sklearn predict: IndexingError: ('Too many indexers', 'occurred at index ')
0
$begingroup$
use_cols = ['FIRST_NAME', 'LAST_NAME', 'PERSON_STATUS', 'DIVISION_NAME',
'PERSON_TYPE', 'JOB_CHANGE', 'JOB_TRANSFER', 'IDENTIFY_DATE',
'SSO', 'USER_ID', 'ASSET_ID', 'ROLE', 'HPA',
'LAST_LOGIN_DATE', 'ASSET_NAME', 'AUDIT_ID',
'REVIEWER_ACTION', 'USER ID', 'LABELED_ROLE', 'REVIEW_ACTION']
prediction_col = ['REVIEW_ACTION']
categorical_features = ['DIVISION_NAME', 'PERSON_TYPE', 'JOB_CHANGE', 'JOB_TRANSFER',
'SSO', 'USER_ID', 'ASSET_ID', 'ROLE', 'HPA', 'LAST_LOGIN_DATE',
'USER ID', 'LABELED_ROLE']
categorical_transformer = Pipeline(steps=[
('si', SimpleImputer(strategy='constant', fill_value='missing')),
('ohe', OneHotEncoder(handle_unknown='ignore'))],remainder='passthrough')
preprocessor = ColumnTransformer(
transformers=[
('cat', categorical_transformer, categorical_features)])
rf = Pipeline(steps=[('preprocessor', preprocessor),
('rfc', RandomForestClassifier(n_estimators=100))
])
So I let several fields
Then I try to run the prediction
df['rf_prediction'] = df[categorical_features].apply(rf.predict)
and get the error:
IndexingError: ('Too many indexers', 'occurred at index DIVISION_NAME')
I suspect that this has something to do with the columns being 'passthrough' but I'm not sure how to resolve it. I don't want to process some of these columns but want them in the results when I write the file so that I can validate the results.
scikit-learn pipelines
asked 8 mins ago
HarveyHarvey
11
New contributor
Harvey 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 |
0
$begingroup$
use_cols = ['FIRST_NAME', 'LAST_NAME', 'PERSON_STATUS', 'DIVISION_NAME',
'PERSON_TYPE', 'JOB_CHANGE', 'JOB_TRANSFER', 'IDENTIFY_DATE',
'SSO', 'USER_ID', 'ASSET_ID', 'ROLE', 'HPA',
'LAST_LOGIN_DATE', 'ASSET_NAME', 'AUDIT_ID',
'REVIEWER_ACTION', 'USER ID', 'LABELED_ROLE', 'REVIEW_ACTION']
prediction_col = ['REVIEW_ACTION']
categorical_features = ['DIVISION_NAME', 'PERSON_TYPE', 'JOB_CHANGE', 'JOB_TRANSFER',
'SSO', 'USER_ID', 'ASSET_ID', 'ROLE', 'HPA', 'LAST_LOGIN_DATE',
'USER ID', 'LABELED_ROLE']
categorical_transformer = Pipeline(steps=[
('si', SimpleImputer(strategy='constant', fill_value='missing')),
('ohe', OneHotEncoder(handle_unknown='ignore'))],remainder='passthrough')
preprocessor = ColumnTransformer(
transformers=[
('cat', categorical_transformer, categorical_features)])
rf = Pipeline(steps=[('preprocessor', preprocessor),
('rfc', RandomForestClassifier(n_estimators=100))
])
So I let several fields
Then I try to run the prediction
df['rf_prediction'] = df[categorical_features].apply(rf.predict)
and get the error:
IndexingError: ('Too many indexers', 'occurred at index DIVISION_NAME')
I suspect that this has something to do with the columns being 'passthrough' but I'm not sure how to resolve it. I don't want to process some of these columns but want them in the results when I write the file so that I can validate the results.
scikit-learn pipelines
asked 8 mins ago
HarveyHarvey
11
New contributor
Harvey 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 |
0
0
0
$begingroup$
use_cols = ['FIRST_NAME', 'LAST_NAME', 'PERSON_STATUS', 'DIVISION_NAME',
'PERSON_TYPE', 'JOB_CHANGE', 'JOB_TRANSFER', 'IDENTIFY_DATE',
'SSO', 'USER_ID', 'ASSET_ID', 'ROLE', 'HPA',
'LAST_LOGIN_DATE', 'ASSET_NAME', 'AUDIT_ID',
'REVIEWER_ACTION', 'USER ID', 'LABELED_ROLE', 'REVIEW_ACTION']
prediction_col = ['REVIEW_ACTION']
categorical_features = ['DIVISION_NAME', 'PERSON_TYPE', 'JOB_CHANGE', 'JOB_TRANSFER',
'SSO', 'USER_ID', 'ASSET_ID', 'ROLE', 'HPA', 'LAST_LOGIN_DATE',
'USER ID', 'LABELED_ROLE']
categorical_transformer = Pipeline(steps=[
('si', SimpleImputer(strategy='constant', fill_value='missing')),
('ohe', OneHotEncoder(handle_unknown='ignore'))],remainder='passthrough')
preprocessor = ColumnTransformer(
transformers=[
('cat', categorical_transformer, categorical_features)])
rf = Pipeline(steps=[('preprocessor', preprocessor),
('rfc', RandomForestClassifier(n_estimators=100))
])
So I let several fields
Then I try to run the prediction
df['rf_prediction'] = df[categorical_features].apply(rf.predict)
and get the error:
IndexingError: ('Too many indexers', 'occurred at index DIVISION_NAME')
I suspect that this has something to do with the columns being 'passthrough' but I'm not sure how to resolve it. I don't want to process some of these columns but want them in the results when I write the file so that I can validate the results.
scikit-learn pipelines
asked 8 mins ago
HarveyHarvey
11
New contributor
Harvey is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
$endgroup$
use_cols = ['FIRST_NAME', 'LAST_NAME', 'PERSON_STATUS', 'DIVISION_NAME',
'PERSON_TYPE', 'JOB_CHANGE', 'JOB_TRANSFER', 'IDENTIFY_DATE',
'SSO', 'USER_ID', 'ASSET_ID', 'ROLE', 'HPA',
'LAST_LOGIN_DATE', 'ASSET_NAME', 'AUDIT_ID',
'REVIEWER_ACTION', 'USER ID', 'LABELED_ROLE', 'REVIEW_ACTION']
prediction_col = ['REVIEW_ACTION']
categorical_features = ['DIVISION_NAME', 'PERSON_TYPE', 'JOB_CHANGE', 'JOB_TRANSFER',
'SSO', 'USER_ID', 'ASSET_ID', 'ROLE', 'HPA', 'LAST_LOGIN_DATE',
'USER ID', 'LABELED_ROLE']
categorical_transformer = Pipeline(steps=[
('si', SimpleImputer(strategy='constant', fill_value='missing')),
('ohe', OneHotEncoder(handle_unknown='ignore'))],remainder='passthrough')
preprocessor = ColumnTransformer(
transformers=[
('cat', categorical_transformer, categorical_features)])
rf = Pipeline(steps=[('preprocessor', preprocessor),
('rfc', RandomForestClassifier(n_estimators=100))
])
So I let several fields
Then I try to run the prediction
df['rf_prediction'] = df[categorical_features].apply(rf.predict)
and get the error:
IndexingError: ('Too many indexers', 'occurred at index DIVISION_NAME')
I suspect that this has something to do with the columns being 'passthrough' but I'm not sure how to resolve it. I don't want to process some of these columns but want them in the results when I write the file so that I can validate the results.
scikit-learn pipelines
scikit-learn pipelines
asked 8 mins ago
HarveyHarvey
11
New contributor
Harvey is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
asked 8 mins ago
HarveyHarvey
11
New contributor
Harvey is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
asked 8 mins ago
HarveyHarvey
11
New contributor
Harvey is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
asked 8 mins ago
HarveyHarvey
11
asked 8 mins ago
HarveyHarvey
11
11
New contributor
Harvey is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
New contributor
Harvey is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
Harvey 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 |
add a comment |
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