feat: Add CGAN for timeseries #108
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fabclmnt
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Nov 3, 2021
jfsantos-ds
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Nov 4, 2021
| @@ -1,45 +1,46 @@ | |||
| # Inverts all preprocessing pipelines provided in the preprocessing examples | |||
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Changes in this script consist in extending the inverse support to the MinMaxScaler
jfsantos-ds
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Nov 4, 2021
| @@ -1,18 +1,31 @@ | |||
| """ | |||
| Get the stock data from Yahoo finance data | |||
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Allowing subsetting of columns
jfsantos-ds
commented
Nov 4, 2021
| """ | ||
| Utility functions to be shared by the time-series preprocessing required to feed the data into the synthesizers | ||
| """ | ||
| import numpy as np |
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Enabling inverse_transform of the timeseries scaler (processor needs to be returned here)
jfsantos-ds
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Nov 4, 2021
fabclmnt
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Nov 5, 2021
src/ydata_synthetic/postprocessing/timeseries/inverse_preprocesser.py
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fabclmnt
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Nov 8, 2021
added 5 commits
December 15, 2021 18:17
remove changes on gitignore removed unused n_feats argument
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Auto regressive timeseries sampling method revert TS data processor integration
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jfsantos-ds
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May 26, 2022
| data_.append(records) | ||
| data_ = hstack(data_)[:, :seq_len] | ||
| data.append(data_) | ||
| return array(vstack(data)) |
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Regarding to a revision of this sample method I think:
- We can force the number of samples to be the same as requested, i.e. truncate the last batch if needed
- Condition converted to tensor could be taken outside the for loop
- It does not seem to be truly auto-regressive since only the condition part of the produced records is being used as condition, we can add this capacity
Do you agree/have anything to add @fabclmnt ?
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Closes #93