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