add lab07 solution

Dockerfile

@@ -12,4 +12,5 @@ COPY ./script2.py ./
 RUN pip3 install -r ./requirements.txt
 CMD python3 script2.py
 CMD python3 lab05.py
-RUN pip3 install gast==0.2.2
+RUN pip3 install gast==0.2.2
+CMD python3 lab07_sacred.py

Jenkinsfile_evaluation

@@ -29,6 +29,7 @@ pipeline {
       stage('archiveArtifacts') {
          steps {
             archiveArtifacts 'eval.txt'
+            archiveArtifacts 'lab07/**'
          }
       }
        stage('sendMail') {

lab07_sacred.py

@@ -0,0 +1,81 @@
+import pandas as pd
+from sklearn.metrics import mean_squared_error
+from sklearn.model_selection import train_test_split
+
+from tensorflow.keras.models import Sequential
+from tensorflow.keras.layers import Dense
+from tensorflow.keras.layers import Dropout
+from tensorflow.keras.callbacks import EarlyStopping
+from datetime import datetime
+from sacred.observers import FileStorageObserver
+from sacred import Experiment
+
+ex = Experiment("file_observer", interactive=False)
+ex.observers.append(FileStorageObserver('lab07/my_runs'))
+
+
+@ex.config
+def my_config():
+    train_size_param = 0.8
+    test_size_param = 0.2
+    epochs = 400
+    batch_size = 128
+
+
+@ex.capture
+def prepare_model(train_size_param, test_size_param, epochs, batch_size, _run):
+    _run.info["prepare_model_ts"] = str(datetime.now())
+    movies_data = pd.read_csv('train.csv', error_bad_lines=False)
+    movies_data.drop(movies_data.columns[0], axis=1, inplace=True)
+    movies_data.dropna(inplace=True)
+    X = movies_data.drop("rating", axis=1)
+    Y = movies_data["rating"]
+
+    print(X, Y.values)
+    # Split set to train/test 8:2 ratio
+    X_train, X_test, Y_train, Y_test = train_test_split(
+        X, Y, test_size=test_size_param, random_state=42
+    )
+
+    test_df = pd.read_csv('test.csv')
+    test_df.drop(test_df.columns[0], axis=1, inplace=True)
+    x_test = test_df.drop("rating", axis=1)
+    y_test = test_df["rating"]
+
+    # Set up model
+    model = Sequential()
+    model.add(Dense(8, activation="relu"))
+    model.add(Dropout(0.5))
+    model.add(Dense(3, activation="relu"))
+    model.add(Dropout(0.5))
+    model.add(Dense(1))
+    model.compile(optimizer="adam", loss="mse")
+
+    early_stop = EarlyStopping(monitor="val_loss", mode="min", verbose=1, patience=10)
+
+    model.fit(
+        x=X_train.values,
+        y=Y_train.values,
+        validation_data=(X_test, Y_test.values),
+        batch_size=batch_size,
+        epochs=epochs,
+        callbacks=[early_stop],
+    )
+    y_pred = model.predict(x_test.values)
+
+    rmse = mean_squared_error(y_test, y_pred)
+
+    _run.info["Final Results: "] = rmse
+
+    model.save('model_movies')
+
+    return rmse
+
+
+@ex.automain
+def my_main(train_size_param, test_size_param, epochs, batch_size):
+    print(prepare_model())
+
+
+r = ex.run()
+ex.add_artifact("model_movies/saved_model.pb")

requirements.txt

@@ -6,4 +6,5 @@ numpy==1.19.5
 pandas==1.0.1
 tensorflow==2.0.0b1
 wget==3.2
-gast==0.3.3
+gast==0.3.3
+sacred==0.8.2