"Setting an array element with a sequence" numpy error

Question

I'm working on a project that involves having to work with preprocessed data in the following form.

Data explanation has been given above too. The goal is to predict whether a written digit matches the audio of said digit or not. First I transform the spoken arrays of form (N,13) to the means over the time axis as such:

This creates a consistent length of (1,13) for every array within spoken. In order to test this in a simple vanilla algorithm I zip the two arrays together such that we create an array of the form (45000, 2), when I insert this into the fit function of the LogisticRegression class it throws me the following error:

What am I doing wrong?

Code:

import numpy as np
from sklearn.linear_model import LogisticRegression

match = np.load("/srv/digits/match_train.npy")
spoken = np.load("/srv/digits/spoken_train.npy")
written = np.load("/srv/digits/written_train.npy")
print(match.shape, spoken.shape, written.shape)
print(spoken[0].shape, spoken[1].shape)

def features(signal, function):
    new = np.copy(signal)
    for i in range(len(signal)):
        new[i] = function(new[i], axis=0)
    return new

spokenMean = features(spoken, np.mean)
print(spokenMean.shape, spokenMean[0])

result = np.array(list(zip(spokenMean,written)))
print(result.shape)

X_train, X_val, y_train, y_val = train_test_split(result, match, test_size = 
0.33, random_state = 123)
model = LogisticRegression()
print(X_train.shape, y_train.shape)
model.fit(X_train, y_train)
yh_val = model.predict(X_val)

@Siddharth spokenMean should not be in the fit function, that should of course be X_train. X_train has a shape of (30150,2); y_train has a shape of (30150,). — Menno Van Dijk, Oct 28 '17 at 13:49
@Siddharth Same error, does not matter what array I throw in there. I've already tried a lot of different shapes / functions. — Menno Van Dijk, Oct 28 '17 at 13:51
Can you please just add the code, instead of screenshots? It is kind of small to read like this. — Chiel, Oct 28 '17 at 13:52
@Chiel You can click on the pictures to enlarge them perhaps? If that doesn't help I can try to put in the code. — Menno Van Dijk, Oct 28 '17 at 13:55
@MennoVanDijk Ideally, you make an example that people can run. It is not possible to copy and paste code from screenshots. — Chiel, Oct 28 '17 at 13:56
@Chiel I mean, you'd need the entire dataset to make that work right? I'll try to put in the code. Not sure if it really helps answer the question though. — Menno Van Dijk, Oct 28 '17 at 13:57
@Chiel Thanks. I eliminated as much code as I possibly could. Do you have any suggestion however how I can fix this issue I'm having? — Menno Van Dijk, Oct 28 '17 at 14:07

Maxim · Accepted Answer · 2017-10-31T20:13:05.377

1

The spokenMean type is object, i.e. it's a 1D array which holds smaller 1D arrays. The first thing you need to do is to convert it to a 2D float array. This worked for me:

spokenMean = features(spoken, np.mean)
spokenMean = np.vstack(spokenMean[:]).astype(np.float32)

Then, list(zip(...)) is not the right way to concatenate the two arrays. Instead, call np.concatenate:

result = np.concatenate([spokenMean, written], axis=1)

This will make X_train a normal 2D array.

edited Oct 31 '17 at 20:13

answered Oct 31 '17 at 19:18

Maxim

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Reshaping written seems to break the concatenation as the dimensions will change. However, when removing that line of code it all seems to work out. Thank you very much for your help! – Menno Van Dijk Oct 31 '17 at 20:03

"Setting an array element with a sequence" numpy error

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