94 lines
3.4 KiB
Python
94 lines
3.4 KiB
Python
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import mlflow
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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.metrics import accuracy_score
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from sklearn.model_selection import train_test_split
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from mlflow.models.signature import infer_signature
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from sklearn.preprocessing import StandardScaler
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import sys
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mlflow.set_experiment("s444465")
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def evaluate_model(model, test_x, test_y):
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test_loss, test_acc, test_rec = model.evaluate(test_x, test_y, verbose=1)
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# print("Accuracy:", test_acc)
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# print("Loss:", test_loss)
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# print("Recall:", test_rec)
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return test_acc, test_loss, test_rec
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def main():
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no_of_epochs = int(sys.argv[1]) if (len(sys.argv) == 2 and sys.argv[1].isdigit()) else 10
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is_testing = (len(sys.argv) == 2) and not sys.argv[1].isdigit() and sys.argv[1] == "test"
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mlflow.log_param("epochs", no_of_epochs)
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scaler = StandardScaler()
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feature_names = ["BMI", "SleepTime", "Sex", "Diabetic", "PhysicalActivity", "Smoking", "AlcoholDrinking"]
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dataset = pd.read_csv('heart_2020_cleaned.csv')
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dataset = dataset.dropna()
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dataset["Diabetic"] = dataset["Diabetic"].apply(lambda x: True if "Yes" in x else False)
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dataset["HeartDisease"] = dataset["HeartDisease"].apply(lambda x: True if x == "Yes" else False)
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dataset["PhysicalActivity"] = dataset["PhysicalActivity"].apply(lambda x: True if x == "Yes" else False)
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dataset["Smoking"] = dataset["Smoking"].apply(lambda x: True if x == "Yes" else False)
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dataset["AlcoholDrinking"] = dataset["AlcoholDrinking"].apply(lambda x: True if x == "Yes" else False)
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dataset["Sex"] = dataset["Sex"].apply(lambda x: 1 if x == "Female" else 0)
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dataset_train, dataset_test = train_test_split(dataset, test_size=.1, train_size=.9, random_state=1)
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print(dataset_test.shape)
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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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acc, loss, rec = evaluate_model(model, test_X, test_Y)
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mlflow.log_metric("accuracy", acc)
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mlflow.log_metric("loss", loss)
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signature = infer_signature(np.array(train_X), np.array(train_Y))
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mlflow.sklearn.log_model(model, "mlflow_model", signature=signature, input_example=np.array(test_X[0]))
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if is_testing:
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predictions = model.predict(np.array(test_X))
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predictions = [int(i > 0.5) for i in predictions]
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accuracy = accuracy_score(np.array(test_Y), predictions)
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mlflow.log_metric("eval_accuracy", accuracy)
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main()
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