diff --git a/examples/tutorial.ipynb b/examples/tutorial.ipynb
index 22d349a..0c66bc3 100644
--- a/examples/tutorial.ipynb
+++ b/examples/tutorial.ipynb
@@ -13,7 +13,7 @@
},
{
"cell_type": "code",
- "execution_count": 1,
+ "execution_count": null,
"id": "2442cb34",
"metadata": {},
"outputs": [],
@@ -26,10 +26,10 @@
"import polars as pl\n",
"from matplotlib import pyplot as plt\n",
"\n",
- "from metasyn import MetaFrame\n",
+ "from metasyn import MetaFrame, demo_file\n",
"from metasyncontrib.disclosure import DisclosurePrivacy\n",
"from metasyn.provider import DistributionProviderList\n",
- "from utils import get_demonstration_fp"
+ "#from utils import get_demonstration_fp"
]
},
{
@@ -45,51 +45,12 @@
},
{
"cell_type": "code",
- "execution_count": 2,
+ "execution_count": null,
"id": "3c2a44b7",
"metadata": {},
- "outputs": [
- {
- "data": {
- "text/html": [
- "
\n",
- "
shape: (5, 12)PassengerId | Name | Sex | Age | Parch | Fare | Cabin | Embarked | Birthday | Board time | Married since | all_NA |
---|
i64 | str | cat | i64 | i64 | f64 | str | cat | date | time | datetime[μs] | str |
1 | "Braund, Mr. Ow… | "male" | 22 | 0 | 7.25 | null | "S" | 1937-10-28 | 15:53:04 | 2022-08-05 04:43:34 | null |
2 | "Cumings, Mrs. … | "female" | 38 | 0 | 71.2833 | "C85" | "C" | null | 12:26:00 | 2022-08-07 01:56:33 | null |
3 | "Heikkinen, Mis… | "female" | 26 | 0 | 7.925 | null | "S" | 1931-09-24 | 16:08:25 | 2022-08-04 20:27:37 | null |
4 | "Futrelle, Mrs.… | "female" | 35 | 0 | 53.1 | "C123" | "S" | 1936-11-30 | null | 2022-08-07 07:05:55 | null |
5 | "Allen, Mr. Wil… | "male" | 35 | 0 | 8.05 | null | "S" | 1918-11-07 | 10:59:08 | 2022-08-02 15:13:34 | null |
"
- ],
- "text/plain": [
- "shape: (5, 12)\n",
- "┌─────────────┬───────────────┬────────┬─────┬───┬────────────┬────────────┬──────────────┬────────┐\n",
- "│ PassengerId ┆ Name ┆ Sex ┆ Age ┆ … ┆ Birthday ┆ Board time ┆ Married ┆ all_NA │\n",
- "│ --- ┆ --- ┆ --- ┆ --- ┆ ┆ --- ┆ --- ┆ since ┆ --- │\n",
- "│ i64 ┆ str ┆ cat ┆ i64 ┆ ┆ date ┆ time ┆ --- ┆ str │\n",
- "│ ┆ ┆ ┆ ┆ ┆ ┆ ┆ datetime[μs] ┆ │\n",
- "╞═════════════╪═══════════════╪════════╪═════╪═══╪════════════╪════════════╪══════════════╪════════╡\n",
- "│ 1 ┆ Braund, Mr. ┆ male ┆ 22 ┆ … ┆ 1937-10-28 ┆ 15:53:04 ┆ 2022-08-05 ┆ null │\n",
- "│ ┆ Owen Harris ┆ ┆ ┆ ┆ ┆ ┆ 04:43:34 ┆ │\n",
- "│ 2 ┆ Cumings, Mrs. ┆ female ┆ 38 ┆ … ┆ null ┆ 12:26:00 ┆ 2022-08-07 ┆ null │\n",
- "│ ┆ John Bradley ┆ ┆ ┆ ┆ ┆ ┆ 01:56:33 ┆ │\n",
- "│ ┆ (Flor… ┆ ┆ ┆ ┆ ┆ ┆ ┆ │\n",
- "│ 3 ┆ Heikkinen, ┆ female ┆ 26 ┆ … ┆ 1931-09-24 ┆ 16:08:25 ┆ 2022-08-04 ┆ null │\n",
- "│ ┆ Miss. Laina ┆ ┆ ┆ ┆ ┆ ┆ 20:27:37 ┆ │\n",
- "│ 4 ┆ Futrelle, ┆ female ┆ 35 ┆ … ┆ 1936-11-30 ┆ null ┆ 2022-08-07 ┆ null │\n",
- "│ ┆ Mrs. Jacques ┆ ┆ ┆ ┆ ┆ ┆ 07:05:55 ┆ │\n",
- "│ ┆ Heath (Li… ┆ ┆ ┆ ┆ ┆ ┆ ┆ │\n",
- "│ 5 ┆ Allen, Mr. ┆ male ┆ 35 ┆ … ┆ 1918-11-07 ┆ 10:59:08 ┆ 2022-08-02 ┆ null │\n",
- "│ ┆ William Henry ┆ ┆ ┆ ┆ ┆ ┆ 15:13:34 ┆ │\n",
- "└─────────────┴───────────────┴────────┴─────┴───┴────────────┴────────────┴──────────────┴────────┘"
- ]
- },
- "execution_count": 2,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
+ "outputs": [],
"source": [
- "demonstration_fp = get_demonstration_fp()\n",
+ "demonstration_fp =demo_file()\n",
"df = pl.read_csv(\n",
" source=demonstration_fp, \n",
" try_parse_dates=True,\n",
@@ -114,54 +75,10 @@
},
{
"cell_type": "code",
- "execution_count": 3,
+ "execution_count": null,
"id": "b2f5eadd",
"metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Lower bound distribution: 2022-07-15 12:21:15\n",
- "Lowest value in dataframe: 2022-07-15 12:21:15\n"
- ]
- },
- {
- "data": {
- "text/html": [
- "\n",
- "
shape: (5, 12)PassengerId | Name | Sex | Age | Parch | Fare | Cabin | Embarked | Birthday | Board time | Married since | all_NA |
---|
i64 | str | cat | i64 | i64 | f64 | str | cat | date | time | datetime[μs] | f32 |
1 | "Kathleen Dean" | "female" | null | 0 | 46.019655 | null | "C" | null | 16:32:24 | null | null |
2 | "Claudia Gonzal… | "male" | 29 | 1 | 56.67445 | "A226" | "S" | 1921-04-09 | 16:24:20 | 2022-07-21 14:31:15 | null |
3 | "Elizabeth Cart… | "male" | null | 0 | 62.881209 | null | "S" | 1928-09-25 | 11:10:36 | 2022-08-02 06:22:13 | null |
4 | "Richard Wright… | "male" | 34 | 1 | 0.964734 | "A396" | "S" | 1921-11-09 | 12:51:06 | 2022-08-05 23:27:28 | null |
5 | "Christian Cox" | "male" | 26 | 0 | 16.932637 | null | "S" | 1919-05-23 | null | 2022-07-21 00:42:00 | null |
"
- ],
- "text/plain": [
- "shape: (5, 12)\n",
- "┌─────────────┬───────────┬────────┬──────┬───┬────────────┬────────────┬─────────────────┬────────┐\n",
- "│ PassengerId ┆ Name ┆ Sex ┆ Age ┆ … ┆ Birthday ┆ Board time ┆ Married since ┆ all_NA │\n",
- "│ --- ┆ --- ┆ --- ┆ --- ┆ ┆ --- ┆ --- ┆ --- ┆ --- │\n",
- "│ i64 ┆ str ┆ cat ┆ i64 ┆ ┆ date ┆ time ┆ datetime[μs] ┆ f32 │\n",
- "╞═════════════╪═══════════╪════════╪══════╪═══╪════════════╪════════════╪═════════════════╪════════╡\n",
- "│ 1 ┆ Kathleen ┆ female ┆ null ┆ … ┆ null ┆ 16:32:24 ┆ null ┆ null │\n",
- "│ ┆ Dean ┆ ┆ ┆ ┆ ┆ ┆ ┆ │\n",
- "│ 2 ┆ Claudia ┆ male ┆ 29 ┆ … ┆ 1921-04-09 ┆ 16:24:20 ┆ 2022-07-21 ┆ null │\n",
- "│ ┆ Gonzales ┆ ┆ ┆ ┆ ┆ ┆ 14:31:15 ┆ │\n",
- "│ 3 ┆ Elizabeth ┆ male ┆ null ┆ … ┆ 1928-09-25 ┆ 11:10:36 ┆ 2022-08-02 ┆ null │\n",
- "│ ┆ Carter ┆ ┆ ┆ ┆ ┆ ┆ 06:22:13 ┆ │\n",
- "│ 4 ┆ Richard ┆ male ┆ 34 ┆ … ┆ 1921-11-09 ┆ 12:51:06 ┆ 2022-08-05 ┆ null │\n",
- "│ ┆ Wright ┆ ┆ ┆ ┆ ┆ ┆ 23:27:28 ┆ │\n",
- "│ 5 ┆ Christian ┆ male ┆ 26 ┆ … ┆ 1919-05-23 ┆ null ┆ 2022-07-21 ┆ null │\n",
- "│ ┆ Cox ┆ ┆ ┆ ┆ ┆ ┆ 00:42:00 ┆ │\n",
- "└─────────────┴───────────┴────────┴──────┴───┴────────────┴────────────┴─────────────────┴────────┘"
- ]
- },
- "execution_count": 3,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
+ "outputs": [],
"source": [
"from metasyn.distribution import RegexDistribution, FakerDistribution\n",
"from metasyn.distribution import DiscreteUniformDistribution\n",
@@ -203,64 +120,10 @@
},
{
"cell_type": "code",
- "execution_count": 4,
+ "execution_count": null,
"id": "b8b96c16",
- "metadata": {
- "scrolled": false
- },
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Lower bound distribution: 2022-07-15 17:12:24\n",
- "Lowest value in dataframe: 2022-07-15 12:21:15\n"
- ]
- },
- {
- "name": "stderr",
- "output_type": "stream",
- "text": [
- "/home/qubix/Documents/shared_work/synthetic/metasyn/metasyn/distribution/categorical.py:37: UserWarning: Multinoulli probabilities do not add up to 1 (0.9831649831649834); they will be rescaled.\n",
- " warnings.warn(\"Multinoulli probabilities do not add up to 1 \"\n"
- ]
- },
- {
- "data": {
- "text/html": [
- "\n",
- "
shape: (5, 12)PassengerId | Name | Sex | Age | Parch | Fare | Cabin | Embarked | Birthday | Board time | Married since | all_NA |
---|
i64 | str | cat | i64 | i64 | f64 | str | cat | date | time | datetime[μs] | f32 |
0 | "Kathleen Dean" | "male" | 25 | 0 | 6.388781 | "A739" | "S" | 1919-06-14 | 13:30:07 | 2022-07-18 07:11:54 | null |
1 | "Claudia Gonzal… | "female" | 22 | 1 | 11.486222 | "E619" | "S" | 1906-05-04 | 14:45:49 | 2022-07-17 23:24:32 | null |
2 | "Elizabeth Cart… | "male" | null | 0 | 2.814815 | null | "C" | 1909-06-17 | 13:37:09 | 2022-08-13 03:10:47 | null |
3 | "Richard Wright… | "male" | 36 | 0 | 58.833168 | null | "S" | 1921-10-12 | 12:54:26 | 2022-07-29 13:25:34 | null |
4 | "Christian Cox" | "male" | 35 | 0 | 5.988215 | "D39" | "S" | 1904-05-29 | 13:35:30 | 2022-07-30 04:01:27 | null |
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- ],
- "text/plain": [
- "shape: (5, 12)\n",
- "┌─────────────┬───────────┬────────┬──────┬───┬────────────┬────────────┬─────────────────┬────────┐\n",
- "│ PassengerId ┆ Name ┆ Sex ┆ Age ┆ … ┆ Birthday ┆ Board time ┆ Married since ┆ all_NA │\n",
- "│ --- ┆ --- ┆ --- ┆ --- ┆ ┆ --- ┆ --- ┆ --- ┆ --- │\n",
- "│ i64 ┆ str ┆ cat ┆ i64 ┆ ┆ date ┆ time ┆ datetime[μs] ┆ f32 │\n",
- "╞═════════════╪═══════════╪════════╪══════╪═══╪════════════╪════════════╪═════════════════╪════════╡\n",
- "│ 0 ┆ Kathleen ┆ male ┆ 25 ┆ … ┆ 1919-06-14 ┆ 13:30:07 ┆ 2022-07-18 ┆ null │\n",
- "│ ┆ Dean ┆ ┆ ┆ ┆ ┆ ┆ 07:11:54 ┆ │\n",
- "│ 1 ┆ Claudia ┆ female ┆ 22 ┆ … ┆ 1906-05-04 ┆ 14:45:49 ┆ 2022-07-17 ┆ null │\n",
- "│ ┆ Gonzales ┆ ┆ ┆ ┆ ┆ ┆ 23:24:32 ┆ │\n",
- "│ 2 ┆ Elizabeth ┆ male ┆ null ┆ … ┆ 1909-06-17 ┆ 13:37:09 ┆ 2022-08-13 ┆ null │\n",
- "│ ┆ Carter ┆ ┆ ┆ ┆ ┆ ┆ 03:10:47 ┆ │\n",
- "│ 3 ┆ Richard ┆ male ┆ 36 ┆ … ┆ 1921-10-12 ┆ 12:54:26 ┆ 2022-07-29 ┆ null │\n",
- "│ ┆ Wright ┆ ┆ ┆ ┆ ┆ ┆ 13:25:34 ┆ │\n",
- "│ 4 ┆ Christian ┆ male ┆ 35 ┆ … ┆ 1904-05-29 ┆ 13:35:30 ┆ 2022-07-30 ┆ null │\n",
- "│ ┆ Cox ┆ ┆ ┆ ┆ ┆ ┆ 04:01:27 ┆ │\n",
- "└─────────────┴───────────┴────────┴──────┴───┴────────────┴────────────┴─────────────────┴────────┘"
- ]
- },
- "execution_count": 4,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
+ "metadata": {},
+ "outputs": [],
"source": [
"meta_frame = MetaFrame.fit_dataframe(\n",
" df=df, \n",
@@ -304,23 +167,21 @@
},
{
"cell_type": "code",
- "execution_count": 5,
+ "execution_count": null,
"id": "6630b9a3",
- "metadata": {
- "scrolled": false
- },
+ "metadata": {},
"outputs": [],
"source": [
"from metasyn.distribution import MultinoulliDistribution\n",
"\n",
"def plot_outliers(dist_type, series_size=50):\n",
" dist_providers = DistributionProviderList([\"builtin\", \"metasyn-disclosure\"])\n",
- " disc_distributions = dist_providers._get_dist_list(var_type=dist_type, privacy=DisclosurePrivacy())\n",
+ " disc_distributions = dist_providers.get_distributions(var_type=dist_type, privacy=DisclosurePrivacy())\n",
" \n",
" for disc_class in disc_distributions:\n",
" if issubclass(disc_class, MultinoulliDistribution):\n",
" continue\n",
- " base_class = dist_providers.find_distribution(disc_class.implements)\n",
+ " base_class = dist_providers.find_distribution(disc_class.implements, disc_class.var_type)\n",
"\n",
" dist = base_class.default_distribution()\n",
" series = pl.Series([dist.draw() for _ in range(series_size)])\n",
@@ -364,123 +225,10 @@
},
{
"cell_type": "code",
- "execution_count": 6,
+ "execution_count": null,
"id": "fd6903c2",
- "metadata": {
- "scrolled": false
- },
- "outputs": [
- {
- "data": {
- "image/png": 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",
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