Troubleshooting & Error Messages
Lists possible errors and how to address them
Issues related to your data
CSV file must not be empty
May occur if upload fails or is interrupted
Provide a CSV with content; try re-uploading
CSV file must contain headers
Headers are used to create your variable names and/or map CSV columns to your variables
Edit your CSV to include headers
Invalid header name
Requirements for header names are included in the Data-to-Model wizard
Address header name issues (such as special characters)
Found null/empty value
Currently, Genius does not handle empty cells/values for CSVs, there is a future feature planned to address this
Either remove rows which have empty values or provide values within those cells
Error parsing CSV file: {contents of CSV file}
Likely to occur if the CSV is malformed or does not conform to CSV data format
Ensure CSV adheres to the required format; if problem persists, report an issue via your Customer Portal
Column {number} appears to not have headers (header name same as data values)
Warning will not prevent additional actions; continuing is likely to result in incorrect inferences
Ensure all column headers are present and correct
Column {number} appears to contain dates; these will be treated as categorical variables and will likely be useless for inference
Warning will not prevent additional actions; continuing is likely to result in incorrect inferences; there is a future feature planned to make use of dates/times
Ensure column does not include dates
Column {number} contains both numeric and categorical data; this may lead to incorrect inference
Warning will not prevent additional actions; continuing is likely to result in incorrect inferences
Ensure values in the column are accurate the represent one set/type of observation data
Issues related to your model
Invalid variable name: {variable}
Update variable names such that they do not contain any spaces or special characters
Variable {variable} must have at least 1 value
Ensure the variable's possible states are inputted
Factor {factor} must have at least one variable
Ensure the factor connects to at least one variable
Factor {factor} must have at least one probability value
Genius does provide assistance for initializing a factor with probabilities before any learning takes place if you do not have values to input manually
Ensure the factor contains some probability (initial or learned)
Factor variable {variable} is not defined in variables
When manually creating a model, it is possible to refer to a variable that has not yet been created
Ensure all variables named in your factors are represented as variables in your model
Factor {factor} 's tensor shape {actual_shape} is incompatible with its variable cardinalities {expected_shape}
Ensure the factor's tensor accounts for the correct number of variable state combinations
Tensor values for each target variable element must sum to 1.0 for categorical distributions. Found sum of {sum} for factor {factor}
Ensure use of the right kind of distribution, update parameter values such that they sum to 1 if correct, otherwise update the distribution type to conditional
There must be a distribution over variable {variable}
Connect the variable to a factor and ensure that factor has an accurate probability table
The graph representing a Bayesian Network must be acyclic
Variables cannot depend (even by an inheritance chain) on variables which depend on them (variable a cannot depend on variable b if variable b also depends on variable a)
Remove any bidirectional dependencies in your model
Variables {variables} are connected by a factor whose distribution must be conditional
Connect the variables via a conditioned factor
Factors involving variable(s) {variables} have role {role} which is undefined for this model type
Likely to occur if the model is specified as a Bayesian Network or Markov Random Field but the model includes an action variable which can currently only be used with POMDPs
Correct the roles associated with the variables to match the model type or update the model type to support the variable roles; likely will require additional updates to the model
Factor {factor} represents a conditional distribution. Markov random fields use symmetric potential functions (categorical distributions)
Specific to Markov Random Fields
Update factors to categorical distributions
State variable(s) {variables} participate in no likelihood factors
Specific to POMDPs; May indicate that the variables are not strictly necessary for the model
Add/Connect likelihood factors to variables
Observation variable(s) {variables} lack likelihood factors
Specific to POMDPs; May indicate that the variables are not strictly necessary for the model
Add/Connect likelihood factors to variables
Role for variable(s) {variables} cannot be inferred from VFG information
Explicitly set the variable role as action, latent, or observed
POMDP contains no transition factors
Add a state-transition factor and connect it to an action
POMDP contains no likelihood factors
Add likelihood factor(s) and connect it/them to observed variables
State factors {variables} not connected to a control variable
Add/Connect a latent state variable to the state-transition factor
Errors occurred during model validation. Catch this exception and check the .errors field for details. VFG.apply_patches(errors) will automatically correct recoverable errors.
General error message when validating your model fails but none of the cases above are found
Varies, see .errors; if error persists, report an issue via your Customer Portal
Other possible issues
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