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https://github.com/OpenBB-finance/OpenBB.git
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42b1acf5c3
* Initial implementation of the API wrapper for stocks/disc
* Create a file for helper classes
* Move models namespace class where it belongs... TO THE HELPER_CLASSES🚀
* Add backtesting api wrapper
* Add ba api wrapper
* Add dps api wrapper
* Fix flake8 warnings in command help strings
* Add comparison analysis models
* Add dd api wrapper
* Add fa api wrapper
* Add gov api wrapper
* Add ins api wrapper *note the note
* Add options api wrapper *note the note
* Add prediction api wrapper
* Add api support for importing models from both local and common contexts
* Add qa api wrapper
* Add screener api wrapper *note the note
* Fix typos in sia controller
* Add sia api wrapper
* Add ta api wrapper
* Disable linting for specific lines that fail in CI
* Remove deprecated parameter
* Add exception handling when prediction packages are not installed
41 lines
1.3 KiB
Python
41 lines
1.3 KiB
Python
"""Inference API."""
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import os
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from gamestonk_terminal.helper_classes import ModelsNamespace as _models
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# flake8: noqa
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# pylint: disable=unused-import
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# Menu commands
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from gamestonk_terminal.common.prediction_techniques.ets_view import (
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display_exponential_smoothing as ets,
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)
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from gamestonk_terminal.common.prediction_techniques.knn_view import (
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display_k_nearest_neighbors as knn,
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)
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from gamestonk_terminal.common.prediction_techniques.regression_view import (
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display_regression as regression,
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)
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from gamestonk_terminal.common.prediction_techniques.arima_view import (
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display_arima as arima,
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)
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from gamestonk_terminal.common.prediction_techniques.neural_networks_view import (
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display_mlp as mlp,
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)
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from gamestonk_terminal.common.prediction_techniques.neural_networks_view import (
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display_rnn as rnn,
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)
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from gamestonk_terminal.common.prediction_techniques.neural_networks_view import (
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display_lstm as lstm,
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)
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from gamestonk_terminal.common.prediction_techniques.neural_networks_view import (
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display_conv1d as conv1d,
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)
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from gamestonk_terminal.common.prediction_techniques.mc_view import (
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display_mc_forecast as mc,
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)
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# Models
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# NOTE: The ets function is used here to point to the commons path where all the
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# inference models are expected to live
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models = _models(os.path.abspath(os.path.dirname(ets.__code__.co_filename)))
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