How to use one hot encoding of string categorical features in keras?
0
$begingroup$
I am dealing with a binary classification problem. The output column of my dataset is already encoded in 0/1. The problem is that I have many categorical features (columns), which are strings and I would like to one-hot encode them.
I have 18 features (few features are integers and others are strings, the categorical ones) and 1 output column.
I tried this:
dataframe = pd.read_csv('basic_df_export.csv', sep=';', encoding = 'ISO-8859-1', header=None)
dataset = dataframe.values
# split into input (X) and output (Y) variables
X = dataset[:,0:17]
Y = dataset[:,17]
# define example
encoded = to_categorical(X)
print(encoded)
but it doesn't work, throwing me this error:
---------------------------------------------------------------------------
ValueError Traceback (most recent call last)
<ipython-input-41-318da09d7033> in <module>()
9
10 # define example
---> 11 encoded = to_categorical(X)
12 print(encoded)
~/anaconda/lib/python3.6/site-packages/keras/utils/np_utils.py in to_categorical(y, num_classes)
21 is placed last.
22 """
---> 23 y = np.array(y, dtype='int')
24 input_shape = y.shape
25 if input_shape and input_shape[-1] == 1 and len(input_shape) > 1:
ValueError: invalid literal for int() with base 10: 'photo'
classification keras feature-engineering encoding
asked Jan 7 at 20:11
ZelelBZelelB
1526
$endgroup$
bumped to the homepage by Community♦ 2 mins ago
This question has answers that may be good or bad; the system has marked it active so that they can be reviewed.
add a comment |
0
$begingroup$
I am dealing with a binary classification problem. The output column of my dataset is already encoded in 0/1. The problem is that I have many categorical features (columns), which are strings and I would like to one-hot encode them.
I have 18 features (few features are integers and others are strings, the categorical ones) and 1 output column.
I tried this:
dataframe = pd.read_csv('basic_df_export.csv', sep=';', encoding = 'ISO-8859-1', header=None)
dataset = dataframe.values
# split into input (X) and output (Y) variables
X = dataset[:,0:17]
Y = dataset[:,17]
# define example
encoded = to_categorical(X)
print(encoded)
but it doesn't work, throwing me this error:
---------------------------------------------------------------------------
ValueError Traceback (most recent call last)
<ipython-input-41-318da09d7033> in <module>()
9
10 # define example
---> 11 encoded = to_categorical(X)
12 print(encoded)
~/anaconda/lib/python3.6/site-packages/keras/utils/np_utils.py in to_categorical(y, num_classes)
21 is placed last.
22 """
---> 23 y = np.array(y, dtype='int')
24 input_shape = y.shape
25 if input_shape and input_shape[-1] == 1 and len(input_shape) > 1:
ValueError: invalid literal for int() with base 10: 'photo'
classification keras feature-engineering encoding
asked Jan 7 at 20:11
ZelelBZelelB
1526
$endgroup$
bumped to the homepage by Community♦ 2 mins ago
This question has answers that may be good or bad; the system has marked it active so that they can be reviewed.
1
$begingroup$
Look at this: pbpython.com/categorical-encoding.html
$endgroup$
– moh
Jan 7 at 21:47
$begingroup$
I suggest that you modify your question and provide some actual data points so that people can see what kind of variable are there. Clearly you do not know how to encode categorical features. You say you have a mix of categorical and numerical columns, but here "encoded = to_categorical(X)", you pass all your features to be encoded. Not to mention that this way of encoding categorical features is rather wrong as well! Otherwise you get not useful answers. A place to start understanding cat. encoding could be: towardsdatascience.com/…
$endgroup$
– Majid Mortazavi
Jan 9 at 6:57
add a comment |
0
0
0
$begingroup$
I am dealing with a binary classification problem. The output column of my dataset is already encoded in 0/1. The problem is that I have many categorical features (columns), which are strings and I would like to one-hot encode them.
I have 18 features (few features are integers and others are strings, the categorical ones) and 1 output column.
I tried this:
dataframe = pd.read_csv('basic_df_export.csv', sep=';', encoding = 'ISO-8859-1', header=None)
dataset = dataframe.values
# split into input (X) and output (Y) variables
X = dataset[:,0:17]
Y = dataset[:,17]
# define example
encoded = to_categorical(X)
print(encoded)
but it doesn't work, throwing me this error:
---------------------------------------------------------------------------
ValueError Traceback (most recent call last)
<ipython-input-41-318da09d7033> in <module>()
9
10 # define example
---> 11 encoded = to_categorical(X)
12 print(encoded)
~/anaconda/lib/python3.6/site-packages/keras/utils/np_utils.py in to_categorical(y, num_classes)
21 is placed last.
22 """
---> 23 y = np.array(y, dtype='int')
24 input_shape = y.shape
25 if input_shape and input_shape[-1] == 1 and len(input_shape) > 1:
ValueError: invalid literal for int() with base 10: 'photo'
classification keras feature-engineering encoding
asked Jan 7 at 20:11
ZelelBZelelB
1526
$endgroup$
I am dealing with a binary classification problem. The output column of my dataset is already encoded in 0/1. The problem is that I have many categorical features (columns), which are strings and I would like to one-hot encode them.
I have 18 features (few features are integers and others are strings, the categorical ones) and 1 output column.
I tried this:
dataframe = pd.read_csv('basic_df_export.csv', sep=';', encoding = 'ISO-8859-1', header=None)
dataset = dataframe.values
# split into input (X) and output (Y) variables
X = dataset[:,0:17]
Y = dataset[:,17]
# define example
encoded = to_categorical(X)
print(encoded)
but it doesn't work, throwing me this error:
---------------------------------------------------------------------------
ValueError Traceback (most recent call last)
<ipython-input-41-318da09d7033> in <module>()
9
10 # define example
---> 11 encoded = to_categorical(X)
12 print(encoded)
~/anaconda/lib/python3.6/site-packages/keras/utils/np_utils.py in to_categorical(y, num_classes)
21 is placed last.
22 """
---> 23 y = np.array(y, dtype='int')
24 input_shape = y.shape
25 if input_shape and input_shape[-1] == 1 and len(input_shape) > 1:
ValueError: invalid literal for int() with base 10: 'photo'
classification keras feature-engineering encoding
classification keras feature-engineering encoding
asked Jan 7 at 20:11
ZelelBZelelB
1526
asked Jan 7 at 20:11
ZelelBZelelB
1526
asked Jan 7 at 20:11
ZelelBZelelB
1526
asked Jan 7 at 20:11
ZelelBZelelB
1526
asked Jan 7 at 20:11
ZelelBZelelB
1526
1526
bumped to the homepage by Community♦ 2 mins ago
This question has answers that may be good or bad; the system has marked it active so that they can be reviewed.
bumped to the homepage by Community♦ 2 mins ago
This question has answers that may be good or bad; the system has marked it active so that they can be reviewed.
1
$begingroup$
Look at this: pbpython.com/categorical-encoding.html
$endgroup$
– moh
Jan 7 at 21:47
$begingroup$
I suggest that you modify your question and provide some actual data points so that people can see what kind of variable are there. Clearly you do not know how to encode categorical features. You say you have a mix of categorical and numerical columns, but here "encoded = to_categorical(X)", you pass all your features to be encoded. Not to mention that this way of encoding categorical features is rather wrong as well! Otherwise you get not useful answers. A place to start understanding cat. encoding could be: towardsdatascience.com/…
$endgroup$
– Majid Mortazavi
Jan 9 at 6:57
add a comment |
1
$begingroup$
Look at this: pbpython.com/categorical-encoding.html
$endgroup$
– moh
Jan 7 at 21:47
$begingroup$
I suggest that you modify your question and provide some actual data points so that people can see what kind of variable are there. Clearly you do not know how to encode categorical features. You say you have a mix of categorical and numerical columns, but here "encoded = to_categorical(X)", you pass all your features to be encoded. Not to mention that this way of encoding categorical features is rather wrong as well! Otherwise you get not useful answers. A place to start understanding cat. encoding could be: towardsdatascience.com/…
$endgroup$
– Majid Mortazavi
Jan 9 at 6:57
1
1
$begingroup$
Look at this: pbpython.com/categorical-encoding.html
$endgroup$
– moh
Jan 7 at 21:47
$begingroup$
Look at this: pbpython.com/categorical-encoding.html
$endgroup$
– moh
Jan 7 at 21:47
$begingroup$
I suggest that you modify your question and provide some actual data points so that people can see what kind of variable are there. Clearly you do not know how to encode categorical features. You say you have a mix of categorical and numerical columns, but here "encoded = to_categorical(X)", you pass all your features to be encoded. Not to mention that this way of encoding categorical features is rather wrong as well! Otherwise you get not useful answers. A place to start understanding cat. encoding could be: towardsdatascience.com/…
$endgroup$
– Majid Mortazavi
Jan 9 at 6:57
$begingroup$
I suggest that you modify your question and provide some actual data points so that people can see what kind of variable are there. Clearly you do not know how to encode categorical features. You say you have a mix of categorical and numerical columns, but here "encoded = to_categorical(X)", you pass all your features to be encoded. Not to mention that this way of encoding categorical features is rather wrong as well! Otherwise you get not useful answers. A place to start understanding cat. encoding could be: towardsdatascience.com/…
$endgroup$
– Majid Mortazavi
Jan 9 at 6:57
add a comment |
1 Answer
1
active
oldest
votes
0
$begingroup$
Try:
X = dataset[:,0:17].astype(float).astype(int)
I think that if you have a string like '45.2', you will have to cast it as a floating-point first and from float you can cast them into integer.
I will be glad if an editor could corroborate/correct this answer.
answered Jan 8 at 2:56
aerijmanaerijman
285
$endgroup$
$begingroup$
no it doesn't work. I dont have any strings like that. All my columns are integers or strings (categorical features, where for example, in the column 'industry' the values could take one of 3 values: 'IT', 'FoodTrade' or 'Automotive') and the column 'content_type' could take one of 4 values: 'photo', 'video', 'link', 'event')
$endgroup$
– ZelelB
Jan 8 at 21:17
$begingroup$
Ok, you can describe your "to_categorical" method together with a few rows of your data? You can't certainly convert 'photo' into a integer. Did you already transformed these strings 'photo', 'video', 'link', 'event' into 1,2,3,4?
$endgroup$
– aerijman
Jan 8 at 22:04
$begingroup$
got it.. I used get_dummies()
$endgroup$
– ZelelB
Jan 10 at 0:22
add a comment |
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0
$begingroup$
Try:
X = dataset[:,0:17].astype(float).astype(int)
I think that if you have a string like '45.2', you will have to cast it as a floating-point first and from float you can cast them into integer.
I will be glad if an editor could corroborate/correct this answer.
answered Jan 8 at 2:56
aerijmanaerijman
285
$endgroup$
$begingroup$
no it doesn't work. I dont have any strings like that. All my columns are integers or strings (categorical features, where for example, in the column 'industry' the values could take one of 3 values: 'IT', 'FoodTrade' or 'Automotive') and the column 'content_type' could take one of 4 values: 'photo', 'video', 'link', 'event')
$endgroup$
– ZelelB
Jan 8 at 21:17
$begingroup$
Ok, you can describe your "to_categorical" method together with a few rows of your data? You can't certainly convert 'photo' into a integer. Did you already transformed these strings 'photo', 'video', 'link', 'event' into 1,2,3,4?
$endgroup$
– aerijman
Jan 8 at 22:04
$begingroup$
got it.. I used get_dummies()
$endgroup$
– ZelelB
Jan 10 at 0:22
add a comment |
0
$begingroup$
Try:
X = dataset[:,0:17].astype(float).astype(int)
I think that if you have a string like '45.2', you will have to cast it as a floating-point first and from float you can cast them into integer.
I will be glad if an editor could corroborate/correct this answer.
answered Jan 8 at 2:56
aerijmanaerijman
285
$endgroup$
$begingroup$
no it doesn't work. I dont have any strings like that. All my columns are integers or strings (categorical features, where for example, in the column 'industry' the values could take one of 3 values: 'IT', 'FoodTrade' or 'Automotive') and the column 'content_type' could take one of 4 values: 'photo', 'video', 'link', 'event')
$endgroup$
– ZelelB
Jan 8 at 21:17
$begingroup$
Ok, you can describe your "to_categorical" method together with a few rows of your data? You can't certainly convert 'photo' into a integer. Did you already transformed these strings 'photo', 'video', 'link', 'event' into 1,2,3,4?
$endgroup$
– aerijman
Jan 8 at 22:04
$begingroup$
got it.. I used get_dummies()
$endgroup$
– ZelelB
Jan 10 at 0:22
add a comment |
0
0
0
$begingroup$
Try:
X = dataset[:,0:17].astype(float).astype(int)
I think that if you have a string like '45.2', you will have to cast it as a floating-point first and from float you can cast them into integer.
I will be glad if an editor could corroborate/correct this answer.
answered Jan 8 at 2:56
aerijmanaerijman
285
$endgroup$
Try:
X = dataset[:,0:17].astype(float).astype(int)
I think that if you have a string like '45.2', you will have to cast it as a floating-point first and from float you can cast them into integer.
I will be glad if an editor could corroborate/correct this answer.
answered Jan 8 at 2:56
aerijmanaerijman
285
answered Jan 8 at 2:56
aerijmanaerijman
285
answered Jan 8 at 2:56
aerijmanaerijman
285
answered Jan 8 at 2:56
aerijmanaerijman
285
285
$begingroup$
no it doesn't work. I dont have any strings like that. All my columns are integers or strings (categorical features, where for example, in the column 'industry' the values could take one of 3 values: 'IT', 'FoodTrade' or 'Automotive') and the column 'content_type' could take one of 4 values: 'photo', 'video', 'link', 'event')
$endgroup$
– ZelelB
Jan 8 at 21:17
$begingroup$
Ok, you can describe your "to_categorical" method together with a few rows of your data? You can't certainly convert 'photo' into a integer. Did you already transformed these strings 'photo', 'video', 'link', 'event' into 1,2,3,4?
$endgroup$
– aerijman
Jan 8 at 22:04
$begingroup$
got it.. I used get_dummies()
$endgroup$
– ZelelB
Jan 10 at 0:22
add a comment |
$begingroup$
no it doesn't work. I dont have any strings like that. All my columns are integers or strings (categorical features, where for example, in the column 'industry' the values could take one of 3 values: 'IT', 'FoodTrade' or 'Automotive') and the column 'content_type' could take one of 4 values: 'photo', 'video', 'link', 'event')
$endgroup$
– ZelelB
Jan 8 at 21:17
$begingroup$
Ok, you can describe your "to_categorical" method together with a few rows of your data? You can't certainly convert 'photo' into a integer. Did you already transformed these strings 'photo', 'video', 'link', 'event' into 1,2,3,4?
$endgroup$
– aerijman
Jan 8 at 22:04
$begingroup$
got it.. I used get_dummies()
$endgroup$
– ZelelB
Jan 10 at 0:22
$begingroup$
no it doesn't work. I dont have any strings like that. All my columns are integers or strings (categorical features, where for example, in the column 'industry' the values could take one of 3 values: 'IT', 'FoodTrade' or 'Automotive') and the column 'content_type' could take one of 4 values: 'photo', 'video', 'link', 'event')
$endgroup$
– ZelelB
Jan 8 at 21:17
$begingroup$
no it doesn't work. I dont have any strings like that. All my columns are integers or strings (categorical features, where for example, in the column 'industry' the values could take one of 3 values: 'IT', 'FoodTrade' or 'Automotive') and the column 'content_type' could take one of 4 values: 'photo', 'video', 'link', 'event')
$endgroup$
– ZelelB
Jan 8 at 21:17
$begingroup$
Ok, you can describe your "to_categorical" method together with a few rows of your data? You can't certainly convert 'photo' into a integer. Did you already transformed these strings 'photo', 'video', 'link', 'event' into 1,2,3,4?
$endgroup$
– aerijman
Jan 8 at 22:04
$begingroup$
Ok, you can describe your "to_categorical" method together with a few rows of your data? You can't certainly convert 'photo' into a integer. Did you already transformed these strings 'photo', 'video', 'link', 'event' into 1,2,3,4?
$endgroup$
– aerijman
Jan 8 at 22:04
$begingroup$
got it.. I used get_dummies()
$endgroup$
– ZelelB
Jan 10 at 0:22
$begingroup$
got it.. I used get_dummies()
$endgroup$
– ZelelB
Jan 10 at 0:22
add a comment |
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1
$begingroup$
Look at this: pbpython.com/categorical-encoding.html
$endgroup$
– moh
Jan 7 at 21:47
$begingroup$
I suggest that you modify your question and provide some actual data points so that people can see what kind of variable are there. Clearly you do not know how to encode categorical features. You say you have a mix of categorical and numerical columns, but here "encoded = to_categorical(X)", you pass all your features to be encoded. Not to mention that this way of encoding categorical features is rather wrong as well! Otherwise you get not useful answers. A place to start understanding cat. encoding could be: towardsdatascience.com/…
$endgroup$
– Majid Mortazavi
Jan 9 at 6:57