def sgd_ons():
x_train, y_train, x_test, y_test = load_processed_data(dir_data)
results = []
alpha = 0.1
gamma = 0.1
radius = 100
T = 10000
_, logger = train_sgd_proj(
a=x_train,
b=y_train,
a_test=x_test,
b_test=y_test,
T=T,
radius=radius,
alpha=alpha,
)
results.append(logger)
_, logger = train_ons(
a=x_train,
b=y_train,
a_test=x_test,
b_test=y_test,
T=T,
gamma=gamma,
radius=radius,
alpha=alpha,
)
results.append(logger)
plot_results_(results)
quit()
def gd_gd_proj_z():
x_train, y_train, x_test, y_test = load_processed_data(dir_data)
results = []
for radius in [0.5, 1.0, 10, 100, 500]:
alpha = 0.33
# gamma = 0.1
# radius = 100
T = 1000
_, logger = train_gd(
a=x_train,
b=y_train,
a_test=x_test,
b_test=y_test,
T=T,
# gamma = gamma,
# radius=radius,
alpha=alpha,
)
results.append(logger)
_, logger = train_gd_proj(
a=x_train,
b=y_train,
a_test=x_test,
b_test=y_test,
T=T,
# gamma = gamma,
radius=radius,
alpha=alpha,
)
results.append(logger)
plot_results_(results)
def plot_hogwild():
x_train, y_train, x_test, y_test = load_processed_data(dir_data)
# --- 2. plot hogwild for various values of K
n_runs = 3
n_workers = 8
T = 1000000
alpha = 0.33
beta = 0.37
theta = 0.2
results = [
AvgLogger([
train_hogwild(a=x_train,
b=y_train,
a_test=x_test,
b_test=y_test,
T=T,
alpha=alpha,
beta=beta,
K=K,
theta=theta,
n_processes=n_workers,
sequential=False,
seed=s)[1] for s in range(n_runs)
]) for K in [3, 10, 50]
]
# --- 1. plot comparison between SGD and hogwild, fixed K
# n_runs = 3
# n_workers = 8
# T = 1000000
# alpha = 0.33
# beta = 0.37
# theta = 0.2
# results = [AvgLogger([
# train_hogwild(a=x_train, b=y_train, a_test=x_test, b_test=y_test, T=T, alpha=alpha, beta=beta,
# K=K, theta=theta, n_processes=n_workers, sequential=False, seed=s)[1]
# for s in range(n_runs)
# ]) for K in [3]]
# results.append(AvgLogger([
# train_hogwild(a=x_train, b=y_train, a_test=x_test, b_test=y_test, T=T, alpha=alpha, beta=beta,
# K=3, theta=theta, n_processes=n_workers, sequential=True, seed=s)[1]
# for s in range(n_runs)
# ]))
# results.append(AvgLogger([
# train_sgd(a=x_train, b=y_train, a_test=x_test, b_test=y_test, T=T, alpha=alpha, return_avg=True, seed=s)[1]
# for s in range(n_runs)
# ]))
plot_results(
results,
add_to_title=
rf" ($\alpha={alpha}, \beta={beta}, \theta={theta}$, n_runs={n_runs})")
def sgd_ons_var():
x_train, y_train, x_test, y_test = load_processed_data(dir_data)
results = []
n_runs = 5
T = 1000
alpha = 0.1
radius = 100
gamma = 0.1
results.append(
AvgLogger([
train_ons(
a=x_train,
b=y_train,
a_test=x_test,
b_test=y_test,
T=T,
gamma=gamma,
radius=radius,
seed=s,
alpha=alpha,
)[1] for s in range(n_runs)
]))
results.append(
AvgLogger([
train_sgd_proj(
a=x_train,
b=y_train,
a_test=x_test,
b_test=y_test,
T=T,
alpha=alpha,
radius=100,
seed=s,
)[1] for s in range(n_runs)
]))
plot_results_(results,
add_to_title=rf" - $\alpha={alpha}$, n_runs={n_runs}")
from datetime import datetime
from pathlib import Path
import pandas as pd
from algos import train_hogwild, train_sgd
from data_utils import load_processed_data
if __name__ == "__main__":
dir_data = Path(__file__).resolve().parents[1].joinpath("data/")
save_folder = dir_data.joinpath("../csv_results")
try:
os.mkdir(save_folder)
except FileExistsError:
pass
x_train, y_train, x_test, y_test = load_processed_data(dir_data)
n_runs = 1
T_config = 2017521
alpha = 0.33
beta = 0.37
theta = 0.2
K = 3
results = {
"algo": [],
"time": [],
"test_error": [],
"T": [],
"n_workers": [],
"K": [],
}
202313, 520268, 628267, 760933, 761105, 761274, 767884, 767948, 768051,
778196, 781774, 790989, 791094, 913179, 1073703, 1132513, 1132676, 1140226,
1141794, 1237426, 1241905, 1387080, 1388043, 1570724, 1585962, 1586097
] # all errors in training
START = 761200
WINDOW = 200
r_filename = "training_data.csv"
p_filename = "training_data_ps.csv"
if not PREPROCESSED:
array, labels = load_raw_data(r_filename, START + WINDOW)
else:
array, labels = load_processed_data(p_filename, START + WINDOW)
array2 = np.zeros(shape=array.shape)
array2[1:, :] = np.copy(array[:-1, :])
array3 = array[START:, :]
if not os.path.exists("images"):
os.makedirs("images")
min = array3.min(axis=0)
max = array3.max(axis=0)
array4 = (array3 - min) / (max - min)
scipy.misc.toimage(array4, cmin=-1.0, cmax=1.0).save('images/data1.jpg')
def main(argv):
if len(argv) < 3:
print("Correct arguments: <model> <data_file> |-<i(interactive)>|")
exit()
model_file = argv[1]
weights_file = argv[1] + ".h5"
data_file = argv[2]
if not (os.path.exists(model_file) and os.path.exists(weights_file)
and os.path.exists(data_file)):
print("One of the specified files {}, {}, {} doesn't exist".format(
model_file, weights_file, data_file))
exit()
print("# Loading data from files {}".format(data_file))
(X, y_test) = load_processed_data(data_file, TEST_ROWS)
print("### Loaded {} test rows".format(X.shape[0]))
print("## X_test shape: ", X.shape)
print("## Y_test shape: ", y_test.shape)
# y_train = np.random.choice([0, 1], size=y_train.shape, p=[0.99, 0.01])
# Modifying labels to time series prediction
print("### Modifying labels")
nonzero_test = np.count_nonzero(y_test)
print("# Number of non-error labels: {}".format(y_test.shape[0] -
nonzero_test))
print("# Number of error labels: {}".format(nonzero_test))
y_test = warp_labels(y_test, PREDICTION_LENGTH, WINDOW_SIZE)
nonzero_test = np.count_nonzero(y_test)
print("## Labels modified")
print("# Number of non-error labels: {}".format(y_test.shape[0] -
nonzero_test))
print("# Number of error labels: {}".format(nonzero_test))
print("### Modified labels a to signal errors in the next {} samples.".
format(PREDICTION_LENGTH))
# Modifying x's to be 3D vectors
X = make_timeseries_instances(X, WINDOW_SIZE)
print("### Modified data to tensors with height {}".format(WINDOW_SIZE))
# Something with adding the channel count
X = np.expand_dims(X, axis=3)
y_test = y_test[:X.shape[0]]
print("### Loading the model from file {}".format(model_file))
json_file = open(model_file, 'r')
model_json = json_file.read()
json_file.close()
model = model_from_json(model_json)
print("### Loading weights from file {}".format(weights_file))
model.load_weights(weights_file)
print("### Loaded model from disk")
model.compile(loss='binary_crossentropy',
optimizer='rmsprop',
metrics=['accuracy'])
print("### Evaluating the model")
if len(argv) < 4:
score = model.evaluate(X, y_test, verbose=1)
print("#### Results ####")
print('Test loss:', score[0])
print('Test accuracy:', score[1])
elif "-i" in argv:
print("Enter sample numbers for prediction:")
while True:
line = sys.stdin.readline()
i = int(line) - PREDICTION_LENGTH
if 0 < i < X.shape[0]:
prediction = model.predict(X[i, ].reshape(
1, WINDOW_SIZE, PCA_TARGET_SIZE, 1))
value = 0 if math.isnan(
np.sum(prediction)) else np.sum(prediction)
if value > 0.0001:
print("Will fail in {}".format(PREDICTION_LENGTH))
else:
print("Will not fail in {}".format(PREDICTION_LENGTH))
PREPROCESSED = True
training_filename = 'training_data.csv'
test_filename = 'test_data.csv'
prep_training_filename = 'training_data_ps.csv'
prep_test_filename = 'test_data_ps.csv'
import sys
# Usually we will use pre-processed data, this is for a special case
if PREPROCESSED:
# Normal turn of events
print("# Loading prepared data from files {} and {}".format(prep_training_filename, prep_test_filename))
(x_train, y_train), (x_test, y_test) = load_processed_data(prep_training_filename, TRAIN_ROWS), \
load_processed_data(prep_test_filename, TEST_ROWS)
else:
# Loading raw unprocessed data
print("# Loading raw data from files {} and {}".format(training_filename, test_filename))
(x_train, y_train), (x_test, y_test) = load_raw_data(training_filename, TRAIN_ROWS), \
load_raw_data(test_filename, TEST_ROWS)
# PCA dimensionality reduction
pca = decomposition.PCA(n_components=PCA_TARGET_SIZE)
pca.fit(x_train)
x_train = pca.transform(x_train)
pca.fit(x_test)
x_test = pca.transform(x_test)
print("# Reduced data to {} dimensions", PCA_TARGET_SIZE)
sampler=sampler.SubsetRandomSampler(
range(NUM_VAL)))
optimizer = optim.Adadelta(model.parameters())
train_model(model, optimizer, loader_train, loader_val, epoches=2)
# Upload data to memory
#data_utils.proccess_data()
regular_train_images, regular_test_images, regular_val_images,\
regular_train_labels, regular_test_labels, regular_val_labels,\
inverted_train_images, inverted_test_images, inverted_val_images,\
inverted_train_labels, inverted_test_labels, inverted_val_labels,\
train_one_group_images, test_one_group_images, val_one_group_images,\
train_one_group_labels, test_one_group_labels, val_one_group_labels = data_utils.load_processed_data()
# Check for GPU availability:
device = my_models.device_gpu_cpu()
print('using device:', device)
dtype = torch.float32 # we will be using float
# Constant to control how frequently we print train loss
print_every = 100
# Create models:
model = my_models.model_2()
my_models.test_model_size(model, dtype) # test model size output:
scene_1()