pip install human-learn

from sklearn.datasets import load_iris
from sklearn.model_selection import train_test_split
import pandas as pd 
# Load data
X, y = load_iris(return_X_y=True, as_frame=True)
X.columns = ['sepal_length', 'sepal_width', 'petal_length', 'petal_width']
# Train test split
X_train, X_test, y_train, y_test = train_test_split(X, y, random_state=1)
# Concatenate features and labels of the training data
train = pd.concat([X_train, pd.DataFrame(y_train)], axis=1)
train

from hulearn.experimental.interactive import InteractiveCharts
charts = InteractiveCharts(train, labels='target')
charts.add_chart(x='sepal_length', y='sepal_width')

charts.add_chart(x='petal_length', y='petal_width')

from hulearn.classification import InteractiveClassifier
model = InteractiveClassifier(json_desc=charts.data())
preds = model.fit(X_train, y_train).predict_proba(X_train)
print(preds.shape) # Output: (150, 3)

print('Classes:', model.classes_)
print('Predictions:\n', preds[:5, :])
"""Output
Classes: [1, 2, 0]
Predictions:
 [[5.71326574e-01 4.28530630e-01 1.42795945e-04]
 [2.00079952e-01 7.99720168e-01 1.99880072e-04]
 [2.00079952e-01 7.99720168e-01 1.99880072e-04]
 [2.49812641e-04 2.49812641e-04 9.99500375e-01]
 [4.99916708e-01 4.99916708e-01 1.66583375e-04]]
"""

# Get the first sample of X_test
new_sample = new_sample = X_test.iloc[:1]
# Predict
pred = model.predict(new_sample)
real = y_test[:1]
print("The prediction is", pred[0])
print("The real label is", real.iloc[0])

def plot_prediction(prediction: int, columns: list):
    """Plot new sample
    Parameters
    ----------
    prediction : int
        prediction of the new sample
    columns : list
        Features to create a scatter plot 
    """    
    index = prediction_to_index[prediction] 
    col1, col2 = columns    
    plt.figure(figsize=(12, 3))
    plt.scatter(X_train[col1], X_train[col2], c=preds[:, index])
    plt.plot(new_sample[col1], new_sample[col2], 'ro', c='red', label='new_sample')    
    plt.xlabel(col1)
    plt.ylabel(col2)
    plt.title(f"Label {model.classes_[index]}")
    plt.colorbar()
    plt.legend()

plot_prediction(0, columns=['petal_length', 'petal_width'])

from sklearn.metrics import confusion_matrix, f1_score
predictions = model.predict(X_test)
confusion_matrix(y_test, predictions, labels=[0,1,2])

array([[13,  0,  0],
       [ 0, 15,  1],
       [ 0,  0,  9]])

f1_score(y_test, predictions, average='micro')