diff --git a/.flake8 b/.flake8 new file mode 100755 index 0000000..58973d4 --- /dev/null +++ b/.flake8 @@ -0,0 +1,9 @@ +[flake8] +ignore = W503, D +max-complexity = 10 +max-line-length = 99 +docstring-convention = google +import-order-style = google +inline-quotes = single +application-import-names = ml_lab + diff --git a/.gitignore b/.gitignore new file mode 100644 index 0000000..cb85bec --- /dev/null +++ b/.gitignore @@ -0,0 +1,20 @@ +# Byte-compiled / optimized / DLL files +__pycache__/ + +# Unit test / coverage reports +.pytest_cache/ +.coverage +coverage.xml + +# mypy +.mypy_cache/ + +# IDE +.vscode/ + +# Distribution / packaging +*.egg-info/ + +# Jupyter Notebook +.ipynb_checkpoints + diff --git a/.isort.cfg b/.isort.cfg new file mode 100755 index 0000000..1968a10 --- /dev/null +++ b/.isort.cfg @@ -0,0 +1,6 @@ +[settings] +profile = google +known_first_party = ml_lab +force_single_line = false +line_length = 99 +multi_line_output = 5 diff --git a/.pre-commit-config.yaml b/.pre-commit-config.yaml new file mode 100755 index 0000000..a0d8226 --- /dev/null +++ b/.pre-commit-config.yaml @@ -0,0 +1,31 @@ +repos: + - repo: local + hooks: + - id: isort + name: isort + stages: [commit] + language: system + entry: isort + types: [python] + + - id: yapf + name: yapf + stages: [commit] + language: system + entry: yapf + types: [python] + args: ['--style=.style.yapf', '--parallel', '--in-place', '--recursive'] + + - id: flake8 + name: flake8 + stages: [commit] + language: system + entry: flake8 + types: [python] + + - id: mypy + name: mypy + stages: [commit] + language: system + entry: mypy + types: [python] \ No newline at end of file diff --git a/.style.yapf b/.style.yapf new file mode 100755 index 0000000..3796b43 --- /dev/null +++ b/.style.yapf @@ -0,0 +1,6 @@ +[style] +based_on_style = google +column_limit = 99 +split_before_logical_operator = true +split_before_arithmetic_operator = true +dedent_closing_brackets = true \ No newline at end of file diff --git a/LICENSE b/LICENSE new file mode 100644 index 0000000..8352ad8 --- /dev/null +++ b/LICENSE @@ -0,0 +1,21 @@ +MIT License + +Copyright (c) 2021 Dmitry Labazkin + +Permission is hereby granted, free of charge, to any person obtaining a copy +of this software and associated documentation files (the "Software"), to deal +in the Software without restriction, including without limitation the rights +to use, copy, modify, merge, publish, distribute, sublicense, and/or sell +copies of the Software, and to permit persons to whom the Software is +furnished to do so, subject to the following conditions: + +The above copyright notice and this permission notice shall be included in all +copies or substantial portions of the Software. + +THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR +IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, +FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE +AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER +LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, +OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE +SOFTWARE. \ No newline at end of file diff --git a/README.md b/README.md new file mode 100644 index 0000000..e69de29 diff --git a/ml_lab/__init__.py b/ml_lab/__init__.py new file mode 100644 index 0000000..b4dd4bc --- /dev/null +++ b/ml_lab/__init__.py @@ -0,0 +1 @@ +"""Implements ML useful tools.""" diff --git a/ml_lab/model_selection.py b/ml_lab/model_selection.py new file mode 100644 index 0000000..6a57cdf --- /dev/null +++ b/ml_lab/model_selection.py @@ -0,0 +1,207 @@ +"""Implements model selection for time series.""" + +from itertools import groupby +from typing import Iterable, Iterator, Literal, Optional, Tuple + +import numpy as np +from sklearn.utils import indexable +from sklearn.utils.validation import _num_samples + + +class GroupTimeSeriesSplit: + """Time series cross validation with custom grouping.""" + + def __init__( + self, + test_size: int, + train_size: Optional[int] = None, + n_splits: Optional[int] = None, + gap: int = 0, + shift_size: int = 1, + window: Literal['rolling', 'expanding'] = 'rolling' + ): + """Initializes cross validation parameters. + + Args: + test_size (int): + Size of test dataset. + train_size (Optional[int], optional): + Size of train dataset. Defaults to None. + n_splits (int, optional): + Number of splits. Defaults to None. + gap (int, optional): + Gap size. Defaults to 0. + shift_size (int, optional): + Step to shift for the next fold. Defaults to 1. + window (str): + Type of the window. Defaults to 'rolling'. + """ + # Check if only one of train_size and n_splits is specified + if (train_size is None) and (n_splits is None): + raise ValueError('Either train_size or n_splits have to be defined') + + # Check if window mode value is correctly specified + if window not in ['rolling', 'expanding']: + raise ValueError('Window can be either "rolling" or "expanding"') + + # Check if train size if specified only with rolling window + if (train_size is not None) and (window == 'expanding'): + raise ValueError('Train size can be specified only with rolling window') + + self.test_size = test_size + self.train_size = train_size + self.n_splits = n_splits + self.gap = gap + self.shift_size = shift_size + self.window = window + + def split(self, + X: Iterable, + y: Optional[Iterable] = None, + groups: Optional[Iterable] = None) -> Iterator[Tuple[np.ndarray, np.ndarray]]: + """Calculates train/test indices based on split parameters. + + Args: + X (Iterable): Dataset with features. + y (Iterable): Dataset with target. + groups (Iterable): Array with group numbers. + + Yields: + Iterator[Tuple[np.ndarray, np.ndarray]]: Train/test dataset indices. + """ + train_size = self.train_size + test_size = self.test_size + n_splits = self.n_splits + gap = self.gap + shift_size = self.shift_size + + # Convert to indexable data structures with additional lengths consistency check + X, y, groups = indexable(X, y, groups) + + # Check if groups are specified + if groups is None: + raise ValueError('Groups must be specified') + + # Check if groups are consecutive in dataset + group_seqs = [group[0] for group in groupby(groups)] + unique_groups, group_starts_idx = np.unique(groups, return_index=True) + n_groups = _num_samples(unique_groups) + + if group_seqs > sorted(unique_groups): + raise ValueError('Groups must be presorted in increasing order') + + # Calculate number of splits if not specified + if n_splits is None: + n_splits = ((n_groups - train_size - gap - test_size) // shift_size) + 1 + self.n_splits = n_splits + + # Check if number of splits is positive + if n_splits <= 0: + raise ValueError( + ( + f'Not enough data to split number of groups ({n_groups})' + f' for number splits ({n_splits})' + f' with train size ({train_size}),' + f' test size ({test_size}), gap ({gap}), shift_size ({shift_size})' + ) + ) + train_start_idx = n_groups - train_size - gap - test_size - (n_splits - 1) * shift_size + # Calculate train size if not specified + elif train_size is None: + train_size = int(n_groups - gap - (test_size + (n_splits - 1) * shift_size)) + self.train_size = train_size + + # Check if train size is not empty + if train_size <= 0: + raise ValueError( + ( + f'Not enough data to split number of groups ({n_groups})' + f' for number splits ({n_splits})' + f' with train size ({train_size}),' + f' test size ({test_size}), gap ({gap}), shift_size ({shift_size})' + ) + ) + train_start_idx = 0 + # Calculate train start index if train size and number of splits ar specified + else: + train_start_idx = n_groups - train_size - gap - test_size - (n_splits - 1) * shift_size + + # Check if data is enough to split + if train_start_idx < 0: + raise ValueError( + ( + f'Not enough data to split number of groups ({n_groups})' + f' for number splits ({n_splits})' + f' with train size ({train_size}),' + f' test size ({test_size}), gap ({gap}), shift_size ({shift_size})' + ) + ) + + # Calculate number of folds + n_folds = n_splits + 1 + + # Check if number of required folds is greater than groups + if n_folds > n_groups: + raise ValueError( + ( + f'Cannot have number of folds ({n_folds}) greater than' + f' the number of groups ({n_groups})' + ) + ) + + # Create mapping between groups and its start indices in array + groups_dict = dict(zip(unique_groups, group_starts_idx)) + + # Calculate number of samples + n_samples = _num_samples(X) + + # Calculate start/end indices for initial train/test datasets + train_end_idx = train_start_idx + train_size + test_start_idx = train_end_idx + gap + test_end_idx = test_start_idx + test_size + + # Process each split + for _ in range(n_splits): + # Calculate train indices range + train_idx = np.r_[slice(groups_dict[train_start_idx], groups_dict[train_end_idx])] + + # Calculate test indices range + if test_end_idx < n_groups: + test_idx = np.r_[slice(groups_dict[test_start_idx], groups_dict[test_end_idx])] + else: + test_idx = np.r_[slice(groups_dict[test_start_idx], n_samples)] + + # Yield train/test indices range + yield (train_idx, test_idx) + + # Shift train dataset start index by shift size for rolling window + if self.window == 'rolling': + train_start_idx = train_start_idx + shift_size + + # Shift train dataset end index by shift size + train_end_idx = train_end_idx + shift_size + + # Shift test dataset indices range by shift size + test_start_idx = test_start_idx + shift_size + test_end_idx = test_end_idx + shift_size + + def get_n_splits( + self, + X: Optional[Iterable] = None, + y: Optional[Iterable] = None, + groups: Optional[Iterable] = None + ) -> int: + """Calculates number of splits given specified parameters. + + Args: + X (Optional[Iterable], optional): Dataset with features. Defaults to None. + y (Optional[Iterable], optional): Dataset with target. Defaults to None. + groups (Optional[Iterable], optional): Array with group numbers. Defaults to None. + + Returns: + int: Calculated number of splits. + """ + if self.n_splits is not None: + return self.n_splits + else: + raise ValueError('Number of splits is not defined') diff --git a/mypy.ini b/mypy.ini new file mode 100755 index 0000000..f095d4d --- /dev/null +++ b/mypy.ini @@ -0,0 +1,2 @@ +[mypy] +ignore_missing_imports = True diff --git a/notebooks/GroupTimeSeriesSplit.ipynb b/notebooks/GroupTimeSeriesSplit.ipynb new file mode 100644 index 0000000..2289cdc --- /dev/null +++ b/notebooks/GroupTimeSeriesSplit.ipynb @@ -0,0 +1,1173 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "%load_ext autoreload\n", + "%autoreload 2" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "from string import ascii_lowercase\n", + "\n", + "from IPython.display import display\n", + "import numpy as np\n", + "import pandas as pd\n", + "\n", + "from ml_lab.model_selection import GroupTimeSeriesSplit" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [], + "source": [ + "RANDOM_STATE = 42" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "tags": [] + }, + "source": [ + "## Prepare sample data" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Monthly data" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "Index(['2021-01', '2021-02', '2021-02', '2021-02', '2021-02', '2021-03',\n", + " '2021-03', '2021-03', '2021-04', '2021-04', '2021-05', '2021-05',\n", + " '2021-06', '2021-06', '2021-06', '2021-06', '2021-07', '2021-08',\n", + " '2021-08', '2021-08', '2021-09', '2021-10', '2021-11', '2021-11',\n", + " '2021-11', '2021-12', '2021-12', '2021-12', '2021-12'],\n", + " dtype='object')" + ] + }, + "execution_count": 4, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "rng = np.random.default_rng(RANDOM_STATE)\n", + "\n", + "start_date = '2021-01-01'\n", + "end_date = '2021-12-01'\n", + "\n", + "unique_months = pd.date_range(start_date, end_date, freq='MS').strftime('%Y-%m')\n", + "times = rng.integers(low=1, high=5, size=len(unique_months))\n", + "months = unique_months.repeat(times)\n", + "months" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**Generate monthly groups**" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([ 0, 1, 1, 1, 1, 2, 2, 2, 3, 3, 4, 4, 5, 5, 5, 5, 6,\n", + " 7, 7, 7, 8, 9, 10, 10, 10, 11, 11, 11, 11])" + ] + }, + "execution_count": 5, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "groups, _ = pd.factorize(months)\n", + "groups" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Features and targets" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
num_feature
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\n", + "
" + ], + "text/plain": [ + " num_feature\n", + "2021-01 0.773956\n", + "2021-02 0.438878\n", + "2021-02 0.858598\n", + "2021-02 0.697368\n", + "2021-02 0.094177" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/plain": [ + "0 1\n", + "1 1\n", + "2 1\n", + "3 1\n", + "4 0\n", + "Name: target, dtype: int64" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "rng = np.random.default_rng(RANDOM_STATE)\n", + "\n", + "data_size = len(groups)\n", + "\n", + "num_feature = rng.random(data_size)\n", + "X = pd.DataFrame({\n", + " 'num_feature': num_feature\n", + "}, index=months)\n", + "\n", + "targets = rng.choice([0, 1], size=data_size)\n", + "y = pd.Series(targets, name='target')\n", + "\n", + "display(X.head(), y.head())" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Define test function" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [], + "source": [ + "def test_cv(X, y, groups, **cv_args):\n", + " cv = GroupTimeSeriesSplit(**cv_args)\n", + " \n", + " for train_idx, test_idx in cv.split(X, groups=groups):\n", + " print('TRAIN INDICES:', train_idx)\n", + " print('TEST INDICES:', test_idx)\n", + " print('TRAIN LENGTH:', len(train_idx))\n", + " print('TEST LENGTH:', len(test_idx))\n", + " print('TRAIN GROUPS:', groups[train_idx]) \n", + " print('TEST GROUPS:', groups[test_idx])\n", + " print('TRAIN GROUP SIZE:', len(set(groups[train_idx]))) \n", + " print('TEST GROUP SIZE:', len(set(groups[test_idx])))\n", + " print('TRAIN GROUP MONTHS:', X.index[train_idx].values) \n", + " print('TEST GROUP MONTHS:', X.index[test_idx].values)\n", + " print()" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "tags": [] + }, + "source": [ + "## Check train/test split" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [], + "source": [ + "cv_args = {\n", + " 'test_size': 3, \n", + " 'n_splits': 1,\n", + " 'gap': 0\n", + "}" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "TRAIN INDICES: [ 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20]\n", + "TEST INDICES: [21 22 23 24 25 26 27 28]\n", + "TRAIN LENGTH: 21\n", + "TEST LENGTH: 8\n", + "TRAIN GROUPS: [0 1 1 1 1 2 2 2 3 3 4 4 5 5 5 5 6 7 7 7 8]\n", + "TEST GROUPS: [ 9 10 10 10 11 11 11 11]\n", + "TRAIN GROUP SIZE: 9\n", + "TEST GROUP SIZE: 3\n", + "TRAIN GROUP MONTHS: ['2021-01' '2021-02' '2021-02' '2021-02' '2021-02' '2021-03' '2021-03'\n", + " '2021-03' '2021-04' '2021-04' '2021-05' '2021-05' '2021-06' '2021-06'\n", + " '2021-06' '2021-06' '2021-07' '2021-08' '2021-08' '2021-08' '2021-09']\n", + "TEST GROUP MONTHS: ['2021-10' '2021-11' '2021-11' '2021-11' '2021-12' '2021-12' '2021-12'\n", + " '2021-12']\n", + "\n" + ] + } + ], + "source": [ + "test_cv(X, y, groups, **cv_args)" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [], + "source": [ + "cv = GroupTimeSeriesSplit(**cv_args)\n", + "train_idx, test_idx = next(cv.split(X, groups=groups))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Check cross-validation split" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [], + "source": [ + "X_train = X.iloc[train_idx]\n", + "y_train = y.iloc[train_idx]\n", + "groups_train = groups[train_idx]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Failed cases" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "- Groups are not sorted\n", + "- Both train_size and n_splits are specified\n", + "- Incorrect window name\n", + "- Specify train_size with expanding window\n", + "- Not enough data (too many number of splits)\n", + "- Not enough data (too large value of shift size)\n", + "- Not enough data (too large value of gap)\n", + "- Repeated usage of the same cv object\n", + "- (TODO) Incorrect range of parameters" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**Groups are not sorted**" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([6, 4, 5, 8, 5, 5, 2, 2, 3, 1, 0, 7, 2, 4, 7, 1, 1, 7, 1, 5, 3])" + ] + }, + "execution_count": 12, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "rng = np.random.default_rng(RANDOM_STATE)\n", + "\n", + "unsorted_groups_train = groups_train.copy()\n", + "rng.shuffle(unsorted_groups_train)\n", + "unsorted_groups_train" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Groups must be presorted in increasing order\n" + ] + } + ], + "source": [ + "try:\n", + " test_cv(X_train, y_train, unsorted_groups_train, **cv_args)\n", + "except ValueError as e:\n", + " print(e)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**Both train_size and n_splits are not specified**" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Either train_size or n_splits have to be defined\n" + ] + } + ], + "source": [ + "cv_args = {\n", + " 'test_size': 3\n", + "}\n", + "\n", + "try:\n", + " test_cv(X_train, y_train, groups_train, **cv_args)\n", + "except ValueError as e:\n", + " print(e)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**Incorrect window name**" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Window can be either \"rolling\" or \"expanding\"\n" + ] + } + ], + "source": [ + "cv_args = {\n", + " 'test_size': 3,\n", + " 'n_splits': 3,\n", + " 'window': 'bad_name'\n", + "}\n", + "\n", + "try:\n", + " test_cv(X_train, y_train, groups_train, **cv_args)\n", + "except ValueError as e:\n", + " print(e)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**Specify train_size with expanding window**" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Train size can be specified only with rolling window\n" + ] + } + ], + "source": [ + "cv_args = {\n", + " 'test_size': 3,\n", + " 'train_size': 3,\n", + " 'window': 'expanding'\n", + "}\n", + "\n", + "try:\n", + " test_cv(X_train, y_train, groups_train, **cv_args)\n", + "except ValueError as e:\n", + " print(e)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**Not enough data (too large train_size and/or n_splits)**" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Not enough data to split number of groups (9) for number splits (1) with train size (7), test size (3), gap (0), shift_size (1)\n" + ] + } + ], + "source": [ + "cv_args = {\n", + " 'test_size': 3,\n", + " 'train_size': 7, \n", + " 'n_splits': 1\n", + "}\n", + "\n", + "try:\n", + " test_cv(X_train, y_train, groups_train, **cv_args)\n", + "except ValueError as e:\n", + " print(e)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**Not enough data (too many number of splits)**" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Not enough data to split number of groups (9) for number splits (10) with train size (-3), test size (3), gap (0), shift_size (1)\n" + ] + } + ], + "source": [ + "cv_args = {\n", + " 'test_size': 3,\n", + " 'n_splits': 10\n", + "}\n", + "\n", + "try:\n", + " test_cv(X_train, y_train, groups_train, **cv_args)\n", + "except ValueError as e:\n", + " print(e)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**Not enough data (too large values of shift size)**" + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Not enough data to split number of groups (9) for number splits (3) with train size (-14), test size (3), gap (0), shift_size (10)\n" + ] + } + ], + "source": [ + "cv_args = {\n", + " 'test_size': 3,\n", + " 'n_splits': 3,\n", + " 'shift_size': 10\n", + "}\n", + "\n", + "try:\n", + " test_cv(X_train, y_train, groups_train, **cv_args)\n", + "except ValueError as e:\n", + " print(e)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**Not enough data (too large values of gap)**" + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Not enough data to split number of groups (9) for number splits (3) with train size (-6), test size (3), gap (10), shift_size (1)\n" + ] + } + ], + "source": [ + "cv_args = {\n", + " 'test_size': 3,\n", + " 'n_splits': 3,\n", + " 'gap': 10\n", + "}\n", + "\n", + "try:\n", + " test_cv(X_train, y_train, groups_train, **cv_args)\n", + "except ValueError as e:\n", + " print(e)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "tags": [] + }, + "source": [ + "### Success cases" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "- test_size + train_size \n", + "- test_size + n_splits\n", + "- test_size + n_splits + gap\n", + "- test_size + n_splits + shift_size\n", + "- test_size + n_splits + expanding window\n", + "- test_size + train_size + n_splits\n", + "- Repeated usage of the same object" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**test_size + train_size**" + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "TRAIN INDICES: [0 1 2 3 4 5 6 7]\n", + "TEST INDICES: [ 8 9 10 11 12 13 14 15]\n", + "TRAIN LENGTH: 8\n", + "TEST LENGTH: 8\n", + "TRAIN GROUPS: [0 1 1 1 1 2 2 2]\n", + "TEST GROUPS: [3 3 4 4 5 5 5 5]\n", + "TRAIN GROUP SIZE: 3\n", + "TEST GROUP SIZE: 3\n", + "TRAIN GROUP MONTHS: ['2021-01' '2021-02' '2021-02' '2021-02' '2021-02' '2021-03' '2021-03'\n", + " '2021-03']\n", + "TEST GROUP MONTHS: ['2021-04' '2021-04' '2021-05' '2021-05' '2021-06' '2021-06' '2021-06'\n", + " '2021-06']\n", + "\n", + "TRAIN INDICES: [1 2 3 4 5 6 7 8 9]\n", + "TEST INDICES: [10 11 12 13 14 15 16]\n", + "TRAIN LENGTH: 9\n", + "TEST LENGTH: 7\n", + "TRAIN GROUPS: [1 1 1 1 2 2 2 3 3]\n", + "TEST GROUPS: [4 4 5 5 5 5 6]\n", + "TRAIN GROUP SIZE: 3\n", + "TEST GROUP SIZE: 3\n", + "TRAIN GROUP MONTHS: ['2021-02' '2021-02' '2021-02' '2021-02' '2021-03' '2021-03' '2021-03'\n", + " '2021-04' '2021-04']\n", + "TEST GROUP MONTHS: ['2021-05' '2021-05' '2021-06' '2021-06' '2021-06' '2021-06' '2021-07']\n", + "\n", + "TRAIN INDICES: [ 5 6 7 8 9 10 11]\n", + "TEST INDICES: [12 13 14 15 16 17 18 19]\n", + "TRAIN LENGTH: 7\n", + "TEST LENGTH: 8\n", + "TRAIN GROUPS: [2 2 2 3 3 4 4]\n", + "TEST GROUPS: [5 5 5 5 6 7 7 7]\n", + "TRAIN GROUP SIZE: 3\n", + "TEST GROUP SIZE: 3\n", + "TRAIN GROUP MONTHS: ['2021-03' '2021-03' '2021-03' '2021-04' '2021-04' '2021-05' '2021-05']\n", + "TEST GROUP MONTHS: ['2021-06' '2021-06' '2021-06' '2021-06' '2021-07' '2021-08' '2021-08'\n", + " '2021-08']\n", + "\n", + "TRAIN INDICES: [ 8 9 10 11 12 13 14 15]\n", + "TEST INDICES: [16 17 18 19 20]\n", + "TRAIN LENGTH: 8\n", + "TEST LENGTH: 5\n", + "TRAIN GROUPS: [3 3 4 4 5 5 5 5]\n", + "TEST GROUPS: [6 7 7 7 8]\n", + "TRAIN GROUP SIZE: 3\n", + "TEST GROUP SIZE: 3\n", + "TRAIN GROUP MONTHS: ['2021-04' '2021-04' '2021-05' '2021-05' '2021-06' '2021-06' '2021-06'\n", + " '2021-06']\n", + "TEST GROUP MONTHS: ['2021-07' '2021-08' '2021-08' '2021-08' '2021-09']\n", + "\n" + ] + } + ], + "source": [ + "cv_args = {\n", + " 'test_size': 3,\n", + " 'train_size': 3\n", + "}\n", + "\n", + "test_cv(X_train, y_train, groups_train, **cv_args)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**test_size + n_splits**" + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "TRAIN INDICES: [0 1 2 3 4 5 6 7 8 9]\n", + "TEST INDICES: [10 11 12 13 14 15 16]\n", + "TRAIN LENGTH: 10\n", + "TEST LENGTH: 7\n", + "TRAIN GROUPS: [0 1 1 1 1 2 2 2 3 3]\n", + "TEST GROUPS: [4 4 5 5 5 5 6]\n", + "TRAIN GROUP SIZE: 4\n", + "TEST GROUP SIZE: 3\n", + "TRAIN GROUP MONTHS: ['2021-01' '2021-02' '2021-02' '2021-02' '2021-02' '2021-03' '2021-03'\n", + " '2021-03' '2021-04' '2021-04']\n", + "TEST GROUP MONTHS: ['2021-05' '2021-05' '2021-06' '2021-06' '2021-06' '2021-06' '2021-07']\n", + "\n", + "TRAIN INDICES: [ 1 2 3 4 5 6 7 8 9 10 11]\n", + "TEST INDICES: [12 13 14 15 16 17 18 19]\n", + "TRAIN LENGTH: 11\n", + "TEST LENGTH: 8\n", + "TRAIN GROUPS: [1 1 1 1 2 2 2 3 3 4 4]\n", + "TEST GROUPS: [5 5 5 5 6 7 7 7]\n", + "TRAIN GROUP SIZE: 4\n", + "TEST GROUP SIZE: 3\n", + "TRAIN GROUP MONTHS: ['2021-02' '2021-02' '2021-02' '2021-02' '2021-03' '2021-03' '2021-03'\n", + " '2021-04' '2021-04' '2021-05' '2021-05']\n", + "TEST GROUP MONTHS: ['2021-06' '2021-06' '2021-06' '2021-06' '2021-07' '2021-08' '2021-08'\n", + " '2021-08']\n", + "\n", + "TRAIN INDICES: [ 5 6 7 8 9 10 11 12 13 14 15]\n", + "TEST INDICES: [16 17 18 19 20]\n", + "TRAIN LENGTH: 11\n", + "TEST LENGTH: 5\n", + "TRAIN GROUPS: [2 2 2 3 3 4 4 5 5 5 5]\n", + "TEST GROUPS: [6 7 7 7 8]\n", + "TRAIN GROUP SIZE: 4\n", + "TEST GROUP SIZE: 3\n", + "TRAIN GROUP MONTHS: ['2021-03' '2021-03' '2021-03' '2021-04' '2021-04' '2021-05' '2021-05'\n", + " '2021-06' '2021-06' '2021-06' '2021-06']\n", + "TEST GROUP MONTHS: ['2021-07' '2021-08' '2021-08' '2021-08' '2021-09']\n", + "\n" + ] + } + ], + "source": [ + "cv_args = {\n", + " 'test_size': 3,\n", + " 'n_splits': 3\n", + "}\n", + "\n", + "test_cv(X_train, y_train, groups_train, **cv_args)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**test_size + n_splits + gap**" + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "TRAIN INDICES: [0 1 2 3 4]\n", + "TEST INDICES: [10 11 12 13 14 15 16]\n", + "TRAIN LENGTH: 5\n", + "TEST LENGTH: 7\n", + "TRAIN GROUPS: [0 1 1 1 1]\n", + "TEST GROUPS: [4 4 5 5 5 5 6]\n", + "TRAIN GROUP SIZE: 2\n", + "TEST GROUP SIZE: 3\n", + "TRAIN GROUP MONTHS: ['2021-01' '2021-02' '2021-02' '2021-02' '2021-02']\n", + "TEST GROUP MONTHS: ['2021-05' '2021-05' '2021-06' '2021-06' '2021-06' '2021-06' '2021-07']\n", + "\n", + "TRAIN INDICES: [1 2 3 4 5 6 7]\n", + "TEST INDICES: [12 13 14 15 16 17 18 19]\n", + "TRAIN LENGTH: 7\n", + "TEST LENGTH: 8\n", + "TRAIN GROUPS: [1 1 1 1 2 2 2]\n", + "TEST GROUPS: [5 5 5 5 6 7 7 7]\n", + "TRAIN GROUP SIZE: 2\n", + "TEST GROUP SIZE: 3\n", + "TRAIN GROUP MONTHS: ['2021-02' '2021-02' '2021-02' '2021-02' '2021-03' '2021-03' '2021-03']\n", + "TEST GROUP MONTHS: ['2021-06' '2021-06' '2021-06' '2021-06' '2021-07' '2021-08' '2021-08'\n", + " '2021-08']\n", + "\n", + "TRAIN INDICES: [5 6 7 8 9]\n", + "TEST INDICES: [16 17 18 19 20]\n", + "TRAIN LENGTH: 5\n", + "TEST LENGTH: 5\n", + "TRAIN GROUPS: [2 2 2 3 3]\n", + "TEST GROUPS: [6 7 7 7 8]\n", + "TRAIN GROUP SIZE: 2\n", + "TEST GROUP SIZE: 3\n", + "TRAIN GROUP MONTHS: ['2021-03' '2021-03' '2021-03' '2021-04' '2021-04']\n", + "TEST GROUP MONTHS: ['2021-07' '2021-08' '2021-08' '2021-08' '2021-09']\n", + "\n" + ] + } + ], + "source": [ + "cv_args = {\n", + " 'test_size': 3,\n", + " 'n_splits': 3,\n", + " 'gap': 2\n", + "}\n", + "\n", + "test_cv(X_train, y_train, groups_train, **cv_args)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**test_size + n_splits + shift_size**" + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "TRAIN INDICES: [0 1 2 3 4]\n", + "TEST INDICES: [ 5 6 7 8 9 10 11]\n", + "TRAIN LENGTH: 5\n", + "TEST LENGTH: 7\n", + "TRAIN GROUPS: [0 1 1 1 1]\n", + "TEST GROUPS: [2 2 2 3 3 4 4]\n", + "TRAIN GROUP SIZE: 2\n", + "TEST GROUP SIZE: 3\n", + "TRAIN GROUP MONTHS: ['2021-01' '2021-02' '2021-02' '2021-02' '2021-02']\n", + "TEST GROUP MONTHS: ['2021-03' '2021-03' '2021-03' '2021-04' '2021-04' '2021-05' '2021-05']\n", + "\n", + "TRAIN INDICES: [5 6 7 8 9]\n", + "TEST INDICES: [10 11 12 13 14 15 16]\n", + "TRAIN LENGTH: 5\n", + "TEST LENGTH: 7\n", + "TRAIN GROUPS: [2 2 2 3 3]\n", + "TEST GROUPS: [4 4 5 5 5 5 6]\n", + "TRAIN GROUP SIZE: 2\n", + "TEST GROUP SIZE: 3\n", + "TRAIN GROUP MONTHS: ['2021-03' '2021-03' '2021-03' '2021-04' '2021-04']\n", + "TEST GROUP MONTHS: ['2021-05' '2021-05' '2021-06' '2021-06' '2021-06' '2021-06' '2021-07']\n", + "\n", + "TRAIN INDICES: [10 11 12 13 14 15]\n", + "TEST INDICES: [16 17 18 19 20]\n", + "TRAIN LENGTH: 6\n", + "TEST LENGTH: 5\n", + "TRAIN GROUPS: [4 4 5 5 5 5]\n", + "TEST GROUPS: [6 7 7 7 8]\n", + "TRAIN GROUP SIZE: 2\n", + "TEST GROUP SIZE: 3\n", + "TRAIN GROUP MONTHS: ['2021-05' '2021-05' '2021-06' '2021-06' '2021-06' '2021-06']\n", + "TEST GROUP MONTHS: ['2021-07' '2021-08' '2021-08' '2021-08' '2021-09']\n", + "\n" + ] + } + ], + "source": [ + "cv_args = {\n", + " 'test_size': 3,\n", + " 'n_splits': 3,\n", + " 'shift_size': 2\n", + "}\n", + "\n", + "test_cv(X_train, y_train, groups_train, **cv_args)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**test_size + n_splits + expanding window**" + ] + }, + { + "cell_type": "code", + "execution_count": 25, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "TRAIN INDICES: [0 1 2 3 4 5 6 7 8 9]\n", + "TEST INDICES: [10 11 12 13 14 15 16]\n", + "TRAIN LENGTH: 10\n", + "TEST LENGTH: 7\n", + "TRAIN GROUPS: [0 1 1 1 1 2 2 2 3 3]\n", + "TEST GROUPS: [4 4 5 5 5 5 6]\n", + "TRAIN GROUP SIZE: 4\n", + "TEST GROUP SIZE: 3\n", + "TRAIN GROUP MONTHS: ['2021-01' '2021-02' '2021-02' '2021-02' '2021-02' '2021-03' '2021-03'\n", + " '2021-03' '2021-04' '2021-04']\n", + "TEST GROUP MONTHS: ['2021-05' '2021-05' '2021-06' '2021-06' '2021-06' '2021-06' '2021-07']\n", + "\n", + "TRAIN INDICES: [ 0 1 2 3 4 5 6 7 8 9 10 11]\n", + "TEST INDICES: [12 13 14 15 16 17 18 19]\n", + "TRAIN LENGTH: 12\n", + "TEST LENGTH: 8\n", + "TRAIN GROUPS: [0 1 1 1 1 2 2 2 3 3 4 4]\n", + "TEST GROUPS: [5 5 5 5 6 7 7 7]\n", + "TRAIN GROUP SIZE: 5\n", + "TEST GROUP SIZE: 3\n", + "TRAIN GROUP MONTHS: ['2021-01' '2021-02' '2021-02' '2021-02' '2021-02' '2021-03' '2021-03'\n", + " '2021-03' '2021-04' '2021-04' '2021-05' '2021-05']\n", + "TEST GROUP MONTHS: ['2021-06' '2021-06' '2021-06' '2021-06' '2021-07' '2021-08' '2021-08'\n", + " '2021-08']\n", + "\n", + "TRAIN INDICES: [ 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15]\n", + "TEST INDICES: [16 17 18 19 20]\n", + "TRAIN LENGTH: 16\n", + "TEST LENGTH: 5\n", + "TRAIN GROUPS: [0 1 1 1 1 2 2 2 3 3 4 4 5 5 5 5]\n", + "TEST GROUPS: [6 7 7 7 8]\n", + "TRAIN GROUP SIZE: 6\n", + "TEST GROUP SIZE: 3\n", + "TRAIN GROUP MONTHS: ['2021-01' '2021-02' '2021-02' '2021-02' '2021-02' '2021-03' '2021-03'\n", + " '2021-03' '2021-04' '2021-04' '2021-05' '2021-05' '2021-06' '2021-06'\n", + " '2021-06' '2021-06']\n", + "TEST GROUP MONTHS: ['2021-07' '2021-08' '2021-08' '2021-08' '2021-09']\n", + "\n" + ] + } + ], + "source": [ + "cv_args = {\n", + " 'test_size': 3,\n", + " 'n_splits': 3,\n", + " 'window': 'expanding'\n", + "}\n", + "\n", + "test_cv(X_train, y_train, groups_train, **cv_args)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**test_size + train_size + n_splits (full usage of data)**" + ] + }, + { + "cell_type": "code", + "execution_count": 26, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "TRAIN INDICES: [0 1 2 3 4 5 6 7]\n", + "TEST INDICES: [ 8 9 10 11 12 13 14 15]\n", + "TRAIN LENGTH: 8\n", + "TEST LENGTH: 8\n", + "TRAIN GROUPS: [0 1 1 1 1 2 2 2]\n", + "TEST GROUPS: [3 3 4 4 5 5 5 5]\n", + "TRAIN GROUP SIZE: 3\n", + "TEST GROUP SIZE: 3\n", + "TRAIN GROUP MONTHS: ['2021-01' '2021-02' '2021-02' '2021-02' '2021-02' '2021-03' '2021-03'\n", + " '2021-03']\n", + "TEST GROUP MONTHS: ['2021-04' '2021-04' '2021-05' '2021-05' '2021-06' '2021-06' '2021-06'\n", + " '2021-06']\n", + "\n", + "TRAIN INDICES: [1 2 3 4 5 6 7 8 9]\n", + "TEST INDICES: [10 11 12 13 14 15 16]\n", + "TRAIN LENGTH: 9\n", + "TEST LENGTH: 7\n", + "TRAIN GROUPS: [1 1 1 1 2 2 2 3 3]\n", + "TEST GROUPS: [4 4 5 5 5 5 6]\n", + "TRAIN GROUP SIZE: 3\n", + "TEST GROUP SIZE: 3\n", + "TRAIN GROUP MONTHS: ['2021-02' '2021-02' '2021-02' '2021-02' '2021-03' '2021-03' '2021-03'\n", + " '2021-04' '2021-04']\n", + "TEST GROUP MONTHS: ['2021-05' '2021-05' '2021-06' '2021-06' '2021-06' '2021-06' '2021-07']\n", + "\n", + "TRAIN INDICES: [ 5 6 7 8 9 10 11]\n", + "TEST INDICES: [12 13 14 15 16 17 18 19]\n", + "TRAIN LENGTH: 7\n", + "TEST LENGTH: 8\n", + "TRAIN GROUPS: [2 2 2 3 3 4 4]\n", + "TEST GROUPS: [5 5 5 5 6 7 7 7]\n", + "TRAIN GROUP SIZE: 3\n", + "TEST GROUP SIZE: 3\n", + "TRAIN GROUP MONTHS: ['2021-03' '2021-03' '2021-03' '2021-04' '2021-04' '2021-05' '2021-05']\n", + "TEST GROUP MONTHS: ['2021-06' '2021-06' '2021-06' '2021-06' '2021-07' '2021-08' '2021-08'\n", + " '2021-08']\n", + "\n", + "TRAIN INDICES: [ 8 9 10 11 12 13 14 15]\n", + "TEST INDICES: [16 17 18 19 20]\n", + "TRAIN LENGTH: 8\n", + "TEST LENGTH: 5\n", + "TRAIN GROUPS: [3 3 4 4 5 5 5 5]\n", + "TEST GROUPS: [6 7 7 7 8]\n", + "TRAIN GROUP SIZE: 3\n", + "TEST GROUP SIZE: 3\n", + "TRAIN GROUP MONTHS: ['2021-04' '2021-04' '2021-05' '2021-05' '2021-06' '2021-06' '2021-06'\n", + " '2021-06']\n", + "TEST GROUP MONTHS: ['2021-07' '2021-08' '2021-08' '2021-08' '2021-09']\n", + "\n" + ] + } + ], + "source": [ + "cv_args = {\n", + " 'test_size': 3,\n", + " 'train_size': 3,\n", + " 'n_splits': 4\n", + "}\n", + "\n", + "test_cv(X_train, y_train, groups_train, **cv_args)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**test_size + train_size + n_splits (partial usage of data)**" + ] + }, + { + "cell_type": "code", + "execution_count": 27, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "TRAIN INDICES: [ 5 6 7 8 9 10 11]\n", + "TEST INDICES: [12 13 14 15 16 17 18 19]\n", + "TRAIN LENGTH: 7\n", + "TEST LENGTH: 8\n", + "TRAIN GROUPS: [2 2 2 3 3 4 4]\n", + "TEST GROUPS: [5 5 5 5 6 7 7 7]\n", + "TRAIN GROUP SIZE: 3\n", + "TEST GROUP SIZE: 3\n", + "TRAIN GROUP MONTHS: ['2021-03' '2021-03' '2021-03' '2021-04' '2021-04' '2021-05' '2021-05']\n", + "TEST GROUP MONTHS: ['2021-06' '2021-06' '2021-06' '2021-06' '2021-07' '2021-08' '2021-08'\n", + " '2021-08']\n", + "\n", + "TRAIN INDICES: [ 8 9 10 11 12 13 14 15]\n", + "TEST INDICES: [16 17 18 19 20]\n", + "TRAIN LENGTH: 8\n", + "TEST LENGTH: 5\n", + "TRAIN GROUPS: [3 3 4 4 5 5 5 5]\n", + "TEST GROUPS: [6 7 7 7 8]\n", + "TRAIN GROUP SIZE: 3\n", + "TEST GROUP SIZE: 3\n", + "TRAIN GROUP MONTHS: ['2021-04' '2021-04' '2021-05' '2021-05' '2021-06' '2021-06' '2021-06'\n", + " '2021-06']\n", + "TEST GROUP MONTHS: ['2021-07' '2021-08' '2021-08' '2021-08' '2021-09']\n", + "\n" + ] + } + ], + "source": [ + "cv_args = {\n", + " 'test_size': 3,\n", + " 'train_size': 3,\n", + " 'n_splits': 2\n", + "}\n", + "\n", + "test_cv(X_train, y_train, groups_train, **cv_args)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**Repeated usage of the same cv object**" + ] + }, + { + "cell_type": "code", + "execution_count": 28, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "First usage: (array([ 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15]), array([16, 17, 18, 19, 20]))\n", + "Repeated usage: (array([ 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15]), array([16, 17, 18, 19, 20]))\n" + ] + } + ], + "source": [ + "cv_args = {\n", + " 'test_size': 3,\n", + " 'n_splits': 1\n", + "}\n", + "cv = GroupTimeSeriesSplit(**cv_args)\n", + "\n", + "print('First usage: ', next(cv.split(X_train, groups=groups_train)))\n", + "print('Repeated usage: ', next(cv.split(X_train, groups=groups_train)))" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "ml_lab", + "language": "python", + "name": "ml_lab" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.9.7" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} diff --git a/poetry.lock 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+setup( + name='ml_lab', + packages=find_packages(), + version='0.1.0', + description='', + author='Dmitry Labazkin ', +)