def test_random_seed(self):
log.info("TEST random seed")
df = pd.read_csv(PEYTON_FILE, nrows=512)
set_random_seed(0)
m = NeuralProphet(epochs=1)
metrics_df = m.fit(df, freq="D")
future = m.make_future_dataframe(df,
periods=10,
n_historic_predictions=10)
forecast = m.predict(future)
checksum1 = sum(forecast["yhat1"].values)
set_random_seed(0)
m = NeuralProphet(epochs=1)
metrics_df = m.fit(df, freq="D")
future = m.make_future_dataframe(df,
periods=10,
n_historic_predictions=10)
forecast = m.predict(future)
checksum2 = sum(forecast["yhat1"].values)
set_random_seed(1)
m = NeuralProphet(epochs=1)
metrics_df = m.fit(df, freq="D")
future = m.make_future_dataframe(df,
periods=10,
n_historic_predictions=10)
forecast = m.predict(future)
checksum3 = sum(forecast["yhat1"].values)
log.debug("should be same: {} and {}".format(checksum1, checksum2))
log.debug("should not be same: {} and {}".format(checksum1, checksum3))
assert math.isclose(checksum1, checksum2)
assert not math.isclose(checksum1, checksum3)
#ensure minimum forecast is zero
# def gt0(x):
# if x > 0:
# return x
# else:
# return 0
#df.loc[:,'Order_Demand'] = df['Order_Demand'].apply(gt0)
df.loc[:,'Order_Demand'] = df['Order_Demand'].apply(lambda x: 0 if x < 0 else x)
df.to_pickle("DATA/forecast-demand.pkl")
if __name__ == '__main__':
from neuralprophet import NeuralProphet, set_random_seed
# clean_and_save()
set_random_seed(42)
df = pd.read_pickle("DATA/forecast-demand.pkl")
# df.loc[:,'Order_Demand'] = df['Order_Demand'].apply(lambda x: math.log(x+1))
products = df.groupby('Product_Code')
# prodmed, prodmax = products.median(),products.max()
# print(prodmin.head(10))
# _ = sns.boxplot(y=prodmed['Order_Demand'])
# _ = sns.boxplot(y=prodmax['Order_Demand'])
# plt.show()
#describe.reset_index().plot(x='Date')
# test one product for forecasting
Product_1766 = (products.resample('D')
.median()
import pandas as pd
from neuralprophet import NeuralProphet, set_random_seed
from pandas import DataFrame
from utils import DAYS_OF_PREDICTION
set_random_seed(0)
def predictions(df: DataFrame) -> DataFrame:
"""
Make predictions about
:param df: Dataframe to make the predictions
:return:
"""
m = NeuralProphet()
m.fit(df, freq='D')
future = m.make_future_dataframe(df, periods=DAYS_OF_PREDICTION)
forecast = m.predict(future)
forecast['ds'] = pd.to_datetime(forecast['ds']).dt.strftime('%Y-%m-%d')
forecast = forecast.set_index('ds')
forecast.rename(columns={'yhat1': 'currency'}, inplace=True)
forecast = forecast.round(2)
return forecast