polish crash isolation in some cases

benchmark/numpy_7/numpy_7_fixed.ipynb

@@ -923,7 +923,6 @@
     "#     plot_sample(dataset['test_images'][image_idx], classes[dataset['test_labels'][image_idx]], classes[pred])\n",
     "    plot_sample(dataset['test_images'][image_idx], classes[dataset['test_labels'][image_idx][0]], classes[pred])\n",
     "\n",
-    "    # fix 2 by temporary commenting because: need to make sure the model is built after every layer\n",
     "#     # extracting the output and appending to outputs\n",
     "#     feature_maps = []\n",
     "#     for name in layer_names:\n",

benchmark/numpy_7/numpy_7_reproduced.ipynb

@@ -867,20 +867,20 @@
     "\n",
     "    plot_sample(dataset['test_images'][image_idx], classes[dataset['test_labels'][image_idx]], classes[pred])\n",
     "\n",
-    "    # extracting the output and appending to outputs\n",
-    "    feature_maps = []\n",
-    "    for name in layer_names:\n",
-    "        tmp_model = Model(inputs=model.input, outputs=model.get_layer(name).output)\n",
-    "        feature_maps.append(tmp_model.predict(image))\n",
-    "\n",
-    "    fig, ax = plt.subplots(nrows=len(feature_maps), ncols=num_features, figsize=(20, 20))\n",
-    "    for i in range(len(feature_maps)):\n",
-    "        for z in range(num_features):\n",
-    "            ax[i][z].imshow(feature_maps[i][0, :, :, z])\n",
-    "            ax[i][z].set_title(layer_names[i])\n",
-    "            ax[i][z].set_xticks([])\n",
-    "            ax[i][z].set_yticks([])\n",
-    "    plt.savefig('feature_maps.png')\n",
+    "#     # extracting the output and appending to outputs\n",
+    "#     feature_maps = []\n",
+    "#     for name in layer_names:\n",
+    "#         tmp_model = Model(inputs=model.input, outputs=model.get_layer(name).output)\n",
+    "#         feature_maps.append(tmp_model.predict(image))\n",
+    "\n",
+    "#     fig, ax = plt.subplots(nrows=len(feature_maps), ncols=num_features, figsize=(20, 20))\n",
+    "#     for i in range(len(feature_maps)):\n",
+    "#         for z in range(num_features):\n",
+    "#             ax[i][z].imshow(feature_maps[i][0, :, :, z])\n",
+    "#             ax[i][z].set_title(layer_names[i])\n",
+    "#             ax[i][z].set_xticks([])\n",
+    "#             ax[i][z].set_yticks([])\n",
+    "#     plt.savefig('feature_maps.png')\n",
     "\n",
     "\n",
     "def plot_weights(model):\n",

benchmark/numpy_8/numpy_8_fixed.ipynb

@@ -2093,7 +2093,7 @@
     "def evaluate_fitness(individual):\n",
     "    # Вычислите значения признаков на основе individual\n",
     "    \n",
-    "    # fix 2 --- shape mismatch, undefined model\n",
+    "    # fix 2 --- shape mismatch\n",
     "#     X_train_gp = transform_gp_structure(individual, X_train)  # Вычисление новых признаков на обучающей выборке\n",
     "#     rf_model_gp.fit(X_train_gp, y_train)  # Обучение модели случайного леса с новыми признаками\n",
     "#     X_test_gp = transform_gp_structure(individual, X_test)  # Вычисление новых признаков на тестовой выборке\n",
@@ -2121,8 +2121,7 @@
    },
    "outputs": [],
    "source": [
-    "# fix 3 --- missing import and variable\n",
-    "import random\n",
+    "import random # fix missing import for crash isolation purposes\n",
     "crossover_prob = 0.5\n",
     "mutation_prob = 0.5\n",
     "\n",

benchmark/numpy_8/numpy_8_reproduced.ipynb

@@ -1202,34 +1202,34 @@
     {
      "data": {
       "text/plain": [
-       "['CNT_CHILDREN',\n",
+       "['DEF_60_CNT_SOCIAL_CIRCLE',\n",
+       " 'DAYS_LAST_PHONE_CHANGE',\n",
        " 'DAYS_BIRTH',\n",
        " 'AMT_INCOME_TOTAL',\n",
-       " 'HOUR_APPR_PROCESS_START',\n",
+       " 'OBS_30_CNT_SOCIAL_CIRCLE',\n",
+       " 'REGION_RATING_CLIENT_W_CITY',\n",
+       " 'AMT_REQ_CREDIT_BUREAU_WEEK',\n",
        " 'AMT_GOODS_PRICE',\n",
-       " 'DEF_30_CNT_SOCIAL_CIRCLE',\n",
+       " 'LIVE_CITY_NOT_WORK_CITY',\n",
+       " 'REG_REGION_NOT_LIVE_REGION',\n",
+       " 'HOUR_APPR_PROCESS_START',\n",
+       " 'REG_CITY_NOT_LIVE_CITY',\n",
        " 'AMT_REQ_CREDIT_BUREAU_QRT',\n",
-       " 'AMT_REQ_CREDIT_BUREAU_YEAR',\n",
-       " 'DEF_60_CNT_SOCIAL_CIRCLE',\n",
-       " 'AMT_REQ_CREDIT_BUREAU_HOUR',\n",
        " 'AMT_REQ_CREDIT_BUREAU_MON',\n",
-       " 'REGION_RATING_CLIENT_W_CITY',\n",
-       " 'EXT_SOURCE_2',\n",
-       " 'AMT_REQ_CREDIT_BUREAU_DAY',\n",
-       " 'OBS_30_CNT_SOCIAL_CIRCLE',\n",
-       " 'DAYS_ID_PUBLISH',\n",
-       " 'DAYS_LAST_PHONE_CHANGE',\n",
-       " 'REG_CITY_NOT_WORK_CITY',\n",
        " 'DAYS_EMPLOYED',\n",
-       " 'REGION_RATING_CLIENT',\n",
-       " 'LIVE_CITY_NOT_WORK_CITY',\n",
-       " 'REG_REGION_NOT_LIVE_REGION',\n",
-       " 'REGION_POPULATION_RELATIVE',\n",
+       " 'AMT_REQ_CREDIT_BUREAU_HOUR',\n",
        " 'DAYS_REGISTRATION',\n",
-       " 'REG_CITY_NOT_LIVE_CITY',\n",
+       " 'DAYS_ID_PUBLISH',\n",
        " 'EXT_SOURCE_3',\n",
+       " 'REGION_RATING_CLIENT',\n",
+       " 'CNT_CHILDREN',\n",
+       " 'DEF_30_CNT_SOCIAL_CIRCLE',\n",
        " 'AMT_ANNUITY',\n",
-       " 'AMT_REQ_CREDIT_BUREAU_WEEK']"
+       " 'AMT_REQ_CREDIT_BUREAU_YEAR',\n",
+       " 'EXT_SOURCE_2',\n",
+       " 'AMT_REQ_CREDIT_BUREAU_DAY',\n",
+       " 'REGION_POPULATION_RELATIVE',\n",
+       " 'REG_CITY_NOT_WORK_CITY']"
       ]
      },
      "execution_count": 27,
@@ -2089,6 +2089,7 @@
     "def evaluate_fitness(individual):\n",
     "    # Вычислите значения признаков на основе individual\n",
     "    X_train_gp = transform_gp_structure(individual, X_train)  # Вычисление новых признаков на обучающей выборке\n",
+    "    rf_model_gp = lr\n",
     "    rf_model_gp.fit(X_train_gp, y_train)  # Обучение модели случайного леса с новыми признаками\n",
     "    X_test_gp = transform_gp_structure(individual, X_test)  # Вычисление новых признаков на тестовой выборке\n",
     "    y_pred_gp = rf_model_gp.predict_proba(X_test_gp)[:, 1]  # Прогнозирование на тестовой выборке с новыми признаками\n",
@@ -2116,15 +2117,19 @@
      "traceback": [
       "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
       "\u001b[0;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
-      "\u001b[0;32m<ipython-input-39-c2fff693920c>\u001b[0m in \u001b[0;36m<cell line: 8>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      9\u001b[0m     \u001b[0;31m# Оценка фитнеса\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     10\u001b[0m     \u001b[0mfitnesses\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mmap\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mevaluate_fitness\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mpopulation\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 11\u001b[0;31m     \u001b[0;32mfor\u001b[0m \u001b[0mindividual\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfitness\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mzip\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mpopulation\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfitnesses\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     12\u001b[0m         \u001b[0mindividual\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfitness\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mvalues\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mfitness\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     13\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0;32m<ipython-input-37-c74419f37cb5>\u001b[0m in \u001b[0;36mevaluate_fitness\u001b[0;34m(individual)\u001b[0m\n\u001b[1;32m      7\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0mevaluate_fitness\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mindividual\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      8\u001b[0m     \u001b[0;31m# Вычислите значения признаков на основе individual\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 9\u001b[0;31m     \u001b[0mX_train_gp\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtransform_gp_structure\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mindividual\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mX_train\u001b[0m\u001b[0;34m)\u001b[0m  \u001b[0;31m# Вычисление новых признаков на обучающей выборке\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     10\u001b[0m     \u001b[0mrf_model_gp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfit\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX_train_gp\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my_train\u001b[0m\u001b[0;34m)\u001b[0m  \u001b[0;31m# Обучение модели случайного леса с новыми признаками\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     11\u001b[0m     \u001b[0mX_test_gp\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtransform_gp_structure\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mindividual\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mX_test\u001b[0m\u001b[0;34m)\u001b[0m  \u001b[0;31m# Вычисление новых признаков на тестовой выборке\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0;32m<ipython-input-37-c74419f37cb5>\u001b[0m in \u001b[0;36mtransform_gp_structure\u001b[0;34m(individual, X)\u001b[0m\n\u001b[1;32m      2\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0mtransform_gp_structure\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mindividual\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mX\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      3\u001b[0m     \u001b[0mexpr\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mgp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcompile\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mindividual\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mpset\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 4\u001b[0;31m     \u001b[0;32mreturn\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0marray\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mexpr\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0mrow\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mrow\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mX\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      5\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      6\u001b[0m \u001b[0;31m# Определение функции оценки фитнеса (ваша собственная функция)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0;32m<ipython-input-37-c74419f37cb5>\u001b[0m in \u001b[0;36m<listcomp>\u001b[0;34m(.0)\u001b[0m\n\u001b[1;32m      2\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0mtransform_gp_structure\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mindividual\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mX\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      3\u001b[0m     \u001b[0mexpr\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mgp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcompile\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mindividual\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mpset\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 4\u001b[0;31m     \u001b[0;32mreturn\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0marray\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mexpr\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0mrow\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mrow\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mX\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      5\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      6\u001b[0m \u001b[0;31m# Определение функции оценки фитнеса (ваша собственная функция)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m<ipython-input-39-c8e552423953>\u001b[0m in \u001b[0;36m<cell line: 12>\u001b[0;34m()\u001b[0m\n\u001b[1;32m     13\u001b[0m     \u001b[0;31m# Оценка фитнеса\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     14\u001b[0m     \u001b[0mfitnesses\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mmap\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mevaluate_fitness\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mpopulation\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 15\u001b[0;31m     \u001b[0;32mfor\u001b[0m \u001b[0mindividual\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfitness\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mzip\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mpopulation\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfitnesses\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     16\u001b[0m         \u001b[0mindividual\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfitness\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mvalues\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mfitness\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     17\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m<ipython-input-37-65cc5406f880>\u001b[0m in \u001b[0;36mevaluate_fitness\u001b[0;34m(individual)\u001b[0m\n\u001b[1;32m      7\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0mevaluate_fitness\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mindividual\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      8\u001b[0m     \u001b[0;31m# Вычислите значения признаков на основе individual\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 9\u001b[0;31m     \u001b[0mX_train_gp\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtransform_gp_structure\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mindividual\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mX_train\u001b[0m\u001b[0;34m)\u001b[0m  \u001b[0;31m# Вычисление новых признаков на обучающей выборке\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     10\u001b[0m     \u001b[0mrf_model_gp\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mlr\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     11\u001b[0m     \u001b[0mrf_model_gp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfit\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX_train_gp\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my_train\u001b[0m\u001b[0;34m)\u001b[0m  \u001b[0;31m# Обучение модели случайного леса с новыми признаками\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m<ipython-input-37-65cc5406f880>\u001b[0m in \u001b[0;36mtransform_gp_structure\u001b[0;34m(individual, X)\u001b[0m\n\u001b[1;32m      2\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0mtransform_gp_structure\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mindividual\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mX\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      3\u001b[0m     \u001b[0mexpr\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mgp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcompile\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mindividual\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mpset\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 4\u001b[0;31m     \u001b[0;32mreturn\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0marray\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mexpr\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0mrow\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mrow\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mX\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      5\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      6\u001b[0m \u001b[0;31m# Определение функции оценки фитнеса (ваша собственная функция)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m<ipython-input-37-65cc5406f880>\u001b[0m in \u001b[0;36m<listcomp>\u001b[0;34m(.0)\u001b[0m\n\u001b[1;32m      2\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0mtransform_gp_structure\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mindividual\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mX\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      3\u001b[0m     \u001b[0mexpr\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mgp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcompile\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mindividual\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mpset\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 4\u001b[0;31m     \u001b[0;32mreturn\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0marray\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mexpr\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0mrow\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mrow\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mX\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      5\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      6\u001b[0m \u001b[0;31m# Определение функции оценки фитнеса (ваша собственная функция)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
       "\u001b[0;31mTypeError\u001b[0m: <lambda>() takes 2 positional arguments but 72 were given"
      ]
     }
    ],
    "source": [
+    "import random # fix missing import and vars for crash isolation purposes\n",
+    "crossover_prob = 0.5\n",
+    "mutation_prob = 0.5\n",
+    "\n",
     "pop_size = 100  # Размер популяции\n",
     "num_generations = 50  # Количество поколений\n",
     "\n",

benchmark/pandas_14/pandas_14_fixed.ipynb

@@ -1265,11 +1265,11 @@
     "for i in range(filas):\n",
     "    for j in range(col):\n",
     "        n = i * col + j\n",
-    "        if n < len(nombres_col):\n",
-    "            # fix --- add a condition\n",
-    "            if n < len(nombres_col.columns): # fix\n",
-    "                axs[i, j].set_title(nombres_col.columns[n])\n",
-    "                axs[i, j].scatter(df[nombres_col.columns[n]][:N], y[:N])\n",
+    "        # fix --- fix the guarding condition\n",
+    "#         if n < len(nombres_col):\n",
+    "        if n < len(nombres_col.columns):\n",
+    "            axs[i, j].set_title(nombres_col.columns[n])\n",
+    "            axs[i, j].scatter(df[nombres_col.columns[n]][:N], y[:N])\n",
     "\n",
     "fig.tight_layout()\n",
     "plt.show()\n"

benchmark/sklearn_10/sklearn_10_fixed.ipynb

@@ -1553,7 +1553,7 @@
     "kf_rmse = []\n",
     "for fold, (train_idx, valid_idx) in enumerate(KFold(n_splits=n_splits, shuffle=True).split(X_train,y_train)):\n",
     "    # Fetch the train-validation indices.\n",
-    "    # fix 1: should iloc row ids, should not rewrite X_train, y_train\n",
+    "    # fix: should iloc row ids, should not rewrite X_train, y_train\n",
     "    X_train_fold, y_train_fold = X_train.iloc[train_idx], y_train.iloc[train_idx]\n",
     "    X_valid_fold, y_valid_fold = X_train.iloc[valid_idx], y_train.iloc[valid_idx]\n",
     "    \n",
@@ -1566,20 +1566,18 @@
     "    # Validation predictions.\n",
     "    valid_pred = model.predict(X_valid_fold)\n",
     "    \n",
-    "    # fix 2: non-existing func\n",
-    "#     rmse = my_rmse(y_valid_fold, valid_pred)\n",
+    "#     rmse = my_rmse(y_valid_fold, valid_pred) # fix for crash isolation purpose, non-existing func\n",
     "    rmse = np.sqrt(mean_squared_error(y_valid_fold, valid_pred))\n",
     "    \n",
     "    print(f'Fold {fold+1}/{n_splits} RMSE: {rmse:.4f}')\n",
     "    kf_rmse.append(rmse)\n",
     "    \n",
-    "    # fix 3: non-existing test variable\n",
     "    # Use the model trained for 1/n_splits of the output predictions.\n",
     "    if test_preds is None:\n",
-    "        test_preds = model.predict(X_test)\n",
+    "        test_preds = model.predict(X_test) # fix for crash isolation purpose: non-existing test_x variable\n",
     "    else:\n",
     "        # This is kind of naughty for numerical accuracy (may overflow on other problems) but slightly quicker.\n",
-    "        test_preds += model.predict(X_test)\n",
+    "        test_preds += model.predict(X_test) # fix for crash isolation purpose: non-existing test_x variable\n",
     "\n",
     "test_preds /= n_splits\n",
     "print(f'Average KFold RMSE: {np.mean(np.array(kf_rmse)):.5f}')"

benchmark/sklearn_10/sklearn_10_reproduced.ipynb

@@ -1539,8 +1539,8 @@
    ],
    "source": [
     "params = {}\n",
-    "params['tree_method'] = 'gpu_hist'\n",
-    "params['predictor'] = 'gpu_predictor'\n",
+    "params['tree_method'] = 'hist' # fix (for testing locally), use cpu instead of gpu\n",
+    "params['predictor'] = 'predictor'\n",
     "params['n_jobs'] = 4\n",
     "\n",
     "\n",
@@ -1561,16 +1561,16 @@
     "    \n",
     "    # Validation predictions.\n",
     "    valid_pred = model.predict(X_valid)\n",
-    "    rmse = my_rmse(y_valid, valid_pred)\n",
-    "    print(f'Fold {fold+1}/{n_splits} RMSE: {rmse:.4f}')\n",
+    "#     rmse = my_rmse(y_valid_fold, valid_pred) # fix for crash isolation purpose, non-existing func\n",
+    "    rmse = np.sqrt(mean_squared_error(y_valid_fold, valid_pred))    print(f'Fold {fold+1}/{n_splits} RMSE: {rmse:.4f}')\n",
     "    kf_rmse.append(rmse)\n",
     "    \n",
     "    # Use the model trained for 1/n_splits of the output predictions.\n",
     "    if test_preds is None:\n",
-    "        test_preds = model.predict(test_x)\n",
+    "        test_preds = model.predict(X_test) # fix for crash isolation purpose: non-existing test_x variable\n",
     "    else:\n",
     "        # This is kind of naughty for numerical accuracy (may overflow on other problems) but slightly quicker.\n",
-    "        test_preds += model.predict(test_x)\n",
+    "        test_preds += model.predict(X_test) # fix for crash isolation purpose: non-existing test_x variable\n",
     "\n",
     "test_preds /= n_splits\n",
     "print(f'Average KFold RMSE: {np.mean(np.array(kf_rmse)):.5f}')"

benchmark/sklearn_6/sklearn_6_fixed.ipynb

@@ -916,7 +916,7 @@
    },
    "outputs": [],
    "source": [
-    "test_ds_ids = test_ds['Id'] # fix --- save ids for submission\n",
+    "test_ds_ids = test_ds['Id'] # fix for crash isolation purpose\n",
     "test_ds.drop(['Id', 'MoSold', 'GarageYrBlt', 'Condition1', 'Condition2'], axis = 1, inplace = True)"
    ]
   },
@@ -1028,7 +1028,7 @@
    "source": [
     "predictions = FReg.predict(test_ds)\n",
     "submissions_df = pd.DataFrame({\n",
-    "    \"ID\" : test_ds_ids, # test_data['ID'], # fix --- wrong variable name\n",
+    "    \"ID\" : test_ds_ids, # test_data['ID'], # fix for crash isolation purpose\n",
     "    \"Predictions\" : predictions\n",
     "})\n",
     "\n",

benchmark/sklearn_6/sklearn_6_reproduced.ipynb

@@ -916,6 +916,7 @@
    },
    "outputs": [],
    "source": [
+    "test_ds_ids = test_ds['Id'] # fix for crash isolation purpose\n",
     "test_ds.drop(['Id', 'MoSold', 'GarageYrBlt', 'Condition1', 'Condition2'], axis = 1, inplace = True)"
    ]
   },
@@ -1051,7 +1052,7 @@
      "traceback": [
       "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
       "\u001b[0;31mValueError\u001b[0m                                Traceback (most recent call last)",
-      "\u001b[0;32m<ipython-input-15-e055db40fbd5>\u001b[0m in \u001b[0;36m<cell line: 1>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mpredictions\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mFReg\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpredict\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtest_ds\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      2\u001b[0m submissions_df = pd.DataFrame({\n\u001b[1;32m      3\u001b[0m     \u001b[0;34m\"ID\"\u001b[0m \u001b[0;34m:\u001b[0m \u001b[0mtest_data\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'ID'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      4\u001b[0m     \u001b[0;34m\"Predictions\"\u001b[0m \u001b[0;34m:\u001b[0m \u001b[0mpredictions\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      5\u001b[0m })\n",
+      "\u001b[0;32m<ipython-input-15-0b091944b5d9>\u001b[0m in \u001b[0;36m<cell line: 1>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mpredictions\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mFReg\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpredict\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtest_ds\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      2\u001b[0m submissions_df = pd.DataFrame({\n\u001b[1;32m      3\u001b[0m     \u001b[0;34m\"ID\"\u001b[0m \u001b[0;34m:\u001b[0m \u001b[0mtest_ds_ids\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;31m# test_data['ID'], # fix for crash isolation purpose\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      4\u001b[0m     \u001b[0;34m\"Predictions\"\u001b[0m \u001b[0;34m:\u001b[0m \u001b[0mpredictions\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      5\u001b[0m })\n",
       "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/sklearn/ensemble/_forest.py\u001b[0m in \u001b[0;36mpredict\u001b[0;34m(self, X)\u001b[0m\n\u001b[1;32m    979\u001b[0m         \u001b[0mcheck_is_fitted\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    980\u001b[0m         \u001b[0;31m# Check data\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 981\u001b[0;31m         \u001b[0mX\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_validate_X_predict\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    982\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    983\u001b[0m         \u001b[0;31m# Assign chunk of trees to jobs\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
       "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/sklearn/ensemble/_forest.py\u001b[0m in \u001b[0;36m_validate_X_predict\u001b[0;34m(self, X)\u001b[0m\n\u001b[1;32m    600\u001b[0m         Validate X whenever one tries to predict, apply, predict_proba.\"\"\"\n\u001b[1;32m    601\u001b[0m         \u001b[0mcheck_is_fitted\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 602\u001b[0;31m         \u001b[0mX\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_validate_data\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdtype\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mDTYPE\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0maccept_sparse\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m\"csr\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mreset\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mFalse\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    603\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0missparse\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mand\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mX\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mindices\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdtype\u001b[0m \u001b[0;34m!=\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mintc\u001b[0m \u001b[0;32mor\u001b[0m \u001b[0mX\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mindptr\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdtype\u001b[0m \u001b[0;34m!=\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mintc\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    604\u001b[0m             \u001b[0;32mraise\u001b[0m \u001b[0mValueError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"No support for np.int64 index based sparse matrices\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
       "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/sklearn/base.py\u001b[0m in \u001b[0;36m_validate_data\u001b[0;34m(self, X, y, reset, validate_separately, **check_params)\u001b[0m\n\u001b[1;32m    546\u001b[0m             \u001b[0mvalidated\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    547\u001b[0m         \"\"\"\n\u001b[0;32m--> 548\u001b[0;31m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_check_feature_names\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mreset\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mreset\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    549\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    550\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0my\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mNone\u001b[0m \u001b[0;32mand\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_get_tags\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m\"requires_y\"\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
@@ -1063,7 +1064,7 @@
    "source": [
     "predictions = FReg.predict(test_ds)\n",
     "submissions_df = pd.DataFrame({\n",
-    "    \"ID\" : test_data['ID'],\n",
+    "    \"ID\" : test_ds_ids, # test_data['ID'], # fix for crash isolation purpose\n",
     "    \"Predictions\" : predictions\n",
     "})\n",
     "\n",

benchmark/sklearn_7/sklearn_7_fixed.ipynb

@@ -768,7 +768,7 @@
     }
    ],
    "source": [
-    "import numpy as np # fix --- imports\n",
+    "import numpy as np # fix for crash isolation purpose (only testing the fix)\n",
     "\n",
     "# Evaluate the model\n",
     "mse = mean_squared_error(y_test, y_pred)\n",

benchmark/torch_1/torch_1_reproduced.ipynb

@@ -577,7 +577,7 @@
     "        iteration_number += 10\n",
     "        counter.append(iteration_number)\n",
     "        loss.append(loss_contrastive.item())\n",
-    "    show_plot(counter, loss)  \n",
+    "#     show_plot(counter, loss)  \n",
     "    return net\n",
     "#set the device to cuda\n",
     "device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')\n",

benchmark/torch_5/torch_5_fixed.ipynb

@@ -249,10 +249,7 @@
     "    total = 0.0\n",
     "    \n",
     "    with torch.no_grad():\n",
-    "        # fix 4 ----- undefined variable \n",
-    "#         for data in test_loader:\n",
-    "        for data in testing_set_loader:\n",
-    "        \n",
+    "        for data in testing_set_loader: # for data in test_loader: # fix for crash isolation reasons\n",
     "            images, labels = data\n",
     "            # run the model on the test set to predict labels\n",
     "            outputs = model(images)\n",
@@ -281,9 +278,7 @@
     "        running_loss = 0.0\n",
     "        running_acc = 0.0\n",
     "\n",
-    "        # fix 2-------should use previously created dataloader\n",
-    "#         for i, (images, classes) in enumerate(dataset, 0):\n",
-    "        for i, (images, classes) in enumerate(training_set_loader, 0):\n",
+    "        for i, (images, classes) in enumerate(training_set_loader, 0): # for i, (images, classes) in enumerate(dataset, 0): # fix for crash isolation reasons\n",
     "            \n",
     "            # fix 1 ---- inputs and labels should be tensors\n",
     "#             # get the inputs\n",
@@ -357,9 +352,7 @@
     "def testBatch():\n",
     "    # get batch of images from the test DataLoader  \n",
     "    \n",
-    "    # fix 5----------undefined variable\n",
-    "#     images, labels = next(iter(test_loader))\n",
-    "    images, labels = next(iter(testing_set_loader))\n",
+    "    images, labels = next(iter(testing_set_loader)) # images, labels = next(iter(test_loader)) # fix for crash isolation reasons\n",
     "    \n",
     "\n",
     "    # show all images as one image grid\n",
@@ -407,7 +400,7 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "<ipython-input-7-9774f697d913>:16: FutureWarning: You are using `torch.load` with `weights_only=False` (the current default value), which uses the default pickle module implicitly. It is possible to construct malicious pickle data which will execute arbitrary code during unpickling (See https://github.com/pytorch/pytorch/blob/main/SECURITY.md#untrusted-models for more details). In a future release, the default value for `weights_only` will be flipped to `True`. This limits the functions that could be executed during unpickling. Arbitrary objects will no longer be allowed to be loaded via this mode unless they are explicitly allowlisted by the user via `torch.serialization.add_safe_globals`. We recommend you start setting `weights_only=True` for any use case where you don't have full control of the loaded file. Please open an issue on GitHub for any issues related to this experimental feature.\n",
+      "<ipython-input-7-e3520913a2eb>:13: FutureWarning: You are using `torch.load` with `weights_only=False` (the current default value), which uses the default pickle module implicitly. It is possible to construct malicious pickle data which will execute arbitrary code during unpickling (See https://github.com/pytorch/pytorch/blob/main/SECURITY.md#untrusted-models for more details). In a future release, the default value for `weights_only` will be flipped to `True`. This limits the functions that could be executed during unpickling. Arbitrary objects will no longer be allowed to be loaded via this mode unless they are explicitly allowlisted by the user via `torch.serialization.add_safe_globals`. We recommend you start setting `weights_only=True` for any use case where you don't have full control of the loaded file. Please open an issue on GitHub for any issues related to this experimental feature.\n",
       "  model.load_state_dict(torch.load(path))\n"
      ]
     },
@@ -438,10 +431,7 @@
     "    print('Finished Training')\n",
     "\n",
     "    # Test which classes performed well\n",
-    "    \n",
-    "    # fix 6----------undefined function\n",
-    "#     testModelAccuracy()\n",
-    "    testAccuracy()\n",
+    "    testAccuracy() # testModelAccuracy() # fix for crash isolation reasons\n",
     "    \n",
     "    # Let's load the model we just created and test the accuracy per label\n",
     "    model = Network()\n",

benchmark/torch_5/torch_5_reproduced.ipynb

@@ -232,7 +232,7 @@
     "    total = 0.0\n",
     "    \n",
     "    with torch.no_grad():\n",
-    "        for data in test_loader:\n",
+    "        for data in testing_set_loader: # for data in test_loader: # fix for crash isolation reasons\n",
     "            images, labels = data\n",
     "            # run the model on the test set to predict labels\n",
     "            outputs = model(images)\n",
@@ -261,7 +261,7 @@
     "        running_loss = 0.0\n",
     "        running_acc = 0.0\n",
     "\n",
-    "        for i, (images, classes) in enumerate(dataset, 0):\n",
+    "        for i, (images, classes) in enumerate(training_set_loader, 0): # for i, (images, classes) in enumerate(dataset, 0): # fix for crash isolation reasons\n",
     "            \n",
     "            # get the inputs\n",
     "            images = Variable(images.to(device))\n",
@@ -327,7 +327,7 @@
     "# Function to test the model with a batch of images and show the labels predictions\n",
     "def testBatch():\n",
     "    # get batch of images from the test DataLoader  \n",
-    "    images, labels = next(iter(test_loader))\n",
+    "    images, labels = next(iter(testing_set_loader)) # images, labels = next(iter(test_loader)) # fix for crash isolation reasons\n",
     "\n",
     "    # show all images as one image grid\n",
     "    imageshow(torchvision.utils.make_grid(images))\n",
@@ -367,6 +367,14 @@
       "The model will be running on cpu device\n"
      ]
     },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "<ipython-input-5-9eca036b5181>:50: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n",
+      "  classes = torch.tensor(classes)\n"
+     ]
+    },
     {
      "ename": "TypeError",
      "evalue": "conv2d() received an invalid combination of arguments - got (torch.device, Parameter, Parameter, tuple, tuple, tuple, int), but expected one of:\n * (Tensor input, Tensor weight, Tensor bias = None, tuple of ints stride = 1, tuple of ints padding = 0, tuple of ints dilation = 1, int groups = 1)\n      didn't match because some of the arguments have invalid types: (\u001b[31;1mtorch.device\u001b[0m, \u001b[31;1mParameter\u001b[0m, \u001b[31;1mParameter\u001b[0m, \u001b[31;1mtuple of (int, int)\u001b[0m, \u001b[31;1mtuple of (int, int)\u001b[0m, \u001b[31;1mtuple of (int, int)\u001b[0m, \u001b[31;1mint\u001b[0m)\n * (Tensor input, Tensor weight, Tensor bias = None, tuple of ints stride = 1, str padding = \"valid\", tuple of ints dilation = 1, int groups = 1)\n      didn't match because some of the arguments have invalid types: (\u001b[31;1mtorch.device\u001b[0m, \u001b[31;1mParameter\u001b[0m, \u001b[31;1mParameter\u001b[0m, \u001b[31;1mtuple of (int, int)\u001b[0m, \u001b[31;1mtuple of (int, int)\u001b[0m, \u001b[31;1mtuple of (int, int)\u001b[0m, \u001b[31;1mint\u001b[0m)\n",
@@ -374,8 +382,8 @@
      "traceback": [
       "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
       "\u001b[0;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
-      "\u001b[0;32m<ipython-input-7-94cb09bf2a34>\u001b[0m in \u001b[0;36m<cell line: 1>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      2\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      3\u001b[0m     \u001b[0;31m# Let's build our model\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 4\u001b[0;31m     \u001b[0mtrain\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m5\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      5\u001b[0m     \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'Finished Training'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      6\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0;32m<ipython-input-5-9ad46287b0ec>\u001b[0m in \u001b[0;36mtrain\u001b[0;34m(num_epochs)\u001b[0m\n\u001b[1;32m     54\u001b[0m             \u001b[0moptimizer\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mzero_grad\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     55\u001b[0m             \u001b[0;31m# predict classes using images from the training set\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 56\u001b[0;31m             \u001b[0moutputs\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mmodel\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdevice\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     57\u001b[0m             \u001b[0;31m# compute the loss based on model output and real labels\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     58\u001b[0m             \u001b[0mloss\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mloss_fn\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0moutputs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mclass_names\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m<ipython-input-7-3a1624416c0c>\u001b[0m in \u001b[0;36m<cell line: 1>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      2\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      3\u001b[0m     \u001b[0;31m# Let's build our model\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 4\u001b[0;31m     \u001b[0mtrain\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m5\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      5\u001b[0m     \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'Finished Training'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      6\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m<ipython-input-5-9eca036b5181>\u001b[0m in \u001b[0;36mtrain\u001b[0;34m(num_epochs)\u001b[0m\n\u001b[1;32m     54\u001b[0m             \u001b[0moptimizer\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mzero_grad\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     55\u001b[0m             \u001b[0;31m# predict classes using images from the training set\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 56\u001b[0;31m             \u001b[0moutputs\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mmodel\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdevice\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     57\u001b[0m             \u001b[0;31m# compute the loss based on model output and real labels\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     58\u001b[0m             \u001b[0mloss\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mloss_fn\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0moutputs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mclass_names\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
       "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/torch/nn/modules/module.py\u001b[0m in \u001b[0;36m_wrapped_call_impl\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m   1551\u001b[0m             \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_compiled_call_impl\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m  \u001b[0;31m# type: ignore[misc]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1552\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1553\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_call_impl\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1554\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1555\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0m_call_impl\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
       "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/torch/nn/modules/module.py\u001b[0m in \u001b[0;36m_call_impl\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m   1560\u001b[0m                 \u001b[0;32mor\u001b[0m \u001b[0m_global_backward_pre_hooks\u001b[0m \u001b[0;32mor\u001b[0m \u001b[0m_global_backward_hooks\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1561\u001b[0m                 or _global_forward_hooks or _global_forward_pre_hooks):\n\u001b[0;32m-> 1562\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0mforward_call\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1563\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1564\u001b[0m         \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
       "\u001b[0;32m<ipython-input-3-0e24d66103f7>\u001b[0m in \u001b[0;36mforward\u001b[0;34m(self, input)\u001b[0m\n\u001b[1;32m     21\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     22\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mforward\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0minput\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 23\u001b[0;31m         \u001b[0moutput\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mF\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrelu\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mbn1\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mconv1\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0minput\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     24\u001b[0m         \u001b[0moutput\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mF\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrelu\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mbn2\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mconv2\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0moutput\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     25\u001b[0m         \u001b[0moutput\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpool\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0moutput\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
@@ -395,7 +403,7 @@
     "    print('Finished Training')\n",
     "\n",
     "    # Test which classes performed well\n",
-    "    testModelAccuracy()\n",
+    "    testAccuracy() # testModelAccuracy() # fix for crash isolation reasons\n",
     "    \n",
     "    # Let's load the model we just created and test the accuracy per label\n",
     "    model = Network()\n",

benchmark/torch_6/torch_6_fixed.ipynb

@@ -1527,7 +1527,7 @@
     "        #print(\"output shape is,\",output.shape)\n",
     "        #print(\"label shape is,\",label.shape)\n",
     "   \n",
-    "        # fix ---- labels had shape [batch_size, 393], but model predicts [batch_size, 6294]. That won’t match\n",
+    "        # fix ---- label is one-hot encoded, but nn.CrossEntropyLoss expects a 1D target tensor of class indices\n",
     "        label = label.argmax(dim=1)\n",
     "    \n",
     "        loss_ = loss(output, label).to(device)\n",