diff --git a/machine_learning/lstm/lstm_prediction.py b/machine_learning/lstm/lstm_prediction.py new file mode 100644 index 000000000..fbf802f4d --- /dev/null +++ b/machine_learning/lstm/lstm_prediction.py @@ -0,0 +1,56 @@ +""" + Create a Long Short Term Memory (LSTM) network model + An LSTM is a type of Recurrent Neural Network (RNN) as discussed at: + * http://colah.github.io/posts/2015-08-Understanding-LSTMs + * https://en.wikipedia.org/wiki/Long_short-term_memory +""" + +from keras.layers import Dense, LSTM +from keras.models import Sequential +import numpy as np +import pandas as pd +from sklearn.preprocessing import MinMaxScaler + + +if __name__ == "__main__": + """ + First part of building a model is to get the data and prepare + it for our model. You can use any dataset for stock prediction + make sure you set the price column on line number 21. Here we + use a dataset which have the price on 3rd column. + """ + df = pd.read_csv("sample_data.csv", header=None) + len_data = df.shape[:1][0] + # If you're using some other dataset input the target column + actual_data = df.iloc[:, 1:2] + actual_data = actual_data.values.reshape(len_data, 1) + actual_data = MinMaxScaler().fit_transform(actual_data) + look_back = 10 + forward_days = 5 + periods = 20 + division = len_data - periods * look_back + train_data = actual_data[:division] + test_data = actual_data[division - look_back :] + train_x, train_y = [], [] + test_x, test_y = [], [] + + for i in range(0, len(train_data) - forward_days - look_back + 1): + train_x.append(train_data[i : i + look_back]) + train_y.append(train_data[i + look_back : i + look_back + forward_days]) + for i in range(0, len(test_data) - forward_days - look_back + 1): + test_x.append(test_data[i : i + look_back]) + test_y.append(test_data[i + look_back : i + look_back + forward_days]) + x_train = np.array(train_x) + x_test = np.array(test_x) + y_train = np.array([list(i.ravel()) for i in train_y]) + y_test = np.array([list(i.ravel()) for i in test_y]) + + model = Sequential() + model.add(LSTM(128, input_shape=(look_back, 1), return_sequences=True)) + model.add(LSTM(64, input_shape=(128, 1))) + model.add(Dense(forward_days)) + model.compile(loss="mean_squared_error", optimizer="adam") + history = model.fit( + x_train, y_train, epochs=150, verbose=1, shuffle=True, batch_size=4 + ) + pred = model.predict(x_test) diff --git a/machine_learning/lstm/sample_data.csv b/machine_learning/lstm/sample_data.csv new file mode 100644 index 000000000..f94db621f --- /dev/null +++ b/machine_learning/lstm/sample_data.csv @@ -0,0 +1,1259 @@ +04/24/2020, 1279.31, 1640394, 1261.17, 1280.4, 1249.45 +04/23/2020, 1276.31, 1566203, 1271.55, 1293.31, 1265.67 +04/22/2020, 1263.21, 2093140, 1245.54, 1285.6133, 1242 +04/21/2020, 1216.34, 2153003, 1247, 1254.27, 1209.71 +04/20/2020, 1266.61, 1695488, 1271, 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mypy numpy>=1.17.4 @@ -14,5 +15,5 @@ requests scikit-fuzzy sklearn sympy -tensorflow; python_version < '3.8' +tensorflow xgboost