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from IPython.core.display import display, HTML
display(HTML("<style>.container { width:80% !important; }</style>"))

pd.set_option('display.max_columns', None)
pd.set_option('display.max_rows', None)

from IPython.display import display, clear_output

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%matplotlib inline
from matplotlib import pyplot as plt
import numpy as np
import pandas as pd
import seaborn as sns
import lightgbm as lgb
import shap
from sklearn.model_selection import train_test_split

shap.initjs()

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new_data['date'] = new_data['date'].apply(lambda x: str(x).replace('-', '')).astype(np.float)

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## Multiple plots in one line

fig, (ax1, ax2, ax3, ax4) = plt.subplots(ncols=4, sharey=False, figsize=(20,5))
ax1.set_ylim([60,90])
sns.barplot(x="pp", y='sc_avgcr', data=high_five, ax=ax1)
ax2.set_ylim([60,90])
sns.barplot(x="pp", y='sc_avgsr', data=high_five, ax=ax2)
ax3.set_ylim([60,90])
sns.barplot(x="pp", y='sc_hisr', data=high_five, ax=ax3)
ax4.set_ylim([60,90])

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feats = ['post', 'odds']
i_start = 0
i_end = 4
total_size = len(classes)

print(classes[i_start:i_end])

while i_start < total_size:
    
    print(pclass)

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import pandas as pd
import numpy as np

def test_cutoffs(start_amount, start_price, buy_fee, sell_fee, end_price):
    bought_fee = start_amount*buy_fee
    bought_amt = (start_amount-bought_fee) / start_price
    
    sold = bought_amt*end_price
    sold_fee = sold*sell_fee
    sold = sold-sold_fee

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import matplotlib.pyplot as plt
import numpy as np
from scipy.stats import gaussian_kde

def plot_prediction_density(target, probs, figsize=(8,5),
                            title='Prediction Density Plot'):

    class_set = sorted(set(target))

    x_grid = np.linspace(0, 1, 1000)

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import matplotlib.pyplot as plt
import numpy as np
import pandas as pd

def get_bins(scores, q=10, adjust_endpoints=False):
    _, bins =  pd.qcut(scores, q=q, retbins=True)
    if adjust_endpoints:
        bins[0] = -0.1
        bins[len(bins)-1] = 1.1
    return bins

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import matplotlib.pyplot as plt
import itertools
import numpy as np
import sklearn.metrics as metrics

def get_confusion_matrix(target, preds):
        return metrics.confusion_matrix(target, preds)

def plot_confusion_matrix(cm, classes,
                          normalize=True,