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72e2b6e | 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 | import pickle
from pathlib import Path
import numpy as np
import torch
import torch.nn as nn
from torch.utils.data import Dataset
class Vocabulary:
def __init__(self, min_freq: int = 1):
self.min_freq = min_freq
self.token2idx = {"<PAD>": 0, "<UNK>": 1}
self.idx2token = {0: "<PAD>", 1: "<UNK>"}
def build(self, texts: list[str]) -> None:
freq = {}
for text in texts:
for token in text.split():
freq[token] = freq.get(token, 0) + 1
for token, count in freq.items():
if count >= self.min_freq and token not in self.token2idx:
idx = len(self.token2idx)
self.token2idx[token] = idx
self.idx2token[idx] = token
def encode(self, text: str, max_length: int) -> list[int]:
tokens = text.split()[:max_length]
ids = [self.token2idx.get(t, 1) for t in tokens]
ids += [0] * (max_length - len(ids))
return ids
def __len__(self) -> int:
return len(self.token2idx)
def save(self, save_path: str) -> None:
path = Path(save_path)
path.parent.mkdir(parents=True, exist_ok=True)
with open(path, "wb") as f:
pickle.dump(self, f)
@staticmethod
def load(load_path: str) -> "Vocabulary":
path = Path(load_path)
if not path.exists():
raise FileNotFoundError(f"Vocabulary not found: {path}")
with open(path, "rb") as f:
return pickle.load(f)
class IntentDatasetNN(Dataset):
def __init__(
self,
texts: list[str],
labels: list[int],
vocab: Vocabulary,
max_length: int = 32,
):
self.labels = labels
self.encodings = [vocab.encode(text, max_length) for text in texts]
def __len__(self) -> int:
return len(self.labels)
def __getitem__(self, idx: int) -> tuple[torch.Tensor, torch.Tensor]:
return (
torch.tensor(self.encodings[idx], dtype=torch.long),
torch.tensor(self.labels[idx], dtype=torch.long),
)
class TextCNN(nn.Module):
def __init__(
self,
vocab_size: int,
embedding_dim: int,
num_filters: int,
kernel_sizes: list[int],
num_classes: int,
dropout: float,
pad_idx: int = 0,
):
super().__init__()
self.embedding = nn.Embedding(vocab_size, embedding_dim, padding_idx=pad_idx)
self.convs = nn.ModuleList(
[
nn.Conv1d(
in_channels=embedding_dim,
out_channels=num_filters,
kernel_size=k,
)
for k in kernel_sizes
]
)
self.dropout = nn.Dropout(dropout)
self.fc = nn.Linear(num_filters * len(kernel_sizes), num_classes)
def forward(self, x: torch.Tensor) -> torch.Tensor:
embedded = self.embedding(x)
embedded = embedded.permute(0, 2, 1)
pooled = []
for conv in self.convs:
activated = torch.relu(conv(embedded))
pool = torch.max(activated, dim=2).values
pooled.append(pool)
concatenated = torch.cat(pooled, dim=1)
dropped = self.dropout(concatenated)
return self.fc(dropped)
def save(self, save_path: str) -> None:
path = Path(save_path)
path.parent.mkdir(parents=True, exist_ok=True)
torch.save(self.state_dict(), path)
def load(self, load_path: str) -> None:
path = Path(load_path)
if not path.exists():
raise FileNotFoundError(f"Model not found: {path}")
self.load_state_dict(torch.load(path, map_location="cpu"))
def predict_proba(self, x: torch.Tensor) -> np.ndarray:
self.eval()
with torch.no_grad():
logits = self.forward(x)
probs = torch.softmax(logits, dim=1)
return probs.cpu().numpy()
class RNNModel(nn.Module):
def __init__(
self,
vocab_size: int,
embedding_dim: int,
hidden_dim: int,
num_layers: int,
num_classes: int,
dropout: float,
pad_idx: int = 0,
):
super().__init__()
self.embedding = nn.Embedding(vocab_size, embedding_dim, padding_idx=pad_idx)
self.rnn = nn.RNN(
input_size=embedding_dim,
hidden_size=hidden_dim,
num_layers=num_layers,
batch_first=True,
dropout=dropout if num_layers > 1 else 0.0,
)
self.dropout = nn.Dropout(dropout)
self.fc = nn.Linear(hidden_dim, num_classes)
def forward(self, x: torch.Tensor) -> torch.Tensor:
embedded = self.dropout(self.embedding(x))
_, hidden = self.rnn(embedded)
out = self.dropout(hidden[-1])
return self.fc(out)
def save(self, save_path: str) -> None:
path = Path(save_path)
path.parent.mkdir(parents=True, exist_ok=True)
torch.save(self.state_dict(), path)
def load(self, load_path: str) -> None:
path = Path(load_path)
if not path.exists():
raise FileNotFoundError(f"Model not found: {path}")
self.load_state_dict(torch.load(path, map_location="cpu"))
def predict_proba(self, x: torch.Tensor) -> np.ndarray:
self.eval()
with torch.no_grad():
logits = self.forward(x)
probs = torch.softmax(logits, dim=1)
return probs.cpu().numpy()
class LSTMModel(nn.Module):
def __init__(
self,
vocab_size: int,
embedding_dim: int,
hidden_dim: int,
num_layers: int,
num_classes: int,
dropout: float,
pad_idx: int = 0,
):
super().__init__()
self.embedding = nn.Embedding(vocab_size, embedding_dim, padding_idx=pad_idx)
self.lstm = nn.LSTM(
input_size=embedding_dim,
hidden_size=hidden_dim,
num_layers=num_layers,
batch_first=True,
dropout=dropout if num_layers > 1 else 0.0,
)
self.dropout = nn.Dropout(dropout)
self.fc = nn.Linear(hidden_dim, num_classes)
def forward(self, x: torch.Tensor) -> torch.Tensor:
embedded = self.dropout(self.embedding(x))
_, (hidden, _) = self.lstm(embedded)
out = self.dropout(hidden[-1])
return self.fc(out)
def save(self, save_path: str) -> None:
path = Path(save_path)
path.parent.mkdir(parents=True, exist_ok=True)
torch.save(self.state_dict(), path)
def load(self, load_path: str) -> None:
path = Path(load_path)
if not path.exists():
raise FileNotFoundError(f"Model not found: {path}")
self.load_state_dict(torch.load(path, map_location="cpu"))
def predict_proba(self, x: torch.Tensor) -> np.ndarray:
self.eval()
with torch.no_grad():
logits = self.forward(x)
probs = torch.softmax(logits, dim=1)
return probs.cpu().numpy()
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