From f495ddd0729c7ac61dc8d8f54fc706178a0d253f Mon Sep 17 00:00:00 2001
From: kwinkunks <kwinkunks@gmail.com>
Date: Sun, 21 Apr 2024 11:31:53 +0200
Subject: [PATCH] Catch up

---
 docs/notebooks/Demo.ipynb | 129 ++++++++++++++++----------------------
 1 file changed, 55 insertions(+), 74 deletions(-)

diff --git a/docs/notebooks/Demo.ipynb b/docs/notebooks/Demo.ipynb
index 2bfbd3f..d39cc31 100644
--- a/docs/notebooks/Demo.ipynb
+++ b/docs/notebooks/Demo.ipynb
@@ -1,26 +1,5 @@
 {
  "cells": [
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import numpy as np\n",
-    "import pandas as pd\n",
-    "import matplotlib.pyplot as plt\n",
-    "import seaborn as sns\n",
-    "\n",
-    "from sklearn.svm import SVC\n",
-    "from sklearn.ensemble import RandomForestClassifier\n",
-    "from sklearn.pipeline import make_pipeline\n",
-    "from sklearn.metrics import accuracy_score, f1_score\n",
-    "from sklearn.model_selection import train_test_split\n",
-    "from sklearn.preprocessing import StandardScaler\n",
-    "\n",
-    "np.set_printoptions(suppress=True)"
-   ]
-  },
   {
    "cell_type": "markdown",
    "metadata": {},
@@ -32,7 +11,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": 1,
    "metadata": {},
    "outputs": [
     {
@@ -209,12 +188,14 @@
        "4  2382.602601  siliciclastic           True  "
       ]
      },
-     "execution_count": 2,
+     "execution_count": 1,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
+    "import pandas as pd\n",
+    "\n",
     "df = pd.read_csv('https://raw.githubusercontent.com/scienxlab/datasets/main/kgs/panoma-training-data.csv')\n",
     "\n",
     "df.head()"
@@ -229,10 +210,12 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": 2,
    "metadata": {},
    "outputs": [],
    "source": [
+    "import numpy as np\n",
+    "\n",
     "rng = np.random.default_rng(42)\n",
     "\n",
     "df['Noise'] = rng.normal(size=len(df))"
@@ -254,7 +237,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": 3,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -264,7 +247,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": 4,
    "metadata": {},
    "outputs": [
     {
@@ -280,7 +263,7 @@
        "Name: count, dtype: int64"
       ]
      },
-     "execution_count": 5,
+     "execution_count": 4,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -300,16 +283,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": 5,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "[<matplotlib.lines.Line2D at 0x135327e00>]"
+       "[<matplotlib.lines.Line2D at 0x11e0ba840>]"
       ]
      },
-     "execution_count": 6,
+     "execution_count": 5,
      "metadata": {},
      "output_type": "execute_result"
     },
@@ -325,6 +308,8 @@
     }
    ],
    "source": [
+    "import matplotlib.pyplot as plt\n",
+    "\n",
     "shrimplin = df['Well Name'] == 'SHRIMPLIN'\n",
     "\n",
     "plt.plot(df.loc[shrimplin, 'Depth'], df.loc[shrimplin, 'GR'])"
@@ -339,7 +324,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": 6,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -355,7 +340,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": 7,
    "metadata": {},
    "outputs": [
     {
@@ -458,7 +443,7 @@
        "max     200.000000  2802.871147     8.094000     3.178854"
       ]
      },
-     "execution_count": 8,
+     "execution_count": 7,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -469,16 +454,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": 8,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "<seaborn.axisgrid.PairGrid at 0x13537c8c0>"
+       "<seaborn.axisgrid.PairGrid at 0x11dfb2cc0>"
       ]
      },
-     "execution_count": 9,
+     "execution_count": 8,
      "metadata": {},
      "output_type": "execute_result"
     },
@@ -494,12 +479,14 @@
     }
    ],
    "source": [
+    "import seaborn as sns\n",
+    "\n",
     "sns.pairplot(df, x_vars=features, y_vars=features, hue='Lithology')"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
+   "execution_count": 9,
    "metadata": {},
    "outputs": [
     {
@@ -508,7 +495,7 @@
        "<Axes: >"
       ]
      },
-     "execution_count": 10,
+     "execution_count": 9,
      "metadata": {},
      "output_type": "execute_result"
     },
@@ -545,7 +532,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
+   "execution_count": 10,
    "metadata": {},
    "outputs": [
     {
@@ -554,7 +541,7 @@
        "'0.4.2rc2.dev14+g54704af.d20240421'"
       ]
      },
-     "execution_count": 11,
+     "execution_count": 10,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -575,7 +562,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 12,
+   "execution_count": 11,
    "metadata": {},
    "outputs": [
     {
@@ -584,7 +571,7 @@
        "True"
       ]
      },
-     "execution_count": 12,
+     "execution_count": 11,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -595,7 +582,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 13,
+   "execution_count": 12,
    "metadata": {},
    "outputs": [
     {
@@ -604,7 +591,7 @@
        "True"
       ]
      },
-     "execution_count": 13,
+     "execution_count": 12,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -615,7 +602,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 14,
+   "execution_count": 13,
    "metadata": {},
    "outputs": [
     {
@@ -624,7 +611,7 @@
        "3.4203357617985457"
       ]
      },
-     "execution_count": 14,
+     "execution_count": 13,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -635,7 +622,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 15,
+   "execution_count": 14,
    "metadata": {},
    "outputs": [
     {
@@ -644,7 +631,7 @@
        "array(['dolomite', 'sandstone', 'mudstone', 'wackestone'], dtype='<U10')"
       ]
      },
-     "execution_count": 15,
+     "execution_count": 14,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -655,7 +642,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 16,
+   "execution_count": 15,
    "metadata": {},
    "outputs": [
     {
@@ -699,7 +686,7 @@
        "       3101, 3109, 3110, 3111, 3112, 3113, 3114, 3115])"
       ]
      },
-     "execution_count": 16,
+     "execution_count": 15,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -717,7 +704,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 17,
+   "execution_count": 16,
    "metadata": {},
    "outputs": [
     {
@@ -726,7 +713,7 @@
        "array(['dolomite', 'sandstone', 'mudstone', 'wackestone'], dtype='<U10')"
       ]
      },
-     "execution_count": 17,
+     "execution_count": 16,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -737,7 +724,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 18,
+   "execution_count": 17,
    "metadata": {},
    "outputs": [
     {
@@ -756,16 +743,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 19,
+   "execution_count": 18,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "array([0.39842062, 0.20974805, 0.32885426, 0.06297706])"
+       "array([0.39633779, 0.21317204, 0.31425213, 0.07623804])"
       ]
      },
-     "execution_count": 19,
+     "execution_count": 18,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -783,7 +770,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 20,
+   "execution_count": 19,
    "metadata": {},
    "outputs": [
     {
@@ -816,7 +803,7 @@
        "                ('rf.dummy', DummyPredictor())])"
       ]
      },
-     "execution_count": 20,
+     "execution_count": 19,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -827,10 +814,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 21,
+   "execution_count": 20,
    "metadata": {},
    "outputs": [],
    "source": [
+    "from sklearn.pipeline import make_pipeline\n",
+    "from sklearn.preprocessing import StandardScaler\n",
+    "from sklearn.ensemble import RandomForestClassifier\n",
+    "\n",
     "model = RandomForestClassifier()\n",
     "\n",
     "pipe = make_pipeline(\n",
@@ -842,7 +833,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 22,
+   "execution_count": 21,
    "metadata": {},
    "outputs": [
     {
@@ -856,7 +847,7 @@
       "ℹ️ Multimodality detection may not have succeeded for all groups in all features.\n",
       "🚩 There are more outliers than expected in the training data (316 vs 31).\n",
       "🚩 Feature 3 has low importance; check for relevance.\n",
-      "ℹ️ Dummy classifier scores: {'f1': 0.25488459423559595, 'roc_auc': 0.5} (most_frequent strategy).\n"
+      "ℹ️ Dummy classifier scores: {'f1': 0.2680354401640276, 'roc_auc': 0.502937195291403} (stratified strategy).\n"
      ]
     }
    ],
@@ -875,7 +866,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 23,
+   "execution_count": 22,
    "metadata": {},
    "outputs": [
     {
@@ -895,19 +886,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 24,
+   "execution_count": 23,
    "metadata": {},
    "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "(-0.19470000000000007, 8.488700000000001)"
-      ]
-     },
-     "execution_count": 24,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
     {
      "data": {
       "image/png": 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@@ -923,7 +904,7 @@
     "fig, axs = plt.subplots(ncols=2, figsize=(15, 5))\n",
     "sns.histplot(X_train.iloc[:, 2], kde=True, lw=0, ax=axs[0])\n",
     "sns.histplot(X_test.iloc[:, 2], kde=True, lw=0, ax=axs[1])\n",
-    "axs[1].set_xlim(axs[0].get_xlim())"
+    "_ = axs[1].set_xlim(axs[0].get_xlim())"
    ]
   },
   {