my question is the following:
I have an array that has feature vectors that correspond to several audio files. So if for example there are 10 audio files than this array would have length 10.
I have a feature that is itself a list (this list comprises the information of a specific feature of the audio file) and for a given audio file the feature vector looks like this:
array([0.03861840871664194, 187.72393405210002, 62.59881268743305,
0.2911392405063291,
array([4963.40332031, 3229.98046875, 2691.65039062, 3208.44726562,
4338.94042969, 4220.5078125 , 4166.67480469, 4801.90429688,
5555.56640625, 5910.86425781, 6115.4296875 , 5706.29882812,
4984.93652344, 2756.25 , 1991.82128906, 2551.68457031,
2734.71679688, 2906.98242188, 3143.84765625, 3219.21386719,
3186.9140625 , 3165.38085938, 3068.48144531, 2465.55175781,
2110.25390625, 2508.61816406, 2993.11523438, 3843.67675781,
4715.77148438, 5652.46582031, 5480.20019531, 5792.43164062,
5932.39746094, 6244.62890625, 6072.36328125, 6201.5625 ,
6158.49609375, 6201.5625 , 6233.86230469, 6061.59667969])],
dtype=object)
Now when I try to feed this data into the svm model:
from sklearn import svm
from sklearn.model_selection import train_test_split
from sklearn.metrics import confusion_matrix
import matplotlib.pyplot as plt
X_train, X_val, y_train, y_val = train_test_split(X,y,test_size=0.3)
model = svm.SVC()
model.fit(X_train,y_train)
yt_p = model.predict(X_train)
yv_p = model.predict(X_val)
I get this error ValueError: setting an array element with a sequence.
How can I structure my feature vector in order to be able to feed it to the svm?
EDIT:
Here I provide with an example of X
if we have 5 audio files then X will be:
array([[0.017455393927437918, 227.66237105624407, 32.42076654734572,
0.3867924528301887,
array([1851.85546875, 2433.25195312, 3057.71484375, 3079.24804688,
3079.24804688, 3068.48144531, 3046.94824219, 3359.1796875 ,
3908.27636719, 4618.87207031, 4618.87207031, 4521.97265625,
4091.30859375, 3111.54785156, 3100.78125 , 2863.91601562,
1561.15722656, 1119.7265625 , 1065.89355469, 947.4609375 ,
979.76074219, 990.52734375, 990.52734375, 1356.59179688,
2077.95410156, 2993.11523438, 3025.41503906, 3068.48144531,
3079.24804688, 3090.01464844, 3100.78125 , 3111.54785156,
2993.11523438, 3100.78125 , 3079.24804688, 2853.14941406,
1205.859375 , 1281.22558594, 1614.99023438, 2131.78710938,
2325.5859375 , 2034.88769531, 1916.45507812, 1744.18945312,
1851.85546875, 2357.88574219, 2368.65234375, 1916.45507812,
1959.52148438, 1959.52148438, 1754.95605469, 1787.25585938,
2207.15332031])],
[0.03861840871664194, 187.72393405210002, 62.59881268743305,
0.2911392405063291,
array([4963.40332031, 3229.98046875, 2691.65039062, 3208.44726562,
4338.94042969, 4220.5078125 , 4166.67480469, 4801.90429688,
5555.56640625, 5910.86425781, 6115.4296875 , 5706.29882812,
4984.93652344, 2756.25 , 1991.82128906, 2551.68457031,
2734.71679688, 2906.98242188, 3143.84765625, 3219.21386719,
3186.9140625 , 3165.38085938, 3068.48144531, 2465.55175781,
2110.25390625, 2508.61816406, 2993.11523438, 3843.67675781,
4715.77148438, 5652.46582031, 5480.20019531, 5792.43164062,
5932.39746094, 6244.62890625, 6072.36328125, 6201.5625 ,
6158.49609375, 6201.5625 , 6233.86230469, 6061.59667969])],
[0.042435441297643324, 128.81225073038124, 20.912528554426807,
0.313953488372093,
array([4349.70703125, 4242.04101562, 4274.34082031, 4123.60839844,
4457.37304688, 4834.20410156, 4661.93847656, 4306.640625 ,
4231.27441406, 4543.50585938, 4435.83984375, 6201.5625 ,
8817.84667969, 8817.84667969, 742.89550781, 721.36230469,
732.12890625, 732.12890625, 710.59570312, 721.36230469,
925.92773438, 1119.7265625 , 1141.25976562, 1431.95800781,
7762.71972656, 7934.98535156, 7891.91894531, 7332.05566406,
3789.84375 , 2799.31640625, 2831.61621094, 2217.91992188,
581.39648438, 602.9296875 , 2217.91992188, 2228.68652344,
2368.65234375, 2519.38476562, 2863.91601562, 3682.17773438,
3649.87792969, 4188.20800781, 4112.84179688])],
[0.006295381642571726, 130.28309914454434, 5.193614287487564,
0.2411764705882353,
array([7978.05175781, 8010.3515625 , 8118.01757812, 8430.24902344,
8257.98339844, 8451.78222656, 8591.74804688, 8677.88085938,
8796.31347656, 8850.14648438, 8796.31347656, 8925.51269531,
6244.62890625, 344.53125 , 344.53125 , 1614.99023438,
2325.5859375 , 2971.58203125, 3316.11328125, 3617.578125 ,
3294.58007812, 2788.54980469, 2637.81738281, 2702.41699219,
2723.95019531, 3133.08105469, 3413.01269531, 5663.23242188,
5770.8984375 , 5577.09960938, 2228.68652344, 1604.22363281,
1690.35644531, 4123.60839844, 5566.33300781, 5803.19824219,
5749.36523438, 5846.26464844, 6772.19238281, 7073.65722656,
7622.75390625, 7859.61914062, 8236.45019531, 8441.015625 ,
8699.4140625 , 8807.08007812, 8742.48046875, 8667.11425781,
8710.18066406, 8947.04589844, 9140.84472656, 9130.078125 ,
8936.27929688, 8925.51269531, 8947.04589844, 8925.51269531,
9097.77832031, 9205.44433594, 9194.67773438, 9140.84472656,
9162.37792969, 9043.9453125 , 9162.37792969, 9108.54492188,
9183.91113281, 9280.81054688, 9270.04394531, 9108.54492188,
9076.24511719, 9356.17675781, 9226.97753906, 9216.2109375 ,
9248.51074219, 9140.84472656, 9237.74414062, 9334.64355469,
9259.27734375, 9226.97753906, 9216.2109375 , 9108.54492188,
9183.91113281, 9216.2109375 , 9248.51074219, 9259.27734375,
9183.91113281])],
[0.017070271599460656, 171.91660927761163, 26.854424936811768,
0.11188811188811189,
array([4715.77148438, 4629.63867188, 4898.80371094, 5275.63476562,
4941.87011719, 4532.73925781, 4618.87207031, 4995.703125 ,
4705.00488281, 4500.43945312, 4188.20800781, 4371.24023438,
4457.37304688, 4188.20800781, 4909.5703125 , 4877.27050781,
6761.42578125, 7708.88671875, 7719.65332031, 7956.51855469,
8484.08203125, 9033.17871094, 9043.9453125 , 9000.87890625,
9011.64550781, 9011.64550781, 9000.87890625, 9108.54492188,
8817.84667969, 6686.05957031, 1808.7890625 , 1830.32226562,
1851.85546875, 1636.5234375 , 1022.82714844, 1281.22558594,
1927.22167969, 1948.75488281, 1302.75878906, 1399.65820312,
1873.38867188, 1959.52148438, 7245.92285156, 9011.64550781,
9420.77636719, 9549.97558594, 9453.07617188, 9431.54296875,
9410.00976562, 9248.51074219, 9151.61132812, 9194.67773438,
8968.57910156, 8634.81445312, 8268.75 , 7439.72167969,
5501.73339844, 5232.56835938, 5103.36914062, 7052.12402344,
7299.75585938, 7127.49023438, 7192.08984375, 5673.99902344,
5523.26660156, 5986.23046875, 6729.12597656, 6309.22851562,
5135.66894531, 5081.8359375 , 5329.46777344, 5404.83398438])]],
dtype=object)
You can feed the feature with the lists inside to your model in two ways:
To try the first option:
# Convert `X` to data frame
X = pd.DataFrame(X)
# Rename columns
X.columns = ['feature_' + str(i + 1) for i in range(X.shape[1])]
# Convert the feature with lists inside to long format
x = X['feature_5'].explode().to_frame()
# Create counter by observation so we can pivot
x['observation_id'] = x.groupby(level=0).cumcount()
# Convert to dataset and rename all columns
x = x.pivot(columns='observation_id', values='feature_5').fillna(0)
x = x.add_prefix('list_element_')
# Drop `feature_5` from X
X.drop(columns='feature_5', axis=1, inplace=True)
# Concatenate X and x together
X = pd.concat([X, x], axis=1)
# Carry on as before
X_train, X_val, y_train, y_val = train_test_split(X,y,test_size=0.3)
model = svm.SVC()
model.fit(X_train,y_train)
There's no right answer to the second option and only you can decide how to do this because only you know what the lists mean. However, if you want to get the mean (for example) of each list and use that as a feature:
# Get the mean of each list
means = [np.mean(array) for array in X[:, 4]]
# Replace the lists with `means`
X[:, 4] = means
And then carry on with the splitting and fitting.