def fit(self, x, Y): self.classes = np.sort(list(set(Y))) x = np.array(x).copy() y = np.array(Y).copy() for f in range(len(self.classes)): y[y == self.classes[f]] = f clf = MLPClassifier(hidden_layer_sizes=self.shape, alpha=self.beta, learning_rate_init=self.eta0) clf.partial_fit(x, Y, classes=self.classes) coefs = clf.coefs_ intercepts = clf.intercepts_ coefs_tot = [np.zeros(np.shape(c)) for c in coefs] intercepts_tot = [np.zeros(np.shape(c)) for c in intercepts] for f in range(self.agg): if self.verbose and (self.agg > 1): print('Passage ' + str(f)) clfi = self.fit1(x, Y, clf, coefs, intercepts) for i in range(len(coefs)): coefs_tot[i] += clfi.coefs_[i] intercepts_tot[i] += clfi.intercepts_[i] coefs = [c / self.agg for c in coefs_tot] intercepts = [c / self.agg for c in intercepts_tot] clf.coefs_ = coefs clf.intercepts_ = intercepts self.clf = clf
def generate_NN(): data_set = load_breast_cancer() X = data_set['data'] y = data_set['target'] X_train, X_test, y_train, y_test = train_test_split(X, y) scaler = StandardScaler() scaler.fit(X_train) X_train = scaler.transform(X_train) X_test = scaler.transform(X_test) mlp = MLPClassifier(hidden_layer_sizes=hidden_layers_size) mlp.fit(X_train, y_train) mlp.intercepts_ = get_zeroes_biases_vectors(input_layer_size, hidden_layers_size, output_layer_size) return X_test, mlp, y_test
def test_fit(): # Test that the algorithm solution is equal to a worked out example. X = np.array([[0.6, 0.8, 0.7]]) y = np.array([0]) mlp = MLPClassifier(solver='sgd', learning_rate_init=0.1, alpha=0.1, activation='logistic', random_state=1, max_iter=1, hidden_layer_sizes=2, momentum=0) # set weights mlp.coefs_ = [0] * 2 mlp.intercepts_ = [0] * 2 mlp.n_outputs_ = 1 mlp.coefs_[0] = np.array([[0.1, 0.2], [0.3, 0.1], [0.5, 0]]) mlp.coefs_[1] = np.array([[0.1], [0.2]]) mlp.intercepts_[0] = np.array([0.1, 0.1]) mlp.intercepts_[1] = np.array([1.0]) mlp._coef_grads = [] * 2 mlp._intercept_grads = [] * 2 # Initialize parameters mlp.n_iter_ = 0 mlp.learning_rate_ = 0.1 # Compute the number of layers mlp.n_layers_ = 3 # Pre-allocate gradient matrices mlp._coef_grads = [0] * (mlp.n_layers_ - 1) mlp._intercept_grads = [0] * (mlp.n_layers_ - 1) mlp.out_activation_ = 'logistic' mlp.t_ = 0 mlp.best_loss_ = np.inf mlp.loss_curve_ = [] mlp._no_improvement_count = 0 mlp._intercept_velocity = [np.zeros_like(intercepts) for intercepts in mlp.intercepts_] mlp._coef_velocity = [np.zeros_like(coefs) for coefs in mlp.coefs_] mlp.partial_fit(X, y, classes=[0, 1]) # Manually worked out example # h1 = g(X1 * W_i1 + b11) = g(0.6 * 0.1 + 0.8 * 0.3 + 0.7 * 0.5 + 0.1) # = 0.679178699175393 # h2 = g(X2 * W_i2 + b12) = g(0.6 * 0.2 + 0.8 * 0.1 + 0.7 * 0 + 0.1) # = 0.574442516811659 # o1 = g(h * W2 + b21) = g(0.679 * 0.1 + 0.574 * 0.2 + 1) # = 0.7654329236196236 # d21 = -(0 - 0.765) = 0.765 # d11 = (1 - 0.679) * 0.679 * 0.765 * 0.1 = 0.01667 # d12 = (1 - 0.574) * 0.574 * 0.765 * 0.2 = 0.0374 # W1grad11 = X1 * d11 + alpha * W11 = 0.6 * 0.01667 + 0.1 * 0.1 = 0.0200 # W1grad11 = X1 * d12 + alpha * W12 = 0.6 * 0.0374 + 0.1 * 0.2 = 0.04244 # W1grad21 = X2 * d11 + alpha * W13 = 0.8 * 0.01667 + 0.1 * 0.3 = 0.043336 # W1grad22 = X2 * d12 + alpha * W14 = 0.8 * 0.0374 + 0.1 * 0.1 = 0.03992 # W1grad31 = X3 * d11 + alpha * W15 = 0.6 * 0.01667 + 0.1 * 0.5 = 0.060002 # W1grad32 = X3 * d12 + alpha * W16 = 0.6 * 0.0374 + 0.1 * 0 = 0.02244 # W2grad1 = h1 * d21 + alpha * W21 = 0.679 * 0.765 + 0.1 * 0.1 = 0.5294 # W2grad2 = h2 * d21 + alpha * W22 = 0.574 * 0.765 + 0.1 * 0.2 = 0.45911 # b1grad1 = d11 = 0.01667 # b1grad2 = d12 = 0.0374 # b2grad = d21 = 0.765 # W1 = W1 - eta * [W1grad11, .., W1grad32] = [[0.1, 0.2], [0.3, 0.1], # [0.5, 0]] - 0.1 * [[0.0200, 0.04244], [0.043336, 0.03992], # [0.060002, 0.02244]] = [[0.098, 0.195756], [0.2956664, # 0.096008], [0.4939998, -0.002244]] # W2 = W2 - eta * [W2grad1, W2grad2] = [[0.1], [0.2]] - 0.1 * # [[0.5294], [0.45911]] = [[0.04706], [0.154089]] # b1 = b1 - eta * [b1grad1, b1grad2] = 0.1 - 0.1 * [0.01667, 0.0374] # = [0.098333, 0.09626] # b2 = b2 - eta * b2grad = 1.0 - 0.1 * 0.765 = 0.9235 assert_almost_equal(mlp.coefs_[0], np.array([[0.098, 0.195756], [0.2956664, 0.096008], [0.4939998, -0.002244]]), decimal=3) assert_almost_equal(mlp.coefs_[1], np.array([[0.04706], [0.154089]]), decimal=3) assert_almost_equal(mlp.intercepts_[0], np.array([0.098333, 0.09626]), decimal=3) assert_almost_equal(mlp.intercepts_[1], np.array(0.9235), decimal=3) # Testing output # h1 = g(X1 * W_i1 + b11) = g(0.6 * 0.098 + 0.8 * 0.2956664 + # 0.7 * 0.4939998 + 0.098333) = 0.677 # h2 = g(X2 * W_i2 + b12) = g(0.6 * 0.195756 + 0.8 * 0.096008 + # 0.7 * -0.002244 + 0.09626) = 0.572 # o1 = h * W2 + b21 = 0.677 * 0.04706 + # 0.572 * 0.154089 + 0.9235 = 1.043 # prob = sigmoid(o1) = 0.739 assert_almost_equal(mlp.predict_proba(X)[0, 1], 0.739, decimal=3)
import numpy as np from sklearn.neural_network import MLPClassifier os.chdir('../data') # Load data X = pd.read_csv('X.csv', header=None) #with open('X.csv') as datafile: # X = [[float(val) for val in line.split(',')] for line in datafile.read().split('\n') if line != ''] y = pd.read_csv('y.csv', header=None).transpose().values[0] #with open('y.csv') as datafile: # y = [float(line) for line in datafile.read().split('\n') if line != ''] Theta1 = pd.read_csv('Theta1.csv', header=None) #with open('Theta1.csv') as datafile: # Theta1 = [[float(val) for val in line.split(',')] for line in datafile.read().split('\n') if line != ''] Theta2 = pd.read_csv('Theta2.csv', header=None) #with open('Theta2.csv') as datafile: # Theta2 = [[float(val) for val in line.split(',')] for line in datafile.read().split('\n') if line != ''] clf = MLPClassifier(algorithm='l-bfgs', alpha=1e-5, hidden_layer_sizes=(25), random_state=1) clf.fit(X, y) #clf.n_outputs_ = 10 #clf.n_layers_ = 3 clf.out_activation_ = 'logistic' clf.intercepts_ = [Theta1[0].values, Theta2[0].values] clf.coefs_ = [Theta1.transpose()[1:].values, Theta2.transpose()[1:].values] p = clf.predict(X) diff = (p == y).mean() print(diff)
def update(self): if not World.executions: raise RuntimeError('Cannot update. No executions left to work on.') # Define workspace (data set of interest). params = World.executions.pop(0) World.params = params t_time = time() print('Data set %s %f' % (params['name'], t_time)) ds = params['dataset']() X, y = ds.data, ds.target if params['settings'].get('pc_decomposing', False): X = decomposition.PCA(whiten=params['settings'].get( 'whiten', False), random_state=0).fit_transform(X) if params['settings']['plotting']: plt.scatter(X[:, 0], X[:, 1], c=y, cmap=plt.cm.Spectral) plt.tight_layout() plt.savefig('report/ds-%s.png' % params['name']) plt.close() # Separate train (80%) and test data (20%). X, X_test, y, y_test = model_selection.train_test_split( X, y, train_size=.8, random_state=random_state) # Set class attributes. We need this for the genetic algorithm. World.X, World.X_test, World.y, World.y_test = X, X_test, y, y_test # Build a regularly trained Neural Network once. # We'll use it as base for our benchmarks. mpl = MLPClassifier(**params['nn_params']) print('Regular training ongoing...') t = time() mpl.fit(X, y) print('Training complete (elapsed: %f s)' % (time() - t)) self.evaluate(mpl, label='NN') best_i, best_model, best_score = -1, None, -np.inf for i, search_params in enumerate(params['searches']): trainer = art.agents.ResponderAgent( search=art.searches.genetic.GeneticAlgorithm, environment=self, search_params=search_params) # Ask agent to find a trained net for us. print('Genetic training has started. Parameters: \n%s' % search_params) t = time() training = trainer.act() print( 'Evolution complete (%i cycles, %f s elapsed, ' 'candidate utility: %f)' % (trainer.search.cycle_, time() - t, trainer.utility(training))) if (params['settings']['plotting'] and search_params.get('debug', False)): # Plotting generations' utilities. plt.plot(trainer.search.lowest_utility_, color='blue', linewidth=4, label='Lowest') plt.plot(trainer.search.average_utility_, color='orange', linewidth=4, label='Average') plt.plot(trainer.search.highest_utility_, color='red', linewidth=4, label='Highest') plt.legend() plt.xlabel('generation') plt.ylabel('utility') plt.tight_layout() plt.savefig('report/ut-%s-%i.png' % (params['name'], i)) plt.close() # Let's cross our fingers! Build a Neural Network with the # parameters selected by the evolutionary process. gmpl = MLPClassifier(**params['nn_params']) gmpl.coefs_ = training.coefs_ gmpl.intercepts_ = training.intercepts_ score = self.evaluate(gmpl, label='GA') if score > best_score: best_i, best_model, best_score = i, gmpl, score gmpl.fit(X, y) self.evaluate(gmpl, label='trained-gnn') print('%s\'s report:\n' '\tBest estimator id: %i\n' 'Score: %.2f\n' 'Total time elapsed: %f s\n' '---\n' % (params['name'], best_i, best_score, (time() - t_time)))
memory[index % memory_size, 0], memory[index % memory_size, 1] = observation[0], observation[1] memory[index % memory_size, 2], memory[index % memory_size, 3], memory[ index % memory_size, 4] = action_0, observation_1[0], observation_1[1] index += 1 observation = observation_1 # choose minimum batch batch_index = np.random.choice(memory_size, size=mini_batch) batch_memory = memory[batch_index, :] e_r_l_new = batch_memory[:, 0:3] e_r_l_old = batch_memory[:, 3:5] q_target = np.zeros((mini_batch, 1)) counter_2 = 0 for element in e_r_l_old: q_target[counter_2] = r_ + GAMMA * max(old_nn.predict([np.append(element, 0)]), \ old_nn.predict([np.append(element, 1)]), \ old_nn.predict([np.append(element, 2)])) counter_2 += 1 q_target = q_target.ravel() new_nn.fit(e_r_l_new, q_target) if step % learning_inter == 0: old_nn.coefs_ = new_nn.coefs_ old_nn.intercepts_ = new_nn.intercepts_ # reach the right top if observation[0] == env.observation_space.high[0]: break
clf = NN(hid_layers, activation='relu') features, encodings, ((x_train, y_train), (x_val, y_val), (x_test, y_test)) = process(all_data, train_frac, val_frac, test_frac, modify=True) print(x_train.shape, y_train.shape) clf.fit(x_train, y_train) print("Simulated annealing:") acc_anneal = [] test_anneal = [] clf.coefs_ = [] clf.intercepts_ = [] #anneal(clf, hid_layers, x_train, y_train) #uses simulated annealing to find the optimal weights anneal = NNAnneal(clf, hid_layers, x_train, y_train) ([clf.coefs_, clf.intercepts_]), e = anneal.anneal() print(accuracy_score(clf.predict(x_train), y_train)) print(accuracy_score(clf.predict(x_val), y_val)) print(accuracy_score(clf.predict(x_test), y_test)) # acc_anneal.append(accuracy_score(clf.predict(x_val), y_val)) # test_anneal.append(accuracy_score(clf.predict(x_test), y_test)) # print(acc_anneal) # print(test_anneal) print("Hill climbing:") clf.coefs_ = []
def update(self): if not World.executions: raise RuntimeError('Cannot update. No executions left to work on.') # Define workspace (data set of interest). params = World.executions.pop(0) World.params = params t_time = time() print('Data set %s %f' % (params['name'], t_time)) ds = params['dataset']() X, y = ds.data, ds.target if params['settings'].get('pc_decomposing', False): X = decomposition.PCA( whiten=params['settings'].get('whiten', False), random_state=0).fit_transform(X) if params['settings']['plotting']: plt.scatter(X[:, 0], X[:, 1], c=y, cmap=plt.cm.Spectral) plt.tight_layout() plt.savefig('report/ds-%s.png' % params['name']) plt.close() # Separate train (80%) and test data (20%). X, X_test, y, y_test = model_selection.train_test_split( X, y, train_size=.8, random_state=random_state) # Set class attributes. We need this for the genetic algorithm. World.X, World.X_test, World.y, World.y_test = X, X_test, y, y_test # Build a regularly trained Neural Network once. # We'll use it as base for our benchmarks. mpl = MLPClassifier(**params['nn_params']) print('Regular training ongoing...') t = time() mpl.fit(X, y) print('Training complete (elapsed: %f s)' % (time() - t)) self.evaluate(mpl, label='NN') best_i, best_model, best_score = -1, None, -np.inf for i, search_params in enumerate(params['searches']): trainer = art.agents.ResponderAgent( search=art.searches.genetic.GeneticAlgorithm, environment=self, search_params=search_params) # Ask agent to find a trained net for us. print('Genetic training has started. Parameters: \n%s' % search_params) t = time() training = trainer.act() print('Evolution complete (%i cycles, %f s elapsed, ' 'candidate utility: %f)' % (trainer.search.cycle_, time() - t, trainer.utility(training))) if (params['settings']['plotting'] and search_params.get('debug', False)): # Plotting generations' utilities. plt.plot(trainer.search.lowest_utility_, color='blue', linewidth=4, label='Lowest') plt.plot(trainer.search.average_utility_, color='orange', linewidth=4, label='Average') plt.plot(trainer.search.highest_utility_, color='red', linewidth=4, label='Highest') plt.legend() plt.xlabel('generation') plt.ylabel('utility') plt.tight_layout() plt.savefig('report/ut-%s-%i.png' % (params['name'], i)) plt.close() # Let's cross our fingers! Build a Neural Network with the # parameters selected by the evolutionary process. gmpl = MLPClassifier(**params['nn_params']) gmpl.coefs_ = training.coefs_ gmpl.intercepts_ = training.intercepts_ score = self.evaluate(gmpl, label='GA') if score > best_score: best_i, best_model, best_score = i, gmpl, score gmpl.fit(X, y) self.evaluate(gmpl, label='trained-gnn') print('%s\'s report:\n' '\tBest estimator id: %i\n' 'Score: %.2f\n' 'Total time elapsed: %f s\n' '---\n' % (params['name'], best_i, best_score, (time() - t_time)))