Mondrian_Tree_AL/repro_var_mt.py at reorganizing · tarungog/Mondrian_Tree_AL · GitHub

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from data_sets.toy_data_var_complexity import toy_data_var_complexity
from Mondrian_Tree import Mondrian_Tree
from sklearn.tree import DecisionTreeRegressor

import numpy as np
import warnings
import matplotlib
import itertools
matplotlib.use('AGG')
import matplotlib.pyplot as plt
import copy
import math


def example_var_mt(seed_index):

    n_points = 40000
    n_test_points = 5000
    n_finals = [500, 1000, 1500, 2000, 2500, 3000, 3500, 4000, 4500, 5000]
    p = 10
    marginal = 'uniform'

    # n_finals = [2000, 3000, 4000, 5000, 6000, 7000, 8000, 9000, 10000]
    # p = 5
    data_seeds = [x * 11 for x in range(12)]
    tree_seeds = [x * 13 for x in range(12)]

    seed_combs = list(itertools.product(data_seeds, tree_seeds))
    data_seed, tree_seed = seed_combs[int(seed_index)]

    std = 1
    low_freq = 0.1
    high_freq = 0.05

    low_mag = 5
    high_mag = 20

    high_area = [[0.1,1]]*p

    MT_al_MSE = np.zeros([len(n_finals)])
    MT_rn_MSE = np.zeros([len(n_finals)])
    MT_oracle_MSE = np.zeros([len(n_finals)])
    MT_uc_MSE = np.zeros([len(n_finals)])

    BT_al_MSE = np.zeros([len(n_finals)])
    BT_rn_MSE = np.zeros([len(n_finals)])
    BT_uc_MSE = np.zeros([len(n_finals)])

    for n_final_ind, n_final in enumerate(n_finals):

        n_start = int(n_final/2)


        X, y, true_labels = toy_data_var_complexity(n=n_points,p=p,high_area=high_area,std=std,
            low_freq=low_freq,high_freq=high_freq, low_mag=low_mag, high_mag=high_mag,
            set_seed=data_seed, marginal=marginal, return_true_labels=True)

        X = np.array(X)
        y = np.array(y)

        np.random.seed(data_seed)

        cv_ind = np.random.permutation(range(X.shape[0]))

        train_ind_al = cv_ind[:n_start]
        train_ind_rn = cv_ind[:n_final]

        X = X[cv_ind,:]
        y = y[cv_ind]

        X_test, y_test = toy_data_var_complexity(n=n_points,p=p,high_area=high_area,std=std,
            low_freq=low_freq,high_freq=high_freq, low_mag=low_mag, high_mag=high_mag,
            set_seed=data_seed+1)

        X_test = np.array(X_test)
        y_test = np.array(y_test)


        print(n_final, data_seed, tree_seed)

        # MT_al and labels for BT_al

        MT_al = Mondrian_Tree([[0,1]]*p)
        MT_al.update_life_time(n_final**(1/(2+p))-1, set_seed=tree_seed)
        MT_rn = copy.deepcopy(MT_al)
        MT_oracle = copy.deepcopy(MT_al)

        MT_al.input_data(X, range(n_start), y[:n_start])
        MT_al.make_full_leaf_list()
        MT_al.make_full_leaf_var_list()
        MT_al.al_set_default_var_global_var()
        # print(MT_al.al_default_var)

        MT_al.al_calculate_leaf_proportions()
        MT_al.al_calculate_leaf_number_new_labels(n_final)

        MT_uc = copy.deepcopy(MT_al)

        new_labelled_points = []
        for i, node in enumerate(MT_al._full_leaf_list):
            # print(i)
            curr_num = len(node.labelled_index)
            tot_num = curr_num + MT_al._al_leaf_number_new_labels[i]
            # print(curr_num,tot_num, MT_al._al_proportions[i] * n_final,node.rounded_linear_dims(2))
            num_new_points = MT_al._al_leaf_number_new_labels[i]
            labels_to_add = node.pick_new_points(num_new_points,self_update = False, set_seed = tree_seed*i)
            # print(labels_to_add)
            new_labelled_points.extend(labels_to_add)
            for ind in labels_to_add:
                MT_al.label_point(ind, y[ind])

        MT_al.set_default_pred_global_mean()

        with warnings.catch_warnings():
            warnings.simplefilter("ignore")
            MT_al_preds = MT_al.predict(X_test)
        MT_al_preds = np.array(MT_al_preds)
        MT_al_MSE[n_final_ind] += sum(1/X_test.shape[0]*(y_test - MT_al_preds)**2)

        # print('Done MT_al')

        # MT_rn

        MT_rn.input_data(X, range(n_final), y[:n_final])
        MT_rn.set_default_pred_global_mean()
        with warnings.catch_warnings():
            warnings.simplefilter("ignore")
            MT_rn_preds = MT_rn.predict(X_test)
        MT_rn_preds = np.array(MT_rn_preds)
        MT_rn_MSE[n_final_ind] += sum(1/X_test.shape[0]*(y_test - MT_rn_preds)**2)

        # MT_uc

        new_labelled_points_uc = []
        MT_uc._al_proportions = [x / sum(MT_uc._full_leaf_var_list) for x in MT_uc._full_leaf_var_list]
        with warnings.catch_warnings():
            warnings.simplefilter("ignore")
            MT_uc.al_calculate_leaf_number_new_labels(n_final)
        for i, node in enumerate(MT_uc._full_leaf_list):
            # print(i)

            num_new_points = MT_uc._al_leaf_number_new_labels[i]
            labels_to_add = node.pick_new_points(num_new_points,self_update = False, set_seed = tree_seed*i)

            new_labelled_points_uc.extend(labels_to_add)
            for ind in labels_to_add:
                MT_uc.label_point(ind, y[ind])

        MT_uc.set_default_pred_global_mean()

        with warnings.catch_warnings():
            warnings.simplefilter("ignore")
            MT_uc_preds = MT_uc.predict(X_test)
        MT_uc_preds = np.array(MT_uc_preds)
        MT_uc_MSE[n_final_ind] += sum(1/X_test.shape[0]*(y_test - MT_uc_preds)**2)

        # BT_al

        BT_al = DecisionTreeRegressor(random_state=tree_seed, max_leaf_nodes = MT_al._num_leaves+1)
        BT_al.fit(X[list(range(n_start)) + new_labelled_points,:], y[list(range(n_start)) + new_labelled_points])
        BT_al_preds = BT_al.predict(X_test)
        BT_al_MSE[n_final_ind] += sum(1/X_test.shape[0]*(y_test - BT_al_preds)**2)
        # print('Done BT_al')

        # BT_rn

        BT_rn = DecisionTreeRegressor(random_state=tree_seed, max_leaf_nodes = MT_rn._num_leaves+1)
        BT_rn.fit(X[list(range(n_final)),:], y[list(range(n_final))])
        BT_rn_preds = BT_rn.predict(X_test)
        BT_rn_MSE[n_final_ind] += sum(1/X_test.shape[0]*(y_test - BT_rn_preds)**2)
        # print('Done BT_rn')

        # BT_uc
        BT_uc = DecisionTreeRegressor(random_state=tree_seed, max_leaf_nodes = MT_uc._num_leaves+1)
        BT_uc.fit(X[list(range(n_start)) + new_labelled_points_uc,:], y[list(range(n_start)) + new_labelled_points_uc])
        BT_uc_preds = BT_uc.predict(X_test)
        BT_uc_MSE[n_final_ind] += sum(1/X_test.shape[0]*(y_test - BT_uc_preds)**2)


    np.savez('graphs/var_mt' +
        str(data_seed) + '_' + str(tree_seed) + '.npz',
        MT_al_MSE=MT_al_MSE, MT_rn_MSE=MT_rn_MSE,
        MT_uc_MSE=MT_uc_MSE, BT_uc_MSE=BT_uc_MSE,
        BT_al_MSE=BT_al_MSE, BT_rn_MSE=BT_rn_MSE
    )


def main():
    import sys
    assert(len(sys.argv) == 2)
    index = sys.argv[1]

    example_var_mt(index)


if __name__ == '__main__':
    main()