chore: fix typo

Fix some typos.
zama-ai · Sep 28, 2023 · 5db5c0f · 5db5c0f
1 parent 274f0e4
commit 5db5c0f
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Showing 5 changed files with 7 additions and 7 deletions.
diff --git a/docs/advanced_examples/GLMComparison.ipynb b/docs/advanced_examples/GLMComparison.ipynb
@@ -97,13 +97,13 @@
     "# Getting the original data-set containing the risk features\n",
     "# Link: https://www.openml.org/d/41214\n",
     "risks_data, _ = fetch_openml(\n",
-    "    data_id=41214, as_frame=True, cache=True, data_home=\"~/.cache/sklean\", return_X_y=True\n",
+    "    data_id=41214, as_frame=True, cache=True, data_home=\"~/.cache/sklearn\", return_X_y=True\n",
     ")\n",
     "\n",
     "# Getting the data set containing claims amount\n",
     "# Link: https://www.openml.org/d/41215\n",
     "claims_data, _ = fetch_openml(\n",
-    "    data_id=41215, as_frame=True, cache=True, data_home=\"~/.cache/sklean\", return_X_y=True\n",
+    "    data_id=41215, as_frame=True, cache=True, data_home=\"~/.cache/sklearn\", return_X_y=True\n",
     ")"
    ]
   },

diff --git a/docs/advanced_examples/PoissonRegression.ipynb b/docs/advanced_examples/PoissonRegression.ipynb
@@ -86,7 +86,7 @@
    "outputs": [],
    "source": [
     "df, _ = fetch_openml(\n",
-    "    data_id=41214, as_frame=True, cache=True, data_home=\"~/.cache/sklean\", return_X_y=True\n",
+    "    data_id=41214, as_frame=True, cache=True, data_home=\"~/.cache/sklearn\", return_X_y=True\n",
     ")\n",
     "df = df.head(50000)"
    ]

diff --git a/docs/advanced_examples/XGBRegressor.ipynb b/docs/advanced_examples/XGBRegressor.ipynb
@@ -1135,11 +1135,11 @@
    "source": [
     "# Evaluation\n",
     "\n",
-    "r2_score_sklean = metrics.r2_score(y_test, y_preds_XGBoost)\n",
+    "r2_score_sklearn = metrics.r2_score(y_test, y_preds_XGBoost)\n",
     "r2_score_clear_concrete = metrics.r2_score(y_test, y_preds_clear)\n",
     "r2_score_fhe_concrete = metrics.r2_score(y_test, y_preds_fhe)\n",
     "\n",
-    "print(f\"R2_score with XGBoost: {r2_score_sklean:.4f}\")\n",
+    "print(f\"R2_score with XGBoost: {r2_score_sklearn:.4f}\")\n",
     "print(f\"R2_score without FHE : {r2_score_clear_concrete:.4f}\")\n",
     "print(f\"R2_score with FHE    : {r2_score_fhe_concrete:.4f}\")"
    ]

diff --git a/use_case_examples/federated_learning/federated_utils.py b/use_case_examples/federated_learning/federated_utils.py
@@ -228,7 +228,7 @@ def get_model_parameters(model: LogisticRegression) -> LogRegParams:
 
 
 def set_model_params(model: LogisticRegression, params: LogRegParams) -> LogisticRegression:
-    """Sets the parameters of a sklean LogisticRegression model."""
+    """Sets the parameters of a sklearn LogisticRegression model."""
     model.coef_ = params[0]
     if model.fit_intercept:
         model.intercept_ = params[1]

diff --git a/use_case_examples/federated_learning/load_to_cml.py b/use_case_examples/federated_learning/load_to_cml.py
@@ -17,7 +17,7 @@
     with path_to_model.open("rb") as file:
         sklearn_model = pickle.load(file)
 
-    # Compile model without data since the server doesn't have acces to it
+    # Compile model without data since the server doesn't have access to it
     # Indeed in this scenario the users have the data but the server doesn't.
     # We then have to compile the model using random input sampled with the same
     # low and high bounds as the real data, in this context [0, 255].