def time_plot_andrews_curves(self):
andrews_curves(self.df, "Name")
def test_andrews_curves(self):
from pandas.plotting import andrews_curves
from matplotlib import cm
df = self.iris
_check_plot_works(andrews_curves, frame=df, class_column='Name')
rgba = ('#556270', '#4ECDC4', '#C7F464')
ax = _check_plot_works(andrews_curves, frame=df,
class_column='Name', color=rgba)
self._check_colors(
ax.get_lines()[:10], linecolors=rgba, mapping=df['Name'][:10])
cnames = ['dodgerblue', 'aquamarine', 'seagreen']
ax = _check_plot_works(andrews_curves, frame=df,
class_column='Name', color=cnames)
self._check_colors(
ax.get_lines()[:10], linecolors=cnames, mapping=df['Name'][:10])
ax = _check_plot_works(andrews_curves, frame=df,
class_column='Name', colormap=cm.jet)
cmaps = lmap(cm.jet, np.linspace(0, 1, df['Name'].nunique()))
self._check_colors(
ax.get_lines()[:10], linecolors=cmaps, mapping=df['Name'][:10])
length = 10
df = DataFrame({"A": random.rand(length),
"B": random.rand(length),
"C": random.rand(length),
"Name": ["A"] * length})
_check_plot_works(andrews_curves, frame=df, class_column='Name')
rgba = ('#556270', '#4ECDC4', '#C7F464')
ax = _check_plot_works(andrews_curves, frame=df,
class_column='Name', color=rgba)
self._check_colors(
ax.get_lines()[:10], linecolors=rgba, mapping=df['Name'][:10])
cnames = ['dodgerblue', 'aquamarine', 'seagreen']
ax = _check_plot_works(andrews_curves, frame=df,
class_column='Name', color=cnames)
self._check_colors(
ax.get_lines()[:10], linecolors=cnames, mapping=df['Name'][:10])
ax = _check_plot_works(andrews_curves, frame=df,
class_column='Name', colormap=cm.jet)
cmaps = lmap(cm.jet, np.linspace(0, 1, df['Name'].nunique()))
self._check_colors(
ax.get_lines()[:10], linecolors=cmaps, mapping=df['Name'][:10])
colors = ['b', 'g', 'r']
df = DataFrame({"A": [1, 2, 3],
"B": [1, 2, 3],
"C": [1, 2, 3],
"Name": colors})
ax = andrews_curves(df, 'Name', color=colors)
handles, labels = ax.get_legend_handles_labels()
self._check_colors(handles, linecolors=colors)
with tm.assert_produces_warning(FutureWarning):
andrews_curves(data=df, class_column='Name')
def test_andrews_curves(self, iris):
from pandas.plotting import andrews_curves
from matplotlib import cm
df = iris
_check_plot_works(andrews_curves, frame=df, class_column="Name")
rgba = ("#556270", "#4ECDC4", "#C7F464")
ax = _check_plot_works(andrews_curves,
frame=df,
class_column="Name",
color=rgba)
self._check_colors(ax.get_lines()[:10],
linecolors=rgba,
mapping=df["Name"][:10])
cnames = ["dodgerblue", "aquamarine", "seagreen"]
ax = _check_plot_works(andrews_curves,
frame=df,
class_column="Name",
color=cnames)
self._check_colors(ax.get_lines()[:10],
linecolors=cnames,
mapping=df["Name"][:10])
ax = _check_plot_works(andrews_curves,
frame=df,
class_column="Name",
colormap=cm.jet)
cmaps = [cm.jet(n) for n in np.linspace(0, 1, df["Name"].nunique())]
self._check_colors(ax.get_lines()[:10],
linecolors=cmaps,
mapping=df["Name"][:10])
length = 10
df = DataFrame({
"A": random.rand(length),
"B": random.rand(length),
"C": random.rand(length),
"Name": ["A"] * length,
})
_check_plot_works(andrews_curves, frame=df, class_column="Name")
rgba = ("#556270", "#4ECDC4", "#C7F464")
ax = _check_plot_works(andrews_curves,
frame=df,
class_column="Name",
color=rgba)
self._check_colors(ax.get_lines()[:10],
linecolors=rgba,
mapping=df["Name"][:10])
cnames = ["dodgerblue", "aquamarine", "seagreen"]
ax = _check_plot_works(andrews_curves,
frame=df,
class_column="Name",
color=cnames)
self._check_colors(ax.get_lines()[:10],
linecolors=cnames,
mapping=df["Name"][:10])
ax = _check_plot_works(andrews_curves,
frame=df,
class_column="Name",
colormap=cm.jet)
cmaps = [cm.jet(n) for n in np.linspace(0, 1, df["Name"].nunique())]
self._check_colors(ax.get_lines()[:10],
linecolors=cmaps,
mapping=df["Name"][:10])
colors = ["b", "g", "r"]
df = DataFrame({
"A": [1, 2, 3],
"B": [1, 2, 3],
"C": [1, 2, 3],
"Name": colors
})
ax = andrews_curves(df, "Name", color=colors)
handles, labels = ax.get_legend_handles_labels()
self._check_colors(handles, linecolors=colors)
with tm.assert_produces_warning(FutureWarning):
andrews_curves(data=df, class_column="Name")
plt.figure(figsize=(15, 10))
parallel_coordinates(dataset.drop("Id", axis=1), "Species")
plt.title("Parallel Coordinates Plot", fontsize=20, fontweight="bold")
plt.xlabel("Features", fontsize=15)
plt.ylabel("Features Values", fontsize=15)
plt.legend(loc=1,
prop={"size": 15},
frameon=True,
shadow=True,
facecolor="White",
edgecolor="black")
plt.show()
## Andrew Curves
from pandas.plotting import andrews_curves
andrews_curves(dataset.drop("Id", axis=1), "Species")
plt.title("Andrews Curve Plot", fontsize=20, fontweight="bold")
plt.xlabel("Features", fontsize=15)
plt.ylabel("Features Values", fontsize=15)
plt.legend(loc=1,
prop={"size": 5},
frameon=True,
shadow=True,
facecolor="White",
edgecolor="black")
plt.show()
## BoxPlots
plt.figure()
dataset.drop("Id", axis=1).boxplot(by="Species", figsize=(15, 10))
plt.show()
def display_andrews_graph(self):
pdplt.andrews_curves(df, "output", ax=None)
plt.show()
def draw_func(self):
if len(self.all_data.index) == 0:
self.show_message('请导入采样数据')
elif len(self.info_data.index) == 0:
self.show_message('请导入采样信息')
elif not self.region_linked:
self.show_message("请点击链接")
elif not self.figure_able:
self.show_message("数据包含非数值类型，不可画图！")
else:
self.clear_func()
self.cur_slice()
# 解决无法显示中文
plt.rcParams['font.sans-serif'] = ['Arial Unicode MS']
# plt.rcParams['font.sans-serif']=['SimHei'] #指定默认字体,SimHei为黑体
# 解决无法显示负号
plt.rcParams['axes.unicode_minus'] = False
plt.title(self.figure_type)
if self.figure_type == "主成分分析":
region_data = self.cur_data.iloc[:, 0].values.tolist()
print(region_data)
regions = list(set(region_data))
print(regions)
region_color = [(int(regions.index(i) * 255 / len(regions))) for i in region_data]
# region_color = [regions.index[i] for i in region_data]
print(region_color)
data = self.cur_data.iloc[:, 1:].values
data = data - np.mean(data, axis=0)
print("data",data.shape)
cov_mat = np.cov(data, rowvar=0)
print("cov:", cov_mat.shape)
eig_vals, eig_vects = np.linalg.eig(np.mat(cov_mat))
low_data_mat = data * eig_vects
print("low:", low_data_mat.shape)
eig_val_indice = np.argsort(eig_vals)
top = 2
n_eig_val_indice = range(top)
print("n_eig_val_indice", n_eig_val_indice)
n_eig_vects = eig_vects[:, n_eig_val_indice]
print("n_eig:",n_eig_vects.shape)
recon_mat = (low_data_mat * eig_vects) + np.mean(data, axis=0)
print("rec:", recon_mat.shape)
x = np.array(low_data_mat)[:, 0]
y = np.array(low_data_mat)[:, 1]
# z = np.array(low_data_mat)[:, 2]
for region in regions:
index = [i for i, data in enumerate(region_data) if data == region]
plt.scatter(x[index], y[index])
plt.legend(regions)
elif self.figure_type == '平行坐标图':
parallel_coordinates(self.cur_data, self.region_method)
elif self.figure_type == "Andrews图":
colors = ['b', 'g', 'r', 'orange']
andrews_curves(self.cur_data, self.region_method, color=colors)
elif self.figure_type == 'Radiv图':
radviz(self.cur_data, self.region_method)
elif self.figure_type == '矩阵散点图':
print("绘制矩阵散点图")
sns.pairplot(data=self.cur_data, hue=self.region_method)
f = plt.gcf()
self.ax = f
self.canvas = FigureCanvas(f)
elif self.figure_type == 'Chernoff脸谱图':
self.cur_data.to_excel('cur_data.xlsx')
print("data out")
# goto_r()
os.system("python ./PyToR.py")
face_info = pd.read_csv('face_info.csv')
# f_str = face_info.to_string()
font = {'weight': 'normal',
'size': 11,
}
plt.text(500, 0 , "脸谱图条目 数据列", fontdict=font)
for index, row in face_info.iterrows():
f_str = row[0] + " : "
plt.text(500, 20 + 20 * index, f_str, fontdict=font)
f_str = row[1]
plt.text(650, 30 + 20 * index, f_str, fontdict=font)
plt.imshow(Image.open('face.png'))
plt.gca().add_patch(plt.Rectangle(xy=(500, 20), width=100, height=300,
edgecolor=[1, 1, 1],
fill=False,
linewidth=2))
# print("文件命名为:face.jpg")
# info=pd.read_csv('face_info.csv',encoding='GBK')
# print("effect of variables:\n{}".format(info))
self.table_view.setVisible(False)
self.canvas.setVisible(True)
self.figure_layout.removeWidget(self.table_view)
self.figure_layout.addWidget(self.canvas)
self.canvas.draw()
self.figure_state = 2
annot_kws={'size': 8},
vmax=1,
vmin=-1,
cmap='GnBu',
center=0)
bottom, top = ax2.get_ylim()
ax2.set_ylim(bottom + 0.5, top - 0.5)
#%%
####### COORDENADAS PARALELAS
from pandas.plotting import parallel_coordinates
caracteristicas_media.append("diagnosis")
parallel_coordinates(cancer_limpio_diagnostico[caracteristicas_media],
"diagnosis",
colormap='cool',
xticks=None)
plt.show()
#%%
####### CURVAS DE ANDREWS
from pandas.plotting import andrews_curves
andrews_curves(cancer_limpio_diagnostico[caracteristicas_media],
"diagnosis",
colormap='rainbow')
plt.show()
from pandas.plotting import andrews_curves
import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
mtcars = pd.read_csv("../data/mtcars.csv")
mtcars.drop(['cars', 'carname'], axis=1, inplace=True)
_, ax = plt.subplots(dpi=100)
andrews_curves(mtcars, 'cyl', colormap='Set1')
sns.despine()
ax.set(xlim=(-3, 3), title='Andrews Curves of mtcars')
ax.grid(alpha=0.3)
plt.show()
def time_plot_andrews_curves(self):
andrews_curves(self.df, "Name")
def test_andrews_curves(self):
from pandas.plotting import andrews_curves
from matplotlib import cm
df = self.iris
_check_plot_works(andrews_curves, frame=df, class_column='Name')
rgba = ('#556270', '#4ECDC4', '#C7F464')
ax = _check_plot_works(andrews_curves,
frame=df,
class_column='Name',
color=rgba)
self._check_colors(ax.get_lines()[:10],
linecolors=rgba,
mapping=df['Name'][:10])
cnames = ['dodgerblue', 'aquamarine', 'seagreen']
ax = _check_plot_works(andrews_curves,
frame=df,
class_column='Name',
color=cnames)
self._check_colors(ax.get_lines()[:10],
linecolors=cnames,
mapping=df['Name'][:10])
ax = _check_plot_works(andrews_curves,
frame=df,
class_column='Name',
colormap=cm.jet)
cmaps = lmap(cm.jet, np.linspace(0, 1, df['Name'].nunique()))
self._check_colors(ax.get_lines()[:10],
linecolors=cmaps,
mapping=df['Name'][:10])
length = 10
df = DataFrame({
"A": random.rand(length),
"B": random.rand(length),
"C": random.rand(length),
"Name": ["A"] * length
})
_check_plot_works(andrews_curves, frame=df, class_column='Name')
rgba = ('#556270', '#4ECDC4', '#C7F464')
ax = _check_plot_works(andrews_curves,
frame=df,
class_column='Name',
color=rgba)
self._check_colors(ax.get_lines()[:10],
linecolors=rgba,
mapping=df['Name'][:10])
cnames = ['dodgerblue', 'aquamarine', 'seagreen']
ax = _check_plot_works(andrews_curves,
frame=df,
class_column='Name',
color=cnames)
self._check_colors(ax.get_lines()[:10],
linecolors=cnames,
mapping=df['Name'][:10])
ax = _check_plot_works(andrews_curves,
frame=df,
class_column='Name',
colormap=cm.jet)
cmaps = lmap(cm.jet, np.linspace(0, 1, df['Name'].nunique()))
self._check_colors(ax.get_lines()[:10],
linecolors=cmaps,
mapping=df['Name'][:10])
colors = ['b', 'g', 'r']
df = DataFrame({
"A": [1, 2, 3],
"B": [1, 2, 3],
"C": [1, 2, 3],
"Name": colors
})
ax = andrews_curves(df, 'Name', color=colors)
handles, labels = ax.get_legend_handles_labels()
self._check_colors(handles, linecolors=colors)
with tm.assert_produces_warning(FutureWarning):
andrews_curves(data=df, class_column='Name')
todo_selectbox = st.sidebar.selectbox(
"Что вы хотите сделать?", ("Ознакомиться с данными", "Кластеризовать"))
if todo_selectbox == "Ознакомиться с данными":
visualize_selectbox = st.sidebar.selectbox(
"Выберите метод визуализции",
("Общие сведения", "Andrew Curves for Gender",
"Распределение по возрасту и доходу", "По gender",
"Распределение по возрасту", "Распределение по доходу",
"Распредленеие по оценке расходов", "Есть ли кореляция",
"Пол и оценка расходов"))
if visualize_selectbox == "Andrew Curves for Gender":
plt.rcParams['figure.figsize'] = (15, 10)
plotting.andrews_curves(data.drop("CustomerID", axis=1),
"Gender",
color=list(["lightsteelblue", "pink"]))
plt.title('Andrew Curves for Gender', fontsize=20)
st.markdown(
'Можно заметить, что линии соответствующие похожим **значениям** также имеют и схожую **форму**.'
)
st.pyplot(plt)
# plt.show()
elif visualize_selectbox == "Общие сведения":
st.markdown('_CustomerID_ - уникальный идентификатор пользователя')
st.markdown('_Gender_ - пол')
st.markdown('_Age_ - возраст')
st.markdown('_Annual Income_ - годовой доход клиента')
st.markdown(
'_Spending Score_ - оценка, присвоенная торговым центром на основе поведения клиентов и характера расходов'
)
import subprocess
import pandas as pd
import numpy as np
import serial
from sklearn.tree import DecisionTreeClassifier, export_graphviz
from tkinter import *
#import plotly.plotly as py
#reading the training data and storing it in pandas dataframe
df = pd.read_csv("/home/scas/Documents/test_file1.csv",
names=['humidity', 'temp', 'moisture', 'LDR', 'output'])
print(df['output'].unique())
sns.pairplot(df, hue="output", size=3)
plt.show()
pdplt.andrews_curves(df, "output", ax=None)
plt.show()
#modifying the dataframe, encoding the categorical variables to integers since the decision_tree classifier takes
#only integer inputs
def encode_target(df, target_column):
df_mod = df.copy()
targets = df_mod[target_column].unique()
map_to_int = {name: n for n, name in enumerate(targets)}
df_mod["Target"] = df_mod[target_column].replace(map_to_int)
return df_mod, targets
df2, targets = encode_target(df, "output")
# In[69]:
sns.kdeplot(df.last_evaluation, df.average_montly_hours, shade=True)
# ## Andrews Curve
# In[36]:
df_andrews = df.select_dtypes(exclude=['object']).astype(int)
df_andrews.head()
# In[37]:
from pandas.plotting import andrews_curves
plt.figure(figsize=(12, 12))
andrews_curves(df_andrews, 'left')
# ## Hexbin Plot
# In[71]:
x, y = df.average_montly_hours, df.satisfaction_level
fig, ax = plt.subplots()
hx = ax.hexbin(x, y, cmap='Greens', gridsize=10)
fig.colorbar(hx)
plt.show()
# In[34]:
sns.jointplot(x='average_montly_hours',
y='satisfaction_level',
