def plot_EFA_relationships(all_results):
EFA_all_results = {k:v.EFA for k,v in all_results.items()}
scores = {k:v.get_scores() for k,v in EFA_all_results.items()}
# quantify relationships using linear regression
for name1, name2 in combinations(scores.keys(), 2):
scores1 = scores[name1]
scores2 = scores[name2]
lr = LinearRegression()
cv_score = np.mean(cross_val_score(lr, scores1, scores2, cv=10))
print(name1, name2, cv_score)
# plot
# plot task factors in task PCA space
pca = PCA(2)
task_pca = pca.fit_transform(scores['task'])
palettes = ['Reds', 'Blues', 'Greens']
all_colors = []
# plot scores in task PCA space
f, ax = plt.subplots(figsize=[12,8])
ax.set_facecolor('white')
for k,v in scores.items():
palette = sns.color_palette(palettes.pop(), n_colors = len(v.columns))
all_colors += palette
lr = LinearRegression()
lr.fit(task_pca, v)
for i, coef in enumerate(lr.coef_):
plt.plot([0,coef[0]], [0, coef[1]], linewidth=3,
c=palette[i], label=k+'_'+str(v.columns[i]))
leg = plt.legend(bbox_to_anchor=(.8, .5))
frame = leg.get_frame()
frame.set_color('black')
beautify_legend(leg, all_colors)
def plot_EFA_relationships(all_results):
EFA_all_results = {k: v.EFA for k, v in all_results.items()}
scores = {k: v.get_scores() for k, v in EFA_all_results.items()}
# quantify relationships using linear regression
for name1, name2 in combinations(scores.keys(), 2):
scores1 = scores[name1]
scores2 = scores[name2]
lr = LinearRegression()
cv_score = np.mean(cross_val_score(lr, scores1, scores2, cv=10))
print(name1, name2, cv_score)
# plot
# plot task factors in task PCA space
pca = PCA(2)
task_pca = pca.fit_transform(scores['task'])
palettes = ['Reds', 'Blues', 'Greens']
all_colors = []
# plot scores in task PCA space
f, ax = plt.subplots(figsize=[12, 8])
ax.set_facecolor('white')
for k, v in scores.items():
palette = sns.color_palette(palettes.pop(), n_colors=len(v.columns))
all_colors += palette
lr = LinearRegression()
lr.fit(task_pca, v)
for i, coef in enumerate(lr.coef_):
plt.plot([0, coef[0]], [0, coef[1]],
linewidth=3,
c=palette[i],
label=k + '_' + str(v.columns[i]))
leg = plt.legend(bbox_to_anchor=(.8, .5))
frame = leg.get_frame()
frame.set_color('black')
beautify_legend(leg, all_colors)
def visualize_task_factors(task_loadings, ax, xticklabels=True, label_size=12,
yticklabels=False, pad=0, ymax=None, legend=True):
"""Plot task loadings on one axis"""
n_measures = len(task_loadings)
colors = sns.hls_palette(len(task_loadings), l=.4, s=.8)
for i, (name, DV) in enumerate(task_loadings.iterrows()):
plot_loadings(ax, abs(DV)+pad, width_scale=1/(n_measures),
colors = [colors[i]], offset=i+.5,
kind='line',
plot_kws={'label': name, 'alpha': .8})
# set up yticks
if ymax:
ax.set_ylim(top=ymax)
ytick_locs = ax.yaxis.get_ticklocs()
new_yticks = np.linspace(0, ytick_locs[-1], 7)
ax.set_yticks(new_yticks)
if yticklabels:
labels = np.round(new_yticks,2)
replace_dict = {i:'' for i in labels[::2]}
labels = [replace_dict.get(i, i) for i in labels]
ax.set_yticklabels(labels)
# set up x ticks
xtick_locs = np.arange(0.0, 2*np.pi, 2*np.pi/len(DV))
ax.set_xticks(xtick_locs)
ax.set_xticks(xtick_locs+np.pi/len(DV), minor=True)
if xticklabels:
labels = task_loadings.columns
if type(labels[0]) != str:
labels = ['Fac %s' % str(i) for i in labels]
scale = 1.2
size = ax.get_position().expanded(scale, scale)
ax2=ax.get_figure().add_axes(size,zorder=2)
max_var_length = max([len(v) for v in labels])
for i, var in enumerate(labels):
offset=.3*25/len(labels)**2
start = (i-offset)*2*np.pi/len(labels)
end = (i+(1-offset))*2*np.pi/len(labels)
curve = [
np.cos(np.linspace(start,end,100)),
np.sin(np.linspace(start,end,100))
]
plt.plot(*curve, alpha=0)
# pad strings to longest length
num_spaces = (max_var_length-len(var))
var = ' '*(num_spaces//2) + var + ' '*(num_spaces-num_spaces//2)
curvetext = CurvedText(
x = curve[0][::-1],
y = curve[1][::-1],
text=var, #'this this is a very, very long text',
va = 'top',
axes = ax2,
fontsize=label_size##calls ax.add_artist in __init__
)
ax2.axis('off')
if legend:
leg = ax.legend(loc='upper center', bbox_to_anchor=(.5,-.15), frameon=False)
beautify_legend(leg, colors[:len(task_loadings)])
def importance_bar_plots(predictions,
target_order=None,
show_sign=True,
colorbar=True,
size=5,
dpi=300,
filename=None):
#palette = sns.cubehelix_palette(100)
# plot
if target_order is None:
target_order = predictions.keys()
n_predictors = len(predictions[list(target_order)[0]]['importances'][0])
#set up color styling
palette = sns.color_palette('Blues_d', n_predictors)
# get max r2
max_r2 = 0
vals = [predictions[i] for i in target_order]
max_r2 = max(max_r2, max([i['scores_cv'][0]['R2'] for i in vals]))
importances = [(i['predvars'], i['importances'][0]) for i in vals]
prediction_df = pd.DataFrame([i[1] for i in importances],
columns=importances[0][0],
index=target_order)
prediction_df.sort_values(axis=1,
by=prediction_df.index[0],
inplace=True,
ascending=False)
# plot
sns.set_style('white')
ax = prediction_df.plot(kind='bar',
edgecolor=None,
linewidth=0,
figsize=(size, size * .67),
color=palette)
fig = ax.get_figure()
ax.tick_params(labelsize=size)
#ax.tick_params(axis='x', rotation=0)
ax.set_ylabel(r'Standardized $\beta$', fontsize=size * 1.5)
# set up legend and other aesthetic
ax.grid(axis='y', linewidth=size / 10)
leg = ax.legend(frameon=False,
fontsize=size * 1.5,
bbox_to_anchor=(1.25, .8),
handlelength=0,
handletextpad=0,
framealpha=1)
beautify_legend(leg, colors=palette)
for name, spine in ax.spines.items():
spine.set_visible(False)
if filename is not None:
save_figure(fig, filename, {'bbox_inches': 'tight', 'dpi': dpi})
plt.close()
else:
return fig
def plot_BIC(all_results, size=4.6, dpi=300, ext='png', plot_dir=None):
""" Plots BIC and SABIC curves
Args:
all_results: a dimensional structure all_results object
dpi: the final dpi for the image
ext: the extension for the saved figure
plot_dir: the directory to save the figure. If none, do not save
"""
all_colors = [sns.color_palette('Blues_d',3)[0:3],
sns.color_palette('Reds_d',3)[0:3],
sns.color_palette('Greens_d',3)[0:3],
sns.color_palette('Oranges_d',3)[0:3]]
height= size*.75/len(all_results)
with sns.axes_style('white'):
fig, axes = plt.subplots(1, len(all_results), figsize=(size, height))
for i, results in enumerate([all_results[key] for key in ['task','survey']]):
ax1 = axes[i]
name = results.ID.split('_')[0].title()
EFA = results.EFA
# Plot BIC and SABIC curves
colors = all_colors[i]
with sns.axes_style('white'):
x = list(EFA.results['cscores_metric-BIC'].keys())
# score keys
keys = [k for k in EFA.results.keys() if 'cscores' in k]
for key in keys:
metric = key.split('-')[-1]
BIC_scores = [EFA.results[key][i] for i in x]
BIC_c = EFA.results['c_metric-%s' % metric]
ax1.plot(x, BIC_scores, 'o-', c=colors[0], lw=size/6, label=metric,
markersize=height*2)
ax1.plot(BIC_c, BIC_scores[BIC_c-1], '.', color='white',
markeredgecolor=colors[0], markeredgewidth=height/2,
markersize=height*4)
if i==0:
if len(keys)>1:
ax1.set_ylabel('Score', fontsize=height*3)
leg = ax1.legend(loc='center right',
fontsize=height*3, markerscale=0)
beautify_legend(leg, colors=colors)
else:
ax1.set_ylabel(metric, fontsize=height*4)
ax1.set_xlabel('# Factors', fontsize=height*4)
ax1.set_xticks(x)
ax1.set_xticklabels(x)
ax1.tick_params(labelsize=height*2, pad=size/4, length=0)
ax1.set_title(name, fontsize=height*4, y=1.01)
ax1.grid(linewidth=size/8)
[i.set_linewidth(size*.1) for i in ax1.spines.values()]
if plot_dir is not None:
save_figure(fig, path.join(plot_dir, 'BIC_curves.%s' % ext),
{'bbox_inches': 'tight', 'dpi': dpi})
plt.close()
def plot_prediction_comparison(results,
size=4.6,
change=False,
dpi=300,
ext='png',
plot_dir=None):
colors = ref_colors[results.ID.split('_')[0]]
R2s = {}
for EFA in [False, True]:
predictions = results.get_prediction_files(EFA=EFA,
change=change,
shuffle=False)
predictions = sorted(predictions, key=path.getmtime)
classifiers = np.unique([i.split('_')[-2] for i in predictions])
# get last prediction file of each type
for classifier in classifiers:
filey = [i for i in predictions if classifier in i][-1]
prediction_object = pickle.load(open(filey, 'rb'))['data']
R2 = [i['scores_cv'][0]['R2'] for i in prediction_object.values()]
R2 = np.nan_to_num(R2)
feature = 'EFA' if EFA else 'IDM'
R2s[feature + '_' + classifier] = R2
if len(R2s) == 0:
print('No prediction objects found')
return
R2s = pd.DataFrame(R2s).melt(var_name='Classifier', value_name='R2')
R2s['Feature'], R2s['Classifier'] = R2s.Classifier.str.split('_', 1).str
f = plt.figure(figsize=(size, size * .62))
sns.barplot(x='Classifier',
y='R2',
data=R2s,
hue='Feature',
palette=colors[:2],
errwidth=size / 5)
ax = plt.gca()
ax.tick_params(axis='y', labelsize=size * 1.8)
ax.tick_params(axis='x', labelsize=size * 1.8)
leg = ax.legend(fontsize=size * 2, loc='upper right')
beautify_legend(leg, colors[:2])
plt.xlabel('Classifier', fontsize=size * 2.2, labelpad=size / 2)
plt.ylabel('R2', fontsize=size * 2.2, labelpad=size / 2)
plt.title('Comparison of Prediction Methods', fontsize=size * 2.5, y=1.05)
if plot_dir is not None:
filename = 'prediction_comparison.%s' % ext
save_figure(f, path.join(plot_dir, filename), {
'bbox_inches': 'tight',
'dpi': dpi
})
plt.close()
def plot_vars(tasks, contrasts, axes=None, xlabel='Value', standardize=False):
colors = sns.hls_palette(4)
desat_colors = [sns.desaturate(c, .5) for c in colors]
for i, task in enumerate(tasks):
subset = contrasts.filter(regex='^' + task)
if subset.shape[1] != 0:
if standardize:
subset = subset / subset.std()
subset.columns = [c.split('.')[1] for c in subset.columns]
subset.columns = format_variable_names(subset.columns)
# add mean value to columns
means = subset.mean()
subset.columns = [
subset.columns[i] + ': %s' % format_num(means.iloc[i])
for i in range(len(means))
]
subset = subset.melt(var_name='Variable', value_name='Value')
sns.stripplot(x='Value',
y='Variable',
hue='Variable',
ax=axes[i],
data=subset,
palette=desat_colors,
jitter=True,
alpha=.75)
# plot central tendency
N = len(means)
axes[i].scatter(means,
range(N),
s=200,
c=colors[:N],
edgecolors='white',
linewidths=2,
zorder=3)
# add legend
leg = axes[i].get_legend()
leg.set_title('')
beautify_legend(leg, colors=colors, fontsize=14)
# change axes
max_val = subset.Value.abs().max()
axes[i].set_xlim(-max_val, max_val)
axes[i].set_xlabel(xlabel, fontsize=16)
axes[i].set_ylabel('')
axes[i].set_yticklabels('')
axes[i].set_title(format_variable_names([task])[0].title(),
fontsize=20)
plt.subplots_adjust(hspace=.3)
def plot_DDM(results,
c,
rotate='oblimin',
dpi=300,
figsize=(20, 8),
ext='png',
plot_dir=None):
EFA = results.EFA
loading = abs(EFA.get_loading(c, rotate=rotate))
cats = []
for i in loading.index:
if 'drift' in i:
cats.append('Drift')
elif 'thresh' in i:
cats.append('Thresh')
elif 'non_decision' in i:
cats.append('Non-Decision')
else:
cats.append('Misc')
loading.insert(0, 'category', cats)
# plotting
colors = sns.color_palette("Set1", 8, .75)
color_map = {v: i for i, v in enumerate(loading.category.unique())}
fig = plt.figure(figsize=(12, 12))
ax = fig.add_subplot(111, projection='3d')
for name, group in loading.groupby('category'):
ax.scatter(group['Speeded IP'],
group['Caution'],
group['Perc / Resp'],
marker='o',
s=150,
c=colors[color_map[name]],
label=name)
ax.tick_params(labelsize=0, length=0)
ax.set_xlabel('Speeded IP', fontsize=20)
ax.set_ylabel('Caution', fontsize=20)
ax.set_zlabel('Perc / Resp', fontsize=20)
ax.view_init(30, 30)
leg = plt.legend(fontsize=20)
beautify_legend(leg, colors)
if plot_dir is not None:
fig.savefig(path.join(plot_dir, 'DDM_factors.%s' % ext),
bbox_inches='tight',
dpi=dpi)
plt.close()
def plot_BIC_SABIC(results, size=2.3, dpi=300, ext='png', plot_dir=None):
""" Plots BIC and SABIC curves
Args:
results: a dimensional structure results object
dpi: the final dpi for the image
ext: the extension for the saved figure
plot_dir: the directory to save the figure. If none, do not save
"""
EFA = results.EFA
# Plot BIC and SABIC curves
colors = ['c', 'm']
with sns.axes_style('white'):
fig, ax1 = plt.subplots(1,1, figsize=(size, size*.75))
x = sorted(list(EFA.results['cscores_metric-BIC'].keys()))
# BIC
BIC_scores = [EFA.results['cscores_metric-BIC'][i] for i in x]
BIC_c = EFA.results['c_metric-BIC']
ax1.plot(x, BIC_scores, 'o-', c=colors[0], lw=3, label='BIC',
markersize=size*2)
ax1.set_xlabel('# Factors', fontsize=size*3)
ax1.set_ylabel('BIC', fontsize=size*3)
ax1.plot(BIC_c, BIC_scores[BIC_c-1], '.', color='white',
markeredgecolor=colors[0], markeredgewidth=size/2,
markersize=size*4)
ax1.tick_params(labelsize=size*2)
if 'cscores_metric-SABIC' in EFA.results.keys():
# SABIC
ax2 = ax1.twinx()
SABIC_scores = list(EFA.results['cscores_metric-SABIC'].values())
SABIC_c = EFA.results['c_metric-SABIC']
ax2.plot(x, SABIC_scores, c=colors[1], lw=3, label='SABIC',
markersize=size*2)
ax2.set_ylabel('SABIC', fontsize=size*4)
ax2.plot(SABIC_c, SABIC_scores[SABIC_c],'k.',
markeredgecolor=colors[0], markeredgewidth=size/2,
markersize=size*4)
# set up legend
ax1.plot(np.nan, c='m', lw=3, label='SABIC')
leg = ax1.legend(loc='right center')
beautify_legend(leg, colors=colors)
if plot_dir is not None:
save_figure(fig, path.join(plot_dir, 'BIC_SABIC_curves.%s' % ext),
{'bbox_inches': 'tight', 'dpi': dpi})
plt.close()
def plot_DDM(results, c, rotate='oblimin',
dpi=300, figsize=(20,8), ext='png', plot_dir=None):
EFA = results.EFA
loading = abs(EFA.get_loading(c, rotate=rotate))
cats = []
for i in loading.index:
if 'drift' in i:
cats.append('Drift')
elif 'thresh' in i:
cats.append('Thresh')
elif 'non_decision' in i:
cats.append('Non-Decision')
else:
cats.append('Misc')
loading.insert(0,'category', cats)
# plotting
colors = sns.color_palette("Set1", 8, .75)
color_map = {v:i for i,v in enumerate(loading.category.unique())}
fig = plt.figure(figsize=(12,12))
ax = fig.add_subplot(111, projection='3d')
for name, group in loading.groupby('category'):
ax.scatter(group['Speeded IP'],
group['Caution'],
group['Perc / Resp'],
marker='o',
s=150,
c=colors[color_map[name]],
label=name)
ax.tick_params(labelsize=0, length=0)
ax.set_xlabel('Speeded IP', fontsize=20)
ax.set_ylabel('Caution', fontsize=20)
ax.set_zlabel('Perc / Resp', fontsize=20)
ax.view_init(30, 30)
leg = plt.legend(fontsize=20)
beautify_legend(leg, colors)
if plot_dir is not None:
fig.savefig(path.join(plot_dir, 'DDM_factors.%s' % ext),
bbox_inches='tight', dpi=dpi)
plt.close()
def plot_prediction_comparison(results, size=4.6, change=False,
dpi=300, ext='png', plot_dir=None):
colors = ref_colors[results.ID.split('_')[0]]
R2s = {}
for EFA in [False, True]:
predictions = results.get_prediction_files(EFA=EFA, change=change,
shuffle=False)
predictions = sorted(predictions, key = path.getmtime)
classifiers = np.unique([i.split('_')[-2] for i in predictions])
# get last prediction file of each type
for classifier in classifiers:
filey = [i for i in predictions if classifier in i][-1]
prediction_object = pickle.load(open(filey, 'rb'))['data']
R2 = [i['scores_cv'][0]['R2'] for i in prediction_object.values()]
R2 = np.nan_to_num(R2)
feature = 'EFA' if EFA else 'IDM'
R2s[feature+'_'+classifier] = R2
if len(R2s) == 0:
print('No prediction objects found')
return
R2s = pd.DataFrame(R2s).melt(var_name='Classifier', value_name='R2')
R2s['Feature'], R2s['Classifier'] = R2s.Classifier.str.split('_', 1).str
f = plt.figure(figsize=(size, size*.62))
sns.barplot(x='Classifier', y='R2', data=R2s, hue='Feature',
palette=colors[:2], errwidth=size/5)
ax = plt.gca()
ax.tick_params(axis='y', labelsize=size*1.8)
ax.tick_params(axis='x', labelsize=size*1.8)
leg = ax.legend(fontsize=size*2, loc='upper right')
beautify_legend(leg, colors[:2])
plt.xlabel('Classifier', fontsize=size*2.2, labelpad=size/2)
plt.ylabel('R2', fontsize=size*2.2, labelpad=size/2)
plt.title('Comparison of Prediction Methods', fontsize=size*2.5, y=1.05)
if plot_dir is not None:
filename = 'prediction_comparison.%s' % ext
save_figure(f, path.join(plot_dir, filename),
{'bbox_inches': 'tight', 'dpi': dpi})
plt.close()
def plot_prediction(predictions,
shuffled_predictions,
target_order=None,
metric='R2',
size=4.6,
dpi=300,
filename=None):
""" Plots predictions resulting from "run_prediction" function
Args:
predictions: dictionary of run_prediction results
shuffled_predictions: dictionary of run_prediction shuffled results
target_order: (optional) a list of targets to order the plot
metric: which metric from the output of run_prediction to use
size: figure size
dpi: dpi to use for saving
ext: extension to use for saving (e.g., pdf)
filename: if provided, save to this location
"""
colors = sns.color_palette('Blues_d', 5)
basefont = max(size, 5)
sns.set_style('white')
if target_order is None:
target_order = predictions.keys()
prediction_keys = predictions.keys()
# get prediction success
# plot
shuffled_grey = [.3, .3, .3, .3]
# plot variables
figsize = (size, size * .75)
fig = plt.figure(figsize=figsize)
# plot bars
width = 1 / (len(prediction_keys) + 1)
ax1 = fig.add_axes([0, 0, 1, .5])
for predictor_i, key in enumerate(prediction_keys):
prediction = predictions[key]
shuffled_prediction = shuffled_predictions[key]
r2s = [[k, prediction[k]['scores_cv'][0][metric]]
for k in target_order]
# get shuffled values
shuffled_r2s = []
for i, k in enumerate(target_order):
# normalize r2s to significance
R2s = [i[metric] for i in shuffled_prediction[k]['scores_cv']]
R2_95 = np.percentile(R2s, 95)
shuffled_r2s.append((k, R2_95))
# convert nans to 0
r2s = [(i, k) if k == k else (i, 0) for i, k in r2s]
shuffled_r2s = [(i, k) if k == k else (i, 0) for i, k in shuffled_r2s]
ind = np.arange(len(r2s)) - (width * (len(prediction_keys) / 2 - 1))
ax1.bar(ind + width * predictor_i, [i[1] for i in r2s],
width,
label='%s Prediction' % ' '.join(key.title().split('_')),
linewidth=0,
color=colors[predictor_i])
# plot shuffled values above
if predictor_i == len(prediction_keys) - 1:
shuffled_label = '95% shuffled prediction'
else:
shuffled_label = None
ax1.bar(ind + width * predictor_i, [i[1] for i in shuffled_r2s],
width,
color=shuffled_grey,
linewidth=0,
label=shuffled_label)
ax1.set_xticks(np.arange(0, len(r2s)) + width / 2)
ax1.set_xticklabels(['\n'.join(i[0].split()) for i in r2s],
rotation=90,
fontsize=basefont * .75,
ha='center')
ax1.tick_params(axis='y', labelsize=size * 1.2)
ax1.tick_params(length=size / 2,
width=size / 10,
pad=size / 2,
bottom=True,
left=True)
xlow, xhigh = ax1.get_xlim()
if metric == 'R2':
ax1.set_ylabel(r'$R^2$', fontsize=basefont * 1.5, labelpad=size * 1.5)
else:
ax1.set_ylabel(metric, fontsize=basefont * 1.5, labelpad=size * 1.5)
# add a legend
leg = ax1.legend(fontsize=basefont * 1.4,
loc='upper right',
bbox_to_anchor=(1.3, 1.1),
frameon=True,
handlelength=0,
handletextpad=0,
framealpha=1)
beautify_legend(leg, colors[:len(predictions)] + [shuffled_grey])
# draw grid
ax1.set_axisbelow(True)
plt.grid(axis='y', linestyle='dotted', linewidth=size / 6)
plt.setp(list(ax1.spines.values()), linewidth=size / 10)
if filename is not None:
save_figure(fig, filename, {'bbox_inches': 'tight', 'dpi': dpi})
plt.close()
else:
return fig
def plot_prediction(predictions, comparison_predictions,
colors=None, EFA=None, comparison_label=None,
target_order=None, metric='R2', size=4.6,
dpi=300, filename=None):
if colors is None:
colors = [sns.color_palette('Purples_d', 4)[i] for i in [1,3]]
if comparison_label is None:
comparison_label = '95% shuffled prediction'
basefont = max(size, 5)
sns.set_style('white')
if target_order is None:
target_order = predictions.keys()
# get prediction success
r2s = [[k,predictions[k]['scores_cv'][0][metric]] for k in target_order]
insample_r2s = [[k, predictions[k]['scores_insample'][0][metric]] for k in target_order]
# get shuffled values
shuffled_r2s = []
insample_shuffled_r2s = []
for i, k in enumerate(target_order):
# normalize r2s to significance
R2s = [i[metric] for i in comparison_predictions[k]['scores_cv']]
R2_95 = np.percentile(R2s, 95)
shuffled_r2s.append((k,R2_95))
# and insample
R2s = [i[metric] for i in comparison_predictions[k]['scores_insample']]
R2_95 = np.percentile(R2s, 95)
insample_shuffled_r2s.append((k,R2_95))
# convert nans to 0
r2s = [(i, k) if k==k else (i,0) for i, k in r2s]
insample_r2s = [(i, k) if k==k else (i,0) for i, k in insample_r2s]
shuffled_r2s = [(i, k) if k==k else (i,0) for i, k in shuffled_r2s]
# plot
shuffled_grey = [.3,.3,.3]
# plot variables
figsize = (size, size*.75)
fig = plt.figure(figsize=figsize)
# plot bars
ind = np.arange(len(r2s))
width=.25
ax1 = fig.add_axes([0,0,1,.5])
ax1.bar(ind, [i[1] for i in r2s], width,
label='Cross-validated prediction', color=colors[0])
ax1.bar(ind+width, [i[1] for i in insample_r2s], width,
label='Insample prediction', color=colors[1])
# plot shuffled values above
ax1.bar(ind, [i[1] for i in shuffled_r2s], width,
color='none', edgecolor=shuffled_grey,
linewidth=size/10, linestyle='--', label=comparison_label)
ax1.bar(ind+width, [i[1] for i in insample_shuffled_r2s], width,
color='none', edgecolor=shuffled_grey,
linewidth=size/10, linestyle='--')
ax1.set_xticks(np.arange(0,len(r2s))+width/2)
ax1.set_xticklabels([i[0] for i in r2s], rotation=15, fontsize=basefont*1.4)
ax1.tick_params(axis='y', labelsize=size*1.2)
ax1.tick_params(length=size/4, width=size/10, pad=size/2, left=True, bottom=True)
xlow, xhigh = ax1.get_xlim()
if metric == 'R2':
ax1.set_ylabel(r'$R^2$', fontsize=basefont*1.5, labelpad=size*1.5)
else:
ax1.set_ylabel(metric, fontsize=basefont*1.5, labelpad=size*1.5)
# add a legend
leg = ax1.legend(fontsize=basefont*1.4, loc='upper left', framealpha=1,
frameon=True, handlelength=0, handletextpad=0)
leg.get_frame().set_linewidth(size/10)
beautify_legend(leg, colors[:2]+[shuffled_grey])
# change y extents
ylim = ax1.get_ylim()
r2_max = max(max(r2s, key=lambda x: x[1])[1],
max(insample_r2s, key=lambda x: x[1])[1])
ymax = r2_max*1.5
ax1.set_ylim(ylim[0], ymax)
# change yticks
if ymax<.15:
ax1.set_ylim(ylim[0], .15)
ax1.yaxis.set_major_locator(ticker.MultipleLocator(.025))
else:
ax1.yaxis.set_major_locator(ticker.MultipleLocator(.05))
ax1.set_yticks(np.append([0, .025, .05, .075, .1, .125], np.arange(.15, .45, .05)))
# draw grid
ax1.set_axisbelow(True)
plt.grid(axis='y', linestyle='dotted', linewidth=size/6)
plt.setp(list(ax1.spines.values()), linewidth=size/10)
# Plot Polar Plots for importances
if EFA is not None:
reorder_vec = EFA.get_factor_reorder(EFA.results['num_factors'])
reorder_fun = lambda x: [x[i] for i in reorder_vec]
# get importances
vals = [predictions[i] for i in target_order]
importances = [(reorder_fun(i['predvars']),
reorder_fun(i['importances'][0])) for i in vals]
# plot
axes=[]
N = len(importances)
best_predictors = sorted(enumerate(r2s), key = lambda x: x[1][1])
#if plot_heights is None:
ylim = ax1.get_ylim(); yrange = np.sum(np.abs(ylim))
zero_place = abs(ylim[0])/yrange
plot_heights = [int(r2s[i][1]>0)
*(max(r2s[i][1],
insample_r2s[i][1],
shuffled_r2s[i][1],
insample_shuffled_r2s[i][1])/yrange)
for i, k in enumerate(target_order)]
plot_heights = [(h+zero_place+.02)*.5 for h in plot_heights]
# mask heights
plot_heights = [plot_heights[i] if r2s[i][1]>max(shuffled_r2s[i][1],0) else np.nan
for i in range(len(plot_heights))]
plot_x = (ax1.get_xticks()-xlow)/(xhigh-xlow)-(1/N/2)
for i, importance in enumerate(importances):
if pd.isnull(plot_heights[i]):
continue
axes.append(fig.add_axes([plot_x[i], plot_heights[i], 1/N,1/N], projection='polar'))
color = colors[0]
visualize_importance(importance, axes[-1],
yticklabels=False, xticklabels=False,
label_size=figsize[1]*1,
color=color,
axes_linewidth=size/10)
# plot top 2 predictions, labeled
if best_predictors[-1][0] < best_predictors[-2][0]:
locs = [.32, .68]
else:
locs = [.68, .32]
label_importance = importances[best_predictors[-1][0]]
# write abbreviation key
pad = 0
text = [(l, shortened_factors.get(l, None)) for l in label_importance[0]] # for abbeviations text
if len([True for t in text if t[1] is not None]) > 0:
pad = .05
text_ax = fig.add_axes([.8,.56,.1,.34])
text_ax.tick_params(labelleft=False, left=False,
labelbottom=False, bottom=False)
for spine in ['top','right','bottom','left']:
text_ax.spines[spine].set_visible(False)
for i, (val, abr) in enumerate(text):
text_ax.text(0, i/len(text), abr+':', fontsize=size*1.2)
text_ax.text(.5, i/len(text), val, fontsize=size*1.2)
ratio = figsize[1]/figsize[0]
axes.append(fig.add_axes([locs[0]-.2*ratio-pad,.56,.3*ratio,.3], projection='polar'))
visualize_importance(label_importance, axes[-1], yticklabels=False,
xticklabels=True,
label_size=max(figsize[1]*1.5, 5),
label_scale=.22,
title=best_predictors[-1][1][0],
color=colors[0],
axes_linewidth=size/10)
# 2nd top
label_importance = importances[best_predictors[-2][0]]
ratio = figsize[1]/figsize[0]
axes.append(fig.add_axes([locs[1]-.2*ratio-pad,.56,.3*ratio,.3], projection='polar'))
visualize_importance(label_importance, axes[-1], yticklabels=False,
xticklabels=True,
label_size=max(figsize[1]*1.5, 5),
label_scale=.22,
title=best_predictors[-2][1][0],
color=colors[0],
axes_linewidth=size/10)
if filename is not None:
save_figure(fig, filename,
{'bbox_inches': 'tight', 'dpi': dpi})
plt.close()
sns.barplot(x='feedback_last',
y='switch',
hue='stage_transition_last',
data=plot_df,
order=['Rewarded', 'Unrewarded'],
hue_order=['Common', 'Rare'],
palette=colors,
ax=axes[0])
axes[0].set_xlabel('')
axes[0].set_ylabel('Stay Probability', fontsize=24)
axes[0].set_title('Two Step Task', y=1.04, fontsize=30)
axes[0].set_ylim([.5, 1])
axes[0].tick_params(labelsize=20)
leg = axes[0].get_legend()
leg.set_title('')
beautify_legend(leg, colors=colors, fontsize=20)
#shift
sns.pointplot('trials_since_switch', 'correct', data=shift_df, ax=axes[1])
axes[1].set_xticks(range(0, 25, 5))
axes[1].set_xticklabels(range(0, 25, 5))
axes[1].set_xlabel('Trials After Change-Point', fontsize=24)
axes[1].set_ylabel('Percent Correct', fontsize=24)
axes[1].set_title('Shift Task', y=1.04, fontsize=30)
axes[1].tick_params(labelsize=20)
save_dir = path.join(base_dir, 'Results', 'replication', 'Plots',
'successful_learning_tasks.%s' % ext)
f.savefig(save_dir, dpi=300, bbox_inches='tight')
plt.close()
# *************************************************************************
def plot_corr_hist(all_results, reps=100, size=4.6,
dpi=300, ext='png', plot_dir=None):
colors = sns.color_palette('Blues_d',3)[0:2] + sns.color_palette('Reds_d',2)[:1]
survey_corr = abs(all_results['survey'].data.corr())
task_corr = abs(all_results['task'].data.corr())
all_data = pd.concat([all_results['task'].data, all_results['survey'].data], axis=1)
datasets = [('survey', all_results['survey'].data),
('task', all_results['task'].data),
('all', all_data)]
# get cross corr
cross_corr = abs(all_data.corr()).loc[survey_corr.columns,
task_corr.columns]
plot_elements = [(extract_tril(survey_corr.values,-1), 'Within Surveys'),
(extract_tril(task_corr.values,-1), 'Within Tasks'),
(cross_corr.values.flatten(), 'Surveys x Tasks')]
# get shuffled 95% correlation
shuffled_95 = []
for label, df in datasets:
shuffled_corr = np.array([])
for _ in range(reps):
# create shuffled
shuffled = df.copy()
for i in shuffled:
shuffle_vec = shuffled[i].sample(len(shuffled)).tolist()
shuffled.loc[:,i] = shuffle_vec
if label == 'all':
shuffled_corr = abs(shuffled.corr()).loc[survey_corr.columns,
task_corr.columns]
else:
shuffled_corr = abs(shuffled.corr())
np.append(shuffled_corr, extract_tril(shuffled_corr.values,-1))
shuffled_95.append(np.percentile(shuffled_corr,95))
# get cross_validated r2
average_r2 = {}
for (slabel, source), (tlabel, target) in product(datasets[:-1], repeat=2):
scores = []
for var, values in target.iteritems():
if var in source.columns:
predictors = source.drop(var, axis=1)
else:
predictors = source
lr = RidgeCV()
cv_score = np.mean(cross_val_score(lr, predictors, values, cv=10))
scores.append(cv_score)
average_r2[(slabel, tlabel)] = np.mean(scores)
# bring everything together
plot_elements = [(extract_tril(survey_corr.values,-1), 'Within Surveys',
average_r2[('survey','survey')]),
(extract_tril(task_corr.values,-1), 'Within Tasks',
average_r2[('task','task')]),
(cross_corr.values.flatten(), 'Surveys x Tasks',
average_r2[('survey', 'task')])]
with sns.axes_style('white'):
f, axes = plt.subplots(1,3, figsize=(10,4))
plt.subplots_adjust(wspace=.3)
for i, (corr, label, r2) in enumerate(plot_elements):
#h = axes[i].hist(corr, normed=True, color=colors[i],
# bins=12, label=label, rwidth=1, alpha=.4)
sns.kdeplot(corr, ax=axes[i], color=colors[i], shade=True,
label=label, linewidth=3)
axes[i].text(.4, axes[i].get_ylim()[1]*.5, 'CV-R2: {0:.2f}'.format(r2))
for i, ax in enumerate(axes):
ax.vlines(shuffled_95[i], *ax.get_ylim(), color=[.2,.2,.2],
linewidth=2, linestyle='dashed', zorder=10)
ax.set_xlim(0,1)
ax.set_ylim(0, ax.get_ylim()[1])
ax.set_xticks([0,.5,1])
ax.set_xticklabels([0,.5,1], fontsize=16)
ax.set_yticks([])
ax.spines['right'].set_visible(False)
#ax.spines['left'].set_visible(False)
ax.spines['top'].set_visible(False)
leg=ax.legend(fontsize=14, loc='upper center')
beautify_legend(leg, [colors[i]])
axes[1].set_xlabel('Pearson Correlation', fontsize=20, labelpad=10)
axes[0].set_ylabel('Normalized Density', fontsize=20, labelpad=10)
# save
if plot_dir is not None:
# make histogram plot
save_figure(f, path.join(plot_dir, 'within-across_correlations.%s' % ext),
{'bbox_inches': 'tight', 'dpi': dpi})
int)
sns.pointplot(x='num_available_measures',
y='corr_score',
hue='pop_size',
data=KNNRpartial_var_summary,
palette=colors,
ax=axes[0],
ci=None,
scale=1.4)
leg = axes[0].legend(loc='best',
frameon=False,
handlelength=0,
handletextpad=0,
fontsize=size * 1.5)
beautify_legend(leg, colors=colors)
leg.get_title().set_fontsize(size * 1.5)
axes[0].set_ylabel('Reconstruction Score', fontsize=size * 3)
axes[0].set_xlabel('# of Measures', fontsize=size * 2)
axes[0].set_title('KNNR with Random Subset', fontsize=size * 3)
axes[0].tick_params(width=2, length=2, labelsize=size * 1.8)
# efficiency subset
closest_files = glob(
path.join(ontology_results_dir, 'KNNRclosest_correlation_summary.pkl'))
closest_summary = pd.read_pickle(closest_files[0])
sns.pointplot(x='num_available_measures',
y='mean',
hue='pop_size',
data=plot_df,
def plot_prediction(predictions,
comparison_predictions,
colors=None,
EFA=None,
comparison_label=None,
target_order=None,
metric='R2',
size=4.6,
dpi=300,
filename=None):
if colors is None:
colors = [sns.color_palette('Purples_d', 4)[i] for i in [1, 3]]
if comparison_label is None:
comparison_label = '95% shuffled prediction'
basefont = max(size, 5)
sns.set_style('white')
if target_order is None:
target_order = predictions.keys()
# get prediction success
r2s = [[k, predictions[k]['scores_cv'][0][metric]] for k in target_order]
insample_r2s = [[k, predictions[k]['scores_insample'][0][metric]]
for k in target_order]
# get shuffled values
shuffled_r2s = []
insample_shuffled_r2s = []
for i, k in enumerate(target_order):
# normalize r2s to significance
R2s = [i[metric] for i in comparison_predictions[k]['scores_cv']]
R2_95 = np.percentile(R2s, 95)
shuffled_r2s.append((k, R2_95))
# and insample
R2s = [i[metric] for i in comparison_predictions[k]['scores_insample']]
R2_95 = np.percentile(R2s, 95)
insample_shuffled_r2s.append((k, R2_95))
# convert nans to 0
r2s = [(i, k) if k == k else (i, 0) for i, k in r2s]
insample_r2s = [(i, k) if k == k else (i, 0) for i, k in insample_r2s]
shuffled_r2s = [(i, k) if k == k else (i, 0) for i, k in shuffled_r2s]
# plot
shuffled_grey = [.3, .3, .3]
# plot variables
figsize = (size, size * .75)
fig = plt.figure(figsize=figsize)
# plot bars
ind = np.arange(len(r2s))
width = .25
ax1 = fig.add_axes([0, 0, 1, .5])
ax1.bar(ind, [i[1] for i in r2s],
width,
label='Cross-validated prediction',
color=colors[0])
ax1.bar(ind + width, [i[1] for i in insample_r2s],
width,
label='Insample prediction',
color=colors[1])
# plot shuffled values above
ax1.bar(ind, [i[1] for i in shuffled_r2s],
width,
color='none',
edgecolor=shuffled_grey,
linewidth=size / 10,
linestyle='--',
label=comparison_label)
ax1.bar(ind + width, [i[1] for i in insample_shuffled_r2s],
width,
color='none',
edgecolor=shuffled_grey,
linewidth=size / 10,
linestyle='--')
ax1.set_xticks(np.arange(0, len(r2s)) + width / 2)
ax1.set_xticklabels([i[0] for i in r2s],
rotation=15,
fontsize=basefont * 1.4)
ax1.tick_params(axis='y', labelsize=size * 1.2)
ax1.tick_params(length=size / 4,
width=size / 10,
pad=size / 2,
left=True,
bottom=True)
xlow, xhigh = ax1.get_xlim()
if metric == 'R2':
ax1.set_ylabel(r'$R^2$', fontsize=basefont * 1.5, labelpad=size * 1.5)
else:
ax1.set_ylabel(metric, fontsize=basefont * 1.5, labelpad=size * 1.5)
# add a legend
leg = ax1.legend(fontsize=basefont * 1.4,
loc='upper left',
framealpha=1,
frameon=True,
handlelength=0,
handletextpad=0)
leg.get_frame().set_linewidth(size / 10)
beautify_legend(leg, colors[:2] + [shuffled_grey])
# change y extents
ylim = ax1.get_ylim()
r2_max = max(
max(r2s, key=lambda x: x[1])[1],
max(insample_r2s, key=lambda x: x[1])[1])
ymax = r2_max * 1.5
ax1.set_ylim(ylim[0], ymax)
# change yticks
if ymax < .15:
ax1.set_ylim(ylim[0], .15)
ax1.yaxis.set_major_locator(ticker.MultipleLocator(.025))
else:
ax1.yaxis.set_major_locator(ticker.MultipleLocator(.05))
ax1.set_yticks(
np.append([0, .025, .05, .075, .1, .125], np.arange(.15, .45,
.05)))
# draw grid
ax1.set_axisbelow(True)
plt.grid(axis='y', linestyle='dotted', linewidth=size / 6)
plt.setp(list(ax1.spines.values()), linewidth=size / 10)
# Plot Polar Plots for importances
if EFA is not None:
reorder_vec = EFA.get_factor_reorder(EFA.results['num_factors'])
reorder_fun = lambda x: [x[i] for i in reorder_vec]
# get importances
vals = [predictions[i] for i in target_order]
importances = [(reorder_fun(i['predvars']),
reorder_fun(i['importances'][0])) for i in vals]
# plot
axes = []
N = len(importances)
best_predictors = sorted(enumerate(r2s), key=lambda x: x[1][1])
#if plot_heights is None:
ylim = ax1.get_ylim()
yrange = np.sum(np.abs(ylim))
zero_place = abs(ylim[0]) / yrange
plot_heights = [
int(r2s[i][1] > 0) *
(max(r2s[i][1], insample_r2s[i][1], shuffled_r2s[i][1],
insample_shuffled_r2s[i][1]) / yrange)
for i, k in enumerate(target_order)
]
plot_heights = [(h + zero_place + .02) * .5 for h in plot_heights]
# mask heights
plot_heights = [
plot_heights[i]
if r2s[i][1] > max(shuffled_r2s[i][1], 0) else np.nan
for i in range(len(plot_heights))
]
plot_x = (ax1.get_xticks() - xlow) / (xhigh - xlow) - (1 / N / 2)
for i, importance in enumerate(importances):
if pd.isnull(plot_heights[i]):
continue
axes.append(
fig.add_axes([plot_x[i], plot_heights[i], 1 / N, 1 / N],
projection='polar'))
color = colors[0]
visualize_importance(importance,
axes[-1],
yticklabels=False,
xticklabels=False,
label_size=figsize[1] * 1,
color=color,
axes_linewidth=size / 10)
# plot top 2 predictions, labeled
if best_predictors[-1][0] < best_predictors[-2][0]:
locs = [.32, .68]
else:
locs = [.68, .32]
label_importance = importances[best_predictors[-1][0]]
# write abbreviation key
pad = 0
text = [(l, shortened_factors.get(l, None))
for l in label_importance[0]] # for abbeviations text
if len([True for t in text if t[1] is not None]) > 0:
pad = .05
text_ax = fig.add_axes([.8, .56, .1, .34])
text_ax.tick_params(labelleft=False,
left=False,
labelbottom=False,
bottom=False)
for spine in ['top', 'right', 'bottom', 'left']:
text_ax.spines[spine].set_visible(False)
for i, (val, abr) in enumerate(text):
text_ax.text(0, i / len(text), abr + ':', fontsize=size * 1.2)
text_ax.text(.5, i / len(text), val, fontsize=size * 1.2)
ratio = figsize[1] / figsize[0]
axes.append(
fig.add_axes([locs[0] - .2 * ratio - pad, .56, .3 * ratio, .3],
projection='polar'))
visualize_importance(label_importance,
axes[-1],
yticklabels=False,
xticklabels=True,
label_size=max(figsize[1] * 1.5, 5),
label_scale=.22,
title=best_predictors[-1][1][0],
color=colors[0],
axes_linewidth=size / 10)
# 2nd top
label_importance = importances[best_predictors[-2][0]]
ratio = figsize[1] / figsize[0]
axes.append(
fig.add_axes([locs[1] - .2 * ratio - pad, .56, .3 * ratio, .3],
projection='polar'))
visualize_importance(label_importance,
axes[-1],
yticklabels=False,
xticklabels=True,
label_size=max(figsize[1] * 1.5, 5),
label_scale=.22,
title=best_predictors[-2][1][0],
color=colors[0],
axes_linewidth=size / 10)
if filename is not None:
save_figure(fig, filename, {'bbox_inches': 'tight', 'dpi': dpi})
plt.close()
def plot_communality(results, c, rotate='oblimin', retest_threshold=.2,
size=4.6, dpi=300, ext='png', plot_dir=None):
EFA = results.EFA
communality = get_communality(EFA, rotate, c)
# load retest data
retest_data = get_retest_data(dataset=results.dataset.replace('Complete','Retest'))
if retest_data is None:
print('No retest data found for datafile: %s' % results.dataset)
return
# reorder data in line with communality
retest_data = retest_data.loc[communality.index]
# reformat variable names
communality.index = format_variable_names(communality.index)
retest_data.index = format_variable_names(retest_data.index)
if len(retest_data) > 0:
adjusted_communality,correlation, noise_ceiling = \
get_adjusted_communality(communality,
retest_data,
retest_threshold)
# plot communality bars woo!
if len(retest_data)>0:
f, axes = plt.subplots(1, 3, figsize=(3*(size/10), size))
plot_bar_factor(communality, axes[0], width=size/10, height=size,
label_rows=True, title='Communality')
plot_bar_factor(noise_ceiling, axes[1], width=size/10, height=size,
label_rows=False, title='Test-Retest')
plot_bar_factor(adjusted_communality, axes[2], width=size/10, height=size,
label_rows=False, title='Adjusted Communality')
else:
f = plot_bar_factor(communality, label_rows=True,
width=size/3, height=size*2, title='Communality')
if plot_dir:
filename = 'communality_bars-EFA%s.%s' % (c, ext)
save_figure(f, path.join(plot_dir, filename),
{'bbox_inches': 'tight', 'dpi': dpi})
plt.close()
# plot communality histogram
if len(retest_data) > 0:
with sns.axes_style('white'):
colors = sns.color_palette(n_colors=2, desat=.75)
f, ax = plt.subplots(1,1,figsize=(size,size))
sns.kdeplot(communality, linewidth=size/4,
shade=True, label='Communality', color=colors[0])
sns.kdeplot(adjusted_communality, linewidth=size/4,
shade=True, label='Adjusted Communality', color=colors[1])
ylim = ax.get_ylim()
ax.vlines(np.mean(communality), ylim[0], ylim[1],
color=colors[0], linewidth=size/4, linestyle='--')
ax.vlines(np.mean(adjusted_communality), ylim[0], ylim[1],
color=colors[1], linewidth=size/4, linestyle='--')
leg=ax.legend(fontsize=size*2, loc='upper right')
beautify_legend(leg, colors)
plt.xlabel('Communality', fontsize=size*2)
plt.ylabel('Normalized Density', fontsize=size*2)
ax.set_yticks([])
ax.tick_params(labelsize=size)
ax.set_ylim(0, ax.get_ylim()[1])
ax.set_xlim(0, ax.get_xlim()[1])
ax.spines['right'].set_visible(False)
#ax.spines['left'].set_visible(False)
ax.spines['top'].set_visible(False)
# add correlation
correlation = format_num(np.mean(correlation))
ax.text(1.1, 1.25, 'Correlation Between Communality \nand Test-Retest: %s' % correlation,
size=size*2)
if plot_dir:
filename = 'communality_dist-EFA%s.%s' % (c, ext)
save_figure(f, path.join(plot_dir, filename),
{'bbox_inches': 'tight', 'dpi': dpi})
plt.close()
def plot_corr_hist(all_results,
reps=100,
size=4.6,
dpi=300,
ext='png',
plot_dir=None):
colors = sns.color_palette('Blues_d', 3)[0:2] + sns.color_palette(
'Reds_d', 2)[:1]
survey_corr = abs(all_results['survey'].data.corr())
task_corr = abs(all_results['task'].data.corr())
all_data = pd.concat(
[all_results['task'].data, all_results['survey'].data], axis=1)
datasets = [('survey', all_results['survey'].data),
('task', all_results['task'].data), ('all', all_data)]
# get cross corr
cross_corr = abs(all_data.corr()).loc[survey_corr.columns,
task_corr.columns]
plot_elements = [(extract_tril(survey_corr.values, -1), 'Within Surveys'),
(extract_tril(task_corr.values, -1), 'Within Tasks'),
(cross_corr.values.flatten(), 'Surveys x Tasks')]
# get shuffled 95% correlation
shuffled_95 = []
for label, df in datasets:
shuffled_corr = np.array([])
for _ in range(reps):
# create shuffled
shuffled = df.copy()
for i in shuffled:
shuffle_vec = shuffled[i].sample(len(shuffled)).tolist()
shuffled.loc[:, i] = shuffle_vec
if label == 'all':
shuffled_corr = abs(shuffled.corr()).loc[survey_corr.columns,
task_corr.columns]
else:
shuffled_corr = abs(shuffled.corr())
np.append(shuffled_corr, extract_tril(shuffled_corr.values, -1))
shuffled_95.append(np.percentile(shuffled_corr, 95))
# get cross_validated r2
average_r2 = {}
for (slabel, source), (tlabel, target) in product(datasets[:-1], repeat=2):
scores = []
for var, values in target.iteritems():
if var in source.columns:
predictors = source.drop(var, axis=1)
else:
predictors = source
lr = RidgeCV()
cv_score = np.mean(cross_val_score(lr, predictors, values, cv=10))
scores.append(cv_score)
average_r2[(slabel, tlabel)] = np.mean(scores)
# bring everything together
plot_elements = [
(extract_tril(survey_corr.values,
-1), 'Within Surveys', average_r2[('survey', 'survey')]),
(extract_tril(task_corr.values,
-1), 'Within Tasks', average_r2[('task', 'task')]),
(cross_corr.values.flatten(), 'Surveys x Tasks', average_r2[('survey',
'task')])
]
with sns.axes_style('white'):
f, axes = plt.subplots(1, 3, figsize=(10, 4))
plt.subplots_adjust(wspace=.3)
for i, (corr, label, r2) in enumerate(plot_elements):
#h = axes[i].hist(corr, normed=True, color=colors[i],
# bins=12, label=label, rwidth=1, alpha=.4)
sns.kdeplot(corr,
ax=axes[i],
color=colors[i],
shade=True,
label=label,
linewidth=3)
axes[i].text(.4, axes[i].get_ylim()[1] * .5,
'CV-R2: {0:.2f}'.format(r2))
for i, ax in enumerate(axes):
ax.vlines(shuffled_95[i],
*ax.get_ylim(),
color=[.2, .2, .2],
linewidth=2,
linestyle='dashed',
zorder=10)
ax.set_xlim(0, 1)
ax.set_ylim(0, ax.get_ylim()[1])
ax.set_xticks([0, .5, 1])
ax.set_xticklabels([0, .5, 1], fontsize=16)
ax.set_yticks([])
ax.spines['right'].set_visible(False)
#ax.spines['left'].set_visible(False)
ax.spines['top'].set_visible(False)
leg = ax.legend(fontsize=14, loc='upper center')
beautify_legend(leg, [colors[i]])
axes[1].set_xlabel('Pearson Correlation', fontsize=20, labelpad=10)
axes[0].set_ylabel('Normalized Density', fontsize=20, labelpad=10)
# save
if plot_dir is not None:
# make histogram plot
save_figure(f,
path.join(plot_dir, 'within-across_correlations.%s' % ext),
{
'bbox_inches': 'tight',
'dpi': dpi
})
def plot_cross_communality(all_results, rotate='oblimin', retest_threshold=.2,
size=4.6, dpi=300, ext='png', plot_dir=None):
retest_data = None
num_cols = 2
num_rows = math.ceil(len(all_results.keys())/2)
with sns.axes_style('white'):
f, axes = plt.subplots(num_rows, num_cols, figsize=(size, size/2*num_rows))
max_y = 0
for i, (name, results) in enumerate(all_results.items()):
if retest_data is None:
# load retest data
retest_data = get_retest_data(dataset=results.dataset.replace('Complete','Retest'))
if retest_data is None:
print('No retest data found for datafile: %s' % results.dataset)
c = results.EFA.get_c()
EFA = results.EFA
loading = EFA.get_loading(c, rotate=rotate)
# get communality from psych out
fa = EFA.results['factor_tree_Rout_%s' % rotate][c]
communality = get_attr(fa, 'communalities')
communality = pd.Series(communality, index=loading.index)
# alternative calculation
#communality = (loading**2).sum(1).sort_values()
communality.index = [i.replace('.logTr','') for i in communality.index]
# reorder data in line with communality
retest_subset= retest_data.loc[communality.index]
# reformat variable names
communality.index = format_variable_names(communality.index)
retest_subset.index = format_variable_names(retest_subset.index)
if len(retest_subset) > 0:
# noise ceiling
noise_ceiling = retest_subset.pearson
# remove very low reliabilities
if retest_threshold:
noise_ceiling[noise_ceiling<retest_threshold]= np.nan
# adjust
adjusted_communality = communality/noise_ceiling
# plot communality histogram
if len(retest_subset) > 0:
ax = axes[i]
ax.set_title(name.title(), fontweight='bold', fontsize=size*2)
colors = sns.color_palette(n_colors=2, desat=.75)
sns.kdeplot(communality, linewidth=size/4, ax=ax, vertical=True,
shade=True, label='Communality', color=colors[0])
sns.kdeplot(adjusted_communality, linewidth=size/4, ax=ax, vertical=True,
shade=True, label='Adjusted Communality', color=colors[1])
xlim = ax.get_xlim()
ax.hlines(np.mean(communality), xlim[0], xlim[1],
color=colors[0], linewidth=size/4, linestyle='--')
ax.hlines(np.mean(adjusted_communality), xlim[0], xlim[1],
color=colors[1], linewidth=size/4, linestyle='--')
ax.set_xticks([])
ax.tick_params(labelsize=size*1.2)
ax.set_ylim(0, ax.get_ylim()[1])
ax.set_xlim(0, ax.get_xlim()[1])
ax.spines['right'].set_visible(False)
ax.spines['top'].set_visible(False)
if (i+1) == len(all_results):
ax.set_xlabel('Normalized Density', fontsize=size*2)
leg=ax.legend(fontsize=size*1.5, loc='upper right',
bbox_to_anchor=(1.2, 1.0),
handlelength=0, handletextpad=0)
beautify_legend(leg, colors)
elif i>=len(all_results)-2:
ax.set_xlabel('Normalized Density', fontsize=size*2)
ax.legend().set_visible(False)
else:
ax.legend().set_visible(False)
if i%2==0:
ax.set_ylabel('Communality', fontsize=size*2)
ax.tick_params(labelleft=True, left=True,
length=size/4, width=size/8)
else:
ax.tick_params(labelleft=False, left=True,
length=0, width=size/8)
# update max_x
if ax.get_ylim()[1] > max_y:
max_y = ax.get_ylim()[1]
ax.grid(False)
[i.set_linewidth(size*.1) for i in ax.spines.values()]
for ax in axes:
ax.set_ylim((0, max_y))
plt.subplots_adjust(wspace=0)
if plot_dir:
filename = 'communality_adjustment.%s' % ext
save_figure(f, path.join(plot_dir, rotate, filename),
{'bbox_inches': 'tight', 'dpi': dpi})
plt.close()
axes[2].set_ylabel(r'Mean $\pm$ SEM reaction time', fontsize=20)
axes[2].set_xticks(range(rt_stats.shape[0]))
axes[2].set_xticklabels(['AX', 'AY', 'BX', 'BY'])
axes[2].set_xlabel('Trial Type', fontsize=20)
axes[2].grid(axis='y')
# plot literature
axes[0].errorbar(range(lit_rt_stats.shape[0]),
lit_acc_stats.loc[:, 'mean'],
yerr=lit_acc_stats.loc[:, 'std'] / (lit_N**.5),
color='#29A6F0',
linewidth=5,
elinewidth=3,
label='Lopez-Garcia et al')
leg = axes[0].legend(handlelength=0)
beautify_legend(leg, colors=['#D3244F', '#29A6F0'])
# plot reaction time
axes[2].errorbar(range(lit_rt_stats.shape[0]),
lit_rt_stats.loc[:, 'mean'],
yerr=lit_rt_stats.loc[:, 'std'] / (lit_N**.5),
color='#29A6F0',
linewidth=5,
elinewidth=3)
# plot comparison to literature
axes[1].scatter(lit_acc_stats.loc[:, 'mean'],
acc_stats.loc[:, 'mean'],
color='k')
max_val = max(max(axes[1].get_xlim()), max(axes[1].get_ylim()))
axes[1].plot([0, max_val], [0, max_val], linestyle='--', color='k')
axes[1].set_ylabel('Our Values', fontsize=20)
def plot_BIC(all_results, size=4.6, dpi=300, ext='png', plot_dir=None):
""" Plots BIC and SABIC curves
Args:
all_results: a dimensional structure all_results object
dpi: the final dpi for the image
ext: the extension for the saved figure
plot_dir: the directory to save the figure. If none, do not save
"""
all_colors = [
sns.color_palette('Blues_d', 3)[0:3],
sns.color_palette('Reds_d', 3)[0:3],
sns.color_palette('Greens_d', 3)[0:3],
sns.color_palette('Oranges_d', 3)[0:3]
]
height = size * .75 / len(all_results)
with sns.axes_style('white'):
fig, axes = plt.subplots(1, len(all_results), figsize=(size, height))
for i, results in enumerate(
[all_results[key] for key in ['task', 'survey']]):
ax1 = axes[i]
name = results.ID.split('_')[0].title()
EFA = results.EFA
# Plot BIC and SABIC curves
colors = all_colors[i]
with sns.axes_style('white'):
x = list(EFA.results['cscores_metric-BIC'].keys())
# score keys
keys = [k for k in EFA.results.keys() if 'cscores' in k]
for key in keys:
metric = key.split('-')[-1]
BIC_scores = [EFA.results[key][i] for i in x]
BIC_c = EFA.results['c_metric-%s' % metric]
ax1.plot(x,
BIC_scores,
'o-',
c=colors[0],
lw=size / 6,
label=metric,
markersize=height * 2)
ax1.plot(BIC_c,
BIC_scores[BIC_c - 1],
'.',
color='white',
markeredgecolor=colors[0],
markeredgewidth=height / 2,
markersize=height * 4)
if i == 0:
if len(keys) > 1:
ax1.set_ylabel('Score', fontsize=height * 3)
leg = ax1.legend(loc='center right',
fontsize=height * 3,
markerscale=0)
beautify_legend(leg, colors=colors)
else:
ax1.set_ylabel(metric, fontsize=height * 4)
ax1.set_xlabel('# Factors', fontsize=height * 4)
ax1.set_xticks(x)
ax1.set_xticklabels(x)
ax1.tick_params(labelsize=height * 2, pad=size / 4, length=0)
ax1.set_title(name, fontsize=height * 4, y=1.01)
ax1.grid(linewidth=size / 8)
[i.set_linewidth(size * .1) for i in ax1.spines.values()]
if plot_dir is not None:
save_figure(fig, path.join(plot_dir, 'BIC_curves.%s' % ext), {
'bbox_inches': 'tight',
'dpi': dpi
})
plt.close()
def plot_BIC_SABIC(results, size=2.3, dpi=300, ext='png', plot_dir=None):
""" Plots BIC and SABIC curves
Args:
results: a dimensional structure results object
dpi: the final dpi for the image
ext: the extension for the saved figure
plot_dir: the directory to save the figure. If none, do not save
"""
EFA = results.EFA
# Plot BIC and SABIC curves
colors = ['c', 'm']
with sns.axes_style('white'):
fig, ax1 = plt.subplots(1, 1, figsize=(size, size * .75))
x = sorted(list(EFA.results['cscores_metric-BIC'].keys()))
# BIC
BIC_scores = [EFA.results['cscores_metric-BIC'][i] for i in x]
BIC_c = EFA.results['c_metric-BIC']
ax1.plot(x,
BIC_scores,
'o-',
c=colors[0],
lw=3,
label='BIC',
markersize=size * 2)
ax1.set_xlabel('# Factors', fontsize=size * 3)
ax1.set_ylabel('BIC', fontsize=size * 3)
ax1.plot(BIC_c,
BIC_scores[BIC_c - 1],
'.',
color='white',
markeredgecolor=colors[0],
markeredgewidth=size / 2,
markersize=size * 4)
ax1.tick_params(labelsize=size * 2)
if 'cscores_metric-SABIC' in EFA.results.keys():
# SABIC
ax2 = ax1.twinx()
SABIC_scores = list(EFA.results['cscores_metric-SABIC'].values())
SABIC_c = EFA.results['c_metric-SABIC']
ax2.plot(x,
SABIC_scores,
c=colors[1],
lw=3,
label='SABIC',
markersize=size * 2)
ax2.set_ylabel('SABIC', fontsize=size * 4)
ax2.plot(SABIC_c,
SABIC_scores[SABIC_c],
'k.',
markeredgecolor=colors[0],
markeredgewidth=size / 2,
markersize=size * 4)
# set up legend
ax1.plot(np.nan, c='m', lw=3, label='SABIC')
leg = ax1.legend(loc='right center')
beautify_legend(leg, colors=colors)
if plot_dir is not None:
save_figure(fig, path.join(plot_dir, 'BIC_SABIC_curves.%s' % ext),
{
'bbox_inches': 'tight',
'dpi': dpi
})
plt.close()
f, axes = plt.subplots(1,2,figsize=(20,8))
# two stage
sns.barplot(x='feedback_last', y='switch', hue='stage_transition_last',
data=plot_df,
order=['Rewarded', 'Unrewarded'],
hue_order=['Common', 'Rare'],
palette=colors,
ax=axes[0])
axes[0].set_xlabel('')
axes[0].set_ylabel('Stay Probability', fontsize=24)
axes[0].set_title('Two Step Task', y=1.04, fontsize=30)
axes[0].set_ylim([.5,1])
axes[0].tick_params(labelsize=20)
leg = axes[0].get_legend()
leg.set_title('')
beautify_legend(leg, colors=colors, fontsize=20)
#shift
sns.pointplot('trials_since_switch', 'correct', data=shift_df, ax=axes[1])
axes[1].set_xticks(range(0,25,5))
axes[1].set_xticklabels(range(0,25,5))
axes[1].set_xlabel('Trials After Change-Point', fontsize=24)
axes[1].set_ylabel('Percent Correct', fontsize= 24)
axes[1].set_title('Shift Task', y=1.04, fontsize=30)
axes[1].tick_params(labelsize=20)
save_dir = path.join(base_dir, 'Results', 'replication', 'Plots', 'successful_learning_tasks.%s' % ext)
f.savefig(save_dir, dpi=300, bbox_inches='tight')
plt.close()
# *************************************************************************
# Unsuccessful replications
def plot_cross_communality(all_results,
rotate='oblimin',
retest_threshold=.2,
size=4.6,
dpi=300,
ext='png',
plot_dir=None):
retest_data = None
num_cols = 2
num_rows = math.ceil(len(all_results.keys()) / 2)
with sns.axes_style('white'):
f, axes = plt.subplots(num_rows,
num_cols,
figsize=(size, size / 2 * num_rows))
max_y = 0
for i, (name, results) in enumerate(all_results.items()):
if retest_data is None:
# load retest data
retest_data = get_retest_data(
dataset=results.dataset.replace('Complete', 'Retest'))
if retest_data is None:
print('No retest data found for datafile: %s' %
results.dataset)
c = results.EFA.get_c()
EFA = results.EFA
loading = EFA.get_loading(c, rotate=rotate)
# get communality from psych out
fa = EFA.results['factor_tree_Rout_%s' % rotate][c]
communality = get_attr(fa, 'communalities')
communality = pd.Series(communality, index=loading.index)
# alternative calculation
#communality = (loading**2).sum(1).sort_values()
communality.index = [
i.replace('.logTr', '') for i in communality.index
]
# reorder data in line with communality
retest_subset = retest_data.loc[communality.index]
# reformat variable names
communality.index = format_variable_names(communality.index)
retest_subset.index = format_variable_names(retest_subset.index)
if len(retest_subset) > 0:
# noise ceiling
noise_ceiling = retest_subset.pearson
# remove very low reliabilities
if retest_threshold:
noise_ceiling[noise_ceiling < retest_threshold] = np.nan
# adjust
adjusted_communality = communality / noise_ceiling
# plot communality histogram
if len(retest_subset) > 0:
ax = axes[i]
ax.set_title(name.title(), fontweight='bold', fontsize=size * 2)
colors = sns.color_palette(n_colors=2, desat=.75)
sns.kdeplot(communality,
linewidth=size / 4,
ax=ax,
vertical=True,
shade=True,
label='Communality',
color=colors[0])
sns.kdeplot(adjusted_communality,
linewidth=size / 4,
ax=ax,
vertical=True,
shade=True,
label='Adjusted Communality',
color=colors[1])
xlim = ax.get_xlim()
ax.hlines(np.mean(communality),
xlim[0],
xlim[1],
color=colors[0],
linewidth=size / 4,
linestyle='--')
ax.hlines(np.mean(adjusted_communality),
xlim[0],
xlim[1],
color=colors[1],
linewidth=size / 4,
linestyle='--')
ax.set_xticks([])
ax.tick_params(labelsize=size * 1.2)
ax.set_ylim(0, ax.get_ylim()[1])
ax.set_xlim(0, ax.get_xlim()[1])
ax.spines['right'].set_visible(False)
ax.spines['top'].set_visible(False)
if (i + 1) == len(all_results):
ax.set_xlabel('Normalized Density', fontsize=size * 2)
leg = ax.legend(fontsize=size * 1.5,
loc='upper right',
bbox_to_anchor=(1.2, 1.0),
handlelength=0,
handletextpad=0)
beautify_legend(leg, colors)
elif i >= len(all_results) - 2:
ax.set_xlabel('Normalized Density', fontsize=size * 2)
ax.legend().set_visible(False)
else:
ax.legend().set_visible(False)
if i % 2 == 0:
ax.set_ylabel('Communality', fontsize=size * 2)
ax.tick_params(labelleft=True,
left=True,
length=size / 4,
width=size / 8)
else:
ax.tick_params(labelleft=False,
left=True,
length=0,
width=size / 8)
# update max_x
if ax.get_ylim()[1] > max_y:
max_y = ax.get_ylim()[1]
ax.grid(False)
[i.set_linewidth(size * .1) for i in ax.spines.values()]
for ax in axes:
ax.set_ylim((0, max_y))
plt.subplots_adjust(wspace=0)
if plot_dir:
filename = 'communality_adjustment.%s' % ext
save_figure(f, path.join(plot_dir, rotate, filename), {
'bbox_inches': 'tight',
'dpi': dpi
})
plt.close()
def plot_communality(results,
c,
rotate='oblimin',
retest_threshold=.2,
size=4.6,
dpi=300,
ext='png',
plot_dir=None):
EFA = results.EFA
communality = get_communality(EFA, rotate, c)
# load retest data
retest_data = get_retest_data(
dataset=results.dataset.replace('Complete', 'Retest'))
if retest_data is None:
print('No retest data found for datafile: %s' % results.dataset)
return
# reorder data in line with communality
retest_data = retest_data.loc[communality.index]
# reformat variable names
communality.index = format_variable_names(communality.index)
retest_data.index = format_variable_names(retest_data.index)
if len(retest_data) > 0:
adjusted_communality,correlation, noise_ceiling = \
get_adjusted_communality(communality,
retest_data,
retest_threshold)
# plot communality bars woo!
if len(retest_data) > 0:
f, axes = plt.subplots(1, 3, figsize=(3 * (size / 10), size))
plot_bar_factor(communality,
axes[0],
width=size / 10,
height=size,
label_rows=True,
title='Communality')
plot_bar_factor(noise_ceiling,
axes[1],
width=size / 10,
height=size,
label_rows=False,
title='Test-Retest')
plot_bar_factor(adjusted_communality,
axes[2],
width=size / 10,
height=size,
label_rows=False,
title='Adjusted Communality')
else:
f = plot_bar_factor(communality,
label_rows=True,
width=size / 3,
height=size * 2,
title='Communality')
if plot_dir:
filename = 'communality_bars-EFA%s.%s' % (c, ext)
save_figure(f, path.join(plot_dir, filename), {
'bbox_inches': 'tight',
'dpi': dpi
})
plt.close()
# plot communality histogram
if len(retest_data) > 0:
with sns.axes_style('white'):
colors = sns.color_palette(n_colors=2, desat=.75)
f, ax = plt.subplots(1, 1, figsize=(size, size))
sns.kdeplot(communality,
linewidth=size / 4,
shade=True,
label='Communality',
color=colors[0])
sns.kdeplot(adjusted_communality,
linewidth=size / 4,
shade=True,
label='Adjusted Communality',
color=colors[1])
ylim = ax.get_ylim()
ax.vlines(np.mean(communality),
ylim[0],
ylim[1],
color=colors[0],
linewidth=size / 4,
linestyle='--')
ax.vlines(np.mean(adjusted_communality),
ylim[0],
ylim[1],
color=colors[1],
linewidth=size / 4,
linestyle='--')
leg = ax.legend(fontsize=size * 2, loc='upper right')
beautify_legend(leg, colors)
plt.xlabel('Communality', fontsize=size * 2)
plt.ylabel('Normalized Density', fontsize=size * 2)
ax.set_yticks([])
ax.tick_params(labelsize=size)
ax.set_ylim(0, ax.get_ylim()[1])
ax.set_xlim(0, ax.get_xlim()[1])
ax.spines['right'].set_visible(False)
#ax.spines['left'].set_visible(False)
ax.spines['top'].set_visible(False)
# add correlation
correlation = format_num(np.mean(correlation))
ax.text(1.1,
1.25,
'Correlation Between Communality \nand Test-Retest: %s' %
correlation,
size=size * 2)
if plot_dir:
filename = 'communality_dist-EFA%s.%s' % (c, ext)
save_figure(f, path.join(plot_dir, filename), {
'bbox_inches': 'tight',
'dpi': dpi
})
plt.close()