LAB 5 FRAMEWORK PYTORCH
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FROM ubuntu:latest
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RUN apt update && apt 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 update && apt install -y python3-pip && pip3 install setuptools && pip3 install numpy && pip3 install pandas && pip3 install wget && pip3 install scikit-learn && pip3 install torch==1.8.1+cu102 torchvision==0.9.1+cu102 torchaudio===0.8.1 -f https://download.pytorch.org/whl/torch_stable.html &&rm -rf /var/lib/apt/lists/*
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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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@ -14,6 +14,7 @@ pipeline {
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stage('Docker'){
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steps{
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sh 'python3 ./create.py'
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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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@ -23,6 +24,7 @@ pipeline {
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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 'data_val.csv'
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archiveArtifacts 'data_test.csv'
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archiveArtifacts 'data_train.csv'
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lab5.ipynb
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lab5.ipynb
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stroke-pytorch.py
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stroke-pytorch.py
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import torch
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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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from sklearn.model_selection import train_test_split
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from sklearn.preprocessing import MinMaxScaler
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from sklearn.metrics import accuracy_score
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import numpy as np
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import pandas as pd
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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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data_train = pd.read_csv("data_train.csv")
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data_test = pd.read_csv("data_test.csv")
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data_val = pd.read_csv("data_val.csv")
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FEATURES = ['age','hypertension','heart_disease','ever_married', 'avg_glucose_level', 'bmi']
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x_train = data_train[FEATURES].astype(np.float32)
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y_train = data_train['stroke'].astype(np.float32)
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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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fTrain = torch.from_numpy(x_train.values)
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tTrain = torch.from_numpy(y_train.values.reshape(2945,1))
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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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learning_rate = 0.001
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input_dim = 6
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output_dim = 1
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model = LogisticRegressionModel(input_dim, output_dim)
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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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model.train()
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optimizer.zero_grad()
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# Forward pass
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y_pred = model(fTrain)
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# Compute Loss
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loss = criterion(y_pred, tTrain)
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print(loss.item())
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# Backward pass
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loss.backward()
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optimizer.step()
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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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stroke.pkl
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stroke.pkl
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