diff --git a/notebooks/octopus_showcase.ipynb b/notebooks/octopus_showcase.ipynb
index 69c7dea..0715c7d 100644
--- a/notebooks/octopus_showcase.ipynb
+++ b/notebooks/octopus_showcase.ipynb
@@ -12,7 +12,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 82,
+   "execution_count": 1,
    "metadata": {
     "ExecuteTime": {
      "end_time": "2020-12-15T06:57:35.530610Z",
@@ -26,18 +26,20 @@
     "import pandas as pd \n",
     "import numpy as np\n",
     "from pandas_summary import DataFrameSummary\n",
+    "import matplotlib.pyplot as plt\n",
     "\n",
     "pd.set_option('display.max_columns', None)  # or 1000\n",
     "pd.set_option('display.max_rows', None)  # or 1000\n",
     "pd.set_option('display.max_colwidth', -1)  # or 199\n",
-    "import octopus as oc \n",
+    "import octopus_ml as oc \n",
     "from sklearn.metrics import classification_report\n",
-    "%matplotlib inline\n"
+    "%matplotlib inline\n",
+    "import seaborn as sns\n"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 83,
+   "execution_count": 2,
    "metadata": {
     "ExecuteTime": {
      "end_time": "2020-12-15T06:27:56.795757Z",
@@ -65,7 +67,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 53,
+   "execution_count": 3,
    "metadata": {
     "ExecuteTime": {
      "end_time": "2020-12-15T06:27:56.802314Z",
@@ -79,7 +81,7 @@
        "(91713, 186)"
       ]
      },
-     "execution_count": 53,
+     "execution_count": 3,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -90,7 +92,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 54,
+   "execution_count": 4,
    "metadata": {
     "ExecuteTime": {
      "end_time": "2020-12-15T06:27:56.921276Z",
@@ -664,7 +666,7 @@
        "      <td>51.0</td>\n",
        "      <td>51.0</td>\n",
        "      <td>54.8</td>\n",
-       "      <td>51.000000</td>\n",
+       "      <td>51.0</td>\n",
        "      <td>37.0</td>\n",
        "      <td>37.0</td>\n",
        "      <td>7.45</td>\n",
@@ -875,384 +877,6 @@
        "      <td>Metabolic</td>\n",
        "      <td>Metabolic</td>\n",
        "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>79267</td>\n",
-       "      <td>46918</td>\n",
-       "      <td>118</td>\n",
-       "      <td>0</td>\n",
-       "      <td>81.0</td>\n",
-       "      <td>22.64</td>\n",
-       "      <td>1</td>\n",
-       "      <td>Caucasian</td>\n",
-       "      <td>F</td>\n",
-       "      <td>165.1</td>\n",
-       "      <td>Operating Room</td>\n",
-       "      <td>Operating Room / Recovery</td>\n",
-       "      <td>92</td>\n",
-       "      <td>admit</td>\n",
-       "      <td>CTICU</td>\n",
-       "      <td>0.000694</td>\n",
-       "      <td>0</td>\n",
-       "      <td>61.7</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>203.0</td>\n",
-       "      <td>1206.03</td>\n",
-       "      <td>1</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>0.6</td>\n",
-       "      <td>4.0</td>\n",
-       "      <td>6.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>5.0</td>\n",
-       "      <td>185.0</td>\n",
-       "      <td>114.0</td>\n",
-       "      <td>25.9</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>60.0</td>\n",
-       "      <td>30.0</td>\n",
-       "      <td>30.0</td>\n",
-       "      <td>142.0</td>\n",
-       "      <td>7.39</td>\n",
-       "      <td>4.0</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>34.8</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>8.0</td>\n",
-       "      <td>62.0</td>\n",
-       "      <td>30.0</td>\n",
-       "      <td>48.0</td>\n",
-       "      <td>42.0</td>\n",
-       "      <td>48.0</td>\n",
-       "      <td>42.0</td>\n",
-       "      <td>116.0</td>\n",
-       "      <td>92.0</td>\n",
-       "      <td>92.0</td>\n",
-       "      <td>52.0</td>\n",
-       "      <td>84.0</td>\n",
-       "      <td>84.0</td>\n",
-       "      <td>84.0</td>\n",
-       "      <td>84.0</td>\n",
-       "      <td>23.0</td>\n",
-       "      <td>7.0</td>\n",
-       "      <td>100.0</td>\n",
-       "      <td>95.0</td>\n",
-       "      <td>164.0</td>\n",
-       "      <td>78.0</td>\n",
-       "      <td>158.0</td>\n",
-       "      <td>84.0</td>\n",
-       "      <td>158.0</td>\n",
-       "      <td>84.0</td>\n",
-       "      <td>38.0</td>\n",
-       "      <td>34.8</td>\n",
-       "      <td>62.0</td>\n",
-       "      <td>44.0</td>\n",
-       "      <td>62.0</td>\n",
-       "      <td>44.0</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>100.0</td>\n",
-       "      <td>96.0</td>\n",
-       "      <td>92.0</td>\n",
-       "      <td>71.0</td>\n",
-       "      <td>92.0</td>\n",
-       "      <td>71.0</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>12.0</td>\n",
-       "      <td>11.0</td>\n",
-       "      <td>100.0</td>\n",
-       "      <td>99.0</td>\n",
-       "      <td>136.0</td>\n",
-       "      <td>106.0</td>\n",
-       "      <td>136.0</td>\n",
-       "      <td>106.0</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>35.6</td>\n",
-       "      <td>34.8</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>185.0</td>\n",
-       "      <td>88.0</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>11.6</td>\n",
-       "      <td>8.9</td>\n",
-       "      <td>34.0</td>\n",
-       "      <td>25.9</td>\n",
-       "      <td>1.6</td>\n",
-       "      <td>1.1</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>198.0</td>\n",
-       "      <td>43.0</td>\n",
-       "      <td>5.0</td>\n",
-       "      <td>3.5</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>9.0</td>\n",
-       "      <td>8.0</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>11.6</td>\n",
-       "      <td>11.6</td>\n",
-       "      <td>34.0</td>\n",
-       "      <td>34.0</td>\n",
-       "      <td>1.6</td>\n",
-       "      <td>1.1</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>43.0</td>\n",
-       "      <td>43.0</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>8.8</td>\n",
-       "      <td>8.8</td>\n",
-       "      <td>37.0</td>\n",
-       "      <td>27.0</td>\n",
-       "      <td>7.44</td>\n",
-       "      <td>7.34</td>\n",
-       "      <td>337.0</td>\n",
-       "      <td>102.0</td>\n",
-       "      <td>342.5</td>\n",
-       "      <td>236.666667</td>\n",
-       "      <td>36.0</td>\n",
-       "      <td>33.0</td>\n",
-       "      <td>7.37</td>\n",
-       "      <td>7.34</td>\n",
-       "      <td>337.0</td>\n",
-       "      <td>265.0</td>\n",
-       "      <td>337.0</td>\n",
-       "      <td>337.0</td>\n",
-       "      <td>0.04</td>\n",
-       "      <td>0.03</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>Cardiovascular</td>\n",
-       "      <td>Cardiovascular</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>92056</td>\n",
-       "      <td>34377</td>\n",
-       "      <td>33</td>\n",
-       "      <td>0</td>\n",
-       "      <td>19.0</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>0</td>\n",
-       "      <td>Caucasian</td>\n",
-       "      <td>M</td>\n",
-       "      <td>188.0</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>Accident &amp; Emergency</td>\n",
-       "      <td>91</td>\n",
-       "      <td>admit</td>\n",
-       "      <td>Med-Surg ICU</td>\n",
-       "      <td>0.073611</td>\n",
-       "      <td>0</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>119.0</td>\n",
-       "      <td>601.01</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>60.0</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>103.0</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>16.0</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>36.7</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>99.0</td>\n",
-       "      <td>57.0</td>\n",
-       "      <td>99.0</td>\n",
-       "      <td>57.0</td>\n",
-       "      <td>89.0</td>\n",
-       "      <td>60.0</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>104.0</td>\n",
-       "      <td>90.0</td>\n",
-       "      <td>104.0</td>\n",
-       "      <td>90.0</td>\n",
-       "      <td>18.0</td>\n",
-       "      <td>16.0</td>\n",
-       "      <td>100.0</td>\n",
-       "      <td>96.0</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>147.0</td>\n",
-       "      <td>120.0</td>\n",
-       "      <td>147.0</td>\n",
-       "      <td>120.0</td>\n",
-       "      <td>37.2</td>\n",
-       "      <td>36.7</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>99.0</td>\n",
-       "      <td>68.0</td>\n",
-       "      <td>99.0</td>\n",
-       "      <td>68.0</td>\n",
-       "      <td>89.0</td>\n",
-       "      <td>76.0</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>104.0</td>\n",
-       "      <td>92.0</td>\n",
-       "      <td>104.0</td>\n",
-       "      <td>92.0</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>100.0</td>\n",
-       "      <td>100.0</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>130.0</td>\n",
-       "      <td>120.0</td>\n",
-       "      <td>130.0</td>\n",
-       "      <td>120.0</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>Trauma</td>\n",
-       "      <td>Trauma</td>\n",
-       "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "</div>"
@@ -1262,351 +886,262 @@
        "0  66154         25312       118          0               68.0  22.73   \n",
        "1  114252        59342       81           0               77.0  27.42   \n",
        "2  119783        50777       118          0               25.0  31.95   \n",
-       "3  79267         46918       118          0               81.0  22.64   \n",
-       "4  92056         34377       33           0               19.0 NaN      \n",
        "\n",
        "   elective_surgery  ethnicity gender  height hospital_admit_source  \\\n",
        "0  0                 Caucasian  M      180.3   Floor                  \n",
        "1  0                 Caucasian  F      160.0   Floor                  \n",
        "2  0                 Caucasian  F      172.7   Emergency Department   \n",
-       "3  1                 Caucasian  F      165.1   Operating Room         \n",
-       "4  0                 Caucasian  M      188.0   NaN                    \n",
        "\n",
-       "            icu_admit_source  icu_id icu_stay_type      icu_type  \\\n",
-       "0  Floor                      92      admit         CTICU          \n",
-       "1  Floor                      90      admit         Med-Surg ICU   \n",
-       "2  Accident & Emergency       93      admit         Med-Surg ICU   \n",
-       "3  Operating Room / Recovery  92      admit         CTICU          \n",
-       "4  Accident & Emergency       91      admit         Med-Surg ICU   \n",
+       "       icu_admit_source  icu_id icu_stay_type      icu_type  pre_icu_los_days  \\\n",
+       "0  Floor                 92      admit         CTICU         0.541667           \n",
+       "1  Floor                 90      admit         Med-Surg ICU  0.927778           \n",
+       "2  Accident & Emergency  93      admit         Med-Surg ICU  0.000694           \n",
        "\n",
-       "   pre_icu_los_days  readmission_status  weight  albumin_apache  \\\n",
-       "0  0.541667          0                   73.9    2.3              \n",
-       "1  0.927778          0                   70.2   NaN               \n",
-       "2  0.000694          0                   95.3   NaN               \n",
-       "3  0.000694          0                   61.7   NaN               \n",
-       "4  0.073611          0                  NaN     NaN               \n",
+       "   readmission_status  weight  albumin_apache  apache_2_diagnosis  \\\n",
+       "0  0                   73.9    2.3             113.0                \n",
+       "1  0                   70.2   NaN              108.0                \n",
+       "2  0                   95.3   NaN              122.0                \n",
        "\n",
-       "   apache_2_diagnosis  apache_3j_diagnosis  apache_post_operative  arf_apache  \\\n",
-       "0  113.0               502.01               0                      0.0          \n",
-       "1  108.0               203.01               0                      0.0          \n",
-       "2  122.0               703.03               0                      0.0          \n",
-       "3  203.0               1206.03              1                      0.0          \n",
-       "4  119.0               601.01               0                      0.0          \n",
+       "   apache_3j_diagnosis  apache_post_operative  arf_apache  bilirubin_apache  \\\n",
+       "0  502.01               0                      0.0         0.4                \n",
+       "1  203.01               0                      0.0        NaN                 \n",
+       "2  703.03               0                      0.0        NaN                 \n",
        "\n",
-       "   bilirubin_apache  bun_apache  creatinine_apache  fio2_apache  \\\n",
-       "0  0.4               31.0        2.51              NaN            \n",
-       "1 NaN                9.0         0.56               1.0           \n",
-       "2 NaN               NaN         NaN                NaN            \n",
-       "3 NaN               NaN         NaN                 0.6           \n",
-       "4 NaN               NaN         NaN                NaN            \n",
+       "   bun_apache  creatinine_apache  fio2_apache  gcs_eyes_apache  \\\n",
+       "0  31.0        2.51              NaN           3.0               \n",
+       "1  9.0         0.56               1.0          1.0               \n",
+       "2 NaN         NaN                NaN           3.0               \n",
        "\n",
-       "   gcs_eyes_apache  gcs_motor_apache  gcs_unable_apache  gcs_verbal_apache  \\\n",
-       "0  3.0              6.0               0.0                4.0                 \n",
-       "1  1.0              3.0               0.0                1.0                 \n",
-       "2  3.0              6.0               0.0                5.0                 \n",
-       "3  4.0              6.0               0.0                5.0                 \n",
-       "4 NaN              NaN               NaN                NaN                  \n",
+       "   gcs_motor_apache  gcs_unable_apache  gcs_verbal_apache  glucose_apache  \\\n",
+       "0  6.0               0.0                4.0                168.0            \n",
+       "1  3.0               0.0                1.0                145.0            \n",
+       "2  6.0               0.0                5.0               NaN               \n",
        "\n",
-       "   glucose_apache  heart_rate_apache  hematocrit_apache  intubated_apache  \\\n",
-       "0  168.0           118.0              27.4               0.0                \n",
-       "1  145.0           120.0              36.9               0.0                \n",
-       "2 NaN              102.0             NaN                 0.0                \n",
-       "3  185.0           114.0              25.9               1.0                \n",
-       "4 NaN              60.0              NaN                 0.0                \n",
+       "   heart_rate_apache  hematocrit_apache  intubated_apache  map_apache  \\\n",
+       "0  118.0              27.4               0.0               40.0         \n",
+       "1  120.0              36.9               0.0               46.0         \n",
+       "2  102.0             NaN                 0.0               68.0         \n",
        "\n",
-       "   map_apache  paco2_apache  paco2_for_ph_apache  pao2_apache  ph_apache  \\\n",
-       "0  40.0       NaN           NaN                  NaN          NaN          \n",
-       "1  46.0        37.0          37.0                 51.0         7.45        \n",
-       "2  68.0       NaN           NaN                  NaN          NaN          \n",
-       "3  60.0        30.0          30.0                 142.0        7.39        \n",
-       "4  103.0      NaN           NaN                  NaN          NaN          \n",
+       "   paco2_apache  paco2_for_ph_apache  pao2_apache  ph_apache  resprate_apache  \\\n",
+       "0 NaN           NaN                  NaN          NaN         36.0              \n",
+       "1  37.0          37.0                 51.0         7.45       33.0              \n",
+       "2 NaN           NaN                  NaN          NaN         37.0              \n",
        "\n",
-       "   resprate_apache  sodium_apache  temp_apache  urineoutput_apache  \\\n",
-       "0  36.0             134.0          39.3        NaN                   \n",
-       "1  33.0             145.0          35.1        NaN                   \n",
-       "2  37.0            NaN             36.7        NaN                   \n",
-       "3  4.0             NaN             34.8        NaN                   \n",
-       "4  16.0            NaN             36.7        NaN                   \n",
+       "   sodium_apache  temp_apache  urineoutput_apache  ventilated_apache  \\\n",
+       "0  134.0          39.3        NaN                  0.0                 \n",
+       "1  145.0          35.1        NaN                  1.0                 \n",
+       "2 NaN             36.7        NaN                  0.0                 \n",
        "\n",
-       "   ventilated_apache  wbc_apache  d1_diasbp_invasive_max  \\\n",
-       "0  0.0                14.1        46.0                     \n",
-       "1  1.0                12.7       NaN                       \n",
-       "2  0.0               NaN         NaN                       \n",
-       "3  1.0                8.0         62.0                     \n",
-       "4  0.0               NaN         NaN                       \n",
+       "   wbc_apache  d1_diasbp_invasive_max  d1_diasbp_invasive_min  d1_diasbp_max  \\\n",
+       "0  14.1        46.0                    32.0                    68.0            \n",
+       "1  12.7       NaN                     NaN                      95.0            \n",
+       "2 NaN         NaN                     NaN                      88.0            \n",
        "\n",
-       "   d1_diasbp_invasive_min  d1_diasbp_max  d1_diasbp_min  \\\n",
-       "0  32.0                    68.0           37.0            \n",
-       "1 NaN                      95.0           31.0            \n",
-       "2 NaN                      88.0           48.0            \n",
-       "3  30.0                    48.0           42.0            \n",
-       "4 NaN                      99.0           57.0            \n",
+       "   d1_diasbp_min  d1_diasbp_noninvasive_max  d1_diasbp_noninvasive_min  \\\n",
+       "0  37.0           68.0                       37.0                        \n",
+       "1  31.0           95.0                       31.0                        \n",
+       "2  48.0           88.0                       48.0                        \n",
        "\n",
-       "   d1_diasbp_noninvasive_max  d1_diasbp_noninvasive_min  d1_heartrate_max  \\\n",
-       "0  68.0                       37.0                       119.0              \n",
-       "1  95.0                       31.0                       118.0              \n",
-       "2  88.0                       48.0                       96.0               \n",
-       "3  48.0                       42.0                       116.0              \n",
-       "4  99.0                       57.0                       89.0               \n",
+       "   d1_heartrate_max  d1_heartrate_min  d1_mbp_invasive_max  \\\n",
+       "0  119.0             72.0              66.0                  \n",
+       "1  118.0             72.0             NaN                    \n",
+       "2  96.0              68.0             NaN                    \n",
        "\n",
-       "   d1_heartrate_min  d1_mbp_invasive_max  d1_mbp_invasive_min  d1_mbp_max  \\\n",
-       "0  72.0              66.0                 40.0                 89.0         \n",
-       "1  72.0             NaN                  NaN                   120.0        \n",
-       "2  68.0             NaN                  NaN                   102.0        \n",
-       "3  92.0              92.0                 52.0                 84.0         \n",
-       "4  60.0             NaN                  NaN                   104.0        \n",
+       "   d1_mbp_invasive_min  d1_mbp_max  d1_mbp_min  d1_mbp_noninvasive_max  \\\n",
+       "0  40.0                 89.0        46.0        89.0                     \n",
+       "1 NaN                   120.0       38.0        120.0                    \n",
+       "2 NaN                   102.0       68.0        102.0                    \n",
        "\n",
-       "   d1_mbp_min  d1_mbp_noninvasive_max  d1_mbp_noninvasive_min  \\\n",
-       "0  46.0        89.0                    46.0                     \n",
-       "1  38.0        120.0                   38.0                     \n",
-       "2  68.0        102.0                   68.0                     \n",
-       "3  84.0        84.0                    84.0                     \n",
-       "4  90.0        104.0                   90.0                     \n",
+       "   d1_mbp_noninvasive_min  d1_resprate_max  d1_resprate_min  d1_spo2_max  \\\n",
+       "0  46.0                    34.0             10.0             100.0         \n",
+       "1  38.0                    32.0             12.0             100.0         \n",
+       "2  68.0                    21.0             8.0              98.0          \n",
        "\n",
-       "   d1_resprate_max  d1_resprate_min  d1_spo2_max  d1_spo2_min  \\\n",
-       "0  34.0             10.0             100.0        74.0          \n",
-       "1  32.0             12.0             100.0        70.0          \n",
-       "2  21.0             8.0              98.0         91.0          \n",
-       "3  23.0             7.0              100.0        95.0          \n",
-       "4  18.0             16.0             100.0        96.0          \n",
+       "   d1_spo2_min  d1_sysbp_invasive_max  d1_sysbp_invasive_min  d1_sysbp_max  \\\n",
+       "0  74.0         122.0                  64.0                   131.0          \n",
+       "1  70.0        NaN                    NaN                     159.0          \n",
+       "2  91.0        NaN                    NaN                     148.0          \n",
        "\n",
-       "   d1_sysbp_invasive_max  d1_sysbp_invasive_min  d1_sysbp_max  d1_sysbp_min  \\\n",
-       "0  122.0                  64.0                   131.0         73.0           \n",
-       "1 NaN                    NaN                     159.0         67.0           \n",
-       "2 NaN                    NaN                     148.0         105.0          \n",
-       "3  164.0                  78.0                   158.0         84.0           \n",
-       "4 NaN                    NaN                     147.0         120.0          \n",
+       "   d1_sysbp_min  d1_sysbp_noninvasive_max  d1_sysbp_noninvasive_min  \\\n",
+       "0  73.0          131.0                     73.0                       \n",
+       "1  67.0          159.0                     67.0                       \n",
+       "2  105.0         148.0                     105.0                      \n",
        "\n",
-       "   d1_sysbp_noninvasive_max  d1_sysbp_noninvasive_min  d1_temp_max  \\\n",
-       "0  131.0                     73.0                      39.9          \n",
-       "1  159.0                     67.0                      36.3          \n",
-       "2  148.0                     105.0                     37.0          \n",
-       "3  158.0                     84.0                      38.0          \n",
-       "4  147.0                     120.0                     37.2          \n",
+       "   d1_temp_max  d1_temp_min  h1_diasbp_invasive_max  h1_diasbp_invasive_min  \\\n",
+       "0  39.9         37.2        NaN                     NaN                       \n",
+       "1  36.3         35.1        NaN                     NaN                       \n",
+       "2  37.0         36.7        NaN                     NaN                       \n",
        "\n",
-       "   d1_temp_min  h1_diasbp_invasive_max  h1_diasbp_invasive_min  h1_diasbp_max  \\\n",
-       "0  37.2        NaN                     NaN                      68.0            \n",
-       "1  35.1        NaN                     NaN                      61.0            \n",
-       "2  36.7        NaN                     NaN                      88.0            \n",
-       "3  34.8         62.0                    44.0                    62.0            \n",
-       "4  36.7        NaN                     NaN                      99.0            \n",
+       "   h1_diasbp_max  h1_diasbp_min  h1_diasbp_noninvasive_max  \\\n",
+       "0  68.0           63.0           68.0                        \n",
+       "1  61.0           48.0           61.0                        \n",
+       "2  88.0           58.0           88.0                        \n",
        "\n",
-       "   h1_diasbp_min  h1_diasbp_noninvasive_max  h1_diasbp_noninvasive_min  \\\n",
-       "0  63.0           68.0                       63.0                        \n",
-       "1  48.0           61.0                       48.0                        \n",
-       "2  58.0           88.0                       58.0                        \n",
-       "3  44.0          NaN                        NaN                          \n",
-       "4  68.0           99.0                       68.0                        \n",
+       "   h1_diasbp_noninvasive_min  h1_heartrate_max  h1_heartrate_min  \\\n",
+       "0  63.0                       119.0             108.0              \n",
+       "1  48.0                       114.0             100.0              \n",
+       "2  58.0                       96.0              78.0               \n",
        "\n",
-       "   h1_heartrate_max  h1_heartrate_min  h1_mbp_invasive_max  \\\n",
-       "0  119.0             108.0            NaN                    \n",
-       "1  114.0             100.0            NaN                    \n",
-       "2  96.0              78.0             NaN                    \n",
-       "3  100.0             96.0              92.0                  \n",
-       "4  89.0              76.0             NaN                    \n",
+       "   h1_mbp_invasive_max  h1_mbp_invasive_min  h1_mbp_max  h1_mbp_min  \\\n",
+       "0 NaN                  NaN                   86.0        85.0         \n",
+       "1 NaN                  NaN                   85.0        57.0         \n",
+       "2 NaN                  NaN                   91.0        83.0         \n",
        "\n",
-       "   h1_mbp_invasive_min  h1_mbp_max  h1_mbp_min  h1_mbp_noninvasive_max  \\\n",
-       "0 NaN                   86.0        85.0        86.0                     \n",
-       "1 NaN                   85.0        57.0        85.0                     \n",
-       "2 NaN                   91.0        83.0        91.0                     \n",
-       "3  71.0                 92.0        71.0       NaN                       \n",
-       "4 NaN                   104.0       92.0        104.0                    \n",
+       "   h1_mbp_noninvasive_max  h1_mbp_noninvasive_min  h1_resprate_max  \\\n",
+       "0  86.0                    85.0                    26.0              \n",
+       "1  85.0                    57.0                    31.0              \n",
+       "2  91.0                    83.0                    20.0              \n",
        "\n",
-       "   h1_mbp_noninvasive_min  h1_resprate_max  h1_resprate_min  h1_spo2_max  \\\n",
-       "0  85.0                    26.0             18.0             100.0         \n",
-       "1  57.0                    31.0             28.0             95.0          \n",
-       "2  83.0                    20.0             16.0             98.0          \n",
-       "3 NaN                      12.0             11.0             100.0         \n",
-       "4  92.0                   NaN              NaN               100.0         \n",
+       "   h1_resprate_min  h1_spo2_max  h1_spo2_min  h1_sysbp_invasive_max  \\\n",
+       "0  18.0             100.0        74.0        NaN                      \n",
+       "1  28.0             95.0         70.0        NaN                      \n",
+       "2  16.0             98.0         91.0        NaN                      \n",
        "\n",
-       "   h1_spo2_min  h1_sysbp_invasive_max  h1_sysbp_invasive_min  h1_sysbp_max  \\\n",
-       "0  74.0        NaN                    NaN                     131.0          \n",
-       "1  70.0        NaN                    NaN                     95.0           \n",
-       "2  91.0        NaN                    NaN                     148.0          \n",
-       "3  99.0         136.0                  106.0                  136.0          \n",
-       "4  100.0       NaN                    NaN                     130.0          \n",
+       "   h1_sysbp_invasive_min  h1_sysbp_max  h1_sysbp_min  \\\n",
+       "0 NaN                     131.0         115.0          \n",
+       "1 NaN                     95.0          71.0           \n",
+       "2 NaN                     148.0         124.0          \n",
        "\n",
-       "   h1_sysbp_min  h1_sysbp_noninvasive_max  h1_sysbp_noninvasive_min  \\\n",
-       "0  115.0         131.0                     115.0                      \n",
-       "1  71.0          95.0                      71.0                       \n",
-       "2  124.0         148.0                     124.0                      \n",
-       "3  106.0        NaN                       NaN                         \n",
-       "4  120.0         130.0                     120.0                      \n",
+       "   h1_sysbp_noninvasive_max  h1_sysbp_noninvasive_min  h1_temp_max  \\\n",
+       "0  131.0                     115.0                     39.5          \n",
+       "1  95.0                      71.0                      36.3          \n",
+       "2  148.0                     124.0                     36.7          \n",
        "\n",
-       "   h1_temp_max  h1_temp_min  d1_albumin_max  d1_albumin_min  d1_bilirubin_max  \\\n",
-       "0  39.5         37.5         2.3             2.3             0.4                \n",
-       "1  36.3         36.3         1.6             1.6             0.5                \n",
-       "2  36.7         36.7        NaN             NaN             NaN                 \n",
-       "3  35.6         34.8        NaN             NaN             NaN                 \n",
-       "4 NaN          NaN          NaN             NaN             NaN                 \n",
+       "   h1_temp_min  d1_albumin_max  d1_albumin_min  d1_bilirubin_max  \\\n",
+       "0  37.5         2.3             2.3             0.4                \n",
+       "1  36.3         1.6             1.6             0.5                \n",
+       "2  36.7        NaN             NaN             NaN                 \n",
        "\n",
        "   d1_bilirubin_min  d1_bun_max  d1_bun_min  d1_calcium_max  d1_calcium_min  \\\n",
        "0  0.4               31.0        30.0        8.5             7.4              \n",
        "1  0.5               11.0        9.0         8.6             8.0              \n",
        "2 NaN               NaN         NaN         NaN             NaN               \n",
-       "3 NaN               NaN         NaN         NaN             NaN               \n",
-       "4 NaN               NaN         NaN         NaN             NaN               \n",
        "\n",
        "   d1_creatinine_max  d1_creatinine_min  d1_glucose_max  d1_glucose_min  \\\n",
        "0  2.51               2.23               168.0           109.0            \n",
        "1  0.71               0.56               145.0           128.0            \n",
        "2 NaN                NaN                NaN             NaN               \n",
-       "3 NaN                NaN                 185.0           88.0             \n",
-       "4 NaN                NaN                NaN             NaN               \n",
        "\n",
        "   d1_hco3_max  d1_hco3_min  d1_hemaglobin_max  d1_hemaglobin_min  \\\n",
        "0  19.0         15.0         8.9                8.9                 \n",
        "1  27.0         26.0         11.3               11.1                \n",
        "2 NaN          NaN          NaN                NaN                  \n",
-       "3 NaN          NaN           11.6               8.9                 \n",
-       "4 NaN          NaN          NaN                NaN                  \n",
        "\n",
        "   d1_hematocrit_max  d1_hematocrit_min  d1_inr_max  d1_inr_min  \\\n",
        "0  27.4               27.4              NaN         NaN           \n",
        "1  36.9               36.1               1.3         1.3          \n",
        "2 NaN                NaN                NaN         NaN           \n",
-       "3  34.0               25.9               1.6         1.1          \n",
-       "4 NaN                NaN                NaN         NaN           \n",
        "\n",
        "   d1_lactate_max  d1_lactate_min  d1_platelets_max  d1_platelets_min  \\\n",
        "0  1.3             1.0             233.0             233.0              \n",
        "1  3.5             3.5             557.0             487.0              \n",
        "2 NaN             NaN             NaN               NaN                 \n",
-       "3 NaN             NaN              198.0             43.0               \n",
-       "4 NaN             NaN             NaN               NaN                 \n",
        "\n",
        "   d1_potassium_max  d1_potassium_min  d1_sodium_max  d1_sodium_min  \\\n",
        "0  4.0               3.4               136.0          134.0           \n",
        "1  4.2               3.8               145.0          145.0           \n",
        "2 NaN               NaN               NaN            NaN              \n",
-       "3  5.0               3.5              NaN            NaN              \n",
-       "4 NaN               NaN               NaN            NaN              \n",
        "\n",
        "   d1_wbc_max  d1_wbc_min  h1_albumin_max  h1_albumin_min  h1_bilirubin_max  \\\n",
        "0  14.1        14.1       NaN             NaN             NaN                 \n",
        "1  23.3        12.7       NaN             NaN             NaN                 \n",
        "2 NaN         NaN         NaN             NaN             NaN                 \n",
-       "3  9.0         8.0        NaN             NaN             NaN                 \n",
-       "4 NaN         NaN         NaN             NaN             NaN                 \n",
        "\n",
        "   h1_bilirubin_min  h1_bun_max  h1_bun_min  h1_calcium_max  h1_calcium_min  \\\n",
        "0 NaN               NaN         NaN         NaN             NaN               \n",
        "1 NaN                9.0         9.0         8.6             8.6              \n",
        "2 NaN               NaN         NaN         NaN             NaN               \n",
-       "3 NaN               NaN         NaN         NaN             NaN               \n",
-       "4 NaN               NaN         NaN         NaN             NaN               \n",
        "\n",
        "   h1_creatinine_max  h1_creatinine_min  h1_glucose_max  h1_glucose_min  \\\n",
        "0 NaN                NaN                NaN             NaN               \n",
        "1  0.56               0.56               145.0           143.0            \n",
        "2 NaN                NaN                NaN             NaN               \n",
-       "3 NaN                NaN                NaN             NaN               \n",
-       "4 NaN                NaN                NaN             NaN               \n",
        "\n",
        "   h1_hco3_max  h1_hco3_min  h1_hemaglobin_max  h1_hemaglobin_min  \\\n",
        "0 NaN          NaN          NaN                NaN                  \n",
        "1  27.0         27.0         11.3               11.3                \n",
        "2 NaN          NaN          NaN                NaN                  \n",
-       "3 NaN          NaN           11.6               11.6                \n",
-       "4 NaN          NaN          NaN                NaN                  \n",
        "\n",
        "   h1_hematocrit_max  h1_hematocrit_min  h1_inr_max  h1_inr_min  \\\n",
        "0 NaN                NaN                NaN         NaN           \n",
        "1  36.9               36.9               1.3         1.3          \n",
        "2 NaN                NaN                NaN         NaN           \n",
-       "3  34.0               34.0               1.6         1.1          \n",
-       "4 NaN                NaN                NaN         NaN           \n",
        "\n",
        "   h1_lactate_max  h1_lactate_min  h1_platelets_max  h1_platelets_min  \\\n",
        "0 NaN             NaN             NaN               NaN                 \n",
        "1  3.5             3.5             557.0             557.0              \n",
        "2 NaN             NaN             NaN               NaN                 \n",
-       "3 NaN             NaN              43.0              43.0               \n",
-       "4 NaN             NaN             NaN               NaN                 \n",
        "\n",
        "   h1_potassium_max  h1_potassium_min  h1_sodium_max  h1_sodium_min  \\\n",
        "0 NaN               NaN               NaN            NaN              \n",
        "1  4.2               4.2               145.0          145.0           \n",
        "2 NaN               NaN               NaN            NaN              \n",
-       "3 NaN               NaN               NaN            NaN              \n",
-       "4 NaN               NaN               NaN            NaN              \n",
        "\n",
        "   h1_wbc_max  h1_wbc_min  d1_arterial_pco2_max  d1_arterial_pco2_min  \\\n",
        "0 NaN         NaN         NaN                   NaN                     \n",
        "1  12.7        12.7        37.0                  37.0                   \n",
        "2 NaN         NaN         NaN                   NaN                     \n",
-       "3  8.8         8.8         37.0                  27.0                   \n",
-       "4 NaN         NaN         NaN                   NaN                     \n",
        "\n",
        "   d1_arterial_ph_max  d1_arterial_ph_min  d1_arterial_po2_max  \\\n",
        "0 NaN                 NaN                 NaN                    \n",
        "1  7.45                7.45                51.0                  \n",
        "2 NaN                 NaN                 NaN                    \n",
-       "3  7.44                7.34                337.0                 \n",
-       "4 NaN                 NaN                 NaN                    \n",
        "\n",
        "   d1_arterial_po2_min  d1_pao2fio2ratio_max  d1_pao2fio2ratio_min  \\\n",
        "0 NaN                  NaN                   NaN                     \n",
-       "1  51.0                 54.8                  51.000000              \n",
+       "1  51.0                 54.8                  51.0                   \n",
        "2 NaN                  NaN                   NaN                     \n",
-       "3  102.0                342.5                 236.666667             \n",
-       "4 NaN                  NaN                   NaN                     \n",
        "\n",
        "   h1_arterial_pco2_max  h1_arterial_pco2_min  h1_arterial_ph_max  \\\n",
        "0 NaN                   NaN                   NaN                   \n",
        "1  37.0                  37.0                  7.45                 \n",
        "2 NaN                   NaN                   NaN                   \n",
-       "3  36.0                  33.0                  7.37                 \n",
-       "4 NaN                   NaN                   NaN                   \n",
        "\n",
        "   h1_arterial_ph_min  h1_arterial_po2_max  h1_arterial_po2_min  \\\n",
        "0 NaN                 NaN                  NaN                    \n",
        "1  7.45                51.0                 51.0                  \n",
        "2 NaN                 NaN                  NaN                    \n",
-       "3  7.34                337.0                265.0                 \n",
-       "4 NaN                 NaN                  NaN                    \n",
        "\n",
        "   h1_pao2fio2ratio_max  h1_pao2fio2ratio_min  apache_4a_hospital_death_prob  \\\n",
        "0 NaN                   NaN                    0.10                            \n",
        "1  51.0                  51.0                  0.47                            \n",
        "2 NaN                   NaN                    0.00                            \n",
-       "3  337.0                 337.0                 0.04                            \n",
-       "4 NaN                   NaN                   NaN                              \n",
        "\n",
        "   apache_4a_icu_death_prob  aids  cirrhosis  diabetes_mellitus  \\\n",
        "0  0.05                      0.0   0.0        1.0                 \n",
        "1  0.29                      0.0   0.0        1.0                 \n",
        "2  0.00                      0.0   0.0        0.0                 \n",
-       "3  0.03                      0.0   0.0        0.0                 \n",
-       "4 NaN                        0.0   0.0        0.0                 \n",
        "\n",
        "   hepatic_failure  immunosuppression  leukemia  lymphoma  \\\n",
        "0  0.0              0.0                0.0       0.0        \n",
        "1  0.0              0.0                0.0       0.0        \n",
        "2  0.0              0.0                0.0       0.0        \n",
-       "3  0.0              0.0                0.0       0.0        \n",
-       "4  0.0              0.0                0.0       0.0        \n",
        "\n",
        "   solid_tumor_with_metastasis apache_3j_bodysystem apache_2_bodysystem  \n",
        "0  0.0                          Sepsis               Cardiovascular      \n",
        "1  0.0                          Respiratory          Respiratory         \n",
-       "2  0.0                          Metabolic            Metabolic           \n",
-       "3  0.0                          Cardiovascular       Cardiovascular      \n",
-       "4  0.0                          Trauma               Trauma              "
+       "2  0.0                          Metabolic            Metabolic           "
       ]
      },
-     "execution_count": 54,
+     "execution_count": 4,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "XY_df.head(5)"
+    "XY_df.head(3)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Target distribution"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 55,
+   "execution_count": 5,
    "metadata": {
     "ExecuteTime": {
      "end_time": "2020-12-15T06:27:56.927779Z",
@@ -1622,7 +1157,7 @@
        "Name: hospital_death, dtype: int64"
       ]
      },
-     "execution_count": 55,
+     "execution_count": 5,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1633,7 +1168,101 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 56,
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 288x216 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "oc.target_pie(XY_df,'hospital_death')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Selected features vs target historgrams "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 466.34x216 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "oc.hist_target(XY_df, 'd1_heartrate_min', 'hospital_death')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 466.34x216 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "oc.hist_target(XY_df, 'age', 'hospital_death')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 466.34x216 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "oc.hist_target(XY_df, 'icu_id', 'hospital_death')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Data summary - and missing values analysis"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
    "metadata": {
     "ExecuteTime": {
      "end_time": "2020-12-15T06:28:11.003524Z",
@@ -5139,7 +4768,7 @@
        "types         categorical         "
       ]
      },
-     "execution_count": 56,
+     "execution_count": 10,
      "metadata": {},
      "output_type": "execute_result"
     },
@@ -5149,20 +4778,18 @@
        "<AxesSubplot:>"
       ]
      },
-     "execution_count": 56,
+     "execution_count": 10,
      "metadata": {},
      "output_type": "execute_result"
     },
     {
      "data": {
-      "image/png": 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       "text/plain": [
        "<Figure size 1800x720 with 2 Axes>"
       ]
      },
-     "metadata": {
-      "needs_background": "light"
-     },
+     "metadata": {},
      "output_type": "display_data"
     }
    ],
@@ -5188,7 +4815,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 57,
+   "execution_count": 11,
    "metadata": {
     "ExecuteTime": {
      "end_time": "2020-12-15T06:28:11.009307Z",
@@ -5211,7 +4838,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 58,
+   "execution_count": 12,
    "metadata": {
     "ExecuteTime": {
      "end_time": "2020-12-15T06:28:11.015383Z",
@@ -5227,52 +4854,41 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 59,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2020-12-15T06:28:11.115768Z",
-     "start_time": "2020-12-15T06:28:11.017872Z"
+   "execution_count": 13,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "['ethnicity', 'gender', 'hospital_admit_source', 'icu_admit_source', 'icu_stay_type', 'icu_type', 'apache_3j_bodysystem', 'apache_2_bodysystem']\n"
+     ]
     }
-   },
-   "outputs": [],
+   ],
    "source": [
-    "X=XY_df[features]\n",
-    "y=XY_df['hospital_death']"
+    "# Categorical features pre-proccesing  \n",
+    "cat_list ,XY_df=oc.cat_features_proccessing(XY_df)\n",
+    "print (cat_list)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 60,
+   "execution_count": 14,
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2020-12-15T06:28:11.193436Z",
-     "start_time": "2020-12-15T06:28:11.117586Z"
+     "end_time": "2020-12-15T06:28:11.115768Z",
+     "start_time": "2020-12-15T06:28:11.017872Z"
     }
    },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "['ethnicity', 'gender', 'hospital_admit_source', 'icu_admit_source', 'icu_stay_type', 'icu_type', 'apache_3j_bodysystem', 'apache_2_bodysystem']\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
-    "#print([c for c in XY_df.columns if (1<XY_df[c].nunique()) & (XY_df[c].dtype != np.number)& (XY_df[c].dtype != int) ])\n",
-    "categorical_features=[]\n",
-    "for c in X.columns:\n",
-    "    col_type = X[c].dtype\n",
-    "    if col_type == 'object' or col_type.name == 'category':\n",
-    "        X[c] = X[c].astype('category')\n",
-    "        categorical_features.append(c)\n",
-    "print (categorical_features)\n",
-    "        "
+    "X=XY_df[features]\n",
+    "y=XY_df['hospital_death']"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 61,
+   "execution_count": 15,
    "metadata": {
     "ExecuteTime": {
      "end_time": "2020-12-15T06:28:11.203536Z",
@@ -5287,7 +4903,7 @@
        "<IPython.core.display.Image object>"
       ]
      },
-     "execution_count": 61,
+     "execution_count": 15,
      "metadata": {
       "image/png": {
        "height": 600,
@@ -5304,7 +4920,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 62,
+   "execution_count": 16,
    "metadata": {
     "ExecuteTime": {
      "end_time": "2020-12-15T06:28:11.420608Z",
@@ -5318,7 +4934,11 @@
      "text": [
       "number of positive instances: 7915 \n",
       "number of negative instance :  83798\n",
-      "Sampled dataset shape:  (12915, 186)\n"
+      "new dataset shape:  (12915, 186)\n",
+      "Method Name       :\u001b[35;1m sampling\u001b[0m\n",
+      "Current memory usage:\u001b[36m 18.766211MB\u001b[0m\n",
+      "Peak                :\u001b[36m 121.594595MB\u001b[0m\n",
+      "Total time taken:  \u001b[36m 198.670 ms \u001b[0m\n"
      ]
     }
    ],
@@ -5347,7 +4967,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 63,
+   "execution_count": 17,
    "metadata": {
     "ExecuteTime": {
      "end_time": "2020-12-15T06:31:00.687777Z",
@@ -5367,6 +4987,8 @@
      "output_type": "stream",
      "text": [
       "[LightGBM] [Info] Number of positive: 6332, number of negative: 67038\n",
+      "[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.075971 seconds.\n",
+      "You can set `force_col_wise=true` to remove the overhead.\n",
       "[LightGBM] [Info] Total Bins 25635\n",
       "[LightGBM] [Info] Number of data points in the train set: 73370, number of used features: 182\n",
       "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.086302 -> initscore=-2.359643\n",
@@ -5377,7 +4999,7 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "1it [00:30, 30.85s/it]"
+      "1it [00:21, 21.58s/it]"
      ]
     },
     {
@@ -5385,6 +5007,8 @@
      "output_type": "stream",
      "text": [
       "[LightGBM] [Info] Number of positive: 6332, number of negative: 67038\n",
+      "[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.104479 seconds.\n",
+      "You can set `force_col_wise=true` to remove the overhead.\n",
       "[LightGBM] [Info] Total Bins 25625\n",
       "[LightGBM] [Info] Number of data points in the train set: 73370, number of used features: 182\n",
       "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.086302 -> initscore=-2.359643\n",
@@ -5395,7 +5019,7 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "2it [00:57, 29.49s/it]"
+      "2it [00:42, 21.40s/it]"
      ]
     },
     {
@@ -5403,6 +5027,9 @@
      "output_type": "stream",
      "text": [
       "[LightGBM] [Info] Number of positive: 6332, number of negative: 67038\n",
+      "[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.014513 seconds.\n",
+      "You can set `force_row_wise=true` to remove the overhead.\n",
+      "And if memory is not enough, you can set `force_col_wise=true`.\n",
       "[LightGBM] [Info] Total Bins 25640\n",
       "[LightGBM] [Info] Number of data points in the train set: 73370, number of used features: 182\n",
       "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.086302 -> initscore=-2.359643\n",
@@ -5413,7 +5040,7 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "3it [01:27, 29.72s/it]"
+      "3it [01:11, 23.73s/it]"
      ]
     },
     {
@@ -5421,6 +5048,8 @@
      "output_type": "stream",
      "text": [
       "[LightGBM] [Info] Number of positive: 6332, number of negative: 67039\n",
+      "[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.076969 seconds.\n",
+      "You can set `force_col_wise=true` to remove the overhead.\n",
       "[LightGBM] [Info] Total Bins 25653\n",
       "[LightGBM] [Info] Number of data points in the train set: 73371, number of used features: 182\n",
       "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.086301 -> initscore=-2.359658\n",
@@ -5431,7 +5060,7 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "4it [02:09, 33.48s/it]"
+      "4it [01:32, 22.84s/it]"
      ]
     },
     {
@@ -5439,6 +5068,8 @@
      "output_type": "stream",
      "text": [
       "[LightGBM] [Info] Number of positive: 6332, number of negative: 67039\n",
+      "[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.103452 seconds.\n",
+      "You can set `force_col_wise=true` to remove the overhead.\n",
       "[LightGBM] [Info] Total Bins 25630\n",
       "[LightGBM] [Info] Number of data points in the train set: 73371, number of used features: 182\n",
       "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.086301 -> initscore=-2.359658\n",
@@ -5449,7 +5080,7 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "5it [02:49, 33.84s/it]"
+      "5it [01:52, 22.47s/it]"
      ]
     },
     {
@@ -5457,9 +5088,9 @@
      "output_type": "stream",
      "text": [
       "Method Name       :\u001b[35;1m cv\u001b[0m\n",
-      "Current memory usage:\u001b[36m 4.779288MB\u001b[0m\n",
-      "Peak                :\u001b[36m 453.719431MB\u001b[0m\n",
-      "Total time taken:  \u001b[36m 169247.596 ms \u001b[0m\n"
+      "Current memory usage:\u001b[36m 5.124197MB\u001b[0m\n",
+      "Peak                :\u001b[36m 453.330765MB\u001b[0m\n",
+      "Total time taken:  \u001b[36m 112355.785 ms \u001b[0m\n"
      ]
     },
     {
@@ -5472,12 +5103,19 @@
    ],
    "source": [
     "params = {\n",
+    "        'boosting_type': 'gbdt',\n",
     "        'objective': 'binary',\n",
-    "        'boosting': 'gbdt',        \n",
+    "        'metric': 'auc',\n",
     "        'learning_rate': 0.01,\n",
-    "        'seed': 100,\n",
-    "        'scale_pos_weight': 0.2,\n",
-    "        'force_col_wise': True\n",
+    "        'subsample': 1,\n",
+    "        'colsample_bytree': 0.2,\n",
+    "        'reg_alpha': 3,\n",
+    "        'reg_lambda': 1,\n",
+    "        'scale_pos_weight': 9,\n",
+    "        'n_estimators': 5000,\n",
+    "        'max_depth': -1,\n",
+    "        'seed':100\n",
+    "        #'verbose': -1,\n",
     "        #'categorical_feature': 'auto'\n",
     "        # 'metric': 'auc',\n",
     "        # 'scale_pos_weight':0.1,\n",
@@ -5491,7 +5129,7 @@
     "        # \"feature_fraction\" : 0.05\n",
     "}\n",
     "\n",
-    "clf,arr_f1_weighted,arr_f1_macro,arr_f1_positive,prediction_folds,preds_folds,y_folds= oc.cv(X,y,0.1,2000,shuffle=True,params=params)"
+    "clf,arr_f1_weighted,arr_f1_macro,arr_f1_positive,prediction_folds,preds_folds,y_folds= oc.cv(X,y,0.1,3000,shuffle=True,params=params)"
    ]
   },
   {
@@ -5515,13 +5153,13 @@
     }
    },
    "source": [
-    "## Plot of the CV folds - F1 macro and F1 for the positive class \n",
+    "### Plot of the CV folds - F1 macro and F1 for the positive class \n",
     "(in this case it's an unbalanced dataset)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 64,
+   "execution_count": 18,
    "metadata": {
     "ExecuteTime": {
      "end_time": "2020-12-15T06:31:00.944005Z",
@@ -5531,7 +5169,7 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
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\n",
       "text/plain": [
        "<Figure size 936x504 with 1 Axes>"
       ]
@@ -5558,7 +5196,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 65,
+   "execution_count": 19,
    "metadata": {
     "ExecuteTime": {
      "end_time": "2020-12-15T06:31:01.132424Z",
@@ -5572,12 +5210,12 @@
      "text": [
       "              precision    recall  f1-score   support\n",
       "\n",
-      "           0       0.96      0.96      0.96     83798\n",
-      "           1       0.57      0.52      0.54      7915\n",
+      "           0       0.99      0.65      0.78     83798\n",
+      "           1       0.20      0.93      0.33      7915\n",
       "\n",
-      "    accuracy                           0.92     91713\n",
-      "   macro avg       0.76      0.74      0.75     91713\n",
-      "weighted avg       0.92      0.92      0.92     91713\n",
+      "    accuracy                           0.67     91713\n",
+      "   macro avg       0.60      0.79      0.56     91713\n",
+      "weighted avg       0.92      0.67      0.74     91713\n",
       "\n"
      ]
     }
@@ -5600,7 +5238,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 66,
+   "execution_count": 20,
    "metadata": {
     "ExecuteTime": {
      "end_time": "2020-12-15T06:31:01.481931Z",
@@ -5610,7 +5248,7 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
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\n",
       "text/plain": [
        "<Figure size 720x432 with 1 Axes>"
       ]
@@ -5637,7 +5275,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 67,
+   "execution_count": 21,
    "metadata": {
     "ExecuteTime": {
      "end_time": "2020-12-15T06:31:02.186153Z",
@@ -5656,7 +5294,7 @@
     },
     {
      "data": {
-      "image/png": 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\n",
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\n",
       "text/plain": [
        "<Figure size 504x396 with 2 Axes>"
       ]
@@ -5675,7 +5313,7 @@
     },
     {
      "data": {
-      "image/png": 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\n",
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\n",
       "text/plain": [
        "<Figure size 504x396 with 2 Axes>"
       ]
@@ -5702,7 +5340,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 68,
+   "execution_count": 22,
    "metadata": {
     "ExecuteTime": {
      "end_time": "2020-12-15T06:31:03.194703Z",
@@ -5712,9 +5350,9 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
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       "text/plain": [
-       "<Figure size 1008x1152 with 1 Axes>"
+       "<Figure size 1152x720 with 1 Axes>"
       ]
      },
      "metadata": {},
@@ -5722,12 +5360,12 @@
     }
    ],
    "source": [
-    "feature_imp_list=oc.plot_imp(clf,X,'LightGBM Mortality Kaggle',num=40)"
+    "feature_imp_list=oc.plot_imp(clf,X,'LightGBM Mortality Kaggle',num=20)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 69,
+   "execution_count": 23,
    "metadata": {
     "ExecuteTime": {
      "end_time": "2020-12-15T06:31:03.201650Z",
@@ -5742,7 +5380,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 70,
+   "execution_count": 24,
    "metadata": {
     "ExecuteTime": {
      "end_time": "2020-12-15T06:31:03.213082Z",
@@ -5778,133 +5416,133 @@
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>171</th>\n",
-       "      <td>1709</td>\n",
+       "      <td>535876.716645</td>\n",
        "      <td>apache_4a_hospital_death_prob</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>1257</td>\n",
-       "      <td>age</td>\n",
+       "      <th>172</th>\n",
+       "      <td>346325.674820</td>\n",
+       "      <td>apache_4a_icu_death_prob</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>50</th>\n",
-       "      <td>1254</td>\n",
-       "      <td>d1_heartrate_min</td>\n",
+       "      <th>27</th>\n",
+       "      <td>168614.263725</td>\n",
+       "      <td>gcs_verbal_apache</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>67</th>\n",
-       "      <td>1166</td>\n",
-       "      <td>d1_temp_max</td>\n",
+       "      <th>41</th>\n",
+       "      <td>151988.522101</td>\n",
+       "      <td>ventilated_apache</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>60</th>\n",
-       "      <td>1050</td>\n",
-       "      <td>d1_spo2_min</td>\n",
+       "      <th>25</th>\n",
+       "      <td>129663.055195</td>\n",
+       "      <td>gcs_motor_apache</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>1040</td>\n",
-       "      <td>bmi</td>\n",
+       "      <th>116</th>\n",
+       "      <td>126158.822219</td>\n",
+       "      <td>d1_lactate_min</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>9</th>\n",
-       "      <td>1005</td>\n",
-       "      <td>icu_id</td>\n",
+       "      <th>1</th>\n",
+       "      <td>123964.845595</td>\n",
+       "      <td>age</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>17</th>\n",
-       "      <td>955</td>\n",
-       "      <td>apache_3j_diagnosis</td>\n",
+       "      <th>60</th>\n",
+       "      <td>114204.442367</td>\n",
+       "      <td>d1_spo2_min</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>116</th>\n",
-       "      <td>857</td>\n",
-       "      <td>d1_lactate_min</td>\n",
+       "      <th>115</th>\n",
+       "      <td>101504.979184</td>\n",
+       "      <td>d1_lactate_max</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>106</th>\n",
-       "      <td>851</td>\n",
-       "      <td>d1_glucose_min</td>\n",
+       "      <th>99</th>\n",
+       "      <td>100641.783117</td>\n",
+       "      <td>d1_bun_max</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>172</th>\n",
-       "      <td>788</td>\n",
-       "      <td>apache_4a_icu_death_prob</td>\n",
+       "      <th>64</th>\n",
+       "      <td>91150.095096</td>\n",
+       "      <td>d1_sysbp_min</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>118</th>\n",
-       "      <td>778</td>\n",
-       "      <td>d1_platelets_min</td>\n",
+       "      <th>66</th>\n",
+       "      <td>87279.921341</td>\n",
+       "      <td>d1_sysbp_noninvasive_min</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>58</th>\n",
-       "      <td>769</td>\n",
-       "      <td>d1_resprate_min</td>\n",
+       "      <th>17</th>\n",
+       "      <td>87168.229081</td>\n",
+       "      <td>apache_3j_diagnosis</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>12</th>\n",
-       "      <td>747</td>\n",
-       "      <td>pre_icu_los_days</td>\n",
+       "      <th>100</th>\n",
+       "      <td>86849.696062</td>\n",
+       "      <td>d1_bun_min</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>40</th>\n",
-       "      <td>740</td>\n",
-       "      <td>urineoutput_apache</td>\n",
+       "      <th>158</th>\n",
+       "      <td>80773.784617</td>\n",
+       "      <td>d1_arterial_ph_min</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>157</th>\n",
-       "      <td>726</td>\n",
-       "      <td>d1_arterial_ph_max</td>\n",
+       "      <th>159</th>\n",
+       "      <td>79423.022331</td>\n",
+       "      <td>d1_arterial_po2_max</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>121</th>\n",
-       "      <td>704</td>\n",
-       "      <td>d1_sodium_max</td>\n",
+       "      <th>182</th>\n",
+       "      <td>76704.551217</td>\n",
+       "      <td>apache_2_bodysystem</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>14</th>\n",
-       "      <td>675</td>\n",
-       "      <td>weight</td>\n",
+       "      <th>57</th>\n",
+       "      <td>73318.616032</td>\n",
+       "      <td>d1_resprate_max</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>161</th>\n",
-       "      <td>668</td>\n",
-       "      <td>d1_pao2fio2ratio_max</td>\n",
+       "      <th>50</th>\n",
+       "      <td>72876.979745</td>\n",
+       "      <td>d1_heartrate_min</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>124</th>\n",
-       "      <td>666</td>\n",
-       "      <td>d1_wbc_min</td>\n",
+       "      <th>21</th>\n",
+       "      <td>70535.882450</td>\n",
+       "      <td>bun_apache</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "</div>"
       ],
       "text/plain": [
-       "     Value                        Feature\n",
-       "171  1709   apache_4a_hospital_death_prob\n",
-       "1    1257   age                          \n",
-       "50   1254   d1_heartrate_min             \n",
-       "67   1166   d1_temp_max                  \n",
-       "60   1050   d1_spo2_min                  \n",
-       "2    1040   bmi                          \n",
-       "9    1005   icu_id                       \n",
-       "17   955    apache_3j_diagnosis          \n",
-       "116  857    d1_lactate_min               \n",
-       "106  851    d1_glucose_min               \n",
-       "172  788    apache_4a_icu_death_prob     \n",
-       "118  778    d1_platelets_min             \n",
-       "58   769    d1_resprate_min              \n",
-       "12   747    pre_icu_los_days             \n",
-       "40   740    urineoutput_apache           \n",
-       "157  726    d1_arterial_ph_max           \n",
-       "121  704    d1_sodium_max                \n",
-       "14   675    weight                       \n",
-       "161  668    d1_pao2fio2ratio_max         \n",
-       "124  666    d1_wbc_min                   "
+       "             Value                        Feature\n",
+       "171  535876.716645  apache_4a_hospital_death_prob\n",
+       "172  346325.674820  apache_4a_icu_death_prob     \n",
+       "27   168614.263725  gcs_verbal_apache            \n",
+       "41   151988.522101  ventilated_apache            \n",
+       "25   129663.055195  gcs_motor_apache             \n",
+       "116  126158.822219  d1_lactate_min               \n",
+       "1    123964.845595  age                          \n",
+       "60   114204.442367  d1_spo2_min                  \n",
+       "115  101504.979184  d1_lactate_max               \n",
+       "99   100641.783117  d1_bun_max                   \n",
+       "64   91150.095096   d1_sysbp_min                 \n",
+       "66   87279.921341   d1_sysbp_noninvasive_min     \n",
+       "17   87168.229081   apache_3j_diagnosis          \n",
+       "100  86849.696062   d1_bun_min                   \n",
+       "158  80773.784617   d1_arterial_ph_min           \n",
+       "159  79423.022331   d1_arterial_po2_max          \n",
+       "182  76704.551217   apache_2_bodysystem          \n",
+       "57   73318.616032   d1_resprate_max              \n",
+       "50   72876.979745   d1_heartrate_min             \n",
+       "21   70535.882450   bun_apache                   "
       ]
      },
-     "execution_count": 70,
+     "execution_count": 24,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -5927,7 +5565,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 71,
+   "execution_count": 26,
    "metadata": {
     "ExecuteTime": {
      "end_time": "2020-12-15T06:31:04.346868Z",
@@ -5935,19 +5573,9 @@
     }
    },
    "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Method Name       :\u001b[35;1m correlations\u001b[0m\n",
-      "Current memory usage:\u001b[36m 1.512899MB\u001b[0m\n",
-      "Peak                :\u001b[36m 31.197333MB\u001b[0m\n",
-      "Total time taken:  \u001b[36m 785.921 ms \u001b[0m\n"
-     ]
-    },
     {
      "data": {
-      "image/png": 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\n",
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\n",
       "text/plain": [
        "<Figure size 792x648 with 2 Axes>"
       ]
@@ -5957,426 +5585,37 @@
     }
    ],
    "source": [
-    "oc.correlations(X,top_features['Feature'])"
+    "list_for_correlations=top_features['Feature'].to_list()\n",
+    "list_for_correlations.append('hospital_death')\n",
+    "oc.correlations(XY_df,list_for_correlations)"
    ]
   },
   {
    "cell_type": "markdown",
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2020-12-02T15:43:21.575532Z",
-     "start_time": "2020-12-02T15:43:21.572171Z"
-    }
-   },
+   "metadata": {},
    "source": [
-    "## Anomaly detection on the training set (on top features alone) "
+    "## Data leakage test "
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 72,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2020-12-15T06:31:11.636794Z",
-     "start_time": "2020-12-15T06:31:04.349447Z"
-    }
-   },
+   "execution_count": 27,
+   "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Method Name       :\u001b[35;1m anomalies\u001b[0m\n",
-      "Current memory usage:\u001b[36m 14.121327MB\u001b[0m\n",
-      "Peak                :\u001b[36m 133.355563MB\u001b[0m\n",
-      "Total time taken:  \u001b[36m 7266.432 ms \u001b[0m\n"
-     ]
-    },
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>apache_4a_hospital_death_prob</th>\n",
-       "      <th>age</th>\n",
-       "      <th>d1_heartrate_min</th>\n",
-       "      <th>d1_temp_max</th>\n",
-       "      <th>d1_spo2_min</th>\n",
-       "      <th>bmi</th>\n",
-       "      <th>icu_id</th>\n",
-       "      <th>apache_3j_diagnosis</th>\n",
-       "      <th>d1_lactate_min</th>\n",
-       "      <th>d1_glucose_min</th>\n",
-       "      <th>apache_4a_icu_death_prob</th>\n",
-       "      <th>d1_platelets_min</th>\n",
-       "      <th>d1_resprate_min</th>\n",
-       "      <th>pre_icu_los_days</th>\n",
-       "      <th>urineoutput_apache</th>\n",
-       "      <th>d1_arterial_ph_max</th>\n",
-       "      <th>d1_sodium_max</th>\n",
-       "      <th>weight</th>\n",
-       "      <th>d1_pao2fio2ratio_max</th>\n",
-       "      <th>d1_wbc_min</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>0.10</td>\n",
-       "      <td>68.0</td>\n",
-       "      <td>72.0</td>\n",
-       "      <td>39.9</td>\n",
-       "      <td>74.0</td>\n",
-       "      <td>22.730000</td>\n",
-       "      <td>92</td>\n",
-       "      <td>502.01</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>109.0</td>\n",
-       "      <td>0.05</td>\n",
-       "      <td>233.0</td>\n",
-       "      <td>10.0</td>\n",
-       "      <td>0.541667</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>136.0</td>\n",
-       "      <td>73.9</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>14.1</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>0.00</td>\n",
-       "      <td>25.0</td>\n",
-       "      <td>68.0</td>\n",
-       "      <td>37.0</td>\n",
-       "      <td>91.0</td>\n",
-       "      <td>31.950000</td>\n",
-       "      <td>93</td>\n",
-       "      <td>703.03</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>0.00</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>8.0</td>\n",
-       "      <td>0.000694</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>95.3</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>0.04</td>\n",
-       "      <td>81.0</td>\n",
-       "      <td>92.0</td>\n",
-       "      <td>38.0</td>\n",
-       "      <td>95.0</td>\n",
-       "      <td>22.640000</td>\n",
-       "      <td>92</td>\n",
-       "      <td>1206.03</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>88.0</td>\n",
-       "      <td>0.03</td>\n",
-       "      <td>43.0</td>\n",
-       "      <td>7.0</td>\n",
-       "      <td>0.000694</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>7.44</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>61.7</td>\n",
-       "      <td>342.5</td>\n",
-       "      <td>8.0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>NaN</td>\n",
-       "      <td>19.0</td>\n",
-       "      <td>60.0</td>\n",
-       "      <td>37.2</td>\n",
-       "      <td>96.0</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>91</td>\n",
-       "      <td>601.01</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>16.0</td>\n",
-       "      <td>0.073611</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>5</th>\n",
-       "      <td>0.05</td>\n",
-       "      <td>67.0</td>\n",
-       "      <td>83.0</td>\n",
-       "      <td>36.8</td>\n",
-       "      <td>91.0</td>\n",
-       "      <td>27.560000</td>\n",
-       "      <td>95</td>\n",
-       "      <td>403.01</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>125.0</td>\n",
-       "      <td>0.02</td>\n",
-       "      <td>159.0</td>\n",
-       "      <td>10.0</td>\n",
-       "      <td>0.000694</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>137.0</td>\n",
-       "      <td>100.0</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>10.9</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>7</th>\n",
-       "      <td>0.11</td>\n",
-       "      <td>70.0</td>\n",
-       "      <td>86.0</td>\n",
-       "      <td>38.5</td>\n",
-       "      <td>92.0</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>91</td>\n",
-       "      <td>501.05</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>129.0</td>\n",
-       "      <td>0.06</td>\n",
-       "      <td>260.0</td>\n",
-       "      <td>12.0</td>\n",
-       "      <td>0.002083</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>7.38</td>\n",
-       "      <td>140.0</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>12.8</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>9</th>\n",
-       "      <td>0.02</td>\n",
-       "      <td>50.0</td>\n",
-       "      <td>57.0</td>\n",
-       "      <td>37.1</td>\n",
-       "      <td>96.0</td>\n",
-       "      <td>25.710000</td>\n",
-       "      <td>114</td>\n",
-       "      <td>107.01</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>134.0</td>\n",
-       "      <td>0.01</td>\n",
-       "      <td>232.0</td>\n",
-       "      <td>14.0</td>\n",
-       "      <td>0.060417</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>139.0</td>\n",
-       "      <td>79.0</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>12.8</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>10</th>\n",
-       "      <td>0.01</td>\n",
-       "      <td>72.0</td>\n",
-       "      <td>67.0</td>\n",
-       "      <td>37.0</td>\n",
-       "      <td>92.0</td>\n",
-       "      <td>28.257052</td>\n",
-       "      <td>113</td>\n",
-       "      <td>1304.08</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>133.0</td>\n",
-       "      <td>0.00</td>\n",
-       "      <td>257.0</td>\n",
-       "      <td>14.0</td>\n",
-       "      <td>0.004861</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>137.0</td>\n",
-       "      <td>67.8</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>10.3</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>11</th>\n",
-       "      <td>0.04</td>\n",
-       "      <td>80.0</td>\n",
-       "      <td>59.0</td>\n",
-       "      <td>36.9</td>\n",
-       "      <td>76.0</td>\n",
-       "      <td>27.382812</td>\n",
-       "      <td>95</td>\n",
-       "      <td>1505.02</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>100.0</td>\n",
-       "      <td>0.01</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>16.0</td>\n",
-       "      <td>1.271528</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>136.0</td>\n",
-       "      <td>70.1</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>12</th>\n",
-       "      <td>0.03</td>\n",
-       "      <td>48.0</td>\n",
-       "      <td>66.0</td>\n",
-       "      <td>36.9</td>\n",
-       "      <td>97.0</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>102</td>\n",
-       "      <td>403.01</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>98.0</td>\n",
-       "      <td>0.01</td>\n",
-       "      <td>199.0</td>\n",
-       "      <td>10.0</td>\n",
-       "      <td>0.020833</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>7.6</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "    apache_4a_hospital_death_prob   age  d1_heartrate_min  d1_temp_max  \\\n",
-       "0   0.10                           68.0  72.0              39.9          \n",
-       "2   0.00                           25.0  68.0              37.0          \n",
-       "3   0.04                           81.0  92.0              38.0          \n",
-       "4  NaN                             19.0  60.0              37.2          \n",
-       "5   0.05                           67.0  83.0              36.8          \n",
-       "7   0.11                           70.0  86.0              38.5          \n",
-       "9   0.02                           50.0  57.0              37.1          \n",
-       "10  0.01                           72.0  67.0              37.0          \n",
-       "11  0.04                           80.0  59.0              36.9          \n",
-       "12  0.03                           48.0  66.0              36.9          \n",
-       "\n",
-       "    d1_spo2_min        bmi  icu_id  apache_3j_diagnosis  d1_lactate_min  \\\n",
-       "0   74.0         22.730000  92      502.01               1.0              \n",
-       "2   91.0         31.950000  93      703.03              NaN               \n",
-       "3   95.0         22.640000  92      1206.03             NaN               \n",
-       "4   96.0        NaN         91      601.01              NaN               \n",
-       "5   91.0         27.560000  95      403.01              NaN               \n",
-       "7   92.0        NaN         91      501.05              NaN               \n",
-       "9   96.0         25.710000  114     107.01              NaN               \n",
-       "10  92.0         28.257052  113     1304.08             NaN               \n",
-       "11  76.0         27.382812  95      1505.02             NaN               \n",
-       "12  97.0        NaN         102     403.01              NaN               \n",
-       "\n",
-       "    d1_glucose_min  apache_4a_icu_death_prob  d1_platelets_min  \\\n",
-       "0   109.0           0.05                      233.0              \n",
-       "2  NaN              0.00                     NaN                 \n",
-       "3   88.0            0.03                      43.0               \n",
-       "4  NaN             NaN                       NaN                 \n",
-       "5   125.0           0.02                      159.0              \n",
-       "7   129.0           0.06                      260.0              \n",
-       "9   134.0           0.01                      232.0              \n",
-       "10  133.0           0.00                      257.0              \n",
-       "11  100.0           0.01                     NaN                 \n",
-       "12  98.0            0.01                      199.0              \n",
-       "\n",
-       "    d1_resprate_min  pre_icu_los_days  urineoutput_apache  d1_arterial_ph_max  \\\n",
-       "0   10.0             0.541667         NaN                 NaN                   \n",
-       "2   8.0              0.000694         NaN                 NaN                   \n",
-       "3   7.0              0.000694         NaN                  7.44                 \n",
-       "4   16.0             0.073611         NaN                 NaN                   \n",
-       "5   10.0             0.000694         NaN                 NaN                   \n",
-       "7   12.0             0.002083         NaN                  7.38                 \n",
-       "9   14.0             0.060417         NaN                 NaN                   \n",
-       "10  14.0             0.004861         NaN                 NaN                   \n",
-       "11  16.0             1.271528         NaN                 NaN                   \n",
-       "12  10.0             0.020833         NaN                 NaN                   \n",
-       "\n",
-       "    d1_sodium_max  weight  d1_pao2fio2ratio_max  d1_wbc_min  \n",
-       "0   136.0          73.9   NaN                    14.1        \n",
-       "2  NaN             95.3   NaN                   NaN          \n",
-       "3  NaN             61.7    342.5                 8.0         \n",
-       "4  NaN            NaN     NaN                   NaN          \n",
-       "5   137.0          100.0  NaN                    10.9        \n",
-       "7   140.0         NaN     NaN                    12.8        \n",
-       "9   139.0          79.0   NaN                    12.8        \n",
-       "10  137.0          67.8   NaN                    10.3        \n",
-       "11  136.0          70.1   NaN                   NaN          \n",
-       "12 NaN            NaN     NaN                    7.6         "
-      ]
-     },
-     "execution_count": 72,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "oc.anomalies(X,top_features['Feature'])"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2020-12-15T06:14:20.654179Z",
-     "start_time": "2020-12-15T06:14:20.652171Z"
-    }
-   },
-   "source": [
-    "## Data leakage test "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 81,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2020-12-15T06:57:46.986523Z",
-     "start_time": "2020-12-15T06:57:46.979066Z"
-    }
-   },
-   "outputs": [
-    {
-     "ename": "AttributeError",
-     "evalue": "module 'octopus' has no attribute 'data_leakage'",
-     "output_type": "error",
-     "traceback": [
-      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
-      "\u001b[0;31mAttributeError\u001b[0m                            Traceback (most recent call last)",
-      "\u001b[0;32m<ipython-input-81-6d8aa383222b>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0moc\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdata_leakage\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mtop_features\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'Feature'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
-      "\u001b[0;31mAttributeError\u001b[0m: module 'octopus' has no attribute 'data_leakage'"
+      "-> \u001b[32m Passed the data leakage test - no duplicate intstances detected \u001b[0m\n",
+      "Method Name       :\u001b[35;1m data_leakage\u001b[0m\n",
+      "Current memory usage:\u001b[36m 0.380243MB\u001b[0m\n",
+      "Peak                :\u001b[36m 32.270963MB\u001b[0m\n",
+      "Total time taken:  \u001b[36m 62.554 ms \u001b[0m\n"
      ]
     }
    ],
    "source": [
-    "oc.data_leakage(X,top_features['Feature'])"
+    "oc.data_leakage(X,top_features['Feature'].to_list())"
    ]
   },
   {
diff --git a/octopus-ml/__pycache__/__init__.cpython-37.pyc b/octopus-ml/__pycache__/__init__.cpython-37.pyc
deleted file mode 100644
index 8a34eec..0000000
Binary files a/octopus-ml/__pycache__/__init__.cpython-37.pyc and /dev/null differ
diff --git a/octopus-ml/octopus_data.py b/octopus-ml/octopus_data.py
deleted file mode 100644
index e1eb57c..0000000
--- a/octopus-ml/octopus_data.py
+++ /dev/null
@@ -1,248 +0,0 @@
-#
-#        _/_/      _/_/_/  _/_/_/_/_/    _/_/    _/_/_/    _/    _/    _/_/_/
-#      _/    _/  _/            _/      _/    _/  _/    _/  _/    _/  _/
-#      _/    _/  _/            _/      _/    _/  _/_/_/    _/    _/    _/_/
-#      _/    _/  _/            _/      _/    _/  _/        _/    _/        _/
-#        _/_/      _/_/_/      _/        _/_/    _/          _/_/    _/_/_/
-#
-#
-#    __          _____            __               _____    __     _ __
-#   / /  __ __  / ___/__ _______ / /  ___  ___    / ___/__ / /__  (_) /_____ ____
-#  / _ \/ // / / (_ / -_) __(_-</ _ \/ _ \/ _ \  / /__/ -_) / _ \/ /  '_/ -_) __/
-# /_.__/\_, /  \___/\__/_/ /___/_//_/\___/_//_/  \___/\__/_/_//_/_/_/\_\\__/_/
-#      /___/
-
-from tqdm import tqdm
-from sklearn.metrics import confusion_matrix, accuracy_score, roc_curve, auc
-from sklearn.metrics import accuracy_score, precision_score, recall_score, f1_score
-from sklearn.metrics import classification_report
-from sklearn.metrics import roc_auc_score
-from sklearn.ensemble import IsolationForest
-from sklearn.model_selection import train_test_split
-from sklearn.model_selection import KFold
-from sklearn.metrics import f1_score
-from sklearn.model_selection import StratifiedKFold
-from .misc import mem_measure, timer
-import matplotlib.pyplot as plt
-import seaborn as sns
-import warnings
-import time
-import pandas as pd
-import numpy as np
-import lightgbm as lgb
-import tracemalloc
-
-
-def anomalies(df, df_cols):
-    clf = IsolationForest(max_samples=100)
-    clf.fit(df[df_cols].fillna)
-
-    y_pred_train = clf.predict(df[df_cols].fillna(0))
-    # y_pred_dev = clf.predict(X_dev)
-    y_pred_test = clf.predict(df[df_cols].fillna(0))
-    return df[y_pred_test == 1][df_cols].head(10)
-
-
-def correlations(df, cols):
-    sns.set(style="white")
-    corr = df[cols].corr()
-    mask = np.zeros_like(corr, dtype=np.bool)
-    mask[np.triu_indices_from(mask)] = True
-    f, ax = plt.subplots(figsize=(11, 9))
-    cmap = sns.diverging_palette(220, 10, as_cmap=True)
-    sns.heatmap(
-        corr,
-        mask=mask,
-        cmap=cmap,
-        vmax=0.3,
-        center=0,
-        square=True,
-        linewidths=0.5,
-        cbar_kws={"shrink": 0.5},
-    )
-
-
-def recieve_fns(df, df_cols, index, y_folds, preds_folds, threshold=5, top=20):
-    # Returns a list of FNs with low score
-    # preds_labels=pd.DataFrame(df[index],y_folds,preds_folds, columns=['index','label','preds'])
-    preds_labels = pd.DataFrame(y_folds, columns=["label"])
-    preds_labels.index = df.loc[:, index]
-    preds_labels["preds_proba"] = preds_folds
-    preds_labels["preds_class"] = preds_labels["preds_proba"].apply(
-        lambda x: 1 if x > threshold else 0
-    )
-    if threshold != 0.5:
-        print("recieved threshold of: ", threshold, "\n")
-    else:
-        print("calculating using threshold of: ", threshold)
-    preds_labels_sorted = preds_labels[preds_labels["label"] == 1].sort_values(
-        by="preds_proba", ascending=True
-    )
-    print(preds_labels_sorted.head(top))
-    return preds_labels_sorted
-
-
-def recieve_fps(df, df_cols, index, y_folds, preds_folds, threshold=5, top=20):
-    # Returns a list of FPs with high score
-    # preds_labels=pd.DataFrame(df[index],y_folds,preds_folds, columns=['index','label','preds'])
-    preds_labels = pd.DataFrame(y_folds, columns=["label"])
-    preds_labels.index = df.loc[:, index]
-    preds_labels["preds_proba"] = preds_folds
-    preds_labels["preds_class"] = preds_labels["preds_proba"].apply(
-        lambda x: 1 if x > threshold else 0
-    )
-    if threshold != 0.5:
-        print("recieved threshold of: ", threshold, "\n")
-    else:
-        print("calculating using threshold of: ", threshold)
-    preds_labels_sorted = preds_labels[preds_labels["label"] == 0].sort_values(
-        by="preds_proba", ascending=True
-    )
-    print(preds_labels_sorted.head(top))
-    return preds_labels_sorted
-
-
-@timer
-@mem_measure
-def sampling_by_group(df, target, num_negative_instances, group_by_name):
-    # Sampling method by
-    grouped_df = df.groupby(group_by_name)
-    print("Number of groups: ", len(grouped_df))
-
-    grouped_df.target.max()
-    g_df = grouped_df.target.max().reset_index()
-    positive = len(g_df[g_df[target] == 1])
-    negative = len(g_df[g_df[target] == 0])
-
-    print(
-        "number of positive groups: ",
-        positive,
-        "\nnumber of negative groups: ",
-        negative,
-    )
-
-    negative_indices = g_df[g_df[target] == 0].index
-    positive_indices = g_df[g_df[target] == 1].index
-    random_indices = np.random.choice(
-        negative_indices, num_negative_instances, replace=False
-    )
-    sample_indices = np.concatenate([positive_indices, random_indices])
-
-    g_df_sampled = g_df.loc[sample_indices]
-    print(g_df_sampled[group_by_name])
-
-    sampled_df = df[df[group_by_name].isin(g_df_sampled[group_by_name])]
-    # print ("new dataset shape: ", df_sampled.shape)
-    return sampled_df
-
-
-@timer
-@mem_measure
-def sampling(df, target, num_instances):
-    positive = len(df[df[target] == 1])
-    negative = len(df[df[target] == 0])
-    print(
-        "number of positive instances:",
-        positive,
-        "\nnumber of negative instance : ",
-        negative,
-    )
-    negative_indices = df[df[target] == 0].index
-    positive_indices = df[df[target] == 1].index
-    random_indices = np.random.choice(negative_indices, num_instances, replace=False)
-    sample_indices = np.concatenate([positive_indices, random_indices])
-    sampled_df = df.loc[sample_indices]
-    print("new dataset shape: ", sampled_df.shape)
-    return sampled_df
-
-
-@timer
-@mem_measure
-def data_leakage(df, cols):
-    CRED = "\033[91m"
-    CEND = "\033[0m"
-    CGREEN = "\33[32m"
-
-    duplicateRowsDF = df[df.duplicated(cols, keep="first")]
-
-    if round(len(duplicateRowsDF) / len(df) * 100, 2) > 0:
-        print(
-            "\n-> Total number of duplicate instances:",
-            len(duplicateRowsDF),
-            "out of",
-            len(df),
-            ":",
-            CRED,
-            round(len(duplicateRowsDF) / len(df) * 100, 2),
-            "%",
-            CEND,
-            "\n",
-        )
-        print("Top duplicate instances:", "\n")
-        df["All_features"] = df[cols].astype(str).apply(" | ".join, axis=1)
-        print(df["All_features"].value_counts().head(12))
-
-    else:
-        print(
-            "->",
-            CGREEN,
-            "Passed the data leakage test - no duplicate intstances detected",
-            CEND,
-        )
-
-
-def sampling_within_group(df, target, num_negative_instances, group_by_name, frac):
-    # Sampling method by
-    grouped_df = df.groupby(group_by_name)
-    print("Original shape: ", df.shape)
-
-    print("Number of groups: ", len(grouped_df))
-
-    grouped_df.target.max()
-    g_df = grouped_df.target.max().reset_index()
-    positive = len(g_df[g_df[target] == 1])
-    negative = len(g_df[g_df[target] == 0])
-
-    print(
-        "number of positive groups: ",
-        positive,
-        "\nnumber of negative groups: ",
-        negative,
-    )
-
-    negative_indices = g_df[g_df[target] == 0].index
-    positive_indices = g_df[g_df[target] == 1].index
-    random_indices = np.random.choice(
-        negative_indices, num_negative_instances, replace=False
-    )
-    # sample_indices = np.concat([positive_indices, random_indices])
-
-    # g_df_sampled = g_df.loc[sample_indices]
-    # print (g_df_sampled[group_by_name])
-
-    # sampled_df = df[df[group_by_name].isin(g_df_sampled[group_by_name])]
-    positive_instances = df[df[target] == 1]
-    negative_instances = df[df[target] == 0]
-    # selected_negative_instances = df.sample(frac=fracture, replace=False, random_state=1)
-    # selected_negative_instances = df.sample(frac=fracture, replace=False, random_state=1)
-    selected_negative_instances = (
-        df[df[target] == 0].groupby("stay").sample(frac=frac, random_state=2)
-    )
-
-    sampled_df = pd.concat([positive_instances, selected_negative_instances])
-
-    # sampled_df = df[df[group_by_name].isin(g_df_sampled[group_by_name])]
-    # print ("new dataset shape: ", df_sampled.shape)
-    # return sampled_df
-
-    print("new dataset shape: ", sampled_df.shape)
-    positive = len(sampled_df[sampled_df[target] == 1])
-    negative = len(sampled_df[sampled_df[target] == 0])
-
-    print(
-        "number of positive instances: ",
-        positive,
-        "\nnumber of negative instances: ",
-        negative,
-    )
-    return sampled_df
diff --git a/octopus-ml/octopus_ml.py b/octopus-ml/octopus_ml.py
deleted file mode 100644
index 1a8bcbf..0000000
--- a/octopus-ml/octopus_ml.py
+++ /dev/null
@@ -1,321 +0,0 @@
-#
-#        _/_/      _/_/_/  _/_/_/_/_/    _/_/    _/_/_/    _/    _/    _/_/_/
-#      _/    _/  _/            _/      _/    _/  _/    _/  _/    _/  _/
-#      _/    _/  _/            _/      _/    _/  _/_/_/    _/    _/    _/_/
-#      _/    _/  _/            _/      _/    _/  _/        _/    _/        _/
-#        _/_/      _/_/_/      _/        _/_/    _/          _/_/    _/_/_/
-#
-#
-#    __          _____            __               _____    __     _ __
-#   / /  __ __  / ___/__ _______ / /  ___  ___    / ___/__ / /__  (_) /_____ ____
-#  / _ \/ // / / (_ / -_) __(_-</ _ \/ _ \/ _ \  / /__/ -_) / _ \/ /  '_/ -_) __/
-# /_.__/\_, /  \___/\__/_/ /___/_//_/\___/_//_/  \___/\__/_/_//_/_/_/\_\\__/_/
-#      /___/
-
-from tqdm import tqdm
-from sklearn.metrics import confusion_matrix, accuracy_score, roc_curve, auc
-from sklearn.metrics import accuracy_score, precision_score, recall_score, f1_score
-from sklearn.metrics import classification_report
-from sklearn.metrics import roc_auc_score
-from sklearn.ensemble import IsolationForest
-from sklearn.model_selection import train_test_split
-from sklearn.model_selection import KFold
-from sklearn.metrics import f1_score
-from sklearn.model_selection import StratifiedKFold
-from .misc import mem_measure, timer
-
-import matplotlib.pyplot as plt
-import seaborn as sns
-import warnings
-import time
-import pandas as pd
-import numpy as np
-import lightgbm as lgb
-import tracemalloc
-
-pd.set_option("display.max_columns", None)  # or 1000
-pd.set_option("display.max_rows", None)  # or 1000
-pd.set_option("display.max_colwidth", -1)  # or 199
-from IPython.core.interactiveshell import InteractiveShell
-
-InteractiveShell.ast_node_interactivity = "all"
-warnings.simplefilter(action="ignore", category=FutureWarning)
-
-
-# ML visualizations
-
-
-def plot_imp(clf, X, title, model="lgbm", num=30):
-    # Feature importance plot supporting LGBM, RN and Catboost, return the list of features importance sorted by their contribution
-    sns.set_style("whitegrid")
-
-    if model == "catboost":
-        feature_imp = pd.DataFrame(
-            {"Value": clf.get_feature_importance(), "Feature": X.columns}
-        )
-    elif model == "lgbm":
-        feature_imp = pd.DataFrame(
-            {
-                "Value": clf.feature_importance(importance_type="gain"),
-                "Feature": X.columns,
-            }
-        )
-    else:
-        feature_imp = pd.DataFrame(
-            {"Value": clf.feature_importance(), "Feature": X.columns}
-        )
-
-    plt.figure(figsize=(16, 20))
-    sns.set(font_scale=1.1)
-    sns.barplot(
-        color="#3498db",
-        x="Value",
-        y="Feature",
-        data=feature_imp.sort_values(by="Value", ascending=False)[0:num],
-    )
-    plt.title(title)
-    plt.tight_layout()
-    plt.show()
-
-    return feature_imp
-
-
-def confusion_matrix_plot(y_test, y_predict):
-    # Confusion Matrix plot, binary classification including both normalized and absolute values plots
-
-    plt.figure()
-    cm = confusion_matrix(y_test, y_predict)
-    cmn = cm.astype("float") / cm.sum(axis=1)[:, np.newaxis] * 100
-    sns.set(font_scale=1.1)
-    labels = ["0", "1"]
-    plt.figure(figsize=(7, 5.5))
-    # sns.heatmap(cm, xticklabels = labels, yticklabels = labels, annot = True, fmt='d', cmap="Blues", vmin = 0.2);
-    sns.heatmap(
-        cmn, xticklabels=labels, yticklabels=labels, annot=True, fmt=".2f", cmap="Blues"
-    )
-    plt.title("Confusion Matrix")
-    plt.ylabel("True Class")
-    plt.xlabel("Predicted Class")
-    plt.show()
-
-    plt.figure()
-    cm = confusion_matrix(y_test, y_predict)
-    cmn = cm.astype("int")
-    sns.set(font_scale=1.1)
-    labels = ["0", "1"]
-    plt.figure(figsize=(7, 5.5))
-    # sns.heatmap(cm, xticklabels = labels, yticklabels = labels, annot = True, fmt='d', cmap="Blues", vmin = 0.2);
-    sns.heatmap(
-        cmn, xticklabels=labels, yticklabels=labels, annot=True, fmt="d", cmap="Blues"
-    )
-    plt.title("Confusion Matrix")
-    plt.ylabel("True Class")
-    plt.xlabel("Predicted Class")
-    plt.show()
-
-
-def roc_curve_plot(y_test, predictions):
-    # Roc curve visualization, binary classification including AUC calculation
-
-    sns.set_style("whitegrid")
-    rf_roc_auc = roc_auc_score(y_test, predictions)
-    rf_fpr, rf_tpr, rf_thresholds = roc_curve(y_test, predictions)
-
-    plt.figure(figsize=(10, 6))
-    plt.plot(rf_fpr, rf_tpr, label="LGBM (area = %0.3f)" % rf_roc_auc)
-    plt.plot([0, 1], [0, 1], "r--")
-    plt.xlim([0.0, 1.0])
-    plt.ylim([0.0, 1.05])
-
-    plt.xlabel("False Positive Rate", fontsize=14)
-    plt.ylabel("True Positive Rate", fontsize=14)
-    plt.title("Receiver operating characteristic", fontsize=17)
-    plt.legend(loc="lower right")
-    plt.show()
-
-
-def cv_plot(arr_f1_weighted, arr_f1_macro, arr_f1_positive, AxisName):
-    # Visualization of the CV folds, F1 macro and F1 positive class
-
-    sns.set_style("whitegrid")
-    plt.figure(figsize=(13, 7))
-    gcbest = ["#3498db", "#2ecc71"]
-    sns.set_palette(gcbest)
-
-    index = np.arange(len(arr_f1_weighted))
-    bar_width = 0.30
-    opacity = 0.8
-
-    plt.bar(
-        index - bar_width / 2, arr_f1_macro, bar_width, alpha=opacity, label="F1 Macro"
-    )
-
-    plt.bar(
-        index + bar_width / 2,
-        arr_f1_positive,
-        bar_width,
-        alpha=opacity,
-        label="F1 Positive",
-    )
-
-    plt.xticks(np.arange(len(arr_f1_weighted)), fontsize=14)
-    plt.ylabel("F1", fontsize=14)
-    plt.xlabel("Folds", fontsize=14)
-    plt.title("%s, 5-Folds Cross Validation" % AxisName[0 : len(AxisName)], fontsize=17)
-    plt.legend(["F1 macro", "F1 positive"], loc="upper right", fontsize=14)
-    plt.grid(True)
-
-
-def lgbm(X_train, y_train, X_test, y_test, num, params=None):
-    # Training function for LGBM with basic categorical features treatment and close to default params
-
-    categorical_features = []
-    for c in X_train.columns:
-        col_type = X_train[c].dtype
-        if col_type == "object" or col_type.name == "category":
-            # an option in case the data(pandas dataframe) isn't passed with the categorical column type
-            # X[c] = X[c].astype('category')
-            categorical_features.append(c)
-
-    lgb_train = lgb.Dataset(X_train, y_train, categorical_feature=categorical_features)
-    lgb_valid = lgb.Dataset(X_test, y_test, categorical_feature=categorical_features)
-
-    if params == None:
-        params = {
-            "objective": "binary",
-            "boosting": "gbdt",
-            "scale_pos_weight": 0.02,
-            "learning_rate": 0.005,
-            "seed": 100
-            # 'categorical_feature': 'auto',
-            # 'metric': 'auc',
-            # 'scale_pos_weight':0.1,
-            # 'learning_rate': 0.02,
-            # 'num_boost_round':2000,
-            # "min_sum_hessian_in_leaf":1,
-            # 'max_depth' : 100,
-            # "bagging_freq": 2,
-            # "num_leaves":31,
-            # "bagging_fraction" : 0.4,
-            # "feature_fraction" : 0.05,
-        }
-
-    clf = lgb.train(params, lgb_train, num)
-
-    return clf
-
-
-def adjusted_classes(y_scores, t):
-    # transformation from prediction probabolity to class given the threshold
-    return [1 if y >= t else 0 for y in y_scores]
-
-
-@timer
-@mem_measure
-def cv(X, y, threshold, iterations, shuffle=True, params=None):
-    # Cross Validation - stratified with and without shuffeling
-    arr_f1_weighted = np.array([])
-    arr_f1_macro = np.array([])
-    arr_f1_positive = np.array([])
-    arr_recall = np.array([])
-    arr_precision = np.array([])
-    prediction_folds = []
-    preds_folds = []
-    y_folds = []
-
-    skf = StratifiedKFold(n_splits=5, random_state=2, shuffle=shuffle)
-
-    for train_index, test_index in tqdm(skf.split(X, y)):
-        X_train, X_test = X.iloc[train_index], X.iloc[test_index]
-        y_train, y_test = y.iloc[train_index], y.iloc[test_index]
-
-        clf = lgbm(X_train, y_train, X_test, y_test, iterations, params)
-        preds = clf.predict(X_test)
-
-        predictions = []
-        predictions = adjusted_classes(preds, threshold)
-
-        """ Multiclass 
-        predictions = clf.predict(X_test)
-        predictions_classes = []
-        for i in predictions:   
-            print (np.argmax(i))
-            predictions_classes.append(np.argmax(i))  
-        """
-
-        prediction_folds.extend(predictions)
-        preds_folds.extend(preds)
-        y_folds.extend(y_test)
-        arr_f1_weighted = np.append(
-            arr_f1_weighted, f1_score(y_test, predictions, average="weighted")
-        )
-        arr_f1_macro = np.append(
-            arr_f1_macro, f1_score(y_test, predictions, average="macro")
-        )
-        arr_f1_positive = np.append(
-            arr_f1_positive, f1_score(y_test, predictions, average="binary")
-        )
-
-    return (
-        clf,
-        arr_f1_weighted,
-        arr_f1_macro,
-        arr_f1_positive,
-        prediction_folds,
-        preds_folds,
-        y_folds,
-    )
-
-
-def cv_grouped(X, y, threshold, iterations, group_name, shuffle=True):
-    arr_f1_weighted = np.array([])
-    arr_f1_macro = np.array([])
-    arr_f1_positive = np.array([])
-    arr_recall = np.array([])
-    arr_precision = np.array([])
-    prediction_folds = []
-    preds_folds = []
-    y_folds = []
-
-    skf = StratifiedKFold(n_splits=5, random_state=2, shuffle=shuffle)
-
-    for train_index, test_index in tqdm(skf.split(skf.split(X, y))):
-
-        X_train, X_test = X.iloc[train_index], X.iloc[test_index]
-        y_train, y_test = y.iloc[train_index], y.iloc[test_index]
-
-        clf = lgbm(X_train, y_train, X_test, y_test, iterations)
-        preds = clf.predict(X_test)
-
-        predictions = []
-        predictions = adjusted_classes(preds, threshold)
-
-        """ Multiclass 
-        predictions = clf.predict(X_test)
-        predictions_classes = []
-        for i in predictions:   
-            print (np.argmax(i))
-            predictions_classes.append(np.argmax(i))  
-        """
-        prediction_folds.extend(predictions)
-        preds_folds.extend(preds)
-        y_folds.extend(y_test)
-        arr_f1_weighted = np.append(
-            arr_f1_weighted, f1_score(y_test, predictions, average="weighted")
-        )
-        arr_f1_macro = np.append(
-            arr_f1_macro, f1_score(y_test, predictions, average="macro")
-        )
-        arr_f1_positive = np.append(
-            arr_f1_positive, f1_score(y_test, predictions, average="binary")
-        )
-
-    return (
-        clf,
-        arr_f1_weighted,
-        arr_f1_macro,
-        arr_f1_positive,
-        prediction_folds,
-        preds_folds,
-        y_folds,
-    )
diff --git a/setup.py b/setup.py
index a30be62..841b8f0 100644
--- a/setup.py
+++ b/setup.py
@@ -5,11 +5,11 @@
 
 setup(
     name="octopus-ml",
-    version="0.1.0",
+    version="0.1.4",
     description="A collection of handy ML and data validation tools",
     long_description=long_description,
     long_description_content_type="text/markdown",
-    packages=["octopus-ml"],
+    packages=["octopus_ml"],
     author="Gershon Celniker",
     author_email="gershonc@gmail.com",
     url="https://github.com/gershonc/octopus-ml",