added sacred

.gitignore

@@ -3,4 +3,5 @@ __pycache__
 suicide_model.h5
 test.csv
 train.csv
-validate.csv
+validate.csv
+sacred_runs/

sacred_training.py

@@ -0,0 +1,67 @@
+import sys
+import pandas as pd
+import numpy as np
+import tensorflow as tf
+from tensorflow.keras import layers
+from tensorflow.keras.layers.experimental import preprocessing
+from sacred import Experiment
+from sacred.observers import FileStorageObserver
+from sklearn.metrics import mean_squared_error
+
+ex = Experiment("434784_sacred_scopes", interactive=True)
+ex.observers.append(FileStorageObserver('sacred_runs'))
+
+
+@ex.config
+def my_config():
+    epochs = 10
+    batch_size = 10
+
+
+@ex.capture
+def prepare(epochs, batch_size, _run):
+    sc = pd.read_csv('who_suicide_statistics.csv')
+    sc.dropna(inplace=True)
+    sc = pd.get_dummies(
+        sc, columns=['age', 'sex', 'country'], prefix='', prefix_sep='')
+
+    train, validate, test = np.split(sc.sample(frac=1, random_state=42),
+                                     [int(.6*len(sc)), int(.8*len(sc))])
+
+    X_train = train.loc[:, train.columns != 'suicides_no']
+    y_train = train[['suicides_no']]
+    X_test = test.loc[:, train.columns != 'suicides_no']
+    y_test = test[['suicides_no']]
+
+    normalizer = preprocessing.Normalization()
+    normalizer.adapt(np.array(X_train))
+
+    model = tf.keras.Sequential([
+        normalizer,
+        layers.Dense(units=1)
+    ])
+
+    model.compile(
+        optimizer=tf.optimizers.Adam(learning_rate=0.1),
+        loss='mean_absolute_error')
+
+    model.fit(
+        X_train, y_train,
+        batch_size=batch_size,
+        epochs=epochs,
+        validation_split=0.2)
+
+    model.save_weights('suicide_model.h5')
+    predictions = model.predict(X_test)
+    error = mean_squared_error(y_test, predictions)
+    _run.info["mean_squared_error"] = str(error)
+    return error
+
+
+@ex.automain
+def my_main(epochs, batch_size):
+    print(prepare())
+
+
+r = ex.run()
+ex.add_artifact('suicide_model.h5')