def setUp(self):
X, y = datasets.make_blobs(n_samples=10000, n_features=5, centers=10)
self.X, self.y = X, y
clustering = KMeans(n_clusters=10)
classification = LinearDiscriminantAnalysis()
steps = [('clustering', clustering),
('classification', classification)]
pgraph = PipeGraph(steps=steps)
pgraph.inject(sink='clustering',
sink_var='X',
source='_External',
source_var='X')
pgraph.inject(sink='classification',
sink_var='X',
source='_External',
source_var='X')
pgraph.inject(sink='classification',
sink_var='y',
source='clustering',
source_var='predict')
self.pgraph = pgraph
'X': slice(0, 1),
'sample_weight': slice(1, 2)
})
steps = [('selector', selector), ('custom_power', custom_power),
('scaler', scaler), ('polynomial_features', polynomial_features),
('linear_model', linear_model)]
pgraph = PipeGraph(steps=steps)
(pgraph.inject(
sink='selector', sink_var='X', source='_External', source_var='X').inject(
'custom_power', 'X', 'selector',
'sample_weight').inject('scaler', 'X', 'selector', 'X').inject(
'polynomial_features', 'X', 'scaler').inject(
'linear_model', 'X',
'polynomial_features').inject('linear_model',
'y',
source_var='y').inject(
'linear_model',
'sample_weight',
'custom_power'))
###############################################################################
# Then we define ``param_grid`` as expected by :class:`GridSearchCV` exploring a few possibilities
# of varying parameters.
param_grid = {
'polynomial_features__degree': range(1, 3),
'linear_model__fit_intercept': [True, False],
'custom_power__power': [1, 5, 10, 20, 30]
}
mlp = MLPClassifier()
concatenator = Concatenator()
steps = [('scaler', scaler), ('gaussian_nb', gaussian_nb), ('svc', svc),
('concat', concatenator), ('mlp', mlp)]
###############################################################################
# In this example we use a :class:`PipeGraphClassifier` because the result is a classification and we want to take advantage of Scikit-Learn default scoring method for classifiers.
pgraph = PipeGraph(steps=steps)
(pgraph.inject(sink='scaler', sink_var='X', source='_External',
source_var='X').inject('gaussian_nb', 'X', 'scaler').inject(
'gaussian_nb', 'y',
source_var='y').inject('svc', 'X', 'scaler').inject(
'svc', 'y',
source_var='y').inject('concat', 'X1', 'scaler').inject(
'concat', 'X2',
'gaussian_nb').inject('concat', 'X3', 'svc').inject(
'mlp', 'X', 'concat').inject('mlp',
'y',
source_var='y'))
param_grid = {
'svc__C': [0.1, 0.5, 1.0],
'mlp__hidden_layer_sizes': [
(3, ),
(6, ),
(9, ),
],
'mlp__max_iter': [5000, 10000]
}
def test_Pipegraph__ex_3_inject(self):
import numpy as np
import pandas as pd
from sklearn.preprocessing import MinMaxScaler
from sklearn.preprocessing import PolynomialFeatures
from sklearn.linear_model import LinearRegression
from sklearn.model_selection import GridSearchCV
from pipegraph.base import PipeGraph
from pipegraph.demo_blocks import CustomPower
X = pd.DataFrame(
dict(X=np.array([1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11]),
sample_weight=np.array([
0.01, 0.95, 0.10, 0.95, 0.95, 0.10, 0.10, 0.95, 0.95,
0.95, 0.01
])))
y = np.array([10, 4, 20, 16, 25, -60, 85, 64, 81, 100, 150])
scaler = MinMaxScaler()
polynomial_features = PolynomialFeatures()
linear_model = LinearRegression()
custom_power = CustomPower()
selector = ColumnSelector(mapping={
'X': slice(0, 1),
'sample_weight': slice(1, 2)
})
steps = [('selector', selector), ('custom_power', custom_power),
('scaler', scaler),
('polynomial_features', polynomial_features),
('linear_model', linear_model)]
pgraph = PipeGraph(steps=steps) #PipeGraphRegressor
self.assertTrue(pgraph.fit_connections is None)
self.assertTrue(pgraph.predict_connections is None)
(pgraph.inject(
sink='selector',
sink_var='X', source='_External', source_var='X').inject(
'custom_power', 'X', 'selector',
'sample_weight').inject('scaler', 'X', 'selector', 'X').inject(
'polynomial_features', 'X', 'scaler').inject(
'linear_model', 'X',
'polynomial_features').inject('linear_model',
'y',
source_var='y').inject(
'linear_model',
'sample_weight',
'custom_power'))
self.assertTrue(pgraph.fit_connections is not None)
self.assertTrue(pgraph.predict_connections is not None)
pgraph.fit(X, y)
self.assertEqual(
pgraph.fit_connections, {
'selector': {
'X': ('_External', 'X')
},
'custom_power': {
'X': ('selector', 'sample_weight')
},
'scaler': {
'X': ('selector', 'X')
},
'polynomial_features': {
'X': ('scaler', 'predict')
},
'linear_model': {
'X': ('polynomial_features', 'predict'),
'y': ('_External', 'y'),
'sample_weight': ('custom_power', 'predict')
}
})
self.assertEqual(
pgraph.predict_connections, {
'selector': {
'X': ('_External', 'X')
},
'custom_power': {
'X': ('selector', 'sample_weight')
},
'scaler': {
'X': ('selector', 'X')
},
'polynomial_features': {
'X': ('scaler', 'predict')
},
'linear_model': {
'X': ('polynomial_features', 'predict'),
'y': ('_External', 'y'),
'sample_weight': ('custom_power', 'predict')
}
})
