![Sheaza](/assets/img/avatar_default.png)
Some checks failed
s464980-training/pipeline/head There was a failure building this commit
59 lines
1.9 KiB
Python
59 lines
1.9 KiB
Python
import pandas as pd
|
|
from tensorflow import keras
|
|
from tensorflow.keras import layers
|
|
import argparse
|
|
import mlflow
|
|
|
|
|
|
class RegressionModel:
|
|
def __init__(self, optimizer="adam", loss="mean_squared_error"):
|
|
self.model = keras.Sequential([
|
|
layers.Input(shape=(5,)), # Input layer
|
|
layers.Dense(32, activation='relu'), # Hidden layer with 32 neurons and ReLU activation
|
|
layers.Dense(1) # Output layer with a single neuron (for regression)
|
|
])
|
|
self.optimizer = optimizer
|
|
self.loss = loss
|
|
self.X_train = None
|
|
self.X_test = None
|
|
self.y_train = None
|
|
self.y_test = None
|
|
|
|
def load_data(self, train_path, test_path):
|
|
data_train = pd.read_csv(train_path)
|
|
data_test = pd.read_csv(test_path)
|
|
self.X_train = data_train.drop("Performance Index", axis=1)
|
|
self.y_train = data_train["Performance Index"]
|
|
self.X_test = data_test.drop("Performance Index", axis=1)
|
|
self.y_test = data_test["Performance Index"]
|
|
|
|
def train(self, epochs=30):
|
|
self.model.compile(optimizer=self.optimizer, loss=self.loss)
|
|
self.model.fit(self.X_train, self.y_train, epochs=epochs, batch_size=32, validation_data=(self.X_test, self.y_test))
|
|
|
|
def predict(self, data):
|
|
prediction = self.model.predict(data)
|
|
return prediction
|
|
|
|
def evaluate(self):
|
|
test_loss = self.model.evaluate(self.X_test, self.y_test)
|
|
print(f"Test Loss: {test_loss:.4f}")
|
|
return test_loss
|
|
|
|
def save_model(self):
|
|
self.model.save("model.keras")
|
|
|
|
|
|
parser = argparse.ArgumentParser()
|
|
parser.add_argument('--epochs')
|
|
|
|
args = parser.parse_args()
|
|
mlflow.set_tracking_uri("http://localhost:5000")
|
|
model = RegressionModel()
|
|
with mlflow.start_run() as run:
|
|
model.train(epochs=int(args.epochs))
|
|
rmse = model.evaluate()
|
|
mlflow.log_param("epoch", int(args.epochs))
|
|
mlflow.log_metric("rmse", rmse)
|
|
model.save_model()
|