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combi_model_11.py
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from keras.layers import Input, Conv1D, Dense, Activation, Dropout, Lambda, Multiply, Add, Concatenate, TimeDistributed, Average, Embedding,Flatten
import os
from utils import *
class CombiModel11:
def __init__(self, args,seed):
self.pred_steps = args.pred_steps
days = 365#1034#365
len = days - self.pred_steps - self.pred_steps
n_filters = args.n_filters # 32
filter_width = args.filter_width # 2
n_dilation = args.n_dilation
dilation_rates = [2 ** i for i in range(n_dilation)] # [2 ** i for i in range(6)]
dense_layer1_nuerons = args.dense_1
dropout_rate = args.dropout_rate
history_seq = Input(shape = (len, 1))
#history_seq = Input(shape=(333, 1))
#history_seq = Input(shape=(748, 1))
x = history_seq
conv_layers = []
for dilation_rate in dilation_rates:
conv_layers.append(Conv1D(filters=n_filters,
kernel_size=filter_width,
padding='causal',
dilation_rate=dilation_rate))
dense_layer1 = TimeDistributed(Dense(dense_layer1_nuerons, activation='relu'))
dropout_layer = Dropout(dropout_rate,seed=seed)
dense_layer2 = TimeDistributed(Dense(1))
# extract the last 14 time steps as the training target
def slice(x, seq_length):
return x[:, -seq_length:, :]
lambda_layer = Lambda(slice, arguments={'seq_length': 1})
lambda_layer2 = Lambda(slice, arguments={'seq_length': len})
#lambda_layer2 = Lambda(slice, arguments={'seq_length': 333})
#lambda_layer2 = Lambda(slice, arguments={'seq_length': 748})
concat_layer = Concatenate(axis=1)
for p in range(self.pred_steps):
x2 = x
for c in conv_layers:
x2 = c(x2)
x2 = dense_layer1(x2)
x2 = dropout_layer(x2)
x2 = dense_layer2(x2)
sliced_x2 = lambda_layer(x2)
x = concat_layer([x, sliced_x2])
x = lambda_layer2(x)
pred_seq_train = Lambda(slice, arguments={'seq_length': self.pred_steps})(x)
self.model = Model(history_seq, pred_seq_train)
def get_train_data(self, series_array, date_to_index, train_enc_start, train_enc_end, train_pred_start,
train_pred_end):
encoder_input_data = get_time_block_series(series_array, date_to_index,
train_enc_start, train_enc_end)
encoder_input_data, encode_series_mean, encode_series_std = transform_series_encode(encoder_input_data)
decoder_target_data = get_time_block_series(series_array, date_to_index,
train_pred_start, train_pred_end)
decoder_target_data = transform_series_decode(decoder_target_data, encode_series_mean, encode_series_std)
return encoder_input_data, decoder_target_data, encode_series_mean, encode_series_std
def get_test_data(self, series_array, date_to_index, val_enc_start, val_enc_end, val_pred_start, val_pred_end):
encoder_input_data = get_time_block_series(series_array, date_to_index, val_enc_start, val_enc_end)
encoder_input_data, encode_series_mean, encode_series_std = transform_series_encode(encoder_input_data)
decoder_target_data = get_time_block_series(series_array, date_to_index, val_pred_start, val_pred_end)
decoder_target_data = transform_series_decode(decoder_target_data, encode_series_mean, encode_series_std)
return encoder_input_data, decoder_target_data, encode_series_mean, encode_series_std
def predict_sequence(self, input_sequence):
history_sequence = input_sequence.copy()
pred_sequence = self.model.predict(history_sequence)[:,-self.pred_steps:]
return pred_sequence
def predict_and_write(self, encoder_input_data, decoder_target_data, sample_ind, path, df, encode_series_mean,
encode_series_std):
encode_series = encoder_input_data[sample_ind:sample_ind + 1, :, :]
pred_series = self.predict_sequence(encode_series)
#print(pred_series.shape)
pred_series = (pred_series.reshape(-1, 1) * encode_series_std[sample_ind]) + encode_series_mean[sample_ind]
target_series = (decoder_target_data[sample_ind, :, :1].reshape(-1, 1) * encode_series_std[sample_ind]) + \
encode_series_mean[sample_ind]
#delo = df.loc[sample_ind]['item_delo'].split('_')[0]
#item = df.loc[sample_ind]['item_delo'].split('_')[1].split('.')[0]
delo = df.loc[sample_ind]['item_str'].split('_')[0]
item = df.loc[sample_ind]['item_str'].split('_')[1]
wdf = pd.DataFrame({'p': pred_series.flatten(), 'real': target_series.flatten()})
if not os.path.exists(path + str(delo)):
os.makedirs(path + str(delo))
wdf.to_csv(path + str(delo) + '/' + str(item) + '.csv', index=False)
def predict_all(self,encoder_input_data, decoder_target_data, path, df, encode_series_mean,
encode_series_std):
encode_series = encoder_input_data
pred_series_all = self.predict_sequence(encode_series)
#df2 = pd.read_csv('data/ah_online_sales_143_764.csv')
# print(pred_series.shape)
for sample_ind in range(df.shape[0]):
if sample_ind%10 ==0:
print(sample_ind)
pred_series = pred_series_all[sample_ind:sample_ind+1,:]
pred_series = (pred_series.reshape(-1, 1) * encode_series_std[sample_ind]) + encode_series_mean[sample_ind]
target_series = (decoder_target_data[sample_ind, :, :1].reshape(-1, 1) * encode_series_std[sample_ind]) + \
encode_series_mean[sample_ind]
#delo = df.loc[sample_ind]['item_delo'].split('_')[0]
#item = df.loc[sample_ind]['item_delo'].split('_')[1].split('.')[0]
delo = df.loc[sample_ind]['item_str'].split('_')[0]
item = df.loc[sample_ind]['item_str'].split('_')[1]
#if df.loc[sample_ind]['item_str'] in df2['item_str'].tolist():
wdf = pd.DataFrame({'p': pred_series.flatten(), 'real': target_series.flatten()})
if not os.path.exists(path + str(delo)):
os.makedirs(path + str(delo))
wdf.to_csv(path + str(delo) + '/' + str(item) + '.csv', index=False)