Welcome to DeepMatch’s documentation!

DeepMatch is a deep matching model library for recommendations, advertising, and search. It’s easy to train models and to export representation vectors for user and item which can be used for ANN search.You can use any complex model with model.fit() and model.predict() .

Let’s Get Started! or Run examples!

You can read the latest code at https://github.com/shenweichen/DeepMatch

News

10/31/2022 : Add ComiRec . Changelog

07/04/2022 : Support different negative sampling strategies, including inbatch , uniform , frequency , adaptive . Changelog

06/17/2022 : Fix some bugs. Changelog

DisscussionGroup

公众号：浅梦学习笔记 wechat ID: deepctrbot

Discussions 学习小组主题集合

Quick-Start

Installation Guide

Now deepmatch is available for python 2.7 and 3.6, 3.7, 3.8.
deepmatch depends on tensorflow, you can specify to install the cpu version or gpu version through pip.

CPU version

$ pip install deepmatch[cpu]

GPU version

$ pip install deepmatch[gpu]

Run examples !!

• Run models on MovieLen1M in Google colab
• YoutubeDNN/MIND with sampled softmax
• SDM with sampled softmax
• DSSM with in batch softmax
• DSSM with negative sampling

Features

Feature Columns

SparseFeat

SparseFeat is a namedtuple with signature SparseFeat(name, vocabulary_size, embedding_dim, use_hash, dtype, embeddings_initializer, embedding_name, group_name, trainable)

• name : feature name
• vocabulary_size : number of unique feature values for sprase feature or hashing space when use_hash=True
• embedding_dim : embedding dimension
• use_hash : defualt False.If True the input will be hashed to space of size vocabulary_size.
• dtype : default int32.dtype of input tensor.
• embeddings_initializer : initializer for the embeddings matrix.
• embedding_name : default None. If None, the embedding_name will be same as name.
• group_name : feature group of this feature.
• trainable: default True.Whether or not the embedding is trainable.

DenseFeat

DenseFeat is a namedtuple with signature DenseFeat(name, dimension, dtype)

• name : feature name
• dimension : dimension of dense feature vector.
• dtype : default float32.dtype of input tensor.

VarLenSparseFeat

VarLenSparseFeat is a namedtuple with signature VarLenSparseFeat(sparsefeat, maxlen, combiner, length_name, weight_name,weight_norm)

• sparsefeat : a instance of SparseFeat
• maxlen : maximum length of this feature for all samples
• combiner : pooling method,can be sum,mean or max
• length_name : feature length name,if None, value 0 in feature is for padding.
• weight_name : default None. If not None, the sequence feature will be multiplyed by the feature whose name is weight_name.
• weight_norm : default True. Whether normalize the weight score or not.

Models

FM (Convolutional Click Prediction Model)

FM Model API

Factorization Machines

DSSM (Deep Structured Semantic Model)

DSSM Model API

Deep Structured Semantic Models for Web Search using Clickthrough Data

YoutubeDNN

YoutubeDNN Model API

Deep Neural Networks for YouTube Recommendations

NCF (Neural Collaborative Filtering)

NCF Model API

Neural Collaborative Filtering

SDM (Sequential Deep Matching Model)

SDM Model API

SDM example

SDM: Sequential Deep Matching Model for Online Large-scale Recommender System

MIND (Multi-Interest Network with Dynamic routing)

MIND Model API

Multi-interest network with dynamic routing for recommendation at Tmall

COMIREC (Controllable Multi-Interest Framework for Recommendation)

COMIREC Model API

Controllable Multi-Interest Framework for Recommendation

Examples

Run models on MovieLen1M in Google colab

YoutubeDNN:

SDM:

DSSM InBatchSoftmax:

YoutubeDNN/MIND with sampled softmax

The MovieLens data has been used for personalized tag recommendation,which contains 668, 953 tag applications of users on movies. Here is a small fraction of data include only sparse field.

This example shows how to use YoutubeDNN to solve a matching task. You can get the demo data movielens_sample.txt and run the following codes.

import pandas as pd
from deepctr.feature_column import SparseFeat, VarLenSparseFeat
from deepmatch.models import *
from deepmatch.utils import sampledsoftmaxloss, NegativeSampler
from preprocess import gen_data_set, gen_model_input
from sklearn.preprocessing import LabelEncoder
from tensorflow.python.keras import backend as K
from tensorflow.python.keras.models import Model

if __name__ == "__main__":

    data = pd.read_csvdata = pd.read_csv("./movielens_sample.txt")
    data['genres'] = list(map(lambda x: x.split('|')[0], data['genres'].values))

    sparse_features = ["movie_id", "user_id",
                       "gender", "age", "occupation", "zip", "genres"]
    SEQ_LEN = 50

    # 1.Label Encoding for sparse features,and process sequence features with `gen_date_set` and `gen_model_input`

    feature_max_idx = {}
    for feature in sparse_features:
        lbe = LabelEncoder()
        data[feature] = lbe.fit_transform(data[feature]) + 1
        feature_max_idx[feature] = data[feature].max() + 1

    user_profile = data[["user_id", "gender", "age", "occupation", "zip"]].drop_duplicates('user_id')

    item_profile = data[["movie_id"]].drop_duplicates('movie_id')

    user_profile.set_index("user_id", inplace=True)

    user_item_list = data.groupby("user_id")['movie_id'].apply(list)

    train_set, test_set = gen_data_set(data, SEQ_LEN, 0)

    train_model_input, train_label = gen_model_input(train_set, user_profile, SEQ_LEN)
    test_model_input, test_label = gen_model_input(test_set, user_profile, SEQ_LEN)

    # 2.count #unique features for each sparse field and generate feature config for sequence feature

    embedding_dim = 16

    user_feature_columns = [SparseFeat('user_id', feature_max_idx['user_id'], embedding_dim),
                            SparseFeat("gender", feature_max_idx['gender'], embedding_dim),
                            SparseFeat("age", feature_max_idx['age'], embedding_dim),
                            SparseFeat("occupation", feature_max_idx['occupation'], embedding_dim),
                            SparseFeat("zip", feature_max_idx['zip'], embedding_dim),
                            VarLenSparseFeat(SparseFeat('hist_movie_id', feature_max_idx['movie_id'], embedding_dim,
                                                        embedding_name="movie_id"), SEQ_LEN, 'mean', 'hist_len'),
                            VarLenSparseFeat(SparseFeat('hist_genres', feature_max_idx['genres'], embedding_dim,
                                                        embedding_name="genres"), SEQ_LEN, 'mean', 'hist_len')
                            ]

    item_feature_columns = [SparseFeat('movie_id', feature_max_idx['movie_id'], embedding_dim)]

    from collections import Counter

    train_counter = Counter(train_model_input['movie_id'])
    item_count = [train_counter.get(i, 0) for i in range(item_feature_columns[0].vocabulary_size)]
    sampler_config = NegativeSampler('frequency', num_sampled=5, item_name='movie_id', item_count=item_count)

    # 3.Define Model and train

    import tensorflow as tf

    if tf.__version__ >= '2.0.0':
        tf.compat.v1.disable_eager_execution()
    else:
        K.set_learning_phase(True)

    model = YoutubeDNN(user_feature_columns, item_feature_columns, user_dnn_hidden_units=(64, embedding_dim),
                       sampler_config=sampler_config)
    # model = MIND(user_feature_columns, item_feature_columns, dynamic_k=False, k_max=2,
    #              user_dnn_hidden_units=(64, embedding_dim), sampler_config=sampler_config)

    model.compile(optimizer="adam", loss=sampledsoftmaxloss)

    history = model.fit(train_model_input, train_label,  # train_label,
                        batch_size=256, epochs=1, verbose=1, validation_split=0.0, )

    # 4. Generate user features for testing and full item features for retrieval
    test_user_model_input = test_model_input
    all_item_model_input = {"movie_id": item_profile['movie_id'].values}

    user_embedding_model = Model(inputs=model.user_input, outputs=model.user_embedding)
    item_embedding_model = Model(inputs=model.item_input, outputs=model.item_embedding)

    user_embs = user_embedding_model.predict(test_user_model_input, batch_size=2 ** 12)
    # user_embs = user_embs[:, i, :]  # i in [0,k_max) if MIND
    item_embs = item_embedding_model.predict(all_item_model_input, batch_size=2 ** 12)

    print(user_embs.shape)
    print(item_embs.shape)

    # 5. [Optional] ANN search by faiss  and evaluate the result

    # test_true_label = {line[0]:[line[1]] for line in test_set}
    #
    # import numpy as np
    # import faiss
    # from tqdm import tqdm
    # from deepmatch.utils import recall_N
    #
    # index = faiss.IndexFlatIP(embedding_dim)
    # # faiss.normalize_L2(item_embs)
    # index.add(item_embs)
    # # faiss.normalize_L2(user_embs)
    # D, I = index.search(np.ascontiguousarray(user_embs), 50)
    # s = []
    # hit = 0
    # for i, uid in tqdm(enumerate(test_user_model_input['user_id'])):
    #     try:
    #         pred = [item_profile['movie_id'].values[x] for x in I[i]]
    #         filter_item = None
    #         recall_score = recall_N(test_true_label[uid], pred, N=50)
    #         s.append(recall_score)
    #         if test_true_label[uid] in pred:
    #             hit += 1
    #     except:
    #         print(i)
    # print("recall", np.mean(s))
    # print("hr", hit / len(test_user_model_input['user_id']))

SDM with sampled softmax

The MovieLens data has been used for personalized tag recommendation,which contains 668, 953 tag applications of users on movies. Here is a small fraction of data include only sparse field.

This example shows how to use SDM to solve a matching task. You can get the demo data movielens_sample.txt and run the following codes.

import pandas as pd
from deepctr.feature_column import SparseFeat, VarLenSparseFeat
from deepmatch.models import SDM
from deepmatch.utils import sampledsoftmaxloss, NegativeSampler
from preprocess import gen_data_set_sdm, gen_model_input_sdm
from sklearn.preprocessing import LabelEncoder
from tensorflow.python.keras import backend as K
from tensorflow.python.keras.models import Model

if __name__ == "__main__":
    data = pd.read_csvdata = pd.read_csv("./movielens_sample.txt")
    data['genres'] = list(map(lambda x: x.split('|')[0], data['genres'].values))

    sparse_features = ["movie_id", "user_id",
                       "gender", "age", "occupation", "zip", "genres"]
    SEQ_LEN_short = 5
    SEQ_LEN_prefer = 50

    # 1.Label Encoding for sparse features,and process sequence features with `gen_date_set` and `gen_model_input`

    feature_max_idx = {}
    for feature in sparse_features:
        lbe = LabelEncoder()
        data[feature] = lbe.fit_transform(data[feature]) + 1
        feature_max_idx[feature] = data[feature].max() + 1

    user_profile = data[["user_id", "gender", "age", "occupation", "zip"]].drop_duplicates('user_id')

    item_profile = data[["movie_id"]].drop_duplicates('movie_id')

    user_profile.set_index("user_id", inplace=True)
    #
    # user_item_list = data.groupby("user_id")['movie_id'].apply(list)

    train_set, test_set = gen_data_set_sdm(data, seq_short_max_len=SEQ_LEN_short, seq_prefer_max_len=SEQ_LEN_prefer)

    train_model_input, train_label = gen_model_input_sdm(train_set, user_profile, SEQ_LEN_short, SEQ_LEN_prefer)
    test_model_input, test_label = gen_model_input_sdm(test_set, user_profile, SEQ_LEN_short, SEQ_LEN_prefer)

    # 2.count #unique features for each sparse field and generate feature config for sequence feature

    embedding_dim = 32
    # for sdm,we must provide `VarLenSparseFeat` with name "prefer_xxx" and "short_xxx" and their length
    user_feature_columns = [SparseFeat('user_id', feature_max_idx['user_id'], 16),
                            SparseFeat("gender", feature_max_idx['gender'], 16),
                            SparseFeat("age", feature_max_idx['age'], 16),
                            SparseFeat("occupation", feature_max_idx['occupation'], 16),
                            SparseFeat("zip", feature_max_idx['zip'], 16),
                            VarLenSparseFeat(SparseFeat('short_movie_id', feature_max_idx['movie_id'], embedding_dim,
                                                        embedding_name="movie_id"), SEQ_LEN_short, 'mean',
                                             'short_sess_length'),
                            VarLenSparseFeat(SparseFeat('prefer_movie_id', feature_max_idx['movie_id'], embedding_dim,
                                                        embedding_name="movie_id"), SEQ_LEN_prefer, 'mean',
                                             'prefer_sess_length'),
                            VarLenSparseFeat(SparseFeat('short_genres', feature_max_idx['genres'], embedding_dim,
                                                        embedding_name="genres"), SEQ_LEN_short, 'mean',
                                             'short_sess_length'),
                            VarLenSparseFeat(SparseFeat('prefer_genres', feature_max_idx['genres'], embedding_dim,
                                                        embedding_name="genres"), SEQ_LEN_prefer, 'mean',
                                             'prefer_sess_length'),
                            ]

    item_feature_columns = [SparseFeat('movie_id', feature_max_idx['movie_id'], embedding_dim)]

    from collections import Counter

    train_counter = Counter(train_model_input['movie_id'])
    item_count = [train_counter.get(i, 0) for i in range(item_feature_columns[0].vocabulary_size)]
    sampler_config = NegativeSampler('frequency', num_sampled=5, item_name='movie_id', item_count=item_count)

    K.set_learning_phase(True)

    import tensorflow as tf

    if tf.__version__ >= '2.0.0':
        tf.compat.v1.disable_eager_execution()
    else:
        K.set_learning_phase(True)

    # units must be equal to item embedding dim!
    model = SDM(user_feature_columns, item_feature_columns, history_feature_list=['movie_id', 'genres'],
                units=embedding_dim, sampler_config=sampler_config)

    model.compile(optimizer='adam', loss=sampledsoftmaxloss)

    history = model.fit(train_model_input, train_label,  # train_label,
                        batch_size=512, epochs=1, verbose=1, validation_split=0.0, )

    K.set_learning_phase(False)
    # 3.Define Model,train,predict and evaluate
    test_user_model_input = test_model_input
    all_item_model_input = {"movie_id": item_profile['movie_id'].values, }

    user_embedding_model = Model(inputs=model.user_input, outputs=model.user_embedding)
    item_embedding_model = Model(inputs=model.item_input, outputs=model.item_embedding)

    user_embs = user_embedding_model.predict(test_user_model_input, batch_size=2 ** 12)
    # user_embs = user_embs[:, i, :]  # i in [0,k_max) if MIND
    item_embs = item_embedding_model.predict(all_item_model_input, batch_size=2 ** 12)

    print(user_embs.shape)
    print(item_embs.shape)

    # test_true_label = {line[0]: [line[1]] for line in test_set}
    #
    # import numpy as np
    # import faiss
    # from tqdm import tqdm
    # from deepmatch.utils import recall_N
    #
    # index = faiss.IndexFlatIP(embedding_dim)
    # # faiss.normalize_L2(item_embs)
    # index.add(item_embs)
    # # faiss.normalize_L2(user_embs)
    # D, I = index.search(np.ascontiguousarray(user_embs), 50)
    # s = []
    # hit = 0
    # for i, uid in tqdm(enumerate(test_user_model_input['user_id'])):
    #     try:
    #         pred = [item_profile['movie_id'].values[x] for x in I[i]]
    #         filter_item = None
    #         recall_score = recall_N(test_true_label[uid], pred, N=50)
    #         s.append(recall_score)
    #         if test_true_label[uid] in pred:
    #             hit += 1
    #     except:
    #         print(i)
    # print("")
    # print("recall", np.mean(s))
    # print("hit rate", hit / len(test_user_model_input['user_id']))

DSSM with in batch softmax

The MovieLens data has been used for personalized tag recommendation,which contains 668, 953 tag applications of users on movies. Here is a small fraction of data include only sparse field.

This example shows how to use DSSM to solve a matching task. You can get the demo data movielens_sample.txt and run the following codes.

import pandas as pd
from deepctr.feature_column import SparseFeat, VarLenSparseFeat
from deepmatch.models import *
from deepmatch.utils import sampledsoftmaxloss, NegativeSampler
from preprocess import gen_data_set, gen_model_input
from sklearn.preprocessing import LabelEncoder
from tensorflow.python.keras.models import Model

if __name__ == "__main__":

    data = pd.read_csvdata = pd.read_csv("./movielens_sample.txt")
    sparse_features = ["movie_id", "user_id",
                       "gender", "age", "occupation", "zip", "genres"]
    SEQ_LEN = 50
    negsample = 10

    # 1.Label Encoding for sparse features,and process sequence features with `gen_date_set` and `gen_model_input`

    feature_max_idx = {}
    for feature in sparse_features:
        lbe = LabelEncoder()
        data[feature] = lbe.fit_transform(data[feature]) + 1
        feature_max_idx[feature] = data[feature].max() + 1

    user_profile = data[["user_id", "gender", "age", "occupation", "zip"]].drop_duplicates('user_id')

    item_profile = data[["movie_id", "genres"]].drop_duplicates('movie_id')

    user_profile.set_index("user_id", inplace=True)

    user_item_list = data.groupby("user_id")['movie_id'].apply(list)

    train_set, test_set = gen_data_set(data, SEQ_LEN, negsample)

    train_model_input, train_label = gen_model_input(train_set, user_profile, SEQ_LEN)
    test_model_input, test_label = gen_model_input(test_set, user_profile, SEQ_LEN)

    # 2.count #unique features for each sparse field and generate feature config for sequence feature

    embedding_dim = 32

    user_feature_columns = [SparseFeat('user_id', feature_max_idx['user_id'], embedding_dim),
                            SparseFeat("gender", feature_max_idx['gender'], embedding_dim),
                            SparseFeat("age", feature_max_idx['age'], embedding_dim),
                            SparseFeat("occupation", feature_max_idx['occupation'], embedding_dim),
                            SparseFeat("zip", feature_max_idx['zip'], embedding_dim),
                            VarLenSparseFeat(SparseFeat('hist_movie_id', feature_max_idx['movie_id'], embedding_dim,
                                                        embedding_name="movie_id"), SEQ_LEN, 'mean', 'hist_len'),
                            VarLenSparseFeat(SparseFeat('hist_genres', feature_max_idx['genres'], embedding_dim,
                                                        embedding_name="genres"), SEQ_LEN, 'mean', 'hist_len'),
                            ]

    item_feature_columns = [SparseFeat('movie_id', feature_max_idx['movie_id'], embedding_dim),
                            SparseFeat('genres', feature_max_idx['genres'], embedding_dim)
                            ]

    from collections import Counter

    train_counter = Counter(train_model_input['movie_id'])
    item_count = [train_counter.get(i, 0) for i in range(item_feature_columns[0].vocabulary_size)]
    sampler_config = NegativeSampler('inbatch', num_sampled=5, item_name='movie_id', item_count=item_count)

    # 3.Define Model and train

    import tensorflow as tf

    if tf.__version__ >= '2.0.0':
        tf.compat.v1.disable_eager_execution()
    else:
        K.set_learning_phase(True)

    model = DSSM(user_feature_columns, item_feature_columns, loss_type="softmax", sampler_config=sampler_config)
    # model = FM(user_feature_columns, item_feature_columns, loss_type="softmax", sampler_config=sampler_config)

    model.compile(optimizer='adagrad', loss=sampledsoftmaxloss)

    history = model.fit(train_model_input, train_label,
                        batch_size=256, epochs=1, verbose=1, validation_split=0.0, )

    # 4. Generate user features for testing and full item features for retrieval
    test_user_model_input = test_model_input
    all_item_model_input = {"movie_id": item_profile['movie_id'].values, "genres": item_profile['genres'].values}

    user_embedding_model = Model(inputs=model.user_input, outputs=model.user_embedding)
    item_embedding_model = Model(inputs=model.item_input, outputs=model.item_embedding)

    user_embs = user_embedding_model.predict(test_user_model_input, batch_size=2 ** 12)
    item_embs = item_embedding_model.predict(all_item_model_input, batch_size=2 ** 12)

    print(user_embs.shape)
    print(item_embs.shape)

    # 5. [Optional] ANN search by faiss and evaluate the result

    # test_true_label = {line[0]:[line[1]] for line in test_set}
    #
    # import numpy as np
    # import faiss
    # from tqdm import tqdm
    # from deepmatch.utils import recall_N
    #
    # index = faiss.IndexFlatIP(embedding_dim)
    # # faiss.normalize_L2(item_embs)
    # index.add(item_embs)
    # # faiss.normalize_L2(user_embs)
    # D, I = index.search(user_embs, 50)
    # s = []
    # hit = 0
    # for i, uid in tqdm(enumerate(test_user_model_input['user_id'])):
    #     try:
    #         pred = [item_profile['movie_id'].values[x] for x in I[i]]
    #         filter_item = None
    #         recall_score = recall_N(test_true_label[uid], pred, N=50)
    #         s.append(recall_score)
    #         if test_true_label[uid] in pred:
    #             hit += 1
    #     except:
    #         print(i)
    # print("recall", np.mean(s))
    # print("hr", hit / len(test_user_model_input['user_id']))

DSSM with negative sampling

The MovieLens data has been used for personalized tag recommendation,which contains 668, 953 tag applications of users on movies. Here is a small fraction of data include only sparse field.

This example shows how to use DSSM to solve a matching task. You can get the demo data movielens_sample.txt and run the following codes.

import pandas as pd
from deepctr.feature_column import SparseFeat, VarLenSparseFeat
from deepmatch.models import *
from preprocess import gen_data_set, gen_model_input
from sklearn.preprocessing import LabelEncoder
from tensorflow.python.keras.models import Model

if __name__ == "__main__":

    data = pd.read_csvdata = pd.read_csv("./movielens_sample.txt")
    sparse_features = ["movie_id", "user_id",
                       "gender", "age", "occupation", "zip", "genres"]
    SEQ_LEN = 50
    negsample = 10

    # 1.Label Encoding for sparse features,and process sequence features with `gen_date_set` and `gen_model_input`

    feature_max_idx = {}
    for feature in sparse_features:
        lbe = LabelEncoder()
        data[feature] = lbe.fit_transform(data[feature]) + 1
        feature_max_idx[feature] = data[feature].max() + 1

    user_profile = data[["user_id", "gender", "age", "occupation", "zip"]].drop_duplicates('user_id')

    item_profile = data[["movie_id", "genres"]].drop_duplicates('movie_id')

    user_profile.set_index("user_id", inplace=True)

    user_item_list = data.groupby("user_id")['movie_id'].apply(list)

    train_set, test_set = gen_data_set(data, SEQ_LEN, negsample)

    train_model_input, train_label = gen_model_input(train_set, user_profile, SEQ_LEN)
    test_model_input, test_label = gen_model_input(test_set, user_profile, SEQ_LEN)

    # 2.count #unique features for each sparse field and generate feature config for sequence feature

    embedding_dim = 32

    user_feature_columns = [SparseFeat('user_id', feature_max_idx['user_id'], 16),
                            SparseFeat("gender", feature_max_idx['gender'], 16),
                            SparseFeat("age", feature_max_idx['age'], 16),
                            SparseFeat("occupation", feature_max_idx['occupation'], 16),
                            SparseFeat("zip", feature_max_idx['zip'], 16),
                            VarLenSparseFeat(SparseFeat('hist_movie_id', feature_max_idx['movie_id'], embedding_dim,
                                                        embedding_name="movie_id"), SEQ_LEN, 'mean', 'hist_len'),
                            VarLenSparseFeat(SparseFeat('hist_genres', feature_max_idx['genres'], embedding_dim,
                                                        embedding_name="genres"), SEQ_LEN, 'mean', 'hist_len'),
                            ]

    item_feature_columns = [SparseFeat('movie_id', feature_max_idx['movie_id'], embedding_dim),
                            SparseFeat('genres', feature_max_idx['genres'], embedding_dim)
                            ]

    # 3.Define Model and train

    import tensorflow as tf

    if tf.__version__ >= '2.0.0':
        tf.compat.v1.disable_eager_execution()
    else:
        K.set_learning_phase(True)

    model = DSSM(user_feature_columns, item_feature_columns, loss_type="logistic")
    # model = FM(user_feature_columns,item_feature_columns)

    model.compile(optimizer='adagrad', loss="binary_crossentropy")

    history = model.fit(train_model_input, train_label,
                        batch_size=256, epochs=1, verbose=1, validation_split=0.0, )

    # 4. Generate user features for testing and full item features for retrieval
    test_user_model_input = test_model_input
    all_item_model_input = {"movie_id": item_profile['movie_id'].values, "genres": item_profile['genres'].values}

    user_embedding_model = Model(inputs=model.user_input, outputs=model.user_embedding)
    item_embedding_model = Model(inputs=model.item_input, outputs=model.item_embedding)

    user_embs = user_embedding_model.predict(test_user_model_input, batch_size=2 ** 12)
    item_embs = item_embedding_model.predict(all_item_model_input, batch_size=2 ** 12)

    print(user_embs.shape)
    print(item_embs.shape)

    # 5. [Optional] ANN search by faiss and evaluate the result

    # test_true_label = {line[0]:[line[1]] for line in test_set}
    #
    # import numpy as np
    # import faiss
    # from tqdm import tqdm
    # from deepmatch.utils import recall_N
    #
    # index = faiss.IndexFlatIP(embedding_dim)
    # # faiss.normalize_L2(item_embs)
    # index.add(item_embs)
    # # faiss.normalize_L2(user_embs)
    # D, I = index.search(user_embs, 50)
    # s = []
    # hit = 0
    # for i, uid in tqdm(enumerate(test_user_model_input['user_id'])):
    #     try:
    #         pred = [item_profile['movie_id'].values[x] for x in I[i]]
    #         filter_item = None
    #         recall_score = recall_N(test_true_label[uid], pred, N=50)
    #         s.append(recall_score)
    #         if test_true_label[uid] in pred:
    #             hit += 1
    #     except:
    #         print(i)
    # print("recall", np.mean(s))
    # print("hr", hit / len(test_user_model_input['user_id']))

FAQ

1. Save or load weights/models

---

To save/load weights,you can write codes just like any other keras models.

model = YoutubeDNN()
model.save_weights('YoutubeDNN_w.h5')
model.load_weights('YoutubeDNN_w.h5')

To save/load models,just a little different.

from tensorflow.python.keras.models import  save_model,load_model
model = DeepFM()
save_model(model, 'YoutubeDNN.h5')# save_model, same as before

from deepmatch.layers import custom_objects
model = load_model('YoutubeDNN.h5',custom_objects)# load_model,just add a parameter

2. Set learning rate and use earlystopping

---

You can use any models in DeepCTR like a keras model object. Here is a example of how to set learning rate and earlystopping:

import deepmatch
from tensorflow.python.keras.optimizers import Adam,Adagrad
from tensorflow.python.keras.callbacks import EarlyStopping

model = deepmatch.models.FM(user_feature_columns,item_feature_columns)
model.compile(Adagrad(0.01),'binary_crossentropy',metrics=['binary_crossentropy'])

es = EarlyStopping(monitor='val_binary_crossentropy')
history = model.fit(model_input, data[target].values,batch_size=256, epochs=10, verbose=2, validation_split=0.2,callbacks=[es] )

History

• 10/31/2022 : v0.3.1 released.Add ComiRec model .
• 07/04/2022 : v0.3.0 released.Support different negative sampling strategies, including inbatch, uniform, frequency, adaptive.
• 06/17/2022 : v0.2.1 released.Fix some bugs.
• 10/12/2020 : v0.2.0 released.Support different initializers for different embedding weights and loading pretrained embeddings.
• 05/17/2020 : v0.1.3 released.Add SDM model .
• 04/10/2020 : v0.1.2 released.Support saving and loading model.
• 04/06/2020 : DeepMatch first version is released on PyPi

DeepMatch Models API

Methods

compile

compile(optimizer, loss=None, metrics=None, loss_weights=None, sample_weight_mode=None, weighted_metrics=None, target_tensors=None)

Configures the model for training.

Arguments

• optimizer: String (name of optimizer) or optimizer instance. See optimizers.
• loss: String (name of objective function) or objective function. See losses. If the model has multiple outputs, you can use a different loss on each output by passing a dictionary or a list of losses. The loss value that will be minimized by the model will then be the sum of all individual losses.
• metrics: List of metrics to be evaluated by the model during training and testing. Typically you will use metrics=['accuracy']. To specify different metrics for different outputs of a multi-output model, you could also pass a dictionary, such as metrics={'output_a': 'accuracy'}.
• loss_weights: Optional list or dictionary specifying scalar coefficients (Python floats) to weight the loss contributions of different model outputs. The loss value that will be minimized by the model will then be the weighted sum of all individual losses, weighted by the loss_weights coefficients. If a list, it is expected to have a 1:1 mapping to the model’s outputs. If a tensor, it is expected to map output names (strings) to scalar coefficients.
• sample_weight_mode: If you need to do timestep-wise sample weighting (2D weights), set this to "temporal". None defaults to sample-wise weights (1D). If the model has multiple outputs, you can use a different sample_weight_mode on each output by passing a dictionary or a list of modes.
• weighted_metrics: List of metrics to be evaluated and weighted by sample_weight or class_weight during training and testing.
• target_tensors: By default, Keras will create placeholders for the model’s target, which will be fed with the target data during training. If instead you would like to use your own target tensors (in turn, Keras will not expect external Numpy data for these targets at training time), you can specify them via the target_tensors argument. It can be a single tensor (for a single-output model), a list of tensors, or a dict mapping output names to target tensors.

Raises

• ValueError: In case of invalid arguments for optimizer, loss, metrics or sample_weight_mode.

fit

fit(x=None, y=None, batch_size=None, epochs=1, verbose=1, callbacks=None, validation_split=0.0, validation_data=None, shuffle=True, class_weight=None, sample_weight=None, initial_epoch=0, steps_per_epoch=None, validation_steps=None, validation_freq=1)

Trains the model for a given number of epochs (iterations on a dataset).

Arguments

• x: Numpy array of training data (if the model has a single input), or list of Numpy arrays (if the model has multiple inputs). If input layers in the model are named, you can also pass a dictionary mapping input names to Numpy arrays. x can be None (default) if feeding from framework-native tensors (e.g. TensorFlow data tensors).
• y: Numpy array of target (label) data (if the model has a single output), or list of Numpy arrays (if the model has multiple outputs). If output layers in the model are named, you can also pass a dictionary mapping output names to Numpy arrays. y can be None (default) if feeding from framework-native tensors (e.g. TensorFlow data tensors).
• batch_size: Integer or None. Number of samples per gradient update. If unspecified, batch_size will default to 32.
• epochs: Integer. Number of epochs to train the model. An epoch is an iteration over the entire x and y data provided. Note that in conjunction with initial_epoch, epochs is to be understood as “final epoch”. The model is not trained for a number of iterations given by epochs, but merely until the epoch of index epochs is reached.
• verbose: Integer. 0, 1, or 2. Verbosity mode. 0 = silent, 1 = progress bar, 2 = one line per epoch.
• callbacks: List of tf.keras.callbacks.Callback instances. List of callbacks to apply during training and validation (if ). See callbacks.
• validation_split: Float between 0 and 1. Fraction of the training data to be used as validation data. The model will set apart this fraction of the training data, will not train on it, and will evaluate the loss and any model metrics on this data at the end of each epoch. The validation data is selected from the last samples in the x and y data provided, before shuffling.
• validation_data: tuple (x_val, y_val) or tuple (x_val, y_val, val_sample_weights) on which to evaluate the loss and any model metrics at the end of each epoch. The model will not be trained on this data. validation_data will override validation_split.
• shuffle: Boolean (whether to shuffle the training data before each epoch) or str (for ‘batch’). ‘batch’ is a special option for dealing with the limitations of HDF5 data; it shuffles in batch-sized chunks. Has no effect when steps_per_epoch is not None.
• class_weight: Optional dictionary mapping class indices (integers) to a weight (float) value, used for weighting the loss function (during training only). This can be useful to tell the model to “pay more attention” to samples from an under-represented class.
• sample_weight: Optional Numpy array of weights for the training samples, used for weighting the loss function (during training only). You can either pass a flat (1D) Numpy array with the same length as the input samples (1:1 mapping between weights and samples), or in the case of temporal data, you can pass a 2D array with shape (samples, sequence_length), to apply a different weight to every timestep of every sample. In this case you should make sure to specify sample_weight_mode="temporal" in compile().
• initial_epoch: Integer. Epoch at which to start training (useful for resuming a previous training run).
• steps_per_epoch: Integer or None. Total number of steps (batches of samples) before declaring one epoch finished and starting the next epoch. When training with input tensors such as TensorFlow data tensors, the default None is equal to the number of samples in your dataset divided by the batch size, or 1 if that cannot be determined. validation_steps: Only relevant if steps_per_epoch is specified. Total number of steps (batches of samples) to validate before stopping.
• validation_freq: Only relevant if validation data is provided. Integer or list/tuple/set. If an integer, specifies how many training epochs to run before a new validation run is performed, e.g. validation_freq=2 runs validation every 2 epochs. If a list, tuple, or set, specifies the epochs on which to run validation, e.g. validation_freq=[1, 2, 10] runs validation at the end of the 1st, 2nd, and 10th epochs.

Returns

• A History object. Its History.history attribute is a record of training loss values and metrics values at successive epochs, as well as validation loss values and validation metrics values (if applicable).

Raises

• RuntimeError: If the model was never compiled. ValueError: In case of mismatch between the provided input data and what the model expects.

evaluate

evaluate(x=None, y=None, batch_size=None, verbose=1, sample_weight=None, steps=None, callbacks=None)

Returns the loss value & metrics values for the model in test mode. Computation is done in batches.

Arguments

• x: Numpy array of test data (if the model has a single input), or list of Numpy arrays (if the model has multiple inputs). If input layers in the model are named, you can also pass a dictionary mapping input names to Numpy arrays. x can be None (default) if feeding from framework-native tensors (e.g. TensorFlow data tensors).
• y: Numpy array of target (label) data (if the model has a single output), or list of Numpy arrays (if the model has multiple outputs). If output layers in the model are named, you can also pass a dictionary mapping output names to Numpy arrays. y can be None (default) if feeding from framework-native tensors (e.g. TensorFlow data tensors).
• batch_size: Integer or None. Number of samples per evaluation step. If unspecified, batch_size will default to 32.
• verbose: 0 or 1. Verbosity mode. 0 = silent, 1 = progress bar.
• sample_weight: Optional Numpy array of weights for the test samples, used for weighting the loss function. You can either pass a flat (1D) Numpy array with the same length as the input samples (1:1 mapping between weights and samples), or in the case of temporal data, you can pass a 2D array with shape (samples, sequence_length), to apply a different weight to every timestep of every sample. In this case you should make sure to specify sample_weight_mode="temporal" in compile().
• steps: Integer or None. Total number of steps (batches of samples) before declaring the evaluation round finished. Ignored with the default value of None.
• callbacks: List of tf.keras.callbacks.Callback instances. List of callbacks to apply during evaluation. See callbacks.

Returns

• Scalar test loss (if the model has a single output and no metrics) or list of scalars (if the model has multiple outputs and/or metrics). The attribute model.metrics_names will give you the display labels for the scalar outputs.

predict

predict(x, batch_size=None, verbose=0, steps=None, callbacks=None)

Generates output predictions for the input samples.

Computation is done in batches.

Arguments

• x: The input data, as a Numpy array (or list of Numpy arrays if the model has multiple inputs). batch_size: Integer. If unspecified, it will default to 32.
• verbose: Verbosity mode, 0 or 1.
• steps: Total number of steps (batches of samples) before declaring the prediction round finished. Ignored with the default value of None.
• callbacks: List of tf.keras.callbacks.Callback instances. List of callbacks to apply during prediction. See callbacks.

Returns

• Numpy array(s) of predictions.

Raises

• ValueError: In case of mismatch between the provided input data and the model’s expectations, or in case a stateful model receives a number of samples that is not a multiple of the batch size.

train_on_batch

train_on_batch(x, y, sample_weight=None, class_weight=None)

Runs a single gradient update on a single batch of data.

Arguments

• x: Numpy array of training data, or list of Numpy arrays if the model has multiple inputs. If all inputs in the model are named, you can also pass a dictionary mapping input names to Numpy arrays.
• y: Numpy array of target data, or list of Numpy arrays if the model has multiple outputs. If all outputs in the model are named, you can also pass a dictionary mapping output names to Numpy arrays.
• sample_weight: Optional array of the same length as x, containing weights to apply to the model’s loss for each sample. In the case of temporal data, you can pass a 2D array with shape (samples, sequence_length), to apply a different weight to every timestep of every sample. In this case you should make sure to specify sample_weight_mode=”temporal” in compile().
• class_weight: Optional dictionary mapping class indices (integers) to a weight (float) to apply to the model’s loss for the samples from this class during training. This can be useful to tell the model to “pay more attention” to samples from an under-represented class.

Returns

• Scalar training loss (if the model has a single output and no metrics) or list of scalars (if the model has multiple outputs and/or metrics). The attribute model.metrics_names will give you the display labels for the scalar outputs.

test_on_batch

test_on_batch(x, y, sample_weight=None)

Test the model on a single batch of samples.

Arguments

• x: Numpy array of test data, or list of Numpy arrays if the model has multiple inputs. If all inputs in the model are named, you can also pass a dictionary mapping input names to Numpy arrays.
• y: Numpy array of target data, or list of Numpy arrays if the model has multiple outputs. If all outputs in the model are named, you can also pass a dictionary mapping output names to Numpy arrays.
• sample_weight: Optional array of the same length as x, containing weights to apply to the model’s loss for each sample. In the case of temporal data, you can pass a 2D array with shape (samples, sequence_length), to apply a different weight to every timestep of every sample. In this case you should make sure to specify sample_weight_mode="temporal" in compile().

Returns

• Scalar test loss (if the model has a single output and no metrics) or list of scalars (if the model has multiple outputs and/or metrics). The attribute model.metrics_names will give you the display labels for the scalar outputs.

predict_on_batch

predict_on_batch(x)

Returns predictions for a single batch of samples.

Arguments

• x: Input samples, as a Numpy array.

Returns

• Numpy array(s) of predictions.

fit_generator

fit_generator(generator, steps_per_epoch=None, epochs=1, verbose=1, callbacks=None, validation_data=None, validation_steps=None, validation_freq=1, class_weight=None, max_queue_size=10, workers=1, use_multiprocessing=False, shuffle=True, initial_epoch=0)

Trains the model on data generated batch-by-batch by a Python generator (or an instance of Sequence). The generator is run in parallel to the model, for efficiency. For instance, this allows you to do real-time data augmentation on images on CPU in parallel to training your model on GPU. The use of tf.keras.utils.Sequence guarantees the ordering and guarantees the single use of every input per epoch when using use_multiprocessing=True.

Arguments

• generator: A generator or an instance of Sequence (tf.keras.utils.Sequence) object in order to avoid duplicate data when using multiprocessing. The output of the generator must be either

  a tuple (inputs, targets) or a tuple (inputs, targets, sample_weights).
  This tuple (a single output of the generator) makes a single batch. Therefore, all arrays in this tuple must have the same length (equal to the size of this batch). Different batches may have different sizes. For example, the last batch of the epoch is commonly smaller than the others, if the size of the dataset is not divisible by the batch size. The generator is expected to loop over its data indefinitely. An epoch finishes when steps_per_epoch batches have been seen by the model.

• steps_per_epoch: Integer. Total number of steps (batches of samples) to yield from generator before declaring one epoch finished and starting the next epoch. It should typically be equal to ceil(num_samples / batch_size) Optional for Sequence: if unspecified, will use the len(generator) as a number of steps.

• epochs: Integer. Number of epochs to train the model. An epoch is an iteration over the entire data provided, as defined by steps_per_epoch. Note that in conjunction with initial_epoch, epochs is to be understood as “final epoch”. The model is not trained for a number of iterations given by epochs, but merely until the epoch of index epochs is reached.

• verbose: Integer. 0, 1, or 2. Verbosity mode. 0 = silent, 1 = progress bar, 2 = one line per epoch.

• callbacks: List of tf.keras.callbacks.Callback instances. List of callbacks to apply during training. See callbacks.

• validation_data: This can be either a generator or a Sequence object for the validation data tuple (x_val, y_val) tuple (x_val, y_val, val_sample_weights) on which to evaluate the loss and any model metrics at the end of each epoch. The model will not be trained on this data.

• validation_steps: Only relevant if validation_data is a generator. Total number of steps (batches of samples) to yield from validation_data generator before stopping at the end of every epoch. It should typically be equal to the number of samples of your validation dataset divided by the batch size. Optional for Sequence: if unspecified, will use the len(validation_data) as a number of steps.

• validation_freq: Only relevant if validation data is provided. Integer or collections.Container instance (e.g. list, tuple, etc.). If an integer, specifies how many training epochs to run before a new validation run is performed, e.g. validation_freq=2 runs validation every 2 epochs. If a Container, specifies the epochs on which to run validation, e.g. validation_freq=[1, 2, 10] runs validation at the end of the 1st, 2nd, and 10th epochs.

• class_weight: Optional dictionary mapping class indices (integers) to a weight (float) value, used for weighting the loss function (during training only). This can be useful to tell the model to “pay more attention” to samples from an under-represented class.

• max_queue_size: Integer. Maximum size for the generator queue. If unspecified, max_queue_size will default to 10.

• workers: Integer. Maximum number of processes to spin up when using process-based threading. If unspecified, workers will default to 1. If 0, will execute the generator on the main thread.

• use_multiprocessing: Boolean. If True, use process-based threading. If unspecified, use_multiprocessing will default to False. Note that because this implementation relies on multiprocessing, you should not pass non-picklable arguments to the generator as they can’t be passed easily to children processes.

• shuffle: Boolean. Whether to shuffle the order of the batches at the beginning of each epoch. Only used with instances of Sequence (tf.keras.utils.Sequence). Has no effect when steps_per_epoch is not None. initial_epoch: Integer. Epoch at which to start training (useful for resuming a previous training run).

Returns

• A History object. Its History.history attribute is a record of training loss values and metrics values at successive epochs, as well as validation loss values and validation metrics values (if applicable).

Raises

• ValueError: In case the generator yields data in an invalid format.

Example

def generate_arrays_from_file(path):
    while True:
        with open(path) as f:
            for line in f:
                # create numpy arrays of input data
                # and labels, from each line in the file
                x1, x2, y = process_line(line)
                yield ({'input_1': x1, 'input_2': x2}, {'output': y})

model.fit_generator(generate_arrays_from_file('/my_file.txt'),
                    steps_per_epoch=10000, epochs=10)

evaluate_generator

evaluate_generator(generator, steps=None, callbacks=None, max_queue_size=10, workers=1, use_multiprocessing=False, verbose=0)

Evaluates the model on a data generator. The generator should return the same kind of data as accepted by test_on_batch.

Arguments

• generator: Generator yielding tuples (inputs, targets) or (inputs, targets, sample_weights) or an instance of Sequence (tf.keras.utils.Sequence) object in order to avoid duplicate data when using multiprocessing.
• steps: Total number of steps (batches of samples) to yield from generator before stopping. Optional for Sequence: if unspecified, will use the len(generator) as a number of steps.
• callbacks: List of tf.keras.callbacks.Callback instances. List of callbacks to apply during training. See callbacks.
• max_queue_size: maximum size for the generator queue
• workers: Integer. Maximum number of processes to spin up when using process based threading. If unspecified, workers will default to 1. If 0, will execute the generator on the main thread.
• use_multiprocessing: if True, use process based threading. Note that because this implementation relies on multiprocessing, you should not pass non picklable arguments to the generator as they can’t be passed easily to children processes.
• verbose: verbosity mode, 0 or 1.

Returns

• Scalar test loss (if the model has a single output and no metrics) or list of scalars (if the model has multiple outputs and/or metrics). The attribute model.metrics_names will give you the display labels for the scalar outputs.

Raises

• ValueError: In case the generator yields data in an invalid format.

predict_generator

predict_generator(generator, steps=None, callbacks=None, max_queue_size=10, workers=1, use_multiprocessing=False, verbose=0)

Generates predictions for the input samples from a data generator. The generator should return the same kind of data as accepted by predict_on_batch.

Arguments

• generator: Generator yielding batches of input samples or an instance of Sequence (tf.keras.utils.Sequence) object in order to avoid duplicate data when using multiprocessing.
• steps: Total number of steps (batches of samples) to yield from generator before stopping. Optional for Sequence: if unspecified, will use the len(generator) as a number of steps.
• callbacks: List of tf.keras.callbacks.Callback instances. List of callbacks to apply during training. See callbacks.
• max_queue_size: Maximum size for the generator queue.
• workers: Integer. Maximum number of processes to spin up when using process based threading. If unspecified, workers will default to 1. If 0, will execute the generator on the main thread.
• use_multiprocessing: If True, use process based threading. Note that because this implementation relies on multiprocessing, you should not pass non picklable arguments to the generator as they can’t be passed easily to children processes.
• verbose: verbosity mode, 0 or 1.

Returns

• Numpy array(s) of predictions.

Raises

• ValueError: In case the generator yields data in an invalid format.

get_layer

get_layer(name=None, index=None)

Retrieves a layer based on either its name (unique) or index. If name and index are both provided, index will take precedence. Indices are based on order of horizontal graph traversal (bottom-up).

Arguments

• name: String, name of layer.
• index: Integer, index of layer.

Returns

• A layer instance.

Raises

• ValueError: In case of invalid layer name or index.

deepmatch.models.fm module

deepmatch.models.dssm module

deepmatch.models.youtubednn module

deepmatch.models.ncf module

deepmatch.models.sdm module

deepmatch.models.mind module

deepmatch.models.comirec module

Indices and tables

• Index
• Module Index
• Search Page