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Newnius 2020-05-02 16:31:23 +08:00
parent 5a71f23db5
commit ee930aa2c5
3 changed files with 72 additions and 83 deletions
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53
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@ -180,6 +182,8 @@
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@ -195,11 +199,11 @@
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11
serve.py
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@ -11,6 +11,17 @@ import os
from sklearn.model_selection import train_test_split from sklearn.model_selection import train_test_split
from model_tensorflow import train, predict from model_tensorflow import train, predict
import csv import csv
from pandas import DataFrame
from pandas import Series
from pandas import concat
from pandas import read_csv
from sklearn.metrics import mean_squared_error
from sklearn.preprocessing import MinMaxScaler
from keras.models import Sequential
from keras.layers import Dense
from keras.layers import LSTM
from math import sqrt
import numpy
class Config: class Config:

91
train.py
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@ -9,7 +9,6 @@ from keras.layers import Dense
from keras.layers import LSTM from keras.layers import LSTM
from math import sqrt from math import sqrt
import numpy import numpy
from keras.optimizers import Adam
# frame a sequence as a supervised learning problem # frame a sequence as a supervised learning problem
@ -75,21 +74,14 @@ def fit_lstm(train, batch_size2, nb_epoch, neurons):
return model return model
# make a one-step forecast # run a experiment
def forecast_lstm(model, batch_size, X): def experiment(series):
X = X.reshape(1, 1, len(X))
yhat = model.predict(X, batch_size=batch_size)
return yhat[0, 0]
# run a repeated experiment
def experiment(repeats, series, seed):
# transform data to be stationary # transform data to be stationary
raw_values = series.values raw_values = series.values
diff_values = difference(raw_values, 1) diff_values = difference(raw_values, 1)
# transform data to be supervised learning # transform data to be supervised learning
lag2 = 4 lag = 4
supervised = timeseries_to_supervised(diff_values, lag2) supervised = timeseries_to_supervised(diff_values, lag)
supervised_values = supervised.values supervised_values = supervised.values
batch_size = 32 batch_size = 32
@ -102,59 +94,40 @@ def experiment(repeats, series, seed):
# split data into train and test-sets # split data into train and test-sets
train, test = supervised_values[0:-test_data_num], supervised_values[-test_data_num:] train, test = supervised_values[0:-test_data_num], supervised_values[-test_data_num:]
# transform the scale of the data # transform the scale of the data
print(test)
scaler, train_scaled, test_scaled = scale(train, test) scaler, train_scaled, test_scaled = scale(train, test)
print(test_scaled)
# run experiment # run experiment
error_scores = list() error_scores = list()
for r in range(repeats): # fit the model
# fit the model t1 = train.shape[0] % batch_size
t1 = train.shape[0] % batch_size t2 = test.shape[0] % batch_size
t2 = test.shape[0] % batch_size
train_trimmed = train_scaled[t1:, :] train_trimmed = train_scaled[t1:, :]
lstm_model = fit_lstm(train_trimmed, batch_size, 30, 4) lstm_model = fit_lstm(train_trimmed, batch_size, 30, 4)
# forecast the entire training dataset to build up state for forecasting
print(train_trimmed) # forecast the entire training dataset to build up state for forecasting
print(train_trimmed[:, 0]) test_reshaped = test_scaled[:, 0:-1]
print(train_trimmed[:, :-1]) test_reshaped = test_reshaped.reshape(len(test_reshaped), 1, lag)
# if seed: output = lstm_model.predict(test_reshaped, batch_size=batch_size)
# train_reshaped = train_trimmed[:, :-1].reshape(len(train_trimmed), 1, lag2) predictions = list()
# lstm_model.predict(train_reshaped, batch_size=batch_size) for i in range(len(output)):
# forecast test dataset yhat = output[i, 0]
test_reshaped = test_scaled[:, 0:-1] X = test_scaled[i, 0:-1]
test_reshaped = test_reshaped.reshape(len(test_reshaped), 1, lag2) # invert scaling
output = lstm_model.predict(test_reshaped, batch_size=batch_size) yhat = invert_scale(scaler, X, yhat)
predictions = list() # invert differencing
for i in range(len(output)): yhat = inverse_difference(raw_values, yhat, len(test_scaled) + 1 - i)
yhat = output[i, 0] # store forecast
X = test_scaled[i, 0:-1] predictions.append(yhat)
# invert scaling # report performance
yhat = invert_scale(scaler, X, yhat) rmse = sqrt(mean_squared_error(raw_values[-test_data_num:], predictions))
# invert differencing print(predictions, raw_values[-test_data_num:])
yhat = inverse_difference(raw_values, yhat, len(test_scaled) + 1 - i) error_scores.append(rmse)
# store forecast
predictions.append(yhat)
# report performance
rmse = sqrt(mean_squared_error(raw_values[-test_data_num:], predictions))
print(predictions, raw_values[-test_data_num:])
print('%d) Test RMSE: %.3f' % (r + 1, rmse))
error_scores.append(rmse)
return error_scores return error_scores
# load dataset # load dataset
series = read_csv('data.csv', header=0, index_col=0, squeeze=True) series = read_csv('data.csv', header=0, index_col=0, squeeze=True)
# experiment
repeats = 1 with_seed = experiment(series)
results = DataFrame()
# with seeding
with_seed = experiment(repeats, series, True)
results['with-seed'] = with_seed
# without seeding
# without_seed = experiment(repeats, series, False)
# results['without-seed'] = without_seed
# summarize results
print(results.describe())
# save boxplot
# results.boxplot()