Saving and loading graphs

by: Kevin Broløs
(Feyn version 1.4 or newer)

Once you've evaluated your graphs and are ready to use these predictions in a production setup, you can simply save the graphs.

from sklearn.datasets import make_classification
import pandas as pd

from feyn import QLattice
from feyn.tools import split

ql = QLattice()

# Generate a dataset and put it into a dataframe
X, y = make_classification()
data = pd.DataFrame(X, columns=[str(i) for i in range(X.shape[1])])
data['target'] = y

# Train/test split
train, test = split(data)

# Get a classifier
qgraph = ql.get_classifier(train, 'target')
qgraph.fit(train)

# Select a graph from your fitted QGraph
best_graph = qgraph.best()[0]

# Save your evaluated graph to a file
best_graph.save('/tmp/my_precious.graph')

And that's that. You can load the graphs again at any time using feyn.Graph.load, and use them to predict on new data.

from feyn import Graph
from feyn.plots import plot_confusion_matrix
loaded_graph = Graph.load('/tmp/my_precious.graph')

prediction = loaded_graph.predict(test)

plot_confusion_matrix(y_true=test['target'],
                                y_pred=prediction.round(),
                                title="Confusion Matrix [Test]")

Once a graph is saved and selected, you can insert them into any (Python) environment to do predictions - you don't need access to your QLattice in order to use the resulting graphs.

You can even do fine-tuning or fitting of that specific graph after loading it, if you want to!