129 lines
5.0 KiB
Python
129 lines
5.0 KiB
Python
"""
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====================================================
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Imputing missing values before building an estimator
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====================================================
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Missing values can be replaced by the mean, the median or the most frequent
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value using the basic ``SimpleImputer``.
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The median is a more robust estimator for data with high magnitude variables
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which could dominate results (otherwise known as a 'long tail').
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Another option is the ``ChainedImputer``. This uses round-robin linear
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regression, treating every variable as an output in turn. The version
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implemented assumes Gaussian (output) variables. If your features are obviously
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non-Normal, consider transforming them to look more Normal so as to improve
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performance.
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"""
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import numpy as np
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import matplotlib.pyplot as plt
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from sklearn.datasets import load_diabetes
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from sklearn.datasets import load_boston
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from sklearn.ensemble import RandomForestRegressor
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from sklearn.pipeline import Pipeline
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from sklearn.impute import SimpleImputer, ChainedImputer
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from sklearn.model_selection import cross_val_score
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rng = np.random.RandomState(0)
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def get_results(dataset):
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X_full, y_full = dataset.data, dataset.target
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n_samples = X_full.shape[0]
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n_features = X_full.shape[1]
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# Estimate the score on the entire dataset, with no missing values
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estimator = RandomForestRegressor(random_state=0, n_estimators=100)
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full_scores = cross_val_score(estimator, X_full, y_full,
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scoring='neg_mean_squared_error')
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# Add missing values in 75% of the lines
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missing_rate = 0.75
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n_missing_samples = int(np.floor(n_samples * missing_rate))
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missing_samples = np.hstack((np.zeros(n_samples - n_missing_samples,
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dtype=np.bool),
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np.ones(n_missing_samples,
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dtype=np.bool)))
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rng.shuffle(missing_samples)
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missing_features = rng.randint(0, n_features, n_missing_samples)
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# Estimate the score after replacing missing values by 0
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X_missing = X_full.copy()
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X_missing[np.where(missing_samples)[0], missing_features] = 0
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y_missing = y_full.copy()
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estimator = RandomForestRegressor(random_state=0, n_estimators=100)
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zero_impute_scores = cross_val_score(estimator, X_missing, y_missing,
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scoring='neg_mean_squared_error')
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# Estimate the score after imputation (mean strategy) of the missing values
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X_missing = X_full.copy()
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X_missing[np.where(missing_samples)[0], missing_features] = 0
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y_missing = y_full.copy()
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estimator = Pipeline([("imputer", SimpleImputer(missing_values=0,
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strategy="mean")),
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("forest", RandomForestRegressor(random_state=0,
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n_estimators=100))])
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mean_impute_scores = cross_val_score(estimator, X_missing, y_missing,
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scoring='neg_mean_squared_error')
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# Estimate the score after chained imputation of the missing values
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estimator = Pipeline([("imputer", ChainedImputer(missing_values=0,
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random_state=0)),
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("forest", RandomForestRegressor(random_state=0,
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n_estimators=100))])
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chained_impute_scores = cross_val_score(estimator, X_missing, y_missing,
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scoring='neg_mean_squared_error')
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return ((full_scores.mean(), full_scores.std()),
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(zero_impute_scores.mean(), zero_impute_scores.std()),
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(mean_impute_scores.mean(), mean_impute_scores.std()),
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(chained_impute_scores.mean(), chained_impute_scores.std()))
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results_diabetes = np.array(get_results(load_diabetes()))
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mses_diabetes = results_diabetes[:, 0] * -1
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stds_diabetes = results_diabetes[:, 1]
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results_boston = np.array(get_results(load_boston()))
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mses_boston = results_boston[:, 0] * -1
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stds_boston = results_boston[:, 1]
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n_bars = len(mses_diabetes)
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xval = np.arange(n_bars)
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x_labels = ['Full data',
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'Zero imputation',
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'Mean Imputation',
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'Chained Imputation']
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colors = ['r', 'g', 'b', 'orange']
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# plot diabetes results
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plt.figure(figsize=(12, 6))
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ax1 = plt.subplot(121)
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for j in xval:
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ax1.barh(j, mses_diabetes[j], xerr=stds_diabetes[j],
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color=colors[j], alpha=0.6, align='center')
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ax1.set_title('Imputation Techniques with Diabetes Data')
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ax1.set_xlim(left=np.min(mses_diabetes) * 0.9,
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right=np.max(mses_diabetes) * 1.1)
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ax1.set_yticks(xval)
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ax1.set_xlabel('MSE')
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ax1.invert_yaxis()
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ax1.set_yticklabels(x_labels)
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# plot boston results
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ax2 = plt.subplot(122)
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for j in xval:
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ax2.barh(j, mses_boston[j], xerr=stds_boston[j],
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color=colors[j], alpha=0.6, align='center')
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ax2.set_title('Imputation Techniques with Boston Data')
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ax2.set_yticks(xval)
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ax2.set_xlabel('MSE')
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ax2.invert_yaxis()
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ax2.set_yticklabels([''] * n_bars)
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plt.show()
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