chore: remove knn from classifier notebook

A new notebook will be dedicated for knn closes zama-ai/concrete-ml-internal#4037
zama-ai · Oct 3, 2023 · 8336b47 · 8336b47
1 parent f46a112
commit 8336b47
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Showing 2 changed files with 16 additions and 77 deletions.
diff --git a/docs/advanced_examples/ClassifierComparison.ipynb b/docs/advanced_examples/ClassifierComparison.ipynb
diff --git a/docs/advanced_examples/utils/classifier_comparison_utils.py b/docs/advanced_examples/utils/classifier_comparison_utils.py
@@ -11,7 +11,6 @@
 
 warnings.simplefilter(action="ignore", category=FutureWarning)
 import time
-from pathlib import Path
 
 import matplotlib.pyplot as plt
 import numpy as np
@@ -21,18 +20,15 @@
 from sklearn.model_selection import train_test_split
 from sklearn.preprocessing import StandardScaler
 
-from concrete.ml.common.utils import get_model_name
 from concrete.ml.sklearn import DecisionTreeClassifier
 
 ALWAYS_USE_SIM = False
 
 # pylint: disable=too-many-locals,too-many-statements,too-many-branches,invalid-name
-def make_classifier_comparison(
-    title, classifiers, decision_level, verbose=False, show_score=False, save_plot=False
-):
+def make_classifier_comparison(title, classifiers, decision_level, verbose=False, save_plot=False):
 
     h = 0.04  # Step size in the mesh
-    n_samples = 25 if get_model_name(classifiers[0][0]) == "KNeighborsClassifier" else 200
+    n_samples = 200
 
     X, y = make_classification(
         n_samples=n_samples,
@@ -123,16 +119,8 @@ def make_classifier_comparison(
             sklearn_y_pred = sklearn_model.predict(X_test)
 
             # Compile the Concrete ML model
-            if get_model_name(classifier) == "KNeighborsClassifier":
-                n_compile_samples = 20
-            else:
-                n_compile_samples = X_train.shape[0]
-
-            if verbose:
-                print(f"Inputset has: {n_compile_samples} samples used for the compilationg.\n")
-
             time_begin = time.time()
-            circuit = concrete_model.compile(X_train[:n_compile_samples])
+            circuit = concrete_model.compile(X_train)
 
             if verbose:
                 print(f"Compilation time: {(time.time() - time_begin):.4f} seconds\n")
@@ -190,14 +178,6 @@ def make_classifier_comparison(
                     np.c_[xx.ravel(), yy.ravel()],
                     fhe="simulate",
                 )
-            # `predict_proba` not implemented yet for Concrete KNN
-            # FIXME: https://github.com/zama-ai/concrete-ml-internal/issues/3962
-            elif get_model_name(classifier) == "KNeighborsClassifier":
-                sklearn_Z = sklearn_model.predict(np.c_[xx.ravel(), yy.ravel()].astype(np.float32))
-                concrete_Z = concrete_model.predict(
-                    np.c_[xx.ravel(), yy.ravel()],
-                    fhe="simulate",
-                )
             else:
                 sklearn_Z = sklearn_model.predict_proba(
                     np.c_[xx.ravel(), yy.ravel()].astype(np.float32)
@@ -249,14 +229,14 @@ def make_classifier_comparison(
 
                 if i == 0:
                     ax.set_title(model_name + f" ({framework})", fontsize=font_size_text)
-                if show_score:
-                    ax.text(
-                        xx.max() - 0.3,
-                        yy.min() + 0.3,
-                        f"{score*100:0.1f}%",
-                        size=font_size_text,
-                        horizontalalignment="right",
-                    )
+
+                ax.text(
+                    xx.max() - 0.3,
+                    yy.min() + 0.3,
+                    f"{score*100:0.1f}%",
+                    size=font_size_text,
+                    horizontalalignment="right",
+                )
 
                 if bitwidth and framework == "Concrete ML":
                     ax.text(