XGBoost.ipynb

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   "source": [
    "https://towardsdatascience.com/regression-prediction-intervals-with-xgboost-428e0a018b?\n",
    "https://colab.research.google.com/drive/1KlRkrLi7JmVpprL94vN96lZU-HyFNkTq?usp=sharing#scrollTo=EbvnttJdyCmI"
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      "[09:00:24] WARNING: /home/conda/feedstock_root/build_artifacts/xgboost_1598185621802/work/src/learner.cc:516: \n",
      "Parameters: { quant_alpha, quant_delta, quant_thres, quant_var, silent } might not be used.\n",
      "\n",
      "  This may not be accurate due to some parameters are only used in language bindings but\n",
      "  passed down to XGBoost core.  Or some parameters are not used but slip through this\n",
      "  verification. Please open an issue if you find above cases.\n",
      "\n",
      "\n"
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      "/home/sergey/anaconda3/lib/python3.7/site-packages/sklearn/base.py:213: FutureWarning: From version 0.24, get_params will raise an AttributeError if a parameter cannot be retrieved as an instance attribute. Previously it would return None.\n",
      "  FutureWarning)\n"
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       "XGBQuantile(colsample_bynode=1, gpu_id=-1, importance_type='gain',\n",
       "            interaction_constraints='', monotone_constraints='()', nthread=1,\n",
       "            num_parallel_tree=1,\n",
       "            objective=functools.partial(<function XGBQuantile.quantile_loss at 0x7fcaf6f5c320>, alpha=0.95, delta=1.0, threshold=1.0, var=1.0),\n",
       "            seed=0, tree_method='exact', validate_parameters=1, verbosity=None)"
      ]
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     "execution_count": 5,
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   "source": [
    "from xgbquantile import XGBQuantile\n",
    "from sklearn.datasets import make_regression\n",
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "clf = XGBQuantile()\n",
    "X,y = make_regression()\n",
    "X_train,X_test, y_train, y_test = train_test_split(X,y)\n",
    "clf.fit(X_train,y_train)"
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    "def collect_prediction(X_train,y_train,X_test,y_test,estimator,alpha,model_name):\n",
    "  estimator.fit(X_train,y_train)\n",
    "  y_pred = estimator.predict(X_test)\n",
    "  print( \"{model_name} alpha = {alpha:.2f},score = {score:.1f}\".format(model_name=model_name, alpha=alpha , score= XGBQuantile.quantile_score(y_test, y_pred, alpha)) )\n",
    "  return y_pred"
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     "text": [
      "[08:59:38] WARNING: /home/conda/feedstock_root/build_artifacts/xgboost_1598185621802/work/src/learner.cc:516: \n",
      "Parameters: { quant_alpha, quant_delta, quant_thres, quant_var, silent } might not be used.\n",
      "\n",
      "  This may not be accurate due to some parameters are only used in language bindings but\n",
      "  passed down to XGBoost core.  Or some parameters are not used but slip through this\n",
      "  verification. Please open an issue if you find above cases.\n",
      "\n",
      "\n",
      "Quantile XGB alpha = 0.05,score = 1824.9\n",
      "[08:59:38] WARNING: /home/conda/feedstock_root/build_artifacts/xgboost_1598185621802/work/src/learner.cc:516: \n",
      "Parameters: { quant_alpha, quant_delta, quant_thres, quant_var, silent } might not be used.\n",
      "\n",
      "  This may not be accurate due to some parameters are only used in language bindings but\n",
      "  passed down to XGBoost core.  Or some parameters are not used but slip through this\n",
      "  verification. Please open an issue if you find above cases.\n",
      "\n",
      "\n",
      "Quantile XGB alpha = 0.95,score = 1663.9\n"
     ]
    }
   ],
   "source": [
    "alpha = 0.95\n",
    "regressor = XGBQuantile(n_estimators=100,max_depth = 3, reg_alpha =5.0,gamma = 0.5,reg_lambda =1.0 )     \n",
    "regressor.set_params(quant_alpha=1.-alpha,quant_delta=1.0,quant_thres=5.0,quant_var=3.2)\n",
    "y_lower = collect_prediction(X_train,y_train,X_test,y_test,estimator=regressor,alpha=1.-alpha,model_name=\"Quantile XGB\")\n",
    "regressor.set_params(quant_alpha=alpha,quant_delta=1.0,quant_thres=6.0,quant_var = 4.2)\n",
    "y_upper = collect_prediction(X_train,y_train,X_test,y_test,estimator=regressor,alpha=alpha,model_name=\"Quantile XGB\")"
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     "text": [
      "[-1.5401597 -0.5655502  1.9827869  3.2481701  1.0095189]\n",
      "[ 0.84138775 -2.622522    2.1667144   3.4823449  -2.7396379 ]\n"
     ]
    }
   ],
   "source": [
    "print(y_lower[:5],y_upper[:5],sep=\"\\n\")"
   ]
  }
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