File "C:\Users\James\PycharmProjects\MATH541 > Project\venv\lib\site-packages\sklearn\utils\__init__.py", line 1084, in > check_pandas_support > raise ImportError( > ImportError: fetch_openml with as_frame=True requires pandas. > > Process finished with exit code 1 > > _______________________________________________ > scikit-learn mailing list > scikit-learn@python.org > https://mail.python.org/mailman/listinfo/scikit-learn >
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