def convert_test_data(filename, alarm, ticket):
'''
Function converts the encoded hex values to numpy arrays.
Returns: converted data in form of numpy array.
Parameters:
filename: prediction filename
alarm: alarm file required by DataProcessor object
ticket: ticket file required by DataProcessor object
'''
dp = DataProcessor(alarm, ticket)
test_alarm_values = []
with open(filename, encoding='utf8') as csv_file:
reader = csv.reader(csv_file)
next(reader)
for row in reader:
test_alarm_values.append(row[3])
result_list = []
for item in test_alarm_values:
encoded_alarm = dp.encode_hex_values(item)
result_list.append(encoded_alarm)
return dp.convert_array_to_np_array(result_list)
def train(raw, flags):
data_processor = DataProcessor(flags.forecast_length, flags.batch_size,
flags.window)
train_loader, val_loader = data_processor.get_train_test_data(
raw, flags.validation_ratio)
model = DeepAR(cov_dim=data_processor.num_features,
hidden_dim=flags.num_units,
num_layers=flags.num_layers,
num_class=len(raw['type'].unique()),
embedding_dim=flags.embedding_size,
batch_first=True,
dropout=flags.dropout)
opt = torch.optim.Adam(model.parameters(), lr=flags.learning_rate)
teacher_ratio = flags.teacher_ratio
loss_history = []
loss_fn = gaussian_likelihood_loss
model, opt, start_epoch = load_checkpoint(flags.checkpoint_path, model,
opt)
if start_epoch >= flags.num_epochs:
print('start_epoch is larger than num_epochs!')
epoch = start_epoch
# TODO: add early stop
for epoch in range(start_epoch, flags.num_epochs):
for step, data in enumerate(train_loader):
avg_loss, _ = _forward(data, model, loss_fn, flags.window,
flags.forecast_length, teacher_ratio)
loss_history.append(avg_loss)
opt.zero_grad()
avg_loss.backward()
opt.step()
teacher_ratio *= flags.teacher_ratio_decay
validation_loss = evaluate(val_loader, model, loss_fn, flags.window,
flags.forecast_length)
print('Epoch: %d' % epoch)
print("Training Loss:%.3f" % avg_loss)
print("Validation Loss:%.3f" % validation_loss)
print('Teacher_ratio: %.3f' % teacher_ratio)
print()
print('Model training completed and save at %s' % flags.checkpoint_path)
state = {
'epoch': epoch + 1,
'state_dict': model.state_dict(),
'optimizer': opt.state_dict()
}
if not os.path.exists(flags.checkpoint_path):
os.mkdir(flags.checkpoint_path)
torch.save(state, flags.checkpoint_path + '/model.pt')
data_processor.save(flags.checkpoint_path)
return model, loss_history
def __init__(self, ip):
self.log = Logger(MAIN_CLIENT_LOG_FILE, D_VERB)
self.log.info('[MAIN THREAD] Instantiated client')
self.receiving = False
self.define_headers()
self.targets = {}
self.transmit = Queue.Queue()
self.data_client = DataClient(self.transmit, ip)
self.data_processor = DataProcessor(self.transmit, self.headers,
self.targets)
self.connect(ip)
def train(raw, flags):
data_processor = DataProcessor(flags.forecast_length, flags.batch_size, flags.window)
train_loader, val_loader = data_processor.get_train_test_data(raw,
flags.validation_ratio)
encoder = EncoderRNN(data_processor.num_features + 1, flags.num_units)
decoder = DecoderRNN(data_processor.num_features + 1, flags.num_units,
output_size=flags.output_dim, batch_first=True)
def init_weights(m):
for name, param in m.named_parameters():
torch.nn.init.uniform_(param.data, -0.1, 0.1)
encoder.apply(init_weights)
decoder.apply(init_weights)
loss_fn = torch.nn.MSELoss()
# loss_fn = SMAPE()
model_params = list(encoder.parameters()) + list(decoder.parameters())
opt = torch.optim.Adam(model_params, lr=flags.learning_rate)
teacher_ratio = flags.teacher_ratio
loss_history = []
# model, opt, start_epoch = load_checkpoint(flags.checkpoint_path, model, opt)
# if start_epoch >= flags.num_epochs:
# print('start_epoch is larger than num_epochs!')
start_epoch = 0
epoch = start_epoch
# TODO: add early stop
for epoch in range(start_epoch, flags.num_epochs):
for step, data in enumerate(train_loader):
avg_loss, _, acc = _forward(data, [encoder, decoder], loss_fn, flags.window,
flags.forecast_length, True, teacher_ratio=teacher_ratio)
loss_history.append(avg_loss)
opt.zero_grad()
avg_loss.backward()
torch.nn.utils.clip_grad_norm_(model_params, 5.0)
opt.step()
teacher_ratio *= flags.teacher_ratio_decay
val_loss, val_acc = evaluate(val_loader, [encoder, decoder], loss_fn, flags.window,
flags.forecast_length)
print('Epoch: %d' % epoch)
print("Training Loss:%.3f" % avg_loss)
print('Training Avg Accuracy:%.3f' % acc)
print("Validation Loss:%.3f" % val_loss)
print("Validation Accuracy:%.3f" % val_acc)
print('Teacher_ratio: %.3f' % teacher_ratio)
# print('Model training completed and save at %s' % flags.checkpoint_path) # state = {'epoch': epoch + 1, 'state_dict': model.state_dict(), 'optimizer': opt.state_dict()} # if not os.path.exists(flags.checkpoint_path): # os.mkdir(flags.checkpoint_path) # torch.save(state, flags.checkpoint_path + '/model.pt') # data_processor.save(flags.checkpoint_path) # return model, loss_history
print('Gradients:%.3f' % torch.mean(
(torch.stack([torch.mean(torch.abs(p.grad)) for p in model_params], 0))))
print()
def main_execute():
trading_advisor = TradingAdvisor()
intervals = [20, 60, 120]
processor = DataProcessor(intervals)
subset_df = processor.get_dataframe_subset(500)
advisor.calc_buy_sell(intervals, subset_df)
processor.plot(subset_df)
processor.db.close()
def main_execute():
advisor = Advisor()
intervals = [60, 120, 720]
processor = DataProcessor(intervals)
subset_df = processor.get_dataframe_subset(3500)
advisor.calc_buy_sell(intervals, subset_df)
processor.plot(subset_df)
processor.db.close()
def __init__(self, split, args):
"""
Initialise the MetaLearningDataset class
Parameters
----------
split : str
'train' or 'test'
args : parsed args
args passed in to the model
"""
self.data_dir = "../../../datascience-projects/internal/multitask_learning/processed_data/Streetbees_Mood/Streetbees_Mood_all.csv"
data = pd.read_csv(self.data_dir, index_col=0)
self.data_processor = DataProcessor(data_dir=None, data_name='Streetbees_Mood', labels=['0','1'])
self.K = args.K
self.num_classes = args.num_classes
self.baseLM_name = args.model_name.split("-")[0]
do_lower_case = False if args.model_name.split("-")[-1] == "cased" else True
self.tokenizer = TOKENIZERS[self.baseLM_name].from_pretrained(args.model_name, do_lower_case=do_lower_case)
self.device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
# Pick categories for dev and test set
ALL_CATEGORIES = np.unique(data['category'])
TEST_CATEGORIES = ['Healthy', 'Positive', 'Unwell', 'Fine']
self.tasks = {}
for category in ALL_CATEGORIES:
if (split == 'train' and category in TEST_CATEGORIES) or (split == 'test' and category not in TEST_CATEGORIES):
continue
# Each category will become a separate task. Explicitly redefine variable below for clarity
task = category
pos_examples, neg_examples = self.get_positive_and_negative_examples(data, category=task)
if task not in self.tasks:
self.tasks[task] = (pos_examples, neg_examples)
task_list = []
task_names = []
for task in self.tasks:
pos_examples = self.tasks[task][0]
neg_examples = self.tasks[task][1]
if len(pos_examples) < self.K or len(neg_examples) < self.K:
print('not enough examples', task)
continue # skip for now if not enough examples
task_list.append((pos_examples, neg_examples))
task_names.append(task)
self.tasks = task_list
self.task_names = task_names
self.num_tasks = len(self.tasks)
def infer(raw, flags):
"""
:param raw:
:param flags:
:return:
"""
data_processor = DataProcessor.load(flags.checkpoint_path)
model = LSTM(data_processor.num_features,
flags.num_units,
output_dim=flags.output_dim,
num_layers=flags.num_layers,
batch_first=True,
dropout=flags.dropout)
model, _, _ = load_checkpoint(flags.checkpoint_path, model, None)
model.eval()
# predict
results = {}
loader, ts_types = data_processor.get_forecast_data(raw)
with torch.no_grad():
for type, data in zip(ts_types, loader):
scale = data[4]
_, outputs = _infer(data, model, flags.window,
flags.forecast_length)
results[type] = [(output * scale).detach().numpy()[0]
for output in outputs]
print(results)
return results
def run_k_folds(self, n_runs=5, n_folds=2):
dp = DataProcessor()
dp.load('data/SQuAD/squad-v7.file')
model = LogRegModel()
model.load_vectors(dp.articles, n_folds=n_folds)
baseline_results = []
sentiment_results = []
for run in range(n_runs):
print("k-fold run:", run)
baseline_results.append(model.run_k_fold(with_sentiment=False))
sentiment_results.append(model.run_k_fold())
model.create_new_folds(n_folds=n_folds)
self.save_results(baseline_results, "results/5x2_baseline")
self.save_results(sentiment_results, "results/5x2_sentiment")
def get_data(
model_args,
training_args,
tokenizer,
text_data_path="../data/test_dataset"): # 경로 변경 ../data/test_dataset
"""
get data
Args:
model_args: model arguments
training_args: training arguments
tokenizer: tokenizer
text_data_path: Defaults to "../data/test_dataset"
Returns:
text_data, val_iter, val_dataset, scores
"""
text_data = load_from_disk(text_data_path)
# run_ lasticsearch
if "elastic" in model_args.retrieval_type:
is_sentence_trainformer = False
if "sentence_trainformer" in model_args.retrieval_type:
is_sentence_trainformer = True
# number of text to concat
concat_num = model_args.retrieval_elastic_num
text_data, scores = run_elasticsearch(text_data, concat_num,
model_args,
is_sentence_trainformer)
elif model_args.retrieval_type == "dense":
concat_num = model_args.retrieval_elastic_num
text_data, scores = run_concat_dense_retrival(text_data, concat_num)
column_names = text_data["validation"].column_names
data_collator = (DataCollatorWithPadding(
tokenizer, pad_to_multiple_of=8 if training_args.fp16 else None))
# 데이터 tokenize(mrc 모델안에 들어 갈 수 있도록)
data_processor = DataProcessor(tokenizer)
val_text = text_data["validation"]
val_dataset = data_processor.val_tokenzier(val_text, column_names)
val_iter = DataLoader(val_dataset, collate_fn=data_collator, batch_size=1)
return text_data, val_iter, val_dataset, scores
def __init__(self, ip):
self.log = Logger(MAIN_CLIENT_LOG_FILE, D_VERB)
self.log.info('[MAIN THREAD] Instantiated client')
self.receiving = False
self.define_headers()
self.targets = {}
self.transmit = Queue.Queue()
self.data_client = DataClient(self.transmit, ip)
self.data_processor = DataProcessor(self.transmit, self.headers, self.targets)
self.connect(ip)
def load_rf_data(cur_path):
data_folder = "data\\titanic"
processed_data_folder = os.path.join(cur_path, data_folder)
# Note: Not using test.csv as it does not provide whether or not the passenger survived; therefore we cannot assess
# how well the model performed.
data_file_path = os.path.join(processed_data_folder, "train.csv")
data = DataProcessor(data_file_path, processed_data_folder)
try:
#Try to load data
data.load_processed_data()
except FileNotFoundError:
#No data found, so process it
# 10% test, 10% validation, 80% training samples from data
splits = (0.1, 0.1, 0.8)
# Only use certain columns
use_cols = ( # 0, #PassengerID
1, # Survived
2, # Pclass
# 3, #Name
4, # Sex
5, # Age
6, # SibSp
7, # Parch
# 8, #Ticket
9, # Fare
# 10, #Cabin
11, # Embarked
)
# Mark features as categorical (so we can one-hot-encode them later)
# categorical_cols = ()
categorical_cols = (2, # Pclass
4, # Sex
11 # Embarked
)
# Convert certain columns to float values (so we can use numpy arrays)
converters = {4: lambda sex: {'male': 0.0, 'female': 1.0}[sex],
11: lambda embarked: {'S': 0.0, 'C': 1.0, 'Q': 2.0}[embarked]}
data.process_data(splits=splits, use_cols=use_cols, categorical_cols=categorical_cols, converters=converters,
filter_missing=True)
return data
def run_gru(s):
x_dims = len(x_cols[s.dataSet]) if s.dataSet in x_cols else s.lookback
random.seed(6)
np.random.seed(6)
rnn = Sequential()
rnn.add(
GRU(s.nodes,
input_shape=(None, x_dims),
kernel_initializer='he_uniform',
stateful=False))
#rnn.add(Dropout(0.15))
rnn.add(Dense(1, kernel_initializer='he_uniform'))
opt = adam(lr=s.lr, decay=0.0) #1e-3)
rnn.compile(loss='mae', optimizer=opt)
# prepare dataset as pyBrain sequential dataset
sequence = readDataSet(s.dataSet, s.dataSetDetailed, s)
if s.limit_to:
sequence = sequence[:s.limit_to]
dp = DataProcessor()
# standardize data by subtracting mean and dividing by std
#(meanSeq, stdSeq) = dp.normalize('data', sequence)
dp.windowed_normalize(sequence)
for key in sequence.keys():
if key != "data":
dp.normalize(key, sequence)
predictedInput = np.zeros((len(sequence), ))
targetInput = np.zeros((len(sequence), ))
trueData = np.zeros((len(sequence), ))
if s.dataSet in differenceSets:
predictedInputNodiff = np.zeros((len(sequence), ))
targetInputNodiff = np.zeros((len(sequence), ))
if s.dataSet in differenceSets:
backup_sequence = sequence
sequence = dp.difference(sequence, s.lookback)
allX = getX(sequence, s)
allY = np.array(sequence['data'])
allX = allX[48:]
allY = allY[48:]
#if s.dataSet not in x_cols:
# allY = allY[s.lookback:]
trainX = allX[0:s.nTrain]
trainY = allY[s.predictionStep:s.nTrain + s.predictionStep]
trainX = np.reshape(trainX, (trainX.shape[0], 1, trainX.shape[1]))
curBatch = 1.0
callback = LossCallback()
temp_set = np.array(sequence['data'])[:48 + s.nTrain + 5]
configure_batches(48, s.batch_size,
np.reshape(temp_set, (temp_set.shape[0], 1, 1)))
rnn.fit(trainX,
trainY,
epochs=s.epochs,
batch_size=s.batch_size,
verbose=min(s.max_verbosity, 2),
callbacks=[callback])
for i in xrange(0, s.nTrain):
targetInput[i] = allY[i + s.predictionStep]
for i in tqdm(xrange(s.nTrain + s.predictionStep, len(allX)),
disable=s.max_verbosity == 0):
if i % s.retrain_interval == 0 and i > s.numLags + s.nTrain and s.online:
trainX = allX[i - s.nTrain - s.predictionStep:i - s.predictionStep]
trainY = allY[i - s.nTrain:i]
trainX = np.reshape(trainX, (trainX.shape[0], 1, trainX.shape[1]))
temp_set = np.array(sequence['data'])[i - s.nTrain -
s.predictionStep - 48:i]
configure_batches(48, s.batch_size,
np.reshape(temp_set, (temp_set.shape[0], 1, 1)))
rnn.fit(trainX,
trainY,
epochs=s.epochs,
batch_size=s.batch_size,
verbose=2,
callbacks=[callback])
targetInput[i] = allY[i + s.predictionStep]
predictedInput[i] = rnn.predict(np.reshape(allX[i], (1, 1, x_dims)))
if s.dataSet in differenceSets:
predictedInputNodiff[i] = predictedInput[i]
targetInputNodiff[i] = targetInput[i]
predictedInput[i] = dp.inverse_difference(backup_sequence['data'],
predictedInput[i], i - 1)
targetInput[i] = dp.inverse_difference(backup_sequence['data'],
targetInput[i], i - 1)
predictedInput[0] = 0
trueData[i] = sequence['data'][i]
#predictedInput = dp.denormalize(predictedInput, meanSeq, stdSeq)
#targetInput = dp.denormalize(targetInput, meanSeq, stdSeq)
dp.windowed_denormalize(predictedInput, targetInput)
if s.dataSet in differenceSets:
# predictedInputNodiff = dp.denormalize(predictedInputNodiff)
# targetInputNodiff = dp.denormalize(targetInputNodiff)
pass
#trueData = (trueData * stdSeq) + meanSeq
dp.saveResultToFile(s.dataSet, predictedInput, targetInput, 'gru',
s.predictionStep, s.max_verbosity)
skipTrain = error_ignore_first[s.dataSet]
from plot import computeSquareDeviation
squareDeviation = computeSquareDeviation(predictedInput, targetInput)
squareDeviation[:skipTrain] = None
nrmse = np.sqrt(np.nanmean(squareDeviation)) / np.nanstd(targetInput)
if s.max_verbosity > 0:
print "", s.nodes, "NRMSE {}".format(nrmse)
mae = np.nanmean(np.abs(targetInput - predictedInput))
if s.max_verbosity > 0:
print "MAE {}".format(mae)
if s.dataSet in differenceSets:
dp.saveResultToFile(s.dataSet, predictedInputNodiff, targetInputNodiff,
'gru_nodiff', s.predictionStep, s.max_verbosity)
squareDeviation = computeSquareDeviation(predictedInputNodiff,
targetInputNodiff)
squareDeviation[:skipTrain] = None
nrmse = np.sqrt(
np.nanmean(squareDeviation)) / np.nanstd(targetInputNodiff)
if s.max_verbosity > 0:
print "", s.nodes, "NRMSE {}".format(nrmse)
mae = np.nanmean(np.abs(targetInputNodiff - predictedInputNodiff))
if s.max_verbosity > 0:
print "MAE {}".format(mae)
mase = errors.get_mase(predictedInput, targetInput,
np.roll(targetInput, 24))
if s.max_verbosity > 0:
print "MAE {}".format(mae)
return nrmse
def train(raw, flags):
data_processor = DataProcessor(flags.forecast_length, flags.batch_size,
flags.window)
train_loader, val_loader = data_processor.get_train_test_data(
raw, if_scale=True, val_ratio=flags.validation_ratio)
model = LSTM(data_processor.num_features,
flags.num_units,
output_dim=flags.output_dim,
num_layers=flags.num_layers,
batch_first=True,
dropout=flags.dropout)
if flags.loss == 'mse':
loss_fn = torch.nn.MSELoss()
else:
loss_fn = SMAPE()
opt = torch.optim.Adam(model.parameters(), lr=flags.learning_rate)
teacher_ratio = flags.teacher_ratio
loss_history = []
model, opt, start_epoch = load_checkpoint(flags.checkpoint_path, model,
opt)
if start_epoch >= flags.num_epochs:
print('start_epoch is larger than num_epochs!')
epoch = start_epoch
# TODO: add early stop
for epoch in range(start_epoch, flags.num_epochs):
for step, data in enumerate(train_loader):
avg_loss, _, acc = _forward(data, model, loss_fn, flags.window,
flags.forecast_length, True,
teacher_ratio)
loss_history.append(avg_loss)
opt.zero_grad()
avg_loss.backward()
torch.nn.utils.clip_grad_norm_(model.parameters(), 5.0)
opt.step()
teacher_ratio *= flags.teacher_ratio_decay
val_loss, val_acc = evaluate(val_loader, model, loss_fn, flags.window,
flags.forecast_length)
print('Epoch: %d' % epoch)
print("Training Loss:%.3f" % avg_loss)
print('Training Avg Accuracy:%.3f' % acc)
print("Validation Loss:%.3f" % val_loss)
print("Validation Accuracy:%.3f" % val_acc)
print('Teacher_ratio: %.3f' % teacher_ratio)
print('Gradients:%.3f' % torch.mean((torch.stack(
[torch.mean(torch.abs(p.grad)) for p in model.parameters()], 0))))
print()
print('Model training completed and save at %s' % flags.checkpoint_path)
state = {
'epoch': epoch + 1,
'state_dict': model.state_dict(),
'optimizer': opt.state_dict()
}
if not os.path.exists(flags.checkpoint_path):
os.mkdir(flags.checkpoint_path)
torch.save(state, flags.checkpoint_path + '/model.pt')
data_processor.save(flags.checkpoint_path)
return model, loss_history
def run_gru(s):
s.print_settings()
prob = tf.placeholder_with_default(
1.0, shape=()) #Retain probability for TF dropout
start_time = timeit.default_timer()
if s.implementation == "keras":
if s.use_binary:
raise Exception("Binary Keras not implemented")
input = Input(shape=(1, s.x_dims))
dense1 = Dense(s.nodes, activation='sigmoid')(input)
dense2 = Dense(s.nodes, activation='sigmoid')(input)
dense3 = Dense(s.nodes, activation='tanh')(input)
mult1 = Multiply()([dense2, dense3])
act1 = Activation('tanh')(mult1)
mult2 = Multiply()([dense1, act1])
reshape = Reshape((s.nodes, ))(mult2)
dropout = Dropout(0.5)(reshape)
dense_out = Dense(1)(dropout)
rnn = Model(inputs=[input], outputs=[dense_out])
opt = adam(lr=s.lr, decay=0.0,
epsilon=s.adam_eps) #, clipvalue=1.)#1e-3)
#opt = rmsprop(lr=s.lr)
rnn.compile(loss=s.loss, optimizer=opt)
if s.max_verbosity > 0:
print(rnn.summary())
else:
raise Exception("Unknown implementation " + s.implementation)
sequence = readDataSet(s.dataSet, s.dataSetDetailed, s).values
if s.limit_to:
sequence = sequence[:s.limit_to]
#Get rid of unneeded columns
sequence = sequence[:, 0:s.feature_count]
#sequence[-1000,0] = 666
#print "Changed -1000 to 666"
"""
We need to leave some values unpredicted in front so that
- We can fill the lookback window for each prediction
- We can get the value from 1 season earlier for MASE
--> Don't use the first `front_buffer` values as prediction
--> Independent from `prediction_step`, so the first actual value predicted is `front_buffer`\
plus however many steps the `prediction_step` is higher than 1
In other words, the most recent X-value for the first prediction will be the final value in the `front_buffer`
"""
first_prediction_index = s.front_buffer + s.predictionStep - 1
targetInput = sequence[
first_prediction_index:,
0].copy() #grab this now to avoid having to denormalize
dp = DataProcessor()
if s.normalization_type == 'default':
(meanSeq, stdSeq) = dp.normalize(
sequence, s.nTrain if s.cutoff_normalize else len(sequence))
elif s.normalization_type == 'windowed':
dp.windowed_normalize(sequence, columns=[0])
if s.feature_count > 1:
dp.normalize(sequence, s.nTrain, columns=range(1, s.feature_count))
elif s.normalization_type == 'AN':
an = AdaptiveNormalizer(s.lookback, s.lookback + s.predictionStep)
an.set_pruning(False)
an.set_source_data(sequence, s.nTrain)
an.do_ma('s')
an.do_stationary()
an.remove_outliers()
seq_norm = an.do_adaptive_normalize()
print seq_norm.shape
if s.feature_count > 1:
dp.normalize(sequence, s.nTrain, columns=range(1, s.feature_count))
start = sequence.shape[0] - seq_norm.shape[
0] - s.lookback - s.predictionStep + 1
for i in range(seq_norm.shape[0]):
seq_norm[i, :,
1:s.feature_count] = sequence[start + i:start + i +
seq_norm.shape[1],
1:s.feature_count]
#an.do_ma('s')
#an.do_stationary()
#an.remove_outliers()
#seq_norm = an.do_adaptive_normalize()
#print seq_norm[15000,0,0]
#exit(1)
else:
raise Exception("Unsupported normalization type: " +
s.normalization_type)
#seq_actual = sequence[s.front_buffer:] #Leave enough headroom for MASE calculation and lookback
#seq_full_norm = np.reshape(sequence[:,0], (sequence.shape[0],))
#seq_actual_norm = seq_full_norm[s.front_buffer:]
if s.normalization_type != "AN":
#Default and windowed change the seq itself but still require creating lookback frames
allX = getX(sequence, s)
allY = sequence[first_prediction_index:, 0]
else:
#AN creates a new array but takes care of lookback internally
allX = seq_norm[:, 0:-s.predictionStep]
allY = np.reshape(seq_norm[:, -1, 0], (-1, ))
predictedInput = np.full((len(allY), ),
np.nan) #Initialize all predictions to NaN
#print "TESTT", allX[15000,0,1:]
print "FIRST", allX[875]
trainX = allX[:s.nTrain]
trainY = allY[:s.nTrain]
#print "FIRST", trainX[0], trainY[0]
trainX = np.reshape(trainX, s.actual_input_shape_train)
trainY = np.reshape(trainY, s.actual_output_shape_train)
#print "FIRST", trainX[0], trainY[0]
if s.implementation == "keras":
#for _ in tqdm(range(s.epochs)):
for _ in range(1):
rnn.fit(
trainX,
trainY,
epochs=s.epochs,
batch_size=s.batch_size,
verbose=min(s.max_verbosity, 2),
shuffle=not s.stateful
) #, validation_data=(trainX, trainY), callbacks=[TensorBoard(log_dir='./logs', histogram_freq=1, write_grads=True)])
if s.stateful:
rnn_layer.reset_states()
# for layer in rnn.layers:
# print layer.get_weights()
#for i in xrange(0, s.nTrain + s.predictionStep):
# rnn.predict(np.reshape(allX[i], (1, 1, x_dims)))
#predictedInput[s.nTrain + s.predictionStep : len(allX)] = rnn.predict(np.reshape(allX[s.nTrain + s.predictionStep : len(allX)], (1, 12510, x_dims)))
latestStart = None
do_non_lookback = True
latest_onego = 0
#buffer = s.retrain_interval / 2
buffer = 0
for i in tqdm(xrange(s.nTrain + s.predictionStep, len(allX)),
disable=s.max_verbosity == 0):
if i % s.retrain_interval == 0 and s.online and i > s.nTrain + s.predictionStep + buffer:
do_non_lookback = True
if s.normalization_type == 'AN':
#print "TEST", seq_norm[15000,0,1]
predictedInput = np.array(
an.do_adaptive_denormalize(
predictedInput, therange=(i - s.retrain_interval, i)))
latestStart = i
an.set_ignore_first_n(i - s.nTrain - s.predictionStep)
an.do_ma('s')
an.do_stationary()
an.remove_outliers()
seq_norm = an.do_adaptive_normalize()
print seq_norm[15000, 0, 0]
print seq_norm.shape
#exit(1)
#print "FIRST", seq_norm[i-s.nTrain -s.predictionStep,0]#, trainY[0]
#print sequence[start+i-s.nTrain-s.predictionStep:start+
if s.feature_count > 1:
#dp.normalize(sequence, s.nTrain, columns=range(1,s.feature_count))
start = sequence.shape[0] - seq_norm.shape[
0] - s.lookback - s.predictionStep + 1
for j in range(seq_norm.shape[0]):
seq_norm[j, :, 1:s.feature_count] = sequence[
start + j:start + j + seq_norm.shape[1],
1:s.feature_count]
#print "FIRST", seq_norm[i-s.nTrain -s.predictionStep,0]#, trainY[0]
allX = seq_norm[:, 0:-s.predictionStep]
allY = np.reshape(seq_norm[:, -1, 0], (-1, ))
if s.lookback:
trainX = allX[i - s.nTrain - s.predictionStep:i -
s.predictionStep]
trainY = allY[i - s.nTrain - s.predictionStep:i -
s.predictionStep]
else:
trainX = allX[i - s.nTrain - s.predictionStep:i -
s.predictionStep]
trainY = allY[i - s.nTrain - s.predictionStep:i -
s.predictionStep]
#print "TESTT", allX[15000,0,:]
print "at", i - s.nTrain - s.predictionStep
print "FIRST", allX[875] #, trainY[0]
#exit(1)
trainX = np.reshape(trainX, s.actual_input_shape_train)
trainY = np.reshape(trainY, s.actual_output_shape_train)
#print "FIRST", trainX[0], trainY[0]
#exit(1)
if s.implementation == "keras":
if s.reset_on_retrain:
input = Input(shape=(1, s.x_dims))
dense1 = Dense(s.nodes, activation='sigmoid')(input)
dense2 = Dense(s.nodes, activation='sigmoid')(input)
dense3 = Dense(s.nodes, activation='tanh')(input)
mult1 = Multiply()([dense2, dense3])
act1 = Activation('tanh')(mult1)
mult2 = Multiply()([dense1, act1])
reshape = Reshape((s.nodes, ))(mult2)
dropout = Dropout(0.5)(reshape)
dense_out = Dense(1)(dropout)
rnn = Model(inputs=[input], outputs=[dense_out])
opt = adam(lr=s.lr, decay=0.0,
epsilon=s.adam_eps) # , clipvalue=1.)#1e-3)
#opt = rmsprop(lr=s.lr)
rnn.compile(loss=s.loss, optimizer=opt)
for _ in range(1):
rnn.fit(trainX,
trainY,
epochs=s.epochs_retrain
if s.epochs_retrain else s.epochs,
batch_size=s.batch_size,
verbose=2,
shuffle=not s.stateful)
if s.stateful:
rnn_layer.reset_states()
if s.lookback:
if s.implementation == "keras":
predictedInput[i] = rnn.predict(
np.reshape(allX[i], s.predict_input_shape))
elif do_non_lookback:
do_non_lookback = False
up_to = min(allX.shape[0], i - (i % s.retrain_interval) +
s.retrain_interval) if s.online else allX.shape[0]
start_time = time.time()
#print allX[0]
start = 0 if s.refeed_on_retrain else latest_onego
new_predicts = rnn.predict(
np.reshape(allX[start:up_to], (1, -1, s.x_dims)))
new_predicts = np.reshape(new_predicts, (new_predicts.shape[1], ))
predictedInput[i:up_to] = new_predicts[-(up_to - i):]
latest_onego = up_to
for i in range(s.nTrain + s.predictionStep):
predictedInput[i] = np.nan
if s.normalization_type == 'default':
predictedInput = dp.denormalize(predictedInput, meanSeq[0], stdSeq[0])
elif s.normalization_type == 'windowed':
dp.windowed_denormalize(predictedInput, pred_step=s.predictionStep)
elif s.normalization_type == 'AN':
if latestStart:
predictedInput = np.array(
an.do_adaptive_denormalize(predictedInput,
therange=(latestStart,
len(predictedInput))))
else:
predictedInput = np.array(
an.do_adaptive_denormalize(predictedInput))
if an.pruning:
targetInput = np.delete(targetInput, an.deletes)
print "Final time", (timeit.default_timer() - start_time)
#print "Last not to change:", predictedInput[-996], targetInput[-996]
#print "First to change:", predictedInput[-995], targetInput[-995]
dp.saveResultToFile(s.dataSet, predictedInput, targetInput, 'gru',
s.predictionStep, s.max_verbosity)
for ignore in s.ignore_for_error:
skipTrain = ignore
from plot import computeSquareDeviation
squareDeviation = computeSquareDeviation(predictedInput, targetInput)
squareDeviation[:skipTrain] = None
nrmse = np.sqrt(np.nanmean(squareDeviation)) / np.nanstd(targetInput)
if s.max_verbosity > 0:
print "", s.nodes, "NRMSE {}".format(nrmse)
mae = np.nanmean(np.abs(targetInput - predictedInput))
if s.max_verbosity > 0:
print "MAE {}".format(mae)
mape = errors.get_mape(predictedInput, targetInput, skipTrain)
if s.max_verbosity > 0:
print "MAPE {}".format(mape)
mase = errors.get_mase(predictedInput, targetInput,
np.roll(targetInput, s.season), skipTrain)
if s.max_verbosity > 0:
print "MASE {}".format(mase)
return mase
def data_preprocess():
with DataProcessor() as dp:
line_file_path = os.path.join(DATA_PATH, LINE_FILE)
conv_file_path = os.path.join(DATA_PATH, CONV_FILE)
dp.prepare_text_data(line_file_path, conv_file_path, PROCESSED_PATH, TESTSET_SIZE)
dp.process_data(PROCESSED_PATH, THRESHOLD, UNK_SYM, SOS, EOS)
import json
from flask import Flask, abort, request
from db_engine import Engine
from data_processing import DataProcessor
app = Flask(__name__)
engine = Engine()
processor = DataProcessor()
@app.after_request
def build_response(resp):
resp.headers['Access-Control-Allow-Origin'] = '*'
resp.headers[
'Access-Control-Allow-Headers'] = 'Origin, X-Requested-With, Content-Type, Accept'
resp.headers['Access-Control-Allow-Methods'] = 'GET, POST, DELETE'
return resp
# Here begins the routing and corresponding calls
@app.route('/scheduleforseason=<int:season>')
def get_schedule(season):
""" send the league's schedule for a season to front end """
# validation: converter <int:x> forces all values to ints, rejects otherwise
# season must be a 4-character year, i.e. 2017
schedule = []
try:
schedule = engine.get_league_schedule_for_season(season)
class MetaLearningDataset(Dataset):
def __init__(self, split, args):
"""
Initialise the MetaLearningDataset class
Parameters
----------
split : str
'train' or 'test'
args : parsed args
args passed in to the model
"""
self.data_dir = "../../../datascience-projects/internal/multitask_learning/processed_data/Streetbees_Mood/Streetbees_Mood_all.csv"
data = pd.read_csv(self.data_dir, index_col=0)
self.data_processor = DataProcessor(data_dir=None, data_name='Streetbees_Mood', labels=['0','1'])
self.K = args.K
self.num_classes = args.num_classes
self.baseLM_name = args.model_name.split("-")[0]
do_lower_case = False if args.model_name.split("-")[-1] == "cased" else True
self.tokenizer = TOKENIZERS[self.baseLM_name].from_pretrained(args.model_name, do_lower_case=do_lower_case)
self.device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
# Pick categories for dev and test set
ALL_CATEGORIES = np.unique(data['category'])
TEST_CATEGORIES = ['Healthy', 'Positive', 'Unwell', 'Fine']
self.tasks = {}
for category in ALL_CATEGORIES:
if (split == 'train' and category in TEST_CATEGORIES) or (split == 'test' and category not in TEST_CATEGORIES):
continue
# Each category will become a separate task. Explicitly redefine variable below for clarity
task = category
pos_examples, neg_examples = self.get_positive_and_negative_examples(data, category=task)
if task not in self.tasks:
self.tasks[task] = (pos_examples, neg_examples)
task_list = []
task_names = []
for task in self.tasks:
pos_examples = self.tasks[task][0]
neg_examples = self.tasks[task][1]
if len(pos_examples) < self.K or len(neg_examples) < self.K:
print('not enough examples', task)
continue # skip for now if not enough examples
task_list.append((pos_examples, neg_examples))
task_names.append(task)
self.tasks = task_list
self.task_names = task_names
self.num_tasks = len(self.tasks)
@staticmethod
def get_positive_and_negative_examples(data, category):
positive_examples = data[(data['category'] == category) & (data['label'] == 1)]
negative_examples = data[(data['category'] == category) & (data['label'] == 0)]
return positive_examples.drop(columns='category'), negative_examples.drop(columns='category')
def __getitem__(self, task_index):
# choose the task indicated by index
pos_examples, neg_examples = self.tasks[task_index]
# for now just choose randomly among examples
pos_indices = np.random.choice(range(len(pos_examples)), size=self.K)
neg_indices = np.random.choice(range(len(neg_examples)), size=self.K)
# # interleave randomly - DIFFERENT FROM RANDOMLY SHUFFLING
# examples = np.empty((self.K*2, 2), dtype=pos.dtype)
# if np.random.uniform() > .5:
# examples[0::2, :] = pos
# examples[1::2, :] = neg
# else:
# examples[0::2, :] = neg
# examples[1::2, :] = pos
# Randomly shuffle positive and negative examples for now
all_examples = pd.concat([pos_examples.iloc[pos_indices, :],
neg_examples.iloc[neg_indices, :]]).sample(frac=1)
train_examples = self.data_processor.get_examples(input_df=all_examples.iloc[:self.K, :], set_type='train')
test_examples = self.data_processor.get_examples(input_df=all_examples.iloc[self.K:, :], set_type='test')
train_features = convert_examples_to_features(train_examples, label_list=['0','1'], max_seq_length=128,
tokenizer=self.tokenizer, task_name='Streetbees_Mood',
model_name=self.baseLM_name, do_logging=False)
all_input_ids = torch.tensor([feature.input_ids for feature in train_features], dtype=torch.long)
all_segment_ids = torch.tensor([feature.segment_ids for feature in train_features], dtype=torch.long)
all_input_masks = torch.tensor([feature.input_mask for feature in train_features], dtype=torch.long)
all_label_ids = torch.tensor([feature.label_id for feature in train_features], dtype=torch.long)
task_train_data = {'input_ids': all_input_ids, 'segment_ids': all_segment_ids,
'input_masks': all_input_masks, 'label_ids': all_label_ids}
test_features = convert_examples_to_features(test_examples, label_list=['0','1'], max_seq_length=128,
tokenizer=self.tokenizer, task_name='Streetbees_Mood',
model_name=self.baseLM_name, do_logging=False)
all_input_ids = torch.tensor([feature.input_ids for feature in train_features], dtype=torch.long)
all_segment_ids = torch.tensor([feature.segment_ids for feature in train_features], dtype=torch.long)
all_input_masks = torch.tensor([feature.input_mask for feature in train_features], dtype=torch.long)
all_label_ids = torch.tensor([feature.label_id for feature in train_features], dtype=torch.long)
task_test_data = {'input_ids': all_input_ids, 'segment_ids': all_segment_ids,
'input_masks': all_input_masks, 'label_ids': all_label_ids}
return task_train_data, task_test_data
def __len__(self):
return len(self.tasks)
def get_data(data_args, training_args, tokenizer):
'''train과 validation의 dataloader와 dataset를 반환하는 함수'''
if data_args.dataset_name == 'basic':
if os.path.isdir("../data/train_dataset"):
dataset = load_from_disk("../data/train_dataset")
else:
raise Exception("Set the data path to 'p3-mrc-team-ikyo/data/.'")
elif data_args.dataset_name == 'preprocessed':
if os.path.isfile("../data/preprocess_train.pkl"):
dataset = get_pickle("../data/preprocess_train.pkl")
else:
dataset = make_custom_dataset("../data/preprocess_train.pkl")
elif data_args.dataset_name == 'concat':
if os.path.isfile("../data/concat_train.pkl"):
dataset = get_pickle("../data/concat_train.pkl")
else:
dataset = make_custom_dataset("../data/concat_train.pkl")
elif data_args.dataset_name == 'korquad':
if os.path.isfile("../data/korquad_train.pkl"):
dataset = get_pickle("../data/korquad_train.pkl")
else:
dataset = make_custom_dataset("../data/korquad_train.pkl")
elif data_args.dataset_name == "question_type":
if os.path.isfile("../data/question_type.pkl"):
dataset = get_pickle("../data/question_type.pkl")
else:
dataset = make_custom_dataset("../data/question_type.pkl")
elif data_args.dataset_name == "ai_hub":
if os.path.isfile("../data/ai_hub_dataset.pkl"):
dataset = get_pickle("../data/ai_hub_dataset.pkl")
else:
dataset = make_custom_dataset("../data/ai_hub_dataset.pkl")
elif data_args.dataset_name == "only_korquad":
dataset = load_dataset("squad_kor_v1")
elif data_args.dataset_name == "random_masking":
if os.path.isfile("../data/random_mask_train.pkl"):
dataset = get_pickle("../data/random_mask_train.pkl")
else:
dataset = make_custom_dataset("../data/random_mask_train.pkl")
elif data_args.dataset_name == "token_masking":
if os.path.isfile("../data/concat_token_mask_top_3.pkl"):
dataset = get_pickle("../data/concat_token_mask_top_3.pkl")
else:
dataset = make_mask_dataset("../data/concat_token_mask_top_3.pkl",
tokenizer)
train_dataset = dataset['train']
val_dataset = dataset['validation']
else:
raise Exception(
"dataset_name have to be one of ['basic', 'preprocessed', 'concat', 'korquad', 'only_korquad', 'question_type', 'ai_hub', 'random_masking', 'token_masking']"
)
if data_args.dataset_name != "token_masking":
train_dataset = dataset['train']
val_dataset = dataset['validation']
train_column_names = train_dataset.column_names
val_column_names = val_dataset.column_names
data_processor = DataProcessor(tokenizer, data_args.max_seq_length,
data_args.doc_stride)
train_dataset = data_processor.train_tokenizer(train_dataset,
train_column_names)
val_dataset = data_processor.val_tokenzier(val_dataset,
val_column_names)
data_collator = (DataCollatorWithPadding(
tokenizer, pad_to_multiple_of=8 if training_args.fp16 else None))
train_iter = DataLoader(
train_dataset,
collate_fn=data_collator,
batch_size=training_args.per_device_train_batch_size)
val_iter = DataLoader(val_dataset,
collate_fn=data_collator,
batch_size=training_args.per_device_eval_batch_size)
return dataset, train_iter, val_iter, train_dataset, val_dataset
def run_gru(s):
global global_step
global increment_global_step_op
global reset_global_step_op
global batches
global images_placeholder
global batches_op
global_step = tf.Variable(0,
name='global_step',
trainable=False,
dtype=tf.int32)
increment_global_step_op = tf.assign(global_step, global_step + 1)
reset_global_step_op = tf.assign(global_step, 0)
batches = tf.get_variable(
"batches", [s.nTrain / int(s.batch_size), s.batch_size, 1, 1],
dtype=tf.float32,
initializer=tf.zeros_initializer)
images_placeholder = tf.placeholder(tf.float32,
shape=(s.nTrain / int(s.batch_size),
s.batch_size, 1, 1))
batches_op = tf.assign(batches, images_placeholder)
x_dims = len(x_cols[s.dataSet]) if s.dataSet in x_cols else s.lookback
random.seed(6)
np.random.seed(6)
rnn = Sequential()
rnn.add(
GRU(s.nodes,
input_shape=(None, x_dims),
kernel_initializer='he_uniform',
stateful=False))
#rnn.add(Dropout(0.15))
rnn.add(Dense(1, kernel_initializer='he_uniform'))
opt = adam(lr=s.lr, decay=0.0) #1e-3)
rnn.compile(loss='mae', optimizer=opt)
# prepare dataset as pyBrain sequential dataset
sequence = readDataSet(s.dataSet, s.dataSetDetailed, s)
if s.limit_to:
sequence = sequence[:s.limit_to]
dp = DataProcessor()
# standardize data by subtracting mean and dividing by std
(meanSeq, stdSeq) = dp.normalize('data', sequence, s.nTrain)
#dp.windowed_normalize(sequence)
for key in sequence.keys():
if key != "data":
dp.normalize(key, sequence, s.nTrain)
if s.dataSet in differenceSets:
predictedInputNodiff = np.zeros((len(sequence), ))
targetInputNodiff = np.zeros((len(sequence), ))
if s.dataSet in differenceSets:
backup_sequence = sequence
sequence = dp.difference(sequence, s.lookback)
seq_full = sequence['data'].values
seq_actual = seq_full[s.front_buffer:]
allX = getX(seq_full, s)
allY = seq_actual[s.predictionStep - 1:]
predictedInput = np.full((len(allY), ), np.nan)
#if s.dataSet not in x_cols:
# allY = allY[s.lookback:]
trainX = allX[:s.nTrain]
trainY = allY[:s.nTrain]
trainX = np.reshape(trainX, (trainX.shape[0], 1, trainX.shape[1]))
rnn.fit(trainX,
trainY,
epochs=s.epochs,
batch_size=s.batch_size,
verbose=min(s.max_verbosity, 2))
#for i in xrange(0,s.nTrain):
# targetInput[i] = allY[i+s.predictionStep]
targetInput = allY
pred_diffs = []
pred_closer_to_actual = []
isFirst = True
for i in tqdm(xrange(s.nTrain + s.predictionStep, len(allX)),
disable=s.max_verbosity == 0):
#for i in tqdm(xrange(0, len(allX)), disable=s.max_verbosity == 0):
if i % s.retrain_interval == 0 and i > s.numLags + s.nTrain and s.online:
trainX = allX[i - s.nTrain - s.predictionStep:i - s.predictionStep]
trainY = allY[i - s.nTrain - s.predictionStep:i - s.predictionStep]
trainX = np.reshape(trainX, (trainX.shape[0], 1, trainX.shape[1]))
rnn.fit(trainX,
trainY,
epochs=s.epochs,
batch_size=s.batch_size,
verbose=0)
#targetInput[i] = allY[i]
predictedInput[i] = rnn.predict(np.reshape(allX[i], (1, 1, x_dims)))
if isFirst:
print predictedInput[i]
isFirst = False
#predictedInput[i] = targetInput[i-1440]
pred_diffs.append(abs(predictedInput[i] - allX[i][-1]))
pred_closer_to_actual.append(
abs(predictedInput[i] - targetInput[i]) < abs(predictedInput[i] -
allX[i][-1]))
if s.dataSet in differenceSets:
predictedInputNodiff[i] = predictedInput[i]
targetInputNodiff[i] = targetInput[i]
predictedInput[i] = dp.inverse_difference(backup_sequence['data'],
predictedInput[i], i - 1)
targetInput[i] = dp.inverse_difference(backup_sequence['data'],
targetInput[i], i - 1)
for i in range(s.nTrain + s.predictionStep):
predictedInput[i] = np.nan
predictedInput = dp.denormalize(predictedInput, meanSeq, stdSeq)
targetInput = dp.denormalize(targetInput, meanSeq, stdSeq)
#dp.windowed_denormalize(predictedInput, targetInput)
print "FINAL", predictedInput[-1], targetInput[-1], len(
predictedInput), len(targetInput)
if s.dataSet in differenceSets:
# predictedInputNodiff = dp.denormalize(predictedInputNodiff)
# targetInputNodiff = dp.denormalize(targetInputNodiff)
pass
dp.saveResultToFile(s.dataSet, predictedInput, targetInput, 'gru',
s.predictionStep, s.max_verbosity)
skipTrain = error_ignore_first[s.dataSet]
from plot import computeSquareDeviation
squareDeviation = computeSquareDeviation(predictedInput, targetInput)
squareDeviation[:skipTrain] = None
nrmse = np.sqrt(np.nanmean(squareDeviation)) / np.nanstd(targetInput)
if s.max_verbosity > 0:
print "", s.nodes, "NRMSE {}".format(nrmse)
mae = np.nanmean(np.abs(targetInput - predictedInput))
if s.max_verbosity > 0:
print "MAE {}".format(mae)
mase = errors.get_mase(predictedInput, targetInput,
np.roll(targetInput, s.season))
if s.max_verbosity > 0:
print "MASE {}".format(mase)
if s.dataSet in differenceSets:
dp.saveResultToFile(s.dataSet, predictedInputNodiff, targetInputNodiff,
'gru_nodiff', s.predictionStep, s.max_verbosity)
squareDeviation = computeSquareDeviation(predictedInputNodiff,
targetInputNodiff)
squareDeviation[:skipTrain] = None
nrmse = np.sqrt(
np.nanmean(squareDeviation)) / np.nanstd(targetInputNodiff)
if s.max_verbosity > 0:
print "", s.nodes, "NRMSE {}".format(nrmse)
mae = np.nanmean(np.abs(targetInputNodiff - predictedInputNodiff))
if s.max_verbosity > 0:
print "MAE {}".format(mae)
closer_rate = pred_closer_to_actual.count(True) / float(
len(pred_closer_to_actual))
if s.max_verbosity > 0:
pred_diffs.sort()
print pred_diffs[0], pred_diffs[-1], pred_diffs[int(0.9 *
len(pred_diffs))]
print "Good results:", closer_rate
return mase, closer_rate
def __init__(self):
self.engine = Engine()
self.processor = DataProcessor()
sensor = model._getSensorRegion()
encoderList = sensor.getSelf().encoder.getEncoderList()
if sensor.getSelf().disabledEncoder is not None:
classifier_encoder = sensor.getSelf().disabledEncoder.getEncoderList()
classifier_encoder = classifier_encoder[0]
else:
classifier_encoder = None
print "Load dataset: ", dataSet
skips = data_skips[dataSet]
df = pd.read_csv(inputData, header=0, skiprows=skips)
df = preprocess(df)
if limit_to:
df = df[:limit_to]
dp = DataProcessor()
#dp.windowed_normalize(df, field_name=predictedField, is_data_field=True)
print " run SP through the first %i samples %i passes " % (nMultiplePass,
nTrain)
model = runMultiplePassSPonly(df, model, nMultiplePass, nTrain)
model._spLearningEnabled = False
maxBucket = classifier_encoder.n - classifier_encoder.w + 1
likelihoodsVecAll = np.zeros((maxBucket, len(df)))
prediction_nstep = None
time_step = []
actual_data = []
patternNZ_track = []
predict_data = np.zeros((_options.stepsAhead, 0))
class LightClient(object):
def __init__(self, ip):
self.log = Logger(MAIN_CLIENT_LOG_FILE, D_VERB)
self.log.info('[MAIN THREAD] Instantiated client')
self.receiving = False
self.define_headers()
self.targets = {}
self.transmit = Queue.Queue()
self.data_client = DataClient(self.transmit, ip)
self.data_processor = DataProcessor(self.transmit, self.headers, self.targets)
self.connect(ip)
def connect(self, ip):
self.soc_ctrl = socket.socket(socket.AF_INET, socket.SOCK_STREAM)
self.soc_ctrl.setsockopt(socket.SOL_SOCKET, socket.SO_REUSEADDR, 1)
my_ip = socket.gethostbyname('')
self.log.debug('[MAIN THREAD] connecting...')
self.soc_ctrl.connect((ip,SOC_PORT_CTRL))
self.log.info('[MAIN THREAD] Client connected to server')
def disconnect(self):
### data processor should not be here
self.data_processor.stop()
self.soc_ctrl.close()
def define_headers(self):
head = {}
head['process'] = PROC_CPU_DATA + PROC_MEM_DATA + TIMESTAMPS
head['system'] = SYS_CPU_OTHER + LOAD_AVG + SYS_CPU_DATA + SYS_MEM_DATA + TIMESTAMPS
self.headers = head
def add_target(self, target, name):
if target in self.targets:
self.targets[target].append(name)
else:
self.targets[target]=[name]
def remove_target(self, target, name):
if target in self.targets:
if name in self.targets[target]:
self.targets[target].remove(name)
self.log.info('[MAIN THREAD] Removed {} named {}'.format(target, name))
else:
self.log.error('[MAIN THREAD] Asked to remove {} named {} while not recorded'.format(target, name))
else:
self.log.error('[MAIN THREAD] Asked to remove {} named {} while not recorded'.format(target, name))
def start_record(self, target, name):
self.log.debug('[MAIN THREAD] Asking server to start recording')
msg = MSG_SEP.join([START_RECORD, target, name])
answer = send_data(self.soc_ctrl,msg)
self.log.info('[MAIN THREAD] Server asked to start recording')
if answer == SYNC:
self.add_target(target, name)
self.log.info('[MAIN THREAD] Added {} named {}'.format(target, name))
else:
self.log.warn('[MAIN THREAD] Could not add {} named {} because of server answer'.format(target, name))
def stop_record(self, target, name):
self.log.debug('[MAIN THREAD] Asking server to stop recording')
msg = MSG_SEP.join([STOP_RECORD, target, name])
answer = send_data(self.soc_ctrl,msg)
self.log.info('[MAIN THREAD] Server asked to stop recording {}'.format(name))
if answer == SYNC:
self.remove_target(target, name)
else:
self.log.warn('[MAIN THREAD] Could not remove {} named {} because of server answer'.format(target, name))
def start_receive(self):
if not self.receiving:
self.receiving = True
self.log.debug('[MAIN THREAD] Asking server to start sending')
status = send_data(self.soc_ctrl,START_SEND)
self.log.info('[MAIN THREAD] Server asked to start sending')
if status == FAIL:
self.log.error('[MAIN THREAD] Client tried to receive but server denied it')
else:
print status
self.data_client.start()
self.log.info('[MAIN THREAD] Client is receiving')
self.log.debug("[MAIN THREAD] DATA THREAD started")
else:
self.log.warn("[MAIN THREAD] Asked to start receiving while already receiving")
def stop_receive(self):
if self.receiving:
self.log.debug('[MAIN THREAD] Closing data channel. Exiting data client thread')
self.data_client.stop()
self.log.info("[MAIN THREAD] Asked server to stop receiving")
self.receiving = False
send_data(self.soc_ctrl,STOP_SEND)
else:
self.log.warn("[MAIN THREAD] Asked to stop receiving while already receiving")
def start_store(self, dirname = 'easy_client'):
return self.data_processor.start_store(dirname)
def stop_store(self):
self.data_processor.stop_store()
def start_print(self):
self.data_processor.start_print()
def stop_print(self):
self.printing = self.data_processor.stop_print()
def stop_process(self):
self.stop_print()
self.stop_store()
self.data_processor.stop()
self.stop_receive()
self.soc_ctrl.close()
def stop_all(self):
self.stop_process()
send_data(self.soc_ctrl, STOP_ALL)
result["conf_matrices"].append(
confusion_matrix(
self.model.predict(test_vectors),
test_targets
))
result["roc_curves"].append(
roc_curve(
test_targets,
self.model.predict_proba(test_vectors)[:,1])
)
result["roc_auc_scores"].append(
roc_auc_score(
test_targets,
self.model.predict(test_vectors))
)
result["coefficients"].append(
self.model.coef_[0]
)
return result
if __name__ == "__main__":
dp = DataProcessor()
dp.load('data/SQuAD/squad-v7.file')
model = LogRegModel()
model.load_vectors(dp.articles)
model.run_k_fold()
class LightClient(object):
def __init__(self, ip):
self.log = Logger(MAIN_CLIENT_LOG_FILE, D_VERB)
self.log.info('[MAIN THREAD] Instantiated client')
self.receiving = False
self.define_headers()
self.targets = {}
self.transmit = Queue.Queue()
self.data_client = DataClient(self.transmit, ip)
self.data_processor = DataProcessor(self.transmit, self.headers,
self.targets)
self.connect(ip)
def connect(self, ip):
self.soc_ctrl = socket.socket(socket.AF_INET, socket.SOCK_STREAM)
self.soc_ctrl.setsockopt(socket.SOL_SOCKET, socket.SO_REUSEADDR, 1)
my_ip = socket.gethostbyname('')
self.log.debug('[MAIN THREAD] connecting...')
self.soc_ctrl.connect((ip, SOC_PORT_CTRL))
self.log.info('[MAIN THREAD] Client connected to server')
def disconnect(self):
### data processor should not be here
self.data_processor.stop()
self.soc_ctrl.close()
def define_headers(self):
head = {}
head['process'] = PROC_CPU_DATA + PROC_MEM_DATA + TIMESTAMPS
head[
'system'] = SYS_CPU_OTHER + LOAD_AVG + SYS_CPU_DATA + SYS_MEM_DATA + TIMESTAMPS
self.headers = head
def add_target(self, target, name):
if target in self.targets:
self.targets[target].append(name)
else:
self.targets[target] = [name]
def remove_target(self, target, name):
if target in self.targets:
if name in self.targets[target]:
self.targets[target].remove(name)
self.log.info('[MAIN THREAD] Removed {} named {}'.format(
target, name))
else:
self.log.error(
'[MAIN THREAD] Asked to remove {} named {} while not recorded'
.format(target, name))
else:
self.log.error(
'[MAIN THREAD] Asked to remove {} named {} while not recorded'.
format(target, name))
def start_record(self, target, name):
self.log.debug('[MAIN THREAD] Asking server to start recording')
msg = MSG_SEP.join([START_RECORD, target, name])
answer = send_data(self.soc_ctrl, msg)
self.log.info('[MAIN THREAD] Server asked to start recording')
if answer == SYNC:
self.add_target(target, name)
self.log.info('[MAIN THREAD] Added {} named {}'.format(
target, name))
else:
self.log.warn(
'[MAIN THREAD] Could not add {} named {} because of server answer'
.format(target, name))
def stop_record(self, target, name):
self.log.debug('[MAIN THREAD] Asking server to stop recording')
msg = MSG_SEP.join([STOP_RECORD, target, name])
answer = send_data(self.soc_ctrl, msg)
self.log.info(
'[MAIN THREAD] Server asked to stop recording {}'.format(name))
if answer == SYNC:
self.remove_target(target, name)
else:
self.log.warn(
'[MAIN THREAD] Could not remove {} named {} because of server answer'
.format(target, name))
def start_receive(self):
if not self.receiving:
self.receiving = True
self.log.debug('[MAIN THREAD] Asking server to start sending')
status = send_data(self.soc_ctrl, START_SEND)
self.log.info('[MAIN THREAD] Server asked to start sending')
if status == FAIL:
self.log.error(
'[MAIN THREAD] Client tried to receive but server denied it'
)
else:
print status
self.data_client.start()
self.log.info('[MAIN THREAD] Client is receiving')
self.log.debug("[MAIN THREAD] DATA THREAD started")
else:
self.log.warn(
"[MAIN THREAD] Asked to start receiving while already receiving"
)
def stop_receive(self):
if self.receiving:
self.log.debug(
'[MAIN THREAD] Closing data channel. Exiting data client thread'
)
self.data_client.stop()
self.log.info("[MAIN THREAD] Asked server to stop receiving")
self.receiving = False
send_data(self.soc_ctrl, STOP_SEND)
else:
self.log.warn(
"[MAIN THREAD] Asked to stop receiving while already receiving"
)
def start_store(self, dirname='easy_client'):
return self.data_processor.start_store(dirname)
def stop_store(self):
self.data_processor.stop_store()
def start_print(self):
self.data_processor.start_print()
def stop_print(self):
self.printing = self.data_processor.stop_print()
def stop_process(self):
self.stop_print()
self.stop_store()
self.data_processor.stop()
self.stop_receive()
self.soc_ctrl.close()
def stop_all(self):
self.stop_process()
send_data(self.soc_ctrl, STOP_ALL)
def train_and_validate(alarm, ticket):
'''
Function will perform the following actions:
1. Data will be converted to numpy arrays to be accepted as
input to the learning model
2. Construct learning model and train on the data provided
3. Generate data predictions using a subset of the ticket data
4. Validate the predictions generated with the test data against the
manually predicted values in the ticket file
Parameters:
alarm: alarm file needed to initialize DataProcessor
ticket: ticket file needed to intialize DataProcessor
'''
# Initialize DataProcessor object
dp = DataProcessor(alarm, ticket)
# Create thread to run progress bar
thread = threading.Thread(target=run_progress_bar, args=(670, ))
# Start thread to run progress bar
thread.start()
'''
Converting lists to numpy array, the number specifies the index in the
array of associated label values
'''
encoded_hex_codes = dp.convert_array_to_np_array(
dp.encode_ticket_hex_codes())
event_cause_options = dp.convert_array_to_np_array(
dp.get_encoded_label_value(dp.event_cause_vals, 0))
detection_method_options = dp.convert_array_to_np_array(
dp.get_encoded_label_value(dp.detection_method, 1))
restore_method_options = dp.convert_array_to_np_array(
dp.get_encoded_label_value(dp.restore_method, 2))
fix_classification_options = dp.convert_array_to_np_array(
dp.get_encoded_label_value(dp.fix_classification, 3))
subsystem_options = dp.convert_array_to_np_array(
dp.get_encoded_label_value(dp.subsystem, 4))
relevance_options = dp.convert_array_to_np_array(
dp.get_encoded_label_value(dp.relevance, 5))
# Train on Data. Training & saving the model for each of the labels
classify_data(encoded_hex_codes, event_cause_options, 'event_cause.hdf5',
101, 110, 0.80, len(dp.event_cause_vals))
classify_data(encoded_hex_codes, detection_method_options,
'detection_method.hdf5', 101, 110, 0.80,
len(dp.detection_method))
classify_data(encoded_hex_codes, restore_method_options,
'restore_method.hdf5', 101, 110, 0.80,
len(dp.restore_method))
classify_data(encoded_hex_codes, fix_classification_options,
'fix_classification.hdf5', 101, 110, 0.80,
len(dp.fix_classification))
classify_data(encoded_hex_codes, subsystem_options, 'subsystem.hdf5', 101,
110, 0.80, len(dp.subsystem))
classify_data(encoded_hex_codes, relevance_options, 'relevance.hdf5', 101,
110, 0.80, len(dp.relevance))
'''
Generate Training Data Predictions
20 percent that needs to be tested for alarm hex and all labels
Calculate the starting place by multiplying length of encoded_hex_codes
which is the input for the model
'''
start_index = int(len(encoded_hex_codes) * 0.8)
predict_input_hex = encoded_hex_codes[start_index:]
predict_event_cause = event_cause_options[start_index:]
predict_detection_method = detection_method_options[start_index:]
predict_restore_method = restore_method_options[start_index:]
predict_fix_classification = fix_classification_options[start_index:]
predict_subsystem = subsystem_options[start_index:]
predict_relevance = relevance_options[start_index:]
# calling prediction from predict.py and returns array of confidence values
event_cause_prediction = prediction(predict_input_hex, 'event_cause.hdf5')
detection_method_prediction = prediction(predict_input_hex,
'detection_method.hdf5')
restore_method_prediction = prediction(predict_input_hex,
'restore_method.hdf5')
fix_classification_prediction = prediction(predict_input_hex,
'fix_classification.hdf5')
subsystem_prediction = prediction(predict_input_hex, 'subsystem.hdf5')
relevance_prediction = prediction(predict_input_hex, 'relevance.hdf5')
'''
Validate Training Data Predictions
Validation calls for all labels using the prediction function returns
'''
validation(predict_event_cause, event_cause_prediction, predict_input_hex,
'event_cause_predictions.txt')
validation(predict_detection_method, detection_method_prediction,
predict_input_hex, 'detection_method_predictions.txt')
validation(predict_restore_method, restore_method_prediction,
predict_input_hex, 'restore_method_predictions.txt')
validation(predict_fix_classification, fix_classification_prediction,
predict_input_hex, 'fix_classication_predictions.txt')
validation(predict_subsystem, subsystem_prediction, predict_input_hex,
'subsystem_predictions.txt')
validation(predict_relevance, relevance_prediction, predict_input_hex,
'relevance_predictions.txt')
# Join thread back to main process
thread.join()
def run_gru(s):
prob = tf.placeholder_with_default(1.0, shape=())
global global_step
global increment_global_step_op
global reset_global_step_op
global batches
global images_placeholder
global batches_op
global_step = tf.Variable(0, name='global_step', trainable=False, dtype=tf.int32)
increment_global_step_op = tf.assign(global_step, global_step + 1)
reset_global_step_op = tf.assign(global_step, 0)
batches = tf.get_variable("batches", [s.nTrain / int(s.batch_size), s.batch_size, 1, 1], dtype=tf.float32,
initializer=tf.zeros_initializer)
images_placeholder = tf.placeholder(tf.float32, shape=(s.nTrain / int(s.batch_size), s.batch_size, 1, 1))
batches_op = tf.assign(batches, images_placeholder)
x_dims = s.lookback
random.seed(6)
np.random.seed(6)
tf.set_random_seed(6)
if s.implementation == "keras":
if s.use_binary:
raise Exception("Binary Keras not implemented")
rnn = Sequential()
if s.rnn_type == "lstm":
rnn.add(LSTM(s.nodes, input_shape=(None,x_dims), kernel_initializer='he_uniform'))
elif s.rnn_type == "gru":
rnn.add(GRU(s.nodes, input_shape=(None, x_dims), kernel_initializer='he_uniform'))
rnn.add(Dropout(0.5))
rnn.add(Dense(1, kernel_initializer='he_uniform'))
opt = rmsprop(lr=s.lr)#1e-3)
rnn.compile(loss='mae', optimizer=opt)
input = Input(shape=(1, x_dims))
dense1 = Dense(s.nodes, activation='sigmoid')(input)
dense2 = Dense(s.nodes, activation='sigmoid')(input)
dense3 = Dense(s.nodes, activation='tanh')(input)
mult1 = Multiply()([dense2, dense3])
act1 = Activation('tanh')(mult1)
mult2 = Multiply()([dense1, act1])
reshape = Reshape((s.nodes,))(mult2)
dropout = Dropout(0.5)(reshape)
dense_out = Dense(1)(dropout)
rnn = Model(inputs=[input], outputs=[dense_out])
opt = adam(lr=s.lr) # 1e-3)
rnn.compile(loss='mae', optimizer=opt)
print rnn.summary()
elif s.implementation == "tf":
data = tf.placeholder(tf.float32, [None, s.lookback, 1]) # Number of examples, number of input, dimension of each input
target = tf.placeholder(tf.float32, [None, 1])
if s.rnn_type == "lstm" and s.use_binary:
cell = rnn_tf.LSTMCell(s.nodes)
elif s.rnn_type == "lstm" and not s.use_binary:
cell = tf.nn.rnn_cell.LSTMCell(s.nodes)
elif s.rnn_type == "gru" and s.use_binary:
cell = rnn_tf.GRUCell(s.nodes)
elif s.rnn_type == "gru" and not s.use_binary:
cell = tf.nn.rnn_cell.GRUCell(s.nodes)
val, _ = tf.nn.dynamic_rnn(cell, data, dtype=tf.float32)
with tf.name_scope('rnn_summaries'):
var = val
mean = tf.reduce_mean(var)
tf.summary.scalar('mean', mean)
with tf.name_scope('stddev'):
stddev = tf.sqrt(tf.reduce_mean(tf.square(var - mean)))
tf.summary.scalar('stddev', stddev)
tf.summary.scalar('max', tf.reduce_max(var))
tf.summary.scalar('min', tf.reduce_min(var))
tf.summary.histogram('histogram', var)
val = tf.nn.dropout(val, prob)
if not s.use_binary:
dense = tf.layers.dense(val, 1)
else:
dense = core_discretize.dense(val, 1)
with tf.name_scope('dense_summaries'):
var = dense
mean = tf.reduce_mean(var)
tf.summary.scalar('mean', mean)
with tf.name_scope('stddev'):
stddev = tf.sqrt(tf.reduce_mean(tf.square(var - mean)))
tf.summary.scalar('stddev', stddev)
tf.summary.scalar('max', tf.reduce_max(var))
tf.summary.scalar('min', tf.reduce_min(var))
tf.summary.histogram('histogram', var)
pred = tf.reshape(dense, (tf.shape(dense)[0], 1))
summary = tf.summary.merge_all()
optimizer = tf.train.AdamOptimizer(learning_rate=s.lr)
#cost = tf.losses.mean_squared_error(target, pred)
cost = tf.reduce_mean(tf.abs(target - pred))
minimize = optimizer.minimize(cost)
else:
raise Exception("Unknown implementation " + s.implementation)
sequence = readDataSet(s.dataSet, s.dataSetDetailed, s)
if s.limit_to:
sequence = sequence[:s.limit_to]
#TEMP SANITY CHECK
# sequence['data'][7001] = 0
# sequence['data'][7002] = 0
# sequence['data'][7003] = 0
# sequence['data'][7004] = 0
# sequence['data'][7005] = 0
seq_full = sequence['data'].values #use .values to copy
targetInput = seq_full[s.front_buffer + s.predictionStep - 1:].copy() #grab this now to avoid having to denormalize
dp = DataProcessor()
if s.normalization_type == 'default':
(meanSeq, stdSeq) = dp.normalize('data', sequence, s.nTrain)
elif s.normalization_type == 'windowed':
dp.windowed_normalize(sequence)
elif s.normalization_type == 'AN':
an = AdaptiveNormalizer(s.lookback, s.lookback + s.predictionStep)
an.set_pruning(False)
an.set_source_data(seq_full, s.nTrain)
an.do_ma('s')
an.do_stationary()
an.remove_outliers()
seq_norm = an.do_adaptive_normalize()
else:
raise Exception("Unsupported normalization type: " + s.normalization_type)
seq_actual = seq_full[s.front_buffer:] #Leave enough headroom for MASE calculation and lookback
seq_full_norm = sequence['data'].values
seq_actual_norm = seq_full_norm[s.front_buffer:]
if s.normalization_type != "AN":
#Default and windowed change the seq itself but still require creating lookback frames
allX = getX(seq_full_norm, s)
allY = seq_actual_norm[s.predictionStep-1:]
else:
#AN creates a new array but takes care of lookback internally
allX= seq_norm[:,0:-s.predictionStep]
allY = np.reshape(seq_norm[:,-1], (-1,))
# TODO FIX PROPERLY (now rolled too far)
too_long = len(allX) - (len(seq_full) - s.front_buffer - s.predictionStep + 1)
if too_long > 0:
allX = allX[too_long:]
allY = allY[too_long:]
print len(allX), len(allY), s.front_buffer
predictedInput = np.full((len(allY),), np.nan) #Initialize all predictions to NaN
trainX = allX[:s.nTrain]
trainY = allY[:s.nTrain]
trainX = np.reshape(trainX, (trainX.shape[0],1, trainX.shape[1]))
trainY = np.reshape(trainY, ( trainY.shape[0],))
if s.implementation == "keras":
rnn.fit(trainX, trainY, epochs=s.epochs, batch_size=s.batch_size, verbose=min(s.max_verbosity, 2))
elif s.implementation == "tf":
sess = tf.Session()
writer = tf.summary.FileWriter("results/", graph=sess.graph)
init = tf.global_variables_initializer()
sess.run(init)
for v in tf.trainable_variables():
print v.name
for epoch in tqdm(range(s.epochs)):
the_cost, _, summ = sess.run([cost, minimize, summary], feed_dict={data: trainX, target: trainY, prob: 0.5})
writer.add_summary(summ, epoch)
if epoch % 10 == 0:
print the_cost
#print(psutil.Process(os.getpid()).memory_percent())
var = [v for v in tf.trainable_variables() if v.name == "rnn/gru_cell/gates/kernel:0"][0]
print sess.run(tf.reduce_min(var))
print sess.run(tf.reduce_max(var))
# var = [v for v in tf.trainable_variables() if v.name == "rnn/gru_cell/gates/bias:0"][0]
# print sess.run(tf.reduce_min(var))
# print sess.run(tf.reduce_max(var))
# var = [v for v in tf.trainable_variables() if v.name == "rnn/gru_cell/candidate/kernel:0"][0]
# print sess.run(tf.reduce_min(var))
# print sess.run(tf.reduce_max(var))
# var = [v for v in tf.trainable_variables() if v.name == "rnn/gru_cell/candidate/bias:0"][0]
# print sess.run(tf.reduce_min(var))
# print sess.run(tf.reduce_max(var))
# print "loop"
var = [v for v in tf.trainable_variables() if v.name == "dense/bias:0"]
print sess.run(var)
minval = 10
latestStart = None
for i in tqdm(xrange(s.nTrain + s.predictionStep, len(allX)), disable=s.max_verbosity == 0):
#for i in tqdm(xrange(0, len(allX)), disable=s.max_verbosity == 0):
#for i in tqdm(xrange(10475, len(allX)), disable=s.max_verbosity == 0):
if i % s.retrain_interval == 0 and i > s.numLags+s.nTrain and s.online:
if s.normalization_type == 'AN':
predictedInput = np.array(an.do_adaptive_denormalize(predictedInput, therange=(i-s.retrain_interval, i)))
latestStart = i
an.set_ignore_first_n(i-s.nTrain-s.predictionStep)
an.do_ma('s')
an.do_stationary()
an.remove_outliers()
seq_norm = an.do_adaptive_normalize()
allX = seq_norm[:, 0:-s.predictionStep]
allY = np.reshape(seq_norm[:, -1], (-1,))
trainX = allX[i-s.nTrain-s.predictionStep:i-s.predictionStep]
trainY = allY[i-s.nTrain-s.predictionStep:i-s.predictionStep]
trainX = np.reshape(trainX, (trainX.shape[0], 1, trainX.shape[1]))
trainY = np.reshape(trainY, (trainY.shape[0], 1))
if s.implementation == "keras":
rnn.fit(trainX, trainY, epochs=s.epochs, batch_size=s.batch_size, verbose=0)
elif s.implementation == "tf":
for epoch in range(s.epochs):
sess.run(minimize, feed_dict={data: trainX, target: trainY, prob: 0.5})
if s.implementation == "keras":
predictedInput[i] = rnn.predict(np.reshape(allX[i], (1,1,x_dims)))
elif s.implementation == "tf":
predictedInput[i] = sess.run(dense, feed_dict={data: np.reshape(allX[i], (1, x_dims, 1))})
#if len(allX) > i+5:
# predictedInput[i] = allY[i-3000]
# if i == 10000:
# print allX[i]
# print "should be ", (targetInput[i] - meanSeq) / stdSeq
# print "predicted as ", predictedInput[i]
# for i in range(s.nTrain + s.predictionStep):
# predictedInput[i] = np.nan
print "SMALLEST", minval
# np.set_printoptions(threshold=np.nan, suppress=True)
# print "ALLY START"
# for val in allY:
# print val
# print "ALLY STOP"
if s.normalization_type == 'default':
predictedInput = dp.denormalize(predictedInput, meanSeq, stdSeq)
#targetInput = dp.denormalize(targetInput, meanSeq, stdSeq)
elif s.normalization_type == 'windowed':
dp.windowed_denormalize(predictedInput, targetInput, pred_step=s.predictionStep)
elif s.normalization_type == 'AN':
if latestStart:
predictedInput = np.array(an.do_adaptive_denormalize(predictedInput, therange=(latestStart, len(predictedInput))))
else:
predictedInput = np.array(an.do_adaptive_denormalize(predictedInput))
if an.pruning:
targetInput = np.delete(targetInput, an.deletes)
print len(predictedInput), len(targetInput), "LENS"
#TEMP SANITY CHECK
#print predictedInput[7005 - s.front_buffer - s.predictionStep +1]
#print predictedInput[7006 - s.front_buffer - s.predictionStep + 1]
dp.saveResultToFile(s.dataSet, predictedInput, targetInput, 'gru', s.predictionStep, s.max_verbosity)
skipTrain = s.ignore_for_error
from plot import computeSquareDeviation
squareDeviation = computeSquareDeviation(predictedInput, targetInput)
squareDeviation[:skipTrain] = None
nrmse = np.sqrt(np.nanmean(squareDeviation)) / np.nanstd(targetInput)
if s.max_verbosity > 0:
print "", s.nodes, "NRMSE {}".format(nrmse)
mae = np.nanmean(np.abs(targetInput-predictedInput))
if s.max_verbosity > 0:
print "MAE {}".format(mae)
mape = errors.get_mape(predictedInput,targetInput, s.ignore_for_error)
if s.max_verbosity > 0:
print "MAPE {}".format(mape)
mase = errors.get_mase(predictedInput, targetInput, np.roll(targetInput, s.season), s.ignore_for_error)
if s.max_verbosity > 0:
print "MASE {}".format(mase)
if s.implementation == "tf":
sess.close()
return mase
# from keras.models import Sequential # Note: included for debug source access
from tensorflow.keras.layers import Dense
# from keras.layers import Dense # Note: included for debug source access
import os
import numpy as np
import sys
from data_processing import DataProcessor
from matplotlib import pyplot
cur_path = os.path.dirname(__file__)
data_folder = "data\\titanic"
processed_data_folder = os.path.join(cur_path, data_folder)
# Note: Not using test.csv as it does not provide whether or not the passenger survived; therefore we cannot assess
# how well the model performed.
data_file_path = os.path.join(processed_data_folder, "train.csv")
data_processor = DataProcessor(data_file_path, processed_data_folder, "ffnn_processed.npz")
# Load data
try:
# Try to load data
data_processor.load_processed_data()
except FileNotFoundError:
# No data found, so process it
# 20% test, 20% validation, 60% training samples from data
splits = (0.2, 0.2, 0.6)
# Only use certain columns
use_cols = ( # 0, #PassengerID
1, # Survived
2, # Pclass
# 3, #Name
class DataCollector:
""" For all data collection. """
def __init__(self):
self.engine = Engine()
self.processor = DataProcessor()
def get_single_box_score(self, game_id):
"""
Gathers a box score from basketball-reference.com and stores to database.
Keyword arguments:
game_id: 12 character long string in form YYYYMMDD0XXX, where
YYYY is the year
MM is a 2 digit numeric representation of the month, with zero padding if necessary
DD is a 2 digit numeric representation of the day, with zero padding if necessary
XXX is the 3-character abbreviation of the home team,
i.e. 'BOS' for Boston Celtics or 'NYK' for New York Knicks
"""
url = BK_REF_URL + game_id + HTML_SUFFIX
page_response = requests.get(url, headers=REQUEST_HEADERS)
page_tree = html.fromstring(page_response.content)
home_stats, away_stats = [], []
for stat in [
'pts', 'fg', 'fga', 'fg3', 'fg3a', 'ft', 'fta', 'orb', 'drb',
'ast', 'stl', 'blk', 'tov'
]:
away, home = page_tree.xpath(BK_REF_XPATH % stat)
away_stats.append(int(away.text.strip()))
home_stats.append(int(home.text.strip()))
minutes = int(page_tree.xpath(BK_REF_XPATH % 'mp')[0].text.strip()) / 5
data_values = tuple([None, None, game_id] + away_stats + home_stats +
[minutes])
self.engine.insert_box_score(data_values)
def get_season_schedule(self, year):
"""
Gathers a full season's game schedule by traversing basketball-reference.com
These will eventually be used by the get_single_box_score method to gather box scores
Keywords arguments:
year: int representing the year that a season concludes in i.e. 1986-1987 season is represented by 1987
"""
schedule = []
for month in [
'october', 'november', 'december', 'january', 'february',
'march', 'april'
]:
url = BK_REF_SCHEDULE_URL % (str(year), month)
page_response = requests.get(url, headers=REQUEST_HEADERS)
if page_response.status_code == 404:
continue
page_tree = html.fromstring(page_response.content)
game_headers = page_tree.xpath(BK_REF_SCHEDULE_XPATH + 'th')
away_xpath = 'td[1]/a' if int(year) <= 2000 else 'td[2]/a'
away_teams = page_tree.xpath(BK_REF_SCHEDULE_XPATH + away_xpath)
# handle special case for april, where playoff games are displayed on the page
if month == 'april':
header_list = page_tree.xpath(BK_REF_SCHEDULE_XPATH + 'th')
try:
end_index = next(index
for index, val in enumerate(header_list)
if not val.get('class', False))
except StopIteration:
end_index = len(game_headers)
else:
end_index = len(game_headers)
for index, game in enumerate(game_headers):
if index == end_index:
break
game_code = game.attrib['csk']
away_url = away_teams[index].attrib['href']
away_team = away_url.split('/')[2]
home_team = game_code[-3:]
game_date = '{}-{}-{}'.format(game_code[:4], game_code[4:6],
game_code[6:8])
schedule.append(
(game_code, game_date, year, away_team, home_team))
self.engine.insert_scheduled_games(schedule)
self.engine.commit_changes()
def gather_all_scheduled_games(self):
"""
simple loop to gather all games from 1986 to the present
"""
print "Loading each season schedule and saving to database:"
for season in SEASON_LIST:
print season
self.get_season_schedule(season)
def gather_all_box_scores(self):
"""
Gather all games on schedule. Save after each because this will likely be interrupted at some point
"""
games = self.engine.get_game_ids_to_gather()
for game_id in games:
print game_id
self.get_single_box_score(game_id)
self.engine.commit_changes()
def fill_database_from_scratch(self):
""" Starting with model but no records, fill in the database """
# start by loading teams table in
self.engine.insert_all_team_data()
# # gather and save all scheduled games into db
self.gather_all_scheduled_games()
# gather and save all box scores into db
self.gather_all_box_scores()
# calculate all team stats from box scores
self.processor.complete_database_setup()
def in_season_update(self):
self.gather_all_box_scores()
self.processor.process_all_stats_for_year(CURRENT_SEASON)