Lr model + docker

learning/Jenkinsfile

@@ -0,0 +1,32 @@
+pipeline {
+    agent {
+        dockerfile true
+    }
+    stages {
+        stage('Copy Archive') {
+            steps {
+                script {
+                    step ([$class: 'CopyArtifact',
+                    projectName: 's434700-create-dataset',
+                    filter: '*.csv',
+                    target: 'datasets'])
+                }
+            }
+        }
+        stage('checkout: Check out from version control') {
+            steps {
+                git 'https://git.wmi.amu.edu.pl/s434700/ium_s434700.git'
+            }
+        }
+        stage('learning') {
+            steps {
+                sh 'python ./learning/ml.py'
+            }
+        }
+        stage('archiveArtifacts') {
+            steps {
+                archiveArtifacts 'model.pt'
+            }
+        }
+    }
+}

learning/ml.py

@@ -0,0 +1,64 @@
+import torch
+import torch.nn as nn
+import numpy as np
+import pandas as pd
+import matplotlib.pyplot as plt
+import seaborn as sns
+import torch
+import datetime
+from torch.autograd import Variable
+
+
+INPUT_DIM = 1
+OUTPUT_DIM = 1
+LEARNING_RATE = 0.01
+EPOCHS = 100
+
+
+dataset = pd.read_csv('datasets/train_set.csv')
+
+
+x_values = [datetime.datetime.strptime(
+    item, "%Y-%m-%d").month for item in dataset['date'].values]
+x_train = np.array(x_values, dtype=np.float32)
+x_train = x_train.reshape(-1, 1)
+
+y_values = [min(dataset['result_1'].values[i]/dataset['result_2'].values[i], dataset['result_2'].values[i] /
+                dataset['result_1'].values[i]) for i in range(len(dataset['result_1'].values))]
+y_train = np.array(y_values, dtype=np.float32)
+y_train = y_train.reshape(-1, 1)
+
+
+class LinearRegression(torch.nn.Module):
+    def __init__(self, inputSize, outputSize):
+        super(LinearRegression, self).__init__()
+        self.linear = torch.nn.Linear(inputSize, outputSize)
+
+    def forward(self, x):
+        out = self.linear(x)
+        return out
+
+
+model = LinearRegression(INPUT_DIM, OUTPUT_DIM)
+
+
+criterion = torch.nn.MSELoss()
+optimizer = torch.optim.SGD(model.parameters(), lr=LEARNING_RATE)
+
+for epoch in range(EPOCHS):
+    inputs = Variable(torch.from_numpy(x_train))
+    labels = Variable(torch.from_numpy(y_train))
+
+    optimizer.zero_grad()
+
+    outputs = model(inputs)
+
+    loss = criterion(outputs, labels)
+    print(loss)
+    loss.backward()
+
+    optimizer.step()
+
+    print('epoch {}, loss {}'.format(epoch, loss.item()))
+
+torch.save(model.state_dict(), 'model.pt')

ml.py

@@ -1,19 +0,0 @@
-import torch
-import torch.nn as nn
-import numpy as np
-import pandas as pd
-import matplotlib.pyplot as plt
-import seaborn as sns
-
-dataset = pd.read_csv('./train_set.csv')
-print(dataset.head())
-
-print(dataset.map_winner)
-
-# teams1 = dataset['team_1'].cat.codes.values
-# teams2 = dataset['team_2'].cat.codes.values
-
-print(dataset.dtypes)
-
-device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
-print(device)