fabclmnt

synth_data = synth.sample(1000)
print(synth_data)

fabclmnt

synth = RegularSynthesizer(modelname='fast')
synth.fit(data=data, num_cols=num_cols, cat_cols=cat_cols)

fabclmnt

data = fetch_data('adult')
num_cols = ['age', 'fnlwgt', 'capital-gain', 'capital-loss', 'hours-per-week']
cat_cols = ['workclass','education', 'education-num', 'marital-status', 'occupation', 'relationship', 'race', 'sex',
           'native-country', 'target']

fabclmnt

from pmlb import fetch_data

from ydata_synthetic.synthesizers.regular import RegularSynthesizer
from ydata_synthetic.synthesizers import ModelParameters, TrainParameters

fabclmnt

`pip install ydata-synthetic==1.2.0`

fabclmnt

2023-03-20 | Val_1 | Val_2 | Val_3
2023-03-19 | Val_1 | Val_2 | Val_3
2023-03-18 | Val_1 | Val_2 | Val_3
2023-03-17 | Val_1 | Val_2 | Val_3
2023-03-16 | Val_1 | Val_2 | Val_3
(...)
2023-02-03 | Val_1 | Val_2 | Val_3
2023-02-02 | Val_1 | Val_2 | Val_3
2023-02-01 | Val_1 | Val_2 | Val_3
(...)

fabclmnt

# Defining general model parameters
batch_size = 128
learning_rate = 5e-4
noise_dim = 128
dim = 128

gan_args = ModelParameters(batch_size=batch_size,
                                                   lr=learning_rate,
                                                   noise_dim=noise_dim,
                                                   layers_dim=dim)

fabclmnt

stock_data_df =
pd.read_csv("https://raw.githubusercontent.com/ydataai/ydata-synthetic/70504e2158a1194bc5075ee8cae7560277c39b4d/data/stock_data.csv")

fabclmnt

import pandas as pd
from ydata_synthetic.synthesizers import ModelParameters
from ydata_synthetic.synthesizers.timeseries import TimeGAN
from ydata_synthetic.preprocessing.timeseries.utils import real_data_loading

fabclmnt

conda create --name synth-env python==3.9 pip

conda activate synth-env

pip install ydata-synthetic==v1.0.1