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_ope... 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 <leibniz01@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

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