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column_data.py
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column_data.py
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from .imports import *
from .torch_imports import *
from .dataset import *
from .learner import *
class PassthruDataset(Dataset):
def __init__(self,*args, is_reg=True, is_multi=False):
*xs,y=args
self.xs,self.y = xs,y
self.is_reg = is_reg
self.is_multi = is_multi
def __len__(self): return len(self.y)
def __getitem__(self, idx): return [o[idx] for o in self.xs] + [self.y[idx]]
@classmethod
def from_data_frame(cls, df, cols_x, col_y, is_reg=True, is_multi=False):
cols = [df[o] for o in cols_x+[col_y]]
return cls(*cols, is_reg=is_reg, is_multi=is_multi)
class ColumnarDataset(Dataset):
def __init__(self, cats, conts, y, is_reg, is_multi):
n = len(cats[0]) if cats else len(conts[0])
self.cats = np.stack(cats, 1).astype(np.int64) if cats else np.zeros((n,1))
self.conts = np.stack(conts, 1).astype(np.float32) if conts else np.zeros((n,1))
self.y = np.zeros((n,1)) if y is None else y
if is_reg:
self.y = self.y[:,None]
self.is_reg = is_reg
self.is_multi = is_multi
def __len__(self): return len(self.y)
def __getitem__(self, idx):
return [self.cats[idx], self.conts[idx], self.y[idx]]
@classmethod
def from_data_frames(cls, df_cat, df_cont, y=None, is_reg=True, is_multi=False):
cat_cols = [c.values for n,c in df_cat.items()]
cont_cols = [c.values for n,c in df_cont.items()]
return cls(cat_cols, cont_cols, y, is_reg, is_multi)
@classmethod
def from_data_frame(cls, df, cat_flds, y=None, is_reg=True, is_multi=False):
return cls.from_data_frames(df[cat_flds], df.drop(cat_flds, axis=1), y, is_reg, is_multi)
class ColumnarModelData(ModelData):
def __init__(self, path, trn_ds, val_ds, bs, test_ds=None, shuffle=True):
test_dl = DataLoader(test_ds, bs, shuffle=False, num_workers=1) if test_ds is not None else None
super().__init__(path, DataLoader(trn_ds, bs, shuffle=shuffle, num_workers=1),
DataLoader(val_ds, bs*2, shuffle=False, num_workers=1), test_dl)
@classmethod
def from_arrays(cls, path, val_idxs, xs, y, is_reg=True, is_multi=False, bs=64, test_xs=None, shuffle=True):
((val_xs, trn_xs), (val_y, trn_y)) = split_by_idx(val_idxs, xs, y)
test_ds = PassthruDataset(*(test_xs.T), [0] * len(test_xs), is_reg=is_reg, is_multi=is_multi) if test_xs is not None else None
return cls(path, PassthruDataset(*(trn_xs.T), trn_y, is_reg=is_reg, is_multi=is_multi),
PassthruDataset(*(val_xs.T), val_y, is_reg=is_reg, is_multi=is_multi),
bs=bs, shuffle=shuffle, test_ds=test_ds)
@classmethod
def from_data_frames(cls, path, trn_df, val_df, trn_y, val_y, cat_flds, bs, is_reg, is_multi, test_df=None):
trn_ds = ColumnarDataset.from_data_frame(trn_df, cat_flds, trn_y, is_reg, is_multi)
val_ds = ColumnarDataset.from_data_frame(val_df, cat_flds, val_y, is_reg, is_multi)
test_ds = ColumnarDataset.from_data_frame(test_df, cat_flds, None, is_reg, is_multi) if test_df is not None else None
return cls(path, trn_ds, val_ds, bs, test_ds=test_ds)
@classmethod
def from_data_frame(cls, path, val_idxs, df, y, cat_flds, bs, is_reg=True, is_multi=False, test_df=None):
((val_df, trn_df), (val_y, trn_y)) = split_by_idx(val_idxs, df, y)
return cls.from_data_frames(path, trn_df, val_df, trn_y, val_y, cat_flds, bs, is_reg, is_multi, test_df=test_df)
def get_learner(self, emb_szs, n_cont, emb_drop, out_sz, szs, drops,
y_range=None, use_bn=False, **kwargs):
model = MixedInputModel(emb_szs, n_cont, emb_drop, out_sz, szs, drops, y_range, use_bn, self.is_reg, self.is_multi)
return StructuredLearner(self, StructuredModel(to_gpu(model)), opt_fn=optim.Adam, **kwargs)
def emb_init(x):
x = x.weight.data
sc = 2/(x.size(1)+1)
x.uniform_(-sc,sc)
class MixedInputModel(nn.Module):
def __init__(self, emb_szs, n_cont, emb_drop, out_sz, szs, drops,
y_range=None, use_bn=False, is_reg=True, is_multi=False):
super().__init__()
self.embs = nn.ModuleList([nn.Embedding(c, s) for c,s in emb_szs])
for emb in self.embs: emb_init(emb)
n_emb = sum(e.embedding_dim for e in self.embs)
self.n_emb, self.n_cont=n_emb, n_cont
szs = [n_emb+n_cont] + szs
self.lins = nn.ModuleList([
nn.Linear(szs[i], szs[i+1]) for i in range(len(szs)-1)])
self.bns = nn.ModuleList([
nn.BatchNorm1d(sz) for sz in szs[1:]])
for o in self.lins: kaiming_normal(o.weight.data)
self.outp = nn.Linear(szs[-1], out_sz)
kaiming_normal(self.outp.weight.data)
self.emb_drop = nn.Dropout(emb_drop)
self.drops = nn.ModuleList([nn.Dropout(drop) for drop in drops])
self.bn = nn.BatchNorm1d(n_cont)
self.use_bn,self.y_range = use_bn,y_range
self.is_reg = is_reg
self.is_multi = is_multi
def forward(self, x_cat, x_cont):
if self.n_emb != 0:
x = [e(x_cat[:,i]) for i,e in enumerate(self.embs)]
x = torch.cat(x, 1)
x = self.emb_drop(x)
if self.n_cont != 0:
x2 = self.bn(x_cont)
x = torch.cat([x, x2], 1) if self.n_emb != 0 else x2
for l,d,b in zip(self.lins, self.drops, self.bns):
x = F.relu(l(x))
if self.use_bn: x = b(x)
x = d(x)
x = self.outp(x)
if not self.is_reg:
if self.is_multi:
x = F.sigmoid(x)
else:
x = F.log_softmax(x)
elif self.y_range:
x = F.sigmoid(x)
x = x*(self.y_range[1] - self.y_range[0])
x = x+self.y_range[0]
return x
class StructuredLearner(Learner):
def __init__(self, data, models, **kwargs):
super().__init__(data, models, **kwargs)
def _get_crit(self, data): return F.mse_loss if data.is_reg else F.binary_cross_entropy if data.is_multi else F.nll_loss
def summary(self):
x = [torch.ones(3, self.data.trn_ds.cats.shape[1], dtype=torch.int64), torch.rand(3, self.data.trn_ds.conts.shape[1])]
return model_summary(self.model, x)
class StructuredModel(BasicModel):
def get_layer_groups(self):
m=self.model
return [m.embs, children(m.lins)+children(m.bns), m.outp]
class CollabFilterDataset(Dataset):
def __init__(self, path, user_col, item_col, ratings):
self.ratings,self.path = ratings.values.astype(np.float32),path
self.n = len(ratings)
(self.users,self.user2idx,self.user_col,self.n_users) = self.proc_col(user_col)
(self.items,self.item2idx,self.item_col,self.n_items) = self.proc_col(item_col)
self.min_score,self.max_score = min(ratings),max(ratings)
self.cols = [self.user_col,self.item_col,self.ratings]
@classmethod
def from_data_frame(cls, path, df, user_name, item_name, rating_name):
return cls(path, df[user_name], df[item_name], df[rating_name])
@classmethod
def from_csv(cls, path, csv, user_name, item_name, rating_name):
df = pd.read_csv(os.path.join(path,csv))
return cls.from_data_frame(path, df, user_name, item_name, rating_name)
def proc_col(self,col):
uniq = col.unique()
name2idx = {o:i for i,o in enumerate(uniq)}
return (uniq, name2idx, np.array([name2idx[x] for x in col]), len(uniq))
def __len__(self): return self.n
def __getitem__(self, idx): return [o[idx] for o in self.cols]
def get_data(self, val_idxs, bs):
val, trn = zip(*split_by_idx(val_idxs, *self.cols))
return ColumnarModelData(self.path, PassthruDataset(*trn), PassthruDataset(*val), bs)
def get_model(self, n_factors):
model = EmbeddingDotBias(n_factors, self.n_users, self.n_items, self.min_score, self.max_score)
return CollabFilterModel(to_gpu(model))
def get_learner(self, n_factors, val_idxs, bs, **kwargs):
return CollabFilterLearner(self.get_data(val_idxs, bs), self.get_model(n_factors), **kwargs)
def get_emb(ni,nf):
e = nn.Embedding(ni, nf)
e.weight.data.uniform_(-0.05,0.05)
return e
class EmbeddingDotBias(nn.Module):
def __init__(self, n_factors, n_users, n_items, min_score, max_score):
super().__init__()
self.min_score,self.max_score = min_score,max_score
(self.u, self.i, self.ub, self.ib) = [get_emb(*o) for o in [
(n_users, n_factors), (n_items, n_factors), (n_users,1), (n_items,1)
]]
def forward(self, users, items):
um = self.u(users)* self.i(items)
res = um.sum(1) + self.ub(users).squeeze() + self.ib(items).squeeze()
return F.sigmoid(res) * (self.max_score-self.min_score) + self.min_score
class CollabFilterLearner(Learner):
def __init__(self, data, models, **kwargs):
super().__init__(data, models, **kwargs)
def _get_crit(self, data): return F.mse_loss
def summary(self): return model_summary(self.model, [torch.ones(3, dtype=torch.int64), torch.ones(3, dtype=torch.int64)])
class CollabFilterModel(BasicModel):
def get_layer_groups(self): return self.model