class Error(widget.OWWidget.Error): number_of_edges = widget.Msg( 'Estimated number of edges is too high ({})')
class Error(widget.OWWidget.Error): no_features = widget.Msg("At least 1 feature is required") no_instances = widget.Msg("At least 1 data instance is required") sparse_data = widget.Msg("Sparse data is not supported")
class Error(widget.OWWidget.Error): empty_data = widget.Msg("Empty dataset") no_disc_vars = widget.Msg("No categorical data")
class Warning(widget.OWWidget.Warning): model_not_appropriate = widget.Msg( 'Model or its settings are not appropriate for this type of data.')
class Error(widget.OWWidget.Error): no_vars_selected = widget.Msg('No independent variables selected') no_class_selected = widget.Msg('No dependent variable selected')
class Warning(widget.OWWidget.Warning): empty_input = widget.Msg("Empty result on input. Nothing to display.")
class Error(widget.OWWidget.Error): proj_and_domain_match = widget.Msg( "Projection and Data domains do not match") no_valid_data = widget.Msg("No projection due to invalid data")
class Warning(OWAnchorProjectionWidget.Warning): invalid_embedding = widget.Msg("No projection for selected features") removed_vars = widget.Msg("Categorical variables with more than" " two values are not shown.") max_vars_selected = widget.Msg( "Maximum number of selected variables reached.")
class Warning(widget.OWWidget.Warning): topic_precedence = widget.Msg( 'Input signal Topic takes priority over Corpus')
class Warning(widget.OWWidget.Warning): no_cont_features = widget.Msg("Plotting requires numeric features") not_enough_components = widget.Msg("Input projection has less than 2 components") trivial_components = widget.Msg( "All components of the PCA are trivial (explain 0 variance). " "Input data is constant (or near constant).")
class Error(widget.OWWidget.Error): unsupported_extension = widget.Msg( "Selected extension is not supported.")
class Info(widget.OWWidget.Information): bow_weights = widget.Msg("Showing bag of words weights.")
class Warning(widget.OWWidget.Warning): no_silhouettes = widget.Msg( "Silhouette scores are not computed for >{} samples".format( SILHOUETTE_MAX_SAMPLES)) not_enough_data = widget.Msg( "Too few ({}) unique data instances for {} clusters")
class Warning(widget.OWWidget.Warning): read_error = widget.Msg("{} 无法读取")
class Warning(OWAnchorProjectionWidget.Warning): removed_features = widget.Msg("Categorical features with more than" " two values are not shown.")
class Error(OWWidget.Error): error = widget.Msg("{}")
class Warning(widget.OWWidget.Warning): duplicate_names = widget.Msg("Duplicate variable names in output.")
class Error(widget.OWWidget.Error): no_features = widget.Msg("At least 1 feature is required") no_instances = widget.Msg("At least 1 data instance is required")
class Error(widget.OWWidget.Error): failed = widget.Msg("Clustering failed\nError: {}") not_enough_data = widget.Msg( "Too few ({}) unique data instances for {} clusters") no_attributes = widget.Msg("Data is missing features.")
class Error(widget.OWWidget.Error): no_webcam = widget.Msg("Couldn't acquire webcam")
class Error(widget.OWWidget.Error): unexpected_error = widget.Msg('Unexpected error: {}')
class Error(widget.OWWidget.Error): need_discrete_data = widget.Msg( "Need some discrete data to work with.") no_disc_features = widget.Msg( "Discrete features required but data has none.")
class Information(widget.OWWidget.Information): nothing_significant = widget.Msg( 'Chosen parameters reveal no significant groups')
class Warning(widget.OWWidget.Warning): cont_attrs = widget.Msg( "Data has continuous attributes which will be skipped.") err_reg_expression = widget.Msg("Error in {} regular expression: {}")
class Warning(widget.OWWidget.Warning): trivial_components = widget.Msg( "All components of the PCA are trivial (explain 0 variance). " "Input data is constant (or near constant).")
class Error(widget.OWWidget.Error): file_not_found = widget.Msg("File not found.") missing_reader = widget.Msg("Missing reader.") sheet_error = widget.Msg("Error listing available sheets.") unknown = widget.Msg("Read error:\n{}")
class Warning(widget.OWWidget.Warning): file_too_big = widget.Msg( "The file is too large to load automatically." " Press Reload to load.")
class Error(widget.OWWidget.Error): duplicate_var_name = widget.Msg("A variable name is duplicated.")
class Error(widget.OWWidget.Error): no_time_variable = widget.Msg('Data contains no time variable')
class Warning(widget.OWWidget.Warning): large_number_of_nodes = widget.Msg( 'Large number of nodes/edges; performance will be hindered')