Problem
Problem
Source code in opti/problem.py
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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 275 276 277 278 279 280 281 282 283 284 285 286 287 288 289 290 291 292 293 294 295 296 297 298 299 300 301 302 303 304 305 306 307 308 309 310 311 312 313 314 315 316 317 318 319 320 321 322 323 324 325 326 327 328 329 330 331 332 333 334 335 |
|
__init__(inputs, outputs, objectives=None, constraints=None, output_constraints=None, f=None, models=None, data=None, optima=None, name=None, **kwargs)
An optimization problem.
Parameters:
Name | Type | Description | Default |
---|---|---|---|
inputs
|
ParametersLike
|
Input parameters. |
required |
outputs
|
ParametersLike
|
Output parameters. |
required |
objectives
|
Optional[ObjectivesLike]
|
Optimization objectives. Defaults to minimization. |
None
|
constraints
|
Optional[ConstraintsLike]
|
Constraints on the inputs. |
None
|
output_constraints
|
Optional[ObjectivesLike]
|
Constraints on the outputs. |
None
|
f
|
Optional[Callable]
|
Function to evaluate the outputs for given inputs. Must have the signature: f(x: pd.DataFrame) -> pd.DataFrame |
None
|
data
|
Optional[DataFrameLike]
|
Experimental data. |
None
|
optima
|
Optional[DataFrameLike]
|
Pareto optima. |
None
|
name
|
Optional[str]
|
Name of the problem. |
None
|
Source code in opti/problem.py
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 |
|
add_data(data)
Add a number of data points.
Source code in opti/problem.py
262 263 264 265 |
|
check_data(data)
Check if data is consistent with input and output parameters.
Source code in opti/problem.py
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 |
|
check_models()
Check if the models are well defined
Source code in opti/problem.py
228 229 230 231 232 233 234 235 236 237 238 239 240 241 |
|
check_problem()
Check if input and output parameters are consistent.
Source code in opti/problem.py
176 177 178 179 180 181 182 183 184 185 186 |
|
create_initial_data(n_samples=10)
Create an initial data set for benchmark problems by sampling uniformly from the input space and evaluating f(x) at the sampled inputs.
Source code in opti/problem.py
329 330 331 332 333 334 335 |
|
from_config(config)
staticmethod
Create a Problem instance from a configuration dict.
Source code in opti/problem.py
137 138 139 140 |
|
from_json(fname)
staticmethod
Read a problem from a JSON file.
Source code in opti/problem.py
163 164 165 166 167 168 |
|
get_X(data=None)
Return the input values in data
or self.data
.
Source code in opti/problem.py
273 274 275 276 277 |
|
get_XY(outputs=None, data=None, continuous='none', discrete='none', categorical='none')
Return the input and output values as numeric numpy arrays.
Rows with missing output values will be dropped. Input values are assumed to be complete. Categorical outputs are one-hot or label encoded.
Parameters:
Name | Type | Description | Default |
---|---|---|---|
outputs
|
optional
|
Subset of the outputs to consider. |
None
|
data
|
optional
|
Dataframe to consider instead of problem.data |
None
|
Source code in opti/problem.py
285 286 287 288 289 290 291 292 293 294 295 296 297 298 299 300 301 302 303 304 305 306 307 308 309 310 311 312 |
|
get_X_bounds()
Return the lower and upper data bounds.
Source code in opti/problem.py
314 315 316 317 318 319 320 321 |
|
get_Y(data=None)
Return the output values in data
or self.data
.
Source code in opti/problem.py
279 280 281 282 283 |
|
get_data()
Return self.data
if it exists or an empty dataframe.
Source code in opti/problem.py
256 257 258 259 260 |
|
sample_inputs(n_samples=10)
Uniformly sample points from the input space subject to the constraints.
Source code in opti/problem.py
323 324 325 326 327 |
|
set_data(data)
Set the data.
Source code in opti/problem.py
243 244 245 246 247 248 249 250 251 252 253 254 |
|
set_optima(optima)
Set the optima / Pareto front.
Source code in opti/problem.py
267 268 269 270 271 |
|
to_config()
Return json-serializable configuration dict.
Source code in opti/problem.py
142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 |
|
to_json(fname)
Save a problem from a JSON file.
Source code in opti/problem.py
170 171 172 173 174 |
|
read_json(filepath)
Read a problem specification from a JSON file.
Source code in opti/problem.py
338 339 340 |
|