7 电力需求预测挑战赛 baseline

2024 iFLYTEK A.I.开发者大赛-讯飞开放平台2022 iFLYTEK A.I.开发者大赛-讯飞开放平台https://challenge.xfyun.cn/topic/info?type=electricity-demand本次baseline分享是253，就是简单的使用MLP网络。

MLP网络

import numpy as np
import pandas as pd
#import lightgbm as lgb
from sklearn.model_selection import StratifiedKFold, KFold, GroupKFold
from sklearn.metrics import mean_squared_error, mean_absolute_error
from sklearn.model_selection import train_test_split

from tqdm import tqdm
from sklearn.preprocessing import LabelEncoder
#import h3
import seaborn as sns
import warnings
warnings.filterwarnings('ignore')
pd.set_option("display.max_rows",None)

from tqdm import tqdm
warnings.filterwarnings('ignore')
from tensorflow.keras.preprocessing.sequence import TimeseriesGenerator
from tensorflow.keras.models import Sequential, Model
from tensorflow.keras.layers import Flatten, Reshape, LSTM, Dropout, Dense, Bidirectional, BatchNormalization, Input, LayerNormalization, GRU, Conv1D, Concatenate, MaxPooling1D, MultiHeadAttention, GlobalAveragePooling1D, Activation, SpatialDropout1D, Lambda
from tensorflow.keras.losses import MeanSquaredError, Huber
from tensorflow.keras.optimizers import Adam
from tensorflow.keras.callbacks import EarlyStopping, ModelCheckpoint, ReduceLROnPlateau
import warnings

train_df = pd.read_csv('../data/dataset/train.csv')
#train_df = train_df[train_df.dt<100].reset_index(drop=True)
test_df = pd.read_csv('../data/dataset/test.csv')
#train_df['target_div_dt'] = train_df['target'] / train_df['dt']


df_all = pd.concat([train_df,test_df])
df_all['dt_max'] = df_all.groupby('id')['dt'].transform('max')
df_all = df_all.sort_values(['id','dt']).reset_index(drop=True)
df_all['new_dt'] = df_all['dt_max']-df_all['dt']
df_all = df_all.sort_values(['id','new_dt']).reset_index(drop=True)
df_all.tail()
train_df = df_all[~df_all['target'].isna()].reset_index(drop=True)
test_df = df_all[df_all['target'].isna()].reset_index(drop=True)

# 数据集合
def create_dataset(X, n_steps_in, n_steps_out):
    print(f"Input data shape before processing: {X.shape}")
    
    Xs, ys = [], []
    for i in range(len(X) - n_steps_in - n_steps_out + 1):
        Xs.append(X[i:(i + n_steps_in)])
        ys.append(X[(i + n_steps_in):(i + n_steps_in + n_steps_out)])
    
    Xs = np.array(Xs)
    ys = np.array(ys)
    
    print(f"Xs shape after processing: {Xs.shape}")
    print(f"ys shape after processing: {ys.shape}")
    
    return Xs, ys


def create_model(input_shape, output_length,lr=1e-3, warehouse="None"):

    model = Sequential()
    model.add(Input(shape=input_shape))
    
    model.add(Flatten())
    
    model.add(Dense(256, activation="relu"))
    model.add(Dropout(0.4)) 
    
    
    model.add(Dense(output_length))
    
    model.compile(loss=MeanSquaredError(), optimizer=tf.keras.optimizers.RMSprop(learning_rate=lr, rho=0.9))
                  
    return model
    
def plot_loss(history, warehouse):
    plt.figure(figsize=(8, 6))

    # training and validation loss
    plt.plot(history.history['loss'], label='Training Loss', color='blue', linewidth=2)
    plt.plot(history.history['val_loss'], label='Validation Loss', color='orange', linewidth=2)
    
    # minimum validation loss
    min_val_loss = min(history.history['val_loss'])
    min_val_loss_epoch = history.history['val_loss'].index(min_val_loss)
    plt.axvline(min_val_loss_epoch, linestyle='--', color='gray', linewidth=1)
    plt.text(min_val_loss_epoch, min_val_loss, f'Min Val Loss: {min_val_loss:.4f}', 
             verticalalignment='bottom', horizontalalignment='right', color='gray', fontsize=10)
    
    plt.title(f'Training and Validation Loss for Warehouse: {warehouse}', fontsize=16)
    plt.xlabel('Epoch', fontsize=14)
    plt.ylabel('Loss', fontsize=14)
    plt.legend(fontsize=12)
    plt.grid(True)

    plt.xticks(fontsize=12)
    plt.yticks(fontsize=12)
    plt.tight_layout()
    
    #plt.savefig(f'training_validation_loss_{warehouse}.png', dpi=300)
    
    plt.show()

n_features = 1  # 因为这是一个一维序列
n_steps_in = 70  # 输入序列的长度
n_steps_out = 10  # 预测序列的长度
# 数据预处理
# 数据预处理

best_sub = pd.read_csv('../data/sub/es_xgb_55_big_diff_fill_es_216.csv')
tf.random.set_seed(42)
import numpy as np
np.random.seed(42)
import random
import os
error_df = {}
def set_random_seed(seed_value):
    # Set `PYTHONHASHSEED` environment variable at a fixed value
    os.environ['PYTHONHASHSEED']=str(seed_value)
    # Set `python` built-in pseudo-random generator at a fixed value
    random.seed(seed_value)
    # Set `numpy` pseudo-random generator at a fixed value
    np.random.seed(seed_value)
    # Set `tensorflow` pseudo-random generator at a fixed value
    tf.random.set_seed(seed_value)
set_random_seed(42)
import keras

class PrintCallback(keras.callbacks.Callback):
    def __init__(self, print_every=1):
        super(PrintCallback, self).__init__()
        self.print_every = print_every

    def on_epoch_end(self, epoch, logs=None):
        if (epoch + 1) % self.print_every == 0:
            print(f"Epoch {epoch + 1}: loss={logs['loss']:.4f}, val_loss={logs['val_loss']:.4f}")

# 使用示例
print_every_n_epochs = 5  # 每 5 个 epoch 打印一次
error_id = []
for id in tqdm(train_df.id.unique().tolist()):
    try:
        temp_df = train_df[train_df.id==id].reset_index(drop=True)
        X = temp_df.target.values
        x_test = X[-n_steps_in:]
        train_X,train_y =  create_dataset(X,n_steps_in,n_steps_out)
        X_train, X_val, y_train, y_val = train_test_split(train_X, train_y, test_size=0.2, shuffle=True)
        model = create_model(input_shape=(n_steps_in, 1),output_length=n_steps_out,lr=1e-3)
    
        callbacks = [
        PrintCallback(print_every=print_every_n_epochs),
        EarlyStopping(monitor='val_loss', patience=25, restore_best_weights=True),]
    
    
        history = model.fit(
                X_train, y_train, 
                epochs=150, 
                batch_size=16, 
                #validation_split=0.2, 
                validation_data=(X_val, y_val), 
                callbacks=callbacks,
                verbose=0
            )
        test_y = model.predict(x_test.reshape((-1,n_steps_in)))
        test_df.loc[test_df.id==id,'target'] = test_y[0]
        error = mean_squared_error(best_sub[best_sub['id']==id]['target'],test_y[0])
        error_df[id] = round(error,4)
        print(f'linear model {id} VS best sb ERROR = {error}')
        
    except Exception as e:
        error_id.append(id)
        print(f'error id = {id}',e)
    pass

# 注意其中有两个序列长度过低，因此没有参与训练
test_df[['id','dt','target']].to_csv('../data/sub/mlp_lgb_19.csv',index=False)