{"metadata":{"kernelspec":{"language":"python","display_name":"Python 3","name":"python3"},"language_info":{"name":"python","version":"3.10.14","mimetype":"text/x-python","codemirror_mode":{"name":"ipython","version":3},"pygments_lexer":"ipython3","nbconvert_exporter":"python","file_extension":".py"},"kaggle":{"accelerator":"none","dataSources":[{"sourceId":84493,"databundleVersionId":9871156,"sourceType":"competition"},{"sourceId":9675091,"sourceType":"datasetVersion","datasetId":5912994}],"dockerImageVersionId":30786,"isInternetEnabled":true,"language":"python","sourceType":"notebook","isGpuEnabled":false}},"nbformat_minor":4,"nbformat":4,"cells":[{"cell_type":"markdown","source":"# About this notebook\n\nI created a simulator based on my explanation(https://www.kaggle.com/code/chumajin/janestreet-easy-to-understand-new-time-series-api) of how the time API works.\nYou can check your memory and time is enough or not in this notebook.\n\n## how to use\n\n### Step 1 : change valid_from in # 1\n\nfrom this discussion, I estimated the period of the public and private data_id.\nhttps://www.kaggle.com/competitions/jane-street-real-time-market-data-forecasting/discussion/541314#3021981\n\nFor public test : you should set this 1577 (This means 6 months).\nFor private test : you should set this 1455 (This means 1 year).\n\n![image.png](attachment:3e1eafb3-b494-4449-9981-50ba103f0237.png)\n\n### Step 2 : change predict function in # 3\n\n![image.png](attachment:47cd0fe9-8743-4344-a147-601c8c0c4591.png)\n\n\nThis code is a sample of my lightGBM result.\n\nHowever, since this is a simulator, it does not provide any guarantees. Please use it carefully and enjoy!","metadata":{},"attachments":{"3e1eafb3-b494-4449-9981-50ba103f0237.png":{"image/png":"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"},"47cd0fe9-8743-4344-a147-601c8c0c4591.png":{"image/png":"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"}}},{"cell_type":"markdown","source":"# 0. import","metadata":{}},{"cell_type":"code","source":"import numpy as np # linear algebra\nimport pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)\nimport os\nimport polars as pl\nimport time\nfrom tqdm import tqdm\nimport matplotlib.pyplot as plt\nimport lightgbm\nimport torch","metadata":{"_uuid":"8f2839f25d086af736a60e9eeb907d3b93b6e0e5","_cell_guid":"b1076dfc-b9ad-4769-8c92-a6c4dae69d19","execution":{"iopub.status.busy":"2024-10-21T12:22:19.125415Z","iopub.execute_input":"2024-10-21T12:22:19.125858Z","iopub.status.idle":"2024-10-21T12:22:19.132034Z","shell.execute_reply.started":"2024-10-21T12:22:19.125817Z","shell.execute_reply":"2024-10-21T12:22:19.130646Z"},"trusted":true},"outputs":[],"execution_count":null},{"cell_type":"markdown","source":"# 1. make valid data","metadata":{}},{"cell_type":"markdown","source":"Note that : you should change this from public test 1577(6 months) to 1455 for private tests(1 year) when you check for private tests","metadata":{}},{"cell_type":"code","source":"valid_from = 1577 # for private you should change to 1455 (1 year)","metadata":{"execution":{"iopub.status.busy":"2024-10-21T12:22:21.79871Z","iopub.execute_input":"2024-10-21T12:22:21.799163Z","iopub.status.idle":"2024-10-21T12:22:21.804746Z","shell.execute_reply.started":"2024-10-21T12:22:21.799108Z","shell.execute_reply":"2024-10-21T12:22:21.803417Z"},"trusted":true},"outputs":[],"execution_count":null},{"cell_type":"code","source":"# Lazy Frame \nalltraindata = pl.scan_parquet(\"/kaggle/input/jane-street-real-time-market-data-forecasting/train.parquet\")","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2024-10-21T12:29:25.393126Z","iopub.execute_input":"2024-10-21T12:29:25.394315Z","iopub.status.idle":"2024-10-21T12:29:25.400268Z","shell.execute_reply.started":"2024-10-21T12:29:25.39426Z","shell.execute_reply":"2024-10-21T12:29:25.398993Z"}},"outputs":[],"execution_count":null},{"cell_type":"code","source":"# Number of times per date\nnum_dtdf = alltraindata.group_by(\"date_id\").agg(pl.col(\"time_id\").max()).sort(\"date_id\").collect()\nprint(num_dtdf.head())\nprint(num_dtdf.tail())","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2024-10-21T12:29:26.293894Z","iopub.execute_input":"2024-10-21T12:29:26.294363Z","iopub.status.idle":"2024-10-21T12:29:27.122394Z","shell.execute_reply.started":"2024-10-21T12:29:26.294322Z","shell.execute_reply":"2024-10-21T12:29:27.121111Z"}},"outputs":[],"execution_count":null},{"cell_type":"code","source":"plt.figure(figsize=(10, 5))\n\n# Line\nplt.plot(num_dtdf[\"date_id\"],num_dtdf[\"time_id\"])\nplt.title('Number of times per date')\nplt.xlabel('Date')\nplt.ylabel('Num of time')\n\n# Discontinuity\nplt.plot(677, 967, 'ko', markersize=5)\nplt.plot(677, 848, 'ko', markersize=5, markerfacecolor='none')\nplt.annotate(f'(677,967)',\n             xy=(677, 967),\n             xytext=(677, 967),\n             horizontalalignment='right',\n             verticalalignment='bottom',\n             fontsize=10,\n             color='black'\n             )\nplt.annotate(f'(677,848)',\n             xy=(677, 848),\n             xytext=(677, 848),\n             horizontalalignment='left',\n             verticalalignment='bottom',\n             fontsize=10,\n             color='black'\n             )\n\nplt.savefig('/kaggle/working/num_dtdf.pdf', format='pdf')\nplt.show()","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2024-10-21T12:32:35.563714Z","iopub.execute_input":"2024-10-21T12:32:35.564179Z","iopub.status.idle":"2024-10-21T12:32:36.39068Z","shell.execute_reply.started":"2024-10-21T12:32:35.564123Z","shell.execute_reply":"2024-10-21T12:32:36.389562Z"}},"outputs":[],"execution_count":null},{"cell_type":"code","source":"# Number of symbols per date\nnum_dsdf = train_data.group_by(\"date_id\").agg(pl.col(\"symbol_id\").max()).sort(\"date_id\").collect()\nprint(num_dsdf.head())\nprint(num_dsdf.tail())","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2024-10-21T12:32:37.669182Z","iopub.execute_input":"2024-10-21T12:32:37.669862Z","iopub.status.idle":"2024-10-21T12:32:38.474565Z","shell.execute_reply.started":"2024-10-21T12:32:37.669814Z","shell.execute_reply":"2024-10-21T12:32:38.473209Z"}},"outputs":[],"execution_count":null},{"cell_type":"code","source":"plt.figure(figsize=(10, 5))\n\n# Line\nplt.plot(num_dsdf[\"date_id\"],num_dsdf[\"symbol_id\"])\nplt.title('Number of symbols per date')\nplt.xlabel('Date')\nplt.ylabel('Num of symbols')\n\nplt.savefig('/kaggle/working/num_dsdf.pdf', format='pdf')\nplt.show()","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2024-10-21T12:32:39.591244Z","iopub.execute_input":"2024-10-21T12:32:39.592358Z","iopub.status.idle":"2024-10-21T12:32:40.036699Z","shell.execute_reply.started":"2024-10-21T12:32:39.592307Z","shell.execute_reply":"2024-10-21T12:32:40.035463Z"}},"outputs":[],"execution_count":null},{"cell_type":"code","source":"num_train_mv = alltraindata.null_count().collect()\nnum_train_mv","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2024-10-21T12:33:31.476895Z","iopub.execute_input":"2024-10-21T12:33:31.477332Z","iopub.status.idle":"2024-10-21T12:34:13.959717Z","shell.execute_reply.started":"2024-10-21T12:33:31.47729Z","shell.execute_reply":"2024-10-21T12:34:13.95836Z"}},"outputs":[],"execution_count":null},{"cell_type":"code","source":"lags_data = pl.read_parquet('/kaggle/input/jane-street-real-time-market-data-forecasting/lags.parquet')\nlags_data","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2024-10-21T12:35:18.936009Z","iopub.execute_input":"2024-10-21T12:35:18.93649Z","iopub.status.idle":"2024-10-21T12:35:18.957614Z","shell.execute_reply.started":"2024-10-21T12:35:18.936448Z","shell.execute_reply":"2024-10-21T12:35:18.956407Z"}},"outputs":[],"execution_count":null},{"cell_type":"code","source":"alltraindata = pl.scan_parquet(\"/kaggle/input/jane-street-real-time-market-data-forecasting/train.parquet\")\nvalid_df = alltraindata.filter(pl.col(\"date_id\")>=valid_from).collect()","metadata":{"execution":{"iopub.status.busy":"2024-10-21T12:29:27.368785Z","iopub.execute_input":"2024-10-21T12:29:27.369805Z","iopub.status.idle":"2024-10-21T12:29:29.412253Z","shell.execute_reply.started":"2024-10-21T12:29:27.36976Z","shell.execute_reply":"2024-10-21T12:29:29.411191Z"},"trusted":true},"outputs":[],"execution_count":null},{"cell_type":"code","source":"valid_df = valid_df.with_columns(pl.Series(range(len(valid_df))).alias(\"row_id\"))\nlen(valid_df)","metadata":{"execution":{"iopub.status.busy":"2024-10-21T12:29:33.518594Z","iopub.execute_input":"2024-10-21T12:29:33.519021Z","iopub.status.idle":"2024-10-21T12:29:33.573441Z","shell.execute_reply.started":"2024-10-21T12:29:33.518984Z","shell.execute_reply":"2024-10-21T12:29:33.572102Z"},"trusted":true},"outputs":[],"execution_count":null},{"cell_type":"markdown","source":"# 2. make lag function","metadata":{}},{"cell_type":"code","source":"lag_sample = pl.read_parquet(\"/kaggle/input/jane-street-real-time-market-data-forecasting/lags.parquet/date_id=0/part-0.parquet\")\ntrain_sample = pl.read_parquet(\"/kaggle/input/jane-street-real-time-market-data-forecasting/train.parquet/partition_id=0/part-0.parquet\",n_rows=1)\nresponder_cols = [s for s in train_sample.columns if \"responder\" in s]\n\ndef makelag(date_id):\n    \"\"\"\n    Making lag at the previout day\n\n    Args:\n    date_id (int): date_id at the previout day\n    \n    Returns:\n    pl.dataframe\n    \"\"\"\n    \n    lag = alltraindata.filter(pl.col(\"date_id\")==date_id).select([\"date_id\",\"time_id\",\"symbol_id\"] + responder_cols).collect()\n    lag.columns = lag_sample.columns\n    \n    return lag    ","metadata":{"execution":{"iopub.status.busy":"2024-10-21T12:29:37.610011Z","iopub.execute_input":"2024-10-21T12:29:37.610436Z","iopub.status.idle":"2024-10-21T12:29:37.79817Z","shell.execute_reply.started":"2024-10-21T12:29:37.610399Z","shell.execute_reply":"2024-10-21T12:29:37.797048Z"},"trusted":true},"outputs":[],"execution_count":null},{"cell_type":"markdown","source":"# 3. predict your function","metadata":{}},{"cell_type":"code","source":"model = torch.load(\"/kaggle/input/janestreet-lgbm-model/model.bin\")","metadata":{"execution":{"iopub.status.busy":"2024-10-21T12:35:46.303636Z","iopub.execute_input":"2024-10-21T12:35:46.304078Z","iopub.status.idle":"2024-10-21T12:35:46.319876Z","shell.execute_reply.started":"2024-10-21T12:35:46.304036Z","shell.execute_reply":"2024-10-21T12:35:46.318509Z"},"trusted":true},"outputs":[],"execution_count":null},{"cell_type":"code","source":"features = [s for s in valid_df.columns if \"feature\" in s]\nlen(features)","metadata":{"execution":{"iopub.status.busy":"2024-10-21T12:35:48.959871Z","iopub.execute_input":"2024-10-21T12:35:48.96072Z","iopub.status.idle":"2024-10-21T12:35:48.9689Z","shell.execute_reply.started":"2024-10-21T12:35:48.960671Z","shell.execute_reply":"2024-10-21T12:35:48.967662Z"},"trusted":true},"outputs":[],"execution_count":null},{"cell_type":"code","source":"lags_ : pl.DataFrame | None = None\n\n\n# Replace this function with your inference code.\n# You can return either a Pandas or Polars dataframe, though Polars is recommended.\n# Each batch of predictions (except the very first) must be returned within 10 minutes of the batch features being provided.\ndef predict(test: pl.DataFrame, lags: pl.DataFrame | None) -> pl.DataFrame | pd.DataFrame:\n    \"\"\"Make a prediction.\"\"\"\n    # All the responders from the previous day are passed in at time_id == 0. We save them in a global variable for access at every time_id.\n    # Use them as extra features, if you like.\n    global lags_\n    if lags is not None:\n        lags_ = lags\n        \n    try:\n        test2 = test.to_pandas()\n    except:\n        pass\n        \n    preds = model.predict(test2[features])\n\n    predictions = test.select(\n        'row_id',\n        pl.Series(preds).alias('responder_6'),\n    )\n\n    # The predict function must return a DataFrame\n    assert isinstance(predictions, pl.DataFrame | pd.DataFrame)\n    # with columns 'row_id', 'responer_6'\n    assert predictions.columns == ['row_id', 'responder_6']\n    # and as many rows as the test data.\n    assert len(predictions) == len(test)\n\n    return predictions","metadata":{"execution":{"iopub.status.busy":"2024-10-21T12:35:52.412323Z","iopub.execute_input":"2024-10-21T12:35:52.412762Z","iopub.status.idle":"2024-10-21T12:35:52.422634Z","shell.execute_reply.started":"2024-10-21T12:35:52.412721Z","shell.execute_reply":"2024-10-21T12:35:52.421443Z"},"trusted":true},"outputs":[],"execution_count":null},{"cell_type":"code","source":"lags_ : pl.DataFrame | None = None\n\n\n# Replace this function with your inference code.\n# You can return either a Pandas or Polars dataframe, though Polars is recommended.\n# Each batch of predictions (except the very first) must be returned within 10 minutes of the batch features being provided.\ndef predict(test: pl.DataFrame, lags: pl.DataFrame | None) -> pl.DataFrame | pd.DataFrame:\n    \"\"\"Make a prediction.\"\"\"\n    # All the responders from the previous day are passed in at time_id == 0. We save them in a global variable for access at every time_id.\n    # Use them as extra features, if you like.\n    global lags_\n    if lags is not None:\n        lags_ = lags\n    \n    predictions = test.select(\n        'row_id',\n        pl.lit(0.0).alias('responder_6'),\n    )\n\n    # The predict function must return a DataFrame\n    assert isinstance(predictions, pl.DataFrame | pd.DataFrame)\n    # with columns 'row_id', 'responer_6'\n    assert predictions.columns == ['row_id', 'responder_6']\n    # and as many rows as the test data.\n    assert len(predictions) == len(test)\n\n    return predictions","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2024-10-21T12:30:29.534748Z","iopub.execute_input":"2024-10-21T12:30:29.535219Z","iopub.status.idle":"2024-10-21T12:30:29.542546Z","shell.execute_reply.started":"2024-10-21T12:30:29.535176Z","shell.execute_reply":"2024-10-21T12:30:29.541389Z"}},"outputs":[],"execution_count":null},{"cell_type":"code","source":"lags_ : pl.DataFrame | None = None\n\n\n# Replace this function with your inference code.\n# You can return either a Pandas or Polars dataframe, though Polars is recommended.\n# Each batch of predictions (except the very first) must be returned within 10 minutes of the batch features being provided.\ndef predict(test: pl.DataFrame, lags: pl.DataFrame | None) -> pl.DataFrame | pd.DataFrame:\n    \"\"\"Make a prediction.\"\"\"\n    # All the responders from the previous day are passed in at time_id == 0. We save them in a global variable for access at every time_id.\n    # Use them as extra features, if you like.\n    global lags_\n    if lags is not None:\n        lags_ = lags\n    \n    predictions = test.select(\n        'row_id',\n        pl.lit(0.0).alias('responder_6'),\n    )\n\n    # The predict function must return a DataFrame\n    assert isinstance(predictions, pl.DataFrame | pd.DataFrame)\n    # with columns 'row_id', 'responer_6'\n    assert predictions.columns == ['row_id', 'responder_6']\n    # and as many rows as the test data.\n    assert len(predictions) == len(test)\n\n    return predictions","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2024-10-21T12:22:41.301846Z","iopub.execute_input":"2024-10-21T12:22:41.303027Z","iopub.status.idle":"2024-10-21T12:22:41.310293Z","shell.execute_reply.started":"2024-10-21T12:22:41.302979Z","shell.execute_reply":"2024-10-21T12:22:41.30889Z"}},"outputs":[],"execution_count":null},{"cell_type":"markdown","source":"# 4. check submission","metadata":{}},{"cell_type":"code","source":"all_submission_dataframe = []","metadata":{"execution":{"iopub.status.busy":"2024-10-21T12:36:08.63127Z","iopub.execute_input":"2024-10-21T12:36:08.631725Z","iopub.status.idle":"2024-10-21T12:36:08.63711Z","shell.execute_reply.started":"2024-10-21T12:36:08.631684Z","shell.execute_reply":"2024-10-21T12:36:08.636008Z"},"trusted":true},"outputs":[],"execution_count":null},{"cell_type":"code","source":"all_inference_times = []","metadata":{"execution":{"iopub.status.busy":"2024-10-21T12:36:09.500667Z","iopub.execute_input":"2024-10-21T12:36:09.501101Z","iopub.status.idle":"2024-10-21T12:36:09.513204Z","shell.execute_reply.started":"2024-10-21T12:36:09.50106Z","shell.execute_reply":"2024-10-21T12:36:09.511728Z"},"trusted":true},"outputs":[],"execution_count":null},{"cell_type":"code","source":"timeout = 600  # 10 minitus","metadata":{"execution":{"iopub.status.busy":"2024-10-21T12:36:10.556027Z","iopub.execute_input":"2024-10-21T12:36:10.556513Z","iopub.status.idle":"2024-10-21T12:36:10.562436Z","shell.execute_reply.started":"2024-10-21T12:36:10.556466Z","shell.execute_reply":"2024-10-21T12:36:10.560993Z"},"trusted":true},"outputs":[],"execution_count":null},{"cell_type":"code","source":"total_iterations = len(valid_df[\"date_id\"].unique())\ntotal_iterations","metadata":{"execution":{"iopub.status.busy":"2024-10-21T12:36:11.603021Z","iopub.execute_input":"2024-10-21T12:36:11.603508Z","iopub.status.idle":"2024-10-21T12:36:11.632964Z","shell.execute_reply.started":"2024-10-21T12:36:11.603466Z","shell.execute_reply":"2024-10-21T12:36:11.631735Z"},"trusted":true},"outputs":[],"execution_count":null},{"cell_type":"code","source":"## Step 1 The data is split by day using group_by.\n\nfor num_days, df_per_day in tqdm(valid_df.group_by(\"date_id\",maintain_order=True),total=total_iterations,desc=\"Processing\"):\n    \n    ## Step 2 The data is split by time_id using group_by, and the lag is generated (for time_id == 0).\n    \n    for time_id, test in df_per_day.group_by(\"time_id\",maintain_order=True):\n        \n        ## when time_id == 0, makelags\n        \n        start_time = time.time()\n        \n        if time_id[0] == 0:\n            lag = makelag(num_days[0] - 1)\n        else:\n            lag = None\n        \n        submission_dataframe = predict(test, lag)\n        \n        all_submission_dataframe.append(submission_dataframe)\n        \n        end_time = time.time()\n        \n        diff = end_time - start_time\n        \n        all_inference_times.append(diff)\n        \n     #   print(f\"{num_days[0]=},{time_id[0]=}{diff=}\")\n        \n        if diff > timeout:\n            print(f\"{num_days[0]=},{time_id[0]=}{diff=}\")\n            assert elapsed_time < timeout, f\"process over {timeout/60} mins. cancelled\"\n        \nall_submission_dataframe = pl.concat(all_submission_dataframe)\nall_submission_dataframe","metadata":{"execution":{"iopub.status.busy":"2024-10-21T12:36:12.944561Z","iopub.execute_input":"2024-10-21T12:36:12.945564Z","iopub.status.idle":"2024-10-21T12:47:16.99796Z","shell.execute_reply.started":"2024-10-21T12:36:12.945517Z","shell.execute_reply":"2024-10-21T12:47:16.996678Z"},"trusted":true},"outputs":[],"execution_count":null},{"cell_type":"markdown","source":"# 5. Visualize the inference time in each time_id submission","metadata":{}},{"cell_type":"code","source":"plt.plot(all_inference_times)","metadata":{"execution":{"iopub.status.busy":"2024-10-21T12:47:26.506024Z","iopub.execute_input":"2024-10-21T12:47:26.506664Z","iopub.status.idle":"2024-10-21T12:47:26.848503Z","shell.execute_reply.started":"2024-10-21T12:47:26.506596Z","shell.execute_reply":"2024-10-21T12:47:26.847407Z"},"trusted":true},"outputs":[],"execution_count":null},{"cell_type":"markdown","source":"# 6. Calculation Score","metadata":{}},{"cell_type":"code","source":"def weighted_zero_mean_r2(y_true, y_pred, weights):\n    \"\"\"\n    Calculate the sample weighted zero-mean R-squared score.\n\n    Parameters:\n    y_true (numpy.ndarray): Ground-truth values for responder_6.\n    y_pred (numpy.ndarray): Predicted values for responder_6.\n    weights (numpy.ndarray): Sample weight vector.\n\n    Returns:\n    float: The weighted zero-mean R-squared score.\n    \"\"\"\n    numerator = np.sum(weights * (y_true - y_pred)**2)\n    denominator = np.sum(weights * y_true**2)\n    \n    r2_score = 1 - numerator / denominator\n    return r2_score","metadata":{"execution":{"iopub.status.busy":"2024-10-21T12:47:36.614923Z","iopub.execute_input":"2024-10-21T12:47:36.615385Z","iopub.status.idle":"2024-10-21T12:47:36.62242Z","shell.execute_reply.started":"2024-10-21T12:47:36.615342Z","shell.execute_reply":"2024-10-21T12:47:36.62091Z"},"trusted":true},"outputs":[],"execution_count":null},{"cell_type":"code","source":"y_true = valid_df.select(\"responder_6\").to_numpy().reshape(-1)\ny_pred = all_submission_dataframe.select(\"responder_6\").to_numpy().reshape(-1)\nweights = valid_df.select(\"weight\").to_numpy().reshape(-1)","metadata":{"execution":{"iopub.status.busy":"2024-10-21T12:47:38.494934Z","iopub.execute_input":"2024-10-21T12:47:38.49593Z","iopub.status.idle":"2024-10-21T12:47:38.629241Z","shell.execute_reply.started":"2024-10-21T12:47:38.495876Z","shell.execute_reply":"2024-10-21T12:47:38.628112Z"},"trusted":true},"outputs":[],"execution_count":null},{"cell_type":"code","source":"len(y_true),len(y_pred),len(weights)","metadata":{"execution":{"iopub.status.busy":"2024-10-21T12:47:40.331456Z","iopub.execute_input":"2024-10-21T12:47:40.331873Z","iopub.status.idle":"2024-10-21T12:47:40.339621Z","shell.execute_reply.started":"2024-10-21T12:47:40.331835Z","shell.execute_reply":"2024-10-21T12:47:40.338358Z"},"trusted":true},"outputs":[],"execution_count":null},{"cell_type":"code","source":"plt.scatter(y_pred,y_true,alpha=0.5)","metadata":{"execution":{"iopub.status.busy":"2024-10-21T12:47:42.222192Z","iopub.execute_input":"2024-10-21T12:47:42.222633Z","iopub.status.idle":"2024-10-21T12:48:06.287558Z","shell.execute_reply.started":"2024-10-21T12:47:42.222595Z","shell.execute_reply":"2024-10-21T12:48:06.286358Z"},"trusted":true},"outputs":[],"execution_count":null},{"cell_type":"code","source":"weighted_zero_mean_r2(y_true, y_pred, weights)","metadata":{"execution":{"iopub.status.busy":"2024-10-21T12:48:15.470623Z","iopub.execute_input":"2024-10-21T12:48:15.471165Z","iopub.status.idle":"2024-10-21T12:48:15.53328Z","shell.execute_reply.started":"2024-10-21T12:48:15.471102Z","shell.execute_reply":"2024-10-21T12:48:15.532014Z"},"trusted":true},"outputs":[],"execution_count":null},{"cell_type":"markdown","source":"Note that : my sample is leakage... (This model is just for simulator)","metadata":{}},{"cell_type":"code","source":"","metadata":{},"outputs":[],"execution_count":null}]}