import numpy as np
from sklearn import tree, linear_model, ensemble
from sklearn.metrics import accuracy_score
from sklearn.utils import shuffle
from sklearn.model_selection import train_test_split
from scipy.sparse import csr_matrix, lil_matrix
import operator
import code
from functools import reduce

class DecisionTreeWithBaggingRegressor:
  def __init__(self, criterion='mse', bagging=0.8, depth=9, trees=5, seed=None):
    self.criterion = criterion
    self.bagging = bagging
    self.trees = trees
    self.seed = seed
    self.random_state = np.random.RandomState(seed=self.seed)
    self.estimators_ = []
    for i in range(trees):
      self.estimators_.append(tree.DecisionTreeRegressor(
        max_depth=depth,
        max_features='auto',
        random_state = self.random_state,
      ))

  def fit(self, X_all, y_all):
    indices_all = np.arange(len(X_all))

    random_state = np.random.RandomState(seed=self.seed)
    for i in range(self.trees):
      X, X_test, y, y_test, indices, indices_test = train_test_split(X_all, y_all, indices_all, test_size=1-self.bagging, random_state=random_state)
      X, y = shuffle(X, y, random_state=self.seed)
      self.estimators_[i].fit(X, y)
    return self

  def decision_path(self, X):
    paths = []
    for i in range(self.trees):
      current_paths = self.estimators_[i].decision_path(X)
      paths = np.append(paths, current_paths)

    paths_csr_dim = reduce(lambda s, x: s+x.shape[1], paths, 0)
    paths_csr = lil_matrix((np.shape(X)[0], paths_csr_dim),dtype=np.float32)
    current_i = 0
    for current_paths in paths:
      paths_csr[:, current_i:current_i + current_paths.shape[1]] = current_paths
      current_i += current_paths.shape[1]

    return paths_csr.tocsr(), []



36fe4de5873350d9f78aba3ac364b372d8e64b0a
<mask token>


class DecisionTreeWithBaggingRegressor:
    <mask token>
    <mask token>
    <mask token>

<mask token>


class DecisionTreeWithBaggingRegressor:

    def __init__(self, criterion='mse', bagging=0.8, depth=9, trees=5, seed
        =None):
        self.criterion = criterion
        self.bagging = bagging
        self.trees = trees
        self.seed = seed
        self.random_state = np.random.RandomState(seed=self.seed)
        self.estimators_ = []
        for i in range(trees):
            self.estimators_.append(tree.DecisionTreeRegressor(max_depth=
                depth, max_features='auto', random_state=self.random_state))
    <mask token>

    def decision_path(self, X):
        paths = []
        for i in range(self.trees):
            current_paths = self.estimators_[i].decision_path(X)
            paths = np.append(paths, current_paths)
        paths_csr_dim = reduce(lambda s, x: s + x.shape[1], paths, 0)
        paths_csr = lil_matrix((np.shape(X)[0], paths_csr_dim), dtype=np.
            float32)
        current_i = 0
        for current_paths in paths:
            paths_csr[:, current_i:current_i + current_paths.shape[1]
                ] = current_paths
            current_i += current_paths.shape[1]
        return paths_csr.tocsr(), []

<mask token>


class DecisionTreeWithBaggingRegressor:

    def __init__(self, criterion='mse', bagging=0.8, depth=9, trees=5, seed
        =None):
        self.criterion = criterion
        self.bagging = bagging
        self.trees = trees
        self.seed = seed
        self.random_state = np.random.RandomState(seed=self.seed)
        self.estimators_ = []
        for i in range(trees):
            self.estimators_.append(tree.DecisionTreeRegressor(max_depth=
                depth, max_features='auto', random_state=self.random_state))

    def fit(self, X_all, y_all):
        indices_all = np.arange(len(X_all))
        random_state = np.random.RandomState(seed=self.seed)
        for i in range(self.trees):
            X, X_test, y, y_test, indices, indices_test = train_test_split(
                X_all, y_all, indices_all, test_size=1 - self.bagging,
                random_state=random_state)
            X, y = shuffle(X, y, random_state=self.seed)
            self.estimators_[i].fit(X, y)
        return self

    def decision_path(self, X):
        paths = []
        for i in range(self.trees):
            current_paths = self.estimators_[i].decision_path(X)
            paths = np.append(paths, current_paths)
        paths_csr_dim = reduce(lambda s, x: s + x.shape[1], paths, 0)
        paths_csr = lil_matrix((np.shape(X)[0], paths_csr_dim), dtype=np.
            float32)
        current_i = 0
        for current_paths in paths:
            paths_csr[:, current_i:current_i + current_paths.shape[1]
                ] = current_paths
            current_i += current_paths.shape[1]
        return paths_csr.tocsr(), []

import numpy as np
from sklearn import tree, linear_model, ensemble
from sklearn.metrics import accuracy_score
from sklearn.utils import shuffle
from sklearn.model_selection import train_test_split
from scipy.sparse import csr_matrix, lil_matrix
import operator
import code
from functools import reduce


class DecisionTreeWithBaggingRegressor:

    def __init__(self, criterion='mse', bagging=0.8, depth=9, trees=5, seed
        =None):
        self.criterion = criterion
        self.bagging = bagging
        self.trees = trees
        self.seed = seed
        self.random_state = np.random.RandomState(seed=self.seed)
        self.estimators_ = []
        for i in range(trees):
            self.estimators_.append(tree.DecisionTreeRegressor(max_depth=
                depth, max_features='auto', random_state=self.random_state))

    def fit(self, X_all, y_all):
        indices_all = np.arange(len(X_all))
        random_state = np.random.RandomState(seed=self.seed)
        for i in range(self.trees):
            X, X_test, y, y_test, indices, indices_test = train_test_split(
                X_all, y_all, indices_all, test_size=1 - self.bagging,
                random_state=random_state)
            X, y = shuffle(X, y, random_state=self.seed)
            self.estimators_[i].fit(X, y)
        return self

    def decision_path(self, X):
        paths = []
        for i in range(self.trees):
            current_paths = self.estimators_[i].decision_path(X)
            paths = np.append(paths, current_paths)
        paths_csr_dim = reduce(lambda s, x: s + x.shape[1], paths, 0)
        paths_csr = lil_matrix((np.shape(X)[0], paths_csr_dim), dtype=np.
            float32)
        current_i = 0
        for current_paths in paths:
            paths_csr[:, current_i:current_i + current_paths.shape[1]
                ] = current_paths
            current_i += current_paths.shape[1]
        return paths_csr.tocsr(), []

import numpy as np
from sklearn import tree, linear_model, ensemble
from sklearn.metrics import accuracy_score
from sklearn.utils import shuffle
from sklearn.model_selection import train_test_split
from scipy.sparse import csr_matrix, lil_matrix
import operator
import code
from functools import reduce

class DecisionTreeWithBaggingRegressor:
  def __init__(self, criterion='mse', bagging=0.8, depth=9, trees=5, seed=None):
    self.criterion = criterion
    self.bagging = bagging
    self.trees = trees
    self.seed = seed
    self.random_state = np.random.RandomState(seed=self.seed)
    self.estimators_ = []
    for i in range(trees):
      self.estimators_.append(tree.DecisionTreeRegressor(
        max_depth=depth,
        max_features='auto',
        random_state = self.random_state,
      ))

  def fit(self, X_all, y_all):
    indices_all = np.arange(len(X_all))

    random_state = np.random.RandomState(seed=self.seed)
    for i in range(self.trees):
      X, X_test, y, y_test, indices, indices_test = train_test_split(X_all, y_all, indices_all, test_size=1-self.bagging, random_state=random_state)
      X, y = shuffle(X, y, random_state=self.seed)
      self.estimators_[i].fit(X, y)
    return self

  def decision_path(self, X):
    paths = []
    for i in range(self.trees):
      current_paths = self.estimators_[i].decision_path(X)
      paths = np.append(paths, current_paths)

    paths_csr_dim = reduce(lambda s, x: s+x.shape[1], paths, 0)
    paths_csr = lil_matrix((np.shape(X)[0], paths_csr_dim),dtype=np.float32)
    current_i = 0
    for current_paths in paths:
      paths_csr[:, current_i:current_i + current_paths.shape[1]] = current_paths
      current_i += current_paths.shape[1]

    return paths_csr.tocsr(), []


