added mlflow

MLProject

@@ -0,0 +1,12 @@
+name: 434804
+
+docker_env: my_env.yaml
+    image: s434804/ium:0.4
+
+entry_points:
+    main:
+        parameters:
+            test_size: {type: int, default: 0.2}
+            epochs: {type: int, default: 100}
+            batch_size: {type: int, default: 32}
+        command: "python3 mlflow.py {test_size} {epochs} {batch_size}"

mlflow_model.py

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+import sys
+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
+import mlflow as mlf
+
+
+def create_model(test_size, epochs, batch_size):
+    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)
+    model.save('vaccines_model')
+    return model, rmse_result
+
+if __name__ == "__main__":
+    test_size = float(sys.argv[1]) if len(sys.argv) > 1 else 0.2
+    epochs = int(sys.argv[2]) if len(sys.argv) > 1 else 100
+    batch_size = int(sys.argv[3]) if len(sys.argv) > 1 else 32
+
+    with mlf.start_run():
+        mlf.log_param("Test size", test_size)
+        mlf.log_param("Epochs", epochs)
+        mlf.log_param("Batch size", batch_size)
+
+        model, rmse_result = create_model(
+            test_size=test_size,
+            epochs=epochs,
+            batch_size=batch_size,
+        )
+
+        mlf.log_metric("RMSE", rmse_result)
+
+        mlf.keras.log_model(model, "country_vaccination")