def POS_Tagger(crf_iteration, L1_coefficient, L2_coefficient):
X_train, y_train, X_test, y_test = data_helpers.load_POS_data(None, True)
labels, crf = Train.train(X_train, y_train, crf_iteration, L1_coefficient,
L2_coefficient)
y_pred = Train.Predict(X_test, crf)
Train.Classification_report_detail(y_test, y_pred)
print("POS_Tagger accuracy:",
Train.Classification_report_accuracy(y_test, y_pred))
print("POS_Tagger weighted f-score:",
Train.Classification_report_fscore_weighted(y_test, y_pred))
print("POS_Tagger macro f-score:",
Train.Classification_report_fscore_macro(y_test, y_pred))
print("POS_Tagger micro f-score:",
Train.Classification_report_fscore_micro(y_test, y_pred))
def Trained_POS2Chunker(crf_iteration, L1_coefficient_p, L2_coefficient_p,
L1_coefficient_c, L2_coefficient_c):
X_train, y_train, X_test, y_test = data_helpers.load_POS_data(None, True)
_, crf = Train.train(X_train, y_train, crf_iteration, L1_coefficient_p,
L2_coefficient_p)
y_pred_pos = Train.Predict(X_train, crf)
X_train, y_train, X_test, y_test = data_helpers.load_Chunk_data(
y_pred_pos, False)
labels, crf = Train.train(X_train, y_train, crf_iteration,
L1_coefficient_c, L2_coefficient_c)
y_pred = Train.Predict(X_test, crf)
Train.Classification_report_detail(y_test, y_pred)
print("Chunker accuracy:",
Train.Classification_report_accuracy(y_test, y_pred))
print("Chunker weighted f-score:",
Train.Classification_report_fscore_weighted(y_test, y_pred))
print("Chunker macro f-score:",
Train.Classification_report_fscore_macro(y_test, y_pred))
print("Chunker micro f-score:",
Train.Classification_report_fscore_micro(y_test, y_pred))
def Iterative_Training(iteration, crf_iteration, L1_coefficient_p,
L2_coefficient_p, L1_coefficient_c, L2_coefficient_c):
X_train_pos, y_train_pos, X_test_pos, y_test_pos = data_helpers.load_POS_data(
None, True)
pos_fscores = []
chunk_fscores = []
for i in range(iteration):
_, crf_pos = Train.train(X_train_pos, y_train_pos, crf_iteration,
L1_coefficient_p, L2_coefficient_p)
y_pred_pos = Train.Predict(X_train_pos, crf_pos)
y_pred_pos_f = Train.Predict(X_test_pos, crf_pos)
X_train_chunk, y_train_chunk, X_test_chunk, y_test_chunk = data_helpers.load_Chunk_data(
y_pred_pos, False)
_, crf_chunk = Train.train(X_train_chunk, y_train_chunk, crf_iteration,
L1_coefficient_c, L2_coefficient_c)
y_pred_chunk = Train.Predict(X_train_chunk, crf_chunk)
y_pred_chunk_f = Train.Predict(X_test_chunk, crf_chunk)
X_train_pos, y_train_pos, X_test_pos, y_test_pos = data_helpers.load_POS_data_with_chunk(
y_pred_chunk, False)
pos_fscores.append(
round(
Train.Classification_report_fscore_weighted(
y_test_pos, y_pred_pos_f), 5))
chunk_fscores.append(
round(
Train.Classification_report_fscore_weighted(
y_test_chunk, y_pred_chunk_f), 5))
print(
"Iteration,", i + 1, "POS_Tagger weighted f-score:",
Train.Classification_report_fscore_weighted(
y_test_pos, y_pred_pos_f), ", Chunker weighted f-score:",
Train.Classification_report_fscore_weighted(
y_test_chunk, y_pred_chunk_f))
print("Classification report after", iteration, "iteration")
Train.Classification_report_detail(y_test_pos, y_pred_pos_f)
print("POS_Tagger accuracy:",
Train.Classification_report_accuracy(y_test_pos, y_pred_pos_f))
print(
"POS_Tagger weighted f-score:",
Train.Classification_report_fscore_weighted(y_test_pos, y_pred_pos_f))
print("POS_Tagger macro f-score:",
Train.Classification_report_fscore_macro(y_test_pos, y_pred_pos_f))
print("POS_Tagger micro f-score:",
Train.Classification_report_fscore_micro(y_test_pos, y_pred_pos_f))
print(
"________________________________________________________________________________"
)
Train.Classification_report_detail(y_test_chunk, y_pred_chunk_f)
print("Chunker accuracy:",
Train.Classification_report_accuracy(y_test_chunk, y_pred_chunk_f))
print(
"Chunker weighted f-score:",
Train.Classification_report_fscore_weighted(y_test_chunk,
y_pred_chunk_f))
print(
"Chunker macro f-score:",
Train.Classification_report_fscore_macro(y_test_chunk, y_pred_chunk_f))
print(
"Chunker micro f-score:",
Train.Classification_report_fscore_micro(y_test_chunk, y_pred_chunk_f))
plt.plot(pos_fscores)
plt.plot(chunk_fscores)
plt.title("CRF iteration: " + str(crf_iteration))
plt.ylabel('weighted f-score')
plt.xlabel('iteration')
plt.legend(['POS test', 'Chunk test'], loc='upper left')
plt.figtext(
.2, .2, " POS Max: " + str(max(pos_fscores) * 100) +
" in iteration: " + str(pos_fscores.index(max(pos_fscores)) + 1) +
" Chunk Max: " + str(max(chunk_fscores) * 100) + " in iteration: " +
str(chunk_fscores.index(max(chunk_fscores)) + 1))
plt.show()