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How to use cat_eval method in fMBT

Best Python code snippet using fMBT_python

cat_train.py

Source:cat_train.py

1# -*- coding: utf-8 --*--2# @Author: Zessay3# @time: 2019.05.09 9:494# @File: cat_train.py5# @Software: PyCharm67import os8import catboost9from catboost import Pool10from utils import sort_val, find_threshold, save_pkl, return_entity, save_as_order11from config import DefaultConfig121314class CATTrain:15    def __init__(self, train, test, params):16        self.train_ = train17        self.test_ = test18        self.params = params19        self.threshold = None20        self.model = None21        self.fe = None22        self.pred = None23        self.opt = DefaultConfig()2425    # è®ç»é¨åè®ç»éæ°æ®ï¼å¨éªè¯éæç´¢åå²éå¼26    def train_for_threshold(self, features, target='label', num=35000):27        train_df = self.train_[self.train_.ID < num]28        val_df = self.train_[self.train_.ID >= num]2930        X_train, y_train = train_df[features].values, train_df[target].values.astype('uint8')31        X_eval, y_eval = val_df[features].values, val_df[target].values.astype('uint8')3233        cat_train = Pool(X_train, y_train)34        cat_eval = Pool(X_eval, y_eval)3536        cat_model = catboost.train(cat_train, self.params, iterations=10000,37                              eval_set=cat_eval,38                              early_stopping_rounds=200,39                              verbose=500)40        y_pred = cat_model.predict(cat_eval, prediction_type='Probability')[:,1]41        ## è·åéªè¯éççå®å®ä½ï¼ä»¥åæé¡ºåºæåºé¢æµçæ¦çåå¯¹åºçåè¯42        gt_ent, pred_words, pred_proba = sort_val(val_df, y_pred)43        ## è·åæç´¢å¾å°çéå¼ç»æ44        self.threshold, _ = find_threshold(gt_ent, pred_words, pred_proba)4546        return self.threshold4748    # è®ç»å¨é¨æ°æ®49    def train_and_predict(self, features, target='label', save=True):50        self.fe = features51        X_train, y_train = self.train_[features].values, self.train_[target].values.astype('uint8')52        X_test = self.test_[self.fe].values5354        cat_all = Pool(X_train, y_train)5556        model = catboost.train(57            cat_all,58            self.params,59            iterations=10000,60            early_stopping_rounds=200,61            verbose=100062        )6364        self.model = model6566        if save:67            save_pkl(model, os.path.join(self.opt['model_train'], 'cat.pkl'))6869        self.pred = model.predict(X_test, prediction_type='Probability')[:, 1]7071        return self.pred7273    # ä¿åæµè¯éå¾å°çç»æ74    def save_result(self):75        pred_id, pred_words, pred_proba = sort_val(self.test_, self.pred, predict=True)76        entities = return_entity(pred_words, pred_proba, self.threshold)7778        save_as_order(pred_id, entities, self.opt, 'cat_result.txt')7980818283
...

eval.py

Source:eval.py

1"""2run evaluation on test dataset3"""4import argparse5import json6from pycocotools.coco import COCO7from pycocotools.cocoeval import COCOeval8import pandas as pd9from .data.coco import category_count10def coco_eval(cocoGt, cocoDt, catIds):11    E = COCOeval(cocoGt, cocoDt, iouType='bbox')  # initialize CocoEval object12    if catIds:13        E.params.catIds = catIds14        print([cocoGt.cats[c] for c in E.params.catIds])15    E.evaluate()16    E.accumulate()17    E.summarize()18    return E.stats19if __name__ == '__main__':20    parser = argparse.ArgumentParser(description=__doc__,21                                     formatter_class=argparse.ArgumentDefaultsHelpFormatter,22                                     fromfile_prefix_chars='@')23    parser.add_argument("--gt", type=str, help='ground truth annotation')24    parser.add_argument('--dt', type=str, default='submission.json', help='detection json')25    parser.add_argument('--dt-images', action='store_true', help='use images of detection only')26    parser.add_argument('--catId', type=lambda s: [int(i) for i in s.split(',')], default=[], help='select catIds')27    parser.add_argument('--each-category', action='store_true', help='evaluate each category')28    args = parser.parse_args()29    cocoGt = COCO(args.gt)30    if args.dt_images:31        dt = json.load(open(args.dt))32        dt_images = set([d['image_id'] for d in dt])33        print('#images:', len(dt_images))34        cocoGt.imgs = {k: v for k, v in cocoGt.imgs.items() if k in dt_images}35    cocoDt = cocoGt.loadRes(args.dt)36    catIds = cocoGt.getCatIds(catIds=args.catId)37    if args.each_category:38        cat_count = category_count(cocoGt)39        print('cat_count', cat_count)40        cat_eval = {c: coco_eval(cocoGt, cocoDt, [c]) for c in catIds}41        cat_eval = {c: v for c, v in cat_eval.items() if (v >= 0).any()}42        columns = ['mAP', 'AP50', 'AP75', 'mAPs', 'mAPm', 'mAPl', 'AR1', 'AR10', 'AR', 'ARs', 'ARm', 'ARl', 'T', 'name']43        values = [v.tolist() + [cat_count[k], cocoGt.cats[k]['name']] for k, v in cat_eval.items()]44        cat_eval = pd.DataFrame(values, index=cat_eval.keys(), columns=columns)45        print("=============================================== summary ===============================================")46        pd.set_option('display.max_columns', None)47        pd.set_option('display.max_rows', None)48        pd.set_option('display.precision', 3)49        pd.set_option('display.width', 120)50        print(cat_eval.sort_values(by=['mAP']))...

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