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()