Made pickles optional

felixriese · Sep 29, 2019 · 4e98393 · 4e98393
1 parent 7ed3c98
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Showing 2 changed files with 42 additions and 14 deletions.
diff --git a/examples/SOMClassifier_Salinas.ipynb b/examples/SOMClassifier_Salinas.ipynb
@@ -195,8 +195,18 @@
     "cv_som = cross_validate(\n",
     "    som, X_salinas_m, y_salinas_m, cv=5, n_jobs=-1,\n",
     "    scoring=scorer,\n",
-    "    return_train_score=True, return_estimator=True, verbose=1)\n",
-    "pickle.dump(cv_som, open(\"pickles/SOMClassifier_cv.p\", \"wb\"))"
+    "    return_train_score=True, return_estimator=True, verbose=1)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# optional\n",
+    "# pickle.dump(cv_som, open(\"pickles/SOMClassifier_cv.p\", \"wb\"))\n",
+    "# cv_som = pickle.load(open(\"pickles/SOMClassifier_cv.p\", \"rb\"))"
    ]
   },
   {
@@ -217,8 +227,6 @@
    "source": [
     "# NBVAL_IGNORE_OUTPUT\n",
     "\n",
-    "cv_som = pickle.load(open(\"pickles/SOMClassifier_cv.p\", \"rb\"))\n",
-    "\n",
     "print(\"OA\\t{0:.2f}\\t{1:.2f}\".format(np.mean(cv_som['train_oa'])*100, np.mean(cv_som['test_oa'])*100))\n",
     "print(\"AA\\t{0:.2f}\\t{1:.2f}\".format(np.mean(cv_som['train_aa'])*100, np.mean(cv_som['test_aa'])*100))\n",
     "print(\"Kappa\\t{0:.2f}\\t{1:.2f}\".format(np.mean(cv_som['train_kappa'])*100, np.mean(cv_som['test_kappa'])*100))"
@@ -471,8 +479,18 @@
     "cv_rf = cross_validate(\n",
     "    rf, X_salinas_m, y_salinas_m, cv=5, n_jobs=-1,\n",
     "    scoring=scorer,\n",
-    "    return_train_score=True, return_estimator=True, verbose=1)\n",
-    "pickle.dump(cv_rf, open(\"pickles/RFClassifier_cv.p\", \"wb\"))"
+    "    return_train_score=True, return_estimator=True, verbose=1)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# optional\n",
+    "# pickle.dump(cv_rf, open(\"pickles/RFClassifier_cv.p\", \"wb\"))\n",
+    "# cv_rf = pickle.load(open(\"pickles/RFClassifier_cv.p\", \"rb\"))"
    ]
   },
   {
@@ -491,8 +509,6 @@
     }
    ],
    "source": [
-    "cv_rf = pickle.load(open(\"pickles/RFClassifier_cv.p\", \"rb\"))\n",
-    "\n",
     "print(\"OA\\t{0:.2f}\\t{1:.2f}\".format(np.mean(cv_rf['train_oa'])*100, np.mean(cv_rf['test_oa'])*100))\n",
     "print(\"AA\\t{0:.2f}\\t{1:.2f}\".format(np.mean(cv_rf['train_aa'])*100, np.mean(cv_rf['test_aa'])*100))\n",
     "print(\"Kappa\\t{0:.2f}\\t{1:.2f}\".format(np.mean(cv_rf['train_kappa'])*100, np.mean(cv_rf['test_kappa'])*100))"
@@ -506,7 +522,9 @@
    "source": [
     "rf_best = cv_rf[\"estimator\"][np.argmax(cv_rf['test_oa'])]\n",
     "y_pred_rf = rf_best.predict(X_salinas_m)\n",
-    "pickle.dump(y_pred_rf, open(\"pickles/RFClassifier_pred.p\", \"wb\"))"
+    "\n",
+    "# optional:\n",
+    "# pickle.dump(y_pred_rf, open(\"pickles/RFClassifier_pred.p\", \"wb\"))"
    ]
   },
   {
@@ -562,8 +580,9 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "y_pred = pickle.load(open(\"pickles/SOMClassifier_pred.p\", \"rb\"))\n",
-    "y_pred_rf = pickle.load(open(\"pickles/RFClassifier_pred.p\", \"rb\"))"
+    "# optional\n",
+    "# y_pred = pickle.load(open(\"pickles/SOMClassifier_pred.p\", \"rb\"))\n",
+    "# y_pred_rf = pickle.load(open(\"pickles/RFClassifier_pred.p\", \"rb\"))"
    ]
   },
   {

diff --git a/examples/SOMRegressor_Hyperspectral.ipynb b/examples/SOMRegressor_Hyperspectral.ipynb
@@ -112,8 +112,18 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "som.fit(X_train, y_train)\n",
-    "pickle.dump(som, open(\"pickles/SOMRegressor.p\", \"wb\"))"
+    "som.fit(X_train, y_train)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# optional\n",
+    "# pickle.dump(som, open(\"pickles/SOMRegressor.p\", \"wb\"))\n",
+    "# som = pickle.load(open(\"pickles/SOMRegressor.p\", \"rb\"))"
    ]
   },
   {
@@ -122,7 +132,6 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "som = pickle.load(open(\"pickles/SOMRegressor.p\", \"rb\"))\n",
     "y_pred = som.predict(X_test)\n",
     "y_pred_train = som.predict(X_train)\n",
     "pickle.dump(y_pred, open(\"pickles/SOMRegressor_pred.p\", \"wb\"))"