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Update notebooks 1 to 8 to latest library versions (in particular Scikit-Learn 0.20)

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Aurélien Geron
2018-12-21 10:18:31 +08:00
parent dc16446c5f
commit b54ee1b608
8 changed files with 694 additions and 586 deletions
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15
04_training_linear_models.ipynb
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@@ -65,7 +65,7 @@
"\n",
"# Ignore useless warnings (see SciPy issue #5998)\n",
"import warnings\n",
"warnings.filterwarnings(action=\"ignore\", module=\"scipy\", message=\"^internal gelsd\")"
"warnings.filterwarnings(action=\"ignore\", message=\"^internal gelsd\")"
]
},
{
@@ -384,7 +384,7 @@
"outputs": [],
"source": [
"from sklearn.linear_model import SGDRegressor\n",
"sgd_reg = SGDRegressor(max_iter=50, penalty=None, eta0=0.1, random_state=42)\n",
"sgd_reg = SGDRegressor(max_iter=50, tol=-np.infty, penalty=None, eta0=0.1, random_state=42)\n",
"sgd_reg.fit(X, y.ravel())"
]
},
@@ -727,7 +727,7 @@
"metadata": {},
"outputs": [],
"source": [
"sgd_reg = SGDRegressor(max_iter=5, penalty=\"l2\", random_state=42)\n",
"sgd_reg = SGDRegressor(max_iter=50, tol=-np.infty, penalty=\"l2\", random_state=42)\n",
"sgd_reg.fit(X, y.ravel())\n",
"sgd_reg.predict([[1.5]])"
]
@@ -810,6 +810,7 @@
"X_val_poly_scaled = poly_scaler.transform(X_val)\n",
"\n",
"sgd_reg = SGDRegressor(max_iter=1,\n",
" tol=-np.infty,\n",
" penalty=None,\n",
" eta0=0.0005,\n",
" warm_start=True,\n",
@@ -854,7 +855,7 @@
"outputs": [],
"source": [
"from sklearn.base import clone\n",
"sgd_reg = SGDRegressor(max_iter=1, warm_start=True, penalty=None,\n",
"sgd_reg = SGDRegressor(max_iter=1, tol=-np.infty, warm_start=True, penalty=None,\n",
" learning_rate=\"constant\", eta0=0.0005, random_state=42)\n",
"\n",
"minimum_val_error = float(\"inf\")\n",
@@ -1043,7 +1044,7 @@
"outputs": [],
"source": [
"from sklearn.linear_model import LogisticRegression\n",
"log_reg = LogisticRegression(random_state=42)\n",
"log_reg = LogisticRegression(solver=\"liblinear\", random_state=42)\n",
"log_reg.fit(X, y)"
]
},
@@ -1123,7 +1124,7 @@
"X = iris[\"data\"][:, (2, 3)] # petal length, petal width\n",
"y = (iris[\"target\"] == 2).astype(np.int)\n",
"\n",
"log_reg = LogisticRegression(C=10**10, random_state=42)\n",
"log_reg = LogisticRegression(solver=\"liblinear\", C=10**10, random_state=42)\n",
"log_reg.fit(X, y)\n",
"\n",
"x0, x1 = np.meshgrid(\n",
@@ -1742,7 +1743,7 @@
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.6.4"
"version": "3.6.6"
},
"nav_menu": {},
"toc": {