import streamlit as st import numpy as np import tensorflow as tf from tensorflow import keras from sklearn.datasets import make_classification from sklearn.model_selection import train_test_split from sklearn.metrics import accuracy_score import random # Define a function to generate a dataset def generate_dataset(task_id): X, y = make_classification(n_samples=100, n_features=10, n_informative=5, n_redundant=3, n_repeated=2, random_state=task_id) X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=task_id) return X_train, X_test, y_train, y_test # Define a neural network class class Net(keras.Model): def __init__(self): super(Net, self).__init__() self.fc1 = keras.layers.Dense(20, activation='relu', input_shape=(10,)) self.fc2 = keras.layers.Dense(10, activation='relu') self.fc3 = keras.layers.Dense(2) def call(self, x): x = self.fc1(x) x = self.fc2(x) x = self.fc3(x) return x # Define a genetic algorithm class class GeneticAlgorithm: def __init__(self, population_size, task_id): self.population_size = population_size self.task_id = task_id self.population = [Net() for _ in range(population_size)] def selection(self): X_train, X_test, y_train, y_test = generate_dataset(self.task_id) fitness = [] for i, net in enumerate(self.population): net.compile(optimizer='adam', loss='sparse_categorical_crossentropy', metrics=['accuracy']) net.fit(X_train, y_train, epochs=10, verbose=0) loss, accuracy = net.evaluate(X_test, y_test, verbose=0) fitness.append(accuracy) if len(fitness) > 0: self.population = [self.population[i] for i in np.argsort(fitness)[-self.population_size//2:]] def crossover(self): offspring = [] for _ in range(self.population_size//2): parent1, parent2 = random.sample(self.population, 2) child = Net() child.compile(optimizer='adam', loss='sparse_categorical_crossentropy', metrics=['accuracy']) # Get the weights of the parent networks parent1_weights = parent1.get_weights() parent2_weights = parent2.get_weights() # Average the weights of the two parents child_weights = [(np.array(w1) + np.array(w2)) / 2 for w1, w2 in zip(parent1_weights, parent2_weights)] child.set_weights(child_weights) offspring.append(child) self.population += offspring def mutation(self): for net in self.population: if random.random() < 0.1: weights = net.get_weights() new_weights = [np.array(w) + np.random.randn(*w.shape) * 0.1 for w in weights] net.set_weights(new_weights) # Streamlit app st.title("Evolution of Sub-Models") # Parameters st.sidebar.header("Parameters") population_size = st.sidebar.slider("Population size", 10, 100, 50) num_tasks = st.sidebar.slider("Number of tasks", 1, 10, 5) num_generations = st.sidebar.slider("Number of generations", 1, 100, 10) gas = None # Run the evolution gas = [] if st.button("Run evolution"): gas = [GeneticAlgorithm(population_size, task_id) for task_id in range(num_tasks)] gas = [GeneticAlgorithm(population_size, task_id) for task_id in range(num_tasks)] for generation in range(num_generations): for ga in gas: ga.selection() ga.crossover() ga.mutation() st.write(f"Generation {generation+1} complete") # Evaluate the final population final_accuracy = [] for task_id, ga in enumerate(gas): X_train, X_test, y_train, y_test = generate_dataset(task_id) accuracy = [] for net in ga.population: net.compile(optimizer='adam', loss='sparse_categorical_crossentropy', metrics=['accuracy']) net.fit(X_train, y_train, epochs=10, verbose=0) loss, acc = net.evaluate(X_test, y_test, verbose=0) accuracy.append(acc) if len(accuracy) > 0: final_accuracy.append(np.mean(accuracy)) # Trade populations between tasks for i in range(len(gas)): for j in range(i+1, len(gas)): ga1 = gas[i] ga2 = gas[j] population1 = ga1.population population2 = ga2.population num_trade = int(0.1 * population_size) trade1 = random.sample(population1, num_trade) trade2 = random.sample(population2, num_trade) ga1.population = population1 + trade2 ga2.population = population2 + trade1 # Evaluate the final population after trading final_accuracy_after_trade = [] for task_id, ga in enumerate(gas): X_train, X_test, y_train, y_test = generate_dataset(task_id) accuracy = [] for net in ga.population: net.compile(optimizer='adam', loss='sparse_categorical_crossentropy', metrics=['accuracy']) net.build(input_shape=(None, 10)) # Compile the model before training net.fit(X_train, y_train, epochs=10, verbose=0) loss, acc = net.evaluate(X_test, y_test, verbose=0) accuracy.append(acc) final_accuracy_after_trade.append(np.mean(accuracy)) if len(final_accuracy) > 0: st.write(f"Final accuracy: {np.mean(final_accuracy)}") st.write(f"Final accuracy after trading: {np.mean(final_accuracy_after_trade)}")