114 KiB
114 KiB
Fasttext --> Feedforward Neural Network
import pandas as pd
import numpy as np
train = pd.read_csv("train.csv")
test = pd.read_csv("test.csv")
valid = pd.read_csv("valid.csv")
train.loc[train["review_score"]==-1, "review_score"]=0
test.loc[test["review_score"]==-1, "review_score"]=0
valid.loc[valid["review_score"]==-1, "review_score"]=0Wczytywanie pliku z wyuczonymi angielskimi embeddingami fasttext (https://fasttext.cc/docs/en/crawl-vectors.html):
from gensim.models.fasttext import load_facebook_model
vectors_file = "cc.en.300.bin"
fb_model = load_facebook_model(vectors_file)
fb_model.wv.get_sentence_vector("Good game")array([ 1.78246237e-02, -1.06189400e-01, -2.44729444e-02, -2.21012142e-02,
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dtype=float32)from numpy.linalg import norm
def cosine_similarity(a, b):
return np.dot(a,b)/(norm(a)*norm(b))
def get_sentence_similarity(sent_a, sent_b):
vec1 = fb_model.wv.get_sentence_vector(sent_a)
vec2 = fb_model.wv.get_sentence_vector(sent_b)
return cosine_similarity(vec1,vec2)
print(get_sentence_similarity("Good game", "Amazing game, I love it!"))
print(get_sentence_similarity("Good game", "Horrible game. A buggy mess."))0.9303907 0.94947904
train["vectorized"] = train["review_text"].apply(lambda x : fb_model.wv.get_sentence_vector(x))train.iloc[0]["vectorized"]array([ 8.71886499e-03, -9.33922902e-02, -3.61323059e-02, -2.44729072e-02,
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dtype=float32)test["vectorized"] = test["review_text"].apply(lambda x : fb_model.wv.get_sentence_vector(x))
valid["vectorized"] = valid["review_text"].apply(lambda x : fb_model.wv.get_sentence_vector(x))import tensorflow as tf
from tensorflow import keras
from keras import layers
from keras.optimizers import Adam
def create_model():
inputs = keras.Input(shape=(300,))
dense1 = layers.Dense(256, activation="relu")(inputs)
dense2 = layers.Dense(128, activation="relu")(dense1)
dense3 = layers.Dense(64, activation="relu")(dense2)
output = layers.Dense(1, activation="sigmoid")(dense3)
model = keras.Model(inputs=inputs, outputs=output)
model.compile(loss='binary_crossentropy', optimizer=Adam(learning_rate=1e-4), metrics=['accuracy'])
return modeltrain_x = np.stack(train["vectorized"].values)
train_y = np.stack(train["review_score"].values)
valid_x = np.stack(valid["vectorized"].values)
valid_y = np.stack(valid["review_score"].values)
test_x = np.stack(test["vectorized"].values)
test_y = np.stack(test["review_score"].values)callback = keras.callbacks.EarlyStopping(monitor='val_loss', mode='min', patience=5, restore_best_weights=True)
model = create_model()
history = model.fit(train_x, train_y, validation_data=(valid_x, valid_y), epochs=25, callbacks=[callback])Epoch 1/25 1351/1351 [==============================] - 3s 1ms/step - loss: 0.6815 - accuracy: 0.5627 - val_loss: 0.6221 - val_accuracy: 0.7079 Epoch 2/25 1351/1351 [==============================] - 2s 1ms/step - loss: 0.6564 - accuracy: 0.6112 - val_loss: 0.6649 - val_accuracy: 0.5497 Epoch 3/25 1351/1351 [==============================] - 2s 1ms/step - loss: 0.6492 - accuracy: 0.6204 - val_loss: 0.6619 - val_accuracy: 0.5553 Epoch 4/25 1351/1351 [==============================] - 2s 1ms/step - loss: 0.6439 - accuracy: 0.6280 - val_loss: 0.6552 - val_accuracy: 0.5702 Epoch 5/25 1351/1351 [==============================] - 2s 1ms/step - loss: 0.6405 - accuracy: 0.6333 - val_loss: 0.6135 - val_accuracy: 0.6346 Epoch 6/25 1351/1351 [==============================] - 2s 1ms/step - loss: 0.6374 - accuracy: 0.6352 - val_loss: 0.7130 - val_accuracy: 0.4843 Epoch 7/25 1351/1351 [==============================] - 2s 1ms/step - loss: 0.6348 - accuracy: 0.6409 - val_loss: 0.6140 - val_accuracy: 0.6315 Epoch 8/25 1351/1351 [==============================] - 2s 1ms/step - loss: 0.6307 - accuracy: 0.6445 - val_loss: 0.5606 - val_accuracy: 0.7186 Epoch 9/25 1351/1351 [==============================] - 2s 1ms/step - loss: 0.6297 - accuracy: 0.6449 - val_loss: 0.6899 - val_accuracy: 0.5398 Epoch 10/25 1351/1351 [==============================] - 2s 1ms/step - loss: 0.6267 - accuracy: 0.6480 - val_loss: 0.6795 - val_accuracy: 0.5511 Epoch 11/25 1351/1351 [==============================] - 2s 1ms/step - loss: 0.6251 - accuracy: 0.6486 - val_loss: 0.6778 - val_accuracy: 0.5430 Epoch 12/25 1351/1351 [==============================] - 2s 1ms/step - loss: 0.6222 - accuracy: 0.6530 - val_loss: 0.5447 - val_accuracy: 0.7297 Epoch 13/25 1351/1351 [==============================] - 2s 1ms/step - loss: 0.6206 - accuracy: 0.6541 - val_loss: 0.5630 - val_accuracy: 0.7080 Epoch 14/25 1351/1351 [==============================] - 2s 1ms/step - loss: 0.6189 - accuracy: 0.6563 - val_loss: 0.5919 - val_accuracy: 0.6746 Epoch 15/25 1351/1351 [==============================] - 2s 1ms/step - loss: 0.6168 - accuracy: 0.6588 - val_loss: 0.6887 - val_accuracy: 0.5485 Epoch 16/25 1351/1351 [==============================] - 2s 1ms/step - loss: 0.6160 - accuracy: 0.6576 - val_loss: 0.5550 - val_accuracy: 0.7132 Epoch 17/25 1351/1351 [==============================] - 2s 1ms/step - loss: 0.6132 - accuracy: 0.6589 - val_loss: 0.6529 - val_accuracy: 0.5999
def create_model():
inputs = keras.Input(shape=(300,))
dense1 = layers.Dense(1024, activation="relu")(inputs)
dense2 = layers.Dense(512, activation="relu")(dense1)
dense3 = layers.Dense(128, activation="relu")(dense2)
output = layers.Dense(1, activation="sigmoid")(dense3)
model = keras.Model(inputs=inputs, outputs=output)
model.compile(loss='binary_crossentropy', optimizer=Adam(learning_rate=1e-4), metrics=['accuracy'])
return modelcallback = keras.callbacks.EarlyStopping(monitor='val_loss', mode='min', patience=5, restore_best_weights=True)
model = create_model()
history = model.fit(train_x, train_y, validation_data=(valid_x, valid_y), epochs=25, callbacks=[callback])Epoch 1/25 1351/1351 [==============================] - 3s 2ms/step - loss: 0.6707 - accuracy: 0.5788 - val_loss: 0.6569 - val_accuracy: 0.5608 Epoch 2/25 1351/1351 [==============================] - 2s 2ms/step - loss: 0.6493 - accuracy: 0.6180 - val_loss: 0.8048 - val_accuracy: 0.3736 Epoch 3/25 1351/1351 [==============================] - 2s 2ms/step - loss: 0.6426 - accuracy: 0.6281 - val_loss: 0.6285 - val_accuracy: 0.6160 Epoch 4/25 1351/1351 [==============================] - 2s 2ms/step - loss: 0.6376 - accuracy: 0.6348 - val_loss: 0.6405 - val_accuracy: 0.5911 Epoch 5/25 1351/1351 [==============================] - 3s 2ms/step - loss: 0.6328 - accuracy: 0.6406 - val_loss: 0.6520 - val_accuracy: 0.5826 Epoch 6/25 1351/1351 [==============================] - 2s 2ms/step - loss: 0.6283 - accuracy: 0.6458 - val_loss: 0.6654 - val_accuracy: 0.5612 Epoch 7/25 1351/1351 [==============================] - 3s 2ms/step - loss: 0.6251 - accuracy: 0.6484 - val_loss: 0.6713 - val_accuracy: 0.5577 Epoch 8/25 1351/1351 [==============================] - 2s 2ms/step - loss: 0.6230 - accuracy: 0.6512 - val_loss: 0.5540 - val_accuracy: 0.7145 Epoch 9/25 1351/1351 [==============================] - 2s 2ms/step - loss: 0.6208 - accuracy: 0.6529 - val_loss: 0.6870 - val_accuracy: 0.5254 Epoch 10/25 1351/1351 [==============================] - 2s 2ms/step - loss: 0.6182 - accuracy: 0.6544 - val_loss: 0.5915 - val_accuracy: 0.6618 Epoch 11/25 1351/1351 [==============================] - 3s 2ms/step - loss: 0.6161 - accuracy: 0.6565 - val_loss: 0.6600 - val_accuracy: 0.5738 Epoch 12/25 1351/1351 [==============================] - 3s 2ms/step - loss: 0.6139 - accuracy: 0.6587 - val_loss: 0.7102 - val_accuracy: 0.5054 Epoch 13/25 1351/1351 [==============================] - 2s 2ms/step - loss: 0.6130 - accuracy: 0.6582 - val_loss: 0.5946 - val_accuracy: 0.6568
from matplotlib import pyplot as plt
plt.plot(history.history['loss'])
plt.plot(history.history['val_loss'])
plt.title('Wartość funkcji straty')
plt.ylabel('Strata')
plt.xlabel('Epoka')
plt.legend(['train', 'valid'], loc='upper left')<matplotlib.legend.Legend at 0x1b6a1ae2400>
from matplotlib import pyplot as plt
plt.plot(history.history['accuracy'])
plt.plot(history.history['val_accuracy'])
plt.title('model accuracy')
plt.ylabel('accuracy')
plt.xlabel('epoch')
plt.legend(['train', 'valid'], loc='upper left')<matplotlib.legend.Legend at 0x1b6a1ab1b50>
model.save("fasttext_model.keras")Pomimo kilku prób dostosowania parametrów model cechuje się stosunkowo niską jakością predykcji, pomimo tego że wygląda na przeuczony
(skacząca wartość accuracy/loss dla zbioru walidacyjnego)
import tensorflow as tf
def test_review_text(sentence):
vectorized = fb_model.wv.get_sentence_vector(sentence)
reshaped = tf.reshape(vectorized,shape=(1,300))
#print(vectorized.shape)
score = float(model(reshaped))
score_rounded = round(score)
print(score)
if score_rounded==0:
print("Negative review")
else:
print("Positive review")test_review_text("A buggy, uninspired mess")0.7820833921432495 Positive review
test_review_text("This game is bad")0.44321733713150024 Negative review
test_review_text("This game destroyed my life")0.8973167538642883 Positive review
test_review_text("Best game I've ever played")0.8987871408462524 Positive review
test_review_text("Fun cooperative play with scalable difficulty. Rapid path to get into a game with friends or open public games. ")0.5772996544837952 Positive review
test_review_text("Deliriously buggy. Fun if/when it works properly. Wait and see if they actually QA the next few patches before you play.")0.6418458819389343 Positive review
test["model_predictions"] = model(np.stack(test["vectorized"].values))test["model_predictions"] = test["model_predictions"].apply(lambda x : round(float(x)))def get_metrics():
df = test
predictions = df["model_predictions"].to_numpy()
true_values = df["review_score"].to_numpy()
accuracy = np.sum(np.rint(predictions) == true_values)/len(true_values)
TN_count = len(df.query("`review_score`==0 and `model_predictions`==0").index)
TP_count = len(df.query("`review_score`==1 and `model_predictions`==1").index)
FP_count = len(df.query("`review_score`==0 and `model_predictions`==1").index)
FN_count = len(df.query("`review_score`==1 and `model_predictions`==0").index)
precision = TP_count/(TP_count+FP_count)
recall = TP_count/(TP_count+FN_count)
F1_score = (2*precision*recall)/(precision+recall)
print(f"Accuracy: {accuracy:.2f}")
print(f"Precision: {precision:.2f}")
print(f"Recall: {recall:.2f}")
print(f"F1 Score: {F1_score:.2f}")
get_metrics()Accuracy: 0.72 Precision: 0.89 Recall: 0.76 F1 Score: 0.82
Możliwe iż model osiągnąłby lepsze wyniki gdyby embeddingi były wyuczone na samych recenzjach gier, a nie pochodziły z gotowych wektorów dla języka angielskiego.