forked from s464914/ium_464914
jenkins evaluation
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parent
6a0b357945
commit
5dfd11b904
20
Jenkinsfile
vendored
20
Jenkinsfile
vendored
@ -6,7 +6,6 @@ pipeline {
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description: 'Build for copying artifacts',
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description: 'Build for copying artifacts',
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name: 'BUILD_SELECTOR'
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name: 'BUILD_SELECTOR'
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)
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)
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string(name: 'EPOCHS', defaultValue: '10', description: 'epochs')
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}
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}
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stages {
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stages {
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stage('Git Checkout') {
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stage('Git Checkout') {
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@ -16,16 +15,27 @@ pipeline {
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}
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}
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stage('Copy Artifacts') {
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stage('Copy Artifacts') {
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steps {
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steps {
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copyArtifacts fingerprintArtifacts: true, projectName: 'z-s464914-create-dataset', selector: buildParameter('BUILD_SELECTOR')
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copyArtifacts filter: '*', projectName: 's464914-training/experiments/', selector: buildParameter('BUILD_SELECTOR')
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}
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}
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}
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}
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stage('Train') {
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stage('Prediction') {
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steps {
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steps {
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script {
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script {
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def customImage = docker.build("custom-image")
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def customImage = docker.build("custom-image")
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customImage.inside {
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customImage.inside {
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sh 'python3 ./model.py ' + params.EPOCHS
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sh 'python3 ./prediction.py'
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archiveArtifacts artifacts: 'model.pth, predictions.txt', onlyIfSuccessful: true
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archiveArtifacts artifacts: 'predictions.txt', onlyIfSuccessful: true
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}
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}
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}
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}
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stage('Metrics') {
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steps {
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script {
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def customImage = docker.build("custom-image")
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customImage.inside {
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sh 'python3 ./metrics.py'
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archiveArtifacts artifacts: 'metrics.txt', 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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25
metrics.py
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25
metrics.py
Normal file
@ -0,0 +1,25 @@
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from sklearn.metrics import accuracy_score, precision_score, recall_score, f1_score, mean_squared_error
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import numpy as np
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true_labels = []
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predicted_labels = []
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f = open("predictions.txt", "r")
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for line in f:
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parts = line.strip().split(' ')
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true_labels.append(int(parts[3]))
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predicted_labels.append(int(parts[1]))
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accuracy = accuracy_score(true_labels, predicted_labels)
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precision_micro = precision_score(true_labels, predicted_labels, average='micro')
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recall_micro = recall_score(true_labels, predicted_labels, average='micro')
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f1_micro = f1_score(true_labels, predicted_labels, average='micro')
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rmse = np.sqrt(mean_squared_error(true_labels, predicted_labels))
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with open(r'metrics.txt', 'a') as fp:
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fp.write(f"Accuracy: {accuracy}\n")
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fp.write(f"Micro-average Precision: {precision_micro}\n")
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fp.write(f"Micro-average Recall: {recall_micro}\n")
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fp.write(f"Micro-average F1-score: {f1_micro}\n")
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fp.write(f"RMSE: {rmse}\n")
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fp.write("--------------------\n")
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@ -6,6 +6,8 @@ import pandas as pd
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from sklearn.model_selection import train_test_split
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from sklearn.model_selection import train_test_split
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from sklearn.preprocessing import LabelEncoder
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from sklearn.preprocessing import LabelEncoder
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import torch.nn.functional as F
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import torch.nn.functional as F
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from sklearn.metrics import accuracy_score, precision_score, recall_score, f1_score, mean_squared_error
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import numpy as np
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device = (
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device = (
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"cuda"
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"cuda"
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@ -41,7 +43,6 @@ def predict(model, input_data):
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return predicted_class.item() # Return the predicted class label
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return predicted_class.item() # Return the predicted class label
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def main():
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def main():
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forest_test = pd.read_csv('forest_test.csv')
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forest_test = pd.read_csv('forest_test.csv')
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@ -55,15 +56,23 @@ def main():
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load_model(model, model_path)
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load_model(model, model_path)
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predictions = []
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predictions = []
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for input_data in X_test:
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correct = 0
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predicted_class = predict(model, input_data)
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total = 0
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predictions.append(predicted_class)
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with torch.no_grad():
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for input_data, target in zip(X_test, y_test):
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output = model(input_data)
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_, predicted_class = torch.max(output, 0)
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prediction_entry = f"predicted: {predicted_class.item()} true_label: {target}"
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predictions.append(prediction_entry)
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total += 1
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if predicted_class.item() == target:
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correct += 1
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with open(r'predictions.txt', 'w') as fp:
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with open(r'predictions.txt', 'w') as fp:
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for item in predictions:
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for item in predictions:
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# write each item on a new line
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# write each item on a new line
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fp.write("%s\n" % item)
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fp.write("%s\n" % item)
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if __name__ == "__main__":
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if __name__ == "__main__":
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main()
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main()
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116203
predictions.txt
Normal file
116203
predictions.txt
Normal file
File diff suppressed because it is too large
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