add scripts for 6

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'
+                    }
+                }
+            }
+        }
+    }
+}

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)
+

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]
+model = tf.keras.models.load_model('grid-stability-dense.h5')
-Y_test_onehot = np.eye(2)[Y_test_one_zero]
+results = model.evaluate(X_test, Y_test, batch_size=64)
 
-model = tf.keras.models.load_model('grid_stability.h5')
+with open('eval.csv', 'a', newline='') as fp:
-
+    wr = csv.writer(fp, dialect='excel')
-results = model.evaluate(X_test, Y_test_onehot, batch_size=64)
+    wr.writerow(results)
-
-f = open('eval.csv', 'a+')
-
-f.write(results[0], ',')
-f.write(results[1], ',')
 

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_valid = X_valid.pop('stabf')
-Y_train_onehot = np.eye(2)[Y_train_one_zero]
+Y_valid = pd.get_dummies(Y_valid)
-
-Y_test_one_zero = [onezero(x) for x in Y_test]
-Y_test_onehot = np.eye(2)[Y_test_one_zero]
 
 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')
+