def dt_log(m, features, label):
trainDT, testDT = train_test_split(data_load, test_size=0.2, random_state=1)
# trainDT, cvDT = train_test_split(trainDT, test_size=0.2, random_state=1)
dt = DecisionTreeClassifier(max_depth=3)
dt.fit(trainDT[features[:m]], trainDT[label])
leaf = dt.apply(trainDT[features[:m]])
leafNode = leaf.reshape(-1, 1)
coder = OneHotEncoder()
coder.fit(leafNode)
newFeature = np.c_[
coder.transform(dt.apply(trainDT[features[:m]]).reshape(-1, 1)).toarray(),
trainDT[features[m:]]]
logit = LogisticRegression()
logit.fit(newFeature[:, 1:], trainDT[label].values.ravel())
testFeature = np.c_[
coder.transform(dt.apply(testDT[features[:m]]).reshape(-1, 1)).toarray(),
testDT[features[m:]]]
y_predprob = logit.predict_proba(testFeature[:, 1:])
y_pred = np.argmax(y_predprob, axis=1)
print(confusion_matrix(testDT[label]['retention_status'].values, y_pred))
print("Accuracy : %.4g" % accuracy_score(testDT[label]['retention_status'].values, y_pred))
print("AUC Score (Test): %f" % roc_auc_score(testDT[label]['retention_status'].values, y_predprob[:, 1]))
res = roc_curve(testDT[label], y_predprob[:, 1])
plot_roc(res)
class TreeDiscretizer:
"""A DecisionTreeClassifier of which the leaf indices are used as cluster indices.
Parameters
----------
X_train : array-like, shape (n_samples, n_features), floats
The feature matrix of the training set.
y_train : array-like, shape (n_samples), nonnegative ints
The labels of X_train.
J : int
The maximum number of desired clusters.
criterion : string, optional
The split criterion of the underlying DecisionTreeClassifier, 'gini' by default.
seed : nonnegative int, optional
This seed for the random number generator makes random splits reproducible.
"""
def __init__(self, X_train, y_train, J, criterion='gini', seed=None):
self.classifier = DecisionTreeClassifier(max_leaf_nodes=J,
criterion=criterion,
random_state=seed)
self.classifier.fit(X_train, y_train)
x_train = self.classifier.apply(X_train)
self.indexmap = dict(
zip(np.unique(x_train), range(len(np.unique(x_train)))))
def discretize(self, X):
x_raw = self.classifier.apply(X) # return the raw leaf indices of X
return np.array([self.indexmap[x] for x in x_raw])
def compute_new_features(X_train,y_train,X_test,y_test):
classifier = DecisionTreeClassifier(max_leaf_nodes=50)
classifier.fit(X_train,y_train)
idx_train = classifier.apply(X_train)
idx_train = idx_train.reshape([-1,1])
enc = OneHotEncoder()
enc.fit(idx_train)
new_features_train = enc.transform(idx_train).toarray()
idx_test = classifier.apply(X_test)
idx_test = idx_test.reshape([-1,1])
new_features_test = enc.transform(idx_test).toarray()
return ([np.hstack([X_train,new_features_train]), np.hstack([X_test,new_features_test])])
class DecisionTreeCalibration(BaseEstimator, TransformerMixin):
"""
Выполнение калибровки решеающим деревом и линейными моделями в листах
"""
def __init__(self, model, tree_max_depth=3, rs=17):
self.model = model
self.rs = rs
self.dt_calib = DecisionTreeClassifier(max_depth=tree_max_depth,
random_state=rs)
self.logits = {}
def fit(self, X: pd.DataFrame, y: pd.Series):
# Обучить дерево решений
self.dt_calib.fit(X[self.model.used_features], y)
leafs = self.dt_calib.apply(X[self.model.used_features])
# Обучить логистическую регрессию для каждого листа
for leaf in np.unique(leafs):
lr = LogisticRegression(random_state=self.rs)
X_sub = X[leafs == leaf]
y_pred_sub = self.model.transform(X_sub)
y_sub = y[leafs == leaf]
lr.fit(y_pred_sub.reshape(-1, 1), y_sub)
self.logits[leaf] = lr
def transform(self, X: pd.DataFrame):
pred_df = pd.DataFrame(
{
"y_pred": self.model.transform(X),
"leaf": self.dt_calib.apply(X[self.model.used_features])
},
index=X.index)
y_calib = pd.Series()
# для каждого листа применить свой логит
for lf in np.unique(pred_df.leaf):
idx_sub = pred_df[pred_df.leaf == lf].index
y_pred_sub = np.array(pred_df[pred_df.leaf == lf].y_pred).reshape(
-1, 1)
y_calib_sub = pd.Series(
self.logits[lf].predict_proba(y_pred_sub)[:, 1], index=idx_sub)
y_calib = y_calib.append(y_calib_sub)
return y_calib
class TreeClassificationTransformer(BaseTransformer):
"""
A class used to transform data from a category to a specialized representation.
Parameters
----------
kwargs : dict, default={}
A dictionary to contain parameters of the tree.
Attributes
----------
transformer : sklearn.tree.DecisionTreeClassifier
an internal sklearn DecisionTreeClassifier
"""
def __init__(self, kwargs={}):
self.kwargs = kwargs
def fit(self, X, y):
"""
Fits the transformer to data X with labels y.
Parameters
----------
X : ndarray
Input data matrix.
y : ndarray
Output (i.e. response data matrix).
Returns
-------
self : TreeClassificationTransformer
The object itself.
"""
X, y = check_X_y(X, y)
self.transformer_ = DecisionTreeClassifier(**self.kwargs).fit(X, y)
return self
def transform(self, X):
"""
Performs inference using the transformer.
Parameters
----------
X : ndarray
Input data matrix.
Returns
-------
X_transformed : ndarray
The transformed input.
Raises
------
NotFittedError
When the model is not fitted.
"""
check_is_fitted(self)
X = check_array(X)
return self.transformer_.apply(X)
class DecisionTreeModel:
# initialize a DecisionTreeModel object with "model" attribute containing an actual DecisionTreeClassifier object from the skLearn module
def __init__(self,*args,**kwargs):
self.model = DecisionTreeClassifier(*args, **kwargs)
def get_model(self):
return self.model
def apply(self,X,check_input=True):
if (isinstance(X,TabularData)):
X=DataConversion.extract_X(X)
return self.model.apply(X,check_input)
def cost_complexity_pruning_path(self,X,y,sample_weight=None):
if (isinstance(X,TabularData)):
X=DataConversion.extract_X(X)
if (isinstance(y,TabularData)):
y=DataConversion.extract_y(y)
return self.model.cost_complexity_pruning_path(X,y,sample_weight)
def decision_path(self,X,check_input=True):
if (isinstance(X,TabularData)):
X=DataConversion.extract_X(X)
return self.model.decision_path(X,check_input)
def fit(self,X,y,sample_weight=None,check_input=True,X_idx_sorted=None):
if (isinstance(X,TabularData)):
X=DataConversion.extract_X(X)
if (isinstance(y,TabularData)):
y=DataConversion.extract_y(y)
self.model.fit(X,y,sample_weight,check_input,X_idx_sorted)
return self
def predict(self,X,check_input=True):
if (isinstance(X,TabularData)):
X=DataConversion.extract_X(X)
return self.model.predict(X,check_input)
def predict_log_proba(self,X):
if (isinstance(X,TabularData)):
X=DataConversion.extract_X(X)
return self.model.predict_log_proba(X)
def predict_proba(self,X,check_input=True):
if (isinstance(X,TabularData)):
X=DataConversion.extract_X(X)
return self.model.predict_proba(X,check_input)
def score(self,X,y,sample_weight=None):
if (isinstance(X,TabularData)):
X=DataConversion.extract_X(X)
if (isinstance(y,TabularData)):
y=DataConversion.extract_y(y)
return self.model.score(X,y,sample_weight)
def __getattribute__(self,item):
try:
return super().__getattribute__(item)
except:
pass;
return getattr(self.model,item)
def findLeafDepthWithoutPrunning(self, df, classes):
values = np.empty([0, 0])
estimator = DecisionTreeClassifier()
estimator.fit(df, classes)
n_nodes = estimator.tree_.node_count
# print(n_nodes)
children_left = estimator.tree_.children_left
children_right = estimator.tree_.children_right
node_depth = np.zeros(shape=n_nodes, dtype=np.int64)
stack = [(0, -1)] # seed is the root node id and its parent depth
while len(stack) > 0:
node_id, parent_depth = stack.pop()
node_depth[node_id] = parent_depth + 1
# If we have a test node
if children_left[node_id] != children_right[node_id]:
stack.append((children_left[node_id], parent_depth + 1))
stack.append((children_right[node_id], parent_depth + 1))
leafsIndexes = estimator.apply(df, check_input=True)
for index in leafsIndexes:
values = np.append(values, np.full((1, 1), node_depth[index]))
return values
def fit_function(self, comb, model):
"""
Fits a single logistic regression or decision tree model
comb: feature combination to fit model over
model: fit logistic regression or a decision tree
"""
X_val = self.X_val.iloc[:, list(comb)]
if np.shape(X_val)[0] == 1:
X_val = X_val.reshape(-1, 1)
# fit decision tree or logistic regression or knn
if model == 'dt':
dt = DecisionTreeClassifier(max_depth=len(comb))
dt.fit(X_val, self.Y_val)
# caculate val_acc
Y_pred = dt.apply(self.X_val.iloc[:, list(comb)])
from sklearn.metrics import accuracy_score
# print("va - ", comb, "\t", accuracy_score(self.Y_val, Y_pred))
return dt
elif model == 'lr':
lr = LogisticRegression(multi_class='auto', n_jobs=self.n_jobs)
lr.fit(X_val, self.Y_val)
return lr
elif model == 'nn':
nn = KNeighborsClassifier(algorithm='kd_tree', n_jobs=self.n_jobs)
nn.fit(X_val, self.Y_val)
return nn
def get_decision_paths(model: tree.DecisionTreeClassifier, data, selection):
selected_rows = data.loc[selection.astype(bool), :]
d_path = model.decision_path(selected_rows)
paths = set()
leaf_id = model.apply(selected_rows)
feature = model.tree_.feature
threshold = model.tree_.threshold
for sample_id in range(len(selected_rows.index)):
node_idx = d_path.indices[d_path.indptr[sample_id]:d_path.
indptr[sample_id + 1]]
rules = []
for node_id in node_idx:
if leaf_id[sample_id] == node_id:
continue
sign = None
if selected_rows.iloc[sample_id,
feature[node_id]] <= threshold[node_id]:
sign = " <= "
else:
sign = " >= "
rule = (data.columns[feature[node_id]] + sign +
str(round(threshold[node_id], 2)))
rules.append(rule)
paths.add(tuple(rules))
paths = [[rule for rule in path] for path in paths]
return paths
def get_leaf(train_x, train_y, val_x):
from sklearn.tree import DecisionTreeClassifier
train_x, train_y, val_x = map(np.array, [train_x, train_y, val_x])
train_x = train_x.reshape(-1, 1)
train_y = train_y.reshape(-1, 1)
val_x = val_x.reshape(-1, 1)
m = DecisionTreeClassifier(min_samples_leaf=0.001, max_leaf_nodes=25)
m.fit(train_x, train_y)
return m.apply(val_x)
def fit_preproc(self, x_train, y_train):
tree_list = []
ohe_list = []
var_names = []
n_predictors = self.n_predictors if self.n_predictors != None else x_train.shape[
1]
self.n_predictors = n_predictors # number of trees
if isinstance(x_train, pd.DataFrame):
self.initial_var_names = x_train.columns
for i in range(n_predictors):
for j in range(i + 1, n_predictors):
# Input data with 2 variables (column)
# print(i,j)
x_input = x_train.iloc[:, [i, j]]
tree_t = DecisionTreeClassifier(max_depth=2,
max_leaf_nodes=3).fit(
x_input, y_train)
# Output of tree data
x_processed = tree_t.apply(x_input)
# onehot encoder transform
ohe = OneHotEncoder().fit(x_processed.reshape((-1, 1)))
# Setting var names : for understanding
c_names = [str(i), str(j)]
categ = ohe.categories_[0]
feature = tree_t.tree_.feature
feature_used = feature[feature >= 0]
if len(feature_used) > 1:
one = feature_used[0]
two = feature_used[1]
var = [
c_names[one],
'(' + c_names[one] + ',' + c_names[two] + ')'
] # First name is the variable used in root and the secode one is the couple of variables used
elif len(feature_used) == 1:
# print(i,j)
var = [c_names[feature_used[0]]]
else: # variables are useless (both)
var = ['ij']
# Record trees and encoder
tree_list.append(tree_t)
ohe_list.append(ohe)
var_names.append(var)
self.tree_list = tree_list
self.ohe_list = ohe_list
self.var_names = var_names
return self
def binning_opt(X, y, depth):
var_name = X.name
df = X.to_frame()
target_df = y.to_frame()
missing_df = df[df[var_name].isnull()]
df = df.merge(missing_df,
left_index=True,
right_index=True,
how='outer',
indicator=True)
df = df[df['_merge'] == 'left_only']
df.drop([var_name + '_y', '_merge'], axis=1, inplace=True)
df.rename(columns={var_name + '_x': var_name}, inplace=True)
target_df = target_df.merge(missing_df,
left_index=True,
right_index=True,
how='outer',
indicator=True)
target_df = target_df[target_df['_merge'] == 'left_only']
target_df.drop([var_name, '_merge'], axis=1, inplace=True)
dt = DecisionTreeClassifier(max_features=1,
max_depth=depth,
min_samples_leaf=0.1)
dt.fit(df, target_df)
df['nodo'] = dt.apply(df)
df['nodo'] = df['nodo'].astype(str)
bins = df.groupby('nodo').agg(['min', 'max'])[var_name]
bins.sort_values('min', inplace=True)
bins['min2'] = bins['max'].shift(1)
bins['min'] = np.where(bins['min2'].isnull(), bins['min'], bins['min2'])
bins.reset_index(inplace=True)
bins['id'] = (bins.index +
1).map(lambda x: '0' + str(x) if x < 10 else str(x))
bins['C_' + var_name] = bins['id'] + '. (' + bins['min'].astype(
str) + ", " + bins['max'].astype(str) + "]"
bins = bins[['nodo', 'C_' + var_name]]
df['in'] = df.index
df = df.merge(bins, how='inner', on='nodo')
df.index = df['in']
df.sort_index(inplace=True)
df.drop(['in', 'nodo'], axis=1, inplace=True)
missing_df['C_' + var_name] = 'Null'
missing_df['in'] = missing_df.index
missing_df.index = missing_df['in']
missing_df.drop('in', axis=1, inplace=True)
df = pd.concat([df, missing_df]).sort_index()
df.index.name = None
return df['C_' + var_name]
def trainModel(data, features, label):
"""
分别使用「逻辑回归」、「决策树」、「逻辑回归+决策树」建模
:param data:
:return:
"""
res = {}
trainData, testData = train_test_split(data, test_size=0.5)
# 单独使用逻辑回归
logitModel = LogisticRegression()
logitModel.fit(trainData[features], trainData[label])
logitProb = logitModel.predict_proba(testData[features])[:, 1]
res["logit"] = roc_curve(testData[label], logitProb)
# 单独使用决策树
dtModel = DecisionTreeClassifier(max_depth=2)
dtModel.fit(trainData[features], trainData[label])
dtProb = dtModel.predict_proba(testData[features])[:, 1]
res["DT"] = roc_curve(testData[label], dtProb)
trainDT, trainLR = train_test_split(trainData, test_size=0.5)
m = 2
_dt = DecisionTreeClassifier(max_depth=2)
_dt.fit(trainDT[features[:m]], trainDT[label])
leafNode = _dt.apply(trainDT[features[: m]]).reshape(-1, 1)
coder = OneHotEncoder()
coder.fit(leafNode)
newFeature = np.c_[
coder.transform(_dt.apply(trainLR[features[:m]]).reshape(-1, 1)).toarray(),
trainLR[features[m:]]]
_logit = LogisticRegression()
_logit.fit(newFeature[:, 1:], trainLR[label])
testFeature = np.c_[
coder.transform(_dt.apply(testData[features[:m]]).reshape(-1, 1)).toarray(),
testData[features[m:]]]
dtLogitProb = _logit.predict_proba(testFeature[:, 1:])[:, 1]
res["DT + logit"] = roc_curve(testData[label], dtLogitProb)
return res
def extract_table(cls, N: np.ndarray, y: np.ndarray,
model: DecisionTreeClassifier) -> np.ndarray:
"""Precompute ``model``, ``table`` and ``tree_depth``.
Parameters
----------
N : :obj:`np.ndarray`
Attributes from fitted data.
y : :obj:`np.ndarray`
Target attribute from fitted data.
random_state : :obj:`int`, optional
If int, random_state is the seed used by the random number
generator; If RandomState instance, random_state is the random
number generator; If None, the random number generator is the
RandomState instance used by np.random.
kwargs:
Additional arguments. May have previously precomputed before this
method from other precomputed methods, so they can help speed up
this precomputation.
Returns
-------
:obj:`np.ndarray`
Tree property table.
- Each line represents a node.
- Column 0: It is the id of the attributed splited in that
node.
- Column 1: It is 1 if the node is a leaf node, otherwise 0.
- Columns 2: It is the number of examples that fall on that
node.
- Columns 3: It is 0 if the node is not a leaf, otherwise is
the class number represented by that leaf node.
"""
table = np.zeros((model.tree_.node_count, 4)) # type: np.ndarray
table[:, 0] = model.tree_.feature
table[:, 2] = model.tree_.n_node_samples
leaves = model.apply(N) # type: DecisionTreeClassifier
if not isinstance(y, np.number):
_, y = np.unique(y, return_inverse=True)
tmp = np.array([leaves, y + 1]) # type: np.ndarray
x = 0 # type: int
for x in set(leaves):
table[x, 3] = list(Counter(tmp[1, tmp[0, :] == x]).keys())[0] + 1
table[x, 1] = 1
return table
def beginWork(home, logger):
constant = CONSTANT(home)
logger.info("========== get subgroup ==========".center(CONSTANT.logLength, "="))
data, featureName = loadData(constant.getDataFilteredPath(), logger)
X, Y = data[:, :-1], data[:, -1]
size = X.shape[0]
subgroupSizes = [4, 8, 16]
cartModelDir = constant.getCartModelDir()
shutil.rmtree(cartModelDir)
os.makedirs(cartModelDir)
for subgroupSize in subgroupSizes:
curCartModelDir = os.path.join(cartModelDir, "subgroup-"+str(subgroupSize))
os.makedirs(curCartModelDir)
dotFilePath = os.path.join(curCartModelDir, "dot.dot")
paramsTxtPath = os.path.join(curCartModelDir, "params.txt")
subgroupsSavedPath = os.path.join(curCartModelDir, "subgroups.pkl")
subgroups = {}
avgSize = size // subgroupSize
param = {
"criterion": "gini",
"min_samples_leaf": avgSize*2 // 3,
"min_samples_split": avgSize*2
}
clf = DecisionTreeClassifier(**param).fit(X, Y)
# 保存树结构&超参数
with open(dotFilePath, "w", encoding="utf-8") as file:
file = export_graphviz(clf, out_file = file, feature_names = featureName,
filled = True, rounded = True, special_characters = True)
logger.info("cart structure saved in {}".format(dotFilePath))
with open(paramsTxtPath, "w", encoding='utf-8') as file:
file.write(str(clf.get_params()))
logger.info("cart params saved in {}".format(paramsTxtPath))
logger.info(param)
# 获得并保存亚组
itemIndex = clf.apply(X)
for sampleIndex, groupIndex in enumerate(itemIndex):
if subgroups.get(groupIndex) is None:
subgroups[groupIndex] = []
subgroups[groupIndex].append(sampleIndex)
with open(subgroupsSavedPath, "wb") as file:
pickle.dump(subgroups, file)
logger.info("the number of subgroup:{}".format(len(subgroups)))
logger.info("subgroup data saved in {}".format(subgroupsSavedPath))
logger.info("{0}{1}".format("subgroup index".center(20), "subgroup size".format(20)))
for subgroupName, subgroup in subgroups.items():
logger.info("{0}{1}".format(str(subgroupName).center(20), str(len(subgroup)).center(20)))
logger.info("==================================".center(CONSTANT.logLength, "="))
def treeBinning(self, trainN, i=6, column="column", cutPoint=0.0):
tree = DecisionTreeClassifier(max_leaf_nodes=i,
min_samples_leaf=np.int(
np.rint(trainN.shape[0] * 0.06)))
X_select = pd.DataFrame(
trainN[trainN[column] > cutPoint][column]
) # this filter assumes all values < 0 as special and bins separately
tree.fit(X_select, self.y_train[X_select.index])
X_select["Node"] = tree.apply(X_select)
X_select = X_select.append(
pd.DataFrame({
column: trainN[trainN[column] <= cutPoint][column],
"Node": -1
}))
X_select = pd.concat([X_select, self.y_train], axis=1)
test = pd.concat([
X_select.pivot_table(index="Node",
values=column,
aggfunc=[np.min, np.max],
margins=True),
X_select.pivot_table(index="Node",
values=self.target,
aggfunc=[np.sum, len],
margins=True)
],
axis=1)
test.rename(columns={"sum": "badCnt", "len": "totalCnt"}, inplace=True)
test["goodCnt"] = test["totalCnt"] - test["badCnt"]
test["popPercentage"] = test["totalCnt"] / test.loc["All",
"totalCnt"] * 100
test["badRate"] = test["badCnt"] / test["totalCnt"] * 100
test.columns = [
"amin", "amax", "badCnt", "totalCnt", "goodCnt", "popPercentage",
"badRate"
]
test = test.sort_values(by="amax")
test["badSign"] = np.sign(test.badRate - test.badRate.shift(-1))
test.iloc[test.shape[0] - 2, 7] = np.nan
test["badDistribution"] = test["badCnt"] / test.loc["All", "badCnt"]
test["goodDistribution"] = test["goodCnt"] / test.loc["All", "goodCnt"]
test["distributedGoodBad"] = test["goodDistribution"] - test[
"badDistribution"]
test["WOE"] = np.log(test["goodDistribution"] /
test["badDistribution"])
test["IV"] = test["WOE"] * test["distributedGoodBad"] * 100
test.loc["All", "IV"] = np.sum(test["IV"])
test["column"] = column
return test[[
"amin", "amax", "popPercentage", "IV", "badRate", "badSign",
"column"
]]
def train_model(data, features, label):
"""
分别使用逻辑回归、决策树和决策树+逻辑回归建模
"""
res = {}
train_data, test_data = train_test_split(data, test_size=0.5)
# 单独使用逻辑回归
logit_model = LogisticRegression()
logit_model.fit(train_data[features], train_data[label])
logit_prob = logit_model.predict_proba(test_data[features])[:, 1]
res["logit"] = roc_curve(test_data[label], logit_prob)
# 单独使用决策树
dt_model = DecisionTreeClassifier(max_depth=2)
dt_model.fit(train_data[features], train_data[label])
dt_prob = dt_model.predict_proba(test_data[features])[:, 1]
res["DT"] = roc_curve(test_data[label], dt_prob)
# 决策树和逻辑回归联结
# 为了防止过拟合，使用不同的数据训练决策树和逻辑回归
train_DT, train_LR = train_test_split(train_data, test_size=0.5)
# 使用决策树对前两个变量做变换
m = 2
_dt = DecisionTreeClassifier(max_depth=2)
_dt.fit(train_DT[features[:m]], train_DT[label])
leaf_node = _dt.apply(train_DT[features[:m]]).reshape(-1, 1)
coder = OneHotEncoder()
coder.fit(leaf_node)
new_feature = np.c_[coder.transform(
_dt.apply(train_LR[features[:m]]).reshape(-1, 1)).toarray(),
train_LR[features[m:]]]
_logit = LogisticRegression()
_logit.fit(new_feature[:, 1:], train_LR[label])
test_feature = np.c_[coder.transform(
_dt.apply(test_data[features[:m]]).reshape(-1, 1)).toarray(),
test_data[features[m:]]]
dt_logit_prob = _logit.predict_proba(test_feature[:, 1:])[:, 1]
res["DT + logit"] = roc_curve(test_data[label], dt_logit_prob)
return res
class TreeClassificationTransformer(BaseTransformer):
def __init__(self, kwargs={}):
"""
Doc strings here.
"""
self.kwargs = kwargs
self._is_fitted = False
def fit(self, X, y):
"""
Doc strings here.
"""
X, y = check_X_y(X, y)
# define the ensemble
self.transformer = DecisionTreeClassifier(**self.kwargs).fit(X, y)
self._is_fitted = True
return self
def transform(self, X):
"""
Doc strings here.
"""
if not self.is_fitted():
msg = (
"This %(name)s instance is not fitted yet. Call 'fit' with "
"appropriate arguments before using this transformer."
)
raise NotFittedError(msg % {"name": type(self).__name__})
X = check_array(X)
return self.transformer.apply(X)
def is_fitted(self):
"""
Doc strings here.
"""
return self._is_fitted
def countDCP(self, df, classes, minimum_impurity_split):
values = np.empty([0, 0])
estimator = DecisionTreeClassifier(min_impurity_split=minimum_impurity_split)
estimator.fit(df, classes)
leafsIndexes = estimator.apply(df, check_input=True)
for index, _ in df.iterrows():
suma = 0
value = 0
for leafIndex, _ in df.iterrows():
if leafsIndexes[index] == leafsIndexes[leafIndex]:
suma += 1
if classes[index] == classes[leafIndex]:
value += 1
values = np.append(values, np.full((1, 1), (value / suma) * -1))
# print("Count DCP for " + repr(index) + ". row of data.")
return values
def learnTrees_and_return_segments(depth):
global dt
global features
global targets
features = list(df.columns)
target_feature = features[-1]
features = list(features[:len(features)-1])
targets = df[target_feature].unique()
print 'targets:', targets
print 'features:', features
y=df[target_feature]
X=df[features]
X_train, X_test, y_train, y_test = train_test_split(X,y,test_size=0.0,random_state=0)
#dt = DecisionTreeRegressor(max_depth=depth)#, min_samples_split=20, random_state=99)
dt = DecisionTreeClassifier(max_depth=depth)#, min_samples_split=20, random_state=99)
dt.fit(X_train,y_train)
prediction = dt.predict(X_train)
print 'accuracy:', accuracy_score(y_train,prediction)
print 'recall:', recall_score(y_train,prediction,average=None)
print 'classification report:'
print classification_report(y_train,prediction)
print 'num correct:', accuracy_score(y_train,prediction) * len(y_train)
print 'num incorrect:', (1-accuracy_score(y_train,prediction)) * len(y_train)
print 'R2 Score:', r2_score(y_train,prediction)
print 'absolute error:', mean_absolute_error(y_train,prediction)*len(X_train)
app = dt.apply(X)
uni=np.unique(app)
segments_set=[[[]] for i in uni]
for i in range(len(app)):
index=int(np.where(uni==app[i])[0])
segments_set[index][0].append(i)
#segments_set=[copy.copy(segments_set) for i in uni]
return targets, segments_set, mean_absolute_error(y_train,prediction)*len(X_train)
def fit(X0, W0, X1, W1, **kwargs):
X = np.concatenate([as_features(X0), as_features(X1)])
Y = np.array([0] * W0.size + [1] * W1.size)
W = np.concatenate([W0, W1])
T = DecisionTreeClassifier(class_weight="balanced", **kwargs)
T.fit(X, Y, sample_weight=W)
_, *shape = X0.shape
tree = T.tree_
feature = [
np.unravel_index(f, shape) if f >= 0 else None
for f in tree.feature
]
leaf = T.apply(X)
pred = np.empty(tree.node_count)
for n in range(tree.node_count):
mask = leaf == n
w0 = (W * mask * (Y == 0)).sum() + 1e-3
w1 = (W * mask * (Y == 1)).sum() + 1e-3
pred[n] = np.log(w1 / w0) / 2
return DTree(feature, tree.threshold, tree.children_left,
tree.children_right, pred)
def countDS(self, df, classes):
values = np.empty([0, 0])
estimator = DecisionTreeClassifier()
estimator.fit(df, classes)
leafsIndexes = estimator.apply(df, check_input=True)
leafs = np.zeros(estimator.tree_.node_count)
# count number of instances for every leaf
for leafIndex in leafsIndexes:
leafs[leafIndex] += 1
biggestDisjunct = max(leafs) - 1
# count fraction for every instance
for leafIndex in leafsIndexes:
values = np.append(
values,
np.full((1, 1),
((leafs[leafIndex] - 1) / biggestDisjunct) * -1))
return values
def tree_bins_func(self, grps=None, pct_size=None):
"""
基于决策树（信息熵）的分组
1.max_grps控制最大分组的个数；
2.pct_size控制每组最低的样本占比
"""
tmp = self.raw.copy().dropna()
if pct_size is None:
smp_size = np.int(len(tmp) * self.argms['pct_size']) + 1
else:
smp_size = np.int(len(tmp) * pct_size) + 1
if grps is None:
grps = self.argms['max_grps']
#当特征的最大取值占比超过阈值时，不做进一步区分，只分为2组
#以决策树为分组的基准工具
clf = DecisionTreeClassifier(min_samples_leaf=smp_size,
max_leaf_nodes=grps)
clf.fit(tmp[[self.ft_name]], tmp['label'])
tmp['grp_prd'] = clf.apply(tmp[[self.ft_name]])
grp_info = tmp.groupby('grp_prd').min()
grp_info.sort_values(self.ft_name, inplace=True, ascending=True)
cuts = list(grp_info[self.ft_name]) + [tmp[self.ft_name].max() + 1]
cuts = self._smpSizeCheck_real(tmp, cuts, smp_size)
self.bins = {self.ft_name: cuts}
self.cap_info = {
'max': tmp[self.ft_name].max(),
'min': tmp[self.ft_name].min()
}
if len(cuts) == 2:
self.woe_check = {
self.ft_name: 'tree_bins_func_failed!-value biased'
}
else:
self.woe_check = {}
def IV(df,
var,
target,
n_levels_to_factor_threshold=5,
calc_type='Categorical',
Min_Category_Share=0.05,
nbins=10):
# cut numeric features
if is_numeric(
df[var]) and len(df[var].unique()) > n_levels_to_factor_threshold:
if calc_type == 'Interval':
df = pd.DataFrame({
var: qcut(df[var], q=nbins, duplicates='drop'),
target: df[target]
})
elif calc_type == 'Categorical':
tree = DecisionTreeClassifier(
criterion='entropy',
min_samples_leaf=int(df.shape[0] * Min_Category_Share),
presort=True,
random_state=1223)
tree.fit(df.loc[~df[var].isna(), [var]], df.loc[~df[var].isna(),
target])
tmp = tree.apply(df.loc[~df[var].isna(), [var]]).astype(str)
tmp = ['leaf_' + e for e in tmp]
tmp2 = ~df[var].isna()
df = pd.DataFrame({var: ['NA'] * df.shape[0], target: df[target]})
df.loc[tmp2, var] = tmp
# calculate IV
rez = WoE_full(df, var, target)
rez = sum(rez['WoE'] * (rez['GR'] - rez['BR']))
# return result
return rez
OUT_FOLDER = "data/COMPAS/holdout/recidivism_%s.csv"
df = pd.read_csv("data/COMPAS/recidivism.csv")
X = df.iloc[:, 0:-1]
y = df.iloc[:, -1]
X_train, X_test, y_train, y_test = train_test_split(X,
y,
test_size=0.165,
random_state=69)
clf = DecisionTreeClassifier(random_state=0, min_samples_leaf=300)
clf.fit(X, y)
l_train = clf.apply(X_train)
l_test = clf.apply(X_test)
l_indexes = np.unique(l_train)
clusters = []
for l_index in l_indexes:
c_train_indexes = np.where(l_train == l_index)
c_test_indexes = np.where(l_test == l_index)
cluster = (l_index, c_train_indexes[0], c_test_indexes[0])
clusters.append(cluster)
clusters.sort(key=lambda x: len(x[1]))
def store_data(outfile, X, y):
df = pd.concat([X, y], axis=1)
df.to_csv(outfile)
feature[i],
threshold[i],
children_right[i],
))
print()
"""
# First let's retrieve the decision path of each sample. The decision_path
# method allows to retrieve the node indicator functions. A non zero element of
# indicator matrix at the position (i, j) indicates that the sample i goes
# through the node j.
"""
node_indicator = tree.decision_path(X)
# Similarly, we can also have the leaves ids reached by each sample.
leave_id = tree.apply(X)
# Now, it's possible to get the tests that were used to predict a sample or
# a group of samples. First, let's make it for the sample.
#sample_id = 0
#node_index = node_indicator.indices[node_indicator.indptr[sample_id]:
# node_indicator.indptr[sample_id + 1]]
#
#print('Rules used to predict sample %s: ' % sample_id)
#for node_id in node_index:
# if leave_id[sample_id] == node_id:
# continue
#
# if (X[sample_id, feature[node_id]] <= threshold[node_id]):
# threshold_sign = "<="
clf.fit(X_train, y_train)
print("showing prediction results (first 10) [1=infected, 0 = non infected]:")
y_pred = clf.predict(X_test)
print(y_pred[:5])
a = X_test[:1]
print("a:")
print(a)
#print('sk_pred: {}'.format(clf.predict(a)))
#print('true: {}'.format(y_test[:3]))
# shows the end point of the tree traverse by a sample
print("Returns the index of the leaf that each sample is predicted as:")
index_of_leaf = clf.apply(a)
print(index_of_leaf)
#decision path shows the nodes of the tree that were traverse by the sample.
print("decision path:")
d_path = clf.decision_path(a)
print(d_path)
print("nodes in the decision path:")
n_d_path = np.unique(np.sort(d_path.indices))
print(n_d_path)
print("probability of each class:")
print(clf.predict_proba(a))
print("Feature importances:")
class TreeClassificationTransformer(BaseTransformer):
"""
A class used to transform data from a category to a specialized representation.
Attributes (object)
----------
kwargs : dict
A dictionary to contain parameters of the tree.
_is_fitted_ : bool
A boolean to identify if the model is currently fitted.
Methods
----------
fit(X, y)
Fits the transformer to data X with labels y.
transform(X)
Performs inference using the transformer.
is_fitted()
Indicates whether the transformer is fitted.
"""
def __init__(self, kwargs={}):
self.kwargs = kwargs
self._is_fitted = False
def fit(self, X, y):
"""
Fits the transformer to data X with labels y.
Parameters
----------
X : ndarray
Input data matrix.
y : ndarray
Output (i.e. response data matrix).
"""
X, y = check_X_y(X, y)
# define the ensemble
self.transformer = DecisionTreeClassifier(**self.kwargs).fit(X, y)
self._is_fitted = True
return self
def transform(self, X):
"""
Performs inference using the transformer.
Parameters
----------
X : ndarray
Input data matrix.
"""
if not self.is_fitted():
msg = ("This %(name)s instance is not fitted yet. Call 'fit' with "
"appropriate arguments before using this transformer.")
raise NotFittedError(msg % {"name": type(self).__name__})
X = check_array(X)
return self.transformer.apply(X)
def is_fitted(self):
"""
Indicates whether the transformer is fitted.
Parameters
----------
None
"""
return self._is_fitted
# ``decision_path`` method outputs an indicator matrix that allows us to
# retrieve the nodes the samples of interest traverse through. A non zero
# element in the indicator matrix at position ``(i, j)`` indicates that
# the sample ``i`` goes through the node ``j``. Or, for one sample ``i``, the
# positions of the non zero elements in row ``i`` of the indicator matrix
# designate the ids of the nodes that sample goes through.
#
# The leaf ids reached by samples of interest can be obtained with the
# ``apply`` method. This returns an array of the node ids of the leaves
# reached by each sample of interest. Using the leaf ids and the
# ``decision_path`` we can obtain the splitting conditions that were used to
# predict a sample or a group of samples. First, let's do it for one sample.
# Note that ``node_index`` is a sparse matrix.
node_indicator = clf.decision_path(X_test)
leaf_id = clf.apply(X_test)
sample_id = 0
# obtain ids of the nodes `sample_id` goes through, i.e., row `sample_id`
node_index = node_indicator.indices[
node_indicator.indptr[sample_id] : node_indicator.indptr[sample_id + 1]
]
print("Rules used to predict sample {id}:\n".format(id=sample_id))
for node_id in node_index:
# continue to the next node if it is a leaf node
if leaf_id[sample_id] == node_id:
continue
# check if value of the split feature for sample 0 is below threshold
if X_test[sample_id, feature[node_id]] <= threshold[node_id]:
@author: Samruddhi Somani
"""
execfile('Original.py')
execfile('tfidf.py')
from sklearn.tree import DecisionTreeClassifier
from sklearn.linear_model import SGDClassifier
from sklearn.naive_bayes import MultinomialNB
from sklearn.ensemble import RandomForestClassifier
import numpy as np
#fitting/examining tree
s2=DecisionTreeClassifier(max_depth=3, random_state=5)
s2.fit(x,cuisine)
leaves2=pd.Series(s2.apply(x),name='leaves')
idk=pd.concat([cuisine,leaves2],axis=1)
m=list(leaves2.value_counts().index.values) #[3, 6, 10, 4, 7, 13, 11, 14]
for y in m:
print y
print idk[leaves2==y]['cuisine'].value_counts()
leaves2.value_counts()
#==============================================================================
# 3 33684: SGD SVM
# 6 2991: SGD SVM
# 10 1848: Naive Bayes/Logistic Regression
# 4 914: Naive Bayes/Logistic Regression
# 7 300: Logistic Regression
# 13 20: Naive Bayes
# 11 14: Naive Bayes
feature[i],
threshold[i],
children_right[i],
))
print()
# First let's retrieve the decision path of each sample. The decision_path
# method allows to retrieve the node indicator functions. A non zero element of
# indicator matrix at the position (i, j) indicates that the sample i goes
# through the node j.
node_indicator = estimator.decision_path(X_test)
# Similarly, we can also have the leaves ids reached by each sample.
leave_id = estimator.apply(X_test)
# Now, it's possible to get the tests that were used to predict a sample or
# a group of samples. First, let's make it for the sample.
sample_id = 0
node_index = node_indicator.indices[
node_indicator.indptr[sample_id]:node_indicator.indptr[sample_id + 1]]
print('Rules used to predict sample %s: ' % sample_id)
for node_id in node_index:
if leave_id[sample_id] != node_id:
continue
if (X_test[sample_id, feature[node_id]] <= threshold[node_id]):
threshold_sign = "<="
# -*- coding: utf-8 -*-
execfile('Original.py')
execfile('tfidf.py')
from sklearn.tree import DecisionTreeClassifier
s=DecisionTreeClassifier(max_depth=2, random_state=5)
s.fit(x,cuisine)
leaves=pd.Series(s.apply(x),name='leaves')
idk=pd.concat([cuisine,leaves],axis=1)
m=list(leaves.value_counts().index.values)
for y in m:
print y
print idk[leaves==y]['cuisine'].value_counts()
leaves.value_counts()
s2=DecisionTreeClassifier(max_depth=3, random_state=5)
s2.fit(x,cuisine)
leaves2=pd.Series(s2.apply(x),name='leaves')
idk=pd.concat([cuisine,leaves2],axis=1)
m=list(leaves2.value_counts().index.values)
for y in m:
print y
print idk[leaves2==y]['cuisine'].value_counts()
leaves2.value_counts()
s3=DecisionTreeClassifier(max_leaf_nodes=8, random_state=5,criterion='entropy')
s3.fit(x,cuisine)
print(
"%snode=%s test node: go to node %s if X[:, %s] <= %ss else to "
"node %s." % (node_depth[i] * "\t", i, children_left[i], feature[i], threshold[i], children_right[i])
)
print()
# First let's retrieve the decision path of each sample. The decision_path
# method allows to retrieve the node indicator functions. A non zero element of
# indicator matrix at the position (i, j) indicates that the sample i goes
# through the node j.
node_indicator = estimator.decision_path(X_test)
# Similarly, we can also have the leaves ids reached by each sample.
leave_id = estimator.apply(X_test)
# Now, it's possible to get the tests that were used to predict a sample or
# a group of samples. First, let's make it for the sample.
sample_id = 0
node_index = node_indicator.indices[node_indicator.indptr[sample_id] : node_indicator.indptr[sample_id + 1]]
print("Rules used to predict sample %s: " % sample_id)
for node_id in node_index:
if leave_id[sample_id] != node_id:
continue
if X_test[sample_id, feature[node_id]] <= threshold[node_id]:
threshold_sign = "<="
else:
#divide each column over sum of rows
df_new=df.div(df.sum(axis=0),axis='columns').fillna(0)
return df_new
def hmwrapper(cm,filename):
h=heatmap(cm).get_figure()
ax=h.add_subplot(111)
ax.set_xlabel('Predictions')
h.tight_layout()
h.set_size_inches(8,5.5)
h.savefig(filename,bbox_inches='tight',dpi=100)
#fitting/examining tree
s2=DecisionTreeClassifier(max_leaf_nodes=10, min_samples_leaf=500, random_state=5)
s2.fit(x,cuisine)
leaves2=pd.Series(s2.apply(x),name='leaves')
idk=pd.concat([cuisine,leaves2],axis=1)
m=list(leaves2.value_counts().index.values) #[3, 6, 10, 4, 7, 13, 11, 14]
for y in m:
print y
print idk[leaves2==y]['cuisine'].value_counts()
leaves2.value_counts()
#==============================================================================
# 3 33684: SGD SVM
# 6 2991: SGD SVM
# 10 1848: Naive Bayes/Logistic Regression
# 4 914: Naive Bayes/Logistic Regression
# 7 300: Logistic Regression
# 13 20: Naive Bayes
# 11 14: Naive Bayes
