{
    "info": {
        "author": "zhangzehua",
        "author_email": "zzh_0729@foxmail.com",
        "bugtrack_url": null,
        "classifiers": [
            "License :: OSI Approved :: MIT License",
            "Operating System :: OS Independent",
            "Programming Language :: Python :: 2",
            "Programming Language :: Python :: 3"
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        "description": "# DeepEX\n\n[TOC]\n\n## Overview\n\n**DeepEX**  is a universal convenient frame with keras and Tensorflow,\n\nYou can get well-known Wide&Deep model such as DeepFM here. \n\nOr, you can define you custom model use this frame.\n\n\n\n\n\n### How to Install\n\n* For Linux:\n\n```shell\npip install deepex\n```\n\n* For Windows:\n\nYou need active cmd first and, \n\n```powershell\npip install deepex\n```\n\nNotice: This frame needs keras and tensorflow, maybe it has some problem on windows with python 2.x because of tensorflow.\n\n\n\n\n\n## DeepEX Class API\n\nIn the functional API, given some parmeters, you can instantiate a `DeepEX` object, via:\n\n```python\nDeepEX(data = None, feature_dim=None, category_index=None,embedding_dict_size=1000,\n      embedding_size=64, depths_size = [1024,256,64],class_num=2,\n      aggregate_flag=False, metrics=None, optimizer='Adam', activation='relu',\n      embedding_way='dense')\n```\n\nOnly data is necessary, other parmeters have default value.\n\n**Arguments**\n\n* **data**: np.array\n\n* **feature_dim**: integer, feature dimension\n\n* **category_index**: \n\n  1. Can be a 2D list, like [A,B], A and B also a list, if len(A)>1, that means the element of A belong to a field, and will be input embedding layer together.\n\n  2. Can be a integer, it specific feature_dim % integer == 0, that means split feature as equal intervals with category_index\n\n  3. If None, all of feature will be embedding\n\n  4. *NEW UPDATE: Can be a mix type, like [A,B,c], A and B is list but c is a integer. It can achive more flexible way of split field, A and B will be split as col.1 and the rest of element will be split as col.2 with parameter c. Notice: c must be the last element, and the count of remaining element can be evenly divisible by c* \n\n* **embedding_dict_size**: embedding dict size of categroy feature\n\n* **embedding_size**: embedding size, it make output size like (?, len(category_index), embedding_size)\n\n* **depths_size**: network of deep part parameter, last dimension means fc7 shape\n\n* **class_num**: multi class or binary class, if class_num < 2, you will get a regression model\n\n* **aggregate_flag**: \n  * if True, first_order and second_order of FM part output as (?,1)\n  * if False, output as (?, len(category_index)) and (?, embedding_size)\n\n* **metrics**: can recive custom metrics, if None, binary class use AUC, multi class use auccary\n\n* **optimizer**: Network optimizer, default adam,  see [optimizer](https://keras.io/optimizers/).\n\n* **activation**: Deep part activation, default relu, see [activations](https://keras.io/activations/).\n\n* **embedding_way**: How network to do embedding, Embedding layer or Dense layer\n\n\n\n\n\n## Methods\n\n### **get_embedding_layer**\n\nembedding input data as class parameter. \n\n**return**\n\n* inputs: A list, which elements are Input layer,  prepare to deep model\n* numerics: A list, which elements are numeric feature tensor\n* embeddings:  A list, which elements are categroy feature embedding tensor\n* embedding_layer: A tensor, which is concate numeric feature tensor and categroy feature embedding tensor\n\n-----\n\n### fm\n\nget fm part\n\n**return**\n\n- A tensor, shape depends on class parameter **aggregate_flag**\n\n-----\n\n### deep\n\nget deep part\n\n**return**\n\n* inputs: A list, which elements are Input layer,  prepare to deep model\n* model: A tensor, which is also a keras functional layer\n\n-----\n\n### deepfm\n\nget deepfm model, fc7 which is the last layer before classifier, it will be a class variable\n\n**return**\n\n* model: A keras functional model\n\n-----\n\n### auc\n\n```python\nauc(y_true, y_pred)\n```\n\nA custom metrics, when class_num=2, use this metrics to evaluate model\n\n-----\n\n### fit\n\n```python\nfit(model, y, save_model_path = None, batch_size=None, epochs=1, verbose=1,\n    callbacks=None,validation_split=0.0, validation_data=None,\n    shuffle=True, class_weight=None,sample_weight=None, \n    initial_epoch=0, steps_per_epoch=None, validation_steps=None)\n```\n\nfit data to train model\n\n**Arguments**\n\n* **model**: a DeepEX model\n* **save_model_path**: A string, where model to save, if None, model will not be saved\n* **others**: see document [keras fit](https://keras.io/models/model/#fit)\n\n\n\n### get_fc7_output\n\n```python\nget_fc7_output(self, model_path = None, layer_name = 'fc7', data = None)\n```\n\nGet model's fc7 layer output, it can use for other operation, such as model ensemble\n\n**Arguments**\n\n- **model_path**: A string, only model_path is necessary,  tell function where model is, the model file should be saved use keras.models.Model.save() function.\n- **layer_name**: A string, default 'fc7'\n- **data**: numpy array, NOTICE: if you declare a DeepEX object use same parameter just like model will be load (actually just need [data] and [category_index] are same), this parametre can be None, data will read from self.data. HOWEVER, if you declare other way, you need split data format as model input\n\n**return**\n\n* intermediate_output: Numpy array(s) of intermediate outputs.\n\n\n\n## How to Use\n\nThis class is very easy to use,  three steps to go\uff1a\n\n```python\n# step 1, declare DeepEX object\ndeepEX = DeepEX(...)\n\n# step 2, get model you want\nmodel = deepEX.deepfm()\n\n# step 3, train and save\ndeepEX.fit(...)\n```\n\n### example\n\n```python\nfrom deepex import *\nimport numpy as np\n\nsamples = 100000    # set samples num\nfeat_dim = 10   # set feat_dim \ncate = np.random.randint(1,6,samples)   # set a categroy feat randomly\nx = np.random.random((samples,feat_dim))    # generate feat randomly\nx[:,3] = cate   # chose a column to be categroy feat\ny = np.random.randint(0,2,samples)  # generate label\n\n# declare DeepEX objects\ndeepEX = DeepEX(data = x, feature_dim=feat_dim, category_index=[0,1],4, embedding_dict_size=1000, \nembedding_size=64, depths_size = [1024,256,64], class_num=2, \naggregate_flag=False, metrics=None, optimizer='Adam', activation='relu', embedding_way='emb')\n\nmodel = deepEX.deepfm()  # get DeepFM\nplot_model(model,'deepFM.png',show_shapes=True) # show model graph\n\n# train deepfm\npath = None\ndeepEX.fit(model, y, save_model_path=path, 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)\n\n# get fc7 output tensor\nfc7 = deepEX.get_fc7_output(model_path=path, layer_name='fc7', data=deepEX.data_split)\n```\n\n\n\n",
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