How to use deeprank - 10 common examples

To help you get started, we’ve selected a few deeprank examples, based on popular ways it is used in public projects.

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github pl8787 / DeepRank_PyTorch / deeprank / select_module / group_pointer_net.py View on Github external
from collections import defaultdict

import numpy as np
import torch
import torch.nn as nn
import torch.nn.functional as F

from deeprank import select_module


class GroupPointerNet(select_module.SelectNet):
    def __init__(self, config, out_device=None):
        super().__init__(config)
        self.output_type = 'LL'

        self.pad_value = self.config['pad_value']
        self.win_size = self.config['win_size']

        self.max_match = self.config['max_match']

        self.embedding = nn.Embedding(
            config['vocab_size'],
            config['embed_dim'],
            padding_idx=self.pad_value
        )

        self.embedding.weight.requires_grad = self.config['finetune_embed']
github pl8787 / DeepRank_PyTorch / deeprank / select_module / identity_net.py View on Github external
from collections import defaultdict

import numpy as np
import torch
import torch.nn as nn
import torch.nn.functional as F

from deeprank import select_module


class IdentityNet(select_module.SelectNet):
    def __init__(self, config):
        super().__init__(config)

    def forward(self, q_data, d_data, q_len, d_len):
        q_data = q_data[:, :self.config['q_limit']]
        d_data = d_data[:, :self.config['d_limit']]
        q_len = torch.clamp(q_len, max=self.config['q_limit'])
        d_len = torch.clamp(d_len, max=self.config['d_limit'])
        return q_data, d_data, q_len, d_len
github pl8787 / DeepRank_PyTorch / deeprank / select_module / query_centric_net.py View on Github external
from collections import defaultdict
from itertools import chain

import numpy as np
import torch
import torch.nn as nn
import torch.nn.functional as F

from deeprank import select_module


class QueryCentricNet(select_module.SelectNet):
    def __init__(self, config, out_device=None):
        super().__init__(config)
        self.output_type = 'LL'

        self.pad_value = self.config['pad_value']

        self.max_match = self.config['max_match']
        self.win_size = self.config['win_size']

        self.q_size = self.config['q_limit']
        self.d_size = self.max_match

        # key (doc_id, q_item)
        self.cache = {}
        self.out_device = out_device
github pl8787 / DeepRank_PyTorch / deeprank / select_module / pointer_net.py View on Github external
from collections import defaultdict

import numpy as np
import torch
import torch.nn as nn
import torch.nn.functional as F

from deeprank import select_module


class PointerNet(select_module.SelectNet):
    def __init__(self, config, out_device=None):
        super().__init__(config)
        self.output_type = 'LL'

        self.pad_value = self.config['pad_value']
        self.win_size = self.config['win_size']

        self.max_match = self.config['max_match']

        self.embedding = nn.Embedding(
            config['vocab_size'],
            config['embed_dim'],
            padding_idx=self.pad_value
        )

        self.embedding.weight.requires_grad = self.config['finetune_embed']
github pl8787 / DeepRank_PyTorch / deeprank / dataset.py View on Github external
def __init__(self, config_file):
        self.config_file = config_file
        self.config = json.loads( open(config_file).read() )

        self.Letor07Path = self.config['data_dir'] #'/home/pangliang/matching/data/letor/r5w/'

        self.word_dict, self.iword_dict = utils.read_word_dict(
            filename=self.Letor07Path + '/word_dict.txt')
        self.query_data = utils.read_data(
            filename=self.Letor07Path + '/qid_query.txt')
        self.doc_data = utils.read_data(
            filename=self.Letor07Path + '/docid_doc.txt')
        self.embed_dict = utils.read_embedding(
            filename=self.Letor07Path + '/embed_wiki-pdc_d50_norm')
        self.idf_dict = utils.read_embedding(
            filename=self.Letor07Path + '/embed.idf')

        self.feat_size = self.config['feat_size']

        self._PAD_ = len(self.word_dict)
        self.word_dict[self._PAD_] = '[PAD]'
        self.iword_dict['[PAD]'] = self._PAD_
github pl8787 / DeepRank_PyTorch / deeprank / dataset.py View on Github external
def __init__(self, config_file):
        self.config_file = config_file
        self.config = json.loads( open(config_file).read() )

        self.Letor07Path = self.config['data_dir'] #'/home/pangliang/matching/data/letor/r5w/'

        self.word_dict, self.iword_dict = utils.read_word_dict(
            filename=self.Letor07Path + '/word_dict.txt')
        self.query_data = utils.read_data(
            filename=self.Letor07Path + '/qid_query.txt')
        self.doc_data = utils.read_data(
            filename=self.Letor07Path + '/docid_doc.txt')
        self.embed_dict = utils.read_embedding(
            filename=self.Letor07Path + '/embed_wiki-pdc_d50_norm')
        self.idf_dict = utils.read_embedding(
            filename=self.Letor07Path + '/embed.idf')

        self.feat_size = self.config['feat_size']

        self._PAD_ = len(self.word_dict)
        self.word_dict[self._PAD_] = '[PAD]'
        self.iword_dict['[PAD]'] = self._PAD_

        self.embed_dict[self._PAD_] = np.zeros((50, ), dtype=np.float32)
        self.W_init_embed = np.float32(
github pl8787 / DeepRank_PyTorch / deeprank / dataset.py View on Github external
def __init__(self, rel_file, config):
        rel = utils.read_relation(filename=rel_file)
        self.pair_list = self.make_pair(rel)
        self.config = config
github pl8787 / DeepRank_PyTorch / deeprank / dataset.py View on Github external
self.feat_size = self.config['feat_size']

        self._PAD_ = len(self.word_dict)
        self.word_dict[self._PAD_] = '[PAD]'
        self.iword_dict['[PAD]'] = self._PAD_

        self.embed_dict[self._PAD_] = np.zeros((50, ), dtype=np.float32)
        self.W_init_embed = np.float32(
            np.random.uniform(-0.02, 0.02, [len(self.word_dict), 50]))
        self.embedding = utils.convert_embed_2_numpy(
            self.embed_dict, embed = self.W_init_embed)

        self.W_init_idf = np.float32(
            np.zeros([len(self.word_dict), 1]))
        self.idf_embedding = utils.convert_embed_2_numpy(
            self.idf_dict, embed = self.W_init_idf)
github pl8787 / DeepRank_PyTorch / deeprank / dataset.py View on Github external
filename=self.Letor07Path + '/docid_doc.txt')
        self.embed_dict = utils.read_embedding(
            filename=self.Letor07Path + '/embed_wiki-pdc_d50_norm')
        self.idf_dict = utils.read_embedding(
            filename=self.Letor07Path + '/embed.idf')

        self.feat_size = self.config['feat_size']

        self._PAD_ = len(self.word_dict)
        self.word_dict[self._PAD_] = '[PAD]'
        self.iword_dict['[PAD]'] = self._PAD_

        self.embed_dict[self._PAD_] = np.zeros((50, ), dtype=np.float32)
        self.W_init_embed = np.float32(
            np.random.uniform(-0.02, 0.02, [len(self.word_dict), 50]))
        self.embedding = utils.convert_embed_2_numpy(
            self.embed_dict, embed = self.W_init_embed)

        self.W_init_idf = np.float32(
            np.zeros([len(self.word_dict), 1]))
        self.idf_embedding = utils.convert_embed_2_numpy(
            self.idf_dict, embed = self.W_init_idf)
github pl8787 / DeepRank_PyTorch / deeprank / dataset.py View on Github external
def __init__(self, config_file):
        self.config_file = config_file
        self.config = json.loads( open(config_file).read() )

        self.Letor07Path = self.config['data_dir'] #'/home/pangliang/matching/data/letor/r5w/'

        self.word_dict, self.iword_dict = utils.read_word_dict(
            filename=self.Letor07Path + '/word_dict.txt')
        self.query_data = utils.read_data(
            filename=self.Letor07Path + '/qid_query.txt')
        self.doc_data = utils.read_data(
            filename=self.Letor07Path + '/docid_doc.txt')
        self.embed_dict = utils.read_embedding(
            filename=self.Letor07Path + '/embed_wiki-pdc_d50_norm')
        self.idf_dict = utils.read_embedding(
            filename=self.Letor07Path + '/embed.idf')

        self.feat_size = self.config['feat_size']

        self._PAD_ = len(self.word_dict)
        self.word_dict[self._PAD_] = '[PAD]'
        self.iword_dict['[PAD]'] = self._PAD_

        self.embed_dict[self._PAD_] = np.zeros((50, ), dtype=np.float32)
        self.W_init_embed = np.float32(
            np.random.uniform(-0.02, 0.02, [len(self.word_dict), 50]))
        self.embedding = utils.convert_embed_2_numpy(

deeprank

Rank Protein-Protein Interactions using Deep Learning

Apache-2.0
Latest version published 3 years ago

Package Health Score

48 / 100
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