Full model: Issue with the fit() function when using naive 3-step and 3-step BCH approaches

[FelixLaliberte]

When using the fit() function on a full model (with mixed descriptors), there seems to be an issue with the naive 3-step and 3-step BCH approaches. With the 1-step, 2-step and 3-step ML approaches, there is no such issue.

However, using the bootstrap() function, there are no error messages with any approach.

Here is an example with the Iris dataset:

packages

import pandas as pd import numpy as np from sklearn.datasets import load_iris from sklearn.metrics import rand_score from stepmix.stepmix import StepMix from stepmix.utils import get_mixed_descriptor from stepmix.bootstrap import bootstrap

IRIS in a dataframe

data, target = load_iris(return_X_y=True, as_frame=True)

Create categorical and binary data based on the Iris data quantiles

for c in data: c_categorical = c.replace("cm", "cat") data[c_categorical] = pd.qcut(data[c], q=3).cat.codes c_binary = c.replace("cm", "binary") data[c_binary] = pd.qcut(data[c], q=2).cat.codes

Create a fake covariate

data['Total length (cm)'] = data["sepal length (cm)"] + data["petal length (cm)"]

Add missing values in all variables, except the covariate

Replace 50% of values with missing values

for i, c in enumerate(data.columns): if c != 'Total length (cm)': data[c] = data[c].sample(frac=.5, random_state=42*i)

Measurement model definition

mm_data, mm_descriptor = get_mixed_descriptor( dataframe=data, continuous_nan=['sepal length (cm)', 'sepal width (cm)'], binary_nan=['sepal length (binary)', 'sepal width (binary)'], categorical_nan=['sepal length (cat)', 'sepal width (cat)'], )

Structural model definition

sm_data, sm_descriptor = get_mixed_descriptor( dataframe=data,

Covariate

covariate=['Total length (cm)'],
#Outcomes
continuous_nan=['petal length (cm)', 'petal width (cm)'],
binary_nan=['petal length (binary)', 'petal width (binary)'],
categorical_nan=['petal length (cat)', 'petal width (cat)'],

)

models to be fit

model1 = StepMix(n_components=3, measurement=mm_descriptor, structural=sm_descriptor, verbose=1, random_state=123, n_steps=3) #naive using bootstrap

model2 = StepMix(n_components=3, measurement=mm_descriptor, structural=sm_descriptor, verbose=1, random_state=123, n_steps=3) #naive using fit()

model3 = StepMix(n_components=3, measurement=mm_descriptor, structural=sm_descriptor, verbose=1, random_state=123, n_steps=3, correction='BCH') #BCH using fit()

model4 = StepMix(n_components=3, measurement=mm_descriptor, structural=sm_descriptor, verbose=1, random_state=123, n_steps=3, correction='ML') #ML using fit

results

model1, bootstrapped_params = bootstrap(model1, mm_data, sm_data, n_repetitions=10) #OK

model2.fit(mm_data, sm_data) #error

model3.fit(mm_data, sm_data) #error

model4.fit(mm_data, sm_data) #OK