def polish_pickle(feature_params, test_accuracy):
filename = tr.make_pickle_filename('trained_models', feature_params,
test_accuracy)
data = tr.load_data(filename)
data['feature_params'] = feature_params
tr.save_data(data, filename)
# Check feature_params were saved correctly.
data = tr.load_data(filename)
print('feature_params:', data['feature_params'].str())
def best_feature(temporal_rel):
"""Look at the accuracies for all features in isolation."""
features = [
"pos", "stem", "aspect", "tense", "distance", "similarity", "polarity",
"modality"
]
accuracies = []
recall = []
precision = []
for feature in features:
X, y = load_data(True, temporal_rel, features=[feature])
X_train, X_test, y_train, y_test = split(X, y)
rf = RandomForestClassifier(n_jobs=2, n_estimators=100)
rf.fit(X_train, y_train)
y_pred = rf.predict(X_test)
accuracies.append({feature: f1_score(y_test, y_pred)})
recall.append({feature: recall_score(y_test, y_pred)})
precision.append({feature: precision_score(y_test, y_pred)})
print "Done with feature"
# Add all features
X, y = load_data(True, temporal_rel)
X_train, X_test, y_train, y_test = split(X, y)
rf = RandomForestClassifier(n_jobs=2, n_estimators=100)
rf.fit(X_train, y_train)
y_pred = rf.predict(X_test)
accuracies.append({"all": f1_score(y_test, y_pred)})
recall.append({"all": recall_score(y_test, y_pred)})
precision.append({"all": precision_score(y_test, y_pred)})
features.append("all")
data = [x.values()[0] for x in accuracies]
if temporal_rel == None:
plot("best_feature_weighted.jpg", "feature", "f1_score", data,
features)
else:
plot("best_feature_" + str(temporal_rel) + ".jpg", "feature",
"f1_score", data, features)
print recall
print precision
def get_distance_data(data, temporal_rel):
"""Extracts the distance feature into the following data structure which will be returned: [{distance : classified_right?}, ...]"""
X, y, distance = load_data(True, temporal_rel, distance=True)
X_train, X_test, y_train, y_test, distance_train, distance_test = split(
X, y, distance)
rf = RandomForestClassifier(n_jobs=2, n_estimators=100)
rf.fit(X_train, y_train)
y_pred = rf.predict(X_test)
# Make array with elements like this {distance : [TruePositive?, TrueNegative?, FalsePositive?, FalseNegative?]}
for i in range(len(X_test)):
if y_test[i] == y_pred[i] and y_test[i] == 1:
# True positive
data.append({distance_test[i]: [True, False, False, False]})
elif y_test[i] == y_pred[i] and y_test[i] == 0:
# True negative
data.append({distance_test[i]: [False, True, False, False]})
elif y_test[i] != y_pred[i] and y_pred[i] == 1:
# False positive
data.append({distance_test[i]: [False, False, True, False]})
elif y_test[i] != y_pred[i] and y_pred[i] == 0:
# False negative
data.append({distance_test[i]: [False, False, False, True]})
def main():
"""Argument parser for making G2P predictions"""
parser = argparse.ArgumentParser()
parser.add_argument('pron_path',
default='./data/prondict_ice.txt',
nargs='?')
parser.add_argument(
'words',
default=['adolfsdóttir', 'skynsemi', 'uppvaxtarskilyrði'],
nargs='?')
parser.add_argument('exp_name', default='g2p_ice', nargs='?')
parser.add_argument('emb_dim', default=500, nargs='?')
parser.add_argument('hidden_dim', default=500, nargs='?')
parser.add_argument('cuda', default=True, nargs='?')
parser.add_argument('seed', default=1337, nargs='?')
parser.add_argument('result_dir', default='./results', nargs='?')
parser.add_argument('data_splits', default=(0.9, 0.05, 0.05), nargs='?')
args = parser.parse_args()
exp_dir = os.path.join(args.result_dir, args.exp_name)
ckp_path = os.path.join(exp_dir, 'mdl.ckpt')
full_ds, _ = load_data(args.pron_path, args.data_splits, **vars(args))
model = load_model(full_ds.num_graphemes, full_ds.num_phonemes, ckp_path,
**vars(args))
for word in args.words:
print(word)
predict(model, word, full_ds)
def get_data(dataset, data_dir, batch_size, test_batch_size):
'''
get data for imagenet
'''
nThread = 1
pin = True # for cuda device
traindir = os.path.join(data_dir, 'train')
valdir = os.path.join(data_dir, 'validation')
print('train_dir is ', traindir)
dataset, dataset_test, train_sampler, test_sampler = load_data(
traindir, valdir, False,
get_world_size() > 1)
train_loader = torch.utils.data.DataLoader(dataset,
batch_size=batch_size,
sampler=train_sampler,
num_workers=nThread,
pin_memory=True)
val_loader = torch.utils.data.DataLoader(dataset_test,
batch_size=test_batch_size,
sampler=test_sampler,
num_workers=nThread,
pin_memory=True)
criterion = torch.nn.CrossEntropyLoss()
return train_loader, val_loader, criterion
def eval_mode_batch(output_tags, confidences, cities):
tagged_data, identifier = load_data(output_tags)
num_tags = len(int2tags) - 1
assert len(tagged_data) == len(confidences)
for i in range(len(tagged_data)):
sentence = tagged_data[i][0]
tags = tagged_data[i][1]
tag_confs = confidences[i]
ident = identifier[i]
gold_ents = ident.split(',')[:num_tags] #Throw away title
output_pred_line, entity_confidences, entity_cnts = predict_mode(sentence, tags, tag_confs, cities)
predictions = output_pred_line.split(" ### ")
# Evaluate the predictions.
evaluateArticle(predictions, gold_ents)
print "------------\nEvaluation Stats: (Precision, Recall, F1):"
for tag in GOLD:
prec = CORRECT[tag]/PRED[tag]
rec = CORRECT[tag]/GOLD[tag]
f1 = (2*prec*rec)/(prec+rec)
print tag, prec, rec, f1, "########", CORRECT[tag], PRED[tag], GOLD[tag]
def main(_):
_, dev, test, samples, _, _, data = load_data(FLAGS.pop_prep_dir,
FLAGS.pop_dataset,
FLAGS.pop_prep_name,
FLAGS.pop_debug)
sorted_items = sort_items_by_popularity(samples)
print_most_popular_items(data, sorted_items)
pop_ranking = np.ones((1, len(sorted_items)))
for i, (item_idx, _) in enumerate(sorted_items):
pop_ranking[0, item_idx] = len(sorted_items) - i
random_items = 100
for title, split in zip(["DEV", "TEST"], [dev, test]):
rank_all, rank_random = random_eval(pop_ranking,
split,
samples,
num_rand=random_items)
metric_all, metric_random = rank_to_metric_dict(
rank_all), rank_to_metric_dict(rank_random)
print(f"Result for {title.upper()}")
print(f"Random items {random_items}: " +
" ".join((f"{k}: {v:.2f}" for k, v in metric_random.items())))
print("All items: " + " ".join((f"{k}: {v:.2f}"
for k, v in metric_all.items())))
def check_model():
# Creating the session
session, img_length, img_height, y_pred_cls, x, weights1, weights2, conv1, conv2 = load_graph(
True, "../model/two_d_cnn_proj.ckpt")
# object names
object_names = object_names_func()
list_of_objects = list(range(29))
list_of_objects.remove(22)
# plotting the weights
plot_conv_weights(session.run(weights1), 0, '../model/weights1.png')
plot_conv_weights(session.run(weights2), 0, '../model/weights2.png')
# Select some images after reading in the data
train_input_encode, train_out_encode, test_input_encode, test_out_encode = load_data(
"../data")
image = train_input_encode[1000]
test_object = object_names[list_of_objects[np.argmax(
train_out_encode[1000])]]
feed_dict = {x: [image]}
# Calculate and retrieve the output values of the layer1
values = session.run(conv1, feed_dict=feed_dict)
plot_conv_layer(values, '../model/{}_1.png'.format(test_object))
# Calculate and retrieve the output values of the layer2
values = session.run(conv2, feed_dict=feed_dict)
plot_conv_layer(values, '../model/{}_2.png'.format(test_object))
print("Object = {}".format(test_object))
def eval_mode_batch(output_tags, confidences, cities):
tagged_data, identifier = load_data(output_tags)
num_tags = len(int2tags) - 1
assert len(tagged_data) == len(confidences)
for i in range(len(tagged_data)):
sentence = tagged_data[i][0]
tags = tagged_data[i][1]
tag_confs = confidences[i]
ident = identifier[i]
gold_ents = ident.split(',')[:num_tags] #Throw away title
output_pred_line, entity_confidences, entity_cnts = predict_mode(
sentence, tags, tag_confs, cities)
predictions = output_pred_line.split(" ### ")
# Evaluate the predictions.
evaluateArticle(predictions, gold_ents)
print "------------\nEvaluation Stats: (Precision, Recall, F1):"
for tag in GOLD:
prec = CORRECT[tag] / PRED[tag]
rec = CORRECT[tag] / GOLD[tag]
f1 = (2 * prec * rec) / (prec + rec)
print tag, prec, rec, f1, "########", CORRECT[tag], PRED[tag], GOLD[
tag]
def run(data_name, num_train, num_test, Phi,
depth, widths, lc_w_range, shift_w_range,
optim_name, optim_args,
num_epochs, batch_size, chkpt_freq):
id = get_info()
identifier_id = '%s%s' % (identifier, id)
train_data, test_data = load_data(data_name, num_train, num_test)
train_ll, test_ll = load_log_ll(data_name, num_train, num_test)
print('Computing ground truth manually because tagged log likelihood is wrong')
from phi_listing import ClaytonPhi
gt_phi = ClaytonPhi(torch.tensor(5.))
cop = Copula(gt_phi)
train_ll = -torch.log(cop(train_data, mode='pdf'))
test_ll = -torch.log(cop(test_data, mode='pdf'))
print('train_ll', torch.mean(train_ll))
print('test_ll', torch.mean(test_ll))
print('Train ideal ll:', torch.mean(train_ll))
print('Test ideal ll:', torch.mean(test_ll))
phi = Phi(depth, widths, lc_w_range, shift_w_range)
net = Copula(phi)
expt(train_data, test_data, net, optim_name,
optim_args, identifier_id, num_epochs, batch_size, chkpt_freq)
def dummy():
config = train_config
data_train = load_data(config, 'train')
config['input_dim'] = data_train.input_[0].shape[-1]
config['output_dim'] = data_train.target[0].shape[-1]
data_train.reshuffle()
rnn_model_class, placeholders = get_model_and_placeholders(config)
rnn_model = RNNModel(config, placeholders, mode='training')
# loop through all training batches
for i, batch in enumerate(data_train.all_batches()):
# get the feed dict for the current batch
feed_dict = rnn_model.get_feed_dict(batch)
for sequence_mask in batch.mask:
if np.sum(sequence_mask) < 35:
print('found it {0}'.format(np.sum(sequence_mask)))
input_padded, target_padded = batch.get_padded_data()
mse = mean_squared_error(input_padded[0], target_padded[0])
mse2 = mean_squared_error(input_padded[1], target_padded[1])
def different_number_of_trees(temporal_rel,
start=5,
end=800,
steps=20,
rerunning=5):
"""How does the accuracy change for different amounts of trees. Plots to different_number_of_trees.jpg"""
X, y = load_data(True, temporal_rel)
X_train, X_test, y_train, y_test = split(X, y)
# Since accuracies for small amounts of trees differ a lot, we take the average over many tries
many_accuracies = []
many_recall = []
many_precision = []
for x in range(rerunning):
accuracies = []
recall = []
precision = []
for i in range(start, end, steps):
rf = RandomForestClassifier(n_jobs=2, n_estimators=i)
rf.fit(X_train, y_train)
y_pred = rf.predict(X_test)
accuracies.append(f1_score(y_test, y_pred))
recall.append(recall_score(y_test, y_pred))
precision.append(precision_score(y_test, y_pred))
many_accuracies.append(accuracies)
many_recall.append(recall)
many_precision.append(precision)
final_accuracies = []
final_recall = []
final_precision = []
# Calculate the mean
for i in range(len(many_accuracies[0])):
mean = []
mean_recall = []
mean_precision = []
for j in range(len(many_accuracies)):
mean.append(many_accuracies[j][i])
mean_recall.append(many_recall[j][i])
mean_precision.append(many_precision[j][i])
final_accuracies.append(np.mean(mean))
final_recall.append(np.mean(mean_recall))
final_precision.append(np.mean(mean_precision))
# xticks
xticks = range(start, end, steps)
if temporal_rel == None:
plot("different_number_of_trees_weighted.jpg", "number_of_trees",
"f1_score", final_accuracies, xticks)
else:
plot("different_number_of_trees_" + str(temporal_rel) + ".jpg",
"number_of_trees", "f1_score", final_accuracies, xticks)
print final_recall
print final_precision
def main(repo_path):
test_csv_path = repo_path / "data/prepared/test.csv"
test_data, labels = load_data(test_csv_path)
model = load(repo_path / "model/model.joblib")
predictions = model.predict(test_data)
accuracy = accuracy_score(labels, predictions)
metrics = {"accuracy": accuracy}
accuracy_path = repo_path / "metrics/accuracy.json"
accuracy_path.write_text(json.dumps(metrics))
def learning_rate(temporal_rel, k=20, new=False):
"""Splits the dataset into k pieces and builds a series out of those k pieces. For every partial sum the accuracy will be calculated to obtain the learning rate. Plots the data to learning_rate.jpg"""
X, y = load_data(new, temporal_rel)
X_train, X_test, y_train, y_test = split(X, y)
# Splitting the training set up into k pieces
len_piece = len(X_train) / k
X_pieces = []
y_pieces = []
data_count = []
recall = []
precision = []
for i in range(k):
data_count.append((i + 1) * len_piece)
offset = i * len_piece
X_piece = X[offset:][:len_piece]
y_piece = y[offset:][:len_piece]
X_pieces.append(X_piece)
y_pieces.append(y_piece)
# Building series (from 0 to k) for those pieces
X_series = []
y_series = []
for i in range(k):
X_sum = X_pieces[0]
y_sum = y_pieces[0]
for j in range(i):
X_sum = np.concatenate((X_sum, X_pieces[j]))
y_sum = np.concatenate((y_sum, y_pieces[j]))
X_series.append(X_sum)
y_series.append(y_sum)
# Calculate the accuracy for each partial sum
rf = RandomForestClassifier(n_jobs=2, n_estimators=1000)
accuracies = []
for partial_X, partial_y in zip(X_series, y_series):
rf.fit(partial_X, partial_y)
y_pred = rf.predict(X_test)
accuracies.append(f1_score(y_test, y_pred))
recall.append(recall_score(y_test, y_pred))
precision.append(precision_score(y_test, y_pred))
if temporal_rel == None:
plot("learning_rate_weighted.jpg", "data_count", "f1_score",
accuracies, data_count)
else:
plot("learning_rate_" + str(temporal_rel) + ".jpg", "data_count",
"f1_score", accuracies, data_count)
print recall
print precision
def main(): args = get_args() weight_path = args.weight_path if not os.path.exists(RESPATH): os.makedirs(RESPATH) viddata, auddata = train.load_data(DATAPATH) net_out = auddata.shape[1] viddata, auddata_norm, auddata_means, auddata_stds = standardize_data(viddata, auddata) print(net_out)
def union_vs_intersected_relations():
"""Looking at the difference in accuracy when all relations (union) are used vs. all events are used which the annotators have in common (intersected)."""
X_union, y_union = load_data(new=True, annotations="union")
X_intersected, y_intersected = load_data(new=True, annotations="intersected")
X_u_train, X_u_test, y_u_train, y_u_test = split(X_union, y_union)
X_i_train, X_i_test, y_i_train, y_i_test = split(X_intersected, y_intersected)
rf_u = RandomForestClassifier(n_jobs=2, n_estimators=100)
rf_u.fit(X_u_train, y_u_train)
rf_i = RandomForestClassifier(n_jobs=2, n_estimators=100)
rf_i.fit(X_i_train, y_i_train)
y_u_pred = rf_u.predict(X_u_test)
y_i_pred = rf_i.predict(X_i_test)
print "Union: " + str(f1_score(y_u_test, y_u_pred))
print "Intersected: " + str(f1_score(y_i_test, y_i_test))
def best_feature(temporal_rel):
"""Look at the accuracies for all features in isolation."""
features = ["pos", "stem", "aspect", "tense", "distance", "similarity", "polarity", "modality"]
accuracies = []
recall = []
precision = []
for feature in features:
X, y = load_data(True, temporal_rel, features=[feature])
X_train, X_test, y_train, y_test = split(X, y)
rf = RandomForestClassifier(n_jobs=2, n_estimators=100)
rf.fit(X_train, y_train)
y_pred = rf.predict(X_test)
accuracies.append({feature : f1_score(y_test, y_pred)})
recall.append({feature : recall_score(y_test, y_pred)})
precision.append({feature : precision_score(y_test, y_pred)})
print "Done with feature"
# Add all features
X, y = load_data(True, temporal_rel)
X_train, X_test, y_train, y_test = split(X, y)
rf = RandomForestClassifier(n_jobs=2, n_estimators=100)
rf.fit(X_train, y_train)
y_pred = rf.predict(X_test)
accuracies.append({"all": f1_score(y_test, y_pred)})
recall.append({"all": recall_score(y_test, y_pred)})
precision.append({"all": precision_score(y_test, y_pred)})
features.append("all")
data = [x.values()[0] for x in accuracies]
if temporal_rel == None:
plot("best_feature_weighted.jpg", "feature", "f1_score", data, features)
else:
plot("best_feature_"+str(temporal_rel)+".jpg", "feature", "f1_score", data, features)
print recall
print precision
def mean_std(train_dir, val_dir):
train_loader, val_loader = load_data(train_dir, val_dir)
mean_train, std_train = process(train_loader)
mean_val, std_val = process(val_loader)
print('mean_train: ', mean_train)
print('std_train: ', std_train)
print('mean_val: ', mean_val)
print('std_val: ', std_val)
def learning_rate(temporal_rel, k=20, new=False):
"""Splits the dataset into k pieces and builds a series out of those k pieces. For every partial sum the accuracy will be calculated to obtain the learning rate. Plots the data to learning_rate.jpg"""
X, y = load_data(new, temporal_rel)
X_train, X_test, y_train, y_test = split(X, y)
# Splitting the training set up into k pieces
len_piece = len(X_train)/k
X_pieces = []
y_pieces = []
data_count = []
recall = []
precision = []
for i in range(k):
data_count.append((i+1)*len_piece)
offset = i*len_piece
X_piece = X[offset:][:len_piece]
y_piece = y[offset:][:len_piece]
X_pieces.append(X_piece)
y_pieces.append(y_piece)
# Building series (from 0 to k) for those pieces
X_series = []
y_series = []
for i in range(k):
X_sum = X_pieces[0]
y_sum = y_pieces[0]
for j in range(i):
X_sum = np.concatenate((X_sum, X_pieces[j]))
y_sum = np.concatenate((y_sum, y_pieces[j]))
X_series.append(X_sum)
y_series.append(y_sum)
# Calculate the accuracy for each partial sum
rf = RandomForestClassifier(n_jobs=2, n_estimators=1000)
accuracies = []
for partial_X, partial_y in zip(X_series, y_series):
rf.fit(partial_X, partial_y)
y_pred = rf.predict(X_test)
accuracies.append(f1_score(y_test, y_pred))
recall.append(recall_score(y_test, y_pred))
precision.append(precision_score(y_test, y_pred))
if temporal_rel == None:
plot("learning_rate_weighted.jpg", "data_count", "f1_score", accuracies, data_count)
else:
plot("learning_rate_"+str(temporal_rel)+".jpg", "data_count", "f1_score", accuracies, data_count)
print recall
print precision
def union_vs_intersected_relations():
"""Looking at the difference in accuracy when all relations (union) are used vs. all events are used which the annotators have in common (intersected)."""
X_union, y_union = load_data(new=True, annotations="union")
X_intersected, y_intersected = load_data(new=True,
annotations="intersected")
X_u_train, X_u_test, y_u_train, y_u_test = split(X_union, y_union)
X_i_train, X_i_test, y_i_train, y_i_test = split(X_intersected,
y_intersected)
rf_u = RandomForestClassifier(n_jobs=2, n_estimators=100)
rf_u.fit(X_u_train, y_u_train)
rf_i = RandomForestClassifier(n_jobs=2, n_estimators=100)
rf_i.fit(X_i_train, y_i_train)
y_u_pred = rf_u.predict(X_u_test)
y_i_pred = rf_i.predict(X_i_test)
print "Union: " + str(f1_score(y_u_test, y_u_pred))
print "Intersected: " + str(f1_score(y_i_test, y_i_test))
def different_number_of_trees(temporal_rel, start=5, end=800, steps=20, rerunning=5):
"""How does the accuracy change for different amounts of trees. Plots to different_number_of_trees.jpg"""
X, y = load_data(True, temporal_rel)
X_train, X_test, y_train, y_test = split(X, y)
# Since accuracies for small amounts of trees differ a lot, we take the average over many tries
many_accuracies = []
many_recall = []
many_precision = []
for x in range(rerunning):
accuracies = []
recall = []
precision = []
for i in range(start, end, steps):
rf = RandomForestClassifier(n_jobs=2, n_estimators=i)
rf.fit(X_train, y_train)
y_pred = rf.predict(X_test)
accuracies.append(f1_score(y_test, y_pred))
recall.append(recall_score(y_test, y_pred))
precision.append(precision_score(y_test, y_pred))
many_accuracies.append(accuracies)
many_recall.append(recall)
many_precision.append(precision)
final_accuracies = []
final_recall = []
final_precision = []
# Calculate the mean
for i in range(len(many_accuracies[0])):
mean = []
mean_recall = []
mean_precision = []
for j in range(len(many_accuracies)):
mean.append(many_accuracies[j][i])
mean_recall.append(many_recall[j][i])
mean_precision.append(many_precision[j][i])
final_accuracies.append(np.mean(mean))
final_recall.append(np.mean(mean_recall))
final_precision.append(np.mean(mean_precision))
# xticks
xticks = range(start, end, steps)
if temporal_rel == None:
plot("different_number_of_trees_weighted.jpg", "number_of_trees", "f1_score", final_accuracies, xticks)
else:
plot("different_number_of_trees_"+str(temporal_rel)+".jpg", "number_of_trees", "f1_score", final_accuracies, xticks)
print final_recall
print final_precision
def main(trained_model,
test_file,
viterbi,
output_tags="output.tag",
output_predictions="output.pred"):
test_data, identifier = load_data(testing_file)
evaluate = True
# extract features
if not "crf" in trained_model:
if not isinstance(trained_model, list):
with open(trained_model, 'rb') as frb:
clf, previous_n, next_n, word_vocab, other_features = pickle.load(
frb)
else:
clf, previous_n, next_n, word_vocab, other_features = trained_model
tic = time.clock()
with open(output_tags, 'w') as fw:
confidences = []
for i in range(len(test_data) + len(identifier)):
if i % 2 == 1:
if "crf" in trained_model:
y, tmp_conf = crf.predict(test_data[i / 2][0],
trained_model)
fw.write(" ".join([
test_data[i / 2][0][j] + "_" + y[j]
for j in range(len(test_data[i / 2][0]))
]))
else:
y, tmp_conf = predict_tags_n(viterbi, previous_n, next_n,
clf, test_data[i / 2][0],
word_vocab, other_features)
fw.write(" ".join([
test_data[i / 2][0][j] + "_" + int2tags[int(y[j])]
for j in range(len(test_data[i / 2][0]))
]))
assert (len(y) == len(tmp_conf))
confidences.append(tmp_conf)
fw.write("\n")
else:
fw.write(identifier[i / 2])
fw.write("\n")
print(time.clock() - tic)
if evaluate:
eval_mode_batch(output_tags, confidences, helper.cities)
else:
predict_mode_batch(output_tags, output_predictions, helper.cityies)
def predict_mode_batch(output_tags, output_predictions, cities):
tagged_data, identifier = load_data(output_tags)
f = open(output_predictions,'w')
for i in range(len(tagged_data)+len(identifier)):
if i%2 == 1:
f.write(predict_mode(tagged_data[i/2][0], tagged_data[i/2][1], cities))
f.write("\n")
else:
f.write(identifier[i/2])
f.write("\n")
return
def main():
args = get_args()
weight_path = args.weight_path
if not os.path.exists(RESPATH):
os.makedirs(RESPATH)
(Xtr, Ytr), (Xte, Yte) = train.load_data(DATAPATH)
net_out = Ytr.shape[1]
Xtr, Ytr_norm, Xte, Yte_norm, Y_means, Y_stds = train.standardize_data(
Xtr, Ytr, Xte, Yte)
model = train.build_model(net_out)
model.compile(loss='mse', optimizer='adam')
model.load_weights(weight_path)
Ytr_pred, Yte_pred = train.predict(model, Xtr, Xte, Y_means, Y_stds)
train.savedata(Ytr, Ytr_pred, Yte, Yte_pred, respath=RESPATH)
def predict_mode_batch(output_tags, output_predictions, cities):
tagged_data, identifier = load_data(output_tags)
with open(output_predictions, 'w') as f:
for i in range(len(tagged_data) + len(identifier)):
if i % 2 == 1:
f.write(
predict_mode(tagged_data[i / 2][0], tagged_data[i / 2][1],
cities))
f.write('\n')
else:
f.write(identifier[i / 2])
f.write('\n')
return
def test_apply_model(self):
"""Tests if we can apply a model to a small test dataset."""
checkpoint_path = os.path.join(os.path.dirname(__file__), 'va0.1',
't0f50.ckpt')
file_pattern = os.path.join(os.path.dirname(__file__), 'testdata',
'Con100_η0.1N300L5_100')
predictions = train.apply_model(checkpoint_path=checkpoint_path,
file_pattern=file_pattern,
time_index=0)
data = train.load_data(file_pattern, 0)
targets = data[0].targets
# correlation_value = np.corrcoef(predictions[0], targets)[0, 1]
print('predictions:',predictions)
print('targets:',targets)
def main():
args = get_args()
weight_path = args.weight_path
if not os.path.exists(RESPATH):
os.makedirs(RESPATH)
viddata, auddata = train.load_data(DATAPATH)
net_out = auddata.shape[1]
viddata, auddata_norm, auddata_means, auddata_stds = standardize_data(
viddata, auddata)
model = train.build_model(net_out)
model.compile(loss='mse', optimizer='adam')
model.load_weights(weight_path)
aud_pred = train.predict(model, viddata, auddata_means, auddata_stds)
np.save(join(RESPATH, 'aud_pred.npy'), aud_pred)
def run(data_name, num_train, num_test, Phi, depth, widths, lc_w_range,
shift_w_range, optim_name, optim_args, num_epochs, batch_size,
chkpt_freq):
id = get_info()
identifier_id = '%s%s' % (identifier, id)
train_data, test_data = load_data(data_name, num_train, num_test)
train_ll, test_ll = load_log_ll(data_name, num_train, num_test)
print('Train ideal ll:', torch.mean(train_ll))
print('Test ideal ll:', torch.mean(test_ll))
phi = Phi(depth, widths, lc_w_range, shift_w_range)
net = Copula(phi)
expt(train_data, test_data, net, optim_name, optim_args, identifier_id,
num_epochs, batch_size, chkpt_freq)
def main():
data, words = load_data()
model = models.load_model('model.couplets.h5')
c = 13
x = np.zeros((1, 2 * c), dtype='uint32')
for i in range(2 * c):
s = max(i - c, 0)
probas = model.predict(x[:, s:s + c], verbose=0)
probas = probas.astype('float64')
probas /= probas.sum()
probas = np.random.multinomial(1, probas[0], 1)
char = np.argmax(probas)
x[0, i] = char
char = words[char]
print(char)
def eval_mode_batch(output_tags, confidences, cities):
tagged_data, identifier = load_data(output_tags)
num_tags = len(int2tags) - 1
correct = [0]* num_tags
guessed = [0] * num_tags
gold_correct = [0] * num_tags
assert len(tagged_data) == len(confidences)
for i in range(len(tagged_data)):
sentence = tagged_data[i][0]
tags = tagged_data[i][1]
tag_confs = confidences[i]
ident = identifier[i]
gold_ents = ident.split(',')[:num_tags] #Throw away title
output_pred_line, entity_confidences, entity_cnts = predict_mode(sentence, tags, tag_confs, cities)
predictions = output_pred_line.split(" ### ")
if not len(gold_ents) == len(predictions):
print 'ident', ident
print 'gold_ents', gold_ents
raw_input()
continue
for index in range(len(gold_ents)):
match = evaluatePrediction(predictions[index], gold_ents[index])
debugCity = False
if index == 3 and debugCity:
print 'predictions[index]', predictions[index]
print 'gold_ents[index]', gold_ents[index]
print 'match', match
raw_input()
if match == 'skip':
continue
else:
gold_correct[index] += 1
if match == "no_predict":
continue
if match == 1:
correct[index] += 1
guessed[index] += 1
helper.printScores(correct, guessed, gold_correct, False)
def yield_chars():
text, char_indices, indices_char = load_data()
model = build_model(indices_char)
load_latest_model(model)
start_index = random.randint(0, len(text) - MAXLEN - 1)
gen = sample_from_model(text, start_index, char_indices, model, indices_char)
next_letter_upper = False
for c in gen:
if next_letter_upper and c.upper() != c:
yield c.upper()
next_letter_upper = False
else:
yield c
if c == '.':
next_letter_upper = True
def eval_mode_batch(output_tags, confidences, cities):
tagged_data, identifier = load_data(output_tags)
num_tags = len(int2tags) - 1
correct = [0] * num_tags
guessed = [0] * num_tags
gold_correct = [0] * num_tags
assert len(tagged_data) == len(confidences)
for i in range(len(tagged_data)):
sentence = tagged_data[i][0]
tags = tagged_data[i][1]
tag_confs = confidences[i]
ident = identifier[i]
gold_ents = ident.split(',')[:num_tags] #Throw away title
output_pred_line, entity_confidences, entity_cnts = predict_mode(
sentence, tags, tag_confs, cities)
predictions = output_pred_line.split(" ### ")
if not len(gold_ents) == len(predictions):
print 'ident', ident
print 'gold_ents', gold_ents
raw_input()
continue
for index in range(len(gold_ents)):
match = evaluatePrediction(predictions[index], gold_ents[index])
debugCity = False
if index == 3 and debugCity:
print 'predictions[index]', predictions[index]
print 'gold_ents[index]', gold_ents[index]
print 'match', match
raw_input()
if match == 'skip':
continue
else:
gold_correct[index] += 1
if match == "no_predict":
continue
if match == 1:
correct[index] += 1
guessed[index] += 1
helper.printScores(correct, guessed, gold_correct, False)
def yield_chars():
text, char_indices, indices_char = load_data()
model = build_model(indices_char)
load_latest_model(model)
start_index = random.randint(0, len(text) - MAXLEN - 1)
gen = sample_from_model(text, start_index, char_indices, model,
indices_char)
next_letter_upper = False
for c in gen:
if next_letter_upper and c.upper() != c:
yield c.upper()
next_letter_upper = False
else:
yield c
if c == '.':
next_letter_upper = True
def evaluate_model(load_model):
args = {}
args['max_pos_embed'] = 512
args['max_num_sentences'] = 32
args['max_summary_length'] = 96
args[
'model_data_dir'] = "/home/alta/summary/pm574/summariser0/lib/model_data/"
val_batch_size = 200 # okay for K80 & RTX 2080 ti
if 'X_SGE_CUDA_DEVICE' in os.environ: # to run on CUED stack machine
print('running on the stack...')
cuda_device = os.environ['X_SGE_CUDA_DEVICE']
print('X_SGE_CUDA_DEVICE is set to {}'.format(cuda_device))
os.environ['CUDA_VISIBLE_DEVICES'] = cuda_device
else:
# pdb.set_trace()
print('running locally...')
os.environ[
"CUDA_VISIBLE_DEVICES"] = '0' # choose the device (GPU) here
device = 'cuda'
val_data = load_data(args, 'val')
val_summary = load_summary(args, 'val')
abs_sum = AbstractiveSummariser(args, device=device)
abs_sum.load_state_dict(torch.load(load_model))
abs_sum.eval() # switch to evaluation mode
print("evaluate model: {}".format(load_model))
vocab_size = abs_sum.decoder.linear_decoder.out_features
with torch.no_grad():
avg_val_loss = evaluate2(abs_sum, val_data, val_summary,
val_batch_size, vocab_size, args, device)
print(
"============================================================================================"
)
print("MODEL = {}".format(load_model))
print("VLOSS = {}".format(avg_val_loss))
print(
"============================================================================================"
)
def predict(dataset, model_file):
"""
Loads a model file and predicts on the test set
"""
classifier = cPickle.load(open(model_file))
predict_model = theano.function(inputs=[classifier.input],
outputs=classifier.y_pred)
datasets = load_data(dataset)
test_set_x, test_set_y = datasets[2]
test_set_x = test_set_x.get_value()
predicted_values = predict_model(test_set_x)
print("Predicted values for the first 10 examples in the test test:")
print(predicted_values[:10])
test_model = theano.function(inputs=[classifier.input],
outputs=classifier.errors(test_set_y))
test_error = test_model(test_set_x)
print("Test error is %f %%" % (test_error * 100))
def predict(dataset, model_file):
"""
Loads a model file and predicts on the test set
"""
classifier = cPickle.load(open(model_file))
predict_model = theano.function(inputs=[classifier.input],
outputs=classifier.y_pred)
datasets = load_data(dataset)
test_set_x, test_set_y = datasets[2]
test_set_x = test_set_x.get_value()
predicted_values = predict_model(test_set_x)
print("Predicted values for the first 10 examples in the test test:")
print(predicted_values[:10])
test_model = theano.function(inputs=[classifier.input],
outputs=classifier.errors(test_set_y))
test_error = test_model(test_set_x)
print("Test error is %f %%" % (test_error * 100))
def load_initial_train(setlist,
version,
fraction,
save=False,
hys_dir=HYS_DIR,
train_set_dir=TRAIN_SET_DIR,
grid_dir=GRID_DIR):
if VERBOSE:
print('Loading initial training data')
(grids, results) = train.load_data(setlist,
hys_dir,
version,
fraction,
grid_dir=grid_dir)
n_grids = grids.shape[0]
indexed_results = np.zeros((n_grids, 2))
indexed_results[:, 0] = np.reshape(np.arange(n_grids), (n_grids, ))
indexed_results[:, 1] = results
if save:
out_path = os.path.join(train_set_dir, 'train_grids00.csv')
np.savetxt(out_path, grids, fmt='%i', delimiter=',')
out_path = os.path.join(train_set_dir, 'train_results00.csv')
np.savetxt(out_path, indexed_results, delimiter=',')
return (grids, indexed_results)
def main(trained_model,testing_file,viterbi,output_tags="output.tag", output_predictions="output.pred"):
test_data, identifier = load_data(testing_file)
evaluate = True
## extract features
if not "crf" in trained_model:
if not isinstance(trained_model, list):
clf, previous_n, next_n, word_vocab,other_features = pickle.load( open( trained_model, "rb" ) )
else:
clf, previous_n, next_n, word_vocab,other_features = trained_model
tic = time.clock()
f = open(output_tags,'w')
confidences = []
for i in range(len(test_data)+len(identifier)):
if i%2 == 1:
if "crf" in trained_model:
y, tmp_conf = crf.predict(test_data[i/2][0], trained_model)
f.write(" ".join([test_data[i/2][0][j]+"_"+y[j] for j in range(len(test_data[i/2][0]))]))
else:
y, tmp_conf = predict_tags_n(viterbi, previous_n,next_n, clf, test_data[i/2][0], word_vocab,other_features)
f.write(" ".join([test_data[i/2][0][j]+"_"+int2tags[int(y[j])] for j in range(len(test_data[i/2][0]))]))
assert(len(y) == len(tmp_conf))
confidences.append(tmp_conf)
f.write("\n")
else:
f.write(identifier[i/2])
f.write("\n")
#print time.clock()-tic
f.close()
if evaluate:
eval_mode_batch(output_tags, confidences, helper.cities)
else:
predict_mode_batch(output_tags, output_predictions, helper.cities)
return
def get_distance_data(data, temporal_rel):
"""Extracts the distance feature into the following data structure which will be returned: [{distance : classified_right?}, ...]"""
X, y, distance = load_data(True, temporal_rel, distance=True)
X_train, X_test, y_train, y_test, distance_train, distance_test = split(X, y, distance)
rf = RandomForestClassifier(n_jobs=2, n_estimators=100)
rf.fit(X_train, y_train)
y_pred = rf.predict(X_test)
# Make array with elements like this {distance : [TruePositive?, TrueNegative?, FalsePositive?, FalseNegative?]}
for i in range(len(X_test)):
if y_test[i] == y_pred[i] and y_test[i] == 1:
# True positive
data.append({distance_test[i] : [True, False, False, False]})
elif y_test[i] == y_pred[i] and y_test[i] == 0:
# True negative
data.append({distance_test[i] : [False, True, False, False]})
elif y_test[i] != y_pred[i] and y_pred[i] == 1:
# False positive
data.append({distance_test[i] : [False, False, True, False]})
elif y_test[i] != y_pred[i] and y_pred[i] == 0:
# False negative
data.append({distance_test[i] : [False, False, False, True]})
def exp_raw(dtype):
shp = (None, 3, 256, 256)
input_var = T.tensor4('input_var', dtype = 'float32')
psp = T.dmatrix("psp")
network = OrderedDict()
network['input'] = lasagne.layers.InputLayer(shape = shp, input_var = input_var)
# network = make_vgg16(network, 'model/vgg16_weights_from_caffe.h5')
# First conv and segmentation part
network['conv1_1'] = lasagne.layers.Conv2DLayer(network['input'],
num_filters = 64, filter_size = (3, 3),nonlinearity = lasagne.nonlinearities.rectify,
W=lasagne.init.GlorotUniform())
network['conv1_2'] = lasagne.layers.Conv2DLayer(network['conv1_1'],
num_filters = 64, filter_size = (3, 3), nonlinearity = lasagne.nonlinearities.rectify)
network['pool1_1'] = lasagne.layers.MaxPool2DLayer(network['conv1_2'], pool_size = (2, 2))
network['norm1_1'] = lasagne.layers.BatchNormLayer(network['pool1_1'])
network['conv1_3'] = lasagne.layers.Conv2DLayer(network['norm1_1'],
num_filters = 128, filter_size = (3, 3), nonlinearity = lasagne.nonlinearities.rectify)
network['conv1_4'] = lasagne.layers.Conv2DLayer(network['conv1_3'],
num_filters = 128, filter_size = (3, 3), nonlinearity = lasagne.nonlinearities.rectify)
network['pool1_2'] = lasagne.layers.MaxPool2DLayer(network['conv1_4'], pool_size = (2, 2))
network['norm1_2'] = lasagne.layers.BatchNormLayer(network['pool1_2'])
network['conv1_5'] = lasagne.layers.Conv2DLayer(network['norm1_2'],
num_filters = 256, filter_size = (3, 3), nonlinearity = lasagne.nonlinearities.rectify)
network['pool1_3'] = lasagne.layers.MaxPool2DLayer(network['conv1_5'], pool_size = (2, 2))
network['conv1_6'] = lasagne.layers.Conv2DLayer(network['pool1_3'],
num_filters = 256, filter_size = (3, 3), nonlinearity = lasagne.nonlinearities.rectify)
network['pool1_4'] = lasagne.layers.MaxPool2DLayer(network['conv1_6'], pool_size = (2, 2))
# Perspective Transform
network['norm2'] = lasagne.layers.BatchNormLayer(network['pool1_4'])
# network['cast'] = CastingLayer(network['norm2'], dtype)
theano.config.floatX = dtype
network['pfc2_1'] = lasagne.layers.DenseLayer(
lasagne.layers.dropout(network['norm2'], p = 0.05),
num_units = 1024, nonlinearity = lasagne.nonlinearities.rectify)
network['pfc2_2'] = lasagne.layers.DenseLayer(
lasagne.layers.dropout(network['pfc2_1'], p=0.05),
num_units = 1024, nonlinearity = lasagne.nonlinearities.rectify)
network['pfc2_3'] = lasagne.layers.DenseLayer(
lasagne.layers.dropout(network['pfc2_2'], p=0.05),
num_units = 1024, nonlinearity = lasagne.nonlinearities.rectify)
# loss target 2
network['pfc_out'] = lasagne.layers.DenseLayer(
lasagne.layers.dropout(network['pfc2_3'], p = 0.05),
num_units = 8, nonlinearity = lasagne.nonlinearities.rectify)
theano.config.floatX = 'float32'
predict = lasagne.layers.get_output(network['pfc_out'])
loss = T.sqrt(lasagne.objectives.squared_error(predict, psp).mean())
paras = lasagne.layers.get_all_params(network['pfc_out'], trainable = True)
updates = adam(loss, paras, [theano.shared(np.float32(0.0001)) for i in range(len(paras))])
ftrain = theano.function([input_var, psp], [loss, predict], updates = updates)
def get_inputs(meta, batch, path):
# batchidx = [keys[i] for i in batch]
input = np.array([read_image(path + 'patch/' + idx + '.jpg', shape = (256, 256))
for idx in batch]).astype(np.float32)
seg = np.array([read_image(path + 'pmask/' + idx + '.jpg', shape = (256, 256))
for idx in batch]).astype(np.float32)
dat = [meta[key] for key in batch]
Ps = np.array([np.array(dat[i][0]).flatten()[0 : 8] for i in range(len(batch))])
for P in Ps:
P[6 : 8] = (P[6 : 8] + 1e-3) * 1e4
return input, Ps
path = '/home/yancz/text_generator/data/real/'
dat, meta = load_data(path, 10000, False)
for epoch in range(10):
loss = 0
trs = 0
for batch in iterate_minibatch(dat['train'], 32, len(dat['train'])):
inputs = get_inputs(meta, batch, path)
l, valp = ftrain(*inputs)
log(l)
print(valp)
loss += l
trs += 1
loss /= trs
log('loss ' + str(epoch) + ' ' + str(l))
return ftrain
import numpy as np
import matplotlib.pyplot as plt
from sklearn.linear_model import LogisticRegression
from sklearn import datasets
from sklearn.preprocessing import StandardScaler
from matlab_port.utils import partition_data, shuffle_data
from train import load_data
X, y = load_data('data/ex4data1_conv.mat', 'numpy')
X, y = shuffle_data(X, y)
X, y, X_test, y_test = partition_data(X, y, split=.8)
classifier = LogisticRegression(C=.1)
classifier.fit(X, y)
z = classifier.predict(X_test)
print("Test accuracy: {acc:.1%}".format(acc=(sum(y_test == z) / y_test.size)))
import joblib
from os import path
from train import load_data
from sklearn.metrics import accuracy_score
DIR_NAME = path.dirname(__file__)
MODELS_FOLDER = path.join('.', 'models')
EXPERIMENT_NAME = path.join(MODELS_FOLDER, 'exp_01_default')
TRANFORMER_NAME = 'tf_std_default_v0.1.pkl'
MODEL_NAME = 'model_mlp_default_v0.1.pkl'
X, y = load_data()
# load models
tf = joblib.load(path.join(EXPERIMENT_NAME, TRANFORMER_NAME))
model = joblib.load(path.join(EXPERIMENT_NAME, MODEL_NAME))
X_tf = tf.transform(X)
y_hat = model.predict(X_tf)
print('accuracy score {}'.format(accuracy_score(y, y_hat)))
# EXTRA_QUERY='(adulterated | scandal | countries | fake)'
# Shooter queries
# EXTRA_QUERY='( injured | wounded | victim )'
# EXTRA_QUERY='( suspect | shooter | identified | arrested | charged )'
if __name__ == "__main__":
trainFile = sys.argv[1]
saveFile = sys.argv[2]
extra_query = sys.argv[3]
# load data and process identifiers
articles, identifiers = load_data(trainFile)
identifiers_tmp = []
titles = []
for e in identifiers:
e = e.split(",")
for i in range(NUM_ENTITIES):
try:
e[i] = int(e[i])
e[i] = inflect_engine.number_to_words(e[i])
except:
pass
identifiers_tmp.append(e[:NUM_ENTITIES])
titles.append(",".join(e[NUM_ENTITIES:]))
identifiers = identifiers_tmp
# download related files
batch_size = 16
nb_epoch_mask = 5
nb_epoch_mask_exist = 4
dropout_mask = .2
dropout_exist_mask = .15
basename = generate_basename(batch_size, nb_epoch_mask, nb_epoch_mask_exist,
dropout_mask, dropout_exist_mask)
weight_load = ''
mask_filename = ''
mask_exist_filename = ''
weight_mask_filename = ''
weight_mask_exist_filename = ''
imgs_train, imgs_mask_train, imgs_patient_train, imgs_test = load_data(data_path)
def multiply_mask(mask_filename, mask_exist_filename):
imgs_mask_test = np.load(mask_filename)
imgs_mask_exist_test = np.load(mask_exist_filename)
for n in range(len(imgs_mask_exist_test)):
if (imgs_mask_exist_test[n] == 0):
imgs_mask_test[n,0] = 0
return imgs_mask_test
score_mask_train = []
score_mask_val = []
score_exist_mask_train = []
score_exist_mask_val = []
for n in range(10):
def test(num_features, model_name):
df = train.load_data()
test_with_data(num_features, model_name, df)
print "reload helper"
reload(helper)
helper.load_constants()
print "end load helper"
retrain = True
if retrain:
num_blocks = 1
## num_blocks = 5
training_file = "../data/tagged_data/EMA/train.tag"
dev_file = "../data/tagged_data/EMA/dev.tag"
test_file = "../data/tagged_data/EMA/test.tag"
trained_model = "trained_model_crf.EMA.p"
print "load files"
train_data, train_identifier = train.load_data(training_file)
test_data, test_identifier = train.load_data(dev_file)
print "End load files"
prev_n = 2
next_n = 2
print "Start Feature extract on train set"
trainX, trainY = featureExtract(train_data,train_identifier, prev_n, next_n )
print "Done Feature extract on train set"
#trainX, trainY = featureExtract(dev_data, prev_n, next_n)
print "Start Feature extract on test set"
testX, testY = featureExtract(test_data, test_identifier, prev_n, next_n)
print "Done Feature extract on test set"
#testX, testY = featureExtract(train_data[split_index:], prev_n, next_n)
trainer = trainModel(1)
