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7af7098 74ddfb9 7af7098 74ddfb9 7af7098 | 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 | from dataclasses import dataclass
from typing import Any, Literal, cast
import torch.nn as nn
import torch.optim as optim
from sympy import sympify
from logic import *
@dataclass
class DatasetOptions:
dataset_type: str
function: str
xmin: float
xmax: float
sigma: float
nsample: int
sample_method: str
csv_path: str
has_header: bool
xcol: int
ycol: int
@dataclass
class OptimizerOptions:
optimizer_type: str
learning_rate: float | None
beta1: float | None
beta2: float | None
momentum: float | None
weight_decay: float | None
batch_size: int | None
class Manager:
def __init__(self) -> None:
self._dataset: Dataset | None = None
self._true_dataset: Dataset | None = None
self._architecture: str | None = None
self._model: nn.Module | None = None
self._optimizer: optim.Optimizer | None = None
self._optimizer_options: OptimizerOptions | None = None
self._batch_size: int | None = None
def handle_set_dataset(self, options: dict[str, Any]) -> PlotData:
if self._model is not None:
self.handle_reset_model()
try:
parsed_options = DatasetOptions(**options)
except TypeError as e:
raise ValueError(f"Invalid dataset options: {e}")
if parsed_options.dataset_type == "Generate":
try:
function_expr = sympify(parsed_options.function)
except Exception as e:
raise ValueError(f"Invalid function expression: {e}")
if parsed_options.sample_method not in ["Grid", "Random"]:
raise ValueError(f"Invalid sample method: {parsed_options.sample_method}")
parsed_options.sample_method = cast(Literal["Grid", "Random"], parsed_options.sample_method)
dataset = generate_dataset(
function_expr,
(parsed_options.xmin, parsed_options.xmax),
DataGenerationOptions(
parsed_options.sample_method,
parsed_options.nsample,
parsed_options.sigma,
),
)
true_dataset = generate_dataset(
function_expr,
(parsed_options.xmin - 1, parsed_options.xmax + 1),
DataGenerationOptions(
"Grid",
1000,
0.0,
),
)
elif parsed_options.dataset_type == "CSV":
dataset = load_dataset_from_csv(
parsed_options.csv_path,
parsed_options.has_header,
parsed_options.xcol,
parsed_options.ycol,
)
true_dataset = Dataset(x=[], y=[])
else:
raise ValueError(f"Unknown dataset type: {parsed_options.dataset_type}")
self._dataset = dataset
self._true_dataset = true_dataset
return self.get_plot_data()
def handle_set_architecture(self, architecture_str: str) -> PlotData:
self._architecture = architecture_str
self._model = build_model_from_architecture(architecture_str)
# important! must reset optimizer
if self._optimizer_options is not None:
self._optimizer = self._build_optimizer()
return self.get_plot_data()
def handle_set_optimizer(self, options: dict[str, Any]) -> PlotData:
try:
parsed_options = OptimizerOptions(**options)
except TypeError as e:
raise ValueError(f"Invalid optimizer options: {e}")
self._optimizer_options = parsed_options
if self._model is None:
raise ValueError("Model must be set before configuring the optimizer.")
self._optimizer = self._build_optimizer()
self._batch_size = self._optimizer_options.batch_size or 32
return self.get_plot_data()
def _build_optimizer(self) -> optim.Optimizer:
if self._model is None:
raise ValueError("Model must be set before configuring the optimizer.")
if self._optimizer_options is None:
raise ValueError("Optimizer options must be set before configuring the optimizer.")
options = self._optimizer_options
if options.optimizer_type == "SGD":
return optim.SGD(
self._model.parameters(),
lr=options.learning_rate or 0.01,
momentum=options.momentum or 0.0,
weight_decay=options.weight_decay or 0.0,
)
elif options.optimizer_type == "Adam":
return optim.Adam(
self._model.parameters(),
lr=options.learning_rate or 0.001,
betas=(
options.beta1 or 0.9,
options.beta2 or 0.999,
),
weight_decay=options.weight_decay or 0.0,
)
else:
raise ValueError(f"Unknown optimizer type: {options.optimizer_type}")
def handle_train_step(self, num_steps: int = 1) -> PlotData:
if self._model is None or self._optimizer is None or self._dataset is None:
raise ValueError("Model, optimizer, and dataset must be set before training.")
train_step(
self._model,
self._optimizer,
self._dataset,
batch_size=self._batch_size or 32,
num_steps=num_steps,
)
return self.get_plot_data()
def handle_reset_model(self) -> PlotData:
if self._architecture is None:
raise ValueError("Architecture must be set before resetting the model.")
self._model = build_model_from_architecture(self._architecture)
self._optimizer = self._build_optimizer()
return self.get_plot_data()
def get_plot_data(self) -> PlotData:
if self._dataset is None:
dataset = Dataset(x=[], y=[])
else:
dataset = self._dataset
if self._true_dataset is None:
test_dataset = Dataset(x=[], y=[])
else:
test_dataset = self._true_dataset
if test_dataset.x and test_dataset.y and self._model is not None:
test_predictions = generate_test_predictions(
test_dataset,
self._model,
)
else:
test_predictions = None
return PlotData(
dataset=dataset,
test_dataset=test_dataset,
test_predictions=test_predictions,
)
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