def __init__ (self):
self.data = []
self.training = (0, 0.6)
self.validation = (0.6, 0.8)
self.testing = (0.8, 1)
self.data_size = 7
self.num_queries = 200
#with open ('timeseriesOnlineRetailCleaned2.csv', 'r') as f:
with open ('../data/randomizedOnlineRetail.csv', 'r' ) as f:
reader = csv.reader(f, delimiter=',')
title_row = True
for row in reader:
if title_row:
self.products = row
title_row = False
else:
integer_data = [int(row[i]) for i in range (self.num_queries)]
self.data.append (integer_data)
#print (self.data)
self.predictor1 = QLearn(threshold=0.5, svd=True, regularization=True)
#self.predictor2 = GRUModel ()
self.baseline1 = NaiveModel()
self.baseline2 = EarliestModel()
self.baseline3 = AverageModel(threshold=0.75, regularization=True)
def __init__(self):
self.data = []
self.training = (0, 0.6)
self.validation = (0.6, 0.8)
self.testing = (0.8, 1)
#self.data_size = 24
self.data_size = 7
self.num_queries = 2000
# q = 1, epochs = 100
# q = 5, epochs = 20
# q = 100, epochs = 10
# q = 2000, epochs = 3
with open(DATA_DIR, 'r') as f:
#with open ('hourlyTimeSeriesOnlineRetailCleaned.csv', 'r') as f:
reader = csv.reader(f, delimiter=',')
title_row = True
for row in reader:
if title_row:
self.products = row
title_row = False
else:
integer_data = [
int(row[i]) for i in range(self.num_queries)
]
self.data.append(integer_data)
#print (self.data)
self.predictor1 = QLearn(threshold=0.5, regularization=True)
#self.predictor2 = RNNModel ()
self.baseline1 = NaiveModel()
self.baseline2 = EarliestModel()
self.predictor3 = simple_model((None, self.num_queries + 1), 2)
def __init__(self):
self.data = []
self.training = (0, 0.6)
self.validation = (0.6, 0.8)
self.testing = (0.8, 0.95)
#self.data_size = 24
self.data_size = 7
self.num_queries = 200
# q = 1, epochs = 100
# q = 5, epochs = 20
# q = 100, epochs = 10
# q = 2000, epochs = 3
#print (self.data)
#self.predictor1 = QLearn(threshold=0.5, regularization=True)
self.baseline1 = NaiveModel()
self.baseline2 = EarliestModel()
saved_models = [(
10, "LSTM", "sgd", 0.001, 2, 32,
"../results/10Qs/LSTM/2L/32HS/sgd-0.001lr/20180731-134128-683987/900epochs.h5"
)]
model_num = 0
self.num_queries = saved_models[model_num][0]
# q = 1, epochs = 100
# q = 5, epochs = 20
# q = 100, epochs = 10
# q = 2000, epochs = 3
with open(DATA_DIR, 'r') as f:
#with open ('hourlyTimeSeriesOnlineRetailCleaned.csv', 'r') as f:
reader = csv.reader(f, delimiter=',')
title_row = True
for row in reader:
if title_row:
self.products = row
title_row = False
else:
integer_data = [
int(row[i]) for i in range(self.num_queries)
]
self.data.append(integer_data)
self.predictor3 = RNNModel(saved_models[model_num][0],
rnn_type=saved_models[model_num][1],
optimizer_type=saved_models[model_num][2],
learning_rate=saved_models[model_num][3],
layers=saved_models[model_num][4],
hidden_size=saved_models[model_num][5])
self.predictor3.load_model(saved_models[model_num][6])
def __init__(self):
self.data = []
self.training = (0, 0.5)
self.validation = (0.5, 0.8)
self.testing = (0.8, 0.95)
#self.data_size = 24
self.data_size = 96
self.num_queries = 10
# q = 1, epochs = 100
# q = 5, epochs = 20
# q = 100, epochs = 10
# q = 2000, epochs = 3
self.ds = Electricity()
data = self.ds.get_data()
transpose = np.transpose(data)
transpose = transpose[:self.num_queries]
data = np.transpose(transpose)
self.training_data = data[int(self.training[0] *
len(data)):int(self.training[1] *
len(data))]
self.validation_data = data[int(self.validation[0] *
len(data)):int(self.validation[1] *
len(data))]
self.testing_data = data[int(self.training[0] *
len(data)):int(self.training[1] *
len(data))]
#for i in range (int(len (transpose) / self.num_queries)):
#print (self.data)
self.predictor1 = QLearn(threshold=0.5, regularization=False)
self.baseline1 = NaiveModel()
self.baseline2 = EarliestModel()
self.predictor3 = RNNModel(self.num_queries,
rnn_type="LSTM",
optimizer_type="adam",
learning_rate=0.001,
layers=2,
hidden_size=64,
recurrent_dropout=0.2)
def __init__(self):
self.data = []
self.training = (0, 0.6)
self.validation = (0.6, 0.8)
self.testing = (0.8, 0.95)
#self.data_size = 24
self.data_size = 7
self.num_queries = 10
# q = 1, epochs = 100
# q = 5, epochs = 20
# q = 100, epochs = 10
# q = 2000, epochs = 3
with open(DATA_DIR, 'r') as f:
#with open ('hourlyTimeSeriesOnlineRetailCleaned.csv', 'r') as f:
reader = csv.reader(f, delimiter=',')
title_row = True
for row in reader:
if title_row:
self.products = row
title_row = False
else:
integer_data = [
int(row[i]) for i in range(self.num_queries)
]
self.data.append(integer_data)
#print (self.data)
self.predictor1 = QLearn(threshold=0.5, regularization=True)
self.baseline1 = NaiveModel()
self.baseline2 = EarliestModel()
self.predictor3 = RNNModel(self.num_queries,
rnn_type="LSTM",
optimizer_type="sgd",
learning_rate=0.001,
layers=1,
hidden_size=128,
recurrent_dropout=0.2)
class QOnlineRetail:
def __init__ (self):
self.data = []
self.training = (0, 0.6)
self.validation = (0.6, 0.8)
self.testing = (0.8, 1)
self.data_size = 7
self.num_queries = 200
#with open ('timeseriesOnlineRetailCleaned2.csv', 'r') as f:
with open ('../data/randomizedOnlineRetail.csv', 'r' ) as f:
reader = csv.reader(f, delimiter=',')
title_row = True
for row in reader:
if title_row:
self.products = row
title_row = False
else:
integer_data = [int(row[i]) for i in range (self.num_queries)]
self.data.append (integer_data)
#print (self.data)
self.predictor1 = QLearn(threshold=0.5, svd=True, regularization=True)
#self.predictor2 = GRUModel ()
self.baseline1 = NaiveModel()
self.baseline2 = EarliestModel()
self.baseline3 = AverageModel(threshold=0.75, regularization=True)
def clean_data (self):
data_used = []
new_data = []
new_header = []
uncleaned_training_data = self.data[int (360*self.training[0]):int(360*self.training[1])]
#print (len (uncleaned_training_data))
sums_of_columns = [ sum(x) for x in zip(*uncleaned_training_data) ]
#print (sums_of_columns)
mean = sum (sums_of_columns) / len (sums_of_columns)
#print ("Mean = " + str(mean))
variance = 0
for i in range (0, len (sums_of_columns)):
variance += (sums_of_columns[i]-mean) ** 2
variance = variance / len (sums_of_columns)
#print ("Variance = " + str(variance))
std_dev = variance ** 0.5
#print ("Standard Deviation = " + str(std_dev))
mean_of_nonzero_columns = 0
num_of_nonzero_columns = 0
for i in range (0, len (sums_of_columns)):
if sums_of_columns[i] > 0:
mean_of_nonzero_columns += sums_of_columns[i]
num_of_nonzero_columns += 1
mean_of_nonzero_columns = mean_of_nonzero_columns / num_of_nonzero_columns
#print ("Mean of non-zero columns = " + str(mean_of_nonzero_columns))
variance_of_nonzero_columns = 0
for i in range (0, len (sums_of_columns)):
if sums_of_columns[i] > 0:
variance_of_nonzero_columns += (sums_of_columns[i]-mean_of_nonzero_columns) ** 2
variance_of_nonzero_columns = variance_of_nonzero_columns / num_of_nonzero_columns
#print ("Variance of non-zero columns = " + str(variance_of_nonzero_columns))
std_dev_of_nonzero_columns = variance_of_nonzero_columns ** 0.5
#print ("Standard Deviation of non-zero columns = " + str(std_dev_of_nonzero_columns))
# Remove data one standard deviation below the non-zero mean
lower_cutoff = mean_of_nonzero_columns - std_dev_of_nonzero_columns +3
print ("Lower cutoff = " + str(lower_cutoff))
for i in range (0, len(sums_of_columns)):
if (sums_of_columns[i] > lower_cutoff):
data_used.append (i)
new_header.append (self.products[i])
for row in range (0, len (self.data)):
new_data.append ([])
for col in range (0, len (self.data[row])):
if (col in data_used):
new_data[row].append (self.data[row][col])
#self.data = new_data
#print (new_data)
print ("Writing into CSV")
with open ('timeseriesOnlineRetailCleaned.csv', "w") as csvfile:
writer = csv.writer(csvfile, delimiter=',')
writer.writerow (new_header)
writer.writerows (new_data)
def validate_predictor (self):
input = []
output = []
week_data = []
for days in range (int (360*self.validation[0]), int (360*self.validation[0])+self.data_size):
today = self.data[days]
week_data = week_data+ today
for days in range (int (360*self.validation[0])+self.data_size, int(360*self.validation[1])):
today = self.data[days]
input.append (week_data)
output.append (today)
week_data = week_data+ today
for i in range (len (self.data[0])):
week_data.pop (0)
print ()
print ("Linear Algebra Model")
#self.predictor1.try_ktruncations (input,output)
self.predictor1.test_model (input, output, verbose=False)
#self.predictor1.print_concepts()
print ()
print ("Previous Day Naive Model")
self.baseline1.test_model (input, output, verbose=False)
print ()
print ("Earliest Day Naive Model")
self.baseline2.test_model (input, output, verbose=False)
print ()
print ("Average of Past Days Model")
self.baseline3.test_model (input, output, verbose=False)
"""
print ()
print ("RNN Model")
self.predictor2.test_model_keras (input, output)
self.predictor2.test_model(input, output, verbose=False)
"""
def train_data (self):
input = []
output = []
week_data = []
for days in range (int (360*self.training[0]), int (360*self.training[0])+self.data_size):
today = self.data[days]
week_data = week_data+ today
for days in range (int (360*self.training[0])+self.data_size, int(360*self.training[1])):
today = self.data[days]
input.append (week_data)
output.append (today)
week_data = week_data+ today
for i in range (len (self.data[0])):
week_data.pop (0)
self.predictor1.set_training_data (input, output)
print ("")
print ("Window size " + str (self.data_size) + " days")
print ("")
print ("Training Model...")
self.predictor1.train()
self.baseline1.train(input, output)
self.baseline2.train (input, output)
self.baseline3.train (input, output)
#self.predictor2.train (input, output)
print ("... Done Training")
def print_concepts (self):
self.predictor1.print_concepts()
class QOnlineRetail:
def __init__(self):
self.data = []
self.training = (0, 0.6)
self.validation = (0.6, 0.8)
self.testing = (0.8, 0.95)
#self.data_size = 24
self.data_size = 7
self.num_queries = 10
# q = 1, epochs = 100
# q = 5, epochs = 20
# q = 100, epochs = 10
# q = 2000, epochs = 3
with open(DATA_DIR, 'r') as f:
#with open ('hourlyTimeSeriesOnlineRetailCleaned.csv', 'r') as f:
reader = csv.reader(f, delimiter=',')
title_row = True
for row in reader:
if title_row:
self.products = row
title_row = False
else:
integer_data = [
int(row[i]) for i in range(self.num_queries)
]
self.data.append(integer_data)
#print (self.data)
self.predictor1 = QLearn(threshold=0.5, regularization=True)
self.baseline1 = NaiveModel()
self.baseline2 = EarliestModel()
self.predictor3 = RNNModel(self.num_queries,
rnn_type="LSTM",
optimizer_type="sgd",
learning_rate=0.001,
layers=1,
hidden_size=128,
recurrent_dropout=0.2)
#self.baseline3 = AverageModel(threshold=0.75, regularization=True)
def validate_predictor(self):
input = []
output = []
week_data = []
for days in range(int(360 * self.validation[0]),
int(360 * self.validation[0]) + self.data_size):
today = self.data[days]
week_data = week_data + today
for days in range(
int(360 * self.validation[0]) + self.data_size,
int(360 * self.validation[1])):
today = self.data[days]
input.append(week_data)
output.append(today)
week_data = week_data + today
for i in range(len(self.data[0])):
week_data.pop(0)
print()
print("Linear Algebra Model")
#self.predictor1.try_ktruncations (input,output)
self.predictor1.test_model(input, output, verbose=False)
#self.predictor1.print_concepts()
print()
print("Previous Day Naive Model")
self.baseline1.test_model(input, output, verbose=False)
print()
print("Earliest Day Naive Model")
self.baseline2.test_model(input, output, verbose=False)
#print ()
#print ("Average of Past Days Model")
#self.baseline3.test_model (input, output, verbose=False)
print()
print("RNN Model")
self.predictor3.test_model(self.xval, self.yval)
#self.predictor2.test_model_keras (input, output)
#self.predictor2.test_model(input, output, verbose=False)
def train_data(self):
input = []
output = []
week_data = []
for days in range(int(360 * self.training[0]),
int(360 * self.training[0]) + self.data_size):
today = self.data[days]
week_data = week_data + today
for days in range(
int(360 * self.training[0]) + self.data_size,
int(360 * self.training[1])):
today = self.data[days]
input.append(week_data)
output.append(today)
week_data = week_data + today
for i in range(len(self.data[0])):
week_data.pop(0)
self.predictor1.set_training_data(input, output)
self.x_data = self.data[:-1]
self.y_data = self.data[1:]
train_length = int(self.training[1] * len(self.y_data)) - int(
self.training[0] * len(self.y_data))
x_train = np.zeros(
(1, train_length, self.num_queries
)) # 1 example, number of time steps, number of queries
for i in range(int(self.training[0] * len(self.x_data)),
int(self.training[0] * len(self.x_data)) +
train_length):
for q in range(self.num_queries):
x_train[0][i][q] = self.x_data[i][
q] # sets the query at the time step to either 0 or 1
y_train = []
for q in range(self.num_queries):
y_train.append(np.zeros((1, train_length, 2)))
for i in range(
int(self.training[0] * len(self.y_data)),
int(self.training[0] * len(self.x_data)) + train_length):
y_train[q][0][i][self.y_data[i][q]] = 1
#Validation
val_length = int(self.validation[1] * len(self.x_data)) - int(
self.validation[0] * len(self.x_data))
x_val = np.zeros((1, val_length, self.num_queries))
for i in range(int(self.validation[0] * len(self.x_data)),
int(self.validation[0] * len(self.x_data)) +
val_length):
for q in range(self.num_queries):
x_val[0][i - int(self.validation[0] *
len(self.x_data))][q] = self.x_data[i][q]
y_val = []
for q in range(self.num_queries):
y_val.append(np.zeros((1, val_length, 2)))
for i in range(
int(self.validation[0] * len(self.y_data)),
int(self.validation[0] * len(self.x_data)) + val_length):
y_val[q][0][i - int(self.validation[0] *
len(self.y_data))][self.y_data[i][q]] = 1
self.xtrain = x_train
self.ytrain = y_train
self.xval = x_val
self.yval = y_val
print("")
print("Window size " + str(self.data_size) + " days")
print("")
print("Training Model...")
self.predictor1.train()
self.baseline1.train(input, output)
self.baseline2.train(input, output)
self.predictor3.train(x_train, y_train, x_val, y_val)
print("... Done Training")
class QOnlineRetail:
def __init__(self):
self.data = []
self.training = (0, 0.6)
self.validation = (0.6, 0.8)
self.testing = (0.8, 1)
#self.data_size = 24
self.data_size = 7
self.num_queries = 2000
# q = 1, epochs = 100
# q = 5, epochs = 20
# q = 100, epochs = 10
# q = 2000, epochs = 3
with open(DATA_DIR, 'r') as f:
#with open ('hourlyTimeSeriesOnlineRetailCleaned.csv', 'r') as f:
reader = csv.reader(f, delimiter=',')
title_row = True
for row in reader:
if title_row:
self.products = row
title_row = False
else:
integer_data = [
int(row[i]) for i in range(self.num_queries)
]
self.data.append(integer_data)
#print (self.data)
self.predictor1 = QLearn(threshold=0.5, regularization=True)
#self.predictor2 = RNNModel ()
self.baseline1 = NaiveModel()
self.baseline2 = EarliestModel()
self.predictor3 = simple_model((None, self.num_queries + 1), 2)
#self.baseline3 = AverageModel(threshold=0.75, regularization=True)
def validate_predictor(self):
input = []
output = []
week_data = []
for days in range(int(360 * self.validation[0]),
int(360 * self.validation[0]) + self.data_size):
today = self.data[days]
week_data = week_data + today
for days in range(
int(360 * self.validation[0]) + self.data_size,
int(360 * self.validation[1])):
today = self.data[days]
input.append(week_data)
output.append(today)
week_data = week_data + today
for i in range(len(self.data[0])):
week_data.pop(0)
print()
print("Linear Algebra Model")
#self.predictor1.try_ktruncations (input,output)
self.predictor1.test_model(input, output, verbose=False)
#self.predictor1.print_concepts()
print()
print("Previous Day Naive Model")
self.baseline1.test_model(input, output, verbose=False)
print()
print("Earliest Day Naive Model")
self.baseline2.test_model(input, output, verbose=False)
#print ()
#print ("Average of Past Days Model")
#self.baseline3.test_model (input, output, verbose=False)
print()
print("RNN Model")
test_model(self.predictor3, self.xval, self.yval)
#self.predictor2.test_model_keras (input, output)
#self.predictor2.test_model(input, output, verbose=False)
def train_data(self):
input = []
output = []
week_data = []
for days in range(int(360 * self.training[0]),
int(360 * self.training[0]) + self.data_size):
today = self.data[days]
week_data = week_data + today
for days in range(
int(360 * self.training[0]) + self.data_size,
int(360 * self.training[1])):
today = self.data[days]
input.append(week_data)
output.append(today)
week_data = week_data + today
for i in range(len(self.data[0])):
week_data.pop(0)
self.predictor1.set_training_data(input, output)
self.x_data = self.data[:-1]
self.y_data = self.data[1:]
#x_train = []
#for q in range (self.num_queries):
# x_train.append(self.x_data [int(self.training[0] * len (self.x_data)):
#int(self.training[1] * len (self.x_data)) ][q])
#x_train = np.array (x_train)
train_length = int(self.training[1] * len(self.y_data)) - int(
self.training[0] * len(self.y_data))
x_train = np.zeros(
(self.num_queries, train_length, 1 + self.num_queries))
for q in range(self.num_queries):
for i in range(int(self.training[0] * len(self.x_data)),
int(self.training[1] * len(self.x_data))):
x_train[q][i - int(self.training[0] *
len(self.x_data))][0] = self.x_data[i][q]
x_train[q][i - int(self.training[0] * len(self.x_data))][q +
1] = 1
y_train = np.zeros((self.num_queries, train_length, 2))
for q in range(self.num_queries):
for i in range(int(self.training[0] * len(self.y_data)),
int(self.training[1] * len(self.y_data))):
y_train[q][i - int(self.training[0] *
len(self.y_data))][self.y_data[i][q]] = 1
#y_train = []
#y_train.append(self.y_data [int(self.training[0] * len (self.y_data)): int(self.training[1] * len (self.y_data) )])
#y_train = np.array (y_train)
#Validation
#x_val = []
#for q in range (self.num_queries):
# x_val.append(self.x_data [int(self.validation[0] * len (self.x_data)): int(self.validation[1] * len (self.x_data) )])
#x_val = np.array (x_val)
x_val = np.zeros(
(self.num_queries, int(self.validation[1] * len(self.x_data)) -
int(self.validation[0] * len(self.x_data)), 1 + self.num_queries))
for q in range(self.num_queries):
for i in range(int(self.validation[0] * len(self.x_data)),
int(self.validation[1] * len(self.x_data))):
x_val[q][i - int(self.validation[0] *
len(self.x_data))][0] = self.x_data[i][q]
x_val[q][i - int(self.validation[0] * len(self.x_data))][q +
1] = 1
#encoding using one-hot method
y_val = np.zeros(
(self.num_queries, int(self.validation[1] * len(self.y_data)) -
int(self.validation[0] * len(self.y_data)), 2))
for q in range(self.num_queries):
for i in range(int(self.validation[0] * len(self.y_data)),
int(self.validation[1] * len(self.y_data))):
y_val[q][i - int(self.validation[0] *
len(self.y_data))][self.y_data[i][q]] = 1
#y_val = []
#y_val.append(self.y_data [int(self.validation[0] * len (self.y_data)): int(self.validation[1] * len (self.y_data) )])
#y_val = np.array (y_val)
print("")
print("Window size " + str(self.data_size) + " days")
print("")
print("Training Model...")
self.predictor1.train()
self.baseline1.train(input, output)
self.baseline2.train(input, output)
train(self.predictor3, x_train, y_train, x_val, y_val)
self.xtrain = x_train
self.ytrain = y_train
self.xval = x_val
self.yval = y_val
#self.baseline3.train (input, output)
#self.predictor2.train (input, output)
print("... Done Training")
class ElectricityPrediction:
def __init__(self):
self.data = []
self.training = (0, 0.5)
self.validation = (0.5, 0.8)
self.testing = (0.8, 0.95)
#self.data_size = 24
self.data_size = 96
self.num_queries = 10
# q = 1, epochs = 100
# q = 5, epochs = 20
# q = 100, epochs = 10
# q = 2000, epochs = 3
self.ds = Electricity()
data = self.ds.get_data()
transpose = np.transpose(data)
transpose = transpose[:self.num_queries]
data = np.transpose(transpose)
self.training_data = data[int(self.training[0] *
len(data)):int(self.training[1] *
len(data))]
self.validation_data = data[int(self.validation[0] *
len(data)):int(self.validation[1] *
len(data))]
self.testing_data = data[int(self.training[0] *
len(data)):int(self.training[1] *
len(data))]
#for i in range (int(len (transpose) / self.num_queries)):
#print (self.data)
self.predictor1 = QLearn(threshold=0.5, regularization=False)
self.baseline1 = NaiveModel()
self.baseline2 = EarliestModel()
self.predictor3 = RNNModel(self.num_queries,
rnn_type="LSTM",
optimizer_type="adam",
learning_rate=0.001,
layers=2,
hidden_size=64,
recurrent_dropout=0.2)
#self.baseline3 = AverageModel(threshold=0.75, regularization=True)
def validate_predictor(self):
input = []
output = []
validation_data = self.validation_data.tolist()
week_data = []
for days in range(0, self.data_size):
today = validation_data[days]
week_data = week_data + today
for days in range(self.data_size, len(self.validation_data)):
today = validation_data[days]
input.append(week_data)
output.append(today)
week_data = week_data + today
week_data = week_data[len(self.validation_data[0]):]
print()
print("Linear Algebra Model")
#self.predictor1.try_ktruncations (input,output)
self.predictor1.test_model(input, output, verbose=False)
#self.predictor1.print_concepts()
print()
print("Previous Day Naive Model")
self.baseline1.test_model(input, output, verbose=False)
print()
print("Earliest Day Naive Model")
self.baseline2.test_model(input, output, verbose=False)
#print ()
#print ("Average of Past Days Model")
#self.baseline3.test_model (input, output, verbose=False)
print()
print("RNN Model")
self.predictor3.test_model(self.xval, self.yval)
#self.predictor2.test_model_keras (input, output)
#self.predictor2.test_model(input, output, verbose=False)
def train_data(self):
input = []
output = []
training_data = self.training_data.tolist()
week_data = []
for days in range(0, self.data_size):
today = training_data[days]
week_data = week_data + today
for days in range(self.data_size, len(self.training_data)):
today = training_data[days]
input.append(week_data)
output.append(today)
week_data = week_data + today
week_data = week_data[len(self.training_data[0]):]
self.predictor1.set_training_data(input, output)
self.x_data = self.data[:-1]
self.y_data = self.data[1:]
x_train = np.array([self.training_data[:-1]])
y_train = np.array([self.training_data[1:]])
x_val = np.array([self.validation_data[:-1]])
y_val = np.array([self.validation_data[1:]])
self.xtrain = x_train
self.ytrain = y_train
self.xval = x_val
self.yval = y_val
print("")
print("Window size: " + str(self.data_size))
print("")
print("Training Model...")
self.predictor1.train()
self.baseline1.train(input, output)
self.baseline2.train(input, output)
self.predictor3.train(x_train, y_train, x_val, y_val)
print("... Done Training")
class QOnlineRetail:
def __init__(self):
self.data = []
self.training = (0, 0.6)
self.validation = (0.6, 0.8)
self.testing = (0.8, 0.95)
#self.data_size = 24
self.data_size = 7
self.num_queries = 200
# q = 1, epochs = 100
# q = 5, epochs = 20
# q = 100, epochs = 10
# q = 2000, epochs = 3
#print (self.data)
#self.predictor1 = QLearn(threshold=0.5, regularization=True)
self.baseline1 = NaiveModel()
self.baseline2 = EarliestModel()
saved_models = [(
10, "LSTM", "sgd", 0.001, 2, 32,
"../results/10Qs/LSTM/2L/32HS/sgd-0.001lr/20180731-134128-683987/900epochs.h5"
)]
model_num = 0
self.num_queries = saved_models[model_num][0]
# q = 1, epochs = 100
# q = 5, epochs = 20
# q = 100, epochs = 10
# q = 2000, epochs = 3
with open(DATA_DIR, 'r') as f:
#with open ('hourlyTimeSeriesOnlineRetailCleaned.csv', 'r') as f:
reader = csv.reader(f, delimiter=',')
title_row = True
for row in reader:
if title_row:
self.products = row
title_row = False
else:
integer_data = [
int(row[i]) for i in range(self.num_queries)
]
self.data.append(integer_data)
self.predictor3 = RNNModel(saved_models[model_num][0],
rnn_type=saved_models[model_num][1],
optimizer_type=saved_models[model_num][2],
learning_rate=saved_models[model_num][3],
layers=saved_models[model_num][4],
hidden_size=saved_models[model_num][5])
self.predictor3.load_model(saved_models[model_num][6])
#self.baseline3 = AverageModel(threshold=0.75, regularization=True)
def validate_predictor(self):
input = []
output = []
week_data = []
for days in range(int(360 * self.validation[0]),
int(360 * self.validation[0]) + self.data_size):
today = self.data[days]
week_data = week_data + today
for days in range(
int(360 * self.validation[0]) + self.data_size,
int(360 * self.validation[1])):
today = self.data[days]
input.append(week_data)
output.append(today)
week_data = week_data + today
for i in range(len(self.data[0])):
week_data.pop(0)
#print ()
#print ("Linear Algebra Model")
#self.predictor1.try_ktruncations (input,output)
#self.predictor1.test_model (input, output, verbose=False)
#self.predictor1.print_concepts()
print()
print("Previous Day Naive Model")
self.baseline1.test_model(input, output, verbose=False)
print()
print("Earliest Day Naive Model")
self.baseline2.test_model(input, output, verbose=False)
#print ()
#print ("Average of Past Days Model")
#self.baseline3.test_model (input, output, verbose=False)
print()
print("RNN Model")
self.predictor3.test_model(self.xval, self.yval)
#self.predictor2.test_model_keras (input, output)
#self.predictor2.test_model(input, output, verbose=False)
def train_data(self):
input = []
output = []
week_data = []
for days in range(int(360 * self.training[0]),
int(360 * self.training[0]) + self.data_size):
today = self.data[days]
week_data = week_data + today
for days in range(
int(360 * self.training[0]) + self.data_size,
int(360 * self.training[1])):
today = self.data[days]
input.append(week_data)
output.append(today)
week_data = week_data + today
for i in range(len(self.data[0])):
week_data.pop(0)
#self.predictor1.set_training_data (input, output)
self.x_data = self.data[:-1]
self.y_data = self.data[1:]
train_length = int(self.training[1] * len(self.y_data)) - int(
self.training[0] * len(self.y_data))
x_train = np.zeros(
(1, train_length, self.num_queries
)) # 1 example, number of time steps, number of queries
for i in range(int(self.training[0] * len(self.x_data)),
int(self.training[0] * len(self.x_data)) +
train_length):
for q in range(self.num_queries):
x_train[0][i][q] = self.x_data[i][
q] # sets the query at the time step to either 0 or 1
y_train = np.zeros(
(1, train_length, self.num_queries
)) # 1 example, number of time steps, number of queries
for i in range(int(self.training[0] * len(self.y_data)),
int(self.training[0] * len(self.y_data)) +
train_length):
for q in range(self.num_queries):
y_train[0][i][q] = self.y_data[i][q]
"""
y_train = []
for q in range (self.num_queries):
y_train.append(np.zeros ((1, train_length, 2)))
for i in range (int(self.training[0]*len (self.y_data)), int(self.training[0] * len (self.x_data)) + train_length):
y_train[q][0][i][self.y_data[i][q]] = 1
"""
#Validation
val_length = int(self.validation[1] * len(self.x_data)) - int(
self.validation[0] * len(self.x_data))
x_val = np.zeros((1, val_length, self.num_queries))
for i in range(int(self.validation[0] * len(self.x_data)),
int(self.validation[0] * len(self.x_data)) +
val_length):
for q in range(self.num_queries):
x_val[0][i - int(self.validation[0] *
len(self.x_data))][q] = self.x_data[i][q]
y_val = np.zeros((1, val_length, self.num_queries))
for i in range(int(self.validation[0] * len(self.y_data)),
int(self.validation[0] * len(self.y_data)) +
val_length):
for q in range(self.num_queries):
y_val[0][i - int(self.validation[0] *
len(self.y_data))][q] = self.y_data[i][q]
self.xtrain = x_train
self.ytrain = y_train
self.xval = x_val
self.yval = y_val
self.baseline1.train(input, output)
self.baseline2.train(input, output)