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Given the following text description, write Python code to implement the functionality described below step by step
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Description:
 Visualization Tutorial
Inside metal/contrib/visualization are a number of simple helper methods for visualizing label matrices.
For example, you can generate heat maps of the label matrix or overlaps/conflicts.
Step1:  We reload the synthetic data from the basics tutorial.
Step2:  In this case, there isn't a lot to see, since these label matrices were created synthetically. However, these visualizations can often help you to discover unexpected phenomena in your label matrices, such as unexpectedly high or low conflict rates between certain labeling functions, or portions of your dataset with surprisingly low or high coverage.
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Python Code:
import sys
sys.path.append('../../metal')
import metal
%load_ext autoreload
%autoreload 2
%matplotlib inline
Explanation: Visualization Tutorial
Inside metal/contrib/visualization are a number of simple helper methods for visualizing label matrices.
For example, you can generate heat maps of the label matrix or overlaps/conflicts.
End of explanation
import pickle
with open("data/basics_tutorial.pkl", 'rb') as f:
    X, Y, L, D = pickle.load(f)
    
from metal.utils import split_data
Ls = split_data(L, splits=[0.8, 0.1, 0.1], seed=123)
Explanation: We reload the synthetic data from the basics tutorial.
End of explanation
import os
from metal.contrib.visualization.analysis import view_label_matrix
# This if statement and others like it are for our continuous integration tests; you can ignore them.
if 'CI' not in os.environ:
    view_label_matrix(Ls[0])
from metal.contrib.visualization.analysis import view_overlaps
if 'CI' not in os.environ:
    view_overlaps(Ls[0])
from metal.contrib.visualization.analysis import view_conflicts
if 'CI' not in os.environ:
    view_conflicts(Ls[0], normalize=False)
Explanation: In this case, there isn't a lot to see, since these label matrices were created synthetically. However, these visualizations can often help you to discover unexpected phenomena in your label matrices, such as unexpectedly high or low conflict rates between certain labeling functions, or portions of your dataset with surprisingly low or high coverage.
End of explanation
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