Matplotlib viz of Simple & Multiple LR

[gumdropsteve]

Simple LR

Getting ValueError: object __array__ method not producing an array when trying to run the Matplotlib viz of simple linear regression.

import matplotlib.pyplot as plt
import cupy

# scatter actual and predicted results
plt.scatter(sY_test, sY_pred)

# label graph
plt.xlabel("Actual Prices: $Y_i$")
plt.ylabel("Predicted prices: $\hat{Y}_i$")
plt.title("Prices vs Predicted prices: $Y_i$ vs $\hat{Y}_i$")

plt.show()

type(sY_pred)==cupy.core.core.ndarray so .to_array() and .to_pandas() don't work, but this can be fixed by adding .to_list().

Full Error Output

---------------------------------------------------------------------------
ValueError                                Traceback (most recent call last)
<ipython-input-10-f0bf19bebebf> in <module>
      2 import cupy
      3 # scatter actual and predicted results
----> 4 plt.scatter(sY_test, sY_pred)
      5 
      6 # label graph

/opt/conda-environments/rapids-stable/lib/python3.7/site-packages/matplotlib/pyplot.py in scatter(x, y, s, c, marker, cmap, norm, vmin, vmax, alpha, linewidths, verts, edgecolors, plotnonfinite, data, **kwargs)
   2814         verts=verts, edgecolors=edgecolors,
   2815         plotnonfinite=plotnonfinite, **({"data": data} if data is not
-> 2816         None else {}), **kwargs)
   2817     sci(__ret)
   2818     return __ret

/opt/conda-environments/rapids-stable/lib/python3.7/site-packages/matplotlib/__init__.py in inner(ax, data, *args, **kwargs)
   1563     def inner(ax, *args, data=None, **kwargs):
   1564         if data is None:
-> 1565             return func(ax, *map(sanitize_sequence, args), **kwargs)
   1566 
   1567         bound = new_sig.bind(ax, *args, **kwargs)

/opt/conda-environments/rapids-stable/lib/python3.7/site-packages/matplotlib/cbook/deprecation.py in wrapper(*args, **kwargs)
    356                 f"%(removal)s.  If any parameter follows {name!r}, they "
    357                 f"should be pass as keyword, not positionally.")
--> 358         return func(*args, **kwargs)
    359 
    360     return wrapper

/opt/conda-environments/rapids-stable/lib/python3.7/site-packages/matplotlib/axes/_axes.py in scatter(self, x, y, s, c, marker, cmap, norm, vmin, vmax, alpha, linewidths, verts, edgecolors, plotnonfinite, **kwargs)
   4375         # np.ma.ravel yields an ndarray, not a masked array,
   4376         # unless its argument is a masked array.
-> 4377         x = np.ma.ravel(x)
   4378         y = np.ma.ravel(y)
   4379         if x.size != y.size:

/opt/conda-environments/rapids-stable/lib/python3.7/site-packages/numpy/ma/core.py in __call__(self, a, *args, **params)
   6678             a, args[0] = args[0], a
   6679 
-> 6680         marr = asanyarray(a)
   6681         method_name = self.__name__
   6682         method = getattr(type(marr), method_name, None)

/opt/conda-environments/rapids-stable/lib/python3.7/site-packages/numpy/ma/core.py in asanyarray(a, dtype)
   7896     if isinstance(a, MaskedArray) and (dtype is None or dtype == a.dtype):
   7897         return a
-> 7898     return masked_array(a, dtype=dtype, copy=False, keep_mask=True, subok=True)
   7899 
   7900 

/opt/conda-environments/rapids-stable/lib/python3.7/site-packages/numpy/ma/core.py in __new__(cls, data, mask, dtype, copy, subok, ndmin, fill_value, keep_mask, hard_mask, shrink, order, **options)
   2793         # Process data.
   2794         _data = np.array(data, dtype=dtype, copy=copy,
-> 2795                          order=order, subok=True, ndmin=ndmin)
   2796         _baseclass = getattr(data, '_baseclass', type(_data))
   2797         # Check that we're not erasing the mask.

ValueError: object __array__ method not producing an array

Multiple LR

Getting this error with the MLR plot;

TypeError: Implicit conversion to a host NumPy array via __array__ is not allowed,             To explicitly construct a GPU array, consider using             cupy.asarray(...)
To explicitly construct a             host array, consider using .to_array()

Adding .to_array() or .to_pandas() makes it work.