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return value.numpy().astype(np.float32) + elif isinstance(value, np.ndarray) and np.issubdtype(value.dtype, np.number): + return value.astype(np.float32) + else: + return None # Ignore non-numeric data + +def convert_weights(npy_path): + """Convert `weights.npy` to a TensorFlow-free HDF5 format.""" + if not os.path.exists(npy_path): + print(f"❌ Error: {npy_path} not found!") + return + + h5_path = npy_path.replace(".npy", "_tf_free.h5") + + # Load the weights + print(f"🔍 Loading {npy_path}...") + weights = np.load(npy_path, allow_pickle=True) + + # Convert all elements to NumPy arrays (remove TensorFlow dtypes) + converted_weights = [convert_to_numpy(w) for w in weights if convert_to_numpy(w) is not None] + + # Save to HDF5 format + with h5py.File(h5_path, "w") as hf: + for i, w in enumerate(converted_weights): + hf.create_dataset(f"weight_{i}", data=w) + + print(f"✅ Converted: {npy_path} -> {h5_path}") + +if __name__ == "__main__": + # Search for all `weights.npy` files and convert them + for root, _, files in os.walk("."): + for file in files: + if file == "weights.npy": + convert_weights(os.path.join(root, file)) + + print("🚀 All weight files converted successfully!") + diff --git a/h origin fix-h5-weights b/h origin fix-h5-weights new file mode 100644 index 0000000..e2d3637 --- /dev/null +++ b/h origin fix-h5-weights @@ -0,0 +1,4244 @@ +diff --git a/.gitignore b/.gitignore +new file mode 100644 +index 0000000..2586c3b +--- /dev/null ++++ b/.gitignore +@@ -0,0 +1,18 @@ ++Data/ ++__pycache__/ ++.idea/ ++README.html ++Embeddings-Discussion.html ++Models-Specifications.html ++Future-Works.html ++train_multilingual.py ++trash_bin.py ++Models/Thai_temp/ ++Models/Burmese_temp/ ++Models/Thai_temp_genvec/ ++Models/Burmese_temp_genvec/ ++Models/Burmese_model4_version2/ ++Models/Other/ ++*~ ++venv/ ++convert_weights.py +diff --git a/Embeddings Discussion.md b/Embeddings Discussion.md +new file mode 100644 +index 0000000..6d71716 +--- /dev/null ++++ b/Embeddings Discussion.md +@@ -0,0 +1,244 @@ ++## Error Analysis for Different Embedding Systems ++Author: Sahand Farhoodi (sahandfr@gmail.com, sahand.farhoodi93@gmail.com) ++ ++In this project, we develop a bi-directional LSTM model for word segmentation. For now, this model is implemented for Thai and Burmese. ++ ++### Background ++In this document, we explore a range of different embedding systems for the [bi-directional LSTM word segmentation algorithm](https://github.com/SahandFarhoodi/word_segmentation) which predicts the word boundaries of a given sentence in **Thai** or **Burmese**. The first layer of this model is the embedding layer, where the input of the LSTM model, which is a sequence of characters or letters, is mapped to a sequence of numerical vectors. There are multiple options for the embedding layer that are thoroughly explained in [this document](https://docs.google.com/document/d/1KXnTvrgISYUplOk1NRbQbJssueeXa8k1Vu8YApMud4k/edit#heading=h.bmtbd2h7j5nt). Here, we consider three of these embedding systems and implement the word segmentation algorithm for them and compare them based on the model accuracy and model size. We also spend some time on error analyses based on these different embedding systems. ++ ++The embedding system highly depends on the units of a sentence that we consider to be used by our model for training. There are two choices: ++* **Unicode code points**: These are often considered as the smallest part of a sentence or a word. Please see [this link](https://en.wikipedia.org/wiki/Code_point) for a formal definition. ++* **Grapheme clusters**: Each grapheme cluster can have one or more code points in it. We can distinguish between these two types of grapheme clusters as single-code-point grapheme clusters and multi-code-point grapheme clusters. The reason that grapheme clusters are in many cases considered as the basic unit for word segmentation algorithms is that a word boundary should never occur in the middle of a grapheme cluster. ++ ++For a given language, e.g. Thai, the three embedding systems explored in this document are: ++ ++* **grapheme clusters vectors** (graph_clust): In this embedding system, first a set of grapheme clusters is found that cover 99% of the text in the given language. We use large corpora in the given language to obtain this set. Then, each grapheme cluster in this set is represented by a single vector of a fixed length. All other grapheme clusters are represented with a shared vector of the same length. For Thai and Burmese, this gives us an embedding matrix with ~350 vectors. These vectors are learned during the training of the LSTM model. ++ ++ ![Figure 1. Grapheme clusters embedding.](Figures/Graphclust_embedding.png) ++ ++* **generalized vectors**: In this embedding system, each Unicode code point (or group of code points) in the given language is represented by a vector of a fixed length. Then, each grapheme cluster is represented by the average of vectors corresponding to code points in it.  ++ ++ ![Figure 2. Generalized vectors embedding.](Figures/Genvec_embedding.png) ++  ++ This embedding system has different versions: ++ * **Buckets 1, 2** (genvec_12): in this version, each code point with type 1 (letters) or 2 (marks) in the given language is represented with a separate vector. Other code points are grouped as follows, and elements of each group share a single vector: ++ * All other letters that are not represented above ++ * All other digits that are not represented above ++ * All marks, punctuations, and symbols that are not represented above ++ * All code points of type 4 (separators) and 7 (others)  ++ ++ In this version, we will have  ++ * 136 embedding vectors for Burmese ++ * 77 embedding vectors for Thai ++ ++ * **Buckets 1, 2, 3** (genvec_123): Same as the "Bucket 1,2" version, but now each code point with type 3 (digits) in the given language is also represented with a separate vector. In this version, we will have ++ * 156 embedding vectors for Burmese ++ * 87 embedding vectors for Thai ++  ++ * **Buckets 1, 2, digit 0** (genvec_12d0): Same as the "Bucket 1,2" version, but now the code points that represent digit 0 in the given language are represented with a new vector. In this version, we will have  ++ * 138 embedding vectors for Burmese ++ * 78 embedding vectors for Thai ++  ++ * **Buckets 1, 2, 5** (genvec_125): Same as the "Bucket 1,2" version, but now each code point with type 5 (punctuations) in the given language is also represented with a separate vector. In this version, we will have  ++ * 142 embedding vectors for Burmese ++ * 80 embedding vectors for Thai ++  ++ * **Buckets 1, 2, 3, 5** (genvec_1235): Same as the "Bucket 1,2" version, but now each code point with type 3 (digits) or type 5 (punctuations) in the given language is also represented with a separate vector. In this version, we will have  ++ * 162 embedding vectors for Burmese ++ * 90 embedding vectors for Thai ++  ++* **Code points**: In this embedding system, each single Unicode code point in a given language is represented with a single vector. This structure potentially lets the model put word boundaries inside a grapheme cluster. Hence, we need an extra normalizer algorithm that checks for this type of word boundaries and fixes them. ++ ++ ![Figure 3. Code points embedding.](Figures/Codepoints_embedding.png) ++ ++### Error Analysis ++All the models described in this section are trained with the same training data sets, where the same number of epochs and batch sizes are used. The value of two important hyperparameters, number of hidden units (hunits) and embedding dimension, are calculated separately for each embedding system using the Bayesian optimization. The computed value for these two hyperparameters are as follows: ++ ++| Embedding | embedding dimension | hunits | ++| :---: | :----: | :---: | ++| Thai graph_clust | 16 | 23 | ++| Thai codepoints | 40 | 27 | ++| Thai genvec | 22 | 40 | ++| Thai genvec light | 22 | 20 | ++| Burmese graph_clust | 28 | 14 | ++| Burmese genvec | 33 | 20 | ++ ++#### Grapheme Clusters vs. Generalized Vectors ++Here, we investigate the difference between grapheme clusters and generalized vectors embedding systems. The following table shows the accuracy and model size of the fitted LSTM models for **Thai**. ++ ++| Embedding | BIES accuracy | F1-Score | Model size | ++| :---: | :----: | :----: | :---: | ++| graph_clust | 92 | 85.3 | 27 KB | ++| graph_clust_light | 91.9 | 85.2 | 22 KB | ++| genvec_12 | 92.2 | 85.5 | 47 KB | ++| genvec_123 | 92.3 | 85.7 | 47 KB | ++| genvec_123_light | 91.9 | 85 | 20 KB | ++| genvec_12d0 | 92.2 | 85.5 | 47 KB | ++| genvec_125 | 92.1 | 85.3 | 47 KB | ++| genvec_1235 | 92.1 | 85.5 | 47 KB | ++ ++The following table shows the accuracy and model size of the fitted LSTM models for **Burmese**. ++ ++| Embedding | BIES accuracy | F1-Score | Model size | ++| :---: | :----: | :----: | :---: | ++| graph_clust | 91.7 | 90.4 | 30 KB | ++| genvec_12 | 91.2 | 89.9 | 28 KB | ++| genvec_123 | 91 | 89.6 | 30 KB | ++| genvec_12d0 | 90.7 | 89.3 | 28 KB | ++| genvec_125 | 91.1 | 89.7 | 28 KB | ++| genvec_1235 | 91.5 | 90.1 | 29 KB | ++ ++Based on these tables, for Thai, it seems that whatever is achievable by generalized vectors can also be achieved by grapheme clusters (compare `graph_clust_light` with `genvec_123_light`, and `graph_clust` with `genvec_123`). For Burmese, it seems that using grapheme clusters is the better choice; it has a smaller data size and shows a better performance in terms of accuracy. However, our analysis shows that the generalized vector approach is also very viable, and can be used to give model accuracy very close to what can be achieved using grapheme clusters. ++ ++#### Grapheme Clusters vs. Code Points ++ ++Here, we investigate the difference between grapheme clusters and code point embedding systems. The following two tables show the accuracy and model size of the fitted LSTM models for Thai and Burmese. Note that for a fair comparison between these two embedding systems, looking at the BIES accuracy can be misleading. This is due to the fact the length of the BIES accuracy computed for code point embedding is often longer than that computed for grapheme clusters and hence the BIES accuracy tends to be higher for code point embeddings. For instance, consider the following example: ++ ++* Correct segmentation: |ใหญ่ซึ่ง|  ++  ++* Our segmentation: |ใหญ่|ซึ่ง| ++ ++In this case, the correct BIES sequence, the estimated BIES sequence, and BIES accuracy for grapheme clusters and code points will be: ++ ++* Grapheme cluster: ++ * Correct segmentation BIES: biiie ++ * Our segmentation BIES: biebe ++ * BIES accuracy = 60 ++ ++* Code point: ++ * Correct segmentation BIES: biiiiiie ++ * Our segmentation BIES: biiebiie ++ * BIES accuracy = 75 ++ ++Therefore, the code point version of BIES accuracy is higher for the same segmentation. For this reason, in this section, we only report F1-scores. The following table shows the accuracy and model size of the fitted LSTM models for **Thai**. Note that our comparison is slightly in favor of grapheme clusters because the value reported for code point is computed for models trained using Thai-specific script. This means that we had to exclude all spaces, marks, and Latin letters from the original data which results in much shorter sentences, and can adversely affect the model accuracy. ++ ++| Embedding | F1-Score | Model size | ++| :---: | :----: | :---: | ++| graph_clust | 88.7 | 28 KB ++| codepoints light | 88.9 | 28 KB | ++| codepoints | 90.1 | 36 KB | ++ ++The following table shows the accuracy and model size of the fitted LSTM models for **Burmese**. Given that there are potentially many spaces in Burmese texts, training models using Burmese-specific script results in considerably lower accuracy. Therefore, in order to make a fair comparison between grapheme clusters and code point embeddings, values reported in the following table are for models trained on the original data sets. ++ ++| Embedding | F1-Score | Model size | ++| :---: | :----: | :---: | ++| graph_clust | 92.9 | 30 KB ++| codepoints light | 93 | 20 KB | ++| codepoints | 93.4 | 45 KB | ++ ++Based on these tables, for models of the same size, we see that code points embedding gives slightly better accuracy (compare `graph_clust` to `codepoint light` for Thai table). Besides, by using larger code points models we get considerably better performance (third row of the Thai table). We weren't able to achieve that level of accuracy using grapheme clusters, even when we train models larger than what we show in these tables. ++ ++#### Case-by-case Analysis ++ ++In what follows, we use some sample examples to see what kind of sentences in **Thai** to see how different algorithms segment them: ++ ++| Algorithm | Output | ++| :---: | :---- | ++| Unsegmented | `การเดินทางใน` | ++| Manually Segmented | `\|การ\|เดินทาง\|ใน\|` | ++| Deepcut | `\|การ\|เดินทาง\|ใน\|` | ++| ICU | `\|การ\|เดิน\|ทางใน\|` | ++| Grapheme Clusters | `\|การ\|เดิน\|ทาง\|ใน\|` | ++| Generalized Vectors | `\|การ\|เดิน\|ทาง\|ใ\|น\|` | ++| Code Points | `\|การ\|เดินทาง\|ใน\|` | ++ ++**Test Case 3** ++ ++| Algorithm | Output | ++| :---: | :---- | ++| Unsegmented | `นั่งนายกฯต่อสมัยหน้า` | ++| Manually Segmented | `\|นั่ง\|นายก\|ฯ\|ต่อ\|สมัย\|หน้า\|` | ++| Deepcut | `\|นั่ง\|นายก\|ฯ\|ต่อ\|สมัย\|หน้า\|` | ++| ICU | `\|นั่ง\|นา\|ยกฯ\|ต่อ\|สมัย\|หน้า\|` | ++| Grapheme Clusters | `\|นั่ง\|นายก\|ฯ\|ต่อ\|สมัย\|หน้า\|` | ++| Generalized Vectors | `\|นั่ง\|นายก\|ฯ\|ต่อสมัยหน้า\|` | ++| Code Points | `\|นั่ง\|นายก\|ฯ\|ต่อ\|สมัย\|หน้า\|` | ++ ++**Test Case 4** ++ ++| Algorithm | Output | ++| :---: | :---- | ++| Unsegmented | `พร้อมจัดตั้ง` | ++| Manually Segmented | `\|พร้อม\|จัดตั้ง\|` | ++| Deepcut | `\|พร้อม\|จัด\|ตั้ง\|` | ++| ICU | `\|พร้อม\|จัด\|ตั้ง\|` | ++| Grapheme Clusters | `\|พร้อม\|จัด\|ตั้ง\|` | ++| Generalized Vectors | `\|พร้อม\|จัด\|ตั้ง\|` | ++| Code Points | `\|พร้อม\|จัดตั้ง\|` | ++ ++**Test Case 5** ++ ++| Algorithm | Output | ++| :---: | :---- | ++| Unsegmented | `เพราะดนตรีที่ชอบนั้น` | ++| Manually Segmented | `\|เพราะ\|ดนตรี\|ที่\|ชอบ\|นั้น\|` | ++| Deepcut | `\|เพราะ\|ดนตรี\|ที่\|ชอบ\|นั้น\|` | ++| ICU | `\|เพราะ\|ดนตรี\|ที่\|ชอบ\|นั้น\|` | ++| Grapheme Clusters | `\|เพราะ\|ดนตรี\|ที่\|ชอบ\|นั้น\|` | ++| Generalized Vectors | `\|เ\|พราะดนตรี\|ที่\|ชอบ\|นั้น\|` | ++| Code Points | `\|เพราะ\|ดนตรี\|ที่\|ชอบ\|นั้น\|` | ++ ++In what follows, we use some sample examples to see what kind of sentences in **Burmese** to see how different algorithms segment them: ++ ++**Test Case 1** ++ ++| Algorithm | Output | ++| :---: | :---- | ++| Unsegmented | `ဖော်ပြထားသည့်` | ++| Manually Segmented | `\|ဖော်ပြ\|ထားသည့်\|` | ++| ICU | `\|ဖော်ပြ\|ထား\|သည့်\|` | ++| Grapheme Clusters | `\|ဖော်\|ပြ\|ထား\|သည့်\| ` | ++| Generalized Vectors | `\|ဖော်\|ပြ\|ထား\|သည့်\|` | ++| Code Points | `\|ဖော်ပြ\|ထား\|သည့်\` | ++ ++**Test Case 2** ++ ++| Algorithm | Output | ++| :---: | :---- | ++| Unsegmented | `အသားအရောင်အားဖြင့်` | ++| Manually Segmented | `\|အသားအရောင်\|အားဖြင့်\|` | ++| ICU | `\|အသား\|အရောင်\|အားဖြင့်\|` | ++| Grapheme Clusters | `\|အသား\|အရောင်\|အား\|ဖြင့်\|` | ++| Generalized Vectors | `\|အသား\|အရောင်\|အား\|ဖြင့်\|` | ++| Code Points | `\|အသားအရောင်\|အားဖြင့်\|` | ++ ++**Test Case 3** ++ ++| Algorithm | Output | ++| :---: | :---- | ++| Unsegmented | `သဘာဝအားဖြင့်` | ++| Manually Segmented | `\|သဘာဝ\|အားဖြင့်\|` | ++| ICU | `\|သဘာ\|ဝ\|အားဖြင့်\|` | ++| Grapheme Clusters | `\|သဘာ\|ဝ\|အား\|ဖြင့်\|` | ++| Generalized Vectors | `\|သဘာ\|ဝအား\|ဖြင့်\|` | ++| Code Points | `\|သဘာ\|ဝ\|အား\|ဖြင့်\|` | ++ ++**Test Case 4** ++ ++| Algorithm | Output | ++| :---: | :---- | ++| Unsegmented | `ထို့ပြင်` | ++| Manually Segmented | `\|ထို့ပြင်\|` | ++| ICU | `\|ထို့ပြင်\|` | ++| Grapheme Clusters | `\|ထို့\|ပြင်\|` | ++| Generalized Vectors | `\|ထို့\|ပြင်\|` | ++| Code Points | `\|ထို့ပြင်\|` | ++ ++**Test Case 5** ++ ++| Algorithm | Output | ++| :---: | :---- | ++| Unsegmented | `နိုင်ငံရေးဆိုင်ရာ` | ++| Manually Segmented | `\|နိုင်ငံရေး\|ဆိုင်ရာ\|` | ++| ICU | `\|နိုင်ငံရေး\|ဆိုင်ရာ\|` | ++| Grapheme Clusters | `\|နိုင်ငံရေး\|ဆိုင်ရာ\|` | ++| Generalized Vectors | `\|နိုင်ငံရေး\|ဆိုင်ရာ\|` | ++| Code Points | `\|နိုင်ငံရေး\|ဆိုင်ရာ\|` | ++ ++### Conclusions ++ ++Based on these analyses, it seems that for both Burmese and Thai, code points embedding is the most favorable option. This embedding system is capable of giving better accuracies and often is easier to implement. It also requires less preprocessing than the grapheme clusters approach at training time where we have to find the most common grapheme clusters first. Furthermore, at the evaluation time where we don't need to segment a sentence into its grapheme clusters anymore. Another advantage of the code point embedding is that in contrast to the grapheme clusters embedding we don't need to store any extra dictionary in the model.  ++ ++One disadvantage of code point embedding is that since sentences are longer when we use code points instead of grapheme clusters, this type of embedding probably requires models to have a larger number of hidden units to learn the same amount of context. This can make them have a larger model size. However, this extra data size is covered in many cases with the reduction that we get in the embedding matrix, by having for example at most 73 columns for code points instead of 350 columns for grapheme clusters for Thai.  ++ ++Another advantage of code points models (shared with generalized vectors models) is that they probably perform better for sentences for rare/unseen grapheme clusters. Such occasions can happen frequently, when we are using our algorithms to segment non-formal texts, such as text messages. For instance, to come up with an English example, one may use "the meeting is tooooo long" instead of "the meeting is too long", which has a word (it can be a grapheme cluster in Thai) that is not a formal word, and therefore can be problematic for grapheme clusters approach. +diff --git a/Figures/Bi-directional LSTM-2.drawio b/Figures/Bi-directional LSTM-2.drawio +new file mode 100644 +index 0000000..b234cdb +--- /dev/null ++++ b/Figures/Bi-directional LSTM-2.drawio +@@ -0,0 +1 @@ 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samples of fixed length (`self.n`). This means that in most cases we have to break long sentences into multiple examples. Having fixed-length samples makes training of the LSTM model easier (multiple samples can be used to update weights which makes the backpropagation algorithm much more stable), but can potentially harm the model accuracy. Another drawback of it is that at the evaluation time, eventually, we are going to use our model to segment sentences of variable lengths. Using TensorFlow prediction functions segments these sentences correctly (we checked it using our manual implementation), but it generates some warnings. Furthermore, our analysis shows that using `tf.predict` function in this case is much slower. One area for development is to write a code that uses variable-length samples for training while it still is capable of merging multiple samples to update weights of the LSTM model. ++ ++### Complete analysis of runtime ++Our initial analysis implies that the current ICU algorithm is approximately 5 times faster than our models when `tf.predict` is used for evaluation. This number for Deepcut, which is considerably larger than our models, is about 39. Given that deep learning models perform a lot of matrix multiplication while ICU algorithms mostly look-up dictionaries, this difference in runtime is more-or-less expected. However, there are some ideas for making our algorithm faster that remain to be explored: ++ ++ * Using ADMM-NN approach to reduce the data size further and making the algorithm faster. The results presented in [this paper](https://arxiv.org/abs/1812.11677) are very promising. ++  ++ * One approach is using fast versions of `sigmoid` and `tanh` functions. Based on our analysis with replacing float32 with float 16 in our moels, we don't expect a decrease in model accuracy when fast versions of `sigmoid` and `tanh` are used. ++  ++ * Multiplying sparse matrices is much faster. One idea is to set small values in model matrices to zero to decrease the run time. To do so, first, we need to see how small are values in our matrices, and second, make sure making matrices sparse doesn't decrease accuracy a lot. ++  ++ * Using fast matrix multiplication approaches that sacrifice precision a little but result in much faster algorithms is another unexplored area. ++ ++### Using other representation of segmented strings ++Our LSTM models are based on representing a segmented string with BIES (B for beginning, I for inside, E for end, and S for single). Therefore, what our algorithm does during the training is maximizing BIES accuracy (the difference between estimated BIES and true BIES). Ideally, a word-segmentation training algorithm should maximize the F1-score because this is the measure that we care about at evaluation time. However, that is not doable because we cannot take derivatives of F1-score at each unit of a sentence (grapheme cluster or code point). Therefore, BIES is used as a proxy for F1-score. An interesting question is what other alternatives exist? For example, some word segmentation models use BE (B for beginning and E for not beginning). Another option is to use a representation that we propose, called BInES. The difference between BInES and BIES is that instead of representing all inside characters with I, we have I1, I2, I3, ... that shows if a character is the first I character, is it the second I character, and so on. This representation has more information in it, but more information doesn't necessarily mean more signals. It is also harder to train a model based on BInES, because the algorithm has a harder task of detecting signals from noise. This area is open to more investigations.  ++ ++### Training multilingual models ++An ongoing project is training models that can segment texts in more than one language. This can be an interesting idea particularly when we have two languages, A and B, with different scripts but similar rules. In this case, at least in theory, the main difference between models trained separately for these two languages must be the embedding layer. Therefore, we can keep the number of hidden units fixed, and only increase the embedding size to train a model that can segment texts in both A and B. This will cause a dramatic decrease in the model size. ++ ++### Other embedding systems ++Another area that has more room to be explored is other types of embedding. We already have investigated three embedding systems thoroughly. There are other ones, such as phonemes (see [here](https://docs.google.com/document/d/1KXnTvrgISYUplOk1NRbQbJssueeXa8k1Vu8YApMud4k/edit#heading=h.bmtbd2h7j5nt)), that can result in interesting findings. +diff --git a/LICENSE b/LICENSE +new file mode 100644 +index 0000000..4f5cd29 +--- /dev/null ++++ b/LICENSE +@@ -0,0 +1,41 @@ ++UNICODE LICENSE V3 ++ ++COPYRIGHT AND PERMISSION NOTICE ++ ++Copyright © 2020-2024 Unicode, Inc. ++ ++NOTICE TO USER: Carefully read the following legal agreement. BY ++DOWNLOADING, INSTALLING, COPYING OR OTHERWISE USING DATA FILES, AND/OR ++SOFTWARE, YOU UNEQUIVOCALLY ACCEPT, AND AGREE TO BE BOUND BY, ALL OF THE ++TERMS AND CONDITIONS OF THIS AGREEMENT. IF YOU DO NOT AGREE, DO NOT ++DOWNLOAD, INSTALL, COPY, DISTRIBUTE OR USE THE DATA FILES OR SOFTWARE. ++ ++Permission is hereby granted, free of charge, to any person obtaining a ++copy of data files and any associated documentation (the "Data Files") or ++software and any associated documentation (the "Software") to deal in the ++Data Files or Software without restriction, including without limitation ++the rights to use, copy, modify, merge, publish, distribute, and/or sell ++copies of the Data Files or Software, and to permit persons to whom the ++Data Files or Software are furnished to do so, provided that either (a) ++this copyright and permission notice appear with all copies of the Data ++Files or Software, or (b) this copyright and permission notice appear in ++associated Documentation. ++ ++THE DATA FILES AND SOFTWARE ARE PROVIDED "AS IS", WITHOUT WARRANTY OF ANY ++KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF ++MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT OF ++THIRD PARTY RIGHTS. ++ ++IN NO EVENT SHALL THE COPYRIGHT HOLDER OR HOLDERS INCLUDED IN THIS NOTICE ++BE LIABLE FOR ANY CLAIM, OR ANY SPECIAL INDIRECT OR CONSEQUENTIAL DAMAGES, ++OR ANY DAMAGES WHATSOEVER RESULTING FROM LOSS OF USE, DATA OR PROFITS, ++WHETHER IN AN ACTION OF CONTRACT, NEGLIGENCE OR OTHER TORTIOUS ACTION, ++ARISING OUT OF OR IN CONNECTION WITH THE USE OR PERFORMANCE OF THE DATA ++FILES OR SOFTWARE. ++ ++Except as contained in this notice, the name of a copyright holder shall ++not be used in advertising or otherwise to promote the sale, use or other ++dealings in these Data Files or Software without prior written ++authorization of the copyright holder. ++ ++SPDX-License-Identifier: Unicode-3.0 +diff --git a/Models Specifications.md b/Models Specifications.md +new file mode 100644 +index 0000000..5426cf4 +--- /dev/null ++++ b/Models Specifications.md +@@ -0,0 +1,122 @@ ++## Model Specifications ++ ++Author: Sahand Farhoodi (sahandfr@gmail.com, sahand.farhoodi93@gmail.com) ++ ++Here, I explain what are different hyper-parameters in each model and what are the specification of trained models in this repository. This information will be very useful for someone who wants to use this repository to train a new model, either in Thai, Burmese, or a new language. ++ ++### Hyper-parameters ++When you make a new instance of `WordSegmenter` using command ++ ++``` python ++model_name = "Thai_codepoints_exclusive_model4_heavy" ++word_segmenter = WordSegmenter(input_name=model_name, input_n=300, input_t=1200000, input_clusters_num=350, ++ input_embedding_dim=16, input_hunits=23, input_dropout_rate=0.2, input_output_dim=4, ++ input_epochs=15, input_training_data="exclusive BEST", ++ input_evaluation_data="exclusive BEST", input_language="Thai", ++ input_embedding_type="codepoints") ++``` ++ ++you need to specify the following hyper-parameters: ++ ++* **input_name:** This is name of the model that you are training. The remaining of this repository works much smoother if you follow a simple convention. I explain this convention by an example: consider name `Thai_codepoints_exclusive_model4_heavy`.  ++ ++ * The first part is the language of the model.  ++ * The second part is `codepoints` which shows the embedding type and is important to be used in this way. You can use `codepoints` if the `input_embedding_type = codepoints`, `graphclust` if `input_embedding_type = grapheme_clusters_tf` or `input_embedding_type = grapheme_clusters_man`, and different versions of `genvec` (such as `genvec123`, `genvec12d0`, etc) if you wish to use `generalized_vectors` for your embedding. The different versions of generalized vectros embedding are explained later in this document. ++ * The next part of the name is `exclusive`. This tells you if you have used Thai-script-only text to train your model or not. Note that it means that you had no spaces, marks, or Latin letters in your training data. If you don't use such training data then it is okay to erase `exclusive` from your model name.  ++ * The next part, `model4` in our example, is what you want to call your model, and is totally up to you. In my trained modes, `model5` and `model7` indicate the most parsimonious and accurate models respectively, and `model4` shows a model that is somewhere between these two.  ++ * The last part shows how much data you used to train your model. This ties to the values of `input_n` and `input_t` that I define later. It can be `heavy`, `heavier`, or nothing.  ++  ++ You can take a look at `pick_lstm_model` function in `word_segmenter.py` to see how these names are used in the code. For example, the following code shows the relation between `heavy` and `heavier` and `input_n` and `input_t`. ++  ++ ```python ++ input_n = None ++ input_t = None ++ if "genvec" in model_name or "graphclust" in model_name: ++ input_n = 50 ++ input_t = 100000 ++ if "heavy" in model_name: ++ input_n = 200 ++ input_t = 600000 ++ elif "heavier" in model_name: ++ input_n = 200 ++ input_t = 2000000 ++ elif "codepoints" in model_name: ++ input_n = 100 ++ input_t = 200000 ++ if "heavy" in model_name: ++ input_n = 300 ++ input_t = 1200000 ++ ``` ++ ++* **input_n:** This is the number of grapheme clusters (or code points if your embedding type is code points) in each example in each batch used to train the model, or simply the number of cells in each one of forward and backward LSTM layers.  ++ ++* **input_t:** This is the total number of all grapheme clusters (or code points if embedding type is code points) used to train the model. ++ ++* **input_clusters_num:** This is the number of grapheme clusters used in the embedding layer if the embedding type is grapheme clusters. If another embedding type is specified then this hyper-parameter plays no role. ++ ++* **input_embedding_dim:** This is the length of each embedding vector and plays a significant role in data size and accuracy. ++ ++* **input_hunits:** This is the number of hidden units in each LSTM cell, and again, plays a significant role in data size and accuracy. ++ ++* **input_dropout_rate:** This is the dropout rate used after the input layer and also before the output layer. ++ ++* **input_output_dim:** This hyper-parameter is always set to 4 in this repository because we always use BIES to represent a segmented string. This value needs to be changed only if you develop some code that uses another representation for a segmented line, such as BE in the Deepcut algorithm. ++ ++* **input_epochs:** This is the number of epochs used to train the model. ++ ++* **input_training_data/ input_evaluation_data:** This is the data used to train and test the model. For Thai, it can be equal to `"BEST"`, `"exclusive BEST"` (for Thai-script-only models), or `"pseudo BEST"` (for models trained/tested using pseudo segmented data generated by ICU). Another option that is only available for evaluation is `"SAFT_Thai"`. For Burmese, the data can be `"my"` (for models trained using the pseudo segmented data generated by ICU), `"exclusive my"` (same as `"my"` but for Burmese-script-only models), or `"SAFT_Burmese"` (when Google SAFT data is accessible). There is also another option, `"BEST_my"`, which is for training multilingual models and is not implemented currently in this repository. Note that it is okay to use different data sets for training and testing as long as they are compatible (both in the same language). Also, it is important to use an appropriate model name based on the data used to train the model, e.g. if "exclusive BEST" is used for training, the model name must have both "Thai" and "exclusive" in it. ++ ++* **input_language:** This is the language of your model. ++ ++* **input_embedding_type:** This determines what type of embedding is used to train the model, and can be one of the followings: ++ * `"grapheme_clusters_tf"`: This option should be used when one uses grapheme clusters as the embedding system. ++ * `"grapheme_clusters_man"`: This is the same as `"grapheme_clusters_tf"`, but the embedding layer is implemented manually. This was mostly part of exploring new embedding systems and have no practical use now. ++ * `"codepoints"`: this option should be used when the embedding is based on code points. ++ * `"generalized_vectors"`: This should be used if one of the generalized vectors embedding systems is used. There are different versions of generalized vectors embedding, and based on the version one of `"_12"`, `"_12"`, `"_12d0"`, `"_125"`, or `"_1235"` should be added to the end of `"generalized_vectors"`. For example, a valid value would be `"generalized_vectors_12"`. Please refer to "Embedding Discussion" to read more about the difference between these versions. ++ ++* **other parameters**: There are some other parameters defined in the `__init__` and `train_model` functions of `WordSegmenter` that are explained briefly in the code, or have self-explanatory names. The most important ones are `batch_size` and `learning_rate`. ++ ++For using Bayesian optimization to estimate `hunits` and `embedding_dim`, you need to make an instance of `LSTMBayesianOptimization` using the following command (from `train_thai.py` or `train_burmese.py`): ++```python ++bayes_optimization = LSTMBayesianOptimization(input_language="Thai", input_n=50, input_t=10000, input_epochs=3, ++ input_embedding_type='grapheme_clusters_tf', input_clusters_num=350, ++ input_training_data="BEST", input_evaluation_data="BEST", ++ input_hunits_lower=4, input_hunits_upper=64, input_embedding_dim_lower=4, ++ input_embedding_dim_upper=64, input_c=0.05, input_iterations=10) ++bayes_optimization.perform_bayesian_optimization() ++``` ++There are a few new hyper-parameters here that I explain: ++ ++* **input_hunits_lower/input_hunits_upper:** These two parameters specify the domain search for `hunits`. ++ ++* **input_embedding_dim_lower/input_embedding_dim_upper:** These two parameters specify the domain search for `embedding_dim`. ++ ++* **input_c:** This value plays an important role in computing the cost function for each candidate model. It should take a value between 0 and 1, and identifies how much we want to penalize models based on their size. By setting it to 0 you will get the model with the best accuracy, and by setting it to values in the range (0.1, 0.2) you will get parsimonious models. Values in the range (0, 0.1) result in intermediate models. ++ ++* **input_iterations:** This parameter determines how many different models the Bayesian optimization algorithm should fit to find the best value for `hunits` and `embedding_dim`. For my models, values above 10 work fine. ++ ++### Models Details ++The following table shows model size, F1-score, and estimated values for `hunits` and `embedding_dim` for different models. These values are estimated using Bayesian optimization. For Thai, we have three non-exclusive models with grapheme clusters embedding, three exclusive models with code points embedding, and one non-exclusive generalized vectors model (version 123). For Burmese, we have three non-exclusive models with grapheme clusters embedding, three exclusive models with code points embedding, and one non-exclusive generalized vectors model (version 1235). ++ ++For Burmese models, the F1-score is computed using the pseudo segmented data (exclusive and non-exclusive based on the model type). For Thai models, the F1-score is computed using BEST data set (exclusive and non-exclusive based on the model type). Based on this table we see that the negative effect of using exclusive models on accuracy is much more noticeable for Burmese, probably because it has more spaces in it. ++ ++ ++| Model | embedding_dim | hunits | F1-score | model size | ++| :---: | :----: | :---: | :---: | :---: | ++| Thai_graphclust_model4_heavy | 16 | 23 | 89.9 | 27 KB | ++| Thai_graphclust_model5_heavy | 15 | 12 | 86.6 | 10 KB | ++| Thai_graphclust_model7_heavy | 29 | 47 | 91.9 | 86 KB | ++| Thai_codepoints_exclusive_model4_heavy | 40 | 27 | 90.1 | 36 KB | ++| Thai_codepoints_exclusive_model5_heavy | 20 | 15 | 86.7 | 12 KB | ++| Thai_codepoints_exclusive_model7_heavy | 34 | 58 | 91.3 | 93 KB | ++| Thai_genvec123_model5_heavy | 22 | 20 | 85.4 | 19 KB | ++| Burmese_graphclust_model4_heavy | 28 | 14 | 92.9 | 30 KB | ++| Burmese_graphclust_model5_heavy | 12 | 12 | 91.1 | 15 KB | ++| Burmese_graphclust_model7_heavy | 54 | 44 | 94.9 | 125 KB | ++| Burmese_codepoints_exclusive_model4_heavy | 40 | 27 | 85.7 | 45 KB | ++| Burmese_codepoints_exclusive_model5_heavy | 20 | 15 | 82.3 | 17 KB | ++| Burmese_codepoints_exclusive_model7_heavy | 29 | 47 | 87.8 | 70 KB | ++| Burmese_genvec1235_model4_heavy | 33 | 20 | 90.3 | 29 KB | ++ ++ ++ +diff --git a/Models/Burmese_codepoints_exclusive_model4_heavy/saved_model.pb b/Models/Burmese_codepoints_exclusive_model4_heavy/saved_model.pb +new file mode 100644 +index 0000000..13f12d3 +Binary files /dev/null and b/Models/Burmese_codepoints_exclusive_model4_heavy/saved_model.pb differ +diff --git a/Models/Burmese_codepoints_exclusive_model4_heavy/variables/variables.data-00000-of-00001 b/Models/Burmese_codepoints_exclusive_model4_heavy/variables/variables.data-00000-of-00001 +new file mode 100644 +index 0000000..6fb59dd +Binary files /dev/null and b/Models/Burmese_codepoints_exclusive_model4_heavy/variables/variables.data-00000-of-00001 differ +diff --git a/Models/Burmese_codepoints_exclusive_model4_heavy/variables/variables.index b/Models/Burmese_codepoints_exclusive_model4_heavy/variables/variables.index +new file mode 100644 +index 0000000..0b02907 +Binary files /dev/null and b/Models/Burmese_codepoints_exclusive_model4_heavy/variables/variables.index differ +diff --git a/Models/Burmese_codepoints_exclusive_model4_heavy/weights.json b/Models/Burmese_codepoints_exclusive_model4_heavy/weights.json +new file mode 100644 +index 0000000..e259e64 +--- /dev/null ++++ b/Models/Burmese_codepoints_exclusive_model4_heavy/weights.json +@@ -0,0 +1 @@ ++{"model": "Burmese_codepoints_exclusive_model4_heavy", "dic": {"\u1000": 0, "\u1001": 1, "\u1002": 2, "\u1003": 3, "\u1004": 4, "\u1005": 5, "\u1006": 6, "\u1007": 7, "\u1008": 8, "\u1009": 9, "\u100a": 10, "\u100b": 11, "\u100c": 12, "\u100d": 13, "\u100e": 14, "\u100f": 15, "\u1010": 16, "\u1011": 17, "\u1012": 18, "\u1013": 19, "\u1014": 20, "\u1015": 21, "\u1016": 22, "\u1017": 23, "\u1018": 24, "\u1019": 25, "\u101a": 26, "\u101b": 27, "\u101c": 28, "\u101d": 29, 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@@ ++## LSTM-based Model for Word Segmentation ++Author: Sahand Farhoodi (sahandfr@gmail.com, sahand.farhoodi93@gmail.com) ++ ++In this project, we develop a bi-directional LSTM model for word segmentation. For now, these models are trained for Thai and Burmese. ++ ++### Quick start ++* **Use a pre-trained model:** To segment an arbitrary line go to file `train_language.py` where `language` is the language you want to use. For example, if the arbitrary line is in Thai, you should use the file `train_thai.py`. Over there, find comment `# Choose one of the saved models to use`. Everything before this line is for training a new model and can be ignored. After this comment, you can use the function `pick_lstm_model` to choose the model you want to use for segmentation: ++ ++ ```python ++ word_segmenter = pick_lstm_model(model_name="Thai_codepoints_exclusive_model4_heavy", embedding="codepoints", ++ train_data="exclusive BEST", eval_data="exclusive BEST") ++ ``` ++ ++ You need to specify three hyper-parameters: `embedding`, `train_data`, and `eval_data`. Please refer to [Models Specicitaions](https://github.com/SahandFarhoodi/word_segmentation/blob/work/Models%20Specifications.md) for a detailed explanation of these hyper-parameters, and also for a list of trained models ready to be used in this repository and their specifications. If you don't have time to do that, just pick one of the trained models and make sure that name of the embedding you choose appears in the model name (`train_data` and `eval-data` doesn't affect segmentation of arbitrary inputs). Next, you can use the following commands to specify your input and segment it: ++ ++ ```python ++ line = "ทำสิ่งต่างๆ ได้มากขึ้นขณะที่อุปกรณ์ล็อกและชาร์จอยู่ด้วยโหมดแอมเบียนท์" ++ word_segmenter.segment_arbitrary_line(line) ++ ``` ++ ++* **Train a new model:** In order to train a new model in Thai or Burmese, you need to use file `train_language.py` where `language` is the language you want to work with. Over there, you need to use the code between comments `# Train a new model -- choose name cautiously to not overwrite other models` and `# Choose one of the saved models to use`. The following code let you define a new model: ++ ++ ```python ++ model_name = "Thai_new_model" ++ word_segmenter = WordSegmenter(input_name=model_name, input_n=50, input_t=10000, input_clusters_num=350, ++ input_embedding_dim=16, input_hunits=23, input_dropout_rate=0.2, input_output_dim=4, ++ input_epochs=1, input_training_data="exclusive BEST", ++ input_evaluation_data="exclusive BEST", input_language="Thai", ++ input_embedding_type="codepoints") ++ ``` ++ ++ There are some hyperparameters need to be specified that are explained in detail in [Models Specifications](https://github.com/SahandFarhoodi/word_segmentation/blob/work/Models%20Specifications.md). After specifying your model, you can use function `word_segmenter.train_model()` to train your model, `word_segmenter.save_model()` to save it, and `word_segmenter.test_model_line_by_line()` to test it: ++ ```python ++ word_segmenter.train_model() ++ word_segmenter.save_model() ++ word_segmenter.test_model_line_by_line(verbose=True) ++ ``` ++ This repository is developed in a way that makes the process of training models in a new language semi-automatic. If you are interested in doing so, you need to find appropriate data sets (or decide to use the unsupervised learning option), add a couple of lines in `word_segmenter.py` and `constants.py` that let you use those data sets, use the `LSTMBayesianOptimization` class to estimate the values of `hunits` and `embedding_dim` (see [Models Specifications](https://github.com/SahandFarhoodi/word_segmentation/blob/work/Models%20Specifications.md) for details), and then train your models as above. You may also need to do some extra preprocessing (see `preproceee.py`) if you decide to use grapheme clusters embedding. Feel free to contact me if you think I can help you with this. ++ ++### Model structure ++Figure 1 illustrates our bi-directional model structure. Below we explain what are different layers: ++ ++* **Input Layer**: In the input layer we have a sequence of letters or characters that we want to segment. To be more specific, when you look at a string you can see it as a sequence of code points or [extended grapheme clusters](https://unicode.org/reports/tr29/). The choice of units for your input sequence (grapheme clusters or code points) results in different models, which should be chosen carefully. The code in this repository supports both of these options. ++ ++* **Embedding Layer**: In the embedding layer, we represent each unit of the input line (grapheme cluster or code point) with a numerical vector so it can be used by the rest of the model. The choice of embedding can significantly affect the model size and performance. The length of each embedding vector is denoted by *embedding size* throughout the rest of this document. In this repository, three types of embeddings are implemented: ++ * **grapheme clusters to vectors**: In this approach, each grapheme cluster is mapped to a single vector. These vectors are learned during model training and need to be stored to be used later for evaluation. Given that the set of possible grapheme clusters is theoretically infinity, we cannot store one vector for each possible grapheme clusters. Hence, we use large corpora to extract all grapheme clusters that actually happen in texts. Then we sort these grapheme clusters based on their frequency in the corpora and store one vector for those grapheme clusters that cover 99% of the text, and one vector for any other grapheme cluster. Using this approach, we need to store about 350 grapheme cluster vectors for Thai and Burmese. ++ * **Generalized encoding vectors**: In this approach, each code point is mapped to a vector that is learned during training, and then the vector computed for a grapheme cluster is the average of vectors corresponding to code points in it. The number of code points in a language is fixed and considerably less than the number of grapheme clusters, and hence the embedding matrix will have a smaller size using this approach. However, this approach typically requires a larger number of hidden units and embedding size in the model. There are variations of this approach (explained in [Embedding Discussion](https://github.com/SahandFarhoodi/word_segmentation/blob/work/Embeddings%20Discussion.md)) that are centered around the idea of having one vector for a group of code points that we believe behave similarly, such as digits. ++ * **code points to vectors**: In this approach, each code point is mapped to a single vector and these vectors are used directly in the remaining of the model. Therefore, in contrast to the two previous methods, here the smallest part of a sentence is code point instead of grapheme clusters. Again, this approach reduces the number of vectors we need to store, but often increases the number of hidden units and also the embedding size. ++* **Forward/Backward LSTM Layers**: The output of the embedding layer is fed into the forward and backward LSTM layers. We show the number of hidden units in each cell of LSTM by *hunits*. ++ ++* **Output Layer**: Here, the output of forward and backward LSTM layers are concatenated and fed into a dense layer with *softmax* activation function to make a vector of length four for each grapheme cluster. The values in each vector add up to 1 and are probabilities of *BIES*, where: ++ * *B* stands for beginning of a word. ++ * *I* stands for inside of a word. ++ * *E* stands for end of a word. ++ * *S* stands for a single grapheme cluster that forms a word by itself. ++ ++* **Dropout Layers**: We have two dropout layers in our model; one right after the embedding layer and one before the output layer. ++ ++ ++![Figure 1. The model structure for a bi-directional LSTM.](Figures/model_structure.png) ++ ++### Estimating hyperparameters of the model ++There are many hyperparameters in the model that need to be estimated before using it. Among different hyper-parameters, there are two that affect the model size and performance more significantly: *hunits* and *embedding size*. We use a stepwise grid-search to decide on all hyper-parameters except these two such as *learning rate*, *batch size*, and *dropout rate*. For *hunits* and *embedding size* we use [Bayesian optimization](https://github.com/fmfn/BayesianOptimization) which is much more computationally expensive, but guarantees a better estimation of these parameters. ++ ++### Data sets ++For some languages, there are manually annotated data sets that can be used to train learning-based models. However, for some other languages, such data sets don't exist. We develop a framework that let us train our model in both scenarios. In this framework (shown in Figure 2), if a manually segmented data set exists then we use it directly to train our model (supervised learning). Otherwise, if such data set doesn't exist (unsupervised learning), we use one of the existing algorithms such as the current ICU algorithm to generate pseudo segmented data, and then use that to train our model. We use ICU specifically because it already supports word segmentation for almost all languages, it is light, fast, and has acceptable accuracy. However, for some specific languages with better word segmentation algorithms ICU can be replaced. Our analysis shows that in the absence of a segmented data set, our algorithm is capable of learning what ICU does, and in a few cases, it can outperform ICU. Below we explain the data sets used to train and test models for Thai and Burmese: ++ ++* **Thai**: We use [NECTEC BEST data set](https://thailang.nectec.or.th/downloadcenter/index4c74.html?option=com_docman&task=cat_view&gid=42&Itemid=61) to train our model. The text files in this dataset use UTF-8 encoding and are manually segmented. There are four different genres of text in this data set: novel, news, encyclopedia, and article. For testing the model, we use both NECTEC BEST data set and Google SAFT data. ++* **Burmese**: For Burmese, we use the [Google corpus crawler](https://github.com/google/corpuscrawler) to collect unsegmented texts, and then use ICU to generate a pseudo segmented data set to be used for training. For testing, we use both pseudo segmented texts and Google SAFT data. ++ ++![Figure 2. The framework for training and testing the model.](Figures/framework.png) ++ ++### Performance summary ++There are two sets of trained models, one set is models trained using the language-specific script (models with `exclusive` in their name) where all other characters, including spaces, marks, and Latin letters are excluded from the data. This forces the model to be trained on much smaller sentences and can lower its accuracy. However, these models are completely compatible with the structure of ICU4C word segmenter, and can replace language engines for Thai and Burmese directly. The second set of models are trained using standard data sets (with spaces, marks, Latin letters in them) and give better accuracies. These models can be used in ICU4X, and also in ICU4C if some changes are made to its current structure. Below we present the performance of the first set of models and compare them to existing algorithms: ++ ++* **Thai**: The following table summarizes the performance of our algorithm alongside that of the state of the art algorithm [Deepcut](https://github.com/rkcosmos/deepcut) and current ICU algorithm. We have different versions of our algorithm, where LSTM model 7 and LSTM model 5 are respectively the most accurate and the most parsimonious models, and LSTM model 4 sits somewhere between these two and provides a high accuracy while still has a small data size. Based on this table, when BEST data is used for evaluation, **all LSTM models outperform ICU based on F1-score**, where models 4 and 7 do it with a considerable margin. When SAFT data is used, we see a considerable drop in performance of our models, which is primarily due to different segmentation rules that BEST and SAFT data have (e.g. 2020 is segmented as |2020| in BEST and |2|0|2|0| in SAFT). However, LSTM models still outperform ICU. **In terms of data size, LSTM models 4, 5, and 7 show respectively 79%, 92%, and 32% reduction**. Deepcut is by far the largest model which makes its applications in industry limited. It also outperforms all other methods in terms of accuracy by a considerable margin when the BEST data is used. However, for the SAFT data, which is not the data used to train Deepcut, this margin drops significantly. ++ ++ | Algorithm | BIES accuracy (BEST) | F1-score (BEST) | BIES accuracy (SAFT) | F1-score (SAFT) | Model size | ++ | :---: | :----: | :---: | :----: | :---: | :---: | ++ | LSTM model 4 | 94.5 | 89.9 | 90.8 | 82.8 | 27 KB | ++ | LSTM model 5 | 92.6 | 86.6 | 88.9 | 79.6 | 10 KB | ++ | LSTM model 7 | 95.7 | 91.9 | 92 | 84.9 | 86 KB | ++ | Deepcut | 97.8 | 95.7 | 92.5 | 86 | 2.2 MB | ++ | ICU | 93 | 86.4 | 90.3 | 81.9 | 126 KB | ++ ++* **Burmese**: ++The following table summarizes the performance of our algorithm and the current ICU algorithm for Burmese. Again, we have different versions of our LSTM models, where LSTM model 7 and LSTM model 5 are respectively the most accurate and the most parsimonious models. Based on this table, LSTM models learn to mimic what the ICU algorithm does pretty well. **For instance, on SAFT data, the relative error with respect to ICU is less than 1% (93.1/92.4) for model 7 and less than 2% for models 4 and 5. In terms of data size, LSTM models 4, 5, and 7 show respectively 88%, 94%, and 51% reduction**. ++ ++ | Algorithm | BIES accuracy (ICU segmented) | F1-score (ICU segmented) | BIES accuracy (SAFT) | F1-score (SAFT) | Model size | ++ | :---: | :----: | :---: | :---: | :---: | :---: | ++ | LSTM model 4 | 94.7 | 92.9 | 91.7 | 90.5 | 30 KB | ++ | LSTM model 5 | 93.4 | 91.1 | 91.4 | 90.1 | 15 KB | ++ | LSTM model 7 | 96.2 | 94.9 | 92.3 | 91.1 | 125 KB | ++ | ICU | 100 | 100 | 93.1 | 92.4 | 254 KB | ++ ++There are several directions for improving this project. Please see [Future Works](https://github.com/SahandFarhoodi/word_segmentation/blob/work/Future%20Works.md) for some ideas we have, and contact [me](http://math.bu.edu/people/sahand/) if you have any idea! ++ ++### Copyright & Licenses ++ ++Copyright © 2020-2024 Unicode, Inc. Unicode and the Unicode Logo are registered trademarks of Unicode, Inc. in the United States and other countries. ++ ++A CLA is required to contribute to this project - please refer to the [CONTRIBUTING.md](https://github.com/unicode-org/.github/blob/main/.github/CONTRIBUTING.md) file (or start a Pull Request) for more information. ++ ++The contents of this repository are governed by the Unicode [Terms of Use](https://www.unicode.org/copyright.html) and are released under [LICENSE](./LICENSE). +diff --git a/convert_lstm_model.py b/convert_lstm_model.py +new file mode 100755 +index 0000000..61f54ac +--- /dev/null ++++ b/convert_lstm_model.py +@@ -0,0 +1,128 @@ ++# Copyright (C) 2021 and later: Unicode, Inc. and others. ++# License & terms of use: http://www.unicode.org/copyright.html ++# Lint as: python3 ++ ++import sys, getopt, json, struct ++ ++""" ++Tool to convert Models/*/weights.json files into a resource file could be build ++into ICU. The result should be copy to icu/icu4c/source/data/brkitr/lstm. ++See https://docs.google.com/document/d/1EVK2CwOmUamJwMOMbbdTz7tuaV0IR21rMoH7a3pyFwE/edit#heading=h.qkedw6o6vy20 ++for detail design. ++""" ++ ++def main(argv): ++ inputfile = '' ++ try: ++ opts, args = getopt.getopt(argv,"hio::",["ifile=","ofile="]) ++ except getopt.GetoptError: ++ print('convert_lstm_model.py -i -o ') ++ sys.exit(2) ++ for opt, arg in opts: ++ if opt == '-h': ++ print('convert_lstm_model.py -i -o ') ++ sys.exit() ++ elif opt in ("-i", "--ifile"): ++ inputfile = arg ++ elif opt in ("-o", "--ofile"): ++ outfile = arg ++ ++ input = json.load(open(inputfile, 'r')) ++ embeddings = input["mat1"]["dim"][1]; ++ hunits = input["mat3"]["dim"][0]; ++ dict_size = len(input["dic"]) ++ model = input["model"] ++ type = "" ++ if str.find(model, "_codepoints_") > 0: ++ type = "codepoints" ++ elif str.find(model, "_graphclust_") > 0: ++ type = "graphclust" ++ ++ if type == "": ++ print("Unknon type specified in the model. Need to be either 'codepoints' or 'graphclust'") ++ sys.exit(2) ++ ++ verify_dimension(input, dict_size, embeddings, hunits) ++ ++ copyright="""\uFEFF// © 2021 and later: Unicode, Inc. and others. ++// License & terms of use: http://www.unicode.org/copyright.html""" ++ with open(outfile, 'w', encoding='utf-8') as f: ++ print(copyright, file=f) ++ print("{model}:table(nofallback){{".format(model=model), file=f) ++ print(" model{{\"{model}\"}}".format(model=model), file=f) ++ print(" type{{\"{type}\"}}".format(type=type), file=f) ++ print(" embeddings:int{{{embeddings}}}".format(embeddings=embeddings), file=f) ++ print(" hunits:int{{{hunits}}}".format(hunits=hunits), file=f) ++ print_dict(input["dic"], file=f) ++ print(" data:intvector{", file=f) ++ print_float_in_int(input["mat1"]["data"], file=f) ++ print_float_in_int(input["mat2"]["data"], file=f) ++ print_float_in_int(input["mat3"]["data"], file=f) ++ print_float_in_int(input["mat4"]["data"], file=f) ++ print_float_in_int(input["mat5"]["data"], file=f) ++ print_float_in_int(input["mat6"]["data"], file=f) ++ print_float_in_int(input["mat7"]["data"], file=f) ++ print_float_in_int(input["mat8"]["data"], file=f) ++ print_float_in_int(input["mat9"]["data"], file=f) ++ print(" }", file=f) ++ print("}", file=f) ++ ++def print_dict(dict, file): ++ print(" dict{", file=file) ++ i = 0 ++ for k in dict: ++ print(" \"{key}\",".format(key=k.replace('"', '\\"')), file=file) ++ if i != dict[k]: ++ print("Incorrect value for dic \"{k}\": {v}- expecting {i}" ++ .format(k=k, v=dict[k], i=i)) ++ sys.exit(2) ++ i += 1 ++ print(" }", file=file) ++ ++def print_float_in_int(data, file): ++ # TODO currently we print each float as 32 bit int. We may later change it to ++ # print two float as float16 into one 32 bit int. ++ for i in data: ++ print(" {f},".format(f=struct.unpack("i", struct.pack("f", i))[0]), file=file) ++ ++def verify_dimension(input, dict_size, embeddings, hunits): ++ hunits4 = 4 * hunits ++ hunits2 = 2 * hunits ++ if (input["mat1"]["dim"][0] != dict_size + 1): ++ dimension_error("mat1", dict_size + 1, input["mat1"]["dim"]) ++ if (input["mat1"]["dim"][1] != embeddings): ++ dimension_error("mat1", embeddings, input["mat1"]["dim"]) ++ if (input["mat2"]["dim"][0] != embeddings): ++ dimension_error("mat2", embeddings, input["mat2"]["dim"]) ++ if (input["mat2"]["dim"][1] != hunits4): ++ dimension_error("mat2", hunits4, input["mat2"]["dim"]) ++ if (input["mat3"]["dim"][0] != hunits): ++ dimension_error("mat3", hunits, input["mat3"]["dim"]) ++ if (input["mat3"]["dim"][1] != hunits4): ++ dimension_error("mat3", hunits4, input["mat3"]["dim"]) ++ if (input["mat4"]["dim"][0] != hunits4): ++ dimension_error("mat4", hunits4, input["mat4"]["dim"]) ++ if (input["mat5"]["dim"][0] != embeddings): ++ dimension_error("mat5", embeddings, input["mat5"]["dim"]) ++ if (input["mat5"]["dim"][1] != hunits4): ++ dimension_error("mat5", hunits4, input["mat5"]["dim"]) ++ if (input["mat6"]["dim"][0] != hunits): ++ dimension_error("mat6", hunits, input["mat6"]["dim"]) ++ if (input["mat6"]["dim"][1] != hunits4): ++ dimension_error("mat6", hunits4, input["mat6"]["dim"]) ++ if (input["mat7"]["dim"][0] != hunits4): ++ dimension_error("mat7", hunits4, input["mat7"]["dim"]) ++ if (input["mat8"]["dim"][0] != hunits2): ++ dimension_error("mat8", hunits2, input["mat8"]["dim"]) ++ if (input["mat8"]["dim"][1] != 4): ++ dimension_error("mat8", 4, input["mat8"]["dim"]) ++ if (input["mat9"]["dim"][0] != 4): ++ dimension_error("mat9", 4, input["mat9"]["dim"]) ++ ++def dimension_error(name, value, dim): ++ print("Dimension mismatch for {name}, expected {value}, but got {dim}" ++ .format(name=name, value=value, dim=dim)) ++ sys.exit(2) ++ ++if __name__ == "__main__": ++ main(sys.argv[1:]) +diff --git a/convert_weights.py b/convert_weights.py +new file mode 100644 +index 0000000..08d5548 +--- /dev/null ++++ b/convert_weights.py +@@ -0,0 +1,48 @@ ++import os ++import numpy as np ++import tensorflow as tf # Needed to handle TF tensors ++import h5py ++ ++def convert_to_numpy(value): ++ """ ++ Convert TensorFlow tensors/variables to NumPy arrays (float32). ++ Ensures we remove any TensorFlow-specific data. ++ """ ++ if isinstance(value, tf.Tensor) or isinstance(value, tf.Variable): ++ return value.numpy().astype(np.float32) ++ elif isinstance(value, np.ndarray) and np.issubdtype(value.dtype, np.number): ++ return value.astype(np.float32) ++ else: ++ return None # Ignore non-numeric data ++ ++def convert_weights(npy_path): ++ """Convert `weights.npy` to a TensorFlow-free HDF5 format.""" ++ if not os.path.exists(npy_path): ++ print(f"❌ Error: {npy_path} not found!") ++ return ++  ++ h5_path = npy_path.replace(".npy", "_tf_free.h5") ++  ++ # Load the weights ++ print(f"🔍 Loading {npy_path}...") ++ weights = np.load(npy_path, allow_pickle=True) ++ ++ # Convert all elements to NumPy arrays (remove TensorFlow dtypes) ++ converted_weights = [convert_to_numpy(w) for w in weights if convert_to_numpy(w) is not None] ++ ++ # Save to HDF5 format ++ with h5py.File(h5_path, "w") as hf: ++ for i, w in enumerate(converted_weights): ++ hf.create_dataset(f"weight_{i}", data=w) ++ ++ print(f"✅ Converted: {npy_path} -> {h5_path}") ++ ++if __name__ == "__main__": ++ # Search for all `weights.npy` files and convert them ++ for root, _, files in os.walk("."): ++ for file in files: ++ if file == "weights.npy": ++ convert_weights(os.path.join(root, file)) ++ ++ print("🚀 All weight files converted successfully!") ++ +diff --git a/error_analysis.py b/error_analysis.py +new file mode 100644 +index 0000000..6b0f887 +--- /dev/null ++++ b/error_analysis.py +@@ -0,0 +1,120 @@ ++from pathlib import Path ++from lstm_word_segmentation.word_segmenter import pick_lstm_model ++from lstm_word_segmentation.text_helpers import get_lines_of_text ++from lstm_word_segmentation.line import Line ++from lstm_word_segmentation.preprocess import evaluate_existing_algorithms ++import deepcut ++import timeit ++ ++ ++# Analyzing how different word segmenters perform for a set of unsegmented lines ++# ''' ++# Picking models for error analysis ++word_segmenter1 = pick_lstm_model(model_name="Thai_graphclust_exclusive_model4_heavy", embedding="grapheme_clusters_tf", ++ train_data="BEST", eval_data="BEST") ++ ++word_segmenter2 = pick_lstm_model(model_name="Thai_graphclust_model5_heavy", embedding="grapheme_clusters_tf", ++ train_data="BEST", eval_data="BEST") ++ ++word_segmenter3 = pick_lstm_model(model_name="Thai_graphclust_model7_heavy", embedding="grapheme_clusters_tf", ++ train_data="BEST", eval_data="BEST") ++ ++word_segmenter4 = pick_lstm_model(model_name="Thai_graphclust_exclusive_model4_heavy", embedding="grapheme_clusters_tf", ++ train_data="exclusive BEST", eval_data="exclusive BEST") ++ ++word_segmenter5 = pick_lstm_model(model_name="Thai_codepoints_exclusive_model4_heavy", embedding="codepoints", ++ train_data="exclusive BEST", eval_data="exclusive BEST") ++ ++word_segmenter6 = pick_lstm_model(model_name="Thai_codepoints_exclusive_model7_heavy", embedding="codepoints", ++ train_data="exclusive BEST", eval_data="exclusive BEST") ++ ++word_segmenters = [word_segmenter1, word_segmenter2, word_segmenter3, word_segmenter4, word_segmenter5, word_segmenter6] ++ ++# Show output of models ++verbose = True ++ ++# Write the output of models into a output_file defined below ++write = False ++if write: ++ output_file = Path.joinpath(Path(__file__).parent.absolute(), 'Data/wiki_thai_sample_exclusive_results.txt') ++ output = open(str(output_file), 'w') ++ ++# Read the input from a file directly. The alternative is inserting them directly below ++read = False ++if read: ++ file = Path.joinpath(Path(__file__).parent.absolute(), 'Data/wiki_thai_sample_exclusive.txt') ++ lines = get_lines_of_text(file, "unsegmented") ++else: ++ inp_lines = ["|เพราะ|เขา|เห็น|โอกาส|ใน|การ|ซื้อ|", "|การ|เดินทาง|ใน|", "|นั่ง|นายก|ฯ|ต่อ|สมัย|หน้า|", ++ "|พร้อม|จัดตั้ง|", ++ "|เพราะ|ดนตรี|ที่|ชอบ|นั้น|"] ++ # inp_lines = ["|ဖော်ပြ|ထားသည့်|", "|အသားအရောင်|အားဖြင့်|", "|သဘာဝ|အားဖြင့်|", "|ထို့ပြင်|", "|နိုင်ငံရေး|ဆိုင်ရာ|"] ++ lines = [] ++ for inp_line in inp_lines: ++ lines.append(Line(inp_line, "man_segmented")) ++ ++# Running different models ++for line in lines: ++ deepcut_words = deepcut.tokenize(line.unsegmented) ++ deepcut_segmented = "|" ++ for word in deepcut_words: ++ deepcut_segmented += word + "|" ++ if verbose: ++ print("***************************************************************************************************") ++ print("{:<40} : {}".format("Unsegmented", line.unsegmented)) ++ print("{:<40} : {}".format("Deepcut", deepcut_segmented)) ++ print("{:<40} : {}".format("ICU", line.icu_segmented)) ++ for word_seg in word_segmenters: ++ print("{:<40} : {}".format(word_seg.name, word_seg.segment_arbitrary_line(line.unsegmented))) ++ print("***************************************************************************************************") ++ if write: ++ output.write("********************************************************************************\n") ++ output.write("{:<40} : {}\n".format("Unsegmented", line.unsegmented)) ++ output.write("{:<40} : {}\n".format("Deepcut", deepcut_segmented)) ++ output.write("{:<40} : {}\n".format("ICU", line.icu_segmented)) ++ for word_seg in word_segmenters: ++ output.write("{:<40} : {}\n".format(word_seg.name, word_seg.segment_arbitrary_line(line.unsegmented))) ++ output.write("********************************************************************************\n") ++# ''' ++ ++# Measuring the time that different models need to evaluate texts ++# ''' ++word_segmenter1 = pick_lstm_model(model_name="Thai_codepoints_exclusive_model4_heavy", embedding="codepoints", ++ train_data="exclusive BEST", eval_data="exclusive BEST") ++ ++word_segmenter2 = pick_lstm_model(model_name="Thai_graphclust_model5_heavy", embedding="grapheme_clusters_tf", ++ train_data="BEST", eval_data="BEST") ++ ++word_segmenter3 = pick_lstm_model(model_name="Thai_graphclust_model7_heavy", embedding="grapheme_clusters_tf", ++ train_data="BEST", eval_data="BEST") ++ ++word_segmenter4 = pick_lstm_model(model_name="Thai_codepoints_exclusive_model4_heavy", embedding="codepoints", ++ train_data="exclusive BEST", eval_data="exclusive BEST") ++ ++word_segmenter5 = pick_lstm_model(model_name="Thai_codepoints_exclusive_model5_heavy", embedding="codepoints", ++ train_data="exclusive BEST", eval_data="exclusive BEST") ++ ++word_segmenter6 = pick_lstm_model(model_name="Thai_codepoints_exclusive_model7_heavy", embedding="codepoints", ++ train_data="exclusive BEST", eval_data="exclusive BEST") ++ ++word_segmenters = [word_segmenter1, word_segmenter2, word_segmenter3, word_segmenter4, word_segmenter5, word_segmenter6] ++ ++# Testing all models using the same BEST data: texts 40-60 ++for word_seg in word_segmenters: ++ start = timeit.default_timer() ++ word_seg.test_model_line_by_line(verbose=False, fast=True) ++ stop = timeit.default_timer() ++ print("{} Time using test_line_by_line: {}".format(word_seg.name, stop - start)) ++ ++# ICU ++start = timeit.default_timer() ++evaluate_existing_algorithms(algorithm="ICU", data="BEST", fast=True) ++stop = timeit.default_timer() ++print("ICU Time: {}".format(stop-start)) ++ ++# Deepcut ++start = timeit.default_timer() ++evaluate_existing_algorithms(algorithm="Deepcut", data="BEST", fast=True) ++stop = timeit.default_timer() ++print("Deepcut Time: {}".format(stop-start)) ++# ''' +diff --git a/lstm_word_segmentation/__init__.py b/lstm_word_segmentation/__init__.py +new file mode 100644 +index 0000000..e69de29 +diff --git a/lstm_word_segmentation/accuracy.py b/lstm_word_segmentation/accuracy.py +new file mode 100644 +index 0000000..8257e14 +--- /dev/null ++++ b/lstm_word_segmentation/accuracy.py +@@ -0,0 +1,87 @@ ++from lstm_word_segmentation.helpers import diff_strings ++ ++ ++class Accuracy: ++ """ ++ A class that helps computing accuracy for word segmentation. It let you compute both BIES accuracy and F1 score. ++ """ ++ def __init__(self): ++ """ ++ The __init__ function creates a new instance of the class. ++ """ ++ self.bies_length = 0 ++ self.bies_mismatch = 0 ++ self.segmented_words = 0 ++ self.true_words = 0 ++ self.correctly_segmented_words = 0 ++ ++ def update(self, true_bies, est_bies): ++ """ ++ This function updates the accuracy with respect to a new segmented line and its true segmentation. ++ Args: ++ true_bies: a string that indicates the true BIES sequence ++ est_bies: a string that indicates the estimated BIES sequence ++ """ ++ if len(true_bies) != len(est_bies): ++ print("Warning: length of true_bies and est_bies are different") ++ self.bies_length += len(true_bies) ++ self.bies_mismatch += diff_strings(true_bies, est_bies) ++ ++ true_word_brkpoints = [] ++ for i in range(len(true_bies)): ++ if true_bies[i] in ['b', 's']: ++ true_word_brkpoints.append(i) ++ true_word_brkpoints.append(len(true_bies)) ++ est_word_brkpoints = [] ++ for i in range(len(est_bies)): ++ if est_bies[i] in ['b', 's']: ++ est_word_brkpoints.append(i) ++ est_word_brkpoints.append(len(est_bies)) ++ true_words_tuples = [(true_word_brkpoints[i], true_word_brkpoints[i + 1]) for i in ++ range(len(true_word_brkpoints) - 1)] ++ est_words_tuples = [(est_word_brkpoints[i], est_word_brkpoints[i + 1]) for i in ++ range(len(est_word_brkpoints) - 1)] ++ self.true_words += len(true_words_tuples) ++ self.segmented_words += len(est_words_tuples) ++ self.correctly_segmented_words += len(set(est_words_tuples).intersection(true_words_tuples)) ++ ++ def merge_accuracy(self, other): ++ """ ++ This function lets us to use another Accuracy instance to update the accuracy of the current instance ++ Args: ++ other: the other Accuracy instance ++ """ ++ self.segmented_words += other.segmented_words ++ self.true_words += other.true_words ++ self.correctly_segmented_words += other.correctly_segmented_words ++ self.bies_length += other.bies_length ++ self.bies_mismatch += other.bies_mismatch ++ ++ def get_f1_score(self): ++ """ ++ This function returns the F1 score ++ """ ++ # Computing precision ++ precision = 0 ++ if self.segmented_words != 0: ++ precision = self.correctly_segmented_words / self.segmented_words ++ ++ # Computing recall ++ recall = 0 ++ if self.true_words != 0: ++ recall = self.correctly_segmented_words / self.true_words ++ ++ # Computing F1 score ++ f1 = 0 ++ if precision + recall != 0: ++ f1 = 2 * precision * recall / (precision + recall) ++ return f1 ++ ++ def get_bies_accuracy(self): ++ """ ++ This function returns the BIES accuracy ++ """ ++ bies_acc = 0 ++ if self.bies_length != 0: ++ return 1 - self.bies_mismatch/self.bies_length ++ return bies_acc +diff --git a/lstm_word_segmentation/bies.py b/lstm_word_segmentation/bies.py +new file mode 100644 +index 0000000..2738fd4 +--- /dev/null ++++ b/lstm_word_segmentation/bies.py +@@ -0,0 +1,86 @@ ++import numpy as np ++ ++ ++class Bies: ++ """ ++ A class that stores a bies sequence in it. ++ """ ++ def __init__(self, input_bies, input_type): ++ """ ++ The __init__ function creates a new instance of the class. ++ Args: ++ input_bies: the input to initialize the instance ++ input_type: determines what is the type of the input. It can be "mat" or "str" ++ """ ++ if input_type == "mat": ++ self.mat = input_bies ++ self.compute_str_from_mat() ++ elif input_type == "str": ++ self.str = input_bies ++ self.mat = None ++ else: ++ print("Warning: this input_type is not known for BIES") ++ ++ def compute_str_from_mat(self): ++ """ ++ This function uses the matrix format of the bies sequence to generate a string version. ++ """ ++ self.str = "" ++ for i in range(self.mat.shape[0]): ++ max_ind = np.argmax(self.mat[i, :]) ++ if max_ind == 0: ++ self.str += "b" ++ elif max_ind == 1: ++ self.str += "i" ++ elif max_ind == 2: ++ self.str += "e" ++ elif max_ind == 3: ++ self.str += "s" ++ ++ def normalize_bies(self): ++ """ ++ This function normalizes the BIES sequence to generate a string that makes sense. For example the output ++ won't have substring such as "biiis", "biese" or "siie". Note that it doesn't change the self.mat ++ """ ++ if len(self.str) == 0: ++ return 's' ++ out_bies = "" ++ start_of_word = True ++ skip = False ++ for i in range(len(self.str)): ++ if skip: ++ skip = False ++ continue ++ if start_of_word: ++ if i == len(self.str) - 1 or self.str[i] == 's': ++ out_bies += 's' ++ start_of_word = True ++ continue ++ elif self.str[i] == 'b': ++ out_bies += self.str[i] ++ start_of_word = False ++ continue ++ elif self.str[i] in ['i', 'e']: ++ if self.str[i + 1] in ['i', 'e']: ++ out_bies += 'b' ++ start_of_word = False ++ continue ++ else: ++ out_bies += 's' ++ start_of_word = True ++ continue ++ if not start_of_word: ++ if self.str[i] == 'e': ++ out_bies += self.str[i] ++ start_of_word = True ++ continue ++ elif self.str[i] in ['b', 'i', 's']: ++ if i == len(self.str) - 1 or self.str[i + 1] in ['b', 's']: ++ out_bies += 'e' ++ start_of_word = True ++ continue ++ else: ++ out_bies += 'i' ++ start_of_word = False ++ continue ++ self.str = out_bies +diff --git a/lstm_word_segmentation/code_point.py b/lstm_word_segmentation/code_point.py +new file mode 100644 +index 0000000..fd0765d +--- /dev/null ++++ b/lstm_word_segmentation/code_point.py +@@ -0,0 +1,22 @@ ++class CodePoint: ++ """ ++ A class to store a code point. It supports the following versions of a code point: ++ 1) code_point_id: an integer that is computed with respect to a dictionary of all accepted code points ++ """ ++ def __init__(self, codepoint, codepoint_dic): ++ """ ++ The __init__ function creates a new instance of the class. ++ Args: ++ codepoint: the input code point ++ codepoint_dic: the dictionary that stores all accepted codepoints in a language ++ """ ++ self.codepoint = codepoint ++ self.num_codepoints = len(codepoint_dic)+1 ++ self.codepoint_id = codepoint_dic.get(self.codepoint, self.num_codepoints - 1) ++ ++ def display(self): ++ """ ++ A function that displays different versions of the current code point ++ """ ++ print("Code point: \n{}".format(self.codepoint)) ++ print("Code point id: \n{}".format(self.codepoint_id)) +diff --git a/lstm_word_segmentation/constants.py b/lstm_word_segmentation/constants.py +new file mode 100755 +index 0000000..d0c90c8 +--- /dev/null ++++ b/lstm_word_segmentation/constants.py +@@ -0,0 +1,68 @@ ++import numpy as np ++from pathlib import Path ++from icu import UCharCategory, UnicodeSet ++ ++# The dictionary that stores grapheme clusters and the frequency they appeared in BEST data set ++path = Path.joinpath(Path(__file__).parent.parent.absolute(), 'Public_Data/Thai_graph_clust_ratio.npy') ++THAI_GRAPH_CLUST_RATIO = np.load(str(path), allow_pickle=True).item() ++ ++# The dictionary that stores only Thai-script grapheme clusters and the frequency they appeared in BEST data set ++path = Path.joinpath(Path(__file__).parent.parent.absolute(), 'Public_Data/Thai_exclusive_graph_clust_ratio.npy') ++THAI_EXCLUSIVE_GRAPH_CLUST_RATIO = np.load(str(path), allow_pickle=True).item() ++ ++# The dictionary for all code points in Unicode Thai boxes ++accepted_code_points = UnicodeSet("[[:Thai:]&[:LineBreak=SA:]]") ++THAI_CODE_POINT_DICTIONARY = {accepted_code_points[i]: i for i in range(len(accepted_code_points))} ++ ++# The dictionary that stores grapheme clusters and the frequency they appeared in "my" data set ++path = Path.joinpath(Path(__file__).parent.parent.absolute(), 'Public_Data/Burmese_graph_clust_ratio.npy') ++BURMESE_GRAPH_CLUST_RATIO = np.load(str(path), allow_pickle=True).item() ++ ++# The dictionary that stores only Burmese-script grapheme clusters and the frequency they appeared in "my" data set ++path = Path.joinpath(Path(__file__).parent.parent.absolute(), 'Public_Data/Burmese_exclusive_graph_clust_ratio.npy') ++BURMESE_EXCLUSIVE_GRAPH_CLUST_RATIO = np.load(str(path), allow_pickle=True).item() ++ ++# The dictionary for all code points in Unicode Burmese boxes ++accepted_code_points = UnicodeSet("[[:Mymr:]&[:LineBreak=SA:]]") ++BURMESE_CODE_POINT_DICTIONARY = {accepted_code_points[i]: i for i in range(len(accepted_code_points))} ++ ++# The dictionary that stores grapheme clusters and the frequency they appeared in BEST and my data sets ++path = Path.joinpath(Path(__file__).parent.parent.absolute(), 'Public_Data/Thai_Burmese_graph_clust_ratio.npy') ++THAI_BURMESE_GRAPH_CLUST_RATIO = np.load(str(path), allow_pickle=True).item() ++ ++# A dictionary that determines how different types of code points are grouped together. This dictionary will be used ++# when generalized vectors are used for embedding. Here is meaning of numbers: ++# 1: Letters, 2: Marks, 3: Digits, 4: Separators, 5: Punctuations, 6: Symbols, 7: Others ++CHAR_TYPE_TO_BUCKET = { ++ UCharCategory.UPPERCASE_LETTER: 1, ++ UCharCategory.LOWERCASE_LETTER: 1, ++ UCharCategory.TITLECASE_LETTER: 1, ++ UCharCategory.MODIFIER_LETTER: 1, ++ UCharCategory.OTHER_LETTER: 1, ++ UCharCategory.NON_SPACING_MARK: 2, ++ UCharCategory.ENCLOSING_MARK: 2, ++ UCharCategory.COMBINING_SPACING_MARK: 2, ++ UCharCategory.DECIMAL_DIGIT_NUMBER: 3, ++ # LETTER_NUMBER and OTHER_NUMBER are more like symbols than digits ++ UCharCategory.LETTER_NUMBER: 6, ++ UCharCategory.OTHER_NUMBER: 6, ++ UCharCategory.SPACE_SEPARATOR: 4, ++ UCharCategory.LINE_SEPARATOR: 4, ++ UCharCategory.PARAGRAPH_SEPARATOR: 4, ++ UCharCategory.DASH_PUNCTUATION: 5, ++ UCharCategory.START_PUNCTUATION: 5, ++ UCharCategory.END_PUNCTUATION: 5, ++ UCharCategory.CONNECTOR_PUNCTUATION: 5, ++ UCharCategory.OTHER_PUNCTUATION: 5, ++ UCharCategory.INITIAL_PUNCTUATION: 5, ++ UCharCategory.FINAL_PUNCTUATION: 5, ++ UCharCategory.MATH_SYMBOL: 6, ++ UCharCategory.CURRENCY_SYMBOL: 6, ++ UCharCategory.MODIFIER_SYMBOL: 6, ++ UCharCategory.OTHER_SYMBOL: 6, ++ UCharCategory.CONTROL_CHAR: 7, ++ UCharCategory.FORMAT_CHAR: 7, ++ UCharCategory.PRIVATE_USE_CHAR: 7, ++ UCharCategory.SURROGATE: 7, ++ 0: 7 ++ } +diff --git a/lstm_word_segmentation/grapheme_cluster.py b/lstm_word_segmentation/grapheme_cluster.py +new file mode 100644 +index 0000000..f654844 +--- /dev/null ++++ b/lstm_word_segmentation/grapheme_cluster.py +@@ -0,0 +1,53 @@ ++import numpy as np ++from . import constants ++from icu import Char ++ ++ ++class GraphemeCluster: ++ """ ++ A class to store a grapheme cluster. It supports three versions of a grapheme cluster: ++ 1) graph_clust_id: an integer that is computed with respect to a dictionary of top grapheme clusters ++ 2) graph_clust_vec: a vector that has only one at the slot associated with graph_clust_id ++ 3) generalized_vec: a vector of a fixed size that has 1/m for each code point found in the grapheme cluster, ++ where m is the total number of code points in the grapheme cluster ++ """ ++ def __init__(self, grapheme_cluster, graph_clust_dic, letters_dic): ++ """ ++ The __init__ function creates a new instance of the class. ++ Args: ++ grapheme_cluster: the input grapheme cluster ++ graph_clust_dic: the dictionary that stores all top graph clusters in a language ++ letters_dic: a dictionary that determines how different code points are mapped to different slots of ++ generalized_vec. ++ """ ++ self.graph_clust = grapheme_cluster ++ self.num_clusters = len(graph_clust_dic)+1 ++ self.graph_clust_id = graph_clust_dic.get(self.graph_clust, self.num_clusters - 1) ++ self.graph_clust_vec = np.zeros(self.num_clusters) ++ self.graph_clust_vec[self.graph_clust_id] = 1 ++ ++ # Making the generalized vectors representation with respect to the letters_dic ++ self.num_letters = len(letters_dic) ++ self.generalized_vec_length = self.num_letters + 4 ++ self.generalized_vec = np.zeros(self.generalized_vec_length) ++ for ch in self.graph_clust: ++ if ch in letters_dic: ++ self.generalized_vec[letters_dic.get(ch)] += 1 ++ elif constants.CHAR_TYPE_TO_BUCKET[Char.charType(ch)] == 1: ++ self.generalized_vec[self.num_letters] += 1 ++ elif constants.CHAR_TYPE_TO_BUCKET[Char.charType(ch)] in [2, 5, 6]: ++ self.generalized_vec[self.num_letters + 1] += 1 ++ elif constants.CHAR_TYPE_TO_BUCKET[Char.charType(ch)] == 3: ++ self.generalized_vec[self.num_letters + 2] += 1 ++ elif constants.CHAR_TYPE_TO_BUCKET[Char.charType(ch)] in [4, 7]: ++ self.generalized_vec[self.num_letters + 3] += 1 ++ self.generalized_vec = self.generalized_vec/np.sum(self.generalized_vec) ++ ++ def display(self): ++ """ ++ A function that displays different versions of the current grapheme_cluster ++ """ ++ print("Grapheme cluster: \n{}".format(self.graph_clust)) ++ print("Grapheme cluster id: \n{}".format(self.graph_clust_id)) ++ print("Grapheme cluster vector: \n{}".format(self.graph_clust_vec)) ++ print("Generalized vector: \n{}".format(self.generalized_vec)) +diff --git a/lstm_word_segmentation/helpers.py b/lstm_word_segmentation/helpers.py +new file mode 100644 +index 0000000..3b5b00c +--- /dev/null ++++ b/lstm_word_segmentation/helpers.py +@@ -0,0 +1,86 @@ ++import numpy as np ++from . import constants ++ ++ ++def is_ascii(input_str): ++ """ ++ A very basic function that checks if all elements of str are ASCII or not ++ Args: ++ input_str: input string ++ """ ++ return all(ord(char) < 128 for char in input_str) ++ ++ ++def diff_strings(str1, str2): ++ """ ++ A function that returns the number of elements of two strings that are not identical ++ Args: ++ str1: the first string ++ str2: the second string ++ """ ++ if len(str1) != len(str2): ++ print("Warning: length of two strings are not equal") ++ return -1 ++ return sum(str1[i] != str2[i] for i in range(len(str1))) ++ ++ ++def sigmoid(inp): ++ """ ++ Computes the sigmoid function of a scalar or a 1d numpy array ++ Args: ++ inp: the input which can be a scalar or a 1d numpy array ++ """ ++ inp = np.asarray(inp) ++ scalar_input = False ++ if inp.ndim == 0: ++ inp = inp[None] ++ scalar_input = True ++ # Checking for case when the input is an array/np.array of arrays. In this case only the first element of inp is ++ # used. A common example is when A = np.array([np.array([1, 2, 3])]). ++ if type(inp[0]) == np.ndarray: ++ inp = inp[0] ++ out = [] ++ for x in inp: ++ if x < -20: ++ out.append(0) ++ else: ++ out.append(1.0/(1.0 + np.exp(-x))) ++ out = np.array(out) ++ if scalar_input: ++ return np.squeeze(out) ++ return out ++ ++ ++def print_grapheme_clusters(thrsh, language, exclusive): ++ """ ++ This function print the grapheme clusters and their frequencies for a given langauge. It also computes what ++ percentage of grapheme clusters form which percent of the text ++ Args: ++ thrsh: shows what percent of the text we want to be covered by grapheme clusters ++ language: shows the language that we are working with ++ exclusive: shows if we only consider grapheme clusters in a single script or not ++ """ ++ ratios = None ++ if language == "Thai" and exclusive is False: ++ ratios = constants.THAI_GRAPH_CLUST_RATIO ++ if language == "Thai" and exclusive is True: ++ ratios = constants.THAI_EXCLUSIVE_GRAPH_CLUST_RATIO ++ if language == "Burmese" and exclusive is False: ++ ratios = constants.BURMESE_GRAPH_CLUST_RATIO ++ if language == "Burmese" and exclusive is True: ++ ratios = constants.BURMESE_EXCLUSIVE_GRAPH_CLUST_RATIO ++ if language == "Thai-Burmese": ++ ratios = constants.THAI_BURMESE_GRAPH_CLUST_RATIO ++ if ratios is None: ++ print("No grapheme cluster dictionary has been computed for the input language.") ++ return ++ cum_sum = 0 ++ cnt = 0 ++ for val in ratios.values(): ++ cum_sum += val ++ cnt += 1 ++ if cum_sum > thrsh: ++ break ++ print(ratios) ++ print("number of different grapheme clusters in {} = {}".format(language, len(ratios.keys()))) ++ print("{} grapheme clusters form {} of the text".format(cnt, thrsh)) +diff --git a/lstm_word_segmentation/line.py b/lstm_word_segmentation/line.py +new file mode 100644 +index 0000000..1a41fc6 +--- /dev/null ++++ b/lstm_word_segmentation/line.py +@@ -0,0 +1,212 @@ ++import numpy as np ++from icu import BreakIterator, Locale ++from .bies import Bies ++from collections import Counter ++import deepcut ++ ++ ++class Line: ++ """ ++ A class that stores different versions of a line: unsegmented, ICU segmented, and manually segmented (if exists). ++ """ ++ def __init__(self, input_line, input_type): ++ """ ++ The __init__ function creates a new instance of the class based on the input line and its type. ++ Args: ++ input_line: the input line. It should be a clean line in a single language. ++ input_type: determines what is the type of the input line. It can be segmented, icu_segmented, or ++ man_segmented. ++ """ ++ if input_type == "unsegmented": ++ self.unsegmented = input_line ++ self._compute_char_brkpoints() ++ self._compute_icu_segmented() ++ self.icu_word_brkpoints = self._compute_word_brkpoints(input_type="icu_segmented") ++ self.man_segmented = None ++ self.man_word_brkpoints = None ++ ++ elif input_type == "icu_segmented": ++ self.icu_segmented = input_line ++ self.icu_word_brkpoints = self._compute_word_brkpoints(input_type="icu_segmented") ++ self.unsegmented = input_line.replace("|", "") ++ self._compute_char_brkpoints() ++ self.man_segmented = None ++ self.man_word_brkpoints = None ++ ++ elif input_type == "man_segmented": ++ self.man_segmented = input_line ++ self.man_word_brkpoints = self._compute_word_brkpoints(input_type="man_segmented") ++ self.unsegmented = input_line.replace("|", "") ++ self._compute_char_brkpoints() ++ self._compute_icu_segmented() ++ self.icu_word_brkpoints = self._compute_word_brkpoints(input_type="icu_segmented") ++ ++ else: ++ print("Warning: this input_type is not implemented") ++ ++ self.deepcut = None ++ ++ def _compute_icu_segmented(self): ++ """ ++ This function computes the ICU segmented version of the line using the unsegmented version. Therefore, in order ++ to use it the unsegmented version must have been already computed. ++ """ ++ words_break_iterator = BreakIterator.createWordInstance(Locale.getRoot()) ++ words_break_iterator.setText(self.unsegmented) ++ self.icu_word_brkpoints = [0] ++ for brkpoint in words_break_iterator: ++ self.icu_word_brkpoints.append(brkpoint) ++ self.icu_segmented = "|" ++ for i in range(len(self.icu_word_brkpoints) - 1): ++ self.icu_segmented += self.unsegmented[self.icu_word_brkpoints[i]: self.icu_word_brkpoints[i + 1]] + "|" ++ ++ def _compute_word_brkpoints(self, input_type): ++ """ ++ This function computes a list of word breakpoints based on the input type. Note that it treats all "|" as a ++ separator between two successive words. ++ Args: ++ input_type: The type of the input. It should be either "man_segmentend" or "icu_segmented" ++ """ ++ input_line = None ++ if input_type == "icu_segmented": ++ input_line = self.icu_segmented ++ elif input_type == "man_segmented": ++ input_line = self.man_segmented ++ elif input_type == "deepcut_segmented": ++ input_line = self.get_deepcut_segmented() ++ else: ++ print("Warning: the _compute_word_breakpoints function is not defined for this input type") ++ ++ word_brkpoints = [] ++ found_bars = 0 ++ for i in range(len(input_line)): ++ if input_line[i] == '|': ++ word_brkpoints.append(i - found_bars) ++ found_bars += 1 ++ return word_brkpoints ++ ++ def _compute_char_brkpoints(self): ++ """ ++ This function uses ICU BreakIterator to identify and store extended grapheme clusters. ++ """ ++ chars_break_iterator = BreakIterator.createCharacterInstance(Locale.getRoot()) ++ chars_break_iterator.setText(self.unsegmented) ++ self.char_brkpoints = [0] ++ for brkpoint in chars_break_iterator: ++ self.char_brkpoints.append(brkpoint) ++ ++ def get_deepcut_segmented(self): ++ """ ++ This function returns a clean string that is the output of applying deepcut on unsegmented version of line ++ """ ++ deepcut_out = deepcut.tokenize(self.unsegmented) ++ out_line = "|" ++ for word in deepcut_out: ++ out_line += word + "|" ++ return out_line ++ ++ def get_bies_grapheme_clusters(self, segmentation_type): ++ """ ++ This function computes the BIES matrix for grapheme clusters that represents the line in this instance. ++ Args: ++ segmentation_type: this can be "icu", "man", or "deep" which indicates which segmentation we want to be used ++ """ ++ word_brkpoints = None ++ if segmentation_type == "icu": ++ word_brkpoints = self.icu_word_brkpoints ++ elif segmentation_type == "man": ++ word_brkpoints = self.man_word_brkpoints ++ elif segmentation_type == "deep": ++ word_brkpoints = self._compute_word_brkpoints(input_type="deepcut_segmented") ++ else: ++ print("Warning: No segmentation exist for the given type") ++ ++ bies_mat = np.zeros(shape=[len(self.char_brkpoints) - 1, 4]) ++ word_ind = 0 ++ for i in range(len(self.char_brkpoints) - 1): ++ word_st = word_brkpoints[word_ind] ++ word_fn = word_brkpoints[word_ind + 1] ++ char_st = self.char_brkpoints[i] ++ char_fn = self.char_brkpoints[i + 1] ++ if char_st == word_st and char_fn != word_fn: ++ bies_mat[i, 0] = 1 ++ continue ++ if char_st != word_st and char_fn != word_fn: ++ bies_mat[i, 1] = 1 ++ continue ++ if char_st != word_st and char_fn == word_fn: ++ bies_mat[i, 2] = 1 ++ word_ind += 1 ++ continue ++ if char_st == word_st and char_fn == word_fn: ++ bies_mat[i, 3] = 1 ++ word_ind += 1 ++ continue ++ return Bies(input_bies=bies_mat, input_type="mat") ++ ++ def get_bies_codepoints(self, segmentation_type): ++ """ ++ This function computes the BIES matrix for code points that represents the line in this instance. ++ Args: ++ segmentation_type: this can be "icu", "man", or "deep" which indicates which segmentation we want to be used ++ """ ++ word_brkpoints = None ++ if segmentation_type == "icu": ++ word_brkpoints = self.icu_word_brkpoints ++ elif segmentation_type == "man": ++ word_brkpoints = self.man_word_brkpoints ++ elif segmentation_type == "deep": ++ word_brkpoints = self._compute_word_brkpoints(input_type="deepcut_segmented") ++ else: ++ print("Warning: No segmentation exist for the given type") ++ ++ bies_mat = np.zeros(shape=[len(self.unsegmented), 4]) ++ word_ind = 0 ++ for i in range(len(self.unsegmented)): ++ word_st = word_brkpoints[word_ind] ++ word_fn = word_brkpoints[word_ind + 1] ++ if i == word_st and i+1 < word_fn: ++ bies_mat[i, 0] = 1 ++ continue ++ if i != word_st and i+1 != word_fn: ++ bies_mat[i, 1] = 1 ++ continue ++ if i != word_st and i+1 == word_fn: ++ bies_mat[i, 2] = 1 ++ word_ind += 1 ++ continue ++ if i == word_st and i+1 == word_fn: ++ bies_mat[i, 3] = 1 ++ word_ind += 1 ++ continue ++ return Bies(input_bies=bies_mat, input_type="mat") ++ ++ def get_grapheme_clusters(self): ++ """ ++ This function returns a Counter dictionary that holds the frequency of different grapheme clusters in the line ++ """ ++ out = Counter() ++ for i in range(len(self.char_brkpoints) - 1): ++ new_graph_clust = self.unsegmented[self.char_brkpoints[i]: self.char_brkpoints[i + 1]] ++ out[new_graph_clust] += 1 ++ return out ++ ++ def get_codepoints(self): ++ out = Counter() ++ for codepoint in self.unsegmented: ++ out[codepoint] += 1 ++ return out ++ ++ def display(self): ++ """ ++ This function displays different versions of the line (unsegmented, icu_segmented, man_segmented), word break ++ points for different types of segmentation, and a list of extended grapheme clusters in the line ++ """ ++ print("*******************************************************") ++ print("unsegmented : {}".format(self.unsegmented)) ++ print("icu_segmented : {}".format(self.icu_segmented)) ++ print("man_segmented : {}".format(self.man_segmented)) ++ print("icu_word_brkpoints: {}".format(self.icu_word_brkpoints)) ++ print("man_word_brkpoints: {}".format(self.man_word_brkpoints)) ++ print("char_brkpoints : {}".format(self.char_brkpoints)) ++ print("*******************************************************") +diff --git a/lstm_word_segmentation/lstm_bayesian_optimization.py b/lstm_word_segmentation/lstm_bayesian_optimization.py +new file mode 100755 +index 0000000..db92b60 +--- /dev/null ++++ b/lstm_word_segmentation/lstm_bayesian_optimization.py +@@ -0,0 +1,85 @@ ++from .word_segmenter import WordSegmenter ++from bayes_opt import BayesianOptimization ++ ++ ++class LSTMBayesianOptimization: ++ """ ++ A class that can be used to run Bayesian optimization algorithm to decide on the best value for parameters `hunits` ++ and `embedding_dim` for bi-directional LSTM models. ++ """ ++ def __init__(self, input_n, input_t, input_language, input_epochs, input_embedding_type, input_clusters_num, ++ input_training_data, input_evaluation_data, input_hunits_lower, input_hunits_upper, ++ input_embedding_dim_lower, input_embedding_dim_upper, input_c, input_iterations): ++ """ ++ The __init__ function creates a new instance of the class based on the input line and its type. ++ Args: ++ input_language: the language of the model ++ input_n: length of lines for training the LSTM model ++ input_t: total length of the text to be used for training the LSTM model ++ input_epochs: number of epochs to fit each individual model ++ input_embedding_type: the type of embedding used to train models ++ input_clusters_num: the number of top grapheme clusters (used if the embedding type is grapheme_clusters) ++ input_training_data: the training data ++ input_evaluation_data: the evaluation data ++ input_hunits_lower and input_hunits_upper: the range of search area for hunits ++ input_embedding_dim_lower and input_embedding_dim_upper: the range of search area for embedding_dim ++ input_c: the constant value used in the penalty function of the lstm_score ++ input_iterations: the number of iterations for Bayesian optimization algorithm ++ """ ++ self.n = input_n ++ self.t = input_t ++ self.language = input_language ++ self.epochs = input_epochs ++ self.embedding_type = input_embedding_type ++ self.clusters_num = input_clusters_num ++ self.training_data = input_training_data ++ self.evaluation_data = input_evaluation_data ++ self.hunits_lower = input_hunits_lower ++ self.hunits_upper = input_hunits_upper ++ self.embedding_dim_lower = input_embedding_dim_lower ++ self.embedding_dim_upper = input_embedding_dim_upper ++ self.c = input_c ++ self.iterations = input_iterations ++ ++ # Setting self.lambda to the number of the parameters of the largest possible model ++ word_segmenter = WordSegmenter(input_name="temp", input_n=50, input_t=10000, ++ input_clusters_num=self.clusters_num, ++ input_embedding_dim=self.embedding_dim_upper, input_hunits=self.hunits_upper, ++ input_dropout_rate=0.2, input_output_dim=4, input_epochs=1, ++ input_training_data=self.training_data, ++ input_evaluation_data=self.evaluation_data, input_language=self.language, ++ input_embedding_type=self.embedding_type) ++ word_segmenter.train_model() ++ self.lam = 1/word_segmenter.model.count_params() ++ ++ def lstm_score(self, hunits, embedding_dim): ++ """ ++ Given the number of hidden units and embedding dimension, this function computes a score for a bi-directional ++ LSTM which is the accuracy of the model minus a weighted penalty function linear in number of parameters ++ Args: ++ hunits: number of LSTM cells in bi-directional LSTM model ++ embedding_dim: length of output of the embedding layer ++ """ ++ hunits = int(round(hunits)) ++ embedding_dim = int(round(embedding_dim)) ++ word_segmenter = WordSegmenter(input_name="temp", input_n=self.n, input_t=self.t, ++ input_clusters_num=self.clusters_num, input_embedding_dim=embedding_dim, ++ input_hunits=hunits, input_dropout_rate=0.2, input_output_dim=4, ++ input_epochs=self.epochs, input_training_data=self.training_data, ++ input_evaluation_data=self.training_data, input_language=self.language, ++ input_embedding_type=self.embedding_type) ++ word_segmenter.train_model() ++ return word_segmenter.test_model_line_by_line(verbose=False).get_f1_score() - self.c * self.lam * \ ++ word_segmenter.model.count_params() ++ ++ def perform_bayesian_optimization(self): ++ """ ++ This function implements uses the socre function given in `lstm_score` to search for the best value for ++ parameters hunits and embedding_dim. ++ """ ++ bounds = {'hunits': (self.hunits_lower, self.hunits_upper), ++ 'embedding_dim': (self.embedding_dim_lower, self.embedding_dim_upper)} ++ optimizer = BayesianOptimization(f=self.lstm_score, pbounds=bounds, random_state=1) ++ optimizer.maximize(init_points=2, n_iter=self.iterations) ++ print(optimizer.max) ++ print(optimizer.res) +diff --git a/lstm_word_segmentation/preprocess.py b/lstm_word_segmentation/preprocess.py +new file mode 100644 +index 0000000..7c64bb1 +--- /dev/null ++++ b/lstm_word_segmentation/preprocess.py +@@ -0,0 +1,143 @@ ++from pathlib import Path ++import numpy as np ++from collections import Counter ++from .text_helpers import get_lines_of_text, compute_accuracy, compute_accuracy_best ++from . import constants ++ ++ ++def evaluate_existing_algorithms(algorithm, data, fast=False): ++ """ ++ This function evaluates the algorithms that are imported, such as ICU and Deepcut for Thai ++ Args: ++ algorithm: the algorithm to be tested. It can be ICU or Deepcut for now ++ data: the data to be used for testing algorithms. ++ fast: determines if we use small amount of text to run the test or not. ++ """ ++ acc = None ++ if algorithm == "ICU": ++ if data == "SAFT Thai": ++ file = Path.joinpath(Path(__file__).parent.parent.absolute(), 'Data/SAFT/test.txt') ++ acc = compute_accuracy(file, "icu") ++ elif data == "BEST": ++ if fast: ++ acc = compute_accuracy_best(starting_text=40, ending_text=45, algorithm="icu", exclusive=False) ++ else: ++ acc = compute_accuracy_best(starting_text=40, ending_text=60, algorithm="icu", exclusive=False) ++ elif data == "exclusive BEST": ++ if fast: ++ acc = compute_accuracy_best(starting_text=40, ending_text=45, algorithm="icu", exclusive=True) ++ else: ++ acc = compute_accuracy_best(starting_text=40, ending_text=60, algorithm="icu", exclusive=True) ++ elif data == "SAFT Burmese": ++ file = Path.joinpath(Path(__file__).parent.parent.absolute(), 'Data/SAFT_burmese_test_limited.txt') ++ acc = compute_accuracy(file, "icu") ++ elif data == "my": ++ file = Path.joinpath(Path(__file__).parent.parent.absolute(), 'Data/my_test_segmented.txt') ++ acc = compute_accuracy(file, "icu") ++ elif data == "exclusive my": ++ file = Path.joinpath(Path(__file__).parent.parent.absolute(), 'Data/my_test_segmented_exclusive.txt') ++ acc = compute_accuracy(file, "icu") ++ ++ if algorithm == "Deepcut": ++ if data == "SAFT Thai": ++ file = Path.joinpath(Path(__file__).parent.parent.absolute(), 'Data/SAFT/test.txt') ++ acc = compute_accuracy(file, "deep") ++ elif data == "BEST": ++ if fast: ++ acc = compute_accuracy_best(starting_text=40, ending_text=45, algorithm="deep", exclusive=False) ++ else: ++ acc = compute_accuracy_best(starting_text=40, ending_text=60, algorithm="deep", exclusive=False) ++ elif data == "exclusive BEST": ++ if fast: ++ acc = compute_accuracy_best(starting_text=40, ending_text=45, algorithm="deep", exclusive=True) ++ else: ++ acc = compute_accuracy_best(starting_text=40, ending_text=60, algorithm="deep", exclusive=True) ++ ++ if acc is None: ++ print("Warning: the evaluation for this combination of data and algorithm is not supported.") ++ else: ++ print( ++ "{} accuracy on {} data set: BIES accuracy = {}, F1-score = {}".format(algorithm, data, ++ acc.get_bies_accuracy(), ++ acc.get_f1_score())) ++ return acc ++ ++ ++def find_grapheme_clusters(language, exclusive, verbose): ++ """ ++ This function uses the BEST data set to ++ 1) compute the grapheme cluster dictionary that holds the frequency of different grapheme clusters ++ Args: ++ language: the language that we want to used to find grapheme clusters. It can be "Thai" or "Burmese" ++ verbose: shows if we want to see how the algorithm is working or not ++ exclusive: determines to use a data set where all code points are in the script associated with the language or ++ not ++ """ ++ grapheme_clusters_dic = Counter() ++ lines = [] ++ ++ # For Thai use BEST data set ++ if language == "Thai": ++ num_texts = 96 ++ for text_num in range(1, num_texts): ++ for cat in ["news", "encyclopedia", "article", "novel"]: ++ text_num_str = "{}".format(text_num).zfill(5) ++ if exclusive: ++ file = Path.joinpath(Path(__file__).parent.parent.absolute(), "Data/exclusive_Best/{}/{}_". ++ format(cat, cat) + text_num_str + ".txt") ++ else: ++ file = Path.joinpath(Path(__file__).parent.parent.absolute(), "Data/Best/{}/{}_".format(cat, cat) + ++ text_num_str + ".txt") ++ ++ lines += get_lines_of_text(file=file, type_of_lines="man_segmented") ++ ++ # For Burmese use "my" data set ++ elif language == "Burmese": ++ if exclusive: ++ file = Path.joinpath(Path(__file__).parent.parent.absolute(), "Data/my_exclusive.txt") ++ else: ++ file = Path.joinpath(Path(__file__).parent.parent.absolute(), "Data/my.txt") ++ lines = get_lines_of_text(file=file, type_of_lines="man_segmented") ++ ++ else: ++ print("Warning: the input language is not supported") ++ ++ for line in lines: ++ grapheme_clusters_dic += line.get_grapheme_clusters() ++ if verbose: ++ line.display() ++ ++ # Saving the dictionary based on frequency of grapheme clusters ++ graph_clust_freq = dict(grapheme_clusters_dic) ++ graph_clust_freq = {k: v for k, v in sorted(graph_clust_freq.items(), key=lambda item: item[1], reverse=True)} ++ graph_clust_ratio = graph_clust_freq ++ total = sum(graph_clust_ratio.values(), 0.0) ++ graph_clust_ratio = {k: v / total for k, v in graph_clust_ratio.items()} ++ ++ # Saving the dictionary ++ save_file = "" ++ if language == "Thai": ++ save_file = Path.joinpath(Path(__file__).parent.parent.absolute(), "Public_Data/Thai_graph_clust_ratio.npy") ++ if exclusive: ++ save_file = Path.joinpath(Path(__file__).parent.parent.absolute(), ++ "Public_Data/Thai_exclusive_graph_clust_ratio.npy") ++ if language == "Burmese": ++ save_file = Path.joinpath(Path(__file__).parent.parent.absolute(), "Public_Data/Burmese_graph_clust_ratio.npy") ++ if exclusive: ++ save_file = Path.joinpath(Path(__file__).parent.parent.absolute(), ++ "Public_Data/Burmese_exclusive_graph_clust_ratio.npy") ++ np.save(str(save_file), graph_clust_ratio) ++ ++ ++def make_thai_burmese_dictionary(): ++ new_dic = Counter() ++ for ch in constants.THAI_GRAPH_CLUST_RATIO.keys(): ++ new_dic[ch] += constants.THAI_GRAPH_CLUST_RATIO[ch] ++ for ch in constants.BURMESE_GRAPH_CLUST_RATIO.keys(): ++ new_dic[ch] += constants.BURMESE_GRAPH_CLUST_RATIO[ch] ++ new_dic = dict(new_dic) ++ new_dic = {k: v for k, v in sorted(new_dic.items(), key=lambda item: item[1], reverse=True)} ++ total = sum(new_dic.values(), 0.0) ++ graph_clust_ratio = {k: v / total for k, v in new_dic.items()} ++ save_file = Path.joinpath(Path(__file__).parent.parent.absolute(), "Public_Data/Thai_Burmese_graph_clust_ratio.npy") ++ np.save(str(save_file), graph_clust_ratio) +diff --git a/lstm_word_segmentation/script_normalizer.py b/lstm_word_segmentation/script_normalizer.py +new file mode 100644 +index 0000000..035ba05 +--- /dev/null ++++ b/lstm_word_segmentation/script_normalizer.py +@@ -0,0 +1,41 @@ ++from .constants import CHAR_TYPE_TO_BUCKET ++from icu import Char, Script, UCharCategory ++ ++ ++def normalize_string(in_str, allowed_scripts): ++ """ ++ Normalizes in_str by replacing letters and digits in other scripts with ++ exemplar values. ++ ++ Args: ++ in_str: String to process ++ allowed_scripts: List of script short names (like "Mymr") to preserve ++ """ ++ # TODO: Consider checking ScriptExtensions here as well ++ output = "" ++ for ch in in_str: ++ ch_script = Script.getScript(ch) ++ ch_type = Char.charType(ch) ++ ch_bucket = CHAR_TYPE_TO_BUCKET[ch_type] ++ ch_digit = Char.digit(ch) ++ if ch_script.getShortName() in allowed_scripts: ++ # ch is in an allowed script: ++ # copy directly to the output ++ output += ch ++ elif ch_bucket == 1: ++ # ch is a letter in a disallowed script: ++ # normalize to the sample char for that script ++ output += Script.getSampleString(ch_script) ++ elif ch_bucket == 3 and ch_digit != -1: ++ # ch is a decimal digit in a disallowed script: ++ # normalize to the zero digit in that numbering system ++ output += chr(ord(ch) - ch_digit) ++ elif ch_type == UCharCategory.CURRENCY_SYMBOL: ++ # ch is a currency symbol in a disallowed script: ++ # normalize to $ ++ output += "$" ++ else: ++ # all other characters: ++ # copy directly to the output ++ output += ch ++ return output +diff --git a/lstm_word_segmentation/text_helpers.py b/lstm_word_segmentation/text_helpers.py +new file mode 100644 +index 0000000..4508076 +--- /dev/null ++++ b/lstm_word_segmentation/text_helpers.py +@@ -0,0 +1,450 @@ ++import numpy as np ++from pathlib import Path ++from .line import Line ++from .accuracy import Accuracy ++from .helpers import is_ascii ++from icu import Char, Script, UCharCategory ++from . import constants ++from icu import UnicodeSet ++ ++ ++def remove_tags(line, st_tag, fn_tag): ++ """ ++ Given a string and two substrings, remove any text between these tags. ++ It handles spaces around tags as follows: ++ abc|def|ghi ---> abc|ghi ++ abc| |def|ghi ---> abc| |ghi ++ abc|def| |ghi ---> abc| |ghi ++ abc| |def| |ghi ---> abc| |ghi ++ Args: ++ line: the input string ++ st_tag: the first substring ++ fn_tag: the secibd substring ++ """ ++ new_line = "" ++ last_bar = 0 ++ ind = 0 ++ while ind < len(line): ++ if line[ind: ind + len(st_tag)] == st_tag: ++ fn_ind = ind ++ while fn_ind + len(fn_tag) <= len(line) and line[fn_ind: fn_ind+len(fn_tag)] != fn_tag: ++ fn_ind += 1 ++ new_bar = fn_ind + len(fn_tag) ++ while new_bar < len(line) and line[new_bar] != '|': ++ new_bar += 1 ++ if new_bar < len(line): ++ last_bar = new_bar ++ else: ++ if line[last_bar] == '|': ++ new_line += '|' ++ break ++ ind = new_bar ++ if ind == len(line)-1: ++ for i in range(last_bar, ind+1): ++ new_line += line[i] ++ elif line[ind] == '|': ++ new_bar = ind ++ for i in range(last_bar, new_bar): ++ new_line += line[i] ++ last_bar = new_bar ++ ind += 1 ++ ++ return new_line ++ ++ ++def clean_line(line, segmented): ++ """ ++ This line cleans a line as follows such that it is ready for process by different components of the code. It returns ++ the clean line or -1, if the line should be omitted. ++ 1) remove tags and https from the line. ++ 2) Put a | at the begining and end of the line if it isn't already there ++ 3) if line is very short (len < 3) or if it is all in English or it has a link in it, return -1 ++ Args: ++ line: the input line ++ segmented: shows if the input line is segmented or not. This is to see if we need to check for bars at the start ++ and end of the line or not. ++ """ ++ line = line.strip() ++ ++ # Remove lines with links ++ if "http" in line or len(line) == 0: ++ return -1 ++ ++ # Remove texts between following tags ++ line = remove_tags(line, "", "") ++ line = remove_tags(line, "", "") ++ line = remove_tags(line, "", "") ++ line = remove_tags(line, "", "") ++ ++ # Remove lines that are all fully in English ++ if is_ascii(line): ++ return -1 ++ ++ if segmented: ++ # Add "|" to the end of each line if it is not there ++ if len(line) >= 1 and line[len(line) - 1] != '|': ++ line += "|" ++ ++ # Adding "|" to the start of each line if it is not there ++ if len(line) >= 0 and line[0] != '|': ++ line = '|' + line ++ ++ return line ++ ++ ++def add_additional_bars(read_filename, write_filename): ++ """ ++ This function reads a segmented file and add bars around each space in it if appropriate bars don't exist. It ++ assumes that spaces are used as breakpoints in the segmentation (just like "|") ++ Args: ++ read_filename: Address of the input file ++ write_filename: Address of the output file ++ """ ++ wfile = open(write_filename, 'w') ++ with open(read_filename) as f: ++ for line in f: ++ line = line.strip() ++ if len(line) == 0: ++ continue ++ new_line = "" ++ for i in range(len(line)): ++ ch = line[i] ++ if ch == " ": ++ if i == 0: ++ if i+1 < len(line) and line[i+1] == "|": ++ new_line = new_line + "|" + ch ++ else: ++ new_line = new_line + "|" + ch + "|" ++ elif i == len(line)-1: ++ if line[i-1] == "|": ++ new_line = new_line + ch + "|" ++ else: ++ new_line = new_line + "|" + ch + "|" ++ else: ++ if line[i-1] != "|" and line[i+1] != "|": ++ new_line = new_line + "|" + ch + "|" ++ if line[i-1] == "|" and line[i+1] != "|": ++ new_line = new_line + ch + "|" ++ if line[i-1] != "|" and line[i+1] == "|": ++ new_line = new_line + "|" + ch ++ if line[i-1] == "|" and line[i+1] == "|": ++ new_line = new_line + ch ++ else: ++ new_line += ch ++ new_line += "\n" ++ wfile.write(new_line) ++ ++ ++def permute_lines_of_text(filename, permutated_filename): ++ """ ++ This function first divides line of an input file into buckets where each bucket is a fixed number of successive ++ lines in the file, and then write down these buckets of lines into a new file randomely (without repetition). ++ Args: ++ filename: the input file ++ permutated_filename: the output file ++ """ ++ num_lines = sum(1 for _line in open(filename)) ++ bucket_size = 20 ++ permutated_buckets = np.random.permutation(num_lines//bucket_size) ++ new_file = open(permutated_filename, 'w') ++ lines_list = [line for line in open(filename)] ++ for bucket_id in permutated_buckets: ++ for line in lines_list[bucket_id*bucket_size: (bucket_id+1)*bucket_size]: ++ new_file.write(line) ++ ++ ++def divide_train_test_data(input_text, train_text, valid_text, test_text, line_limit): ++ """ ++ This function divides a file into three new files, that contain train data, validation data, and test data ++ It first divide lines of the text into small buckets (e.g. 20 lines per each bucket) and then distribute each ++ bucket between train, validation and test texts. This way, we will in each of the resulting texts, we have samples ++ from all ovor the original text. ++ Args: ++ input_text: address of the original file ++ train_text: address to store the train data in it ++ valid_text: address to store the validation data in it ++ test_text: address to store the test file in it ++ line_limit: the number of lines that we want to process ++ """ ++ train_ratio = 0.4 ++ valid_ratio = 0.4 ++ bucket_size = 20 ++ train_file = open(train_text, 'w') ++ valid_file = open(valid_text, 'w') ++ test_file = open(test_text, 'w') ++ line_counter = 0 ++ with open(input_text) as f: ++ for line in f: ++ if line_counter > line_limit: ++ break ++ line_counter += 1 ++ line = line.strip() ++ if is_ascii(line): ++ continue ++ if line_counter % bucket_size <= bucket_size*train_ratio: ++ train_file.write(line + "\n") ++ elif bucket_size*train_ratio < line_counter % bucket_size <= bucket_size*(train_ratio+valid_ratio): ++ valid_file.write(line + "\n") ++ else: ++ test_file.write(line + "\n") ++ ++ ++def get_lines_of_text(file, type_of_lines): ++ """ ++ Given a file, this function returns a list of line objects in that file. ++ Args: ++ file: Address of the file ++ type_of_lines: It shows what is the type of sentences in the file. It can take 3 different values: unsegmented, ++ man_segmented, or icu_segmented ++ """ ++ segmented = True ++ if type_of_lines == "unsegmented": ++ segmented = False ++ out = [] ++ with open(file) as f: ++ for file_line in f: ++ file_line = clean_line(file_line, segmented) ++ if file_line == -1: ++ continue ++ line = Line(file_line, type_of_lines) ++ out.append(line) ++ return out ++ ++ ++def get_segmented_file_in_one_line(filename, input_type, output_type): ++ """ ++ This function returns a single segmented line that contains all lines in a text file. If the output is supposed to ++ be manually segmented, it just combines the manually segmented sentences inside the file. If the output is supposed ++ to be icu segmented, it first combines all unsegmented versions of line of the text into a single line, and then use ++ icu to segment that single line. ++ The reason for this function is that some code points at the beginning of lines in some Burmese texts are not ++ valid code points and need to be merged with previous line (see line 457457 of the my_train.txt). ++ Args: ++ filename: address of the input file ++ input_type: determines if the input is unsegmented, manually segmented, or ICU segmented ++ output_type: determines if the output is manually segmented or ICU segmented ++ """ ++ lines = get_lines_of_text(filename, input_type) ++ all_file_line = "" ++ if output_type == "man_segmented": ++ for i in range(len(lines)): ++ all_file_line += lines[i].man_segmented ++ if i != len(lines) - 1: ++ all_file_line += " " ++ return all_file_line ++ if output_type == "icu_segmented": ++ for i in range(len(lines)): ++ all_file_line += lines[i].unsegmented ++ if i != len(lines)-1: ++ all_file_line += " " ++ all_file_line = Line(all_file_line, "unsegmented") ++ return all_file_line.icu_segmented ++ ++ ++def get_best_data_text(starting_text, ending_text, pseudo, exclusive): ++ """ ++ Gives a long string, that contains all lines (separated by a single space) from BEST data. This function uses data ++ from all genres (news, encyclopedia, article, and novel) with text numbr in a given range. ++ It removes all texts between pair of tags such as (, ), assures that the string starts and ends with "|", ++ and ignores empty lines, lines with "http" in them, and lines that are all in ascii (since these are not segmented ++ in the BEST data set) ++ Args: ++ starting_text: number or the smallest text ++ ending_text: number or the largest text + 1 ++ pseudo: if True, it means we use pseudo segmented data, if False, we use BEST manually segmentation ++ exclusive: determines if we want use original BEST data set or exclusive BEST data set where any non-thai code ++ point is excluded from texts ++ """ ++ out_str = "" ++ category = ["news", "encyclopedia", "article", "novel"] ++ for text_num in range(starting_text, ending_text): ++ for cat in category: ++ text_num_str = "{}".format(text_num).zfill(5) ++ if exclusive: ++ file = Path.joinpath(Path(__file__).parent.parent.absolute(), "Data/exclusive_Best/{}/{}_". ++ format(cat, cat) + text_num_str + ".txt") ++ else: ++ file = Path.joinpath(Path(__file__).parent.parent.absolute(), "Data/Best/{}/{}_".format(cat, cat) + ++ text_num_str + ".txt") ++ output_type = "man_segmented" ++ if pseudo: ++ output_type = "icu_segmented" ++ new_line = get_segmented_file_in_one_line(filename=file, input_type="man_segmented", ++ output_type=output_type) ++ if len(out_str) == 0: ++ out_str = new_line ++ else: ++ out_str = out_str + " " + new_line ++ return out_str ++ ++ ++def compute_accuracy(file, segmentation_type): ++ """ ++ This function uses a file with manually segmented lines to compute the accuracy of existing algorithms such as ICU ++ and Deepcut. ++ Args: ++ file: The file to be used for computing accuracy ++ segmentation_type: Indicates what algorithm we want to test. For now, it can be "icu" or "deep". ++ """ ++ accuracy = Accuracy() ++ lines = get_lines_of_text(file, "man_segmented") ++ for line in lines: ++ true_bies = line.get_bies_grapheme_clusters(segmentation_type="man") ++ algo_bies = line.get_bies_grapheme_clusters(segmentation_type=segmentation_type) ++ accuracy.update(true_bies=true_bies.str, est_bies=algo_bies.str) ++ return accuracy ++ ++ ++def compute_accuracy_best(starting_text, ending_text, algorithm, exclusive): ++ """ ++ This function uses BEST data set to compute the accuracy of an existing algorithm such as ICU or Deepcut. ++ Args: ++ starting_text: number or the smallest text ++ ending_text: number or the largest text + 1 ++ algorithm: the algorithm to be tested. It can be "icu" or "deep" for now. ++ exclusive: identifies to use BEST data or exclusive BEST data ++ """ ++ category = ["news", "encyclopedia", "article", "novel"] ++ accuracy = Accuracy() ++ for text_num in range(starting_text, ending_text): ++ print("comuting accuracy for text number {}".format(text_num)) ++ for cat in category: ++ text_num_str = "{}".format(text_num).zfill(5) ++ file = Path.joinpath(Path(__file__).parent.parent.absolute(), "Data/Best/{}/{}_".format(cat, cat) + ++ text_num_str + ".txt") ++ if exclusive: ++ file = Path.joinpath(Path(__file__).parent.parent.absolute(), "Data/exclusive_Best/{}/{}_".format( ++ cat, cat) + text_num_str + ".txt") ++ accuracy.merge_accuracy(compute_accuracy(file, segmentation_type=algorithm)) ++ return accuracy ++ ++ ++# Function normalize_string is written with the goal of increasing accuracy for grapheme cluster based models. In the ++# current implementation we don't use it, but we don't delete it as it may be used later. ++def normalize_string(in_str, allowed_scripts): ++ """ ++ Normalizes in_str by replacing letters and digits in other scripts with ++ exemplar values. ++ ++ Args: ++ in_str: String to process ++ allowed_scripts: List of script short names (like "Mymr") to preserve ++ """ ++ # TODO: Consider checking ScriptExtensions here as well ++ output = "" ++ for ch in in_str: ++ ch_script = Script.getScript(ch) ++ ch_type = Char.charType(ch) ++ ch_bucket = constants.CHAR_TYPE_TO_BUCKET[ch_type] ++ ch_digit = Char.digit(ch) ++ if ch_script.getShortName() in allowed_scripts: ++ # ch is in an allowed script: ++ # copy directly to the output ++ output += ch ++ elif ch_bucket == 1: ++ # ch is a letter in a disallowed script: ++ # normalize to the sample char for that script ++ output += Script.getSampleString(ch_script) ++ elif ch_bucket == 3 and ch_digit != -1: ++ # ch is a decimal digit in a disallowed script: ++ # normalize to the zero digit in that numbering system ++ output += chr(ord(ch) - ch_digit) ++ elif ch_type == UCharCategory.CURRENCY_SYMBOL: ++ # ch is a currency symbol in a disallowed script: ++ # normalize to $ ++ output += "$" ++ else: ++ # all other characters: ++ # copy directly to the output ++ output += ch ++ return output ++ ++ ++def merge_two_texts(input_texts1, input_texts2, output_text, line_limit): ++ """ ++ This function merges two texts into one new text. Every line of text 1 appears before every line of text 2. ++ Args: ++ input_texts1: list of address of the first group of the input files ++ input_texts2: list of address of the second group of the input files ++ output_text: address of the resulting file ++ line_limit: number of lines to be included from each group of input texts ++ """ ++ output_file = open(output_text, 'w') ++ line_counter = 0 ++ for input_text in input_texts1: ++ if line_counter >= line_limit: ++ break ++ with open(input_text) as f: ++ for line in f: ++ if line_counter >= line_limit: ++ break ++ output_file.write(line) ++ line_counter += 1 ++ ++ line_counter = 0 ++ for input_text in input_texts2: ++ if line_counter >= line_limit: ++ break ++ with open(input_text) as f: ++ for line in f: ++ if line_counter >= line_limit: ++ break ++ output_file.write(line) ++ line_counter += 1 ++ ++ ++def only_one_script_text(input_text, output_text, script, segmented): ++ """ ++ This function uses lines of an input text and divide it into pieces where each piece has only code point in the ++ specific script. Each piece is then written in a new line. This is the type of input that current ICU language ++ engines accept, and is what we call "exclusive" data in this repository. ++ Args: ++ input_text: the path to the input text ++ output_text: the path to the output text ++ script: the specific script ++ segmented: shows if the input_text is a segmented or not, to handle '|' appropriately ++ """ ++ accepted_code_points = [] ++ if script == "Thai": ++ accepted_code_points = UnicodeSet("[[:Thai:]&[:LineBreak=SA:]]") ++ elif script == "Burmese": ++ accepted_code_points = UnicodeSet("[[:Mymr:]&[:LineBreak=SA:]]") ++ else: ++ print("Warning: the input language is not supported") ++ accepted_code_points = list(accepted_code_points) ++ accepted_code_points.append('|') ++ output = open(output_text, 'w') ++ with open(input_text) as f: ++ for line in f: ++ line = clean_line(line, segmented=segmented) ++ if line == -1: ++ continue ++ new_str = "" ++ for i in range(len(line)): ++ ch = line[i] ++ if ch in accepted_code_points: ++ new_str += ch ++ if i == len(line)-1: ++ if not is_ascii(new_str): ++ output.write(new_str + "\n") ++ new_str = "" ++ if ch not in accepted_code_points: ++ if not is_ascii(new_str): ++ output.write(new_str + "\n") ++ new_str = "" ++ ++ ++def make_thai_specific_best_data(): ++ """ ++ This function makes a copy of BEST data with only those code points that are identified by ICU Thai engine. This ++ copy is called "exclusive BEST" ++ """ ++ category = ["news", "encyclopedia", "article", "novel"] ++ for text_num in range(1, 96): ++ for cat in category: ++ text_num_str = "{}".format(text_num).zfill(5) ++ input_text = Path.joinpath(Path(__file__).parent.parent.absolute(), "Data/Best/{}/{}_".format(cat, cat) + ++ text_num_str + ".txt") ++ output_text = Path.joinpath(Path(__file__).parent.parent.absolute(), "Data/exclusive_Best/{}/{}_". ++ format(cat, cat) + text_num_str + ".txt") ++ only_one_script_text(input_text=input_text, output_text=output_text, script="Thai", segmented=True) +diff --git a/lstm_word_segmentation/word_segmenter.py b/lstm_word_segmentation/word_segmenter.py +new file mode 100644 +index 0000000..a06e0a8 +--- /dev/null ++++ b/lstm_word_segmentation/word_segmenter.py +@@ -0,0 +1,704 @@ ++from pathlib import Path ++import numpy as np ++import json ++from icu import Char ++from keras.models import Sequential ++from keras.layers import LSTM, Dense, TimeDistributed, Bidirectional, Embedding, Dropout ++from tensorflow import keras ++import tensorflow as tf ++ ++from . import constants ++from .helpers import sigmoid ++from .text_helpers import get_segmented_file_in_one_line, get_best_data_text, get_lines_of_text ++from .accuracy import Accuracy ++from .line import Line ++from .bies import Bies ++from .grapheme_cluster import GraphemeCluster ++from .code_point import CodePoint ++ ++ ++class KerasBatchGenerator(object): ++ """ ++ A batch generator component, which is used to generate batches for training, validation, and evaluation. ++ Args: ++ x_data: A list of GraphemeCluster objects that is the input of the model ++ y_data: A np array that contains output of the model ++ n: length of the input and output in each batch ++ batch_size: number of batches ++ """ ++ def __init__(self, x_data, y_data, n, batch_size): ++ self.x_data = x_data ++ self.y_data = y_data ++ self.n = n ++ self.batch_size = batch_size ++ self.dim_output = self.y_data.shape[1] ++ if len(x_data) != y_data.shape[0]: ++ print("Warning: x_data and y_data have not compatible sizes!") ++ if len(x_data) < batch_size * n: ++ print("Warning: x_data or y_data is not large enough!") ++ ++ def generate(self, embedding_type): ++ """ ++ This function generates batches used for training and validation ++ """ ++ x, y = self.generate_once(embedding_type) ++ while True: ++ yield x, y ++ ++ def generate_once(self, embedding_type): ++ """ ++ This function generates batches only once and is used for testing ++ """ ++ y = np.zeros([self.batch_size, self.n, self.dim_output]) ++ x = None ++ if embedding_type == "grapheme_clusters_tf": ++ x = np.zeros([self.batch_size, self.n]) ++ elif embedding_type == "grapheme_clusters_man": ++ x = np.zeros([self.batch_size, self.n, self.x_data[0].num_clusters]) ++ elif embedding_type == "generalized_vectors": ++ x = np.zeros([self.batch_size, self.n, self.x_data[0].generalized_vec_length]) ++ elif embedding_type == "codepoints": ++ x = np.zeros([self.batch_size, self.n]) ++ else: ++ print("Warning: the embedding type is not valid") ++ for i in range(self.batch_size): ++ for j in range(self.n): ++ if embedding_type == "grapheme_clusters_tf": ++ x[i, j] = self.x_data[self.n*i + j].graph_clust_id ++ if embedding_type == "grapheme_clusters_man": ++ x[i, j, :] = self.x_data[self.n*i + j].graph_clust_vec ++ if embedding_type == "generalized_vectors": ++ x[i, j, :] = self.x_data[self.n*i + j].generalized_vec ++ if embedding_type == "codepoints": ++ x[i, j] = self.x_data[self.n * i + j].codepoint_id ++ y[i, :, :] = self.y_data[self.n * i: self.n * (i + 1), :] ++ return x, y ++ ++ ++class WordSegmenter: ++ """ ++ A class that let you make a bi-directional LSTM, train it, and test it. ++ Args: ++ input_n: Length of the input for LSTM model ++ input_t: The total length of data used to train and validate the model. It is equal to number of batches times n ++ input_clusters_num: number of top grapheme clusters used to train the model ++ input_embedding_dim: length of the embedding vectors for each grapheme cluster ++ input_hunits: number of hidden units used in each cell of LSTM ++ input_dropout_rate: dropout rate used in layers after the embedding and after the bidirectional LSTM ++ input_output_dim: dimension of the output layer ++ input_epochs: number of epochs used to train the model ++ input_training_data: name of the data used to train the model ++ input_evaluation_data: name of the data used to evaluate the model ++ input_language: shows what is the language used to train the model (e.g. Thai, Burmese, ...) ++ input_embedding_type: determines what type of embedding to be used in the model. Possible values are ++ "grapheme_clusters_tf", "grapheme_clusters_man", and "generalized_vectors" ++ """ ++ def __init__(self, input_name, input_n, input_t, input_clusters_num, input_embedding_dim, input_hunits, ++ input_dropout_rate, input_output_dim, input_epochs, input_training_data, input_evaluation_data, ++ input_language, input_embedding_type): ++ self.name = input_name ++ self.n = input_n ++ self.t = input_t ++ if self.t % self.n != 0: ++ print("Warning: t is not divided by n") ++ self.clusters_num = input_clusters_num ++ # batch_size is the number of batches used in each iteration of back propagation to update model weights ++ # The default value is self.t/self.n, but it can be set to other values as well. The only constraint is that ++ # self.t should always be greater than self.batch_size * self.n ++ self.batch_size = self.t // self.n ++ self.embedding_dim = input_embedding_dim ++ self.hunits = input_hunits ++ self.dropout_rate = input_dropout_rate ++ self.output_dim = input_output_dim ++ self.epochs = input_epochs ++ self.training_data = input_training_data ++ self.evaluation_data = input_evaluation_data ++ self.language = input_language ++ self.embedding_type = input_embedding_type ++ self.model = None ++ ++ # Constructing the grapheme cluster dictionary -- this will be used if self.embedding_type is Grapheme Clusters ++ ratios = None ++ if self.language == "Thai": ++ if "exclusive" in self.training_data: ++ ratios = constants.THAI_EXCLUSIVE_GRAPH_CLUST_RATIO ++ else: ++ ratios = constants.THAI_GRAPH_CLUST_RATIO ++ elif self.language == "Burmese": ++ if "exclusive" in self.training_data: ++ ratios = constants.BURMESE_EXCLUSIVE_GRAPH_CLUST_RATIO ++ else: ++ ratios = constants.BURMESE_GRAPH_CLUST_RATIO ++ elif self.language == "Thai_Burmese": ++ ratios = constants.THAI_BURMESE_GRAPH_CLUST_RATIO ++ else: ++ print("Warning: the input language is not supported") ++ cnt = 0 ++ self.graph_clust_dic = dict() ++ for key in ratios.keys(): ++ if cnt < self.clusters_num - 1: ++ self.graph_clust_dic[key] = cnt ++ if cnt == self.clusters_num - 1: ++ break ++ cnt += 1 ++ ++ # Loading the code points dictionary -- this will be used if self.embedding_type is Code Points ++ # If you want to group some of the code points into buckets, that code should go here to change ++ # self.codepoint_dic appropriately ++ if self.language == "Thai": ++ self.codepoint_dic = constants.THAI_CODE_POINT_DICTIONARY ++ if self.language == "Burmese": ++ self.codepoint_dic = constants.BURMESE_CODE_POINT_DICTIONARY ++ self.codepoints_num = len(self.codepoint_dic) + 1 ++ ++ # Constructing the letters dictionary -- this will be used if self.embedding_type is Generalized Vectors ++ self.letters_dic = dict() ++ if self.language in ["Thai", "Burmese"]: ++ smallest_unicode_dec = None ++ largest_unicode_dec = None ++ ++ # Defining the Unicode box for model's language ++ if self.language == "Thai": ++ smallest_unicode_dec = int("0E01", 16) ++ largest_unicode_dec = int("0E5B", 16) ++ elif self.language == "Burmese": ++ smallest_unicode_dec = int("1000", 16) ++ largest_unicode_dec = int("109F", 16) ++ ++ # Defining the code point buckets that will get their own individual embedding vector ++ # 1: Letters, 2: Marks, 3: Digits, 4: Separators, 5: Punctuations, 6: Symbols, 7: Others ++ separate_slot_buckets = [] ++ separate_codepoints = [] ++ if self.embedding_type == "generalized_vectors_123": ++ separate_slot_buckets = [1, 2, 3] ++ elif self.embedding_type == "generalized_vectors_12": ++ separate_slot_buckets = [1, 2] ++ elif self.embedding_type == "generalized_vectors_12d0": ++ separate_slot_buckets = [1, 2] ++ if self.language == "Burmese": ++ separate_codepoints = [4160, 4240] ++ if self.language == "Thai": ++ separate_codepoints = [3664] ++ elif self.embedding_type == "generalized_vectors_125": ++ separate_slot_buckets = [1, 2, 5] ++ elif self.embedding_type == "generalized_vectors_1235": ++ separate_slot_buckets = [1, 2, 3, 5] ++ ++ # Constructing letters dictionary ++ cnt = 0 ++ for i in range(smallest_unicode_dec, largest_unicode_dec + 1): ++ ch = chr(i) ++ if constants.CHAR_TYPE_TO_BUCKET[Char.charType(ch)] in separate_slot_buckets: ++ self.letters_dic[ch] = cnt ++ cnt += 1 ++ for unicode_dec in separate_codepoints: ++ ch = chr(unicode_dec) ++ self.letters_dic[ch] = cnt ++ cnt += 1 ++ ++ # After making the letters dictionary, we can call different versions of the generalized vectors same thing ++ if "generalized_vectors" in self.embedding_type: ++ self.embedding_type = "generalized_vectors" ++ ++ else: ++ print("Warning: the generalized_vectros embedding type is not supported for this language") ++ ++ def _get_trainable_data(self, input_line): ++ """ ++ Given a segmented line, generates a list of input data (with respect to the embedding type) and a n*4 np array ++ that represents BIES where n is the length of the unsegmented line. ++ Args: ++ input_line: the unsegmented line ++ """ ++ # Finding word breakpoints ++ # Note that it is possible that input is segmented manually instead of icu. However, for both cases we set that ++ # input_type equal to "icu_segmented" because that doesn't affect performance of this function. This way we ++ # won't need unnecessary if/else for "man_segmented" and "icu_segmented" throughout rest of this function. ++ line = Line(input_line, "icu_segmented") ++ ++ # x_data and y_data will be code point based if self.embedding_type is codepoints ++ if self.embedding_type == "codepoints": ++ true_bies = line.get_bies_codepoints("icu") ++ y_data = true_bies.mat ++ line_len = len(line.unsegmented) ++ x_data = [] ++ for i in range(line_len): ++ x_data.append(CodePoint(line.unsegmented[i], self.codepoint_dic)) ++ ++ # x_data and y_data will be grapheme clusters based if self.embedding type is grapheme_clusters or generalized_ ++ # vectors ++ else: ++ true_bies = line.get_bies_grapheme_clusters("icu") ++ y_data = true_bies.mat ++ line_len = len(line.char_brkpoints) - 1 ++ x_data = [] ++ for i in range(line_len): ++ char_start = line.char_brkpoints[i] ++ char_finish = line.char_brkpoints[i + 1] ++ curr_char = line.unsegmented[char_start: char_finish] ++ x_data.append(GraphemeCluster(curr_char, self.graph_clust_dic, self.letters_dic)) ++ ++ return x_data, y_data ++ ++ def train_model(self): ++ """ ++ This function trains the model using the dataset specified in the __init__ function. It combine all lines in ++ the data set with a space between them and then divide this large string into batches of fixed length self.n. ++ in reading files, if `pseudo` is True then we use icu segmented text instead of manually segmented texts to ++ train the model. ++ """ ++ # Get training data of length self.t ++ input_str = None ++ if self.training_data == "BEST": ++ input_str = get_best_data_text(starting_text=1, ending_text=10, pseudo=False, exclusive=False) ++ elif self.training_data == "exclusive BEST": ++ input_str = get_best_data_text(starting_text=1, ending_text=10, pseudo=False, exclusive=True) ++ elif self.training_data == "pseudo BEST": ++ input_str = get_best_data_text(starting_text=1, ending_text=10, pseudo=True, exclusive=False) ++ elif self.training_data == "my": ++ file = Path.joinpath(Path(__file__).parent.parent.absolute(), 'Data/my_train.txt') ++ input_str = get_segmented_file_in_one_line(file, input_type="unsegmented", output_type="icu_segmented") ++ elif self.training_data == "exclusive my": ++ file = Path.joinpath(Path(__file__).parent.parent.absolute(), 'Data/my_train_exclusive.txt') ++ input_str = get_segmented_file_in_one_line(file, input_type="unsegmented", output_type="icu_segmented") ++ elif self.training_data == "SAFT_Burmese": ++ file = Path.joinpath(Path(__file__).parent.parent.absolute(), 'Data/SAFT_burmese_train.txt') ++ input_str = get_segmented_file_in_one_line(file, input_type="man_segmented", output_type="man_segmented") ++ elif self.training_data == "BEST_my": ++ file = Path.joinpath(Path(__file__).parent.parent.absolute(), 'Data/Best_my_train.txt') ++ input_str = get_segmented_file_in_one_line(file, input_type="man_segmented", output_type="man_segmented") ++ else: ++ print("Warning: no implementation for this training data exists!") ++ x_data, y_data = self._get_trainable_data(input_str) ++ if self.t > len(x_data): ++ print("Warning: size of the training data is less than self.t") ++ x_data = x_data[:self.t] ++ y_data = y_data[:self.t, :] ++ train_generator = KerasBatchGenerator(x_data, y_data, n=self.n, batch_size=self.batch_size) ++ ++ # Get validation data of length self.t ++ if self.training_data == "BEST": ++ input_str = get_best_data_text(starting_text=10, ending_text=20, pseudo=False, exclusive=False) ++ elif self.training_data == "exclusive BEST": ++ input_str = get_best_data_text(starting_text=10, ending_text=20, pseudo=False, exclusive=True) ++ elif self.training_data == "pseudo BEST": ++ input_str = get_best_data_text(starting_text=10, ending_text=20, pseudo=True, exclusive=False) ++ elif self.training_data == "my": ++ file = Path.joinpath(Path(__file__).parent.parent.absolute(), 'Data/my_valid.txt') ++ input_str = get_segmented_file_in_one_line(file, input_type="unsegmented", output_type="icu_segmented") ++ elif self.training_data == "exclusive my": ++ file = Path.joinpath(Path(__file__).parent.parent.absolute(), 'Data/my_valid_exclusive.txt') ++ input_str = get_segmented_file_in_one_line(file, input_type="unsegmented", output_type="icu_segmented") ++ elif self.training_data == "SAFT_Burmese": ++ file = Path.joinpath(Path(__file__).parent.parent.absolute(), 'Data/SAFT_burmese_test.txt') ++ input_str = get_segmented_file_in_one_line(file, input_type="man_segmented", output_type="man_segmented") ++ elif self.training_data == "BEST_my": ++ file = Path.joinpath(Path(__file__).parent.parent.absolute(), 'Data/Best_my_valid.txt') ++ input_str = get_segmented_file_in_one_line(file, input_type="man_segmented", output_type="man_segmented") ++ else: ++ print("Warning: no implementation for this validation data exists!") ++ x_data, y_data = self._get_trainable_data(input_str) ++ ++ if self.t > len(x_data): ++ print("Warning: size of the validation data is less than self.t") ++ x_data = x_data[:self.t] ++ y_data = y_data[:self.t, :] ++ valid_generator = KerasBatchGenerator(x_data, y_data, n=self.n, batch_size=self.batch_size) ++ ++ # Building the model ++ model = Sequential() ++ if self.embedding_type == "grapheme_clusters_tf": ++ model.add(Embedding(input_dim=self.clusters_num, output_dim=self.embedding_dim, input_length=self.n)) ++ elif self.embedding_type == "grapheme_clusters_man": ++ model.add(TimeDistributed(Dense(input_dim=self.clusters_num, units=self.embedding_dim, use_bias=False, ++ kernel_initializer='uniform'))) ++ elif self.embedding_type == "generalized_vectors": ++ model.add(TimeDistributed(Dense(self.embedding_dim, activation=None, use_bias=False, ++ kernel_initializer='uniform'))) ++ elif self.embedding_type == "codepoints": ++ model.add(Embedding(input_dim=self.codepoints_num, output_dim=self.embedding_dim, input_length=self.n)) ++ else: ++ print("Warning: the embedding_type is not implemented") ++ model.add(Dropout(self.dropout_rate)) ++ model.add(Bidirectional(LSTM(self.hunits, return_sequences=True), input_shape=(self.n, 1))) ++ model.add(Dropout(self.dropout_rate)) ++ model.add(TimeDistributed(Dense(self.output_dim, activation='softmax'))) ++ opt = keras.optimizers.Adam(learning_rate=0.1) ++ # opt = keras.optimizers.SGD(learning_rate=0.4, momentum=0.9) ++ model.compile(loss='categorical_crossentropy', optimizer=opt, metrics=['accuracy']) ++ ++ # Fitting the model ++ model.fit(train_generator.generate(embedding_type=self.embedding_type), ++ steps_per_epoch=self.t // self.batch_size, epochs=self.epochs, ++ validation_data=valid_generator.generate(embedding_type=self.embedding_type), ++ validation_steps=self.t // self.batch_size) ++ self.model = model ++ ++ def _test_text_line_by_line(self, file, line_limit, verbose): ++ """ ++ This function tests the model fitted in self.train() line by line, using the lines in file. These lines must be ++ already segmented so we can compute the performance of model. ++ Args: ++ file: the address of the file that is going to be tested ++ line_limit: number of lines to be tested. If set to -1, all lines will be tested. ++ verbose: determines if we want to show results line by line ++ """ ++ lines = get_lines_of_text(file, "man_segmented") ++ if len(lines) < line_limit: ++ print("Warning: not enough lines in the test file") ++ accuracy = Accuracy() ++ for line in lines[:line_limit]: ++ x_data, y_data = self._get_trainable_data(line.man_segmented) ++ ++ # Using the manual predict function for lines because they are not necessarily self.n long ++ y_hat = Bies(input_bies=self._manual_predict(x_data), input_type="mat") ++ y_hat.normalize_bies() ++ ++ # Updating overall accuracy using the new line ++ actual_y = Bies(input_bies=y_data, input_type="mat") ++ accuracy.update(true_bies=actual_y.str, est_bies=y_hat.str) ++ if verbose: ++ print("The BIES accuracy (line by line) for file {} : {:.3f}".format(file, accuracy.get_bies_accuracy())) ++ print("The F1 score (line by line) for file {} : {:.3f}".format(file, accuracy.get_f1_score())) ++ return accuracy ++ ++ def test_model_line_by_line(self, verbose, fast=False): ++ """ ++ This function uses the evaluating data to test the model line by line. ++ Args: ++ verbose: determines if we want to see the the accuracy of each text that is being tested. ++ fast: determines if we use small amount of text to run the test or not. ++ """ ++ line_limit = -1 ++ if fast: ++ line_limit = 1000 ++ accuracy = Accuracy() ++ if self.evaluation_data in ["BEST", "exclusive BEST"]: ++ texts_range = range(40, 60) ++ if fast: ++ texts_range = range(40, 45) ++ category = ["news", "encyclopedia", "article", "novel"] ++ for text_num in texts_range: ++ if verbose: ++ print("testing text {}".format(text_num)) ++ for cat in category: ++ text_num_str = "{}".format(text_num).zfill(5) ++ file = None ++ if self.evaluation_data == "BEST": ++ file = Path.joinpath(Path(__file__).parent.parent.absolute(), ++ "Data/Best/{}/{}_".format(cat, cat) + text_num_str + ".txt") ++ elif self.evaluation_data == "exclusive BEST": ++ file = Path.joinpath(Path(__file__).parent.parent.absolute(), ++ "Data/exclusive_Best/{}/{}_".format(cat, cat) + text_num_str + ".txt") ++ text_acc = self._test_text_line_by_line(file=file, line_limit=-1, verbose=verbose) ++ accuracy.merge_accuracy(text_acc) ++ ++ elif self.evaluation_data == "SAFT_Thai": ++ if self.language != "Thai": ++ print("Warning: the current SAFT data is in Thai and you are testing a model in another language") ++ file = Path.joinpath(Path(__file__).parent.parent.absolute(), 'Data/SAFT/test.txt') ++ text_acc = self._test_text_line_by_line(file=file, line_limit=-1, verbose=verbose) ++ accuracy.merge_accuracy(text_acc) ++ elif self.evaluation_data == "my": ++ if self.language != "Burmese": ++ print("Warning: the my data is in Burmese and you are testing a model in another language") ++ file = Path.joinpath(Path(__file__).parent.parent.absolute(), 'Data/my_test_segmented.txt') ++ text_acc = self._test_text_line_by_line(file=file, line_limit=line_limit, verbose=verbose) ++ accuracy.merge_accuracy(text_acc) ++ elif self.evaluation_data == "exclusive my": ++ if self.language != "Burmese": ++ print("Warning: the exvlusive my data is in Burmese and you are testing a model in another language") ++ file = Path.joinpath(Path(__file__).parent.parent.absolute(), 'Data/my_test_segmented_exclusive.txt') ++ text_acc = self._test_text_line_by_line(file=file, line_limit=line_limit, verbose=verbose) ++ accuracy.merge_accuracy(text_acc) ++ elif self.evaluation_data == "SAFT_Burmese": ++ if self.language != "Burmese": ++ print("Warning: the my.text data is in Burmese and you are testing a model in another language") ++ file = Path.joinpath(Path(__file__).parent.parent.absolute(), 'Data/SAFT_burmese_test.txt') ++ text_acc = self._test_text_line_by_line(file=file, line_limit=line_limit, verbose=verbose) ++ accuracy.merge_accuracy(text_acc) ++ ++ elif self.evaluation_data == "BEST_my": ++ if self.language != "Thai_Burmese": ++ print("Warning: the current data should be used only for Thai_Burmese multilingual models") ++ # Testing for BEST ++ acc1 = Accuracy() ++ category = ["news", "encyclopedia", "article", "novel"] ++ for text_num in range(40, 45): ++ print("testing text {}".format(text_num)) ++ for cat in category: ++ text_num_str = "{}".format(text_num).zfill(5) ++ file = Path.joinpath(Path(__file__).parent.parent.absolute(), "Data/Best/{}/{}_".format(cat, cat) ++ + text_num_str + ".txt") ++ text_acc = self._test_text_line_by_line(file=file, line_limit=-1, verbose=verbose) ++ acc1.merge_accuracy(text_acc) ++ if verbose: ++ print("The BIES accuracy by test_model_line_by_line function (Thai): {:.3f}". ++ format(acc1.get_bies_accuracy())) ++ print("The F1 score by test_model_line_by_line function (Thai): {:.3f}".format(acc1.get_f1_score())) ++ # Testing for my ++ file = Path.joinpath(Path(__file__).parent.parent.absolute(), 'Data/my_test_segmented.txt') ++ acc2 = self._test_text_line_by_line(file, line_limit=line_limit, verbose=verbose) ++ if verbose: ++ print("The BIES accuracy by test_model_line_by_line function (Burmese): {:.3f}". ++ format(acc2.get_bies_accuracy())) ++ print("The F1 score by test_model_line_by_line function (Burmese): {:.3f}".format(acc2.get_f1_score())) ++ accuracy.merge_accuracy(acc1) ++ accuracy.merge_accuracy(acc2) ++ ++ else: ++ print("Warning: no implementation for line by line evaluating this data exists") ++ if verbose: ++ print("The BIES accuracy by test_model_line_by_line function: {:.3f}".format(accuracy.get_bies_accuracy())) ++ print("The F1 score by test_model_line_by_line function: {:.3f}".format(accuracy.get_f1_score())) ++ return accuracy ++ ++ def _manual_predict(self, test_input): ++ """ ++ Implementation of the tf.predict function manually. This function works for inputs of any length, and only uses ++ model weights obtained from self.model.weights. ++ Args: ++ test_input: the input text ++ """ ++ # Forward LSTM ++ dtype = np.float32 ++ embedarr = self.model.weights[0].numpy().astype(dtype) ++ lstm_weights = [self.model.weights[1].numpy().astype(dtype), self.model.weights[2].numpy().astype(dtype), ++ self.model.weights[3].numpy().astype(dtype)] ++ c_fw = np.zeros([1, self.hunits], dtype=dtype) ++ h_fw = np.zeros([1, self.hunits], dtype=dtype) ++ all_h_fw = np.zeros([len(test_input), self.hunits], dtype=dtype) ++ for i in range(len(test_input)): ++ x_t = None ++ if self.embedding_type == "grapheme_clusters_tf": ++ input_graph_id = test_input[i].graph_clust_id ++ x_t = embedarr[input_graph_id, :] ++ x_t = x_t.reshape(1, x_t.shape[0]) ++ elif self.embedding_type == "grapheme_clusters_man": ++ input_graph_vec = test_input[i].graph_clust_vec ++ x_t = input_graph_vec.dot(embedarr) ++ elif self.embedding_type == "generalized_vectors": ++ input_generalized_vec = test_input[i].generalized_vec ++ x_t = input_generalized_vec.dot(embedarr) ++ elif self.embedding_type == "codepoints": ++ input_codepoint_id = test_input[i].codepoint_id ++ x_t = embedarr[input_codepoint_id, :] ++ x_t = x_t.reshape(1, x_t.shape[0]) ++ else: ++ print("Warning: this embedding type is not implemented for manual prediction") ++ h_fw, c_fw = self._compute_hc(lstm_weights, x_t, h_fw, c_fw) ++ all_h_fw[i, :] = h_fw ++ ++ # Backward LSTM ++ lstm_weights = [self.model.weights[4].numpy().astype(dtype), self.model.weights[5].numpy().astype(dtype), ++ self.model.weights[6].numpy().astype(dtype)] ++ c_bw = np.zeros([1, self.hunits], dtype=dtype) ++ h_bw = np.zeros([1, self.hunits], dtype=dtype) ++ all_h_bw = np.zeros([len(test_input), self.hunits]) ++ for i in range(len(test_input) - 1, -1, -1): ++ x_t = None ++ if self.embedding_type == "grapheme_clusters_tf": ++ input_graph_id = test_input[i].graph_clust_id ++ x_t = embedarr[input_graph_id, :] ++ x_t = x_t.reshape(1, x_t.shape[0]) ++ elif self.embedding_type == "grapheme_clusters_man": ++ input_graph_vec = test_input[i].graph_clust_vec ++ x_t = input_graph_vec.dot(embedarr) ++ elif self.embedding_type == "generalized_vectors": ++ input_generalized_vec = test_input[i].generalized_vec ++ x_t = input_generalized_vec.dot(embedarr) ++ elif self.embedding_type == "codepoints": ++ input_codepoint_id = test_input[i].codepoint_id ++ x_t = embedarr[input_codepoint_id, :] ++ x_t = x_t.reshape(1, x_t.shape[0]) ++ else: ++ print("Warning: this embedding type is not implemented for manual prediction") ++ h_bw, c_bw = self._compute_hc(lstm_weights, x_t, h_bw, c_bw) ++ all_h_bw[i, :] = h_bw ++ ++ # Combining Forward and Backward layers through dense time-distributed layer ++ timew = self.model.weights[7].numpy().astype(dtype) ++ timeb = self.model.weights[8].numpy().astype(dtype) ++ est = np.zeros([len(test_input), 4], dtype=dtype) ++ for i in range(len(test_input)): ++ final_h = np.concatenate((all_h_fw[i, :], all_h_bw[i, :]), axis=0) ++ final_h = final_h.reshape(1, 2 * self.hunits) ++ curr_est = final_h.dot(timew) + timeb ++ curr_est = curr_est[0] ++ curr_est = np.exp(curr_est) / sum(np.exp(curr_est)) ++ est[i, :] = curr_est ++ return est ++ ++ def _compute_hc(self, weights, x_t, h_tm1, c_tm1): ++ """ ++ Given weights of a LSTM model, the input at time t, and values for h and c at time t-1, this function compute ++ the values of h and c at time t. ++ Args: ++ weights: a list of three matrices, which are W (from input to cell), U (from h to cell), and b (bias) ++ respectively. ++ x_t: the input at time t ++ h_tm1: value of h for time t-1 ++ c_tm1: value of c for time t-1 ++ """ ++ # Implementing forget, input, and output gates in LSTM model ++ warr, uarr, barr = weights ++ s_t = (x_t.dot(warr) + h_tm1.dot(uarr) + barr) ++ hunit = uarr.shape[0] ++ i = sigmoid(s_t[:, :hunit]) ++ f = sigmoid(s_t[:, 1 * hunit:2 * hunit]) ++ _c = np.tanh(s_t[:, 2 * hunit:3 * hunit]) ++ o = sigmoid(s_t[:, 3 * hunit:]) ++ c_t = i * _c + f * c_tm1 ++ h_t = o * np.tanh(c_t) ++ return [h_t, c_t] ++ ++ def segment_arbitrary_line(self, input_line): ++ """ ++ This function uses the LSTM model to segment an unsegmented line and compare it to ICU and deepcut. ++ Args: ++ input_line: the string that needs to be segmented. It is supposed to be unsegmented ++ """ ++ line = Line(input_line, "unsegmented") ++ grapheme_clusters_in_line = len(line.char_brkpoints) - 1 ++ ++ # Using LSTM model to segment the input line ++ if self.embedding_type == "codepoints": ++ x_data = [] ++ for i in range(len(line.unsegmented)): ++ x_data.append(CodePoint(line.unsegmented[i], self.codepoint_dic)) ++ else: ++ x_data = [] ++ for i in range(grapheme_clusters_in_line): ++ char_start = line.char_brkpoints[i] ++ char_finish = line.char_brkpoints[i + 1] ++ curr_char = line.unsegmented[char_start: char_finish] ++ x_data.append(GraphemeCluster(curr_char, self.graph_clust_dic, self.letters_dic)) ++ y_hat = Bies(input_bies=self._manual_predict(x_data), input_type="mat") ++ ++ # Making a pretty version of the output of the LSTM, where bars show the boundaries of words ++ y_hat_pretty = "" ++ if self.embedding_type == "codepoints": ++ for i in range(len(line.unsegmented)): ++ if y_hat.str[i] in ['b', 's']: ++ y_hat_pretty += "|" ++ y_hat_pretty += line.unsegmented[i] ++ y_hat_pretty += "|" ++ else: ++ y_hat_pretty = "" ++ for i in range(grapheme_clusters_in_line): ++ char_start = line.char_brkpoints[i] ++ char_finish = line.char_brkpoints[i + 1] ++ curr_char = line.unsegmented[char_start: char_finish] ++ if y_hat.str[i] in ['b', 's']: ++ y_hat_pretty += "|" ++ y_hat_pretty += curr_char ++ y_hat_pretty += "|" ++ ++ return y_hat_pretty ++ ++ def save_model(self): ++ """ ++ This function saves the current trained model of this word_segmenter instance. ++ """ ++ # Save the model using Keras ++ model_path = (Path.joinpath(Path(__file__).parent.parent.absolute(), "Models/" + self.name)) ++ tf.saved_model.save(self.model, model_path) ++ ++ # Save one np array that holds all weights ++ file = Path.joinpath(Path(__file__).parent.parent.absolute(), "Models/" + self.name + "/weights") ++ np.save(str(file), self.model.weights) ++ ++ # Save the model in json format, that has both weights and grapheme clusters dictionary ++ json_file = Path.joinpath(Path(__file__).parent.parent.absolute(), "Models/" + self.name + "/weights.json") ++ with open(str(json_file), 'w') as wfile: ++ output = dict() ++ output["model"] = self.name ++ if "grapheme_clusters" in self.embedding_type: ++ output["dic"] = self.graph_clust_dic ++ elif "codepoints" in self.embedding_type: ++ if self.language == "Thai": ++ output["dic"] = constants.THAI_CODE_POINT_DICTIONARY ++ if self.language == "Burmese": ++ output["dic"] = constants.BURMESE_CODE_POINT_DICTIONARY ++ for i in range(len(self.model.weights)): ++ dic_model = dict() ++ dic_model["v"] = 1 ++ mat = self.model.weights[i].numpy() ++ dim0 = mat.shape[0] ++ dim1 = 1 ++ if len(mat.shape) == 1: ++ dic_model["dim"] = [dim0] ++ else: ++ dim1 = mat.shape[1] ++ dic_model["dim"] = [dim0, dim1] ++ serial_mat = np.reshape(mat, newshape=[dim0 * dim1]) ++ serial_mat = serial_mat.tolist() ++ dic_model["data"] = serial_mat ++ output["mat{}".format(i+1)] = dic_model ++ json.dump(output, wfile) ++ ++ def set_model(self, input_model): ++ """ ++ This function set the current model to an input model ++ input_model: the input model ++ """ ++ self.model = input_model ++ ++ ++def pick_lstm_model(model_name, embedding, train_data, eval_data): ++ """ ++ This function returns a saved word segmentation instance w.r.t input specifics ++ Args: ++ model_name: name of the model ++ embedding: embedding type used to train the model ++ train_data: the data set used to train the model ++ eval_data: the data set to test the model. Often, it should have the same structure as training data set. ++ """ ++ file = Path.joinpath(Path(__file__).parent.parent.absolute(), 'Models/' + model_name) ++ model = keras.layers.TFSMLayer(file, call_endpoint='serving_default') ++ ++ # Figuring out name of the model ++ language = None ++ if "Thai" in model_name: ++ language = "Thai" ++ elif "Burmese" in model_name: ++ language = "Burmese" ++ if language is None: ++ print("This model name is not valid because it doesn't have name of a valid language in it") ++ ++ # Letting the user know how this model has been trained ++ if "exclusive" in model_name: ++ print("Note: model {} has been trained using an exclusive data set. However, if you like you can still test" ++ " it by other types of data sets (not recommended).".format(model_name)) ++ ++ # Figuring out values for different hyper-parameters ++ input_clusters_num = model.weights[0].shape[0] ++ input_embedding_dim = model.weights[0].shape[1] ++ input_hunits = model.weights[1].shape[1]//4 ++ input_n = None ++ input_t = None ++ if "genvec" in model_name or "graphclust" in model_name: ++ input_n = 50 ++ input_t = 100000 ++ if "heavy" in model_name: ++ input_n = 200 ++ input_t = 600000 ++ elif "heavier" in model_name: ++ input_n = 200 ++ input_t = 2000000 ++ elif "codepoints" in model_name: ++ input_n = 100 ++ input_t = 200000 ++ if "heavy" in model_name: ++ input_n = 300 ++ input_t = 1200000 ++ if input_n is None: ++ print("This model name is not valid because it doesn't have name of the embedding type in it") ++ ++ word_segmenter = WordSegmenter(input_name=model_name, input_n=input_n, input_t=input_t, ++ input_clusters_num=input_clusters_num, input_embedding_dim=input_embedding_dim, ++ input_hunits=input_hunits, input_dropout_rate=0.2, input_output_dim=4, ++ input_epochs=15, input_training_data=train_data, input_evaluation_data=eval_data, ++ input_language=language, input_embedding_type=embedding) ++ word_segmenter.set_model(model) ++ return word_segmenter +diff --git a/segment_text.py b/segment_text.py +new file mode 100644 +index 0000000..a8a58db +--- /dev/null ++++ b/segment_text.py +@@ -0,0 +1,77 @@ ++# Copyright (C) 2021 and later: Unicode, Inc. and others. ++# License & terms of use: http://www.unicode.org/copyright.html ++# Lint as: python3 ++from lstm_word_segmentation.word_segmenter import pick_lstm_model ++import glob, sys, getopt ++ ++""" ++A sample / simple program to segment the text from standard input ++and output the input with segmented result to standout output. ++""" ++ ++model_name = "Thai_codepoints_exclusive_model4_heavy" ++ ++def available_models(): ++ return [ m.replace("Models/", "") \ ++ .replace("/weights.json", "") \ ++ for m in glob.glob("Models/*/weights.json")] ++ ++def print_models(): ++ print("Supported Models") ++ for m in sorted(available_models()): ++ if m == model_name: ++ print(" ", m, "[DEFAULT]") ++ else: ++ print(" ", m) ++ ++def print_usage(): ++ print('segment_text.py -h -l -m model') ++ print(""" ++ -h \tHelp / Usage ++ -l \tList models ++ -m model\tSpecify model ++ """) ++ print_models() ++ ++def embedding_from_name(name): ++ if "_codepoints_" in name: ++ return "codepoints" ++ else: ++ return "grapheme_clusters_tf" ++ ++def main(argv): ++ global model_name ++ try: ++ opts, args = getopt.getopt(argv,"hlm::") ++ except getopt.GetoptError: ++ print_usage() ++ sys.exit(2) ++ for opt, arg in opts: ++ if opt == '-m': ++ model_name = arg ++ if opt == '-h': ++ print_usage() ++ sys.exit() ++ if opt == '-l': ++ print_models() ++ sys.exit() ++ ++ file1 = sys.stdin ++ Lines = file1.readlines() ++ ++ embedding = embedding_from_name(model_name) ++ word_segmenter = pick_lstm_model(model_name=model_name, embedding=embedding, ++ train_data="", eval_data="") ++ ++ print("Model:", model_name, sep='\t') ++ print("Embedding:", embedding, sep='\t') ++ ++ count = 0 ++ # Strips the newline character ++ for line in Lines: ++ line = line.strip() ++ print("Input:", line, sep='\t') ++ print("Output:", word_segmenter.segment_arbitrary_line(line), sep='\t') ++ ++if __name__ == "__main__": ++ main(sys.argv[1:]) +diff --git a/study_languages.py b/study_languages.py +new file mode 100755 +index 0000000..efb6fac +--- /dev/null ++++ b/study_languages.py +@@ -0,0 +1,79 @@ ++from pathlib import Path ++from lstm_word_segmentation.preprocess import evaluate_existing_algorithms, find_grapheme_clusters, \ ++ make_thai_burmese_dictionary ++from lstm_word_segmentation.helpers import print_grapheme_clusters ++from lstm_word_segmentation.text_helpers import make_thai_specific_best_data, permute_lines_of_text, \ ++ divide_train_test_data, only_one_script_text, get_lines_of_text ++ ++ ++# Making exclusive BEST data set: a version of BEST data that has only Thai-script code points. ++# make_thai_specific_best_data() ++ ++ ++# Dividing the my.txt data to train, validation, and test data sets. ++''' ++permute_lines_of_text('Data/my.txt', 'Data/my_permutated.txt') ++divide_train_test_data(input_text=Path.joinpath(Path(__file__).parent.absolute(), 'Data/my_permutated.txt'), ++ train_text=Path.joinpath(Path(__file__).parent.absolute(), 'Data/my_train.txt'), ++ valid_text=Path.joinpath(Path(__file__).parent.absolute(), 'Data/my_valid.txt'), ++ test_text=Path.joinpath(Path(__file__).parent.absolute(), 'Data/my_test.txt'), ++ line_limit=30000) ++''' ++ ++# Making exclusive my data set: a version of my data that has only Burmese-script code points. ++''' ++# Whole "my" data ++input_text = Path.joinpath(Path(__file__).parent.absolute(), "Data/my.txt") ++output_text = Path.joinpath(Path(__file__).parent.absolute(), "Data/my_exclusive.txt") ++only_one_script_text(input_text=input_text, output_text=output_text, script="Burmese", segmented=False) ++ ++# Train data ++input_text = Path.joinpath(Path(__file__).parent.absolute(), "Data/my_train.txt") ++output_text = Path.joinpath(Path(__file__).parent.absolute(), "Data/my_train_exclusive.txt") ++only_one_script_text(input_text=input_text, output_text=output_text, script="Burmese", segmented=False) ++ ++# Validation data ++input_text = Path.joinpath(Path(__file__).parent.absolute(), "Data/my_valid.txt") ++output_text = Path.joinpath(Path(__file__).parent.absolute(), "Data/my_valid_exclusive.txt") ++only_one_script_text(input_text=input_text, output_text=output_text, script="Burmese", segmented=False) ++ ++# Test data ++input_text = Path.joinpath(Path(__file__).parent.absolute(), "Data/my_test.txt") ++output_text = Path.joinpath(Path(__file__).parent.absolute(), "Data/my_test_exclusive.txt") ++only_one_script_text(input_text=input_text, output_text=output_text, script="Burmese", segmented=False) ++ ++# Test data segmented ++input_text = Path.joinpath(Path(__file__).parent.absolute(), "Data/my_test_exclusive.txt") ++lines = get_lines_of_text(file=input_text, type_of_lines="unsegmented") ++output_text = Path.joinpath(Path(__file__).parent.absolute(), "Data/my_test_segmented_exclusive.txt") ++output_file = open(str(output_text), 'w') ++for line in lines: ++ output_file.write(line.icu_segmented+"\n") ++''' ++ ++ ++# Evaluate ICU and Deepcut for Thai ++# ''' ++# evaluate_existing_algorithms(algorithm="ICU", data="BEST") ++# evaluate_existing_algorithms(algorithm="ICU", data="exclusive BEST") ++# evaluate_existing_algorithms(algorithm="ICU", data="my") ++# evaluate_existing_algorithms(algorithm="ICU", data="exclusive my") ++evaluate_existing_algorithms(algorithm="Deepcut", data="BEST") ++# evaluate_existing_algorithms(algorithm="Deepcut", data="exclusive BEST") ++# evaluate_existing_algorithms(algorithm="ICU", data="SAFT Burmese") ++# ''' ++ ++# Using BEST data set and Google Corpus crawler to make grapheme clusters for Thai and Burmese ++''' ++# Thai ++find_grapheme_clusters(language="Thai", exclusive=True, verbose=False) ++print_grapheme_clusters(thrsh=0.99, language="Thai", exclusive=True) ++ ++# Burmese ++find_grapheme_clusters(language="Burmese", exclusive=True, verbose=False) ++print_grapheme_clusters(thrsh=0.99, language="Burmese", exclusive=True) ++ ++# Multilingual Thai/Burmese ++make_thai_burmese_dictionary() ++print_grapheme_clusters(thrsh=0.99, language="Thai-Burmese", exclusive=True) ++''' +diff --git a/test/__init__.py b/test/__init__.py +new file mode 100644 +index 0000000..e69de29 +diff --git a/test/test_bies.py b/test/test_bies.py +new file mode 100644 +index 0000000..50c3e06 +--- /dev/null ++++ b/test/test_bies.py +@@ -0,0 +1,45 @@ ++from collections import namedtuple ++import unittest ++from lstm_word_segmentation.bies import Bies ++ ++ ++class TestBies(unittest.TestCase): ++ def test_normalize_bies(self): ++ TestCase = namedtuple("TestCase", ["str", "expected"]) ++ cases = [ ++ TestCase("biiie", "biiie"), ++ TestCase("biiiebie", "biiiebie"), ++ TestCase("bebiie", "bebiie"), ++ TestCase("bies", "bies"), ++ TestCase("ssbesbie", "ssbesbie"), ++ TestCase("ssssss", "ssssss"), ++ TestCase("bebebebes", "bebebebes"), ++ TestCase("e", "s"), ++ TestCase("b", "s"), ++ TestCase("i", "s"), ++ TestCase("s", "s"), ++ TestCase("bi", "be"), ++ TestCase("ii", "be"), ++ TestCase("ie", "be"), ++ TestCase("is", "ss"), ++ TestCase("biiis", "biies"), ++ TestCase("bbiiis", "biiies"), ++ TestCase("sbiiis", "sbiies"), ++ TestCase("biisiiie", "biesbiie"), ++ TestCase("biisiiie", "biesbiie"), ++ TestCase("ibiie", "sbiie"), ++ TestCase("iibiie", "bebiie"), ++ TestCase("iiebiie", "biebiie"), ++ TestCase("isbiie", "ssbiie"), ++ TestCase("eebiie", "bebiie"), ++ TestCase("eibiie", "bebiie"), ++ TestCase("esbiie", "ssbiie"), ++ TestCase("biieesbiie", "biiessbiie"), ++ ] ++ for cas in cases: ++ bies = Bies(input_bies=cas.str, input_type="str") ++ bies.normalize_bies() ++ self.assertEqual(cas.expected, bies.str) ++ ++if __name__ == "__main__": ++ unittest.main() +diff --git a/test/test_helpers.py b/test/test_helpers.py +new file mode 100644 +index 0000000..a2d3784 +--- /dev/null ++++ b/test/test_helpers.py +@@ -0,0 +1,78 @@ ++from collections import namedtuple ++import unittest ++from lstm_word_segmentation.helpers import is_ascii, diff_strings, sigmoid ++import numpy as np ++ ++class TestIsAscii(unittest.TestCase): ++ def test_is_ascii(self): ++ TestCase = namedtuple("TestCase", ["str", "expected"]) ++ cases = [ ++ TestCase("Unicode", True), ++ TestCase("123", True), ++ TestCase("(hello!)", True), ++ TestCase("\n", True), ++ TestCase("", True), ++ TestCase(" ", True), ++ TestCase("กระสับกระส่ายและปวดศีรษะ", False), ++ TestCase("กระสับ 123", False), ++ TestCase("กร|ะสับ|กระส่ายและ|ปวดศี|รษะ", False), ++ TestCase("Unicodeบ", False), ++ ] ++ for cas in cases: ++ computed = is_ascii(input_str=cas.str) ++ self.assertEqual(cas.expected, computed) ++ ++ ++class TestDiffStrings(unittest.TestCase): ++ def test_diff_string(self): ++ TestCase = namedtuple("TestCase", ["str1", "str2", "expected"]) ++ cases = [ ++ TestCase("Google", "Google", 0), ++ TestCase("Google", "Googee", 1), ++ TestCase("Google", "google", 1), ++ TestCase("Gooogl", "Google", 3), ++ TestCase("Googl", "Google", -1), ++ TestCase("", "Google", -1), ++ TestCase("Google", "word segmentation", -1), ++ TestCase("word segmentation", "werd segmentation", 1), ++ TestCase("word segmentation", "wordsegmentation ", 13), ++ TestCase("word segmentation", "wordsegmentation", -1), ++ TestCase("sooo long", " sooolong", 3), ++ ] ++ for cas in cases: ++ computed = diff_strings(str1=cas.str1, str2=cas.str2) ++ self.assertEqual(cas.expected, computed) ++ ++# ''' ++class TestSigmoid(unittest.TestCase): ++ def test_sigmoid(self): ++ TestCase = namedtuple("TestCase", ["input", "expected"]) ++ cases = [ ++ TestCase(0, 0.5), ++ TestCase(1, 0.73105858), ++ TestCase(2, 0.88079708), ++ TestCase(3, 0.95257413), ++ TestCase(10, 0.99995460), ++ TestCase(100, 1), ++ TestCase(1000, 1), ++ TestCase(-1, 0.26894142), ++ TestCase(-2, 0.11920292), ++ TestCase(-3, 0.04742587), ++ TestCase(-10, 0.00004540), ++ TestCase(-100, 0), ++ TestCase(-1000, 0), ++ TestCase(np.array([0, 1, 100, -1, -10]), np.array([0.5, 0.73105858, 1, 0.26894142, 0.00004540])), ++ TestCase(np.array([0]), np.array([0.5])), ++ TestCase(np.array([0]), 0.5), ++ TestCase(0, np.array(0.5)), ++ TestCase(np.array([0, -1000]), np.array([0.5, 0])), ++ TestCase(np.array([[0, 1, 100, -1, -10]]), np.array([0.5, 0.73105858, 1, 0.26894142, 0.00004540])), ++ TestCase(np.array([np.array([0, 1, 100, -1, -10]), np.array([1, 2, 3])]), np.array([0.5, 0.73105858, 1, 0.26894142, 0.00004540])), ++ ] ++ for cas in cases: ++ computed = sigmoid(inp=cas.input) ++ np.testing.assert_almost_equal(cas.expected, computed) ++# ''' ++ ++if __name__ == "__main__": ++ unittest.main() +diff --git a/test/test_script_normalizer.py b/test/test_script_normalizer.py +new file mode 100644 +index 0000000..89d3aa6 +--- /dev/null ++++ b/test/test_script_normalizer.py +@@ -0,0 +1,30 @@ ++from collections import namedtuple ++import unittest ++from lstm_word_segmentation.script_normalizer import normalize_string ++ ++class TestScriptNormalizer(unittest.TestCase): ++ def test_normalize_string(self): ++ TestCase = namedtuple("TestCase", ["in_str", "scripts", "out_str"]) ++ cases = [ ++ TestCase("abc", ["Latn"], "abc"), ++ TestCase("abc", [], "LLL"), ++ TestCase("a𑄌", ["Latn", "Cakm"], "a𑄌"), ++ TestCase("a𑄌", ["Latn"], "a𑄃"), ++ TestCase("a𑄌", ["Cakm"], "L𑄌"), ++ TestCase("a𑄌", [], "L𑄃"), ++ # NOTE: ASCII digits have script Common, not Latin ++ TestCase("123", ["Latn"], "000"), ++ TestCase("၁၁၁", ["Mymr"], "၁၁၁"), ++ TestCase("၁၁၁", [], "၀၀၀"), ++ # NOTE: Currency symbols have script Common ++ TestCase("฿100", ["Thai"], "$000"), ++ TestCase("฿100", [], "$000"), ++ TestCase("พันโทหญิง สมเด็จพระนางเจ้าอินทรศักดิศจี พระวรราชชายา (พระนามเดิม: ประไพ; 10 มิถุนายน พ.ศ. 2445 — 30 พฤศจิกายน พ.ศ. 2518) พระวรราชชายาในพระบาทสมเด็จพระมงกุฎเกล้าเจ้าอยู่หัว", ["Thai"], "พันโทหญิง สมเด็จพระนางเจ้าอินทรศักดิศจี พระวรราชชายา (พระนามเดิม: ประไพ; 00 มิถุนายน พ.ศ. 0000 — 00 พฤศจิกายน พ.ศ. 0000) พระวรราชชายาในพระบาทสมเด็จพระมงกุฎเกล้าเจ้าอยู่หัว"), ++ TestCase("พันโทหญิง สมเด็จพระนางเจ้าอินทรศักดิศจี พระวรราชชายา (พระนามเดิม: ประไพ; 10 มิถุนายน พ.ศ. 2445 — 30 พฤศจิกายน พ.ศ. 2518) พระวรราชชายาในพระบาทสมเด็จพระมงกุฎเกล้าเจ้าอยู่หัว", [], "ทัทททททิท ทททท็ททททททททท้ททิททททัททิทที ทททททททททททท (ททททททททิท: ททททท; 00 ทิทุทททท ท.ท. 0000 — 00 ททททิทททท ท.ท. 0000) ทททททททททททททททททททททททท็ทททททททุทททท้ททท้ทททู่ทัท"), ++ ] ++ for cas in cases: ++ actual = normalize_string(cas.in_str, cas.scripts) ++ self.assertEqual(cas.out_str, actual, cas) ++ ++if __name__ == "__main__": ++ unittest.main() +diff --git a/test/test_text_helpers.py b/test/test_text_helpers.py +new file mode 100644 +index 0000000..0434573 +--- /dev/null ++++ b/test/test_text_helpers.py +@@ -0,0 +1,146 @@ ++from collections import namedtuple ++import unittest ++from lstm_word_segmentation.text_helpers import remove_tags, clean_line, normalize_string ++ ++ ++class TestRemoveTags(unittest.TestCase): ++ def test_remove_tags(self): ++ TestCase = namedtuple("TestCase", ["line", "st_tag", "fn_tag", "expected"]) ++ cases = [ ++ TestCase("Word segmentation is an interesting problem.", "", "", ++ "Word segmentation is an interesting problem."), ++ TestCase("|Word| |segmentation| |is| |an| |interesting| |problem|.|", "", "", ++ "|Word| |segmentation| |is| |an| |interesting| |problem|.|"), ++ TestCase("Word| |segmentation| |is| |an| |interesting| |problem|.|", "", "", ++ "Word| |segmentation| |is| |an| |interesting| |problem|.|"), ++ TestCase("Word| |segmentation| |is| |an| |interesting| |problem|.", "", "", ++ "Word| |segmentation| |is| |an| |interesting| |problem|."), ++ TestCase("|Word| |segmentation| |is| |an| |interesting| |problem|.|", "", "", ++ "|Word| |segmentation| |is| |an| |interesting| |problem|.|"), ++ TestCase("|Word| |segmentation is| |an| |interesting| |problem|.|", "", "", ++ "|Word| | |an| |interesting| |problem|.|"), ++ TestCase("|Word| |segmentation is|an| |interesting| |problem|.|", "", "", ++ "|Word| |an| |interesting| |problem|.|"), ++ TestCase("|Word|segmentation is| |an| |interesting| |problem|.|", "", "", ++ "|Word| |an| |interesting| |problem|.|"), ++ TestCase("|Word|segmentation is|an| |interesting| |problem|.|", "", "", ++ "|Word|an| |interesting| |problem|.|"), ++ TestCase("segmentation is|an| |interesting| |problem|.|", "", "", ++ "|an| |interesting| |problem|.|"), ++ TestCase("|segmentation is|an| |interesting| |problem|.|", "", "", ++ "|an| |interesting| |problem|.|"), ++ TestCase("|word| |segmentation is|an interesting| |problem|.|", "", "", ++ "|word| | |problem|.|"), ++ TestCase("|word| |segmentation is| an interesting| |problem|.|", "", "", ++ "|word| | |problem|.|"), ++ TestCase("|word| |segmentation is| an interesting blah | |problem|.|", "", "", ++ "|word| | |problem|.|"), ++ TestCase("|word| |segmentation is| |an interesting| |problem|.|", "", "", ++ "|word| | | |problem|.|"), ++ TestCase("|word|segmentation is|an interesting| |problem|.|", "", "", ++ "|word| |problem|.|"), ++ TestCase("|word| |segmentation is|", "", "", ++ "|word| |"), ++ TestCase("|word| |segmentation is", "", "", ++ "|word| |"), ++ TestCase("|word| segmentation is", "", "", ++ "|word|"), ++ TestCase("|word| segmentation is", "", "|", ++ "|word|"), ++ TestCase("|segmentation is", "", "", ++ "|"), ++ TestCase("|segmentation is|", "", "", ++ "|"), ++ TestCase("segmentation is", "", "", ++ ""), ++ TestCase("|segmentation is|", "", "", ++ "|"), ++ TestCase("word|segmentation| |is|an|interesting|problem|", "", "", ++ "word|an|interesting|problem|"), ++ TestCase("word|segmentation is|an interesting|problem|", "", "", ++ "word|an interesting|problem|"), ++ TestCase("word|segmentation is|an interesting|problem| ", "", "", ++ "word|an interesting|problem| "), ++ TestCase("|แม้จะกะเวลาเอา|วลาเอ|แม้จะกะเ| ", "", "", ++ "|วลาเอ|แม้จะกะเ| "), ++ TestCase("|วลาเอ|แม้จะกะเ| ", "", "", ++ "|วลาเอ| "), ++ ] ++ for cas in cases: ++ computed = remove_tags(line=cas.line, st_tag=cas.st_tag, fn_tag=cas.fn_tag) ++ self.assertEqual(cas.expected, computed) ++ ++ ++class TestCleanLine(unittest.TestCase): ++ def test_clean_line(self): ++ TestCase = namedtuple("TestCase", ["line", "segmented", "expected"]) ++ cases = [ ++ TestCase("Word segmentation is https an interesting problem.", True, -1), ++ TestCase("Word segmentation is an interesting problem.", True, -1), ++ TestCase("|Word| |segmentation| |is| |an| |interesting| |problem|.|", True, -1), ++ TestCase("|", True, -1), ++ TestCase("", True, -1), ++ TestCase(" ", True, -1), ++ TestCase("| |", True, -1), ++ TestCase("|แม้จะกะเวลาเอา|", True, -1), ++ TestCase("|แม้จะกะเวลาเอา| |แม้จะกะเวลาเอ/AB>", True, -1), ++ TestCase("|แม้จะกะเวลาเอา|วลาเอ|แม้จะกะเ", True, "|วลาเอ|"), ++ TestCase("|แม้จะกะเวลาเอา|วลาเอ|แม้จะกะเ|", True, "|วลาเอ|"), ++ TestCase("|แม้จะกะเวลาเอา|วลาเอ|แม้จะกะเ| |", True, "|วลาเอ| |"), ++ TestCase("|แม้จะกะเวลาเอา|วลาเอ|แม้จะ https กะเ| |", True, -1), ++ TestCase("|แม้จะกะเวลาเอา|วลาเอ|แม้จะกะเ|a", True, "|วลาเอ|a|"), ++ TestCase("|แม้จะกะเวลาเอา|วลาเอ|แม้จะกะเ|ab", True, "|วลาเอ|ab|"), ++ TestCase("|แม้จะกะเวลาเอา|วลาเอ|แม้จะกะเ|ab|", True, "|วลาเอ|ab|"), ++ TestCase("|แม้จะกะเวลาเอา|วลาเอ|แม้จะกะเ|ab |", True, "|วลาเอ|ab |"), ++ TestCase("|แม้จะกะเวลาเอา|วลาเอ|แม้จะกะเ| |", True, "|วลาเอ| |"), ++ TestCase("|แม้จะกะเวลาเอา|วลาเอ|แม้จะกะเ| ", True, "|วลาเอ|"), ++ TestCase("แม้จะกะเวลาเอา|วลาเอ|แม้จะกะเ| ", True, "|วลาเอ|"), ++ TestCase("word segmentation is interesting วลาเอ ", True, "|word segmentation is interesting วลาเอ|"), ++ TestCase(" word segmentation is interesting วลาเอ ", True, "|word segmentation is interesting วลาเอ|"), ++ TestCase(" |word segmentation is interesting วลาเอ ", True, "|word segmentation is interesting วลาเอ|"), ++ TestCase("|แม้จะกะเวลาเอา| |แม้จะกะเวลาเอ/AB>", False, -1), ++ TestCase("แม้จะกะเวลาเอา| |แม้จะกะเวลาเอ/AB>", False, -1), ++ TestCase("word segmentation is interesting วลาเอ ", False, "word segmentation is interesting วลาเอ"), ++ TestCase("|แม้จะกะเวลาเอา|วลาเอ|แม้จะกะเ| ", False, "|วลาเอ|"), ++ TestCase(" |word segmentation is interesting วลาเอ ", False, "|word segmentation is interesting วลาเอ"), ++ TestCase(" word segmentation is วลาเอ |", False, "word segmentation is วลาเอ |"), ++ ++ ] ++ for cas in cases: ++ computed = clean_line(line=cas.line, segmented=cas.segmented) ++ self.assertEqual(cas.expected, computed) ++ ++ ++class TestNormalizeString(unittest.TestCase): ++ def test_normalize_string(self): ++ TestCase = namedtuple("TestCase", ["in_str", "scripts", "out_str"]) ++ cases = [ ++ TestCase("abc", ["Latn"], "abc"), ++ TestCase("abc", [], "LLL"), ++ TestCase("a𑄌", ["Latn", "Cakm"], "a𑄌"), ++ TestCase("a𑄌", ["Latn"], "a𑄃"), ++ TestCase("a𑄌", ["Cakm"], "L𑄌"), ++ TestCase("a𑄌", [], "L𑄃"), ++ # NOTE: ASCII digits have script Common, not Latin ++ TestCase("123", ["Latn"], "000"), ++ TestCase("၁၁၁", ["Mymr"], "၁၁၁"), ++ TestCase("၁၁၁", [], "၀၀၀"), ++ # NOTE: Currency symbols have script Common ++ TestCase("฿100", ["Thai"], "$000"), ++ TestCase("฿100", [], "$000"), ++ TestCase( ++ "พันโทหญิง สมเด็จพระนางเจ้าอินทรศักดิศจี พระวรราชชายา (พระนามเดิม: ประไพ; 10 มิถุนายน พ.ศ. 2445 — 30 พฤศจิกายน พ.ศ. 2518) พระวรราชชายาในพระบาทสมเด็จพระมงกุฎเกล้าเจ้าอยู่หัว", ++ ["Thai"], ++ "พันโทหญิง สมเด็จพระนางเจ้าอินทรศักดิศจี พระวรราชชายา (พระนามเดิม: ประไพ; 00 มิถุนายน พ.ศ. 0000 — 00 พฤศจิกายน พ.ศ. 0000) พระวรราชชายาในพระบาทสมเด็จพระมงกุฎเกล้าเจ้าอยู่หัว"), ++ TestCase( ++ "พันโทหญิง สมเด็จพระนางเจ้าอินทรศักดิศจี พระวรราชชายา (พระนามเดิม: ประไพ; 10 มิถุนายน พ.ศ. 2445 — 30 พฤศจิกายน พ.ศ. 2518) พระวรราชชายาในพระบาทสมเด็จพระมงกุฎเกล้าเจ้าอยู่หัว", ++ [], ++ "ทัทททททิท ทททท็ททททททททท้ททิททททัททิทที ทททททททททททท (ททททททททิท: ททททท; 00 ทิทุทททท ท.ท. 0000 — 00 ททททิทททท ท.ท. 0000) ทททททททททททททททททททททททท็ทททททททุทททท้ททท้ทททู่ทัท"), ++ ] ++ for cas in cases: ++ actual = normalize_string(in_str=cas.in_str, allowed_scripts=cas.scripts) ++ self.assertEqual(cas.out_str, actual, cas) ++ ++ ++if __name__ == "__main__": ++ unittest.main() +diff --git a/train_burmese.py b/train_burmese.py +new file mode 100755 +index 0000000..744d285 +--- /dev/null ++++ b/train_burmese.py +@@ -0,0 +1,36 @@ ++from lstm_word_segmentation.lstm_bayesian_optimization import LSTMBayesianOptimization ++from lstm_word_segmentation.word_segmenter import pick_lstm_model ++from lstm_word_segmentation.word_segmenter import WordSegmenter ++ ++ ++# Use Bayesian optimization to decide on values of hunits and embedding_dim ++''' ++bayes_optimization = LSTMBayesianOptimization(input_language="Burmese", input_n=50, input_t=10000, input_epochs=2, ++ input_embedding_type='grapheme_clusters_tf', input_clusters_num=350, ++ input_training_data="my", input_evaluation_data="my", ++ input_hunits_lower=4, input_hunits_upper=64, input_embedding_dim_lower=4, ++ input_embedding_dim_upper=64, input_c=0.05, input_iterations=1) ++bayes_optimization.perform_bayesian_optimization() ++''' ++ ++# Train a new model -- choose name cautiously to not overwrite other models ++''' ++model_name = "Burmese_temp" ++word_segmenter = WordSegmenter(input_name=model_name, input_n=200, input_t=600000, input_clusters_num=350, ++ input_embedding_dim=28, input_hunits=14, input_dropout_rate=0.2, input_output_dim=4, ++ input_epochs=20, input_training_data="exclusive my", input_evaluation_data="exclusive my", ++ input_language="Burmese", input_embedding_type="grapheme_clusters_tf") ++word_segmenter.train_model() ++word_segmenter.save_model() ++word_segmenter.test_model_line_by_line(verbose=True) ++''' ++ ++# Choose one of the saved models to use ++# ''' ++word_segmenter = pick_lstm_model(model_name="Burmese_graphclust_model5_heavy", embedding="grapheme_clusters_tf", ++ train_data="my", eval_data="my") ++print("model_name = {}, embedding dim = {}, hunits = {}".format(word_segmenter.name, word_segmenter.embedding_dim, ++ word_segmenter.hunits)) ++# word_segmenter.save_model() ++word_segmenter.test_model_line_by_line(verbose=True, fast=True) ++# ''' +diff --git a/train_multilingual.py b/train_multilingual.py +new file mode 100755 +index 0000000..0212ba2 +--- /dev/null ++++ b/train_multilingual.py +@@ -0,0 +1,75 @@ ++from pathlib import Path ++from tensorflow import keras ++from lstm_word_segmentation.lstm_bayesian_optimization import LSTMBayesianOptimization ++from lstm_word_segmentation.word_segmenter import WordSegmenter ++from lstm_word_segmentation.text_helpers import merge_two_texts, add_additional_bars ++ ++ ++# Making new files with bars around spaces in my_train.txt and my_valid.txt, so it is compatible with the format of BEST ++# data sets. ++# ''' ++input_file = Path.joinpath(Path(__file__).parent.absolute(), "Data/my_train.txt") ++output_file = Path.joinpath(Path(__file__).parent.absolute(), "Data/my_train_with_bars.txt") # You have to recompute these ++add_additional_bars(read_filename=input_file, write_filename=output_file) ++ ++input_file = Path.joinpath(Path(__file__).parent.absolute(), "Data/my_valid.txt") ++output_file = Path.joinpath(Path(__file__).parent.absolute(), "Data/my_valid_with_bars.txt") ++add_additional_bars(read_filename=input_file, write_filename=output_file) ++# ''' ++ ++# Making a dataset that has both Thai (BEST) and Burmese (my) in it ++# ''' ++thai_texts = [] ++category = ["news", "encyclopedia", "article", "novel"] ++for text_num in range(10, 20): ++ for cat in category: ++ text_num_str = "{}".format(text_num).zfill(5) ++ file = Path.joinpath(Path(__file__).parent.absolute(), "Data/Best/{}/{}_".format(cat, cat) + ++ text_num_str + ".txt") ++ thai_texts.append(file) ++burmese_texts = [Path.joinpath(Path(__file__).parent.absolute(), "Data/my_valid_with_bars.txt")] ++output_text = file = Path.joinpath(Path(__file__).parent.absolute(), "Data/Best_my_valid.txt") ++merge_two_texts(input_texts1=thai_texts, input_texts2=burmese_texts, output_text=output_text, line_limit=1200) ++# ''' ++ ++# Train a new model -- choose name cautiously to not overwrite other models ++# ''' ++model_name = "Thai_Burmese_temp" ++word_segmenter = WordSegmenter(input_name=model_name, input_n=100, input_t=200000, input_clusters_num=500, ++ input_embedding_dim=40, input_hunits=40, input_dropout_rate=0.2, input_output_dim=4, ++ input_epochs=15, input_training_data="BEST_my", input_evaluation_data="BEST_my", ++ input_language="Thai_Burmese", input_embedding_type="grapheme_clusters_tf") ++ ++# Training, testing, and saving the model ++word_segmenter.train_model() ++word_segmenter.save_model() ++# ''' ++ ++# Choose one of the saved models to use ++model_name = "Thai_Burmese_temp" ++input_embedding_type = "grapheme_clusters_tf" ++file = Path.joinpath(Path(__file__).parent.absolute(), 'Models/' + model_name) ++model = keras.models.load_model(file) ++input_clusters_num = model.weights[0].shape[0] ++input_embedding_dim = model.weights[0].shape[1] ++input_hunits = model.weights[1].shape[1]//4 ++if "heavy" in model_name: ++ input_n = 200 ++ input_t = 600000 ++elif "heavier" in model_name: ++ input_n = 200 ++ input_t = 2000000 ++else: ++ input_n = 50 ++ input_t = 100000 ++ ++print("embedding dim = {}".format(input_embedding_dim)) ++print("hunits = {}".format(input_hunits)) ++ ++word_segmenter = WordSegmenter(input_name=model_name, input_n=input_n, input_t=input_t, ++ input_clusters_num=input_clusters_num, input_embedding_dim=input_embedding_dim, ++ input_hunits=input_hunits, input_dropout_rate=0.2, input_output_dim=4, input_epochs=15, ++ input_training_data="BEST_my", input_evaluation_data="BEST_my", ++ input_language="Thai_Burmese", input_embedding_type=input_embedding_type) ++word_segmenter.set_model(model) ++word_segmenter.test_model_line_by_line(verbose=True) +diff --git a/train_thai.py b/train_thai.py +new file mode 100755 +index 0000000..07fce15 +--- /dev/null ++++ b/train_thai.py +@@ -0,0 +1,36 @@ ++from lstm_word_segmentation.lstm_bayesian_optimization import LSTMBayesianOptimization ++from lstm_word_segmentation.word_segmenter import WordSegmenter ++from lstm_word_segmentation.word_segmenter import pick_lstm_model ++ ++# Use Bayesian optimization to decide on values of hunits and embedding_dim ++''' ++bayes_optimization = LSTMBayesianOptimization(input_language="Thai", input_n=50, input_t=10000, input_epochs=1, ++ input_embedding_type='grapheme_clusters_tf', input_clusters_num=350, ++ input_training_data="BEST", input_evaluation_data="BEST", ++ input_hunits_lower=4, input_hunits_upper=64, input_embedding_dim_lower=4, ++ input_embedding_dim_upper=64, input_c=0.05, input_iterations=2) ++bayes_optimization.perform_bayesian_optimization() ++''' ++ ++# Train a new model -- choose name cautiously to not overwrite other models ++''' ++model_name = "Thai_codepoints" ++word_segmenter = WordSegmenter(input_name=model_name, input_n=50, input_t=10000, input_clusters_num=350, ++ input_embedding_dim=16, input_hunits=23, input_dropout_rate=0.2, input_output_dim=4, ++ input_epochs=1, input_training_data="exclusive BEST", ++ input_evaluation_data="exclusive BEST", input_language="Thai", ++ input_embedding_type="codepoints") ++word_segmenter.train_model() ++word_segmenter.save_model() ++word_segmenter.test_model_line_by_line(verbose=True) ++''' ++ ++# Choose one of the saved models to use ++# ''' ++word_segmenter = pick_lstm_model(model_name="Thai_graphclust_model4_heavy", embedding="grapheme_clusters_tf", ++ train_data="BEST", eval_data="BEST") ++print("model_name = {}, embedding dim = {}, hunits = {}".format(word_segmenter.name, word_segmenter.embedding_dim, ++ word_segmenter.hunits)) ++# word_segmenter.save_model() ++word_segmenter.test_model_line_by_line(verbose=True, fast=True) ++# ''' diff --git a/lstm_word_segmentation/word_segmenter.py b/lstm_word_segmentation/word_segmenter.py index a06e0a8..0b8bef9 100644 --- a/lstm_word_segmentation/word_segmenter.py +++ b/lstm_word_segmentation/word_segmenter.py @@ -601,41 +601,41 @@ def save_model(self): This function saves the current trained model of this word_segmenter instance. """ # Save the model using Keras - model_path = (Path.joinpath(Path(__file__).parent.parent.absolute(), "Models/" + self.name)) - tf.saved_model.save(self.model, model_path) - - # Save one np array that holds all weights - file = Path.joinpath(Path(__file__).parent.parent.absolute(), "Models/" + self.name + "/weights") - np.save(str(file), self.model.weights) - - # Save the model in json format, that has both weights and grapheme clusters dictionary - json_file = Path.joinpath(Path(__file__).parent.parent.absolute(), "Models/" + self.name + "/weights.json") - with open(str(json_file), 'w') as wfile: - output = dict() - output["model"] = self.name + model_dir = Path(__file__).parent.parent / "Models" / self.name + model_dir.mkdir(parents=True, exist_ok=True) + + # Save model as .h5 file + self.model.save(model_dir / "weights.h5") + + # Save weights as a NumPy file + np.save(model_dir / "weights.npy", np.array([w.numpy() for w in self.model.weights], dtype=object)) + + # Save the model in JSON format + json_file = model_dir / "weights.json" + with open(json_file, 'w') as wfile: + output = {"model": self.name} + + # Store grapheme clusters or codepoints if "grapheme_clusters" in self.embedding_type: - output["dic"] = self.graph_clust_dic + output["dic"] = self.graph_clust_dic elif "codepoints" in self.embedding_type: - if self.language == "Thai": - output["dic"] = constants.THAI_CODE_POINT_DICTIONARY - if self.language == "Burmese": - output["dic"] = constants.BURMESE_CODE_POINT_DICTIONARY - for i in range(len(self.model.weights)): - dic_model = dict() - dic_model["v"] = 1 - mat = self.model.weights[i].numpy() - dim0 = mat.shape[0] - dim1 = 1 - if len(mat.shape) == 1: - dic_model["dim"] = [dim0] - else: - dim1 = mat.shape[1] - dic_model["dim"] = [dim0, dim1] - serial_mat = np.reshape(mat, newshape=[dim0 * dim1]) - serial_mat = serial_mat.tolist() - dic_model["data"] = serial_mat - output["mat{}".format(i+1)] = dic_model - json.dump(output, wfile) + output["dic"] = ( + constants.THAI_CODE_POINT_DICTIONARY if self.language == "Thai" + else constants.BURMESE_CODE_POINT_DICTIONARY if self.language == "Burmese" + else {} + ) + + # Serialized weights into JSON format + for i, weight in enumerate(self.model.weights, start=1): + mat = weight.numpy() + output[f"mat{i}"] = { + "v": 1, + "dim": list(mat.shape), + "data": mat.flatten().tolist() + } + + # Write JSON output to file + json.dump(output, wfile, indent=4) def set_model(self, input_model): """