def call_data():
loading = instant_data()
df, mode = loading.hourly_instant(), 'hour'
df = df[start_p:stop_p]
data = df
data = data.interpolate(limit=300000000, limit_direction='both').astype(
'float32'
) #interpolate neighbor first, for rest NA fill with mean() #.apply(lambda x: x.fillna(x.mean()),axis=0)
data['Day'] = data.index.dayofyear
# # MARS
mars_cutoff = 0.3
data_mar = call_mar(data, target, mode, cutoff=mars_cutoff)
data_mar = move_column_inplace(data_mar, target, 0)
return data_mar
def datapreprocess():
loading = instant_data()
df,mode = loading.hourly_instant(),'hour'
df = df[start_p:stop_p]
data = df
data = data.interpolate(limit=300000000,limit_direction='both').astype('float32')#interpolate neighbor first, for rest NA fill with mean() #.apply(lambda x: x.fillna(x.mean()),axis=0)
data[target].plot()
# # MARS
mars_cutoff = 0.3
data_mar = call_mar(data,target,mode,cutoff=mars_cutoff)
data_mar = move_column_inplace(data_mar,target,0)
# # SCALE
# scaler = MinMaxScaler()
# df_scaled = pd.DataFrame(scaler.fit_transform(data_mar), columns=data_mar.columns,index=data_mar.index)
scaler_tar = MinMaxScaler()
scaler_tar.fit(data[target].to_numpy().reshape(-1,1))
return data_mar,scaler_tar
print('incorrect input')
st = 'CPY012'
target, start_p, stop_p, host_path = station_sel(st, mode)
start_p = '2016-01-01'
stop_p = '2017-01-01'
#-----------------------------
data = df[start_p:stop_p]
split_date = int(len(data) * .7)
data = data.interpolate(limit=3000000000,
limit_direction='both').astype('float32')
data['Day'] = data.index.dayofyear #add day
#-----------------------------
cutoff = .3
data_mar = call_mar(data, target, mode, cutoff=cutoff)
data_mar = move_column_inplace(data_mar, target, 0)
n_features = len(data_mar.columns)
#----------------------------
out_t_step = 1
X, Y, _ = to_supervise(data_mar, target, out_t_step)
#trainX, testX = X[:split_date].dropna(),X[split_date:].dropna()
#trainY, testY = Y[:split_date].dropna(),Y[split_date:].dropna()
trainX, testX = X.iloc[:split_date].dropna(), X.iloc[split_date:].dropna()
trainY, testY = Y.iloc[:split_date].dropna(), Y.iloc[split_date:].dropna()
#--------------------------------------------#
scaler = StandardScaler()
trainX = scaler.fit_transform(trainX)
testX = scaler.transform(testX)