GMM_adult_income.ipynb

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        "<a href=\"https://colab.research.google.com/github/gabitza-tech/Machine-Learning-Collection/blob/master/GMM_adult_income.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
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        "id": "CCq4bMymepI1"
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      "source": [
        "import numpy as np \n",
        "import pandas as pd \n",
        "import matplotlib.pyplot as plt \n",
        "from pandas import DataFrame \n",
        "from sklearn.preprocessing import StandardScaler, normalize\n",
        "from sklearn.decomposition import PCA\n",
        "from sklearn.mixture import GaussianMixture \n",
        "from sklearn.metrics import silhouette_score\n",
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn import metrics\n",
        "from sklearn.preprocessing import LabelEncoder"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "raw_df = pd.read_csv('/content/adult.csv')\n",
        "raw_df = raw_df.drop('income', axis = 1) \n",
        "features_string=[\"workclass\",  \"education\",  \"marital-status\", \"occupation\", \"relationship\", \"race\", \"gender\", \"native-country\"]\n",
        "\n",
        "raw_df = raw_df.replace('?', np.NaN)\n",
        "raw_df.fillna(method ='ffill', inplace = True) \n",
        "\n",
        "print(raw_df)"
      ],
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      "outputs": [
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          "text": [
            "       age         workclass  fnlwgt     education  educational-num  \\\n",
            "0       25           Private  226802          11th                7   \n",
            "1       38           Private   89814       HS-grad                9   \n",
            "2       28         Local-gov  336951    Assoc-acdm               12   \n",
            "3       44           Private  160323  Some-college               10   \n",
            "4       18           Private  103497  Some-college               10   \n",
            "...    ...               ...     ...           ...              ...   \n",
            "38459   42           Private  341757  Some-college               10   \n",
            "38460   36           Private  218542       HS-grad                9   \n",
            "38461   44         Local-gov  190020     Bachelors               13   \n",
            "38462   27           Private  221436     Bachelors               13   \n",
            "38463   39  Self-emp-not-inc   52187       HS-grad                9   \n",
            "\n",
            "           marital-status         occupation   relationship   race  gender  \\\n",
            "0           Never-married  Machine-op-inspct      Own-child  Black    Male   \n",
            "1      Married-civ-spouse    Farming-fishing        Husband  White    Male   \n",
            "2      Married-civ-spouse    Protective-serv        Husband  White    Male   \n",
            "3      Married-civ-spouse  Machine-op-inspct        Husband  Black    Male   \n",
            "4           Never-married  Machine-op-inspct      Own-child  White  Female   \n",
            "...                   ...                ...            ...    ...     ...   \n",
            "38459       Never-married  Machine-op-inspct  Not-in-family  White  Female   \n",
            "38460           Separated       Adm-clerical      Unmarried  Black  Female   \n",
            "38461  Married-civ-spouse     Prof-specialty        Husband  White    Male   \n",
            "38462  Married-civ-spouse       Adm-clerical           Wife  White  Female   \n",
            "38463  Married-civ-spouse            Exec-ma           Wife  White  Female   \n",
            "\n",
            "       capital-gain  capital-loss  hours-per-week native-country  \n",
            "0               0.0           0.0            40.0  United-States  \n",
            "1               0.0           0.0            50.0  United-States  \n",
            "2               0.0           0.0            40.0  United-States  \n",
            "3            7688.0           0.0            40.0  United-States  \n",
            "4               0.0           0.0            30.0  United-States  \n",
            "...             ...           ...             ...            ...  \n",
            "38459           0.0           0.0            40.0  United-States  \n",
            "38460           0.0           0.0            40.0  United-States  \n",
            "38461           0.0           0.0            50.0  United-States  \n",
            "38462           0.0           0.0            40.0           Cuba  \n",
            "38463           0.0           0.0            40.0           Cuba  \n",
            "\n",
            "[38464 rows x 14 columns]\n"
          ]
        }
      ]
    },
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      "cell_type": "code",
      "source": [
        "labelEncoder = LabelEncoder()\n",
        "for feature in features_string:\n",
        "  print(feature)\n",
        "  labelEncoder.fit(raw_df[feature])\n",
        "  raw_df[feature] = labelEncoder.transform(raw_df[feature])\n",
        "\n",
        "print(raw_df)"
      ],
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      "execution_count": 6,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "workclass\n",
            "education\n",
            "marital-status\n",
            "occupation\n",
            "relationship\n",
            "race\n",
            "gender\n",
            "native-country\n",
            "       age  workclass  fnlwgt  education  educational-num  marital-status  \\\n",
            "0       25          3  226802          1                7               4   \n",
            "1       38          3   89814         11                9               2   \n",
            "2       28          1  336951          7               12               2   \n",
            "3       44          3  160323         15               10               2   \n",
            "4       18          3  103497         15               10               4   \n",
            "...    ...        ...     ...        ...              ...             ...   \n",
            "38459   42          3  341757         15               10               4   \n",
            "38460   36          3  218542         11                9               5   \n",
            "38461   44          1  190020          9               13               2   \n",
            "38462   27          3  221436          9               13               2   \n",
            "38463   39          5   52187         11                9               2   \n",
            "\n",
            "       occupation  relationship  race  gender  capital-gain  capital-loss  \\\n",
            "0               7             3     2       1           0.0           0.0   \n",
            "1               5             0     4       1           0.0           0.0   \n",
            "2              11             0     4       1           0.0           0.0   \n",
            "3               7             0     2       1        7688.0           0.0   \n",
            "4               7             3     4       0           0.0           0.0   \n",
            "...           ...           ...   ...     ...           ...           ...   \n",
            "38459           7             1     4       0           0.0           0.0   \n",
            "38460           0             4     2       0           0.0           0.0   \n",
            "38461          10             0     4       1           0.0           0.0   \n",
            "38462           0             5     4       0           0.0           0.0   \n",
            "38463           3             5     4       0           0.0           0.0   \n",
            "\n",
            "       hours-per-week  native-country  \n",
            "0                40.0              38  \n",
            "1                50.0              38  \n",
            "2                40.0              38  \n",
            "3                40.0              38  \n",
            "4                30.0              38  \n",
            "...               ...             ...  \n",
            "38459            40.0              38  \n",
            "38460            40.0              38  \n",
            "38461            50.0              38  \n",
            "38462            40.0               4  \n",
            "38463            40.0               4  \n",
            "\n",
            "[38464 rows x 14 columns]\n"
          ]
        }
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        "# Standardize data\n",
        "scaler = StandardScaler() \n",
        "scaled_df = scaler.fit_transform(raw_df) \n",
        "  \n",
        "# Normalizing the Data \n",
        "normalized_df = normalize(scaled_df) \n",
        "  \n",
        "# Converting the numpy array into a pandas DataFrame \n",
        "normalized_df = pd.DataFrame(normalized_df) \n",
        "  \n",
        "# Reducing the dimensions of the data \n",
        "pca = PCA(n_components = 2) \n",
        "X_principal = pca.fit_transform(normalized_df) \n",
        "X_principal = pd.DataFrame(X_principal) \n",
        "X_principal.columns = ['P1', 'P2'] \n",
        "  \n",
        "X_principal.head(2)"
      ],
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      "execution_count": 7,
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              "         P1        P2\n",
              "0  0.391866 -0.084570\n",
              "1 -0.656572 -0.206337"
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        "gmm = GaussianMixture(n_components = 2) \n",
        "gmm.fit(X_principal)"
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              "GaussianMixture(n_components=2)"
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          "execution_count": 8
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        "# Visualizing the clustering \n",
        "plt.scatter(X_principal['P1'], X_principal['P2'],  \n",
        "           c = GaussianMixture(n_components = 2).fit_predict(X_principal), cmap =plt.cm.winter, alpha = 0.6) \n",
        "plt.show() "
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              "<Figure size 432x288 with 1 Axes>"
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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "def SelBest(arr:list, X:int)->list:\n",
        "    '''\n",
        "    returns the set of X configurations with shorter distance\n",
        "    '''\n",
        "    dx=np.argsort(arr)[:X]\n",
        "    return arr[dx]"
      ],
      "metadata": {
        "id": "JPqGVcEEllhH"
      },
      "execution_count": 10,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "n_clusters=np.arange(2, 8)\n",
        "sils=[]\n",
        "sils_err=[]\n",
        "iterations=20\n",
        "for n in n_clusters:\n",
        "    tmp_sil=[]\n",
        "    for _ in range(iterations):\n",
        "        gmm=GaussianMixture(n, n_init=2).fit(X_principal) \n",
        "        labels=gmm.predict(X_principal)\n",
        "        sil=metrics.silhouette_score(X_principal, labels, metric='euclidean')\n",
        "        tmp_sil.append(sil)\n",
        "    val=np.mean(SelBest(np.array(tmp_sil), int(iterations/5)))\n",
        "    err=np.std(tmp_sil)\n",
        "    sils.append(val)\n",
        "    sils_err.append(err)"
      ],
      "metadata": {
        "id": "pSfHnOtTlmQe"
      },
      "execution_count": 11,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "plt.errorbar(n_clusters, sils, yerr=sils_err)\n",
        "plt.title(\"Silhouette Scores\", fontsize=20)\n",
        "plt.xticks(n_clusters)\n",
        "plt.xlabel(\"N. of clusters\")\n",
        "plt.ylabel(\"Score\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 317
        },
        "id": "IAsnSidHlpKm",
        "outputId": "b7ee5848-b601-47f8-80b2-b114f2b87ce2"
      },
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(0, 0.5, 'Score')"
            ]
          },
          "metadata": {},
          "execution_count": 12
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#Courtesy of https://stackoverflow.com/questions/26079881/kl-divergence-of-two-gmms. Here the difference is that we take the squared root, so it's a proper metric\n",
        "\n",
        "def gmm_js(gmm_p, gmm_q, n_samples=10**5):\n",
        "    X = gmm_p.sample(n_samples)[0]\n",
        "    log_p_X = gmm_p.score_samples(X)\n",
        "    log_q_X = gmm_q.score_samples(X)\n",
        "    log_mix_X = np.logaddexp(log_p_X, log_q_X)\n",
        "\n",
        "    Y = gmm_q.sample(n_samples)[0]\n",
        "    log_p_Y = gmm_p.score_samples(Y)\n",
        "    log_q_Y = gmm_q.score_samples(Y)\n",
        "    log_mix_Y = np.logaddexp(log_p_Y, log_q_Y)\n",
        "\n",
        "    return np.sqrt((log_p_X.mean() - (log_mix_X.mean() - np.log(2))\n",
        "            + log_q_Y.mean() - (log_mix_Y.mean() - np.log(2))) / 2)"
      ],
      "metadata": {
        "id": "q2F9PfPdtDkO"
      },
      "execution_count": 13,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "n_clusters=np.arange(2, 8)\n",
        "iterations=20\n",
        "results=[]\n",
        "res_sigs=[]\n",
        "for n in n_clusters:\n",
        "    dist=[]\n",
        "    \n",
        "    for iteration in range(iterations):\n",
        "        train, test=train_test_split(X_principal, test_size=0.5)\n",
        "        \n",
        "        gmm_train=GaussianMixture(n, n_init=2).fit(train) \n",
        "        gmm_test=GaussianMixture(n, n_init=2).fit(test) \n",
        "        dist.append(gmm_js(gmm_train, gmm_test))\n",
        "    selec=SelBest(np.array(dist), int(iterations/5))\n",
        "    result=np.mean(selec)\n",
        "    res_sig=np.std(selec)\n",
        "    results.append(result)\n",
        "    res_sigs.append(res_sig)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "7d7c4Rc0tG2E",
        "outputId": "17204e4d-bf30-4308-a52b-ccb577d6de7c"
      },
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.8/dist-packages/sklearn/base.py:450: UserWarning: X does not have valid feature names, but GaussianMixture was fitted with feature names\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.8/dist-packages/sklearn/base.py:450: UserWarning: X does not have valid feature names, but GaussianMixture was fitted with feature names\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.8/dist-packages/sklearn/base.py:450: UserWarning: X does not have valid feature names, but GaussianMixture was fitted with feature names\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.8/dist-packages/sklearn/base.py:450: UserWarning: X does not have valid feature names, but GaussianMixture was fitted with feature names\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.8/dist-packages/sklearn/base.py:450: UserWarning: X does not have valid feature names, but GaussianMixture was fitted with feature names\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.8/dist-packages/sklearn/base.py:450: UserWarning: X does not have valid feature names, but GaussianMixture was fitted with feature names\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.8/dist-packages/sklearn/base.py:450: UserWarning: X does not have valid feature names, but GaussianMixture was fitted with feature names\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.8/dist-packages/sklearn/base.py:450: UserWarning: X does not have valid feature names, but GaussianMixture was fitted with feature names\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.8/dist-packages/sklearn/base.py:450: UserWarning: X does not have valid feature names, but GaussianMixture was fitted with feature names\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.8/dist-packages/sklearn/base.py:450: UserWarning: X does not have valid feature names, but GaussianMixture was fitted with feature names\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.8/dist-packages/sklearn/base.py:450: UserWarning: X does not have valid feature names, but GaussianMixture was fitted with feature names\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.8/dist-packages/sklearn/base.py:450: UserWarning: X does not have valid feature names, but GaussianMixture was fitted with feature names\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.8/dist-packages/sklearn/base.py:450: UserWarning: X does not have valid feature names, but GaussianMixture was fitted with feature names\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.8/dist-packages/sklearn/base.py:450: UserWarning: X does not have valid feature names, but GaussianMixture was fitted with feature names\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.8/dist-packages/sklearn/base.py:450: UserWarning: X does not have valid feature names, but GaussianMixture was fitted with feature names\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.8/dist-packages/sklearn/base.py:450: UserWarning: X does not have valid feature names, but GaussianMixture was fitted with feature names\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.8/dist-packages/sklearn/base.py:450: UserWarning: X does not have valid feature names, but GaussianMixture was fitted with feature names\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.8/dist-packages/sklearn/base.py:450: UserWarning: X does not have valid feature names, but GaussianMixture was fitted with feature names\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.8/dist-packages/sklearn/base.py:450: UserWarning: X does not have valid feature names, but GaussianMixture was fitted with feature names\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.8/dist-packages/sklearn/base.py:450: UserWarning: X does not have valid feature names, but GaussianMixture was fitted with feature names\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.8/dist-packages/sklearn/base.py:450: UserWarning: X does not have valid feature names, but GaussianMixture was fitted with feature names\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.8/dist-packages/sklearn/base.py:450: UserWarning: X does not have valid feature names, but GaussianMixture was fitted with feature names\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.8/dist-packages/sklearn/base.py:450: UserWarning: X does not have valid feature names, but GaussianMixture was fitted with feature names\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.8/dist-packages/sklearn/base.py:450: UserWarning: X does not have valid feature names, but GaussianMixture was fitted with feature names\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.8/dist-packages/sklearn/base.py:450: UserWarning: X does not have valid feature names, but GaussianMixture was fitted with feature names\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.8/dist-packages/sklearn/base.py:450: UserWarning: X does not have valid feature names, but GaussianMixture was fitted with feature names\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.8/dist-packages/sklearn/base.py:450: UserWarning: X does not have valid feature names, but GaussianMixture was fitted with feature names\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.8/dist-packages/sklearn/base.py:450: UserWarning: X does not have valid feature names, but GaussianMixture was fitted with feature names\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.8/dist-packages/sklearn/base.py:450: UserWarning: X does not have valid feature names, but GaussianMixture was fitted with feature names\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.8/dist-packages/sklearn/base.py:450: UserWarning: X does not have valid feature names, but GaussianMixture was fitted with feature names\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.8/dist-packages/sklearn/base.py:450: UserWarning: X does not have valid feature names, but GaussianMixture was fitted with feature names\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.8/dist-packages/sklearn/base.py:450: UserWarning: X does not have valid feature names, but GaussianMixture was fitted with feature names\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.8/dist-packages/sklearn/base.py:450: UserWarning: X does not have valid feature names, but GaussianMixture was fitted with feature names\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.8/dist-packages/sklearn/base.py:450: UserWarning: X does not have valid feature names, but GaussianMixture was fitted with feature names\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.8/dist-packages/sklearn/base.py:450: UserWarning: X does not have valid feature names, but GaussianMixture was fitted with feature names\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.8/dist-packages/sklearn/base.py:450: UserWarning: X does not have valid feature names, but GaussianMixture was fitted with feature names\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.8/dist-packages/sklearn/base.py:450: UserWarning: X does not have valid feature names, but GaussianMixture was fitted with feature names\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.8/dist-packages/sklearn/base.py:450: UserWarning: X does not have valid feature names, but GaussianMixture was fitted with feature names\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.8/dist-packages/sklearn/base.py:450: UserWarning: X does not have valid feature names, but GaussianMixture was fitted with feature names\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.8/dist-packages/sklearn/base.py:450: UserWarning: X does not have valid feature names, but GaussianMixture was fitted with feature names\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.8/dist-packages/sklearn/base.py:450: UserWarning: X does not have valid feature names, but GaussianMixture was fitted with feature names\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.8/dist-packages/sklearn/base.py:450: UserWarning: X does not have valid feature names, but GaussianMixture was fitted with feature names\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.8/dist-packages/sklearn/base.py:450: UserWarning: X does not have valid feature names, but GaussianMixture was fitted with feature names\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.8/dist-packages/sklearn/base.py:450: UserWarning: X does not have valid feature names, but GaussianMixture was fitted with feature names\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.8/dist-packages/sklearn/base.py:450: UserWarning: X does not have valid feature names, but GaussianMixture was fitted with feature names\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.8/dist-packages/sklearn/base.py:450: UserWarning: X does not have valid feature names, but GaussianMixture was fitted with feature names\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.8/dist-packages/sklearn/base.py:450: UserWarning: X does not have valid feature names, but GaussianMixture was fitted with feature names\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.8/dist-packages/sklearn/base.py:450: UserWarning: X does not have valid feature names, but GaussianMixture was fitted with feature names\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.8/dist-packages/sklearn/base.py:450: UserWarning: X does not have valid feature names, but GaussianMixture was fitted with feature names\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.8/dist-packages/sklearn/base.py:450: UserWarning: X does not have valid feature names, but GaussianMixture was fitted with feature names\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.8/dist-packages/sklearn/base.py:450: UserWarning: X does not have valid feature names, but GaussianMixture was fitted with feature names\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.8/dist-packages/sklearn/base.py:450: UserWarning: X does not have valid feature names, but GaussianMixture was fitted with feature names\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.8/dist-packages/sklearn/base.py:450: UserWarning: X does not have valid feature names, but GaussianMixture was fitted with feature names\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.8/dist-packages/sklearn/base.py:450: UserWarning: X does not have valid feature names, but GaussianMixture was fitted with feature names\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.8/dist-packages/sklearn/base.py:450: UserWarning: X does not have valid feature names, but GaussianMixture was fitted with feature names\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.8/dist-packages/sklearn/base.py:450: UserWarning: X does not have valid feature names, but GaussianMixture was fitted with feature names\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.8/dist-packages/sklearn/base.py:450: UserWarning: X does not have valid feature names, but GaussianMixture was fitted with feature names\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.8/dist-packages/sklearn/base.py:450: UserWarning: X does not have valid feature names, but GaussianMixture was fitted with feature names\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.8/dist-packages/sklearn/base.py:450: UserWarning: X does not have valid feature names, but GaussianMixture was fitted with feature names\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.8/dist-packages/sklearn/base.py:450: UserWarning: X does not have valid feature names, but GaussianMixture was fitted with feature names\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.8/dist-packages/sklearn/base.py:450: UserWarning: X does not have valid feature names, but GaussianMixture was fitted with feature names\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.8/dist-packages/sklearn/base.py:450: UserWarning: X does not have valid feature names, but GaussianMixture was fitted with feature names\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.8/dist-packages/sklearn/base.py:450: UserWarning: X does not have valid feature names, but GaussianMixture was fitted with feature names\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.8/dist-packages/sklearn/base.py:450: UserWarning: X does not have valid feature names, but GaussianMixture was fitted with feature names\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.8/dist-packages/sklearn/base.py:450: UserWarning: X does not have valid feature names, but GaussianMixture was fitted with feature names\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.8/dist-packages/sklearn/base.py:450: UserWarning: X does not have valid feature names, but GaussianMixture was fitted with feature names\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.8/dist-packages/sklearn/base.py:450: UserWarning: X does not have valid feature names, but GaussianMixture was fitted with feature names\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.8/dist-packages/sklearn/base.py:450: UserWarning: X does not have valid feature names, but GaussianMixture was fitted with feature names\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.8/dist-packages/sklearn/base.py:450: UserWarning: X does not have valid feature names, but GaussianMixture was fitted with feature names\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.8/dist-packages/sklearn/base.py:450: UserWarning: X does not have valid feature names, but GaussianMixture was fitted with feature names\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.8/dist-packages/sklearn/base.py:450: UserWarning: X does not have valid feature names, but GaussianMixture was fitted with feature names\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.8/dist-packages/sklearn/base.py:450: UserWarning: X does not have valid feature names, but GaussianMixture was fitted with feature names\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.8/dist-packages/sklearn/base.py:450: UserWarning: X does not have valid feature names, but GaussianMixture was fitted with feature names\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.8/dist-packages/sklearn/base.py:450: UserWarning: X does not have valid feature names, but GaussianMixture was fitted with feature names\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.8/dist-packages/sklearn/base.py:450: UserWarning: X does not have valid feature names, but GaussianMixture was fitted with feature names\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.8/dist-packages/sklearn/base.py:450: UserWarning: X does not have valid feature names, but GaussianMixture was fitted with feature names\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.8/dist-packages/sklearn/base.py:450: UserWarning: X does not have valid feature names, but GaussianMixture was fitted with feature names\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.8/dist-packages/sklearn/base.py:450: UserWarning: X does not have valid feature names, but GaussianMixture was fitted with feature names\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.8/dist-packages/sklearn/base.py:450: UserWarning: X does not have valid feature names, but GaussianMixture was fitted with feature names\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.8/dist-packages/sklearn/base.py:450: UserWarning: X does not have valid feature names, but GaussianMixture was fitted with feature names\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.8/dist-packages/sklearn/base.py:450: UserWarning: X does not have valid feature names, but GaussianMixture was fitted with feature names\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.8/dist-packages/sklearn/base.py:450: UserWarning: X does not have valid feature names, but GaussianMixture was fitted with feature names\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.8/dist-packages/sklearn/base.py:450: UserWarning: X does not have valid feature names, but GaussianMixture was fitted with feature names\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.8/dist-packages/sklearn/base.py:450: UserWarning: X does not have valid feature names, but GaussianMixture was fitted with feature names\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.8/dist-packages/sklearn/base.py:450: UserWarning: X does not have valid feature names, but GaussianMixture was fitted with feature names\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.8/dist-packages/sklearn/base.py:450: UserWarning: X does not have valid feature names, but GaussianMixture was fitted with feature names\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.8/dist-packages/sklearn/base.py:450: UserWarning: X does not have valid feature names, but GaussianMixture was fitted with feature names\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.8/dist-packages/sklearn/base.py:450: UserWarning: X does not have valid feature names, but GaussianMixture was fitted with feature names\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.8/dist-packages/sklearn/base.py:450: UserWarning: X does not have valid feature names, but GaussianMixture was fitted with feature names\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.8/dist-packages/sklearn/base.py:450: UserWarning: X does not have valid feature names, but GaussianMixture was fitted with feature names\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.8/dist-packages/sklearn/base.py:450: UserWarning: X does not have valid feature names, but GaussianMixture was fitted with feature names\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.8/dist-packages/sklearn/base.py:450: UserWarning: X does not have valid feature names, but GaussianMixture was fitted with feature names\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.8/dist-packages/sklearn/base.py:450: UserWarning: X does not have valid feature names, but GaussianMixture was fitted with feature names\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.8/dist-packages/sklearn/base.py:450: UserWarning: X does not have valid feature names, but GaussianMixture was fitted with feature names\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.8/dist-packages/sklearn/base.py:450: UserWarning: X does not have valid feature names, but GaussianMixture was fitted with feature names\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.8/dist-packages/sklearn/base.py:450: UserWarning: X does not have valid feature names, but GaussianMixture was fitted with feature names\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.8/dist-packages/sklearn/base.py:450: UserWarning: X does not have valid feature names, but GaussianMixture was fitted with feature names\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.8/dist-packages/sklearn/base.py:450: UserWarning: X does not have valid feature names, but GaussianMixture was fitted with feature names\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.8/dist-packages/sklearn/base.py:450: UserWarning: X does not have valid feature names, but GaussianMixture was fitted with feature names\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.8/dist-packages/sklearn/base.py:450: UserWarning: X does not have valid feature names, but GaussianMixture was fitted with feature names\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.8/dist-packages/sklearn/base.py:450: UserWarning: X does not have valid feature names, but GaussianMixture was fitted with feature names\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.8/dist-packages/sklearn/base.py:450: UserWarning: X does not have valid feature names, but GaussianMixture was fitted with feature names\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.8/dist-packages/sklearn/base.py:450: UserWarning: X does not have valid feature names, but GaussianMixture was fitted with feature names\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.8/dist-packages/sklearn/base.py:450: UserWarning: X does not have valid feature names, but GaussianMixture was fitted with feature names\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.8/dist-packages/sklearn/base.py:450: UserWarning: X does not have valid feature names, but GaussianMixture was fitted with feature names\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.8/dist-packages/sklearn/base.py:450: UserWarning: X does not have valid feature names, but GaussianMixture was fitted with feature names\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.8/dist-packages/sklearn/base.py:450: UserWarning: X does not have valid feature names, but GaussianMixture was fitted with feature names\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.8/dist-packages/sklearn/base.py:450: UserWarning: X does not have valid feature names, but GaussianMixture was fitted with feature names\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.8/dist-packages/sklearn/base.py:450: UserWarning: X does not have valid feature names, but GaussianMixture was fitted with feature names\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.8/dist-packages/sklearn/base.py:450: UserWarning: X does not have valid feature names, but GaussianMixture was fitted with feature names\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.8/dist-packages/sklearn/base.py:450: UserWarning: X does not have valid feature names, but GaussianMixture was fitted with feature names\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.8/dist-packages/sklearn/base.py:450: UserWarning: X does not have valid feature names, but GaussianMixture was fitted with feature names\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.8/dist-packages/sklearn/base.py:450: UserWarning: X does not have valid feature names, but GaussianMixture was fitted with feature names\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.8/dist-packages/sklearn/base.py:450: UserWarning: X does not have valid feature names, but GaussianMixture was fitted with feature names\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.8/dist-packages/sklearn/base.py:450: UserWarning: X does not have valid feature names, but GaussianMixture was fitted with feature names\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.8/dist-packages/sklearn/base.py:450: UserWarning: X does not have valid feature names, but GaussianMixture was fitted with feature names\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.8/dist-packages/sklearn/base.py:450: UserWarning: X does not have valid feature names, but GaussianMixture was fitted with feature names\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.8/dist-packages/sklearn/base.py:450: UserWarning: X does not have valid feature names, but GaussianMixture was fitted with feature names\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.8/dist-packages/sklearn/base.py:450: UserWarning: X does not have valid feature names, but GaussianMixture was fitted with feature names\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.8/dist-packages/sklearn/base.py:450: UserWarning: X does not have valid feature names, but GaussianMixture was fitted with feature names\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.8/dist-packages/sklearn/base.py:450: UserWarning: X does not have valid feature names, but GaussianMixture was fitted with feature names\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.8/dist-packages/sklearn/base.py:450: UserWarning: X does not have valid feature names, but GaussianMixture was fitted with feature names\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.8/dist-packages/sklearn/base.py:450: UserWarning: X does not have valid feature names, but GaussianMixture was fitted with feature names\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.8/dist-packages/sklearn/base.py:450: UserWarning: X does not have valid feature names, but GaussianMixture was fitted with feature names\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.8/dist-packages/sklearn/base.py:450: UserWarning: X does not have valid feature names, but GaussianMixture was fitted with feature names\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.8/dist-packages/sklearn/base.py:450: UserWarning: X does not have valid feature names, but GaussianMixture was fitted with feature names\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.8/dist-packages/sklearn/base.py:450: UserWarning: X does not have valid feature names, but GaussianMixture was fitted with feature names\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.8/dist-packages/sklearn/base.py:450: UserWarning: X does not have valid feature names, but GaussianMixture was fitted with feature names\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.8/dist-packages/sklearn/base.py:450: UserWarning: X does not have valid feature names, but GaussianMixture was fitted with feature names\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.8/dist-packages/sklearn/base.py:450: UserWarning: X does not have valid feature names, but GaussianMixture was fitted with feature names\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.8/dist-packages/sklearn/base.py:450: UserWarning: X does not have valid feature names, but GaussianMixture was fitted with feature names\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.8/dist-packages/sklearn/base.py:450: UserWarning: X does not have valid feature names, but GaussianMixture was fitted with feature names\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.8/dist-packages/sklearn/base.py:450: UserWarning: X does not have valid feature names, but GaussianMixture was fitted with feature names\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.8/dist-packages/sklearn/base.py:450: UserWarning: X does not have valid feature names, but GaussianMixture was fitted with feature names\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.8/dist-packages/sklearn/base.py:450: UserWarning: X does not have valid feature names, but GaussianMixture was fitted with feature names\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.8/dist-packages/sklearn/base.py:450: UserWarning: X does not have valid feature names, but GaussianMixture was fitted with feature names\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.8/dist-packages/sklearn/base.py:450: UserWarning: X does not have valid feature names, but GaussianMixture was fitted with feature names\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.8/dist-packages/sklearn/base.py:450: UserWarning: X does not have valid feature names, but GaussianMixture was fitted with feature names\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.8/dist-packages/sklearn/base.py:450: UserWarning: X does not have valid feature names, but GaussianMixture was fitted with feature names\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.8/dist-packages/sklearn/base.py:450: UserWarning: X does not have valid feature names, but GaussianMixture was fitted with feature names\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.8/dist-packages/sklearn/base.py:450: UserWarning: X does not have valid feature names, but GaussianMixture was fitted with feature names\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.8/dist-packages/sklearn/base.py:450: UserWarning: X does not have valid feature names, but GaussianMixture was fitted with feature names\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.8/dist-packages/sklearn/base.py:450: UserWarning: X does not have valid feature names, but GaussianMixture was fitted with feature names\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.8/dist-packages/sklearn/base.py:450: UserWarning: X does not have valid feature names, but GaussianMixture was fitted with feature names\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.8/dist-packages/sklearn/base.py:450: UserWarning: X does not have valid feature names, but GaussianMixture was fitted with feature names\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.8/dist-packages/sklearn/base.py:450: UserWarning: X does not have valid feature names, but GaussianMixture was fitted with feature names\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.8/dist-packages/sklearn/base.py:450: UserWarning: X does not have valid feature names, but GaussianMixture was fitted with feature names\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.8/dist-packages/sklearn/base.py:450: UserWarning: X does not have valid feature names, but GaussianMixture was fitted with feature names\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.8/dist-packages/sklearn/base.py:450: UserWarning: X does not have valid feature names, but GaussianMixture was fitted with feature names\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.8/dist-packages/sklearn/base.py:450: UserWarning: X does not have valid feature names, but GaussianMixture was fitted with feature names\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.8/dist-packages/sklearn/base.py:450: UserWarning: X does not have valid feature names, but GaussianMixture was fitted with feature names\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.8/dist-packages/sklearn/base.py:450: UserWarning: X does not have valid feature names, but GaussianMixture was fitted with feature names\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.8/dist-packages/sklearn/base.py:450: UserWarning: X does not have valid feature names, but GaussianMixture was fitted with feature names\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.8/dist-packages/sklearn/base.py:450: UserWarning: X does not have valid feature names, but GaussianMixture was fitted with feature names\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.8/dist-packages/sklearn/base.py:450: UserWarning: X does not have valid feature names, but GaussianMixture was fitted with feature names\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.8/dist-packages/sklearn/base.py:450: UserWarning: X does not have valid feature names, but GaussianMixture was fitted with feature names\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.8/dist-packages/sklearn/base.py:450: UserWarning: X does not have valid feature names, but GaussianMixture was fitted with feature names\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.8/dist-packages/sklearn/base.py:450: UserWarning: X does not have valid feature names, but GaussianMixture was fitted with feature names\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.8/dist-packages/sklearn/base.py:450: UserWarning: X does not have valid feature names, but GaussianMixture was fitted with feature names\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.8/dist-packages/sklearn/base.py:450: UserWarning: X does not have valid feature names, but GaussianMixture was fitted with feature names\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.8/dist-packages/sklearn/base.py:450: UserWarning: X does not have valid feature names, but GaussianMixture was fitted with feature names\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.8/dist-packages/sklearn/base.py:450: UserWarning: X does not have valid feature names, but GaussianMixture was fitted with feature names\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.8/dist-packages/sklearn/base.py:450: UserWarning: X does not have valid feature names, but GaussianMixture was fitted with feature names\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.8/dist-packages/sklearn/base.py:450: UserWarning: X does not have valid feature names, but GaussianMixture was fitted with feature names\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.8/dist-packages/sklearn/base.py:450: UserWarning: X does not have valid feature names, but GaussianMixture was fitted with feature names\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.8/dist-packages/sklearn/base.py:450: UserWarning: X does not have valid feature names, but GaussianMixture was fitted with feature names\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.8/dist-packages/sklearn/base.py:450: UserWarning: X does not have valid feature names, but GaussianMixture was fitted with feature names\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.8/dist-packages/sklearn/base.py:450: UserWarning: X does not have valid feature names, but GaussianMixture was fitted with feature names\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.8/dist-packages/sklearn/base.py:450: UserWarning: X does not have valid feature names, but GaussianMixture was fitted with feature names\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.8/dist-packages/sklearn/base.py:450: UserWarning: X does not have valid feature names, but GaussianMixture was fitted with feature names\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.8/dist-packages/sklearn/base.py:450: UserWarning: X does not have valid feature names, but GaussianMixture was fitted with feature names\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.8/dist-packages/sklearn/base.py:450: UserWarning: X does not have valid feature names, but GaussianMixture was fitted with feature names\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.8/dist-packages/sklearn/base.py:450: UserWarning: X does not have valid feature names, but GaussianMixture was fitted with feature names\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.8/dist-packages/sklearn/base.py:450: UserWarning: X does not have valid feature names, but GaussianMixture was fitted with feature names\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.8/dist-packages/sklearn/base.py:450: UserWarning: X does not have valid feature names, but GaussianMixture was fitted with feature names\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.8/dist-packages/sklearn/base.py:450: UserWarning: X does not have valid feature names, but GaussianMixture was fitted with feature names\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.8/dist-packages/sklearn/base.py:450: UserWarning: X does not have valid feature names, but GaussianMixture was fitted with feature names\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.8/dist-packages/sklearn/base.py:450: UserWarning: X does not have valid feature names, but GaussianMixture was fitted with feature names\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.8/dist-packages/sklearn/base.py:450: UserWarning: X does not have valid feature names, but GaussianMixture was fitted with feature names\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.8/dist-packages/sklearn/base.py:450: UserWarning: X does not have valid feature names, but GaussianMixture was fitted with feature names\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.8/dist-packages/sklearn/base.py:450: UserWarning: X does not have valid feature names, but GaussianMixture was fitted with feature names\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.8/dist-packages/sklearn/base.py:450: UserWarning: X does not have valid feature names, but GaussianMixture was fitted with feature names\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.8/dist-packages/sklearn/base.py:450: UserWarning: X does not have valid feature names, but GaussianMixture was fitted with feature names\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.8/dist-packages/sklearn/base.py:450: UserWarning: X does not have valid feature names, but GaussianMixture was fitted with feature names\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.8/dist-packages/sklearn/base.py:450: UserWarning: X does not have valid feature names, but GaussianMixture was fitted with feature names\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.8/dist-packages/sklearn/base.py:450: UserWarning: X does not have valid feature names, but GaussianMixture was fitted with feature names\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.8/dist-packages/sklearn/base.py:450: UserWarning: X does not have valid feature names, but GaussianMixture was fitted with feature names\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.8/dist-packages/sklearn/base.py:450: UserWarning: X does not have valid feature names, but GaussianMixture was fitted with feature names\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.8/dist-packages/sklearn/base.py:450: UserWarning: X does not have valid feature names, but GaussianMixture was fitted with feature names\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.8/dist-packages/sklearn/base.py:450: UserWarning: X does not have valid feature names, but GaussianMixture was fitted with feature names\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.8/dist-packages/sklearn/base.py:450: UserWarning: X does not have valid feature names, but GaussianMixture was fitted with feature names\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.8/dist-packages/sklearn/base.py:450: UserWarning: X does not have valid feature names, but GaussianMixture was fitted with feature names\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.8/dist-packages/sklearn/base.py:450: UserWarning: X does not have valid feature names, but GaussianMixture was fitted with feature names\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.8/dist-packages/sklearn/base.py:450: UserWarning: X does not have valid feature names, but GaussianMixture was fitted with feature names\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.8/dist-packages/sklearn/base.py:450: UserWarning: X does not have valid feature names, but GaussianMixture was fitted with feature names\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.8/dist-packages/sklearn/base.py:450: UserWarning: X does not have valid feature names, but GaussianMixture was fitted with feature names\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.8/dist-packages/sklearn/base.py:450: UserWarning: X does not have valid feature names, but GaussianMixture was fitted with feature names\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.8/dist-packages/sklearn/base.py:450: UserWarning: X does not have valid feature names, but GaussianMixture was fitted with feature names\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.8/dist-packages/sklearn/base.py:450: UserWarning: X does not have valid feature names, but GaussianMixture was fitted with feature names\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.8/dist-packages/sklearn/base.py:450: UserWarning: X does not have valid feature names, but GaussianMixture was fitted with feature names\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.8/dist-packages/sklearn/base.py:450: UserWarning: X does not have valid feature names, but GaussianMixture was fitted with feature names\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.8/dist-packages/sklearn/base.py:450: UserWarning: X does not have valid feature names, but GaussianMixture was fitted with feature names\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.8/dist-packages/sklearn/base.py:450: UserWarning: X does not have valid feature names, but GaussianMixture was fitted with feature names\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.8/dist-packages/sklearn/base.py:450: UserWarning: X does not have valid feature names, but GaussianMixture was fitted with feature names\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.8/dist-packages/sklearn/base.py:450: UserWarning: X does not have valid feature names, but GaussianMixture was fitted with feature names\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.8/dist-packages/sklearn/base.py:450: UserWarning: X does not have valid feature names, but GaussianMixture was fitted with feature names\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.8/dist-packages/sklearn/base.py:450: UserWarning: X does not have valid feature names, but GaussianMixture was fitted with feature names\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.8/dist-packages/sklearn/base.py:450: UserWarning: X does not have valid feature names, but GaussianMixture was fitted with feature names\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.8/dist-packages/sklearn/base.py:450: UserWarning: X does not have valid feature names, but GaussianMixture was fitted with feature names\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.8/dist-packages/sklearn/base.py:450: UserWarning: X does not have valid feature names, but GaussianMixture was fitted with feature names\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.8/dist-packages/sklearn/base.py:450: UserWarning: X does not have valid feature names, but GaussianMixture was fitted with feature names\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.8/dist-packages/sklearn/base.py:450: UserWarning: X does not have valid feature names, but GaussianMixture was fitted with feature names\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.8/dist-packages/sklearn/base.py:450: UserWarning: X does not have valid feature names, but GaussianMixture was fitted with feature names\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.8/dist-packages/sklearn/base.py:450: UserWarning: X does not have valid feature names, but GaussianMixture was fitted with feature names\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.8/dist-packages/sklearn/base.py:450: UserWarning: X does not have valid feature names, but GaussianMixture was fitted with feature names\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.8/dist-packages/sklearn/base.py:450: UserWarning: X does not have valid feature names, but GaussianMixture was fitted with feature names\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.8/dist-packages/sklearn/base.py:450: UserWarning: X does not have valid feature names, but GaussianMixture was fitted with feature names\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.8/dist-packages/sklearn/base.py:450: UserWarning: X does not have valid feature names, but GaussianMixture was fitted with feature names\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.8/dist-packages/sklearn/base.py:450: UserWarning: X does not have valid feature names, but GaussianMixture was fitted with feature names\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.8/dist-packages/sklearn/base.py:450: UserWarning: X does not have valid feature names, but GaussianMixture was fitted with feature names\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.8/dist-packages/sklearn/base.py:450: UserWarning: X does not have valid feature names, but GaussianMixture was fitted with feature names\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.8/dist-packages/sklearn/base.py:450: UserWarning: X does not have valid feature names, but GaussianMixture was fitted with feature names\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.8/dist-packages/sklearn/base.py:450: UserWarning: X does not have valid feature names, but GaussianMixture was fitted with feature names\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.8/dist-packages/sklearn/base.py:450: UserWarning: X does not have valid feature names, but GaussianMixture was fitted with feature names\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.8/dist-packages/sklearn/base.py:450: UserWarning: X does not have valid feature names, but GaussianMixture was fitted with feature names\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.8/dist-packages/sklearn/base.py:450: UserWarning: X does not have valid feature names, but GaussianMixture was fitted with feature names\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.8/dist-packages/sklearn/base.py:450: UserWarning: X does not have valid feature names, but GaussianMixture was fitted with feature names\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.8/dist-packages/sklearn/base.py:450: UserWarning: X does not have valid feature names, but GaussianMixture was fitted with feature names\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.8/dist-packages/sklearn/base.py:450: UserWarning: X does not have valid feature names, but GaussianMixture was fitted with feature names\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.8/dist-packages/sklearn/base.py:450: UserWarning: X does not have valid feature names, but GaussianMixture was fitted with feature names\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.8/dist-packages/sklearn/base.py:450: UserWarning: X does not have valid feature names, but GaussianMixture was fitted with feature names\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.8/dist-packages/sklearn/base.py:450: UserWarning: X does not have valid feature names, but GaussianMixture was fitted with feature names\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.8/dist-packages/sklearn/base.py:450: UserWarning: X does not have valid feature names, but GaussianMixture was fitted with feature names\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.8/dist-packages/sklearn/base.py:450: UserWarning: X does not have valid feature names, but GaussianMixture was fitted with feature names\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.8/dist-packages/sklearn/base.py:450: UserWarning: X does not have valid feature names, but GaussianMixture was fitted with feature names\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.8/dist-packages/sklearn/base.py:450: UserWarning: X does not have valid feature names, but GaussianMixture was fitted with feature names\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.8/dist-packages/sklearn/base.py:450: UserWarning: X does not have valid feature names, but GaussianMixture was fitted with feature names\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.8/dist-packages/sklearn/base.py:450: UserWarning: X does not have valid feature names, but GaussianMixture was fitted with feature names\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.8/dist-packages/sklearn/base.py:450: UserWarning: X does not have valid feature names, but GaussianMixture was fitted with feature names\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.8/dist-packages/sklearn/base.py:450: UserWarning: X does not have valid feature names, but GaussianMixture was fitted with feature names\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.8/dist-packages/sklearn/base.py:450: UserWarning: X does not have valid feature names, but GaussianMixture was fitted with feature names\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.8/dist-packages/sklearn/base.py:450: UserWarning: X does not have valid feature names, but GaussianMixture was fitted with feature names\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.8/dist-packages/sklearn/base.py:450: UserWarning: X does not have valid feature names, but GaussianMixture was fitted with feature names\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.8/dist-packages/sklearn/base.py:450: UserWarning: X does not have valid feature names, but GaussianMixture was fitted with feature names\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.8/dist-packages/sklearn/base.py:450: UserWarning: X does not have valid feature names, but GaussianMixture was fitted with feature names\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.8/dist-packages/sklearn/base.py:450: UserWarning: X does not have valid feature names, but GaussianMixture was fitted with feature names\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.8/dist-packages/sklearn/base.py:450: UserWarning: X does not have valid feature names, but GaussianMixture was fitted with feature names\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.8/dist-packages/sklearn/base.py:450: UserWarning: X does not have valid feature names, but GaussianMixture was fitted with feature names\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.8/dist-packages/sklearn/base.py:450: UserWarning: X does not have valid feature names, but GaussianMixture was fitted with feature names\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.8/dist-packages/sklearn/base.py:450: UserWarning: X does not have valid feature names, but GaussianMixture was fitted with feature names\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.8/dist-packages/sklearn/base.py:450: UserWarning: X does not have valid feature names, but GaussianMixture was fitted with feature names\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.8/dist-packages/sklearn/base.py:450: UserWarning: X does not have valid feature names, but GaussianMixture was fitted with feature names\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.8/dist-packages/sklearn/base.py:450: UserWarning: X does not have valid feature names, but GaussianMixture was fitted with feature names\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.8/dist-packages/sklearn/base.py:450: UserWarning: X does not have valid feature names, but GaussianMixture was fitted with feature names\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.8/dist-packages/sklearn/base.py:450: UserWarning: X does not have valid feature names, but GaussianMixture was fitted with feature names\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.8/dist-packages/sklearn/base.py:450: UserWarning: X does not have valid feature names, but GaussianMixture was fitted with feature names\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.8/dist-packages/sklearn/base.py:450: UserWarning: X does not have valid feature names, but GaussianMixture was fitted with feature names\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.8/dist-packages/sklearn/base.py:450: UserWarning: X does not have valid feature names, but GaussianMixture was fitted with feature names\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.8/dist-packages/sklearn/base.py:450: UserWarning: X does not have valid feature names, but GaussianMixture was fitted with feature names\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.8/dist-packages/sklearn/base.py:450: UserWarning: X does not have valid feature names, but GaussianMixture was fitted with feature names\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.8/dist-packages/sklearn/base.py:450: UserWarning: X does not have valid feature names, but GaussianMixture was fitted with feature names\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.8/dist-packages/sklearn/base.py:450: UserWarning: X does not have valid feature names, but GaussianMixture was fitted with feature names\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.8/dist-packages/sklearn/base.py:450: UserWarning: X does not have valid feature names, but GaussianMixture was fitted with feature names\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.8/dist-packages/sklearn/base.py:450: UserWarning: X does not have valid feature names, but GaussianMixture was fitted with feature names\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.8/dist-packages/sklearn/base.py:450: UserWarning: X does not have valid feature names, but GaussianMixture was fitted with feature names\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.8/dist-packages/sklearn/base.py:450: UserWarning: X does not have valid feature names, but GaussianMixture was fitted with feature names\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.8/dist-packages/sklearn/base.py:450: UserWarning: X does not have valid feature names, but GaussianMixture was fitted with feature names\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.8/dist-packages/sklearn/base.py:450: UserWarning: X does not have valid feature names, but GaussianMixture was fitted with feature names\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.8/dist-packages/sklearn/base.py:450: UserWarning: X does not have valid feature names, but GaussianMixture was fitted with feature names\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.8/dist-packages/sklearn/base.py:450: UserWarning: X does not have valid feature names, but GaussianMixture was fitted with feature names\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.8/dist-packages/sklearn/base.py:450: UserWarning: X does not have valid feature names, but GaussianMixture was fitted with feature names\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.8/dist-packages/sklearn/base.py:450: UserWarning: X does not have valid feature names, but GaussianMixture was fitted with feature names\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.8/dist-packages/sklearn/base.py:450: UserWarning: X does not have valid feature names, but GaussianMixture was fitted with feature names\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.8/dist-packages/sklearn/base.py:450: UserWarning: X does not have valid feature names, but GaussianMixture was fitted with feature names\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.8/dist-packages/sklearn/base.py:450: UserWarning: X does not have valid feature names, but GaussianMixture was fitted with feature names\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.8/dist-packages/sklearn/base.py:450: UserWarning: X does not have valid feature names, but GaussianMixture was fitted with feature names\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.8/dist-packages/sklearn/base.py:450: UserWarning: X does not have valid feature names, but GaussianMixture was fitted with feature names\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.8/dist-packages/sklearn/base.py:450: UserWarning: X does not have valid feature names, but GaussianMixture was fitted with feature names\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.8/dist-packages/sklearn/base.py:450: UserWarning: X does not have valid feature names, but GaussianMixture was fitted with feature names\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.8/dist-packages/sklearn/base.py:450: UserWarning: X does not have valid feature names, but GaussianMixture was fitted with feature names\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.8/dist-packages/sklearn/base.py:450: UserWarning: X does not have valid feature names, but GaussianMixture was fitted with feature names\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.8/dist-packages/sklearn/base.py:450: UserWarning: X does not have valid feature names, but GaussianMixture was fitted with feature names\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.8/dist-packages/sklearn/base.py:450: UserWarning: X does not have valid feature names, but GaussianMixture was fitted with feature names\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.8/dist-packages/sklearn/base.py:450: UserWarning: X does not have valid feature names, but GaussianMixture was fitted with feature names\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.8/dist-packages/sklearn/base.py:450: UserWarning: X does not have valid feature names, but GaussianMixture was fitted with feature names\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.8/dist-packages/sklearn/base.py:450: UserWarning: X does not have valid feature names, but GaussianMixture was fitted with feature names\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.8/dist-packages/sklearn/base.py:450: UserWarning: X does not have valid feature names, but GaussianMixture was fitted with feature names\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.8/dist-packages/sklearn/base.py:450: UserWarning: X does not have valid feature names, but GaussianMixture was fitted with feature names\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.8/dist-packages/sklearn/base.py:450: UserWarning: X does not have valid feature names, but GaussianMixture was fitted with feature names\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.8/dist-packages/sklearn/base.py:450: UserWarning: X does not have valid feature names, but GaussianMixture was fitted with feature names\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.8/dist-packages/sklearn/base.py:450: UserWarning: X does not have valid feature names, but GaussianMixture was fitted with feature names\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.8/dist-packages/sklearn/base.py:450: UserWarning: X does not have valid feature names, but GaussianMixture was fitted with feature names\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.8/dist-packages/sklearn/base.py:450: UserWarning: X does not have valid feature names, but GaussianMixture was fitted with feature names\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.8/dist-packages/sklearn/base.py:450: UserWarning: X does not have valid feature names, but GaussianMixture was fitted with feature names\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.8/dist-packages/sklearn/base.py:450: UserWarning: X does not have valid feature names, but GaussianMixture was fitted with feature names\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.8/dist-packages/sklearn/base.py:450: UserWarning: X does not have valid feature names, but GaussianMixture was fitted with feature names\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.8/dist-packages/sklearn/base.py:450: UserWarning: X does not have valid feature names, but GaussianMixture was fitted with feature names\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.8/dist-packages/sklearn/base.py:450: UserWarning: X does not have valid feature names, but GaussianMixture was fitted with feature names\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.8/dist-packages/sklearn/base.py:450: UserWarning: X does not have valid feature names, but GaussianMixture was fitted with feature names\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.8/dist-packages/sklearn/base.py:450: UserWarning: X does not have valid feature names, but GaussianMixture was fitted with feature names\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.8/dist-packages/sklearn/base.py:450: UserWarning: X does not have valid feature names, but GaussianMixture was fitted with feature names\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.8/dist-packages/sklearn/base.py:450: UserWarning: X does not have valid feature names, but GaussianMixture was fitted with feature names\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.8/dist-packages/sklearn/base.py:450: UserWarning: X does not have valid feature names, but GaussianMixture was fitted with feature names\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.8/dist-packages/sklearn/base.py:450: UserWarning: X does not have valid feature names, but GaussianMixture was fitted with feature names\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.8/dist-packages/sklearn/base.py:450: UserWarning: X does not have valid feature names, but GaussianMixture was fitted with feature names\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.8/dist-packages/sklearn/base.py:450: UserWarning: X does not have valid feature names, but GaussianMixture was fitted with feature names\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.8/dist-packages/sklearn/base.py:450: UserWarning: X does not have valid feature names, but GaussianMixture was fitted with feature names\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.8/dist-packages/sklearn/base.py:450: UserWarning: X does not have valid feature names, but GaussianMixture was fitted with feature names\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.8/dist-packages/sklearn/base.py:450: UserWarning: X does not have valid feature names, but GaussianMixture was fitted with feature names\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.8/dist-packages/sklearn/base.py:450: UserWarning: X does not have valid feature names, but GaussianMixture was fitted with feature names\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.8/dist-packages/sklearn/base.py:450: UserWarning: X does not have valid feature names, but GaussianMixture was fitted with feature names\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.8/dist-packages/sklearn/base.py:450: UserWarning: X does not have valid feature names, but GaussianMixture was fitted with feature names\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.8/dist-packages/sklearn/base.py:450: UserWarning: X does not have valid feature names, but GaussianMixture was fitted with feature names\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.8/dist-packages/sklearn/base.py:450: UserWarning: X does not have valid feature names, but GaussianMixture was fitted with feature names\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.8/dist-packages/sklearn/base.py:450: UserWarning: X does not have valid feature names, but GaussianMixture was fitted with feature names\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.8/dist-packages/sklearn/base.py:450: UserWarning: X does not have valid feature names, but GaussianMixture was fitted with feature names\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.8/dist-packages/sklearn/base.py:450: UserWarning: X does not have valid feature names, but GaussianMixture was fitted with feature names\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.8/dist-packages/sklearn/base.py:450: UserWarning: X does not have valid feature names, but GaussianMixture was fitted with feature names\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.8/dist-packages/sklearn/base.py:450: UserWarning: X does not have valid feature names, but GaussianMixture was fitted with feature names\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.8/dist-packages/sklearn/base.py:450: UserWarning: X does not have valid feature names, but GaussianMixture was fitted with feature names\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.8/dist-packages/sklearn/base.py:450: UserWarning: X does not have valid feature names, but GaussianMixture was fitted with feature names\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.8/dist-packages/sklearn/base.py:450: UserWarning: X does not have valid feature names, but GaussianMixture was fitted with feature names\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.8/dist-packages/sklearn/base.py:450: UserWarning: X does not have valid feature names, but GaussianMixture was fitted with feature names\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.8/dist-packages/sklearn/base.py:450: UserWarning: X does not have valid feature names, but GaussianMixture was fitted with feature names\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.8/dist-packages/sklearn/base.py:450: UserWarning: X does not have valid feature names, but GaussianMixture was fitted with feature names\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.8/dist-packages/sklearn/base.py:450: UserWarning: X does not have valid feature names, but GaussianMixture was fitted with feature names\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.8/dist-packages/sklearn/base.py:450: UserWarning: X does not have valid feature names, but GaussianMixture was fitted with feature names\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.8/dist-packages/sklearn/base.py:450: UserWarning: X does not have valid feature names, but GaussianMixture was fitted with feature names\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.8/dist-packages/sklearn/base.py:450: UserWarning: X does not have valid feature names, but GaussianMixture was fitted with feature names\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.8/dist-packages/sklearn/base.py:450: UserWarning: X does not have valid feature names, but GaussianMixture was fitted with feature names\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.8/dist-packages/sklearn/base.py:450: UserWarning: X does not have valid feature names, but GaussianMixture was fitted with feature names\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.8/dist-packages/sklearn/base.py:450: UserWarning: X does not have valid feature names, but GaussianMixture was fitted with feature names\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.8/dist-packages/sklearn/base.py:450: UserWarning: X does not have valid feature names, but GaussianMixture was fitted with feature names\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.8/dist-packages/sklearn/base.py:450: UserWarning: X does not have valid feature names, but GaussianMixture was fitted with feature names\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.8/dist-packages/sklearn/base.py:450: UserWarning: X does not have valid feature names, but GaussianMixture was fitted with feature names\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.8/dist-packages/sklearn/base.py:450: UserWarning: X does not have valid feature names, but GaussianMixture was fitted with feature names\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.8/dist-packages/sklearn/base.py:450: UserWarning: X does not have valid feature names, but GaussianMixture was fitted with feature names\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.8/dist-packages/sklearn/base.py:450: UserWarning: X does not have valid feature names, but GaussianMixture was fitted with feature names\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.8/dist-packages/sklearn/base.py:450: UserWarning: X does not have valid feature names, but GaussianMixture was fitted with feature names\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.8/dist-packages/sklearn/base.py:450: UserWarning: X does not have valid feature names, but GaussianMixture was fitted with feature names\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.8/dist-packages/sklearn/base.py:450: UserWarning: X does not have valid feature names, but GaussianMixture was fitted with feature names\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.8/dist-packages/sklearn/base.py:450: UserWarning: X does not have valid feature names, but GaussianMixture was fitted with feature names\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.8/dist-packages/sklearn/base.py:450: UserWarning: X does not have valid feature names, but GaussianMixture was fitted with feature names\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.8/dist-packages/sklearn/base.py:450: UserWarning: X does not have valid feature names, but GaussianMixture was fitted with feature names\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.8/dist-packages/sklearn/base.py:450: UserWarning: X does not have valid feature names, but GaussianMixture was fitted with feature names\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.8/dist-packages/sklearn/base.py:450: UserWarning: X does not have valid feature names, but GaussianMixture was fitted with feature names\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.8/dist-packages/sklearn/base.py:450: UserWarning: X does not have valid feature names, but GaussianMixture was fitted with feature names\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.8/dist-packages/sklearn/base.py:450: UserWarning: X does not have valid feature names, but GaussianMixture was fitted with feature names\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.8/dist-packages/sklearn/base.py:450: UserWarning: X does not have valid feature names, but GaussianMixture was fitted with feature names\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.8/dist-packages/sklearn/base.py:450: UserWarning: X does not have valid feature names, but GaussianMixture was fitted with feature names\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.8/dist-packages/sklearn/base.py:450: UserWarning: X does not have valid feature names, but GaussianMixture was fitted with feature names\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.8/dist-packages/sklearn/base.py:450: UserWarning: X does not have valid feature names, but GaussianMixture was fitted with feature names\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.8/dist-packages/sklearn/base.py:450: UserWarning: X does not have valid feature names, but GaussianMixture was fitted with feature names\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.8/dist-packages/sklearn/base.py:450: UserWarning: X does not have valid feature names, but GaussianMixture was fitted with feature names\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.8/dist-packages/sklearn/base.py:450: UserWarning: X does not have valid feature names, but GaussianMixture was fitted with feature names\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.8/dist-packages/sklearn/base.py:450: UserWarning: X does not have valid feature names, but GaussianMixture was fitted with feature names\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.8/dist-packages/sklearn/base.py:450: UserWarning: X does not have valid feature names, but GaussianMixture was fitted with feature names\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.8/dist-packages/sklearn/base.py:450: UserWarning: X does not have valid feature names, but GaussianMixture was fitted with feature names\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.8/dist-packages/sklearn/base.py:450: UserWarning: X does not have valid feature names, but GaussianMixture was fitted with feature names\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.8/dist-packages/sklearn/base.py:450: UserWarning: X does not have valid feature names, but GaussianMixture was fitted with feature names\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.8/dist-packages/sklearn/base.py:450: UserWarning: X does not have valid feature names, but GaussianMixture was fitted with feature names\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.8/dist-packages/sklearn/base.py:450: UserWarning: X does not have valid feature names, but GaussianMixture was fitted with feature names\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.8/dist-packages/sklearn/base.py:450: UserWarning: X does not have valid feature names, but GaussianMixture was fitted with feature names\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.8/dist-packages/sklearn/base.py:450: UserWarning: X does not have valid feature names, but GaussianMixture was fitted with feature names\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.8/dist-packages/sklearn/base.py:450: UserWarning: X does not have valid feature names, but GaussianMixture was fitted with feature names\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.8/dist-packages/sklearn/base.py:450: UserWarning: X does not have valid feature names, but GaussianMixture was fitted with feature names\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.8/dist-packages/sklearn/base.py:450: UserWarning: X does not have valid feature names, but GaussianMixture was fitted with feature names\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.8/dist-packages/sklearn/base.py:450: UserWarning: X does not have valid feature names, but GaussianMixture was fitted with feature names\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.8/dist-packages/sklearn/base.py:450: UserWarning: X does not have valid feature names, but GaussianMixture was fitted with feature names\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.8/dist-packages/sklearn/base.py:450: UserWarning: X does not have valid feature names, but GaussianMixture was fitted with feature names\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.8/dist-packages/sklearn/base.py:450: UserWarning: X does not have valid feature names, but GaussianMixture was fitted with feature names\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.8/dist-packages/sklearn/base.py:450: UserWarning: X does not have valid feature names, but GaussianMixture was fitted with feature names\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.8/dist-packages/sklearn/base.py:450: UserWarning: X does not have valid feature names, but GaussianMixture was fitted with feature names\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.8/dist-packages/sklearn/base.py:450: UserWarning: X does not have valid feature names, but GaussianMixture was fitted with feature names\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.8/dist-packages/sklearn/base.py:450: UserWarning: X does not have valid feature names, but GaussianMixture was fitted with feature names\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.8/dist-packages/sklearn/base.py:450: UserWarning: X does not have valid feature names, but GaussianMixture was fitted with feature names\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.8/dist-packages/sklearn/base.py:450: UserWarning: X does not have valid feature names, but GaussianMixture was fitted with feature names\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.8/dist-packages/sklearn/base.py:450: UserWarning: X does not have valid feature names, but GaussianMixture was fitted with feature names\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.8/dist-packages/sklearn/base.py:450: UserWarning: X does not have valid feature names, but GaussianMixture was fitted with feature names\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.8/dist-packages/sklearn/base.py:450: UserWarning: X does not have valid feature names, but GaussianMixture was fitted with feature names\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.8/dist-packages/sklearn/base.py:450: UserWarning: X does not have valid feature names, but GaussianMixture was fitted with feature names\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.8/dist-packages/sklearn/base.py:450: UserWarning: X does not have valid feature names, but GaussianMixture was fitted with feature names\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.8/dist-packages/sklearn/base.py:450: UserWarning: X does not have valid feature names, but GaussianMixture was fitted with feature names\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.8/dist-packages/sklearn/base.py:450: UserWarning: X does not have valid feature names, but GaussianMixture was fitted with feature names\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.8/dist-packages/sklearn/base.py:450: UserWarning: X does not have valid feature names, but GaussianMixture was fitted with feature names\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.8/dist-packages/sklearn/base.py:450: UserWarning: X does not have valid feature names, but GaussianMixture was fitted with feature names\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.8/dist-packages/sklearn/base.py:450: UserWarning: X does not have valid feature names, but GaussianMixture was fitted with feature names\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.8/dist-packages/sklearn/base.py:450: UserWarning: X does not have valid feature names, but GaussianMixture was fitted with feature names\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.8/dist-packages/sklearn/base.py:450: UserWarning: X does not have valid feature names, but GaussianMixture was fitted with feature names\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.8/dist-packages/sklearn/base.py:450: UserWarning: X does not have valid feature names, but GaussianMixture was fitted with feature names\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.8/dist-packages/sklearn/base.py:450: UserWarning: X does not have valid feature names, but GaussianMixture was fitted with feature names\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.8/dist-packages/sklearn/base.py:450: UserWarning: X does not have valid feature names, but GaussianMixture was fitted with feature names\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.8/dist-packages/sklearn/base.py:450: UserWarning: X does not have valid feature names, but GaussianMixture was fitted with feature names\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.8/dist-packages/sklearn/base.py:450: UserWarning: X does not have valid feature names, but GaussianMixture was fitted with feature names\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.8/dist-packages/sklearn/base.py:450: UserWarning: X does not have valid feature names, but GaussianMixture was fitted with feature names\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.8/dist-packages/sklearn/base.py:450: UserWarning: X does not have valid feature names, but GaussianMixture was fitted with feature names\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.8/dist-packages/sklearn/base.py:450: UserWarning: X does not have valid feature names, but GaussianMixture was fitted with feature names\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.8/dist-packages/sklearn/base.py:450: UserWarning: X does not have valid feature names, but GaussianMixture was fitted with feature names\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.8/dist-packages/sklearn/base.py:450: UserWarning: X does not have valid feature names, but GaussianMixture was fitted with feature names\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.8/dist-packages/sklearn/base.py:450: UserWarning: X does not have valid feature names, but GaussianMixture was fitted with feature names\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.8/dist-packages/sklearn/base.py:450: UserWarning: X does not have valid feature names, but GaussianMixture was fitted with feature names\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.8/dist-packages/sklearn/base.py:450: UserWarning: X does not have valid feature names, but GaussianMixture was fitted with feature names\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.8/dist-packages/sklearn/base.py:450: UserWarning: X does not have valid feature names, but GaussianMixture was fitted with feature names\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.8/dist-packages/sklearn/base.py:450: UserWarning: X does not have valid feature names, but GaussianMixture was fitted with feature names\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.8/dist-packages/sklearn/base.py:450: UserWarning: X does not have valid feature names, but GaussianMixture was fitted with feature names\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.8/dist-packages/sklearn/base.py:450: UserWarning: X does not have valid feature names, but GaussianMixture was fitted with feature names\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.8/dist-packages/sklearn/base.py:450: UserWarning: X does not have valid feature names, but GaussianMixture was fitted with feature names\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.8/dist-packages/sklearn/base.py:450: UserWarning: X does not have valid feature names, but GaussianMixture was fitted with feature names\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.8/dist-packages/sklearn/base.py:450: UserWarning: X does not have valid feature names, but GaussianMixture was fitted with feature names\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.8/dist-packages/sklearn/base.py:450: UserWarning: X does not have valid feature names, but GaussianMixture was fitted with feature names\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.8/dist-packages/sklearn/base.py:450: UserWarning: X does not have valid feature names, but GaussianMixture was fitted with feature names\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.8/dist-packages/sklearn/base.py:450: UserWarning: X does not have valid feature names, but GaussianMixture was fitted with feature names\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.8/dist-packages/sklearn/base.py:450: UserWarning: X does not have valid feature names, but GaussianMixture was fitted with feature names\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.8/dist-packages/sklearn/base.py:450: UserWarning: X does not have valid feature names, but GaussianMixture was fitted with feature names\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.8/dist-packages/sklearn/base.py:450: UserWarning: X does not have valid feature names, but GaussianMixture was fitted with feature names\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.8/dist-packages/sklearn/base.py:450: UserWarning: X does not have valid feature names, but GaussianMixture was fitted with feature names\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.8/dist-packages/sklearn/base.py:450: UserWarning: X does not have valid feature names, but GaussianMixture was fitted with feature names\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.8/dist-packages/sklearn/base.py:450: UserWarning: X does not have valid feature names, but GaussianMixture was fitted with feature names\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.8/dist-packages/sklearn/base.py:450: UserWarning: X does not have valid feature names, but GaussianMixture was fitted with feature names\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.8/dist-packages/sklearn/base.py:450: UserWarning: X does not have valid feature names, but GaussianMixture was fitted with feature names\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.8/dist-packages/sklearn/base.py:450: UserWarning: X does not have valid feature names, but GaussianMixture was fitted with feature names\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.8/dist-packages/sklearn/base.py:450: UserWarning: X does not have valid feature names, but GaussianMixture was fitted with feature names\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.8/dist-packages/sklearn/base.py:450: UserWarning: X does not have valid feature names, but GaussianMixture was fitted with feature names\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.8/dist-packages/sklearn/base.py:450: UserWarning: X does not have valid feature names, but GaussianMixture was fitted with feature names\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.8/dist-packages/sklearn/base.py:450: UserWarning: X does not have valid feature names, but GaussianMixture was fitted with feature names\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.8/dist-packages/sklearn/base.py:450: UserWarning: X does not have valid feature names, but GaussianMixture was fitted with feature names\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.8/dist-packages/sklearn/base.py:450: UserWarning: X does not have valid feature names, but GaussianMixture was fitted with feature names\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.8/dist-packages/sklearn/base.py:450: UserWarning: X does not have valid feature names, but GaussianMixture was fitted with feature names\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.8/dist-packages/sklearn/base.py:450: UserWarning: X does not have valid feature names, but GaussianMixture was fitted with feature names\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.8/dist-packages/sklearn/base.py:450: UserWarning: X does not have valid feature names, but GaussianMixture was fitted with feature names\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.8/dist-packages/sklearn/base.py:450: UserWarning: X does not have valid feature names, but GaussianMixture was fitted with feature names\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.8/dist-packages/sklearn/base.py:450: UserWarning: X does not have valid feature names, but GaussianMixture was fitted with feature names\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.8/dist-packages/sklearn/base.py:450: UserWarning: X does not have valid feature names, but GaussianMixture was fitted with feature names\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.8/dist-packages/sklearn/base.py:450: UserWarning: X does not have valid feature names, but GaussianMixture was fitted with feature names\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.8/dist-packages/sklearn/base.py:450: UserWarning: X does not have valid feature names, but GaussianMixture was fitted with feature names\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.8/dist-packages/sklearn/base.py:450: UserWarning: X does not have valid feature names, but GaussianMixture was fitted with feature names\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.8/dist-packages/sklearn/base.py:450: UserWarning: X does not have valid feature names, but GaussianMixture was fitted with feature names\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.8/dist-packages/sklearn/base.py:450: UserWarning: X does not have valid feature names, but GaussianMixture was fitted with feature names\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.8/dist-packages/sklearn/base.py:450: UserWarning: X does not have valid feature names, but GaussianMixture was fitted with feature names\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.8/dist-packages/sklearn/base.py:450: UserWarning: X does not have valid feature names, but GaussianMixture was fitted with feature names\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.8/dist-packages/sklearn/base.py:450: UserWarning: X does not have valid feature names, but GaussianMixture was fitted with feature names\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.8/dist-packages/sklearn/base.py:450: UserWarning: X does not have valid feature names, but GaussianMixture was fitted with feature names\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.8/dist-packages/sklearn/base.py:450: UserWarning: X does not have valid feature names, but GaussianMixture was fitted with feature names\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.8/dist-packages/sklearn/base.py:450: UserWarning: X does not have valid feature names, but GaussianMixture was fitted with feature names\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.8/dist-packages/sklearn/base.py:450: UserWarning: X does not have valid feature names, but GaussianMixture was fitted with feature names\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.8/dist-packages/sklearn/base.py:450: UserWarning: X does not have valid feature names, but GaussianMixture was fitted with feature names\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.8/dist-packages/sklearn/base.py:450: UserWarning: X does not have valid feature names, but GaussianMixture was fitted with feature names\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.8/dist-packages/sklearn/base.py:450: UserWarning: X does not have valid feature names, but GaussianMixture was fitted with feature names\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.8/dist-packages/sklearn/base.py:450: UserWarning: X does not have valid feature names, but GaussianMixture was fitted with feature names\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.8/dist-packages/sklearn/base.py:450: UserWarning: X does not have valid feature names, but GaussianMixture was fitted with feature names\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.8/dist-packages/sklearn/base.py:450: UserWarning: X does not have valid feature names, but GaussianMixture was fitted with feature names\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.8/dist-packages/sklearn/base.py:450: UserWarning: X does not have valid feature names, but GaussianMixture was fitted with feature names\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.8/dist-packages/sklearn/base.py:450: UserWarning: X does not have valid feature names, but GaussianMixture was fitted with feature names\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.8/dist-packages/sklearn/base.py:450: UserWarning: X does not have valid feature names, but GaussianMixture was fitted with feature names\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.8/dist-packages/sklearn/base.py:450: UserWarning: X does not have valid feature names, but GaussianMixture was fitted with feature names\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.8/dist-packages/sklearn/base.py:450: UserWarning: X does not have valid feature names, but GaussianMixture was fitted with feature names\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.8/dist-packages/sklearn/base.py:450: UserWarning: X does not have valid feature names, but GaussianMixture was fitted with feature names\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.8/dist-packages/sklearn/base.py:450: UserWarning: X does not have valid feature names, but GaussianMixture was fitted with feature names\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.8/dist-packages/sklearn/base.py:450: UserWarning: X does not have valid feature names, but GaussianMixture was fitted with feature names\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.8/dist-packages/sklearn/base.py:450: UserWarning: X does not have valid feature names, but GaussianMixture was fitted with feature names\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.8/dist-packages/sklearn/base.py:450: UserWarning: X does not have valid feature names, but GaussianMixture was fitted with feature names\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.8/dist-packages/sklearn/base.py:450: UserWarning: X does not have valid feature names, but GaussianMixture was fitted with feature names\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.8/dist-packages/sklearn/base.py:450: UserWarning: X does not have valid feature names, but GaussianMixture was fitted with feature names\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.8/dist-packages/sklearn/base.py:450: UserWarning: X does not have valid feature names, but GaussianMixture was fitted with feature names\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.8/dist-packages/sklearn/base.py:450: UserWarning: X does not have valid feature names, but GaussianMixture was fitted with feature names\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.8/dist-packages/sklearn/base.py:450: UserWarning: X does not have valid feature names, but GaussianMixture was fitted with feature names\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.8/dist-packages/sklearn/base.py:450: UserWarning: X does not have valid feature names, but GaussianMixture was fitted with feature names\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.8/dist-packages/sklearn/base.py:450: UserWarning: X does not have valid feature names, but GaussianMixture was fitted with feature names\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.8/dist-packages/sklearn/base.py:450: UserWarning: X does not have valid feature names, but GaussianMixture was fitted with feature names\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.8/dist-packages/sklearn/base.py:450: UserWarning: X does not have valid feature names, but GaussianMixture was fitted with feature names\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.8/dist-packages/sklearn/base.py:450: UserWarning: X does not have valid feature names, but GaussianMixture was fitted with feature names\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.8/dist-packages/sklearn/base.py:450: UserWarning: X does not have valid feature names, but GaussianMixture was fitted with feature names\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.8/dist-packages/sklearn/base.py:450: UserWarning: X does not have valid feature names, but GaussianMixture was fitted with feature names\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.8/dist-packages/sklearn/base.py:450: UserWarning: X does not have valid feature names, but GaussianMixture was fitted with feature names\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.8/dist-packages/sklearn/base.py:450: UserWarning: X does not have valid feature names, but GaussianMixture was fitted with feature names\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.8/dist-packages/sklearn/base.py:450: UserWarning: X does not have valid feature names, but GaussianMixture was fitted with feature names\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.8/dist-packages/sklearn/base.py:450: UserWarning: X does not have valid feature names, but GaussianMixture was fitted with feature names\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.8/dist-packages/sklearn/base.py:450: UserWarning: X does not have valid feature names, but GaussianMixture was fitted with feature names\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.8/dist-packages/sklearn/base.py:450: UserWarning: X does not have valid feature names, but GaussianMixture was fitted with feature names\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.8/dist-packages/sklearn/base.py:450: UserWarning: X does not have valid feature names, but GaussianMixture was fitted with feature names\n",
            "  warnings.warn(\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "plt.errorbar(n_clusters, results, yerr=res_sigs)\n",
        "plt.title(\"Distance between Train and Test GMMs\", fontsize=20)\n",
        "plt.xticks(n_clusters)\n",
        "plt.xlabel(\"N. of clusters\")\n",
        "plt.ylabel(\"Distance\")\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 300
        },
        "id": "WdGFwd0NtsZF",
        "outputId": "4ffc3cf2-2c52-4e06-e0b0-22d0e07287ea"
      },
      "execution_count": 15,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "n_clusters=np.arange(2, 8)\n",
        "bics=[]\n",
        "bics_err=[]\n",
        "iterations=20\n",
        "for n in n_clusters:\n",
        "    tmp_bic=[]\n",
        "    for _ in range(iterations):\n",
        "        gmm=GaussianMixture(n, n_init=2).fit(X_principal) \n",
        "        \n",
        "        tmp_bic.append(gmm.bic(X_principal))\n",
        "    val=np.mean(SelBest(np.array(tmp_bic), int(iterations/5)))\n",
        "    err=np.std(tmp_bic)\n",
        "    bics.append(val)\n",
        "    bics_err.append(err)"
      ],
      "metadata": {
        "id": "qReu3Qt8twnf"
      },
      "execution_count": 16,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "plt.errorbar(n_clusters,bics, yerr=bics_err, label='BIC')\n",
        "plt.title(\"BIC Scores\", fontsize=20)\n",
        "plt.xticks(n_clusters)\n",
        "plt.xlabel(\"N. of clusters\")\n",
        "plt.ylabel(\"Score\")\n",
        "plt.legend()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 317
        },
        "id": "29QAsQOZt4fY",
        "outputId": "80367ead-56e3-4e13-a9f7-65537110aa4c"
      },
      "execution_count": 17,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.legend.Legend at 0x7f949ef02790>"
            ]
          },
          "metadata": {},
          "execution_count": 17
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    }
  ]
}