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"}}},{"cell_type":"code","source":"import numpy as np \nimport pandas as pd \nimport datetime\nimport os\nimport time\nimport matplotlib.pyplot as plt\nimport seaborn as sns\nimport gc\n%matplotlib inline","metadata":{"execution":{"iopub.status.busy":"2022-09-29T14:14:21.310782Z","iopub.execute_input":"2022-09-29T14:14:21.311277Z","iopub.status.idle":"2022-09-29T14:14:22.538336Z","shell.execute_reply.started":"2022-09-29T14:14:21.311179Z","shell.execute_reply":"2022-09-29T14:14:22.537056Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"It will have happened more than once among us kagglers to preprocess a large database and pray that our kernel does not reset due to **over-allocation** of memory.\n\nFortunately, there are tricks, which consist of preprocessing the data in **chunks**, so that the calculations do not stress the RAM.\n\nObviously, the division of data into chunks does not follow a standard rule, but depends on the case study and the type of calculations we have to perform.\n\nFor example, usually when dealing with time series of bank data, it is very important to preprocess by grouping the data by **customer id**.\n\n\nThe simplest strategy is to remove the id of **duplicate** customers, and split the data into chunks of the **same size**. Other times we need to **keep** the duplicates to see if customers have different behaviour over time; for example, it is very common to count (**count()**) how many times a customer occurs in the data.","metadata":{}},{"cell_type":"markdown","source":"**READ EXAMPLE DATASET**","metadata":{}},{"cell_type":"markdown","source":"For demonstration purposes, we choose a small dataset. There are **100000** rows, and we select 2 columns (**ip** and **channel**)","metadata":{}},{"cell_type":"code","source":"#wanted columns\ncolumns = ['ip', 'channel']\ndtypes = {\n        'ip'      : 'uint32',\n        'channel' : 'uint16',\n        }\n\nips_df = pd.read_csv('../input/talkingdata-adtracking-fraud-detection/train_sample.csv', usecols=columns, dtype=dtypes)","metadata":{"execution":{"iopub.status.busy":"2022-09-29T14:14:43.202479Z","iopub.execute_input":"2022-09-29T14:14:43.203022Z","iopub.status.idle":"2022-09-29T14:14:43.348445Z","shell.execute_reply.started":"2022-09-29T14:14:43.202974Z","shell.execute_reply":"2022-09-29T14:14:43.346968Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"print(ips_df.info())\nprint(ips_df.head())","metadata":{"execution":{"iopub.status.busy":"2022-09-29T14:14:45.203504Z","iopub.execute_input":"2022-09-29T14:14:45.203903Z","iopub.status.idle":"2022-09-29T14:14:45.238865Z","shell.execute_reply.started":"2022-09-29T14:14:45.203871Z","shell.execute_reply":"2022-09-29T14:14:45.237371Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"**CHUNKS OF THE SAME SIZE** (common approach)","metadata":{}},{"cell_type":"code","source":"# Dividing our dataset into 4 parts we obtain 25000 rows per chunk\nnum_parts = 4\nall_rows = len(ips_df)\nchunk = all_rows//num_parts","metadata":{"execution":{"iopub.status.busy":"2022-09-29T14:14:47.807335Z","iopub.execute_input":"2022-09-29T14:14:47.807737Z","iopub.status.idle":"2022-09-29T14:14:47.813859Z","shell.execute_reply.started":"2022-09-29T14:14:47.807705Z","shell.execute_reply":"2022-09-29T14:14:47.812470Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"We want to perform a single calculation, i.e. **count** how many times the variable **channel** occurs for each **ip**. But remember that we can also calculate the sum (sum()), standard deviation (std()), number of unique (nunique()) etc...","metadata":{}},{"cell_type":"code","source":"for p in range(0,num_parts):\n    start = p*chunk\n    end = p*chunk + chunk\n    if end < all_rows:\n        group = ips_df[start:end][['ip', 'channel']].groupby('ip', as_index=False).count()\n    else:\n        group = ips_df[start:][['ip', 'channel']].groupby('ip', as_index=False).count()\n    print(group)","metadata":{"execution":{"iopub.status.busy":"2022-09-29T14:14:49.657505Z","iopub.execute_input":"2022-09-29T14:14:49.658013Z","iopub.status.idle":"2022-09-29T14:14:49.712098Z","shell.execute_reply.started":"2022-09-29T14:14:49.657975Z","shell.execute_reply":"2022-09-29T14:14:49.710840Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"**LOOK CAREFULLY**, the **ip = 10** occurs in the **first**, **second** and **third** group/chunk. Specifically, the ip = 10 occurs **once** in the first 3 groups, so what we have to do is **merge** the groups and do the sum per row, so that we get 3 (1 + 1 + 1).\n\nThis process is annoying and would increase the complexity of the calculation, and consequently the RAM used. To remedy this, we can create variable chunks. ","metadata":{}},{"cell_type":"markdown","source":"**CHUNKS OF DIFFERENT SIZE** (improved approach)","metadata":{}},{"cell_type":"code","source":"#sort the dataset by ip and reset the index \nips_df.sort_values(by=['ip'], inplace  = True)\nips_df.reset_index(drop = True, inplace = True)\nips_df.head()","metadata":{"execution":{"iopub.status.busy":"2022-09-29T14:14:53.736995Z","iopub.execute_input":"2022-09-29T14:14:53.737493Z","iopub.status.idle":"2022-09-29T14:14:53.772862Z","shell.execute_reply.started":"2022-09-29T14:14:53.737450Z","shell.execute_reply":"2022-09-29T14:14:53.771537Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"Create a **window** of values so as to be sure not to cut out the most frequent values\n\nEXAMPLE: \n\nSuppose we have a list, and **imagine** that this list is actually an ID column of customers:\n\nlist_ID = [1, 2, 2, 3, 3, 3, 4, 4, 4, 4, 4, 8, 8, 9, 9, 10, 12, 18, 19, 20]\n\n\nWe divide the list into 2 chunks or batches of data, and we get:\n\nchunk1 = [1, 2, 2, 3, 3, 3, 4, 4, 4, 4]\nchunk2 = [4, 8, 8, 9, 9, 10, 12, 18, 19]\n\nAs you can see, the customer with **ID = 4** is in both chunks, and we want to avoid this. What we need to do is update chunk1, so that the client with **ID = 4** is only in this chunk, and consequently both chunks will have a different size.\n\nWhat we need to do is to calculate the **unique values** of chunk1. Yes, very fast and simple, but if we have an ID column of 5 million customers and we need to optimise the calculation to avoid excessive RAM usage, how can we do it?\n\nCreate a **WINDOW** of rows, so that we only work on the last part of the chunk. Let us see it in practice\n\nWINDOW = 5 (number of times the most frequent value (ID = 4) occurs in list_ID)\n\nThis means that we only need to work on the **last 5 rows + 1**\n\nchunk_window = [3, 3, 4, 4, 4, 4]\n\nWe now calculate the **unique values**, and following the **second last value**\n\nlist_unique = [3, 4]\n\nsecond_last = [3]\n\nPerfect now we can update **chunk1** with a new size. The last row must correspond to exactly this value.\n\nchunk1 = [1, 2, 2, 3, 3, 3]\n\nchunk2 = [4, 4, 4, 4, 4, 8, 8, 9, 9, 10, 12, 18, 19]\n\n\nThis way we are sure that customers do not end up in separate chunks. Now let us move on to the code ","metadata":{}},{"cell_type":"code","source":"#calculate the most common value with the \"mode\", and the \"window\"\nimport operator as op\na_list = list(ips_df['ip'])\nmost_common = int(ips_df['ip'].mode())\nwindow = op.countOf(a_list,most_common)","metadata":{"execution":{"iopub.status.busy":"2022-09-29T14:14:58.052296Z","iopub.execute_input":"2022-09-29T14:14:58.052822Z","iopub.status.idle":"2022-09-29T14:14:58.078053Z","shell.execute_reply.started":"2022-09-29T14:14:58.052783Z","shell.execute_reply":"2022-09-29T14:14:58.076033Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"#function to get the unique values\ndef unique(list1):\n    x = np.array(list1)\n    return np.unique(x)","metadata":{"execution":{"iopub.status.busy":"2022-09-29T14:15:00.993602Z","iopub.execute_input":"2022-09-29T14:15:00.994750Z","iopub.status.idle":"2022-09-29T14:15:01.001433Z","shell.execute_reply.started":"2022-09-29T14:15:00.994691Z","shell.execute_reply":"2022-09-29T14:15:01.000425Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"#set start = 0\nstart = 0\n#new dataframe to append the results of the for loop\nnew_ips_df=pd.DataFrame()\nfor p in range(0,num_parts):\n    end = p*chunk + chunk\n    if end < all_rows:\n        chunk_window = ips_df[start:end].tail(window+1)\n        list_unique = unique(chunk_window['ip'])\n        second_last = list_unique[-2]\n        new_end = chunk_window[chunk_window['ip'] == second_last].tail(1).index.tolist()[0]\n        new_chunk = ips_df[start:new_end+1][['ip', 'channel']].groupby('ip', as_index=False).count()\n    else:\n        new_chunk = ips_df[start:][['ip', 'channel']].groupby('ip', as_index=False).count()\n    start = new_end+1\n    new_ips_df = new_ips_df.append(new_chunk, ignore_index=True)","metadata":{"execution":{"iopub.status.busy":"2022-09-29T14:15:07.734205Z","iopub.execute_input":"2022-09-29T14:15:07.734708Z","iopub.status.idle":"2022-09-29T14:15:07.777562Z","shell.execute_reply.started":"2022-09-29T14:15:07.734670Z","shell.execute_reply":"2022-09-29T14:15:07.776398Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"new_ips_df","metadata":{"execution":{"iopub.status.busy":"2022-09-29T14:15:11.734528Z","iopub.execute_input":"2022-09-29T14:15:11.735080Z","iopub.status.idle":"2022-09-29T14:15:11.752654Z","shell.execute_reply.started":"2022-09-29T14:15:11.735034Z","shell.execute_reply":"2022-09-29T14:15:11.751172Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"Well done, now try it yourself with the **7.54GB** dataset (train.csv)","metadata":{}},{"cell_type":"code","source":"#wanted columns\ncolumns = ['ip', 'channel']\ndtypes = {\n        'ip'      : 'uint32',\n        'channel' : 'uint16',\n        }\n\nips_df = pd.read_csv('../input/talkingdata-adtracking-fraud-detection/train.csv', usecols=columns, dtype=dtypes)","metadata":{"execution":{"iopub.status.busy":"2022-09-29T14:16:03.879737Z","iopub.execute_input":"2022-09-29T14:16:03.880197Z","iopub.status.idle":"2022-09-29T14:18:58.211034Z","shell.execute_reply.started":"2022-09-29T14:16:03.880162Z","shell.execute_reply":"2022-09-29T14:18:58.208661Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"print(ips_df.info())","metadata":{"execution":{"iopub.status.busy":"2022-09-29T14:19:18.923963Z","iopub.execute_input":"2022-09-29T14:19:18.924484Z","iopub.status.idle":"2022-09-29T14:19:18.943276Z","shell.execute_reply.started":"2022-09-29T14:19:18.924441Z","shell.execute_reply":"2022-09-29T14:19:18.941778Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"# Dividing our dataset into 4 parts we obtain 46225972 rows per chunk\nnum_parts = 4\nall_rows = len(ips_df)\nchunk = all_rows//num_parts\n#sort the dataset by ip and reset the index \nips_df.sort_values(by=['ip'], inplace  = True)\nips_df.reset_index(drop = True, inplace = True)\nips_df.head()","metadata":{"execution":{"iopub.status.busy":"2022-09-29T14:20:02.399191Z","iopub.execute_input":"2022-09-29T14:20:02.400038Z","iopub.status.idle":"2022-09-29T14:21:26.493222Z","shell.execute_reply.started":"2022-09-29T14:20:02.399966Z","shell.execute_reply":"2022-09-29T14:21:26.491706Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"#calculate the most common value with the \"mode\", and the \"window\"\nimport operator as op\na_list = list(ips_df['ip'])\nmost_common = int(ips_df['ip'].mode())\nwindow = op.countOf(a_list,most_common)\n\ndel a_list\n\ngc.collect()","metadata":{"execution":{"iopub.status.busy":"2022-09-29T14:23:57.315936Z","iopub.execute_input":"2022-09-29T14:23:57.316394Z","iopub.status.idle":"2022-09-29T14:24:27.824093Z","shell.execute_reply.started":"2022-09-29T14:23:57.316360Z","shell.execute_reply":"2022-09-29T14:24:27.822980Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"#function to get the unique values\ndef unique(list1):\n    x = np.array(list1)\n    return np.unique(x)","metadata":{"execution":{"iopub.status.busy":"2022-09-29T14:25:02.787544Z","iopub.execute_input":"2022-09-29T14:25:02.788088Z","iopub.status.idle":"2022-09-29T14:25:02.796248Z","shell.execute_reply.started":"2022-09-29T14:25:02.788047Z","shell.execute_reply":"2022-09-29T14:25:02.794439Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"#set start = 0\nstart = 0\n#new dataframe to append the results of the for loop\nnew_ips_df=pd.DataFrame()\nfor p in range(0,num_parts):\n    end = p*chunk + chunk\n    if end < all_rows:\n        chunk_window = ips_df[start:end].tail(window+1)\n        list_unique = unique(chunk_window['ip'])\n        second_last = list_unique[-2]\n        new_end = chunk_window[chunk_window['ip'] == second_last].tail(1).index.tolist()[0]\n        new_chunk = ips_df[start:new_end+1][['ip', 'channel']].groupby('ip', as_index=False).count()\n    else:\n        new_chunk = ips_df[start:][['ip', 'channel']].groupby('ip', as_index=False).count()\n    start = new_end+1\n    new_ips_df = new_ips_df.append(new_chunk, ignore_index=True)\n    del new_chunk","metadata":{"execution":{"iopub.status.busy":"2022-09-29T14:27:30.320548Z","iopub.execute_input":"2022-09-29T14:27:30.321236Z","iopub.status.idle":"2022-09-29T14:27:39.018455Z","shell.execute_reply.started":"2022-09-29T14:27:30.321196Z","shell.execute_reply":"2022-09-29T14:27:39.017169Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"new_ips_df","metadata":{"execution":{"iopub.status.busy":"2022-09-29T14:28:00.980344Z","iopub.execute_input":"2022-09-29T14:28:00.980786Z","iopub.status.idle":"2022-09-29T14:28:00.994581Z","shell.execute_reply.started":"2022-09-29T14:28:00.980753Z","shell.execute_reply":"2022-09-29T14:28:00.993341Z"},"trusted":true},"execution_count":null,"outputs":[]}]}