# Setup the PSO algorithm and run
# list of lengthh 10000, 1 or 0. More 1s towards end of list.
# to graph, look at average over some increment.
# for example, graph the average over the first 1000 episodes, then the next.
# avg will be number in [0, 1]. This corresponds to "fitness"
IR = [q.run_session(random.random(), random.random()) for _ in range(30)]
IRAvg = sum([sum(i[-1000:])/1000 for i in IR])/30
InitialRewardPerEpisode = IR
print ("Before tuning, QLearner achieves averages fitness of ", IRAvg, "(S.D. = ", statistics.stdev([sum(i[-1000:])/1000 for i in IR]), ") in 30 runs of 10,000 episodes.")
dataplot = DataPlotter()
# Eval function/fitness is average of the last 1000 episodes rewards.
# Should converge to ~0.7. In other words, gets a reward 70% of the time.
pso = PSO.ParticleSwarm((lambda a, b : sum(q.run_session(a, b)[-1000:])/1000), 5, 100, 0.00, 1.00, dataplot)
pso.minV = -1
pso.maxV = 1
particles = [PSO.Particle(0.00, 1.00, dataplot) for _ in range(pso.numParticles)]
pso.particles = particles
pso.display_particles()
alpha, gamma = pso.algorithm()
FR = [q.run_session(alpha, gamma) for _ in range(30)]
FRAvg = sum([sum(i[-1000:])/1000 for i in FR])/30
FinalRewardPerEpisode = FR
print ("After tuning, QLearner achieves average fitness of ", FRAvg, "(S.D. = ", statistics.stdev([sum(i[-1000:])/1000 for i in FR]), ")in 30 runs of 10,000 episodes.")
dataplot.appendqlRVals(IR, FR)
dataplot.outputGraphs()
def run_test(test_number, hidden_layers, Export=False):
# Start timing
time_started = time.strftime("%Y-%m-%d-%H-%M-%S")
num_inputs = X.shape[1]
# Variables
no_iterations = 4000
shape = [num_inputs] + hidden_layers + [10]
no_particles = 25
swarm_inertia = 0.9
swarm_phi_p = 1
swarm_phi_g = 3
swarm_v_max = 3
accuracies = [0.1]
measured_times = [
time_delta(time_started, time.strftime("%Y-%m-%d-%H-%M-%S"))
]
# Initialize swarm
cost_func = functools.partial(eval_neural_network,
shape=shape,
X=X,
y=train_labels_one_hot)
swarm = PSO.ParticleSwarm(
cost_func,
num_dimensions=dim_weights(shape),
num_particles=no_particles,
inertia=swarm_inertia,
phi_p=swarm_phi_p,
phi_g=swarm_phi_g,
v_max=swarm_v_max,
)
# Train...
i = 0
best_scores = [(i, swarm.best_score)]
while swarm.best_score > 1e-6 and i < no_iterations:
swarm.update(num_iterations=no_iterations, current_iter=i + 1)
i = i + 1
# if swarm.best_score < best_scores[-1][1]:
if i % 10 == 0:
best_scores.append((i, swarm.best_score))
corrects, wrongs, predictions = eval_accuracy(
vector_to_weights(swarm.g, shape), shape, X_test, y_test)
accuracy = corrects / (corrects + wrongs)
current_time = time_delta(time_started,
time.strftime("%Y-%m-%d-%H-%M-%S"))
accuracies.append(accuracy)
measured_times.append(current_time)
print(
best_scores[-1][0],
"- Time(s):",
current_time,
"Error:",
best_scores[-1][1],
"Accuracy:",
accuracy,
)
# End time
time_ended = time.strftime("%Y-%m-%d-%H-%M-%S")
time_elapsed = time_delta(time_started, time_ended)
# Plot
iters = [tup[0] for tup in best_scores]
errors = [tup[1] for tup in best_scores]
"""
figure = plt.figure()
errorplot = plt.subplot(2, 1, 1)
errorplot.plot(iters, errors)
plt.title("PSO")
plt.ylabel("Mean squared error")
accuracyplot = plt.subplot(2, 1, 2)
accuracyplot.plot(iters, accuracies)
plt.xlabel("Iterations")
plt.ylabel("Accuracy")
"""
# Test...
best_weights = vector_to_weights(swarm.g, shape)
best_nn = MultiLayerPerceptron(shape, weights=best_weights)
y_test_pred = np.round(best_nn.run(X_test))
print(
sklearn.metrics.classification_report(test_labels_one_hot,
y_test_pred.T))
print("Accuracy:", accuracy)
print("Time(s):", time_elapsed)
# Output results
report = sklearn.metrics.classification_report(test_labels_one_hot,
y_test_pred.T,
output_dict=True)
report = DataFrame(report).transpose().values.tolist()
# start CSV file
ExportToFile = ("results/PSO/" + str(len(train_imgs)) +
str(hidden_layers) + "/" + str(test_number) +
"-accuracy-" + str(accuracy) + "-time-" + time_started +
".csv")
if Export == True:
# figure.savefig("results/PSO/figures/"+str(len(train_imgs))+str(hidden_layers)+"/"+str(test_number)+"-"+"-time-"+time_started+".png")
# plt.clf()
with open(ExportToFile, "a", newline="\n") as out:
writer = csv.writer(out, delimiter=",")
writer.writerow(["Accuracies"] + accuracies)
writer.writerow(["Errors"] + errors)
writer.writerow(["Iters"] + iters)
writer.writerow(["Time(s)"] + measured_times)
writer.writerow([])
header = [
"#Particles",
"#Iterations",
"Inertia",
"Phi_p",
"Phi_g",
"V_max",
"Accuracy",
"Time Elapsed (s)",
"Inputs",
"Hidden nodes",
"Outputs",
"Time started",
"Time ended",
]
writer.writerow(header)
line = [
no_particles,
no_iterations,
swarm_inertia,
swarm_phi_p,
swarm_phi_g,
swarm_v_max,
accuracy,
time_elapsed,
shape[0],
shape[1],
shape[2],
time_started,
time_ended,
]
writer.writerow(line)
writer.writerow("\n")
writer.writerow(
["#", "Precision", "Recall", "f1-score", "support"])
for x in range(10):
writer.writerow([
x, report[x][0], report[x][1], report[x][2], report[x][3]
])
writer.writerow([
"Micro avg",
report[10][0],
report[10][1],
report[10][2],
report[10][3],
])
writer.writerow([
"Macro avg",
report[11][0],
report[11][1],
report[11][2],
report[11][3],
])
writer.writerow([
"Weighted avg",
report[12][0],
report[12][1],
report[12][2],
report[12][3],
])
writer.writerow([
"Samples avg",
report[13][0],
report[13][1],
report[13][2],
report[13][3],
])
out.close()
