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import matplotlib.pyplot as plt
import os
def update_num_variable(dataset_choice):
reset_value = 1
if not dataset_choice :
return gr.update(minimum=1, maximum=10, step=1, value=reset_value)
if dataset_choice in ['Electricity', 'Traffic']:
num_variable_range = (1,10,1)
elif dataset_choice in ['ETTh1', 'ETTh2', 'ETTm1', 'ETTm2', 'National_illness']:
num_variable_range = (1,7,1)
elif dataset_choice == "Weather":
num_variable_range = (1,21,1)
elif dataset_choice == "Exchange":
num_variable_range = (1,8,1)
return gr.update(minimum=num_variable_range[0], maximum=num_variable_range[1], step=num_variable_range[2],
value=reset_value)
def update_time_horizon(dataset_choice):
if dataset_choice == "National_illness":
choices = [24, 36, 48, 60]
else:
choices = [96, 192, 336, 720]
#default value = maximum choice
default_value = max(choices)
return gr.update(choices=choices, value=default_value)
def update_starting_point(model_choice, dataset_choice, time_horizon):
reset_value = 0
starting_point_range = (0, 300, 20) # Standardwert
if not model_choice or not dataset_choice or not time_horizon:
return gr.update(minimum=0, maximum=300, step=20, value = reset_value)
if model_choice == "NLinear":
if dataset_choice == "Electricity":
if time_horizon == 96:
starting_point_range = (0, 160, 20)
elif time_horizon in [192, 336, 720]:
starting_point_range = (0, 140, 20)
elif dataset_choice == "ETTh1":
if time_horizon in [96, 192]:
starting_point_range = (0, 80, 20)
else:
starting_point_range = (0, 60, 20)
elif dataset_choice == "ETTh2":
if time_horizon in [96, 192]:
starting_point_range = (0, 80, 20)
else:
starting_point_range = (0, 60, 20)
elif dataset_choice == "Traffic":
if time_horizon in [96, 192]:
starting_point_range = (0, 100, 20)
else:
starting_point_range = (0, 80, 20)
elif dataset_choice == "Weather":
if time_horizon in [96, 192]:
starting_point_range = (0, 320, 20)
else:
starting_point_range = (0, 300, 20)
elif dataset_choice == "Exchange":
if time_horizon in [96, 192]:
starting_point_range = (0,40,20)
else:
starting_point_range = (0, 20, 20)
elif dataset_choice == "National_illness":
starting_point_range = (0, 0, 0)
elif dataset_choice == "ETTm2":
if time_horizon == 720:
starting_point_range = (0, 320, 20)
else:
starting_point_range = (0,340,20)
elif dataset_choice == "ETTm1":
if time_horizon == 720:
starting_point_range = (0, 320, 20)
else:
starting_point_range = (0,340,20)
else:
if dataset_choice == "National_illness":
if time_horizon == 24:
starting_point_range = (0, 160, 20)
elif time_horizon in [36, 48]:
starting_point_range = (0, 140, 20)
elif time_horizon == 60:
starting_point_range = (0, 120, 20)
elif dataset_choice == "Electricity":
starting_point_range = (0,4320, 720)
elif dataset_choice in ["ETTh1", "ETTh2"]:
if time_horizon == 96:
starting_point_range = (0, 2700, 100)
elif time_horizon == 192:
starting_point_range = (0, 2600, 100)
elif time_horizon == 336:
starting_point_range = (0, 2300, 100)
elif time_horizon == 720:
starting_point_range = (0, 2100, 100)
elif dataset_choice in ["ETTm1", "ETTm2"]:
starting_point_range = (0,10800, 720)
elif dataset_choice == "Exchange":
if time_horizon == 96:
starting_point_range = (0, 1300, 100)
elif time_horizon == 192:
starting_point_range = (0, 1100, 100)
elif time_horizon == 336:
starting_point_range = (0, 1000, 100)
elif time_horizon == 720:
starting_point_range = (0, 700, 100)
elif dataset_choice == "Traffic":
if time_horizon == 96:
starting_point_range = (0, 3300, 100)
elif time_horizon == 192:
starting_point_range = (0, 3200, 100)
elif time_horizon == 336:
starting_point_range = (0, 3000, 100)
elif time_horizon == 720:
starting_point_range = (0, 2100, 100)
elif dataset_choice == "Weather":
starting_point_range = (0, 9360, 720)
else:
starting_point_range = (0, 300, 20)
return gr.update(minimum = starting_point_range[0], maximum = starting_point_range[1], step = starting_point_range[2], value = reset_value)
def find_csv_file(dataset_choice, time_horizon, look_back_window):
dir = "../../../Desktop/Project/Models/LTBoost"
for root, dirs, files in os.walk(dir):
for dir_name in dirs:
if dataset_choice.lower() in dir_name.lower():
full_dir_path = os.path.join(root, dir_name)
for file_name in os.listdir(full_dir_path):
if f"pl_{time_horizon}" in file_name:
csv_file_path = os.path.join(full_dir_path, file_name)
if os.path.exists(csv_file_path):
return csv_file_path
return None
def plot_forecast_vs_ground_truth(df_forecast, df_ground_truth, dataset_choice, model_choice, lookback_window,time_horizon, starting_point, num_variable):
plt.figure(figsize=(12, 6))
plt.plot(df_ground_truth, label='Forecast')
plt.plot(df_forecast, label='Ground Truth')
# Marking the transition between look-back window and forecast horizon
transition_point = lookback_window
plt.axvline(x=transition_point, color='black', linestyle='-')
plt.title(f'Forecast vs Ground Truth for {dataset_choice} using {model_choice} Model')
#plt.xlabel('Time')
#plt.ylabel('Value')
plt.legend()
plt.grid(True)
# Create directory path based on dataset name, lookback window, and time horizon
save_dir = f"results_ltboost/{dataset_choice}_sl{lookback_window}_pl{time_horizon}/{num_variable}"
os.makedirs(save_dir, exist_ok=True) # Ensure directory exists, create if not
# Save the plot as PNG file
path = os.path.join(save_dir, f"img_{starting_point}.png")
plt.savefig(path)
plt.close()
return path
def find_image_path(dataset_choice, model_choice, time_horizon, look_back_window, starting_point, num_variable):
directory = "results_nlinear"
for root, dirs, files in os.walk(directory):
for dir_name in dirs:
if dataset_choice.lower() in dir_name.lower() and model_choice in dir_name and f"pl{time_horizon}" in dir_name and f"sl{look_back_window}" in dir_name:
variable_dir = os.path.join(root, dir_name, str(num_variable))
if os.path.exists(variable_dir):
image_path = os.path.join(variable_dir, f"{starting_point}.png")
if os.path.exists(image_path):
return image_path
return None
def find_image_path_LTBoost(dataset_choice, time_horizon, look_back_window, starting_point, num_variable):
directory = "results_ltboost"
for root, dirs, files in os.walk(directory):
for dir_name in dirs:
# Check if the folder name contains the desired dataset, time horizon, and look-back window
if (dataset_choice.lower() in dir_name.lower() and
f"pl{time_horizon}" in dir_name and
f"sl{look_back_window}" in dir_name):
variable_dir = os.path.join(root, dir_name, str(num_variable))
if os.path.exists(variable_dir):
image_path = os.path.join(variable_dir, f"img_{starting_point}.png")
if os.path.exists(image_path):
return image_path
return None
def perform_forecasting(dataset_choice, model_choice, time_horizon, starting_point, num_variable):
if model_choice == "NLinear":
if dataset_choice == "National_illness":
look_back_window = 104
else:
look_back_window = 336
else:
if dataset_choice == "National_illness":
look_back_window = 104
elif dataset_choice == "Exchange" or dataset_choice == "ETTh1":
look_back_window = 336
else:
look_back_window = 720
if model_choice == "NLinear":
# check if already have a png image
img = find_image_path(dataset_choice, model_choice, time_horizon, look_back_window, starting_point, num_variable)
if img:
return img
else:
return "No image found"
elif model_choice == "LTBoost":
# check if already have a png image
img = find_image_path_LTBoost(dataset_choice, time_horizon, look_back_window, starting_point, num_variable)
if img:
return img
else:
return "Image not found"
def main():
theme = gr.themes.Base(
primary_hue="blue",
secondary_hue="blue",
neutral_hue="gray"
)
theme.set(
body_background_fill="*primary_50",
body_background_fill_dark="*checkbox_background_color_focus",
body_text_color_dark="white",
body_text_color="*neutral_800",
background_fill_secondary_dark="*checkbox_border_color_hover",
block_background_fill="*background_fill_primary",
block_background_fill_dark="*neutral_800",
block_border_color_dark="*primary_100",
block_border_width_dark="4px",
block_border_width="4px",
block_border_color="*secondary_200"
)
with gr.Blocks(theme=theme) as demo:
gr.Markdown("""
<h1 style="text-align: center; font-size: 1.5em;">LTBoost</h1>
<div style="font-size: 16px; letter-spacing: 1.2px; line-height: 1.8; text-align: justify;">
LTBoost: Boosted Hybrids of Ensemble Linear and Gradient Algorithms for the Long-term Time Series Forecasting<br>
<strong>LTBoost repository:</strong> <a href="https://github.com/hubtru/LTBoost" target="_blank">LTBoost</a><br>
You can choose between the dataset, model, time horizon, and starting point. <br>
<strong>Options:</strong> <br>
<ul>
<li><strong>Datasets:</strong> Electricity, Exchange Rate, Traffic, Weather, ILI, 4 ETTs</li>
<li><strong>Models:</strong> LTBoost or NLinear</li>
<li><strong>Time horizon:</strong> {96, 192, 336, 720} (different for ILI)</li>
<li><strong>Starting point:</strong> {0, ..., end}</li>
<li><strong>Variate:</strong> 10 last variates (including the OT which is always the last)</li>
</ul>
Number of figures for each of the dataset depends on the dataset_trainset length and the starting_point_resolution (offset).<br>
Due to the high number of the figures the starting_point_resolution has been limited to {20, 100, 720}.<br>
</div>
""")
with gr.Row():
with gr.Column():
dataset_choice = gr.Dropdown(
["Electricity", "Traffic", "Weather", "Exchange", "National_illness", "ETTh1", "ETTh2", "ETTm1", "ETTm2"],
label="Dataset Choice"
)
model_choice = gr.Dropdown(["NLinear", "LTBoost"], label="Model Choice", value= "LTBoost")
time_horizon = gr.Dropdown([96, 192, 336, 720], label="Time Horizon")
starting_point = gr.Slider(minimum=0, maximum=300, step=20, label="Starting Point", value= 0)
num_variable = gr.Slider(minimum=1,maximum=10,step=1,label="Variable")
input_components = [dataset_choice, model_choice, time_horizon, starting_point, num_variable]
dataset_choice.change(update_time_horizon, inputs=dataset_choice, outputs=time_horizon)
dataset_choice.change(update_starting_point, inputs=[model_choice, dataset_choice, time_horizon],
outputs=starting_point)
model_choice.change(update_starting_point, inputs=[model_choice, dataset_choice, time_horizon],
outputs=starting_point)
time_horizon.change(update_starting_point, inputs=[model_choice, dataset_choice, time_horizon],
outputs=starting_point)
dataset_choice.change(update_num_variable, inputs = dataset_choice, outputs = num_variable)
gr.ClearButton(input_components)
with gr.Column():
# Automatically update the output image whenever any input component changes
output_png = gr.Image(label="Forecast Results")
for component in input_components:
component.change(perform_forecasting, inputs=input_components, outputs=output_png)
with gr.Row():
with gr.Column():
gr.Markdown("Examples for the user input:")
gr.Examples([
['ETTh1', 'NLinear', 720, 0],
['ETTh2', 'NLinear', 720, 0],
['ETTm1', 'NLinear', 720, 0],
['ETTm2', 'NLinear', 720, 0],
['Weather', 'NLinear', 720, 0],
['Traffic', 'NLinear', 720, 0],
['Electricity', 'NLinear', 720, 0],
['ETTh1', 'LTBoost', 720, 0],
['ETTh2', 'LTBoost', 720, 0],
['ETTm1', 'LTBoost', 720, 0],
['ETTm2', 'LTBoost', 720, 0],
['Weather', 'LTBoost', 720, 0],
['Traffic', 'LTBoost', 720, 0],
['Electricity', 'LTBoost', 720, 0],
],
inputs=input_components)
demo.launch()
if __name__ == "__main__":
main()
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