Fix Wordfreq Errors: Unsupported Languages & Strict Settings

Ever run into a snag when working with language data, especially when the tools you're using haven't quite caught up to the linguistic diversity you're exploring? This can be particularly frustrating when you're trying to generate non-words or analyze language patterns. Recently, an issue surfaced with the nonwordgen GUI, specifically when attempting to use languages not natively supported by the wordfreq library. When a user selected a language like Thai and cranked up the strictness setting to "strict" or higher, the program would throw exceptions and errors directly into the terminal. It's a classic case of software encountering an unexpected situation – a language it doesn't have the necessary data or processing tools for – and failing to handle it gracefully. This kind of problem can halt your workflow and leave you scratching your head, wondering why your perfectly valid input is causing such a fuss. The goal here is to understand why this happens and how we can prevent these kinds of exceptions from derailing our creative or analytical processes when dealing with language generation and analysis tools.

Understanding the Core Problem: Unsupported Languages and Strictness

The crux of the issue lies in the interaction between the nonwordgen tool, the wordfreq library, and the specific language selected. The wordfreq library is designed to provide word frequency data, which is incredibly useful for tasks like generating realistic-sounding non-words or analyzing the statistical properties of a language. However, wordfreq relies on pre-existing datasets and tokenizers for the languages it supports. When you select a language that isn't in its database, like Thai (represented by the language code 'th' in the provided logs), wordfreq simply doesn't have the information it needs. The error message, "The language 'th' is in the 'Thai' script, which we don't have a tokenizer for. The results will be bad," is quite explicit. It tells us that wordfreq lacks the fundamental components to process Thai words accurately.

Adding the "strict" setting into the mix exacerbates the problem. In nonwordgen, strictness levels likely dictate how rigorously generated words must adhere to certain linguistic rules or known word patterns. A higher strictness means the tool will try harder to ensure generated words resemble real words, often by cross-referencing against frequency dictionaries. When wordfreq fails to look up a word's frequency (because the language isn't supported), and the nonwordgen tool is set to be strict, it interprets this lack of data as a failure. Instead of gracefully acknowledging that it can't verify the word's frequency in that language, it tries to process the error, leading to a cascade of exceptions, including KeyError and LookupError. The LookupError: No wordlist 'best' available for language 'th' is a particularly damning indictment of the situation, showing that the fundamental data wordfreq needs to operate simply isn't present for the chosen language.

This scenario highlights a common challenge in software development when dealing with global or diverse data: the need for comprehensive support. For nonwordgen to function smoothly with a wider range of languages, the underlying wordfreq library would need to expand its linguistic repertoire. Without this, users are confined to languages with pre-existing wordfreq data, or they risk encountering these disruptive errors, especially when aiming for high-quality, strictly validated output. The implications are significant for anyone working with multilingual text generation or analysis, as it underscores the dependency on the breadth of language support within the tools they employ. It's not just about the tool itself, but the ecosystem of libraries and data it relies upon.

Debugging the Tracebacks: A Closer Look at the Errors

Let's dive a bit deeper into the provided tracebacks to fully grasp what's happening under the hood when nonwordgen encounters an unsupported language with strict settings. The sequence of errors reveals a clear chain reaction initiated by wordfreq's inability to process the 'th' (Thai) language.

Initial Warnings and Cache Failures

The first lines of the log output are crucial: "The language 'th' is in the 'Thai' script, which we don't have a tokenizer for. The results will be bad." and "wordfreq lookup failed for language 'th'; disabling WordfreqDictionary." These are not exceptions yet, but rather informative messages from wordfreq itself. They indicate that wordfreq has detected the unsupported language and is preemptively warning the user that its functionality will be compromised. It explicitly states it cannot tokenize Thai words, meaning it can't even break them down into meaningful units for analysis. Consequently, it disables its own dictionary for that language, signaling that frequency lookups will be impossible. This is a good start in terms of user feedback, but it doesn't prevent the subsequent failures.

The KeyError Cascade

Following these warnings, we see the first actual exception: KeyError: ('luengyian', 'th', 'best', 1e-09). This KeyError occurs within the wordfreq.word_frequency function. Essentially, wordfreq tries to look up a word ('luengyian' in this instance, likely a generated non-word) in its internal cache using a specific set of arguments (the word itself, the language code 'th', the wordlist type 'best', and a minimum frequency threshold). Since the language 'th' is not supported and its dictionary was disabled, this cache lookup fails because the necessary data structure for 'th' doesn't exist or is incomplete, leading to the KeyError.

The LookupError Deep Dive

The KeyError triggers a chain of events within wordfreq. The word_frequency function is designed to handle such cache misses by attempting to fetch the required data. It calls _word_frequency, which in turn calls get_frequency_dict, and finally get_frequency_list. It's within get_frequency_list(lang, wordlist) that the more specific LookupError is raised: LookupError: No wordlist 'best' available for language 'th'. This exception is the most direct explanation for the failure. It unequivocally states that wordfreq cannot find the requested wordlist ('best', which likely refers to a curated list of common words used for frequency analysis) for the specified language ('th').

The Exception within an Exception

What's particularly interesting is that the LookupError is raised during the handling of the initial KeyError. This means that wordfreq's internal error handling mechanisms, while attempting to recover from the cache KeyError, encountered another fundamental problem: the absence of the required language data. The traceback shows this clearly: "During handling of the above exception, another exception occurred." This nesting of exceptions often indicates that a program is struggling to cope with an unexpected condition, trying to recover but hitting another roadblock.

Repercussions in nonwordgen

Finally, these exceptions bubble up from wordfreq back into nonwordgen's dictionaries.py file, specifically within the is_real_word function, which relies on zipf_frequency (a wrapper around wordfreq's functionality). When zipf_frequency fails due to the LookupError, the is_real_word function cannot determine if the generated word is