def main(loader, name):
    """
    Here we iterate through the datasets and score them with a classifier using different encodings.

    """

    scores = []
    raw_scores_ds = {}

    # first get the dataset
    X, y, mapping = loader()

    clf = linear_model.LogisticRegression(solver='lbfgs', multi_class='auto', max_iter=200, random_state=0)

    # try each encoding method available, which works on multiclass problems
    encoders = (set(category_encoders.__all__) - {'WOEEncoder'})  # WoE is currently only for binary targets

    for encoder_name in encoders:
        encoder = getattr(category_encoders, encoder_name)
        start_time = time.time()
        score, stds, raw_scores, dim = score_models(clf, X, y, encoder)
        scores.append([encoder_name, name, dim, score, stds, time.time() - start_time])
        raw_scores_ds[encoder_name] = raw_scores
        gc.collect()

    results = pd.DataFrame(scores, columns=['Encoding', 'Dataset', 'Dimensionality', 'Avg. Score', 'Score StDev', 'Elapsed Time'])

    raw = pd.DataFrame.from_dict(raw_scores_ds)
    ax = raw.plot(kind='box', return_type='axes')
    plt.title('Scores for Encodings on %s Dataset' % (name,))
    plt.ylabel('Score (higher is better)')
    for tick in ax.get_xticklabels():
        tick.set_rotation(90)
    plt.grid()
    plt.tight_layout()
    plt.show()

    return results, raw
def main(loader, name):
    """
    Here we iterate through the datasets and score them with a classifier using different encodings.

    """

    scores = []
    raw_scores_ds = {}

    # first get the dataset
    X, y, mapping = loader()

    clf = linear_model.LogisticRegression(solver='lbfgs', multi_class='auto', max_iter=200, random_state=0)

    # try each encoding method available, which works on multiclass problems
    encoders = (set(category_encoders.__all__) - {'WOEEncoder'})  # WoE is currently only for binary targets

    for encoder_name in encoders:
        encoder = getattr(category_encoders, encoder_name)
        start_time = time.time()
        score, stds, raw_scores, dim = score_models(clf, X, y, encoder)
        scores.append([encoder_name, name, dim, score, stds, time.time() - start_time])
        raw_scores_ds[encoder_name] = raw_scores
        gc.collect()

    results = pd.DataFrame(scores, columns=['Encoding', 'Dataset', 'Dimensionality', 'Avg. Score', 'Score StDev', 'Elapsed Time'])

    raw = pd.DataFrame.from_dict(raw_scores_ds)
    ax = raw.plot(kind='box', return_type='axes')
    plt.title('Scores for Encodings on %s Dataset' % (name,))
    plt.ylabel('Score (higher is better)')
    for tick in ax.get_xticklabels():
        tick.set_rotation(90)
    plt.grid()
    plt.tight_layout()
    plt.show()

    return results, raw
def main ( loader , name ) : scores = [ ] raw_scores_ds = { } # first get the dataset X , y , mapping = loader ( ) clf = linear_model . LogisticRegression ( solver = 'lbfgs' , multi_class = 'auto' , max_iter = 200 , random_state = 0 ) # try each encoding method available, which works on multiclass problems encoders = ( set ( category_encoders . __all__ ) - { 'WOEEncoder' } ) # WoE is currently only for binary targets for encoder_name in encoders : encoder = getattr ( category_encoders , encoder_name ) start_time = time . time ( ) score , stds , raw_scores , dim = score_models ( clf , X , y , encoder ) scores . append ( [ encoder_name , name , dim , score , stds , time . time ( ) - start_time ] ) raw_scores_ds [ encoder_name ] = raw_scores gc . collect ( ) results = pd . DataFrame ( scores , columns = [ 'Encoding' , 'Dataset' , 'Dimensionality' , 'Avg. Score' , 'Score StDev' , 'Elapsed Time' ] ) raw = pd . DataFrame . from_dict ( raw_scores_ds ) ax = raw . plot ( kind = 'box' , return_type = 'axes' ) plt . title ( 'Scores for Encodings on %s Dataset' % ( name , ) ) plt . ylabel ( 'Score (higher is better)' ) for tick in ax . get_xticklabels ( ) : tick . set_rotation ( 90 ) plt . grid ( ) plt . tight_layout ( ) plt . show ( ) return results , raw
Here we iterate through the datasets and score them with a classifier using different encodings.
Here we iterate through the datasets and score them with a classifier using different encodings .