diff --git a/docs/source/api/metasynth.distribution.regex.rst b/docs/source/api/metasynth.distribution.regex.rst deleted file mode 100644 index 15696efd..00000000 --- a/docs/source/api/metasynth.distribution.regex.rst +++ /dev/null @@ -1,37 +0,0 @@ -metasynth.distribution.regex package -==================================== - -Submodules ----------- - -metasynth.distribution.regex.base module ----------------------------------------- - -.. automodule:: metasynth.distribution.regex.base - :members: - :undoc-members: - :show-inheritance: - -metasynth.distribution.regex.element module -------------------------------------------- - -.. automodule:: metasynth.distribution.regex.element - :members: - :undoc-members: - :show-inheritance: - -metasynth.distribution.regex.optimizer module ---------------------------------------------- - -.. automodule:: metasynth.distribution.regex.optimizer - :members: - :undoc-members: - :show-inheritance: - -Module contents ---------------- - -.. automodule:: metasynth.distribution.regex - :members: - :undoc-members: - :show-inheritance: diff --git a/docs/source/api/metasynth.distribution.rst b/docs/source/api/metasynth.distribution.rst index 69b9e2f3..b5b5d567 100644 --- a/docs/source/api/metasynth.distribution.rst +++ b/docs/source/api/metasynth.distribution.rst @@ -1,14 +1,6 @@ metasynth.distribution package ============================== -Subpackages ------------ - -.. toctree:: - :maxdepth: 4 - - metasynth.distribution.regex - Submodules ---------- @@ -60,6 +52,15 @@ metasynth.distribution.faker module :undoc-members: :show-inheritance: +metasynth.distribution.regex module +----------------------------------- + +.. automodule:: metasynth.distribution.regex + :members: + :undoc-members: + :show-inheritance: + + Module contents --------------- diff --git a/examples/advanced_tutorial.ipynb b/examples/advanced_tutorial.ipynb index b9d4fc78..51d850f5 100644 --- a/examples/advanced_tutorial.ipynb +++ b/examples/advanced_tutorial.ipynb @@ -12,13 +12,13 @@ }, { "cell_type": "markdown", - "source": [ - "## Step 0: Install the metasynth package and import required packages" - ], + "id": "f5c6597b", "metadata": { "collapsed": false }, - "id": "f5c6597b" + "source": [ + "## Step 0: Install the metasynth package and import required packages" + ] }, { "cell_type": "code", @@ -105,22 +105,22 @@ { "cell_type": "code", "execution_count": null, - "outputs": [], - "source": [ - "df.describe()" - ], + "id": "c72c2acb55fca193", "metadata": { "collapsed": false }, - "id": "c72c2acb55fca193" + "outputs": [], + "source": [ + "df.describe()" + ] }, { "cell_type": "markdown", - "source": [], + "id": "5df3f1e974e84da4", "metadata": { "collapsed": false }, - "id": "5df3f1e974e84da4" + "source": [] }, { "cell_type": "markdown", @@ -162,26 +162,26 @@ }, { "cell_type": "markdown", - "source": [ - "Alternatively, we can preview the MetaFrame as it would be output to a file" - ], + "id": "aeb3edae1eedf4b8", "metadata": { "collapsed": false }, - "id": "aeb3edae1eedf4b8" + "source": [ + "Alternatively, we can preview the MetaFrame as it would be output to a file" + ] }, { "cell_type": "code", "execution_count": null, + "id": "cbb6f59f1d439189", + "metadata": { + "collapsed": false + }, "outputs": [], "source": [ "json_preview = repr(mf)\n", "print(json_preview)" - ], - "metadata": { - "collapsed": false - }, - "id": "cbb6f59f1d439189" + ] }, { "cell_type": "markdown", @@ -226,25 +226,25 @@ { "cell_type": "code", "execution_count": null, + "id": "c5eac7eeb3326f03", + "metadata": { + "collapsed": false + }, "outputs": [], "source": [ "#load previously exported MetaFrame (.json) file\n", "mf = MetaFrame.from_json(file_path)" - ], - "metadata": { - "collapsed": false - }, - "id": "c5eac7eeb3326f03" + ] }, { "cell_type": "markdown", - "source": [ - "Once a MetaFrame is loaded, synthetic data can be generated from it. The `synthesize` method takes the number of rows to be generated as parameter and returns a DataFrame with the synthetic data." - ], + "id": "85201666a67a73fd", "metadata": { "collapsed": false }, - "id": "85201666a67a73fd" + "source": [ + "Once a MetaFrame is loaded, synthetic data can be generated from it. The `synthesize` method takes the number of rows to be generated as parameter and returns a DataFrame with the synthetic data." + ] }, { "cell_type": "code", @@ -423,7 +423,7 @@ "# To create a regex distribution, you need a list of tuples, where each tuple is an element.\n", "# The first part of the tuple is a string representation of the regex, while the second is the proportion of the\n", "# time the regex element is used.\n", - "cabin_distribution = RegexDistribution(r\"[ABCDEF]\\d{2,3}\") # Add the r so that it becomes a literal string.\n", + "cabin_distribution = RegexDistribution(r\"[ABCDEF][0-9]{2,3}\") # Add the r so that it becomes a literal string.\n", "# just for completeness: data generated from this distribution will always match the regex [ABCDEF]?(\\d{2,3})?\n", "\n", "var_spec = {\n", @@ -519,7 +519,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.9.10" + "version": "3.11.1" }, "vscode": { "interpreter": { diff --git a/examples/getting_started.ipynb b/examples/getting_started.ipynb index c052c1c3..5a043969 100644 --- a/examples/getting_started.ipynb +++ b/examples/getting_started.ipynb @@ -12,14 +12,14 @@ }, { "cell_type": "markdown", - "source": [ - "## Step 0: Install the metasynth package and import required packages\n", - "First, install the metasynth package in your session:" - ], + "id": "f5c6597b", "metadata": { "collapsed": false }, - "id": "f5c6597b" + "source": [ + "## Step 0: Install the metasynth package and import required packages\n", + "First, install the metasynth package in your session:" + ] }, { "cell_type": "code", @@ -152,26 +152,26 @@ }, { "cell_type": "markdown", - "source": [ - "Alternatively, we can preview the MetaFrame as it would be output to a file" - ], + "id": "572128238aa26d67", "metadata": { "collapsed": false }, - "id": "572128238aa26d67" + "source": [ + "Alternatively, we can preview the MetaFrame as it would be output to a file" + ] }, { "cell_type": "code", "execution_count": null, + "id": "42470906a0d25575", + "metadata": { + "collapsed": false + }, "outputs": [], "source": [ "json_preview = repr(mf)\n", "print(json_preview)" - ], - "metadata": { - "collapsed": false - }, - "id": "42470906a0d25575" + ] }, { "cell_type": "markdown", @@ -226,26 +226,26 @@ }, { "cell_type": "markdown", - "source": [ - "Once a MetaFrame is loaded, synthetic data can be generated from it. The `synthesize` method takes the number of rows to be generated as parameter and returns a DataFrame with the synthetic data." - ], + "id": "48bc803ba4e76a1c", "metadata": { "collapsed": false }, - "id": "48bc803ba4e76a1c" + "source": [ + "Once a MetaFrame is loaded, synthetic data can be generated from it. The `synthesize` method takes the number of rows to be generated as parameter and returns a DataFrame with the synthetic data." + ] }, { "cell_type": "code", "execution_count": null, + "id": "4ccf451c", + "metadata": { + "collapsed": false + }, "outputs": [], "source": [ "# generate synthetic data\n", "mf.synthesize(5)" - ], - "metadata": { - "collapsed": false - }, - "id": "4ccf451c" + ] }, { "cell_type": "markdown", @@ -275,7 +275,7 @@ " # Manually set a distribution for age \n", " \"Age\": {\"distribution\": DiscreteUniformDistribution(20, 40)},\n", " # Manually set a regex distribution for cabin\n", - " \"Cabin\": {\"distribution\": RegexDistribution(r\"[ABCDEF]\\d{2,3}\")}\n", + " \"Cabin\": {\"distribution\": RegexDistribution(r\"[ABCDEF][0-9]{2,3}\")}\n", "}\n", "\n", "# create the high-quality metadata\n", @@ -313,6 +313,14 @@ "source": [ "syn_df.describe()" ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "f1e2244c", + "metadata": {}, + "outputs": [], + "source": [] } ], "metadata": { @@ -331,7 +339,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.9.10" + "version": "3.11.1" }, "vscode": { "interpreter": { diff --git a/examples/titanic.ipynb b/examples/titanic.ipynb index adae4de7..fe4cec4f 100644 --- a/examples/titanic.ipynb +++ b/examples/titanic.ipynb @@ -1,6 +1,7 @@ { "cells": [ { + "attachments": {}, "cell_type": "markdown", "id": "ff21c5ce", "metadata": {}, @@ -9,6 +10,7 @@ ] }, { + "attachments": {}, "cell_type": "markdown", "id": "529373c2", "metadata": {}, @@ -17,6 +19,7 @@ ] }, { + "attachments": {}, "cell_type": "markdown", "id": "388d5eb5", "metadata": {}, @@ -26,7 +29,7 @@ }, { "cell_type": "code", - "execution_count": 1, + "execution_count": null, "id": "9ffe3c40", "metadata": {}, "outputs": [], @@ -41,6 +44,7 @@ ] }, { + "attachments": {}, "cell_type": "markdown", "id": "872c6e15", "metadata": {}, @@ -50,7 +54,7 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": null, "id": "46ba6b89", "metadata": {}, "outputs": [], @@ -61,6 +65,7 @@ ] }, { + "attachments": {}, "cell_type": "markdown", "id": "8b0ddc0a", "metadata": {}, @@ -72,7 +77,7 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": null, "id": "5e060a1d", "metadata": {}, "outputs": [], @@ -91,6 +96,7 @@ ] }, { + "attachments": {}, "cell_type": "markdown", "id": "fa7cd2ce", "metadata": {}, @@ -100,7 +106,7 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": null, "id": "c319763e", "metadata": {}, "outputs": [], @@ -109,6 +115,7 @@ ] }, { + "attachments": {}, "cell_type": "markdown", "id": "d7549f77", "metadata": {}, @@ -120,28 +127,16 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": null, "id": "e02c6c59", "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/Users/qubix/Documents/work/metasynth/metasynth/metasynth/provider.py:328: UserWarning: \n", - "Variable PassengerId seems unique, but not set to be unique.\n", - "Set the variable to be either unique or not unique to remove this warning.\n", - "\n", - " warnings.warn(f\"\\nVariable {series.name} seems unique, but not set to be unique.\\n\"\n" - ] - } - ], + "outputs": [], "source": [ - "dataset = MetaFrame.fit_dataframe(df, spec={\"Cabin\": {\"distribution\": \"regex\", \"fit_kwargs\": {\"mode\": \"fast\"}}\n", - " })" + "dataset = MetaFrame.fit_dataframe(df)" ] }, { + "attachments": {}, "cell_type": "markdown", "id": "a30a428e", "metadata": {}, @@ -151,269 +146,16 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": null, "id": "ef13c521", "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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"execution_count": 9, + "execution_count": null, "id": "22863b6b", "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "# Rows: 891\n", - "# Columns: 12\n", - "\n", - "{'name': 'PassengerId', 'description': None, 'type': 'discrete', 'dtype': 'Int64', 'prop_missing': 0.0, 'distribution': \"{'implements': 'core.discrete_uniform', 'provenance': 'builtin', 'class_name': 'DiscreteUniformDistribution', 'parameters': {'low': 1, 'high': 892}}\"}\n", - "\n", - "{'name': 'Survived', 'description': None, 'type': 'categorical', 'dtype': 'Categorical', 'prop_missing': 0.0, 'distribution': \"{'implements': 'core.multinoulli', 'provenance': 'builtin', 'class_name': 'MultinoulliDistribution', 'parameters': {'labels': ['0', '1'], 'probs': [0.6161616161616161, 0.3838383838383838]}}\"}\n", - "\n", - "{'name': 'Pclass', 'description': None, 'type': 'categorical', 'dtype': 'Categorical', 'prop_missing': 0.0, 'distribution': \"{'implements': 'core.multinoulli', 'provenance': 'builtin', 'class_name': 'MultinoulliDistribution', 'parameters': {'labels': ['1', '2', '3'], 'probs': [0.24242424242424243, 0.20650953984287318, 0.5510662177328844]}}\"}\n", - "\n", - "{'name': 'Name', 'description': None, 'type': 'string', 'dtype': 'Utf8', 'prop_missing': 0.0, 'distribution': '.[]{12,82}'}\n", - "\n", - "{'name': 'Sex', 'description': None, 'type': 'categorical', 'dtype': 'Categorical', 'prop_missing': 0.0, 'distribution': \"{'implements': 'core.multinoulli', 'provenance': 'builtin', 'class_name': 'MultinoulliDistribution', 'parameters': {'labels': ['female', 'male'], 'probs': [0.35241301907968575, 0.6475869809203143]}}\"}\n", - "\n", - "{'name': 'Age', 'description': None, 'type': 'continuous', 'dtype': 'Float64', 'prop_missing': 0.19865319865319866, 'distribution': \"{'implements': 'core.truncated_normal', 'provenance': 'builtin', 'class_name': 'TruncatedNormalDistribution', 'parameters': {'lower_bound': 0.41999999, 'upper_bound': 80.00000001, 'mu': 28.286441105482094, 'sigma': 15.964614689648158}}\"}\n", - "\n", - "{'name': 'SibSp', 'description': None, 'type': 'categorical', 'dtype': 'Categorical', 'prop_missing': 0.0, 'distribution': \"{'implements': 'core.multinoulli', 'provenance': 'builtin', 'class_name': 'MultinoulliDistribution', 'parameters': {'labels': ['0', '1', '2', '3', '4', '5', '8'], 'probs': array([0.68237935, 0.2345679 , 0.03142536, 0.01795735, 0.02020202,\\n 0.00561167, 0.00785634])}}\"}\n", - "\n", - "{'name': 'Parch', 'description': None, 'type': 'categorical', 'dtype': 'Categorical', 'prop_missing': 0.0, 'distribution': \"{'implements': 'core.multinoulli', 'provenance': 'builtin', 'class_name': 'MultinoulliDistribution', 'parameters': {'labels': ['0', '1', '2', '3', '4', '5', '6'], 'probs': array([0.76094276, 0.13243547, 0.08978676, 0.00561167, 0.00448934,\\n 0.00561167, 0.00112233])}}\"}\n", - "\n", - "{'name': 'Ticket', 'description': None, 'type': 'string', 'dtype': 'Utf8', 'prop_missing': 0.0, 'distribution': '[A-Z]{1,5}[ ./123456789a][A-Z]{1,5}[ ./0123456789a][A-Z]{1,1}[a-z]{3,4}[ ./0123456789][A-Z]{1,5}[ .0123456789].[]{1,8}'}\n", - "\n", - "{'name': 'Fare', 'description': None, 'type': 'continuous', 'dtype': 'Float64', 'prop_missing': 0.0, 'distribution': \"{'implements': 'core.exponential', 'provenance': 'builtin', 'class_name': 'ExponentialDistribution', 'parameters': {'rate': 0.03052908440177665}}\"}\n", - "\n", - "{'name': 'Cabin', 'description': None, 'type': 'string', 'dtype': 'Utf8', 'prop_missing': 0.7710437710437711, 'distribution': '[ABCDEFGT].[]{1,14}'}\n", - "\n", - "{'name': 'Embarked', 'description': None, 'type': 'categorical', 'dtype': 'Categorical', 'prop_missing': 0.002244668911335578, 'distribution': \"{'implements': 'core.multinoulli', 'provenance': 'builtin', 'class_name': 'MultinoulliDistribution', 'parameters': {'labels': ['C', 'Q', 'S'], 'probs': [0.1889763779527559, 0.08661417322834646, 0.7244094488188977]}}\"}\n", - "\n" - ] - } - ], + "outputs": [], "source": [ "print(MetaFrame.from_json(\"test.json\"))" ] }, { + "attachments": {}, "cell_type": "markdown", "id": "26e6ac69", "metadata": {}, @@ -586,144 +224,12 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": null, "id": "d76c6882", "metadata": { "scrolled": false }, - "outputs": [ - { - "data": { - "text/plain": [ - "{'n_rows': 891,\n", - " 'n_columns': 12,\n", - " 'provenance': {'created by': {'name': 'MetaSynth',\n", - " 'version': '0.2.3.dev14+g19376b6.d20230418'},\n", - " 'creation time': '2023-04-18T20:05:02.107155'},\n", - " 'vars': [{'name': 'PassengerId',\n", - " 'type': 'discrete',\n", - " 'dtype': 'Int64',\n", - " 'prop_missing': 0.0,\n", - " 'distribution': {'implements': 'core.discrete_uniform',\n", - " 'provenance': 'builtin',\n", - " 'class_name': 'DiscreteUniformDistribution',\n", - " 'parameters': {'low': 1, 'high': 892}}},\n", - " {'name': 'Survived',\n", - " 'type': 'categorical',\n", - " 'dtype': 'Categorical',\n", - " 'prop_missing': 0.0,\n", - " 'distribution': {'implements': 'core.multinoulli',\n", - " 'provenance': 'builtin',\n", - " 'class_name': 'MultinoulliDistribution',\n", - " 'parameters': {'labels': array(['0', '1'], dtype=' Path: dframe["all_NA"] = [pd.NA for _ in range(len(dframe))] # Remove some columns for brevity and write to a file. - dframe = dframe.drop(["SibSp", "Pclass", "Ticket", "Survived"], axis=1) + dframe = dframe.drop(["SibSp", "Pclass", "Survived"], axis=1) dframe.to_csv(output_fp, index=False) return output_fp diff --git a/metasynth/demo/demo_titanic.csv b/metasynth/demo/demo_titanic.csv index e3fb9159..5bbed7da 100644 --- a/metasynth/demo/demo_titanic.csv +++ b/metasynth/demo/demo_titanic.csv @@ -1,892 +1,892 @@ -PassengerId,Name,Sex,Age,Parch,Fare,Cabin,Embarked,Birthday,Board time,Married since,all_NA -1,"Braund, Mr. Owen Harris",male,22,0,7.25,,S,1937-10-28,15:53:04,2022-08-05 04:43:34, -2,"Cumings, Mrs. John Bradley (Florence Briggs Thayer)",female,38,0,71.2833,C85,C,,12:26:00,2022-08-07 01:56:33, -3,"Heikkinen, Miss. Laina",female,26,0,7.925,,S,1931-09-24,16:08:25,2022-08-04 20:27:37, -4,"Futrelle, Mrs. Jacques Heath (Lily May Peel)",female,35,0,53.1,C123,S,1936-11-30,,2022-08-07 07:05:55, -5,"Allen, Mr. William Henry",male,35,0,8.05,,S,1918-11-07,10:59:08,2022-08-02 15:13:34, -6,"Moran, Mr. James",male,,0,8.4583,,Q,1923-06-02,16:28:33,2022-07-28 14:37:30, -7,"McCarthy, Mr. Timothy J",male,54,0,51.8625,E46,S,1928-07-02,17:20:04,2022-07-18 20:20:09, -8,"Palsson, Master. Gosta Leonard",male,2,1,21.075,,S,1918-03-12,17:56:40,2022-08-02 03:42:58, -9,"Johnson, Mrs. Oscar W (Elisabeth Vilhelmina Berg)",female,27,2,11.1333,,S,1909-11-16,16:33:50,2022-08-07 04:06:24, -10,"Nasser, Mrs. Nicholas (Adele Achem)",female,14,0,30.0708,,C,,17:28:21,2022-07-17 22:55:10, -11,"Sandstrom, Miss. Marguerite Rut",female,4,1,16.7,G6,S,1903-10-30,16:41:34,2022-07-24 19:18:18, -12,"Bonnell, Miss. Elizabeth",female,58,0,26.55,C103,S,1938-12-31,17:32:25,2022-07-23 03:46:49, -13,"Saundercock, Mr. William Henry",male,20,0,8.05,,S,1932-05-01,11:09:30,2022-07-27 10:47:33, -14,"Andersson, Mr. Anders Johan",male,39,5,31.275,,S,1933-03-06,14:16:15,2022-08-04 18:49:36, -15,"Vestrom, Miss. Hulda Amanda Adolfina",female,14,0,7.8542,,S,1908-05-26,18:36:24,2022-08-10 22:17:32, -16,"Hewlett, Mrs. (Mary D Kingcome) ",female,55,0,16.0,,S,1906-05-01,17:46:34,, -17,"Rice, Master. Eugene",male,2,1,29.125,,Q,1909-05-19,15:39:25,2022-08-06 22:46:12, -18,"Williams, Mr. Charles Eugene",male,,0,13.0,,S,1916-12-15,15:00:45,2022-07-24 10:54:46, -19,"Vander Planke, Mrs. Julius (Emelia Maria Vandemoortele)",female,31,0,18.0,,S,1938-02-27,17:27:11,2022-08-15 03:39:59, -20,"Masselmani, Mrs. Fatima",female,,0,7.225,,C,1921-08-01,13:01:05,2022-08-09 03:06:51, -21,"Fynney, Mr. Joseph J",male,35,0,26.0,,S,1920-09-17,12:11:48,2022-07-31 12:30:35, -22,"Beesley, Mr. Lawrence",male,34,0,13.0,D56,S,1928-09-03,18:30:54,2022-07-15 12:58:08, -23,"McGowan, Miss. Anna ""Annie""",female,15,0,8.0292,,Q,1939-03-28,14:10:42,, -24,"Sloper, Mr. William Thompson",male,28,0,35.5,A6,S,1926-03-20,12:19:12,2022-07-25 06:31:32, -25,"Palsson, Miss. Torborg Danira",female,8,1,21.075,,S,1938-06-17,,2022-07-25 00:25:51, -26,"Asplund, Mrs. Carl Oscar (Selma Augusta Emilia Johansson)",female,38,5,31.3875,,S,,18:15:57,2022-08-10 05:31:28, -27,"Emir, Mr. Farred Chehab",male,,0,7.225,,C,1921-12-24,,2022-08-09 15:16:19, -28,"Fortune, Mr. Charles Alexander",male,19,2,263.0,C23 C25 C27,S,1934-06-08,11:47:08,2022-07-22 07:34:41, -29,"O'Dwyer, Miss. Ellen ""Nellie""",female,,0,7.8792,,Q,1938-03-20,,2022-07-17 12:20:31, -30,"Todoroff, Mr. Lalio",male,,0,7.8958,,S,1928-02-07,11:06:55,2022-07-17 11:29:11, -31,"Uruchurtu, Don. Manuel E",male,40,0,27.7208,,C,,11:18:08,2022-08-01 09:00:50, -32,"Spencer, Mrs. William Augustus (Marie Eugenie)",female,,0,146.5208,B78,C,1904-05-23,17:38:32,2022-08-12 00:23:17, -33,"Glynn, Miss. Mary Agatha",female,,0,7.75,,Q,1939-10-03,15:15:06,2022-07-26 20:12:13, -34,"Wheadon, Mr. Edward H",male,66,0,10.5,,S,1907-10-10,18:15:37,2022-07-16 14:14:18, -35,"Meyer, Mr. Edgar Joseph",male,28,0,82.1708,,C,1919-08-24,15:49:03,2022-07-19 00:44:15, -36,"Holverson, Mr. Alexander Oskar",male,42,0,52.0,,S,1939-05-24,17:33:36,2022-08-12 14:24:05, -37,"Mamee, Mr. Hanna",male,,0,7.2292,,C,1917-04-14,17:14:57,2022-08-08 22:40:30, -38,"Cann, Mr. Ernest Charles",male,21,0,8.05,,S,1934-11-16,11:51:09,2022-08-03 22:37:37, -39,"Vander Planke, Miss. Augusta Maria",female,18,0,18.0,,S,1914-04-19,15:05:30,2022-08-14 13:54:54, -40,"Nicola-Yarred, Miss. Jamila",female,14,0,11.2417,,C,1927-09-20,14:07:06,2022-07-18 02:10:45, -41,"Ahlin, Mrs. Johan (Johanna Persdotter Larsson)",female,40,0,9.475,,S,1907-08-06,15:09:05,, -42,"Turpin, Mrs. William John Robert (Dorothy Ann Wonnacott)",female,27,0,21.0,,S,1909-04-06,18:17:30,2022-08-11 18:57:36, -43,"Kraeff, Mr. Theodor",male,,0,7.8958,,C,1929-01-19,,2022-07-30 02:04:47, -44,"Laroche, Miss. Simonne Marie Anne Andree",female,3,2,41.5792,,C,1939-01-23,,2022-07-25 17:40:43, -45,"Devaney, Miss. Margaret Delia",female,19,0,7.8792,,Q,1910-09-23,18:36:50,2022-07-16 21:31:16, -46,"Rogers, Mr. William John",male,,0,8.05,,S,1913-06-12,10:48:51,2022-08-01 00:12:07, -47,"Lennon, Mr. Denis",male,,0,15.5,,Q,1908-08-05,13:24:32,2022-07-19 06:11:27, -48,"O'Driscoll, Miss. Bridget",female,,0,7.75,,Q,,16:00:34,2022-07-27 09:24:55, -49,"Samaan, Mr. Youssef",male,,0,21.6792,,C,1920-09-13,,2022-07-19 15:04:12, -50,"Arnold-Franchi, Mrs. Josef (Josefine Franchi)",female,18,0,17.8,,S,1907-07-30,13:47:45,2022-08-02 04:21:34, -51,"Panula, Master. Juha Niilo",male,7,1,39.6875,,S,1923-04-26,14:59:39,2022-07-21 06:35:49, -52,"Nosworthy, Mr. Richard Cater",male,21,0,7.8,,S,,18:15:31,2022-08-09 08:14:03, -53,"Harper, Mrs. Henry Sleeper (Myna Haxtun)",female,49,0,76.7292,D33,C,1929-11-03,,2022-08-14 16:38:12, -54,"Faunthorpe, Mrs. Lizzie (Elizabeth Anne Wilkinson)",female,29,0,26.0,,S,1930-11-19,,2022-08-04 07:10:22, -55,"Ostby, Mr. Engelhart Cornelius",male,65,1,61.9792,B30,C,1919-04-01,12:29:33,2022-08-13 13:54:25, -56,"Woolner, Mr. Hugh",male,,0,35.5,C52,S,1905-08-28,18:06:13,2022-08-04 18:15:31, -57,"Rugg, Miss. Emily",female,21,0,10.5,,S,1905-04-01,10:40:18,2022-07-23 20:19:47, -58,"Novel, Mr. Mansouer",male,28,0,7.2292,,C,,,2022-07-21 05:22:04, -59,"West, Miss. Constance Mirium",female,5,2,27.75,,S,1907-07-04,12:45:12,2022-07-22 14:30:14, -60,"Goodwin, Master. William Frederick",male,11,2,46.9,,S,1910-12-18,15:39:59,2022-07-26 03:24:34, -61,"Sirayanian, Mr. Orsen",male,22,0,7.2292,,C,1923-11-01,13:27:13,2022-07-23 20:39:22, -62,"Icard, Miss. Amelie",female,38,0,80.0,B28,,1918-11-02,11:37:34,2022-07-29 05:50:51, -63,"Harris, Mr. Henry Birkhardt",male,45,0,83.475,C83,S,1939-04-18,13:08:10,2022-08-08 21:01:36, -64,"Skoog, Master. Harald",male,4,2,27.9,,S,1922-12-21,16:39:09,2022-08-03 20:21:38, -65,"Stewart, Mr. Albert A",male,,0,27.7208,,C,1913-02-11,11:48:25,2022-07-23 13:46:04, -66,"Moubarek, Master. Gerios",male,,1,15.2458,,C,,11:20:44,2022-07-16 04:33:36, -67,"Nye, Mrs. (Elizabeth Ramell)",female,29,0,10.5,F33,S,1912-03-27,14:31:49,2022-07-31 10:35:43, -68,"Crease, Mr. Ernest James",male,19,0,8.1583,,S,1904-11-07,13:24:11,2022-07-21 01:10:14, -69,"Andersson, Miss. 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Gerda Ulrika",female,22,0,7552,10.5167,,S,,15:36:36,2022-08-05 13:26:26, +884,"Banfield, Mr. Frederick James",male,28,0,C.A./SOTON 34068,10.5,,S,1934-09-04,16:10:24,2022-08-06 03:44:50, +885,"Sutehall, Mr. Henry Jr",male,25,0,SOTON/OQ 392076,7.05,,S,1938-03-07,17:36:02,2022-08-02 02:29:17, +886,"Rice, Mrs. William (Margaret Norton)",female,39,5,382652,29.125,,Q,1923-05-27,15:55:39,, +887,"Montvila, Rev. Juozas",male,27,0,211536,13.0,,S,1918-09-16,13:58:18,2022-08-02 17:29:12, +888,"Graham, Miss. Margaret Edith",female,19,0,112053,30.0,B42,S,1911-04-28,11:43:34,2022-07-30 03:35:42, +889,"Johnston, Miss. Catherine Helen ""Carrie""",female,,2,W./C. 6607,23.45,,S,1939-03-18,14:41:36,, +890,"Behr, Mr. Karl Howell",male,26,0,111369,30.0,C148,C,1905-04-16,13:37:08,2022-07-23 08:04:22, +891,"Dooley, Mr. Patrick",male,32,0,370376,7.75,,Q,,17:21:32,2022-08-01 11:07:59, diff --git a/metasynth/distribution/base.py b/metasynth/distribution/base.py index 4ede9de6..c4d21061 100644 --- a/metasynth/distribution/base.py +++ b/metasynth/distribution/base.py @@ -23,6 +23,7 @@ class BaseDistribution(ABC): provenance: str = "builtin" privacy: str = "none" is_unique: bool = False + version: str = "1.0" @classmethod def fit(cls, series: Union[Sequence, pl.Series], @@ -68,17 +69,20 @@ def draw(self) -> object: def draw_reset(self) -> None: """Reset the drawing of elements to start again.""" - def __str__(self) -> str: - """Return an easy to read formatted string for the distribution.""" - params_formatted = "\n".join( + @property + def _params_formatted(self) -> str: + return "\n".join( f"\t- {param}: {value}" for param, value in self._param_dict().items() ) + + def __str__(self) -> str: + """Return an easy to read formatted string for the distribution.""" return ( f"- Type: {self.implements}\n" f"- Provenance: {self.provenance}\n" f"- Parameters:\n" - f"{params_formatted}\n" + f"{self._params_formatted}\n" ) @abstractmethod @@ -89,6 +93,7 @@ def to_dict(self) -> dict: """Convert the distribution to a dictionary.""" return { "implements": self.implements, + "version": self.version, "provenance": self.provenance, "class_name": self.__class__.__name__, "parameters": deepcopy(self._param_dict()), @@ -106,6 +111,7 @@ def schema(cls) -> dict: "type": "object", "properties": { "implements": {"const": cls.implements}, + "version": {"type": "string"}, "provenance": {"const": cls.provenance}, "class_name": {"const": cls.__name__}, "parameters": { @@ -164,6 +170,7 @@ def metadist( provenance: Optional[str] = None, var_type: Optional[Union[str, list[str]]] = None, is_unique: Optional[bool] = None, + version: Optional[str] = None, privacy: Optional[str] = None): """Decorate class to create a distribution with the right properties. @@ -179,6 +186,9 @@ def metadist( Whether the distribution is unique or not. privacy: Privacy class/implementation of the distribution. + version: + Version of the distribution. Increment this to ensure that compatibility is + properly handled. Returns ------- @@ -196,6 +206,8 @@ def _wrap(cls): cls.is_unique = is_unique if privacy is not None: cls.privacy = privacy + if version is not None: + cls.version = version return cls return _wrap diff --git a/metasynth/distribution/legacy/__init__.py b/metasynth/distribution/legacy/__init__.py new file mode 100644 index 00000000..2a8074c9 --- /dev/null +++ b/metasynth/distribution/legacy/__init__.py @@ -0,0 +1,7 @@ +"""Legacy distributions that will be removed in the future.""" + +from metasynth.distribution.legacy.regex import RegexDistribution +from metasynth.distribution.legacy.regex import UniqueRegexDistribution + + +__all__ = ["RegexDistribution", "UniqueRegexDistribution"] diff --git a/metasynth/distribution/legacy/regex/__init__.py b/metasynth/distribution/legacy/regex/__init__.py new file mode 100644 index 00000000..2150934d --- /dev/null +++ b/metasynth/distribution/legacy/regex/__init__.py @@ -0,0 +1,6 @@ +"""Package for fitting regexes to strings.""" + +from metasynth.distribution.legacy.regex.base import (RegexDistribution, + UniqueRegexDistribution) + +__all__ = ["RegexDistribution", "UniqueRegexDistribution"] diff --git a/metasynth/distribution/regex/base.py b/metasynth/distribution/legacy/regex/base.py similarity index 94% rename from metasynth/distribution/regex/base.py rename to metasynth/distribution/legacy/regex/base.py index 3bb6bafa..9411bb75 100644 --- a/metasynth/distribution/regex/base.py +++ b/metasynth/distribution/legacy/regex/base.py @@ -7,11 +7,11 @@ import numpy as np from metasynth.distribution.base import metadist, BaseDistribution -from metasynth.distribution.regex.element import (AlphaNumericRegex, AnyRegex, - BaseRegexElement, DigitRegex, - LettersRegex, LowercaseRegex, - SingleRegex, UppercaseRegex) -from metasynth.distribution.regex.optimizer import RegexOptimizer +from metasynth.distribution.legacy.regex.element import (AlphaNumericRegex, AnyRegex, + BaseRegexElement, DigitRegex, + LettersRegex, LowercaseRegex, + SingleRegex, UppercaseRegex) +from metasynth.distribution.legacy.regex.optimizer import RegexOptimizer def _get_gradient_start(values: Sequence[str], new_values: Sequence[str], diff --git a/metasynth/distribution/regex/element.py b/metasynth/distribution/legacy/regex/element.py similarity index 99% rename from metasynth/distribution/regex/element.py rename to metasynth/distribution/legacy/regex/element.py index cc97085e..d7368c58 100644 --- a/metasynth/distribution/regex/element.py +++ b/metasynth/distribution/legacy/regex/element.py @@ -11,7 +11,7 @@ import numpy as np from sklearn.feature_extraction.text import CountVectorizer -from metasynth.distribution.regex.optimizer import RegexOptimizer +from metasynth.distribution.legacy.regex.optimizer import RegexOptimizer class BaseRegexElement(ABC): diff --git a/metasynth/distribution/regex/optimizer.py b/metasynth/distribution/legacy/regex/optimizer.py similarity index 100% rename from metasynth/distribution/regex/optimizer.py rename to metasynth/distribution/legacy/regex/optimizer.py diff --git a/metasynth/distribution/regex.py b/metasynth/distribution/regex.py new file mode 100644 index 00000000..b02e6676 --- /dev/null +++ b/metasynth/distribution/regex.py @@ -0,0 +1,114 @@ +"""Distribution for structured strings, using regexes.""" +from __future__ import annotations + +from typing import Union, Optional + +from regexmodel import RegexModel, NotFittedError + +from metasynth.distribution.base import metadist, BaseDistribution, UniqueDistributionMixin + + +@metadist(implements="core.regex", var_type="string", version="2.0") +class RegexDistribution(BaseDistribution): + """Distribution for structured strings. + + Main implementation details in the regexmodel package: + https://github.com/sodascience/regexmodel + + This distribution tries to create a regex that fits the variable. This + regex also contains statistical information about the probabilities if the + regex has multiple options (e.g. a|b). The regex is only a subset what is provided + by the python re package. What is currently implemented: + + - Parentheses with multiple options and no modifiers, e.g. ([a]|[b]|[c]) + - Square brackets without negation, e.g. [abc] + - Ranges [A-Z], [a-z], [0-9], but not subranges (e.g. [0-3]) + - Repetition quantifiers (curly brackets) with minimum and maximum [A-Z]{3,6}, but not [A-Z]{6}. + + When fitting the RegexDistribution using the fit method, pay attention to the + count_thres and method parameters. By default these will be dynamic and take reasonable + values for the input, but in some cases it can be important to set them manually. + The count_thres parameter sets the minimum number of times a regex element needs to + be used. So, if count_thres=2, and there is only one value starting with "a", then the regex + will never start with "a". In effect, a higher value will provide more privacy, less + utility and a faster fit. The other parameter "method" has a small effect on the accuracy + of the regex, and a larger effect on the worst case time consumption for fitting. + Set to "accurate" for the best result, and "fast" for the fastest result. + + Examples that this distribution should work reasonably for are: email, + ID's, telephone numbers, ip addresses, etc. + + Parameters + ---------- + regex_data: + Valid inputs for the regex model are: + - str: String with a regex (that falls within the specifications, see above). + - dict: Serialized version of the regex model, as it is coming from a JSON file. + - RegexModel: Initialized regex model. + """ + + def __init__(self, regex_data: Union[str, dict, RegexModel]): + self.regex_model = RegexModel(regex_data) + + @classmethod + def _fit(cls, values, count_thres: Optional[int] = None, method: str = "auto"): + """Fit a regex to structured strings. + + Arguments + --------- + values: + Values to be fitted (pl.Series). + count_thres: + Threshold for regex elements, so that a regex element can only be used if + the number of values satisfying said element is higher than the threshold. + method: + Method for fitting the regex model. Possible values are ["accurate", "fast", "auto"] + The "auto" method switches between the "accurate" and "fast" methods depending on + the number of characters (fast if #char > 10000) in the series. + """ + if method == "auto": + if values.str.lengths().mean() > 10: + method = "fast" + else: + method = "accurate" + + # Make count_thres ~= #values/100 up to 50 if in auto mode. + if count_thres is None: + count_thres = min(50, max(2, round(len(values)/50))) + + # Try to fit the values, if it cannot be fit, then use the default distribution. + try: + model = RegexModel.fit(values, count_thres=count_thres, method=method) + except NotFittedError: + return cls.default_distribution() + return cls(model) + + def draw(self): + return self.regex_model.draw() + + def _param_dict(self): + return {"regex_data": self.regex_model.serialize()} + + @property + def _params_formatted(self): + return f"\t- regex: {self.regex_model.regex}" + + @classmethod + def _param_schema(cls): + return { + "regex": { + "type": "string" + }, + "counts": { + "type": "array" + } + } + + @classmethod + def default_distribution(cls): + return cls(r"[ABC][0-9]{3,4}") + + +@metadist(implements="core.unique_regex", var_type="string", is_unique=True) +class UniqueRegexDistribution(UniqueDistributionMixin, RegexDistribution): + """Unique variant of the regex distribution.""" diff --git a/metasynth/distribution/regex/__init__.py b/metasynth/distribution/regex/__init__.py deleted file mode 100644 index 7ea8d7f2..00000000 --- a/metasynth/distribution/regex/__init__.py +++ /dev/null @@ -1,6 +0,0 @@ -"""Package for fitting regexes to strings.""" - -from metasynth.distribution.regex.base import (RegexDistribution, - UniqueRegexDistribution) - -__all__ = ["RegexDistribution", "UniqueRegexDistribution"] diff --git a/metasynth/provider.py b/metasynth/provider.py index 1293811e..66403941 100644 --- a/metasynth/provider.py +++ b/metasynth/provider.py @@ -36,8 +36,9 @@ from metasynth.distribution.faker import (FakerDistribution, UniqueFakerDistribution, FreeTextDistribution) -from metasynth.distribution.regex.base import (RegexDistribution, - UniqueRegexDistribution) +from metasynth.distribution.regex import (RegexDistribution, + UniqueRegexDistribution) +from metasynth.distribution import legacy from metasynth.privacy import BasePrivacy, BasicPrivacy @@ -50,6 +51,7 @@ class BaseDistributionProvider(ABC): name = "" version = "" distributions: list[type[BaseDistribution]] = [] + legacy_distributions: list[type[BaseDistribution]] = [] def __init__(self): # Perform internal consistency check. @@ -57,13 +59,16 @@ def __init__(self): assert len(self.version) > 0 assert len(self.distributions) > 0 - def get_dist_list(self, var_type: str) -> List[Type[BaseDistribution]]: + def get_dist_list(self, var_type: str, + use_legacy: bool = False) -> List[Type[BaseDistribution]]: """Get all distributions for a certain variable type. Parameters ---------- var_type: Variable type to get the distributions for. + use_legacy: + Whether to find the distributions in the legacy distribution list. Returns ------- @@ -71,7 +76,11 @@ def get_dist_list(self, var_type: str) -> List[Type[BaseDistribution]]: List of distributions with that variable type. """ dist_list = [] - for dist_class in self.distributions: + if use_legacy: + distributions = self.legacy_distributions + else: + distributions = self.distributions + for dist_class in distributions: str_chk = (isinstance(dist_class.var_type, str) and var_type == dist_class.var_type) lst_chk = (not isinstance(dist_class.var_type, str) and var_type in dist_class.var_type) if str_chk or lst_chk: @@ -94,7 +103,7 @@ class BuiltinDistributionProvider(BaseDistributionProvider): """Distribution tree that includes the builtin distributions.""" name = "builtin" - version = "1.0" + version = "1.1" distributions = [ DiscreteUniformDistribution, PoissonDistribution, UniqueKeyDistribution, UniformDistribution, NormalDistribution, LogNormalDistribution, @@ -106,6 +115,9 @@ class BuiltinDistributionProvider(BaseDistributionProvider): UniformTimeDistribution, UniformDateTimeDistribution, ] + legacy_distributions = [ + legacy.RegexDistribution, legacy.UniqueRegexDistribution + ] class DistributionProviderList(): @@ -226,8 +238,11 @@ def _find_best_fit(self, series: pl.Series, var_type: str, f"that have unique == {unique}.") return dist_instances[np.argmin(dist_aic)] - def find_distribution(self, dist_name: str, privacy: BasePrivacy = BasicPrivacy(), - ) -> type[BaseDistribution]: + def find_distribution(self, # pylint: disable=too-many-branches + dist_name: str, + privacy: BasePrivacy = BasicPrivacy(), + var_type: Optional[str] = None, + version: Optional[str] = None) -> type[BaseDistribution]: """Find a distribution and fit keyword arguments from a name. Parameters @@ -237,6 +252,11 @@ def find_distribution(self, dist_name: str, privacy: BasePrivacy = BasicPrivacy( uniform distribution: "uniform", "core.uniform", "UniformDistribution". privacy: Type of privacy to be applied. + var_type: + Type of the variable to find. If var_type is None, then do not check the + variable type. + version: + Version of the distribution to get. If necessary get them from legacy. Returns ------- @@ -246,10 +266,43 @@ def find_distribution(self, dist_name: str, privacy: BasePrivacy = BasicPrivacy( if NADistribution.matches_name(dist_name): return NADistribution - for dist_class in self._get_dist_list(privacy) + [NADistribution]: - if dist_class.matches_name(dist_name) and dist_class.privacy == privacy.name: - return dist_class - raise ValueError(f"Cannot find distribution with name '{dist_name}'.") + versions_found = [] + for dist_class in self._get_dist_list(privacy, var_type=var_type) + [NADistribution]: + if dist_class.matches_name(dist_name): + if version is None or version == dist_class.version: + return dist_class + versions_found.append(dist_class) + + # Look for distribution in legacy + warnings.simplefilter("always") + legacy_versions: list[Type[BaseDistribution]] = [] + for dist_class in self._get_dist_list(privacy, use_legacy=True, var_type=var_type): + if dist_class.matches_name(dist_name): + if version is None or version == dist_class.version: + if len(versions_found) == 0: + warnings.warn("Distribution with name '{dist_name}' is deprecated and " + "will be removed in the future.") + return dist_class + warnings.warn("Version ({version}) of distribution with name '{dist_name}'" + " is deprecated and will be removed in the future.") + if version is not None: + major_version = version.split(".")[0] + for dist_class in versions_found: + if dist_class.version.split(".")[0] == major_version: + warnings.warn("Version mismatch ({version}) versus ({dist_class.version})") + return dist_class + for dist_class in legacy_versions: + if dist_class.version.split(".")[0] == major_version: + warnings.warn("Version mismatch ({version}) versus ({dist_class.version})" + " for '{dist_name}") + warnings.warn("(Major) Version ({dist_classversion}) of distribution is" + " deprecated") + return dist_class + if len(legacy_versions+versions_found) == 0: + raise ValueError(f"Cannot find distribution with name '{dist_name}'.") + raise ValueError( + f"Cannot find compatible version for distribution '{dist_name}', available: " + f"{legacy_versions+versions_found}") def _fit_distribution(self, series: pl.Series, dist: Union[str, Type[BaseDistribution], BaseDistribution], @@ -293,13 +346,15 @@ def _fit_distribution(self, series: pl.Series, return dist_instance def _get_dist_list(self, privacy: Optional[BasePrivacy] = None, - var_type: Optional[str] = None) -> list[type[BaseDistribution]]: + var_type: Optional[str] = None, + use_legacy: bool = False) -> list[type[BaseDistribution]]: dist_list = [] for dist_provider in self.dist_packages: if var_type is None: dist_list.extend(dist_provider.distributions) else: - dist_list.extend(dist_provider.get_dist_list(var_type)) + dist_list.extend(dist_provider.get_dist_list(var_type, + use_legacy=use_legacy)) if privacy is None: return dist_list @@ -319,12 +374,11 @@ def from_dict(self, var_dict: dict[str, Any]) -> BaseDistribution: BaseDistribution: Distribution representing the dictionary. """ - for dist_class in self._get_dist_list(var_type=var_dict["type"]) + [NADistribution]: - if dist_class.implements == var_dict["distribution"]["implements"]: - return dist_class.from_dict(var_dict["distribution"]) - raise ValueError(f"Cannot find distribution with name " - f"'{var_dict['distribution']['implements']}'" - f"and type '{var_dict['type']}'.") + dist_name = var_dict["distribution"]["implements"] + version = var_dict["distribution"].get("version", "1.0") + var_type = var_dict["type"] + dist_class = self.find_distribution(dist_name, version=version, var_type=var_type) + return dist_class.from_dict(var_dict["distribution"]) def _get_all_providers() -> dict[str, EntryPoint]: diff --git a/pyproject.toml b/pyproject.toml index d45bffd8..8d03e51d 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -37,6 +37,7 @@ dependencies = [ "importlib-metadata;python_version<'3.10'", "importlib-resources;python_version<'3.9'", "wget", + "regexmodel>=0.2.1" ] dynamic = ["version"] @@ -79,6 +80,7 @@ ignore_missing_imports = true [tool.pylint.'MASTER'] ignore-patterns="_version.py" +ignore-paths = "^metasynth/distribution/legacy.*$" [tool.pylint.'FORMAT'] max-line-length=100 diff --git a/tests/test_regex.py b/tests/test_regex.py deleted file mode 100644 index 2b4caebe..00000000 --- a/tests/test_regex.py +++ /dev/null @@ -1,106 +0,0 @@ -import string -from random import choice - -import numpy as np -import pandas as pd -import polars as pl -from pytest import mark, raises - -from metasynth.distribution.regex import RegexDistribution, UniqueRegexDistribution -from metasynth.distribution.regex.element import SingleRegex,\ - DigitRegex, AlphaNumericRegex, LettersRegex, LowercaseRegex, UppercaseRegex,\ - AnyRegex - - -@mark.parametrize("series_type", [pd.Series, pl.Series]) -def test_regex_single_digit(series_type): - series = series_type(["R123", "R837", "R354", "R456", "R578", "R699"]) - dist = RegexDistribution.fit(series) - dist_unique = UniqueRegexDistribution.fit(series) - assert dist.information_criterion(series) < dist_unique.information_criterion(series) - - def check_regex_dist(dist): - assert len(dist.re_list) == 2 - assert isinstance(dist.re_list[0], SingleRegex) - assert isinstance(dist.re_list[1], DigitRegex) - assert dist.re_list[0].character_selection == ["R"] - assert dist.re_list[1].min_digit == 3 - assert dist.re_list[1].max_digit == 3 - assert dist.regex_string == r"[R]\d{3,3}" - - for draw_str in [dist.draw() for _ in range(10)]: - assert len(draw_str) == 4 - assert draw_str[0] == "R" - - check_regex_dist(dist) - - re_string_list = dist.to_dict()["parameters"]["re_list"] - new_dist = RegexDistribution(re_string_list) - check_regex_dist(new_dist) - - -@mark.parametrize("series_type", [pd.Series, pl.Series]) -def test_regex_unique(series_type): - series = series_type(["R1", "R2", "R3", "R4", "R5", "R6"]) - dist = UniqueRegexDistribution.fit(series) - values = [dist.draw() for _ in range(10)] - assert len(set(values)) == 10 - assert set(values) == set(["R" + x for x in string.digits]) - with raises(ValueError): - dist.draw() - dist.draw_reset() - dist.draw() - - -@mark.parametrize( - "dist_class,regex_str,regex_str_alt", - [ - (AlphaNumericRegex, r"\w{1,1}", r"\w"), - (LettersRegex, r"[a-zA-Z]{1,1}", r"[a-zA-Z]"), - (LowercaseRegex, r"[a-z]{1,1}", r"[a-z]"), - (UppercaseRegex, r"[A-Z]{1,1}", r"[A-Z]"), - (DigitRegex, r"\d{1,1}", r"\d"), - (AnyRegex, r".[]{1,1}", r".[]"), - ] -) -def test_optional_length(dist_class, regex_str, regex_str_alt): - dist, _ = dist_class.from_string(regex_str) - dist_alt, _ = dist_class.from_string(regex_str_alt) - assert str(dist) == str(dist_alt) - - -@mark.parametrize( - "digit_set,dist_class,regex_str,n_digits", - [ - (string.ascii_letters+string.digits, AlphaNumericRegex, r"\w{10,10}", 10), - (string.ascii_letters, LettersRegex, r"[a-zA-Z]{10,10}", 10), - (string.digits, DigitRegex, r"\d{10,10}", 10), - (string.ascii_lowercase, LowercaseRegex, r"[a-z]{10,10}", 10), - (string.ascii_uppercase, UppercaseRegex, r"[A-Z]{10,10}", 10), - ] -) -@mark.parametrize("series_type", [pd.Series, pl.Series]) -def test_digits(digit_set, dist_class, regex_str, n_digits, series_type): - def draw(): - draw_str = "" - for _ in range(n_digits): - draw_str += choice(digit_set) - return draw_str - - series = series_type([draw() for _ in range(100)]) - dist = RegexDistribution.fit(series) - assert len(dist.re_list) == 1 - assert isinstance(dist.re_list[0], dist_class) - assert dist.re_list[0].min_digit == n_digits - assert dist.re_list[0].max_digit == n_digits - assert dist.to_dict()["parameters"]["re_list"][0][0] == regex_str - assert np.all([len(dist.draw()) == n_digits for _ in range(100)]) - assert np.all([c in digit_set for c in dist.draw()]) - new_dist = dist_class.from_string(dist.regex_string, 1.0)[0] - with raises(ValueError): - dist_class.from_string(dist.regex_string, 3)[0] - assert isinstance(new_dist, dist_class) - assert new_dist.min_digit == dist.re_list[0].min_digit - assert new_dist.max_digit == dist.re_list[0].max_digit - - diff --git a/tests/test_var.py b/tests/test_var.py index 404fd4d2..445e2126 100644 --- a/tests/test_var.py +++ b/tests/test_var.py @@ -4,13 +4,12 @@ import polars as pl import numpy as np from metasynth.var import MetaVar -from metasynth.distribution import NormalDistribution, RegexDistribution +from metasynth.distribution import NormalDistribution, RegexDistribution, UniqueRegexDistribution from metasynth.distribution import DiscreteUniformDistribution from metasynth.distribution import UniformDistribution from pytest import mark, raises from metasynth.dataset import _jsonify from metasynth.distribution.discrete import UniqueKeyDistribution -from metasynth.distribution.regex import UniqueRegexDistribution from metasynth.distribution.continuous import TruncatedNormalDistribution from metasynth.distribution.categorical import MultinoulliDistribution