Dask categorical encoding applied to train and test dataframes
$begingroup$
I am relatively new to dask and I’m trying to ensure my training and test dataframes share the same categorical information for label and onehot encoding. Obviously if this is not the case then my test predictions will likely be completely incorrect.
Approach 1 (failed)
I concatenated the training and test sets together with
train_test = dd.concat([train,test], axis=0)
I then completed the label and one hot encoding successfully on the new combined dataframe (test_train).
train_test = train_test.categorize(columns=categorical_list+onehot_list)
oe = OrdinalEncoder()
oe.fit(train_test[categorical_list])
train_test[categorical_list] = oe.transform(train_test[categorical_list])
enc = OneHotEncoder()
enc.fit(train_test[onehot_list])
onehot_df = enc.transform(train_test[onehot_list])
train_test[onehot_cols]=onehot_df
However, I then found that I couldn’t then split the dataframe back into a training and test set with: -
train = train_test[:len_train]
test = train_test[len_train:]
len(train) #should be 8921483, ends up being higher (train+test)
This code runs but it fails silently without an error. The length of the dataframe remained unchanged. I realise row-wise operations are hard with dask but without being able to slice the two sets back to train and test I’m at a brick-wall with this approach.
Note: I cannot compute() the entire training dataframe as the resulting pandas dataframe will not fit in memory (it’s a big dataset). I plan to fit my model on a random sample of the train data.
Approach 2 (failed)
I kept train and test as separate dataframes. I tried to set the test categories to be the same as the training categories, but the syntax that might work in Panda’s does not behave the same in dask.
for n in test.columns:
if (n in train.columns) and (train[n].dtype.name=='category'):
test[n] = test[n].astype('category').cat.as_ordered()
test[n].cat.set_categories(train[n].cat.categories, ordered=True, inplace=True)
It’s possible that both of my approaches are at fault and I’m missing something basic. I just wish to ensure that my categorical label and onehot encoding is the same in the test set as the training set. I’d be very grateful for any help!
machine-learning bigdata categorical-data
asked 10 mins ago
Nigel AdamsNigel Adams
1
New contributor
Nigel Adams is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
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add a comment |
$begingroup$
I am relatively new to dask and I’m trying to ensure my training and test dataframes share the same categorical information for label and onehot encoding. Obviously if this is not the case then my test predictions will likely be completely incorrect.
Approach 1 (failed)
I concatenated the training and test sets together with
train_test = dd.concat([train,test], axis=0)
I then completed the label and one hot encoding successfully on the new combined dataframe (test_train).
train_test = train_test.categorize(columns=categorical_list+onehot_list)
oe = OrdinalEncoder()
oe.fit(train_test[categorical_list])
train_test[categorical_list] = oe.transform(train_test[categorical_list])
enc = OneHotEncoder()
enc.fit(train_test[onehot_list])
onehot_df = enc.transform(train_test[onehot_list])
train_test[onehot_cols]=onehot_df
However, I then found that I couldn’t then split the dataframe back into a training and test set with: -
train = train_test[:len_train]
test = train_test[len_train:]
len(train) #should be 8921483, ends up being higher (train+test)
This code runs but it fails silently without an error. The length of the dataframe remained unchanged. I realise row-wise operations are hard with dask but without being able to slice the two sets back to train and test I’m at a brick-wall with this approach.
Note: I cannot compute() the entire training dataframe as the resulting pandas dataframe will not fit in memory (it’s a big dataset). I plan to fit my model on a random sample of the train data.
Approach 2 (failed)
I kept train and test as separate dataframes. I tried to set the test categories to be the same as the training categories, but the syntax that might work in Panda’s does not behave the same in dask.
for n in test.columns:
if (n in train.columns) and (train[n].dtype.name=='category'):
test[n] = test[n].astype('category').cat.as_ordered()
test[n].cat.set_categories(train[n].cat.categories, ordered=True, inplace=True)
It’s possible that both of my approaches are at fault and I’m missing something basic. I just wish to ensure that my categorical label and onehot encoding is the same in the test set as the training set. I’d be very grateful for any help!
machine-learning bigdata categorical-data
asked 10 mins ago
Nigel AdamsNigel Adams
1
New contributor
Nigel Adams is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
$endgroup$
add a comment |
$begingroup$
I am relatively new to dask and I’m trying to ensure my training and test dataframes share the same categorical information for label and onehot encoding. Obviously if this is not the case then my test predictions will likely be completely incorrect.
Approach 1 (failed)
I concatenated the training and test sets together with
train_test = dd.concat([train,test], axis=0)
I then completed the label and one hot encoding successfully on the new combined dataframe (test_train).
train_test = train_test.categorize(columns=categorical_list+onehot_list)
oe = OrdinalEncoder()
oe.fit(train_test[categorical_list])
train_test[categorical_list] = oe.transform(train_test[categorical_list])
enc = OneHotEncoder()
enc.fit(train_test[onehot_list])
onehot_df = enc.transform(train_test[onehot_list])
train_test[onehot_cols]=onehot_df
However, I then found that I couldn’t then split the dataframe back into a training and test set with: -
train = train_test[:len_train]
test = train_test[len_train:]
len(train) #should be 8921483, ends up being higher (train+test)
This code runs but it fails silently without an error. The length of the dataframe remained unchanged. I realise row-wise operations are hard with dask but without being able to slice the two sets back to train and test I’m at a brick-wall with this approach.
Note: I cannot compute() the entire training dataframe as the resulting pandas dataframe will not fit in memory (it’s a big dataset). I plan to fit my model on a random sample of the train data.
Approach 2 (failed)
I kept train and test as separate dataframes. I tried to set the test categories to be the same as the training categories, but the syntax that might work in Panda’s does not behave the same in dask.
for n in test.columns:
if (n in train.columns) and (train[n].dtype.name=='category'):
test[n] = test[n].astype('category').cat.as_ordered()
test[n].cat.set_categories(train[n].cat.categories, ordered=True, inplace=True)
It’s possible that both of my approaches are at fault and I’m missing something basic. I just wish to ensure that my categorical label and onehot encoding is the same in the test set as the training set. I’d be very grateful for any help!
machine-learning bigdata categorical-data
asked 10 mins ago
Nigel AdamsNigel Adams
1
New contributor
Nigel Adams is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
$endgroup$
I am relatively new to dask and I’m trying to ensure my training and test dataframes share the same categorical information for label and onehot encoding. Obviously if this is not the case then my test predictions will likely be completely incorrect.
Approach 1 (failed)
I concatenated the training and test sets together with
train_test = dd.concat([train,test], axis=0)
I then completed the label and one hot encoding successfully on the new combined dataframe (test_train).
train_test = train_test.categorize(columns=categorical_list+onehot_list)
oe = OrdinalEncoder()
oe.fit(train_test[categorical_list])
train_test[categorical_list] = oe.transform(train_test[categorical_list])
enc = OneHotEncoder()
enc.fit(train_test[onehot_list])
onehot_df = enc.transform(train_test[onehot_list])
train_test[onehot_cols]=onehot_df
However, I then found that I couldn’t then split the dataframe back into a training and test set with: -
train = train_test[:len_train]
test = train_test[len_train:]
len(train) #should be 8921483, ends up being higher (train+test)
This code runs but it fails silently without an error. The length of the dataframe remained unchanged. I realise row-wise operations are hard with dask but without being able to slice the two sets back to train and test I’m at a brick-wall with this approach.
Note: I cannot compute() the entire training dataframe as the resulting pandas dataframe will not fit in memory (it’s a big dataset). I plan to fit my model on a random sample of the train data.
Approach 2 (failed)
I kept train and test as separate dataframes. I tried to set the test categories to be the same as the training categories, but the syntax that might work in Panda’s does not behave the same in dask.
for n in test.columns:
if (n in train.columns) and (train[n].dtype.name=='category'):
test[n] = test[n].astype('category').cat.as_ordered()
test[n].cat.set_categories(train[n].cat.categories, ordered=True, inplace=True)
It’s possible that both of my approaches are at fault and I’m missing something basic. I just wish to ensure that my categorical label and onehot encoding is the same in the test set as the training set. I’d be very grateful for any help!
machine-learning bigdata categorical-data
machine-learning bigdata categorical-data
asked 10 mins ago
Nigel AdamsNigel Adams
1
New contributor
Nigel Adams is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
asked 10 mins ago
Nigel AdamsNigel Adams
1
New contributor
Nigel Adams is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
asked 10 mins ago
Nigel AdamsNigel Adams
1
New contributor
Nigel Adams is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
asked 10 mins ago
Nigel AdamsNigel Adams
1
asked 10 mins ago
Nigel AdamsNigel Adams
1
1
New contributor
Nigel Adams is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
New contributor
Nigel Adams is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
Nigel Adams is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
add a comment |
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