From 63159ed8bf6fafc44fe3a97d5a58fcc359cbc278 Mon Sep 17 00:00:00 2001
From: Wojciech Lidwin <59863785+Halal37@users.noreply.github.com>
Date: Fri, 12 May 2023 03:20:45 +0200
Subject: [PATCH] XD

---
 ium_sacred.py | 122 ++++++++++++++++++++++++++++++++++++++++++++++++++
 1 file changed, 122 insertions(+)
 create mode 100644 ium_sacred.py

diff --git a/ium_sacred.py b/ium_sacred.py
new file mode 100644
index 0000000..22120b9
--- /dev/null
+++ b/ium_sacred.py
@@ -0,0 +1,122 @@
+from keras.models import Sequential, load_model
+from keras.layers import Dense
+from keras.optimizers import Adam
+import pandas as pd
+import tensorflow as tf
+import numpy as np
+from sklearn.preprocessing import LabelEncoder
+import argparse
+import shutil
+from sacred.observers import FileStorageObserver, MongoObserver
+from sacred import Experiment
+from sklearn import metrics
+import math
+
+ex = Experiment('s487197-train', save_git_info=False,interactive=True)
+ex.observers.append(FileStorageObserver('sacred_results'))
+ex.observers.append(MongoObserver(url='mongodb://admin:IUM_2021@172.17.0.1:27017', db_name='sacred'))
+#ex.observers.append(MongoObserver(url='mongodb://admin:IUM_2021@172.17.0.1:27017', db_name='sacred'))
+
+
+def write_list(names):
+    with open('listfile.txt', 'w') as fp:
+        fp.write("\n".join(str(item) for item in names))
+
+def get_x_y(data):
+    lb = LabelEncoder()
+    data = data.drop(["Location 1"], axis=1)
+    data = data.drop(
+        columns=["Longitude", "Latitude", "Location", "Total Incidents", "CrimeTime", "Neighborhood", "Post",
+                 "CrimeDate", "Inside/Outside"], axis=1)
+    for column_name in data.columns:
+        data[column_name] = lb.fit_transform(data[column_name])
+    x = data.drop('Weapon', axis=1)
+    y = data['Weapon']
+
+    return data, x, y
+
+
+@ex.config
+def my_config():
+    epochs = 20
+    lr = 0.01
+    validation_split = 0.2
+    #parser = argparse.ArgumentParser(description='Train')
+
+   # parser.add_argument('-epochs', type=int, default=20)
+   # parser.add_argument('-lr', type=float, default=0.01)
+    #parser.add_argument('-validation_split', type=float, default=0.2)
+    #args = parser.parse_args()
+   # epochs = args.epochs
+  #  lr = args.lr
+   # validation_split = args.validation_split
+
+
+@ex.main
+def predict(epochs, lr, validation_split):
+
+    print("ble")
+    model = load_model('baltimore_model')
+
+    train = pd.read_csv('baltimore_train.csv')
+    baltimore_data_test =pd.read_csv('baltimore_test.csv')
+    baltimore_data_test.columns = train.columns
+    baltimore_data_test, x_test, y_test = get_x_y(baltimore_data_test)
+    scores = model.evaluate(x_test, y_test)
+    print("\n%s: %.2f%%" % (model.metrics_names[1], scores[1] * 100))
+
+    y_predicted = model.predict(x_test)
+    y_predicted = np.argmax(y_predicted, axis=1)
+    test_results = {}
+    test_results['Weapon'] = model.evaluate(
+        x_test,
+        y_test, verbose=0)
+    write_list(y_predicted)
+    print('Accuracy : ', scores[1] * 100)
+    print('Mean Absolute Error : ', metrics.mean_absolute_error(y_test, y_predicted))
+    print('Root Mean Squared Error : ', math.sqrt(metrics.mean_squared_error(y_test, y_predicted)))
+
+    data = {
+            'mse': metrics.mean_squared_error(y_test, y_predicted),
+            'rmse': math.sqrt(metrics.mean_squared_error(y_test, y_predicted)),
+            'accuracy': scores[1] * 100
+        }
+    ex.log_scalar('accuracy', data['accuracy'])
+    ex.log_scalar('rmse', data['rmse'])
+    ex.log_scalar('accuracy', data['accuracy'])
+
+    ex.add_artifact('baltimore_model')
+
+
+
+
+@ex.capture
+def train_model(epochs, lr, validation_split):
+
+
+    train = pd.read_csv('baltimore_train.csv')
+
+    data_train, x_train, y_train = get_x_y(train)
+    normalizer = tf.keras.layers.Normalization(axis=1)
+    normalizer.adapt(np.array(x_train))
+    model = Sequential(normalizer)
+    model.add(Dense(64, activation="relu"))
+    model.add(Dense(10, activation='relu'))
+    model.add(Dense(10, activation='relu'))
+    model.add(Dense(10, activation='relu'))
+    model.add(Dense(5, activation="softmax"))
+    model.compile(Adam(learning_rate=lr), loss='sparse_categorical_crossentropy', metrics=['accuracy'])
+    model.summary()
+
+    history = model.fit(
+        x_train,
+        y_train,
+        epochs=epochs,
+        validation_split=validation_split)
+    hist = pd.DataFrame(history.history)
+    hist['epoch'] = history.epoch
+    model.save('baltimore_model')
+    shutil.make_archive('baltimore', 'zip', 'baltimore_model')
+
+ex.run()
+