def create_converters(r, in_size):
"""
:param r:
:param in_size:
:return:
"""
if in_size != -1:
pca_model = pickle.load(
open("models/pca_" + r + "_" + str(in_size) + ".p", 'r'))
else:
pca_model = None
# end if
# >> 1. Choose a text to symbol converter.
if r == "pos":
converter = RCNLPPosConverter(resize=-1,
pca_model=pca_model,
fill_in=True)
elif r == "tag":
converter = RCNLPTagConverter(resize=-1,
pca_model=pca_model,
fill_in=True)
elif r == "fw":
converter = RCNLPFuncWordConverter(resize=-1,
pca_model=pca_model,
fill_in=True)
else:
converter = RCNLPWordVectorConverter(resize=-1, pca_model=pca_model)
# end if
return converter
# For each size
for in_size in reps[r]:
print("For representation %s size %d" % (r, in_size))
if in_size != -1:
pca_model = pickle.load(open("models/pca_" + r + "_" + str(in_size) + ".p", 'r'))
else:
pca_model = None
# end if
# >> 1. Choose a text to symbol converter.
if r == "pos":
converter = RCNLPPosConverter(resize=-1, pca_model=pca_model)
elif r == "tag":
converter = RCNLPTagConverter(resize=-1, pca_model=pca_model)
elif r == "fw":
converter = RCNLPFuncWordConverter(resize=-1, pca_model=pca_model)
elif r == "letter":
converter = LetterConverter(resize=-1, pca_model=pca_model)
else:
converter = RCNLPWordVectorConverter(resize=-1, pca_model=pca_model)
# end if
# >> 3. Array for results
average_success_rate = np.array([])
# For each samples
for s in range(0, args.samples):
print("#")
# >> 5. Prepare training and test set.
training_set_indexes = np.arange(0, 100, 1)[s:s+args.training_size]
test_set_indexes = np.delete(np.arange(0, 100, 1), training_set_indexes, axis=0)[:args.test_size]
# PCA model
pca_model = None
if args.pca_model != "":
pca_model = pickle.load(open(args.pca_model, 'r'))
# end if
# >> 1. Choose a text to symbol converter.
if args.converter == "pos":
converter = RCNLPPosConverter(resize=args.in_components,
pca_model=pca_model)
elif args.converter == "tag":
converter = RCNLPTagConverter(resize=args.in_components,
pca_model=pca_model)
elif args.converter == "fw":
converter = RCNLPFuncWordConverter(resize=args.in_components,
pca_model=pca_model)
else:
converter = RCNLPWordVectorConverter(resize=args.in_components,
pca_model=pca_model)
# end if
# >> 3. Array for results
doc_success_rate_avg = np.array([])
sen_success_rate_avg = np.array([])
doc_success_rate_std = np.array([])
sen_success_rate_std = np.array([])
# Training set sizes
training_set_sizes = np.arange(1, 96, args.step)
# For each training size
logging = RCNLPLogging(exp_name=ex_name,
exp_inst=ex_instance,
exp_value=RCNLPLogging.generate_experience_name(
locals()))
logging.save_globals()
logging.save_variables(locals())
# Reduce POS
pca_reduction(RCNLPPosConverter()(io.open(args.text, 'r').read()),
title="POS",
ncomponents=args.poscomponents)
# Reduce Tags
pca_reduction(RCNLPTagConverter()(io.open(args.text, 'r').read()),
title="Tags",
ncomponents=args.tagcomponents)
# Reduce Word vectors
pca_reduction(RCNLPWordVectorConverter()(io.open(args.text, 'r').read()),
title="Word vectors",
ncomponents=args.wvcomponents)
# Reduce FW
pca_reduction(RCNLPFuncWordConverter()(io.open(args.text, 'r').read()),
title="Function words",
ncomponents=args.fwcomponents)
# Open logging dir
logging.open_dir()
# end if
def clustering_states(args,
texts1,
texts2,
ex_name,
ex_instance,
size,
input_scaling,
leak_rate,
spectral_radius,
input_sparsity,
w_sparsity,
logging,
save_graph=False,
pca_model=None,
flow=None):
# >> 1. Convert the text to symbolic or continuous representations
if args.converter == "pos":
converter = RCNLPPosConverter(resize=args.in_components,
pca_model=pca_model)
elif args.converter == "tag":
converter = RCNLPTagConverter(resize=args.in_components,
pca_model=pca_model)
elif args.converter == "fw":
converter = RCNLPFuncWordConverter(resize=args.in_components,
pca_model=pca_model)
else:
converter = RCNLPWordVectorConverter(resize=args.in_components,
pca_model=pca_model)
# end if
# >> 2. Create an echo state network
if flow is None:
flow = create_reservoir(converter.get_n_inputs(), size, input_scaling,
leak_rate, spectral_radius, input_sparsity,
w_sparsity)
# end if
# >> 3. Generate Temporal Representations
# Generate "temporal representations" for first author
a1_states, a1_index, a1_n_samples = generate_documents_states(
converter, flow, texts1, args)
if save_graph:
generate_plot(logging,
ex_name,
ex_instance,
"Temporal representations for Author 1",
"Time",
"Neurons",
a1_states[:args.show_states],
transpose=True,
cmap='Greys')
# end if
# Generate "temporal representations" for second author
a2_states, a2_index, a2_n_samples = generate_documents_states(
converter, flow, texts2, args)
if save_graph:
generate_plot(logging,
ex_name,
ex_instance,
"Temporal representations for Author 2",
"Time",
"Neurons",
a2_states[:args.show_states],
transpose=True,
cmap='Greys')
# end if
# >> 4. Complete states.
complete_states = np.vstack((a1_states, a2_states))
if save_graph:
generate_plot(logging,
ex_name,
ex_instance,
"Complete joined Reservoir states",
"Time",
"Neurons",
complete_states,
transpose=True,
cmap='Greys')
# end if
# Get average and std dev
logging.save_results("Average neural activations",
np.average(complete_states))
logging.save_results("Std dev of neural activations",
np.std(complete_states))
# Same size for each authors in needed
if args.homogene:
if a1_states.shape[0] > a2_states.shape[0]:
a1_states = a1_states[:a2_states.shape[0]]
elif a2_states.shape[0] > a1_states.shape[0]:
a2_states = a2_states[:a1_states.shape[0]]
# end if
# end if
# >> 5. Join states.
join_states = np.vstack((a1_states, a2_states))
if save_graph:
generate_plot(logging,
ex_name,
ex_instance,
"Joined Reservoir states",
"Time",
"Neurons",
join_states,
transpose=True,
cmap='Greys')
# end if
# >> 6. Clustering
if args.out_components != -1:
# PCA
pca = PCA(n_components=args.out_components)
pca.fit(join_states)
# Generate PCA image of principal components
if args.pca_images and save_graph:
# Generate PCA
a1_states_pca = pca.transform(a1_states)
a2_states_pca = pca.transform(a2_states)
for c in np.arange(0, 8):
save_pca_image(logging, a1_states_pca, a2_states_pca, c, c + 1)
# end for
# end if
# Reduce whole states
join_states_reduced = pca.transform(join_states)
# Get centroids for the whole components
centroids, _ = kmeans(join_states_reduced, 2)
# Assign each sample to a cluster
idx, _ = vq(pca.transform(complete_states), centroids)
a1_idx = idx[:a1_n_samples]
a2_idx = idx[a1_n_samples:]
else:
# Get centroids for the whole components
centroids, _ = kmeans(join_states, 2)
# Assign each sample to a cluster
idx, _ = vq(complete_states, centroids)
a1_idx = idx[:a1_n_samples]
a2_idx = idx[a1_n_samples:]
# end if
# Compute average precision
return get_v_measure_score(a1_idx, a2_idx, a1_index, a2_index)
if __name__ == "__main__":
# Argument parser
parser = argparse.ArgumentParser(
description=
"RCNLP - Authorship attribution with Part-Of-Speech to Echo State Network"
)
# Argument
parser.add_argument("--file", type=str, help="Input text file")
parser.add_argument("--lang",
type=str,
help="Language (ar, en, es, pt)",
default='en')
parser.add_argument("--sample-size",
type=int,
help="Word vector sample size",
default=300)
args = parser.parse_args()
# Convert the text to Temporal Vector Representation
converter = RCNLPFuncWordConverter()
doc_array = converter(io.open(args.file, 'r').read())
# Display the Temporal Vector Representation
RCNLPFuncWordConverter.display_representations(doc_array)
# Transform the TVR to ESN learning input
data_set = RCNLPFuncWordConverter.generate_data_set_inputs(doc_array, 2, 0)
# end if