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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

ClementC

import notion
from notion.client import NotionClient

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

from getpass import getpass

ClementC

import notion
from notion.client import NotionClient

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

from getpass import getpass

ClementC

# 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:

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ClementC

# coding: utf-8

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


# Twitter API token

ClementC

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()