def getTopWeightedFeatures(exp_id, instance_id, size):
instance_id = int(instance_id)
classifier = get_classifier(exp_id)
# get the features
exp = update_curr_exp(exp_id)
features_from_exp = FeaturesFromExp(exp)
features_names, features_values = features_from_exp.get_instance(
instance_id)
features_values = [float(value) for value in features_values]
# scale the features
scaled_values = classifier.named_steps['scaler'].transform(
np.reshape(features_values, (1, -1)))
weighted_values = np.multiply(scaled_values,
classifier.named_steps['model'].coef_)
features = list(
map(lambda name, value, w_value: (name, value, w_value),
features_names, features_values, weighted_values[0]))
features.sort(key=lambda tup: abs(tup[2]))
features = features[:-int(size) - 1:-1]
features_names = [x[0] for x in features]
features_values = [x[1] for x in features]
features_weighted_values = [x[2] for x in features]
labels = [str(name) for name in features_names]
tooltips = [
'%s (%.2f)' % (name, features_values[i])
for i, name in enumerate(features_names)
]
barplot = BarPlot(labels)
dataset = PlotDataset(features_weighted_values, None)
dataset.set_color(red)
barplot.add_dataset(dataset)
return jsonify(barplot.to_json(tooltip_data=tooltips))
def getFeatures(exp_id, instance_id):
instance_id = int(instance_id)
experiment = update_curr_exp(exp_id)
features_from_exp = FeaturesFromExp(experiment)
features_names, features_values = features_from_exp.get_instance(
instance_id)
features = {features_names[i]: v for i, v in enumerate(features_values)}
return jsonify(features)
def getFeatures(exp_id, instance_id):
instance_id = int(instance_id)
experiment = update_curr_exp(exp_id)
f_names, f_values = FeaturesFromExp.get_instance(experiment, instance_id)
return jsonify({f_names[i]: v for i, v in enumerate(f_values)})