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"}}},{"cell_type":"markdown","source":"# 1.Introduction 💳\n\n![image.png](attachment:6f3c2a65-8e45-4aeb-8662-a2048342d780.png)\n\nCredit cards has made life easier, whether shopping online or having dinner outing, the convenience of the credit card comes forward. [American Express](https://www.americanexpress.com) company (AKA AMEX) is multinational coporation with focus in payments solutions including but not limited to credit cards. Instead of carrying large amount of cash, one credit card can do the job just fine by paying in advance and and pay the loaned money back over a certain period of time. That raise an important question, how do card issues know that the money will be paid back in a specified time frame?\n\n**That age old question has peaked the curiosity of bankers, economists, statisticians and recently data scientist.** 👨‍💻\n\nIn this notebook, We'll go over the data of AMEX customers to find out key patterns that identify customer who weren't able to pay the money back, this act is called  **Default**. The goal is to predict in the future with data trend the possibility of default customers.\n","metadata":{},"attachments":{"6f3c2a65-8e45-4aeb-8662-a2048342d780.png":{"image/png":"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"}}},{"cell_type":"markdown","source":"# 2.Importing Libraries 📚","metadata":{}},{"cell_type":"code","source":"import numpy as np\nimport pandas as pd\nimport matplotlib.pyplot as plt\nimport seaborn as sns\nimport math\nimport gc\nimport warnings\n\nimport cupy, cudf #pandas and numpy GPU alternative, Requires GPU Accelerator\nfrom sklearn.model_selection import KFold\nimport xgboost as xgb\n\nwarnings.filterwarnings('ignore')  #ignore all warnings \n#%config Completer.use_jedi = False  # kaggle notebook autocomplete","metadata":{"execution":{"iopub.status.busy":"2022-09-09T17:12:27.864755Z","iopub.execute_input":"2022-09-09T17:12:27.865088Z","iopub.status.idle":"2022-09-09T17:12:32.678386Z","shell.execute_reply.started":"2022-09-09T17:12:27.864996Z","shell.execute_reply":"2022-09-09T17:12:32.677437Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"","metadata":{},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"plt.style.use('https://raw.githubusercontent.com/MightyStud/matplotlib-stylesheets/master/pitayasmoothie-light.mplstyle')","metadata":{"execution":{"iopub.status.busy":"2022-09-09T12:20:40.676269Z","iopub.execute_input":"2022-09-09T12:20:40.677359Z","iopub.status.idle":"2022-09-09T12:20:40.869975Z","shell.execute_reply.started":"2022-09-09T12:20:40.677318Z","shell.execute_reply":"2022-09-09T12:20:40.868770Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"## 2.1 The dataset 🔢\nThe original dataset of AMEX default prediction is above 50GB, Making it hard to fit as a whole into memory. Fortunately the data can be compressed into **parquet** format, which is much smaller file format size and easier to work with. A link to the new dataset can be found [here](https://www.kaggle.com/datasets/raddar/amex-data-integer-dtypes-parquet-format).\n\nThe objective is to predict the probability that a customer does not pay back their credit card balance amount in the future based on their monthly customer profile. The target binary variable is calculated by observing 18 months performance window after the latest credit card statement, and if the customer does not pay due amount in 120 days after their latest statement date it is considered a **default** event.\n\nThe dataset contains aggregated profile features for each customer at each statement date. Features are anonymized and normalized, and fall into the following general categories:\n\n- D_* = Delinquency variables\n- S_* = Spend variables\n- P_* = Payment variables\n- B_* = Balance variables\n- R_* = Risk variables\n\nwith the following features being categorical:\n\n\\['B_30', 'B_38', 'D_114', 'D_116', 'D_117', 'D_120', 'D_126', 'D_63', 'D_64', 'D_66', 'D_68']\n\n*More details on the variables are kept secret to keep the work more purely data focused.*\n\n*The task is to predict, for each customer_ID, the probability of a future payment default (target = 1).*\n\n*It is also worth noting that the negative class has been subsampled for this dataset at 5%, and thus receives a 20x weighting in the scoring metric.*\n\n___\n\nThere was also artificial  noise added, it had been explained and dealt with in this dataset, more can be found [here](https://www.kaggle.com/competitions/amex-default-prediction/discussion/328514).","metadata":{}},{"cell_type":"code","source":"categorical_features = ['B_30', 'B_38', 'D_63', 'D_64', 'D_66', 'D_68', 'D_114', 'D_116', 'D_117', 'D_120', 'D_126']\n\ntest_path = '../input/amex-data-integer-dtypes-parquet-format/test.parquet'\ntrain_path = '../input/amex-data-integer-dtypes-parquet-format/train.parquet'\nlabels_path = '../input/amex-default-prediction/train_labels.csv'","metadata":{"execution":{"iopub.status.busy":"2022-09-09T12:20:42.092077Z","iopub.execute_input":"2022-09-09T12:20:42.092495Z","iopub.status.idle":"2022-09-09T12:20:42.099239Z","shell.execute_reply.started":"2022-09-09T12:20:42.092462Z","shell.execute_reply":"2022-09-09T12:20:42.098110Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"# 3. Exploratory Data Analysis 🔎","metadata":{}},{"cell_type":"markdown","source":"## 3.1 Test Data 🧪\nWe'll start with the Test data and try to find patterns and key insights","metadata":{}},{"cell_type":"code","source":"%%time \n\ntest = pd.read_parquet(test_path)\nprint(\"Memory usage of Test data = {:.2f} GB\".format(test.memory_usage().sum()/1024/1024/1024))","metadata":{"execution":{"iopub.status.busy":"2022-09-09T12:20:43.399305Z","iopub.execute_input":"2022-09-09T12:20:43.399712Z","iopub.status.idle":"2022-09-09T12:21:30.739025Z","shell.execute_reply.started":"2022-09-09T12:20:43.399679Z","shell.execute_reply":"2022-09-09T12:21:30.737699Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"___\n**Remakrs**:\n- Even after compressing, the size of test data is quite large 5+GB.\n- We should be careful dealing with memory limitation\n___","metadata":{}},{"cell_type":"code","source":"#assign types\ntest['S_2'] = pd.to_datetime(test['S_2'])\ntest[categorical_features] = test[categorical_features].astype(\"category\")","metadata":{"execution":{"iopub.status.busy":"2022-09-09T12:21:35.285312Z","iopub.execute_input":"2022-09-09T12:21:35.285861Z","iopub.status.idle":"2022-09-09T12:21:43.969007Z","shell.execute_reply.started":"2022-09-09T12:21:35.285813Z","shell.execute_reply":"2022-09-09T12:21:43.967399Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"#missing values\ntest.info(max_cols=199, show_counts=True)","metadata":{"_kg_hide-output":true,"execution":{"iopub.status.busy":"2022-09-09T12:21:43.971657Z","iopub.execute_input":"2022-09-09T12:21:43.972203Z","iopub.status.idle":"2022-09-09T12:21:50.265757Z","shell.execute_reply.started":"2022-09-09T12:21:43.972154Z","shell.execute_reply":"2022-09-09T12:21:50.264427Z"},"collapsed":true,"jupyter":{"outputs_hidden":true},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"temp = test.isna().sum().div(len(test)).mul(100).sort_values(ascending = False)\ntemp = temp[temp != 0]\n\nfig, ax = plt.subplots(1,1, figsize=(20,5))\nsns.barplot(x=temp.index, y=temp.values, ax=ax, color='#7A76C2', alpha = 0.8)\nax.set_ylabel('Percentage [%]')\nax.tick_params(axis='x', rotation=90)\nplt.suptitle(\"Missing Data Per Column in Test Dataset [%]\", fontsize = 20)\nplt.tight_layout()\nplt.show()\n\ndel temp\n_ = gc.collect()","metadata":{"execution":{"iopub.status.busy":"2022-09-09T13:04:46.619043Z","iopub.execute_input":"2022-09-09T13:04:46.619424Z","iopub.status.idle":"2022-09-09T13:04:51.896997Z","shell.execute_reply.started":"2022-09-09T13:04:46.619393Z","shell.execute_reply":"2022-09-09T13:04:51.895530Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"___\n**Remakrs**:\n- There is a quite amount of missing values in test data especially in variables:\n - \\[D_88, D_110, B_39, D_73, B_42, D_134, D_76, D_132, D_42, D_142, B_29, D_53]\n- Mostly in D and B variables\n- Imputation methods or a model that can deal with missing values must be utilized\n___","metadata":{}},{"cell_type":"markdown","source":"___\n**Inspection:**\n- Variable S_2 is datatime data\n- We will examine the possibility  of time series data analysis \n___","metadata":{}},{"cell_type":"code","source":"print(f'Test dates range is from {test.S_2.min()} to {test.S_2.max()} spanning aduration of {test.S_2.max() - test.S_2.min()}.')","metadata":{"execution":{"iopub.status.busy":"2022-09-08T15:54:54.444419Z","iopub.execute_input":"2022-09-08T15:54:54.445100Z","iopub.status.idle":"2022-09-08T15:54:54.576284Z","shell.execute_reply.started":"2022-09-08T15:54:54.445061Z","shell.execute_reply":"2022-09-08T15:54:54.575217Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"fig, ax = plt.subplots(1,1, figsize=(20,6))\ntemp = test.S_2.groupby(test.customer_ID).max().reset_index().sort_values('S_2').set_index('customer_ID')\nsns.histplot(x='S_2', data=temp, bins=len(pd.date_range(temp.min()[0], temp.max()[0], freq=\"d\")), ax=ax,  color='#7A76C2', alpha = 0.8)\nax.set_title('Last Statement Dates of Test Data', fontsize=20)\nax.set_xlabel('last statement date per customer')\nax.set_ylabel('count of statements')\nplt.show()\n\ndel temp\n_ = gc.collect()","metadata":{"execution":{"iopub.status.busy":"2022-09-09T13:04:55.991076Z","iopub.execute_input":"2022-09-09T13:04:55.991517Z","iopub.status.idle":"2022-09-09T13:05:01.735267Z","shell.execute_reply.started":"2022-09-09T13:04:55.991479Z","shell.execute_reply":"2022-09-09T13:05:01.733968Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"___\n**Remarks:**\n- The last statement of test customer occured in 2019-04 and 2019-10\n___\n","metadata":{}},{"cell_type":"markdown","source":"It makes sense to train our models based only on the latest statement available for each customer. \n\nBecause, the provided target variables describe the latest default status of each customer. So it will be wise to remove old statements from training dataset.","metadata":{}},{"cell_type":"code","source":"print(f'Number of unique customers in test data: {test[\"customer_ID\"].nunique()}')","metadata":{"execution":{"iopub.status.busy":"2022-09-08T15:54:54.577783Z","iopub.execute_input":"2022-09-08T15:54:54.578472Z","iopub.status.idle":"2022-09-08T15:54:56.234785Z","shell.execute_reply.started":"2022-09-08T15:54:54.578433Z","shell.execute_reply":"2022-09-08T15:54:56.233585Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"fig, ax = plt.subplots(1,1, figsize=(20,6))\ntemp = test.groupby(['customer_ID']).size().reset_index().rename(columns={0:'frequency'})\nsns.histplot(x='frequency', stat='percent', bins=np.arange(0,14), data=temp, ax=ax, color='#7A76C2', alpha = 0.8)\nax.bar_label(ax.containers[0], fmt='%.2f%%')\nax.set_title('Customer Presence in the Test Data', fontsize = 25)\nplt.show()\n\ndel temp\n_ = gc.collect()","metadata":{"execution":{"iopub.status.busy":"2022-09-09T13:04:29.901969Z","iopub.execute_input":"2022-09-09T13:04:29.902414Z","iopub.status.idle":"2022-09-09T13:04:33.331088Z","shell.execute_reply.started":"2022-09-09T13:04:29.902377Z","shell.execute_reply":"2022-09-09T13:04:33.330038Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"___\n**Remarks:**\n- Most customers had 13 statements \n- A few (about 10%) had 12 or less card statments \n___","metadata":{}},{"cell_type":"markdown","source":"## 3.2 train Data 👟\nWe'll do an intense EDA with the train data and try to find patterns and key insights to help with model choosing/prediction","metadata":{}},{"cell_type":"code","source":"del test\n_ = gc.collect()","metadata":{"execution":{"iopub.status.busy":"2022-09-09T13:10:24.608646Z","iopub.execute_input":"2022-09-09T13:10:24.609156Z","iopub.status.idle":"2022-09-09T13:10:24.893791Z","shell.execute_reply.started":"2022-09-09T13:10:24.609116Z","shell.execute_reply":"2022-09-09T13:10:24.892945Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"%%time \n\ntrain = pd.read_parquet(train_path)\nlabels = pd.read_csv(labels_path)\ntrain = train.merge(labels, left_on='customer_ID', right_on='customer_ID')\n\nprint(\"Memory usage of Train data = {:.2f} GB\".format(train.memory_usage().sum()/1024/1024/1024))\n\ndel labels\n_ = gc.collect()","metadata":{"execution":{"iopub.status.busy":"2022-09-09T13:10:26.057186Z","iopub.execute_input":"2022-09-09T13:10:26.058063Z","iopub.status.idle":"2022-09-09T13:13:38.802615Z","shell.execute_reply.started":"2022-09-09T13:10:26.058015Z","shell.execute_reply":"2022-09-09T13:13:38.801166Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"___\n**Remakrs**:\n- The parquet file of train data is still considerably big.\n- We should be careful dealing with memory limitation\n___","metadata":{}},{"cell_type":"code","source":"#assign types\ntrain['S_2'] = pd.to_datetime(train['S_2'])\ntrain[categorical_features] = train[categorical_features].astype(\"category\")","metadata":{"execution":{"iopub.status.busy":"2022-09-09T13:13:38.804559Z","iopub.execute_input":"2022-09-09T13:13:38.805226Z","iopub.status.idle":"2022-09-09T13:13:46.219671Z","shell.execute_reply.started":"2022-09-09T13:13:38.805183Z","shell.execute_reply":"2022-09-09T13:13:46.218270Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"#missing values\ntrain.info(max_cols=199, show_counts=True)","metadata":{"_kg_hide-output":true,"execution":{"iopub.status.busy":"2022-09-09T13:13:46.221135Z","iopub.execute_input":"2022-09-09T13:13:46.221518Z","iopub.status.idle":"2022-09-09T13:13:48.664836Z","shell.execute_reply.started":"2022-09-09T13:13:46.221483Z","shell.execute_reply":"2022-09-09T13:13:48.663594Z"},"collapsed":true,"jupyter":{"outputs_hidden":true},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"temp = train.isna().sum().div(len(train)).mul(100).sort_values(ascending = False)\ntemp = temp[temp != 0]\n\nfig, ax = plt.subplots(1,1, figsize=(20,5))\nsns.barplot(x=temp.index, y=temp.values, ax=ax, color='#ff6e9c', alpha=0.8)\nax.set_ylabel('Percentage [%]')\nax.tick_params(axis='x', rotation=90)\nplt.suptitle(\"Missing Data Per Column in Train Dataset [%]\", fontsize = 20)\nplt.tight_layout()\nplt.show()\n\ndel temp\n_ = gc.collect()","metadata":{"execution":{"iopub.status.busy":"2022-09-09T13:13:48.677260Z","iopub.execute_input":"2022-09-09T13:13:48.677756Z","iopub.status.idle":"2022-09-09T13:13:51.786453Z","shell.execute_reply.started":"2022-09-09T13:13:48.677709Z","shell.execute_reply":"2022-09-09T13:13:51.785378Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"___\n**Remakrs**:\n- There is a quite amount of missing values in train data too especially in variables:\n - \\[D_88, D_110, B_39, D_73, B_42, D_134, B_29, D_132, D_76, D_42, D_142, D_53]\n- Mostly in D and B variables\n- Imputation methods or a model that can deal with missing values must be utilized\n___","metadata":{}},{"cell_type":"code","source":"temp = train['target'].value_counts()\nfig, ax = plt.subplots(1,1, figsize=(10,10))\nplt.pie(temp.values, labels=['Paid', 'Default'], textprops={'fontsize': 20}, autopct='%.2f%%' )\nplt.legend()\nplt.title('Distribution of the Target Variable [%]',fontsize=25)\nplt.show()\n\ndel temp\n_ = gc.collect()","metadata":{"execution":{"iopub.status.busy":"2022-09-08T15:57:54.509467Z","iopub.execute_input":"2022-09-08T15:57:54.509806Z","iopub.status.idle":"2022-09-08T15:57:54.941233Z","shell.execute_reply.started":"2022-09-08T15:57:54.509772Z","shell.execute_reply":"2022-09-08T15:57:54.940210Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"___\n**Remarks:**\n- The train data is imbalanced with 75% - 25% distribution \n- A StratifiedKFold for CV will be used\n- The usual accuracy will not be suitable for imbalanced data\n- We will use the [suggested metric](https://www.kaggle.com/competitions/amex-default-prediction/overview/evaluation) that is a mix of AUC of ROC curve and % of recall (will be explained later)\n___","metadata":{}},{"cell_type":"code","source":"print(f'Train dates range is from {train.S_2.min()} to {train.S_2.max()} spanning aduration of {train.S_2.max() - train.S_2.min()}.')","metadata":{"execution":{"iopub.status.busy":"2022-09-08T15:57:54.942794Z","iopub.execute_input":"2022-09-08T15:57:54.943151Z","iopub.status.idle":"2022-09-08T15:57:55.012356Z","shell.execute_reply.started":"2022-09-08T15:57:54.943115Z","shell.execute_reply":"2022-09-08T15:57:55.011121Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"#last month statement\nfig, ax = plt.subplots(1,1, figsize=(20,6))\ntemp = train.S_2.groupby(train.customer_ID).max().reset_index().sort_values('S_2').set_index('customer_ID')\nsns.histplot(x='S_2', data=temp, bins=len(pd.date_range(temp.min()[0], temp.max()[0], freq=\"d\")), ax=ax,kde=True, color='#ff6e9c', alpha=0.8)\nax.set_title('Last Statement Dates of Train Data', fontsize=20)\nax.set_xlabel('last statement date per customer')\nax.set_ylabel('count of statements')\nplt.show()\n\ndel temp\n_ = gc.collect()","metadata":{"execution":{"iopub.status.busy":"2022-09-09T13:18:39.401327Z","iopub.execute_input":"2022-09-09T13:18:39.401865Z","iopub.status.idle":"2022-09-09T13:18:43.948502Z","shell.execute_reply.started":"2022-09-09T13:18:39.401828Z","shell.execute_reply":"2022-09-09T13:18:43.946978Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"___\n**Remarks:**\n- The last statement of all the customers happened between 2018-03-01 and 2018-04-01\n- We cannot cross-validate with a TimeSeriesSplit because all training happens in the same month.\n___\n","metadata":{}},{"cell_type":"code","source":"print(f'Number of unique customers in train data: {train[\"customer_ID\"].nunique()}')","metadata":{"execution":{"iopub.status.busy":"2022-09-08T15:57:55.014030Z","iopub.execute_input":"2022-09-08T15:57:55.014446Z","iopub.status.idle":"2022-09-08T15:57:55.998904Z","shell.execute_reply.started":"2022-09-08T15:57:55.014398Z","shell.execute_reply":"2022-09-08T15:57:55.997650Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"#number of unique customers and their presence in train data\nfig, ax = plt.subplots(1,1, figsize=(20,6))\ntemp = train.groupby(['customer_ID', 'target']).size().reset_index().rename(columns={0:'frequency'})\nsns.histplot(x='frequency',hue='target',hue_order=[1,0], stat='percent', bins=np.arange(0,14), data=temp, ax=ax, multiple='dodge',alpha = 0.8)\nax.bar_label(ax.containers[0], fmt='%.2f%%')\nax.bar_label(ax.containers[1], fmt='%.2f%%')\nax.set_title('Customer Presence in the Train Data', fontsize = 25)\nax.legend(loc=0, labels=['Paid','Default'])\nplt.show()\n\ndel temp\n_ = gc.collect()","metadata":{"execution":{"iopub.status.busy":"2022-09-09T13:20:04.185001Z","iopub.execute_input":"2022-09-09T13:20:04.186293Z","iopub.status.idle":"2022-09-09T13:20:07.299769Z","shell.execute_reply.started":"2022-09-09T13:20:04.186232Z","shell.execute_reply":"2022-09-09T13:20:07.297626Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"___\n**Remarks:**\n- Most customers had 13 statements \n- A few (about 15%) had 12 or less card statments \n___","metadata":{}},{"cell_type":"code","source":"train_last = train.groupby('customer_ID').tail(1).reset_index(drop=True)\ntrain_last.shape\n\ndel train\n_ = gc.collect()","metadata":{"execution":{"iopub.status.busy":"2022-09-09T13:33:12.599937Z","iopub.execute_input":"2022-09-09T13:33:12.600511Z","iopub.status.idle":"2022-09-09T13:33:15.730668Z","shell.execute_reply.started":"2022-09-09T13:33:12.600471Z","shell.execute_reply":"2022-09-09T13:33:15.729340Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"## 3.3.1 Distributions, the kdeplots 🌊","metadata":{}},{"cell_type":"code","source":"# Distributions, the kdeplots\ndef dist_kdeplots(letter, figsize):\n    cols = [c for c in train_last.columns if (c.startswith((letter,'t'))) & (c not in categorical_features) & (c != \"S_2\")]\n    temp = train_last[cols]\n    plot_cols = 4\n    plot_rows = math.ceil((len(cols) - 1)/plot_cols)\n    \n    fig, axes = plt.subplots(plot_rows, plot_cols, figsize=figsize)\n    for i, ax in enumerate(axes.reshape(-1)):\n        if i<len(cols)-1:\n            sns.kdeplot(x=cols[i], hue ='target', data=temp, fill=True,hue_order=[1,0], linewidth=2, \n                       ax=ax)\n        ax.tick_params(left=False, bottom=False, labelsize=5)\n        ax.xaxis.get_label().set_fontsize(10)\n        ax.set_ylabel('')\n        if i==0:\n            ax.legend(loc=2, bbox_to_anchor=(-0.35,1), labels=['Paid','Default'])\n        else:\n            ax.legend().remove() \n    sns.despine(bottom=True, trim=True)\n    plt.suptitle('Distributions of {}_* Variables'.format(letter), fontsize=20)\n    plt.tight_layout(rect=[0, 0.2, 1, 0.98])\n    plt.show()\n    ","metadata":{"execution":{"iopub.status.busy":"2022-09-08T15:58:03.757619Z","iopub.execute_input":"2022-09-08T15:58:03.758223Z","iopub.status.idle":"2022-09-08T15:58:03.770018Z","shell.execute_reply.started":"2022-09-08T15:58:03.758181Z","shell.execute_reply":"2022-09-08T15:58:03.768804Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"%time dist_kdeplots('D', (20,70))","metadata":{"execution":{"iopub.status.busy":"2022-09-08T15:58:03.771373Z","iopub.execute_input":"2022-09-08T15:58:03.772083Z","iopub.status.idle":"2022-09-08T16:00:33.748109Z","shell.execute_reply.started":"2022-09-08T15:58:03.772044Z","shell.execute_reply":"2022-09-08T16:00:33.747242Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"___\n**Remarks:**\n- The bigger the distance and the less overlapping between two distribution curves the better discriminator this feature is.\n- There are some variables that are skewed towards the end\n___","metadata":{}},{"cell_type":"code","source":"%time dist_kdeplots('S', (20,15))","metadata":{"execution":{"iopub.status.busy":"2022-09-08T16:00:33.749088Z","iopub.execute_input":"2022-09-08T16:00:33.749434Z","iopub.status.idle":"2022-09-08T16:01:12.007279Z","shell.execute_reply.started":"2022-09-08T16:00:33.749399Z","shell.execute_reply":"2022-09-08T16:01:12.006395Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"___\n**Remarks:**\n- The bigger the distance and the less overlapping between two distribution curves the better discriminator this feature is.\n- There are some variables that are skewed towards the end\n___","metadata":{}},{"cell_type":"code","source":"%time dist_kdeplots('P', (20,5))","metadata":{"execution":{"iopub.status.busy":"2022-09-08T16:01:12.008730Z","iopub.execute_input":"2022-09-08T16:01:12.009277Z","iopub.status.idle":"2022-09-08T16:01:18.243388Z","shell.execute_reply.started":"2022-09-08T16:01:12.009237Z","shell.execute_reply":"2022-09-08T16:01:18.242389Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"___\n**Remarks:**\n- The bigger the distance and the less overlapping between two distribution curves the better discriminator this feature is.\n- There are some variables that are skewed towards the end\n___","metadata":{}},{"cell_type":"code","source":"%time dist_kdeplots('B', (20,30))","metadata":{"execution":{"iopub.status.busy":"2022-09-08T16:01:18.245367Z","iopub.execute_input":"2022-09-08T16:01:18.246065Z","iopub.status.idle":"2022-09-08T16:02:25.075874Z","shell.execute_reply.started":"2022-09-08T16:01:18.246024Z","shell.execute_reply":"2022-09-08T16:02:25.074843Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"___\n**Remarks:**\n- The bigger the distance and the less overlapping between two distribution curves the better discriminator this feature is.\n- There are some variables that are skewed towards the end\n___","metadata":{}},{"cell_type":"code","source":"%time dist_kdeplots('R', (20,20))","metadata":{"execution":{"iopub.status.busy":"2022-09-08T16:02:25.077639Z","iopub.execute_input":"2022-09-08T16:02:25.078831Z","iopub.status.idle":"2022-09-08T16:03:17.222809Z","shell.execute_reply.started":"2022-09-08T16:02:25.078773Z","shell.execute_reply":"2022-09-08T16:03:17.221819Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"___\n**Remarks:**\n- The bigger the distance and the less overlapping between two distribution curves the better discriminator this feature is.\n- There are some variables that are skewed towards the end\n___","metadata":{}},{"cell_type":"code","source":"# distrubution of categorical values\ncols = categorical_features + ['target']\ntemp=train_last[cols]\nfig, axes = plt.subplots(3, 4, figsize=(20,20))\nfor i, ax in enumerate(axes.reshape(-1)):\n    if i<len(cols)-1:\n        sns.countplot(x=cols[i], hue='target', data=temp, ax=ax,)\nplt.suptitle(\"Distribution of Categorical Data\", fontsize=20)\nfig.tight_layout()\nfig.subplots_adjust(top=0.95)\nplt.show()\n\ndel temp\n_ = gc.collect()","metadata":{"execution":{"iopub.status.busy":"2022-09-08T16:03:17.230570Z","iopub.execute_input":"2022-09-08T16:03:17.231531Z","iopub.status.idle":"2022-09-08T16:03:19.941097Z","shell.execute_reply.started":"2022-09-08T16:03:17.231478Z","shell.execute_reply":"2022-09-08T16:03:19.940058Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"___\n**Remarks:**\n- The most number of classes in categorical varaibles is 8\n- The distributions for target (0,1) differ by a quite a margin, this is helpful for modeling \n___","metadata":{}},{"cell_type":"markdown","source":"## 3.3.2 Correleation Matrix 🎭","metadata":{}},{"cell_type":"code","source":"#Correleation matrix \ndef corr_mat_plot(letter, figsize):\n    cols = [c for c in train_last.columns if (c.startswith((letter))) & (c != \"S_2\")]\n    temp = train_last[cols].corr()\n    mask = np.triu(np.ones_like(temp))[1:,:-1]\n    temp=temp.iloc[1:,:-1].copy()\n    \n    fig, ax = plt.subplots(figsize=figsize)\n    sns.heatmap(temp, mask=mask, vmin=-1, vmax=1, center=0, annot=True, fmt=\".2f\",\n                cmap='RdBu_r', cbar=True)\n\n    ax.tick_params(left=False, bottom=False)\n    ax.set_xticklabels(ax.get_xticklabels(),horizontalalignment='right', fontsize=12, rotation=45)\n    ax.set_yticklabels(ax.get_yticklabels(), fontsize=12)\n    plt.title(\"Correlation Between {}_* Variables\".format(letter), fontsize=20)\n    plt.show()","metadata":{"execution":{"iopub.status.busy":"2022-09-08T16:03:19.942694Z","iopub.execute_input":"2022-09-08T16:03:19.943284Z","iopub.status.idle":"2022-09-08T16:03:19.952410Z","shell.execute_reply.started":"2022-09-08T16:03:19.943248Z","shell.execute_reply":"2022-09-08T16:03:19.951552Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"%time corr_mat_plot('D', (70,70))","metadata":{"execution":{"iopub.status.busy":"2022-09-08T16:03:19.953557Z","iopub.execute_input":"2022-09-08T16:03:19.954108Z","iopub.status.idle":"2022-09-08T16:03:44.300270Z","shell.execute_reply.started":"2022-09-08T16:03:19.954070Z","shell.execute_reply":"2022-09-08T16:03:44.299230Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"___\n**Remarks:**\n- A large number of positively & negatively correlated variables \n- We might need to handle multicollinearity between variables to better the model performance\n___","metadata":{}},{"cell_type":"code","source":"%time corr_mat_plot('S', (23,23))","metadata":{"execution":{"iopub.status.busy":"2022-09-08T16:03:44.301914Z","iopub.execute_input":"2022-09-08T16:03:44.302941Z","iopub.status.idle":"2022-09-08T16:03:46.155521Z","shell.execute_reply.started":"2022-09-08T16:03:44.302894Z","shell.execute_reply":"2022-09-08T16:03:46.154479Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"___\n**Remarks:**\n- A large number of positively & negatively correlated variables \n- We might need to handle multicollinearity between variables to better the model performance\n___","metadata":{}},{"cell_type":"code","source":"%time corr_mat_plot('P', (8,8))","metadata":{"execution":{"iopub.status.busy":"2022-09-08T16:03:46.157221Z","iopub.execute_input":"2022-09-08T16:03:46.158016Z","iopub.status.idle":"2022-09-08T16:03:46.428381Z","shell.execute_reply.started":"2022-09-08T16:03:46.157969Z","shell.execute_reply":"2022-09-08T16:03:46.427354Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"___\n**Remarks:**\n- A large number of positively & negatively correlated variables \n- We might need to handle multicollinearity between variables to better the model performance\n___","metadata":{}},{"cell_type":"code","source":"%time corr_mat_plot('B', (45,45))","metadata":{"execution":{"iopub.status.busy":"2022-09-08T16:03:46.430031Z","iopub.execute_input":"2022-09-08T16:03:46.430726Z","iopub.status.idle":"2022-09-08T16:03:51.517528Z","shell.execute_reply.started":"2022-09-08T16:03:46.430684Z","shell.execute_reply":"2022-09-08T16:03:51.516383Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"___\n**Remarks:**\n- A large number of positively & negatively correlated variables \n- We might need to handle multicollinearity between variables to better the model performance\n___","metadata":{}},{"cell_type":"code","source":"%time corr_mat_plot('R', (30,30))","metadata":{"execution":{"iopub.status.busy":"2022-09-08T16:03:51.519161Z","iopub.execute_input":"2022-09-08T16:03:51.519783Z","iopub.status.idle":"2022-09-08T16:03:54.301477Z","shell.execute_reply.started":"2022-09-08T16:03:51.519747Z","shell.execute_reply":"2022-09-08T16:03:54.300576Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"___\n**Remarks:**\n- A large number of positively & negatively correlated variables \n- We might need to handle multicollinearity between variables to better the model performance\n___","metadata":{}},{"cell_type":"markdown","source":"## 3.3.3 Feature Correlation with Target 🎯","metadata":{}},{"cell_type":"code","source":"#Correlation between variables and target\ntemp=train_last.corr()\ntemp=temp['target'].sort_values(ascending=False)\nfig, ax = plt.subplots(figsize=(10,80))\nsns.barplot(x=temp[1:], y=temp.index[1:], orient='h', palette= ['#ff6e9c70'if y>0 else '#7A76C270' for y in temp[1:]])\nax.bar_label(ax.containers[0], fmt=\"%.2f\")\nplt.title('Feature Correleation with Target', fontsize=20)\nplt.show()\n\ndel temp\n_ = gc.collect()","metadata":{"execution":{"iopub.status.busy":"2022-09-08T16:03:54.302841Z","iopub.execute_input":"2022-09-08T16:03:54.303466Z","iopub.status.idle":"2022-09-08T16:04:32.036800Z","shell.execute_reply.started":"2022-09-08T16:03:54.303418Z","shell.execute_reply":"2022-09-08T16:04:32.035672Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"___\n**Remarks:**\n- Variables \\[D_48, D_55, B_9] are the highest postiviely correlated with target\n- Variables \\[P_2, B_2, B_18] are the highest negatively correlated with target\n___","metadata":{}},{"cell_type":"markdown","source":"## 3.3.4 Outlier plot (boxplot) 📦\n\nAn outlier is defined as a data point that is located outside the whiskers of the box plot. For example, outside 1.5 times the interquartile range above the upper quartile and below the lower quartile (Q1 - 1.5 * IQR or Q3 + 1.5 * 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plot, boxplot\ndef outlier_plot(letter, figsize):\n    cols = [c for c in train_last.columns if (c.startswith((letter,'t'))) & (c not in categorical_features) & (c != \"S_2\")]\n    temp = train_last[cols]\n    plot_cols = 4\n    plot_rows = math.ceil((len(cols) - 1)/plot_cols)\n    \n    fig, axes = plt.subplots(plot_rows, plot_cols, figsize=figsize)\n    for i, ax in enumerate(axes.reshape(-1)):\n        if i<len(cols)-1:\n            sns.boxplot(x=cols[i], data=temp, dodge=True, \n                       ax=ax)\n \n    plt.suptitle('Outlier plot of {}_* Variables'.format(letter), fontsize=20)\n    plt.tight_layout(rect=[0, 0.2, 1, 0.98])\n    plt.show()","metadata":{"execution":{"iopub.status.busy":"2022-09-08T16:04:32.038340Z","iopub.execute_input":"2022-09-08T16:04:32.038715Z","iopub.status.idle":"2022-09-08T16:04:32.045990Z","shell.execute_reply.started":"2022-09-08T16:04:32.038676Z","shell.execute_reply":"2022-09-08T16:04:32.045080Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"%time outlier_plot('D', (20,50))","metadata":{"execution":{"iopub.status.busy":"2022-09-08T16:04:32.047535Z","iopub.execute_input":"2022-09-08T16:04:32.048155Z","iopub.status.idle":"2022-09-08T16:04:44.786021Z","shell.execute_reply.started":"2022-09-08T16:04:32.048117Z","shell.execute_reply":"2022-09-08T16:04:44.785023Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"___\n**Remarks:**\n- From the boxplot, lots of variables seems to have large number of outliers \n- Some techniques can be used to deal with outliners such as:\n    - Deleting the observations\n    - Transforming values\n    - Imputation\n    - Separetely training\n___","metadata":{}},{"cell_type":"code","source":"%time outlier_plot('S', (20,15))","metadata":{"execution":{"iopub.status.busy":"2022-09-08T16:04:44.787687Z","iopub.execute_input":"2022-09-08T16:04:44.788471Z","iopub.status.idle":"2022-09-08T16:04:49.145909Z","shell.execute_reply.started":"2022-09-08T16:04:44.788428Z","shell.execute_reply":"2022-09-08T16:04:49.144989Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"___\n**Remarks:**\n- From the boxplot, lots of variables seems to have large number of outliers \n- Some techniques can be used to deal with outliners such as:\n    - Deleting the observations\n    - Transforming values\n    - Imputation\n    - Separetely training\n___","metadata":{}},{"cell_type":"code","source":"%time outlier_plot('P', (20,5))","metadata":{"execution":{"iopub.status.busy":"2022-09-08T16:04:49.147569Z","iopub.execute_input":"2022-09-08T16:04:49.148242Z","iopub.status.idle":"2022-09-08T16:04:49.845836Z","shell.execute_reply.started":"2022-09-08T16:04:49.148201Z","shell.execute_reply":"2022-09-08T16:04:49.844840Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"___\n**Remarks:**\n- From the boxplot, lots of variables seems to have large number of outliers \n- Some techniques can be used to deal with outliners such as:\n    - Deleting the observations\n    - Transforming values\n    - Imputation\n    - Separetely training\n___","metadata":{}},{"cell_type":"code","source":"%time outlier_plot('B', (20,30))","metadata":{"execution":{"iopub.status.busy":"2022-09-08T16:04:49.847492Z","iopub.execute_input":"2022-09-08T16:04:49.848469Z","iopub.status.idle":"2022-09-08T16:04:56.412669Z","shell.execute_reply.started":"2022-09-08T16:04:49.848426Z","shell.execute_reply":"2022-09-08T16:04:56.410873Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"___\n**Remarks:**\n- From the boxplot, lots of variables seems to have large number of outliers \n- Some techniques can be used to deal with outliners such as:\n    - Deleting the observations\n    - Transforming values\n    - Imputation\n    - Separetely training\n___","metadata":{}},{"cell_type":"code","source":"%time outlier_plot('R', (20,20))","metadata":{"execution":{"iopub.status.busy":"2022-09-08T16:04:56.414643Z","iopub.execute_input":"2022-09-08T16:04:56.415310Z","iopub.status.idle":"2022-09-08T16:05:00.854728Z","shell.execute_reply.started":"2022-09-08T16:04:56.415265Z","shell.execute_reply":"2022-09-08T16:05:00.853650Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"___\n**Remarks:**\n- From the boxplot, lots of variables seems to have large number of outliers \n- Some techniques can be used to deal with outliners such as:\n    - Deleting the observations\n    - Transforming values\n    - Imputation\n    - Separetely training\n___","metadata":{}},{"cell_type":"code","source":"del train_last\n_ = gc.collect()","metadata":{"execution":{"iopub.status.busy":"2022-09-08T16:05:00.858548Z","iopub.execute_input":"2022-09-08T16:05:00.859690Z","iopub.status.idle":"2022-09-08T16:05:01.174820Z","shell.execute_reply.started":"2022-09-08T16:05:00.859618Z","shell.execute_reply":"2022-09-08T16:05:01.173616Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"# 4. XGBoost Prediction Model ❎","metadata":{}},{"cell_type":"code","source":"# Train XGBoost parameters \nseed = 194\nfolds = 5 # number of K in k str folds\nver = 4 # for ver name for saved model\nnan_value = -127 # lowest int8 number\nnum_test_chunks = 8\n\nxgboost_parameters = {\n    'max_depth':4, \n    'learning_rate':0.04, \n    'subsample':0.71,\n    'colsample_bytree':0.72, \n    'eval_metric':'logloss',\n    'objective':'binary:logistic',\n    'tree_method':'gpu_hist',\n    'predictor':'gpu_predictor',\n    'random_state':seed\n}","metadata":{"execution":{"iopub.status.busy":"2022-09-08T16:05:01.180190Z","iopub.execute_input":"2022-09-08T16:05:01.183348Z","iopub.status.idle":"2022-09-08T16:05:01.192447Z","shell.execute_reply.started":"2022-09-08T16:05:01.183294Z","shell.execute_reply":"2022-09-08T16:05:01.191313Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"# read data with GPU\ndef read_GPU(path='', usecols=None, reduce_str=False):\n    if usecols is not None:\n        temp = cudf.read_parquet(path, columns=usecols)\n    else:\n        temp = cudf.read_parquet(path)\n        \n    # cudf can't work with nun-numric data properly, transfer customer_id.string to int64\n    # https://github.com/rapidsai/cudf/issues/5688\n    if reduce_str:temp['customer_ID'] = temp['customer_ID'].str[-16:].str.hex_to_int().astype('int64')\n        \n    temp.S_2 = cudf.to_datetime(temp.S_2)\n    #temp = temp.fillna(nan_value)\n    \n    print('shape of read data:', temp.shape)\n    return temp","metadata":{"execution":{"iopub.status.busy":"2022-09-08T16:05:01.198294Z","iopub.execute_input":"2022-09-08T16:05:01.199106Z","iopub.status.idle":"2022-09-08T16:05:01.212887Z","shell.execute_reply.started":"2022-09-08T16:05:01.199071Z","shell.execute_reply":"2022-09-08T16:05:01.211601Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"train = read_GPU(path=train_path)","metadata":{"execution":{"iopub.status.busy":"2022-09-08T16:05:01.218387Z","iopub.execute_input":"2022-09-08T16:05:01.221510Z","iopub.status.idle":"2022-09-08T16:05:04.902949Z","shell.execute_reply.started":"2022-09-08T16:05:01.221464Z","shell.execute_reply":"2022-09-08T16:05:04.901650Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"# preprocessing and feautre engineering with GPU libraries \ndef preprocess_and_feautre_engineer_GPU(data):\n    used_cols = [c for c in list(data.columns) if c not in ['customer_ID', 'S_2']]\n    numerical_features = [col for col in used_cols if col not in categorical_features]\n    \n    num_agg = data.groupby('customer_ID')[numerical_features].agg(['mean', 'std', 'min', 'max', 'last'])\n    num_agg.columns = ['_'.join(x) for x in num_agg.columns]\n    \n    cat_agg = data.groupby('customer_ID')[categorical_features].agg(['count', 'last', 'nunique'])\n    cat_agg.columns = ['_'.join(x) for x in cat_agg.columns]\n    \n    data = cudf.concat([num_agg, cat_agg], axis=1)\n    del num_agg, cat_agg\n    _ = gc.collect()\n    print('shape after feature engineering: ', data.shape)\n    \n    return data","metadata":{"execution":{"iopub.status.busy":"2022-09-08T16:05:04.908700Z","iopub.execute_input":"2022-09-08T16:05:04.909090Z","iopub.status.idle":"2022-09-08T16:05:04.927630Z","shell.execute_reply.started":"2022-09-08T16:05:04.909051Z","shell.execute_reply":"2022-09-08T16:05:04.926413Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"train = preprocess_and_feautre_engineer_GPU(train)","metadata":{"execution":{"iopub.status.busy":"2022-09-08T16:05:04.933275Z","iopub.execute_input":"2022-09-08T16:05:04.935861Z","iopub.status.idle":"2022-09-08T16:05:09.617110Z","shell.execute_reply.started":"2022-09-08T16:05:04.935823Z","shell.execute_reply":"2022-09-08T16:05:09.616141Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"# add labels to train\nlabels = cudf.read_csv(labels_path)\nlabels = labels.set_index('customer_ID')\ntrain = train.merge(labels, left_index=True, right_index=True, how='left')\ntrain.target = train.target.astype('int8')\ndel labels\n_ = gc.collect()\n\ntrain = train.sort_index().reset_index()\n\nfeatures = train.columns[1:-1]\nprint('number of features = ', len(features))","metadata":{"execution":{"iopub.status.busy":"2022-09-08T16:05:09.618917Z","iopub.execute_input":"2022-09-08T16:05:09.619300Z","iopub.status.idle":"2022-09-08T16:05:10.794186Z","shell.execute_reply.started":"2022-09-08T16:05:09.619264Z","shell.execute_reply":"2022-09-08T16:05:10.793057Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"train.head(10)","metadata":{"execution":{"iopub.status.busy":"2022-09-08T16:05:10.795669Z","iopub.execute_input":"2022-09-08T16:05:10.797745Z","iopub.status.idle":"2022-09-08T16:05:11.836514Z","shell.execute_reply.started":"2022-09-08T16:05:10.797702Z","shell.execute_reply":"2022-09-08T16:05:11.835374Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"# data loader DeviceQuantileDMatrix A data iterator for XGBoost DMatrix\n# https://xgboost.readthedocs.io/en/latest/python/examples/quantile_data_iterator.html\nclass IterLoadForDMatrix(xgb.core.DataIter):\n    def __init__(self, df=None, features=None, target=None, batch_size=256*1024):\n        self.features = features\n        self.target = target\n        self.df = df\n        self.it = 0 # set iterator to 0\n        self.batch_size = batch_size\n        self.batches = int( np.ceil( len(df) / self.batch_size ) )\n        super().__init__()\n\n    def reset(self):\n        '''Reset the iterator'''\n        self.it = 0\n\n    def next(self, input_data):\n        '''Yield next batch of data.'''\n        if self.it == self.batches:\n            return 0 # Return 0 when there's no more batch.\n        \n        a = self.it * self.batch_size\n        b = min( (self.it + 1) * self.batch_size, len(self.df) )\n        dt = cudf.DataFrame(self.df.iloc[a:b])\n        input_data(data=dt[self.features], label=dt[self.target]) #, weight=dt['weight'])\n        self.it += 1\n        return 1","metadata":{"execution":{"iopub.status.busy":"2022-09-08T16:05:11.838296Z","iopub.execute_input":"2022-09-08T16:05:11.838719Z","iopub.status.idle":"2022-09-08T16:05:11.848254Z","shell.execute_reply.started":"2022-09-08T16:05:11.838682Z","shell.execute_reply":"2022-09-08T16:05:11.847180Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"## 4.1 AMEX Default Prediction Evaluating Metric 📏\n\nThe evaluation metric *M* for this task is the mean of two measures of rank ordering: Normalized Gini Coefficient, *G* , and default rate captured at 4%, *D*.\n\n![image.png](attachment:b4b7930d-56e9-42b7-985b-6bdd745079ac.png)\n\n\nThe default rate captured at 4% is the percentage of the positive labels (defaults) captured within the highest-ranked 4% of the predictions, and represents a Sensitivity/Recall statistic.\n\nFor both of the sub-metrics *G* and *D* , the negative labels are given a weight of 20 to adjust for downsampling.\n\nThis metric has a maximum value of 1.0.\n\n![image.png](attachment:7b36983c-2b5f-4186-af20-f950bce3dff3.png)\n\n","metadata":{},"attachments":{"7b36983c-2b5f-4186-af20-f950bce3dff3.png":{"image/png":"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"},"b4b7930d-56e9-42b7-985b-6bdd745079ac.png":{"image/png":"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"}}},{"cell_type":"code","source":"# defining the used metric for evaluation\n# https://www.kaggle.com/competitions/amex-default-prediction/discussion/328020\n# https://www.kaggle.com/competitions/amex-default-prediction/overview/evaluation\n# https://www.kaggle.com/competitions/amex-default-prediction/discussion/328020\n# https://www.kaggle.com/competitions/amex-default-prediction/discussion/327464\n# https://www.kaggle.com/competitions/amex-default-prediction/discussion/327116\n# https://theblog.github.io/post/gini-coefficient-intuitive-explanation/\n\ndef amex_metric(y_true, y_pred):\n\n    labels     = np.transpose(np.array([y_true, y_pred]))\n    labels     = labels[labels[:, 1].argsort()[::-1]]\n    weights    = np.where(labels[:,0]==0, 20, 1)\n    cut_vals   = labels[np.cumsum(weights) <= int(0.04 * np.sum(weights))]\n    top_four   = np.sum(cut_vals[:,0]) / np.sum(labels[:,0])\n\n    gini = [0,0]\n    for i in [1,0]:\n        labels         = np.transpose(np.array([y_true, y_pred]))\n        labels         = labels[labels[:, i].argsort()[::-1]]\n        weight         = np.where(labels[:,0]==0, 20, 1)\n        weight_random  = np.cumsum(weight / np.sum(weight))\n        total_pos      = np.sum(labels[:, 0] *  weight)\n        cum_pos_found  = np.cumsum(labels[:, 0] * weight)\n        lorentz        = cum_pos_found / total_pos\n        gini[i]        = np.sum((lorentz - weight_random) * weight)\n\n    return 0.5 * (gini[1]/gini[0] + top_four)","metadata":{"execution":{"iopub.status.busy":"2022-09-08T16:05:11.850021Z","iopub.execute_input":"2022-09-08T16:05:11.850798Z","iopub.status.idle":"2022-09-08T16:05:11.863465Z","shell.execute_reply.started":"2022-09-08T16:05:11.850761Z","shell.execute_reply":"2022-09-08T16:05:11.862110Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"## 4.2 Model Training and Initialization 🔃","metadata":{}},{"cell_type":"code","source":"%%time\n# model train\nimportances, oof = [], [] # oof: out-of-fold\ntrain = train.to_pandas()\ntrain_subsample = 1\n_ = gc.collect()\n\nskf = KFold(n_splits=folds, shuffle=True, random_state=seed)\nfor fold, (train_idx, valid_idx) in enumerate(skf.split(train, train.target)):\n    \n    # train with subsample of train fold data\n    if train_subsample<1.0:\n        cupy.random.seed(seed)\n        train_idx = cupy.random.choice(train_idx,\n                                      int(len(train_idx)*train_subsample), replace=False)\n        cupy.random.seed(None)\n        \n    print('#'*25)\n    print('### Fold',fold+1)\n    print('### Train size',len(train_idx),'Valid size',len(valid_idx))\n    print(f'### Training with {int(train_subsample*100)}% fold data...')\n    print('#'*25)\n    \n    #train, valid, test for fold k\n    xy_train = IterLoadForDMatrix(train.loc[train_idx], features, 'target')\n    x_valid = train.loc[valid_idx, features]\n    y_valid = train.loc[valid_idx, 'target']\n    \n    dtrain = xgb.DeviceQuantileDMatrix(xy_train, max_bin=256)\n    dvalid = xgb.DMatrix(data=x_valid, label=y_valid)\n    \n    # Train model fold k\n    model = xgb.train(xgboost_parameters,\n                     dtrain=dtrain,\n                     evals=[(dtrain, 'train'), (dvalid,'valid')],\n                     num_boost_round=9999,\n                     early_stopping_rounds=100,\n                     verbose_eval=100)\n    model.save_model(f'xgb_v{ver}_fold{fold}.xgb')\n    \n    # calculate feature importance for fold k \n    dd = model.get_score(importance_type='weight')\n    df = pd.DataFrame({'feature':dd.keys(), f'importance_{fold}':dd.values()})\n    importances.append(df)\n    \n    # infer the OOF (out of fold) of k fold\n    oof_preds = model.predict(dvalid)\n    acc = amex_metric(y_valid.values, oof_preds)\n    print('AMEX Kaggle Metric =', acc, \"\\n\")\n    \n    # Save the oof preds\n    df = train.loc[valid_idx, ['customer_ID', 'target']].copy()\n    df['oof_pred'] = oof_preds\n    oof.append(df)\n    \n    del dtrain, xy_train, dd, df, x_valid, y_valid, dvalid, model\n    _ = gc.collect()\n    \nprint('#'*25)\noof = pd.concat(oof, axis=0, ignore_index=True).set_index('customer_ID')\nacc = amex_metric(oof.target.values, oof.oof_pred.values)\nprint('Overall cv kaggle metric =', acc)","metadata":{"execution":{"iopub.status.busy":"2022-09-08T16:05:11.865672Z","iopub.execute_input":"2022-09-08T16:05:11.866604Z","iopub.status.idle":"2022-09-08T16:16:30.877290Z","shell.execute_reply.started":"2022-09-08T16:05:11.866566Z","shell.execute_reply":"2022-09-08T16:16:30.876087Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"del train\n_ = gc.collect()","metadata":{"scrolled":true,"execution":{"iopub.status.busy":"2022-09-08T16:16:30.878964Z","iopub.execute_input":"2022-09-08T16:16:30.879545Z","iopub.status.idle":"2022-09-08T16:16:31.065289Z","shell.execute_reply.started":"2022-09-08T16:16:30.879512Z","shell.execute_reply":"2022-09-08T16:16:31.063751Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"oof.head()","metadata":{"execution":{"iopub.status.busy":"2022-09-08T16:16:31.066784Z","iopub.execute_input":"2022-09-08T16:16:31.068042Z","iopub.status.idle":"2022-09-08T16:16:31.083866Z","shell.execute_reply.started":"2022-09-08T16:16:31.067998Z","shell.execute_reply":"2022-09-08T16:16:31.082456Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"# save oof predictions\noof_xgb = pd.read_parquet(train_path, columns=['customer_ID']).drop_duplicates()\noof_xgb = oof_xgb.set_index('customer_ID')\noof_xgb = oof_xgb.merge(oof, left_index=True, right_index=True)\noof_xgb = oof_xgb.sort_index().reset_index(drop=False)\noof_xgb.to_csv(f'oof_xgb_v{ver}.csv',index=False)\noof_xgb.head(10)","metadata":{"execution":{"iopub.status.busy":"2022-09-08T16:16:31.085557Z","iopub.execute_input":"2022-09-08T16:16:31.086417Z","iopub.status.idle":"2022-09-08T16:16:35.174541Z","shell.execute_reply.started":"2022-09-08T16:16:31.086377Z","shell.execute_reply":"2022-09-08T16:16:35.173341Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"fig, ax = plt.subplots(1,1, figsize=(20,6))\nsns.histplot(x='oof_pred', data=oof_xgb, bins = 150)\nax.set_title('Out-of-fold Predictions of Train Data', fontsize=20)\nax.set_xlabel('Prediction Score')\nax.set_ylabel('Number of Customers')\nplt.show()","metadata":{"execution":{"iopub.status.busy":"2022-09-08T16:16:35.175838Z","iopub.execute_input":"2022-09-08T16:16:35.176613Z","iopub.status.idle":"2022-09-08T16:16:35.683295Z","shell.execute_reply.started":"2022-09-08T16:16:35.176581Z","shell.execute_reply":"2022-09-08T16:16:35.682237Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"del oof_xgb, oof\n_ = gc.collect()","metadata":{"execution":{"iopub.status.busy":"2022-09-08T16:16:35.684941Z","iopub.execute_input":"2022-09-08T16:16:35.686091Z","iopub.status.idle":"2022-09-08T16:16:35.870865Z","shell.execute_reply.started":"2022-09-08T16:16:35.686049Z","shell.execute_reply":"2022-09-08T16:16:35.869356Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"df = importances[0].copy()\nfor k in range(1,folds): df = df.merge(importances[k], on='feature', how='left')\ndf['importances'] = df.iloc[:,1:].mean(axis=1)\ndf = df.sort_values('importances', ascending=False)\ndf.to_csv(f'xgb_feature_importance_v{ver}.csv', index=False)","metadata":{"execution":{"iopub.status.busy":"2022-09-08T16:16:35.872586Z","iopub.execute_input":"2022-09-08T16:16:35.874946Z","iopub.status.idle":"2022-09-08T16:16:35.904517Z","shell.execute_reply.started":"2022-09-08T16:16:35.874903Z","shell.execute_reply":"2022-09-08T16:16:35.903382Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"df.head()","metadata":{"execution":{"iopub.status.busy":"2022-09-08T16:16:35.905817Z","iopub.execute_input":"2022-09-08T16:16:35.906140Z","iopub.status.idle":"2022-09-08T16:16:35.926220Z","shell.execute_reply.started":"2022-09-08T16:16:35.906112Z","shell.execute_reply":"2022-09-08T16:16:35.925344Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"## 4.3 Model Feature Importance ⚡","metadata":{}},{"cell_type":"code","source":"num_importances = 50\nfig, ax = plt.subplots(figsize=(10,num_importances*3//5))\nsns.barplot(x=df['importances'][:num_importances], y=df['feature'][:num_importances], orient='h', color = \"#7A76C2\")\nax.bar_label(ax.containers[0], fmt='%.0f')\nplt.title('XGBoost Feature Importance', fontsize=20)\nplt.show()","metadata":{"execution":{"iopub.status.busy":"2022-09-08T16:16:35.927644Z","iopub.execute_input":"2022-09-08T16:16:35.928349Z","iopub.status.idle":"2022-09-08T16:16:36.912361Z","shell.execute_reply.started":"2022-09-08T16:16:35.928295Z","shell.execute_reply":"2022-09-08T16:16:36.911320Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"del df\n_ = gc.collect()","metadata":{"execution":{"iopub.status.busy":"2022-09-08T16:16:36.913840Z","iopub.execute_input":"2022-09-08T16:16:36.914672Z","iopub.status.idle":"2022-09-08T16:16:37.085987Z","shell.execute_reply.started":"2022-09-08T16:16:36.914631Z","shell.execute_reply":"2022-09-08T16:16:37.084818Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"## 4.4 Test Inferences 🎓 ","metadata":{}},{"cell_type":"code","source":"# GPU VRAM cleaning\n#gc.collect()","metadata":{"execution":{"iopub.status.busy":"2022-09-08T16:16:37.088137Z","iopub.execute_input":"2022-09-08T16:16:37.088562Z","iopub.status.idle":"2022-09-08T16:16:37.098249Z","shell.execute_reply.started":"2022-09-08T16:16:37.088489Z","shell.execute_reply":"2022-09-08T16:16:37.097198Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"# load test data into GPU in chunks\ndef rows_chunks(customers, test, num_chunks=4):\n    chunk = len(customers)//num_chunks\n    print(f'chunk size of: {chunk} each (except last chunk) in {num_chunks} parts')\n    rows = []\n    \n    for k in range(num_chunks):\n        if k == num_chunks-1:\n            cc = customers[k*chunk:]\n        else:\n            cc = customers[k*chunk:(k+1)*chunk]\n        s = test.loc[test.customer_ID.isin(cc)].shape[0]\n        rows.append(s)    \n    print('# of rows in each part: ',rows)\n    return rows, chunk","metadata":{"execution":{"iopub.status.busy":"2022-09-08T16:16:37.099807Z","iopub.execute_input":"2022-09-08T16:16:37.101148Z","iopub.status.idle":"2022-09-08T16:16:37.109043Z","shell.execute_reply.started":"2022-09-08T16:16:37.101102Z","shell.execute_reply":"2022-09-08T16:16:37.108091Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"test = read_GPU(path = test_path, usecols=['customer_ID', 'S_2'], reduce_str=True)\ncustomers = test[['customer_ID']].drop_duplicates().sort_index().values.flatten()\nrows, num_cust = rows_chunks(customers, test[['customer_ID']], num_chunks=num_test_chunks)","metadata":{"execution":{"iopub.status.busy":"2022-09-08T16:16:37.110410Z","iopub.execute_input":"2022-09-08T16:16:37.110878Z","iopub.status.idle":"2022-09-08T16:16:40.467052Z","shell.execute_reply.started":"2022-09-08T16:16:37.110842Z","shell.execute_reply":"2022-09-08T16:16:40.465013Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"%%time\n#inferance of test data\nskip_rows = 0\nskip_cust = 0\ntest_preds = []\n\nfor k in range(num_test_chunks):\n    # read\n    print('\\nLoading Test Data:')\n    test = read_GPU(path=test_path, reduce_str=True)\n    test = test.iloc[skip_rows:skip_rows+rows[k]]\n    skip_rows += rows[k]\n    print(f'Chunk part {k+1} out of {num_test_chunks} with test shape:', test.shape)\n    \n    #feature engineering\n    test = preprocess_and_feautre_engineer_GPU(test)\n    if k == num_test_chunks - 1:\n        test = test.loc[customers[skip_cust:]]\n    else:\n        test = test.loc[customers[skip_cust:skip_cust+num_cust]]\n    skip_cust += num_cust\n    \n    #test data load into gpu dmatric \n    x_test = test[features]\n    dtest = xgb.DMatrix(data = x_test)\n    test = test[[\"P_2_mean\"]]\n    del x_test\n    gc.collect()\n    \n    # infer XGB\n    for m in range(0, folds):\n        if m==0:\n            model = xgb.Booster()\n            model.load_model(f'xgb_v{ver}_fold{m}.xgb')\n            preds = model.predict(dtest)\n        else:    \n            model.load_model(f'xgb_v{ver}_fold{m}.xgb')\n            preds += model.predict(dtest)\n            \n    preds /= folds\n    test_preds.append(preds)\n    \n    del dtest, model\n    _ = gc.collect()","metadata":{"execution":{"iopub.status.busy":"2022-09-08T16:16:40.468779Z","iopub.execute_input":"2022-09-08T16:16:40.469154Z","iopub.status.idle":"2022-09-08T16:19:25.535645Z","shell.execute_reply.started":"2022-09-08T16:16:40.469115Z","shell.execute_reply":"2022-09-08T16:19:25.534578Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"# submission for competition\ntest_preds = np.concatenate(test_preds)\ntest = cudf.DataFrame(index=customers,data={'prediction':test_preds})\nsub = cudf.read_csv('../input/amex-default-prediction/sample_submission.csv')[['customer_ID']]\nsub['customer_ID_hash'] = sub['customer_ID'].str[-16:].str.hex_to_int().astype('int64')\nsub = sub.set_index('customer_ID_hash')\nsub = sub.merge(test[['prediction']], left_index=True, right_index=True, how='left')\nsub = sub.reset_index(drop=True)\n\nsub.to_csv(f'AMEX_XGBoost_v{ver}_submission.csv', index=False)\nprint('submission csv shape:', sub.shape)\nsub.head()","metadata":{"execution":{"iopub.status.busy":"2022-09-08T16:19:25.538516Z","iopub.execute_input":"2022-09-08T16:19:25.538901Z","iopub.status.idle":"2022-09-08T16:19:26.627932Z","shell.execute_reply.started":"2022-09-08T16:19:25.538865Z","shell.execute_reply":"2022-09-08T16:19:26.626794Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"fig, ax = plt.subplots(1,1, figsize=(20,6))\nsns.histplot(x='prediction', data=sub.to_pandas(), bins = 150)\nax.set_title('Final Test Data Prediction Distribution', fontsize=20)\nax.set_xlabel('Prediction Score')\nax.set_ylabel('Number of Customers')\nplt.show()","metadata":{"execution":{"iopub.status.busy":"2022-09-08T16:19:26.629819Z","iopub.execute_input":"2022-09-08T16:19:26.630208Z","iopub.status.idle":"2022-09-08T16:19:27.621107Z","shell.execute_reply.started":"2022-09-08T16:19:26.630170Z","shell.execute_reply":"2022-09-08T16:19:27.620056Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"# 5.Future work 🔮\n\n- After running standard XGBoost model, outputing a score of 0.80250 with the best model outputing a scoring 0.80977.\n- Since the data is quite large, running a RandomizedSearchCV on the XGBoost model parameter will most likely achieve better results\n- Observation from EDA can be utilized more in improving the model such as:\n    - Giving weights to highly correlated and important varaibles with target\n    - Reducing columns through dimensionality reduction techniques\n    - Removing highly correlated varaibles (multicollinearity)\n    - Dealing with outliers and missing variables\n    - Time seires analysis with monthly statements","metadata":{}}]}