# This Python 3 environment comes with many helpful analytics libraries installed
# It is defined by the kaggle/python Docker image: https://github.com/kaggle/docker-python
# For example, here's several helpful packages to load

import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)

# Input data files are available in the read-only "../input/" directory
# For example, running this (by clicking run or pressing Shift+Enter) will list all files under the input directory

import os
for dirname, _, filenames in os.walk('/kaggle/input'):
    for filename in filenames:
        print(os.path.join(dirname, filename))

# You can write up to 5GB to the current directory (/kaggle/working/) that gets preserved as output when you create a version using "Save & Run All" 
# You can also write temporary files to /kaggle/temp/, but they won't be saved outside of the current session

!pip install libra


from libra import client #this is the only import you'll ever have to worry about :)

from libra import client
a_client = client('../input/preprocess-choc/dfn.csv')
print(a_client)

a_client.neural_network_query('review_date', epochs=20) #lets say I want to run this query fairly quickly, so I limit the epochs / number of iterations

import matplotlib.pyplot as plt
a_client.analyze()
plt.figure(figsize=(3,4))