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zgolebiewska 2024-05-26 14:23:37 +02:00
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from sacred import Experiment
from sacred.observers import MongoObserver, FileStorageObserver
import tensorflow as tf
import pandas as pd
from sklearn.preprocessing import LabelEncoder, StandardScaler
from sklearn.model_selection import train_test_split
import json
import mlflow
mlflow.set_tracking_uri("http://localhost:5000") # Ustawienie adresu MLflow Tracking Server
ex = Experiment("s464906_experiment")
df = pd.read_csv('OrangeQualityData.csv')
mongo_url = "mongodb://admin:IUM_2021@tzietkiewicz.vm.wmi.amu.edu.pl:27017"
ex.observers.append(MongoObserver(url=mongo_url, db_name='sacred'))
ex.observers.append(FileStorageObserver('logs'))
encoder = LabelEncoder()
df["Color"] = encoder.fit_transform(df["Color"])
df["Variety"] = encoder.fit_transform(df["Variety"])
df["Blemishes"] = df["Blemishes (Y/N)"].apply(lambda x: 1 if x.startswith("Y") else 0)
@ex.config
def cfg():
epochs = 100
df.drop(columns=["Blemishes (Y/N)"], inplace=True)
@ex.automain
def train_model(epochs):
df = pd.read_csv('OrangeQualityData.csv')
X = df.drop(columns=["Quality (1-5)"])
y = df["Quality (1-5)"]
encoder = LabelEncoder()
df["Color"] = encoder.fit_transform(df["Color"])
df["Variety"] = encoder.fit_transform(df["Variety"])
df["Blemishes"] = df["Blemishes (Y/N)"].apply(lambda x: 1 if x.startswith("Y") else 0)
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)
df.drop(columns=["Blemishes (Y/N)"], inplace=True)
scaler = StandardScaler()
X_train_scaled = scaler.fit_transform(X_train)
X_test_scaled = scaler.transform(X_test)
X = df.drop(columns=["Quality (1-5)"])
y = df["Quality (1-5)"]
model = tf.keras.Sequential([
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)
scaler = StandardScaler()
X_train_scaled = scaler.fit_transform(X_train)
X_test_scaled = scaler.transform(X_test)
model = tf.keras.Sequential([
tf.keras.layers.Dense(64, activation='relu', input_shape=(X_train_scaled.shape[1],)),
tf.keras.layers.Dense(32, activation='relu'),
tf.keras.layers.Dense(1)
])
])
model.compile(optimizer='sgd', loss='mse')
model.compile(optimizer='sgd', loss='mse')
with mlflow.start_run():
mlflow.log_param("optimizer", 'sgd')
mlflow.log_param("loss_function", 'mse')
mlflow.log_param("epochs", 100)
history = model.fit(X_train_scaled, y_train, epochs=epochs, verbose=0, validation_data=(X_test_scaled, y_test))
history = model.fit(X_train_scaled, y_train, epochs=100, verbose=0, validation_data=(X_test_scaled, y_test))
ex.log_scalar("epochs", epochs)
for key, value in history.history.items():
mlflow.log_metric(key, value[-1]) # Logujemy ostatnią wartość metryki
ex.add_artifact(__file__)
model.save('orange_quality_model_tf.h5')
ex.add_artifact('orange_quality_model_tf.h5')
for key, value in history.history.items():
ex.log_scalar(key, value[-1])
predictions = model.predict(X_test_scaled)
with open('predictions_tf.json', 'w') as f:
json.dump(predictions.tolist(), f, indent=4)
ex.add_artifact('predictions_tf.json')
return 'Training completed successfully'