Function plot

numpy_ml/plots/trees_plots.py:20–161

()

def plot():
    fig, axes = plt.subplots(4, 4)
    fig.set_size_inches(10, 10)
    for ax in axes.flatten():
        n_ex = 100
        n_trees = 50
        n_feats = np.random.randint(2, 100)
        max_depth_d = np.random.randint(1, 100)
        max_depth_r = np.random.randint(1, 10)

        classifier = np.random.choice([True, False])
        if classifier:
            # create classification problem
            n_classes = np.random.randint(2, 10)
            X, Y = make_blobs(n_samples=n_ex, centers=n_classes, n_features=2)
            X, X_test, Y, Y_test = train_test_split(X, Y, test_size=0.3)
            n_feats = min(n_feats, X.shape[1])

            # initialize model
            def loss(yp, y):
                return accuracy_score(yp, y)

            # initialize model
            criterion = np.random.choice(["entropy", "gini"])
            mine = RandomForest(
                classifier=classifier,
                n_feats=n_feats,
                n_trees=n_trees,
                criterion=criterion,
                max_depth=max_depth_r,
            )
            mine_d = DecisionTree(
                criterion=criterion, max_depth=max_depth_d, classifier=classifier
            )
            mine_g = GradientBoostedDecisionTree(
                n_trees=n_trees,
                max_depth=max_depth_d,
                classifier=classifier,
                learning_rate=1,
                loss="crossentropy",
                step_size="constant",
                split_criterion=criterion,
            )

        else:
            # create regeression problem
            X, Y = make_regression(n_samples=n_ex, n_features=1)
            X, X_test, Y, Y_test = train_test_split(X, Y, test_size=0.3)
            n_feats = min(n_feats, X.shape[1])

            # initialize model
            criterion = "mse"
            loss = mean_squared_error
            mine = RandomForest(
                criterion=criterion,
                n_feats=n_feats,
                n_trees=n_trees,
                max_depth=max_depth_r,