Update app.py

app.py

@@ -1,16 +1,10 @@
 import gradio as gr
 import yfinance as yf
 from prophet import Prophet
-from sklearn.linear_model import LinearRegression, BayesianRidge
-from sklearn.svm import SVR
-from sklearn.preprocessing import MinMaxScaler
-from statsmodels.tsa.arima.model import ARIMA
+from sklearn.linear_model import LinearRegression
 import pandas as pd
-import numpy as np
 from datetime import datetime
 import plotly.graph_objects as go
-from tensorflow.keras.models import Sequential
-from tensorflow.keras.layers import LSTM, Dense
 
 def download_data(ticker, start_date='2010-01-01'):
     """
@@ -40,82 +34,26 @@ def predict_future_prices(ticker, periods=1825):
     
     # Linear Regression 모델 생성 및 학습
     model_lr = LinearRegression()
-    X = pd.Series(range(len(data))).values.reshape(-1, 1)
+    X = pd.to_numeric(pd.Series(range(len(data))))
     y = data['y'].values
-    model_lr.fit(X, y)
+    model_lr.fit(X.values.reshape(-1, 1), y)
     
     # 미래 데이터 프레임 생성 및 예측
-    future_dates = pd.date_range(start=data['ds'].iloc[-1], periods=periods+1, freq='D')[1:].strftime('%Y-%m-%d')
-    X_future = pd.Series(range(len(data), len(data) + len(future_dates))).values.reshape(-1, 1)
-    future_lr = pd.DataFrame({'ds': future_dates, 'yhat': model_lr.predict(X_future)})
+    future_dates = pd.date_range(start=data['ds'].iloc[-1], periods=periods+1, freq='D')[1:]
+    future_lr = pd.DataFrame({'ds': future_dates})
+    future_lr['ds'] = future_lr['ds'].dt.strftime('%Y-%m-%d')
+    X_future = pd.to_numeric(pd.Series(range(len(data), len(data) + len(future_lr))))
+    future_lr['yhat'] = model_lr.predict(X_future.values.reshape(-1, 1))
     
-    
-    # ARIMA 모델 생성 및 학습
-    model_arima = ARIMA(data['y'], order=(1, 1, 1))
-    model_arima_fit = model_arima.fit()
-    forecast_arima = model_arima_fit.forecast(steps=periods)
-    future_arima = pd.DataFrame({'ds': future_dates, 'yhat': forecast_arima})
-    
-
-    # LSTM 모델 생성 및 학습
-    scaler = MinMaxScaler(feature_range=(0, 1))
-    scaled_data = scaler.fit_transform(data['y'].values.reshape(-1, 1))
-    X_train, y_train = [], []
-    for i in range(60, len(scaled_data)):
-        X_train.append(scaled_data[i-60:i, 0])
-        y_train.append(scaled_data[i, 0])
-    X_train, y_train = np.array(X_train), np.array(y_train)
-    X_train = np.reshape(X_train, (X_train.shape[0], X_train.shape[1], 1))
-
-    model_lstm = Sequential()
-    model_lstm.add(LSTM(units=50, return_sequences=True, input_shape=(X_train.shape[1], 1)))
-    model_lstm.add(LSTM(units=50))
-    model_lstm.add(Dense(1))
-    model_lstm.compile(loss='mean_squared_error', optimizer='adam')
-    model_lstm.fit(X_train, y_train, epochs=10, batch_size=32)
-
-    pred_lstm = []
-    last_60_days = scaled_data[-60:]
-    for i in range(periods):
-        X_test = last_60_days.reshape(1, 60, 1)
-        pred = model_lstm.predict(X_test)
-        last_60_days = np.append(last_60_days[1:], pred)
-        pred_lstm.append(pred[0, 0])
-
-    pred_lstm = scaler.inverse_transform(np.array(pred_lstm).reshape(-1, 1))
-    future_lstm = pd.DataFrame({'ds': future_dates[:len(pred_lstm)], 'yhat': pred_lstm.flatten()})
-    
-#  # XGBoost 모델 생성 및 학습
-#   model_xgb = XGBRegressor(n_estimators=100, learning_rate=0.1)
-#   model_xgb.fit(X.reshape(-1, 1), y)
-#   future_xgb = pd.DataFrame({'ds': future_dates, 'yhat': model_xgb.predict(X_future)})
-    
-#    # SVR 모델 생성 및 학습
-#   model_svr = SVR(kernel='rbf', C=1e3, gamma=0.1)
-#   model_svr.fit(X.reshape(-1, 1), y)
-#   future_svr = pd.DataFrame({'ds': future_dates, 'yhat': model_svr.predict(X_future)})
-    
-#    # Bayesian Regression 모델 생성 및 학습
-#   model_bayes = BayesianRidge()
-#   model_bayes.fit(X.reshape(-1, 1), y)
-#   future_bayes = pd.DataFrame({'ds': future_dates, 'yhat': model_bayes.predict(X_future)})
-    
-
     # 예측 결과 그래프 생성
     forecast_prophet['ds'] = forecast_prophet['ds'].dt.strftime('%Y-%m-%d')
     fig = go.Figure()
     fig.add_trace(go.Scatter(x=forecast_prophet['ds'], y=forecast_prophet['yhat'], mode='lines', name='Prophet Forecast (Blue)'))
     fig.add_trace(go.Scatter(x=future_lr['ds'], y=future_lr['yhat'], mode='lines', name='Linear Regression Forecast (Red)', line=dict(color='red')))
-    fig.add_trace(go.Scatter(x=future_arima['ds'], y=future_arima['yhat'], mode='lines', name='ARIMA Forecast (Green)', line=dict(color='green')))
-    fig.add_trace(go.Scatter(x=future_lstm['ds'], y=future_lstm['yhat'], mode='lines', name='LSTM Forecast (Orange)', line=dict(color='orange')))
-#   fig.add_trace(go.Scatter(x=future_xgb['ds'], y=future_xgb['yhat'], mode='lines', name='XGBoost Forecast (Purple)', line=dict(color='purple')))
-#   fig.add_trace(go.Scatter(x=future_svr['ds'], y=future_svr['yhat'], mode='lines', name='SVR Forecast (Brown)', line=dict(color='brown')))
-#   fig.add_trace(go.Scatter(x=future_bayes['ds'], y=future_bayes['yhat'], mode='lines', name='Bayesian Regression Forecast (Pink)', line=dict(color='pink')))
     fig.add_trace(go.Scatter(x=data['ds'], y=data['y'], mode='lines', name='Actual (Black)', line=dict(color='black')))
     
-    return fig, forecast_prophet[['ds', 'yhat', 'yhat_lower', 'yhat_upper']], future_lr[['ds', 'yhat']], future_arima[['ds', 'yhat']], future_lstm[['ds', 'yhat']], future_xgb[['ds', 'yhat']], future_svr[['ds', 'yhat']], future_bayes[['ds', 'yhat']]
+    return fig, forecast_prophet[['ds', 'yhat', 'yhat_lower', 'yhat_upper']], future_lr[['ds', 'yhat']]
 
-    
 # Gradio 인터페이스 설정 및 실행
 with gr.Blocks() as app:
     with gr.Row():
@@ -126,16 +64,11 @@ with gr.Blocks() as app:
     forecast_chart = gr.Plot(label="Forecast Chart")
     forecast_data_prophet = gr.Dataframe(label="Prophet Forecast Data")
     forecast_data_lr = gr.Dataframe(label="Linear Regression Forecast Data")
-    forecast_data_arima = gr.Dataframe(label="ARIMA Forecast Data")
-    forecast_data_lstm = gr.Dataframe(label="LSTM Forecast Data")
-#   forecast_data_xgb = gr.Dataframe(label="XGBoost Forecast Data")
-#   forecast_data_svr = gr.Dataframe(label="SVR Forecast Data")
-#   forecast_data_bayes = gr.Dataframe(label="Bayesian Regression Forecast Data")
     
     forecast_button.click(
         fn=predict_future_prices,
         inputs=[ticker_input, periods_input],
-        outputs=[forecast_chart, forecast_data_prophet, forecast_data_lr, forecast_data_arima, forecast_data_lstm] #,forecast_data_xgb, forecast_data_svr, forecast_data_bayes]
+        outputs=[forecast_chart, forecast_data_prophet, forecast_data_lr]
     )
 
 app.launch()