Learning to Rank.ipynb

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   "cells": [
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "%matplotlib inline\n",
      "from os.path import expanduser, join\n",
      "from time import time\n",
      "import numpy as np\n",
      "import matplotlib.pyplot as plt\n",
      "\n",
      "from sklearn.metrics import r2_score\n",
      "\n",
      "from sklearn.externals import joblib\n",
      "from sklearn.ensemble import ExtraTreesRegressor\n",
      "from sklearn.linear_model import LinearRegression\n",
      "\n",
      "from pyrallel.ensemble import EnsembleGrower\n",
      "from pyrallel.ensemble import sub_ensemble"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 2
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "from IPython.parallel import Client\n",
      "lb_view = Client().load_balanced_view()\n",
      "len(lb_view)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 3,
       "text": [
        "95"
       ]
      }
     ],
     "prompt_number": 3
    },
    {
     "cell_type": "heading",
     "level": 2,
     "metadata": {},
     "source": [
      "Loading the dataset"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "This is a NumPy array version of Fold1 of the [MSLR-WEB10K](http://research.microsoft.com/en-us/projects/mslr/) dataset."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "%%time\n",
      "\n",
      "data = np.load(expanduser('~/data/MSLR-WEB10K/mslr-web10k_fold1.npz'))\n",
      "X_train, y_train, qid_train = data['X_train'], data['y_train'], data['qid_train']\n",
      "X_vali, y_vali, qid_vali = data['X_vali'], data['y_vali'], data['qid_vali']\n",
      "X_test, y_test, qid_test = data['X_test'], data['y_test'], data['qid_test']"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "CPU times: user 3.82 s, sys: 960 ms, total: 4.78 s\n",
        "Wall time: 15.4 s\n"
       ]
      }
     ],
     "prompt_number": 5
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Total size in bytes, total number of search results and number of queries:"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "(X_train.nbytes + X_vali.nbytes + X_test.nbytes) / 1e6"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 6,
       "text": [
        "652.904448"
       ]
      }
     ],
     "prompt_number": 6
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "len(X_train) + len(X_vali) + len(X_test)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 7,
       "text": [
        "1200192"
       ]
      }
     ],
     "prompt_number": 7
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "len(np.unique(qid_train)) + len(np.unique(qid_vali)) + len(np.unique(qid_test))"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 8,
       "text": [
        "10000"
       ]
      }
     ],
     "prompt_number": 8
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Concatenate the training and validation sets as a big development set."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "X_dev = np.vstack([X_train, X_vali])\n",
      "y_dev = np.concatenate([y_train, y_vali])\n",
      "qid_dev = np.concatenate([qid_train, qid_vali])"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 9
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "X_dev.shape"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 10,
       "text": [
        "(958671, 136)"
       ]
      }
     ],
     "prompt_number": 10
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "X_dev.dtype"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 11,
       "text": [
        "dtype('float32')"
       ]
      }
     ],
     "prompt_number": 11
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Extract a subset of 500 queries to speed up the learning when prototyping"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "def subsample(X, y, qid, size, seed=None):\n",
      "    rng = np.random.RandomState(seed)\n",
      "    unique_qid = np.unique(qid)\n",
      "    qid_mask = rng.permutation(len(unique_qid))[:size]\n",
      "    subset_mask = np.in1d(qid_train, unique_qid[qid_mask])\n",
      "    return X[subset_mask], y[subset_mask], qid[subset_mask]\n",
      "\n",
      "\n",
      "X_train_small, y_train_small, qid_train_small = subsample(\n",
      "    X_train, y_train, qid_train, 500, seed=0)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 12
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "X_train_small.shape"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 13,
       "text": [
        "(62244, 136)"
       ]
      }
     ],
     "prompt_number": 13
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Sanity check:"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "len(np.unique(qid_train_small))"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 14,
       "text": [
        "500"
       ]
      }
     ],
     "prompt_number": 14
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "X_train_medium, y_train_medium, qid_train_medium = subsample(\n",
      "    X_train, y_train, qid_train, 1000, seed=0)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 37
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "def balance_irrelevant(X, y, qid, seed=None):\n",
      "    \"\"\"Subsample the zero-scored entries\"\"\"\n",
      "    rng = np.random.RandomState(seed)\n",
      "    unique_qid = np.unique(qid)\n",
      "    final_mask = np.ones(shape=y.shape, dtype=np.bool)\n",
      "    for this_qid in unique_qid:\n",
      "        this_mask = qid == this_qid\n",
      "        this_y = y[this_mask]\n",
      "        relevant = this_y >= 2\n",
      "        ratio = float(np.mean(relevant))\n",
      "        if ratio > 0.5:\n",
      "            # already balanced\n",
      "            continue\n",
      "            \n",
      "        final_mask[this_mask] = np.logical_or(\n",
      "            relevant, np.random.random(len(this_y)) > 0.7) \n",
      "    return X[final_mask], y[final_mask], qid[final_mask]\n",
      "\n",
      "X_balanced_small, y_balanced_small, qid_balanced_small = balance_irrelevant(\n",
      "    X_train_small, y_train_small, qid_train_small)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 15
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "print(len(y_train_small))\n",
      "print(len(y_balanced_small))"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "62244\n",
        "25307\n"
       ]
      }
     ],
     "prompt_number": 16
    },
    {
     "cell_type": "heading",
     "level": 2,
     "metadata": {},
     "source": [
      "Quantifying ranking success with NDCG"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "def dcg(relevances, rank=10):\n",
      "    \"\"\"Discounted cumulative gain at rank (DCG)\"\"\"\n",
      "    relevances = np.asarray(relevances)[:rank]\n",
      "    n_relevances = len(relevances)\n",
      "    if n_relevances == 0:\n",
      "        return 0.\n",
      "\n",
      "    discounts = np.log2(np.arange(n_relevances) + 2)\n",
      "    return np.sum(relevances / discounts)\n",
      " \n",
      " \n",
      "def ndcg(relevances, rank=10):\n",
      "    \"\"\"Normalized discounted cumulative gain (NDGC)\"\"\"\n",
      "    best_dcg = dcg(sorted(relevances, reverse=True), rank)\n",
      "    if best_dcg == 0:\n",
      "        return 0.\n",
      "\n",
      "    return dcg(relevances, rank) / best_dcg"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 17
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "ndcg([2, 4, 0, 1, 1, 0, 0], rank=5)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 18,
       "text": [
        "0.86253003992915656"
       ]
      }
     ],
     "prompt_number": 18
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "ndcg([0, 0, 0, 1, 1, 2, 4], rank=5)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 19,
       "text": [
        "0.13201850690866795"
       ]
      }
     ],
     "prompt_number": 19
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "ndcg([0, 0, 0, 1, 1, 2, 4], rank=3)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 20,
       "text": [
        "0.0"
       ]
      }
     ],
     "prompt_number": 20
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "ndcg([4, 2, 1, 1, 0, 0, 0], rank=5)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 21,
       "text": [
        "1.0"
       ]
      }
     ],
     "prompt_number": 21
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "def mean_ndcg(y_true, y_pred, query_ids, rank=10):\n",
      "    y_true = np.asarray(y_true)\n",
      "    y_pred = np.asarray(y_pred)\n",
      "    query_ids = np.asarray(query_ids)\n",
      "    # assume query_ids are sorted\n",
      "    ndcg_scores = []\n",
      "    previous_qid = query_ids[0]\n",
      "    previous_loc = 0\n",
      "    for loc, qid in enumerate(query_ids):\n",
      "        if previous_qid != qid:\n",
      "            chunk = slice(previous_loc, loc)\n",
      "            ranked_relevances = y_true[chunk][np.argsort(y_pred[chunk])[::-1]]\n",
      "            ndcg_scores.append(ndcg(ranked_relevances, rank=rank))\n",
      "            previous_loc = loc\n",
      "        previous_qid = qid\n",
      "\n",
      "    chunk = slice(previous_loc, loc + 1)\n",
      "    ranked_relevances = y_true[chunk][np.argsort(y_pred[chunk])[::-1]]\n",
      "    ndcg_scores.append(ndcg(ranked_relevances, rank=rank))\n",
      "    return np.mean(ndcg_scores)\n",
      "\n",
      "\n",
      "mean_ndcg([4, 3, 1, 4, 3], [4, 0, 1, 4, 2], [0, 0, 0, 2, 2], rank=10)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 22,
       "text": [
        "0.9795191506818377"
       ]
      }
     ],
     "prompt_number": 22
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "def print_evaluation(model, X, y, qid):\n",
      "    tic = time()\n",
      "    y_predicted = model.predict(X)\n",
      "    prediction_time = time() - tic\n",
      "    print(\"Prediction time: {:.3f}s\".format(prediction_time))\n",
      "    print(\"NDCG@5 score: {:.3f}\".format(\n",
      "    mean_ndcg(y, y_predicted, qid, rank=5)))\n",
      "    print(\"NDCG@10 score: {:.3f}\".format(\n",
      "    mean_ndcg(y, y_predicted, qid, rank=10)))\n",
      "    print(\"NDCG score: {:.3f}\".format(\n",
      "    mean_ndcg(y, y_predicted, qid, rank=None)))\n",
      "    print(\"R2 score: {:.3f}\".format(r2_score(y, y_predicted)))"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 23
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "def plot_ndcg_by_trees(model, X, y, qid, rank=10):\n",
      "    max_n_trees = len(model.estimators_)\n",
      "    scores = []\n",
      "    \n",
      "    if hasattr(model, 'staged_predict'):\n",
      "        # stage-wise score computation for boosted ensembles\n",
      "        n_trees = np.arange(max_n_trees) + 1\n",
      "        for y_predicted in model.staged_predict(X):\n",
      "            scores.append(mean_ndcg(y, y_predicted, qid, rank=10))\n",
      "    else:\n",
      "        # assume forest-type of tree ensemble: use a log scale to speedup\n",
      "        # the computation\n",
      "        # XXX: partial predictions could be reused\n",
      "        n_trees = np.logspace(0, np.log10(max_n_trees), 10).astype(int)\n",
      "        for j, n in enumerate(n_trees):\n",
      "            y_predicted = sub_ensemble(model, n).predict(X)\n",
      "            scores.append(mean_ndcg(y, y_predicted, qid, rank=rank))\n",
      "            \n",
      "    plt.plot(n_trees, scores)\n",
      "    plt.xlabel(\"Number of trees\")\n",
      "    plt.ylabel(\"Average NDCG@%d\" % rank)\n",
      "    _ = plt.title(\"Impact of the number of trees\")"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 24
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "%%time\n",
      "\n",
      "from sklearn.ensemble import ExtraTreesRegressor\n",
      "\n",
      "etr = ExtraTreesRegressor(n_estimators=200, min_samples_split=5, random_state=1, n_jobs=-1)\n",
      "etr.fit(X_train_small, y_train_small)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "CPU times: user 28min 11s, sys: 1.01 s, total: 28min 12s\n",
        "Wall time: 57 s\n"
       ]
      }
     ],
     "prompt_number": 25
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "print_evaluation(etr, X_test, y_test, qid_test)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "Prediction time: 1.117s\n",
        "NDCG@5 score: 0.470"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "NDCG@10 score: 0.478"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "NDCG score: 0.730"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "R2 score: 0.136\n"
       ]
      }
     ],
     "prompt_number": 26
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "plot_ndcg_by_trees(etr, X_test, y_test, qid_test)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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QAtOmTYOfn5/O+i5duuDFF19s04FNjd1URETGobfFERwcjP3798PW1lZnvUql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\nhImDiIhahImDiIha5P8D3cqCcsAWhm8AAAAASUVORK5CYII=\n",
       "text": [
        "<matplotlib.figure.Figure at 0x43567d0>"
       ]
      }
     ],
     "prompt_number": 27
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "%%time\n",
      "\n",
      "from sklearn.ensemble import RandomForestRegressor\n",
      "\n",
      "rfr = RandomForestRegressor(n_estimators=200, min_samples_split=5, random_state=1, n_jobs=-1)\n",
      "rfr.fit(X_balanced_small, y_balanced_small)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "CPU times: user 8min 24s, sys: 172 ms, total: 8min 25s\n",
        "Wall time: 46.4 s\n"
       ]
      }
     ],
     "prompt_number": 136
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "print_evaluation(rfr, X_test, y_test, qid_test)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "Prediction time: 1.555s\n",
        "NDCG@5 score: 0.476"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "NDCG@10 score: 0.486"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "NDCG score: 0.733"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "R2 score: -0.010\n"
       ]
      }
     ],
     "prompt_number": 137
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "plot_ndcg_by_trees(rfr, X_test, y_test, qid_test)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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zXFFRUUZvFezZsycGDRoEQD+6S6VS4eLFiw06rykXLgBaLeDl1azDEBHd00wm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\n",
       "text": [
        "<matplotlib.figure.Figure at 0x4c4a5d0>"
       ]
      }
     ],
     "prompt_number": 138
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "%%time\n",
      "\n",
      "from sklearn.ensemble import RandomForestRegressor\n",
      "\n",
      "rfr2 = RandomForestRegressor(n_estimators=200, min_samples_split=5, random_state=1, n_jobs=-1)\n",
      "rfr2.fit(X_train_small, y_train_small)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "CPU times: user 24min 21s, sys: 512 ms, total: 24min 21s\n",
        "Wall time: 2min 18s\n"
       ]
      }
     ],
     "prompt_number": 139
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "#rfr.set_params(n_jobs=-1)\n",
      "print_evaluation(rfr2, X_test, y_test, qid_test)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "Prediction time: 1.644s\n",
        "NDCG@5 score: 0.484"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "NDCG@10 score: 0.493"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "NDCG score: 0.734"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "R2 score: 0.142\n"
       ]
      }
     ],
     "prompt_number": 140
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "plot_ndcg_by_trees(rfr2, X_test, y_test, qid_test)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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p9ElNpTEfPjUVePddID1doqCIiFo4yabjVldX10kagHqKbnV1daNPaG6cUUVE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\nGMrAh5cUmgAAAABJRU5ErkJggg==\n",
       "text": [
        "<matplotlib.figure.Figure at 0x6071590>"
       ]
      }
     ],
     "prompt_number": 141
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "%%time\n",
      "\n",
      "from sklearn.ensemble import GradientBoostingRegressor\n",
      "\n",
      "gbr = GradientBoostingRegressor(n_estimators=200, random_state=1, verbose=1)\n",
      "gbr.fit(X_train_small, y_train_small)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "      Iter       Train Loss   Remaining Time \n",
        "         1           0.6470            5.41m"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "         2           0.6361            4.32m"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "         3           0.6264            3.93m"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "         4           0.6184            3.75m"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "         5           0.6115            3.64m"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "         6           0.6055            3.50m"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "         7           0.5999            3.40m"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "         8           0.5954            3.32m"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "         9           0.5910            3.26m"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "        10           0.5875            3.20m"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "        20           0.5646            2.91m"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "        30           0.5528            2.66m"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "        40           0.5457            2.45m"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "        50           0.5402            2.27m"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "        60           0.5354            2.10m"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "        70           0.5316            1.94m"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "        80           0.5284            1.78m"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "        90           0.5244            1.64m"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "       100           0.5219            1.50m"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "       200           0.5002            0.00s"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "CPU times: user 2min 57s, sys: 32 ms, total: 2min 57s\n",
        "Wall time: 2min 57s\n"
       ]
      }
     ],
     "prompt_number": 146
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "print_evaluation(gbr, X_test, y_test, qid_test)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "Prediction time: 3.031s\n",
        "NDCG@5 score: 0.502"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "NDCG@10 score: 0.510"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "NDCG score: 0.743"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "R2 score: 0.155\n"
       ]
      }
     ],
     "prompt_number": 147
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "plot_ndcg_by_trees(gbr, X_test, y_test, qid_test)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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/XL62bQOUlUq9RER1h87b3ubNmxEfH6997+joiGPHjkGtVqNbt26YMWNGuQd2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\n0gsTBxER6eX/AS9HtfvwwlKmAAAAAElFTkSuQmCC\n",
       "text": [
        "<matplotlib.figure.Figure at 0x4bee890>"
       ]
      }
     ],
     "prompt_number": 148
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "%%time\n",
      "\n",
      "from sklearn.ensemble import GradientBoostingRegressor\n",
      "\n",
      "gbr2 = GradientBoostingRegressor(n_estimators=300, max_depth=3,\n",
      "                                 learning_rate=0.1, loss='ls',\n",
      "                                 random_state=1, verbose=1)\n",
      "gbr2.fit(X_dev, y_dev)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "      Iter       Train Loss   Remaining Time \n",
        "         1           0.6662          163.32m"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "         2           0.6556          131.63m"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "         3           0.6464          121.04m"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "         4           0.6388          117.25m"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "         5           0.6322          114.55m"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "         6           0.6268          112.25m"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "         7           0.6219          109.67m"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "         8           0.6174          108.07m"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "         9           0.6136          106.45m"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "        10           0.6105          104.90m"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "        20           0.5904           96.37m"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "        30           0.5807           92.51m"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "        40           0.5751           87.38m"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "        50           0.5712           81.67m"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "        60           0.5682           76.44m"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "        70           0.5659           71.63m"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "        80           0.5641           67.39m"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "        90           0.5626           63.37m"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "       100           0.5613          481.66m"
       ]
      }
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "print_evaluation(gbr2, X_test, y_test, qid_test)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": []
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "plot_ndcg_by_trees(gbr2, X_test, y_test, qid_test)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": []
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "print_evaluation(gbr2, X_dev, y_dev, qid_dev)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": []
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "%%time\n",
      "\n",
      "from sklearn.ensemble import LambdaMART\n",
      "\n",
      "lmart= LambdaMART(n_estimators=300, max_depth=3,\n",
      "                  learning_rate=0.1, random_state=1, verbose=1)\n",
      "lmart.fit(X_train_small, y_train_small, group=qid_train_small)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "      Iter       Train Loss   Remaining Time \n",
        "         1           0.4105            8.01m"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "         2           0.4427            6.38m"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "         3           0.4445            5.84m"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "         4           0.4447            5.40m"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "         5           0.4441            5.18m"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "         6           0.4438            4.98m"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "         7           0.4529            4.84m"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "         8           0.4550            4.71m"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "         9           0.4557            4.60m"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "        10           0.4572            4.51m"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "        20           0.4659            4.00m"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "        30           0.4828            3.73m"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "        40           0.4927            3.53m"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "        50           0.5017            3.35m"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "        60           0.5069            3.20m"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "        70           0.5164            3.05m"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "        80           0.5205            2.91m"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "        90           0.5262            2.76m"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "       100           0.5303            2.63m"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "       200           0.5645            1.31m"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "       300           0.5844            0.00s"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "CPU times: user 4min 2s, sys: 349 ms, total: 4min 2s\n",
        "Wall time: 4min 2s\n"
       ]
      }
     ],
     "prompt_number": 32
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "print_evaluation(lmart, X_test, y_test, qid_test)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "Prediction time: 2.294s\n",
        "NDCG@5 score: 0.490"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "NDCG@10 score: 0.495"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "NDCG score: 0.737"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "R2 score: -1.423\n"
       ]
      }
     ],
     "prompt_number": 33
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "plot_ndcg_by_trees(lmart, X_test, y_test, qid_test)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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Ul0Pr1rB7dxpSJCKS4VIxH8c+5+92oDs2ZtVhiZ4wFVQxLiLiHy91HI8DLYDf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\nIiIiIiIiIiIiIiIiIiIiIiIi2eT/AdfE1W536PzeAAAAAElFTkSuQmCC\n",
       "text": [
        "<matplotlib.figure.Figure at 0x11d283290>"
       ]
      }
     ],
     "prompt_number": 34
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "print_evaluation(lmart, X_train_small, y_train_small, qid_train_small)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "Prediction time: 0.844s\n",
        "NDCG@5 score: 0.584"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "NDCG@10 score: 0.563"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "NDCG score: 0.756"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "R2 score: -1.441\n"
       ]
      }
     ],
     "prompt_number": 35
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "%%time\n",
      "\n",
      "lmart= LambdaMART(n_estimators=300, max_depth=3,\n",
      "                  learning_rate=0.1, random_state=1, verbose=1)\n",
      "lmart.fit(X_dev, y_dev, group=qid_dev)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "      Iter       Train Loss   Remaining Time \n",
        "         1           0.4552          158.83m"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "         2           0.4555          123.79m"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "         3           0.4555          111.86m"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "         4           0.4561          105.25m"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "         5           0.4582          101.11m"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "         6           0.4637           98.34m"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "         7           0.4646           96.19m"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "         8           0.4651           94.57m"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "         9           0.4656           93.31m"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "        10           0.4666           92.22m"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "        20           0.4810           85.55m"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "        30           0.4917           81.51m"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "        40           0.5044           79.35m"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "        50           0.5120           76.18m"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "        60           0.5183           73.14m"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "        70           0.5231           70.31m"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "        80           0.5273           67.35m"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "        90           0.5301           64.28m"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "       100           0.5338           61.26m"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "       200           0.5490           30.89m"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "       300           0.5562            0.00s"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "CPU times: user 1h 29min 50s, sys: 8.55 s, total: 1h 29min 58s\n",
        "Wall time: 2h 29min 34s\n"
       ]
      }
     ],
     "prompt_number": 48
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "print_evaluation(lmart, X_test, y_test, qid_test)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "Prediction time: 2.441s\n",
        "NDCG@5 score: 0.522"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "NDCG@10 score: 0.525"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "NDCG score: 0.750"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "R2 score: -1.176\n"
       ]
      }
     ],
     "prompt_number": 49
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "print_evaluation(lmart, X_dev, y_dev, qid_dev)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "Prediction time: 9.917s\n",
        "NDCG@5 score: 0.541"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "NDCG@10 score: 0.539"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "NDCG score: 0.757"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "R2 score: -1.149\n"
       ]
      }
     ],
     "prompt_number": 56
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "plot_ndcg_by_trees(lmart, X_test, y_test, qid_test)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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DO+9Y57mPP4YvvoDTT4c5c6wPxUUXWUV19+5wzDHBGdxE0o2KqkSqad06q8yeMMFGd+3e\nHY49FkaM0MB6IpEoxyF1Tnm5FUFNmQIffmhzSF98sY3metZZtTv3s4gX1KpKgUMi8PttbKZ586yI\nqV07q7geOxYGD4ZGjayH9RlnWM/rhg29TrFI7VHgUOCQMIsXw9VXw/bt0K2b1VtUVFhzWZ/PJgjq\n2VM9sKXuUh2HiGP5cnjqKRg3Dh56CK67ziqvt22z9c2aKViI1AS1CZG05/db5fZxx1nv7OnT4YYb\ngi2e9t3XXgoaIjVDOQ5JK3PnWlFTaakNHb50qc1Sl5NjkxQddZTXKRTJfOn+G0x1HHVEUZFVar/1\nFjz8sA0UWFRkf1u2tKHGVcEtkhhVjitwZCy/H15+GSZOtA55AwbYEB/t2nmdMpH0psChwJFxiouh\nfn248Uabh+L2223AwIMP9jplIpmhuoFDleOSUkaOhK5d4bDDrA5j+nS4/HIFDZFUohyHpIwFC2ze\n7M8/tzoLtYIScYeKqhQ4MsaFF1ov7jvu8DolIplNgUOBI62VlMDo0fDcczbX9rx5GppcxG2q45C0\n9PXXcN550Lq1tZp67jmbl1tBQyT1xQoczYHBwGJgC7DZeT/YWSdSJYWFcP75cMEFsHYtvPeetZrK\nzvY6ZSKSiFiB43UsYPiA/Z3XKcBWZ51I0hYtslFp//Uv65PRpInXKRKRZMUq41oCdK7CutqkOo4U\ntnYtvPmm9eiuV8+mYh07Fp5+Gvr18zp1InWXm6Pjfg/8HRgFrHeWtQauBFZW9YSS2X780XIVCxbY\nCLU+n819UVYGv/qVFVO1b+91KkWkOmJFnP2BgcD5QCtn2XrgHayeY7O7SUuIchwpZPp0a1J70EHW\nga9fP5soSURSS6o3xz0bGAJkA0OBx8LW+4AJwHLn85vAwwnuCwocnpszxyZN2rbNJkp69lkLHiKS\nuryayOkqYEScbbKBZ4DTgdXATCy3sihsu6lYrqYq+4pHtm6FIUMsUAwZAscea7kMtYwSyXxV7cfx\nYALb9ACWAcXAHmAc0DvCdpGiXqL7igcKCmw485UrrT/GZZfB4YcraIjUFbFyHN/GWNcygWO3A34I\n+bwKOC5sGz/we2AelrO4E1iY4L7igd27bXa9YcOgt0K5SJ0UK3C0xOoZtkRY92UCx06k8mE20AEo\nBXoBb5NkM9/8/Pxf3vt8Pnw+XzK7SwL8fstlrF1r83l36mQd+EQkPRQUFFBQUFBjx4tVOTIcq8f4\nPMK6sUDfOMc+HsjHgg/AIKCCyJXcASuAY7Hgkci+qhx30a5d8NVXkJ8P69bB0UdDt242oVJurtep\nE5GqSuVWVfWB74DTgDXADCzYhFZwtwI2YLmTHliP9LwE9wUFjhpTXm5/s7NtXu9HHoFJk+CQQ+CW\nW+DKK21yJRFJf7XVquoAoCPWj+OHONsGlAE3A5OxVlLDsAf/9c76F4CLgBudbUuBS+PsKy6oqIA+\nfWDGDOukt2AB3HMPvPgi7Lef16kTkVQTL+IcAjyBFRMtxeo9WmLNcTe6m7SEKMdRA/Lz4cMP4ckn\nbarWXr00Sq1IJnMzx9EeeA24HCs2Cvg18E/gDazllYYfSVPbtlll9/DhMHMmtGoVfx8RkVj9OB4A\n7saCxnhgO/A1MA0rPloL/I/bCZSaV1YGd91lc3q/9hpMmKCgISKJi5Xj6AZc67z3YzmNlcBBWPHV\nbNS3Iq289Zb19G7aFHbsgIULoU0br1MlIukmVuCo77zKsLqOrc7yrc5ngHL3kiY1YcMGG8p84kQL\nFPffD998Y9O1Nm3qdepEJB3FChwFwAVYMdUDwEdAEdAJ+F9sHKnpLqdPqmHOHOuod+qp1tv7jDMU\nLESk+mLVqrcCPgCuAOZj9SEtgE3Ar4Ax2OCEq11OYyxqVRWmtBSWLLGmtG+8YUVTF1/sdapEJJW4\n2apqPdAHeA5revsV1iz3OKye4zK8DRoSZt48mzjpwAOtX8bChfZeRKQmJRpxOgNHY5Xk84HFrqUo\nOcpxOIqKrP/FAw/YaLUiItHUVs/xJVjP7mwseDTAhjsXD+3ZY0OFTJsGl1wC992noCEi7osVce7B\nAsT/Op9XAtuAHGAk8A9XU5aYOpvj8Pvhz3+GqVNtyJCxY+GUU7xOlYikAzdzHH2Ak0I+/wh0xXId\nn5EagaNO8vvhmWegsBDGjLE+GQoaIlJb4hVV7Qx5/7TztxzQSEYe8fvh+uvhyy/hv/+1QQlFRGpT\nrMDRGCuW2u18Hun8bQioN4BH/vEPmD3bRrLVnBgi4oVYY1WNB/6DBZCAJthw6OPdTJREdu+9MHIk\nvP22goaIeCdW4Lgfm2Tpe2xcqtlAMda/4z7XUyaVvPuuDUj45ZfQvr3XqRGRuiyRWvVc4FDn/TKs\nWW6qqBOtqn7+GQ47zMaXUiW4iFSXm62q+jkHHg0Uhi0vB16t6kklOa+9BkceqaAhIqkhVuAYgM35\nHe4trDmuAofLpk2DYcNssMIHH/Q6NSIiJlYdRwNgR4TlO5114qLiYrjoInufnQ3nnONpckREfhEr\nx9EIa0W1M2x5UxQ4XPPzz7BunQ2HPnAg3Hqr1ykSEaksVo5jGDaveF7Iso7YPOTDXExTnbVpE3Tr\nBt272yi3t9zidYpERPYWr1b9BmAQwQ5/O7GhRp53M1FJyJhWVWVlFixOPBEGD/Y6NSKSyarbqirR\nHQOBI1Kdh5cyJnDcd5/1Bv/gA6gXKx8oIlJNbg+rfjhwnfMXYCHwEvBdVU8oe7vxRvj0U3spaIhI\nqov1mPod8CmWy3gRCxil2Fzkv3M9ZXVEYSG89x7MmgVt2nidGhGR+GJlVSYBg7FAEaonMBDo5VKa\nkpH2RVV33QUNGsCjj3qdEhGpK9ys41iCTRkbyXdAKgzondaBo6QEOneGjz6CLl28To2I1BXVDRyx\niqrC+2+ESqXxqtLSqlXW7Pa88xQ0RCS9xKoc7wD8i8hRqV2Cxz8bGILNGjgUeCzKdt2Br4BLgDed\nZYOAy4EK4FvgKmBXgudNaTt2wB/+AP36waBBXqdGRCQ5sbIq/QF/lG38wKg4x87GirROB1YDM4G+\nwKII232I5WJGYIEjD/gE6IIFi9eA9yOcM+2KqhYtgj594NRT4emnIavKmUURkapxsznuyKoe1NED\nG4a92Pk8DujN3oFjADYxVPeQZduBPdiQ7uXO39XVTI+nBg6ETz6BFSvg4YfhuusUNEQkPcUKHCOi\nLA/8xL86zrHbAT+EfF4FHBdhm97AqVjgCBx7M/AEsBL4CZgMfBTnfCnr449h3DgYMwYOPxxatPA6\nRSIiVRcrcEykclGVH6v3uB0rXoonkTKkIVjT3sB5AufqBPwNK7Laho2ZdRnwSvgB8vPzf3nv8/nw\n+XwJnLb2+P1wxx0wZIgNJyIiUtsKCgooKCioseMlWljSCausPhl4ChvkcHecfY4H8rEKcpz9K6hc\nQb48JA0tsHqO64CGwJnAX5x1/Zzj3RR2jpSv45g5Ey65BJYtU69wEUkNbjbHBaucHgO8C0wDjsAG\nOIwXNABmAYdhuYYcrMXUO2HbHIKNuNsRq+e4EZiAVaofD+yDXdzp2HAnaWfoULjmGgUNEckcsYqq\nxgPdsLqG27FK6mYh6zfHOXYZcDNWP5GN5VIWAdc761+Ise88bMraWVguZTY27ElaKSmBN96Ab7/1\nOiUiIjUnVlal2PkbqSzIj+UWvJbSRVWjRsHrr8PEiV6nREQkyM3muHlVPaiYYcPgttu8ToWISM1S\nybtLpk2zecPPO8/rlIiI1CwFDhcEmuA+8oiNfCsikkkUOFwwZIj9vewyb9MhIuKGRCtHTgIOxXqT\nHwg0AVa4lagkpFzl+JIl1tFv+nTo2NHr1IiI7M3tfhxgnfj+jnXgA+uTMaaqJ8x077xjgxgqaIhI\npkokcPwRG0+qxPm8GmjqWorS3Cef2Mi3IiKZKpHAsQvrhBfQ2KW0pL3du+GLL+CUU7xOiYiIexIJ\nHG9gvbybY+NIfYxNyiRhvvgCDjsM9t/f65SIiLgn0cqRM50X2BAiH7qTnKSlTOX4d99ZEdXjj0Pf\nvl6nRkQkuupWjqf7VEIpEzj+9jdo2hQeesjrlIiIxFYbrap2RHitAt4iNcarSgkzZsBpp3mdChER\n9yUScR7GZvIb63y+FJufYw5wA+BzJWWJSYkcx5490Lw5rFtnuQ4RkVRWG0VVhcBvwpbNBY7Bhj8/\nuqonrwEpETjmzIHLL4cFC7xOiYhIfLVRVFWKTcJUz3ldDPzsrPP+qZ0CZsyAHj28ToWISO1IJHBc\nhk3dusF5XQFcjs3Od7N7SUsP5eUwZgz07Ol1SkREaodaVVXDggXw1FNQVAQffQTZ2Z4lRUQkYW5O\n5BSwD3ANNt94o5DlV1f1pJmguNhyGf36wdixChoiUnckUlT1MtAKOBuYCnQAdrqZqFTn98O118Kd\nd1qOo3Vrr1MkIlJ7EsmqBFpQBVpXNQC+AI5zMV2J8qSoavhwePZZGzq9fiJ5NhGRFFIbrap2O3+3\nAUdhY1YdWNUTpqNZs2DwYNi1CzZuhIEDLXgoaIhIXZRIxPkL8F8saIzEJnG6D/iPe8lKWK3kOM49\n1+o0cnPhqKOgcWP4979dP62IiCvcrhyvhw0xshmr36hT0xP99a+waZP101i5MpjTKCryOmUiIt5J\nJOJ8AxzrdkKqyNUcR14etGwJZ5wBjzxileLr1kGbNq6dUkTEdbUx5MhgYBPwGsFZAMFyIV5zLXCs\nWgVdu8KGDfY5K917vIiIOGqjH8el2NAiN4Utz+hiq2nT4IQTFDBERMIlEjjy3E5EKpo2DU480etU\niIiknkSa4zbGWlG95Hw+DDjPtRSlgIoKeO89m9FPREQqSyRwjMD6cvze+bwGeCTB458NLAaWAnfH\n2K47UAZcGLKsOTAeWAQsBI5P8JzVNmmSzRvetWttnVFEJH0kEjg6AY8R7AhYEmPbUNnAM1jwOALo\nC3SJst1jwCQqV9Y8Dbzv7PMbLIDUimefhZtuUv2GiEgkiQSOXdhAhwGdnGXx9ACWAcXAHmAc0DvC\ndgOwnMXGkGX7AicBw53PZVjPdVetWgXLl1u/jUsvdftsIiLpKZHAkY/lBtoDrwKfELvYKaAdNuVs\nwCpnWfg2vYHnnc+BtrUdsUAyApiN1a/kJnDOKps8GQ4+GK65Bvr3h332ibuLiEidlEirqinYwztQ\nx3ArlXMH0STSwWIIMNDZNotgUVV9oBs2UdTMkO3uDz9Afn7+L+99Ph8+ny+B04Yl1A/33QcDBsBL\nL8HQoUkfQkQkZRUUFFBQUFBjx0ukFP9dYCwwgcTrN8ACTT5WxwEwCKjA6jMCloekoQU2Te21wHTg\na4J9RU7EAkd4a64a6QD48svw5JPwzTewezc0ahR/HxGRdFUbo+M+gdU3LMTqIi6i8oRO0czCmu7m\nATnYvOXvhG1zCBYcOjrHvtHZZj1WzNXZ2e50YEEC50zasmVwxx02BlW9egoaIiLxJFJUVeC86gOn\nYDmC4UCzOPuVYUVNk7GWU8OwllHXO+tfiLP/AOAVLOgUAVclkNakfPihzeD36KNqeisikqhEsyr7\nAOcDF2N1D+9hD3avVbmo6pVX4K67bNrXnj1rOFUiIimsNgY5fB2b7W8S1qR2KlZXkQqqFDj8fjji\nCHjxRTjpJBdSJSKSwmpjkMPhWOe9cufzSdjAh+GDHqaNGTOgvFxjUYmIVEUigWMSVjzVFyuqWgG8\n6Wai3DZqFFx5pXqGi4hURazA8SssWFyC9dt4A8va+NxPlnv8fnj/ffjgA69TIiKSnmIFjkVYJfhZ\nwEpn2e2up8hlS5daMdXhh3udEhGR9BSrH8eFwE/AZ8B/gNOoRmVKqpgyxaaCVTGViEjVxAocb2PF\nVL8GPgduAw7ExpU60/2k1bx77oGnn4Yz0zL1IiKpIdnf3ftjPccvBVJhmqOEm+P6/dC4MQwZApdf\nDrmuDpkoIpK6aqMfRypLOHDs3AktW0JJiYqpRKRuq42xqjLChg0WOBQ0RESqp84FDhERqR4FDhER\nSYoCh4iIJEWBQ0REkqLAISIiSVHgEBGRpChwiIhIUhQ4REQkKQocIiKSlEQmckprZWXw0kuweTO0\naOF1akQzTuEsAAAJ20lEQVRE0l/G5zjGjIEXXoC33oKcHK9TIyKS/tJ95KaYgxyWlUGXLpbj8Plq\nL1EiIqlMgxxGUVoKV1wBHTtCz55ep0ZEJHNkbOB4/HHYvh0mTNCIuCIiNSndH6kRi6rKyiynMXEi\n/OY3HqRKRCSFqagqgokT4aCDFDRERNyQkYHjq6/gnHO8ToWISGbKyMCxZg20a+d1KkREMpPbgeNs\nYDGwFLg7xnbdgTLgwrDl2cAc4N1kTrp2LbRpk8weIiKSKDcDRzbwDBY8jgD6Al2ibPcYMIm9K2tu\nBRYC0TtrRLBmDbRtm2xyRUQkEW4Gjh7AMqAY2AOMA3pH2G4AMB7YGLa8PXAOMJQka/+V4xARcY+b\ngaMd8EPI51XOsvBtegPPO59DcxZPAXcBFcmc9KefoKQEDjggucSKiEhi3AwciRQvDQEGOttmEcxZ\nnAdswOo3ksptrFsHrVur05+IiFvcHB13NdAh5HMHLNcR6lisCAugBdALqyQ/DjgfK6pqBDQDRgNX\nhJ8kPz//l/c+n48GDXyq3xARCVFQUEBBQUGNHc/N3+X1ge+A04A1wAysgnxRlO1HYK2n/hu2vCdw\nJ/CHCPvs1XN8/Hh49VX4b/hRREQEqH7PcTdzHGXAzcBkrOXUMCxoXO+sfyGJYyXcqkotqkRE3JXu\nNQF75TgGDYImTeDeez1KkYhIitNYVWGmT7c5OERExB0ZleMoKoLjj4dVq6BhQw9TJSKSwlK5jqPW\nLFgAn38OkydDv34KGiIibkr7oqrFi+Hkk2HmTJuD4847vU6RiEhmS/uiql69/JxxBtx2m9dJERFJ\nD3W+crxBA7jpJq9TISJSd6R9jiPS1LEiIhJdnc9xiIhI7VLgEBGRpChwiIhIUhQ4REQkKQocIiKS\nFAUOERFJigKHiIgkRYFDRESSosAhIiJJUeAQEZGkKHCIiEhSFDhERCQpChwiIpIUBQ4REUmKAoeI\niCRFgUNERJKiwCEiIklR4BARkaQocIiISFIUOEREJCkKHCIikpTaCBxnA4uBpcDdMbbrDpQBFzqf\nOwCfAguA+cAtLqZRREQS5HbgyAaewYLHEUBfoEuU7R4DJgFZzrI9wG3AkcDxwE1R9s1YBQUFXifB\nVbq+9JbJ15fJ11YT3A4cPYBlQDEWCMYBvSNsNwAYD2wMWbYOmOu83wksAtq6ldBUlOn/eHV96S2T\nry+Tr60muB042gE/hHxe5SwL36Y38Lzz2R/hOHlAV2B6DadPRESS5HbgiBQEwg0BBjrbZhEsqgpo\nguVGbsVyHiIi4qHwh3RNOx7Ix+o4AAYBFVh9RsDykHS0AEqBa4F3gAbAe8AHWIAJtwzoVNOJFhHJ\ncEXAoV4nIpr6WALzgBysziJWBfcIgq2qsoDRwFMupk9ERFJQL+A7LHcwyFl2vfMKFxo4TsRyJ3OB\nOc7r7Aj7iIiIiIiI1LxEOxamk2KgEMtdzXCW7Q98CCwBpgDNPUlZ8oYD64FvQ5bFupZB2L1cDJxZ\nS2msjkjXl4+1HAzkkHuFrEu364vWATdT7mG068sn/e9hI6wF6lxgIfAPZ3mm3Lsqy8aKvvKwCvR4\ndSfpYgV2c0P9H/B35/3dwOBaTVHVnYQ1oQ59sEa7liOwe9gAu6fLSP3hcCJd3wPA7RG2Tcfraw0c\n47xvghU3dyFz7mG068uUe5jr/K0PfI0V/dfYvUvlC48l0Y6F6Si8pdv5wCjn/SjggtpNTpV9DmwJ\nWxbtWnoDY7F7WYzd2x7uJ7FaIl0fRG6pmI7XF6kDbjsy5x5Guz7IjHtY6vzNwX5ob6EG7126Bo5E\nOhamIz/wETALa5IM0AorEsH528qDdNWUaNfSFruHAel8PwcA84BhBIsC0v368gh2wM3Ee5iHXd/X\nzudMuIf1sMC4nmCRXI3du3QNHIl0LExHJ2D/gHthY3OdFLbeT+Zce7xrScfrfB7oiBWBrAWeiLFt\nulxfE+BNrAPujrB1mXAPwzsYZ8o9rMCuoT1wMnBK2Ppq3bt0DRyrscqtgA5Ujpjpaq3zdyPwFpZd\nXI+VxwK0ATZ4kK6aEu1awu9ne2dZutlA8D/kUILZ/XS9vgZY0HgZeNtZlkn3MHB9YwheX6bdw23A\nROBYMuveVUmyHQvTQS7Q1HnfGJiGtW74P4KtxgaSPpXjYPcnvHI80rUEKudysF97Rbg/qkFNyKPy\n9bUJeX8b8KrzPh2vL1oH3Ey5h9GuLxPuYQuCRWz7AJ8Bp5E5965aInUsTGcdsZs3F2seGLim/bF6\nj3RrjjsWWAPsxuqjriL2tdyD3cvFwFm1mtKqCb++q7EHUSFWPv42leuj0u36onXAzZR7GOn6epEZ\n9/AoYDZ2bYXAXc7yTLl3IiIiIiIiIiIiIiIiIiIiIiIiIiIiIuKNCuDxkM93YqOb1oSRwJ9q6Fix\n9MGGvv44bPnBQN9aOL9I0tJ1yBERsM53fwQOcD7X5NhB1TlW/SS2vQb4C9azN1RH4M81cHyRGqfA\nIelsD/AiNjREuJFUzjHsdP76gKlYr+AibNiFftjEWYXAISH7nA7MxEYoONdZlg3809l+HnBdyHE/\nByZgI5GG6+sc/1uCQz3cjw1sORwbDiLUYGyQyznA34ArgXewnMmH2BA1w7ERa2djQ2bHSl8bbOiJ\nOU4aToyQRhGRjLcDG99rBdAMuINgUdUIKgeOwMiuPmxuglbY2DyrsVnfwGaBC4xdNBJ433l/KDas\nSEPsQXyvs7whFljynOPuxIqYwrUFvsdyRtnYwz8wf8ynQLcI+/QE3g353N9JQ2CYiEeBy5z3zbHg\nlhsjfbdjw0qAjUPUJMI5RRKiLK+kux3Y+EK3AD8luM9MgvMSLAMmO+/nExx+2g+8HrLNcuBwbODJ\no4CLnHXNsMBShv3K/z7C+bpjAeJH5/Mr2FDXE5zPkQaUC1/mx3IaW53PZwJ/wOp1wILEQTHSNxPL\noTTAclvzIpxTJCEKHJIJhmDFNSNClpURLIqth+UuAnaFvK8I+VxB7P8TgXqPm7GHeCgfUBJjv9BA\nkEXlOpRE61PCj38hNk90uEjpAyv6Og/LTT2JDZcukjTVcUgm2ILlDq4h+BAuxuYgACv/b5DkMbOw\nFk9ZQCes7mMxljv5K8EA05ng/M7RzMSKngJFVZdi9SyxbCc4zH4gPaEmY7msgK4hyyOl7yBsnpeh\nzqsrIlWkHIeks9Bf6k9gv7QDXsKKguYCkwhWjofvF348f8j7lVjxUzPgeqwV11CszmA29jDfgLXs\nijWj2lps/oNPnX3eo3L9RSSFQLmT/pFYcAw9/kNYTqsQ+wG4HAuQ0dLnw4bX3oMV710R5/wiIiIi\nIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIikk7+H3DflhUfBn/tAAAAAElFTkSuQmCC\n",
       "text": [
        "<matplotlib.figure.Figure at 0x11d274090>"
       ]
      }
     ],
     "prompt_number": 51
    },
    {
     "cell_type": "heading",
     "level": 2,
     "metadata": {},
     "source": [
      "Growing Randomized Trees to predict relevance scores"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "from sklearn.ensemble import RandomForestRegressor\n",
      "\n",
      "base_estimator = RandomForestRegressor(n_estimators=1, min_samples_split=10)\n",
      "grower = EnsembleGrower(lb_view, base_estimator)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 29
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "grower.launch(X_dev, y_dev, n_estimators=200, folder=\"web10k\",\n",
      "              dump_models=False)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 50,
       "text": [
        "Progress: 00% (000/200), elapsed: 0.413s\n"
       ]
      }
     ],
     "prompt_number": 50
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "grower"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 59,
       "text": [
        "Progress: 100% (200/200), elapsed: 1051.006s\n"
       ]
      }
     ],
     "prompt_number": 59
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "#grower.wait()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 57
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "%%time\n",
      "\n",
      "rfr = grower.aggregate_model()\n",
      "print(\"Number of trees: {}\".format(len(rfr.estimators_)))"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "Number of trees: 200\n",
        "CPU times: user 4 ms, sys: 0 ns, total: 4 ms\n",
        "Wall time: 3.03 ms\n"
       ]
      }
     ],
     "prompt_number": 60
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "print_evaluation(rfr, X_test, y_test, qid_test)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "Prediction time: 21.488s\n",
        "NDCG@5 score: 0.528"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "NDCG@10 score: 0.531"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "NDCG score: 0.751"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "R2 score: 0.190\n"
       ]
      }
     ],
     "prompt_number": 61
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "plot_ndcg_by_trees(rfr, X_test, y_test, qid_test)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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PHktEZKp4AcQIhACOHgWWLwfc3IB//Us+WCARkTlgcBjYuXPA0qXAgwfy1kV4OJ+1ICLz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\nRU5ErkJggg==\n",
       "text": [
        "<matplotlib.figure.Figure at 0x25ae3210>"
       ]
      }
     ],
     "prompt_number": 62
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Evaluation of the overfitting of the ensemble:"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "print_evaluation(rfr, X_train_medium, y_train_medium, qid_train_medium)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "Prediction time: 11.140s\n",
        "NDCG@5 score: 0.949"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "NDCG@10 score: 0.953"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "NDCG score: 0.963"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "R2 score: 0.806\n"
       ]
      }
     ],
     "prompt_number": 63
    },
    {
     "cell_type": "heading",
     "level": 2,
     "metadata": {},
     "source": [
      "Comparing with a baseline linear regression models (with different optimizers and input scaling)"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "%time lr = LinearRegression().fit(X_dev, y_dev)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "CPU times: user 25.4 s, sys: 1.47 s, total: 26.9 s\n",
        "Wall time: 19.9 s\n"
       ]
      }
     ],
     "prompt_number": 30
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "%time y_test_lr = lr.predict(X_test)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "CPU times: user 136 ms, sys: 96.5 ms, total: 232 ms\n",
        "Wall time: 214 ms\n"
       ]
      }
     ],
     "prompt_number": 33
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "print_evaluation(lr, X_test, y_test, qid_test)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "Prediction time: 0.221s\n",
        "NDCG@5 score: 0.433"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "NDCG@10 score: 0.450"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "NDCG score: 0.722"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "R2 score: 0.127\n"
       ]
      }
     ],
     "prompt_number": 34
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Evaluate overfitting by comparing with training set:"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "print_evaluation(lr, X_dev, y_dev, qid_dev)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "Prediction time: 1.035s\n",
        "NDCG@5 score: 0.432"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "NDCG@10 score: 0.451"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "NDCG score: 0.722"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "R2 score: 0.131\n"
       ]
      }
     ],
     "prompt_number": 40
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Interestingly enough, a slight overfitting of the training set from a regression standpoint (higher r2 score) does not seem to cause overfitting from a ranking standpoint. This would have to be checked with cross-validation though.\n",
      "\n",
      "Let's evaluate the impact of imput feature normalization:"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "%%time\n",
      "\n",
      "nlr = LinearRegression(normalize=True).fit(X_dev, y_dev)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "CPU times: user 27.2 s, sys: 2.62 s, total: 29.8 s\n",
        "Wall time: 23.9 s\n"
       ]
      }
     ],
     "prompt_number": 47
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "print_evaluation(nlr, X_test, y_test, qid_test)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "Prediction time: 0.235s\n",
        "NDCG@5 score: 0.434"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "NDCG@10 score: 0.450"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "NDCG score: 0.722"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "R2 score: 0.127\n"
       ]
      }
     ],
     "prompt_number": 49
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "%%time\n",
      "\n",
      "nlr = LinearRegression(normalize=True).fit(X_train_small, y_train_small)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "CPU times: user 1.59 s, sys: 44.5 ms, total: 1.63 s\n",
        "Wall time: 1.22 s\n"
       ]
      }
     ],
     "prompt_number": 53
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "print_evaluation(nlr, X_test, y_test, qid_test)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "Prediction time: 0.228s\n",
        "NDCG@5 score: 0.431"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "NDCG@10 score: 0.448"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "NDCG score: 0.719"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "R2 score: 0.098\n"
       ]
      }
     ],
     "prompt_number": 54
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "from sklearn.linear_model import SGDRegressor\n",
      "from sklearn.preprocessing import StandardScaler\n",
      "\n",
      "scaler = StandardScaler()\n",
      "X_train_small_scaled = scaler.fit_transform(X_train_small)\n",
      "X_test_scaled = scaler.transform(X_test)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 55
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "%%time\n",
      "\n",
      "sgdlr = SGDRegressor(alpha=1e-7, learning_rate='constant', eta0=1e-5, n_iter=50).fit(X_train_small_scaled, y_train_small)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "CPU times: user 1.26 s, sys: 15 ms, total: 1.27 s\n",
        "Wall time: 1.27 s\n"
       ]
      }
     ],
     "prompt_number": 96
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "print_evaluation(sgdlr, X_test_scaled, y_test, qid_test)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "Prediction time: 0.250s\n",
        "NDCG@5 score: 0.433"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "NDCG@10 score: 0.448"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "NDCG score: 0.720"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "R2 score: 0.118\n"
       ]
      }
     ],
     "prompt_number": 98
    },
    {
     "cell_type": "heading",
     "level": 2,
     "metadata": {},
     "source": [
      "Comparing with a classification to NDCG ranking reduction models"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "from sklearn.linear_model import LogisticRegression\n",
      "from sklearn.base import RegressorMixin\n",
      "from sklearn.preprocessing import StandardScaler\n",
      "from sklearn.base import clone"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 100
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "def proba_to_relevance(probas):\n",
      "    \"\"\"MCRank-like reduction of classification proba to DCG predictions\"\"\"\n",
      "    rel = np.zeros(probas.shape[0], dtype=np.float32)\n",
      "    for i in range(probas.shape[1]):\n",
      "        rel += i * probas[:, i]\n",
      "    return rel\n",
      "        \n",
      "        \n",
      "class ClassificationRanker(RegressorMixin):\n",
      "    \n",
      "    def __init__(self, base_estimator=None):\n",
      "        self.base_estimator = base_estimator\n",
      "        \n",
      "    def fit(self, X, y):\n",
      "        self.estimator_ = clone(self.base_estimator)\n",
      "        self.scaler_ = StandardScaler()\n",
      "        X = self.scaler_.fit_transform(X)\n",
      "        self.estimator_.fit(X, y)\n",
      "        \n",
      "    def predict(self, X):\n",
      "        X_scaled = self.scaler_.transform(X)\n",
      "        probas = self.estimator_.predict_proba(X_scaled)\n",
      "        return proba_to_relevance(probas)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 105
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "%%time\n",
      "\n",
      "logr = ClassificationRanker(LogisticRegression(C=1000))\n",
      "logr.fit(X_train_small, y_train_small)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "CPU times: user 20min 29s, sys: 2.97 s, total: 20min 32s\n",
        "Wall time: 8h 30min 23s\n"
       ]
      }
     ],
     "prompt_number": 44
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "print_evaluation(logr, X_test, y_test, qid_test)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "Prediction time: 1.326s\n",
        "NDCG@5 score: 0.440"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "NDCG@10 score: 0.456"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "NDCG score: 0.724"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "R2 score: 0.126\n"
       ]
      }
     ],
     "prompt_number": 45
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "%%time\n",
      "\n",
      "from sklearn.linear_model import SGDClassifier\n",
      "\n",
      "sgdlogr = SGDClassifier(loss='modified_huber', alpha=1e-8, n_iter=200, learning_rate='constant', eta0=1e-6, n_jobs=-1)\n",
      "sgdlogrr = ClassificationRanker(sgdlogr)\n",
      "sgdlogrr.fit(X_train_small_scaled, y_train_small)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "CPU times: user 860 ms, sys: 993 ms, total: 1.85 s\n",
        "Wall time: 13.9 s\n"
       ]
      }
     ],
     "prompt_number": 146
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "print_evaluation(sgdlogrr, X_test_scaled, y_test, qid_test)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "Prediction time: 0.564s\n",
        "NDCG@5 score: 0.440"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "NDCG@10 score: 0.454"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "NDCG score: 0.723"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "R2 score: 0.127\n"
       ]
      }
     ],
     "prompt_number": 147
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "%%time\n",
      "\n",
      "from sklearn.ensemble import RandomForestClassifier\n",
      "\n",
      "rfc = RandomForestClassifier(n_estimators=200, max_features=20, min_samples_split=5,\n",
      "                            random_state=1, n_jobs=-1)\n",
      "rfr = ClassificationRanker(rfc)\n",
      "rfr.fit(X_train_small, y_train_small)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "CPU times: user 3min 34s, sys: 1.3 s, total: 3min 36s\n",
        "Wall time: 1min\n"
       ]
      }
     ],
     "prompt_number": 122
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "print_evaluation(rfr, X_test, y_test, qid_test)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "Prediction time: 5.751s\n",
        "NDCG@5 score: 0.468"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "NDCG@10 score: 0.477"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "NDCG score: 0.730"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "R2 score: 0.138\n"
       ]
      }
     ],
     "prompt_number": 123
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "%%time\n",
      "\n",
      "from sklearn.ensemble import GradientBoostingClassifier\n",
      "\n",
      "gbc = GradientBoostingClassifier(n_estimators=100, random_state=1)\n",
      "gbr = ClassificationRanker(gbc)\n",
      "gbr.fit(X_train_small, y_train_small)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "CPU times: user 5min 52s, sys: 1.45 s, total: 5min 54s\n",
        "Wall time: 5min 54s\n"
       ]
      }
     ],
     "prompt_number": 124
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "print_evaluation(gbr, X_test, y_test, qid_test)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "Prediction time: 5.175s\n",
        "NDCG@5 score: 0.500"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "NDCG@10 score: 0.506"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "NDCG score: 0.743"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "R2 score: 0.149\n"
       ]
      }
     ],
     "prompt_number": 125
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "%%time\n",
      "\n",
      "from sklearn.ensemble import ExtraTreesClassifier\n",
      "\n",
      "etc = ClassificationRanker(ExtraTreesClassifier(n_estimators=200, random_state=1, n_jobs=-1))\n",
      "etc.fit(X_train_small, y_train_small)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "CPU times: user 2min 5s, sys: 1.8 s, total: 2min 7s\n",
        "Wall time: 36.4 s\n"
       ]
      }
     ],
     "prompt_number": 111
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "print_evaluation(etc, X_test, y_test, qid_test)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "Prediction time: 7.620s\n",
        "NDCG@5 score: 0.433"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "NDCG@10 score: 0.447"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "NDCG score: 0.719"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "R2 score: 0.124\n"
       ]
      }
     ],
     "prompt_number": 112
    },
    {
     "cell_type": "heading",
     "level": 2,
     "metadata": {},
     "source": [
      "Introspecting the distribution of relevance scores predictions"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "subset = np.random.permutation(y_test.shape[0])[:10000]"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 66
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "plt.title('Extra Trees predictions')\n",
      "plt.scatter(y_test[subset], y_test_etr[subset], alpha=0.1, s=100)\n",
      "plt.xlabel('True relevance')\n",
      "plt.ylabel('Predicted relevance')\n",
      "plt.ylim(-2, 5)\n",
      "plt.xlim(-2, 5)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 67,
       "text": [
        "(-2, 5)"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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ItbU+UVb8JX/5y1/mvvvuY3h4mIsuuoinn36ayy+/nMcee2w15FOcQmzbxveT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\n",
       "text": [
        "<matplotlib.figure.Figure at 0x98b655d0>"
       ]
      }
     ],
     "prompt_number": 67
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "plt.title('Linear Regression predictions')\n",
      "plt.scatter(y_test[subset], y_test_lr[subset], alpha=0.1, s=100)\n",
      "plt.xlabel('True relevance')\n",
      "plt.ylabel('Predicted relevance')\n",
      "plt.ylim(-2, 5)\n",
      "plt.xlim(-2, 5)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 68,
       "text": [
        "(-2, 5)"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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I328BAl13kUzm2Lixh2BwAMty1l0KSC8/mbQIhwMUCgWGh0eIxzMIIYhE/NTV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\n",
       "text": [
        "<matplotlib.figure.Figure at 0x98b7b4d0>"
       ]
      }
     ],
     "prompt_number": 68
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "plt.hist(y_test, bins=5, alpha=.3, color='b', label='True relevance')\n",
      "plt.hist(y_test_etr, bins=5, alpha=.3, color='g', label='ET predicted relevance')\n",
      "plt.legend(loc='best')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 69,
       "text": [
        "<matplotlib.legend.Legend at 0x98b5c950>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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nZwOQnZ3NunXrACgqKiIzMxOHw0FUVBTR0dGUl5dTX1/PoUOHiI+PB2DGjBnm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\nISIiligwRETEkv8HQ6Nwd5RAkl8AAAAASUVORK5CYII=\n",
       "text": [
        "<matplotlib.figure.Figure at 0x98b42410>"
       ]
      }
     ],
     "prompt_number": 69
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "For each query, count the number of results with rank 0, 1, 2, 3 or 4."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "unique_qids_test = np.unique(qid_test)\n",
      "for qid in unique_qids_test[:5]:\n",
      "    qids = y_test[qid_test == qid].astype(np.int)\n",
      "    print(np.bincount(qids, minlength=5))"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "[45 54 31  8  0]\n",
        "[59 25  8  2  0]\n",
        "[52 20  9  2  3]\n",
        "[97 45  2  2  2]\n",
        "[32 56 24  7  4]\n"
       ]
      }
     ],
     "prompt_number": 70
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 44
    }
   ],
   "metadata": {}
  }
 ]
}