quiz 1번
from sklearn.datasets import fetch_openml
import ssl
ssl._create_default_https_context = ssl._create_unverified_context
from sklearn.model_selection import GridSearchCV
from sklearn.tree import DecisionTreeClassifier
from sklearn.neighbors import KNeighborsClassifier
mnist = fetch_openml('mnist_784', as_frame=False)
x, y = mnist.data, mnist.target
# print(mnist)
import matplotlib.pyplot as plt
import numpy as np
def plot_digit(image_data):
image = image_data.reshape(28, 28)
plt.imshow(image, cmap='binary')
plt.axis('off')
some_digit = x[0]
plot_digit(some_digit)
plt.figure(figsize=(9,9))
for idx, image_data in enumerate(x[:100]):
plt.subplot(10,10, idx+1)
plot_digit(image_data)
plt.subplots_adjust(wspace=0,hspace=0)
plt.show()
knn_clf = KNeighborsClassifier()
params = [{'n_neighbors':range(3,8,1), 'weights':['uniform','distance']}]
from sklearn.model_selection import train_test_split
x_train, x_test, y_train, y_test= train_test_split(x[:10000], y[:10000], test_size=0.2, random_state=0)
grid_search = GridSearchCV(knn_clf, params, cv=5)
grid_search.fit(x_train, y_train)
print(grid_search.best_score_)
print(grid_search.best_params_)
print()
bestmodel = grid_search.best_estimator_
bestmodel.fit(x_train,y_train)
print(bestmodel.score(x_test,y_test))
0.94525
{'n_neighbors': 4, 'weights': 'distance'}
0.9495
quiz 2번
import pandas as pd
passengers = pd.read_csv('titanic_data.csv')
passengers.info()
print(passengers['Pclass'])
print()
dummies = pd.get_dummies(passengers['Pclass'])
print(dummies)
print()
del passengers['Pclass']
passengers = pd.concat([passengers, dummies], axis=1, join='inner')
passengers.info()
print()
print(passengers['Sex'])
'''
1. age 누락값을 평균으로 채움
2. Sex의 male = > 0, female = > 1
3. 1 => FirstClass 2 => SecondClass 3 => EtcClass
4. Sex,Age,FirstClass,SecondClass,EtcClass 를 특성 데이터로 이용해서 Survived 를 예측하는 모델을 구성
5. LogisticRegression 을 이용
6. 데이터 train, test 비율 8 : 2
7. 특성 데이터(x_train, x_test)는 standarScaler를 이용해 스케일 작업을 한다
8. 학습 완료 후
kim = np.array([0.0,20.0,0.0,0.0,1.0])
hyo = np.array([1.0,17.0,1.0,0.0,0.0])
choi = np.array([0.0,32.0,0.0,1.0,0.0])
'''
# 1. age 누락값을 평균으로 채움
meanage = passengers['Age'].mean()
print(meanage)
passengers.fillna({'Age':int(meanage)},inplace=True)
print(passengers['Age'])
# 2. Sex의 male = > 0, female = > 1
passengers['Sex'] = passengers['Sex'].map({'male':0,'female':1})
print(passengers['Sex'])
# 3. 1 => FirstClass 2 => SecondClass 3 => EtcClass
passengers.rename(columns={1:'FirstClass',2:'SecondClass',3:'EtcClass'}, inplace=True)
passengers.info()
# 4. Sex,Age,FirstClass,SecondClass,EtcClass 를 특성 데이터로 이용해서 Survived 를 예측하는 모델을 구성
# 5. LogisticRegression 을 이용
# 6. 데이터 train, test 비율 8 : 2
x_input = passengers.loc[:,['Sex','Age','FirstClass','SecondClass','EtcClass']].values
x_target = passengers['Survived'].to_numpy()
print(x_input.shape)
from sklearn.model_selection import train_test_split
train_input, test_input, train_target, test_target = train_test_split(
x_input, x_target, test_size=0.2, random_state=42
)
from sklearn.preprocessing import StandardScaler
ss = StandardScaler()
ss.fit(train_input)
train_scaled = ss.transform(train_input)
test_scaled = ss.transform(test_input)
from sklearn.linear_model import LogisticRegression
import numpy as np
kim = np.array([0.0,20.0,0.0,0.0,1.0])
hyo = np.array([1.0,17.0,1.0,0.0,0.0])
choi = np.array([0.0,32.0,0.0,1.0,0.0])
A = np.stack((kim,hyo,choi),axis=0)
A_scaled = ss.transform(A)
lr = LogisticRegression()
lr.fit(train_scaled, train_target)
print('train data accuracy: ', lr.score(train_scaled, train_target))
print('test data accuracy: ', lr.score(test_scaled, test_target))
y_pred = lr.predict(A_scaled)
print(y_pred)
<class 'pandas.core.frame.DataFrame'>
RangeIndex: 891 entries, 0 to 890
Data columns (total 12 columns):
# Column Non-Null Count Dtype
--- ------ -------------- -----
0 PassengerId 891 non-null int64
1 Survived 891 non-null int64
2 Pclass 891 non-null int64
3 Name 891 non-null object
4 Sex 891 non-null object
5 Age 714 non-null float64
6 SibSp 891 non-null int64
7 Parch 891 non-null int64
8 Ticket 891 non-null object
9 Fare 891 non-null float64
10 Cabin 204 non-null object
11 Embarked 889 non-null object
dtypes: float64(2), int64(5), object(5)
memory usage: 83.7+ KB
0 3
1 1
2 3
3 1
4 3
..
886 2
887 1
888 3
889 1
890 3
Name: Pclass, Length: 891, dtype: int64
1 2 3
0 False False True
1 True False False
2 False False True
3 True False False
4 False False True
.. ... ... ...
886 False True False
887 True False False
888 False False True
889 True False False
890 False False True
[891 rows x 3 columns]
<class 'pandas.core.frame.DataFrame'>
RangeIndex: 891 entries, 0 to 890
Data columns (total 14 columns):
# Column Non-Null Count Dtype
--- ------ -------------- -----
0 PassengerId 891 non-null int64
1 Survived 891 non-null int64
2 Name 891 non-null object
3 Sex 891 non-null object
4 Age 714 non-null float64
5 SibSp 891 non-null int64
6 Parch 891 non-null int64
7 Ticket 891 non-null object
8 Fare 891 non-null float64
9 Cabin 204 non-null object
10 Embarked 889 non-null object
11 1 891 non-null bool
12 2 891 non-null bool
13 3 891 non-null bool
dtypes: bool(3), float64(2), int64(4), object(5)
memory usage: 79.3+ KB
0 male
1 female
2 female
3 female
4 male
...
886 male
887 female
888 female
889 male
890 male
Name: Sex, Length: 891, dtype: object
29.69911764705882
0 22.0
1 38.0
2 26.0
3 35.0
4 35.0
...
886 27.0
887 19.0
888 29.0
889 26.0
890 32.0
Name: Age, Length: 891, dtype: float64
0 0
1 1
2 1
3 1
4 0
..
886 0
887 1
888 1
889 0
890 0
Name: Sex, Length: 891, dtype: int64
<class 'pandas.core.frame.DataFrame'>
RangeIndex: 891 entries, 0 to 890
Data columns (total 14 columns):
# Column Non-Null Count Dtype
--- ------ -------------- -----
0 PassengerId 891 non-null int64
1 Survived 891 non-null int64
2 Name 891 non-null object
3 Sex 891 non-null int64
4 Age 891 non-null float64
5 SibSp 891 non-null int64
6 Parch 891 non-null int64
7 Ticket 891 non-null object
8 Fare 891 non-null float64
9 Cabin 204 non-null object
10 Embarked 889 non-null object
11 FirstClass 891 non-null bool
12 SecondClass 891 non-null bool
13 EtcClass 891 non-null bool
dtypes: bool(3), float64(2), int64(5), object(4)
memory usage: 79.3+ KB
(891, 5)
train data accuracy: 0.7949438202247191
test data accuracy: 0.8044692737430168
[0 1 0]quiz 3번
from sklearn.datasets import load_wine
from sklearn.svm import LinearSVC
wine = load_wine(as_frame=True)
print(wine)
print(wine.data)
print(type(wine.data))
print(wine.DESCR)
from sklearn.model_selection import train_test_split
x_train, x_test, y_train, y_test = train_test_split(wine.data, wine.target,random_state=42)
# (scaler LinearSVC) pipeline
from sklearn.pipeline import Pipeline
from sklearn.svm import LinearSVC
from sklearn.preprocessing import StandardScaler, PolynomialFeatures
svm_clf = Pipeline([
('scalar',StandardScaler()),
('svm_clf',LinearSVC(C=10, random_state=42))
])
svm_clf.fit(x_train, y_train)
print(svm_clf.predict(x_train[:10])) # 예측 값 10번째 까지
print(svm_clf.score(x_test,y_test)) # accuracy
{'data': alcohol malic_acid ash ... hue od280/od315_of_diluted_wines proline
0 14.23 1.71 2.43 ... 1.04 3.92 1065.0
1 13.20 1.78 2.14 ... 1.05 3.40 1050.0
2 13.16 2.36 2.67 ... 1.03 3.17 1185.0
3 14.37 1.95 2.50 ... 0.86 3.45 1480.0
4 13.24 2.59 2.87 ... 1.04 2.93 735.0
.. ... ... ... ... ... ... ...
173 13.71 5.65 2.45 ... 0.64 1.74 740.0
174 13.40 3.91 2.48 ... 0.70 1.56 750.0
175 13.27 4.28 2.26 ... 0.59 1.56 835.0
176 13.17 2.59 2.37 ... 0.60 1.62 840.0
177 14.13 4.10 2.74 ... 0.61 1.60 560.0
[178 rows x 13 columns], 'target': 0 0
1 0
2 0
3 0
4 0
..
173 2
174 2
175 2
176 2
177 2
Name: target, Length: 178, dtype: int64, 'frame': alcohol malic_acid ash ... od280/od315_of_diluted_wines proline target
0 14.23 1.71 2.43 ... 3.92 1065.0 0
1 13.20 1.78 2.14 ... 3.40 1050.0 0
2 13.16 2.36 2.67 ... 3.17 1185.0 0
3 14.37 1.95 2.50 ... 3.45 1480.0 0
4 13.24 2.59 2.87 ... 2.93 735.0 0
.. ... ... ... ... ... ... ...
173 13.71 5.65 2.45 ... 1.74 740.0 2
174 13.40 3.91 2.48 ... 1.56 750.0 2
175 13.27 4.28 2.26 ... 1.56 835.0 2
176 13.17 2.59 2.37 ... 1.62 840.0 2
177 14.13 4.10 2.74 ... 1.60 560.0 2
[178 rows x 14 columns], 'target_names': array(['class_0', 'class_1', 'class_2'], dtype='<U7'), 'DESCR': '.. _wine_dataset:\n\nWine recognition dataset\n------------------------\n\n**Data Set Characteristics:**\n\n:Number of Instances: 178\n:Number of Attributes: 13 numeric, predictive attributes and the class\n:Attribute Information:\n - Alcohol\n - Malic acid\n - Ash\n - Alcalinity of ash\n - Magnesium\n - Total phenols\n - Flavanoids\n - Nonflavanoid phenols\n - Proanthocyanins\n - Color intensity\n - Hue\n - OD280/OD315 of diluted wines\n - Proline\n - class:\n - class_0\n - class_1\n - class_2\n\n:Summary Statistics:\n\n============================= ==== ===== ======= =====\n Min Max Mean SD\n============================= ==== ===== ======= =====\nAlcohol: 11.0 14.8 13.0 0.8\nMalic Acid: 0.74 5.80 2.34 1.12\nAsh: 1.36 3.23 2.36 0.27\nAlcalinity of Ash: 10.6 30.0 19.5 3.3\nMagnesium: 70.0 162.0 99.7 14.3\nTotal Phenols: 0.98 3.88 2.29 0.63\nFlavanoids: 0.34 5.08 2.03 1.00\nNonflavanoid Phenols: 0.13 0.66 0.36 0.12\nProanthocyanins: 0.41 3.58 1.59 0.57\nColour Intensity: 1.3 13.0 5.1 2.3\nHue: 0.48 1.71 0.96 0.23\nOD280/OD315 of diluted wines: 1.27 4.00 2.61 0.71\nProline: 278 1680 746 315\n============================= ==== ===== ======= =====\n\n:Missing Attribute Values: None\n:Class Distribution: class_0 (59), class_1 (71), class_2 (48)\n:Creator: R.A. Fisher\n:Donor: Michael Marshall (MARSHALL%PLU@io.arc.nasa.gov)\n:Date: July, 1988\n\nThis is a copy of UCI ML Wine recognition datasets.\nhttps://archive.ics.uci.edu/ml/machine-learning-databases/wine/wine.data\n\nThe data is the results of a chemical analysis of wines grown in the same\nregion in Italy by three different cultivators. There are thirteen different\nmeasurements taken for different constituents found in the three types of\nwine.\n\nOriginal Owners:\n\nForina, M. et al, PARVUS -\nAn Extendible Package for Data Exploration, Classification and Correlation.\nInstitute of Pharmaceutical and Food Analysis and Technologies,\nVia Brigata Salerno, 16147 Genoa, Italy.\n\nCitation:\n\nLichman, M. (2013). UCI Machine Learning Repository\n[https://archive.ics.uci.edu/ml]. Irvine, CA: University of California,\nSchool of Information and Computer Science.\n\n.. dropdown:: References\n\n (1) S. Aeberhard, D. Coomans and O. de Vel,\n Comparison of Classifiers in High Dimensional Settings,\n Tech. Rep. no. 92-02, (1992), Dept. of Computer Science and Dept. of\n Mathematics and Statistics, James Cook University of North Queensland.\n (Also submitted to Technometrics).\n\n The data was used with many others for comparing various\n classifiers. The classes are separable, though only RDA\n has achieved 100% correct classification.\n (RDA : 100%, QDA 99.4%, LDA 98.9%, 1NN 96.1% (z-transformed data))\n (All results using the leave-one-out technique)\n\n (2) S. Aeberhard, D. Coomans and O. de Vel,\n "THE CLASSIFICATION PERFORMANCE OF RDA"\n Tech. Rep. no. 92-01, (1992), Dept. of Computer Science and Dept. of\n Mathematics and Statistics, James Cook University of North Queensland.\n (Also submitted to Journal of Chemometrics).\n', 'feature_names': ['alcohol', 'malic_acid', 'ash', 'alcalinity_of_ash', 'magnesium', 'total_phenols', 'flavanoids', 'nonflavanoid_phenols', 'proanthocyanins', 'color_intensity', 'hue', 'od280/od315_of_diluted_wines', 'proline']}
alcohol malic_acid ash ... hue od280/od315_of_diluted_wines proline
0 14.23 1.71 2.43 ... 1.04 3.92 1065.0
1 13.20 1.78 2.14 ... 1.05 3.40 1050.0
2 13.16 2.36 2.67 ... 1.03 3.17 1185.0
3 14.37 1.95 2.50 ... 0.86 3.45 1480.0
4 13.24 2.59 2.87 ... 1.04 2.93 735.0
.. ... ... ... ... ... ... ...
173 13.71 5.65 2.45 ... 0.64 1.74 740.0
174 13.40 3.91 2.48 ... 0.70 1.56 750.0
175 13.27 4.28 2.26 ... 0.59 1.56 835.0
176 13.17 2.59 2.37 ... 0.60 1.62 840.0
177 14.13 4.10 2.74 ... 0.61 1.60 560.0
[178 rows x 13 columns]
<class 'pandas.core.frame.DataFrame'>
.. _wine_dataset:
Wine recognition dataset
------------------------
**Data Set Characteristics:**
:Number of Instances: 178
:Number of Attributes: 13 numeric, predictive attributes and the class
:Attribute Information:
- Alcohol
- Malic acid
- Ash
- Alcalinity of ash
- Magnesium
- Total phenols
- Flavanoids
- Nonflavanoid phenols
- Proanthocyanins
- Color intensity
- Hue
- OD280/OD315 of diluted wines
- Proline
- class:
- class_0
- class_1
- class_2
:Summary Statistics:
============================= ==== ===== ======= =====
Min Max Mean SD
============================= ==== ===== ======= =====
Alcohol: 11.0 14.8 13.0 0.8
Malic Acid: 0.74 5.80 2.34 1.12
Ash: 1.36 3.23 2.36 0.27
Alcalinity of Ash: 10.6 30.0 19.5 3.3
Magnesium: 70.0 162.0 99.7 14.3
Total Phenols: 0.98 3.88 2.29 0.63
Flavanoids: 0.34 5.08 2.03 1.00
Nonflavanoid Phenols: 0.13 0.66 0.36 0.12
Proanthocyanins: 0.41 3.58 1.59 0.57
Colour Intensity: 1.3 13.0 5.1 2.3
Hue: 0.48 1.71 0.96 0.23
OD280/OD315 of diluted wines: 1.27 4.00 2.61 0.71
Proline: 278 1680 746 315
============================= ==== ===== ======= =====
:Missing Attribute Values: None
:Class Distribution: class_0 (59), class_1 (71), class_2 (48)
:Creator: R.A. Fisher
:Donor: Michael Marshall (MARSHALL%PLU@io.arc.nasa.gov)
:Date: July, 1988
This is a copy of UCI ML Wine recognition datasets.
https://archive.ics.uci.edu/ml/machine-learning-databases/wine/wine.data
The data is the results of a chemical analysis of wines grown in the same
region in Italy by three different cultivators. There are thirteen different
measurements taken for different constituents found in the three types of
wine.
Original Owners:
Forina, M. et al, PARVUS -
An Extendible Package for Data Exploration, Classification and Correlation.
Institute of Pharmaceutical and Food Analysis and Technologies,
Via Brigata Salerno, 16147 Genoa, Italy.
Citation:
Lichman, M. (2013). UCI Machine Learning Repository
[https://archive.ics.uci.edu/ml]. Irvine, CA: University of California,
School of Information and Computer Science.
.. dropdown:: References
(1) S. Aeberhard, D. Coomans and O. de Vel,
Comparison of Classifiers in High Dimensional Settings,
Tech. Rep. no. 92-02, (1992), Dept. of Computer Science and Dept. of
Mathematics and Statistics, James Cook University of North Queensland.
(Also submitted to Technometrics).
The data was used with many others for comparing various
classifiers. The classes are separable, though only RDA
has achieved 100% correct classification.
(RDA : 100%, QDA 99.4%, LDA 98.9%, 1NN 96.1% (z-transformed data))
(All results using the leave-one-out technique)
(2) S. Aeberhard, D. Coomans and O. de Vel,
"THE CLASSIFICATION PERFORMANCE OF RDA"
Tech. Rep. no. 92-01, (1992), Dept. of Computer Science and Dept. of
Mathematics and Statistics, James Cook University of North Queensland.
(Also submitted to Journal of Chemometrics).
[0 1 1 2 0 1 0 0 2 2]
0.9777777777777777quiz 4번
import pandas as pd
import matplotlib.pyplot as plt
from day4.clusteringEx7 import cluster
frame = pd.read_csv('Mall_Customers.csv')
frame.info()
from sklearn.preprocessing import StandardScaler
data = frame[['Annual Income (k$)','Spending Score (1-100)']]
scaler = StandardScaler()
df_scale = pd.DataFrame(scaler.fit_transform(data), columns=data.columns)
from sklearn.cluster import DBSCAN
model = DBSCAN(eps=0.5, min_samples=2)
model.fit(df_scale)
df_scale['cluster'] = model.fit_predict(df_scale)
print(df_scale)
plt.figure(figsize=(8,8))
for i in range(-1, df_scale['cluster'].max() + 1):
plt.scatter(df_scale.loc[df_scale['cluster'] == i, 'Annual Income (k$)'],
df_scale.loc[df_scale['cluster'] == i, 'Spending Score (1-100)'],
label=f'cluster {i}')
plt.legend(loc='best')
plt.title('eps=0.5 min_samples=2', fontsize=19)
plt.xlabel('Annual Income', fontsize=14)
plt.ylabel('Spending Score', fontsize=14)
plt.show()
+AI to AI+