ML 7.2 - Mean Shift - example - titanic dataset.ipynb

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   "source": [
    "Internet Resources:\n",
    "\n",
    "[Python Programming.net - machine learning episodes 39-42](https://pythonprogramming.net/hierarchical-clustering-mean-shift-machine-learning-tutorial/)"
   ]
  },
  {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>pclass</th>\n",
       "      <th>survived</th>\n",
       "      <th>sex</th>\n",
       "      <th>age</th>\n",
       "      <th>sibsp</th>\n",
       "      <th>parch</th>\n",
       "      <th>ticket</th>\n",
       "      <th>fare</th>\n",
       "      <th>cabin</th>\n",
       "      <th>embarked</th>\n",
       "      <th>boat</th>\n",
       "      <th>home.dest</th>\n",
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       "      <th>0</th>\n",
       "      <td>1</td>\n",
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       "      <td>29.0000</td>\n",
       "      <td>0</td>\n",
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       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>30.0000</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>511</td>\n",
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      "text/plain": [
       "   pclass  survived  sex      age  sibsp  parch  ticket      fare  cabin  \\\n",
       "0       1         1    0  29.0000      0      0     761  211.3375     83   \n",
       "1       1         1    1   0.9167      1      2     511  151.5500     20   \n",
       "2       1         0    0   2.0000      1      2     511  151.5500     20   \n",
       "3       1         0    1  30.0000      1      2     511  151.5500     20   \n",
       "4       1         0    0  25.0000      1      2     511  151.5500     20   \n",
       "\n",
       "   embarked  boat  home.dest  \n",
       "0         3     1        259  \n",
       "1         3     4        277  \n",
       "2         3     0        277  \n",
       "3         3     0        277  \n",
       "4         3     0        277  "
      ]
     },
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     "metadata": {},
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   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "from matplotlib import style\n",
    "style.use('ggplot')\n",
    "import numpy as np\n",
    "from sklearn.cluster import MeanShift\n",
    "from sklearn import preprocessing\n",
    "import pandas as pd\n",
    "\n",
    "# data from https://pythonprogramming.net/static/downloads/machine-learning-data/titanic.xls\n",
    "df = pd.read_excel('data/titanic.xls') \n",
    "'''\n",
    "Pclass Passenger Class (1 = 1st; 2 = 2nd; 3 = 3rd)\n",
    "survival Survival (0 = No; 1 = Yes)\n",
    "name Name\n",
    "sex Sex\n",
    "age Age\n",
    "sibsp Number of Siblings/Spouses Aboard\n",
    "parch Number of Parents/Children Aboard\n",
    "ticket Ticket Number\n",
    "fare Passenger Fare (British pound)\n",
    "cabin Cabin\n",
    "embarked Port of Embarkation (C = Cherbourg; Q = Queenstown; S = Southampton)\n",
    "boat Lifeboat\n",
    "body Body Identification Number\n",
    "home.dest Home/Destination\n",
    "'''\n",
    "\n",
    "original_df = pd.DataFrame.copy(df)\n",
    "\n",
    "df.drop(['body','name'], 1, inplace=True)\n",
    "df.apply(pd.to_numeric, errors=\"ignore\")\n",
    "df.fillna(0, inplace=True)\n",
    "\n",
    "# handling non linear data:\n",
    "# every unique feature value gets a different integer number\n",
    "# e.g. female becomes 0, male becomes 1\n",
    "def handle_non_numerical_data(df):\n",
    "    columns = df.columns.values\n",
    "    for column in columns:\n",
    "        text_digit_vals = {}\n",
    "        def convert_to_int(val):\n",
    "            return text_digit_vals[val]\n",
    "\n",
    "        if df[column].dtype != np.int64 and df[column].dtype != np.float64:\n",
    "            column_contents = df[column].values.tolist()\n",
    "            unique_elements = set(column_contents)\n",
    "            x = 0\n",
    "            for unique in unique_elements:\n",
    "                if unique not in text_digit_vals:\n",
    "                    text_digit_vals[unique] = x\n",
    "                    x+=1\n",
    "\n",
    "            df[column] = list(map(convert_to_int, df[column]))\n",
    "\n",
    "    return df\n",
    "\n",
    "df = handle_non_numerical_data(df)\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Cluster  0 :  0.3783151326053042\n",
      "Cluster  1 :  0.8235294117647058\n",
      "Cluster  2 :  0.1\n"
     ]
    }
   ],
   "source": [
    "# train\n",
    "X = np.array(df.drop(['survived'], 1).astype(float))\n",
    "X = preprocessing.scale(X) # https://scikit-learn.org/stable/modules/preprocessing.html\n",
    "y = np.array(df['survived'])\n",
    "\n",
    "clf = MeanShift()\n",
    "clf.fit(X)\n",
    "\n",
    "# lets take a look at the different clusters mean shift has found\n",
    "labels = clf.labels_\n",
    "cluster_centers = clf.cluster_centers_\n",
    "original_df['cluster_group']=np.nan\n",
    "\n",
    "for i in range(len(X)):\n",
    "    original_df.at[i, 'cluster_group'] = labels[i] \n",
    "\n",
    "n_clusters_ = len(np.unique(labels))\n",
    "\n",
    "survival_rates = {}\n",
    "for i in range(n_clusters_):\n",
    "    temp_df = original_df[ (original_df['cluster_group']==float(i)) ] # temp only has data with cluster_group i\n",
    "    #print(temp_df.head())\n",
    "    \n",
    "    survival_cluster = temp_df[  (temp_df['survived'] == 1) ] # temp df just with only suvivors\n",
    "    \n",
    "    survival_rate = len(survival_cluster) / len(temp_df)\n",
    "    print(\"Cluster \", i,\": \",survival_rate)\n",
    "    survival_rates[i] = survival_rate"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Cluster 0:\n",
      "\n",
      "            pclass     survived          age        sibsp        parch  \\\n",
      "count  1282.000000  1282.000000  1021.000000  1282.000000  1282.000000   \n",
      "mean      2.306552     0.378315    29.689194     0.485959     0.322933   \n",
      "std       0.830961     0.485156    14.383434     1.038767     0.670736   \n",
      "min       1.000000     0.000000     0.166700     0.000000     0.000000   \n",
      "25%       2.000000     0.000000    21.000000     0.000000     0.000000   \n",
      "50%       3.000000     0.000000    28.000000     0.000000     0.000000   \n",
      "75%       3.000000     1.000000    38.000000     1.000000     0.000000   \n",
      "max       3.000000     1.000000    80.000000     8.000000     4.000000   \n",
      "\n",
      "              fare        body  cluster_group  \n",
      "count  1281.000000  119.000000         1282.0  \n",
      "mean     29.398523  159.571429            0.0  \n",
      "std      38.294234   97.302914            0.0  \n",
      "min       0.000000    1.000000            0.0  \n",
      "25%       7.895800   71.000000            0.0  \n",
      "50%      13.900000  155.000000            0.0  \n",
      "75%      30.000000  255.500000            0.0  \n",
      "max     247.520800  328.000000            0.0  \n",
      "\n",
      "\n",
      "\n",
      "\n",
      "Cluster 1:\n",
      "\n",
      "       pclass   survived        age      sibsp      parch        fare  body  \\\n",
      "count    17.0  17.000000  17.000000  17.000000  17.000000   17.000000   0.0   \n",
      "mean      1.0   0.823529  36.705882   1.294118   1.764706  321.408341   NaN   \n",
      "std       0.0   0.392953  16.642433   1.212678   1.300452  109.163539   NaN   \n",
      "min       1.0   0.000000  13.000000   0.000000   0.000000  262.375000   NaN   \n",
      "25%       1.0   1.000000  23.000000   0.000000   1.000000  262.375000   NaN   \n",
      "50%       1.0   1.000000  35.000000   1.000000   2.000000  263.000000   NaN   \n",
      "75%       1.0   1.000000  48.000000   2.000000   2.000000  263.000000   NaN   \n",
      "max       1.0   1.000000  64.000000   3.000000   4.000000  512.329200   NaN   \n",
      "\n",
      "       cluster_group  \n",
      "count           17.0  \n",
      "mean             1.0  \n",
      "std              0.0  \n",
      "min              1.0  \n",
      "25%              1.0  \n",
      "50%              1.0  \n",
      "75%              1.0  \n",
      "max              1.0  \n",
      "\n",
      "\n",
      "\n",
      "\n",
      "Cluster 2:\n",
      "\n",
      "       pclass   survived        age      sibsp      parch       fare  \\\n",
      "count    10.0  10.000000   8.000000  10.000000  10.000000  10.000000   \n",
      "mean      3.0   0.100000  39.875000   0.800000   6.000000  42.703750   \n",
      "std       0.0   0.316228   1.552648   0.421637   1.632993  15.590194   \n",
      "min       3.0   0.000000  38.000000   0.000000   5.000000  29.125000   \n",
      "25%       3.0   0.000000  39.000000   1.000000   5.000000  31.303125   \n",
      "50%       3.0   0.000000  39.500000   1.000000   5.000000  35.537500   \n",
      "75%       3.0   0.000000  40.250000   1.000000   6.000000  46.900000   \n",
      "max       3.0   1.000000  43.000000   1.000000   9.000000  69.550000   \n",
      "\n",
      "             body  cluster_group  \n",
      "count    2.000000           10.0  \n",
      "mean   234.500000            2.0  \n",
      "std    130.814755            0.0  \n",
      "min    142.000000            2.0  \n",
      "25%    188.250000            2.0  \n",
      "50%    234.500000            2.0  \n",
      "75%    280.750000            2.0  \n",
      "max    327.000000            2.0  \n",
      "\n",
      "\n",
      "\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# detailed look at clusters\n",
    "for i in range(n_clusters_):\n",
    "    print(\"Cluster \"+str(i)+ \":\\n\")\n",
    "    print(original_df[(original_df['cluster_group'] == float(i))].describe())\n",
    "    print(\"\\n\\n\\n\")"
   ]
  }
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