def train(self, train_csv: str, mode: str):
self.config["task"] = "train"
self.config["mode"] = mode
self.config["model"] = {}
self.config["ensemble"] = {"lgb": 1}
self.config.tmp_dir = self.config.model_dir + "/tmp"
os.makedirs(self.config.tmp_dir, exist_ok=True)
# load holiday
path_holiday = './holiday.csv'
holiday = pd.read_csv(path_holiday, \
encoding='utf-8', low_memory=False, dtype={'holiday':str})['holiday'].values
self.config['holiday'] = set(holiday)
df = read_df(train_csv, self.config)
print(df.shape)
holiday_detect(df, self.config)
preprocess(df, self.config)
y = df["target"]
X = df.drop("target", axis=1)
train(X, y, self.config)
def train(self, train_csv: str, mode: str):
self.config["task"] = "train"
self.config["mode"] = mode
self.config.tmp_dir = self.config.model_dir + "/tmp"
os.makedirs(self.config.tmp_dir, exist_ok=True)
df = read_df(train_csv, self.config)
preprocess(df, self.config)
y = df["target"]
X = df.drop("target", axis=1)
train(X, y, self.config)
def train(self, train_csv: str, mode: str):
self.config["task"] = "train"
self.config["mode"] = mode
self.config.tmp_dir = self.config.model_dir + "/tmp"
os.makedirs(self.config.tmp_dir, exist_ok=True)
## prepare data
df = read_df(train_csv, self.config)
## preprecessing
preprocess(df, self.config)
y = df["target"]
X = df.drop("target", axis=1)
log('drop target')
log('####### cur time = ' + str(datetime.datetime.now().strftime("%Y/%m/%d %H:%M:%S")))
log('################## after FE #########################')
log(X.shape)
log('#####################################################')
train(X, y, self.config)
def train(self, train_csv: str, mode: str):
self.config["task"] = "train"
self.config["mode"] = mode
self.config[
"objective"] = "regression" if mode == "regression" else "binary"
self.config["metric"] = "rmse" if mode == "regression" else "auc"
self.config.tmp_dir = self.config.model_dir + "/tmp"
os.makedirs(self.config.tmp_dir, exist_ok=True)
df = read_df(train_csv, self.config)
df = preprocess(df, self.config)
y = df["target"].copy()
X = df.drop("target", axis=1).copy()
del df
gc.collect()
self.config["columns"] = list(X)
train(X, y, self.config)
### Formating Data
y_train_cat = np_utils.to_categorical(y_train)
y_test_cat = np_utils.to_categorical(y_test)
x_train = x_train.reshape((x_train.shape[0], depth, width, height)).astype('float32')
x_test = x_test.reshape((x_test.shape[0], depth, width, height)).astype('float32')
################################ PART 1 ################################
print("\n[PART 1]\n")
### Parameters
NUM_EPOCHS = 1
SIZE_BATCHS = 128
NUM_BATCHS = 60000 // SIZE_BATCHS
### Training Model
(trained_model, history) = model.train(x_train, y_train_cat, SIZE_BATCHS, NUM_EPOCHS,
width, height, depth, num_classes)
### Testing Model
(y_pred, accuracy) = model.fit(x_test, y_test_cat, trained_model, "output_0")
### Save and Plot results
graph.output_graphs(y_test, y_pred, history, NUM_BATCHS, "output_0")
################################ PART 2 ################################
print("\n[PART 2]\n")
### Store results
error_p2 = list()
std_list = list()
import pprint
import random
from lib import csv_helper
from lib import vectors
from modifiers import credit
from lib import model
pp = pprint.PrettyPrinter(width=150)
training_data_csv = csv_helper.csv_to_dict_list('datasets/training.csv')
test_data_csv = csv_helper.csv_to_dict_list('datasets/test.csv')
training_data = vectors.to_vectors(credit.modifier, training_data_csv)
test_data = vectors.to_vectors(credit.modifier, test_data_csv)
params = model.train(training_data, 8, 'class')
weights = params['weights']
bias = params['bias']
features = params['features']
# pp.pprint(bias)
# pp.pprint(weights)
predictions = list(
map(
lambda example: model.test(weights, bias, features, example['class'],
example), test_data))
correct = list(filter(lambda pred: pred == True, predictions))
accuracy = (len(correct) / len(test_data)) * 100
conv_2_2 = Conv(128,
128,
adjs_dist=self.placeholders['adj_dist_3'],
adjs_rad=self.placeholders['adj_rad_3'],
logging=self.logging)
max_pool_2 = MaxPool(size=4)
average_pool = AveragePool()
fc_1 = FC(128,
data.num_classes,
act=lambda x: x,
bias=False,
dropout=self.placeholders['dropout'],
logging=self.logging)
self.layers = [
conv_1_1, conv_1_2, max_pool_1, conv_2_1, conv_2_2, max_pool_2,
average_pool, fc_1
]
placeholders = generate_placeholders(BATCH_SIZE, LEVELS, NUM_FEATURES,
data.num_classes)
model = Model(placeholders=placeholders,
learning_rate=LEARNING_RATE,
train_dir=TRAIN_DIR,
log_dir=LOG_DIR)
train(model, data, preprocess_algorithm, BATCH_SIZE, DROPOUT,
AUGMENT_TRAIN_EXAMPLES, MAX_STEPS, PREPROCESS_FIRST, DISPLAY_STEP)
std_max = 0
burn_in = 0
if 'num_burn_in_steps' in optim_params:
burn_in = optim_params['num_burn_in_steps']
batch_evaluator = lib.evaluation.BatchEvaluator(test_loader,
burn_in=burn_in,
thinning=100)
# print('burn_in: ', burn_in)
state_accum = []
for epoch in range(1, epochs + 1):
t0 = time.time()
print('current_lr: ', current_lr)
model.train()
for data, target in train_loader:
step += 1
data = data.cuda()
target = target.cuda()
optimizer.zero_grad()
output = model(data)
loss = F.nll_loss(output, target)
loss.backward()
if precond:
precond.step()
if block_size > 0 and block_decay > 0 and lr_param:
optimizer.step(lr=current_lr)
else:
optimizer.step()
#!/usr/bin/env python3
import argparse
import logging
import pathlib
from fse import IndexedLineDocument
from fse.models import SIF
from lib import data, utils, model
import gensim.downloader as api
log = logging.getLogger("train_model")
EXPECTED_LINES = 66836199
if __name__ == "__main__":
utils.setup_logging()
parser = argparse.ArgumentParser()
parser.add_argument("-d", "--data", default=data.DEFAULT_OUTPUT_PREFIX,
help="Prefix of input data to read, default=" + data.DEFAULT_OUTPUT_PREFIX)
parser.add_argument("-o", "--output", default=model.DEFAULT_MODEL_FILE,
help="File name to save model, default=" + model.DEFAULT_MODEL_FILE)
args = parser.parse_args()
glove = api.load("glove-wiki-gigaword-100")
input_path = pathlib.Path(args.data).with_suffix(".txt")
sents = IndexedLineDocument(str(input_path))
model = SIF(glove, workers=2)
model.train(sents)
model.save(args.output)
