Updated references for files

Author: zilbermanor

demos/location-based-recommendation/06-creating-grafana-dashboard.ipynb

@@ -128,7 +128,7 @@
     "* Open up the Grafana service (click on the HTTPS link)\n",
     "* Click on + and then \"Import\"\n",
     "* On the righ hand side - click on the Upload .Json file\n",
-    "* Upload the Json file under location_based_recommendation/dashboards\n",
+    "* Upload the Json file under location-based-recommendation/dashboards\n",
     "* It could take few minutes till the data will be displayed in the dashboard"
    ]
   },

demos/location-based-recommendation/generate-model/03-train-prod2vec-skipgram.ipynb

@@ -190,7 +190,7 @@
     "                    print('CV',cv_loss / cv_steps)\n",
     "\n",
     "    def save_model(self, step):\n",
-    "        self.saver.save(self.sess, os.path.join(self.models_dir, 'prod2vec_skip_gram'), global_step=step)\n",
+    "        self.saver.save(self.sess, os.path.join(self.models_dir, 'prod2vec-skip-gram'), global_step=step)\n",
     "\n",
     "    def load_model(self, path):\n",
     "        self.saver = tf.train.import_meta_graph(self.models_dir + path)\n",
@@ -335,30 +335,13 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 35,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "ename": "NotFoundError",
-     "evalue": "/Users/orz/projects/iguazio/tutorials/demos/location_based_recommendation/models; No such file or directory",
-     "output_type": "error",
-     "traceback": [
-      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
-      "\u001b[0;31mNotFoundError\u001b[0m                             Traceback (most recent call last)",
-      "\u001b[0;32m<ipython-input-35-488b1ef660e6>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0mtf\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mreset_default_graph\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      2\u001b[0m \u001b[0mmodel\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mProduct2VecSkipGram\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdf_train\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdf_cv\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mbatch_size\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmax\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mproducts\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mproduct_id\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 3\u001b[0;31m \u001b[0mmodel\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mload_model\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'/trained/prod2vec_skip_gram-80000.meta'\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      4\u001b[0m \u001b[0mmodel\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtrain\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m120001\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m20000\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdf_cv\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m//\u001b[0m \u001b[0mbatch_size\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mrates\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-23-070b0fe01d04>\u001b[0m in \u001b[0;36mload_model\u001b[0;34m(self, path)\u001b[0m\n\u001b[1;32m     64\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mload_model\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mpath\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     65\u001b[0m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msaver\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtf\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtrain\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mimport_meta_graph\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmodels_dir\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0mpath\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 66\u001b[0;31m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msaver\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrestore\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msess\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtf\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtrain\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mlatest_checkpoint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmodels_dir\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     67\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     68\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mbuild_graph\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[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0;32m~/.pythonlibs/lib/python3.6/site-packages/tensorflow/python/training/checkpoint_management.py\u001b[0m in \u001b[0;36mlatest_checkpoint\u001b[0;34m(checkpoint_dir, latest_filename)\u001b[0m\n\u001b[1;32m    341\u001b[0m     v1_path = _prefix_to_checkpoint_path(ckpt.model_checkpoint_path,\n\u001b[1;32m    342\u001b[0m                                          saver_pb2.SaverDef.V1)\n\u001b[0;32m--> 343\u001b[0;31m     if file_io.get_matching_files(v2_path) or file_io.get_matching_files(\n\u001b[0m\u001b[1;32m    344\u001b[0m         v1_path):\n\u001b[1;32m    345\u001b[0m       \u001b[0;32mreturn\u001b[0m \u001b[0mckpt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmodel_checkpoint_path\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0;32m~/.pythonlibs/lib/python3.6/site-packages/tensorflow/python/lib/io/file_io.py\u001b[0m in \u001b[0;36mget_matching_files\u001b[0;34m(filename)\u001b[0m\n\u001b[1;32m    359\u001b[0m     \u001b[0merrors\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mOpError\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mIf\u001b[0m \u001b[0mthere\u001b[0m \u001b[0mare\u001b[0m \u001b[0mfilesystem\u001b[0m \u001b[0;34m/\u001b[0m \u001b[0mdirectory\u001b[0m \u001b[0mlisting\u001b[0m \u001b[0merrors\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    360\u001b[0m   \"\"\"\n\u001b[0;32m--> 361\u001b[0;31m   \u001b[0;32mreturn\u001b[0m \u001b[0mget_matching_files_v2\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfilename\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    362\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    363\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0;32m~/.pythonlibs/lib/python3.6/site-packages/tensorflow/python/lib/io/file_io.py\u001b[0m in \u001b[0;36mget_matching_files_v2\u001b[0;34m(pattern)\u001b[0m\n\u001b[1;32m    387\u001b[0m           \u001b[0;31m# Convert the filenames to string from bytes.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    388\u001b[0m           \u001b[0mcompat\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mas_str_any\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmatching_filename\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 389\u001b[0;31m           \u001b[0;32mfor\u001b[0m \u001b[0msingle_filename\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mpattern\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    390\u001b[0m           for matching_filename in pywrap_tensorflow.GetMatchingFiles(\n\u001b[1;32m    391\u001b[0m               compat.as_bytes(single_filename), status)\n",
-      "\u001b[0;32m~/.pythonlibs/lib/python3.6/site-packages/tensorflow/python/framework/errors_impl.py\u001b[0m in \u001b[0;36m__exit__\u001b[0;34m(self, type_arg, value_arg, traceback_arg)\u001b[0m\n\u001b[1;32m    526\u001b[0m             \u001b[0;32mNone\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    527\u001b[0m             \u001b[0mcompat\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mas_text\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mc_api\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mTF_Message\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mstatus\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mstatus\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[0;32m--> 528\u001b[0;31m             c_api.TF_GetCode(self.status.status))\n\u001b[0m\u001b[1;32m    529\u001b[0m     \u001b[0;31m# Delete the underlying status object from memory otherwise it stays alive\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    530\u001b[0m     \u001b[0;31m# as there is a reference to status from this from the traceback due to\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0;31mNotFoundError\u001b[0m: /Users/orz/projects/iguazio/tutorials/demos/location_based_recommendation/models; No such file or directory"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "tf.reset_default_graph()\n",
     "model = Product2VecSkipGram(df_train, df_cv, batch_size, 1, 1, np.max(products.product_id) + 1)\n",
-    "model.load_model('/trained/prod2vec_skip_gram-80000.meta')\n",
+    "model.load_model(os.path.join(MODELS_DIR, 'trained', 'prod2vec-skip-gram-80000.meta'))\n",
     "model.train(120001, 20000, len(df_cv) // batch_size, rates)"
    ]
   },
@@ -377,7 +360,7 @@
    "source": [
     "tf.reset_default_graph()\n",
     "model = Product2VecSkipGram(df_train, df_cv, len(products), 1, 1, np.max(products.product_id) + 1)\n",
-    "model.load_model('prod2vec_skip_gram-80000.meta')\n",
+    "model.load_model(os.path.join(MODELS_DIR, 'trained', 'prod2vec-skip_gram-80000.meta'))\n",
     "embd = model.predict(products.product_id.values)\n",
     "products = pd.concat([products, pd.DataFrame(embd)], axis=1)\n",
     "products.to_pickle(DATA_DIR + 'product_embeddings.pkl')"
@@ -388,9 +371,7 @@
    "execution_count": null,
    "metadata": {},
    "outputs": [],
-   "source": [
-    "tutorials/demos/location_based_recommendation/models/trained/prod2vec_skip_gram-80000.meta"
-   ]
+   "source": []
   }
  ],
  "metadata": {
@@ -413,5 +394,5 @@
   }
  },
  "nbformat": 4,
- "nbformat_minor": 2
+ "nbformat_minor": 4
 }

demos/location-based-recommendation/generate-model/04-lgbm.ipynb

@@ -816,7 +816,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "model = lgb.Booster(model_file=MODELS_DIR + 'lgb.model')"
+    "model = lgb.Booster(model_file=os.path.join(MODELS_DIR,'lgb.model'))"
    ]
   }
  ],
@@ -840,5 +840,5 @@
   }
  },
  "nbformat": 4,
- "nbformat_minor": 2
+ "nbformat_minor": 4
 }

demos/location-based-recommendation/models/checkpoint

@@ -1,6 +1,6 @@
-model_checkpoint_path: "/Users/orz/projects/iguazio/tutorials/demos/location_based_recommendation/models/trained/prod2vec_skip_gram-80000"
-all_model_checkpoint_paths: "/Users/orz/projects/iguazio/tutorials/demos/location_based_recommendation/models/trained/prod2vec_skip_gram-0"
-all_model_checkpoint_paths: "/Users/orz/projects/iguazio/tutorials/demos/location_based_recommendation/models/trained/prod2vec_skip_gram-20000"
-all_model_checkpoint_paths: "/Users/orz/projects/iguazio/tutorials/demos/location_based_recommendation/models/trained/prod2vec_skip_gram-40000"
-all_model_checkpoint_paths: "/Users/orz/projects/iguazio/tutorials/demos/location_based_recommendation/models/trained/prod2vec_skip_gram-60000"
-all_model_checkpoint_paths: "/Users/orz/projects/iguazio/tutorials/demos/location_based_recommendation/models/trained/prod2vec_skip_gram-80000"
+model_checkpoint_path: "prod2vec-skip-gram-80000"
+all_model_checkpoint_paths: "prod2vec-skip-gram-0"
+all_model_checkpoint_paths: "prod2vec-skip_gram-20000"
+all_model_checkpoint_paths: "prod2vec-skip-gram-40000"
+all_model_checkpoint_paths: "prod2vec-skip-gram-60000"
+all_model_checkpoint_paths: "prod2vec-skip-gram-80000"