added sacred

Jenkinsfile_evaluation

@@ -31,12 +31,15 @@ pipeline {
                 sh 'chmod +x ./tensor-eval.py'
                 sh 'python3 ./tensor-eval.py $BUILD_NUMBER >> evaluation.txt'
                 sh 'python3 ./tensor-plot.py'
+                sh 'python3 ./sacred1.py'
+                sh 'python3 ./sacred2.py'
 			}
 		}
 		stage('archiveArtifacts') {
 			steps{
 				archiveArtifacts 'evaluation.txt'
                 archiveArtifacts 'evaluation_plot.png'
+                archiveArtifacts 'sacred/**'
 			}
 		}
     }

sacred1.py

@@ -0,0 +1,56 @@
+import pandas as pd
+from tensorflow import keras 
+from sklearn.model_selection import train_test_split
+from sklearn.metrics import mean_squared_error as rmse
+from sacred import Experiment
+from datetime import datetime
+from sacred.observers import FileStorageObserver
+ex = Experiment("file_observer", interactive=False, save_git_info=False)
+ex.observers.append(FileStorageObserver('sacred/my_runs'))
+
+
+@ex.config
+def my_config():
+    test_size = 0.2
+    epochs = 100
+    batch_size = 32
+
+
+@ex.capture
+def create_model(test_size, epochs, batch_size, _run):
+    _run.info["prepare_model_ts"] = str(datetime.now())
+    df = pd.read_csv('country_vaccinations.csv').dropna()
+    dataset = df.iloc[:, 3:-3]
+    dataset = df.groupby(by=["country"], dropna=True).sum()
+    X = dataset.loc[:,dataset.columns != "daily_vaccinations"]
+    y = dataset.loc[:,dataset.columns == "daily_vaccinations"]
+
+    X_train, X_test, y_train, y_test = train_test_split(X, y, test_size = test_size, random_state = 6)
+
+    model = keras.Sequential([
+        keras.layers.Dense(512,input_dim = X_train.shape[1],kernel_initializer='normal', activation='relu'),
+        keras.layers.Dense(512,kernel_initializer='normal', activation='relu'),
+        keras.layers.Dense(256,kernel_initializer='normal', activation='relu'),
+        keras.layers.Dense(256,kernel_initializer='normal', activation='relu'),
+        keras.layers.Dense(128,kernel_initializer='normal', activation='relu'),
+        keras.layers.Dense(1,kernel_initializer='normal', activation='linear'),
+    ])
+
+    model.compile(loss='mean_absolute_error', optimizer='adam', metrics=['mean_absolute_error'])
+
+    model.fit(X_train, y_train, epochs = epochs, validation_split = 0.3, batch_size = batch_size)
+
+    prediction = model.predict(X_test)
+    rmse_result = rmse(y_test, prediction, squared = False)
+    print(prediction)
+    _run.info["Results: "] = rmse_result
+    model.save('vaccines_model')
+    return rmse_result
+
+
+@ex.automain
+def my_main(test_size, epochs, batch_size):
+    print(create_model())
+
+r = ex.run()
+ex.add_artifact("vaccines_model/saved_model.pb")

sacred2.py

@@ -0,0 +1,56 @@
+import pandas as pd
+from tensorflow import keras 
+from sklearn.model_selection import train_test_split
+from sklearn.metrics import mean_squared_error as rmse
+from sacred import Experiment
+from datetime import datetime
+from sacred.observers import MongoObserver
+ex = Experiment("file_observer", interactive=False, save_git_info=False)
+ex.observers.append(MongoObserver(url='mongodb://mongo_user:mongo_password_IUM_2021@172.17.0.1:27017', db_name='sacred'))
+
+
+@ex.config
+def my_config():
+    test_size = 0.2
+    epochs = 100
+    batch_size = 32
+
+
+@ex.capture
+def create_model(test_size, epochs, batch_size, _run):
+    _run.info["prepare_model_ts"] = str(datetime.now())
+    df = pd.read_csv('country_vaccinations.csv').dropna()
+    dataset = df.iloc[:, 3:-3]
+    dataset = df.groupby(by=["country"], dropna=True).sum()
+    X = dataset.loc[:,dataset.columns != "daily_vaccinations"]
+    y = dataset.loc[:,dataset.columns == "daily_vaccinations"]
+
+    X_train, X_test, y_train, y_test = train_test_split(X, y, test_size = test_size, random_state = 6)
+
+    model = keras.Sequential([
+        keras.layers.Dense(512,input_dim = X_train.shape[1],kernel_initializer='normal', activation='relu'),
+        keras.layers.Dense(512,kernel_initializer='normal', activation='relu'),
+        keras.layers.Dense(256,kernel_initializer='normal', activation='relu'),
+        keras.layers.Dense(256,kernel_initializer='normal', activation='relu'),
+        keras.layers.Dense(128,kernel_initializer='normal', activation='relu'),
+        keras.layers.Dense(1,kernel_initializer='normal', activation='linear'),
+    ])
+
+    model.compile(loss='mean_absolute_error', optimizer='adam', metrics=['mean_absolute_error'])
+
+    model.fit(X_train, y_train, epochs = epochs, validation_split = 0.3, batch_size = batch_size)
+
+    prediction = model.predict(X_test)
+    rmse_result = rmse(y_test, prediction, squared = False)
+    print(prediction)
+    _run.info["Results: "] = rmse_result
+    model.save('vaccines_model')
+    return rmse_result
+
+
+@ex.automain
+def my_main(test_size, epochs, batch_size):
+    print(create_model())
+
+r = ex.run()
+ex.add_artifact("vaccines_model/saved_model.pb")