from collections import OrderedDict import gradio as gr import numpy as np from data import TEST_EQUATIONS from gradio.components.base import Component from plots import plot_example_data, plot_pareto_curve from processing import processing class ExampleData: def __init__(self, demo: gr.Blocks) -> None: with gr.Column(): self.example_plot = gr.Plot() with gr.Column(): self.test_equation = gr.Radio( TEST_EQUATIONS, value=TEST_EQUATIONS[0], label="Test Equation" ) self.num_points = gr.Slider( minimum=10, maximum=1000, value=200, label="Number of Data Points", step=1, ) self.noise_level = gr.Slider( minimum=0, maximum=1, value=0.05, label="Noise Level" ) self.data_seed = gr.Number(value=0, label="Random Seed") # Set up plotting: eqn_components = [ self.test_equation, self.num_points, self.noise_level, self.data_seed, ] for eqn_component in eqn_components: eqn_component.change( plot_example_data, eqn_components, self.example_plot, show_progress=False, ) demo.load(plot_example_data, eqn_components, self.example_plot) class UploadData: def __init__(self) -> None: self.file_input = gr.File(label="Upload a CSV File") self.label = gr.Markdown( "The rightmost column of your CSV file will be used as the target variable." ) class Data: def __init__(self, demo: gr.Blocks) -> None: with gr.Tab("Example Data"): self.example_data = ExampleData(demo) with gr.Tab("Upload Data"): self.upload_data = UploadData() class BasicSettings: def __init__(self) -> None: self.binary_operators = gr.CheckboxGroup( choices=["+", "-", "*", "/", "^", "max", "min", "mod", "cond"], label="Binary Operators", value=["+", "-", "*", "/"], ) self.unary_operators = gr.CheckboxGroup( choices=[ "sin", "cos", "tan", "exp", "log", "square", "cube", "sqrt", "abs", "erf", "relu", "round", "sign", ], label="Unary Operators", value=["sin"], ) self.niterations = gr.Slider( minimum=1, maximum=1000, value=40, label="Number of Iterations", step=1, ) self.maxsize = gr.Slider( minimum=7, maximum=100, value=20, label="Maximum Complexity", step=1, ) self.parsimony = gr.Number( value=0.0032, label="Parsimony Coefficient", ) class AdvancedSettings: def __init__(self) -> None: self.populations = gr.Slider( minimum=2, maximum=100, value=15, label="Number of Populations", step=1, ) self.population_size = gr.Slider( minimum=2, maximum=1000, value=33, label="Population Size", step=1, ) self.ncycles_per_iteration = gr.Number( value=550, label="Cycles per Iteration", ) self.elementwise_loss = gr.Radio( ["L2DistLoss()", "L1DistLoss()", "LogitDistLoss()", "HuberLoss()"], value="L2DistLoss()", label="Loss Function", ) self.adaptive_parsimony_scaling = gr.Number( value=20.0, label="Adaptive Parsimony Scaling", ) self.optimizer_algorithm = gr.Radio( ["BFGS", "NelderMead"], value="BFGS", label="Optimizer Algorithm", ) self.optimizer_iterations = gr.Slider( minimum=1, maximum=100, value=8, label="Optimizer Iterations", step=1, ) self.batching = gr.Checkbox( value=False, label="Batching", ) self.batch_size = gr.Slider( minimum=2, maximum=1000, value=50, label="Batch Size", step=1, ) class GradioSettings: def __init__(self) -> None: self.plot_update_delay = gr.Slider( minimum=1, maximum=100, value=3, label="Plot Update Delay", ) self.force_run = gr.Checkbox( value=False, label="Ignore Warnings", ) class Settings: def __init__(self): with gr.Tab("Basic Settings"): self.basic_settings = BasicSettings() with gr.Tab("Advanced Settings"): self.advanced_settings = AdvancedSettings() with gr.Tab("Gradio Settings"): self.gradio_settings = GradioSettings() class Results: def __init__(self): with gr.Tab("Pareto Front"): self.pareto = gr.Plot() with gr.Tab("Predictions"): self.predictions_plot = gr.Plot() self.df = gr.Dataframe( headers=["complexity", "loss", "equation"], datatype=["number", "number", "str"], wrap=True, column_widths=[75, 75, 200], interactive=False, ) self.messages = gr.Textbox(label="Messages", value="", interactive=False) def flatten_attributes( component_group, absolute_name: str, d: OrderedDict ) -> OrderedDict: if not hasattr(component_group, "__dict__"): return d for name, elem in component_group.__dict__.items(): new_absolute_name = absolute_name + "." + name if name.startswith("_"): # Private attribute continue elif elem in d.values(): # Don't duplicate any tiems continue elif isinstance(elem, Component): # Only add components to dict d[new_absolute_name] = elem else: flatten_attributes(elem, new_absolute_name, d) return d class AppInterface: def __init__(self, demo: gr.Blocks) -> None: with gr.Row(): with gr.Column(): with gr.Row(): self.data = Data(demo) with gr.Row(): self.settings = Settings() with gr.Column(): self.results = Results() self.run = gr.Button() # Update plot when dataframe is updated: self.results.df.change( plot_pareto_curve, inputs=[self.results.df, self.settings.basic_settings.maxsize], outputs=[self.results.pareto], show_progress=False, ) ignore = ["df", "predictions_plot", "pareto", "messages"] self.run.click( create_processing_function(self, ignore=ignore), inputs=[ v for k, v in flatten_attributes(self, "interface", OrderedDict()).items() if last_part(k) not in ignore ], outputs=[ self.results.df, self.results.predictions_plot, self.results.messages, ], show_progress=True, ) def last_part(k: str) -> str: return k.split(".")[-1] def create_processing_function(interface: AppInterface, ignore=[]): d = flatten_attributes(interface, "interface", OrderedDict()) keys = [k for k in map(last_part, d.keys()) if k not in ignore] _, idx, counts = np.unique(keys, return_index=True, return_counts=True) if np.any(counts > 1): raise AssertionError("Bad keys: " + ",".join(np.array(keys)[idx[counts > 1]])) def f(*components): n = len(components) assert n == len(keys) for output in processing(**{keys[i]: components[i] for i in range(n)}): yield output return f def main(): with gr.Blocks(theme="default") as demo: _ = AppInterface(demo) demo.launch(debug=True) if __name__ == "__main__": main()