NN_Diabetes.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "provenance": [],
      "collapsed_sections": [],
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/gabriela0821/Portafolio/blob/main/NN_Diabetes.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Camilo Gomez y Gabriela Lara"
      ],
      "metadata": {
        "id": "HQsf9Hiur6D2"
      }
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "pj4pfuZsAfud",
        "outputId": "49c47106-088e-4c60-8954-659be617a44c"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "     Pregnancies  Glucose  BloodPressure  SkinThickness  Insulin   BMI  \\\n",
            "0              6      148             72             35        0  33.6   \n",
            "1              1       85             66             29        0  26.6   \n",
            "2              8      183             64              0        0  23.3   \n",
            "3              1       89             66             23       94  28.1   \n",
            "4              0      137             40             35      168  43.1   \n",
            "..           ...      ...            ...            ...      ...   ...   \n",
            "763           10      101             76             48      180  32.9   \n",
            "764            2      122             70             27        0  36.8   \n",
            "765            5      121             72             23      112  26.2   \n",
            "766            1      126             60              0        0  30.1   \n",
            "767            1       93             70             31        0  30.4   \n",
            "\n",
            "     DiabetesPedigreeFunction  Age  Outcome  \n",
            "0                       0.627   50        1  \n",
            "1                       0.351   31        0  \n",
            "2                       0.672   32        1  \n",
            "3                       0.167   21        0  \n",
            "4                       2.288   33        1  \n",
            "..                        ...  ...      ...  \n",
            "763                     0.171   63        0  \n",
            "764                     0.340   27        0  \n",
            "765                     0.245   30        0  \n",
            "766                     0.349   47        1  \n",
            "767                     0.315   23        0  \n",
            "\n",
            "[768 rows x 9 columns]\n"
          ]
        }
      ],
      "source": [
        "\n",
        "import pandas as pd\n",
        "from sklearn.neural_network import MLPClassifier\n",
        "from sklearn.datasets import make_classification\n",
        "from sklearn.model_selection import train_test_split\n",
        "\n",
        "\n",
        "import matplotlib.pyplot as plt\n",
        "import numpy as np\n",
        "\n",
        "\n",
        "datosauxd = pd.read_excel('diabetes (1).xlsx')\n",
        "print(datosauxd)"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "plt.scatter(datosauxd[\"Pregnancies\"],datosauxd[\"Outcome\"])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 282
        },
        "id": "Cio760Q6BEY4",
        "outputId": "254703c0-0586-4add-9f4c-a7cadce83f99"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.collections.PathCollection at 0x7ff7e546ac90>"
            ]
          },
          "metadata": {},
          "execution_count": 2
        },
        {
          "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": [
        "plt.scatter(datosauxd[\"BloodPressure\"],datosauxd[\"Outcome\"])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 282
        },
        "id": "i1RaLOmlBSAj",
        "outputId": "9de14fd7-0b0d-451f-b4ca-ab81df10bdb3"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.collections.PathCollection at 0x7ff7e4ef0fd0>"
            ]
          },
          "metadata": {},
          "execution_count": 5
        },
        {
          "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": [
        "plt.scatter(datosauxd[\"Glucose\"],datosauxd[\"Outcome\"])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 282
        },
        "id": "QnREHtz4Bqu0",
        "outputId": "f9e32871-612b-4776-d4dc-8d4aa049ff2c"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.collections.PathCollection at 0x7ff7e4e69e50>"
            ]
          },
          "metadata": {},
          "execution_count": 6
        },
        {
          "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": [
        "plt.scatter(datosauxd[\"SkinThickness\"],datosauxd[\"Outcome\"])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 282
        },
        "id": "qdwn3b1hBxsY",
        "outputId": "7f114e09-2fe2-450a-fa1a-1ef418068789"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.collections.PathCollection at 0x7ff7e4e2ad50>"
            ]
          },
          "metadata": {},
          "execution_count": 7
        },
        {
          "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": [
        "plt.scatter(datosauxd[\"Insulin\"],datosauxd[\"Outcome\"])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 282
        },
        "id": "dlQyJEZPCESZ",
        "outputId": "1ba592c9-df9b-481c-c8a4-bc2341799cd7"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.collections.PathCollection at 0x7ff7e4f48b10>"
            ]
          },
          "metadata": {},
          "execution_count": 8
        },
        {
          "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": [
        "plt.scatter(datosauxd[\"BMI\"],datosauxd[\"Outcome\"])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 282
        },
        "id": "APAnzChbCWMh",
        "outputId": "18fa35f4-04ff-4677-ec53-61d6d4271a88"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.collections.PathCollection at 0x7ff7e4e54f50>"
            ]
          },
          "metadata": {},
          "execution_count": 9
        },
        {
          "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": [
        "plt.scatter(datosauxd[\"DiabetesPedigreeFunction\"],datosauxd[\"Outcome\"])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 282
        },
        "id": "iDJ69lHDCdv3",
        "outputId": "f9958d05-ddf1-44c6-f323-feb33a9362dc"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.collections.PathCollection at 0x7ff7e4d35150>"
            ]
          },
          "metadata": {},
          "execution_count": 10
        },
        {
          "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": [
        "plt.scatter(datosauxd[\"Age\"],datosauxd[\"Outcome\"])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 282
        },
        "id": "tNPr8f15DLhp",
        "outputId": "e57b6e46-2a89-4955-a815-55eeda928752"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.collections.PathCollection at 0x7ff7e558ed10>"
            ]
          },
          "metadata": {},
          "execution_count": 11
        },
        {
          "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": [
        "Mcorrelacion=datosauxd.corr()\n",
        "print(Mcorrelacion)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "eNaPh2lYDWr5",
        "outputId": "f554da6f-1c08-4201-f250-f86ed2d7d606"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "                          Pregnancies   Glucose  BloodPressure  SkinThickness  \\\n",
            "Pregnancies                  1.000000  0.129459       0.141282      -0.081672   \n",
            "Glucose                      0.129459  1.000000       0.152590       0.057328   \n",
            "BloodPressure                0.141282  0.152590       1.000000       0.207371   \n",
            "SkinThickness               -0.081672  0.057328       0.207371       1.000000   \n",
            "Insulin                     -0.073535  0.331357       0.088933       0.436783   \n",
            "BMI                          0.017683  0.221071       0.281805       0.392573   \n",
            "DiabetesPedigreeFunction    -0.033523  0.137337       0.041265       0.183928   \n",
            "Age                          0.544341  0.263514       0.239528      -0.113970   \n",
            "Outcome                      0.221898  0.466581       0.065068       0.074752   \n",
            "\n",
            "                           Insulin       BMI  DiabetesPedigreeFunction  \\\n",
            "Pregnancies              -0.073535  0.017683                 -0.033523   \n",
            "Glucose                   0.331357  0.221071                  0.137337   \n",
            "BloodPressure             0.088933  0.281805                  0.041265   \n",
            "SkinThickness             0.436783  0.392573                  0.183928   \n",
            "Insulin                   1.000000  0.197859                  0.185071   \n",
            "BMI                       0.197859  1.000000                  0.140647   \n",
            "DiabetesPedigreeFunction  0.185071  0.140647                  1.000000   \n",
            "Age                      -0.042163  0.036242                  0.033561   \n",
            "Outcome                   0.130548  0.292695                  0.173844   \n",
            "\n",
            "                               Age   Outcome  \n",
            "Pregnancies               0.544341  0.221898  \n",
            "Glucose                   0.263514  0.466581  \n",
            "BloodPressure             0.239528  0.065068  \n",
            "SkinThickness            -0.113970  0.074752  \n",
            "Insulin                  -0.042163  0.130548  \n",
            "BMI                       0.036242  0.292695  \n",
            "DiabetesPedigreeFunction  0.033561  0.173844  \n",
            "Age                       1.000000  0.238356  \n",
            "Outcome                   0.238356  1.000000  \n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "dtxd= datosauxd[[ 'Glucose','BloodPressure' ,'Insulin','BMI','DiabetesPedigreeFunction',\"Outcome\"]]\n",
        "\n",
        "\n",
        "plt.scatter(dtxd[\"Glucose\"],dtxd[\"BloodPressure\"],dtxd[\"BMI\"],dtxd[\"Insulin\"] )"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 282
        },
        "id": "c1vfcAEODetC",
        "outputId": "1b3e63e1-7f28-41e8-c8ea-c4aece3ac4a0"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.collections.PathCollection at 0x7ff7e4c62e90>"
            ]
          },
          "metadata": {},
          "execution_count": 27
        },
        {
          "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": [
        "plt.scatter(dtxd[\"Glucose\"],dtxd[\"BloodPressure\"],dtxd[\"BMI\"],dtxd[\"Outcome\"] )"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 282
        },
        "id": "iClTm8VhDw2w",
        "outputId": "927891bc-6593-4f27-e545-bd4616880cb3"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.collections.PathCollection at 0x7ff7e4d99750>"
            ]
          },
          "metadata": {},
          "execution_count": 28
        },
        {
          "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": [
        "plt.scatter(dtxd[\"Glucose\"],dtxd[\"Insulin\"],dtxd[\"BMI\"],dtxd[\"DiabetesPedigreeFunction\"] )"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 282
        },
        "id": "DqdTSrgSIXst",
        "outputId": "066da3be-81a9-4836-aed3-0dd8c369e906"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.collections.PathCollection at 0x7ff7e4830110>"
            ]
          },
          "metadata": {},
          "execution_count": 29
        },
        {
          "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": [
        "x=dtxd= datosauxd[[ 'Glucose','Insulin','BMI','DiabetesPedigreeFunction']]\n",
        "y=dtxd= datosauxd[[ 'Outcome']]"
      ],
      "metadata": {
        "id": "74_sQOvAG2te"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "clf = MLPClassifier(solver='lbfgs', alpha=1e-5, hidden_layer_sizes=(10,8,20))"
      ],
      "metadata": {
        "id": "ow1TYSl8JSDp"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "clf.fit(x,y)\n",
        "c=clf.n_layers_\n",
        "d=clf.coefs_\n",
        "#print(c)\n",
        "#print(d)\n",
        "contandor=0\n",
        "for i in d:\n",
        "  print('coeficientes de la capa numero',contandor)\n",
        "  print(len(i))\n",
        "  print(i)\n",
        "  contandor=contandor+1\n",
        "  "
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "vT3Z2j8yJmXM",
        "outputId": "53c330c8-9fab-4a9b-83e1-af5386830632"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/sklearn/neural_network/_multilayer_perceptron.py:1109: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
            "  y = column_or_1d(y, warn=True)\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "coeficientes de la capa numero 0\n",
            "4\n",
            "[[-0.29796868 -0.52074015 -0.33504226  1.389299   -0.18104923 -1.08460816\n",
            "  -1.2966503   0.25223913 -0.44841684 -0.81762766]\n",
            " [-0.27094571 -0.4043638  -0.15276054  0.45315683 -0.23164278  0.54853857\n",
            "  -0.30794844  0.32470042  0.0171657  -0.14962218]\n",
            " [ 0.10024496 -0.07845132  0.59351524  0.09769864 -0.33418545 -0.53574464\n",
            "   0.20277044 -0.99335984  0.57968484 -0.60847333]\n",
            " [ 0.41856869  0.40101497 -0.50225961  0.17972188  0.58738551  0.4274163\n",
            "   0.15004664  0.59012156  0.44474574  0.29321396]]\n",
            "coeficientes de la capa numero 1\n",
            "10\n",
            "[[-0.11256785 -0.21416815 -0.39161899 -0.28307425  0.00483767  0.27235094\n",
            "   0.31307686 -0.42367949]\n",
            " [-0.50045518 -0.06295373  0.0311979  -0.1044376   0.13131763 -0.00962789\n",
            "   0.17672869  0.33029796]\n",
            " [ 0.07103355  0.16844049 -0.41131549 -0.24859813 -0.19761573 -0.2361345\n",
            "  -0.45363099 -0.21585167]\n",
            " [ 0.47311804 -0.6093986  -0.08783502  0.34296864 -0.52462821 -0.24712354\n",
            "  -0.53203501 -0.01935614]\n",
            " [ 0.25094585  0.35090779 -0.4391898   0.00898335  0.15699338 -0.05419504\n",
            "   0.33506531  0.1444121 ]\n",
            " [ 0.21216255 -0.41024232  0.32275556 -0.53455117 -0.40886506  0.53556818\n",
            "   0.38701411  0.13193953]\n",
            " [-0.22474392  0.68422198 -0.0349204  -0.29001567 -0.3341502  -0.09829465\n",
            "   0.13653931 -0.29137676]\n",
            " [-1.46025768  0.21765245 -0.55486757  1.82912544 -0.1244896  -0.10685916\n",
            "  -0.23188992 -0.56054125]\n",
            " [-0.36460585  0.24234615 -0.52341387  0.29909951  0.27324068 -0.56954878\n",
            "  -0.47021737 -0.44144779]\n",
            " [-0.36080858 -0.53719823 -0.22938268  0.49060998  0.05135598 -0.24061143\n",
            "  -0.22279367 -0.04613614]]\n",
            "coeficientes de la capa numero 2\n",
            "8\n",
            "[[ 0.23736749 -0.36803616  0.10899387  0.2619983  -0.45466496  0.01040157\n",
            "  -0.15292788 -0.10638221 -0.08253697  0.2349395   0.09287165  0.16828765\n",
            "  -0.31562341 -0.02286275 -0.06283458  0.02758963  0.24574398  0.26609508\n",
            "  -0.27899223 -0.27590946]\n",
            " [-0.35942916  0.17846267 -0.0342279   0.25858269  0.30458664 -0.40825633\n",
            "  -0.21121452 -0.00240371 -0.27797047 -0.04128876 -0.10396195 -0.08886975\n",
            "   0.00682335 -0.28141318  0.1342583   0.11784086  0.02718285 -0.33893907\n",
            "   0.2763226  -0.17866545]\n",
            " [-0.4488559  -0.13563277  0.13431124  0.09970751 -0.03331031  0.31122482\n",
            "   0.0113007  -0.01640683  0.15368972 -0.19592431  0.29152897 -0.23912638\n",
            "   0.17039536  0.06774129 -0.27403453  0.17653581  0.24510671  0.27110457\n",
            "  -0.44291599 -0.40944343]\n",
            " [-0.37503765  0.39506271  0.19300273 -0.36677442 -0.04062879  0.73608379\n",
            "   0.5103308  -0.0460536  -0.26738624  0.61422228 -0.33493389  0.2739115\n",
            "  -0.39357133  0.77827179 -0.75266847 -0.32744006 -0.4501338  -0.01164787\n",
            "  -0.85494626 -0.05256265]\n",
            " [ 0.38930165  0.11673341 -0.32813008  0.09709367 -0.0587547   0.0732341\n",
            "   0.28794867  0.10923351  0.43884741 -0.42945846 -0.1145993   0.28470438\n",
            "  -0.11836607  0.06187407 -0.20826323  0.45773926  0.44693943  0.40187207\n",
            "   0.18735019  0.44856695]\n",
            " [ 0.22853045  0.3413405  -0.11295918 -0.1732918  -0.14804772  0.45182117\n",
            "  -0.37033918  0.03008149  0.14354131  0.18033465 -0.19663618 -0.22936774\n",
            "  -0.45817642 -0.41085773 -0.04560594  0.31678506 -0.09694729  0.24834187\n",
            "   0.07945577  0.11551238]\n",
            " [ 0.07777549  0.0522245  -0.05822766 -0.44273697 -0.18998111 -0.21514866\n",
            "   0.13519261  0.27449554 -0.51789547  0.0836436   0.29719744 -0.22911629\n",
            "   0.01549535 -0.31422434  0.19393815 -0.36346631  0.05310054  0.46632873\n",
            "  -0.153867   -0.19405264]\n",
            " [ 0.24532865 -0.23396072  0.25154601  0.38521223 -0.39633433  0.06594638\n",
            "  -0.35182368  0.16515841  0.125694    0.14550894  0.4056137  -0.21943842\n",
            "  -0.25659539  0.27849428  0.22517794  0.28788428 -0.44339877  0.23492553\n",
            "   0.37539491  0.19381572]]\n",
            "coeficientes de la capa numero 3\n",
            "20\n",
            "[[ 0.23671106]\n",
            " [ 0.21436868]\n",
            " [ 0.20291447]\n",
            " [ 0.21345353]\n",
            " [ 0.41584382]\n",
            " [-0.66831464]\n",
            " [-0.67605999]\n",
            " [-0.11502574]\n",
            " [ 0.34829711]\n",
            " [ 1.01618981]\n",
            " [-0.30124182]\n",
            " [-0.15462422]\n",
            " [-0.05695991]\n",
            " [ 0.19009545]\n",
            " [ 0.16571757]\n",
            " [-0.0365236 ]\n",
            " [ 0.00649595]\n",
            " [-0.69691628]\n",
            " [ 0.0525542 ]\n",
            " [-0.17130389]]\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/sklearn/neural_network/_multilayer_perceptron.py:549: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
            "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
            "\n",
            "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
            "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
            "  self.n_iter_ = _check_optimize_result(\"lbfgs\", opt_res, self.max_iter)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "X_train, X_test, y_train, y_test = train_test_split(x, y, stratify=y,train_size=0.7,)\n",
        "\n",
        "clf = MLPClassifier(#random_state=1, \n",
        "                    max_iter=100).fit(X_train, y_train)\n",
        "#paso de 100\n",
        "clf.score(X_test, y_test)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Vq5pJb1OJ1N5",
        "outputId": "4ced91d1-5e29-4217-af4b-2b8937b0beb4"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/sklearn/neural_network/_multilayer_perceptron.py:1109: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
            "  y = column_or_1d(y, warn=True)\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.6320346320346321"
            ]
          },
          "metadata": {},
          "execution_count": 37
        }
      ]
    }
  ]
}