I'm trying to load in my models into my second notebook using a simple for loop like this:
\nmodelling_list = {}
\nmodel_list = XGBClassifier()
for t in range(1,19):
\nmodelling_list = model_list.load_model(f\"/kaggle/input/student-performance-notebook/XGB_question{t}.xgb\") \nprint(modelling_list)
\nBut it doesn't end up saving anything into the list after I run the code. I have attached an image of the outputs I'm trying to read in, and it works if I just load one model (e.g. \"/kaggle/input/student-performance-notebook/XGB_question1.xgb\"). I'm an R user by trade, so if I'm messing up some Python syntax please let me know. Thanks for any help.
", "messages": [ { "id": "2226564", "postDate": "04/19/2023 03:27:16", "content": "I'm trying to load in my models into my second notebook using a simple for loop like this:
\nmodelling_list = {}
\nmodel_list = XGBClassifier()
for t in range(1,19):
\nmodelling_list = model_list.load_model(f\"/kaggle/input/student-performance-notebook/XGB_question{t}.xgb\") \nprint(modelling_list)
\nBut it doesn't end up saving anything into the list after I run the code. I have attached an image of the outputs I'm trying to read in, and it works if I just load one model (e.g. \"/kaggle/input/student-performance-notebook/XGB_question1.xgb\"). I'm an R user by trade, so if I'm messing up some Python syntax please let me know. Thanks for any help.
", "rawMarkdown": "I'm trying to load in my models into my second notebook using a simple for loop like this:\n\nmodelling_list = {}\nmodel_list = XGBClassifier()\n\nfor t in range(1,19):\n\n modelling_list = model_list.load_model(f\"/kaggle/input/student-performance-notebook/XGB_question{t}.xgb\") \n \n\nprint(modelling_list)\n\n\nBut it doesn't end up saving anything into the list after I run the code. I have attached an image of the outputs I'm trying to read in, and it works if I just load one model (e.g. \"/kaggle/input/student-performance-notebook/XGB_question1.xgb\"). I'm an R user by trade, so if I'm messing up some Python syntax please let me know. Thanks for any help.", "votes": null }, { "id": "2226582", "postDate": "04/19/2023 03:52:02", "content": "Hi @paulmerica if you want to load multiple XGBClassifier models, you should use a list to store them. Here's what it's supposed to look like:
\n\nmodel_list = []\n t (, ):\n \n model = XGBClassifier()\n model.load_model()\n model_list.append(model)\n\n\nprediction = model_list[].predict(\n(prediction)\nHope this helps ✌️
\n(Extra: You can also save XGB in JSON format, work fine as well)
I had the same question and I have documented what worked for me here. https://www.kaggle.com/competitions/predict-student-performance-from-game-play/discussion/402178
", "rawMarkdown": "I had the same question and I have documented what worked for me here. https://www.kaggle.com/competitions/predict-student-performance-from-game-play/discussion/402178", "votes": null }, { "id": "2227396", "postDate": "04/19/2023 18:25:00", "content": "\n\nHi @paulmerica if you want to load multiple XGBClassifier models, you should use a list to store them. Here's what it's supposed to look like:
\n\n\nmodel_list = []\n t (, ):\n \n model = XGBClassifier()\n model.load_model()\n model_list.append(model)\n\n\nprediction = model_list[].predict(\n(prediction)\nHope this helps ✌️
\n
\n (Extra: You can also save XGB in JSON format, work fine as well)
This solved my problem. Thanks so much for the help, I'm new to python so using .append was not something I knew I needed to use. Thanks again! I might end up making an inference notebook to help others soon.
", "rawMarkdown": "> Hi @paulmerica if you want to load multiple XGBClassifier models, you should use a list to store them. Here's what it's supposed to look like:\n> \n> ```python\n> # Save each model to list\n> model_list = []\n> for t in range(1, 19):\n> # Includes XGBClassifier() inside so it loads the new model on every loop\n> model = XGBClassifier()\n> model.load_model(f\"/kaggle/input/student-performance-notebook/XGB_question{t}.xgb\")\n> model_list.append(model)\n> \n> # Call model with prefer number\n> prediction = model_list[0].predict(###your target feature###)\n> print(prediction)\n> ```\n> \n> Hope this helps ✌️\n> (Extra: You can also save [XGB in JSON format](https://www.kaggle.com/competitions/predict-student-performance-from-game-play/discussion/398500#2202834), work fine as well)\n\nThis solved my problem. Thanks so much for the help, I'm new to python so using .append was not something I knew I needed to use. Thanks again! I might end up making an inference notebook to help others soon.", "votes": null }, { "id": "2227397", "postDate": "04/19/2023 18:25:20", "content": "\n\nI had the same question and I have documented what worked for me here. https://www.kaggle.com/competitions/predict-student-performance-from-game-play/discussion/402178
\n
Hey I got this part, but the other user answered my question above.
", "rawMarkdown": "> I had the same question and I have documented what worked for me here. https://www.kaggle.com/competitions/predict-student-performance-from-game-play/discussion/402178\n\nHey I got this part, but the other user answered my question above.", "votes": null }, { "id": "2229528", "postDate": "04/21/2023 12:56:01", "content": "To load multiple XGBClassifier models, use a list to store them. You can use a for loop to iterate over each model, load it into the list and then access it later on like this:
\nmodels = []\nfor t in range(1, 19):\n model = XGBClassifier()\n model.load_model(f\"/kaggle/input/student-performance-notebook/XGB_question{t}.xgb\")\n models.append(model)\n\ny_pred = np.mean(np.stack([model.predict(df) for model in models]), axis = 0)\nThis will load all models and take the avg of their predictions as the final prediction.
\nLet me know if there is anything else you need.
\nThe Devastator.
", "rawMarkdown": "To load multiple XGBClassifier models, use a list to store them. You can use a for loop to iterate over each model, load it into the list and then access it later on like this:\n\n```\nmodels = []\nfor t in range(1, 19):\n model = XGBClassifier()\n model.load_model(f\"/kaggle/input/student-performance-notebook/XGB_question{t}.xgb\")\n models.append(model)\n\ny_pred = np.mean(np.stack([model.predict(df) for model in models]), axis = 0)\n``` \n\nThis will load all models and take the avg of their predictions as the final prediction.\n\nLet me know if there is anything else you need.\n\nThe Devastator.", "votes": null } ], "comments": [ { "id": 2226582, "author_name": "thaweewatboy", "author_url": "", "post_date": "04/19/2023 03:52:02", "content": "Hi @paulmerica if you want to load multiple XGBClassifier models, you should use a list to store them. Here's what it's supposed to look like:
\n\nmodel_list = []\n t (, ):\n \n model = XGBClassifier()\n model.load_model()\n model_list.append(model)\n\n\nprediction = model_list[].predict(\n(prediction)\nHope this helps ✌️
\n(Extra: You can also save XGB in JSON format, work fine as well)
\n\nHi @paulmerica if you want to load multiple XGBClassifier models, you should use a list to store them. Here's what it's supposed to look like:
\n\n\nmodel_list = []\n t (, ):\n \n model = XGBClassifier()\n model.load_model()\n model_list.append(model)\n\n\nprediction = model_list[].predict(\n(prediction)\nHope this helps ✌️
\n
\n (Extra: You can also save XGB in JSON format, work fine as well)
This solved my problem. Thanks so much for the help, I'm new to python so using .append was not something I knew I needed to use. Thanks again! I might end up making an inference notebook to help others soon.
", "votes": null, "replies": [] } ] }, { "id": 2226584, "author_name": "spacehorse", "author_url": "", "post_date": "04/19/2023 03:53:55", "content": "I had the same question and I have documented what worked for me here. https://www.kaggle.com/competitions/predict-student-performance-from-game-play/discussion/402178
", "votes": null, "replies": [ { "id": 2227397, "author_name": "paulmerica", "author_url": "", "post_date": "04/19/2023 18:25:20", "content": "\n\nI had the same question and I have documented what worked for me here. https://www.kaggle.com/competitions/predict-student-performance-from-game-play/discussion/402178
\n
Hey I got this part, but the other user answered my question above.
", "votes": null, "replies": [] } ] }, { "id": 2229528, "author_name": "thedevastator", "author_url": "", "post_date": "04/21/2023 12:56:01", "content": "To load multiple XGBClassifier models, use a list to store them. You can use a for loop to iterate over each model, load it into the list and then access it later on like this:
\nmodels = []\nfor t in range(1, 19):\n model = XGBClassifier()\n model.load_model(f\"/kaggle/input/student-performance-notebook/XGB_question{t}.xgb\")\n models.append(model)\n\ny_pred = np.mean(np.stack([model.predict(df) for model in models]), axis = 0)\nThis will load all models and take the avg of their predictions as the final prediction.
\nLet me know if there is anything else you need.
\nThe Devastator.
", "votes": null, "replies": [] } ], "raw_markdown_by_id": { "2226564": "I'm trying to load in my models into my second notebook using a simple for loop like this:\n\nmodelling_list = {}\nmodel_list = XGBClassifier()\n\nfor t in range(1,19):\n\n modelling_list = model_list.load_model(f\"/kaggle/input/student-performance-notebook/XGB_question{t}.xgb\") \n \n\nprint(modelling_list)\n\n\nBut it doesn't end up saving anything into the list after I run the code. I have attached an image of the outputs I'm trying to read in, and it works if I just load one model (e.g. \"/kaggle/input/student-performance-notebook/XGB_question1.xgb\"). I'm an R user by trade, so if I'm messing up some Python syntax please let me know. Thanks for any help.", "2226582": "Hi @paulmerica if you want to load multiple XGBClassifier models, you should use a list to store them. Here's what it's supposed to look like:\n\n```python\n# Save each model to list\nmodel_list = []\nfor t in range(1, 19):\n # Includes XGBClassifier() inside so it loads the new model on every loop\n model = XGBClassifier()\n model.load_model(f\"/kaggle/input/student-performance-notebook/XGB_question{t}.xgb\")\n model_list.append(model)\n\n# Call model with prefer number\nprediction = model_list[0].predict(###your target feature###)\nprint(prediction)\n```\n\nHope this helps ✌️\n(Extra: You can also save [XGB in JSON format](https://www.kaggle.com/competitions/predict-student-performance-from-game-play/discussion/398500#2202834), work fine as well)", "2226584": "I had the same question and I have documented what worked for me here. https://www.kaggle.com/competitions/predict-student-performance-from-game-play/discussion/402178", "2227396": "> Hi @paulmerica if you want to load multiple XGBClassifier models, you should use a list to store them. Here's what it's supposed to look like:\n> \n> ```python\n> # Save each model to list\n> model_list = []\n> for t in range(1, 19):\n> # Includes XGBClassifier() inside so it loads the new model on every loop\n> model = XGBClassifier()\n> model.load_model(f\"/kaggle/input/student-performance-notebook/XGB_question{t}.xgb\")\n> model_list.append(model)\n> \n> # Call model with prefer number\n> prediction = model_list[0].predict(###your target feature###)\n> print(prediction)\n> ```\n> \n> Hope this helps ✌️\n> (Extra: You can also save [XGB in JSON format](https://www.kaggle.com/competitions/predict-student-performance-from-game-play/discussion/398500#2202834), work fine as well)\n\nThis solved my problem. Thanks so much for the help, I'm new to python so using .append was not something I knew I needed to use. Thanks again! I might end up making an inference notebook to help others soon.", "2227397": "> I had the same question and I have documented what worked for me here. https://www.kaggle.com/competitions/predict-student-performance-from-game-play/discussion/402178\n\nHey I got this part, but the other user answered my question above.", "2229528": "To load multiple XGBClassifier models, use a list to store them. You can use a for loop to iterate over each model, load it into the list and then access it later on like this:\n\n```\nmodels = []\nfor t in range(1, 19):\n model = XGBClassifier()\n model.load_model(f\"/kaggle/input/student-performance-notebook/XGB_question{t}.xgb\")\n models.append(model)\n\ny_pred = np.mean(np.stack([model.predict(df) for model in models]), axis = 0)\n``` \n\nThis will load all models and take the avg of their predictions as the final prediction.\n\nLet me know if there is anything else you need.\n\nThe Devastator." }, "source": "meta" }