<reponame>nimasadri11/capreolus
import torch
import torch.nn as nn

from capreolus import ConfigOption, Dependency
from capreolus.reranker import Reranker
from capreolus.utils.loginit import get_logger

logger = get_logger(__name__)


class DSSM_class(nn.Module):
    def __init__(self, extractor, config):
        super(DSSM_class, self).__init__()
        p = config
        nvocab = len(extractor.stoi)
        nhiddens = [nvocab] + list(map(int, p["nhiddens"].split()))
        self.ffw = nn.Sequential()
        for i in range(len(nhiddens) - 1):
            self.ffw.add_module("linear%d" % i, nn.Linear(nhiddens[i], nhiddens[i + 1]))
            self.ffw.add_module("activate%d" % i, nn.ReLU())
            self.ffw.add_module("dropout%i" % i, nn.Dropout(0.5))

        self.output_layer = nn.Sigmoid()

    def forward(self, sentence, query, query_idf):
        query = query.float()
        sentence = sentence.float()
        query = self.ffw(query)
        sentence = self.ffw(sentence)

        query_norm = query.norm(dim=-1)[:, None] + 1e-7
        sentence_norm = sentence.norm(dim=-1)[:, None] + 1e-7

        query = query / query_norm
        sentence = sentence / sentence_norm

        cos_x = (query * sentence).sum(dim=-1, keepdim=True)

        score = self.output_layer(cos_x)
        return score


dtype = torch.FloatTensor


@Reranker.register
class DSSM(Reranker):
    """<NAME>, <NAME>, <NAME>, <NAME>, <NAME>, and <NAME>. 2013. Learning deep structured semantic models for web search using clickthrough data. In CIKM'13."""

    module_name = "DSSM"
    dependencies = [
        Dependency(key="extractor", module="extractor", name="bagofwords"),
        Dependency(key="trainer", module="trainer", name="pytorch", default_config_overrides={"lr": 0.0001}),
    ]
    config_spec = [
        ConfigOption(
            "nhiddens",
            "56",
            "list of hidden layer sizes (eg '56 128'), where the i'th value indicates the output size of the i'th layer",
        )
    ]

    def build_model(self):
        if not hasattr(self, "model"):
            self.model = DSSM_class(self.extractor, self.config)
        return self.model

    def score(self, d):
        query_idf = d["query_idf"]
        query_sentence = d["query"]
        pos_sentence, neg_sentence = d["posdoc"], d["negdoc"]
        return [
            self.model(pos_sentence, query_sentence, query_idf).view(-1),
            self.model(neg_sentence, query_sentence, query_idf).view(-1),
        ]

    def test(self, d):
        query_idf = d["query_idf"]
        query_sentence = d["query"]
        pos_sentence = d["posdoc"]
        return self.model(pos_sentence, query_sentence, query_idf).view(-1)

import torch
import torch.nn as nn

from capreolus import ConfigOption, Dependency
from capreolus.reranker import Reranker
from capreolus.utils.loginit import get_logger

logger = get_logger(__name__)


class DSSM_class(nn.Module):
    def __init__(self, extractor, config):
        super(DSSM_class, self).__init__()
        p = config
        nvocab = len(extractor.stoi)
        nhiddens = [nvocab] + list(map(int, p["nhiddens"].split()))
        self.ffw = nn.Sequential()
        for i in range(len(nhiddens) - 1):
            self.ffw.add_module("linear%d" % i, nn.Linear(nhiddens[i], nhiddens[i + 1]))
            self.ffw.add_module("activate%d" % i, nn.ReLU())
            self.ffw.add_module("dropout%i" % i, nn.Dropout(0.5))

        self.output_layer = nn.Sigmoid()

    def forward(self, sentence, query, query_idf):
        query = query.float()
        sentence = sentence.float()
        query = self.ffw(query)
        sentence = self.ffw(sentence)

        query_norm = query.norm(dim=-1)[:, None] + 1e-7
        sentence_norm = sentence.norm(dim=-1)[:, None] + 1e-7

        query = query / query_norm
        sentence = sentence / sentence_norm

        cos_x = (query * sentence).sum(dim=-1, keepdim=True)

        score = self.output_layer(cos_x)
        return score


dtype = torch.FloatTensor


@Reranker.register
class DSSM(Reranker):
    """<NAME>, <NAME>, <NAME>, <NAME>, <NAME>, and <NAME>. 2013. Learning deep structured semantic models for web search using clickthrough data. In CIKM'13."""

    module_name = "DSSM"
    dependencies = [
        Dependency(key="extractor", module="extractor", name="bagofwords"),
        Dependency(key="trainer", module="trainer", name="pytorch", default_config_overrides={"lr": 0.0001}),
    ]
    config_spec = [
        ConfigOption(
            "nhiddens",
            "56",
            "list of hidden layer sizes (eg '56 128'), where the i'th value indicates the output size of the i'th layer",
        )
    ]

    def build_model(self):
        if not hasattr(self, "model"):
            self.model = DSSM_class(self.extractor, self.config)
        return self.model

    def score(self, d):
        query_idf = d["query_idf"]
        query_sentence = d["query"]
        pos_sentence, neg_sentence = d["posdoc"], d["negdoc"]
        return [
            self.model(pos_sentence, query_sentence, query_idf).view(-1),
            self.model(neg_sentence, query_sentence, query_idf).view(-1),
        ]

    def test(self, d):
        query_idf = d["query_idf"]
        query_sentence = d["query"]
        pos_sentence = d["posdoc"]
        return self.model(pos_sentence, query_sentence, query_idf).view(-1)
<NAME>, <NAME>, <NAME>, <NAME>, <NAME>, and <NAME>. 2013. Learning deep structured semantic models for web search using clickthrough data. In CIKM'13.