def plot_feature_importances_cancer(model, cancer):
n_features = cancer.data.shape[1]
plt.barh(np.arange(n_features), model.feature_importances_, align='center')
plt.yticks(np.arange(n_features), cancer.feature_names)
plt.xlabel("Feature importance")
plt.ylabel("Feature")
plt.ylim(-1, n_features)
def check_sanity():
fig = plt.figure(figsize=[10, 5])
# Axes for flower image
ax = fig.add_axes([.5, .4, .5, .5])
# Displaay image
result = process_image('flowers/test/1/image_06743.jpg')
ax = imshow(result, ax)
ax.axis('off')
index = 77
ax.set_title(cat_to_name[str(index)])
# Prediction of image
predictions, classes = predict('flowers/test/1/image_06743.jpg',
model,
device=device)
# Create bar graph
# Axis x and y
ax1 = fig.add_axes([0, -.4, .888, .888])
# Classes probability
y_pos = np.arange(len(classes))
# Horizontal bar chart to see it better
plt.barh(y_pos, predictions, align='center', alpha=0.5)
plt.yticks(y_pos, classes)
plt.xlabel('probabilities')
plt.show()
def plot_feature_importances_cancer(scores):
names, val_scores = [name for name, _, _, _, _, _, _ in scores
], [score for _, score, _, _, _, _, _ in scores]
plt.rcParams["figure.figsize"] = [15, 9]
n_features = len(names)
plt.barh(range(n_features), val_scores, align='center')
plt.yticks(np.arange(n_features), names)
plt.xlabel("Accuracy")
plt.ylabel("Model")
path = WORKING_PATH + "/___comparison_cancer_model.png"
plt.savefig(path)
plt.clf()
return path
def plotting_feature_importance(importance, model):
"""Plot the feature importances of the forest"""
std = np.std([modelo.feature_importances_ for modelo in model.estimators_],
axis=0)
index = np.argsort(feten)
plt.figure(figsize=(15, 15))
plt.title("Feature importances")
plt.barh(range(X_train.values.shape[1]),
feten[index],
color="r",
xerr=std[index],
align="center")
plt.yticks(range(X_train.values.shape[1]), index)
plt.ylim([-1, X_train.values.shape[1]])
return plt.show()
# TODO: Display an image along with the top 5 classes
model.eval()
class_names = []
# Process Image
image_path = 'input2.png'
# Give image to model to predict output
probs, classes, image = predict(image_path, model)
print('probs: {} and classes: {}'.format(probs, classes))
print(probs, classes)
print(cat_to_name)
print(model.class_to_idx)
#probs=probs.detach.numpy()
#classes=classes.detach.numpy()
print(probs, classes)
print(classes[0])
for i in classes[0]:
class_names.append(model.class_to_idx.item(int(classes[0, i])))
print(class_names)
for c in classes:
class_names.append(cat_to_name[c])
print('classnames: {}'.format(class_names))
# Show the image
ax = imshow(image)
plt.barh(probs, class_names)
plt.xlabel('Probability')
plt.title('Predicted Flower Names')
plt.show()
zz=yy.iloc[:,[5,6,7,8]] d=zz[(zz.differ_day_standard!=0)|(zz.differ_day_source_copy2!=0)|(zz.differ_day_source!=0)|(zz.differ_day_standard_copy2!=0)] standard=to_list(d["differ_day_standard"]) source=to_list(d["differ_day_source"]) source_copy2=to_list(d["differ_day_source_copy2"]) standard_copy2=to_list(d["differ_day_standard_copy2"]) zhaoyang=[0]*len(standard) y=range(len(standard)) x1=sorted(standard) x2=sorted(source) x3=sorted(source_copy2) x4=sorted(standard_copy2) plt.barh(range(len(x1)), x1) plt.barh(range(len(x2)), x2) plt.barh(range(len(x3)), x3) plt.barh(range(len(x4)), x4) plt.show()
def main(argv):
print("hello")
# 读取表格
data = xlrd.open_workbook("developers.xlsx")
# 获取表格的sheets
table = data.sheets()[0]
# 输出行数量
print(table.nrows) # 8
# 输出列数量
print(table.ncols) # 4
# 获取第一行数据
row1data = table.row_values(0)
print(row1data) # ['列1', '列2', '列3', '列4']
print(row1data[0]) # 列1
from pyecharts.charts import Bar
# 读取表格
# data = xlrd.open_workbook("developers.xlsx")
# 获取表格的sheets
table = data.sheets()[0]
# 输出行数量
print(table.nrows)
# 输出列数量
print(table.ncols)
# 获取第一行数据
row1data = table.row_values(0)
print(row1data) # ['列1', '列2', '列3', '列4']
print(row1data[0]) # 列1
xdata = []
ydata = []
for i in range(1, table.nrows):
print(table.row_values(i))
xdata.append(table.row_values(i)[0])
ydata.append(table.row_values(i)[1])
print(xdata)
print(ydata)
# 数据可视化，柱状图
bar = Bar()
bar.add_xaxis(xdata)
bar.add_yaxis("名称1", ydata)
bar.render("show.html")
plt.bar(xdata, ydata)
x = [2010, 2011, 2012, 2013, 2014, 2015, 2016, 2017, 2018, 2019]
y = [5, 3, 6, 20, 17, 16, 19, 30, 32, 35]
plt.plot(x, y)
plt.show()
a = np.random.randn(100)
s = pd.Series(a)
plt.hist(s)
plt.show()
x = ['Cat1', 'Cat2', 'Cat3', 'Cat4', 'Cat5']
y = [5, 4, 8, 12, 7]
plt.bar(x, y)
plt.show()
x = ['Cat1', 'Cat2', 'Cat3', 'Cat4', 'Cat5']
y = [5, 4, 8, 12, 7]
plt.barh(x, y)
plt.show()
nums = [25, 37, 33, 37, 6]
labels = ['High-school', 'Bachelor', 'Master', 'Ph.d', 'Others']
plt.pie(x=nums, labels=labels)
plt.show()
# 生成0-1之间的10*4维度数据
data = np.random.normal(size=(10, 4))
lables = ['A', 'B', 'C', 'D']
# 用Matplotlib画箱线图
plt.boxplot(data, labels=lables)
plt.show()
# flights = sns.load_dataset("flights")
# data = flights.pivot('year', 'month', 'passengers')
# sns.heatmap(data)
# plt.show()
N = 1000
x = np.random.randn(N)
y = np.random.randn(N)
plt.scatter(x, y, marker='x')
plt.show()
N = 10000
x = np.random.randn(N)
y = np.random.randn(N)
plt.scatter(x, y, marker='x')
plt.show()
labels = np.array([u"推进", "KDA", u"生存", u"团战", u"发育", u"输出"])
stats = [83, 61, 95, 67, 76, 88]
# 画图数据准备，角度、状态值
angles = np.linspace(0, 2 * np.pi, len(labels), endpoint=False)
stats = np.concatenate((stats, [stats[0]]))
angles = np.concatenate((angles, [angles[0]]))
# 用Matplotlib画蜘蛛图
fig = plt.figure()
ax = fig.add_subplot(111, polar=True)
ax.plot(angles, stats, 'o-', linewidth=2)
ax.fill(angles, stats, alpha=0.25)
# 设置中文字体
# font = FontProperties(fname=r"/System/Library/Fonts/PingFang.ttc", size=14)
# ax.set_thetagrids(angles * 180/np.pi, labels, FontProperties=font)
plt.show()
# tips = sns.load_dataset("tips")
# tips.head(10)
# # 散点图
# sns.jointplot(x="total_bill", y="tip", data=tips, kind='scatter')
#
# # Hexbin图
# sns.jointplot(x="total_bill", y="tip", data=tips, kind='hex')
# plt.show()
df = pd.DataFrame(np.random.rand(10, 4), columns=['a', 'b', 'c', 'd'])
# 堆面积图
df.plot.area()
# 面积图
df.plot.area(stacked=False)
df = pd.DataFrame(np.random.randn(1000, 2), columns=['a', 'b'])
df['b'] = df['b'] + np.arange(1000)
# 关键字参数gridsize；它控制x方向上的六边形数量，默认为100，较大的gridsize意味着更多，更小的bin
df.plot.hexbin(x='a', y='b', gridsize=25)
print("end")
import matplotlib as plt
import seaborn as sns
sns.set()
config = configparser.ConfigParser()
config.read('willy.ini')
#
postgresql_pwd = config['POSTGRESQL'][
'PASSWORD_POSTGRESQL'] # PostgreSQL password
#
# # Make connection tot PostgreSQL database
#
db_string = 'postgresql+psycopg2://postgres:' + postgresql_pwd + '@10.10.1.35/Willy'
# # Make connection to database with correct string
#
conn = create_engine(db_string)
teprinten = pd.read_sql_query('select * from navigation_geometry', con=conn)
fig = plt.figure()
plt.barh(teprinten['id'], teprinten['linear_x'], color='blue', align='center')
plt.title('Geometrie van navigatie', fontsize=16)
plt.xlabel('ID', fontsize=13)
plt.ylabel('Lineair X', fontsize=13)
plt.show()
naam = 'geometrie,jpg'
grid.fig.tight_layout(w_pad=1)
fig.savefig(naam)
plt.show()
plot([1,2,3,4,5,6]) #y轴点图 plot([4,3,2,1],[1,2,3,4]) #(y,x)轴的值 import matplotlib.pyplot as plt x = [1,2,3,4] #some data y = [5,4,3,2] # create new figure plt.figure() plt.subplot(231) # plot折线图 plt.plot(x, y) plt.subplot(232) # 柱状图 plt.bar(x, y) plt.subplot(233) # 条状图 plt.barh(x, y) plt.subplot(234) # stacked bar charts堆叠柱状图 plt.bar(x, y) y1 = [7,8,5,3] # more data for stacked bar charts plt.bar(x, y1, bottom=y, color = 'r') # 底线[5,4,3,2] + 柱长[7,8,5,3] plt.subplot(235) # 箱线图 plt.boxplot(x) plt.subplot(236) # 散点图 plt.scatter(x,y) dataset = [113,115,119,121,124,