__author__ = 'Thong_Le'
from libs.features import extractFeature, randomSample
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))
__author__ = 'Thong_Le'
from libs.features import extractFeature, randomSample
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
def exc():
# store.loadFeatureCSV()
# 4.1 Random Training and Testing Data
x = randomSample()
# store.saveTrainingTestingData(x)
store.saveTrainingTestingDataCSV(x, getFeatureNames())
from libs import store
from libs.features import extractFeature, randomSample
from libs.models import buildClassifer
from config import *
# 1. Read txt data
tupleData = store.loadTxtData()
# 2. Extract features
featureTuples = extractFeature(feature_func)
store.saveFeature(featureTuples)
accs = []
if (standard_data):
X_train, y_train, X_test, y_test = randomSample()
X_train, y_train, X_test, y_test = np.asarray(X_train), np.asarray(
y_train), np.asarray(X_test), np.asarray(y_test)
print('=> Testing model with static data...')
for i in range(nTesting):
# 3.3 Train model
print('==> Training model ' + str(i) + '...')
clf_model = buildClassifer()
clf_model.fit(X_train, y_train)
y_hat = clf_model.predict(X_test)
tacc = sum([1 for (y1, y2) in zip(y_test, y_hat) if (y1 == y2)
]) / len(y_hat)
print('<== Acc = ', tacc)
accs.append(tacc)
from libs.features import extractFeature, randomSample
from libs.models import buildClassifer
from libs.config import *
import numpy as np
# 1. Read txt data
tupleData = store.loadTxtData()
# 2. Extract features
featureTuples = extractFeature(feature_func, preprocessing_func, tupleData)
store.saveFeature(featureTuples)
accs = []
if (standard_data):
X_train, y_train, X_test, y_test = randomSample()
X_train, y_train, X_test, y_test = np.asarray(X_train), np.asarray(y_train), np.asarray(X_test), np.asarray(y_test)
print('=> Testing model with static data...')
for i in range(nTesting):
# 3.3 Train model
print('==> Training model ' + str(i) + '...')
clf_model = buildClassifer()
clf_model.fit(X_train, y_train)
y_hat = clf_model.predict(X_test)
tacc = sum([1 for (y1, y2) in zip(y_test, y_hat) if (y1 == y2)]) / len(y_hat)
print('<== Acc = ', tacc)
accs.append(tacc)
else:
print('=> Testing model with random data...')