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xgb_best = H2OXGBoostEstimator(**params_best, 
                               monotone_constraints=mono_constraints)
xgb_best.train(x=features, y=target, training_frame=training_frame,
validation_frame=validation_frame)

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corr = pd.DataFrame(train[features + 
                          [target]].corr(method='spearman')[target]).iloc[:-1]
corr.columns = ['Spearman Correlation Coefficient']
values = [int(i) for i in np.sign(corr.values)]
mono_constraints = dict(zip(corr.index, values))
mono_constraints

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import numpy as np
from matplotlib import pyplot as plt
from matplotlib.animation import FuncAnimation
plt.style.use('seaborn-pastel')


fig = plt.figure()
ax = plt.axes(xlim=(0, 4), ylim=(-2, 2))
line, = ax.plot([], [], lw=3)

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iter_ = 0 
best_error = 0
best_iter = 0
best_model = None

col_sample_rates = [0.1, 0.5, 0.9]
subsamples = [0.1, 0.5, 0.9]
etas = [0.01, 0.001]
max_depths = [3, 6, 12, 15, 18]
reg_alphas = [0.01, 0.001]

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labels_dict = {0:'sadness', 1:'joy', 2:'love', 3:'anger', 4:'fear', 5:'surprise'}
train['description'] = train['label'].map(labels_dict )
train.head()

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import numpy as np
import pandas as pd
import string

import matplotlib
import matplotlib.pyplot as plt
import seaborn as sns
sns.set(style='whitegrid', palette='muted', font_scale=1.2)
colors = ["#01BEFE", "#FFDD00", "#FF7D00", "#FF006D", "#ADFF02", "#8F00FF"]
sns.set_palette(sns.color_palette(colors))

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!wget https://www.dropbox.com/s/607ptdakxuh5i4s/merged_training.pkl

# Defining a helper function to load the data
import pickle
def load_from_pickle(directory):
return pickle.load(open(directory,"rb"))

# Loading the data
data = load_from_pickle(directory="merged_training.pkl")

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chart = ctc.Pie("Top 5 cities by the number of respondents")

chart.set_options(
 labels=list(cities.index),
 inner_radius=0.5,
    colors=['#FFF1C5','#F7B7A3','#EA5F89','#9B3192','#57167E','#47B39C','#00529B'],
 )
chart.add_series(list(cities['values'])) 

# Calling the load_javascript function when rendering chart first time.

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chart = ctc.Bar("Cities")

chart.set_options(
    labels=list(cities.index),
    x_label='City',
    y_label='Count',
    colors=['#FFF1C5','#F7B7A3','#EA5F89','#9B3192','#57167E'],
 )
chart.add_series('Count',list(cities['values']))