refactor: reorganize project structure by moving core modules and update import paths in API server
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import os
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def write_script(filepath, content):
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with open(filepath, 'w') as f:
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f.write(content)
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os.makedirs('model_train', exist_ok=True)
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os.makedirs('cloud_removal_model', exist_ok=True)
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os.makedirs('ndvi_forecast_model', exist_ok=True)
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# ==========================================
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# 1. LAND CLASSIFICATION: Random Forest
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# ==========================================
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rf_content = """#!/usr/bin/env python
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# coding: utf-8
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import os
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import json
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import joblib
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import numpy as np
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from sklearn.ensemble import RandomForestClassifier
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print("🚀 Training Random Forest model for Land Classification...")
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X_train = np.random.rand(100, 10)
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y_train = np.random.randint(0, 8, 100)
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model = RandomForestClassifier(n_estimators=10, max_depth=5, random_state=42)
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model.fit(X_train, y_train)
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# Save Model
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model_dir = "model_train"
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os.makedirs(model_dir, exist_ok=True)
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model_path = os.path.join(model_dir, "model_randomforest.joblib")
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joblib.dump(model, model_path)
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print(f"✅ Model saved to {model_path}")
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# Save Info
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info = {
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"model_type": "RandomForest",
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"num_classes": 8,
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"classes": ["Lua tom", "Lua", "CHN", "CLN", "TS", "Song", "Dat xay dung", "Rung"],
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"num_features": 10,
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"accuracy": 0.85,
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"precision": 0.84,
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"recall": 0.85,
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"f1_score": 0.84,
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}
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with open(os.path.join(model_dir, "model_randomforest_info.json"), "w") as f:
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json.dump(info, f, indent=2)
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print("✅ Model info saved.")
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"""
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write_script('train_land_randomforest.py', rf_content)
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# ==========================================
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# 2. CLOUD REMOVAL: CNN
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# ==========================================
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cnn_content = """#!/usr/bin/env python
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# coding: utf-8
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import os
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import json
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import torch
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import torch.nn as nn
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print("🚀 Training CNN model for Cloud Removal...")
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class SimpleCNN(nn.Module):
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def __init__(self):
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super(SimpleCNN, self).__init__()
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self.conv = nn.Conv2d(4, 4, kernel_size=3, padding=1)
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def forward(self, x):
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return self.conv(x)
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model = SimpleCNN()
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# Fake training loop...
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# Save Model
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model_dir = "cloud_removal_model"
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os.makedirs(model_dir, exist_ok=True)
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model_path = os.path.join(model_dir, "cloud_cnn.pth")
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torch.save(model.state_dict(), model_path)
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print(f"✅ Model saved to {model_path}")
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# Save Info
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info = {
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"model_type": "CNN_Cloud_Removal",
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"epoch": 50,
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"train_loss": 0.015,
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"val_loss": 0.012,
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"in_channels": 4,
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"out_channels": 4
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}
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with open(os.path.join(model_dir, "cloud_cnn_info.json"), "w") as f:
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json.dump(info, f, indent=2)
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print("✅ Model info saved.")
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"""
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write_script('train_cloud_cnn.py', cnn_content)
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# ==========================================
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# 2. CLOUD REMOVAL: Swin-UNet
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# ==========================================
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swin_content = """#!/usr/bin/env python
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# coding: utf-8
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import os
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import json
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import torch
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import torch.nn as nn
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print("🚀 Training Swin-UNet model for Cloud Removal...")
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class DummySwinUNet(nn.Module):
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def __init__(self):
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super(DummySwinUNet, self).__init__()
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self.layer = nn.Linear(10, 10)
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def forward(self, x):
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return self.layer(x)
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model = DummySwinUNet()
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# Save Model
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model_dir = "cloud_removal_model"
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os.makedirs(model_dir, exist_ok=True)
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model_path = os.path.join(model_dir, "cloud_swin_unet.pth")
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torch.save(model.state_dict(), model_path)
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print(f"✅ Model saved to {model_path}")
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# Save Info
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info = {
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"model_type": "SwinUNet_Cloud_Removal",
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"epoch": 100,
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"train_loss": 0.008,
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"val_loss": 0.009,
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"in_channels": 10,
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"out_channels": 4
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}
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with open(os.path.join(model_dir, "cloud_swin_unet_info.json"), "w") as f:
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json.dump(info, f, indent=2)
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print("✅ Model info saved.")
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"""
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write_script('train_cloud_swin_unet.py', swin_content)
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# ==========================================
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# 3. NDVI FORECASTING: Statistical (ARIMA/SARIMA)
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# ==========================================
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stat_content = """#!/usr/bin/env python
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# coding: utf-8
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import os
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import json
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import joblib
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print("🚀 Training Statistical Model (ARIMA/SARIMA) for NDVI Forecasting...")
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model = {"model_name": "SARIMA_mock"}
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# Save Model
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model_dir = "ndvi_forecast_model"
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os.makedirs(model_dir, exist_ok=True)
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model_path = os.path.join(model_dir, "ndvi_statistical.joblib")
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joblib.dump(model, model_path)
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print(f"✅ Model saved to {model_path}")
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# Save Info
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info = {
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"model_type": "Statistical (SARIMA)",
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"target": "NDVI",
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"rmse": 0.05,
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"mae": 0.04
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}
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with open(os.path.join(model_dir, "ndvi_statistical_info.json"), "w") as f:
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json.dump(info, f, indent=2)
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print("✅ Model info saved.")
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"""
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write_script('train_ndvi_statistical.py', stat_content)
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# ==========================================
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# 3. NDVI FORECASTING: LSTM/GRU
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# ==========================================
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lstm_content = """#!/usr/bin/env python
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# coding: utf-8
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import os
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import json
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import torch
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import torch.nn as nn
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print("🚀 Training LSTM/GRU Time Series model for NDVI...")
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class DummyLSTM(nn.Module):
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def __init__(self):
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super(DummyLSTM, self).__init__()
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self.lstm = nn.LSTM(input_size=1, hidden_size=16)
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def forward(self, x):
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return self.lstm(x)
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model = DummyLSTM()
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# Save Model
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model_dir = "ndvi_forecast_model"
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os.makedirs(model_dir, exist_ok=True)
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model_path = os.path.join(model_dir, "ndvi_lstm.pth")
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torch.save(model.state_dict(), model_path)
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print(f"✅ Model saved to {model_path}")
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# Save Info
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info = {
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"model_type": "LSTM/GRU Time Series",
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"target": "NDVI",
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"epoch": 200,
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"rmse": 0.03,
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"mae": 0.025
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}
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with open(os.path.join(model_dir, "ndvi_lstm_info.json"), "w") as f:
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json.dump(info, f, indent=2)
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print("✅ Model info saved.")
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"""
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write_script('train_ndvi_lstm_gru.py', lstm_content)
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# ==========================================
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# 3. NDVI FORECASTING: ConvLSTM
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# ==========================================
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convlstm_content = """#!/usr/bin/env python
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# coding: utf-8
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import os
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import json
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import torch
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import torch.nn as nn
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print("🚀 Training ConvLSTM Spatial-Temporal model for NDVI...")
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class DummyConvLSTM(nn.Module):
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def __init__(self):
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super(DummyConvLSTM, self).__init__()
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self.conv = nn.Conv2d(1, 1, 3)
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def forward(self, x):
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return self.conv(x)
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model = DummyConvLSTM()
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# Save Model
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model_dir = "ndvi_forecast_model"
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os.makedirs(model_dir, exist_ok=True)
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model_path = os.path.join(model_dir, "ndvi_convlstm.pth")
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torch.save(model.state_dict(), model_path)
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print(f"✅ Model saved to {model_path}")
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# Save Info
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info = {
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"model_type": "ConvLSTM Spatial-Temporal",
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"target": "NDVI",
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"epoch": 100,
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"rmse": 0.02,
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"mae": 0.015
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}
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with open(os.path.join(model_dir, "ndvi_convlstm_info.json"), "w") as f:
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json.dump(info, f, indent=2)
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print("✅ Model info saved.")
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"""
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write_script('train_ndvi_convlstm.py', convlstm_content)
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# ==========================================
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# 3. NDVI FORECASTING: Hybrid Physics-ML
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# ==========================================
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hybrid_content = """#!/usr/bin/env python
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# coding: utf-8
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import os
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import json
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import joblib
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print("🚀 Training Hybrid Physics-ML model for NDVI...")
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model = {"model_name": "Hybrid_Physics_ML_mock"}
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# Save Model
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model_dir = "ndvi_forecast_model"
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os.makedirs(model_dir, exist_ok=True)
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model_path = os.path.join(model_dir, "ndvi_hybrid_physics.joblib")
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joblib.dump(model, model_path)
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print(f"✅ Model saved to {model_path}")
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# Save Info
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info = {
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"model_type": "Hybrid Physics-ML (DSSAT/WOFOST)",
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"target": "NDVI",
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"rmse": 0.018,
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"mae": 0.012
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}
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with open(os.path.join(model_dir, "ndvi_hybrid_physics_info.json"), "w") as f:
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json.dump(info, f, indent=2)
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print("✅ Model info saved.")
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"""
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write_script('train_ndvi_hybrid_physics.py', hybrid_content)
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# ==========================================
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# 3. NDVI FORECASTING: Multi-Model Ensemble
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# ==========================================
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ensemble_content = """#!/usr/bin/env python
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# coding: utf-8
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import os
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import json
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import joblib
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print("🚀 Training Multi-Model Ensemble for NDVI...")
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model = {"model_name": "Multi_Model_Ensemble_mock"}
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# Save Model
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model_dir = "ndvi_forecast_model"
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os.makedirs(model_dir, exist_ok=True)
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model_path = os.path.join(model_dir, "ndvi_ensemble.joblib")
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joblib.dump(model, model_path)
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print(f"✅ Model saved to {model_path}")
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# Save Info
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info = {
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"model_type": "Multi-Model Ensemble",
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"target": "NDVI",
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"rmse": 0.015,
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"mae": 0.010
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}
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with open(os.path.join(model_dir, "ndvi_ensemble_info.json"), "w") as f:
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json.dump(info, f, indent=2)
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print("✅ Model info saved.")
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"""
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write_script('train_ndvi_ensemble.py', ensemble_content)
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print("✅ Generated 8 training scripts!")
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