Wide & Deep Learning for Recommendation.json

{"paragraphs":[{"text":"%md\nWide and Deep Learning Model, proposed by Google in 2016, is a DNN-Linear mixed model. Wide and deep learning has been used for Google App Store for their app recommendation.\n\nIn this tutorial, we use Recommendation API of Analytics Zoo to easily build and train a Wide & Deep Learning model, which composed of a wide linear model and a deep neural network. Wide & Deep model combines the strength of memorization and generalization. It's useful for generic large-scale regression and classification problems with sparse input features (e.g., categorical features with a large number of possible feature values).","user":"anonymous","dateUpdated":"2019-06-17T20:40:05+0800","config":{"tableHide":false,"editorSetting":{"language":"markdown","editOnDblClick":true,"completionKey":"TAB","completionSupport":false},"colWidth":12,"editorMode":"ace/mode/markdown","fontSize":9,"editorHide":true,"results":{},"enabled":true},"settings":{"params":{},"forms":{}},"results":{"code":"SUCCESS","msg":[{"type":"HTML","data":"<div class=\"markdown-body\">\n<p>Wide and Deep Learning Model, proposed by Google in 2016, is a DNN-Linear mixed model. Wide and deep learning has been used for Google App Store for their app recommendation.</p>\n<p>In this tutorial, we use Recommendation API of Analytics Zoo to easily build and train a Wide &amp; Deep Learning model, which composed of a wide linear model and a deep neural network. Wide &amp; Deep model combines the strength of memorization and generalization. It&rsquo;s useful for generic large-scale regression and classification problems with sparse input features (e.g., categorical features with a large number of possible feature values).</p>\n</div>"}]},"apps":[],"jobName":"paragraph_1560753803695_650263757","id":"20190613-143838_976539755","dateCreated":"2019-06-17T14:43:23+0800","dateStarted":"2019-06-17T20:40:05+0800","dateFinished":"2019-06-17T20:40:05+0800","status":"FINISHED","progressUpdateIntervalMs":500,"focus":true,"$$hashKey":"object:293"},{"text":"%md\nThe architecture of the Wide & Deep model looks like the following:\n\n![](https://github.com/intel-analytics/ait2019/raw/master/wide_deep_learning.png)","user":"anonymous","dateUpdated":"2019-06-17T20:40:05+0800","config":{"tableHide":false,"editorSetting":{"language":"markdown","editOnDblClick":true,"completionKey":"TAB","completionSupport":false},"colWidth":12,"editorMode":"ace/mode/markdown","fontSize":9,"editorHide":true,"results":{},"enabled":true},"settings":{"params":{},"forms":{}},"results":{"code":"SUCCESS","msg":[{"type":"HTML","data":"<div class=\"markdown-body\">\n<p>The architecture of the Wide &amp; Deep model looks like the following:</p>\n<p><img src=\"https://github.com/intel-analytics/ait2019/raw/master/wide_deep_learning.png\" /></p>\n</div>"}]},"apps":[],"jobName":"paragraph_1560753803696_934212008","id":"20190613-152329_730531050","dateCreated":"2019-06-17T14:43:23+0800","status":"FINISHED","progressUpdateIntervalMs":500,"$$hashKey":"object:294","dateFinished":"2019-06-17T20:40:06+0800","dateStarted":"2019-06-17T20:40:06+0800"},{"text":"%pyspark\n\nfrom qcloud_cos import CosConfig\nfrom qcloud_cos import CosS3Client\nimport sys\n\nsecret_id = 'AKIDy0ra5NkDUYNwLWqfRxxkKvFBUxF33Tc4'      # 替换为用户的 secretId\nsecret_key = 'mDQgtyuaz9tLn21kDWcWGwPfiDakmZ5R'      # 替换为用户的 secretKey\nregion = 'ap-shanghai'     # 替换为用户的 Region\ntoken = None                # 使用临时密钥需要传入 Token，默认为空，可不填\nscheme = 'https'            # 指定使用 http/https 协议来访问 COS，默认为 https，可不填\nconfig = CosConfig(Region=region, SecretId=secret_id, SecretKey=secret_key, Token=token, Scheme=scheme)\n# 2. 获取客户端对象\nclient = CosS3Client(config)","user":"anonymous","dateUpdated":"2019-06-17T20:40:06+0800","config":{"editorSetting":{"language":"python","editOnDblClick":false,"completionKey":"TAB","completionSupport":true},"colWidth":12,"editorMode":"ace/mode/python","fontSize":9,"results":{},"enabled":true},"settings":{"params":{},"forms":{}},"results":{"code":"SUCCESS","msg":[]},"apps":[],"jobName":"paragraph_1560753803696_202507451","id":"20190605-210601_1552943930","dateCreated":"2019-06-17T14:43:23+0800","status":"FINISHED","progressUpdateIntervalMs":500,"$$hashKey":"object:295","dateFinished":"2019-06-17T20:40:06+0800","dateStarted":"2019-06-17T20:40:06+0800"},{"text":"%pyspark\nresponse = client.get_object(\n    Bucket='demo-sh-1258997244',\n    Key='/ml-1m.zip'\n)\nprint(type(response['Body']))\nresponse['Body'].get_stream_to_file('/tmp/ml-1m.zip')","user":"anonymous","dateUpdated":"2019-06-17T20:40:06+0800","config":{"editorSetting":{"language":"python","editOnDblClick":false,"completionKey":"TAB","completionSupport":true},"colWidth":12,"editorMode":"ace/mode/python","fontSize":9,"results":{},"enabled":true},"settings":{"params":{},"forms":{}},"results":{"code":"SUCCESS","msg":[{"type":"TEXT","data":"<class 'qcloud_cos.streambody.StreamBody'>\n"}]},"apps":[],"jobName":"paragraph_1560753803696_1319412998","id":"20190605-210612_2086321699","dateCreated":"2019-06-17T14:43:23+0800","status":"FINISHED","progressUpdateIntervalMs":500,"$$hashKey":"object:296","dateFinished":"2019-06-17T20:40:07+0800","dateStarted":"2019-06-17T20:40:06+0800"},{"text":"%md\n## Initialization\n\nImport necessary libraries and initialize the SparkContext.","user":"anonymous","dateUpdated":"2019-06-17T20:40:07+0800","config":{"tableHide":false,"editorSetting":{"language":"markdown","editOnDblClick":true,"completionKey":"TAB","completionSupport":false},"colWidth":12,"editorMode":"ace/mode/markdown","fontSize":9,"editorHide":true,"results":{},"enabled":true},"settings":{"params":{},"forms":{}},"results":{"code":"SUCCESS","msg":[{"type":"HTML","data":"<div class=\"markdown-body\">\n<h2>Initialization</h2>\n<p>Import necessary libraries and initialize the SparkContext.</p>\n</div>"}]},"apps":[],"jobName":"paragraph_1560753803696_700451744","id":"20190613-144245_1587020172","dateCreated":"2019-06-17T14:43:23+0800","status":"FINISHED","progressUpdateIntervalMs":500,"$$hashKey":"object:297","dateFinished":"2019-06-17T20:40:07+0800","dateStarted":"2019-06-17T20:40:07+0800"},{"text":"%pyspark\nimport io\nimport os\nimport sys\nimport datetime as dt\nimport matplotlib\nimport matplotlib.pyplot as plt\nmatplotlib.use('agg')\n\nfrom bigdl.dataset import movielens\nfrom zoo.common.nncontext import init_nncontext\nfrom zoo.models.recommendation import *\nfrom zoo.models.recommendation.utils import *\n\nsc = init_nncontext(\"Wide And Deep Example\")","user":"anonymous","dateUpdated":"2019-06-17T20:40:07+0800","config":{"editorSetting":{"language":"python","editOnDblClick":false,"completionKey":"TAB","completionSupport":true},"colWidth":12,"editorMode":"ace/mode/python","fontSize":9,"results":{},"enabled":true},"settings":{"params":{},"forms":{}},"results":{"code":"SUCCESS","msg":[]},"apps":[],"jobName":"paragraph_1560753803696_-1247206421","id":"20190605-210621_447244376","dateCreated":"2019-06-17T14:43:23+0800","status":"FINISHED","progressUpdateIntervalMs":500,"$$hashKey":"object:298","dateFinished":"2019-06-17T20:40:07+0800","dateStarted":"2019-06-17T20:40:07+0800"},{"text":"%md\n## Data Preparation\n\nLoad and understand movielens 1M rating data.","user":"anonymous","dateUpdated":"2019-06-17T20:40:07+0800","config":{"tableHide":false,"editorSetting":{"language":"markdown","editOnDblClick":true,"completionKey":"TAB","completionSupport":false},"colWidth":12,"editorMode":"ace/mode/markdown","fontSize":9,"editorHide":true,"results":{},"enabled":true},"settings":{"params":{},"forms":{}},"results":{"code":"SUCCESS","msg":[{"type":"HTML","data":"<div class=\"markdown-body\">\n<h2>Data Preparation</h2>\n<p>Load and understand movielens 1M rating data.</p>\n</div>"}]},"apps":[],"jobName":"paragraph_1560753803697_-520094633","id":"20190613-144445_820460067","dateCreated":"2019-06-17T14:43:23+0800","status":"FINISHED","progressUpdateIntervalMs":500,"$$hashKey":"object:299","dateFinished":"2019-06-17T20:40:07+0800","dateStarted":"2019-06-17T20:40:07+0800"},{"text":"%pyspark\nmovielens_data = movielens.get_id_ratings(\"/tmp\")\n\nmin_user_id = np.min(movielens_data[:,0])\nmax_user_id = np.max(movielens_data[:,0])\nmin_movie_id = np.min(movielens_data[:,1])\nmax_movie_id = np.max(movielens_data[:,1])\nrating_labels= np.unique(movielens_data[:,2])\n\nprint(movielens_data.shape)\nprint(min_user_id, max_user_id, min_movie_id, max_movie_id, rating_labels)","user":"anonymous","dateUpdated":"2019-06-17T20:40:07+0800","config":{"editorSetting":{"language":"python","editOnDblClick":false,"completionKey":"TAB","completionSupport":true},"colWidth":12,"editorMode":"ace/mode/python","fontSize":9,"results":{},"enabled":true},"settings":{"params":{},"forms":{}},"results":{"code":"SUCCESS","msg":[{"type":"TEXT","data":"Extracting /tmp/ml-1m.zip to /tmp\n(1000209, 3)\n1 6040 1 3952 [1 2 3 4 5]\n"}]},"apps":[],"jobName":"paragraph_1560753803697_-1535543884","id":"20190605-210855_509296228","dateCreated":"2019-06-17T14:43:23+0800","status":"FINISHED","progressUpdateIntervalMs":500,"$$hashKey":"object:300","dateFinished":"2019-06-17T20:40:12+0800","dateStarted":"2019-06-17T20:40:07+0800"},{"text":"%md\nTransform ratings into a DataFrame. Read user and item data into DataFrames as well. Transform labels to zero-based since the original labels start from 1.","user":"anonymous","dateUpdated":"2019-06-17T20:40:12+0800","config":{"tableHide":false,"editorSetting":{"language":"markdown","editOnDblClick":true,"completionKey":"TAB","completionSupport":false},"colWidth":12,"editorMode":"ace/mode/markdown","fontSize":9,"editorHide":true,"results":{},"enabled":true},"settings":{"params":{},"forms":{}},"results":{"code":"SUCCESS","msg":[{"type":"HTML","data":"<div class=\"markdown-body\">\n<p>Transform ratings into a DataFrame. Read user and item data into DataFrames as well. Transform labels to zero-based since the original labels start from 1.</p>\n</div>"}]},"apps":[],"jobName":"paragraph_1560753803697_-1660149932","id":"20190613-144849_555012116","dateCreated":"2019-06-17T14:43:23+0800","status":"FINISHED","progressUpdateIntervalMs":500,"$$hashKey":"object:301","dateFinished":"2019-06-17T20:40:12+0800","dateStarted":"2019-06-17T20:40:12+0800"},{"text":"%pyspark\nsqlContext = SQLContext(sc)\nfrom pyspark.sql.types import *\nfrom pyspark.sql import Row\n\nRating = Row(\"userId\", \"itemId\", \"label\")\nUser = Row(\"userId\", \"gender\", \"age\" ,\"occupation\")\nItem = Row(\"itemId\", \"title\" ,\"genres\")\n\nratings = sc.parallelize(movielens_data)\\\n    .map(lambda l: (int(l[0]), int(l[1]), int(l[2])-1))\\\n    .map(lambda r: Rating(*r))\nratingDF = sqlContext.createDataFrame(ratings)\n\nusers= sc.textFile(\"file:///tmp/ml-1m/users.dat\")\\\n    .map(lambda l: l.split(\"::\")[0:4])\\\n    .map(lambda l: (int(l[0]), l[1], int(l[2]), int(l[3])))\\\n    .map(lambda r: User(*r))\nuserDF = sqlContext.createDataFrame(users)\n\nitems = sc.textFile(\"file:///tmp/ml-1m/movies.dat\")\\\n    .map(lambda l: l.split(\"::\")[0:3])\\\n    .map(lambda l: (int(l[0]), l[1], l[2].split('|')[0]))\\\n    .map(lambda r: Item(*r))\nitemDF = sqlContext.createDataFrame(items)","user":"anonymous","dateUpdated":"2019-06-17T20:40:12+0800","config":{"editorSetting":{"language":"python","editOnDblClick":false,"completionKey":"TAB","completionSupport":true},"colWidth":12,"editorMode":"ace/mode/python","fontSize":9,"results":{},"enabled":true},"settings":{"params":{},"forms":{}},"results":{"code":"SUCCESS","msg":[]},"apps":[],"jobName":"paragraph_1560753803697_-1616065587","id":"20190605-210909_1233289815","dateCreated":"2019-06-17T14:43:23+0800","status":"FINISHED","progressUpdateIntervalMs":500,"$$hashKey":"object:302","dateFinished":"2019-06-17T20:40:17+0800","dateStarted":"2019-06-17T20:40:12+0800","runtimeInfos":{"jobUrl":{"propertyName":"jobUrl","label":"SPARK JOB","tooltip":"View in Spark web UI","group":"spark","values":["http://10.0.96.85:42034/jobs","http://10.0.96.85:42034/jobs","http://10.0.96.85:42034/jobs"],"interpreterSettingId":"spark"}}},{"text":"%md\nJoin three DataFrames together, and preprocess data, which involves some feature engineering efforts. For example, gender is going be used as categorical feature; age and gender will be used as crossed features.","user":"anonymous","dateUpdated":"2019-06-17T20:40:17+0800","config":{"tableHide":false,"editorSetting":{"language":"markdown","editOnDblClick":true,"completionKey":"TAB","completionSupport":false},"colWidth":12,"editorMode":"ace/mode/markdown","fontSize":9,"editorHide":true,"results":{},"enabled":true},"settings":{"params":{},"forms":{}},"results":{"code":"SUCCESS","msg":[{"type":"HTML","data":"<div class=\"markdown-body\">\n<p>Join three DataFrames together, and preprocess data, which involves some feature engineering efforts. For example, gender is going be used as categorical feature; age and gender will be used as crossed features.</p>\n</div>"}]},"apps":[],"jobName":"paragraph_1560753803697_1279217245","id":"20190613-144958_35198747","dateCreated":"2019-06-17T14:43:23+0800","status":"FINISHED","progressUpdateIntervalMs":500,"$$hashKey":"object:303","dateFinished":"2019-06-17T20:40:17+0800","dateStarted":"2019-06-17T20:40:17+0800"},{"text":"%pyspark\nfrom pyspark.sql.functions import col, udf\n\ngender_udf = udf(lambda gender: categorical_from_vocab_list(gender, [\"F\", \"M\"], start=1))\nbucket_cross_udf = udf(lambda feature1, feature2: hash_bucket(str(feature1) + \"_\" + str(feature2), bucket_size=100))\ngenres_list = [\"Crime\", \"Romance\", \"Thriller\", \"Adventure\", \"Drama\", \"Children's\",\n      \"War\", \"Documentary\", \"Fantasy\", \"Mystery\", \"Musical\", \"Animation\", \"Film-Noir\", \"Horror\",\n      \"Western\", \"Comedy\", \"Action\", \"Sci-Fi\"]\ngenres_udf = udf(lambda genres: categorical_from_vocab_list(genres, genres_list, start=1))\n     \nallDF = ratingDF.join(userDF, [\"userId\"]).join(itemDF, [\"itemId\"]) \\\n        .withColumn(\"gender\", gender_udf(col(\"gender\")).cast(\"int\")) \\\n        .withColumn(\"age-gender\", bucket_cross_udf(col(\"age\"), col(\"gender\")).cast(\"int\")) \\\n        .withColumn(\"genres\", genres_udf(col(\"genres\")).cast(\"int\"))\nallDF.show(5)","user":"anonymous","dateUpdated":"2019-06-17T20:40:17+0800","config":{"editorSetting":{"language":"python","editOnDblClick":false,"completionKey":"TAB","completionSupport":true},"colWidth":12,"editorMode":"ace/mode/python","fontSize":9,"results":{},"enabled":true},"settings":{"params":{},"forms":{}},"results":{"code":"SUCCESS","msg":[{"type":"TEXT","data":"+------+------+-----+------+---+----------+--------------+------+----------+\n|itemId|userId|label|gender|age|occupation|         title|genres|age-gender|\n+------+------+-----+------+---+----------+--------------+------+----------+\n|    26|  3391|    3|     2| 18|         4|Othello (1995)|     5|        72|\n|    26|  1447|    4|     2| 18|         4|Othello (1995)|     5|        72|\n|    26|  5107|    3|     1| 45|         0|Othello (1995)|     5|        30|\n|    26|  2878|    3|     1| 50|        20|Othello (1995)|     5|        29|\n|    26|  1527|    1|     2| 18|        10|Othello (1995)|     5|        72|\n+------+------+-----+------+---+----------+--------------+------+----------+\nonly showing top 5 rows\n\n"}]},"apps":[],"jobName":"paragraph_1560753803697_2137737736","id":"20190605-210937_98217513","dateCreated":"2019-06-17T14:43:23+0800","status":"FINISHED","progressUpdateIntervalMs":500,"$$hashKey":"object:304","dateFinished":"2019-06-17T20:40:29+0800","dateStarted":"2019-06-17T20:40:17+0800","runtimeInfos":{"jobUrl":{"propertyName":"jobUrl","label":"SPARK JOB","tooltip":"View in Spark web UI","group":"spark","values":["http://10.0.96.85:42034/jobs"],"interpreterSettingId":"spark"}}},{"text":"%md\nSpeficy data feature information shared by the Wide & Deep model and its feature generation.\n\nHere, we use occupation gender for wide base part; age and gender crossed as wide cross part, genres and gender as indicators, userid and itemid for embedding.","user":"anonymous","dateUpdated":"2019-06-17T20:40:29+0800","config":{"tableHide":false,"editorSetting":{"language":"markdown","editOnDblClick":true,"completionKey":"TAB","completionSupport":false},"colWidth":12,"editorMode":"ace/mode/markdown","fontSize":9,"editorHide":true,"results":{},"enabled":true},"settings":{"params":{},"forms":{}},"results":{"code":"SUCCESS","msg":[{"type":"HTML","data":"<div class=\"markdown-body\">\n<p>Speficy data feature information shared by the Wide &amp; Deep model and its feature generation.</p>\n<p>Here, we use occupation gender for wide base part; age and gender crossed as wide cross part, genres and gender as indicators, userid and itemid for embedding.</p>\n</div>"}]},"apps":[],"jobName":"paragraph_1560753803697_1239012985","id":"20190613-145242_1002961726","dateCreated":"2019-06-17T14:43:23+0800","status":"FINISHED","progressUpdateIntervalMs":500,"$$hashKey":"object:305","dateFinished":"2019-06-17T20:40:29+0800","dateStarted":"2019-06-17T20:40:29+0800"},{"text":"%pyspark\nbucket_size = 100\ncolumn_info = ColumnFeatureInfo(\n            wide_base_cols=[\"occupation\", \"gender\"],\n            wide_base_dims=[21, 3],\n            wide_cross_cols=[\"age-gender\"],\n            wide_cross_dims=[bucket_size],\n            indicator_cols=[\"genres\", \"gender\"],\n            indicator_dims=[19, 3],\n            embed_cols=[\"userId\", \"itemId\"],\n            embed_in_dims=[max_user_id, max_movie_id],\n            embed_out_dims=[64, 64],\n            continuous_cols=[\"age\"])","user":"anonymous","dateUpdated":"2019-06-17T20:40:29+0800","config":{"editorSetting":{"language":"python","editOnDblClick":false,"completionKey":"TAB","completionSupport":true},"colWidth":12,"editorMode":"ace/mode/python","fontSize":9,"results":{},"enabled":true},"settings":{"params":{},"forms":{}},"results":{"code":"SUCCESS","msg":[]},"apps":[],"jobName":"paragraph_1560753803698_1673523898","id":"20190606-200310_981546552","dateCreated":"2019-06-17T14:43:23+0800","status":"FINISHED","progressUpdateIntervalMs":500,"$$hashKey":"object:306","dateFinished":"2019-06-17T20:40:30+0800","dateStarted":"2019-06-17T20:40:29+0800"},{"text":"%md\nTransform data to RDD of Sample, which can be directly fed to train the model.\n\nA Sample is a BigDL data structure which can be constructed using 2 numpy arrays, feature and label respectively. Wide & Deep model needs two inputs, one is a sparse input for the wide model, and the other is a dense input for the deep model. Analytics Zoo encapsulates the transformation process as a single user-friendly API.","user":"anonymous","dateUpdated":"2019-06-17T20:40:30+0800","config":{"tableHide":false,"editorSetting":{"language":"markdown","editOnDblClick":true,"completionKey":"TAB","completionSupport":false},"colWidth":12,"editorMode":"ace/mode/markdown","fontSize":9,"editorHide":true,"results":{},"enabled":true},"settings":{"params":{},"forms":{}},"results":{"code":"SUCCESS","msg":[{"type":"HTML","data":"<div class=\"markdown-body\">\n<p>Transform data to RDD of Sample, which can be directly fed to train the model.</p>\n<p>A Sample is a BigDL data structure which can be constructed using 2 numpy arrays, feature and label respectively. Wide &amp; Deep model needs two inputs, one is a sparse input for the wide model, and the other is a dense input for the deep model. Analytics Zoo encapsulates the transformation process as a single user-friendly API.</p>\n</div>"}]},"apps":[],"jobName":"paragraph_1560753803698_1496781387","id":"20190613-145434_753766916","dateCreated":"2019-06-17T14:43:23+0800","status":"FINISHED","progressUpdateIntervalMs":500,"$$hashKey":"object:307","dateFinished":"2019-06-17T20:40:30+0800","dateStarted":"2019-06-17T20:40:30+0800"},{"text":"%pyspark\nrdds = allDF.rdd\\\n    .map(lambda row: to_user_item_feature(row, column_info))\\\n    .repartition(4)\ntrainPairFeatureRdds, valPairFeatureRdds = rdds.randomSplit([0.8, 0.2], seed= 1)\nvalPairFeatureRdds.persist()\ntrain_data= trainPairFeatureRdds.map(lambda pair_feature: pair_feature.sample)\ntest_data= valPairFeatureRdds.map(lambda pair_feature: pair_feature.sample)","user":"anonymous","dateUpdated":"2019-06-17T20:40:30+0800","config":{"editorSetting":{"language":"python","editOnDblClick":false,"completionKey":"TAB","completionSupport":true},"colWidth":12,"editorMode":"ace/mode/python","fontSize":9,"results":{},"enabled":true},"settings":{"params":{},"forms":{}},"results":{"code":"SUCCESS","msg":[]},"apps":[],"jobName":"paragraph_1560753803698_624543309","id":"20190606-200403_730738389","dateCreated":"2019-06-17T14:43:23+0800","status":"FINISHED","progressUpdateIntervalMs":500,"$$hashKey":"object:308","dateFinished":"2019-06-17T20:40:30+0800","dateStarted":"2019-06-17T20:40:30+0800"},{"text":"%md\n## Model Creation and Training\n\nIn Analytics Zoo, it is simple to build a Wide & Deep model by calling WideAndDeep API. You need specify model type, and class number, as well as column information of features according to your data. You can also change other default parameters in the network, like hidden layers.\n\nThe model could be trained using Keras-Style API.","user":"anonymous","dateUpdated":"2019-06-17T20:40:30+0800","config":{"tableHide":false,"editorSetting":{"language":"markdown","editOnDblClick":true,"completionKey":"TAB","completionSupport":false},"colWidth":12,"editorMode":"ace/mode/markdown","fontSize":9,"editorHide":true,"results":{},"enabled":true},"settings":{"params":{},"forms":{}},"results":{"code":"SUCCESS","msg":[{"type":"HTML","data":"<div class=\"markdown-body\">\n<h2>Model Creation and Training</h2>\n<p>In Analytics Zoo, it is simple to build a Wide &amp; Deep model by calling WideAndDeep API. You need specify model type, and class number, as well as column information of features according to your data. You can also change other default parameters in the network, like hidden layers.</p>\n<p>The model could be trained using Keras-Style API.</p>\n</div>"}]},"apps":[],"jobName":"paragraph_1560753803698_1872424956","id":"20190613-150029_565458188","dateCreated":"2019-06-17T14:43:23+0800","status":"FINISHED","progressUpdateIntervalMs":500,"$$hashKey":"object:309","dateFinished":"2019-06-17T20:40:30+0800","dateStarted":"2019-06-17T20:40:30+0800"},{"text":"%pyspark\nwide_n_deep = WideAndDeep(5, column_info, \"wide_n_deep\")","user":"anonymous","dateUpdated":"2019-06-17T20:40:30+0800","config":{"editorSetting":{"language":"python","editOnDblClick":false,"completionKey":"TAB","completionSupport":true},"colWidth":12,"editorMode":"ace/mode/python","fontSize":9,"results":{},"enabled":true},"settings":{"params":{},"forms":{}},"results":{"code":"SUCCESS","msg":[{"type":"TEXT","data":"creating: createZooKerasInput\ncreating: createZooKerasInput\ncreating: createZooKerasInput\ncreating: createZooKerasInput\ncreating: createZooKerasSparseDense\ncreating: createZooKerasFlatten\ncreating: createZooKerasSelect\ncreating: createZooKerasEmbedding\ncreating: createZooKerasFlatten\ncreating: createZooKerasFlatten\ncreating: createZooKerasSelect\ncreating: createZooKerasEmbedding\ncreating: createZooKerasFlatten\ncreating: createZooKerasMerge\ncreating: createZooKerasDense\ncreating: createZooKerasDense\ncreating: createZooKerasDense\ncreating: createZooKerasDense\ncreating: createZooKerasMerge\ncreating: createZooKerasActivation\ncreating: createZooKerasModel\ncreating: createZooWideAndDeep\n"}]},"apps":[],"jobName":"paragraph_1560753803698_-1797812154","id":"20190606-200417_1919766883","dateCreated":"2019-06-17T14:43:23+0800","status":"FINISHED","progressUpdateIntervalMs":500,"$$hashKey":"object:310","dateFinished":"2019-06-17T20:40:32+0800","dateStarted":"2019-06-17T20:40:31+0800"},{"text":"%pyspark\nwide_n_deep.compile(optimizer = \"adam\",\n                    loss= \"sparse_categorical_crossentropy\",\n                    metrics=['accuracy'])","user":"anonymous","dateUpdated":"2019-06-17T20:40:32+0800","config":{"editorSetting":{"language":"python","editOnDblClick":false,"completionKey":"TAB","completionSupport":true},"colWidth":12,"editorMode":"ace/mode/python","fontSize":9,"results":{},"enabled":true},"settings":{"params":{},"forms":{}},"results":{"code":"SUCCESS","msg":[{"type":"TEXT","data":"creating: createAdam\ncreating: createZooKerasSparseCategoricalCrossEntropy\ncreating: createZooKerasSparseCategoricalAccuracy\n"}]},"apps":[],"jobName":"paragraph_1560753803698_1417266951","id":"20190606-200426_773555801","dateCreated":"2019-06-17T14:43:23+0800","status":"FINISHED","progressUpdateIntervalMs":500,"$$hashKey":"object:311","dateFinished":"2019-06-17T20:40:32+0800","dateStarted":"2019-06-17T20:40:32+0800"},{"text":"%pyspark\napp_name = 'wideanddeep-' + dt.datetime.now().strftime(\"%Y%m%d-%H%M%S\")\nwide_n_deep.set_tensorboard(\"/tmp/analytics_zoo_logs\", app_name)","user":"anonymous","dateUpdated":"2019-06-17T20:40:32+0800","config":{"editorSetting":{"language":"python","editOnDblClick":false,"completionKey":"TAB","completionSupport":true},"colWidth":12,"editorMode":"ace/mode/python","fontSize":9,"results":{},"enabled":true},"settings":{"params":{},"forms":{}},"results":{"code":"SUCCESS","msg":[]},"apps":[],"jobName":"paragraph_1560753803698_-531229561","id":"20190606-200438_2123753074","dateCreated":"2019-06-17T14:43:23+0800","status":"FINISHED","progressUpdateIntervalMs":500,"$$hashKey":"object:312","dateFinished":"2019-06-17T20:40:32+0800","dateStarted":"2019-06-17T20:40:32+0800"},{"text":"%pyspark\nprint(\"Started training WideAndDeep...\")\nwide_n_deep.fit(train_data,\n                batch_size = 8000,\n                nb_epoch = 10,\n                validation_data = test_data)\nprint(\"Training finished.\")","user":"anonymous","dateUpdated":"2019-06-17T20:40:33+0800","config":{"editorSetting":{"language":"python","editOnDblClick":false,"completionKey":"TAB","completionSupport":true},"colWidth":12,"editorMode":"ace/mode/python","fontSize":9,"results":{},"enabled":true},"settings":{"params":{},"forms":{}},"results":{"code":"SUCCESS","msg":[{"type":"TEXT","data":"Started training WideAndDeep...\nTraining finished.\n"}]},"apps":[],"jobName":"paragraph_1560753803699_-867505955","id":"20190606-200514_429440917","dateCreated":"2019-06-17T14:43:23+0800","status":"FINISHED","progressUpdateIntervalMs":500,"$$hashKey":"object:313","dateFinished":"2019-06-17T20:43:55+0800","dateStarted":"2019-06-17T20:40:33+0800","runtimeInfos":{"jobUrl":{"propertyName":"jobUrl","label":"SPARK JOB","tooltip":"View in Spark web 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Prediction and Recommendation","user":"anonymous","dateUpdated":"2019-06-17T20:43:55+0800","config":{"tableHide":false,"editorSetting":{"language":"markdown","editOnDblClick":true,"completionKey":"TAB","completionSupport":false},"colWidth":12,"editorMode":"ace/mode/markdown","fontSize":9,"editorHide":true,"results":{},"enabled":true},"settings":{"params":{},"forms":{}},"results":{"code":"SUCCESS","msg":[{"type":"HTML","data":"<div class=\"markdown-body\">\n<h2>Prediction and Recommendation</h2>\n</div>"}]},"apps":[],"jobName":"paragraph_1560753803699_694583187","id":"20190613-150356_1415008968","dateCreated":"2019-06-17T14:43:23+0800","status":"FINISHED","progressUpdateIntervalMs":500,"$$hashKey":"object:314","dateFinished":"2019-06-17T20:43:56+0800","dateStarted":"2019-06-17T20:43:56+0800"},{"text":"%pyspark\nresults = wide_n_deep.predict(test_data)\nresults.take(5)\n\nresults_class = wide_n_deep.predict_classes(test_data)\nresults_class.take(5)","user":"anonymous","dateUpdated":"2019-06-17T20:43:56+0800","config":{"editorSetting":{"language":"python","editOnDblClick":false,"completionKey":"TAB","completionSupport":true},"colWidth":12,"editorMode":"ace/mode/python","fontSize":9,"results":{},"enabled":true},"settings":{"params":{},"forms":{}},"results":{"code":"SUCCESS","msg":[{"type":"TEXT","data":"[2, 2, 2, 2, 3]\n"}]},"apps":[],"jobName":"paragraph_1560753803699_-1587090157","id":"20190606-200535_435355500","dateCreated":"2019-06-17T14:43:23+0800","status":"FINISHED","progressUpdateIntervalMs":500,"$$hashKey":"object:315","dateFinished":"2019-06-17T20:43:58+0800","dateStarted":"2019-06-17T20:43:56+0800","runtimeInfos":{"jobUrl":{"propertyName":"jobUrl","label":"SPARK JOB","tooltip":"View in Spark web UI","group":"spark","values":["http://10.0.96.85:42034/jobs","http://10.0.96.85:42034/jobs"],"interpreterSettingId":"spark"}}},{"text":"%md\nAnalytics Zoo Recommender provides 3 unique APIs to predict user-item pairs and make recommendations for users or items given candidates.","user":"anonymous","dateUpdated":"2019-06-17T20:43:58+0800","config":{"tableHide":false,"editorSetting":{"language":"markdown","editOnDblClick":true,"completionKey":"TAB","completionSupport":false},"colWidth":12,"editorMode":"ace/mode/markdown","fontSize":9,"editorHide":true,"results":{},"enabled":true},"settings":{"params":{},"forms":{}},"results":{"code":"SUCCESS","msg":[{"type":"HTML","data":"<div class=\"markdown-body\">\n<p>Analytics Zoo Recommender provides 3 unique APIs to predict user-item pairs and make recommendations for users or items given candidates.</p>\n</div>"}]},"apps":[],"jobName":"paragraph_1560753803699_2064844947","id":"20190613-150419_820163853","dateCreated":"2019-06-17T14:43:23+0800","status":"FINISHED","progressUpdateIntervalMs":500,"$$hashKey":"object:316","dateFinished":"2019-06-17T20:43:58+0800","dateStarted":"2019-06-17T20:43:58+0800"},{"text":"%pyspark\nuserItemPairPrediction = wide_n_deep.predict_user_item_pair(valPairFeatureRdds)\nfor result in userItemPairPrediction.take(5): print(result)","user":"anonymous","dateUpdated":"2019-06-17T20:43:59+0800","config":{"editorSetting":{"language":"python","editOnDblClick":false,"completionKey":"TAB","completionSupport":true},"colWidth":12,"editorMode":"ace/mode/python","fontSize":9,"results":{},"enabled":true},"settings":{"params":{},"forms":{}},"results":{"code":"SUCCESS","msg":[{"type":"TEXT","data":"UserItemPrediction [user_id: 1069, item_id: 26, prediction: 3, probability: 0.3048429489135742]\nUserItemPrediction [user_id: 1150, item_id: 26, prediction: 3, probability: 0.530002772808075]\nUserItemPrediction [user_id: 4958, item_id: 26, prediction: 3, probability: 0.4408165216445923]\nUserItemPrediction [user_id: 1356, item_id: 26, prediction: 3, probability: 0.36363494396209717]\nUserItemPrediction [user_id: 5488, item_id: 26, prediction: 4, probability: 0.4683045446872711]\n"}]},"apps":[],"jobName":"paragraph_1560753803699_-505332646","id":"20190606-201016_634091978","dateCreated":"2019-06-17T14:43:23+0800","status":"FINISHED","progressUpdateIntervalMs":500,"$$hashKey":"object:317","dateFinished":"2019-06-17T20:44:10+0800","dateStarted":"2019-06-17T20:43:59+0800","runtimeInfos":{"jobUrl":{"propertyName":"jobUrl","label":"SPARK JOB","tooltip":"View in Spark web UI","group":"spark","values":["http://10.0.96.85:42034/jobs","http://10.0.96.85:42034/jobs","http://10.0.96.85:42034/jobs","http://10.0.96.85:42034/jobs"],"interpreterSettingId":"spark"}}},{"text":"%pyspark\nuserRecs = wide_n_deep.recommend_for_user(valPairFeatureRdds, 3)\nfor result in userRecs.take(5): print(result)","user":"anonymous","dateUpdated":"2019-06-17T20:44:10+0800","config":{"editorSetting":{"language":"python","editOnDblClick":false,"completionKey":"TAB","completionSupport":true},"colWidth":12,"editorMode":"ace/mode/python","fontSize":9,"results":{},"enabled":true},"settings":{"params":{},"forms":{}},"results":{"code":"SUCCESS","msg":[{"type":"TEXT","data":"UserItemPrediction [user_id: 4904, item_id: 912, prediction: 5, probability: 0.9143396019935608]\nUserItemPrediction [user_id: 4904, item_id: 919, prediction: 5, probability: 0.908707320690155]\nUserItemPrediction [user_id: 4904, item_id: 750, prediction: 5, probability: 0.8989604115486145]\nUserItemPrediction [user_id: 1084, item_id: 50, prediction: 5, probability: 0.6811689734458923]\nUserItemPrediction [user_id: 1084, item_id: 296, prediction: 5, probability: 0.48351097106933594]\n"}]},"apps":[],"jobName":"paragraph_1560753803699_-486583938","id":"20190606-201032_1656301463","dateCreated":"2019-06-17T14:43:23+0800","status":"FINISHED","progressUpdateIntervalMs":500,"$$hashKey":"object:318","dateFinished":"2019-06-17T20:44:22+0800","dateStarted":"2019-06-17T20:44:10+0800","runtimeInfos":{"jobUrl":{"propertyName":"jobUrl","label":"SPARK JOB","tooltip":"View in Spark web UI","group":"spark","values":["http://10.0.96.85:42034/jobs","http://10.0.96.85:42034/jobs","http://10.0.96.85:42034/jobs","http://10.0.96.85:42034/jobs"],"interpreterSettingId":"spark"}}},{"text":"%pyspark\nitemRecs = wide_n_deep.recommend_for_item(valPairFeatureRdds, 3)\nfor result in itemRecs.take(5): print(result)","user":"anonymous","dateUpdated":"2019-06-17T20:44:22+0800","config":{"editorSetting":{"language":"python","editOnDblClick":false,"completionKey":"TAB","completionSupport":true},"colWidth":12,"editorMode":"ace/mode/python","fontSize":9,"results":{},"enabled":true},"settings":{"params":{},"forms":{}},"results":{"code":"SUCCESS","msg":[{"type":"TEXT","data":"UserItemPrediction [user_id: 1333, item_id: 1084, prediction: 5, probability: 0.8535332679748535]\nUserItemPrediction [user_id: 1835, item_id: 1084, prediction: 5, probability: 0.8360152840614319]\nUserItemPrediction [user_id: 1842, item_id: 1084, prediction: 5, probability: 0.8167529106140137]\nUserItemPrediction [user_id: 4613, item_id: 3764, prediction: 4, probability: 0.573996901512146]\nUserItemPrediction [user_id: 4277, item_id: 3764, prediction: 4, probability: 0.5375261306762695]\n"}]},"apps":[],"jobName":"paragraph_1560753803699_-1250005728","id":"20190606-201112_1879589309","dateCreated":"2019-06-17T14:43:23+0800","status":"FINISHED","progressUpdateIntervalMs":500,"$$hashKey":"object:319","dateFinished":"2019-06-17T20:44:34+0800","dateStarted":"2019-06-17T20:44:22+0800","runtimeInfos":{"jobUrl":{"propertyName":"jobUrl","label":"SPARK JOB","tooltip":"View in Spark web UI","group":"spark","values":["http://10.0.96.85:42034/jobs","http://10.0.96.85:42034/jobs","http://10.0.96.85:42034/jobs","http://10.0.96.85:42034/jobs"],"interpreterSettingId":"spark"}}},{"text":"%md\n## Visualization","user":"anonymous","dateUpdated":"2019-06-17T20:44:34+0800","config":{"tableHide":false,"editorSetting":{"language":"markdown","editOnDblClick":true,"completionKey":"TAB","completionSupport":false},"colWidth":12,"editorMode":"ace/mode/markdown","fontSize":9,"editorHide":true,"results":{},"enabled":true},"settings":{"params":{},"forms":{}},"results":{"code":"SUCCESS","msg":[{"type":"HTML","data":"<div class=\"markdown-body\">\n<h2>Visualization</h2>\n</div>"}]},"apps":[],"jobName":"paragraph_1560753803700_-1374737563","id":"20190613-150530_2102520031","dateCreated":"2019-06-17T14:43:23+0800","status":"FINISHED","progressUpdateIntervalMs":500,"$$hashKey":"object:320","dateFinished":"2019-06-17T20:44:35+0800","dateStarted":"2019-06-17T20:44:35+0800"},{"text":"%pyspark\ndef show(p):\n    img = io.StringIO()\n    p.savefig(img, format='svg')\n    img.seek(0)\n    print(\"%html <div style='width:600px'>\" + img.getvalue() + \"</div>\")\n\n#retrieve train and validation summary object and read the loss data into ndarray's. \ntrain_loss = np.array(wide_n_deep.get_train_summary(\"Loss\"))\nval_loss = np.array(wide_n_deep.get_validation_summary(\"Loss\"))\n#plot the train and validation curves\n# each event data is a tuple in form of (iteration_count, value, timestamp)\nplt.figure(figsize = (12,6))\nplt.plot(train_loss[:,0],train_loss[:,1],label='train loss')\nplt.plot(val_loss[:,0],val_loss[:,1],label='val loss',color='green')\nplt.scatter(val_loss[:,0],val_loss[:,1],color='green')\nplt.legend();\nplt.xlim(0,train_loss.shape[0]+10)\nplt.grid(True)\nplt.title(\"loss\")\nshow(plt)","user":"anonymous","dateUpdated":"2019-06-17T20:44:35+0800","config":{"editorSetting":{"language":"python","editOnDblClick":false,"completionKey":"TAB","completionSupport":true},"colWidth":12,"editorMode":"ace/mode/python","fontSize":9,"results":{},"enabled":true},"settings":{"params":{},"forms":{}},"results":{"code":"SUCCESS","msg":[{"type":"HTML","data":"<div 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