Fifth Example: Demultiplexor - multiplexor

An imaginative layout using a classifier to predict the cluster labels and fitting a separate model for each cluster.

Steps of the PipeGraph:

• scaler: A MinMaxScaler data preprocessor
• classifier: A GaussianMixture classifier
• demux: A custom Demultiplexer class in charge of splitting the input arrays accordingly to the selection input vector
• lm_0: A LinearRegression model
• lm_1: A LinearRegression model
• lm_2: A LinearRegression model
• mux: A custom Multiplexer class in charge of combining different input arrays into a single one accordingly to the selection input vector

Figure 1. PipeGraph diagram showing the steps and their connections

import numpy as np
import pandas as pd
from sklearn.preprocessing import MinMaxScaler
from sklearn.linear_model import LinearRegression
from sklearn.model_selection import GridSearchCV
from pipegraph.base import PipeGraph, Demultiplexer, Multiplexer
import matplotlib.pyplot as plt
from sklearn.mixture import GaussianMixture

X_first = pd.Series(np.random.rand(100,))
y_first = pd.Series(4 * X_first + 0.5*np.random.randn(100,))
X_second = pd.Series(np.random.rand(100,) + 3)
y_second = pd.Series(-4 * X_second + 0.5*np.random.randn(100,))
X_third = pd.Series(np.random.rand(100,) + 6)
y_third = pd.Series(2 * X_third + 0.5*np.random.randn(100,))

X = pd.concat([X_first, X_second, X_third], axis=0).to_frame()
y = pd.concat([y_first, y_second, y_third], axis=0).to_frame()

scaler = MinMaxScaler()
gaussian_mixture = GaussianMixture(n_components=3)
demux = Demultiplexer()
lm_0 = LinearRegression()
lm_1 = LinearRegression()
lm_2 = LinearRegression()
mux = Multiplexer()


steps = [('scaler', scaler),
         ('classifier', gaussian_mixture),
         ('demux', demux),
         ('lm_0', lm_0),
         ('lm_1', lm_1),
         ('lm_2', lm_2),
         ('mux', mux), ]

Instead of using inject as in previous example, in this one we are going to pass a dictionary describing the connections to PipeGraph constructor

connections = { 'scaler': {'X': 'X'},
                'classifier': {'X': 'scaler'},
                'demux': {'X': 'scaler',
                          'y': 'y',
                          'selection': 'classifier'},
                'lm_0': {'X': ('demux', 'X_0'),
                         'y': ('demux', 'y_0')},
                'lm_1': {'X': ('demux', 'X_1'),
                         'y': ('demux', 'y_1')},
                'lm_2': {'X': ('demux', 'X_2'),
                         'y': ('demux', 'y_2')},
                'mux': {'0': 'lm_0',
                        '1': 'lm_1',
                        '2': 'lm_2',
                        'selection': 'classifier'}}

pgraph = PipeGraph(steps=steps, fit_connections=connections)
pgraph.fit(X, y)
#%%
y_pred = pgraph.predict(X)
plt.scatter(X, y)
plt.scatter(X, y_pred)

Total running time of the script: ( 0 minutes 0.031 seconds)