Iris 꽃 종류 분류

 

 

 

 

Iris 꽃 종류 분류¶

 

In [1]:

import pandas as pd
import numpy as np
import matplotlib.pyplot as plt


np.random.seed(2021)

 

 

1. Data¶

 

 

1.1 Data Load¶

데이터는 sklearn.datasets 의 load_iris 함수를 이용해 받을 수 있습니다.

 

In [2]:

from sklearn.datasets import load_iris

iris = load_iris()

 

 

데이터에서 사용되는 변수는 암술과 수술의 길이와 넓이입니다.

• sepal length (cm)
• sepal width (cm)
• petal length (cm)
• petal width (cm)

 

In [3]:

iris["feature_names"]

 

Out[3]:

['sepal length (cm)',
 'sepal width (cm)',
 'petal length (cm)',
 'petal width (cm)']

 

 

정답은 iris 꽃의 종류입니다.

 

In [4]:

iris["target_names"]

 

Out[4]:

array(['setosa', 'versicolor', 'virginica'], dtype='<U10')

 

In [5]:

data, target = iris["data"], iris["target"]

 

 

1.2 데이터 EDA¶

 

In [6]:

pd.DataFrame(data, columns=iris["feature_names"]).describe()

 

Out[6]:

	sepal length (cm)	sepal width (cm)	petal length (cm)	petal width (cm)
count	150.000000	150.000000	150.000000	150.000000
mean	5.843333	3.057333	3.758000	1.199333
std	0.828066	0.435866	1.765298	0.762238
min	4.300000	2.000000	1.000000	0.100000
25%	5.100000	2.800000	1.600000	0.300000
50%	5.800000	3.000000	4.350000	1.300000
75%	6.400000	3.300000	5.100000	1.800000
max	7.900000	4.400000	6.900000	2.500000

 

 

1.3 Data Split¶

 

In [7]:

from sklearn.model_selection import train_test_split

train_data, test_data, train_target, test_target = train_test_split(
    data, target, train_size=0.7, random_state=2021, stratify=target
)

 

In [8]:

print("train data 개수:", len(train_data))
print("train data 개수:", len(test_data))

 

 

train data 개수: 105
train data 개수: 45

 

 

1.4 시각화¶

 

In [9]:

fig, axes = plt.subplots(nrows=2, ncols=3, figsize=(15,10))

pair_combs = [
    [0, 1], [0, 2], [0, 3], [1, 2], [1, 3], [2, 3]
]
for idx, pair in enumerate(pair_combs):
    x, y = pair
    ax = axes[idx//3, idx%3]
    ax.scatter(
        x=train_data[:, x], y=train_data[:, y], c=train_target, edgecolor='black', s=15
    )
    ax.set_xlabel(iris["feature_names"][x])
    ax.set_ylabel(iris["feature_names"][y])

 

 

 

 

2. Decision Tree¶

 

In [10]:

from sklearn.tree import DecisionTreeClassifier, plot_tree

gini_tree = DecisionTreeClassifier()

 

 

2.1 학습¶

 

In [11]:

gini_tree.fit(train_data, train_target)

 

Out[11]:

DecisionTreeClassifier()

In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook.
On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.

 

In [12]:

plt.figure(figsize=(10,10))
plot_tree(gini_tree, feature_names=iris["feature_names"], class_names=iris["target_names"])

 

Out[12]:

[Text(0.4, 0.875, 'petal width (cm) <= 0.8\ngini = 0.667\nsamples = 105\nvalue = [35, 35, 35]\nclass = setosa'),
 Text(0.2, 0.625, 'gini = 0.0\nsamples = 35\nvalue = [35, 0, 0]\nclass = setosa'),
 Text(0.6, 0.625, 'petal width (cm) <= 1.65\ngini = 0.5\nsamples = 70\nvalue = [0, 35, 35]\nclass = versicolor'),
 Text(0.4, 0.375, 'petal length (cm) <= 5.35\ngini = 0.102\nsamples = 37\nvalue = [0, 35, 2]\nclass = versicolor'),
 Text(0.2, 0.125, 'gini = 0.0\nsamples = 35\nvalue = [0, 35, 0]\nclass = versicolor'),
 Text(0.6, 0.125, 'gini = 0.0\nsamples = 2\nvalue = [0, 0, 2]\nclass = virginica'),
 Text(0.8, 0.375, 'gini = 0.0\nsamples = 33\nvalue = [0, 0, 33]\nclass = virginica')]

 

 

 

2.2 Arguments¶

 

 

DecisionTreeClassifier에서 주로 탐색하는 argument들은 다음과 같습니다.

• criterion
  • 어떤 정보 이득을 기준으로 데이터를 나눌지 정합니다.
  • "gini", "entropy"
• max_depth
  • 나무의 최대 깊이를 정해줍니다.
• min_samples_split
  • 노드가 나눠질 수 있는 최소 데이터 개수를 정합니다.

 

 

2.2.1 max_depth¶

 

In [13]:

depth_1_tree = DecisionTreeClassifier(max_depth=1)
depth_1_tree.fit(train_data, train_target)

plot_tree(depth_1_tree, feature_names=iris["feature_names"], class_names=iris["target_names"])

 

Out[13]:

[Text(0.5, 0.75, 'petal length (cm) <= 2.5\ngini = 0.667\nsamples = 105\nvalue = [35, 35, 35]\nclass = setosa'),
 Text(0.25, 0.25, 'gini = 0.0\nsamples = 35\nvalue = [35, 0, 0]\nclass = setosa'),
 Text(0.75, 0.25, 'gini = 0.5\nsamples = 70\nvalue = [0, 35, 35]\nclass = versicolor')]

 

 

 

2.2.2 min_samples_split¶

 

In [14]:

sample_50_tree = DecisionTreeClassifier(min_samples_split=50)
sample_50_tree.fit(train_data, train_target)

plot_tree(sample_50_tree, feature_names=iris["feature_names"], class_names=iris["target_names"])

 

Out[14]:

[Text(0.4, 0.8333333333333334, 'petal length (cm) <= 2.5\ngini = 0.667\nsamples = 105\nvalue = [35, 35, 35]\nclass = setosa'),
 Text(0.2, 0.5, 'gini = 0.0\nsamples = 35\nvalue = [35, 0, 0]\nclass = setosa'),
 Text(0.6, 0.5, 'petal width (cm) <= 1.65\ngini = 0.5\nsamples = 70\nvalue = [0, 35, 35]\nclass = versicolor'),
 Text(0.4, 0.16666666666666666, 'gini = 0.102\nsamples = 37\nvalue = [0, 35, 2]\nclass = versicolor'),
 Text(0.8, 0.16666666666666666, 'gini = 0.0\nsamples = 33\nvalue = [0, 0, 33]\nclass = virginica')]

 

 

 

2.2.3 criterion¶

 

In [15]:

entropy_tree = DecisionTreeClassifier(criterion="entropy")
entropy_tree.fit(train_data, train_target)

fig, axes = plt.subplots(nrows=1, ncols=2, figsize=(15, 10))
plot_tree(gini_tree, feature_names=iris["feature_names"], class_names=iris["target_names"], ax=axes[0])
plot_tree(entropy_tree, feature_names=iris["feature_names"], class_names=iris["target_names"], ax=axes[1])
plt.show()

 

 

 

 

2.3 예측¶

 

In [16]:

trees = [
    ("gini tree", gini_tree),
    ("entropy tree", entropy_tree),
    ("depth=1 tree", depth_1_tree),
    ("sample=50 tree" ,sample_50_tree),
]

 

In [17]:

train_preds = []
test_preds = []
for tree_name, tree in trees:
    train_pred = tree.predict(train_data)
    test_pred =  tree.predict(test_data)
    train_preds += [train_pred]
    test_preds += [test_pred]

 

In [18]:

train_preds

 

Out[18]:

[array([0, 2, 0, 0, 0, 2, 0, 2, 1, 1, 2, 1, 0, 2, 1, 1, 1, 2, 2, 2, 2, 1,
        2, 0, 2, 0, 2, 1, 0, 2, 2, 1, 1, 0, 1, 0, 1, 2, 0, 0, 2, 2, 0, 0,
        2, 2, 2, 0, 0, 1, 1, 1, 1, 2, 1, 0, 1, 0, 2, 2, 0, 2, 0, 0, 1, 0,
        0, 2, 2, 1, 2, 2, 2, 0, 0, 1, 1, 1, 0, 1, 0, 2, 0, 2, 2, 1, 1, 2,
        1, 0, 1, 1, 0, 0, 2, 1, 1, 0, 2, 0, 1, 0, 1, 1, 1]),
 array([0, 2, 0, 0, 0, 2, 0, 2, 1, 1, 2, 1, 0, 2, 1, 1, 1, 2, 2, 2, 2, 1,
        2, 0, 2, 0, 2, 1, 0, 2, 2, 1, 1, 0, 1, 0, 1, 2, 0, 0, 2, 2, 0, 0,
        2, 2, 2, 0, 0, 1, 1, 1, 1, 2, 1, 0, 1, 0, 2, 2, 0, 2, 0, 0, 1, 0,
        0, 2, 2, 1, 2, 2, 2, 0, 0, 1, 1, 1, 0, 1, 0, 2, 0, 2, 2, 1, 1, 2,
        1, 0, 1, 1, 0, 0, 2, 1, 1, 0, 2, 0, 1, 0, 1, 1, 1]),
 array([0, 1, 0, 0, 0, 1, 0, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1,
        1, 0, 1, 0, 1, 1, 0, 1, 1, 1, 1, 0, 1, 0, 1, 1, 0, 0, 1, 1, 0, 0,
        1, 1, 1, 0, 0, 1, 1, 1, 1, 1, 1, 0, 1, 0, 1, 1, 0, 1, 0, 0, 1, 0,
        0, 1, 1, 1, 1, 1, 1, 0, 0, 1, 1, 1, 0, 1, 0, 1, 0, 1, 1, 1, 1, 1,
        1, 0, 1, 1, 0, 0, 1, 1, 1, 0, 1, 0, 1, 0, 1, 1, 1]),
 array([0, 2, 0, 0, 0, 2, 0, 2, 1, 1, 2, 1, 0, 2, 1, 1, 1, 2, 2, 2, 2, 1,
        2, 0, 1, 0, 2, 1, 0, 2, 2, 1, 1, 0, 1, 0, 1, 1, 0, 0, 2, 2, 0, 0,
        2, 2, 2, 0, 0, 1, 1, 1, 1, 2, 1, 0, 1, 0, 2, 2, 0, 2, 0, 0, 1, 0,
        0, 2, 2, 1, 2, 2, 2, 0, 0, 1, 1, 1, 0, 1, 0, 2, 0, 2, 2, 1, 1, 2,
        1, 0, 1, 1, 0, 0, 2, 1, 1, 0, 2, 0, 1, 0, 1, 1, 1])]

 

 

2.3 평가하기¶

 

In [19]:

from sklearn.metrics import accuracy_score

 

In [20]:

for idx, (tree_name, tree) in enumerate(trees):
    train_acc = accuracy_score(train_target, train_preds[idx])
    test_acc =  accuracy_score(test_target, test_preds[idx])
    print(tree_name)
    print("\t", f"train accuracy is {train_acc:.2f}")
    print("\t", f"test accuracy is {test_acc:.2f}")

 

 

gini tree
	 train accuracy is 1.00
	 test accuracy is 0.91
entropy tree
	 train accuracy is 1.00
	 test accuracy is 0.91
depth=1 tree
	 train accuracy is 0.67
	 test accuracy is 0.67
sample=50 tree
	 train accuracy is 0.98
	 test accuracy is 0.91

 

 

2.4 Feature Importance¶

 

In [21]:

iris["feature_names"]

 

Out[21]:

['sepal length (cm)',
 'sepal width (cm)',
 'petal length (cm)',
 'petal width (cm)']

 

In [22]:

gini_tree.feature_importances_

 

Out[22]:

array([0.        , 0.        , 0.05405405, 0.94594595])

 

In [23]:

gini_feature_importance = pd.Series(gini_tree.feature_importances_, index=iris["feature_names"])

 

In [24]:

gini_feature_importance

 

Out[24]:

sepal length (cm)    0.000000
sepal width (cm)     0.000000
petal length (cm)    0.054054
petal width (cm)     0.945946
dtype: float64

 

In [25]:

gini_feature_importance.plot(kind="barh", title="gini tree feature importance")

 

Out[25]:

<AxesSubplot: title={'center': 'gini tree feature importance'}>

 

 

In [26]:

sample_50_feature_importance = pd.Series(
    sample_50_tree.feature_importances_,
    index=iris["feature_names"]
)

 

In [27]:

sample_50_feature_importance.plot(kind="barh", title="sample=50 tree feature importance")

 

Out[27]:

<AxesSubplot: title={'center': 'sample=50 tree feature importance'}>

 

 

 

3. 시각화¶

 

In [28]:

def plot_decision_boundary(pair_data, pair_tree, ax):
    x_min, x_max = pair_data[:, 0].min() - 1, pair_data[:, 0].max() + 1
    y_min, y_max = pair_data[:, 1].min() - 1, pair_data[:, 1].max() + 1
    xx, yy = np.meshgrid(np.arange(x_min, x_max, 0.02),
                         np.arange(y_min, y_max, 0.02))

    Z = pair_tree.predict(np.c_[xx.ravel(), yy.ravel()])
    Z = Z.reshape(xx.shape)
    cs = ax.contourf(xx, yy, Z, cmap=plt.cm.RdYlBu)

    # Plot the training points
    for i, color in zip(range(3), "ryb"):
        idx = np.where(train_target == i)
        ax.scatter(pair_data[idx, 0], pair_data[idx, 1], c=color, label=iris["target_names"][i],
                    cmap=plt.cm.RdYlBu, edgecolor='black', s=15)
    return ax

 

In [29]:

fig, axes = plt.subplots(nrows=2, ncols=3, figsize=(15,10))

pair_combs = [
    [0, 1], [0, 2], [0, 3], [1, 2], [1, 3], [2, 3]
]
for idx, pair in enumerate(pair_combs):
    x, y = pair
    pair_data = train_data[:, pair]
    pair_tree = DecisionTreeClassifier().fit(pair_data, train_target)

    ax = axes[idx//3, idx%3]
    ax = plot_decision_boundary(pair_data, pair_tree, ax)
    ax.set_xlabel(iris["feature_names"][x])
    ax.set_ylabel(iris["feature_names"][y])

 

 

C:\Users\sjy99\AppData\Local\Temp\ipykernel_7596\2413102993.py:14: UserWarning: No data for colormapping provided via 'c'. Parameters 'cmap' will be ignored
  ax.scatter(pair_data[idx, 0], pair_data[idx, 1], c=color, label=iris["target_names"][i],
C:\Users\sjy99\AppData\Local\Temp\ipykernel_7596\2413102993.py:14: UserWarning: No data for colormapping provided via 'c'. Parameters 'cmap' will be ignored
  ax.scatter(pair_data[idx, 0], pair_data[idx, 1], c=color, label=iris["target_names"][i],
C:\Users\sjy99\AppData\Local\Temp\ipykernel_7596\2413102993.py:14: UserWarning: No data for colormapping provided via 'c'. Parameters 'cmap' will be ignored
  ax.scatter(pair_data[idx, 0], pair_data[idx, 1], c=color, label=iris["target_names"][i],
C:\Users\sjy99\AppData\Local\Temp\ipykernel_7596\2413102993.py:14: UserWarning: No data for colormapping provided via 'c'. Parameters 'cmap' will be ignored
  ax.scatter(pair_data[idx, 0], pair_data[idx, 1], c=color, label=iris["target_names"][i],
C:\Users\sjy99\AppData\Local\Temp\ipykernel_7596\2413102993.py:14: UserWarning: No data for colormapping provided via 'c'. Parameters 'cmap' will be ignored
  ax.scatter(pair_data[idx, 0], pair_data[idx, 1], c=color, label=iris["target_names"][i],
C:\Users\sjy99\AppData\Local\Temp\ipykernel_7596\2413102993.py:14: UserWarning: No data for colormapping provided via 'c'. Parameters 'cmap' will be ignored
  ax.scatter(pair_data[idx, 0], pair_data[idx, 1], c=color, label=iris["target_names"][i],

 

 

In [ ]: