TruncatedSVD.transform returns an array, not a sparse matrix. In fact, in the present version of scikit-learn, only the vectorizers return sparse matrices.
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Python Coding help- keep recieving error message"AttributeError: 'numpy.ndarray' object has no attribute 'MESSAGE_A'"
Error creating numpy v-stack, 'AttributeError: 'numpy.ndarray' object has no attribute 'np'
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Hello,
Linked is the screenshot of the two error messages I keep recieving as well as the code leading up to it. I'm trying to run an uplift on a classification tree. Any help is appreciated, I am still fairly new to python. Thank you!!
Hi,
I'm trying to create a numpy v-stack and creating 3 np.array's for it, by filling them with a loop:
I get the error: 'AttributeError: 'numpy.ndarray' object has no attribute 'np' . I think I'm using the wrong notation to append to the empty arrays:
neighbor_id = [id_ for id_ in range(1, n_obs) if id_ != user_id]
neighbor_id_arr = np.array(neighbor_id)
similarity = np.array([])
num_interactions = np.array([])
# get similarity and num_interactions
for id_ in neighbor_id:
similarity.np.append(np.dot(user_item.loc[user_id],user_item.loc[id_])) #The issue is here, I think
num_interactions.np.append(user_interactions.loc[id_])
c = numpy.vstack((neighbor_id_arr, similarity,num_interactions))
Thanks!
James
if you need to flatten you can directly use flatten() on numpy.ndarray object
X = X.flatten()
Link to doc: https://numpy.org/doc/stable/reference/generated/numpy.ndarray.flatten.html
DO NOT VOTE UP RESPONSE MAIN WORK DONE BY @wayne above:
Did you run print(type(X)) on X prior to that line that gave an error? Given you got an error of AttributeError: 'numpy.ndarray' object has no attribute 'to_numpy', X appears to already be numpy object. Also, I don't think the to_numpy comes from numpy. Pandas has one. Polars has one. This drops us in the end of the problem and so we cannot provide much help without more code upstream.
solution
reload data set.
df = pd.read_csv(file_name, header=0)
[str(i) for i in ([["CPU_frequency"]])]
code above resolves.
The problem is that train_test_split(X, y, ...) returns numpy arrays and not pandas dataframes. Numpy arrays have no attribute named columns
If you want to see what features SelectFromModel kept, you need to substitute X_train (which is a numpy.array) with X which is a pandas.DataFrame.
selected_feat= X.columns[(sel.get_support())]
This will return a list of the columns kept by the feature selector.
If you wanted to see how many features were kept you can just run this:
sel.get_support().sum() # by default this will count 'True' as 1 and 'False' as 0
because this :
X = df.iloc[:,:24481].values
y = df.iloc[:, -1].values
you should remove .values or make extra X_col, y_col like that
X_col = df.iloc[:,:24481]
y_col = df.iloc[:, -1]
If you debug your program by simply printing ax, you'll quickly find out that ax is a two-dimensional array: one dimension for the rows, one for the columns.
Thus, you need two indices to index ax to retrieve the actual AxesSubplot instance, like:
ax[1,1].plot(...)
If you want to iterate through the subplots in the way you do it now, by flattening ax first:
ax = ax.flatten()
and now ax is a one dimensional array. I don't know if rows or columns are stepped through first, but if it's the wrong around, use the transpose:
ax = ax.T.flatten()
Of course, by now it makes more sense to simply create each subplot on the fly, because that already has an index, and the other two numbers are fixed:
for x < plots_tot:
ax = plt.subplot(nrows, ncols, x+1)
Note: you have x <= plots_tot, but with x starting at 0, you'll get an IndexError next with your current code (after flattening your array). Matplotlib is (unfortunately) 1-indexed for subplots. I prefer using a 0-indexed variable (Python style), and just add +1 for the subplot index (like above).
The problem here is with how matplotlib handles subplots. Just do the following:
fig, axes = plt.subplots(nrows=1, ncols=2)
for axis in axes:
print(type(axis))
you will get a matplotlib object which is actually a 1D array which can be traversed using single index i.e. axis[0], axis[1]...and so on. But if you do
fig, axes = plt.subplots(nrows=2, ncols=2)
for axis in axes:
print(type(axis))
you will get a numpy ndarray object which is actually a 2D array which can be traversed only using 2 indices i.e. axis[0, 0], axis[1, 0]...and so on. So be mindful how you incorporate your for loop to traverse through axes object.