<reponame>ketyi/dgl
import torch
from BGNN import BGNNPredictor
import pandas as pd
import numpy as np
import json
import os
from dgl.data.utils import load_graphs
from dgl.nn.pytorch import GATConv as GATConvDGL, GraphConv, ChebConv as ChebConvDGL, \
    AGNNConv as AGNNConvDGL, APPNPConv
from torch.nn import Dropout, ELU, Sequential, Linear, ReLU
import torch.nn.functional as F
from category_encoders import CatBoostEncoder
from sklearn import preprocessing

class GNNModelDGL(torch.nn.Module):
    def __init__(self, in_dim, hidden_dim, out_dim,
                 dropout=0., name='gat', residual=True, use_mlp=False, join_with_mlp=False):
        super(GNNModelDGL, self).__init__()
        self.name = name
        self.use_mlp = use_mlp
        self.join_with_mlp = join_with_mlp
        self.normalize_input_columns = True
        if name == 'gat':
            self.l1 = GATConvDGL(in_dim, hidden_dim//8, 8, feat_drop=dropout, attn_drop=dropout, residual=False,
                              activation=F.elu)
            self.l2 = GATConvDGL(hidden_dim, out_dim, 1, feat_drop=dropout, attn_drop=dropout, residual=residual, activation=None)
        elif name == 'gcn':
            self.l1 = GraphConv(in_dim, hidden_dim, activation=F.elu)
            self.l2 = GraphConv(hidden_dim, out_dim, activation=F.elu)
            self.drop = Dropout(p=dropout)
        elif name == 'cheb':
            self.l1 = ChebConvDGL(in_dim, hidden_dim, k = 3)
            self.l2 = ChebConvDGL(hidden_dim, out_dim, k = 3)
            self.drop = Dropout(p=dropout)
        elif name == 'agnn':
            self.lin1 = Sequential(Dropout(p=dropout), Linear(in_dim, hidden_dim), ELU())
            self.l1 = AGNNConvDGL(learn_beta=False)
            self.l2 = AGNNConvDGL(learn_beta=True)
            self.lin2 = Sequential(Dropout(p=dropout), Linear(hidden_dim, out_dim), ELU())
        elif name == 'appnp':
            self.lin1 = Sequential(Dropout(p=dropout), Linear(in_dim, hidden_dim),
                       ReLU(), Dropout(p=dropout), Linear(hidden_dim, out_dim))
            self.l1 = APPNPConv(k=10, alpha=0.1, edge_drop=0.)


    def forward(self, graph, features):
        h = features
        if self.use_mlp:
            if self.join_with_mlp:
                h = torch.cat((h, self.mlp(features)), 1)
            else:
                h = self.mlp(features)
        if self.name == 'gat':
            h = self.l1(graph, h).flatten(1)
            logits = self.l2(graph, h).mean(1)
        elif self.name in ['appnp']:
            h = self.lin1(h)
            logits = self.l1(graph, h)
        elif self.name == 'agnn':
            h = self.lin1(h)
            h = self.l1(graph, h)
            h = self.l2(graph, h)
            logits = self.lin2(h)
        elif self.name == 'che3b':
            lambda_max = dgl.laplacian_lambda_max(graph)
            h = self.drop(h)
            h = self.l1(graph, h, lambda_max)
            logits = self.l2(graph, h, lambda_max)
        elif self.name == 'gcn':
            h = self.drop(h)
            h = self.l1(graph, h)
            logits = self.l2(graph, h)

        return logits

def read_input(input_folder):
    X = pd.read_csv(f'{input_folder}/X.csv')
    y = pd.read_csv(f'{input_folder}/y.csv')

    categorical_columns = []
    if os.path.exists(f'{input_folder}/cat_features.txt'):
        with open(f'{input_folder}/cat_features.txt') as f:
            for line in f:
                if line.strip():
                    categorical_columns.append(line.strip())

    cat_features = None
    if categorical_columns:
        columns = X.columns
        cat_features = np.where(columns.isin(categorical_columns))[0]

        for col in list(columns[cat_features]):
            X[col] = X[col].astype(str)

    gs, _ = load_graphs(f'{input_folder}/graph.dgl')
    graph = gs[0]

    with open(f'{input_folder}/masks.json') as f:
        masks = json.load(f)

    return graph, X, y, cat_features, masks

def normalize_features(X, train_mask, val_mask, test_mask):
    min_max_scaler = preprocessing.MinMaxScaler()
    A = X.to_numpy(copy=True)
    A[train_mask] = min_max_scaler.fit_transform(A[train_mask])
    A[val_mask + test_mask] = min_max_scaler.transform(A[val_mask + test_mask])
    return pd.DataFrame(A, columns=X.columns).astype(float)

def replace_na(X, train_mask):
    if X.isna().any().any():
        return X.fillna(X.iloc[train_mask].min() - 1)
    return X

def encode_cat_features(X, y, cat_features, train_mask, val_mask, test_mask):
    enc = CatBoostEncoder()
    A = X.to_numpy(copy=True)
    b = y.to_numpy(copy=True)
    A[np.ix_(train_mask, cat_features)] = enc.fit_transform(A[np.ix_(train_mask, cat_features)], b[train_mask])
    A[np.ix_(val_mask + test_mask, cat_features)] = enc.transform(A[np.ix_(val_mask + test_mask, cat_features)])
    A = A.astype(float)
    return pd.DataFrame(A, columns=X.columns)

if __name__ == '__main__':
    # datasets can be found here: https://www.dropbox.com/s/verx1evkykzli88/datasets.zip
    # Read dataset
    input_folder = 'datasets/avazu'
    graph, X, y, cat_features, masks = read_input(input_folder)
    train_mask, val_mask, test_mask = masks['0']['train'], masks['0']['val'], masks['0']['test']

    encoded_X = X.copy()
    normalizeFeatures = False
    replaceNa = True

    if len(cat_features):
        encoded_X = encode_cat_features(encoded_X, y, cat_features, train_mask, val_mask, test_mask)
    if normalizeFeatures:
        encoded_X = normalize_features(encoded_X, train_mask, val_mask, test_mask)
    if replaceNa:
        encoded_X = replace_na(encoded_X, train_mask)



    # specify parameters
    task = 'regression'
    hidden_dim = 128
    trees_per_epoch = 5 # 5-10 are good values to try
    backprop_per_epoch = 5 # 5-10 are good values to try
    lr = 0.1 # 0.01-0.1 are good values to try
    append_gbdt_pred = False # this can be important for performance (try True and False)
    train_input_features = False
    gbdt_depth = 6
    gbdt_lr = 0.1

    out_dim = y.shape[1] if task == 'regression' else len(set(y.iloc[test_mask, 0]))
    in_dim = out_dim + X.shape[1] if append_gbdt_pred else out_dim

    # specify GNN model
    gnn_model = GNNModelDGL(in_dim, hidden_dim, out_dim)


    # initialize BGNN model
    bgnn = BGNNPredictor(gnn_model, task=task,
                         loss_fn=None,
                         trees_per_epoch=trees_per_epoch,
                         backprop_per_epoch=backprop_per_epoch,
                         lr=lr,
                         append_gbdt_pred=append_gbdt_pred,
                         train_input_features=train_input_features,
                         gbdt_depth=gbdt_depth,
                         gbdt_lr=gbdt_lr)

    # train
    metrics = bgnn.fit(graph, encoded_X, y, train_mask, val_mask, test_mask,
                       original_X = X, cat_features=cat_features,
                       num_epochs=100, patience=10, metric_name='loss')

    bgnn.plot_interactive(metrics, legend=['train', 'valid', 'test'], title='Avazu', metric_name='loss')

import torch
from BGNN import BGNNPredictor
import pandas as pd
import numpy as np
import json
import os
from dgl.data.utils import load_graphs
from dgl.nn.pytorch import GATConv as GATConvDGL, GraphConv, ChebConv as ChebConvDGL, \
    AGNNConv as AGNNConvDGL, APPNPConv
from torch.nn import Dropout, ELU, Sequential, Linear, ReLU
import torch.nn.functional as F
from category_encoders import CatBoostEncoder
from sklearn import preprocessing

class GNNModelDGL(torch.nn.Module):
    def __init__(self, in_dim, hidden_dim, out_dim,
                 dropout=0., name='gat', residual=True, use_mlp=False, join_with_mlp=False):
        super(GNNModelDGL, self).__init__()
        self.name = name
        self.use_mlp = use_mlp
        self.join_with_mlp = join_with_mlp
        self.normalize_input_columns = True
        if name == 'gat':
            self.l1 = GATConvDGL(in_dim, hidden_dim//8, 8, feat_drop=dropout, attn_drop=dropout, residual=False,
                              activation=F.elu)
            self.l2 = GATConvDGL(hidden_dim, out_dim, 1, feat_drop=dropout, attn_drop=dropout, residual=residual, activation=None)
        elif name == 'gcn':
            self.l1 = GraphConv(in_dim, hidden_dim, activation=F.elu)
            self.l2 = GraphConv(hidden_dim, out_dim, activation=F.elu)
            self.drop = Dropout(p=dropout)
        elif name == 'cheb':
            self.l1 = ChebConvDGL(in_dim, hidden_dim, k = 3)
            self.l2 = ChebConvDGL(hidden_dim, out_dim, k = 3)
            self.drop = Dropout(p=dropout)
        elif name == 'agnn':
            self.lin1 = Sequential(Dropout(p=dropout), Linear(in_dim, hidden_dim), ELU())
            self.l1 = AGNNConvDGL(learn_beta=False)
            self.l2 = AGNNConvDGL(learn_beta=True)
            self.lin2 = Sequential(Dropout(p=dropout), Linear(hidden_dim, out_dim), ELU())
        elif name == 'appnp':
            self.lin1 = Sequential(Dropout(p=dropout), Linear(in_dim, hidden_dim),
                       ReLU(), Dropout(p=dropout), Linear(hidden_dim, out_dim))
            self.l1 = APPNPConv(k=10, alpha=0.1, edge_drop=0.)


    def forward(self, graph, features):
        h = features
        if self.use_mlp:
            if self.join_with_mlp:
                h = torch.cat((h, self.mlp(features)), 1)
            else:
                h = self.mlp(features)
        if self.name == 'gat':
            h = self.l1(graph, h).flatten(1)
            logits = self.l2(graph, h).mean(1)
        elif self.name in ['appnp']:
            h = self.lin1(h)
            logits = self.l1(graph, h)
        elif self.name == 'agnn':
            h = self.lin1(h)
            h = self.l1(graph, h)
            h = self.l2(graph, h)
            logits = self.lin2(h)
        elif self.name == 'che3b':
            lambda_max = dgl.laplacian_lambda_max(graph)
            h = self.drop(h)
            h = self.l1(graph, h, lambda_max)
            logits = self.l2(graph, h, lambda_max)
        elif self.name == 'gcn':
            h = self.drop(h)
            h = self.l1(graph, h)
            logits = self.l2(graph, h)

        return logits

def read_input(input_folder):
    X = pd.read_csv(f'{input_folder}/X.csv')
    y = pd.read_csv(f'{input_folder}/y.csv')

    categorical_columns = []
    if os.path.exists(f'{input_folder}/cat_features.txt'):
        with open(f'{input_folder}/cat_features.txt') as f:
            for line in f:
                if line.strip():
                    categorical_columns.append(line.strip())

    cat_features = None
    if categorical_columns:
        columns = X.columns
        cat_features = np.where(columns.isin(categorical_columns))[0]

        for col in list(columns[cat_features]):
            X[col] = X[col].astype(str)

    gs, _ = load_graphs(f'{input_folder}/graph.dgl')
    graph = gs[0]

    with open(f'{input_folder}/masks.json') as f:
        masks = json.load(f)

    return graph, X, y, cat_features, masks

def normalize_features(X, train_mask, val_mask, test_mask):
    min_max_scaler = preprocessing.MinMaxScaler()
    A = X.to_numpy(copy=True)
    A[train_mask] = min_max_scaler.fit_transform(A[train_mask])
    A[val_mask + test_mask] = min_max_scaler.transform(A[val_mask + test_mask])
    return pd.DataFrame(A, columns=X.columns).astype(float)

def replace_na(X, train_mask):
    if X.isna().any().any():
        return X.fillna(X.iloc[train_mask].min() - 1)
    return X

def encode_cat_features(X, y, cat_features, train_mask, val_mask, test_mask):
    enc = CatBoostEncoder()
    A = X.to_numpy(copy=True)
    b = y.to_numpy(copy=True)
    A[np.ix_(train_mask, cat_features)] = enc.fit_transform(A[np.ix_(train_mask, cat_features)], b[train_mask])
    A[np.ix_(val_mask + test_mask, cat_features)] = enc.transform(A[np.ix_(val_mask + test_mask, cat_features)])
    A = A.astype(float)
    return pd.DataFrame(A, columns=X.columns)

if __name__ == '__main__':
    # datasets can be found here: https://www.dropbox.com/s/verx1evkykzli88/datasets.zip
    # Read dataset
    input_folder = 'datasets/avazu'
    graph, X, y, cat_features, masks = read_input(input_folder)
    train_mask, val_mask, test_mask = masks['0']['train'], masks['0']['val'], masks['0']['test']

    encoded_X = X.copy()
    normalizeFeatures = False
    replaceNa = True

    if len(cat_features):
        encoded_X = encode_cat_features(encoded_X, y, cat_features, train_mask, val_mask, test_mask)
    if normalizeFeatures:
        encoded_X = normalize_features(encoded_X, train_mask, val_mask, test_mask)
    if replaceNa:
        encoded_X = replace_na(encoded_X, train_mask)



    # specify parameters
    task = 'regression'
    hidden_dim = 128
    trees_per_epoch = 5 # 5-10 are good values to try
    backprop_per_epoch = 5 # 5-10 are good values to try
    lr = 0.1 # 0.01-0.1 are good values to try
    append_gbdt_pred = False # this can be important for performance (try True and False)
    train_input_features = False
    gbdt_depth = 6
    gbdt_lr = 0.1

    out_dim = y.shape[1] if task == 'regression' else len(set(y.iloc[test_mask, 0]))
    in_dim = out_dim + X.shape[1] if append_gbdt_pred else out_dim

    # specify GNN model
    gnn_model = GNNModelDGL(in_dim, hidden_dim, out_dim)


    # initialize BGNN model
    bgnn = BGNNPredictor(gnn_model, task=task,
                         loss_fn=None,
                         trees_per_epoch=trees_per_epoch,
                         backprop_per_epoch=backprop_per_epoch,
                         lr=lr,
                         append_gbdt_pred=append_gbdt_pred,
                         train_input_features=train_input_features,
                         gbdt_depth=gbdt_depth,
                         gbdt_lr=gbdt_lr)

    # train
    metrics = bgnn.fit(graph, encoded_X, y, train_mask, val_mask, test_mask,
                       original_X = X, cat_features=cat_features,
                       num_epochs=100, patience=10, metric_name='loss')

    bgnn.plot_interactive(metrics, legend=['train', 'valid', 'test'], title='Avazu', metric_name='loss')
# datasets can be found here: https://www.dropbox.com/s/verx1evkykzli88/datasets.zip # Read dataset # specify parameters # 5-10 are good values to try # 5-10 are good values to try # 0.01-0.1 are good values to try # this can be important for performance (try True and False) # specify GNN model # initialize BGNN model # train