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@ -1,10 +1,10 @@
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FROM ubuntu:latest
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RUN apt-get update && apt-get install -y python3-pip && pip3 install setuptools && pip3 install numpy && pip3 install pandas && pip3 install wget && pip3 install scikit-learn && rm -rf /var/lib/apt/lists/*
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RUN apt-get update && apt-get install -y python3-pip && pip3 install setuptools && pip3 install numpy && pip3 install pandas && pip3 install wget && pip3 install scikit-learn && pip3 install matplotlib && rm -rf /var/lib/apt/lists/*
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RUN pip3 install torch torchvision torchaudio
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WORKDIR /app
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COPY ./create.py ./
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COPY ./stats.py ./
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COPY ./stroke-pytorch.py ./
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COPY ./stroke-pytorch-eval.py ./
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lab5.ipynb
97
lab5.ipynb
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@ -1,10 +1,11 @@
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FROM ubuntu:latest
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RUN apt-get update && apt-get install -y python3-pip && pip3 install setuptools && pip3 install numpy && pip3 install pandas && pip3 install wget && pip3 install scikit-learn && rm -rf /var/lib/apt/lists/*
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RUN apt-get update && apt-get install -y python3-pip && pip3 install setuptools && pip3 install numpy && pip3 install pandas && pip3 install wget && pip3 install scikit-learn && pip3 install matplotlib && rm -rf /var/lib/apt/lists/*
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RUN pip3 install torch torchvision torchaudio
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WORKDIR /app
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COPY ./create.py ./
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COPY ./stats.py ./
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COPY ./stroke-pytorch.py ./
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COPY ./stroke-pytorch-eval.py ./
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32
pytorch-training-eval/Jenkinsfile-eval
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pytorch-training-eval/Jenkinsfile-eval
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pipeline {
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agent {
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dockerfile true
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}
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stages {
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stage('checkout') {
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steps {
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git 'https://git.wmi.amu.edu.pl/s434766/ium_434766.git'
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copyArtifacts fingerprintArtifacts: true, projectName: 's434766-create-dataset'
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copyArtifacts fingerprintArtifacts: true, projectName: 's434766-training'
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}
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}
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stage('Docker'){
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steps{
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sh 'python3 "./stroke-pytorch-eval.py" >> eval.txt'
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}
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}
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stage('archiveArtifacts') {
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steps {
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archiveArtifacts 'eval.txt'
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}
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post {
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success {
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emailext body: 'Evaluation of stroke predictions is finished',
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subject: 's434766 evaluation finished',
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to: '26ab8f35.uam.onmicrosoft.com@emea.teams.ms'
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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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stage('Docker'){
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steps{
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sh 'python3 "./stroke-pytorch.py" > model.txt'
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}
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}
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stage('checkout: Check out from version control') {
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steps {
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git 'https://git.wmi.amu.edu.pl/s434766/ium_434766.git'
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sh 'python3 "./stroke-pytorch.py" ${BATCH_SIZE} ${EPOCHS} > pred.txt'
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}
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}
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stage('archiveArtifacts') {
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steps {
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archiveArtifacts 'model.txt'
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archiveArtifacts 'pred.txt'
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archiveArtifacts 'stroke.pkl'
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}
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post {
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success {
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emailext body: 'Training of stroke predictions is finished',
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subject: 's434766 training finished',
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to: '26ab8f35.uam.onmicrosoft.com@emea.teams.ms'
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}
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}
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}
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}
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}
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50
stroke-pytorch-eval.py
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50
stroke-pytorch-eval.py
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import torch
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import torch.nn as nn
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import numpy as np
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from os import path
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import torch.nn.functional as F
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from torch import nn
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from torch.autograd import Variable
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import torchvision.transforms as transforms
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import pandas as pd
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from sklearn.metrics import accuracy_score
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from sklearn.metrics import mean_squared_error
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from sklearn.metrics import classification_report
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import matplotlib.pyplot as plt
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class LogisticRegressionModel(nn.Module):
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def __init__(self, input_dim, output_dim):
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super(LogisticRegressionModel, self).__init__()
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self.linear = nn.Linear(input_dim, output_dim)
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self.sigmoid = nn.Sigmoid()
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def forward(self, x):
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out = self.linear(x)
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return self.sigmoid(out)
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np.set_printoptions(suppress=False)
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data_test = pd.read_csv("data_test.csv")
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FEATURES = ['age','hypertension','heart_disease','ever_married', 'avg_glucose_level', 'bmi']
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x_test = data_test[FEATURES].astype(np.float32)
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y_test = data_test['stroke'].astype(np.float32)
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fTest= torch.from_numpy(x_test.values)
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tTest = torch.from_numpy(y_test.values)
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model = LogisticRegressionModel(6,1)
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model.load_state_dict(torch.load('stroke.pth'))
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y_pred = model(fTest)
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rmse = mean_squared_error(tTest, y_pred.detach().numpy())
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acc = accuracy_score(tTest, np.argmax(y_pred.detach().numpy(), axis=1))
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print('-' * 60)
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print(classification_report(tTest, y_pred.detach().numpy().round()))
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print(f" RMSE: {rmse}")
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print(f" Accuracy: {acc}")
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print('-' * 60)
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import torch
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import sys
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import torch.nn.functional as F
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from torch import nn
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from torch.autograd import Variable
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import numpy as np
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import pandas as pd
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np.set_printoptions(suppress=False)
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class LogisticRegressionModel(nn.Module):
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def __init__(self, input_dim, output_dim):
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super(LogisticRegressionModel, self).__init__()
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fTest= torch.from_numpy(x_test.values)
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tTest = torch.from_numpy(y_test.values)
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batch_size = 95
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n_iters = 1000
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num_epochs = int(n_iters / (len(x_train) / batch_size))
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batch_size = int(sys.argv[1]) if len(sys.argv) > 1 else 16
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num_epochs = int(sys.argv[2]) if len(sys.argv) > 2 else 5
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learning_rate = 0.001
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input_dim = 6
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output_dim = 1
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@ -48,7 +50,7 @@ criterion = torch.nn.BCELoss(reduction='mean')
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optimizer = torch.optim.SGD(model.parameters(), lr = learning_rate)
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for epoch in range(num_epochs):
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print ("Epoch #",epoch)
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# print ("Epoch #",epoch)
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model.train()
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optimizer.zero_grad()
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# Forward pass
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y_pred = model(fTest)
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print("predicted Y value: ", y_pred.data)
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print ("The accuracy is", accuracy_score(tTest, np.argmax(y_pred.detach().numpy(), axis=1)))
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torch.save(model, 'stroke.pkl')
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torch.save(model.state_dict(), 'stroke.pth')
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stroke.pkl
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stroke.pkl
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stroke.pth
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stroke.pth
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