def test_Pipegraph__example_1_no_connections(self):
import numpy as np
from sklearn.preprocessing import MinMaxScaler
from sklearn.linear_model import LinearRegression
from pipegraph import PipeGraph
X = np.random.rand(100, 1)
y = 4 * X + 0.5 * np.random.randn(100, 1)
scaler = MinMaxScaler()
linear_model = LinearRegression()
steps = [('scaler', scaler), ('linear_model', linear_model)]
pgraph = PipeGraph(steps=steps)
self.assertTrue(pgraph.fit_connections is None)
self.assertTrue(pgraph.predict_connections is None)
pgraph.fit(X, y)
y_pred = pgraph.predict(X)
self.assertEqual(y_pred.shape[0], y.shape[0])
self.assertEqual(
pgraph.fit_connections,
dict(scaler={'X': 'X'},
linear_model={
'X': ('scaler', 'predict'),
'y': 'y'
}))
self.assertEqual(
pgraph.predict_connections,
dict(scaler={'X': 'X'},
linear_model={
'X': ('scaler', 'predict'),
'y': 'y'
}))
def test_compositable__isinstance(self):
X = self.X
y = self.y
new_graph = PipeGraph(steps=[('pgraph', self.pgraph)])
self.assertEqual(new_graph.named_steps, {'pgraph': self.pgraph})
new_graph.fit(X, y)
result = new_graph.predict(X)['predict']
expected = self.pgraph.predict(X)['predict']
self.assertEqual(result.shape[0], expected.shape[0])
class TestPipeGraphCompositable(unittest.TestCase):
def setUp(self):
self.size = 100
self.X = np.random.rand(self.size, 1)
self.y = 2 * self.X
lm = LinearRegression()
steps = [('linear_model', lm)]
self.lm = lm
self.steps = steps
self.pgraph = PipeGraph(steps=steps)
def test_compositable__isinstance(self):
X = self.X
y = self.y
new_graph = PipeGraph(steps=[('pgraph', self.pgraph)])
self.assertEqual(new_graph.named_steps, {'pgraph': self.pgraph})
new_graph.fit(X, y)
result = new_graph.predict(X)['predict']
expected = self.pgraph.predict(X)['predict']
self.assertEqual(result.shape[0], expected.shape[0])
'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)
