def replace_missing_with_kde_samples(data_frame, attribute):
""" Replace missing values based on samples from KDE function
:param data_frame: Pandas dataframe holding the attribute
:type data_frame: pandas.DataFrame
:param attribute: The attribute for which missing values should be replaced
:type attribute: str
"""
minimum = data_frame[attribute].min()
maximum = data_frame[attribute].max()
values = np.array(data_frame[attribute].dropna())
kde = KernelDensity(kernel='gaussian', bandwidth=0.2).fit(
values.reshape(-1, 1))
missing_values = data_frame.loc[
data_frame[attribute].isnull(), attribute]
samples = [num for num in
kde.sample(n_samples=len(data_frame[attribute].dropna()))
if minimum <= num <= maximum]
while len(samples) < 2*len(missing_values):
samples.extend([num for num in
kde.sample(n_samples=len(data_frame[attribute].dropna()))
if minimum <= num <= maximum])
samples = [samples[i] for i in
sorted(random.sample(xrange(len(samples)), len(missing_values)))]
for index, value in enumerate(samples):
missing_values[index] = samples[index]
data_frame.update(pd.DataFrame(missing_values))
def kde_sampler_life(enc,
X,
y,
batch_size,
bandwidth=0.5,
nn_subset_size=None):
while True:
log_time("get_z_enc")
if nn_subset_size is None:
imgs = X
else:
rand_idxs = np.random.randint(0, len(X), nn_subset_size)
imgs = X[rand_idxs]
z_enc = ld_gan.utils.model_handler.apply_model(enc,
imgs,
batch_size=500)
log_time("get_z_enc")
batch_idxs = np.random.randint(0, len(z_enc), batch_size)
img_batch = imgs[batch_idxs]
y_batch = y[batch_idxs]
kde = KernelDensity(bandwidth=bandwidth).fit(z_enc)
z_batch = kde.sample(batch_size)
yield img_batch, y_batch, z_batch, z_batch
def find_max_density(point_list):
point_list, _ = remove_nan(point_list)
if point_list.shape[0] == 0:
return [float('nan'),float('nan'),float('nan')]
kde = KernelDensity(kernel='gaussian', bandwidth=0.2).fit(point_list)
points = kde.sample(100000)
prob_list = kde.score_samples(points)
max_point = points[np.argmax(prob_list)]
# print "max", max_point
return max_point
def train_rlos(data, show_chart=False):
"""Train LOS estimator"""
"""Train patient LOS for triplet (sex, age, sline)"""
freq = {}
for row in data:
sex = int(row["sex"])
age = fp.split_age(int(row["age"]))
sline = row["sline"]
rlos = int(row["rlos"])
if rlos == 0:
print "RLOS equals zero for sex %d, age %d, SL %s" % (sex, age, sline)
tuple = (sex, age, sline)
freq.setdefault(tuple, [])
freq[tuple].append(rlos)
result = {}
for tuple, train_data in freq.items():
(sex, age, sline) = tuple
if len(train_data) < training_threshold:
print "Too small training set (<%d) for sex %d, age %d, SL %s. Data will be skipped. " % \
(training_threshold, sex, age, sline)
continue
X = np.array([train_data]).transpose()
kde = KernelDensity(kernel='tophat', bandwidth=0.5).fit(X)
kdef = lambda size: [round(l[0]) for l in kde.sample(size).tolist()]
result[tuple] = kde
if show_chart:
# print "Sex=%d, Age=%d, SL=%s" % (sex, age, sline)
# print_freq(ages)
samples = kdef(len(train_data)) if len(train_data) < 500 else kdef(500)
# print_freq(samples)
# hist for train data
plt.subplot(211)
plt.title("RLOS train data for Sex=%d, Age=%d, SL=%s" % (sex, age, sline))
plt.ylabel('freq')
plt.xlabel('RLOS')
plt.hist(train_data)
# estimated density
plt.subplot(212)
plt.title("Estimated density Sex=%d, Age=%d, SL=%s" % (sex, age, sline))
plt.ylabel('freq')
plt.xlabel('RLOS')
plt.hist(samples)
plt.show()
return result
def get_numerical_signature(values, S):
'''
Learns a distribution of the values
Then generates a sample of size S
'''
# Transform data to numpy array
Xnumpy = np.asarray(values)
X = Xnumpy.reshape(-1, 1)
# Learn kernel
kde = KernelDensity(kernel=C.kd["kernel"],
bandwidth=C.kd["bandwidth"]).fit(X)
sig_v = [kde.sample()[0][0] for x in range(S)]
return sig_v
def train_admit_count(data, show_chart=False):
"""Train patient admittance number for triplet (sex, age, sline)"""
freq = {}
for row in data:
sex = int(row["sex"])
age = fp.split_age(int(row["age"]))
sline = row["sline"]
admit = row["admit"]
tuple = (sex, age, sline)
freq.setdefault(tuple, {})
freq[tuple].setdefault(admit, 0)
freq[tuple][admit] += 1
result = {}
for tuple, days in freq.items():
(sex, age, sline) = tuple
train_data = days.values()
if len(train_data) < training_threshold:
print "Too small training set (<%d) for sex %d, age %d, SL %s. Data will be skipped. " % \
(training_threshold, sex, age, sline)
continue
X = np.array([train_data]).transpose()
kde = KernelDensity(kernel='tophat', bandwidth=0.5).fit(X)
kdef = lambda size: [int(round(l[0])) for l in kde.sample(size).tolist()]
result[tuple] = kde
if show_chart:
# print "Sex=%d, Age=%d, SL=%s" % (sex, age, sline)
# print_freq(ages)
samples = kdef(len(train_data)) if len(train_data) < 500 else kdef(500)
# print_freq(samples)
# hist for train data
plt.subplot(211)
plt.title("Admit count train data for Sex=%d, Age=%d, SL=%s" % (sex, age, sline))
plt.ylabel('freq')
plt.xlabel('admittance count')
plt.hist(train_data)
# estimated density
plt.subplot(212)
plt.title("Estimated density Sex=%d, Age=%d, SL=%s" % (sex, age, sline))
plt.ylabel('freq')
plt.xlabel('admittance count')
plt.hist(samples)
plt.show()
return result
class KDEModel(object):
"""
Wrapper class for Scikit Learn's Kernel Density Estimation model.
Attributes
----------
model : KernelDensity
Wrapped class model.
"""
def __init__(self, kernel='gaussian', bandwidth=.001):
self.model = KernelDensity(kernel='gaussian', bandwidth=bandwidth)
def fit(self, train_X):
"""
Wrapper method for fit() method of Kernel Density model.
Parameters
----------
train_X : {array-like, sparse matrix}, shape = [n_samples, n_features]
"""
self.model.fit(train_X)
def generate_samples(self, n_samples):
"""
Generates the random samples according to the fitted distribution.
Returns
-------
list
List of numpy arrays of randomly generated observations.
"""
points = self.model.sample(n_samples)
return points
def score_samples(self, X):
"""
Predicts the log likelihood score of the samples in X.
Parameters
----------
X : {array-like, sparse matrix}, shape = [n_samples, n_features]
"""
return self.model.score_samples(X)
def get_kde_pdf(X, bandwidth=2, step=.1, num_samples=200, optimize=False):
"""
return kde and pdf from a data sample
"""
if len(X) == 0:
return [], np.array([]), []
if optimize:
bandwidths = 10**np.linspace(-1, 1, 10)
grid = GridSearchCV(KernelDensity(kernel='gaussian'),
{'bandwidth': bandwidths},
cv=LeaveOneOut(len(X)))
grid.fit(X[:, None])
kde = KernelDensity(kernel='gaussian',
bandwidth=grid.best_params_['bandwidth']).fit(
X[:, None])
else:
kde = KernelDensity(kernel='gaussian', bandwidth=2).fit(X[:, None])
pdf = np.exp(kde.score_samples(np.arange(0, 100, step)[:, None]))
samples = kde.sample(num_samples)
return kde, np.array(pdf), samples
def get_sampled_feature(dict_ids,
article_ids,
ndim=100,
type_features='second',
features=['intervals'],
bw=1):
'''
Each article has 'intervals' (second) between two adjacent retweets.
ndim : feature dimension. Thus, the number of samplings
'''
X = np.zeros((len(article_ids), ndim * len(features)))
for i, id_ in enumerate(article_ids):
article_id = id_
stats = dict_ids[id_]
for find, feature in enumerate(features):
raw_data = change_resolution(stats[feature], type_features)
kde = KernelDensity(kernel='gaussian',
bandwidth=bw).fit(raw_data.reshape(-1, 1))
X[i,
find * ndim:(find + 1) * ndim] = np.sort(kde.sample(ndim)[:, 0])
return X
def resample_state(D, w):
w_norm = np.sum(w) # Normalization factor for weights
w_ecdf = np.cumsum(w) / w_norm # New weight given the new measurement
# Resample the points
D_new, ind = np.empty_like(D), np.empty_like(D)
for i, q in enumerate(D):
ind[i] = bisect.bisect_left(w_ecdf, np.random.uniform(
0, 1)) # Indexes for new samples
D_new[i] = D[int(
ind[i]
)] # New weighted particles (samples) from previous step given new measuremnt
# Regularize it!
# std = np.std(D_new)
bandwidth = 0.05 #1.06*std*len(D_new)**-0.2 ## used to be 0.08
kde = KernelDensity(
bandwidth=bandwidth, kernel='gaussian', algorithm='ball_tree'
) # Bandwidth = 0.006 is calculated based on Silverman's Rule of Thumb
kde.fit(D_new[:, np.newaxis])
return kde.sample(num_particles).flatten(), ind
class DensityEstimator:
def __init__(self,
training_set,
method_name,
n_components=None,
log_dir=None,
second_stage_beta=None):
self.log_dir = log_dir
self.training_set = training_set
self.fitting_done = False
self.method_name = method_name
self.second_density_mdl = None
self.skip_fitting_and_sampling = False
if method_name == "GMM_Dirichlet":
self.model = mixture.BayesianGaussianMixture(
n_components=n_components,
covariance_type='full',
weight_concentration_prior=1.0 / n_components)
elif method_name == "GMM":
self.model = mixture.GaussianMixture(n_components=n_components,
covariance_type='full',
max_iter=2000,
verbose=2,
tol=1e-3)
elif method_name == "GMM_1":
self.model = mixture.GaussianMixture(n_components=1,
covariance_type='full',
max_iter=2000,
verbose=2,
tol=1e-3)
elif method_name == "GMM_10":
self.model = mixture.GaussianMixture(n_components=10,
covariance_type='full',
max_iter=2000,
verbose=2,
tol=1e-3)
elif method_name == "GMM_20":
self.model = mixture.GaussianMixture(n_components=20,
covariance_type='full',
max_iter=2000,
verbose=2,
tol=1e-3)
elif method_name == "GMM_100":
self.model = mixture.GaussianMixture(n_components=100,
covariance_type='full',
max_iter=2000,
verbose=2,
tol=1e-3)
elif method_name == "GMM_200":
self.model = mixture.GaussianMixture(n_components=200,
covariance_type='full',
max_iter=2000,
verbose=2,
tol=1e-3)
elif method_name.find("aux_vae") >= 0:
have_2nd_density_est = False
if method_name[8:] != "":
self.second_density_mdl = method_name[8:]
have_2nd_density_est = True
self.model = VaeModelWrapper(
input_shape=(training_set.shape[-1], ),
latent_space_dim=training_set.shape[-1],
have_2nd_density_est=have_2nd_density_est,
log_dir=self.log_dir,
sec_stg_beta=second_stage_beta)
elif method_name == "given_zs":
files = os.listdir(log_dir)
for z_smpls in files:
if z_smpls.endswith('.npy'):
break
self.z_smps = np.load(os.path.join(log_dir, z_smpls))
self.skip_fitting_and_sampling = True
elif method_name.upper() == "KDE":
self.model = KernelDensity(kernel='gaussian', bandwidth=0.425)
# self.model = KernelDensity(kernel='tophat', bandwidth=15)
else:
raise NotImplementedError("Method specified : " +
str(method_name) +
" doesn't have an implementation yet.")
def fitorload(self, file_name=None):
if not self.skip_fitting_and_sampling:
if file_name is None:
self.model.fit(self.training_set, self.second_density_mdl)
else:
self.model.load(file_name)
self.fitting_done = True
def score(self, X, y=None):
if self.method_name.upper().find(
"AUX_VAE") >= 0 or self.skip_fitting_and_sampling:
raise NotImplementedError(
"Log likelihood evaluation for VAE is difficult. or skipped")
else:
return self.model.score(X, y)
def save(self, file_name):
if not self.skip_fitting_and_sampling:
if self.method_name.find('vae') >= 0:
self.model.save(file_name)
else:
with open(file_name, 'wb') as f:
pickle.dump(self.model, f)
def reconstruct(self, input_batch):
if self.method_name.upper().find("AUX_VAE") < 0:
raise ValueError("Non autoencoder style density estimator: " +
self.method_name)
return self.model.reconstruct(input_batch)
def get_samples(self, n_samples):
if not self.skip_fitting_and_sampling:
if not self.fitting_done:
self.fitorload()
scrmb_idx = np.array(range(n_samples))
np.random.shuffle(scrmb_idx)
if self.log_dir is not None:
pickle_path = os.path.join(self.log_dir,
self.method_name + '_mdl.pkl')
with open(pickle_path, 'wb') as f:
pickle.dump(self.model, f)
if self.method_name.upper() == "GMM_DIRICHLET" or self.method_name.upper() == "AUX_VAE" \
or self.method_name.upper() == "GMM" or self.method_name.upper() == "GMM_1" \
or self.method_name.upper() == "GMM_10" or self.method_name.upper() == "GMM_20" \
or self.method_name.upper() == "GMM_100" or self.method_name.upper() == "GMM_200"\
or self.method_name.upper().find("AUX_VAE") >= 0:
return self.model.sample(n_samples)[0][scrmb_idx, :]
else:
return np.random.shuffle(
self.model.sample(n_samples))[scrmb_idx, :]
else:
return self.z_smps
def estimate(): kde = KernelDensity(kernel = "gaussian", bandwidth = 0.1).fit(X) #get random sample samples = kde.sample()
# In[34]:
pca1 = PCA(n_components=n1, whiten=True)
dt = pca1.fit_transform(digits.data)
# print(dt.shape)
# In[35]:
kde_model = KernelDensity(kernel='gaussian', bandwidth=bandwidth)
kde_model.fit(dt)
# In[38]:
d1new = kde_model.sample(n_samples=100, random_state=0)
digits1k_new = pca1.inverse_transform(d1new)
plot_digits(digits1k_new)
# In[40]:
n_comps = np.arange(50, 210, 10)
clf_gauss_models = [
GaussianMixture(n_components=n, covariance_type='full', random_state=0)
for n in n_comps
]
aics = [model.fit(dt).aic(dt) for model in clf_gauss_models]
lbd = aics.index(min(aics))
print("Optimal Number of Components for GMM =", n_comps[lbd])
# In[44]:
def kernel_smoother(all_data, bandwidth, sample_size):
X = np.array(all_data)
kde = KernelDensity(kernel='gaussian', bandwidth=bandwidth).fit(X)
return kde.sample(sample_size)
def train_age(data, show_chart=False):
"""Train age estimator for each SL"""
def print_freq(data):
freq = {}
length = float(len(data))
for x in data:
xcat = fp.split_age(x)
freq.setdefault(xcat, 0)
freq[xcat] += 1
for x in sorted(freq.keys()):
print "%d: %.2f" % (x, round(freq[x]/length, 2)),
print
sline_ages = {}
bad_sl = set()
for row in data:
sline = row['sline']
age = int(row["age"])
if age <= 0:
bad_sl.add(sline)
continue
sline_ages.setdefault(sline, [])
sline_ages[sline].append(age)
for sl in bad_sl:
print "SL=%s has age values equal or less than zero. Values were ignored" % sl
for sline, ages in sline_ages.items():
if len(ages) < alert_count:
print "SL=%s has less(%d) than %d samples and will be excluded" % (sline, len(ages), alert_count)
del sline_ages[sline]
result = {}
for sline,ages in sline_ages.items():
X = np.array([ages]).transpose()
kde = KernelDensity(kernel='tophat', bandwidth=1.0).fit(X)
kdef = lambda size: [round(l[0]) for l in kde.sample(size).tolist()]
result[sline] = kdef
if show_chart:
print "SL=%s" % sline
print_freq(ages)
samples = kdef(len(ages)) if len(ages) < 500 else kdef(500)
print_freq(samples)
# hist for train data
plt.subplot(211)
plt.title("Age train data for SL=%s" %(sline))
plt.ylabel('freq')
plt.xlabel('age category')
plt.hist(ages)
# estimated density
plt.subplot(212)
plt.title("Estimated density %s" % sline)
plt.ylabel('freq')
plt.xlabel('age category')
plt.hist(samples)
plt.show()
return result
# In[25]:
get_ipython().run_line_magic('pinfo', 'kde.sample')
# Basically, that means we can use this model to predict what the next output of the 3 arms (constituting the Gaussian problem) will be.
#
# Let see this with one example.
# In[26]:
np.random.seed(1)
one_sample = kde.sample()
one_sample
# In[27]:
one_draw = M.draw_each()
one_draw
# Of course, the next random rewards from the arms have no reason to be close to predicted ones...
#
# But maybe we can use the prediction to choose the arm with highest sample?
# And hopefully this will be the best arm, *at least in average*!
class CSGM(torch.nn.Module):
def __init__(self,
target,
filter,
G,
num_samples,
BS=64,
init_threshold=1e-2,
threshold=0.05,
bandwidth=0.1,
lr=1e-2):
super(CSGM, self).__init__()
self.target = torch.FloatTensor(target).cuda()
self.A = torch.FloatTensor(filter).cuda()
self.num_samples = num_samples
self.G = G
self.n_pixels = np.sum(filter)
self.threshold = threshold
self.init_threshold = init_threshold
self.BS = BS
# determine the points for KDE
self.z, self.init_samples, self.init_bg = reconstruct_batch(
target,
filter,
self.n_pixels,
G,
num_samples,
threshold=init_threshold,
lr=lr)
self.Dz = KernelDensity(kernel='gaussian', bandwidth=bandwidth).fit(
self.z.reshape(num_samples, 100))
def update_sampler(self, bandwidth):
self.Dz = KernelDensity(kernel='gaussian', bandwidth=bandwidth).fit(
self.z.reshape(-1, 100))
def sample(self, num_samples):
count = 0
z_samples = []
gen_samples = []
bg_samples = []
while count < num_samples:
Z = self.Dz.sample(self.BS)
Z = torch.FloatTensor(Z).cuda().view(-1, 100, 1, 1)
gen = self.G(Z).view(Z.shape[0], 28, 28)
yhat = gen * self.A
error = i_se(yhat,
self.target.unsqueeze(0).repeat(Z.shape[0], 1,
1)) / self.n_pixels
Z = Z[error <= self.threshold]
gen = gen[error <= self.threshold]
end = min(Z.shape[0], num_samples - count)
bg = gen[:end] * (1 - self.A)
z_samples.append(Z[:end].data.cpu().numpy())
gen_samples.append(gen[:end].data.cpu().numpy())
bg_samples.append(bg.data.cpu().numpy())
count += end
z_samples = np.concatenate(z_samples, axis=0)
gen_samples = np.concatenate(gen_samples, axis=0)
bg_samples = np.concatenate(bg_samples, axis=0)
return z_samples, gen_samples, bg_samples
class SkewExploreKDE(SkewExploreBase):
"""
Class for state density estimation using sklearn kernel density estimator, goal proposing and result plotting.
The goal proposing distribution is computed using Skew-fit algorithm (https://arxiv.org/abs/1903.03698)
with a sklearn kernel density estimation.
:param env: the environment, used to access environment properties such as state range,
pass proposed goals and pass the on-line mean and standard diviation for state normalization
:param args: additional arguments for configuring result plotting
"""
def __init__(self, env, args):
super().__init__(env, args)
self.density_estimator = KernelDensity(kernel='gaussian',
bandwidth=self.bandwidth)
self.density_estimator_raw = KernelDensity(kernel='gaussian',
bandwidth=0.1)
self.sample_prob = []
## for coverage computation
self.obs_in_use = None
self.num_points_estimator = 50000 #70000
def fit_model(self):
"""
fit the kernel density model
"""
self.count += 1
logging.info('Activate buffer')
self.init_buffer = True
selected_index = np.random.randint(len(self.obs_hist),
size=self.num_points_estimator)
self.obs_in_use = self.obs_hist[selected_index]
# only yumi environments need to normalize the observation states on-line
if self.args.use_auto_scale:
if self.args.env == 'yumi' or self.args.env == 'yumi_box_pick' or self.args.env == 'yumi_door_button':
if self.count % 2 == 0:
self.obs_mean = self.obs_rms.mean[
0] #np.mean(self.obs_in_use, axis=0)
self.obs_std = np.sqrt(
self.obs_rms.var[0]
) + 1e-8 #np.std(self.obs_in_use, axis=0) + 0.000000001
self.obs_nomalized = (self.obs_in_use - self.obs_mean) / self.obs_std
self.density_estimator.fit(self.obs_nomalized)
# scale the observation for entropy computation
self.obs_scaled = (self.obs_in_use -
self.entropy_shift) / self.entropy_scale
self.density_estimator_raw.fit(self.obs_scaled)
if self.plot_density:
if self.args.env == 'yumi':
self.xy_estimator.fit(self.obs_nomalized[:, 0:2])
self.doorangle_estimator.fit(self.obs_nomalized[:, -1:])
elif self.args.env == 'yumi_box_pick' or self.args.env == 'yumi_door_button':
self.xy_estimator.fit(self.obs_nomalized[:, 0:2])
self.doorangle_estimator.fit(self.obs_nomalized[:, -2:])
def get_samples_and_density(self, sample_num):
"""
Sample states from the density model and compute the sample density
"""
samples = self.density_estimator.sample(self.skew_sample_num)
samples_density = np.exp(self.density_estimator.score_samples(samples))
return samples, samples_density
def get_log_density(self, obs_test):
"""
Compute log density
"""
log_density = self.density_estimator.score_samples(obs_test)
return log_density
def get_density(self, obs_test):
"""
Compute density
"""
density = np.exp(self.density_estimator.score_samples(obs_test))
return density
class Texture3D:
def __init__(self, size):
self.rocks = []
self.size = size
self.data = []
'''
add a rock to the texture if the rock doesn't any rock in self.rocks
returns bool
'''
def add(self, rock):
if not self.intersect(rock):
self.rocks.append(rock)
self.data.append(rock.data())
return True
return False
'''
Create the tree for the kernelDensity
'''
def learn(self):
self.kde = KernelDensity(kernel='gaussian',
bandwidth=0.04).fit(self.data)
'''
samples rocks from kde one by one making sure there are no intersection
returns a Texture
'''
def sample(self, n_rocks=None):
length = n_rocks
if length == None:
length = len(self.data)
mtexture = Texture3D(self.size)
i = 0
while i < length:
new_rock = rock.dataToRock3D(
self.kde.sample(1, random_state=None)[0])
if mtexture.add(new_rock):
i = i + 1
return mtexture
'''
compute the distance between two points
return float
'''
def __distance(self, center1, center2):
center1 = np.array(center1)
center2 = np.array(center2)
sub = center1 - center2
sub = sub**2
return math.sqrt(np.sum(sub))
'''
returns a string defining the texture. (for saving)
format:
size
c1,c2,c3#rad1,rad2,rad3#col1,col2,col3#rot1,rot2,rot3
c1,c2,c3#rad1,rad2,rad3#col1,col2,col3#rot1,rot2,rot3
'''
def toString(self):
result = str(self.size) + '\n'
for rock in self.rocks:
center = '' + str(rock.center[0]) + ',' + str(
rock.center[1]) + ',' + str(rock.center[2])
radius = '' + str(rock.radius[0]) + ',' + str(
rock.radius[1]) + ',' + str(rock.radius[2])
color = '' + str(rock.color[0]) + ',' + str(
rock.color[1]) + ',' + str(rock.color[2] + ',' +
str(rock.color[3]))
rotation = '' + str(rock.rotation[0]) + ',' + str(
rock.rotation[1]) + ',' + str(rock.rotation[2])
result = result + center + '#' + radius + '#' + color + '#' + rotation + '\n'
return result
'''
Test if two rocks intersect.
returns bool
'''
def __intersect(self, rock1, rock2):
return self.__distance(
rock1.center, rock2.center) < max(rock1.radius) + max(rock2.radius)
'''
Test if a rock intersects another rock in self.rocks
'''
def intersect(self, rock):
for r in self.rocks:
if self.__intersect(rock, r):
return True
return False
import numpy as np
import matplotlib.pyplot as plt
from sklearn.neighbors.kde import KernelDensity
#load data
xtrain = np.genfromtxt('../../contest_data/train.csv', delimiter=',')[1:, 1:-1]
ytrain = np.genfromtxt('../../contest_data/train.csv', delimiter=',')[1:, -1]
ytrain = np.asmatrix(ytrain).T
xtrain_linear_imputed = np.genfromtxt(
'../../contest_data/xtrain_linear_imputed.csv', delimiter=',')
#imputing by sampling from class conditioned density estimate
#class conditional density estimate of column 1
for k in range(500):
finite = np.isfinite(xtrain[:, k])
nans = np.isnan(xtrain[:, k])
y = np.array(ytrain[finite].T)
X = xtrain[finite, k][:, np.newaxis]
print k
for i in range(29):
#X_plot=np.linspace(0,1,1000)[:,np.newaxis]
ind = y == float(i)
kde = KernelDensity(kernel='gaussian', bandwidth=0.1).fit(X[ind[0]])
nans_i = np.isnan(xtrain[:, k]) * np.array((ytrain == float(i)).T)
xtrain[nans_i[0],
k] = np.array(kde.sample(sum(nans_i[0]), random_state=0).T)
log_dens = kde.score_samples(X_plot)
#dens=np.exp(log_dens)
#plt.plot(X_plot,dens)
#plt.show()
class SkewExploreBase():
"""
Class for state density estimation, goal proposing and result plotting.
The goal proposing distribution is computed using Skew-fit algorithm (https://arxiv.org/abs/1903.03698)
with a sklearn kernel density estimation.
:param env: the environment, used to access environment properties such as state range,
pass proposed goals and pass the on-line mean and standard diviation for state normalization
:param args: additional arguments for configuring result plotting
"""
def __init__(self, env, args):
self.density_estimator_raw = None #KernelDensity(kernel='gaussian', bandwidth=0.1)
self.density_estimator = None
self.args = args
self.env = env
self.obs_rms = None #RunningMeanStd(shape=env.observation_space)
self.skew_sample_num = 10000 #25000
self.skew_alpha = args.skew_alpha #-2.5 #-2.3 #-2.1
self.goal_sampling_num = 100
self.init_buffer = False
self.obs_hist = None
self.obs_next_hist = None
self.dones = None
self.obs_in_use = None
self.obs_new = None
self.plot_coverage = False
self.plot_density = False
self.plot_overall_coverage = False
self.plot_entropy = False
self.count = 0
self.coverages = []
self.entropy = []
self.task_reward = []
self.obs_mean = None
self.obs_std = None
self.plot_coverage = self.args.plot_coverage
self.plot_density = self.args.plot_density
self.plot_overall_coverage = self.args.plot_overall_coverage
self.plot_entropy = self.args.plot_entropy
# for coverage plotting
if self.args.env == 'maze':
self.bandwidth = 0.1
self.init_maze_plotting_params()
sigma = 0.1
elif self.args.env == 'yumi':
# self.bandwidth = 0.003
self.bandwidth = 0.1
self.init_door_plotting_params()
sigma = 0.1
elif self.args.env == 'yumi_box_pick' or self.args.env == 'yumi_door_button':
self.bandwidth = 0.11
self.init_boxpick_plotting_params()
sigma = 0.005
self.beta = 1 / (sigma**2 * 2)
def init_maze_plotting_params(self):
"""
Initialize parameters to evaluate and plot results of point maze environment
"""
xbins = 50j
ybins = 50j
x_start = -6
x_end = 6
y_start = -12
y_end = 4
self.xx, self.yy = np.mgrid[x_start:x_end:xbins, y_start:y_end:ybins]
self.eval_sample = np.vstack([self.yy.ravel(), self.xx.ravel()]).T
self.eval_sample_min_dist = np.ones(len(self.eval_sample))
self.skewed_estimator = KernelDensity(kernel='gaussian',
bandwidth=self.bandwidth)
self.entropy_shift = np.array([y_start, x_start])
self.entropy_scale = np.array([(y_end - y_start, x_end - x_start)])
def init_door_plotting_params(self):
"""
Initialize parameters to evaluate and plot results of yumi door opening environment
"""
xbins, ybins, zbins, gbins, dbins = 10j, 10j, 10j, 2j, 10j
self.x_start, self.y_start, self.z_start = self.env.xyz_start
self.x_end, self.y_end, self.z_end = self.env.xyz_end
self.g_start, self.g_end = self.env.gripper_start, self.env.gripper_end
self.d_start, self.d_end = self.env.door_start, self.env.door_end
# for xy and door angle plotting
self.mesh_xx, self.mesh_yy = np.mgrid[self.x_start:self.x_end:xbins,
self.y_start:self.y_end:ybins]
self.dd = np.mgrid[self.d_start:self.d_end:dbins]
self.xy_eval_sample = np.vstack(
[self.mesh_xx.ravel(), self.mesh_yy.ravel()]).T
self.door_eval_sample = np.vstack([self.dd.ravel()]).T
self.door_eval_sample_min_dist = np.ones(len(self.door_eval_sample))
# for coverage plotting
self.xx, self.yy, self.zz, self.gg, self.dd = np.mgrid[
self.x_start:self.x_end:xbins, self.y_start:self.y_end:ybins,
self.z_start:self.z_end:zbins, self.g_start:self.g_end:gbins,
self.d_start:self.d_end:dbins]
self.eval_sample = np.vstack([
self.xx.ravel(),
self.yy.ravel(),
self.zz.ravel(),
self.gg.ravel(),
self.dd.ravel()
]).T
self.eval_sample_min_dist = np.ones(len(self.eval_sample))
self.xy_estimator = KernelDensity(kernel='gaussian',
bandwidth=self.bandwidth)
self.doorangle_estimator = KernelDensity(kernel='gaussian',
bandwidth=self.bandwidth)
self.skewed_estimator = KernelDensity(kernel='gaussian',
bandwidth=self.bandwidth)
self.entropy_shift = np.array([
self.x_start, self.y_start, self.z_start, self.g_start,
self.d_start
])
self.entropy_scale = np.array([
self.x_start - self.x_end, self.y_start - self.y_end,
self.z_start - self.z_end, self.g_start - self.g_end,
self.d_start - self.d_end
])
def init_boxpick_plotting_params(self):
"""
Initialize parameters to evaluate and plot results of yumi door button environment
"""
xbins, ybins, zbins, gbins, dlbins, drbins = 1j, 1j, 1j, 1j, 10j, 5j
self.x_start, self.y_start, self.z_start = self.env.xyz_start
self.x_end, self.y_end, self.z_end = self.env.xyz_end
self.g_start, self.g_end = self.env.gripper_start, self.env.gripper_end
self.dl_start, self.dl_end = self.env.door_l_start, self.env.door_l_end
self.dr_start, self.dr_end = self.env.door_r_start, self.env.door_r_end
# for xy and door angle plotting
self.mesh_xx, self.mesh_yy = np.mgrid[self.x_start:self.x_end:xbins,
self.y_start:self.y_end:ybins]
self.xy_eval_sample = np.vstack(
[self.mesh_xx.ravel(), self.mesh_yy.ravel()]).T
self.mesh_ld, self.mesh_rd = np.mgrid[self.dl_start:self.dl_end:dlbins,
self.dr_start:self.dr_end:drbins]
self.door_eval_sample = np.vstack(
[self.mesh_ld.ravel(), self.mesh_rd.ravel()]).T
self.door_eval_sample_min_dist = np.ones(len(self.door_eval_sample))
# for coverage plotting
self.xx, self.yy, self.zz, self.gg, self.dl, self.dr = np.mgrid[
self.x_start:self.x_end:xbins, self.y_start:self.y_end:ybins,
self.z_start:self.z_end:zbins, self.g_start:self.g_end:gbins,
self.dl_start:self.dl_end:dlbins, self.dr_start:self.dr_end:drbins]
self.eval_sample = np.vstack([
self.xx.ravel(),
self.yy.ravel(),
self.zz.ravel(),
self.gg.ravel(),
self.dl.ravel(),
self.dr.ravel()
]).T
self.eval_sample_min_dist = np.ones(len(self.eval_sample))
self.xy_estimator = KernelDensity(kernel='gaussian',
bandwidth=self.bandwidth)
self.doorangle_estimator = KernelDensity(kernel='gaussian',
bandwidth=self.bandwidth)
self.skewed_estimator = KernelDensity(kernel='gaussian',
bandwidth=self.bandwidth)
self.entropy_shift = np.array([
self.x_start, self.y_start, self.z_start, self.g_start,
self.dl_start, self.dr_start
])
self.entropy_scale = np.array([
self.x_start - self.x_end, self.y_start - self.y_end,
self.z_start - self.z_end, self.g_start - self.g_end,
self.dl_start - self.dl_end, self.dr_start - self.dr_end
])
def plot_maze_metrics(self):
"""
Plot intermediate result for point maze environment
"""
fig, (ax1, ax2, ax3, ax4, ax5) = plt.subplots(5, 1, figsize=(5, 20))
if self.plot_entropy or self.plot_density:
eval_sample_log_density = self.get_log_density(self.eval_sample)
eval_sample_density = np.exp(eval_sample_log_density)
if self.plot_density:
zz_density = np.reshape(eval_sample_density, self.xx.shape)
im = ax1.pcolormesh(self.yy, self.xx, zz_density)
if self.plot_entropy:
entropy = self.compute_entropy(eval_sample_density,
eval_sample_log_density)
self.entropy.append(entropy)
ax3.plot(self.entropy)
# np.save(self.args.save_path + '/entropy.npy', self.entropy)
if self.plot_coverage:
z_coverage = self.get_coverage()
zz_coverage = np.reshape(z_coverage, self.xx.shape)
im = ax2.pcolormesh(self.yy, self.xx, zz_coverage, vmin=0, vmax=1)
# if the use_extrinsic_reward flag is true, it will only plot the curve of task_reward
if self.args.use_extrinsic_reward:
ax4.plot(self.task_reward)
elif self.plot_overall_coverage:
self.coverages.append(z_coverage.mean())
ax4.plot(self.coverages)
# np.save(self.args.save_path + '/coverage.npy', self.coverages)
sample_goal = self.sample_goal(200)
ax5.scatter(sample_goal[:, 0], sample_goal[:, 1], s=10, color='red')
ax5.set_xlim([-12, 4])
ax5.set_ylim([-6, 6])
if self.plot_density or self.plot_coverage or self.plot_overall_coverage or self.plot_entropy:
plt.savefig(self.args.save_path + '/coverage_' + str(self.count) +
'.svg')
plt.close()
def plot_door_metrics(self):
"""
Plot intermediate result for door opening environment
"""
fig, ax = plt.subplots(3, 2, figsize=(10, 15))
if self.plot_density:
xy_eval_sample_norm = (self.xy_eval_sample -
self.obs_mean[:2]) / self.obs_std[:2]
xy_sample_density = np.exp(
self.xy_estimator.score_samples(xy_eval_sample_norm))
xy_density = np.reshape(xy_sample_density, self.mesh_xx.shape)
door_eval_sample_norm = (self.door_eval_sample -
self.obs_mean[-1]) / self.obs_std[-1]
door_sample_density = np.exp(
self.doorangle_estimator.score_samples(door_eval_sample_norm))
im = ax[0][0].pcolormesh(self.mesh_xx, self.mesh_yy, xy_density)
im = ax[0][1].scatter(self.door_eval_sample, door_sample_density)
ax[0][0].set_xlim([self.x_start - 0.05, self.x_end + 0.05])
ax[0][0].set_ylim([self.y_start - 0.05, self.y_end + 0.05])
ax[0][1].set_xlim([self.d_start - 0.05, self.d_end + 0.05])
ax[0][1].set_ylim([-0.05, 1])
if self.plot_coverage:
door_sample_coverage = self.get_door_coverage(
self.door_eval_sample, -1)
ax[2][1].scatter(self.door_eval_sample, door_sample_coverage)
# if the use_extrinsic_reward flag is true, it will only plot the curve of task_reward
if self.args.use_extrinsic_reward:
ax[2][0].plot(self.task_reward)
elif self.plot_overall_coverage:
eval_sample_coverage = self.get_coverage()
self.coverages.append(eval_sample_coverage.mean())
ax[2][0].plot(self.coverages)
if self.plot_entropy:
eval_sample_scaled = (self.eval_sample -
self.entropy_shift) / self.entropy_scale
eval_sample_log_density = self.density_estimator_raw.score_samples(
eval_sample_scaled) #self.get_log_density(eval_sample_norm)
eval_sample_density = np.exp(eval_sample_log_density)
entropy = self.compute_entropy(eval_sample_density,
eval_sample_log_density)
self.entropy.append(entropy)
ax[0][0].plot(self.entropy)
np.save(self.args.save_path + '/entropy', np.array(self.entropy))
sample_goal = self.sample_goal(200)
ax[1][0].scatter(sample_goal[:, 0],
sample_goal[:, 1],
s=10,
color='red')
ax[1][0].set_xlim([self.x_start - 0.05, self.x_end + 0.05])
ax[1][0].set_ylim([self.y_start - 0.05, self.y_end + 0.05])
ax[1][1].scatter(sample_goal[:, -1],
np.ones(len(sample_goal)),
s=1,
color='red')
ax[1][1].set_xlim([self.d_start - 0.05, self.d_end + 0.05])
if self.plot_density or self.plot_coverage or self.plot_overall_coverage or self.plot_entropy:
plt.savefig(self.args.save_path + '/coverage_' + str(self.count) +
'.svg')
plt.close()
def plot_boxpick_metrics(self):
"""
Plot intermediate result for door button environment
"""
fig, ax = plt.subplots(3, 2, figsize=(15, 15))
if self.plot_density:
xy_eval_sample_norm = (self.xy_eval_sample -
self.obs_mean[:2]) / self.obs_std[:2]
xy_sample_density = np.exp(
self.xy_estimator.score_samples(xy_eval_sample_norm))
xy_density = np.reshape(xy_sample_density, self.mesh_xx.shape)
door_eval_sample_norm = (self.door_eval_sample -
self.obs_mean[-2:]) / self.obs_std[-2:]
door_sample_density = np.exp(
self.doorangle_estimator.score_samples(door_eval_sample_norm))
door_density = np.reshape(door_sample_density, self.mesh_ld.shape)
im = ax[0][0].pcolormesh(self.mesh_xx, self.mesh_yy, xy_density)
im = ax[0][1].pcolormesh(self.mesh_ld, self.mesh_rd, door_density)
ax[0][0].set_xlim([self.x_start - 0.05, self.x_end + 0.05])
ax[0][0].set_ylim([self.y_start - 0.05, self.y_end + 0.05])
ax[0][1].set_xlim([self.dl_start, self.dl_end])
ax[0][1].set_ylim([self.dr_start, self.dr_end])
# if the use_extrinsic_reward flag is true, it will only plot the curve of task_reward
if self.args.use_extrinsic_reward:
ax[2][0].plot(self.task_reward)
elif self.plot_overall_coverage:
eval_sample_coverage = self.get_coverage()
self.coverages.append(eval_sample_coverage.mean())
ax[2][0].plot(self.coverages)
if self.plot_coverage:
door_sample_coverage = self.get_door_coverage(
self.door_eval_sample, -2)
door_coverage = np.reshape(door_sample_coverage,
self.mesh_ld.shape)
ax[2][1].pcolormesh(self.mesh_ld,
self.mesh_rd,
door_coverage,
vmin=0,
vmax=1)
if self.plot_entropy:
eval_sample_norm = (self.eval_sample -
self.obs_mean) / self.obs_std
eval_sample_log_density = self.get_log_density(eval_sample_norm)
eval_sample_density = np.exp(eval_sample_log_density)
entropy = self.compute_entropy(eval_sample_density,
eval_sample_log_density)
self.entropy.append(entropy)
np.save(self.args.save_path + '/entropy', np.array(self.entropy))
sample_goal = self.sample_goal(200)
ax[1][0].scatter(sample_goal[:, 0],
sample_goal[:, 1],
s=10,
color='red')
ax[1][0].set_xlim([self.x_start - 0.05, self.x_end + 0.05])
ax[1][0].set_ylim([self.y_start - 0.05, self.y_end + 0.05])
ax[1][1].scatter(sample_goal[:, -2],
sample_goal[:, -1],
s=1,
color='red')
ax[1][1].set_xlim([self.dl_start, self.dl_end])
ax[1][1].set_ylim([self.dr_start, self.dr_end])
if self.plot_density or self.plot_coverage or self.plot_overall_coverage or self.plot_entropy:
plt.savefig(self.args.save_path + '/coverage_' + str(self.count) +
'.svg')
plt.close()
def activate_buffer(self):
"""
Update the history buffer,
update the state density estimation model
update the goal proposing distribution model
"""
start_time = time.time()
if self.obs_hist is None:
self.obs_hist = self.obs_new
# self.obs_next_hist = obs_next
# self.done_hist = dones
else:
self.obs_hist = np.concatenate((self.obs_hist, self.obs_new),
axis=0)
self.fit_model()
fitmodel_time = time.time()
logging.info("fit model time cost: %f" % (fitmodel_time - start_time))
self.train_skew_generator()
fitskew_time = time.time()
logging.info("fit skew-model time cost: %f" %
(fitskew_time - start_time))
# update goal samples in the environment and update the obs mean and std
if self.args.use_auto_scale:
self.env.update_reward_scale(self.obs_mean, self.obs_std)
sampled_goal = self.sample_goal(self.goal_sampling_num)
self.env.set_goals(sampled_goal)
self.env.set_density_estimator(self.density_estimator)
# compute task_reward
if self.args.use_extrinsic_reward:
# dones = self.dones.astype(int)
task_reward = self.env.get_extrinsic_reward(
self.obs_new) # * dones
self.task_reward.append(task_reward.mean())
# plotting
if self.plot_density or self.plot_coverage or self.plot_overall_coverage or self.plot_entropy:
if self.args.env == 'maze':
self.plot_maze_metrics()
elif self.args.env == 'yumi':
self.plot_door_metrics()
elif self.args.env == 'yumi_box_pick' or self.args.env == 'yumi_door_button':
self.plot_boxpick_metrics()
self.obs_new = None
self.dones = None
finish_time = time.time()
logging.info('time cost: %f' % (finish_time - start_time))
np.save(self.args.save_path + '/entropy', np.array(self.entropy))
np.save(self.args.save_path + '/coverage', np.array(self.coverages))
np.save(self.args.save_path + '/task_reward',
np.array(self.task_reward))
logging.info('end of activate buffer')
def train_skew_generator(self):
"""
Update the goal proposing distribution using the Skew-fit algorithm
(https://arxiv.org/abs/1903.03698)
"""
# NOTE: The skewed samples are sampled from density estimator
self.skew_samples, skew_samples_density = self.get_samples_and_density(
self.skew_sample_num)
# self.skew_samples = self.density_estimator.sample(self.skew_sample_num)
# skew_samples_density = np.exp(self.density_estimator.score_samples(self.skew_samples))
skew_unnormalized_weights = skew_samples_density * skew_samples_density**self.skew_alpha
skew_zeta_alpha = np.sum(skew_unnormalized_weights)
self.skew_weights = skew_unnormalized_weights / skew_zeta_alpha
self.skewed_estimator.fit(self.skew_samples,
sample_weight=self.skew_weights)
def sample_goal(self, goal_num):
"""
Sample goal states from the goal proposing distribution
"""
sampled_data = self.skewed_estimator.sample(goal_num)
sampled_data = sampled_data * self.obs_std + self.obs_mean
return sampled_data #sampled_data[goal_index]
def get_samples_and_density(self, sample_num):
raise NotImplementedError()
def fit_model(self):
raise NotImplementedError()
def get_pvisited(self, obs_test):
raise NotImplementedError()
def get_log_density(self, obs_test):
raise NotImplementedError()
def get_coverage(self):
"""
Compute the current coverage of the states used for evaluation
"""
p_coverage = np.zeros(len(self.eval_sample))
for i in range(len(self.eval_sample)):
obs = self.eval_sample[i]
obs_diff = self.obs_new - obs
diff_norm = LA.norm(obs_diff, axis=1)
min_dist = diff_norm.min()
current_min_dist = self.eval_sample_min_dist[i]
new_min_dist = np.minimum(current_min_dist, min_dist)
self.eval_sample_min_dist[i] = new_min_dist
pv = np.exp(-new_min_dist * new_min_dist * self.beta)
p_coverage[i] = 1 - pv
return p_coverage
def get_door_coverage(self, door_eval_sample, index):
"""
Compute the current coverage of the door states used for evaluation
"""
p_coverage = np.zeros(len(door_eval_sample))
for i in range(len(door_eval_sample)):
obs = door_eval_sample[i]
obs_diff = self.obs_new[:, index:] - obs
diff_norm = LA.norm(obs_diff, axis=1)
min_dist = diff_norm.min()
current_min_dist = self.door_eval_sample_min_dist[i]
new_min_dist = np.minimum(current_min_dist, min_dist)
self.door_eval_sample_min_dist[i] = new_min_dist
pv = np.exp(-new_min_dist * new_min_dist * self.beta)
p_coverage[i] = pv
return p_coverage
def compute_entropy(self, density, log_density):
"""
Compute the entropy
"""
d_mul_logd = density * log_density
entropy = -np.sum(d_mul_logd)
return entropy
def get_preach(self, obs_from):
raise NotImplementedError()
def get_preal(self, obs_test):
raise NotImplementedError()
def compute_reward(self, obs_test, use_sampling=False):
raise NotImplementedError()
def update_history(self, obs, dones):
"""
Save the new states in the self.obs_new buffer.
the self.obs_new buffer will be merged to the self.obs buffer
in activate_buffer() function.
"""
if self.args.use_index:
obs = obs[:, :-1]
if self.obs_mean is None:
self.obs_mean = np.zeros_like(obs)[0]
self.obs_std = np.ones_like(obs)[0]
self.obs_rms = RunningMeanStd(shape=obs.shape)
if self.obs_new is None:
self.obs_new = obs
# self.obs_next_hist = obs_next
self.dones = dones
else:
self.obs_new = np.concatenate((self.obs_new, obs), axis=0)
# self.obs_next_hist = np.concatenate((self.obs_next_hist, obs_next), axis=0)
self.dones = np.concatenate((self.dones, dones), axis=0)
self.obs_rms.update(obs)
class GAE():
def __init__(self,
img_shape=(48, 96, 96, 1),
encoded_dim=8,
optimizer=SGD(0.001, momentum=.9),
optimizer_discriminator=SGD(0.0001, momentum=.9),
optimizer_autoencoder=Adam(0.0001)):
self.encoded_dim = encoded_dim
self.optimizer = optimizer
self.optimizer_discriminator = optimizer_discriminator
self.optimizer_autoencoder = optimizer_autoencoder
self.img_shape = img_shape
self.initializer = RandomNormal(mean=0., stddev=1.)
self._initAndCompileFullModel(img_shape, encoded_dim)
def _genEncoderModel(self, img_shape, encoded_dim):
""" Build Encoder Model Based on Paper Configuration
Args:
img_shape (tuple) : shape of input image
encoded_dim (int) : number of latent variables
Return:
A sequential keras model
"""
encoder = Sequential()
encoder.add(
keras.layers.Conv3D(input_shape=img_shape,
filters=16,
kernel_size=3,
strides=(1, ) * 3,
padding="SAME",
activation='relu'))
encoder.add(keras.layers.Dropout(0.2))
encoder.add(
keras.layers.Conv3D(filters=16,
kernel_size=3,
strides=(2, ) * 3,
padding="SAME",
activation='relu'))
#encoder.add(keras.layers.MaxPool3D(pool_size=(2,)*3, padding="SAME"))
encoder.add(
keras.layers.Conv3D(filters=32,
kernel_size=3,
strides=(1, ) * 3,
padding="SAME",
activation='relu'))
encoder.add(keras.layers.Dropout(0.2))
encoder.add(
keras.layers.Conv3D(filters=32,
kernel_size=3,
strides=(2, ) * 3,
padding="SAME",
activation='relu'))
#encoder.add(keras.layers.MaxPool3D(pool_size=(2,)*3, padding="SAME"))
encoder.add(
keras.layers.Conv3D(filters=64,
kernel_size=3,
strides=(1, ) * 3,
padding="SAME",
activation='relu'))
encoder.add(keras.layers.Dropout(0.2))
encoder.add(
keras.layers.Conv3D(filters=64,
kernel_size=3,
strides=(2, ) * 3,
padding="SAME",
activation='relu'))
#encoder.add(keras.layers.MaxPool3D(pool_size=(2,)*3, padding="SAME"))
encoder.add(keras.layers.GlobalAvgPool3D())
encoder.add(keras.layers.Flatten())
encoder.add(Dense(encoded_dim))
encoder.summary()
return encoder
def _getDecoderModel(self, encoded_dim, img_shape):
""" Build Decoder Model Based on Paper Configuration
Args:
encoded_dim (int) : number of latent variables
img_shape (tuple) : shape of target images
Return:
A sequential keras model
"""
decoder = Sequential()
decoder.add(Dense(128, activation='relu', input_dim=encoded_dim))
decoder.add(Reshape((128, 1)))
decoder.add(
keras.layers.Conv1D(filters=108,
kernel_size=3,
strides=1,
padding="SAME",
activation='relu'))
decoder.add(Reshape([3, 6, 6, 128]))
decoder.add(
Conv3DTranspose(filters=64,
kernel_size=3,
strides=(2, ) * 3,
padding="SAME",
activation='relu'))
decoder.add(
Conv3DTranspose(filters=32,
kernel_size=3,
strides=(2, ) * 3,
padding="SAME",
activation='relu'))
decoder.add(
Conv3DTranspose(filters=16,
kernel_size=3,
strides=(2, ) * 3,
padding="SAME",
activation='relu'))
decoder.add(
Conv3DTranspose(filters=1,
kernel_size=3,
strides=(2, ) * 3,
padding="SAME",
activation='relu'))
#decoder.add(Dense(1000, activation='relu'))
#decoder.add(Dense(np.prod(img_shape), activation='sigmoid'))
decoder.summary()
return decoder
def _getDescriminator(self, img_shape):
""" Build Descriminator Model Based on Paper Configuration
Args:
encoded_dim (int) : number of latent variables
Return:
A sequential keras model
"""
discriminator = Sequential()
discriminator.add(
keras.layers.Conv3D(input_shape=img_shape,
filters=16,
kernel_size=3,
strides=(1, ) * 3,
padding="SAME",
activation='relu'))
discriminator.add(
keras.layers.MaxPool3D(pool_size=(2, ) * 3, padding="SAME"))
discriminator.add(
keras.layers.Conv3D(input_shape=img_shape,
filters=32,
kernel_size=3,
strides=(1, ) * 3,
padding="SAME",
activation='relu'))
discriminator.add(
keras.layers.MaxPool3D(pool_size=(2, ) * 3, padding="SAME"))
discriminator.add(
keras.layers.Conv3D(input_shape=img_shape,
filters=64,
kernel_size=3,
strides=(1, ) * 3,
padding="SAME",
activation='relu'))
discriminator.add(
keras.layers.MaxPool3D(pool_size=(2, ) * 3, padding="SAME"))
discriminator.add(
keras.layers.Conv3D(input_shape=img_shape,
filters=128,
kernel_size=3,
strides=(1, ) * 3,
padding="SAME",
activation='relu'))
discriminator.add(
keras.layers.MaxPool3D(pool_size=(2, ) * 3, padding="SAME"))
discriminator.add(keras.layers.GlobalAvgPool3D())
discriminator.add(keras.layers.Flatten())
discriminator.add(Dense(32, activation="relu"))
discriminator.add(Dense(1, activation="sigmoid"))
discriminator.summary()
return discriminator
def _initAndCompileFullModel(self, img_shape, encoded_dim):
self.encoder = self._genEncoderModel(img_shape, encoded_dim)
self.decoder = self._getDecoderModel(encoded_dim, img_shape)
self.discriminator = self._getDescriminator(img_shape)
img = Input(shape=img_shape)
encoded_repr = self.encoder(img)
gen_img = self.decoder(encoded_repr)
self.autoencoder = Model(img, gen_img)
self.autoencoder.compile(optimizer=self.optimizer_autoencoder,
loss='mse')
self.discriminator.compile(optimizer=self.optimizer_discriminator,
loss='binary_crossentropy',
metrics=['accuracy'])
for layer in self.discriminator.layers:
layer.trainable = False
is_real = self.discriminator(gen_img)
self.autoencoder_discriminator = Model(img, is_real)
self.autoencoder_discriminator.compile(optimizer=self.optimizer,
loss='binary_crossentropy',
metrics=['accuracy'])
def imagegrid(self, epochnumber):
fig = plt.figure(figsize=[20, 20])
for i in range(-5, 5):
for j in range(-5, 5):
topred = np.array((i * 0.5, j * 0.5))
topred = topred.reshape((1, 2))
img = self.decoder.predict(topred)
img = img.reshape(self.img_shape)
ax = fig.add_subplot(10, 10, (i + 5) * 10 + j + 5 + 1)
ax.set_axis_off()
ax.imshow(img, cmap="gray")
fig.savefig(str(epochnumber) + ".png")
plt.show()
plt.close(fig)
def train(self, x_train, batch_size=4, epochs=5):
self.autoencoder.fit(x_train, x_train, epochs=1)
for epoch in range(epochs):
#---------------Train Discriminator -------------
# Select a random half batch of images
idx = np.random.randint(0, x_train.shape[0], batch_size)
imgs_real = x_train[idx]
idx = np.random.randint(0, x_train.shape[0], batch_size)
imgs_real2 = x_train[idx]
# Generate a half batch of new images
#gen_imgs = self.decoder.predict(latent_fake)
imgs_fake = self.autoencoder.predict(imgs_real2)
valid = np.ones((batch_size, 1))
fake = np.zeros((batch_size, 1))
# Train the discriminator
d_loss_real = self.discriminator.train_on_batch(imgs_real, valid)
d_loss_fake = self.discriminator.train_on_batch(imgs_fake, fake)
d_loss = 0.5 * np.add(d_loss_real, d_loss_fake)
#d_loss = (0,0)
idx = np.random.randint(0, x_train.shape[0], batch_size)
imgs_real = x_train[idx]
# Generator wants the discriminator to label the generated representations as valid
valid_y = np.ones((batch_size, 1))
# Train generator
g_logg_similarity = self.autoencoder_discriminator.train_on_batch(
imgs_real, valid_y)
# Plot the progress
print(
"%d [D loss = %.2f, accuracy: %.2f] [G loss = %f, accuracy: %.2f]"
% (epoch, d_loss[0], d_loss[1], g_logg_similarity[0],
g_logg_similarity[1]))
# if(epoch % save_interval == 0):
# self.imagegrid(epoch)
codes = self.encoder.predict(x_train)
# params = {'bandwidth': [3.16]}#np.logspace(0, 2, 5)}
# grid = GridSearchCV(KernelDensity(), params, n_jobs=4)
# grid.fit(codes)
# print grid.best_params_
# self.kde = grid.best_estimator_
self.kde = KernelDensity(kernel='gaussian', bandwidth=3.16).fit(codes)
def generate(self, n=10000):
codes = self.kde.sample(n)
images = self.decoder.predict(codes)
return images
def autoEncode(self, image):
codes = self.encoder(image)
gen_image = self.decoder(codes)
return gen_image
def generateAndPlot(self, x_train, n=10, fileName="generated.png"):
fig = plt.figure(figsize=[20, 20])
images = self.generate(n * n)
index = 1
for image in images:
image = image.reshape(self.img_shape)
ax = fig.add_subplot(n, n + 1, index)
index = index + 1
ax.set_axis_off()
ax.imshow(image, cmap="gray")
if ((index) % (n + 1) == 0):
nearest = helpers.findNearest(x_train, image)
ax = fig.add_subplot(n, n + 1, index)
index = index + 1
ax.imshow(nearest, cmap="gray")
fig.savefig(fileName)
plt.show()
def meanLogLikelihood(self, x_test):
KernelDensity(kernel='gaussian', bandwidth=0.2).fit(codes)
class Texture:
def __init__(self):
self.rocks = []
self.data = []
'''
add a rock to the texture if the rock doesn't any rock in self.rocks
returns bool
'''
def add(self, rock):
if not self.intersect(rock):
self.rocks.append(rock)
self.data.append(rock.data())
return True
return False
'''
Create the tree for the kernelDensity
'''
def learn(self):
self.kde = KernelDensity(kernel='gaussian',
bandwidth=0.02).fit(self.data)
'''
samples rocks from kde one by one making sure there are no intersection
returns a Texture
'''
def sample(self, n_rocks=None):
length = n_rocks
if length == None:
length = len(self.data)
mtexture = Texture()
i = 0
while i < length:
new_rock = rock.dataToRock(
self.kde.sample(1, random_state=None)[0])
if mtexture.add(new_rock):
i = i + 1
return mtexture
'''
compute the distance between two points
return float
'''
def __distance(self, center1, center2):
center1 = np.array(center1)
center2 = np.array(center2)
sub = center1 - center2
sub = sub**2
return math.sqrt(np.sum(sub))
'''
Test if two rocks intersect.
returns bool
'''
def __intersect(self, rock1, rock2):
return self.__distance(rock1.center,
rock2.center) < rock1.radius + rock2.radius
'''
Test if a rock intersects another rock in self.rocks
'''
def intersect(self, rock):
for r in self.rocks:
if self.__intersect(rock, r):
return True
return False
class skde(object):
r"""
Custom wrapper around `sklearn.neighbors.kde.KernelDensity` to conform
to our prefered syntax calling (following scipy conventions)
"""
def __init__(self, data, mirror=False, **kwds):
self.mirror = mirror
if kwds is None:
if self.mirror:
self.kde_object = KernelDensity(kernel='gaussian').fit( np.vstack([-data, data]) )
else:
self.kde_object = KernelDensity(kernel='gaussian').fit(data)
else:
if self.mirror:
self.kde_object = KernelDensity(**kwds).fit( np.vstack([-data, data]) )
else:
self.kde_object = KernelDensity(**kwds).fit(data)
try:
self.d = data.shape[1]
except IndexError:
self.d = 1
self.n = data.shape[0]
def rvs(self, size=1):
r"""
Generates random variables from a kde object. Wrapper function for
`sklearn.neighbors.kde.KernelDensity.sample`.
:param int size: number of random samples to generate
:param tuple size: number of samples is taken to be the first argument
"""
if type(size) is tuple:
size=size[0]
if self.mirror: # have to generate twice as many samples
num_samps = 0
samps = []
while num_samps < size:
samp_proposal = self.kde_object.sample()
if samp_proposal > 0:
samps.append(samp_proposal)
num_samps += 1
samps = np.array(samps).reshape(size,self.d)
else:
samps = self.kde_object.sample(size)
return samps
#TODO write a test that makes sure this returns the correct shape
def pdf(self, eval_points):
r"""
Generates random variables from a kde object. Wrapper function for
`sklearn.neighbors.kde.KernelDensity.score_samples`.
:param eval_points: points on which to evaluate the density.
:type eval_points: :class:`numpy.ndarray` of shape (num, dim)
"""
#: TODO write a test that makes sure this returns the correct shape
num_samples = eval_points.shape[0]
if self.mirror:
p = 2*np.exp( self.kde_object.score_samples( eval_points ) ).reshape(num_samples)
else:
try:
p = np.exp( self.kde_object.score_samples( eval_points ) )
except ValueError:
p = np.exp( self.kde_object.score_samples( eval_points.reshape(-1,1) ) )
return p
class GAE():
def __init__(self, img_shape=(28, 28), encoded_dim=2):
self.encoded_dim = encoded_dim
self.optimizer = Adam(0.001)
self.optimizer_discriminator = Adam(0.00001)
self._initAndCompileFullModel(img_shape, encoded_dim)
self.img_shape = img_shape
def _genEncoderModel(self, img_shape, encoded_dim):
""" Build Encoder Model Based on Paper Configuration
Args:
img_shape (tuple) : shape of input image
encoded_dim (int) : number of latent variables
Return:
A sequential keras model
"""
encoder = Sequential()
encoder.add(Flatten(input_shape=img_shape))
encoder.add(Dense(1000, activation='relu'))
encoder.add(Dense(1000, activation='relu'))
encoder.add(Dense(encoded_dim))
encoder.summary()
return encoder
def _getDecoderModel(self, encoded_dim, img_shape):
""" Build Decoder Model Based on Paper Configuration
Args:
encoded_dim (int) : number of latent variables
img_shape (tuple) : shape of target images
Return:
A sequential keras model
"""
decoder = Sequential()
decoder.add(Dense(1000, activation='relu', input_dim=encoded_dim))
decoder.add(Dense(1000, activation='relu'))
decoder.add(Dense(np.prod(img_shape), activation='sigmoid'))
decoder.add(Reshape(img_shape))
decoder.summary()
return decoder
def _getDescriminator(self, img_shape):
""" Build Descriminator Model Based on Paper Configuration
Args:
encoded_dim (int) : number of latent variables
Return:
A sequential keras model
"""
discriminator = Sequential()
discriminator.add(Flatten(input_shape=img_shape))
discriminator.add(
Dense(1000,
activation='relu',
kernel_initializer=initializer,
bias_initializer=initializer))
discriminator.add(
Dense(1000,
activation='relu',
kernel_initializer=initializer,
bias_initializer=initializer))
discriminator.add(
Dense(1,
activation='sigmoid',
kernel_initializer=initializer,
bias_initializer=initializer))
discriminator.summary()
return discriminator
def _initAndCompileFullModel(self, img_shape, encoded_dim):
self.encoder = self._genEncoderModel(img_shape, encoded_dim)
self.decoder = self._getDecoderModel(encoded_dim, img_shape)
self.discriminator = self._getDescriminator(img_shape)
img = Input(shape=img_shape)
encoded_repr = self.encoder(img)
gen_img = self.decoder(encoded_repr)
self.autoencoder = Model(img, gen_img)
self.autoencoder.compile(optimizer=self.optimizer, loss='mse')
self.discriminator.compile(optimizer=self.optimizer,
loss='binary_crossentropy',
metrics=['accuracy'])
for layer in self.discriminator.layers:
layer.trainable = False
latent = Input(shape=(encoded_dim, ))
gen_image_from_latent = self.decoder(latent)
is_real = self.discriminator(gen_image_from_latent)
self.decoder_discriminator = Model(latent, is_real)
self.decoder_discriminator.compile(
optimizer=self.optimizer_discriminator,
loss='binary_crossentropy',
metrics=['accuracy'])
def imagegrid(self, epochnumber):
fig = plt.figure(figsize=[20, 20])
for i in range(-5, 5):
for j in range(-5, 5):
topred = np.array((i * 0.5, j * 0.5))
topred = topred.reshape((1, 2))
img = self.decoder.predict(topred)
img = img.reshape(self.img_shape)
ax = fig.add_subplot(10, 10, (i + 5) * 10 + j + 5 + 1)
ax.set_axis_off()
ax.imshow(img, cmap="gray")
fig.savefig(str(epochnumber) + ".png")
plt.show()
plt.close(fig)
def train(self, x_train, batch_size=32, epochs=5):
fileNames = glob.glob('models/GAE/weights_mnist_autoencoder.*')
fileNames.sort()
if (len(fileNames) != 0):
savedEpoch = int(fileNames[-1].split('.')[1])
self.autoencoder.load_weights(fileNames[-1])
else:
savedEpoch = -1
if (savedEpoch < epochs - 1):
self.autoencoder.fit(
x_train,
x_train,
batch_size=batch_size,
epochs=epochs,
callbacks=[
keras.callbacks.ModelCheckpoint(
'models/GAE/weights_autoencoder.{epoch:02d}.hdf5',
verbose=0,
save_best_only=False,
save_weights_only=False,
mode='auto',
period=1)
])
print "Training KDE"
codes = self.encoder.predict(x_train)
# params = {'bandwidth': [3.16]}#np.logspace(0, 2, 5)}
# grid = GridSearchCV(KernelDensity(), params, n_jobs=4)
# grid.fit(codes)
# print grid.best_params_
# self.kde = grid.best_estimator_
self.kde = KernelDensity(kernel='gaussian', bandwidth=3.16).fit(codes)
print "Initial Training of discriminator"
fileNames = glob.glob('models/GAE/weights_mnist_discriminator.*')
fileNames.sort()
if (len(fileNames) != 0):
savedEpoch = int(fileNames[-1].split('.')[1])
self.discriminator.load_weights(fileNames[-1])
else:
savedEpoch = -1
if (savedEpoch < epochs - 1):
imgs_fake = self.generate(n=len(x_train))
#gen_imgs = self.decoder.predict(latent_fake)
valid = np.ones((len(x_train), 1))
fake = np.zeros((len(x_train), 1))
labels = np.vstack([valid, fake])
images = np.vstack([x_train, imgs_fake])
# Train the discriminator
self.discriminator.fit(
images,
labels,
epochs=epochs,
batch_size=batch_size,
shuffle=True,
callbacks=[
keras.callbacks.ModelCheckpoint(
'models/GAE/weights_discriminator.{epoch:02d}.hdf5',
verbose=0,
save_best_only=False,
save_weights_only=False,
mode='auto',
period=1)
])
print "Training GAN"
self.generateAndPlot(x_train, fileName="before_gan.png")
self.trainGAN(x_train,
epochs=len(x_train) / batch_size,
batch_size=batch_size)
self.generateAndPlot(x_train, fileName="after_gan.png")
def trainGAN(self, x_train, epochs=1000, batch_size=32):
half_batch = batch_size / 2
for epoch in range(epochs):
#---------------Train Discriminator -------------
# Select a random half batch of images
idx = np.random.randint(0, x_train.shape[0], half_batch)
imgs_real = x_train[idx]
# Generate a half batch of new images
imgs_fake = self.generate(n=half_batch)
#gen_imgs = self.decoder.predict(latent_fake)
valid = np.ones((half_batch, 1))
fake = np.zeros((half_batch, 1))
# Train the discriminator
d_loss_real = self.discriminator.train_on_batch(imgs_real, valid)
d_loss_fake = self.discriminator.train_on_batch(imgs_fake, fake)
d_loss = 0.5 * np.add(d_loss_real, d_loss_fake)
#d_loss = (0,0)
codes = self.kde.sample(batch_size)
# Generator wants the discriminator to label the generated representations as valid
valid_y = np.ones((batch_size, 1))
# Train generator
g_logg_similarity = self.decoder_discriminator.train_on_batch(
codes, valid_y)
# Plot the progress
print("%d [D accuracy: %.2f] [G accuracy: %.2f]" %
(epoch, d_loss[1], g_logg_similarity[1]))
# if(epoch % save_interval == 0):
# self.imagegrid(epoch)
def generate(self, n=10000):
codes = self.kde.sample(n)
images = self.decoder.predict(codes)
return images
def generateAndPlot(self, x_train, n=10, fileName="generated.png"):
fig = plt.figure(figsize=[20, 20])
images = self.generate(n * n)
index = 1
for image in images:
image = image.reshape(self.img_shape)
ax = fig.add_subplot(n, n + 1, index)
index = index + 1
ax.set_axis_off()
ax.imshow(image, cmap="gray")
if ((index) % (n + 1) == 0):
nearest = helpers.findNearest(x_train, image)
ax = fig.add_subplot(n, n + 1, index)
index = index + 1
ax.imshow(nearest, cmap="gray")
fig.savefig(fileName)
plt.show()
def meanLogLikelihood(self, x_test):
KernelDensity(kernel='gaussian', bandwidth=0.2).fit(codes)
class KDEestimator:
"""
An interface for generating random numbers according
to a given Kernel Density Estimation (KDE) parametrization based on the
data.
"""
def __init__(self, bandwidth=1.0):
from sklearn.neighbors.kde import KernelDensity
self.bandwidth = bandwidth
self.model = KernelDensity(bandwidth=self.bandwidth)
def _botev_fixed_point(self, t, M, I, a2):
# Find the largest float available for this numpy
if hasattr(np, 'float128'):
large_float = np.float128
elif hasattr(np, 'float96'):
large_float = np.float96
else:
large_float = np.float64
l = 7
I = large_float(I)
M = large_float(M)
a2 = large_float(a2)
f = 2 * np.pi**(2 * l) * np.sum(I**l * a2 * np.exp(-I * np.pi**2 * t))
for s in range(l, 1, -1):
K0 = np.prod(np.arange(1, 2 * s, 2)) / np.sqrt(2 * np.pi)
const = (1 + (1 / 2)**(s + 1 / 2)) / 3
time = (2 * const * K0 / M / f)**(2 / (3 + 2 * s))
f = 2 * np.pi ** (2 * s) * \
np.sum(I ** s * a2 * np.exp(-I * np.pi ** 2 * time))
return t - (2 * M * np.sqrt(np.pi) * f)**(-2 / 5)
def finite(self, val):
""" Checks if a value is finite or not """
return val is not None and np.isfinite(val)
def botev_bandwidth(self, data):
""" Implementation of the KDE bandwidth selection method outline in:
Z. I. Botev, J. F. Grotowski, and D. P. Kroese. *Kernel density estimation via diffusion.* The Annals of Statistics, 38(5):2916-2957, 2010.
Based on the implementation of Daniel B. Smith, PhD. The object is a callable returning the bandwidth for a 1D kernel.
Forked from the package `PyQT_fit <https://code.google.com/archive/p/pyqt-fit/>`_.
:param data: 1D array containing the data to model with a 1D KDE.
:type data: numpy.ndarray
:returns: Optimal bandwidth according to the data.
"""
from scipy import fftpack, optimize
# def __init__(self, N=None, **kword):
# if 'lower' in kword or 'upper' in kword:
# print("Warning, using 'lower' and 'upper' for botev bandwidth is "
# "deprecated. Argument is ignored")
# self.N = N
#
# def __call__(self, data):#, model):
# """
# Returns the optimal bandwidth based on the data
# """
N = 2**10 #if self.N is None else int(2 ** np.ceil(np.log2(self.N)))
# lower = getattr(model, 'lower', None)
# upper = getattr(model, 'upper', None)
# if not finite(lower) or not finite(upper):
minimum = np.min(data)
maximum = np.max(data)
span = maximum - minimum
lower = minimum - span / 10 #if not finite(lower) else lower
upper = maximum + span / 10 #if not finite(upper) else upper
# Range of the data
span = upper - lower
# Histogram of the data to get a crude approximation of the density
# weights = model.weights
# if not weights.shape:
weights = None
M = len(data)
DataHist, bins = np.histogram(data,
bins=N,
range=(lower, upper),
weights=weights)
DataHist = DataHist / M
DCTData = fftpack.dct(DataHist, norm=None)
I = np.arange(1, N, dtype=int)**2
SqDCTData = (DCTData[1:] / 2)**2
guess = 0.1
try:
t_star = optimize.brentq(self._botev_fixed_point,
0,
guess,
args=(M, I, SqDCTData))
except ValueError:
t_star = .28 * N**(-.4)
return np.sqrt(t_star) * span
def fit(self, x):
self.bandwidth = self.botev_bandwidth(x.flatten())
self.model.set_params(**{'bandwidth': self.bandwidth})
self.model.fit(x.reshape(-1, 1))
def sample(self, dimension=1.0):
return self.model.sample(dimension)
def pdf(self, x):
return self.model.score_samples(x)
class GAE:
def __init__(self, img_shape=(28, 28), encoded_dim=2):
self.img_shape = img_shape
self.encoded_dim = encoded_dim
self.optimizer = Adam(0.001)
self.optimizer_discriminator = Adam(0.00001)
self.discriminator = self.get_discriminator_model(img_shape)
self.decoder = self.get_decoder_model(encoded_dim, img_shape)
self.encoder = self.get_encoder_model(img_shape, encoded_dim)
# Initialize Autoencoder
img = Input(shape=self.img_shape)
encoded_repr = self.encoder(img)
gen_img = self.decoder(encoded_repr)
self.autoencoder = Model(img, gen_img)
# Initialize Discriminator
latent = Input(shape=(encoded_dim,))
gen_image_from_latent = self.decoder(latent)
is_real = self.discriminator(gen_image_from_latent)
self.decoder_discriminator = Model(latent, is_real)
# Finally compile models
self.initialize_full_model(encoded_dim)
def initialize_full_model(self, encoded_dim):
self.autoencoder.compile(optimizer=self.optimizer, loss='mse')
self.discriminator.compile(optimizer=self.optimizer,
loss='binary_crossentropy',
metrics=['accuracy'])
# Default start discriminator is not trainable
for layer in self.discriminator.layers:
layer.trainable = False
self.decoder_discriminator.compile(optimizer=self.optimizer_discriminator,
loss='binary_crossentropy',
metrics=['accuracy'])
@staticmethod
def get_encoder_model(img_shape, encoded_dim):
encoder = Sequential()
encoder.add(Flatten(input_shape=img_shape))
encoder.add(Dense(1000, activation='relu'))
encoder.add(Dense(1000, activation='relu'))
encoder.add(Dense(encoded_dim))
encoder.summary()
return encoder
@staticmethod
def get_decoder_model(encoded_dim, img_shape):
decoder = Sequential()
decoder.add(Dense(1000, activation='relu', input_dim=encoded_dim))
decoder.add(Dense(1000, activation='relu'))
decoder.add(Dense(np.prod(img_shape), activation='sigmoid'))
decoder.add(Reshape(img_shape))
decoder.summary()
return decoder
@staticmethod
def get_discriminator_model(img_shape):
discriminator = Sequential()
discriminator.add(Flatten(input_shape=img_shape))
discriminator.add(Dense(1000, activation='relu',
kernel_initializer=initializer,
bias_initializer=initializer))
discriminator.add(Dense(1000, activation='relu', kernel_initializer=initializer,
bias_initializer=initializer))
discriminator.add(Dense(1, activation='sigmoid', kernel_initializer=initializer,
bias_initializer=initializer))
discriminator.summary()
return discriminator
def imagegrid(self, epochnumber):
fig = plt.figure(figsize=[20, 20])
for i in range(-5, 5):
for j in range(-5, 5):
topred = np.array((i * 0.5, j * 0.5))
topred = topred.reshape((1, 2))
img = self.decoder.predict(topred)
img = img.reshape(self.img_shape)
ax = fig.add_subplot(10, 10, (i + 5) * 10 + j + 5 + 1)
ax.set_axis_off()
ax.imshow(img)
fig.savefig(str(epochnumber) + ".png")
plt.show()
plt.close(fig)
def train(self, x_train_input, batch_size=128, epochs=5):
fileNames = glob.glob('models/weights_mnist_autoencoder.*')
fileNames.sort()
if len(fileNames) != 0:
saved_epoch = int(fileNames[-1].split('.')[1])
self.autoencoder.load_weights(fileNames[-1])
else:
saved_epoch = -1
if saved_epoch < epochs - 1:
self.autoencoder.fit(x_train_input, x_train_input, batch_size=batch_size,
epochs=epochs,
callbacks=[
keras.callbacks.ModelCheckpoint('models/weights_autoencoder.{epoch:02d}.hdf5',
verbose=0,
save_best_only=False,
save_weights_only=False,
mode='auto',
period=1),
keras.callbacks.EarlyStopping(monitor='loss', patience=3, min_delta=1e-4,
restore_best_weights=True)])
print("Training KDE")
codes = self.encoder.predict(x_train_input)
self.kde = KernelDensity(kernel='gaussian', bandwidth=3.16).fit(codes)
print("Initial Training of discriminator")
fileNames = glob.glob('models/weights_mnist_discriminator.*')
fileNames.sort()
if len(fileNames) != 0:
saved_epoch = int(fileNames[-1].split('.')[1])
self.discriminator.load_weights(fileNames[-1])
else:
saved_epoch = -1
train_count = len(x_train_input)
if saved_epoch < epochs - 1:
# Combine real and fake images for discriminator training
imgs_fake = self.generate(n=train_count)
valid = np.ones((train_count, 1)) # result for training images
fake = np.zeros((train_count, 1)) # result for generated fakes
labels = np.vstack([valid, fake]) # combine together
images = np.vstack([x_train_input, imgs_fake])
# Train the discriminator
self.discriminator.fit(images, labels, epochs=epochs, batch_size=batch_size, shuffle=True,
callbacks=[
keras.callbacks.ModelCheckpoint(
'models/weights_discriminator.{epoch:02d}.hdf5',
verbose=0,
save_best_only=False,
save_weights_only=False,
mode='auto',
period=1),
keras.callbacks.EarlyStopping(monitor='loss', patience=3, min_delta=1e-4,
restore_best_weights=True)])
print("Training GAN")
self.generateAndPlot(x_train_input, fileName="before_gan.png")
self.trainGAN(x_train_input, epochs=int(train_count / batch_size), batch_size=batch_size)
self.generateAndPlot(x_train_input, fileName="after_gan.png")
def trainGAN(self, x_train_input, epochs=1000, batch_size=128):
half_batch = int(batch_size / 2)
for epoch in range(epochs):
# ---------------Train Discriminator -------------
# Select a random half batch of images
idx = np.random.randint(0, x_train_input.shape[0], half_batch)
imgs_real = x_train_input[idx]
# Generate a half batch of new images
imgs_fake = self.generate(n=half_batch)
valid = np.ones((half_batch, 1))
fake = np.zeros((half_batch, 1))
# Train the discriminator
d_loss_real = self.discriminator.train_on_batch(imgs_real, valid)
d_loss_fake = self.discriminator.train_on_batch(imgs_fake, fake)
d_loss = 0.5 * np.add(d_loss_real, d_loss_fake)
codes = self.kde.sample(batch_size)
# Generator wants the discriminator to label the generated representations as valid
valid_y = np.ones((batch_size, 1))
# Train generator
g_logg_similarity = self.decoder_discriminator.train_on_batch(codes, valid_y)
# Plot the progress
if epoch % 50 == 0:
print("epoch %d [D accuracy: %.2f] [G accuracy: %.2f]" % (epoch, d_loss[1], g_logg_similarity[1]))
def generate(self, n=10000):
codes = self.kde.sample(n)
images = self.decoder.predict(codes)
return images
def generateAndPlot(self, x_train_input, n=10, fileName="generated.png"):
fig = plt.figure(figsize=[20, 20])
images = self.generate(n * n)
index = 1
for image in images:
image = image.reshape(self.img_shape)
ax = fig.add_subplot(n, n + 1, index)
index = index + 1
ax.set_axis_off()
ax.imshow(image)
if index % (n + 1) == 0:
nearest = findNearest(x_train_input, image)
ax = fig.add_subplot(n, n + 1, index)
index = index + 1
ax.imshow(nearest)
fig.savefig(fileName)
plt.show()
@staticmethod
def mean_log_likelihood(x_test_input):
KernelDensity(kernel='gaussian', bandwidth=0.2).fit(x_test_input)
counts = np.array(counts).reshape(-1, 1)
data['day'] = [s[:2] for s in data.Date]
day = np.array(data['day']).reshape(-1, 1)
trans = np.array(data.Amount).reshape(-1, 1)
X1 = np.linspace(0, 120, 1000)[:, np.newaxis]
X2 = np.linspace(0, 32, 1000)[:, np.newaxis]
X3 = np.linspace(min(trans), max(trans)+1, 1000)[:, np.newaxis]
for kernel in ['gaussian', 'tophat']:
kde1 = KernelDensity(kernel=kernel, bandwidth=5).fit(counts)
kde2 = KernelDensity(kernel=kernel, bandwidth=5).fit(day)
kde3 = KernelDensity(kernel=kernel, bandwidth=5).fit(trans)
log_dens1 = kde1.score_samples(X1)
log_dens2 = kde2.score_samples(X2)
log_dens3 = kde3.score_samples(X3)
samples = int(kde1.sample(1)[0][0])
print('There are', samples, 'transactions', '\n')
num_days = kde2.sample(samples)
for m in range(len(num_days)):
num_days[m] = int(round(num_days[m][0]))
while num_days[m] <= 0 or num_days[m] > 31:
num_days[m] = int(round(kde2.sample(1)[0][0]))
print('The days are:')
print(num_days, '\n')
num_trans = kde3.sample(samples)
print('The transactions are:')
print(num_trans, '\n')
#Plotting density of number of transactions in a month
plt.plot(X1[:, 0], np.exp(log_dens1), '-',
label="kernel = '{0}'".format(kernel))
for i, label, color, bw in zip([64, 16, 4, 1], labels, colors, bandwidths):
print(i)
data = np.loadtxt(
'STP_09_lm55_t{:02d}_joint_Lframe_resampled.dat'.format(i))
ra, dec = data[:, 8], data[:, 9]
#ra -= np.pi
# create the KDE estimator
# we could use grid-search cross validation to estimate the bandwidth here
radec = np.vstack((dec, ra)).T
kde = KernelDensity(kernel='tophat', bandwidth=bw,
metric='haversine').fit(radec)
# find the density levels corresponding to 1, 2, 3 sigmas
n = 10000
sample = kde.sample(n)
sample_densities = np.sort(np.exp(kde.score_samples(sample)))
# Note: are those levels appropriate for 2d ?
levels = [
sample_densities[int(n * (1 - p))] for p in [0.9973, 0.9545, 0.6827]
]
gra = np.linspace(-np.pi, np.pi, 300)
gdec = np.linspace(-np.pi / 2, np.pi / 2, 300)
ggra, ggdec = np.meshgrid(gra, gdec)
d = np.exp(kde.score_samples(np.vstack((ggdec.ravel(), ggra.ravel())).T))
d = np.reshape(d, ggra.shape)
cs = ax.contour(gra,
gdec,
d,
colors=color,
transformer = RobustScaler(quantile_range=(25, 75))
transformer.fit(X)
X = transformer.transform(X)
from sklearn.model_selection import GridSearchCV
bandwidths = 10**np.linspace(-2, 0, 20)
grid = GridSearchCV(KernelDensity(kernel='gaussian'),
{'bandwidth': bandwidths},
cv=2)
grid.fit(X)
kde = KernelDensity(kernel='gaussian', bandwidth=.021)
kde.fit(X)
samples = kde.sample(n_samples=45 * 198)
samples = transformer.inverse_transform(samples)
x_int = r_0_g * np.cos(phi_0_g)
y_int = r_0_g * np.sin(phi_0_g)
X_int = sp.interpolate.griddata(samples[:, 1:],
samples[:, 0],
(x_int.flatten(), y_int.flatten()),
method='linear')
im = plt.contourf(x_int, y_int, np.reshape(X_int, r_0_g.shape), cmap='jet')
plt.colorbar(im)
with open('K_CNR.pkl', 'wb') as writer:
pickle.dump(K_CNR, writer)
#####Dataframes
