In [2]:

    

import numpy as np
import pandas as pd
import sys
import sys
import os
sys.path.append('../')
os.getcwd()
import matplotlib.pyplot as plt 
import seaborn as sns
sns.set_style('white')
%matplotlib inline


    

In [3]:

    

import src
import src.recommendation
reload(src.recommendation)
from src.recommendation import *


    

In [102]:

    

# our lib
from lib.resnet50 import ResNet50
from lib.imagenet_utils import preprocess_input, decode_predictions

#keras 
from keras.preprocessing import image
from keras.models import Model

import glob

def preprocess_img(img_path):
    img = image.load_img(img_path, target_size=(224, 224))
    x = image.img_to_array(img)
    x = np.expand_dims(x, axis=0)
    x = preprocess_input(x)
    return(x,img)


    

In [100]:

    

# instantiate the model
base_model = ResNet50(include_top=False, weights='imagenet') #this will pull the weights from the folder 

# cut the model to lower levels only 
model = Model(input=base_model.input, output=base_model.get_layer('avg_pool').output)


    

    




../lib/resnet50.py:165: UserWarning: Update your `Conv2D` call to the Keras 2 API: `Conv2D(64, (7, 7), strides=(2, 2), name="conv1")`
  x = Convolution2D(64, 7, 7, subsample=(2, 2), name='conv1')(x)
../lib/resnet50.py:90: UserWarning: Update your `Conv2D` call to the Keras 2 API: `Conv2D(64, (1, 1), strides=(1, 1), name="res2a_branch2a")`
  name=conv_name_base + '2a')(input_tensor)
../lib/resnet50.py:95: UserWarning: Update your `Conv2D` call to the Keras 2 API: `Conv2D(64, (3, 3), padding="same", name="res2a_branch2b")`
  name=conv_name_base + '2b')(x)
../lib/resnet50.py:99: UserWarning: Update your `Conv2D` call to the Keras 2 API: `Conv2D(256, (1, 1), name="res2a_branch2c")`
  x = Convolution2D(nb_filter3, 1, 1, name=conv_name_base + '2c')(x)
../lib/resnet50.py:103: UserWarning: Update your `Conv2D` call to the Keras 2 API: `Conv2D(256, (1, 1), strides=(1, 1), name="res2a_branch1")`
  name=conv_name_base + '1')(input_tensor)
../lib/resnet50.py:106: UserWarning: The `merge` function is deprecated and will be removed after 08/2017. Use instead layers from `keras.layers.merge`, e.g. `add`, `concatenate`, etc.
  x = merge([x, shortcut], mode='sum')
/Users/chris/anaconda/envs/virtenv/lib/python2.7/site-packages/keras/legacy/layers.py:456: UserWarning: The `Merge` layer is deprecated and will be removed after 08/2017. Use instead layers from `keras.layers.merge`, e.g. `add`, `concatenate`, etc.
  name=name)
../lib/resnet50.py:51: UserWarning: Update your `Conv2D` call to the Keras 2 API: `Conv2D(64, (1, 1), name="res2b_branch2a")`
  x = Convolution2D(nb_filter1, 1, 1, name=conv_name_base + '2a')(input_tensor)
../lib/resnet50.py:56: UserWarning: Update your `Conv2D` call to the Keras 2 API: `Conv2D(64, (3, 3), padding="same", name="res2b_branch2b")`
  border_mode='same', name=conv_name_base + '2b')(x)
../lib/resnet50.py:60: UserWarning: Update your `Conv2D` call to the Keras 2 API: `Conv2D(256, (1, 1), name="res2b_branch2c")`
  x = Convolution2D(nb_filter3, 1, 1, name=conv_name_base + '2c')(x)
../lib/resnet50.py:63: UserWarning: The `merge` function is deprecated and will be removed after 08/2017. Use instead layers from `keras.layers.merge`, e.g. `add`, `concatenate`, etc.
  x = merge([x, input_tensor], mode='sum')
../lib/resnet50.py:51: UserWarning: Update your `Conv2D` call to the Keras 2 API: `Conv2D(64, (1, 1), name="res2c_branch2a")`
  x = Convolution2D(nb_filter1, 1, 1, name=conv_name_base + '2a')(input_tensor)
../lib/resnet50.py:56: UserWarning: Update your `Conv2D` call to the Keras 2 API: `Conv2D(64, (3, 3), padding="same", name="res2c_branch2b")`
  border_mode='same', name=conv_name_base + '2b')(x)
../lib/resnet50.py:60: UserWarning: Update your `Conv2D` call to the Keras 2 API: `Conv2D(256, (1, 1), name="res2c_branch2c")`
  x = Convolution2D(nb_filter3, 1, 1, name=conv_name_base + '2c')(x)
../lib/resnet50.py:90: UserWarning: Update your `Conv2D` call to the Keras 2 API: `Conv2D(128, (1, 1), strides=(2, 2), name="res3a_branch2a")`
  name=conv_name_base + '2a')(input_tensor)
../lib/resnet50.py:95: UserWarning: Update your `Conv2D` call to the Keras 2 API: `Conv2D(128, (3, 3), padding="same", name="res3a_branch2b")`
  name=conv_name_base + '2b')(x)
../lib/resnet50.py:99: UserWarning: Update your `Conv2D` call to the Keras 2 API: `Conv2D(512, (1, 1), name="res3a_branch2c")`
  x = Convolution2D(nb_filter3, 1, 1, name=conv_name_base + '2c')(x)
../lib/resnet50.py:103: UserWarning: Update your `Conv2D` call to the Keras 2 API: `Conv2D(512, (1, 1), strides=(2, 2), name="res3a_branch1")`
  name=conv_name_base + '1')(input_tensor)
../lib/resnet50.py:51: UserWarning: Update your `Conv2D` call to the Keras 2 API: `Conv2D(128, (1, 1), name="res3b_branch2a")`
  x = Convolution2D(nb_filter1, 1, 1, name=conv_name_base + '2a')(input_tensor)
../lib/resnet50.py:56: UserWarning: Update your `Conv2D` call to the Keras 2 API: `Conv2D(128, (3, 3), padding="same", name="res3b_branch2b")`
  border_mode='same', name=conv_name_base + '2b')(x)
../lib/resnet50.py:60: UserWarning: Update your `Conv2D` call to the Keras 2 API: `Conv2D(512, (1, 1), name="res3b_branch2c")`
  x = Convolution2D(nb_filter3, 1, 1, name=conv_name_base + '2c')(x)
../lib/resnet50.py:51: UserWarning: Update your `Conv2D` call to the Keras 2 API: `Conv2D(128, (1, 1), name="res3c_branch2a")`
  x = Convolution2D(nb_filter1, 1, 1, name=conv_name_base + '2a')(input_tensor)
../lib/resnet50.py:56: UserWarning: Update your `Conv2D` call to the Keras 2 API: `Conv2D(128, (3, 3), padding="same", name="res3c_branch2b")`
  border_mode='same', name=conv_name_base + '2b')(x)
../lib/resnet50.py:60: UserWarning: Update your `Conv2D` call to the Keras 2 API: `Conv2D(512, (1, 1), name="res3c_branch2c")`
  x = Convolution2D(nb_filter3, 1, 1, name=conv_name_base + '2c')(x)
../lib/resnet50.py:51: UserWarning: Update your `Conv2D` call to the Keras 2 API: `Conv2D(128, (1, 1), name="res3d_branch2a")`
  x = Convolution2D(nb_filter1, 1, 1, name=conv_name_base + '2a')(input_tensor)
../lib/resnet50.py:56: UserWarning: Update your `Conv2D` call to the Keras 2 API: `Conv2D(128, (3, 3), padding="same", name="res3d_branch2b")`
  border_mode='same', name=conv_name_base + '2b')(x)
../lib/resnet50.py:60: UserWarning: Update your `Conv2D` call to the Keras 2 API: `Conv2D(512, (1, 1), name="res3d_branch2c")`
  x = Convolution2D(nb_filter3, 1, 1, name=conv_name_base + '2c')(x)
../lib/resnet50.py:90: UserWarning: Update your `Conv2D` call to the Keras 2 API: `Conv2D(256, (1, 1), strides=(2, 2), name="res4a_branch2a")`
  name=conv_name_base + '2a')(input_tensor)
../lib/resnet50.py:95: UserWarning: Update your `Conv2D` call to the Keras 2 API: `Conv2D(256, (3, 3), padding="same", name="res4a_branch2b")`
  name=conv_name_base + '2b')(x)
../lib/resnet50.py:99: UserWarning: Update your `Conv2D` call to the Keras 2 API: `Conv2D(1024, (1, 1), name="res4a_branch2c")`
  x = Convolution2D(nb_filter3, 1, 1, name=conv_name_base + '2c')(x)
../lib/resnet50.py:103: UserWarning: Update your `Conv2D` call to the Keras 2 API: `Conv2D(1024, (1, 1), strides=(2, 2), name="res4a_branch1")`
  name=conv_name_base + '1')(input_tensor)
../lib/resnet50.py:51: UserWarning: Update your `Conv2D` call to the Keras 2 API: `Conv2D(256, (1, 1), name="res4b_branch2a")`
  x = Convolution2D(nb_filter1, 1, 1, name=conv_name_base + '2a')(input_tensor)
../lib/resnet50.py:56: UserWarning: Update your `Conv2D` call to the Keras 2 API: `Conv2D(256, (3, 3), padding="same", name="res4b_branch2b")`
  border_mode='same', name=conv_name_base + '2b')(x)
../lib/resnet50.py:60: UserWarning: Update your `Conv2D` call to the Keras 2 API: `Conv2D(1024, (1, 1), name="res4b_branch2c")`
  x = Convolution2D(nb_filter3, 1, 1, name=conv_name_base + '2c')(x)
../lib/resnet50.py:51: UserWarning: Update your `Conv2D` call to the Keras 2 API: `Conv2D(256, (1, 1), name="res4c_branch2a")`
  x = Convolution2D(nb_filter1, 1, 1, name=conv_name_base + '2a')(input_tensor)
../lib/resnet50.py:56: UserWarning: Update your `Conv2D` call to the Keras 2 API: `Conv2D(256, (3, 3), padding="same", name="res4c_branch2b")`
  border_mode='same', name=conv_name_base + '2b')(x)
../lib/resnet50.py:60: UserWarning: Update your `Conv2D` call to the Keras 2 API: `Conv2D(1024, (1, 1), name="res4c_branch2c")`
  x = Convolution2D(nb_filter3, 1, 1, name=conv_name_base + '2c')(x)
../lib/resnet50.py:51: UserWarning: Update your `Conv2D` call to the Keras 2 API: `Conv2D(256, (1, 1), name="res4d_branch2a")`
  x = Convolution2D(nb_filter1, 1, 1, name=conv_name_base + '2a')(input_tensor)
../lib/resnet50.py:56: UserWarning: Update your `Conv2D` call to the Keras 2 API: `Conv2D(256, (3, 3), padding="same", name="res4d_branch2b")`
  border_mode='same', name=conv_name_base + '2b')(x)
../lib/resnet50.py:60: UserWarning: Update your `Conv2D` call to the Keras 2 API: `Conv2D(1024, (1, 1), name="res4d_branch2c")`
  x = Convolution2D(nb_filter3, 1, 1, name=conv_name_base + '2c')(x)
../lib/resnet50.py:51: UserWarning: Update your `Conv2D` call to the Keras 2 API: `Conv2D(256, (1, 1), name="res4e_branch2a")`
  x = Convolution2D(nb_filter1, 1, 1, name=conv_name_base + '2a')(input_tensor)
../lib/resnet50.py:56: UserWarning: Update your `Conv2D` call to the Keras 2 API: `Conv2D(256, (3, 3), padding="same", name="res4e_branch2b")`
  border_mode='same', name=conv_name_base + '2b')(x)
../lib/resnet50.py:60: UserWarning: Update your `Conv2D` call to the Keras 2 API: `Conv2D(1024, (1, 1), name="res4e_branch2c")`
  x = Convolution2D(nb_filter3, 1, 1, name=conv_name_base + '2c')(x)
../lib/resnet50.py:51: UserWarning: Update your `Conv2D` call to the Keras 2 API: `Conv2D(256, (1, 1), name="res4f_branch2a")`
  x = Convolution2D(nb_filter1, 1, 1, name=conv_name_base + '2a')(input_tensor)
../lib/resnet50.py:56: UserWarning: Update your `Conv2D` call to the Keras 2 API: `Conv2D(256, (3, 3), padding="same", name="res4f_branch2b")`
  border_mode='same', name=conv_name_base + '2b')(x)
../lib/resnet50.py:60: UserWarning: Update your `Conv2D` call to the Keras 2 API: `Conv2D(1024, (1, 1), name="res4f_branch2c")`
  x = Convolution2D(nb_filter3, 1, 1, name=conv_name_base + '2c')(x)
../lib/resnet50.py:90: UserWarning: Update your `Conv2D` call to the Keras 2 API: `Conv2D(512, (1, 1), strides=(2, 2), name="res5a_branch2a")`
  name=conv_name_base + '2a')(input_tensor)
../lib/resnet50.py:95: UserWarning: Update your `Conv2D` call to the Keras 2 API: `Conv2D(512, (3, 3), padding="same", name="res5a_branch2b")`
  name=conv_name_base + '2b')(x)
../lib/resnet50.py:99: UserWarning: Update your `Conv2D` call to the Keras 2 API: `Conv2D(2048, (1, 1), name="res5a_branch2c")`
  x = Convolution2D(nb_filter3, 1, 1, name=conv_name_base + '2c')(x)
../lib/resnet50.py:103: UserWarning: Update your `Conv2D` call to the Keras 2 API: `Conv2D(2048, (1, 1), strides=(2, 2), name="res5a_branch1")`
  name=conv_name_base + '1')(input_tensor)
../lib/resnet50.py:51: UserWarning: Update your `Conv2D` call to the Keras 2 API: `Conv2D(512, (1, 1), name="res5b_





    





limit_output extension: Maximum message size of 10000 exceeded with 10692 characters

In [110]:

    

user_id = 106144465 

#get images 
folder = '../data_img_sample_item_view_sequences/'
img_files = glob.glob(folder+'*'+str(user_id)+'*')
print(img_files)


# make features 
trajectory_features = np.empty((len(img_files),2048))
for i,img_file in enumerate(img_files):
    x,img = preprocess_img(img_file) # preprocess
    trajectory_features[i,:] = model.predict(x)[0,0,0,:]


    

    




['../data_img_sample_item_view_sequences/106144465_1_82548633538732181_0_view_.jpg', '../data_img_sample_item_view_sequences/106144465_2_310261911240101889_11_view_.jpg', '../data_img_sample_item_view_sequences/106144465_3_75793218577661959_2_view_.jpg', '../data_img_sample_item_view_sequences/106144465_4_76074708321099781_5_view_.jpg', '../data_img_sample_item_view_sequences/106144465_5_98029795868614676_4_view_.jpg', '../data_img_sample_item_view_sequences/106144465_6_22313013691846692_3_view_.jpg', '../data_img_sample_item_view_sequences/106144465_7_467887899726037038_4_view_.jpg', '../data_img_sample_item_view_sequences/106144465_8_216812239953170449_21_buy_.jpg']

In [111]:

    

red_traj = np.dot(trajectory_features,red_weights)


    

In [145]:

    

print('target class')
plt.figure(figsize=(12,6))
len_seq = len(img_files)

fig,axes = plt.subplots(2,len_seq)

# make color
color_red_black = pd.Series(red_traj>0).map({False:'k',True:'r'}).as_matrix()

for i in range(len_seq):
    img = image.load_img(img_files[i], target_size=(224, 224))

    # images
    axes[0,i].imshow(img)
    axes[0,i].set_xticklabels([])
    #axes[0,i].get_xaxis().set_visible(False)
    axes[0,i].get_xaxis().set_ticks([])
    axes[0,i].get_yaxis().set_visible(False) 
    if i<(len_seq-1):
        axes[0,i].set_xlabel('view '+str(i))
    else:
        axes[0,i].set_xlabel('buy')
         
    # bar 
    axes[1,i].bar(0,red_traj[i],color=color_red_black[i])
    axes[1,i].set_ylim([-10,5])
    axes[1,i].get_xaxis().set_visible(False)
    axes[1,i].axhline(y=0,linestyle='--',color='w')
    if i==0:
        print('here')
        axes[1,i].set_ylabel('red classification')
    else:
        axes[1,i].get_yaxis().set_visible(False) 
    sns.despine()
savefile = '../figures/example_sequence_interpretable_features_ui_'+str(user_id)+'.png'
plt.savefig(savefile,dpi=300)


    

    




target class
here






    






<matplotlib.figure.Figure at 0x1202735d0>






    

In [152]:

    

reload(src.s3_data_management)
from src import s3_data_management
s3_data_management.push_results_to_s3(os.path.basename(savefile),savefile)


    

    




.........
https://s3-us-west-2.amazonaws.com/bishopbucket/proj_cdips/Results/example_sequence_interpretable_features_ui_106144465.png

In [4]:

    

# load weights from the nn
red_weights = np.loadtxt('../data_nn_features/class_weights_LR_redpink.txt')


    

In [5]:

    

# load smaller user behavior dataset
user_profile = pd.read_pickle('../data_user_view_buy/user_profile_items_nonnull_features_20_mins_5_views_v2_sample1000.pkl')
user_sample = user_profile.user_id.unique()
print(len(user_profile))
print(len(user_sample))
user_profile.head()


    

    




40141
961






    
Out[5]:







  

    

      

      
user_id
      
buy_spu
      
buy_sn
      
buy_ct3
      
view_spu
      
view_sn
      
view_ct3
      
time_interval
      
view_cnt
      
view_seconds
      
index
      
spu_id
      
view_spu_count
      
drop
    
  
  

    

      
1226
      
3787002243
      
14994645517246498
      
10006541
      
334
      
221597279072104476
      
10005949
      
334
      
389705
      
4
      
98
      
87616.0
      
2.215973e+17
      
11
      
0
    
    

      
1227
      
3787002243
      
14994645517246498
      
10006541
      
334
      
29349802448457744
      
10004542
      
334
      
7342
      
1
      
10
      
73880.0
      
2.934980e+16
      
11
      
0
    
    

      
2261
      
132681117
      
6831894477217820
      
10001155
      
334
      
27661017974767638
      
10004048
      
334
      
32451
      
1
      
6
      
21676.0
      
2.766102e+16
      
63
      
0
    
    

      
2262
      
132681117
      
6831894477217820
      
10001155
      
334
      
288869796837830741
      
10000351
      
334
      
33318
      
2
      
30
      
6475.0
      
2.888698e+17
      
63
      
0
    
    

      
2263
      
132681117
      
6831894477217820
      
10001155
      
334
      
301817675098247170
      
10020640
      
334
      
428149
      
1
      
9
      
25612.0
      
3.018177e+17
      
63
      
0
    
  

In [6]:

    

# read nn features 
spu_fea = pd.read_pickle("../data_nn_features/spu_fea_sample1000.pkl")


    

In [7]:

    

spu_fea.head()


    

    
Out[7]:







  

    

      

      
index
      
spu_id
      
features
      
view_spu
    
  
  

    

      
2
      
4
      
100281553839460375
      
[0.661, 1.84, 0.028, 1.128, 0.172, 1.097, 1.45...
      
100281553839460375
    
    

      
10
      
17
      
102533309850574975
      
[0.351, 0.066, 0.044, 0.648, 0.011, 0.0, 0.207...
      
102533309850574975
    
    

      
11
      
18
      
102533356906119172
      
[0.474, 0.903, 0.03, 0.495, 0.01, 0.003, 0.2, ...
      
102533356906119172
    
    

      
16
      
26
      
1029993181497487363
      
[0.029, 0.347, 0.175, 0.789, 0.09, 0.0, 0.181,...
      
1029993181497487363
    
    

      
17
      
27
      
103096330169212941
      
[1.117, 0.826, 0.01, 0.058, 0.251, 0.783, 0.12...
      
103096330169212941
    
  

In [32]:

    

# sample users
size = 10
np.random.seed(1000)
user_ids = np.random.choice(user_profile.user_id.unique(),size=size)


fig,axes = plt.subplots(size,1,figsize=(16,3*size),sharex=True,sharey=True)

for ui,user_id in enumerate(user_ids):
    # get his trajectory
    trajectory = user_profile.loc[user_profile.user_id==user_id,]
    
    # get trajectory features (make a separate function # )
    trajectory_features = np.empty((len(trajectory),2048))
    for i,(index,row) in enumerate(trajectory.iterrows()):
        trajectory_features[i,:] = spu_fea.loc[spu_fea.spu_id==row['view_spu'],'features'].as_matrix()[0]
        
    # project onto red dimension
    red_traj = np.dot(trajectory_features,red_weights)

    # plot 
    axes[ui].plot(np.arange(len(red_traj)),red_traj)
    axes[ui].axhline(y=0,linestyle='--',color='k')
    axes[ui].set_ylabel('red features')
    sns.despine()
plt.xlabel('positition in sequence')
savefile = '../figures/example_sequences_red_10_users.png'
plt.savefig(savefile,dpi=300)


    

In [36]:

    

from sklearn.decomposition import PCA


    

In [33]:

    

# load smaller user behavior dataset
user_profile = pd.read_pickle('../data_user_view_buy/user_profile_items_nonnull_features_20_mins_5_views_v2_sample1000.pkl')
user_sample = user_profile.user_id.unique()
print(len(user_profile))
print(len(user_sample))
user_profile.head()


    

    




40141
961






    
Out[33]:







  

    

      

      
user_id
      
buy_spu
      
buy_sn
      
buy_ct3
      
view_spu
      
view_sn
      
view_ct3
      
time_interval
      
view_cnt
      
view_seconds
      
index
      
spu_id
      
view_spu_count
      
drop
    
  
  

    

      
1226
      
3787002243
      
14994645517246498
      
10006541
      
334
      
221597279072104476
      
10005949
      
334
      
389705
      
4
      
98
      
87616.0
      
2.215973e+17
      
11
      
0
    
    

      
1227
      
3787002243
      
14994645517246498
      
10006541
      
334
      
29349802448457744
      
10004542
      
334
      
7342
      
1
      
10
      
73880.0
      
2.934980e+16
      
11
      
0
    
    

      
2261
      
132681117
      
6831894477217820
      
10001155
      
334
      
27661017974767638
      
10004048
      
334
      
32451
      
1
      
6
      
21676.0
      
2.766102e+16
      
63
      
0
    
    

      
2262
      
132681117
      
6831894477217820
      
10001155
      
334
      
288869796837830741
      
10000351
      
334
      
33318
      
2
      
30
      
6475.0
      
2.888698e+17
      
63
      
0
    
    

      
2263
      
132681117
      
6831894477217820
      
10001155
      
334
      
301817675098247170
      
10020640
      
334
      
428149
      
1
      
9
      
25612.0
      
3.018177e+17
      
63
      
0
    
  

In [34]:

    

# read nn features 
spu_fea = pd.read_pickle("../data_nn_features/spu_fea_sample1000.pkl")


    

In [ ]:

    

# reduce dimensionality 
pca = pickle.load(open('../data_nn_features/pca_all_items_sample1000.pkl','rb'))


    

In [41]:

    

pca.components_.shape


    

    
Out[41]:





(1000, 2048)

In [65]:

    

projection = pca.transform(X_item_feature[0,:].reshape(-1,1).T)
projection.shape
plt.plot(np.arange(100),projection[0,0:100])
plt.xlabel('component')
plt.ylabel('projection')
sns.despine()


    

In [73]:

    

# sample users
size = 10
np.random.seed(1000)
user_ids = np.random.choice(user_profile.user_id.unique(),size=size)


fig,axes = plt.subplots(size,1,figsize=(16,3*size),sharex=True,sharey=True)

for ui,user_id in enumerate(user_ids):
    # get his trajectory
    trajectory = user_profile.loc[user_profile.user_id==user_id,]
    
    # get trajectory features (make a separate function # )
    trajectory_features = np.empty((len(trajectory),2048))
    for i,(index,row) in enumerate(trajectory.iterrows()):
        trajectory_features[i,:] = spu_fea.loc[spu_fea.spu_id==row['view_spu'],'features'].as_matrix()[0]
        
    # project onto pca dimension
    projected_traj = pca.transform(trajectory_features)
    
    # get first dimension 
    traj_PC1 = projected_traj[:,0]
    traj_PC2 = projected_traj[:,1]
    traj_PC3 = projected_traj[:,2]

    # plot 
    axes[ui].plot(traj_PC1,label='PC1')
    axes[ui].plot(traj_PC2,label='PC2')
    axes[ui].plot(traj_PC3,label='PC3')
    
    plt.legend()
    
    axes[ui].axhline(y=0,linestyle='--',color='k')
    axes[ui].set_ylabel('red features')
    sns.despine()
plt.xlabel('positition in sequence')
savefile = '../figures/example_sequences_PCA_10_users.png'
plt.savefig(savefile,dpi=300)


    

In [153]:

    

%%bash 
#jupyter nbconvert --to Plotting_Sequences_in_low_dimensions.ipynb && mv Plotting_Sequences_in_low_dimensions.slides.html ../notebook_slides/Plotting_Sequences_in_low_dimensions_v1.slides.html
jupyter nbconvert --to html Plotting_Sequences_in_low_dimensions.ipynb && mv Exploring_Data.html ../notebook_htmls/Plotting_Sequences_in_low_dimensions_v1.html
cp Plotting_Sequences_in_low_dimensions.ipynb ../notebook_versions/Plotting_Sequences_in_low_dimensions_v1.ipynb


    

    




[NbConvertApp] Converting notebook Plotting_Sequences_in_low_dimensions.ipynb to html
[NbConvertApp] Writing 357480 bytes to Plotting_Sequences_in_low_dimensions.html
mv: rename Exploring_Data.html to ../notebook_htmls/Plotting_Sequences_in_low_dimensions_v1.html: No such file or directory

In [ ]: