cross_val_score has created three different models for cross-validation.
Which did you want to use to impute?
After cross-validation you can fit the model on the whole dataset, although
this may be bad practice depending on how you want to use the model.
GridSearchCV is the common way to use cross-validation to evaluate
different parameter settings, then fit a final model with the best settings.
On 9 January 2015 at 08:50, Edward Fine <edward.f...@gmail.com> wrote:
> import numpy as np
>
> from sklearn.datasets import load_boston from sklearn.ensemble import
> RandomForestRegressor from sklearn.pipeline import Pipeline from
> sklearn.preprocessing import Imputer from sklearn.cross_validation import
> cross_val_score
>
> rng = np.random.RandomState(0)
>
> dataset = load_boston() X_full, y_full = dataset.data, dataset.target
> n_samples = X_full.shape[0] n_features = X_full.shape[1]
>
> estimator = RandomForestRegressor(random_state=0, n_estimators=100) score
> = cross_val_score(estimator, X_full, y_full).mean() print("Score with the
> entire dataset = %.2f" % score)
> Add missing values in 75% of the lines
>
> missing_rate = 0.75 n_missing_samples = np.floor(n_samples * missing_rate)
> missing_samples = np.hstack((np.zeros(n_samples - n_missing_samples,
> dtype=np.bool), np.ones(n_missing_samples, dtype=np.bool)))
> rng.shuffle(missing_samples) missing_features = rng.randint(0, n_features,
> n_missing_samples)
> Estimate the score without the lines containing missing values
>
> X_filtered = X_full[~missing_samples, :] y_filtered =
> y_full[~missing_samples] estimator = RandomForestRegressor(random_state=0,
> n_estimators=100) score = cross_val_score(estimator, X_filtered,
> y_filtered).mean() print("Score without the samples containing missing
> values = %.2f" % score)
> Estimate the score after imputation of the missing values
>
> X_missing = X_full.copy() X_missing[np.where(missing_samples)[0],
> missing_features] = 0 y_missing = y_full.copy() estimator =
> Pipeline([("imputer", Imputer(missing_values=0, strategy="mean", axis=0)),
> ("forest", RandomForestRegressor(random_state=0, n_estimators=100))]) score
> = cross_val_score(estimator, X_missing, y_missing).mean() print("Score
> after imputation of the missing values = %.2f" % score)
>
> This all runs fine.
>
> Now I want to predict, which should be simple, but
> estimator.predict(X=X_filtered[1:10,:])
>
> returns the following error:
>
> "AttributeError: 'Imputer' object has no attribute 'statistics_'"
>
> What is wrong here?
>
>
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sponsored by Intel and developed in partnership with Slashdot Media, is your
hub for all things parallel software development, from weekly thought
leadership blogs to news, videos, case studies, tutorials and more. Take a
look and join the conversation now. http://goparallel.sourceforge.net
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