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question_type_anlaysis.py
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question_type_anlaysis.py
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from lcquad_test import Orchestrator
from parser.lc_quad import LC_QaudParser
from learning.classifier.svmclassifier import SVMClassifier
from parser.qald import Qald
from parser.lc_quad_linked import LC_Qaud_Linked
from sklearn.metrics import classification_report
from sklearn.metrics import accuracy_score
import sys
import numpy as np
import json
from sklearn.metrics import confusion_matrix
import matplotlib.pyplot as plt
if __name__ == "__main__":
parser = LC_QaudParser()
classifier1 = SVMClassifier('./output/question_type_classifier/svm.model')
classifier2 = SVMClassifier('./output/double_relation_classifier/svm.model')
query_builder = Orchestrator(None, classifier1, classifier2, parser, None, auto_train=True)
print("train_question_classifier")
scores = query_builder.train_question_classifier(file_path="./data/LC-QUAD/data.json", test_size=0.8)
print(scores)
y_pred = query_builder.question_classifier.predict(query_builder.X_test)
print(accuracy_score(query_builder.y_test, y_pred))
print(classification_report(query_builder.y_test, y_pred, digits=3))
ds = LC_Qaud_Linked(path="./data/LC-QUAD/linked_test.json")
ds.load()
ds.parse()
lcquad = []
lc_y = []
for qapair in ds.qapairs:
lcquad.append(qapair.question.text)
if "COUNT(" in qapair.sparql.query:
lc_y.append(2)
elif "ASK" in qapair.sparql.query:
lc_y.append(1)
else:
lc_y.append(0)
lc_y = np.array(lc_y)
print('LIST: ', sum(lc_y==0))
print('ASK: ', sum(lc_y == 1))
print('COUNT: ', sum(lc_y == 2))
np.savetxt('lcquad_question_type.csv', lc_y, delimiter=',')
lc_pred = query_builder.question_classifier.predict(lcquad)
print('LC-QUAD question_classifier')
print(accuracy_score(lc_y, lc_pred))
print(classification_report(lc_y, lc_pred, digits=4))
classes = ['List', 'Count', 'Boolean']
cm = confusion_matrix(lc_y, lc_pred)
print('Before Normalization')
print(cm)
print('Accuracy by class: ')
c_acc = cm.diagonal() / cm.sum(axis=1)
print(c_acc)
cm = cm.astype('float') / cm.sum(axis=1)[:, np.newaxis]
print('After Normalization')
print(cm)
fig, ax = plt.subplots()
im = ax.imshow(cm, interpolation='nearest', cmap=plt.cm.Blues)
ax.figure.colorbar(im, ax=ax)
# We want to show all ticks...
ax.set(xticks=np.arange(cm.shape[1]),
yticks=np.arange(cm.shape[0]),
# ... and label them with the respective list entries
xticklabels=classes, yticklabels=classes,
ylabel='True label',
xlabel='Predicted label')
# Rotate the tick labels and set their alignment.
plt.setp(ax.get_xticklabels(), rotation=45, ha="right",
rotation_mode="anchor")
# Loop over data dimensions and create text annotations.
fmt = '.2f'
thresh = cm.max() / 2.
for i in range(cm.shape[0]):
for j in range(cm.shape[1]):
ax.text(j, i, format(cm[i, j], fmt),
ha="center", va="center",
color="white" if cm[i, j] > thresh else "black")
fig.tight_layout()
plt.savefig('confusion_matrix_lcquad.png')
q_ds = Qald(Qald.qald_7)
q_ds.load()
q_ds.parse()
qald = []
q_y = []
for qapair in q_ds.qapairs:
qald.append(qapair.question.text)
if "COUNT(" in qapair.sparql.query:
q_y.append(2)
elif "ASK" in qapair.sparql.query:
q_y.append(1)
x = ascii(qapair.sparql.query.replace('\n', ' ').replace('\t', ' '))
print(x)
else:
q_y.append(0)
q_y = np.array(q_y)
print('LIST: ', sum(q_y==0))
print('ASK: ', sum(q_y == 1))
print('COUNT: ', sum(q_y == 2))
np.savetxt('qald_question_type.csv', q_y, delimiter=',')
q_pred = query_builder.question_classifier.predict(qald)
print('QALD question_classifier')
print(accuracy_score(q_y, q_pred))
print(classification_report(q_y, q_pred, digits=4))
classes = ['List', 'Count', 'Boolean']
cm = confusion_matrix(q_y, q_pred)
print('Before Normalization')
print(cm)
print('Accuracy by class: ')
c_acc = cm.diagonal() / cm.sum(axis=1)
print(c_acc)
cm = cm.astype('float') / cm.sum(axis=1)[:, np.newaxis]
print('After Normalization')
print(cm)
fig, ax = plt.subplots()
im = ax.imshow(cm, interpolation='nearest', cmap=plt.cm.Blues)
ax.figure.colorbar(im, ax=ax)
# We want to show all ticks...
ax.set(xticks=np.arange(cm.shape[1]),
yticks=np.arange(cm.shape[0]),
# ... and label them with the respective list entries
xticklabels=classes, yticklabels=classes,
ylabel='True label',
xlabel='Predicted label')
# Rotate the tick labels and set their alignment.
plt.setp(ax.get_xticklabels(), rotation=45, ha="right",
rotation_mode="anchor")
# Loop over data dimensions and create text annotations.
fmt = '.2f'
thresh = cm.max() / 2.
for i in range(cm.shape[0]):
for j in range(cm.shape[1]):
ax.text(j, i, format(cm[i, j], fmt),
ha="center", va="center",
color="white" if cm[i, j] > thresh else "black")
fig.tight_layout()
plt.savefig('confusion_matrix_qald.png')
ds = LC_Qaud_Linked(path="./data/LC-QUAD/linked_answer.json")
ds.load()
ds.parse()
lcquad = []
lc_y = []
for qapair in ds.qapairs:
lcquad.append(qapair.question.text)
if "COUNT(" in qapair.sparql.query:
lc_y.append(2)
elif "ASK" in qapair.sparql.query:
lc_y.append(1)
else:
lc_y.append(0)
lc_y = np.array(lc_y)
print('LIST: ', sum(lc_y==0))
print('ASK: ', sum(lc_y == 1))
print('COUNT: ', sum(lc_y == 2))
np.savetxt('lcquad_question_type_all.csv', lc_y, delimiter=',')
lc_pred = query_builder.question_classifier.predict(lcquad)
print('LC-QUAD question_classifier')
print(accuracy_score(lc_y, lc_pred))
print(classification_report(lc_y, lc_pred, digits=4))
classes = ['List', 'Count', 'Boolean']
cm = confusion_matrix(lc_y, lc_pred)
print('Before Normalization')
print(cm)
print('Accuracy by class: ')
c_acc = cm.diagonal() / cm.sum(axis=1)
print(c_acc)
cm = cm.astype('float') / cm.sum(axis=1)[:, np.newaxis]
print('After Normalization')
print(cm)
fig, ax = plt.subplots()
im = ax.imshow(cm, interpolation='nearest', cmap=plt.cm.Blues)
ax.figure.colorbar(im, ax=ax)
# We want to show all ticks...
ax.set(xticks=np.arange(cm.shape[1]),
yticks=np.arange(cm.shape[0]),
# ... and label them with the respective list entries
xticklabels=classes, yticklabels=classes,
ylabel='True label',
xlabel='Predicted label')
# Rotate the tick labels and set their alignment.
plt.setp(ax.get_xticklabels(), rotation=45, ha="right",
rotation_mode="anchor")
# Loop over data dimensions and create text annotations.
fmt = '.2f'
thresh = cm.max() / 2.
for i in range(cm.shape[0]):
for j in range(cm.shape[1]):
ax.text(j, i, format(cm[i, j], fmt),
ha="center", va="center",
color="white" if cm[i, j] > thresh else "black")
fig.tight_layout()
plt.savefig('confusion_matrix_lcquad_all.png')