In this Python tutorial, we will learn How to create a scikit learn Markov model in python and we will also cover these examples related to the Markov model. And, we will cover these topics.
- What is scikit learn Markov model?
- What made scikit learn Markov model hidden
- Scikit learn hidden Markov model example
What is scikit learn Markov model?
In this section, we will learn about the Scikit learn Markov model in python and how its works.
A Markov model is defined as a stochastic process whereas the considered probability of the future state depends upon the current process state.
Example:
Consider you want to make a model future probability that our cat is in the three-state given its current state.
We consider the cat we have been very lazy. We define its state sleeping eating, Pooping. We set the initial probabilities to 45%,35%, and20% resp.
Code:
import numpy as np
import pandas as pd
import networkx as nx
import matplotlib.pyplot as plot
%matplotlib inline
states = ['sleeping', 'eating', 'pooping']
pi = [0.45, 0.35, 0.2]
state_space = pd.Series(pi, index=states, name='states')
print(state_space)
print(state_space.sum())
After running the above code we get the following output in which we can see that the probability of the assuming state of the cat.
The next step is to define the probability simply in the same state or moving to a different state given the current state.
q_df = pd.DataFrame(columns=states, index=states)
q_df.loc[states[0]] = [0.4, 0.2, 0.4]
q_df.loc[states[1]] = [0.40, 0.40, 0.2]
q_df.loc[states[2]] = [0.40, 0.20, .4]
print(q_df)
q_f = q_df.values
print('\n', q_f, q_f.shape, '\n')
print(q_df.sum(axis=1))
After doing the above process we have initial and transition probabilities and now we are creating a Markov diagram using Networkx package.
from pprint import pprint
def _get_markov_edges(Q):
edge = {}
for column in Q.columns:
for index in Q.index:
edge[(index,column)] = Q.loc[index,column]
return edge
edge_wt = _get_markov_edges(q_df)
pprint(edge_wt)
Now we can create a scikit learn Markov model graph.
- Graph.add_nodes_from(states) is used to create nodes corresponding to the states.
- Graph.add_edge(tmp_origin, tmp_destination, weight=v, label=v) edges represent the transition probabilities.
Graph = nx.MultiDiGraph()
Graph.add_nodes_from(states)
print(f'Nodes:\n{Graph.nodes()}\n')
for k, v in edge_wt.items():
tmp_origin, tmp_destination = k[0], k[1]
Graph.add_edge(tmp_origin, tmp_destination, weight=v, label=v)
print(f'Edges:')
pprint(Graph.edges(data=True))
position = nx.drawing.nx_pydot.graphviz_layout(Graph, prog='dot')
nx.draw_networkx(Graph, position)
edge_labels = {(n1,n2):d['label'] for n1,n2,d in Graph.edges(data=True)}
nx.draw_networkx_edge_labels(Graph , position, edge_labels=edge_labels)
nx.drawing.nx_pydot.write_dot(Graph, 'pet_dog_markov.dot')
Read: Scikit-learn logistic regression
In this section, we will learn about the scikit learn model hidden and who made the Markov model hidden.
Consider that our cat is acting strangely and we find that why they act like that our cat behavior is due to sickness or simply they act like that.
Code:
In this, we create a space in which we can see that our cat is healthy or sick.
hidden_state = ['healthy', 'sick']
pi = [0.55, 0.45]
state_space = pd.Series(pi, index=hidden_state, name='states')
print(state_space)
print('\n', state_space.sum())
In the next step, we are creating a transition matrix for the hidden state.
a1_df = pd.DataFrame(columns=hidden_state, index=hidden_state)
a1_df.loc[hidden_state[0]] = [0.7, 0.3]
a1_df.loc[hidden_state[1]] = [0.4, 0.6]
print(a1_df)
a1 = a1_df.values
print('\n', a1, a1.shape, '\n')
print(a1_df.sum(axis=1))
Here in this step, we create an emission and observation matrix. The matrix we are creating is the size of the axb. a is the number of hidden states and b is the number of observable states.
observable_state = states
b1_df = pd.DataFrame(columns=observable_state, index=hidden_state)
b1_df.loc[hidden_state[0]] = [0.3, 0.5, 0.2]
b1_df.loc[hidden_state[1]] = [0.3, 0.3, 0.4]
print(b1_df)
b1 = b1_df.values
print('\n', b1, b1.shape, '\n')
print(b1_df.sum(axis=1))
In this step, we are creating the graph edges and the graph object from which we can create a complete graph.
hide_edges_wt = _get_markov_edges(a1_df)
pprint(hide_edges_wt)
emit_edges_wt = _get_markov_edges(b1_df)
pprint(emit_edges_wt)
Here we can plot the complete graph of the hidden Markov model.
G = nx.MultiDiGraph()
G.add_nodes_from(hidden_state)
print(f'Nodes:\n{G.nodes()}\n')
for k, v in hide_edges_wt.items():
tmp_origin, tmp_destination = k[0], k[1]
G.add_edge(tmp_origin, tmp_destination, weight=v, label=v)
for k, v in emit_edges_wt.items():
tmp_origin, tmp_destination = k[0], k[1]
G.add_edge(tmp_origin, tmp_destination, weight=v, label=v)
print(f'Edges:')
pprint(G.edges(data=True))
pos = nx.drawing.nx_pydot.graphviz_layout(G, prog='neato')
nx.draw_networkx(G, pos)
Read: Scikit learn Decision Tree
In this section, we will learn about scikit learn hidden Markov model example in python.
The scikit learn hidden Markov model is a process whereas the future probability of future depends upon the current state.
Code:
In the following code, we will import some libraries from which we are creating a hidden Markov model.
state_space = pd.Series(pi, index=states, name=’states’) is used to create a state space and initial state space probability.
edge[(index,column)] = Q.loc[index,column] is used to create a function that maps transition probability dataframe.
Graph.add_nodes_from(states) is used to add the node corresponding to the data frame.
Graph.add_edge(tmp_origin, tmp_destination, weight=v, label=v) edges is used to represent the transition property.
import numpy as np
import pandas as pd
import networkx as nx
import matplotlib.pyplot as plot
%matplotlib inline
states = ['sleeping', 'eating', 'pooping']
pi = [0.45, 0.35, 0.2]
state_space = pd.Series(pi, index=states, name='states')
print(state_space)
print(state_space.sum())
q_df = pd.DataFrame(columns=states, index=states)
q_df.loc[states[0]] = [0.4, 0.2, 0.4]
q_df.loc[states[1]] = [0.40, 0.40, 0.2]
q_df.loc[states[2]] = [0.40, 0.20, .4]
print(q_df)
q_f = q_df.values
print('\n', q_f, q_f.shape, '\n')
print(q_df.sum(axis=1))
from pprint import pprint
def _get_markov_edges(Q):
edge = {}
for column in Q.columns:
for index in Q.index:
edge[(index,column)] = Q.loc[index,column]
return edge
edge_wt = _get_markov_edges(q_df)
pprint(edge_wt)
Graph = nx.MultiDiGraph()
Graph.add_nodes_from(states)
print(f'Nodes:\n{Graph.nodes()}\n')
for k, v in edge_wt.items():
tmp_origin, tmp_destination = k[0], k[1]
Graph.add_edge(tmp_origin, tmp_destination, weight=v, label=v)
print(f'Edges:')
pprint(Graph.edges(data=True))
position = nx.drawing.nx_pydot.graphviz_layout(Graph, prog='dot')
nx.draw_networkx(Graph, position)
edge_labels = {(n1,n2):d['label'] for n1,n2,d in Graph.edges(data=True)}
nx.draw_networkx_edge_labels(Graph , position, edge_labels=edge_labels)
nx.drawing.nx_pydot.write_dot(Graph, 'pet_dog_markov.dot')
Output:
After running the above code, we get the following output in which we can see that the Markov model is plotted on the screen.
You may also like to read the following Scikit learn tutorials.
- Scikit-learn Vs Tensorflow
- Scikit learn Ridge Regression
- Scikit learn accuracy_score
- Scikit learn Image Processing
- Scikit learn Hyperparameter Tuning
- Scikit learn Hierarchical Clustering
So, in this tutorial we discussed the scikit learn hidden Markov model and we have also covered different examples related to its implementation. Here is the list of examples that we have covered.
- What is scikit learn Markov model?
- What made scikit learn Markov model hidden
- Scikit learn hidden Markov model example.
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