def exc():
# 4.2 Load Training and Testing Data
modelData = store.loadTrainingTestingDataCSV()
# 4.3 Train model
print('=> Training model...')
clf_model = buildClassifer()
clf_model.fit(modelData['X_train'], modelData['y_train'])
y_hat = clf_model.predict(modelData['X_test'])
tacc = sum([1 for (y1, y2) in zip(modelData['y_test'], y_hat) if (y1 == y2)]) / len(y_hat)
print('Acc = ', tacc)
store.saveClassifier(clf_model)
return tacc
def exc():
# 4.2 Load Training and Testing Data
modelData = store.loadTrainingTestingDataCSV()
# 4.3 Train model
print('=> Training model...')
clf_model = buildClassifer()
clf_model.fit(modelData['X_train'], modelData['y_train'])
y_hat = clf_model.predict(modelData['X_test'])
tacc = sum([
1 for (y1, y2) in zip(modelData['y_test'], y_hat) if (y1 == y2)
]) / len(y_hat)
print('Acc = ', tacc)
store.saveClassifier(clf_model)
return tacc
tupleData = store.loadTxtData()
# 2. Extract features
featureTuples = extractFeature(feature_func, preprocessing_func, tupleData)
store.saveFeature(featureTuples)
# 3.1 Random Training and Testing Data
x = randomSample()
store.saveTrainingTestingData(x)
# 3.2 Load Training and Testing Data
modelData = store.loadTrainingTestingData()
# 3.3 Train model
print('=> Training model...')
clf_model = buildClassifer()
clf_model.fit(modelData['X_train'], modelData['y_train'])
y_hat = clf_model.predict(modelData['X_test'])
print(
'Acc = ',
sum([1 for (y1, y2) in zip(modelData['y_test'], y_hat) if (y1 == y2)]) /
len(y_hat))
store.saveClassifier(clf_model)
# 4. Test
import test_address_segment
import test_term_classifier_model
None
from libs.models import buildClassifer
from libs.config import *
# 1. Read txt data
tupleData = store.loadTxtData()
# 2. Extract features
featureTuples = extractFeature(feature_func, preprocessing_func, tupleData)
store.saveFeature(featureTuples)
# 3.1 Random Training and Testing Data
x = randomSample()
store.saveTrainingTestingData(x)
# 3.2 Load Training and Testing Data
modelData = store.loadTrainingTestingData()
# 3.3 Train model
print('=> Training model...')
clf_model = buildClassifer()
clf_model.fit(modelData['X_train'], modelData['y_train'])
y_hat = clf_model.predict(modelData['X_test'])
print('Acc = ', sum([1 for (y1, y2) in zip(modelData['y_test'], y_hat) if (y1 == y2)]) / len(y_hat))
store.saveClassifier(clf_model)
# 4. Test
import test
None