Update app2.py

app2.py

@@ -1,127 +1,5 @@
 import pandas as pd
-import numpy as np
-import baostock as bs
-from sklearn.preprocessing import MinMaxScaler, StandardScaler
-from sklearn.model_selection import train_test_split
-from sklearn.metrics import accuracy_score, precision_score, recall_score, f1_score, mean_absolute_error
-from neuralprophet import NeuralProphet, set_log_level
-from torch.optim import Adam
-from torch.nn import LSTM
-import torch
-import torch.nn as nn
-import os
 
-# Baostock API settings
-bs.login()
-
-# Collect historical data
-data = bs.query_history_k_data_plus(
-    "sz.000001",  # Shanghai Composite Index
-    "date,open,high,low,close,volume",
-    start_date="2005-05-30",
-    end_date="2024-01-31",
-    frequency="d"
-)
-
-# Convert ResultData object to pandas DataFrame
-data_list = []
-while (data.error_code == '0') & data.next():
-    # 获取一条记录，将记录合并在一起
-    data_list.append(data.get_row_data())
-data_df = pd.DataFrame(data_list, columns=data.fields)
-
-# Convert 'open' and 'close' columns to numeric type
-data_df['open'] = pd.to_numeric(data_df['open'])
-data_df['close'] = pd.to_numeric(data_df['close'])
-
-# Filter out stocks that meet the conditions
-# Added fillna(0) to handle the None value introduced by shift(1)
-data_df = data_df[(data_df["open"] >= 0.98 * data_df["close"].shift(1).fillna(0)) & (data_df["open"] <= 1.02 * data_df["close"].shift(1).fillna(0))] 
-data_df = data_df[(data_df["high"] == data_df["close"]) & (data_df["low"] == data_df["close"])]  # limit-up condition
-data_df = data_df[(data_df["open"]!= 0) & (data_df["close"]!= 0)]  # exclude zero prices
-
-# Check if data_df is empty before proceeding
-if data_df.empty:
-    print("Warning: data_df is empty after filtering. Check your filtering conditions.")
-    # Optionally, you can raise an exception to stop execution:
-    # raise ValueError("data_df is empty after filtering.")
-else:
-    # Now use data_df (the DataFrame) in train_test_split
-    train_data, val_data = train_test_split(data_df, test_size=0.2, random_state=42)
-
-# Set log level to ERROR to suppress unnecessary warnings
-set_log_level("ERROR")
-
-# Specify the custom layer configuration
-custom_layer = LSTM(input_size=1, hidden_size=128, num_layers=1, batch_first=True)
-
-# Now initialize the NeuralProphet model
-model_np = NeuralProphet(
-    n_forecasts=1,
-    n_lags=30, 
-    n_changepoints=10, 
-    changepoints_range=0.8, 
-    learning_rate=1e-3,
-    optimizer=Adam,
-)
-
-# Create a custom model by combining NeuralProphet with PyTorch's LSTM
-class CustomModel(nn.Module):
-    def __init__(self):
-        super(CustomModel, self).__init__()
-        self.neural_prophet = NeuralProphet(
-            n_forecasts=1,
-            n_lags=30, 
-            n_changepoints=10, 
-            changepoints_range=0.8, 
-            learning_rate=1e-3,
-            optimizer=Adam,
-        )
-        self.lstm = LSTM(input_size=1, hidden_size=128, num_layers=1, batch_first=True)
-
-    def forward(self, x):
-        x = self.neural_prophet(x)
-        x = self.lstm(x)
-        return x
-
-    def predict(self, df):
-        """
-        Custom predict method for CustomModel. Utilizes NeuralProphet's prediction.
-
-        Args:
-            df: The input DataFrame for prediction.
-
-        Returns:
-            Predictions from the NeuralProphet model.
-        """
-        # Assuming your NeuralProphet model expects a DataFrame in a specific format
-        # You might need to adjust this based on your data and model setup
-        future = self.neural_prophet.make_future_dataframe(df, periods=1)  # Adjust periods as needed
-        forecast = self.neural_prophet.predict(future)
-        return forecast['yhat1'].values  # Or access the relevant prediction column
-
-# Instantiate your model
-model = CustomModel()
-
-# Define loss function and optimizer
-criterion = nn.BCELoss()  
-optimizer = optim.Adam(model.parameters(), lr=1e-3)
-
-# Training loop
-def fit(model, train_data, epochs, batch_size, validation_data):
-    """
-    Custom training loop for the CustomModel.
-
-    Args:
-        model: The Custom
-        
-def predict_stock_codes(data_df):
-    # Scale the data using MinMaxScaler
-    scaler = MinMaxScaler()
-    data_df[['open', 'high', 'low', 'close', 'volume']] = scaler.fit_transform(data_df[['open', 'high', 'low', 'close', 'volume']])
-
-    #... (rest of the code to predict stock codes)
-
-    return top_5_stocks
-        
-		
+def predict_stock_codes(y_pred, data_df):
+    top_5_stocks = data_df.iloc[y_pred.argsort()[-5:]]
+    return top_5_stocks['code'].tolist()