solve task 6+7

import matplotlib.pyplot as plt

from matplotlib.colors import ListedColormap

from sklearn.datasets import make_moons, make_circles, make_classification

from sklearn.tree import DecisionTreeClassifier

from sklearn.tree import DecisionTreeClassifier,plot_tree

from sklearn.ensemble import RandomForestClassifier

from sklearn.metrics import accuracy_score

def generate_datasets(random_state):

    ax.set_xticks(())

    ax.set_yticks(())

def plot_results(optimal_classifiers,default_classifiers,*datasets):

def plot_results(classifiers,*datasets):

    figure = plt.figure(figsize=(27, 11)) 

    position_idx =1

    plotted_datasets_name = ["x_train","x_test"]

    plotted_datasets_name = [ "X_train with maxium depth","X_test with maxium depth",

                              "X_train with default values","X_test with default values",

                              "X_train with a random forest","X_test with a random forest"]

    dataset_count = 0

    for x_train,x_test,labels_train,labels_test in zip(*datasets):

    for pair_of_cls,x_train,x_test,labels_train,labels_test in zip(classifiers,*datasets):

        xx,yy = create_meshgrid(x_train)

        cm = plt.cm.RdBu

        cm_bright = ListedColormap(['#FF0000', '#0000FF'])

        ax = plt.subplot(3,5,position_idx)

        ax = plt.subplot(3,7,position_idx)

        if position_idx == 1:

            ax.set_title("Input data")

        # Plot the training points

                edgecolors='k')

        set_axis(ax,xx,yy)

        position_idx+=1

        opt_cls = optimal_classifiers[dataset_count]

        cls = default_classifiers[dataset_count]

        for cls in [opt_cls,cls]:    

            for name,data,labels in zip(plotted_datasets_name,[x_train,x_test],[labels_train,labels_test]):

                ax = plt.subplot(3,5,position_idx)

        cls_name_idx = 0

        for cls in pair_of_cls:

            for data,labels in zip([x_train,x_test],[labels_train,labels_test]):

                ax = plt.subplot(3,7,position_idx)

                Z= cls.predict_proba(np.c_[xx.ravel(), yy.ravel()])[:, 1]

                Z = Z.reshape(xx.shape)

                ax.contourf(xx, yy, Z, cmap=cm, alpha=.8)

                ax.scatter(data[:, 0], data[:, 1], c=labels, cmap=cm_bright,edgecolors='k')

                set_axis(ax,xx,yy)

                if dataset_count ==0:

                name = plotted_datasets_name[cls_name_idx]

                if cls_name_idx in (0,1):

                    name+=" "+str(cls.tree_.max_depth)

                # first or second column or entire first row 

                if cls_name_idx in(0,1) or dataset_count==0:

                    ax.set_title(name)

                #ax.text(xx.max() - .3, yy.min() + .3, ('%.2f' % score).lstrip('0'),

                #    size=15, horizontalalignment='right')

                position_idx += 1

                cls_name_idx+=1

        dataset_count+=1

if __name__ =="__main__":

    datasets = get_train_test_datasets()

    optimal_classifiers = [ DecisionTreeClassifier(max_depth=5),DecisionTreeClassifier(max_depth=4),DecisionTreeClassifier(max_depth=2)]

    print("default classifiers")

    default_classifiers = [DecisionTreeClassifier() for _ in range(0,3)]

    default_classifiers = train_classifiers(default_classifiers,datasets)

    combined_classifiers = list(zip(optimal_classifiers,default_classifiers))

    plot_results(optimal_classifiers,default_classifiers,*datasets)

    random_forest = [RandomForestClassifier() for _ in range(0,3)]

    print("random forest classifier")

    train_classifiers(random_forest,datasets)

    combined_classifiers =list(zip(optimal_classifiers,default_classifiers,random_forest))

    plot_results(combined_classifiers,*datasets)

    #plot_tree(optimal_classifiers[0],filled=True)

    plt.tight_layout()

    plt.show()