Pushing the docs to dev/ for branch: main, commit b0f86e797190d1fd62331eac8b88a5b0dfa522b9

Author: sklearn-ci

dev/_downloads/07fcc19ba03226cd3d83d4e40ec44385/auto_examples_python.zip

@@ -87,18 +87,7 @@
       },
       "outputs": [],
       "source": [
-        "from sklearn.utils.fixes import parse_version, sp_version\n\n# This is line is to avoid incompatibility if older SciPy version.\n# You should use `solver=\"highs\"` with recent version of SciPy.\nsolver = \"highs\" if sp_version >= parse_version(\"1.6.0\") else \"interior-point\""
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "collapsed": false
-      },
-      "outputs": [],
-      "source": [
-        "from sklearn.linear_model import QuantileRegressor\n\nquantiles = [0.05, 0.5, 0.95]\npredictions = {}\nout_bounds_predictions = np.zeros_like(y_true_mean, dtype=np.bool_)\nfor quantile in quantiles:\n    qr = QuantileRegressor(quantile=quantile, alpha=0, solver=solver)\n    y_pred = qr.fit(X, y_normal).predict(X)\n    predictions[quantile] = y_pred\n\n    if quantile == min(quantiles):\n        out_bounds_predictions = np.logical_or(\n            out_bounds_predictions, y_pred >= y_normal\n        )\n    elif quantile == max(quantiles):\n        out_bounds_predictions = np.logical_or(\n            out_bounds_predictions, y_pred <= y_normal\n        )"
+        "from sklearn.linear_model import QuantileRegressor\n\nquantiles = [0.05, 0.5, 0.95]\npredictions = {}\nout_bounds_predictions = np.zeros_like(y_true_mean, dtype=np.bool_)\nfor quantile in quantiles:\n    qr = QuantileRegressor(quantile=quantile, alpha=0)\n    y_pred = qr.fit(X, y_normal).predict(X)\n    predictions[quantile] = y_pred\n\n    if quantile == min(quantiles):\n        out_bounds_predictions = np.logical_or(\n            out_bounds_predictions, y_pred >= y_normal\n        )\n    elif quantile == max(quantiles):\n        out_bounds_predictions = np.logical_or(\n            out_bounds_predictions, y_pred <= y_normal\n        )"
       ]
     },
     {
@@ -134,7 +123,7 @@
       },
       "outputs": [],
       "source": [
-        "quantiles = [0.05, 0.5, 0.95]\npredictions = {}\nout_bounds_predictions = np.zeros_like(y_true_mean, dtype=np.bool_)\nfor quantile in quantiles:\n    qr = QuantileRegressor(quantile=quantile, alpha=0, solver=solver)\n    y_pred = qr.fit(X, y_pareto).predict(X)\n    predictions[quantile] = y_pred\n\n    if quantile == min(quantiles):\n        out_bounds_predictions = np.logical_or(\n            out_bounds_predictions, y_pred >= y_pareto\n        )\n    elif quantile == max(quantiles):\n        out_bounds_predictions = np.logical_or(\n            out_bounds_predictions, y_pred <= y_pareto\n        )"
+        "quantiles = [0.05, 0.5, 0.95]\npredictions = {}\nout_bounds_predictions = np.zeros_like(y_true_mean, dtype=np.bool_)\nfor quantile in quantiles:\n    qr = QuantileRegressor(quantile=quantile, alpha=0)\n    y_pred = qr.fit(X, y_pareto).predict(X)\n    predictions[quantile] = y_pred\n\n    if quantile == min(quantiles):\n        out_bounds_predictions = np.logical_or(\n            out_bounds_predictions, y_pred >= y_pareto\n        )\n    elif quantile == max(quantiles):\n        out_bounds_predictions = np.logical_or(\n            out_bounds_predictions, y_pred <= y_pareto\n        )"
       ]
     },
     {
@@ -163,7 +152,7 @@
       },
       "outputs": [],
       "source": [
-        "from sklearn.linear_model import LinearRegression\nfrom sklearn.metrics import mean_absolute_error, mean_squared_error\n\nlinear_regression = LinearRegression()\nquantile_regression = QuantileRegressor(quantile=0.5, alpha=0, solver=solver)\n\ny_pred_lr = linear_regression.fit(X, y_pareto).predict(X)\ny_pred_qr = quantile_regression.fit(X, y_pareto).predict(X)\n\nprint(\n    f\"\"\"Training error (in-sample performance)\n    {linear_regression.__class__.__name__}:\n    MAE = {mean_absolute_error(y_pareto, y_pred_lr):.3f}\n    MSE = {mean_squared_error(y_pareto, y_pred_lr):.3f}\n    {quantile_regression.__class__.__name__}:\n    MAE = {mean_absolute_error(y_pareto, y_pred_qr):.3f}\n    MSE = {mean_squared_error(y_pareto, y_pred_qr):.3f}\n    \"\"\"\n)"
+        "from sklearn.linear_model import LinearRegression\nfrom sklearn.metrics import mean_absolute_error, mean_squared_error\n\nlinear_regression = LinearRegression()\nquantile_regression = QuantileRegressor(quantile=0.5, alpha=0)\n\ny_pred_lr = linear_regression.fit(X, y_pareto).predict(X)\ny_pred_qr = quantile_regression.fit(X, y_pareto).predict(X)\n\nprint(\n    f\"\"\"Training error (in-sample performance)\n    {linear_regression.__class__.__name__}:\n    MAE = {mean_absolute_error(y_pareto, y_pred_lr):.3f}\n    MSE = {mean_squared_error(y_pareto, y_pred_lr):.3f}\n    {quantile_regression.__class__.__name__}:\n    MAE = {mean_absolute_error(y_pareto, y_pred_qr):.3f}\n    MSE = {mean_squared_error(y_pareto, y_pred_qr):.3f}\n    \"\"\"\n)"
       ]
     },
     {

dev/_downloads/10754505339af88c10b8a48127535a4a/plot_quantile_regression.ipynb

@@ -109,11 +109,6 @@
 #
 # We will use the quantiles at 5% and 95% to find the outliers in the training
 # sample beyond the central 90% interval.
-from sklearn.utils.fixes import parse_version, sp_version
-
-# This is line is to avoid incompatibility if older SciPy version.
-# You should use `solver="highs"` with recent version of SciPy.
-solver = "highs" if sp_version >= parse_version("1.6.0") else "interior-point"
 
 # %%
 from sklearn.linear_model import QuantileRegressor
@@ -122,7 +117,7 @@
 predictions = {}
 out_bounds_predictions = np.zeros_like(y_true_mean, dtype=np.bool_)
 for quantile in quantiles:
-    qr = QuantileRegressor(quantile=quantile, alpha=0, solver=solver)
+    qr = QuantileRegressor(quantile=quantile, alpha=0)
     y_pred = qr.fit(X, y_normal).predict(X)
     predictions[quantile] = y_pred
 
@@ -184,7 +179,7 @@
 predictions = {}
 out_bounds_predictions = np.zeros_like(y_true_mean, dtype=np.bool_)
 for quantile in quantiles:
-    qr = QuantileRegressor(quantile=quantile, alpha=0, solver=solver)
+    qr = QuantileRegressor(quantile=quantile, alpha=0)
     y_pred = qr.fit(X, y_pareto).predict(X)
     predictions[quantile] = y_pred
 
@@ -254,7 +249,7 @@
 from sklearn.metrics import mean_absolute_error, mean_squared_error
 
 linear_regression = LinearRegression()
-quantile_regression = QuantileRegressor(quantile=0.5, alpha=0, solver=solver)
+quantile_regression = QuantileRegressor(quantile=0.5, alpha=0)
 
 y_pred_lr = linear_regression.fit(X, y_pareto).predict(X)
 y_pred_qr = quantile_regression.fit(X, y_pareto).predict(X)