You don't want to pickle the predictions but rather the fit.
Change joblib.dump(nb_predict_train, "trained-model.pkl") to joblib.dump(nb_model, "trained-model.pkl")
python - AttributeError: 'numpy.ndarray' object has no attribute 'predict' - Data Science Stack Exchange
Numpy has not attribute?
python - AttributeError: 'numpy.ndarray' object has no attribute 'predict' - Stack Overflow
AttributeError: 'numpy.ndarray' object has no attribute 'predict'
I am trying to plot a .txt file of lines of the form: filename.txt date magnitude V098550.txt 362.0 3.34717962317 but I am getting the error "numpy.float64' object has no attribute 'replace'". Does anyone know if this is a syntax error with numpy, or how I can resolve my issue?
import numpy as np
import matplotlib.pyplot as plt
x, y = np.loadtxt ("condensed.txt", usecols=(1, 2),
delimiter=",", unpack=True)
for ii in range (len(x)): x[ii].replace('.txt', '.lc\n') jd, npmag = np.loadtxt ("/net/jovan/export/jovan/oelkerrj/Vela/rotation/Vela/"+x[ii], usecols= (1, 2), unpack=True)
plt.scatter (jd, npmag)
plt.xlabel ('Time')
plt.ylabel ('Mag')
plt.ylim ([max (npmag), min (npmag)])
plt.show() # aftertest comment this out
fileName = x[ii][:-3] + ".png"
plt.savefig(fileName)print "done"
The answer is already provided in the comments by @mattdmo and @tdelaney:
NumPy 1.20 (release notes) deprecated
numpy.float,numpy.int, and similar aliases, causing them to issue a deprecation warningNumPy 1.24 (release notes) removed these aliases altogether, causing an error when they are used
In many cases you can simply replace the deprecated NumPy types by the equivalent Python built-in type, e.g., numpy.float becomes a "plain" Python float.
For detailed guidelines on how to deal with various deprecated types, have a closer look at the table and guideline in the release notes for 1.20:
...
To give a clear guideline for the vast majority of cases, for the types
bool,object,str(andunicode) using the plain version is shorter and clear, and generally a good replacement. Forfloatandcomplexyou can usefloat64andcomplex128if you wish to be more explicit about the precision.For
np.inta direct replacement withnp.int_orintis also good and will not change behavior, but the precision will continue to depend on the computer and operating system. If you want to be more explicit and review the current use, you have the following alternatives:
np.int64ornp.int32to specify the precision exactly. This ensures that results cannot depend on the computer or operating system.np.int_orint(the default), but be aware that it depends on the computer and operating system.- The C types:
np.cint(int),np.int_(long),np.longlong.np.intpwhich is 32bit on 32bit machines 64bit on 64bit machines. This can be the best type to use for indexing....
If you have dependencies that use the deprecated types, a quick workaround would be to roll back your NumPy version to 1.24 or less (as suggested in some of the other answers), while waiting for the dependency to catch up. Alternatively, you could create a patch yourself and open a pull request, or monkey patch the dependency in your own code.
In the 1.24 version:
The deprecation for the aliases np.object, np.bool, np.float, np.complex, np.str, and np.int is expired (introduces NumPy 1.20). Some of these will now give a FutureWarning in addition to raising an error since they will be mapped to the NumPy scalars in the future.
pip install "numpy<1.24" to work around it.
In [1]: import numpy as np
In [2]: np.__version__
Out[2]: '1.23.5'
In [3]: np.float(3)
<ipython-input-3-8262e04d58e1>:1: DeprecationWarning: `np.float` is a deprecated alias for the builtin `float`. To silence this warning, use `float` by itself. Doing this will not modify any behavior and is safe. If you specifically wanted the numpy scalar type, use `np.float64` here.
Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations
np.float(3)
Out[3]: 3.0