demo/event-sequence-local-embeddings.ipynb

{
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "8a795c70"
      },
      "source": [
        "# Colab setup"
      ],
      "id": "8a795c70"
    },
    {
      "cell_type": "code",
      "source": [
        "from IPython.display import clear_output"
      ],
      "metadata": {
        "id": "oC0IXH4APJCb"
      },
      "id": "oC0IXH4APJCb",
      "execution_count": 1,
      "outputs": []
    },
    {
      "cell_type": "code",
      "execution_count": 2,
      "metadata": {
        "id": "8b6cedd5"
      },
      "outputs": [],
      "source": [
        "import sys\n",
        "if 'google.colab' in str(get_ipython()):\n",
        "    ! {sys.executable} -m pip install pytorch-lifestream\n",
        "\n",
        "clear_output()"
      ],
      "id": "8b6cedd5"
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "a7431993"
      },
      "source": [
        "## Data load"
      ],
      "id": "a7431993"
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "9e91a902"
      },
      "source": [
        "## Setup"
      ],
      "id": "9e91a902"
    },
    {
      "cell_type": "code",
      "execution_count": 3,
      "metadata": {
        "id": "587df1ea"
      },
      "outputs": [],
      "source": [
        "%load_ext autoreload\n",
        "%autoreload 2\n",
        "\n",
        "# import logging\n",
        "import torch\n",
        "import pytorch_lightning as pl\n",
        "# import warnings\n",
        "\n",
        "# warnings.filterwarnings('ignore')\n",
        "# logging.getLogger(\"pytorch_lightning\").setLevel(logging.ERROR)"
      ],
      "id": "587df1ea"
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "82f989bc"
      },
      "source": [
        "## Data preproccessing"
      ],
      "id": "82f989bc"
    },
    {
      "cell_type": "code",
      "source": [
        "import os\n",
        "import pandas as pd\n",
        "\n",
        "source_data = pd.read_csv('https://huggingface.co/datasets/dllllb/age-group-prediction/resolve/main/transactions_train.csv.gz?download=true',\n",
        "                          compression='gzip')\n",
        "source_data.head(2)"
      ],
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          "height": 112
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      "id": "s08H8cQSn7nJ",
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   client_id  trans_date  small_group  amount_rur\n",
              "0      33172           6            4      71.463\n",
              "1      33172           6           35      45.017"
            ],
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              "variable_name": "source_data"
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          },
          "metadata": {},
          "execution_count": 4
        }
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 5,
      "metadata": {
        "id": "8615b722"
      },
      "outputs": [],
      "source": [
        "from ptls.preprocessing import PandasDataPreprocessor\n",
        "\n",
        "preprocessor = PandasDataPreprocessor(\n",
        "    col_id='client_id',\n",
        "    col_event_time='trans_date',\n",
        "    event_time_transformation='none',\n",
        "    cols_category=['small_group'],\n",
        "    cols_numerical=['amount_rur'],\n",
        "    return_records=True,\n",
        ")"
      ],
      "id": "8615b722"
    },
    {
      "cell_type": "code",
      "execution_count": 6,
      "metadata": {
        "id": "fca72f6e",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "7937011e-ac9e-4044-85cc-39ead77450a7"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "CPU times: user 55.9 s, sys: 13.8 s, total: 1min 9s\n",
            "Wall time: 1min 10s\n"
          ]
        }
      ],
      "source": [
        "%%time\n",
        "\n",
        "dataset = preprocessor.fit_transform(source_data)"
      ],
      "id": "fca72f6e"
    },
    {
      "cell_type": "code",
      "execution_count": 7,
      "metadata": {
        "id": "4a747c9b"
      },
      "outputs": [],
      "source": [
        "import pickle\n",
        "\n",
        "with open('preprocessor.p', 'wb') as f:\n",
        "    pickle.dump(preprocessor, f)"
      ],
      "id": "4a747c9b"
    },
    {
      "cell_type": "code",
      "execution_count": 8,
      "metadata": {
        "id": "0e4ca12d"
      },
      "outputs": [],
      "source": [
        "dataset = sorted(dataset, key=lambda x: x['client_id'])"
      ],
      "id": "0e4ca12d"
    },
    {
      "cell_type": "code",
      "execution_count": 9,
      "metadata": {
        "id": "98e7d39d",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "9f2c0e21-70ec-4fd9-a96c-605f8827f4c1"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(24000, 6000)"
            ]
          },
          "metadata": {},
          "execution_count": 9
        }
      ],
      "source": [
        "from sklearn.model_selection import train_test_split\n",
        "\n",
        "train, test = train_test_split(dataset, test_size=0.2, random_state=42)\n",
        "\n",
        "len(train), len(test)"
      ],
      "id": "98e7d39d"
    },
    {
      "cell_type": "code",
      "execution_count": 10,
      "metadata": {
        "id": "5af110e1",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "d7e23816-c3d8-4bde-dc06-7534bddb286b"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "dict_keys(['client_id', 'trans_date', 'event_time', 'small_group', 'amount_rur'])"
            ]
          },
          "metadata": {},
          "execution_count": 10
        }
      ],
      "source": [
        "train[0].keys()"
      ],
      "id": "5af110e1"
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "f9080a6f"
      },
      "source": [
        "## Embedding training"
      ],
      "id": "f9080a6f"
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "56493c0b"
      },
      "source": [
        "Model training in our framework organised via pytorch-lightning (pl) framework.\n",
        "The key parts of neural networks training in pl are:\n",
        "\n",
        "    * model (`pytorch_lightning.LightningModule`)\n",
        "    * data loader (`torch.utils.data.DataLoader`)\n",
        "    * trainer (`pytorch_lightning.Trainer`)\n",
        "    \n",
        "For futher details check https://pytorchlightning.ai/"
      ],
      "id": "56493c0b"
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "43a6ee58"
      },
      "source": [
        "### Model definition"
      ],
      "id": "43a6ee58"
    },
    {
      "cell_type": "code",
      "execution_count": 11,
      "metadata": {
        "id": "988c508d",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "5b7acf7a-e4e1-41f8-c033-70fa7b741a5f"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/torchmetrics/utilities/prints.py:36: UserWarning: Metric `AUROC` will save all targets and predictions in buffer. For large datasets this may lead to large memory footprint.\n",
            "  warnings.warn(*args, **kwargs)\n"
          ]
        }
      ],
      "source": [
        "from functools import partial\n",
        "from ptls.nn import TrxEncoder, RnnSeqEncoder\n",
        "from ptls.frames.bert import RtdModule\n",
        "\n",
        "trx_encoder_params = dict(\n",
        "    embeddings_noise=0.003,\n",
        "    numeric_values={'amount_rur': 'identity'},\n",
        "    embeddings={\n",
        "        # It is better to remove trans_date,\n",
        "        # since using the date it is easy to notice the replacement in the sequence\n",
        "        #'trans_date': {'in': 800, 'out': 16},\n",
        "        'small_group': {'in': 250, 'out': 16},\n",
        "    },\n",
        ")\n",
        "\n",
        "seq_encoder = RnnSeqEncoder(\n",
        "    trx_encoder=TrxEncoder(**trx_encoder_params),\n",
        "    hidden_size=64,\n",
        "    type='gru',\n",
        ")\n",
        "\n",
        "model = RtdModule(\n",
        "    seq_encoder=seq_encoder,\n",
        "    optimizer_partial=partial(torch.optim.Adam, lr=0.001),\n",
        "    lr_scheduler_partial=partial(torch.optim.lr_scheduler.StepLR, step_size=30, gamma=0.9),\n",
        ")"
      ],
      "id": "988c508d"
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "87997ac0"
      },
      "source": [
        "### Data loader"
      ],
      "id": "87997ac0"
    },
    {
      "cell_type": "code",
      "execution_count": 12,
      "metadata": {
        "id": "624065bf"
      },
      "outputs": [],
      "source": [
        "from ptls.data_load.datasets import MemoryMapDataset\n",
        "from ptls.data_load.iterable_processing import SeqLenFilter\n",
        "from ptls.frames.bert import RtdDataset\n",
        "from ptls.frames.coles.split_strategy import SampleSlices\n",
        "from ptls.frames import PtlsDataModule\n",
        "\n",
        "train_dl = PtlsDataModule(\n",
        "    train_data=RtdDataset(\n",
        "        MemoryMapDataset(\n",
        "            data=train,\n",
        "            i_filters=[\n",
        "                SeqLenFilter(min_seq_len=25),\n",
        "            ],\n",
        "        ),\n",
        "        min_len = 600,\n",
        "        max_len = 600,\n",
        "    ),\n",
        "    train_num_workers=2,\n",
        "    train_batch_size=256,\n",
        ")"
      ],
      "id": "624065bf"
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "fc9a09be"
      },
      "source": [
        "### Trainer"
      ],
      "id": "fc9a09be"
    },
    {
      "cell_type": "code",
      "execution_count": 73,
      "metadata": {
        "id": "8fdbb67d",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "495a2dfa-5c3b-4a6c-ba7f-118820829e0b"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "INFO:pytorch_lightning.utilities.rank_zero:GPU available: True (cuda), used: True\n",
            "INFO:pytorch_lightning.utilities.rank_zero:TPU available: False, using: 0 TPU cores\n",
            "INFO:pytorch_lightning.utilities.rank_zero:IPU available: False, using: 0 IPUs\n",
            "INFO:pytorch_lightning.utilities.rank_zero:HPU available: False, using: 0 HPUs\n"
          ]
        }
      ],
      "source": [
        "import torch\n",
        "import pytorch_lightning as pl\n",
        "\n",
        "import logging\n",
        "\n",
        "trainer = pl.Trainer(\n",
        "    max_epochs=50,\n",
        "    accelerator=\"cuda\" if torch.cuda.is_available() else \"cpu\",\n",
        "    devices=1 if torch.cuda.is_available() else \"auto\",\n",
        "    enable_progress_bar=False,\n",
        ")"
      ],
      "id": "8fdbb67d"
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "a88078a3"
      },
      "source": [
        "### Training"
      ],
      "id": "a88078a3"
    },
    {
      "cell_type": "code",
      "execution_count": 74,
      "metadata": {
        "id": "f40877df",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "8dee8c7f-9105-4166-fb19-832537511af5"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/pytorch_lightning/trainer/configuration_validator.py:108: PossibleUserWarning: You defined a `validation_step` but have no `val_dataloader`. Skipping val loop.\n",
            "  rank_zero_warn(\n",
            "INFO:pytorch_lightning.accelerators.cuda:LOCAL_RANK: 0 - CUDA_VISIBLE_DEVICES: [0]\n",
            "INFO:pytorch_lightning.callbacks.model_summary:\n",
            "  | Name               | Type          | Params\n",
            "-----------------------------------------------------\n",
            "0 | _loss              | BCELoss       | 0     \n",
            "1 | _seq_encoder       | RnnSeqEncoder | 20.0 K\n",
            "2 | _validation_metric | AUROC         | 0     \n",
            "3 | _head              | Sequential    | 65    \n",
            "-----------------------------------------------------\n",
            "20.1 K    Trainable params\n",
            "0         Non-trainable params\n",
            "20.1 K    Total params\n",
            "0.080     Total estimated model params size (MB)\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "logger.version = 3\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "INFO:pytorch_lightning.utilities.rank_zero:`Trainer.fit` stopped: `max_epochs=50` reached.\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "{'loss': tensor(0.3536), 'seq_len': tensor(600.)}\n",
            "CPU times: user 3min 19s, sys: 24.2 s, total: 3min 43s\n",
            "Wall time: 5min 53s\n"
          ]
        }
      ],
      "source": [
        "%%time\n",
        "print(f'logger.version = {trainer.logger.version}')\n",
        "trainer.fit(model, train_dl)\n",
        "print(trainer.logged_metrics)"
      ],
      "id": "f40877df"
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "7aaa35bc"
      },
      "source": [
        "### Save sequence encoder for other experiments"
      ],
      "id": "7aaa35bc"
    },
    {
      "cell_type": "code",
      "execution_count": 75,
      "metadata": {
        "id": "50b76d62"
      },
      "outputs": [],
      "source": [
        "torch.save(seq_encoder.state_dict(), \"rtd-emb.pt\")"
      ],
      "id": "50b76d62"
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Inference\n"
      ],
      "metadata": {
        "id": "8atWeQ2zl0kP"
      },
      "id": "8atWeQ2zl0kP"
    },
    {
      "cell_type": "code",
      "source": [
        "from ptls.data_load.utils import collate_feature_dict"
      ],
      "metadata": {
        "id": "233vRO88Cvpi"
      },
      "id": "233vRO88Cvpi",
      "execution_count": 76,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "import numpy as np\n",
        "import torch\n",
        "from collections import defaultdict\n",
        "from functools import reduce\n",
        "\n",
        "from ptls.data_load.feature_dict import FeatureDict\n",
        "from ptls.data_load.padded_batch import PaddedBatch\n",
        "\n",
        "from transformers.tokenization_utils_base import BatchEncoding"
      ],
      "metadata": {
        "id": "OxonqMO6AZMN"
      },
      "id": "OxonqMO6AZMN",
      "execution_count": 77,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "inference_data = train[:3000]"
      ],
      "metadata": {
        "id": "MDI2nEIIoLzz"
      },
      "id": "MDI2nEIIoLzz",
      "execution_count": 78,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "inference_dataset = MemoryMapDataset(\n",
        "    data=inference_data,\n",
        ")"
      ],
      "metadata": {
        "id": "x2vRWINrkfi5"
      },
      "id": "x2vRWINrkfi5",
      "execution_count": 79,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "inference_dl = torch.utils.data.DataLoader(\n",
        "    dataset=inference_dataset,\n",
        "    collate_fn=collate_feature_dict,\n",
        "    shuffle=False,\n",
        "    batch_size=32,\n",
        "    num_workers=2,\n",
        ")"
      ],
      "metadata": {
        "id": "C9M5z36aCd5F"
      },
      "id": "C9M5z36aCd5F",
      "execution_count": 80,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from ptls.frames.inference_module import InferenceModule\n",
        "from ptls.data_load.padded_batch import PaddedBatch\n",
        "from itertools import chain"
      ],
      "metadata": {
        "id": "K5QMDiUxN1_9"
      },
      "id": "K5QMDiUxN1_9",
      "execution_count": 81,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "inference_module = InferenceModule(\n",
        "    model=seq_encoder,\n",
        "    pandas_output=True,\n",
        "    drop_seq_features=False,\n",
        "    model_out_name='emb')"
      ],
      "metadata": {
        "id": "RQjqQtuMNror"
      },
      "id": "RQjqQtuMNror",
      "execution_count": 83,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "inference_module.model.seq_encoder.is_reduce_sequence = False"
      ],
      "metadata": {
        "id": "hD41f0qNN7Hl"
      },
      "id": "hD41f0qNN7Hl",
      "execution_count": 84,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "predict = pl.Trainer(\n",
        "    accelerator=\"cuda\" if torch.cuda.is_available() else \"cpu\",\n",
        "    devices=1 if torch.cuda.is_available() else \"auto\"\n",
        ").predict(inference_module, inference_dl)"
      ],
      "metadata": {
        "id": "yH5PKWyxN7Xk",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 136,
          "referenced_widgets": [
            "adf702e482054d37aecf3915f735cf46",
            "555b7bed380344f79e857c1bd8b4f29f",
            "b1ef3adfb6844940a2a27d999035acdf",
            "bcc701ad82434723b0bc364ecedad0c8",
            "a1318f60ff5442fab8208b7954c7fc57",
            "1ace04960cb046ce968b2b3593b85403",
            "16bfbb631a2445c799a1c2687e28658f",
            "2006a02d77b049c6a79537bf35ceb83d",
            "b5161e9391fd42ce9341380626f9268c",
            "fefe296c125a44bb99dcd3ea7bfa850b",
            "839f378e9d494b67b7edf5d3342f9c5f"
          ]
        },
        "outputId": "d046b997-a4cd-491b-f1fe-8fe1b296d648"
      },
      "id": "yH5PKWyxN7Xk",
      "execution_count": 85,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "INFO:pytorch_lightning.utilities.rank_zero:GPU available: True (cuda), used: True\n",
            "INFO:pytorch_lightning.utilities.rank_zero:TPU available: False, using: 0 TPU cores\n",
            "INFO:pytorch_lightning.utilities.rank_zero:IPU available: False, using: 0 IPUs\n",
            "INFO:pytorch_lightning.utilities.rank_zero:HPU available: False, using: 0 HPUs\n",
            "INFO:pytorch_lightning.accelerators.cuda:LOCAL_RANK: 0 - CUDA_VISIBLE_DEVICES: [0]\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Predicting: 0it [00:00, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "adf702e482054d37aecf3915f735cf46"
            }
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          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "full_predict = pd.concat(predict, axis=0)\n",
        "full_predict.shape"
      ],
      "metadata": {
        "id": "5d7HcxRV7QUm",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "35f18fd9-d624-4c8b-b73f-fbeba6b88745"
      },
      "id": "5d7HcxRV7QUm",
      "execution_count": 86,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(2646251, 69)"
            ]
          },
          "metadata": {},
          "execution_count": 86
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "full_predict.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 256
        },
        "id": "BIc08So5dSfD",
        "outputId": "6001c05f-d8e5-4d7f-ec41-16ab65a5267b"
      },
      "id": "BIc08So5dSfD",
      "execution_count": 87,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   client_id  trans_date  event_time  small_group  amount_rur  emb_0000  \\\n",
              "0      36253           0           0            1      40.756 -0.999944   \n",
              "1      36253           1           1            1      40.413 -0.999998   \n",
              "2      36253           2           2            1      36.788 -1.000000   \n",
              "3      36253           3           3           11     212.640 -0.977772   \n",
              "4      36253           3           3           31     255.538 -0.748471   \n",
              "\n",
              "   emb_0001  emb_0002  emb_0003  emb_0004  ...  emb_0054  emb_0055  emb_0056  \\\n",
              "0  0.689444  0.436590 -0.861628 -0.942182  ... -0.281197  0.657984 -0.971727   \n",
              "1  0.648132  0.376979 -0.666376 -0.996325  ... -0.373447  0.581358 -0.997420   \n",
              "2  0.588498  0.435898 -0.714104 -0.997517  ... -0.372284  0.518096 -0.998287   \n",
              "3  0.604587  0.572005 -0.935599  0.403538  ... -0.679253  0.524378 -0.799694   \n",
              "4  0.628531  0.765505 -0.987866  0.183479  ... -0.842937  0.528587 -0.870956   \n",
              "\n",
              "   emb_0057  emb_0058  emb_0059  emb_0060  emb_0061  emb_0062  emb_0063  \n",
              "0 -0.284884 -0.852577 -0.499774 -0.456695 -0.465291 -0.870806 -0.122840  \n",
              "1 -0.403388 -0.849408 -0.418344 -0.463797  0.251035 -0.860770 -0.128784  \n",
              "2 -0.458272 -0.844440 -0.323835 -0.474915  0.615958 -0.866145 -0.134356  \n",
              "3 -0.453984 -0.545881 -0.397622 -0.244209 -0.275014 -0.873881 -0.171591  \n",
              "4 -0.461824 -0.739728 -0.426225 -0.122032 -0.499951 -0.877514 -0.206213  \n",
              "\n",
              "[5 rows x 69 columns]"
            ],
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              "      <th></th>\n",
              "      <th>client_id</th>\n",
              "      <th>trans_date</th>\n",
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              "      <th>emb_0003</th>\n",
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              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-8390b10f-673d-4479-af3b-6c9c46031ffd button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-8390b10f-673d-4479-af3b-6c9c46031ffd');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "\n",
              "<div id=\"df-03a76982-5957-4412-b032-e45e12ea0b77\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-03a76982-5957-4412-b032-e45e12ea0b77')\"\n",
              "            title=\"Suggest charts\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "     width=\"24px\">\n",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-03a76982-5957-4412-b032-e45e12ea0b77 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "full_predict"
            }
          },
          "metadata": {},
          "execution_count": 87
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "ids_lst = [1187, 36253, 396, 38243, 1002]\n",
        "vecs = full_predict.loc[full_predict['client_id'].isin(ids_lst)].copy()"
      ],
      "metadata": {
        "id": "ZRT_lAcNPsg1"
      },
      "id": "ZRT_lAcNPsg1",
      "execution_count": 88,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "cols_to_drop = ['client_id',\n",
        "                'trans_date',\n",
        "                'event_time',\n",
        "                'small_group',\n",
        "                'amount_rur']"
      ],
      "metadata": {
        "id": "GJmxJ7FUUp9S"
      },
      "id": "GJmxJ7FUUp9S",
      "execution_count": 89,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.manifold import TSNE\n",
        "\n",
        "tsne = TSNE(random_state=1, n_iter=250, metric=\"cosine\")\n",
        "embs = tsne.fit_transform(vecs.drop(columns=cols_to_drop))"
      ],
      "metadata": {
        "id": "Z12WkZtIHYhU"
      },
      "id": "Z12WkZtIHYhU",
      "execution_count": 90,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "vecs['x'] = embs[:, 0]\n",
        "vecs['y'] = embs[:, 1]"
      ],
      "metadata": {
        "id": "MjXmoZ-nTht2"
      },
      "id": "MjXmoZ-nTht2",
      "execution_count": 91,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from matplotlib import pyplot as plt\n",
        "FS = (7, 5)\n",
        "fig, ax = plt.subplots(figsize=FS)\n",
        "users = [1187, 36253, 396, 38243, 1002]\n",
        "colors = ['b', 'r', 'g', 'purple', 'yellow', 'brown']\n",
        "for usr, clr in zip(users, colors):\n",
        "    ax.scatter(vecs.loc[vecs['client_id'] == usr].x,\n",
        "               vecs.loc[vecs['client_id'] == usr].y,\n",
        "               alpha=.3,\n",
        "               color = clr,\n",
        "               label = f'client_id = {usr}')\n",
        "ax.set_title('tsne for client_id in [1187, 36253, 396, 38243, 1002]')\n",
        "ax.legend()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 485
        },
        "id": "mwfrMe82LMFJ",
        "outputId": "e5b4a56e-5fcf-442f-d94d-a990b88f3fd4"
      },
      "id": "mwfrMe82LMFJ",
      "execution_count": 92,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.legend.Legend at 0x79e465f14640>"
            ]
          },
          "metadata": {},
          "execution_count": 92
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 700x500 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import matplotlib.colors as colors\n",
        "FS = (7, 5)\n",
        "mccs = vecs[vecs['client_id'] == 1187].small_group.unique()\n",
        "colors_list = list(colors.cnames.values())\n",
        "fig, ax = plt.subplots(figsize=FS)\n",
        "clr = 0\n",
        "for mcc in mccs:\n",
        "    ax.scatter(vecs.loc[(vecs['client_id'] == 1187) & (vecs['small_group'] == mcc)].x,\n",
        "               vecs.loc[(vecs['client_id'] == 1187) & (vecs['small_group'] == mcc)].y,\n",
        "               alpha = .6,\n",
        "               color = colors_list[clr],\n",
        "               label = f'mcc: {mcc}')\n",
        "    clr += len(colors_list)//len(mccs)\n",
        "ax.set_title('tsne for mcc for client_id=1187')\n",
        "ax.legend(ncols = 2,\n",
        "          loc=\"center left\",\n",
        "          bbox_to_anchor=(0.9, 0.5),\n",
        "          bbox_transform=fig.transFigure )"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 465
        },
        "id": "cvrmFr_iQiLc",
        "outputId": "3315a466-5b4a-46ba-a23d-5ae2324675cd"
      },
      "id": "cvrmFr_iQiLc",
      "execution_count": 93,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.legend.Legend at 0x79e465f23d90>"
            ]
          },
          "metadata": {},
          "execution_count": 93
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 700x500 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "plt.figure(figsize=(8, 5))\n",
        "plt.scatter(vecs.loc[(vecs['client_id'] == 1187)].x,\n",
        "           vecs.loc[(vecs['client_id'] == 1187)].y,\n",
        "           c = vecs.loc[(vecs['client_id'] == 1187)].trans_date,\n",
        "           alpha = .6)\n",
        "plt.title('tsne for trans_date for client_id=1187')\n",
        "cbar = plt.colorbar()\n",
        "cbar.ax.set_ylabel('trans_date')\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 485
        },
        "id": "WEEkNUtHGvAm",
        "outputId": "e011d16b-b538-41eb-f039-9531b64f6ffd"
      },
      "id": "WEEkNUtHGvAm",
      "execution_count": 94,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(0, 0.5, 'trans_date')"
            ]
          },
          "metadata": {},
          "execution_count": 94
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 800x500 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "baafa0c0"
      },
      "source": [
        "Obtained embeddings can be used as features for model training\n",
        "\n",
        "For example:"
      ],
      "id": "baafa0c0"
    },
    {
      "cell_type": "code",
      "source": [
        "full_predict['next_small_group'] = full_predict.groupby('client_id')['small_group'].shift(-1)\n",
        "full_predict = full_predict.dropna()\n",
        "full_predict = full_predict.astype({'next_small_group': 'int64'})\n",
        "cols_to_drop.append('next_small_group')"
      ],
      "metadata": {
        "id": "UAttBb-XxDft"
      },
      "id": "UAttBb-XxDft",
      "execution_count": 96,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "X = full_predict.drop(columns = cols_to_drop).values[:120000]\n",
        "y = full_predict['next_small_group'].values[:120000]"
      ],
      "metadata": {
        "id": "lpgaZRBbgxKG"
      },
      "id": "lpgaZRBbgxKG",
      "execution_count": 97,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "X_train, X_test = X[:100000], X[100000:]\n",
        "y_train, y_test = y[:100000], y[100000:]"
      ],
      "metadata": {
        "id": "x5fSILCuatP2"
      },
      "id": "x5fSILCuatP2",
      "execution_count": 98,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "import lightgbm as lgb\n",
        "clf = lgb.LGBMClassifier(learning_rate = 0.01, random_state = 42)\n",
        "clf.fit(X_train, y_train)\n",
        "clear_output()"
      ],
      "metadata": {
        "id": "uBFdDgTcqBr7"
      },
      "id": "uBFdDgTcqBr7",
      "execution_count": 99,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.metrics import accuracy_score\n",
        "y_pred=clf.predict(X_test)\n",
        "accuracy_score(y_pred, y_test)"
      ],
      "metadata": {
        "id": "zsHd1By0rrLL",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "f651a1cd-1f5c-4227-c9d9-8a74ed94ca60"
      },
      "id": "zsHd1By0rrLL",
      "execution_count": 100,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.34215"
            ]
          },
          "metadata": {},
          "execution_count": 100
        }
      ]
    }
  ],
  "metadata": {
    "colab": {
      "provenance": [],
      "gpuType": "T4"
    },
    "kernelspec": {
      "display_name": "Python 3",
      "name": "python3"
    },
    "language_info": {
      "codemirror_mode": {
        "name": "ipython",
        "version": 3
      },
      "file_extension": ".py",
      "mimetype": "text/x-python",
      "name": "python",
      "nbconvert_exporter": "python",
      "pygments_lexer": "ipython3",
      "version": "3.8.16"
    },
    "vscode": {
      "interpreter": {
        "hash": "e153ad723e521bdc1aff37b3bab9b1f3df31355f5a727a9b9eb7d0282ae9ca8c"
      }
    },
    "accelerator": "GPU",
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            "_view_count": null,
            "_view_module": "@jupyter-widgets/controls",
            "_view_module_version": "1.5.0",
            "_view_name": "HBoxView",
            "box_style": "",
            "children": [
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        "555b7bed380344f79e857c1bd8b4f29f": {
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