Train/Test Split Python
0
There are 250 randomly generated data points that are obtained as follows:
[X, y] = getDataSet() # getDataSet() randomly generates 250 data points
X looks like:
[array([[-2.44141527e-01, 8.39016956e-01],
[ 1.37468561e+00, 4.97114860e-01],
[ 3.08071887e-02, -2.03260255e-01],...
While y looks like:
y is array([[0.],
[0.],
[0.],...
(it also contains 1s)
So, I'm trying to split [X, y] into training and testing sets. The training set is suppose to be a random selection of 120 of the randomly generated data points. Here is how I'm generating the training set:
nTrain = 120
maxIndex = len(X)
randomTrainingSamples = np.random.choice(maxIndex, nTrain, replace=False)
trainX = X[randomTrainingSamples, :] # training samples
trainY = y[randomTrainingSamples, :] # labels of training samples nTrain X 1
Now, what I can't seem to figure out is, how to get the testing set, which is the 130 other randomly generated data points that are not included in the training set:
testX = # testing samples
testY = # labels of testing samples nTest x 1
Suggestions are much appreciated. Thank you!
python numpy machine-learning
asked Dec 28 '18 at 5:45
MatthewSpireMatthewSpire
1591111
add a comment |
0
There are 250 randomly generated data points that are obtained as follows:
[X, y] = getDataSet() # getDataSet() randomly generates 250 data points
X looks like:
[array([[-2.44141527e-01, 8.39016956e-01],
[ 1.37468561e+00, 4.97114860e-01],
[ 3.08071887e-02, -2.03260255e-01],...
While y looks like:
y is array([[0.],
[0.],
[0.],...
(it also contains 1s)
So, I'm trying to split [X, y] into training and testing sets. The training set is suppose to be a random selection of 120 of the randomly generated data points. Here is how I'm generating the training set:
nTrain = 120
maxIndex = len(X)
randomTrainingSamples = np.random.choice(maxIndex, nTrain, replace=False)
trainX = X[randomTrainingSamples, :] # training samples
trainY = y[randomTrainingSamples, :] # labels of training samples nTrain X 1
Now, what I can't seem to figure out is, how to get the testing set, which is the 130 other randomly generated data points that are not included in the training set:
testX = # testing samples
testY = # labels of testing samples nTest x 1
Suggestions are much appreciated. Thank you!
python numpy machine-learning
asked Dec 28 '18 at 5:45
MatthewSpireMatthewSpire
1591111
add a comment |
0
0
0
There are 250 randomly generated data points that are obtained as follows:
[X, y] = getDataSet() # getDataSet() randomly generates 250 data points
X looks like:
[array([[-2.44141527e-01, 8.39016956e-01],
[ 1.37468561e+00, 4.97114860e-01],
[ 3.08071887e-02, -2.03260255e-01],...
While y looks like:
y is array([[0.],
[0.],
[0.],...
(it also contains 1s)
So, I'm trying to split [X, y] into training and testing sets. The training set is suppose to be a random selection of 120 of the randomly generated data points. Here is how I'm generating the training set:
nTrain = 120
maxIndex = len(X)
randomTrainingSamples = np.random.choice(maxIndex, nTrain, replace=False)
trainX = X[randomTrainingSamples, :] # training samples
trainY = y[randomTrainingSamples, :] # labels of training samples nTrain X 1
Now, what I can't seem to figure out is, how to get the testing set, which is the 130 other randomly generated data points that are not included in the training set:
testX = # testing samples
testY = # labels of testing samples nTest x 1
Suggestions are much appreciated. Thank you!
python numpy machine-learning
asked Dec 28 '18 at 5:45
MatthewSpireMatthewSpire
1591111
There are 250 randomly generated data points that are obtained as follows:
[X, y] = getDataSet() # getDataSet() randomly generates 250 data points
X looks like:
[array([[-2.44141527e-01, 8.39016956e-01],
[ 1.37468561e+00, 4.97114860e-01],
[ 3.08071887e-02, -2.03260255e-01],...
While y looks like:
y is array([[0.],
[0.],
[0.],...
(it also contains 1s)
So, I'm trying to split [X, y] into training and testing sets. The training set is suppose to be a random selection of 120 of the randomly generated data points. Here is how I'm generating the training set:
nTrain = 120
maxIndex = len(X)
randomTrainingSamples = np.random.choice(maxIndex, nTrain, replace=False)
trainX = X[randomTrainingSamples, :] # training samples
trainY = y[randomTrainingSamples, :] # labels of training samples nTrain X 1
Now, what I can't seem to figure out is, how to get the testing set, which is the 130 other randomly generated data points that are not included in the training set:
testX = # testing samples
testY = # labels of testing samples nTest x 1
Suggestions are much appreciated. Thank you!
python numpy machine-learning
python numpy machine-learning
asked Dec 28 '18 at 5:45
MatthewSpireMatthewSpire
1591111
asked Dec 28 '18 at 5:45
MatthewSpireMatthewSpire
1591111
asked Dec 28 '18 at 5:45
MatthewSpireMatthewSpire
1591111
asked Dec 28 '18 at 5:45
MatthewSpireMatthewSpire
1591111
asked Dec 28 '18 at 5:45
MatthewSpireMatthewSpire
1591111
1591111
add a comment |
add a comment |
3 Answers
3
active
oldest
votes
2
You can try this.
randomTestingSamples = [i for i in range(maxIndex) if i not in randomTrainingSamples]
testX = X[randomTestingSamples, :] # testing samples
testY = y[randomTestingSamples, :] # labels of testing samples nTest x 1
answered Dec 28 '18 at 6:02
feed liufeed liu
415
I think this worked. Thank you!
– MatthewSpire
Dec 28 '18 at 12:51
I wanted to let you know this was for an assignment and I have attempted to give credit for your help.
– MatthewSpire
Dec 29 '18 at 17:28
add a comment |
2
You can use sklearn.model_selection.train_test_split:
import numpy as np
from sklearn.model_selection import train_test_split
X, y = np.ndarray((250, 2)), np.ndarray((250, 1))
trainX, testX, trainY, testY = train_test_split(X, y, test_size= 130)
trainX.shape
# (120, 2)
testX.shape
# (130, 2)
trainY.shape
# (120, 1)
testY.shape
# (130, 1)
answered Dec 28 '18 at 5:47
ChrisChris
722211
Cannot use sklearn, otherwise I would have. Thank you!
– MatthewSpire
Dec 28 '18 at 12:46
@MatthewSpire Only numpy then?
– Chris
Dec 28 '18 at 12:48
Yes sir. I've already got the training, but I can't seem to figure out how to select the other 130 for testing.
– MatthewSpire
Dec 28 '18 at 12:50
I think feed liu got it 'cause that code seems to work.
– MatthewSpire
Dec 28 '18 at 12:52
add a comment |
0
You can shuffle the index and pick the first 120 as train and the next 130 as test
random_index = np.random.shuffle(np.arange(len(X)))
randomTrainingSamples = random_index[:120]
randomTestSamples = random_index[120:250]
trainX = X[randomTrainingSamples, :]
trainY = y[randomTrainingSamples, :]
testX = X[randomTestSamples, :]
testY = y[randomTestSamples, :]
answered Dec 28 '18 at 5:52
Ernest S KirubakaranErnest S Kirubakaran
90759
add a comment |
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3 Answers
3
active
oldest
votes
3 Answers
3
active
oldest
votes
active
oldest
votes
active
oldest
votes
2
You can try this.
randomTestingSamples = [i for i in range(maxIndex) if i not in randomTrainingSamples]
testX = X[randomTestingSamples, :] # testing samples
testY = y[randomTestingSamples, :] # labels of testing samples nTest x 1
answered Dec 28 '18 at 6:02
feed liufeed liu
415
I think this worked. Thank you!
– MatthewSpire
Dec 28 '18 at 12:51
I wanted to let you know this was for an assignment and I have attempted to give credit for your help.
– MatthewSpire
Dec 29 '18 at 17:28
add a comment |
2
You can try this.
randomTestingSamples = [i for i in range(maxIndex) if i not in randomTrainingSamples]
testX = X[randomTestingSamples, :] # testing samples
testY = y[randomTestingSamples, :] # labels of testing samples nTest x 1
answered Dec 28 '18 at 6:02
feed liufeed liu
415
I think this worked. Thank you!
– MatthewSpire
Dec 28 '18 at 12:51
I wanted to let you know this was for an assignment and I have attempted to give credit for your help.
– MatthewSpire
Dec 29 '18 at 17:28
add a comment |
2
2
2
You can try this.
randomTestingSamples = [i for i in range(maxIndex) if i not in randomTrainingSamples]
testX = X[randomTestingSamples, :] # testing samples
testY = y[randomTestingSamples, :] # labels of testing samples nTest x 1
answered Dec 28 '18 at 6:02
feed liufeed liu
415
You can try this.
randomTestingSamples = [i for i in range(maxIndex) if i not in randomTrainingSamples]
testX = X[randomTestingSamples, :] # testing samples
testY = y[randomTestingSamples, :] # labels of testing samples nTest x 1
answered Dec 28 '18 at 6:02
feed liufeed liu
415
answered Dec 28 '18 at 6:02
feed liufeed liu
415
answered Dec 28 '18 at 6:02
feed liufeed liu
415
answered Dec 28 '18 at 6:02
feed liufeed liu
415
415
I think this worked. Thank you!
– MatthewSpire
Dec 28 '18 at 12:51
I wanted to let you know this was for an assignment and I have attempted to give credit for your help.
– MatthewSpire
Dec 29 '18 at 17:28
add a comment |
I think this worked. Thank you!
– MatthewSpire
Dec 28 '18 at 12:51
I wanted to let you know this was for an assignment and I have attempted to give credit for your help.
– MatthewSpire
Dec 29 '18 at 17:28
I think this worked. Thank you!
– MatthewSpire
Dec 28 '18 at 12:51
I think this worked. Thank you!
– MatthewSpire
Dec 28 '18 at 12:51
I wanted to let you know this was for an assignment and I have attempted to give credit for your help.
– MatthewSpire
Dec 29 '18 at 17:28
I wanted to let you know this was for an assignment and I have attempted to give credit for your help.
– MatthewSpire
Dec 29 '18 at 17:28
add a comment |
2
You can use sklearn.model_selection.train_test_split:
import numpy as np
from sklearn.model_selection import train_test_split
X, y = np.ndarray((250, 2)), np.ndarray((250, 1))
trainX, testX, trainY, testY = train_test_split(X, y, test_size= 130)
trainX.shape
# (120, 2)
testX.shape
# (130, 2)
trainY.shape
# (120, 1)
testY.shape
# (130, 1)
answered Dec 28 '18 at 5:47
ChrisChris
722211
Cannot use sklearn, otherwise I would have. Thank you!
– MatthewSpire
Dec 28 '18 at 12:46
@MatthewSpire Only numpy then?
– Chris
Dec 28 '18 at 12:48
Yes sir. I've already got the training, but I can't seem to figure out how to select the other 130 for testing.
– MatthewSpire
Dec 28 '18 at 12:50
I think feed liu got it 'cause that code seems to work.
– MatthewSpire
Dec 28 '18 at 12:52
add a comment |
2
You can use sklearn.model_selection.train_test_split:
import numpy as np
from sklearn.model_selection import train_test_split
X, y = np.ndarray((250, 2)), np.ndarray((250, 1))
trainX, testX, trainY, testY = train_test_split(X, y, test_size= 130)
trainX.shape
# (120, 2)
testX.shape
# (130, 2)
trainY.shape
# (120, 1)
testY.shape
# (130, 1)
answered Dec 28 '18 at 5:47
ChrisChris
722211
Cannot use sklearn, otherwise I would have. Thank you!
– MatthewSpire
Dec 28 '18 at 12:46
@MatthewSpire Only numpy then?
– Chris
Dec 28 '18 at 12:48
Yes sir. I've already got the training, but I can't seem to figure out how to select the other 130 for testing.
– MatthewSpire
Dec 28 '18 at 12:50
I think feed liu got it 'cause that code seems to work.
– MatthewSpire
Dec 28 '18 at 12:52
add a comment |
2
2
2
You can use sklearn.model_selection.train_test_split:
import numpy as np
from sklearn.model_selection import train_test_split
X, y = np.ndarray((250, 2)), np.ndarray((250, 1))
trainX, testX, trainY, testY = train_test_split(X, y, test_size= 130)
trainX.shape
# (120, 2)
testX.shape
# (130, 2)
trainY.shape
# (120, 1)
testY.shape
# (130, 1)
answered Dec 28 '18 at 5:47
ChrisChris
722211
You can use sklearn.model_selection.train_test_split:
import numpy as np
from sklearn.model_selection import train_test_split
X, y = np.ndarray((250, 2)), np.ndarray((250, 1))
trainX, testX, trainY, testY = train_test_split(X, y, test_size= 130)
trainX.shape
# (120, 2)
testX.shape
# (130, 2)
trainY.shape
# (120, 1)
testY.shape
# (130, 1)
answered Dec 28 '18 at 5:47
ChrisChris
722211
answered Dec 28 '18 at 5:47
ChrisChris
722211
answered Dec 28 '18 at 5:47
ChrisChris
722211
answered Dec 28 '18 at 5:47
ChrisChris
722211
722211
Cannot use sklearn, otherwise I would have. Thank you!
– MatthewSpire
Dec 28 '18 at 12:46
@MatthewSpire Only numpy then?
– Chris
Dec 28 '18 at 12:48
Yes sir. I've already got the training, but I can't seem to figure out how to select the other 130 for testing.
– MatthewSpire
Dec 28 '18 at 12:50
I think feed liu got it 'cause that code seems to work.
– MatthewSpire
Dec 28 '18 at 12:52
add a comment |
Cannot use sklearn, otherwise I would have. Thank you!
– MatthewSpire
Dec 28 '18 at 12:46
@MatthewSpire Only numpy then?
– Chris
Dec 28 '18 at 12:48
Yes sir. I've already got the training, but I can't seem to figure out how to select the other 130 for testing.
– MatthewSpire
Dec 28 '18 at 12:50
I think feed liu got it 'cause that code seems to work.
– MatthewSpire
Dec 28 '18 at 12:52
Cannot use sklearn, otherwise I would have. Thank you!
– MatthewSpire
Dec 28 '18 at 12:46
Cannot use sklearn, otherwise I would have. Thank you!
– MatthewSpire
Dec 28 '18 at 12:46
@MatthewSpire Only numpy then?
– Chris
Dec 28 '18 at 12:48
@MatthewSpire Only numpy then?
– Chris
Dec 28 '18 at 12:48
Yes sir. I've already got the training, but I can't seem to figure out how to select the other 130 for testing.
– MatthewSpire
Dec 28 '18 at 12:50
Yes sir. I've already got the training, but I can't seem to figure out how to select the other 130 for testing.
– MatthewSpire
Dec 28 '18 at 12:50
I think feed liu got it 'cause that code seems to work.
– MatthewSpire
Dec 28 '18 at 12:52
I think feed liu got it 'cause that code seems to work.
– MatthewSpire
Dec 28 '18 at 12:52
add a comment |
0
You can shuffle the index and pick the first 120 as train and the next 130 as test
random_index = np.random.shuffle(np.arange(len(X)))
randomTrainingSamples = random_index[:120]
randomTestSamples = random_index[120:250]
trainX = X[randomTrainingSamples, :]
trainY = y[randomTrainingSamples, :]
testX = X[randomTestSamples, :]
testY = y[randomTestSamples, :]
answered Dec 28 '18 at 5:52
Ernest S KirubakaranErnest S Kirubakaran
90759
add a comment |
0
You can shuffle the index and pick the first 120 as train and the next 130 as test
random_index = np.random.shuffle(np.arange(len(X)))
randomTrainingSamples = random_index[:120]
randomTestSamples = random_index[120:250]
trainX = X[randomTrainingSamples, :]
trainY = y[randomTrainingSamples, :]
testX = X[randomTestSamples, :]
testY = y[randomTestSamples, :]
answered Dec 28 '18 at 5:52
Ernest S KirubakaranErnest S Kirubakaran
90759
add a comment |
0
0
0
You can shuffle the index and pick the first 120 as train and the next 130 as test
random_index = np.random.shuffle(np.arange(len(X)))
randomTrainingSamples = random_index[:120]
randomTestSamples = random_index[120:250]
trainX = X[randomTrainingSamples, :]
trainY = y[randomTrainingSamples, :]
testX = X[randomTestSamples, :]
testY = y[randomTestSamples, :]
answered Dec 28 '18 at 5:52
Ernest S KirubakaranErnest S Kirubakaran
90759
You can shuffle the index and pick the first 120 as train and the next 130 as test
random_index = np.random.shuffle(np.arange(len(X)))
randomTrainingSamples = random_index[:120]
randomTestSamples = random_index[120:250]
trainX = X[randomTrainingSamples, :]
trainY = y[randomTrainingSamples, :]
testX = X[randomTestSamples, :]
testY = y[randomTestSamples, :]
answered Dec 28 '18 at 5:52
Ernest S KirubakaranErnest S Kirubakaran
90759
answered Dec 28 '18 at 5:52
Ernest S KirubakaranErnest S Kirubakaran
90759
answered Dec 28 '18 at 5:52
Ernest S KirubakaranErnest S Kirubakaran
90759
answered Dec 28 '18 at 5:52
Ernest S KirubakaranErnest S Kirubakaran
90759
90759
add a comment |
add a comment |
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