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IUM_06

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AWieczarek 2024-05-06 19:51:16 +02:00
parent 9a77c82ebb
commit 4ee651cb5d
1 changed files with 8 additions and 11 deletions
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IUM_05-predict.py
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@ -2,31 +2,28 @@ import pandas as pd
import numpy as np import numpy as np
import tensorflow as tf import tensorflow as tf
# Load the test data
test_data = pd.read_csv('./beer_reviews_test.csv') test_data = pd.read_csv('./beer_reviews_test.csv')
X_test = test_data[['review_aroma', 'review_appearance', 'review_palate', 'review_taste']] X_test = test_data[['review_aroma', 'review_appearance', 'review_palate', 'review_taste']]
y_test = test_data['review_overall'] y_test = test_data['review_overall']
# Load the model
model = tf.keras.models.load_model('beer_review_sentiment_model.h5') model = tf.keras.models.load_model('beer_review_sentiment_model.h5')
# Preprocess the test data
tokenizer = tf.keras.preprocessing.text.Tokenizer(num_words=10000) tokenizer = tf.keras.preprocessing.text.Tokenizer(num_words=10000)
tokenizer.fit_on_texts(X_test)
X_test_seq = tokenizer.texts_to_sequences(X_test) X_test_list = X_test.values.tolist()
tokenizer.fit_on_texts(X_test_list)
X_test_seq = tokenizer.texts_to_sequences(X_test_list)
X_test_pad = tf.keras.preprocessing.sequence.pad_sequences(X_test_seq, maxlen=100) X_test_pad = tf.keras.preprocessing.sequence.pad_sequences(X_test_seq, maxlen=100)
# Make predictions
predictions = model.predict(X_test_pad) predictions = model.predict(X_test_pad)
print(model.summary())
print(f'X_test_pad shape: {X_test_pad.shape}')
# Check the shape of the predictions
print(f'Predictions shape: {predictions.shape}') print(f'Predictions shape: {predictions.shape}')
# If predictions have more than one dimension, select only one dimension
if len(predictions.shape) > 1: if len(predictions.shape) > 1:
predictions = predictions[:, 0] predictions = predictions[:, 0]
# Save predictions and actual data to a CSV file
results = pd.DataFrame({'Predictions': predictions, 'Actual': y_test}) results = pd.DataFrame({'Predictions': predictions, 'Actual': y_test})
results.to_csv('beer_review_sentiment_predictions.csv', index=False) results.to_csv('beer_review_sentiment_predictions.csv', index=False)