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evaluate.py
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evaluate.py
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import pandas as pd
import numpy as np
from tobow import tobow
from sklearn.preprocessing import LabelEncoder
from keras.models import Sequential
from keras.models import model_from_json
def evaluate(model_path, weights_path, test_data_path, test_label_path, encoder_path):
X_test = pd.read_csv(test_data_path, header=None)
Y_test = pd.read_csv(test_label_path, header=None)
test_data = X_test.values
test_label = Y_test.values
#load class encoder
encoder = LabelEncoder()
encoder.classes_ = np.load(encoder_path)
json_file = open(model_path, "r")
loaded_model_json = json_file.read()
json_file.close()
#load classifier model
model = model_from_json(loaded_model_json)
model.load_weights(weights_path)
model.compile(loss="categorical_crossentropy",
optimizer="adam",
metrics=['accuracy'])
#classification process
labels = []
for sentence in test_data:
vector = tobow(sentence[0])
predict = model.predict(np.array([vector[0]]))
index = np.argmax(predict[0])
labels.append(encoder.classes_[index])
#evaluate accuracy
correct = 0
for i in range(len(labels)):
print(test_label[i]+"===>"+labels[i])
if test_label[i] == labels[i]:
correct += 1
print("correct"+str(correct)+" from "+str(len(test_data)))
print("Accuracy: "+str(float(correct)/len(test_data)*100))
return correct/len(test_data)
model_path = "classifier.json"
weights_path = "weights.h5"
encoder_path = "encoder.npy"
test_data_path = "data/test/test-data.csv"
test_label_path = "data/test/test-label.csv"
evaluate(model_path,weights_path,test_data_path,test_label_path,encoder_path)