+ "print(__doc__)\n\nimport numpy as np\nimport matplotlib.pyplot as plt\n\n# To use the experimental IterativeImputer, we need to explicitly ask for it:\nfrom sklearn.experimental import enable_iterative_imputer # noqa\nfrom sklearn.datasets import load_diabetes\nfrom sklearn.datasets import load_boston\nfrom sklearn.ensemble import RandomForestRegressor\nfrom sklearn.pipeline import make_pipeline, make_union\nfrom sklearn.impute import SimpleImputer, IterativeImputer, MissingIndicator\nfrom sklearn.model_selection import cross_val_score\n\nrng = np.random.RandomState(0)\n\nN_SPLITS = 5\nREGRESSOR = RandomForestRegressor(random_state=0)\n\n\ndef get_scores_for_imputer(imputer, X_missing, y_missing):\n estimator = make_pipeline(\n make_union(imputer, MissingIndicator(missing_values=0)),\n REGRESSOR)\n impute_scores = cross_val_score(estimator, X_missing, y_missing,\n scoring='neg_mean_squared_error',\n cv=N_SPLITS)\n return impute_scores\n\n\ndef get_results(dataset):\n X_full, y_full = dataset.data, dataset.target\n n_samples = X_full.shape[0]\n n_features = X_full.shape[1]\n\n # Estimate the score on the entire dataset, with no missing values\n full_scores = cross_val_score(REGRESSOR, X_full, y_full,\n scoring='neg_mean_squared_error',\n cv=N_SPLITS)\n\n # Add missing values in 75% of the lines\n missing_rate = 0.75\n n_missing_samples = int(np.floor(n_samples * missing_rate))\n missing_samples = np.hstack((np.zeros(n_samples - n_missing_samples,\n dtype=np.bool),\n np.ones(n_missing_samples,\n dtype=np.bool)))\n rng.shuffle(missing_samples)\n missing_features = rng.randint(0, n_features, n_missing_samples)\n X_missing = X_full.copy()\n X_missing[np.where(missing_samples)[0], missing_features] = 0\n y_missing = y_full.copy()\n\n # Estimate the score after replacing missing values by 0\n imputer = SimpleImputer(missing_values=0,\n strategy='constant',\n fill_value=0)\n zero_impute_scores = get_scores_for_imputer(imputer, X_missing, y_missing)\n\n # Estimate the score after imputation (mean strategy) of the missing values\n imputer = SimpleImputer(missing_values=0, strategy=\"mean\")\n mean_impute_scores = get_scores_for_imputer(imputer, X_missing, y_missing)\n\n # Estimate the score after iterative imputation of the missing values\n imputer = IterativeImputer(missing_values=0,\n random_state=0,\n n_nearest_features=5,\n sample_posterior=True)\n iterative_impute_scores = get_scores_for_imputer(imputer,\n X_missing,\n y_missing)\n\n return ((full_scores.mean(), full_scores.std()),\n (zero_impute_scores.mean(), zero_impute_scores.std()),\n (mean_impute_scores.mean(), mean_impute_scores.std()),\n (iterative_impute_scores.mean(), iterative_impute_scores.std()))\n\n\nresults_diabetes = np.array(get_results(load_diabetes()))\nmses_diabetes = results_diabetes[:, 0] * -1\nstds_diabetes = results_diabetes[:, 1]\n\nresults_boston = np.array(get_results(load_boston()))\nmses_boston = results_boston[:, 0] * -1\nstds_boston = results_boston[:, 1]\n\nn_bars = len(mses_diabetes)\nxval = np.arange(n_bars)\n\nx_labels = ['Full data',\n 'Zero imputation',\n 'Mean Imputation',\n 'Multivariate Imputation']\ncolors = ['r', 'g', 'b', 'orange']\n\n# plot diabetes results\nplt.figure(figsize=(12, 6))\nax1 = plt.subplot(121)\nfor j in xval:\n ax1.barh(j, mses_diabetes[j], xerr=stds_diabetes[j],\n color=colors[j], alpha=0.6, align='center')\n\nax1.set_title('Imputation Techniques with Diabetes Data')\nax1.set_xlim(left=np.min(mses_diabetes) * 0.9,\n right=np.max(mses_diabetes) * 1.1)\nax1.set_yticks(xval)\nax1.set_xlabel('MSE')\nax1.invert_yaxis()\nax1.set_yticklabels(x_labels)\n\n# plot boston results\nax2 = plt.subplot(122)\nfor j in xval:\n ax2.barh(j, mses_boston[j], xerr=stds_boston[j],\n color=colors[j], alpha=0.6, align='center')\n\nax2.set_title('Imputation Techniques with Boston Data')\nax2.set_yticks(xval)\nax2.set_xlabel('MSE')\nax2.invert_yaxis()\nax2.set_yticklabels([''] * n_bars)\n\nplt.show()"
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