ML done

Dockerfile

@@ -4,6 +4,9 @@ RUN apt update && apt install -y python3 python3-pip
 
 RUN pip3 install kaggle
 RUN pip3 install pandas
+RUN pip3 install tensorflow
+RUN pip3 install numpy
+RUN pip3 install matplotlib
 
 
 

Jenkinsfile

@@ -2,11 +2,6 @@ pipeline{
     agent any
     properties([parameters([text(defaultValue: '50', description: 'Number of lines to cutoff', name: 'CUTOFF')])])
     stages{
-        stage('Stage 1'){
-            steps{
-                echo 'Hello World!'
-            }
-        }
         stage('checkout: Check out from version control'){
             steps{
                 git url: 'https://github.com/jfrogdev/project-examples.git'

Jenkinsfile-stats

@@ -0,0 +1,29 @@
+    pipeline{
+    agent any
+    properties([parameters([
+  buildSelector(
+      defaultSelector: lastSuccessful(),
+      description: 'Which build to use for copying artifacts',
+      name: 'BUILD_SELECTOR')
+])])
+    stages{
+        stage('checkout: Check out from version control'){
+            steps{
+                git credentialsId: 'b4ba8ec9-8fc6-4f68-bf24-695634cec73e', url: 'https://git.wmi.amu.edu.pl/s437622/ium_s437622.git'
+            }
+        }
+        stage('copy artifacts'){
+            copyArtifacts filter: 'dev.csv, train.csv, test.csv', fingerprintArtifacts: true, projectName: 's437622-create-dataset', selector: buildParameter('BUILD_SELECTOR')
+        }
+        stage('sh: Shell Script'){
+            steps{
+                ./stats.sh
+            }
+        }
+        stage('Archive artifacts'){
+            steps{
+                archiveArtifacts 'stats.txt'
+            }
+        }
+    }
+}

README.md

@@ -1 +1,5 @@
-test3
+15.05
+ML - uczenie działa
+przewiduje same zera (czyli nie działa)
+wynik jest zapisywany do pliku results.csv
+do Dockera dodane są biblioteki potrzebne do ML

zad5.py

@@ -1,7 +1,54 @@
 import os
 import matplotlib.pyplot as plt
+import numpy as np
 import tensorflow as tf
+from tensorflow import keras
+from tensorflow.keras import layers
+import pandas as pd
 
-print("TensorFlow version: {}".format(tf.__version__))
-print("Eager execution: {}".format(tf.executing_eagerly()))
+
+model_name="model"
+
+train=pd.read_csv('train.csv', header=None, skiprows=1)
+indexNames = train[train[1] ==2].index
+train.drop(indexNames, inplace=True)
+cols=[0,2,3]
+X=train[cols].to_numpy()
+y=train[1].to_numpy()
+X=np.asarray(X).astype('float32')
+
+
+
+
+model = keras.Sequential(name="winner")
+model.add(keras.Input(shape=(3), name="game_info"))
+model.add(layers.Dense(4, activation="relu", name="layer1"))
+model.add(layers.Dense(8, activation="relu", name="layer2"))
+model.add(layers.Dense(8, activation="relu", name="layer3"))
+model.add(layers.Dense(5, activation="relu", name="layer4"))
+model.add(layers.Dense(1, activation="relu", name="output"))
+
+model.compile(
+    optimizer=keras.optimizers.RMSprop(),
+    loss=keras.losses.MeanSquaredError(),
+    )
+
+history = model.fit(
+    X,
+    y,
+    batch_size=16,
+    epochs=15,)
+
+model.save(model_name)
+
+test=pd.read_csv('test.csv', header=None, skiprows=1)
+cols=[0,2,3]
+indexNames = test[test[1] ==2].index
+test.drop(indexNames, inplace=True)
+X_test=test[cols].to_numpy()
+y_test=test[1].to_numpy()
+X_test=np.asarray(X_test).astype('float32')
+
+predictions=model.predict(X_test)
+pd.DataFrame(predictions).to_csv('results.csv', sep='\t', index=False, header=False)