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{}")
