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import pandas as pd
from sklearn.tree import DecisionTreeClassifier
from sklearn.model_selection import cross_val_score
import numpy as np
# from warnings import warn
# 2018.11.17 Created by Eamon.Zhang
# ChiMerge method modeified from https://github.com/tatsumiw/ChiMerge/blob/master/ChiMerge.py
# TODO: add more constraits to the discretized result.
class ChiMerge():
"""
supervised discretization using the ChiMerge method.
Parameters
----------
confidenceVal: number
default=3.841, correspond to p=0.05 dof=1
num_of_bins: int
number of bins after discretize
col: str
the column to be performed
"""
def __init__(self, col=None, bins=None, confidenceVal=3.841, num_of_bins=10):
self.col = col
self._dim = None
self.confidenceVal = confidenceVal
self.bins = bins
self.num_of_bins = num_of_bins
def fit(self, X, y, **kwargs):
"""Fit encoder according to X and y.
Parameters
----------
X : array-like, shape = [n_samples, n_features]
Training vectors, where n_samples is the number of samples
and n_features is the number of features.
y : array-like, shape = [n_samples]
Target values.
Returns
-------
self : encoder
Returns self.
"""
self._dim = X.shape[1]
_, bins = self.chimerge(
X_in=X,
y=y,
confidenceVal=self.confidenceVal,
col=self.col,
num_of_bins=self.num_of_bins
)
self.bins = bins
return self
def transform(self, X):
"""Perform the transformation to new data.
Will use the tree model and the column list to discretize the
column.
Parameters
----------
X : array-like, shape = [n_samples, n_features]
Returns
-------
X : new dataframe with discretized new column.
"""
if self._dim is None:
raise ValueError('Must train encoder before it can be used to transform data.')
# make sure that it is the right size
if X.shape[1] != self._dim:
raise ValueError('Unexpected input dimension %d, expected %d' % (X.shape[1], self._dim,))
X, _ = self.chimerge(
X_in=X,
col=self.col,
bins=self.bins
)
return X
def chimerge(self, X_in, y=None, confidenceVal=None, num_of_bins=None, col=None, bins=None):
"""
discretize a variable using ChiMerge
"""
X = X_in.copy(deep=True)
if bins is not None: # transform
try:
X[col+'_chimerge'] = pd.cut(X[col],bins=bins,include_lowest=True)
except Exception as e:
print(e)
else: # fit
try:
# create an array which save the num of 0/1 samples of the column to be chimerge
total_num = X.groupby([col])[y].count()
total_num = pd.DataFrame({'total_num': total_num})
positive_class = X.groupby([col])[y].sum()
positive_class = pd.DataFrame({'positive_class': positive_class})
regroup = pd.merge(total_num, positive_class, left_index=True, right_index=True,how='inner')
regroup.reset_index(inplace=True)
regroup['negative_class'] = regroup['total_num'] - regroup['positive_class']
regroup = regroup.drop('total_num', axis=1)
np_regroup = np.array(regroup)
# merge interval that have 0 pos/neg samples
i = 0
while (i <= np_regroup.shape[0] - 2):
if ((np_regroup[i, 1] == 0 and np_regroup[i + 1, 1] == 0) or ( np_regroup[i, 2] == 0 and np_regroup[i + 1, 2] == 0)):
np_regroup[i, 1] = np_regroup[i, 1] + np_regroup[i + 1, 1] # pos
np_regroup[i, 2] = np_regroup[i, 2] + np_regroup[i + 1, 2] # neg
np_regroup[i, 0] = np_regroup[i + 1, 0]
np_regroup = np.delete(np_regroup, i + 1, 0)
i = i - 1
i = i + 1
# calculate chi for neighboring intervals
# ∑[(yA-yB)²/yB]
chi_table = np.array([])
for i in np.arange(np_regroup.shape[0] - 1):
chi = (np_regroup[i, 1] * np_regroup[i + 1, 2] - np_regroup[i, 2] * np_regroup[i + 1, 1]) ** 2 \
* (np_regroup[i, 1] + np_regroup[i, 2] + np_regroup[i + 1, 1] + np_regroup[i + 1, 2]) / \
((np_regroup[i, 1] + np_regroup[i, 2]) * (np_regroup[i + 1, 1] + np_regroup[i + 1, 2]) * (
np_regroup[i, 1] + np_regroup[i + 1, 1]) * (np_regroup[i, 2] + np_regroup[i + 1, 2]))
chi_table = np.append(chi_table, chi)
# merge intervals that have closing chi
while (1):
if (len(chi_table) <= (num_of_bins - 1) and min(chi_table) >= confidenceVal):
break
chi_min_index = np.argwhere(chi_table == min(chi_table))[0]
np_regroup[chi_min_index, 1] = np_regroup[chi_min_index, 1] + np_regroup[chi_min_index + 1, 1]
np_regroup[chi_min_index, 2] = np_regroup[chi_min_index, 2] + np_regroup[chi_min_index + 1, 2]
np_regroup[chi_min_index, 0] = np_regroup[chi_min_index + 1, 0]
np_regroup = np.delete(np_regroup, chi_min_index + 1, 0)
if (chi_min_index == np_regroup.shape[0] - 1):
chi_table[chi_min_index - 1] = (np_regroup[chi_min_index - 1, 1] * np_regroup[chi_min_index, 2] - np_regroup[chi_min_index - 1, 2] * np_regroup[chi_min_index, 1]) ** 2 \
* (np_regroup[chi_min_index - 1, 1] + np_regroup[chi_min_index - 1, 2] + np_regroup[chi_min_index, 1] + np_regroup[chi_min_index, 2]) / \
((np_regroup[chi_min_index - 1, 1] + np_regroup[chi_min_index - 1, 2]) * (np_regroup[chi_min_index, 1] + np_regroup[chi_min_index, 2]) * (np_regroup[chi_min_index - 1, 1] + np_regroup[chi_min_index, 1]) * (np_regroup[chi_min_index - 1, 2] + np_regroup[chi_min_index, 2]))
chi_table = np.delete(chi_table, chi_min_index, axis=0)
else:
chi_table[chi_min_index - 1] = (np_regroup[chi_min_index - 1, 1] * np_regroup[chi_min_index, 2] - np_regroup[chi_min_index - 1, 2] * np_regroup[chi_min_index, 1]) ** 2 \
* (np_regroup[chi_min_index - 1, 1] + np_regroup[chi_min_index - 1, 2] + np_regroup[chi_min_index, 1] + np_regroup[chi_min_index, 2]) / \
((np_regroup[chi_min_index - 1, 1] + np_regroup[chi_min_index - 1, 2]) * (np_regroup[chi_min_index, 1] + np_regroup[chi_min_index, 2]) * (np_regroup[chi_min_index - 1, 1] + np_regroup[chi_min_index, 1]) * (np_regroup[chi_min_index - 1, 2] + np_regroup[chi_min_index, 2]))
chi_table[chi_min_index] = (np_regroup[chi_min_index, 1] * np_regroup[chi_min_index + 1, 2] - np_regroup[chi_min_index, 2] * np_regroup[chi_min_index + 1, 1]) ** 2 \
* (np_regroup[chi_min_index, 1] + np_regroup[chi_min_index, 2] + np_regroup[chi_min_index + 1, 1] + np_regroup[chi_min_index + 1, 2]) / \
((np_regroup[chi_min_index, 1] + np_regroup[chi_min_index, 2]) * (np_regroup[chi_min_index + 1, 1] + np_regroup[chi_min_index + 1, 2]) * (np_regroup[chi_min_index, 1] + np_regroup[chi_min_index + 1, 1]) * (np_regroup[chi_min_index, 2] + np_regroup[chi_min_index + 1, 2]))
chi_table = np.delete(chi_table, chi_min_index + 1, axis=0)
result_data = pd.DataFrame()
result_data['variable'] = [col] * np_regroup.shape[0]
bins = []
tmp = []
for i in np.arange(np_regroup.shape[0]):
if i == 0:
y = '-inf' + ',' + str(np_regroup[i, 0])
#x = np_regroup[i, 0]
#list_temp.append(x)
elif i == np_regroup.shape[0] - 1:
y = str(np_regroup[i - 1, 0]) + '+'
#x = 100000000.
#list_temp.append(x)
else:
y = str(np_regroup[i - 1, 0]) + ',' + str(np_regroup[i, 0])
#x = np_regroup[i, 0]
#list_temp.append(x)
bins.append(np_regroup[i - 1, 0])
tmp.append(y)
#list_temp.append(df[variable].max()+0.1)
bins.append(X[col].min()-0.1)
result_data['interval'] = tmp
result_data['flag_0'] = np_regroup[:, 2]
result_data['flag_1'] = np_regroup[:, 1]
bins.sort(reverse=False)
print('Interval for variable %s' % col)
print(result_data)
except Exception as e:
print(e)
return X, bins
# 2018.11.15 Created by Eamon.Zhang
class DiscretizeByDecisionTree():
"""
Discretisation with Decision Trees consists of using a decision tree
to identify the optimal splitting points that would determine the bins
or contiguous intervals:
1.train a decision tree of limited depth (2, 3 or 4) using the variable
we want to discretise to predict the target.
2.the original variable values are then replaced by the
probability returned by the tree.
Parameters
----------
col: str
column to discretise
max_depth: int or list of int
max depth of the tree. Can be an int or a list of int we want the tree model to search
for the optimal depth.
"""
def __init__(self, col=None, max_depth=None, tree_model=None):
self.col = col
self._dim = None
self.max_depth = max_depth
self.tree_model = tree_model
def fit(self, X, y, **kwargs):
"""Fit encoder according to X and y.
Parameters
----------
X : array-like, shape = [n_samples, n_features]
Training vectors, where n_samples is the number of samples
and n_features is the number of features.
y : array-like, shape = [n_samples]
Target values.
Returns
-------
self : encoder
Returns self.
"""
self._dim = X.shape[1]
_, tree = self.discretize(
X_in=X,
y=y,
max_depth=self.max_depth,
col=self.col,
tree_model=self.tree_model
)
self.tree_model = tree
return self
def transform(self, X):
"""Perform the transformation to new categorical data.
Will use the tree model and the column list to discretize the
column.
Parameters
----------
X : array-like, shape = [n_samples, n_features]
Returns
-------
X : new dataframe with discretized new column.
"""
if self._dim is None:
raise ValueError('Must train encoder before it can be used to transform data.')
# make sure that it is the right size
if X.shape[1] != self._dim:
raise ValueError('Unexpected input dimension %d, expected %d' % (X.shape[1], self._dim,))
X, _ = self.discretize(
X_in=X,
col=self.col,
tree_model=self.tree_model
)
return X
def discretize(self, X_in, y=None, max_depth=None, tree_model=None, col=None):
"""
discretize a variable using DecisionTreeClassifier
"""
X = X_in.copy(deep=True)
if tree_model is not None: # transform
X[col+'_tree_discret'] = tree_model.predict_proba(X[col].to_frame())[:,1]
else: # fit
if isinstance(max_depth,int):
tree_model = DecisionTreeClassifier(max_depth=max_depth)
tree_model.fit(X[col].to_frame(), y)
# X[col+'_tree_discret'] = tree_model.predict_proba(X[col].to_frame())[:,1]
#print(x.tree_discret.unique())
# bins = pd.concat( [X.groupby([col+'_tree_discret'])[col].min(),
# X.groupby([col+'_tree_discret'])[col].max()], axis=1)
# print('bins:')
# print(bins)
elif len(max_depth)>1:
score_ls = [] # here I will store the roc auc
score_std_ls = [] # here I will store the standard deviation of the roc_auc
for tree_depth in max_depth:
tree_model = DecisionTreeClassifier(max_depth=tree_depth)
scores = cross_val_score(tree_model, X[col].to_frame(), y, cv=3, scoring='roc_auc')
score_ls.append(np.mean(scores))
score_std_ls.append(np.std(scores))
temp = pd.concat([pd.Series(max_depth), pd.Series(score_ls), pd.Series(score_std_ls)], axis=1)
temp.columns = ['depth', 'roc_auc_mean', 'roc_auc_std']
print('result ROC-AUC for each depth')
print(temp)
max_roc = temp.roc_auc_mean.max()
optimal_depth=temp[temp.roc_auc_mean==max_roc]['depth'].values
print('optimal_depth:',optimal_depth)
tree_model = DecisionTreeClassifier(max_depth=optimal_depth)
tree_model.fit(X[col].to_frame(), y)
# bins = pd.concat( [X.groupby([col+'_tree_discret'])[col].min(),
# X.groupby([col+'_tree_discret'])[col].max()], axis=1)
# print('bins:')
# print(bins)
else:
raise ValueError('max_depth of a tree must be an integer or a list')
return X, tree_model
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