import sys
import os
if 'google.colab' in sys.modules:
!pip install -q dtreeviz
import sys
import os
# add library module to PYTHONPATH
print(os.getcwd())
sys.path.append(f"{os.getcwd()}/../")
/content
from sklearn.datasets import *
from dtreeviz.trees import *
from IPython.display import Image, display_svg, SVG
model = tree.DecisionTreeRegressor(max_depth=3)
boston = load_boston()
# 데이터 나누기
X_train = boston.data
y_train = boston.target
# 모델 학습
model.fit(X_train, y_train)
# 모델 시각화
viz = dtreeviz(model,
X_train,
y_train,
target_name='price', # leaf node에 보여지는 target 표시
feature_names=boston.feature_names,
title="Boston data set regression", # 제목
fontname="Arial", # 글씨 폰트
title_fontsize=16, # 타이틀 폰트 사이즈
colors = {"title":"purple"}
)
viz
/usr/local/lib/python3.7/dist-packages/sklearn/utils/deprecation.py:87: FutureWarning: Function load_boston is deprecated; `load_boston` is deprecated in 1.0 and will be removed in 1.2.
The Boston housing prices dataset has an ethical problem. You can refer to
the documentation of this function for further details.
The scikit-learn maintainers therefore strongly discourage the use of this
dataset unless the purpose of the code is to study and educate about
ethical issues in data science and machine learning.
In this special case, you can fetch the dataset from the original
source::
import pandas as pd
import numpy as np
data_url = "http://lib.stat.cmu.edu/datasets/boston"
raw_df = pd.read_csv(data_url, sep="\s+", skiprows=22, header=None)
data = np.hstack([raw_df.values[::2, :], raw_df.values[1::2, :2]])
target = raw_df.values[1::2, 2]
Alternative datasets include the California housing dataset (i.e.
:func:`~sklearn.datasets.fetch_california_housing`) and the Ames housing
dataset. You can load the datasets as follows::
from sklearn.datasets import fetch_california_housing
housing = fetch_california_housing()
for the California housing dataset and::
from sklearn.datasets import fetch_openml
housing = fetch_openml(name="house_prices", as_frame=True)
for the Ames housing dataset.
warnings.warn(msg, category=FutureWarning)
WARNING:matplotlib.font_manager:findfont: Font family ['Arial'] not found. Falling back to DejaVu Sans.
dtreeviz(model,
X_train,
y_train,
target_name='price', # leaf node에 보여지는 target 표시
feature_names=boston.feature_names,
scale=0.7 # scale를 통해 이미지의 크기를 조절
)
model = tree.DecisionTreeClassifier(max_depth=2)
# 데이터 나누기
iris = load_iris()
X_train = iris.data
y_train = iris.target
# 모델 학습
model.fit(X_train, y_train)
# 모델 시각화
viz = dtreeviz(model,
X_train,
y_train,
target_name='iris type',
fontname="Arial",
feature_names=iris.feature_names,
class_names=["setosa", "versicolor", "virginica"],
histtype= 'barstacked') # 히스토 그램 형태 : 기본(barstacked)
viz
WARNING:matplotlib.font_manager:findfont: Font family ['Arial'] not found. Falling back to DejaVu Sans. /usr/local/lib/python3.7/dist-packages/numpy/core/fromnumeric.py:3208: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray. return asarray(a).size /usr/local/lib/python3.7/dist-packages/matplotlib/cbook/__init__.py:1376: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray. X = np.atleast_1d(X.T if isinstance(X, np.ndarray) else np.asarray(X)) WARNING:matplotlib.font_manager:findfont: Font family ['Arial'] not found. Falling back to DejaVu Sans. WARNING:matplotlib.font_manager:findfont: Font family ['Arial'] not found. Falling back to DejaVu Sans.
model = tree.DecisionTreeClassifier(max_depth=2)
cancer = load_breast_cancer()
X_train = cancer.data
y_train = cancer.target
model.fit(X_train, y_train)
viz = dtreeviz(model,
X_train,
y_train,
target_name='cancer',
feature_names=cancer.feature_names,
class_names=["malignant", "benign"],
orientation='LR')
viz
/usr/local/lib/python3.7/dist-packages/numpy/core/_asarray.py:83: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray return array(a, dtype, copy=False, order=order)
| 컬럼명 | 설명 | 데이터 유형 |
|---|---|---|
| age | 나이 | 숫자 |
| sex | 성별 | 명목형 |
| bmi | 체질량 지수 | 숫자 |
| bp | 평균 혈압 | 숫자 |
| s1 | 혈청 측정값1 | 숫자 |
| s2 | 혈청 측정값2 | 숫자 |
| s3 | 혈청 측정값3 | 숫자 |
| s4 | 혈청 측정값4 | 숫자 |
| s5 | 혈청 측정값5 | 숫자 |
| s6 | 혈청 측정값6 | 숫자 |
| Y | 10개변수 측정 후, 당뇨병 진행도 | 숫자 |
model = tree.DecisionTreeRegressor(max_depth=3)
# 데이터 나누기
diabetes = load_diabetes()
X_train = diabetes.data
y_train = diabetes.target
# 모델 학습
model.fit(X_train, y_train)
X = diabetes.data[np.random.randint(0, len(diabetes.data)),:]
viz = dtreeviz(model,
X_train,
y_train,
target_name='progress',
feature_names=diabetes.feature_names,
X=X,
show_node_labels = True,
scale=.7
)
viz
viz.save("decision_tree_diabetes.svg")
from google.colab import files
files.download("decision_tree_diabetes.svg")
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