How to save ColumnTransfers ( scaler , one hot encoder , imputer..etc) for later predictions
0
$begingroup$
here is my code for a neural network I'm testing. I like to save my scales & encoders for later use in prediction. But Cannot figure out how
cat_steps = Pipeline([
('si', SimpleImputer(strategy='constant', fill_value='MISSING')),
('imputer', SimpleImputer(strategy='constant', fill_value='missing')),
('ohehot', OneHotEncoder(sparse=False, handle_unknown='ignore'))
])
categoricalColumns = [
'advCode',
'
'Totalbonus',
]
# *********************************** Numerical Transformer ***************************************
num_steps = Pipeline([
('imputer', SimpleImputer(strategy='median')),
('scaler', StandardScaler())
])
numericalColumns = [
'height',
"weight",
]
# Collect all terformers in one
preprocessor = ColumnTransformer(
transformers=[
('num', num_steps, numericalColumns),
('cat', cat_steps, categoricalColumns)
]
)
X_train = preprocessor.fit_transform(X_train)
I tried saving and loading column transfer like this
from sklearn.externals import joblib
joblib.dump(preprocessor, 'filename.pkl')
preprocessor = load('filename.pkl')
Loading the file returns KeyError: 0
Is it possible for me to save these values in order to use it later for predictions.
scikit-learn
asked 19 hours ago
Noob CoderNoob Coder
133
$endgroup$
add a comment |
0
$begingroup$
here is my code for a neural network I'm testing. I like to save my scales & encoders for later use in prediction. But Cannot figure out how
cat_steps = Pipeline([
('si', SimpleImputer(strategy='constant', fill_value='MISSING')),
('imputer', SimpleImputer(strategy='constant', fill_value='missing')),
('ohehot', OneHotEncoder(sparse=False, handle_unknown='ignore'))
])
categoricalColumns = [
'advCode',
'
'Totalbonus',
]
# *********************************** Numerical Transformer ***************************************
num_steps = Pipeline([
('imputer', SimpleImputer(strategy='median')),
('scaler', StandardScaler())
])
numericalColumns = [
'height',
"weight",
]
# Collect all terformers in one
preprocessor = ColumnTransformer(
transformers=[
('num', num_steps, numericalColumns),
('cat', cat_steps, categoricalColumns)
]
)
X_train = preprocessor.fit_transform(X_train)
I tried saving and loading column transfer like this
from sklearn.externals import joblib
joblib.dump(preprocessor, 'filename.pkl')
preprocessor = load('filename.pkl')
Loading the file returns KeyError: 0
Is it possible for me to save these values in order to use it later for predictions.
scikit-learn
asked 19 hours ago
Noob CoderNoob Coder
133
$endgroup$
$begingroup$
Have you tried to pickle them?
$endgroup$
– Simon Larsson
18 hours ago
$begingroup$
yes . I tried saving and loading column transfer like this from sklearn.externals import joblib joblib.dump(preprocessor, 'filename.pkl') preprocessor = load('filename.pkl') Loading the file returns KeyError: 0
$endgroup$
– Noob Coder
18 hours ago
add a comment |
0
0
0
$begingroup$
here is my code for a neural network I'm testing. I like to save my scales & encoders for later use in prediction. But Cannot figure out how
cat_steps = Pipeline([
('si', SimpleImputer(strategy='constant', fill_value='MISSING')),
('imputer', SimpleImputer(strategy='constant', fill_value='missing')),
('ohehot', OneHotEncoder(sparse=False, handle_unknown='ignore'))
])
categoricalColumns = [
'advCode',
'
'Totalbonus',
]
# *********************************** Numerical Transformer ***************************************
num_steps = Pipeline([
('imputer', SimpleImputer(strategy='median')),
('scaler', StandardScaler())
])
numericalColumns = [
'height',
"weight",
]
# Collect all terformers in one
preprocessor = ColumnTransformer(
transformers=[
('num', num_steps, numericalColumns),
('cat', cat_steps, categoricalColumns)
]
)
X_train = preprocessor.fit_transform(X_train)
I tried saving and loading column transfer like this
from sklearn.externals import joblib
joblib.dump(preprocessor, 'filename.pkl')
preprocessor = load('filename.pkl')
Loading the file returns KeyError: 0
Is it possible for me to save these values in order to use it later for predictions.
scikit-learn
asked 19 hours ago
Noob CoderNoob Coder
133
$endgroup$
here is my code for a neural network I'm testing. I like to save my scales & encoders for later use in prediction. But Cannot figure out how
cat_steps = Pipeline([
('si', SimpleImputer(strategy='constant', fill_value='MISSING')),
('imputer', SimpleImputer(strategy='constant', fill_value='missing')),
('ohehot', OneHotEncoder(sparse=False, handle_unknown='ignore'))
])
categoricalColumns = [
'advCode',
'
'Totalbonus',
]
# *********************************** Numerical Transformer ***************************************
num_steps = Pipeline([
('imputer', SimpleImputer(strategy='median')),
('scaler', StandardScaler())
])
numericalColumns = [
'height',
"weight",
]
# Collect all terformers in one
preprocessor = ColumnTransformer(
transformers=[
('num', num_steps, numericalColumns),
('cat', cat_steps, categoricalColumns)
]
)
X_train = preprocessor.fit_transform(X_train)
I tried saving and loading column transfer like this
from sklearn.externals import joblib
joblib.dump(preprocessor, 'filename.pkl')
preprocessor = load('filename.pkl')
Loading the file returns KeyError: 0
Is it possible for me to save these values in order to use it later for predictions.
scikit-learn
scikit-learn
asked 19 hours ago
Noob CoderNoob Coder
133
asked 19 hours ago
Noob CoderNoob Coder
133
edited 16 hours ago
Noob Coder
asked 19 hours ago
Noob CoderNoob Coder
133
asked 19 hours ago
Noob CoderNoob Coder
133
asked 19 hours ago
Noob CoderNoob Coder
133
133
$begingroup$
Have you tried to pickle them?
$endgroup$
– Simon Larsson
18 hours ago
$begingroup$
yes . I tried saving and loading column transfer like this from sklearn.externals import joblib joblib.dump(preprocessor, 'filename.pkl') preprocessor = load('filename.pkl') Loading the file returns KeyError: 0
$endgroup$
– Noob Coder
18 hours ago
add a comment |
$begingroup$
Have you tried to pickle them?
$endgroup$
– Simon Larsson
18 hours ago
$begingroup$
yes . I tried saving and loading column transfer like this from sklearn.externals import joblib joblib.dump(preprocessor, 'filename.pkl') preprocessor = load('filename.pkl') Loading the file returns KeyError: 0
$endgroup$
– Noob Coder
18 hours ago
$begingroup$
Have you tried to pickle them?
$endgroup$
– Simon Larsson
18 hours ago
$begingroup$
Have you tried to pickle them?
$endgroup$
– Simon Larsson
18 hours ago
$begingroup$
yes . I tried saving and loading column transfer like this from sklearn.externals import joblib joblib.dump(preprocessor, 'filename.pkl') preprocessor = load('filename.pkl') Loading the file returns KeyError: 0
$endgroup$
– Noob Coder
18 hours ago
$begingroup$
yes . I tried saving and loading column transfer like this from sklearn.externals import joblib joblib.dump(preprocessor, 'filename.pkl') preprocessor = load('filename.pkl') Loading the file returns KeyError: 0
$endgroup$
– Noob Coder
18 hours ago
add a comment |
0
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$begingroup$
Have you tried to pickle them?
$endgroup$
– Simon Larsson
18 hours ago
$begingroup$
yes . I tried saving and loading column transfer like this from sklearn.externals import joblib joblib.dump(preprocessor, 'filename.pkl') preprocessor = load('filename.pkl') Loading the file returns KeyError: 0
$endgroup$
– Noob Coder
18 hours ago