LukasHaas

class LightGCN(torch.nn.Module):
    def __init__(self, train_data, num_layers, emb_size=16, initialize_with_words=False):
        super(LightGCN, self).__init__()
        self.convs = nn.ModuleList()
        assert (num_layers >= 1), 'Number of layers is not >=1'
        for l in range(num_layers):
            self.convs.append(LightGCNConv(input_dim, input_dim))

        # Initialize using custom embeddings if provided
        num_nodes = train_data.node_label_index.size()[0]

LukasHaas

class LightGCNConv(MessagePassing):
    def __init__(self, aggregation='mean', **kwargs):  
        super(LightGCNConv, self).__init__(**kwargs)
        self.aggregation = aggregation

    def forward(self, x, edge_index, size = None):
        out = self.propagate(edge_index, x=(x, x))
        return out

    def message(self, x_j):

LukasHaas

import pandas as pd
import numpy as np

from sklearn.base import BaseEstimator, clone
from sklearn.metrics import r2_score

from decimal import Decimal
from typing import Tuple, Union, Dict

class QuantileRegressor(BaseEstimator):

LukasHaas

import numpy as np
import pandas as pd
import sklearn

from typing import Dict, Tuple
from sklearn.base import BaseEstimator

class RuleAugmentedEstimator(BaseEstimator):
    """Augments sklearn estimators with rule-based logic.