ClusteringWIthDBSCAN.ipynb - machinelearning

ClusteringWIthDBSCAN.ipynb (86332B)
      1 {
      2  "cells": [
      3   {
      4    "cell_type": "code",
      5    "execution_count": 20,
      6    "metadata": {},
      7    "outputs": [
      8     {
      9      "data": {
     10       "text/html": [
     11        "<style>#sk-container-id-7 {\n",
     12        "  /* Definition of color scheme common for light and dark mode */\n",
     13        "  --sklearn-color-text: black;\n",
     14        "  --sklearn-color-line: gray;\n",
     15        "  /* Definition of color scheme for unfitted estimators */\n",
     16        "  --sklearn-color-unfitted-level-0: #fff5e6;\n",
     17        "  --sklearn-color-unfitted-level-1: #f6e4d2;\n",
     18        "  --sklearn-color-unfitted-level-2: #ffe0b3;\n",
     19        "  --sklearn-color-unfitted-level-3: chocolate;\n",
     20        "  /* Definition of color scheme for fitted estimators */\n",
     21        "  --sklearn-color-fitted-level-0: #f0f8ff;\n",
     22        "  --sklearn-color-fitted-level-1: #d4ebff;\n",
     23        "  --sklearn-color-fitted-level-2: #b3dbfd;\n",
     24        "  --sklearn-color-fitted-level-3: cornflowerblue;\n",
     25        "\n",
     26        "  /* Specific color for light theme */\n",
     27        "  --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
     28        "  --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, white)));\n",
     29        "  --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
     30        "  --sklearn-color-icon: #696969;\n",
     31        "\n",
     32        "  @media (prefers-color-scheme: dark) {\n",
     33        "    /* Redefinition of color scheme for dark theme */\n",
     34        "    --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
     35        "    --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, #111)));\n",
     36        "    --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
     37        "    --sklearn-color-icon: #878787;\n",
     38        "  }\n",
     39        "}\n",
     40        "\n",
     41        "#sk-container-id-7 {\n",
     42        "  color: var(--sklearn-color-text);\n",
     43        "}\n",
     44        "\n",
     45        "#sk-container-id-7 pre {\n",
     46        "  padding: 0;\n",
     47        "}\n",
     48        "\n",
     49        "#sk-container-id-7 input.sk-hidden--visually {\n",
     50        "  border: 0;\n",
     51        "  clip: rect(1px 1px 1px 1px);\n",
     52        "  clip: rect(1px, 1px, 1px, 1px);\n",
     53        "  height: 1px;\n",
     54        "  margin: -1px;\n",
     55        "  overflow: hidden;\n",
     56        "  padding: 0;\n",
     57        "  position: absolute;\n",
     58        "  width: 1px;\n",
     59        "}\n",
     60        "\n",
     61        "#sk-container-id-7 div.sk-dashed-wrapped {\n",
     62        "  border: 1px dashed var(--sklearn-color-line);\n",
     63        "  margin: 0 0.4em 0.5em 0.4em;\n",
     64        "  box-sizing: border-box;\n",
     65        "  padding-bottom: 0.4em;\n",
     66        "  background-color: var(--sklearn-color-background);\n",
     67        "}\n",
     68        "\n",
     69        "#sk-container-id-7 div.sk-container {\n",
     70        "  /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n",
     71        "     but bootstrap.min.css set `[hidden] { display: none !important; }`\n",
     72        "     so we also need the `!important` here to be able to override the\n",
     73        "     default hidden behavior on the sphinx rendered scikit-learn.org.\n",
     74        "     See: https://github.com/scikit-learn/scikit-learn/issues/21755 */\n",
     75        "  display: inline-block !important;\n",
     76        "  position: relative;\n",
     77        "}\n",
     78        "\n",
     79        "#sk-container-id-7 div.sk-text-repr-fallback {\n",
     80        "  display: none;\n",
     81        "}\n",
     82        "\n",
     83        "div.sk-parallel-item,\n",
     84        "div.sk-serial,\n",
     85        "div.sk-item {\n",
     86        "  /* draw centered vertical line to link estimators */\n",
     87        "  background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));\n",
     88        "  background-size: 2px 100%;\n",
     89        "  background-repeat: no-repeat;\n",
     90        "  background-position: center center;\n",
     91        "}\n",
     92        "\n",
     93        "/* Parallel-specific style estimator block */\n",
     94        "\n",
     95        "#sk-container-id-7 div.sk-parallel-item::after {\n",
     96        "  content: \"\";\n",
     97        "  width: 100%;\n",
     98        "  border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n",
     99        "  flex-grow: 1;\n",
    100        "}\n",
    101        "\n",
    102        "#sk-container-id-7 div.sk-parallel {\n",
    103        "  display: flex;\n",
    104        "  align-items: stretch;\n",
    105        "  justify-content: center;\n",
    106        "  background-color: var(--sklearn-color-background);\n",
    107        "  position: relative;\n",
    108        "}\n",
    109        "\n",
    110        "#sk-container-id-7 div.sk-parallel-item {\n",
    111        "  display: flex;\n",
    112        "  flex-direction: column;\n",
    113        "}\n",
    114        "\n",
    115        "#sk-container-id-7 div.sk-parallel-item:first-child::after {\n",
    116        "  align-self: flex-end;\n",
    117        "  width: 50%;\n",
    118        "}\n",
    119        "\n",
    120        "#sk-container-id-7 div.sk-parallel-item:last-child::after {\n",
    121        "  align-self: flex-start;\n",
    122        "  width: 50%;\n",
    123        "}\n",
    124        "\n",
    125        "#sk-container-id-7 div.sk-parallel-item:only-child::after {\n",
    126        "  width: 0;\n",
    127        "}\n",
    128        "\n",
    129        "/* Serial-specific style estimator block */\n",
    130        "\n",
    131        "#sk-container-id-7 div.sk-serial {\n",
    132        "  display: flex;\n",
    133        "  flex-direction: column;\n",
    134        "  align-items: center;\n",
    135        "  background-color: var(--sklearn-color-background);\n",
    136        "  padding-right: 1em;\n",
    137        "  padding-left: 1em;\n",
    138        "}\n",
    139        "\n",
    140        "\n",
    141        "/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is\n",
    142        "clickable and can be expanded/collapsed.\n",
    143        "- Pipeline and ColumnTransformer use this feature and define the default style\n",
    144        "- Estimators will overwrite some part of the style using the `sk-estimator` class\n",
    145        "*/\n",
    146        "\n",
    147        "/* Pipeline and ColumnTransformer style (default) */\n",
    148        "\n",
    149        "#sk-container-id-7 div.sk-toggleable {\n",
    150        "  /* Default theme specific background. It is overwritten whether we have a\n",
    151        "  specific estimator or a Pipeline/ColumnTransformer */\n",
    152        "  background-color: var(--sklearn-color-background);\n",
    153        "}\n",
    154        "\n",
    155        "/* Toggleable label */\n",
    156        "#sk-container-id-7 label.sk-toggleable__label {\n",
    157        "  cursor: pointer;\n",
    158        "  display: block;\n",
    159        "  width: 100%;\n",
    160        "  margin-bottom: 0;\n",
    161        "  padding: 0.5em;\n",
    162        "  box-sizing: border-box;\n",
    163        "  text-align: center;\n",
    164        "}\n",
    165        "\n",
    166        "#sk-container-id-7 label.sk-toggleable__label-arrow:before {\n",
    167        "  /* Arrow on the left of the label */\n",
    168        "  content: \"▸\";\n",
    169        "  float: left;\n",
    170        "  margin-right: 0.25em;\n",
    171        "  color: var(--sklearn-color-icon);\n",
    172        "}\n",
    173        "\n",
    174        "#sk-container-id-7 label.sk-toggleable__label-arrow:hover:before {\n",
    175        "  color: var(--sklearn-color-text);\n",
    176        "}\n",
    177        "\n",
    178        "/* Toggleable content - dropdown */\n",
    179        "\n",
    180        "#sk-container-id-7 div.sk-toggleable__content {\n",
    181        "  max-height: 0;\n",
    182        "  max-width: 0;\n",
    183        "  overflow: hidden;\n",
    184        "  text-align: left;\n",
    185        "  /* unfitted */\n",
    186        "  background-color: var(--sklearn-color-unfitted-level-0);\n",
    187        "}\n",
    188        "\n",
    189        "#sk-container-id-7 div.sk-toggleable__content.fitted {\n",
    190        "  /* fitted */\n",
    191        "  background-color: var(--sklearn-color-fitted-level-0);\n",
    192        "}\n",
    193        "\n",
    194        "#sk-container-id-7 div.sk-toggleable__content pre {\n",
    195        "  margin: 0.2em;\n",
    196        "  border-radius: 0.25em;\n",
    197        "  color: var(--sklearn-color-text);\n",
    198        "  /* unfitted */\n",
    199        "  background-color: var(--sklearn-color-unfitted-level-0);\n",
    200        "}\n",
    201        "\n",
    202        "#sk-container-id-7 div.sk-toggleable__content.fitted pre {\n",
    203        "  /* unfitted */\n",
    204        "  background-color: var(--sklearn-color-fitted-level-0);\n",
    205        "}\n",
    206        "\n",
    207        "#sk-container-id-7 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
    208        "  /* Expand drop-down */\n",
    209        "  max-height: 200px;\n",
    210        "  max-width: 100%;\n",
    211        "  overflow: auto;\n",
    212        "}\n",
    213        "\n",
    214        "#sk-container-id-7 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
    215        "  content: \"▾\";\n",
    216        "}\n",
    217        "\n",
    218        "/* Pipeline/ColumnTransformer-specific style */\n",
    219        "\n",
    220        "#sk-container-id-7 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
    221        "  color: var(--sklearn-color-text);\n",
    222        "  background-color: var(--sklearn-color-unfitted-level-2);\n",
    223        "}\n",
    224        "\n",
    225        "#sk-container-id-7 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
    226        "  background-color: var(--sklearn-color-fitted-level-2);\n",
    227        "}\n",
    228        "\n",
    229        "/* Estimator-specific style */\n",
    230        "\n",
    231        "/* Colorize estimator box */\n",
    232        "#sk-container-id-7 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
    233        "  /* unfitted */\n",
    234        "  background-color: var(--sklearn-color-unfitted-level-2);\n",
    235        "}\n",
    236        "\n",
    237        "#sk-container-id-7 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
    238        "  /* fitted */\n",
    239        "  background-color: var(--sklearn-color-fitted-level-2);\n",
    240        "}\n",
    241        "\n",
    242        "#sk-container-id-7 div.sk-label label.sk-toggleable__label,\n",
    243        "#sk-container-id-7 div.sk-label label {\n",
    244        "  /* The background is the default theme color */\n",
    245        "  color: var(--sklearn-color-text-on-default-background);\n",
    246        "}\n",
    247        "\n",
    248        "/* On hover, darken the color of the background */\n",
    249        "#sk-container-id-7 div.sk-label:hover label.sk-toggleable__label {\n",
    250        "  color: var(--sklearn-color-text);\n",
    251        "  background-color: var(--sklearn-color-unfitted-level-2);\n",
    252        "}\n",
    253        "\n",
    254        "/* Label box, darken color on hover, fitted */\n",
    255        "#sk-container-id-7 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
    256        "  color: var(--sklearn-color-text);\n",
    257        "  background-color: var(--sklearn-color-fitted-level-2);\n",
    258        "}\n",
    259        "\n",
    260        "/* Estimator label */\n",
    261        "\n",
    262        "#sk-container-id-7 div.sk-label label {\n",
    263        "  font-family: monospace;\n",
    264        "  font-weight: bold;\n",
    265        "  display: inline-block;\n",
    266        "  line-height: 1.2em;\n",
    267        "}\n",
    268        "\n",
    269        "#sk-container-id-7 div.sk-label-container {\n",
    270        "  text-align: center;\n",
    271        "}\n",
    272        "\n",
    273        "/* Estimator-specific */\n",
    274        "#sk-container-id-7 div.sk-estimator {\n",
    275        "  font-family: monospace;\n",
    276        "  border: 1px dotted var(--sklearn-color-border-box);\n",
    277        "  border-radius: 0.25em;\n",
    278        "  box-sizing: border-box;\n",
    279        "  margin-bottom: 0.5em;\n",
    280        "  /* unfitted */\n",
    281        "  background-color: var(--sklearn-color-unfitted-level-0);\n",
    282        "}\n",
    283        "\n",
    284        "#sk-container-id-7 div.sk-estimator.fitted {\n",
    285        "  /* fitted */\n",
    286        "  background-color: var(--sklearn-color-fitted-level-0);\n",
    287        "}\n",
    288        "\n",
    289        "/* on hover */\n",
    290        "#sk-container-id-7 div.sk-estimator:hover {\n",
    291        "  /* unfitted */\n",
    292        "  background-color: var(--sklearn-color-unfitted-level-2);\n",
    293        "}\n",
    294        "\n",
    295        "#sk-container-id-7 div.sk-estimator.fitted:hover {\n",
    296        "  /* fitted */\n",
    297        "  background-color: var(--sklearn-color-fitted-level-2);\n",
    298        "}\n",
    299        "\n",
    300        "/* Specification for estimator info (e.g. \"i\" and \"?\") */\n",
    301        "\n",
    302        "/* Common style for \"i\" and \"?\" */\n",
    303        "\n",
    304        ".sk-estimator-doc-link,\n",
    305        "a:link.sk-estimator-doc-link,\n",
    306        "a:visited.sk-estimator-doc-link {\n",
    307        "  float: right;\n",
    308        "  font-size: smaller;\n",
    309        "  line-height: 1em;\n",
    310        "  font-family: monospace;\n",
    311        "  background-color: var(--sklearn-color-background);\n",
    312        "  border-radius: 1em;\n",
    313        "  height: 1em;\n",
    314        "  width: 1em;\n",
    315        "  text-decoration: none !important;\n",
    316        "  margin-left: 1ex;\n",
    317        "  /* unfitted */\n",
    318        "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
    319        "  color: var(--sklearn-color-unfitted-level-1);\n",
    320        "}\n",
    321        "\n",
    322        ".sk-estimator-doc-link.fitted,\n",
    323        "a:link.sk-estimator-doc-link.fitted,\n",
    324        "a:visited.sk-estimator-doc-link.fitted {\n",
    325        "  /* fitted */\n",
    326        "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
    327        "  color: var(--sklearn-color-fitted-level-1);\n",
    328        "}\n",
    329        "\n",
    330        "/* On hover */\n",
    331        "div.sk-estimator:hover .sk-estimator-doc-link:hover,\n",
    332        ".sk-estimator-doc-link:hover,\n",
    333        "div.sk-label-container:hover .sk-estimator-doc-link:hover,\n",
    334        ".sk-estimator-doc-link:hover {\n",
    335        "  /* unfitted */\n",
    336        "  background-color: var(--sklearn-color-unfitted-level-3);\n",
    337        "  color: var(--sklearn-color-background);\n",
    338        "  text-decoration: none;\n",
    339        "}\n",
    340        "\n",
    341        "div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,\n",
    342        ".sk-estimator-doc-link.fitted:hover,\n",
    343        "div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,\n",
    344        ".sk-estimator-doc-link.fitted:hover {\n",
    345        "  /* fitted */\n",
    346        "  background-color: var(--sklearn-color-fitted-level-3);\n",
    347        "  color: var(--sklearn-color-background);\n",
    348        "  text-decoration: none;\n",
    349        "}\n",
    350        "\n",
    351        "/* Span, style for the box shown on hovering the info icon */\n",
    352        ".sk-estimator-doc-link span {\n",
    353        "  display: none;\n",
    354        "  z-index: 9999;\n",
    355        "  position: relative;\n",
    356        "  font-weight: normal;\n",
    357        "  right: .2ex;\n",
    358        "  padding: .5ex;\n",
    359        "  margin: .5ex;\n",
    360        "  width: min-content;\n",
    361        "  min-width: 20ex;\n",
    362        "  max-width: 50ex;\n",
    363        "  color: var(--sklearn-color-text);\n",
    364        "  box-shadow: 2pt 2pt 4pt #999;\n",
    365        "  /* unfitted */\n",
    366        "  background: var(--sklearn-color-unfitted-level-0);\n",
    367        "  border: .5pt solid var(--sklearn-color-unfitted-level-3);\n",
    368        "}\n",
    369        "\n",
    370        ".sk-estimator-doc-link.fitted span {\n",
    371        "  /* fitted */\n",
    372        "  background: var(--sklearn-color-fitted-level-0);\n",
    373        "  border: var(--sklearn-color-fitted-level-3);\n",
    374        "}\n",
    375        "\n",
    376        ".sk-estimator-doc-link:hover span {\n",
    377        "  display: block;\n",
    378        "}\n",
    379        "\n",
    380        "/* \"?\"-specific style due to the `<a>` HTML tag */\n",
    381        "\n",
    382        "#sk-container-id-7 a.estimator_doc_link {\n",
    383        "  float: right;\n",
    384        "  font-size: 1rem;\n",
    385        "  line-height: 1em;\n",
    386        "  font-family: monospace;\n",
    387        "  background-color: var(--sklearn-color-background);\n",
    388        "  border-radius: 1rem;\n",
    389        "  height: 1rem;\n",
    390        "  width: 1rem;\n",
    391        "  text-decoration: none;\n",
    392        "  /* unfitted */\n",
    393        "  color: var(--sklearn-color-unfitted-level-1);\n",
    394        "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
    395        "}\n",
    396        "\n",
    397        "#sk-container-id-7 a.estimator_doc_link.fitted {\n",
    398        "  /* fitted */\n",
    399        "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
    400        "  color: var(--sklearn-color-fitted-level-1);\n",
    401        "}\n",
    402        "\n",
    403        "/* On hover */\n",
    404        "#sk-container-id-7 a.estimator_doc_link:hover {\n",
    405        "  /* unfitted */\n",
    406        "  background-color: var(--sklearn-color-unfitted-level-3);\n",
    407        "  color: var(--sklearn-color-background);\n",
    408        "  text-decoration: none;\n",
    409        "}\n",
    410        "\n",
    411        "#sk-container-id-7 a.estimator_doc_link.fitted:hover {\n",
    412        "  /* fitted */\n",
    413        "  background-color: var(--sklearn-color-fitted-level-3);\n",
    414        "}\n",
    415        "</style><div id=\"sk-container-id-7\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>DBSCAN(eps=0.2)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-7\" type=\"checkbox\" checked><label for=\"sk-estimator-id-7\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;&nbsp;DBSCAN<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.4/modules/generated/sklearn.cluster.DBSCAN.html\">?<span>Documentation for DBSCAN</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></label><div class=\"sk-toggleable__content fitted\"><pre>DBSCAN(eps=0.2)</pre></div> </div></div></div></div>"
    416       ],
    417       "text/plain": [
    418        "DBSCAN(eps=0.2)"
    419       ]
    420      },
    421      "execution_count": 20,
    422      "metadata": {},
    423      "output_type": "execute_result"
    424     }
    425    ],
    426    "source": [
    427     "from sklearn.cluster import DBSCAN\n",
    428     "from sklearn.datasets import make_moons\n",
    429     "\n",
    430     "X,y = make_moons(n_samples=1000, noise=.05)\n",
    431     "dbscan = DBSCAN(eps=.2, min_samples=5)\n",
    432     "dbscan.fit(X)"
    433    ]
    434   },
    435   {
    436    "cell_type": "code",
    437    "execution_count": 21,
    438    "metadata": {},
    439    "outputs": [
    440     {
    441      "name": "stdout",
    442      "output_type": "stream",
    443      "text": [
    444       "(1000, 2)\n"
    445      ]
    446     },
    447     {
    448      "data": {
    449       "text/plain": [
    450        "<matplotlib.collections.PathCollection at 0x7f4a9c4f4210>"
    451       ]
    452      },
    453      "execution_count": 21,
    454      "metadata": {},
    455      "output_type": "execute_result"
    456     },
    457     {
    458      "data": {
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",
    460       "text/plain": [
    461        "<Figure size 640x480 with 1 Axes>"
    462       ]
    463      },
    464      "metadata": {},
    465      "output_type": "display_data"
    466     }
    467    ],
    468    "source": [
    469     "import matplotlib.pyplot as plt\n",
    470     "\n",
    471     "print(X.shape)\n",
    472     "plt.scatter(X[:,0],X[:,1], c=dbscan.labels_, cmap='jet')"
    473    ]
    474   }
    475  ],
    476  "metadata": {
    477   "kernelspec": {
    478    "display_name": "notebook",
    479    "language": "python",
    480    "name": "notebook"
    481   },
    482   "language_info": {
    483    "codemirror_mode": {
    484     "name": "ipython",
    485     "version": 3
    486    },
    487    "file_extension": ".py",
    488    "mimetype": "text/x-python",
    489    "name": "python",
    490    "nbconvert_exporter": "python",
    491    "pygments_lexer": "ipython3",
    492    "version": "3.11.2"
    493   }
    494  },
    495  "nbformat": 4,
    496  "nbformat_minor": 2
    497 }
	machinelearning Machine learning code
	git clone git://git.laack.co/machinelearning.git
	Log \| Files \| Refs