def silhouette_samples(X, labels, verbose=False, cuda=False):
if not cuda:
return s_sil(X, labels)
X, labels = check_X_y(X, labels, accept_sparse=['csc', 'csr'])
le = LabelEncoder()
labels = le.fit_transform(labels)
n_samples = len(labels)
label_freqs = np.bincount(labels)
check_number_of_labels(len(le.classes_), n_samples)
reduce_func = functools.partial(_silhouette_reduce, labels=labels, label_freqs=label_freqs)
results = zip(*pairwise_distances_chunked_cuda(X, reduce_func=reduce_func, verbose=verbose))
intra_clust_dists, inter_clust_dists = results
intra_clust_dists = np.concatenate(intra_clust_dists)
inter_clust_dists = np.concatenate(inter_clust_dists)
denom = (label_freqs - 1).take(labels, mode='clip')
with np.errstate(divide="ignore", invalid="ignore"):
intra_clust_dists /= denom
sil_samples = inter_clust_dists - intra_clust_dists
with np.errstate(divide="ignore", invalid="ignore"):
sil_samples /= np.maximum(intra_clust_dists, inter_clust_dists)
# nan values are for clusters of size 1, and should be 0
return np.nan_to_num(sil_samples)
def test_label_encoder_negative_ints():
le = LabelEncoder()
le.fit([1, 1, 4, 5, -1, 0])
assert_array_equal(le.classes_, [-1, 0, 1, 4, 5])
assert_array_equal(le.transform([0, 1, 4, 4, 5, -1, -1]),
[1, 2, 3, 3, 4, 0, 0])
assert_array_equal(le.inverse_transform([1, 2, 3, 3, 4, 0, 0]),
[0, 1, 4, 4, 5, -1, -1])
with pytest.raises(ValueError):
le.transform([0, 6])
def test_label_encoder_empty_array(values):
le = LabelEncoder()
le.fit(values)
# test empty transform
transformed = le.transform([])
assert_array_equal(np.array([]), transformed)
# test empty inverse transform
inverse_transformed = le.inverse_transform([])
assert_array_equal(np.array([]), inverse_transformed)
def test_label_encoder_errors():
# Check that invalid arguments yield ValueError
le = LabelEncoder()
with pytest.raises(ValueError):
le.transform([])
with pytest.raises(ValueError):
le.inverse_transform([])
# Fail on unseen labels
le = LabelEncoder()
le.fit([1, 2, 3, -1, 1])
msg = "contains previously unseen labels"
with pytest.raises(ValueError, match=msg):
le.inverse_transform([-2])
with pytest.raises(ValueError, match=msg):
le.inverse_transform([-2, -3, -4])
# Fail on inverse_transform("")
msg = "bad input shape ()"
with pytest.raises(ValueError, match=msg):
le.inverse_transform("")
def test_label_encoder_str_bad_shape(dtype):
le = LabelEncoder()
le.fit(np.array(["apple", "orange"], dtype=dtype))
msg = "bad input shape"
with pytest.raises(ValueError, match=msg):
le.transform("apple")
def test_label_encoder(values, classes, unknown):
# Test LabelEncoder's transform, fit_transform and
# inverse_transform methods
le = LabelEncoder()
le.fit(values)
assert_array_equal(le.classes_, classes)
assert_array_equal(le.transform(values), [1, 0, 2, 0, 2])
assert_array_equal(le.inverse_transform([1, 0, 2, 0, 2]), values)
le = LabelEncoder()
ret = le.fit_transform(values)
assert_array_equal(ret, [1, 0, 2, 0, 2])
with pytest.raises(ValueError, match="unseen labels"):
le.transform(unknown)
def mainFunc(request):
studentData = pd.read_csv('https://raw.githubusercontent.com/pyh3887/Django_final_project/master/student.csv',encoding='cp949')
# print(studentData.head(3))
# studentData.성적.value_counts()
# plot_div = sns.countplot(x='부모의학교만족도',data = studentData, hue='성적',palette='bright')
# fig = plt.gcf()
# fig.savefig('C:/work/py_sou/chartdb/mychart/static/image/chart.png', dpi=fig.dpi)
# df = studentData.groupby(['성적','부모의학교만족도'])
print(studentData.groupby(['성적'])['부모의학교만족도'].size())
fig = go.Figure(data=[
go.Bar(name='Good', x=['H','L','M'],y=studentData[studentData['부모의학교만족도']=='Good'].groupby(['성적','부모의학교만족도']).size(),
marker_color='#9bb1d6'),
go.Bar(name='Bad', x=['H','L','M'],y=studentData[studentData['부모의학교만족도']=='Bad'].groupby(['성적','부모의학교만족도']).size(),
marker_color='#a39bd6'),
])
# Change the bar mode
fig = fig.update_layout(barmode ='group',width=500,
height=500,
title='부모의학교만족도에 따른 성적 그래프',
xaxis_title='성적',
yaxis_title='합계',plot_bgcolor='rgba(0,0,0,0)',paper_bgcolor='rgba(0,0,0,0)',font=dict(family='Courier New, monospace')
) #데이터를 그룹화하여 표에 적용
# fig = px.bar(studentData, x='성적', y='부모의학교만족도', barmode='group',height=400)
plot_div = plot(fig,output_type='div')
#전공별 비율
fig2 = go.Figure(data=[go.Pie(labels=['Arabic','Biology','Chemistry','English','French','Geology','History','IT','Math','Quran','Science','Spanish'], values=studentData.groupby(['전공']).size(), textinfo='label+percent',
insidetextorientation='radial'
)])
fig2 = fig2.update_layout(width=500,height=600)
pie_div = plot(fig2,output_type='div')
# print('룰루랄ㄹ라\n',studentData.groupby(['전공']).size())
#전공별 성적 비교----의미가 있는지 없는지 확인받기
majors = ['Arabic','Biology','Chemistry','English','French','Geology','History','IT','Math','Quran','Science','Spanish']
fig3 = go.Figure()
fig3.add_trace(go.Scatter(
x=studentData[studentData['성적']=='H'].groupby(['전공']).size(),
y=majors,
marker=dict(color="crimson", size=12),
mode="markers",
name="High",
))
fig3.add_trace(go.Scatter(
x=studentData[studentData['성적']=='M'].groupby(['전공']).size(),
y=majors,
marker=dict(color="gold", size=12),
mode="markers",
name="Middle",
))
fig3.add_trace(go.Scatter(
x=studentData[studentData['성적']=='L'].groupby(['전공']).size(),
y=majors,
marker=dict(color="black", size=12),
mode="markers",
name="Low",
))
fig3.update_layout(title="전공에 따른 성적 그래프",
xaxis_title="학생수",
yaxis_title="전공",width=700,height=500)
last_div = plot(fig3,output_type='div')
fig4 = go.Figure(data=[go.Bar(name='7일이하',
x=['H','L','M'], y=studentData[studentData['결석일수']=='Under-7'].groupby(['성적']).size(),
text=studentData[studentData['결석일수']=='Under-7'].groupby(['성적']).size(),
textposition='auto',
marker_color='rgb(204,153,153)'
),
go.Bar(name='7일이상',
x=['H','L','M'], y=studentData[studentData['결석일수']=='Above-7'].groupby(['성적']).size(),
text=studentData[studentData['결석일수']=='Above-7'].groupby(['성적']).size(),
textposition='auto',
marker_color='rgb(255,204,204)'
)
])
print(studentData[studentData['결석일수']=='Above-7'].groupby(['성적']).size())
fig4.update_layout(title="결석일수에 따른 성적 분포",
xaxis_title="성적",
yaxis_title="학생수",width=500,height=500,paper_bgcolor='rgba(0,0,0,0)',plot_bgcolor='rgba(0,0,0,0)',font=dict(family='Courier New, monospace', color='#000'))
ab_plot = plot(fig4,output_type='div')
fig5 = px.scatter_matrix(studentData,
dimensions=["발표수", "과정반복수", "새공지사항확인수", "토론참여수"],
color="성적",width=1200, height=1000)
plot5_div = plot(fig5,output_type='div')
fig6 = px.strip(studentData, x="발표수", y="발표수", orientation="v", color="성적")
plot6_div = plot(fig6,output_type='div')
#찬규씨
plt.clf()
data = pd.read_csv('https://raw.githubusercontent.com/pyh3887/Django_final_project/master/student.csv', encoding='euc-kr')
data['성적'] = data['성적'].map({'H':2,'M':1,'L':0})
# 데이터 상위 5개 행 읽기
# print(data.head())
# 데이터의 컬럼별 요약 통계량 확인
# print(data.describe())
# 데이터의 행과 열의 개수
# print(data.shape)
# 결측값 확인
# print(data.isnull().sum())
# # 국적 인원 분포도 (1)
# print(data['국적'].value_counts())
# # 국적 인원 분포도 퍼센트로 막대그래프 출력 (2)
# print('인원수\n',data.국적.value_counts(normalize=False))
# plt.subplot(1, 2, 1)
# data.국적.value_counts(normalize=False).plot(kind='bar')
#
# # 국적별 성적 분포도
# print('인원수\n',data.성적.value_counts(normalize=False))
# plt.subplot(1, 2, 2)
# data.성적.value_counts(normalize=False).plot(kind='pie')
# 국적별 발표 수
# print('발표 수 : \n', data.발표수.value_counts())
# counts 값 percentage 로 변경
# print(data['국적'].unique())
# for i in data['국적'].unique():
# for i in len(data['국적'].value_counts()):
# i = i / 480 * 100
# 이미지 저장 작업
# plt.gcf()
# 1행 1열에 국적별 성적 출력
df = pd.DataFrame({"국적":data['국적'],"성적":data['성적']})
df7 = pd.crosstab(df.성적, df.국적, margins=True)
# print(df7.columns)
for i in df7.columns:
df7[i] = df7[i].values / df7.loc['All', i] * 100
# result = df7['Egypt'].values / df7.loc['All', 'Egypt'] * 100
# print(result)
df7 = df7.drop(['All'])
fig8 = go.Figure(data=[
go.Bar(name='H', x=['Egypt', 'Iran', 'Iraq', 'Jordan', 'KW', 'Lybia', 'Morocco',
'Palestine', 'SaudiArabia', 'Syria', 'Tunis', 'USA', 'lebanon',
'venzuela'],y=df7.iloc[0,:17].values),
go.Bar(name='M', x=['Egypt', 'Iran', 'Iraq', 'Jordan', 'KW', 'Lybia', 'Morocco',
'Palestine', 'SaudiArabia', 'Syria', 'Tunis', 'USA', 'lebanon',
'venzuela'],y=df7.iloc[1,:17].values),
go.Bar(name='L', x=['Egypt', 'Iran', 'Iraq', 'Jordan', 'KW', 'Lybia', 'Morocco',
'Palestine', 'SaudiArabia', 'Syria', 'Tunis', 'USA', 'lebanon',
'venzuela'],y=df7.iloc[2,:17].values),
])
# Change the bar mode
fig8.update_layout(barmode ='stack')
plot10_div = plot(fig8,output_type='div')
fig9 = px.violin(data, y="과정반복수", x="결석일수", color="성별", box=True, points="all", hover_data=df.columns)
fig9.update_layout(width=1000)
plot11_div = plot(fig9,output_type='div')
fig = px.scatter_3d(data, x='발표수', y='토론참여수', z='새공지사항확인수',
color='성적', size_max=1,
symbol='성적', opacity=0.7)
# tight layout
fig.update_layout(margin=dict(l=0, r=0, b=0, t=0),width=500,height=500)
plot12_div = plot(fig,output_type='div')
# =================================
#경석씨
df = data
print(data.columns)
# LabelEncoder
le = LabelEncoder()
# apply "le.fit_transform"
df = df.apply(le.fit_transform)
# 성적 순으로 숫자를 재배치
df.loc[df['성적'] == 0, '성적'] = 3
df.loc[df['성적'] == 2, '성적'] = 2
df.loc[df['성적'] == 1, '성적'] = 1
print(df['성적'].head())
# print(df)
print(df['발표수'].head(20))
print(np.corrcoef(df['발표수'], df['국적']))
print(df.corr())
# 피어슨의 상관계수는 일반적으로,
# 값이 -1.0 ~ -0.7 이면, 강한 음적 상관관계
# 값이 -0.7 ~ -0.3 이면, 뚜렷한 음적 상관관계
# 값이 -0.3 ~ -0.1 이면, 약한 음적 상관관계
# 값이 -0.1 ~ +0.1 이면, 없다고 할 수 있는 상관관계
# 값이 +0.1 ~ +0.3 이면, 약한 양적 상관관계
# 값이 +0.3 ~ +0.7 이면, 뚜렷한 양적 상관관계
# 값이 +0.7 ~ +1.0 이면, 강한 양적 상관관계로 해석됩니다.
fig20 = px.imshow(df.corr(),
x=['성별', '국적', '출생지', '교육단계', '학년', '학급', '전공', '학기', '담당부모', '발표수', '과정반복수', '새공지사항확인수', '토론참여수', '부모의학교만족도', '결석일수', '성적'],
y=['성별', '국적', '출생지', '교육단계', '학년', '학급', '전공', '학기', '담당부모', '발표수', '과정반복수', '새공지사항확인수', '토론참여수', '부모의학교만족도', '결석일수', '성적'],
width=1000, height=900, color_continuous_scale='RdBu_r'
)
plot20_div = plot(fig20,output_type='div')
# print(studentData[studentData['성적']=='M'].groupby(['전공']).size())
return render(request,'index.html', context={'plot_div': plot_div,'pie_div': pie_div,'last_div': last_div,'ab_plot': ab_plot,'plot5_div': plot5_div,'plot6_div': plot6_div,'plot10_div': plot10_div,'plot11_div': plot11_div,'plot12_div': plot12_div,'plot20_div': plot20_div})
