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198 lines
7.4 KiB
Python
198 lines
7.4 KiB
Python
"""Nhits View"""
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__docformat__ = "numpy"
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import logging
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from typing import Union, Optional, List, Tuple
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from datetime import datetime
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import pandas as pd
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import matplotlib.pyplot as plt
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from openbb_terminal.forecast import nhits_model
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from openbb_terminal.decorators import log_start_end
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from openbb_terminal.forecast import helpers
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logger = logging.getLogger(__name__)
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# pylint: disable=too-many-arguments
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@log_start_end(log=logger)
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def display_nhits_forecast(
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data: Union[pd.Series, pd.DataFrame],
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target_column: str = "close",
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dataset_name: str = "",
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n_predict: int = 5,
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past_covariates: str = None,
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train_split: float = 0.85,
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forecast_horizon: int = 5,
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input_chunk_length: int = 14,
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output_chunk_length: int = 5,
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num_stacks: int = 3,
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num_blocks: int = 1,
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num_layers: int = 2,
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layer_widths: int = 512,
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pooling_kernel_sizes: Optional[Tuple[Tuple[int]]] = None,
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n_freq_downsample: Optional[Tuple[Tuple[int]]] = None,
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dropout: float = 0.1,
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activation: str = "ReLU",
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max_pool_1d: bool = True,
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batch_size: int = 32,
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n_epochs: int = 100,
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learning_rate: float = 1e-3,
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model_save_name: str = "nhits_model",
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force_reset: bool = True,
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save_checkpoints: bool = True,
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export: str = "",
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residuals: bool = False,
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forecast_only: bool = False,
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start_date: Optional[datetime] = None,
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end_date: Optional[datetime] = None,
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naive: bool = False,
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export_pred_raw: bool = False,
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external_axes: Optional[List[plt.axes]] = None,
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):
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"""Display Nhits forecast
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Parameters
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----------
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data: Union[pd.Series, pd.DataFrame]
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Input Data
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target_column: str
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Target column to forecast. Defaults to "close".
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dataset_name: str
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The name of the ticker to be predicted
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n_predict: int
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Days to predict. Defaults to 5.
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train_split: float
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Train/val split. Defaults to 0.85.
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past_covariates: str
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Multiple secondary columns to factor in when forecasting. Defaults to None.
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forecast_horizon: int
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Forecast horizon when performing historical forecasting. Defaults to 5.
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input_chunk_length: int
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Number of past time steps that are fed to the forecasting module at prediction time. Defaults to 14.
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output_chunk_length: int
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The length of the forecast of the model. Defaults to 5.
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num_stacks: int
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The number of stacks that make up the whole model.
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num_blocks: int
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The number of blocks making up every stack.
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num_layers: int
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The number of fully connected layers preceding the final forking layers in each block
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of every stack.
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layer_widths: int
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Determines the number of neurons that make up each fully connected layer in each
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block of every stack. If a list is passed, it must have a length equal to num_stacks
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and every entry in that list corresponds to the layer width of the corresponding stack.
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If an integer is passed, every stack will have blocks with FC layers of the same width.
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pooling_kernel_size: Optional[Tuple[Tuple[int]]]
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If set, this parameter must be a tuple of tuples, of size (num_stacks x num_blocks),
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specifying the kernel size for each block in each stack used for the input pooling
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layer. If left to None, some default values will be used based on input_chunk_length.
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n_freq_downsample: Optional[Tuple[Tuple[int]]]
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If set, this parameter must be a tuple of tuples, of size (num_stacks x num_blocks),
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specifying the downsampling factors before interpolation, for each block in each stack.
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If left to None, some default values will be used based on output_chunk_length.
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dropout: float
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The dropout probability to be used in fully connected layers.
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activation: str
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Supported activations: [[‘ReLU’,’RReLU’, ‘PReLU’, ‘Softplus’, ‘Tanh’, ‘SELU’, ‘LeakyReLU’, ‘Sigmoid’]
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max_pool_1d: bool
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Use max_pool_1d pooling. False uses AvgPool1d.
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batch_size: int
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Number of time series (input and output sequences) used in each training pass. Defaults to 32.
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n_epochs: int
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Number of epochs over which to train the model. Defaults to 100.
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learning_rate: float
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Defaults to 1e-3.
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model_save_name: str
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Name for model. Defaults to "brnn_model".
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force_reset: bool
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If set to True, any previously-existing model with the same name will be reset
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(all checkpoints will be discarded). Defaults to True.
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save_checkpoints: bool
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Whether or not to automatically save the untrained model and checkpoints from training.
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Defaults to True.
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export: str
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Format to export data
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residuals: bool
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Whether to show residuals for the model. Defaults to False.
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forecast_only: bool
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Whether to only show dates in the forecasting range. Defaults to False.
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start_date: Optional[datetime]
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The starting date to perform analysis, data before this is trimmed. Defaults to None.
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end_date: Optional[datetime]
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The ending date to perform analysis, data after this is trimmed. Defaults to None.
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naive: bool
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Whether to show the naive baseline. This just assumes the closing price will be the same
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as the previous day's closing price. Defaults to False.
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external_axes: Optional[List[plt.axes]]
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External axes to plot on
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"""
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data = helpers.clean_data(
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data, start_date, end_date, target_column, past_covariates
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)
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if not helpers.check_data(data, target_column, past_covariates):
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return
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output_chunk_length = helpers.check_output(
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output_chunk_length, n_predict, bool(past_covariates)
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)
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(
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ticker_series,
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historical_fcast,
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predicted_values,
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precision,
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_model,
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) = nhits_model.get_nhits_data(
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data=data,
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n_predict=n_predict,
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target_column=target_column,
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past_covariates=past_covariates,
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train_split=train_split,
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forecast_horizon=forecast_horizon,
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input_chunk_length=input_chunk_length,
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output_chunk_length=output_chunk_length,
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num_stacks=num_stacks,
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num_blocks=num_blocks,
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num_layers=num_layers,
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layer_widths=layer_widths,
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pooling_kernel_sizes=pooling_kernel_sizes,
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n_freq_downsample=n_freq_downsample,
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dropout=dropout,
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activation=activation,
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max_pool_1d=max_pool_1d,
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batch_size=batch_size,
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n_epochs=n_epochs,
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learning_rate=learning_rate,
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model_save_name=model_save_name,
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force_reset=force_reset,
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save_checkpoints=save_checkpoints,
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)
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if ticker_series == []:
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return
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probabilistic = False
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helpers.plot_forecast(
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name="NHITS",
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target_col=target_column,
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historical_fcast=historical_fcast,
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predicted_values=predicted_values,
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ticker_series=ticker_series,
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ticker_name=dataset_name,
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data=data,
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n_predict=n_predict,
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forecast_horizon=forecast_horizon,
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past_covariates=past_covariates,
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precision=precision,
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probabilistic=probabilistic,
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export=export,
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forecast_only=forecast_only,
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naive=naive,
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export_pred_raw=export_pred_raw,
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external_axes=external_axes,
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)
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if residuals:
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helpers.plot_residuals(
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_model, past_covariates, ticker_series, forecast_horizon=forecast_horizon
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)
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