dmlc--dgl
Mufei Li
ff519f98c3
* Update * Update * Update * Update * Update * Update * Update * Fix * Update * Fix subgraph tests * Capture stdout for distributed test * Capture stdout for distributed test * Update * Update * Update * Update subgraph.cc Co-authored-by: Ubuntu <ubuntu@ip-172-31-28-17.us-west-2.compute.internal> Co-authored-by: Jinjing Zhou <VoVAllen@users.noreply.github.com>
63 行
2.6 KiB
Python
63 行
2.6 KiB
Python
import torch
|
|
import dgl
|
|
import numpy as np
|
|
import scipy.sparse as ssp
|
|
import tqdm
|
|
import dask.dataframe as dd
|
|
|
|
# This is the train-test split method most of the recommender system papers running on MovieLens
|
|
# takes. It essentially follows the intuition of "training on the past and predict the future".
|
|
# One can also change the threshold to make validation and test set take larger proportions.
|
|
def train_test_split_by_time(df, timestamp, user):
|
|
df['train_mask'] = np.ones((len(df),), dtype=np.bool)
|
|
df['val_mask'] = np.zeros((len(df),), dtype=np.bool)
|
|
df['test_mask'] = np.zeros((len(df),), dtype=np.bool)
|
|
df = dd.from_pandas(df, npartitions=10)
|
|
def train_test_split(df):
|
|
df = df.sort_values([timestamp])
|
|
if df.shape[0] > 1:
|
|
df.iloc[-1, -3] = False
|
|
df.iloc[-1, -1] = True
|
|
if df.shape[0] > 2:
|
|
df.iloc[-2, -3] = False
|
|
df.iloc[-2, -2] = True
|
|
return df
|
|
df = df.groupby(user, group_keys=False).apply(train_test_split).compute(scheduler='processes').sort_index()
|
|
print(df[df[user] == df[user].unique()[0]].sort_values(timestamp))
|
|
return df['train_mask'].to_numpy().nonzero()[0], \
|
|
df['val_mask'].to_numpy().nonzero()[0], \
|
|
df['test_mask'].to_numpy().nonzero()[0]
|
|
|
|
def build_train_graph(g, train_indices, utype, itype, etype, etype_rev):
|
|
train_g = g.edge_subgraph(
|
|
{etype: train_indices, etype_rev: train_indices},
|
|
relabel_nodes=False)
|
|
|
|
# copy features
|
|
for ntype in g.ntypes:
|
|
for col, data in g.nodes[ntype].data.items():
|
|
train_g.nodes[ntype].data[col] = data
|
|
for etype in g.etypes:
|
|
for col, data in g.edges[etype].data.items():
|
|
train_g.edges[etype].data[col] = data[train_g.edges[etype].data[dgl.EID]]
|
|
|
|
return train_g
|
|
|
|
def build_val_test_matrix(g, val_indices, test_indices, utype, itype, etype):
|
|
n_users = g.number_of_nodes(utype)
|
|
n_items = g.number_of_nodes(itype)
|
|
val_src, val_dst = g.find_edges(val_indices, etype=etype)
|
|
test_src, test_dst = g.find_edges(test_indices, etype=etype)
|
|
val_src = val_src.numpy()
|
|
val_dst = val_dst.numpy()
|
|
test_src = test_src.numpy()
|
|
test_dst = test_dst.numpy()
|
|
val_matrix = ssp.coo_matrix((np.ones_like(val_src), (val_src, val_dst)), (n_users, n_items))
|
|
test_matrix = ssp.coo_matrix((np.ones_like(test_src), (test_src, test_dst)), (n_users, n_items))
|
|
|
|
return val_matrix, test_matrix
|
|
|
|
def linear_normalize(values):
|
|
return (values - values.min(0, keepdims=True)) / \
|
|
(values.max(0, keepdims=True) - values.min(0, keepdims=True))
|