model_complexity.ipynb

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      "source": [
        "https://scikit-learn.org/stable/auto_examples/model_selection/plot_grid_search_refit_callable.html"
      ]
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    {
      "cell_type": "markdown",
      "metadata": {
        "id": "-7cwp-1732Iq",
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      "source": [
        "https://scikit-learn.org/stable/auto_examples/applications/plot_model_complexity_influence.html"
      ]
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      "cell_type": "markdown",
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        "id": "TgEzlNT43zWP",
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      "source": [
        "Model Complexity Influence\n",
        "Demonstrate how model complexity influences both prediction accuracy and computational performance.\n",
        "\n",
        "The dataset is the Boston Housing dataset (resp. 20 Newsgroups) for regression (resp. classification).\n",
        "\n",
        "For each class of models we make the model complexity vary through the choice of relevant model parameters and measure the influence on both computational performance (latency) and predictive power (MSE or Hamming Loss)."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "SYLclFF038y_",
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      "source": [
        "import time\n",
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "from mpl_toolkits.axes_grid1.parasite_axes import host_subplot\n",
        "from mpl_toolkits.axisartist.axislines import Axes\n",
        "from scipy.sparse.csr import csr_matrix\n",
        "\n",
        "from sklearn import datasets\n",
        "from sklearn.utils import shuffle\n",
        "from sklearn.metrics import mean_squared_error\n",
        "from sklearn.svm.classes import NuSVR\n",
        "from sklearn.ensemble.gradient_boosting import GradientBoostingRegressor\n",
        "from sklearn.linear_model.stochastic_gradient import SGDClassifier\n",
        "from sklearn.metrics import hamming_loss"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
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        "id": "ff86jxUC3_wJ",
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      "source": [
        "# #############################################################################\n",
        "# Routines\n",
        "\n",
        "\n",
        "# Initialize random generator\n",
        "np.random.seed(0)\n",
        "\n",
        "\n",
        "def generate_data(case, sparse=False):\n",
        "    \"\"\"Generate regression/classification data.\"\"\"\n",
        "    bunch = None\n",
        "    if case == 'regression':\n",
        "        bunch = datasets.load_boston()\n",
        "    elif case == 'classification':\n",
        "        bunch = datasets.fetch_20newsgroups_vectorized(subset='all')\n",
        "    X, y = shuffle(bunch.data, bunch.target)\n",
        "    offset = int(X.shape[0] * 0.8)\n",
        "    X_train, y_train = X[:offset], y[:offset]\n",
        "    X_test, y_test = X[offset:], y[offset:]\n",
        "    if sparse:\n",
        "        X_train = csr_matrix(X_train)\n",
        "        X_test = csr_matrix(X_test)\n",
        "    else:\n",
        "        X_train = np.array(X_train)\n",
        "        X_test = np.array(X_test)\n",
        "    y_test = np.array(y_test)\n",
        "    y_train = np.array(y_train)\n",
        "    data = {'X_train': X_train, 'X_test': X_test, 'y_train': y_train,\n",
        "            'y_test': y_test}\n",
        "    return data\n",
        "\n",
        "\n",
        "def benchmark_influence(conf):\n",
        "    \"\"\"\n",
        "    Benchmark influence of :changing_param: on both MSE and latency.\n",
        "    \"\"\"\n",
        "    prediction_times = []\n",
        "    prediction_powers = []\n",
        "    complexities = []\n",
        "    for param_value in conf['changing_param_values']:\n",
        "        conf['tuned_params'][conf['changing_param']] = param_value\n",
        "        estimator = conf['estimator'](**conf['tuned_params'])\n",
        "        print(\"Benchmarking %s\" % estimator)\n",
        "        estimator.fit(conf['data']['X_train'], conf['data']['y_train'])\n",
        "        conf['postfit_hook'](estimator)\n",
        "        complexity = conf['complexity_computer'](estimator)\n",
        "        complexities.append(complexity)\n",
        "        start_time = time.time()\n",
        "        for _ in range(conf['n_samples']):\n",
        "            y_pred = estimator.predict(conf['data']['X_test'])\n",
        "        elapsed_time = (time.time() - start_time) / float(conf['n_samples'])\n",
        "        prediction_times.append(elapsed_time)\n",
        "        pred_score = conf['prediction_performance_computer'](\n",
        "            conf['data']['y_test'], y_pred)\n",
        "        prediction_powers.append(pred_score)\n",
        "        print(\"Complexity: %d | %s: %.4f | Pred. Time: %fs\\n\" % (\n",
        "            complexity, conf['prediction_performance_label'], pred_score,\n",
        "            elapsed_time))\n",
        "    return prediction_powers, prediction_times, complexities\n"
      ],
      "execution_count": 0,
      "outputs": []
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      "source": [
        "\n",
        "\n",
        "def plot_influence(conf, mse_values, prediction_times, complexities):\n",
        "    \"\"\"\n",
        "    Plot influence of model complexity on both accuracy and latency.\n",
        "    \"\"\"\n",
        "    plt.figure(figsize=(12, 6))\n",
        "    host = host_subplot(111, axes_class=Axes)\n",
        "    plt.subplots_adjust(right=0.75)\n",
        "    par1 = host.twinx()\n",
        "    host.set_xlabel('Model Complexity (%s)' % conf['complexity_label'])\n",
        "    y1_label = conf['prediction_performance_label']\n",
        "    y2_label = \"Time (s)\"\n",
        "    host.set_ylabel(y1_label)\n",
        "    par1.set_ylabel(y2_label)\n",
        "    p1, = host.plot(complexities, mse_values, 'b-', label=\"prediction error\")\n",
        "    p2, = par1.plot(complexities, prediction_times, 'r-',\n",
        "                    label=\"latency\")\n",
        "    host.legend(loc='upper right')\n",
        "    host.axis[\"left\"].label.set_color(p1.get_color())\n",
        "    par1.axis[\"right\"].label.set_color(p2.get_color())\n",
        "    plt.title('Influence of Model Complexity - %s' % conf['estimator'].__name__)\n",
        "    plt.show()\n",
        "\n",
        "\n",
        "def _count_nonzero_coefficients(estimator):\n",
        "    a = estimator.coef_.toarray()\n",
        "    return np.count_nonzero(a)\n",
        "\n",
        "# #############################################################################\n",
        "# Main code\n",
        "regression_data = generate_data('regression')\n",
        "classification_data = generate_data('classification', sparse=True)\n",
        "configurations = [\n",
        "    {'estimator': SGDClassifier,\n",
        "     'tuned_params': {'penalty': 'elasticnet', 'alpha': 0.001, 'loss':\n",
        "                      'modified_huber', 'fit_intercept': True, 'tol': 1e-3},\n",
        "     'changing_param': 'l1_ratio',\n",
        "     'changing_param_values': [0.25, 0.5, 0.75, 0.9],\n",
        "     'complexity_label': 'non_zero coefficients',\n",
        "     'complexity_computer': _count_nonzero_coefficients,\n",
        "     'prediction_performance_computer': hamming_loss,\n",
        "     'prediction_performance_label': 'Hamming Loss (Misclassification Ratio)',\n",
        "     'postfit_hook': lambda x: x.sparsify(),\n",
        "     'data': classification_data,\n",
        "     'n_samples': 30},\n",
        "    {'estimator': NuSVR,\n",
        "     'tuned_params': {'C': 1e3, 'gamma': 2 ** -15},\n",
        "     'changing_param': 'nu',\n",
        "     'changing_param_values': [0.1, 0.25, 0.5, 0.75, 0.9],\n",
        "     'complexity_label': 'n_support_vectors',\n",
        "     'complexity_computer': lambda x: len(x.support_vectors_),\n",
        "     'data': regression_data,\n",
        "     'postfit_hook': lambda x: x,\n",
        "     'prediction_performance_computer': mean_squared_error,\n",
        "     'prediction_performance_label': 'MSE',\n",
        "     'n_samples': 30},\n",
        "    {'estimator': GradientBoostingRegressor,\n",
        "     'tuned_params': {'loss': 'ls'},\n",
        "     'changing_param': 'n_estimators',\n",
        "     'changing_param_values': [10, 50, 100, 200, 500],\n",
        "     'complexity_label': 'n_trees',\n",
        "     'complexity_computer': lambda x: x.n_estimators,\n",
        "     'data': regression_data,\n",
        "     'postfit_hook': lambda x: x,\n",
        "     'prediction_performance_computer': mean_squared_error,\n",
        "     'prediction_performance_label': 'MSE',\n",
        "     'n_samples': 30},\n",
        "]\n",
        "for conf in configurations:\n",
        "    prediction_performances, prediction_times, complexities = \\\n",
        "        benchmark_influence(conf)\n",
        "    plot_influence(conf, prediction_performances, prediction_times,\n",
        "                   complexities)"
      ],
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Benchmarking SGDClassifier(alpha=0.001, average=False, class_weight=None,\n",
            "              early_stopping=False, epsilon=0.1, eta0=0.0, fit_intercept=True,\n",
            "              l1_ratio=0.25, learning_rate='optimal', loss='modified_huber',\n",
            "              max_iter=1000, n_iter_no_change=5, n_jobs=None,\n",
            "              penalty='elasticnet', power_t=0.5, random_state=None,\n",
            "              shuffle=True, tol=0.001, validation_fraction=0.1, verbose=0,\n",
            "              warm_start=False)\n",
            "Complexity: 4466 | Hamming Loss (Misclassification Ratio): 0.2491 | Pred. Time: 0.026187s\n",
            "\n",
            "Benchmarking SGDClassifier(alpha=0.001, average=False, class_weight=None,\n",
            "              early_stopping=False, epsilon=0.1, eta0=0.0, fit_intercept=True,\n",
            "              l1_ratio=0.5, learning_rate='optimal', loss='modified_huber',\n",
            "              max_iter=1000, n_iter_no_change=5, n_jobs=None,\n",
            "              penalty='elasticnet', power_t=0.5, random_state=None,\n",
            "              shuffle=True, tol=0.001, validation_fraction=0.1, verbose=0,\n",
            "              warm_start=False)\n",
            "Complexity: 1663 | Hamming Loss (Misclassification Ratio): 0.2915 | Pred. Time: 0.020132s\n",
            "\n",
            "Benchmarking SGDClassifier(alpha=0.001, average=False, class_weight=None,\n",
            "              early_stopping=False, epsilon=0.1, eta0=0.0, fit_intercept=True,\n",
            "              l1_ratio=0.75, learning_rate='optimal', loss='modified_huber',\n",
            "              max_iter=1000, n_iter_no_change=5, n_jobs=None,\n",
            "              penalty='elasticnet', power_t=0.5, random_state=None,\n",
            "              shuffle=True, tol=0.001, validation_fraction=0.1, verbose=0,\n",
            "              warm_start=False)\n",
            "Complexity: 880 | Hamming Loss (Misclassification Ratio): 0.3180 | Pred. Time: 0.015669s\n",
            "\n",
            "Benchmarking SGDClassifier(alpha=0.001, average=False, class_weight=None,\n",
            "              early_stopping=False, epsilon=0.1, eta0=0.0, fit_intercept=True,\n",
            "              l1_ratio=0.9, learning_rate='optimal', loss='modified_huber',\n",
            "              max_iter=1000, n_iter_no_change=5, n_jobs=None,\n",
            "              penalty='elasticnet', power_t=0.5, random_state=None,\n",
            "              shuffle=True, tol=0.001, validation_fraction=0.1, verbose=0,\n",
            "              warm_start=False)\n",
            "Complexity: 639 | Hamming Loss (Misclassification Ratio): 0.3337 | Pred. Time: 0.014430s\n",
            "\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
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CrK0RzKyDmX1vZj+a2bUZ3H6RmX1jZjPNbJqZ1Ymub2tm06PbpptZ62THmtOK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\nqkDJlIiIiEgV/D9r0YFkKUgCLgAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<Figure size 864x432 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "Benchmarking NuSVR(C=1000.0, cache_size=200, coef0=0.0, degree=3, gamma=3.0517578125e-05,\n",
            "      kernel='rbf', max_iter=-1, nu=0.1, shrinking=True, tol=0.001,\n",
            "      verbose=False)\n",
            "Complexity: 69 | MSE: 31.8139 | Pred. Time: 0.000315s\n",
            "\n",
            "Benchmarking NuSVR(C=1000.0, cache_size=200, coef0=0.0, degree=3, gamma=3.0517578125e-05,\n",
            "      kernel='rbf', max_iter=-1, nu=0.25, shrinking=True, tol=0.001,\n",
            "      verbose=False)\n",
            "Complexity: 136 | MSE: 25.6140 | Pred. Time: 0.000584s\n",
            "\n",
            "Benchmarking NuSVR(C=1000.0, cache_size=200, coef0=0.0, degree=3, gamma=3.0517578125e-05,\n",
            "      kernel='rbf', max_iter=-1, nu=0.5, shrinking=True, tol=0.001,\n",
            "      verbose=False)\n",
            "Complexity: 244 | MSE: 22.3375 | Pred. Time: 0.000995s\n",
            "\n",
            "Benchmarking NuSVR(C=1000.0, cache_size=200, coef0=0.0, degree=3, gamma=3.0517578125e-05,\n",
            "      kernel='rbf', max_iter=-1, nu=0.75, shrinking=True, tol=0.001,\n",
            "      verbose=False)\n",
            "Complexity: 351 | MSE: 21.3688 | Pred. Time: 0.001403s\n",
            "\n",
            "Benchmarking NuSVR(C=1000.0, cache_size=200, coef0=0.0, degree=3, gamma=3.0517578125e-05,\n",
            "      kernel='rbf', max_iter=-1, nu=0.9, shrinking=True, tol=0.001,\n",
            "      verbose=False)\n",
            "Complexity: 404 | MSE: 21.1033 | Pred. Time: 0.001619s\n",
            "\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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4cCATJ07cbyVpxowZjBgxgqlTp+K954wzzqBp06aULFmSpUuX8u677zJs2DCuuuoqxo4d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\nc64oMBbohm3B9QL6HOg+ycnJJCYm7v0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ca/nzz8OyZW2H7f7wh9QxXjFoUGuvU7H3afRo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\n4STJzMzMrISTJDMzM7MSTpLMzMzMSvw/027kvP75zT0AAAAASUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 864x432 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "Benchmarking GradientBoostingRegressor(alpha=0.9, criterion='friedman_mse', init=None,\n",
            "                          learning_rate=0.1, loss='ls', max_depth=3,\n",
            "                          max_features=None, max_leaf_nodes=None,\n",
            "                          min_impurity_decrease=0.0, min_impurity_split=None,\n",
            "                          min_samples_leaf=1, min_samples_split=2,\n",
            "                          min_weight_fraction_leaf=0.0, n_estimators=10,\n",
            "                          n_iter_no_change=None, presort='auto',\n",
            "                          random_state=None, subsample=1.0, tol=0.0001,\n",
            "                          validation_fraction=0.1, verbose=0, warm_start=False)\n",
            "Complexity: 10 | MSE: 29.0148 | Pred. Time: 0.000164s\n",
            "\n",
            "Benchmarking GradientBoostingRegressor(alpha=0.9, criterion='friedman_mse', init=None,\n",
            "                          learning_rate=0.1, loss='ls', max_depth=3,\n",
            "                          max_features=None, max_leaf_nodes=None,\n",
            "                          min_impurity_decrease=0.0, min_impurity_split=None,\n",
            "                          min_samples_leaf=1, min_samples_split=2,\n",
            "                          min_weight_fraction_leaf=0.0, n_estimators=50,\n",
            "                          n_iter_no_change=None, presort='auto',\n",
            "                          random_state=None, subsample=1.0, tol=0.0001,\n",
            "                          validation_fraction=0.1, verbose=0, warm_start=False)\n",
            "Complexity: 50 | MSE: 8.9630 | Pred. Time: 0.000272s\n",
            "\n",
            "Benchmarking GradientBoostingRegressor(alpha=0.9, criterion='friedman_mse', init=None,\n",
            "                          learning_rate=0.1, loss='ls', max_depth=3,\n",
            "                          max_features=None, max_leaf_nodes=None,\n",
            "                          min_impurity_decrease=0.0, min_impurity_split=None,\n",
            "                          min_samples_leaf=1, min_samples_split=2,\n",
            "                          min_weight_fraction_leaf=0.0, n_estimators=100,\n",
            "                          n_iter_no_change=None, presort='auto',\n",
            "                          random_state=None, subsample=1.0, tol=0.0001,\n",
            "                          validation_fraction=0.1, verbose=0, warm_start=False)\n",
            "Complexity: 100 | MSE: 7.7187 | Pred. Time: 0.000311s\n",
            "\n",
            "Benchmarking GradientBoostingRegressor(alpha=0.9, criterion='friedman_mse', init=None,\n",
            "                          learning_rate=0.1, loss='ls', max_depth=3,\n",
            "                          max_features=None, max_leaf_nodes=None,\n",
            "                          min_impurity_decrease=0.0, min_impurity_split=None,\n",
            "                          min_samples_leaf=1, min_samples_split=2,\n",
            "                          min_weight_fraction_leaf=0.0, n_estimators=200,\n",
            "                          n_iter_no_change=None, presort='auto',\n",
            "                          random_state=None, subsample=1.0, tol=0.0001,\n",
            "                          validation_fraction=0.1, verbose=0, warm_start=False)\n",
            "Complexity: 200 | MSE: 6.6955 | Pred. Time: 0.000482s\n",
            "\n",
            "Benchmarking GradientBoostingRegressor(alpha=0.9, criterion='friedman_mse', init=None,\n",
            "                          learning_rate=0.1, loss='ls', max_depth=3,\n",
            "                          max_features=None, max_leaf_nodes=None,\n",
            "                          min_impurity_decrease=0.0, min_impurity_split=None,\n",
            "                          min_samples_leaf=1, min_samples_split=2,\n",
            "                          min_weight_fraction_leaf=0.0, n_estimators=500,\n",
            "                          n_iter_no_change=None, presort='auto',\n",
            "                          random_state=None, subsample=1.0, tol=0.0001,\n",
            "                          validation_fraction=0.1, verbose=0, warm_start=False)\n",
            "Complexity: 500 | MSE: 7.1437 | Pred. Time: 0.001044s\n",
            "\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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r3HADnHQSrFsH778Po0crQcoHJUmBzBFuxawzv4iIlGQffQSNGlnVaOBAG+Lf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\nK+AgyczMzKyAgyQzMzOzAv8Pum9W7G1SXh0AAAAASUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 864x432 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Osr3LrvS4Bk6",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        ""
      ],
      "execution_count": 0,
      "outputs": []
    }
  ]
}