def main():
# Set the log file for debuging use
utils.set_logger(os.path.join(os.getcwd(), 'train.log'))
logging.info('Loading datasets...')
data_loader = DataLoader(DATA_PATH)
X_train, Y_train, X_val, Y_val = data_loader.get_train_data()
X_test, Y_test = data_loader.get_test_data()
logging.info('Building the model...')
my_model = seq2class() # NEED TO PASS PARAMETERS SHIT
print("Here is our model: ")
print(my_model.model.summary())
logging.info('Training....')
history = my_model.model.fit(X_train, Y_train, epochs=EPOCHS, verbose=1, batch_size=BATCH_SIZE, validation_data=(X_val, Y_val))
logging.info(f"train loss: {history.history['loss']}")
logging.info(f"val loss: {history.history['val_loss']}")
logging.info(f"train accuracy: {history.history['acc']}")
logging.info(f"val accuracy: {history.history['val_acc']}")
# Plotting the loss history #
plot = utils.Plotting(history)
# plot.plot_loss()
# plot.plot_accuracy()
print('Testing...')
loss, accuracy = my_model.model.evaluate(X_test, Y_test)
logging.info('Testing loss', loss)
logging.info("Test accuracy", accuracy)
def evaluate_from_workspace(workspace_dir):
global args, data_loader
"""
Evaluate the model on the test set.
"""
data_dir = workspace_dir
model_dir = os.path.join(data_dir, "model")
# Load the parameters
args = parser.parse_args()
json_path = os.path.join(model_dir, 'params.json')
assert os.path.isfile(
json_path), "No json configuration file found at {}".format(json_path)
params = utils.Params(json_path)
params.data_dir = data_dir if data_dir else args.data_dir
params.model_dir = model_dir if model_dir else args.model_dir
# use GPU if available
params.cuda = torch.cuda.is_available() # use GPU is available
# Set the random seed for reproducible experiments
torch.manual_seed(230)
if params.cuda: torch.cuda.manual_seed(230)
# Get the logger
utils.set_logger(os.path.join(params.model_dir, 'evaluate.log'))
# Create the input data pipeline
logging.info("Creating the dataset...")
# load data
data_loader = DataLoader(params.data_dir, params)
data = data_loader.load_data_from_dir(['test'], params.data_dir)
test_data = data['test']
# specify the test set size
params.test_size = test_data['size']
test_data_iterator = data_loader.data_iterator(test_data, params)
logging.info("- done.")
# Define the model
model = net.Net(params).cuda() if params.cuda else net.Net(params)
loss_fn = net.loss_fn
metrics = net.metrics
logging.info("Starting evaluation")
# Reload weights from the saved file
utils.load_checkpoint(
os.path.join(params.model_dir, args.restore_file + '.pth.tar'), model)
# Evaluate
num_steps = (params.test_size + 1) // params.batch_size
test_metrics = evaluate(model, loss_fn, test_data_iterator, metrics,
params, num_steps)
save_path = os.path.join(params.model_dir,
"metrics_test_{}.json".format(args.restore_file))
utils.save_dict_to_json(test_metrics, save_path)
def setUp(self):
self.files = list(Path("../data").iterdir())
self.files = sorted(
self.files,
key=lambda x: int(
str(x).split("/")[-1].split(".")[0].split("_")[-1]))
self.dataloader = DataLoader(files=self.files)
from torch import device as dev
MODEL_DIR = 'experiments/base_model/'
DATA_DIR = 'data/'
params = utils.Params(MODEL_DIR + 'params.json')
params.vocab_size = 25
params.number_of_classes = 10
params.cuda = torch.cuda.is_available()
weights = MODEL_DIR + 'best.pth'
model = net.Net(params).cuda() if params.cuda else net.Net(params)
checkpoint = torch.load(weights, map_location=dev('cpu'))
model.load_state_dict(checkpoint['state_dict'])
data_loader = DataLoader(DATA_DIR, params)
data = data_loader.load_data(['train', 'val'], DATA_DIR)
train_data = data['train']
train_data_iterator = data_loader.data_iterator(train_data,
params,
shuffle=True)
train_batch, _ = next(train_data_iterator)
val_data = data['val']
val_data_iterator = data_loader.data_iterator(val_data, params, shuffle=False)
val_batch, _ = next(val_data_iterator)
explainer = shap.KernelExplainer(model.forward, train_batch[:1])
vals = train_batch[:10]
shap_values = explainer.shap_values(train_batch[:10])
shap.force_plot(explainer.expected_value[0], shap_values[0][0],
import test_context
import re
from model.data_loader import DataLoader
dl = DataLoader('data/xml/CoreOnly.xml')
for mon in dl.monsters:
for atype in [mon.traits, mon.actions, mon.reactions, mon.legendaries]:
for act in atype:
if type(act.text) is list:
t = ''
for at in act.text:
if at is not None:
t += at
elif act.text is not None:
t = act.text
if t:
m = re.search(r'[0-9]+d[0-9]+', t)
if m and act.attack is None:
print('{}: {}'.format(mon.name, act.name))
# use GPU if available
params.cuda = torch.cuda.is_available()
# Set the random seed for reproducible experiments
# torch.manual_seed(230)
# if params.cuda:
# torch.cuda.manual_seed(230)
# Set the logger
utils.set_logger(os.path.join(args.model_dir, 'train.log'))
# Create the input data pipeline
logging.info("Loading the datasets...")
# load data
data_loader = DataLoader(params)
data = data_loader.load_data(['train', 'dev'])
train_data = data['train']
val_data = data['dev']
# specify the train and val dataset sizes
params.train_size = train_data['size']
params.val_size = val_data['size']
logging.info("- done.")
# Define the model and optimizer
model = net.Net(params).cuda() if params.cuda else net.Net(params)
optimizer = optim.Adam(model.parameters(), lr=params.learning_rate)
# fetch loss function and metrics
# use GPU if available
params.cuda = torch.cuda.is_available()
# Set the random seed for reproducible experiments
# torch.manual_seed(230)
# if params.cuda: torch.cuda.manual_seed(230)
# Set the logger
utils.set_logger(os.path.join(args.model_dir, 'train.log'))
# Create the input data pipeline
logging.info("Loading the datasets...")
# load data
data_loader = DataLoader(params)
train_data_path = os.path.join(args.data_dir, 'train', 'train_data.json')
data_loader.load_data(train_data_path,
split='train',
size_limit=args.dataset_size_limit)
# data_loader.split_data(split_ratio=params.split_ratio)
val_data_path = os.path.join(args.data_dir, 'val', 'val_data.json')
data_loader.load_data(val_data_path,
'val',
size_limit=args.dataset_size_limit)
# specify the train and val dataset sizes( append in data_loader
params.train_size = data_loader.get_dataset_size('train')
params.val_size = data_loader.get_dataset_size('val')
logging.info("- done.")
params = utils.Params(json_path)
params.cuda = torch.cuda.is_available()
torch.manual_seed(230)
if params.cuda:
torch.cuda.manual_seed(230)
# Set the logger
utils.set_logger(os.path.join(args.model_dir, 'train.log'))
# Create the input data pipeline
logging.info("Loading the datasets...")
# load data
data_loader = DataLoader('data/', params)
data = data_loader.load_data(['train', 'val'], 'data/')
train_data = data['train']
val_data = data['val']
# specify the train and val dataset sizes
params.train_size = train_data['size']
params.val_size = val_data['size']
logging.info('- done.')
# Define the model and optimizer
model = net.Net(params).cuda() if params.cuda else net.Net(params)
optimizer = optim.Adam(model.parameters(), lr=params.learning_rate)
# fetch loss function and metrics
params = utils.Params(json_path) # In[3]: # use GPU if available params.cuda = torch.cuda.is_available() params.dict # In[4]: # load data data_loader = DataLoader(data_dir, params) data = data_loader.load_data(['train', 'val', 'test']) train_data = data['train'] val_data = data['val'] test_data = data['test'] # specify the train and val dataset sizes params.train_size = train_data['size'] params.val_size = val_data['size'] params.test_size = test_data['size'] params.pad_tag_ind = data_loader.tag_map[params.pad_tag] # In[5]:
# use GPU if available
params.cuda = torch.cuda.is_available() # use GPU is available
# Set the random seed for reproducible experiments
torch.manual_seed(230)
if params.cuda:
torch.cuda.manual_seed(230)
# Get the logger
utils.set_logger(os.path.join(args.model_dir, 'evaluate.log'))
# Create the input data pipeline
logging.info("Creating the dataset...")
# load data
data_loader = DataLoader('data/', params)
data = data_loader.load_data(['test'], 'data/')
test_data = data['test']
# specify the test set size
params.test_size = test_data['size']
test_data_iterator = data_loader.data_iterator(test_data, params)
logging.info("- done.")
# Define the model
model = net.Net(params).cuda() if params.cuda else net.Net(params)
loss_fn = net.loss_fn
metrics = net.metrics
params.number_of_classes = 10
params.cuda = torch.cuda.is_available()
# weights = MODEL_DIR + 'best.pth'
classes = [
'Extracellular', 'Plastid', 'Cytoplasm', 'Mitochondrion', 'Nucleus',
'Endoplasmic.reticulum', 'Golgi.apparatus', 'Cell.membrane',
'Lysosome/Vacuole', 'Peroxisome'
]
# model = net.Net(params).cuda() if params.cuda else net.Net(params)
# checkpoint = torch.load(weights, map_location=dev('cpu'))
# model.load_state_dict(checkpoint['state_dict'])
# model.eval()
loader = DataLoader(DATA_DIR, params)
# data = loader.load_data(['train', 'val'], DATA_DIR)
# train_data = data['train']
# train_data_iterator = loader.data_iterator(train_data, params, shuffle=False)
# train_batch, label_batch = next(train_data_iterator)
# sentences_file = DATA_DIR + 'train/sentences.txt'
# labels_file = DATA_DIR + 'train/labels.txt'
# sentences = []
# with open(sentences_file) as f:
# for sent in f.read().splitlines():
# sentences.append(sent)
# labels = []
# with open(labels_file) as f:
# for lab in f.read().splitlines():
# labels.append(lab)
token_reference = TokenReferenceBase(reference_token_idx=loader.pad_ind)
@staticmethod
def exact_match_score(outputs, data_batch):
preds = outputs_to_preds(outputs)
preds = preds_to_answers(preds, data_batch['tkd_c'])
scores = [
metric_max_over_ground_truths(exact_match_score, pred,
data_batch['gts'][i])
for i, pred in enumerate(preds)
]
return np.mean(scores)
@staticmethod
def f1_score(outputs, data_batch):
preds = outputs_to_preds(outputs)
preds = preds_to_answers(preds, data_batch['tkd_c'])
scores = [
metric_max_over_ground_truths(f1_score, pred, data_batch['gts'][i])
for i, pred in enumerate(preds)
]
return np.mean(scores)
if __name__ == '__main__':
from utils.model_utils import Params
params = Params('../model_dir/configs.json')
params.update('../data/dataset_configs.json')
from model.data_loader import DataLoader
data_loader = DataLoader(params)
_model = Model(params)
print(_model)
job_name = "emb{}_lr{}_k{}_bs{}_f{}_dr{}_{}_{}".format(
params.emb, params.learning_rate, params.kernels, params.batch_size,
params.filters, params.dropout, params.model, params.task)
# Create a new folder in parent_dir with unique_name "job_name"
model_dir = os.path.join(params.save_path, 'fold' + str(params.fold),
job_name)
if not os.path.exists(model_dir):
os.makedirs(model_dir)
# Set the logger
utils.set_logger(os.path.join(model_dir, 'train.log'))
logging.info("Loading the datasets...")
# load data
data_loader = DataLoader(params.local_data, params)
data = data_loader.load_data(['train', 'val', 'test'], params.local_data)
train_data = data['train']
val_data = data['val']
test_data = data['test']
# specify the train and val dataset sizes
params.train_size = train_data['size']
params.val_size = val_data['size']
logging.info("- done.")
# Define the model and optimizer
device = torch.device(
"cuda:0" if params.cuda else sys.exit("gpu unavailable"))
# Set the logger
job_name = "emb{}_lr{}_k{}_bs{}_f{}_dr{}_{}_{}".format(
params.emb, params.learning_rate, params.kernels, params.batch_size,
params.filters, params.dropout, params.model, params.task)
# Create a new folder in parent_dir with unique_name "job_name"
model_dir = os.path.join(params.save_path, 'fold' + str(params.fold),
job_name)
if not os.path.exists(model_dir):
os.makedirs(model_dir)
utils.set_logger(os.path.join(model_dir, 'eval.log'))
logging.info("Loading the datasets...")
# load data
data_loader = DataLoader(params.data_dir, params)
data = data_loader.load_data(['test'], params.data_dir)
test_data = data['test']
# specify the train and val dataset sizes
params.test_size = test_data['size']
logging.info("- done.")
# Define the model and optimizer
device = torch.device(
"cuda:0" if params.cuda else sys.exit("gpu unavailable"))
if params.model == "cnn_text":
if params.emb == 'w2v':
model = models.CNN_Text(data_loader.weights_w2v, params)
def predict_from_workspace(workspace_dir, input_data):
"""
Evaluate the model on the test set.
"""
global args, data_loader
data_dir = workspace_dir
model_dir = os.path.join(data_dir, "model")
# Load the parameters
args = parser.parse_args()
trgt_json_path = os.path.join(model_dir, 'params.json')
assert os.path.isfile(
trgt_json_path), "No json configuration file found at {}".format(
trgt_json_path)
params = utils.Params(trgt_json_path)
params.data_dir = data_dir if data_dir else args.data_dir
params.model_dir = model_dir if model_dir else args.model_dir
# use GPU if available
params.cuda = torch.cuda.is_available() # use GPU is available
# Set the random seed for reproducible experiments
torch.manual_seed(230)
if params.cuda: torch.cuda.manual_seed(230)
# Get the logger
utils.set_logger(os.path.join(params.model_dir, 'evaluate.log'))
# Create the input data pipeline
logging.info("Creating the dataset...")
# load data
data_loader = DataLoader(params.data_dir, params)
data = data_loader.load_data_for_predict(input_data)
batch_sentences = data["predict"]["data"]
# compute length of longest sentence in batch
batch_max_len = max([len(s) for s in batch_sentences])
# prepare a numpy array with the data, initialising the data with pad_ind and all labels with -1
# initialising labels to -1 differentiates tokens with tags from PADding tokens
batch_data = data_loader.pad_ind * np.ones(
(len(batch_sentences), batch_max_len))
# copy the data to the numpy array
for j in range(len(batch_sentences)):
cur_len = len(batch_sentences[j])
batch_data[j][:cur_len] = batch_sentences[j]
logging.info("- done.")
# Define the model
model = net.Net(params).cuda() if params.cuda else net.Net(params)
logging.info("Starting prediction")
# Reload weights from the saved file
utils.load_checkpoint(
os.path.join(params.model_dir, args.restore_file + '.pth.tar'), model)
# Evaluate
results = predict(model, batch_data)
return results
def train_from_workspace(workspace_dir):
global args, data_loader
data_dir = workspace_dir
model_dir = os.path.join(data_dir, "model")
# Load the parameters from json file
args = parser.parse_args()
src_json_path = os.path.join(args.model_dir, 'params.json')
assert os.path.isfile(src_json_path), "No json configuration file found at {}".format(src_json_path)
trgt_json_path = os.path.join(model_dir, 'params.json')
if not os.path.exists(model_dir):
print("Workspace Model Directory does not exist! Making directory {}".format(model_dir))
os.mkdir(model_dir)
else:
print("Workspace Model Directory exists! ")
shutil.copyfile(src_json_path, trgt_json_path)
params = utils.Params(trgt_json_path)
params.data_dir = data_dir if data_dir else args.data_dir
params.model_dir = model_dir if model_dir else args.model_dir
# use GPU if available
params.cuda = torch.cuda.is_available()
# Set the random seed for reproducible experiments
torch.manual_seed(230)
if params.cuda: torch.cuda.manual_seed(230)
# Set the logger
utils.set_logger(os.path.join(params.model_dir, 'train.log'))
# Create the input data pipeline
logging.info("Loading the datasets...")
# load data
data_loader = DataLoader(params.data_dir, params)
data = data_loader.load_data_from_dir(['train', 'val'], params.data_dir)
train_data = data['train']
val_data = data['val']
# specify the train and val dataset sizes
params.train_size = train_data['size']
params.val_size = val_data['size']
logging.info("- done.")
# Define the model and optimizer
model = net.Net(params).cuda() if params.cuda else net.Net(params)
optimizer = optim.Adam(model.parameters(), lr=params.learning_rate)
# fetch loss function and metrics
loss_fn = net.loss_fn
metrics = net.metrics
# Train the model
logging.info("Starting training for {} epoch(s)".format(params.num_epochs))
best_eval_acc = train_and_evaluate(model, train_data, val_data, optimizer, loss_fn, metrics, params, params.model_dir,
args.restore_file)
return best_eval_acc
from model.analize import Analize
from model.data_loader import DataLoader
import numpy as np
import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
import itertools as it
from pandas import DataFrame
X_trained = ['Milk', 'Frozen']
label = ['Customer']
from model.preprocess import Preprocess
data = DataLoader('./data.csv', ';', ',')
file = data.read_file()
prepared = Preprocess().prepare_input(X_trained, file)
model = Analize().fit(prepared)
rs = file['Milk']
rs = rs.to_frame(name='Milk')
rs2 = file['Frozen']
rs2 = rs2.to_frame(name='Frozen')
lab = file['Customer ']
lab = lab.to_frame(name='Customer')
rs3 = pd.DataFrame(model)
rs3.columns = ['Cluster']
# use GPU if available
params.cuda = torch.cuda.is_available() # use GPU is available
# Set the random seed for reproducible experiments
torch.manual_seed(230)
if params.cuda: torch.cuda.manual_seed(230)
# Get the logger
utils.set_logger(os.path.join(args.model_dir, 'evaluate.log'))
# Create the input data pipeline
logging.info("Creating the dataset...")
# load data
data_loader = DataLoader(args.data_dir, params)
data = data_loader.load_data(['test'], args.data_dir)
test_data = data['test']
# specify the test set size
params.test_size = test_data['size']
test_data_iterator = data_loader.data_iterator(test_data, params)
logging.info("- done.")
# Load embeddings
gen_emb = np.load(os.path.join(args.emb_dir, 'gen.npy'))
domain_emb = np.load(os.path.join(args.emb_dir, 'domain.npy'))
# Define the model
model = net.Net(params, gen_emb, domain_emb).cuda() if params.cuda else net.Net(params, gen_emb, domain_emb)
import test_context
from model.data_loader import DataLoader
dl = DataLoader('data/xml/CoreOnly.xml')
dl.print_stats()
# use GPU if available
params.cuda = torch.cuda.is_available()
# Set the random seed for reproducible experiments
torch.manual_seed(230)
if params.cuda: torch.cuda.manual_seed(230)
# Set the logger
utils.set_logger(os.path.join(args.model_dir, 'train.log'))
# Create the input data pipeline
logging.info("Loading the datasets...")
# load data
data_loader = DataLoader(args.data_dir, params)
data = data_loader.load_data(['train', 'val'], args.data_dir)
train_data = data['train']
val_data = data['val']
# specify the train and val dataset sizes
params.train_size = train_data['size']
params.val_size = val_data['size']
logging.info("- done.")
# Define the model and optimizer
model = net.Net(params).cuda() if params.cuda else net.Net(params)
torch.save(model, 'model_for_visulization.pth')
optimizer = optim.Adam(model.parameters(), lr=params.learning_rate)
# use GPU if available
params.cuda = torch.cuda.is_available() # use GPU is available
# Set the random seed for reproducible experiments
torch.manual_seed(230)
if params.cuda: torch.cuda.manual_seed(230)
# Get the logger
utils.set_logger(os.path.join(args.model_dir, 'evaluate.log'))
# Create the input data pipeline
logging.info("Creating the dataset...")
# load data
print(params)
data_loader = DataLoader(params)
data = data_loader.load_data(args.test_data_path)
# specify the test set size
params.test_size = data_loader.get_dataset_size('all')
test_data_iterator = data_loader.data_iterator(split='all', batch_size=params.batch_size)
logging.info("- done.")
# Define the model
model = net.Model(params).cuda() if params.cuda else net.Model(params)
loss_fn = model.loss_fn
metrics = {
'EM': model.exact_match_score,
'f1': model.f1_score
