{"metadata":{"kernelspec":{"language":"python","display_name":"Python 3","name":"python3"},"language_info":{"pygments_lexer":"ipython3","nbconvert_exporter":"python","version":"3.6.4","file_extension":".py","codemirror_mode":{"name":"ipython","version":3},"name":"python","mimetype":"text/x-python"}},"nbformat_minor":4,"nbformat":4,"cells":[{"cell_type":"markdown","source":"# What is about ?\n\n### Briefly - downsample and make quick experiments\n\nHere we will downsample the data. I.e. select 10% of data as a kind of \"Playground\" - for quick experiments. \nAnd will train/tune different models on that playground first.\n\n\n### About CV scheme\n\nPublic and private test sets are quite different. (Private - contains new DAY (and donor), while public ONLY new donor).\nSo one should be careful with the validation schemes.\nSome proposals are described here: \n\nNotebook: https://www.kaggle.com/code/alexandervc/mmscel-crossvalidation-schemes\nTopic: https://www.kaggle.com/competitions/open-problems-multimodal/discussion/358860\n\nWe will be based on them. \n\n### Start with selection of \"Playground\" - 10% part of data - to quickly test models, ideas\n\nData is quite big, and so training models, tuning params might take long time. That is not always affordable.\nIn the present script we first choose some 10% part of data - to make quick experiments.\nThat part is chosen to have SAME proporitions of key characteristics: donors, days, cell types as the initial data\n\nSo we can create CV scheme for that \"playground\" part.\nBut we can also simplify even further - for start - use  not 6-fold scheme but just splite by days. And have only 1 train subset for quick experiments. \n\nTo simplify even further we can start play with just one target only. \n\n\n\n### Versions\n\n#### 9 Blend and BIG SURPRISE(!) - how to explain ? ideas - welcome ! \n\n    added blending part for all solutions - and got suprise:\n    and the WORST/TERRIBLE (r2 negative, mse solution 10 times worse than others -   KernelRidge\n    enters the blends and improve it ! \n    \n    The only thing - that it is very uncorrelated with the other solutions. \n\n#### 8: Added - statistics on all methods - collected in one table (dataframe)\n    \n    Still Ridge, SVR are the best. Cat\n\n#### 7: added XGBoost+Optuna, CatBoost, MLP\n    \n    CatBoost is quite good with default params\n\n#### 3,4,5,6 - search for optimal LightGBM params; added: models and param tuning for other models,\n    Surprise - Ridge is better than LightGBM ! Even tuning of params of LightGBM does not help much !\n    \n    Version 5 - changed number of features to 100 - Ridge - quite improved, boosting - less. \n    Version 6 - seems around 40 PCA features is better for LightGBM  - current_best_r2 = 0.46792863038644295 # \n    \n#### 1,2 Test several models - Ridge,LGB, SVR, RF etc...\n\n    Target chosen - only CD31\n    No params tuning - just fist look \n","metadata":{}},{"cell_type":"code","source":"tune_RF = False # True # Takes 4 minutes for 6 params ","metadata":{"execution":{"iopub.status.busy":"2022-11-05T11:28:09.372852Z","iopub.execute_input":"2022-11-05T11:28:09.373436Z","iopub.status.idle":"2022-11-05T11:28:09.378203Z","shell.execute_reply.started":"2022-11-05T11:28:09.373404Z","shell.execute_reply":"2022-11-05T11:28:09.377165Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"# Install/import modules, load technical data\n","metadata":{}},{"cell_type":"code","source":"# This Python 3 environment comes with many helpful analytics libraries installed\n# It is defined by the kaggle/python Docker image: https://github.com/kaggle/docker-python\n# For example, here's several helpful packages to load\n\nimport numpy as np # linear algebra\nimport pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)\n\n# Input data files are available in the read-only \"../input/\" directory\n# For example, running this (by clicking run or pressing Shift+Enter) will list all files under the input directory\n\nimport os\nfor dirname, _, filenames in os.walk('/kaggle/input'):\n    for filename in filenames:\n        print(os.path.join(dirname, filename))\n\n# You can write up to 20GB to the current directory (/kaggle/working/) that gets preserved as output when you create a version using \"Save & Run All\" \n# You can also write temporary files to /kaggle/temp/, but they won't be saved outside of the current session","metadata":{"_uuid":"8f2839f25d086af736a60e9eeb907d3b93b6e0e5","_cell_guid":"b1076dfc-b9ad-4769-8c92-a6c4dae69d19","execution":{"iopub.status.busy":"2022-11-05T11:28:09.381560Z","iopub.execute_input":"2022-11-05T11:28:09.381848Z","iopub.status.idle":"2022-11-05T11:28:09.426937Z","shell.execute_reply.started":"2022-11-05T11:28:09.381825Z","shell.execute_reply":"2022-11-05T11:28:09.425993Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"import time\nt0start = time.time()\n\nimport pandas as pd\nimport numpy as np\nimport os\nimport sys\n\nimport matplotlib.pyplot as plt\n#plt.style.use('dark_background')\nimport seaborn as sns\n\n#If you see a urllib warning running this cell, go to \"Settings\" on the right hand side, \n#and turn on internet. Note, you need to be phone verified.\n!pip install --quiet tables\n\n\nimport h5py\n!pip install hdf5plugin~=2.0 # https://forum.hdfgroup.org/t/cant-open-directory-usr-local-hdf5-lib-plugin/9738/4\nimport hdf5plugin\n\n# !pip install scanpy\n# import scanpy as sc\n# import anndata\n\nDATA_DIR = \"/kaggle/input/open-problems-multimodal/\"\nFP_CELL_METADATA = os.path.join(DATA_DIR,\"metadata.csv\")\n\nFP_CITE_TRAIN_INPUTS = os.path.join(DATA_DIR,\"train_cite_inputs.h5\")\nFP_CITE_TRAIN_TARGETS = os.path.join(DATA_DIR,\"train_cite_targets.h5\")\nFP_CITE_TEST_INPUTS = os.path.join(DATA_DIR,\"test_cite_inputs.h5\")\n\nFP_MULTIOME_TRAIN_INPUTS = os.path.join(DATA_DIR,\"train_multi_inputs.h5\")\nFP_MULTIOME_TRAIN_TARGETS = os.path.join(DATA_DIR,\"train_multi_targets.h5\")\nFP_MULTIOME_TEST_INPUTS = os.path.join(DATA_DIR,\"test_multi_inputs.h5\")\n\nFP_SUBMISSION = os.path.join(DATA_DIR,\"sample_submission.csv\")\nFP_EVALUATION_IDS = os.path.join(DATA_DIR,\"evaluation_ids.csv\")\n\ndf_cell = pd.read_csv(FP_CELL_METADATA)\ndf_cell","metadata":{"execution":{"iopub.status.busy":"2022-11-05T11:28:09.429078Z","iopub.execute_input":"2022-11-05T11:28:09.429776Z","iopub.status.idle":"2022-11-05T11:28:28.724093Z","shell.execute_reply.started":"2022-11-05T11:28:09.429736Z","shell.execute_reply":"2022-11-05T11:28:28.723194Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"import time\nt0start = time.time()\n\nimport pandas as pd\nimport numpy as np\nimport os\nimport sys\n\nimport matplotlib.pyplot as plt\n#plt.style.use('dark_background')\nimport seaborn as sns\n\n#If you see a urllib warning running this cell, go to \"Settings\" on the right hand side, \n#and turn on internet. Note, you need to be phone verified.\n!pip install --quiet tables\n\n\nimport h5py\n!pip install hdf5plugin~=2.0 # https://forum.hdfgroup.org/t/cant-open-directory-usr-local-hdf5-lib-plugin/9738/4\nimport hdf5plugin\n\n# !pip install scanpy\n# import scanpy as sc\n# import anndata\n\nDATA_DIR = \"/kaggle/input/open-problems-multimodal/\"\nFP_CELL_METADATA = os.path.join(DATA_DIR,\"metadata.csv\")\n\nFP_CITE_TRAIN_INPUTS = os.path.join(DATA_DIR,\"train_cite_inputs.h5\")\nFP_CITE_TRAIN_TARGETS = os.path.join(DATA_DIR,\"train_cite_targets.h5\")\nFP_CITE_TEST_INPUTS = os.path.join(DATA_DIR,\"test_cite_inputs.h5\")\n\nFP_MULTIOME_TRAIN_INPUTS = os.path.join(DATA_DIR,\"train_multi_inputs.h5\")\nFP_MULTIOME_TRAIN_TARGETS = os.path.join(DATA_DIR,\"train_multi_targets.h5\")\nFP_MULTIOME_TEST_INPUTS = os.path.join(DATA_DIR,\"test_multi_inputs.h5\")\n\nFP_SUBMISSION = os.path.join(DATA_DIR,\"sample_submission.csv\")\nFP_EVALUATION_IDS = os.path.join(DATA_DIR,\"evaluation_ids.csv\")\n\ndf_cell = pd.read_csv(FP_CELL_METADATA)\ndf_cell","metadata":{"execution":{"iopub.status.busy":"2022-11-05T11:28:28.725329Z","iopub.execute_input":"2022-11-05T11:28:28.725606Z","iopub.status.idle":"2022-11-05T11:28:45.591287Z","shell.execute_reply.started":"2022-11-05T11:28:28.725580Z","shell.execute_reply":"2022-11-05T11:28:45.590194Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"# Load prepared Features for CITE-seq part of task","metadata":{}},{"cell_type":"code","source":"%%time\n\nprint('Load prepared features for CITE-seq')\n# These files contain both train and test parts .\n# For CITEseq part - first 70988 elements - train, and later 48663 - test. Overall 119651 samples.\nfn = '/kaggle/input/feature-shop-for-multimodal-singlecell-competition/citeseq_train_and_test_TruncatedSVD200_niter7_rs42.csv'\nfn = '/kaggle/input/feature-shop-for-multimodal-singlecell-competition/citeseq_train_and_test_PCA500.csv'\ndf_cite = pd.read_csv(fn,index_col = 0)\ndisplay(df_cite)\n","metadata":{"execution":{"iopub.status.busy":"2022-11-05T11:28:45.593692Z","iopub.execute_input":"2022-11-05T11:28:45.593936Z","iopub.status.idle":"2022-11-05T11:29:01.009868Z","shell.execute_reply.started":"2022-11-05T11:28:45.593913Z","shell.execute_reply":"2022-11-05T11:29:01.008986Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"df_cite.mean(axis = 0)","metadata":{"execution":{"iopub.status.busy":"2022-11-05T11:29:01.010913Z","iopub.execute_input":"2022-11-05T11:29:01.011202Z","iopub.status.idle":"2022-11-05T11:29:01.120965Z","shell.execute_reply.started":"2022-11-05T11:29:01.011178Z","shell.execute_reply":"2022-11-05T11:29:01.119721Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"df_cite.std(axis=0)","metadata":{"execution":{"iopub.status.busy":"2022-11-05T11:29:01.122572Z","iopub.execute_input":"2022-11-05T11:29:01.122930Z","iopub.status.idle":"2022-11-05T11:29:01.487712Z","shell.execute_reply.started":"2022-11-05T11:29:01.122895Z","shell.execute_reply":"2022-11-05T11:29:01.486631Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"# Load Targets for CITE-seq","metadata":{}},{"cell_type":"code","source":"%%time\n#if 1:\nprint('Load CITE-seq targets and ')\ndf_cite_train_y = pd.read_hdf(FP_CITE_TRAIN_TARGETS)\ndisplay(df_cite_train_y)","metadata":{"execution":{"iopub.status.busy":"2022-11-05T11:29:01.488860Z","iopub.execute_input":"2022-11-05T11:29:01.489165Z","iopub.status.idle":"2022-11-05T11:29:02.254945Z","shell.execute_reply.started":"2022-11-05T11:29:01.489139Z","shell.execute_reply":"2022-11-05T11:29:02.253903Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"## Load and start prepare some metadata (cut only CITE-seq train part)","metadata":{}},{"cell_type":"code","source":"%%time\nfn2 = '/kaggle/input/feature-shop-for-multimodal-singlecell-competition/_citeseq_meta_all_text_also.csv'\ndf_meta_full = pd.read_csv(fn2,index_col = 0)\ndisplay(df_meta_full)\n#if 1:\ndf_meta = pd.DataFrame(index = df_cite_train_y.index) \ndf_meta = df_meta.join(df_cell.set_index('cell_id') )\ndisplay(df_meta)","metadata":{"execution":{"iopub.status.busy":"2022-11-05T11:29:02.256534Z","iopub.execute_input":"2022-11-05T11:29:02.256846Z","iopub.status.idle":"2022-11-05T11:29:02.560693Z","shell.execute_reply.started":"2022-11-05T11:29:02.256815Z","shell.execute_reply":"2022-11-05T11:29:02.559830Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"# Create \"Playground\"  (i.e. downsample)\n\nSmall 10% of data of data where we can make prelimanary experiments. \n\nit will be labeled by special column \"Playground\" in df_meta (meta data for CITE-seq train only)","metadata":{}},{"cell_type":"code","source":"# Prepare for creation of additional holdout folds with 10% of samples \n# We will use stratified Kfold to achieve that days, cell_types and donors are equally distributed \nimport numpy as np\nfrom sklearn.model_selection import StratifiedKFold\nscol = 'donor&day&CT'\ndf_meta[scol] =df_meta['donor'].apply(lambda x:str(x)+'_') + df_meta['day'].apply(lambda x:str(x)+'_') + df_meta['cell_type']\n\n\nskf = StratifiedKFold(n_splits=10,  shuffle=True, random_state=40)\nskf.get_n_splits(df_meta, df_meta[scol] )\n\n\n\ny = df_meta[scol] \nfor train_index, test_index in skf.split(df_meta, df_meta[scol]):\n    print(\"TRAIN:\", len(train_index), \"TEST:\", len(test_index) ); \n    break\nprint(test_index)\nprint(df_meta[scol].value_counts().head(5)   )\nprint(df_meta.iloc[test_index,:][scol].value_counts().head(5)    )\n\n\nflagged_column_name = 'Playground'\ndf_meta[flagged_column_name] = 0 \ndf_meta.loc[df_meta.index[test_index],flagged_column_name]  = 1\ndf_meta","metadata":{"execution":{"iopub.status.busy":"2022-11-05T11:29:02.561789Z","iopub.execute_input":"2022-11-05T11:29:02.562052Z","iopub.status.idle":"2022-11-05T11:29:02.741337Z","shell.execute_reply.started":"2022-11-05T11:29:02.562027Z","shell.execute_reply":"2022-11-05T11:29:02.740215Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"# Create X,y, X_train, y_train, etc - INSIDE \"Playground\"\n","metadata":{}},{"cell_type":"code","source":"selected_target = 'CD31'\nn_features = 40\n\nX= df_cite.iloc[:70988,:n_features][df_meta['Playground']==1]\ny= df_cite_train_y[df_meta['Playground']==1][selected_target]\nprint('X.shape, y.shape', X.shape, y.shape )\n\n# Create simplfied validation scheme - like real test data - with two test-sets private-like, public-like:\n# Private like test - new DAY, and donor, \n# While public like - only new donor (days are the same as in train):\n# Step 1: \nmask_train = (df_meta['Playground']==1)&(df_meta['day']!=4)&(df_meta['donor']!=31800) \nX_train = df_cite.iloc[:70988,:n_features][mask_train]\ny_train = df_cite_train_y[mask_train][ selected_target ]\n# Step 2:\nmask_test_private_like = (df_meta['Playground']==1)&(df_meta['day']==4)\nX_test_private_like = df_cite.iloc[:70988,:n_features][ mask_test_private_like  ]\ny_test_private_like = df_cite_train_y[mask_test_private_like][ selected_target ]\nX_test = X_test_private_like\ny_test = y_test_private_like\n# Step 3: \nmask_test_public_like = (df_meta['Playground']==1)&(df_meta['day']!=4)  &(df_meta['donor']==31800) \nX_test_public_like = df_cite.iloc[:70988,:n_features][mask_test_public_like ]\ny_test_public_like = df_cite_train_y[mask_test_public_like][ selected_target ]\nX_test2 = X_test_public_like\ny_test2 = y_test_public_like\n\nmask_out_of_playground = (df_meta['Playground']==0)\nX_oop = df_cite.iloc[:70988,:n_features][mask_out_of_playground]\ny_oop = df_cite_train_y[mask_out_of_playground][ selected_target ]\n\n\n\nX.shape,y.shape, X_train.shape, X_test_private_like.shape, X_test_public_like.shape, y_train.shape, y_test_private_like.shape, y_test_public_like.shape\n\n","metadata":{"execution":{"iopub.status.busy":"2022-11-05T11:29:02.745384Z","iopub.execute_input":"2022-11-05T11:29:02.745683Z","iopub.status.idle":"2022-11-05T11:29:02.876785Z","shell.execute_reply.started":"2022-11-05T11:29:02.745658Z","shell.execute_reply":"2022-11-05T11:29:02.875923Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"","metadata":{},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"# Modeling Preparations","metadata":{}},{"cell_type":"code","source":"from sklearn.metrics import mean_squared_error\nfrom sklearn.metrics import r2_score","metadata":{"execution":{"iopub.status.busy":"2022-11-05T11:29:02.877886Z","iopub.execute_input":"2022-11-05T11:29:02.878886Z","iopub.status.idle":"2022-11-05T11:29:02.883935Z","shell.execute_reply.started":"2022-11-05T11:29:02.878856Z","shell.execute_reply":"2022-11-05T11:29:02.882458Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"df_models_stat = pd.DataFrame()# columns = [ 'r2_score','mse','Time',  'n_feat', 'Target' ])\n#IXmdl = 0\ndf_models_stat","metadata":{"execution":{"iopub.status.busy":"2022-11-05T11:29:02.885356Z","iopub.execute_input":"2022-11-05T11:29:02.885698Z","iopub.status.idle":"2022-11-05T11:29:02.899493Z","shell.execute_reply.started":"2022-11-05T11:29:02.885666Z","shell.execute_reply":"2022-11-05T11:29:02.898468Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"dict_save_predictions = {}\ndef update_models_stat(model, model_ID, t0 ):\n    \n    IXmdl = model_ID # df_models_stat.shape[0] + 1\n    #df_models_stat.loc[IXmdl,'Model'] = model_ID\n    y_pred_loc = model.predict(X_test)\n    df_models_stat.loc[IXmdl,'r2_score'] = r2_score(y_test, y_pred_loc) \n    df_models_stat.loc[IXmdl,'mse'] =  mean_squared_error(y_test, y_pred_loc)\n    df_models_stat.loc[IXmdl,'Time'] = np.round( time.time() - t0,3)\n    df_models_stat.loc[IXmdl,'Target'] = selected_target\n    df_models_stat.loc[IXmdl,'n_feat'] = X_train.shape[1]\n    df_models_stat.loc[IXmdl,'n_samples_train'] = X_train.shape[0]\n    \n    y_pred_loc = model.predict(X_test_public_like)\n    df_models_stat.loc[IXmdl,'r2_score Test2 PublLike'] = r2_score(y_test_public_like, y_pred_loc) \n    df_models_stat.loc[IXmdl,'mse Test2 PublLike'] =  mean_squared_error(y_test_public_like, y_pred_loc)\n    \n    y_pred_loc = model.predict(X_oop)\n    df_models_stat.loc[IXmdl,'r2_score Out of Playgr'] = r2_score(y_oop, y_pred_loc) \n    df_models_stat.loc[IXmdl,'mse Out of Playgr'] =  mean_squared_error(y_oop, y_pred_loc)\n\n    y_pred_loc = model.predict(X_train)\n    df_models_stat.loc[IXmdl,'r2_score Train'] = r2_score(y_train, y_pred_loc) \n    df_models_stat.loc[IXmdl,'mse Train'] =  mean_squared_error(y_train, y_pred_loc)\n    \n    dict_save_predictions[model_ID] = (model.predict(X_train), model.predict(X_test), \n                                       model.predict(X_test_public_like), model.predict(X_oop)   )\n    \n","metadata":{"execution":{"iopub.status.busy":"2022-11-05T11:29:02.900905Z","iopub.execute_input":"2022-11-05T11:29:02.901229Z","iopub.status.idle":"2022-11-05T11:29:02.912226Z","shell.execute_reply.started":"2022-11-05T11:29:02.901201Z","shell.execute_reply":"2022-11-05T11:29:02.911172Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"# XGBoost , with Optuna","metadata":{}},{"cell_type":"code","source":"%%time\nimport xgboost as xgb\nprint('Default params')\nt0 = time.time()\nmodel = xgb.XGBRegressor()# tree_method=\"gpu_hist\")(n_neighbors=15)\nmodel.fit(X_train,y_train)\ny_pred = model.predict(X_test)\nupdate_models_stat(model, 'XGBoost Default', t0 ) # Updates df_models_stat\nr2_score(y_test, y_pred), mean_squared_error(y_test, y_pred)","metadata":{"execution":{"iopub.status.busy":"2022-11-05T11:29:02.913841Z","iopub.execute_input":"2022-11-05T11:29:02.914286Z","iopub.status.idle":"2022-11-05T11:29:04.130449Z","shell.execute_reply.started":"2022-11-05T11:29:02.914248Z","shell.execute_reply":"2022-11-05T11:29:04.129595Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"df_models_stat\n","metadata":{"execution":{"iopub.status.busy":"2022-11-05T11:29:04.133532Z","iopub.execute_input":"2022-11-05T11:29:04.134433Z","iopub.status.idle":"2022-11-05T11:29:04.148979Z","shell.execute_reply.started":"2022-11-05T11:29:04.134402Z","shell.execute_reply":"2022-11-05T11:29:04.147782Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"model.get_params()","metadata":{"execution":{"iopub.status.busy":"2022-11-05T11:29:04.150594Z","iopub.execute_input":"2022-11-05T11:29:04.151522Z","iopub.status.idle":"2022-11-05T11:29:04.163376Z","shell.execute_reply.started":"2022-11-05T11:29:04.151483Z","shell.execute_reply":"2022-11-05T11:29:04.162467Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"# # Simplest example to use optuna: https://optuna.org/#code_examples\n# \n# import optuna\n# def objective(trial):\n#     x = trial.suggest_float('x', -10, 10)\n#     return (x - 2) ** 2\n# study = optuna.create_study()\n# study.optimize(objective, n_trials=100)\n# study.best_params  # E.g. {'x': 2.002108042}\n\n# Some example of Optuna with Lightgbm\n# https://www.kaggle.com/code/xiafire/lb0-830-lgbm-optuna-msci-citeseq\n","metadata":{"execution":{"iopub.status.busy":"2022-11-05T11:29:04.164250Z","iopub.execute_input":"2022-11-05T11:29:04.164518Z","iopub.status.idle":"2022-11-05T11:29:04.169530Z","shell.execute_reply.started":"2022-11-05T11:29:04.164493Z","shell.execute_reply":"2022-11-05T11:29:04.168481Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"import optuna\n\ndef objective(trial):\n    params = {\n        'n_estimators' :  trial.suggest_categorical('n_estimators', [100, 200, 500]) ,\n       'max_depth': trial.suggest_categorical('max_depth', [6, 10,20,100]),\n#        'max_leaves' : trial.suggest_int('max_leaves', 0, 1000),\n#         'min_child_samples': trial.suggest_int('min_child_samples', 1, 300),\n    }\n    \n    model = xgb.XGBRegressor(**params)# tree_method=\"gpu_hist\")(n_neighbors=15)\n    model.fit(X_train,y_train)\n    y_pred = model.predict(X_test)\n    #r2_score(y_test, y_pred), \n    mse = mean_squared_error(y_test, y_pred)\n    \n    return mse","metadata":{"execution":{"iopub.status.busy":"2022-11-05T11:29:04.170631Z","iopub.execute_input":"2022-11-05T11:29:04.170857Z","iopub.status.idle":"2022-11-05T11:29:04.182933Z","shell.execute_reply.started":"2022-11-05T11:29:04.170835Z","shell.execute_reply":"2022-11-05T11:29:04.182248Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"%%time\nfind_params = True\nif find_params:\n#     study = optuna.create_study(\n#         direction='minimize', \n#         pruner=optuna.pruners.MedianPruner(n_warmup_steps=20),\n#         study_name='small')\n#     study.optimize(objective, n_trials=20)\n    study = optuna.create_study()\n    study.optimize(objective, n_trials=10)\n    \n    # Output for best found params: \n    print(); print('Best params:')\n    print(study.best_params)  # E.g. {'x': 2.002108042}\n    t0  = time.time()\n    model = xgb.XGBRegressor(**study.best_params)# tree_method=\"gpu_hist\")(n_neighbors=15)\n    model.fit(X_train,y_train)\n    y_pred = model.predict(X_test)\n    #r2_score(y_test, y_pred), \n    \n    update_models_stat(model, 'XGBoost Tuned', t0 ) # Updates df_models_stat\n\n    print('Best r2:%.6f'%r2_score(y_test, y_pred),'Mse:%.3f'%mean_squared_error(y_test, y_pred) )\n","metadata":{"execution":{"iopub.status.busy":"2022-11-05T11:29:04.183717Z","iopub.execute_input":"2022-11-05T11:29:04.183948Z","iopub.status.idle":"2022-11-05T11:29:36.247603Z","shell.execute_reply.started":"2022-11-05T11:29:04.183926Z","shell.execute_reply":"2022-11-05T11:29:36.246918Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"# Catboost","metadata":{}},{"cell_type":"code","source":"%%time\nfrom catboost import CatBoostRegressor\nprint('Default params')\nt0 = time.time()\nmodel = CatBoostRegressor(verbose = 0 ) # iterations=2, learning_rate=1, depth=2)\nmodel.fit(X_train,y_train)\ny_pred = model.predict(X_test)\nupdate_models_stat(model, 'CatBoost Default', t0 ) # Updates df_models_stat\nr2_score(y_test, y_pred), mean_squared_error(y_test, y_pred)","metadata":{"execution":{"iopub.status.busy":"2022-11-05T11:29:36.248856Z","iopub.execute_input":"2022-11-05T11:29:36.249325Z","iopub.status.idle":"2022-11-05T11:29:40.845003Z","shell.execute_reply.started":"2022-11-05T11:29:36.249298Z","shell.execute_reply":"2022-11-05T11:29:40.844130Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"df_models_stat\n","metadata":{"execution":{"iopub.status.busy":"2022-11-05T11:29:40.846050Z","iopub.execute_input":"2022-11-05T11:29:40.846321Z","iopub.status.idle":"2022-11-05T11:29:40.861270Z","shell.execute_reply.started":"2022-11-05T11:29:40.846289Z","shell.execute_reply":"2022-11-05T11:29:40.860270Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"model.get_all_params()","metadata":{"execution":{"iopub.status.busy":"2022-11-05T11:29:40.862276Z","iopub.execute_input":"2022-11-05T11:29:40.862496Z","iopub.status.idle":"2022-11-05T11:29:40.872782Z","shell.execute_reply.started":"2022-11-05T11:29:40.862474Z","shell.execute_reply":"2022-11-05T11:29:40.871848Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"import optuna\nfrom catboost import CatBoostRegressor\n\ndef objective(trial):\n    params = {\n        'n_estimators' :  trial.suggest_categorical('n_estimators', [100, 200, 500]) ,\n       'max_depth': trial.suggest_categorical('max_depth', [6,8,10,12,16]),\n#        'max_leaves' : trial.suggest_int('max_leaves', 0, 1000),\n#         'min_child_samples': trial.suggest_int('min_child_samples', 1, 300),\n    }\n    \n    model = CatBoostRegressor(verbose = 0, random_seed=42)# tree_method=\"gpu_hist\")(n_neighbors=15)\n    model.fit(X_train,y_train)\n    y_pred = model.predict(X_test)\n    #r2_score(y_test, y_pred), \n    mse = mean_squared_error(y_test, y_pred)\n    \n    return mse","metadata":{"execution":{"iopub.status.busy":"2022-11-05T14:44:16.626988Z","iopub.execute_input":"2022-11-05T14:44:16.627414Z","iopub.status.idle":"2022-11-05T14:44:16.634865Z","shell.execute_reply.started":"2022-11-05T14:44:16.627383Z","shell.execute_reply":"2022-11-05T14:44:16.633618Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"%%time\nfind_params = True\nif find_params:\n#     study = optuna.create_study(\n#         direction='minimize', \n#         pruner=optuna.pruners.MedianPruner(n_warmup_steps=20),\n#         study_name='small')\n#     study.optimize(objective, n_trials=20)\n    study = optuna.create_study()\n    study.optimize(objective, n_trials=10)\n    \n    # Output for best found params: \n    print(); print('Best params:')\n    print(study.best_params)  # E.g. {'x': 2.002108042}\n    t0  = time.time()\n    model = CatBoostRegressor(**study.best_params, verbose=0)# tree_method=\"gpu_hist\")(n_neighbors=15)\n    model.fit(X_train,y_train)\n    y_pred = model.predict(X_test)\n    #r2_score(y_test, y_pred), \n    \n    update_models_stat(model, 'CatBoost Tuned', t0 ) # Updates df_models_stat\n\n    print('Best r2:%.6f'%r2_score(y_test, y_pred),'Mse:%.3f'%mean_squared_error(y_test, y_pred) )\n","metadata":{"execution":{"iopub.status.busy":"2022-11-05T14:44:19.027422Z","iopub.execute_input":"2022-11-05T14:44:19.027804Z","iopub.status.idle":"2022-11-05T14:45:06.023256Z","shell.execute_reply.started":"2022-11-05T14:44:19.027769Z","shell.execute_reply":"2022-11-05T14:45:06.021853Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"# Show summary stat","metadata":{}},{"cell_type":"code","source":"df_models_stat.sort_values('r2_score',ascending = False)","metadata":{"execution":{"iopub.status.busy":"2022-11-05T14:47:29.625839Z","iopub.execute_input":"2022-11-05T14:47:29.626333Z","iopub.status.idle":"2022-11-05T14:47:29.646870Z","shell.execute_reply.started":"2022-11-05T14:47:29.626296Z","shell.execute_reply":"2022-11-05T14:47:29.645576Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"df_models_stat.sort_values('r2_score',ascending = False).to_csv('df_models_stat.csv')","metadata":{"execution":{"iopub.status.busy":"2022-10-31T14:16:41.350978Z","iopub.execute_input":"2022-10-31T14:16:41.351497Z","iopub.status.idle":"2022-10-31T14:16:41.362144Z","shell.execute_reply.started":"2022-10-31T14:16:41.351448Z","shell.execute_reply":"2022-10-31T14:16:41.360908Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"","metadata":{},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"print('%.1f seconds passed total '%(time.time()-t0start) )","metadata":{"execution":{"iopub.status.busy":"2022-10-31T14:16:41.364231Z","iopub.execute_input":"2022-10-31T14:16:41.364766Z","iopub.status.idle":"2022-10-31T14:16:41.375895Z","shell.execute_reply.started":"2022-10-31T14:16:41.364731Z","shell.execute_reply":"2022-10-31T14:16:41.374758Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"","metadata":{},"execution_count":null,"outputs":[]}]}