In [6]:
# 데이터 불러오기
from model.snoopers import load_excel
price, data, scaler = load_excel('./data/coffeedata.xlsx')
data.head()
/home/buffet/Coding/Python/Pytorch/lib/python3.8/site-packages/pandas/core/indexing.py:1738: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame. Try using .loc[row_indexer,col_indexer] = value instead See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy self._setitem_single_column(loc, value[:, i].tolist(), pi)
Out[6]:
| air | gas | gasgap | gram | bean | |
|---|---|---|---|---|---|
| 0 | 196 | 10 | 10 | 80 | 179 |
| 1 | 195 | 10 | 10 | 80 | 177 |
| 2 | 195 | 10 | 10 | 80 | 176 |
| 3 | 193 | 10 | 10 | 80 | 173 |
| 4 | 192 | 10 | 10 | 80 | 171 |
In [2]:
# 데이터 전처리 및 모델의 활용
from model.snoopers import split_data, lstm_pytorch_model
X, y = split_data(price, lookback = 60)
model = lstm_pytorch_model('./model/coffee.pth')
model.eval()
Out[2]:
LSTM( (lstm): LSTM(1, 32, num_layers=2, batch_first=True) (fc): Linear(in_features=32, out_features=1, bias=True) )
In [7]:
# CSV 파일로 저장하기
file_csv = 'bean_lstm.csv'
# 데이터로 예측 데이터 학습 및 결과값 출력
from model.snoopers import r2_score_and_save_csv
y_pred = model(X)
lstm_predict, predict, original = r2_score_and_save_csv(y, y_pred, scaler, data)
lstm_predict.to_csv(file_csv, index=None)
print(f"결과값을 {file_csv} 파일로 저장하였습니다.")
lstm_predict.tail()
r2_score is : 0.9903256085226195
Out[7]:
| air | gas | gasgap | gram | bean | lstm | |
|---|---|---|---|---|---|---|
| 20184 | 249 | 90 | 40 | 800 | 219 | 217.821106 |
| 20185 | 249 | 90 | 40 | 800 | 220 | 217.944443 |
| 20186 | 249 | 90 | 40 | 800 | 220 | 218.024628 |
| 20187 | 250 | 90 | 40 | 800 | 220 | 218.215454 |
| 20188 | 250 | 90 | 40 | 800 | 220 | 218.418716 |
In [8]:
%matplotlib inline
# 결과값 시각화 출력하기
from model.snoopers import plot_data
plot_data(predict, original)
