o h+@sddlZddlZejeZejeZejeZejeddlZddlZddlZddl m Z m Z m Z m Z ddlmZddlmZddlZddlmZddlZddlmZddlmZddlmZddlmZdd lmZdd l m!Z!dd l"m#Z#e#Z$e%d Z&d e e'e e(e)ffde e'e e'e'ffde)fddZ*d'de e de e de fddZ+gfdej,dej-de e e(de e e'dej-f ddZ.d(dej,de(d e/d!e/de e e e)e)ff d"d#Z0d)d$d%Z1e2d&kre1dSdS)*N)AnyListTupleMapping) defaultdict)random)default_device)utils)LemmaClassifier)LemmaClassifierLSTM)LemmaClassifierWithTransformer)format_confusion)get_tqdmzstanza.lemmaclassifier mcc_results confusionreturncCsPd}d}|D]}||d}t||}|||7}||7}q||S)z Computes the weighted F1 score across an evaluation set. The weight of a class's F1 score is equal to the number of examples in evaluation. This makes classes that have more examples in the evaluation more impactful to the weighted f1. rf1)getsumvalues)rrnum_total_examples weighted_f1class_idclass_f1num_class_examplesri/var/www/html/env_mimamsha/lib/python3.10/site-packages/stanza/models/lemma_classifier/evaluate_models.pyget_weighted_f1#s  rTgold_tag_sequencespred_tag_sequences label_decoderc st|t|ksJdt|dt|tdddd|D}t||D]\}}||||d7<q*tdd}D]jzid tfd d D}Wn tynd }Ynwzid ti} Wn tyd } Ynwz d || || } Wn tyd } Ynw|| | d|<qG|r|D]%} t d| dd|| ddd|| ddd|| dqt |} || fS)a- Evaluates a model's predicted tags against a set of gold tags. Computes precision, recall, and f1 for all classes. Precision = true positives / true positives + false positives Recall = true positives / true positives + false negatives F1 = 2 * (Precision * Recall) / (Precision + Recall) Returns: 1. Multi class result dictionary, where each class is a key and maps to another map of its F1, precision, and recall scores. e.g. multiclass_results[0]["precision"] would give class 0's precision. 2. Confusion matrix, where each key is a gold tag and its value is another map with a key of the predicted tag with value of that (gold, pred) count. e.g. confusion[0][1] = 6 would mean that for gold tag 0, the model predicted tag 1 a total of 6 times. z Length of gold tag sequences is z,, while length of predicted tag sequence is cSttSN)rintrrrrGz$evaluate_sequences..cSsi|]\}}||qSrr).0xyrrr Isz&evaluate_sequences..cSr!r")rfloatrrrrr$Mr%rcs g|] }|idqS)r)r)r&krgold_tagrr Rs z&evaluate_sequences..g) precisionrecallrzLemma 'z' had precision dr1z , recall r2z and F1 score of r) lenritemszipkeysrrZeroDivisionErrorrloggerinfor) rrr verbosereverse_label_decodergoldpredmulti_class_resultprecr2rlemmarrr-revaluate_sequences6sB   4 ,   H  rBmodelposition_indices sentences upos_tagscCsHt||||}tj|dd}Wd|S1swY|S)aS A LemmaClassifierLSTM or LemmaClassifierWithTransformer is used to predict on a single text example, given the position index of the target token. Args: model (LemmaClassifier): A trained LemmaClassifier that is able to predict on a target token. position_indices (Tensor[int]): A tensor of the (zero-indexed) position of the target token in `text` for each example in the batch. sentences (List[List[str]]): A list of lists of the tokenized strings of the input sentences. Returns: (int): The index of the predicted class in `model`'s output. r*)dimN)torchno_gradargmax)rCrDrErFlogitspredicted_classrrr model_predictos  rMF eval_pathr; is_trainingc Cst}|||s|tj||jdd}td|d\}}|jg}} t |dD]'\} } } } t || | | }|| |k}|t |7}|t |7}| |7} q.td||}t|| |j|d\}}}|rtd|d |d |d td |j||||fS) ah Helper function for model evaluation Args: model (LemmaClassifierLSTM or LemmaClassifierWithTransformer): An instance of the LemmaClassifier class that has architecture initialized which matches the model saved in `model_path`. model_path (str): Path to the saved model weights that will be loaded into `model`. eval_path (str): Path to the saved evaluation dataset. verbose (bool, optional): True if `evaluate_sequences()` should print the F1, Precision, and Recall for each class. Defaults to True. is_training (bool, optional): Whether the model is in training mode. If the model is training, we do not change it to eval mode. Returns: 1. Multi-class results (Mapping[int, Mapping[str, float]]): first map has keys as the classes (lemma indices) and value is another map with key of "f1", "precision", or "recall" with corresponding values. 2. Confusion Matrix (Mapping[int, Mapping[int, int]]): A confusion matrix with keys equal to the index of the gold tag, and a value of the map with the key as the predicted tag and corresponding count of that (gold, pred) pair. 3. Accuracy (float): the total accuracy (num correct / total examples) across the evaluation set. F)r shufflezEvaluating on evaluation file )rrz"Evaluating examples from data filez3Finished evaluating on dataset. Computing scores...)r; Accuracy: z (/)zLabel decoder: )rtoevalr Datasetr r9r:labelstqdmrMrHrr4tolistrB)rCrNr;rOdevicedatasetcorrecttotal gold_tags pred_tagsrE pos_indicesrFrWr> correct_predsaccuracy mc_resultsrrrrrevaluate_models*     rdc Cst}|jdtddd|jdtddd|jdtd d d|jd tdd d|jd dddd|jdtddd|jdttjtjt dddd|jdttjtjt dddd|jdttjtjt dddd|jdtd d!d|jd"tdd#d|jd$td%d&|s| |n|}t d't |}|D]}t |d(||qt d)t d*|d+d,|d-t|d+|}t||d-\}}}}t d.t|t d/t d0t|t d/t d1|t d/t d2|||||fS)3Nz --vocab_sizei'zNumber of tokens in vocab)typedefaulthelpz--embedding_dimr3z?Number of dimensions in word embeddings (currently using GloVe)z --hidden_dimzSize of hidden layerz--wordvec_pretrain_filez'Exact name of the pretrain file to readz--charlm store_trueFz(Whether not to use the charlm embeddings)actionrfrgz--charlm_shorthandz=Shorthand for character-level language model training corpus.z--charlm_forward_file charlm_filesz1billion_forward.ptzPath to forward charlm filez--charlm_backward_filez1billion_backwards.ptzPath to backward charlm filez --save_name saved_modelszlemma_classifier_model.ptzPath to model save filez --model_typerobertaz8Which transformer to use ('bert' or 'roberta' or 'lstm')z --bert_modelz>Use a specific transformer instead of the default bert/robertaz --eval_filezpath to evaluation file)rergz0Running training script with the following args:z: z<------------------------------------------------------------z$Attempting evaluation of model from save_namez on file eval_filez MCC Results: .______________________________________________z Confusion: %srQz Weighted f1: )argparseArgumentParser add_argumentr#strospathjoindirname__file__ parse_argsr9r:varsr loadrddictr ) argspredefined_argsparserargrCrraccrrrrmains<&&&      r__main__)T)TF)NN)3rusysrvrxry parentdirappendloggingrqtypingrrrr collectionsrnumpyrrHtorch.nnnnstanzastanza.models.common.utilsrstanza.models.lemma_classifierr )stanza.models.lemma_classifier.base_modelr )stanza.models.lemma_classifier.lstm_modelr 0stanza.models.lemma_classifier.transformer_modelr stanza.utils.confusionr stanza.utils.get_tqdmrrX getLoggerr9r#rtr+rrBModuleTensorrMboolrdr__name__rrrrs@               6 89. 5,