class Information(widget.OWWidget.Information): sampled = Msg("Data has been sampled") discrete_ignored = Msg("{} categorical feature{} ignored") row_clust = Msg("{}") col_clust = Msg("{}") sparse_densified = Msg("Showing this data may require a lot of memory")
class Warning(OWWidget.Warning): outdated_learner = Msg("Press Apply to submit changes.")
class Error(OWWidget.Error): too_many_folds = Msg("Number of folds exceeds data size") sample_larger_than_data = Msg("Sample can't be larger than data") not_enough_to_stratify = Msg("Data is too small to stratify") no_data = Msg("Dataset is empty")
class Error(OWWidget.Error): no_backends = Msg("Please install cx_Oracle package. It is either missing or not working properly")
class Error(widget.OWWidget.Error): empty_dataset = Msg("No features in data")
class Warning(OWWidget.Warning): renamed_variables = Msg( "Variables with duplicated names have been renamed.")
class Information(OWDataProjectionWidget.Information): modified = Msg("The parameter settings have been changed. Press " "\"Start\" to rerun with the new settings.")
class Information(OWWidget.Information): hidden_instances = Msg("Instances with unknown values are not shown.") too_many_features = Msg("Data has too many features. Only first {}" " are shown.".format(MAX_FEATURES))
class Warning(OWWidget.Warning): no_input_variables = Msg("Input data has no variables") continuous_target = Msg("Continuous target value can not be used.") sparse_not_supported = Msg("Sparse data is ignored.")
class Error(OWWidget.Error): not_enough_attrs = Msg("Need at least one numeric feature.") no_valid_data = Msg("No plot due to no valid data.")
class Warning(OWWidget.Warning): no_display_option = Msg("No display option is selected.")
class Warning(OWWidget.Warning): no_feats_search = Msg('No features included in search.') no_feats_display = Msg('No features selected for display.')
class Error(OWWidget.Error): not_enough_rows = Msg("Input data needs at least 2 rows") constant_data = Msg("Input data is constant") no_attributes = Msg("Data has no attributes") out_of_memory = Msg("Out of memory") optimization_error = Msg("Error during optimization\n{}")
class Warning(widget.OWWidget.Warning): empty_clusters = Msg("Empty clusters were removed")
class Information(OWWidget.Information): no_target_var = Msg("Data does not have a (single) target variable.") missings_imputed = Msg('Missing values will be imputed as needed.')
class Warning(OWWidget.Warning): no_input_variables = Msg("Input data has no variables") continuous_target = Msg("Continuous target value can not be used.")
class Error(OWWidget.Error): invalid_type = Msg("Cannot handle target variable type {}") inadequate_learner = Msg("Scorer {} inadequate: {}") no_attributes = Msg("Data does not have a single attribute.")
class Information(OWWidget.Information): use_first_two = \ Msg("Paint Data uses data from the first two attributes.")
class Warning(OWBaseLearner.Warning): sparse_data = Msg('Input data is sparse, default preprocessing is to scale it.')
class Warning(widget.OWWidget.Warning): mismatching_domain = Msg("Features and data domain do not match")
class Error(widget.OWWidget.Error): not_enough_instances = Msg("Not enough unique data instances. " "At least two are required.")
class Error(widget.OWWidget.Error): file_not_found = Msg("File not found.") missing_reader = Msg("Missing reader.") sheet_error = Msg("Error listing available sheets.") unknown = Msg("Read error:\n{}")
class Information(widget.OWWidget.Information): modified = Msg("Press commit to recompute clusters and send new data")
class Warning(OWWidget.Warning): threshold_error = Msg("Low slider should be less than High")
class Error(OWWidget.Error): data_error = Msg("{}") fitting_failed = Msg("Fitting failed.\n{}") sparse_not_supported = Msg("Sparse data is not supported.") out_of_memory = Msg("Out of memory.")
class Error(OWWidget.Error): image_too_big = Msg("Image for chosen features is too big ({} x {}).")
class Warning(OWWidget.Warning): could_not_stratify = Msg("Stratification failed\n{}") bigger_sample = Msg('Sample is bigger than input')
class Information(OWWidget.Information): not_shown = Msg("Undefined positions: {} data point(s) are not shown.")
class Error(OWWidget.Error): data_error = Msg("{}")
class Error(OWDataProjectionWidget.Error): sparse_data = Msg("Sparse data is not supported") no_valid_data = Msg("No projection due to no valid data") no_instances = Msg("At least two data instances are required") proj_error = Msg("An error occurred while projecting data.\n{}")