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evaluation
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import pandas as pd
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import pandas as pd
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import numpy as np
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import tensorflow as tf
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import tensorflow as tf
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test_data = pd.read_csv('./beer_reviews_test.csv')
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test_data = pd.read_csv('./beer_reviews_test.csv')
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X_test = test_data[['review_aroma', 'review_appearance', 'review_palate', 'review_taste']]
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X_test = test_data[['review_aroma', 'review_appearance', 'review_palate', 'review_taste']]
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y_test = test_data['review_overall']
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model = tf.keras.models.load_model('beer_review_sentiment_model.h5')
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model = tf.keras.models.load_model('beer_review_sentiment_model.h5')
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tokenizer = tf.keras.preprocessing.text.Tokenizer(num_words=10000)
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predictions = model.predict(X_test)
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print(f'Predictions shape: {predictions.shape}')
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X_test_seq = tokenizer.texts_to_sequences(X_test)
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if len(predictions.shape) > 1:
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X_test_pad = tf.keras.preprocessing.sequence.pad_sequences(X_test_seq, maxlen=100)
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predictions = predictions[:, 0]
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predictions = model.predict(X_test_pad)
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results = pd.DataFrame({'Predictions': predictions, 'Actual': y_test})
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results.to_csv('beer_review_sentiment_predictions.csv', index=False)
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np.savetxt('beer_review_sentiment_predictions.csv', predictions, delimiter=',', fmt='%.10f')
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24
IUM_06-metrics.py
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IUM_06-metrics.py
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import pandas as pd
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from sklearn.metrics import accuracy_score, precision_recall_fscore_support, mean_squared_error
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from math import sqrt
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import sys
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data = pd.read_csv('beer_review_sentiment_predictions.csv')
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y_pred = data['Predictions']
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y_test = data['Actual']
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y_test_binary = (y_test >= 3).astype(int)
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build_number = sys.argv[1]
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accuracy = accuracy_score(y_test_binary, y_pred.round())
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precision, recall, f1, _ = precision_recall_fscore_support(y_test_binary, y_pred.round(), average='micro')
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rmse = sqrt(mean_squared_error(y_test, y_pred))
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print(f'Accuracy: {accuracy}')
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print(f'Micro-avg Precision: {precision}')
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print(f'Micro-avg Recall: {recall}')
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print(f'F1 Score: {f1}')
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print(f'RMSE: {rmse}')
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with open(r"beer_metrics.txt", "a") as f:
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f.write(f"{accuracy},{build_number}\n")
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24
IUM_06-plot.py
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IUM_06-plot.py
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import matplotlib.pyplot as plt
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def main():
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accuracy = []
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build_numbers = []
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with open("beer_metrics.txt") as f:
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for line in f:
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accuracy.append(float(line.split(",")[0]))
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build_numbers.append(int(line.split(",")[1]))
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plt.plot(build_numbers, accuracy)
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plt.xlabel("Build Number")
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plt.ylabel("Accuracy")
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plt.title("Accuracy of the model over time")
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plt.xticks(range(min(build_numbers), max(build_numbers) + 1))
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plt.show()
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plt.savefig("acc.png")
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if __name__ == "__main__":
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main()
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68
Jenkinsfile
vendored
68
Jenkinsfile
vendored
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pipeline {
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pipeline {
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agent any
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agent {
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dockerfile true
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}
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triggers {
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upstream(upstreamProjects: 's464979-training/training', threshold: hudson.model.Result.SUCCESS)
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}
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parameters {
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parameters {
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string(name: 'CUTOFF', defaultValue: '10000', description: 'Liczba wierszy do obcięcia ze zbioru danych')
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buildSelector(defaultSelector: lastSuccessful(), description: 'Which build to use for copying artifacts', name: 'BUILD_SELECTOR')
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string(name: 'KAGGLE_USERNAME', defaultValue: '', description: 'Kaggle username')
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gitParameter branchFilter: 'origin/(.*)', defaultValue: 'training', name: 'BRANCH', type: 'PT_BRANCH'
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password(name: 'KAGGLE_KEY', defaultValue: '', description: 'Kaggle API key')
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}
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}
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stages {
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stages {
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stage('Clone Repository') {
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stage('Clone Repository') {
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steps {
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steps {
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git url: "https://git.wmi.amu.edu.pl/s464979/ium_464979"
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git branch: 'evaluation', url: "https://git.wmi.amu.edu.pl/s464979/ium_464979"
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}
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}
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}
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}
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stage('Download dataset') {
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stage('Copy Dataset Artifacts') {
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steps {
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steps {
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withEnv(["KAGGLE_USERNAME=${env.KAGGLE_USERNAME}", "KAGGLE_KEY=${env.KAGGLE_KEY}"]) {
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copyArtifacts filter: 'beer_reviews.csv,beer_reviews_train.csv,beer_reviews_test.csv', projectName: 'z-s464979-create-dataset', selector: buildParameter('BUILD_SELECTOR')
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sh "kaggle datasets download -d thedevastator/1-5-million-beer-reviews-from-beer-advocate --unzip"
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}
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}
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}
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stage('Process and Split Dataset') {
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agent {
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dockerfile {
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filename 'Dockerfile'
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reuseNode true
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}
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}
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}
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}
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stage('Copy Training Artifacts') {
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steps {
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steps {
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sh "chmod +x ./IUM_05-split.py"
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copyArtifacts filter: 'beer_review_sentiment_model.h5', projectName: 's464979-training/' + params.BRANCH, selector: buildParameter('BUILD_SELECTOR')
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sh "python3 ./IUM_05-split.py"
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archiveArtifacts artifacts: 'beer_reviews.csv,beer_reviews_train.csv,beer_reviews_test.csv', onlyIfSuccessful: true
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}
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}
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stage("Run") {
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agent {
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dockerfile {
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filename 'Dockerfile'
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reuseNode true
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}
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}
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}
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}
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stage('Copy Evaluation Artifacts') {
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steps {
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copyArtifacts filter: 'beer_metrics.txt', projectName: '_s464979-evaluation/evaluation', selector: buildParameter('BUILD_SELECTOR'), optional: true
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}
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}
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stage("Run predictions") {
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steps {
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steps {
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sh "chmod +x ./IUM_05-model.py"
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sh "chmod +x ./IUM_05-predict.py"
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sh "chmod +x ./IUM_05-predict.py"
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sh "python3 ./IUM_05-model.py"
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sh "python3 ./IUM_05-predict.py"
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sh "python3 ./IUM_05-predict.py"
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archiveArtifacts artifacts: 'beer_review_sentiment_model.h5,beer_review_sentiment_predictions.csv', onlyIfSuccessful: true
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archiveArtifacts artifacts: 'beer_review_sentiment_predictions.csv', onlyIfSuccessful: true
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}
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}
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stage('Run metrics') {
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steps {
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sh 'chmod +x ./IUM_06-metrics.py'
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sh "python3 ./IUM_06-metrics.py ${currentBuild.number}"
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}
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}
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stage('Run plot') {
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steps {
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sh 'chmod +x ./IUM_06-plot.py'
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sh 'python3 ./IUM_06-plot.py'
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}
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}
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stage('Archive Artifacts') {
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steps {
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archiveArtifacts artifacts: '*', onlyIfSuccessful: true
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}
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}
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}
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}
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}
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}
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