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https://github.com/luxfi/torus-ml.git
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zeekay
e037e8f7ea
The distribution called itself torus-ml while shipping a package directory named concrete, so the name a user installs and the name they import disagreed — and most of the tree already said torus.ml, which could not resolve against src/concrete. Moved src/concrete to src/torus and pointed the include at it. The dependency was Zama's concrete-python from their index. It is torus-fhe from ours, which is what luxfhe/torus already ships: that frontend declares torus-fhe and ships a torus/fhe module, so every 'from torus.fhe' in this tree was already naming our compiler and only the dependency lagged. Two distributions share one namespace, which is the shape the upstream this forked from used and the reason the split works: torus-fhe gives torus.fhe, torus-ml gives torus.ml. Swept every other spelling — identifiers, enum members, workflow variables, filenames, notebooks. LICENSE and NOTICE keep the upstream attribution a fork owes; the figures carry the old name in JPEG metadata a text sweep cannot reach.
105 lines
3.4 KiB
Python
105 lines
3.4 KiB
Python
import matplotlib.pyplot as plt
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import numpy as np
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from matplotlib.colors import ListedColormap
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from sklearn.metrics import accuracy_score
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# pylint: disable=too-many-locals
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def plot_data(axs, X_train, y_train, X_test, y_test, model, name, h=0.04, font_size_text=20):
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# Train the model and retrieve both the Torus ML model and its equivalent one from
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# scikit-learn
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torus_model, sklearn_model = model.fit_benchmark(X_train, y_train)
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# Compute the predictions in clear using the scikit-learn model
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sklearn_y_pred = sklearn_model.predict(X_test)
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# Compile the Torus ML model
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circuit = torus_model.compile(X_test)
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torus_y_pred = torus_model.predict(X_test, fhe="simulate")
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# Measure the accuracy scores
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sklearn_score = accuracy_score(sklearn_y_pred, y_test)
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torus_score = accuracy_score(torus_y_pred, y_test)
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bitwidth = circuit.graph.maximum_integer_bit_width()
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x_min, x_max = X_test[:, 0].min() - 0.5, X_test[:, 0].max() + 0.5
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y_min, y_max = X_test[:, 1].min() - 0.5, X_test[:, 1].max() + 0.5
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xx, yy = np.meshgrid(np.arange(x_min, x_max, h), np.arange(y_min, y_max, h))
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# pylint: disable-next=no-member
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cm = plt.cm.RdBu
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cm_bright = ListedColormap(["#FF0000", "#0000FF"])
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raveled_input = np.c_[xx.ravel(), yy.ravel()]
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# Plot the decision boundaries.
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# For that, a color is assigned to each point in the mesh, which is obtained as a
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# cartesian product of [x_min, x_max] with [y_min, y_max].
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if hasattr(sklearn_model, "decision_function"):
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sklearn_Z = sklearn_model.decision_function(raveled_input)
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torus_Z = torus_model.decision_function(raveled_input, fhe="simulate")
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else:
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sklearn_Z = sklearn_model.predict_proba(raveled_input.astype(np.float32))[:, 1]
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torus_Z = torus_model.predict_proba(raveled_input, fhe="simulate")[:, 1]
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for _, (ax, framework, score, Z) in enumerate(
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zip(
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axs,
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["scikit-learn", "Torus ML"],
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[sklearn_score, torus_score],
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[sklearn_Z, torus_Z],
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)
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):
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# Put the result into a color plot
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Z = Z.reshape(xx.shape)
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ax.contourf(xx, yy, Z, cmap=cm, alpha=0.8)
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# Plot the training points
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ax.scatter(X_test[:, 0], X_test[:, 1], c=y_test, cmap=cm_bright, edgecolors="k")
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# Plot the testing points
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ax.scatter(
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X_test[:, 0],
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X_test[:, 1],
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c=y_test,
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marker="D",
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cmap=cm_bright,
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edgecolors="k",
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alpha=0.6,
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)
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ax.contour(
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xx,
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yy,
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Z,
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levels=[0.5],
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linewidths=2,
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)
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ax.set_xlim(xx.min(), xx.max())
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ax.set_ylim(yy.min(), yy.max())
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ax.tick_params(axis='x', labelsize=font_size_text)
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ax.tick_params(axis='y', labelsize=font_size_text)
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ax.set_title(
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f"Logistic Regression ({framework}) with {name} data.", fontsize=font_size_text
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)
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ax.text(
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xx.max() - 0.3,
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yy.min() + 0.3,
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f"{score*100:0.1f}%",
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size=font_size_text,
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horizontalalignment="right",
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)
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if bitwidth and framework == "Torus ML":
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ax.text(
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xx.max() - 0.3,
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yy.min() + 1.0,
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f"bit-width={bitwidth}",
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size=font_size_text,
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horizontalalignment="right",
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)
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