WIP: Regression for gas prices

main.py

@@ -1,16 +1,4 @@
-# This is a sample Python script.
+import sql_connection_handler as sql
 
-# Press Shift+F10 to execute it or replace it with your code.
-# Press Double Shift to search everywhere for classes, files, tool windows, actions, and settings.
-
-
-def print_hi(name):
-    # Use a breakpoint in the code line below to debug your script.
-    print(f'Hi, {name}')  # Press Ctrl+F8 to toggle the breakpoint.
-
-
-# Press the green button in the gutter to run the script.
 if __name__ == '__main__':
-    print_hi('PyCharm')
-
-# See PyCharm help at https://www.jetbrains.com/help/pycharm/
+    conn = sql.get_db_connection()

regression_gas_prices.py

@@ -0,0 +1,132 @@
+import pymysql
+import matplotlib.pyplot as plt
+import numpy as np
+import pandas as pd
+import seaborn as sns
+import tensorflow as tf
+from tensorflow import keras
+from tensorflow.keras import layers
+
+import sql_connection_handler as sql
+
+np.set_printoptions(precision=3, suppress=True)
+
+
+def get_data_from_sql() -> pd.DataFrame:
+	conn = sql.get_db_connection()
+	cur = conn.cursor()
+
+	query = 'SELECT timestamp, price FROM prices WHERE fuel_type = "E5" AND station = 1'
+	if not cur.execute(query):
+		raise pymysql.Error("Error loading data from SQL")
+
+	res = cur.fetchall()
+
+	raw_data = pd.DataFrame(res)
+
+	return raw_data
+
+
+def prepare_data(dataset: pd.DataFrame):
+	dataset = dataset.dropna()
+
+	# Split into training and test data
+	train_dataset = dataset.sample(frac=0.8, random_state=0)
+	test_dataset = dataset.drop(train_dataset.index)
+
+	# Split into features and labels
+	train_features = train_dataset.copy()
+	test_features = test_dataset.copy()
+
+	train_labels = train_features.pop(1)
+	test_labels = test_features.pop(1)
+
+	return train_features, test_features, train_labels, test_labels
+
+
+def normalize_data(train_features, test_features, train_labels, test_labels):
+	normalizer = tf.keras.layers.Normalization(axis=-1)
+	normalizer.adapt(np.asarray(train_features).astype('float32'))
+
+	price = np.asarray(train_features[0]).astype('float32')
+
+	price_normalizer = layers.Normalization(input_shape=[1, ], axis=None)
+	price_normalizer.adapt(price)
+
+	return price_normalizer
+
+
+def generate_model(price_normalizer):
+	price_model = tf.keras.Sequential([
+		price_normalizer,
+		layers.Dense(units=1)
+	])
+
+	price_model.compile(
+		optimizer=tf.optimizers.Adam(learning_rate=0.1),
+		loss='mean_absolute_error'
+	)
+
+	return price_model
+
+
+def train_model(price_model, train_features, train_labels):
+	history = price_model.fit(
+		np.asarray(train_features[0]).astype('float32'),
+		train_labels,
+		epochs=100,
+		verbose=0,
+		validation_split=0.2
+	)
+
+	# Show loss plot
+	plot_loss(history)
+
+	return price_model
+
+
+def collect_results(price_model, test_features, test_labels):
+	test_results = {}
+
+	test_results['price_model'] = price_model.evaluate(
+		np.asarray(train_features[0]).astype('float32'),
+		test_labels,
+		verbose=0
+	)
+
+	print(test_results)
+
+
+def predict_prices(price_model, train_features, train_labels):
+	x = tf.linspace(0.0, 250, 251)
+	y = price_model.predict(x)
+	plot_prices(x, y, train_features, train_labels)
+
+
+def plot_loss(history):
+	plt.plot(history.history['loss'], label='loss')
+	plt.plot(history.history['val_loss'], label='val_loss')
+	plt.ylim([0, 10])
+	plt.xlabel('Epoch')
+	plt.ylabel('Error [MPG]')
+	plt.legend()
+	plt.grid(True)
+	plt.show()
+
+def plot_prices(x, y, train_features, train_labels):
+	plt.scatter(train_features[0], train_labels, label='Data')
+	plt.plot(x, y, color='k', label='Predictions')
+	plt.xlabel('Datetime')
+	plt.ylabel('Price')
+	plt.legend()
+	plt.show()
+
+
+if __name__ == '__main__':
+	dataset = get_data_from_sql().copy()
+	train_features, test_features, train_labels, test_labels = prepare_data(dataset)
+	price_normalizer = normalize_data(train_features, test_features, train_labels, test_labels)
+	price_model = generate_model(price_normalizer)
+	price_model = train_model(price_model, train_features, train_labels)
+	collect_results(price_model, test_features, test_labels)
+	predict_prices(price_model)

requirements.txt

@@ -3,4 +3,6 @@ tensorflow
 seaborn
 matplotlib
 numpy
-pandas
+pandas
+python-dotenv
+pymysql

sql_connection_handler.py

@@ -0,0 +1,22 @@
+import logging
+import pymysql
+import os
+from dotenv import load_dotenv
+
+
+def get_db_connection() -> pymysql.Connection:
+	conn = None
+	try:
+		load_dotenv(".env")
+
+		conn = pymysql.connect(
+			user=os.environ.get('DB_USER'),
+			password=os.environ.get('DB_PASSWORD'),
+			host=os.environ.get('DB_HOST'),
+			port=int(os.environ.get('DB_PORT')),
+			database=os.environ.get('DB_NAME')
+		)
+	except Exception as e:
+		logging.error("SQL Connection Error:%s", e)
+
+	return conn