Python apply_function Examples

Example #1

File: results-dexterity-prediction-network.py Project: robbisg/mvpa_itab_wu

pipeline = 'dexterity+prediction+network'

path = "/media/robbis/DATA/meg/viviana-hcp/derivatives/pipeline-%s/" % (
    pipeline)
dataframe = get_results_bids(path,
                             field_list=loaded_keywords,
                             pipeline=[pipeline],
                             scores=scores)

for score in scores:
    score_key = 'score_' + score
    for clf in np.unique(dataframe['estimator__clf']):
        for t in np.unique(dataframe['task']):
            df = filter_dataframe(dataframe,
                                  estimator__clf=[clf],
                                  fx=[score],
                                  task=[t])

            df = apply_function(
                df,
                keys=['nodes_1', 'nodes_2', 'band', 'estimator__clf', 'fx'],
                attr=score_key,
                fx=lambda x: np.vstack(x).mean())

            grid = sns.FacetGrid(df, col="band")
            grid.map(plot_heatmap, "nodes_1", "nodes_2", score_key)

            fname = "/home/robbis/Dropbox/PhD/experiments/blp-hcp-viviana/figure-regression-%s.png" % (
                t)
            grid.savefig(fname, dpi=150)

Example #2

File: results.py Project: robbisg/mvpa_itab_wu

from pyitab.results import get_results_bids, filter_dataframe, apply_function
import seaborn as sns
import h5py

path = "/media/robbis/DATA/meg/viviana-hcp/derivatives/pipeline-blp-dexterity-trial"
dataframe = get_results_bids(path, 
                field_list=['sample_slicer','estimator__clf', 'feature_slicer'], 
                pipeline=['raw'],
                )

dataframe['networks'] = dataframe['nodes_1']+'+'+dataframe['nodes_2']

average_df = apply_function(dataframe, 
                            keys=['nodes_1', 'nodes_2', 'band', 'task', 'estimator__clf'], 
                            attr='score_accuracy', 
                            fx=lambda x:np.vstack(x).mean())

grid = sns.FacetGrid(dataframe, col="band", row="task", palette="tab20c",
                     height=1.5)
grid.map(sns.barplot, "networks", 'score_accuracy')

def plot_heatmap(nodes1, nodes2, accuracy, **kwargs):

    df = dict(n1=nodes1, n2=nodes2, a=accuracy)
    df = pd.DataFrame(df)
    pdf = df.pivot("n1", "n2", "a")

    nz = np.nonzero(np.isnan(pdf.values))
    pdf.values[nz] = pdf.values[nz[::-1]]

    sns.heatmap(pdf, annot=True, cmap="RdBu", fmt=".2f", center=.5, vmax=.8)

Example #3

File: results-dexterity-prediction.py Project: robbisg/mvpa_itab_wu

                       markersize=10,
                       markeredgecolor=None,
                       palette='Dark2')

    axes = grid.axes

    score_key = 'score_' + score

    for clf in ['Lasso(alpha=0.5)']:

        score_df = filter_dataframe(dataframe,
                                    fx=[score],
                                    estimator__clf=[clf])
        average_df_score = apply_function(
            score_df,
            keys=['band', 'task', 'target_transformer__attr'],
            attr=score_key,
            fx=lambda x: np.nanmean(np.vstack(x)))

        perm_df = filter_dataframe(dataframe_perm,
                                   fx=[score],
                                   estimator__clf=[clf])

        average_df = apply_function(perm_df,
                                    keys=[
                                        'band', 'task', 'perm',
                                        'estimator__clf',
                                        'target_transformer__attr'
                                    ],
                                    attr=score_key,
                                    fx=lambda x: np.nanmean(np.vstack(x)))