From 4ee651cb5d8df95262cef1ea050e6e1f3745cf92 Mon Sep 17 00:00:00 2001 From: AWieczarek Date: Mon, 6 May 2024 19:51:16 +0200 Subject: [PATCH] IUM_06 --- IUM_05-predict.py | 19 ++++++++----------- 1 file changed, 8 insertions(+), 11 deletions(-) diff --git a/IUM_05-predict.py b/IUM_05-predict.py index 63b0a9f..20f65da 100644 --- a/IUM_05-predict.py +++ b/IUM_05-predict.py @@ -2,31 +2,28 @@ import pandas as pd import numpy as np import tensorflow as tf -# Load the test data test_data = pd.read_csv('./beer_reviews_test.csv') X_test = test_data[['review_aroma', 'review_appearance', 'review_palate', 'review_taste']] y_test = test_data['review_overall'] -# Load the model 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.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) -# Make predictions 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}') -# If predictions have more than one dimension, select only one dimension if len(predictions.shape) > 1: predictions = predictions[:, 0] -# Save predictions and actual data to a CSV file results = pd.DataFrame({'Predictions': predictions, 'Actual': y_test}) results.to_csv('beer_review_sentiment_predictions.csv', index=False) \ No newline at end of file