User Guide¶

Make a classification problem¶

from sklearn.datasets import make_classification
from sklearn.model_selection import train_test_split
import numpy as np
import pandas as pd
from sklearn.metrics import roc_auc_score
from calfcv import CalfCV

seed = 45
X, y = make_classification(
    n_samples=100,
    n_features=5,
    n_informative=2,
    n_redundant=2,
    n_classes=2,
    random_state=seed
)
X_train, X_test, y_train, y_test = train_test_split(X, y, random_state=seed)

Train the classifier¶

cls = CalfCV().fit(X_train, y_train)

Get the score for class prediction on unseen data¶

cls.score(X_test, y_test)

0.92

Class probabilities¶

We vertically stack the ground truth on the top with the probabilities of class 1 on the bottom. The first five entries are shown.

np.round(np.vstack((y_train, cls.predict_proba(X_train).T))[:, 0:5], 2)

array([[1.  , 1.  , 0.  , 0.  , 0.  ],
       [0.21, 0.46, 1.  , 0.8 , 0.71],
       [0.79, 0.54, 0.  , 0.2 , 0.29]])

roc_auc_score(y_true=y_train, y_score=cls.predict_proba(X_train)[:, 1])

0.9722617354196301

Predict the classes¶

The ground truth is on the top and the predicted classes are on the bottom. The first five entries are shown.

y_pred = cls.predict(X_test)
np.vstack((y_test, y_pred))[:, 0:5]

array([[0, 0, 0, 1, 0],
       [0, 0, 0, 1, 0]])

The class prediction is expected to be lower than the auc prediction.

roc_auc_score(y_true=y_test, y_score=y_pred)

0.9198717948717948

Reproduce the AUC from example of the Calf paper [1]¶

While calfpy yields an auc of 0.875 in example 1 from the paper, calfcv produces an auc of 0.9796875.

=========== References:

Jeffries, C.D., Ford, J.R., Tilson, J.L. et al. A greedy regression algorithm with coarse weights offers novel advantages. Sci Rep 12, 5440 (2022). https://doi.org/10.1038/s41598-022-09415-2

input_file = "../../data/n2.csv"
df = pd.read_csv(input_file, header=0, sep=",")

# The input data is everything except the first column
X = df.loc[:, df.columns != 'ctrl/case']
# The outcome or diagnoses are in the first ctrl/case column
Y = df['ctrl/case']

# The header row is the feature set
features = list(X.columns)

# label the outcomes
Y_names = Y.replace({0: 'non_psychotic', 1: 'pre_psychotic'})

# glmnet requires float64
x = X.to_numpy(dtype='float64')
y = Y.to_numpy(dtype='float64')

Features¶

Here we look at the feature names, number of features, shape, category balance, and probability of choosing the positive category by chance.

features[0:5]

['ADIPOQ', 'SERPINA3', 'AMBP', 'A2M', 'ACE']

x.size

x.shape

(72, 135)

Category Balance¶

print(list(Y).count(1), list(Y).count(0))

32 40

len(y)

AUC improvement¶

CalfCV improves on the calfpy auc of 0.875 from example 1 of the paper.

y_pred = CalfCV().fit(x, y).predict_proba(x)
roc_auc_score(y, y_pred[:, 1])

0.9796875