In this simple tutorial written in Python 2.7, they have a line loading the numpy array.
train_data = np.load(open('../musicnet.npz','rb'))
Then, they get the data by calling different keys
X,Y = train_data['2494']
Everything works well in python 2.7
Data type of train_data is numpy.lib.npyio.NpzFile
My problem
However, whenever I try to do the same in Python 3.5, most of the lines work fine, except when it comes to the line of X,Y = train_data['2494'], it just freezes there forever. I would like to use Python 3.5 because my other projects are written in python 3.5.
How to rewrite this line so that it runs with Python 3.5?
Error Message
I finally managed to get the error message in terminal
It freezes there because there's tons of output right after the error message, my jupyter notebook just cannot handle that much information.
Solution
Change the encoding to 'bytes'
train_data = np.load('../musicnet.npz', encoding='bytes')
Then everything works fine.
You first said things crashed, now you say it freezes when trying to access a specific array. numpy has the same syntax in 3.5 compared to 2.7. You shouldn't have to rewrite anything.
np.load does have a couple of parameters that deal with differences between Py2 and Py3. But I'm not sure these are an issue for you.
fix_imports : bool, optional
Only useful when loading Python 2 generated pickled files on Python 3,
which includes npy/npz files containing object arrays. If `fix_imports`
is True, pickle will try to map the old Python 2 names to the new names
used in Python 3.
encoding : str, optional
What encoding to use when reading Python 2 strings. Only useful when
loading Python 2 generated pickled files in Python 3, which includes
npy/npz files containing object arrays. Values other than 'latin1',
'ASCII', and 'bytes' are not allowed, as they can corrupt numerical
data. Default: 'ASCII'
Try
print(list(train_data.keys()))
This should show the array names that were saved to the zip archive. Do they match the names in the Py2 load? Do they include the '2494' name?
A couple of things are unusual about:
X,Y = train_data['2494']
Naming an array in the zip archive by a string number, and unpacking the load into two variables.
Do you know anything about how this was savez? What was saved?
Another question - are you loading this file from the same machine that Py2 worked on? Or has the file been transferred from another machine, and possibly corrupted?
As those parameters indicate, there are differences in the pickle code between Py2 and Py3. If the original save included object dtype arrays, or non-array objects, then they will be pickled and there might be incompatibilities in the pickle versions.
Try this,
with np.load('../musicnet.npz') as train_data:
X,Y = train_data['2494']
There are 2 ways out in my point of view:
re-edit your code
train_data = np.load(open('../musicnet.npz','rb'))
to
train_data = np.load(open('../musicnet.npz','r'))
Because the mode of r/rb in python2.7 / 3.5 is a difference in your situation.
Using the default debugger to pointing the significant error. (Usually, work on my experience)
Related
I am using machine learning in my Python (version 3.8.5) code. In the preprocessing part, I need to hash encode few features. So earlier I have dumped a hash encoder pickle file using the features in the training phase. Saved the file with the name of 'hash_encoder.pkl'. Now in the testing phase, I need to transform the features using this pickle file. I'm using the following code given in screenshot to hash encode three string features as given in the first line.
In the encoder.transform line, I'm getting the error of "data_lock=mutiprocessing.Manager().Lock()".
At the end I'm also getting 'raise EOF error'.
I have tried using same version of pandas (1.1.3) to dump the hash_encoder file and also to load it. I'm not sure why is this coming up.
Can someone help me in understand or debugging this part?
I have added the screenshot of the error.
When I parse the example rss link provided by BBC weather it gives only an empty feed, the example link is: "https://weather-broker-cdn.api.bbci.co.uk/en/forecast/rss/3day/2643123"
Ive tried using the feedparser module in python, I would like to do this in either python or c++ but python seemed easier. Ive also tried rewriting the URL without https:// and with .xml and it still doesn't work.
import feedparser
d = feedparser.parse('https://weather-broker-cdn.api.bbci.co.uk/en/forecast/rss/3day/2643123')
print(d)
Should give a result similar to the RSS feed which is on the link, but it just gets an empty feed
First, I know you got no result - not an error like me. Perhaps you are running a different version. As I mentioned, it yields a result on an older version in Python 2, using a program that has been running solidly every night for about 5 years, but it throws an exception on a freshly installed feedparser 5.2.1 on Python 3.7.4 64 bit.
I'm not entirely sure what is going on, but the function called _gen_georss_coords which is throwing a StopIteration on the first call. I have noted some references to this error due to the implementation of PEP479. It is written as a generator, but for your rss feed it only has to return 1 tuple. Here is the offending function.
def _gen_georss_coords(value, swap=True, dims=2):
# A generator of (lon, lat) pairs from a string of encoded GeoRSS
# coordinates. Converts to floats and swaps order.
latlons = map(float, value.strip().replace(',', ' ').split())
nxt = latlons.__next__
while True:
t = [nxt(), nxt()][::swap and -1 or 1]
if dims == 3:
t.append(nxt())
yield tuple(t)
There is something curious going on, perhaps to do with PEP479 and the fact that there are two separate generators happening in the same function, that is causing StopIteration to bubble up to the calling function. Anyway, I rewrote it is a somewhat more straightforward way.
def _gen_georss_coords(value, swap=True, dims=2):
# A generator of (lon, lat) pairs from a string of encoded GeoRSS
# coordinates. Converts to floats and swaps order.
latlons = list(map(float, value.strip().replace(',', ' ').split()))
for i in range(0, len(latlons), 3):
t = [latlons[i], latlons[i+1]][::swap and -1 or 1]
if dims == 3:
t.append(latlons[i+2])
yield tuple(t)
You can define the above new function in your code, then execute the following to patch it into feedparser
saveit, feedparser._gen_georss_coords = (feedparser._gen_georss_coords, _gen_georss_coords)
Once you're done with it, you can restore feedparser to its previous state with
feedparser._gen_georss_coords, _gen_georss_coords = (saveit, feedparser._gen_georss_coords)
Or if you're confident that this is solid, you can modify feedparser itself. Anyway I did this trick and your rss feed suddenly started working. Perhaps in your case it will also result in some improvement.
I have set up a google cloud account
I want to perform my deep learning much more faster on a jupyter notebook, but
I cannot find a way to read my csv file
I downloaded it with wget from my github account and afterwards I tried
dataset = pd.read_csv('/home/user/.jupyter/SIEMENSTRAIN.csv')
but I get the following error
pandas.parser.CParserError: Error tokenizing data. C error: Expected 2 fields in line 3, saw 12
Why? When I read it on my laptop using my jupyter notebooks, everything runs well
Any suggestions?
I tried the recommended solutions for this error and I got the next warning
/home/user/anaconda3/lib/python3.5/site-packages/ipykernel/main.py:1: ParserWarning: Falling back to the 'python' engine because the 'c' engine does not support regex separators; you can avoid this warning by specifying engine='python'.
if name == 'main':
When I ran dataset.head() this is what appeared
Any help please?
There are a number of possibilities that could be causing the problem... I would first always make sure that Pandas (pd)'s version is updated and compatible.
The more likely cause is that the CSV itself is not right, so pd.read_csv() is not able to work correctly (thus a Parse Error). This may have something to do with the headers, though I'm not sure what your original CSV file looks like. It's worth playing around with read_csv, for example:
df = pandas.read_csv(fileName, sep='delimiter', header=None)
This tampers with 2 things - the delimiter, and if pd is reading a header from CSV or not.
I go through some pd.read_csv() stuff in my book about Stock Prediction (another cool Machine Learning problem) and Deep Learning, feel free to check it out.
Good Luck!
I tried what you proposed and this is what I got
So, any suggestions?
I suppose that the path is ok, but it just won't be read properly, or am I wrong?
I have a pickle file that was created (I don't know how exactly) in python 2. It is intended to be loaded by the following python 2 lines, which when used in python 3 (unsurprisingly) do not work:
with open('filename','r') as f:
foo, bar = pickle.load(f)
Result:
'ascii' codec can't decode byte 0xc2 in position 1219: ordinal not in range(128)
Manual inspection of the file indicates it is utf-8 encoded, therefore:
with open('filename','r', encoding='utf-8') as f:
foo, bar = pickle.load(f)
Result:
TypeError: a bytes-like object is required, not 'str'
With binary encoding:
with open('filename','rb', encoding='utf-8') as f:
foo, bar = pickle.load(f)
Result:
ValueError: binary mode doesn't take an encoding argument
Without binary encoding:
with open('filename','rb') as f:
foo, bar = pickle.load(f)
Result:
UnpicklingError: invalid load key, '
'.
Is this pickle file just broken? If not, how can I pry this thing open in python 3? (I have browsed the extensive collection of related questions and not found anything that works yet.)
Finally, note that the original
import cPickle as pickle
has been replaced with
import _pickle as pickle
The loading of python2 pickles in python3 (version 3.7.2 in this example) can be helped using the fix_imports parameter in the pickle.load function, but in my case it also worked without setting that parameter to True.
I was attempting to load a scipy.sparse.csr.csr_matrix contained in pickle generated using Python2.
When inspecting the file format using the UNIX command file it says:
>file -bi python2_generated.pckl
application/octet-stream; charset=binary
I could load the pickle in Python3 using the following code:
with open("python2_generated.pckl", "rb") as fd:
bh01 = pickle.load(fd, fix_imports=True, encoding="latin1")
Note that the loading was successful with and without setting fix_imports to True
As for the "latin1" encoding, the Python3 documentation (version 3.7.2) for the pickle.load function says:
Using encoding='latin1' is required for unpickling NumPy arrays and instances of datetime, date and time pickled by Python 2
Although this is specifically for scipy matrixes (or Numpy arrays), and since Novak is not clarifing what his pickle file contained,
I hope this could of help to other users :)
Two errors were conflating each other.
First: By the time the .p file reached me, it had almost certainly been corrupted in transit, likely by FTP-ing (or similar) in ASCII rather than binary mode. I was able to get my hands on a properly transmitted copy, which allowed me to discover...
Second: Whatever the file might have implied on the inside, the proper encoding was 'latin1' not 'utf-8'.
So in a sense, yes, the file was broken, and even after that I was doing it wrong. I leave this here as a reminder to whoever eventually has the next bizarre pickle/python2/python3 issue that there can be multiple things gone wrong, and they have to be solved in the correct orderr.
I wonder, how to save and load numpy.array data properly. Currently I'm using the numpy.savetxt() method. For example, if I got an array markers, which looks like this:
I try to save it by the use of:
numpy.savetxt('markers.txt', markers)
In other script I try to open previously saved file:
markers = np.fromfile("markers.txt")
And that's what I get...
Saved data first looks like this:
0.000000000000000000e+00
0.000000000000000000e+00
0.000000000000000000e+00
0.000000000000000000e+00
0.000000000000000000e+00
0.000000000000000000e+00
0.000000000000000000e+00
0.000000000000000000e+00
0.000000000000000000e+00
0.000000000000000000e+00
But when I save just loaded data by the use of the same method, ie. numpy.savetxt() it looks like this:
1.398043286095131769e-76
1.398043286095288860e-76
1.396426376485745879e-76
1.398043286055061908e-76
1.398043286095288860e-76
1.182950697433698368e-76
1.398043275797188953e-76
1.398043286095288860e-76
1.210894289234927752e-99
1.398040649781712473e-76
What am I doing wrong? PS there are no other "backstage" operation which I perform. Just saving and loading, and that's what I get. Thank you in advance.
The most reliable way I have found to do this is to use np.savetxt with np.loadtxt and not np.fromfile which is better suited to binary files written with tofile. The np.fromfile and np.tofile methods write and read binary files whereas np.savetxt writes a text file.
So, for example:
a = np.array([1, 2, 3, 4])
np.savetxt('test1.txt', a, fmt='%d')
b = np.loadtxt('test1.txt', dtype=int)
a == b
# array([ True, True, True, True], dtype=bool)
Or:
a.tofile('test2.dat')
c = np.fromfile('test2.dat', dtype=int)
c == a
# array([ True, True, True, True], dtype=bool)
I use the former method even if it is slower and creates bigger files (sometimes): the binary format can be platform dependent (for example, the file format depends on the endianness of your system).
There is a platform independent format for NumPy arrays, which can be saved and read with np.save and np.load:
np.save('test3.npy', a) # .npy extension is added if not given
d = np.load('test3.npy')
a == d
# array([ True, True, True, True], dtype=bool)
np.save('data.npy', num_arr) # save
new_num_arr = np.load('data.npy') # load
The short answer is: you should use np.save and np.load.
The advantage of using these functions is that they are made by the developers of the Numpy library and they already work (plus are likely optimized nicely for processing speed).
For example:
import numpy as np
from pathlib import Path
path = Path('~/data/tmp/').expanduser()
path.mkdir(parents=True, exist_ok=True)
lb,ub = -1,1
num_samples = 5
x = np.random.uniform(low=lb,high=ub,size=(1,num_samples))
y = x**2 + x + 2
np.save(path/'x', x)
np.save(path/'y', y)
x_loaded = np.load(path/'x.npy')
y_load = np.load(path/'y.npy')
print(x is x_loaded) # False
print(x == x_loaded) # [[ True True True True True]]
Expanded answer:
In the end it really depends in your needs because you can also save it in a human-readable format (see Dump a NumPy array into a csv file) or even with other libraries if your files are extremely large (see best way to preserve numpy arrays on disk for an expanded discussion).
However, (making an expansion since you use the word "properly" in your question) I still think using the numpy function out of the box (and most code!) most likely satisfy most user needs. The most important reason is that it already works. Trying to use something else for any other reason might take you on an unexpectedly LONG rabbit hole to figure out why it doesn't work and force it work.
Take for example trying to save it with pickle. I tried that just for fun and it took me at least 30 minutes to realize that pickle wouldn't save my stuff unless I opened & read the file in bytes mode with wb. It took time to google the problem, test potential solutions, understand the error message, etc... It's a small detail, but the fact that it already required me to open a file complicated things in unexpected ways. To add to that, it required me to re-read this (which btw is sort of confusing): Difference between modes a, a+, w, w+, and r+ in built-in open function?.
So if there is an interface that meets your needs, use it unless you have a (very) good reason (e.g. compatibility with matlab or for some reason your really want to read the file and printing in Python really doesn't meet your needs, which might be questionable). Furthermore, most likely if you need to optimize it, you'll find out later down the line (rather than spending ages debugging useless stuff like opening a simple Numpy file).
So use the interface/numpy provide. It might not be perfect, but it's most likely fine, especially for a library that's been around as long as Numpy.
I already spent the saving and loading data with numpy in a bunch of way so have fun with it. Hope this helps!
import numpy as np
import pickle
from pathlib import Path
path = Path('~/data/tmp/').expanduser()
path.mkdir(parents=True, exist_ok=True)
lb,ub = -1,1
num_samples = 5
x = np.random.uniform(low=lb,high=ub,size=(1,num_samples))
y = x**2 + x + 2
# using save (to npy), savez (to npz)
np.save(path/'x', x)
np.save(path/'y', y)
np.savez(path/'db', x=x, y=y)
with open(path/'db.pkl', 'wb') as db_file:
pickle.dump(obj={'x':x, 'y':y}, file=db_file)
## using loading npy, npz files
x_loaded = np.load(path/'x.npy')
y_load = np.load(path/'y.npy')
db = np.load(path/'db.npz')
with open(path/'db.pkl', 'rb') as db_file:
db_pkl = pickle.load(db_file)
print(x is x_loaded)
print(x == x_loaded)
print(x == db['x'])
print(x == db_pkl['x'])
print('done')
Some comments on what I learned:
np.save as expected, this already compresses it well (see https://stackoverflow.com/a/55750128/1601580), works out of the box without any file opening. Clean. Easy. Efficient. Use it.
np.savez uses a uncompressed format (see docs) Save several arrays into a single file in uncompressed .npz format. If you decide to use this (you were warned about going away from the standard solution so expect bugs!) you might discover that you need to use argument names to save it, unless you want to use the default names. So don't use this if the first already works (or any works use that!)
Pickle also allows for arbitrary code execution. Some people might not want to use this for security reasons.
Human-readable files are expensive to make etc. Probably not worth it.
There is something called hdf5 for large files. Cool! https://stackoverflow.com/a/9619713/1601580
Note that this is not an exhaustive answer. But for other resources check this:
For pickle (guess the top answer is don't use pickle, use np.save): Save Numpy Array using Pickle
For large files (great answer! compares storage size, loading save and more!): https://stackoverflow.com/a/41425878/1601580
For matlab (we have to accept matlab has some freakin' nice plots!): "Converting" Numpy arrays to Matlab and vice versa
For saving in human-readable format: Dump a NumPy array into a csv file
np.fromfile() has a sep= keyword argument:
Separator between items if file is a text file. Empty (“”) separator means the file should be treated as binary. Spaces (” ”) in the separator match zero or more whitespace characters. A separator consisting only of spaces must match at least one whitespace.
The default value of sep="" means that np.fromfile() tries to read it as a binary file rather than a space-separated text file, so you get nonsense values back. If you use np.fromfile('markers.txt', sep=" ") you will get the result you are looking for.
However, as others have pointed out, np.loadtxt() is the preferred way to convert text files to numpy arrays, and unless the file needs to be human-readable it is usually better to use binary formats instead (e.g. np.load()/np.save()).