StanとRでベイズ統計モデリング(アヒル本)をPythonにしてみる - 5.2 二項ロジスティック回帰

実行環境

インポート

import numpy as np
import pandas as pd
import pystan
import matplotlib.pyplot as plt
from matplotlib.figure import figaspect
from matplotlib import colors
from matplotlib.markers import MarkerStyle
%matplotlib inline

データ読み込み

attendance2 = pd.read_csv('./data/data-attendance-2.txt')
attendance2['A'] = pd.Categorical(attendance2['A'])

5.2 二項ロジスティック回帰

5.2.2 データの分布の確認

def pairplot(df, **kwargs):
    import numpy as np
    import matplotlib.pyplot as plt
    from matplotlib.figure import figaspect
    import seaborn as sns
    
    def corrplot(x, y, data, cmap=None, correlation='pearson', **kwargs):
        from scipy import stats
        from matplotlib.patches import Ellipse
        
        if correlation is 'spearman':
            r = stats.spearmanr(data[x], data[y])[0]
        else:
            r = stats.pearsonr(data[x], data[y])[0]
        if cmap is None:
            cmap = 'coolwarm'
        if type(cmap) is str:
            cmap = plt.get_cmap(cmap)
        color = cmap((r+1)/2)
        ax.axis('off')
        ax.add_artist(Ellipse((0.5, 0.5), width=np.sqrt(1+r), height=np.sqrt(1-r), angle=45, color=color))
        ax.text(0.5, 0.5, '{:.2f}'.format(r), size='x-large', horizontalalignment='center', verticalalignment='center')
    
    def crosstabplot(x, y, data, ax, **kwargs):
        import pandas as pd

        cross = pd.crosstab(data[x], data[y]).values
        size = cross / cross.max() * 500
        crosstab_kws = kwargs['crosstab_kws'] if 'crosstab_kws' in kwargs else {}
        scatter_kws = dict(color=sns.color_palette()[0], alpha=0.3)
        scatter_kws.update(crosstab_kws['scatter_kws'] if 'scatter_kws' in crosstab_kws else {})
        text_kws = dict(size='x-large')
        text_kws.update(crosstab_kws['text_kws'] if 'text_kws' in crosstab_kws else {})
        for (xx, yy), count in np.ndenumerate(cross):
            ax.scatter(xx, yy, s=size[xx, yy], **scatter_kws)
            ax.text(xx, yy, count, horizontalalignment='center', verticalalignment='center', **text_kws)
    
    def catplot(x, y, hue, data, orient, ax, **kwargs):
        box_kws = dict(color='w')
        box_kws.update(kwargs['box_kws'] if 'box_kws' in kwargs else {})
        sns.boxplot(x, y, data=data, orient=orient, ax=ax, **box_kws)
        strip_kws = dict(color=None if hue else sns.color_palette()[0])
        strip_kws.update(kwargs['strip_kws'] if 'strip_kws' in kwargs else {})
        sns.stripplot(x, y, hue, data, orient=orient, ax=ax, **strip_kws)
        legend = ax.get_legend()
        if legend:
            plt.setp(legend, visible=False)
    
    def scatterplot(x, y, hue, data, ax, **kwargs):
        if hue:
            hues = data[hue].unique()
            colors = sns.color_palette(n_colors=hues.size)
            for h, c in zip(hues, colors):
                ax.scatter(x, y, data=data.query('{col}=={value}'.format(col=hue, value=h)), color=c, **kwargs)
        else:
            ax.scatter(x, y, data=data, **kwargs)
    
    n_variables = df.columns.size
    hue = kwargs['hue'] if 'hue' in kwargs else None
    figsize = kwargs['figsize'] if 'figsize' in kwargs else figaspect(1) * 0.5 * n_variables
    _, axes = plt.subplots(n_variables, n_variables, figsize=figsize)
    plt.subplots_adjust(hspace=0.1, wspace=0.1)
    
    for i in range(n_variables):
        axes[i, i].get_shared_x_axes().join(*axes[i:n_variables, i])
        if i > 1:
            axes[i, 0].get_shared_y_axes().join(*axes[i, :i-1])

    for (row, col), ax in np.ndenumerate(axes):
        x = df.columns[col]
        y = df.columns[row]
        x_data = df[x]
        y_data = df[y]
        x_dtype = x_data.dtype.name
        y_dtype = y_data.dtype.name
        if x_dtype == 'category':
            x_categories = x_data.cat.categories
        if y_dtype == 'category':
            y_categories = y_data.cat.categories
        
        if row == col: # diagonal
            hue_data = df[hue] if hue else None
            if x_dtype == 'category':
                bar_kws = dict(alpha=0.4)
                bar_kws.update(kwargs['bar_kws'] if 'bar_kws' in kwargs else {})
                if hue:
                    cross = pd.crosstab(x_data, hue_data)
                    cross.index = cross.index.categories
                    cross.columns = cross.columns.categories if hue_data.dtype.name == 'category' else hue_data.unique()
                    cross.reset_index(inplace=True)
                    melt = pd.melt(cross, id_vars='index',var_name='hue')
                    sns.barplot('index', 'value', 'hue', data=melt, ci=None, orient='v', dodge=False, ax=ax, **bar_kws)
                    plt.setp(ax.get_legend(), visible=False)
                else:
                    cross = pd.crosstab(x_data, []).values.ravel()
                    sns.barplot(x_data.cat.categories, cross, ci=None, orient='v', color=sns.color_palette()[0], ax=ax, **bar_kws)
            else:
                dist_kws = kwargs['dist_kws'] if 'dist_kws' in kwargs else {}
                if hue:
                    hist_kws = dict(color=sns.color_palette(n_colors=hue_data.unique().size), alpha=0.4)
                    hist_kws.update(dist_kws['hist_kws'] if 'hist_kws' in dist_kws else {})
                    hue_values = df[hue].cat.categories if hue_data.dtype.name == 'category' else df[hue].unique()
                    ax.hist([df.query('{hue}=={v}'.format(hue=hue, v=v))[x] for v in hue_values], density=True, histtype='barstacked', **hist_kws)
                    sns.distplot(x_data, hist=False, ax=ax, **dist_kws)
                else:
                    sns.distplot(x_data, ax=ax, **dist_kws)
        elif row < col: # upper
            corr_kws = kwargs['corr_kws'] if 'corr_kws' in kwargs else {}
            corrplot(x, y, data=df, **corr_kws)
        else: # lower
            if x_dtype == 'category' and y_dtype == 'category':
                crosstabplot(x, y, data=df, ax=ax)
            else:
                cat_kws = kwargs['cat_kws'] if 'cat_kws' in kwargs else {}
                if x_dtype == 'category':
                    catplot(x, y, hue, df, 'v', ax, **cat_kws)
                elif y_dtype == 'category':
                    catplot(x, y, hue, df, 'h', ax, **cat_kws)
                else:
                    scatter_kws = kwargs['scatter_kws'] if 'scatter_kws' in kwargs else {}
                    scatterplot(x, y, hue, df, ax, **scatter_kws)
        if row < n_variables-1:
            plt.setp(ax, xlabel='')
            plt.setp(ax.get_xticklabels(), visible=False)
        else:
            plt.setp(ax, xlabel=x)
            if x_dtype == 'category':
                plt.setp(ax, xticks=np.arange(x_categories.size), xticklabels=x_data.cat.categories)
        if col > 0:
            plt.setp(ax, ylabel='')
            plt.setp(ax.get_yticklabels(), visible=False)
        else:
            plt.setp(ax, ylabel=y)
            if row > 0 and y_dtype == 'category':
                plt.setp(ax, yticks=np.arange(y_categories.size), yticklabels=y_data.cat.categories)
    
    return axes

attendance2['ratio'] = attendance2['Y'] / attendance2['M']
pairplot(attendance2.loc[:, 'A':'ratio'], hue='A', corr_kws=dict(correlation='spearman'))
plt.show()

5.2.5 Stanで実装

data = dict(
    N=attendance2.index.size,
    A=attendance2['A'],
    Score=attendance2['Score']/200,
    M=attendance2['M'],
    Y=attendance2['Y']
)
fit = pystan.stan('./stan/model5-4.stan', data=data, seed=1234)

5.2.6 推定結果の解釈

fit

出力は省略

5.2.7 図によるモデルのチェック

ms = fit.extract()

prob = [10, 50, 90]
d_qua = np.percentile(ms['y_pred'], prob, axis=0).T
d_qua = pd.DataFrame(np.hstack((attendance2, d_qua)), columns=attendance2.columns.tolist()+['p{}'.format(p) for p in prob])

plt.figure(figsize=figaspect(1))
ax = plt.axes()
c = ['black', 'gray']
for v in d_qua['A'].unique().astype(int):
    d_part = d_qua.query('A==@v')
    err_lower = d_part['p50'] - d_part['p10']
    err_upper = d_part['p90'] - d_part['p50']
    ax.errorbar('Y', 'p50', data=d_part, yerr=[err_lower, err_upper], fmt='.', color=colors.to_rgb(c[v]), marker=MarkerStyle.filled_markers[int(v)], label='A={}'.format(v))
# ax.axline(1, 1)
_, xmax = ax.get_xlim()
_, ymax = ax.get_ylim()
lim = (0, xmax if xmax > ymax else ymax)
ax.plot(lim, lim, c='k', alpha=3/5)
ax.legend()
plt.setp(ax, xlim=lim, ylim=lim, xlabel='Observed', ylabel='Predicted')
plt.show()