压缩试验只在必要时训练模型

tests/book/ch05/plot_compressed_f1.py

@@ -4,20 +4,24 @@
 # 《自然语言处理入门》5.6.3 特征裁剪与模型压缩
 # 配套书籍：http://nlp.hankcs.com/book.php
 # 讨论答疑：https://bbs.hankcs.com/
-import sys
+import os
+
+import matplotlib.pyplot as plt
 from jpype import JClass
+
 from tests.book.ch03.eval_bigram_cws import CWSEvaluator
 from tests.book.ch03.msr import msr_train, msr_model, msr_gold, msr_dict, msr_output, msr_test
 from tests.book.ch05.perceptron_cws import CWSTrainer, PerceptronLexicalAnalyzer
-import matplotlib.pyplot as plt
 
 plt.rcParams['font.sans-serif'] = ['SimHei']  # 用来正常显示中文标签
 plt.rcParams['axes.unicode_minus'] = False  # 用来正常显示负号
 
 
 def train_evaluate(ratios):
-    model = CWSTrainer().train(msr_train, msr_train, msr_model, 0, 10, 8).getModel()  # 训练模型
-    # model = JClass('com.hankcs.hanlp.model.perceptron.model.LinearModel')(msr_model)
+    if not os.path.isfile(msr_model):
+        model = CWSTrainer().train(msr_train, msr_train, msr_model, 0, 10, 8).getModel()  # 训练模型
+    else:
+        model = JClass('com.hankcs.hanlp.model.perceptron.model.LinearModel')(msr_model)
     pre = -1
     scores = []
     for c in ratios: