Comments (3)
Hi @nathanlim45,
Could you provide more details on how you tried the tool with your regression model?
Was it explicitly in the Jupyter environment that no results were shown?
Or the regression example in the web demo didn't work for you either?
from manifold.
@nathanlim45 I'm thinking of adding a regression example for the Jupyter binding. Give the following code snippet a try
from mlvis import Manifold
import sys, json, math
import pandas as pd
from random import uniform, normalvariate
def generate_random_categorical_value(categories):
return categories[int(math.floor(uniform(0, 1) * len(categories)))]
num_instances = 100
categories = ['A', 'B', 'C', 'D']
domain = [1, 1000]
x = [{'feature_0': math.floor(uniform(*domain)),
'feature_1': generate_random_categorical_value(categories)}
for i in range(0, num_instances)]
# yPred is an list of tablular data in which each table contains the regression results for a model
yPred = [pd.DataFrame([{'arbitrary_name': normalvariate(i, 0.1 + i * 0.1)} for j in range(0, num_instances)]) for i in range(0, 3)]
yTrue = [normalvariate(0, 0.1) for i in range(0, num_instances)]
Manifold(props={'data': {
'x': x,
'yPred': yPred,
'yTrue': yTrue
}})
from manifold.
@kenns29 it worked. Thank you. The problem was that I put different format for yPred
from manifold.
Related Issues (20)
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from manifold.