machinelearning

Machine learning code
git clone git://git.laack.co/machinelearning.git
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MLPHousingSciKitLearn.ipynb (2315B)

      1 {
      2  "cells": [
      3   {
      4    "cell_type": "code",
      5    "execution_count": 4,
      6    "metadata": {},
      7    "outputs": [],
      8    "source": [
      9     "from sklearn.datasets import fetch_california_housing\n",
     10     "from sklearn.metrics import root_mean_squared_error\n",
     11     "from sklearn.model_selection import train_test_split\n",
     12     "from sklearn.neural_network import MLPRegressor\n",
     13     "from sklearn.pipeline import make_pipeline\n",
     14     "from sklearn.preprocessing import StandardScaler\n",
     15     "\n",
     16     "housing = fetch_california_housing()\n",
     17     "\n",
     18     "# Split train, validation, and test (in that order).\n",
     19     "X_train_full, X_test, y_train_full, y_test = train_test_split(housing.data,housing.target,random_state=10)\n",
     20     "X_train, X_valid, y_train, y_valid = train_test_split(X_train_full, y_train_full, random_state=10)"
     21    ]
     22   },
     23   {
     24    "cell_type": "code",
     25    "execution_count": 5,
     26    "metadata": {},
     27    "outputs": [
     28     {
     29      "name": "stderr",
     30      "output_type": "stream",
     31      "text": [
     32       "/home/andrew/gitRepos/myvenv/lib/python3.11/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n",
     33       "  warnings.warn(\n"
     34      ]
     35     }
     36    ],
     37    "source": [
     38     "mlp_reg = MLPRegressor(hidden_layer_sizes=[50,50,50], random_state=10)\n",
     39     "pipline = make_pipeline(StandardScaler(), mlp_reg)\n",
     40     "pipline.fit(X_train, y_train)\n",
     41     "y_pred = pipline.predict(X_valid)\n",
     42     "rmse = root_mean_squared_error(y_valid,y_pred)"
     43    ]
     44   },
     45   {
     46    "cell_type": "code",
     47    "execution_count": 6,
     48    "metadata": {},
     49    "outputs": [
     50     {
     51      "data": {
     52       "text/plain": [
     53        "0.52747850210309"
     54       ]
     55      },
     56      "execution_count": 6,
     57      "metadata": {},
     58      "output_type": "execute_result"
     59     }
     60    ],
     61    "source": [
     62     "rmse"
     63    ]
     64   }
     65  ],
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