From b7556dedb02b58eb5e625e463aecb797c50d6328 Mon Sep 17 00:00:00 2001
From: s430705 <miczar1@st.amu.edu.pl>
Date: Wed, 12 May 2021 09:42:24 +0200
Subject: [PATCH] Split sacred and MLflow to separate files

---
 lab07_sacred.py                        | 80 ++++++++++++++++++++++++++
 lab07_08_sacred_mfl.py => lab08_mfl.py | 34 +----------
 2 files changed, 81 insertions(+), 33 deletions(-)
 create mode 100644 lab07_sacred.py
 rename lab07_08_sacred_mfl.py => lab08_mfl.py (73%)

diff --git a/lab07_sacred.py b/lab07_sacred.py
new file mode 100644
index 0000000..8caffe7
--- /dev/null
+++ b/lab07_sacred.py
@@ -0,0 +1,80 @@
+from datetime import datetime
+
+import pandas as pd
+from sacred import Experiment
+from sacred.observers import FileStorageObserver
+from sklearn.metrics import mean_squared_error
+from sklearn.model_selection import train_test_split
+from tensorflow.keras.callbacks import EarlyStopping
+from tensorflow.keras.layers import Dense, Dropout
+from tensorflow.keras.models import Sequential
+
+ex = Experiment("file_observer", interactive=False, save_git_info=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")
diff --git a/lab07_08_sacred_mfl.py b/lab08_mfl.py
similarity index 73%
rename from lab07_08_sacred_mfl.py
rename to lab08_mfl.py
index 2ed778a..72ab479 100644
--- a/lab07_08_sacred_mfl.py
+++ b/lab08_mfl.py
@@ -2,41 +2,20 @@ import sys
 import mlflow
 import pandas as pd
 
-from sacred import Experiment
-from sacred.observers import FileStorageObserver
 from sklearn.metrics import mean_squared_error
 from sklearn.model_selection import train_test_split
 from tensorflow.keras.callbacks import EarlyStopping
 from tensorflow.keras.layers import Dense, Dropout
 from tensorflow.keras.models import Sequential
 
-'''
-If you want to use it as sacred, you have to 
-uncomment lines, for now it's set up for MLFlow
-'''
-# ex = Experiment("file_observer", interactive=False, save_git_info=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())
+def prepare_model(train_size_param, test_size_param, epochs, batch_size):
     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
     )
@@ -69,22 +48,11 @@ def prepare_model(train_size_param, test_size_param, epochs, batch_size):  # _ru
 
     rmse = mean_squared_error(y_test, y_pred)
 
-    # _run.info["Final Results: "] = rmse
-
     model.save("model_movies")
 
     return model, 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")
-
-
 train_size_param = float(sys.argv[1]) if len(sys.argv) > 1 else 0.8
 test_size_param = float(sys.argv[2]) if len(sys.argv) > 1 else 0.2
 epochs = int(sys.argv[3]) if len(sys.argv) > 1 else 400