From ce2af02fe4edf08016ddafa6028796adf353b863 Mon Sep 17 00:00:00 2001
From: Diwakar Gupta <39624018+Diwakar-Gupta@users.noreply.github.com>
Date: Sat, 4 Jun 2022 19:30:08 +0530
Subject: [PATCH] K-Means & Semi Supervised

---
 22-06-03-KMeans/April_K_Means.ipynb | 264 +++++++++++++++-------------
 1 file changed, 141 insertions(+), 123 deletions(-)

diff --git a/22-06-03-KMeans/April_K_Means.ipynb b/22-06-03-KMeans/April_K_Means.ipynb
index 36b7ee9..4a2854b 100644
--- a/22-06-03-KMeans/April_K_Means.ipynb
+++ b/22-06-03-KMeans/April_K_Means.ipynb
@@ -6,7 +6,7 @@
       "name": "April K-Means.ipynb",
       "provenance": [],
       "collapsed_sections": [],
-      "authorship_tag": "ABX9TyN3z2LGygaTT5NcO/bRp0hq",
+      "authorship_tag": "ABX9TyOmHbgO/5OfCQpLF4QMpYNK",
       "include_colab_link": true
     },
     "kernelspec": {
@@ -30,7 +30,7 @@
     },
     {
       "cell_type": "code",
-      "execution_count": 50,
+      "execution_count": 1,
       "metadata": {
         "id": "ZiWlYYayRlLA"
       },
@@ -53,29 +53,29 @@
           "height": 175
         },
         "id": "h8Se3i01RpXL",
-        "outputId": "3a31b670-f2f7-4026-d75a-840ad142110a"
+        "outputId": "b9b64f0a-b5a4-4651-9b7e-e9047dd2d3a8"
       },
-      "execution_count": 51,
+      "execution_count": 2,
       "outputs": [
         {
           "output_type": "execute_result",
           "data": {
             "text/plain": [
               "    sepal_length  sepal_width  petal_length  petal_width     species  \\\n",
+              "87           6.3          2.3           4.4          1.3  versicolor   \n",
+              "86           6.7          3.1           4.7          1.5  versicolor   \n",
+              "24           4.8          3.4           1.9          0.2      setosa   \n",
               "33           5.5          4.2           1.4          0.2      setosa   \n",
-              "85           6.0          3.4           4.5          1.6  versicolor   \n",
-              "10           5.4          3.7           1.5          0.2      setosa   \n",
-              "31           5.4          3.4           1.5          0.4      setosa   \n",
               "\n",
               "    species_id  \n",
-              "33           1  \n",
-              "85           2  \n",
-              "10           1  \n",
-              "31           1  "
+              "87           2  \n",
+              "86           2  \n",
+              "24           1  \n",
+              "33           1  "
             ],
             "text/html": [
               "\n",
-              "  <div id=\"df-ff1e0c3b-ba92-401c-a43d-1a2fa0731116\">\n",
+              "  <div id=\"df-06c7b11b-d7a5-4b2f-9f76-78398d33770f\">\n",
               "    <div class=\"colab-df-container\">\n",
               "      <div>\n",
               "<style scoped>\n",
@@ -105,45 +105,45 @@
               "  </thead>\n",
               "  <tbody>\n",
               "    <tr>\n",
-              "      <th>33</th>\n",
-              "      <td>5.5</td>\n",
-              "      <td>4.2</td>\n",
-              "      <td>1.4</td>\n",
-              "      <td>0.2</td>\n",
-              "      <td>setosa</td>\n",
-              "      <td>1</td>\n",
+              "      <th>87</th>\n",
+              "      <td>6.3</td>\n",
+              "      <td>2.3</td>\n",
+              "      <td>4.4</td>\n",
+              "      <td>1.3</td>\n",
+              "      <td>versicolor</td>\n",
+              "      <td>2</td>\n",
               "    </tr>\n",
               "    <tr>\n",
-              "      <th>85</th>\n",
-              "      <td>6.0</td>\n",
-              "      <td>3.4</td>\n",
-              "      <td>4.5</td>\n",
-              "      <td>1.6</td>\n",
+              "      <th>86</th>\n",
+              "      <td>6.7</td>\n",
+              "      <td>3.1</td>\n",
+              "      <td>4.7</td>\n",
+              "      <td>1.5</td>\n",
               "      <td>versicolor</td>\n",
               "      <td>2</td>\n",
               "    </tr>\n",
               "    <tr>\n",
-              "      <th>10</th>\n",
-              "      <td>5.4</td>\n",
-              "      <td>3.7</td>\n",
-              "      <td>1.5</td>\n",
+              "      <th>24</th>\n",
+              "      <td>4.8</td>\n",
+              "      <td>3.4</td>\n",
+              "      <td>1.9</td>\n",
               "      <td>0.2</td>\n",
               "      <td>setosa</td>\n",
               "      <td>1</td>\n",
               "    </tr>\n",
               "    <tr>\n",
-              "      <th>31</th>\n",
-              "      <td>5.4</td>\n",
-              "      <td>3.4</td>\n",
-              "      <td>1.5</td>\n",
-              "      <td>0.4</td>\n",
+              "      <th>33</th>\n",
+              "      <td>5.5</td>\n",
+              "      <td>4.2</td>\n",
+              "      <td>1.4</td>\n",
+              "      <td>0.2</td>\n",
               "      <td>setosa</td>\n",
               "      <td>1</td>\n",
               "    </tr>\n",
               "  </tbody>\n",
               "</table>\n",
               "</div>\n",
-              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-ff1e0c3b-ba92-401c-a43d-1a2fa0731116')\"\n",
+              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-06c7b11b-d7a5-4b2f-9f76-78398d33770f')\"\n",
               "              title=\"Convert this dataframe to an interactive table.\"\n",
               "              style=\"display:none;\">\n",
               "        \n",
@@ -194,12 +194,12 @@
               "\n",
               "      <script>\n",
               "        const buttonEl =\n",
-              "          document.querySelector('#df-ff1e0c3b-ba92-401c-a43d-1a2fa0731116 button.colab-df-convert');\n",
+              "          document.querySelector('#df-06c7b11b-d7a5-4b2f-9f76-78398d33770f button.colab-df-convert');\n",
               "        buttonEl.style.display =\n",
               "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
               "\n",
               "        async function convertToInteractive(key) {\n",
-              "          const element = document.querySelector('#df-ff1e0c3b-ba92-401c-a43d-1a2fa0731116');\n",
+              "          const element = document.querySelector('#df-06c7b11b-d7a5-4b2f-9f76-78398d33770f');\n",
               "          const dataTable =\n",
               "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
               "                                                     [key], {});\n",
@@ -222,7 +222,7 @@
             ]
           },
           "metadata": {},
-          "execution_count": 51
+          "execution_count": 2
         }
       ]
     },
@@ -252,9 +252,9 @@
           "height": 368
         },
         "id": "liV1OuoTRzvk",
-        "outputId": "d2ba6a80-8201-46d0-fc9b-b90deca3de31"
+        "outputId": "55d5e13a-d28f-4ee5-eba1-83a3af97f358"
       },
-      "execution_count": 52,
+      "execution_count": 3,
       "outputs": [
         {
           "output_type": "execute_result",
@@ -264,7 +264,7 @@
             ]
           },
           "metadata": {},
-          "execution_count": 52
+          "execution_count": 3
         },
         {
           "output_type": "display_data",
@@ -295,9 +295,9 @@
           "base_uri": "https://localhost:8080/"
         },
         "id": "sIf1qT2sSa3O",
-        "outputId": "2ad3ceec-2073-4f70-8f60-7387cbad38b6"
+        "outputId": "ac8caa81-0a2a-480d-bcb1-3f254e4c62de"
       },
-      "execution_count": 53,
+      "execution_count": 4,
       "outputs": [
         {
           "output_type": "execute_result",
@@ -307,7 +307,7 @@
             ]
           },
           "metadata": {},
-          "execution_count": 53
+          "execution_count": 4
         }
       ]
     },
@@ -321,9 +321,9 @@
           "base_uri": "https://localhost:8080/"
         },
         "id": "PjIFh5Wyde87",
-        "outputId": "bbf42f82-5792-4efe-d3a3-46dcd3aca583"
+        "outputId": "ff4e9c23-d85f-4d94-f277-8d7386edb319"
       },
-      "execution_count": 54,
+      "execution_count": 5,
       "outputs": [
         {
           "output_type": "execute_result",
@@ -335,7 +335,7 @@
             ]
           },
           "metadata": {},
-          "execution_count": 54
+          "execution_count": 5
         }
       ]
     },
@@ -353,9 +353,9 @@
           "height": 265
         },
         "id": "w-RLjy1tdyOf",
-        "outputId": "d5dab9e8-c2d7-43f8-b1bf-dcd91fbe6da4"
+        "outputId": "8f91b274-493c-4aa6-f1cb-b6521fcb531e"
       },
-      "execution_count": 55,
+      "execution_count": 6,
       "outputs": [
         {
           "output_type": "display_data",
@@ -385,9 +385,9 @@
           "height": 265
         },
         "id": "B7bhoGczeTLT",
-        "outputId": "edcb74f2-aaec-4746-d700-fbc047e7af13"
+        "outputId": "1ffa5f34-fd18-4c31-8f5a-efb8718ab2ae"
       },
-      "execution_count": 56,
+      "execution_count": 7,
       "outputs": [
         {
           "output_type": "display_data",
@@ -413,9 +413,9 @@
           "base_uri": "https://localhost:8080/"
         },
         "id": "_gonaC4OejFf",
-        "outputId": "7906113a-41a3-4cec-b910-fdadab7cc42f"
+        "outputId": "534e5223-99a0-4407-d173-9fa656b6bcc0"
       },
-      "execution_count": 57,
+      "execution_count": 8,
       "outputs": [
         {
           "output_type": "execute_result",
@@ -425,7 +425,7 @@
             ]
           },
           "metadata": {},
-          "execution_count": 57
+          "execution_count": 8
         }
       ]
     },
@@ -442,7 +442,7 @@
       "metadata": {
         "id": "9X8Q5trPg5MS"
       },
-      "execution_count": 58,
+      "execution_count": 9,
       "outputs": []
     },
     {
@@ -456,19 +456,19 @@
           "height": 282
         },
         "id": "4gLRluQ3hKvT",
-        "outputId": "bf11b050-2132-4cd4-d35f-4e043383c080"
+        "outputId": "79dd5898-5fd4-46d4-83eb-f613376c42d7"
       },
-      "execution_count": 59,
+      "execution_count": 10,
       "outputs": [
         {
           "output_type": "execute_result",
           "data": {
             "text/plain": [
-              "[<matplotlib.lines.Line2D at 0x7fb90e589f50>]"
+              "[<matplotlib.lines.Line2D at 0x7f13838f6dd0>]"
             ]
           },
           "metadata": {},
-          "execution_count": 59
+          "execution_count": 10
         },
         {
           "output_type": "display_data",
@@ -476,7 +476,7 @@
             "text/plain": [
               "<Figure size 432x288 with 1 Axes>"
             ],
-            "image/png": 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\n"
+            "image/png": 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\n"
           },
           "metadata": {
             "needs_background": "light"
@@ -500,7 +500,7 @@
       "metadata": {
         "id": "fOMKfaxauIwR"
       },
-      "execution_count": 61,
+      "execution_count": 11,
       "outputs": []
     },
     {
@@ -514,19 +514,19 @@
           "height": 282
         },
         "id": "M-CMasbEuX7s",
-        "outputId": "fd17a80b-ff02-497d-a217-c723b5ac19ab"
+        "outputId": "68fff139-0ed1-458d-d8de-39924ce61149"
       },
-      "execution_count": 62,
+      "execution_count": 12,
       "outputs": [
         {
           "output_type": "execute_result",
           "data": {
             "text/plain": [
-              "[<matplotlib.lines.Line2D at 0x7fb90e5431d0>]"
+              "[<matplotlib.lines.Line2D at 0x7f13966f7410>]"
             ]
           },
           "metadata": {},
-          "execution_count": 62
+          "execution_count": 12
         },
         {
           "output_type": "display_data",
@@ -534,7 +534,7 @@
             "text/plain": [
               "<Figure size 432x288 with 1 Axes>"
             ],
-            "image/png": 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\n"
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\n"
           },
           "metadata": {
             "needs_background": "light"
@@ -560,7 +560,7 @@
       "metadata": {
         "id": "g4KNxB6LiOzO"
       },
-      "execution_count": 27,
+      "execution_count": 13,
       "outputs": []
     },
     {
@@ -571,7 +571,7 @@
       "metadata": {
         "id": "lCALhze_nEUA"
       },
-      "execution_count": 25,
+      "execution_count": 14,
       "outputs": []
     },
     {
@@ -584,9 +584,9 @@
           "base_uri": "https://localhost:8080/"
         },
         "id": "82OpycKAnGRO",
-        "outputId": "4cfe459e-52aa-4ec3-f996-653477d88646"
+        "outputId": "b3243829-1870-4bd4-e288-1e0a9af8dc4f"
       },
-      "execution_count": 26,
+      "execution_count": 15,
       "outputs": [
         {
           "output_type": "execute_result",
@@ -596,7 +596,7 @@
             ]
           },
           "metadata": {},
-          "execution_count": 26
+          "execution_count": 15
         }
       ]
     },
@@ -609,7 +609,7 @@
       "metadata": {
         "id": "MzH5X0gAnLLw"
       },
-      "execution_count": 28,
+      "execution_count": 16,
       "outputs": []
     },
     {
@@ -622,9 +622,9 @@
           "base_uri": "https://localhost:8080/"
         },
         "id": "qRdFvEA1nbtY",
-        "outputId": "c9be1797-9798-40a4-a854-297a4c524eca"
+        "outputId": "6f710e45-fa63-4697-a391-35f112ea90d5"
       },
-      "execution_count": 29,
+      "execution_count": 17,
       "outputs": [
         {
           "output_type": "execute_result",
@@ -634,21 +634,32 @@
             ]
           },
           "metadata": {},
-          "execution_count": 29
+          "execution_count": 17
         }
       ]
     },
+    {
+      "cell_type": "code",
+      "source": [
+        "SEED=90"
+      ],
+      "metadata": {
+        "id": "OpdoMcIXw6GY"
+      },
+      "execution_count": 21,
+      "outputs": []
+    },
     {
       "cell_type": "code",
       "source": [
         "from sklearn.model_selection import train_test_split\n",
         "\n",
-        "xtrain, xtest, ytrain, ytest = train_test_split(X, y, random_state=90, test_size=0.5)"
+        "xtrain, xtest, ytrain, ytest = train_test_split(X, y, random_state=SEED, test_size=0.5)"
       ],
       "metadata": {
         "id": "PeqhC7KBneZ8"
       },
-      "execution_count": 31,
+      "execution_count": 18,
       "outputs": []
     },
     {
@@ -658,7 +669,7 @@
         "\n",
         "n_labeled = 50\n",
         "\n",
-        "model = LogisticRegression().fit(xtrain[:n_labeled], ytrain[:n_labeled])\n",
+        "model = LogisticRegression(random_state=SEED).fit(xtrain[:n_labeled], ytrain[:n_labeled])\n",
         "model.score(xtest, ytest)"
       ],
       "metadata": {
@@ -666,9 +677,9 @@
           "base_uri": "https://localhost:8080/"
         },
         "id": "FnHbjF05nnEF",
-        "outputId": "12f978da-3d05-4ce1-f622-1f692315d4df"
+        "outputId": "dc02c318-9452-4118-c031-727d9e1c9867"
       },
-      "execution_count": 32,
+      "execution_count": 22,
       "outputs": [
         {
           "output_type": "execute_result",
@@ -678,7 +689,7 @@
             ]
           },
           "metadata": {},
-          "execution_count": 32
+          "execution_count": 22
         }
       ]
     },
@@ -688,47 +699,15 @@
         "from sklearn.cluster import KMeans\n",
         "\n",
         "k = 50\n",
-        "kmeans = KMeans(n_clusters=k)\n",
+        "kmeans = KMeans(n_clusters=k, random_state=SEED)\n",
         "X_digits_dist = kmeans.fit_transform(xtrain)"
       ],
       "metadata": {
         "id": "GNSp-lDMn6CD"
       },
-      "execution_count": 33,
+      "execution_count": 23,
       "outputs": []
     },
-    {
-      "cell_type": "code",
-      "source": [
-        "for i in range(8):\n",
-        "    img = kmeans.cluster_centers_[i].reshape(28, 28)\n",
-        "    plt.subplot(2, 4, i+1)\n",
-        "    plt.imshow(img)"
-      ],
-      "metadata": {
-        "colab": {
-          "base_uri": "https://localhost:8080/",
-          "height": 242
-        },
-        "id": "hTuxB5bIowVV",
-        "outputId": "79921fb4-c6cf-4dd1-92b1-445c5aa2fa28"
-      },
-      "execution_count": 39,
-      "outputs": [
-        {
-          "output_type": "display_data",
-          "data": {
-            "text/plain": [
-              "<Figure size 432x288 with 8 Axes>"
-            ],
-            "image/png": 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\n"
-          },
-          "metadata": {
-            "needs_background": "light"
-          }
-        }
-      ]
-    },
     {
       "cell_type": "code",
       "source": [
@@ -749,9 +728,9 @@
           "height": 282
         },
         "id": "RX8qkSP7qSKX",
-        "outputId": "a8b93134-a48e-45c8-8a89-3e89c2e81c32"
+        "outputId": "cf187641-693f-45bf-f212-8e371bec9aac"
       },
-      "execution_count": 46,
+      "execution_count": 24,
       "outputs": [
         {
           "output_type": "display_data",
@@ -759,7 +738,7 @@
             "text/plain": [
               "<Figure size 432x288 with 1 Axes>"
             ],
-            "image/png": 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\n"
+            "image/png": "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\n"
           },
           "metadata": {
             "needs_background": "light"
@@ -769,15 +748,28 @@
           "name": "stdout",
           "output_type": "stream",
           "text": [
-            "5\n"
+            "2\n"
           ]
         }
       ]
     },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "```\n",
+        "ytrain = array([1., 7., 6., 0., 5., 1., 9., 3., 5., 0., 2., 0., 6., 8., 3., 9., 3.,\n",
+        "       1., 8., 0., 4., 9., 8., 6., 5., 4., 4., 1., 6., 0., 6., 9., 7., 2.,\n",
+        "       3., 7., 6., 3., 5., 7., 8., 4., 3., 2., 7., 2., 0., 4., 0., 2.])\n",
+        "```"
+      ],
+      "metadata": {
+        "id": "XZqYl1kiyNmb"
+      }
+    },
     {
       "cell_type": "code",
       "source": [
-        "model = LogisticRegression().fit(kmeans.cluster_centers_, ytrain)\n",
+        "model = LogisticRegression(random_state=SEED).fit(kmeans.cluster_centers_, ytrain)\n",
         "model.score(xtest, ytest)"
       ],
       "metadata": {
@@ -785,9 +777,9 @@
           "base_uri": "https://localhost:8080/"
         },
         "id": "TfOJcci9rXlz",
-        "outputId": "27d1d113-ca2e-41ab-efa1-8b546b59a398"
+        "outputId": "ddbd1605-a071-4969-c4f2-df88aa2bbcb0"
       },
-      "execution_count": 49,
+      "execution_count": 27,
       "outputs": [
         {
           "output_type": "execute_result",
@@ -797,7 +789,33 @@
             ]
           },
           "metadata": {},
-          "execution_count": 49
+          "execution_count": 27
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "model.score(xtest, ytest.astype('int8'))"
+      ],
+      "metadata": {
+        "id": "VA5koSYlr8Ab",
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "outputId": "609afda0-786b-48bd-bb06-21a3c532b02a"
+      },
+      "execution_count": 31,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "0.7681142857142857"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 31
         }
       ]
     },
@@ -807,7 +825,7 @@
         ""
       ],
       "metadata": {
-        "id": "VA5koSYlr8Ab"
+        "id": "TUiHsKekytiR"
       },
       "execution_count": null,
       "outputs": []