diff --git a/竞赛优胜技巧/.ipynb_checkpoints/AutoAI Tools-checkpoint.ipynb b/竞赛优胜技巧/.ipynb_checkpoints/AutoAI Tools-checkpoint.ipynb index 3744e19..b42ab74 100644 --- a/竞赛优胜技巧/.ipynb_checkpoints/AutoAI Tools-checkpoint.ipynb +++ b/竞赛优胜技巧/.ipynb_checkpoints/AutoAI Tools-checkpoint.ipynb @@ -28,7 +28,7 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": 1, "id": "f5e0e977", "metadata": {}, "outputs": [], @@ -181,85 +181,160 @@ "name": "stdout", "output_type": "stream", "text": [ - "训练集： (13932, 8)\n", - "验证集： (4644, 8)\n", + "训练集： (18576, 8)\n", "测试集： (2064, 8)\n" ] } ], "source": [ "# 切分训练和测试集\n", - "train_valid_x, test_x, train_valid_y, test_y = train_test_split(X, y,random_state=42,test_size=0.1)\n", + "train_x, test_x, train_y, test_y = train_test_split(X, y,random_state=42,test_size=0.1)\n", + "train_y = pd.DataFrame(train_y,columns=['result'])\n", + "test_y = pd.DataFrame(test_y,columns=['result'])\n", "\n", - "# 切分训练和验证集\n", - "train_x, valid_x, train_y, valid_y = train_test_split(train_valid_x, train_valid_y,random_state=42)\n", "print('训练集：',train_x.shape)\n", - "print('验证集：',valid_x.shape)\n", "print('测试集：', test_x.shape)" ] }, + { + "cell_type": "code", + "execution_count": 6, + "id": "009efc5d", + "metadata": {}, + "outputs": [], + "source": [ + "from sklearn.model_selection import KFold\n", + "skf = KFold(n_splits=5, shuffle=True, random_state=42)" + ] + }, { "cell_type": "markdown", "id": "e87a2121", "metadata": {}, "source": [ - "### 使用LGB作为模型，不使用optuna调参" + "### 使用LGB作为模型并CV，不使用optuna调参" ] }, { "cell_type": "code", - "execution_count": 12, + "execution_count": 7, "id": "5cad8967", - "metadata": {}, + "metadata": { + "scrolled": true + }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.001822 seconds.\n", + "[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000866 seconds.\n", "You can set `force_col_wise=true` to remove the overhead.\n", "[LightGBM] [Info] Total Bins 1837\n", - "[LightGBM] [Info] Number of data points in the train set: 13932, number of used features: 8\n", - "[LightGBM] [Info] Start training from score 2.072422\n", + "[LightGBM] [Info] Number of data points in the train set: 14860, number of used features: 8\n", + "[LightGBM] [Info] Start training from score 2.073247\n", "Training until validation scores don't improve for 20 rounds\n", "[50]\tvalid_0's rmse: 0.492877\n", - "[100]\tvalid_0's rmse: 0.471803\n", - "[150]\tvalid_0's rmse: 0.46445\n", - "[200]\tvalid_0's rmse: 0.459716\n", - "[250]\tvalid_0's rmse: 0.456658\n", - "[300]\tvalid_0's rmse: 0.454223\n", - "[350]\tvalid_0's rmse: 0.452644\n", + "[100]\tvalid_0's rmse: 0.469235\n", + "[150]\tvalid_0's rmse: 0.460357\n", + "[200]\tvalid_0's rmse: 0.455302\n", + "[250]\tvalid_0's rmse: 0.452674\n", + "[300]\tvalid_0's rmse: 0.451179\n", + "Early stopping, best iteration is:\n", + "[313]\tvalid_0's rmse: 0.450303\n", + "[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000494 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 1838\n", + "[LightGBM] [Info] Number of data points in the train set: 14861, number of used features: 8\n", + "[LightGBM] [Info] Start training from score 2.070396\n", + "Training until validation scores don't improve for 20 rounds\n", + "[50]\tvalid_0's rmse: 0.496484\n", + "[100]\tvalid_0's rmse: 0.476225\n", + "[150]\tvalid_0's rmse: 0.469582\n", + "[200]\tvalid_0's rmse: 0.464643\n", + "[250]\tvalid_0's rmse: 0.463244\n", + "[300]\tvalid_0's rmse: 0.461886\n", + "[350]\tvalid_0's rmse: 0.460305\n", + "[400]\tvalid_0's rmse: 0.4596\n", + "[450]\tvalid_0's rmse: 0.458645\n", + "Early stopping, best iteration is:\n", + "[435]\tvalid_0's rmse: 0.458368\n", + "[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000800 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 1837\n", + "[LightGBM] [Info] Number of data points in the train set: 14861, number of used features: 8\n", + "[LightGBM] [Info] Start training from score 2.069154\n", + "Training until validation scores don't improve for 20 rounds\n", + "[50]\tvalid_0's rmse: 0.48676\n", + "[100]\tvalid_0's rmse: 0.466911\n", + "[150]\tvalid_0's rmse: 0.459693\n", + "[200]\tvalid_0's rmse: 0.457631\n", + "[250]\tvalid_0's rmse: 0.452574\n", + "Early stopping, best iteration is:\n", + "[264]\tvalid_0's rmse: 0.451947\n", + "[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000779 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 1838\n", + "[LightGBM] [Info] Number of data points in the train set: 14861, number of used features: 8\n", + "[LightGBM] [Info] Start training from score 2.063366\n", + "Training until validation scores don't improve for 20 rounds\n", + "[50]\tvalid_0's rmse: 0.492482\n", + "[100]\tvalid_0's rmse: 0.468929\n", + "[150]\tvalid_0's rmse: 0.459713\n", + "[200]\tvalid_0's rmse: 0.456203\n", + "[250]\tvalid_0's rmse: 0.454063\n", + "[300]\tvalid_0's rmse: 0.451901\n", + "Early stopping, best iteration is:\n", + "[322]\tvalid_0's rmse: 0.450961\n", + "[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000694 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 1838\n", + "[LightGBM] [Info] Number of data points in the train set: 14861, number of used features: 8\n", + "[LightGBM] [Info] Start training from score 2.066924\n", + "Training until validation scores don't improve for 20 rounds\n", + "[50]\tvalid_0's rmse: 0.488385\n", + "[100]\tvalid_0's rmse: 0.464851\n", + "[150]\tvalid_0's rmse: 0.455183\n", + "[200]\tvalid_0's rmse: 0.450506\n", + "[250]\tvalid_0's rmse: 0.4461\n", + "[300]\tvalid_0's rmse: 0.44438\n", + "[350]\tvalid_0's rmse: 0.441808\n", + "[400]\tvalid_0's rmse: 0.44079\n", "Early stopping, best iteration is:\n", - "[342]\tvalid_0's rmse: 0.452522\n" + "[411]\tvalid_0's rmse: 0.440543\n", + "Wall time: 3.44 s\n" ] } ], "source": [ + "%%time\n", + "test_predict = np.zeros(shape=[test_x.shape[0], 5],dtype=float)\n", "params = {'boosting_type': 'gbdt',\n", " 'objective': 'regression',\n", " \"metric\": 'rmse'}\n", - "dtrain = lgb.Dataset(train_x, label=train_y)\n", - "dvalid = lgb.Dataset(valid_x, label=valid_y)\n", - "model = lgb.train(params=params, train_set=dtrain,valid_sets=[dvalid],\n", - " verbose_eval=50,\n", - " early_stopping_rounds=20,\n", - " num_boost_round=5000)\n", - "predict = model.predict(test_x)" + "for i, (trn_idx, val_idx) in enumerate(skf.split(train_x, train_y)):\n", + " dtrain = lgb.Dataset(train_x.iloc[trn_idx], label=train_y.iloc[trn_idx])\n", + " dvalid = lgb.Dataset(train_x.iloc[val_idx], label=train_y.iloc[val_idx])\n", + " model = lgb.train(params=params, train_set=dtrain,valid_sets=[dvalid],\n", + " verbose_eval=50,\n", + " early_stopping_rounds=20,\n", + " num_boost_round=5000)\n", + " test_predict[:,i] = model.predict(test_x)\n", + "predict = np.mean(test_predict,axis=1)" ] }, { "cell_type": "code", - "execution_count": 14, + "execution_count": 8, "id": "886bfdad", "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "0.4531666044672748" + "0.4346521330333544" ] }, - "execution_count": 14, + "execution_count": 8, "metadata": {}, "output_type": "execute_result" } @@ -274,80 +349,24 @@ "id": "c271442c", "metadata": {}, "source": [ - "### 使用LGB作为模型，使用optuna调参" + "### 使用LGB作为模型并CV，使用optuna调参" ] }, { "cell_type": "code", - "execution_count": 41, + "execution_count": 9, "id": "9a87f9db", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Looking in indexes: https://pypi.tuna.tsinghua.edu.cn/simple\n", - "Collecting optuna\n", - " Downloading https://pypi.tuna.tsinghua.edu.cn/packages/2a/b4/061c8721b5da1614794d1b66fcb212eee156efd5284f66854d02f295b0be/optuna-2.9.1-py3-none-any.whl (302 kB)\n", - "Requirement already satisfied: tqdm in d:\\programdata\\anaconda3\\lib\\site-packages (from optuna) (4.59.0)\n", - "Collecting cmaes>=0.8.2\n", - 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" Stored in directory: c:\\users\\administrator\\appdata\\local\\pip\\cache\\wheels\\30\\c0\\21\\bc13df81c8b032076577671a8ef05db4e168a335e07e64d9a7\n", - "Successfully built pyperclip\n", - "Installing collected packages: pyreadline3, pyperclip, pbr, stevedore, PrettyTable, Mako, importlib-resources, cmd2, autopage, colorlog, cmaes, cliff, alembic, optuna\n", - "Successfully installed Mako-1.1.5 PrettyTable-2.2.0 alembic-1.7.1 autopage-0.4.0 cliff-3.9.0 cmaes-0.8.2 cmd2-2.1.2 colorlog-6.4.1 importlib-resources-5.2.2 optuna-2.9.1 pbr-5.6.0 pyperclip-1.8.2 pyreadline3-3.3 stevedore-3.4.0\n", - "Note: you may need to restart the kernel to use updated packages.\n" - ] - } - ], + "metadata": { + "scrolled": true + }, + "outputs": [], "source": [ - "pip install optuna" + "# pip install optuna" ] }, { "cell_type": "code", - "execution_count": 15, + "execution_count": 10, "id": "d8a0279a", "metadata": {}, "outputs": [], @@ -357,12 +376,12 @@ }, { "cell_type": "code", - "execution_count": 18, + "execution_count": 11, "id": "7433fdcb", "metadata": {}, "outputs": [], "source": [ - "def objective(trial,train_x, valid_x, train_y, valid_y):\n", + "def objective(trial,train_x, train_y, valid_x, valid_y):\n", " dtrain = lgb.Dataset(train_x, label=train_y)\n", " dvalid = lgb.Dataset(valid_x, label=valid_y)\n", "\n", @@ -395,7 +414,7 @@ }, { "cell_type": "code", - "execution_count": 19, + "execution_count": 12, "id": "b9018adb", "metadata": { "scrolled": true @@ -405,119 +424,119 @@ "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32m[I 2021-09-07 09:26:30,952]\u001b[0m A new study created in memory with name: no-name-f2147511-069d-495f-90ec-5990dc3c3716\u001b[0m\n", - "\u001b[32m[I 2021-09-07 09:26:31,284]\u001b[0m Trial 0 finished with value: 0.5579165649228477 and parameters: {'lambda_l1': 1.6814939560853405e-06, 'lambda_l2': 8.772634980486007, 'num_leaves': 79, 'feature_fraction': 0.4415746779386105, 'bagging_fraction': 0.5306839081914155, 'bagging_freq': 5, 'min_child_samples': 13}. Best is trial 0 with value: 0.5579165649228477.\u001b[0m\n", - "\u001b[32m[I 2021-09-07 09:26:31,523]\u001b[0m Trial 1 finished with value: 0.5577111827415846 and parameters: {'lambda_l1': 0.00013684423280171766, 'lambda_l2': 0.010116712675880523, 'num_leaves': 42, 'feature_fraction': 0.4758659908396936, 'bagging_fraction': 0.8916447942940564, 'bagging_freq': 7, 'min_child_samples': 32}. Best is trial 1 with value: 0.5577111827415846.\u001b[0m\n", - "\u001b[32m[I 2021-09-07 09:26:31,986]\u001b[0m Trial 2 finished with value: 0.5382832732103667 and parameters: {'lambda_l1': 1.849141022226266, 'lambda_l2': 9.297088403542385e-06, 'num_leaves': 94, 'feature_fraction': 0.6894755218120849, 'bagging_fraction': 0.9503182129837274, 'bagging_freq': 2, 'min_child_samples': 98}. Best is trial 2 with value: 0.5382832732103667.\u001b[0m\n", - "\u001b[32m[I 2021-09-07 09:26:32,156]\u001b[0m Trial 3 finished with value: 0.5786765226592324 and parameters: {'lambda_l1': 4.448151837364203e-05, 'lambda_l2': 1.8955982808109254e-08, 'num_leaves': 19, 'feature_fraction': 0.493099451522876, 'bagging_fraction': 0.6742062281671596, 'bagging_freq': 5, 'min_child_samples': 35}. Best is trial 2 with value: 0.5382832732103667.\u001b[0m\n", - "\u001b[32m[I 2021-09-07 09:26:32,266]\u001b[0m Trial 4 finished with value: 0.6139367553603606 and parameters: {'lambda_l1': 0.0002791229907486381, 'lambda_l2': 5.269713608638115, 'num_leaves': 5, 'feature_fraction': 0.8303631732661438, 'bagging_fraction': 0.942024874372832, 'bagging_freq': 4, 'min_child_samples': 61}. Best is trial 2 with value: 0.5382832732103667.\u001b[0m\n", - "\u001b[32m[I 2021-09-07 09:26:32,826]\u001b[0m Trial 5 finished with value: 0.5453617455111696 and parameters: {'lambda_l1': 0.06860061560766911, 'lambda_l2': 0.2723830502667369, 'num_leaves': 98, 'feature_fraction': 0.7105587363977491, 'bagging_fraction': 0.5899933873219961, 'bagging_freq': 4, 'min_child_samples': 71}. Best is trial 2 with value: 0.5382832732103667.\u001b[0m\n", - "\u001b[32m[I 2021-09-07 09:26:32,880]\u001b[0m Trial 6 pruned. Trial was pruned at iteration 10.\u001b[0m\n", - "\u001b[32m[I 2021-09-07 09:26:33,189]\u001b[0m Trial 7 finished with value: 0.5588682864793495 and parameters: {'lambda_l1': 0.4689515611654304, 'lambda_l2': 1.5494838308859912e-07, 'num_leaves': 65, 'feature_fraction': 0.5583496504073044, 'bagging_fraction': 0.6569918432147945, 'bagging_freq': 5, 'min_child_samples': 58}. Best is trial 2 with value: 0.5382832732103667.\u001b[0m\n", - "\u001b[32m[I 2021-09-07 09:26:33,462]\u001b[0m Trial 8 finished with value: 0.5447098867148504 and parameters: {'lambda_l1': 0.0010101561223389774, 'lambda_l2': 0.01515390989225731, 'num_leaves': 47, 'feature_fraction': 0.6245633996226619, 'bagging_fraction': 0.419624870667929, 'bagging_freq': 3, 'min_child_samples': 48}. Best is trial 2 with value: 0.5382832732103667.\u001b[0m\n", - "\u001b[32m[I 2021-09-07 09:26:33,693]\u001b[0m Trial 9 pruned. Trial was pruned at iteration 80.\u001b[0m\n", - "\u001b[32m[I 2021-09-07 09:26:34,181]\u001b[0m Trial 10 finished with value: 0.541110883425217 and parameters: {'lambda_l1': 1.4756346287368392e-08, 'lambda_l2': 1.3544296321997672e-05, 'num_leaves': 189, 'feature_fraction': 0.9751158697609862, 'bagging_fraction': 0.8348333900598492, 'bagging_freq': 1, 'min_child_samples': 100}. Best is trial 2 with value: 0.5382832732103667.\u001b[0m\n", - "\u001b[32m[I 2021-09-07 09:26:34,761]\u001b[0m Trial 11 finished with value: 0.5430244062824532 and parameters: {'lambda_l1': 1.2282790653635484e-08, 'lambda_l2': 8.373619608433318e-06, 'num_leaves': 196, 'feature_fraction': 0.9236444858767681, 'bagging_fraction': 0.8122951589315216, 'bagging_freq': 1, 'min_child_samples': 100}. Best is trial 2 with value: 0.5382832732103667.\u001b[0m\n", - "\u001b[32m[I 2021-09-07 09:26:35,237]\u001b[0m Trial 12 finished with value: 0.5479941202014348 and parameters: {'lambda_l1': 8.66531632367352, 'lambda_l2': 2.7356786615324263e-05, 'num_leaves': 182, 'feature_fraction': 0.7257944800491923, 'bagging_fraction': 0.8151059105693412, 'bagging_freq': 1, 'min_child_samples': 98}. Best is trial 2 with value: 0.5382832732103667.\u001b[0m\n", - "\u001b[32m[I 2021-09-07 09:26:36,132]\u001b[0m Trial 13 finished with value: 0.5426396090105863 and parameters: {'lambda_l1': 1.5311804262482413e-08, 'lambda_l2': 8.31070641463058e-06, 'num_leaves': 241, 'feature_fraction': 0.9996713520233095, 'bagging_fraction': 0.9659578077631608, 'bagging_freq': 2, 'min_child_samples': 84}. Best is trial 2 with value: 0.5382832732103667.\u001b[0m\n", - "\u001b[32m[I 2021-09-07 09:26:36,961]\u001b[0m Trial 14 finished with value: 0.5367665957770634 and parameters: {'lambda_l1': 0.014095420115571123, 'lambda_l2': 0.0002563644914171101, 'num_leaves': 149, 'feature_fraction': 0.7883099949736571, 'bagging_fraction': 0.8086023984951658, 'bagging_freq': 2, 'min_child_samples': 85}. Best is trial 14 with value: 0.5367665957770634.\u001b[0m\n", - "\u001b[32m[I 2021-09-07 09:26:37,824]\u001b[0m Trial 15 finished with value: 0.5348886658006932 and parameters: {'lambda_l1': 0.020071941122612545, 'lambda_l2': 0.0003014852059198554, 'num_leaves': 146, 'feature_fraction': 0.7936923799556383, 'bagging_fraction': 0.756430674635471, 'bagging_freq': 3, 'min_child_samples': 83}. Best is trial 15 with value: 0.5348886658006932.\u001b[0m\n", - "\u001b[32m[I 2021-09-07 09:26:38,622]\u001b[0m Trial 16 finished with value: 0.5372333761766732 and parameters: {'lambda_l1': 0.015173696268463625, 'lambda_l2': 0.0016757129971514612, 'num_leaves': 138, 'feature_fraction': 0.8107104336197675, 'bagging_fraction': 0.7376130779961089, 'bagging_freq': 3, 'min_child_samples': 78}. Best is trial 15 with value: 0.5348886658006932.\u001b[0m\n", - "\u001b[32m[I 2021-09-07 09:26:39,451]\u001b[0m Trial 17 finished with value: 0.5376252190781058 and parameters: {'lambda_l1': 0.007357005023837469, 'lambda_l2': 0.000250874148036676, 'num_leaves': 147, 'feature_fraction': 0.7829205508785075, 'bagging_fraction': 0.7478270069878126, 'bagging_freq': 3, 'min_child_samples': 67}. Best is trial 15 with value: 0.5348886658006932.\u001b[0m\n", - "\u001b[32m[I 2021-09-07 09:26:39,567]\u001b[0m Trial 18 pruned. Trial was pruned at iteration 10.\u001b[0m\n", - "\u001b[32m[I 2021-09-07 09:26:40,910]\u001b[0m Trial 19 finished with value: 0.5315793527934071 and parameters: {'lambda_l1': 0.013209811084277438, 'lambda_l2': 0.06726287443306718, 'num_leaves': 231, 'feature_fraction': 0.637876115312563, 'bagging_fraction': 0.8716817438233221, 'bagging_freq': 3, 'min_child_samples': 44}. Best is trial 19 with value: 0.5315793527934071.\u001b[0m\n", - "\u001b[32m[I 2021-09-07 09:26:42,324]\u001b[0m Trial 20 finished with value: 0.5262550702976 and parameters: {'lambda_l1': 0.003113216663103784, 'lambda_l2': 0.24783337352027449, 'num_leaves': 242, 'feature_fraction': 0.6137607392321572, 'bagging_fraction': 0.8936473883645825, 'bagging_freq': 3, 'min_child_samples': 44}. Best is trial 20 with value: 0.5262550702976.\u001b[0m\n", - "\u001b[32m[I 2021-09-07 09:26:43,182]\u001b[0m Trial 21 finished with value: 0.5297204439253479 and parameters: {'lambda_l1': 0.003978575421253158, 'lambda_l2': 0.11658574784670588, 'num_leaves': 254, 'feature_fraction': 0.6168955771891016, 'bagging_fraction': 0.8787433941466242, 'bagging_freq': 3, 'min_child_samples': 43}. Best is trial 20 with value: 0.5262550702976.\u001b[0m\n", - "\u001b[32m[I 2021-09-07 09:26:43,310]\u001b[0m Trial 22 pruned. Trial was pruned at iteration 13.\u001b[0m\n", - "\u001b[32m[I 2021-09-07 09:26:44,194]\u001b[0m Trial 23 finished with value: 0.5288803542960898 and parameters: {'lambda_l1': 8.341315535148706e-06, 'lambda_l2': 0.24030532904199514, 'num_leaves': 221, 'feature_fraction': 0.5901567080654435, 'bagging_fraction': 0.8875920940685119, 'bagging_freq': 3, 'min_child_samples': 25}. Best is trial 20 with value: 0.5262550702976.\u001b[0m\n", - "\u001b[32m[I 2021-09-07 09:26:44,331]\u001b[0m Trial 24 pruned. Trial was pruned at iteration 10.\u001b[0m\n", - "\u001b[32m[I 2021-09-07 09:26:45,248]\u001b[0m Trial 25 finished with value: 0.5310533243420329 and parameters: {'lambda_l1': 1.9500743317527624e-07, 'lambda_l2': 0.0502751774074574, 'num_leaves': 214, 'feature_fraction': 0.5734739546088348, 'bagging_fraction': 0.9115976551406307, 'bagging_freq': 2, 'min_child_samples': 24}. Best is trial 20 with value: 0.5262550702976.\u001b[0m\n" + "\u001b[32m[I 2021-09-07 16:27:35,965]\u001b[0m A new study created in memory with name: no-name-cc424a48-83c6-4329-92a3-261d08a5edb9\u001b[0m\n", + "\u001b[32m[I 2021-09-07 16:27:36,779]\u001b[0m Trial 0 finished with value: 0.5178928385298298 and parameters: {'lambda_l1': 0.9464949700025425, 'lambda_l2': 1.4692321446797693e-05, 'num_leaves': 135, 'feature_fraction': 0.6448685329355106, 'bagging_fraction': 0.9048970068857253, 'bagging_freq': 7, 'min_child_samples': 30}. Best is trial 0 with value: 0.5178928385298298.\u001b[0m\n", + "\u001b[32m[I 2021-09-07 16:27:37,641]\u001b[0m Trial 1 finished with value: 0.5336017055299863 and parameters: {'lambda_l1': 0.6354399847075458, 'lambda_l2': 4.87700419620481, 'num_leaves': 237, 'feature_fraction': 0.895991820221385, 'bagging_fraction': 0.6704364287317786, 'bagging_freq': 3, 'min_child_samples': 49}. Best is trial 0 with value: 0.5178928385298298.\u001b[0m\n", + "\u001b[32m[I 2021-09-07 16:27:38,288]\u001b[0m Trial 2 finished with value: 0.5345721353318472 and parameters: {'lambda_l1': 9.52099217128906e-05, 'lambda_l2': 1.1454590646374039e-07, 'num_leaves': 170, 'feature_fraction': 0.7899599630691868, 'bagging_fraction': 0.5059741439893897, 'bagging_freq': 2, 'min_child_samples': 50}. Best is trial 0 with value: 0.5178928385298298.\u001b[0m\n", + "\u001b[32m[I 2021-09-07 16:27:38,956]\u001b[0m Trial 3 finished with value: 0.543693048566443 and parameters: {'lambda_l1': 0.1481577233973877, 'lambda_l2': 3.179336503731134e-07, 'num_leaves': 128, 'feature_fraction': 0.9783519466402901, 'bagging_fraction': 0.6889127950806107, 'bagging_freq': 5, 'min_child_samples': 37}. Best is trial 0 with value: 0.5178928385298298.\u001b[0m\n", + "\u001b[32m[I 2021-09-07 16:27:39,424]\u001b[0m Trial 4 finished with value: 0.5501760993688642 and parameters: {'lambda_l1': 3.7638689664291936, 'lambda_l2': 0.020384770050374573, 'num_leaves': 175, 'feature_fraction': 0.4840841073088404, 'bagging_fraction': 0.8334033110992205, 'bagging_freq': 5, 'min_child_samples': 35}. Best is trial 0 with value: 0.5178928385298298.\u001b[0m\n", + "\u001b[32m[I 2021-09-07 16:27:39,493]\u001b[0m Trial 5 pruned. Trial was pruned at iteration 10.\u001b[0m\n", + "\u001b[32m[I 2021-09-07 16:27:39,774]\u001b[0m Trial 6 pruned. Trial was pruned at iteration 33.\u001b[0m\n", + "\u001b[32m[I 2021-09-07 16:27:39,828]\u001b[0m Trial 7 pruned. Trial was pruned at iteration 10.\u001b[0m\n", + "\u001b[32m[I 2021-09-07 16:27:40,187]\u001b[0m Trial 8 finished with value: 0.5249490156742078 and parameters: {'lambda_l1': 8.186728837628555e-05, 'lambda_l2': 2.6479736415938615, 'num_leaves': 72, 'feature_fraction': 0.7599981859922114, 'bagging_fraction': 0.9692259725506919, 'bagging_freq': 5, 'min_child_samples': 38}. Best is trial 0 with value: 0.5178928385298298.\u001b[0m\n", + "\u001b[32m[I 2021-09-07 16:27:40,299]\u001b[0m Trial 9 pruned. Trial was pruned at iteration 11.\u001b[0m\n", + "\u001b[32m[I 2021-09-07 16:27:40,353]\u001b[0m Trial 10 pruned. Trial was pruned at iteration 10.\u001b[0m\n", + "\u001b[32m[I 2021-09-07 16:27:40,398]\u001b[0m Trial 11 pruned. Trial was pruned at iteration 10.\u001b[0m\n", + "\u001b[32m[I 2021-09-07 16:27:40,470]\u001b[0m Trial 12 pruned. Trial was pruned at iteration 10.\u001b[0m\n", + "\u001b[32m[I 2021-09-07 16:27:41,280]\u001b[0m Trial 13 finished with value: 0.5325142394678348 and parameters: {'lambda_l1': 0.009551573559223583, 'lambda_l2': 0.0003114297759461472, 'num_leaves': 152, 'feature_fraction': 0.8236610445865283, 'bagging_fraction': 0.8389170404125451, 'bagging_freq': 4, 'min_child_samples': 20}. Best is trial 0 with value: 0.5178928385298298.\u001b[0m\n", + "\u001b[32m[I 2021-09-07 16:27:41,391]\u001b[0m Trial 14 pruned. Trial was pruned at iteration 10.\u001b[0m\n", + "\u001b[32m[I 2021-09-07 16:27:42,015]\u001b[0m Trial 15 finished with value: 0.5314302398651312 and parameters: {'lambda_l1': 0.024227381422510946, 'lambda_l2': 0.33276527010426776, 'num_leaves': 91, 'feature_fraction': 0.7874026441153832, 'bagging_fraction': 0.7899555875604796, 'bagging_freq': 7, 'min_child_samples': 19}. Best is trial 0 with value: 0.5178928385298298.\u001b[0m\n", + "\u001b[32m[I 2021-09-07 16:27:42,091]\u001b[0m Trial 16 pruned. Trial was pruned at iteration 10.\u001b[0m\n", + "\u001b[32m[I 2021-09-07 16:27:42,154]\u001b[0m Trial 17 pruned. Trial was pruned at iteration 10.\u001b[0m\n", + "\u001b[32m[I 2021-09-07 16:27:42,226]\u001b[0m Trial 18 pruned. Trial was pruned at iteration 10.\u001b[0m\n", + "\u001b[32m[I 2021-09-07 16:27:42,338]\u001b[0m Trial 19 pruned. Trial was pruned at iteration 14.\u001b[0m\n", + "\u001b[32m[I 2021-09-07 16:27:42,384]\u001b[0m Trial 20 pruned. Trial was pruned at iteration 10.\u001b[0m\n", + "\u001b[32m[I 2021-09-07 16:27:42,829]\u001b[0m Trial 21 finished with value: 0.5245233417218278 and parameters: {'lambda_l1': 0.07296901453015396, 'lambda_l2': 0.19272462963641587, 'num_leaves': 90, 'feature_fraction': 0.752955332537125, 'bagging_fraction': 0.7944607588474272, 'bagging_freq': 7, 'min_child_samples': 15}. Best is trial 0 with value: 0.5178928385298298.\u001b[0m\n", + "\u001b[32m[I 2021-09-07 16:27:42,901]\u001b[0m Trial 22 pruned. Trial was pruned at iteration 10.\u001b[0m\n", + "\u001b[32m[I 2021-09-07 16:27:43,287]\u001b[0m Trial 23 pruned. Trial was pruned at iteration 97.\u001b[0m\n", + "\u001b[32m[I 2021-09-07 16:27:43,371]\u001b[0m Trial 24 pruned. Trial was pruned at iteration 10.\u001b[0m\n", + "\u001b[32m[I 2021-09-07 16:27:43,489]\u001b[0m Trial 25 pruned. Trial was pruned at iteration 10.\u001b[0m\n", + "\u001b[32m[I 2021-09-07 16:27:43,572]\u001b[0m Trial 26 pruned. Trial was pruned at iteration 10.\u001b[0m\n", + "\u001b[32m[I 2021-09-07 16:27:43,632]\u001b[0m Trial 27 pruned. Trial was pruned at iteration 10.\u001b[0m\n", + "\u001b[32m[I 2021-09-07 16:27:44,171]\u001b[0m Trial 28 finished with value: 0.5255422897694476 and parameters: {'lambda_l1': 0.028260092955639447, 'lambda_l2': 0.08413723627697263, 'num_leaves': 74, 'feature_fraction': 0.8042469147694201, 'bagging_fraction': 0.7548217217698655, 'bagging_freq': 3, 'min_child_samples': 42}. Best is trial 0 with value: 0.5178928385298298.\u001b[0m\n", + "\u001b[32m[I 2021-09-07 16:27:44,331]\u001b[0m Trial 29 pruned. Trial was pruned at iteration 13.\u001b[0m\n", + "\u001b[32m[I 2021-09-07 16:27:44,516]\u001b[0m Trial 30 pruned. Trial was pruned at iteration 16.\u001b[0m\n", + "\u001b[32m[I 2021-09-07 16:27:44,637]\u001b[0m Trial 31 pruned. Trial was pruned at iteration 10.\u001b[0m\n", + "\u001b[32m[I 2021-09-07 16:27:44,723]\u001b[0m Trial 32 pruned. Trial was pruned at iteration 10.\u001b[0m\n", + "\u001b[32m[I 2021-09-07 16:27:45,592]\u001b[0m Trial 33 finished with value: 0.5218146276752049 and parameters: {'lambda_l1': 0.0016497886750146257, 'lambda_l2': 0.21484669274687151, 'num_leaves': 133, 'feature_fraction': 0.6885034648040802, 'bagging_fraction': 0.9281320338482011, 'bagging_freq': 3, 'min_child_samples': 42}. Best is trial 0 with value: 0.5178928385298298.\u001b[0m\n", + "\u001b[32m[I 2021-09-07 16:27:45,711]\u001b[0m Trial 34 pruned. Trial was pruned at iteration 10.\u001b[0m\n", + "\u001b[32m[I 2021-09-07 16:27:46,669]\u001b[0m Trial 35 finished with value: 0.5205542553680208 and parameters: {'lambda_l1': 4.3493248665689235e-05, 'lambda_l2': 3.680853885692289, 'num_leaves': 169, 'feature_fraction': 0.7104567429350184, 'bagging_fraction': 0.8672533271233644, 'bagging_freq': 1, 'min_child_samples': 23}. Best is trial 0 with value: 0.5178928385298298.\u001b[0m\n", + "\u001b[32m[I 2021-09-07 16:27:47,676]\u001b[0m Trial 36 finished with value: 0.5206023202395521 and parameters: {'lambda_l1': 0.0006187306261428898, 'lambda_l2': 6.130428276943865e-08, 'num_leaves': 168, 'feature_fraction': 0.7036284508576037, 'bagging_fraction': 0.868428878180794, 'bagging_freq': 2, 'min_child_samples': 15}. Best is trial 0 with value: 0.5178928385298298.\u001b[0m\n", + "\u001b[32m[I 2021-09-07 16:27:47,808]\u001b[0m Trial 37 pruned. Trial was pruned at iteration 10.\u001b[0m\n", + "\u001b[32m[I 2021-09-07 16:27:47,937]\u001b[0m Trial 38 pruned. Trial was pruned at iteration 10.\u001b[0m\n", + "\u001b[32m[I 2021-09-07 16:27:48,844]\u001b[0m Trial 39 finished with value: 0.5191042263340773 and parameters: {'lambda_l1': 0.0005562321404093257, 'lambda_l2': 2.232696278872472e-08, 'num_leaves': 161, 'feature_fraction': 0.710527336424428, 'bagging_fraction': 0.9309118257124477, 'bagging_freq': 1, 'min_child_samples': 24}. Best is trial 0 with value: 0.5178928385298298.\u001b[0m\n", + "\u001b[32m[I 2021-09-07 16:27:49,889]\u001b[0m Trial 40 finished with value: 0.5168505162425636 and parameters: {'lambda_l1': 0.0004441190594738051, 'lambda_l2': 1.0707946566246148e-08, 'num_leaves': 190, 'feature_fraction': 0.7132737394337006, 'bagging_fraction': 0.8296082050311246, 'bagging_freq': 1, 'min_child_samples': 16}. Best is trial 40 with value: 0.5168505162425636.\u001b[0m\n", + "\u001b[32m[I 2021-09-07 16:27:50,929]\u001b[0m Trial 41 finished with value: 0.5262832333602245 and parameters: {'lambda_l1': 0.0004892025280466791, 'lambda_l2': 1.89573604152611e-08, 'num_leaves': 189, 'feature_fraction': 0.7134600565845959, 'bagging_fraction': 0.8334328552244646, 'bagging_freq': 1, 'min_child_samples': 16}. Best is trial 40 with value: 0.5168505162425636.\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32m[I 2021-09-07 09:26:46,126]\u001b[0m Trial 26 finished with value: 0.5310399189972692 and parameters: {'lambda_l1': 5.65218762871428e-06, 'lambda_l2': 1.6944446205708956, 'num_leaves': 256, 'feature_fraction': 0.6693624417062835, 'bagging_fraction': 0.8606541857435407, 'bagging_freq': 4, 'min_child_samples': 25}. Best is trial 20 with value: 0.5262550702976.\u001b[0m\n", - "\u001b[32m[I 2021-09-07 09:26:47,541]\u001b[0m Trial 27 finished with value: 0.5289141422774571 and parameters: {'lambda_l1': 3.372780137131952e-07, 'lambda_l2': 0.004231573716537988, 'num_leaves': 222, 'feature_fraction': 0.5834642111209242, 'bagging_fraction': 0.9283224808721449, 'bagging_freq': 3, 'min_child_samples': 33}. Best is trial 20 with value: 0.5262550702976.\u001b[0m\n", - "\u001b[32m[I 2021-09-07 09:26:47,734]\u001b[0m Trial 28 pruned. Trial was pruned at iteration 10.\u001b[0m\n", - "\u001b[32m[I 2021-09-07 09:26:47,883]\u001b[0m Trial 29 pruned. Trial was pruned at iteration 10.\u001b[0m\n", - "\u001b[32m[I 2021-09-07 09:26:48,032]\u001b[0m Trial 30 pruned. Trial was pruned at iteration 10.\u001b[0m\n", - "\u001b[32m[I 2021-09-07 09:26:48,233]\u001b[0m Trial 31 pruned. Trial was pruned at iteration 10.\u001b[0m\n", - "\u001b[32m[I 2021-09-07 09:26:48,470]\u001b[0m Trial 32 pruned. Trial was pruned at iteration 12.\u001b[0m\n", - "\u001b[32m[I 2021-09-07 09:26:50,256]\u001b[0m Trial 33 finished with value: 0.5307511312800164 and parameters: {'lambda_l1': 8.331346294111473e-08, 'lambda_l2': 0.018173663934055492, 'num_leaves': 238, 'feature_fraction': 0.5874989674601676, 'bagging_fraction': 0.8876263960076438, 'bagging_freq': 2, 'min_child_samples': 30}. Best is trial 20 with value: 0.5262550702976.\u001b[0m\n", - "\u001b[32m[I 2021-09-07 09:26:52,081]\u001b[0m Trial 34 finished with value: 0.5364692667074208 and parameters: {'lambda_l1': 8.939808862490256e-07, 'lambda_l2': 1.3094120587455667, 'num_leaves': 249, 'feature_fraction': 0.739630943171398, 'bagging_fraction': 0.9999691110926188, 'bagging_freq': 3, 'min_child_samples': 17}. Best is trial 20 with value: 0.5262550702976.\u001b[0m\n", - "\u001b[32m[I 2021-09-07 09:26:52,228]\u001b[0m Trial 35 pruned. Trial was pruned at iteration 10.\u001b[0m\n", - "\u001b[32m[I 2021-09-07 09:26:52,401]\u001b[0m Trial 36 pruned. Trial was pruned at iteration 10.\u001b[0m\n", - "\u001b[32m[I 2021-09-07 09:26:52,527]\u001b[0m Trial 37 pruned. Trial was pruned at iteration 10.\u001b[0m\n", - "\u001b[32m[I 2021-09-07 09:26:52,646]\u001b[0m Trial 38 pruned. Trial was pruned at iteration 10.\u001b[0m\n", - "\u001b[32m[I 2021-09-07 09:26:52,746]\u001b[0m Trial 39 pruned. Trial was pruned at iteration 10.\u001b[0m\n", - "\u001b[32m[I 2021-09-07 09:26:53,973]\u001b[0m Trial 40 finished with value: 0.5256364275490798 and parameters: {'lambda_l1': 0.0710216045724967, 'lambda_l2': 0.10020172057765622, 'num_leaves': 206, 'feature_fraction': 0.6441352660567405, 'bagging_fraction': 0.8538258125595672, 'bagging_freq': 5, 'min_child_samples': 8}. Best is trial 40 with value: 0.5256364275490798.\u001b[0m\n", - "\u001b[32m[I 2021-09-07 09:26:54,896]\u001b[0m Trial 41 finished with value: 0.5233364787469251 and parameters: {'lambda_l1': 0.23671365201549266, 'lambda_l2': 0.0749439220683321, 'num_leaves': 210, 'feature_fraction': 0.6436961501856725, 'bagging_fraction': 0.8521968194803821, 'bagging_freq': 5, 'min_child_samples': 5}. Best is trial 41 with value: 0.5233364787469251.\u001b[0m\n", - "\u001b[32m[I 2021-09-07 09:26:55,831]\u001b[0m Trial 42 finished with value: 0.5293150200028972 and parameters: {'lambda_l1': 0.6574943326302751, 'lambda_l2': 0.0361443234291568, 'num_leaves': 209, 'feature_fraction': 0.6596272574104582, 'bagging_fraction': 0.850673456207462, 'bagging_freq': 6, 'min_child_samples': 5}. Best is trial 41 with value: 0.5233364787469251.\u001b[0m\n", - "\u001b[32m[I 2021-09-07 09:26:56,858]\u001b[0m Trial 43 finished with value: 0.5308606295175099 and parameters: {'lambda_l1': 0.07618036054920922, 'lambda_l2': 0.22544030283002933, 'num_leaves': 226, 'feature_fraction': 0.6984185669610476, 'bagging_fraction': 0.7845150865412134, 'bagging_freq': 5, 'min_child_samples': 10}. Best is trial 41 with value: 0.5233364787469251.\u001b[0m\n", - "\u001b[32m[I 2021-09-07 09:26:57,019]\u001b[0m Trial 44 pruned. Trial was pruned at iteration 13.\u001b[0m\n", - "\u001b[32m[I 2021-09-07 09:26:57,787]\u001b[0m Trial 45 finished with value: 0.5331475267867624 and parameters: {'lambda_l1': 1.4967349278475324, 'lambda_l2': 0.0008951722845905283, 'num_leaves': 207, 'feature_fraction': 0.7339992851750987, 'bagging_fraction': 0.919959739142468, 'bagging_freq': 5, 'min_child_samples': 26}. Best is trial 41 with value: 0.5233364787469251.\u001b[0m\n", - "\u001b[32m[I 2021-09-07 09:26:57,865]\u001b[0m Trial 46 pruned. Trial was pruned at iteration 10.\u001b[0m\n", - "\u001b[32m[I 2021-09-07 09:26:57,971]\u001b[0m Trial 47 pruned. Trial was pruned at iteration 10.\u001b[0m\n", - "\u001b[32m[I 2021-09-07 09:26:58,069]\u001b[0m Trial 48 pruned. Trial was pruned at iteration 10.\u001b[0m\n", - "\u001b[32m[I 2021-09-07 09:26:58,168]\u001b[0m Trial 49 pruned. Trial was pruned at iteration 10.\u001b[0m\n", - "\u001b[32m[I 2021-09-07 09:26:58,317]\u001b[0m Trial 50 pruned. Trial was pruned at iteration 30.\u001b[0m\n", - "\u001b[32m[I 2021-09-07 09:26:58,445]\u001b[0m Trial 51 pruned. Trial was pruned at iteration 13.\u001b[0m\n", - "\u001b[32m[I 2021-09-07 09:26:59,558]\u001b[0m Trial 52 finished with value: 0.5287423259473492 and parameters: {'lambda_l1': 0.44209025556975773, 'lambda_l2': 0.02237337201530471, 'num_leaves': 180, 'feature_fraction': 0.6816257587535688, 'bagging_fraction': 0.8645245816570197, 'bagging_freq': 7, 'min_child_samples': 5}. Best is trial 41 with value: 0.5233364787469251.\u001b[0m\n", - "\u001b[32m[I 2021-09-07 09:27:00,776]\u001b[0m Trial 53 finished with value: 0.5248984867652216 and parameters: {'lambda_l1': 0.02970872423104034, 'lambda_l2': 0.0197319338622143, 'num_leaves': 177, 'feature_fraction': 0.7150825328146555, 'bagging_fraction': 0.8972252525350597, 'bagging_freq': 7, 'min_child_samples': 12}. Best is trial 41 with value: 0.5233364787469251.\u001b[0m\n", - "\u001b[32m[I 2021-09-07 09:27:01,886]\u001b[0m Trial 54 finished with value: 0.5310139507545261 and parameters: {'lambda_l1': 0.03922536031082296, 'lambda_l2': 1.7618527190204045e-06, 'num_leaves': 180, 'feature_fraction': 0.7089331672809696, 'bagging_fraction': 0.8021837464466384, 'bagging_freq': 7, 'min_child_samples': 11}. Best is trial 41 with value: 0.5233364787469251.\u001b[0m\n", - "\u001b[32m[I 2021-09-07 09:27:02,043]\u001b[0m Trial 55 pruned. Trial was pruned at iteration 13.\u001b[0m\n", - "\u001b[32m[I 2021-09-07 09:27:02,158]\u001b[0m Trial 56 pruned. Trial was pruned at iteration 10.\u001b[0m\n", - "\u001b[32m[I 2021-09-07 09:27:02,937]\u001b[0m Trial 57 pruned. Trial was pruned at iteration 78.\u001b[0m\n", - "\u001b[32m[I 2021-09-07 09:27:03,912]\u001b[0m Trial 58 finished with value: 0.5310990808119093 and parameters: {'lambda_l1': 0.008908141404368873, 'lambda_l2': 1.0024604099274968e-08, 'num_leaves': 156, 'feature_fraction': 0.7135958388327297, 'bagging_fraction': 0.8918708775644101, 'bagging_freq': 7, 'min_child_samples': 10}. Best is trial 41 with value: 0.5233364787469251.\u001b[0m\n", - "\u001b[32m[I 2021-09-07 09:27:05,509]\u001b[0m Trial 59 finished with value: 0.5303075036750562 and parameters: {'lambda_l1': 0.0036415543019748704, 'lambda_l2': 0.060450481568631226, 'num_leaves': 245, 'feature_fraction': 0.6322922933331399, 'bagging_fraction': 0.8119277832780933, 'bagging_freq': 6, 'min_child_samples': 18}. Best is trial 41 with value: 0.5233364787469251.\u001b[0m\n", - "\u001b[32m[I 2021-09-07 09:27:05,675]\u001b[0m Trial 60 pruned. Trial was pruned at iteration 10.\u001b[0m\n", - "\u001b[32m[I 2021-09-07 09:27:06,717]\u001b[0m Trial 61 finished with value: 0.5266068329070613 and parameters: {'lambda_l1': 0.08187221145217663, 'lambda_l2': 0.12038514531389062, 'num_leaves': 230, 'feature_fraction': 0.5893130926352276, 'bagging_fraction': 0.9218811557192532, 'bagging_freq': 3, 'min_child_samples': 30}. Best is trial 41 with value: 0.5233364787469251.\u001b[0m\n", - "\u001b[32m[I 2021-09-07 09:27:06,839]\u001b[0m Trial 62 pruned. Trial was pruned at iteration 10.\u001b[0m\n", - "\u001b[32m[I 2021-09-07 09:27:06,957]\u001b[0m Trial 63 pruned. Trial was pruned at iteration 10.\u001b[0m\n", - "\u001b[32m[I 2021-09-07 09:27:07,060]\u001b[0m Trial 64 pruned. Trial was pruned at iteration 10.\u001b[0m\n", - "\u001b[32m[I 2021-09-07 09:27:07,235]\u001b[0m Trial 65 pruned. Trial was pruned at iteration 13.\u001b[0m\n", - "\u001b[32m[I 2021-09-07 09:27:07,371]\u001b[0m Trial 66 pruned. Trial was pruned at iteration 13.\u001b[0m\n", - "\u001b[32m[I 2021-09-07 09:27:08,252]\u001b[0m Trial 67 finished with value: 0.524615381244906 and parameters: {'lambda_l1': 0.0007372437461333507, 'lambda_l2': 0.07197819258619385, 'num_leaves': 217, 'feature_fraction': 0.7085126717217893, 'bagging_fraction': 0.9783185592011208, 'bagging_freq': 4, 'min_child_samples': 7}. Best is trial 41 with value: 0.5233364787469251.\u001b[0m\n" + "\u001b[32m[I 2021-09-07 16:27:51,023]\u001b[0m Trial 42 pruned. Trial was pruned at iteration 10.\u001b[0m\n", + "\u001b[32m[I 2021-09-07 16:27:51,805]\u001b[0m Trial 43 finished with value: 0.516065766031832 and parameters: {'lambda_l1': 0.00021869661941011055, 'lambda_l2': 1.0674672013946999e-08, 'num_leaves': 201, 'feature_fraction': 0.7039965594752932, 'bagging_fraction': 0.8165783106186105, 'bagging_freq': 1, 'min_child_samples': 10}. Best is trial 43 with value: 0.516065766031832.\u001b[0m\n", + "\u001b[32m[I 2021-09-07 16:27:51,940]\u001b[0m Trial 44 pruned. Trial was pruned at iteration 10.\u001b[0m\n", + "\u001b[32m[I 2021-09-07 16:27:52,044]\u001b[0m Trial 45 pruned. Trial was pruned at iteration 10.\u001b[0m\n", + "\u001b[32m[I 2021-09-07 16:27:52,218]\u001b[0m Trial 46 pruned. Trial was pruned at iteration 10.\u001b[0m\n", + "\u001b[32m[I 2021-09-07 16:27:53,067]\u001b[0m Trial 47 finished with value: 0.5256815758791472 and parameters: {'lambda_l1': 6.162602167779085e-05, 'lambda_l2': 1.6462819728079273e-07, 'num_leaves': 201, 'feature_fraction': 0.7163097853297453, 'bagging_fraction': 0.9420618119436382, 'bagging_freq': 1, 'min_child_samples': 24}. Best is trial 43 with value: 0.516065766031832.\u001b[0m\n", + "\u001b[32m[I 2021-09-07 16:27:53,178]\u001b[0m Trial 48 pruned. Trial was pruned at iteration 10.\u001b[0m\n", + "\u001b[32m[I 2021-09-07 16:27:53,762]\u001b[0m Trial 49 finished with value: 0.5213791636070063 and parameters: {'lambda_l1': 2.774750232046531e-07, 'lambda_l2': 2.4404405074646332e-08, 'num_leaves': 154, 'feature_fraction': 0.6882189607163384, 'bagging_fraction': 0.9871043449165129, 'bagging_freq': 1, 'min_child_samples': 9}. Best is trial 43 with value: 0.516065766031832.\u001b[0m\n", + "\u001b[32m[I 2021-09-07 16:27:53,875]\u001b[0m Trial 50 pruned. Trial was pruned at iteration 10.\u001b[0m\n", + "\u001b[32m[I 2021-09-07 16:27:54,587]\u001b[0m Trial 51 finished with value: 0.5174618905113623 and parameters: {'lambda_l1': 0.000381507052985562, 'lambda_l2': 4.901891994722806e-08, 'num_leaves': 166, 'feature_fraction': 0.7117978094083592, 'bagging_fraction': 0.8956613846537137, 'bagging_freq': 1, 'min_child_samples': 12}. Best is trial 43 with value: 0.516065766031832.\u001b[0m\n", + "\u001b[32m[I 2021-09-07 16:27:55,304]\u001b[0m Trial 52 finished with value: 0.5219780933938188 and parameters: {'lambda_l1': 0.0003055316771241415, 'lambda_l2': 1.0975399369852818e-08, 'num_leaves': 175, 'feature_fraction': 0.7709159871191618, 'bagging_fraction': 0.8946659725371712, 'bagging_freq': 1, 'min_child_samples': 12}. Best is trial 43 with value: 0.516065766031832.\u001b[0m\n", + "\u001b[32m[I 2021-09-07 16:27:56,481]\u001b[0m Trial 53 finished with value: 0.522019841081264 and parameters: {'lambda_l1': 3.0853077659712017e-06, 'lambda_l2': 3.167435996190143e-08, 'num_leaves': 199, 'feature_fraction': 0.700370780125537, 'bagging_fraction': 0.9659440325481246, 'bagging_freq': 1, 'min_child_samples': 5}. Best is trial 43 with value: 0.516065766031832.\u001b[0m\n", + "\u001b[32m[I 2021-09-07 16:27:57,422]\u001b[0m Trial 54 finished with value: 0.5210662969810745 and parameters: {'lambda_l1': 7.857450656938453e-05, 'lambda_l2': 4.5159973730477044e-07, 'num_leaves': 153, 'feature_fraction': 0.7263108852936352, 'bagging_fraction': 0.936630215722916, 'bagging_freq': 1, 'min_child_samples': 19}. Best is trial 43 with value: 0.516065766031832.\u001b[0m\n", + "\u001b[32m[I 2021-09-07 16:27:57,568]\u001b[0m Trial 55 pruned. Trial was pruned at iteration 10.\u001b[0m\n", + "\u001b[32m[I 2021-09-07 16:27:57,678]\u001b[0m Trial 56 pruned. Trial was pruned at iteration 10.\u001b[0m\n", + "\u001b[32m[I 2021-09-07 16:27:57,803]\u001b[0m Trial 57 pruned. Trial was pruned at iteration 10.\u001b[0m\n", + "\u001b[32m[I 2021-09-07 16:27:57,889]\u001b[0m Trial 58 pruned. Trial was pruned at iteration 10.\u001b[0m\n", + "\u001b[32m[I 2021-09-07 16:27:58,284]\u001b[0m Trial 59 pruned. Trial was pruned at iteration 10.\u001b[0m\n", + "\u001b[32m[I 2021-09-07 16:27:58,510]\u001b[0m Trial 60 pruned. Trial was pruned at iteration 15.\u001b[0m\n", + "\u001b[32m[I 2021-09-07 16:27:59,683]\u001b[0m Trial 61 finished with value: 0.5238780309760502 and parameters: {'lambda_l1': 0.0005622649168938934, 'lambda_l2': 7.041773458860283e-08, 'num_leaves': 165, 'feature_fraction': 0.6971774484774765, 'bagging_fraction': 0.8845735572162268, 'bagging_freq': 2, 'min_child_samples': 16}. Best is trial 43 with value: 0.516065766031832.\u001b[0m\n", + "\u001b[32m[I 2021-09-07 16:27:59,867]\u001b[0m Trial 62 pruned. Trial was pruned at iteration 10.\u001b[0m\n", + "\u001b[32m[I 2021-09-07 16:28:01,236]\u001b[0m Trial 63 finished with value: 0.5172018048571797 and parameters: {'lambda_l1': 4.4774427854289624e-05, 'lambda_l2': 1.0431434685856767e-07, 'num_leaves': 175, 'feature_fraction': 0.7067550902029666, 'bagging_fraction': 0.7960327498051027, 'bagging_freq': 2, 'min_child_samples': 16}. Best is trial 43 with value: 0.516065766031832.\u001b[0m\n", + "\u001b[32m[I 2021-09-07 16:28:01,369]\u001b[0m Trial 64 pruned. Trial was pruned at iteration 10.\u001b[0m\n", + "\u001b[32m[I 2021-09-07 16:28:01,513]\u001b[0m Trial 65 pruned. Trial was pruned at iteration 10.\u001b[0m\n", + "\u001b[32m[I 2021-09-07 16:28:02,393]\u001b[0m Trial 66 pruned. Trial was pruned at iteration 76.\u001b[0m\n", + "\u001b[32m[I 2021-09-07 16:28:02,529]\u001b[0m Trial 67 pruned. Trial was pruned at iteration 10.\u001b[0m\n", + "\u001b[32m[I 2021-09-07 16:28:02,721]\u001b[0m Trial 68 pruned. Trial was pruned at iteration 12.\u001b[0m\n", + "\u001b[32m[I 2021-09-07 16:28:03,060]\u001b[0m Trial 69 pruned. Trial was pruned at iteration 44.\u001b[0m\n", + "\u001b[32m[I 2021-09-07 16:28:03,135]\u001b[0m Trial 70 pruned. Trial was pruned at iteration 10.\u001b[0m\n", + "\u001b[32m[I 2021-09-07 16:28:03,486]\u001b[0m Trial 71 pruned. Trial was pruned at iteration 56.\u001b[0m\n", + "\u001b[32m[I 2021-09-07 16:28:03,587]\u001b[0m Trial 72 pruned. Trial was pruned at iteration 10.\u001b[0m\n", + "\u001b[32m[I 2021-09-07 16:28:04,107]\u001b[0m Trial 73 pruned. Trial was pruned at iteration 68.\u001b[0m\n", + "\u001b[32m[I 2021-09-07 16:28:04,203]\u001b[0m Trial 74 pruned. Trial was pruned at iteration 11.\u001b[0m\n", + "\u001b[32m[I 2021-09-07 16:28:04,307]\u001b[0m Trial 75 pruned. Trial was pruned at iteration 10.\u001b[0m\n", + "\u001b[32m[I 2021-09-07 16:28:04,631]\u001b[0m Trial 76 pruned. Trial was pruned at iteration 39.\u001b[0m\n", + "\u001b[32m[I 2021-09-07 16:28:04,725]\u001b[0m Trial 77 pruned. Trial was pruned at iteration 10.\u001b[0m\n", + "\u001b[32m[I 2021-09-07 16:28:04,831]\u001b[0m Trial 78 pruned. Trial was pruned at iteration 10.\u001b[0m\n", + "\u001b[32m[I 2021-09-07 16:28:04,920]\u001b[0m Trial 79 pruned. Trial was pruned at iteration 10.\u001b[0m\n", + "\u001b[32m[I 2021-09-07 16:28:04,992]\u001b[0m Trial 80 pruned. Trial was pruned at iteration 10.\u001b[0m\n", + "\u001b[32m[I 2021-09-07 16:28:05,099]\u001b[0m Trial 81 pruned. Trial was pruned at iteration 11.\u001b[0m\n", + "\u001b[32m[I 2021-09-07 16:28:05,766]\u001b[0m Trial 82 finished with value: 0.5178938881576146 and parameters: {'lambda_l1': 1.2203483524616316e-08, 'lambda_l2': 5.507928324956204e-07, 'num_leaves': 185, 'feature_fraction': 0.7219415365227362, 'bagging_fraction': 0.9438920094595108, 'bagging_freq': 1, 'min_child_samples': 17}. Best is trial 43 with value: 0.516065766031832.\u001b[0m\n", + "\u001b[32m[I 2021-09-07 16:28:05,866]\u001b[0m Trial 83 pruned. Trial was pruned at iteration 10.\u001b[0m\n", + "\u001b[32m[I 2021-09-07 16:28:05,961]\u001b[0m Trial 84 pruned. Trial was pruned at iteration 10.\u001b[0m\n", + "\u001b[32m[I 2021-09-07 16:28:06,778]\u001b[0m Trial 85 finished with value: 0.5122528701381253 and parameters: {'lambda_l1': 6.371822640459341e-07, 'lambda_l2': 3.03967794068636e-08, 'num_leaves': 189, 'feature_fraction': 0.7066521328434867, 'bagging_fraction': 0.8972190570020028, 'bagging_freq': 1, 'min_child_samples': 11}. Best is trial 85 with value: 0.5122528701381253.\u001b[0m\n", + "\u001b[32m[I 2021-09-07 16:28:07,709]\u001b[0m Trial 86 finished with value: 0.5128398316765018 and parameters: {'lambda_l1': 1.5059254193489838e-07, 'lambda_l2': 2.6171717314455622e-08, 'num_leaves': 192, 'feature_fraction': 0.711268040370677, 'bagging_fraction': 0.9174432395706226, 'bagging_freq': 1, 'min_child_samples': 11}. Best is trial 85 with value: 0.5122528701381253.\u001b[0m\n", + "\u001b[32m[I 2021-09-07 16:28:09,411]\u001b[0m Trial 87 finished with value: 0.5191456815789754 and parameters: {'lambda_l1': 5.687878085793806e-08, 'lambda_l2': 2.6772763929664963e-08, 'num_leaves': 216, 'feature_fraction': 0.7413398142797374, 'bagging_fraction': 0.9993988673163303, 'bagging_freq': 1, 'min_child_samples': 11}. Best is trial 85 with value: 0.5122528701381253.\u001b[0m\n", + "\u001b[32m[I 2021-09-07 16:28:09,813]\u001b[0m Trial 88 pruned. Trial was pruned at iteration 10.\u001b[0m\n", + "\u001b[32m[I 2021-09-07 16:28:12,526]\u001b[0m Trial 89 finished with value: 0.5102536883069437 and parameters: {'lambda_l1': 1.6446541932580473e-07, 'lambda_l2': 1.0734412329614426e-07, 'num_leaves': 193, 'feature_fraction': 0.8123395009389368, 'bagging_fraction': 0.9184649865458648, 'bagging_freq': 1, 'min_child_samples': 10}. Best is trial 89 with value: 0.5102536883069437.\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32m[I 2021-09-07 09:27:09,210]\u001b[0m Trial 68 finished with value: 0.524942396458539 and parameters: {'lambda_l1': 0.0011590093830295628, 'lambda_l2': 0.06353809127053359, 'num_leaves': 198, 'feature_fraction': 0.7106295012909083, 'bagging_fraction': 0.973655849802224, 'bagging_freq': 5, 'min_child_samples': 7}. Best is trial 41 with value: 0.5233364787469251.\u001b[0m\n", - "\u001b[32m[I 2021-09-07 09:27:09,572]\u001b[0m Trial 69 pruned. Trial was pruned at iteration 29.\u001b[0m\n", - "\u001b[32m[I 2021-09-07 09:27:10,511]\u001b[0m Trial 70 finished with value: 0.5227354154865925 and parameters: {'lambda_l1': 0.0019204609823774911, 'lambda_l2': 0.006957053438847566, 'num_leaves': 200, 'feature_fraction': 0.7174135338967843, 'bagging_fraction': 0.9747628414441452, 'bagging_freq': 5, 'min_child_samples': 8}. Best is trial 70 with value: 0.5227354154865925.\u001b[0m\n", - "\u001b[32m[I 2021-09-07 09:27:11,445]\u001b[0m Trial 71 pruned. Trial was pruned at iteration 72.\u001b[0m\n", - "\u001b[32m[I 2021-09-07 09:27:12,707]\u001b[0m Trial 72 finished with value: 0.5273794666616337 and parameters: {'lambda_l1': 0.00253464769953144, 'lambda_l2': 0.04250761519699366, 'num_leaves': 203, 'feature_fraction': 0.723989872880134, 'bagging_fraction': 0.9954485306248265, 'bagging_freq': 5, 'min_child_samples': 10}. Best is trial 70 with value: 0.5227354154865925.\u001b[0m\n", - "\u001b[32m[I 2021-09-07 09:27:14,118]\u001b[0m Trial 73 finished with value: 0.5290081420408789 and parameters: {'lambda_l1': 0.0014187815549416435, 'lambda_l2': 0.12791352246804705, 'num_leaves': 218, 'feature_fraction': 0.7758653557130019, 'bagging_fraction': 0.9488928693793105, 'bagging_freq': 5, 'min_child_samples': 18}. Best is trial 70 with value: 0.5227354154865925.\u001b[0m\n", - "\u001b[32m[I 2021-09-07 09:27:14,196]\u001b[0m Trial 74 pruned. Trial was pruned at iteration 10.\u001b[0m\n", - "\u001b[32m[I 2021-09-07 09:27:15,517]\u001b[0m Trial 75 finished with value: 0.5325397214424649 and parameters: {'lambda_l1': 0.0005872739303922063, 'lambda_l2': 0.0096136159649407, 'num_leaves': 211, 'feature_fraction': 0.8012899648531023, 'bagging_fraction': 0.931501707649951, 'bagging_freq': 4, 'min_child_samples': 13}. Best is trial 70 with value: 0.5227354154865925.\u001b[0m\n", - "\u001b[32m[I 2021-09-07 09:27:16,903]\u001b[0m Trial 76 finished with value: 0.5296781377317934 and parameters: {'lambda_l1': 0.006787205212552698, 'lambda_l2': 0.0790698959620889, 'num_leaves': 239, 'feature_fraction': 0.7454753912863651, 'bagging_fraction': 0.9799306593714261, 'bagging_freq': 5, 'min_child_samples': 7}. Best is trial 70 with value: 0.5227354154865925.\u001b[0m\n", - "\u001b[32m[I 2021-09-07 09:27:17,956]\u001b[0m Trial 77 finished with value: 0.5309336374271006 and parameters: {'lambda_l1': 0.0037427478770181, 'lambda_l2': 0.8130901726120456, 'num_leaves': 232, 'feature_fraction': 0.7224358819185711, 'bagging_fraction': 0.9520121758601578, 'bagging_freq': 4, 'min_child_samples': 12}. Best is trial 70 with value: 0.5227354154865925.\u001b[0m\n", - "\u001b[32m[I 2021-09-07 09:27:18,071]\u001b[0m Trial 78 pruned. Trial was pruned at iteration 13.\u001b[0m\n", - "\u001b[32m[I 2021-09-07 09:27:19,373]\u001b[0m Trial 79 finished with value: 0.5347521802448649 and parameters: {'lambda_l1': 0.017570019491239652, 'lambda_l2': 0.05059260326524094, 'num_leaves': 226, 'feature_fraction': 0.6981808494111595, 'bagging_fraction': 0.9993645575870086, 'bagging_freq': 4, 'min_child_samples': 20}. Best is trial 70 with value: 0.5227354154865925.\u001b[0m\n", - "\u001b[32m[I 2021-09-07 09:27:19,471]\u001b[0m Trial 80 pruned. Trial was pruned at iteration 10.\u001b[0m\n", - "\u001b[32m[I 2021-09-07 09:27:21,230]\u001b[0m Trial 81 finished with value: 0.5227732830528738 and parameters: {'lambda_l1': 0.0003058538457597125, 'lambda_l2': 0.04324937485563167, 'num_leaves': 202, 'feature_fraction': 0.73206644303025, 'bagging_fraction': 0.9911170826848787, 'bagging_freq': 5, 'min_child_samples': 10}. Best is trial 70 with value: 0.5227354154865925.\u001b[0m\n", - "\u001b[32m[I 2021-09-07 09:27:22,266]\u001b[0m Trial 82 finished with value: 0.5279916258627607 and parameters: {'lambda_l1': 0.0002580487853233371, 'lambda_l2': 0.015002378165105253, 'num_leaves': 213, 'feature_fraction': 0.665112654945876, 'bagging_fraction': 0.9341515771267435, 'bagging_freq': 5, 'min_child_samples': 11}. Best is trial 70 with value: 0.5227354154865925.\u001b[0m\n", - "\u001b[32m[I 2021-09-07 09:27:26,040]\u001b[0m Trial 83 pruned. Trial was pruned at iteration 51.\u001b[0m\n", - "\u001b[32m[I 2021-09-07 09:27:29,359]\u001b[0m Trial 84 finished with value: 0.5257703648298483 and parameters: {'lambda_l1': 0.09313058634431053, 'lambda_l2': 0.0794176561682967, 'num_leaves': 174, 'feature_fraction': 0.7330255912724464, 'bagging_fraction': 0.9556166881594967, 'bagging_freq': 6, 'min_child_samples': 14}. Best is trial 70 with value: 0.5227354154865925.\u001b[0m\n", - "\u001b[32m[I 2021-09-07 09:27:29,749]\u001b[0m Trial 85 pruned. Trial was pruned at iteration 30.\u001b[0m\n", - "\u001b[32m[I 2021-09-07 09:27:29,919]\u001b[0m Trial 86 pruned. Trial was pruned at iteration 17.\u001b[0m\n", - "\u001b[32m[I 2021-09-07 09:27:30,026]\u001b[0m Trial 87 pruned. Trial was pruned at iteration 10.\u001b[0m\n", - "\u001b[32m[I 2021-09-07 09:27:30,861]\u001b[0m Trial 88 finished with value: 0.5270306752854106 and parameters: {'lambda_l1': 0.0003495273937948516, 'lambda_l2': 0.057992141622577896, 'num_leaves': 167, 'feature_fraction': 0.7406493057210397, 'bagging_fraction': 0.8812843020741449, 'bagging_freq': 5, 'min_child_samples': 10}. Best is trial 70 with value: 0.5227354154865925.\u001b[0m\n", - "\u001b[32m[I 2021-09-07 09:27:30,933]\u001b[0m Trial 89 pruned. Trial was pruned at iteration 11.\u001b[0m\n", - "\u001b[32m[I 2021-09-07 09:27:31,029]\u001b[0m Trial 90 pruned. Trial was pruned at iteration 10.\u001b[0m\n", - "\u001b[32m[I 2021-09-07 09:27:31,358]\u001b[0m Trial 91 pruned. Trial was pruned at iteration 48.\u001b[0m\n", - "\u001b[32m[I 2021-09-07 09:27:31,714]\u001b[0m Trial 92 pruned. Trial was pruned at iteration 41.\u001b[0m\n", - "\u001b[32m[I 2021-09-07 09:27:32,543]\u001b[0m Trial 93 finished with value: 0.5264391883178012 and parameters: {'lambda_l1': 0.055429809241931895, 'lambda_l2': 0.013315511259061849, 'num_leaves': 235, 'feature_fraction': 0.6209901742544794, 'bagging_fraction': 0.9603625243176316, 'bagging_freq': 5, 'min_child_samples': 9}. Best is trial 70 with value: 0.5227354154865925.\u001b[0m\n", - "\u001b[32m[I 2021-09-07 09:27:32,825]\u001b[0m Trial 94 pruned. Trial was pruned at iteration 40.\u001b[0m\n", - "\u001b[32m[I 2021-09-07 09:27:32,940]\u001b[0m Trial 95 pruned. Trial was pruned at iteration 13.\u001b[0m\n", - "\u001b[32m[I 2021-09-07 09:27:33,525]\u001b[0m Trial 96 finished with value: 0.521566218012535 and parameters: {'lambda_l1': 0.001072724146880552, 'lambda_l2': 0.043446953857601264, 'num_leaves': 142, 'feature_fraction': 0.6922532844601723, 'bagging_fraction': 0.9898097804978216, 'bagging_freq': 5, 'min_child_samples': 5}. Best is trial 96 with value: 0.521566218012535.\u001b[0m\n", - "\u001b[32m[I 2021-09-07 09:27:34,149]\u001b[0m Trial 97 finished with value: 0.5196727190561812 and parameters: {'lambda_l1': 0.0025915189209523223, 'lambda_l2': 0.37941398551753863, 'num_leaves': 159, 'feature_fraction': 0.6942886423659413, 'bagging_fraction': 0.9888819332176355, 'bagging_freq': 6, 'min_child_samples': 5}. Best is trial 97 with value: 0.5196727190561812.\u001b[0m\n", - "\u001b[32m[I 2021-09-07 09:27:34,855]\u001b[0m Trial 98 finished with value: 0.5265000531725292 and parameters: {'lambda_l1': 0.0006868362572582551, 'lambda_l2': 0.040525833784093586, 'num_leaves': 143, 'feature_fraction': 0.6930600995921747, 'bagging_fraction': 0.9897374239207342, 'bagging_freq': 6, 'min_child_samples': 6}. Best is trial 97 with value: 0.5196727190561812.\u001b[0m\n", - "\u001b[32m[I 2021-09-07 09:27:34,935]\u001b[0m Trial 99 pruned. Trial was pruned at iteration 10.\u001b[0m\n" + "\u001b[32m[I 2021-09-07 16:28:12,792]\u001b[0m Trial 90 pruned. Trial was pruned at iteration 10.\u001b[0m\n", + "\u001b[32m[I 2021-09-07 16:28:13,099]\u001b[0m Trial 91 pruned. Trial was pruned at iteration 13.\u001b[0m\n", + "\u001b[32m[I 2021-09-07 16:28:13,456]\u001b[0m Trial 92 pruned. Trial was pruned at iteration 14.\u001b[0m\n", + "\u001b[32m[I 2021-09-07 16:28:13,664]\u001b[0m Trial 93 pruned. Trial was pruned at iteration 10.\u001b[0m\n", + "\u001b[32m[I 2021-09-07 16:28:14,351]\u001b[0m Trial 94 pruned. Trial was pruned at iteration 45.\u001b[0m\n", + "\u001b[32m[I 2021-09-07 16:28:14,508]\u001b[0m Trial 95 pruned. Trial was pruned at iteration 10.\u001b[0m\n", + "\u001b[32m[I 2021-09-07 16:28:14,828]\u001b[0m Trial 96 pruned. Trial was pruned at iteration 10.\u001b[0m\n", + "\u001b[32m[I 2021-09-07 16:28:14,962]\u001b[0m Trial 97 pruned. Trial was pruned at iteration 10.\u001b[0m\n", + "\u001b[32m[I 2021-09-07 16:28:16,197]\u001b[0m Trial 98 finished with value: 0.5171555001746302 and parameters: {'lambda_l1': 1.0103137663726183e-08, 'lambda_l2': 1.7418953701287595e-06, 'num_leaves': 203, 'feature_fraction': 0.7265189263478641, 'bagging_fraction': 0.9317931154547426, 'bagging_freq': 1, 'min_child_samples': 10}. Best is trial 89 with value: 0.5102536883069437.\u001b[0m\n", + "\u001b[32m[I 2021-09-07 16:28:16,557]\u001b[0m Trial 99 pruned. Trial was pruned at iteration 16.\u001b[0m\n" ] }, { @@ -526,108 +545,635 @@ "text": [ "Number of finished trials: 100\n", "Best trial:\n", - " Value: 0.5196727190561812\n", + " Value: 0.5102536883069437\n", " Params: \n", - " lambda_l1: 0.0025915189209523223\n", - " lambda_l2: 0.37941398551753863\n", - " num_leaves: 159\n", - " feature_fraction: 0.6942886423659413\n", - " bagging_fraction: 0.9888819332176355\n", - " bagging_freq: 6\n", - " min_child_samples: 5\n" + " lambda_l1: 1.6446541932580473e-07\n", + " lambda_l2: 1.0734412329614426e-07\n", + " num_leaves: 193\n", + " feature_fraction: 0.8123395009389368\n", + " bagging_fraction: 0.9184649865458648\n", + " bagging_freq: 1\n", + " min_child_samples: 10\n", + "[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000712 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 1837\n", + "[LightGBM] [Info] Number of data points in the train set: 14860, number of used features: 8\n", + "[LightGBM] [Info] Start training from score 2.073247\n", + "Training until validation scores don't improve for 20 rounds\n", + "[50]\tvalid_0's rmse: 0.453164\n", + "[100]\tvalid_0's rmse: 0.442958\n", + "Early stopping, best iteration is:\n", + "[110]\tvalid_0's rmse: 0.442157\n" ] - } - ], - "source": [ - "if __name__ == \"__main__\":\n", - " study = optuna.create_study(\n", - " pruner=optuna.pruners.MedianPruner(n_warmup_steps=10), direction=\"minimize\"\n", - " )\n", - " study.optimize(lambda trial: objective(trial, train_x, valid_x, train_y, valid_y), n_trials=100)\n", - "\n", - " print(\"Number of finished trials: {}\".format(len(study.trials)))\n", - "\n", - " print(\"Best trial:\")\n", - " trial = study.best_trial\n", - "\n", - " print(\" Value: {}\".format(trial.value))\n", - "\n", - " print(\" Params: \")\n", - " for key, value in trial.params.items():\n", - " print(\" {}: {}\".format(key, value))" - ] - }, - { - "cell_type": "code", - "execution_count": 24, - "id": "681d9cc2", - "metadata": {}, - "outputs": [ + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\u001b[32m[I 2021-09-07 16:28:17,677]\u001b[0m Trial 100 pruned. Trial was pruned at iteration 10.\u001b[0m\n", + "\u001b[32m[I 2021-09-07 16:28:17,796]\u001b[0m Trial 101 pruned. Trial was pruned at iteration 10.\u001b[0m\n", + "\u001b[32m[I 2021-09-07 16:28:17,941]\u001b[0m Trial 102 pruned. Trial was pruned at iteration 10.\u001b[0m\n", + "\u001b[32m[I 2021-09-07 16:28:18,090]\u001b[0m Trial 103 pruned. Trial was pruned at iteration 10.\u001b[0m\n", + "\u001b[32m[I 2021-09-07 16:28:18,293]\u001b[0m Trial 104 pruned. Trial was pruned at iteration 10.\u001b[0m\n", + "\u001b[32m[I 2021-09-07 16:28:18,412]\u001b[0m Trial 105 pruned. Trial was pruned at iteration 10.\u001b[0m\n", + "\u001b[32m[I 2021-09-07 16:28:18,524]\u001b[0m Trial 106 pruned. Trial was pruned at iteration 10.\u001b[0m\n", + "\u001b[32m[I 2021-09-07 16:28:18,647]\u001b[0m Trial 107 pruned. Trial was pruned at iteration 10.\u001b[0m\n", + "\u001b[32m[I 2021-09-07 16:28:18,797]\u001b[0m Trial 108 pruned. Trial was pruned at iteration 10.\u001b[0m\n", + "\u001b[32m[I 2021-09-07 16:28:18,911]\u001b[0m Trial 109 pruned. Trial was pruned at iteration 10.\u001b[0m\n", + "\u001b[32m[I 2021-09-07 16:28:19,029]\u001b[0m Trial 110 pruned. Trial was pruned at iteration 10.\u001b[0m\n", + "\u001b[32m[I 2021-09-07 16:28:19,211]\u001b[0m Trial 111 pruned. Trial was pruned at iteration 16.\u001b[0m\n", + "\u001b[32m[I 2021-09-07 16:28:19,595]\u001b[0m Trial 112 pruned. Trial was pruned at iteration 30.\u001b[0m\n", + "\u001b[32m[I 2021-09-07 16:28:19,744]\u001b[0m Trial 113 pruned. Trial was pruned at iteration 10.\u001b[0m\n", + "\u001b[32m[I 2021-09-07 16:28:19,853]\u001b[0m Trial 114 pruned. Trial was pruned at iteration 10.\u001b[0m\n", + "\u001b[32m[I 2021-09-07 16:28:20,146]\u001b[0m Trial 115 pruned. Trial was pruned at iteration 23.\u001b[0m\n", + "\u001b[32m[I 2021-09-07 16:28:20,267]\u001b[0m Trial 116 pruned. Trial was pruned at iteration 10.\u001b[0m\n", + "\u001b[32m[I 2021-09-07 16:28:20,420]\u001b[0m Trial 117 pruned. Trial was pruned at iteration 10.\u001b[0m\n", + "\u001b[32m[I 2021-09-07 16:28:20,594]\u001b[0m Trial 118 pruned. Trial was pruned at iteration 10.\u001b[0m\n", + "\u001b[32m[I 2021-09-07 16:28:20,780]\u001b[0m Trial 119 pruned. Trial was pruned at iteration 10.\u001b[0m\n", + "\u001b[32m[I 2021-09-07 16:28:20,942]\u001b[0m Trial 120 pruned. Trial was pruned at iteration 10.\u001b[0m\n", + "\u001b[32m[I 2021-09-07 16:28:21,073]\u001b[0m Trial 121 pruned. Trial was pruned at iteration 10.\u001b[0m\n", + "\u001b[32m[I 2021-09-07 16:28:21,410]\u001b[0m Trial 122 pruned. Trial was pruned at iteration 10.\u001b[0m\n", + "\u001b[32m[I 2021-09-07 16:28:21,601]\u001b[0m Trial 123 pruned. Trial was pruned at iteration 10.\u001b[0m\n", + "\u001b[32m[I 2021-09-07 16:28:21,799]\u001b[0m Trial 124 pruned. Trial was pruned at iteration 10.\u001b[0m\n", + "\u001b[32m[I 2021-09-07 16:28:21,944]\u001b[0m Trial 125 pruned. Trial was pruned at iteration 10.\u001b[0m\n", + "\u001b[32m[I 2021-09-07 16:28:22,080]\u001b[0m Trial 126 pruned. Trial was pruned at iteration 10.\u001b[0m\n", + "\u001b[32m[I 2021-09-07 16:28:22,209]\u001b[0m Trial 127 pruned. Trial was pruned at iteration 10.\u001b[0m\n", + "\u001b[32m[I 2021-09-07 16:28:22,367]\u001b[0m Trial 128 pruned. Trial was pruned at iteration 10.\u001b[0m\n", + "\u001b[32m[I 2021-09-07 16:28:22,509]\u001b[0m Trial 129 pruned. Trial was pruned at iteration 10.\u001b[0m\n", + "\u001b[32m[I 2021-09-07 16:28:22,673]\u001b[0m Trial 130 pruned. Trial was pruned at iteration 10.\u001b[0m\n", + "\u001b[32m[I 2021-09-07 16:28:22,754]\u001b[0m Trial 131 pruned. Trial was pruned at iteration 10.\u001b[0m\n", + "\u001b[32m[I 2021-09-07 16:28:22,923]\u001b[0m Trial 132 pruned. Trial was pruned at iteration 10.\u001b[0m\n", + "\u001b[32m[I 2021-09-07 16:28:23,074]\u001b[0m Trial 133 pruned. Trial was pruned at iteration 10.\u001b[0m\n", + "\u001b[32m[I 2021-09-07 16:28:23,442]\u001b[0m Trial 134 pruned. Trial was pruned at iteration 10.\u001b[0m\n", + "\u001b[32m[I 2021-09-07 16:28:23,682]\u001b[0m Trial 135 pruned. Trial was pruned at iteration 16.\u001b[0m\n", + "\u001b[32m[I 2021-09-07 16:28:23,824]\u001b[0m Trial 136 pruned. Trial was pruned at iteration 10.\u001b[0m\n", + "\u001b[32m[I 2021-09-07 16:28:24,010]\u001b[0m Trial 137 pruned. Trial was pruned at iteration 10.\u001b[0m\n", + "\u001b[32m[I 2021-09-07 16:28:24,160]\u001b[0m Trial 138 pruned. Trial was pruned at iteration 10.\u001b[0m\n", + "\u001b[32m[I 2021-09-07 16:28:24,458]\u001b[0m Trial 139 pruned. Trial was pruned at iteration 10.\u001b[0m\n", + "\u001b[32m[I 2021-09-07 16:28:24,678]\u001b[0m Trial 140 pruned. Trial was pruned at iteration 10.\u001b[0m\n", + "\u001b[32m[I 2021-09-07 16:28:24,849]\u001b[0m Trial 141 pruned. Trial was pruned at iteration 10.\u001b[0m\n", + "\u001b[32m[I 2021-09-07 16:28:25,081]\u001b[0m Trial 142 pruned. Trial was pruned at iteration 10.\u001b[0m\n", + "\u001b[32m[I 2021-09-07 16:28:25,256]\u001b[0m Trial 143 pruned. Trial was pruned at iteration 10.\u001b[0m\n", + "\u001b[32m[I 2021-09-07 16:28:25,501]\u001b[0m Trial 144 pruned. Trial was pruned at iteration 10.\u001b[0m\n", + "\u001b[32m[I 2021-09-07 16:28:26,208]\u001b[0m Trial 145 pruned. Trial was pruned at iteration 10.\u001b[0m\n", + "\u001b[32m[I 2021-09-07 16:28:26,709]\u001b[0m Trial 146 pruned. Trial was pruned at iteration 10.\u001b[0m\n", + "\u001b[32m[I 2021-09-07 16:28:26,919]\u001b[0m Trial 147 pruned. Trial was pruned at iteration 10.\u001b[0m\n", + "\u001b[32m[I 2021-09-07 16:28:27,534]\u001b[0m Trial 148 pruned. 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Trial was pruned at iteration 10.\u001b[0m\n", + "\u001b[32m[I 2021-09-07 16:28:35,002]\u001b[0m Trial 197 pruned. Trial was pruned at iteration 10.\u001b[0m\n", + "\u001b[32m[I 2021-09-07 16:28:35,242]\u001b[0m Trial 198 pruned. Trial was pruned at iteration 10.\u001b[0m\n", + "\u001b[32m[I 2021-09-07 16:28:35,408]\u001b[0m Trial 199 pruned. Trial was pruned at iteration 10.\u001b[0m\n" + ] + }, { "name": "stdout", "output_type": "stream", "text": [ - "{'boosting_type': 'gbdt', 'objective': 'regression', 'metric': 'rmse', 'lambda_l1': 0.0025915189209523223, 'lambda_l2': 0.37941398551753863, 'num_leaves': 159, 'feature_fraction': 0.6942886423659413, 'bagging_fraction': 0.9888819332176355, 'bagging_freq': 6, 'min_child_samples': 5}\n" + "Number of finished trials: 200\n", + "Best trial:\n", + " Value: 0.5102536883069437\n", + " Params: \n", + " lambda_l1: 1.6446541932580473e-07\n", + " lambda_l2: 1.0734412329614426e-07\n", + " num_leaves: 193\n", + " feature_fraction: 0.8123395009389368\n", + " bagging_fraction: 0.9184649865458648\n", + " bagging_freq: 1\n", + " min_child_samples: 10\n", + "[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000777 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 1838\n", + "[LightGBM] [Info] Number of data points in the train set: 14861, number of used features: 8\n", + "[LightGBM] [Info] Start training from score 2.070396\n", + "Training until validation scores don't improve for 20 rounds\n", + "[50]\tvalid_0's rmse: 0.465875\n", + "Early stopping, best iteration is:\n", + "[76]\tvalid_0's rmse: 0.460117\n" ] - } - ], - "source": [ - "params = {'boosting_type': 'gbdt',\n", - " 'objective': 'regression',\n", - " \"metric\": 'rmse'}\n", - "for key, value in trial.params.items():\n", - " params[key]=value\n", - "print(params)" - ] - }, - { - "cell_type": "code", - "execution_count": 25, - "id": "f5534bab", - "metadata": {}, - "outputs": [ + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\u001b[32m[I 2021-09-07 16:28:36,810]\u001b[0m Trial 200 pruned. 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Trial was pruned at iteration 10.\u001b[0m\n" + ] + }, { "name": "stdout", "output_type": "stream", "text": [ - "[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000585 seconds.\n", - "You can set `force_row_wise=true` to remove the overhead.\n", - "And if memory is not enough, you can set `force_col_wise=true`.\n", + "Number of finished trials: 300\n", + "Best trial:\n", + " Value: 0.5102536883069437\n", + " Params: \n", + " lambda_l1: 1.6446541932580473e-07\n", + " lambda_l2: 1.0734412329614426e-07\n", + " num_leaves: 193\n", + " feature_fraction: 0.8123395009389368\n", + " bagging_fraction: 0.9184649865458648\n", + " bagging_freq: 1\n", + " min_child_samples: 10\n", + "[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000797 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", "[LightGBM] [Info] Total Bins 1837\n", - "[LightGBM] [Info] Number of data points in the train set: 13932, number of used features: 8\n", - "[LightGBM] [Info] Start training from score 2.072422\n", + "[LightGBM] [Info] Number of data points in the train set: 14861, number of used features: 8\n", + "[LightGBM] [Info] Start training from score 2.069154\n", + "Training until validation scores don't improve for 20 rounds\n", + "[50]\tvalid_0's rmse: 0.451573\n", + "Early stopping, best iteration is:\n", + "[63]\tvalid_0's rmse: 0.44707\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\u001b[32m[I 2021-09-07 16:28:51,004]\u001b[0m Trial 300 pruned. 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"[150]\tvalid_0's rmse: 0.448356\n", + "[50]\tvalid_0's rmse: 0.444941\n", + "[100]\tvalid_0's rmse: 0.437147\n", "Early stopping, best iteration is:\n", - "[166]\tvalid_0's rmse: 0.447872\n" + "[90]\tvalid_0's rmse: 0.43678\n", + "Wall time: 1min 46s\n" ] } ], "source": [ - "dtrain = lgb.Dataset(train_x, label=train_y)\n", - "dvalid = lgb.Dataset(valid_x, label=valid_y)\n", - "model = lgb.train(params=params, train_set=dtrain,valid_sets=[dvalid],\n", - " verbose_eval=50,\n", - " early_stopping_rounds=20,\n", - " num_boost_round=5000)\n", - "predict = model.predict(test_x)" + "%%time\n", + "if __name__ == \"__main__\":\n", + " test_predict = np.zeros(shape=[test_x.shape[0], 5],dtype=float)\n", + " study = optuna.create_study(\n", + " pruner=optuna.pruners.MedianPruner(n_warmup_steps=10), direction=\"minimize\" # 指定是越小越好\n", + " )\n", + " for i, (trn_idx, val_idx) in enumerate(skf.split(train_x, train_y)):\n", + " study.optimize(lambda trial: objective(trial, train_x.iloc[trn_idx], train_y.iloc[trn_idx], \n", + " train_x.iloc[val_idx], train_y.iloc[val_idx]), n_trials=100)\n", + "\n", + " print(\"Number of finished trials: {}\".format(len(study.trials)))\n", + "\n", + " print(\"Best trial:\")\n", + " trial = study.best_trial\n", + "\n", + " print(\" Value: {}\".format(trial.value))\n", + "\n", + " print(\" Params: \")\n", + " for key, value in trial.params.items():\n", + " print(\" {}: {}\".format(key, value))\n", + " \n", + " params = {'boosting_type': 'gbdt',\n", + " 'objective': 'regression',\n", + " \"metric\": 'rmse'}\n", + " for key, value in trial.params.items():\n", + " params[key]=value\n", + " \n", + " dtrain = lgb.Dataset(train_x.iloc[trn_idx], label=train_y.iloc[trn_idx])\n", + " dvalid = lgb.Dataset(train_x.iloc[val_idx], label=train_y.iloc[val_idx])\n", + " model = lgb.train(params=params, train_set=dtrain,valid_sets=[dvalid],\n", + " verbose_eval=50,\n", + " early_stopping_rounds=20,\n", + " num_boost_round=5000)\n", + " test_predict[:,i] = model.predict(test_x)\n", + " predict = np.mean(test_predict,axis=1)" ] }, { "cell_type": "code", - "execution_count": 26, + "execution_count": 13, "id": "f28d82da", "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "0.44403838770137805" + "0.432341765333029" ] }, - "execution_count": 26, + "execution_count": 13, "metadata": {}, "output_type": "execute_result" } @@ -639,17 +1185,17 @@ }, { "cell_type": "markdown", - "id": "5e292bdb", + "id": "0c7ee3c5", "metadata": {}, "source": [ "### 回归任务的结论\n", - "不使用optuna的分数是0.4531666044672748，使用的分数是0.44403838770137805，提升了0.00912821676589675。" + "不使用optuna的分数是0.4346521330333544，使用的分数是0.432341765333029，提升了0.0023103677003254。作者测了很多次，基本再0.003-0.002之间，感兴趣的可以多跑几次。" ] }, { "cell_type": "code", "execution_count": null, - "id": "36384535", + "id": "82ea6cab", "metadata": {}, "outputs": [], "source": [] diff --git a/竞赛优胜技巧/AutoAI Tools.ipynb b/竞赛优胜技巧/AutoAI Tools.ipynb index 3744e19..7388f26 100644 --- a/竞赛优胜技巧/AutoAI Tools.ipynb +++ b/竞赛优胜技巧/AutoAI Tools.ipynb @@ -28,7 +28,7 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": 1, "id": "f5e0e977", "metadata": {}, "outputs": [], @@ -181,85 +181,160 @@ "name": "stdout", "output_type": "stream", "text": [ - "训练集： (13932, 8)\n", - "验证集： (4644, 8)\n", + "训练集： (18576, 8)\n", "测试集： (2064, 8)\n" ] } ], "source": [ "# 切分训练和测试集\n", - "train_valid_x, test_x, train_valid_y, test_y = train_test_split(X, y,random_state=42,test_size=0.1)\n", + "train_x, test_x, train_y, test_y = train_test_split(X, y,random_state=42,test_size=0.1)\n", + "train_y = pd.DataFrame(train_y,columns=['result'])\n", + "test_y = pd.DataFrame(test_y,columns=['result'])\n", "\n", - "# 切分训练和验证集\n", - "train_x, valid_x, train_y, valid_y = train_test_split(train_valid_x, train_valid_y,random_state=42)\n", "print('训练集：',train_x.shape)\n", - "print('验证集：',valid_x.shape)\n", "print('测试集：', test_x.shape)" ] }, + { + "cell_type": "code", + "execution_count": 6, + "id": "69c4867e", + "metadata": {}, + "outputs": [], + "source": [ + "from sklearn.model_selection import KFold\n", + "skf = KFold(n_splits=5, shuffle=True, random_state=42)" + ] + }, { "cell_type": "markdown", "id": "e87a2121", "metadata": {}, "source": [ - "### 使用LGB作为模型，不使用optuna调参" + "### 使用LGB作为模型并CV，不使用optuna调参" ] }, { "cell_type": "code", - "execution_count": 12, + "execution_count": 7, "id": "5cad8967", - "metadata": {}, + "metadata": { + "scrolled": true + }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.001822 seconds.\n", + "[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000866 seconds.\n", "You can set `force_col_wise=true` to remove the overhead.\n", "[LightGBM] [Info] Total Bins 1837\n", - "[LightGBM] [Info] Number of data points in the train set: 13932, number of used features: 8\n", - "[LightGBM] [Info] Start training from score 2.072422\n", + "[LightGBM] [Info] Number of data points in the train set: 14860, number of used features: 8\n", + "[LightGBM] [Info] Start training from score 2.073247\n", "Training until validation scores don't improve for 20 rounds\n", "[50]\tvalid_0's rmse: 0.492877\n", - "[100]\tvalid_0's rmse: 0.471803\n", - "[150]\tvalid_0's rmse: 0.46445\n", - "[200]\tvalid_0's rmse: 0.459716\n", - "[250]\tvalid_0's rmse: 0.456658\n", - "[300]\tvalid_0's rmse: 0.454223\n", - "[350]\tvalid_0's rmse: 0.452644\n", + "[100]\tvalid_0's rmse: 0.469235\n", + "[150]\tvalid_0's rmse: 0.460357\n", + "[200]\tvalid_0's rmse: 0.455302\n", + "[250]\tvalid_0's rmse: 0.452674\n", + "[300]\tvalid_0's rmse: 0.451179\n", + "Early stopping, best iteration is:\n", + "[313]\tvalid_0's rmse: 0.450303\n", + "[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000494 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 1838\n", + "[LightGBM] [Info] Number of data points in the train set: 14861, number of used features: 8\n", + "[LightGBM] [Info] Start training from score 2.070396\n", + "Training until validation scores don't improve for 20 rounds\n", + "[50]\tvalid_0's rmse: 0.496484\n", + "[100]\tvalid_0's rmse: 0.476225\n", + "[150]\tvalid_0's rmse: 0.469582\n", + "[200]\tvalid_0's rmse: 0.464643\n", + "[250]\tvalid_0's rmse: 0.463244\n", + "[300]\tvalid_0's rmse: 0.461886\n", + "[350]\tvalid_0's rmse: 0.460305\n", + "[400]\tvalid_0's rmse: 0.4596\n", + "[450]\tvalid_0's rmse: 0.458645\n", + "Early stopping, best iteration is:\n", + "[435]\tvalid_0's rmse: 0.458368\n", + "[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000800 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 1837\n", + "[LightGBM] [Info] Number of data points in the train set: 14861, number of used features: 8\n", + "[LightGBM] [Info] Start training from score 2.069154\n", + "Training until validation scores don't improve for 20 rounds\n", + "[50]\tvalid_0's rmse: 0.48676\n", + "[100]\tvalid_0's rmse: 0.466911\n", + "[150]\tvalid_0's rmse: 0.459693\n", + "[200]\tvalid_0's rmse: 0.457631\n", + "[250]\tvalid_0's rmse: 0.452574\n", "Early stopping, best iteration is:\n", - "[342]\tvalid_0's rmse: 0.452522\n" + "[264]\tvalid_0's rmse: 0.451947\n", + "[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000779 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 1838\n", + "[LightGBM] [Info] Number of data points in the train set: 14861, number of used features: 8\n", + "[LightGBM] [Info] Start training from score 2.063366\n", + "Training until validation scores don't improve for 20 rounds\n", + "[50]\tvalid_0's rmse: 0.492482\n", + "[100]\tvalid_0's rmse: 0.468929\n", + "[150]\tvalid_0's rmse: 0.459713\n", + "[200]\tvalid_0's rmse: 0.456203\n", + "[250]\tvalid_0's rmse: 0.454063\n", + "[300]\tvalid_0's rmse: 0.451901\n", + "Early stopping, best iteration is:\n", + "[322]\tvalid_0's rmse: 0.450961\n", + "[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000694 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 1838\n", + "[LightGBM] [Info] Number of data points in the train set: 14861, number of used features: 8\n", + "[LightGBM] [Info] Start training from score 2.066924\n", + "Training until validation scores don't improve for 20 rounds\n", + "[50]\tvalid_0's rmse: 0.488385\n", + "[100]\tvalid_0's rmse: 0.464851\n", + "[150]\tvalid_0's rmse: 0.455183\n", + "[200]\tvalid_0's rmse: 0.450506\n", + "[250]\tvalid_0's rmse: 0.4461\n", + "[300]\tvalid_0's rmse: 0.44438\n", + "[350]\tvalid_0's rmse: 0.441808\n", + "[400]\tvalid_0's rmse: 0.44079\n", + "Early stopping, best iteration is:\n", + "[411]\tvalid_0's rmse: 0.440543\n", + "Wall time: 3.44 s\n" ] } ], "source": [ + "%%time\n", + "test_predict = np.zeros(shape=[test_x.shape[0], 5],dtype=float)\n", "params = {'boosting_type': 'gbdt',\n", " 'objective': 'regression',\n", " \"metric\": 'rmse'}\n", - "dtrain = lgb.Dataset(train_x, label=train_y)\n", - "dvalid = lgb.Dataset(valid_x, label=valid_y)\n", - "model = lgb.train(params=params, train_set=dtrain,valid_sets=[dvalid],\n", - " verbose_eval=50,\n", - " early_stopping_rounds=20,\n", - " num_boost_round=5000)\n", - "predict = model.predict(test_x)" + "for i, (trn_idx, val_idx) in enumerate(skf.split(train_x, train_y)):\n", + " dtrain = lgb.Dataset(train_x.iloc[trn_idx], label=train_y.iloc[trn_idx])\n", + " dvalid = lgb.Dataset(train_x.iloc[val_idx], label=train_y.iloc[val_idx])\n", + " model = lgb.train(params=params, train_set=dtrain,valid_sets=[dvalid],\n", + " verbose_eval=50,\n", + " early_stopping_rounds=20,\n", + " num_boost_round=5000)\n", + " test_predict[:,i] = model.predict(test_x)\n", + "predict = np.mean(test_predict,axis=1)" ] }, { "cell_type": "code", - "execution_count": 14, + "execution_count": 8, "id": "886bfdad", "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "0.4531666044672748" + "0.4346521330333544" ] }, - "execution_count": 14, + "execution_count": 8, "metadata": {}, "output_type": "execute_result" } @@ -274,80 +349,24 @@ "id": "c271442c", "metadata": {}, "source": [ - "### 使用LGB作为模型，使用optuna调参" + "### 使用LGB作为模型并CV，使用optuna调参" ] }, { "cell_type": "code", - "execution_count": 41, + "execution_count": 9, "id": "9a87f9db", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Looking in indexes: https://pypi.tuna.tsinghua.edu.cn/simple\n", - "Collecting optuna\n", - " Downloading https://pypi.tuna.tsinghua.edu.cn/packages/2a/b4/061c8721b5da1614794d1b66fcb212eee156efd5284f66854d02f295b0be/optuna-2.9.1-py3-none-any.whl (302 kB)\n", - "Requirement already satisfied: tqdm in d:\\programdata\\anaconda3\\lib\\site-packages (from optuna) (4.59.0)\n", - "Collecting cmaes>=0.8.2\n", - " Downloading https://pypi.tuna.tsinghua.edu.cn/packages/01/1f/43b01223a0366171f474320c6e966c39a11587287f098a5f09809b45e05f/cmaes-0.8.2-py3-none-any.whl (15 kB)\n", - "Requirement already satisfied: scipy!=1.4.0 in d:\\programdata\\anaconda3\\lib\\site-packages (from optuna) (1.4.1)\n", - 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"Successfully built pyperclip\n", - "Installing collected packages: pyreadline3, pyperclip, pbr, stevedore, PrettyTable, Mako, importlib-resources, cmd2, autopage, colorlog, cmaes, cliff, alembic, optuna\n", - "Successfully installed Mako-1.1.5 PrettyTable-2.2.0 alembic-1.7.1 autopage-0.4.0 cliff-3.9.0 cmaes-0.8.2 cmd2-2.1.2 colorlog-6.4.1 importlib-resources-5.2.2 optuna-2.9.1 pbr-5.6.0 pyperclip-1.8.2 pyreadline3-3.3 stevedore-3.4.0\n", - "Note: you may need to restart the kernel to use updated packages.\n" - ] - } - ], + "metadata": { + "scrolled": true + }, + "outputs": [], "source": [ - "pip install optuna" + "# pip install optuna" ] }, { "cell_type": "code", - "execution_count": 15, + "execution_count": 10, "id": "d8a0279a", "metadata": {}, "outputs": [], @@ -357,12 +376,12 @@ }, { "cell_type": "code", - "execution_count": 18, + "execution_count": 11, "id": "7433fdcb", "metadata": {}, "outputs": [], "source": [ - "def objective(trial,train_x, valid_x, train_y, valid_y):\n", + "def objective(trial,train_x, train_y, valid_x, valid_y):\n", " dtrain = lgb.Dataset(train_x, label=train_y)\n", " dvalid = lgb.Dataset(valid_x, label=valid_y)\n", "\n", @@ -395,7 +414,7 @@ }, { "cell_type": "code", - "execution_count": 19, + "execution_count": 12, "id": "b9018adb", "metadata": { "scrolled": true @@ -405,119 +424,119 @@ "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32m[I 2021-09-07 09:26:30,952]\u001b[0m A new study created in memory with name: no-name-f2147511-069d-495f-90ec-5990dc3c3716\u001b[0m\n", - "\u001b[32m[I 2021-09-07 09:26:31,284]\u001b[0m Trial 0 finished with value: 0.5579165649228477 and parameters: {'lambda_l1': 1.6814939560853405e-06, 'lambda_l2': 8.772634980486007, 'num_leaves': 79, 'feature_fraction': 0.4415746779386105, 'bagging_fraction': 0.5306839081914155, 'bagging_freq': 5, 'min_child_samples': 13}. Best is trial 0 with value: 0.5579165649228477.\u001b[0m\n", - "\u001b[32m[I 2021-09-07 09:26:31,523]\u001b[0m Trial 1 finished with value: 0.5577111827415846 and parameters: {'lambda_l1': 0.00013684423280171766, 'lambda_l2': 0.010116712675880523, 'num_leaves': 42, 'feature_fraction': 0.4758659908396936, 'bagging_fraction': 0.8916447942940564, 'bagging_freq': 7, 'min_child_samples': 32}. Best is trial 1 with value: 0.5577111827415846.\u001b[0m\n", - "\u001b[32m[I 2021-09-07 09:26:31,986]\u001b[0m Trial 2 finished with value: 0.5382832732103667 and parameters: {'lambda_l1': 1.849141022226266, 'lambda_l2': 9.297088403542385e-06, 'num_leaves': 94, 'feature_fraction': 0.6894755218120849, 'bagging_fraction': 0.9503182129837274, 'bagging_freq': 2, 'min_child_samples': 98}. Best is trial 2 with value: 0.5382832732103667.\u001b[0m\n", - "\u001b[32m[I 2021-09-07 09:26:32,156]\u001b[0m Trial 3 finished with value: 0.5786765226592324 and parameters: {'lambda_l1': 4.448151837364203e-05, 'lambda_l2': 1.8955982808109254e-08, 'num_leaves': 19, 'feature_fraction': 0.493099451522876, 'bagging_fraction': 0.6742062281671596, 'bagging_freq': 5, 'min_child_samples': 35}. Best is trial 2 with value: 0.5382832732103667.\u001b[0m\n", - "\u001b[32m[I 2021-09-07 09:26:32,266]\u001b[0m Trial 4 finished with value: 0.6139367553603606 and parameters: {'lambda_l1': 0.0002791229907486381, 'lambda_l2': 5.269713608638115, 'num_leaves': 5, 'feature_fraction': 0.8303631732661438, 'bagging_fraction': 0.942024874372832, 'bagging_freq': 4, 'min_child_samples': 61}. Best is trial 2 with value: 0.5382832732103667.\u001b[0m\n", - "\u001b[32m[I 2021-09-07 09:26:32,826]\u001b[0m Trial 5 finished with value: 0.5453617455111696 and parameters: {'lambda_l1': 0.06860061560766911, 'lambda_l2': 0.2723830502667369, 'num_leaves': 98, 'feature_fraction': 0.7105587363977491, 'bagging_fraction': 0.5899933873219961, 'bagging_freq': 4, 'min_child_samples': 71}. Best is trial 2 with value: 0.5382832732103667.\u001b[0m\n", - "\u001b[32m[I 2021-09-07 09:26:32,880]\u001b[0m Trial 6 pruned. Trial was pruned at iteration 10.\u001b[0m\n", - "\u001b[32m[I 2021-09-07 09:26:33,189]\u001b[0m Trial 7 finished with value: 0.5588682864793495 and parameters: {'lambda_l1': 0.4689515611654304, 'lambda_l2': 1.5494838308859912e-07, 'num_leaves': 65, 'feature_fraction': 0.5583496504073044, 'bagging_fraction': 0.6569918432147945, 'bagging_freq': 5, 'min_child_samples': 58}. Best is trial 2 with value: 0.5382832732103667.\u001b[0m\n", - "\u001b[32m[I 2021-09-07 09:26:33,462]\u001b[0m Trial 8 finished with value: 0.5447098867148504 and parameters: {'lambda_l1': 0.0010101561223389774, 'lambda_l2': 0.01515390989225731, 'num_leaves': 47, 'feature_fraction': 0.6245633996226619, 'bagging_fraction': 0.419624870667929, 'bagging_freq': 3, 'min_child_samples': 48}. Best is trial 2 with value: 0.5382832732103667.\u001b[0m\n", - "\u001b[32m[I 2021-09-07 09:26:33,693]\u001b[0m Trial 9 pruned. Trial was pruned at iteration 80.\u001b[0m\n", - "\u001b[32m[I 2021-09-07 09:26:34,181]\u001b[0m Trial 10 finished with value: 0.541110883425217 and parameters: {'lambda_l1': 1.4756346287368392e-08, 'lambda_l2': 1.3544296321997672e-05, 'num_leaves': 189, 'feature_fraction': 0.9751158697609862, 'bagging_fraction': 0.8348333900598492, 'bagging_freq': 1, 'min_child_samples': 100}. Best is trial 2 with value: 0.5382832732103667.\u001b[0m\n", - "\u001b[32m[I 2021-09-07 09:26:34,761]\u001b[0m Trial 11 finished with value: 0.5430244062824532 and parameters: {'lambda_l1': 1.2282790653635484e-08, 'lambda_l2': 8.373619608433318e-06, 'num_leaves': 196, 'feature_fraction': 0.9236444858767681, 'bagging_fraction': 0.8122951589315216, 'bagging_freq': 1, 'min_child_samples': 100}. Best is trial 2 with value: 0.5382832732103667.\u001b[0m\n", - "\u001b[32m[I 2021-09-07 09:26:35,237]\u001b[0m Trial 12 finished with value: 0.5479941202014348 and parameters: {'lambda_l1': 8.66531632367352, 'lambda_l2': 2.7356786615324263e-05, 'num_leaves': 182, 'feature_fraction': 0.7257944800491923, 'bagging_fraction': 0.8151059105693412, 'bagging_freq': 1, 'min_child_samples': 98}. Best is trial 2 with value: 0.5382832732103667.\u001b[0m\n", - "\u001b[32m[I 2021-09-07 09:26:36,132]\u001b[0m Trial 13 finished with value: 0.5426396090105863 and parameters: {'lambda_l1': 1.5311804262482413e-08, 'lambda_l2': 8.31070641463058e-06, 'num_leaves': 241, 'feature_fraction': 0.9996713520233095, 'bagging_fraction': 0.9659578077631608, 'bagging_freq': 2, 'min_child_samples': 84}. Best is trial 2 with value: 0.5382832732103667.\u001b[0m\n", - "\u001b[32m[I 2021-09-07 09:26:36,961]\u001b[0m Trial 14 finished with value: 0.5367665957770634 and parameters: {'lambda_l1': 0.014095420115571123, 'lambda_l2': 0.0002563644914171101, 'num_leaves': 149, 'feature_fraction': 0.7883099949736571, 'bagging_fraction': 0.8086023984951658, 'bagging_freq': 2, 'min_child_samples': 85}. Best is trial 14 with value: 0.5367665957770634.\u001b[0m\n", - "\u001b[32m[I 2021-09-07 09:26:37,824]\u001b[0m Trial 15 finished with value: 0.5348886658006932 and parameters: {'lambda_l1': 0.020071941122612545, 'lambda_l2': 0.0003014852059198554, 'num_leaves': 146, 'feature_fraction': 0.7936923799556383, 'bagging_fraction': 0.756430674635471, 'bagging_freq': 3, 'min_child_samples': 83}. Best is trial 15 with value: 0.5348886658006932.\u001b[0m\n", - "\u001b[32m[I 2021-09-07 09:26:38,622]\u001b[0m Trial 16 finished with value: 0.5372333761766732 and parameters: {'lambda_l1': 0.015173696268463625, 'lambda_l2': 0.0016757129971514612, 'num_leaves': 138, 'feature_fraction': 0.8107104336197675, 'bagging_fraction': 0.7376130779961089, 'bagging_freq': 3, 'min_child_samples': 78}. Best is trial 15 with value: 0.5348886658006932.\u001b[0m\n", - "\u001b[32m[I 2021-09-07 09:26:39,451]\u001b[0m Trial 17 finished with value: 0.5376252190781058 and parameters: {'lambda_l1': 0.007357005023837469, 'lambda_l2': 0.000250874148036676, 'num_leaves': 147, 'feature_fraction': 0.7829205508785075, 'bagging_fraction': 0.7478270069878126, 'bagging_freq': 3, 'min_child_samples': 67}. Best is trial 15 with value: 0.5348886658006932.\u001b[0m\n", - "\u001b[32m[I 2021-09-07 09:26:39,567]\u001b[0m Trial 18 pruned. Trial was pruned at iteration 10.\u001b[0m\n", - "\u001b[32m[I 2021-09-07 09:26:40,910]\u001b[0m Trial 19 finished with value: 0.5315793527934071 and parameters: {'lambda_l1': 0.013209811084277438, 'lambda_l2': 0.06726287443306718, 'num_leaves': 231, 'feature_fraction': 0.637876115312563, 'bagging_fraction': 0.8716817438233221, 'bagging_freq': 3, 'min_child_samples': 44}. Best is trial 19 with value: 0.5315793527934071.\u001b[0m\n", - "\u001b[32m[I 2021-09-07 09:26:42,324]\u001b[0m Trial 20 finished with value: 0.5262550702976 and parameters: {'lambda_l1': 0.003113216663103784, 'lambda_l2': 0.24783337352027449, 'num_leaves': 242, 'feature_fraction': 0.6137607392321572, 'bagging_fraction': 0.8936473883645825, 'bagging_freq': 3, 'min_child_samples': 44}. Best is trial 20 with value: 0.5262550702976.\u001b[0m\n", - "\u001b[32m[I 2021-09-07 09:26:43,182]\u001b[0m Trial 21 finished with value: 0.5297204439253479 and parameters: {'lambda_l1': 0.003978575421253158, 'lambda_l2': 0.11658574784670588, 'num_leaves': 254, 'feature_fraction': 0.6168955771891016, 'bagging_fraction': 0.8787433941466242, 'bagging_freq': 3, 'min_child_samples': 43}. Best is trial 20 with value: 0.5262550702976.\u001b[0m\n", - "\u001b[32m[I 2021-09-07 09:26:43,310]\u001b[0m Trial 22 pruned. Trial was pruned at iteration 13.\u001b[0m\n", - "\u001b[32m[I 2021-09-07 09:26:44,194]\u001b[0m Trial 23 finished with value: 0.5288803542960898 and parameters: {'lambda_l1': 8.341315535148706e-06, 'lambda_l2': 0.24030532904199514, 'num_leaves': 221, 'feature_fraction': 0.5901567080654435, 'bagging_fraction': 0.8875920940685119, 'bagging_freq': 3, 'min_child_samples': 25}. Best is trial 20 with value: 0.5262550702976.\u001b[0m\n", - "\u001b[32m[I 2021-09-07 09:26:44,331]\u001b[0m Trial 24 pruned. Trial was pruned at iteration 10.\u001b[0m\n", - "\u001b[32m[I 2021-09-07 09:26:45,248]\u001b[0m Trial 25 finished with value: 0.5310533243420329 and parameters: {'lambda_l1': 1.9500743317527624e-07, 'lambda_l2': 0.0502751774074574, 'num_leaves': 214, 'feature_fraction': 0.5734739546088348, 'bagging_fraction': 0.9115976551406307, 'bagging_freq': 2, 'min_child_samples': 24}. Best is trial 20 with value: 0.5262550702976.\u001b[0m\n" + "\u001b[32m[I 2021-09-07 16:27:35,965]\u001b[0m A new study created in memory with name: no-name-cc424a48-83c6-4329-92a3-261d08a5edb9\u001b[0m\n", + "\u001b[32m[I 2021-09-07 16:27:36,779]\u001b[0m Trial 0 finished with value: 0.5178928385298298 and parameters: {'lambda_l1': 0.9464949700025425, 'lambda_l2': 1.4692321446797693e-05, 'num_leaves': 135, 'feature_fraction': 0.6448685329355106, 'bagging_fraction': 0.9048970068857253, 'bagging_freq': 7, 'min_child_samples': 30}. Best is trial 0 with value: 0.5178928385298298.\u001b[0m\n", + "\u001b[32m[I 2021-09-07 16:27:37,641]\u001b[0m Trial 1 finished with value: 0.5336017055299863 and parameters: {'lambda_l1': 0.6354399847075458, 'lambda_l2': 4.87700419620481, 'num_leaves': 237, 'feature_fraction': 0.895991820221385, 'bagging_fraction': 0.6704364287317786, 'bagging_freq': 3, 'min_child_samples': 49}. Best is trial 0 with value: 0.5178928385298298.\u001b[0m\n", + "\u001b[32m[I 2021-09-07 16:27:38,288]\u001b[0m Trial 2 finished with value: 0.5345721353318472 and parameters: {'lambda_l1': 9.52099217128906e-05, 'lambda_l2': 1.1454590646374039e-07, 'num_leaves': 170, 'feature_fraction': 0.7899599630691868, 'bagging_fraction': 0.5059741439893897, 'bagging_freq': 2, 'min_child_samples': 50}. Best is trial 0 with value: 0.5178928385298298.\u001b[0m\n", + "\u001b[32m[I 2021-09-07 16:27:38,956]\u001b[0m Trial 3 finished with value: 0.543693048566443 and parameters: {'lambda_l1': 0.1481577233973877, 'lambda_l2': 3.179336503731134e-07, 'num_leaves': 128, 'feature_fraction': 0.9783519466402901, 'bagging_fraction': 0.6889127950806107, 'bagging_freq': 5, 'min_child_samples': 37}. Best is trial 0 with value: 0.5178928385298298.\u001b[0m\n", + "\u001b[32m[I 2021-09-07 16:27:39,424]\u001b[0m Trial 4 finished with value: 0.5501760993688642 and parameters: {'lambda_l1': 3.7638689664291936, 'lambda_l2': 0.020384770050374573, 'num_leaves': 175, 'feature_fraction': 0.4840841073088404, 'bagging_fraction': 0.8334033110992205, 'bagging_freq': 5, 'min_child_samples': 35}. Best is trial 0 with value: 0.5178928385298298.\u001b[0m\n", + "\u001b[32m[I 2021-09-07 16:27:39,493]\u001b[0m Trial 5 pruned. Trial was pruned at iteration 10.\u001b[0m\n", + "\u001b[32m[I 2021-09-07 16:27:39,774]\u001b[0m Trial 6 pruned. Trial was pruned at iteration 33.\u001b[0m\n", + "\u001b[32m[I 2021-09-07 16:27:39,828]\u001b[0m Trial 7 pruned. Trial was pruned at iteration 10.\u001b[0m\n", + "\u001b[32m[I 2021-09-07 16:27:40,187]\u001b[0m Trial 8 finished with value: 0.5249490156742078 and parameters: {'lambda_l1': 8.186728837628555e-05, 'lambda_l2': 2.6479736415938615, 'num_leaves': 72, 'feature_fraction': 0.7599981859922114, 'bagging_fraction': 0.9692259725506919, 'bagging_freq': 5, 'min_child_samples': 38}. Best is trial 0 with value: 0.5178928385298298.\u001b[0m\n", + "\u001b[32m[I 2021-09-07 16:27:40,299]\u001b[0m Trial 9 pruned. Trial was pruned at iteration 11.\u001b[0m\n", + "\u001b[32m[I 2021-09-07 16:27:40,353]\u001b[0m Trial 10 pruned. Trial was pruned at iteration 10.\u001b[0m\n", + "\u001b[32m[I 2021-09-07 16:27:40,398]\u001b[0m Trial 11 pruned. Trial was pruned at iteration 10.\u001b[0m\n", + "\u001b[32m[I 2021-09-07 16:27:40,470]\u001b[0m Trial 12 pruned. Trial was pruned at iteration 10.\u001b[0m\n", + "\u001b[32m[I 2021-09-07 16:27:41,280]\u001b[0m Trial 13 finished with value: 0.5325142394678348 and parameters: {'lambda_l1': 0.009551573559223583, 'lambda_l2': 0.0003114297759461472, 'num_leaves': 152, 'feature_fraction': 0.8236610445865283, 'bagging_fraction': 0.8389170404125451, 'bagging_freq': 4, 'min_child_samples': 20}. Best is trial 0 with value: 0.5178928385298298.\u001b[0m\n", + "\u001b[32m[I 2021-09-07 16:27:41,391]\u001b[0m Trial 14 pruned. Trial was pruned at iteration 10.\u001b[0m\n", + "\u001b[32m[I 2021-09-07 16:27:42,015]\u001b[0m Trial 15 finished with value: 0.5314302398651312 and parameters: {'lambda_l1': 0.024227381422510946, 'lambda_l2': 0.33276527010426776, 'num_leaves': 91, 'feature_fraction': 0.7874026441153832, 'bagging_fraction': 0.7899555875604796, 'bagging_freq': 7, 'min_child_samples': 19}. Best is trial 0 with value: 0.5178928385298298.\u001b[0m\n", + "\u001b[32m[I 2021-09-07 16:27:42,091]\u001b[0m Trial 16 pruned. Trial was pruned at iteration 10.\u001b[0m\n", + "\u001b[32m[I 2021-09-07 16:27:42,154]\u001b[0m Trial 17 pruned. Trial was pruned at iteration 10.\u001b[0m\n", + "\u001b[32m[I 2021-09-07 16:27:42,226]\u001b[0m Trial 18 pruned. Trial was pruned at iteration 10.\u001b[0m\n", + "\u001b[32m[I 2021-09-07 16:27:42,338]\u001b[0m Trial 19 pruned. Trial was pruned at iteration 14.\u001b[0m\n", + "\u001b[32m[I 2021-09-07 16:27:42,384]\u001b[0m Trial 20 pruned. Trial was pruned at iteration 10.\u001b[0m\n", + "\u001b[32m[I 2021-09-07 16:27:42,829]\u001b[0m Trial 21 finished with value: 0.5245233417218278 and parameters: {'lambda_l1': 0.07296901453015396, 'lambda_l2': 0.19272462963641587, 'num_leaves': 90, 'feature_fraction': 0.752955332537125, 'bagging_fraction': 0.7944607588474272, 'bagging_freq': 7, 'min_child_samples': 15}. Best is trial 0 with value: 0.5178928385298298.\u001b[0m\n", + "\u001b[32m[I 2021-09-07 16:27:42,901]\u001b[0m Trial 22 pruned. Trial was pruned at iteration 10.\u001b[0m\n", + "\u001b[32m[I 2021-09-07 16:27:43,287]\u001b[0m Trial 23 pruned. Trial was pruned at iteration 97.\u001b[0m\n", + "\u001b[32m[I 2021-09-07 16:27:43,371]\u001b[0m Trial 24 pruned. Trial was pruned at iteration 10.\u001b[0m\n", + "\u001b[32m[I 2021-09-07 16:27:43,489]\u001b[0m Trial 25 pruned. Trial was pruned at iteration 10.\u001b[0m\n", + "\u001b[32m[I 2021-09-07 16:27:43,572]\u001b[0m Trial 26 pruned. Trial was pruned at iteration 10.\u001b[0m\n", + "\u001b[32m[I 2021-09-07 16:27:43,632]\u001b[0m Trial 27 pruned. Trial was pruned at iteration 10.\u001b[0m\n", + "\u001b[32m[I 2021-09-07 16:27:44,171]\u001b[0m Trial 28 finished with value: 0.5255422897694476 and parameters: {'lambda_l1': 0.028260092955639447, 'lambda_l2': 0.08413723627697263, 'num_leaves': 74, 'feature_fraction': 0.8042469147694201, 'bagging_fraction': 0.7548217217698655, 'bagging_freq': 3, 'min_child_samples': 42}. Best is trial 0 with value: 0.5178928385298298.\u001b[0m\n", + "\u001b[32m[I 2021-09-07 16:27:44,331]\u001b[0m Trial 29 pruned. Trial was pruned at iteration 13.\u001b[0m\n", + "\u001b[32m[I 2021-09-07 16:27:44,516]\u001b[0m Trial 30 pruned. Trial was pruned at iteration 16.\u001b[0m\n", + "\u001b[32m[I 2021-09-07 16:27:44,637]\u001b[0m Trial 31 pruned. Trial was pruned at iteration 10.\u001b[0m\n", + "\u001b[32m[I 2021-09-07 16:27:44,723]\u001b[0m Trial 32 pruned. Trial was pruned at iteration 10.\u001b[0m\n", + "\u001b[32m[I 2021-09-07 16:27:45,592]\u001b[0m Trial 33 finished with value: 0.5218146276752049 and parameters: {'lambda_l1': 0.0016497886750146257, 'lambda_l2': 0.21484669274687151, 'num_leaves': 133, 'feature_fraction': 0.6885034648040802, 'bagging_fraction': 0.9281320338482011, 'bagging_freq': 3, 'min_child_samples': 42}. Best is trial 0 with value: 0.5178928385298298.\u001b[0m\n", + "\u001b[32m[I 2021-09-07 16:27:45,711]\u001b[0m Trial 34 pruned. Trial was pruned at iteration 10.\u001b[0m\n", + "\u001b[32m[I 2021-09-07 16:27:46,669]\u001b[0m Trial 35 finished with value: 0.5205542553680208 and parameters: {'lambda_l1': 4.3493248665689235e-05, 'lambda_l2': 3.680853885692289, 'num_leaves': 169, 'feature_fraction': 0.7104567429350184, 'bagging_fraction': 0.8672533271233644, 'bagging_freq': 1, 'min_child_samples': 23}. Best is trial 0 with value: 0.5178928385298298.\u001b[0m\n", + "\u001b[32m[I 2021-09-07 16:27:47,676]\u001b[0m Trial 36 finished with value: 0.5206023202395521 and parameters: {'lambda_l1': 0.0006187306261428898, 'lambda_l2': 6.130428276943865e-08, 'num_leaves': 168, 'feature_fraction': 0.7036284508576037, 'bagging_fraction': 0.868428878180794, 'bagging_freq': 2, 'min_child_samples': 15}. Best is trial 0 with value: 0.5178928385298298.\u001b[0m\n", + "\u001b[32m[I 2021-09-07 16:27:47,808]\u001b[0m Trial 37 pruned. Trial was pruned at iteration 10.\u001b[0m\n", + "\u001b[32m[I 2021-09-07 16:27:47,937]\u001b[0m Trial 38 pruned. Trial was pruned at iteration 10.\u001b[0m\n", + "\u001b[32m[I 2021-09-07 16:27:48,844]\u001b[0m Trial 39 finished with value: 0.5191042263340773 and parameters: {'lambda_l1': 0.0005562321404093257, 'lambda_l2': 2.232696278872472e-08, 'num_leaves': 161, 'feature_fraction': 0.710527336424428, 'bagging_fraction': 0.9309118257124477, 'bagging_freq': 1, 'min_child_samples': 24}. Best is trial 0 with value: 0.5178928385298298.\u001b[0m\n", + "\u001b[32m[I 2021-09-07 16:27:49,889]\u001b[0m Trial 40 finished with value: 0.5168505162425636 and parameters: {'lambda_l1': 0.0004441190594738051, 'lambda_l2': 1.0707946566246148e-08, 'num_leaves': 190, 'feature_fraction': 0.7132737394337006, 'bagging_fraction': 0.8296082050311246, 'bagging_freq': 1, 'min_child_samples': 16}. Best is trial 40 with value: 0.5168505162425636.\u001b[0m\n", + "\u001b[32m[I 2021-09-07 16:27:50,929]\u001b[0m Trial 41 finished with value: 0.5262832333602245 and parameters: {'lambda_l1': 0.0004892025280466791, 'lambda_l2': 1.89573604152611e-08, 'num_leaves': 189, 'feature_fraction': 0.7134600565845959, 'bagging_fraction': 0.8334328552244646, 'bagging_freq': 1, 'min_child_samples': 16}. Best is trial 40 with value: 0.5168505162425636.\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32m[I 2021-09-07 09:26:46,126]\u001b[0m Trial 26 finished with value: 0.5310399189972692 and parameters: {'lambda_l1': 5.65218762871428e-06, 'lambda_l2': 1.6944446205708956, 'num_leaves': 256, 'feature_fraction': 0.6693624417062835, 'bagging_fraction': 0.8606541857435407, 'bagging_freq': 4, 'min_child_samples': 25}. Best is trial 20 with value: 0.5262550702976.\u001b[0m\n", - "\u001b[32m[I 2021-09-07 09:26:47,541]\u001b[0m Trial 27 finished with value: 0.5289141422774571 and parameters: {'lambda_l1': 3.372780137131952e-07, 'lambda_l2': 0.004231573716537988, 'num_leaves': 222, 'feature_fraction': 0.5834642111209242, 'bagging_fraction': 0.9283224808721449, 'bagging_freq': 3, 'min_child_samples': 33}. Best is trial 20 with value: 0.5262550702976.\u001b[0m\n", - "\u001b[32m[I 2021-09-07 09:26:47,734]\u001b[0m Trial 28 pruned. Trial was pruned at iteration 10.\u001b[0m\n", - "\u001b[32m[I 2021-09-07 09:26:47,883]\u001b[0m Trial 29 pruned. Trial was pruned at iteration 10.\u001b[0m\n", - "\u001b[32m[I 2021-09-07 09:26:48,032]\u001b[0m Trial 30 pruned. Trial was pruned at iteration 10.\u001b[0m\n", - "\u001b[32m[I 2021-09-07 09:26:48,233]\u001b[0m Trial 31 pruned. Trial was pruned at iteration 10.\u001b[0m\n", - "\u001b[32m[I 2021-09-07 09:26:48,470]\u001b[0m Trial 32 pruned. Trial was pruned at iteration 12.\u001b[0m\n", - "\u001b[32m[I 2021-09-07 09:26:50,256]\u001b[0m Trial 33 finished with value: 0.5307511312800164 and parameters: {'lambda_l1': 8.331346294111473e-08, 'lambda_l2': 0.018173663934055492, 'num_leaves': 238, 'feature_fraction': 0.5874989674601676, 'bagging_fraction': 0.8876263960076438, 'bagging_freq': 2, 'min_child_samples': 30}. Best is trial 20 with value: 0.5262550702976.\u001b[0m\n", - "\u001b[32m[I 2021-09-07 09:26:52,081]\u001b[0m Trial 34 finished with value: 0.5364692667074208 and parameters: {'lambda_l1': 8.939808862490256e-07, 'lambda_l2': 1.3094120587455667, 'num_leaves': 249, 'feature_fraction': 0.739630943171398, 'bagging_fraction': 0.9999691110926188, 'bagging_freq': 3, 'min_child_samples': 17}. Best is trial 20 with value: 0.5262550702976.\u001b[0m\n", - "\u001b[32m[I 2021-09-07 09:26:52,228]\u001b[0m Trial 35 pruned. Trial was pruned at iteration 10.\u001b[0m\n", - "\u001b[32m[I 2021-09-07 09:26:52,401]\u001b[0m Trial 36 pruned. Trial was pruned at iteration 10.\u001b[0m\n", - "\u001b[32m[I 2021-09-07 09:26:52,527]\u001b[0m Trial 37 pruned. Trial was pruned at iteration 10.\u001b[0m\n", - "\u001b[32m[I 2021-09-07 09:26:52,646]\u001b[0m Trial 38 pruned. Trial was pruned at iteration 10.\u001b[0m\n", - "\u001b[32m[I 2021-09-07 09:26:52,746]\u001b[0m Trial 39 pruned. Trial was pruned at iteration 10.\u001b[0m\n", - "\u001b[32m[I 2021-09-07 09:26:53,973]\u001b[0m Trial 40 finished with value: 0.5256364275490798 and parameters: {'lambda_l1': 0.0710216045724967, 'lambda_l2': 0.10020172057765622, 'num_leaves': 206, 'feature_fraction': 0.6441352660567405, 'bagging_fraction': 0.8538258125595672, 'bagging_freq': 5, 'min_child_samples': 8}. Best is trial 40 with value: 0.5256364275490798.\u001b[0m\n", - "\u001b[32m[I 2021-09-07 09:26:54,896]\u001b[0m Trial 41 finished with value: 0.5233364787469251 and parameters: {'lambda_l1': 0.23671365201549266, 'lambda_l2': 0.0749439220683321, 'num_leaves': 210, 'feature_fraction': 0.6436961501856725, 'bagging_fraction': 0.8521968194803821, 'bagging_freq': 5, 'min_child_samples': 5}. Best is trial 41 with value: 0.5233364787469251.\u001b[0m\n", - "\u001b[32m[I 2021-09-07 09:26:55,831]\u001b[0m Trial 42 finished with value: 0.5293150200028972 and parameters: {'lambda_l1': 0.6574943326302751, 'lambda_l2': 0.0361443234291568, 'num_leaves': 209, 'feature_fraction': 0.6596272574104582, 'bagging_fraction': 0.850673456207462, 'bagging_freq': 6, 'min_child_samples': 5}. Best is trial 41 with value: 0.5233364787469251.\u001b[0m\n", - "\u001b[32m[I 2021-09-07 09:26:56,858]\u001b[0m Trial 43 finished with value: 0.5308606295175099 and parameters: {'lambda_l1': 0.07618036054920922, 'lambda_l2': 0.22544030283002933, 'num_leaves': 226, 'feature_fraction': 0.6984185669610476, 'bagging_fraction': 0.7845150865412134, 'bagging_freq': 5, 'min_child_samples': 10}. Best is trial 41 with value: 0.5233364787469251.\u001b[0m\n", - "\u001b[32m[I 2021-09-07 09:26:57,019]\u001b[0m Trial 44 pruned. Trial was pruned at iteration 13.\u001b[0m\n", - "\u001b[32m[I 2021-09-07 09:26:57,787]\u001b[0m Trial 45 finished with value: 0.5331475267867624 and parameters: {'lambda_l1': 1.4967349278475324, 'lambda_l2': 0.0008951722845905283, 'num_leaves': 207, 'feature_fraction': 0.7339992851750987, 'bagging_fraction': 0.919959739142468, 'bagging_freq': 5, 'min_child_samples': 26}. Best is trial 41 with value: 0.5233364787469251.\u001b[0m\n", - "\u001b[32m[I 2021-09-07 09:26:57,865]\u001b[0m Trial 46 pruned. Trial was pruned at iteration 10.\u001b[0m\n", - "\u001b[32m[I 2021-09-07 09:26:57,971]\u001b[0m Trial 47 pruned. Trial was pruned at iteration 10.\u001b[0m\n", - "\u001b[32m[I 2021-09-07 09:26:58,069]\u001b[0m Trial 48 pruned. Trial was pruned at iteration 10.\u001b[0m\n", - "\u001b[32m[I 2021-09-07 09:26:58,168]\u001b[0m Trial 49 pruned. Trial was pruned at iteration 10.\u001b[0m\n", - "\u001b[32m[I 2021-09-07 09:26:58,317]\u001b[0m Trial 50 pruned. Trial was pruned at iteration 30.\u001b[0m\n", - "\u001b[32m[I 2021-09-07 09:26:58,445]\u001b[0m Trial 51 pruned. Trial was pruned at iteration 13.\u001b[0m\n", - "\u001b[32m[I 2021-09-07 09:26:59,558]\u001b[0m Trial 52 finished with value: 0.5287423259473492 and parameters: {'lambda_l1': 0.44209025556975773, 'lambda_l2': 0.02237337201530471, 'num_leaves': 180, 'feature_fraction': 0.6816257587535688, 'bagging_fraction': 0.8645245816570197, 'bagging_freq': 7, 'min_child_samples': 5}. Best is trial 41 with value: 0.5233364787469251.\u001b[0m\n", - "\u001b[32m[I 2021-09-07 09:27:00,776]\u001b[0m Trial 53 finished with value: 0.5248984867652216 and parameters: {'lambda_l1': 0.02970872423104034, 'lambda_l2': 0.0197319338622143, 'num_leaves': 177, 'feature_fraction': 0.7150825328146555, 'bagging_fraction': 0.8972252525350597, 'bagging_freq': 7, 'min_child_samples': 12}. Best is trial 41 with value: 0.5233364787469251.\u001b[0m\n", - "\u001b[32m[I 2021-09-07 09:27:01,886]\u001b[0m Trial 54 finished with value: 0.5310139507545261 and parameters: {'lambda_l1': 0.03922536031082296, 'lambda_l2': 1.7618527190204045e-06, 'num_leaves': 180, 'feature_fraction': 0.7089331672809696, 'bagging_fraction': 0.8021837464466384, 'bagging_freq': 7, 'min_child_samples': 11}. Best is trial 41 with value: 0.5233364787469251.\u001b[0m\n", - "\u001b[32m[I 2021-09-07 09:27:02,043]\u001b[0m Trial 55 pruned. Trial was pruned at iteration 13.\u001b[0m\n", - "\u001b[32m[I 2021-09-07 09:27:02,158]\u001b[0m Trial 56 pruned. Trial was pruned at iteration 10.\u001b[0m\n", - "\u001b[32m[I 2021-09-07 09:27:02,937]\u001b[0m Trial 57 pruned. Trial was pruned at iteration 78.\u001b[0m\n", - "\u001b[32m[I 2021-09-07 09:27:03,912]\u001b[0m Trial 58 finished with value: 0.5310990808119093 and parameters: {'lambda_l1': 0.008908141404368873, 'lambda_l2': 1.0024604099274968e-08, 'num_leaves': 156, 'feature_fraction': 0.7135958388327297, 'bagging_fraction': 0.8918708775644101, 'bagging_freq': 7, 'min_child_samples': 10}. Best is trial 41 with value: 0.5233364787469251.\u001b[0m\n", - "\u001b[32m[I 2021-09-07 09:27:05,509]\u001b[0m Trial 59 finished with value: 0.5303075036750562 and parameters: {'lambda_l1': 0.0036415543019748704, 'lambda_l2': 0.060450481568631226, 'num_leaves': 245, 'feature_fraction': 0.6322922933331399, 'bagging_fraction': 0.8119277832780933, 'bagging_freq': 6, 'min_child_samples': 18}. Best is trial 41 with value: 0.5233364787469251.\u001b[0m\n", - "\u001b[32m[I 2021-09-07 09:27:05,675]\u001b[0m Trial 60 pruned. Trial was pruned at iteration 10.\u001b[0m\n", - "\u001b[32m[I 2021-09-07 09:27:06,717]\u001b[0m Trial 61 finished with value: 0.5266068329070613 and parameters: {'lambda_l1': 0.08187221145217663, 'lambda_l2': 0.12038514531389062, 'num_leaves': 230, 'feature_fraction': 0.5893130926352276, 'bagging_fraction': 0.9218811557192532, 'bagging_freq': 3, 'min_child_samples': 30}. Best is trial 41 with value: 0.5233364787469251.\u001b[0m\n", - "\u001b[32m[I 2021-09-07 09:27:06,839]\u001b[0m Trial 62 pruned. Trial was pruned at iteration 10.\u001b[0m\n", - "\u001b[32m[I 2021-09-07 09:27:06,957]\u001b[0m Trial 63 pruned. Trial was pruned at iteration 10.\u001b[0m\n", - "\u001b[32m[I 2021-09-07 09:27:07,060]\u001b[0m Trial 64 pruned. Trial was pruned at iteration 10.\u001b[0m\n", - "\u001b[32m[I 2021-09-07 09:27:07,235]\u001b[0m Trial 65 pruned. Trial was pruned at iteration 13.\u001b[0m\n", - "\u001b[32m[I 2021-09-07 09:27:07,371]\u001b[0m Trial 66 pruned. Trial was pruned at iteration 13.\u001b[0m\n", - "\u001b[32m[I 2021-09-07 09:27:08,252]\u001b[0m Trial 67 finished with value: 0.524615381244906 and parameters: {'lambda_l1': 0.0007372437461333507, 'lambda_l2': 0.07197819258619385, 'num_leaves': 217, 'feature_fraction': 0.7085126717217893, 'bagging_fraction': 0.9783185592011208, 'bagging_freq': 4, 'min_child_samples': 7}. Best is trial 41 with value: 0.5233364787469251.\u001b[0m\n" + "\u001b[32m[I 2021-09-07 16:27:51,023]\u001b[0m Trial 42 pruned. Trial was pruned at iteration 10.\u001b[0m\n", + "\u001b[32m[I 2021-09-07 16:27:51,805]\u001b[0m Trial 43 finished with value: 0.516065766031832 and parameters: {'lambda_l1': 0.00021869661941011055, 'lambda_l2': 1.0674672013946999e-08, 'num_leaves': 201, 'feature_fraction': 0.7039965594752932, 'bagging_fraction': 0.8165783106186105, 'bagging_freq': 1, 'min_child_samples': 10}. Best is trial 43 with value: 0.516065766031832.\u001b[0m\n", + "\u001b[32m[I 2021-09-07 16:27:51,940]\u001b[0m Trial 44 pruned. Trial was pruned at iteration 10.\u001b[0m\n", + "\u001b[32m[I 2021-09-07 16:27:52,044]\u001b[0m Trial 45 pruned. Trial was pruned at iteration 10.\u001b[0m\n", + "\u001b[32m[I 2021-09-07 16:27:52,218]\u001b[0m Trial 46 pruned. Trial was pruned at iteration 10.\u001b[0m\n", + "\u001b[32m[I 2021-09-07 16:27:53,067]\u001b[0m Trial 47 finished with value: 0.5256815758791472 and parameters: {'lambda_l1': 6.162602167779085e-05, 'lambda_l2': 1.6462819728079273e-07, 'num_leaves': 201, 'feature_fraction': 0.7163097853297453, 'bagging_fraction': 0.9420618119436382, 'bagging_freq': 1, 'min_child_samples': 24}. Best is trial 43 with value: 0.516065766031832.\u001b[0m\n", + "\u001b[32m[I 2021-09-07 16:27:53,178]\u001b[0m Trial 48 pruned. Trial was pruned at iteration 10.\u001b[0m\n", + "\u001b[32m[I 2021-09-07 16:27:53,762]\u001b[0m Trial 49 finished with value: 0.5213791636070063 and parameters: {'lambda_l1': 2.774750232046531e-07, 'lambda_l2': 2.4404405074646332e-08, 'num_leaves': 154, 'feature_fraction': 0.6882189607163384, 'bagging_fraction': 0.9871043449165129, 'bagging_freq': 1, 'min_child_samples': 9}. Best is trial 43 with value: 0.516065766031832.\u001b[0m\n", + "\u001b[32m[I 2021-09-07 16:27:53,875]\u001b[0m Trial 50 pruned. Trial was pruned at iteration 10.\u001b[0m\n", + "\u001b[32m[I 2021-09-07 16:27:54,587]\u001b[0m Trial 51 finished with value: 0.5174618905113623 and parameters: {'lambda_l1': 0.000381507052985562, 'lambda_l2': 4.901891994722806e-08, 'num_leaves': 166, 'feature_fraction': 0.7117978094083592, 'bagging_fraction': 0.8956613846537137, 'bagging_freq': 1, 'min_child_samples': 12}. Best is trial 43 with value: 0.516065766031832.\u001b[0m\n", + "\u001b[32m[I 2021-09-07 16:27:55,304]\u001b[0m Trial 52 finished with value: 0.5219780933938188 and parameters: {'lambda_l1': 0.0003055316771241415, 'lambda_l2': 1.0975399369852818e-08, 'num_leaves': 175, 'feature_fraction': 0.7709159871191618, 'bagging_fraction': 0.8946659725371712, 'bagging_freq': 1, 'min_child_samples': 12}. Best is trial 43 with value: 0.516065766031832.\u001b[0m\n", + "\u001b[32m[I 2021-09-07 16:27:56,481]\u001b[0m Trial 53 finished with value: 0.522019841081264 and parameters: {'lambda_l1': 3.0853077659712017e-06, 'lambda_l2': 3.167435996190143e-08, 'num_leaves': 199, 'feature_fraction': 0.700370780125537, 'bagging_fraction': 0.9659440325481246, 'bagging_freq': 1, 'min_child_samples': 5}. Best is trial 43 with value: 0.516065766031832.\u001b[0m\n", + "\u001b[32m[I 2021-09-07 16:27:57,422]\u001b[0m Trial 54 finished with value: 0.5210662969810745 and parameters: {'lambda_l1': 7.857450656938453e-05, 'lambda_l2': 4.5159973730477044e-07, 'num_leaves': 153, 'feature_fraction': 0.7263108852936352, 'bagging_fraction': 0.936630215722916, 'bagging_freq': 1, 'min_child_samples': 19}. Best is trial 43 with value: 0.516065766031832.\u001b[0m\n", + "\u001b[32m[I 2021-09-07 16:27:57,568]\u001b[0m Trial 55 pruned. Trial was pruned at iteration 10.\u001b[0m\n", + "\u001b[32m[I 2021-09-07 16:27:57,678]\u001b[0m Trial 56 pruned. Trial was pruned at iteration 10.\u001b[0m\n", + "\u001b[32m[I 2021-09-07 16:27:57,803]\u001b[0m Trial 57 pruned. Trial was pruned at iteration 10.\u001b[0m\n", + "\u001b[32m[I 2021-09-07 16:27:57,889]\u001b[0m Trial 58 pruned. Trial was pruned at iteration 10.\u001b[0m\n", + "\u001b[32m[I 2021-09-07 16:27:58,284]\u001b[0m Trial 59 pruned. Trial was pruned at iteration 10.\u001b[0m\n", + "\u001b[32m[I 2021-09-07 16:27:58,510]\u001b[0m Trial 60 pruned. Trial was pruned at iteration 15.\u001b[0m\n", + "\u001b[32m[I 2021-09-07 16:27:59,683]\u001b[0m Trial 61 finished with value: 0.5238780309760502 and parameters: {'lambda_l1': 0.0005622649168938934, 'lambda_l2': 7.041773458860283e-08, 'num_leaves': 165, 'feature_fraction': 0.6971774484774765, 'bagging_fraction': 0.8845735572162268, 'bagging_freq': 2, 'min_child_samples': 16}. Best is trial 43 with value: 0.516065766031832.\u001b[0m\n", + "\u001b[32m[I 2021-09-07 16:27:59,867]\u001b[0m Trial 62 pruned. Trial was pruned at iteration 10.\u001b[0m\n", + "\u001b[32m[I 2021-09-07 16:28:01,236]\u001b[0m Trial 63 finished with value: 0.5172018048571797 and parameters: {'lambda_l1': 4.4774427854289624e-05, 'lambda_l2': 1.0431434685856767e-07, 'num_leaves': 175, 'feature_fraction': 0.7067550902029666, 'bagging_fraction': 0.7960327498051027, 'bagging_freq': 2, 'min_child_samples': 16}. Best is trial 43 with value: 0.516065766031832.\u001b[0m\n", + "\u001b[32m[I 2021-09-07 16:28:01,369]\u001b[0m Trial 64 pruned. Trial was pruned at iteration 10.\u001b[0m\n", + "\u001b[32m[I 2021-09-07 16:28:01,513]\u001b[0m Trial 65 pruned. Trial was pruned at iteration 10.\u001b[0m\n", + "\u001b[32m[I 2021-09-07 16:28:02,393]\u001b[0m Trial 66 pruned. Trial was pruned at iteration 76.\u001b[0m\n", + "\u001b[32m[I 2021-09-07 16:28:02,529]\u001b[0m Trial 67 pruned. Trial was pruned at iteration 10.\u001b[0m\n", + "\u001b[32m[I 2021-09-07 16:28:02,721]\u001b[0m Trial 68 pruned. Trial was pruned at iteration 12.\u001b[0m\n", + "\u001b[32m[I 2021-09-07 16:28:03,060]\u001b[0m Trial 69 pruned. Trial was pruned at iteration 44.\u001b[0m\n", + "\u001b[32m[I 2021-09-07 16:28:03,135]\u001b[0m Trial 70 pruned. Trial was pruned at iteration 10.\u001b[0m\n", + "\u001b[32m[I 2021-09-07 16:28:03,486]\u001b[0m Trial 71 pruned. Trial was pruned at iteration 56.\u001b[0m\n", + "\u001b[32m[I 2021-09-07 16:28:03,587]\u001b[0m Trial 72 pruned. Trial was pruned at iteration 10.\u001b[0m\n", + "\u001b[32m[I 2021-09-07 16:28:04,107]\u001b[0m Trial 73 pruned. Trial was pruned at iteration 68.\u001b[0m\n", + "\u001b[32m[I 2021-09-07 16:28:04,203]\u001b[0m Trial 74 pruned. Trial was pruned at iteration 11.\u001b[0m\n", + "\u001b[32m[I 2021-09-07 16:28:04,307]\u001b[0m Trial 75 pruned. Trial was pruned at iteration 10.\u001b[0m\n", + "\u001b[32m[I 2021-09-07 16:28:04,631]\u001b[0m Trial 76 pruned. Trial was pruned at iteration 39.\u001b[0m\n", + "\u001b[32m[I 2021-09-07 16:28:04,725]\u001b[0m Trial 77 pruned. Trial was pruned at iteration 10.\u001b[0m\n", + "\u001b[32m[I 2021-09-07 16:28:04,831]\u001b[0m Trial 78 pruned. Trial was pruned at iteration 10.\u001b[0m\n", + "\u001b[32m[I 2021-09-07 16:28:04,920]\u001b[0m Trial 79 pruned. Trial was pruned at iteration 10.\u001b[0m\n", + "\u001b[32m[I 2021-09-07 16:28:04,992]\u001b[0m Trial 80 pruned. Trial was pruned at iteration 10.\u001b[0m\n", + "\u001b[32m[I 2021-09-07 16:28:05,099]\u001b[0m Trial 81 pruned. Trial was pruned at iteration 11.\u001b[0m\n", + "\u001b[32m[I 2021-09-07 16:28:05,766]\u001b[0m Trial 82 finished with value: 0.5178938881576146 and parameters: {'lambda_l1': 1.2203483524616316e-08, 'lambda_l2': 5.507928324956204e-07, 'num_leaves': 185, 'feature_fraction': 0.7219415365227362, 'bagging_fraction': 0.9438920094595108, 'bagging_freq': 1, 'min_child_samples': 17}. Best is trial 43 with value: 0.516065766031832.\u001b[0m\n", + "\u001b[32m[I 2021-09-07 16:28:05,866]\u001b[0m Trial 83 pruned. Trial was pruned at iteration 10.\u001b[0m\n", + "\u001b[32m[I 2021-09-07 16:28:05,961]\u001b[0m Trial 84 pruned. Trial was pruned at iteration 10.\u001b[0m\n", + "\u001b[32m[I 2021-09-07 16:28:06,778]\u001b[0m Trial 85 finished with value: 0.5122528701381253 and parameters: {'lambda_l1': 6.371822640459341e-07, 'lambda_l2': 3.03967794068636e-08, 'num_leaves': 189, 'feature_fraction': 0.7066521328434867, 'bagging_fraction': 0.8972190570020028, 'bagging_freq': 1, 'min_child_samples': 11}. Best is trial 85 with value: 0.5122528701381253.\u001b[0m\n", + "\u001b[32m[I 2021-09-07 16:28:07,709]\u001b[0m Trial 86 finished with value: 0.5128398316765018 and parameters: {'lambda_l1': 1.5059254193489838e-07, 'lambda_l2': 2.6171717314455622e-08, 'num_leaves': 192, 'feature_fraction': 0.711268040370677, 'bagging_fraction': 0.9174432395706226, 'bagging_freq': 1, 'min_child_samples': 11}. Best is trial 85 with value: 0.5122528701381253.\u001b[0m\n", + "\u001b[32m[I 2021-09-07 16:28:09,411]\u001b[0m Trial 87 finished with value: 0.5191456815789754 and parameters: {'lambda_l1': 5.687878085793806e-08, 'lambda_l2': 2.6772763929664963e-08, 'num_leaves': 216, 'feature_fraction': 0.7413398142797374, 'bagging_fraction': 0.9993988673163303, 'bagging_freq': 1, 'min_child_samples': 11}. Best is trial 85 with value: 0.5122528701381253.\u001b[0m\n", + "\u001b[32m[I 2021-09-07 16:28:09,813]\u001b[0m Trial 88 pruned. Trial was pruned at iteration 10.\u001b[0m\n", + "\u001b[32m[I 2021-09-07 16:28:12,526]\u001b[0m Trial 89 finished with value: 0.5102536883069437 and parameters: {'lambda_l1': 1.6446541932580473e-07, 'lambda_l2': 1.0734412329614426e-07, 'num_leaves': 193, 'feature_fraction': 0.8123395009389368, 'bagging_fraction': 0.9184649865458648, 'bagging_freq': 1, 'min_child_samples': 10}. Best is trial 89 with value: 0.5102536883069437.\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32m[I 2021-09-07 09:27:09,210]\u001b[0m Trial 68 finished with value: 0.524942396458539 and parameters: {'lambda_l1': 0.0011590093830295628, 'lambda_l2': 0.06353809127053359, 'num_leaves': 198, 'feature_fraction': 0.7106295012909083, 'bagging_fraction': 0.973655849802224, 'bagging_freq': 5, 'min_child_samples': 7}. Best is trial 41 with value: 0.5233364787469251.\u001b[0m\n", - "\u001b[32m[I 2021-09-07 09:27:09,572]\u001b[0m Trial 69 pruned. Trial was pruned at iteration 29.\u001b[0m\n", - "\u001b[32m[I 2021-09-07 09:27:10,511]\u001b[0m Trial 70 finished with value: 0.5227354154865925 and parameters: {'lambda_l1': 0.0019204609823774911, 'lambda_l2': 0.006957053438847566, 'num_leaves': 200, 'feature_fraction': 0.7174135338967843, 'bagging_fraction': 0.9747628414441452, 'bagging_freq': 5, 'min_child_samples': 8}. Best is trial 70 with value: 0.5227354154865925.\u001b[0m\n", - "\u001b[32m[I 2021-09-07 09:27:11,445]\u001b[0m Trial 71 pruned. Trial was pruned at iteration 72.\u001b[0m\n", - "\u001b[32m[I 2021-09-07 09:27:12,707]\u001b[0m Trial 72 finished with value: 0.5273794666616337 and parameters: {'lambda_l1': 0.00253464769953144, 'lambda_l2': 0.04250761519699366, 'num_leaves': 203, 'feature_fraction': 0.723989872880134, 'bagging_fraction': 0.9954485306248265, 'bagging_freq': 5, 'min_child_samples': 10}. Best is trial 70 with value: 0.5227354154865925.\u001b[0m\n", - "\u001b[32m[I 2021-09-07 09:27:14,118]\u001b[0m Trial 73 finished with value: 0.5290081420408789 and parameters: {'lambda_l1': 0.0014187815549416435, 'lambda_l2': 0.12791352246804705, 'num_leaves': 218, 'feature_fraction': 0.7758653557130019, 'bagging_fraction': 0.9488928693793105, 'bagging_freq': 5, 'min_child_samples': 18}. Best is trial 70 with value: 0.5227354154865925.\u001b[0m\n", - "\u001b[32m[I 2021-09-07 09:27:14,196]\u001b[0m Trial 74 pruned. Trial was pruned at iteration 10.\u001b[0m\n", - "\u001b[32m[I 2021-09-07 09:27:15,517]\u001b[0m Trial 75 finished with value: 0.5325397214424649 and parameters: {'lambda_l1': 0.0005872739303922063, 'lambda_l2': 0.0096136159649407, 'num_leaves': 211, 'feature_fraction': 0.8012899648531023, 'bagging_fraction': 0.931501707649951, 'bagging_freq': 4, 'min_child_samples': 13}. Best is trial 70 with value: 0.5227354154865925.\u001b[0m\n", - "\u001b[32m[I 2021-09-07 09:27:16,903]\u001b[0m Trial 76 finished with value: 0.5296781377317934 and parameters: {'lambda_l1': 0.006787205212552698, 'lambda_l2': 0.0790698959620889, 'num_leaves': 239, 'feature_fraction': 0.7454753912863651, 'bagging_fraction': 0.9799306593714261, 'bagging_freq': 5, 'min_child_samples': 7}. Best is trial 70 with value: 0.5227354154865925.\u001b[0m\n", - "\u001b[32m[I 2021-09-07 09:27:17,956]\u001b[0m Trial 77 finished with value: 0.5309336374271006 and parameters: {'lambda_l1': 0.0037427478770181, 'lambda_l2': 0.8130901726120456, 'num_leaves': 232, 'feature_fraction': 0.7224358819185711, 'bagging_fraction': 0.9520121758601578, 'bagging_freq': 4, 'min_child_samples': 12}. Best is trial 70 with value: 0.5227354154865925.\u001b[0m\n", - "\u001b[32m[I 2021-09-07 09:27:18,071]\u001b[0m Trial 78 pruned. Trial was pruned at iteration 13.\u001b[0m\n", - "\u001b[32m[I 2021-09-07 09:27:19,373]\u001b[0m Trial 79 finished with value: 0.5347521802448649 and parameters: {'lambda_l1': 0.017570019491239652, 'lambda_l2': 0.05059260326524094, 'num_leaves': 226, 'feature_fraction': 0.6981808494111595, 'bagging_fraction': 0.9993645575870086, 'bagging_freq': 4, 'min_child_samples': 20}. Best is trial 70 with value: 0.5227354154865925.\u001b[0m\n", - "\u001b[32m[I 2021-09-07 09:27:19,471]\u001b[0m Trial 80 pruned. Trial was pruned at iteration 10.\u001b[0m\n", - "\u001b[32m[I 2021-09-07 09:27:21,230]\u001b[0m Trial 81 finished with value: 0.5227732830528738 and parameters: {'lambda_l1': 0.0003058538457597125, 'lambda_l2': 0.04324937485563167, 'num_leaves': 202, 'feature_fraction': 0.73206644303025, 'bagging_fraction': 0.9911170826848787, 'bagging_freq': 5, 'min_child_samples': 10}. Best is trial 70 with value: 0.5227354154865925.\u001b[0m\n", - "\u001b[32m[I 2021-09-07 09:27:22,266]\u001b[0m Trial 82 finished with value: 0.5279916258627607 and parameters: {'lambda_l1': 0.0002580487853233371, 'lambda_l2': 0.015002378165105253, 'num_leaves': 213, 'feature_fraction': 0.665112654945876, 'bagging_fraction': 0.9341515771267435, 'bagging_freq': 5, 'min_child_samples': 11}. Best is trial 70 with value: 0.5227354154865925.\u001b[0m\n", - "\u001b[32m[I 2021-09-07 09:27:26,040]\u001b[0m Trial 83 pruned. Trial was pruned at iteration 51.\u001b[0m\n", - "\u001b[32m[I 2021-09-07 09:27:29,359]\u001b[0m Trial 84 finished with value: 0.5257703648298483 and parameters: {'lambda_l1': 0.09313058634431053, 'lambda_l2': 0.0794176561682967, 'num_leaves': 174, 'feature_fraction': 0.7330255912724464, 'bagging_fraction': 0.9556166881594967, 'bagging_freq': 6, 'min_child_samples': 14}. Best is trial 70 with value: 0.5227354154865925.\u001b[0m\n", - "\u001b[32m[I 2021-09-07 09:27:29,749]\u001b[0m Trial 85 pruned. Trial was pruned at iteration 30.\u001b[0m\n", - "\u001b[32m[I 2021-09-07 09:27:29,919]\u001b[0m Trial 86 pruned. Trial was pruned at iteration 17.\u001b[0m\n", - "\u001b[32m[I 2021-09-07 09:27:30,026]\u001b[0m Trial 87 pruned. Trial was pruned at iteration 10.\u001b[0m\n", - "\u001b[32m[I 2021-09-07 09:27:30,861]\u001b[0m Trial 88 finished with value: 0.5270306752854106 and parameters: {'lambda_l1': 0.0003495273937948516, 'lambda_l2': 0.057992141622577896, 'num_leaves': 167, 'feature_fraction': 0.7406493057210397, 'bagging_fraction': 0.8812843020741449, 'bagging_freq': 5, 'min_child_samples': 10}. Best is trial 70 with value: 0.5227354154865925.\u001b[0m\n", - "\u001b[32m[I 2021-09-07 09:27:30,933]\u001b[0m Trial 89 pruned. Trial was pruned at iteration 11.\u001b[0m\n", - "\u001b[32m[I 2021-09-07 09:27:31,029]\u001b[0m Trial 90 pruned. Trial was pruned at iteration 10.\u001b[0m\n", - "\u001b[32m[I 2021-09-07 09:27:31,358]\u001b[0m Trial 91 pruned. Trial was pruned at iteration 48.\u001b[0m\n", - "\u001b[32m[I 2021-09-07 09:27:31,714]\u001b[0m Trial 92 pruned. Trial was pruned at iteration 41.\u001b[0m\n", - "\u001b[32m[I 2021-09-07 09:27:32,543]\u001b[0m Trial 93 finished with value: 0.5264391883178012 and parameters: {'lambda_l1': 0.055429809241931895, 'lambda_l2': 0.013315511259061849, 'num_leaves': 235, 'feature_fraction': 0.6209901742544794, 'bagging_fraction': 0.9603625243176316, 'bagging_freq': 5, 'min_child_samples': 9}. Best is trial 70 with value: 0.5227354154865925.\u001b[0m\n", - "\u001b[32m[I 2021-09-07 09:27:32,825]\u001b[0m Trial 94 pruned. Trial was pruned at iteration 40.\u001b[0m\n", - "\u001b[32m[I 2021-09-07 09:27:32,940]\u001b[0m Trial 95 pruned. Trial was pruned at iteration 13.\u001b[0m\n", - "\u001b[32m[I 2021-09-07 09:27:33,525]\u001b[0m Trial 96 finished with value: 0.521566218012535 and parameters: {'lambda_l1': 0.001072724146880552, 'lambda_l2': 0.043446953857601264, 'num_leaves': 142, 'feature_fraction': 0.6922532844601723, 'bagging_fraction': 0.9898097804978216, 'bagging_freq': 5, 'min_child_samples': 5}. Best is trial 96 with value: 0.521566218012535.\u001b[0m\n", - "\u001b[32m[I 2021-09-07 09:27:34,149]\u001b[0m Trial 97 finished with value: 0.5196727190561812 and parameters: {'lambda_l1': 0.0025915189209523223, 'lambda_l2': 0.37941398551753863, 'num_leaves': 159, 'feature_fraction': 0.6942886423659413, 'bagging_fraction': 0.9888819332176355, 'bagging_freq': 6, 'min_child_samples': 5}. Best is trial 97 with value: 0.5196727190561812.\u001b[0m\n", - "\u001b[32m[I 2021-09-07 09:27:34,855]\u001b[0m Trial 98 finished with value: 0.5265000531725292 and parameters: {'lambda_l1': 0.0006868362572582551, 'lambda_l2': 0.040525833784093586, 'num_leaves': 143, 'feature_fraction': 0.6930600995921747, 'bagging_fraction': 0.9897374239207342, 'bagging_freq': 6, 'min_child_samples': 6}. Best is trial 97 with value: 0.5196727190561812.\u001b[0m\n", - "\u001b[32m[I 2021-09-07 09:27:34,935]\u001b[0m Trial 99 pruned. Trial was pruned at iteration 10.\u001b[0m\n" + "\u001b[32m[I 2021-09-07 16:28:12,792]\u001b[0m Trial 90 pruned. Trial was pruned at iteration 10.\u001b[0m\n", + "\u001b[32m[I 2021-09-07 16:28:13,099]\u001b[0m Trial 91 pruned. Trial was pruned at iteration 13.\u001b[0m\n", + "\u001b[32m[I 2021-09-07 16:28:13,456]\u001b[0m Trial 92 pruned. Trial was pruned at iteration 14.\u001b[0m\n", + "\u001b[32m[I 2021-09-07 16:28:13,664]\u001b[0m Trial 93 pruned. Trial was pruned at iteration 10.\u001b[0m\n", + "\u001b[32m[I 2021-09-07 16:28:14,351]\u001b[0m Trial 94 pruned. Trial was pruned at iteration 45.\u001b[0m\n", + "\u001b[32m[I 2021-09-07 16:28:14,508]\u001b[0m Trial 95 pruned. Trial was pruned at iteration 10.\u001b[0m\n", + "\u001b[32m[I 2021-09-07 16:28:14,828]\u001b[0m Trial 96 pruned. Trial was pruned at iteration 10.\u001b[0m\n", + "\u001b[32m[I 2021-09-07 16:28:14,962]\u001b[0m Trial 97 pruned. Trial was pruned at iteration 10.\u001b[0m\n", + "\u001b[32m[I 2021-09-07 16:28:16,197]\u001b[0m Trial 98 finished with value: 0.5171555001746302 and parameters: {'lambda_l1': 1.0103137663726183e-08, 'lambda_l2': 1.7418953701287595e-06, 'num_leaves': 203, 'feature_fraction': 0.7265189263478641, 'bagging_fraction': 0.9317931154547426, 'bagging_freq': 1, 'min_child_samples': 10}. Best is trial 89 with value: 0.5102536883069437.\u001b[0m\n", + "\u001b[32m[I 2021-09-07 16:28:16,557]\u001b[0m Trial 99 pruned. Trial was pruned at iteration 16.\u001b[0m\n" ] }, { @@ -526,108 +545,635 @@ "text": [ "Number of finished trials: 100\n", "Best trial:\n", - " Value: 0.5196727190561812\n", + " Value: 0.5102536883069437\n", " Params: \n", - " lambda_l1: 0.0025915189209523223\n", - " lambda_l2: 0.37941398551753863\n", - " num_leaves: 159\n", - " feature_fraction: 0.6942886423659413\n", - " bagging_fraction: 0.9888819332176355\n", - " bagging_freq: 6\n", - " min_child_samples: 5\n" + " lambda_l1: 1.6446541932580473e-07\n", + " lambda_l2: 1.0734412329614426e-07\n", + " num_leaves: 193\n", + " feature_fraction: 0.8123395009389368\n", + " bagging_fraction: 0.9184649865458648\n", + " bagging_freq: 1\n", + " min_child_samples: 10\n", + "[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000712 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 1837\n", + "[LightGBM] [Info] Number of data points in the train set: 14860, number of used features: 8\n", + "[LightGBM] [Info] Start training from score 2.073247\n", + "Training until validation scores don't improve for 20 rounds\n", + "[50]\tvalid_0's rmse: 0.453164\n", + "[100]\tvalid_0's rmse: 0.442958\n", + "Early stopping, best iteration is:\n", + "[110]\tvalid_0's rmse: 0.442157\n" ] - } - ], - "source": [ - "if __name__ == \"__main__\":\n", - " study = optuna.create_study(\n", - " pruner=optuna.pruners.MedianPruner(n_warmup_steps=10), direction=\"minimize\"\n", - " )\n", - " study.optimize(lambda trial: objective(trial, train_x, valid_x, train_y, valid_y), n_trials=100)\n", - "\n", - " print(\"Number of finished trials: {}\".format(len(study.trials)))\n", - "\n", - " print(\"Best trial:\")\n", - " trial = study.best_trial\n", - "\n", - " print(\" Value: {}\".format(trial.value))\n", - "\n", - " print(\" Params: \")\n", - " for key, value in trial.params.items():\n", - " print(\" {}: {}\".format(key, value))" - ] - }, - { - "cell_type": "code", - "execution_count": 24, - "id": "681d9cc2", - "metadata": {}, - "outputs": [ + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\u001b[32m[I 2021-09-07 16:28:17,677]\u001b[0m Trial 100 pruned. Trial was pruned at iteration 10.\u001b[0m\n", + "\u001b[32m[I 2021-09-07 16:28:17,796]\u001b[0m Trial 101 pruned. Trial was pruned at iteration 10.\u001b[0m\n", + "\u001b[32m[I 2021-09-07 16:28:17,941]\u001b[0m Trial 102 pruned. Trial was pruned at iteration 10.\u001b[0m\n", + "\u001b[32m[I 2021-09-07 16:28:18,090]\u001b[0m Trial 103 pruned. Trial was pruned at iteration 10.\u001b[0m\n", + "\u001b[32m[I 2021-09-07 16:28:18,293]\u001b[0m Trial 104 pruned. Trial was pruned at iteration 10.\u001b[0m\n", + "\u001b[32m[I 2021-09-07 16:28:18,412]\u001b[0m Trial 105 pruned. Trial was pruned at iteration 10.\u001b[0m\n", + "\u001b[32m[I 2021-09-07 16:28:18,524]\u001b[0m Trial 106 pruned. Trial was pruned at iteration 10.\u001b[0m\n", + "\u001b[32m[I 2021-09-07 16:28:18,647]\u001b[0m Trial 107 pruned. Trial was pruned at iteration 10.\u001b[0m\n", + "\u001b[32m[I 2021-09-07 16:28:18,797]\u001b[0m Trial 108 pruned. Trial was pruned at iteration 10.\u001b[0m\n", + "\u001b[32m[I 2021-09-07 16:28:18,911]\u001b[0m Trial 109 pruned. Trial was pruned at iteration 10.\u001b[0m\n", + "\u001b[32m[I 2021-09-07 16:28:19,029]\u001b[0m Trial 110 pruned. Trial was pruned at iteration 10.\u001b[0m\n", + "\u001b[32m[I 2021-09-07 16:28:19,211]\u001b[0m Trial 111 pruned. Trial was pruned at iteration 16.\u001b[0m\n", + "\u001b[32m[I 2021-09-07 16:28:19,595]\u001b[0m Trial 112 pruned. Trial was pruned at iteration 30.\u001b[0m\n", + "\u001b[32m[I 2021-09-07 16:28:19,744]\u001b[0m Trial 113 pruned. Trial was pruned at iteration 10.\u001b[0m\n", + "\u001b[32m[I 2021-09-07 16:28:19,853]\u001b[0m Trial 114 pruned. Trial was pruned at iteration 10.\u001b[0m\n", + "\u001b[32m[I 2021-09-07 16:28:20,146]\u001b[0m Trial 115 pruned. Trial was pruned at iteration 23.\u001b[0m\n", + "\u001b[32m[I 2021-09-07 16:28:20,267]\u001b[0m Trial 116 pruned. Trial was pruned at iteration 10.\u001b[0m\n", + "\u001b[32m[I 2021-09-07 16:28:20,420]\u001b[0m Trial 117 pruned. Trial was pruned at iteration 10.\u001b[0m\n", + "\u001b[32m[I 2021-09-07 16:28:20,594]\u001b[0m Trial 118 pruned. Trial was pruned at iteration 10.\u001b[0m\n", + "\u001b[32m[I 2021-09-07 16:28:20,780]\u001b[0m Trial 119 pruned. Trial was pruned at iteration 10.\u001b[0m\n", + "\u001b[32m[I 2021-09-07 16:28:20,942]\u001b[0m Trial 120 pruned. Trial was pruned at iteration 10.\u001b[0m\n", + "\u001b[32m[I 2021-09-07 16:28:21,073]\u001b[0m Trial 121 pruned. Trial was pruned at iteration 10.\u001b[0m\n", + "\u001b[32m[I 2021-09-07 16:28:21,410]\u001b[0m Trial 122 pruned. Trial was pruned at iteration 10.\u001b[0m\n", + "\u001b[32m[I 2021-09-07 16:28:21,601]\u001b[0m Trial 123 pruned. Trial was pruned at iteration 10.\u001b[0m\n", + "\u001b[32m[I 2021-09-07 16:28:21,799]\u001b[0m Trial 124 pruned. Trial was pruned at iteration 10.\u001b[0m\n", + "\u001b[32m[I 2021-09-07 16:28:21,944]\u001b[0m Trial 125 pruned. Trial was pruned at iteration 10.\u001b[0m\n", + "\u001b[32m[I 2021-09-07 16:28:22,080]\u001b[0m Trial 126 pruned. Trial was pruned at iteration 10.\u001b[0m\n", + "\u001b[32m[I 2021-09-07 16:28:22,209]\u001b[0m Trial 127 pruned. Trial was pruned at iteration 10.\u001b[0m\n", + "\u001b[32m[I 2021-09-07 16:28:22,367]\u001b[0m Trial 128 pruned. Trial was pruned at iteration 10.\u001b[0m\n", + "\u001b[32m[I 2021-09-07 16:28:22,509]\u001b[0m Trial 129 pruned. Trial was pruned at iteration 10.\u001b[0m\n", + "\u001b[32m[I 2021-09-07 16:28:22,673]\u001b[0m Trial 130 pruned. Trial was pruned at iteration 10.\u001b[0m\n", + "\u001b[32m[I 2021-09-07 16:28:22,754]\u001b[0m Trial 131 pruned. Trial was pruned at iteration 10.\u001b[0m\n", + "\u001b[32m[I 2021-09-07 16:28:22,923]\u001b[0m Trial 132 pruned. 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Trial was pruned at iteration 10.\u001b[0m\n", + "\u001b[32m[I 2021-09-07 16:28:35,002]\u001b[0m Trial 197 pruned. Trial was pruned at iteration 10.\u001b[0m\n", + "\u001b[32m[I 2021-09-07 16:28:35,242]\u001b[0m Trial 198 pruned. Trial was pruned at iteration 10.\u001b[0m\n", + "\u001b[32m[I 2021-09-07 16:28:35,408]\u001b[0m Trial 199 pruned. Trial was pruned at iteration 10.\u001b[0m\n" + ] + }, { "name": "stdout", "output_type": "stream", "text": [ - "{'boosting_type': 'gbdt', 'objective': 'regression', 'metric': 'rmse', 'lambda_l1': 0.0025915189209523223, 'lambda_l2': 0.37941398551753863, 'num_leaves': 159, 'feature_fraction': 0.6942886423659413, 'bagging_fraction': 0.9888819332176355, 'bagging_freq': 6, 'min_child_samples': 5}\n" + "Number of finished trials: 200\n", + "Best trial:\n", + " Value: 0.5102536883069437\n", + " Params: \n", + " lambda_l1: 1.6446541932580473e-07\n", + " lambda_l2: 1.0734412329614426e-07\n", + " num_leaves: 193\n", + " feature_fraction: 0.8123395009389368\n", + " bagging_fraction: 0.9184649865458648\n", + " bagging_freq: 1\n", + " min_child_samples: 10\n", + "[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000777 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 1838\n", + "[LightGBM] [Info] Number of data points in the train set: 14861, number of used features: 8\n", + "[LightGBM] [Info] Start training from score 2.070396\n", + "Training until validation scores don't improve for 20 rounds\n", + "[50]\tvalid_0's rmse: 0.465875\n", + "Early stopping, best iteration is:\n", + "[76]\tvalid_0's rmse: 0.460117\n" ] - } - ], - "source": [ - "params = {'boosting_type': 'gbdt',\n", - " 'objective': 'regression',\n", - " \"metric\": 'rmse'}\n", - "for key, value in trial.params.items():\n", - " params[key]=value\n", - "print(params)" - ] - }, - { - "cell_type": "code", - "execution_count": 25, - "id": "f5534bab", - "metadata": {}, - "outputs": [ + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\u001b[32m[I 2021-09-07 16:28:36,810]\u001b[0m Trial 200 pruned. 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Trial was pruned at iteration 10.\u001b[0m\n" + ] + }, { "name": "stdout", "output_type": "stream", "text": [ - "[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000585 seconds.\n", - "You can set `force_row_wise=true` to remove the overhead.\n", - "And if memory is not enough, you can set `force_col_wise=true`.\n", + "Number of finished trials: 300\n", + "Best trial:\n", + " Value: 0.5102536883069437\n", + " Params: \n", + " lambda_l1: 1.6446541932580473e-07\n", + " lambda_l2: 1.0734412329614426e-07\n", + " num_leaves: 193\n", + " feature_fraction: 0.8123395009389368\n", + " bagging_fraction: 0.9184649865458648\n", + " bagging_freq: 1\n", + " min_child_samples: 10\n", + "[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000797 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", "[LightGBM] [Info] Total Bins 1837\n", - "[LightGBM] [Info] Number of data points in the train set: 13932, number of used features: 8\n", - "[LightGBM] [Info] Start training from score 2.072422\n", + "[LightGBM] [Info] Number of data points in the train set: 14861, number of used features: 8\n", + "[LightGBM] [Info] Start training from score 2.069154\n", + "Training until validation scores don't improve for 20 rounds\n", + "[50]\tvalid_0's rmse: 0.451573\n", + "Early stopping, best iteration is:\n", + "[63]\tvalid_0's rmse: 0.44707\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\u001b[32m[I 2021-09-07 16:28:51,004]\u001b[0m Trial 300 pruned. 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