Spaces:
Running
Running
File size: 7,574 Bytes
9fa2182 519fcb9 c6a43c4 88a78a4 86e9755 dd65136 86e9755 dd65136 f751163 9d6017e f751163 dd65136 e487754 dd65136 fea9443 dd65136 86e9755 9d6017e 0dc382d 9d6017e d3c4f72 9d6017e 0dc382d 9d6017e 8a2bd53 9d6017e 8a2bd53 9d6017e 8a2bd53 9d6017e 8a2bd53 9d6017e 8a2bd53 dd65136 9d6017e 8a2bd53 dd65136 f072863 86e9755 dd65136 46fdaa6 dd65136 46fdaa6 dd65136 46fdaa6 146981e c353ada bb76c1f 4eac491 146981e c353ada 46fdaa6 dd65136 519fcb9 46fdaa6 dd65136 bb76c1f dd65136 fea9443 dd65136 9d6017e 8a2bd53 dd65136 46fdaa6 fea9443 46fdaa6 dd65136 fea9443 dd65136 edbcfa6 fea9443 758e952 fea9443 5a5a76f |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 |
from .data import test_equations
from .plots import replot, replot_pareto
from .processing import processing
def get_gr():
import gradio as gr
return gr
def _data_layout():
gr = get_gr()
with gr.Tab("Example Data"):
# Plot of the example data:
with gr.Row():
with gr.Column():
example_plot = gr.Plot()
with gr.Column():
test_equation = gr.Radio(
test_equations, value=test_equations[0], label="Test Equation"
)
num_points = gr.Slider(
minimum=10,
maximum=1000,
value=200,
label="Number of Data Points",
step=1,
)
noise_level = gr.Slider(
minimum=0, maximum=1, value=0.05, label="Noise Level"
)
data_seed = gr.Number(value=0, label="Random Seed")
with gr.Tab("Upload Data"):
file_input = gr.File(label="Upload a CSV File")
gr.Markdown(
"The rightmost column of your CSV file will be used as the target variable."
)
return dict(
file_input=file_input,
test_equation=test_equation,
num_points=num_points,
noise_level=noise_level,
data_seed=data_seed,
example_plot=example_plot,
)
def _settings_layout():
gr = get_gr()
with gr.Tab("Basic Settings"):
binary_operators = gr.CheckboxGroup(
choices=["+", "-", "*", "/", "^", "max", "min", "mod", "cond"],
label="Binary Operators",
value=["+", "-", "*", "/"],
)
unary_operators = gr.CheckboxGroup(
choices=[
"sin",
"cos",
"tan",
"exp",
"log",
"square",
"cube",
"sqrt",
"abs",
"erf",
"relu",
"round",
"sign",
],
label="Unary Operators",
value=["sin"],
)
niterations = gr.Slider(
minimum=1,
maximum=1000,
value=40,
label="Number of Iterations",
step=1,
)
maxsize = gr.Slider(
minimum=7,
maximum=100,
value=20,
label="Maximum Complexity",
step=1,
)
parsimony = gr.Number(
value=0.0032,
label="Parsimony Coefficient",
)
with gr.Tab("Advanced Settings"):
populations = gr.Slider(
minimum=2,
maximum=100,
value=15,
label="Number of Populations",
step=1,
)
population_size = gr.Slider(
minimum=2,
maximum=1000,
value=33,
label="Population Size",
step=1,
)
ncycles_per_iteration = gr.Number(
value=550,
label="Cycles per Iteration",
)
elementwise_loss = gr.Radio(
["L2DistLoss()", "L1DistLoss()", "LogitDistLoss()", "HuberLoss()"],
value="L2DistLoss()",
label="Loss Function",
)
adaptive_parsimony_scaling = gr.Number(
value=20.0,
label="Adaptive Parsimony Scaling",
)
optimizer_algorithm = gr.Radio(
["BFGS", "NelderMead"],
value="BFGS",
label="Optimizer Algorithm",
)
optimizer_iterations = gr.Slider(
minimum=1,
maximum=100,
value=8,
label="Optimizer Iterations",
step=1,
)
# Bool:
batching = gr.Checkbox(
value=False,
label="Batching",
)
batch_size = gr.Slider(
minimum=2,
maximum=1000,
value=50,
label="Batch Size",
step=1,
)
with gr.Tab("Gradio Settings"):
plot_update_delay = gr.Slider(
minimum=1,
maximum=100,
value=3,
label="Plot Update Delay",
)
force_run = gr.Checkbox(
value=False,
label="Ignore Warnings",
)
return dict(
binary_operators=binary_operators,
unary_operators=unary_operators,
niterations=niterations,
maxsize=maxsize,
force_run=force_run,
plot_update_delay=plot_update_delay,
parsimony=parsimony,
populations=populations,
population_size=population_size,
ncycles_per_iteration=ncycles_per_iteration,
elementwise_loss=elementwise_loss,
adaptive_parsimony_scaling=adaptive_parsimony_scaling,
optimizer_algorithm=optimizer_algorithm,
optimizer_iterations=optimizer_iterations,
batching=batching,
batch_size=batch_size,
)
def main():
gr = get_gr()
blocks = {}
with gr.Blocks() as demo:
with gr.Row():
with gr.Column():
with gr.Row():
blocks = {**blocks, **_data_layout()}
with gr.Row():
blocks = {**blocks, **_settings_layout()}
with gr.Column():
with gr.Tab("Pareto Front"):
blocks["pareto"] = gr.Plot()
with gr.Tab("Predictions"):
blocks["predictions_plot"] = gr.Plot()
blocks["df"] = gr.Dataframe(
headers=["complexity", "loss", "equation"],
datatype=["number", "number", "str"],
wrap=True,
column_widths=[75, 75, 200],
interactive=False,
)
blocks["run"] = gr.Button()
blocks["run"].click(
processing,
inputs=[
blocks[k]
for k in [
"file_input",
"force_run",
"test_equation",
"num_points",
"noise_level",
"data_seed",
"niterations",
"maxsize",
"binary_operators",
"unary_operators",
"plot_update_delay",
"parsimony",
"populations",
"population_size",
"ncycles_per_iteration",
"elementwise_loss",
"adaptive_parsimony_scaling",
"optimizer_algorithm",
"optimizer_iterations",
"batching",
"batch_size",
]
],
outputs=blocks["df"],
)
# Any update to the equation choice will trigger a replot:
eqn_components = [
blocks["test_equation"],
blocks["num_points"],
blocks["noise_level"],
blocks["data_seed"],
]
for eqn_component in eqn_components:
eqn_component.change(replot, eqn_components, blocks["example_plot"])
# Update plot when dataframe is updated:
blocks["df"].change(
replot_pareto,
inputs=[blocks["df"], blocks["maxsize"]],
outputs=[blocks["pareto"]],
)
demo.load(replot, eqn_components, blocks["example_plot"])
demo.launch(debug=True)
|