Classifying Iris Species Using Logistic Regression

import numpy as np
import cv2
from sklearn import datasets
from sklearn import model_selection
from sklearn import metrics
import matplotlib.pyplot as plt
%matplotlib inline
plt.style.use('ggplot')
iris = datasets.load_iris()
idx = iris.target != 2
data = iris.data[idx].astype(np.float32)
target = iris.target[idx].astype(np.float32)
plt.figure(figsize=(10, 6))
plt.scatter(data[:, 0], data[:, 1], c=target, cmap=plt.cm.Paired, s=100)
plt.xlabel(iris.feature_names[0])
plt.ylabel(iris.feature_names[1]);
X_train, X_test, y_train, y_test = model_selection.train_test_split(
    data, target, test_size=0.1, random_state=42)
lr = cv2.ml.LogisticRegression_create()
lr.setTrainMethod(cv2.ml.LogisticRegression_MINI_BATCH)
lr.setMiniBatchSize(1)
lr.setIterations(100)
lr.train(X_train, cv2.ml.ROW_SAMPLE, y_train);
lr.get_learnt_thetas()
ret, y_pred = lr.predict(X_train)
metrics.accuracy_score(y_train, y_pred)
ret, y_pred = lr.predict(X_test)
metrics.accuracy_score(y_test, y_pred)