From 9036cd24c358c7b65963ebf59e2897b46d70a470 Mon Sep 17 00:00:00 2001
From: rp <rp@outlook.com>
Date: 星期一, 14 四月 2025 00:26:24 +0800
Subject: [PATCH] 备份
---
testonly.py | 210 ++++++++++++++++++++++++++++++++++++++++++++++++++++
1 files changed, 210 insertions(+), 0 deletions(-)
diff --git a/testonly.py b/testonly.py
new file mode 100644
index 0000000..4527a7f
--- /dev/null
+++ b/testonly.py
@@ -0,0 +1,210 @@
+import sys
+import pandas as pd
+import numpy as np
+import matplotlib.pyplot as plt
+from sklearn.preprocessing import MinMaxScaler
+from sklearn.metrics import mean_absolute_error, mean_squared_error
+
+# Check numpy version
+if np.__version__ < '1.23.5':
+ print(f"Warning: Current numpy version {np.__version__} may cause compatibility issues.")
+ print("Please upgrade numpy to version 1.23.5 or higher.")
+
+try:
+ import tensorflow as tf
+ print(f"TensorFlow version: {tf.__version__}")
+except ImportError as e:
+ print("Error importing TensorFlow:", e)
+ sys.exit(1)
+
+# Check TensorFlow version
+if tf.__version__ < '2.10.0':
+ print(f"Warning: Current TensorFlow version {tf.__version__} may cause compatibility issues.")
+ print("Please upgrade TensorFlow to version 2.10.0 or higher.")
+
+from tensorflow.keras.models import Model
+from tensorflow.keras.layers import Input, Conv1D, LSTM, Dense, Layer
+from tensorflow.keras.callbacks import EarlyStopping
+
+# ================================
+# 1. 鏁版嵁棰勫鐞嗘ā鍧�
+# ================================
+class DataPreprocessor:
+ def __init__(self, file_path, target_col='Value'):
+ # 浣跨敤姝ｇ‘鐨勭紪鐮佸拰鍒楀悕
+ self.df = pd.read_csv(file_path, encoding='utf-8-sig', parse_dates=['DateTime'], index_col='DateTime')
+ self.target_col = target_col
+ self.scaler = MinMaxScaler(feature_range=(0, 1))
+
+ def preprocess(self, resample_freq='h'): # 浣跨敤灏忓啓鐨�'h'浠ｆ浛澶у啓鐨�'H'
+ """鏁版嵁閲嶉噰鏍蜂笌褰掍竴鍖�"""
+ # 鍙�夋嫨Value鍒楄繘琛屽鐞�
+ value_series = self.df[self.target_col]
+
+ # 澶勭悊闈炵瓑闂撮殧閲囨牱
+ df_resampled = value_series.resample(resample_freq).mean()
+ df_filled = df_resampled.fillna(method='ffill').fillna(method='bfill') # 鍙屽悜濉厖
+
+ # 褰掍竴鍖栧鐞�
+ self.scaled_data = self.scaler.fit_transform(df_filled.values.reshape(-1, 1))
+ self.dates = df_filled.index
+ return self.scaled_data, self.dates
+
+ def create_sequences(self, data, look_back=72, pred_steps=120):
+ """鍒涘缓鐩戠潱瀛︿範搴忓垪"""
+ X, Y = [], []
+ for i in range(len(data) - look_back - pred_steps):
+ X.append(data[i:(i + look_back)])
+ Y.append(data[(i + look_back):(i + look_back + pred_steps)])
+ return np.array(X), np.array(Y)
+
+# ================================
+# 2. 妯″瀷鏋勫缓妯″潡
+# ================================
+class TemporalAttention(Layer):
+ """鏃堕棿娉ㄦ剰鍔涙満鍒跺眰"""
+ def __init__(self, units):
+ super(TemporalAttention, self).__init__()
+ self.W1 = Dense(units)
+ self.W2 = Dense(units)
+ self.V = Dense(1)
+
+ def call(self, encoder_output, lstm_output):
+ lstm_output = tf.expand_dims(lstm_output, 1)
+ score = self.V(tf.nn.tanh(
+ self.W1(encoder_output) + self.W2(lstm_output)))
+ attention_weights = tf.nn.softmax(score, axis=1)
+ context_vector = attention_weights * encoder_output
+ context_vector = tf.reduce_sum(context_vector, axis=1)
+ return context_vector, attention_weights
+
+class SalinityPredictor:
+ def __init__(self, look_back=72, pred_steps=120):
+ self.look_back = look_back # 鍘嗗彶绐楀彛锛堝皬鏃讹級
+ self.pred_steps = pred_steps # 棰勬祴姝ラ暱锛�5澶�=120灏忔椂锛�
+
+ def build_model(self):
+ """鏋勫缓CNN-LSTM-Attention娣峰悎妯″瀷"""
+ inputs = Input(shape=(self.look_back, 1))
+
+ # CNN鐗瑰緛鎻愬彇
+ cnn = Conv1D(64, 3, activation='relu', padding='same')(inputs)
+ cnn = Conv1D(32, 3, activation='relu', padding='same')(cnn)
+
+ # LSTM鏃跺簭寤烘ā
+ lstm_out = LSTM(128, return_sequences=True)(cnn)
+ lstm_out = LSTM(64, return_sequences=False)(lstm_out)
+
+ # 娉ㄦ剰鍔涙満鍒�
+ context_vector, _ = TemporalAttention(64)(cnn, lstm_out)
+
+ # 杈撳嚭灞�
+ outputs = Dense(self.pred_steps)(context_vector)
+
+ self.model = Model(inputs=inputs, outputs=outputs)
+ self.model.compile(optimizer='adam', loss='mse')
+ return self.model
+
+ def dynamic_split(self, data, dates, cutoff_date):
+ """鍔ㄦ�佸垝鍒嗚缁冮泦"""
+ cutoff_idx = np.where(dates <= cutoff_date)[0][-self.look_back]
+ train_data = data[:cutoff_idx]
+ return train_data
+
+ def train(self, X_train, y_train, epochs=200, batch_size=32):
+ """妯″瀷璁粌"""
+ early_stop = EarlyStopping(monitor='val_loss', patience=20)
+ history = self.model.fit(
+ X_train, y_train,
+ epochs=epochs,
+ batch_size=batch_size,
+ validation_split=0.2,
+ callbacks=[early_stop],
+ verbose=1
+ )
+ return history
+
+ def predict(self, last_sequence):
+ """閫掑綊澶氭棰勬祴"""
+ predictions = []
+ current_seq = last_sequence.copy()
+
+ for _ in range(self.pred_steps):
+ pred = self.model.predict(current_seq[np.newaxis, :, :], verbose=0)
+ predictions.append(pred[0][0]) # 鍙彇绗竴涓娴嬪��
+ current_seq = np.roll(current_seq, -1, axis=0)
+ current_seq[-1] = pred[0][0] # 浣跨敤鍗曚釜棰勬祴鍊兼洿鏂板簭鍒�
+
+ return np.array(predictions)
+
+# ================================
+# 3. 瀹屾暣娴佺▼鎵ц
+# ================================
+if __name__ == "__main__":
+ # 鍙傛暟閰嶇疆
+ DATA_PATH = 'D:\opencv\.venv\涓�鍙栨按.csv'
+ CUTOFF_DATE = '2024-12-20 00:00' # 鐢ㄦ埛鎸囧畾鍒嗗壊鏃堕棿鐐�
+ LOOK_BACK = 72 # 3澶╁巻鍙叉暟鎹�
+ PRED_STEPS = 120 # 棰勬祴5澶�
+
+ # 鏁版嵁棰勫鐞�
+ preprocessor = DataPreprocessor(DATA_PATH)
+ scaled_data, dates = preprocessor.preprocess()
+ X, Y = preprocessor.create_sequences(scaled_data, LOOK_BACK, PRED_STEPS)
+
+ # 妯″瀷鏋勫缓
+ predictor = SalinityPredictor(LOOK_BACK, PRED_STEPS)
+ model = predictor.build_model()
+ model.summary()
+
+ # 鍔ㄦ�佽缁�
+ train_data = predictor.dynamic_split(scaled_data, dates, pd.to_datetime(CUTOFF_DATE))
+ X_train, y_train = preprocessor.create_sequences(train_data, LOOK_BACK, PRED_STEPS)
+ history = predictor.train(X_train, y_train)
+
+ # 棰勬祴楠岃瘉
+ cutoff_idx = np.where(dates <= pd.to_datetime(CUTOFF_DATE))[0][-1]
+ last_seq = scaled_data[cutoff_idx-LOOK_BACK:cutoff_idx] # 浣跨敤鍒嗗壊鐐瑰墠鐨勬暟鎹綔涓鸿緭鍏�
+ scaled_pred = predictor.predict(last_seq)
+ predictions = preprocessor.scaler.inverse_transform(scaled_pred.reshape(-1, 1))
+
+ # 缁撴灉鍙鍖�
+ true_dates = pd.date_range(start=pd.to_datetime(CUTOFF_DATE), periods=PRED_STEPS, freq='h')
+ plt.figure(figsize=(15, 6))
+
+ # 缁樺埗鍒嗗壊鐐瑰墠鐨勫巻鍙叉暟鎹�
+ plt.plot(dates[cutoff_idx-PRED_STEPS:cutoff_idx],
+ preprocessor.scaler.inverse_transform(scaled_data[cutoff_idx-PRED_STEPS:cutoff_idx]),
+ 'b-', label='鍘嗗彶鏁版嵁锛堝垎鍓茬偣鍓嶏級')
+
+ # 缁樺埗鍒嗗壊鐐瑰悗鐨勫疄闄呮暟鎹�
+ plt.plot(dates[cutoff_idx:cutoff_idx+PRED_STEPS],
+ preprocessor.scaler.inverse_transform(scaled_data[cutoff_idx:cutoff_idx+PRED_STEPS]),
+ 'g-', label='瀹為檯鏁版嵁锛堝垎鍓茬偣鍚庯級')
+
+ # 缁樺埗棰勬祴鏁版嵁
+ plt.plot(true_dates, predictions, 'r--', label='棰勬祴鏁版嵁')
+
+ # 娣诲姞鍒嗗壊绾�
+ plt.axvline(x=pd.to_datetime(CUTOFF_DATE), color='k', linestyle='--', label='鍒嗗壊鏃堕棿鐐�')
+
+ plt.title(f'鐩愬害棰勬祴瀵规瘮锛坽CUTOFF_DATE}鍚�5澶╋級')
+ plt.xlabel('鏃堕棿')
+ plt.ylabel('鐩愬害鍊�')
+ plt.legend()
+ plt.grid(True)
+ plt.xticks(rotation=45)
+ plt.tight_layout()
+ plt.show()
+
+ # 鎬ц兘鎸囨爣
+ true_values = preprocessor.scaler.inverse_transform(scaled_data[cutoff_idx:cutoff_idx+PRED_STEPS])
+ mae = mean_absolute_error(true_values, predictions)
+ rmse = np.sqrt(mean_squared_error(true_values, predictions))
+ print(f'楠岃瘉鎸囨爣 => MAE: {mae:.3f}, RMSE: {rmse:.3f}')
+
+# ================================
+# 4. 妯″瀷淇濆瓨涓庡姞杞斤紙鍙�夛級
+# ================================
+# model.save('salinity_predictor.h5')
+# loaded_model = tf.keras.models.load_model('salinity_predictor.h5', custom_objects={'TemporalAttention': TemporalAttention})
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