From 1801d9ba2ac3890e2272cd8f7ce41a33d69ed9d3 Mon Sep 17 00:00:00 2001 From: Alex Chen Date: Tue, 5 Mar 2024 10:34:54 +0800 Subject: [PATCH 01/17] Explore the bug --- demo/dev/executable_smartnoise.ipynb | 211 +++++++++++++++++++++++++++ 1 file changed, 211 insertions(+) create mode 100644 demo/dev/executable_smartnoise.ipynb diff --git a/demo/dev/executable_smartnoise.ipynb b/demo/dev/executable_smartnoise.ipynb new file mode 100644 index 00000000..42acbe6c --- /dev/null +++ b/demo/dev/executable_smartnoise.ipynb @@ -0,0 +1,211 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "import pandas as pd\n", + "import numpy as np" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "/Users/alex/PETsARD\n" + ] + } + ], + "source": [ + "%cd /Users/alex/PETsARD" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Users/alex/miniforge3/envs/dev310/lib/python3.10/site-packages/tqdm/auto.py:21: TqdmWarning: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html\n", + " from .autonotebook import tqdm as notebook_tqdm\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Loader - Benchmarker: file benchmark/adult.csv already exist and match SHA-256.\n", + " PETsARD will ignore download and use local data directly.\n" + ] + } + ], + "source": [ + "from PETsARD import (\n", + " Loader,\n", + " Processor,\n", + " Synthesizer\n", + ")\n", + "\n", + "\n", + "load = Loader(\n", + " filepath='benchmark://adult',\n", + " na_values={k: '?' for k in [\n", + " 'workclass',\n", + " 'occupation',\n", + " 'native-country'\n", + " ]}\n", + ")\n", + "load.load()" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [], + "source": [ + "import pprint\n", + "\n", + "\n", + "def issue332(\n", + " load: Loader,\n", + " synthesizing_method: str,\n", + " scaler_inhibit: bool = False\n", + "):\n", + " pp = pprint.PrettyPrinter(depth=2)\n", + "\n", + " proc_discretizing = Processor(\n", + " metadata=load.metadata,\n", + " )\n", + "\n", + " if scaler_inhibit:\n", + " print(\"Preproc config of Scaler before update as ...\")\n", + " pp.pprint(proc_discretizing.get_config()['scaler'])\n", + " proc_discretizing.update_config(\n", + " {'scaler': {\n", + " col: None for col in load.data.columns\n", + " }}\n", + " )\n", + " print(\"Preproc config of Scaler before after as ...\")\n", + " pp.pprint(proc_discretizing.get_config()['scaler'])\n", + "\n", + " proc_discretizing.fit(\n", + " data=load.data,\n", + " sequence=[\n", + " 'missing',\n", + " 'outlier',\n", + " 'scaler',\n", + " 'discretizing'\n", + " ]\n", + " )\n", + " preproc_discretizing_data = proc_discretizing.transform(\n", + " data=load.data\n", + " )\n", + " print(\"Preproc data as ...\")\n", + " print(preproc_discretizing_data.head(1))\n", + "\n", + "\n", + "\n", + " syn = Synthesizer(\n", + " method=synthesizing_method,\n", + " epsilon=10.0,\n", + " )\n", + " syn.create(data=preproc_discretizing_data)\n", + " syn.fit_sample()\n", + " print(\"Sync data as ...\")\n", + " print(syn.data_syn.head(1))" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Preproc data as ...\n", + " age workclass fnlwgt education educational-num marital-status \\\n", + "0 0.0 3 0.0 1 0.0 4 \n", + "\n", + " occupation relationship race gender capital-gain capital-loss \\\n", + "0 6 3 2 1 0.0 0.0 \n", + "\n", + " hours-per-week native-country income \n", + "0 0.0 38 0 \n", + "Synthesizer (SmartNoise): Fitting aim.\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Users/alex/miniforge3/envs/dev310/lib/python3.10/site-packages/mbi/__init__.py:15: UserWarning: MixtureInference disabled, please install jax and jaxlib\n", + " warnings.warn('MixtureInference disabled, please install jax and jaxlib')\n" + ] + }, + { + "ename": "ValueError", + "evalue": "The transformer appears to have some continuous columns. Please provide only categorical or ordinal.", + "output_type": "error", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mValueError\u001b[0m Traceback (most recent call last)", + "Cell \u001b[0;32mIn[6], line 1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m \u001b[43missue332\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m 2\u001b[0m \u001b[43m \u001b[49m\u001b[43mload\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mload\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 3\u001b[0m \u001b[43m \u001b[49m\u001b[43msynthesizing_method\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43msmartnoise-aim\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m,\u001b[49m\n\u001b[1;32m 4\u001b[0m \u001b[43m)\u001b[49m\n", + "Cell \u001b[0;32mIn[5], line 48\u001b[0m, in \u001b[0;36missue332\u001b[0;34m(load, synthesizing_method, scaler_inhibit)\u001b[0m\n\u001b[1;32m 43\u001b[0m syn \u001b[38;5;241m=\u001b[39m Synthesizer(\n\u001b[1;32m 44\u001b[0m method\u001b[38;5;241m=\u001b[39msynthesizing_method,\n\u001b[1;32m 45\u001b[0m epsilon\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m10.0\u001b[39m,\n\u001b[1;32m 46\u001b[0m )\n\u001b[1;32m 47\u001b[0m syn\u001b[38;5;241m.\u001b[39mcreate(data\u001b[38;5;241m=\u001b[39mpreproc_discretizing_data)\n\u001b[0;32m---> 48\u001b[0m \u001b[43msyn\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mfit_sample\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 49\u001b[0m \u001b[38;5;28mprint\u001b[39m(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mSync data as ...\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[1;32m 50\u001b[0m \u001b[38;5;28mprint\u001b[39m(syn\u001b[38;5;241m.\u001b[39mdata_syn\u001b[38;5;241m.\u001b[39mhead(\u001b[38;5;241m1\u001b[39m))\n", + "File \u001b[0;32m~/PETsARD/PETsARD/synthesizer/synthesizer.py:121\u001b[0m, in \u001b[0;36mSynthesizer.fit_sample\u001b[0;34m(self, **kwargs)\u001b[0m\n\u001b[1;32m 119\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mdata_syn \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mloader\u001b[38;5;241m.\u001b[39mdata\n\u001b[1;32m 120\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m--> 121\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mdata_syn \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mSynthesizer\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mfit_sample\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n", + "File \u001b[0;32m~/PETsARD/PETsARD/synthesizer/smartnoise.py:130\u001b[0m, in \u001b[0;36mSmartNoise.fit_sample\u001b[0;34m(self, sample_num_rows, reset_sampling, output_file_path)\u001b[0m\n\u001b[1;32m 111\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mfit_sample\u001b[39m(\n\u001b[1;32m 112\u001b[0m \u001b[38;5;28mself\u001b[39m,\n\u001b[1;32m 113\u001b[0m sample_num_rows: \u001b[38;5;28mint\u001b[39m \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m,\n\u001b[1;32m 114\u001b[0m reset_sampling: \u001b[38;5;28mbool\u001b[39m \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mFalse\u001b[39;00m,\n\u001b[1;32m 115\u001b[0m output_file_path: \u001b[38;5;28mstr\u001b[39m \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m\n\u001b[1;32m 116\u001b[0m ) \u001b[38;5;241m-\u001b[39m\u001b[38;5;241m>\u001b[39m pd\u001b[38;5;241m.\u001b[39mDataFrame:\n\u001b[1;32m 117\u001b[0m \u001b[38;5;250m \u001b[39m\u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[1;32m 118\u001b[0m \u001b[38;5;124;03m Fit and sample from the synthesizer.\u001b[39;00m\n\u001b[1;32m 119\u001b[0m \u001b[38;5;124;03m The combination of the methods `fit()` and `sample()`.\u001b[39;00m\n\u001b[0;32m (...)\u001b[0m\n\u001b[1;32m 128\u001b[0m \u001b[38;5;124;03m data_syn (pd.DataFrame): The synthesized data.\u001b[39;00m\n\u001b[1;32m 129\u001b[0m \u001b[38;5;124;03m \"\"\"\u001b[39;00m\n\u001b[0;32m--> 130\u001b[0m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mfit\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 131\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39msample(sample_num_rows, reset_sampling, output_file_path)\n", + "File \u001b[0;32m~/PETsARD/PETsARD/synthesizer/smartnoise.py:47\u001b[0m, in \u001b[0;36mSmartNoise.fit\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m 42\u001b[0m t \u001b[38;5;241m=\u001b[39m TableTransformer([IdentityTransformer() \n\u001b[1;32m 43\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m i \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mrange\u001b[39m(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mdata\u001b[38;5;241m.\u001b[39mshape[\u001b[38;5;241m1\u001b[39m])])\n\u001b[1;32m 45\u001b[0m \u001b[38;5;66;03m# TODO - Only support cube-style synthesizer. \u001b[39;00m\n\u001b[1;32m 46\u001b[0m \u001b[38;5;66;03m# GAN-style synthesizer needed to be implemented.\u001b[39;00m\n\u001b[0;32m---> 47\u001b[0m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_Synthesizer\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mfit\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mdata\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mtransformer\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mt\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 48\u001b[0m \u001b[38;5;28mprint\u001b[39m(\n\u001b[1;32m 49\u001b[0m \u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mSynthesizer (SmartNoise): \u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m 50\u001b[0m \u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mFitting \u001b[39m\u001b[38;5;132;01m{\u001b[39;00m\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39msyn_method\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m spent \u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m 51\u001b[0m \u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;132;01m{\u001b[39;00m\u001b[38;5;28mround\u001b[39m(time\u001b[38;5;241m.\u001b[39mtime()\u001b[38;5;241m-\u001b[39mtime_start\u001b[38;5;250m \u001b[39m,\u001b[38;5;241m4\u001b[39m)\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m sec.\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m 52\u001b[0m )\n\u001b[1;32m 53\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n", + "File \u001b[0;32m~/miniforge3/envs/dev310/lib/python3.10/site-packages/snsynth/aim/aim.py:121\u001b[0m, in \u001b[0;36mAIMSynthesizer.fit\u001b[0;34m(self, data, transformer, categorical_columns, ordinal_columns, continuous_columns, preprocessor_eps, nullable, *ignore)\u001b[0m\n\u001b[1;32m 119\u001b[0m cards \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_transformer\u001b[38;5;241m.\u001b[39mcardinality\n\u001b[1;32m 120\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28many\u001b[39m(c \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m \u001b[38;5;28;01mfor\u001b[39;00m c \u001b[38;5;129;01min\u001b[39;00m cards):\n\u001b[0;32m--> 121\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mValueError\u001b[39;00m(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mThe transformer appears to have some continuous columns. Please provide only categorical or ordinal.\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[1;32m 123\u001b[0m dimensionality \u001b[38;5;241m=\u001b[39m np\u001b[38;5;241m.\u001b[39mprod(cards)\n\u001b[1;32m 124\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mverbose:\n", + "\u001b[0;31mValueError\u001b[0m: The transformer appears to have some continuous columns. Please provide only categorical or ordinal." + ] + } + ], + "source": [ + "issue332(\n", + " load=load,\n", + " synthesizing_method='smartnoise-aim',\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "dev310", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.10.13" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} From 5f9345614ededc1c10b78bb89fa5c75ddc390336 Mon Sep 17 00:00:00 2001 From: Alex Chen Date: Tue, 5 Mar 2024 16:03:44 +0800 Subject: [PATCH 02/17] change the output of transform to categorical --- PETsARD/processor/discretizing.py | 2 +- PETsARD/processor/encoder.py | 2 +- 2 files changed, 2 insertions(+), 2 deletions(-) diff --git a/PETsARD/processor/discretizing.py b/PETsARD/processor/discretizing.py index ae82fda3..4bc70eaa 100644 --- a/PETsARD/processor/discretizing.py +++ b/PETsARD/processor/discretizing.py @@ -50,7 +50,7 @@ def transform(self, data: pd.Series) -> np.ndarray: if not self._is_fitted: raise UnfittedError('The object is not fitted. Use .fit() first.') - return self._transform(data) + return pd.Categorical(self._transform(data)) def _transform(): """ diff --git a/PETsARD/processor/encoder.py b/PETsARD/processor/encoder.py index de78aff0..dbcf3c5b 100644 --- a/PETsARD/processor/encoder.py +++ b/PETsARD/processor/encoder.py @@ -64,7 +64,7 @@ def transform(self, data: pd.Series) -> np.ndarray: " in the fitting process.", " Please check the data categories again.") - return self._transform(data) + return pd.Categorical(self._transform(data)) def _transform(): """ From 8994fc61e27cad8b252eb70f74e6886b183f4e72 Mon Sep 17 00:00:00 2001 From: Alex Chen Date: Tue, 5 Mar 2024 16:05:17 +0800 Subject: [PATCH 03/17] edit TableTransformer in smartnoise --- PETsARD/synthesizer/smartnoise.py | 7 +------ 1 file changed, 1 insertion(+), 6 deletions(-) diff --git a/PETsARD/synthesizer/smartnoise.py b/PETsARD/synthesizer/smartnoise.py index 788118f7..eca86d98 100644 --- a/PETsARD/synthesizer/smartnoise.py +++ b/PETsARD/synthesizer/smartnoise.py @@ -37,14 +37,9 @@ def fit(self) -> None: f"Synthesizer (SmartNoise): Fitting {self.syn_method}." ) - # TODO - for mst and pacsynth, - # we need to implement other transformer rather than IdentityTransformer - t = TableTransformer([IdentityTransformer() - for i in range(self.data.shape[1])]) - # TODO - Only support cube-style synthesizer. # GAN-style synthesizer needed to be implemented. - self._Synthesizer.fit(self.data, transformer=t) + self._Synthesizer.fit(self.data, categorical_columns=self.data.columns) print( f"Synthesizer (SmartNoise): " f"Fitting {self.syn_method} spent " From 987447eaf14f06e1b46f9fd05f12eddeb9020750 Mon Sep 17 00:00:00 2001 From: Alex Chen Date: Tue, 5 Mar 2024 16:14:04 +0800 Subject: [PATCH 04/17] Edit inverse_transform dtype --- PETsARD/processor/encoder.py | 14 +++++++------- 1 file changed, 7 insertions(+), 7 deletions(-) diff --git a/PETsARD/processor/encoder.py b/PETsARD/processor/encoder.py index dbcf3c5b..51b3a37b 100644 --- a/PETsARD/processor/encoder.py +++ b/PETsARD/processor/encoder.py @@ -92,7 +92,7 @@ def inverse_transform(self, data: pd.Series) -> pd.Series | np.ndarray: if not self._is_fitted: raise UnfittedError('The object is not fitted. Use .fit() first.') - return self._inverse_transform(data) + return self._inverse_transform(data.to_numpy()) def _inverse_transform(): """ @@ -168,12 +168,12 @@ def _transform(self, data: pd.Series) -> np.ndarray: uniform(self.cat_to_val[x][0], self.cat_to_val[x][1], size=1)[0]).values - def _inverse_transform(self, data: pd.Series) -> pd.Series: + def _inverse_transform(self, data: np.ndarray) -> pd.Series: """ Inverse the transformed data to the categorical data. Args: - data (pd.Series): The categorical data needed to + data (np.ndarray): The categorical data needed to be transformed inversely. Return: @@ -231,12 +231,12 @@ def _transform(self, data: pd.Series) -> np.ndarray: return self.model.transform(data) - def _inverse_transform(self, data: pd.Series) -> np.ndarray: + def _inverse_transform(self, data: np.ndarray) -> np.ndarray: """ Inverse the transformed data to the categorical data. Args: - data (pd.Series): The categorical data needed to + data (np.ndarray): The categorical data needed to be transformed inversely. Return: @@ -285,13 +285,13 @@ def _transform(self, data: pd.Series) -> None: return data - def _inverse_transform(self, data: pd.Series) -> None: + def _inverse_transform(self, data: np.ndarray) -> None: """ Inverse the transformed data to the categorical data. This is a dummy method, and it is implemented in MediatorEncoder. Args: - data (pd.Series): The categorical data needed to + data (np.ndarray): The categorical data needed to be transformed inversely. Return: From af94b5e16a4d077f3e444a875c28a124151d4f15 Mon Sep 17 00:00:00 2001 From: Alex Chen Date: Tue, 5 Mar 2024 16:14:44 +0800 Subject: [PATCH 05/17] demo file --- demo/dev/AlexTest.ipynb | 905 ++++++++++----------------- demo/dev/executable_smartnoise.ipynb | 110 ++-- 2 files changed, 399 insertions(+), 616 deletions(-) diff --git a/demo/dev/AlexTest.ipynb b/demo/dev/AlexTest.ipynb index 3140f461..736639be 100644 --- a/demo/dev/AlexTest.ipynb +++ b/demo/dev/AlexTest.ipynb @@ -42,7 +42,16 @@ "cell_type": "code", "execution_count": 3, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Users/alex/miniforge3/envs/dev310/lib/python3.10/site-packages/tqdm/auto.py:21: TqdmWarning: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html\n", + " from .autonotebook import tqdm as notebook_tqdm\n" + ] + } + ], "source": [ "import PETsARD" ] @@ -85,7 +94,7 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": 6, "metadata": {}, "outputs": [], "source": [ @@ -100,7 +109,7 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": 7, "metadata": {}, "outputs": [], "source": [ @@ -109,7 +118,7 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": 8, "metadata": {}, "outputs": [], "source": [ @@ -118,7 +127,7 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": 9, "metadata": {}, "outputs": [], "source": [ @@ -127,7 +136,7 @@ }, { "cell_type": "code", - "execution_count": 11, + "execution_count": 10, "metadata": {}, "outputs": [], "source": [ @@ -136,7 +145,7 @@ }, { "cell_type": "code", - "execution_count": 12, + "execution_count": 11, "metadata": {}, "outputs": [ { @@ -181,7 +190,7 @@ "0 4190 12 0" ] }, - "execution_count": 12, + "execution_count": 11, "metadata": {}, "output_type": "execute_result" } @@ -192,7 +201,7 @@ }, { "cell_type": "code", - "execution_count": 13, + "execution_count": 12, "metadata": {}, "outputs": [ { @@ -524,7 +533,7 @@ "qm NaN 0 4.0 NaN " ] }, - "execution_count": 13, + "execution_count": 12, "metadata": {}, "output_type": "execute_result" } @@ -535,7 +544,7 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 13, "metadata": {}, "outputs": [], "source": [ @@ -544,7 +553,7 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 14, "metadata": {}, "outputs": [], "source": [ @@ -554,7 +563,7 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": 15, "metadata": {}, "outputs": [], "source": [ @@ -582,24 +591,16 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": 16, "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "No self-defined config passed. Generate a config automatically.\n" - ] - } - ], + "outputs": [], "source": [ "pm = PETsARD.Processor(m)" ] }, { "cell_type": "code", - "execution_count": 9, + "execution_count": 17, "metadata": {}, "outputs": [], "source": [ @@ -611,7 +612,7 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": 18, "metadata": {}, "outputs": [], "source": [ @@ -621,7 +622,7 @@ }, { "cell_type": "code", - "execution_count": 11, + "execution_count": 19, "metadata": {}, "outputs": [], "source": [ @@ -633,7 +634,7 @@ }, { "cell_type": "code", - "execution_count": 12, + "execution_count": 20, "metadata": {}, "outputs": [], "source": [ @@ -642,75 +643,75 @@ }, { "cell_type": "code", - "execution_count": 13, + "execution_count": 21, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "{'missing': {'gen': ,\n", - " 'age': ,\n", - " 'race': ,\n", - " 'edu': ,\n", - " 'mar': ,\n", - " 'bmi': ,\n", - " 'dep': ,\n", - " 'pir': ,\n", - " 'gh': ,\n", - " 'mets': ,\n", - " 'qm': ,\n", - " 'dia': },\n", + "{'missing': {'gen': ,\n", + " 'age': ,\n", + " 'race': ,\n", + " 'edu': ,\n", + " 'mar': ,\n", + " 'bmi': ,\n", + " 'dep': ,\n", + " 'pir': ,\n", + " 'gh': ,\n", + " 'mets': ,\n", + " 'qm': ,\n", + " 'dia': },\n", " 'outlier': {'gen': None,\n", - " 'age': ,\n", + " 'age': ,\n", " 'race': None,\n", " 'edu': None,\n", " 'mar': None,\n", - " 'bmi': ,\n", - " 'dep': ,\n", - " 'pir': ,\n", - " 'gh': ,\n", - " 'mets': ,\n", + " 'bmi': ,\n", + " 'dep': ,\n", + " 'pir': ,\n", + " 'gh': ,\n", + " 'mets': ,\n", " 'qm': None,\n", - " 'dia': },\n", - " 'encoder': {'gen': ,\n", + " 'dia': },\n", + " 'encoder': {'gen': ,\n", " 'age': None,\n", - " 'race': ,\n", - " 'edu': ,\n", - " 'mar': ,\n", + " 'race': ,\n", + " 'edu': ,\n", + " 'mar': ,\n", " 'bmi': None,\n", " 'dep': None,\n", " 'pir': None,\n", " 'gh': None,\n", " 'mets': None,\n", - " 'qm': ,\n", + " 'qm': ,\n", " 'dia': None},\n", " 'scaler': {'gen': None,\n", - " 'age': ,\n", + " 'age': ,\n", " 'race': None,\n", " 'edu': None,\n", " 'mar': None,\n", - " 'bmi': ,\n", - " 'dep': ,\n", - " 'pir': ,\n", - " 'gh': ,\n", - " 'mets': ,\n", + " 'bmi': ,\n", + " 'dep': ,\n", + " 'pir': ,\n", + " 'gh': ,\n", + " 'mets': ,\n", " 'qm': None,\n", - " 'dia': },\n", - " 'discretizing': {'gen': ,\n", - " 'age': ,\n", - " 'race': ,\n", - " 'edu': ,\n", - " 'mar': ,\n", - " 'bmi': ,\n", - " 'dep': ,\n", - " 'pir': ,\n", - " 'gh': ,\n", - " 'mets': ,\n", - " 'qm': ,\n", - " 'dia': }}" + " 'dia': },\n", + " 'discretizing': {'gen': ,\n", + " 'age': ,\n", + " 'race': ,\n", + " 'edu': ,\n", + " 'mar': ,\n", + " 'bmi': ,\n", + " 'dep': ,\n", + " 'pir': ,\n", + " 'gh': ,\n", + " 'mets': ,\n", + " 'qm': ,\n", + " 'dia': }}" ] }, - "execution_count": 13, + "execution_count": 21, "metadata": {}, "output_type": "execute_result" } @@ -721,7 +722,7 @@ }, { "cell_type": "code", - "execution_count": 14, + "execution_count": 22, "metadata": {}, "outputs": [], "source": [ @@ -730,43 +731,16 @@ }, { "cell_type": "code", - "execution_count": 15, + "execution_count": 23, "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/Users/alex/PETsARD/PETsARD/processor/mediator.py:169: FutureWarning: Setting an item of incompatible dtype is deprecated and will raise in a future error of pandas. Value '['Male', 'Male', 'Male', 'Female', 'Female', ..., 'Male', 'Female', 'Female', 'Male', 'Female']\n", - "Length: 4190\n", - "Categories (2, object): ['Female', 'Male']' has dtype incompatible with bool, please explicitly cast to a compatible dtype first.\n", - " transformed.loc[:, col] = self._config.get(col,\n", - "/Users/alex/PETsARD/PETsARD/processor/mediator.py:169: FutureWarning: Setting an item of incompatible dtype is deprecated and will raise in a future error of pandas. Value '['White', 'White', 'White', 'White', 'Black', ..., 'Other', 'White', 'White', 'White', 'White']\n", - "Length: 4190\n", - "Categories (5, object): ['Black', 'Hispanic', 'Mexican', 'Other', 'White']' has dtype incompatible with bool, please explicitly cast to a compatible dtype first.\n", - " transformed.loc[:, col] = self._config.get(col,\n", - "/Users/alex/PETsARD/PETsARD/processor/mediator.py:169: FutureWarning: Setting an item of incompatible dtype is deprecated and will raise in a future error of pandas. Value '['Graduate', 'HighSchool', 'HighSchool', 'Graduate', 'College', ..., 'Graduate', 'HighSchool', 'College', '9th', 'Graduate']\n", - "Length: 4190\n", - "Categories (5, object): ['11th', '9th', 'College', 'Graduate', 'HighSchool']' has dtype incompatible with bool, please explicitly cast to a compatible dtype first.\n", - " transformed.loc[:, col] = self._config.get(col,\n", - "/Users/alex/PETsARD/PETsARD/processor/mediator.py:169: FutureWarning: Setting an item of incompatible dtype is deprecated and will raise in a future error of pandas. Value '['Married', 'Divorced', 'Married', 'Parther', 'Divorced', ..., 'Never', 'Widowed', 'Widowed', 'Married', 'Never']\n", - "Length: 4190\n", - "Categories (6, object): ['Divorced', 'Married', 'Never', 'Parther', 'Separated', 'Widowed']' has dtype incompatible with bool, please explicitly cast to a compatible dtype first.\n", - " transformed.loc[:, col] = self._config.get(col,\n", - "/Users/alex/PETsARD/PETsARD/processor/mediator.py:169: FutureWarning: Setting an item of incompatible dtype is deprecated and will raise in a future error of pandas. Value '['Q2', 'Q1', 'Q3', 'Q3', 'Q4', ..., 'Q1', 'Q1', 'Q1', 'Q1', 'Q3']\n", - "Length: 4190\n", - "Categories (4, object): ['Q1', 'Q2', 'Q3', 'Q4']' has dtype incompatible with bool, please explicitly cast to a compatible dtype first.\n", - " transformed.loc[:, col] = self._config.get(col,\n" - ] - } - ], + "outputs": [], "source": [ "df_transformed = pm.transform(df)" ] }, { "cell_type": "code", - "execution_count": 16, + "execution_count": 24, "metadata": {}, "outputs": [ { @@ -813,14 +787,14 @@ " \n", " 0\n", " 0.700000\n", - " 0.501014\n", - " 0.510496\n", + " 0.528095\n", + " 0.331861\n", " -0.202041\n", " -0.514504\n", " -0.517100\n", " 0.0\n", " 0.0\n", - " 0.439481\n", + " 0.353997\n", " 1.949241\n", " 0.0\n", " 1.0\n", @@ -833,14 +807,14 @@ " \n", " 1\n", " 0.550000\n", - " 0.593540\n", - " 0.722505\n", + " 0.683797\n", + " 0.697654\n", " 0.235906\n", " -0.514504\n", " 1.933861\n", " 0.0\n", " 0.0\n", - " 0.221088\n", + " 0.021236\n", " -0.513020\n", " 0.0\n", " 1.0\n", @@ -853,14 +827,14 @@ " \n", " 2\n", " 0.966667\n", - " 0.570027\n", - " 0.511464\n", + " 0.619753\n", + " 0.256406\n", " -0.056059\n", " -0.514504\n", " -0.517100\n", " 0.0\n", " 0.0\n", - " 0.941341\n", + " 0.983815\n", " 1.949241\n", " 0.0\n", " 1.0\n", @@ -873,14 +847,14 @@ " \n", " 3\n", " 0.600000\n", - " 0.440567\n", - " 0.843059\n", + " 0.485426\n", + " 0.837900\n", " 1.929305\n", " 1.943618\n", " -0.517100\n", " 0.0\n", " 0.0\n", - " 0.878165\n", + " 0.984964\n", " -0.513020\n", " 1.0\n", " 0.0\n", @@ -893,14 +867,14 @@ " \n", " 4\n", " 0.366667\n", - " 0.250450\n", - " 0.798162\n", + " 0.195866\n", + " 0.726425\n", " -1.296911\n", " 1.943618\n", " -0.517100\n", " 0.0\n", " 0.0\n", - " 0.571218\n", + " 0.589998\n", " -0.513020\n", " 1.0\n", " 0.0\n", @@ -933,14 +907,14 @@ " \n", " 4162\n", " 0.083333\n", - " 0.309831\n", - " 0.691554\n", + " 0.321372\n", + " 0.674615\n", " -1.194723\n", " -0.514504\n", " -0.517100\n", " 0.0\n", " 0.0\n", - " 0.171890\n", + " 0.244652\n", " -0.513020\n", " 0.0\n", " 1.0\n", @@ -953,14 +927,14 @@ " \n", " 4163\n", " 0.933333\n", - " 0.571986\n", - " 0.940840\n", + " 0.655997\n", + " 0.907691\n", " -1.121732\n", " 1.943618\n", " -0.517100\n", " 0.0\n", " 0.0\n", - " 0.230822\n", + " 0.089844\n", " -0.513020\n", " 1.0\n", " 0.0\n", @@ -973,14 +947,14 @@ " \n", " 4164\n", " 1.000000\n", - " 0.198564\n", - " 0.934402\n", + " 0.195312\n", + " 0.923717\n", " 0.265103\n", " -0.514504\n", " -0.517100\n", " 0.0\n", " 0.0\n", - " 0.265541\n", + " 0.067132\n", " -0.513020\n", " 1.0\n", " 0.0\n", @@ -993,14 +967,14 @@ " \n", " 4165\n", " 0.250000\n", - " 0.834683\n", - " 0.126036\n", + " 0.846496\n", + " 0.476420\n", " -0.464810\n", " -0.514504\n", " 1.933861\n", " 0.0\n", " 0.0\n", - " 0.056893\n", + " 0.213837\n", " -0.513020\n", " 0.0\n", " 1.0\n", @@ -1013,14 +987,14 @@ " \n", " 4166\n", " 0.066667\n", - " 0.484015\n", - " 0.626134\n", + " 0.331822\n", + " 0.621483\n", " -1.136330\n", " -0.514504\n", " -0.517100\n", " 0.0\n", " 0.0\n", - " 0.797874\n", + " 0.799186\n", " -0.513020\n", " 1.0\n", " 0.0\n", @@ -1037,30 +1011,30 @@ ], "text/plain": [ " age edu mar bmi dep pir gh mets \\\n", - "0 0.700000 0.501014 0.510496 -0.202041 -0.514504 -0.517100 0.0 0.0 \n", - "1 0.550000 0.593540 0.722505 0.235906 -0.514504 1.933861 0.0 0.0 \n", - "2 0.966667 0.570027 0.511464 -0.056059 -0.514504 -0.517100 0.0 0.0 \n", - "3 0.600000 0.440567 0.843059 1.929305 1.943618 -0.517100 0.0 0.0 \n", - "4 0.366667 0.250450 0.798162 -1.296911 1.943618 -0.517100 0.0 0.0 \n", + "0 0.700000 0.528095 0.331861 -0.202041 -0.514504 -0.517100 0.0 0.0 \n", + "1 0.550000 0.683797 0.697654 0.235906 -0.514504 1.933861 0.0 0.0 \n", + "2 0.966667 0.619753 0.256406 -0.056059 -0.514504 -0.517100 0.0 0.0 \n", + "3 0.600000 0.485426 0.837900 1.929305 1.943618 -0.517100 0.0 0.0 \n", + "4 0.366667 0.195866 0.726425 -1.296911 1.943618 -0.517100 0.0 0.0 \n", "... ... ... ... ... ... ... ... ... \n", - "4162 0.083333 0.309831 0.691554 -1.194723 -0.514504 -0.517100 0.0 0.0 \n", - "4163 0.933333 0.571986 0.940840 -1.121732 1.943618 -0.517100 0.0 0.0 \n", - "4164 1.000000 0.198564 0.934402 0.265103 -0.514504 -0.517100 0.0 0.0 \n", - "4165 0.250000 0.834683 0.126036 -0.464810 -0.514504 1.933861 0.0 0.0 \n", - "4166 0.066667 0.484015 0.626134 -1.136330 -0.514504 -0.517100 0.0 0.0 \n", + "4162 0.083333 0.321372 0.674615 -1.194723 -0.514504 -0.517100 0.0 0.0 \n", + "4163 0.933333 0.655997 0.907691 -1.121732 1.943618 -0.517100 0.0 0.0 \n", + "4164 1.000000 0.195312 0.923717 0.265103 -0.514504 -0.517100 0.0 0.0 \n", + "4165 0.250000 0.846496 0.476420 -0.464810 -0.514504 1.933861 0.0 0.0 \n", + "4166 0.066667 0.331822 0.621483 -1.136330 -0.514504 -0.517100 0.0 0.0 \n", "\n", " qm dia gen_Female gen_Male race_Black race_Hispanic \\\n", - "0 0.439481 1.949241 0.0 1.0 0.0 0.0 \n", - "1 0.221088 -0.513020 0.0 1.0 0.0 0.0 \n", - "2 0.941341 1.949241 0.0 1.0 0.0 0.0 \n", - "3 0.878165 -0.513020 1.0 0.0 0.0 0.0 \n", - "4 0.571218 -0.513020 1.0 0.0 1.0 0.0 \n", + "0 0.353997 1.949241 0.0 1.0 0.0 0.0 \n", + "1 0.021236 -0.513020 0.0 1.0 0.0 0.0 \n", + "2 0.983815 1.949241 0.0 1.0 0.0 0.0 \n", + "3 0.984964 -0.513020 1.0 0.0 0.0 0.0 \n", + "4 0.589998 -0.513020 1.0 0.0 1.0 0.0 \n", "... ... ... ... ... ... ... \n", - "4162 0.171890 -0.513020 0.0 1.0 0.0 0.0 \n", - "4163 0.230822 -0.513020 1.0 0.0 0.0 0.0 \n", - "4164 0.265541 -0.513020 1.0 0.0 0.0 0.0 \n", - "4165 0.056893 -0.513020 0.0 1.0 0.0 0.0 \n", - "4166 0.797874 -0.513020 1.0 0.0 0.0 0.0 \n", + "4162 0.244652 -0.513020 0.0 1.0 0.0 0.0 \n", + "4163 0.089844 -0.513020 1.0 0.0 0.0 0.0 \n", + "4164 0.067132 -0.513020 1.0 0.0 0.0 0.0 \n", + "4165 0.213837 -0.513020 0.0 1.0 0.0 0.0 \n", + "4166 0.799186 -0.513020 1.0 0.0 0.0 0.0 \n", "\n", " race_Mexican race_Other race_White \n", "0 0.0 0.0 1.0 \n", @@ -1078,7 +1052,7 @@ "[4167 rows x 17 columns]" ] }, - "execution_count": 16, + "execution_count": 24, "metadata": {}, "output_type": "execute_result" } @@ -1089,7 +1063,7 @@ }, { "cell_type": "code", - "execution_count": 17, + "execution_count": 25, "metadata": {}, "outputs": [], "source": [ @@ -1098,7 +1072,7 @@ }, { "cell_type": "code", - "execution_count": 18, + "execution_count": 26, "metadata": {}, "outputs": [ { @@ -1337,7 +1311,7 @@ "[4167 rows x 12 columns]" ] }, - "execution_count": 18, + "execution_count": 26, "metadata": {}, "output_type": "execute_result" } @@ -1348,7 +1322,7 @@ }, { "cell_type": "code", - "execution_count": 19, + "execution_count": 27, "metadata": {}, "outputs": [ { @@ -1370,15 +1344,15 @@ }, { "cell_type": "code", - "execution_count": 20, + "execution_count": 29, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - " age workclass fnlwgt education educational-num marital-status \\\n", - "0 25 Private 226802 11th 7 Never-married \n", + " age workclass fnlwgt education educational-num marital-status \\\n", + "0 25.0 Private 226802 11th 7 Never-married \n", "\n", " occupation relationship race gender capital-gain capital-loss \\\n", "0 Machine-op-inspct Own-child Black Male 0 0 \n", @@ -1396,12 +1370,14 @@ " ]\n", " }\n", " )\n", + "loader2.load()\n", + "\n", "print(loader2.data.head(1))" ] }, { "cell_type": "code", - "execution_count": 21, + "execution_count": 30, "metadata": {}, "outputs": [], "source": [ @@ -1410,7 +1386,7 @@ }, { "cell_type": "code", - "execution_count": 22, + "execution_count": 31, "metadata": {}, "outputs": [], "source": [ @@ -1441,7 +1417,7 @@ }, { "cell_type": "code", - "execution_count": 23, + "execution_count": 32, "metadata": {}, "outputs": [], "source": [ @@ -1451,107 +1427,99 @@ }, { "cell_type": "code", - "execution_count": 24, + "execution_count": 33, "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "No self-defined config passed. Generate a config automatically.\n" - ] - } - ], + "outputs": [], "source": [ "pm2 = PETsARD.Processor(m2)" ] }, { "cell_type": "code", - "execution_count": 25, + "execution_count": 34, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "{'missing': {'age': ,\n", - " 'workclass': ,\n", - " 'fnlwgt': ,\n", - " 'education': ,\n", - " 'educational-num': ,\n", - " 'marital-status': ,\n", - " 'occupation': ,\n", - " 'relationship': ,\n", - " 'race': ,\n", - " 'gender': ,\n", - " 'capital-gain': ,\n", - " 'capital-loss': ,\n", - " 'hours-per-week': ,\n", - " 'native-country': ,\n", - " 'income': },\n", - " 'outlier': {'age': ,\n", + "{'missing': {'age': ,\n", + " 'workclass': ,\n", + " 'fnlwgt': ,\n", + " 'education': ,\n", + " 'educational-num': ,\n", + " 'marital-status': ,\n", + " 'occupation': ,\n", + " 'relationship': ,\n", + " 'race': ,\n", + " 'gender': ,\n", + " 'capital-gain': ,\n", + " 'capital-loss': ,\n", + " 'hours-per-week': ,\n", + " 'native-country': ,\n", + " 'income': },\n", + " 'outlier': {'age': ,\n", " 'workclass': None,\n", - " 'fnlwgt': ,\n", + " 'fnlwgt': ,\n", " 'education': None,\n", - " 'educational-num': ,\n", + " 'educational-num': ,\n", " 'marital-status': None,\n", " 'occupation': None,\n", " 'relationship': None,\n", " 'race': None,\n", " 'gender': None,\n", - " 'capital-gain': ,\n", - " 'capital-loss': ,\n", - " 'hours-per-week': ,\n", + " 'capital-gain': ,\n", + " 'capital-loss': ,\n", + " 'hours-per-week': ,\n", " 'native-country': None,\n", " 'income': None},\n", " 'encoder': {'age': None,\n", - " 'workclass': ,\n", + " 'workclass': ,\n", " 'fnlwgt': None,\n", - " 'education': ,\n", + " 'education': ,\n", " 'educational-num': None,\n", - " 'marital-status': ,\n", - " 'occupation': ,\n", - " 'relationship': ,\n", - " 'race': ,\n", - " 'gender': ,\n", + " 'marital-status': ,\n", + " 'occupation': ,\n", + " 'relationship': ,\n", + " 'race': ,\n", + " 'gender': ,\n", " 'capital-gain': None,\n", " 'capital-loss': None,\n", " 'hours-per-week': None,\n", - " 'native-country': ,\n", - " 'income': },\n", - " 'scaler': {'age': ,\n", + " 'native-country': ,\n", + " 'income': },\n", + " 'scaler': {'age': ,\n", " 'workclass': None,\n", - " 'fnlwgt': ,\n", + " 'fnlwgt': ,\n", " 'education': None,\n", - " 'educational-num': ,\n", + " 'educational-num': ,\n", " 'marital-status': None,\n", " 'occupation': None,\n", " 'relationship': None,\n", " 'race': None,\n", " 'gender': None,\n", - " 'capital-gain': ,\n", - " 'capital-loss': ,\n", - " 'hours-per-week': ,\n", + " 'capital-gain': ,\n", + " 'capital-loss': ,\n", + " 'hours-per-week': ,\n", " 'native-country': None,\n", " 'income': None},\n", - " 'discretizing': {'age': ,\n", - " 'workclass': ,\n", - " 'fnlwgt': ,\n", - " 'education': ,\n", - " 'educational-num': ,\n", - " 'marital-status': ,\n", - " 'occupation': ,\n", - " 'relationship': ,\n", - " 'race': ,\n", - " 'gender': ,\n", - " 'capital-gain': ,\n", - " 'capital-loss': ,\n", - " 'hours-per-week': ,\n", - " 'native-country': ,\n", - " 'income': }}" + " 'discretizing': {'age': ,\n", + " 'workclass': ,\n", + " 'fnlwgt': ,\n", + " 'education': ,\n", + " 'educational-num': ,\n", + " 'marital-status': ,\n", + " 'occupation': ,\n", + " 'relationship': ,\n", + " 'race': ,\n", + " 'gender': ,\n", + " 'capital-gain': ,\n", + " 'capital-loss': ,\n", + " 'hours-per-week': ,\n", + " 'native-country': ,\n", + " 'income': }}" ] }, - "execution_count": 25, + "execution_count": 34, "metadata": {}, "output_type": "execute_result" } @@ -1562,7 +1530,7 @@ }, { "cell_type": "code", - "execution_count": 26, + "execution_count": 35, "metadata": {}, "outputs": [], "source": [ @@ -1571,59 +1539,16 @@ }, { "cell_type": "code", - "execution_count": 27, + "execution_count": 36, "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/Users/alex/PETsARD/PETsARD/processor/mediator.py:169: FutureWarning: Setting an item of incompatible dtype is deprecated and will raise in a future error of pandas. Value '['Private', 'Private', 'Local-gov', 'Private', 'Private', ..., 'Private', 'Private', 'Private', 'Private', 'Self-emp-inc']\n", - "Length: 45222\n", - "Categories (8, object): ['Federal-gov', 'Local-gov', 'Never-worked', 'Private', 'Self-emp-inc', 'Self-emp-not-inc', 'State-gov', 'Without-pay']' has dtype incompatible with bool, please explicitly cast to a compatible dtype first.\n", - " transformed.loc[:, col] = self._config.get(col,\n", - "/Users/alex/PETsARD/PETsARD/processor/mediator.py:169: FutureWarning: Setting an item of incompatible dtype is deprecated and will raise in a future error of pandas. Value '['11th', 'HS-grad', 'Assoc-acdm', 'Some-college', '10th', ..., 'Assoc-acdm', 'HS-grad', 'HS-grad', 'HS-grad', 'HS-grad']\n", - "Length: 45222\n", - "Categories (16, object): ['10th', '11th', '12th', '1st-4th', ..., 'Masters', 'Preschool', 'Prof-school', 'Some-college']' has dtype incompatible with bool, please explicitly cast to a compatible dtype first.\n", - " transformed.loc[:, col] = self._config.get(col,\n", - "/Users/alex/PETsARD/PETsARD/processor/mediator.py:169: FutureWarning: Setting an item of incompatible dtype is deprecated and will raise in a future error of pandas. Value '['Never-married', 'Married-civ-spouse', 'Married-civ-spouse', 'Married-civ-spouse', 'Never-married', ..., 'Married-civ-spouse', 'Married-civ-spouse', 'Widowed', 'Never-married', 'Married-civ-spouse']\n", - "Length: 45222\n", - "Categories (7, object): ['Divorced', 'Married-AF-spouse', 'Married-civ-spouse', 'Married-spouse-absent', 'Never-married', 'Separated', 'Widowed']' has dtype incompatible with bool, please explicitly cast to a compatible dtype first.\n", - " transformed.loc[:, col] = self._config.get(col,\n", - "/Users/alex/PETsARD/PETsARD/processor/mediator.py:169: FutureWarning: Setting an item of incompatible dtype is deprecated and will raise in a future error of pandas. Value '['Machine-op-inspct', 'Farming-fishing', 'Protective-serv', 'Machine-op-inspct', 'Other-service', ..., 'Tech-support', 'Machine-op-inspct', 'Adm-clerical', 'Adm-clerical', 'Exec-managerial']\n", - "Length: 45222\n", - "Categories (14, object): ['Adm-clerical', 'Armed-Forces', 'Craft-repair', 'Exec-managerial', ..., 'Protective-serv', 'Sales', 'Tech-support', 'Transport-moving']' has dtype incompatible with bool, please explicitly cast to a compatible dtype first.\n", - " transformed.loc[:, col] = self._config.get(col,\n", - "/Users/alex/PETsARD/PETsARD/processor/mediator.py:169: FutureWarning: Setting an item of incompatible dtype is deprecated and will raise in a future error of pandas. Value '['Own-child', 'Husband', 'Husband', 'Husband', 'Not-in-family', ..., 'Wife', 'Husband', 'Unmarried', 'Own-child', 'Wife']\n", - "Length: 45222\n", - "Categories (6, object): ['Husband', 'Not-in-family', 'Other-relative', 'Own-child', 'Unmarried', 'Wife']' has dtype incompatible with bool, please explicitly cast to a compatible dtype first.\n", - " transformed.loc[:, col] = self._config.get(col,\n", - "/Users/alex/PETsARD/PETsARD/processor/mediator.py:169: FutureWarning: Setting an item of incompatible dtype is deprecated and will raise in a future error of pandas. Value '['Black', 'White', 'White', 'Black', 'White', ..., 'White', 'White', 'White', 'White', 'White']\n", - "Length: 45222\n", - "Categories (5, object): ['Amer-Indian-Eskimo', 'Asian-Pac-Islander', 'Black', 'Other', 'White']' has dtype incompatible with bool, please explicitly cast to a compatible dtype first.\n", - " transformed.loc[:, col] = self._config.get(col,\n", - "/Users/alex/PETsARD/PETsARD/processor/mediator.py:169: FutureWarning: Setting an item of incompatible dtype is deprecated and will raise in a future error of pandas. Value '['Male', 'Male', 'Male', 'Male', 'Male', ..., 'Female', 'Male', 'Female', 'Male', 'Female']\n", - "Length: 45222\n", - "Categories (2, object): ['Female', 'Male']' has dtype incompatible with bool, please explicitly cast to a compatible dtype first.\n", - " transformed.loc[:, col] = self._config.get(col,\n", - "/Users/alex/PETsARD/PETsARD/processor/mediator.py:169: FutureWarning: Setting an item of incompatible dtype is deprecated and will raise in a future error of pandas. Value '['United-States', 'United-States', 'United-States', 'United-States', 'United-States', ..., 'United-States', 'United-States', 'United-States', 'United-States', 'United-States']\n", - "Length: 45222\n", - "Categories (41, object): ['Cambodia', 'Canada', 'China', 'Columbia', ..., 'Trinadad&Tobago', 'United-States', 'Vietnam', 'Yugoslavia']' has dtype incompatible with bool, please explicitly cast to a compatible dtype first.\n", - " transformed.loc[:, col] = self._config.get(col,\n", - "/Users/alex/PETsARD/PETsARD/processor/mediator.py:169: FutureWarning: Setting an item of incompatible dtype is deprecated and will raise in a future error of pandas. Value '['<=50K', '<=50K', '>50K', '>50K', '<=50K', ..., '<=50K', '>50K', '<=50K', '<=50K', '>50K']\n", - "Length: 45222\n", - "Categories (2, object): ['<=50K', '>50K']' has dtype incompatible with bool, please explicitly cast to a compatible dtype first.\n", - " transformed.loc[:, col] = self._config.get(col,\n" - ] - } - ], + "outputs": [], "source": [ "df_transformed2 = pm2.transform(df2)" ] }, { "cell_type": "code", - "execution_count": 28, + "execution_count": 37, "metadata": {}, "outputs": [ { @@ -1869,49 +1794,49 @@ "" ], "text/plain": [ - " age workclass fnlwgt education educational-num marital-status \\\n", - "0 0.0 3 0.0 1 2.0 4 \n", - "1 1.0 3 0.0 11 2.0 2 \n", - "2 0.0 1 1.0 7 3.0 2 \n", - "3 1.0 3 0.0 15 3.0 2 \n", - "4 1.0 3 0.0 0 1.0 4 \n", - "... ... ... ... ... ... ... \n", - "45217 0.0 3 0.0 7 3.0 2 \n", - "45218 1.0 3 0.0 11 2.0 2 \n", - "45219 2.0 3 0.0 11 2.0 6 \n", - "45220 0.0 3 0.0 11 2.0 4 \n", - "45221 2.0 4 0.0 11 2.0 2 \n", + " age workclass fnlwgt education educational-num marital-status \\\n", + "0 0.0 3 0.0 1 2.0 4 \n", + "1 1.0 3 0.0 11 2.0 2 \n", + "2 0.0 1 1.0 7 3.0 2 \n", + "3 1.0 3 0.0 15 3.0 2 \n", + "4 1.0 3 0.0 0 1.0 4 \n", + "... ... ... ... ... ... ... \n", + "45217 0.0 3 0.0 7 3.0 2 \n", + "45218 1.0 3 0.0 11 2.0 2 \n", + "45219 2.0 3 0.0 11 2.0 6 \n", + "45220 0.0 3 0.0 11 2.0 4 \n", + "45221 2.0 4 0.0 11 2.0 2 \n", "\n", - " occupation relationship race gender capital-gain capital-loss \\\n", - "0 6 3 2 1 0.0 0.0 \n", - "1 4 0 4 1 0.0 0.0 \n", - "2 10 0 4 1 0.0 0.0 \n", - "3 6 0 2 1 0.0 0.0 \n", - "4 7 1 4 1 0.0 0.0 \n", - "... ... ... ... ... ... ... \n", - "45217 12 5 4 0 0.0 0.0 \n", - "45218 6 0 4 1 0.0 0.0 \n", - "45219 0 4 4 0 0.0 0.0 \n", - "45220 0 3 4 1 0.0 0.0 \n", - "45221 3 5 4 0 0.0 0.0 \n", + " occupation relationship race gender capital-gain capital-loss \\\n", + "0 6 3 2 1 0.0 0.0 \n", + "1 4 0 4 1 0.0 0.0 \n", + "2 10 0 4 1 0.0 0.0 \n", + "3 6 0 2 1 0.0 0.0 \n", + "4 7 1 4 1 0.0 0.0 \n", + "... ... ... ... ... ... ... \n", + "45217 12 5 4 0 0.0 0.0 \n", + "45218 6 0 4 1 0.0 0.0 \n", + "45219 0 4 4 0 0.0 0.0 \n", + "45220 0 3 4 1 0.0 0.0 \n", + "45221 3 5 4 0 0.0 0.0 \n", "\n", - " hours-per-week native-country income \n", - "0 1.0 38 0 \n", - "1 2.0 38 0 \n", - "2 1.0 38 1 \n", - "3 1.0 38 1 \n", - "4 1.0 38 0 \n", - "... ... ... ... \n", - "45217 1.0 38 0 \n", - "45218 1.0 38 1 \n", - "45219 1.0 38 0 \n", - "45220 0.0 38 0 \n", - "45221 1.0 38 1 \n", + " hours-per-week native-country income \n", + "0 1.0 38 0 \n", + "1 2.0 38 0 \n", + "2 1.0 38 1 \n", + "3 1.0 38 1 \n", + "4 1.0 38 0 \n", + "... ... ... ... \n", + "45217 1.0 38 0 \n", + "45218 1.0 38 1 \n", + "45219 1.0 38 0 \n", + "45220 0.0 38 0 \n", + "45221 1.0 38 1 \n", "\n", "[45222 rows x 15 columns]" ] }, - "execution_count": 28, + "execution_count": 37, "metadata": {}, "output_type": "execute_result" } @@ -1922,7 +1847,7 @@ }, { "cell_type": "code", - "execution_count": 29, + "execution_count": 38, "metadata": {}, "outputs": [], "source": [ @@ -1931,7 +1856,7 @@ }, { "cell_type": "code", - "execution_count": 30, + "execution_count": 39, "metadata": {}, "outputs": [ { @@ -2102,12 +2027,12 @@ " \n", " 45218\n", " 38.9\n", - " Private\n", + " NaN\n", " 160096.5\n", " HS-grad\n", " 8.5\n", " Married-civ-spouse\n", - " Machine-op-inspct\n", + " NaN\n", " Husband\n", " White\n", " Male\n", @@ -2185,7 +2110,7 @@ "4 38.9 Private 160096.5 10th 5.5 \n", "... ... ... ... ... ... \n", "45217 24.3 Private 160096.5 Assoc-acdm 11.5 \n", - "45218 38.9 Private 160096.5 HS-grad 8.5 \n", + "45218 38.9 NaN 160096.5 HS-grad 8.5 \n", "45219 53.5 Private 160096.5 HS-grad 8.5 \n", "45220 24.3 Private 160096.5 HS-grad 8.5 \n", "45221 53.5 Self-emp-inc 160096.5 HS-grad 8.5 \n", @@ -2198,7 +2123,7 @@ "4 Never-married Other-service Not-in-family White Male \n", "... ... ... ... ... ... \n", "45217 Married-civ-spouse Tech-support Wife White Female \n", - "45218 Married-civ-spouse Machine-op-inspct Husband White Male \n", + "45218 Married-civ-spouse NaN Husband White Male \n", "45219 Widowed Adm-clerical Unmarried White Female \n", "45220 Never-married Adm-clerical Own-child White Male \n", "45221 Married-civ-spouse Exec-managerial Wife White Female \n", @@ -2219,7 +2144,7 @@ "[45222 rows x 15 columns]" ] }, - "execution_count": 30, + "execution_count": 39, "metadata": {}, "output_type": "execute_result" } @@ -2230,7 +2155,7 @@ }, { "cell_type": "code", - "execution_count": 31, + "execution_count": 40, "metadata": {}, "outputs": [ { @@ -2274,7 +2199,7 @@ " \n", " \n", " 0\n", - " 25\n", + " 25.0\n", " Private\n", " 226802\n", " 11th\n", @@ -2292,7 +2217,7 @@ " \n", " \n", " 1\n", - " 38\n", + " 38.0\n", " Private\n", " 89814\n", " HS-grad\n", @@ -2310,7 +2235,7 @@ " \n", " \n", " 2\n", - " 28\n", + " 28.0\n", " Local-gov\n", " 336951\n", " Assoc-acdm\n", @@ -2328,7 +2253,7 @@ " \n", " \n", " 3\n", - " 44\n", + " 44.0\n", " Private\n", " 160323\n", " Some-college\n", @@ -2346,7 +2271,7 @@ " \n", " \n", " 4\n", - " 18\n", + " 18.0\n", " NaN\n", " 103497\n", " Some-college\n", @@ -2382,7 +2307,7 @@ " \n", " \n", " 48837\n", - " 27\n", + " 27.0\n", " Private\n", " 257302\n", " Assoc-acdm\n", @@ -2400,7 +2325,7 @@ " \n", " \n", " 48838\n", - " 40\n", + " 40.0\n", " Private\n", " 154374\n", " HS-grad\n", @@ -2418,7 +2343,7 @@ " \n", " \n", " 48839\n", - " 58\n", + " 58.0\n", " Private\n", " 151910\n", " HS-grad\n", @@ -2436,7 +2361,7 @@ " \n", " \n", " 48840\n", - " 22\n", + " 22.0\n", " Private\n", " 201490\n", " HS-grad\n", @@ -2454,7 +2379,7 @@ " \n", " \n", " 48841\n", - " 52\n", + " 52.0\n", " Self-emp-inc\n", " 287927\n", " HS-grad\n", @@ -2476,18 +2401,18 @@ "" ], "text/plain": [ - " age workclass fnlwgt education educational-num \\\n", - "0 25 Private 226802 11th 7 \n", - "1 38 Private 89814 HS-grad 9 \n", - "2 28 Local-gov 336951 Assoc-acdm 12 \n", - "3 44 Private 160323 Some-college 10 \n", - "4 18 NaN 103497 Some-college 10 \n", - "... ... ... ... ... ... \n", - "48837 27 Private 257302 Assoc-acdm 12 \n", - "48838 40 Private 154374 HS-grad 9 \n", - "48839 58 Private 151910 HS-grad 9 \n", - "48840 22 Private 201490 HS-grad 9 \n", - "48841 52 Self-emp-inc 287927 HS-grad 9 \n", + " age workclass fnlwgt education educational-num \\\n", + "0 25.0 Private 226802 11th 7 \n", + "1 38.0 Private 89814 HS-grad 9 \n", + "2 28.0 Local-gov 336951 Assoc-acdm 12 \n", + "3 44.0 Private 160323 Some-college 10 \n", + "4 18.0 NaN 103497 Some-college 10 \n", + "... ... ... ... ... ... \n", + "48837 27.0 Private 257302 Assoc-acdm 12 \n", + "48838 40.0 Private 154374 HS-grad 9 \n", + "48839 58.0 Private 151910 HS-grad 9 \n", + "48840 22.0 Private 201490 HS-grad 9 \n", + "48841 52.0 Self-emp-inc 287927 HS-grad 9 \n", "\n", " marital-status occupation relationship race gender \\\n", "0 Never-married Machine-op-inspct Own-child Black Male \n", @@ -2518,7 +2443,7 @@ "[48842 rows x 15 columns]" ] }, - "execution_count": 31, + "execution_count": 40, "metadata": {}, "output_type": "execute_result" } @@ -2529,7 +2454,7 @@ }, { "cell_type": "code", - "execution_count": 32, + "execution_count": 41, "metadata": {}, "outputs": [ { @@ -2553,7 +2478,7 @@ "dtype: float64" ] }, - "execution_count": 32, + "execution_count": 41, "metadata": {}, "output_type": "execute_result" } @@ -2564,16 +2489,16 @@ }, { "cell_type": "code", - "execution_count": 33, + "execution_count": 42, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "0.07098314979434789" + "0.0712706204944496" ] }, - "execution_count": 33, + "execution_count": 42, "metadata": {}, "output_type": "execute_result" } @@ -2584,60 +2509,60 @@ }, { "cell_type": "code", - "execution_count": 34, + "execution_count": 43, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "{'missing': {'gen': ,\n", - " 'age': ,\n", - " 'race': ,\n", - " 'edu': ,\n", - " 'mar': ,\n", - " 'bmi': ,\n", - " 'dep': ,\n", - " 'pir': ,\n", - " 'gh': ,\n", - " 'mets': ,\n", - " 'qm': ,\n", - " 'dia': },\n", - " 'outlier': {'gen': ,\n", - " 'age': ,\n", - " 'race': ,\n", - " 'edu': ,\n", - " 'mar': ,\n", - " 'bmi': ,\n", - " 'dep': ,\n", - " 'pir': ,\n", - " 'gh': ,\n", - " 'mets': ,\n", - " 'qm': ,\n", - " 'dia': },\n", - " 'encoder': {'gen': ,\n", + "{'missing': {'gen': ,\n", + " 'age': ,\n", + " 'race': ,\n", + " 'edu': ,\n", + " 'mar': ,\n", + " 'bmi': ,\n", + " 'dep': ,\n", + " 'pir': ,\n", + " 'gh': ,\n", + " 'mets': ,\n", + " 'qm': ,\n", + " 'dia': },\n", + " 'outlier': {'gen': ,\n", + " 'age': ,\n", + " 'race': ,\n", + " 'edu': ,\n", + " 'mar': ,\n", + " 'bmi': ,\n", + " 'dep': ,\n", + " 'pir': ,\n", + " 'gh': ,\n", + " 'mets': ,\n", + " 'qm': ,\n", + " 'dia': },\n", + " 'encoder': {'gen': ,\n", " 'age': None,\n", - " 'race': ,\n", - " 'edu': ,\n", - " 'mar': ,\n", + " 'race': ,\n", + " 'edu': ,\n", + " 'mar': ,\n", " 'bmi': None,\n", " 'dep': None,\n", " 'pir': None,\n", " 'gh': None,\n", " 'mets': None,\n", - " 'qm': ,\n", + " 'qm': ,\n", " 'dia': None},\n", " 'scaler': {'gen': None,\n", - " 'age': ,\n", + " 'age': ,\n", " 'race': None,\n", " 'edu': None,\n", " 'mar': None,\n", - " 'bmi': ,\n", - " 'dep': ,\n", - " 'pir': ,\n", - " 'gh': ,\n", - " 'mets': ,\n", + " 'bmi': ,\n", + " 'dep': ,\n", + " 'pir': ,\n", + " 'gh': ,\n", + " 'mets': ,\n", " 'qm': None,\n", - " 'dia': ,\n", + " 'dia': ,\n", " 'gen_Female': None,\n", " 'gen_Male': None,\n", " 'race_Black': None,\n", @@ -2645,21 +2570,21 @@ " 'race_Mexican': None,\n", " 'race_Other': None,\n", " 'race_White': None},\n", - " 'discretizing': {'gen': ,\n", - " 'age': ,\n", - " 'race': ,\n", - " 'edu': ,\n", - " 'mar': ,\n", - " 'bmi': ,\n", - " 'dep': ,\n", - " 'pir': ,\n", - " 'gh': ,\n", - " 'mets': ,\n", - " 'qm': ,\n", - " 'dia': }}" + " 'discretizing': {'gen': ,\n", + " 'age': ,\n", + " 'race': ,\n", + " 'edu': ,\n", + " 'mar': ,\n", + " 'bmi': ,\n", + " 'dep': ,\n", + " 'pir': ,\n", + " 'gh': ,\n", + " 'mets': ,\n", + " 'qm': ,\n", + " 'dia': }}" ] }, - "execution_count": 34, + "execution_count": 43, "metadata": {}, "output_type": "execute_result" } @@ -2670,7 +2595,7 @@ }, { "cell_type": "code", - "execution_count": 35, + "execution_count": 44, "metadata": {}, "outputs": [ { @@ -2829,7 +2754,7 @@ "14 encoder race EncoderOneHot EncoderUniform" ] }, - "execution_count": 35, + "execution_count": 44, "metadata": {}, "output_type": "execute_result" } @@ -2838,188 +2763,6 @@ "pm.get_changes()" ] }, - { - "cell_type": "code", - "execution_count": 36, - "metadata": {}, - "outputs": [], - "source": [ - "from PETsARD.synthesizer import Synthesizer" - ] - }, - { - "cell_type": "code", - "execution_count": 37, - "metadata": {}, - "outputs": [], - "source": [ - "syn = Synthesizer(df_transformed2[['age', 'workclass', 'marital-status']], synthesizing_method='smartnoise-mwem')" - ] - }, - { - "cell_type": "code", - "execution_count": 38, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Synthesizer (SmartNoise): Fitting mwem.\n", - "Synthesizer (SmartNoise): Fitting mwem spent 10.7503 sec.\n" - ] - } - ], - "source": [ - "syn.fit()" - ] - }, - { - "cell_type": "code", - "execution_count": 39, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Synthesizer (SmartNoise): Sampling mwem # 45222 rows (same as raw) in 0.6409 sec.\n" - ] - } - ], - "source": [ - "syn.sample()" - ] - }, - { - "cell_type": "code", - "execution_count": 40, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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45222 rows × 3 columns

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" - ], - "text/plain": [ - " age workclass marital-status\n", - "0 2 4 2\n", - "1 1 3 0\n", - "2 0 3 4\n", - "3 3 5 2\n", - "4 2 3 2\n", - "... ... ... ...\n", - "45217 0 5 2\n", - "45218 1 6 4\n", - "45219 0 3 4\n", - "45220 1 5 2\n", - "45221 1 3 2\n", - "\n", - "[45222 rows x 3 columns]" - ] - }, - "execution_count": 40, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "syn.data_syn" - ] - }, { "cell_type": "code", "execution_count": null, diff --git a/demo/dev/executable_smartnoise.ipynb b/demo/dev/executable_smartnoise.ipynb index 42acbe6c..828a3d48 100644 --- a/demo/dev/executable_smartnoise.ipynb +++ b/demo/dev/executable_smartnoise.ipynb @@ -2,7 +2,7 @@ "cells": [ { "cell_type": "code", - "execution_count": 2, + "execution_count": 1, "metadata": {}, "outputs": [], "source": [ @@ -12,7 +12,7 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": 2, "metadata": {}, "outputs": [ { @@ -29,7 +29,7 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": 3, "metadata": {}, "outputs": [ { @@ -70,7 +70,16 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 9, + "metadata": {}, + "outputs": [], + "source": [ + "load.data = load.data.loc[:1000, :]" + ] + }, + { + "cell_type": "code", + "execution_count": 4, "metadata": {}, "outputs": [], "source": [ @@ -115,7 +124,6 @@ " print(preproc_discretizing_data.head(1))\n", "\n", "\n", - "\n", " syn = Synthesizer(\n", " method=synthesizing_method,\n", " epsilon=10.0,\n", @@ -128,7 +136,7 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 11, "metadata": {}, "outputs": [ { @@ -136,49 +144,81 @@ "output_type": "stream", "text": [ "Preproc data as ...\n", + " age workclass fnlwgt education educational-num marital-status occupation \\\n", + "0 0.0 2 0.0 1 0.0 4 6 \n", + "\n", + " relationship race gender capital-gain capital-loss hours-per-week \\\n", + "0 3 2 1 0.0 0.0 0.0 \n", + "\n", + " native-country income \n", + "0 25 0 \n", + "age category\n", + "workclass category\n", + "fnlwgt category\n", + "education category\n", + "educational-num category\n", + "marital-status category\n", + "occupation category\n", + "relationship category\n", + "race category\n", + "gender category\n", + "capital-gain category\n", + "capital-loss category\n", + "hours-per-week category\n", + "native-country category\n", + "income category\n", + "dtype: object\n", + "Synthesizer (SmartNoise): Fitting mst.\n", + "Synthesizer (SmartNoise): Fitting mst spent 41.2776 sec.\n", + "Synthesizer (SmartNoise): Sampling mst # 558 rows (same as raw) in 0.0326 sec.\n", + "Sync data as ...\n", " age workclass fnlwgt education educational-num marital-status \\\n", - "0 0.0 3 0.0 1 0.0 4 \n", + "0 0.0 2 0.0 8 0.0 2 \n", "\n", " occupation relationship race gender capital-gain capital-loss \\\n", - "0 6 3 2 1 0.0 0.0 \n", + "0 2 0 4 1 0.0 0.0 \n", "\n", " hours-per-week native-country income \n", - "0 0.0 38 0 \n", - "Synthesizer (SmartNoise): Fitting aim.\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/Users/alex/miniforge3/envs/dev310/lib/python3.10/site-packages/mbi/__init__.py:15: UserWarning: MixtureInference disabled, please install jax and jaxlib\n", - " warnings.warn('MixtureInference disabled, please install jax and jaxlib')\n" - ] - }, - { - "ename": "ValueError", - "evalue": "The transformer appears to have some continuous columns. Please provide only categorical or ordinal.", - "output_type": "error", - "traceback": [ - "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[0;31mValueError\u001b[0m Traceback (most recent call last)", - "Cell \u001b[0;32mIn[6], line 1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m \u001b[43missue332\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m 2\u001b[0m \u001b[43m \u001b[49m\u001b[43mload\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mload\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 3\u001b[0m \u001b[43m \u001b[49m\u001b[43msynthesizing_method\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43msmartnoise-aim\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m,\u001b[49m\n\u001b[1;32m 4\u001b[0m \u001b[43m)\u001b[49m\n", - "Cell \u001b[0;32mIn[5], line 48\u001b[0m, in \u001b[0;36missue332\u001b[0;34m(load, synthesizing_method, scaler_inhibit)\u001b[0m\n\u001b[1;32m 43\u001b[0m syn \u001b[38;5;241m=\u001b[39m Synthesizer(\n\u001b[1;32m 44\u001b[0m method\u001b[38;5;241m=\u001b[39msynthesizing_method,\n\u001b[1;32m 45\u001b[0m epsilon\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m10.0\u001b[39m,\n\u001b[1;32m 46\u001b[0m )\n\u001b[1;32m 47\u001b[0m syn\u001b[38;5;241m.\u001b[39mcreate(data\u001b[38;5;241m=\u001b[39mpreproc_discretizing_data)\n\u001b[0;32m---> 48\u001b[0m \u001b[43msyn\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mfit_sample\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 49\u001b[0m \u001b[38;5;28mprint\u001b[39m(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mSync data as ...\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[1;32m 50\u001b[0m \u001b[38;5;28mprint\u001b[39m(syn\u001b[38;5;241m.\u001b[39mdata_syn\u001b[38;5;241m.\u001b[39mhead(\u001b[38;5;241m1\u001b[39m))\n", - "File \u001b[0;32m~/PETsARD/PETsARD/synthesizer/synthesizer.py:121\u001b[0m, in \u001b[0;36mSynthesizer.fit_sample\u001b[0;34m(self, **kwargs)\u001b[0m\n\u001b[1;32m 119\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mdata_syn \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mloader\u001b[38;5;241m.\u001b[39mdata\n\u001b[1;32m 120\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m--> 121\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mdata_syn \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mSynthesizer\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mfit_sample\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n", - "File \u001b[0;32m~/PETsARD/PETsARD/synthesizer/smartnoise.py:130\u001b[0m, in \u001b[0;36mSmartNoise.fit_sample\u001b[0;34m(self, sample_num_rows, reset_sampling, output_file_path)\u001b[0m\n\u001b[1;32m 111\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mfit_sample\u001b[39m(\n\u001b[1;32m 112\u001b[0m \u001b[38;5;28mself\u001b[39m,\n\u001b[1;32m 113\u001b[0m sample_num_rows: \u001b[38;5;28mint\u001b[39m \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m,\n\u001b[1;32m 114\u001b[0m reset_sampling: \u001b[38;5;28mbool\u001b[39m \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mFalse\u001b[39;00m,\n\u001b[1;32m 115\u001b[0m output_file_path: \u001b[38;5;28mstr\u001b[39m \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m\n\u001b[1;32m 116\u001b[0m ) \u001b[38;5;241m-\u001b[39m\u001b[38;5;241m>\u001b[39m pd\u001b[38;5;241m.\u001b[39mDataFrame:\n\u001b[1;32m 117\u001b[0m \u001b[38;5;250m \u001b[39m\u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[1;32m 118\u001b[0m \u001b[38;5;124;03m Fit and sample from the synthesizer.\u001b[39;00m\n\u001b[1;32m 119\u001b[0m \u001b[38;5;124;03m The combination of the methods `fit()` and `sample()`.\u001b[39;00m\n\u001b[0;32m (...)\u001b[0m\n\u001b[1;32m 128\u001b[0m \u001b[38;5;124;03m data_syn (pd.DataFrame): The synthesized data.\u001b[39;00m\n\u001b[1;32m 129\u001b[0m \u001b[38;5;124;03m \"\"\"\u001b[39;00m\n\u001b[0;32m--> 130\u001b[0m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mfit\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 131\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39msample(sample_num_rows, reset_sampling, output_file_path)\n", - "File \u001b[0;32m~/PETsARD/PETsARD/synthesizer/smartnoise.py:47\u001b[0m, in \u001b[0;36mSmartNoise.fit\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m 42\u001b[0m t \u001b[38;5;241m=\u001b[39m TableTransformer([IdentityTransformer() \n\u001b[1;32m 43\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m i \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mrange\u001b[39m(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mdata\u001b[38;5;241m.\u001b[39mshape[\u001b[38;5;241m1\u001b[39m])])\n\u001b[1;32m 45\u001b[0m \u001b[38;5;66;03m# TODO - Only support cube-style synthesizer. \u001b[39;00m\n\u001b[1;32m 46\u001b[0m \u001b[38;5;66;03m# GAN-style synthesizer needed to be implemented.\u001b[39;00m\n\u001b[0;32m---> 47\u001b[0m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_Synthesizer\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mfit\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mdata\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mtransformer\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mt\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 48\u001b[0m \u001b[38;5;28mprint\u001b[39m(\n\u001b[1;32m 49\u001b[0m \u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mSynthesizer (SmartNoise): \u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m 50\u001b[0m \u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mFitting \u001b[39m\u001b[38;5;132;01m{\u001b[39;00m\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39msyn_method\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m spent \u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m 51\u001b[0m \u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;132;01m{\u001b[39;00m\u001b[38;5;28mround\u001b[39m(time\u001b[38;5;241m.\u001b[39mtime()\u001b[38;5;241m-\u001b[39mtime_start\u001b[38;5;250m \u001b[39m,\u001b[38;5;241m4\u001b[39m)\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m sec.\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m 52\u001b[0m )\n\u001b[1;32m 53\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n", - "File \u001b[0;32m~/miniforge3/envs/dev310/lib/python3.10/site-packages/snsynth/aim/aim.py:121\u001b[0m, in \u001b[0;36mAIMSynthesizer.fit\u001b[0;34m(self, data, transformer, categorical_columns, ordinal_columns, continuous_columns, preprocessor_eps, nullable, *ignore)\u001b[0m\n\u001b[1;32m 119\u001b[0m cards \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_transformer\u001b[38;5;241m.\u001b[39mcardinality\n\u001b[1;32m 120\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28many\u001b[39m(c \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m \u001b[38;5;28;01mfor\u001b[39;00m c \u001b[38;5;129;01min\u001b[39;00m cards):\n\u001b[0;32m--> 121\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mValueError\u001b[39;00m(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mThe transformer appears to have some continuous columns. Please provide only categorical or ordinal.\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[1;32m 123\u001b[0m dimensionality \u001b[38;5;241m=\u001b[39m np\u001b[38;5;241m.\u001b[39mprod(cards)\n\u001b[1;32m 124\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mverbose:\n", - "\u001b[0;31mValueError\u001b[0m: The transformer appears to have some continuous columns. Please provide only categorical or ordinal." + "0 0.0 25 0 \n" ] } ], "source": [ "issue332(\n", " load=load,\n", - " synthesizing_method='smartnoise-aim',\n", + " synthesizing_method='smartnoise-mst',\n", ")" ] }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [], + "source": [ + "a = np.array([1,2,3])" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([1, 2, 3])" + ] + }, + "execution_count": 15, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "pd.Categorical(a).to_numpy()" + ] + }, { "cell_type": "code", "execution_count": null, From 2be8c03ba5dc60d33ba1f20e189426fac9157e8a Mon Sep 17 00:00:00 2001 From: Alex Chen Date: Tue, 5 Mar 2024 16:25:50 +0800 Subject: [PATCH 06/17] remove mwem part --- docs/_posts/2024-02-20-Synthesizer.md | 7 ------- 1 file changed, 7 deletions(-) diff --git a/docs/_posts/2024-02-20-Synthesizer.md b/docs/_posts/2024-02-20-Synthesizer.md index 0ad55bed..0e95abf8 100644 --- a/docs/_posts/2024-02-20-Synthesizer.md +++ b/docs/_posts/2024-02-20-Synthesizer.md @@ -91,7 +91,6 @@ In this section, we provide a comprehensive list of supported synthesizer types | `sdv` | `GaussianCopulaSynthesizer` | 'sdv-singletable-gaussiancopula' | | | | `sdv` | `TVAESynthesizer` | 'sdv-singletable-tvae' | | | | `smartnoise` | `SmartNoiseCreator` (AIM) | 'smartnoise-aim' | ✅ | ✅ | -| `smartnoise` | `SmartNoiseCreator` (MWEM) | 'smartnoise-mwem' | ✅ | ✅ | | `smartnoise` | `SmartNoiseCreator` (MST) | 'smartnoise-mst' | ✅ | ✅ | | `smartnoise` | `SmartNoiseCreator` (PAC-Synth) | 'smartnoise-pacsynth' | ✅ | ✅ | @@ -159,12 +158,6 @@ Use the Adaptive Iterative Mechanism (AIM) algorithm provided in `smartnoise`. S See [document](https://docs.smartnoise.org/synth/synthesizers/aim.html) for further details. 詳見[說明文件](https://docs.smartnoise.org/synth/synthesizers/aim.html)。 -### `'smartnoise-mwem'` - -Use the Multiplicative Weights Exponential Mechanism (MWEM) algorithm provided in `smartnoise`. See [document](https://docs.smartnoise.org/synth/synthesizers/mwem.html) for further details. - -使用 `smartnoise` 提供的 Multiplicative Weights Exponential Mechanism (MWEM) 演算法。詳見[說明文件](https://docs.smartnoise.org/synth/synthesizers/mwem.html)。 - **Parameters** See [document](https://docs.smartnoise.org/synth/synthesizers/mwem.html) for further details. 詳見[說明文件](https://docs.smartnoise.org/synth/synthesizers/mwem.html)。 From cb0ce893d03f5caef2ce2e36f797e880421a725f Mon Sep 17 00:00:00 2001 From: Alex Chen Date: Thu, 7 Mar 2024 10:42:14 +0800 Subject: [PATCH 07/17] add GAN method --- PETsARD/synthesizer/smartnoise.py | 22 +++++++++++++++++++--- 1 file changed, 19 insertions(+), 3 deletions(-) diff --git a/PETsARD/synthesizer/smartnoise.py b/PETsARD/synthesizer/smartnoise.py index eca86d98..a0fcd272 100644 --- a/PETsARD/synthesizer/smartnoise.py +++ b/PETsARD/synthesizer/smartnoise.py @@ -1,8 +1,7 @@ import time import pandas as pd -from snsynth.transform import NoTransformer, TableTransformer -from snsynth.transform.identity import IdentityTransformer +from snsynth.transform import TableTransformer, MinMaxTransformer from snsynth import Synthesizer as SNSyn from PETsARD.error import UnfittedError, UnsupportedMethodError @@ -17,6 +16,9 @@ class SmartNoise: as well as common functionality. """ + CUBE = ['aim', 'mwem', 'mst', 'pacsynth'] + GAN = ['dpctgan', 'patectgan'] + def __init__(self, data: pd.DataFrame, **kwargs) -> None: """ Args: @@ -39,7 +41,21 @@ def fit(self) -> None: # TODO - Only support cube-style synthesizer. # GAN-style synthesizer needed to be implemented. - self._Synthesizer.fit(self.data, categorical_columns=self.data.columns) + + if self.syn_method in self.CUBE: + self._Synthesizer.fit( + self.data, + categorical_columns=self.data.columns + ) + else: + tt = TableTransformer([ + MinMaxTransformer(lower=self.data[col].min(), + upper=self.data[col].max(), + negative=False) + for col in self.data.columns + ]) + + self._Synthesizer.fit(self.data, transformer=tt) print( f"Synthesizer (SmartNoise): " f"Fitting {self.syn_method} spent " From 279524d109255fc3469603a54a82f3e338b75091 Mon Sep 17 00:00:00 2001 From: Alex Chen Date: Thu, 7 Mar 2024 10:48:47 +0800 Subject: [PATCH 08/17] revert transform output type to numerical rather than categorical --- PETsARD/processor/discretizing.py | 2 +- PETsARD/processor/encoder.py | 16 ++++++++-------- 2 files changed, 9 insertions(+), 9 deletions(-) diff --git a/PETsARD/processor/discretizing.py b/PETsARD/processor/discretizing.py index 4bc70eaa..ae82fda3 100644 --- a/PETsARD/processor/discretizing.py +++ b/PETsARD/processor/discretizing.py @@ -50,7 +50,7 @@ def transform(self, data: pd.Series) -> np.ndarray: if not self._is_fitted: raise UnfittedError('The object is not fitted. Use .fit() first.') - return pd.Categorical(self._transform(data)) + return self._transform(data) def _transform(): """ diff --git a/PETsARD/processor/encoder.py b/PETsARD/processor/encoder.py index 51b3a37b..de78aff0 100644 --- a/PETsARD/processor/encoder.py +++ b/PETsARD/processor/encoder.py @@ -64,7 +64,7 @@ def transform(self, data: pd.Series) -> np.ndarray: " in the fitting process.", " Please check the data categories again.") - return pd.Categorical(self._transform(data)) + return self._transform(data) def _transform(): """ @@ -92,7 +92,7 @@ def inverse_transform(self, data: pd.Series) -> pd.Series | np.ndarray: if not self._is_fitted: raise UnfittedError('The object is not fitted. Use .fit() first.') - return self._inverse_transform(data.to_numpy()) + return self._inverse_transform(data) def _inverse_transform(): """ @@ -168,12 +168,12 @@ def _transform(self, data: pd.Series) -> np.ndarray: uniform(self.cat_to_val[x][0], self.cat_to_val[x][1], size=1)[0]).values - def _inverse_transform(self, data: np.ndarray) -> pd.Series: + def _inverse_transform(self, data: pd.Series) -> pd.Series: """ Inverse the transformed data to the categorical data. Args: - data (np.ndarray): The categorical data needed to + data (pd.Series): The categorical data needed to be transformed inversely. Return: @@ -231,12 +231,12 @@ def _transform(self, data: pd.Series) -> np.ndarray: return self.model.transform(data) - def _inverse_transform(self, data: np.ndarray) -> np.ndarray: + def _inverse_transform(self, data: pd.Series) -> np.ndarray: """ Inverse the transformed data to the categorical data. Args: - data (np.ndarray): The categorical data needed to + data (pd.Series): The categorical data needed to be transformed inversely. Return: @@ -285,13 +285,13 @@ def _transform(self, data: pd.Series) -> None: return data - def _inverse_transform(self, data: np.ndarray) -> None: + def _inverse_transform(self, data: pd.Series) -> None: """ Inverse the transformed data to the categorical data. This is a dummy method, and it is implemented in MediatorEncoder. Args: - data (np.ndarray): The categorical data needed to + data (pd.Series): The categorical data needed to be transformed inversely. Return: From b426ed32ad08fb065ef3fa281f8e875f0955b0cc Mon Sep 17 00:00:00 2001 From: Alex Chen Date: Thu, 7 Mar 2024 10:49:06 +0800 Subject: [PATCH 09/17] demo update --- demo/dev/executable_smartnoise.ipynb | 182 ++++++++++++++++----------- 1 file changed, 109 insertions(+), 73 deletions(-) diff --git a/demo/dev/executable_smartnoise.ipynb b/demo/dev/executable_smartnoise.ipynb index 828a3d48..16a6b941 100644 --- a/demo/dev/executable_smartnoise.ipynb +++ b/demo/dev/executable_smartnoise.ipynb @@ -29,17 +29,9 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": 26, "metadata": {}, "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/Users/alex/miniforge3/envs/dev310/lib/python3.10/site-packages/tqdm/auto.py:21: TqdmWarning: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html\n", - " from .autonotebook import tqdm as notebook_tqdm\n" - ] - }, { "name": "stdout", "output_type": "stream", @@ -70,7 +62,7 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": 27, "metadata": {}, "outputs": [], "source": [ @@ -79,7 +71,7 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": 28, "metadata": {}, "outputs": [], "source": [ @@ -131,12 +123,77 @@ " syn.create(data=preproc_discretizing_data)\n", " syn.fit_sample()\n", " print(\"Sync data as ...\")\n", - " print(syn.data_syn.head(1))" + " print(syn.data_syn.head(1))\n", + " postproc_discretizing_data = proc_discretizing.inverse_transform(\n", + " data=syn.data_syn\n", + " )\n", + " print(postproc_discretizing_data.head(1))" ] }, { "cell_type": "code", - "execution_count": 11, + "execution_count": 24, + "metadata": {}, + "outputs": [], + "source": [ + "import pprint\n", + "\n", + "\n", + "def issue332_gan(\n", + " load: Loader,\n", + " synthesizing_method: str,\n", + " scaler_inhibit: bool = False\n", + "):\n", + " pp = pprint.PrettyPrinter(depth=2)\n", + "\n", + " proc_discretizing = Processor(\n", + " metadata=load.metadata,\n", + " )\n", + "\n", + " if scaler_inhibit:\n", + " print(\"Preproc config of Scaler before update as ...\")\n", + " pp.pprint(proc_discretizing.get_config()['scaler'])\n", + " proc_discretizing.update_config(\n", + " {'scaler': {\n", + " col: None for col in load.data.columns\n", + " }}\n", + " )\n", + " print(\"Preproc config of Scaler before after as ...\")\n", + " pp.pprint(proc_discretizing.get_config()['scaler'])\n", + "\n", + " proc_discretizing.fit(\n", + " data=load.data,\n", + " sequence=[\n", + " 'missing',\n", + " 'outlier',\n", + " 'encoder',\n", + " 'scaler'\n", + " ]\n", + " )\n", + " preproc_discretizing_data = proc_discretizing.transform(\n", + " data=load.data\n", + " )\n", + " print(\"Preproc data as ...\")\n", + " print(preproc_discretizing_data.head(1))\n", + "\n", + "\n", + " syn = Synthesizer(\n", + " method=synthesizing_method,\n", + " epsilon=10.0,\n", + " )\n", + " syn.create(data=preproc_discretizing_data)\n", + " syn.fit_sample()\n", + " print(\"Sync data as ...\")\n", + " print(syn.data_syn.head(1))\n", + " postproc_discretizing_data = proc_discretizing.inverse_transform(\n", + " data=syn.data_syn\n", + " )\n", + " print(postproc_discretizing_data.head(1))" + ] + }, + { + "cell_type": "code", + "execution_count": 29, "metadata": {}, "outputs": [ { @@ -144,87 +201,66 @@ "output_type": "stream", "text": [ "Preproc data as ...\n", - " age workclass fnlwgt education educational-num marital-status occupation \\\n", - "0 0.0 2 0.0 1 0.0 4 6 \n", + " age workclass fnlwgt education educational-num marital-status \\\n", + "0 0.0 2 0.0 1 0.0 4 \n", "\n", - " relationship race gender capital-gain capital-loss hours-per-week \\\n", - "0 3 2 1 0.0 0.0 0.0 \n", + " occupation relationship race gender capital-gain capital-loss \\\n", + "0 6 3 2 1 0.0 0.0 \n", "\n", - " native-country income \n", - "0 25 0 \n", - "age category\n", - "workclass category\n", - "fnlwgt category\n", - "education category\n", - "educational-num category\n", - "marital-status category\n", - "occupation category\n", - "relationship category\n", - "race category\n", - "gender category\n", - "capital-gain category\n", - "capital-loss category\n", - "hours-per-week category\n", - "native-country category\n", - "income category\n", - "dtype: object\n", - "Synthesizer (SmartNoise): Fitting mst.\n", - "Synthesizer (SmartNoise): Fitting mst spent 41.2776 sec.\n", - "Synthesizer (SmartNoise): Sampling mst # 558 rows (same as raw) in 0.0326 sec.\n", + " hours-per-week native-country income \n", + "0 0.0 25 0 \n", + "Synthesizer (SmartNoise): Fitting aim.\n", + "15\n", + "Initial Sigma 11.056041228448832\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Users/alex/miniforge3/envs/dev310/lib/python3.10/site-packages/mbi/__init__.py:15: UserWarning: MixtureInference disabled, please install jax and jaxlib\n", + " warnings.warn('MixtureInference disabled, please install jax and jaxlib')\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Synthesizer (SmartNoise): Fitting aim spent 103.6998 sec.\n", + "Synthesizer (SmartNoise): Sampling aim # 558 rows (same as raw) in 0.0282 sec.\n", "Sync data as ...\n", " age workclass fnlwgt education educational-num marital-status \\\n", - "0 0.0 2 0.0 8 0.0 2 \n", + "0 0.0 2 0.0 7 0.0 2 \n", "\n", " occupation relationship race gender capital-gain capital-loss \\\n", - "0 2 0 4 1 0.0 0.0 \n", + "0 4 0 4 1 0.0 0.0 \n", "\n", " hours-per-week native-country income \n", - "0 0.0 25 0 \n" + "0 0.0 25 1 \n", + " age workclass fnlwgt education educational-num \\\n", + "0 371.264583 Private 8.479563e+09 Assoc-acdm 16.57591 \n", + "\n", + " marital-status occupation relationship race gender \\\n", + "0 Married-civ-spouse Farming-fishing Husband White Male \n", + "\n", + " capital-gain capital-loss hours-per-week native-country income \n", + "0 9.268677e+07 96578.239577 172.18284 United-States >50K \n" ] } ], "source": [ "issue332(\n", " load=load,\n", - " synthesizing_method='smartnoise-mst',\n", + " synthesizing_method='smartnoise-aim'\n", ")" ] }, { - "cell_type": "code", - "execution_count": 12, - "metadata": {}, - "outputs": [], - "source": [ - "a = np.array([1,2,3])" - ] - }, - { - "cell_type": "code", - "execution_count": 15, + "cell_type": "markdown", "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "array([1, 2, 3])" - ] - }, - "execution_count": 15, - "metadata": {}, - "output_type": "execute_result" - } - ], "source": [ - "pd.Categorical(a).to_numpy()" + "上述執行會出現 `RuntimeError: all elements of input should be between 0 and 1`，原因是 input 中含有 NA 值，來自於前處理過程中將離群值去除掉，導致 `capital-gain`, `capital-loss` 兩個欄位的值皆相同，在 `smartnoise` 中進行 `MinMaxTransformer` 時會讓運算結果為 NA （分母為 0）。因此針對 adult 資料集，將其中的 `outlier` 前處理拿掉，就可以正常運行了。" ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] } ], "metadata": { From b79a33af987c569e297e171e5d68abfb1df1bc10 Mon Sep 17 00:00:00 2001 From: Alex Chen Date: Thu, 7 Mar 2024 11:01:27 +0800 Subject: [PATCH 10/17] update doc for the new methods --- docs/_posts/2024-02-20-Synthesizer.md | 24 ++++++++++++++++++++++-- 1 file changed, 22 insertions(+), 2 deletions(-) diff --git a/docs/_posts/2024-02-20-Synthesizer.md b/docs/_posts/2024-02-20-Synthesizer.md index 0e95abf8..cabdf2c0 100644 --- a/docs/_posts/2024-02-20-Synthesizer.md +++ b/docs/_posts/2024-02-20-Synthesizer.md @@ -93,6 +93,8 @@ In this section, we provide a comprehensive list of supported synthesizer types | `smartnoise` | `SmartNoiseCreator` (AIM) | 'smartnoise-aim' | ✅ | ✅ | | `smartnoise` | `SmartNoiseCreator` (MST) | 'smartnoise-mst' | ✅ | ✅ | | `smartnoise` | `SmartNoiseCreator` (PAC-Synth) | 'smartnoise-pacsynth' | ✅ | ✅ | +| `smartnoise` | `SmartNoiseCreator` (DP-CTGAN) | 'smartnoise-dpctgan' | ✅ | | +| `smartnoise` | `SmartNoiseCreator` (PATE-CTGAN) | 'smartnoise-patectgan' | ✅ | | [^1]: In the `Processor` within `PETsARD`, whether `'discretizing'` should be in the `sequence`. If so, it should be the last elements in the `'sequence'`, and `'encoder'` should not be in the `'sequence'`. 若您使用 `PETsARD` 的 `Processor`，請確認在 `sequence` 中的最後一個元素為 `'discretizing'`，且`'encoder'` 不能在 `sequence` 中。 @@ -144,9 +146,9 @@ See [document](https://docs.sdv.dev/sdv/single-table-data/modeling/synthesizers/ ## SmartNoise -`smartnoise` is a synthetic data generation package that emphasizes differential privacy (DP), thereby enhancing privacy protection. For more details, please refer to its official [document](https://docs.smartnoise.org/synth/index.html#synthesizers-reference) and [GitHub](https://github.com/opendp/smartnoise-sdk/tree/main/synth). Within our package, we intentionally disable the default preprocessing procedure in `smartnoise` to enhance customization flexibility. it's worth noting that we only support the utilization of cube-style (histogram-based) synthesizers, as listed above. Please ensure that your input data is exclusively in categorical format before initiating training. Specifically, in the `Processor` within `PETsARD`, ensure that the last element in the `sequence` is set to `'discretizing'`, and `'encoder'` must not be in the `'sequence'`. +`smartnoise` is a synthetic data generation package that emphasizes differential privacy (DP), thereby enhancing privacy protection. For more details, please refer to its official [document](https://docs.smartnoise.org/synth/index.html#synthesizers-reference) and [GitHub](https://github.com/opendp/smartnoise-sdk/tree/main/synth). Within our package, we intentionally disable the default preprocessing procedure in `smartnoise` to enhance customization flexibility. It's worth noting that for the methods which `discretizing` is needed, please ensure that your input data is exclusively in categorical format before initiating training. Specifically, in the `Processor` within `PETsARD`, ensure that the last element in the `sequence` is set to `'discretizing'`, and `'encoder'` must not be in the `'sequence'`. Also noted that if you use the method which `discretizing` is not needed, the instance will perform min-max scaling implicitly to make the programme functional. -`smartnoise` 是一個著重在差分隱私 (DP) 的合成資料套件，以提升隱私保護力。詳見其官方[說明文件](https://docs.smartnoise.org/synth/index.html#synthesizers-reference)及 [GitHub](https://github.com/opendp/smartnoise-sdk/tree/main/synth)。在本套件中，我們抑制了 `smartnoise` 原生的資料前處理流程以提升客製化的彈性。同時我們也只支援 cube-style (基於長條圖的) 合成資料演算法，如上表所附。在訓練前請確保您的資料皆是類別格式。亦即，若您使用 `PETsARD` 的 `Processor`，請確認在 `sequence` 中的最後一個元素為 `'discretizing'`，且`'encoder'` 不能在 `sequence` 中。 +`smartnoise` 是一個著重在差分隱私 (DP) 的合成資料套件，以提升隱私保護力。詳見其官方[說明文件](https://docs.smartnoise.org/synth/index.html#synthesizers-reference)及 [GitHub](https://github.com/opendp/smartnoise-sdk/tree/main/synth)。在本套件中，我們抑制了 `smartnoise` 原生的資料前處理流程以提升客製化的彈性。請注意，若您使用需要 `discretizing` 的演算法，在訓練前請確保您的資料皆是類別格式。亦即，若您使用 `PETsARD` 的 `Processor`，請確認在 `sequence` 中的最後一個元素為 `'discretizing'`，且`'encoder'` 不能在 `sequence` 中。另外提醒，若使用不需要 `discretizing` 的演算法，該物件會內隱執行 min-max 轉換，以確保程式能正常執行。 ### `'smartnoise-aim'` @@ -182,4 +184,22 @@ Use the PAC-Synth algorithm provided in `smartnoise`. See [document](https://doc See [document](https://docs.smartnoise.org/synth/synthesizers/pac_synth.html) for further details. 詳見[說明文件](https://docs.smartnoise.org/synth/synthesizers/pac_synth.html)。 +### `'smartnoise-dpctgan'` +Use the DP-CTGAN algorithm provided in `smartnoise`. See [document](https://docs.smartnoise.org/synth/synthesizers/dpctgan.html) for further details. + +使用 `smartnoise` 提供的 DP-CTGAN 演算法。詳見[說明文件](https://docs.smartnoise.org/synth/synthesizers/dpctgan.html)。 + +**Parameters** + +See [document](https://docs.smartnoise.org/synth/synthesizers/dpctgan.html) for further details. 詳見[說明文件](https://docs.smartnoise.org/synth/synthesizers/dpctgan.html)。 + +### `'smartnoise-patectgan'` + +Use the PATE-CTGAN algorithm provided in `smartnoise`. See [document](https://docs.smartnoise.org/synth/synthesizers/patectgan.html) for further details. + +使用 `smartnoise` 提供的 PATE-CTGAN 演算法。詳見[說明文件](https://docs.smartnoise.org/synth/synthesizers/patectgan.html)。 + +**Parameters** + +See [document](https://docs.smartnoise.org/synth/synthesizers/patectgan.html) for further details. 詳見[說明文件](https://docs.smartnoise.org/synth/synthesizers/patectgan.html)。 \ No newline at end of file From 08eb8da8d8db6fd438744ddf1dbd902d904821d8 Mon Sep 17 00:00:00 2001 From: matheme-justyn Date: Thu, 7 Mar 2024 17:08:03 +0800 Subject: [PATCH 11/17] 20240307, 2024-01-11-Synthesizer.md by #350 --- .../{2024-02-20-Synthesizer.md => 2024-01-11-Synthesizer.md} | 0 1 file changed, 0 insertions(+), 0 deletions(-) rename docs/_posts/{2024-02-20-Synthesizer.md => 2024-01-11-Synthesizer.md} (100%) diff --git a/docs/_posts/2024-02-20-Synthesizer.md b/docs/_posts/2024-01-11-Synthesizer.md similarity index 100% rename from docs/_posts/2024-02-20-Synthesizer.md rename to 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matheme-justyn Date: Fri, 8 Mar 2024 10:58:48 +0800 Subject: [PATCH 13/17] 20240308, update poetry require poetry install --- pyproject.toml | 3 +++ 1 file changed, 3 insertions(+) diff --git a/pyproject.toml b/pyproject.toml index 5e9351fb..7d5a9baa 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -17,6 +17,9 @@ # - Activate virtual environment. # - 啟動虛擬環境。 # 4. poetry lock +# - update poetry.lock based on the current pyproject.toml. +# - 根據當前的 pyproject.toml 更新 poetry.lock。 +# 5. poetry install # - Install the required packages based on poetry.lock. # - 依據 poetry.lock 安裝所需的套件。 # From b536c874de0763932db871ec5ffd669c0cbd63d7 Mon Sep 17 00:00:00 2001 From: matheme-justyn Date: Fri, 8 Mar 2024 13:54:37 +0800 Subject: [PATCH 14/17] 20240308, reset requirements.txt and related work --- demo/dev/executable_smartnoise.ipynb | 1213 +++++++++++++++++++++- poetry.lock | 8 +- pyproject.toml | 18 - requirements-dev.txt | 1444 -------------------------- requirements.txt | Bin 96931 -> 3354 bytes 5 files changed, 1187 insertions(+), 1496 deletions(-) delete mode 100644 requirements-dev.txt diff --git a/demo/dev/executable_smartnoise.ipynb b/demo/dev/executable_smartnoise.ipynb index 16a6b941..aad74b2d 100644 --- a/demo/dev/executable_smartnoise.ipynb +++ b/demo/dev/executable_smartnoise.ipynb @@ -19,25 +19,31 @@ "name": "stdout", "output_type": "stream", "text": [ - "/Users/alex/PETsARD\n" + "d:\\Dropbox\\89_other_application\\GitHub\\PETsARD\n" ] } ], "source": [ - "%cd /Users/alex/PETsARD" + "# %cd /Users/alex/PETsARD\n", + "\n", + "import os\n", + "import sys\n", + "\n", + "path_petsard = os.path.dirname(os.path.dirname(os.getcwd()))\n", + "print(path_petsard)\n", + "sys.path.append(path_petsard)" ] }, { "cell_type": "code", - "execution_count": 26, + "execution_count": 3, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "Loader - Benchmarker: file benchmark/adult.csv already exist and match SHA-256.\n", - " PETsARD will ignore download and use local data directly.\n" + "Loader - Benchmarker : Success download the benchmark dataset from https://petsard-benchmark.s3.amazonaws.com/adult-income.csv.\n" ] } ], @@ -50,7 +56,7 @@ "\n", "\n", "load = Loader(\n", - " filepath='benchmark://adult',\n", + " filepath='benchmark://adult-income',\n", " na_values={k: '?' for k in [\n", " 'workclass',\n", " 'occupation',\n", @@ -62,7 +68,7 @@ }, { "cell_type": "code", - "execution_count": 27, + "execution_count": 4, "metadata": {}, "outputs": [], "source": [ @@ -71,7 +77,7 @@ }, { "cell_type": "code", - "execution_count": 28, + "execution_count": 5, "metadata": {}, "outputs": [], "source": [ @@ -132,7 +138,7 @@ }, { "cell_type": "code", - "execution_count": 24, + "execution_count": 7, "metadata": {}, "outputs": [], "source": [ @@ -142,7 +148,8 @@ "def issue332_gan(\n", " load: Loader,\n", " synthesizing_method: str,\n", - " scaler_inhibit: bool = False\n", + " scaler_inhibit: bool = False,\n", + " outlier_inhibit: bool = False\n", "):\n", " pp = pprint.PrettyPrinter(depth=2)\n", "\n", @@ -161,6 +168,18 @@ " print(\"Preproc config of Scaler before after as ...\")\n", " pp.pprint(proc_discretizing.get_config()['scaler'])\n", "\n", + " if outlier_inhibit:\n", + " print(\"Preproc config of Outlier before update as ...\")\n", + " pp.pprint(proc_discretizing.get_config()['scaler'])\n", + " proc_discretizing.update_config(\n", + " {'outlier': {\n", + " col: None for col in load.data.columns\n", + " }}\n", + " )\n", + " print(\"Preproc config of Outlier before after as ...\")\n", + " pp.pprint(proc_discretizing.get_config()['scaler'])\n", + "\n", + "\n", " proc_discretizing.fit(\n", " data=load.data,\n", " sequence=[\n", @@ -193,7 +212,7 @@ }, { "cell_type": "code", - "execution_count": 29, + "execution_count": 8, "metadata": {}, "outputs": [ { @@ -201,24 +220,21 @@ "output_type": "stream", "text": [ "Preproc data as ...\n", - " age workclass fnlwgt education educational-num marital-status \\\n", - "0 0.0 2 0.0 1 0.0 4 \n", + " age workclass fnlwgt education educational-num marital-status \\\n", + "0 0.0 2 0.0 1 0.0 4 \n", "\n", - " occupation relationship race gender capital-gain capital-loss \\\n", - "0 6 3 2 1 0.0 0.0 \n", + " occupation relationship race gender capital-gain capital-loss \\\n", + "0 6 3 2 1 0.0 0.0 \n", "\n", - " hours-per-week native-country income \n", - "0 0.0 25 0 \n", - "Synthesizer (SmartNoise): Fitting aim.\n", - "15\n", - "Initial Sigma 11.056041228448832\n" + " hours-per-week native-country income \n", + "0 0.0 25 0 \n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "/Users/alex/miniforge3/envs/dev310/lib/python3.10/site-packages/mbi/__init__.py:15: UserWarning: MixtureInference disabled, please install jax and jaxlib\n", + "d:\\Dropbox\\89_other_application\\GitHub\\PETsARD\\.venv\\lib\\site-packages\\mbi\\__init__.py:15: UserWarning: MixtureInference disabled, please install jax and jaxlib\n", " warnings.warn('MixtureInference disabled, please install jax and jaxlib')\n" ] }, @@ -226,22 +242,25 @@ "name": "stdout", "output_type": "stream", "text": [ - "Synthesizer (SmartNoise): Fitting aim spent 103.6998 sec.\n", - "Synthesizer (SmartNoise): Sampling aim # 558 rows (same as raw) in 0.0282 sec.\n", + "Synthesizer (SmartNoise): Fitting aim.\n", + "15\n", + "Initial Sigma 11.056041228448832\n", + "Synthesizer (SmartNoise): Fitting aim spent 270.8398 sec.\n", + "Synthesizer (SmartNoise): Sampling aim # 558 rows (same as raw) in 0.5135 sec.\n", "Sync data as ...\n", " age workclass fnlwgt education educational-num marital-status \\\n", - "0 0.0 2 0.0 7 0.0 2 \n", + "0 0.0 2 0.0 4 0.0 2 \n", "\n", " occupation relationship race gender capital-gain capital-loss \\\n", - "0 4 0 4 1 0.0 0.0 \n", + "0 6 0 4 1 0.0 0.0 \n", "\n", " hours-per-week native-country income \n", "0 0.0 25 1 \n", - " age workclass fnlwgt education educational-num \\\n", - "0 371.264583 Private 8.479563e+09 Assoc-acdm 16.57591 \n", + " age workclass fnlwgt education educational-num \\\n", + "0 371.264583 Private 8.479563e+09 5th-6th 16.57591 \n", "\n", - " marital-status occupation relationship race gender \\\n", - "0 Married-civ-spouse Farming-fishing Husband White Male \n", + " marital-status occupation relationship race gender \\\n", + "0 Married-civ-spouse Machine-op-inspct Husband White Male \n", "\n", " capital-gain capital-loss hours-per-week native-country income \n", "0 9.268677e+07 96578.239577 172.18284 United-States >50K \n" @@ -261,6 +280,1140 @@ "source": [ "上述執行會出現 `RuntimeError: all elements of input should be between 0 and 1`，原因是 input 中含有 NA 值，來自於前處理過程中將離群值去除掉，導致 `capital-gain`, `capital-loss` 兩個欄位的值皆相同，在 `smartnoise` 中進行 `MinMaxTransformer` 時會讓運算結果為 NA （分母為 0）。因此針對 adult 資料集，將其中的 `outlier` 前處理拿掉，就可以正常運行了。" ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Preproc data as ...\n", + " age workclass fnlwgt education educational-num marital-status \\\n", + "0 0.0 2 0.0 1 0.0 4 \n", + "\n", + " occupation relationship race gender capital-gain capital-loss \\\n", + "0 6 3 2 1 0.0 0.0 \n", + "\n", + " hours-per-week native-country income \n", + "0 0.0 25 0 \n", + "Synthesizer (SmartNoise): Fitting mst.\n", + "Synthesizer (SmartNoise): Fitting mst spent 113.5179 sec.\n", + "Synthesizer (SmartNoise): Sampling mst # 558 rows (same as raw) in 0.4945 sec.\n", + "Sync data as ...\n", + " age workclass fnlwgt education educational-num marital-status \\\n", + "0 0.0 0 0.0 9 0.0 2 \n", + "\n", + " occupation relationship race gender capital-gain capital-loss \\\n", + "0 11 0 4 1 0.0 0.0 \n", + "\n", + " hours-per-week native-country income \n", + "0 0.0 25 0 \n", + " age workclass fnlwgt education educational-num \\\n", + "0 371.264583 Federal-gov 8.479563e+09 Bachelors 16.57591 \n", + "\n", + " marital-status occupation relationship race gender capital-gain \\\n", + "0 Married-civ-spouse Sales Husband White Male 9.268677e+07 \n", + "\n", + " capital-loss hours-per-week native-country income \n", + "0 96578.239577 172.18284 United-States <=50K \n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "d:\\Dropbox\\89_other_application\\GitHub\\PETsARD\\.venv\\lib\\site-packages\\snsynth\\mst\\mst.py:241: FutureWarning: Setting an item of incompatible dtype is deprecated and will raise an error in a future version of pandas. Value '[0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0\n", + " 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0\n", + " 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0\n", + " 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0\n", + " 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0\n", + " 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0\n", + " 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0\n", + " 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0\n", + " 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0\n", + " 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0\n", + " 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0\n", + " 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0\n", + " 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0\n", + " 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0\n", + " 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0\n", + " 0 0 0]' has dtype incompatible with int32, please explicitly cast to a compatible dtype first.\n", + " df.loc[~mask, col] = idx[df.loc[~mask, col]]\n", + "d:\\Dropbox\\89_other_application\\GitHub\\PETsARD\\.venv\\lib\\site-packages\\snsynth\\mst\\mst.py:241: FutureWarning: Setting an item of incompatible dtype is deprecated and will raise an error in a future version of pandas. Value '[0 2 2 0 1 2 2 2 1 2 1 2 2 2 0 2 2 2 2 1 2 2 2 2 2 4 2 2 2 0 2 3 2 4 2 2 4\n", + " 2 2 2 2 3 2 2 2 2 2 5 2 2 2 2 2 2 2 2 5 2 2 2 2 2 3 4 2 2 2 2 2 2 2 2 5 5\n", + " 2 2 2 2 3 2 4 0 2 2 2 2 3 2 2 2 2 4 2 2 0 2 0 2 2 2 2 1 1 2 2 5 2 2 2 2 1\n", + " 2 1 2 2 2 2 2 2 4 3 2 2 5 5 2 2 2 2 1 2 2 2 4 2 5 2 2 2 2 2 2 2 2 2 2 5 2\n", + " 2 5 2 3 5 2 2 0 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 3 2 2 2 0 4 2 2 2 2 3 2 1\n", + " 1 5 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 1 2 1 4 2 5 2 1 2 2 2 1 2 2 2 2 2 1\n", + " 2 1 3 2 2 0 3 5 4 2 2 2 1 2 2 2 2 2 2 2 2 2 2 3 2 2 0 4 2 0 2 2 2 2 1 2 1\n", + " 2 2 2 2 2 2 2 2 2 2 2 0 2 2 1 2 2 2 5 2 2 4 2 2 5 2 2 2 2 2 2 2 2 2 3 2 5\n", + " 2 2 2 2 2 4 1 2 1 2 3 2 2 5 2 1 2 2 2 2 2 2 2 0 1 2 2 5 2 2 2 5 2 5 2 0 2\n", + " 2 0 5 2 2 1 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 4 2 5 2 2 2 2 2 2 2 5 2\n", + " 2 4 2 1 2 4 2 2 2 2 2 2 2 3 2 3 2 2 2 2 2 2 2 1 2 2 5 2 2 2 2 2 2 2 2 2 3\n", + " 4 2 2 4 2 1 2 2 4 2 5 2 2 2 2 2 4 2 2 2 5 2 2 2 3 2 2 2 1 2 4 2 1 2 2 2 2\n", + " 2 1 2 2 2 2 2 2 2 2 2 4 4 3 4 0 2 2 2 2 2 1 2 2 2 2 2 1 2 2 2 1 1 2 2 2 1\n", + " 4 2 1 2 3 2 1 2 2 2 2 0 4 2 2 4 2 2 1 2 2 2 2 2 1 0 2 2 2 2 0 2 2 4 1 2 1\n", + " 2 2 2 3 2 2 2 2 2 4 1 2 2 2 2 2 2 1 0 2 1 2 2 2 3 2 2 2 2 2 2 2 2 2 2 2 2\n", + " 2 0 2]' has dtype incompatible with int32, please explicitly cast to a compatible dtype first.\n", + " df.loc[~mask, col] = idx[df.loc[~mask, col]]\n", + "d:\\Dropbox\\89_other_application\\GitHub\\PETsARD\\.venv\\lib\\site-packages\\snsynth\\mst\\mst.py:241: FutureWarning: Setting an item of incompatible dtype is deprecated and will raise an error in a future version of pandas. Value '[0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0\n", + " 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0\n", + " 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0\n", + " 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0\n", + " 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0\n", + " 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0\n", + " 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0\n", + " 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0\n", + " 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0\n", + " 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0\n", + " 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0\n", + " 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0\n", + " 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0\n", + " 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0\n", + " 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0\n", + " 0 0 0]' has dtype incompatible with int32, please explicitly cast to a compatible dtype first.\n", + " df.loc[~mask, col] = idx[df.loc[~mask, col]]\n", + "d:\\Dropbox\\89_other_application\\GitHub\\PETsARD\\.venv\\lib\\site-packages\\snsynth\\mst\\mst.py:240: FutureWarning: Setting an item of incompatible dtype is deprecated and will raise an error in a future version of pandas. Value '[ 9 12 5 12 9 3 3 4 3 5 5 5 3 2 4 12 3 5 2 3 12 9 2 9\n", + " 5 12 12 9 9 5 5 12 12 12 2 2 9 12 12]' has dtype incompatible with int32, please explicitly cast to a compatible dtype first.\n", + " df.loc[mask, col] = np.random.choice(extra, mask.sum())\n", + "d:\\Dropbox\\89_other_application\\GitHub\\PETsARD\\.venv\\lib\\site-packages\\snsynth\\mst\\mst.py:241: FutureWarning: Setting an item of incompatible dtype is deprecated and will raise an error in a future version of pandas. Value '[0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0\n", + " 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0\n", + " 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0\n", + " 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0\n", + " 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0\n", + " 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0\n", + " 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0\n", + " 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0\n", + " 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0\n", + " 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0\n", + " 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0\n", + " 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0\n", + " 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0\n", + " 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0\n", + " 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0\n", + " 0 0 0]' has dtype incompatible with int32, please explicitly cast to a compatible dtype first.\n", + " df.loc[~mask, col] = idx[df.loc[~mask, col]]\n", + "d:\\Dropbox\\89_other_application\\GitHub\\PETsARD\\.venv\\lib\\site-packages\\snsynth\\mst\\mst.py:240: FutureWarning: Setting an item of incompatible dtype is deprecated and will raise an error in a future version of pandas. Value '[5 2 2 2 5 5 2 2 2 2 5 2 5 2 5 5]' has dtype incompatible with int32, please explicitly cast to a compatible dtype first.\n", + " df.loc[mask, col] = np.random.choice(extra, mask.sum())\n", + "d:\\Dropbox\\89_other_application\\GitHub\\PETsARD\\.venv\\lib\\site-packages\\snsynth\\mst\\mst.py:240: FutureWarning: Setting an item of incompatible dtype is deprecated and will raise an error in a future version of pandas. Value '[1 8 1 1 1 1 1 4 1 4 1 1 8 1 1 8]' has dtype incompatible with int32, please explicitly cast to a compatible dtype first.\n", + " df.loc[mask, col] = np.random.choice(extra, mask.sum())\n", + "d:\\Dropbox\\89_other_application\\GitHub\\PETsARD\\.venv\\lib\\site-packages\\snsynth\\mst\\mst.py:241: FutureWarning: Setting an item of incompatible dtype is deprecated and will raise an error in a future version of pandas. Value '[0 0 0 0 0 0 0 1 0 5 2 0 1 0 3 0 0 0 0 2 0 4 3 0 1 1 4 1 4 0 0 0 5 0 0 0 3\n", + " 1 0 4 1 1 3 0 1 4 1 0 3 1 3 1 0 0 0 1 3 0 2 4 0 1 3 0 0 0 1 1 0 0 0 4 1 1\n", + " 3 4 1 5 1 2 1 0 1 3 1 4 3 4 0 3 0 1 5 0 0 0 1 1 4 0 0 0 1 1 1 1 5 5 0 0 1\n", + " 3 4 0 2 0 0 1 2 4 5 1 0 3 0 1 1 0 0 0 1 0 5 0 0 1 5 0 1 5 1 1 3 0 0 4 0 0\n", + " 0 1 0 1 2 5 1 3 1 0 0 3 4 4 3 0 0 0 4 4 1 5 0 0 0 0 1 1 4 0 0 0 1 0 0 0 1\n", + " 0 4 0 3 1 1 4 3 1 0 0 0 0 1 1 1 0 0 0 0 0 0 1 1 3 2 0 1 0 0 0 0 1 0 0 1 0\n", + " 4 3 3 1 0 4 0 3 1 0 3 3 0 3 0 0 1 0 3 1 0 0 0 3 0 4 4 1 0 0 1 1 4 0 0 5 3\n", + " 0 1 3 3 4 1 0 3 1 1 5 0 0 2 4 0 0 1 0 0 0 4 3 5 3 0 3 1 3 0 0 3 3 1 0 0 0\n", + " 3 3 0 3 1 0 1 0 1 4 0 0 0 1 0 0 1 1 0 1 0 3 0 0 4 1 0 0 1 0 1 3 0 3 3 1 1\n", + " 2 1 3 1 0 0 4 4 0 0 1 1 0 1 0 0 0 0 4 0 3 1 0 2 5 0 0 1 0 3 2 1 1 0 0 0 5\n", + " 4 1 4 1 0 3 1 3 4 0 3 0 0 1 0 1 0 3 0 3 1 3 0 3 2 0 3 1 4 1 0 0 2 3 1 1 0\n", + " 0 0 1 0 1 0 0 0 1 0 4 1 1 0 1 3 0 4 1 0 5 1 1 4 0 1 0 0 0 1 1 4 1 0 1 1 3\n", + " 4 3 3 0 0 2 0 0 0 1 0 4 0 2 0 4 1 3 0 0 3 1 1 0 3 1 0 0 3 3 0 0 1 1 0 1 1\n", + " 4 0 3 1 0 0 1 5 0 0 3 3 3 4 4 4 3 1 1 0 1 4 4 1 1 0 1 0 3 5 1 0 0 0 4 0 0\n", + " 0 1 4 4 0 0 3 4 0 2 1 1 0 1 0 1 3 0 0 1 4 1 5 0 3 1 1 1 3 0 0 3 1 1 0 4 0\n", + " 0 0 0]' has dtype incompatible with int32, please explicitly cast to a compatible dtype first.\n", + " df.loc[~mask, col] = idx[df.loc[~mask, col]]\n", + "d:\\Dropbox\\89_other_application\\GitHub\\PETsARD\\.venv\\lib\\site-packages\\snsynth\\mst\\mst.py:240: FutureWarning: Setting an item of incompatible dtype is deprecated and will raise an error in a future version of pandas. Value '[3 1 0 3 3 1 3 1 0 0 1 0 1 1 3 3 1 0 0 3 3 0 3 3]' has dtype incompatible with int32, please explicitly cast to a compatible dtype first.\n", + " df.loc[mask, col] = np.random.choice(extra, mask.sum())\n", + "d:\\Dropbox\\89_other_application\\GitHub\\PETsARD\\.venv\\lib\\site-packages\\snsynth\\mst\\mst.py:241: FutureWarning: Setting an item of incompatible dtype is deprecated and will raise an error in a future version of pandas. Value '[1 1 1 1 1 1 1 1 1 0 0 1 1 1 0 1 1 1 1 1 1 0 1 1 1 1 0 0 0 1 1 1 0 1 1 1 1\n", + " 1 1 0 1 1 0 1 1 0 1 1 1 1 1 1 1 1 1 0 1 1 1 1 1 0 0 1 1 1 1 1 1 1 1 0 1 1\n", + " 1 1 1 1 0 0 0 1 1 1 1 0 0 0 1 1 1 0 0 1 1 1 0 1 1 1 1 1 0 1 1 0 0 1 1 1 1\n", + " 1 0 1 0 1 1 0 0 0 1 1 1 1 1 1 1 1 1 1 1 1 0 1 1 0 0 1 0 0 0 0 0 1 1 0 1 1\n", + " 1 1 1 0 0 0 1 1 0 1 1 0 0 0 1 1 1 1 0 0 1 0 1 1 1 1 1 1 0 1 1 1 0 1 1 1 0\n", + " 1 0 1 1 1 1 0 1 0 1 1 1 1 0 1 0 1 1 1 1 1 1 0 0 0 1 1 0 1 1 1 1 0 1 1 1 1\n", + " 0 1 0 0 1 0 1 1 1 1 0 1 1 0 1 1 1 1 1 1 1 1 1 0 1 0 0 1 1 1 0 0 1 1 1 0 1\n", + " 1 1 0 1 0 0 1 1 1 0 0 1 1 0 0 1 1 0 1 1 1 0 1 0 0 1 1 0 0 1 1 0 1 1 1 1 1\n", + " 0 1 1 1 1 1 0 1 1 0 1 1 1 0 1 1 1 0 1 1 1 0 1 1 0 1 1 1 0 1 1 1 1 1 1 0 0\n", + " 1 0 0 0 1 1 0 0 1 1 1 0 1 1 1 1 1 1 0 1 1 0 1 0 0 1 1 1 1 1 0 0 0 1 1 1 0\n", + " 1 0 0 1 1 0 1 1 1 1 0 1 1 1 1 1 1 1 1 0 1 1 1 1 1 1 1 0 0 1 1 1 0 1 0 0 1\n", + " 1 1 1 1 1 1 1 1 1 1 0 1 1 1 0 1 1 1 1 1 0 1 1 0 1 1 1 1 1 1 1 0 0 1 0 0 0\n", + " 0 0 1 1 1 0 1 1 1 1 1 0 1 0 1 1 0 0 1 1 0 1 1 1 1 1 1 1 0 0 1 1 1 1 1 0 0\n", + " 0 1 1 1 1 1 1 0 1 1 1 0 0 0 0 0 1 1 1 1 0 0 0 1 1 1 0 1 0 0 1 1 1 1 1 1 1\n", + " 1 0 0 1 1 1 1 0 1 0 0 1 1 0 1 1 0 1 1 1 0 0 0 1 1 0 0 1 0 1 1 0 1 1 1 0 1\n", + " 1 1 1]' has dtype incompatible with int32, please explicitly cast to a compatible dtype first.\n", + " df.loc[~mask, col] = idx[df.loc[~mask, col]]\n", + "d:\\Dropbox\\89_other_application\\GitHub\\PETsARD\\.venv\\lib\\site-packages\\snsynth\\mst\\mst.py:241: FutureWarning: Setting an item of incompatible dtype is deprecated and will raise an error in a future version of pandas. Value '[0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0\n", + " 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0\n", + " 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0\n", + " 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0\n", + " 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0\n", + " 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0\n", + " 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0\n", + " 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0\n", + " 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0\n", + " 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0\n", + " 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0\n", + " 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0\n", + " 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0\n", + " 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0\n", + " 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0\n", + " 0 0 0]' has dtype incompatible with int32, please explicitly cast to a compatible dtype first.\n", + " df.loc[~mask, col] = idx[df.loc[~mask, col]]\n", + "d:\\Dropbox\\89_other_application\\GitHub\\PETsARD\\.venv\\lib\\site-packages\\snsynth\\mst\\mst.py:241: FutureWarning: Setting an item of incompatible dtype is deprecated and will raise an error in a future version of pandas. Value '[0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0\n", + " 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0\n", + " 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0\n", + " 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0\n", + " 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0\n", + " 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0\n", + " 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0\n", + " 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0\n", + " 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0\n", + " 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0\n", + " 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0\n", + " 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0\n", + " 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0\n", + " 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0\n", + " 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0\n", + " 0 0 0]' has dtype incompatible with int32, please explicitly cast to a compatible dtype first.\n", + " df.loc[~mask, col] = idx[df.loc[~mask, col]]\n", + "d:\\Dropbox\\89_other_application\\GitHub\\PETsARD\\.venv\\lib\\site-packages\\snsynth\\mst\\mst.py:241: FutureWarning: Setting an item of incompatible dtype is deprecated and will raise an error in a future version of pandas. Value '[0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0\n", + " 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0\n", + " 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0\n", + " 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0\n", + " 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0\n", + " 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0\n", + " 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0\n", + " 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0\n", + " 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0\n", + " 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0\n", + " 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0\n", + " 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0\n", + " 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0\n", + " 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0\n", + " 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0\n", + " 0 0 0]' has dtype incompatible with int32, please explicitly cast to a compatible dtype first.\n", + " df.loc[~mask, col] = idx[df.loc[~mask, col]]\n", + "d:\\Dropbox\\89_other_application\\GitHub\\PETsARD\\.venv\\lib\\site-packages\\snsynth\\mst\\mst.py:240: FutureWarning: Setting an item of incompatible dtype is deprecated and will raise an error in a future version of pandas. Value '[13 16 8 20 11 10 11 6 15 9 17 9 10 20 4 3 15 1 17 14 16 15 18 18\n", + " 7 3 20 18 7 16 11 4 0 20 15 14 12 20 14 14 18 12 1 12 9]' has dtype incompatible with int32, please explicitly cast to a compatible dtype first.\n", + " df.loc[mask, col] = np.random.choice(extra, mask.sum())\n", + "d:\\Dropbox\\89_other_application\\GitHub\\PETsARD\\.venv\\lib\\site-packages\\snsynth\\mst\\mst.py:241: FutureWarning: Setting an item of incompatible dtype is deprecated and will raise an error in a future version of pandas. Value '[0 0 0 0 0 1 0 0 1 1 0 1 0 1 0 1 1 0 0 0 1 0 0 0 0 0 0 1 0 0 1 1 0 1 1 0 0\n", + " 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 1 0 1 0 0 0 0 0 0 0 0 0 1 0 0 0 1 0 0 0 0 0\n", + " 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 1 1 0 0 1 1 0 0 0 0 0 0 1 0 1 0\n", + " 0 0 1 0 0 1 1 0 1 1 0 0 0 0 0 0 0 0 1 0 0 1 1 0 0 0 0 0 1 0 0 0 1 1 0 1 1\n", + " 0 0 1 0 0 1 0 0 0 0 0 0 0 0 0 0 1 0 1 0 0 0 1 0 0 0 0 0 0 1 0 1 0 0 0 0 0\n", + " 0 1 0 0 0 0 0 0 0 0 0 1 0 0 1 0 0 0 0 0 1 0 0 0 0 1 0 1 1 1 1 1 0 0 1 0 1\n", + " 0 1 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 1 0 0 1 0 1 1\n", + " 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 1 0 1 0 0 0 0 1 0 0 0 0 0 0 0 0 0 1 0\n", + " 0 0 1 0 0 0 0 1 0 0 0 1 1 0 1 0 0 0 1 1 0 0 0 1 0 0 0 0 0 1 0 0 0 0 0 0 0\n", + " 0 0 0 0 0 0 1 0 0 0 0 0 0 0 1 0 1 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 1 0 0 0\n", + " 1 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0\n", + " 1 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 1 1 0 0 0 0 0 1 1 1 1 0 0 0 0 0 0 0\n", + " 0 0 0 1 0 0 0 0 0 0 0 0 1 0 0 1 0 0 0 0 0 0 0 0 0 0 0 1 0 0 1 0 1 0 0 0 0\n", + " 0 0 0 0 1 0 0 1 1 1 0 0 0 0 0 0 0 0 0 1 0 0 0 1 0 0 0 1 0 0 0 1 0 1 0 0 0\n", + " 1 0 0 0 1 0 0 0 1 0 0 0 0 0 1 0 0 1 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 1 0 0 0\n", + " 0 1 0]' has dtype incompatible with int32, please explicitly cast to a compatible dtype first.\n", + " df.loc[~mask, col] = idx[df.loc[~mask, col]]\n" + ] + } + ], + "source": [ + "issue332(\n", + " load=load,\n", + " synthesizing_method='smartnoise-mst'\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Preproc data as ...\n", + " age workclass fnlwgt education educational-num marital-status \\\n", + "0 0.0 2 0.0 1 0.0 4 \n", + "\n", + " occupation relationship race gender capital-gain capital-loss \\\n", + "0 6 3 2 1 0.0 0.0 \n", + "\n", + " hours-per-week native-country income \n", + "0 0.0 25 0 \n", + "Synthesizer (SmartNoise): Fitting pacsynth.\n", + "Synthesizer (SmartNoise): Fitting pacsynth spent 0.2158 sec.\n", + "Synthesizer (SmartNoise): Sampling pacsynth # 558 rows (same as raw) in 1.6921 sec.\n", + "Sync data as ...\n", + " age workclass fnlwgt education educational-num marital-status \\\n", + "0 0.0 2.0 0.0 11.0 0.0 2.0 \n", + "\n", + " occupation relationship race gender capital-gain capital-loss \\\n", + "0 6.0 0.0 4.0 1.0 0.0 0.0 \n", + "\n", + " hours-per-week native-country income \n", + "0 0.0 25.0 0.0 \n" + ] + }, + { + "ename": "ValueError", + "evalue": "Input contains NaN.", + "output_type": "error", + "traceback": [ + "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[1;31mValueError\u001b[0m Traceback (most recent call last)", + "Cell \u001b[1;32mIn[10], line 1\u001b[0m\n\u001b[1;32m----> 1\u001b[0m \u001b[43missue332\u001b[49m\u001b[43m(\u001b[49m\n\u001b[0;32m 2\u001b[0m \u001b[43m \u001b[49m\u001b[43mload\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mload\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m 3\u001b[0m \u001b[43m \u001b[49m\u001b[43msynthesizing_method\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43msmartnoise-pacsynth\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\n\u001b[0;32m 4\u001b[0m \u001b[43m)\u001b[49m\n", + "Cell \u001b[1;32mIn[5], line 50\u001b[0m, in \u001b[0;36missue332\u001b[1;34m(load, synthesizing_method, scaler_inhibit)\u001b[0m\n\u001b[0;32m 48\u001b[0m \u001b[38;5;28mprint\u001b[39m(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mSync data as ...\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[0;32m 49\u001b[0m \u001b[38;5;28mprint\u001b[39m(syn\u001b[38;5;241m.\u001b[39mdata_syn\u001b[38;5;241m.\u001b[39mhead(\u001b[38;5;241m1\u001b[39m))\n\u001b[1;32m---> 50\u001b[0m postproc_discretizing_data \u001b[38;5;241m=\u001b[39m \u001b[43mproc_discretizing\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43minverse_transform\u001b[49m\u001b[43m(\u001b[49m\n\u001b[0;32m 51\u001b[0m \u001b[43m \u001b[49m\u001b[43mdata\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43msyn\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mdata_syn\u001b[49m\n\u001b[0;32m 52\u001b[0m \u001b[43m\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m 53\u001b[0m \u001b[38;5;28mprint\u001b[39m(postproc_discretizing_data\u001b[38;5;241m.\u001b[39mhead(\u001b[38;5;241m1\u001b[39m))\n", + "File \u001b[1;32md:\\Dropbox\\89_other_application\\GitHub\\PETsARD\\PETsARD\\processor\\base.py:483\u001b[0m, in \u001b[0;36mProcessor.inverse_transform\u001b[1;34m(self, data)\u001b[0m\n\u001b[0;32m 480\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m obj \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[0;32m 481\u001b[0m \u001b[38;5;28;01mcontinue\u001b[39;00m\n\u001b[1;32m--> 483\u001b[0m transformed[col] \u001b[38;5;241m=\u001b[39m \u001b[43mobj\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43minverse_transform\u001b[49m\u001b[43m(\u001b[49m\u001b[43mtransformed\u001b[49m\u001b[43m[\u001b[49m\u001b[43mcol\u001b[49m\u001b[43m]\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m 485\u001b[0m logging\u001b[38;5;241m.\u001b[39minfo(\u001b[38;5;124mf\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;132;01m{\u001b[39;00mprocessor\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m inverse transformation done.\u001b[39m\u001b[38;5;124m'\u001b[39m)\n\u001b[0;32m 486\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m 487\u001b[0m \u001b[38;5;66;03m# if the processor is not a string,\u001b[39;00m\n\u001b[0;32m 488\u001b[0m \u001b[38;5;66;03m# it should be a mediator, which transforms the data directly.\u001b[39;00m\n", + "File \u001b[1;32md:\\Dropbox\\89_other_application\\GitHub\\PETsARD\\PETsARD\\processor\\discretizing.py:81\u001b[0m, in \u001b[0;36mDiscretizingHandler.inverse_transform\u001b[1;34m(self, data)\u001b[0m\n\u001b[0;32m 78\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_is_fitted:\n\u001b[0;32m 79\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m UnfittedError(\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mThe object is not fitted. Use .fit() first.\u001b[39m\u001b[38;5;124m'\u001b[39m)\n\u001b[1;32m---> 81\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_inverse_transform\u001b[49m\u001b[43m(\u001b[49m\u001b[43mdata\u001b[49m\u001b[43m)\u001b[49m\n", + "File \u001b[1;32md:\\Dropbox\\89_other_application\\GitHub\\PETsARD\\PETsARD\\processor\\discretizing.py:152\u001b[0m, in \u001b[0;36mDiscretizingKBins._inverse_transform\u001b[1;34m(self, data)\u001b[0m\n\u001b[0;32m 140\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21m_inverse_transform\u001b[39m(\u001b[38;5;28mself\u001b[39m, data: pd\u001b[38;5;241m.\u001b[39mSeries) \u001b[38;5;241m-\u001b[39m\u001b[38;5;241m>\u001b[39m np\u001b[38;5;241m.\u001b[39mndarray:\n\u001b[0;32m 141\u001b[0m \u001b[38;5;250m \u001b[39m\u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[0;32m 142\u001b[0m \u001b[38;5;124;03m Inverse the transformed data to the numerical data.\u001b[39;00m\n\u001b[0;32m 143\u001b[0m \n\u001b[1;32m (...)\u001b[0m\n\u001b[0;32m 149\u001b[0m \u001b[38;5;124;03m (np.ndarray): The inverse transformed data.\u001b[39;00m\n\u001b[0;32m 150\u001b[0m \u001b[38;5;124;03m \"\"\"\u001b[39;00m\n\u001b[1;32m--> 152\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mmodel\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43minverse_transform\u001b[49m\u001b[43m(\u001b[49m\u001b[43mdata\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mvalues\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mreshape\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m-\u001b[39;49m\u001b[38;5;241;43m1\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m1\u001b[39;49m\u001b[43m)\u001b[49m\u001b[43m)\u001b[49m\u001b[38;5;241m.\u001b[39mravel()\n", + "File \u001b[1;32md:\\Dropbox\\89_other_application\\GitHub\\PETsARD\\.venv\\lib\\site-packages\\sklearn\\preprocessing\\_discretization.py:430\u001b[0m, in \u001b[0;36mKBinsDiscretizer.inverse_transform\u001b[1;34m(self, Xt)\u001b[0m\n\u001b[0;32m 427\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124monehot\u001b[39m\u001b[38;5;124m\"\u001b[39m \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mencode:\n\u001b[0;32m 428\u001b[0m Xt \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_encoder\u001b[38;5;241m.\u001b[39minverse_transform(Xt)\n\u001b[1;32m--> 430\u001b[0m Xinv \u001b[38;5;241m=\u001b[39m \u001b[43mcheck_array\u001b[49m\u001b[43m(\u001b[49m\u001b[43mXt\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mcopy\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43;01mTrue\u001b[39;49;00m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mdtype\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43m(\u001b[49m\u001b[43mnp\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mfloat64\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mnp\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mfloat32\u001b[49m\u001b[43m)\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m 431\u001b[0m n_features \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mn_bins_\u001b[38;5;241m.\u001b[39mshape[\u001b[38;5;241m0\u001b[39m]\n\u001b[0;32m 432\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m Xinv\u001b[38;5;241m.\u001b[39mshape[\u001b[38;5;241m1\u001b[39m] \u001b[38;5;241m!=\u001b[39m n_features:\n", + "File \u001b[1;32md:\\Dropbox\\89_other_application\\GitHub\\PETsARD\\.venv\\lib\\site-packages\\sklearn\\utils\\validation.py:1049\u001b[0m, in \u001b[0;36mcheck_array\u001b[1;34m(array, accept_sparse, accept_large_sparse, dtype, order, copy, force_all_finite, ensure_2d, allow_nd, ensure_min_samples, ensure_min_features, estimator, input_name)\u001b[0m\n\u001b[0;32m 1043\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mValueError\u001b[39;00m(\n\u001b[0;32m 1044\u001b[0m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mFound array with dim \u001b[39m\u001b[38;5;132;01m%d\u001b[39;00m\u001b[38;5;124m. \u001b[39m\u001b[38;5;132;01m%s\u001b[39;00m\u001b[38;5;124m expected <= 2.\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[0;32m 1045\u001b[0m \u001b[38;5;241m%\u001b[39m (array\u001b[38;5;241m.\u001b[39mndim, estimator_name)\n\u001b[0;32m 1046\u001b[0m )\n\u001b[0;32m 1048\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m force_all_finite:\n\u001b[1;32m-> 1049\u001b[0m \u001b[43m_assert_all_finite\u001b[49m\u001b[43m(\u001b[49m\n\u001b[0;32m 1050\u001b[0m \u001b[43m \u001b[49m\u001b[43marray\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m 1051\u001b[0m \u001b[43m \u001b[49m\u001b[43minput_name\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43minput_name\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m 1052\u001b[0m \u001b[43m \u001b[49m\u001b[43mestimator_name\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mestimator_name\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m 1053\u001b[0m \u001b[43m \u001b[49m\u001b[43mallow_nan\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mforce_all_finite\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m==\u001b[39;49m\u001b[43m \u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mallow-nan\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\n\u001b[0;32m 1054\u001b[0m \u001b[43m \u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m 1056\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m copy:\n\u001b[0;32m 1057\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m _is_numpy_namespace(xp):\n\u001b[0;32m 1058\u001b[0m \u001b[38;5;66;03m# only make a copy if `array` and `array_orig` may share memory`\u001b[39;00m\n", + "File \u001b[1;32md:\\Dropbox\\89_other_application\\GitHub\\PETsARD\\.venv\\lib\\site-packages\\sklearn\\utils\\validation.py:126\u001b[0m, in \u001b[0;36m_assert_all_finite\u001b[1;34m(X, allow_nan, msg_dtype, estimator_name, input_name)\u001b[0m\n\u001b[0;32m 123\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m first_pass_isfinite:\n\u001b[0;32m 124\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m\n\u001b[1;32m--> 126\u001b[0m \u001b[43m_assert_all_finite_element_wise\u001b[49m\u001b[43m(\u001b[49m\n\u001b[0;32m 127\u001b[0m \u001b[43m \u001b[49m\u001b[43mX\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m 128\u001b[0m \u001b[43m \u001b[49m\u001b[43mxp\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mxp\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m 129\u001b[0m \u001b[43m \u001b[49m\u001b[43mallow_nan\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mallow_nan\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m 130\u001b[0m \u001b[43m \u001b[49m\u001b[43mmsg_dtype\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mmsg_dtype\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m 131\u001b[0m \u001b[43m \u001b[49m\u001b[43mestimator_name\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mestimator_name\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m 132\u001b[0m \u001b[43m \u001b[49m\u001b[43minput_name\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43minput_name\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m 133\u001b[0m \u001b[43m\u001b[49m\u001b[43m)\u001b[49m\n", + "File \u001b[1;32md:\\Dropbox\\89_other_application\\GitHub\\PETsARD\\.venv\\lib\\site-packages\\sklearn\\utils\\validation.py:175\u001b[0m, in \u001b[0;36m_assert_all_finite_element_wise\u001b[1;34m(X, xp, allow_nan, msg_dtype, estimator_name, input_name)\u001b[0m\n\u001b[0;32m 158\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m estimator_name \u001b[38;5;129;01mand\u001b[39;00m input_name \u001b[38;5;241m==\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mX\u001b[39m\u001b[38;5;124m\"\u001b[39m \u001b[38;5;129;01mand\u001b[39;00m has_nan_error:\n\u001b[0;32m 159\u001b[0m \u001b[38;5;66;03m# Improve the error message on how to handle missing values in\u001b[39;00m\n\u001b[0;32m 160\u001b[0m \u001b[38;5;66;03m# scikit-learn.\u001b[39;00m\n\u001b[0;32m 161\u001b[0m msg_err \u001b[38;5;241m+\u001b[39m\u001b[38;5;241m=\u001b[39m (\n\u001b[0;32m 162\u001b[0m \u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;130;01m\\n\u001b[39;00m\u001b[38;5;132;01m{\u001b[39;00mestimator_name\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m does not accept missing values\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[0;32m 163\u001b[0m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m encoded as NaN natively. For supervised learning, you might want\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m (...)\u001b[0m\n\u001b[0;32m 173\u001b[0m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m#estimators-that-handle-nan-values\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[0;32m 174\u001b[0m )\n\u001b[1;32m--> 175\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mValueError\u001b[39;00m(msg_err)\n", + "\u001b[1;31mValueError\u001b[0m: Input contains NaN." + ] + } + ], + "source": [ + "issue332(\n", + " load=load,\n", + " synthesizing_method='smartnoise-pacsynth'\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Preproc config of Outlier before update as ...\n", + "{'age': ,\n", + " 'capital-gain': ,\n", + " 'capital-loss': ,\n", + " 'education': None,\n", + " 'educational-num': ,\n", + " 'fnlwgt': ,\n", + " 'gender': None,\n", + " 'hours-per-week': ,\n", + " 'income': None,\n", + " 'marital-status': None,\n", + " 'native-country': None,\n", + " 'occupation': None,\n", + " 'race': None,\n", + " 'relationship': None,\n", + " 'workclass': None}\n", + "Preproc config of Outlier before after as ...\n", + "{'age': ,\n", + " 'capital-gain': ,\n", + " 'capital-loss': ,\n", + " 'education': None,\n", + " 'educational-num': ,\n", + " 'fnlwgt': ,\n", + " 'gender': None,\n", + " 'hours-per-week': ,\n", + " 'income': None,\n", + " 'marital-status': None,\n", + " 'native-country': None,\n", + " 'occupation': None,\n", + " 'race': None,\n", + " 'relationship': None,\n", + " 'workclass': None}\n", + "Preproc data as ...\n", + " age workclass fnlwgt education educational-num marital-status \\\n", + "0 -0.953119 0.366136 0.409053 0.776959 -1.152735 0.810663 \n", + "\n", + " occupation relationship race gender capital-gain capital-loss \\\n", + "0 0.74892 0.757864 0.955584 0.48598 -0.159918 -0.219043 \n", + "\n", + " hours-per-week native-country income \n", + "0 -0.044504 0.216869 0.212391 \n", + "Synthesizer (SmartNoise): Fitting dpctgan.\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "d:\\Dropbox\\89_other_application\\GitHub\\PETsARD\\.venv\\lib\\site-packages\\opacus\\privacy_engine.py:638: UserWarning: The sample rate will be defined from ``batch_size`` and ``sample_size``.The returned privacy budget will be incorrect.\n", + " warnings.warn(\n", + "d:\\Dropbox\\89_other_application\\GitHub\\PETsARD\\.venv\\lib\\site-packages\\opacus\\privacy_engine.py:229: UserWarning: Secure RNG turned off. This is perfectly fine for experimentation as it allows for much faster training performance, but remember to turn it on and retrain one last time before production with ``secure_rng`` turned on.\n", + " warnings.warn(\n", + "d:\\Dropbox\\89_other_application\\GitHub\\PETsARD\\.venv\\lib\\site-packages\\torch\\nn\\modules\\module.py:1352: UserWarning: Using a non-full backward hook when the forward contains multiple autograd Nodes is deprecated and will be removed in future versions. This hook will be missing some grad_input. Please use register_full_backward_hook to get the documented behavior.\n", + " warnings.warn(\"Using a non-full backward hook when the forward contains multiple autograd Nodes \"\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Epoch 1, Loss G: 0.7107, Loss D: 1.3900\n", + "epsilon is 0.08246410416899833, alpha is 63.0\n", + "Epoch 2, Loss G: 0.7097, Loss D: 1.3898\n", + "epsilon is 0.6154321240229542, alpha is 21.0\n", + "Epoch 3, Loss G: 0.7048, Loss D: 1.3908\n", + "epsilon is 0.8717652885709554, alpha is 16.0\n", + "Epoch 4, Loss G: 0.7016, Loss D: 1.3897\n", + "epsilon is 1.0734433928301887, alpha is 14.0\n", + "Epoch 5, Loss G: 0.7059, Loss D: 1.3874\n", + "epsilon is 1.2474424384805656, alpha is 12.0\n", + "Epoch 6, Loss G: 0.7015, Loss D: 1.3895\n", + "epsilon is 1.403078289939324, alpha is 10.9\n", + "Epoch 7, Loss G: 0.6987, Loss D: 1.3876\n", + "epsilon is 1.5430774752709655, alpha is 10.6\n", + "Epoch 8, Loss G: 0.6922, Loss D: 1.3928\n", + "epsilon is 1.6748565545536702, alpha is 10.0\n", + "Epoch 9, Loss G: 0.6947, Loss D: 1.3898\n", + "epsilon is 1.7986930762464894, alpha is 9.4\n", + "Epoch 10, Loss G: 0.6898, Loss D: 1.3850\n", + "epsilon is 1.9160035051686128, alpha is 9.0\n", + "Epoch 11, Loss G: 0.6888, Loss D: 1.3885\n", + "epsilon is 2.027907968415127, alpha is 8.7\n", + "Epoch 12, Loss G: 0.6854, Loss D: 1.3891\n", + "epsilon is 2.135121781113915, alpha is 8.3\n", + "Epoch 13, Loss G: 0.6822, Loss D: 1.3884\n", + "epsilon is 2.2382637160168204, alpha is 8.1\n", + "Epoch 14, Loss G: 0.6828, Loss D: 1.3872\n", + "epsilon is 2.337827882631375, alpha is 7.8\n", + "Epoch 15, Loss G: 0.6835, Loss D: 1.3950\n", + "epsilon is 2.434237194672732, alpha is 7.6\n", + "Epoch 16, Loss G: 0.6794, Loss D: 1.3912\n", + "epsilon is 2.5278163462034815, alpha is 7.4\n", + "Epoch 17, Loss G: 0.6722, Loss D: 1.3944\n", + "epsilon is 2.6188388524326234, alpha is 7.2\n", + "Epoch 18, Loss G: 0.6723, Loss D: 1.3989\n", + "epsilon is 2.7076108306108346, alpha is 7.0\n", + "Epoch 19, Loss G: 0.6683, Loss D: 1.3920\n", + "epsilon is 2.7940664707675626, alpha is 6.9\n", + "Epoch 20, Loss G: 0.6670, Loss D: 1.3951\n", + "epsilon is 2.878763468386405, alpha is 6.8\n", + "Epoch 21, Loss G: 0.6687, Loss D: 1.3902\n", + "epsilon is 2.961464297854409, alpha is 6.6\n", + "Epoch 22, Loss G: 0.6657, Loss D: 1.3872\n", + "epsilon is 3.0425452652856495, alpha is 6.5\n", + "Epoch 23, Loss G: 0.6616, Loss D: 1.3926\n", + "epsilon is 3.122103446988952, alpha is 6.4\n", + "Epoch 24, Loss G: 0.6631, Loss D: 1.3951\n", + "epsilon is 3.200208539731727, alpha is 6.3\n", + "Epoch 25, Loss G: 0.6614, Loss D: 1.3910\n", + "epsilon is 3.2769350559807005, alpha is 6.2\n", + "Epoch 26, Loss G: 0.6637, Loss D: 1.3855\n", + "epsilon is 3.3523628060835255, alpha is 6.1\n", + "Epoch 27, Loss G: 0.6664, Loss D: 1.3881\n", + "epsilon is 3.4265774389666657, alpha is 6.0\n", + "Epoch 28, Loss G: 0.6632, Loss D: 1.3884\n", + "epsilon is 3.4996710497868895, alpha is 5.9\n", + "Epoch 29, Loss G: 0.6609, Loss D: 1.3892\n", + "epsilon is 3.5717428643995515, alpha is 5.8\n", + "Epoch 30, Loss G: 0.6594, Loss D: 1.3910\n", + "epsilon is 3.6428935706241528, alpha is 5.8\n", + "Epoch 31, Loss G: 0.6567, Loss D: 1.3863\n", + "epsilon is 3.712751905883079, alpha is 5.7\n", + "Epoch 32, Loss G: 0.6577, Loss D: 1.3827\n", + "epsilon is 3.7818141415719784, alpha is 5.6\n", + "Epoch 33, Loss G: 0.6609, Loss D: 1.3858\n", + "epsilon is 3.850205948937656, alpha is 5.5\n", + "Epoch 34, Loss G: 0.6532, Loss D: 1.3819\n", + "epsilon is 3.917468189518828, alpha is 5.5\n", + "Epoch 35, Loss G: 0.6551, Loss D: 1.3896\n", + "epsilon is 3.9840352850839897, alpha is 5.4\n", + "Epoch 36, Loss G: 0.6527, Loss D: 1.3815\n", + "epsilon is 4.050006670613625, alpha is 5.4\n", + "Epoch 37, Loss G: 0.6516, Loss D: 1.3873\n", + "epsilon is 4.114903091238218, alpha is 5.3\n", + "Epoch 38, Loss G: 0.6477, Loss D: 1.3901\n", + "epsilon is 4.179582102216282, alpha is 5.2\n", + "Epoch 39, Loss G: 0.6504, Loss D: 1.3830\n", + "epsilon is 4.242979684763249, alpha is 5.2\n", + "Epoch 40, Loss G: 0.6503, Loss D: 1.3904\n", + "epsilon is 4.306336090935168, alpha is 5.1\n", + "Epoch 41, Loss G: 0.6467, Loss D: 1.3944\n", + "epsilon is 4.368450711228463, alpha is 5.1\n", + "Epoch 42, Loss G: 0.6481, Loss D: 1.3939\n", + "epsilon is 4.430565331521756, alpha is 5.1\n", + "Epoch 43, Loss G: 0.6474, Loss D: 1.3883\n", + "epsilon is 4.491519093988257, alpha is 5.0\n", + "Epoch 44, Loss G: 0.6422, Loss D: 1.3922\n", + "epsilon is 4.552353368662634, alpha is 5.0\n", + "Epoch 45, Loss G: 0.6401, Loss D: 1.3860\n", + "epsilon is 4.612407286797646, alpha is 4.9\n", + "Epoch 46, Loss G: 0.6392, Loss D: 1.3932\n", + "epsilon is 4.671963825364106, alpha is 4.9\n", + "Epoch 47, Loss G: 0.6358, Loss D: 1.3937\n", + "epsilon is 4.731359885691218, alpha is 4.8\n", + "Epoch 48, Loss G: 0.6367, Loss D: 1.3966\n", + "epsilon is 4.789641290552096, alpha is 4.8\n", + "Epoch 49, Loss G: 0.6344, Loss D: 1.4051\n", + "epsilon is 4.847922695412973, alpha is 4.8\n", + "Epoch 50, Loss G: 0.6350, Loss D: 1.4007\n", + "epsilon is 4.905655535140821, alpha is 4.7\n", + "Epoch 51, Loss G: 0.6294, Loss D: 1.4034\n", + "epsilon is 4.9626644016048695, alpha is 4.7\n", + "Epoch 52, Loss G: 0.6293, Loss D: 1.3913\n", + "epsilon is 5.0196732680689164, alpha is 4.7\n", + "Epoch 53, Loss G: 0.6228, Loss D: 1.4126\n", + "epsilon is 5.0760439796888255, alpha is 4.6\n", + "Epoch 54, Loss G: 0.6300, Loss D: 1.4028\n", + "epsilon is 5.131782895986607, alpha is 4.6\n", + "Epoch 55, Loss G: 0.6118, Loss D: 1.4130\n", + "epsilon is 5.1875218122843885, alpha is 4.6\n", + "Epoch 56, Loss G: 0.6162, Loss D: 1.4176\n", + "epsilon is 5.242864373609633, alpha is 4.5\n", + "Epoch 57, Loss G: 0.6124, Loss D: 1.4177\n", + "epsilon is 5.297335920908565, alpha is 4.5\n", + "Epoch 58, Loss G: 0.6036, Loss D: 1.4231\n", + "epsilon is 5.351807468207497, alpha is 4.5\n", + "Epoch 59, Loss G: 0.6017, Loss D: 1.4224\n", + "epsilon is 5.406279015506429, alpha is 4.5\n", + "Epoch 60, Loss G: 0.6025, Loss D: 1.4320\n", + "epsilon is 5.4597004033036685, alpha is 4.4\n", + "Epoch 61, Loss G: 0.5997, Loss D: 1.4292\n", + "epsilon is 5.512907155723584, alpha is 4.4\n", + "Epoch 62, Loss G: 0.5974, Loss D: 1.4378\n", + "epsilon is 5.566113908143499, alpha is 4.4\n", + "Epoch 63, Loss G: 0.5980, Loss D: 1.4396\n", + "epsilon is 5.619296859313559, alpha is 4.3\n", + "Epoch 64, Loss G: 0.5923, Loss D: 1.4463\n", + "epsilon is 5.671241383942272, alpha is 4.3\n", + "Epoch 65, Loss G: 0.5817, Loss D: 1.4429\n", + "epsilon is 5.723185908570986, alpha is 4.3\n", + "Epoch 66, Loss G: 0.5879, Loss D: 1.4591\n", + "epsilon is 5.775130433199701, alpha is 4.3\n", + "Epoch 67, Loss G: 0.5835, Loss D: 1.4501\n", + "epsilon is 5.827074957828414, alpha is 4.3\n", + "Epoch 68, Loss G: 0.5829, Loss D: 1.4513\n", + "epsilon is 5.877966017103227, alpha is 4.2\n", + "Epoch 69, Loss G: 0.5753, Loss D: 1.4468\n", + "epsilon is 5.928650874011896, alpha is 4.2\n", + "Epoch 70, Loss G: 0.5799, Loss D: 1.4541\n", + "epsilon is 5.979335730920565, alpha is 4.2\n", + "Epoch 71, Loss G: 0.5725, Loss D: 1.4617\n", + "epsilon is 6.030020587829234, alpha is 4.2\n", + "Epoch 72, Loss G: 0.5712, Loss D: 1.4637\n", + "epsilon is 6.080411733176044, alpha is 4.1\n", + "Epoch 73, Loss G: 0.5642, Loss D: 1.4602\n", + "epsilon is 6.129839475434501, alpha is 4.1\n", + "Epoch 74, Loss G: 0.5707, Loss D: 1.4774\n", + "epsilon is 6.17926721769296, alpha is 4.1\n", + "Epoch 75, Loss G: 0.5642, Loss D: 1.4687\n", + "epsilon is 6.228694959951418, alpha is 4.1\n", + "Epoch 76, Loss G: 0.5691, Loss D: 1.4757\n", + "epsilon is 6.278122702209876, alpha is 4.1\n", + "Epoch 77, Loss G: 0.5707, Loss D: 1.4758\n", + "epsilon is 6.327357790594008, alpha is 4.0\n", + "Epoch 78, Loss G: 0.5639, Loss D: 1.4835\n", + "epsilon is 6.375530964286589, alpha is 4.0\n", + "Epoch 79, Loss G: 0.5652, Loss D: 1.4719\n", + "epsilon is 6.42370413797917, alpha is 4.0\n", + "Epoch 80, Loss G: 0.5628, Loss D: 1.4748\n", + "epsilon is 6.471877311671752, alpha is 4.0\n", + "Epoch 81, Loss G: 0.5608, Loss D: 1.4772\n", + "epsilon is 6.520050485364332, alpha is 4.0\n", + "Epoch 82, Loss G: 0.5557, Loss D: 1.4777\n", + "epsilon is 6.568223659056913, alpha is 4.0\n", + "Epoch 83, Loss G: 0.5670, Loss D: 1.4892\n", + "epsilon is 6.615734837571576, alpha is 3.9\n", + "Epoch 84, Loss G: 0.5594, Loss D: 1.4800\n", + "epsilon is 6.662655981812787, alpha is 3.9\n", + "Epoch 85, Loss G: 0.5555, Loss D: 1.4830\n", + "epsilon is 6.709577126053995, alpha is 3.9\n", + "Epoch 86, Loss G: 0.5576, Loss D: 1.4863\n", + "epsilon is 6.7564982702952046, alpha is 3.9\n", + "Epoch 87, Loss G: 0.5547, Loss D: 1.4850\n", + "epsilon is 6.803419414536414, alpha is 3.9\n", + "Epoch 88, Loss G: 0.5603, Loss D: 1.4947\n", + "epsilon is 6.850340558777624, alpha is 3.9\n", + "Epoch 89, Loss G: 0.5570, Loss D: 1.4874\n", + "epsilon is 6.896912311217361, alpha is 3.8\n", + "Epoch 90, Loss G: 0.5555, Loss D: 1.4854\n", + "epsilon is 6.942583958167586, alpha is 3.8\n", + "Epoch 91, Loss G: 0.5516, Loss D: 1.4906\n", + "epsilon is 6.98825560511781, alpha is 3.8\n", + "Epoch 92, Loss G: 0.5562, Loss D: 1.4936\n", + "epsilon is 7.033927252068035, alpha is 3.8\n", + "Epoch 93, Loss G: 0.5531, Loss D: 1.4931\n", + "epsilon is 7.079598899018261, alpha is 3.8\n", + "Epoch 94, Loss G: 0.5540, Loss D: 1.4899\n", + "epsilon is 7.125270545968486, alpha is 3.8\n", + "Epoch 95, Loss G: 0.5474, Loss D: 1.4922\n", + "epsilon is 7.170942192918711, alpha is 3.8\n", + "Epoch 96, Loss G: 0.5483, Loss D: 1.4962\n", + "epsilon is 7.216225114747892, alpha is 3.7\n", + "Epoch 97, Loss G: 0.5495, Loss D: 1.4945\n", + "epsilon is 7.260649789629172, alpha is 3.7\n", + "Epoch 98, Loss G: 0.5478, Loss D: 1.4869\n", + "epsilon is 7.305074464510453, alpha is 3.7\n", + "Epoch 99, Loss G: 0.5472, Loss D: 1.4911\n", + "epsilon is 7.349499139391733, alpha is 3.7\n", + "Epoch 100, Loss G: 0.5532, Loss D: 1.4938\n", + "epsilon is 7.3939238142730135, alpha is 3.7\n", + "Epoch 101, Loss G: 0.5451, Loss D: 1.4891\n", + "epsilon is 7.4383484891542935, alpha is 3.7\n", + "Epoch 102, Loss G: 0.5529, Loss D: 1.4897\n", + "epsilon is 7.482773164035574, alpha is 3.7\n", + "Epoch 103, Loss G: 0.5484, Loss D: 1.4863\n", + "epsilon is 7.527197838916854, alpha is 3.7\n", + "Epoch 104, Loss G: 0.5478, Loss D: 1.4795\n", + "epsilon is 7.570985182837946, alpha is 3.6\n", + "Epoch 105, Loss G: 0.5577, Loss D: 1.4863\n", + "epsilon is 7.614165403949305, alpha is 3.6\n", + "Epoch 106, Loss G: 0.5572, Loss D: 1.4800\n", + "epsilon is 7.657345625060665, alpha is 3.6\n", + "Epoch 107, Loss G: 0.5546, Loss D: 1.4823\n", + "epsilon is 7.700525846172025, alpha is 3.6\n", + "Epoch 108, Loss G: 0.5574, Loss D: 1.4715\n", + "epsilon is 7.743706067283383, alpha is 3.6\n", + "Epoch 109, Loss G: 0.5554, Loss D: 1.4778\n", + "epsilon is 7.786886288394741, alpha is 3.6\n", + "Epoch 110, Loss G: 0.5621, Loss D: 1.4794\n", + "epsilon is 7.830066509506101, alpha is 3.6\n", + "Epoch 111, Loss G: 0.5558, Loss D: 1.4784\n", + "epsilon is 7.873246730617461, alpha is 3.6\n", + "Epoch 112, Loss G: 0.5632, Loss D: 1.4766\n", + "epsilon is 7.916426951728819, alpha is 3.6\n", + "Epoch 113, Loss G: 0.5602, Loss D: 1.4731\n", + "epsilon is 7.95868193355541, alpha is 3.5\n", + "Epoch 114, Loss G: 0.5532, Loss D: 1.4708\n", + "epsilon is 8.000620212289654, alpha is 3.5\n", + "Epoch 115, Loss G: 0.5629, Loss D: 1.4671\n", + "epsilon is 8.042558491023897, alpha is 3.5\n", + "Epoch 116, Loss G: 0.5605, Loss D: 1.4703\n", + "epsilon is 8.084496769758141, alpha is 3.5\n", + "Epoch 117, Loss G: 0.5661, Loss D: 1.4603\n", + "epsilon is 8.126435048492386, alpha is 3.5\n", + "Epoch 118, Loss G: 0.5667, Loss D: 1.4750\n", + "epsilon is 8.168373327226629, alpha is 3.5\n", + "Epoch 119, Loss G: 0.5659, Loss D: 1.4663\n", + "epsilon is 8.210311605960872, alpha is 3.5\n", + "Epoch 120, Loss G: 0.5597, Loss D: 1.4697\n", + "epsilon is 8.252249884695114, alpha is 3.5\n", + "Epoch 121, Loss G: 0.5606, Loss D: 1.4597\n", + "epsilon is 8.294188163429357, alpha is 3.5\n", + "Epoch 122, Loss G: 0.5615, Loss D: 1.4584\n", + "epsilon is 8.336126442163602, alpha is 3.5\n", + "Epoch 123, Loss G: 0.5618, Loss D: 1.4627\n", + "epsilon is 8.377016439122203, alpha is 3.4\n", + "Epoch 124, Loss G: 0.5616, Loss D: 1.4609\n", + "epsilon is 8.417715279980845, alpha is 3.4\n", + "Epoch 125, Loss G: 0.5624, Loss D: 1.4777\n", + "epsilon is 8.458414120839487, alpha is 3.4\n", + "Epoch 126, Loss G: 0.5613, Loss D: 1.4678\n", + "epsilon is 8.49911296169813, alpha is 3.4\n", + "Epoch 127, Loss G: 0.5576, Loss D: 1.4605\n", + "epsilon is 8.539811802556772, alpha is 3.4\n", + "Epoch 128, Loss G: 0.5654, Loss D: 1.4663\n", + "epsilon is 8.580510643415414, alpha is 3.4\n", + "Epoch 129, Loss G: 0.5651, Loss D: 1.4615\n", + "epsilon is 8.621209484274056, alpha is 3.4\n", + "Epoch 130, Loss G: 0.5654, Loss D: 1.4641\n", + "epsilon is 8.661908325132698, alpha is 3.4\n", + "Epoch 131, Loss G: 0.5639, Loss D: 1.4569\n", + "epsilon is 8.702607165991338, alpha is 3.4\n", + "Epoch 132, Loss G: 0.5672, Loss D: 1.4536\n", + "epsilon is 8.74330600684998, alpha is 3.4\n", + "Epoch 133, Loss G: 0.5713, Loss D: 1.4582\n", + "epsilon is 8.784004847708623, alpha is 3.4\n", + "Epoch 134, Loss G: 0.5650, Loss D: 1.4524\n", + "epsilon is 8.823944600709858, alpha is 3.3\n", + "Epoch 135, Loss G: 0.5589, Loss D: 1.4599\n", + "epsilon is 8.863406501319506, alpha is 3.3\n", + "Epoch 136, Loss G: 0.5631, Loss D: 1.4620\n", + "epsilon is 8.902868401929158, alpha is 3.3\n", + "Epoch 137, Loss G: 0.5668, Loss D: 1.4641\n", + "epsilon is 8.94233030253881, alpha is 3.3\n", + "Epoch 138, Loss G: 0.5698, Loss D: 1.4604\n", + "epsilon is 8.98179220314846, alpha is 3.3\n", + "Epoch 139, Loss G: 0.5650, Loss D: 1.4606\n", + "epsilon is 9.02125410375811, alpha is 3.3\n", + "Epoch 140, Loss G: 0.5700, Loss D: 1.4558\n", + "epsilon is 9.060716004367762, alpha is 3.3\n", + "Epoch 141, Loss G: 0.5640, Loss D: 1.4623\n", + "epsilon is 9.100177904977413, alpha is 3.3\n", + "Epoch 142, Loss G: 0.5664, Loss D: 1.4496\n", + "epsilon is 9.139639805587063, alpha is 3.3\n", + "Epoch 143, Loss G: 0.5707, Loss D: 1.4586\n", + "epsilon is 9.179101706196715, alpha is 3.3\n", + "Epoch 144, Loss G: 0.5654, Loss D: 1.4485\n", + "epsilon is 9.218563606806365, alpha is 3.3\n", + "Epoch 145, Loss G: 0.5691, Loss D: 1.4478\n", + "epsilon is 9.258025507416015, alpha is 3.3\n", + "Epoch 146, Loss G: 0.5750, Loss D: 1.4537\n", + "epsilon is 9.297487408025667, alpha is 3.3\n", + "Epoch 147, Loss G: 0.5737, Loss D: 1.4539\n", + "epsilon is 9.33595966014038, alpha is 3.2\n", + "Epoch 148, Loss G: 0.5714, Loss D: 1.4532\n", + "epsilon is 9.374187111269075, alpha is 3.2\n", + "Epoch 149, Loss G: 0.5723, Loss D: 1.4538\n", + "epsilon is 9.41241456239777, alpha is 3.2\n", + "Epoch 150, Loss G: 0.5719, Loss D: 1.4523\n", + "epsilon is 9.450642013526464, alpha is 3.2\n", + "Epoch 151, Loss G: 0.5674, Loss D: 1.4542\n", + "epsilon is 9.48886946465516, alpha is 3.2\n", + "Epoch 152, Loss G: 0.5661, Loss D: 1.4573\n", + "epsilon is 9.527096915783854, alpha is 3.2\n", + "Epoch 153, Loss G: 0.5722, Loss D: 1.4540\n", + "epsilon is 9.56532436691255, alpha is 3.2\n", + "Epoch 154, Loss G: 0.5742, Loss D: 1.4563\n", + "epsilon is 9.603551818041245, alpha is 3.2\n", + "Epoch 155, Loss G: 0.5725, Loss D: 1.4476\n", + "epsilon is 9.64177926916994, alpha is 3.2\n", + "Epoch 156, Loss G: 0.5703, Loss D: 1.4535\n", + "epsilon is 9.680006720298634, alpha is 3.2\n", + "Epoch 157, Loss G: 0.5690, Loss D: 1.4478\n", + "epsilon is 9.71823417142733, alpha is 3.2\n", + "Epoch 158, Loss G: 0.5698, Loss D: 1.4475\n", + "epsilon is 9.756461622556024, alpha is 3.2\n", + "Epoch 159, Loss G: 0.5766, Loss D: 1.4464\n", + "epsilon is 9.79468907368472, alpha is 3.2\n", + "Epoch 160, Loss G: 0.5784, Loss D: 1.4552\n", + "epsilon is 9.832916524813415, alpha is 3.2\n", + "Epoch 161, Loss G: 0.5751, Loss D: 1.4438\n", + "epsilon is 9.871016847131145, alpha is 3.1\n", + "Epoch 162, Loss G: 0.5799, Loss D: 1.4512\n", + "epsilon is 9.908012332703517, alpha is 3.1\n", + "Epoch 163, Loss G: 0.5725, Loss D: 1.4488\n", + "epsilon is 9.94500781827589, alpha is 3.1\n", + "Epoch 164, Loss G: 0.5762, Loss D: 1.4466\n", + "epsilon is 9.982003303848263, alpha is 3.1\n", + "Synthesizer (SmartNoise): Fitting dpctgan spent 115.4473 sec.\n", + "Synthesizer (SmartNoise): Sampling dpctgan # 927 rows (same as raw) in 0.4688 sec.\n", + "Sync data as ...\n", + " age workclass fnlwgt education educational-num marital-status \\\n", + "0 3.663019 0.997224 4.606915 0.747578 2.269901 0.984679 \n", + "\n", + " occupation relationship race gender capital-gain capital-loss \\\n", + "0 0.990903 0.95497 0.806221 0.997782 -0.159918 -0.219043 \n", + "\n", + " hours-per-week native-country income \n", + "0 2.634771 0.88122 0.993726 \n", + " age workclass fnlwgt education educational-num \\\n", + "0 88.295354 Federal-gov 647932.096102 Masters 15.972689 \n", + "\n", + " marital-status occupation relationship race gender capital-gain \\\n", + "0 Widowed Protective-serv Wife White Female 0.0 \n", + "\n", + " capital-loss hours-per-week native-country income \n", + "0 0.0 72.657481 United-States >50K \n" + ] + } + ], + "source": [ + "issue332_gan(\n", + " load = load,\n", + " synthesizing_method = 'smartnoise-dpctgan',\n", + " outlier_inhibit = True\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Preproc config of Outlier before update as ...\n", + "{'age': ,\n", + " 'capital-gain': ,\n", + " 'capital-loss': ,\n", + " 'education': None,\n", + " 'educational-num': ,\n", + " 'fnlwgt': ,\n", + " 'gender': None,\n", + " 'hours-per-week': ,\n", + " 'income': None,\n", + " 'marital-status': None,\n", + " 'native-country': None,\n", + " 'occupation': None,\n", + " 'race': None,\n", + " 'relationship': None,\n", + " 'workclass': None}\n", + "Preproc config of Outlier before after as ...\n", + "{'age': ,\n", + " 'capital-gain': ,\n", + " 'capital-loss': ,\n", + " 'education': None,\n", + " 'educational-num': ,\n", + " 'fnlwgt': ,\n", + " 'gender': None,\n", + " 'hours-per-week': ,\n", + " 'income': None,\n", + " 'marital-status': None,\n", + " 'native-country': None,\n", + " 'occupation': None,\n", + " 'race': None,\n", + " 'relationship': None,\n", + " 'workclass': None}\n", + "Preproc data as ...\n", + " age workclass fnlwgt education educational-num marital-status \\\n", + "0 -0.953119 0.006814 0.409053 0.780317 -1.152735 0.507641 \n", + "\n", + " occupation relationship race gender capital-gain capital-loss \\\n", + "0 0.750354 0.787001 0.864054 0.199314 -0.159918 -0.219043 \n", + "\n", + " hours-per-week native-country income \n", + "0 -0.044504 0.099702 0.61332 \n", + "Synthesizer (SmartNoise): Fitting patectgan.\n", + "using loss cross_entropy and regularization None\n", + "eps: 0.102479 \t G: 0.713781 \t D: 0.716145\n", + "eps: 0.457732 \t G: 0.713048 \t D: 0.712165\n", + "eps: 0.650248 \t G: 0.689282 \t D: 0.698435\n", + "eps: 0.799151 \t G: 0.698342 \t D: 0.710174\n", + "eps: 0.925562 \t G: 0.716226 \t D: 0.704634\n", + "eps: 1.037397 \t G: 0.750504 \t D: 0.704282\n", + "eps: 1.139329 \t G: 0.733702 \t D: 0.708267\n", + "eps: 1.232819 \t G: 0.742357 \t D: 0.711618\n", + "eps: 1.320496 \t G: 0.698668 \t D: 0.703252\n", + "eps: 1.403195 \t G: 0.737517 \t D: 0.699576\n", + "eps: 1.481995 \t G: 0.681477 \t D: 0.705583\n", + "eps: 1.556995 \t G: 0.672663 \t D: 0.702600\n", + "eps: 1.628302 \t G: 0.745140 \t D: 0.694179\n", + "eps: 1.698302 \t G: 0.690847 \t D: 0.712576\n", + "eps: 1.763994 \t G: 0.727325 \t D: 0.703433\n", + "eps: 1.828994 \t G: 0.709237 \t D: 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0.694982\n", + "eps: 9.975977 \t G: 0.731646 \t D: 0.697401\n", + "eps: 9.995977 \t G: 0.709171 \t D: 0.694371\n", + "Synthesizer (SmartNoise): Fitting patectgan spent 93.3619 sec.\n", + "Synthesizer (SmartNoise): Sampling patectgan # 927 rows (same as raw) in 0.1432 sec.\n", + "Sync data as ...\n", + " age workclass fnlwgt education educational-num marital-status \\\n", + "0 0.24186 0.103012 1.088126 0.554129 0.519998 0.581233 \n", + "\n", + " occupation relationship race gender capital-gain capital-loss \\\n", + "0 0.236864 0.475109 0.705326 0.676615 0.890221 -0.115185 \n", + "\n", + " hours-per-week native-country income \n", + "0 1.13638 0.654151 0.748044 \n", + " age workclass fnlwgt education educational-num \\\n", + "0 41.385254 Private 294926.71711 Bachelors 11.385191 \n", + "\n", + " marital-status occupation relationship race gender capital-gain \\\n", + "0 Never-married Prof-specialty Not-in-family White Male 9733.339667 \n", + "\n", + " capital-loss hours-per-week native-country income \n", + "0 41.004116 54.393705 United-States <=50K \n" + ] + } + ], + "source": [ + "issue332_gan(\n", + " load = load,\n", + " synthesizing_method = 'smartnoise-patectgan',\n", + " outlier_inhibit = True\n", + ")" + ] } ], "metadata": { @@ -279,7 +1432,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.10.13" + "version": "3.10.11" } }, "nbformat": 4, diff --git a/poetry.lock b/poetry.lock index ea021467..21e53784 100644 --- a/poetry.lock +++ b/poetry.lock @@ -1,4 +1,4 @@ -# This file is automatically @generated by Poetry 1.7.1 and should not be changed by hand. +# This file is automatically @generated by Poetry 1.8.2 and should not be changed by hand. [[package]] name = "anonymeter" @@ -2233,11 +2233,11 @@ colorama = {version = "*", markers = "sys_platform == \"win32\""} exceptiongroup = {version = ">=1.0.0rc8", markers = "python_version < \"3.11\""} iniconfig = "*" packaging = "*" -pluggy = ">=1.4,<2.0" -tomli = {version = ">=1", markers = "python_version < \"3.11\""} +pluggy = ">=1.3.0,<2.0" +tomli = {version = ">=1.0.0", markers = "python_version < \"3.11\""} [package.extras] -testing = ["argcomplete", "attrs (>=19.2)", "hypothesis (>=3.56)", "mock", "pygments (>=2.7.2)", "requests", "setuptools", "xmlschema"] +testing = ["argcomplete", "attrs (>=19.2.0)", "hypothesis (>=3.56)", "mock", "nose", "pygments (>=2.7.2)", "requests", "setuptools", "xmlschema"] [[package]] name = "python-dateutil" diff --git a/pyproject.toml b/pyproject.toml index 8f6eec6f..ff8301a6 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -106,24 +106,6 @@ authors = [ ] readme = "README.md" -[tool.poetry.dependencies] -python = ">=3.8,<3.11" -wheel = "^0.42.0" -pyyaml = "^5.4.1" -sdv = "^1.9.0" -smartnoise-synth = "^0.3.3" -anonymeter = "^1.0.0" -sdmetrics = "^0.13.0" -requests = "^2.31.0" -boto3 = "^1.34.37" -toml = "^0.10.2" -tqdm = "^4.66.1" -urllib3 = "<2" # https://github.com/python-poetry/poetry-plugin-export/issues/239 - -[tool.poetry.group.dev.dependencies] -ipykernel = "^6.29.2" -pytest = "^8.0.0" - [build-system] requires = ["poetry-core"] build-backend = "poetry.core.masonry.api" diff --git a/requirements-dev.txt b/requirements-dev.txt deleted file mode 100644 index 5d71c8a1..00000000 --- a/requirements-dev.txt +++ /dev/null @@ -1,1444 +0,0 @@ -anonymeter==1.0.0 ; python_version >= "3.8" and python_version < "3.11" \ - --hash=sha256:b4144ddf5d4ca51fd464479d963eb3959f6bbf990affdde7a778ffb89096641e \ - --hash=sha256:bba90478b266733a9dd41aa085b38de5d4666d5dc9373404923c27a3012a504c -antlr4-python3-runtime==4.9.3 ; python_version >= "3.8" and python_version < "3.11" \ - 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--hash=sha256:6cd52cdf0fef0e0f543299cfc96fec90d7b8a7e88745f411ec33eb44d5ed3531 -traitlets==5.14.1 ; python_version >= "3.8" and python_version < "3.11" \ - --hash=sha256:2e5a030e6eff91737c643231bfcf04a65b0132078dad75e4936700b213652e74 \ - --hash=sha256:8585105b371a04b8316a43d5ce29c098575c2e477850b62b848b964f1444527e -triton==2.2.0 ; platform_system == "Linux" and platform_machine == "x86_64" and python_version < "3.11" and python_version >= "3.8" \ - --hash=sha256:0af58716e721460a61886668b205963dc4d1e4ac20508cc3f623aef0d70283d5 \ - --hash=sha256:227cc6f357c5efcb357f3867ac2a8e7ecea2298cd4606a8ba1e931d1d5a947df \ - --hash=sha256:a2294514340cfe4e8f4f9e5c66c702744c4a117d25e618bd08469d0bfed1e2e5 \ - --hash=sha256:b8ce26093e539d727e7cf6f6f0d932b1ab0574dc02567e684377630d86723ace \ - --hash=sha256:da58a152bddb62cafa9a857dd2bc1f886dbf9f9c90a2b5da82157cd2b34392b0 \ - --hash=sha256:e8fe46d3ab94a8103e291bd44c741cc294b91d1d81c1a2888254cbf7ff846dab -typing-extensions==4.10.0 ; python_version >= "3.8" and python_version < "3.11" \ - --hash=sha256:69b1a937c3a517342112fb4c6df7e72fc39a38e7891a5730ed4985b5214b5475 \ - --hash=sha256:b0abd7c89e8fb96f98db18d86106ff1d90ab692004eb746cf6eda2682f91b3cb -tzdata==2024.1 ; python_version >= "3.8" and python_version < "3.11" \ - --hash=sha256:2674120f8d891909751c38abcdfd386ac0a5a1127954fbc332af6b5ceae07efd \ - --hash=sha256:9068bc196136463f5245e51efda838afa15aaeca9903f49050dfa2679db4d252 -urllib3==1.26.18 ; python_version >= "3.8" and python_version < "3.11" \ - --hash=sha256:34b97092d7e0a3a8cf7cd10e386f401b3737364026c45e622aa02903dffe0f07 \ - --hash=sha256:f8ecc1bba5667413457c529ab955bf8c67b45db799d159066261719e328580a0 -wcwidth==0.2.13 ; python_version >= "3.8" and python_version < "3.11" \ - --hash=sha256:3da69048e4540d84af32131829ff948f1e022c1c6bdb8d6102117aac784f6859 \ - --hash=sha256:72ea0c06399eb286d978fdedb6923a9eb47e1c486ce63e9b4e64fc18303972b5 -wheel==0.42.0 ; python_version >= "3.8" and python_version < "3.11" \ - 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z2xvn{!qkR;@M7@HkbFCom25MU8Ud6*9*_WXdD5e)Dj?m;U;n-9TmH?3n8>*}{Ce}r zaqy!3`J(`u-C&-g9DCHOg&t^)dy)>2KnS3caUc(xsF+pMf$tu>P0!?OhrNtT*~}7= oP<*Y0qC5degr*C+(bdM6LAcy~gWR+jSy!g%4e?h|s-zD83&xgD4*&oF From 8b793392aee70a4a8607834b19bd22e1df31d1a5 Mon Sep 17 00:00:00 2001 From: matheme-justyn Date: Fri, 8 Mar 2024 13:54:37 +0800 Subject: [PATCH 15/17] 20240308, reset requirements.txt and related work --- demo/dev/executable_smartnoise.ipynb | 1213 +++++++++- poetry.lock | 3127 -------------------------- pyproject.toml | 18 - requirements-dev.txt | 1444 ------------ requirements.txt | Bin 96931 -> 3354 bytes 5 files changed, 1183 insertions(+), 4619 deletions(-) delete mode 100644 poetry.lock delete mode 100644 requirements-dev.txt diff --git a/demo/dev/executable_smartnoise.ipynb b/demo/dev/executable_smartnoise.ipynb index 16a6b941..aad74b2d 100644 --- a/demo/dev/executable_smartnoise.ipynb +++ b/demo/dev/executable_smartnoise.ipynb @@ -19,25 +19,31 @@ "name": "stdout", "output_type": "stream", "text": [ - "/Users/alex/PETsARD\n" + "d:\\Dropbox\\89_other_application\\GitHub\\PETsARD\n" ] } ], "source": [ - "%cd /Users/alex/PETsARD" + "# %cd /Users/alex/PETsARD\n", + "\n", + "import os\n", + "import sys\n", + "\n", + "path_petsard = os.path.dirname(os.path.dirname(os.getcwd()))\n", + "print(path_petsard)\n", + "sys.path.append(path_petsard)" ] }, { "cell_type": "code", - "execution_count": 26, + "execution_count": 3, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "Loader - Benchmarker: file benchmark/adult.csv already exist and match SHA-256.\n", - " PETsARD will ignore download and use local data directly.\n" + "Loader - Benchmarker : Success download the benchmark dataset from https://petsard-benchmark.s3.amazonaws.com/adult-income.csv.\n" ] } ], @@ -50,7 +56,7 @@ "\n", "\n", "load = Loader(\n", - " filepath='benchmark://adult',\n", + " filepath='benchmark://adult-income',\n", " na_values={k: '?' for k in [\n", " 'workclass',\n", " 'occupation',\n", @@ -62,7 +68,7 @@ }, { "cell_type": "code", - "execution_count": 27, + "execution_count": 4, "metadata": {}, "outputs": [], "source": [ @@ -71,7 +77,7 @@ }, { "cell_type": "code", - "execution_count": 28, + "execution_count": 5, "metadata": {}, "outputs": [], "source": [ @@ -132,7 +138,7 @@ }, { "cell_type": "code", - "execution_count": 24, + "execution_count": 7, "metadata": {}, "outputs": [], "source": [ @@ -142,7 +148,8 @@ "def issue332_gan(\n", " load: Loader,\n", " synthesizing_method: str,\n", - " scaler_inhibit: bool = False\n", + " scaler_inhibit: bool = False,\n", + " outlier_inhibit: bool = False\n", "):\n", " pp = pprint.PrettyPrinter(depth=2)\n", "\n", @@ -161,6 +168,18 @@ " print(\"Preproc config of Scaler before after as ...\")\n", " pp.pprint(proc_discretizing.get_config()['scaler'])\n", "\n", + " if outlier_inhibit:\n", + " print(\"Preproc config of Outlier before update as ...\")\n", + " pp.pprint(proc_discretizing.get_config()['scaler'])\n", + " proc_discretizing.update_config(\n", + " {'outlier': {\n", + " col: None for col in load.data.columns\n", + " }}\n", + " )\n", + " print(\"Preproc config of Outlier before after as ...\")\n", + " pp.pprint(proc_discretizing.get_config()['scaler'])\n", + "\n", + "\n", " proc_discretizing.fit(\n", " data=load.data,\n", " sequence=[\n", @@ -193,7 +212,7 @@ }, { "cell_type": "code", - "execution_count": 29, + "execution_count": 8, "metadata": {}, "outputs": [ { @@ -201,24 +220,21 @@ "output_type": "stream", "text": [ "Preproc data as ...\n", - " age workclass fnlwgt education educational-num marital-status \\\n", - "0 0.0 2 0.0 1 0.0 4 \n", + " age workclass fnlwgt education educational-num marital-status \\\n", + "0 0.0 2 0.0 1 0.0 4 \n", "\n", - " occupation relationship race gender capital-gain capital-loss \\\n", - "0 6 3 2 1 0.0 0.0 \n", + " occupation relationship race gender capital-gain capital-loss \\\n", + "0 6 3 2 1 0.0 0.0 \n", "\n", - " hours-per-week native-country income \n", - "0 0.0 25 0 \n", - "Synthesizer (SmartNoise): Fitting aim.\n", - "15\n", - "Initial Sigma 11.056041228448832\n" + " hours-per-week native-country income \n", + "0 0.0 25 0 \n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "/Users/alex/miniforge3/envs/dev310/lib/python3.10/site-packages/mbi/__init__.py:15: UserWarning: MixtureInference disabled, please install jax and jaxlib\n", + "d:\\Dropbox\\89_other_application\\GitHub\\PETsARD\\.venv\\lib\\site-packages\\mbi\\__init__.py:15: UserWarning: MixtureInference disabled, please install jax and jaxlib\n", " warnings.warn('MixtureInference disabled, please install jax and jaxlib')\n" ] }, @@ -226,22 +242,25 @@ "name": "stdout", "output_type": "stream", "text": [ - "Synthesizer (SmartNoise): Fitting aim spent 103.6998 sec.\n", - "Synthesizer (SmartNoise): Sampling aim # 558 rows (same as raw) in 0.0282 sec.\n", + "Synthesizer (SmartNoise): Fitting aim.\n", + "15\n", + "Initial Sigma 11.056041228448832\n", + "Synthesizer (SmartNoise): Fitting aim spent 270.8398 sec.\n", + "Synthesizer (SmartNoise): Sampling aim # 558 rows (same as raw) in 0.5135 sec.\n", "Sync data as ...\n", " age workclass fnlwgt education educational-num marital-status \\\n", - "0 0.0 2 0.0 7 0.0 2 \n", + "0 0.0 2 0.0 4 0.0 2 \n", "\n", " occupation relationship race gender capital-gain capital-loss \\\n", - "0 4 0 4 1 0.0 0.0 \n", + "0 6 0 4 1 0.0 0.0 \n", "\n", " hours-per-week native-country income \n", "0 0.0 25 1 \n", - " age workclass fnlwgt education educational-num \\\n", - "0 371.264583 Private 8.479563e+09 Assoc-acdm 16.57591 \n", + " age workclass fnlwgt education educational-num \\\n", + "0 371.264583 Private 8.479563e+09 5th-6th 16.57591 \n", "\n", - " marital-status occupation relationship race gender \\\n", - "0 Married-civ-spouse Farming-fishing Husband White Male \n", + " marital-status occupation relationship race gender \\\n", + "0 Married-civ-spouse Machine-op-inspct Husband White Male \n", "\n", " capital-gain capital-loss hours-per-week native-country income \n", "0 9.268677e+07 96578.239577 172.18284 United-States >50K \n" @@ -261,6 +280,1140 @@ "source": [ "上述執行會出現 `RuntimeError: all elements of input should be between 0 and 1`，原因是 input 中含有 NA 值，來自於前處理過程中將離群值去除掉，導致 `capital-gain`, `capital-loss` 兩個欄位的值皆相同，在 `smartnoise` 中進行 `MinMaxTransformer` 時會讓運算結果為 NA （分母為 0）。因此針對 adult 資料集，將其中的 `outlier` 前處理拿掉，就可以正常運行了。" ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Preproc data as ...\n", + " age workclass fnlwgt education educational-num marital-status \\\n", + "0 0.0 2 0.0 1 0.0 4 \n", + "\n", + " occupation relationship race gender capital-gain capital-loss \\\n", + "0 6 3 2 1 0.0 0.0 \n", + "\n", + " hours-per-week native-country income \n", + "0 0.0 25 0 \n", + "Synthesizer (SmartNoise): Fitting mst.\n", + "Synthesizer (SmartNoise): Fitting mst spent 113.5179 sec.\n", + "Synthesizer (SmartNoise): Sampling mst # 558 rows (same as raw) in 0.4945 sec.\n", + "Sync data as ...\n", + " age workclass fnlwgt education educational-num marital-status \\\n", + "0 0.0 0 0.0 9 0.0 2 \n", + "\n", + " occupation relationship race gender capital-gain capital-loss \\\n", + "0 11 0 4 1 0.0 0.0 \n", + "\n", + " hours-per-week native-country income \n", + "0 0.0 25 0 \n", + " age workclass fnlwgt education educational-num \\\n", + "0 371.264583 Federal-gov 8.479563e+09 Bachelors 16.57591 \n", + "\n", + " marital-status occupation relationship race gender capital-gain \\\n", + "0 Married-civ-spouse Sales Husband White Male 9.268677e+07 \n", + "\n", + " capital-loss hours-per-week native-country income \n", + "0 96578.239577 172.18284 United-States <=50K \n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "d:\\Dropbox\\89_other_application\\GitHub\\PETsARD\\.venv\\lib\\site-packages\\snsynth\\mst\\mst.py:241: FutureWarning: Setting an item of incompatible dtype is deprecated and will raise an error in a future version of pandas. Value '[0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0\n", + " 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0\n", + " 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0\n", + " 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0\n", + " 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0\n", + " 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0\n", + " 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0\n", + " 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0\n", + " 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0\n", + " 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0\n", + " 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0\n", + " 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0\n", + " 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0\n", + " 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0\n", + " 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0\n", + " 0 0 0]' has dtype incompatible with int32, please explicitly cast to a compatible dtype first.\n", + " df.loc[~mask, col] = idx[df.loc[~mask, col]]\n", + "d:\\Dropbox\\89_other_application\\GitHub\\PETsARD\\.venv\\lib\\site-packages\\snsynth\\mst\\mst.py:241: FutureWarning: Setting an item of incompatible dtype is deprecated and will raise an error in a future version of pandas. Value '[0 2 2 0 1 2 2 2 1 2 1 2 2 2 0 2 2 2 2 1 2 2 2 2 2 4 2 2 2 0 2 3 2 4 2 2 4\n", + " 2 2 2 2 3 2 2 2 2 2 5 2 2 2 2 2 2 2 2 5 2 2 2 2 2 3 4 2 2 2 2 2 2 2 2 5 5\n", + " 2 2 2 2 3 2 4 0 2 2 2 2 3 2 2 2 2 4 2 2 0 2 0 2 2 2 2 1 1 2 2 5 2 2 2 2 1\n", + " 2 1 2 2 2 2 2 2 4 3 2 2 5 5 2 2 2 2 1 2 2 2 4 2 5 2 2 2 2 2 2 2 2 2 2 5 2\n", + " 2 5 2 3 5 2 2 0 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 3 2 2 2 0 4 2 2 2 2 3 2 1\n", + " 1 5 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 1 2 1 4 2 5 2 1 2 2 2 1 2 2 2 2 2 1\n", + " 2 1 3 2 2 0 3 5 4 2 2 2 1 2 2 2 2 2 2 2 2 2 2 3 2 2 0 4 2 0 2 2 2 2 1 2 1\n", + " 2 2 2 2 2 2 2 2 2 2 2 0 2 2 1 2 2 2 5 2 2 4 2 2 5 2 2 2 2 2 2 2 2 2 3 2 5\n", + " 2 2 2 2 2 4 1 2 1 2 3 2 2 5 2 1 2 2 2 2 2 2 2 0 1 2 2 5 2 2 2 5 2 5 2 0 2\n", + " 2 0 5 2 2 1 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 4 2 5 2 2 2 2 2 2 2 5 2\n", + " 2 4 2 1 2 4 2 2 2 2 2 2 2 3 2 3 2 2 2 2 2 2 2 1 2 2 5 2 2 2 2 2 2 2 2 2 3\n", + " 4 2 2 4 2 1 2 2 4 2 5 2 2 2 2 2 4 2 2 2 5 2 2 2 3 2 2 2 1 2 4 2 1 2 2 2 2\n", + " 2 1 2 2 2 2 2 2 2 2 2 4 4 3 4 0 2 2 2 2 2 1 2 2 2 2 2 1 2 2 2 1 1 2 2 2 1\n", + " 4 2 1 2 3 2 1 2 2 2 2 0 4 2 2 4 2 2 1 2 2 2 2 2 1 0 2 2 2 2 0 2 2 4 1 2 1\n", + " 2 2 2 3 2 2 2 2 2 4 1 2 2 2 2 2 2 1 0 2 1 2 2 2 3 2 2 2 2 2 2 2 2 2 2 2 2\n", + " 2 0 2]' has dtype incompatible with int32, please explicitly cast to a compatible dtype first.\n", + " df.loc[~mask, col] = idx[df.loc[~mask, col]]\n", + "d:\\Dropbox\\89_other_application\\GitHub\\PETsARD\\.venv\\lib\\site-packages\\snsynth\\mst\\mst.py:241: FutureWarning: Setting an item of incompatible dtype is deprecated and will raise an error in a future version of pandas. Value '[0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0\n", + " 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0\n", + " 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0\n", + " 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0\n", + " 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0\n", + " 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0\n", + " 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0\n", + " 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0\n", + " 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0\n", + " 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0\n", + " 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0\n", + " 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0\n", + " 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0\n", + " 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0\n", + " 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0\n", + " 0 0 0]' has dtype incompatible with int32, please explicitly cast to a compatible dtype first.\n", + " df.loc[~mask, col] = idx[df.loc[~mask, col]]\n", + "d:\\Dropbox\\89_other_application\\GitHub\\PETsARD\\.venv\\lib\\site-packages\\snsynth\\mst\\mst.py:240: FutureWarning: Setting an item of incompatible dtype is deprecated and will raise an error in a future version of pandas. Value '[ 9 12 5 12 9 3 3 4 3 5 5 5 3 2 4 12 3 5 2 3 12 9 2 9\n", + " 5 12 12 9 9 5 5 12 12 12 2 2 9 12 12]' has dtype incompatible with int32, please explicitly cast to a compatible dtype first.\n", + " df.loc[mask, col] = np.random.choice(extra, mask.sum())\n", + "d:\\Dropbox\\89_other_application\\GitHub\\PETsARD\\.venv\\lib\\site-packages\\snsynth\\mst\\mst.py:241: FutureWarning: Setting an item of incompatible dtype is deprecated and will raise an error in a future version of pandas. Value '[0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0\n", + " 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0\n", + " 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0\n", + " 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0\n", + " 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0\n", + " 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0\n", + " 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0\n", + " 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0\n", + " 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0\n", + " 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0\n", + " 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0\n", + " 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0\n", + " 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0\n", + " 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0\n", + " 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0\n", + " 0 0 0]' has dtype incompatible with int32, please explicitly cast to a compatible dtype first.\n", + " df.loc[~mask, col] = idx[df.loc[~mask, col]]\n", + "d:\\Dropbox\\89_other_application\\GitHub\\PETsARD\\.venv\\lib\\site-packages\\snsynth\\mst\\mst.py:240: FutureWarning: Setting an item of incompatible dtype is deprecated and will raise an error in a future version of pandas. Value '[5 2 2 2 5 5 2 2 2 2 5 2 5 2 5 5]' has dtype incompatible with int32, please explicitly cast to a compatible dtype first.\n", + " df.loc[mask, col] = np.random.choice(extra, mask.sum())\n", + "d:\\Dropbox\\89_other_application\\GitHub\\PETsARD\\.venv\\lib\\site-packages\\snsynth\\mst\\mst.py:240: FutureWarning: Setting an item of incompatible dtype is deprecated and will raise an error in a future version of pandas. Value '[1 8 1 1 1 1 1 4 1 4 1 1 8 1 1 8]' has dtype incompatible with int32, please explicitly cast to a compatible dtype first.\n", + " df.loc[mask, col] = np.random.choice(extra, mask.sum())\n", + "d:\\Dropbox\\89_other_application\\GitHub\\PETsARD\\.venv\\lib\\site-packages\\snsynth\\mst\\mst.py:241: FutureWarning: Setting an item of incompatible dtype is deprecated and will raise an error in a future version of pandas. Value '[0 0 0 0 0 0 0 1 0 5 2 0 1 0 3 0 0 0 0 2 0 4 3 0 1 1 4 1 4 0 0 0 5 0 0 0 3\n", + " 1 0 4 1 1 3 0 1 4 1 0 3 1 3 1 0 0 0 1 3 0 2 4 0 1 3 0 0 0 1 1 0 0 0 4 1 1\n", + " 3 4 1 5 1 2 1 0 1 3 1 4 3 4 0 3 0 1 5 0 0 0 1 1 4 0 0 0 1 1 1 1 5 5 0 0 1\n", + " 3 4 0 2 0 0 1 2 4 5 1 0 3 0 1 1 0 0 0 1 0 5 0 0 1 5 0 1 5 1 1 3 0 0 4 0 0\n", + " 0 1 0 1 2 5 1 3 1 0 0 3 4 4 3 0 0 0 4 4 1 5 0 0 0 0 1 1 4 0 0 0 1 0 0 0 1\n", + " 0 4 0 3 1 1 4 3 1 0 0 0 0 1 1 1 0 0 0 0 0 0 1 1 3 2 0 1 0 0 0 0 1 0 0 1 0\n", + " 4 3 3 1 0 4 0 3 1 0 3 3 0 3 0 0 1 0 3 1 0 0 0 3 0 4 4 1 0 0 1 1 4 0 0 5 3\n", + " 0 1 3 3 4 1 0 3 1 1 5 0 0 2 4 0 0 1 0 0 0 4 3 5 3 0 3 1 3 0 0 3 3 1 0 0 0\n", + " 3 3 0 3 1 0 1 0 1 4 0 0 0 1 0 0 1 1 0 1 0 3 0 0 4 1 0 0 1 0 1 3 0 3 3 1 1\n", + " 2 1 3 1 0 0 4 4 0 0 1 1 0 1 0 0 0 0 4 0 3 1 0 2 5 0 0 1 0 3 2 1 1 0 0 0 5\n", + " 4 1 4 1 0 3 1 3 4 0 3 0 0 1 0 1 0 3 0 3 1 3 0 3 2 0 3 1 4 1 0 0 2 3 1 1 0\n", + " 0 0 1 0 1 0 0 0 1 0 4 1 1 0 1 3 0 4 1 0 5 1 1 4 0 1 0 0 0 1 1 4 1 0 1 1 3\n", + " 4 3 3 0 0 2 0 0 0 1 0 4 0 2 0 4 1 3 0 0 3 1 1 0 3 1 0 0 3 3 0 0 1 1 0 1 1\n", + " 4 0 3 1 0 0 1 5 0 0 3 3 3 4 4 4 3 1 1 0 1 4 4 1 1 0 1 0 3 5 1 0 0 0 4 0 0\n", + " 0 1 4 4 0 0 3 4 0 2 1 1 0 1 0 1 3 0 0 1 4 1 5 0 3 1 1 1 3 0 0 3 1 1 0 4 0\n", + " 0 0 0]' has dtype incompatible with int32, please explicitly cast to a compatible dtype first.\n", + " df.loc[~mask, col] = idx[df.loc[~mask, col]]\n", + "d:\\Dropbox\\89_other_application\\GitHub\\PETsARD\\.venv\\lib\\site-packages\\snsynth\\mst\\mst.py:240: FutureWarning: Setting an item of incompatible dtype is deprecated and will raise an error in a future version of pandas. Value '[3 1 0 3 3 1 3 1 0 0 1 0 1 1 3 3 1 0 0 3 3 0 3 3]' has dtype incompatible with int32, please explicitly cast to a compatible dtype first.\n", + " df.loc[mask, col] = np.random.choice(extra, mask.sum())\n", + "d:\\Dropbox\\89_other_application\\GitHub\\PETsARD\\.venv\\lib\\site-packages\\snsynth\\mst\\mst.py:241: FutureWarning: Setting an item of incompatible dtype is deprecated and will raise an error in a future version of pandas. Value '[1 1 1 1 1 1 1 1 1 0 0 1 1 1 0 1 1 1 1 1 1 0 1 1 1 1 0 0 0 1 1 1 0 1 1 1 1\n", + " 1 1 0 1 1 0 1 1 0 1 1 1 1 1 1 1 1 1 0 1 1 1 1 1 0 0 1 1 1 1 1 1 1 1 0 1 1\n", + " 1 1 1 1 0 0 0 1 1 1 1 0 0 0 1 1 1 0 0 1 1 1 0 1 1 1 1 1 0 1 1 0 0 1 1 1 1\n", + " 1 0 1 0 1 1 0 0 0 1 1 1 1 1 1 1 1 1 1 1 1 0 1 1 0 0 1 0 0 0 0 0 1 1 0 1 1\n", + " 1 1 1 0 0 0 1 1 0 1 1 0 0 0 1 1 1 1 0 0 1 0 1 1 1 1 1 1 0 1 1 1 0 1 1 1 0\n", + " 1 0 1 1 1 1 0 1 0 1 1 1 1 0 1 0 1 1 1 1 1 1 0 0 0 1 1 0 1 1 1 1 0 1 1 1 1\n", + " 0 1 0 0 1 0 1 1 1 1 0 1 1 0 1 1 1 1 1 1 1 1 1 0 1 0 0 1 1 1 0 0 1 1 1 0 1\n", + " 1 1 0 1 0 0 1 1 1 0 0 1 1 0 0 1 1 0 1 1 1 0 1 0 0 1 1 0 0 1 1 0 1 1 1 1 1\n", + " 0 1 1 1 1 1 0 1 1 0 1 1 1 0 1 1 1 0 1 1 1 0 1 1 0 1 1 1 0 1 1 1 1 1 1 0 0\n", + " 1 0 0 0 1 1 0 0 1 1 1 0 1 1 1 1 1 1 0 1 1 0 1 0 0 1 1 1 1 1 0 0 0 1 1 1 0\n", + " 1 0 0 1 1 0 1 1 1 1 0 1 1 1 1 1 1 1 1 0 1 1 1 1 1 1 1 0 0 1 1 1 0 1 0 0 1\n", + " 1 1 1 1 1 1 1 1 1 1 0 1 1 1 0 1 1 1 1 1 0 1 1 0 1 1 1 1 1 1 1 0 0 1 0 0 0\n", + " 0 0 1 1 1 0 1 1 1 1 1 0 1 0 1 1 0 0 1 1 0 1 1 1 1 1 1 1 0 0 1 1 1 1 1 0 0\n", + " 0 1 1 1 1 1 1 0 1 1 1 0 0 0 0 0 1 1 1 1 0 0 0 1 1 1 0 1 0 0 1 1 1 1 1 1 1\n", + " 1 0 0 1 1 1 1 0 1 0 0 1 1 0 1 1 0 1 1 1 0 0 0 1 1 0 0 1 0 1 1 0 1 1 1 0 1\n", + " 1 1 1]' has dtype incompatible with int32, please explicitly cast to a compatible dtype first.\n", + " df.loc[~mask, col] = idx[df.loc[~mask, col]]\n", + "d:\\Dropbox\\89_other_application\\GitHub\\PETsARD\\.venv\\lib\\site-packages\\snsynth\\mst\\mst.py:241: FutureWarning: Setting an item of incompatible dtype is deprecated and will raise an error in a future version of pandas. Value '[0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0\n", + " 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0\n", + " 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0\n", + " 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0\n", + " 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0\n", + " 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0\n", + " 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0\n", + " 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0\n", + " 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0\n", + " 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0\n", + " 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0\n", + " 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0\n", + " 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0\n", + " 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0\n", + " 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0\n", + " 0 0 0]' has dtype incompatible with int32, please explicitly cast to a compatible dtype first.\n", + " df.loc[~mask, col] = idx[df.loc[~mask, col]]\n", + "d:\\Dropbox\\89_other_application\\GitHub\\PETsARD\\.venv\\lib\\site-packages\\snsynth\\mst\\mst.py:241: FutureWarning: Setting an item of incompatible dtype is deprecated and will raise an error in a future version of pandas. Value '[0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0\n", + " 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0\n", + " 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0\n", + " 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0\n", + " 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0\n", + " 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0\n", + " 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0\n", + " 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0\n", + " 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0\n", + " 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0\n", + " 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0\n", + " 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0\n", + " 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0\n", + " 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0\n", + " 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0\n", + " 0 0 0]' has dtype incompatible with int32, please explicitly cast to a compatible dtype first.\n", + " df.loc[~mask, col] = idx[df.loc[~mask, col]]\n", + "d:\\Dropbox\\89_other_application\\GitHub\\PETsARD\\.venv\\lib\\site-packages\\snsynth\\mst\\mst.py:241: FutureWarning: Setting an item of incompatible dtype is deprecated and will raise an error in a future version of pandas. Value '[0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0\n", + " 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0\n", + " 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0\n", + " 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0\n", + " 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0\n", + " 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0\n", + " 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0\n", + " 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0\n", + " 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0\n", + " 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0\n", + " 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0\n", + " 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0\n", + " 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0\n", + " 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0\n", + " 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0\n", + " 0 0 0]' has dtype incompatible with int32, please explicitly cast to a compatible dtype first.\n", + " df.loc[~mask, col] = idx[df.loc[~mask, col]]\n", + "d:\\Dropbox\\89_other_application\\GitHub\\PETsARD\\.venv\\lib\\site-packages\\snsynth\\mst\\mst.py:240: FutureWarning: Setting an item of incompatible dtype is deprecated and will raise an error in a future version of pandas. Value '[13 16 8 20 11 10 11 6 15 9 17 9 10 20 4 3 15 1 17 14 16 15 18 18\n", + " 7 3 20 18 7 16 11 4 0 20 15 14 12 20 14 14 18 12 1 12 9]' has dtype incompatible with int32, please explicitly cast to a compatible dtype first.\n", + " df.loc[mask, col] = np.random.choice(extra, mask.sum())\n", + "d:\\Dropbox\\89_other_application\\GitHub\\PETsARD\\.venv\\lib\\site-packages\\snsynth\\mst\\mst.py:241: FutureWarning: Setting an item of incompatible dtype is deprecated and will raise an error in a future version of pandas. Value '[0 0 0 0 0 1 0 0 1 1 0 1 0 1 0 1 1 0 0 0 1 0 0 0 0 0 0 1 0 0 1 1 0 1 1 0 0\n", + " 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 1 0 1 0 0 0 0 0 0 0 0 0 1 0 0 0 1 0 0 0 0 0\n", + " 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 1 1 0 0 1 1 0 0 0 0 0 0 1 0 1 0\n", + " 0 0 1 0 0 1 1 0 1 1 0 0 0 0 0 0 0 0 1 0 0 1 1 0 0 0 0 0 1 0 0 0 1 1 0 1 1\n", + " 0 0 1 0 0 1 0 0 0 0 0 0 0 0 0 0 1 0 1 0 0 0 1 0 0 0 0 0 0 1 0 1 0 0 0 0 0\n", + " 0 1 0 0 0 0 0 0 0 0 0 1 0 0 1 0 0 0 0 0 1 0 0 0 0 1 0 1 1 1 1 1 0 0 1 0 1\n", + " 0 1 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 1 0 0 1 0 1 1\n", + " 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 1 0 1 0 0 0 0 1 0 0 0 0 0 0 0 0 0 1 0\n", + " 0 0 1 0 0 0 0 1 0 0 0 1 1 0 1 0 0 0 1 1 0 0 0 1 0 0 0 0 0 1 0 0 0 0 0 0 0\n", + " 0 0 0 0 0 0 1 0 0 0 0 0 0 0 1 0 1 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 1 0 0 0\n", + " 1 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0\n", + " 1 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 1 1 0 0 0 0 0 1 1 1 1 0 0 0 0 0 0 0\n", + " 0 0 0 1 0 0 0 0 0 0 0 0 1 0 0 1 0 0 0 0 0 0 0 0 0 0 0 1 0 0 1 0 1 0 0 0 0\n", + " 0 0 0 0 1 0 0 1 1 1 0 0 0 0 0 0 0 0 0 1 0 0 0 1 0 0 0 1 0 0 0 1 0 1 0 0 0\n", + " 1 0 0 0 1 0 0 0 1 0 0 0 0 0 1 0 0 1 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 1 0 0 0\n", + " 0 1 0]' has dtype incompatible with int32, please explicitly cast to a compatible dtype first.\n", + " df.loc[~mask, col] = idx[df.loc[~mask, col]]\n" + ] + } + ], + "source": [ + "issue332(\n", + " load=load,\n", + " synthesizing_method='smartnoise-mst'\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Preproc data as ...\n", + " age workclass fnlwgt education educational-num marital-status \\\n", + "0 0.0 2 0.0 1 0.0 4 \n", + "\n", + " occupation relationship race gender capital-gain capital-loss \\\n", + "0 6 3 2 1 0.0 0.0 \n", + "\n", + " hours-per-week native-country income \n", + "0 0.0 25 0 \n", + "Synthesizer (SmartNoise): Fitting pacsynth.\n", + "Synthesizer (SmartNoise): Fitting pacsynth spent 0.2158 sec.\n", + "Synthesizer (SmartNoise): Sampling pacsynth # 558 rows (same as raw) in 1.6921 sec.\n", + "Sync data as ...\n", + " age workclass fnlwgt education educational-num marital-status \\\n", + "0 0.0 2.0 0.0 11.0 0.0 2.0 \n", + "\n", + " occupation relationship race gender capital-gain capital-loss \\\n", + "0 6.0 0.0 4.0 1.0 0.0 0.0 \n", + "\n", + " hours-per-week native-country income \n", + "0 0.0 25.0 0.0 \n" + ] + }, + { + "ename": "ValueError", + "evalue": "Input contains NaN.", + "output_type": "error", + "traceback": [ + "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[1;31mValueError\u001b[0m Traceback (most recent call last)", + "Cell \u001b[1;32mIn[10], line 1\u001b[0m\n\u001b[1;32m----> 1\u001b[0m \u001b[43missue332\u001b[49m\u001b[43m(\u001b[49m\n\u001b[0;32m 2\u001b[0m \u001b[43m \u001b[49m\u001b[43mload\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mload\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m 3\u001b[0m \u001b[43m \u001b[49m\u001b[43msynthesizing_method\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43msmartnoise-pacsynth\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\n\u001b[0;32m 4\u001b[0m \u001b[43m)\u001b[49m\n", + "Cell \u001b[1;32mIn[5], line 50\u001b[0m, in \u001b[0;36missue332\u001b[1;34m(load, synthesizing_method, scaler_inhibit)\u001b[0m\n\u001b[0;32m 48\u001b[0m \u001b[38;5;28mprint\u001b[39m(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mSync data as ...\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[0;32m 49\u001b[0m \u001b[38;5;28mprint\u001b[39m(syn\u001b[38;5;241m.\u001b[39mdata_syn\u001b[38;5;241m.\u001b[39mhead(\u001b[38;5;241m1\u001b[39m))\n\u001b[1;32m---> 50\u001b[0m postproc_discretizing_data \u001b[38;5;241m=\u001b[39m \u001b[43mproc_discretizing\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43minverse_transform\u001b[49m\u001b[43m(\u001b[49m\n\u001b[0;32m 51\u001b[0m \u001b[43m \u001b[49m\u001b[43mdata\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43msyn\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mdata_syn\u001b[49m\n\u001b[0;32m 52\u001b[0m \u001b[43m\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m 53\u001b[0m \u001b[38;5;28mprint\u001b[39m(postproc_discretizing_data\u001b[38;5;241m.\u001b[39mhead(\u001b[38;5;241m1\u001b[39m))\n", + "File \u001b[1;32md:\\Dropbox\\89_other_application\\GitHub\\PETsARD\\PETsARD\\processor\\base.py:483\u001b[0m, in \u001b[0;36mProcessor.inverse_transform\u001b[1;34m(self, data)\u001b[0m\n\u001b[0;32m 480\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m obj \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[0;32m 481\u001b[0m \u001b[38;5;28;01mcontinue\u001b[39;00m\n\u001b[1;32m--> 483\u001b[0m transformed[col] \u001b[38;5;241m=\u001b[39m \u001b[43mobj\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43minverse_transform\u001b[49m\u001b[43m(\u001b[49m\u001b[43mtransformed\u001b[49m\u001b[43m[\u001b[49m\u001b[43mcol\u001b[49m\u001b[43m]\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m 485\u001b[0m logging\u001b[38;5;241m.\u001b[39minfo(\u001b[38;5;124mf\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;132;01m{\u001b[39;00mprocessor\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m inverse transformation done.\u001b[39m\u001b[38;5;124m'\u001b[39m)\n\u001b[0;32m 486\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m 487\u001b[0m \u001b[38;5;66;03m# if the processor is not a string,\u001b[39;00m\n\u001b[0;32m 488\u001b[0m \u001b[38;5;66;03m# it should be a mediator, which transforms the data directly.\u001b[39;00m\n", + "File \u001b[1;32md:\\Dropbox\\89_other_application\\GitHub\\PETsARD\\PETsARD\\processor\\discretizing.py:81\u001b[0m, in \u001b[0;36mDiscretizingHandler.inverse_transform\u001b[1;34m(self, data)\u001b[0m\n\u001b[0;32m 78\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_is_fitted:\n\u001b[0;32m 79\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m UnfittedError(\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mThe object is not fitted. Use .fit() first.\u001b[39m\u001b[38;5;124m'\u001b[39m)\n\u001b[1;32m---> 81\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_inverse_transform\u001b[49m\u001b[43m(\u001b[49m\u001b[43mdata\u001b[49m\u001b[43m)\u001b[49m\n", + "File \u001b[1;32md:\\Dropbox\\89_other_application\\GitHub\\PETsARD\\PETsARD\\processor\\discretizing.py:152\u001b[0m, in \u001b[0;36mDiscretizingKBins._inverse_transform\u001b[1;34m(self, data)\u001b[0m\n\u001b[0;32m 140\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21m_inverse_transform\u001b[39m(\u001b[38;5;28mself\u001b[39m, data: pd\u001b[38;5;241m.\u001b[39mSeries) \u001b[38;5;241m-\u001b[39m\u001b[38;5;241m>\u001b[39m np\u001b[38;5;241m.\u001b[39mndarray:\n\u001b[0;32m 141\u001b[0m \u001b[38;5;250m \u001b[39m\u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[0;32m 142\u001b[0m \u001b[38;5;124;03m Inverse the transformed data to the numerical data.\u001b[39;00m\n\u001b[0;32m 143\u001b[0m \n\u001b[1;32m (...)\u001b[0m\n\u001b[0;32m 149\u001b[0m \u001b[38;5;124;03m (np.ndarray): The inverse transformed data.\u001b[39;00m\n\u001b[0;32m 150\u001b[0m \u001b[38;5;124;03m \"\"\"\u001b[39;00m\n\u001b[1;32m--> 152\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mmodel\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43minverse_transform\u001b[49m\u001b[43m(\u001b[49m\u001b[43mdata\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mvalues\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mreshape\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m-\u001b[39;49m\u001b[38;5;241;43m1\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m1\u001b[39;49m\u001b[43m)\u001b[49m\u001b[43m)\u001b[49m\u001b[38;5;241m.\u001b[39mravel()\n", + "File \u001b[1;32md:\\Dropbox\\89_other_application\\GitHub\\PETsARD\\.venv\\lib\\site-packages\\sklearn\\preprocessing\\_discretization.py:430\u001b[0m, in \u001b[0;36mKBinsDiscretizer.inverse_transform\u001b[1;34m(self, Xt)\u001b[0m\n\u001b[0;32m 427\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124monehot\u001b[39m\u001b[38;5;124m\"\u001b[39m \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mencode:\n\u001b[0;32m 428\u001b[0m Xt \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_encoder\u001b[38;5;241m.\u001b[39minverse_transform(Xt)\n\u001b[1;32m--> 430\u001b[0m Xinv \u001b[38;5;241m=\u001b[39m \u001b[43mcheck_array\u001b[49m\u001b[43m(\u001b[49m\u001b[43mXt\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mcopy\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43;01mTrue\u001b[39;49;00m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mdtype\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43m(\u001b[49m\u001b[43mnp\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mfloat64\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mnp\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mfloat32\u001b[49m\u001b[43m)\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m 431\u001b[0m n_features \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mn_bins_\u001b[38;5;241m.\u001b[39mshape[\u001b[38;5;241m0\u001b[39m]\n\u001b[0;32m 432\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m Xinv\u001b[38;5;241m.\u001b[39mshape[\u001b[38;5;241m1\u001b[39m] \u001b[38;5;241m!=\u001b[39m n_features:\n", + "File \u001b[1;32md:\\Dropbox\\89_other_application\\GitHub\\PETsARD\\.venv\\lib\\site-packages\\sklearn\\utils\\validation.py:1049\u001b[0m, in \u001b[0;36mcheck_array\u001b[1;34m(array, accept_sparse, accept_large_sparse, dtype, order, copy, force_all_finite, ensure_2d, allow_nd, ensure_min_samples, ensure_min_features, estimator, input_name)\u001b[0m\n\u001b[0;32m 1043\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mValueError\u001b[39;00m(\n\u001b[0;32m 1044\u001b[0m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mFound array with dim \u001b[39m\u001b[38;5;132;01m%d\u001b[39;00m\u001b[38;5;124m. \u001b[39m\u001b[38;5;132;01m%s\u001b[39;00m\u001b[38;5;124m expected <= 2.\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[0;32m 1045\u001b[0m \u001b[38;5;241m%\u001b[39m (array\u001b[38;5;241m.\u001b[39mndim, estimator_name)\n\u001b[0;32m 1046\u001b[0m )\n\u001b[0;32m 1048\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m force_all_finite:\n\u001b[1;32m-> 1049\u001b[0m \u001b[43m_assert_all_finite\u001b[49m\u001b[43m(\u001b[49m\n\u001b[0;32m 1050\u001b[0m \u001b[43m \u001b[49m\u001b[43marray\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m 1051\u001b[0m \u001b[43m \u001b[49m\u001b[43minput_name\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43minput_name\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m 1052\u001b[0m \u001b[43m \u001b[49m\u001b[43mestimator_name\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mestimator_name\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m 1053\u001b[0m \u001b[43m \u001b[49m\u001b[43mallow_nan\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mforce_all_finite\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m==\u001b[39;49m\u001b[43m \u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mallow-nan\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\n\u001b[0;32m 1054\u001b[0m \u001b[43m \u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m 1056\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m copy:\n\u001b[0;32m 1057\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m _is_numpy_namespace(xp):\n\u001b[0;32m 1058\u001b[0m \u001b[38;5;66;03m# only make a copy if `array` and `array_orig` may share memory`\u001b[39;00m\n", + "File \u001b[1;32md:\\Dropbox\\89_other_application\\GitHub\\PETsARD\\.venv\\lib\\site-packages\\sklearn\\utils\\validation.py:126\u001b[0m, in \u001b[0;36m_assert_all_finite\u001b[1;34m(X, allow_nan, msg_dtype, estimator_name, input_name)\u001b[0m\n\u001b[0;32m 123\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m first_pass_isfinite:\n\u001b[0;32m 124\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m\n\u001b[1;32m--> 126\u001b[0m \u001b[43m_assert_all_finite_element_wise\u001b[49m\u001b[43m(\u001b[49m\n\u001b[0;32m 127\u001b[0m \u001b[43m \u001b[49m\u001b[43mX\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m 128\u001b[0m \u001b[43m \u001b[49m\u001b[43mxp\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mxp\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m 129\u001b[0m \u001b[43m \u001b[49m\u001b[43mallow_nan\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mallow_nan\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m 130\u001b[0m \u001b[43m \u001b[49m\u001b[43mmsg_dtype\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mmsg_dtype\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m 131\u001b[0m \u001b[43m \u001b[49m\u001b[43mestimator_name\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mestimator_name\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m 132\u001b[0m \u001b[43m \u001b[49m\u001b[43minput_name\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43minput_name\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m 133\u001b[0m \u001b[43m\u001b[49m\u001b[43m)\u001b[49m\n", + "File \u001b[1;32md:\\Dropbox\\89_other_application\\GitHub\\PETsARD\\.venv\\lib\\site-packages\\sklearn\\utils\\validation.py:175\u001b[0m, in \u001b[0;36m_assert_all_finite_element_wise\u001b[1;34m(X, xp, allow_nan, msg_dtype, estimator_name, input_name)\u001b[0m\n\u001b[0;32m 158\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m estimator_name \u001b[38;5;129;01mand\u001b[39;00m input_name \u001b[38;5;241m==\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mX\u001b[39m\u001b[38;5;124m\"\u001b[39m \u001b[38;5;129;01mand\u001b[39;00m has_nan_error:\n\u001b[0;32m 159\u001b[0m \u001b[38;5;66;03m# Improve the error message on how to handle missing values in\u001b[39;00m\n\u001b[0;32m 160\u001b[0m \u001b[38;5;66;03m# scikit-learn.\u001b[39;00m\n\u001b[0;32m 161\u001b[0m msg_err \u001b[38;5;241m+\u001b[39m\u001b[38;5;241m=\u001b[39m (\n\u001b[0;32m 162\u001b[0m \u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;130;01m\\n\u001b[39;00m\u001b[38;5;132;01m{\u001b[39;00mestimator_name\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m does not accept missing values\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[0;32m 163\u001b[0m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m encoded as NaN natively. For supervised learning, you might want\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m (...)\u001b[0m\n\u001b[0;32m 173\u001b[0m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m#estimators-that-handle-nan-values\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[0;32m 174\u001b[0m )\n\u001b[1;32m--> 175\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mValueError\u001b[39;00m(msg_err)\n", + "\u001b[1;31mValueError\u001b[0m: Input contains NaN." + ] + } + ], + "source": [ + "issue332(\n", + " load=load,\n", + " synthesizing_method='smartnoise-pacsynth'\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Preproc config of Outlier before update as ...\n", + "{'age': ,\n", + " 'capital-gain': ,\n", + " 'capital-loss': ,\n", + " 'education': None,\n", + " 'educational-num': ,\n", + " 'fnlwgt': ,\n", + " 'gender': None,\n", + " 'hours-per-week': ,\n", + " 'income': None,\n", + " 'marital-status': None,\n", + " 'native-country': None,\n", + " 'occupation': None,\n", + " 'race': None,\n", + " 'relationship': None,\n", + " 'workclass': None}\n", + "Preproc config of Outlier before after as ...\n", + "{'age': ,\n", + " 'capital-gain': ,\n", + " 'capital-loss': ,\n", + " 'education': None,\n", + " 'educational-num': ,\n", + " 'fnlwgt': ,\n", + " 'gender': None,\n", + " 'hours-per-week': ,\n", + " 'income': None,\n", + " 'marital-status': None,\n", + " 'native-country': None,\n", + " 'occupation': None,\n", + " 'race': None,\n", + " 'relationship': None,\n", + " 'workclass': None}\n", + "Preproc data as ...\n", + " age workclass fnlwgt education educational-num marital-status \\\n", + "0 -0.953119 0.366136 0.409053 0.776959 -1.152735 0.810663 \n", + "\n", + " occupation relationship race gender capital-gain capital-loss \\\n", + "0 0.74892 0.757864 0.955584 0.48598 -0.159918 -0.219043 \n", + "\n", + " hours-per-week native-country income \n", + "0 -0.044504 0.216869 0.212391 \n", + "Synthesizer (SmartNoise): Fitting dpctgan.\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "d:\\Dropbox\\89_other_application\\GitHub\\PETsARD\\.venv\\lib\\site-packages\\opacus\\privacy_engine.py:638: UserWarning: The sample rate will be defined from ``batch_size`` and ``sample_size``.The returned privacy budget will be incorrect.\n", + " warnings.warn(\n", + "d:\\Dropbox\\89_other_application\\GitHub\\PETsARD\\.venv\\lib\\site-packages\\opacus\\privacy_engine.py:229: UserWarning: Secure RNG turned off. This is perfectly fine for experimentation as it allows for much faster training performance, but remember to turn it on and retrain one last time before production with ``secure_rng`` turned on.\n", + " warnings.warn(\n", + "d:\\Dropbox\\89_other_application\\GitHub\\PETsARD\\.venv\\lib\\site-packages\\torch\\nn\\modules\\module.py:1352: UserWarning: Using a non-full backward hook when the forward contains multiple autograd Nodes is deprecated and will be removed in future versions. This hook will be missing some grad_input. Please use register_full_backward_hook to get the documented behavior.\n", + " warnings.warn(\"Using a non-full backward hook when the forward contains multiple autograd Nodes \"\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Epoch 1, Loss G: 0.7107, Loss D: 1.3900\n", + "epsilon is 0.08246410416899833, alpha is 63.0\n", + "Epoch 2, Loss G: 0.7097, Loss D: 1.3898\n", + "epsilon is 0.6154321240229542, alpha is 21.0\n", + "Epoch 3, Loss G: 0.7048, Loss D: 1.3908\n", + "epsilon is 0.8717652885709554, alpha is 16.0\n", + "Epoch 4, Loss G: 0.7016, Loss D: 1.3897\n", + "epsilon is 1.0734433928301887, alpha is 14.0\n", + "Epoch 5, Loss G: 0.7059, Loss D: 1.3874\n", + "epsilon is 1.2474424384805656, alpha is 12.0\n", + "Epoch 6, Loss G: 0.7015, Loss D: 1.3895\n", + "epsilon is 1.403078289939324, alpha is 10.9\n", + "Epoch 7, Loss G: 0.6987, Loss D: 1.3876\n", + "epsilon is 1.5430774752709655, alpha is 10.6\n", + "Epoch 8, Loss G: 0.6922, Loss D: 1.3928\n", + "epsilon is 1.6748565545536702, alpha is 10.0\n", + "Epoch 9, Loss G: 0.6947, Loss D: 1.3898\n", + "epsilon is 1.7986930762464894, alpha is 9.4\n", + "Epoch 10, Loss G: 0.6898, Loss D: 1.3850\n", + "epsilon is 1.9160035051686128, alpha is 9.0\n", + "Epoch 11, Loss G: 0.6888, Loss D: 1.3885\n", + "epsilon is 2.027907968415127, alpha is 8.7\n", + "Epoch 12, Loss G: 0.6854, Loss D: 1.3891\n", + "epsilon is 2.135121781113915, alpha is 8.3\n", + "Epoch 13, Loss G: 0.6822, Loss D: 1.3884\n", + "epsilon is 2.2382637160168204, alpha is 8.1\n", + "Epoch 14, Loss G: 0.6828, Loss D: 1.3872\n", + "epsilon is 2.337827882631375, alpha is 7.8\n", + "Epoch 15, Loss G: 0.6835, Loss D: 1.3950\n", + "epsilon is 2.434237194672732, alpha is 7.6\n", + "Epoch 16, Loss G: 0.6794, Loss D: 1.3912\n", + "epsilon is 2.5278163462034815, alpha is 7.4\n", + "Epoch 17, Loss G: 0.6722, Loss D: 1.3944\n", + "epsilon is 2.6188388524326234, alpha is 7.2\n", + "Epoch 18, Loss G: 0.6723, Loss D: 1.3989\n", + "epsilon is 2.7076108306108346, alpha is 7.0\n", + "Epoch 19, Loss G: 0.6683, Loss D: 1.3920\n", + "epsilon is 2.7940664707675626, alpha is 6.9\n", + "Epoch 20, Loss G: 0.6670, Loss D: 1.3951\n", + "epsilon is 2.878763468386405, alpha is 6.8\n", + "Epoch 21, Loss G: 0.6687, Loss D: 1.3902\n", + "epsilon is 2.961464297854409, alpha is 6.6\n", + "Epoch 22, Loss G: 0.6657, Loss D: 1.3872\n", + "epsilon is 3.0425452652856495, alpha is 6.5\n", + "Epoch 23, Loss G: 0.6616, Loss D: 1.3926\n", + "epsilon is 3.122103446988952, alpha is 6.4\n", + "Epoch 24, Loss G: 0.6631, Loss D: 1.3951\n", + "epsilon is 3.200208539731727, alpha is 6.3\n", + "Epoch 25, Loss G: 0.6614, Loss D: 1.3910\n", + "epsilon is 3.2769350559807005, alpha is 6.2\n", + "Epoch 26, Loss G: 0.6637, Loss D: 1.3855\n", + "epsilon is 3.3523628060835255, alpha is 6.1\n", + "Epoch 27, Loss G: 0.6664, Loss D: 1.3881\n", + "epsilon is 3.4265774389666657, alpha is 6.0\n", + "Epoch 28, Loss G: 0.6632, Loss D: 1.3884\n", + "epsilon is 3.4996710497868895, alpha is 5.9\n", + "Epoch 29, Loss G: 0.6609, Loss D: 1.3892\n", + "epsilon is 3.5717428643995515, alpha is 5.8\n", + "Epoch 30, Loss G: 0.6594, Loss D: 1.3910\n", + "epsilon is 3.6428935706241528, alpha is 5.8\n", + "Epoch 31, Loss G: 0.6567, Loss D: 1.3863\n", + "epsilon is 3.712751905883079, alpha is 5.7\n", + "Epoch 32, Loss G: 0.6577, Loss D: 1.3827\n", + "epsilon is 3.7818141415719784, alpha is 5.6\n", + "Epoch 33, Loss G: 0.6609, Loss D: 1.3858\n", + "epsilon is 3.850205948937656, alpha is 5.5\n", + "Epoch 34, Loss G: 0.6532, Loss D: 1.3819\n", + "epsilon is 3.917468189518828, alpha is 5.5\n", + "Epoch 35, Loss G: 0.6551, Loss D: 1.3896\n", + "epsilon is 3.9840352850839897, alpha is 5.4\n", + "Epoch 36, Loss G: 0.6527, Loss D: 1.3815\n", + "epsilon is 4.050006670613625, alpha is 5.4\n", + "Epoch 37, Loss G: 0.6516, Loss D: 1.3873\n", + "epsilon is 4.114903091238218, alpha is 5.3\n", + "Epoch 38, Loss G: 0.6477, Loss D: 1.3901\n", + "epsilon is 4.179582102216282, alpha is 5.2\n", + "Epoch 39, Loss G: 0.6504, Loss D: 1.3830\n", + "epsilon is 4.242979684763249, alpha is 5.2\n", + "Epoch 40, Loss G: 0.6503, Loss D: 1.3904\n", + "epsilon is 4.306336090935168, alpha is 5.1\n", + "Epoch 41, Loss G: 0.6467, Loss D: 1.3944\n", + "epsilon is 4.368450711228463, alpha is 5.1\n", + "Epoch 42, Loss G: 0.6481, Loss D: 1.3939\n", + "epsilon is 4.430565331521756, alpha is 5.1\n", + "Epoch 43, Loss G: 0.6474, Loss D: 1.3883\n", + "epsilon is 4.491519093988257, alpha is 5.0\n", + "Epoch 44, Loss G: 0.6422, Loss D: 1.3922\n", + "epsilon is 4.552353368662634, alpha is 5.0\n", + "Epoch 45, Loss G: 0.6401, Loss D: 1.3860\n", + "epsilon is 4.612407286797646, alpha is 4.9\n", + "Epoch 46, Loss G: 0.6392, Loss D: 1.3932\n", + "epsilon is 4.671963825364106, alpha is 4.9\n", + "Epoch 47, Loss G: 0.6358, Loss D: 1.3937\n", + "epsilon is 4.731359885691218, alpha is 4.8\n", + "Epoch 48, Loss G: 0.6367, Loss D: 1.3966\n", + "epsilon is 4.789641290552096, alpha is 4.8\n", + "Epoch 49, Loss G: 0.6344, Loss D: 1.4051\n", + "epsilon is 4.847922695412973, alpha is 4.8\n", + "Epoch 50, Loss G: 0.6350, Loss D: 1.4007\n", + "epsilon is 4.905655535140821, alpha is 4.7\n", + "Epoch 51, Loss G: 0.6294, Loss D: 1.4034\n", + "epsilon is 4.9626644016048695, alpha is 4.7\n", + "Epoch 52, Loss G: 0.6293, Loss D: 1.3913\n", + "epsilon is 5.0196732680689164, alpha is 4.7\n", + "Epoch 53, Loss G: 0.6228, Loss D: 1.4126\n", + "epsilon is 5.0760439796888255, alpha is 4.6\n", + "Epoch 54, Loss G: 0.6300, Loss D: 1.4028\n", + "epsilon is 5.131782895986607, alpha is 4.6\n", + "Epoch 55, Loss G: 0.6118, Loss D: 1.4130\n", + "epsilon is 5.1875218122843885, alpha is 4.6\n", + "Epoch 56, Loss G: 0.6162, Loss D: 1.4176\n", + "epsilon is 5.242864373609633, alpha is 4.5\n", + "Epoch 57, Loss G: 0.6124, Loss D: 1.4177\n", + "epsilon is 5.297335920908565, alpha is 4.5\n", + "Epoch 58, Loss G: 0.6036, Loss D: 1.4231\n", + "epsilon is 5.351807468207497, alpha is 4.5\n", + "Epoch 59, Loss G: 0.6017, Loss D: 1.4224\n", + "epsilon is 5.406279015506429, alpha is 4.5\n", + "Epoch 60, Loss G: 0.6025, Loss D: 1.4320\n", + "epsilon is 5.4597004033036685, alpha is 4.4\n", + "Epoch 61, Loss G: 0.5997, Loss D: 1.4292\n", + "epsilon is 5.512907155723584, alpha is 4.4\n", + "Epoch 62, Loss G: 0.5974, Loss D: 1.4378\n", + "epsilon is 5.566113908143499, alpha is 4.4\n", + "Epoch 63, Loss G: 0.5980, Loss D: 1.4396\n", + "epsilon is 5.619296859313559, alpha is 4.3\n", + "Epoch 64, Loss G: 0.5923, Loss D: 1.4463\n", + "epsilon is 5.671241383942272, alpha is 4.3\n", + "Epoch 65, Loss G: 0.5817, Loss D: 1.4429\n", + "epsilon is 5.723185908570986, alpha is 4.3\n", + "Epoch 66, Loss G: 0.5879, Loss D: 1.4591\n", + "epsilon is 5.775130433199701, alpha is 4.3\n", + "Epoch 67, Loss G: 0.5835, Loss D: 1.4501\n", + "epsilon is 5.827074957828414, alpha is 4.3\n", + "Epoch 68, Loss G: 0.5829, Loss D: 1.4513\n", + "epsilon is 5.877966017103227, alpha is 4.2\n", + "Epoch 69, Loss G: 0.5753, Loss D: 1.4468\n", + "epsilon is 5.928650874011896, alpha is 4.2\n", + "Epoch 70, Loss G: 0.5799, Loss D: 1.4541\n", + "epsilon is 5.979335730920565, alpha is 4.2\n", + "Epoch 71, Loss G: 0.5725, Loss D: 1.4617\n", + "epsilon is 6.030020587829234, alpha is 4.2\n", + "Epoch 72, Loss G: 0.5712, Loss D: 1.4637\n", + "epsilon is 6.080411733176044, alpha is 4.1\n", + "Epoch 73, Loss G: 0.5642, Loss D: 1.4602\n", + "epsilon is 6.129839475434501, alpha is 4.1\n", + "Epoch 74, Loss G: 0.5707, Loss D: 1.4774\n", + "epsilon is 6.17926721769296, alpha is 4.1\n", + "Epoch 75, Loss G: 0.5642, Loss D: 1.4687\n", + "epsilon is 6.228694959951418, alpha is 4.1\n", + "Epoch 76, Loss G: 0.5691, Loss D: 1.4757\n", + "epsilon is 6.278122702209876, alpha is 4.1\n", + "Epoch 77, Loss G: 0.5707, Loss D: 1.4758\n", + "epsilon is 6.327357790594008, alpha is 4.0\n", + "Epoch 78, Loss G: 0.5639, Loss D: 1.4835\n", + "epsilon is 6.375530964286589, alpha is 4.0\n", + "Epoch 79, Loss G: 0.5652, Loss D: 1.4719\n", + "epsilon is 6.42370413797917, alpha is 4.0\n", + "Epoch 80, Loss G: 0.5628, Loss D: 1.4748\n", + "epsilon is 6.471877311671752, alpha is 4.0\n", + "Epoch 81, Loss G: 0.5608, Loss D: 1.4772\n", + "epsilon is 6.520050485364332, alpha is 4.0\n", + "Epoch 82, Loss G: 0.5557, Loss D: 1.4777\n", + "epsilon is 6.568223659056913, alpha is 4.0\n", + "Epoch 83, Loss G: 0.5670, Loss D: 1.4892\n", + "epsilon is 6.615734837571576, alpha is 3.9\n", + "Epoch 84, Loss G: 0.5594, Loss D: 1.4800\n", + "epsilon is 6.662655981812787, alpha is 3.9\n", + "Epoch 85, Loss G: 0.5555, Loss D: 1.4830\n", + "epsilon is 6.709577126053995, alpha is 3.9\n", + "Epoch 86, Loss G: 0.5576, Loss D: 1.4863\n", + "epsilon is 6.7564982702952046, alpha is 3.9\n", + "Epoch 87, Loss G: 0.5547, Loss D: 1.4850\n", + "epsilon is 6.803419414536414, alpha is 3.9\n", + "Epoch 88, Loss G: 0.5603, Loss D: 1.4947\n", + "epsilon is 6.850340558777624, alpha is 3.9\n", + "Epoch 89, Loss G: 0.5570, Loss D: 1.4874\n", + "epsilon is 6.896912311217361, alpha is 3.8\n", + "Epoch 90, Loss G: 0.5555, Loss D: 1.4854\n", + "epsilon is 6.942583958167586, alpha is 3.8\n", + "Epoch 91, Loss G: 0.5516, Loss D: 1.4906\n", + "epsilon is 6.98825560511781, alpha is 3.8\n", + "Epoch 92, Loss G: 0.5562, Loss D: 1.4936\n", + "epsilon is 7.033927252068035, alpha is 3.8\n", + "Epoch 93, Loss G: 0.5531, Loss D: 1.4931\n", + "epsilon is 7.079598899018261, alpha is 3.8\n", + "Epoch 94, Loss G: 0.5540, Loss D: 1.4899\n", + "epsilon is 7.125270545968486, alpha is 3.8\n", + "Epoch 95, Loss G: 0.5474, Loss D: 1.4922\n", + "epsilon is 7.170942192918711, alpha is 3.8\n", + "Epoch 96, Loss G: 0.5483, Loss D: 1.4962\n", + "epsilon is 7.216225114747892, alpha is 3.7\n", + "Epoch 97, Loss G: 0.5495, Loss D: 1.4945\n", + "epsilon is 7.260649789629172, alpha is 3.7\n", + "Epoch 98, Loss G: 0.5478, Loss D: 1.4869\n", + "epsilon is 7.305074464510453, alpha is 3.7\n", + "Epoch 99, Loss G: 0.5472, Loss D: 1.4911\n", + "epsilon is 7.349499139391733, alpha is 3.7\n", + "Epoch 100, Loss G: 0.5532, Loss D: 1.4938\n", + "epsilon is 7.3939238142730135, alpha is 3.7\n", + "Epoch 101, Loss G: 0.5451, Loss D: 1.4891\n", + "epsilon is 7.4383484891542935, alpha is 3.7\n", + "Epoch 102, Loss G: 0.5529, Loss D: 1.4897\n", + "epsilon is 7.482773164035574, alpha is 3.7\n", + "Epoch 103, Loss G: 0.5484, Loss D: 1.4863\n", + "epsilon is 7.527197838916854, alpha is 3.7\n", + "Epoch 104, Loss G: 0.5478, Loss D: 1.4795\n", + "epsilon is 7.570985182837946, alpha is 3.6\n", + "Epoch 105, Loss G: 0.5577, Loss D: 1.4863\n", + "epsilon is 7.614165403949305, alpha is 3.6\n", + "Epoch 106, Loss G: 0.5572, Loss D: 1.4800\n", + "epsilon is 7.657345625060665, alpha is 3.6\n", + "Epoch 107, Loss G: 0.5546, Loss D: 1.4823\n", + "epsilon is 7.700525846172025, alpha is 3.6\n", + "Epoch 108, Loss G: 0.5574, Loss D: 1.4715\n", + "epsilon is 7.743706067283383, alpha is 3.6\n", + "Epoch 109, Loss G: 0.5554, Loss D: 1.4778\n", + "epsilon is 7.786886288394741, alpha is 3.6\n", + "Epoch 110, Loss G: 0.5621, Loss D: 1.4794\n", + "epsilon is 7.830066509506101, alpha is 3.6\n", + "Epoch 111, Loss G: 0.5558, Loss D: 1.4784\n", + "epsilon is 7.873246730617461, alpha is 3.6\n", + "Epoch 112, Loss G: 0.5632, Loss D: 1.4766\n", + "epsilon is 7.916426951728819, alpha is 3.6\n", + "Epoch 113, Loss G: 0.5602, Loss D: 1.4731\n", + "epsilon is 7.95868193355541, alpha is 3.5\n", + "Epoch 114, Loss G: 0.5532, Loss D: 1.4708\n", + "epsilon is 8.000620212289654, alpha is 3.5\n", + "Epoch 115, Loss G: 0.5629, Loss D: 1.4671\n", + "epsilon is 8.042558491023897, alpha is 3.5\n", + "Epoch 116, Loss G: 0.5605, Loss D: 1.4703\n", + "epsilon is 8.084496769758141, alpha is 3.5\n", + "Epoch 117, Loss G: 0.5661, Loss D: 1.4603\n", + "epsilon is 8.126435048492386, alpha is 3.5\n", + "Epoch 118, Loss G: 0.5667, Loss D: 1.4750\n", + "epsilon is 8.168373327226629, alpha is 3.5\n", + "Epoch 119, Loss G: 0.5659, Loss D: 1.4663\n", + "epsilon is 8.210311605960872, alpha is 3.5\n", + "Epoch 120, Loss G: 0.5597, Loss D: 1.4697\n", + "epsilon is 8.252249884695114, alpha is 3.5\n", + "Epoch 121, Loss G: 0.5606, Loss D: 1.4597\n", + "epsilon is 8.294188163429357, alpha is 3.5\n", + "Epoch 122, Loss G: 0.5615, Loss D: 1.4584\n", + "epsilon is 8.336126442163602, alpha is 3.5\n", + "Epoch 123, Loss G: 0.5618, Loss D: 1.4627\n", + "epsilon is 8.377016439122203, alpha is 3.4\n", + "Epoch 124, Loss G: 0.5616, Loss D: 1.4609\n", + "epsilon is 8.417715279980845, alpha is 3.4\n", + "Epoch 125, Loss G: 0.5624, Loss D: 1.4777\n", + "epsilon is 8.458414120839487, alpha is 3.4\n", + "Epoch 126, Loss G: 0.5613, Loss D: 1.4678\n", + "epsilon is 8.49911296169813, alpha is 3.4\n", + "Epoch 127, Loss G: 0.5576, Loss D: 1.4605\n", + "epsilon is 8.539811802556772, alpha is 3.4\n", + "Epoch 128, Loss G: 0.5654, Loss D: 1.4663\n", + "epsilon is 8.580510643415414, alpha is 3.4\n", + "Epoch 129, Loss G: 0.5651, Loss D: 1.4615\n", + "epsilon is 8.621209484274056, alpha is 3.4\n", + "Epoch 130, Loss G: 0.5654, Loss D: 1.4641\n", + "epsilon is 8.661908325132698, alpha is 3.4\n", + "Epoch 131, Loss G: 0.5639, Loss D: 1.4569\n", + "epsilon is 8.702607165991338, alpha is 3.4\n", + "Epoch 132, Loss G: 0.5672, Loss D: 1.4536\n", + "epsilon is 8.74330600684998, alpha is 3.4\n", + "Epoch 133, Loss G: 0.5713, Loss D: 1.4582\n", + "epsilon is 8.784004847708623, alpha is 3.4\n", + "Epoch 134, Loss G: 0.5650, Loss D: 1.4524\n", + "epsilon is 8.823944600709858, alpha is 3.3\n", + "Epoch 135, Loss G: 0.5589, Loss D: 1.4599\n", + "epsilon is 8.863406501319506, alpha is 3.3\n", + "Epoch 136, Loss G: 0.5631, Loss D: 1.4620\n", + "epsilon is 8.902868401929158, alpha is 3.3\n", + "Epoch 137, Loss G: 0.5668, Loss D: 1.4641\n", + "epsilon is 8.94233030253881, alpha is 3.3\n", + "Epoch 138, Loss G: 0.5698, Loss D: 1.4604\n", + "epsilon is 8.98179220314846, alpha is 3.3\n", + "Epoch 139, Loss G: 0.5650, Loss D: 1.4606\n", + "epsilon is 9.02125410375811, alpha is 3.3\n", + "Epoch 140, Loss G: 0.5700, Loss D: 1.4558\n", + "epsilon is 9.060716004367762, alpha is 3.3\n", + "Epoch 141, Loss G: 0.5640, Loss D: 1.4623\n", + "epsilon is 9.100177904977413, alpha is 3.3\n", + "Epoch 142, Loss G: 0.5664, Loss D: 1.4496\n", + "epsilon is 9.139639805587063, alpha is 3.3\n", + "Epoch 143, Loss G: 0.5707, Loss D: 1.4586\n", + "epsilon is 9.179101706196715, alpha is 3.3\n", + "Epoch 144, Loss G: 0.5654, Loss D: 1.4485\n", + "epsilon is 9.218563606806365, alpha is 3.3\n", + "Epoch 145, Loss G: 0.5691, Loss D: 1.4478\n", + "epsilon is 9.258025507416015, alpha is 3.3\n", + "Epoch 146, Loss G: 0.5750, Loss D: 1.4537\n", + "epsilon is 9.297487408025667, alpha is 3.3\n", + "Epoch 147, Loss G: 0.5737, Loss D: 1.4539\n", + "epsilon is 9.33595966014038, alpha is 3.2\n", + "Epoch 148, Loss G: 0.5714, Loss D: 1.4532\n", + "epsilon is 9.374187111269075, alpha is 3.2\n", + "Epoch 149, Loss G: 0.5723, Loss D: 1.4538\n", + "epsilon is 9.41241456239777, alpha is 3.2\n", + "Epoch 150, Loss G: 0.5719, Loss D: 1.4523\n", + "epsilon is 9.450642013526464, alpha is 3.2\n", + "Epoch 151, Loss G: 0.5674, Loss D: 1.4542\n", + "epsilon is 9.48886946465516, alpha is 3.2\n", + "Epoch 152, Loss G: 0.5661, Loss D: 1.4573\n", + "epsilon is 9.527096915783854, alpha is 3.2\n", + "Epoch 153, Loss G: 0.5722, Loss D: 1.4540\n", + "epsilon is 9.56532436691255, alpha is 3.2\n", + "Epoch 154, Loss G: 0.5742, Loss D: 1.4563\n", + "epsilon is 9.603551818041245, alpha is 3.2\n", + "Epoch 155, Loss G: 0.5725, Loss D: 1.4476\n", + "epsilon is 9.64177926916994, alpha is 3.2\n", + "Epoch 156, Loss G: 0.5703, Loss D: 1.4535\n", + "epsilon is 9.680006720298634, alpha is 3.2\n", + "Epoch 157, Loss G: 0.5690, Loss D: 1.4478\n", + "epsilon is 9.71823417142733, alpha is 3.2\n", + "Epoch 158, Loss G: 0.5698, Loss D: 1.4475\n", + "epsilon is 9.756461622556024, alpha is 3.2\n", + "Epoch 159, Loss G: 0.5766, Loss D: 1.4464\n", + "epsilon is 9.79468907368472, alpha is 3.2\n", + "Epoch 160, Loss G: 0.5784, Loss D: 1.4552\n", + "epsilon is 9.832916524813415, alpha is 3.2\n", + "Epoch 161, Loss G: 0.5751, Loss D: 1.4438\n", + "epsilon is 9.871016847131145, alpha is 3.1\n", + "Epoch 162, Loss G: 0.5799, Loss D: 1.4512\n", + "epsilon is 9.908012332703517, alpha is 3.1\n", + "Epoch 163, Loss G: 0.5725, Loss D: 1.4488\n", + "epsilon is 9.94500781827589, alpha is 3.1\n", + "Epoch 164, Loss G: 0.5762, Loss D: 1.4466\n", + "epsilon is 9.982003303848263, alpha is 3.1\n", + "Synthesizer (SmartNoise): Fitting dpctgan spent 115.4473 sec.\n", + "Synthesizer (SmartNoise): Sampling dpctgan # 927 rows (same as raw) in 0.4688 sec.\n", + "Sync data as ...\n", + " age workclass fnlwgt education educational-num marital-status \\\n", + "0 3.663019 0.997224 4.606915 0.747578 2.269901 0.984679 \n", + "\n", + " occupation relationship race gender capital-gain capital-loss \\\n", + "0 0.990903 0.95497 0.806221 0.997782 -0.159918 -0.219043 \n", + "\n", + " hours-per-week native-country income \n", + "0 2.634771 0.88122 0.993726 \n", + " age workclass fnlwgt education educational-num \\\n", + "0 88.295354 Federal-gov 647932.096102 Masters 15.972689 \n", + "\n", + " marital-status occupation relationship race gender capital-gain \\\n", + "0 Widowed Protective-serv Wife White Female 0.0 \n", + "\n", + " capital-loss hours-per-week native-country income \n", + "0 0.0 72.657481 United-States >50K \n" + ] + } + ], + "source": [ + "issue332_gan(\n", + " load = load,\n", + " synthesizing_method = 'smartnoise-dpctgan',\n", + " outlier_inhibit = True\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Preproc config of Outlier before update as ...\n", + "{'age': ,\n", + " 'capital-gain': ,\n", + " 'capital-loss': ,\n", + " 'education': None,\n", + " 'educational-num': ,\n", + " 'fnlwgt': ,\n", + " 'gender': None,\n", + " 'hours-per-week': ,\n", + " 'income': None,\n", + " 'marital-status': None,\n", + " 'native-country': None,\n", + " 'occupation': None,\n", + " 'race': None,\n", + " 'relationship': None,\n", + " 'workclass': None}\n", + "Preproc config of Outlier before after as ...\n", + "{'age': ,\n", + " 'capital-gain': ,\n", + " 'capital-loss': ,\n", + " 'education': None,\n", + " 'educational-num': ,\n", + " 'fnlwgt': ,\n", + " 'gender': None,\n", + " 'hours-per-week': ,\n", + " 'income': None,\n", + " 'marital-status': None,\n", + " 'native-country': None,\n", + " 'occupation': None,\n", + " 'race': None,\n", + " 'relationship': None,\n", + " 'workclass': None}\n", + "Preproc data as ...\n", + " age workclass fnlwgt education educational-num marital-status \\\n", + "0 -0.953119 0.006814 0.409053 0.780317 -1.152735 0.507641 \n", + "\n", + " occupation relationship race gender capital-gain capital-loss \\\n", + "0 0.750354 0.787001 0.864054 0.199314 -0.159918 -0.219043 \n", + "\n", + " hours-per-week native-country income \n", + "0 -0.044504 0.099702 0.61332 \n", + "Synthesizer (SmartNoise): Fitting patectgan.\n", + "using loss cross_entropy and regularization None\n", + "eps: 0.102479 \t G: 0.713781 \t D: 0.716145\n", + "eps: 0.457732 \t G: 0.713048 \t D: 0.712165\n", + "eps: 0.650248 \t G: 0.689282 \t D: 0.698435\n", + "eps: 0.799151 \t G: 0.698342 \t D: 0.710174\n", + "eps: 0.925562 \t G: 0.716226 \t D: 0.704634\n", + "eps: 1.037397 \t G: 0.750504 \t D: 0.704282\n", + "eps: 1.139329 \t G: 0.733702 \t D: 0.708267\n", + "eps: 1.232819 \t G: 0.742357 \t D: 0.711618\n", + "eps: 1.320496 \t G: 0.698668 \t D: 0.703252\n", + "eps: 1.403195 \t G: 0.737517 \t D: 0.699576\n", + "eps: 1.481995 \t G: 0.681477 \t D: 0.705583\n", + "eps: 1.556995 \t G: 0.672663 \t D: 0.702600\n", + "eps: 1.628302 \t G: 0.745140 \t D: 0.694179\n", + "eps: 1.698302 \t G: 0.690847 \t D: 0.712576\n", + "eps: 1.763994 \t G: 0.727325 \t D: 0.703433\n", + "eps: 1.828994 \t G: 0.709237 \t D: 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native-country income \n", + "0 41.004116 54.393705 United-States <=50K \n" + ] + } + ], + "source": [ + "issue332_gan(\n", + " load = load,\n", + " synthesizing_method = 'smartnoise-patectgan',\n", + " outlier_inhibit = True\n", + ")" + ] } ], "metadata": { @@ -279,7 +1432,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.10.13" + "version": "3.10.11" } }, "nbformat": 4, diff --git a/poetry.lock b/poetry.lock deleted file mode 100644 index ea021467..00000000 --- a/poetry.lock +++ /dev/null @@ -1,3127 +0,0 @@ -# This file is automatically @generated by Poetry 1.7.1 and should not be changed by hand. - -[[package]] -name = "anonymeter" -version = "1.0.0" -description = "Measure singling out, linkability, and inference risk for synthetic data." -optional = false -python-versions = "<3.12,>3.7" -files = [ - {file = "anonymeter-1.0.0-py3-none-any.whl", hash = "sha256:b4144ddf5d4ca51fd464479d963eb3959f6bbf990affdde7a778ffb89096641e"}, - 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-[tool.poetry.dependencies] -python = ">=3.8,<3.11" -wheel = "^0.42.0" -pyyaml = "^5.4.1" -sdv = "^1.9.0" -smartnoise-synth = "^0.3.3" -anonymeter = "^1.0.0" -sdmetrics = "^0.13.0" -requests = "^2.31.0" -boto3 = "^1.34.37" -toml = "^0.10.2" -tqdm = "^4.66.1" -urllib3 = "<2" # https://github.com/python-poetry/poetry-plugin-export/issues/239 - -[tool.poetry.group.dev.dependencies] -ipykernel = "^6.29.2" -pytest = "^8.0.0" - [build-system] requires = ["poetry-core"] build-backend = "poetry.core.masonry.api" diff --git a/requirements-dev.txt b/requirements-dev.txt deleted file mode 100644 index 5d71c8a1..00000000 --- a/requirements-dev.txt +++ /dev/null @@ -1,1444 +0,0 @@ -anonymeter==1.0.0 ; python_version >= "3.8" and python_version < "3.11" \ - --hash=sha256:b4144ddf5d4ca51fd464479d963eb3959f6bbf990affdde7a778ffb89096641e \ - --hash=sha256:bba90478b266733a9dd41aa085b38de5d4666d5dc9373404923c27a3012a504c -antlr4-python3-runtime==4.9.3 ; python_version >= "3.8" and python_version < "3.11" \ - --hash=sha256:f224469b4168294902bb1efa80a8bf7855f24c99aef99cbefc1bcd3cce77881b -appnope==0.1.4 ; python_version >= "3.8" and python_version < "3.11" and (platform_system == "Darwin" or sys_platform == "darwin") \ - --hash=sha256:1de3860566df9caf38f01f86f65e0e13e379af54f9e4bee1e66b48f2efffd1ee \ - --hash=sha256:502575ee11cd7a28c0205f379b525beefebab9d161b7c964670864014ed7213c -asttokens==2.4.1 ; python_version >= "3.8" and python_version < "3.11" \ - --hash=sha256:051ed49c3dcae8913ea7cd08e46a606dba30b79993209636c4875bc1d637bc24 \ - --hash=sha256:b03869718ba9a6eb027e134bfdf69f38a236d681c83c160d510768af11254ba0 -backcall==0.2.0 ; python_version >= "3.8" and python_version < "3.11" \ - --hash=sha256:5cbdbf27be5e7cfadb448baf0aa95508f91f2bbc6c6437cd9cd06e2a4c215e1e \ - --hash=sha256:fbbce6a29f263178a1f7915c1940bde0ec2b2a967566fe1c65c1dfb7422bd255 -boto3==1.34.54 ; python_version >= "3.8" and python_version < "3.11" \ - --hash=sha256:8b3f5cc7fbedcbb22271c328039df8a6ab343001e746e0cdb24774c426cadcf8 \ - --hash=sha256:f201b6a416f809283d554c652211eecec9fe3a52ed4063dab3f3e7aea7571d9c -botocore==1.34.54 ; python_version >= "3.8" and python_version < "3.11" \ - --hash=sha256:4061ff4be3efcf53547ebadf2c94d419dfc8be7beec24e9fa1819599ffd936fa \ - --hash=sha256:bf215d93e9d5544c593962780d194e74c6ee40b883d0b885e62ef35fc0ec01e5 -certifi==2024.2.2 ; python_version >= "3.8" and python_version < "3.11" \ - --hash=sha256:0569859f95fc761b18b45ef421b1290a0f65f147e92a1e5eb3e635f9a5e4e66f \ - --hash=sha256:dc383c07b76109f368f6106eee2b593b04a011ea4d55f652c6ca24a754d1cdd1 -cffi==1.16.0 ; python_version >= "3.8" and python_version < "3.11" and implementation_name == "pypy" \ - --hash=sha256:0c9ef6ff37e974b73c25eecc13952c55bceed9112be2d9d938ded8e856138bcc \ - --hash=sha256:131fd094d1065b19540c3d72594260f118b231090295d8c34e19a7bbcf2e860a \ - --hash=sha256:1b8ebc27c014c59692bb2664c7d13ce7a6e9a629be20e54e7271fa696ff2b417 \ - --hash=sha256:2c56b361916f390cd758a57f2e16233eb4f64bcbeee88a4881ea90fca14dc6ab \ - --hash=sha256:2d92b25dbf6cae33f65005baf472d2c245c050b1ce709cc4588cdcdd5495b520 \ - --hash=sha256:31d13b0f99e0836b7ff893d37af07366ebc90b678b6664c955b54561fc36ef36 \ - --hash=sha256:32c68ef735dbe5857c810328cb2481e24722a59a2003018885514d4c09af9743 \ - --hash=sha256:3686dffb02459559c74dd3d81748269ffb0eb027c39a6fc99502de37d501faa8 \ - --hash=sha256:582215a0e9adbe0e379761260553ba11c58943e4bbe9c36430c4ca6ac74b15ed \ - --hash=sha256:5b50bf3f55561dac5438f8e70bfcdfd74543fd60df5fa5f62d94e5867deca684 \ - --hash=sha256:5bf44d66cdf9e893637896c7faa22298baebcd18d1ddb6d2626a6e39793a1d56 \ - --hash=sha256:6602bc8dc6f3a9e02b6c22c4fc1e47aa50f8f8e6d3f78a5e16ac33ef5fefa324 \ - --hash=sha256:673739cb539f8cdaa07d92d02efa93c9ccf87e345b9a0b556e3ecc666718468d \ - --hash=sha256:68678abf380b42ce21a5f2abde8efee05c114c2fdb2e9eef2efdb0257fba1235 \ - --hash=sha256:68e7c44931cc171c54ccb702482e9fc723192e88d25a0e133edd7aff8fcd1f6e \ - --hash=sha256:6b3d6606d369fc1da4fd8c357d026317fbb9c9b75d36dc16e90e84c26854b088 \ - --hash=sha256:748dcd1e3d3d7cd5443ef03ce8685043294ad6bd7c02a38d1bd367cfd968e000 \ - --hash=sha256:7651c50c8c5ef7bdb41108b7b8c5a83013bfaa8a935590c5d74627c047a583c7 \ - --hash=sha256:7b78010e7b97fef4bee1e896df8a4bbb6712b7f05b7ef630f9d1da00f6444d2e \ - --hash=sha256:7e61e3e4fa664a8588aa25c883eab612a188c725755afff6289454d6362b9673 \ - --hash=sha256:80876338e19c951fdfed6198e70bc88f1c9758b94578d5a7c4c91a87af3cf31c \ - --hash=sha256:8895613bcc094d4a1b2dbe179d88d7fb4a15cee43c052e8885783fac397d91fe \ - --hash=sha256:88e2b3c14bdb32e440be531ade29d3c50a1a59cd4e51b1dd8b0865c54ea5d2e2 \ - --hash=sha256:8f8e709127c6c77446a8c0a8c8bf3c8ee706a06cd44b1e827c3e6a2ee6b8c098 \ - --hash=sha256:9cb4a35b3642fc5c005a6755a5d17c6c8b6bcb6981baf81cea8bfbc8903e8ba8 \ - --hash=sha256:9f90389693731ff1f659e55c7d1640e2ec43ff725cc61b04b2f9c6d8d017df6a \ - --hash=sha256:a09582f178759ee8128d9270cd1344154fd473bb77d94ce0aeb2a93ebf0feaf0 \ - --hash=sha256:a6a14b17d7e17fa0d207ac08642c8820f84f25ce17a442fd15e27ea18d67c59b \ - --hash=sha256:a72e8961a86d19bdb45851d8f1f08b041ea37d2bd8d4fd19903bc3083d80c896 \ - --hash=sha256:abd808f9c129ba2beda4cfc53bde801e5bcf9d6e0f22f095e45327c038bfe68e \ - --hash=sha256:ac0f5edd2360eea2f1daa9e26a41db02dd4b0451b48f7c318e217ee092a213e9 \ - --hash=sha256:b29ebffcf550f9da55bec9e02ad430c992a87e5f512cd63388abb76f1036d8d2 \ - --hash=sha256:b2ca4e77f9f47c55c194982e10f058db063937845bb2b7a86c84a6cfe0aefa8b \ - --hash=sha256:b7be2d771cdba2942e13215c4e340bfd76398e9227ad10402a8767ab1865d2e6 \ - --hash=sha256:b84834d0cf97e7d27dd5b7f3aca7b6e9263c56308ab9dc8aae9784abb774d404 \ - --hash=sha256:b86851a328eedc692acf81fb05444bdf1891747c25af7529e39ddafaf68a4f3f \ - --hash=sha256:bcb3ef43e58665bbda2fb198698fcae6776483e0c4a631aa5647806c25e02cc0 \ - --hash=sha256:c0f31130ebc2d37cdd8e44605fb5fa7ad59049298b3f745c74fa74c62fbfcfc4 \ - --hash=sha256:c6a164aa47843fb1b01e941d385aab7215563bb8816d80ff3a363a9f8448a8dc \ - --hash=sha256:d8a9d3ebe49f084ad71f9269834ceccbf398253c9fac910c4fd7053ff1386936 \ - --hash=sha256:db8e577c19c0fda0beb7e0d4e09e0ba74b1e4c092e0e40bfa12fe05b6f6d75ba \ - --hash=sha256:dc9b18bf40cc75f66f40a7379f6a9513244fe33c0e8aa72e2d56b0196a7ef872 \ - --hash=sha256:e09f3ff613345df5e8c3667da1d918f9149bd623cd9070c983c013792a9a62eb \ - --hash=sha256:e4108df7fe9b707191e55f33efbcb2d81928e10cea45527879a4749cbe472614 \ - --hash=sha256:e6024675e67af929088fda399b2094574609396b1decb609c55fa58b028a32a1 \ - --hash=sha256:e70f54f1796669ef691ca07d046cd81a29cb4deb1e5f942003f401c0c4a2695d \ - --hash=sha256:e715596e683d2ce000574bae5d07bd522c781a822866c20495e52520564f0969 \ - --hash=sha256:e760191dd42581e023a68b758769e2da259b5d52e3103c6060ddc02c9edb8d7b \ - --hash=sha256:ed86a35631f7bfbb28e108dd96773b9d5a6ce4811cf6ea468bb6a359b256b1e4 \ - --hash=sha256:ee07e47c12890ef248766a6e55bd38ebfb2bb8edd4142d56db91b21ea68b7627 \ - --hash=sha256:fa3a0128b152627161ce47201262d3140edb5a5c3da88d73a1b790a959126956 \ - --hash=sha256:fcc8eb6d5902bb1cf6dc4f187ee3ea80a1eba0a89aba40a5cb20a5087d961357 -charset-normalizer==3.3.2 ; python_version >= "3.8" and python_version < "3.11" \ - --hash=sha256:06435b539f889b1f6f4ac1758871aae42dc3a8c0e24ac9e60c2384973ad73027 \ - --hash=sha256:06a81e93cd441c56a9b65d8e1d043daeb97a3d0856d177d5c90ba85acb3db087 \ - --hash=sha256:0a55554a2fa0d408816b3b5cedf0045f4b8e1a6065aec45849de2d6f3f8e9786 \ - --hash=sha256:0b2b64d2bb6d3fb9112bafa732def486049e63de9618b5843bcdd081d8144cd8 \ - --hash=sha256:10955842570876604d404661fbccbc9c7e684caf432c09c715ec38fbae45ae09 \ - --hash=sha256:122c7fa62b130ed55f8f285bfd56d5f4b4a5b503609d181f9ad85e55c89f4185 \ - --hash=sha256:1ceae2f17a9c33cb48e3263960dc5fc8005351ee19db217e9b1bb15d28c02574 \ - --hash=sha256:1d3193f4a680c64b4b6a9115943538edb896edc190f0b222e73761716519268e \ - --hash=sha256:1f79682fbe303db92bc2b1136016a38a42e835d932bab5b3b1bfcfbf0640e519 \ - --hash=sha256:2127566c664442652f024c837091890cb1942c30937add288223dc895793f898 \ - 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--hash=sha256:8f93ddf3001ecec16568390b507652644a3a103baa72de3ad3b9c530e3277098 \ - --hash=sha256:91a1b598055ba06b2c386415d2e7f6ac818545e94c5def597a74754940188513 \ - --hash=sha256:ada53aebede1c89570e56861b08d12ba4518a1f8b82d467c32665ec4d1f4b3c8 \ - --hash=sha256:b6d78338acabf1fb2e88bf4559d837d30230cf9c3e4337261f4d83200df1fcbe \ - --hash=sha256:be21d4c41ecebed9e99430dac87de1439a8c7882faf23bba7fea3fea7b906ac1 \ - --hash=sha256:c47bc25744c743f3835831a20efdcfd60aeb7c3f9804a213f61e45803d16c2a5 \ - --hash=sha256:d6227060f268894f92c61af0a44c0d8212e19cb98d05c20141c73312d923bc0a \ - --hash=sha256:d86664ec85902967d902e78272e97d1aff1d331f7619d398d3ffab1c9b8e9157 \ - --hash=sha256:ed9e29eb94cd493b36bca9cb0b1fd7f06a0688215ad1e4b3ab4931726e0ec092 \ - --hash=sha256:f1b90ac61f862634039265cd0f746cc9879feee03ff962c803486301b778714b -tornado==6.4 ; python_version >= "3.8" and python_version < "3.11" \ - --hash=sha256:02ccefc7d8211e5a7f9e8bc3f9e5b0ad6262ba2fbb683a6443ecc804e5224ce0 \ - --hash=sha256:10aeaa8006333433da48dec9fe417877f8bcc21f48dda8d661ae79da357b2a63 \ - --hash=sha256:27787de946a9cffd63ce5814c33f734c627a87072ec7eed71f7fc4417bb16263 \ - --hash=sha256:6f8a6c77900f5ae93d8b4ae1196472d0ccc2775cc1dfdc9e7727889145c45052 \ - --hash=sha256:71ddfc23a0e03ef2df1c1397d859868d158c8276a0603b96cf86892bff58149f \ - --hash=sha256:72291fa6e6bc84e626589f1c29d90a5a6d593ef5ae68052ee2ef000dfd273dee \ - --hash=sha256:88b84956273fbd73420e6d4b8d5ccbe913c65d31351b4c004ae362eba06e1f78 \ - --hash=sha256:e43bc2e5370a6a8e413e1e1cd0c91bedc5bd62a74a532371042a18ef19e10579 \ - --hash=sha256:f0251554cdd50b4b44362f73ad5ba7126fc5b2c2895cc62b14a1c2d7ea32f212 \ - --hash=sha256:f7894c581ecdcf91666a0912f18ce5e757213999e183ebfc2c3fdbf4d5bd764e \ - --hash=sha256:fd03192e287fbd0899dd8f81c6fb9cbbc69194d2074b38f384cb6fa72b80e9c2 -tqdm==4.66.2 ; python_version >= "3.8" and python_version < "3.11" \ - --hash=sha256:1ee4f8a893eb9bef51c6e35730cebf234d5d0b6bd112b0271e10ed7c24a02bd9 \ - --hash=sha256:6cd52cdf0fef0e0f543299cfc96fec90d7b8a7e88745f411ec33eb44d5ed3531 -traitlets==5.14.1 ; python_version >= "3.8" and python_version < "3.11" \ - --hash=sha256:2e5a030e6eff91737c643231bfcf04a65b0132078dad75e4936700b213652e74 \ - --hash=sha256:8585105b371a04b8316a43d5ce29c098575c2e477850b62b848b964f1444527e -triton==2.2.0 ; platform_system == "Linux" and platform_machine == "x86_64" and python_version < "3.11" and python_version >= "3.8" \ - --hash=sha256:0af58716e721460a61886668b205963dc4d1e4ac20508cc3f623aef0d70283d5 \ - --hash=sha256:227cc6f357c5efcb357f3867ac2a8e7ecea2298cd4606a8ba1e931d1d5a947df \ - --hash=sha256:a2294514340cfe4e8f4f9e5c66c702744c4a117d25e618bd08469d0bfed1e2e5 \ - --hash=sha256:b8ce26093e539d727e7cf6f6f0d932b1ab0574dc02567e684377630d86723ace \ - --hash=sha256:da58a152bddb62cafa9a857dd2bc1f886dbf9f9c90a2b5da82157cd2b34392b0 \ - --hash=sha256:e8fe46d3ab94a8103e291bd44c741cc294b91d1d81c1a2888254cbf7ff846dab -typing-extensions==4.10.0 ; python_version >= "3.8" and python_version < "3.11" \ - --hash=sha256:69b1a937c3a517342112fb4c6df7e72fc39a38e7891a5730ed4985b5214b5475 \ - --hash=sha256:b0abd7c89e8fb96f98db18d86106ff1d90ab692004eb746cf6eda2682f91b3cb -tzdata==2024.1 ; python_version >= "3.8" and python_version < "3.11" \ - --hash=sha256:2674120f8d891909751c38abcdfd386ac0a5a1127954fbc332af6b5ceae07efd \ - --hash=sha256:9068bc196136463f5245e51efda838afa15aaeca9903f49050dfa2679db4d252 -urllib3==1.26.18 ; python_version >= "3.8" and python_version < "3.11" \ - --hash=sha256:34b97092d7e0a3a8cf7cd10e386f401b3737364026c45e622aa02903dffe0f07 \ - --hash=sha256:f8ecc1bba5667413457c529ab955bf8c67b45db799d159066261719e328580a0 -wcwidth==0.2.13 ; python_version >= "3.8" and python_version < "3.11" \ - --hash=sha256:3da69048e4540d84af32131829ff948f1e022c1c6bdb8d6102117aac784f6859 \ - --hash=sha256:72ea0c06399eb286d978fdedb6923a9eb47e1c486ce63e9b4e64fc18303972b5 -wheel==0.42.0 ; python_version >= "3.8" and python_version < "3.11" \ - 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z2xvn{!qkR;@M7@HkbFCom25MU8Ud6*9*_WXdD5e)Dj?m;U;n-9TmH?3n8>*}{Ce}r zaqy!3`J(`u-C&-g9DCHOg&t^)dy)>2KnS3caUc(xsF+pMf$tu>P0!?OhrNtT*~}7= oP<*Y0qC5degr*C+(bdM6LAcy~gWR+jSy!g%4e?h|s-zD83&xgD4*&oF From a6d4b2a527a367d3f2088c21ac9ee3942865e2b4 Mon Sep 17 00:00:00 2001 From: matheme-justyn Date: Fri, 8 Mar 2024 06:22:10 +0000 Subject: [PATCH 16/17] 20240308, failed test on SageMaker --- demo/dev/executable_smartnoise.ipynb | 1332 ++++---------------------- requirements.txt | Bin 3354 -> 1587 bytes 2 files changed, 168 insertions(+), 1164 deletions(-) diff --git a/demo/dev/executable_smartnoise.ipynb b/demo/dev/executable_smartnoise.ipynb index aad74b2d..76d5f0e8 100644 --- a/demo/dev/executable_smartnoise.ipynb +++ b/demo/dev/executable_smartnoise.ipynb @@ -3,7 +3,133 @@ { "cell_type": "code", "execution_count": 1, - "metadata": {}, + "metadata": { + "tags": [] + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Collecting private-pgm@ git+https://github.com/ryan112358/private-pgm.git@5b9126295c110b741e5426ddbff419ea1e60e788 (from -r ../../requirements.txt (line 56))\n", + " Cloning https://github.com/ryan112358/private-pgm.git (to revision 5b9126295c110b741e5426ddbff419ea1e60e788) to /tmp/pip-install-vn8m0m34/private-pgm_80e5de69f17543b59c34dff626211cba\n", + " Running command git clone --filter=blob:none --quiet https://github.com/ryan112358/private-pgm.git /tmp/pip-install-vn8m0m34/private-pgm_80e5de69f17543b59c34dff626211cba\n", + " Running command git rev-parse -q --verify 'sha^5b9126295c110b741e5426ddbff419ea1e60e788'\n", + " Running command git fetch -q https://github.com/ryan112358/private-pgm.git 5b9126295c110b741e5426ddbff419ea1e60e788\n", + " Resolved https://github.com/ryan112358/private-pgm.git to commit 5b9126295c110b741e5426ddbff419ea1e60e788\n", + " Preparing metadata (setup.py) ... \u001b[?25ldone\n", + "\u001b[?25hCollecting anonymeter==1.0.0 (from -r ../../requirements.txt (line 1))\n", + " Using cached anonymeter-1.0.0-py3-none-any.whl.metadata (10 kB)\n", + "Collecting antlr4-python3-runtime==4.9.3 (from -r ../../requirements.txt (line 2))\n", + " Using cached antlr4-python3-runtime-4.9.3.tar.gz (117 kB)\n", + " Preparing metadata (setup.py) ... \u001b[?25ldone\n", + "\u001b[?25hRequirement already satisfied: asttokens==2.4.1 in /home/ec2-user/anaconda3/envs/pytorch_p310/lib/python3.10/site-packages (from -r ../../requirements.txt (line 3)) (2.4.1)\n", + "Collecting boto3==1.34.58 (from -r ../../requirements.txt (line 4))\n", + " Using cached boto3-1.34.58-py3-none-any.whl.metadata (6.6 kB)\n", + "Collecting botocore==1.34.58 (from -r ../../requirements.txt (line 5))\n", + " Using cached botocore-1.34.58-py3-none-any.whl.metadata (5.7 kB)\n", + "Collecting certifi==2024.2.2 (from -r ../../requirements.txt (line 6))\n", + " Using cached certifi-2024.2.2-py3-none-any.whl.metadata (2.2 kB)\n", + "Requirement already satisfied: charset-normalizer==3.3.2 in /home/ec2-user/anaconda3/envs/pytorch_p310/lib/python3.10/site-packages (from -r ../../requirements.txt (line 7)) (3.3.2)\n", + "Requirement already satisfied: cloudpickle==2.2.1 in /home/ec2-user/anaconda3/envs/pytorch_p310/lib/python3.10/site-packages (from -r ../../requirements.txt (line 8)) (2.2.1)\n", + "Collecting colorama==0.4.6 (from -r ../../requirements.txt (line 9))\n", + " Using cached colorama-0.4.6-py2.py3-none-any.whl.metadata (17 kB)\n", + "Requirement already satisfied: comm==0.2.1 in /home/ec2-user/anaconda3/envs/pytorch_p310/lib/python3.10/site-packages (from -r ../../requirements.txt (line 10)) (0.2.1)\n", + "Requirement already satisfied: contourpy==1.2.0 in /home/ec2-user/anaconda3/envs/pytorch_p310/lib/python3.10/site-packages (from -r ../../requirements.txt (line 11)) (1.2.0)\n", + "Collecting copulas==0.9.2 (from -r ../../requirements.txt (line 12))\n", + " Using cached copulas-0.9.2-py2.py3-none-any.whl.metadata (21 kB)\n", + "Collecting ctgan==0.9.0 (from -r ../../requirements.txt (line 13))\n", + " Using cached ctgan-0.9.0-py2.py3-none-any.whl.metadata (22 kB)\n", + "Requirement already satisfied: cycler==0.12.1 in /home/ec2-user/anaconda3/envs/pytorch_p310/lib/python3.10/site-packages (from -r ../../requirements.txt (line 14)) (0.12.1)\n", + "Collecting debugpy==1.8.1 (from -r ../../requirements.txt (line 15))\n", + " Using cached debugpy-1.8.1-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl.metadata (1.1 kB)\n", + "Requirement already satisfied: decorator==5.1.1 in /home/ec2-user/anaconda3/envs/pytorch_p310/lib/python3.10/site-packages (from -r ../../requirements.txt (line 16)) (5.1.1)\n", + "Collecting deepecho==0.5.0 (from -r ../../requirements.txt (line 17))\n", + " Using cached deepecho-0.5.0-py2.py3-none-any.whl.metadata (15 kB)\n", + "Collecting disjoint-set==0.7.4 (from -r ../../requirements.txt (line 18))\n", + " Using cached disjoint_set-0.7.4-py3-none-any.whl.metadata (1.9 kB)\n", + "Requirement already satisfied: exceptiongroup==1.2.0 in /home/ec2-user/anaconda3/envs/pytorch_p310/lib/python3.10/site-packages (from -r ../../requirements.txt (line 19)) (1.2.0)\n", + "Requirement already satisfied: executing==2.0.1 in /home/ec2-user/anaconda3/envs/pytorch_p310/lib/python3.10/site-packages (from -r ../../requirements.txt (line 20)) (2.0.1)\n", + "Collecting Faker==15.3.4 (from -r ../../requirements.txt (line 21))\n", + " Using cached Faker-15.3.4-py3-none-any.whl.metadata (15 kB)\n", + "Requirement already satisfied: filelock==3.13.1 in /home/ec2-user/anaconda3/envs/pytorch_p310/lib/python3.10/site-packages (from -r ../../requirements.txt (line 22)) (3.13.1)\n", + "Collecting fonttools==4.49.0 (from -r ../../requirements.txt (line 23))\n", + " Using cached fonttools-4.49.0-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl.metadata (159 kB)\n", + "Collecting fsspec==2024.2.0 (from -r ../../requirements.txt (line 24))\n", + " Using cached fsspec-2024.2.0-py3-none-any.whl.metadata (6.8 kB)\n", + "Collecting graphviz==0.17 (from -r ../../requirements.txt (line 25))\n", + " Using cached graphviz-0.17-py3-none-any.whl.metadata (8.1 kB)\n", + "Collecting greenlet==3.0.3 (from -r ../../requirements.txt (line 26))\n", + " Using cached greenlet-3.0.3-cp310-cp310-manylinux_2_24_x86_64.manylinux_2_28_x86_64.whl.metadata (3.8 kB)\n", + "Requirement already satisfied: idna==3.6 in /home/ec2-user/anaconda3/envs/pytorch_p310/lib/python3.10/site-packages (from -r ../../requirements.txt (line 27)) (3.6)\n", + "Collecting ipykernel==6.29.3 (from -r ../../requirements.txt (line 28))\n", + " Using cached ipykernel-6.29.3-py3-none-any.whl.metadata (6.3 kB)\n", + "Collecting ipython==8.22.2 (from -r ../../requirements.txt (line 29))\n", + " Using cached ipython-8.22.2-py3-none-any.whl.metadata (4.8 kB)\n", + "Requirement already satisfied: jedi==0.19.1 in /home/ec2-user/anaconda3/envs/pytorch_p310/lib/python3.10/site-packages (from -r ../../requirements.txt (line 30)) (0.19.1)\n", + "Requirement already satisfied: Jinja2==3.1.3 in /home/ec2-user/anaconda3/envs/pytorch_p310/lib/python3.10/site-packages (from -r ../../requirements.txt (line 31)) (3.1.3)\n", + "Requirement already satisfied: jmespath==1.0.1 in /home/ec2-user/anaconda3/envs/pytorch_p310/lib/python3.10/site-packages (from -r ../../requirements.txt (line 32)) (1.0.1)\n", + "Requirement already satisfied: joblib==1.3.2 in /home/ec2-user/anaconda3/envs/pytorch_p310/lib/python3.10/site-packages (from -r ../../requirements.txt (line 33)) (1.3.2)\n", + "Requirement already satisfied: jupyter_client==8.6.0 in /home/ec2-user/anaconda3/envs/pytorch_p310/lib/python3.10/site-packages (from -r ../../requirements.txt (line 34)) (8.6.0)\n", + "Requirement already satisfied: jupyter_core==5.7.1 in /home/ec2-user/anaconda3/envs/pytorch_p310/lib/python3.10/site-packages (from -r ../../requirements.txt (line 35)) (5.7.1)\n", + "Requirement already satisfied: kiwisolver==1.4.5 in /home/ec2-user/anaconda3/envs/pytorch_p310/lib/python3.10/site-packages (from -r ../../requirements.txt (line 36)) (1.4.5)\n", + "Requirement already satisfied: llvmlite==0.42.0 in /home/ec2-user/anaconda3/envs/pytorch_p310/lib/python3.10/site-packages (from -r ../../requirements.txt (line 37)) (0.42.0)\n", + "Collecting MarkupSafe==2.1.5 (from -r ../../requirements.txt (line 38))\n", + " Using cached MarkupSafe-2.1.5-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl.metadata (3.0 kB)\n", + "Collecting matplotlib==3.8.3 (from -r ../../requirements.txt (line 39))\n", + " Using cached matplotlib-3.8.3-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl.metadata (5.8 kB)\n", + "Requirement already satisfied: matplotlib-inline==0.1.6 in /home/ec2-user/anaconda3/envs/pytorch_p310/lib/python3.10/site-packages (from -r ../../requirements.txt (line 40)) (0.1.6)\n", + "Requirement already satisfied: mpmath==1.3.0 in /home/ec2-user/anaconda3/envs/pytorch_p310/lib/python3.10/site-packages (from -r ../../requirements.txt (line 41)) (1.3.0)\n", + "Requirement already satisfied: nest-asyncio==1.6.0 in /home/ec2-user/anaconda3/envs/pytorch_p310/lib/python3.10/site-packages (from -r ../../requirements.txt (line 42)) (1.6.0)\n", + "Requirement already satisfied: networkx==3.2.1 in /home/ec2-user/anaconda3/envs/pytorch_p310/lib/python3.10/site-packages (from -r ../../requirements.txt (line 43)) (3.2.1)\n", + "Collecting nose==1.3.7 (from -r ../../requirements.txt (line 44))\n", + " Using cached nose-1.3.7-py3-none-any.whl.metadata (1.7 kB)\n", + "Requirement already satisfied: numba==0.59.0 in /home/ec2-user/anaconda3/envs/pytorch_p310/lib/python3.10/site-packages (from -r ../../requirements.txt (line 45)) (0.59.0)\n", + "Collecting numpy==1.26.4 (from -r ../../requirements.txt (line 46))\n", + " Using cached numpy-1.26.4-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl.metadata (61 kB)\n", + "Collecting opacus==0.14.0 (from -r ../../requirements.txt (line 47))\n", + " Using cached opacus-0.14.0-py3-none-any.whl.metadata (5.5 kB)\n", + "Collecting opendp==0.8.0 (from -r ../../requirements.txt (line 48))\n", + " Using cached opendp-0.8.0-py3-none-any.whl.metadata (4.6 kB)\n", + "Collecting pac-synth==0.0.8 (from -r ../../requirements.txt (line 49))\n", + " Using cached pac_synth-0.0.8-cp37-abi3-manylinux_2_17_x86_64.manylinux2014_x86_64.whl.metadata (5.4 kB)\n", + "Collecting packaging==23.2 (from -r ../../requirements.txt (line 50))\n", + " Using cached packaging-23.2-py3-none-any.whl.metadata (3.2 kB)\n", + "Collecting pandas==2.2.1 (from -r ../../requirements.txt (line 51))\n", + " Using cached pandas-2.2.1-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl.metadata (19 kB)\n", + "Requirement already satisfied: parso==0.8.3 in /home/ec2-user/anaconda3/envs/pytorch_p310/lib/python3.10/site-packages (from -r ../../requirements.txt (line 52)) (0.8.3)\n", + "Requirement already satisfied: pillow==10.2.0 in /home/ec2-user/anaconda3/envs/pytorch_p310/lib/python3.10/site-packages (from -r ../../requirements.txt (line 53)) (10.2.0)\n", + "Requirement already satisfied: platformdirs==4.2.0 in /home/ec2-user/anaconda3/envs/pytorch_p310/lib/python3.10/site-packages (from -r ../../requirements.txt (line 54)) (4.2.0)\n", + "Collecting plotly==5.19.0 (from -r ../../requirements.txt (line 55))\n", + " Using cached plotly-5.19.0-py3-none-any.whl.metadata (7.0 kB)\n", + "Collecting prompt-toolkit==3.0.43 (from -r ../../requirements.txt (line 57))\n", + " Using cached prompt_toolkit-3.0.43-py3-none-any.whl.metadata (6.5 kB)\n", + "Requirement already satisfied: psutil==5.9.8 in /home/ec2-user/anaconda3/envs/pytorch_p310/lib/python3.10/site-packages (from -r ../../requirements.txt (line 58)) (5.9.8)\n", + "Requirement already satisfied: pure-eval==0.2.2 in /home/ec2-user/anaconda3/envs/pytorch_p310/lib/python3.10/site-packages (from -r ../../requirements.txt (line 59)) (0.2.2)\n", + "Requirement already satisfied: Pygments==2.17.2 in /home/ec2-user/anaconda3/envs/pytorch_p310/lib/python3.10/site-packages (from -r ../../requirements.txt (line 60)) (2.17.2)\n", + "Collecting pyparsing==3.1.2 (from -r ../../requirements.txt (line 61))\n", + " Using cached pyparsing-3.1.2-py3-none-any.whl.metadata (5.1 kB)\n", + "Collecting python-dateutil==2.9.0.post0 (from -r ../../requirements.txt (line 62))\n", + " Using cached python_dateutil-2.9.0.post0-py2.py3-none-any.whl.metadata (8.4 kB)\n", + "Collecting pytz==2024.1 (from -r ../../requirements.txt (line 63))\n", + " Using cached pytz-2024.1-py2.py3-none-any.whl.metadata (22 kB)\n", + "\u001b[31mERROR: Ignored the following versions that require a different python version: 0.1.3 Requires-Python >=3.6,<3.9; 0.1.3.dev0 Requires-Python >=3.6,<3.9; 0.1.3.dev1 Requires-Python >=3.6,<3.9; 0.1.4 Requires-Python >=3.6,<3.9; 0.1.4.dev0 Requires-Python >=3.6,<3.9; 0.2.0 Requires-Python >=3.6,<3.9; 0.2.0.dev0 Requires-Python >=3.6,<3.9; 0.2.1 Requires-Python >=3.6,<3.9; 0.2.1.dev0 Requires-Python >=3.6,<3.9; 0.2.2 Requires-Python >=3.6,<3.9; 0.2.2.dev0 Requires-Python >=3.6,<3.9; 0.2.2.dev1 Requires-Python >=3.5,<3.9; 0.2.2.dev2 Requires-Python >=3.6,<3.9; 0.2.2.dev3 Requires-Python >=3.6,<3.9; 0.3.0 Requires-Python >=3.6,<3.10; 0.3.0 Requires-Python >=3.6,<3.9; 0.3.0.dev0 Requires-Python >=3.5,<3.9; 0.3.0.dev0 Requires-Python >=3.6,<3.10; 0.3.0.dev1 Requires-Python >=3.6,<3.9; 0.3.0.post1 Requires-Python >=3.6,<3.10; 0.3.1 Requires-Python >=3.5,<3.8; 0.3.1 Requires-Python >=3.6,<3.9; 0.3.1.dev0 Requires-Python >=3.5,<3.8; 0.3.1.dev0 Requires-Python >=3.6,<3.9; 0.3.1.dev1 Requires-Python >=3.6,<3.9; 0.3.1.dev2 Requires-Python >=3.6,<3.9; 0.3.2 Requires-Python >=3.5,<3.9; 0.3.2.dev0 Requires-Python >=3.5,<3.8; 0.3.2.dev0 Requires-Python >=3.6,<3.9; 0.3.2.dev1 Requires-Python >=3.5,<3.9; 0.3.3 Requires-Python >=3.5,<3.9; 0.3.3.dev0 Requires-Python >=3.5,<3.9; 0.4.0 Requires-Python >=3.5,<3.9; 0.4.0 Requires-Python >=3.6,<3.9; 0.4.0.dev0 Requires-Python >=3.5,<3.9; 0.4.0.dev0 Requires-Python >=3.6,<3.9; 0.4.0.dev1 Requires-Python >=3.6,<3.9; 0.4.1 Requires-Python >=3.6,<3.9; 0.4.1.dev0 Requires-Python >=3.6,<3.9; 0.4.1.dev1 Requires-Python >=3.6,<3.9; 0.4.2 Requires-Python >=3.6,<3.9; 0.4.2.dev0 Requires-Python >=3.6,<3.9; 0.4.3 Requires-Python >=3.6,<3.9; 0.4.3.dev0 Requires-Python >=3.6,<3.9; 0.4.3.dev1 Requires-Python >=3.6,<3.9; 0.4.4.dev0 Requires-Python >=3.6,<3.9; 0.5.0 Requires-Python >=3.6,<3.10; 0.5.0 Requires-Python >=3.6,<3.9; 0.5.0.dev0 Requires-Python >=3.6,<3.9; 0.5.0.dev1 Requires-Python >=3.6,<3.10; 0.5.0.dev1 Requires-Python >=3.6,<3.9; 0.5.1 Requires-Python >=3.6,<3.10; 0.5.1 Requires-Python >=3.6,<3.9; 0.5.1.dev0 Requires-Python >=3.6,<3.10; 0.5.1.dev0 Requires-Python >=3.6,<3.9; 0.5.1.dev1 Requires-Python >=3.6,<3.10; 0.5.1.dev1 Requires-Python >=3.6,<3.9; 0.5.1.dev2 Requires-Python >=3.6,<3.10; 0.5.1.dev3 Requires-Python >=3.6,<3.10; 0.5.2 Requires-Python >=3.6,<3.10; 0.5.2.dev0 Requires-Python >=3.6,<3.10; 0.5.2.dev0 Requires-Python >=3.6,<3.9; 0.5.2.dev1 Requires-Python >=3.6,<3.10; 0.5.3.dev0 Requires-Python >=3.6,<3.10; 0.6.0 Requires-Python >=3.6,<3.10; 0.6.0.dev0 Requires-Python >=3.6,<3.10; 0.6.1 Requires-Python >=3.6,<3.10; 0.6.1.dev0 Requires-Python >=3.6,<3.10; 0.7.0 Requires-Python >=3.6,<3.10; 0.7.0.dev0 Requires-Python >=3.6,<3.10\u001b[0m\u001b[31m\n", + "\u001b[0m\u001b[31mERROR: Could not find a version that satisfies the requirement pywin32==306 (from versions: none)\u001b[0m\u001b[31m\n", + "\u001b[0m\u001b[31mERROR: No matching distribution found for pywin32==306\u001b[0m\u001b[31m\n", + "\u001b[0m" + ] + } + ], + "source": [ + "!pip install -r ../../requirements.txt" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": { + "tags": [] + }, "outputs": [], "source": [ "import pandas as pd\n", @@ -12,14 +138,16 @@ }, { "cell_type": "code", - "execution_count": 2, - "metadata": {}, + "execution_count": 3, + "metadata": { + "tags": [] + }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "d:\\Dropbox\\89_other_application\\GitHub\\PETsARD\n" + "/home/ec2-user/SageMaker/PETsARD\n" ] } ], @@ -36,14 +164,24 @@ }, { "cell_type": "code", - "execution_count": 3, - "metadata": {}, + "execution_count": 4, + "metadata": { + "tags": [] + }, "outputs": [ { - "name": "stdout", - "output_type": "stream", - "text": [ - "Loader - Benchmarker : Success download the benchmark dataset from https://petsard-benchmark.s3.amazonaws.com/adult-income.csv.\n" + "ename": "ModuleNotFoundError", + "evalue": "No module named 'sdv'", + "output_type": "error", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mModuleNotFoundError\u001b[0m Traceback (most recent call last)", + "Cell \u001b[0;32mIn[4], line 1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mPETsARD\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m (\n\u001b[1;32m 2\u001b[0m Loader,\n\u001b[1;32m 3\u001b[0m Processor,\n\u001b[1;32m 4\u001b[0m Synthesizer\n\u001b[1;32m 5\u001b[0m )\n\u001b[1;32m 8\u001b[0m load \u001b[38;5;241m=\u001b[39m Loader(\n\u001b[1;32m 9\u001b[0m filepath\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mbenchmark://adult-income\u001b[39m\u001b[38;5;124m'\u001b[39m,\n\u001b[1;32m 10\u001b[0m na_values\u001b[38;5;241m=\u001b[39m{k: \u001b[38;5;124m'\u001b[39m\u001b[38;5;124m?\u001b[39m\u001b[38;5;124m'\u001b[39m \u001b[38;5;28;01mfor\u001b[39;00m k \u001b[38;5;129;01min\u001b[39;00m [\n\u001b[0;32m (...)\u001b[0m\n\u001b[1;32m 14\u001b[0m ]}\n\u001b[1;32m 15\u001b[0m )\n\u001b[1;32m 16\u001b[0m load\u001b[38;5;241m.\u001b[39mload()\n", + "File \u001b[0;32m~/SageMaker/PETsARD/PETsARD/__init__.py:23\u001b[0m\n\u001b[1;32m 21\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mPETsARD\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mloader\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m Loader, Metadata, Splitter\n\u001b[1;32m 22\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mPETsARD\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mprocessor\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m Processor\n\u001b[0;32m---> 23\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mPETsARD\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01msynthesizer\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m Synthesizer\n\u001b[1;32m 24\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mPETsARD\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mevaluator\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m Evaluator, Describer\n\u001b[1;32m 25\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mPETsARD\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mreporter\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m Reporter\n", + "File \u001b[0;32m~/SageMaker/PETsARD/PETsARD/synthesizer/__init__.py:1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mPETsARD\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01msynthesizer\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01msynthesizer\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m Synthesizer\n\u001b[1;32m 3\u001b[0m __all__ \u001b[38;5;241m=\u001b[39m [\n\u001b[1;32m 4\u001b[0m \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mSynthesizer\u001b[39m\u001b[38;5;124m'\u001b[39m\n\u001b[1;32m 5\u001b[0m ]\n", + "File \u001b[0;32m~/SageMaker/PETsARD/PETsARD/synthesizer/synthesizer.py:6\u001b[0m\n\u001b[1;32m 3\u001b[0m \u001b[38;5;28;01mimport\u001b[39;00m \u001b[38;5;21;01mpandas\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m \u001b[38;5;21;01mpd\u001b[39;00m\n\u001b[1;32m 5\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mPETsARD\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m Loader\n\u001b[0;32m----> 6\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mPETsARD\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01msynthesizer\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01msdv\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m SDVFactory\n\u001b[1;32m 7\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mPETsARD\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01msynthesizer\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01msmartnoise\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m SmartNoiseFactory\n\u001b[1;32m 8\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mPETsARD\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01merror\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m ConfigError, UnsupportedMethodError\n", + "File \u001b[0;32m~/SageMaker/PETsARD/PETsARD/synthesizer/sdv.py:5\u001b[0m\n\u001b[1;32m 2\u001b[0m \u001b[38;5;28;01mimport\u001b[39;00m \u001b[38;5;21;01mtime\u001b[39;00m\n\u001b[1;32m 4\u001b[0m \u001b[38;5;28;01mimport\u001b[39;00m \u001b[38;5;21;01mpandas\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m \u001b[38;5;21;01mpd\u001b[39;00m\n\u001b[0;32m----> 5\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01msdv\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mmetadata\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m SingleTableMetadata\n\u001b[1;32m 6\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01msdv\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01msingle_table\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m (\n\u001b[1;32m 7\u001b[0m CopulaGANSynthesizer,\n\u001b[1;32m 8\u001b[0m CTGANSynthesizer,\n\u001b[1;32m 9\u001b[0m GaussianCopulaSynthesizer,\n\u001b[1;32m 10\u001b[0m TVAESynthesizer\n\u001b[1;32m 11\u001b[0m )\n\u001b[1;32m 13\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mPETsARD\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01merror\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m UnfittedError, UnsupportedMethodError\n", + "\u001b[0;31mModuleNotFoundError\u001b[0m: No module named 'sdv'" ] } ], @@ -68,7 +206,7 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -77,7 +215,7 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -138,7 +276,7 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -212,61 +350,9 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Preproc data as ...\n", - " age workclass fnlwgt education educational-num marital-status \\\n", - "0 0.0 2 0.0 1 0.0 4 \n", - "\n", - " occupation relationship race gender capital-gain capital-loss \\\n", - "0 6 3 2 1 0.0 0.0 \n", - "\n", - " hours-per-week native-country income \n", - "0 0.0 25 0 \n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "d:\\Dropbox\\89_other_application\\GitHub\\PETsARD\\.venv\\lib\\site-packages\\mbi\\__init__.py:15: UserWarning: MixtureInference disabled, please install jax and jaxlib\n", - " warnings.warn('MixtureInference disabled, please install jax and jaxlib')\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Synthesizer (SmartNoise): Fitting aim.\n", - "15\n", - "Initial Sigma 11.056041228448832\n", - "Synthesizer (SmartNoise): Fitting aim spent 270.8398 sec.\n", - "Synthesizer (SmartNoise): Sampling aim # 558 rows (same as raw) in 0.5135 sec.\n", - "Sync data as ...\n", - " age workclass fnlwgt education educational-num marital-status \\\n", - "0 0.0 2 0.0 4 0.0 2 \n", - "\n", - " occupation relationship race gender capital-gain capital-loss \\\n", - "0 6 0 4 1 0.0 0.0 \n", - "\n", - " hours-per-week native-country income \n", - "0 0.0 25 1 \n", - " age workclass fnlwgt education educational-num \\\n", - "0 371.264583 Private 8.479563e+09 5th-6th 16.57591 \n", - "\n", - " marital-status occupation relationship race gender \\\n", - "0 Married-civ-spouse Machine-op-inspct Husband White Male \n", - "\n", - " capital-gain capital-loss hours-per-week native-country income \n", - "0 9.268677e+07 96578.239577 172.18284 United-States >50K \n" - ] - } - ], + "outputs": [], "source": [ "issue332(\n", " load=load,\n", @@ -283,233 +369,9 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Preproc data as ...\n", - " age workclass fnlwgt education educational-num marital-status \\\n", - "0 0.0 2 0.0 1 0.0 4 \n", - "\n", - " occupation relationship race gender capital-gain capital-loss \\\n", - "0 6 3 2 1 0.0 0.0 \n", - "\n", - " hours-per-week native-country income \n", - "0 0.0 25 0 \n", - "Synthesizer (SmartNoise): Fitting mst.\n", - "Synthesizer (SmartNoise): Fitting mst spent 113.5179 sec.\n", - "Synthesizer (SmartNoise): Sampling mst # 558 rows (same as raw) in 0.4945 sec.\n", - "Sync data as ...\n", - " age workclass fnlwgt education educational-num marital-status \\\n", - "0 0.0 0 0.0 9 0.0 2 \n", - "\n", - " occupation relationship race gender capital-gain capital-loss \\\n", - "0 11 0 4 1 0.0 0.0 \n", - "\n", - " hours-per-week native-country income \n", - "0 0.0 25 0 \n", - " age workclass fnlwgt education educational-num \\\n", - "0 371.264583 Federal-gov 8.479563e+09 Bachelors 16.57591 \n", - "\n", - " marital-status occupation relationship race gender capital-gain \\\n", - "0 Married-civ-spouse Sales Husband White Male 9.268677e+07 \n", - "\n", - " capital-loss hours-per-week native-country income \n", - "0 96578.239577 172.18284 United-States <=50K \n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "d:\\Dropbox\\89_other_application\\GitHub\\PETsARD\\.venv\\lib\\site-packages\\snsynth\\mst\\mst.py:241: FutureWarning: Setting an item of incompatible dtype is deprecated and will raise an error in a future version of pandas. Value '[0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0\n", - " 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0\n", - " 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0\n", - " 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0\n", - " 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0\n", - " 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0\n", - " 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0\n", - " 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0\n", - " 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0\n", - " 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0\n", - " 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0\n", - " 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0\n", - " 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0\n", - " 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0\n", - " 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0\n", - " 0 0 0]' has dtype incompatible with int32, please explicitly cast to a compatible dtype first.\n", - " df.loc[~mask, col] = idx[df.loc[~mask, col]]\n", - "d:\\Dropbox\\89_other_application\\GitHub\\PETsARD\\.venv\\lib\\site-packages\\snsynth\\mst\\mst.py:241: FutureWarning: Setting an item of incompatible dtype is deprecated and will raise an error in a future version of pandas. Value '[0 2 2 0 1 2 2 2 1 2 1 2 2 2 0 2 2 2 2 1 2 2 2 2 2 4 2 2 2 0 2 3 2 4 2 2 4\n", - " 2 2 2 2 3 2 2 2 2 2 5 2 2 2 2 2 2 2 2 5 2 2 2 2 2 3 4 2 2 2 2 2 2 2 2 5 5\n", - " 2 2 2 2 3 2 4 0 2 2 2 2 3 2 2 2 2 4 2 2 0 2 0 2 2 2 2 1 1 2 2 5 2 2 2 2 1\n", - " 2 1 2 2 2 2 2 2 4 3 2 2 5 5 2 2 2 2 1 2 2 2 4 2 5 2 2 2 2 2 2 2 2 2 2 5 2\n", - " 2 5 2 3 5 2 2 0 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 3 2 2 2 0 4 2 2 2 2 3 2 1\n", - " 1 5 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 1 2 1 4 2 5 2 1 2 2 2 1 2 2 2 2 2 1\n", - " 2 1 3 2 2 0 3 5 4 2 2 2 1 2 2 2 2 2 2 2 2 2 2 3 2 2 0 4 2 0 2 2 2 2 1 2 1\n", - " 2 2 2 2 2 2 2 2 2 2 2 0 2 2 1 2 2 2 5 2 2 4 2 2 5 2 2 2 2 2 2 2 2 2 3 2 5\n", - " 2 2 2 2 2 4 1 2 1 2 3 2 2 5 2 1 2 2 2 2 2 2 2 0 1 2 2 5 2 2 2 5 2 5 2 0 2\n", - " 2 0 5 2 2 1 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 4 2 5 2 2 2 2 2 2 2 5 2\n", - " 2 4 2 1 2 4 2 2 2 2 2 2 2 3 2 3 2 2 2 2 2 2 2 1 2 2 5 2 2 2 2 2 2 2 2 2 3\n", - " 4 2 2 4 2 1 2 2 4 2 5 2 2 2 2 2 4 2 2 2 5 2 2 2 3 2 2 2 1 2 4 2 1 2 2 2 2\n", - " 2 1 2 2 2 2 2 2 2 2 2 4 4 3 4 0 2 2 2 2 2 1 2 2 2 2 2 1 2 2 2 1 1 2 2 2 1\n", - " 4 2 1 2 3 2 1 2 2 2 2 0 4 2 2 4 2 2 1 2 2 2 2 2 1 0 2 2 2 2 0 2 2 4 1 2 1\n", - " 2 2 2 3 2 2 2 2 2 4 1 2 2 2 2 2 2 1 0 2 1 2 2 2 3 2 2 2 2 2 2 2 2 2 2 2 2\n", - " 2 0 2]' has dtype incompatible with int32, please explicitly cast to a compatible dtype first.\n", - " df.loc[~mask, col] = idx[df.loc[~mask, col]]\n", - "d:\\Dropbox\\89_other_application\\GitHub\\PETsARD\\.venv\\lib\\site-packages\\snsynth\\mst\\mst.py:241: FutureWarning: Setting an item of incompatible dtype is deprecated and will raise an error in a future version of pandas. Value '[0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0\n", - " 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0\n", - " 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0\n", - " 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0\n", - " 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0\n", - " 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0\n", - " 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0\n", - " 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0\n", - " 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0\n", - " 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0\n", - " 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0\n", - " 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0\n", - " 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0\n", - " 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0\n", - " 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0\n", - " 0 0 0]' has dtype incompatible with int32, please explicitly cast to a compatible dtype first.\n", - " df.loc[~mask, col] = idx[df.loc[~mask, col]]\n", - "d:\\Dropbox\\89_other_application\\GitHub\\PETsARD\\.venv\\lib\\site-packages\\snsynth\\mst\\mst.py:240: FutureWarning: Setting an item of incompatible dtype is deprecated and will raise an error in a future version of pandas. Value '[ 9 12 5 12 9 3 3 4 3 5 5 5 3 2 4 12 3 5 2 3 12 9 2 9\n", - " 5 12 12 9 9 5 5 12 12 12 2 2 9 12 12]' has dtype incompatible with int32, please explicitly cast to a compatible dtype first.\n", - " df.loc[mask, col] = np.random.choice(extra, mask.sum())\n", - "d:\\Dropbox\\89_other_application\\GitHub\\PETsARD\\.venv\\lib\\site-packages\\snsynth\\mst\\mst.py:241: FutureWarning: Setting an item of incompatible dtype is deprecated and will raise an error in a future version of pandas. Value '[0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0\n", - " 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0\n", - " 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0\n", - " 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0\n", - " 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0\n", - " 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0\n", - " 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0\n", - " 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0\n", - " 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0\n", - " 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0\n", - " 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0\n", - " 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0\n", - " 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0\n", - " 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0\n", - " 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0\n", - " 0 0 0]' has dtype incompatible with int32, please explicitly cast to a compatible dtype first.\n", - " df.loc[~mask, col] = idx[df.loc[~mask, col]]\n", - "d:\\Dropbox\\89_other_application\\GitHub\\PETsARD\\.venv\\lib\\site-packages\\snsynth\\mst\\mst.py:240: FutureWarning: Setting an item of incompatible dtype is deprecated and will raise an error in a future version of pandas. Value '[5 2 2 2 5 5 2 2 2 2 5 2 5 2 5 5]' has dtype incompatible with int32, please explicitly cast to a compatible dtype first.\n", - " df.loc[mask, col] = np.random.choice(extra, mask.sum())\n", - "d:\\Dropbox\\89_other_application\\GitHub\\PETsARD\\.venv\\lib\\site-packages\\snsynth\\mst\\mst.py:240: FutureWarning: Setting an item of incompatible dtype is deprecated and will raise an error in a future version of pandas. Value '[1 8 1 1 1 1 1 4 1 4 1 1 8 1 1 8]' has dtype incompatible with int32, please explicitly cast to a compatible dtype first.\n", - " df.loc[mask, col] = np.random.choice(extra, mask.sum())\n", - "d:\\Dropbox\\89_other_application\\GitHub\\PETsARD\\.venv\\lib\\site-packages\\snsynth\\mst\\mst.py:241: FutureWarning: Setting an item of incompatible dtype is deprecated and will raise an error in a future version of pandas. Value '[0 0 0 0 0 0 0 1 0 5 2 0 1 0 3 0 0 0 0 2 0 4 3 0 1 1 4 1 4 0 0 0 5 0 0 0 3\n", - " 1 0 4 1 1 3 0 1 4 1 0 3 1 3 1 0 0 0 1 3 0 2 4 0 1 3 0 0 0 1 1 0 0 0 4 1 1\n", - " 3 4 1 5 1 2 1 0 1 3 1 4 3 4 0 3 0 1 5 0 0 0 1 1 4 0 0 0 1 1 1 1 5 5 0 0 1\n", - " 3 4 0 2 0 0 1 2 4 5 1 0 3 0 1 1 0 0 0 1 0 5 0 0 1 5 0 1 5 1 1 3 0 0 4 0 0\n", - " 0 1 0 1 2 5 1 3 1 0 0 3 4 4 3 0 0 0 4 4 1 5 0 0 0 0 1 1 4 0 0 0 1 0 0 0 1\n", - " 0 4 0 3 1 1 4 3 1 0 0 0 0 1 1 1 0 0 0 0 0 0 1 1 3 2 0 1 0 0 0 0 1 0 0 1 0\n", - " 4 3 3 1 0 4 0 3 1 0 3 3 0 3 0 0 1 0 3 1 0 0 0 3 0 4 4 1 0 0 1 1 4 0 0 5 3\n", - " 0 1 3 3 4 1 0 3 1 1 5 0 0 2 4 0 0 1 0 0 0 4 3 5 3 0 3 1 3 0 0 3 3 1 0 0 0\n", - " 3 3 0 3 1 0 1 0 1 4 0 0 0 1 0 0 1 1 0 1 0 3 0 0 4 1 0 0 1 0 1 3 0 3 3 1 1\n", - " 2 1 3 1 0 0 4 4 0 0 1 1 0 1 0 0 0 0 4 0 3 1 0 2 5 0 0 1 0 3 2 1 1 0 0 0 5\n", - " 4 1 4 1 0 3 1 3 4 0 3 0 0 1 0 1 0 3 0 3 1 3 0 3 2 0 3 1 4 1 0 0 2 3 1 1 0\n", - " 0 0 1 0 1 0 0 0 1 0 4 1 1 0 1 3 0 4 1 0 5 1 1 4 0 1 0 0 0 1 1 4 1 0 1 1 3\n", - " 4 3 3 0 0 2 0 0 0 1 0 4 0 2 0 4 1 3 0 0 3 1 1 0 3 1 0 0 3 3 0 0 1 1 0 1 1\n", - " 4 0 3 1 0 0 1 5 0 0 3 3 3 4 4 4 3 1 1 0 1 4 4 1 1 0 1 0 3 5 1 0 0 0 4 0 0\n", - " 0 1 4 4 0 0 3 4 0 2 1 1 0 1 0 1 3 0 0 1 4 1 5 0 3 1 1 1 3 0 0 3 1 1 0 4 0\n", - " 0 0 0]' has dtype incompatible with int32, please explicitly cast to a compatible dtype first.\n", - " df.loc[~mask, col] = idx[df.loc[~mask, col]]\n", - "d:\\Dropbox\\89_other_application\\GitHub\\PETsARD\\.venv\\lib\\site-packages\\snsynth\\mst\\mst.py:240: FutureWarning: Setting an item of incompatible dtype is deprecated and will raise an error in a future version of pandas. Value '[3 1 0 3 3 1 3 1 0 0 1 0 1 1 3 3 1 0 0 3 3 0 3 3]' has dtype incompatible with int32, please explicitly cast to a compatible dtype first.\n", - " df.loc[mask, col] = np.random.choice(extra, mask.sum())\n", - "d:\\Dropbox\\89_other_application\\GitHub\\PETsARD\\.venv\\lib\\site-packages\\snsynth\\mst\\mst.py:241: FutureWarning: Setting an item of incompatible dtype is deprecated and will raise an error in a future version of pandas. Value '[1 1 1 1 1 1 1 1 1 0 0 1 1 1 0 1 1 1 1 1 1 0 1 1 1 1 0 0 0 1 1 1 0 1 1 1 1\n", - " 1 1 0 1 1 0 1 1 0 1 1 1 1 1 1 1 1 1 0 1 1 1 1 1 0 0 1 1 1 1 1 1 1 1 0 1 1\n", - " 1 1 1 1 0 0 0 1 1 1 1 0 0 0 1 1 1 0 0 1 1 1 0 1 1 1 1 1 0 1 1 0 0 1 1 1 1\n", - " 1 0 1 0 1 1 0 0 0 1 1 1 1 1 1 1 1 1 1 1 1 0 1 1 0 0 1 0 0 0 0 0 1 1 0 1 1\n", - " 1 1 1 0 0 0 1 1 0 1 1 0 0 0 1 1 1 1 0 0 1 0 1 1 1 1 1 1 0 1 1 1 0 1 1 1 0\n", - " 1 0 1 1 1 1 0 1 0 1 1 1 1 0 1 0 1 1 1 1 1 1 0 0 0 1 1 0 1 1 1 1 0 1 1 1 1\n", - " 0 1 0 0 1 0 1 1 1 1 0 1 1 0 1 1 1 1 1 1 1 1 1 0 1 0 0 1 1 1 0 0 1 1 1 0 1\n", - " 1 1 0 1 0 0 1 1 1 0 0 1 1 0 0 1 1 0 1 1 1 0 1 0 0 1 1 0 0 1 1 0 1 1 1 1 1\n", - " 0 1 1 1 1 1 0 1 1 0 1 1 1 0 1 1 1 0 1 1 1 0 1 1 0 1 1 1 0 1 1 1 1 1 1 0 0\n", - " 1 0 0 0 1 1 0 0 1 1 1 0 1 1 1 1 1 1 0 1 1 0 1 0 0 1 1 1 1 1 0 0 0 1 1 1 0\n", - " 1 0 0 1 1 0 1 1 1 1 0 1 1 1 1 1 1 1 1 0 1 1 1 1 1 1 1 0 0 1 1 1 0 1 0 0 1\n", - " 1 1 1 1 1 1 1 1 1 1 0 1 1 1 0 1 1 1 1 1 0 1 1 0 1 1 1 1 1 1 1 0 0 1 0 0 0\n", - " 0 0 1 1 1 0 1 1 1 1 1 0 1 0 1 1 0 0 1 1 0 1 1 1 1 1 1 1 0 0 1 1 1 1 1 0 0\n", - " 0 1 1 1 1 1 1 0 1 1 1 0 0 0 0 0 1 1 1 1 0 0 0 1 1 1 0 1 0 0 1 1 1 1 1 1 1\n", - " 1 0 0 1 1 1 1 0 1 0 0 1 1 0 1 1 0 1 1 1 0 0 0 1 1 0 0 1 0 1 1 0 1 1 1 0 1\n", - " 1 1 1]' has dtype incompatible with int32, please explicitly cast to a compatible dtype first.\n", - " df.loc[~mask, col] = idx[df.loc[~mask, col]]\n", - "d:\\Dropbox\\89_other_application\\GitHub\\PETsARD\\.venv\\lib\\site-packages\\snsynth\\mst\\mst.py:241: FutureWarning: Setting an item of incompatible dtype is deprecated and will raise an error in a future version of pandas. Value '[0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0\n", - " 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0\n", - " 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0\n", - " 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0\n", - " 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0\n", - " 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0\n", - " 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0\n", - " 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0\n", - " 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0\n", - " 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0\n", - " 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0\n", - " 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0\n", - " 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0\n", - " 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0\n", - " 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0\n", - " 0 0 0]' has dtype incompatible with int32, please explicitly cast to a compatible dtype first.\n", - " df.loc[~mask, col] = idx[df.loc[~mask, col]]\n", - "d:\\Dropbox\\89_other_application\\GitHub\\PETsARD\\.venv\\lib\\site-packages\\snsynth\\mst\\mst.py:241: FutureWarning: Setting an item of incompatible dtype is deprecated and will raise an error in a future version of pandas. Value '[0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0\n", - " 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0\n", - " 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0\n", - " 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0\n", - " 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0\n", - " 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0\n", - " 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0\n", - " 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0\n", - " 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0\n", - " 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0\n", - " 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0\n", - " 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0\n", - " 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0\n", - " 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0\n", - " 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0\n", - " 0 0 0]' has dtype incompatible with int32, please explicitly cast to a compatible dtype first.\n", - " df.loc[~mask, col] = idx[df.loc[~mask, col]]\n", - "d:\\Dropbox\\89_other_application\\GitHub\\PETsARD\\.venv\\lib\\site-packages\\snsynth\\mst\\mst.py:241: FutureWarning: Setting an item of incompatible dtype is deprecated and will raise an error in a future version of pandas. Value '[0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0\n", - " 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0\n", - " 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0\n", - " 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0\n", - " 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0\n", - " 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0\n", - " 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0\n", - " 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0\n", - " 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0\n", - " 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0\n", - " 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0\n", - " 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0\n", - " 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0\n", - " 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0\n", - " 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0\n", - " 0 0 0]' has dtype incompatible with int32, please explicitly cast to a compatible dtype first.\n", - " df.loc[~mask, col] = idx[df.loc[~mask, col]]\n", - "d:\\Dropbox\\89_other_application\\GitHub\\PETsARD\\.venv\\lib\\site-packages\\snsynth\\mst\\mst.py:240: FutureWarning: Setting an item of incompatible dtype is deprecated and will raise an error in a future version of pandas. Value '[13 16 8 20 11 10 11 6 15 9 17 9 10 20 4 3 15 1 17 14 16 15 18 18\n", - " 7 3 20 18 7 16 11 4 0 20 15 14 12 20 14 14 18 12 1 12 9]' has dtype incompatible with int32, please explicitly cast to a compatible dtype first.\n", - " df.loc[mask, col] = np.random.choice(extra, mask.sum())\n", - "d:\\Dropbox\\89_other_application\\GitHub\\PETsARD\\.venv\\lib\\site-packages\\snsynth\\mst\\mst.py:241: FutureWarning: Setting an item of incompatible dtype is deprecated and will raise an error in a future version of pandas. Value '[0 0 0 0 0 1 0 0 1 1 0 1 0 1 0 1 1 0 0 0 1 0 0 0 0 0 0 1 0 0 1 1 0 1 1 0 0\n", - " 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 1 0 1 0 0 0 0 0 0 0 0 0 1 0 0 0 1 0 0 0 0 0\n", - " 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 1 1 0 0 1 1 0 0 0 0 0 0 1 0 1 0\n", - " 0 0 1 0 0 1 1 0 1 1 0 0 0 0 0 0 0 0 1 0 0 1 1 0 0 0 0 0 1 0 0 0 1 1 0 1 1\n", - " 0 0 1 0 0 1 0 0 0 0 0 0 0 0 0 0 1 0 1 0 0 0 1 0 0 0 0 0 0 1 0 1 0 0 0 0 0\n", - " 0 1 0 0 0 0 0 0 0 0 0 1 0 0 1 0 0 0 0 0 1 0 0 0 0 1 0 1 1 1 1 1 0 0 1 0 1\n", - " 0 1 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 1 0 0 1 0 1 1\n", - " 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 1 0 1 0 0 0 0 1 0 0 0 0 0 0 0 0 0 1 0\n", - " 0 0 1 0 0 0 0 1 0 0 0 1 1 0 1 0 0 0 1 1 0 0 0 1 0 0 0 0 0 1 0 0 0 0 0 0 0\n", - " 0 0 0 0 0 0 1 0 0 0 0 0 0 0 1 0 1 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 1 0 0 0\n", - " 1 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0\n", - " 1 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 1 1 0 0 0 0 0 1 1 1 1 0 0 0 0 0 0 0\n", - " 0 0 0 1 0 0 0 0 0 0 0 0 1 0 0 1 0 0 0 0 0 0 0 0 0 0 0 1 0 0 1 0 1 0 0 0 0\n", - " 0 0 0 0 1 0 0 1 1 1 0 0 0 0 0 0 0 0 0 1 0 0 0 1 0 0 0 1 0 0 0 1 0 1 0 0 0\n", - " 1 0 0 0 1 0 0 0 1 0 0 0 0 0 1 0 0 1 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 1 0 0 0\n", - " 0 1 0]' has dtype incompatible with int32, please explicitly cast to a compatible dtype first.\n", - " df.loc[~mask, col] = idx[df.loc[~mask, col]]\n" - ] - } - ], + "outputs": [], "source": [ "issue332(\n", " load=load,\n", @@ -519,56 +381,9 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Preproc data as ...\n", - " age workclass fnlwgt education educational-num marital-status \\\n", - "0 0.0 2 0.0 1 0.0 4 \n", - "\n", - " occupation relationship race gender capital-gain capital-loss \\\n", - "0 6 3 2 1 0.0 0.0 \n", - "\n", - " hours-per-week native-country income \n", - "0 0.0 25 0 \n", - "Synthesizer (SmartNoise): Fitting pacsynth.\n", - "Synthesizer (SmartNoise): Fitting pacsynth spent 0.2158 sec.\n", - "Synthesizer (SmartNoise): Sampling pacsynth # 558 rows (same as raw) in 1.6921 sec.\n", - "Sync data as ...\n", - " age workclass fnlwgt education educational-num marital-status \\\n", - "0 0.0 2.0 0.0 11.0 0.0 2.0 \n", - "\n", - " occupation relationship race gender capital-gain capital-loss \\\n", - "0 6.0 0.0 4.0 1.0 0.0 0.0 \n", - "\n", - " hours-per-week native-country income \n", - "0 0.0 25.0 0.0 \n" - ] - }, - { - "ename": "ValueError", - "evalue": "Input contains NaN.", - "output_type": "error", - "traceback": [ - "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[1;31mValueError\u001b[0m Traceback (most recent call last)", - "Cell \u001b[1;32mIn[10], line 1\u001b[0m\n\u001b[1;32m----> 1\u001b[0m \u001b[43missue332\u001b[49m\u001b[43m(\u001b[49m\n\u001b[0;32m 2\u001b[0m \u001b[43m \u001b[49m\u001b[43mload\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mload\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m 3\u001b[0m \u001b[43m \u001b[49m\u001b[43msynthesizing_method\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43msmartnoise-pacsynth\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\n\u001b[0;32m 4\u001b[0m \u001b[43m)\u001b[49m\n", - "Cell \u001b[1;32mIn[5], line 50\u001b[0m, in \u001b[0;36missue332\u001b[1;34m(load, synthesizing_method, scaler_inhibit)\u001b[0m\n\u001b[0;32m 48\u001b[0m \u001b[38;5;28mprint\u001b[39m(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mSync data as ...\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[0;32m 49\u001b[0m \u001b[38;5;28mprint\u001b[39m(syn\u001b[38;5;241m.\u001b[39mdata_syn\u001b[38;5;241m.\u001b[39mhead(\u001b[38;5;241m1\u001b[39m))\n\u001b[1;32m---> 50\u001b[0m postproc_discretizing_data \u001b[38;5;241m=\u001b[39m \u001b[43mproc_discretizing\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43minverse_transform\u001b[49m\u001b[43m(\u001b[49m\n\u001b[0;32m 51\u001b[0m \u001b[43m \u001b[49m\u001b[43mdata\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43msyn\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mdata_syn\u001b[49m\n\u001b[0;32m 52\u001b[0m \u001b[43m\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m 53\u001b[0m \u001b[38;5;28mprint\u001b[39m(postproc_discretizing_data\u001b[38;5;241m.\u001b[39mhead(\u001b[38;5;241m1\u001b[39m))\n", - "File \u001b[1;32md:\\Dropbox\\89_other_application\\GitHub\\PETsARD\\PETsARD\\processor\\base.py:483\u001b[0m, in \u001b[0;36mProcessor.inverse_transform\u001b[1;34m(self, data)\u001b[0m\n\u001b[0;32m 480\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m obj \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[0;32m 481\u001b[0m \u001b[38;5;28;01mcontinue\u001b[39;00m\n\u001b[1;32m--> 483\u001b[0m transformed[col] \u001b[38;5;241m=\u001b[39m \u001b[43mobj\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43minverse_transform\u001b[49m\u001b[43m(\u001b[49m\u001b[43mtransformed\u001b[49m\u001b[43m[\u001b[49m\u001b[43mcol\u001b[49m\u001b[43m]\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m 485\u001b[0m logging\u001b[38;5;241m.\u001b[39minfo(\u001b[38;5;124mf\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;132;01m{\u001b[39;00mprocessor\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m inverse transformation done.\u001b[39m\u001b[38;5;124m'\u001b[39m)\n\u001b[0;32m 486\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m 487\u001b[0m \u001b[38;5;66;03m# if the processor is not a string,\u001b[39;00m\n\u001b[0;32m 488\u001b[0m \u001b[38;5;66;03m# it should be a mediator, which transforms the data directly.\u001b[39;00m\n", - "File \u001b[1;32md:\\Dropbox\\89_other_application\\GitHub\\PETsARD\\PETsARD\\processor\\discretizing.py:81\u001b[0m, in \u001b[0;36mDiscretizingHandler.inverse_transform\u001b[1;34m(self, data)\u001b[0m\n\u001b[0;32m 78\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_is_fitted:\n\u001b[0;32m 79\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m UnfittedError(\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mThe object is not fitted. Use .fit() first.\u001b[39m\u001b[38;5;124m'\u001b[39m)\n\u001b[1;32m---> 81\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_inverse_transform\u001b[49m\u001b[43m(\u001b[49m\u001b[43mdata\u001b[49m\u001b[43m)\u001b[49m\n", - "File \u001b[1;32md:\\Dropbox\\89_other_application\\GitHub\\PETsARD\\PETsARD\\processor\\discretizing.py:152\u001b[0m, in \u001b[0;36mDiscretizingKBins._inverse_transform\u001b[1;34m(self, data)\u001b[0m\n\u001b[0;32m 140\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21m_inverse_transform\u001b[39m(\u001b[38;5;28mself\u001b[39m, data: pd\u001b[38;5;241m.\u001b[39mSeries) \u001b[38;5;241m-\u001b[39m\u001b[38;5;241m>\u001b[39m np\u001b[38;5;241m.\u001b[39mndarray:\n\u001b[0;32m 141\u001b[0m \u001b[38;5;250m \u001b[39m\u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[0;32m 142\u001b[0m \u001b[38;5;124;03m Inverse the transformed data to the numerical data.\u001b[39;00m\n\u001b[0;32m 143\u001b[0m \n\u001b[1;32m (...)\u001b[0m\n\u001b[0;32m 149\u001b[0m \u001b[38;5;124;03m (np.ndarray): The inverse transformed data.\u001b[39;00m\n\u001b[0;32m 150\u001b[0m \u001b[38;5;124;03m \"\"\"\u001b[39;00m\n\u001b[1;32m--> 152\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mmodel\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43minverse_transform\u001b[49m\u001b[43m(\u001b[49m\u001b[43mdata\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mvalues\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mreshape\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m-\u001b[39;49m\u001b[38;5;241;43m1\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m1\u001b[39;49m\u001b[43m)\u001b[49m\u001b[43m)\u001b[49m\u001b[38;5;241m.\u001b[39mravel()\n", - "File \u001b[1;32md:\\Dropbox\\89_other_application\\GitHub\\PETsARD\\.venv\\lib\\site-packages\\sklearn\\preprocessing\\_discretization.py:430\u001b[0m, in \u001b[0;36mKBinsDiscretizer.inverse_transform\u001b[1;34m(self, Xt)\u001b[0m\n\u001b[0;32m 427\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124monehot\u001b[39m\u001b[38;5;124m\"\u001b[39m \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mencode:\n\u001b[0;32m 428\u001b[0m Xt \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_encoder\u001b[38;5;241m.\u001b[39minverse_transform(Xt)\n\u001b[1;32m--> 430\u001b[0m Xinv \u001b[38;5;241m=\u001b[39m \u001b[43mcheck_array\u001b[49m\u001b[43m(\u001b[49m\u001b[43mXt\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mcopy\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43;01mTrue\u001b[39;49;00m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mdtype\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43m(\u001b[49m\u001b[43mnp\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mfloat64\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mnp\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mfloat32\u001b[49m\u001b[43m)\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m 431\u001b[0m n_features \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mn_bins_\u001b[38;5;241m.\u001b[39mshape[\u001b[38;5;241m0\u001b[39m]\n\u001b[0;32m 432\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m Xinv\u001b[38;5;241m.\u001b[39mshape[\u001b[38;5;241m1\u001b[39m] \u001b[38;5;241m!=\u001b[39m n_features:\n", - "File \u001b[1;32md:\\Dropbox\\89_other_application\\GitHub\\PETsARD\\.venv\\lib\\site-packages\\sklearn\\utils\\validation.py:1049\u001b[0m, in \u001b[0;36mcheck_array\u001b[1;34m(array, accept_sparse, accept_large_sparse, dtype, order, copy, force_all_finite, ensure_2d, allow_nd, ensure_min_samples, ensure_min_features, estimator, input_name)\u001b[0m\n\u001b[0;32m 1043\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mValueError\u001b[39;00m(\n\u001b[0;32m 1044\u001b[0m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mFound array with dim \u001b[39m\u001b[38;5;132;01m%d\u001b[39;00m\u001b[38;5;124m. \u001b[39m\u001b[38;5;132;01m%s\u001b[39;00m\u001b[38;5;124m expected <= 2.\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[0;32m 1045\u001b[0m \u001b[38;5;241m%\u001b[39m (array\u001b[38;5;241m.\u001b[39mndim, estimator_name)\n\u001b[0;32m 1046\u001b[0m )\n\u001b[0;32m 1048\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m force_all_finite:\n\u001b[1;32m-> 1049\u001b[0m \u001b[43m_assert_all_finite\u001b[49m\u001b[43m(\u001b[49m\n\u001b[0;32m 1050\u001b[0m \u001b[43m \u001b[49m\u001b[43marray\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m 1051\u001b[0m \u001b[43m \u001b[49m\u001b[43minput_name\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43minput_name\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m 1052\u001b[0m \u001b[43m \u001b[49m\u001b[43mestimator_name\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mestimator_name\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m 1053\u001b[0m \u001b[43m \u001b[49m\u001b[43mallow_nan\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mforce_all_finite\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m==\u001b[39;49m\u001b[43m \u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mallow-nan\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\n\u001b[0;32m 1054\u001b[0m \u001b[43m \u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m 1056\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m copy:\n\u001b[0;32m 1057\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m _is_numpy_namespace(xp):\n\u001b[0;32m 1058\u001b[0m \u001b[38;5;66;03m# only make a copy if `array` and `array_orig` may share memory`\u001b[39;00m\n", - "File \u001b[1;32md:\\Dropbox\\89_other_application\\GitHub\\PETsARD\\.venv\\lib\\site-packages\\sklearn\\utils\\validation.py:126\u001b[0m, in \u001b[0;36m_assert_all_finite\u001b[1;34m(X, allow_nan, msg_dtype, estimator_name, input_name)\u001b[0m\n\u001b[0;32m 123\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m first_pass_isfinite:\n\u001b[0;32m 124\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m\n\u001b[1;32m--> 126\u001b[0m \u001b[43m_assert_all_finite_element_wise\u001b[49m\u001b[43m(\u001b[49m\n\u001b[0;32m 127\u001b[0m \u001b[43m \u001b[49m\u001b[43mX\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m 128\u001b[0m \u001b[43m \u001b[49m\u001b[43mxp\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mxp\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m 129\u001b[0m \u001b[43m \u001b[49m\u001b[43mallow_nan\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mallow_nan\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m 130\u001b[0m \u001b[43m \u001b[49m\u001b[43mmsg_dtype\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mmsg_dtype\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m 131\u001b[0m \u001b[43m \u001b[49m\u001b[43mestimator_name\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mestimator_name\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m 132\u001b[0m \u001b[43m \u001b[49m\u001b[43minput_name\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43minput_name\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m 133\u001b[0m \u001b[43m\u001b[49m\u001b[43m)\u001b[49m\n", - "File \u001b[1;32md:\\Dropbox\\89_other_application\\GitHub\\PETsARD\\.venv\\lib\\site-packages\\sklearn\\utils\\validation.py:175\u001b[0m, in \u001b[0;36m_assert_all_finite_element_wise\u001b[1;34m(X, xp, allow_nan, msg_dtype, estimator_name, input_name)\u001b[0m\n\u001b[0;32m 158\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m estimator_name \u001b[38;5;129;01mand\u001b[39;00m input_name \u001b[38;5;241m==\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mX\u001b[39m\u001b[38;5;124m\"\u001b[39m \u001b[38;5;129;01mand\u001b[39;00m has_nan_error:\n\u001b[0;32m 159\u001b[0m \u001b[38;5;66;03m# Improve the error message on how to handle missing values in\u001b[39;00m\n\u001b[0;32m 160\u001b[0m \u001b[38;5;66;03m# scikit-learn.\u001b[39;00m\n\u001b[0;32m 161\u001b[0m msg_err \u001b[38;5;241m+\u001b[39m\u001b[38;5;241m=\u001b[39m (\n\u001b[0;32m 162\u001b[0m \u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;130;01m\\n\u001b[39;00m\u001b[38;5;132;01m{\u001b[39;00mestimator_name\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m does not accept missing values\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[0;32m 163\u001b[0m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m encoded as NaN natively. For supervised learning, you might want\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m (...)\u001b[0m\n\u001b[0;32m 173\u001b[0m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m#estimators-that-handle-nan-values\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[0;32m 174\u001b[0m )\n\u001b[1;32m--> 175\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mValueError\u001b[39;00m(msg_err)\n", - "\u001b[1;31mValueError\u001b[0m: Input contains NaN." - ] - } - ], + "outputs": [], "source": [ "issue332(\n", " load=load,\n", @@ -578,423 +393,9 @@ }, { "cell_type": "code", - "execution_count": 12, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Preproc config of Outlier before update as ...\n", - "{'age': ,\n", - " 'capital-gain': ,\n", - " 'capital-loss': ,\n", - " 'education': None,\n", - " 'educational-num': ,\n", - " 'fnlwgt': ,\n", - " 'gender': None,\n", - " 'hours-per-week': ,\n", - " 'income': None,\n", - " 'marital-status': None,\n", - " 'native-country': None,\n", - " 'occupation': None,\n", - " 'race': None,\n", - " 'relationship': None,\n", - " 'workclass': None}\n", - "Preproc config of Outlier before after as ...\n", - "{'age': ,\n", - " 'capital-gain': ,\n", - " 'capital-loss': ,\n", - " 'education': None,\n", - " 'educational-num': ,\n", - " 'fnlwgt': ,\n", - " 'gender': None,\n", - " 'hours-per-week': ,\n", - " 'income': None,\n", - " 'marital-status': None,\n", - " 'native-country': None,\n", - " 'occupation': None,\n", - " 'race': None,\n", - " 'relationship': None,\n", - " 'workclass': None}\n", - "Preproc data as ...\n", - " age workclass fnlwgt education educational-num marital-status \\\n", - "0 -0.953119 0.366136 0.409053 0.776959 -1.152735 0.810663 \n", - "\n", - " occupation relationship race gender capital-gain capital-loss \\\n", - "0 0.74892 0.757864 0.955584 0.48598 -0.159918 -0.219043 \n", - "\n", - " hours-per-week native-country income \n", - "0 -0.044504 0.216869 0.212391 \n", - "Synthesizer (SmartNoise): Fitting dpctgan.\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "d:\\Dropbox\\89_other_application\\GitHub\\PETsARD\\.venv\\lib\\site-packages\\opacus\\privacy_engine.py:638: UserWarning: The sample rate will be defined from ``batch_size`` and ``sample_size``.The returned privacy budget will be incorrect.\n", - " warnings.warn(\n", - "d:\\Dropbox\\89_other_application\\GitHub\\PETsARD\\.venv\\lib\\site-packages\\opacus\\privacy_engine.py:229: UserWarning: Secure RNG turned off. This is perfectly fine for experimentation as it allows for much faster training performance, but remember to turn it on and retrain one last time before production with ``secure_rng`` turned on.\n", - " warnings.warn(\n", - "d:\\Dropbox\\89_other_application\\GitHub\\PETsARD\\.venv\\lib\\site-packages\\torch\\nn\\modules\\module.py:1352: UserWarning: Using a non-full backward hook when the forward contains multiple autograd Nodes is deprecated and will be removed in future versions. This hook will be missing some grad_input. Please use register_full_backward_hook to get the documented behavior.\n", - " warnings.warn(\"Using a non-full backward hook when the forward contains multiple autograd Nodes \"\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Epoch 1, Loss G: 0.7107, Loss D: 1.3900\n", - "epsilon is 0.08246410416899833, alpha is 63.0\n", - "Epoch 2, Loss G: 0.7097, Loss D: 1.3898\n", - "epsilon is 0.6154321240229542, alpha is 21.0\n", - "Epoch 3, Loss G: 0.7048, Loss D: 1.3908\n", - "epsilon is 0.8717652885709554, alpha is 16.0\n", - "Epoch 4, Loss G: 0.7016, Loss D: 1.3897\n", - "epsilon is 1.0734433928301887, alpha is 14.0\n", - "Epoch 5, Loss G: 0.7059, Loss D: 1.3874\n", - "epsilon is 1.2474424384805656, alpha is 12.0\n", - "Epoch 6, Loss G: 0.7015, Loss D: 1.3895\n", - "epsilon is 1.403078289939324, alpha is 10.9\n", - "Epoch 7, Loss G: 0.6987, Loss D: 1.3876\n", - "epsilon is 1.5430774752709655, alpha is 10.6\n", - "Epoch 8, Loss G: 0.6922, Loss D: 1.3928\n", - "epsilon is 1.6748565545536702, alpha is 10.0\n", - "Epoch 9, Loss G: 0.6947, Loss D: 1.3898\n", - "epsilon is 1.7986930762464894, alpha is 9.4\n", - "Epoch 10, Loss G: 0.6898, Loss D: 1.3850\n", - "epsilon is 1.9160035051686128, alpha is 9.0\n", - "Epoch 11, Loss G: 0.6888, Loss D: 1.3885\n", - "epsilon is 2.027907968415127, alpha is 8.7\n", - "Epoch 12, Loss G: 0.6854, Loss D: 1.3891\n", - "epsilon is 2.135121781113915, alpha is 8.3\n", - "Epoch 13, Loss G: 0.6822, Loss D: 1.3884\n", - "epsilon is 2.2382637160168204, alpha is 8.1\n", - "Epoch 14, Loss G: 0.6828, Loss D: 1.3872\n", - "epsilon is 2.337827882631375, alpha is 7.8\n", - "Epoch 15, Loss G: 0.6835, Loss D: 1.3950\n", - "epsilon is 2.434237194672732, alpha is 7.6\n", - "Epoch 16, Loss G: 0.6794, Loss D: 1.3912\n", - "epsilon is 2.5278163462034815, alpha is 7.4\n", - "Epoch 17, Loss G: 0.6722, Loss D: 1.3944\n", - "epsilon is 2.6188388524326234, alpha is 7.2\n", - "Epoch 18, Loss G: 0.6723, Loss D: 1.3989\n", - "epsilon is 2.7076108306108346, alpha is 7.0\n", - "Epoch 19, Loss G: 0.6683, Loss D: 1.3920\n", - "epsilon is 2.7940664707675626, alpha is 6.9\n", - "Epoch 20, Loss G: 0.6670, Loss D: 1.3951\n", - "epsilon is 2.878763468386405, alpha is 6.8\n", - "Epoch 21, Loss G: 0.6687, Loss D: 1.3902\n", - "epsilon is 2.961464297854409, alpha is 6.6\n", - "Epoch 22, Loss G: 0.6657, Loss D: 1.3872\n", - "epsilon is 3.0425452652856495, alpha is 6.5\n", - "Epoch 23, Loss G: 0.6616, Loss D: 1.3926\n", - "epsilon is 3.122103446988952, alpha is 6.4\n", - "Epoch 24, Loss G: 0.6631, Loss D: 1.3951\n", - "epsilon is 3.200208539731727, alpha is 6.3\n", - "Epoch 25, Loss G: 0.6614, Loss D: 1.3910\n", - "epsilon is 3.2769350559807005, alpha is 6.2\n", - "Epoch 26, Loss G: 0.6637, Loss D: 1.3855\n", - "epsilon is 3.3523628060835255, alpha is 6.1\n", - "Epoch 27, Loss G: 0.6664, Loss D: 1.3881\n", - "epsilon is 3.4265774389666657, alpha is 6.0\n", - "Epoch 28, Loss G: 0.6632, Loss D: 1.3884\n", - "epsilon is 3.4996710497868895, alpha is 5.9\n", - "Epoch 29, Loss G: 0.6609, Loss D: 1.3892\n", - "epsilon is 3.5717428643995515, alpha is 5.8\n", - "Epoch 30, Loss G: 0.6594, Loss D: 1.3910\n", - "epsilon is 3.6428935706241528, alpha is 5.8\n", - "Epoch 31, Loss G: 0.6567, Loss D: 1.3863\n", - "epsilon is 3.712751905883079, alpha is 5.7\n", - "Epoch 32, Loss G: 0.6577, Loss D: 1.3827\n", - "epsilon is 3.7818141415719784, alpha is 5.6\n", - "Epoch 33, Loss G: 0.6609, Loss D: 1.3858\n", - "epsilon is 3.850205948937656, alpha is 5.5\n", - "Epoch 34, Loss G: 0.6532, Loss D: 1.3819\n", - "epsilon is 3.917468189518828, alpha is 5.5\n", - "Epoch 35, Loss G: 0.6551, Loss D: 1.3896\n", - "epsilon is 3.9840352850839897, alpha is 5.4\n", - "Epoch 36, Loss G: 0.6527, Loss D: 1.3815\n", - "epsilon is 4.050006670613625, alpha is 5.4\n", - "Epoch 37, Loss G: 0.6516, Loss D: 1.3873\n", - "epsilon is 4.114903091238218, alpha is 5.3\n", - "Epoch 38, Loss G: 0.6477, Loss D: 1.3901\n", - "epsilon is 4.179582102216282, alpha is 5.2\n", - "Epoch 39, Loss G: 0.6504, Loss D: 1.3830\n", - "epsilon is 4.242979684763249, alpha is 5.2\n", - "Epoch 40, Loss G: 0.6503, Loss D: 1.3904\n", - "epsilon is 4.306336090935168, alpha is 5.1\n", - "Epoch 41, Loss G: 0.6467, Loss D: 1.3944\n", - "epsilon is 4.368450711228463, alpha is 5.1\n", - "Epoch 42, Loss G: 0.6481, Loss D: 1.3939\n", - "epsilon is 4.430565331521756, alpha is 5.1\n", - "Epoch 43, Loss G: 0.6474, Loss D: 1.3883\n", - "epsilon is 4.491519093988257, alpha is 5.0\n", - "Epoch 44, Loss G: 0.6422, Loss D: 1.3922\n", - "epsilon is 4.552353368662634, alpha is 5.0\n", - "Epoch 45, Loss G: 0.6401, Loss D: 1.3860\n", - "epsilon is 4.612407286797646, alpha is 4.9\n", - "Epoch 46, Loss G: 0.6392, Loss D: 1.3932\n", - "epsilon is 4.671963825364106, alpha is 4.9\n", - "Epoch 47, Loss G: 0.6358, Loss D: 1.3937\n", - "epsilon is 4.731359885691218, alpha is 4.8\n", - "Epoch 48, Loss G: 0.6367, Loss D: 1.3966\n", - "epsilon is 4.789641290552096, alpha is 4.8\n", - "Epoch 49, Loss G: 0.6344, Loss D: 1.4051\n", - "epsilon is 4.847922695412973, alpha is 4.8\n", - "Epoch 50, Loss G: 0.6350, Loss D: 1.4007\n", - "epsilon is 4.905655535140821, alpha is 4.7\n", - "Epoch 51, Loss G: 0.6294, Loss D: 1.4034\n", - "epsilon is 4.9626644016048695, alpha is 4.7\n", - "Epoch 52, Loss G: 0.6293, Loss D: 1.3913\n", - "epsilon is 5.0196732680689164, alpha is 4.7\n", - "Epoch 53, Loss G: 0.6228, Loss D: 1.4126\n", - "epsilon is 5.0760439796888255, alpha is 4.6\n", - "Epoch 54, Loss G: 0.6300, Loss D: 1.4028\n", - "epsilon is 5.131782895986607, alpha is 4.6\n", - "Epoch 55, Loss G: 0.6118, Loss D: 1.4130\n", - "epsilon is 5.1875218122843885, alpha is 4.6\n", - "Epoch 56, Loss G: 0.6162, Loss D: 1.4176\n", - "epsilon is 5.242864373609633, alpha is 4.5\n", - "Epoch 57, Loss G: 0.6124, Loss D: 1.4177\n", - "epsilon is 5.297335920908565, alpha is 4.5\n", - "Epoch 58, Loss G: 0.6036, Loss D: 1.4231\n", - "epsilon is 5.351807468207497, alpha is 4.5\n", - "Epoch 59, Loss G: 0.6017, Loss D: 1.4224\n", - "epsilon is 5.406279015506429, alpha is 4.5\n", - "Epoch 60, Loss G: 0.6025, Loss D: 1.4320\n", - "epsilon is 5.4597004033036685, alpha is 4.4\n", - "Epoch 61, Loss G: 0.5997, Loss D: 1.4292\n", - "epsilon is 5.512907155723584, alpha is 4.4\n", - "Epoch 62, Loss G: 0.5974, Loss D: 1.4378\n", - "epsilon is 5.566113908143499, alpha is 4.4\n", - "Epoch 63, Loss G: 0.5980, Loss D: 1.4396\n", - "epsilon is 5.619296859313559, alpha is 4.3\n", - "Epoch 64, Loss G: 0.5923, Loss D: 1.4463\n", - "epsilon is 5.671241383942272, alpha is 4.3\n", - "Epoch 65, Loss G: 0.5817, Loss D: 1.4429\n", - "epsilon is 5.723185908570986, alpha is 4.3\n", - "Epoch 66, Loss G: 0.5879, Loss D: 1.4591\n", - "epsilon is 5.775130433199701, alpha is 4.3\n", - "Epoch 67, Loss G: 0.5835, Loss D: 1.4501\n", - "epsilon is 5.827074957828414, alpha is 4.3\n", - "Epoch 68, Loss G: 0.5829, Loss D: 1.4513\n", - "epsilon is 5.877966017103227, alpha is 4.2\n", - "Epoch 69, Loss G: 0.5753, Loss D: 1.4468\n", - "epsilon is 5.928650874011896, alpha is 4.2\n", - "Epoch 70, Loss G: 0.5799, Loss D: 1.4541\n", - "epsilon is 5.979335730920565, alpha is 4.2\n", - "Epoch 71, Loss G: 0.5725, Loss D: 1.4617\n", - "epsilon is 6.030020587829234, alpha is 4.2\n", - "Epoch 72, Loss G: 0.5712, Loss D: 1.4637\n", - "epsilon is 6.080411733176044, alpha is 4.1\n", - "Epoch 73, Loss G: 0.5642, Loss D: 1.4602\n", - "epsilon is 6.129839475434501, alpha is 4.1\n", - "Epoch 74, Loss G: 0.5707, Loss D: 1.4774\n", - "epsilon is 6.17926721769296, alpha is 4.1\n", - "Epoch 75, Loss G: 0.5642, Loss D: 1.4687\n", - "epsilon is 6.228694959951418, alpha is 4.1\n", - "Epoch 76, Loss G: 0.5691, Loss D: 1.4757\n", - "epsilon is 6.278122702209876, alpha is 4.1\n", - "Epoch 77, Loss G: 0.5707, Loss D: 1.4758\n", - "epsilon is 6.327357790594008, alpha is 4.0\n", - "Epoch 78, Loss G: 0.5639, Loss D: 1.4835\n", - "epsilon is 6.375530964286589, alpha is 4.0\n", - "Epoch 79, Loss G: 0.5652, Loss D: 1.4719\n", - "epsilon is 6.42370413797917, alpha is 4.0\n", - "Epoch 80, Loss G: 0.5628, Loss D: 1.4748\n", - "epsilon is 6.471877311671752, alpha is 4.0\n", - "Epoch 81, Loss G: 0.5608, Loss D: 1.4772\n", - "epsilon is 6.520050485364332, alpha is 4.0\n", - "Epoch 82, Loss G: 0.5557, Loss D: 1.4777\n", - "epsilon is 6.568223659056913, alpha is 4.0\n", - "Epoch 83, Loss G: 0.5670, Loss D: 1.4892\n", - "epsilon is 6.615734837571576, alpha is 3.9\n", - "Epoch 84, Loss G: 0.5594, Loss D: 1.4800\n", - "epsilon is 6.662655981812787, alpha is 3.9\n", - "Epoch 85, Loss G: 0.5555, Loss D: 1.4830\n", - "epsilon is 6.709577126053995, alpha is 3.9\n", - "Epoch 86, Loss G: 0.5576, Loss D: 1.4863\n", - "epsilon is 6.7564982702952046, alpha is 3.9\n", - "Epoch 87, Loss G: 0.5547, Loss D: 1.4850\n", - "epsilon is 6.803419414536414, alpha is 3.9\n", - "Epoch 88, Loss G: 0.5603, Loss D: 1.4947\n", - "epsilon is 6.850340558777624, alpha is 3.9\n", - "Epoch 89, Loss G: 0.5570, Loss D: 1.4874\n", - "epsilon is 6.896912311217361, alpha is 3.8\n", - "Epoch 90, Loss G: 0.5555, Loss D: 1.4854\n", - "epsilon is 6.942583958167586, alpha is 3.8\n", - "Epoch 91, Loss G: 0.5516, Loss D: 1.4906\n", - "epsilon is 6.98825560511781, alpha is 3.8\n", - "Epoch 92, Loss G: 0.5562, Loss D: 1.4936\n", - "epsilon is 7.033927252068035, alpha is 3.8\n", - "Epoch 93, Loss G: 0.5531, Loss D: 1.4931\n", - "epsilon is 7.079598899018261, alpha is 3.8\n", - "Epoch 94, Loss G: 0.5540, Loss D: 1.4899\n", - "epsilon is 7.125270545968486, alpha is 3.8\n", - "Epoch 95, Loss G: 0.5474, Loss D: 1.4922\n", - "epsilon is 7.170942192918711, alpha is 3.8\n", - "Epoch 96, Loss G: 0.5483, Loss D: 1.4962\n", - "epsilon is 7.216225114747892, alpha is 3.7\n", - "Epoch 97, Loss G: 0.5495, Loss D: 1.4945\n", - "epsilon is 7.260649789629172, alpha is 3.7\n", - "Epoch 98, Loss G: 0.5478, Loss D: 1.4869\n", - "epsilon is 7.305074464510453, alpha is 3.7\n", - "Epoch 99, Loss G: 0.5472, Loss D: 1.4911\n", - "epsilon is 7.349499139391733, alpha is 3.7\n", - "Epoch 100, Loss G: 0.5532, Loss D: 1.4938\n", - "epsilon is 7.3939238142730135, alpha is 3.7\n", - "Epoch 101, Loss G: 0.5451, Loss D: 1.4891\n", - "epsilon is 7.4383484891542935, alpha is 3.7\n", - "Epoch 102, Loss G: 0.5529, Loss D: 1.4897\n", - "epsilon is 7.482773164035574, alpha is 3.7\n", - "Epoch 103, Loss G: 0.5484, Loss D: 1.4863\n", - "epsilon is 7.527197838916854, alpha is 3.7\n", - "Epoch 104, Loss G: 0.5478, Loss D: 1.4795\n", - "epsilon is 7.570985182837946, alpha is 3.6\n", - "Epoch 105, Loss G: 0.5577, Loss D: 1.4863\n", - "epsilon is 7.614165403949305, alpha is 3.6\n", - "Epoch 106, Loss G: 0.5572, Loss D: 1.4800\n", - "epsilon is 7.657345625060665, alpha is 3.6\n", - "Epoch 107, Loss G: 0.5546, Loss D: 1.4823\n", - "epsilon is 7.700525846172025, alpha is 3.6\n", - "Epoch 108, Loss G: 0.5574, Loss D: 1.4715\n", - "epsilon is 7.743706067283383, alpha is 3.6\n", - "Epoch 109, Loss G: 0.5554, Loss D: 1.4778\n", - "epsilon is 7.786886288394741, alpha is 3.6\n", - "Epoch 110, Loss G: 0.5621, Loss D: 1.4794\n", - "epsilon is 7.830066509506101, alpha is 3.6\n", - "Epoch 111, Loss G: 0.5558, Loss D: 1.4784\n", - "epsilon is 7.873246730617461, alpha is 3.6\n", - "Epoch 112, Loss G: 0.5632, Loss D: 1.4766\n", - "epsilon is 7.916426951728819, alpha is 3.6\n", - "Epoch 113, Loss G: 0.5602, Loss D: 1.4731\n", - "epsilon is 7.95868193355541, alpha is 3.5\n", - "Epoch 114, Loss G: 0.5532, Loss D: 1.4708\n", - "epsilon is 8.000620212289654, alpha is 3.5\n", - "Epoch 115, Loss G: 0.5629, Loss D: 1.4671\n", - "epsilon is 8.042558491023897, alpha is 3.5\n", - "Epoch 116, Loss G: 0.5605, Loss D: 1.4703\n", - "epsilon is 8.084496769758141, alpha is 3.5\n", - "Epoch 117, Loss G: 0.5661, Loss D: 1.4603\n", - "epsilon is 8.126435048492386, alpha is 3.5\n", - "Epoch 118, Loss G: 0.5667, Loss D: 1.4750\n", - "epsilon is 8.168373327226629, alpha is 3.5\n", - "Epoch 119, Loss G: 0.5659, Loss D: 1.4663\n", - "epsilon is 8.210311605960872, alpha is 3.5\n", - "Epoch 120, Loss G: 0.5597, Loss D: 1.4697\n", - "epsilon is 8.252249884695114, alpha is 3.5\n", - "Epoch 121, Loss G: 0.5606, Loss D: 1.4597\n", - "epsilon is 8.294188163429357, alpha is 3.5\n", - "Epoch 122, Loss G: 0.5615, Loss D: 1.4584\n", - "epsilon is 8.336126442163602, alpha is 3.5\n", - "Epoch 123, Loss G: 0.5618, Loss D: 1.4627\n", - "epsilon is 8.377016439122203, alpha is 3.4\n", - "Epoch 124, Loss G: 0.5616, Loss D: 1.4609\n", - "epsilon is 8.417715279980845, alpha is 3.4\n", - "Epoch 125, Loss G: 0.5624, Loss D: 1.4777\n", - "epsilon is 8.458414120839487, alpha is 3.4\n", - "Epoch 126, Loss G: 0.5613, Loss D: 1.4678\n", - "epsilon is 8.49911296169813, alpha is 3.4\n", - "Epoch 127, Loss G: 0.5576, Loss D: 1.4605\n", - "epsilon is 8.539811802556772, alpha is 3.4\n", - "Epoch 128, Loss G: 0.5654, Loss D: 1.4663\n", - "epsilon is 8.580510643415414, alpha is 3.4\n", - "Epoch 129, Loss G: 0.5651, Loss D: 1.4615\n", - "epsilon is 8.621209484274056, alpha is 3.4\n", - "Epoch 130, Loss G: 0.5654, Loss D: 1.4641\n", - "epsilon is 8.661908325132698, alpha is 3.4\n", - "Epoch 131, Loss G: 0.5639, Loss D: 1.4569\n", - "epsilon is 8.702607165991338, alpha is 3.4\n", - "Epoch 132, Loss G: 0.5672, Loss D: 1.4536\n", - "epsilon is 8.74330600684998, alpha is 3.4\n", - "Epoch 133, Loss G: 0.5713, Loss D: 1.4582\n", - "epsilon is 8.784004847708623, alpha is 3.4\n", - "Epoch 134, Loss G: 0.5650, Loss D: 1.4524\n", - "epsilon is 8.823944600709858, alpha is 3.3\n", - "Epoch 135, Loss G: 0.5589, Loss D: 1.4599\n", - "epsilon is 8.863406501319506, alpha is 3.3\n", - "Epoch 136, Loss G: 0.5631, Loss D: 1.4620\n", - "epsilon is 8.902868401929158, alpha is 3.3\n", - "Epoch 137, Loss G: 0.5668, Loss D: 1.4641\n", - "epsilon is 8.94233030253881, alpha is 3.3\n", - "Epoch 138, Loss G: 0.5698, Loss D: 1.4604\n", - "epsilon is 8.98179220314846, alpha is 3.3\n", - "Epoch 139, Loss G: 0.5650, Loss D: 1.4606\n", - "epsilon is 9.02125410375811, alpha is 3.3\n", - "Epoch 140, Loss G: 0.5700, Loss D: 1.4558\n", - "epsilon is 9.060716004367762, alpha is 3.3\n", - "Epoch 141, Loss G: 0.5640, Loss D: 1.4623\n", - "epsilon is 9.100177904977413, alpha is 3.3\n", - "Epoch 142, Loss G: 0.5664, Loss D: 1.4496\n", - "epsilon is 9.139639805587063, alpha is 3.3\n", - "Epoch 143, Loss G: 0.5707, Loss D: 1.4586\n", - "epsilon is 9.179101706196715, alpha is 3.3\n", - "Epoch 144, Loss G: 0.5654, Loss D: 1.4485\n", - "epsilon is 9.218563606806365, alpha is 3.3\n", - "Epoch 145, Loss G: 0.5691, Loss D: 1.4478\n", - "epsilon is 9.258025507416015, alpha is 3.3\n", - "Epoch 146, Loss G: 0.5750, Loss D: 1.4537\n", - "epsilon is 9.297487408025667, alpha is 3.3\n", - "Epoch 147, Loss G: 0.5737, Loss D: 1.4539\n", - "epsilon is 9.33595966014038, alpha is 3.2\n", - "Epoch 148, Loss G: 0.5714, Loss D: 1.4532\n", - "epsilon is 9.374187111269075, alpha is 3.2\n", - "Epoch 149, Loss G: 0.5723, Loss D: 1.4538\n", - "epsilon is 9.41241456239777, alpha is 3.2\n", - "Epoch 150, Loss G: 0.5719, Loss D: 1.4523\n", - "epsilon is 9.450642013526464, alpha is 3.2\n", - "Epoch 151, Loss G: 0.5674, Loss D: 1.4542\n", - "epsilon is 9.48886946465516, alpha is 3.2\n", - "Epoch 152, Loss G: 0.5661, Loss D: 1.4573\n", - "epsilon is 9.527096915783854, alpha is 3.2\n", - "Epoch 153, Loss G: 0.5722, Loss D: 1.4540\n", - "epsilon is 9.56532436691255, alpha is 3.2\n", - "Epoch 154, Loss G: 0.5742, Loss D: 1.4563\n", - "epsilon is 9.603551818041245, alpha is 3.2\n", - "Epoch 155, Loss G: 0.5725, Loss D: 1.4476\n", - "epsilon is 9.64177926916994, alpha is 3.2\n", - "Epoch 156, Loss G: 0.5703, Loss D: 1.4535\n", - "epsilon is 9.680006720298634, alpha is 3.2\n", - "Epoch 157, Loss G: 0.5690, Loss D: 1.4478\n", - "epsilon is 9.71823417142733, alpha is 3.2\n", - "Epoch 158, Loss G: 0.5698, Loss D: 1.4475\n", - "epsilon is 9.756461622556024, alpha is 3.2\n", - "Epoch 159, Loss G: 0.5766, Loss D: 1.4464\n", - "epsilon is 9.79468907368472, alpha is 3.2\n", - "Epoch 160, Loss G: 0.5784, Loss D: 1.4552\n", - "epsilon is 9.832916524813415, alpha is 3.2\n", - "Epoch 161, Loss G: 0.5751, Loss D: 1.4438\n", - "epsilon is 9.871016847131145, alpha is 3.1\n", - "Epoch 162, Loss G: 0.5799, Loss D: 1.4512\n", - "epsilon is 9.908012332703517, alpha is 3.1\n", - "Epoch 163, Loss G: 0.5725, Loss D: 1.4488\n", - "epsilon is 9.94500781827589, alpha is 3.1\n", - "Epoch 164, Loss G: 0.5762, Loss D: 1.4466\n", - "epsilon is 9.982003303848263, alpha is 3.1\n", - "Synthesizer (SmartNoise): Fitting dpctgan spent 115.4473 sec.\n", - "Synthesizer (SmartNoise): Sampling dpctgan # 927 rows (same as raw) in 0.4688 sec.\n", - "Sync data as ...\n", - " age workclass fnlwgt education educational-num marital-status \\\n", - "0 3.663019 0.997224 4.606915 0.747578 2.269901 0.984679 \n", - "\n", - " occupation relationship race gender capital-gain capital-loss \\\n", - "0 0.990903 0.95497 0.806221 0.997782 -0.159918 -0.219043 \n", - "\n", - " hours-per-week native-country income \n", - "0 2.634771 0.88122 0.993726 \n", - " age workclass fnlwgt education educational-num \\\n", - "0 88.295354 Federal-gov 647932.096102 Masters 15.972689 \n", - "\n", - " marital-status occupation relationship race gender capital-gain \\\n", - "0 Widowed Protective-serv Wife White Female 0.0 \n", - "\n", - " capital-loss hours-per-week native-country income \n", - "0 0.0 72.657481 United-States >50K \n" - ] - } - ], + "outputs": [], "source": [ "issue332_gan(\n", " load = load,\n", @@ -1005,408 +406,11 @@ }, { "cell_type": "code", - "execution_count": 13, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Preproc config of Outlier before update as ...\n", - "{'age': ,\n", - " 'capital-gain': ,\n", - " 'capital-loss': ,\n", - " 'education': None,\n", - " 'educational-num': ,\n", - " 'fnlwgt': ,\n", - " 'gender': None,\n", - " 'hours-per-week': ,\n", - " 'income': None,\n", - " 'marital-status': None,\n", - " 'native-country': None,\n", - " 'occupation': None,\n", - " 'race': None,\n", - " 'relationship': None,\n", - " 'workclass': None}\n", - "Preproc config of Outlier before after as ...\n", - "{'age': ,\n", - " 'capital-gain': ,\n", - " 'capital-loss': ,\n", - " 'education': None,\n", - " 'educational-num': ,\n", - " 'fnlwgt': ,\n", - " 'gender': None,\n", - " 'hours-per-week': ,\n", - " 'income': None,\n", - " 'marital-status': None,\n", - " 'native-country': None,\n", - " 'occupation': None,\n", - " 'race': None,\n", - " 'relationship': None,\n", - " 'workclass': None}\n", - "Preproc data as ...\n", - " age workclass fnlwgt education educational-num marital-status \\\n", - "0 -0.953119 0.006814 0.409053 0.780317 -1.152735 0.507641 \n", - "\n", - " occupation relationship race gender capital-gain capital-loss \\\n", - "0 0.750354 0.787001 0.864054 0.199314 -0.159918 -0.219043 \n", - "\n", - " hours-per-week native-country income \n", - "0 -0.044504 0.099702 0.61332 \n", - "Synthesizer (SmartNoise): Fitting patectgan.\n", - "using loss cross_entropy and regularization None\n", - "eps: 0.102479 \t G: 0.713781 \t D: 0.716145\n", - "eps: 0.457732 \t G: 0.713048 \t D: 0.712165\n", - "eps: 0.650248 \t G: 0.689282 \t D: 0.698435\n", - "eps: 0.799151 \t G: 0.698342 \t D: 0.710174\n", - "eps: 0.925562 \t G: 0.716226 \t D: 0.704634\n", - "eps: 1.037397 \t G: 0.750504 \t D: 0.704282\n", - "eps: 1.139329 \t G: 0.733702 \t D: 0.708267\n", - "eps: 1.232819 \t G: 0.742357 \t D: 0.711618\n", - "eps: 1.320496 \t G: 0.698668 \t D: 0.703252\n", - "eps: 1.403195 \t G: 0.737517 \t D: 0.699576\n", - "eps: 1.481995 \t G: 0.681477 \t D: 0.705583\n", - "eps: 1.556995 \t G: 0.672663 \t D: 0.702600\n", - "eps: 1.628302 \t G: 0.745140 \t D: 0.694179\n", - "eps: 1.698302 \t G: 0.690847 \t D: 0.712576\n", - "eps: 1.763994 \t G: 0.727325 \t D: 0.703433\n", - "eps: 1.828994 \t G: 0.709237 \t D: 0.703511\n", - "eps: 1.891630 \t G: 0.708808 \t D: 0.702042\n", - 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\t G: 0.709171 \t D: 0.694371\n", - "Synthesizer (SmartNoise): Fitting patectgan spent 93.3619 sec.\n", - "Synthesizer (SmartNoise): Sampling patectgan # 927 rows (same as raw) in 0.1432 sec.\n", - "Sync data as ...\n", - " age workclass fnlwgt education educational-num marital-status \\\n", - "0 0.24186 0.103012 1.088126 0.554129 0.519998 0.581233 \n", - "\n", - " occupation relationship race gender capital-gain capital-loss \\\n", - "0 0.236864 0.475109 0.705326 0.676615 0.890221 -0.115185 \n", - "\n", - " hours-per-week native-country income \n", - "0 1.13638 0.654151 0.748044 \n", - " age workclass fnlwgt education educational-num \\\n", - "0 41.385254 Private 294926.71711 Bachelors 11.385191 \n", - "\n", - " marital-status occupation relationship race gender capital-gain \\\n", - "0 Never-married Prof-specialty Not-in-family White Male 9733.339667 \n", - "\n", - " capital-loss hours-per-week native-country income \n", - "0 41.004116 54.393705 United-States <=50K \n" - ] - } - ], + "execution_count": null, + "metadata": { + "tags": [] + }, + "outputs": [], "source": [ "issue332_gan(\n", " load = load,\n", @@ -1418,9 +422,9 @@ ], "metadata": { "kernelspec": { - "display_name": "dev310", + "display_name": "conda_pytorch_p310", "language": "python", - "name": "python3" + "name": "conda_pytorch_p310" }, "language_info": { "codemirror_mode": { @@ -1432,9 +436,9 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.10.11" + "version": "3.10.13" } }, "nbformat": 4, - "nbformat_minor": 2 + "nbformat_minor": 4 } diff --git a/requirements.txt b/requirements.txt index e4ff6b2d8963f37b4ae5374150765a814a83e832..82b25d7b5e196d40cf8186e260aa85ed7861b11d 100644 GIT binary patch literal 1587 zcmY*Z%dXov4BhK1x*u5A@*|G{478hB6fMv}SF#XB22wwA=NtbQxs<7sZcWo6Hm(8q)q2ov1GMRGrH|^3eG`!=2oqYB! zy=^<*AIeer2>+iJrDHaefq&g}yx|Q~7a5bzmGg0y*1S{hmbY|c{W1olZWaOHiulS~ zALT5Ep+>$P&ht$F+nvjPiD9K|4X`U87At|R23Um4XX&;B?p9?fQ_Tgv9rbBBZ!Zt! 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requirements.txt | 1 - 2 files changed, 58 insertions(+), 6 deletions(-) diff --git a/demo/dev/executable_smartnoise.ipynb b/demo/dev/executable_smartnoise.ipynb index 76d5f0e8..6ed7401c 100644 --- a/demo/dev/executable_smartnoise.ipynb +++ b/demo/dev/executable_smartnoise.ipynb @@ -12,8 +12,8 @@ "output_type": "stream", "text": [ "Collecting private-pgm@ git+https://github.com/ryan112358/private-pgm.git@5b9126295c110b741e5426ddbff419ea1e60e788 (from -r ../../requirements.txt (line 56))\n", - " Cloning https://github.com/ryan112358/private-pgm.git (to revision 5b9126295c110b741e5426ddbff419ea1e60e788) to /tmp/pip-install-vn8m0m34/private-pgm_80e5de69f17543b59c34dff626211cba\n", - " Running command git clone --filter=blob:none --quiet https://github.com/ryan112358/private-pgm.git /tmp/pip-install-vn8m0m34/private-pgm_80e5de69f17543b59c34dff626211cba\n", + " Cloning https://github.com/ryan112358/private-pgm.git (to revision 5b9126295c110b741e5426ddbff419ea1e60e788) to /tmp/pip-install-hpexqwll/private-pgm_aa8d511759a64cca9e4e4574d5df15e1\n", + " Running command git clone --filter=blob:none --quiet https://github.com/ryan112358/private-pgm.git /tmp/pip-install-hpexqwll/private-pgm_aa8d511759a64cca9e4e4574d5df15e1\n", " Running command git rev-parse -q --verify 'sha^5b9126295c110b741e5426ddbff419ea1e60e788'\n", " Running command git fetch -q https://github.com/ryan112358/private-pgm.git 5b9126295c110b741e5426ddbff419ea1e60e788\n", " Resolved https://github.com/ryan112358/private-pgm.git to commit 5b9126295c110b741e5426ddbff419ea1e60e788\n", @@ -113,9 +113,62 @@ " Using cached python_dateutil-2.9.0.post0-py2.py3-none-any.whl.metadata (8.4 kB)\n", "Collecting pytz==2024.1 (from -r ../../requirements.txt (line 63))\n", " Using cached pytz-2024.1-py2.py3-none-any.whl.metadata (22 kB)\n", - "\u001b[31mERROR: Ignored the following versions that require a different python version: 0.1.3 Requires-Python >=3.6,<3.9; 0.1.3.dev0 Requires-Python >=3.6,<3.9; 0.1.3.dev1 Requires-Python >=3.6,<3.9; 0.1.4 Requires-Python >=3.6,<3.9; 0.1.4.dev0 Requires-Python >=3.6,<3.9; 0.2.0 Requires-Python >=3.6,<3.9; 0.2.0.dev0 Requires-Python >=3.6,<3.9; 0.2.1 Requires-Python >=3.6,<3.9; 0.2.1.dev0 Requires-Python >=3.6,<3.9; 0.2.2 Requires-Python >=3.6,<3.9; 0.2.2.dev0 Requires-Python >=3.6,<3.9; 0.2.2.dev1 Requires-Python >=3.5,<3.9; 0.2.2.dev2 Requires-Python >=3.6,<3.9; 0.2.2.dev3 Requires-Python >=3.6,<3.9; 0.3.0 Requires-Python >=3.6,<3.10; 0.3.0 Requires-Python >=3.6,<3.9; 0.3.0.dev0 Requires-Python >=3.5,<3.9; 0.3.0.dev0 Requires-Python >=3.6,<3.10; 0.3.0.dev1 Requires-Python >=3.6,<3.9; 0.3.0.post1 Requires-Python >=3.6,<3.10; 0.3.1 Requires-Python >=3.5,<3.8; 0.3.1 Requires-Python >=3.6,<3.9; 0.3.1.dev0 Requires-Python >=3.5,<3.8; 0.3.1.dev0 Requires-Python >=3.6,<3.9; 0.3.1.dev1 Requires-Python >=3.6,<3.9; 0.3.1.dev2 Requires-Python >=3.6,<3.9; 0.3.2 Requires-Python >=3.5,<3.9; 0.3.2.dev0 Requires-Python >=3.5,<3.8; 0.3.2.dev0 Requires-Python >=3.6,<3.9; 0.3.2.dev1 Requires-Python >=3.5,<3.9; 0.3.3 Requires-Python >=3.5,<3.9; 0.3.3.dev0 Requires-Python >=3.5,<3.9; 0.4.0 Requires-Python >=3.5,<3.9; 0.4.0 Requires-Python >=3.6,<3.9; 0.4.0.dev0 Requires-Python >=3.5,<3.9; 0.4.0.dev0 Requires-Python >=3.6,<3.9; 0.4.0.dev1 Requires-Python >=3.6,<3.9; 0.4.1 Requires-Python >=3.6,<3.9; 0.4.1.dev0 Requires-Python >=3.6,<3.9; 0.4.1.dev1 Requires-Python >=3.6,<3.9; 0.4.2 Requires-Python >=3.6,<3.9; 0.4.2.dev0 Requires-Python >=3.6,<3.9; 0.4.3 Requires-Python >=3.6,<3.9; 0.4.3.dev0 Requires-Python >=3.6,<3.9; 0.4.3.dev1 Requires-Python >=3.6,<3.9; 0.4.4.dev0 Requires-Python >=3.6,<3.9; 0.5.0 Requires-Python >=3.6,<3.10; 0.5.0 Requires-Python >=3.6,<3.9; 0.5.0.dev0 Requires-Python >=3.6,<3.9; 0.5.0.dev1 Requires-Python >=3.6,<3.10; 0.5.0.dev1 Requires-Python >=3.6,<3.9; 0.5.1 Requires-Python >=3.6,<3.10; 0.5.1 Requires-Python >=3.6,<3.9; 0.5.1.dev0 Requires-Python >=3.6,<3.10; 0.5.1.dev0 Requires-Python >=3.6,<3.9; 0.5.1.dev1 Requires-Python >=3.6,<3.10; 0.5.1.dev1 Requires-Python >=3.6,<3.9; 0.5.1.dev2 Requires-Python >=3.6,<3.10; 0.5.1.dev3 Requires-Python >=3.6,<3.10; 0.5.2 Requires-Python >=3.6,<3.10; 0.5.2.dev0 Requires-Python >=3.6,<3.10; 0.5.2.dev0 Requires-Python >=3.6,<3.9; 0.5.2.dev1 Requires-Python >=3.6,<3.10; 0.5.3.dev0 Requires-Python >=3.6,<3.10; 0.6.0 Requires-Python >=3.6,<3.10; 0.6.0.dev0 Requires-Python >=3.6,<3.10; 0.6.1 Requires-Python >=3.6,<3.10; 0.6.1.dev0 Requires-Python >=3.6,<3.10; 0.7.0 Requires-Python >=3.6,<3.10; 0.7.0.dev0 Requires-Python >=3.6,<3.10\u001b[0m\u001b[31m\n", - "\u001b[0m\u001b[31mERROR: Could not find a version that satisfies the requirement pywin32==306 (from versions: none)\u001b[0m\u001b[31m\n", - "\u001b[0m\u001b[31mERROR: No matching distribution found for pywin32==306\u001b[0m\u001b[31m\n", + "Requirement already satisfied: PyYAML==6.0.1 in /home/ec2-user/anaconda3/envs/pytorch_p310/lib/python3.10/site-packages (from -r ../../requirements.txt (line 64)) 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scikit_learn-1.4.1.post1-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl.metadata (11 kB)\n", + "Requirement already satisfied: scipy==1.12.0 in /home/ec2-user/anaconda3/envs/pytorch_p310/lib/python3.10/site-packages (from -r ../../requirements.txt (line 70)) (1.12.0)\n", + "Collecting sdmetrics==0.13.0 (from -r ../../requirements.txt (line 71))\n", + " Downloading sdmetrics-0.13.0-py2.py3-none-any.whl.metadata (43 kB)\n", + "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m43.2/43.2 kB\u001b[0m \u001b[31m8.8 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n", + "\u001b[?25hCollecting sdv==1.10.0 (from -r ../../requirements.txt (line 72))\n", + " Downloading sdv-1.10.0-py2.py3-none-any.whl.metadata (104 kB)\n", + "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m104.3/104.3 kB\u001b[0m \u001b[31m18.8 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n", + "\u001b[?25hRequirement already satisfied: six==1.16.0 in /home/ec2-user/anaconda3/envs/pytorch_p310/lib/python3.10/site-packages (from -r ../../requirements.txt (line 73)) (1.16.0)\n", + "Collecting smartnoise-sql==1.0.3 (from -r ../../requirements.txt (line 74))\n", + " Downloading smartnoise_sql-1.0.3-py3-none-any.whl.metadata (9.5 kB)\n", + "Collecting smartnoise-synth==1.0.3 (from -r ../../requirements.txt (line 75))\n", + " Downloading smartnoise_synth-1.0.3-py3-none-any.whl.metadata (2.7 kB)\n", + "Collecting SQLAlchemy==2.0.28 (from -r ../../requirements.txt (line 76))\n", + " Downloading SQLAlchemy-2.0.28-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl.metadata (9.6 kB)\n", + "Collecting stack-data==0.6.3 (from -r ../../requirements.txt (line 77))\n", + " Using cached stack_data-0.6.3-py3-none-any.whl.metadata (18 kB)\n", + "Requirement already satisfied: sympy==1.12 in /home/ec2-user/anaconda3/envs/pytorch_p310/lib/python3.10/site-packages (from -r ../../requirements.txt (line 78)) (1.12)\n", + "Requirement already satisfied: tenacity==8.2.3 in /home/ec2-user/anaconda3/envs/pytorch_p310/lib/python3.10/site-packages (from -r ../../requirements.txt (line 79)) (8.2.3)\n", + "Collecting threadpoolctl==3.3.0 (from -r ../../requirements.txt (line 80))\n", + " Downloading threadpoolctl-3.3.0-py3-none-any.whl.metadata (13 kB)\n", + "Collecting torch==2.2.1 (from -r ../../requirements.txt (line 81))\n", + " Downloading torch-2.2.1-cp310-cp310-manylinux1_x86_64.whl.metadata (26 kB)\n", + "Collecting tornado==6.4 (from -r ../../requirements.txt (line 82))\n", + " Using cached tornado-6.4-cp38-abi3-manylinux_2_5_x86_64.manylinux1_x86_64.manylinux_2_17_x86_64.manylinux2014_x86_64.whl.metadata (2.5 kB)\n", + "Collecting tqdm==4.66.2 (from -r ../../requirements.txt (line 83))\n", + " Using cached tqdm-4.66.2-py3-none-any.whl.metadata (57 kB)\n", + "Requirement already satisfied: traitlets==5.14.1 in /home/ec2-user/anaconda3/envs/pytorch_p310/lib/python3.10/site-packages (from -r ../../requirements.txt (line 84)) (5.14.1)\n", + "Collecting typing_extensions==4.10.0 (from -r ../../requirements.txt (line 85))\n", + " Using cached typing_extensions-4.10.0-py3-none-any.whl.metadata (3.0 kB)\n", + "Collecting tzdata==2024.1 (from -r ../../requirements.txt (line 86))\n", + " Using cached tzdata-2024.1-py2.py3-none-any.whl.metadata (1.4 kB)\n", + "Collecting urllib3==2.0.7 (from -r ../../requirements.txt (line 87))\n", + " Using cached urllib3-2.0.7-py3-none-any.whl.metadata (6.6 kB)\n", + "Requirement already satisfied: wcwidth==0.2.13 in /home/ec2-user/anaconda3/envs/pytorch_p310/lib/python3.10/site-packages (from -r ../../requirements.txt (line 88)) (0.2.13)\n", + "Requirement already satisfied: pexpect>4.3 in /home/ec2-user/anaconda3/envs/pytorch_p310/lib/python3.10/site-packages (from ipython==8.22.2->-r ../../requirements.txt (line 29)) (4.9.0)\n", + "INFO: pip is looking at multiple versions of rdt to determine which version is compatible with other requirements. This could take a while.\n", + "\u001b[31mERROR: Cannot install -r ../../requirements.txt (line 66) and Faker==15.3.4 because these package versions have conflicting dependencies.\u001b[0m\u001b[31m\n", + "\u001b[0m\n", + "The conflict is caused by:\n", + " The user requested Faker==15.3.4\n", + " rdt 1.9.2 depends on Faker<20 and >=17\n", + "\n", + "To fix this you could try to:\n", + "1. loosen the range of package versions you've specified\n", + "2. remove package versions to allow pip attempt to solve the dependency conflict\n", + "\n", + "\u001b[31mERROR: ResolutionImpossible: for help visit https://pip.pypa.io/en/latest/topics/dependency-resolution/#dealing-with-dependency-conflicts\u001b[0m\u001b[31m\n", "\u001b[0m" ] } diff --git a/requirements.txt b/requirements.txt index 82b25d7b..78f3cac1 100644 --- a/requirements.txt +++ b/requirements.txt @@ -61,7 +61,6 @@ Pygments==2.17.2 pyparsing==3.1.2 python-dateutil==2.9.0.post0 pytz==2024.1 -pywin32==306 PyYAML==6.0.1 pyzmq==25.1.2 rdt==1.9.2