def model_test():
print("here")
sentiments = np.load('sentiments.npy', allow_pickle=True)
texts = np.load('texts.npy', allow_pickle=True)
all_texts = np.load('text_cache.npy', allow_pickle=True)
neutral = []
_, X_test, _, Y_test = train_test_split(texts, sentiments, test_size=0.01)
print("here ",len(Y_test))
airline_data = CSVReader.dataframe_from_file("Tweets.csv",['airline_sentiment','text'])
airline_text = np.array(airline_data.text)
airline_sentiment = np.array(airline_data.airline_sentiment)
count = 0
for i in range(len(airline_text)):
if(count > 1000):
break
if(airline_sentiment[i] == "neutral"):
neutral = np.append(neutral,airline_text[i])
count+=1
X_test = np.append(X_test,neutral)
Y_test = np.append(Y_test,[0]*len(neutral))
Y_test[Y_test==-1] = 4
Y_test[Y_test==-2] = 3
# categ_test = to_categorical(Y_test,num_classes=5)
tokenizer = Tokenizer(num_words=300000)
tokenizer.fit_on_texts(all_texts)
model = load_model("savedModel2/saved-model3-60.h5")
result = model.predict_on_batch(pad_sequences(tokenizer.texts_to_sequences(X_test),maxlen=75))
result = np.argmax(result,axis=-1)
# cat_result = to_categorical(result,num_classes=5)
print("f1 ",precision_score(Y_test,result, average=None))
"everything is great, i have lost some weight",
"awesome, really cool", "should I play cards",
"I am full and inshape", "is it okay to be that hungry at night?"
]),
maxlen=75))
print("result: ", np.argmax(result, axis=-1), "\n")
if __name__ == "__main__":
embeddings = np.load('text_embedding.npy', allow_pickle=True)
sentiments = np.load('sentiments.npy', allow_pickle=True)
texts = np.load('texts.npy', allow_pickle=True)
all_texts = np.load('text_cache.npy', allow_pickle=True)
_, X_test, _, Y_test = train_test_split(texts, sentiments, test_size=0.01)
airline_data = CSVReader.dataframe_from_file("Tweets.csv",
['airline_sentiment', 'text'])
airline_text = np.array(airline_data.text)
airline_sentiment = np.array(airline_data.airline_sentiment)
count = 0
for i in range(len(airline_text)):
if (count > 1000):
break
if (airline_sentiment[i] == "neutral"):
X_test = np.append(X_test, airline_text[i])
Y_test = np.append(Y_test, [0])
count += 1
models = []
models = np.append(models, load_model("ensemble_bgru.h5"))
models = np.append(models, load_model("ensemble_gru.h5"))
models = np.append(models, load_model("ensemble_gru.h5"))
models = np.append(models, load_model("ensemble_lstm.h5"))
def __init__(self):
#self.emotion_data = CSVReader.dataframe_from_file("VentDataset/emotions.csv", ['id', 'emotion_category_id'])
# self.emotion_data = self.emotion_data[self.emotion_data.enabled == 'TRUE']
self.vent_data = CSVReader.dataframe_from_file("VentDataset/vents.csv",
['emotion_id', 'text'])
self.textPreProcessing = TextPreprocessing()
