Machine_Learning | classical algorithms in Machine Learning | Machine Learning library

You have a leading space before 'maint', so your actual key should be ' maint'.
Either fix the csv file, or flag skipinitialspace=True in pd.read_csv():

I think you can pass a dictionary to the node_color parameter of the draw function. If you construct that dictionary such that the keys are the node-names and the values are the colours you want to associate with those node-names, then you should be able to get the formatting you want.

e.g. if you have been able to run some SPARQL to generate a list of nodes you want to be green, and another list that you want to be blue, and assuming you've got a green_list and blue_list pair of lists of these nodenames, then you could construct your dict something like this:

So I just tried an experiment, and discovered that it was the output shape of the LSTM, and having a smaller output length and then expanding it with a Dense Layer removes the error.

using ax.patches you can achieve it.

This will do:

This may help
create new environment don't install packages in base environment (optional but it fixed tensorflow installation bugs for me)

conda install -c conda-forge tensorflow to install tensorflow
then pip install keras or conda install -c conda-forge keras

First check for all the python version available to install using conda search python. It will give list like below.

I think the issue is with the Lambda layer that was taking model.ouput. based on your eval_data and eval_target, I updated the model. So, please check the following model.

This is a problem with the name. The error message is clearly stating that 'Factor_2' is not valid. So you need to figure out what the actual column name is.

Use df.columns to get the column names.

Check for leading and trailing spaces. Is the comma part of the name?

Alternatively you could rename the columns.

In the example you have shared, the goal is to fit a linear regression to the discovered factor values so that a novel set of factor values can be used to predict the rating. In this kind of setup, you don't want information about which samples are being used; the only crucial information is the training features (the factor scores) and the rating (the training/test label). For more on this topic, take a look at "Dimensionality reduction using non-negative matrix factorization for information retrieval."

If you included the movie ids and user ids in as features, your regression would try to learn on those, which would either add noise to the model or learn that low ids = lower score etc. This is possible, especially if this ids are in some kind of order you're not aware of, such as chronological or by genre.

Note: You could use movie-specific or user-specific information to build a model, but you would have many, many dimensions of data, and that tends to create poorly performing models. The idea here is to avoid the problem of dimensionality by first reducing the dimensionality of the problem space. Matrix factorization is just one method among many to do this. See, for example, PCA, LDA, and word2vec.