55 lines
1.7 KiB
Python
55 lines
1.7 KiB
Python
import numpy as np
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import pandas as pd
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import tensorflow as tf
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from sklearn.model_selection import train_test_split
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from sklearn.preprocessing import StandardScaler
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import sys
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def main():
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no_of_epochs = int(sys.argv[1]) if len(sys.argv) == 2 else 10
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feature_names = ["BMI", "SleepTime", "Sex", "Diabetic", "PhysicalActivity", "Smoking", "AlcoholDrinking",
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"HeartDisease"]
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scaler = StandardScaler()
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dataset_train = pd.read_csv("training_data.csv")
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dataset_test = pd.read_csv("test_data.csv")
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model = tf.keras.Sequential([
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tf.keras.layers.Dense(16, activation='relu'),
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tf.keras.layers.Dense(8, activation='relu'),
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tf.keras.layers.Dense(4, activation='relu'),
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tf.keras.layers.Dense(1, activation='sigmoid')
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])
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model.compile(
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loss=tf.keras.losses.binary_crossentropy,
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optimizer=tf.keras.optimizers.Adam(lr=0.01),
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metrics=["accuracy", tf.keras.metrics.Recall(name='recall')]
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)
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train_X = dataset_train[feature_names].astype(np.float32)
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train_Y = dataset_train["HeartDisease"].astype(np.float32)
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test_X = dataset_test[feature_names].astype(np.float32)
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test_Y = dataset_test["HeartDisease"].astype(np.float32)
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train_X = scaler.fit_transform(train_X)
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# train_Y = scaler.fit_transform(train_Y)
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test_X = scaler.fit_transform(test_X)
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# test_Y = scaler.fit_transform(test_Y)
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print(train_Y.value_counts())
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train_X = tf.convert_to_tensor(train_X)
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train_Y = tf.convert_to_tensor(train_Y)
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test_X = tf.convert_to_tensor(test_X)
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test_Y = tf.convert_to_tensor(test_Y)
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model.fit(train_X, train_Y, epochs=no_of_epochs)
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model.save("trained_model")
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main()
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