train_batch_multiRank_inductive_reddit_onelayer.py

from __future__ import division
from __future__ import print_function

import time
import tensorflow as tf
import scipy.sparse as sp

from utils import *
from models import GCN, MLP, GCN_APPRO_Onelayer
import json
from networkx.readwrite import json_graph

# Set random seed
seed = 123
np.random.seed(seed)
tf.set_random_seed(seed)

# Settings
flags = tf.app.flags
FLAGS = flags.FLAGS
flags.DEFINE_string('dataset', 'pubmed', 'Dataset string.')  # 'cora', 'citeseer', 'pubmed'
flags.DEFINE_string('model', 'gcn_appr', 'Model string.')  # 'gcn', 'gcn_appr'
flags.DEFINE_float('learning_rate', 0.01, 'Initial learning rate.')
flags.DEFINE_integer('epochs', 300, 'Number of epochs to train.')
flags.DEFINE_integer('hidden1', 64, 'Number of units in hidden layer 1.')
flags.DEFINE_float('dropout', 0.1, 'Dropout rate (1 - keep probability).')
flags.DEFINE_float('weight_decay', 1e-4, 'Weight for L2 loss on embedding matrix.')
flags.DEFINE_integer('early_stopping', 30, 'Tolerance for early stopping (# of epochs).')
flags.DEFINE_integer('max_degree', 3, 'Maximum Chebyshev polynomial degree.')
rank1 = 300
rank0 = 300
# Load data


def iterate_minibatches_listinputs(inputs, batchsize, shuffle=False):
    assert inputs is not None
    numSamples = inputs[0].shape[0]
    if shuffle:
        indices = np.arange(numSamples)
        np.random.shuffle(indices)
    for start_idx in range(0, numSamples - batchsize + 1, batchsize):
        if shuffle:
            excerpt = indices[start_idx:start_idx + batchsize]
        else:
            excerpt = slice(start_idx, start_idx + batchsize)
        yield [input[excerpt] for input in inputs]


def loadRedditFromG(dataset_dir, inputfile):
    f= open(dataset_dir+inputfile)
    objects = []
    for _ in range(pkl.load(f)):
        objects.append(pkl.load(f))
    adj, train_labels, val_labels, test_labels, train_index, val_index, test_index = tuple(objects)
    feats = np.load(dataset_dir + "/reddit-feats.npy")
    return sp.csr_matrix(adj), sp.lil_matrix(feats), train_labels, val_labels, test_labels, train_index, val_index, test_index


def loadRedditFromNPZ(dataset_dir):
    adj = sp.load_npz(dataset_dir+"reddit_adj.npz")
    data = np.load(dataset_dir+"reddit.npz")

    return adj, data['feats'], data['y_train'], data['y_val'], data['y_test'], data['train_index'], data['val_index'], data['test_index']



def transferRedditDataFormat(dataset_dir, output_file):
    G = json_graph.node_link_graph(json.load(open(dataset_dir + "/reddit-G.json")))
    labels = json.load(open(dataset_dir + "/reddit-class_map.json"))

    train_ids = [n for n in G.nodes() if not G.node[n]['val'] and not G.node[n]['test']]
    test_ids = [n for n in G.nodes() if G.node[n]['test']]
    val_ids = [n for n in G.nodes() if G.node[n]['val']]
    train_labels = [labels[i] for i in train_ids]
    test_labels = [labels[i] for i in test_ids]
    val_labels = [labels[i] for i in val_ids]
    feats = np.load(dataset_dir + "/reddit-feats.npy")
    ## Logistic gets thrown off by big counts, so log transform num comments and score
    feats[:, 0] = np.log(feats[:, 0] + 1.0)
    feats[:, 1] = np.log(feats[:, 1] - min(np.min(feats[:, 1]), -1))
    feat_id_map = json.load(open(dataset_dir + "reddit-id_map.json"))
    feat_id_map = {id: val for id, val in feat_id_map.iteritems()}

    # train_feats = feats[[feat_id_map[id] for id in train_ids]]
    # test_feats = feats[[feat_id_map[id] for id in test_ids]]

    # numNode = len(feat_id_map)
    # adj = sp.lil_matrix(np.zeros((numNode,numNode)))
    # for edge in G.edges():
    #     adj[feat_id_map[edge[0]], feat_id_map[edge[1]]] = 1

    train_index = [feat_id_map[id] for id in train_ids]
    val_index = [feat_id_map[id] for id in val_ids]
    test_index = [feat_id_map[id] for id in test_ids]
    np.savez(output_file, feats = feats, y_train=train_labels, y_val=val_labels, y_test = test_labels, train_index = train_index,
             val_index=val_index, test_index = test_index)


def transferLabel2Onehot(labels, N):
    y = np.zeros((len(labels),N))
    for i in range(len(labels)):
        pos = labels[i]
        y[i,pos] =1
    return y



def run_regression(train_embeds, train_labels, test_embeds, test_labels):
    np.random.seed(1)
    from sklearn.linear_model import SGDClassifier
    from sklearn.dummy import DummyClassifier
    from sklearn.metrics import accuracy_score
    dummy = DummyClassifier()
    dummy.fit(train_embeds, train_labels)
    log = SGDClassifier(loss="log", n_jobs=55)
    log.fit(train_embeds, train_labels)
    print("Test scores")
    print(accuracy_score(test_labels, log.predict(test_embeds)))
    print("Train scores")
    print(accuracy_score(train_labels, log.predict(train_embeds)))
    print("Random baseline")
    print(accuracy_score(test_labels, dummy.predict(test_embeds)))

def main(rank1):
    adj, features, y_train, y_val, y_test,train_index, val_index, test_index = loadRedditFromNPZ("data/")

    adj = adj+adj.T

    # train_index = train_index[:10000]
    # val_index = val_index[:5000]
    # test_index = test_index[:10000]
    # y_train = transferLabel2Onehot(y_train, 50)[:10000]
    # y_val = transferLabel2Onehot(y_val, 50)[:5000]
    # y_test = transferLabel2Onehot(y_test, 50)[:10000]

    y_train = transferLabel2Onehot(y_train, 50)
    y_val = transferLabel2Onehot(y_val, 50)
    y_test = transferLabel2Onehot(y_test, 50)



    # adj, features, y_train, y_val, y_test, train_mask, val_mask, test_mask = load_data(FLAGS.dataset)

    features = sp.lil_matrix(features)


    adj_train = adj[train_index, :][:, train_index]


    adj_val = adj[val_index, :][:, val_index]

    adj_test = adj[test_index, :][:, test_index]
    numNode_train = adj_train.shape[0]

    train_mask = np.ones((numNode_train,))
    val_mask = np.ones((adj_val.shape[0],))
    test_mask = np.ones((adj_test.shape[0],))


    # print("numNode", numNode)

    # Some preprocessing
    features = nontuple_preprocess_features(features)
    train_features = features[train_index]

    if FLAGS.model == 'gcn_appr':
        normADJ_train = nontuple_preprocess_adj(adj_train)
        normADJ = nontuple_preprocess_adj(adj)
        # normADJ_val = nontuple_preprocess_adj(adj_val)
        # normADJ_test = nontuple_preprocess_adj(adj_test)

        num_supports = 2
        model_func = GCN_APPRO_Onelayer
    else:
        raise ValueError('Invalid argument for model: ' + str(FLAGS.model))

    # Define placeholders
    placeholders = {
        'support': [tf.sparse_placeholder(tf.float32) for _ in range(num_supports)],
        'features': tf.sparse_placeholder(tf.float32),
        'labels': tf.placeholder(tf.float32, shape=(None, y_train.shape[1])),
        'labels_mask': tf.placeholder(tf.int32),
        'dropout': tf.placeholder_with_default(0., shape=()),
        'num_features_nonzero': tf.placeholder(tf.int32)  # helper variable for sparse dropout
    }

    # Create model
    model = model_func(placeholders, input_dim=features.shape[-1], logging=True)

    # Initialize session
    sess = tf.Session()

    # Define model evaluation function
    def evaluate(features, support, labels, mask, placeholders):
        t_test = time.time()
        feed_dict_val = construct_feed_dict(features, support, labels, mask, placeholders)
        outs_val = sess.run([model.loss, model.accuracy], feed_dict=feed_dict_val)
        return outs_val[0], outs_val[1], (time.time() - t_test)

    # Init variables
    sess.run(tf.global_variables_initializer())

    cost_val = []

    p0 = column_prop(normADJ_train)

    # testSupport = [sparse_to_tuple(normADJ), sparse_to_tuple(normADJ)]
    valSupport = [sparse_to_tuple(normADJ[val_index, :])]
    testSupport = [sparse_to_tuple(normADJ[test_index, :])]

    t = time.time()
    # Train model
    for epoch in range(FLAGS.epochs):
        t1 = time.time()

        n = 0
        for batch in iterate_minibatches_listinputs([normADJ_train, y_train, train_mask], batchsize=5120, shuffle=True):
            [normADJ_batch, y_train_batch, train_mask_batch] = batch
            if sum(train_mask_batch) < 1:
                continue
            p1 = column_prop(normADJ_batch)
            if rank1 is not None:
                q1 = np.random.choice(np.arange(numNode_train), rank1, p=p1)  # top layer
                # q0 = np.random.choice(np.arange(numNode_train), rank0, p=p0)  # bottom layer
                support1 = sparse_to_tuple(normADJ_batch[:, q1].dot(sp.diags(1.0 / (p1[q1] * rank1))))

                features_inputs = sparse_to_tuple(train_features[q1, :])  # selected nodes for approximation
            else:
                support1 = sparse_to_tuple(normADJ_batch)
                features_inputs = sparse_to_tuple(train_features)
            # Construct feed dictionary
            feed_dict = construct_feed_dict(features_inputs, [support1], y_train_batch, train_mask_batch,
                                            placeholders)
            feed_dict.update({placeholders['dropout']: FLAGS.dropout})

            # Training step
            outs = sess.run([model.opt_op, model.loss, model.accuracy], feed_dict=feed_dict)


        # Validation
        cost, acc, duration = evaluate(sparse_to_tuple(features), valSupport, y_val, val_mask, placeholders)
        cost_val.append(cost)

        # Print results
        print("Epoch:", '%04d' % (epoch + 1), "train_loss=", "{:.5f}".format(outs[1]),
              "train_acc=", "{:.5f}".format(outs[2]), "val_loss=", "{:.5f}".format(cost),
              "val_acc=", "{:.5f}".format(acc), "time=", "{:.5f}".format(time.time() - t1))

        if epoch > FLAGS.early_stopping and cost_val[-1] > np.mean(cost_val[-(FLAGS.early_stopping + 1):-1]):
            # print("Early stopping...")
            break

    train_duration = time.time() - t
    # Testing
    test_cost, test_acc, test_duration = evaluate(sparse_to_tuple(features), testSupport, y_test, test_mask,
                                                  placeholders)
    print("rank1 = {}".format(rank1), "rank0 = {}".format(rank0), "cost=", "{:.5f}".format(test_cost),
          "accuracy=", "{:.5f}".format(test_acc), "training time=", "{:.5f}".format(train_duration))

def transferG2ADJ():
    G = json_graph.node_link_graph(json.load(open("reddit/reddit-G.json")))
    feat_id_map = json.load(open("reddit/reddit-id_map.json"))
    feat_id_map = {id: val for id, val in feat_id_map.iteritems()}
    numNode = len(feat_id_map)
    adj = np.zeros((numNode, numNode))
    newEdges0 = [feat_id_map[edge[0]] for edge in G.edges()]
    newEdges1 = [feat_id_map[edge[1]] for edge in G.edges()]

    # for edge in G.edges():
    #     adj[feat_id_map[edge[0]], feat_id_map[edge[1]]] = 1
    adj = sp.csr_matrix((np.ones((len(newEdges0),)), (newEdges0, newEdges1)), shape=(numNode, numNode))
    sp.save_npz("reddit_adj.npz", adj)


def original():
    adj, features, y_train, y_val, y_test, train_index, val_index, test_index = loadRedditFromNPZ("data/")
    adj = adj+adj.T
    normADJ = nontuple_preprocess_adj(adj)
    features = adj.dot(features)

    train_feats = features[train_index, :]
    test_feats = features[test_index, :]

    from sklearn.preprocessing import StandardScaler

    scaler = StandardScaler()
    scaler.fit(train_feats)
    train_feats = scaler.transform(train_feats)
    test_feats = scaler.transform(test_feats)
    run_regression(train_feats, y_train, test_feats, y_test)

if __name__=="__main__":
    # transferRedditDataFormat("reddit/","data/reddit.npz")

    # original()
    main(50)