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| import gradio as gr | |
| import pandas as pd | |
| import numpy as np | |
| import matplotlib.pyplot as plt | |
| from statsmodels.tsa.holtwinters import ExponentialSmoothing | |
| from model import PredictionModel | |
| data_path = './data/data_daily.csv' | |
| damped_model_path = './models/damped_hw_model.pkl' | |
| model_path = './models/hw_model.pkl' | |
| months = {1: [31, "2022-01-31"], 2: [59, "2022-02-28"], 3: [90, "2022-03-31"], 4: [120, "2022-04-30"], 5: [151, "2022-05-31"], 6: [181, "2022-06-30"], 7: [212, "2022-07-31"], 8: [243, "2022-08-31"], 9: [273, "2022-09-30"], 10: [304, "2022-010-31"], 11: [334, "2022-11-30"], 12: [365, "2022-12-31"]} | |
| damped_model = PredictionModel(damped_model_path, data_path) | |
| model = PredictionModel(model_path, data_path) | |
| def plot_daily_chart(result, month): | |
| fig, axes = plt.subplots(2,2, figsize=(15,15)) | |
| axes[0][0].plot(result.ds, result.y, label='Train') | |
| axes[0][0].plot(result.ds, result.yhat, label='yhat') | |
| axes[0][0].plot(result.ds, result.upper_bound, label='Upper Bound') | |
| axes[0][0].plot(result.ds, result.lower_bound, label='Lower Bound') | |
| axes[0][0].set_xticklabels(result.ds.dt.date, rotation=45) | |
| axes[0][0].legend(loc='best') | |
| axes[0][0].set_title("Daily Forecast") | |
| axes[0][0].set_xlabel("Date") | |
| axes[0][0].set_ylabel("Receipt Count") | |
| axes[0][0].grid(True) | |
| monthly_result = result.drop('ds', axis=1).groupby(['year', 'month']).sum().reset_index() | |
| monthly_result['monthly'] = monthly_result['year'].astype(str) + '-' + monthly_result['month'].astype(str) | |
| monthly_result['y'].iloc[-int(month):] = np.nan | |
| monthly_result['yhat'].iloc[:12] = np.nan | |
| monthly_result['upper_bound'].iloc[:12] = np.nan | |
| monthly_result['lower_bound'].iloc[:12] = np.nan | |
| axes[1][0].plot(monthly_result.monthly, monthly_result.y, label='Train') | |
| axes[1][0].plot(monthly_result.monthly, monthly_result.yhat, label='yhat') | |
| axes[1][0].plot(monthly_result.monthly, monthly_result.upper_bound, label='Upper Bound') | |
| axes[1][0].plot(monthly_result.monthly, monthly_result.lower_bound, label='Lower Bound') | |
| axes[1][0].set_xticklabels(monthly_result.monthly, rotation=45) | |
| axes[1][0].legend(loc='best') | |
| axes[1][0].set_title("Monthly Forecast") | |
| axes[1][0].set_xlabel("Date") | |
| axes[1][0].set_ylabel("Receipt Count") | |
| axes[1][0].grid(True) | |
| axes[0][1].plot(result.ds, result.y, label='Train') | |
| axes[0][1].plot(result.ds, result.damped_yhat, label='yhat') | |
| axes[0][1].plot(result.ds, result.damped_upper_bound, label='Upper Bound') | |
| axes[0][1].plot(result.ds, result.damped_lower_bound, label='Lower Bound') | |
| axes[0][1].set_xticklabels(result.ds.dt.date, rotation=45) | |
| axes[0][1].legend(loc='best') | |
| axes[0][1].set_title("Damped Model - Daily Forecast") | |
| axes[0][1].set_xlabel("Date") | |
| axes[0][1].set_ylabel("Receipt Count") | |
| axes[0][1].grid(True) | |
| monthly_result['damped_yhat'].iloc[:12] = np.nan | |
| monthly_result['damped_upper_bound'].iloc[:12] = np.nan | |
| monthly_result['damped_lower_bound'].iloc[:12] = np.nan | |
| axes[1][1].plot(monthly_result.monthly, monthly_result.y, label='Train') | |
| axes[1][1].plot(monthly_result.monthly, monthly_result.damped_yhat, label='yhat') | |
| axes[1][1].plot(monthly_result.monthly, monthly_result.damped_upper_bound, label='Upper Bound') | |
| axes[1][1].plot(monthly_result.monthly, monthly_result.damped_lower_bound, label='Lower Bound') | |
| axes[1][1].set_xticklabels(monthly_result.monthly, rotation=45) | |
| axes[1][1].legend(loc='best') | |
| axes[1][1].set_title("Damped Model - Monthly Forecast") | |
| axes[1][1].set_xlabel("Date") | |
| axes[1][1].set_ylabel("Receipt Count") | |
| axes[1][1].grid(True) | |
| fig.tight_layout() | |
| return fig | |
| def cal_result(alpha, month): | |
| alpha = float(alpha) | |
| period = months[int(month)][0] | |
| train_data_len = model.df.shape[0] | |
| yhat, intervals = model.predict(period, alpha) | |
| damped_yhat, damped_intervals = damped_model.predict(period, alpha) | |
| START = "2021-01-01" | |
| END = months[int(month)][1] | |
| result = pd.DataFrame({ | |
| 'ds': pd.date_range(START, END, freq='D'), | |
| 'y': list(model.df.receipt_count.values) + [np.nan] * period, | |
| 'yhat': [np.nan] * train_data_len + list(yhat), | |
| 'upper_bound': [np.nan] * train_data_len + list(intervals[1]), | |
| 'lower_bound': [np.nan] * train_data_len + list(intervals[0]), | |
| 'damped_yhat': [np.nan] * train_data_len + list(damped_yhat), | |
| 'damped_upper_bound': [np.nan] * train_data_len + list(damped_intervals[1]), | |
| 'damped_lower_bound': [np.nan] * train_data_len + list(damped_intervals[0]) | |
| }) | |
| result['year'] = result.ds.dt.year | |
| result['month'] = result.ds.dt.month | |
| daily_chart = plot_daily_chart(result, month) | |
| return daily_chart | |
| with gr.Blocks() as demo: | |
| gr.Markdown("""There are two models with the difference with the damped parameter. Why are there two models? Why do we use damping? | |
| **Realistic Forecasts**: In many real-world scenarios, it's unlikely for a trend to continue indefinitely at the same rate. For example, if sales of a product are increasing, they might not keep increasing forever at the same rate. After a certain point, the growth might slow down. Damping takes this into account. | |
| **Avoid Over-optimistic or Pessimistic Predictions**: Without damping, the model could make overly optimistic (for upward trends) or overly pessimistic (for downward trends) predictions for long-term forecasts. | |
| **Stability**: Damped models often provide more stable long-term forecasts, especially when the data has some inherent variability or noise.""") | |
| with gr.Row(): | |
| alpha = gr.Dropdown(choices=["0.1", "0.05", "0.01"], label="Significance level", info="The significance level for the confidence intervals.", value="0.05") | |
| month = gr.Dropdown(choices=["1", "2", "3", "4", "5", "6", "7", "8", "9", "10", "11", "12"], label="Forecast period", info="The number of months to forecast.", value="12") | |
| submit = gr.Button("Submit") | |
| daily = gr.Plot() | |
| submit.click(fn=cal_result, inputs=[alpha, month], outputs=daily) | |
| demo.launch(server_name="0.0.0.0", server_port=7860) |