def main():
"""Program uses LU-decomposition to solve Au=g"""
# getting dimensions of matrix, and labeling data:
global N
N = int(eval(input("Give value for N: ")))
name = "LUtest%i" % int(np.log10(N))
# generating data:
x = np.linspace(0, 1, N)
h = (x[-1] - x[0]) / N
gen.generate_data(x, name)
g = np.loadtxt("%s/data_files/%s.dat" % (dir, name)) * h**2
A = np.zeros((N, N)) # creating zero matrix.
A = Afunc(A) # populating the three diagonals.
start = time.default_timer() # timing solve.
LU, piv = sp.lu_factor(A) # decomp and forward sub.
u = sp.lu_solve((LU, piv), g) # backward sub.
end = time.default_timer()
print("Time spent on LU %g" % (end - start)) # printing elapsed time.
# saving numerical solution:
np.savetxt("%s/data_files/solution_%s.dat" % (dir, name), u, fmt="%g")
def init_data():
"""Initialising data for program as global variables."""
global dir, N, name, x, h, anal_sol, u, d, d_prime, a, b, g, g_prime
dir = os.path.dirname(os.path.realpath(__file__)) # current directory.
# defining number of rows and columns in matrix:
N = int(eval(input("Specify number of data points N: ")))
# defining common label for data files:
name = input("Label of data-sets without file extension: ")
x = np.linspace(0, 1, N) # array of normalized positions.
h = (x[0] - x[-1]) / N # defining step-siz.
gen.generate_data(x, name) # generating dataanal_name set.
anal_sol = np.loadtxt("%s/data_files/anal_solution_for_%s.dat" %
(dir, name))
u = np.empty(N) # array for unkown values.
d = np.full(N, 2) # array for diagonal elements.
d_prime = np.empty(N) # array for diagonal after decom. and sub.
a = np.full(N - 1, -1) # array for upper, off-center diagonal.
b = np.full(N - 1, -1) # array for lower, off-center diagonal.
# array for g in matrix eq. Au=g.
f = np.loadtxt("%s/data_files/%s.dat" % (dir, name))
g = f * h**2
g_prime = np.empty(N) # array for g after decomp. and sub.
def get_model_generate(f):
data = generate_data(f)
probs = []
for o in states:
probs.append(sum(o == t[0] for t in data) / len(data))
trans = [[0 for x in range(len(states))] for y in range(len(states))]
states_i = np.zeros(len(states))
for i in range(len(data) - 1):
trans[states.index(data[i][0])][states.index(data[i + 1][0])] += 1
states_i[states.index(data[i][0])] += 1
p = data.pop()
for i in range(len(states)):
for j in range(len(states)):
trans[i][j] /= states_i[i]
data.append(p)
probs_est = [[0 for x in range(len(states))] for y in range(len(colors))]
for i in range(len(colors)):
for j in range(len(states)):
probs_est[i][j] = data.count((states[j], colors[i])) / sum(states[j] == t[0] for t in data)
model = hmm.MultinomialHMM(len(states))
model.n_features = len(colors)
model.startprob_ = np.array(probs)
model.transmat_ = np.array(trans)
model.emissionprob_ = np.array(probs_est)
#model = hmm.MultinomialHMM(len(states), np.array(probs), np.array(trans))
#model = model.fit(np.array([colors.index(o[1]) for o in data]).reshape(-1, 1) )
return model
def get_model_predictions(
model_type: type(Model),
num_data: int,
num_models: int,
model_kwargs: Optional[Dict] = None,
) -> np.ndarray:
"""Generate many model predictions.
Args:
model_type: The type of model to create
num_data: The number of data points to generate
num_models: The number of models to generate
model_kwargs: Keyword arguments for building the model.
Returns:
predictions: A 2D numpy array of shape (num_models, num_data)
where each row is the predictions for a different model
"""
if not model_kwargs:
model_kwargs = {}
predictions = np.zeros((num_models, len(X_TEST)))
for model_num in range(num_models):
data = data_generator.generate_data(num_data)
model = model_type(data[:, 0], data[:, 1], **model_kwargs)
model.fit()
y_hat = model.predict(X_TEST)
predictions[model_num] = y_hat
return predictions
def main(args):
blues, reds = generate_data(args.num_datapoints)
data = np.vstack([blues, reds])
data = np.array([[1] + list(i) for i in data])
labels = np.array([0] * len(blues) + [1] * len(reds))
if args.model is Perceptron:
labels = labels * 2 - 1
inds = np.arange(len(labels))
np.random.shuffle(inds)
labels, data = labels[inds], data[inds]
plt.ion()
fig = plt.figure(figsize=(9, 9))
ax = fig.add_subplot(111)
if args.model is Perceptron:
kwargs = {}
else:
kwargs = {'update_params': args.update_params}
if args.update_method is not None:
kwargs['update_method'] = args.update_method
if args.epochs is not None:
kwargs['epochs'] = args.epochs
model = args.model(data.shape[1], **kwargs)
ax.scatter(*blues.T, c='blue')
ax.scatter(*reds.T, c='red')
ln, = plt.plot([], [], c='black')
lmaxx = np.max(data[:, 1])
lmaxy = np.max(data[:, 2])
lminx = np.min(data[:, 1])
lminy = np.min(data[:, 2])
plt.xlim([lminx - 1, lmaxx + 1])
plt.ylim([lminy - 1, lmaxy + 1])
losses = []
for w in model.fit(data, labels):
losses.append(model.loss(data, labels))
plotw(ln, w, [lminx - 1, lmaxx + 1], [lminy - 1, lmaxy + 1])
fig.canvas.draw()
plt.pause(args.sleep_time)
# time.sleep(args.sleep_time)
if args.model is LogisticRegression:
xs = np.linspace(lminx - 1, lmaxx + 1, 100)
ys = np.linspace(lminx - 1, lmaxx + 1, 100)
X, Y = np.meshgrid(xs, ys)
Z = np.array([
model.calculate_probabilities(
np.array([np.ones(100), X_, Y_]).T)
for X_, Y_ in zip(X, Y)
])
plt.pcolormesh(X,
Y,
Z,
shading='gouraud',
cmap=plt.cm.seismic,
vmin=0,
vmax=1,
zorder=0)
plt.show(block=True)
plt.plot(losses)
plt.show(block=True)
def prob_5_2_2(N, d, k, sigma, lam = 300, seed=12231):
# N = 50, d = 75, k = 5, sigma = 10
#lambda^* = 300
(y, X, wg, eps) = dg.generate_data(N, d, k, sigma, seed)
##########
w = randn(1 + d)
prob = ls.initialize_computation(X, w, y)
prob[3] = lam
ls.round_robin(prob, 1000)
return (prob, sum(abs(prob[0][1:k + 1]) > 0), sum(abs(prob[0][1:]) > 0))
def init_data():
"""Initialising data for program as global variables."""
global dir, N, name, x, h, anal_sol, u, d, d_prime, a, b, g, g_prime
dir = os.path.dirname(os.path.realpath(__file__)) # current directory.
# defining number of rows and columns in matrix:
N = int(eval(input("Specify number of data points N: ")))
# defining common label for data files:
name = input("Label of data-sets without file extension: ")
x = np.linspace(0, 1, N) # array of normalized positions.
h = (x[0] - x[-1]) / N # defining step-siz.
gen.generate_data(x, name) # generating dataanal_name set.
anal_sol = np.loadtxt("%s/data_files/anal_solution_for_%s.dat" %
(dir, name))
u = np.empty(N) # array for unkown values.
s = np.arange(1, N + 1)
d_prime = 2 * (s) / (2 * (s + 1)) # pre-calculating the 1/d_prime factors.
f = np.loadtxt("%s/data_files/%s.dat" % (dir, name))
g = f * h**2
g_prime = np.empty(N) # array for g after decomp. and sub.
def average_calculator(model, k, kwargs, gen_data=True):
if gen_data:
generate_data(kwargs)
p_1 = 0.0
r_1 = 0.0
f_1 = 0.0
f_scores = []
train_data = ATEDataProcessor(kwargs["train_file"], **kwargs)
test_data = ATEDataProcessor(kwargs["test_file"],
pos_id=get_count(
train_data.annotated_sentences),
**kwargs)
for i in range(k):
print("Run number: {}".format(i))
test_set = test_data.annotated_sentences
train_set, dev_set = split(train_data.annotated_sentences,
test_size=kwargs["test_size"])
train = DataIterator(train_set, **kwargs)
dev = DataIterator(dev_set, **kwargs)
test = DataIterator(test_set, **kwargs)
if model == "lstm":
model = LSTMNetwork(**kwargs)
elif model == "cnn":
model = CNNNetwork(max_sentence_length=train_data.max_sentence_len,
**kwargs)
model.build()
model.train(train, dev)
model.restore_session(model.model_directory)
results = model.evaluate(test)
f_scores.append(results["f_1"])
p_1 += float(results["p_1"])
r_1 += float(results["r_1"])
f_1 += float(results["f_1"])
model.close_session()
print("p_1: {}\nr_1: {}\nf_1: {}".format(p_1 / k, r_1 / k, f_1 / k))
print(mean_confidence_interval(f_scores))
return {"precision": p_1 / k, "recall": r_1 / k, "fscore": f_1 / k}
def test_student_exact():
'''
MCTS is now working.
The number of rollouts required to be optimal grows very fast as a function of the horizon.
Still, even if not fully optimal, MCTS is an extremely good approximation.
Default student with horizon 10 needs about 50 rollouts is good
learn prob 0.15 student with horizon 40 needs about 150 rollouts is good; gets about 0.94 which is 0.02 off from 0.96
'''
import concept_dependency_graph as cdg
from simple_mdp import create_custom_dependency
r_type = DENSE
n_concepts = 4
learn_prob = 0.5
horizon = 6
n_rollouts = 50
n_trajectories = 100
n_jobs = 8
traj_per_job = n_trajectories // n_jobs
#dgraph = create_custom_dependency()
dgraph = cdg.ConceptDependencyGraph()
dgraph.init_default_tree(n_concepts)
#student = st.Student(n=n_concepts,p_trans_satisfied=learn_prob, p_trans_not_satisfied=0.0, p_get_ex_correct_if_concepts_learned=1.0)
student2 = st.Student2(n_concepts, transition_after)
test_student = student2
accs = Parallel(n_jobs=n_jobs)(delayed(test_student_exact_chunk)(
traj_per_job, dgraph, test_student, horizon, n_rollouts, sparse_r)
for _ in range(n_jobs))
avg = sum(accs) / (n_jobs * traj_per_job)
test_data = dg.generate_data(dgraph,
student=test_student,
n_students=1000,
seqlen=horizon,
policy='expert',
filename=None,
verbose=False)
print('Number of jobs {}'.format(n_jobs))
print('Trajectory per job {}'.format(traj_per_job))
print('Average posttest true: {}'.format(expected_reward(test_data)))
print('Average posttest mcts: {}'.format(avg))
def raw_data_scatterplot():
savedir = os.path.join("images", "raw", "scatter")
os.makedirs(savedir, exist_ok=True)
print("Raw Data")
counts = np.geomspace(8, 100000, 200)
for index, i in tqdm(enumerate(counts), total=len(counts)):
i = int(i)
points = data_generator.generate_data(i, "fixed")
plt.scatter(
points[:, 0],
points[:, 1],
s=10,
edgecolor='k',
facecolor="none",
)
plt.xlabel("X")
plt.ylabel("Y")
plt.ylim(-105, 205)
plt.xlim(-0.5, 10.5)
plt.savefig(os.path.join(savedir, f"scatter.{i:06d}.{index}.png"))
plt.close()
def prob_5_2_1(N, d, k, sigma, rat=9/10., seed=12231):
# N = 50, d = 75, k = 5, sigma = 1
(y, X, wg, eps) = dg.generate_data(N, d, k, sigma, seed)
##########
w = randn(1 + d)
prob = ls.initialize_computation(X, w, y)
ret = []
coeffs = []
for i in range(100):
ls.round_robin(prob, 1000)
ret.append([prob[3], sum(abs(prob[0][1:k + 1]) > 0), sum(abs(prob[0][1:]) > 0)])
coeffs.append(prob[0].copy())
prob[3] *= rat
ret = array(ret)
coeffs = array(coeffs)
#Precision
ret[:, 2] = ret[:, 1]/ret[:, 2]
#Recall
ret[:, 1] = ret[:, 1]/float(k)
return (prob, array(ret), coeffs)
if __name__ == '__main__':
'''
N: number of figures waiting for sort
M: number of processes
'''
if (len(sys.argv) < 2):
print("Deficiency of N[,M] !")
sys.exit()
N = int(sys.argv[1])
if (len(sys.argv) == 2):
M = os.cpu_count()
else:
M = int(sys.argv[2])
data = generate_data(N)
#split data into M parts based on bucket-sort
split_linspace = np.linspace(data.min(), data.max() + 1, M + 1)
split_data = [0] * M
for i in range(M):
f = filter(
lambda x: x >= split_linspace[i] and x < split_linspace[i + 1],
data)
split_data[i] = np.fromiter(f, dtype=np.int32).tolist()
'''
这里之所以使用了fromiter+tolist函数将iterable转为list是因为数字类型为np.int32，该类型不能序列化为json格式。
如果只用：split_data[i] = list(f)，数字格式仍为np.int32，在调用函数call_slave_...时无法转为json格式。
'''
slaves_config = config.slaves()
def init(self, duration):
self.content = data_generator.generate_data(duration)
import data_generator
import learn
if __name__ == '__main__':
# Generate sample data
data_generator.generate_data()
# Train the linear regression model
learn.train_model()
def compare(File1, File2):
file1 = open(File1, 'r')
file2 = open(File2, 'r')
ans1 = file1.readlines()
ans2 = file2.readlines()
file1.close()
file2.close()
if len(ans1) != len(ans2):
print("length does not match")
return False
for i in range(min(len(ans1), len(ans2))):
if ans1[i] != ans2[i]:
print("In line {}".format(i + 1))
print(ans1[i])
print(ans2[i])
print()
return False
if __name__ == "__main__":
for i in range(100):
gen.generate_data()
python()
[t1, t2] = java()
if t2 - t1 > 2.2:
print("CPU TIME EXCEED with CPU TIME is {0}".format(t2 - t1))
exit(0)
if compare('python_ans.txt', 'java_ans.txt') == False:
exit(0)
print("No.{} has done.".format(i + 1))
def raw_data_intro():
savedir = os.path.join("images", "raw", "intro2")
os.makedirs(savedir, exist_ok=True)
n = N_POINTS
m = 5
s = 50
points = data_generator.generate_data(n, "fixed")
ylim = (-105, 205)
xlim = (-0.5, 10.5)
cmap = get_cmap("gnuplot2")
# just points
plt.figure()
plt.scatter(
points[:, 0],
points[:, 1],
s=s,
edgecolor='k',
facecolor="none",
)
plt.xlabel("X")
plt.ylabel("Y")
plt.ylim(*ylim)
plt.xlim(*xlim)
plt.tight_layout()
plt.savefig(os.path.join(savedir, f"scatter.png"))
plt.close()
predictions = []
plt.figure()
plt.scatter(
points[:, 0],
points[:, 1],
s=s,
edgecolor='k',
facecolor="none",
zorder=60,
)
for deg in range(m):
model = PolyfitModel(x_vals=points[:, 0], y_vals=points[:, 1], deg=deg)
model.fit()
_predictions = model.predict(evaluate_model.X_TEST)
predictions.append(_predictions)
mse = np.mean((model.predict(points[:, 0]) - points[:, 1])**2)
plt.plot(
evaluate_model.X_TEST,
predictions[-1],
label=f"Deg {deg}",
c=cmap((1 + deg) / (1 + m)),
zorder=50,
)
plt.xlabel("X")
plt.ylabel("Y")
plt.ylim(*ylim)
plt.xlim(*xlim)
legend = plt.legend(loc='upper center', framealpha=1.0)
legend.get_frame().set_alpha(0.8)
legend.get_frame().set_facecolor((1, 1, 1, 0.8))
legend.set_zorder(100)
plt.tight_layout()
plt.savefig(os.path.join(savedir, f"poly.png"))
plt.close()
os.makedirs(savedir, exist_ok=True)
model_type = PolyfitModel
num_data = 8
num_models = 100
model_kwargs = {"deg": 2}
predictions = evaluate_model.get_model_predictions(
model_type=model_type,
num_data=num_data,
num_models=num_models,
model_kwargs=model_kwargs,
)
## Upper Plot: Many Models
for i in range(predictions.shape[0]):
label = "Models" if i == 0 else None
plt.plot(
evaluate_model.X_TEST,
predictions[i, :],
c='blue',
alpha=0.8,
linewidth=0.1,
zorder=50,
label=label,
)
plt.plot(evaluate_model.X_TEST,
np.mean(predictions, axis=0),
c='red',
alpha=1,
zorder=55,
label="Average Model")
plt.plot(
evaluate_model.X_TEST,
evaluate_model.Y_TEST,
c='k',
alpha=1,
zorder=60,
label="Truth",
)
legend = plt.legend(loc='upper left', framealpha=1.0)
legend.get_frame().set_alpha(0.8)
legend.get_frame().set_facecolor((1, 1, 1, 0.8))
legend.set_zorder(100)
plt.ylim(-55, 205)
plt.xlim(-0.5, 10.5)
plt.suptitle(f'Polynomial (Deg={2})', fontsize=16)
plt.tight_layout()
plt.savefig(os.path.join(savedir, f"combined.png"))
plt.close()
columns=alpha_list).to_csv(f'../results/{data}/regret_alpha.csv')
if __name__ == "__main__":
warnings.filterwarnings("ignore")
tf.get_logger().setLevel("ERROR")
args = parser.parse_args()
# hyper-parameters
iters = args.iters
n_trials = args.n_trials
alpha_list = args.alpha_list
# run simulations
# generate and preprocess semi-synthetic datasets
generate_data(iters=iters)
# prediction by meta-learners, parameter estimation by metrics.
run_preds(alpha_list=alpha_list)
estimate_metrics_on_val() # model evaluation by metrics.
# calculate and save the evaluation performacen of metrics.
save_results()
if alpha_list:
estimate_alpha_metrics_on_val(alpha_list=alpha_list)
save_results_alpha(alpha_list=alpha_list)
main_tuner(iters=iters, n_trials=n_trials) # optuna experiments
visualizer = Visualizer(iters=iters, alpha_list=alpha_list)
visualizer.plot_prediction_mse()
combinations = list(itertools.product(
params_values["lstm_size"]
))
i = 1
j = len(combinations)
with open("cnn_hybrid_fasttext100d_rest_lstm_size_tuning_results.txt", "w") as fp:
for lstm_size in combinations:
print("{} out of {} combinations done".format(i-1, j))
i += 1
params_opt["lstm_size"] = lstm_size
use_params = merge_dicts(params, params_opt)
print("*******************************")
print("Configuation:")
print(json.dumps(params_opt, indent=2))
if i == 2:
generate_data(use_params)
results = average_calculator(model=sys.argv[1], k=10, gen_data=False,
kwargs=use_params)
f_score = results["fscore"]
fp.write("********************\n")
if f_score > best_fscore:
best_fscore = f_score
best_configuration = use_params
best_results = results
fp.write("Best configuration so far!\n")
print("Best configuration so far!\n")
fp.write(json.dumps(results, indent=2))
fp.write("\nConfiguation:\n")
fp.write(json.dumps(use_params, indent=2))
fp.write("\n*********************\n")
print("Results:")
import sys
from argument_parser import AnalysisSettings
from display import display_data
from data_generator import generate_data
sys.argv[1:] = [
"-k=4.0", "-k_end=9.0", "-k_step=0.1", "-tests=1000000",
"-pattern_trials=100", "-d=100", "-m=512"
]
#sys.argv[1:] = ["-tests=10", "-d=110", "-d_end=150", "-d_step=10", "-n=110", "-che", "-crs", "-source=random", "-source=random"]
if __name__ == '__main__':
settings = AnalysisSettings(sys.argv)
result = generate_data(settings)
display_data(result, settings)
def query_property_history_by_name(property_name, n_prev_day):
"""
Example for location:
Get the historical values for the car status property
:param property_name: status property, currently accept: Score, Speed, Odometer or Location
:param n_prev_day: <last n_prev_day> day values will be returned
:return:
if property_name is Location:
{"series": [ [<timestamp>,{"latitude": <float_val>, "longitude": <float_val>}], ... ] }
else:
{"series": [ [<timestamp>, <value>], ...] }
Example for location:
Example for other property:
{
"series": [
[
1447647446000,
9
],
[
1447654646000,
13.405531792385005
],
[
1447661846000,
15.905465020438427
]
] }
"""
if property_name == 'Score':
if n_prev_day < 4:
start_val = random.randrange(30, 36)
elif n_prev_day < 7:
start_val = random.randrange(36, 41)
elif n_prev_day < 14:
start_val = random.randrange(40, 61)
else:
start_val = 75
series_data = generate_data(start_val, 0, 100, n_prev_day, 120, [
[datetime.now() - timedelta(days=21), 0.4, 1.0],
[datetime.now() - timedelta(days=10), -0.1, 0.5],
[datetime.now() - timedelta(days=14), -0.4, 0.3],
[datetime.now() - timedelta(days=7), -0.8, 0.3],
[datetime.now() - timedelta(days=2), -1.5, 0.4]
])
elif property_name == 'Speed':
print('SPEED')
if n_prev_day < 4:
start_val = random.randrange(20, 30)
elif n_prev_day < 7:
start_val = random.randrange(30, 35)
elif n_prev_day < 14:
start_val = random.randrange(35, 40)
else:
start_val = 30
series_data = generate_data(start_val, 0, 120, n_prev_day, 120, [
[datetime.now() - timedelta(days=21), 0.4, 1.0],
[datetime.now() - timedelta(days=20), 0.1, 0.3],
[datetime.now() - timedelta(days=10), -1.0, 0.5],
[datetime.now() - timedelta(days=7), 0.3, 1.2],
[datetime.now() - timedelta(days=5), -0.8, 0.5],
[datetime.now() - timedelta(days=4), 0.3, 0.2],
[datetime.now() - timedelta(days=2), 3, 1.3]
])
elif property_name == 'Odometer':
if n_prev_day < 4:
start_val = random.randrange(0, 20)
elif n_prev_day < 7:
start_val = random.randrange(20, 50)
elif n_prev_day < 14:
start_val = random.randrange(50, 120)
else:
start_val = 120
series_data = generate_data(start_val, 0, 1000, n_prev_day, 120, [
[datetime.now() - timedelta(days=21), 2, 1.0],
[datetime.now() - timedelta(days=20), 0.5, 0.3],
[datetime.now() - timedelta(days=10), 2, 0.5],
[datetime.now() - timedelta(days=7), 2, 1],
[datetime.now() - timedelta(days=5), 3.5, 1],
[datetime.now() - timedelta(days=2), 3.5, 2.0]
])
elif property_name == 'Location':
series_data = generate_location(n_prev_day)
else:
lam = min(random.random(), 0.005)
series_data = create_poisson_series(lam=lam, n_prev_day=n_prev_day)
# series_data_sorted = sorted(series_data, key=lambda x: x[0])
# if property_name == 'Odometer':
# property_name = 'Distance (km)'
# elif property_name == 'Speed':
# property_name = 'Max Speed (km/h)'
# elif property_name == 'HarshBrake':
# property_name = 'Harsh Brake'
# elif property_name == 'SuddenTurn':
# property_name = 'Sudden Turn'
#
# chart_options = {
# 'chart': {'zoomType': 'x'},
# 'title': {'text': property_name + ' Over Time'},
# 'xAxis': {'type': 'datetime'},
# 'yAxis': {'title': {'text': property_name}},
# 'legend': {'enabled': False},
# 'series': [{'name': property_name,
# 'data': series_data_sorted}]
# }
#
# # For score color the chart differently for diff y-axis
# if property_name == 'Score':
# chart_options["series"][0].update(
# {'zones': [
# {
# 'value': 50,
# 'color': '#FC8662'
# },
# {
# 'value': 75,
# 'color': '#FADE25'
# },
# {
# 'color': '#ADF005'
# }
# ]}
# )
#
# if property_name == 'Score':
# chart_options['yAxis'].update({'max': 100})
return jsonify({"series": series_data})
def main(argv=None):
ds = generate_data()
evaluate(ds)
def main(argv=None):
print('argv: ', argv)
ds = generate_data(recreate=False)
train(ds)
def main():
args = parser.parse_args()
args.device = None
if torch.cuda.is_available():
args.device = torch.device('cuda')
else:
args.device = torch.device('cpu')
pprint.pprint(vars(args))
train_data, train_labels, test_data, test_labels = generate_data(
args.block_size, args.use_complex)
test_data = test_data.to(args.device)
test_labels = test_labels.to(args.device)
if not args.use_autoencoder:
train_data = train_data.unsqueeze(1)
train_labels = train_labels.unsqueeze(1)
test_data = test_data.unsqueeze(1)
test_labels = test_labels.unsqueeze(1)
# Data loading
params = {
'batch_size': args.batch_size,
'shuffle': True,
'num_workers': args.workers
}
training_set = Dataset(train_data, train_labels)
training_loader = torch.utils.data.DataLoader(training_set, **params)
loss_fn = nn.BCEWithLogitsLoss()
if args.use_autoencoder:
model = Autoencoder(channel_use=args.channel_use,
block_size=args.block_size,
snr=args.snr,
cuda=args.device,
use_lpf=args.use_lpf,
use_complex=args.use_complex,
channel_type=args.channel_type)
else:
model = RNN(channel_use=args.channel_use,
block_size=args.block_size,
n_layers=1,
snr=args.snr,
num_taps=args.lpf_num_taps,
cuda=args.device,
channel_type=args.channel_type)
model = model.to(args.device)
optimizer = Adam(filter(lambda p: p.requires_grad, model.parameters()),
lr=args.lr)
for epoch in range(args.epochs):
if args.lpf_shift_type == 'cont':
cutoff = max(args.lpf_cutoff,
(args.epochs - epoch - 1) / args.epochs)
else:
if epoch % (args.epochs / 10) == 0:
cutoff = max(args.lpf_cutoff,
(args.epochs - epoch - 1) / args.epochs)
# Filter weights (if enabled) should not change
for name, param in model.named_parameters():
if 'conv' in name:
param.requires_grad = False
model.train()
for batch_idx, (batch, labels) in tqdm(
enumerate(training_loader),
total=int(training_set.__len__() / args.batch_size)):
batch = batch.to(args.device)
labels = labels.to(args.device)
if args.use_autoencoder:
output = model(batch)
else:
output, hidden = model(batch)
loss = loss_fn(output, labels)
optimizer.zero_grad()
loss.backward()
optimizer.step()
if batch_idx % (args.batch_size - 1) == 0:
pred = torch.round(torch.sigmoid(output))
acc = model.accuracy(pred, labels)
print(
'Epoch %2d for SNR %s, shift type %s: loss=%.4f, acc=%.2f'
% (epoch, args.snr, args.lpf_shift_type, loss.item(), acc))
# Validation
model.eval()
if epoch % 10 == 0:
if args.use_autoencoder:
val_output = model(test_data)
else:
val_output, _ = model(test_data)
val_loss = loss_fn(val_output, test_labels)
val_pred = torch.round(torch.sigmoid(val_output))
val_acc = model.accuracy(val_pred, test_labels)
print(
'Validation: Epoch %2d for SNR %s and cutoff %s: loss=%.4f, acc=%.5f'
% (epoch, args.snr, cutoff, val_loss.item(), val_acc))
model.train()
if args.use_complex:
torch.save(
model.state_dict(), './models/complex_(%s,%s)_%s' %
(str(args.channel_use), str(args.block_size), str(args.snr)))
else:
torch.save(
model.state_dict(), './models/real_(%s,%s)_%s' %
(str(args.channel_use), str(args.block_size), str(args.snr)))
def main():
args = argprocessor.process()
data_array = data_generator.generate_data(args)
file_writer.write_output_file(args.output_file, data_array)
sequence_length = 20
num_train, num_valid, num_test = 2000, 500, 500
#cell_type = 'simple'
#cell_type = 'gru'
cell_type = 'lstm'
num_hidden = 20
batch_size = 40
learning_rate = 0.001
max_epoch = 100
# ----------------------------------------------------------------------
# Generate delayed XOR samples
X_train, y_train = generate_data(num_train, sequence_length)
sl_train = sequence_length * np.ones(num_train) # NEW
X_valid, y_valid = generate_data(num_valid, sequence_length)
sl_valid = sequence_length * np.ones(num_valid) # NEW
X_test, y_test = generate_data(num_test, sequence_length)
sl_test = sequence_length * np.ones(num_test) # NEW
# Crop data
# Artificially define variable sequence lengths
# for demo-purposes
for i in range(num_train):
ll = 10 + random.randint(0, sequence_length - 10)
sl_train[i] = ll
AGENT_COUNT = ConfigReader.readconfigfile('config', 'agent_count')
EVENT_COUNT = ConfigReader.readconfigfile('config', 'event_count')
VALID_DATA_RATIO= ConfigReader.readconfigfile('config', 'valid_data_ratio')
API_URL = ConfigReader.readconfigfile('config', 'api_url')
#Reset server
res = requests.get(ConfigReader.readconfigfile('config', 'server_stats_reset_url'))
if res.status_code == 200:
print "Server Reset successfully"
else:
print "Failed to Reset server"
sys.exit(-1)
#data generation
agent_stats = generate_data(AGENT_COUNT, EVENT_COUNT, VALID_DATA_RATIO)
print "Total data generated: %d" % agent_stats['Total']
print "Valid data: %d" % agent_stats['Valid']
print "Invalid data: %d\n" % agent_stats['Invalid']
#Agents Simulation
print "*****Agents Simulation*****\nTotal Agents Simulated: %d" % AGENT_COUNT
# start = time.clock()
q = Queue(AGENT_COUNT)
for i in range(AGENT_COUNT):
t = Thread(target=do_work, args=(q,))
t.daemon = True
t.start()
for i in range(1, AGENT_COUNT + 1):
q.put("Agent-"+str(i))
'rotating_XY', 'rotating_YZ', 'random'
]
job_number = 1
greedy = False # Greedy sweep rule
correlated = False # Correlated errors
rates = [1] # How many times to apply the sweep rule per syndrome measurement
for rate in rates:
for l in l_list:
timeouts = [32 * l
] # How many times to apply rule in perfect decoding phase
# sweep_limit = (int) (round(pow(l, 0.5))) # How many sweeps before changing direction in error suppression phase
sweep_limit = (int)(
round(math.log(l))
) # How many sweeps before changing direction in error suppression phase
for timeout in timeouts:
for N in cycles_list:
for p in p_list:
q = p # Measurement error probability
start_time = time.time()
data_generator.generate_data(lattice_type, l, p, q,
sweep_limit,
sweep_schedules[0], timeout,
N, trials, job_number, greedy,
correlated, rate)
# Data will be saved as a json file
finish_time = round(time.time() - start_time, 2)
print('l={} p=q={} cycles={} trials={} job done in {} s'.
format(l, p, N, trials, finish_time))
job_number += 1
def query_score(n_prev_day):
"""
Get each driver score parameter
:param n_prev_day: <last n_prev_day> day score will be returned
:return: {"score": [<distance_score>,
<harsh_break_score>,
<sudden_turn_score>,
<harsh_accel_score>,
<speed_score>]
}
"""
# Get the speed series
if n_prev_day < 4:
start_val = random.randrange(20, 30)
elif n_prev_day < 7:
start_val = random.randrange(30, 35)
elif n_prev_day < 14:
start_val = random.randrange(35, 40)
else:
start_val = 30
speed_series = generate_data(start_val, 0, 120, n_prev_day, 120, [
[datetime.now() - timedelta(days=21), 0.4, 1.0],
[datetime.now() - timedelta(days=20), 0.1, 0.3],
[datetime.now() - timedelta(days=10), -1.0, 0.5],
[datetime.now() - timedelta(days=7), 0.3, 1.2],
[datetime.now() - timedelta(days=5), -0.8, 0.5],
[datetime.now() - timedelta(days=4), 0.3, 0.2],
[datetime.now() - timedelta(days=2), 3, 1.3]
])
speed_scores = [score_speed(s[1]) for s in speed_series]
speed_score = np.average(speed_scores)
print('SPEED_SCORE: {}'.format(speed_score))
# Get the odometer series
if n_prev_day < 4:
start_val = random.randrange(0, 20)
elif n_prev_day < 7:
start_val = random.randrange(20, 50)
elif n_prev_day < 14:
start_val = random.randrange(50, 120)
else:
start_val = 120
odomoter_series = generate_data(start_val, 0, 1000, n_prev_day, 120, [
[datetime.now() - timedelta(days=21), 2, 1.0],
[datetime.now() - timedelta(days=20), 0.5, 0.3],
[datetime.now() - timedelta(days=10), 2, 0.5],
[datetime.now() - timedelta(days=7), 2, 1],
[datetime.now() - timedelta(days=5), 3.5, 1],
[datetime.now() - timedelta(days=2), 3.5, 2.0]
])
odomoter_series_sorted = sorted(odomoter_series, key=lambda x: x[0],
reverse=True)
day_dist_series = []
odo_iter = iter(odomoter_series_sorted)
day = 1
cur_odo = next(odo_iter)
while day <= n_prev_day:
try:
next_odo = next(odo_iter)
except StopIteration:
break
day_diff = (datetime.fromtimestamp(cur_odo[0] / 1000) -
datetime.fromtimestamp(next_odo[0] / 1000)).days
if day_diff == 1:
day += 1
day_dist_series.append(
(cur_odo[1] - next_odo[1]))
cur_odo = next_odo
if len(day_dist_series) < 1 or len(day_dist_series) < n_prev_day:
day_dist_series.append((cur_odo[1] - next_odo[1]) / 10.0)
day_dist_scores = [score_day_dist(x) for x in day_dist_series]
day_dist_score = np.average(day_dist_scores)
# Array order
# 'Distance', 'Harsh Break', 'Sudden Turn', 'Harsh Accel', 'Speed'
def rand_score():
return random.randrange(50, 100)
if n_prev_day < 2:
speed_score = max(10, speed_score - 45)
day_dist_score = max(12, day_dist_score - 45)
a, b, c = rand_score() - 40, rand_score() - 30, rand_score() - 35
elif n_prev_day < 4:
speed_score = max(15, speed_score - 40)
day_dist_score = max(15, day_dist_score - 40)
a, b, c = rand_score() - 25, rand_score() - 20, rand_score() - 30
elif n_prev_day < 8:
speed_score = max(20, speed_score - 30)
day_dist_score = max(25, day_dist_score - 30)
a, b, c = rand_score() - 10, rand_score() - 10, rand_score() - 10
else:
speed_score = max(20, speed_score - 12)
day_dist_score = max(25, day_dist_score - 12)
a, b, c = rand_score(), rand_score(), rand_score()
return jsonify({'score': [day_dist_score, a, b, c, speed_score]})
color_map, size_map = get_page_rank_and_colors(G)
print("--[x] SUCCESS")
success_log[
'--4. Trying to compute color and size maps...'] = 'SUCCESS'
except Exception as e:
pull_the_plug = True
print("--4. Error at generating color and size maps...", e)
success_log['--4. Trying to compute color and size maps...'] = 'FAIL'
try:
print(
'5. Trying to architect network visualization json for sigma.js...'
)
converted_data_for_sigmajs = generate_data(G, embeddings, color_map,
size_map)
print("--5. [x] SUCCESS")
success_log[
'--5. Trying to architect network visualization json for sigma.js...'] = 'SUCCESS'
except Exception as e:
pull_the_plug = True
print("--5. Error at converting network visualization json..", e)
success_log[
'--5. Trying to architect network visualization json for sigma.js...'] = 'FAIL'
if not pull_the_plug:
try:
print("6. Trying to open the file and dump the json data...")
with open('./network/data.json', 'w', encoding='utf-8') as f:
json.dump(converted_data_for_sigmajs, f)
def gen():
yield from data_generator.generate_data(DATA_DIR, VOCAB_DIR, VOCAB_SIZE,
CONEXT_SIZE)
gutils.construct_kd_tree(vectors, save_file=cnt.WV_KD_TREE_FILE)
group_indices = gutils.load_data_pkl(cnt.GROUP_INDICES_FILE)
print(items[group_indices[0]][0], items[group_indices[0]][5])
print(gutils.get_item_text(items[group_indices[0]]))
kdtree = gutils.load_data_pkl(cnt.WV_KD_TREE_FILE)
query_vector = gutils.get_wv_embeddings([group_indices[0]])[0]
u = gutils.get_nearest_neighbors_count(kdtree, query_vector, count=5)
for x in u:
print(items[group_indices[x]][0], items[group_indices[x]][5])
print(gutils.get_item_text(items[group_indices[x]]))
print("Generating data...")
num_train, num_test, num_validation = dg.generate_data(test_pct=0.2,
validation_pct=0.2)
print(num_train, num_test, num_validation)
del (items)
print("Training Siamese...")
sapi = SiameseAPI()
sapi.train_model()
print(sapi.get_distance_threshold(threshold=0.95))
print("Inserting embeddings...")
sapi.insert_embeddings_pytables()
print("Constructing KD-Tree...")
vectors = sapi.fetch_embeddings_pytables()
def main():
fp = open('results.csv', 'w')
col_names = [
'dataset', 'number of classes', 'number of runs', 'epochs',
'number of parameters-total', 'model', 'number of experts', 'loss',
'training accuracy', 'validation accuracy'
]
fp.write(','.join(col_names) + '\n')
num_runs = 2
#dataset = 'expert_0_gate_0_checker_board-1'
# X, y, trainset, trainloader, testset, testloader, num_classes = generate_data(dataset)
# total_experts = 2
# epochs = 2
# runs = []
# for r in range(0, num_runs):
# models = run_experiment(dataset, trainset, trainloader, testset, testloader, num_classes, total_experts, epochs)
# runs.append(models)
# results = runs[0]
# if num_runs > 1:
# results = aggregate_results(runs, total_experts)
# pickle.dump(results,open('../results/'+dataset+'_results.pkl','wb'))
# log_results(results, total_experts, num_classes, num_runs, epochs, dataset, fp)
# plot_results(X, y, num_classes, trainset, trainloader, testset, testloader, runs[0], dataset, total_experts)
# plot_error_rate(results, total_experts, 'figures/all/accuracy_'+dataset+'_'+ str(num_classes)+'_experts.png')
# dataset = 'expert_0_gate_0_checker_board-2'
#X, y, trainset, trainloader, testset, testloader, num_classes = generate_data(dataset)
# total_experts = 2
# epochs = 2
# runs = []
# for r in range(0, num_runs):
# models = run_experiment(dataset, trainset, trainloader, testset, testloader, num_classes, total_experts, epochs)
# runs.append(models)
# results = runs[0]
# if num_runs > 1:
# results = aggregate_results(runs, total_experts)
# pickle.dump(results,open('../results/'+dataset+'_results.pkl','wb'))
# log_results(results, total_experts, num_classes, num_runs, epochs, dataset, fp)
# plot_results(X, y, num_classes, trainset, trainloader, testset, testloader, runs[0], dataset, total_experts)
# plot_error_rate(results, total_experts, 'figures/all/accuracy_'+dataset+'_'+ str(num_classes)+'_experts.png')
# total_experts = 10
# epochs = 40
# for size in [3000]:#, 5000, 8000]:
# dataset = 'expert_1_gate_1_single_shallow_checker_board_rotated_'+str(size)
# X, y, trainset, trainloader, testset, testloader, num_classes = data_generator.generate_data(dataset, size, 128, 6)
# runs = []
# for r in range(0, num_runs):
# models = run_experiment(dataset, single_model_shallow, trainset, trainloader, testset, testloader, num_classes, total_experts, epochs, loss_importance=True)
# runs.append(models)
# results = runs[0]
# if num_runs > 1:
# results = aggregate_results(runs, total_experts)
# torch.save(results,open('../results/'+dataset+'_results.pt','wb'))
# log_results(results, total_experts, num_classes, num_runs, epochs, dataset, fp)
# generated_data = data_generator.create_meshgrid(X)
# plot_results(X, y, generated_data, num_classes, trainset, trainloader, testset, testloader, runs[0], dataset, total_experts)
# plot_error_rate(results, total_experts, 'figures/all/accuracy_'+dataset+'_'+ str(num_classes)+'_experts.png')
# total_experts = 5
# epochs = 40
# for size in [3000]:#, 5000, 8000]:
# dataset = 'expert_1_gate_1_single_deep_checker_board_rotated_'+str(size)
# X, y, trainset, trainloader, testset, testloader, num_classes = data_generator.generate_data(dataset, size)
# runs = []
# for r in range(0, num_runs):
# models = run_experiment_1(dataset, single_model_deep, trainset, trainloader, testset, testloader, num_classes, total_experts, epochs)
# runs.append(models)
# results = runs[0]
# if num_runs > 1:
# results = aggregate_results(runs, total_experts)
# torch.save(results,open('../results/'+dataset+'_results.pt','wb'))
# log_results(results, total_experts, num_classes, num_runs, epochs, dataset, fp)
# generated_data = data_generator.create_meshgrid(X)
# plot_results(X, y, generated_data, num_classes, trainset, trainloader, testset, testloader, runs[0], dataset, total_experts)
# plot_error_rate(results, total_experts, 'figures/all/accuracy_'+dataset+'_'+ str(num_classes)+'_experts.png')
for size in [3000]: #, 5000, 8000]:
dataset = 'expert_1_gate_1_single_deep_checker_board_rotated_' + str(
size)
X, y, trainset, trainloader, testset, testloader, num_classes = data_generator.generate_data(
dataset, size)
total_experts = 10
epochs = 40
runs = []
for r in range(0, num_runs):
models = run_experiment_1(dataset, single_model_deep, trainset,
trainloader, testset, testloader,
num_classes, total_experts, epochs, True,
True)
runs.append(models)
results = runs[0]
if num_runs > 1:
results = aggregate_results(runs, total_experts)
torch.save(results, open('../results/' + dataset + '_results.pt',
'wb'))
log_results(results, total_experts, num_classes, num_runs, epochs,
dataset, fp)
generated_data = data_generator.create_meshgrid(X)
plot_results(X, y, generated_data, num_classes, trainset, trainloader,
testset, testloader, runs[0], dataset, total_experts)
plot_error_rate(
results, total_experts, 'figures/all/accuracy_' + dataset + '_' +
str(num_classes) + '_experts.png')
fp.close()
#Takes in a model and ciphertext and asks the neural network what cipher it is
def predict(m, ct):
test = np.array([func(ct) for func in statistics.stats_funcs])
test = (np.expand_dims(test, 0))
prediction = m.predict(test)[0]
for probability in sorted(zip(prediction,
range(len(data_generator.config.values()))),
key=lambda x: x[0],
reverse=True):
print(data_generator.reverse_config[probability[1]], probability[0])
print("Generating data...")
data_generator.generate_data(1000, "data/train_data.dat",
"data/train_labels.dat")
data_generator.generate_data(100, "data/test_data.dat", "data/test_labels.dat")
#Load in data
train_data = np.load("data/train_data.dat")
train_labels = np.load("data/train_labels.dat")
test_data = np.load("data/test_data.dat")
test_labels = np.load("data/test_labels.dat")
model = create_model()
print("Training model...")
model.fit(train_data, train_labels, epochs=5, callbacks=[cp_callback])
model.load_weights(checkpoint_path)
print("Testing model...")
def test_dkt(model_id,
n_concepts,
transition_after,
horizon,
n_rollouts,
n_trajectories,
r_type,
use_real,
use_mem,
checkpoints=[]):
'''
Test DKT+MCTS
Can accept a number of checkpoints, meaning to use an ensemble if more than one.
'''
import concept_dependency_graph as cdg
from simple_mdp import create_custom_dependency
#learn_prob = 0.5
n_jobs = 8
traj_per_job = n_trajectories // n_jobs
#dgraph = create_custom_dependency()
dgraph = cdg.ConceptDependencyGraph()
dgraph.init_default_tree(n_concepts)
#student = st.Student(n=n_concepts,p_trans_satisfied=learn_prob, p_trans_not_satisfied=0.0, p_get_ex_correct_if_concepts_learned=1.0)
student2 = st.Student2(n_concepts, transition_after)
test_student = student2
test_student.reset()
test_student.knowledge[0] = 1 # initialize the first concept to be known
sim = st.StudentExactSim(test_student.copy(), dgraph)
# create a shared dktcache across all processes
dktcache_manager = mp.Manager()
dktcache = dktcache_manager.dict()
print('Testing model: {}'.format(model_id))
print('horizon: {}'.format(horizon))
print('rollouts: {}'.format(n_rollouts))
accs = np.array(
Parallel(n_jobs=n_jobs)(delayed(test_dkt_chunk)(traj_per_job,
dgraph,
sim,
model_id,
checkpoints,
horizon,
n_rollouts,
r_type,
dktcache=dktcache,
use_real=use_real,
use_mem=use_mem)
for _ in range(n_jobs)))
results = np.sum(accs, axis=0) / (n_jobs * traj_per_job)
avg_acc, avg_best_q = results[0], results[1]
test_data = dg.generate_data(dgraph,
student=test_student,
n_students=1000,
seqlen=horizon,
policy='expert',
filename=None,
verbose=False)
print('Average posttest true: {}'.format(expected_reward(test_data)))
print('Average posttest mcts: {}'.format(avg_acc))
print('Average best q: {}'.format(avg_best_q))
return avg_acc, avg_best_q