print("### Obtained Scores ###")
print("### (full dataset, top 15)###")
print("###")
print("### Precision : %.4f" % precision_top)
print("### Recall : %.4f" % recall_top)
print("### F1 : %.4f" % f1_top)
print("### ###")
if DATASET == Semeval2017:
print("### SEMEVAL ANNOTATOR ###")
print("### All ###")
from eval import anno_generator
anno_generator.write_anno("/tmp/simplernn", test_doc_str, obtained_words)
from data.Semeval2017 import eval
eval.calculateMeasures("data/Semeval2017/test",
"/tmp/simplernn-all",
remove_anno=["types"])
print("### Filtered ###")
anno_generator.write_anno("/tmp/simplernn-clean", test_doc_str,
clean_words)
eval.calculateMeasures("data/Semeval2017/test",
"/tmp/simplernn-clean",
remove_anno=["types"])
print("### Top 15 ###")
anno_generator.write_anno("/tmp/simplernn-15", test_doc_str,
keras_f1 = keras_metrics.keras_f1(test_y,output)
print("### Obtained Scores ###")
print("### (fixed dataset) ###")
print("###")
print("### Precision : %.4f" % keras_precision)
print("### Recall : %.4f" % keras_recall)
print("### F1 : %.4f" % keras_f1)
print("### ###")
clean_words = postprocessing.get_valid_patterns(obtained_words)
precision = metrics.precision(test_answer,clean_words)
recall = metrics.recall(test_answer,clean_words)
f1 = metrics.f1(precision,recall)
print("### Obtained Scores ###")
print("### (full dataset, ###")
print("### pos patterns filter) ###")
print("###")
print("### Precision : %.4f" % precision)
print("### Recall : %.4f" % recall)
print("### F1 : %.4f" % f1)
print("### ###")
if DATASET == Semeval2017:
from eval import anno_generator
anno_generator.write_anno("/tmp/simplernn",test_doc_str,obtained_words)
from data.Semeval2017 import eval
eval.calculateMeasures("data/Semeval2017/test","/tmp/simplernn",remove_anno=["types"])
print("### Obtained Scores ###")
print("### (full dataset, top 10)###")
print("###")
print("### Precision : %.4f" % precision_top)
print("### Recall : %.4f" % recall_top)
print("### F1 : %.4f" % f1_top)
print("### ###")
obtained_words_top = postprocessing.get_top_words(test_doc, output, 15)
precision_top = metrics.precision(test_answer, obtained_words_top, STEM_MODE)
recall_top = metrics.recall(test_answer, obtained_words_top, STEM_MODE)
f1_top = metrics.f1(precision_top, recall_top)
print("### Obtained Scores ###")
print("### (full dataset, top 15)###")
print("###")
print("### Precision : %.4f" % precision_top)
print("### Recall : %.4f" % recall_top)
print("### F1 : %.4f" % f1_top)
print("### ###")
if DATASET == Semeval2017:
from eval import anno_generator
anno_generator.write_anno("/tmp/mergernn2", test_doc_str, clean_words)
from data.Semeval2017 import eval
eval.calculateMeasures("data/Semeval2017/test",
"/tmp/simplernn",
remove_anno=["types"])