Do sacred

Dockerfile

@@ -2,7 +2,7 @@ FROM ubuntu:latest
 
 RUN apt-get update && \
     apt-get install -y python3-pip && \
-    pip3 install kaggle pandas scikit-learn tensorflow matplotlib mlflow
+    pip3 install kaggle pandas scikit-learn tensorflow matplotlib mlflow git sacred pymongo
 
 RUN useradd -ms /bin/bash jenkins
 

JenkinsfileSacred

@@ -0,0 +1,44 @@
+pipeline {
+    agent {
+        dockerfile true
+    }
+
+    parameters{
+        buildSelector(
+            defaultSelector: lastSuccessful(),
+            description: 'Which build to use for copying artifacts',
+            name: 'BUILD_SELECTOR'
+        )
+    }
+
+    triggers {
+        upstream(upstreamProjects: 'z-s495719-create-dataset', threshold: hudson.model.Result.SUCCESS)
+    }
+
+    stages {
+        stage('Git') {
+            steps {
+                git(
+                    url: "https://git.wmi.amu.edu.pl/s495719/ium_495719.git",
+                    branch: "main"
+                )
+            }
+        }
+        stage('CopyArtifacts') {
+            steps {
+                copyArtifacts fingerprintArtifacts: true, projectName: 'z-s495719-create-dataset', selector: buildParameter('BUILD_SELECTOR')
+            }
+        }
+        stage('Script') {
+            steps {
+                sh 'chmod 777 sacred/create_model.py'
+                sh "python3 sacred/create_model.py"
+            }
+        }
+        stage('CreateArtifacts') {
+            steps {
+                archiveArtifacts artifacts: 'sacred/hp_model.h5'
+            }
+        }
+    }
+}

sacred/create_model.py

@@ -0,0 +1,69 @@
+import numpy as np
+import pandas as pd
+from keras.models import Sequential
+from keras.layers import Dense
+from keras.optimizers import Adam
+from keras import regularizers
+from sacred import Experiment
+from sacred.observers import MongoObserver, FileStorageObserver
+from sklearn.metrics import mean_squared_error, mean_absolute_error, r2_score
+
+from helper import prepare_tensors
+
+ex = Experiment('495719')
+
+ex.observers.append(MongoObserver(url='mongodb://admin:IUM_2021@tzietkiewicz.vm.wmi.amu.edu.pl:27017'))
+ex.observers.append(FileStorageObserver('my_runs'))
+
+@ex.config
+def config():
+    epochs = 10
+    learning_rate = 0.001
+    batch_size = 32
+
+@ex.main
+def main(epochs, learning_rate, batch_size, _run):
+    with _run.open_resource("../hp_train.csv") as f:
+        hp_train = pd.read_csv(f)
+    with _run.open_resource("../hp_dev.csv") as f:
+        hp_dev = pd.read_csv(f)
+
+    X_train, Y_train = prepare_tensors(hp_train)
+    X_dev, Y_dev = prepare_tensors(hp_dev)
+
+    model = Sequential()
+    model.add(Dense(64, input_dim=7, activation='relu', kernel_regularizer=regularizers.l2(0.01)))
+    model.add(Dense(32, activation='relu', kernel_regularizer=regularizers.l2(0.01)))
+    model.add(Dense(16, activation='relu', kernel_regularizer=regularizers.l2(0.01)))
+    model.add(Dense(8, activation='relu', kernel_regularizer=regularizers.l2(0.01)))
+    model.add(Dense(1, activation='linear'))
+
+    adam = Adam(learning_rate=learning_rate, beta_1=0.9, beta_2=0.999, epsilon=1e-7)
+    model.compile(optimizer=adam, loss='mean_squared_error')
+
+    model.fit(X_train, Y_train, epochs=epochs, batch_size=batch_size, validation_data=(X_dev, Y_dev))
+
+    model.save('hp_model.h5')
+    ex.add_artifact("hp_model.h5")
+
+    with _run.open_resource("../hp_test.csv") as f:
+        hp_test = pd.read_csv(f)
+
+    X_test, Y_test = prepare_tensors(hp_test)
+
+    test_predictions = model.predict(X_test)
+
+    predictions_df = pd.DataFrame(test_predictions, columns=["Predicted_Price"])
+    predictions_df.to_csv('hp_test_predictions.csv', index=False)
+
+    rmse = np.sqrt(mean_squared_error(Y_test, test_predictions))
+    mae = mean_absolute_error(Y_test, test_predictions)
+    r2 = r2_score(Y_test, test_predictions)
+
+    _run.log_scalar("rmse", rmse)
+    _run.log_scalar("mae", mae)
+    _run.log_scalar("r2", r2)
+
+
+if __name__ == '__main__':
+    ex.run()