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IUM_06

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AWieczarek 2024-05-06 21:13:39 +02:00
parent 89f3b6ec3d
commit 652ca06460
2 changed files with 0 additions and 9 deletions
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7
IUM_05-predict.py
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@ -1,25 +1,18 @@
import pandas as pd import pandas as pd
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')
# Make predictions
predictions = model.predict(X_test) predictions = model.predict(X_test)
# 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)

2
IUM_06-metrics.py
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@ -3,7 +3,6 @@ from sklearn.metrics import accuracy_score, precision_recall_fscore_support, mea
from math import sqrt from math import sqrt
import sys import sys
# Load the predictions data
data = pd.read_csv('beer_review_sentiment_predictions.csv') data = pd.read_csv('beer_review_sentiment_predictions.csv')
y_pred = data['Predictions'] y_pred = data['Predictions']
y_test = data['Actual'] y_test = data['Actual']
@ -11,7 +10,6 @@ y_test_binary = (y_test >= 3).astype(int)
build_number = sys.argv[1] build_number = sys.argv[1]
# Calculate metrics
accuracy = accuracy_score(y_test_binary, y_pred.round()) accuracy = accuracy_score(y_test_binary, y_pred.round())
precision, recall, f1, _ = precision_recall_fscore_support(y_test_binary, y_pred.round(), average='micro') precision, recall, f1, _ = precision_recall_fscore_support(y_test_binary, y_pred.round(), average='micro')
rmse = sqrt(mean_squared_error(y_test, y_pred)) rmse = sqrt(mean_squared_error(y_test, y_pred))