hierarchical-based-methods.ipynb

{"cells":[{"metadata":{"trusted":true},"cell_type":"code","source":"from warnings import filterwarnings\nfilterwarnings('ignore')\nimport numpy as np\nimport pandas as pd \nimport seaborn as sns\nimport matplotlib.pyplot as plt\nimport scipy as sp\nfrom sklearn.cluster import KMeans","execution_count":1,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"df=pd.read_csv(\"../input/usarrests/USArrests.csv\").copy()\ndf.index = df.iloc[:,0]\ndf = df.iloc[:,1:5]\ndf.index.name = None\ndf.head()","execution_count":2,"outputs":[{"output_type":"execute_result","execution_count":2,"data":{"text/plain":"            Murder  Assault  UrbanPop  Rape\nAlabama       13.2      236        58  21.2\nAlaska        10.0      263        48  44.5\nArizona        8.1      294        80  31.0\nArkansas       8.8      190        50  19.5\nCalifornia     9.0      276        91  40.6","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>Murder</th>\n      <th>Assault</th>\n      <th>UrbanPop</th>\n      <th>Rape</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>Alabama</th>\n      <td>13.2</td>\n      <td>236</td>\n      <td>58</td>\n      <td>21.2</td>\n    </tr>\n    <tr>\n      <th>Alaska</th>\n      <td>10.0</td>\n      <td>263</td>\n      <td>48</td>\n      <td>44.5</td>\n    </tr>\n    <tr>\n      <th>Arizona</th>\n      <td>8.1</td>\n      <td>294</td>\n      <td>80</td>\n      <td>31.0</td>\n    </tr>\n    <tr>\n      <th>Arkansas</th>\n      <td>8.8</td>\n      <td>190</td>\n      <td>50</td>\n      <td>19.5</td>\n    </tr>\n    <tr>\n      <th>California</th>\n      <td>9.0</td>\n      <td>276</td>\n      <td>91</td>\n      <td>40.6</td>\n    </tr>\n  </tbody>\n</table>\n</div>"},"metadata":{}}]},{"metadata":{"trusted":true},"cell_type":"code","source":"from scipy.cluster.hierarchy import linkage\nhc_complete = linkage(df, \"complete\")\nhc_average = linkage(df, \"average\")\nhc_single = linkage(df, \"single\")","execution_count":3,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"from scipy.cluster.hierarchy import dendrogram\n\nplt.figure(figsize=(15, 10))\nplt.title('Hierarchical clustering - Dendrogram')\nplt.xlabel('Index')\nplt.ylabel('Distance')\ndendrogram(\n    hc_complete,\n    leaf_font_size=10\n);","execution_count":4,"outputs":[{"output_type":"display_data","data":{"text/plain":"<Figure size 1080x720 with 1 Axes>","image/png":"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\n"},"metadata":{"needs_background":"light"}}]},{"metadata":{"trusted":true},"cell_type":"code","source":"from scipy.cluster.hierarchy import dendrogram\n\nplt.figure(figsize=(15, 10))\nplt.title('HHierarchical clustering - Dendrogram')\nplt.xlabel('Cardinality')\nplt.ylabel('Distance')\ndendrogram(\n    hc_complete,\n    truncate_mode = \"lastp\",\n    p = 4,                       #for 4 clusters\n    show_contracted = True\n);","execution_count":5,"outputs":[{"output_type":"display_data","data":{"text/plain":"<Figure size 1080x720 with 1 Axes>","image/png":"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\n"},"metadata":{"needs_background":"light"}}]},{"metadata":{"trusted":true},"cell_type":"code","source":"df.shape","execution_count":6,"outputs":[{"output_type":"execute_result","execution_count":6,"data":{"text/plain":"(50, 4)"},"metadata":{}}]},{"metadata":{"trusted":true},"cell_type":"code","source":"from sklearn.cluster import AgglomerativeClustering\nCluster=AgglomerativeClustering(n_clusters=4, affinity=\"euclidean\",linkage=\"ward\")   #We indicated that 4 is more preferable","execution_count":7,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"Cluster.fit_predict(df)","execution_count":8,"outputs":[{"output_type":"execute_result","execution_count":8,"data":{"text/plain":"array([0, 0, 0, 2, 0, 2, 3, 0, 0, 2, 1, 3, 0, 3, 1, 3, 3, 0, 1, 0, 2, 0,\n       1, 0, 2, 3, 3, 0, 1, 2, 0, 0, 0, 1, 3, 2, 2, 3, 2, 0, 1, 2, 2, 3,\n       1, 2, 2, 1, 1, 2])"},"metadata":{}}]},{"metadata":{"trusted":true},"cell_type":"code","source":"pd.DataFrame({\"States\" : df.index, \"Clusters\": Cluster.fit_predict(df)})[0:10]","execution_count":9,"outputs":[{"output_type":"execute_result","execution_count":9,"data":{"text/plain":"        States  Clusters\n0      Alabama         0\n1       Alaska         0\n2      Arizona         0\n3     Arkansas         2\n4   California         0\n5     Colorado         2\n6  Connecticut         3\n7     Delaware         0\n8      Florida         0\n9      Georgia         2","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>States</th>\n      <th>Clusters</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>Alabama</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>Alaska</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>Arizona</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>Arkansas</td>\n      <td>2</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>California</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>5</th>\n      <td>Colorado</td>\n      <td>2</td>\n    </tr>\n    <tr>\n      <th>6</th>\n      <td>Connecticut</td>\n      <td>3</td>\n    </tr>\n    <tr>\n      <th>7</th>\n      <td>Delaware</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>8</th>\n      <td>Florida</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>9</th>\n      <td>Georgia</td>\n      <td>2</td>\n    </tr>\n  </tbody>\n</table>\n</div>"},"metadata":{}}]},{"metadata":{"trusted":true},"cell_type":"code","source":"df[\"Clusters' Number\"]=Cluster.fit_predict(df)\ndf.head()","execution_count":11,"outputs":[{"output_type":"execute_result","execution_count":11,"data":{"text/plain":"            Murder  Assault  UrbanPop  Rape  Clusters Number  Clusters' Number\nAlabama       13.2      236        58  21.2                0                 0\nAlaska        10.0      263        48  44.5                0                 0\nArizona        8.1      294        80  31.0                0                 0\nArkansas       8.8      190        50  19.5                2                 2\nCalifornia     9.0      276        91  40.6                0                 0","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>Murder</th>\n      <th>Assault</th>\n      <th>UrbanPop</th>\n      <th>Rape</th>\n      <th>Clusters Number</th>\n      <th>Clusters' Number</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>Alabama</th>\n      <td>13.2</td>\n      <td>236</td>\n      <td>58</td>\n      <td>21.2</td>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>Alaska</th>\n      <td>10.0</td>\n      <td>263</td>\n      <td>48</td>\n      <td>44.5</td>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>Arizona</th>\n      <td>8.1</td>\n      <td>294</td>\n      <td>80</td>\n      <td>31.0</td>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>Arkansas</th>\n      <td>8.8</td>\n      <td>190</td>\n      <td>50</td>\n      <td>19.5</td>\n      <td>2</td>\n      <td>2</td>\n    </tr>\n    <tr>\n      <th>California</th>\n      <td>9.0</td>\n      <td>276</td>\n      <td>91</td>\n      <td>40.6</td>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n  </tbody>\n</table>\n</div>"},"metadata":{}}]},{"metadata":{},"cell_type":"markdown","source":"Thanks to https://github.com/mvahit/DSMLBC"},{"metadata":{"trusted":true},"cell_type":"code","source":"","execution_count":null,"outputs":[]}],"metadata":{"kernelspec":{"language":"python","display_name":"Python 3","name":"python3"},"language_info":{"pygments_lexer":"ipython3","nbconvert_exporter":"python","version":"3.6.4","file_extension":".py","codemirror_mode":{"name":"ipython","version":3},"name":"python","mimetype":"text/x-python"}},"nbformat":4,"nbformat_minor":4}