73 lines
1.9 KiB
Python
73 lines
1.9 KiB
Python
import pandas as pd
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import numpy as np
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import matplotlib.pyplot as plt
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import os
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from sklearn.model_selection import train_test_split
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from keras import Sequential, layers, optimizers
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# Get the CWD for pathing due to being called from C# now
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SCRIPT_DIR = os.path.dirname(os.path.abspath(__file__))
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DATA_DIR = os.path.join(SCRIPT_DIR, "data")
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# Load the dataset
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dataset = pd.read_parquet(os.path.join(DATA_DIR, "stocks.parquet"))
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X = dataset.drop('Volatility_5', axis=1)
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Y = dataset['Volatility_5']
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# Show the datatypes
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print(dataset.dtypes)
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# Split out the test and train
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train_features, test_features, train_labels, test_labels = train_test_split(X, Y, test_size=0.2)
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# Create a normalizer to nomralize the data
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normalizer = layers.Normalization(axis=-1)
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normalizer.adapt(np.array(train_features))
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# Start with a linear model
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dnn_linear_model = Sequential([
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layers.Input(shape=(9,)), # Explicitly tell Keras there are 9 features
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normalizer,
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layers.Dense(64, activation='relu'),
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layers.Dense(64, activation='relu'),
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layers.Dense(1)
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])
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# Configure the model
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dnn_linear_model.compile(
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optimizer=optimizers.Adam(learning_rate=0.001),
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loss='mean_absolute_error'
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)
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# Show the summary before training the model
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dnn_linear_model.summary()
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# Train the model
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Training_Data = dnn_linear_model.fit(
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train_features,
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train_labels,
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epochs=100,
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# Show progress
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verbose=1,
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# Calculate validation results on 20% of the training data.
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validation_split = 0.2
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)
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# Predict
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test_predictions = dnn_linear_model.predict(test_features).flatten()
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a = plt.axes(aspect='equal')
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plt.scatter(test_labels, test_predictions)
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plt.xlabel('True Values')
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plt.ylabel('Predictions')
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lims = [0, 50]
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plt.xlim(lims)
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plt.ylim(lims)
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_ = plt.plot(lims, lims)
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test_results = dnn_linear_model.evaluate(
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test_features, test_labels, verbose=0
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)
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# Save the model
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dnn_linear_model.save(os.path.join(DATA_DIR, "model.keras")) |