From f29c55c0e1e92c2b3410f262227fd35ffceb13a9 Mon Sep 17 00:00:00 2001
From: jakubknczny <jakubknczny@gmail.com>
Date: Fri, 14 May 2021 21:52:14 +0200
Subject: [PATCH] add scripts for 6

---
 lab5/create/Jenkinsfile       | 37 +++++++++++++++++++++++++++++++++++
 lab5/create/create_dataset.py |  1 +
 lab5/eval/eval.py             | 22 ++++++++-------------
 lab5/train/train.py           | 27 ++++++++++++-------------
 4 files changed, 59 insertions(+), 28 deletions(-)
 create mode 100644 lab5/create/Jenkinsfile

diff --git a/lab5/create/Jenkinsfile b/lab5/create/Jenkinsfile
new file mode 100644
index 0000000..5c103cf
--- /dev/null
+++ b/lab5/create/Jenkinsfile
@@ -0,0 +1,37 @@
+pipeline {
+    agent none
+    stages {
+        stage('copy files') {
+            agent any
+                steps {
+                    sh '''
+                        cp ./lab5/script.sh .
+                        cp ./lab5/create_dataset.py .
+                        cp ./lab5/Dockerfile .
+                        cp ./lab5/requirements.txt .
+                        '''
+                }
+        }
+        stage('docker') {
+            agent {
+                dockerfile true
+            }
+            stages {
+                stage('script') {
+                    steps {
+                    sh '''
+                    chmod +x script.sh
+                    ./script.sh'''
+                    }
+                }
+                stage('archive artifact') {
+                    steps {
+                        archiveArtifacts 'train.csv'
+                        archiveArtifacts 'test.csv'
+                        archiveArtifacts 'valid.csv'
+                    }
+                }
+            }
+        }
+    }
+}
diff --git a/lab5/create/create_dataset.py b/lab5/create/create_dataset.py
index e600bcd..45d451c 100644
--- a/lab5/create/create_dataset.py
+++ b/lab5/create/create_dataset.py
@@ -33,3 +33,4 @@ valid = pd.concat([X_valid, Y_valid], axis=1)
 train.to_csv('train.cs', index_col = False)
 test.to_csv('test.csv', index_col = False)
 valid.to_csv('valid.csv', index_col = False)
+
diff --git a/lab5/eval/eval.py b/lab5/eval/eval.py
index 57a1545..5a20969 100644
--- a/lab5/eval/eval.py
+++ b/lab5/eval/eval.py
@@ -1,23 +1,17 @@
+import csv
 import pandas as pd
-import numpy as np
 import tensorflow as tf
 
-def onezero(label):
-    return 0 if label == 'unstable' else 1
-
 
 X_test = pd.read_csv('test.csv')
+
 Y_test = X_test.pop('stabf')
+Y_test = pd.get_dummies(Y_test)
 
-Y_test_one_zero = [onezero(x) for x in Y_test]
-Y_test_onehot = np.eye(2)[Y_test_one_zero]
+model = tf.keras.models.load_model('grid-stability-dense.h5')
+results = model.evaluate(X_test, Y_test, batch_size=64)
 
-model = tf.keras.models.load_model('grid_stability.h5')
-
-results = model.evaluate(X_test, Y_test_onehot, batch_size=64)
-
-f = open('eval.csv', 'a+')
-
-f.write(results[0], ',')
-f.write(results[1], ',')
+with open('eval.csv', 'a', newline='') as fp:
+    wr = csv.writer(fp, dialect='excel')
+    wr.writerow(results)
 
diff --git a/lab5/train/train.py b/lab5/train/train.py
index 686b0bf..408a7db 100644
--- a/lab5/train/train.py
+++ b/lab5/train/train.py
@@ -1,36 +1,35 @@
-import numpy as np
 import pandas as pd
 import tensorflow as tf
 from tensorflow.keras import layers
 
-def onezero(label):
-  return 0 if label == 'unstable' else 1
-
-
 X_train = pd.read_csv('train.csv')
 X_test = pd.read_csv('test.csv')
+X_valid = pd.read_csv('valid.csv')
 
 Y_train = X_train.pop('stabf')
+Y_train = pd.get_dummies(Y_train)
+
 Y_test = X_test.pop('stabf')
+Y_test = pd.get_dummies(Y_test)
 
-Y_train_one_zero = [onezero(x) for x in Y_train]
-Y_train_onehot = np.eye(2)[Y_train_one_zero]
-
-Y_test_one_zero = [onezero(x) for x in Y_test]
-Y_test_onehot = np.eye(2)[Y_test_one_zero]
+Y_valid = X_valid.pop('stabf')
+Y_valid = pd.get_dummies(Y_valid)
 
 model = tf.keras.Sequential([
                           layers.Input(shape=(12,)),
                           layers.Dense(32),
                           layers.Dense(16),
-                          layers.Dense(2, activation='softmax')])
+                          layers.Dense(2, activation='softmax')
+])
 
 model.compile(
     loss=tf.losses.BinaryCrossentropy(),
     optimizer=tf.optimizers.Adam(),
     metrics=[tf.keras.metrics.BinaryAccuracy()])
 
-history = model.fit(tf.convert_to_tensor(X_train, np.float32),
-          Y_train_onehot, epochs=5)
 
-model.save('grid_stability.h5')
+history = model.fit(tf.convert_to_tensor(X_train, np.float32),
+      Y_train, epochs=2, validation_data=(X_valid, Y_valid))
+
+model.save('grid-stability-dense.h5')
+