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SessionRecommender python api and example (#1465)
* session recommender python
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pyzoo/test/zoo/models/recommendation/test_sessionrecommender.py
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# | ||
# Copyright 2018 Analytics Zoo Authors. | ||
# | ||
# Licensed under the Apache License, Version 2.0 (the "License"); | ||
# you may not use this file except in compliance with the License. | ||
# You may obtain a copy of the License at | ||
# | ||
# http://www.apache.org/licenses/LICENSE-2.0 | ||
# | ||
# Unless required by applicable law or agreed to in writing, software | ||
# distributed under the License is distributed on an "AS IS" BASIS, | ||
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. | ||
# See the License for the specific language governing permissions and | ||
# limitations under the License. | ||
# | ||
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import pytest | ||
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import random | ||
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from zoo.pipeline.api.keras.layers import * | ||
from zoo.models.recommendation.session_recommender import SessionRecommender | ||
from test.zoo.pipeline.utils.test_utils import ZooTestCase | ||
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np.random.seed(1337) # for reproducibility | ||
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class TestSessionRecommender(ZooTestCase): | ||
def test_forward_backward_without_history(self): | ||
model = SessionRecommender(30, 5, [10, 5], 2) | ||
input_data = np.random.randint(1, 30, size=(10, 2)) | ||
self.assert_forward_backward(model, input_data) | ||
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def test_forward_backward_with_history(self): | ||
model = SessionRecommender(30, 5, [10, 5], 2, True, [6, 3], 5) | ||
input_data = [np.random.randint(1, 30, size=(10, 2)), | ||
np.random.randint(1, 30, size=(10, 5))] | ||
self.assert_forward_backward(model, input_data) | ||
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def test_save_load(self): | ||
model = SessionRecommender(30, 5, [10, 5], 2, True, [6, 3], 5) | ||
input_data = [np.random.randint(1, 30, size=(10, 2)), | ||
np.random.randint(1, 30, size=(10, 5))] | ||
self.assert_zoo_model_save_load(model, input_data) | ||
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def test_compile_fit(self): | ||
model = SessionRecommender(30, 5, [10, 5], 2, True, [6, 3], 5) | ||
input_data = [[np.random.randint(1, 30, size=(2)), | ||
np.random.randint(1, 30, size=(5)), | ||
np.random.randint(1, 30)] for i in range(100)] | ||
samples = self.sc.parallelize(input_data)\ | ||
.map(lambda x: Sample.from_ndarray((x[0], x[1]), np.array(x[2]))) | ||
train, test = samples.randomSplit([0.8, 0.2], seed=1) | ||
model.compile(loss='sparse_categorical_crossentropy', | ||
optimizer='rmsprop', | ||
metrics=['top5Accuracy']) | ||
model.fit(train, batch_size=4, nb_epoch=1, validation_data=test) | ||
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def test_recommed_predict(self): | ||
model = SessionRecommender(30, 5, [10, 5], 2, True, [6, 3], 5) | ||
input_data = [[np.random.randint(1, 30, size=(2)), | ||
np.random.randint(1, 30, size=(5)), | ||
np.random.randint(1, 30)] for i in range(100)] | ||
samples = [Sample.from_ndarray((input_data[i][0], input_data[i][1]), | ||
np.array(input_data[i][2])) for i in range(100)] | ||
rdd = self.sc.parallelize(samples) | ||
results1 = model.predict(rdd).collect() | ||
print(results1[0]) | ||
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recommendations1 = model.recommend_for_session(rdd, 3, zero_based_label=False).collect() | ||
print(recommendations1[0]) | ||
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recommendations2 = model.recommend_for_session(samples, 3, zero_based_label=False) | ||
print(recommendations2[0]) | ||
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if __name__ == "__main__": | ||
pytest.main([__file__]) |
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# | ||
# Copyright 2018 Analytics Zoo Authors. | ||
# | ||
# Licensed under the Apache License, Version 2.0 (the "License"); | ||
# you may not use this file except in compliance with the License. | ||
# You may obtain a copy of the License at | ||
# | ||
# http://www.apache.org/licenses/LICENSE-2.0 | ||
# | ||
# Unless required by applicable law or agreed to in writing, software | ||
# distributed under the License is distributed on an "AS IS" BASIS, | ||
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. | ||
# See the License for the specific language governing permissions and | ||
# limitations under the License. | ||
# | ||
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import sys | ||
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from zoo.models.common import KerasZooModel | ||
from zoo.models.recommendation import Recommender | ||
from zoo.pipeline.api.keras.layers import * | ||
from zoo.pipeline.api.keras.models import * | ||
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if sys.version >= '3': | ||
long = int | ||
unicode = str | ||
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class SessionRecommender(Recommender): | ||
""" | ||
The Session Recommender model used for recommendation. | ||
# Arguments | ||
item_ount: The number of distinct items. Positive integer. | ||
item_embed: The output size of embedding layer. Positive integer. | ||
rnn_hidden_layers: Units of hidden layers for the mlp model. Array of positive integers. | ||
session_length: The max number of items in the sequence of a session | ||
include_history: Whether to include purchase history. Boolean. Default is true. | ||
mlp_hidden_layers: Units of hidden layers for the mlp model. Array of positive integers. | ||
history_length: The max number of items in the sequence of historical purchase | ||
""" | ||
def __init__(self, item_count, item_embed, rnn_hidden_layers, session_length, | ||
include_history=False, mlp_hidden_layers=[10, 5], his_length=2, | ||
bigdl_type="float"): | ||
self.item_count = int(item_count) | ||
self.item_embed = int(item_embed) | ||
self.mlp_hidden_layers = [int(unit) for unit in mlp_hidden_layers] | ||
self.rnn_hidden_layers = [int(unit) for unit in rnn_hidden_layers] | ||
self.include_history = include_history | ||
self.session_length = int(session_length) | ||
self.his_length = int(his_length) | ||
self.bigdl_type = bigdl_type | ||
self.model = self.build_model() | ||
super(SessionRecommender, self).__init__(None, self.bigdl_type, | ||
self.item_count, | ||
self.item_embed, | ||
self.rnn_hidden_layers, | ||
self.session_length, | ||
self.include_history, | ||
self.mlp_hidden_layers, | ||
self.his_length, | ||
self.model) | ||
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def build_model(self): | ||
input_rnn = Input(shape=(self.session_length,)) | ||
session_table = Embedding(self.item_count + 1, self.item_embed, init="uniform")(input_rnn) | ||
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gru = GRU(self.rnn_hidden_layers[0], return_sequences=True)(session_table) | ||
for hidden in range(1, len(self.rnn_hidden_layers) - 1): | ||
gru = GRU(self.rnn_hidden_layers[hidden], return_sequences=True)(gru) | ||
gru_last = GRU(self.rnn_hidden_layers[-1], return_sequences=False)(gru) | ||
rnn = Dense(self.item_count)(gru_last) | ||
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if self.include_history: | ||
input_mlp = Input(shape=(self.his_length,)) | ||
his_table = Embedding(self.item_count + 1, self.item_embed, init="uniform")(input_mlp) | ||
embedSum = KerasLayerWrapper(Sum(dimension=2))(his_table) | ||
flatten = Flatten()(embedSum) | ||
mlp = Dense(self.mlp_hidden_layers[0], activation="relu")(flatten) | ||
for hidden in range(1, len(self.mlp_hidden_layers)): | ||
mlp = Dense(self.mlp_hidden_layers[hidden], activation="relu")(mlp) | ||
mlp_last = Dense(self.item_count)(mlp) | ||
merged = merge(inputs=[rnn, mlp_last], mode="sum") | ||
out = Activation(activation="softmax")(merged) | ||
model = Model(input=[input_rnn, input_mlp], output=out) | ||
else: | ||
out = Activation(activation="softmax")(rnn) | ||
model = Model(input=input_rnn, output=out) | ||
return model | ||
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def recommend_for_user(self, feature_rdd, max_items): | ||
raise Exception("recommend_for_user: Unsupported for SessionRecommender") | ||
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def recommend_for_item(self, feature_rdd, max_users): | ||
raise Exception("recommend_for_item: Unsupported for SessionRecommender") | ||
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def predict_user_item_pair(self, feature_rdd): | ||
raise Exception("predict_user_item_pair: Unsupported for SessionRecommender") | ||
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def recommend_for_session(self, sessions, max_items, zero_based_label): | ||
""" | ||
recommend for sessions given rdd of samples or list of samples. | ||
# Arguments | ||
sessions: rdd of samples or list of samples. | ||
max_items: Number of items to be recommended to each user. Positive integer. | ||
zero_based_label: True if data starts from 0, False if data starts from 1 | ||
:return rdd of list of list(item, probability), | ||
""" | ||
if isinstance(sessions, list): | ||
sc = get_spark_context() | ||
sessions_rdd = sc.parallelize(sessions) | ||
elif (isinstance(sessions, RDD)): | ||
sessions_rdd = sessions | ||
else: | ||
raise TypeError("Unsupported training data type: %s" % type(sessions)) | ||
results = callBigDlFunc(self.bigdl_type, "recommendForSession", | ||
self.value, | ||
sessions_rdd, | ||
max_items, | ||
zero_based_label) | ||
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if isinstance(sessions, list): | ||
return results.collect() | ||
else: | ||
return results | ||
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@staticmethod | ||
def load_model(path, weight_path=None, bigdl_type="float"): | ||
""" | ||
Load an existing SessionRecommender model (with weights). | ||
# Arguments | ||
path: The path for the pre-defined model. | ||
Local file system, HDFS and Amazon S3 are supported. | ||
HDFS path should be like 'hdfs://[host]:[port]/xxx'. | ||
Amazon S3 path should be like 's3a://bucket/xxx'. | ||
weight_path: The path for pre-trained weights if any. Default is None. | ||
""" | ||
jmodel = callBigDlFunc(bigdl_type, "loadSessionRecommender", path, weight_path) | ||
model = KerasZooModel._do_load(jmodel, bigdl_type) | ||
model.__class__ = SessionRecommender | ||
return model |
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