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bugfix/granularity handling fix #10

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merged 4 commits into from
Feb 21, 2024
Merged

bugfix/granularity handling fix #10

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6435586
granularity handling fix + new tutorial part
elineii Feb 12, 2024
f5a6a38
fix bugs
elineii Feb 19, 2024
dcbdb15
fix merge conflict
elineii Feb 19, 2024
907439d
fix bug with dataset.seq_data
elineii Feb 19, 2024
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6 changes: 5 additions & 1 deletion .gitignore
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Original file line number Diff line number Diff line change
Expand Up @@ -144,5 +144,9 @@ cython_debug/
.vscode/

temp/
additional_datasets/
catboost_info/
poetry.lock
poetry.lock
*check*.ipynb
*temp*
*demand*
216 changes: 172 additions & 44 deletions Tutorial_1_Basics.ipynb
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -21,20 +21,20 @@
},
{
"cell_type": "code",
"execution_count": 2,
"execution_count": 3,
"metadata": {},
"outputs": [],
"source": [
"def get_results(\n",
" cv: int,\n",
" regime: str,\n",
" y_true: Optional[List[NDArray[np.float]]] = None,\n",
" y_pred: Optional[List[NDArray[np.float]]] = None,\n",
" y_true: Optional[List[NDArray[np.floating]]] = None,\n",
" y_pred: Optional[List[NDArray[np.floating]]] = None,\n",
" ids: Optional[List[Union[float, str]]] = None,\n",
") -> pd.DataFrame:\n",
" def _get_fold_value(\n",
" value: Optional[Union[float, NDArray[np.float]]], idx: int\n",
" ) -> List[Optional[Union[float, NDArray[np.float]]]]:\n",
" value: Optional[Union[float, NDArray[np.floating]]], idx: int\n",
" ) -> List[Optional[Union[float, NDArray[np.floating]]]]:\n",
" if value is None:\n",
" return [None]\n",
" if isinstance(value[idx], float):\n",
Expand All @@ -59,6 +59,13 @@
" return df_res"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Pipeline setup"
]
},
{
"cell_type": "markdown",
"metadata": {},
Expand Down Expand Up @@ -93,7 +100,7 @@
},
{
"cell_type": "code",
"execution_count": 7,
"execution_count": 4,
"metadata": {},
"outputs": [],
"source": [
Expand Down Expand Up @@ -180,7 +187,7 @@
},
{
"cell_type": "code",
"execution_count": 8,
"execution_count": 5,
"metadata": {},
"outputs": [],
"source": [
Expand All @@ -189,9 +196,17 @@
},
{
"cell_type": "code",
"execution_count": 9,
"execution_count": 6,
"metadata": {},
"outputs": [],
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"freq: Day; period: 1\n"
]
}
],
"source": [
"dataset = TSDataset(\n",
" data=pd.read_csv(df_path),\n",
Expand All @@ -202,36 +217,44 @@
"strategy = strategies_factory[strategy_params]"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Backtest validation of pipeline"
]
},
{
"cell_type": "code",
"execution_count": 10,
"execution_count": 7,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"0:\tlearn: 0.0001731\ttest: 0.0001793\tbest: 0.0001793 (0)\ttotal: 2.23ms\tremaining: 2.23s\n",
"500:\tlearn: 0.0000015\ttest: 0.0000015\tbest: 0.0000015 (500)\ttotal: 689ms\tremaining: 687ms\n",
"999:\tlearn: 0.0000011\ttest: 0.0000011\tbest: 0.0000011 (999)\ttotal: 1.35s\tremaining: 0us\n",
"freq: Day; period: 1\n",
"0:\tlearn: 0.0001731\ttest: 0.0001793\tbest: 0.0001793 (0)\ttotal: 63.7ms\tremaining: 1m 3s\n",
"500:\tlearn: 0.0000015\ttest: 0.0000015\tbest: 0.0000015 (500)\ttotal: 1.29s\tremaining: 1.29s\n",
"999:\tlearn: 0.0000011\ttest: 0.0000011\tbest: 0.0000011 (999)\ttotal: 2.48s\tremaining: 0us\n",
"\n",
"bestTest = 1.110360723e-06\n",
"bestIteration = 999\n",
"\n",
"Fold 0:\n",
"MultiRMSE: 1.110360722708484e-06\n",
"0:\tlearn: 0.0001756\ttest: 0.0001744\tbest: 0.0001744 (0)\ttotal: 2.53ms\tremaining: 2.52s\n",
"500:\tlearn: 0.0000015\ttest: 0.0000016\tbest: 0.0000016 (500)\ttotal: 626ms\tremaining: 623ms\n",
"999:\tlearn: 0.0000011\ttest: 0.0000012\tbest: 0.0000012 (999)\ttotal: 1.28s\tremaining: 0us\n",
"0:\tlearn: 0.0001756\ttest: 0.0001744\tbest: 0.0001744 (0)\ttotal: 6.77ms\tremaining: 6.77s\n",
"500:\tlearn: 0.0000015\ttest: 0.0000016\tbest: 0.0000016 (500)\ttotal: 1.31s\tremaining: 1.31s\n",
"999:\tlearn: 0.0000011\ttest: 0.0000012\tbest: 0.0000012 (999)\ttotal: 2.51s\tremaining: 0us\n",
"\n",
"bestTest = 1.164605569e-06\n",
"bestIteration = 999\n",
"\n",
"Fold 1:\n",
"MultiRMSE: 1.1646055692725877e-06\n",
"0:\tlearn: 0.0001767\ttest: 0.0001718\tbest: 0.0001718 (0)\ttotal: 1.57ms\tremaining: 1.57s\n",
"500:\tlearn: 0.0000015\ttest: 0.0000015\tbest: 0.0000015 (500)\ttotal: 628ms\tremaining: 625ms\n",
"999:\tlearn: 0.0000011\ttest: 0.0000012\tbest: 0.0000012 (999)\ttotal: 1.26s\tremaining: 0us\n",
"0:\tlearn: 0.0001767\ttest: 0.0001718\tbest: 0.0001718 (0)\ttotal: 4.55ms\tremaining: 4.54s\n",
"500:\tlearn: 0.0000015\ttest: 0.0000015\tbest: 0.0000015 (500)\ttotal: 1.37s\tremaining: 1.37s\n",
"999:\tlearn: 0.0000011\ttest: 0.0000012\tbest: 0.0000012 (999)\ttotal: 2.94s\tremaining: 0us\n",
"\n",
"bestTest = 1.166647188e-06\n",
"bestIteration = 999\n",
Expand All @@ -249,7 +272,7 @@
},
{
"cell_type": "code",
"execution_count": 11,
"execution_count": 8,
"metadata": {},
"outputs": [
{
Expand Down Expand Up @@ -367,7 +390,7 @@
"[70 rows x 3 columns]"
]
},
"execution_count": 11,
"execution_count": 8,
"metadata": {},
"output_type": "execute_result"
}
Expand All @@ -380,46 +403,39 @@
"cell_type": "markdown",
"metadata": {},
"source": [
"---"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"It is also possible to use the classic fit and predict methods."
"## Fit and predict interface of pipeline"
]
},
{
"cell_type": "code",
"execution_count": 12,
"execution_count": 9,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"0:\tlearn: 0.0001722\ttest: 0.0001803\tbest: 0.0001803 (0)\ttotal: 1.89ms\tremaining: 1.89s\n",
"500:\tlearn: 0.0000015\ttest: 0.0000016\tbest: 0.0000016 (500)\ttotal: 629ms\tremaining: 626ms\n",
"999:\tlearn: 0.0000010\ttest: 0.0000012\tbest: 0.0000012 (999)\ttotal: 1.28s\tremaining: 0us\n",
"0:\tlearn: 0.0001722\ttest: 0.0001803\tbest: 0.0001803 (0)\ttotal: 9.59ms\tremaining: 9.58s\n",
"500:\tlearn: 0.0000015\ttest: 0.0000016\tbest: 0.0000016 (500)\ttotal: 1.65s\tremaining: 1.64s\n",
"999:\tlearn: 0.0000010\ttest: 0.0000012\tbest: 0.0000012 (999)\ttotal: 3.05s\tremaining: 0us\n",
"\n",
"bestTest = 1.219564131e-06\n",
"bestIteration = 999\n",
"\n",
"Fold 0:\n",
"MultiRMSE: 1.2195641307026147e-06\n",
"0:\tlearn: 0.0001757\ttest: 0.0001733\tbest: 0.0001733 (0)\ttotal: 1.56ms\tremaining: 1.56s\n",
"500:\tlearn: 0.0000015\ttest: 0.0000016\tbest: 0.0000016 (500)\ttotal: 645ms\tremaining: 642ms\n",
"999:\tlearn: 0.0000010\ttest: 0.0000012\tbest: 0.0000012 (999)\ttotal: 1.27s\tremaining: 0us\n",
"0:\tlearn: 0.0001757\ttest: 0.0001733\tbest: 0.0001733 (0)\ttotal: 6.25ms\tremaining: 6.24s\n",
"500:\tlearn: 0.0000015\ttest: 0.0000016\tbest: 0.0000016 (500)\ttotal: 2.09s\tremaining: 2.08s\n",
"999:\tlearn: 0.0000010\ttest: 0.0000012\tbest: 0.0000012 (999)\ttotal: 3.71s\tremaining: 0us\n",
"\n",
"bestTest = 1.162857853e-06\n",
"bestIteration = 999\n",
"\n",
"Fold 1:\n",
"MultiRMSE: 1.1628578528684804e-06\n",
"0:\tlearn: 0.0001768\ttest: 0.0001711\tbest: 0.0001711 (0)\ttotal: 1.8ms\tremaining: 1.8s\n",
"500:\tlearn: 0.0000015\ttest: 0.0000015\tbest: 0.0000015 (500)\ttotal: 651ms\tremaining: 648ms\n",
"999:\tlearn: 0.0000010\ttest: 0.0000011\tbest: 0.0000011 (999)\ttotal: 1.27s\tremaining: 0us\n",
"0:\tlearn: 0.0001768\ttest: 0.0001711\tbest: 0.0001711 (0)\ttotal: 3.75ms\tremaining: 3.75s\n",
"500:\tlearn: 0.0000015\ttest: 0.0000015\tbest: 0.0000015 (500)\ttotal: 1.85s\tremaining: 1.84s\n",
"999:\tlearn: 0.0000010\ttest: 0.0000011\tbest: 0.0000011 (999)\ttotal: 3.83s\tremaining: 0us\n",
"\n",
"bestTest = 1.086497806e-06\n",
"bestIteration = 999\n",
Expand All @@ -438,7 +454,7 @@
},
{
"cell_type": "code",
"execution_count": 13,
"execution_count": 10,
"metadata": {},
"outputs": [
{
Expand Down Expand Up @@ -556,7 +572,7 @@
"[70 rows x 3 columns]"
]
},
"execution_count": 13,
"execution_count": 10,
"metadata": {},
"output_type": "execute_result"
}
Expand All @@ -565,12 +581,124 @@
"current_pred"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Working with raw time series' granularity"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Time series come in different granularities, from hourly and daily time series to more complex ones such as the end of each quarter.\n",
"\n",
"If the rows do not contain segments that are too short (that are shorter than history + horizon), then `tsururu` will try to extract the row granularity on its own. We currently support the following types:\n",
"\n",
"- Yearly (and YearlyEnd)\n",
"- Quarterly (and Quarterly)\n",
"- Monthly (and MonthlyEnd)\n",
"- Weekly\n",
"- Daily\n",
"- Hourly\n",
"- Minlutely\n",
"- Secondly\n",
"- Microsecondly\n",
"\n",
"There is also support for compound granularities (10 days, 15 minutes, 32 seconds, etc.). The correctness of the selected granularity can be checked from the output after the `Dataset` class has been created.\n",
"\n",
"However, there are tricky situations (e.g. 28 days) where the monthly granularity may be guessed incorrectly. Therefore, it is possible to set your own granularity using the `pd.DateOffset` class or related classes from `pandas.tseries.offsets`, which must be fed as `delta` parameter into the `Dataset` class. Then the time column will be processed according to the user's settings."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Consider a time series where each point is exactly __28 daily points away__ from each other"
]
},
{
"cell_type": "code",
"execution_count": null,
"execution_count": 31,
"metadata": {},
"outputs": [],
"source": []
"source": [
"df_path_2 = \"datasets/global/simulated_data_to_check_28D.csv\"\n",
"\n",
"# Configure the features settings\n",
"columns_and_features_params_2 = {\n",
" \"target\": {\n",
" \"column\": [\"value\"],\n",
" \"type\": \"continious\",\n",
" },\n",
" \"date\": {\n",
" \"column\": [\"date\"],\n",
" \"type\": \"datetime\",\n",
" },\n",
" \"id\": {\n",
" \"column\": [\"id\"],\n",
" \"type\": \"categorical\",\n",
" }\n",
"}"
]
},
{
"cell_type": "code",
"execution_count": 32,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"freq: Month; period: 1.0\n"
]
}
],
"source": [
"dataset_2 = TSDataset(\n",
" data=pd.read_csv(df_path_2),\n",
" columns_and_features_params=columns_and_features_params_2,\n",
" history=30,\n",
")"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"We see that the frequency of the series is incorrectly defined as monthly. Let's try to pass the `delta` parameter."
]
},
{
"cell_type": "code",
"execution_count": 33,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Custom OffSet: <DateOffset: days=28>\n"
]
}
],
"source": [
"dataset_2 = TSDataset(\n",
" data=pd.read_csv(df_path_2),\n",
" columns_and_features_params=columns_and_features_params_2,\n",
" history=30,\n",
" delta=pd.DateOffset(days=28),\n",
")"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Now it's all detected correctly."
]
}
],
"metadata": {
Expand All @@ -592,7 +720,7 @@
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.9.0"
"version": "3.9.18"
},
"orig_nbformat": 4
},
Expand Down
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