diff --git a/datasets/glass.py b/datasets/glass.py
index fdf83ab..4518269 100644
--- a/datasets/glass.py
+++ b/datasets/glass.py
@@ -12,4 +12,4 @@ class Dataset(BaseDataset):
     def get_data(self):
         data = np.load("datasets/14_glass.npz")
         X, y = data["X"], data["y"]
-        return dict(X=X, y=y)
+        return dict(X=X, y=y, X_test=None)
diff --git a/datasets/simulated.py b/datasets/simulated.py
index 9886f68..f489d4d 100644
--- a/datasets/simulated.py
+++ b/datasets/simulated.py
@@ -1,7 +1,6 @@
 from benchopt import BaseDataset, safe_import_context
 
 with safe_import_context() as import_ctx:
-    # import module to generate normal 1d data
     from sklearn.datasets import make_regression
     import numpy as np
 
diff --git a/datasets/smap.py b/datasets/smap.py
new file mode 100644
index 0000000..884c96e
--- /dev/null
+++ b/datasets/smap.py
@@ -0,0 +1,24 @@
+from benchopt import BaseDataset
+from benchopt import safe_import_context
+
+with safe_import_context() as import_ctx:
+    import os
+    import pandas as pd
+
+
+class Dataset(BaseDataset):
+    name = "SMAP"
+
+    install_cmd = "conda"
+    requirements = ["pandas"]
+
+    def get_data(self):
+
+        path = "/storage/store/work/jyehya/Benchmarks/processing/processed/SMAP"
+        dataset = "SMAP"
+
+        X_train = pd.read_pickle(os.path.join(path, dataset + "_train.pkl"))
+        X_test = pd.read_pickle(os.path.join(path, dataset + "_test.pkl"))
+        y_test = pd.read_pickle(os.path.join(path, dataset + "_test_label.pkl"))
+
+        return dict(X=X_train, y=y_test, X_test=X_test)
diff --git a/exploratory/data_exploration.ipynb b/exploratory/data_exploration.ipynb
index 8880220..e897147 100644
--- a/exploratory/data_exploration.ipynb
+++ b/exploratory/data_exploration.ipynb
@@ -2,7 +2,7 @@
  "cells": [
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": 1,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -12,6 +12,7 @@
     "import pickle\n",
     "import os\n",
     "from tqdm import tqdm\n",
+    "import re\n",
     "\n",
     "path = './processed'\n",
     "dataset = 'SMAP'"
@@ -19,7 +20,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": 2,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -32,7 +33,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": 3,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -41,7 +42,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": 4,
    "metadata": {},
    "outputs": [
     {
@@ -50,7 +51,7 @@
        "(array([0, 1]), array([372921,  54696]))"
       ]
      },
-     "execution_count": 5,
+     "execution_count": 4,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -61,7 +62,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": 5,
    "metadata": {},
    "outputs": [
     {
@@ -360,7 +361,7 @@
        "[8 rows x 25 columns]"
       ]
      },
-     "execution_count": 6,
+     "execution_count": 5,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -379,7 +380,7 @@
     {
      "data": {
       "text/plain": [
-       "<matplotlib.legend.Legend at 0x7f303613a420>"
+       "<matplotlib.legend.Legend at 0x7fc98a26a8a0>"
       ]
      },
      "execution_count": 7,
@@ -424,7 +425,7 @@
     {
      "data": {
       "text/plain": [
-       "[<matplotlib.lines.Line2D at 0x7f302f1ac680>]"
+       "[<matplotlib.lines.Line2D at 0x7fc977432210>]"
       ]
      },
      "execution_count": 8,
@@ -713,7 +714,7 @@
     {
      "data": {
       "text/plain": [
-       "[<matplotlib.lines.Line2D at 0x7f3024aca7e0>]"
+       "[<matplotlib.lines.Line2D at 0x7fae2d871af0>]"
       ]
      },
      "execution_count": 14,
@@ -722,7 +723,7 @@
     },
     {
      "data": {
-      "image/png": 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",
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",
       "text/plain": [
        "<Figure size 640x480 with 1 Axes>"
       ]
@@ -747,7 +748,7 @@
     {
      "data": {
       "text/plain": [
-       "0.8671755332458719"
+       "0.866993127027223"
       ]
      },
      "execution_count": 15,
@@ -768,7 +769,7 @@
     {
      "data": {
       "text/plain": [
-       "(array([0, 1]), array([424927,   2690]))"
+       "(array([0, 1]), array([424819,   2798]))"
       ]
      },
      "execution_count": 16,
@@ -788,7 +789,7 @@
     {
      "data": {
       "text/plain": [
-       "[<matplotlib.lines.Line2D at 0x7f3024afaab0>]"
+       "[<matplotlib.lines.Line2D at 0x7fae2d5343b0>]"
       ]
      },
      "execution_count": 17,
@@ -1414,7 +1415,7 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "100%|██████████| 134/134 [00:42<00:00,  3.19it/s]\n"
+      "100%|██████████| 134/134 [02:10<00:00,  1.03it/s]\n"
      ]
     }
    ],
@@ -1436,7 +1437,14 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "100%|██████████| 34/34 [00:00<00:00, 114.49it/s]\n"
+      "  0%|          | 0/34 [00:00<?, ?it/s]"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "100%|██████████| 34/34 [00:00<00:00, 37.64it/s]\n"
      ]
     }
    ],
@@ -1458,7 +1466,7 @@
      "data": {
       "text/plain": [
        "array([ 1,  1,  1,  1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1,\n",
-       "       -1,  1,  1,  1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1,  1])"
+       "       -1,  1,  1,  1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1])"
       ]
      },
      "execution_count": 32,
@@ -1479,18 +1487,25 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "100%|██████████| 134/134 [00:01<00:00, 120.35it/s]\n"
+      "  0%|          | 0/134 [00:00<?, ?it/s]"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "100%|██████████| 134/134 [00:02<00:00, 47.96it/s]\n"
      ]
     },
     {
      "data": {
       "text/plain": [
-       "array([-1, -1, -1, -1, -1, -1, -1, -1,  1, -1, -1, -1, -1,  1, -1, -1, -1,\n",
+       "array([-1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1,\n",
+       "       -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1,\n",
+       "       -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1,\n",
        "       -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1,\n",
-       "       -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1,  1, -1, -1, -1,\n",
        "       -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1,\n",
        "       -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1,\n",
-       "       -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1,  1, -1,\n",
        "       -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1,\n",
        "       -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1])"
       ]
@@ -2249,10 +2264,10 @@
     {
      "data": {
       "text/plain": [
-       "(0.883383743167546,\n",
-       " 0.08316633266533066,\n",
-       " 0.010687612670615503,\n",
-       " 0.01894112277498859)"
+       "(0.8911418844687979,\n",
+       " 0.22573839662447256,\n",
+       " 0.01377800669585372,\n",
+       " 0.025970873786407767)"
       ]
      },
      "execution_count": 40,
@@ -2273,7 +2288,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 41,
+   "execution_count": 12,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -2291,7 +2306,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 42,
+   "execution_count": 13,
    "metadata": {},
    "outputs": [
     {
@@ -2307,7 +2322,7 @@
        "      dtype=float32)"
       ]
      },
-     "execution_count": 42,
+     "execution_count": 13,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -2318,7 +2333,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 50,
+   "execution_count": 6,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -2331,16 +2346,32 @@
     "        machine (str, optional): _description_. machine number for SMD dataset. Defaults to None.\n",
     "        format : machine-1-1\n",
     "    \"\"\"\n",
+    "    path = './processed'\n",
+    "    \n",
     "    if dataset not in ['SMD', 'MSL', 'SMAP']:\n",
     "        raise ValueError(\"Dataset name should be one of ['SMD', 'MSL', 'SMAP']\")\n",
     "    \n",
     "    if machine is not None and dataset != 'SMD':\n",
     "        raise ValueError(\"Machine number is only for SMD dataset\")\n",
-    "    \n",
+    "\n",
+    "    if machine == 'all':\n",
+    "        machine = set()\n",
+    "        pattern = re.compile(r'^machine-(\\d+-\\d+)_')\n",
+    "        files = os.listdir(os.path.join(path, dataset))\n",
+    "        \n",
+    "        for file in files:\n",
+    "            match = pattern.match(file)\n",
+    "            if match:\n",
+    "                machine.add(match.group(1))\n",
+    "        \n",
+    "        machine = sorted(machine)\n",
+    "\n",
+    "    # print(machine)    \n",
+    "\n",
     "    if type(machine) == str:\n",
     "        # Only one machine\n",
     "        machine = [machine]\n",
-    "        \n",
+    "\n",
     "    name = []\n",
     "    if machine is not None:\n",
     "        for m in machine:\n",
@@ -2348,8 +2379,6 @@
     "    else:\n",
     "        name.append(dataset)\n",
     "        \n",
-    "    path = './processed'\n",
-    "    \n",
     "    X_train = []\n",
     "    X_test = []\n",
     "    y_test = []\n",
@@ -2373,16 +2402,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 51,
+   "execution_count": 7,
    "metadata": {},
    "outputs": [],
    "source": [
-    "X_train, X_test, y_test = load_data('SMD', ['1-1', '1-2', '1-3'])"
+    "X_train, X_test, y_test = load_data('SMD', 'all')"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 52,
+   "execution_count": 8,
    "metadata": {},
    "outputs": [
     {
@@ -2391,7 +2420,7 @@
        "list"
       ]
      },
-     "execution_count": 52,
+     "execution_count": 8,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -2402,7 +2431,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 53,
+   "execution_count": 17,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -2416,7 +2445,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 54,
+   "execution_count": 18,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -2427,7 +2456,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 55,
+   "execution_count": 19,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -2437,31 +2466,26 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 56,
+   "execution_count": 20,
    "metadata": {},
    "outputs": [
     {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "0.09461262906511203\n",
-      "1.0\n",
-      "0.17286960985626282\n",
-      "\n",
-      "0.03249341349726407\n",
-      "0.8874538745387454\n",
-      "0.06269143043336592\n",
-      "\n",
-      "0.40503432494279173\n",
-      "0.21664626682986537\n",
-      "0.2822966507177033\n",
-      "\n"
+     "ename": "NameError",
+     "evalue": "name 'precision_score' is not defined",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
+      "Cell \u001b[0;32mIn[20], line 2\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m i \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mrange\u001b[39m(\u001b[38;5;28mlen\u001b[39m(y_pred)):\n\u001b[0;32m----> 2\u001b[0m     \u001b[38;5;28mprint\u001b[39m(\u001b[43mprecision_score\u001b[49m(y_test[i], y_pred[i]), recall_score(y_test[i], y_pred[i]), f1_score(y_test[i], y_pred[i]), sep\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;130;01m\\t\u001b[39;00m\u001b[38;5;124m'\u001b[39m, end\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;130;01m\\n\u001b[39;00m\u001b[38;5;124m'\u001b[39m)\n",
+      "\u001b[0;31mNameError\u001b[0m: name 'precision_score' is not defined"
      ]
     }
    ],
    "source": [
+    "from sklearn.metrics import precision_score, recall_score, f1_score\n",
+    "\n",
     "for i in range(len(y_pred)):\n",
-    "    print(precision_score(y_test[i], y_pred[i]), recall_score(y_test[i], y_pred[i]), f1_score(y_test[i], y_pred[i]), sep='\\n', end='\\n\\n')"
+    "    print(precision_score(y_test[i], y_pred[i]), recall_score(y_test[i], y_pred[i]), f1_score(y_test[i], y_pred[i]), sep='\\t', end='\\n')"
    ]
   },
   {
@@ -2473,7 +2497,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 64,
+   "execution_count": 21,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -2485,19 +2509,39 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 65,
+   "execution_count": 22,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(708405, 708420, 708420)"
+      ]
+     },
+     "execution_count": 22,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "len(X_train), len(X_test), len(y_test)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 32,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/html": [
-       "<style>#sk-container-id-2 {color: black;}#sk-container-id-2 pre{padding: 0;}#sk-container-id-2 div.sk-toggleable {background-color: white;}#sk-container-id-2 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-2 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-2 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-2 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-2 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-2 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-2 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-2 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-2 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-2 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-2 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-2 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-2 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-2 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-2 div.sk-item {position: relative;z-index: 1;}#sk-container-id-2 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-2 div.sk-item::before, #sk-container-id-2 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-2 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-2 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-2 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-2 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-2 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-2 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-2 div.sk-label-container {text-align: center;}#sk-container-id-2 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-2 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-2\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>OneClassSVM(nu=0.01)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-2\" type=\"checkbox\" checked><label for=\"sk-estimator-id-2\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">OneClassSVM</label><div class=\"sk-toggleable__content\"><pre>OneClassSVM(nu=0.01)</pre></div></div></div></div></div>"
+       "<style>#sk-container-id-3 {color: black;}#sk-container-id-3 pre{padding: 0;}#sk-container-id-3 div.sk-toggleable {background-color: white;}#sk-container-id-3 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-3 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-3 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-3 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-3 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-3 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-3 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-3 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-3 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-3 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-3 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-3 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-3 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-3 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-3 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-3 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-3 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-3 div.sk-item {position: relative;z-index: 1;}#sk-container-id-3 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-3 div.sk-item::before, #sk-container-id-3 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-3 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-3 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-3 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-3 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-3 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-3 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-3 div.sk-label-container {text-align: center;}#sk-container-id-3 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-3 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-3\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>OneClassSVM(nu=0.01)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-3\" type=\"checkbox\" checked><label for=\"sk-estimator-id-3\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">OneClassSVM</label><div class=\"sk-toggleable__content\"><pre>OneClassSVM(nu=0.01)</pre></div></div></div></div></div>"
       ],
       "text/plain": [
        "OneClassSVM(nu=0.01)"
       ]
      },
-     "execution_count": 65,
+     "execution_count": 32,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -2508,19 +2552,12 @@
     "\n",
     "ocsvm = OneClassSVM(nu=0.01)\n",
     "\n",
-    "ocsvm.fit(X_train)\n",
-    "\n",
-    "# for X in X_train:\n",
-    "#     ocsvm.fit(X)\n",
-    "\n",
-    "# y_pred = []\n",
-    "# for X in X_test:\n",
-    "#     y_pred.append(ocsvm.predict(X))"
+    "ocsvm.fit(X_train[:100000])"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 66,
+   "execution_count": 33,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -2529,7 +2566,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 67,
+   "execution_count": 34,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -2538,24 +2575,168 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 68,
+   "execution_count": 35,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "(0.3377910998385981,\n",
-       " 0.3614606464347397,\n",
-       " 0.3492252681764005,\n",
-       " 0.9280404871105488)"
+       "(0.042995515920999594, 0.6955916315717973, 0.08098521933744038)"
       ]
      },
-     "execution_count": 68,
+     "execution_count": 35,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
+    "from sklearn.metrics import precision_score, recall_score, f1_score\n",
+    "precision_score(y_test, y_pred), recall_score(y_test, y_pred), f1_score(y_test, y_pred)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Deep Isolation Forest"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 44,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from pyod.models.dif import DIF\n",
+    "\n",
+    "X_train, X_test, y_test = load_data('SMD', ['1-1', '1-2', '1-3', '1-4', '1-5', '1-6', '1-7', '1-8'])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 45,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "X_train = pd.concat(X_train)\n",
+    "X_test = pd.concat(X_test)\n",
+    "y_test = np.concatenate(y_test)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 46,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "pandas.core.frame.DataFrame"
+      ]
+     },
+     "execution_count": 46,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "type(X_train)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 47,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(194369, 194374, 194374)"
+      ]
+     },
+     "execution_count": 47,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "len(X_train), len(X_test), len(y_test)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 52,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "DIF(batch_size=1000, contamination=0.05, device=device(type='cpu'),\n",
+       "  hidden_activation='tanh', hidden_neurons=[500, 100], max_samples=256,\n",
+       "  n_ensemble=50, n_estimators=6, random_state=None, representation_dim=20,\n",
+       "  skip_connection=False)"
+      ]
+     },
+     "execution_count": 52,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "dif = DIF(contamination=0.05)\n",
+    "\n",
+    "dif.fit(X_train)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 53,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "y_pred = dif.predict(X_test)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 54,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "((array([0, 1]), array([178170,  16204])),\n",
+       " (array([0, 1]), array([182632,  11742])))"
+      ]
+     },
+     "execution_count": 54,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "np.unique(y_pred, return_counts=True), np.unique(y_test, return_counts=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 55,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(0.2184645766477413, 0.3014818599897803, 0.2533457382094038)"
+      ]
+     },
+     "execution_count": 55,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "from sklearn.metrics import precision_score, recall_score, f1_score\n",
+    "\n",
     "precision_score(y_test, y_pred), recall_score(y_test, y_pred), f1_score(y_test, y_pred)"
    ]
   },
diff --git a/objective.py b/objective.py
index 7ae5d7b..3145993 100644
--- a/objective.py
+++ b/objective.py
@@ -14,9 +14,10 @@ def get_one_result(self):
         """
         return np.zeros(self.X.shape[0])
 
-    def set_data(self, X, y):
+    def set_data(self, X, y, X_test):
         "Set the data to compute the objective."
         self.X, self.y = X, y
+        self.X_test = X_test
 
     def evaluate_result(self, y_hat):
         "Evaluate the result provided by the solver."
diff --git a/solvers/dif.py b/solvers/dif.py
new file mode 100644
index 0000000..f0ab1df
--- /dev/null
+++ b/solvers/dif.py
@@ -0,0 +1,33 @@
+# Deep Isolation Forest
+from benchopt import BaseSolver
+from benchopt import safe_import_context
+
+with safe_import_context() as import_ctx:
+    from pyod.models.dif import DIF
+
+
+class Solver(BaseSolver):
+    name = "DIF"
+
+    install_cmd = "conda"
+    requirements = ["pyod"]
+
+    parameters = {
+        "contamination": [0.05, 0.1, 0.2],
+    }
+
+    sampling_strategy = "run_once"
+
+    def set_objective(self, X, y, X_test=None):
+        # y is y_test, the learning is unsupervised
+        self.X = X
+        self.X_test = X_test
+        self.y = y
+
+    def run(self, _):
+        clf = DIF(contamination=self.contamination)
+        clf.fit(self.X)
+        self.y_hat = clf.predict(self.X_test)
+
+    def get_result(self):
+        return {"y_hat": self.y_hat}