def data_plot(customers):
customers.head()
customers.describe()
customers.info()
sns.set_palette("GnBu_d")
sns.set_style('whitegrid')
sns.jointplot(x='Time on Website', y='Yearly Amount Spent', data=customers)
sns.show()
sns.jointplot(x='Time on App', y='Yearly Amount Spent', data=customers)
sns.show()
sns.jointplot(x='Time on App',
y='Length of Membership',
kind='hex',
data=customers)
sns.show()
sns.pairplot(customers)
sns.show()
sns.lmplot(x='Length of Membership',
y='Yearly Amount Spent',
data=customers)
sns.show()
def data_plot(df):
sns.set_style('whitegrid')
sns.lmplot('Room.Board',
'Grad.Rate',
data=df,
hue='Private',
palette='coolwarm',
size=6,
aspect=1,
fit_reg=False)
sns.show()
sns.set_style('whitegrid')
sns.lmplot('Outstate',
'F.Undergrad',
data=df,
hue='Private',
palette='coolwarm',
size=6,
aspect=1,
fit_reg=False)
sns.show()
sns.set_style('darkgrid')
g = sns.FacetGrid(df, hue="Private", palette='coolwarm', size=6, aspect=2)
g = g.map(plt.hist, 'Outstate', bins=20, alpha=0.7)
sns.show()
sns.set_style('darkgrid')
g = sns.FacetGrid(df, hue="Private", palette='coolwarm', size=6, aspect=2)
g = g.map(plt.hist, 'Grad.Rate', bins=20, alpha=0.7)
sns.show()
def data_plot(ad_data):
sns.set_style('whitegrid')
ad_data['Age'].hist(bins=30)
plt.xlabel('Age')
sns.jointplot(x='Age',y='Area Income',data=ad_data)
sns.show()
sns.jointplot(x='Age',y='Daily Time Spent on Site',data=ad_data,color='red',kind='kde');
sns.show()
sns.jointplot(x='Daily Time Spent on Site',y='Daily Internet Usage',data=ad_data,color='green')
sns.show()
sns.pairplot(ad_data,hue='Clicked on Ad',palette='bwr')
sns.show()
def plot_data(yelp):
sns.set_style('white')
get_ipython().run_line_magic('matplotlib', 'inline')
sns.show()
g = sns.FacetGrid(yelp,col='stars')
g.map(plt.hist,'text length')
sns.show()
sns.boxplot(x='stars',y='text length',data=yelp,palette='rainbow')
sns.show()
sns.countplot(x='stars',data=yelp,palette='rainbow')
sns.show()
stars = yelp.groupby('stars').mean()
stars.corr().show()
sns.heatmap(stars.corr(),cmap='coolwarm',annot=True).show()
merged_model.save("whole_model.h5")
f = open("binarizer.pkl", "wb")
f.write(pickle.dumps(lb))
f.close()
print("Saved model to disk")
predicted_classes = merged_model.predict_classes(test_imgs)
cm = confusion_matrix([np.where(r == 1)[0][0]
for r in labels_test], predicted_classes)
plt.figure(figsize=(14, 10))
sns.heatmap(cm, annot=True)
sns.show()
sns.savefig("confusion_matrix.png")
plt.style.use("ggplot")
plt.figure(figsize=(14, 10))
N = EPOCHS
plt.plot(np.arange(0, N), train.history["loss"], label="train_loss")
plt.plot(np.arange(0, N), train.history["val_loss"], label="val_loss")
plt.plot(np.arange(0, N), train.history["acc"], label="acc")
plt.plot(np.arange(0, N), train.history["val_acc"], label="acc")
plt.title("Training Loss and Accuracy")
plt.xlabel("Epoch #")
plt.ylabel("Loss/Accuracy")
plt.legend(loc="upper left")
plt.show()
#!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Created on Mon Mar 18 09:57:49 2019
@author: chance
"""
import matplotlib.pyplot as plt
import seaborn as sns
# data prepare
iris = sns.load_dataset("iris")
# use seaborn
sns.pairplot(iris)
sns.show(s)