Numpy Future Warning when using plot_decision_regions function about python-machine-learning-book HOT 3 CLOSED

Hi, seth814,
hope we can figure on what's going on here in your case. I think the easiest way would be if you could upload your script (if you are okay with sharing it) so that I can take a look and inspect what's going on inside the plot_decision_regions function that may cause this behavior on your dataset.

One thing that I could think of may be non-supported input shapes of the numpy arrays "X" and "y" in plot_decision_regions(X, y, classifier, resolution=0.02). This decision region plotting functions expects these X and y in the shape that scikit-learn works with. E.g., the y-array has to be a 1D integer type array. And X has to be a 2D float (or integer) type array.

Would be nice if you could check your input data and let me know what the result of the "print" functions (see below) looks like -- that would be very helpful

Input:

import numpy as np

y = np.array([1, 2, 0, 0, 2])
X = np.array([[1., 2.],  
              [3., 4.],
              [5., 6.],
              [8., 9.],
              [7., 8.]])

print('y:', y.shape, y.dtype)
print('X:', X.shape, X.dtype)

Output:

y: (5,) int64
X: (5, 2) float64

Above is an example of how the expected shape may look like.

PS: I have a slightly more sophisticated function implemented here: http://rasbt.github.io/mlxtend/user_guide/evaluate/plot_decision_regions/

I am currently a bit busy (at SciPy 2016), but several people asked me about 3D decision spaces recently, which I am going to add soon!

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No worries. I'm not in a huge hurry, but I am curious as to what is going on in the function.

I uploaded the data and file under Vertical Abduction in my repo. I tried to upload a zip but it said the format wasn't supported. The shapes and datatypes are both correct so it's probably something else.

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About the FutureWarning, I think that's not an issue here; it comes from the fact that y_train is a Pandas DataFrame, not a NumPy array. I'd just recommend putting a y_train = y_train.values into your code.

Hm, about the plot itself, I don't think this is a bug. This is how the decision region of the SVM looks like in this case -- you may want to do some hyperparameter tuning here.
E.g., when I plot the first 2 dimensions of the input data, it kind of looks like this:

plt.scatter(X_train.values[:, 0], X_train.values[:, 1])

So, for a more visually pleasing analysis, you could maybe try a non-linear dimensionality reduction technique (e.g., Kernel SVM or other algorithms for manifold learning that are implemented in scikit-learn: http://scikit-learn.org/stable/modules/manifold.html)

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