market_segment_analysis_mcdonalds.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "9c78f49e",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Loading Libraries\n",
    "import pandas as pd\n",
    "from sklearn.decomposition import PCA\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "from sklearn.cluster import KMeans\n",
    "from yellowbrick.cluster import KElbowVisualizer\n",
    "from collections import Counter\n",
    "from sklearn.metrics import adjusted_rand_score\n",
    "from sklearn.utils import resample\n",
    "from sklearn.metrics import silhouette_samples\n",
    "\n",
    "from sklearn.mixture import GaussianMixture\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "\n",
    "from scipy.cluster.hierarchy import linkage, dendrogram\n",
    "\n",
    "import seaborn as sns\n",
    "from sklearn.tree import DecisionTreeClassifier, plot_tree\n",
    "from sklearn.preprocessing import LabelEncoder\n",
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "from statsmodels.graphics.mosaicplot import mosaic\n",
    "from itertools import product"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "8084379f",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Step 3: Exploring Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "092f654d",
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_csv(\"C:/Users/HP/Downloads/mcdonalds.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "047d6b38",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['yummy', 'convenient', 'spicy', 'fattening', 'greasy', 'fast', 'cheap',\n",
       "       'tasty', 'expensive', 'healthy', 'disgusting', 'Like', 'Age',\n",
       "       'VisitFrequency', 'Gender'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "de3048f6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1453, 15)"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "ff271791",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>yummy</th>\n",
       "      <th>convenient</th>\n",
       "      <th>spicy</th>\n",
       "      <th>fattening</th>\n",
       "      <th>greasy</th>\n",
       "      <th>fast</th>\n",
       "      <th>cheap</th>\n",
       "      <th>tasty</th>\n",
       "      <th>expensive</th>\n",
       "      <th>healthy</th>\n",
       "      <th>disgusting</th>\n",
       "      <th>Like</th>\n",
       "      <th>Age</th>\n",
       "      <th>VisitFrequency</th>\n",
       "      <th>Gender</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>-3</td>\n",
       "      <td>61</td>\n",
       "      <td>Every three months</td>\n",
       "      <td>Female</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>+2</td>\n",
       "      <td>51</td>\n",
       "      <td>Every three months</td>\n",
       "      <td>Female</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>+1</td>\n",
       "      <td>62</td>\n",
       "      <td>Every three months</td>\n",
       "      <td>Female</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  yummy convenient spicy fattening greasy fast cheap tasty expensive healthy  \\\n",
       "0    No        Yes    No       Yes     No  Yes   Yes    No       Yes      No   \n",
       "1   Yes        Yes    No       Yes    Yes  Yes   Yes   Yes       Yes      No   \n",
       "2    No        Yes   Yes       Yes    Yes  Yes    No   Yes       Yes     Yes   \n",
       "\n",
       "  disgusting Like  Age      VisitFrequency  Gender  \n",
       "0         No   -3   61  Every three months  Female  \n",
       "1         No   +2   51  Every three months  Female  \n",
       "2         No   +1   62  Every three months  Female  "
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "2ac68374",
   "metadata": {},
   "outputs": [],
   "source": [
    "MD_x = df.iloc[:, :11].applymap(lambda x: 1 if x == \"Yes\" else 0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "2b02a59e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "yummy         0.55\n",
       "convenient    0.91\n",
       "spicy         0.09\n",
       "fattening     0.87\n",
       "greasy        0.53\n",
       "fast          0.90\n",
       "cheap         0.60\n",
       "tasty         0.64\n",
       "expensive     0.36\n",
       "healthy       0.20\n",
       "disgusting    0.24\n",
       "dtype: float64"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "col_means = MD_x.mean().round(2)\n",
    "col_means"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "5f534360",
   "metadata": {},
   "outputs": [],
   "source": [
    "pca = PCA()\n",
    "MD_pca = pca.fit_transform(MD_x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "5925cda3",
   "metadata": {},
   "outputs": [],
   "source": [
    "std_dev = np.sqrt(pca.explained_variance_)\n",
    "proportion_variance = pca.explained_variance_ratio_\n",
    "cumulative_proportion = np.cumsum(proportion_variance)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "d5990734",
   "metadata": {},
   "outputs": [],
   "source": [
    "pca_summary = pd.DataFrame({\n",
    "    'Standard Deviation': std_dev,\n",
    "    'Proportion of Variance': proportion_variance,\n",
    "    'Cumulative Proportion': cumulative_proportion\n",
    "})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "e020d1ed",
   "metadata": {},
   "outputs": [],
   "source": [
    "pca_summary = pca_summary.round(5) \n",
    "pca_summary.index = [f'PC{i+1}' for i in range(len(pca_summary))]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "5203eda1",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Standard Deviation</th>\n",
       "      <th>Proportion of Variance</th>\n",
       "      <th>Cumulative Proportion</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>PC1</th>\n",
       "      <td>0.75705</td>\n",
       "      <td>0.29945</td>\n",
       "      <td>0.29945</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PC2</th>\n",
       "      <td>0.60746</td>\n",
       "      <td>0.19280</td>\n",
       "      <td>0.49224</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PC3</th>\n",
       "      <td>0.50462</td>\n",
       "      <td>0.13305</td>\n",
       "      <td>0.62529</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PC4</th>\n",
       "      <td>0.39880</td>\n",
       "      <td>0.08310</td>\n",
       "      <td>0.70839</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PC5</th>\n",
       "      <td>0.33741</td>\n",
       "      <td>0.05948</td>\n",
       "      <td>0.76787</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PC6</th>\n",
       "      <td>0.31027</td>\n",
       "      <td>0.05030</td>\n",
       "      <td>0.81817</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PC7</th>\n",
       "      <td>0.28970</td>\n",
       "      <td>0.04385</td>\n",
       "      <td>0.86201</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PC8</th>\n",
       "      <td>0.27512</td>\n",
       "      <td>0.03955</td>\n",
       "      <td>0.90156</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PC9</th>\n",
       "      <td>0.26525</td>\n",
       "      <td>0.03676</td>\n",
       "      <td>0.93832</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PC10</th>\n",
       "      <td>0.24884</td>\n",
       "      <td>0.03235</td>\n",
       "      <td>0.97068</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PC11</th>\n",
       "      <td>0.23690</td>\n",
       "      <td>0.02932</td>\n",
       "      <td>1.00000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      Standard Deviation  Proportion of Variance  Cumulative Proportion\n",
       "PC1              0.75705                 0.29945                0.29945\n",
       "PC2              0.60746                 0.19280                0.49224\n",
       "PC3              0.50462                 0.13305                0.62529\n",
       "PC4              0.39880                 0.08310                0.70839\n",
       "PC5              0.33741                 0.05948                0.76787\n",
       "PC6              0.31027                 0.05030                0.81817\n",
       "PC7              0.28970                 0.04385                0.86201\n",
       "PC8              0.27512                 0.03955                0.90156\n",
       "PC9              0.26525                 0.03676                0.93832\n",
       "PC10             0.24884                 0.03235                0.97068\n",
       "PC11             0.23690                 0.02932                1.00000"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pca_summary"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "6afb95f1",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Standard deviations (1, .., p=11):\n",
      "[0.8 0.6 0.5 0.4 0.3 0.3 0.3 0.3 0.3 0.2 0.2]\n"
     ]
    }
   ],
   "source": [
    "print(\"Standard deviations (1, .., p={}):\".format(len(std_dev)))\n",
    "print(np.round(std_dev, 1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "9b2afd2b",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>PC1</th>\n",
       "      <th>PC2</th>\n",
       "      <th>PC3</th>\n",
       "      <th>PC4</th>\n",
       "      <th>PC5</th>\n",
       "      <th>PC6</th>\n",
       "      <th>PC7</th>\n",
       "      <th>PC8</th>\n",
       "      <th>PC9</th>\n",
       "      <th>PC10</th>\n",
       "      <th>PC11</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>yummy</th>\n",
       "      <td>-0.477</td>\n",
       "      <td>0.364</td>\n",
       "      <td>-0.304</td>\n",
       "      <td>0.055</td>\n",
       "      <td>-0.308</td>\n",
       "      <td>0.171</td>\n",
       "      <td>-0.281</td>\n",
       "      <td>0.013</td>\n",
       "      <td>0.572</td>\n",
       "      <td>-0.110</td>\n",
       "      <td>0.045</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>convenient</th>\n",
       "      <td>-0.155</td>\n",
       "      <td>0.016</td>\n",
       "      <td>-0.063</td>\n",
       "      <td>-0.142</td>\n",
       "      <td>0.278</td>\n",
       "      <td>-0.348</td>\n",
       "      <td>-0.060</td>\n",
       "      <td>-0.113</td>\n",
       "      <td>-0.018</td>\n",
       "      <td>-0.666</td>\n",
       "      <td>-0.542</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>spicy</th>\n",
       "      <td>-0.006</td>\n",
       "      <td>0.019</td>\n",
       "      <td>-0.037</td>\n",
       "      <td>0.198</td>\n",
       "      <td>0.071</td>\n",
       "      <td>-0.355</td>\n",
       "      <td>0.708</td>\n",
       "      <td>0.376</td>\n",
       "      <td>0.400</td>\n",
       "      <td>-0.076</td>\n",
       "      <td>0.142</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>fattening</th>\n",
       "      <td>0.116</td>\n",
       "      <td>-0.034</td>\n",
       "      <td>-0.322</td>\n",
       "      <td>-0.354</td>\n",
       "      <td>-0.073</td>\n",
       "      <td>-0.407</td>\n",
       "      <td>-0.386</td>\n",
       "      <td>0.590</td>\n",
       "      <td>-0.161</td>\n",
       "      <td>-0.005</td>\n",
       "      <td>0.251</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>greasy</th>\n",
       "      <td>0.304</td>\n",
       "      <td>-0.064</td>\n",
       "      <td>-0.802</td>\n",
       "      <td>0.254</td>\n",
       "      <td>0.361</td>\n",
       "      <td>0.209</td>\n",
       "      <td>0.036</td>\n",
       "      <td>-0.138</td>\n",
       "      <td>-0.003</td>\n",
       "      <td>0.009</td>\n",
       "      <td>0.002</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>fast</th>\n",
       "      <td>-0.108</td>\n",
       "      <td>-0.087</td>\n",
       "      <td>-0.065</td>\n",
       "      <td>-0.097</td>\n",
       "      <td>0.108</td>\n",
       "      <td>-0.595</td>\n",
       "      <td>-0.087</td>\n",
       "      <td>-0.628</td>\n",
       "      <td>0.166</td>\n",
       "      <td>0.240</td>\n",
       "      <td>0.339</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>cheap</th>\n",
       "      <td>-0.337</td>\n",
       "      <td>-0.611</td>\n",
       "      <td>-0.149</td>\n",
       "      <td>0.119</td>\n",
       "      <td>-0.129</td>\n",
       "      <td>-0.103</td>\n",
       "      <td>-0.040</td>\n",
       "      <td>0.140</td>\n",
       "      <td>0.076</td>\n",
       "      <td>0.428</td>\n",
       "      <td>-0.489</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>tasty</th>\n",
       "      <td>-0.472</td>\n",
       "      <td>0.307</td>\n",
       "      <td>-0.287</td>\n",
       "      <td>-0.003</td>\n",
       "      <td>-0.211</td>\n",
       "      <td>-0.077</td>\n",
       "      <td>0.360</td>\n",
       "      <td>-0.073</td>\n",
       "      <td>-0.639</td>\n",
       "      <td>0.079</td>\n",
       "      <td>0.020</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>expensive</th>\n",
       "      <td>0.329</td>\n",
       "      <td>0.601</td>\n",
       "      <td>0.024</td>\n",
       "      <td>0.068</td>\n",
       "      <td>-0.003</td>\n",
       "      <td>-0.261</td>\n",
       "      <td>-0.068</td>\n",
       "      <td>0.030</td>\n",
       "      <td>0.067</td>\n",
       "      <td>0.454</td>\n",
       "      <td>-0.490</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>healthy</th>\n",
       "      <td>-0.214</td>\n",
       "      <td>0.077</td>\n",
       "      <td>0.192</td>\n",
       "      <td>0.763</td>\n",
       "      <td>0.288</td>\n",
       "      <td>-0.178</td>\n",
       "      <td>-0.350</td>\n",
       "      <td>0.176</td>\n",
       "      <td>-0.186</td>\n",
       "      <td>-0.038</td>\n",
       "      <td>0.158</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>disgusting</th>\n",
       "      <td>0.375</td>\n",
       "      <td>-0.140</td>\n",
       "      <td>-0.089</td>\n",
       "      <td>0.370</td>\n",
       "      <td>-0.729</td>\n",
       "      <td>-0.211</td>\n",
       "      <td>-0.027</td>\n",
       "      <td>-0.167</td>\n",
       "      <td>-0.072</td>\n",
       "      <td>-0.290</td>\n",
       "      <td>-0.041</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              PC1    PC2    PC3    PC4    PC5    PC6    PC7    PC8    PC9  \\\n",
       "yummy      -0.477  0.364 -0.304  0.055 -0.308  0.171 -0.281  0.013  0.572   \n",
       "convenient -0.155  0.016 -0.063 -0.142  0.278 -0.348 -0.060 -0.113 -0.018   \n",
       "spicy      -0.006  0.019 -0.037  0.198  0.071 -0.355  0.708  0.376  0.400   \n",
       "fattening   0.116 -0.034 -0.322 -0.354 -0.073 -0.407 -0.386  0.590 -0.161   \n",
       "greasy      0.304 -0.064 -0.802  0.254  0.361  0.209  0.036 -0.138 -0.003   \n",
       "fast       -0.108 -0.087 -0.065 -0.097  0.108 -0.595 -0.087 -0.628  0.166   \n",
       "cheap      -0.337 -0.611 -0.149  0.119 -0.129 -0.103 -0.040  0.140  0.076   \n",
       "tasty      -0.472  0.307 -0.287 -0.003 -0.211 -0.077  0.360 -0.073 -0.639   \n",
       "expensive   0.329  0.601  0.024  0.068 -0.003 -0.261 -0.068  0.030  0.067   \n",
       "healthy    -0.214  0.077  0.192  0.763  0.288 -0.178 -0.350  0.176 -0.186   \n",
       "disgusting  0.375 -0.140 -0.089  0.370 -0.729 -0.211 -0.027 -0.167 -0.072   \n",
       "\n",
       "             PC10   PC11  \n",
       "yummy      -0.110  0.045  \n",
       "convenient -0.666 -0.542  \n",
       "spicy      -0.076  0.142  \n",
       "fattening  -0.005  0.251  \n",
       "greasy      0.009  0.002  \n",
       "fast        0.240  0.339  \n",
       "cheap       0.428 -0.489  \n",
       "tasty       0.079  0.020  \n",
       "expensive   0.454 -0.490  \n",
       "healthy    -0.038  0.158  \n",
       "disgusting -0.290 -0.041  "
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "loadings = np.round(pca.components_.T, 3)\n",
    "loadings_df = pd.DataFrame(loadings, \n",
    "                           columns=[f'PC{i+1}' for i in range(loadings.shape[1])], \n",
    "                           index=df.columns[:11])\n",
    "loadings_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "f5bbf650",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'PCA of McDonalds Dataset')"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 800x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(8, 6))\n",
    "plt.scatter(MD_pca[:, 0], MD_pca[:, 1], color='purple', alpha=0.5)\n",
    "plt.xlabel('PC1')\n",
    "plt.ylabel('PC2')\n",
    "plt.title('PCA of McDonalds Dataset')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "84480879",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Step 5: Extracting Segments"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "22e174f1",
   "metadata": {},
   "outputs": [],
   "source": [
    "#using kmeans clustering"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "13fc4e5c",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\ProgramData\\anaconda3\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1382: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=6.\n",
      "  warnings.warn(\n",
      "C:\\ProgramData\\anaconda3\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1382: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=6.\n",
      "  warnings.warn(\n",
      "C:\\ProgramData\\anaconda3\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1382: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=6.\n",
      "  warnings.warn(\n",
      "C:\\ProgramData\\anaconda3\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1382: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=6.\n",
      "  warnings.warn(\n",
      "C:\\ProgramData\\anaconda3\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1382: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=6.\n",
      "  warnings.warn(\n",
      "C:\\ProgramData\\anaconda3\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1382: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=6.\n",
      "  warnings.warn(\n",
      "C:\\ProgramData\\anaconda3\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1382: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=6.\n",
      "  warnings.warn(\n"
     ]
    }
   ],
   "source": [
    "MD_x_np = MD_x.values\n",
    "np.random.seed(1234)\n",
    "\n",
    "cluster_range = range(2, 9)\n",
    "best_cluster_labels = None\n",
    "best_score = -1\n",
    "for n_clusters in cluster_range:\n",
    "    kmeans = KMeans(n_clusters=n_clusters, n_init=10, random_state=1234)\n",
    "    cluster_labels = kmeans.fit_predict(MD_x_np)\n",
    "    score = kmeans.score(MD_x_np)\n",
    "    if score > best_score:\n",
    "        best_score = score\n",
    "        best_cluster_labels = cluster_labels\n",
    "\n",
    "MD_km28 = best_cluster_labels"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "9ac412f9",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\ProgramData\\anaconda3\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1382: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=6.\n",
      "  warnings.warn(\n",
      "C:\\ProgramData\\anaconda3\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1382: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=6.\n",
      "  warnings.warn(\n",
      "C:\\ProgramData\\anaconda3\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1382: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=6.\n",
      "  warnings.warn(\n",
      "C:\\ProgramData\\anaconda3\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1382: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=6.\n",
      "  warnings.warn(\n",
      "C:\\ProgramData\\anaconda3\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1382: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=6.\n",
      "  warnings.warn(\n",
      "C:\\ProgramData\\anaconda3\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1382: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=6.\n",
      "  warnings.warn(\n",
      "C:\\ProgramData\\anaconda3\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1382: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=6.\n",
      "  warnings.warn(\n"
     ]
    }
   ],
   "source": [
    "inertia_values = []\n",
    "segment_range = range(2, 9)\n",
    "for n_segments in segment_range:\n",
    "    kmeans = KMeans(n_clusters=n_segments, n_init=10, random_state=1234)\n",
    "    kmeans.fit(MD_x_np)\n",
    "    inertia_values.append(kmeans.inertia_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "433cee8a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 800x550 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.bar(segment_range, inertia_values)\n",
    "plt.xlabel('Number of Segments')\n",
    "plt.ylabel('Sum of within cluster')\n",
    "plt.title('Scree Plot')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "aa703268",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\ProgramData\\anaconda3\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
      "  warnings.warn(\n",
      "C:\\ProgramData\\anaconda3\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1382: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=6.\n",
      "  warnings.warn(\n",
      "C:\\ProgramData\\anaconda3\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
      "  warnings.warn(\n",
      "C:\\ProgramData\\anaconda3\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1382: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=6.\n",
      "  warnings.warn(\n",
      "C:\\ProgramData\\anaconda3\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
      "  warnings.warn(\n",
      "C:\\ProgramData\\anaconda3\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1382: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=6.\n",
      "  warnings.warn(\n",
      "C:\\ProgramData\\anaconda3\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
      "  warnings.warn(\n",
      "C:\\ProgramData\\anaconda3\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1382: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=6.\n",
      "  warnings.warn(\n",
      "C:\\ProgramData\\anaconda3\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
      "  warnings.warn(\n",
      "C:\\ProgramData\\anaconda3\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1382: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=6.\n",
      "  warnings.warn(\n",
      "C:\\ProgramData\\anaconda3\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
      "  warnings.warn(\n",
      "C:\\ProgramData\\anaconda3\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1382: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=6.\n",
      "  warnings.warn(\n",
      "C:\\ProgramData\\anaconda3\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
      "  warnings.warn(\n",
      "C:\\ProgramData\\anaconda3\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1382: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=6.\n",
      "  warnings.warn(\n",
      "C:\\ProgramData\\anaconda3\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
      "  warnings.warn(\n",
      "C:\\ProgramData\\anaconda3\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1382: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=6.\n",
      "  warnings.warn(\n",
      "C:\\ProgramData\\anaconda3\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
      "  warnings.warn(\n",
      "C:\\ProgramData\\anaconda3\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1382: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=6.\n",
      "  warnings.warn(\n",
      "C:\\ProgramData\\anaconda3\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
      "  warnings.warn(\n",
      "C:\\ProgramData\\anaconda3\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1382: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=6.\n",
      "  warnings.warn(\n",
      "C:\\ProgramData\\anaconda3\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
      "  warnings.warn(\n",
      "C:\\ProgramData\\anaconda3\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1382: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=6.\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 800x550 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "model = KMeans()\n",
    "visualizer = KElbowVisualizer(model, k=(1,12)).fit(MD_x)\n",
    "visualizer.show();"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "96e69dc6",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\ProgramData\\anaconda3\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
      "  warnings.warn(\n",
      "C:\\ProgramData\\anaconda3\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1382: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=6.\n",
      "  warnings.warn(\n"
     ]
    }
   ],
   "source": [
    "kmeans = KMeans(n_clusters=4, init='k-means++', random_state=0).fit(MD_x)\n",
    "df['cluster_num'] = kmeans.labels_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "4a09d2dd",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([2, 0, 0, ..., 0, 1, 3])"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "kmeans.labels_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "2a903952",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1603.060444055892"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "kmeans.inertia_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "58f91c46",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "8"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "kmeans.n_iter_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "463e6734",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0.85448916, 0.9628483 , 0.13312693, 0.90712074, 0.61919505,\n",
       "        0.86068111, 0.10835913, 0.93188854, 0.89783282, 0.20433437,\n",
       "        0.10526316],\n",
       "       [0.88793103, 0.98103448, 0.0862069 , 0.79482759, 0.32931034,\n",
       "        0.96034483, 0.92241379, 0.97586207, 0.01724138, 0.32068966,\n",
       "        0.04310345],\n",
       "       [0.02302632, 0.89144737, 0.07236842, 0.92434211, 0.66776316,\n",
       "        0.96381579, 0.93421053, 0.15460526, 0.01315789, 0.07236842,\n",
       "        0.38815789],\n",
       "       [0.0203252 , 0.68292683, 0.08536585, 0.91463415, 0.69512195,\n",
       "        0.73170732, 0.06504065, 0.08943089, 0.87804878, 0.06097561,\n",
       "        0.71544715]])"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "kmeans.cluster_centers_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "23e4854c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Counter({2: 304, 0: 323, 1: 580, 3: 246})"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Counter(kmeans.labels_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "acb4303b",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\ProgramData\\anaconda3\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1382: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=6.\n",
      "  warnings.warn(\n",
      "C:\\ProgramData\\anaconda3\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1382: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=6.\n",
      "  warnings.warn(\n",
      "C:\\ProgramData\\anaconda3\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1382: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=6.\n",
      "  warnings.warn(\n",
      "C:\\ProgramData\\anaconda3\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1382: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=6.\n",
      "  warnings.warn(\n",
      "C:\\ProgramData\\anaconda3\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1382: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=6.\n",
      "  warnings.warn(\n",
      "C:\\ProgramData\\anaconda3\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1382: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=6.\n",
      "  warnings.warn(\n",
      "C:\\ProgramData\\anaconda3\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1382: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=6.\n",
      "  warnings.warn(\n",
      "C:\\ProgramData\\anaconda3\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1382: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=6.\n",
      "  warnings.warn(\n",
      "C:\\ProgramData\\anaconda3\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1382: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=6.\n",
      "  warnings.warn(\n",
      "C:\\ProgramData\\anaconda3\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1382: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=6.\n",
      "  warnings.warn(\n",
      "C:\\ProgramData\\anaconda3\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1382: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=6.\n",
      "  warnings.warn(\n",
      "C:\\ProgramData\\anaconda3\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1382: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=6.\n",
      "  warnings.warn(\n",
      "C:\\ProgramData\\anaconda3\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1382: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=6.\n",
      "  warnings.warn(\n",
      "C:\\ProgramData\\anaconda3\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1382: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=6.\n",
      "  warnings.warn(\n",
      "C:\\ProgramData\\anaconda3\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1382: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=6.\n",
      "  warnings.warn(\n",
      "C:\\ProgramData\\anaconda3\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1382: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=6.\n",
      "  warnings.warn(\n",
      "C:\\ProgramData\\anaconda3\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1382: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=6.\n",
      "  warnings.warn(\n",
      "C:\\ProgramData\\anaconda3\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1382: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=6.\n",
      "  warnings.warn(\n",
      "C:\\ProgramData\\anaconda3\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1382: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=6.\n",
      "  warnings.warn(\n",
      "C:\\ProgramData\\anaconda3\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1382: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=6.\n",
      "  warnings.warn(\n",
      "C:\\ProgramData\\anaconda3\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1382: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=6.\n",
      "  warnings.warn(\n",
      "C:\\ProgramData\\anaconda3\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1382: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=6.\n",
      "  warnings.warn(\n",
      "C:\\ProgramData\\anaconda3\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1382: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=6.\n",
      "  warnings.warn(\n",
      "C:\\ProgramData\\anaconda3\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1382: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=6.\n",
      "  warnings.warn(\n",
      "C:\\ProgramData\\anaconda3\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1382: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=6.\n",
      "  warnings.warn(\n",
      "C:\\ProgramData\\anaconda3\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1382: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=6.\n",
      "  warnings.warn(\n",
      "C:\\ProgramData\\anaconda3\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1382: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=6.\n",
      "  warnings.warn(\n",
      "C:\\ProgramData\\anaconda3\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1382: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=6.\n",
      "  warnings.warn(\n",
      "C:\\ProgramData\\anaconda3\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1382: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=6.\n",
      "  warnings.warn(\n",
      "C:\\ProgramData\\anaconda3\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1382: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=6.\n",
      "  warnings.warn(\n",
      "C:\\ProgramData\\anaconda3\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1382: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=6.\n",
      "  warnings.warn(\n",
      "C:\\ProgramData\\anaconda3\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1382: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=6.\n",
      "  warnings.warn(\n",
      "C:\\ProgramData\\anaconda3\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1382: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=6.\n",
      "  warnings.warn(\n",
      "C:\\ProgramData\\anaconda3\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1382: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=6.\n",
      "  warnings.warn(\n",
      "C:\\ProgramData\\anaconda3\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1382: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=6.\n",
      "  warnings.warn(\n",
      "C:\\ProgramData\\anaconda3\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1382: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=6.\n",
      "  warnings.warn(\n",
      "C:\\ProgramData\\anaconda3\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1382: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=6.\n",
      "  warnings.warn(\n",
      "C:\\ProgramData\\anaconda3\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1382: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=6.\n",
      "  warnings.warn(\n",
      "C:\\ProgramData\\anaconda3\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1382: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=6.\n",
      "  warnings.warn(\n",
      "C:\\ProgramData\\anaconda3\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1382: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=6.\n",
      "  warnings.warn(\n",
      "C:\\ProgramData\\anaconda3\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1382: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=6.\n",
      "  warnings.warn(\n",
      "C:\\ProgramData\\anaconda3\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1382: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=6.\n",
      "  warnings.warn(\n",
      "C:\\ProgramData\\anaconda3\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1382: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=6.\n",
      "  warnings.warn(\n",
      "C:\\ProgramData\\anaconda3\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1382: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=6.\n",
      "  warnings.warn(\n",
      "C:\\ProgramData\\anaconda3\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1382: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=6.\n",
      "  warnings.warn(\n",
      "C:\\ProgramData\\anaconda3\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1382: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=6.\n",
      "  warnings.warn(\n",
      "C:\\ProgramData\\anaconda3\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1382: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=6.\n",
      "  warnings.warn(\n",
      "C:\\ProgramData\\anaconda3\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1382: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=6.\n",
      "  warnings.warn(\n",
      "C:\\ProgramData\\anaconda3\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1382: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=6.\n",
      "  warnings.warn(\n",
      "C:\\ProgramData\\anaconda3\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1382: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=6.\n",
      "  warnings.warn(\n",
      "C:\\ProgramData\\anaconda3\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1382: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=6.\n",
      "  warnings.warn(\n",
      "C:\\ProgramData\\anaconda3\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1382: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=6.\n",
      "  warnings.warn(\n",
      "C:\\ProgramData\\anaconda3\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1382: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=6.\n",
      "  warnings.warn(\n",
      "C:\\ProgramData\\anaconda3\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1382: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=6.\n",
      "  warnings.warn(\n",
      "C:\\ProgramData\\anaconda3\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1382: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=6.\n",
      "  warnings.warn(\n",
      "C:\\ProgramData\\anaconda3\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1382: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=6.\n",
      "  warnings.warn(\n",
      "C:\\ProgramData\\anaconda3\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1382: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=6.\n",
      "  warnings.warn(\n",
      "C:\\ProgramData\\anaconda3\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1382: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=6.\n",
      "  warnings.warn(\n",
      "C:\\ProgramData\\anaconda3\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1382: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=6.\n",
      "  warnings.warn(\n",
      "C:\\ProgramData\\anaconda3\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1382: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=6.\n",
      "  warnings.warn(\n",
      "C:\\ProgramData\\anaconda3\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1382: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=6.\n",
      "  warnings.warn(\n",
      "C:\\ProgramData\\anaconda3\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1382: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=6.\n",
      "  warnings.warn(\n",
      "C:\\ProgramData\\anaconda3\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1382: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=6.\n",
      "  warnings.warn(\n",
      "C:\\ProgramData\\anaconda3\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1382: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=6.\n",
      "  warnings.warn(\n",
      "C:\\ProgramData\\anaconda3\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1382: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=6.\n",
      "  warnings.warn(\n",
      "C:\\ProgramData\\anaconda3\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1382: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=6.\n",
      "  warnings.warn(\n",
      "C:\\ProgramData\\anaconda3\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1382: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=6.\n",
      "  warnings.warn(\n",
      "C:\\ProgramData\\anaconda3\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1382: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=6.\n",
      "  warnings.warn(\n",
      "C:\\ProgramData\\anaconda3\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1382: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=6.\n",
      "  warnings.warn(\n",
      "C:\\ProgramData\\anaconda3\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1382: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=6.\n",
      "  warnings.warn(\n",
      "C:\\ProgramData\\anaconda3\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1382: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=6.\n",
      "  warnings.warn(\n",
      "C:\\ProgramData\\anaconda3\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1382: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=6.\n",
      "  warnings.warn(\n",
      "C:\\ProgramData\\anaconda3\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1382: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=6.\n",
      "  warnings.warn(\n",
      "C:\\ProgramData\\anaconda3\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1382: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=6.\n",
      "  warnings.warn(\n",
      "C:\\ProgramData\\anaconda3\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1382: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=6.\n",
      "  warnings.warn(\n",
      "C:\\ProgramData\\anaconda3\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1382: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=6.\n",
      "  warnings.warn(\n",
      "C:\\ProgramData\\anaconda3\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1382: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=6.\n",
      "  warnings.warn(\n",
      "C:\\ProgramData\\anaconda3\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1382: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=6.\n",
      "  warnings.warn(\n",
      "C:\\ProgramData\\anaconda3\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1382: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=6.\n",
      "  warnings.warn(\n",
      "C:\\ProgramData\\anaconda3\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1382: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=6.\n",
      "  warnings.warn(\n",
      "C:\\ProgramData\\anaconda3\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1382: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=6.\n",
      "  warnings.warn(\n",
      "C:\\ProgramData\\anaconda3\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1382: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=6.\n",
      "  warnings.warn(\n",
      "C:\\ProgramData\\anaconda3\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1382: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=6.\n",
      "  warnings.warn(\n",
      "C:\\ProgramData\\anaconda3\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1382: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=6.\n",
      "  warnings.warn(\n",
      "C:\\ProgramData\\anaconda3\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1382: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=6.\n",
      "  warnings.warn(\n",
      "C:\\ProgramData\\anaconda3\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1382: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=6.\n",
      "  warnings.warn(\n",
      "C:\\ProgramData\\anaconda3\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1382: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=6.\n",
      "  warnings.warn(\n",
      "C:\\ProgramData\\anaconda3\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1382: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=6.\n",
      "  warnings.warn(\n",
      "C:\\ProgramData\\anaconda3\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1382: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=6.\n",
      "  warnings.warn(\n",
      "C:\\ProgramData\\anaconda3\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1382: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=6.\n",
      "  warnings.warn(\n",
      "C:\\ProgramData\\anaconda3\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1382: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=6.\n",
      "  warnings.warn(\n",
      "C:\\ProgramData\\anaconda3\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1382: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=6.\n",
      "  warnings.warn(\n",
      "C:\\ProgramData\\anaconda3\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1382: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=6.\n",
      "  warnings.warn(\n",
      "C:\\ProgramData\\anaconda3\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1382: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=6.\n",
      "  warnings.warn(\n",
      "C:\\ProgramData\\anaconda3\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1382: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=6.\n",
      "  warnings.warn(\n",
      "C:\\ProgramData\\anaconda3\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1382: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=6.\n",
      "  warnings.warn(\n",
      "C:\\ProgramData\\anaconda3\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1382: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=6.\n",
      "  warnings.warn(\n",
      "C:\\ProgramData\\anaconda3\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1382: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=6.\n",
      "  warnings.warn(\n",
      "C:\\ProgramData\\anaconda3\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1382: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=6.\n",
      "  warnings.warn(\n",
      "C:\\ProgramData\\anaconda3\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1382: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=6.\n",
      "  warnings.warn(\n",
      "C:\\ProgramData\\anaconda3\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1382: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=6.\n",
      "  warnings.warn(\n",
      "C:\\ProgramData\\anaconda3\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1382: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=6.\n",
      "  warnings.warn(\n",
      "C:\\ProgramData\\anaconda3\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1382: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=6.\n",
      "  warnings.warn(\n",
      "C:\\ProgramData\\anaconda3\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1382: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=6.\n",
      "  warnings.warn(\n",
      "C:\\ProgramData\\anaconda3\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1382: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=6.\n",
      "  warnings.warn(\n",
      "C:\\ProgramData\\anaconda3\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1382: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=6.\n",
      "  warnings.warn(\n",
      "C:\\ProgramData\\anaconda3\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1382: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=6.\n",
      "  warnings.warn(\n",
      "C:\\ProgramData\\anaconda3\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1382: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=6.\n",
      "  warnings.warn(\n",
      "C:\\ProgramData\\anaconda3\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1382: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=6.\n",
      "  warnings.warn(\n",
      "C:\\ProgramData\\anaconda3\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1382: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=6.\n",
      "  warnings.warn(\n",
      "C:\\ProgramData\\anaconda3\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1382: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=6.\n",
      "  warnings.warn(\n",
      "C:\\ProgramData\\anaconda3\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1382: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=6.\n",
      "  warnings.warn(\n",
      "C:\\ProgramData\\anaconda3\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1382: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=6.\n",
      "  warnings.warn(\n",
      "C:\\ProgramData\\anaconda3\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1382: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=6.\n",
      "  warnings.warn(\n",
      "C:\\ProgramData\\anaconda3\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1382: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=6.\n",
      "  warnings.warn(\n",
      "C:\\ProgramData\\anaconda3\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1382: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=6.\n",
      "  warnings.warn(\n",
      "C:\\ProgramData\\anaconda3\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1382: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=6.\n",
      "  warnings.warn(\n",
      "C:\\ProgramData\\anaconda3\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1382: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=6.\n",
      "  warnings.warn(\n",
      "C:\\ProgramData\\anaconda3\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1382: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=6.\n",
      "  warnings.warn(\n",
      "C:\\ProgramData\\anaconda3\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1382: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=6.\n",
      "  warnings.warn(\n",
      "C:\\ProgramData\\anaconda3\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1382: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=6.\n",
      "  warnings.warn(\n",
      "C:\\ProgramData\\anaconda3\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1382: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=6.\n",
      "  warnings.warn(\n",
      "C:\\ProgramData\\anaconda3\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1382: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=6.\n",
      "  warnings.warn(\n",
      "C:\\ProgramData\\anaconda3\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1382: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=6.\n",
      "  warnings.warn(\n",
      "C:\\ProgramData\\anaconda3\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1382: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=6.\n",
      "  warnings.warn(\n",
      "C:\\ProgramData\\anaconda3\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1382: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=6.\n",
      "  warnings.warn(\n",
      "C:\\ProgramData\\anaconda3\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1382: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=6.\n",
      "  warnings.warn(\n",
      "C:\\ProgramData\\anaconda3\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1382: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=6.\n",
      "  warnings.warn(\n",
      "C:\\ProgramData\\anaconda3\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1382: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=6.\n",
      "  warnings.warn(\n",
      "C:\\ProgramData\\anaconda3\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1382: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=6.\n",
      "  warnings.warn(\n",
      "C:\\ProgramData\\anaconda3\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1382: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=6.\n",
      "  warnings.warn(\n",
      "C:\\ProgramData\\anaconda3\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1382: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=6.\n",
      "  warnings.warn(\n",
      "C:\\ProgramData\\anaconda3\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1382: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=6.\n",
      "  warnings.warn(\n",
      "C:\\ProgramData\\anaconda3\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1382: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=6.\n",
      "  warnings.warn(\n",
      "C:\\ProgramData\\anaconda3\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1382: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=6.\n",
      "  warnings.warn(\n",
      "C:\\ProgramData\\anaconda3\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1382: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=6.\n",
      "  warnings.warn(\n",
      "C:\\ProgramData\\anaconda3\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1382: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=6.\n",
      "  warnings.warn(\n",
      "C:\\ProgramData\\anaconda3\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1382: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=6.\n",
      "  warnings.warn(\n",
      "C:\\ProgramData\\anaconda3\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1382: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=6.\n",
      "  warnings.warn(\n",
      "C:\\ProgramData\\anaconda3\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1382: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=6.\n",
      "  warnings.warn(\n",
      "C:\\ProgramData\\anaconda3\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1382: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=6.\n",
      "  warnings.warn(\n",
      "C:\\ProgramData\\anaconda3\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1382: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=6.\n",
      "  warnings.warn(\n",
      "C:\\ProgramData\\anaconda3\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1382: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=6.\n",
      "  warnings.warn(\n",
      "C:\\ProgramData\\anaconda3\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1382: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=6.\n",
      "  warnings.warn(\n",
      "C:\\ProgramData\\anaconda3\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1382: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=6.\n",
      "  warnings.warn(\n",
      "C:\\ProgramData\\anaconda3\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1382: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=6.\n",
      "  warnings.warn(\n",
      "C:\\ProgramData\\anaconda3\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1382: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=6.\n",
      "  warnings.warn(\n",
      "C:\\ProgramData\\anaconda3\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1382: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=6.\n",
      "  warnings.warn(\n",
      "C:\\ProgramData\\anaconda3\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1382: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=6.\n",
      "  warnings.warn(\n",
      "C:\\ProgramData\\anaconda3\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1382: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=6.\n",
      "  warnings.warn(\n",
      "C:\\ProgramData\\anaconda3\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1382: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=6.\n",
      "  warnings.warn(\n",
      "C:\\ProgramData\\anaconda3\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1382: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=6.\n",
      "  warnings.warn(\n",
      "C:\\ProgramData\\anaconda3\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1382: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=6.\n",
      "  warnings.warn(\n",
      "C:\\ProgramData\\anaconda3\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1382: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=6.\n",
      "  warnings.warn(\n",
      "C:\\ProgramData\\anaconda3\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1382: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=6.\n",
      "  warnings.warn(\n",
      "C:\\ProgramData\\anaconda3\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1382: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=6.\n",
      "  warnings.warn(\n",
      "C:\\ProgramData\\anaconda3\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1382: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=6.\n",
      "  warnings.warn(\n",
      "C:\\ProgramData\\anaconda3\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1382: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=6.\n",
      "  warnings.warn(\n",
      "C:\\ProgramData\\anaconda3\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1382: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=6.\n",
      "  warnings.warn(\n",
      "C:\\ProgramData\\anaconda3\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1382: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=6.\n",
      "  warnings.warn(\n",
      "C:\\ProgramData\\anaconda3\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1382: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=6.\n",
      "  warnings.warn(\n",
      "C:\\ProgramData\\anaconda3\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1382: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=6.\n",
      "  warnings.warn(\n",
      "C:\\ProgramData\\anaconda3\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1382: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=6.\n",
      "  warnings.warn(\n",
      "C:\\ProgramData\\anaconda3\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1382: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=6.\n",
      "  warnings.warn(\n",
      "C:\\ProgramData\\anaconda3\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1382: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=6.\n",
      "  warnings.warn(\n",
      "C:\\ProgramData\\anaconda3\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1382: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=6.\n",
      "  warnings.warn(\n",
      "C:\\ProgramData\\anaconda3\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1382: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=6.\n",
      "  warnings.warn(\n",
      "C:\\ProgramData\\anaconda3\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1382: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=6.\n",
      "  warnings.warn(\n",
      "C:\\ProgramData\\anaconda3\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1382: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=6.\n",
      "  warnings.warn(\n",
      "C:\\ProgramData\\anaconda3\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1382: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=6.\n",
      "  warnings.warn(\n",
      "C:\\ProgramData\\anaconda3\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1382: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=6.\n",
      "  warnings.warn(\n",
      "C:\\ProgramData\\anaconda3\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1382: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=6.\n",
      "  warnings.warn(\n",
      "C:\\ProgramData\\anaconda3\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1382: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=6.\n",
      "  warnings.warn(\n",
      "C:\\ProgramData\\anaconda3\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1382: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=6.\n",
      "  warnings.warn(\n",
      "C:\\ProgramData\\anaconda3\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1382: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=6.\n",
      "  warnings.warn(\n",
      "C:\\ProgramData\\anaconda3\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1382: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=6.\n",
      "  warnings.warn(\n",
      "C:\\ProgramData\\anaconda3\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1382: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=6.\n",
      "  warnings.warn(\n",
      "C:\\ProgramData\\anaconda3\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1382: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=6.\n",
      "  warnings.warn(\n",
      "C:\\ProgramData\\anaconda3\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1382: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=6.\n",
      "  warnings.warn(\n",
      "C:\\ProgramData\\anaconda3\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1382: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=6.\n",
      "  warnings.warn(\n",
      "C:\\ProgramData\\anaconda3\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1382: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=6.\n",
      "  warnings.warn(\n",
      "C:\\ProgramData\\anaconda3\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1382: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=6.\n",
      "  warnings.warn(\n",
      "C:\\ProgramData\\anaconda3\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1382: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=6.\n",
      "  warnings.warn(\n",
      "C:\\ProgramData\\anaconda3\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1382: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=6.\n",
      "  warnings.warn(\n",
      "C:\\ProgramData\\anaconda3\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1382: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=6.\n",
      "  warnings.warn(\n",
      "C:\\ProgramData\\anaconda3\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1382: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=6.\n",
      "  warnings.warn(\n",
      "C:\\ProgramData\\anaconda3\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1382: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=6.\n",
      "  warnings.warn(\n",
      "C:\\ProgramData\\anaconda3\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1382: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=6.\n",
      "  warnings.warn(\n",
      "C:\\ProgramData\\anaconda3\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1382: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=6.\n",
      "  warnings.warn(\n",
      "C:\\ProgramData\\anaconda3\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1382: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=6.\n",
      "  warnings.warn(\n",
      "C:\\ProgramData\\anaconda3\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1382: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=6.\n",
      "  warnings.warn(\n",
      "C:\\ProgramData\\anaconda3\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1382: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=6.\n",
      "  warnings.warn(\n",
      "C:\\ProgramData\\anaconda3\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1382: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=6.\n",
      "  warnings.warn(\n",
      "C:\\ProgramData\\anaconda3\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1382: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=6.\n",
      "  warnings.warn(\n",
      "C:\\ProgramData\\anaconda3\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1382: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=6.\n",
      "  warnings.warn(\n",
      "C:\\ProgramData\\anaconda3\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1382: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=6.\n",
      "  warnings.warn(\n",
      "C:\\ProgramData\\anaconda3\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1382: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=6.\n",
      "  warnings.warn(\n",
      "C:\\ProgramData\\anaconda3\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1382: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=6.\n",
      "  warnings.warn(\n",
      "C:\\ProgramData\\anaconda3\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1382: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=6.\n",
      "  warnings.warn(\n",
      "C:\\ProgramData\\anaconda3\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1382: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=6.\n",
      "  warnings.warn(\n",
      "C:\\ProgramData\\anaconda3\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1382: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=6.\n",
      "  warnings.warn(\n",
      "C:\\ProgramData\\anaconda3\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1382: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=6.\n",
      "  warnings.warn(\n",
      "C:\\ProgramData\\anaconda3\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1382: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=6.\n",
      "  warnings.warn(\n",
      "C:\\ProgramData\\anaconda3\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1382: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=6.\n",
      "  warnings.warn(\n",
      "C:\\ProgramData\\anaconda3\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1382: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=6.\n",
      "  warnings.warn(\n",
      "C:\\ProgramData\\anaconda3\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1382: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=6.\n",
      "  warnings.warn(\n",
      "C:\\ProgramData\\anaconda3\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1382: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=6.\n",
      "  warnings.warn(\n",
      "C:\\ProgramData\\anaconda3\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1382: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=6.\n",
      "  warnings.warn(\n",
      "C:\\ProgramData\\anaconda3\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1382: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=6.\n",
      "  warnings.warn(\n",
      "C:\\ProgramData\\anaconda3\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1382: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=6.\n",
      "  warnings.warn(\n",
      "C:\\ProgramData\\anaconda3\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1382: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=6.\n",
      "  warnings.warn(\n",
      "C:\\ProgramData\\anaconda3\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1382: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=6.\n",
      "  warnings.warn(\n",
      "C:\\ProgramData\\anaconda3\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1382: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=6.\n",
      "  warnings.warn(\n",
      "C:\\ProgramData\\anaconda3\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1382: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=6.\n",
      "  warnings.warn(\n",
      "C:\\ProgramData\\anaconda3\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1382: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=6.\n",
      "  warnings.warn(\n",
      "C:\\ProgramData\\anaconda3\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1382: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=6.\n",
      "  warnings.warn(\n",
      "C:\\ProgramData\\anaconda3\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1382: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=6.\n",
      "  warnings.warn(\n",
      "C:\\ProgramData\\anaconda3\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1382: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=6.\n",
      "  warnings.warn(\n",
      "C:\\ProgramData\\anaconda3\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1382: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=6.\n",
      "  warnings.warn(\n",
      "C:\\ProgramData\\anaconda3\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1382: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=6.\n",
      "  warnings.warn(\n",
      "C:\\ProgramData\\anaconda3\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1382: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=6.\n",
      "  warnings.warn(\n",
      "C:\\ProgramData\\anaconda3\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1382: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=6.\n",
      "  warnings.warn(\n",
      "C:\\ProgramData\\anaconda3\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1382: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=6.\n",
      "  warnings.warn(\n",
      "C:\\ProgramData\\anaconda3\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1382: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=6.\n",
      "  warnings.warn(\n",
      "C:\\ProgramData\\anaconda3\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1382: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=6.\n",
      "  warnings.warn(\n",
      "C:\\ProgramData\\anaconda3\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1382: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=6.\n",
      "  warnings.warn(\n",
      "C:\\ProgramData\\anaconda3\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1382: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=6.\n",
      "  warnings.warn(\n",
      "C:\\ProgramData\\anaconda3\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1382: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=6.\n",
      "  warnings.warn(\n",
      "C:\\ProgramData\\anaconda3\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1382: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=6.\n",
      "  warnings.warn(\n",
      "C:\\ProgramData\\anaconda3\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1382: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=6.\n",
      "  warnings.warn(\n",
      "C:\\ProgramData\\anaconda3\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1382: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=6.\n",
      "  warnings.warn(\n",
      "C:\\ProgramData\\anaconda3\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1382: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=6.\n",
      "  warnings.warn(\n",
      "C:\\ProgramData\\anaconda3\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1382: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=6.\n",
      "  warnings.warn(\n",
      "C:\\ProgramData\\anaconda3\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1382: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=6.\n",
      "  warnings.warn(\n",
      "C:\\ProgramData\\anaconda3\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1382: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=6.\n",
      "  warnings.warn(\n",
      "C:\\ProgramData\\anaconda3\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1382: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=6.\n",
      "  warnings.warn(\n",
      "C:\\ProgramData\\anaconda3\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1382: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=6.\n",
      "  warnings.warn(\n",
      "C:\\ProgramData\\anaconda3\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1382: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=6.\n",
      "  warnings.warn(\n",
      "C:\\ProgramData\\anaconda3\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1382: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=6.\n",
      "  warnings.warn(\n",
      "C:\\ProgramData\\anaconda3\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1382: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=6.\n",
      "  warnings.warn(\n",
      "C:\\ProgramData\\anaconda3\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1382: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=6.\n",
      "  warnings.warn(\n",
      "C:\\ProgramData\\anaconda3\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1382: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=6.\n",
      "  warnings.warn(\n",
      "C:\\ProgramData\\anaconda3\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1382: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=6.\n",
      "  warnings.warn(\n",
      "C:\\ProgramData\\anaconda3\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1382: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=6.\n",
      "  warnings.warn(\n",
      "C:\\ProgramData\\anaconda3\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1382: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=6.\n",
      "  warnings.warn(\n",
      "C:\\ProgramData\\anaconda3\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1382: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=6.\n",
      "  warnings.warn(\n",
      "C:\\ProgramData\\anaconda3\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1382: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=6.\n",
      "  warnings.warn(\n",
      "C:\\ProgramData\\anaconda3\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1382: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=6.\n",
      "  warnings.warn(\n",
      "C:\\ProgramData\\anaconda3\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1382: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=6.\n",
      "  warnings.warn(\n",
      "C:\\ProgramData\\anaconda3\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1382: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=6.\n",
      "  warnings.warn(\n",
      "C:\\ProgramData\\anaconda3\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1382: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=6.\n",
      "  warnings.warn(\n",
      "C:\\ProgramData\\anaconda3\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1382: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=6.\n",
      "  warnings.warn(\n",
      "C:\\ProgramData\\anaconda3\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1382: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=6.\n",
      "  warnings.warn(\n",
      "C:\\ProgramData\\anaconda3\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1382: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=6.\n",
      "  warnings.warn(\n",
      "C:\\ProgramData\\anaconda3\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1382: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=6.\n",
      "  warnings.warn(\n",
      "C:\\ProgramData\\anaconda3\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1382: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=6.\n",
      "  warnings.warn(\n",
      "C:\\ProgramData\\anaconda3\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1382: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=6.\n",
      "  warnings.warn(\n",
      "C:\\ProgramData\\anaconda3\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1382: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=6.\n",
      "  warnings.warn(\n",
      "C:\\ProgramData\\anaconda3\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1382: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=6.\n",
      "  warnings.warn(\n",
      "C:\\ProgramData\\anaconda3\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1382: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=6.\n",
      "  warnings.warn(\n",
      "C:\\ProgramData\\anaconda3\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1382: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=6.\n",
      "  warnings.warn(\n",
      "C:\\ProgramData\\anaconda3\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1382: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=6.\n",
      "  warnings.warn(\n",
      "C:\\ProgramData\\anaconda3\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1382: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=6.\n",
      "  warnings.warn(\n",
      "C:\\ProgramData\\anaconda3\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1382: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=6.\n",
      "  warnings.warn(\n",
      "C:\\ProgramData\\anaconda3\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1382: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=6.\n",
      "  warnings.warn(\n",
      "C:\\ProgramData\\anaconda3\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1382: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=6.\n",
      "  warnings.warn(\n",
      "C:\\ProgramData\\anaconda3\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1382: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=6.\n",
      "  warnings.warn(\n",
      "C:\\ProgramData\\anaconda3\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1382: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=6.\n",
      "  warnings.warn(\n",
      "C:\\ProgramData\\anaconda3\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1382: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=6.\n",
      "  warnings.warn(\n",
      "C:\\ProgramData\\anaconda3\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1382: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=6.\n",
      "  warnings.warn(\n",
      "C:\\ProgramData\\anaconda3\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1382: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=6.\n",
      "  warnings.warn(\n",
      "C:\\ProgramData\\anaconda3\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1382: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=6.\n",
      "  warnings.warn(\n",
      "C:\\ProgramData\\anaconda3\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1382: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=6.\n",
      "  warnings.warn(\n",
      "C:\\ProgramData\\anaconda3\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1382: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=6.\n",
      "  warnings.warn(\n",
      "C:\\ProgramData\\anaconda3\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1382: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=6.\n",
      "  warnings.warn(\n",
      "C:\\ProgramData\\anaconda3\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1382: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=6.\n",
      "  warnings.warn(\n",
      "C:\\ProgramData\\anaconda3\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1382: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=6.\n",
      "  warnings.warn(\n",
      "C:\\ProgramData\\anaconda3\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1382: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=6.\n",
      "  warnings.warn(\n",
      "C:\\ProgramData\\anaconda3\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1382: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=6.\n",
      "  warnings.warn(\n",
      "C:\\ProgramData\\anaconda3\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1382: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=6.\n",
      "  warnings.warn(\n",
      "C:\\ProgramData\\anaconda3\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1382: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=6.\n",
      "  warnings.warn(\n",
      "C:\\ProgramData\\anaconda3\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1382: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=6.\n",
      "  warnings.warn(\n",
      "C:\\ProgramData\\anaconda3\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1382: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=6.\n",
      "  warnings.warn(\n",
      "C:\\ProgramData\\anaconda3\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1382: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=6.\n",
      "  warnings.warn(\n",
      "C:\\ProgramData\\anaconda3\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1382: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=6.\n",
      "  warnings.warn(\n",
      "C:\\ProgramData\\anaconda3\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1382: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=6.\n",
      "  warnings.warn(\n",
      "C:\\ProgramData\\anaconda3\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1382: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=6.\n",
      "  warnings.warn(\n",
      "C:\\ProgramData\\anaconda3\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1382: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=6.\n",
      "  warnings.warn(\n",
      "C:\\ProgramData\\anaconda3\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1382: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=6.\n",
      "  warnings.warn(\n",
      "C:\\ProgramData\\anaconda3\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1382: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=6.\n",
      "  warnings.warn(\n",
      "C:\\ProgramData\\anaconda3\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1382: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=6.\n",
      "  warnings.warn(\n",
      "C:\\ProgramData\\anaconda3\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1382: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=6.\n",
      "  warnings.warn(\n",
      "C:\\ProgramData\\anaconda3\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1382: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=6.\n",
      "  warnings.warn(\n",
      "C:\\ProgramData\\anaconda3\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1382: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=6.\n",
      "  warnings.warn(\n",
      "C:\\ProgramData\\anaconda3\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1382: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=6.\n",
      "  warnings.warn(\n",
      "C:\\ProgramData\\anaconda3\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1382: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=6.\n",
      "  warnings.warn(\n",
      "C:\\ProgramData\\anaconda3\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1382: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=6.\n",
      "  warnings.warn(\n",
      "C:\\ProgramData\\anaconda3\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1382: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=6.\n",
      "  warnings.warn(\n",
      "C:\\ProgramData\\anaconda3\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1382: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=6.\n",
      "  warnings.warn(\n",
      "C:\\ProgramData\\anaconda3\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1382: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=6.\n",
      "  warnings.warn(\n",
      "C:\\ProgramData\\anaconda3\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1382: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=6.\n",
      "  warnings.warn(\n",
      "C:\\ProgramData\\anaconda3\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1382: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=6.\n",
      "  warnings.warn(\n",
      "C:\\ProgramData\\anaconda3\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1382: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=6.\n",
      "  warnings.warn(\n",
      "C:\\ProgramData\\anaconda3\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1382: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=6.\n",
      "  warnings.warn(\n",
      "C:\\ProgramData\\anaconda3\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1382: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=6.\n",
      "  warnings.warn(\n",
      "C:\\ProgramData\\anaconda3\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1382: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=6.\n",
      "  warnings.warn(\n",
      "C:\\ProgramData\\anaconda3\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1382: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=6.\n",
      "  warnings.warn(\n",
      "C:\\ProgramData\\anaconda3\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1382: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=6.\n",
      "  warnings.warn(\n",
      "C:\\ProgramData\\anaconda3\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1382: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=6.\n",
      "  warnings.warn(\n",
      "C:\\ProgramData\\anaconda3\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1382: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=6.\n",
      "  warnings.warn(\n",
      "C:\\ProgramData\\anaconda3\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1382: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=6.\n",
      "  warnings.warn(\n",
      "C:\\ProgramData\\anaconda3\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1382: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=6.\n",
      "  warnings.warn(\n",
      "C:\\ProgramData\\anaconda3\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1382: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=6.\n",
      "  warnings.warn(\n",
      "C:\\ProgramData\\anaconda3\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1382: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=6.\n",
      "  warnings.warn(\n",
      "C:\\ProgramData\\anaconda3\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1382: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=6.\n",
      "  warnings.warn(\n",
      "C:\\ProgramData\\anaconda3\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1382: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=6.\n",
      "  warnings.warn(\n",
      "C:\\ProgramData\\anaconda3\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1382: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=6.\n",
      "  warnings.warn(\n",
      "C:\\ProgramData\\anaconda3\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1382: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=6.\n",
      "  warnings.warn(\n",
      "C:\\ProgramData\\anaconda3\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1382: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=6.\n",
      "  warnings.warn(\n",
      "C:\\ProgramData\\anaconda3\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1382: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=6.\n",
      "  warnings.warn(\n",
      "C:\\ProgramData\\anaconda3\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1382: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=6.\n",
      "  warnings.warn(\n",
      "C:\\ProgramData\\anaconda3\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1382: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=6.\n",
      "  warnings.warn(\n",
      "C:\\ProgramData\\anaconda3\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1382: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=6.\n",
      "  warnings.warn(\n",
      "C:\\ProgramData\\anaconda3\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1382: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=6.\n",
      "  warnings.warn(\n",
      "C:\\ProgramData\\anaconda3\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1382: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=6.\n",
      "  warnings.warn(\n",
      "C:\\ProgramData\\anaconda3\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1382: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=6.\n",
      "  warnings.warn(\n",
      "C:\\ProgramData\\anaconda3\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1382: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=6.\n",
      "  warnings.warn(\n",
      "C:\\ProgramData\\anaconda3\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1382: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=6.\n",
      "  warnings.warn(\n",
      "C:\\ProgramData\\anaconda3\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1382: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=6.\n",
      "  warnings.warn(\n",
      "C:\\ProgramData\\anaconda3\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1382: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=6.\n",
      "  warnings.warn(\n",
      "C:\\ProgramData\\anaconda3\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1382: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=6.\n",
      "  warnings.warn(\n",
      "C:\\ProgramData\\anaconda3\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1382: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=6.\n",
      "  warnings.warn(\n",
      "C:\\ProgramData\\anaconda3\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1382: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=6.\n",
      "  warnings.warn(\n",
      "C:\\ProgramData\\anaconda3\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1382: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=6.\n",
      "  warnings.warn(\n",
      "C:\\ProgramData\\anaconda3\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1382: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=6.\n",
      "  warnings.warn(\n",
      "C:\\ProgramData\\anaconda3\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1382: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=6.\n",
      "  warnings.warn(\n",
      "C:\\ProgramData\\anaconda3\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1382: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=6.\n",
      "  warnings.warn(\n",
      "C:\\ProgramData\\anaconda3\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1382: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=6.\n",
      "  warnings.warn(\n",
      "C:\\ProgramData\\anaconda3\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1382: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=6.\n",
      "  warnings.warn(\n",
      "C:\\ProgramData\\anaconda3\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1382: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=6.\n",
      "  warnings.warn(\n",
      "C:\\ProgramData\\anaconda3\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1382: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=6.\n",
      "  warnings.warn(\n",
      "C:\\ProgramData\\anaconda3\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1382: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=6.\n",
      "  warnings.warn(\n",
      "C:\\ProgramData\\anaconda3\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1382: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=6.\n",
      "  warnings.warn(\n",
      "C:\\ProgramData\\anaconda3\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1382: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=6.\n",
      "  warnings.warn(\n",
      "C:\\ProgramData\\anaconda3\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1382: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=6.\n",
      "  warnings.warn(\n",
      "C:\\ProgramData\\anaconda3\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1382: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=6.\n",
      "  warnings.warn(\n",
      "C:\\ProgramData\\anaconda3\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1382: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=6.\n",
      "  warnings.warn(\n",
      "C:\\ProgramData\\anaconda3\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1382: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=6.\n",
      "  warnings.warn(\n",
      "C:\\ProgramData\\anaconda3\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1382: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=6.\n",
      "  warnings.warn(\n",
      "C:\\ProgramData\\anaconda3\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1382: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=6.\n",
      "  warnings.warn(\n",
      "C:\\ProgramData\\anaconda3\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1382: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=6.\n",
      "  warnings.warn(\n",
      "C:\\ProgramData\\anaconda3\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1382: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=6.\n",
      "  warnings.warn(\n",
      "C:\\ProgramData\\anaconda3\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1382: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=6.\n",
      "  warnings.warn(\n",
      "C:\\ProgramData\\anaconda3\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1382: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=6.\n",
      "  warnings.warn(\n",
      "C:\\ProgramData\\anaconda3\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1382: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=6.\n",
      "  warnings.warn(\n",
      "C:\\ProgramData\\anaconda3\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1382: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=6.\n",
      "  warnings.warn(\n",
      "C:\\ProgramData\\anaconda3\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1382: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=6.\n",
      "  warnings.warn(\n",
      "C:\\ProgramData\\anaconda3\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1382: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=6.\n",
      "  warnings.warn(\n",
      "C:\\ProgramData\\anaconda3\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1382: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=6.\n",
      "  warnings.warn(\n",
      "C:\\ProgramData\\anaconda3\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1382: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=6.\n",
      "  warnings.warn(\n",
      "C:\\ProgramData\\anaconda3\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1382: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=6.\n",
      "  warnings.warn(\n",
      "C:\\ProgramData\\anaconda3\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1382: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=6.\n",
      "  warnings.warn(\n",
      "C:\\ProgramData\\anaconda3\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1382: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=6.\n",
      "  warnings.warn(\n",
      "C:\\ProgramData\\anaconda3\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1382: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=6.\n",
      "  warnings.warn(\n",
      "C:\\ProgramData\\anaconda3\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1382: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=6.\n",
      "  warnings.warn(\n",
      "C:\\ProgramData\\anaconda3\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1382: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=6.\n",
      "  warnings.warn(\n",
      "C:\\ProgramData\\anaconda3\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1382: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=6.\n",
      "  warnings.warn(\n",
      "C:\\ProgramData\\anaconda3\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1382: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=6.\n",
      "  warnings.warn(\n",
      "C:\\ProgramData\\anaconda3\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1382: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=6.\n",
      "  warnings.warn(\n",
      "C:\\ProgramData\\anaconda3\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1382: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=6.\n",
      "  warnings.warn(\n",
      "C:\\ProgramData\\anaconda3\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1382: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=6.\n",
      "  warnings.warn(\n",
      "C:\\ProgramData\\anaconda3\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1382: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=6.\n",
      "  warnings.warn(\n",
      "C:\\ProgramData\\anaconda3\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1382: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=6.\n",
      "  warnings.warn(\n",
      "C:\\ProgramData\\anaconda3\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1382: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=6.\n",
      "  warnings.warn(\n",
      "C:\\ProgramData\\anaconda3\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1382: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=6.\n",
      "  warnings.warn(\n",
      "C:\\ProgramData\\anaconda3\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1382: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=6.\n",
      "  warnings.warn(\n",
      "C:\\ProgramData\\anaconda3\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1382: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=6.\n",
      "  warnings.warn(\n",
      "C:\\ProgramData\\anaconda3\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1382: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=6.\n",
      "  warnings.warn(\n",
      "C:\\ProgramData\\anaconda3\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1382: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=6.\n",
      "  warnings.warn(\n",
      "C:\\ProgramData\\anaconda3\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1382: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=6.\n",
      "  warnings.warn(\n",
      "C:\\ProgramData\\anaconda3\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1382: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=6.\n",
      "  warnings.warn(\n",
      "C:\\ProgramData\\anaconda3\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1382: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=6.\n",
      "  warnings.warn(\n",
      "C:\\ProgramData\\anaconda3\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1382: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=6.\n",
      "  warnings.warn(\n",
      "C:\\ProgramData\\anaconda3\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1382: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=6.\n",
      "  warnings.warn(\n",
      "C:\\ProgramData\\anaconda3\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1382: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=6.\n",
      "  warnings.warn(\n",
      "C:\\ProgramData\\anaconda3\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1382: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=6.\n",
      "  warnings.warn(\n",
      "C:\\ProgramData\\anaconda3\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1382: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=6.\n",
      "  warnings.warn(\n",
      "C:\\ProgramData\\anaconda3\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1382: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=6.\n",
      "  warnings.warn(\n",
      "C:\\ProgramData\\anaconda3\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1382: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=6.\n",
      "  warnings.warn(\n",
      "C:\\ProgramData\\anaconda3\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1382: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=6.\n",
      "  warnings.warn(\n",
      "C:\\ProgramData\\anaconda3\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1382: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=6.\n",
      "  warnings.warn(\n",
      "C:\\ProgramData\\anaconda3\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1382: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=6.\n",
      "  warnings.warn(\n",
      "C:\\ProgramData\\anaconda3\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1382: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=6.\n",
      "  warnings.warn(\n",
      "C:\\ProgramData\\anaconda3\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1382: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=6.\n",
      "  warnings.warn(\n",
      "C:\\ProgramData\\anaconda3\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1382: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=6.\n",
      "  warnings.warn(\n",
      "C:\\ProgramData\\anaconda3\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1382: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=6.\n",
      "  warnings.warn(\n",
      "C:\\ProgramData\\anaconda3\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1382: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=6.\n",
      "  warnings.warn(\n",
      "C:\\ProgramData\\anaconda3\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1382: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=6.\n",
      "  warnings.warn(\n",
      "C:\\ProgramData\\anaconda3\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1382: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=6.\n",
      "  warnings.warn(\n",
      "C:\\ProgramData\\anaconda3\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1382: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=6.\n",
      "  warnings.warn(\n",
      "C:\\ProgramData\\anaconda3\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1382: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=6.\n",
      "  warnings.warn(\n",
      "C:\\ProgramData\\anaconda3\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1382: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=6.\n",
      "  warnings.warn(\n",
      "C:\\ProgramData\\anaconda3\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1382: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=6.\n",
      "  warnings.warn(\n",
      "C:\\ProgramData\\anaconda3\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1382: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=6.\n",
      "  warnings.warn(\n",
      "C:\\ProgramData\\anaconda3\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1382: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=6.\n",
      "  warnings.warn(\n",
      "C:\\ProgramData\\anaconda3\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1382: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=6.\n",
      "  warnings.warn(\n",
      "C:\\ProgramData\\anaconda3\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1382: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=6.\n",
      "  warnings.warn(\n",
      "C:\\ProgramData\\anaconda3\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1382: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=6.\n",
      "  warnings.warn(\n",
      "C:\\ProgramData\\anaconda3\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1382: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=6.\n",
      "  warnings.warn(\n",
      "C:\\ProgramData\\anaconda3\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1382: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=6.\n",
      "  warnings.warn(\n",
      "C:\\ProgramData\\anaconda3\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1382: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=6.\n",
      "  warnings.warn(\n",
      "C:\\ProgramData\\anaconda3\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1382: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=6.\n",
      "  warnings.warn(\n",
      "C:\\ProgramData\\anaconda3\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1382: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=6.\n",
      "  warnings.warn(\n",
      "C:\\ProgramData\\anaconda3\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1382: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=6.\n",
      "  warnings.warn(\n",
      "C:\\ProgramData\\anaconda3\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1382: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=6.\n",
      "  warnings.warn(\n",
      "C:\\ProgramData\\anaconda3\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1382: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=6.\n",
      "  warnings.warn(\n",
      "C:\\ProgramData\\anaconda3\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1382: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=6.\n",
      "  warnings.warn(\n",
      "C:\\ProgramData\\anaconda3\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1382: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=6.\n",
      "  warnings.warn(\n",
      "C:\\ProgramData\\anaconda3\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1382: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=6.\n",
      "  warnings.warn(\n",
      "C:\\ProgramData\\anaconda3\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1382: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=6.\n",
      "  warnings.warn(\n",
      "C:\\ProgramData\\anaconda3\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1382: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=6.\n",
      "  warnings.warn(\n",
      "C:\\ProgramData\\anaconda3\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1382: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=6.\n",
      "  warnings.warn(\n",
      "C:\\ProgramData\\anaconda3\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1382: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=6.\n",
      "  warnings.warn(\n",
      "C:\\ProgramData\\anaconda3\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1382: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=6.\n",
      "  warnings.warn(\n",
      "C:\\ProgramData\\anaconda3\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1382: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=6.\n",
      "  warnings.warn(\n",
      "C:\\ProgramData\\anaconda3\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1382: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=6.\n",
      "  warnings.warn(\n",
      "C:\\ProgramData\\anaconda3\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1382: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=6.\n",
      "  warnings.warn(\n",
      "C:\\ProgramData\\anaconda3\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1382: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=6.\n",
      "  warnings.warn(\n",
      "C:\\ProgramData\\anaconda3\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1382: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=6.\n",
      "  warnings.warn(\n",
      "C:\\ProgramData\\anaconda3\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1382: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=6.\n",
      "  warnings.warn(\n",
      "C:\\ProgramData\\anaconda3\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1382: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=6.\n",
      "  warnings.warn(\n",
      "C:\\ProgramData\\anaconda3\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1382: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=6.\n",
      "  warnings.warn(\n",
      "C:\\ProgramData\\anaconda3\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1382: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=6.\n",
      "  warnings.warn(\n",
      "C:\\ProgramData\\anaconda3\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1382: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=6.\n",
      "  warnings.warn(\n",
      "C:\\ProgramData\\anaconda3\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1382: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=6.\n",
      "  warnings.warn(\n",
      "C:\\ProgramData\\anaconda3\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1382: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=6.\n",
      "  warnings.warn(\n",
      "C:\\ProgramData\\anaconda3\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1382: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=6.\n",
      "  warnings.warn(\n",
      "C:\\ProgramData\\anaconda3\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1382: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=6.\n",
      "  warnings.warn(\n",
      "C:\\ProgramData\\anaconda3\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1382: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=6.\n",
      "  warnings.warn(\n",
      "C:\\ProgramData\\anaconda3\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1382: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=6.\n",
      "  warnings.warn(\n",
      "C:\\ProgramData\\anaconda3\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1382: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=6.\n",
      "  warnings.warn(\n",
      "C:\\ProgramData\\anaconda3\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1382: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=6.\n",
      "  warnings.warn(\n",
      "C:\\ProgramData\\anaconda3\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1382: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=6.\n",
      "  warnings.warn(\n",
      "C:\\ProgramData\\anaconda3\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1382: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=6.\n",
      "  warnings.warn(\n",
      "C:\\ProgramData\\anaconda3\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1382: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=6.\n",
      "  warnings.warn(\n",
      "C:\\ProgramData\\anaconda3\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1382: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=6.\n",
      "  warnings.warn(\n",
      "C:\\ProgramData\\anaconda3\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1382: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=6.\n",
      "  warnings.warn(\n",
      "C:\\ProgramData\\anaconda3\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1382: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=6.\n",
      "  warnings.warn(\n",
      "C:\\ProgramData\\anaconda3\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1382: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=6.\n",
      "  warnings.warn(\n",
      "C:\\ProgramData\\anaconda3\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1382: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=6.\n",
      "  warnings.warn(\n",
      "C:\\ProgramData\\anaconda3\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1382: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=6.\n",
      "  warnings.warn(\n",
      "C:\\ProgramData\\anaconda3\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1382: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=6.\n",
      "  warnings.warn(\n",
      "C:\\ProgramData\\anaconda3\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1382: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=6.\n",
      "  warnings.warn(\n",
      "C:\\ProgramData\\anaconda3\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1382: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=6.\n",
      "  warnings.warn(\n",
      "C:\\ProgramData\\anaconda3\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1382: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=6.\n",
      "  warnings.warn(\n",
      "C:\\ProgramData\\anaconda3\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1382: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=6.\n",
      "  warnings.warn(\n",
      "C:\\ProgramData\\anaconda3\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1382: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=6.\n",
      "  warnings.warn(\n",
      "C:\\ProgramData\\anaconda3\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1382: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=6.\n",
      "  warnings.warn(\n",
      "C:\\ProgramData\\anaconda3\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1382: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=6.\n",
      "  warnings.warn(\n",
      "C:\\ProgramData\\anaconda3\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1382: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=6.\n",
      "  warnings.warn(\n",
      "C:\\ProgramData\\anaconda3\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1382: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=6.\n",
      "  warnings.warn(\n",
      "C:\\ProgramData\\anaconda3\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1382: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=6.\n",
      "  warnings.warn(\n",
      "C:\\ProgramData\\anaconda3\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1382: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=6.\n",
      "  warnings.warn(\n",
      "C:\\ProgramData\\anaconda3\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1382: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=6.\n",
      "  warnings.warn(\n",
      "C:\\ProgramData\\anaconda3\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1382: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=6.\n",
      "  warnings.warn(\n",
      "C:\\ProgramData\\anaconda3\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1382: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=6.\n",
      "  warnings.warn(\n",
      "C:\\ProgramData\\anaconda3\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1382: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=6.\n",
      "  warnings.warn(\n",
      "C:\\ProgramData\\anaconda3\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1382: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=6.\n",
      "  warnings.warn(\n",
      "C:\\ProgramData\\anaconda3\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1382: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=6.\n",
      "  warnings.warn(\n",
      "C:\\ProgramData\\anaconda3\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1382: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=6.\n",
      "  warnings.warn(\n",
      "C:\\ProgramData\\anaconda3\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1382: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=6.\n",
      "  warnings.warn(\n",
      "C:\\ProgramData\\anaconda3\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1382: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=6.\n",
      "  warnings.warn(\n",
      "C:\\ProgramData\\anaconda3\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1382: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=6.\n",
      "  warnings.warn(\n",
      "C:\\ProgramData\\anaconda3\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1382: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=6.\n",
      "  warnings.warn(\n",
      "C:\\ProgramData\\anaconda3\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1382: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=6.\n",
      "  warnings.warn(\n",
      "C:\\ProgramData\\anaconda3\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1382: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=6.\n",
      "  warnings.warn(\n",
      "C:\\ProgramData\\anaconda3\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1382: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=6.\n",
      "  warnings.warn(\n",
      "C:\\ProgramData\\anaconda3\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1382: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=6.\n",
      "  warnings.warn(\n",
      "C:\\ProgramData\\anaconda3\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1382: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=6.\n",
      "  warnings.warn(\n",
      "C:\\ProgramData\\anaconda3\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1382: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=6.\n",
      "  warnings.warn(\n",
      "C:\\ProgramData\\anaconda3\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1382: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=6.\n",
      "  warnings.warn(\n",
      "C:\\ProgramData\\anaconda3\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1382: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=6.\n",
      "  warnings.warn(\n",
      "C:\\ProgramData\\anaconda3\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1382: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=6.\n",
      "  warnings.warn(\n",
      "C:\\ProgramData\\anaconda3\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1382: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=6.\n",
      "  warnings.warn(\n",
      "C:\\ProgramData\\anaconda3\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1382: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=6.\n",
      "  warnings.warn(\n",
      "C:\\ProgramData\\anaconda3\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1382: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=6.\n",
      "  warnings.warn(\n",
      "C:\\ProgramData\\anaconda3\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1382: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=6.\n",
      "  warnings.warn(\n",
      "C:\\ProgramData\\anaconda3\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1382: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=6.\n",
      "  warnings.warn(\n",
      "C:\\ProgramData\\anaconda3\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1382: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=6.\n",
      "  warnings.warn(\n",
      "C:\\ProgramData\\anaconda3\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1382: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=6.\n",
      "  warnings.warn(\n",
      "C:\\ProgramData\\anaconda3\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1382: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=6.\n",
      "  warnings.warn(\n",
      "C:\\ProgramData\\anaconda3\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1382: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=6.\n",
      "  warnings.warn(\n",
      "C:\\ProgramData\\anaconda3\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1382: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=6.\n",
      "  warnings.warn(\n",
      "C:\\ProgramData\\anaconda3\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1382: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=6.\n",
      "  warnings.warn(\n",
      "C:\\ProgramData\\anaconda3\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1382: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=6.\n",
      "  warnings.warn(\n",
      "C:\\ProgramData\\anaconda3\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1382: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=6.\n",
      "  warnings.warn(\n",
      "C:\\ProgramData\\anaconda3\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1382: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=6.\n",
      "  warnings.warn(\n",
      "C:\\ProgramData\\anaconda3\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1382: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=6.\n",
      "  warnings.warn(\n",
      "C:\\ProgramData\\anaconda3\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1382: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=6.\n",
      "  warnings.warn(\n",
      "C:\\ProgramData\\anaconda3\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1382: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=6.\n",
      "  warnings.warn(\n",
      "C:\\ProgramData\\anaconda3\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1382: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=6.\n",
      "  warnings.warn(\n",
      "C:\\ProgramData\\anaconda3\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1382: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=6.\n",
      "  warnings.warn(\n",
      "C:\\ProgramData\\anaconda3\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1382: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=6.\n",
      "  warnings.warn(\n",
      "C:\\ProgramData\\anaconda3\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1382: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=6.\n",
      "  warnings.warn(\n",
      "C:\\ProgramData\\anaconda3\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1382: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=6.\n",
      "  warnings.warn(\n",
      "C:\\ProgramData\\anaconda3\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1382: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=6.\n",
      "  warnings.warn(\n",
      "C:\\ProgramData\\anaconda3\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1382: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=6.\n",
      "  warnings.warn(\n",
      "C:\\ProgramData\\anaconda3\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1382: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=6.\n",
      "  warnings.warn(\n",
      "C:\\ProgramData\\anaconda3\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1382: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=6.\n",
      "  warnings.warn(\n",
      "C:\\ProgramData\\anaconda3\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1382: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=6.\n",
      "  warnings.warn(\n",
      "C:\\ProgramData\\anaconda3\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1382: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=6.\n",
      "  warnings.warn(\n",
      "C:\\ProgramData\\anaconda3\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1382: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=6.\n",
      "  warnings.warn(\n",
      "C:\\ProgramData\\anaconda3\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1382: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=6.\n",
      "  warnings.warn(\n",
      "C:\\ProgramData\\anaconda3\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1382: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=6.\n",
      "  warnings.warn(\n",
      "C:\\ProgramData\\anaconda3\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1382: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=6.\n",
      "  warnings.warn(\n",
      "C:\\ProgramData\\anaconda3\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1382: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=6.\n",
      "  warnings.warn(\n",
      "C:\\ProgramData\\anaconda3\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1382: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=6.\n",
      "  warnings.warn(\n",
      "C:\\ProgramData\\anaconda3\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1382: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=6.\n",
      "  warnings.warn(\n",
      "C:\\ProgramData\\anaconda3\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1382: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=6.\n",
      "  warnings.warn(\n",
      "C:\\ProgramData\\anaconda3\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1382: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=6.\n",
      "  warnings.warn(\n",
      "C:\\ProgramData\\anaconda3\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1382: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=6.\n",
      "  warnings.warn(\n",
      "C:\\ProgramData\\anaconda3\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1382: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=6.\n",
      "  warnings.warn(\n",
      "C:\\ProgramData\\anaconda3\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1382: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=6.\n",
      "  warnings.warn(\n",
      "C:\\ProgramData\\anaconda3\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1382: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=6.\n",
      "  warnings.warn(\n",
      "C:\\ProgramData\\anaconda3\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1382: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=6.\n",
      "  warnings.warn(\n",
      "C:\\ProgramData\\anaconda3\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1382: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=6.\n",
      "  warnings.warn(\n",
      "C:\\ProgramData\\anaconda3\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1382: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=6.\n",
      "  warnings.warn(\n",
      "C:\\ProgramData\\anaconda3\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1382: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=6.\n",
      "  warnings.warn(\n",
      "C:\\ProgramData\\anaconda3\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1382: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=6.\n",
      "  warnings.warn(\n",
      "C:\\ProgramData\\anaconda3\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1382: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=6.\n",
      "  warnings.warn(\n",
      "C:\\ProgramData\\anaconda3\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1382: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=6.\n",
      "  warnings.warn(\n",
      "C:\\ProgramData\\anaconda3\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1382: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=6.\n",
      "  warnings.warn(\n",
      "C:\\ProgramData\\anaconda3\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1382: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=6.\n",
      "  warnings.warn(\n",
      "C:\\ProgramData\\anaconda3\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1382: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=6.\n",
      "  warnings.warn(\n",
      "C:\\ProgramData\\anaconda3\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1382: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=6.\n",
      "  warnings.warn(\n",
      "C:\\ProgramData\\anaconda3\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1382: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=6.\n",
      "  warnings.warn(\n",
      "C:\\ProgramData\\anaconda3\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1382: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=6.\n",
      "  warnings.warn(\n",
      "C:\\ProgramData\\anaconda3\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1382: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=6.\n",
      "  warnings.warn(\n",
      "C:\\ProgramData\\anaconda3\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1382: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=6.\n",
      "  warnings.warn(\n",
      "C:\\ProgramData\\anaconda3\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1382: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=6.\n",
      "  warnings.warn(\n",
      "C:\\ProgramData\\anaconda3\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1382: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=6.\n",
      "  warnings.warn(\n",
      "C:\\ProgramData\\anaconda3\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1382: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=6.\n",
      "  warnings.warn(\n",
      "C:\\ProgramData\\anaconda3\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1382: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=6.\n",
      "  warnings.warn(\n",
      "C:\\ProgramData\\anaconda3\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1382: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=6.\n",
      "  warnings.warn(\n",
      "C:\\ProgramData\\anaconda3\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1382: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=6.\n",
      "  warnings.warn(\n",
      "C:\\ProgramData\\anaconda3\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1382: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=6.\n",
      "  warnings.warn(\n",
      "C:\\ProgramData\\anaconda3\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1382: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=6.\n",
      "  warnings.warn(\n",
      "C:\\ProgramData\\anaconda3\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1382: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=6.\n",
      "  warnings.warn(\n",
      "C:\\ProgramData\\anaconda3\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1382: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=6.\n",
      "  warnings.warn(\n",
      "C:\\ProgramData\\anaconda3\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1382: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=6.\n",
      "  warnings.warn(\n",
      "C:\\ProgramData\\anaconda3\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1382: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=6.\n",
      "  warnings.warn(\n",
      "C:\\ProgramData\\anaconda3\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1382: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=6.\n",
      "  warnings.warn(\n",
      "C:\\ProgramData\\anaconda3\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1382: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=6.\n",
      "  warnings.warn(\n",
      "C:\\ProgramData\\anaconda3\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1382: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=6.\n",
      "  warnings.warn(\n",
      "C:\\ProgramData\\anaconda3\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1382: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=6.\n",
      "  warnings.warn(\n",
      "C:\\ProgramData\\anaconda3\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1382: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=6.\n",
      "  warnings.warn(\n",
      "C:\\ProgramData\\anaconda3\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1382: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=6.\n",
      "  warnings.warn(\n",
      "C:\\ProgramData\\anaconda3\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1382: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=6.\n",
      "  warnings.warn(\n",
      "C:\\ProgramData\\anaconda3\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1382: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=6.\n",
      "  warnings.warn(\n",
      "C:\\ProgramData\\anaconda3\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1382: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=6.\n",
      "  warnings.warn(\n",
      "C:\\ProgramData\\anaconda3\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1382: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=6.\n",
      "  warnings.warn(\n",
      "C:\\ProgramData\\anaconda3\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1382: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=6.\n",
      "  warnings.warn(\n",
      "C:\\ProgramData\\anaconda3\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1382: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=6.\n",
      "  warnings.warn(\n",
      "C:\\ProgramData\\anaconda3\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1382: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=6.\n",
      "  warnings.warn(\n",
      "C:\\ProgramData\\anaconda3\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1382: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=6.\n",
      "  warnings.warn(\n",
      "C:\\ProgramData\\anaconda3\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1382: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=6.\n",
      "  warnings.warn(\n",
      "C:\\ProgramData\\anaconda3\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1382: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=6.\n",
      "  warnings.warn(\n",
      "C:\\ProgramData\\anaconda3\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1382: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=6.\n",
      "  warnings.warn(\n",
      "C:\\ProgramData\\anaconda3\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1382: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=6.\n",
      "  warnings.warn(\n",
      "C:\\ProgramData\\anaconda3\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1382: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=6.\n",
      "  warnings.warn(\n",
      "C:\\ProgramData\\anaconda3\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1382: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=6.\n",
      "  warnings.warn(\n",
      "C:\\ProgramData\\anaconda3\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1382: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=6.\n",
      "  warnings.warn(\n",
      "C:\\ProgramData\\anaconda3\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1382: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=6.\n",
      "  warnings.warn(\n",
      "C:\\ProgramData\\anaconda3\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1382: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=6.\n",
      "  warnings.warn(\n",
      "C:\\ProgramData\\anaconda3\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1382: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=6.\n",
      "  warnings.warn(\n",
      "C:\\ProgramData\\anaconda3\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1382: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=6.\n",
      "  warnings.warn(\n",
      "C:\\ProgramData\\anaconda3\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1382: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=6.\n",
      "  warnings.warn(\n",
      "C:\\ProgramData\\anaconda3\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1382: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=6.\n",
      "  warnings.warn(\n",
      "C:\\ProgramData\\anaconda3\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1382: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=6.\n",
      "  warnings.warn(\n",
      "C:\\ProgramData\\anaconda3\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1382: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=6.\n",
      "  warnings.warn(\n",
      "C:\\ProgramData\\anaconda3\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1382: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=6.\n",
      "  warnings.warn(\n",
      "C:\\ProgramData\\anaconda3\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1382: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=6.\n",
      "  warnings.warn(\n",
      "C:\\ProgramData\\anaconda3\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1382: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=6.\n",
      "  warnings.warn(\n",
      "C:\\ProgramData\\anaconda3\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1382: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=6.\n",
      "  warnings.warn(\n",
      "C:\\ProgramData\\anaconda3\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1382: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=6.\n",
      "  warnings.warn(\n",
      "C:\\ProgramData\\anaconda3\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1382: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=6.\n",
      "  warnings.warn(\n",
      "C:\\ProgramData\\anaconda3\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1382: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=6.\n",
      "  warnings.warn(\n",
      "C:\\ProgramData\\anaconda3\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1382: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=6.\n",
      "  warnings.warn(\n",
      "C:\\ProgramData\\anaconda3\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1382: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=6.\n",
      "  warnings.warn(\n",
      "C:\\ProgramData\\anaconda3\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1382: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=6.\n",
      "  warnings.warn(\n",
      "C:\\ProgramData\\anaconda3\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1382: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=6.\n",
      "  warnings.warn(\n",
      "C:\\ProgramData\\anaconda3\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1382: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=6.\n",
      "  warnings.warn(\n",
      "C:\\ProgramData\\anaconda3\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1382: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=6.\n",
      "  warnings.warn(\n",
      "C:\\ProgramData\\anaconda3\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1382: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=6.\n",
      "  warnings.warn(\n",
      "C:\\ProgramData\\anaconda3\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1382: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=6.\n",
      "  warnings.warn(\n",
      "C:\\ProgramData\\anaconda3\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1382: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=6.\n",
      "  warnings.warn(\n",
      "C:\\ProgramData\\anaconda3\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1382: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=6.\n",
      "  warnings.warn(\n",
      "C:\\ProgramData\\anaconda3\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1382: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=6.\n",
      "  warnings.warn(\n",
      "C:\\ProgramData\\anaconda3\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1382: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=6.\n",
      "  warnings.warn(\n",
      "C:\\ProgramData\\anaconda3\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1382: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=6.\n",
      "  warnings.warn(\n",
      "C:\\ProgramData\\anaconda3\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1382: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=6.\n",
      "  warnings.warn(\n",
      "C:\\ProgramData\\anaconda3\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1382: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=6.\n",
      "  warnings.warn(\n",
      "C:\\ProgramData\\anaconda3\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1382: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=6.\n",
      "  warnings.warn(\n",
      "C:\\ProgramData\\anaconda3\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1382: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=6.\n",
      "  warnings.warn(\n",
      "C:\\ProgramData\\anaconda3\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1382: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=6.\n",
      "  warnings.warn(\n",
      "C:\\ProgramData\\anaconda3\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1382: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=6.\n",
      "  warnings.warn(\n",
      "C:\\ProgramData\\anaconda3\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1382: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=6.\n",
      "  warnings.warn(\n",
      "C:\\ProgramData\\anaconda3\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1382: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=6.\n",
      "  warnings.warn(\n",
      "C:\\ProgramData\\anaconda3\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1382: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=6.\n",
      "  warnings.warn(\n",
      "C:\\ProgramData\\anaconda3\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1382: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=6.\n",
      "  warnings.warn(\n",
      "C:\\ProgramData\\anaconda3\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1382: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=6.\n",
      "  warnings.warn(\n",
      "C:\\ProgramData\\anaconda3\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1382: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=6.\n",
      "  warnings.warn(\n",
      "C:\\ProgramData\\anaconda3\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1382: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=6.\n",
      "  warnings.warn(\n",
      "C:\\ProgramData\\anaconda3\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1382: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=6.\n",
      "  warnings.warn(\n",
      "C:\\ProgramData\\anaconda3\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1382: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=6.\n",
      "  warnings.warn(\n",
      "C:\\ProgramData\\anaconda3\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1382: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=6.\n",
      "  warnings.warn(\n",
      "C:\\ProgramData\\anaconda3\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1382: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=6.\n",
      "  warnings.warn(\n",
      "C:\\ProgramData\\anaconda3\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1382: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=6.\n",
      "  warnings.warn(\n",
      "C:\\ProgramData\\anaconda3\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1382: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=6.\n",
      "  warnings.warn(\n",
      "C:\\ProgramData\\anaconda3\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1382: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=6.\n",
      "  warnings.warn(\n",
      "C:\\ProgramData\\anaconda3\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1382: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=6.\n",
      "  warnings.warn(\n",
      "C:\\ProgramData\\anaconda3\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1382: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=6.\n",
      "  warnings.warn(\n",
      "C:\\ProgramData\\anaconda3\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1382: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=6.\n",
      "  warnings.warn(\n",
      "C:\\ProgramData\\anaconda3\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1382: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=6.\n",
      "  warnings.warn(\n",
      "C:\\ProgramData\\anaconda3\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1382: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=6.\n",
      "  warnings.warn(\n",
      "C:\\ProgramData\\anaconda3\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1382: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=6.\n",
      "  warnings.warn(\n",
      "C:\\ProgramData\\anaconda3\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1382: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=6.\n",
      "  warnings.warn(\n",
      "C:\\ProgramData\\anaconda3\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1382: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=6.\n",
      "  warnings.warn(\n",
      "C:\\ProgramData\\anaconda3\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1382: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=6.\n",
      "  warnings.warn(\n",
      "C:\\ProgramData\\anaconda3\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1382: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=6.\n",
      "  warnings.warn(\n",
      "C:\\ProgramData\\anaconda3\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1382: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=6.\n",
      "  warnings.warn(\n",
      "C:\\ProgramData\\anaconda3\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1382: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=6.\n",
      "  warnings.warn(\n",
      "C:\\ProgramData\\anaconda3\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1382: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=6.\n",
      "  warnings.warn(\n",
      "C:\\ProgramData\\anaconda3\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1382: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=6.\n",
      "  warnings.warn(\n",
      "C:\\ProgramData\\anaconda3\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1382: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=6.\n",
      "  warnings.warn(\n",
      "C:\\ProgramData\\anaconda3\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1382: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=6.\n",
      "  warnings.warn(\n",
      "C:\\ProgramData\\anaconda3\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1382: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=6.\n",
      "  warnings.warn(\n",
      "C:\\ProgramData\\anaconda3\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1382: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=6.\n",
      "  warnings.warn(\n",
      "C:\\ProgramData\\anaconda3\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1382: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=6.\n",
      "  warnings.warn(\n",
      "C:\\ProgramData\\anaconda3\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1382: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=6.\n",
      "  warnings.warn(\n",
      "C:\\ProgramData\\anaconda3\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1382: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=6.\n",
      "  warnings.warn(\n",
      "C:\\ProgramData\\anaconda3\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1382: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=6.\n",
      "  warnings.warn(\n",
      "C:\\ProgramData\\anaconda3\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1382: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=6.\n",
      "  warnings.warn(\n",
      "C:\\ProgramData\\anaconda3\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1382: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=6.\n",
      "  warnings.warn(\n",
      "C:\\ProgramData\\anaconda3\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1382: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=6.\n",
      "  warnings.warn(\n",
      "C:\\ProgramData\\anaconda3\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1382: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=6.\n",
      "  warnings.warn(\n",
      "C:\\ProgramData\\anaconda3\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1382: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=6.\n",
      "  warnings.warn(\n",
      "C:\\ProgramData\\anaconda3\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1382: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=6.\n",
      "  warnings.warn(\n",
      "C:\\ProgramData\\anaconda3\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1382: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=6.\n",
      "  warnings.warn(\n",
      "C:\\ProgramData\\anaconda3\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1382: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=6.\n",
      "  warnings.warn(\n",
      "C:\\ProgramData\\anaconda3\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1382: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=6.\n",
      "  warnings.warn(\n",
      "C:\\ProgramData\\anaconda3\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1382: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=6.\n",
      "  warnings.warn(\n",
      "C:\\ProgramData\\anaconda3\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1382: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=6.\n",
      "  warnings.warn(\n",
      "C:\\ProgramData\\anaconda3\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1382: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=6.\n",
      "  warnings.warn(\n",
      "C:\\ProgramData\\anaconda3\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1382: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=6.\n",
      "  warnings.warn(\n",
      "C:\\ProgramData\\anaconda3\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1382: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=6.\n",
      "  warnings.warn(\n",
      "C:\\ProgramData\\anaconda3\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1382: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=6.\n",
      "  warnings.warn(\n",
      "C:\\ProgramData\\anaconda3\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1382: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=6.\n",
      "  warnings.warn(\n",
      "C:\\ProgramData\\anaconda3\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1382: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=6.\n",
      "  warnings.warn(\n",
      "C:\\ProgramData\\anaconda3\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1382: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=6.\n",
      "  warnings.warn(\n",
      "C:\\ProgramData\\anaconda3\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1382: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=6.\n",
      "  warnings.warn(\n",
      "C:\\ProgramData\\anaconda3\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1382: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=6.\n",
      "  warnings.warn(\n",
      "C:\\ProgramData\\anaconda3\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1382: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=6.\n",
      "  warnings.warn(\n",
      "C:\\ProgramData\\anaconda3\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1382: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=6.\n",
      "  warnings.warn(\n",
      "C:\\ProgramData\\anaconda3\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1382: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=6.\n",
      "  warnings.warn(\n",
      "C:\\ProgramData\\anaconda3\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1382: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=6.\n",
      "  warnings.warn(\n",
      "C:\\ProgramData\\anaconda3\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1382: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=6.\n",
      "  warnings.warn(\n",
      "C:\\ProgramData\\anaconda3\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1382: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=6.\n",
      "  warnings.warn(\n",
      "C:\\ProgramData\\anaconda3\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1382: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=6.\n",
      "  warnings.warn(\n",
      "C:\\ProgramData\\anaconda3\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1382: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=6.\n",
      "  warnings.warn(\n",
      "C:\\ProgramData\\anaconda3\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1382: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=6.\n",
      "  warnings.warn(\n",
      "C:\\ProgramData\\anaconda3\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1382: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=6.\n",
      "  warnings.warn(\n",
      "C:\\ProgramData\\anaconda3\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1382: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=6.\n",
      "  warnings.warn(\n",
      "C:\\ProgramData\\anaconda3\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1382: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=6.\n",
      "  warnings.warn(\n",
      "C:\\ProgramData\\anaconda3\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1382: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=6.\n",
      "  warnings.warn(\n",
      "C:\\ProgramData\\anaconda3\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1382: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=6.\n",
      "  warnings.warn(\n",
      "C:\\ProgramData\\anaconda3\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1382: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=6.\n",
      "  warnings.warn(\n",
      "C:\\ProgramData\\anaconda3\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1382: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=6.\n",
      "  warnings.warn(\n",
      "C:\\ProgramData\\anaconda3\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1382: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=6.\n",
      "  warnings.warn(\n",
      "C:\\ProgramData\\anaconda3\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1382: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=6.\n",
      "  warnings.warn(\n",
      "C:\\ProgramData\\anaconda3\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1382: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=6.\n",
      "  warnings.warn(\n",
      "C:\\ProgramData\\anaconda3\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1382: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=6.\n",
      "  warnings.warn(\n",
      "C:\\ProgramData\\anaconda3\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1382: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=6.\n",
      "  warnings.warn(\n",
      "C:\\ProgramData\\anaconda3\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1382: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=6.\n",
      "  warnings.warn(\n",
      "C:\\ProgramData\\anaconda3\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1382: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=6.\n",
      "  warnings.warn(\n",
      "C:\\ProgramData\\anaconda3\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1382: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=6.\n",
      "  warnings.warn(\n",
      "C:\\ProgramData\\anaconda3\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1382: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=6.\n",
      "  warnings.warn(\n",
      "C:\\ProgramData\\anaconda3\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1382: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=6.\n",
      "  warnings.warn(\n",
      "C:\\ProgramData\\anaconda3\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1382: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=6.\n",
      "  warnings.warn(\n",
      "C:\\ProgramData\\anaconda3\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1382: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=6.\n",
      "  warnings.warn(\n",
      "C:\\ProgramData\\anaconda3\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1382: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=6.\n",
      "  warnings.warn(\n",
      "C:\\ProgramData\\anaconda3\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1382: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=6.\n",
      "  warnings.warn(\n",
      "C:\\ProgramData\\anaconda3\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1382: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=6.\n",
      "  warnings.warn(\n",
      "C:\\ProgramData\\anaconda3\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1382: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=6.\n",
      "  warnings.warn(\n",
      "C:\\ProgramData\\anaconda3\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1382: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=6.\n",
      "  warnings.warn(\n",
      "C:\\ProgramData\\anaconda3\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1382: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=6.\n",
      "  warnings.warn(\n",
      "C:\\ProgramData\\anaconda3\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1382: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=6.\n",
      "  warnings.warn(\n",
      "C:\\ProgramData\\anaconda3\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1382: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=6.\n",
      "  warnings.warn(\n",
      "C:\\ProgramData\\anaconda3\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1382: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=6.\n",
      "  warnings.warn(\n",
      "C:\\ProgramData\\anaconda3\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1382: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=6.\n",
      "  warnings.warn(\n",
      "C:\\ProgramData\\anaconda3\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1382: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=6.\n",
      "  warnings.warn(\n",
      "C:\\ProgramData\\anaconda3\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1382: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=6.\n",
      "  warnings.warn(\n",
      "C:\\ProgramData\\anaconda3\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1382: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=6.\n",
      "  warnings.warn(\n",
      "C:\\ProgramData\\anaconda3\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1382: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=6.\n",
      "  warnings.warn(\n",
      "C:\\ProgramData\\anaconda3\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1382: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=6.\n",
      "  warnings.warn(\n",
      "C:\\ProgramData\\anaconda3\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1382: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=6.\n",
      "  warnings.warn(\n",
      "C:\\ProgramData\\anaconda3\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1382: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=6.\n",
      "  warnings.warn(\n"
     ]
    }
   ],
   "source": [
    "np.random.seed(1234)\n",
    "cluster_range = range(2, 9)\n",
    "n_bootstraps = 100\n",
    "bootstrapped_ari_values = []\n",
    "\n",
    "original_labels_dict = {}\n",
    "for n_clusters in cluster_range:\n",
    "    kmeans = KMeans(n_clusters=n_clusters, n_init=10, random_state=1234).fit(MD_x_np)\n",
    "    original_labels_dict[n_clusters] = kmeans.labels_\n",
    "\n",
    "for n_clusters in cluster_range:\n",
    "    ari_values = []\n",
    "    for _ in range(n_bootstraps):\n",
    "        resampled_data, resampled_indices = resample(MD_x_np, np.arange(len(MD_x_np)))\n",
    "        kmeans_resampled = KMeans(n_clusters=n_clusters, n_init=10, random_state=1234)\n",
    "        resampled_cluster_labels = kmeans_resampled.fit_predict(resampled_data)\n",
    "        original_labels_resampled = original_labels_dict[n_clusters][resampled_indices]\n",
    "        ari = adjusted_rand_score(original_labels_resampled, resampled_cluster_labels)\n",
    "        ari_values.append(ari)\n",
    "    bootstrapped_ari_values.append(ari_values)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "a270718f",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10, 6))\n",
    "plt.boxplot(bootstrapped_ari_values, labels=cluster_range)\n",
    "plt.xlabel('Number of Segments')\n",
    "plt.ylabel('Adjusted Rand Index')\n",
    "plt.title('Box Plot of Adjusted Rand Index for Each Number of Segments')\n",
    "plt.ylim(0.4, 1.0) \n",
    "plt.grid(True)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "19947f0d",
   "metadata": {},
   "outputs": [],
   "source": [
    "cluster_4_data = MD_x[df['cluster_num'] == 3] "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "3f7b7671",
   "metadata": {},
   "outputs": [],
   "source": [
    "similarity_scores = cluster_4_data.mean(axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "f4c7edbf",
   "metadata": {},
   "outputs": [],
   "source": [
    "num_clusters = 4"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "29e8dee1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1400x1000 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, axes = plt.subplots(2, 2, figsize=(14, 10)) \n",
    "for cluster in range(num_clusters):\n",
    "    cluster_data = MD_x[df['cluster_num'] == cluster]\n",
    "    similarity_scores = cluster_data.mean(axis=1)\n",
    "    ax = axes[cluster // 2, cluster % 2]\n",
    "    ax.hist(similarity_scores, bins=30, range=(0.2, 0.8), edgecolor='black', alpha=0.7, \n",
    "            weights=np.ones(len(similarity_scores)) / len(similarity_scores) * 100)\n",
    "    ax.set_xlim(0.2, 0.8)\n",
    "    ax.set_ylim(0, 30)\n",
    "    ax.set_xlabel('Similarity')\n",
    "    ax.set_ylabel('Percent of Total')\n",
    "    ax.set_title(f'Cluster {cluster + 1}')\n",
    "    ax.grid(True)\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "cb13a9b0",
   "metadata": {},
   "outputs": [],
   "source": [
    "MD_k4 = kmeans.labels_\n",
    "silhouette_vals = silhouette_samples(MD_x, MD_k4)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "a2645376",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "cluster_silhouette_vals = [silhouette_vals[MD_k4 == i] for i in range(4)]\n",
    "plt.figure(figsize=(10, 6))\n",
    "plt.boxplot(cluster_silhouette_vals, notch=True, patch_artist=True)\n",
    "plt.axhline(y=np.mean(silhouette_vals), color=\"red\", linestyle=\"--\")\n",
    "plt.ylim(0, 1)\n",
    "plt.xlabel('Segment Number')\n",
    "plt.ylabel('Segment Stability ')\n",
    "plt.title('Segment level stability within solutions (SLSW) plot for the fast food data set')\n",
    "plt.xticks([1, 2, 3, 4], ['1', '2', '3', '4']) \n",
    "plt.grid(True)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "56918d7c",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Using Mixtures of Distributions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "438f207e",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\ProgramData\\anaconda3\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1382: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=6.\n",
      "  warnings.warn(\n",
      "C:\\ProgramData\\anaconda3\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1382: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=6.\n",
      "  warnings.warn(\n",
      "C:\\ProgramData\\anaconda3\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1382: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=6.\n",
      "  warnings.warn(\n",
      "C:\\ProgramData\\anaconda3\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1382: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=6.\n",
      "  warnings.warn(\n",
      "C:\\ProgramData\\anaconda3\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1382: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=6.\n",
      "  warnings.warn(\n",
      "C:\\ProgramData\\anaconda3\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1382: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=6.\n",
      "  warnings.warn(\n",
      "C:\\ProgramData\\anaconda3\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1382: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=6.\n",
      "  warnings.warn(\n"
     ]
    }
   ],
   "source": [
    "aic_values = []\n",
    "bic_values = []\n",
    "icl_values = []\n",
    "\n",
    "for k in range(2, 9):\n",
    "    gmm = GaussianMixture(n_components=k, covariance_type='full', random_state=1234)\n",
    "    gmm.fit(MD_x)\n",
    "    aic_values.append(gmm.aic(MD_x))\n",
    "    bic_values.append(gmm.bic(MD_x))\n",
    "    icl_values.append(gmm.lower_bound_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "88ccd12a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 800x550 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(range(2, 9), aic_values, label='AIC')\n",
    "plt.plot(range(2, 9), bic_values, label='BIC')\n",
    "plt.plot(range(2, 9), icl_values, label='ICL')\n",
    "plt.xlabel('Number of Components')\n",
    "plt.ylabel('Information Criteria Value')\n",
    "plt.title('Information Criteria for Gaussian Mixture Models')\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "b61f65d8",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\ProgramData\\anaconda3\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1382: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=6.\n",
      "  warnings.warn(\n",
      "C:\\ProgramData\\anaconda3\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
      "  warnings.warn(\n",
      "C:\\ProgramData\\anaconda3\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1382: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=6.\n",
      "  warnings.warn(\n"
     ]
    }
   ],
   "source": [
    "num_segments = 4\n",
    "gmm = GaussianMixture(n_components=num_segments, covariance_type='full', random_state=1234)\n",
    "gmm.fit(MD_x)\n",
    "gmm_clusters = gmm.predict(MD_x)\n",
    "kmeans = KMeans(n_clusters=num_segments, random_state=1234).fit(MD_x)\n",
    "kmeans_clusters = kmeans.labels_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "cd84651c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "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>mixture</th>\n",
       "      <th>0</th>\n",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
       "      <th>3</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>kmeans</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>3</td>\n",
       "      <td>123</td>\n",
       "      <td>7</td>\n",
       "      <td>380</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>6</td>\n",
       "      <td>6</td>\n",
       "      <td>261</td>\n",
       "      <td>93</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>202</td>\n",
       "      <td>0</td>\n",
       "      <td>24</td>\n",
       "      <td>16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>18</td>\n",
       "      <td>74</td>\n",
       "      <td>6</td>\n",
       "      <td>234</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "mixture    0    1    2    3\n",
       "kmeans                     \n",
       "0          3  123    7  380\n",
       "1          6    6  261   93\n",
       "2        202    0   24   16\n",
       "3         18   74    6  234"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cross_tab = pd.crosstab(index=kmeans_clusters, columns=gmm_clusters, rownames=['kmeans'], colnames=['mixture'])\n",
    "cross_tab"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 113,
   "id": "e04269e8",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Using Mixtures of Regression Models"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "id": "e221e0be",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "4      58\n",
       "5      59\n",
       "7      71\n",
       "6      73\n",
       "10    143\n",
       "9     152\n",
       "0     152\n",
       "3     160\n",
       "8     169\n",
       "1     187\n",
       "2     229\n",
       "Name: Like, dtype: int64"
      ]
     },
     "execution_count": 69,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "like_counts = df['Like'].value_counts()\n",
    "reversed_like_counts = like_counts.iloc[::-1]\n",
    "reversed_like_counts"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "id": "62418801",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " 4    229\n",
      " 5    187\n",
      "-2    169\n",
      " 3    160\n",
      " 6    152\n",
      "-3    152\n",
      "-4    143\n",
      " 0     73\n",
      "-1     71\n",
      " 1     59\n",
      " 2     58\n",
      "Name: Like.n, dtype: int64\n"
     ]
    }
   ],
   "source": [
    "df['Like.n'] = 6 - df['Like'].astype(int)\n",
    "print(df['Like.n'].value_counts())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "id": "4b9b7630",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'Like.n ~ yummy + convenient + spicy + fattening + greasy + fast + cheap + tasty + expensive + healthy + disgusting'"
      ]
     },
     "execution_count": 73,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "formula = \"Like.n ~ \" + \" + \".join(df.columns[:11])\n",
    "formula"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 119,
   "id": "f237bd60",
   "metadata": {},
   "outputs": [],
   "source": [
    "#step-6 Profiling segments"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "a6c99367",
   "metadata": {},
   "outputs": [],
   "source": [
    "MD_vclust = linkage(MD_x.T, method='complete')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "17086aa6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10, 6))\n",
    "dendrogram(MD_vclust, labels=MD_x.columns)\n",
    "plt.title('Hierarchical Clustering Dendrogram')\n",
    "plt.xlabel('Variables')\n",
    "plt.ylabel('Distance')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "88a08cb3",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\ProgramData\\anaconda3\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
      "  warnings.warn(\n",
      "C:\\ProgramData\\anaconda3\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1382: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=6.\n",
      "  warnings.warn(\n"
     ]
    }
   ],
   "source": [
    "kmeans = KMeans(n_clusters=4, random_state=1234)\n",
    "MD_k4 = kmeans.fit_predict(MD_x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "ed16b725",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 800x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(8, 6))\n",
    "plt.hist(MD_k4, bins=4, color='purple', edgecolor='black', alpha=0.7)\n",
    "plt.title('Cluster Membership')\n",
    "plt.xlabel('Cluster')\n",
    "plt.ylabel('Count')\n",
    "plt.xticks(range(4))\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "2a0adfab",
   "metadata": {},
   "outputs": [],
   "source": [
    "pca = PCA(n_components=2)\n",
    "MD_pca = pca.fit_transform(MD_x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "65bb5933",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 800x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(8, 6))\n",
    "for i in range(4):\n",
    "    plt.scatter(MD_pca[MD_k4 == i, 0], MD_pca[MD_k4 == i, 1], label=f'Cluster {i+1}')\n",
    "plt.xlabel('Principal Component 1')\n",
    "plt.ylabel('Principal Component 2')\n",
    "plt.title('Scatter Plot of PCA with Cluster Membership')\n",
    "plt.legend()\n",
    "plt.grid(True)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "dd63123a",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                 PC1       PC2\n",
      "yummy      -0.476933  0.363790\n",
      "convenient -0.155332  0.016414\n",
      "spicy      -0.006356  0.018809\n",
      "fattening   0.116232 -0.034094\n",
      "greasy      0.304443 -0.063839\n",
      "fast       -0.108493 -0.086972\n",
      "cheap      -0.337186 -0.610633\n",
      "tasty      -0.471514  0.307318\n",
      "expensive   0.329042  0.601286\n",
      "healthy    -0.213711  0.076593\n",
      "disgusting  0.374753 -0.139656\n"
     ]
    }
   ],
   "source": [
    "proj_axes = pca.components_.T\n",
    "proj_axes_df = pd.DataFrame(proj_axes, columns=['PC1', 'PC2'], index=MD_x.columns)\n",
    "print(proj_axes_df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "28bfa070",
   "metadata": {},
   "outputs": [],
   "source": [
    "#step-7 Describing Segments"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "id": "0fb1faeb",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
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       "\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>Like</th>\n",
       "      <th>+1</th>\n",
       "      <th>+2</th>\n",
       "      <th>+3</th>\n",
       "      <th>+4</th>\n",
       "      <th>-1</th>\n",
       "      <th>-2</th>\n",
       "      <th>-3</th>\n",
       "      <th>-4</th>\n",
       "      <th>0</th>\n",
       "      <th>I hate it!-5</th>\n",
       "      <th>I love it!+5</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>cluster_num</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>42</td>\n",
       "      <td>60</td>\n",
       "      <td>66</td>\n",
       "      <td>47</td>\n",
       "      <td>7</td>\n",
       "      <td>6</td>\n",
       "      <td>7</td>\n",
       "      <td>3</td>\n",
       "      <td>36</td>\n",
       "      <td>5</td>\n",
       "      <td>44</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>65</td>\n",
       "      <td>90</td>\n",
       "      <td>143</td>\n",
       "      <td>111</td>\n",
       "      <td>13</td>\n",
       "      <td>6</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "      <td>43</td>\n",
       "      <td>4</td>\n",
       "      <td>99</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>31</td>\n",
       "      <td>31</td>\n",
       "      <td>12</td>\n",
       "      <td>2</td>\n",
       "      <td>25</td>\n",
       "      <td>28</td>\n",
       "      <td>34</td>\n",
       "      <td>36</td>\n",
       "      <td>51</td>\n",
       "      <td>54</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>14</td>\n",
       "      <td>6</td>\n",
       "      <td>8</td>\n",
       "      <td>0</td>\n",
       "      <td>13</td>\n",
       "      <td>19</td>\n",
       "      <td>30</td>\n",
       "      <td>28</td>\n",
       "      <td>39</td>\n",
       "      <td>89</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Like         +1  +2   +3   +4  -1  -2  -3  -4   0  I hate it!-5  I love it!+5\n",
       "cluster_num                                                                  \n",
       "0            42  60   66   47   7   6   7   3  36             5            44\n",
       "1            65  90  143  111  13   6   2   4  43             4            99\n",
       "2            31  31   12    2  25  28  34  36  51            54             0\n",
       "3            14   6    8    0  13  19  30  28  39            89             0"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "crosstab =pd.crosstab(df['cluster_num'],df['Like'])\n",
    "crosstab "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "id": "bb589a04",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 700x500 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.rcParams['figure.figsize'] = (7,5)\n",
    "mosaic(crosstab.stack())\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "id": "1267a366",
   "metadata": {},
   "outputs": [],
   "source": [
    "#step-8 Selecting the Target Segments"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "id": "6c070f52",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "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>cluster_num</th>\n",
       "      <th>VisitFrequency</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>2.547988</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>2.584483</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>2.822368</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>2.654472</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   cluster_num  VisitFrequency\n",
       "0            0        2.547988\n",
       "1            1        2.584483\n",
       "2            2        2.822368\n",
       "3            3        2.654472"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['VisitFrequency'] = LabelEncoder().fit_transform(df['VisitFrequency'])\n",
    "visit = df.groupby('cluster_num')['VisitFrequency'].mean()\n",
    "visit = visit.to_frame().reset_index()\n",
    "visit"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "id": "fd3ff3ce",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "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>cluster_num</th>\n",
       "      <th>Like</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>3.798762</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>3.794828</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>5.430921</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>6.739837</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   cluster_num      Like\n",
       "0            0  3.798762\n",
       "1            1  3.794828\n",
       "2            2  5.430921\n",
       "3            3  6.739837"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['Like'] = LabelEncoder().fit_transform(df['Like'])\n",
    "Like = df.groupby('cluster_num')['Like'].mean()\n",
    "Like = Like.to_frame().reset_index()\n",
    "Like"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "id": "61f46863",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "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>cluster_num</th>\n",
       "      <th>Gender</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>0.523220</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>0.398276</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>0.411184</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>0.569106</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   cluster_num    Gender\n",
       "0            0  0.523220\n",
       "1            1  0.398276\n",
       "2            2  0.411184\n",
       "3            3  0.569106"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['Gender'] = LabelEncoder().fit_transform(df['Gender'])\n",
    "Gender = df.groupby('cluster_num')['Gender'].mean()\n",
    "Gender = Gender.to_frame().reset_index()\n",
    "Gender"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "id": "4a24e8af",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "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>cluster_num</th>\n",
       "      <th>Gender</th>\n",
       "      <th>Like</th>\n",
       "      <th>VisitFrequency</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>0.523220</td>\n",
       "      <td>3.798762</td>\n",
       "      <td>2.547988</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>0.398276</td>\n",
       "      <td>3.794828</td>\n",
       "      <td>2.584483</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>0.411184</td>\n",
       "      <td>5.430921</td>\n",
       "      <td>2.822368</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>0.569106</td>\n",
       "      <td>6.739837</td>\n",
       "      <td>2.654472</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   cluster_num    Gender      Like  VisitFrequency\n",
       "0            0  0.523220  3.798762        2.547988\n",
       "1            1  0.398276  3.794828        2.584483\n",
       "2            2  0.411184  5.430921        2.822368\n",
       "3            3  0.569106  6.739837        2.654472"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "segment = Gender.merge(Like, on='cluster_num', how='left').merge(visit, on='cluster_num', how='left')\n",
    "segment"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "id": "8345167b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 900x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(9, 4))\n",
    "sns.scatterplot(x=\"VisitFrequency\", y=\"Like\", data=segment, s=400, color=\"pink\")\n",
    "plt.title(\"Simple segment evaluation plot for the fast food data set\", fontsize=15)\n",
    "plt.xlabel(\"Visit\", fontsize=12)\n",
    "plt.ylabel(\"Like\", fontsize=12)\n",
    "for i, txt in enumerate(range(1, 5)):\n",
    "    plt.text(segment.iloc[i]['VisitFrequency'], segment.iloc[i]['Like'], str(txt), fontsize=12, ha='center', va='center')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "85c7c1a9",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.10.9"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}