{"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":"# [G-Reaserch] Beginner's Try for simple LGBM\n\nThis is a Table Competition to predict future price of cryptocurrencies. A bit similar to the \"Optiver\" competition a couple of months ago.  \nStarted from Public Score **0.0077** and improved to **0.4278** so far, experiencing various difficulties...\n\n暗号資産の価格を予想する今回のテーブルコンペ。つい数か月前にあった「Optiver」（株価のボラティリティを予測する）にちょっと似ていて面白そう、と思いトライしてみました。Public Score、**0.0077**からスタートし、四苦八苦しながら、とりあえず**0.7005**まで上げてきました。。。\n\n![image.png](attachment:53dad4f7-f44f-4bc5-9bab-7710686467d9.png)","metadata":{},"attachments":{"53dad4f7-f44f-4bc5-9bab-7710686467d9.png":{"image/png":"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"}}},{"cell_type":"markdown","source":"# (1) (2) Build simple model\n\n**(1) Build simple model using Ridge()**  \nAt first, anyway use the as-is data into the simple model, and let's see how good score I can get.  \nReferring to KONRAD BANACHEWICZ's code in below link, tried to feed the training data into Ridge model.  \nThe result is Public score **0.007**, which is a little bit better than original code since I added VWAP as a feature.  \nWell, this seems not so bad as a starting score.\n\nとりあえず、あまり何も考えず、シンプルなモデルでどの程度のスコアが出るか見てみよう。  \nKONRAD BANACHEWICZさんのコードを参考に、ModelとしてRidgeを使いTraining Dataを投入してみる。  \n結果は、Public Score：**0.007**。投入データとしてVWAPも追加したので参考コード（0.005）に比べて少し良かった。  \n最初は、ま、こんなもんでしょ。  \nhttps://www.kaggle.com/konradb/super-simple-baseline","metadata":{}},{"cell_type":"code","source":"# model = Ridge()\n# model.fit(trn_x[col_subset],trn_y)","metadata":{"execution":{"iopub.status.busy":"2021-11-20T03:27:51.127507Z","iopub.execute_input":"2021-11-20T03:27:51.128938Z","iopub.status.idle":"2021-11-20T03:27:51.151395Z","shell.execute_reply.started":"2021-11-20T03:27:51.128744Z","shell.execute_reply":"2021-11-20T03:27:51.150182Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"**(2) Use LightGBM**  \nNext, tried to use LGBMRegressor, simple model to fit with x/y only without eval_set.  \nWhat I was confused here is, I encountered frequent Memory-over error which did not happen in Ridge model.  \nOf course, the original training data itself is very huge, and memory-over error makes sense...  \nReferring to the below Discussion, introduced \"reduce_mem_usage\" function.  \nThe result is, Public score was improved to **0.039**.  Well!, it works so good to simply change from Ridge to LGBM.  \nLet's start to see the actual code!\n\n次に、LGBMを使ってみる（eval_setはセットせず、x・yだけ投入してfitするシンプルなモデル）。ここで困ったのが、Ridgeモデルの時は問題なかった「メモリーオーバーエラー」。  \nまぁ、元のデータが巨大なので、仕方ないと思いますが。。  \n以下のDiscussion（のリンク先）を参考に、Trainingデータの容量を削減する関数（reduce_mem_usage）を追加。  \nhttps://www.kaggle.com/c/g-research-crypto-forecasting/discussion/285719  \n結果は、Public Score＝**0.039**。へぇー、LGBMにするだけでこんなにスコア良くなるんだ。\n\nとりあえずこのあたりで、コードをご紹介していきます。","metadata":{}},{"cell_type":"code","source":"import gresearch_crypto\nimport pandas as pd\nimport numpy as np\nimport lightgbm as lgb\nimport warnings\nwarnings.filterwarnings('ignore')\nfrom sklearn.model_selection import train_test_split","metadata":{"_uuid":"8f2839f25d086af736a60e9eeb907d3b93b6e0e5","_cell_guid":"b1076dfc-b9ad-4769-8c92-a6c4dae69d19","execution":{"iopub.status.busy":"2021-11-20T03:27:51.153401Z","iopub.execute_input":"2021-11-20T03:27:51.153906Z","iopub.status.idle":"2021-11-20T03:27:53.242459Z","shell.execute_reply.started":"2021-11-20T03:27:51.15387Z","shell.execute_reply":"2021-11-20T03:27:53.241505Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"df_train = pd.read_csv('../input/g-research-crypto-forecasting/train.csv')\ndf_test = pd.read_csv('../input/g-research-crypto-forecasting/example_test.csv')","metadata":{"execution":{"iopub.status.busy":"2021-11-20T03:27:53.245028Z","iopub.execute_input":"2021-11-20T03:27:53.24525Z","iopub.status.idle":"2021-11-20T03:28:56.215756Z","shell.execute_reply.started":"2021-11-20T03:27:53.245223Z","shell.execute_reply":"2021-11-20T03:28:56.214885Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"df_train.info()","metadata":{"execution":{"iopub.status.busy":"2021-11-20T03:28:56.217621Z","iopub.execute_input":"2021-11-20T03:28:56.217864Z","iopub.status.idle":"2021-11-20T03:28:56.243631Z","shell.execute_reply.started":"2021-11-20T03:28:56.217835Z","shell.execute_reply":"2021-11-20T03:28:56.242494Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"Hmmm.. As mentioned in above (2), Training data is huge. It may be because of unnecessary Float64 of Dtypes. (e.g. \"Count\" doesn't need to be Float64.) Let's reduce the data memory later.\n\nふむふむ。上記(2)で書きましたが、Training Dataが巨大なのは、むだにデータタイプがFloat64で保持されていたりするからなんでしょうね (Countとか件数なんだからFloat64である必要性ない）。のちほど、軽くします。","metadata":{}},{"cell_type":"code","source":"df_train.describe()","metadata":{"execution":{"iopub.status.busy":"2021-11-20T03:28:56.245214Z","iopub.execute_input":"2021-11-20T03:28:56.245559Z","iopub.status.idle":"2021-11-20T03:29:05.735946Z","shell.execute_reply.started":"2021-11-20T03:28:56.245513Z","shell.execute_reply":"2021-11-20T03:29:05.735066Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"# (3) Try to replace Nan, Inf, -Inf by Mean  \nFrom a look at Training data, I can see NaN, Inf, -inf. They were dropped so far (code [12]), but come up with the idea that it may improved the score if replace them by the other value such as \"Mean\". Let's try :-)  \nThe result is, Failed... Public score became worse to **0.028**. OK, I'm convinced that they should be dropped rather than change.\n\nDescribeでtrainingデータを概観すると、NaN、Inf、-infが見える。これまで、これらのレコードはDrop（コード[12]）してたけど、平均値に置き換えたりしたら少しはスコア改善するかな？と思い、試してみる。  \nが、見事に惨敗。。スコアは**0.028**に低下。なるほど、変に適当な数値に変えるよりも落とせ、ということですね。納得。","metadata":{}},{"cell_type":"code","source":"df_train.isnull().sum()","metadata":{"execution":{"iopub.status.busy":"2021-11-20T03:29:05.737467Z","iopub.execute_input":"2021-11-20T03:29:05.73777Z","iopub.status.idle":"2021-11-20T03:29:06.218452Z","shell.execute_reply.started":"2021-11-20T03:29:05.737729Z","shell.execute_reply":"2021-11-20T03:29:06.217483Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"a1 = df_train[\"VWAP\"].isin([np.inf])\na2 = df_train[\"VWAP\"].isin([-np.inf])\na3 = df_train[\"VWAP\"].isin([np.nan])\nprint (\"inf:\", a1.sum(), \"  -inf:\", a2.sum(), \"  nan:\", a3.sum())","metadata":{"execution":{"iopub.status.busy":"2021-11-20T03:29:06.219655Z","iopub.execute_input":"2021-11-20T03:29:06.219885Z","iopub.status.idle":"2021-11-20T03:29:06.491533Z","shell.execute_reply.started":"2021-11-20T03:29:06.219858Z","shell.execute_reply":"2021-11-20T03:29:06.490604Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"df_train[df_train[\"Target\"].isna()].head()","metadata":{"execution":{"iopub.status.busy":"2021-11-20T03:29:06.49322Z","iopub.execute_input":"2021-11-20T03:29:06.493445Z","iopub.status.idle":"2021-11-20T03:29:06.782466Z","shell.execute_reply.started":"2021-11-20T03:29:06.493416Z","shell.execute_reply":"2021-11-20T03:29:06.781504Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"\n#df_train[\"VWAP\"]=df_train[\"VWAP\"].replace([np.inf, -np.inf], np.nan)\n\n#vwap_mean = df_train[[\"Asset_ID\", \"VWAP\"]].groupby(\"Asset_ID\").mean().reset_index()\n#vwap_mean.columns = [\"Asset_ID\", \"vwap_mean\"]\n\n#df_train = pd.merge(df_train, vwap_mean, on = \"Asset_ID\", how = \"left\")\n#df_train.loc[(df_train[\"VWAP\"].isnull()), \"VWAP\"] = df_train[\"vwap_mean\"]\n#df_train = df_train.drop(\"vwap_mean\", axis = 1)\n#df_train.describe()","metadata":{"execution":{"iopub.status.busy":"2021-11-20T03:29:06.783787Z","iopub.execute_input":"2021-11-20T03:29:06.784046Z","iopub.status.idle":"2021-11-20T03:29:06.789394Z","shell.execute_reply.started":"2021-11-20T03:29:06.784016Z","shell.execute_reply":"2021-11-20T03:29:06.78841Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"# (4) Try to add \"Weight\" as one of the features  \nI can see \"asset_details\" file as well as Training data. It is small file basically to show which Asset ID is which cryptocurrency, but I'm not sure what the \"Weight\" shows.. Let's add it to features, and see how Score becomes.  \nHowever, no, the score went slightly down to **0.036**. (Code [11] is just FYI, actually not used)\n\nTrainingデータ以外に、asset_detailsというファイルも用意されている。どのAssetIDが実際には何の暗号通貨かを示したりするSmallなファイルだけど、ここにあるWeightというのが何に使うのかよくわからない。とりあえず特徴量の一つに加えてスコアが良くなるか見てみる。  \n。。。ダメですね。Scoreは**0.036**と僅かに悪化しました。（コード[11]はその名残です。。結果的には使ってません。）","metadata":{}},{"cell_type":"code","source":"df_asset = pd.read_csv('../input/g-research-crypto-forecasting/asset_details.csv')\ndf_asset","metadata":{"execution":{"iopub.status.busy":"2021-11-20T03:29:06.792613Z","iopub.execute_input":"2021-11-20T03:29:06.79294Z","iopub.status.idle":"2021-11-20T03:29:06.820801Z","shell.execute_reply.started":"2021-11-20T03:29:06.792905Z","shell.execute_reply":"2021-11-20T03:29:06.819921Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"def feature_adjust(df) :\n    feature_cols = ['Asset_ID', 'Close', 'Count', 'High', 'Low', 'Open', 'VWAP','Volume', 'timestamp'] \n                #\"Upper_shadow\", \"Lower_shadow\"] #Bull_Bear\n    categories = [\"Asset_ID\", \"timestamp\"] #Bull_Bear\n    \n    x = df[feature_cols].copy()\n    #df = pd.merge(df, df_asset, on = \"Asset_ID\", how = \"left\")\n    #df = df.drop(\"Asset_Name\", axis = 1)\n    return x","metadata":{"execution":{"iopub.status.busy":"2021-11-20T03:29:06.822086Z","iopub.execute_input":"2021-11-20T03:29:06.822307Z","iopub.status.idle":"2021-11-20T03:29:06.828571Z","shell.execute_reply.started":"2021-11-20T03:29:06.822279Z","shell.execute_reply":"2021-11-20T03:29:06.82704Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"df_train = df_train[~df_train.isin([np.nan, np.inf, -np.inf]).any(1)].reset_index(drop=True)\n#df_train = feature_adjust(df_train)","metadata":{"execution":{"iopub.status.busy":"2021-11-20T03:29:06.829613Z","iopub.execute_input":"2021-11-20T03:29:06.829843Z","iopub.status.idle":"2021-11-20T03:29:12.421177Z","shell.execute_reply.started":"2021-11-20T03:29:06.829816Z","shell.execute_reply":"2021-11-20T03:29:12.42025Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"df_train","metadata":{"execution":{"iopub.status.busy":"2021-11-20T03:29:12.422753Z","iopub.execute_input":"2021-11-20T03:29:12.424348Z","iopub.status.idle":"2021-11-20T03:29:12.446641Z","shell.execute_reply.started":"2021-11-20T03:29:12.4243Z","shell.execute_reply":"2021-11-20T03:29:12.445831Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"#feature_cols = ['Asset_ID', 'Close', 'Count', 'High', 'Low', 'Open', 'VWAP','Volume', 'timestamp'] # \"Weight\"\n#categories = [\"Asset_ID\", \"timestamp\"]","metadata":{"execution":{"iopub.status.busy":"2021-11-20T03:29:12.448147Z","iopub.execute_input":"2021-11-20T03:29:12.448596Z","iopub.status.idle":"2021-11-20T03:29:12.464568Z","shell.execute_reply.started":"2021-11-20T03:29:12.448552Z","shell.execute_reply":"2021-11-20T03:29:12.46367Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"def reduce_mem_usage(df):\n  \n    start_mem = df.memory_usage().sum() / 1024**2\n    print('Memory usage of dataframe is {:.2f} MB'.format(start_mem))\n    \n    for col in df.columns:\n        col_type = df[col].dtype\n        \n        if col_type != object:\n            c_min = df[col].min()\n            c_max = df[col].max()\n            if str(col_type)[:3] == 'int':\n                if c_min > np.iinfo(np.int8).min and c_max < np.iinfo(np.int8).max:\n                    df[col] = df[col].astype(np.int8)\n                elif c_min > np.iinfo(np.int16).min and c_max < np.iinfo(np.int16).max:\n                    df[col] = df[col].astype(np.int16)\n                elif c_min > np.iinfo(np.int32).min and c_max < np.iinfo(np.int32).max:\n                    df[col] = df[col].astype(np.int32)\n                elif c_min > np.iinfo(np.int64).min and c_max < np.iinfo(np.int64).max:\n                    df[col] = df[col].astype(np.int64)  \n            else:\n                if c_min > np.finfo(np.float16).min and c_max < np.finfo(np.float16).max:\n                    df[col] = df[col].astype(np.float16)\n                elif c_min > np.finfo(np.float32).min and c_max < np.finfo(np.float32).max:\n                    df[col] = df[col].astype(np.float32)\n                else:\n                    df[col] = df[col].astype(np.float64)\n#         else:\n#             df[col] = df[col].astype('category')\n\n    df[\"Count\"] = df[\"Count\"].astype(np.int16)\n\n    end_mem = df.memory_usage().sum() / 1024**2\n    \n    print('Memory usage after optimization is: {:.2f} MB'.format(end_mem))\n    print('Decreased by {:.1f}%'.format(100 * (start_mem - end_mem) / start_mem))\n     \n    return df\n\ndf_train = reduce_mem_usage(df_train)","metadata":{"execution":{"iopub.status.busy":"2021-11-20T03:29:12.46592Z","iopub.execute_input":"2021-11-20T03:29:12.46675Z","iopub.status.idle":"2021-11-20T03:29:16.470514Z","shell.execute_reply.started":"2021-11-20T03:29:12.4667Z","shell.execute_reply":"2021-11-20T03:29:16.469988Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"Above [15] is the function to reduce the data memory. Data type Float64 is changed, and then Training data was reduced from 1.8GB to 0.5GB. Data type change works well like this. I see.\n\n上記の[15]が、データメモリーを削減する関数で、Float64のデータタイプを変えることでTrainingデータが1.8GBから0.5GBへと削減されています。データタイプを変えるだけでこんなに違うんだ。へー。","metadata":{}},{"cell_type":"code","source":"df_test = reduce_mem_usage(df_test)","metadata":{"execution":{"iopub.status.busy":"2021-11-20T03:29:16.471701Z","iopub.execute_input":"2021-11-20T03:29:16.472119Z","iopub.status.idle":"2021-11-20T03:29:16.486547Z","shell.execute_reply.started":"2021-11-20T03:29:16.472088Z","shell.execute_reply":"2021-11-20T03:29:16.485612Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"#x_train = df_train[feature_cols].copy()\n#y_train = df_train['Target'].copy()","metadata":{"execution":{"iopub.status.busy":"2021-11-20T03:29:16.487716Z","iopub.execute_input":"2021-11-20T03:29:16.487953Z","iopub.status.idle":"2021-11-20T03:29:16.499706Z","shell.execute_reply.started":"2021-11-20T03:29:16.487926Z","shell.execute_reply":"2021-11-20T03:29:16.49896Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"#train_x = x_train.copy()\n#train_y = y_train.copy()\n#train_x, valid_x, train_y, valid_y = train_test_split(x_train, y_train, test_size = 0.4)","metadata":{"execution":{"iopub.status.busy":"2021-11-20T03:29:16.500925Z","iopub.execute_input":"2021-11-20T03:29:16.5017Z","iopub.status.idle":"2021-11-20T03:29:16.511914Z","shell.execute_reply.started":"2021-11-20T03:29:16.501661Z","shell.execute_reply":"2021-11-20T03:29:16.511047Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"# (5) Try to use \"eval_set\" in LGBM  \nTried to improve the model in the latter half of code[19], adding \"eval_set\" to study with (train_x, train_y) and evaluate with (valid_x, valid_y). Then, always Memory-over error happens.  \nWhen make the test size bigger to 0.4 (= make dataset for fitting smaller to 0.6), somehow it worked without error. Let's see the Public score.  \nOh, miserable.. Public score went down to **0.018**. Once sit down and consider again, it may make sense because only 60% of entire training data is used.\n\nコード[19]の後半では、eval_setもつけて(train_x, train_y)で学習、(valid_x, valid_y)で評価して学習モデルを向上させようとしてみた。。。頻繁にメモリーオーバーが発生。test_sizeを0.4と大きめにする（学習に使うデータセットを0.6と小さくする）と、なんとか動いたので、これでスコアを見てみる。。。  \n惨敗ですね、スコアは**0.018**へと大きく悪化。そりゃそうか、トレーニングデータの6割しか使ってないんだもんね。","metadata":{}},{"cell_type":"markdown","source":"# (6) LGBM (test_size = 0, N_estimators = 200)  \nAfter all, I changed the model back to simple fit(x,y).  \nIf so, don't need to split the training data for validation, hence no use for train_test_split.  \nIn the above (5) process, I confirmed that the epoch of training went over 100 without early stopping and arrived at almost 200. Thus increased the n_estimators from the default 100 to 200. Now, let's see.  \nThe result is... Public score shows **0.069**, much improved. Good! (^o^)/\n\nということで、modelは単純なfit(x,y)に戻した。そうなると、トレーニングデータの一部をvalidate用に取り分ける必要もないので、train_test_splitも使わず。上記(5)で、学習が100回を超えてもearly stoppingがかからず200回近くまで学習が進んだことは確認できたので、学習回数をデフォルトの100から200に増やす。  \nすると、結果は。。。Public Scoreは**0.069**への大きく改善。良しよし。　(^o^)/","metadata":{}},{"cell_type":"code","source":"lgb_params = {\n    \"objective\": \"regression\",\n    \"n_estimators\" : 5000,     # <-- (9) change from 200 to 500\n    \"num_leaves\" : 300,       # <-- (10) Added parameter\n    \"learning_rate\" : 0.09,   # <-- (10) Added parameter\n    \"random_seed\" : 1234}\n    \n#model = lgb.LGBMRegressor(**lgb_params)\n    \n#model.fit(train_x, train_y)\n\n#model.fit(\n#    train_x, \n#    train_y,\n#    eval_set = (valid_x, valid_y),    \n#    early_stopping_rounds = 20,\n#    verbose = 10,\n#    categorical_feature = categories,\n#    )\n\n","metadata":{"execution":{"iopub.status.busy":"2021-11-20T03:29:16.513231Z","iopub.execute_input":"2021-11-20T03:29:16.513824Z","iopub.status.idle":"2021-11-20T03:29:16.525563Z","shell.execute_reply.started":"2021-11-20T03:29:16.513776Z","shell.execute_reply":"2021-11-20T03:29:16.524895Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"# (7) Modeling for each Asset ID\n\nSo far, in the above (1)-(6), I took an easy way to feed the entire Training data into the model at one shot.  \nHowever, the price trend is of course varied for each Asset, thus tried to fit per each Asset.  \nThe result of Public score is... **0.1938**. I see, only modeling change from \"for all data\" to \"for each asset\" can improve the score like this!\n\n上記(1)～(6)では、簡易的にTrainingデータをまとめてモデルに投入していたが、Asset毎に価格傾向も違うので、Asset毎に分けて学習させてみる。  \n結果は、**0.1938**へと大きく上昇。へぇー、個別アセット毎のモデルにするだけで、こんなに改善するんだ。","metadata":{}},{"cell_type":"code","source":"def get_model (asset_id) :\n    \n    df = df_train[df_train[\"Asset_ID\"] == asset_id]\n    \n    x = feature_adjust(df)\n    #x = df[feature_cols].copy()\n    y = df['Target'].copy()\n        \n    model = lgb.LGBMRegressor(**lgb_params)\n    model.fit(x, y)\n    \n    return model","metadata":{"execution":{"iopub.status.busy":"2021-11-20T03:29:16.526756Z","iopub.execute_input":"2021-11-20T03:29:16.527554Z","iopub.status.idle":"2021-11-20T03:29:16.543643Z","shell.execute_reply.started":"2021-11-20T03:29:16.527518Z","shell.execute_reply":"2021-11-20T03:29:16.542567Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"asset_ids = list(df_asset[\"Asset_ID\"].unique())\nasset_ids.sort()\nmodels = {}\n\nfor asset_id in asset_ids :\n        \n    print(f\"Training model for  ID={asset_id}\")\n    \n    model = get_model(asset_id)\n    models[asset_id] = model","metadata":{"execution":{"iopub.status.busy":"2021-11-20T03:29:16.545134Z","iopub.execute_input":"2021-11-20T03:29:16.54541Z","iopub.status.idle":"2021-11-20T03:30:40.457895Z","shell.execute_reply.started":"2021-11-20T03:29:16.545373Z","shell.execute_reply":"2021-11-20T03:30:40.456792Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"env = gresearch_crypto.make_env()\niter_test = env.iter_test()\n\n#for (test_df, sample_prediction_df) in iter_test:\n    \n#    test_df = feature_adjust(test_df)\n#    test_sub = test_df[feature_cols]\n#    sample_prediction_df['Target'] = model.predict(test_sub)\n#    env.predict(sample_prediction_df)","metadata":{"execution":{"iopub.status.busy":"2021-11-20T03:30:40.45922Z","iopub.execute_input":"2021-11-20T03:30:40.459581Z","iopub.status.idle":"2021-11-20T03:30:40.466211Z","shell.execute_reply.started":"2021-11-20T03:30:40.459549Z","shell.execute_reply":"2021-11-20T03:30:40.463325Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"for i, (df_test, df_pred) in enumerate(iter_test) :\n    df_test = reduce_mem_usage(df_test)\n    \n    for j , row in df_test.iterrows():        \n        \n        model = models[row['Asset_ID']]\n        x_test = feature_adjust(row)\n        #x_test = x_test.drop([\"row_id\"])\n        y_pred = model.predict(pd.DataFrame([x_test]))[0]\n        df_pred.loc[df_pred['row_id'] == row['row_id'], 'Target'] = y_pred\n    \n    env.predict(df_pred)","metadata":{"execution":{"iopub.status.busy":"2021-11-20T03:30:40.469309Z","iopub.execute_input":"2021-11-20T03:30:40.469709Z","iopub.status.idle":"2021-11-20T03:30:40.70192Z","shell.execute_reply.started":"2021-11-20T03:30:40.469669Z","shell.execute_reply":"2021-11-20T03:30:40.701129Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"# (8) Add \"Upper_shadow\", \"Lower shadow\" as features  \n\nSo far, I used as-is fields in the existing Training data, but try to create new features.  \nFrom a look at some Web-sites to explain the price of Stock, many sites seem to be explaining Candle Stick (refer to below diagram), with regard to Open , High, Low and Close price.  \nFirst, try to create \"Upper shadow\", \"Lower shadow\" using the eixiting these existing 4 prices.  \nLet's see how Public Score shows... the result is **0.1761** ...Why? Score have got worse. I'm wondering if simply adding them doesn't work?   \n\nこれまでは、Trainingデータにある項目をそのまま特徴量として使ってきたが、これらの項目を使って新しい特徴量を作ってみる。  \n株価を開設しているWebサイトを見ると、既存の特徴量である始値・高値・安値・終値に関してローソク足（下図参照）を解説しているサイトが多い。  \nまずは、ローソク足の特徴である「上ヒゲ」「下ヒゲ」を作ってみる。  \nPublic Scoreの結果は。。。**0.1761**。あれ？スコアが悪くなってる。単純に加えただけだとだめなのか？\n\n![image.png](attachment:501b8a24-758c-4af5-8a9d-0eaaea089274.png)","metadata":{},"attachments":{"501b8a24-758c-4af5-8a9d-0eaaea089274.png":{"image/png":"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"}}},{"cell_type":"code","source":"# FYI, for \"Upper_shadow\" and \"Lower_shadow\", following codes were used.\n# ご参考まで、上ヒゲ・下ヒゲには以下のコードを使いました。\n    \n    #df[\"Upper_shadow\"] = df[\"High\"] - np.maximum(df[\"Open\"], df['Close'])\n    #df[\"Lower_shadow\"] = np.minimum(df[\"Open\"], df['Close']) - df[\"Low\"]","metadata":{},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"# (9) Increase n_estimators from 200 to 500\n\nAbove (8) was less efficient than expected (rather got worse), thus tried more simple way which is to increase n_estimators from 200 to 500 (In[19]).  \nThe result is, Public score has got better to **0.2516**. This is the simple method but much more efficient than expected.\n\n上記(8)が思ったほど効果がなかった（むしろ悪化した）ので、シンプルに学習回数を200→500に増やしてみる（In[19]）。  \n結果は、Public Scoreが**0.2516**へと上昇。単純だけど思った以上の効果があった。","metadata":{}},{"cell_type":"markdown","source":"# (10) Adjust Hyperparameters\n\nSince the above(9) was much more improved than expected, tried to adjust other hyperparameters(num_leaves, learning_rate) as well, refering to the below link.  \nThe result is, Public score has been much improved to **0.4278**. It is surprising that just a slight parameter adjustment contributes to much improvement like this. (Wait, actually the biggest improvement so far...)  \n\n上記(9)でn_estimatorsを変えるだけで予想以上の改善となったので、他のHyperparameter(num_leavesとlearning_rate)も少し調整してみる。  \n調整項目については、以下のリンクを参考にさせていただきました。  \nhttps://www.kaggle.com/swaralipibose/lgdm-model-with-new-features-better-generalization\n\n驚くことに、結果は**0.4278**へと大きく改善。Hyperparameter変えるだけでこんなにスコアが改善するんだ。  \n（というか、これまでで一番大きな改善幅なんですけど。。）","metadata":{}},{"cell_type":"markdown","source":"# (11) Gradual increasing n_estimator from 500 to 5000\n\nBased on the result of the above (9), I'll attempt to gradually increase n_estimator to see how the score is improved.  \nI had assumed the score will get worse due to over-fitting if increased the number too much, however, the more and more n_estimator is increased the higher and higher the score becomes, which is the interesting result.  \n(it takes 4-5 hours to see the score result after submission, hence this experiment required 1 week... got tired..)\n\nここから少し実験し、上記(9)の結果を踏まえ、n_estimatorを徐々に増やしていき、どこまでScoreが伸びるかを試してみる。  \nどこかで過学習の影響が出てスコアは悪くなると思ったが、意に反してn_estimatorを増やせば増やすほどスコアは改善し、なかなか面白い結果となった。  \n（提出後スコアが出るのに4・5時間かかるので、この実験、一週間ほど掛かりました。。疲れた；；）\n\n![image.png](attachment:98d7a295-4a49-454e-a887-2c48968b10b0.png)","metadata":{},"attachments":{"98d7a295-4a49-454e-a887-2c48968b10b0.png":{"image/png":"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"}}},{"cell_type":"markdown","source":"\n\n","metadata":{}}]}