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BaselinePrerocess.py
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#!/usr/bin/env python3
# -*- coding: UTF-8 -*-
"""
@ Project : WaLeF
@ FileName: BaselinePrerocess.py
@ IDE : PyCharm
@ Author : Jimeng Shi
@ Time : 6/25/22 15:55
"""
import numpy as np
import pandas as pd
from pandas import DataFrame, concat, read_csv
from tensorflow import keras
from sklearn.preprocessing import MinMaxScaler
from math import sqrt
from helper import series_to_supervised, stage_series_to_supervised
def baseline_process(n_hours, K, masked_value, split_1, split_2):
# ==================== import dataset ====================
dataset = pd.read_csv('data/Merged-update_hourly.csv', index_col=0)
dataset.fillna(0, inplace=True)
print(dataset.columns)
# ==================== convert dataset to supervised mode ====================
data = dataset[['MEAN_RAIN', 'WS_S4',
'GATE_S25A', 'GATE_S25B', 'GATE_S25B2', 'GATE_S26_1', 'GATE_S26_2',
'PUMP_S25B', 'PUMP_S26',
#'FLOW_S25A', 'FLOW_S25B', 'FLOW_S26',
'HWS_S25A', 'HWS_S25B', 'HWS_S26',
'WS_S1', 'TWS_S25A', 'TWS_S25B', 'TWS_S26']]
features = data.shape[1]
#print("data.shape:", data.shape)
data_supervised = series_to_supervised(data, n_hours, K)
#print("data_supervised.shape:", data_supervised.shape)
col_names = ['MEAN_RAIN', 'WS_S4',
'GATE_S25A', 'GATE_S25B', 'GATE_S25B2', 'GATE_S26_1', 'GATE_S26_2',
'PUMP_S25B', 'PUMP_S26',
#'FLOW_S25A', 'FLOW_S25B', 'FLOW_S26',
'HWS_S25A', 'HWS_S25B', 'HWS_S26',
'WS_S1', 'TWS_S25A', 'TWS_S25B', 'TWS_S26'] * (n_hours+K)
data_supervised.reset_index(drop=True, inplace=True)
data_supervised.columns = [[i + '_' + j for i, j in zip(col_names, list(data_supervised.columns))]]
#print("data_supervised:", data_supervised)
# ==================== past & future ====================
past = data_supervised.iloc[:, :n_hours*data.shape[1]]
past = past.to_numpy(dtype='float32')
past = past.reshape((-1, n_hours, data.shape[1]))
future = data_supervised.iloc[:, n_hours*data.shape[1]:]
future = future.to_numpy(dtype='float32')
future = future.reshape((-1, K, data.shape[1]))
past_future = np.concatenate((past, future), axis=1)
past_future = past_future.astype(np.float32)
# ==================== masking ====================
mask_gate_start_index = 2
mask_gate_end_index = 6
mask_pump_start_index = 7
mask_pump_end_index = 8
mask_hws_start_index = 9
mask_hws_end_index = 11
mask_tws_start_index = 12
mask_tws_end_index = 15
past_future_mask = past_future.copy()
past_future_mask[:, n_hours:, mask_hws_start_index:mask_tws_end_index+1] = masked_value # masking ws
X_mask = past_future_mask
ws_true = past_future[:, n_hours:, mask_tws_start_index:mask_tws_end_index+1]
X_mask_reshape = X_mask.reshape((X_mask.shape[0], -1))
ws_true_reshape = ws_true.reshape((ws_true.shape[0], -1))
split1 = int(len(X_mask_reshape)*split_1)
split2 = int(len(X_mask_reshape)*split_2)
# train / val / test
train_X_mask = X_mask_reshape[:split1]
val_X_mask = X_mask_reshape[split1:split2]
test_X_mask = X_mask_reshape[split1:]
train_ws_true = ws_true_reshape[:split1]
val_ws_true = ws_true_reshape[split1:split2]
test_ws_true = ws_true_reshape[split1:]
# ==================== normalization ====================
scaler = MinMaxScaler(feature_range=(0, 1))
train_X_mask_scaled = scaler.fit_transform(train_X_mask)
val_X_mask_scaled = scaler.fit_transform(val_X_mask)
test_X_mask_scaled = scaler.fit_transform(test_X_mask)
ws_scaler = MinMaxScaler(feature_range=(0, 1))
train_ws_true_scaled = ws_scaler.fit_transform(train_ws_true)
val_ws_true_scaled = ws_scaler.fit_transform(val_ws_true)
test_ws_true_scaled = ws_scaler.fit_transform(test_ws_true)
# final train / val / test
train_X_mask = train_X_mask_scaled.reshape((-1, n_hours+K, features))
val_X_mask = val_X_mask_scaled.reshape((-1, n_hours+K, features))
test_X_mask = test_X_mask_scaled.reshape((-1, n_hours+K, features))
train_ws_y = train_ws_true_scaled
val_ws_y = val_ws_true_scaled
test_ws_y = test_ws_true_scaled
return train_X_mask, val_X_mask, test_X_mask, train_ws_y, val_ws_y, test_ws_y, scaler, ws_scaler
def gcn_process(n_hours, K, masked_value, split_1, split_2):
# ==================== import dataset ====================
dataset = pd.read_csv('data/Merged-update_hourly.csv', index_col=0)
dataset.fillna(0, inplace=True)
print(dataset.columns)
# ==================== convert dataset to supervised mode ====================
data = dataset[['MEAN_RAIN', 'WS_S4',
'GATE_S25A', 'GATE_S25B', 'GATE_S25B2', 'GATE_S26_1', 'GATE_S26_2',
#'PUMP_S25B', 'PUMP_S26',
#'FLOW_S25A', 'FLOW_S25B', 'FLOW_S26',
'HWS_S25A', 'HWS_S25B', 'HWS_S26',
'WS_S1', 'TWS_S25A', 'TWS_S25B', 'TWS_S26']]
features = data.shape[1]
#print("data.shape:", data.shape)
data_supervised = series_to_supervised(data, n_hours, K)
#print("data_supervised.shape:", data_supervised.shape)
col_names = ['MEAN_RAIN', 'WS_S4',
'GATE_S25A', 'GATE_S25B', 'GATE_S25B2', 'GATE_S26_1', 'GATE_S26_2',
#'PUMP_S25B', 'PUMP_S26',
#'FLOW_S25A', 'FLOW_S25B', 'FLOW_S26',
'HWS_S25A', 'HWS_S25B', 'HWS_S26',
'WS_S1', 'TWS_S25A', 'TWS_S25B', 'TWS_S26'] * (n_hours+K)
data_supervised.reset_index(drop=True, inplace=True)
data_supervised.columns = [[i + '_' + j for i, j in zip(col_names, list(data_supervised.columns))]]
#print("data_supervised:", data_supervised)
# ==================== past & future ====================
past = data_supervised.iloc[:, :n_hours*data.shape[1]]
past = past.to_numpy(dtype='float32')
past = past.reshape((-1, n_hours, data.shape[1]))
future = data_supervised.iloc[:, n_hours*data.shape[1]:]
future = future.to_numpy(dtype='float32')
future = future.reshape((-1, K, data.shape[1]))
past_future = np.concatenate((past, future), axis=1)
past_future = past_future.astype(np.float32)
# ==================== masking ====================
mask_gate_start_index = 2
mask_gate_end_index = 6
mask_hws_start_index = 7
mask_hws_end_index = 9
mask_tws_start_index = 10
mask_tws_end_index = 13
past_future_mask = past_future.copy()
past_future_mask[:, n_hours:, mask_hws_start_index:mask_tws_end_index+1] = masked_value # masking ws
X_mask = past_future_mask
ws_true = past_future[:, n_hours:, mask_tws_start_index:mask_tws_end_index+1]
X_mask_reshape = X_mask.reshape((X_mask.shape[0], -1))
ws_true_reshape = ws_true.reshape((ws_true.shape[0], -1))
split1 = int(len(X_mask_reshape)*split_1)
split2 = int(len(X_mask_reshape)*split_2)
# train / val / test
train_X_mask = X_mask_reshape[:split1]
val_X_mask = X_mask_reshape[split1:split2]
test_X_mask = X_mask_reshape[split1:]
train_ws_true = ws_true_reshape[:split1]
val_ws_true = ws_true_reshape[split1:split2]
test_ws_true = ws_true_reshape[split1:]
# ==================== normalization ====================
scaler = MinMaxScaler(feature_range=(0, 1))
train_X_mask_scaled = scaler.fit_transform(train_X_mask)
val_X_mask_scaled = scaler.fit_transform(val_X_mask)
test_X_mask_scaled = scaler.fit_transform(test_X_mask)
ws_scaler = MinMaxScaler(feature_range=(0, 1))
train_ws_true_scaled = ws_scaler.fit_transform(train_ws_true)
val_ws_true_scaled = ws_scaler.fit_transform(val_ws_true)
test_ws_true_scaled = ws_scaler.fit_transform(test_ws_true)
# final train / val / test
train_X_mask = train_X_mask_scaled.reshape((-1, features, n_hours+K))
val_X_mask = val_X_mask_scaled.reshape((-1, features, n_hours+K))
test_X_mask = test_X_mask_scaled.reshape((-1, features, n_hours+K))
train_ws_y = train_ws_true_scaled
val_ws_y = val_ws_true_scaled
test_ws_y = test_ws_true_scaled
# Graph & distance
return train_X_mask, val_X_mask, test_X_mask, train_ws_y, val_ws_y, test_ws_y, scaler, ws_scaler