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    "info": {
        "author": "Yinxiao Li",
        "author_email": "liyinxiao1227@gmail.com",
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        "description": "LambdaRankNN\n==========\n\nPython library for training pairwise Learning-To-Rank Neural Network models (RankNet NN, LambdaRank NN).\n\n## Supported model structure\n\nIt supports pairwise Learning-To-Rank (LTR) algorithms such as Ranknet and LambdaRank, where the underlying model (hidden layers) is a neural network (NN) model. \n\n## Installation\n```\npip install LambdaRankNN\n```\n\n## Example\n\nExample on a LambdaRank NN model. \n\n```python\nimport numpy as np\nfrom LambdaRankNN import LambdaRankNN\n\n# generate query data\nX = np.array([[0.2, 0.3, 0.4],\n              [0.1, 0.7, 0.4],\n              [0.3, 0.4, 0.1],\n              [0.8, 0.4, 0.3],\n              [0.9, 0.35, 0.25]])\ny = np.array([0, 1, 0, 0, 2])\nqid = np.array([1, 1, 1, 2, 2])\n\n# train model\nranker = LambdaRankNN(input_size=X.shape[1], hidden_layer_sizes=(16,8,), activation=('relu', 'relu',), solver='adam')\nranker.fit(X, y, qid, epochs=5)\ny_pred = ranker.predict(X)\nranker.evaluate(X, y, qid, eval_at=2)\n```\n\n## Converting model to pmml\n\nThe trained model can be conveniently converted to pmml, with Python library rankerNN2pmml. \n\n```python\nfrom rankerNN2pmml import rankerNN2pmml\nparams = {\n    'feature_names': ['Feature1', 'Feature2', 'Feature3'],\n    'target_name': 'score'\n}\n\nrankerNN2pmml(estimator=ranker.model, file='Model_example.xml', **params)\n```\n\n\n",
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