From acd7ee81b21a682e3e83488ed5af7dd7f3108f1c Mon Sep 17 00:00:00 2001
From: Wojciech Lidwin <59863785+Halal37@users.noreply.github.com>
Date: Fri, 12 May 2023 03:37:36 +0200
Subject: [PATCH] Fix

---
 Jenkinsfile_train |  2 +-
 ium_sacred.py     | 86 ++++++++++++++++++++---------------------------
 2 files changed, 37 insertions(+), 51 deletions(-)

diff --git a/Jenkinsfile_train b/Jenkinsfile_train
index f668e95..41da832 100644
--- a/Jenkinsfile_train
+++ b/Jenkinsfile_train
@@ -25,7 +25,7 @@ node {
      checkout([$class: 'GitSCM', branches: [[name: '*/master']], extensions: [], userRemoteConfigs: [[credentialsId: 's487197', url: 'https://git.wmi.amu.edu.pl/s487197/ium_487197']]])
  }
  stage('Dockerfile'){
-    def testImage = docker.image('s487197/ium:39')
+    def testImage = docker.image('s487197/ium:40')
 	testImage.inside{ 
          copyArtifacts filter: 'baltimore_train.csv', projectName: 's487197-create-dataset'
 		 sh "python3 ium_sacred.py -epochs $EPOCHS -lr $LR -validation_split $VALIDATION_SPLIT"
diff --git a/ium_sacred.py b/ium_sacred.py
index 22120b9..2c74042 100644
--- a/ium_sacred.py
+++ b/ium_sacred.py
@@ -38,27 +38,44 @@ def get_x_y(data):
 
 @ex.config
 def my_config():
-    epochs = 20
-    lr = 0.01
-    validation_split = 0.2
-    #parser = argparse.ArgumentParser(description='Train')
+    parser = argparse.ArgumentParser(description='Train')
 
-   # parser.add_argument('-epochs', type=int, default=20)
-   # parser.add_argument('-lr', type=float, default=0.01)
-    #parser.add_argument('-validation_split', type=float, default=0.2)
-    #args = parser.parse_args()
-   # epochs = args.epochs
-  #  lr = args.lr
-   # validation_split = args.validation_split
+    parser.add_argument('-epochs', type=int, default=20)
+    parser.add_argument('-lr', type=float, default=0.01)
+    parser.add_argument('-validation_split', type=float, default=0.2)
+    args = parser.parse_args()
+    epochs = args.epochs
+    lr = args.lr
+    validation_split = args.validation_split
 
+@ex.capture
+def prepare_message(epochs, lr, validation_split):
+    return "{0} {1} {2}!".format(epochs, lr, validation_split)
 
 @ex.main
-def predict(epochs, lr, validation_split):
-
-    print("ble")
-    model = load_model('baltimore_model')
-
+def my_main(epochs, lr, validation_split, _run):
     train = pd.read_csv('baltimore_train.csv')
+
+    data_train, x_train, y_train = get_x_y(train)
+    normalizer = tf.keras.layers.Normalization(axis=1)
+    normalizer.adapt(np.array(x_train))
+    model = Sequential(normalizer)
+    model.add(Dense(64, activation="relu"))
+    model.add(Dense(10, activation='relu'))
+    model.add(Dense(10, activation='relu'))
+    model.add(Dense(10, activation='relu'))
+    model.add(Dense(5, activation="softmax"))
+    model.compile(Adam(learning_rate=lr), loss='sparse_categorical_crossentropy', metrics=['accuracy'])
+    model.summary()
+
+    history = model.fit(
+        x_train,
+        y_train,
+        epochs=epochs,
+        validation_split=validation_split)
+    hist = pd.DataFrame(history.history)
+    hist['epoch'] = history.epoch
+
     baltimore_data_test =pd.read_csv('baltimore_test.csv')
     baltimore_data_test.columns = train.columns
     baltimore_data_test, x_test, y_test = get_x_y(baltimore_data_test)
@@ -81,42 +98,11 @@ def predict(epochs, lr, validation_split):
             'rmse': math.sqrt(metrics.mean_squared_error(y_test, y_predicted)),
             'accuracy': scores[1] * 100
         }
-    ex.log_scalar('accuracy', data['accuracy'])
-    ex.log_scalar('rmse', data['rmse'])
-    ex.log_scalar('accuracy', data['accuracy'])
+    _run.log_scalar('accuracy', data['accuracy'])
+    _run.log_scalar('rmse', data['rmse'])
+    _run.log_scalar('accuracy', data['accuracy'])
 
     ex.add_artifact('baltimore_model')
 
-
-
-
-@ex.capture
-def train_model(epochs, lr, validation_split):
-
-
-    train = pd.read_csv('baltimore_train.csv')
-
-    data_train, x_train, y_train = get_x_y(train)
-    normalizer = tf.keras.layers.Normalization(axis=1)
-    normalizer.adapt(np.array(x_train))
-    model = Sequential(normalizer)
-    model.add(Dense(64, activation="relu"))
-    model.add(Dense(10, activation='relu'))
-    model.add(Dense(10, activation='relu'))
-    model.add(Dense(10, activation='relu'))
-    model.add(Dense(5, activation="softmax"))
-    model.compile(Adam(learning_rate=lr), loss='sparse_categorical_crossentropy', metrics=['accuracy'])
-    model.summary()
-
-    history = model.fit(
-        x_train,
-        y_train,
-        epochs=epochs,
-        validation_split=validation_split)
-    hist = pd.DataFrame(history.history)
-    hist['epoch'] = history.epoch
-    model.save('baltimore_model')
-    shutil.make_archive('baltimore', 'zip', 'baltimore_model')
-
 ex.run()