Clément Chastagnol ClementC

## overfitting_illustration_generator.py
import numpy as np
import matplotlib.pyplot as plt
import warnings


np.random.seed(1)   # This line is here to get a nice plot on the first try, you can comment it
warnings.simplefilter('ignore', np.RankWarning)
%matplotlib inline

# Main parameters

## search_and_replace_notion.py
import notion
from notion.client import NotionClient

from collections import Counter
from tqdm import tqdm
from pprint import pprint

from getpass import getpass


## search_and_replace_notion.py
import notion
from notion.client import NotionClient

from collections import Counter
from tqdm import tqdm
from pprint import pprint

from getpass import getpass


## save_matplotlib_animation_from_notebook.py
# Matplotlib animations can be saved as a widget in a Jupyter notebook.
# It's unclear to how to recover it in Python once the notebook kernel has been restarted.
# But the image data is there in the notebook, embedded in a JS script in the output of the cell
# with the individual frames encoded as base64 png images.
# So here's the way I found to extract the images and make a GIF out of them.
# It's not all automatic and needs some manual action.
# Steps:
#   1/ Open the notebook in a web browser. Open the developer tools, and in the console, start typing "anim"
# then TAB to autocomplete. A variable with a unique name looking like "animae693007f74d44b0b917c61003e9490f" should appear.
# Cut this variable name, and copy it in the following command that you can then execute:

## cross_val_predict_issue.ipynb

      
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                ClementC
                / cross_val_predict_issue.ipynb
            
            
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              April 26, 2017 14:57
            
              
                Minimal reproducible example with solutions proposals and benchmark.
              
          
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## check_twitter_followers.py
# coding: utf-8

import twitter
from tqdm import tqdm
from collections import defaultdict
import pickle
import pandas as pd


# Twitter API token

## print_cm.py
from sklearn.metrics import confusion_matrix

def print_cm(cm, labels):
    """pretty print for confusion matrixes"""
    columnwidth = max([len(x) for x in labels])
    # Print header
    print(" " * columnwidth, end="\t")
    for label in labels:
        print("%{0}s".format(columnwidth) % label, end="\t")
    print()
	import numpy as np
	import matplotlib.pyplot as plt
	import warnings


	np.random.seed(1) # This line is here to get a nice plot on the first try, you can comment it
	warnings.simplefilter('ignore', np.RankWarning)
	%matplotlib inline

	# Main parameters
	import notion
	from notion.client import NotionClient

	from collections import Counter
	from tqdm import tqdm
	from pprint import pprint

	from getpass import getpass
	# Matplotlib animations can be saved as a widget in a Jupyter notebook.
	# It's unclear to how to recover it in Python once the notebook kernel has been restarted.
	# But the image data is there in the notebook, embedded in a JS script in the output of the cell
	# with the individual frames encoded as base64 png images.
	# So here's the way I found to extract the images and make a GIF out of them.
	# It's not all automatic and needs some manual action.
	# Steps:
	# 1/ Open the notebook in a web browser. Open the developer tools, and in the console, start typing "anim"
	# then TAB to autocomplete. A variable with a unique name looking like "animae693007f74d44b0b917c61003e9490f" should appear.
	# Cut this variable name, and copy it in the following command that you can then execute:
	# coding: utf-8

	import twitter
	from tqdm import tqdm
	from collections import defaultdict
	import pickle
	import pandas as pd


	# Twitter API token
	from sklearn.metrics import confusion_matrix

	def print_cm(cm, labels):
	"""pretty print for confusion matrixes"""
	columnwidth = max([len(x) for x in labels])
	# Print header
	print(" " * columnwidth, end="\t")
	for label in labels:
	print("%{0}s".format(columnwidth) % label, end="\t")
	print()